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Measurement and prediction of herbicide transport into shallow groundwater

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Title:
Measurement and prediction of herbicide transport into shallow groundwater
Creator:
Smith, Matthew Clay, 1957-
Publication Date:
Language:
English
Physical Description:
xv, 214 leaves : ill. ; 28 cm.

Subjects

Subjects / Keywords:
Bromides ( jstor )
Chemicals ( jstor )
Groundwater ( jstor )
Modeling ( jstor )
Nitrates ( jstor )
Pesticides ( jstor )
Soil samples ( jstor )
Soil solution ( jstor )
Soils ( jstor )
Water tables ( jstor )
Agricultural Engineering thesis Ph. D
Agricultural pollution -- Environmental aspects ( lcsh )
Atrazine -- Environmental aspects ( lcsh )
Dissertations, Academic -- Agricultural Engineering -- UF
Groundwater -- Pollution ( lcsh )
Herbicides -- Environmental aspects ( lcsh )
City of Lakeland ( local )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1988.
Bibliography:
Includes bibliographical references.
Additional Physical Form:
Also available online.
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Matthew Clay Smith.

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University of Florida
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University of Florida
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Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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025018511 ( ALEPH )
20233935 ( OCLC )

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MEASUREMT AND PREDICTION OF HERBICIDE
TRANSPORT INTO SHALOW GMNDWATER






By

NATIHEW CIAY SMITH


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENT
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY




UNIVERSITY OF FLODRIDA


1988


F















DEDICATION



To my mother,

Corrine L. Smith



She was taken from her family by illness during this research. Her

wisdom, courage, and strength continue to inspire me. Without that inner

strength, I would never have made it this far.

I love you Mom.















ACKNWEDGMENIS

I would like to express my sincere appreciation to the following:



Dr. A. B. (Del) Bottcher, chairman of my advisory ccmittee, for

his friendship, patience, and guidance. He provided valuable advice and

philosophy during the highs and lows encountered in my Ph.D. program.

Dr. K. L. Campbell, cochairman of my advisory committee, for his

guidance and support. His calm and reasoned approach to problem solving

is a model which I need to emulate.

Dr. E. D. Threadgill, for serving on my advisory committee, for

making available the field site on which this research was conducted,

and for being the best engineer/scientist/administrator that I could

ever hope to be associated with.

Dr. W. C. Huber, for serving on my advisory coanittee, and being

one of the very best instructors that I have encountered during many

years of college. If all instructors were that good, my GPAs would have

been higher.

Dr. P. S. C. Rao, for serving on my advisory committee, for showing

a sincere interest in my research, and for teaching me several very

valuable lessons during the qualifying exam.

Ms. M. W. Smith, for being a wonderful, loving, and supportive

wife. She assumed a greatly disproportionate share of child care and

household duties so that I could devote time to this research.


iii









Nicholas and Sarah Smith, my children, for their love and for

providing meaning and purpose to my life and education.

Dr. G. W. Isaacs, for financial support in the form of a research

assistantship.

Dr. D. L. Thomas, for his friendship and support. He shielded me

from many distractions at work while I mcopleted this document.

Dr. W. B. Wheeler and Ms. S. J. Scherer, at the UF Pesticide

Residue Laboratory, for allowing me to use their equipment and guiding

me through the intricacies of pesticide residue analysis.

Mr. L. A. Asmussen, Dr. R. A. Leonard, Dr. W. G. Knisel, Ms. L. R.

Marti, and others at the USDA Southeast Watershed Research laboratory for

their advice, encouragement, and support.

USDA, Southern Region Pesticide Impact Assessment Program, for

financial support in the form of a grant.

Many others, too numerous to list here, in both Gainesville and

Tifton who contributed in many ways to the success of the project.









TABLE OF COENETS

Page

ACKNOWLEDGEMENTS.................................................. iii

LIST OF TABLES .................................................... vii

LIST OF FIGURES .................................................. viii

ABSTRACT' ........................................................... xiv

CHAPTERS

1 INTRODUCTION ................................................ 1

2 OBJECTIVES.................................................. 4

3 REVIEW OF THE LITERATURE.................................... 5

3.1 Evidence of Pesticide Residues in Groundwater......... 5

3.2 Factors Which Influence Pesticide Transport to

to Groundwater........................................ 7

3.3 Predicting Pesticide Transport....................... 15

3.4 Field Studies of Pesticide Transport................. 27

3.5 Summary .............................................. 36

4 EXPDERIMENTAL METHODS ....................................... 39

4.1 Field Site Description............................... 39

4.2 Site Instrumentation................................. 42

4.3 Chemical Applications................................ 49

4.4 Sample Collection and Storage........................ 52

4.5 Sample Analysis...................................... 55

5 MODELING THE EDPERIMENTAL SITE............................. 66

5.1 Selection of Ccommon Input Parameter Values........... 67

5.2 Parameters Unique to PRZM............................ 73

5.3 Parameters Unique to GLEAMS.......................... 77









6 RESULTS AND DISCUSSION..................................... 79

6.1 Data Collection ...................................... 79

6.2 Sample Analysis ...................................... 83

6.3 Chemical Applications................................ 85

6.4 Chemicals in the Unsaturated Zone.................... 91

6.5 Chemicals in the Saturated Zone...................... 107

6.6 Model Results and Comparisons....................... 142

7 SUMMARY AND CONCLUSICS ................................... 157

8 RECMMENDATICNS FOR IMF3OVEME3S AND FURTHER STUDY........ 160

REFERENCES........................................................ 162

APPENDICES

A. MO41IORING WELL STATISTICS................................ 170

B. HOW TO GET COMPLETE DATA SET ............................. 173

C. WATER BAIANCE PROGRAMS.................................... 176

D. SURFACE PLOTS OF ATRAZINE CONCENTRATION IN GROUNDWATER.... 200

BIOGRAPHICAL SKETCH............................................... 213









LIST OF TABLES


Page

5.1. Soil properties used in simulations........................ 69

5.2. Chemical properties used in simulations ..................... 70

5.3. Chemical applications summary.............................. 73

6.1. Chronological summary of field site activities..............80

6.2. Chemical application results............................... 88

6.3. Measured application rates and soil surface concentrations

of atrazine and alachlor................................... 91

6.4. Mean concentrations of atrazine (mg/kg) in soil samples..... 96

6.5. Mean concentrations of alachlor (mg/kg) in soil samples..... 97

6.6. Mean concentration of bromide (mg/L) at each sampling

following first application............................... 105

6.7. Mean concentration of bromide (mg/L) at each sampling

following second application............................... 106

6.8. Total mass of chemicals in the saturated zone............... 133

6.9. Simulated mass flux of chemicals........................... 142

A.1. Monitoring well statistics................................ 171

B.1. Example listing of the water sample data set............... 174

B.2. Example listing of the soil sample data set................ 175

C.i. Sample output from program ANALYZE........................ 191

C.2. Sample output from program FLUX........................... 198








IST OF FIGURES


4.1. Contour maps of soil surface and restricting layer

showing locations and ID labels of monitoring wells..........41

4.2. Cross-section of soil profile through application area

showing locations of monitoring wells and soil solution

samplers.................................................... 45

6.1. Uniformity of atrazine application.......................... 86

6.2. Uniformity of alachlor application.......................... 87

6.3. Uniformity of bromide application on 4/27/87.................89

6.4. Comparison of alachlor and atrazine concentrations in

application samples......................................... 90

6.5. Correlation between atrazine concentrations in the top 5 cm

of soil and application solution concentrations.............. 93

6.6. Correlation between atrazine concentrations in the top 5 cm

of soil and application rate................................ 93

6.7. Correlation between alachlor concentrations in the top 5 ancm

of soil and application solution concentrations.............. 94

6.8. Correlation between alachlor concentrations in the top 5 ancm

of soil and application rate................................ 94

6.9. Bromide and atrazine concentrations in solution sampler

09N-2 as a function of total water applied since

application................................................. 98

6.10. Bromide concentration for six sampling locations following

the first application at a 61 cmn depth..................... 101


viii









6.11. Bromide concentration for six sampling locations following

the first application at a 122 on depth ..................... 101

6.12. Bromide concentration for six sampling locations following

the first application at a 183 an depth.................... 102

6.13. Average bromide concentration for the three sampling depths

following the first application............................ 102

6.14. Bromide concentration for six sampling locations following

the second application at a 61 cm depth..................... 103

6.15. Bromide concentration for six sampling locations following

the second application at a 122 cmn depth..................... 103

6.16. Bramide concentration for six sampling locations following

the second application at a 183 cm depth.................... 104

6.17. Average bromide concentration for the three sampling depths

following the second application............................ 104

6.18. Bromide concentration in the groundwater on 12/15/86......... 108


concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration


the

the

the

the

the

the

the

the

the

the


groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater


12/22/86.........108

12/29/86.........109

01/05/87.........109

1/12/87. .........110

1/19/87 .........110

5/05/87 .........111

5/08/87. ........111

5/13/87.........112

5/18/87.........112

5/25/87.........113


6.29. Bromide concentration in the groundwater on 6/01/87......... 113


6.19.

6.20.

6.21.

6.22.

6.23.

6.24.

6.25.

6.26.

6.27.

6.28.


Bromide

Bromide

Bromide

Bromide

Bromide

Bromide

Bromide

Bromide

Bromide

Bromide








6.30. Atrazine concentration in the groundwater on 2/23/87.........115

6.31. Atrazine concentration in the groundwater on 3/02/87.........115

6.32. Atrazine concentration in the groundwater on 3/09/87.........116

6.33. Atrazine concentration in the groundwater on 3/16/87......... 116

6.34. Atrazine concentration in the groundwater on 3/23/87......... 117

6.35. Atrazine concentration in the groundwater on 3/31/87......... 117

6.36. Contour plot of water table elevation on 5/08/87 showing

direction of flow ............................................ 118

6.37. Concentration of atrazine and water table elevation in

well 09-11.................................................. 120

6.38. Nitrate concentration in groundwater on 5/01/87............. 122

6.39. Nitrate concentration in groundwater on 5/03/87............. 122

6.40. Nitrate concentration in groundwater on 5/05/87..............123

6.41. Nitrate concentration in groundwater on 5/08/87.............. 123

6.42. Nitrate concentration in groundwater on 5/13/87............. 124

6.43. Nitrate concentration in groundwater on 5/18/87............. 124

6.44. Nitrate concentration in groundwater on 5/25/87..............125

6.45. Nitrate concentration in groundwater on 6/01/87..............125

6.46. Chloride concentration in groundwater on 5/01/87............. 127

6.47. Chloride concentration in groundwater on 5/03/87............. 127

6.48. Chloride concentration in groundwater on 5/05/87............ 128

6.49. Chloride concentration in groundwater on 5/08/87............. 128

6.50. Chloride concentration in groundwater on 5/13/87............. 129

6.51. Chloride concentration in groundwater on 5/18/87............ 129

6.52. Chloride concentration in groundwater on 5/25/87............. 130

6.53. Chloride concentration in groundwater on 6/01/87.............130









Total mass of atrazine stored in the saturated zone......... 133

Subareas used in water balance............................. 135

Comparison of percolation volumes predicted by GLEAMS and

PRZM ........................................................ 144


6.57. Catparison of measured and PRZM predicted atrazine


concentrations in the soil

6.58. Comparison of measured and

concentrations in the soil

6.59. Comparison of measured and

concentrations in the soil

6.60. OCaparison of measured and

concentrations in the soil

6.61. Comparison of measured and

concentrations in the soil

6.62. Comparison of measured and

concentrations in the soil

6.63. Ccnparison of measured and

concentrations in the soil

6.64. Ocparison of measured and

concentrations in the soil

6.65. COoparlson of measured and

concentrations in the soil

6.66. Comparison of measured and

concentrations in the soil

6.67. Comparison of measured and

concentrations in the soil


on 11/18/86 ...................... 145

PRZM predicted atrazine

on 11/24/86......................145

PRZM predicted atrazine

on 12/22/86......................146

PRZM predicted atrazine

on 2/09/87.......................146

PRZM predicted atrazine

on 3/16/87.......................147

PRZM predicted atrazine

on 5/25/87.......................147

PRZM predicted alachlor

on 11/18/86...................... 148

PRZM predicted alachlor

on 11/24/86......................148

PRZM predicted aladchlor

on 12/22/86......................149

PRZM predicted alachlor

on 2/09/87....................... 149

PRZM predicted alachlor

on 3/16/87.......................150


6.54.

6.55.

6.56.









6.68. Oumparison of measured and IRZM predicted alachlor

ccentraticns in the soil on 5/25/87 ................. ...... 150

6.69. Measured and RZM predicted bromide concentrations in the

soil solution at a 61 ncm depth following the first

application................................................ 152

6.70. Measured and FPRZM predicted bromide concentrations in the

soil solution at a 122 ncm depth following the first

application................................................ 152

6.71. Measured and PRZM predicted bromide concentrations in the

soil solution at a 183 cm depth following the first

application................................................ 153

6.72. Measured and PRZM predicted bromide concentrations in the

soil solution at a 61 cman depth following the second

application................................................ 153

6.73. Measured and PRZM predicted bromide concentrations in the

soil solution at a 122 cm depth following the second

application................................................ 154

6.74. Measured and FRZM predicted bromide concentrations in the

soil solution at a 183 cm depth following the second

application................................................ 154

6.75. Measured and PRZM predicted concentrations of atrazine in

the soil solution at a 61 cm depth ..........................155

C. 1. Listing of program to calculate water and chemical

fluxes and storages........................................ .177

C.2. Program to calculate mass balance between sampling periods.. 194

D.I. Atrazine concentration in the groundwater on 1/19/87.........201


xii









D. 2.

D.3.

D.4.

D.5.

D.6.

D.7.

D.8.

D.9.

D.10.

D. 11.

D.12.

D.13.

D. 14.

D.15.

D. 16.

D.17.

D.18.

D.19.

D.20.

D.21.

D.22.

D.23.


Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine

Atrazine


concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration

concentration


in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the

in the


xiii


groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater

groundwater


1/26/87.........201

2/02/87.........202

2/09/87.........202

2/16/87.........203

2/23/87.........203

3/02/87.........204

3/09/87.........204

3/16/87.........205

3/23/87.........205

3/31/87.........206

4/06/87.........206

4/13/87.........207

4/20/87.........207

4/30/87.........208

5/01/87.........208

5/03/87.........209

5/05/87.........209

5/08/87.........210

5/13/87.........210

5/18/87.........211

5/25/87.........211

6/01/87.........212















Abstract of Dissertation Presented to the Graduate Sdchool
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

MEASUREMET AND PREDICTION OF HERBICIDE
TRANSPORT INTO SHALLOW GRNOWATER

By

Matthew Clay Smith

December 1988

Chairman: Dr. A. B. Bottcher
Major Department: Agricultural Engineering

A field study was conducted to observe the movement of the

herbicides atrazine and alachlor within the soil profile and a shallow

water table aquifer following a surface application. Surface-applied

bromide was used as a nonadsorbed tracer of water movement. The movement

of nitrate from a fertilizer application to the site was also monitored.

Measurements of chemical concentration were made using soil core samples,

samples of soil water fram the unsaturated zone, and water samples from

monitoring wells below the water table.

Atrazine was observed to move rapidly with both saturated and

unsaturated flows. Concentrations of atrazine exceeded 350 pq/L in soil

water samples at a depth of 61 can. Samples of shallow groundwater

contained atrazine residues as high as 90 jig/L. Measurable

concentrations of alachlor did not move below a depth of 45 ancm in the

soil.


xiv








Bromide and nitrate concentrations in soil water demonstrated large

variability between sampling locations. Nitrate concentrations in the

groundwater exceeded 40 mg/L after fertilization of the field.

Two root/vadose zone pesticide transport models, GLEAMS and

PRZM, were used to simulate the conditions on the site. Comparisons were

made between model simulation results and observed data. The

uncalibrated models predicted concentrations that were generally within

an order of magnitude, and often were within a factor of 2 to 3, of

observed values. Differences between the predictions of the models

appear to be due to the relative detail by which each model describes

soil properties as a function of depth.














CHAPTER 1
INTR3DUCHON


Agricultural production systems have undergone many changes since

the 1940s. The capability of post-war industry to synthesize almost

unlimited varieties of chemicals in great quantities has provided

farmers with an arsenal with which to battle insects, weeds, and

disease. These chemicals can also provide plants with required

nutrients and regulate their growth. The result of using these chemical

tools along with improved machines for planting, cultivating, and

harvesting has been a dramatic increase in the productivity of the

American farmer.

However, scme serious problems associated with the use of agricul-

tural chemicals have been identified in recent years. Many of these

chemicals are toxic to non-target organisms such as birds, fish, and the

person who is applying them. Rachel Carson (1962) brought to the

attention of the public the potential harmful effects of using these

chemicals in large quantities over large land areas. Since the

publication of her book 'Silent Spring' there has been much research

conducted to evaluate the fate of agricultural chemicals in the

environment. IXDuring the 1960s and 1970s, great effort was initiated to

determine the quantities of soil, fertilizers and pesticides which were

entering our lakes and rivers. Through these efforts methods have been










developed (though only a few have been implemented) to minimize or

eliminate agricultural impacts on surface waters.

The discovery of abandoned waste sites such as the famous Love

Canal and the discovery of the pesticide aldicarb in the shallow

groundwater on long Island in New York generated concern for the

protection of groundwater. These concerns led to analyzing samples of

groundwater for toxic wastes and pesticides.

As more groundwater samples were analyzed, it became apparent that

agricultural chemicals had somehow entered groundwater aquifers in

several areas of the country. These results were publicized, and

citizens throughout the nation expressed alarm that they were drinking

potentially toxic chemicals. The federal and state governmnnts

responded to these concerns by intensifying monitoring efforts and

reviewing data on the many agricultural chemicals to determine potential

for leaching to groundwater axnd the extent of the current problem.

Several federal agencies, along with university and private industry

scientists, began developing computer models to help explain why these

chemicals were moving beyond their target locations and to screen

chemicals for their relative mobilities in soil. A limiting factor in

the development and utilization of these models was a lack of the

detailed data needed to test and validate model predictions.

To date, data on the presence of agricultural chemicals in

groundwater have been limited primarily to sampling of municipal and

domestic wells, providing only a large scale picture of contamination.

There have been only a few intensive monitoring studies completed which

provide sufficient data for model parameter estimation. Currently, there









3

are increased efforts at specific sites to provide more detailed

monitoring of chemical movement through the soil profile and within

groundwater aquifers.

The study described here is an effort to develop data for use in

the development and testing of models which describe the fate and

transport of pesticides and nutrients used in agriculture.















CHEAPER 2
OBJECTIVES

The overall objective of this research is to develop data on the

movement of two widely used herbicides through the crop root zone,

through the vadose zone (unsaturated zone between the crop root zone and

the top of the unconfined water table aquifer), and within the shallow

water table aquifer. These data will then be compared with the

predictions of two root/vadose zone pesticide transport models.

The specific objectives are to perform the following:

1. Instrument a field site for monitoring the movement of water and

chemicals through the soil profile and a shallow, water table

aquifer.

2. Observe the fate and transport of surface-applied herbicides

(atrazine and alachlor) over time.

3. Use surface-applied bromide as a nonadsorbed tracer of water

movement.

4. Observe transport of the nitrate ccuponent of fertilizers

applied to field.

5. Omrpile the observations of chemical movement into a data base

for use in testing pesticide transport models.

6. Simulate the field conditions present during the monitored

period with two pesticide transport models and compare

to observed results.












CHAPTER 3
REVIEW OF THE LTERATURE

3.1 Evidence of Pesticide Residues in Groundwater

The U. S. Environmental Protection Agency estimates that at least 19

pesticides have been detected in groundwater in 24 states as a result of

agricultural practices (U.S. EPA, 1987a). The total number of

pesticides detected in groundwater is greater than nineteen. These

additional detections are the result of manufacturing, storage, and

loading activities which are not included in the above figures. The

number of reported cases of pesticides in groundwater is increasing.

Cohen et al. (1986) suggest that the increase is due to improvements in

the quality and quantity of studies instead of an increase in the

problem.

The insecticide/nematicide aldicarb was detected in the shallow

groundwater on Long Island, New York in 1979 (Wartenburg, 1988).

Aldicarb was used as an insecticide on potatoes. Aldicarb was detected

in 20 of 31 wells tested in 1978 and the presence was confirmed through a

repeat sampling of the wells in June, 1979. According to the U.S. EPA

(1987a), aldicarb residues have been detected in almost 2000 wells on

Long Island. Concentrations of aldicarb exceeded the state health

guideline of 7 pg/L (ppb) in nearly 50% of those wells. Concentrations

as high as 515 Ig/L were recorded in the Long Island wells as reported by

Ritter (1986). Aldicarb residues have been found in groundwater in 15

states (Cohen et al., 1986) with typical concentrations reported in the

range of 0.3 3 pg/L.










In the state of Florida, residues of the nematicide EDB have been

detected in over 10% of the public and private drinking water wells which

have been sampled (U.S. EPA, 1987a). Approximately 1,200 wells require

treatment or have been closed as sources of drinking water. Marti et al.

(1984) detected EDB in the groundwater in southwest Georgia. EDB

residues have been detected in groundwater in at least 6 other states.

In California, residues of approximately 57 pesticides have been

detected in groundwater (U.S. EPA, 1987a). Most of these detections

were related to factors other than routine use on agricultural fields.

The nematicide DBCP has been detected at levels exceeding 1 Vg/L in

approximately 60% of the 2500 drinking water wells tested.

Residues of the widely used herbicide atrazine are commonly

identified in groundwater. Ritter (1986) reports that approximately

36,000,000 kg of atrazine were used in the United States in 1982.

Atrazine has been detected in PA, IA, NE, WI, MN, and MD (U.S. EPA,

1987; COhen et al., 1986; Ritter, 1986). According to Berteau and Spath

(1986), atrazine has also been detected in concentrations of up to 2 iq/L

in California groundwater. Pionke et al. (1988) reported atrazine

concentrations in groundwater at levels up to 1.1 gq/L in an agricultural

watershed in Pennsylvania. The wells sampled by Pionke et al. (1988)

were all located in unconfined aquifers with depths to the water table

ranging from 2 to 21 m. Atrazine concentrations of 10 J/L have been

reported in a karst aquifer in Iowa (Libra et al., 1986; Kelley et al.,

1986).

Residues of the herbicide alachlor have been found in groundwater in

the states listed for atrazine, excluding WI. According to Ritter










(1986), alachlor arnd atrazine together accounted for 25% of all

pesticides sold in the U. S. in 1982. Ritter (1986) reported that in

1982, approximately 38,600,000 kg of alachlor were applied to cropland in

the United States. Kslley et al. (1986) reported aladclor concentrations

in groundwater as high as 16 gg/L in Iowa.

There are many individual cases of pesticide residue detection in

groundwater that could be cited. The reader is referred to the

proceedings of the Agricultural Impacts on Groundwater Conferences

(National Water Well Assoc., 1986, 1988) for a variety of reports

concerning the detection of pesticide residues in groundwater. A book

edited by Garner et al. (1986) is also a good source of information

related to this subject.



3.2 Factors Which Influence Pesticide Transport To Groundwater

Donigian and Rao (1986a) list five processes which affect the fate

and movement of chemicals within the soil. These processes are

transport, sorption, transformation/degradation, volatilization, and

plant uptake. Other authors, e.g., Cheng and Koskinen (1986), use

slightly varied groupings which include the processes named above. The

interactions of these processes over time and space determine the fate of

chemicals in the soil. The spatial variability of soil properties and

other factors is an important consideration when interpreting field data

and model predictions (Donigian and Rao, 1986a). This section will

describe each of the processes and same of the factors which influence or

regulate them.










3.2.1 Transport

The chemicals can be transported in at least three different phases:

adsorbed to solid materials, in solution, and as a vapor. Surface runoff

can transport chemicals in both the adsorbed and solution phases.

Erosion and transport of soil particles during rainfall and irrigation

can result in significant transport of highly-adsorbed, low-solubility

pesticides (Waudcope, 1978). For most pesticides, the majority of

transport in runoff is in the solution phase (Wauchqpe, 1978; Rao and

Davidson, 1980). Chemicals in the solution phase can be transported

within the soil profile by saturated and unsaturated water flows.

Water percolating through the soil profile can transport chemicals

in the solution phase beneath the root zone and possibly into

groundwater. The most common methods used to describe the transport of

water and solutes in porous media are based on the assumptions that

Darcian flow conditions exist, and that solute movement is controlled by

advective and dispersive processes (Freeze and Cherry, 1979; Jury, 1986a;

Wagenet, 1986; Wang and Anderson, 1982). Darcian flow conditions imply

that the flow equations are representative of some volume of soil. All

soil properties such as pore size distribution and volumetric water

content are assumed to be uniform throughout the volume of soil (Jury,

1986b).

The advective component of transport is usually based upon Richards'

equation and describes the average rate of water flow. The average rate

of solute flow is the product of the solute concentration and the average

pore-water velocity. Additional transport of solute can occur due to

mechanical mixing of water in adjoining pores during advective transport









9

and molecular diffusion of solute from pores with high concentrations to

adjoinirng pores with lower concentrations (Freeze and Cherry, 1979).

Molecular diffusion can be significant when average pore-water

velocities are low; otherwise, the processes involved in mechanical

dispersion usually dominate. Differences in pore-water velocities in the

direction of bulk flow cause a spreading out, and consequently a lowering

of peak concentrations, of the solute plume. Some of the solute will

arrive at a reference point earlier, and some will arrive later, than

would be predicted based upon the average linear flow velocity. This is

referred to as longitudinal dispersion. The tortuosity and

interconnection of pores in the soil will also cause the solute to move

perpendicularly to the bulk flow direction. This is referred to as

transverse dispersion. Freeze and Cherry (1979) and Rao et al. (1988)

provide detailed discussions of the dispersive-diffusive processes and

their mathematical formulations.

Transport predictions based upon the methods described above have

not caqpared well with field observations, e.g. Jury et al. (1986).

Numerous modifications to the general theory have been proposed in order

to help explain and reduce the differences between field observations and

theoretical predictions. Rao et al. (1980) proposed separating pore-

water in soils into two regions: inter-aggregate and intra-aggregate.

Convective-dispersive transport was assumed to be limited to the water in

the inter-aggregate region. The intra-aggregate region was assumed to

act as a source/sink of solute. Diffusion between the regions could

remove or add solute to the water in the inter-aggregate region. Jury

(1982) proposed a method of predicting the transport times of solutes to








10

specified depths which is not based on physical processes. Rather, the

transfer function theory uses measured values to develop a probability

density function of travel times and uses it to predict solute travel

times to depths greater than those that were measured. Gish (1987)

combined the ocnvective-diffusive transport theory with a stochastic

representation of water velocities to describe field-scale (average)

concentrations of bromide at various depths in the soil profile.

Camparisons between the average measured bromide concentrations and

predicted values showed good agreement.

Volatilization from soil and plant surfaces transports the chemicals

into the atmosphere and reduces the amount remaining for transport with

runoff or percolation. Diffusion of chemicals in the vapor phase can

also transport the chemical within the soil profile. Most transport

models do not consider vapor diffusion.

Many of the factors which are assumed to influence the transport of

chemicals through porous media are summarized by Jury (1986b). Some of

the soil properties influencing transport are water content, bulk

density, permeability, clay content, organic matter content, and water

retention (field capacity). Helling and Gish (1986) present results of a

simple modeling exercise to demonstrate the effect of several soil

properties on pesticide transport. Some of the environmental factors

which have been shown to influence transport are precipitation,

evapotranspiration, and temperature (Jury, 1986b).

3.2.2 Sorption

The partitioning of solutes between the liquid and solid phases

(dissolved and adsorbed) is a major factor determining the mass of










solute available for advective-dispersive transport through the soil

profile. If the chemical of interest is strongly adsorbed, then only a

small fraction will exist in the solution phase at a given time and be

available for transport. Non-adsorbed chemicals, however, will exist

entirely in the solution phase and are available for transport with

percolating water.

Many pesticides are nonpolar and adsorption occurs primarily on

organic matter surfaces. Pesticides and chemicals which are polar will

adsorb primarily to clay surfaces (Jury, 1986c). Since the majority of

pesticides are nonpolar, the adsorption onto soils has been primarily

related to the organic matter or organic carbon content of the soil. A

detailed discussion of the factors influencing pesticide adsorption onto

soils is given by Jury (1986c). The relationship between concentrations

of a pesticide in the dissolved and sorbed phases is often represented by

the Freundlich equation:

S=KCn 3.1

where S = adsorbed concentration (pq/g of soil), C = solution

concentration (Ag/mL), and K and n are empirical constants for the soil-

pesticide system. The exponent n in equation 3.1 is often assumed to be

equal to 1.0 which results in a linear relationship of the form:

S = KC 3.2
where Kd = partition coefficient (miL/g of soil).

For most uses of equation 3.2 in transport modeling, the

partitioning between the solution and adsorbed phases is assumed to be

instantaneous and reversible. Errors associated with the assumptions of








12

linearity, instantaneos equilibrium, and reversibility are discussed by

Rao and Davidscn (1980).

The partition coefficient, Kd, is unique to a given pesticide-soil

combination. However, Rao and Davidson (1980) report that investigators

have shown that when Kd is normalized for the organic carbon content of

the soil, the resulting value is independent of the soil type and can be

considered a property of the pesticide. The normalized partition

coefficient is represented as Koc and is defined by:

Kc = Kd 100 / %OC 3.3

where %OC is the percent organic carbon content of the soil and Kd is the

measured partition coefficient. Methods for measurement of the

partition coefficient are reviewed by Rao and Davidson (1980).



3.2.3 Volatilization

Volatilization of pesticides from the soil and plant surfaces

reduces the amount of the pesticide available to be leached or

transported with runoff. Volatilization also determines the quantity of

the pesticide which exists in the vapor phase within the soil and thus

the amount available for diffusive vapor transport. Thus, as noted by

Donigian and Rao (1986a), volatilization affects both the fate and

transport of a pesticide.

Same of the factors which influence volatilization are summarized by

Jury and Valentine (1986). They are Henry's constant KH, chemical

concentration, adsorption site density, temperature, water content, wind

speed, and water evaporation. Henry's constant, KH is the ratio of

saturated vapor density to solubility and is an index of the partitioning










between the vapor and solution phases. A larger KH implies increased

volatilization. Increasing concentrations of the pesticide will increase

the volatilization as long as the vapor density is not saturated.

Adsorption of the pesticide will reduce volatilization. Volatilization

increases as temperature increases. As the soil water content decreases,

the rate of volatilization increases. Increasing wind speed can increase

the volatilization of pesticides, particularly those with low KH values.

Evaporation of water from the soil surface can transport pesticides from

within the soil profile to the surface where temperature and wind

effects can increase the volatilization. Each of these factors is

discussed in more detail by Jury (1986d).



3.2.4 Transformation/degradation

Transformation of a pesticide is the change in structure or

composition of the original compound and degradation is the breakdown of

the compound into smaller fragments with eventual inorganic endproducts

such as H20 and CO2 (Cheng and Koskinen, 1986). The transformation and

degradation processes represent a loss of the original (or parent)

compound, thus reducing the amount remaining in the soil for transport by

surface runoff and percolation.

In general, transformation and degradation processes occur at faster

rates on plant surfaces and in the top few centimeters of soil than in

the deeper soil zones (Donigian and Rao, 1986a). Thus, a chemical that

is foliar or surface-applied will likely degrade more rapidly than if it

were incorporated. For non-persistent pesticides (half-lives less than

15-20 days), the timing of rainfall events or irrigation is important in









14
determining the fraction of the applied mass that will be available for

transport. Events occurring shortly after application will likely result

in the largest concentrations in runoff and percolation water (Wauchope,

1979; Donigian and Rao, 1986a). The timing of rainfall and irrigation

events is less critical for persistent (half-lives in excess of 100 days)

pesticides since they will reside in the soil for longer periods

(Donigian and Rao, 1986a).

The major processes involved in transformation and degradation

include biotransformation, chemical hydrolysis, photolysis, and

oxidation-reduction (Donigian and Rao, 1986a). Factors influencing

transformation and degradation of pesticides, as summarized by Jury and

Valentine (1986), include: microbial populations, chemical concentration,

temperature, oxygen, pH, soil water content, and light. Detailed

descriptions of the many factors influencing these processes are

presented by Rao and Davidson (1980), Valentine and Schnoor (1986), and

Valentine (1986).



3.2.5 Plant processes

Plant processes relating to pesticides are very complex. The

uptake, translocation, accumulation, and transformation of pesticides by

plants affect the availability of pesticides for transport processes and

the potential exposure to pesticide residues by the consumers of the

vegetation, fruits, etc. (Donigian and Rao, 1986a). Plant processes can

serve as both a sink and a source of pesticide residues available for

transport.









15
Pesticides applied to plant foliage will likely be transformed or

degraded more rapidly than if the pesticide were applied to the soil or

incorporated. Pesticides on the foliage may also be absorbed into the

plant. Pesticides in the solution phase may be taken up by plant roots

and translocated to various parts of the plant. These processes serve as

a loss mechanism reducing the availability of the pesticide for

transport.

Pesticide residues on plant foliage can be dislodged by rainfall and

irrigation and washed onto the soil surface, resulting in an increased

mass of pesticide available for transport in runoff and percolation

(Smith et al., 1981; Donigian and Rao, 1986a). Plant residues which are

left on the soil surface or incorporated can also serve as a source of

pesticide which may be available for transport.



3.3 Predicting Pesticide Transport

There are many methods that can be used to predict the mobility of a

given pesticide. These range fram simple indices to complex research

models. Each method has value when used for the purpose for which it was

designed and with recognition of the assumptions and limitations

associated with it.



3.3.1 Indices of contamination potential

Rao et al. (1985) compared several methods for computing indices of

the contamination potential of pesticides. Most of the indices are based

on one or more chemical properties such as Koc and solubility. Three

indices also included distance to groundwater and recharge rate. The








16
attenuation factor (AF) proposed by Rao et al. (1985) can also be used to

estimate the mass of pesticide which will leach below the root zone or

out of the vadose zone and into groundwater. The AF indr1ex is the only

one of the irxndices compared by Rao et al. (1985) which incorporated mass

loadings.

Dean et al. (1984) developed a methodology in which soil

characteristics, crop type, chemical properties, and management practices

(e.g. tillage type) can be combined to find a cumulative frequency

distribution of the percentage of applied pesticide mass leadching below

the crop root zone. These frequency distributions were derived from

hundreds of 25-year simulations of pesticide leaching using the FpRZM

model (Carsel et al., 1984). The methodology was applied to four crop

types (corn, soybean, cotton, and wheat) and 19 representative growing

regions. The results are somewhat unique in that the user is presented

with a statistical probability of leaching based on a 25-year

meteorologic record.

Another index which is currently receiving much attention is the

DRASFTIC index developed by Aller et al. (1985). This irxndex does not

consider chemical properties; instead, it is an index of the

vulnerability of groundwater at a given location to contamination from

surface-applied chemicals. The DRASTIC index assigns numerical scores or

weights to seven factors which could influence pollution potential. The

factors are depth to water, net recharge, aquifer media, soil media,

topography, impact of vadose zone, and conductivity of the aquifer. The

final DRASTIC score is used to describe an area as having high, medium,

or low susceptibility to groundwater pollution.










ERASTIC has been used to map every county in the United States

(Alexander and Little, 1986) as part of the first stage of a national

survey of pesticide contamination of drinking water (U.S. EPA, 1987b).

In the first county-level assessment the Southeast Coastal Plain, which

covers all of Florida and southern Georgia, was shown to be the

groundwater region of highest vulnerability.

Indices like those mentioned above are beneficial to regulatory

agencies as a method to screen great numbers of compounds and locate

vulnerable areas. This information can aid in allocating funding for

further studies concentrating on the chemicals of interest in regions

which may be most vulnerable. These studies may involve detailed

modeling of pesticide transport to groundwater, modeling of surface and

groundwater hydrology, and collection of data from test locations.



3.3.2 Transport models

There are many models of pesticide transport reported in the

literature. These range from steady-state screening models, e.g. PESTAN

(Enfield et al., 1982), to very complex process-oriented research models,

e.g. LEACHMP (Wagenet and Hutson, 1986). Donigian and Rao (1986a), and

Shoemaker and Magette (1987) review a number of the models which are

available. Most models which are capable of simulating pesticide fate

and transport do not explicitly represent agricultural management and

cropping practice effects on runoff and leaching. Modifications of

management practices will be one method of reducing pesticide leaching to

groundwater. The models used for regulatory purposes should reflect the

trade-offs between surface water and groundwater quality. There is scme










evidence that no-till farming will increase pesticide transport to

groundwater while reducing surface runoff and thereby reduce pollution

of surface waters (Dick et al., 1986). Thus, solutions to one problem

may exacerbate another problem. The models discussed below are capable

of simulating, to sane degree, agricultural management practice effects

on pesticide fate and transport.

According to Donigian and Rao (1986a), SESOIL (The Seasonal Soil

Caqpartment model) is designed for long-term simulation of pesticide fate

in the soil environment. It was developed for the EPA and is used as a

screening model. SESOIL can simulate many ccnponents of the hydrologic

cycle including precipitation, evapotranspiration (ET) and surface

runoff. The model considers transport within the unsaturated zone

extending from the soil surface to the top of the saturated zone. The

hydrologic responses are determined using physically based equations in

which uncertainty has been included. The water balance used in the

model is a statistical representation of the hydrologic cCnponents over a

"season." A season is the time step of the model, e.g., month or year.

Erosion is simulated using the Universal Soil Loss Equation (Wischmeier

and Smith, 1978). Hetrick and Travis (1988) coupled SESOIL with EROS (a

submodel of the CREAMS (Knisel, 1980) watershed model) to predict surface

runoff and sediment yield from small watersheds. The pesticide ccanponent

of the model considers most of the processes and factors influencing

pesticide transport which were described in previous sections.

SESOIL requires calibration and represents long-term averages. Some

of the results of use and testing of SESOIL reported by Donigian and Rao

(1986a) indicate that it should not be used for short-term predictions.










The combined SESOI/EROS model of Hetrick and Travis (1988) adequately

predicted long-term (several months) average runoff and sediment yields

when tested against data from three small watersheds. However, monthly

predictions were not good, especially when most of the runoff occurred

due to large single storm events.

MUSE (Method Of Underground Solute Evaluation) was developed at

Cornell University as a management model and a training tool for students

and professionals (Steenhuis et al., 1987). MNOSE is an interactive,

menu-driven program which can run on a IBM-PC or compatible computer.

The model can read daily historical weather files or generate synthetic

rainfall, air, and soil temperature patterns. Surface runoff is computed

using the SCS curve number method. Erosion is not considered. Solute

movement in the vadose zone considers advective and dispersive flux, as

well as degradation and adsorption. MOUSE will also simulate the

movement of a pesticide in a two-dimensional unconfined aquifer.

Extensive graphics illustrate movement as it is being simulated.

PRZM (Pesticide Root Zone Model) is a field-scale hydrology and

transport model developed by the EPA (Carsel et al., 1984). PRZM is a

continuous model capable of simulating water and chemical fluxes over

many years of historical daily weather records. Runoff is predicted

based on the SCS curve number equation, and erosion is simulated using a

modification of the Universal Soil Loss Equation for daily time steps.

The model can simulate the entire vadose zone (soil surface to

groundwater). The vadose zone can be characterized by several layers

with varying properties. For calculations, the vadose zone is divided

into many cmpartments of equal depth. The model simulates crop growth








20

(leaf area and rooting depth). Water in the root zone can be removed by

percolation, evaporation, or transpiration by the plant. Percolation is

calculated based on the water-holding capacity of the soil. When the

water content in a layer exceeds field capacity (1/10 1/3 bar tension)

the excess water drains into the next lower cnpartment. There is an

option in PRZM that allows the draining of the profile to ocur over a

longer period than one day.

Pesticide processes represented include advective and dispersive

flux, sorption, degradation in soil and on plant foliage, and plant

uptake. Volatilization and transport in the vapor phase are not

considered. Application of pesticides can be partitioned between the

soil surface and plant foliage. Applications to the soil can be

incorporated by tillage. Different degradation rates can be specified

for soil and foliar pesticide residues. The degradation rate within the

soil can also be varied by soil layer.

PRZM will simulate multiple applications of one pesticide each year

for many years of continuous climatic record. Thus it can predict

temporal variations in leaching and runoff. Effects of agricultural

management and cropping practices can be simulated. PRZM allows the user

to request time series results for many variables in the model. This

feature is useful for observing processes at intermediate locations

within the vadose zone over time.

GLEAMS (Groundwater Loading Effects of Agricultural Management

Systems) is also a field-scale hydrology and transport model (Leonard et

al., 1987). It is based upon the extensively documented and applied

CREAMS model (Knisel, 1980). CREAMS is a nonpoint source model for









21
predicting sediment, nutrient, and pesticide losses with surface runoff

from agricultural management systems. GIEAMS builds upon the foundations

in CREAMS by adding components to simulate movement of water and

chemicals within the crop root zone. Like CREAMS, GLEAMS is a

continuous, daily simulation model.

GLEAMS predicts runoff using the SCS curve number method. Erosion

is predicted based upon modifications to the Universal Soil Loss

Equation. Surface runoff and eroded sediment, with chemicals in both the

dissolved and sorbed phases, can be routed overland, in channels, and

through impoundents.

GLEAMS divides the crop root zone into seven layers. The vadose

zone between the root zone and the water table is not considered. The

first layer has a thickness of 1 an. This layer is assumed to be the

portion of the soil that determines the mass of pesticide available for

extraction into surface runoff. The thickness of the second layer is

1/6 of the root zone depth minus 1 cm for the first layer. The remaining

5 layers are each 1/6 of the root zone in thickness. This layering

structure is fixed by the program. A strong correlation has been

observed between pesticide concentrations in runoff and the concentration

of pesticides in the top 1 cm of the soil profile (leonard, 1988). Thus

the authors of CRFAMS and GLEAMS believe that a 1 cm active zone at the

soil surface is required to maintain sensitivity of runoff concentrations

to soil concentrations (leonard and Knisel, 1987). Soil profile

properties can vary with depth. The model weights the input values to

establish average properties for each layer in the model. Percolation

through the profile is based on the water-holding capacity of the soil as










in PRZM. Water in excess of field capacity drains to the next lower

layer.

Pesticide transport within the root zone is by advection. No

dispersive flux ccponents are included. Volatilization is not

considered. Pesticide applications can be partitioned between plant

foliage and the soil surface. Different degradation rates can be

specified for chemicals on the foliage and within the soil. Soil-applied

chemicals can be incorporated to a specified depth.

The model will simulate up to ten chemicals simultaneously. This

feature makes possible the observation of the effect of changes in

chemical properties, e.g. partition coefficient, on resulting leaching

losses with one model run. The model can also simulate the formation,

fate, and transport of degradation products of the parent chemicals.



3.3.3 Coupled saturated/unsaturated zone transport models

The root/vadose zone models described above are important tools for

assessing the mobility of pesticides within the unsaturated zone. The

concern expressed by the public and governmental officials relate to

pesticide residues in grourndkwater supplies used for drinking water. Thus

the unsaturated zone models need to be linked to saturated zone transport

models so that the concentrations and transport of pesticides within

aquifers can be calculated.

As described above, MNUSE (Steenhuis et al., 1987) can predict the

transport of pesticides in a vertical, two-dimensional cross section of

a water table aquifer. Jones et al. (1987) coupled the PRZM model to a

two-dimensional saturated zone transport model to predict the movement of









23
aldicarb residues within a shallow water table aquifer. Dean and Carsel

(1988) reported on progress in linking PRZM to a two-dimensional

saturated transport model. The linked model will use PRZM for simulating

the root zone, a one-dimensional transport model for the vadose zone, and

a two-dimensional saturated zone model which can simulate confined,

unconfined, and leaky confined aquifers. The saturated zone model will

also simulate pumping from the aquifer(s). The linked model is expected

to be released for testing during the fall of 1988.



3.3.4 Model testing and uncertainty

Methods (such as described above) to predict the transport of the

chemicals used by agriculture are needed by regulators in order to assess

the impacts on surface water and groundwater quality from the use of

these chemicals. Field testing of the contamination potential of all

chemicals registered for use by agriculture would be infeasible due to

both cost and time constraints. Since decisions regarding chemical use

will be made based upon the results of transport models, it is important

to understand the errors and uncertainty associated with the models.

Each model or method will have errors associated with the assumptions

relating to the description of the system, choice of equations to

represent the system, and input parameters required by the models. There

are some general points that can be made regarding the uncertainties

inherent in deterministic, physically based models such as PWZM and

GIEAMS which were described previously. An excellent discussion of the

uncertainty in transport models is presented by leonard and Knisel

(1988).










Limitations of the conceptual formulation of the system that is

described by a model lead to uncertainty in the results. Most

deterministic models assume that same of the physical properties of the

system are uniform with respect to location. That is, soil properties

such as porosity, water-holding capacity, and organic matter content are

constant across the field, although most models will allow these types of

properties to vary with depth in the soil profile. Furthermore,

variations in pore sizes within the soil, which can cause uneven water

velocity distributions, are usually not considered. Each model will

represent a system (ie. the crop root zone) in varying levels of detail.

The equations which are used to represent the fate and transport

processes have limitations and uncertainties. For example, PRZM and

GLEAMS both use the SCS curve number method for partitioning rainfall

between runoff and infiltration. This method utilizes daily rainfall

records and does not consider the effects of rainfall intensity on the

runoff process. Testing of the CREAMS model (Knisel, 1980), frcm which

GLEAMS was derived, showed that the daily hydrology option represented

average annual runoff volumes well, but did not do as well with daily and

monthly runoff volumes (Smith and Williams, 1980). PRZM and GLEAMS both

utilize modified versions of the USLE (Wischmeier and Smith, 1978) for

prediction of daily soil erosion. The USLE was developed for long-term

(20-yr) average annual erosion rates. Although these modifications have

been tested, the basis of these methods is the USLE which is an empirical

equation derived for long-term average predictions. Many transport

models utilize the linear form of the Freundlich equation (Equation 3.1)

to describe the partitioning of a chemical between the dissolved and









25
adsorbed phases. Rao and Davidson (1980) indicate that the assumption of

linearity can lead to errors on the order of a factor of two to three.

The previous examples illustrate that the mathematical representations of

processes used in models often have inherent inaccuracies and

uncertainties.

The parameters which are required by the models to solve the

equations which describe the system also have inherent uncertainties

associated with them. Jury, (1985), in a review of the literature,

reported coefficients of variation (CV) from field measurements of

several soil physical properties. Soil porosity was found to have a mean

CV of 10%. Bulk density measurement had a CV of 9%. Determinations of

particle size fractions had an average CV of 28%, with a range of 3 to

55%. The water-holding capacity of the soil at matric potentials of 0.1

and 15 bars had a CV of 15 and 25%, respectively. Measurements of

saturated hydraulic conductivity and infiltration rates showed a CV of

124 and 72%, respectively. In a study by Jury et al. (1986), the

partition coefficient, Kd, of napropamide measured on 36 samples from a

1.44-ha field had a CV of 31%. Rao and Davidson (1980) reported that the

normalized partition coefficient, Koc, reported from individual studies

generally had a CV of 40-60%. The variability of the properties reported

above are not necessarily due to the fact the samples were taken over

large areas. Jury (1985) reported that one author had reported that up

to 50% of the variation in a parameter over a field occurred within a 1

m2 area.

Given the uncertainties associated with models and the parameters

required by the models as outlined briefly above, how closely must a










model match field observations in order to be considered validated

(determined to accurately represent the system)? Leonard and Knisel

(1988) indicate that there are no standard criteria for model validation.

Since the spatial distribution of variations in soil properties is

usually unknown, it would be unreasonable to expect any model to exactly

match point measurements from a field study. Hedden (1986) reported that

participants of the Predictive Exposure Assessment Workshop sponsored by

the U. S. EPA in Atlanta, GA, on April 27-29, 1982, agreed on two

criteria for model acceptance. For screening applications of the model

(limited site-specific data and not calibrated to previous data from the

site), the model should be able to replicate measured field data within

an order of magnitude. For site-specific applications (parameters

measured on-site and with the model calibrated to the site), the model

should be able to match the field observations within a factor of two.

The screening level criteria seems to be quite reasonable when all of the

sources of error and uncertainty are considered. The site-specific

criteria, however, may be difficult to meet by even the best models using

carefully measured site-specific parameters.

The fact that models can not be expected to exactly replicate field

measured values does not mean that they have no value. Models that have

been verified (ie. their response to changes in parameters reflect what

would be expected based on knowledge of the system) can be used with some

confidence to evaluate the differences in predicted leaching of different

chemicals or, perhaps, differences in leaching of a single chemical due

to different climatic or management conditions.










3.4 Field Studies of Pesticide Transport

Field-scale studies of pesticide runoff and leaching have, until

recently, been very limited. Studies of sufficient detail to be used for

the validation and testing of transport models have been limited due to

three factors, according to Donigian and Rao (1986b):

1. Most field studies have focused on either chemical leaching or

surface runoff from watersheds; few studies have examined both.

2. Since sampling and analysis costs associated with pesticide

leaching are high, most studies are of short duration.

3. Most studies were designed for purposes other than developing

data sets for model testing and therefore are incomplete with

regard to measurement of all the required model input parameters.

In this section, four field studies are reviewed. Methods of

sampling and monitoring will be emphasized. Some of the studies were

conducted specifically for model validation and testing purposes. A

review of the Big Spring Basin study is included due to the unique

conditions in the study area.



3.4.1 Aldicarb in Florida citrus Qroves

A three-year field study of the movement and degradation of

aldicarb in both the unsaturated zone and a shallow water table was

conducted at two locations in Florida (Hornsby et al., 1983; Jones et

al., 1987). The two locations are referred to as the Oviedo site and the

Lake Hamilton site. At both locations, aldicarb was applied to bedded

citrus trees located on coarse-textured soils. Scme of the soils at the

Oviedo site had a thick organic layer overlying the coarse textured









28
subsoil. The Oviedo site is in the flatwoods area and is poorly drained.

The Lake Hamilton site was located on a sand ridge. Drainage of the

upper soil layers was very rapid. The Oviedo site encrmpassed 3.6 ha of

treated area and the other site encmpassed approximately 1.7 ha.

Aldicarb was banded along each side of the trees and incorporated to

a depth of 5 cm. At the Oviedo site, soil samples were collected in 30

cm increments to a depth of 150 cm. At the lake Hamilton site, soil

samples were collected in 30 cm increments to a depth of 60 cm, and then

in 60 am increments to a depth of 300 cman. Soil samples were collected

using a bucket auger and were transferred into plastic bags for storage

at -20C until analyzed. The soil samples were collected monthly for six

months following the first application of aldicarb in 1983. Urndisturbed

soil cores and bulk soil samples were also collected from untreated areas

at each site for laboratory determination of soil and chemical

properties.

At each site, two clusters of observation wells were installed

within the treated area, and additional well clusters were located

upgradient and downgradient of the treated area. The wells were screened

at depths of 2, 3, 5, and 6 m at the Oviedo site, and at depths ranging

from 5 to 15 m, depending on location, at the Lake Hamilton site. Water

samples were usually collected with a peristaltic pump. At least 5 well

volumes were pumped prior to collecting a sample for analysis. When

flowrates into the well were too slow to permit pumping of 5 well

volumes, the wells were evacuated and the water entering the wells was

pumped for an additional 5 minutes prior to collection of the sample.

The groundwater temperature, pH, and conductivity were measured during










sampling. Water samples were collected monthly throughout the study.

The storage conditions prior to analysis were not specified.

Results from the first 6 months of the study were reported by

Hornsby et al. (1983). Results are reported in terms of total toxic

residues (TTR), which is the sum of residues of aldicarb, aldicarb

sulfoxide, and aldicarb sulfone. Fifteen days after application, TTR

were not detected below 30 ncm at the Oviedo site, but were detected in

all replicates at the Lake Hamilton site to a depth of 120 cm. After 120

days, TMR were detected at the deepest sampling depths (150 cm at Oviedo,

and 300 can at Lake Hamilton) at both sites. No TmR were observed at the

Oviedo site in any of the observation wells during the first six months.

Two wells within the treated area at the Lake Hamilton site showed

measurable levels of TMR. In one well, TmR levels as high as 1.2 mg/L

were measured approximately 130 days after application. Data relating to

rainfall or irrigation during the study were not presented.

Jones et al. (1983) used the data franom the aldicarb study above to

evaluate three pesticide transport models. One of the models tested was

PRZM (Carsel et al., 1984). Agreement between PRZM predicted movement of

aldicarb and the observed data was good. A sensitivity analysis was

performed to demonstrate the response of FPRZM to variations in several

soil and pesticide properties. Jones et al. (1987) presented results

from the Lake Hamilton site in which water samples from the shallow water

table were collected franom a network of 174 wells over a period of three

years. These data were used to evaluate predictions from a linkage of

the PRZM model to a one- or two-dimensional saturated transport model.

The linked model was developed to allow predictions of the extent of









30
lateral transport of TIR which have entered a shallow water table. FPRZM

was used to predict daily mass loadings of aldicarb to the top of the

water table. Results of the saturated zone modeling were in reasonably

good agreement with the observed concentrations in the wells and the rate

of lateral movement.



3.4.2 COmparison of tillagce effects in Maryland

Paired watersheds were established in 1984 on the Eastern Shore of

Maryland to observe the impacts of tillage practices on agricultural

chemical leaching and runoff. The experimental design and early results

have been reported by Brinsfield et al. (1987, 1988). One of the

objectives of the experiments was to generate a data set for testing and

validation of pesticide transport models. One watershed was used to grow

corn using conventional tillage (CT) practices, and on the other

watershed corn was grown using no-till (NT) methods. The CT watershed is

approximately 6 ha in size, and the NT watershed covers approximately 8.9

ha. The soils in both watersheds are silty, well-drained, and nearly

level.

Soil samples were collected at 5 locations in each watershed 4 times

per year. The samples were collected using a hand auger in 15 cm

increments to a depth of 120 m.

Monitoring wells made of 6.35 cm diameter PVC were installed to

depths of 3-4 m on 30 m centers in each field. The wells were arranged

to divide each field into four quadrants. The wells were installed so

that only the top of the water table would be sampled. A bentonite-soil

mixture was used to seal the top 0.5 m of each well. Well samples were










collected monthly. The wells were pumped for 5 minutes prior to

collecting a sample for analysis.

To monitor surface runoff, H-flumes with automatic samplers and

recorders were installed in the outlets of the watersheds. All samples

were frozen for transport and storage prior to analysis. Details of the

analytical procedures used for pesticide residue analysis are presented

by Brinsfield et al. (1987).

Gravity-fed lysimeters were installed horizontally through the walls

of a reinforced pit to collect samples of percolating water. The

lysimeters were installed at a depth of 1 m below the soil surface.

Three lysimeters were installed in each pit. These lysimeters collect

samples of saturated flow percolating through the root zone. Samples

were collected from these lysimeters after each rainfall event.

Herbicide treatments were the same for both watersheds. Atrazine,

metolachlor, and simazine were applied pre-emergence at the rate of 1.68

kg AI (active ingredient)/ha. Cyanazine was applied at 2.24 kg AI/ha,

and carbofuran at 1.12 kg AI/ha, both pre-emergence. Dicamba was

applied post-emergence at a rate of 0.55 kg AI/ha.

leachate collected in the lysimeters during January, 1985, had

atrazine concentrations as high as 2 Vg/L. After planting in 1985, no

leachate samples were collected in the CT watershed until September.

Atrazine and metolachlor concentrations in those samples ranged from 1-2

gg/L. Simazine was detected in only one sample, and cyanazine was not

detected. In the NT watershed, samples collected in July had

concentrations of atrazine at 8-10 &g/L, simazine at 7-10 jg/L, cyanazine

at less than 1 jig/L, and metolachlor at less than 2 jg/L. Leachate from









32
the NT watershed in the fall of 1985 had similar concentrations to those

collected from the CT watershed.

Groundwater samples collected 15 days after application in 1984

showed levels of atrazine, simazine, and cyanazine exceeding 1 Jg/L. The

concentration of atrazine in one well reached 7 A/L on this date.

During late summer and fall, atrazine was the only pesticide detected in

the wells. Pesticide concentrations in the groundwater were similar for

the two watersheds, although the concentrations were somewhat higher

during the growing season in the NT watershed. In April, 1986, (prior to

application) atrazine concentrations of 4 mg/L were observed in the water

table on both watersheds. This indicates that atrazine may be persistent

in the lower soil zones and in the water table.



3.4.3 Aldicarb movement in the Dougherty Plain of Georgia

The U. S. Environental Protection Agency and the U. S. Geological

Survey began a study in 1982 to observe the movement of aldicarb and its

residues in the unsaturated and saturated zones at a field site in Lee

County, in southwestern Georgia. Aldicarb is used as an insecticide and

nematicide on peanuts grown on the site. One of the primary objectives

was to develop an extensive database of soil, land use, geologic, and

pesticide data for use in the validation and testing of the PRZM (Carsel

et al., 1984) model. Different aspects of this study have been reported

by a number of investigators (e.g. Cooper, 1986; Rao et al., 1986; Hook,

1987; Hedden, 1986; Smith and Carsel, 1986).

The study site covers an area of approximately 4.5 ha. Four soils

are mapped in the field. All have a fine-loamy texture. Below a depth








33

of approximately 1 m there are layers of clay separating zones of coarser

sand and gravel. The conductivity of the clay layers is very low, which

suggests that perched water tables may form on these layers and cause

lateral transport of leached pesticides. There is a shallow water table

at the site which can vary in position by as much a 6 m during a year.

Soil samples were collected from the site to characterize soil

properties and pesticide sorption and decay characteristics. Geologic

information was collected from holes bored to the top of the Ocala

Limestone. The residuum was approximately 13 m thick. The Ocala

Limestone is a part of the Floridan aquifer system. Four wells were

cased into the Floridan aquifer for measurement of water levels.

A weather station was installed at the site to collect meteorological

data.

Statistical procedures were used to determine that 20 monitoring

sites would accurately reflect the fate and transport of aldicarb at this

site. The locations of the monitoring sites were randomly selected and

distributed between the three major soil series based upon the relative

area in each series.

Each monitoring site was equipped with an impressive array of

instruments for monitoring of conditions anid collecting samples. Five

tensiometers were installed to a depth of 150 ancm for monitoring soil

water content. The tensicmeters were monitored and serviced three times

per week. Five thermistors were installed to a depth of 115 cm for

measurement of soil temperatures. The thermistors were monitored three

times per week during the middle of the afternoon in order to estimate

maximum soil temperatures. Three soil solution samplers were installed









34
at depths of 1.5, 2.1, and 2.7 m. The samplers were made using stainless

steel for the body, a high-flow ceramic cup, and teflon tubing for sample

collection and applying the vacuum. A silica flour slurry was poured

around each sampler to insure good contact with the surrounding soil.

The vacuum and sample lines were buried to avoid interference during

field operations. Vacuum was applied to the samplers for a period of 24

hours prior to collection of the sample.

At 15 of the monitoring sites, permanent stainless steel monitoring

wells were drilled to a depth of approximately 4.6 m. A stainless steel

screen 0.6 m in length was placed at the bottom of each well. A gravel

pack was placed around the screen and a cement grout was placed from the

gravel to the top of the finished well. The wells were finished so that

the top of the well was approximately 0.6 m below the soil surface so as

not to interfere with cropping operations. After field operations are

complete, the wells are extended above the soil surface. Water samples

are collected using a dedicated teflon tube (one per well) and a

peristaltic pump. The 250 mL glass sample containers were connected in

line between the well and the pump to prevent cross contamination. The

wells were typically pumped for 5-7 minutes prior to collecting a sample.

Soil samples were collected using a hand auger in 15 cm incremnts

to a depth of 120 ancm. After each sample was collected, gravel and plant

material was removed and then the sample was sealed in a metal container.

The references which were obtained for this review did not report

any results of pesticide monitoring from the field site. The detailed

monitoring network and experimental design used at this site should

produce a valuable data set for model validation and testing.












3.4.4 Big Sprind Basin in Iowa.

The Big Spring Basin is a 267 km2 groundwater basin located in

northeastern Iowa. The basin is entirely agricultural with

approximately 60% of the land area in row crops. The basin has been

extensively monitored and characterized, and results from these studies

have been presented by a number of investigators (Libra et al., 1986;

Kelley et al., 1986; Hallberg, 1986; Libra et al., 1987). Over 85% of

the basin's groundwater is discharged through Big Spring. Thus,

investigators have an easily accessible point for monitoring average

groundwater quality in the basin as affected by agricultural activities.

Big Spring is a karst spring with an estimated 10% of flow contributed by

directed recharge through sinkholes. By using hydrograph separation

techniques, the investigators can determine the water quality effects of

both normal infiltration and direct recharge through the sinkholes.

Four years of monitoring have shown flow-weighted mean

concentrations of NO3-N ranging from 7-11 mg/L. Combined losses of N03-N

in both surface water and groundwater amount to 33-55% of the average

annual fertilizer nitrogen applied in the basin. Historic records

indicate that the magnitude of N03-N concentrations have increased by

200-300% over the last 20 years. This corresponds to a 200-300% increase

in the application of nitrogen fertilizers during this period.

The herbicide atrazine is the only pesticide that is always present

in the discharge from Big Spring. Flow-weighted mean concentrations of

atrazine are less than 1 g/L, but the average concentrations have

steadily increased during four years of monitoring. Total losses of










atrazine in the groundwater amount to less than 0.1% of the annual

application. Pesticide concentrations in surface waters are generally an

order of magnitude higher than observed in the discharge from Big Spring.

Peak concentrations of pesticides occur following recharge events in the

spring after crops have been planted.



3.5 Sumr

There is a growing body of evidence that pesticide residues are

being leached to groundwater in some areas of the country. The U. S. EPA

recognizes that at least 19 pesticides have been found in groundwater in

24 states as a result of agricultural practices (U. S. EPA, 1987a). Many

of the pesticide detections in groundwater have been from the use of

nematicides, which in general need to be very soluble in order to be

effective. The other major class of compounds found in groundwater from

agricultural practices is herbicides. The herbicides atrazine and

alachlor are used over large areas in many regions of the country, and

have been frequently detected in groundwater. Nitrates from

agriculturally applied fertilizers have also been found in groundwater.

The health risks associated with the low concentrations (typically less

than 10 pg/L) of pesticides in drinking water are still unknown. There

are documented health risks, however, to infants drinking water with high

levels of nitrates. The agricultural production systems in this country

will continue to require large irputs of chemical fertilizers and

pesticides. Scientists and governmental regulators must identify ways to

protect groundwater supplies from contamination while allowing farmnners to

use the chemical inputs required to maintain yields. Simulation models











will play an important role in evaluating the contamination potential

from applications of pesticides and fertilizers and assess the benefits

of modifications to current agricultural management practices

(application methods, timing, chemical formulations, tillage practices,

etc.).

iThere are many factors which influence the mobility and persistence

of pesticides in the environment. Soil physical properties, chemical

properties, crop characteristics, environmental factors, and the

interactions among them determine the fate of agriculturally applied

chemicals. Many of the processes involved in the transport and

degradation of pesticides in the environment are poorly understood, and

the mathematical equations used to represent these processes are

simplifications based on experimental observations. The soil properties

and environmental factors which strongly influence the fate of a chemical

can vary significantly within small areas.

Methods to predict the fate of agriculturally applied chemicals

range from simple indices to complex mathematical research models.

Models which can represent the effects of agricultural management

practices on the transport of chemicals are being used by governmental

regulators as part of the chemical registration process. Most of the

management models developed to date are designed to represent the

processes on the soil and plant surfaces, within the crop root zone and

possibly the unsaturated zone between the root zone and the water table.

New model developments will incorporate groundwater transport models to

assess how far and at what rate a chemical that leaches into groundwater

will move. There are many sources of uncertainty and error associated










with model predictions. Thus models should not be expected to provide

exact predictions of transport and fate within a field. There have been

a limited number of studies however, which provide data of sufficient

detail to assess how well the models do represent average transport

within a field. In order to gain confidence in the use of models for

pesticide fate and transport predictions, many new studies in various

regions throughout the country are needed. To be of real value in model

testing and validation, these studies should last for several years. For

models which require calibration, a year or more of data may be needed to

calibrate the models and then several additional years of data from that

site will be needed to test the predictions of the calibrated models.














CHAPTER 4
EXPERIMENTAL METHODS

4.1 Field Site Description

The study site is located near Tifton, Georgia, in the coastal plain

physiographic region of the southeastern U.S. The geology of this area

has been described by Asmussen et al. (1986). The site is part of a farm

rented by the University of Georgia Coastal Plain Experiment Station

which is referred to as Gopher Ridge in honor of the many gopher

tortoises which make their home on the edges of the fields and in the

woods nearby.

The study site covers 0.7 ha in the northwest corner of a 2.3-ha

field. The field was developed for irrigated agricultural research.

Irrigation sprinkler risers are located on a 12 by 12 m grid. Supply

lines for irrigation water are buried approximately 1 m below the soil

surface. The field was removed from active crop research in 1982. At

that time the field was deep tilled in both north-south and east-west

directions to remove possible residual tillage effects. A permanent

bahia grass cover was established and fertilized and irrigated as needed.

No pesticides were applied after the grass cover was established. The

field was maintained in this condition until the spring of 1986 when

instrumentation for this research was installed. The site is bounded on

the north and west by trees. Just beyond the tree line on the western

edge of the field, the surface elevation drops rapidly into a seepage









40
area where subsurface flows reemerge as surface water that flows to the

Little River.

The soil on the study site is classified as a Lakeland sand (Typic

Quartzipsamrnents, thermic, coated). The soil profile depth on this site

ranges from 1.9 to 4.4 meters, Underlying the soil is a restricting

layer consisting of tight clays. This restricting layer is the top of

the Hawthorne formation which forms the confining layer over much of the

aquifer system (Floridan) of southern Georgia and Florida. The presence

of this restricting layer causes percolating rainfall or irrigation to

saturate the soil above the layer and form a transient water table.

Previous work by Asmussen et al. (1986) had shown, through the use

of ground penetrating radar (GPR), that the restricting layer below the

study site would cause saturated flow above it to converge into channels.

The presence of these channels made the site look promising for saturated

zone monitoring because samples from wells located in these channels

would be very likely to show the presence of any chemicals which were

moving with the saturated flow. The maps drawn from the (GPR)

descriptions also indicated that the slope of the restricting layer near

the study area boundaries was generally away from the study area. This

would minimize influences of saturated flows coming into the site from

other areas.

Figure 4.1 shows the elevations of both the soil surface and

restricting layer for the 0.7-ha study site. The elevations given are

relative to a local bench mark which was assigned an elevation of 30.48

m. The soil surface has a relatively uniform slope of approximately 4%

towards the west. The restricting layer shows a more complicated









Soil Surface


Elevation


3 up- ', -1, I I
0
/_, ,, o/ ,/ i l
07-09 07-10 07-1 0 7-1 07-1 /
ff 0 0 / 0 0, /
0 D 0. 0010 /(D ) G III
17 o / I j /
I8-07 0-09 05- 0 OB-11/ 08-11 06-13 0 /
I I
_oi 0 0,' 0 9-
S37 2 ( p D (/
*/ <5ii/ 'i
U/
Z 09-0o9 09 10 01, 920,3 91 / /
Q j10-8 1 10 10-111 10 12 1?-13 10414
g 0 O P 0
0I \
57 -1-07 "- \ "- /
0 a 0 G I
70 90 110 130 150 170 190
Distance (m)


Restricting


Layer Elevation


(m)


90 110 130 150 170 190


Distance


(m)


Figure 4.1. Contour maps of soil surface and restricting layer
showing locations and ID labels of monitoring wells.


(m)


-3


'17
C3

-37


57









42

topography. Slopes of the restricting layer range from 1 to 15 percent,

with the general direction of the slope also being towards the west. The

fact that the soil is a coarse sand also was considered to be

advantageous for monitoring pesticide movement. The high hydraulic

conductivities and low clay and organic matter contents should result in

increased leaching and movement of applied chemicals as compared with

tighter soils. Another advantage of this site was the availability of

irrigation which could be used to supplement the natural rainfall if

required to maintain a dynamic flow regime.

This study was not intended to represent typical agricultural

chemical usage and practices in the coastal plain of Georgia. The

primary objective was to observe chemical movement within the unsaturated

soil and in a shallow water table aquifer (shallow groundwater). The

properties of the soil and restricting layer described above were suited

for this objective.



4.2 Site Instrumentation

The movement of the applied chemicals was monitored using a

combination of soil samples, soil-water (soil solution) samples from the

unsaturated zone, and saturated zone samples. These samples were

collected using augers, soil solution samplers, and monitoring wells,

respectively. Soil sample collection required no permanent

instrumentation, and the collection procedure will be described in a

later section.

Samples of the water from unsaturated soil were collected using

soil solution samplers. An excellent review of soil solution samplers is









43
presented by Litaor (1988). Smith and Carsel (1988) presented a design

for a stainless steel solution sampler that do not react with pesticides

during sampling. Samplers constructed of stainless steel were not

economically feasible in this research.

The samplers used in this study were constructed of 4.2 cman OD,

schedule 40 PVC and a 1 bar, high flow ceramic cup, 3.99 can diameter by

19.05 cm long attached to the PVC pipe with epoxy. A 0.64 cm

polypropylene tube was extended from the inside bottanom of the ceramic cup

to a bulkhead fitting on the top of the sampler. A second fitting was

placed on the side of sampler, near the top, for attaching a vacuum line.

The samplers were constructed in two lengths, 1.08 and 2.09 m. The

polypropylene tubing used in the samplers was not tested for adsorption

of atrazine and alachlor. This was not done since the solution samplers

were to be used for collection of samples to be analyzed for inorganic

chemicals such as bromide and nitrate. The volumes of samples collected

from the solution samples were in general too small (less than 50 mL) to

permit reliable quantification of pesticide residues.

The samplers were placed such that the center of the ceramic cup

was located at depths of 61, 122, and 183 cm. The samplers were

installed by augering a 5 cm hole to a depth that was approximately 10

ancm less than the desired sampling depth. A piece of thin wall aluminum

tubing with ID slightly smaller than the ceramic cup was then driven 20

cm beyond the augered hole, removing a core of soil. The sampler was

pushed into the hole until seated on the bottan. A slurry of water and

soil taken from a borrow pit adjacent to the site was then poured into

the amnnulus between the PVC and soil. The top 12 cm of the hole was










widened to approximately 10 an in diameter and was filled with a

bentonite slurry to form a seal around the sampler and prevent direct

flow from the surface down the side of the PVC pipe to the ceramic cup.

Figure 4.1 shows the location of the application area, which is the

strip to which the herbicides and bromide tracer ware applied. The

samplers and three monitoring wells (08-09, 09-09, and 10-09) are within

this area. The samplers were installed in groups of three at a distance

of 3.05 m on either side of each well along a north-south axis. The

samplers were assigned identification labels that indicated their

position with respect to the wells and the depth to which they were

installed, eg. the group of samplers located 3.05 m south of well 09-09

were assigned labels of 09S-2, 09S-4, and 09S-6 for depths of 61, 122,

and 183 an (2, 4, and 6 ft), respectively. A cross-section of the

application area showing the locations of solution samplers and

monitoring wells is shown in Figure 4.2.

A small instrument trailer was located approximately 4 m east of the

application area. A vacuum pump located in the trailer was used to

apply a vacuum to all samplers simultaneously through a manifold. The

pump was capable of creating a vacuum of approximately 28 cm of Hg. A

0.64 an diameter polypropylene sample tube was connected to the sample

fitting on each sampler and routed back to the trailer where it was

connected to a 50 mL polycarbonate sample container. All sample tubing

was enclosed in a protective PVC conduit which was located 1.5 m from the

samplers to avoid interference with chemical application in the vicinity

of the samplers. Between the samplers and the PVC conduit, the stiff

polypropylene vacuum and sample lines were fastened together and held











<- 36.6 M

-> 3 r, 1<2- M ., >< 12.2 --- > 3
0. n Inn
0.6 m
I-

1-t~
0.6 C

0.6 - SOLUTION
0.6 M T SAMPLERS %3
_,k_ .CR -. ,2 M

'-'^ ^ CELL ^^^ '^^\

__ RESTRICTING LAYER


Figure 4.2. Cross-section of soil profile through application area
showing locations of monitoring wells and soil solution
samplers.



about 0.6 m above the soil surface to minimize interference around the

samplers. When it was time to collect the samples from the samplers,

the vacuum was switched to the sample collection side and the vacuum side

was opened to atmospheric pressure. Any water that had accumulated in

the samplers would then be pulled into the sample containers.

Monitoring wells for collection of samples frcm the saturated zone

and measurement of the water table elevation were installed during May

of 1986. The wells were constructed of 6.3 cm diameter, schedule 40 PVC

and were slotted using a veneer blade on a radial arm saw. Three rows of

slots were cut along the circumference of the well. Each slot was

approximately one-sixth as long as the pipe circumference. The slots

were spaced approximately 2.5 cm apart over the bottComn 1.22 m of the

well. Although several manufacturers offer slotted PVC well screens,










these were beyond the budgetary constraints of this project. Scmne

existing wells, installed by the USDA-ARS Southeast Watershed Research

Laboratory in conjunction with the GFPR mapping by Asmussen et al. (1986)

were also used. Conversations with the technicians involved with the

installation revealed that the wells were constructed of the same

materials as described above but were perforated with tiny drill holes

instead of being slotted. They were unable to recall the diameter of the

holes or the extent of the perforation above the bottom of the well.

Appendix A lists statistics associated with each well and indicates which

wells were installed by USDA.

The wells were installed by hand augering a 10 an diameter hole

down to the top of the restricting layer. The restricting layer was

identified by a sudden change from yellcowish-white sand to red clay

mixed with small rocks. The change was very abnrupt and the effort

required to auger through the clay was significantly greater than to

auger through the coarse sand. A PVC end cap was slipped onto the end

of the well and held in place by friction during installation of the

well. Fine gravel was placed around the wells in the zone of the slots

in an attempt to minimize sand migration into the wells through the

relatively coarse slots. Soil from a nearby borrow pit was used to fill

the hole around the well to within 12 cm of the surface. Ten centimeters

of a water and bentonite slurry was then added to form a seal around the

well. The final 2 cm was filled with soil. The USDA augered down to

the restricting layer with a hollow stemn auger on a small drill rig.

The well was slipped into the center of the auger and the auger was










withdrawn. No details on backfill procedures used by the USDA are

available.

The wells were continuous from the restricting layer to a height of

approximately 50 an above the soil surface. No solvent weld joints or

connections were used. Each well was fitted with a 0.64 ncm diameter

polypropylene tube which extended from approximately 2.5 an above the

bottom of the well through a #2 rubber stopper placed in a vented PVC

cap. The polypropylene tubes were not tested for adsorption of atrazine

and alachlor. The caps were vented by milling small channels into

opposite sides of the inside of the cap. This was done to prevent a

vacuum from forming in the well during sampling.

The wells were installed on a 12 by 12 m grid corresponding to the

locations of existing sprinkler risers. The wells were offset by

approximately 50 cm to the west of the sprinkler risers to avoid hitting

supply lines beneath the soil surface. In the application area the

wells and solution samplers were offset from the sprinkler risers by

about 3.05 m so that the risers and supply lines would not interfere

with the downward movement of chemicals within the vicinity of the wells

and samplers. The monitoring wells were assigned ID labels to indicate

their position relative to the sprinkler riser in the SE corner of the

2.7 ha field on which the experimental site is located. Figure 4.1 shows

the locations of the wells and the associated ID labels. Well 08-15 is

located at the edge of the tree line bordering this side of the study

site. One well is located in the woods approximately 30 m west northwest

of well 08-15. This area is a seepage zone where subsurface flows











reemerge as surface water and flow to the Little River. This well is

referred to as "I1'".

Additional instrumentation installed at the site consists of a

weighing rain gage which was located near well 09-12, a water table

recorder which was located near well 08-12, tensicneters for measuring

the water content of the unsaturated soil, and a "deep" well located

between wells 08-13 and 09-13.

The rain gage is a US Weather Bureau standard weighing bucket rain

gage with a seven day chart and 30.5 ancm capacity. The rain gage was

calibrated using standard calibration weights after installation.

The water table recorder was installed to provide a continuous

record of the water table elevation to supplement the weekly

measurements taken on all wells. The recorder was equipped with a seven

day clock and a drum type chart recorder.

Eight tensicmeters were installed around each set of solution

samplers. The tensicmeters were water filled and connected with very

small tubing to a mercury manometer board. The tensicmeters were

installed at depths of 30, 60, 90, 110, 122, 140, and 183 cmn. There were

two tensiameters located at the 60 ancm depth. The tubing connecting the

tensiumeters to the manometer boards was routed above the soil surface

with the vacuum and sample lines as noted above.

The "deep" well was installed by drilling a 10 ancm diameter hole

inside of a 11.4 ancm diameter PVC casing. The casing was pushed down as

the auger advanced. The casing was installed in this manner using 1.5 m

sections to a depth of approximately 12.2 m below soil surface. The

last 3 m of the well was located in a saturated formation that liquified









49
during drilling. The observation well was made of a 1.5 m section of

6.3 cm diameter commercial PVC well screening solvent-welded to two 6.1 m

sections of schedule 40 PVC. This was jetted down inside of the casing

until it bottomed cut at the bottom of the previously augered hole.

After placement of the well, a mixture of bentonite and water was poured

into the area between the casing and well up to the top of the casing.

The purpose of this well was to observe the piezcmetric head difference

across the restricting layer.



4.3 Chemical Applications

The first application of chemicals to the experimental site

occurred on 11/12/86. On the day preceding this first application, the

entire field site was rotary mowed. The grass within the application

area was cut to a height of approximately 5 cm. All dead grass and

clippings were raked up and removed from the area prior to application.

The application area consists of a strip 36.6 m long by 9.14 m wide as

shown in Figure 4.1.

Atrazine and alachlor were applied simultaneously on November 12.

Atrazine was applied in the form of AAtrex (Ciba-Geigy Corp.) which is

an emulsifiable concentrate containing 0.479 kg AI (active ingredient)/L

(4 lb/gal). Alachlor was applied in the form of Lasso (Monsanto

Agricultural Products Co.) which is also an emulsifiable concentrate

containing 0.479 kg AI/L. The herbicides were applied using a self-

propelled born sprayer with provisions for injecting chemicals directly

into the water stream (Sumner et al., 1987). The boom length on the

sprayer is 9.14 m.









50

The intended application rate of the herbicides was 4.5 kg AI/ha (4

lb/ac). One liter of herbicide solution was prepared by mixing 313 mL of

each of the chemicals with 374 mL of deionized water. The sprayer was

operated at a velocity of 3.05 m/min moving from south to north along the

edge of the application area. The chemical solution was injected into

the water stream of the sprayer using an injection pump calibrated to

deliver 83 ml/min. The water pressure at the inlet of the sprayer was

207 kPa. The pressure at the spray nozzles was maintained at 138 kPa.

Despite the calibration of the injection pump and measurement of sprayer

ground speed, the chemical solution ran out as the sprayer reached well

10-09 which is 6.1 m short of the intended end of the application area.

The application of the herbicides was considered complete at this point,

and the remaining portion of the application area was left untreated.

Fourteen 0.35 1 plastic cups were randomly placed within the

application area. The cups were supported upright in holders at a

height of approximately 35 cm above the soil surface. The cups were

used to collect samples of the application solution for analysis of

chemical concentrations, determination of the depth of water applied to

the area during application, and uniformity of chemical and water

application. The use of these cups and holders has been tested and

found to accurately reflect water application amounts (Stansell et al.,

1982).

Immediately after completing the application of the herbicides, the

sprinkler irrigation system was started and 5.1 cm of water was applied

over the entire study site. This irrigation was intended to wash the









51
herbicides off of the grass foliage and move them into the soil profile

to minimize volatilization losses from the soil and plant surfaces.

Bromide in the form of potassium brmanide (KBr) solution was applied

to the application area using the same sprayer on 11/17/86. There was a

light drizzle of rain during application. Five hundred grams of KBr was

dissolved in 1050 mL of deionized water and injected into the sprayer as

described above. This was equivalent to an application rate of 10 kg/ha

of bromide. The sprayer again was operated at a nozzle pressure of 138

kPa, and a ground speed of 3.05 m/min. Fourteen collectors in the same

locations as for the herbicide application were used to collect

application samples.

No further chemical applications were made prior to fertilization

of the grass on 4/16/87. The fertilizer was applied at a rate of 560

kg/ha of 5-10-15 using a tractor-nounted broadcast spreader. Fifteen

percent of the nitrogen in the fertilizer was in the form of nitrate

nitrogen (NO3-N). This is equivalent to applying 18.6 kg/ha of nitrate

(NO3).

In an effort to further characterize bromide movement within the

soil profile and determine flow velocities within the grmrdwater, an

additional bromide application and a separate chloride application were

made. On 4/27/87, KBr was applied to the application area using the

sprayer utilized during the previous application. The application

solution was made by dissolving 1.2 kg of KBr in 2.3 1 of deionized

water. The sprayer made two passes across the application area during

which it applied approximately 1.6 1 of the KBr solution. This was

equivalent to an application rate of 17 kg/ha of bromide. Fifteen









52
plastic cups were used to catch application solution samples in the same

manner as previously described. Immediately following application the

field was irrigated. Irrigations continued daily for a week in order to

raise the water table and provide plenty of infiltrating water to

transport the bromide through the soil.

Prior to the beginning of irrigations on April 27, most of the wells

on the site were dry. The water table began to rise on May 1 at which

time 10 1 of solution containing 500 g of potassium chloride (KCI) was

poured directly into well 07-09 which is near the top of the site. It

was anticipated that the high concentration of chloride in the solution

could be followed downslope with the saturated flow and allow for

determination of water table flow velocities.



4.4 Sample Collection and Storage

Samples were collected weekly (Monday) throughout the study. There

were occasional periods of more frequent sampling immediatelyy after

initial applications and during the last week of April through the

middle of May).

The flint glass containers used for well sample collection and

storage throughout this study did not have teflon lined caps. Therefore,

a small piece of parafilm (a common laboratory film used for sealing

beakers and containers to prevent evaporation and contamination) was

stretched over the mouth of the bottle prior to screwing on the caps.

Samples of the application solutions of the herbicides and bromide

were transferred to glass containers and placed in a cooler with ice









53
packs immediately after the sprayer had passed completely by the sample

collector.

Soil samples were collected in one of two ways: taking 2.5 ncm

diameter by 5 cm long cores using a soil sampling probe, or using a 5 cmn

diameter stainless steel bucket auger. The soil sampling probe was

generally used for collection of samples from the top 5 cm of the soil

profile. When this method was used, 5 to 8 samples would be collected

franom a small area and composited into a plastic sample bag. This would

represent one final sample for the soil surface at that location. For

deeper samples the auger was used. The auger was used to remove soil to

within approximately 5 cm of the desired sample depth. The auger was

then cleaned to remove traces of soil, and a sample was collected which

represented soil from a depth of 5 cm above to 5 cm below the target

depth. The sample was carefully poured from the top of bucket into a

plastic sample bag and sealed. The number of locations within the

application area sampled depended on the number of depths to be sampled

and ranged from 5 to 14. After collection, the soil samples were placed

into coolers with ice packs for transport to Gainesville. Soil samples

franom outside of the application area were collected several times during

the study to use as blanks and spikes during soil residue analysis.

Vacuum was applied to the solution samplers immediately upon

arrival at the study site (7:00 8:00 am). The vacuum would typically

be left on for 8 to 10 hours prior to collecting the soil solution

samples. The vacuum applied to the samplers was approximately 28 cm of

Hg. Near the end of the day the vacuum would be released and the samples

would be drawn directly into individual containers. The containers are










made of polycarbonate with a capacity of 50 mL. The containers had

polycarbonate screw caps for sealing. Immediately after all soil

solution samples were collected, the sample containers were placed in a

cooler for transport.

The elevation of the water table was measured using a well depth

indicator with a stainless steel probe. The depth indicator would sound

an audible alarm when the probe contacted water in the wells. The probe

was attached to a flat cable with markings in feet and inches, with the

smallest division being 1/4 in. Water depths were recorded early in the

morning after arrival.

After the water table elevations in all wells were measured, the

wells were pumped out to insure that the water to be sampled was

representative of the surrounding water in the saturated zone at that

time. When possible, the well was pumped dry. When the depth of water

in the well exceeded approximately 0.4 to 0.5 m, the pumping rate was

less than the rate of flow into the well. When this occurred, the well

was pumped long enough to insure that 2-3 well volumes had been pulled

from the well. A small, 12v battery powered, peristaltic pump was used

to pump the wells and collect samples.

Samples were collected after all wells had been pumped as described

above. A rubber stopper was attached to the intake side of the pump

through a 1 m length of 1.25 cm OD Tygon tubing. The stopper also had a

piece of Tygon tubing approximately 10 cm in length connected to it for

attachment to the sampling tube in each well. The stopper was placed in

a sample bottle and the short piece of tygon tubing was attached to the

well sample tube. Since the pump was downstream from the sample con-










trainer, the only components common to each sample collection were the

rubber stopper and the short piece of Tygon tubing. When a sample was

collected, the initial water from the well was swirled around the bottle,

and the bottle was then inverted to remove this rinse water. This served

to rinse the Tygon tubing, stopper, and bottle. Thus, cross

contamination between wells was minimized. The sample bottles used for

the collection and storage of well samples had a capacity of 250 mL.

Well samples were placed in coolers with ice packs for storage until they

could be refrigerated.

All soil samples were frozen as soon as possible after collection

and maintained at -18C until preparation for analysis. All water

samples were refrigerated after collection and maintained at 4C until

prepared for analysis.



4.5 Sample Analysis

4.5.1 Inorganic tracer analysis

The concentrations of the three inorganic chemicals of interest

which were applied during this study were quantified using ion

chromatography. A DIONEX QIC ion chrcmatograph (IC) was used for all

analyses.

A DICNEX AS4A anion separation column was used with an AG4A guard

column in place to remove organic contaminants prior to reaching the

separation column. The IC was equipped with a conductivity detector

connected to an external integrator/recorder. The IC was also equipped

with an anion micromembrane suppressor to reduce the background









56
conductivity of the eluant and thus allow for greater sensitivity to the

conductivity changes caused by the presence of anions in the sample.

There was no automatic sampling device with this instrument, so

each sample was injected by hand. Approximately 1 mIL of sample was

loaded onto a sample loop prior to injection onto the column. The

sample loop retained approximately 0.25 mL of the sample with the rest

being discarded to a waste line. Between injections, the sample loop

was flushed with 1 mL of deionized (DI) water. Prior to the guard

column, the sample was passed through two filters rated at 30 and 5

microns, respectively, to remove solids from the sample. These filters

were an integral part of the IC and the filter elements were changed

whenever the system pressure was observed to be increasing above nominal

levels.

The eluant used in these analyses was 2.2 millimolar (mM) sodium

carbonate (0.933 g Na2CO3 in 4 1 DI H20) and 0.75 mM sodium bicarbonate

(0.25 g NaHCD3 in the same 4 1 of DI water). This solution was made

fresh as needed. All references herein to deionized (DI) water refer to

water that had been passed through carbon filters and ion exchange resins

to produce DI water with a resistivity of at least 1 megohm. The

regenerant solution was 0.025 N sulfuric acid (2.8 mL of concentrated

H2SO4 in 4 1 of DI water). The regenerant solution was passed through

the micromembrane suppressor to reduce background conductivity.

In normal operation the eluant flowrate was approximately 2.0-2.5

mL/min, and regenerant flowrate was approximately 4 mL/min. The

pressure at the discharge of the pump at the stated eluant flowrate was

approximately 8,274 kPa (1200 psi).








57
A single injection could reveal the presence and concentrations (if

standards were prepared) of the following anions (in order of increasing

retention times): fluoride (F-), chloride (Cl-), bromide (Br), nitrate

(NO3-), phoshate (P043"), and sulfate (SO42-).

In ion chrImatograpy, the retention times of the various anions are

dependent upon total ionic strength of the sample, ionic strength of the

individual ions, eluant flowrate, and temperature of the sample and

eluant. Thus, the retention times could, and did, vary. When the

integrator was set to calculate the areas under the peaks, it was found

that the automatic selection of the beginning and ending points of peaks

was not consistent. A small change in retention time could

significantly alter the reported area for a given ion even when using

duplicate injections. Better results were achieved by setting the

integrator to report maximum peak height. This value was not sensitive

to variations in the starting and stopping times of a peak as long the

baseline was relatively stable.

Initial injections of water table samples from the experimental
site revealed the presence Cl- and S042- in all samples, and relatively

high concentrations of N03- in samples from well IOW. Standards were

prepared using DI H20 and oven-dried quantities of certified or primary

standard grades of potassium chloride, potassium bromide, potassium

nitrate, and potassium sulfate. The standards initially included Cl-

and S042- because of their presence in the samples. They were not used

to quantify the concentrations of either species during initial sample

analysis. When C- was introduced into the water table as a tracer late

in the experiment, sample concentrations of Cl- were quantified. It was









58
observed that the response of the chromatogram to Cl- was approximately

twice the response to the other anions of interest. Thus, standards were

prepared with Cl- at half of the concentrations of the other species so

that the responses of all species would be similar and on scale

simultaneously. Standards were prepared with concentrations of the main

species of interest ranging frcum 0.01 to 100.0 mg/L. A typical standard

would contain 0.5 mg/L Cl- and 1.0 mg/L of Br, N03-, and SO42-. It

should be noted here that all references to nitrate (N03-) indicate

concentrations of N03- and not N03-N (nitrate-nitrogen).

Samples were taken from the refrigerator and allowed to cme to

room temperature prior to injection. No sample preparation was

performed. The analysis protocol typically followed the pattern of 3

standards, 10 samples, 3 standards, etc. The standards used were

selected based upon the concentrations of the analytes being observed in

the samples. Thus, standards were selected to bracket the observed

values as closely as possible. Occasionally a sample would be reinjected

to observe the repeatability of the analysis procedures. Samples from

wells located away from the experimental site, including the deep well

which supplies irrigation water to the farm, were regularly analyzed as

field blanks (no Br- would be expected; however, C-, N03-, and S042-

could be present).

A piecewise linear fit to each sequence of injected standards was

used to generate a function of the form: concentration = f(peak height).

The piecewise fit was selected instead of linear regression because for

some species (particularly high concentrations of Cl-) a linear

regression resulted in a negative y-axis intercept. This implies that










the species can generate a noticeable positive response and yet be

calculated to have a negative concentration. It is expected that a

positive intercept would be a normal result, i.e. there is sane threshold

concentration of the species required before the detector indicates a

response. The piecewise fit insured that any positive response would

result in a positive calculated concentration. Conversations with an

organic chemist at the Pesticide Residue Laboratory of the USDA-ARS

Southeast Watershed Research Laboratory indicated that the piecewise fit

would be an acceptable method of generating a standard curve (Marti,

1987).

The standard curves generated on either end of the analysis of 10

samples were used to compute the concentrations of the species in the

samples. The resulting concentrations were then linearly weighted based

on sample position with respect to either set of standards, i.e. the

first sample following the standards would be weighted towards the curve

generated by the preceding standards and only slightly effected by the

standard curve generated 9 samples later. The sample midway between

standards would be evenly weighted between the two standard curves. In

general, there was little drift in the instrument and the standard

curves did not vary much over a period of hours.



4.5.2 Herbicide residue extraction

Analysis of samples for residues of atrazine and alachlor required

different extraction procedures for soil and water samples. Once the

extractions were completed, all samples were analyzed on a ccnmmon gas

chrcnatograph with identical operating conditions.










There are many published methods for preparing samples for

determination of atrazine and alachlor residues (e.g., Rdhde et al.,

1981; Voznakova and Tatar, 1983). In general, the procedures are rather

detailed and costly in terms of time and supplies. Dr. Willis Wheeler,

Director of the Pesticide Residue Laboratory in the Food Science and

Human Nutrition Department at the University of Florida, Gainesville,

indicated that a simpler method of sample preparation might be developed

which would be satisfactory in terms of the recovery of the pesticide of

interest while being more economical in both the time required for sample

preparation and expense (Wheeler, 1987). The more steps involved in the

extraction and preparation of a sample, the more likely it is that

something can go wrong and the residues lost. With these factors in

mind, methods for extraction of the two herbicides from soil and water

samples were developed which were in keeping with the budgetary

constraints of the project and the time constraints on the analyst.

Extraction of the herbicide residues from water samples was done

using a liquid-liquid extraction procedure. One hundred fifty

milliliters of the sample water were placed in a quart mason jar along

with 150 mL of pesticide-grade hexane. The jar was sealed with a

standard mason jar lid which had been lined with a self-adhesive teflon

film. The jar was agitated on a shaker table for thirty minutes after

which the contents of the jar were poured into a labeled 500 mL

separatory funnel. The sample water was drained through the separatory

funnel back into the jar, leaving the hexane in the funnel. Another 150

mL of hexane was added to the jar and it was again agitated for 30

minutes. The contents of the jar were again added to the funnel. The









61

water was then drained and discarded. The hexane was filtered into a 500

mL boiling flask through sodium sulfate to remove any remaining water.

Next, the boiling flask was placed on a rotary evaporator and the

reduced in volume to near dryness. A few mL of fresh hexane was then

added to the flask and swirled to wash the sides of the flask. This

hexane was placed in a 10 mL volumetric flask. A few mL of hexane was

again added to the boiling flask and the swirling repeated. After two

rinses of the flask, the hexane in the volumetric flask was brought to a

final volume of 10 mL. The finished sample was stored under

refrigeration in test tubes with teflon lined screw closures. The

initial sample volume of 150 mL and the final extracted volume of 10 mL

indicate that this procedure concentrated the residues by a factor of 15.

Blanks taken from remote wells as well as DI water were extracted as a

check on the procedure. Spiked samples of well water and DI water were

extracted to determine the recovery levels of the herbicides using the

procedure described above.

Soil samples were removed from the freezer and allowed to air dry

for one day prior to extraction. This was done to allow excess moisture

to evaporate since the acetone used in the extraction process can absorb

water which will not be removed by filtration through sodium sulfate as

was done for the hexane extractions. Acetone was chosen as the solvent

for soil extractions because it was observed that the water in the soil

samples, or sane other factor, tended to repel hexane. It did not

appear that the hexane was adequately dispersing the soil particles.

A fifty gram portion of the air-dried soil sample was weighed and

placed into the mason jar. One hundred mL of pesticide grade acetone










was then added to the soil sample. The sample was agitated for 30

minutes and the acetone was carefully decanted into a separate labeled

glass container. Another 100 mL portion of acetone was added to the soil

and the agitation was repeated for another 30 minutes. The acetone was

poured into the container with the previous portion. Several rinses of

acetone were poured through the extracted soil to remove any acetone

left from the extractions. The acetone rinses were added to the acetone

frcm the extractions and the entire volume was vacuum filtered through a

47 nmm diameter filter with a 0.45 micron pore size. The filtrate was

then reduced to near dryness on the rotary evaporator and brought to a

final volume of 10 mL in acetone. Soil samples taken from outside the

study area were extracted as field blanks and were spiked with known

quantities of the herbicides and extracted as a check on the extraction

and analysis procedures.

A subsample of approximately 10 g of each soil sample was taken to

determine the soil-water content so that the concentrations of the

herbicides in the soil could be reported on dry weight basis. Soil

samples for soil-water content determinations were dried for 24 hours in

an oven at 100C.



4.5.3. Herbicide residue analysis

All herbicide residue samples were analyzed on a Hewlett Packard

model 5840A gas chromatograph (GC). The GC was equipped with a 35

position autosampler and a model 5840 GC terminal (integrator). The GC

had both electron capture (EC) and nitrogen-phosphorus (NP) detectors.

IDuiring the early phases of the study (methods development), the electron











capture detector was used. This detector had a high sensitivity to

alachlor and a lower sensitivity to atrazine. However, when field

samples were analyzed, as opposed to DI water spiked with pesticides, it

was observed that the EC detector was sensitive to many naturally-

occurring cmpourds (the EC detector is sensitive to chlorine molecules)

which had been extracted along with the pesticides. Thus, the output was

so crowded with peaks that the quantities of atrazine and alachlor

present could no longer be reliably determined. The NP detector is very

sensitive to molecules of nitrogen and phosphorus and insensitive to

almost anything else. When the samples were analyzed on this detector,

the chromatograms were much cleaner and atrazine and alachlor were often

the only visible peaks. The NP detector is somwhere between 5 and 10

times more sensitive to atrazine than it is to alachlor due to the

greater number of nitrogen atoms in the structure of atrazine. The NP

detector was operated with detector gas flowrates of air at 50 mL/min and

hydrogen at 3 mL/min. The carrier gas was helium with a flowrate of 30

mL/min.

The column used in the GC was 1.83 m long with a 4 mm ID. The

column was packed with 3% OV-17 (50% phenyl, methyl silicone) on 100/120

mesh Gas Chrom Q. The operating conditions were: injector port

temperature set at 2400C, column temperature at 200C, and detector

temperature at 300C.

The sample protocol used with the auto sampler was usually of the

form: 4 standards, 1 solvent blank, 10 samples, 4 standards, 1 solvent

blank, 10 samples, 4 standards, 1 solvent blank. The solvent blank was

used to help prevent carryover from the last (and highest) standard into










the following sample. Carryover was normally not a problem, but the

solvent blank would often show some trace of the herbicides.

Working standards of the two herbicides were prepared using

reference standards provided by the EPA in Research Triangle Park, North

Carolina. The initial standard was made by dissolving 10 mg of the

reference into 10 mL of solvent yielding a concentration of 1 mg/mL

(1000 mg/L). This standard was then used as the basis for subsequent

working standards. The standards were prepared with the concentration

of alachlor being 5 times greater than the concentration of atrazine so

that the detector response to the chemicals would be nearly equal.

Working standards of atrazine ranged from 0.01-500.0 mg/L with the

concentrations of aladhlor generally being 5 times higher as noted above

(very high atrazine standards did not include alachlor). The 0.01 mg/L

standard of atrazine represented the smallest concentration which was

detectable without pushing the limits of machine and detector. The

autosampler injected a volume of 4.8 AL. So an injection of 4.8 AL of

0.01 mg/L atrazine represented a lowest detectable mass of 10 nanograms

of atrazine.

Whenever new standards were prepared, they were compared with the

previous standards to assure similar response and that no mistakes had

been made during the dilutions. Two sets of standards were made. One

set was made in acetone for use in analyzing the soil sample extracts.

The other set was made in hexane for use in analyzing the water sample

extracts. Comparisons between the two sets of standards revealed no

differences in response, but recommended procedures (Wheeler, 1987)

dictate that the standards be made up in the same solvent as the samples.











The integrator available with the GC did not work properly. In

order to quantify the amount of herbicides present in the samples, all

peak heights were hand measured (a peak height mode, as used with the IC

was not available on this integrator).

A linear regression was performed from the standards to give a

relationship of the form: concentration = f(peak height). There were no

problems with negative intercepts in these regressions as discussed in

the section on ion chromatography. Sample concentrations were computed

with the regression equations calculated from standards on either side of

the samples. In order to account for drift in the detector response, the

final concentrations were calculated by linearly weighting the

concentrations computed from each set of standards as discussed

previously. The NP detector had a tendency to drift and this correction

for drift was usually needed.















CHAPTER 5
MOELIG THE EXPERDfIMETAL SITE



Two models were selected for the purpose of simulating the movement

of the chemicals applied to the experimental site. The models selected

for use were the Pesticide Root Zone Model, PRZM (Carsel et al., 1984),

version 2, and Grourxdwater Loading Effects of Agricultural Management

Systems, GLEAMS (leonard et al., 1987), version 1.8.53. These models

were selected because they were designed specifically to simulate

pesticide transport through the root zone of a crop and reflect

influences of agricultural management practices such as tillage.

Reference to the manual for GLEAMS includes the documentation for the

CREAMS model (Knisel, 1980) from which GLEAMS was derived as well as the

supplementary GLEAMS user manual which is provided with the model code

and describes the differences in input data sets between CREAMS and

GLEAMS.

This chapter will discuss the selection of input values used in the

models. Only the parameters relevant to the results will be discussed.

There are parameters in both models for which values were selected, but

which had no bearing on the results to be presented. For example the

models utilize the Universal Soil Loss Equation, USLE, (Wischmeier and

Smith, 1978) for prediction of soil erosion, however, in the simulations

surface runoff was never predicted and therefore the parameters chosen

for use with the USLE did not affect the reported results. There are

66










many parameters such as rainfall, pesticide properties, and soil

characteristics which are common to both models. Thus, the selection of

common parameter values will be discussed first, followed by selection of

values for parameters which are unique to each model.



5.1 Selection of Common Input Parameter Values

5.1.1 Rainfall and irrigation

Both models use daily rainfall data. The formats of the rainfall

files for the models are different and PRZM reads in values in

centimeters while GLEAMS reads in values in hundredths of an inch.

The data used to create the precipitation files came from a

combination of two rain gages. As stated previously, one weighing rain

gage is centrally located within the experimental site. This rain gage

was installed in June of 1986. A second rain gage is located

approximately 300 m north-northeast of the experimental site. This gage

is a tipping bucket type rain gage that is connected to a data logger.

All rainfall data used in the simulations came from this off-site gage

since the data fram it was already on a computer and available. The

rainfall amounts recorded in both gages agreed well. The gage located on

the site was used to supplement the rainfall record with amounts of

irrigation applied to the site. This rainfall plus irrigation file was

then manipulated into the units and formats required by each model, and

checked to insure that the two formats contained identical information.











5.1.2 Soil properties

Both models use the Soil Conservation Service (SCS) curve number

method (USDA-SCS, 1972) to partition rainfall between runoff and

infiltration. The runoff curve number was selected from Table 9 in the

PRZM manual and was chosen based on the assumption that the bahia grass

cover could be assumed to be similar to a pasture in good condition

without contouring (Lakeland sand is classified as being in hydrologic

soil group A).

Physical descriptions of the soil profile were obtained from two

sources. Hook (1985) measured properties of the soil in another section

of the farm on which the experimental site is located. The data

collected by Hook (1985) were measured within the top 120 cm of the soil

profile. The soils on the farm are uniformly classified as either a

Lakeland sand or Bonifay sand. The primary differentiation between the

soils is the depth to the restricting layer with the Lakeland soil being

deeper than the Bonifay. Soil physical properties such as water

retention and hydraulic conductivities are considered to be the same

between these soils on this farm. The second source of data used to

determine soil properties is a soil characterization report for Florida

soils prepared by the Soil Science Department at the University of

Florida (Carlisle et al., 1978). This report contains characterization

data for a Lakeland sand.

The properties that were selected to represent the soil profile of

the experimental site are summarized in Table 5.1. The average depth to

the restricting layer for the three wells within the application area is

2.62 m and this was chosen to represent the profile depth.










Table 5.1 Soil properties used in simulations.1

Organic Bulk Hydraulic Water Content (%)
Depth Carbon Density Conductivity Effective Field Wiltng
(cm) (%) (g/cm3) (cm/hr) Saturation Capacity2 Point

0- 13 0.55 1.45 13.3 33.0 10.0 2.1

13- 20 0.28 1.60 13.3 33.0 10.0 2.1

20- 51 0.08 1.58 22.9 33.3 10.0 2.1

51-102 0.04 1.59 36.5 33.8 9.0 2.1

102-262 0.03 1.59 45.0 34.0 9.0 2.1

Data frcm Hook (1985) and Carlisle, et al., (1978).
2Field gravimetric water content following 48 hours of drainage under
plastic following 16 hours of flooding.
3Water content at 15 bars tension.



5.1.3 Pesticide chemical properties and applications

The pesticide chemical properties required by the models are

solubility, partition coefficient, degradation rates on foliage and in

the soil, and a coefficient of plant uptake. The models may require

these parameters in slightly different forms, but the basic information

required is the same. In addition to the model manuals, the manual for

LEACH (Dean et al., 1984) was a good source of estimates of the required

parameters. No exhaustive search of possible values of parameters for

each chemical was undertaken. At this point in the study, the models

were run using values that a user could obtain frcm the manuals. This

may result in less than optimum performance of the models, but probably

reflects the way in which a typical user would apply them.

Reported properties of pesticides are often widely variable. For

properties such as partition coefficient and degradation rates, the










variability is understandable and expected. However, even water

solubility was found to vary by a factor of two. The solubility of

atrazine is not available from the PRZM manual. The CFEAMS manual lists

a value of 33 mg/L. However, the Agrichemical Handbook (1983) gives a

value of 70 mg/L at 20C. The solubility of alachlor is given in the

PRZM manual as 220 mg/L at 20-25C. The CEEAMS manual reports a value of

242 mg/L at an unspecified temperature. The Farm Chemical Harxndbook

(1984) does not report a value for alachlor and the Agrichemical Handbook

(1983) gives the solubility of alachlor in water as 148 mg/L at room

temperature. The final values selected for all chemical properties are

shown in Table 5.2.

The solubilities of brmnide, nitrate, and chloride listed in Table

5.2 are considerably less than the actual solubilities. However,

solubilities of ionic species are seldom listed for reference. The

values in Table 5.2, while less than the actual solubility values, are

sufficiently high such that solubility was not a limiting factor in the

mobility of the chemicals.



Table 5.2 Chemical properties used in simulations.
Property Atrazine Alachlor Bromide Nitrate Chloride

Solubility 33 148 800 800 800
(m/L)

Partition coef., 163 268 0 0 0
Koc (an3/g)

Half-life 78 18 999 999 999
(days)

Plant Uptake Coef. 1.0 1.0 1.0 1.0 1.0
0.65 0.52
0.0 0.0 0.0 0.0 0.0











The user manuals for the models contain tables drawn from many

sources which provide values of the partition coefficient for many

pesticides. The IEAL H manual also contains a number of tables. The

tables in CREAMS and IEACH are more extensive than those in FPRZM.

Different tables within the same manual my give differing values. Table

values often list coefficients of variation (CV) on the order of 50-130

percent. PRZM presents equations by which the organic carbon partition

coefficient, Koc, can be calculated if solubility or the octanol-water

partition coefficient, Kow, is known.

The partition coefficients of alachlor and atrazine were not

measured on this soil and thus values were chosen from the sources

described above. A Koc value for atrazine of 163 cm3/g with a CV of 49%

was found in the IEACH manual. No listing of a Koc was found for

alachlor. PRZM did list a value for log(Kow) which was 2.78. PRZM

presents a relationship between log(KFw) and Koc which is

log Koc = 1.00 (log Kow) 0.21 5.1

Using this equation a Koc of 371 was calculated for alachlor. IEACH

lists a Kw value for alachlor as 434, using this and equation 5.1, a Koc

value of 268 was calculated. Since alachlor has been reported as one of

the pesticides ocamnmly found in groundwater, it was decided to choose

the lower Koc value for use in the model as this would tend to cause

higher predicted leaching losses.

The inorganic tracers bromide, chloride, and nitrate were assumed to

act as non-adsorbed pesticides. Thus they were assigned a Koc value of

zero.









72

Mhe degradation rate constant of atrazine in soil was given in the

LEACH manual as ranging fromn 0.0149 to 0.0063 days-1, corresponding to a

half-life of between 46 and 110 days respectively. The midpoint value

of 78 days was chosen for use in these simulations. A degradation rate

constant of 0.0384 days-1, which corresponds to a half-life of 18 days,

was given for alachlor in both the CREAMS and IEACH manuals.

The coefficient of plant uptake of pesticides with transpiration was

determined using the relationship given in the PRZM manual in which the

uptake factor is a function of KOw and is given as

UPlKF = 0.784 exp [(log Kow 1.78)2/2.44] 5.2

where UPMKF = plant uptake efficiency factor.

Using the Kow for alachlor of 434 as discussed above, the uptake factor

was calculated to be 0.52. Back calculating a Kow for atrazine, based

upon the chosen Koc of 163 using equation 5.1, yields a KOw of 264.

Using this value in equation 5.2 gives a plant uptake factor of 0.65.

The models were also run using uptake factors for both atrazine and

alachlor of 0.0 and 1.0 in order to assess the sensitivity to these

factors. GLEAMS currently recmmends that in the absence of well defined

uptake coefficients, a value of 1.0 should be used. Uptake factors for

the inorganic tracers were assigned values of both 0.0 and 1.0 to observe

the sensitivity of these non-adsorbed chemicals to plant uptake.

The dates and amounts of the chemicals applied to the soil surface

of the experimental site were entered into the models in a direct manner,

ie. there were no calculations or transformations required to convert the

actual values into model parameters. The application rates used in the

simulations correspond to the intended application rates as opposed to









73

the measured rates. The dates and application rates used as model inputs

are shown in Table 5.3. The applications were assumed to reach the soil

surface with no chemical residues remaining on plant surfaces. The

amount applied was further assumed to be uniformly incorporated into the

top 1 cm of the soil.



5.1.4 Length of simulations

Both models simulated the two year time period from 1/1/86 through

12/31/87. This was done to allow the models to overcome any effects of

initial conditions prior to beginning simulation of chemical

applications. Continuing simulations for six months beyond the end of

field data collection would show if any significant leaching would likely

have occurred after sampling was terminated.


Table 5.3 Chemical applications summary.


Date Chemical Applied Application Rate
(kg/ha)
11/12/86 Atrazine 4.9
Alachlor 4.9

11/17/86 Brcnide 10.0

4/16/87 Nitrate 18.6

4/27/87 Braomide 17.0


5.2 Parameters Unique to PRZM

5.2.1 Evapotranspiration prediction.

PRZM uses either measured daily pan evaporation or daily mean

temperature to determine evaporation from the soil and plant surfaces,

and transpiration by the crop. In these simulations, daily pan











evaporation values recorded at a weather station located on the

University of Georgia Coastal Plain Experiment Station campus were used.

This weather station is located approximately 8 km from the research

site. PRZM requires a pan coefficient which is a multiplier used to

adjust pan readings to represent total daily potential evapotranspiration

(PET). A pan factor of 0.75 was selected front a figure provided in the

manual showing pan factors as related to geographic location.

Two additional parameters required for evapotranspiration (ET)

prediction are the depth in the soil profile fram which evaporation can

occur, and the depth of rainfall and irrigation that can be stored on the

plant canopy (interception storage). A figure in the manual indicates

that for southern Georgia, the depth of soil contributing to evaporation

is approximately 25 cm. Data pertaining to the interception storage

capacity of bahia grass was not included in the manual. Values presented

in the manual for various crops ranged fram 0.0-0.3 an. Assuming that

the storage capacity of bahia grass is relatively small, a value of 0.05

cm was assumed.



5.2.2 Crop related parameters

Characteristics of the crop(s) grown during the simulation period

are defined by parameters related to rooting depth, maximum canopy

coverage, and dates for emergence, maturation, and harvest. A rooting

depth for bahia grass was difficult to obtain. Most references to bahia

grass simply stated that it is a deep or very deep rooted plant. Based

on such generalizations and without more specific information, an active

rooting depth (depth containing 90% of root mass) of 91 cm (3 ft) was











selected. Based on observations at the research site, a maximum areal

coverage of the bahia grass foliage was estimated as 98 percent.

Dates of crop emergence, maturation, and harvest are used in the

model to define the development and growth of roots. Roots are assumed

to begin growing at crop emergence and reach the maximum rooting depth at

plant maturation. The roots stay at maximum depth until harvest when

they are set back to zero. These parameters are somewhat difficult to

define in relation to a perennial crop such as bahia grass. In view of

the use made of the selected dates, some artificial dates for emergence

and harvest could be selected. Since the emergence date really defines

the beginning of plant transpiration as a component of the water balance,

an emergence date of March 1 was selected to correspond to the time when

the grass begins to "green up" in the spring. Since the majority of the

roots already exist when the grass begins active growth in the spring, a

maturation date of April 1 was selected to reflect water extraction from

the full maximum rooting depth shortly after growth for the new year

begins. The harvest date was chosen to be December 1 which reflects the

approxiate date when the grass turns brown in the winter and active

growth ceases.



5.2.3 Soil related parameters

PRZM will allow simulation of a larger section of the profile than

just the root zone. A soil core depth of 2.62 m was chosen to represent

the average profile depth beneath the application area. The model will

allow the user to select the number of opartments used to represent the

profile. Too many compartments will increase simulation times and too











few will lead to increased numerical errors in the solution of the

equations used within the model. The manual suggests that no fewer than

30 compartments be used. Thirty-five ccpartments were selected because

this gives a compartment depth that is one-half the crpartment depth in

GLEAMS for a root zone depth of 91 cman (see section 5.3.3).

PRZM4 and GLEAMS both normnnally operate on the assumption that any

soil water in excess of field capacity will drain into the next lower

compartment. This drainage will continue until any excess water drains

from the bottom of the simulated profile. This drainage is assumed to

occur within one day. In the PRZM model, this type of drainage is called

'free drainage'. PRZM has a feature to allow selection of a drainage

rate parameter that serves to slow down the percolation process. This is

an empirical constant and requires calibration. A figure is provided in

the manual whereby the user can make a first cut selection of the

drainage parameter based upon the soil type (sand, clay loam, etc.) and

the number of simulation coapartments selected as described above.

Observations at the experimental site clearly show that there is a delay

of 2-4 days from the time excess water is applied to the time that the

water table responds. Therefore a few simulations were made using the

restricted drainage option to compare with the experimental data. The

drainage rate parameter for this case was selected from the graph in the

manual for a sand soil with 35 ccmpartments to be 2.63 day-1.











5.3 Parameters Unique to ES

5.3.1 Evapotranspiration prediction

Potential evapotranspiration (PET) is computed in GLEAMS based on

mean monthly maximum and minimum temperatures and mean monthly solar

radiation. The mean monthly temperatures and solar radiation data were

obtained from the same weather station that was used for the pan

evaporation data input to PRZM (section 5.2.1). GLEAMS also

differentiates between soil evaporation and plant transpiration. A soil

evaporation parameter is required as a model input and the manual gives a

suggested value of 3.3 (no units) for this parameter for sands.



5.3.2 Crop related parameters

Crop related parameters which are different from PRZM are leaf area

index (IAI), and winter cover factor. The IAI data are used to reflect

changes in plant transpiration with stage of growth. In keeping with the

observed behavior of bahia grass discussed in section 5.2.2, the IAI of

bahia grass was assumed to be zero until March 1 of each year and to

return to zero on December 1st. The maximum IAI for bahia grass was

assumed to be similar to the IAI for pasture as presented in the CRFAMS

manual which had a maximum IAI of 3.0. The IAI for the bahia grass was

assumed to reach 2.0 on April 1 and to reach 3.0 May 1 where it remained

until it began to decline again to a value of 2.0 on Nov. 1.

The winter cover factor is used to moderate evaporation from the

soil surface during the winter due to presence of a bare surface or cover

crops. Since the soil surface at the site was not bare during winter,

the recommended value of the winter cover factor of 0.5 was used.













5.3.3 Soil related parameters

GLEAMS was designed to simulate only the root zone of a crop. The

vadose zone below the crop is ignored. Thus, the soil profile

description input to GLEAMS covers only the 91 cm root zone as opposed to

the 2.62 m profile input to PRZM. There are no soil parameters which are

significant to the results presented in this work which differ from those

discussed in section 5.1.2. In order to highlight one of the major

differences between the two models, a short discussion of the

computational layering of the soil profile in GLEAMS will be presented

here. GLEAMS divides the crop root zone into seven camputational layers.

The first layer is defined to be 1 cm in depth and the other 6 layers are

equal in thickness (layer 2 is 1 an less than layers 3-7) and together

account for the rest of the root zone. The 1 cm upper layer is

considered an important feature of CREAMS and GLEAMS because it increases

the sensitivity of erosion and runoff to chemical concentrations near the

soil surface. PRZM divides the entire profile into a specified number of

ccompartments of equal depth. For the case presented here, GLEAMS layers

would be 15 cm thick after the surface layer (91 can / 6 layers). For

FPRZM with a 2.62 m profile and 35 ccmpartments, each compartment will be

7.5 cm thick. Thus, PRZM may be better able to describe effects of

actual soil layering since properties of adjoining layers would not be

averaged over as large a layer as in GLEAMS. However the surface layer

in PRZM will also be 7.5 cm thick and pesticide storage will be averaged

over this whole layer effectively lowering the mass at the surface which

is available for transport with eroded sediment and runoff water.















CHAPrER 6
RESULTS AND DISCUSSION



There are many areas in this study for which results can be

reported. The approach used in this chapter is to report significant

results relative to each area such as sample collection, sample analysis,

simulation results, etc., and then relate the individual results where

appropriate. A summary of activities at the field site is presented in

Table 6.1 for reference.



6.1 Data Collection

The site was visited on at least a weekly basis throughout the

study. At each visit, samples were collected from the monitoring wells

and soil solution samplers. The water table elevation was also recorded

for every monitoring well on the site and for several wells located

adjacent to the study area. Soil samples were collected on an irregular

basis during the study.

The procedure used for collecting samples from the groundwater

worked well in general. Sand intrusion into a few of the wells, however,

was a consistent problem. It was possible in some instances to fill the

250 mL sampling bottle with sand. This problem was overcome by slowly

raising the bottom of the sampling tube in order to stay above the sand.

These wells were pumped out using a 1.6 cm diameter suction line to

remove accumulated sand as needed. The problem could have been avoided

79











Table 6.1 Chronological summary of field site activities.

Date Event

11/12/86 Apply 4.9 kg/ha of atrazine and alachlor, apply 5.1 an

of irrigation after herbicide application.

11/17/86 Apply 500 g of KBr (10 kg/ha Br-), collect soil solution

samples.

11/18/86 Collect soil solution samples. First soil samples taken

frcm top 5 an in application area.


Collect soil solution and soil samples.


Collect soil

Collect soil

irrigation.

Collect soil

Collect soil

Collect soil

Collect soil

Collect soil

Collect soil

Collect soil

Collect soil

Collect soil

Collect soil

Collect soil

Collect soil

Collect soil


solution

solution


samples.

samples,


apply 5.1 cm of


solution and well samples.

solution, well, and soil samples.

solution and well samples.

solution and well samples.

solution and well samples.

solution and well samples.

solution and well samples.

solution and well samples.

solution, well, and soil samples.

solution and well samples.

solution and well samples.

solution and well samples.

solution and well samples.


11/24/86

12/01/86

12/08/86



12/15/86

12/22/86

12/29/86

1/05/87

1/12/87

1/19/87

1/26/87

2/02/87

2/09/87

2/16/87

2/23/87

3/02/87

3/09/87

3/16/87


Collect soil solution, well, and soil samples.











Table 6.1 Contir

Date

3/23/87

3/31/87

4/06/87

4/13/87

4/16/87

4/20/87



4/27/87


ned.


Event

Collect soil solution and well samples.

Collect soil solution and well samples.

Collect soil solution and well samples.

Collect soil solution and well samples.

Apply 560 kg/ha of 5-10-15 to entire study site.

Collect soil solution and well samples, Very few wells

have water.

Collect soil solution and well samples, Apply 2.5 an o,

irrigation, apply approximately 17 kg/ha Br-, apply 2.


5


ancm of irrigation.

Collect soil solution and

irrigation.

Collect soil solution and

irrigation.

Collect soil solution and

irrigation.

Collect soil solution and

solution containing 500 g

an of irrigation.

Collect soil solution and

irrigation.

Collect soil solution and

irrigation.


well samples, apply 4.2 can of


well samples, apply 3 can of



well samples, apply 3 man of


well samples,

KCl into well


pour 10 1 of

07-09, apply 2.7


well samples, apply 3.2 ancm of



well samples, apply 1.9 an of


4/28/87



4/29/87



4/30/87



5/01/87





5/02/87



5/03/87











Table 6.1

Date

5/05/87



5/08/87



5/11/87



5/13/87

5/18/87

5/25/87

6/01/87


continued.


Collect soil

irrigation.

Collect soil

irrigation.

Collect soil

irrigation.

Collect soil

Collect soil

Collect soil

Collect soil


solution ar


Event

rd well


samples, apply 0.9 an of


solution and well samples, apply 1.9 an of



solution and well samples, apply 5.2 an of



solution and well samples.

solution and well samples.

solution, well, and soil samples.

solution and well samples.


by using

wells.


cxmercially available PVC well screening in the monitoring


No problems were observed with the operation of the soil solution

samplers. The volumes of water collected during sampling varied from 0

to greater than 50 mL. Even though the samples were pulled back to the

instrument trailer through as much as 35 m of 0.64 ancm ID polypropelene

tubing, the vacuum applied to the sample lines was sufficient to pull

very small droplets of water along the wall of the tube and into the

containers. No carryover of the tracers between sampling periods was

observed.

The tensicneters were difficult to maintain properly on the weekly

sampling schedule. Even when the tensicmeters were working, their

usefulness was limited by the short length of time during which they


Continued.









83

could be observed. If an irrigation was initiated early in the morning,

a few of the 30 cm tensiameters might have responded by the end of the

day. Weekly observations of soil-water tension were insufficient for the

purpose of calculating the flux of water through the unsaturated zone.



6.2 Sample Analysis

Water samples collected from the monitoring wells were analyzed for

residues of atrazine and alachlor and concentrations of the inorganic

tracers utilized in this research (Br-, NO3-, and Cl-). The volume of

samples from the solution samplers ranged from 0 to more than 50 mL, with

the average sample volume being approximately 10 mL. Water samples from

the soil solution samplers were generally analyzed for the inorganic

tracers only. Most of the solution samples were not extracted for

residues of the herbicides due to the small sample volumes.

Approximately 50 of the soil solution samples were extracted and analyzed

for residues of the herbicides. Soil samples were extracted and analyzed

for residues of atrazine and alachlor. The presence of alachlor and

atrazine residues in the soil samples was confirmed in a composite of

soil sample extracts that was analyzed using gas chrcamiatography/mass

spectrometry (GC/MS).

During extraction of water samples with hexane, an emulsion of

hexane and water sometimes formed. This would usually separate if

allowed to sit undisturbed for a few minutes. If the enulsion did not

separate, herbicide residues contained within the emulsion could have

been discarded. There was an apparent contamination of the glassware

used for water sample collection or extraction. Samples of deionized











water were extracted and they showed concentrations of atrazine of

approximately 1 gg/L. All glassware was routinely washed with a

cumnercial glassware detergent, triple rinsed with deionized water, and

rinsed with acetone. The contamination levels appeared to be consistent

in that water samples, in which atrazine was not expected, showed a

concentration of approximately 1 J/L. The source of the contamination

was not determined. Several attempts were made to isolate and identify

the source of the contamination, however these attempts were

unsuccessful. To account for the contamination, the measured

concentrations were reduced by 1 pg/L. Thus no concentrations of less

than 1 Mg/L are reported even though the sensitivity of the GC would

allow detection down to the range of tenths of a pq/L.

The gas crciduatograph performed well except that the N-P detector

response had a tendency to drift. This was accounted for by running

standards frequently and adjusting calculated concentrations as described

in the methods section.

The recoveries of atrazine and alachlor from spiked water samples

were nearly identical and ranged frcm 68 to 104% with a mean of 79% and a

standard deviation of 12 percent. The standard deviation would probably

be reduced if these analyses had been performed by an experienced

analyst. Experience contributes significantly to the ability to produce

accurate and consistent results.

The recoveries of atrazine and alachlor from spiked soil samples

could not be determined from the data collected. Moisture contents of

the soils used for spiking were not recorded. The spike levels were

based on the moist weight of the soil. After spiking, the soil was air-











dried for 1 day in order to allow the solvent from the herbicide

standards to evaporate prior to taking subsamples for analysis. The loss

of water from the samples during this time resulted in an increased mass

of pesticide per unit weight of wet soil. Since the initial moisture

contents were unknown, it was not possible to calculate the expected

concentrations in the air dried samples. Calculations based on the

spiking levels of the moist soil resulted in apparent recoveries that

exceeded 100 percent. It can be concluded from the data that recoveries

from the soil were high. When the missions in the soil recovery study

were discovered, the facilities used to perform extractions and analyses

were no longer available.

All concentrations (water and soil) reported in the following

sections are the analytically determined concentrations and have not been

adjusted for expected recovery rates.



6.3 Chemical Applications

Applications of the herbicides and bromide tracer were monitored to

determine the concentration of the chemicals in the application water and

the volume of water applied. Concentrations of the chemicals were

multiplied by the volume of water collected and divided by the opening

area of the collectors in order to determine the application rate on a

mass per unit area basis. Table 6.2 summarizes the measured chemical

application data. The intended application rates were presented in Table

5.3.

The application of atrazine and alachlor to the designated

application area was not uniform as can be seen in Figures 6.1 and 6.2.




Full Text
100
results are referenced. These figures demonstrate the observed
variability between sampling locations. Figure 6.13 shews the average
concentration, from the 6 sampling locations, of bromide at each sample
depth following the first bromide application. Figures 6.14-6.16 show
the concentrations of bromide following the second application in the
61, 123, and 183 cm solution samplers, respectively. Again, the
variability between sampling locations is evident. Figure 6.17 shows the
average concentration of bromide at each sample depth following the
second bromide application.
Tables 6.6 and 6.7 present the mean concentrations and coefficients
of variation between sampling locations of bromide at each sampling depth
on selected sampling dates following the first and second applications of
bromide, respectively. These tables shew that the coefficient of
variation between sampling locations ranges from 23 to over 200%.
The results of all analyses for tracers and herbicides in both soil
and water samples can be provided on magnetic media. Refer to Appendix B
for information on how to request this data and a sample of the data
sets.
6.4.3 Nitrate
The entire field was fertilized on 4/16/87 at a rate of 560 kg/ha of
5-10-15. Concentrations of nitrate (as NO3) moving through the vadose
zone following the fertilizer application exhibited extreme variability.
There were virtually no detections of nitrate due to the fertilizer
application in the samplers located at a 61 cm depth. Two samplers
located at a depth of 122 cm shewed a response to the fertilizer


80
Table 6.1 Chronological summary of field site activities.
Date
Event
11/12/86
Apply 4.9 kg/ha of atrazine and alachlor, apply 5.1 cm
of irrigation after herbicide application.
11/17/86
Apply 500 g of KBr (10 kg/ha Br-), collect soil solution
samples.
11/18/86
Collect soil solution samples. First soil samples taken
from top 5 cm in application area.
11/24/86
Collect soil solution and soil samples.
12/01/86
Collect soil solution samples.
12/08/86
Collect soil solution samples, apply 5.1 cm of
irrigation.
12/15/86
Collect soil solution and well samples.
12/22/86
Collect soil solution, well, and soil samples.
12/29/86
Collect soil solution and well samples.
1/05/87
Collect soil solution and well samples.
1/12/87
Collect soil solution and well samples.
1/19/87
Collect soil solution and well samples.
1/26/87
Collect soil solution and well samples.
2/02/87
Collect soil solution and well samples.
2/09/87
Collect soil solution, well, and soil samples.
2/16/87
Collect soil solution and well samples.
2/23/87
Collect soil solution and well samples.
3/02/87
Collect soil solution and well samples.
3/09/87
Collect soil solution and well samples.
3/16/87
Collect soil solution, well, and soil samples.


16
attenuation factor (AF) preposed by Rao et al. (1985) can also be used to
estimate the mass of pesticide which will leach below the root zone or
out of the vadose zone and into groundwater. The AF index is the only
one of the indices compared by Rao et al. (1985) which incorporated mass
loadings.
Dean et al. (1984) developed a methodology in which soil
characteristics, crop type, chemical properties, and management practices
(e.g. tillage type) can be combined to find a cumulative frequency
distribution of the percentage of applied pesticide mass leaching below
the crop root zone. These frequency distributions were derived from
hundreds of 25-year simulations of pesticide leaching using the PRZM
model (Carsel et al., 1984). The methodology was applied to four crop
types (com, soybean, cotton, and wheat) and 19 representative growing
regions. The results are somewhat unique in that the user is presented
with a statistical probability of leaching based on a 25-year
meteorologic record.
Another index which is currently receiving much attention is the
DRASTIC index developed by Aller et al. (1985). This index does not
consider chemical properties; instead, it is an index of the
vulnerability of groundwater at a given location to contamination from
surface-applied chemicals. The DRASTIC index assigns numerical scores or
weights to seven factors which could influence pollution potential. The
factors are depth to water, net recharge, aquifer media, soil media,
topography, inpact of vadose zone, and conductivity of the aquifer. The
final DRASTIC score is used to describe an area as having high, medium,
or lew susceptibility to groundwater pollution.


CHAPTER 2
OBJECTIVES
The overall objective of this research is to develop data on the
movement of two widely used herbicides through the crop root zone,
through the vadose zone (unsaturated zone between the crop root zone and
the top of the unconfined water table aquifer), and within the shallow
water table aquifer. These data will then be compared with the
predictions of two root/vadose zone pesticide transport models.
The specific objectives are to perform the following:
1. Instrument a field site for monitoring the movement of water and
chemicals through the soil profile and a shallow, water table
aquifer.
2. Observe the fate and transport of surface-applied herbicides
(atrazine and alachlor) over time.
3. Use surface-applied bromide as a nonadsorbed tracer of water
movement.
4. Observe transport of the nitrate component of fertilizers
applied to field.
5. Compile the observations of chemical movement into a data base
for use in testing pesticide transport models.
6. Simulate the field conditions present during the monitored
period with two pesticide transport models and compare
to observed results.
4


14
determining the fraction of the applied mass that will be available for
transport. Events occurring shortly after application will likely result
in the largest concentrations in runoff and percolation water (Wauchope,
1979; Donigian and Rao, 1986a). The timing of rainfall and irrigation
events is less critical for persistent (half-lives in excess of 100 days)
pesticides since they will reside in the soil for longer periods
(Donigian and Rao, 1986a).
The major processes involved in transformation and degradation
include biotransformation, chemical hydrolysis, photolysis, and
oxidation-reduction (Donigian and Rao, 1986a). Factors influencing
transformation and degradation of pesticides, as summarized by Jury and
Valentine (1986), include: microbial populations, chemical concentration,
temperature, oxygen, pH, soil water content, and light. Detailed
descriptions of the many factors influencing these processes are
presented by Rao and Davidson (1980), Valentine and Schnoor (1986), and
Valentine (1986).
3.2.5 Plant processes
Plant processes relating to pesticides are very complex. The
uptake, translocation, accumulation, and transformation of pesticides by
plants affect the availability of pesticides for transport processes and
the potential exposure to pesticide residues by the consumers of the
vegetation, fruits, etc. (Donigian and Rao, 1986a). Plant processes can
serve as both a sink and a source of pesticide residues available for
transport.


74
evaporation values recorded at a weather station located on the
University of Georgia Coastal Plain Experiment Station carpas were used.
This weather station is located approximately 8 km from the research
site. PRZM requires a pan coefficient which is a multiplier used to
adjust pan readings to represent total daily potential evapotranspiration
(PET). A pan factor of 0.75 was selected from a figure provided in the
manual showing pan factors as related to geographic location.
Two additional parameters required for evapotranspiration (ET)
prediction are the depth in the soil profile from which evaporation can
occur, and the depth of rainfall and irrigation that can be stored on the
plant canopy (interception storage). A figure in the manual indicates
that for southern Georgia, the depth of soil contributing to evaporation
is approximately 25 cm. Data pertaining to the interception storage
capacity of bahia grass was not included in the manual. Values presented
in the manual for various crops ranged from 0.0-0.3 cm. Assuming that
the storage capacity of bahia grass is relatively small, a value of 0.05
cm was assumed.
5.2.2 Croo related parameters
Characteristics of the crop(s) grown during the simulation period
are defined by parameters related to rooting depth, maximum canopy
coverage, and dates for emergence, maturation, and harvest. A rooting
depth for bahia grass was difficult to obtain. Most references to bahia
grass simply stated that it is a deep or very deep rooted plant. Based
on such generalizations and without more specific information, an active
rooting depth (depth containing 90% of root mass) of 91 cm (3 ft) was


Cone. (ug/L)
116
09-11
08-09
Date: 03/09/87
09-14
\T
A30 ASO 170
Figure 6.32. Atrazine concentration in the groundwater on 3/09/87.
Vertical bars indicate sampling locations.
09-11
Date: 03/16/87
\T
A30 no A70
WSTAUCE. {xx\)
*
Figure 6.33. Atrazine concentration in the groundwater on 3/16/87.
Vertical bars indicate sampling locations.


98
1986, which was 16 days after application. A plot of the atrazine and
bromide concentrations, from these samples, as a function of the total
depth of water applied since application of each chemical is presented in
Figure 6.9. Since the bromide was applied 5 days after the atrazine, the
concentrations were plotted as a function of the total water applied
since each chemical was applied to provide a common base for comparison.
From this figure it can be seen that approximately 2.4 times as much
water had to be applied to move the peak concentration of atrazine past
the 61 on depth as was required to move the bromide peak past the same
point. This can be interpreted as an approximation of the retardation
factor of atrazine in this soil (assuming that the bromide
Figure 6.9. Bromide and atrazine concentrations in solution
sampler 09N-2 as a function of total water applied
since application.


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
MEASUREMENT AND PREDICTION OF HERBICIDE
TRANSPORT INTO SHALLOW GROUNDWATER
By
Matthew Clay Smith
December 1988
Chairman: Dr. A. B. Bottcher
Major Department: Agricultural Engineering
A field study was conducted to observe the movement of the
herbicides atrazine and alachlor within the soil profile and a shallow
water table aquifer following a surface application. Surface-applied
bromide was used as a nonadsorbed tracer of water movement. The movement
of nitrate from a fertilizer application to the site was also monitored.
Measurements of chemical concentration were made using soil core samples,
samples of soil water from the unsaturated zone, and water samples from
monitoring wells belcw the water table.
Atrazine was observed to move rapidly with both saturated and
unsaturated flews. Concentrations of atrazine exceeded 350 g/L in soil
water samples at a depth of 61 cm. Samples of shallow groundwater
contained atrazine residues as high as 90 jug/L. Measurable
concentrations of alachlor did not move below a depth of 45 cm in the
soil.
xiv


Cone. (mg/L) | Cone. (mg/L)
0 20 40 ro 0 20 40 60 80
130
08-1 1
Date: 05/25/87
07-14
1,1
6.52. Chloride concentration in the groundwater on 5/25/87.
Vertical bars indicate sampling locations.
Figure 6.53. Chloride concentration in the groundwater on 6/01/87.
Vertical bars indicate sampling locations.


APPENDIX C
WATER BALANCE PROGRAMS


This dissertation was submitted to the Graduate Faculty of the
College of Engineering and to the Graduate School and was accepted as
partial fulfillment of the requirements for the degree of Doctor of
Philosophy.
December, 1988
Dean, College of
Dean, Graduate School


Water Table Elevation (m) 5/08/87
118
Figure 6.36. Contour plot of water table elevation on 5/08/87
shewing direction of flew.
saturated porosity of 0.36 (Table 5.1), it is possible to calculate an
estimated water velocity between the wells. The average pore water
velocity is calculated as
v = k s / pe 6.1
where v = average pore water velocity, m/day
k = saturated hydraulic conductivity, m/day
s = hydraulic gradient, m/m
pe = effective saturated porosity, cm3/cm3
Using Equation 6.1 and the values of the variables described above,
a pore-water velocity of 2 m/day was calculated. The distance between
wells 09-11 and 09-14 is 36 m. Based on the calculated pore-water
velocity, water should move the distance between the wells in 18 days.
Atrazine was observed to have moved the distance between the wells in 14


181
Figure C.l Continued.
DO 50, J= 2, (NY-1)
DO 50, 1= 2, (NX-1)
SLPX(I,J) = -( WT(I+1,J) WT(I-1,J) ) / ( 2.0*DELX )
SLPY(I,J) = -( WT(I,J+l) WT(I,J-l) ) / ( 2.0*DELY )
CCCCCCC
C GOMRJTE MAGNITUDE OF RESULTANT SLOPE
CCCCCCC
MAGNITUDE (I, J) = SQRT ( SLPX(I,J)**2 + SLPY(I,J)**2 )
CCCCCCC
C GCMEUTE DIRECTTCN OF RESULTANT SLOPE
C ANGLE IS MEASURED wrt +X AXIS, ie ANGIE = 90 = +Y AXIS
CCCCCCC
ANGLE(I,J) = ATAN2 ( SLPY(I,J) SLPX(I,J) ) 180.0 / PI
IF (ANGIE(I,J) .LT.0.0) THEN
DIEECnON(I,J) = 360.0 + ANGLE(I, J)
ELSE
DIRECnCN(I,J) = ANGLE (I, J)
ENDIF
CCCCCC
C CALCULATE WATER TABLE DEPIH (cm) AT EACH NODE
CCCCCC
WTD(I,J) = 100.0 (WT(I, J) IMP(I, J))
CCCCCC
C CALCULATE DRAINED VOLME (m**3) AT EACH NODE
CCCCCC
WTDEPIH = SURF(I,J) WT(I,J)
DKVOL(I,J) = VOLDRAIN(WTDEPIH) DELX DELY / 100.0
CCCCCC
C CALCULATE MAXIMUM STORAGE VOLUME (m**3) AT EACH NODE
CCCCCC
MAXSTOR(I,J) = (SURF(I,J) IMP(I,J)) PORO DELX DELY
CCCCCC
C CALCULATE VELOCITY, AND FLOWRATE IN X AND Y DIRECTIONS
C AT EACH NODE.
C VELX(I,J) = VELOCITY IN X-DIEECITON (m/day)
C FIOWX(I,J) = VOLUMETRIC FLOWRATE IN X-DIRECITON (m**3/day)
CCCCCC
VELX(I,J) = OCND(J) SLPX(I,J) / PORO
VELY(I,J) = OQND(J) SLPY(I,J) / PORO
FLCWX(I,J) = COND(J) SLPX(I,J) DELX WTD(I,J) / 100.0
FLCWY(I, J) = OOND(J) SLPY (I, J) DELY WTD(I,J) / 100.0
CFI£WX(I,J) = aiC(I,J) FIOWX(I,J)
CFT£WY(I,J) = OONC(I,J) FLCWY(I,J)
CCCCCC
C CALCULATE MASS OF WATER AND CHEMICAL AT EACH NODE
C WMASS = WATER STORAGE IN SATURATED ZONE (m**3) IN NODAL AREA
C CWMASS = MASS OF CHEMICAL IN WATER (mg) IN NODAL AREA
C CSMASS = MASS OF CHEMICAL ON SOIL (irg) IN NODAL AREA
C CMASS = TOTAL MASS OF CHEMICAL (mg) " "
CCCCCC


163
Carsel, R. F., C. N. Smith, L. A. Mulkey, J. D. Dean, and P. P.
Jcwise. 1984. Users manual for the pesticide root zone model
(FRZM) release 1. U. S. Environmental Protection Agency, Athens,
GA. EPA-600/3-84-109.
Carson, R. L. 1962. Silent Spring. Houghton Mifflin, Boston, MA.
Cheng, H. H. and W. C. Koskinen. 1986. Processes and factors
affecting transport of pesticides to ground water, in Evaluation
of Pesticides in Groundwater. W. Y. Gamer, R. C. Honey cut, and H.
N. Nigg, Eds. ACS Symposium Series No. 315, American Chemical
Society, Washington D.C. pp. 170-196.
Cohen, S. Z., C. Eiden, and M. N. Lorber. 1986. Monitoring ground
water for pesticides, in Evaluation of Pesticides in Groundwater.
W. Y. Gamer, R. C. Honeycut, and H. N. Nigg, Eds. ACS Symposium
Series No. 315, American Chemical Society, Washington D.C.
pp. 170-196.
Cooper, S. C. 1986. Design and installation of a monitoring
network for measuring the movement of aldicarb and its residues
in the unsaturated and saturated zones, Lee County, Georgia, in
Agricultural Impacts On Groundwater QualityA conference.
Omaha, NE. National Water Well Association. Water Well Publishing
Co., Dublin, OH. pp. 194-223.
Dean, J. D., P. P. Jcwise, and A. S. Donigian, Jr. 1984. Leaching
Evaluation of Agricultural Chemicals (LEACH) Handbook. U. S.
Environmental Protection Agency, Athens, Georgia.
EPA-600/3-84-068. 407 pp.
Dean, J. D., and R. F. Carsel. 1988. A linked modeling system for
evaluating impacts of agricultural chemical use. in Agricultural
Impacts On Groundwater QualityA conference. Des Moines, IA.
National Water Well Association. Water Well Publishing Co.,
Dublin, OH. pp. 477-502.
Dick, W. A., W. M. Edwards, and F. Haghiri. 1986. Water movement
through soil to which no-tillage cropping practices have been
continuously applied, in Agricultural Impacts On Groundwater
QualityA conference. Omaha, NE. National Water Well
Association. Water Well Publishing CO., Dublin, OH. pp. 243-252.
Donigian, A. S., Jr. and P. S. C. Rao. 1986a. Overview of
terrestrial processes and modeling, in Vadose Zone Modeling of
Organic Pollutants. S. C. Hem and S. M. Mel ancon, Eds. Lewis
Publishers, Inc. Chelsea, MI. pp. 3-35.


22
in FRZM. Water in excess of field capacity drains to the next lower
layer.
Pesticide transport within the root zone is by advection. No
dispersive flux components are included. Volatilization is not
considered. Pesticide applications can be partitioned between plant
foliage and the soil surface. Different degradation rates can be
specified for chemicals on the foliage and within the soil. Soil-applied
chemicals can be incorporated to a specified depth.
The model will simulate up to ten chemicals simultaneously. This
feature makes possible the observation of the effect of changes in
chemical properties, e.g. partition coefficient, on resulting leaching
losses with one model run. The model can also simulate the formation,
fate, and transport of degradation products of the parent chemicals.
3.3.3 Coupled saturated/unsaturated zone transport models
The root/vadose zone models described above are important tools for
assessing the mobility of pesticides within the unsaturated zone. The
concern expressed by the public and governmental officials relate to
pesticide residues in groundwater supplies used for drinking water. Thus
the unsaturated zone models need to be linked to saturated zone transport
models so that the concentrations and transport of pesticides within
aquifers can be calculated.
As described above, MOUSE (Steenhuis et al., 1987) can predict the
transport of pesticides in a vertical, two-dimensional cross section of
a water table aquifer. Jones et al. (1987) coupled the PRZM model to a
two-dimensional saturated zone transport model to predict the movement of


189
Figure C.l Continued.
156
157
158
160
161
162
163
164
165
166
170
171
180
448
181
182
183
421
184
185
186
422
187
188
191
447
192
411
412
195
196
401
452
402
403
404
405
406
451
731
732
921
922
933
934
FORMAT(' ,4X, 'SIDPE MAGNITUDE' ,9X,E10.4,5X,E10.4)
FORMAT(' \4X, 'FLOW IN X-DIRECITCN',3X,E10.4,5X,E10.4)
FORMAT(1 1, 4X, 'FLOW IN Y-DIRECnON' ,3X,E10.4,5X,E10.4)
FORMATC ,4X,53() ,//,5X, 'INHJT FIIES'f14X, 'OUTEUT FIIE',/,
# 5X,11(1-'),14X,11('-'))
FORMATC
FORMATC
# 'UNIT 4
FORMATC
FORMATC
FORMATC
FORMATC
I I
/
I I
/
I I
/
I I
',4X, 'UNIT 1 = ',A12,4X, 'UNIT 5 = ',A12)
' 4X, 'UNIT 2 = \A12,//5X, 'UNIT 3 = ',A12,/,5X,
= ',A12,/,5X,'UNIT 6 = ',A12)
' 4X, 'CHEM FLOW IN X-DIR',3X,E10.4,5X,E10.4)
',4X,'CHEM FLOW IN Y-DIR' ,3X,E10.4,5X,E10.4)
',4X, 'CHEMICAL MASS STORAGE' ,3X,E10.4,5X,E10.4)
',4X, 'CHEMICAL CONCENTRATION' ,3X,E10.4,5X,E10.4)
FORMAT(/,4X, 'FROGRAM RUN ON ,12.2,'/' ,12.2,'/' ,14,' AT
# 12.2,'.',12.2,'.',12.2,'.'12.2)
FORMAT(4X, 'PROGRAM PUN O ,12.2,'/',12.2,'/',14, AT ',
# 12.2,'.',12.2,'.',12.2,'.'12.2)
'2X, BOUNDARY FLUXES',/,2X, 110('-'))
AREA(I) ',7(F10.5,2X))
BF1GWX1 (I) ,7 (F10.5,2X))
BFLCWX2 (I) ,7 (F10.5,2X))
BFICWYl(I) ',7(F10.5,2X))
',' BFTCWY2(I) ',7(F10.5,2X))
',/,' BCFLCWXl(I) ,7 (F10.5,2X))
',' BCETOWX2(I)',7(F10.5,2X))
',' BCFLOWYl(I)',7(F10.5,2X))
',' BCFLDWY2(I)',7(F10.5,2X))
',' NETFIGW(I) ,7(F10.5,2X))
',' NETCFLOW(I) ,7(F10.5,2X))
TVJMASS(I) ,7 (F10.5,2X))
',7(F10.5,2X))
',7(F10.5,2X) ,/,2X,110('-'))
',7(F10.5,2X))
',7(F10.5,2X))
',7(F5.2,2X))
',F5.2)
',7(F10.5,2X),A8)
: ',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
X-NODE BOUNDARIES : ',213)
Y-NODE BOUNDARIES : ',713)
PARTIONING COEFF : ,F7.5)
BUIK DENSITY : ,F7.3)
FORMATC
FORMAT('
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMAT(/,'
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMAT(7A12)
FORMATC
/
i i
MEANWTD(I)
TCMASS(I)
DRVOL(I)
MAXSTOR(I)
HYD COND.
POROSITY
NETFIOW(I) :
AREA(I)
IWMASS(I)
NETCFIOW(I)
TCMASS(I)
ERVOL(I)
MAXSTOR(I)
MEANWID(I)
CXM1ENT : ', 7A12)


179
Figure C.l Continued.
READ(2,102)XX(1),XX(2)
READ(2,102)(YY(I),1=1,7)
READ(2,108)FLDXFIIE
READ(2,933)(CCMMENT(I),1=1,14)
CCCCCC
C INTERPOLATE CONDUCTIVITLES AT EACH Y NODE
CCCCCC
DO 621, I = 1,45
DO 621, J = 1,6
IF(I.GE.YY(J) .AND.I.LE.YY(J+1)) THEN
COND(I) = NODEQOND(J) + FLOAT(I-YY (J) )* (NODEOOND(J+1) -
# NODECOND(J)) / (YY (J+l) YY(J))
EI£>EIF(I.LE. YY(1)) THEN
OOND(I) = NODECOND(l)
ELSEIF(I.GE.YY(7)) THEN
COND(I) = NODECOND(7)
ENDIF
621 CONTINUE
CCCCCC
C OPEN OOTRJT FILE POR TRANSFER OF DATA TO WEEKLY FIDX CALCULATION
C PROGRAM
CCCCCC
OPEN (UNIT=7, FILE=FLUXFILE, STATUS=' UNKNOWN')
WRTTE(7,934)(OCMMENT(I),1=1,14)
CALL GEITTM(IHR,IMIN,ISEC, I100TH)
CALL GETDAT (IYR, IMON, IDAY)
WRITE(7,171) IMON,IDAY,IYR,IHR,IMIN,ISEC,I100TH
WRITE(7,195)(NODECOND(I),1=1,7)
WRITE(7,196)PORO
WRITE(7,921)KD
WRITE(7,922)BULKDEN
WRITE(7,731)XX(1),XX(2)
WRITE(7,732)(YY(I),1=1,7)
CCCCCC
C BEGIN MAIN LOOP TO PERFORM CALCULATIONS FOR EACH OF 28 OBSERVATION
C PERIODS
CCCCCC
30 DO 15, K=l,50
READ(2,100,END=20) MONTH,DAY,YEAR,CHEMFILE
DATE = MONTH // '/ // DAY // '/' // YEAR
INFILE4 = MONIH // // DAY // 'ELV // .GRD'
INFILE6 = CHEMFILE // '.GRD'
OOTFTLE = MONTH // '-' // DAY // 'WTD* // '.SUM'
WRITE(*,*) INFILE4,INFILE6,OOTFILE
CCCCCCC
C OPEN INRJT AND OUTHJT FIIES
CCCCCCC
OPEN (UNIT=4, FILE=INFILE4, STATUS^' OLD')
OPEN (UNIT=5, FILE=OUTFILE, STATUS=' UNKNOWN')
OPEN (UNIT=6, FILE=INFILE6, STATUS=' OLD')


6
In the state of Florida, residues of the nematicide EDB have been
detected in over 10% of the public and private drinking water wells which
have been sampled (U.S. EPA, 1987a). Approximately 1,200 wells require
treatment or have been closed as sources of drinking water. Marti et al.
(1984) detected EDB in the groundwater in southwest Georgia. EDB
residues have been detected in groundwater in at least 6 other states.
In California, residues of approximately 57 pesticides have been
detected in groundwater (U.S. EPA, 1987a). Most of these detections
were related to factors other than routine use on agricultural fields.
The nematicide DBCP has been detected at levels exceeding 1 /xg/L in
approximately 60% of the 2500 drinking water wells tested.
Residues of the widely used herbicide atrazine are commonly
identified in groundwater. Ritter (1986) reports that approximately
36,000,000 kg of atrazine were used in the United States in 1982.
Atrazine has been detected in PA, IA, NE, WI, MN, and MD (U.S. EPA,
1987; Cohen et al., 1986; Ritter, 1986). According to Berteau and Spath
(1986), atrazine has also been detected in concentrations of 15) to 2 /xg/L
in California groundwater. Pionke et al. (1988) reported atrazine
concentrations in groundwater at levels 15) to 1.1 /xg/L in an agricultural
watershed in Pennsylvania. The wells sampled by Pionke et al. (1988)
were all located in unconfined aquifers with depths to the water table
ranging from 2 to 21 m. Atrazine concentrations of 10 /xg/L have been
reported in a karst aquifer in Iowa (Libra et al., 1986; Kelley et al.,
1986).
Residues of the herbicide alachlor have been found in groundwater in
the states listed for atrazine, excluding WI. According to Ritter


211
10-09
Figure D.21. Atrazine concentration in the groundwater on 5/18/87.
Vertical bars indicate sampling locations.
09-11 07-14
Date: 05/25/87
Figure D.22. Atrazine concentration in the groundwater on 5/25/87.
Vertical bars indicate sampling locations.


Cone. (ug/L) ^3 Cone. (jj.g/L)
205
nntp- n^/iK/7
D.9. Atrazine concentration in the groundwater on 3/16/87.
Vertical bars indicate sampling locations.
Date: 03/23/87
o
cn
o
T-*
o
Figure D.10. Atrazine concentration in the groundwater on 3/23/87.
Vertical bars indicate sampling locations.


Cone, (jug/L) £ Cone. (jxg/L)
203
Date: 02/16/87
o
CN
o
T
o
re D.5.
Atrazine concentration in the groundwater on 2/16/87.
Vertical bars indicate sampling locations.
09-11
Date: 02/23/87
Figure D.6. Atrazine concentration in the groundwater on 2/23/87.
Vertical bars indicate sampling locations.


31
collected monthly. The wells were pumped for 5 minutes prior to
collecting a sample for analysis.
To monitor surface runoff, H-flumes with automatic samplers and
recorders were installed in the outlets of the watersheds. All samples
were frozen for transport and storage prior to analysis. Details of the
analytical procedures used for pesticide residue analysis are presented
by Brinsfield et al. (1987).
Gravity-fed lysimeters were installed horizontally through the walls
of a reinforced pit to collect samples of percolating water. The
lysimeters were installed at a depth of 1 m below the soil surface.
Three lysimeters were installed in each pit. These lysimeters collect
samples of saturated flow percolating through the root zone. Samples
were collected from these lysimeters after each rainfall event.
Herbicide treatments were the same for both watersheds. Atrazine,
metolachlor, and simazine were applied pre-emergence at the rate of 1.68
kg AI (active ingredient)/ha. Cyanazine was applied at 2.24 kg Al/ha,
and carbofuran at 1.12 kg Al/ha, both pre-emergence. Dicamba was
applied post-emergence at a rate of 0.55 kg Al/ha.
Leachate collected in the lysimeters during January, 1985, had
atrazine concentrations as high as 2 /ug/L. After planting in 1985, no
leachate samples were collected in the CT watershed until September.
Atrazine and metolachlor concentrations in those samples ranged from 1-2
/ug/L. Simazine was detected in only one sample, and cyanazine was not
detected. In the NT watershed, samples collected in July had
concentrations of atrazine at 8-10 /ug/L, simazine at 7-10 /ug/L, cyanazine
at less than 1 /ug/L, and metolachlor at less than 2 /ug/L. Leachate from


12
linearity, instantaneous equilibrium, and reversibility are discussed by
Rao and Davidson (1980).
Hie partition coefficient, K, is unique to a given pesticide-soil
combination. However, Rao and Davidson (1980) report that investigators
have shewn that when is normalized for the organic carbon content of
the soil, the resulting value is independent of the soil type and can be
considered a property of the pesticide. The normalized partition
coefficient is represented as Kq,-. and is defined by:
Kqc = % 100 / %OC 3.3
where %OC is the percent organic carbon content of the soil and is the
measured partition coefficient. Methods for measurement of the
partition coefficient are reviewed by Rao and Davidson (1980).
3.2.3 Volatilization
Volatilization of pesticides from the soil and plant surfaces
reduces the amount of the pesticide available to be leached or
transported with runoff. Volatilization also determines the quantity of
the pesticide which exists in the vapor phase within the soil and thus
the amount available for diffusive vapor transport. Thus, as noted by
Donigian and Rao (1986a), volatilization affects both the fate and
transport of a pesticide.
Seme of the factors which influence volatilization are summarized by
Jury and Valentine (1986). They are Henry's constant Kjp chemical
concentration, adsorption site density, temperature, water content, wind
speed, and water evaporation. Henry's constant, % is the ratio of
saturated vapor density to solubility and is an index of the partitioning


144
6.63-6.68 present similar data for alachlor. Results frcsn GLEAMS are not
included in these figures due to the fact that GLEAMS only outputs
results for days on which there was sufficient rainfall to cause
percolation beneath the root zone. There were no GLEAMS results
available for most of the days shewn in Figures 6.57-6.68. The results
in Figures 6.57-6.62 suggest that the selected partition coefficient,
for atrazine may be too lew. The model appears to predict that
atrazine would leach more rapidly than the observed rate of movement. It
also appears that atrazine is persistent in the soil and that the 78 day
half-life used as a model input may also be too lew. Figures 6.63-6.68
clearly show that the half-life selected for alachlor was too low. It is
difficult to assess the influence of the Kgy used for alachlor since the
lew half-life degraded the alachlor so rapidly that very little was
available to be leached.
Figure 6.56. Comparison of percolation volumes predicted by GLEAMS
and FRZM.


115
09-11
Date: 02/23/87
Figure 6.30. Atrazine concentration in the groundwater on 2/23/87.
Vertical bars indicate sampling locations.
Date: 03/02/87
Figure 6.31. Atrazine concentration in the groundwater on 3/02/87.
Vertical bars indicate sampling locations.


190
Figure C.l Continued.
CCCCCC
C THE END !!!!!
CCCCCC
END
CCCCCC
C FUNCTION TO CALCULATE THE VOLUME DRAINED IN CM FOR A GIVEN WATER TABLE
C DEPIH. FUNCTION CREATED BY USING LINEAR REGRESSION ON THE VOLUME
C DRAINED WATER TABLE DEPTH CURVE POR WT DEPIH VALUES IN THE RANGE
C OF 1.5 3.5 METERS
CCCCCC
FUNCTION VOLDRAIN (WTDEPIH)
VOLERAIN = -12.5887 + 29.048 WTDEPIH
RETURN
END


26
model match field observations in order to be considered validated
(determined to accurately represent the system)? Leonard and Knisel
(1988) indicate that there are no standard criteria for model validation.
Since the spatial distribution of variations in soil properties is
usually unknown, it would be unreasonable to expect any model to exactly
match point measurements from a field study. Hedden (1986) reported that
participants of the Predictive Esposare Assessment Workshop sponsored by
the U. S. EPA in Atlanta, GA, on April 27-29, 1982, agreed on two
criteria for model acceptance. For screening applications of the model
(limited site-specific data and not calibrated to previous data from the
site), the model should be able to replicate measured field data within
an order of magnitude. For site-specific applications (parameters
measured on-site and with the model calibrated to the site), the model
should be able to match the field observations within a factor of two.
The screening level criteria seems to be quite reasonable when all of the
sources of error and uncertainty are considered. The site-specific
criteria, however, may be difficult to meet by even the best models using
carefully measured site-specific parameters.
The fact that models can not be expected to exactly replicate field
measured values does not mean that they have no value. Models that have
been verified (ie. their response to changes in parameters reflect what
would be expected based on knowledge of the system) can be used with same
confidence to evaluate the differences in predicted leaching of different
chemicals or, perhaps, differences in leaching of a single chemical due
to different climatic or management conditions.


Table C.l. Sample ouput from program ANALYZE
GOPHER RIDGE WATER TABLE DATA
DATE = 5/05/87
NODE
I J
STATION ID
LOCATION (m)
X Y
----ELEVATION
SURF IMP
(m)
WT
WT DEPTH
(cm)
WATER
X
TABLE SLOPE
Y
(m/m)
MAGNITUDE
DIRECTION
(degrees)
2
2
6.00- 7.00
.00
73.15
30.420
27.710
28.037
32.7
-.5500E-02
-. 1263E-02
.5643E-02
193.
6
2
7.00- 7.00
12.19
73.15
30.405
27.770
27.997
22.7
.5601E-02
-.6131E-02
.8304E-02
312.
10
2
8.00- 7.00
24.38
73.15
30.400
28.000
28.000
.0
-.7234E-02
-.8363E-02
.1106E-01
229.
14
2
9.00- 7.00
36.58
73.15
30.334
27.945
28.094
14.9
- .2259E-02
-.1603E-01
.1618E-01
262.
18
2
10.00- 7.00
48.77
73.15
30.270
27.951
28.112
16.1
-.4843E-02
-.7401E-02
.8845E-02
237.
22
2
11.00- 7.00
60.96
73.15
30.240
28.110
28.230
12.0
-.5080E-02
-.7938E- 02
.9424E-02
237.
2
6
6.00- 8.00
.00
85.34
30.368
27.593
27.937
34.3
.2883E- 02
.1717E-02
.3356E-02
31.
6
6
7.00- 8.00
12.19
85.34
30.376
27.606
27.933
32.7
-.1626E-02
.1012E-01
.1025E-01
99.
10
6
8.00- 8.00
24.38
85.34
30.383
27.789
28.024
23.5
-.1604E-01
.7099E-02
.1754E-01
156.
14
6
9.00- 8.00
36.58
85.34
30.410
28.020
28.218
19.9
-.2878E-02
.6295E-02
.6921E-02
115.
18
6
10.00- 8.00
48.77
85.34
30.320
27.926
28.142
21.6
.3308E-02
.5636E-02
.6535E-02
60.
22
6
11.00- 8.00
60.96
85.34
30.261
28.022
28.189
16.7
-.7888E-02
-.3624E-02
.8681E-02
205.
I J MAXIMUM DRAINED SATURATED CHEMICAL CHEMICAL STORAGE X DIRECTION Y DIRECTION
STORAGE VOLUME STORAGE CONC. WATER SOIL TOTAL FLOWRATE VELOCITY CHEMICAL FLOWRATE VELOCITY CHEMICAL
(m**3) (m**3) (m**3) (ppb) (mg) (mg) (mg) (m**3/day) (m/day) (mg/day) (m**3/day) (m/day) (mg/day)
2
2
9.0636
5.2613
1.0937
17.38
19.010
4.106
23.116
-.077
-.214
-1.334
-.018
-.049
-.306
6
2
8.8152
5.3305
.7595
20.64
15.676
3.386
19.062
.054
.218
1.120
-.059
-.238
-1.226
10
2
8.0269
5.3073
.0000
22.03
.000
.000
.000
.000
-.281
.000
.000
-.325
.000
14
2
7.9896
4.8762
.4970
19.05
9.466
2.044
11.510
-.014
-.088
-.273
-.102
-.623
-1.936
18
2
7.7569
4.6551
.5383
14.72
7.922
1.711
9.633
-.033
-.188
-.490
-.051
-.288
-.748
22
2
7.1239
4.2553
.4007
11.89
4.762
1.029
5.791
-.026
-.198
-.309
-.041
-.309
-.482
2
6
9.2798
5.3920
1.1479
17.50
20.083
4.337
24.421
.042
.112
.739
.025
.067
.440
6
6
9.2629
5.4230
1.0926
25.47
27.830
6.011
33.841
-.023
-.063
-.577
.141
.394
3.595
10
6
8.6776
5.1975
.7867
32.77
25.785
5.569
31.354
-.161
-.624
-5.276
.071
.276
2.335
14
6
7.9938
4.7446
.6642
22.91
15.215
3.286
18.500
-.024
-.112
-.559
.053
.245
1.222
18
6
8.0052
4.7072
.7220
12.93
9.334
2.016
11.350
.030
.129
.394
.052
.219
.671
22
6
7.4898
4.4228
.5591
9.74
5.448
1.177
6.625
-.056
-.307
-.548
-.026
-.141
-.252


99
tracer is nonadsorbed). Hie retardation factor can be written as
R = 1 + (Kd) (Pb)
fc
where R = retardation factor, dimensionless, = partition coefficient,
Pb = soil bulk density, and 9fc = soil-water content at field capacity
(Dean et. al, 1984). Hie retardation factor indicates the velocity of
water or a nonadsorbed chemical relative to the velocity of an adsorbed
chemical. Thus a retardation factor of 2 would indicate that the
adsorbed chemical would move at 1/2 of the velocity of a nonadsorbed
species.
Using the soil properties listed in Table 5.1 and a normalized
partition coefficient for atrazine of 163 cm3/g), a weighted average
retardation factor for the top 61 cm of the soil profile was calculated
to be approximately 6. Hie properties listed for the top 13 cm yield a
retardation factor of 14 and the properties of the layer from 51-62 cm
give a retardation factor of 2.
Alachlor was not detected in any of the extracted soil solution
samples.
6.4.2 Bromide
Hie concentrations of bromide (Br) in the 61, 122, and 183 cm deep
solution samplers following the first application of bromide to the site
are shown in Figures 6.10-6.12, respectively. Hie first application of
brcsid.de was made 5 days after the application of atrazine and alachlor.
Hie date of the application of the herbicides to the application site
(11/12/86) is used as the benchmark time to which all observations and


184
Figure C.l Continued.
210 CONTINUE
ccecee
C CAICUIATE NET MASS FLUXES IN EACH OF THE SIX SUB-AREAS AND FOR TOTAL
C AREA.
CCCCCC
DO 220, MM = 1,6
NETFIOW (MM) =BFI£WY (MM) + BFICWXl(MM) BFT£WX2(MM) BFLCWY (MMfl)
NETCFIOW (MM) =BCFL£WY (MM) + BCFTCWXl(MM) BCETCWX2(MM) -
# BCFICWY(MMfl)
220 CONTINUE
NETFIOW(7) = BFLOWY(l) + BFI0WX1(7) BFI£WX2(7) BFIOWY(7)
NETCFIOW(7) = BCFIOWY(l) + BCFI0WX1(7) BCFIOWX2(7) BCFLCWY(7)
CCCCCCC
C COMEUIE MEAN WTD, TOTAL WATER AND CHEMICAL MASSES IN EACH AREA
CCCCCCC
DO 310, KK = 1,7
TWMASS(KK) = 0.0
TCMASS(KK) = 0.0
DRVOISUM(KK) =0.0
MSTORSUM(KK) =0.0
MEANWTD(KK) = 0.0
AREA(KK)=0.0
STRT = YY(KK)
STP = YY(KK+1)
IF(KK.EQ.7) THEN
STRT = YY(1)
STP = YY(7)
ENDIF
DO 300, J = STRT,STP
DO 300, I = XX(1),XX(2)
IF(I.EQ.XX(1).OR.I.EQ.XX(2)) THEN
IF(J.EQ.STRT.OR.J.EQ.STP) THEN
C ** QUARTER AREA CONTRIBUTION IN CORNERS **
TWMASS(KK) = TWMASS(KK) + WMASS(I,J)/4.0
TCMASS(KK) = TCMASS(KK) + CMASS(I, J)/4.0
MSTORSUM(KK) = MSTORSUM(KK) + MAXSTOR(I,J)/4.0
DRVOISUM (KK) = DKVOLSUM (KK) + ERVOL(I, J)/4.0
AREA(KK) = AREA(KK) + DELX DELY / 4.0
MEANWTD(KK) = MEANWTD(KK) + WTD (I, J) *DELX*DELY/4.0
C WRITE(5,951)KK,I,J,TWMASS(KK) ,WMASS(I,J)
C951 F0EMAT(1X,3I3,2(4X,F10.5) QUARTER AREA')
ELSE
C **HALF AREA CONTRIBUTING ON BOUNDARIES**
TWMASS(KK) = TWMASS(KK) + WMASS(I, J)/2.0
TCMASS(KK) = TCMASS(KK) + CMASS(I,J)/2.0
MSTORSUM(KK) = MSTORSUM(KK) + MAXSTOR(I, J)/2.0
DRVOISUM (KK) = DRVOISUM (KK) + DRVOL(I,J)/2.0
AREA(KK) = AREA(KK) + DELX DELY / 2.0
MEANWTD(KK) = MEANWTD(KK) + WTD(I, J) *DELX*DELY/2.0
C WRITE(5,952)KK,I,J,TWMASS(KK) ,WMASS(I,J)


60
There are many published methods for preparing samples for
determination of atrazine and alachlor residues (e.g., Rohde et al.,
1981; Voznakova and Tatar, 1983). In general, the procedures are rather
detailed and costly in terms of time and supplies. Dr. Willis Wheeler,
Director of the Pesticide Residue laboratory in the Food Science and
Human Nutrition Department at the University of Florida, Gainesville,
indicated that a simpler method of sample preparation might be developed
which would be satisfactory in terms of the recovery of the pesticide of
interest while being more economical in both the time required for sample
preparation and expense (Wheeler, 1987). The more steps involved in the
extraction and preparation of a sample, the more likely it is that
something can go wrong and the residues lost. With these factors in
mind, methods for extraction of the two herbicides from soil and water
samples were developed which were in keeping with the budgetary
constraints of the project and the time constraints on the analyst.
Extraction of the herbicide residues from water samples was done
using a liquid-liquid extraction procedure. One hundred fifty
milliliters of the sample water were placed in a quart mason jar along
with 150 mL of pesticide-grade hexane. The jar was sealed with a
standard mason jar lid which had been lined with a self-adhesive teflon
film. The jar was agitated on a shaker table for thirty minutes after
which the contents of the jar were poured into a labeled 500 mL
separatory funnel. The sample water was drained through the separatory
funnel back into the jar, leaving the hexane in the funnel. Another 150
mL of hexane was added to the jar and it was again agitated for 30
minutes. The contents of the jar were again added to the funnel. The


17
DRASTIC has been used to map every county in the United States
(Alexander and Little, 1986) as part of the first stage of a national
survey of pesticide contamination of drinking water (U.S. EPA, 1987b).
In the first county-level assessment the Southeast Coastal Plain, which
covers all of Florida and southern Georgia, was shewn to be the
groundwater region of highest vulnerability.
Indices like those mentioned above are beneficial to regulatory
agencies as a method to screen great numbers of compounds and locate
vulnerable areas. This information can aid in allocating funding for
further studies concentrating on the chemicals of interest in regions
which may be most vulnerable. These studies may involve detailed
modeling of pesticide transport to groundwater, modeling of surface and
groundwater hydrology, and collection of data from test locations.
3.3.2 Transport models
There are many models of pesticide transport reported in the
literature. These range from steady-state screening models, e.g. PESTAN
(Enfield et al., 1982), to very complex process-oriented research models,
e.g. IEACHMP (Wagenet and Hutson, 1986). Donigian and Rao (1986a), and
Shoemaker and Magette (1987) review a number of the models which are
available. Most models which are capable of simulating pesticide fate
and transport do not explicitly represent agricultural management and
cropping practice effects on runoff and leaching. Modifications of
management practices will be one method of reducing pesticide leaching to
groundwater. The models used for regulatory purposes should reflect the
trade-offs between surface water and groundwater quality. There is seme


187
Figure C.l Continued.
CCCCCC
C WRITE MAXIMUMS AND MINIMUMS
CCCCCC
WRITE(5,149)
WRirE(5,150) SURFMIN,SURFMAX
WRITE (5,151) IMEMIN, IMEMAX
WRITE(5,152)WTMIN,WIMAX
WRITE(5,153)WTCMIN/WrCMAX
WRITE (5,154) SLPXMIN, SLPXMAX
WRITE (5,155) SLPYMIN, SLPYMAX
WRITE (5,15 6) MAQGN, MAGMAX
WRITE (5,157) FLUWXMIN, FIDWXMAX
WRITE (5,158) FL3WYMIN,FI£WYMAX
WRITE (5,163) CFICWXMIN, CFICWXMAX
WRITE (5,164) CFD3WYMIN, CFICWYMAX
WRITE (5,165) CMASSMIN, CMASSMAX
WRITE (5,166) CmCMIN, CONCMAX
CCCCCC
C POST FILENAMES USED IN THIS ANALYSIS
CCCCCC
WRITE(5,160)
WRITE (5,161) INFILE1, CXJTFILE
WRITE (5,162) INFILE2, INFILE3, INFIIE4, INFIIE6
WRITE(5,195)(NODECOND(I),1=1,7)
WRITE(5,196)PORO
WRITE(5,921)KD
WRITE(5,922)BUIEDEN
WRITE(5,731)XX(1),XX(2)
WRITE(5,732)(YY(I),1=1,7)
WRITE(5,934) (COyiMENT(I) ,1=1,14)
CCCCCC
C CALL DATE AND TIME TO MARK OUTPUT FOR LATER REFERENCE
CCCCCC
CALL GETTTM(IHR, IMIN, ISEC, I100TH)
CALL GETDAT (IYR, IMON, IDAY)
WRITE(5,170) IM3N,IDAY,IYR,IHR,IMIN,ISEC,I100TH
ENDIF
CCCCCC
C WRITE TO FLUX SUMMARY OUTPUT FILE
CCCCCC
WRITE(7,452) (AREA(I) ,1=1,7) ,DATE
WRITE(7,401) (NETFLCW(I) ,1=1,7) ,DATE
WRITE(7,402) (TWMASS(I) ,1=1,7) ,DATE
WRITE(7,405) (DRVOISUM(I) ,1=1,7) ,DATE
WRITE(7,406)(MSTORSUM(I),1=1,7),DATE
WRITE(7,451) (MEANWTD(I) ,1=1,7) ,DATE
WRITE(7,403) (NETCFICW(I) ,1=1,7) ,DATE
WRTIE(7,404)(TCMASS(I),1=1,7), DATE


49
during drilling. Hie observation well was made of a 1.5 m section of
6.3 cm diameter commercial PVC well screening solvent-welded to two 6.1 m
sections of schedule 40 PVC. Hiis was jetted down inside of the casing
until it bottomed out at the bottom of the previously augered hole.
After placement of the well, a mixture of bentonite and water was poured
into the area between the casing and well up to the top of the casing.
Hie purpose of this well was to observe the piezcmetric head difference
across the restricting layer.
4.3 Chemical Applications
Hie first application of chemicals to the experimental site
occurred on 11/12/86. On the day preceding this first application, the
entire field site was rotary mowed. Hie grass within the application
area was cut to a height of approximately 5 cm. All dead grass and
clippings were raked up and removed from the area prior to application.
Hie application area consists of a strip 36.6 m long by 9.14 m wide as
shewn in Figure 4.1.
Atrazine and alachlor were applied simultaneously on November 12.
Atrazine was applied in the form of AAtrex (Ciba-Geigy Corp.) which is
an emulsifiable concentrate containing 0.479 kg AI (active ingredient)/L
(4 lb/gal). Alachlor was applied in the form of Lasso (Monsanto
Agricultural Products Co.) which is also an emulsifiable concentrate
containing 0.479 kg AI/L. Hie herbicides were applied using a self-
propelled boom sprayer with provisions for injecting chemicals directly
into the water stream (Sumner et al., 1987). Hie boom length on the
sprayer is 9.14 m.


97
Table 6.5. Mean concentrations of alachlor (mg/kg) in soil samples.
Sample Depth
(can)
11/18/86
11/24/86
Sample Date
12/22/86
2/9/87
3/16/87
5/25/87
0-5
3.26
3.11
NS
NS
1.67
NS
(58)
(59)
(55)
0-15
NS
NS
NS
0.73
0.68
0.12
(104)
(23)
(65)
5-15
NS
0.17
NS
NS
NS
NS
(96)
25-36
NS
NS
0.00
0.00
0.02
0.01
(224)
(141)
Calculated
Mass in
Profile (g)
79
84
0
53
69
13
rate of 3.41 kg/ha over an application area of 334 m2 (114 g). The
maximum mass of alachlor within the soil profile was calculated to be 79
g on 11/18/86. This represents 53% of the intended application rate, and
in excess of 300% of the calculated application rate based on the
collection of the application solution as described above.
The complete data set of soil sample concentrations can be provided
upon request. Refer to Appendix B for information on how to request the
data and a sample of the data set.
Approximately 30 samples from the soil solution samplers were
extracted and analyzed for residues of atrazine and alachlor. The
samples from sampler 09N-2 showed a pulse of atrazine moving past the 61
cm depth. The peak concentration of atrazine in samples from this
sampler was approximately 0.35 mg/L and this peak occurred on Nov. 24,


APPENDIX A
MONITORING WELL STATISTICS


159
parameters. When the models were run using pesticide properties obtained
from the manuals, simulated leaching and degradation of the herbicides
exceeded field observations. The models were not calibrated to the
observed data.
The data collected during this study provide a picture of the
concentrations of various chemicals within the soil profile and
groundwater on specific dates. Data to accurately quantify the movement
of water on the site, however, are missing. The models predict the mass
flux of chemicals past a given point (usually the bottom of the root
zone). The observed data from this first year study are insufficient to
determine mass fluxes and therefore can not be used to adequately test
the mass flux predictions of the models. PRZM simulation results were
used to compare observed and predicted concentrations of the chemicals.
This is because PRZM will report the simulated concentrations at any
depth on a daily basis. GLEAMS will only output soil concentration data
(/ig/g) on days with a storm event that causes leaching below the root
zone. This makes it difficult to compare GLEAMS predictions with samples
taken between storm events.
The results presented in this dissertation represent data from the
first year of a field study and application of two pesticide transport
models to the conditions present at the field site during the study.
Much has been learned about sample collection, sample analysis, and the
limitations of the data which have been collected, for both describing
the movement of the chemicals and testing model predictions.


72
The degradation rate constant of atrazine in soil was given in the
LEACH manual as ranging iron 0.0149 to 0.0063 days-1, corresponding to a
half-life of between 46 and 110 days respectively. The midpoint value
of 78 days was chosen for use in these simulations. A degradation rate
constant of 0.0384 days-1, which corresponds to a half-life of 18 days,
was given for alachlor in both the CREAMS and LEACH manuals.
The coefficient of plant uptake of pesticides with transpiration was
determined using the relationship given in the FKZM manual in which the
uptake factor is a function of and is given as
UPTKF = 0.784 exp [(log % 1.78)2/2.44] 5.2
where UPTKF = plant uptake efficiency factor.
Using the for alachlor of 434 as discussed above, the uptake factor
was calculated to be 0.52. Back calculating a for atrazine, based
upon the chosen of 163 using equation 5.1, yields a of 264.
Using this value in equation 5.2 gives a plant uptake factor of 0.65.
The models were also run using uptake factors for both atrazine and
alachlor of 0.0 and 1.0 in order to assess the sensitivity to these
factors. GLEAMS currently recommends that in the absence of well defined
uptake coefficients, a value of 1.0 should be used. Uptake factors for
the inorganic tracers were assigned values of both 0.0 and 1.0 to observe
the sensitivity of these non-adsorbed chemicals to plant uptake.
The dates and amounts of the chemicals applied to the soil surface
of the experimental site were entered into the models in a direct manner,
ie. there were no calculations or transformations required to convert the
actual values into model parameters. The application rates used in the
simulations correspond to the intended application rates as opposed to


164
Donigian, A. S., Jr. and P. S. C. Rao. 1986b. Example model testing
studies, in Vadose Zone Modeling of Organic Pollutants. S. C.
Hem and S. M. Melancon, Eds. Lewis Publishers, Inc. Chelsea, MI.
pp. 103-131.
Enfield, C. G., R. F. Carsel, S. Z. Cohen, T. Fhan, and D. M.
Walters. 1982. Approximating pollutant transport to ground water.
Ground Water. 20:711-722.
Farm Chemical Handbook. 1984. Meister Publishing Company,
Willoughby, OH.
Freeze, R. A. and J. A. Cherry. 1979. Groundwater. Prentice-Hall,
Englewood Cliffs, NJ. 604 pp.
Gamer W. Y., R. C. Honeycut, and H. N. Nigg, Eds. 1986. Evaluation of
Pesticides in Groundwater. ACS Symposium Series No. 315, American
Chemical Society, Washington D.C. pp. 100-115.
Gish, T. J. 1987. Bromide transport in structured soils. ASAE Paper
No. 87-2625. ASAE, St. Joseph, MI. 10 pp.
Gish, T. J., C. S. Helling and P. C. Kearney. 1986. Simultaneous
leaching of bromide and atrazine under field conditions, in
Agricultural Inpacts On Groundwater QualityA conference.
Omaha, NE. National Water Well Association. Water Well Publishing
Co., Dublin, OH. pp. 286-297.
Golden Software, Inc. 1987. Surfer Reference Manual. Golden, 00.
329 pp.
Hallberg, G. R., 1986. Overview of agricultural chemicals in ground
water, in Agricultural Inpacts On Groundwater QualityA
conference. Omaha, NE. National Water Well Association. Water
Well Publishing Co., Dublin, OH. pp. 1-63.
Hedden, K. F. 1986. Example field testing of soil fate and
transport model, PRZM, Dougherty Plain, Georgia, in Vadose Zone
Modeling of Organic Pollutants. S. C. Hem and S. M. Melancon,
Eds. Lewis Publishers, Inc. Chelsea, MI. pp. 81-101.
Helling, C. S. and T. J. Gish. 1986. Soil characteristics affecting
pjesticide movement into groundwater, in Evaluation of Pesticides
in Groundwater. W. Y. Gamer, R. C. Honeycut, and H. N. Nigg,
Eds. ACS Symposium Series No. 315, American Chemical Society,
Washington D.C. pp. 14-38.
Hetrick, D. M. and C. C. Travis. 1988. Model predictions of
watershed erosion components. Water Resources Bulletin.
24(2)413-419.


47
withdrawn. No details on backfill procedures used by the USDA are
available.
The wells were continuous from the restricting layer to a height of
approximately 50 cm above the soil surface. No solvent weld joints or
connections were used. Each well was fitted with a 0.64 cm diameter
polypropylene tube which extended from approximately 2.5 cm above the
bottom of the well through a #2 rubber stepper placed in a vented PVC
cap. The polypropylene tubes were not tested for adsorption of atrazine
and alachlor. The caps were vented by milling small channels into
opposite sides of the inside of the cap. This was done to prevent a
vacuum from forming in the well during sampling.
The wells were installed on a 12 by 12 m grid corresponding to the
locations of existing sprinkler risers. The wells were offset by
approximately 50 cm to the west of the sprinkler risers to avoid hitting
supply lines beneath the soil surface. In the application area the
wells and solution samplers were offset from the sprinkler risers by
about 3.05 m so that the risers and supply lines would not interfere
with the downward movement of chemicals within the vicinity of the wells
and samplers. The monitoring wells were assigned ID labels to indicate
their position relative to the sprinkler riser in the SE comer of the
2.7 ha field on which the experimental site is located. Figure 4.1 shows
the locations of the wells and the associated ID labels. Well 08-15 is
located at the edge of the tree line bordering this side of the study
site. One well is located in the woods approximately 30 m west northwest
of well 08-15. This area is a seepage zone where subsurface flows


165
Hook, J. E. 1985. Irrigated com management for the coastal plain.
Georgia Agricultural Experiment Stations Research Bulletin
No. 335. Athens, GA. 34 pp.
Hook, J. E. 1987. Soil physical properties of a pesticide study
site in the Dougherty Plain of Georgia. Final Report. U. S.
Environmental Protection Agency, Athens, GA.
Hornsby, A. G., P. S. C. Rao, W. B. Wheeler, P. Nkedi-Kizza, and R.
L. Jones. 1983. Fate of aldicarb in Florida citrus soils: 1.
field and laboratory studies, in Proceedings of the
Characterization and Monitoring of the Vadose (Unsaturated) Zone.
Las Vegas, NV. D. M. Nielson and M. Carl, Eds. National Water
Well Association. Water Well Publishing Co., Dublin, OH.
pp. 936-958.
Jones, R. L., A. G. Hornsby, P. S. C. Rao, and M. P. Anderson.
1987. Movement and degradation of aldicarb residues in the
saturated zone under citrus groves on the Florida Ridge. J.
Contaminant Hydrology. 1:265-285.
Jones, R. L., P. S. C. Rao, and A. G. Hornsby. 1983. Fate of
aldicarb in Florida citrus soils: 2. model evaluation, in
Proceedings of the Characterization and Monitoring of the Vadose
(Unsaturated) Zone. Las Vegas, NV. D. M. Nielson and M. Carl
Eds. National Water Well Association. Water Well Publishing
Co., Dublin, OH. pp. 959-978.
Jury, W. A. 1982. Simulation of solute transport using a transfer
function model. Water Resources Research. 18(2):363-368.
Jury, W. A. 1985. Spatial Variability of Soil Physical Parameters in
Solute Migration: A Critical Literature Review. Electric Fewer Research
Institute. Palo Alto, CA. EPRI EA-4228.
Jury, W. A. 1986a. Mathematical derivation of chemical transport
equations, in Vadose Zone Modeling of Organic Pollutants. S. C.
Hem, and S. M. Melancon, Eds. lewis Publishers, Inc. Chelsea,
MI. pp. 271-288.
Jury, W. A. 1986b. Chemical movement through soil, in Vadose Zone
Modeling of Organic Pollutants. S. C. Hem, and S. M. Melancon,
Eds. Lewis Publishers, Inc. Chelsea, MI. pp. 135-158.
Jury, W. A. 1986c. Adsorption of organic chemicals onto soil, in
Vadose Zone Modeling of Organic Pollutants. S. C. Hem, and S. M.
Melancon, Eds. Lewis Publishers, Inc. Chelsea, MI. pp. 177-189.
Jury, W. A. 1986d. Volatilization from soil, in Vadose Zone
Modeling of Organic Pollutants. S. C. Hem, and S. M. Melancon,
Eds. Lewis Publishers, Inc. Chelsea, MI. pp. 159-176.


APPENDIX D
SURFACE PLOTS OF ATRAZINE CONCENTRATION IN GROUNDWATER


153
DAYS SINCE 11/12/86
Figure 6.71. Measured and PRZM predicted brcsnide concentrations in
the soil solution at a 183 cm depth following the
first application.
Figure 6.72. Measured and PRZM predicted brcsnide concentrations in
the soil solution at a 61 cm depth following the
second application.


76
few will lead to increased numerical errors in the solution of the
equations used within the model. The manual suggests that no fewer than
30 compartments be used. Thirty-five compartments were selected because
this gives a compartment depth that is one-half the compartment depth in
GLEAMS for a root zone depth of 91 cm (see section 5.3.3).
FRZM and GLEAMS both normally operate on the assumption that any
soil water in excess of field capacity will drain into the next lower
compartment. This drainage will continue until any excess water drains
from the bottcmi of the simulated profile. This drainage is assumed to
occur within one day. In the PRZM model, this type of drainage is called
' free drainage'. IRZM has a feature to allow selection of a drainage
rate parameter that serves to slow down the percolation process. This is
an empirical constant and requires calibration. A figure is provided in
the manual whereby the user can make a first cut selection of the
drainage parameter based upon the soil type (sand, clay loam, etc.) and
the number of simulation compartments selected as described above.
Observations at the experimental site clearly shew that there is a delay
of 2-4 days from the time excess water is applied to the time that the
water table responds. Therefore a few simulations were made using the
restricted drainage option to compare with the experimental data. The
drainage rate parameter for this case was selected fremi the grafa in the
manual for a sand soil with 35 compartments to be 2.63 day-1.


21
predicting sediment, nutrient, and pesticide losses with surface runoff
from agricultural management systems. GLEAMS builds upon the foundations
in CREAMS by adding components to simulate movement of water and
chemicals within the crop root zone. Like CREAMS, GIEAMS is a
continuous, daily simulation model.
GIEAMS predicts runoff using the SCS curve number method. Erosion
is predicted based upon modifications to the Universal Soil Loss
Equation. Surface runoff and eroded sediment, with chemicals in both the
dissolved and sorbed filases, can be routed overland, in channels, and
through impoundments.
GIEAMS divides the crop root zone into seven layers. The vadose
zone between the root zone and the water table is not considered. The
first layer has a thickness of 1 cm. This layer is assumed to be the
portion of the soil that determines the mass of pesticide available for
extraction into surface runoff. The thickness of the second layer is
1/6 of the root zone depth minus 1 cm for the first layer. The remaining
5 layers are each 1/6 of the root zone in thickness. This layering
structure is fixed by the program. A strong correlation has been
observed between pesticide concentrations in runoff and the concentration
of pesticides in the top 1 cm of the soil profile (Leonard, 1988). Thus
the authors of CREAMS and GIEAMS believe that a 1 cm active zone at the
soil surface is required to maintain sensitivity of runoff concentrations
to soil concentrations (Leonard and Knisel, 1987). Soil profile
properties can vary with depth. The model weights the input values to
establish average properties for each layer in the model. Percolation
through the profile is based on the water-holding capacity of the soil as


75
selected. Based on observations at the research site, a maximum areal
coverage of the bahia grass foliage was estimated as 98 percent.
Dates of crop emergence, maturation, and harvest are used in the
model to define the development and growth of roots. Roots are assumed
to begin growing at crop emergence and reach the maximum rooting depth at
plant maturation. The roots stay at maximum depth until harvest when
they are set back to zero. These parameters are somewhat difficult to
define in relation to a perennial crop such as bahia grass. In view of
the use made of the selected dates, same artificial dates for emergence
and harvest could be selected. Since the emergence date really defines
the beginning of plant transpiration as a component of the water balance,
an emergence date of March 1 was selected to correspond to the time when
the grass begins to "green up" in the spring. Since the majority of the
roots already exist when the grass begins active growth in the spring, a
maturation date of April 1 was selected to reflect water extraction from
the full maximum rooting depth shortly after growth for the new year
begins. The harvest date was chosen to be December 1 which reflects the
approximate date when the grass turns brown in the winter and active
growth ceases.
5.2.3 Soil related parameters
PRZM will allow simulation of a larger section of the profile than
just the root zone. A soil core depth of 2.62 m was chosen to represent
the average profile depth beneath the application area. The model will
allow the user to select the number of compartments used to represent the
profile. Too many compartments will increase simulation times and too


87
08-09
b) Application Rate
Figure 6.2. Uniformity of alachlor application, a) concentration in
application solution (vertical bars shew sampling
locations), b) application rate (vertical bars shew
location of monitoring wells)


15
Pesticides applied to plant foliage will likely be transformed or
degraded more rapidly than if the pesticide were applied to the soil or
incorporated. Pesticides on the foliage may also be absorbed into the
plant. Pesticides in the solution phase may be taken up by plant roots
and translocated to various parts of the plant. These processes serve as
a loss mechanism reducing the availability of the pesticide for
transport.
Pesticide residues on plant foliage can be dislodged by rainfall and
irrigation and washed onto the soil surface, resulting in an increased
mass of pesticide available for transport in runoff and percolation
(Smith et al., 1981; Donigian and Rao, 1986a). Plant residues which are
left on the soil surface or incorporated can also serve as a source of
pesticide which may be available for transport.
3.3 Predicting Pesticide Transport
There are many methods that can be used to predict the nobility of a
given pesticide. These range from simple indices to complex research
models. Each method has value when used for the purpose for which it was
designed and with recognition of the assumptions and limitations
associated with it.
3.3.1 Indices of contamination potential
Rao et al. (1985) compared several methods for computing indices of
the contamination potential of pesticides. Most of the indices are based
on one or more chemical properties such as and solubility. Three
indices also included distance to groundwater and recharge rate. The


61
water was then drained and discarded. The hexane was filtered into a 500
inL boiling flask through sodium sulfate to remove any remaining water.
Next, the boiling flask was placed on a rotary evaporator and the
reduced in volume to near dryness. A few mL of fresh hexane was then
added to the flask and swirled to wash the sides of the flask. This
hexane was placed in a 10 mL volumetric flask. A few mL of hexane was
again added to the boiling flask and the swirling repeated. After two
rinses of the flask, the hexane in the volumetric flask was brought to a
final volume of 10 mL. The finished sample was stored under
refrigeration in test tubes with teflon lined screw closures. The
initial sample volume of 150 mL and the final extracted volume of 10 mL
indicate that this procedure concentrated the residues by a factor of 15.
Blanks taken from remote wells as well as DI water were extracted as a
check on the procedure. Spiked samples of well water and DI water were
extracted to determine the recovery levels of the herbicides using the
procedure described above.
Soil samples were removed from the freezer and allowed to air dry
for one day prior to extraction. This was done to allow excess moisture
to evaporate since the acetone used in the extraction process can absorb
water which will not be removed by filtration through sodium sulfate as
was done for the hexane extractions. Acetone was chosen as the solvent
for soil extractions because it was observed that the water in the soil
samples, or seme other factor, tended to repel hexane. It did not
appear that the hexane was adequately dispersing the soil particles.
A fifty gram portion of the air-dried soil sample was weighed and
placed into the mason jar. One hundred mL of pesticide grade acetone


149
0.0
04-
25 "i
i
100t
E
H 125 4
CU
g .50]
1 75 4
200-
ALACHLOR CONC. (mg/kg)
0.5 1.0 1.5 2.0
11 i i i i i i i i i i i i i iiii
\
>
Sample Date : 12/22/86
MEASURED
PRZM
Notes:
No Samples Above 25 cm.
Measured Values are Avg.
of 6 Samples/Depth
Alachlor not Detected in
These Samples
Figure 6.65.
Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 12/22/86.
0.0
0
25 z'
E
E-*
CU
H
Q
50
100 4
_
1 25 z
150^
1 75 z
200 J
ALACHLOR CONC. (mg/kg)
0.5 1.0 1.5 2.0
J 1 1 1 U I I I I I I I I I I l i I
Sample Date: 2/09/87
MEASURED
PRZM
Notes:
No Samples Below 135 cm.
Measured Values are Avg.
of 6 Samples/Depth
Figure 6.66. Comparison of measured and FRZM predicted alachlor
concentrations in the soil on 02/09/87.


96
Table 6.4. Mean concentrations of atrazine (mg/kg) in soil samples.
Sample Depth
(cm)
11/18/86
11/24/86
Sample Date
12/22/86
2/9/87
3/16/87
5/25/87
0-5
0.291
0.23
CM
£
NS
0.06
NS
(68)3
(61)
(42)
0-15
NS
NS
NS
0.16
0.15
0.04
(53)
(29)
(15)
5-15
NS
0.55
NS
NS
NS
NS
(47)
25-36
NS
NS
0.11
0.23
0.13
0.07
(45)
(61)
(51)
(20)
41-51
NS
NS
NS
0.15
0.08
NS
(30)
(71)
56-66
NS
NS
0.02
0.09
0.05
0.04
(45)
(49)
(65)
(78)
71-81
NS
NS
NS
0.05
NS
NS
(53)
86-97
NS
NS
0.02
0.04
0.03
0.03
(63)
(40)
(94)
(61)
117-127
NS
NS
NS
0.03
0.03
NS
(58)
(125)
132-142
NS
NS
0.02
NS
NS
0.02
(49)
(31)
147-157
NS
NS
0.01
NS
0.02
NS
(77)
(38)
178-188
NS
NS
0.01
NS
0.04
0.01
(64)
(67)
(38)
Calculated
7
32
40
70
58
32
Mass in
Profile (g)
No Sample
^Coefficient of variation (%)
^Mean


151
Figures 6.69-6.71 shew the measured and predicted concentrations of
bromide in the soil solution at depths of 61, 122, and 183 cm following
the first application of bremide. Figures 6.72-6.74 shew the bromide
concentrations at the same depths follcwing the second bremide
application. PRZM predicted concentrations are shewn for both free
drainage and restricted drainage options (see Section 5.2.3 for
discussion of drainage options in FRZM). In almost every case shewn in
Figures 6.69-6.74, the time to peak concentration of the measured data
falls between the predicted time to peak concentration for the two
drainage options. Thus it would appear that a drainage rate parameter
could be selected which would match the measured time to peak
concentration more closely. This would be part of a calibration
procedure. Calibration of the models was not attempted since there is
only one year of data from this study and if this data is used for
calibration, there would be no independent data to test the calibrated
models against.
It should also be noted in Figures 6.69-6.74 that in all cases the
measured and predicted peak concentrations agreed to within an order of
magnitude, and that in most cases they agreed to within a factor of 2 to
3. Thus for the case of the bromide applications, PRZM would meet the
criteria for acceptance suggested by Hedden (1986). In the work
presented here PRZM and GLEAMS were essentially run in a screening mode.
Many of the model parameters were selected from tables and other
information contained the model's users manuals. Parameters were not
optimized or calibrated to produce the best fit. At this stage of data
collection and model use, it was decided to investigate how the models


64
the following sample. Carryover was normally not a problem, but the
solvent blank would often shew some trace of the herbicides.
Working standards of the two herbicides were prepared using
reference standards provided by the EPA in Research Triangle Park, North
Carolina. The initial standard was made by dissolving 10 mg of the
reference into 10 mL of solvent yielding a concentration of 1 mg/mL
(1000 mg/L). This standard was then used as the basis for subsequent
working standards. Ihe standards were prepared with the concentration
of alachlor being 5 times greater than the concentration of atrazine so
that the detector response to the chemicals would be nearly equal.
Working standards of atrazine ranged from 0.01-500.0 mg/L with the
concentrations of alachlor generally being 5 times higher as noted above
(very high atrazine standards did not include alachlor). The 0.01 mg/L
standard of atrazine represented the smallest concentration which was
detectable without pushing the limits of machine and detector. Ihe
autosampler injected a volume of 4.8 fiL. So an injection of 4.8 /L of
0.01 mg/L atrazine represented a lowest detectable mass of 10 nanograms
of atrazine.
Whenever new standards were prepared, they were compared with the
previous standards to assure similar response and that no mistakes had
been made during the dilutions. Two sets of standards were made. One
set was made in acetone for use in analyzing the soil sample extracts.
The other set was made in hexane for use in analyzing the water sample
extracts. Comparisons between the two sets of standards revealed no
differences in response, but recommended procedures (Wheeler, 1987)
dictate that the standards be made up in the same solvent as the samples.


46
these were beyond the budgetary constraints of this project. Seme
existing wells, installed by the USDA-ARS Southeast Watershed Research
Laboratory in conjunction with the GPR mapping by Asmussen et al. (1986)
were also used. Conversations with the technicians involved with the
installation revealed that the wells were constructed of the same
materials as described above but were perforated with tiny drill holes
instead of being slotted. They were unable to recall the diameter of the
holes or the extent of the perforation above the bottom of the well.
Appendix A lists statistics associated with each well and indicates which
wells were installed by USDA.
The wells were installed by hand augering a 10 cm diameter hole
down to the top of the restricting layer. The restricting layer was
identified by a sudden change from yellcwish-white sand to red clay
mixed with small rocks. The change was very abrupt and the effort
required to auger through the clay was significantly greater than to
auger through the coarse sand. A PVC end cap was slipped onto the end
of the well and held in place by friction during installation of the
well. Fine gravel was placed around the wells in the zone of the slots
in an attempt to minimize sand migration into the wells through the
relatively coarse slots. Soil from a nearby borrow pit was used to fill
the hole around the well to within 12 cm of the surface. Ten centimeters
of a water and bentonite slurry was then added to form a seal around the
well. The final 2 cm was filled with soil. The USDA augered down to
the restricting layer with a hollow stem auger on a small drill rig.
The well was slipped into the center of the auger and the auger was


19
The combined SESOII/EROS model of Hetrick and Travis (1988) adequately
predicted long-term (several months) average runoff and sediment yields
when tested against data from three small watersheds. However, monthly
predictions were not good, especially when most of the runoff occurred
due to large single storm events.
M3USE (Method Of Underground Solute Evaluation) was developed at
Cornell University as a management model and a training tool for students
and professionals (Steenhuis et al., 1987). M3JSE is an interactive,
menu-driven program which can run on a IEM-PC or compatible computer.
The model can read daily historical weather files or generate synthetic
rainfall, air, and soil temperature patterns. Surface runoff is computed
using the SCS curve number method. Erosion is not considered. Solute
movement in the vadose zone considers advective and dispersive flux, as
well as degradation and adsorption. MDUSE will also simulate the
movement of a pesticide in a two-dimensional unconfined aquifer.
Extensive graphics illustrate movement as it is being simulated.
PRZM (Pesticide Root Zone Model) is a field-scale hydrology and
transport model developed by the EPA (Carsel et al., 1984). PRZM is a
continuous model capable of simulating water and chemical fluxes over
many years of historical daily weather records. Runoff is predicted
based on the SCS curve number equation, and erosion is simulated using a
modification of the Universal Soil Loss Equation for daily time steps.
The model can simulate the entire vadose zone (soil surface to
groundwater). The vadose zone can be characterized by several layers
with varying properties. For calculations, the vadose zone is divided
into many compartments of equal depth. The model simulates crop growth


68
5.1.2 Soil properties
Both models use the Soil Conservation Service (SCS) curve number
method (USDA-SCS, 1972) to partition rainfall between runoff and
infiltration. Ihe runoff curve number was selected from Table 9 in the
PRZM manual and was chosen based on the assumption that the bahia grass
cover could be assumed to be similar to a pasture in good condition
without contouring (Lakeland sand is classified as being in hydrologic
soil group A).
Physical descriptions of the soil profile were obtained from two
sources. Hook (1985) measured properties of the soil in another section
of the farm on which the experimental site is located. The data
collected by Hook (1985) were measured within the top 120 cm of the soil
profile. The soils on the farm are uniformly classified as either a
Lakeland sand or Bonifay sand. The primary differentiation between the
soils is the depth to the restricting layer with the lakeland soil being
deeper than the Bonifay. Soil physical properties such as water
retention and hydraulic conductivities are considered to be the same
between these soils on this farm. The second source of data used to
determine soil properties is a soil characterization report for Florida
soils prepared by the Soil Science Department at the University of
Florida (Carlisle et al., 1978). This report contains characterization
data for a lakeland sand.
The properties that were selected to represent the soil profile of
the experimental site are summarized in Table 5.1. The average depth to
the restricting layer for the three wells within the application area is
2.62 m and this was chosen to represent the profile depth.


70
variability is understandable and expected. However, even water
solubility was found to vary by a factor of two. The solubility of
atrazine is not available from the PRZM manual. The CREAMS manual lists
a value of 33 mg/L. However, the Agrichemical Handbook (1983) gives a
value of 70 mg/L at 20C. The solubility of alachlor is given in the
PRZM manual as 220 mg/L at 20-25C. The CREAMS manual reports a value of
242 mg/L at an unspecified temperature. The Farm Chemical Handbook
(1984) does not report a value for alachlor and the Agrichemical Handbook
(1983) gives the solubility of alachlor in water as 148 mg/L at room
temperature. The final values selected for all chemical properties are
shown in Table 5.2.
The solubilities of bromide, nitrate, and chloride listed in Table
5.2 are considerably less than the actual solubilities. However,
solubilities of ionic species are seldom listed for reference. The
values in Table 5.2, while less than the actual solubility values, are
sufficiently high such that solubility was not a limiting factor in the
mobility of the chemicals.
Table 5.2 Chemical properties used in simulations.
Property
Atrazine
Alachlor
Bromide
Nitrate
Chloride
Solubility
33
148
800
800
800
(mg/L)
Partition coef.,
163
268
0
0
0
Kqc (cair/g)
Half-life
78
18
999
999
999
(days)
Plant Uptake Coef.
1.0
0.65
1.0
0.52
1.0
1.0
1.0
0.0
0.0
0.0
0.0
0.0


29
sampling. Water samples were collected monthly throughout the study.
The storage conditions prior to analysis were not specified.
Results from the first 6 months of the study were reported by
Hornsby et al. (1983). Results are reported in terms of total toxic
residues (TTR), which is the sum of residues of aldicarb, aldicarb
sulfoxide, and aldicarb sulfone. Fifteen days after application, TTR
were not detected belcw 30 cm at the Oviedo site, but were detected in
all replicates at the Lake Hamilton site to a depth of 120 cm. After 120
days, TTR were detected at the deepest sampling depths (150 cm at Oviedo,
and 300 cm at Lake Hamilton) at both sites. No TTR were observed at the
Oviedo site in any of the observation wells during the first six months.
Two wells within the treated area at the Lake Hamilton site showed
measurable levels of TTR. In one well, TTR levels as high as 1.2 mg/L
were measured approximately 130 days after application. Data relating to
rainfall or irrigation during the study were not presented.
Jones et al. (1983) used the data from the aldicarb study above to
evaluate three pesticide transport models. One of the models tested was
PRZM (Carsel et al., 1984). Agreement between PRZM predicted movement of
aldicarb and the observed data was good. A sensitivity analysis was
performed to demonstrate the response of PRZM to variations in several
soil and pesticide properties. Jones et al. (1987) presented results
from the Lake Hamilton site in which water samples from the shallow water
table were collected from a network of 174 wells over a period of three
years. These data were used to evaluate predictions from a linkage of
the FRZM model to a one- or two-dimensional saturated transport model.
The linked model was developed to allow predictions of the extent of


180
Figure C.l Continued.
CCCCOOC
C READ IN VALUES FROM INHJT FIIES
C WIMIN AND WTMAX ARE MIN AND MAX VALUES OF WT ELEVATION
C CONCMIN AND CONCMAX ARE MIN AND MAX CHEM CONCENTRATION VALUES
ccccccc
READ(4,*)
READ(4,*)
READ(4,*)
READ(4,*)
READ(4,*) WIMIN, WIMAX
CCCCCC
READ(6,*)
READ(6,*)
READ(6,*)
READ(6,*)
READ(6,*) OONCMIN, CONCMAX
CCCCCC
C ZERO OUT OLD VALUES AT THE NODES AND READ IN NEW VALUES OF WT(I,J)
C AND C0NC(I,J)
CCCCCC
DO 35 1=1,23
DO 35 J=l,45
WT(I,J) = 0.0
WTD(I,J) = 0.0
cmc(i,j) =o.o
SLPX(I,J) = 0.0
SLPY(I,J) = 0.0
VELX(I,J) = 0.0
VELY (I,J) = 0.0
FLCWX(I, J) = 0.0
FLCWY (I, J) = 0.0
CFD0WX(I, J) = 0.0
CFICWY (I, J) = 0.0
MAGNITUDE (I, J) = 0.0
DIRECTION (I, J) = 0.0
DRVOL(I, J) =0.0
MAXST0R(I, J) = 0.0
35 CONTINUE
DO 45, J= 1,NY
READ(4,*) (WT(I,J), 1=1,NX)
READ(4,*)
READ(6, *) (CmC(I,J), 1=1,NX)
READ(6,*)
45 CONTINUE
CCCCCC
C NOW COMPUTE SLOPE AND MAGNITUDE AT INTERIOR NODES USING CENTERED
C DIFFERENCE TECHNIQUES
C FIRST COMPUTE SLOPE INDIVIDUALLY IN X AND Y DIRECTIONS
C MINUS SIGN GIVES ACTUAL SLOPE AND NOT GRADIENT
CCCCCC


169
Valentine, R. L. and J. L. Schnoor. 1986. Biotransformation. in
Vadose Zone Modeling of Organic Pollutants. S. C. Hem, and S.
M. Melancon, Eds. Lewis Publishers, Inc. Chelsea, MI.
pp. 191-222.
Voznakova, M. P. and V. Tatar. 1983. Determination of triazines in
water by GC and DC. J. Oxrcmatographic Science. 21:39-42.
Wagenet, R. J. 1986. Principles of modeling pesticide movement in
the unsaturated zone, in Evaluation of Pesticides in Groundwater.
W. Y. Gamer, R. C. Honey cut, and H. N. Nigg, Eds. ACS Symposium
Series No. 315, American Chemical Society, Washington D.C.
pp. 170-196.
Wagenet, R. J., and J. L. Hutson. 1986. Predicting the fate of
nonvolatile pesticides in the unsaturated zone. J. Environ. Qual.
15:315-322.
Wang, H. F., and M. P. Anderson. 1982. Introduction to Groundwater
Modeling, Finite Difference and Finite Element Methods. W. H.
Freeman and Company, San Francisco, CA. 233 pp.
Wartenberg, D. 1988. Groundwater contamination by Temik aldicarb
pesticide: the first 8 months. Water Resources Research.
24(2):185-194.
Wauchope, R. D. 1978. The pesticide content of surface water
draining frcan agricultural fields a review. J. Environ. Qual.
7(4):459-472.
Wheeler, W. B. 1987. Personal communication. Professor of Food Science
and Human Nutrition. University of Florida, Gainesville, FL.
Wischmeier, W. H., and D. D. Smith. 1978. Predicting rainfall
erosion losses. U. S. Department of Agriculture, Agriculture
Handbook No. 537. Washington, DC. 58 pp.


9
and molecular diffusion of solute from pores with high concentrations to
adjoining pores with lower concentrations (Freeze and Cherry, 1979).
Molecular diffusion can be significant when average pore-water
velocities are lew; otherwise, the processes involved in mechanical
dispersion usually dominate. Differences in pore-water velocities in the
direction of bulk flew cause a spreading out, and consequently a lowering
of peak concentrations, of the solute plume. Seme of the solute will
arrive at a reference point earlier, and seme will arrive later, than
would be predicted based upon the average linear flew velocity. This is
referred to as longitudinal dispersion. The tortuosity and
interconnection of pores in the soil will also cause the solute to move
perpendicularly to the bulk flew direction. This is referred to as
transverse dispersion. Freeze and Cherry (1979) and Rao et al. (1988)
provide detailed discussions of the dispersive-diffusive processes and
their mathematical formulations.
Transport predictions based upon the methods described above have
not compared well with field observations, e.g. Jury et al. (1986).
Numerous modifications to the general theory have been proposed in order
to help explain and reduce the differences between field observations and
theoretical predictions. Rao et al. (1980) proposed separating pore-
water in soils into two regions: inter-aggregate and intra-aggregate.
Convective-dispersive transport was assumed to be limited to the water in
the inter-aggregate region. Ihe intra-aggregate region was assumed to
act as a source/sink of solute. Diffusion between the regions could
remove or add solute to the water in the inter-aggregate region. Jury
(1982) proposed a method of predicting the transport times of solutes to


150
a
o
K
E-
CU
0.0
0-
ALACHLOR CONC. (mg/kg)
0.5 1.0 1.5
2.0
i 1 i L 1 i i i t I i i i i 1 i i 1
in
Sample Date: 3/16/87
MEASURED
PRZM
Notes:
li.ii il i uU-inilii
Measured Values are Avg.
of 5 Samples/Depth
Figure 6.67.
Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 03/16/87.
a
o
K
E-
cu
w
Q
0.0
odS1
25^]
50 £
75 ~
100^
125-E
ALACHLOR CONC. (mg/kg)
0.5 1.0 1.5 2.0
jili i i i l i i i i iiiiii
Sample Date: 5/25/87
MEASURED
PRZM
Notes:
Measured Values are Avg.
of 7 Samples/Depth
150
175 i.
200
Figure 6.68. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 05/25/87.


DEPTH (cm) $ DEPTH (
148
a
0.0
o-=
25 -j
50!
-
75i
WOi
125 t
150 t
1 75 t
200-
ALACHLOR CONC. (mg/kg)
0.5 1.0 1.5 2.0 2.5 5.0 5.5
l.i l i i i i,i.l i i | i
Sample Date: 11/18/86
MEASURED
PRZM
Notes:
No Samples Below 5 cm.
Measured Value is Avg.
of 14 Samples
gure 6.63.
Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 11/18/86.
0.0
75-,
WOi
1 25 i
150i
175i
200-
ALACHLOR CONC.
0.5 1.0 1.5 2.0
l i I i i i i
(mg/kg)
2.5 5.0
l i i
5.5
l
Sample Date: 1 1 /24/86
MEASURED
PRZM
Notes:
No Samples Below 20 cm.
Measured Values are Avg.
of 8 Samples/Depth
Figure 6.64. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 11/24/86.


Distance (m) Distance
41
Soil Surface Elevation (m)
Distance (m)
Restricting Layer Elevation (m)
Distance (m)
Figure 4.1.
Contour maps of soil surface and restricting layer
shewing locations and ID labels of monitoring wells.


92
only be achieved if the actual application rate were at least 2.5 kg/ha.
If the planned application rate of 4.9 kg/ha were contained within the
top 5 cm of soil, the expected concentration in the soil would be 6.75
mg/kg. The observed soil concentrations suggest that the alachlor in the
application samples either volatilized rapidly or adsorbed to the
collection containers prior to transfer into glass containers. If
volatilization from the sample containers was the reason for the lew
concentrations, it would be expected that there would also have been
significant volatilization fran the grass foliage and soil surface prior
to irrigating the site and moving the chemical off of the foliage and
into the soil profile.
The low concentration of atrazine within the top 5 cm of soil is
probably due to movement below this depth with percolating water before
collection of these samples. Soil samples collected six days later
(11/24/86) showed considerably more atrazine in the zone from 5 to 15 cm
than frem 0 to 5 cm. Samples from the 5-15 cm depth range were ret
collected on November 18.
Figures 6.5 and 6.6 show the relationship between the measured soil
concentration of atrazine in the top 5 cm of soil on 11/18/86 and the
concentration of the application solution and the application rate,
respectively. Figures 6.7 and 6.8 show the same relationships for
alachlor. Figure 6.5 shews that there was a poor correlation between the
concentration of the application solution and the concentration in the
upper 5 cm of soil measured at the same locations 6 days after
application. Figure 6.6 shews that there was a higher correlation
between application rate and the soil concentration than there was for


134
6.5.6 Water Balance Calculations
An attempt was made to calculate a water and chemical budget for the
study site. The primary objective of this exercise was to calculate the
mass flux of chemicals moving off the site. The mass of a chemical
stored in the saturated zone at the end of the monitoring period plus the
mass of chemical transported off-site should be comparable to the total
mass of chemical leached from the root zone as predicted by the models.
6.5.6.1 Nodes and subareas
As noted in section 6.5.5, the data for water table elevation,
restricting layer elevation, and chemical concentrations were gridded on
a 3 m by 3 m spacing. Each grid point was considered to be a node for
the calculations described below. The study area was divided into six
subareas as shown in Figure 6.55. The subareas were numbered from the
top of the slope. The chemical application area was entirely contained
within subarea number 2 as shown in Figure 6.55.
6.5.6.2 Nodal calculations
The slope of the water table in both the x and y directions was
calculated at every node within the site using centered-difference
techniques, ie. along the y-axis the slope at node j is calculated as:
slope(j) = [elev(j-l) elev(j+l)] / [2 <5y] 6.1
where slqpe(j) = slope of the water table in ny/m, elev(j-l) and elev(j+l)
= elevation of the water table at nodes j-1 and j+1, respectively, in m,
and 6y is the spacing between nodes in m. The gradient of the water


51
herbicides off of the grass foliage and move them into the soil profile
to minimize volatilization losses from the soil and plant surfaces.
Bromide in the form of potassium bromide (KBr) solution was applied
to the application area using the same sprayer on 11/17/86. There was a
light drizzle of rain during application. Five hundred grains of KBr was
dissolved in 1050 mL of deionized water and injected into the sprayer as
described above. This was equivalent to an application rate of 10 kg/ha
of bromide. The sprayer again was operated at a nozzle pressure of 138
kPa, and a ground speed of 3.05 m/min. Fourteen collectors in the same
locations as for the herbicide application were used to collect
application samples.
No further chemical applications were made prior to fertilization
of the grass on 4/16/87. The fertilizer was applied at a rate of 560
kg/ha of 5-10-15 using a tractor-mounted broadcast spreader. Fifteen
percent of the nitrogen in the fertilizer was in the form of nitrate
nitrogen (N03-N). This is equivalent to applying 18.6 kg/ha of nitrate
(N03).
In an effort to further characterize bromide movement within the
soil profile and determine flow velocities within the groundwater, an
additional bromide application and a separate chloride application were
made. On 4/27/87, KBr was applied to the application area using the
sprayer utilized during the previous application. The application
solution was made by dissolving 1.2 kg of KBr in 2.3 1 of deionized
water. The sprayer made two passes across the application area during
which it applied approximately 1.6 1 of the KBr solution. This was
equivalent to an application rate of 17 kg/ha of bromide. Fifteen


6.54. Total mass of atrazine stored in the saturated zone 133
6.55. Subareas used in water balance 135
6.56. Comparison of percolation volumes predicted by GLEAMS and
FRZM 144
6.57. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 11/18/86 145
6.58. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 11/24/86 145
6.59. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 12/22/86 146
6.60. Comparison of measured and FPZM predicted atrazine
concentrations in the soil on 2/09/87 146
6.61. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 3/16/87 147
6.62. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 5/25/87 147
6.63. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 11/18/86 148
6.64. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 11/24/86 148
6.65. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 12/22/86 149
6.66. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 2/09/87 149
6.67. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 3/16/87 150
xi


48
reemerge as surface water and flew to the Little River. This well is
referred to as "LCW".
Additional instrumentation installed at the site consists of a
weighing rain gage which was located near well 09-12, a water table
recorder which was located near well 08-12, tensiometers for measuring
the water content of the unsaturated soil, and a "deep" well located
between wells 08-13 and 09-13.
The rain gage is a US Weather Bureau standard weighing bucket rain
gage with a seven day chart and 30.5 cm capacity. The rain gage was
calibrated using standard calibration weights after installation.
The water table recorder was installed to provide a continuous
record of the water table elevation to supplement the weekly
measurements taken on all wells. The recorder was equipped with a seven
day clock and a drum type chart recorder.
Eight tensiometers were installed around each set of solution
samplers. The tensiometers were water filled and connected with very
small tubing to a mercury manometer board. The tensiometers were
installed at depths of 30, 60, 90, 110, 122, 140, and 183 cm. There were
two tensiometers located at the 60 cm depth. The tubing connecting the
tensiometers to the manometer boards was routed above the soil surface
with the vacuum and sample lines as noted above.
The "deep" well was installed by drilling a 10 cm diameter hole
inside of a 11.4 cm diameter PVC casing. The casing was pushed down as
the auger advanced. The casing was installed in this manner using 1.5 m
sections to a depth of approximately 12.2 m below soil surface. The
last 3 m of the well was located in a saturated formation that liquified


Cone. (/ug/L) g Cone. (/ug/L)
202
Date: 02/02/87
08-14
10-09
re D.3.
Atrazine concentration in the groundwater on 2/02/87.
Vertical bars indicate sampling locations.
07-11
Date: 02/09/87
Figure D.4. Atrazine concentration in the groundwater on 2/09/87.
Vertical bars indicate sampling locations.


Cone. (mg/L) g Cone. (mg/L)
124
09-14 Date: 05/13/87
o
n ~~
6.42. Nitrate concentration in the groundwater on 5/13/87.
Vertical bars indicate sampling locations.
Date: 05/18/87
Figure 6.43. Nitrate concentration in the groundwater on 5/18/87.
Vertical bars indicate sampling locations.


85
dried for 1 day in order to allow the solvent from the herbicide
standards to evaporate prior to taking subsamples for analysis. The loss
of water from the sairples during this time resulted in an increased mass
of pesticide per unit weight of wet soil. Since the initial moisture
contents were unknown, it was not possible to calculate the expected
concentrations in the air dried sairples. Calculations based on the
spiking levels of the moist soil resulted in apparent recoveries that
exceeded 100 percent. It can be concluded from the data that recoveries
from the soil were high. When the emissions in the soil recovery study
were discovered, the facilities used to perform extractions and analyses
were no longer available.
All concentrations (water and soil) reported in the following
sections are the analytically determined concentrations and have not been
adjusted for expected recovery rates.
6.3 Chemical Applications
Applications of the herbicides and bromide tracer were monitored to
determine the concentration of the chemicals in the application water and
the volume of water applied. Concentrations of the chemicals were
multiplied by the volume of water collected and divided by the opening
area of the collectors in order to determine the application rate on a
mass per unit area basis. Table 6.2 summarizes the measured chemical
application data. The intended application rates were presented in Table
5.3.
The application of atrazine and alachlor to the designated
application area was not uniform as can be seen in Figures 6.1 and 6.2.


Cone. (mg/L) 'g Cone. (mg/L)
111
Date: 05/05/87
re 6.24.
Bromide concentration in the groundwater on 5/05/87.
Vertical bars indicate sampling locations.
Date: 05/08/87
08-09
Figure 6.25. Bromide concentration in the groundwater on 5/08/87.
Vertical bars indicate sampling locations.


42
topography. Slopes of the restricting layer range from 1 to 15 percent,
with the general direction of the slope also being towards the west. The
fact that the soil is a coarse sand also was considered to be
advantageous for monitoring pesticide movement. The high hydraulic
conductivities and lew clay and organic matter contents should result in
increased leaching and movement of applied chemicals as compared with
tighter soils. Another advantage of this site was the availability of
irrigation which could be used to supplement the natural rainfall if
required to maintain a dynamic flew regime.
This study was not intended to represent typical agricultural
chemical usage and practices in the coastal plain of Georgia. The
primary objective was to observe chemical movement within the unsaturated
soil and in a shallow water table aquifer (shallow groundwater). The
properties of the soil and restricting layer described above were suited
for this objective.
4.2 Site Instrumentation
The movement of the applied chemicals was monitored using a
combination of soil samples, soil-water (soil solution) samples from the
unsaturated zone, and saturated zone samples. These samples were
collected using augers, soil solution samplers, and monitoring wells,
respectively. Soil sample collection required no permanent
instrumentation, and the collection procedure will be described in a
later section.
Samples of the water from unsaturated soil were collected using
soil solution samplers. An excellent review of soil solution samplers is


77
5.3 Parameters Unique to GTFAMS
5.3.1 Evapotranspiration prediction
Potential evapotranspiration (PET) is computed in GLEAMS based on
mean monthly maximum and minimum temperatures and mean monthly solar
radiation. The mean monthly temperatures and solar radiation data were
obtained from the same weather station that was used for the pan
evaporation data input to PRZM (section 5.2.1). GLEAMS also
differentiates between soil evaporation and plant transpiration. A soil
evaporation parameter is required as a model input and the manual gives a
suggested value of 3.3 (no units) for this parameter for sands.
5.3.2 Crop related parameters
Crop related parameters which are different from PRZM are leaf area
index (LAI), and winter cover factor. The LAI data are used to reflect
changes in plant transpiration with stage of growth. In keeping with the
observed behavior of bahia grass discussed in section 5.2.2, the LAI of
bahia grass was assumed to be zero until March 1 of each year and to
return to zero on December 1st. The maximum LAI for bahia grass was
assumed to be similar to the LAI for pasture as presented in the CREAMS
manual which had a maximum LAI of 3.0. The LAI for the bahia grass was
assumed to reach 2.0 on April 1 and to reach 3.0 May 1 where it remained
until it began to decline again to a value of 2.0 on Nov. 1.
The winter cover factor is used to moderate evaporation from the
soil surface during the winter due to presence of a bare surface or cover
crops. Since the soil surface at the site was not bare during winter,
the recommended value of the winter cover factor of 0.5 was used.


Bromide and nitrate concentrations in soil water demonstrated large
variability between sampling locations. Nitrate concentrations in the
groundwater exceeded 40 mg/L after fertilization of the field.
Two root/vadose zone pesticide transport models, GLEAMS and
PRZM, were used to simulate the conditions on the site. Comparisons were
made between model simulation results and observed data. The
uncalibrated models predicted concentrations that were generally within
an order of magnitude, and often were within a factor of 2 to 3, of
observed values. Differences between the predictions of the models
appear to be due to the relative detail by which each model describes
soil properties as a function of depth.
xv


Cone. (jug/L) E Cone. (jug/O
08-09
Date: 01/19/87
o
D.l. Atrazine concentration in the groundwater on 1/19/87.
Vertical bars indicate sampling locations.
08-09
o
M-
o
n
o
CN
O
O
Figure D.2. Atrazine concentration in the groundwater on 1/26/87.
Vertical bars indicate sampling locations.
201


133
Table 6.8
Total mass of
chemicals in the :
saturated zone
Chemical
Date of
Maximum Mass
in Saturated Zone (g)
Maximum
Method l-*-
Method 21
Atrazine
5/18/87
8
4
(5)
(3)
Bromide
5/13/87
308
198
(54)
(35)
Nitrate
5/05/87
2916
1873
(23)
(15)
Chloride
5/13/87
16367
11812
(6882)
(4967)
--Method 1: using only measured values in gridding procedure
Method 2: assuming that wells without observations had a concentration
of zero
2Numbers in () are percent of total application
DAYS SINCE 11/12/86
id
>
>
2:
+

id
id
>
p
o
B
Figure 6.54. Total mass of atrazine stored in the saturated zone.
Atrazine was applied to the site on 11/12/86.


BROMIDE CONC. (mg/L) & BROMIDE CONC. (mg/L)
154
DAYS SINCE 11/12/86
6.73. Measured and PRZM predicted bromide concentrations in
the soil solution at a 122 cm depth following the
second application.
DAYS SINCE 11/12/86
Figure 6.74. Measured and PRZM predicted bromide concentrations in
the soil solution at a 183 cm depth following the
second application.


36
atrazine in the groundwater amount to less than 0.1% of the annual
application. Pesticide concentrations in surface waters are generally an
order of magnitude higher than observed in the discharge from Big Spring.
Peak concentrations of pesticides occur following recharge events in the
spring after crops have been planted.
3.5 Summary
There is a growing body of evidence that pesticide residues are
being leached to groundwater in some areas of the country. The U. S. EPA
recognizes that at least 19 pesticides have been found in groundwater in
24 states as a result of agricultural practices (U. S. EPA, 1987a). Many
of the pesticide detections in groundwater have been from the use of
nematic ides, which in general need to be very soluble in order to be
effective. The other major class of compounds found in groundwater from
agricultural practices is herbicides. The herbicides atrazine and
alachlor are used over large areas in many regions of the country, and
have been frequently detected in groundwater. Nitrates from
agriculturally applied fertilizers have also been found in groundwater.
The health risks associated with the low concentrations (typically less
than 10 /xg/L) of pesticides in drinking water are still unknown. There
are documented health risks, however, to infants drinking water with high
levels of nitrates. The agricultural production systems in this country
will continue to require large inputs of chemical fertilizers and
pesticides. Scientists and governmental regulators must identify ways to
protect groundwater supplies from contamination while allowing farmers to
use the chemical inputs required to maintain yields. Simulation models


52
plastic cups were used to catch application solution samples in the same
manner as previously described. Immediately following application the
field was irrigated. Irrigations continued daily for a week in order to
raise the water table and provide plenty of infiltrating water to
transport the bromide through the soil.
Prior to the beginning of irrigations on April 27, most of the wells
on the site were dry. The water table began to rise on May 1 at which
time 10 1 of solution containing 500 g of potassium chloride (KC1) was
poured directly into well 07-09 which is near the top of the site. It
was anticipated that the high concentration of chloride in the solution
could be followed dcwnslcpe with the saturated flew and allcw for
determination of water table flew velocities.
4.4 Sample Collection and Storage
Samples were collected weekly (Monday) throughout the study. There
were occasional periods of more frequent sampling (immediately after
initial applications and during the last week of April through the
middle of May).
The flint glass containers used for well sample collection and
storage throughout this study did not have teflon lined caps. Therefore,
a small piece of parafilm (a common laboratory film used for sealing
beakers and containers to prevent evaporation and contamination) was
stretched over the mouth of the bottle prior to screwing on the caps.
Samples of the application solutions of the herbicides and bromide
were transferred to glass containers and placed in a cooler with ice


CHAPTER 7
SUMMARY AND CONCLUSIONS
A field experiment was conducted to observe the movement of two
surface applied herbicides (atrazine and alachlor) through the soil
profile and within a shallow water table aquifer. Bromide was applied to
the soil surface to act as a non-adsorbed tracer of water movement. The
nitrate component of a surface applied fertilizer was also monitored
through the soil profile and within the water table.
Instrumentation was installed on a 0.7 ha field for collection of
soil water samples from the unsaturated zone and water samples from the
saturated zone. Soil water samples were collected with soil solution
samplers installed in six group of three samplers each at depths of 61,
122, and 183 cm. Shallcw groundwater samples were collected frcm 5 cm
diameter FVC monitoring wells. The wells were installed on a 12 by 12 m
grid over the study site. Soil samples were collected several times
during the study from various depths beneath the application area.
Methods were developed to extract the herbicide residues from soil
and water samples. Herbicide residue samples were analyzed on a gas
chromatograph using a nitrogen-phosphorus detector. Concentrations of
the inorganic tracers were analyzed using an ion chrcmatograph.
The uniformities of application of atrazine, alachlor, arri brcmide
were measured. There was considerable variability observed in the
application rates (kg/ha) of the three chemicals. The chemicals were
157


Complete data sets shewing the concentrations of all chemicals in
both water and soil samples can be obtained by writing to:
Matt C. Smith
Agricultural Engineering Department
University of Georgia
P.0. Box 748
Tifton, Georgia 31793
The data sets can be made available on tape, or floppy disk (5-1/4
or 3-1/2 inch). Data sets will be in ASCII format.
An example of the water sample data set is given in Table B.l The
water sample data set also contains data on the elevation of the water
table at each well that was sampled. This data set contains over 900
records. An example of the soil sample data set is presented in Table
B.2. This data set contains approximately 240 records.
Table B.l Example listing of the water sample data set.
Sample Station Water Table Chemical Concentration (mg/1)
Date
ID
Elevation
(m)
Atrazine
Bromide
Nitrate
Chloride
5/25/87
08-09
27.46
0.006
0.01
0.47
13.3
5/25/87
08-10
27.26
0.005
0.04
3.52
41.5
5/25/87
08-11
26.92
0.013
0.00
0.00
82.3
5/25/87
08-12
25.97
#1



5/25/87
08-13
25.60
0.011
0.02
0.11
22.87
^No sample
174


206
Date: 03/31/87
Figure D.ll. Atrazine concentration in the groundwater on
Vertical bars indicate sampling locations.
3/31/87.
09-11
o H
Figure D.12. Atrazine concentration in the groundwater on 4/06/87.
Vertical bars indicate sampling locations.


45
Figure 4.2. Cross-section of soil profile through application area
shewing locations of monitoring wells and soil solution
samplers.
about 0.6 m above the soil surface to minimize interference around the
samplers. When it was time to collect the samples from the samplers,
the vacuum was switched to the sample collection side and the vacuum side
was opened to atmospheric pressure. Any water that had accumulated in
the samplers would then be pulled into the sample containers.
Monitoring wells for collection of samples from the saturated zone
and measurement of the water table elevation were installed during May
of 1986. The wells were constructed of 6.3 cm diameter, schedule 40 PVC
and were slotted using a veneer blade on a radial arm saw. Three rows of
slots were cut along the circumference of the well. Each slot was
approximately one-sixth as long as the pipe circumference. The slots
were spaced approximately 2.5 cm apart over the bottom 1.22 m of the
well. Although several manufacturers offer slotted PVC well screens,


128
07-10
Date: 05/05/87
^c ^
Figure 6.48. Chloride concentration in the groundwater on 5/05/87.
Vertical bars indicate sampling locations.
07 10 Date: 05/08/87
08-12
07-14
no no no
D\ST\UCE. (/M)
Figure 6.49. Chloride concentration in the groundwater on 5/08/87.
Vertical bars indicate sampling locations.


SOIL CONC. VS. APPLICATION CONC.
93
Figure 6.5. Correlation between atrazine concentrations in the top
5 cm of soil and application solution concentrations.
SOIL CONC. VS. APPLICATION RATE
Figure 6.6. Correlation between atrazine concentrations in the top
5 cm of soil and application rate.


207
10-13
Figure D.13. Atrazine ¡concentration in the groundwater on 4/13/87.
Vertical bars indicate sampling locations.
=1
O
u
c
o
O
o
08-10
Date: 04/20/87
Figure D.14. Atrazine concentration in the groundwater on 4/20/87.
Vertical bars indicate sampling locations.


Cone. (/j.g/L) g Cone. (jug/L)
209
Date: 05/01/R7
:e D.17. Atrazine concentration in the groundwater on 5/03/87.
Vertical bars indicate sampling locations.
08-09
Figure D.18. Atrazine concentration in the groundwater on 5/05/87.
Vertical bars indicate sampling locations.


204
Date: 03/02/87
'STANce: (m) 170 iso SjJ¡C ^
Figure D.7. Atrazine concentration in the groundwater on 3/02/87.
Vertical bars indicate sampling locations.
09-11
08-09
09-14
0!s-rANCE. -- ,7o
Date: 03/09/87
190 5?,cfAN^
Figure D.8. Atrazine concentration in the groundwater on 3/09/87.
Vertical bars indicate sampling locations.


186
Figure C.l Continued.
ENDIF
XPOS = FLOAT(I-l) DELX + XMIN
YPOS = FLOAT(J-l) DELY + YMIN
ROW = 6.0 + FLOAT (1-2) / 4.0
COL = 7.0 + FLOAT (J-2) / 4.0
WRITE(5,130)I,J,ROW,COL,XPOS,YPOS,SURF(I,J) ,IMP(I,J) ,
# WT(I,J) ,WTD(I,J) ,SLPX(I,J) ,SLPY(I,J) ,MAGNITUDE^, J) ,
# DIRECTION (I,J)
60 CONTINUE
WRITE(5,140)
WRITE(5,431)
WRITE(5,432)
WRITE(5,433)
DO 320, J= 2, (NY-1), INC
DO 320, 1= 2, (NX-1), INC
IF(J.EQ.14.AND.INC.Gr.l.AND.I.EQ.2) THEN
JJ = 11
DO 380 II = 10,18,4
WRITE(5,142) II,JJ,MAXSTOR(II,JJ) ,EKVOL(II, JJ) ,WMASS(II,JJ) ,
# OONC(II,JJ),CWMASS(II,JJ),CSMASS(II,JJ),CMASS(II,JJ),
# F!OWX(II,JJ) ,VELX(II,JJ) ,CFI£WX(II,JJ) ,FLOWY(II, JJ) ,
# VELY(II,JJ) ,CFLCWY(II,JJ)
380 CONTINUE
ENDIF
WRITE(5,142)I,J,MAXSTOR(I,J) ,DRVOL(I,J) ,WMASS(I,J) ,C0NC(I,J) ,
# CWMASS(I,J) ,CSMASS(I,J) ,CMASS(I,J) ,FD0WX(I,J) ,VELX(I,J),
# CFLCWX(I,J) ,FIOWY(I,J) ,VELY(I,J) ,CFLCWY(I,J)
320 cmriNUE
cccccc
C WRITE BOUNDARY FLUX AND STORAGE VALUES
CCCCCC
WRITE(5,140)
WRITE(5,180)
WRITE(5,448)(AREA(I),1=1,7)
WRITE(5,181)(BFIOWXl(I),1=1,7)
WRITE(5,182)(BFIOWX2(I),1=1,7)
WRITE(5,183)(BFLCWY(I),1=1,6),BFLOWY(l)
WRITE(5,421)(BFLCWY(I),1=2,7),BFLOWY(7)
WRITE(5,187) (NETFICW(I) ,1=1,7)
WRITE(5,412)(MSTORSUM(I),1=1,7)
WRITE(5,411) (DfM)LSUM(I) ,1=1,7)
WRTIE(5,191)(TWMASS(I),1=1,7)
WRITE(5,447) (MEANWTD(I) ,1=1,7)
WRITE(5,184)(BCFLOWXl(I),1=1,7)
WRTIE(5,185)(BCFIOWX2(I),1=1,7)
WRITE(5,186)(BCFLCWY(I),1=1,6),BCFLOWY(l)
WRITE(5,422)(BCFIOWY(I),1=2,7),BCFLOWY(7)
WRITE(5,188) (NETCFLOW(I) ,1=1,7)
WRITE(5,192)(TCMASS(I),1=1,7)


Table C.2. Continued
DAYS SINCE APPLICATION
DELTA WSTORAGE (m3)
PRED DELTA WSTORAGE
DAYS SINCE APPLICATION
DELTA WSTORAGE (m3)
PRED DELTA WSTORAGE
DAYS SINCE APPLICATION
DELTA WSTORAGE (m'3)
PRED DELTA WSTORAGE
DAYS SINCE APPLICATION
DELTA WSTORAGE (m3)
PRED DELTA WSTORAGE
DAYS SINCE APPLICATION
DELTA WSTORAGE (m3)
PRED DELTA WSTORAGE
DAYS SINCE APPLICATION
DELTA WSTORAGE (m3)
PRED DELTA WSTORAGE
TOT ERROR IN WSTORAGE
174, PERCOLATION
SINCE
5/03/87 = 1
.41
cm
10.9312
29.1446
7.8003
12.2948
16.2689
15.6362
92.0759
5.4211
10.4245
-2.4286
13.6757
7.6882
15.4218
50.2027
177, PERCOLATION
SINCE
5/05/87 =
.00
cm
-7.2057
-3.5609
-.2990
.2405
9.0190
16.4747
14.6681
-5.7125
-2.0040
-12.0272
8.0393
.3808
11.0193
-.3043
182, PERCOLATION
SINCE
5/08/87 = 7
.54
cm
15.6433
16.1147
4.6156
-.0760
-7.6467
-9.7187
18.9330
40.1281
46.0137
13.9722
50.9695
28.3841
51.0628
230.5303
187, PERCOLATION
SINCE
5/13/87 =
.64
cm
-21.3326
20.2321
-12.0260
-9.1887
.8297
1.8801
-60.0701
-4.7329
1.1261
-11.7398
12.8980
-4.4958
23.3991
16.4547
194, PERCOLATION
SINCE
5/18/87 =
.73
cm
-14.5931
23.8722
-15.4708
-22.5782
-24.5092
-15.6859
-116.7080
-2.6033
-1.1742
-6.8849
13.6701
-22.8199
29.7160
9.9038
201, PERCOLATION
SINCE
5/25/87 =
o
o
cm
-5.4107
10.3354
-4.0324
-3.4908
-9.9932
-14.0623
-47.3250
-5.2955
-5.4908
-4.8896
14.2750
-22.6561
13.5992
-10.4578
271.1864 338.8987
71.6449
620.3365
-97.0587
568.4956
1773.5030


121
All concentrations of atrazine in the groundwater were below 0.1
mg/L and in roost cases were below 0.01 mg/L. The highest observed
concentration was 0.09 mg/L and occurred on 5/5/87 in well 08-09 which is
located within the application area. This concentration was observed
after the water table had risen due to the application of approximately
24 cm of irrigation water within a p)eriod of 8 days. There were high
concentrations of atrazine in the groundwater when sampling was
discontinued on 6/1/87. Thus, the data collected do not indicate hew
long atrazine residues persisted on the site. Figures D.1-D.23 in
Appendix D shew the concentration of atrazine in the groundwater on every
sampling date from 1/19/87 through 6/01/87.
6.5.3 Nitrate
Background concentrations of nitrate (as N03) in the groundwater
were generally below 1 mg/L. Well LOW, which may respond to drainage
from other parts of the research farm, often had background
concentrations exceeding 15 mg/L. The study site was fertilized on April
16, 1987, and was heavily irrigated beginning on April 27th. The water
table on the site started to rise on May 1st. Concentrations of nitrate
in the groundwater began increasing on May 3rd. Nitrate concentration
levels in the groundwater were observed to be as high as 35 mg/L.
Figures 6.38-6.45 show the nitrate concentrations in the groundwater on
selected dates.


27
3.4 Field Studies of Pesticide Transport
Field-scale studies of pesticide runoff and leaching have, until
recently, been very limited. Studies of sufficient detail to be used for
the validation and testing of transport models have been limited due to
three factors, according to Donigian and Rao (1986b):
1. Most field studies have focused on either chemical leaching or
surface runoff iron watersheds; few studies have examined both.
2. Since sampling and analysis costs associated with pesticide
leaching are high, most studies are of short duration.
3. Most studies were designed for purposes other than developing
data sets for model testing and therefore are incomplete with
regard to measurement of all the required model input parameters.
In this section, four field studies are reviewed. Methods of
sampling and monitoring will be emphasized. Same of the studies were
conducted specifically for model validation and testing purposes. A
review of the Big Spring Basin study is included due to the unique
conditions in the study area.
3.4.1 Aldicarb in Florida citrus groves
A three-year field study of the movement and degradation of
aldicarb in both the unsaturated zone and a shallow water table was
conducted at two locations in Florida (Hornsby et al., 1983; Jones et
al., 1987). The two locations are referred to as the Oviedo site and the
Lake Hamilton site. At both locations, aldicarb was applied to bedded
citrus trees located on coarse-textured soils. Scare of the soils at the
Oviedo site had a thick organic layer overlying the coarse textured


30
lateral transport of TTR which have entered a shallow water table. PRZM
was used to predict daily mass loadings of aldicarb to the top of the
water table. Results of the saturated zone modeling were in reasonably
good agreement with the observed concentrations in the wells and the rate
of lateral movement.
3.4.2 Comparison of tillage effects in Maryland
Paired watersheds were established in 1984 on the Eastern Shore of
Maryland to observe the impacts of tillage practices on agricultural
chemical leaching and runoff. The experimental design and early results
have been reported by Brinsfield et al. (1987, 1988). One of the
objectives of the experiments was to generate a data set for testing and
validation of pesticide transport models. One watershed was used to grow
com using conventional tillage (CT) practices, and on the other
watershed com was grown using no-till (NT) methods. The CT watershed is
approximately 6 ha in size, and the NT watershed cavers approximately 8.9
ha. The soils in both watersheds are silty, well-drained, and nearly
level.
Soil samples were collected at 5 locations in each watershed 4 times
per year. The samples were collected using a hand auger in 15 cm
increments to a depth of 120 m.
Monitoring wells made of 6.35 cm diameter FVC were installed to
depths of 3-4 m on 30 m centers in each field. The wells were arranged
to divide each field into four quadrants. The wells were installed so
that only the top of the water table would be sampled. A bentonite-soil
mixture was used to seal the top 0.5 m of each well. Well samples were


135
Distance m)
Figure 6.55. Subareas used in water balance calculations.
table, as used in flux calculations, is the negative of the slope defined
above. The flux of the water at any node was calculated as:
flux(j) = -cond(j) slope(j) 6.2
where flux(j) = the water flux at node j in m/day, cond(j) = the
saturated conductivity at node j in m/day, and slope(j) is as defined
above. The volumetric flowrate of water through the area represented by
node j was calculated as
Q(j) = flux(j) wtd(j) 6y 6.3
where Q(j) = volumetric flowrate past node j in m3/day, wtd(j) =
groundwater depth at node j in m, and flux(j) and Sy are as defined
above. The mass of chemical moving past a node was calculated as the
product of the volumetric flowrate, Q, and the solution concentration of
the chemical at that node.
To compute the change in water storage between sampling periods, it
was necessary to calculate the relationship between drained volume and


158
applied using a toon sprayer developed for vise in chemigation research.
The results indicate that chemical application rates from conventional
farm equipment are likely to be highly variable. The variability of
application, in addition to the variability of soil properties, makes
prediction of field measured concentrations difficult.
Concentrations of bromide and nitrate at all three monitored depths
showed a high degree of variability. Ihe peak concentration and the time
required to reach the peak concentration varied significantly between
sampling locations. The effect of this variability may be to reduce
maximum concentrations and increase the duration of loading reaching a
water table.
Atrazine moved rapidly through the sandy soil on the study site.
Concentrations of atrazine in the soil water at a depth of 61 cm reached
0.35 mg/L 19 days after application. Detectable levels of atrazine
reached the water table 2 months after application. Atrazine
concentrations as high as 0.09 mg/L were observed in the groundwater
nearly 6 months after application.
Alachlor was not detected in the soil belcw a depth of 45 cm. No
trace of alachlor was detected in the groundwater samples.
As much as 20 percent of the nitrate from the fertilization of the
study site was observed to be in the groundwater on a given date. Ihe
decreasing concentrations of nitrate in the groundwater with continued
percolation of water suggests that most of the nitrate was leached from
the soil profile.
The PRZM and GLEAMS models were found to be easy to use. Sufficient
information is provided in the user manuals for estimation of required


95
application solution concentration and soil concentration. Figures 6.7
and 6.8 shew that there was essentially no correlation between either
alachlor concentration in the application solution or application rate
and the concentration of alachlor in the top 5 cm of soil.
Additional soil samples were collected four more times during the
study. In order to reduce the total number of samples collected, the
number of depths sampled was increased and the number of sampling
locations was decreased with each successive sampling date. Alachlor was
not observed to move belcw a depth of 36 cm in the soil. No trace of
alachlor was detected in the shallow groundwater samples. Consequently,
most of the results discussed in subsequent sections will be limited to
atrazine. Results of soil sample analyses for atrazine and alachlor
residues are presented in Tables 6.4 and 6.5, respectively. Table 6.4
shows that the sampling strategy to increase the depth of sampling with
time since application resulted in missing the passage of the initial
atrazine front past any point. Samples from a depth of 178-188 cm on
12/22/87 already showed atrazine residues. Tables 6.4 and 6.5 include a
calculation of the total mass of atrazine and alachlor in the soil
profile beneath the application area, respectively. For these
calculations, the concentration was assumed to vary linearly between
sample points. The concentration of atrazine within the profile appears
to increase with time for the first 4 sampling dates due to the way in
which the samples were collected ( increasing depth of sampling over
time. The maximum mass of atrazine within the soil profile was
calculated to be 70 g on 2/9/87 which is approximately 47% of the
intended application of 150 g, or 62% of the measured application


43
presented by Litaor (1988). Smith and Carsel (1988) presented a design
for a stainless steel solution sampler that do not react with pesticides
during sampling. Samplers constructed of stainless steel were not
economically feasible in this research.
The samplers used in this study were constructed of 4.2 cm OD,
schedule 40 FVC and a 1 bar, high flew ceramic exp, 3.99 cm diameter by
19.05 cm long attached to the FVC pipe with epoxy. A 0.64 cm
polypropylene tube was extended from the inside bottom of the ceramic cip
to a bulkhead fitting on the top of the sampler. A second fitting was
placed on the side of sampler, near the top, for attaching a vacuum line.
The samplers were constructed in two lengths, 1.08 and 2.09 m. The
polypropylene tubing used in the samplers was not tested for adsorption
of atrazine and alachlor. This was not done since the solution samplers
were to be used for collection of samples to be analyzed for inorganic
chemicals such as bromide and nitrate. The volumes of samples collected
from the solution samples were in general too small (less than 50 mL) to
permit reliable quantification of pesticide residues.
The samplers were placed such that the center of the ceramic cup
was located at depths of 61, 122, and 183 cm. The samplers were
installed by augering a 5 cm hole to a depth that was approximately 10
cm less than the desired sampling depth. A piece of thin wall aluminum
tubing with ID slightly smaller than the ceramic exp was then driven 20
cm beyond the augered hole, removing a core of soil. The sampler was
pushed into the hole until seated on the bottom. A slurry of water and
soil taken from a borrow pit adjacent to the site was then poured into
the annulus between the FVC and soil. The top 12 cm of the hole was


BROMIDE CONC. (tng/L) § BROMIDE CONC. (mg/L)
103
DAYS SINCE 11/12/86
are 6.14. Brani.de concentration for six sampling locations
following the second application at a 61 cm depth.
Figure 6.15. Bronide concentration for six sampling locations
following the second application at a 122 cm depth.


54
made of polycarbonate with a capacity of 50 mL. The containers had
polycarbonate screw caps for sealing. Immediately after all soil
solution samples were collected, the sample containers were placed in a
cooler for transport.
The elevation of the water table was measured using a well depth
indicator with a stainless steel probe. The depth indicator would sound
an audible alarm when the probe contacted water in the wells. The probe
was attached to a flat cable with markings in feet and inches, with the
smallest division being 1/4 in. Water depths were recorded early in the
morning after arrival.
After the water table elevations in all wells were measured, the
wells were pumped out to insure that the water to be sampled was
representative of the surrounding water in the saturated zone at that
time. When possible, the well was pumped dry. When the depth of water
in the well exceeded approximately 0.4 to 0.5 m, the pumping rate was
less than the rate of flew into the well. When this occurred, the well
was pumped long enough to insure that 2-3 well volumes had been pulled
from the well. A small, 12v battery powered, peristaltic pump was used
to pump the wells and collect samples.
Samples were collected after all wells had been pumped as described
above. A rubber stopper was attached to the intake side of the pump
through aim length of 1.25 cm OD Tygon tubing. The stepper also had a
piece of Tygon tubing approximately 10 cm in length connected to it for
attachment to the sampling tube in each well. The stopper was placed in
a sample bottle and the short piece of tygon tubing was attached to the
well sample tube. Since the pump was downstream from the sample con-


156
The maximum observed concentration of atrazine at the 61 cm depth
was approximately 0.35 mg/L and the maximum concentration predicted by
PRZM was 0.57 mg/L. The predicted and observed maximum concentrations
are again within a factor of 2. The 0.35 mg/1 maximum observed value
however was from one of three samplers from which seme samples were
extracted. The data from the other two samplers do not shew any distinct
peak concentration, however, the concentrations are within an order of
magnitude of the predicted values. The models were run with the intended
application rate of 4.9 kg/ha, but the actual application samples suggest
the true application rate was 3.4 kg/ha. The time to peak concentration
between predicted and observed was reasonably close; however FRZM
simulated a much broader peak than was observed.
The simulated transport of applied chemicals by the two models were
in reasonable agreement with each other. The differences are probably
due to the detail of discretization of the root zone and the method of
calculated chemical transport as discussed previously. FRZM can more
nearly match the observed soil layering than can GLEAMS due to the fact
that GLEAMS describes the entire root zone with only seven layers. PRZM
and GLEAMS both simulated large losses of atrazine from the root zone (40
and 62% of application, respectively). The FRZM simulated transport of
atrazine to a depth of 2.6 m amounted to 24% of the mass applied. As
discussed previously, less than 4% of the applied atrazine could be
accounted for on a given day within the saturated zone. This figure
does not account for storage in the vadose zone or mass transported off
site.


107
the instrumented application area due to the presence of the vacuum and
sample tubing and the instrument trailer. As a result, the application
rate of fertilizer in this area was reduced. Refer to Appendix B for
information on the availability of the data set containing results of all
nitrate analyses.
6.5 Chemicals in the Saturated Zone
6.5.1 Bromide
There was no shallow groundwater present on the site between the
date of herbicide application (11/12/1986) and Dec. 15, 1986. Bromide
was observed in the groundwater on this first well sampling date at a
concentration of 0.8 mg/L in well 08-09 (see Figure 4.1 for well
locations) which is within the application area. The continued detection
of bromide in the groundwater following the first application was
sporadic, and the maximum concentration of 3.9 mg/L was detected in well
09-11 on January 12, 1987. The maximum concentration of bromide in the
groundwater following the second application (4/27/87) was 5.2 mg/L on
May 13th, in well 08-09. Figures 6.18-6.23 shew the concentration of
bromide within the groundwater over six sampling periods following the
first application of bromide. Figures 6.24-6.29 show the concentration
of bromide within the groundwater for six sampling periods following the
second application of bromide.


119
days. This would suggest that atrazine was moving faster than the
velocity of the water. In Figures 6.32 and 6.33 it is observed that
atrazine moved the distance iron well 09-14 to well LCW in a period of 7
days. The water table gradient between these wells was approximately
0.7 rn/m. The average pore water velocity for the area between these
wells was calculated to be approximately 3 m/day. These wells are
located a distance of 42 m apart. Based on the calculated water velocity,
it should take 14 days for water to move the distance between the wells.
Thus, using this method, atrazine would not be expected to have traversed
the distance between these wells in 7 days. These simple calculations
indicate that the conductivity value is probably too lew. It is also
possible that atrazine is moving along the top of the restricting layer
in large pores or conduits which are not represented by the conductivity
value used in the calculations. Without measured values of the saturated
conductivity in the saturated zone, it is difficult to explain the rapid
movement of atrazine within the groundwater.
Since samples were collected on 7-day intervals and the wells are
spaced 12 m apart, there is considerable error associated with estimates
of the distance traveled between successive dates and the time required
for atrazine to have moved a specific distance. There are also
significant errors associated with assuming conductivity values for this
site from general soil characterization data. The above examples
illustrate that additional site specific data (hydraulic conductivity,
effective porosity, etc.) are needed to adequately characterize the
observed atrazine transport.


131
6.5.5 Total mass of chemicals within the saturated zone
In addition to observations of the concentrations of the chemicals
within the groundwater, calculations of the total mass of chemicals
within the saturated zone were performed. An inverse distance weighting
method was used for each observation period to generate a square grid of
values over the site. One potential option for gridding data was kriging
which has been shewn to be more accurate than the inverse distance method
(Golden Software, 1987). When kriging was used, hewever, it was observed
that negative concentration values were generated at several intermediate
grid points. With the inverse distance method, negative concentrations
were not a problem. The data from observation wells spaced 12 m by 12 m
apart were gridded to produce data on a 3m by 3m spacing. This was done
initially for illustrative purposes.
The data from the wells were gridded in two ways. First, only the
wells from which samples were obtained and analyzed were included in the
input data set for the gridding process. Second, all wells were included
by assigning the concentration in wells without samples a value of zero.
The second method was used as an estimate of the minimum mass of a
chemical within the saturated zone. The three-dimensional views of the
concentrations were judged to better convey to the viewer the values at
individual wells when the second method was used as compared to the first
method.
Using the concentrations and groundwater depths computed on a 3 m
square grid, calculations of water storage and chemical mass storage
within the groundwater were performed. These calculations are described
in more detail in Section 6.5.6.


Cone. (mg/L) g Cone. (mg/L)
123
Date: 05/05/87
6.40. Nitrate concentration in the groundwater on 5/05/87.
Vertical bars indicate sampling locations.
Date: 05/08/87
Figure 6.41. Nitrate concentration in the groundwater on 5/08/87.
Vertical bars indicate sampling locations.


ACKNOWLEDGMENTS
I would like to express my sincere appreciation to the following:
Dr. A. B. (Del) Bottcher, chairman of my advisory committee, for
his friendship, patience, and guidance. He provided valuable advice and
philosophy during the highs and lews encountered in my Fh.D. program.
Dr. K. L. Campbell, cochairman of my advisory committee, for his
guidance and support. His calm and reasoned approach to problem solving
is a model which I need to emulate.
Dr. E. D. Threadgill, for serving on my advisory committee, for
making available the field site on which this research was conducted,
and for being the best engineer/scientist/administrator that I could
ever hopo to be associated with.
Dr. W. C. Huber, for serving on my advisory committee, and being
one of the very best instructors that I have encountered during many
years of college. If all instructors were that good, my GPAs would have
been higher.
Dr. P. S. C. Rao, for serving on my advisory committee, for showing
. 1 .
a sincere interest in my research, and for teaching me several very
valuable lessons during the qualifying exam.
Ms. M. W. Smith, for being a wonderful, laving, and supportive
wife. She assumed a greatly disproportionate share of child care and
household duties so that I could devote time to this research.
iii


167
Marti, L. R., J. de Kanel, and R. C. Dougherty. 1984. Screening
for organic contamination of groundwater: ethylene dibromide in
Georgia irrigation wells. Environ. Sci. Technol. 18(12):973-974.
National Water Well Association. 1986. Proceedings of the
Agricultural Impacts On Groundwater QualityA conference.
Omaha, NE. Water Well Publishing Co., Dublin, OH. 685 pp.
National Water Well Association. 1988. Proceedings of the
Agricultural Impacts On Groundwater QualityA conference. Des
Moines, IA. Water Well Publishing Co., Dublin, OH. 658 pp.
Pionke, H. B., D. E. Glotfelty, A. D. Lucas, and J. B. Urban. 1988.
Pesticide contamination of groundwaters in the Mahantango Creek
Watershed. J. Environmental Quality. 17(1):76-84.
Rao, P. S. C. and J. M. Davidson, (Eds.). 1980. Retention and
transformations of selected pesticides and phosphorus in
soil-water systems: a critical review. U. S. Environmental
Protection Agency, Athens, GA. EPA-600/3-82-060.
Rao, P. S. C., D. E. Rolston, R. E. Jessup, and J. H. Davidson.
1980. Solute transport in aggregated porous media: theoretical
and experimental evaluation. Soil Sci. Soc. Amer. J.
44(6):1139-1146.
Rao, P. S. C., A. G. Hornsby, and R. E. Jessup. 1985. Indices for
ranking the potential for pesticide contamination of groundwater.
Soil and Crop Sci. Soc. Florida Proc. 44:1-8.
Rao, P. S. C., K. S. V. Edvardsson, L. T. Ou, R. E. Jessup,
P. Nkedi-Kizza, and A. G. Hornsby. 1986. Spatial variability of
pesticide sorption and degradation parameters, in Evaluation of
Pesticides in Groundwater. W. Y. Gamer, R. C. Honeycut, and H.
N. Nigg, Eds. ACS Symposium Series No. 315, American Chemical
Society, Washington D.C. pp. 100-115.
Rao, P. S. C., R. E. Jessup, and J. M. Davidson. 1988. Mass flow
and dispersion, in Environmental Chemistry of Herbicides, Volume
1. R. Grover, Ed. CRC Press, Boca Raton, FL. pp. 21-44.
Ritter, W. F. 1986. Pesticide contamination of groundwatera
review. ASAE Paper No. 86-2028. ASAE, St. Joseph, MI 49085.
Rohde, W. A., L. E. Asmussen, E. W. Hauser, M. L. Hester, and H. D.
Allison. 1981. Atrazine persistence in soil and transport in
surface and subsurface runoff from plots in the coastal plain of
the southern United States. Agro-Ecosystems. 7(1981):225-238.


18
evidence that no-till farming will increase pesticide transport to
groundwater while reducing surface runoff and thereby reduce pollution
of surface waters (Dick et al., 1986). Thus, solutions to one problem
may exacerbate another problem. The models discussed belcw are capable
of simulating, to seme degree, agricultural management practice effects
on pesticide fate and transport.
According to Donigian and Rao (1986a), SESOIL (The Seasonal Soil
Compartment model) is designed for long-term simulation of pesticide fate
in the soil environment. It was developed for the EPA and is used as a
screening model. SESOIL can simulate many components of the hydrologic
cycle including precipitation, evapotranspiration (ET) and surface
runoff. The model considers transport within the unsaturated zone
extending iron the soil surface to the top of the saturated zone. The
hydrologic responses are determined losing physically based equations in
which uncertainty has been included. The water balance used in the
model is a statistical representation of the hydrologic components over a
"season." A season is the time step of the model, e.g., month or year.
Erosion is simulated using the Universal Soil Loss Equation (Wischmeier
and Smith, 1978). Hetrick and Travis (1988) coupled SESOIL with EROS (a
submodel of the CREAMS (Knisel, 1980) watershed model) to predict surface
runoff and sediment yield from small watersheds. The pesticide component
of the model considers most of the processes and factors influencing
pesticide transport which were described in previous sections.
SESOIL requires calibration and represents long-term averages. Same
of the results of use and testing of SESOIL reported by Donigian and Rao
(1986a) indicate that it should not be used for short-term predictions.


32
the NT watershed in the fall of 1985 had similar concentrations to those
collected from the CT watershed.
Groundwater samples collected 15 days after application in 1984
showed levels of atrazine, simazine, and cyanazine exceeding 1 /jg/L. The
concentration of atrazine in one well reached 7 /jg/L on this date.
During late summer and fall, atrazine was the only pesticide detected in
the wells. Pesticide concentrations in the groundwater were similar for
the two watersheds, although the concentrations were somewhat higher
during the growing season in the NT watershed. In April, 1986, (prior to
application) atrazine concentrations of 4 /ig/L were observed in the water
table on both watersheds. This indicates that atrazine may be persistent
in the lower soil zones and in the water table.
3.4.3 Aldicarb movement in the Dougherty Plain of Georgia
The U. S. Environmental Protection Agency and the U. S. Geological
Survey began a study in 1982 to observe the movement of aldicarb and its
residues in the unsaturated and saturated zones at a field site in Lee
County, in southwestern Georgia. Aldicarb is used as an insecticide and
nematicide on peanuts grown on the site. One of the primary objectives
was to develop an extensive database of soil, land use, geologic, and
pesticide data for use in the validation and testing of the PRZM (Carsel
et al., 1984) model. Different aspects of this study have been reported
by a number of investigators (e.g. Cooper, 1986; Rao et al., 1986; Hook,
1987; Hedden, 1986; Smith and Carsel, 1986).
The study site covers an area of approximately 4.5 ha. Four soils
are mapped in the field. All have a fine-loamy texture. Below a depth


84
water were extracted and they shewed concentrations of atrazine of
approximately 1 /xg/L. All glassware was routinely washed with a
commercial glassware detergent, triple rinsed with deionized water, and
rinsed with acetone. The contamination levels appeared to be consistent
in that water samples, in which atrazine was not expected, shewed a
concentration of approximately 1 xg/L. The source of the contamination
was not determined. Several attempts were made to isolate and identify
the source of the contamination, however these attempts were
unsuccessful. To account for the contamination, the measured
concentrations were reduced by 1 /xg/L. Thus no concentrations of less
than 1 /xg/L are reported even though the sensitivity of the GC would
allow detection down to the range of tenths of a /xg/L.
The gas chromatograph performed well except that the N-P detector
response had a tendency to drift. This was accounted for by running
standards frequently and adjusting calculated concentrations as described
in the methods section.
The recoveries of atrazine and alachlor from spiked water samples
were nearly identical and ranged from 68 to 104% with a mean of 79% and a
standard deviation of 12 percent. The standard deviation would probably
be reduced if these analyses had been performed by an experienced
analyst. Experience contributes significantly to the ability to produce
accurate and consistent results.
The recoveries of atrazine and alachlor from spiked soil samples
could not be determined from the data collected. Moisture contents of
the soils used for spiking were not recorded. The spike levels were
based on the moist weight of the soil. After spiking, the soil was air-


CHAPTER 5
MODELING THE EXPERIMENTAL SITE
Two models were selected for the purpose of simulating the movement
of the chemicals applied to the experimental site. The models selected
for use were the Pesticide Root Zone Model, PRZM (Carsel et al., 1984),
version 2, and Groundwater Loading Effects of Agricultural Management
Systems, GLEAMS (Leonard et al., 1987), version 1.8.53. These models
were selected because they were designed specifically to simulate
pesticide transport through the root zone of a crop and reflect
influences of agricultural management practices such as tillage.
Reference to the manual for GIEAMS includes the documentation for the
CREAMS model (Knisel, 1980) from which GIEAMS was derived as well as the
supplementary GIEAMS user manual which is provided with the model code
and describes the differences in input data sets between CREAMS and
GIEAMS.
This chapter will discuss the selection of input values used in the
models. Only the parameters relevant to the results will be discussed.
There are parameters in both models for which values were selected, but
which had no bearing on the results to be presented. For example the
models utilize the Universal Soil Loss Equation, USIE, (Wischmeier and
Smith, 1978) for prediction of soil erosion, however, in the simulations
surface runoff was never predicted and therefore the parameters chosen
for use with the USIE did not affect the reported results. There are
66


Cone. (mg/L) £ Cone. (mg/L)
109
09-11 Date: 12/29/86
o
ire 6.20.
Bromide concentration in the groundwater on 12/29/86.
Vertical bars indicate sampling locations.
Date: 01/05/87
Figure 6.21. Bromide concentration in the groundwater on 1/05/87.
Vertical bars indicate sampling locations.


DEPTH cm) M DEPTH
146
ATRAZINE CONC. (mg/kg)
0.0 0.1
Q J I I I I I L
a
100^
125
150
175
200 d
0.2 0.3 0.4 0.5
J 1 I I I L J I I 1 I I 1 1 1 i I
Sample Date : 12/22/86
MEASURED
PRZM
Notes:
No Samples Above 25 cm.
Measured Values are Avg.
of 6 Samples/Depth
rigure 6.59.
Comparison of measured and FRZM predicted atrazine
concentrations in the soil on 12/22/86.
ATRAZINE CONC. (mg/kg)
0.0 0.1 0.2 0.3 0.4 0.5
Figure 6.60. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 02/09/87.


140
layer of 5 cm. Since the depth of flew on this site was often less than
10 cm, an error of +/- 5 cm in the location of the restricting layer
would significantly affect the calculated depth of flow. As noted above,
the restricting layer was determined by visual observation of color and
texture changes. The depth at which the vertical hydraulic conductivity
of this layer becomes small enough to caase saturation may not correspond
to the visually determined top of the layer. There may also be seme
small channels on the surface of the restricting layer in which
significant flow could occur that are not shewn by the wells. This is
demonstrated by the fact that conductivities along the two boundaries
mentioned above had to be increased significantly above the values used
elsewhere in order to move sufficient quantities of water across those
boundaries. The potential water movement into the restricting layer was
also not accounted for.
There is much additional work to be done before an adequate water
balance can be performed on this site. Additional characterization of
soil properties in the saturated zone and improved estimates of the
location of the restricting layer are needed.
Since the water balance attempts were generally unsuccessful,
chemical balances, although calculated, were not considered further.
Two FORTRAN programs were written to perform the calculation
described in the previous section. Program ANALYZE performs nodal and
boundary flux calculations for each observation date and is presented in
Figure C.l in Appendix C along with a sample output (Table C.l). Program
FLUX computes the water and chemical balances between sampling periods


6.11. Bromide concentration for six sampling locations following
the first application at a 122 cm depth 101
6.12. Bromide concentration for six sampling locations following
the first application at a 183 cm depth 102
6.13. Average bromide concentration for the three sampling depths
following the first application 102
6.14. Bromide concentration for six sampling locations following
the second application at a 61 cm depth 103
6.15. Bromide concentration for six sampling locations following
the second application at a 122 cm depth 103
6.16. Bromide concentration for six sampling locations following
the second application at a 183 cm depth 104
6.17. Average bromide concentration for the three sampling depths
following the second application 104
6.18. Bromide concentration in the groundwater on 12/15/86 108
6.19. Bromide concentration in the groundwater on 12/22/86 108
6.20. Bromide concentration in the groundwater on 12/29/86 109
6.21. Bromide concentration in the groundwater on 01/05/87 109
6.22. Bromide concentration in the groundwater on 1/12/87 110
6.23. Bromide concentration in the groundwater on 1/19/87 110
6.24. Bromide concentration in the groundwater on 5/05/87 Ill
6.25. Bromide concentration in the groundwater on 5/08/87 Ill
6.26. Bromide concentration in the groundwater on 5/13/87 112
6.27. Bromide concentration in the groundwater on 5/18/87 112
6.28. Bromide concentration in the groundwater on 5/25/87 113
6.29. Bromide concentration in the groundwater on 6/01/87 113
ix


BROMIDE CONC. (mg/L) S BROMIDE CONC. (mg/L)
102
DAYS SINCE 11/12/86
ire 6.12.
Bromide concentration for six sampling locations
following the first application at a 183 cm depth.
AVERAGE CONC. AT EACH SAMPLING DEPTH
DAYS SINCE 11/12/86
Figure 6.13. Average bromide concentration for the three sampling
depths following the first application.


33
of approximately 1 m there are layers of clay separating zones of coarser
sand and gravel. The conductivity of the clay layers is very lew, which
suggests that perched water tables may form on these layers and cause
lateral transport of leached pesticides. There is a shallow water table
at the site which can vary in position by as much a 6 m during a year.
Soil samples were collected from the site to characterize soil
properties and pesticide sorption and decay characteristics. Geologic
information was collected from holes bored to the top of the Ocala
Limestone. The residuum was approximately 13 m thick. The Ocala
Limestone is a part of the Floridan aquifer system. Four wells were
cased into the Floridan aquifer for measurement of water levels.
A weather station was installed at the site to collect meteorological
data.
Statistical procedures were used to determine that 20 monitoring
sites would accurately reflect the fate and transport of aldicarb at this
site. The locations of the monitoring sites were randomly selected and
distributed between the three major soil series based upon the relative
area in each series.
Each monitoring site was equipped with an impressive array of
instruments for monitoring of conditions and collecting samples. Five
tensiometers were installed to a depth of 150 cm for monitoring soil
water content. The tensiometers were monitored and serviced three times
per week. Five thermistors were installed to a depth of 115 cm for
measurement of soil temperatures. The thermistors were monitored three
times per week during the middle of the afternoon in order to estimate
maximum soil temperatures. Three soil solution samplers were installed


168
Shoemaker, L. L. and W. L. Magette. 1987. Ground water models for
assessing agricultural best management practice. ASAE Paper
No. 87-2021. ASAE, St. Joseph, MI 49085.
Skaggs, R. W. 1978. A water management model for shallow water
table soils. Water Resources Research Institute of the University
of North Carolina. Report No. 134. 178 pp.
Smith, C. N., W. R. Payne, Jr., L. A. Mulkey, J. E. Benner, R. S.
Parrish, and M. C. Smith. 1981. The persistence and disappearance
by washoff and dryfall of methoxychlor from soybean foliagea
preliminary study. J. Env. Sci. and Health. B16(6):779-794.
Smith, C. N. and R. F. Carsel. 1986. A stainless steel soil
solution sampler for monitoring pesticides in the vadose zone.
Soil Sci. Soc. Am. J. 50(1):263-265.
Smith, R. E. and J. R. Williams. 1980. Simulation of the surface water
hydrology, in Khisel, W. G. (Ed). CREAMS: A Field-Scale Model for
Chemicals, Runoff, and Erosion from Agricultural Management
Systems. U.S. Department of Agriculture, Conservation Research
Report No. 26, Washington, DC. 640 pp.
Stansell, J. R., C. L. Butts, K. A. Harrison, and J. C. Garner.
1982. Irrigation system efficiency survey for Georgia.
Environmental Resources Center, Georgia Institute of Technology,
Atlanta, GA. ERC 10-82. 32 pp.
Steenhuis, T. S., S. Pacenka, and K. S. Porter. 1987. MOUSE: a
management model for evaluating groundwater contamination from
diffuse surface sources aided by computer graphics. Applied Agr.
Research. 2(4):277-289.
Sumner, H. R., E. D. Ihreadgill, J. R. Young, and D. L. Cochran.
1987. Irrigation system simulator for small plot research. ASAE
Paper No. SER-87-201. ASAE, St. Joseph, MI 49085.
U. S. Department of Agriculture, Soil Conservation Service. 1972.
SCS National Engineering Handbook, Section 4, Hydrology.
Washington, DC. 458 pp.
U. S. Environmental Protection Agency. 1987a. Agricultural
Chemicals in Ground Water: Preposed Pesticide Strategy. Office of
Pesticides and Toxic Substances, Washington, DC. 150 pp.
U. S. Environmental Protection Agency. 1987b. Environmental News.
Office of Public Affairs, Washington, DC. Feb. 4, 1987.
Valentine, R. L. 1986. Nonbiological transformation, in Vadose Zone
Modeling of Organic Pollutants. S. C. Hem, and S. M. Melancon,
Eds. Lewis Publishers, Inc. Chelsea, MI. pp. 223-244.


MEASUREMENT AND PREDICTION OF HERBICIDE
TRANSPORT INTO SHALLOW GROUNDWATER
By
MATTHEW CLAY SMITH
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF FHILOSOFHY
UNIVERSITY OF FLORIDA
1988
|j QF F LIBRAS?


55
tainer, the only components common to each sample collection were the
rubber stepper and the short piece of Tygon tubing. When a sample was
collected, the initial water from the well was swirled around the bottle,
and the bottle was then inverted to remove this rinse water. This served
to rinse the Tygon tubing, stepper, and bottle. Thus, cross
contamination between wells was minimized. The sample bottles used for
the collection and storage of well samples had a capacity of 250 mL.
Well samples were placed in coolers with ice packs for storage until they
could be refrigerated.
All soil samples were frozen as soon as possible after collection
and maintained at -18C until preparation for analysis. All water
samples were refrigerated after collection and maintained at 4C until
prepared for analysis.
4.5 Sample Analysis
4.5.1 Inorganic tracer analysis
The concentrations of the three inorganic chemicals of interest
which were applied during this study were quantified using ion
chromatography. A DIONEX QIC ion chromatograph (IC) was used for all
analyses.
A DIONEX AS4A anion separation column was used with an AG4A guard
column in place to remove organic contaminants prior to reaching the
separation column. The IC was equipped with a conductivity detector
connected to an external integrator/recorder. The IC was also equipped
with an anion micromembrane suppressor to reduce the background


24
Limitations of the conceptual formulation of the system that is
described by a model lead to uncertainty in the results. Most
deterministic models assume that seme of the physical properties of the
system are uniform with respect to location. That is, soil properties
such as porosity, water-holding capacity, and organic matter content are
constant across the field, although most models will allow these types of
properties to vary with depth in the soil profile. Furthermore,
variations in pore sizes within the soil, which can cause uneven water
velocity distributions, are usually not considered. Each model will
represent a system (ie. the crop root zone) in varying levels of detail.
The equations which are used to represent the fate and transport
processes have limitations and uncertainties. For example, PRZM and
GLEAMS both use the SCS curve number method for partitioning rainfall
between runoff and infiltration. This method utilizes daily rainfall
records and does not consider the effects of rainfall intensity on the
runoff process. Testing of the CREAMS model (Knisel, 1980), from which
GLEAMS was derived, shewed that the daily hydrology option represented
average annual runoff volumes well, but did not do as well with daily and
monthly runoff volumes (Smith and Williams, 1980). PRZM and GLEAMS both
utilize modified versions of the USLE (Wischmeier and Smith, 1978) for
prediction of daily soil erosion. The USIE was developed for long-term
(20-yr) average annual erosion rates. Although these modifications have
been tested, the basis of these methods is the USIE which is an empirical
equation derived for long-term average predictions. Many transport
models utilize the linear form of the Freundlich equation (Equation 3.1)
to describe the partitioning of a chemical between the dissolved and


37
will play an important role in evaluating the contamination potential
from applications of pesticides and fertilizers and assess the benefits
of modifications to current agricultural management practices
(application methods, timing, chemical formulations, tillage practices,
etc.).
There are many factors which influence the mobility and persistence
of pesticides in the environment. Soil physical properties, chemical
properties, crop characteristics, environmental factors, and the
interactions among them determine the fate of agriculturally applied
chemicals. Many of the processes involved in the transport and
degradation of pesticides in the environment are poorly understood, and
the mathematical equations used to represent these processes are
simplifications based on experimental observations. The soil properties
and environmental factors which strongly influence the fate of a chemical
can vary significantly within small areas.
Methods to predict the fate of agriculturally applied chemicals
range from simple indices to complex mathematical research models.
Models which can represent the effects of agricultural management
practices on the transport of chemicals are being used by governmental
regulators as part of the chemical registration process. Most of the
management models developed to date are designed to represent the
processes on the soil and plant surfaces, within the crop root zone and
possibly the unsaturated zone between the root zone and the water table.
New model developments will incorporate groundwater transport models to
assess how far and at what rate a chemical that leaches into groundwater
will move. There are many sources of uncertainty and error associated


86
a) Solution Concentration
08-09
Figure 6.1. Uniformity of atrazine application, a) concentration in
application solution (vertical bars shew sampling
locations), b) application rate (vertical bars show
location of monitoring wells)


6.68. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 5/25/87 150
6.69. Measured and FRZM predicted bromide concentrations in the
soil solution at a 61 cm depth following the first
application 152
6.70. Measured and FRZM predicted bromide concentrations in the
soil solution at a 122 cm depth following the first
application 152
6.71. Measured and PRZM predicted bromide concentrations in the
soil solution at a 183 cm depth following the first
application 153
6.72. Measured and PRZM predicted bromide concentrations in the
soil solution at a 61 cm depth following the second
application 153
6.73. Measured and PRZM predicted bromide concentrations in the
soil solution at a 122 cm depth following the second
application 154
6.74. Measured and PRZM predicted bromide concentrations in the
soil solution at a 183 cm depth following the second
application 154
6.75. Measured and PRZM predicted concentrations of atrazine in
the soil solution at a 61 cm depth 155
C.l. Listing of program to calculate water and chemical
fluxes and storages 177
C.2. Program to calculate mass balance between sampling periods.. 194
D.l. Atrazine concentration in the groundwater on 1/19/87 201
xii


BROMIDE CONC. (mg/L) f BROMIDE CONC. (mg/L)
104
DAYS SINCE 11/12/86
6.16. Bromide concentration for six sampling locations
following the second application at a 183 cm depth.
DAYS SINCE 11/12/86
Figure 6.17. Average bromide concentration for the three sampling
depths following the second application.


126
6.5.4 Chloride
In order to further characterize the flew velocities within the
groundwater, 10 L of solution containing 23.8 g/L of chloride (50 g/L of
KC1) was poured into well 07-09 on 5/1/87. The chloride was added to the
groundwater at a time when nearly continuous percolation was occurring
due to irrigation, and the water table was beginning to rise. Chloride
concentrations in wells downslope frem well 07-09 showed chloride levels
exceeding 80 mg/L following the chloride application.
Figures 6.46-6.53 shew the chloride concentrations within the
saturated zone on selected dates. In Figure 6.46 the background
concentration of chloride on May 1, 1987 can be seen to be on the order
of 1 mg/L or less. Figure 6.47 shews that chloride from other sources
(KC1 in the fertilizer) is also entering the groundwater. This is most
clearly demonstrated by the high concentration of chloride in well 11-10
which is on the opposite side of the study site fretn well 07-09 which is
where the chloride was introduced into the groundwater. Figures 6.46-
6.53 show the general movement of chloride in the groundwater over a
period of 29 days.
Discussions presented in Section 6.5.6 will show that additional
characterization of soil properties near the restricting layer is needed
before the observations of chemical movement within the groundwater can
be used to describe flew patterns and velocities within the groundwater.
No calculations of flew velocities were performed with the chloride data
and no conclusions were drawn from it for presentation here.


35
3.4.4 Big Spring Basin in Icwa.
The Big Spring Basin is a 267 km2 groundwater basin located in
northeastern Iowa. The basin is entirely agricultural with
approximately 60% of the land area in rcw crops. The basin has been
extensively monitored and characterized, and results from these studies
have been presented by a number of investigators (Libra et al., 1986;
Kelley et al., 1986; Hallberg, 1986; Libra et al., 1987). Over 85% of
the basin's groundwater is discharged through Big Spring. Thus,
investigators have an easily accessible point for monitoring average
groundwater quality in the basin as affected by agricultural activities.
Big Spring is a karst spring with an estimated 10% of flow contributed by
directed recharge through sinkholes. By losing hydrograph separation
techniques, the investigators can determine the water quality effects of
both normal infiltration and direct recharge through the sinkholes.
Four years of monitoring have shown flow-weighted mean
concentrations of N03-N ranging from 7-11 mg/L. Combined losses of N03-N
in both surface water and groundwater amount to 33-55% of the average
annual fertilizer nitrogen applied in the basin. Historic records
indicate that the magnitude of N03-N concentrations have increased by
200-300% over the last 20 years. This corresponds to a 200-300% increase
in the application of nitrogen fertilizers during this period.
The herbicide atrazine is the only pesticide that is always present
in the discharge from Big Spring. Flow-weighted mean concentrations of
atrazine are less than 1 /ug/L, but the average concentrations have
steadily increased during four years of monitoring. Total losses of


LIST OF TABLES
Page
5.1. Soil properties used in simulations 69
5.2. Chemical properties used in simulations 70
5.3. Chemical applications summary 73
6.1. Chronological summary of field site activities 80
6.2. Chemical application results 88
6.3. Measured application rates and soil surface concentrations
of atrazine and alachlor 91
6.4. Mean concentrations of atrazine (mg/kg) in soil samples 96
6.5. Mean concentrations of alachlor (mg/kg) in soil samples 97
6.6. Mean concentration of bromide (mg/L) at each sampling
following first application 105
6.7. Mean concentration of bromide (mg/L) at each sampling
following second application 106
6.8. Total mass of chemicals in the saturated zone 133
6.9. Simulated mass flux of chemicals 142
A. 1. Monitoring well statistics 171
B.l. Example listing of the water sample data set 174
B.2. Example listing of the soil sample data set 175
C.l. Sample output from program ANALYZE 191
C.2. Sample output from program FLUX 198
vii


113
10-09
Date: 05/25/87
Figure 6.28. Bromide concentration in the groundwater on 5/25/87.
Vertical bars indicate sampling locations.
09-11
Figure 6.29. Bromide concentration in the groundwater on 6/01/87.
Vertical bars indicate sampling locations.


83
could be observed. If an irrigation was initiated early in the morning,
a few of the 30 cm tensiometers might have responded by the end of the
day. Weekly observations of soil-water tension were insufficient for the
purpose of calculating the flux of water through the unsaturated zone.
6.2 Sample Analysis
Water samples collected from the monitoring wells were analyzed for
residues of atrazine and alachlor and concentrations of the inorganic
tracers utilized in this research (Br-, NO3-, and Cl-). The volume of
samples from the solution samplers ranged from 0 to more than 50 xnL, with
the average sample volume being approximately 10 mL. Water samples from
the soil solution samplers were generally analyzed for the Inorganic
tracers only. Most of the solution samples were not extracted for
residues of the herbicides due to the small sample volumes.
Approximately 50 of the soil solution samples were extracted and analyzed
for residues of the herbicides. Soil samples were extracted and analyzed
for residues of atrazine and alachlor. The presence of alachlor and
atrazine residues in the soil samples was confirmed in a composite of
soil sample extracts that was analyzed using gas chromatography/mass
spectrometry (GC/MS).
During extraction of water samples with hexane, an emulsion of
hexane and water sometimes formed. This would usually separate if
allowed to sit undisturbed for a few minutes. If the emulsion did not
separate, herbicide residues contained within the emulsion could have
been discarded. There was an apparent contamination of the glassware
used for water sample collection or extraction. Samples of deionized


136
water table depth. The slope of the drained volume-water table depth
relationship is called the drainable porosity. A program called DVOIWTD
was used to calculate the drainable porosity. The DVOIWTD program is
provided with the water table management model ERAINMOD (Skaggs, 1978).
The program uses the soil-water characteristic data for the profile and
computes the volume of water drained as the water table falls. The soil
water characteristic data input to DVOIWTD was obtained frcm Hook
(1985). Graphs of the drained volume-^water table depth relationship and
the soil-water characteristic curve are presented in Figures C.3 and C.4
in Appendix C. The drained volume was calculated at every node based
upon the depth to the water table at that node.
The mass of chemical stored (sorbed and in solution) in the volume
represented by each node was calculated based upon the groundwater depth
at the node, the concentration of the chemical in solution, and the
partitioning coefficient for the chemical. The for atrazine used in
this study is 163 cm3/g (Table 5.2). The organic carbon content of the
soil above the restricting layer was assumed to be 0.03% (Table 5.1).
Thus the Kq for atrazine in the lower soil zones is 0.049. The drained
volume and chemical storage values calculated as described above were
summed over each subarea.
6.5.6,2 Boundary and subarea flux calculations
The total volumetric flowrate of water was calculated for each
boundary in a subarea. This was done by summing the flowrates calculated
above for all the nodes along a boundary. The sum of the flowrates
calculated for the four boundaries yields the net flowrate into or out of


LIST OF FIGURES
Page
4.1. Contour maps of soil surface and restricting layer
showing locations and ID labels of monitoring wells 41
4.2. Cross-section of soil profile through application area
showing locations of monitoring wells and soil solution
samplers 45
6.1. Uniformity of atrazine application 86
6.2. Uniformity of alachlor application 87
6.3. Uniformity of bromide application on 4/27/87 89
6.4. Comparison of alachlor and atrazine concentrations in
application samples 90
6.5. Correlation between atrazine concentrations in the top 5 cm
of soil and application solution concentrations 93
6.6. Correlation between atrazine concentrations in the top 5 cm
of soil and application rate 93
6.7. Correlation between alachlor concentrations in the top 5 cm
of soil and application solution concentrations 94
6.8. Correlation between alachlor concentrations in the top 5 cm
of soil and application rate 94
6.9. Bromide and atrazine concentrations in solution sampler
09N-2 as a function of total water applied since
application 98
6.10. Bromide concentration for six sampling locations following
the first application at a 61 cm depth 101
viii


7
(1986), alachlor and atrazine together accounted for 25% of all
pesticides sold in the U. S. in 1982. Ritter (1986) reported that in
1982, approximately 38,600,000 kg of alachlor were applied to cropland in
the United States. Kelley et al. (1986) reported alachlor concentrations
in groundwater as high as 16 jug/L in Iowa.
There are many individual cases of pesticide residue detection in
groundwater that could be cited. The reader is referred to the
proceedings of the Agricultural Impacts on Groundwater Conferences
(National Water Well Assoc., 1986, 1988) for a variety of reports
concerning the detection of pesticide residues in groundwater. A book
edited by Gamer et al. (1986) is also a good source of information
related to this subject.
3.2 Factors Which Influence Pesticide Transport To Groundwater
Donigian and Rao (1986a) list five processes which affect the fate
and movement of chemicals within the soil. These processes are
transport, sorption, transformation/degradation, volatilization, and
plant uptake. Other authors, e.g., Cheng and Koskinen (1986), use
slightly varied groupings which include the processes named above. The
interactions of these processes over time and space determine the fate of
chemicals in the soil. The spatial variability of soil properties and
other factors is an important consideration when interpreting field data
and model predictions (Donigian and Rao, 1986a). This section will
describe each of the processes and same of the factors which -influence or
regulate them.


APPENDIX B
HCW TO GET COMPLETE DATA SET


105
Table 6.6. Mean concentration of bromide (mg/L) at each sampling depth
following first application1.
Sample Date
61
Sample Depth (cm)
122
183
11/17/86
0.03z
: (115)J
0.00
(0)
0.00
"FT-
11/18/86
0.05
(114)
0.10
(82)
0.10
(*)
11/24/86
1.92
(106)
0.72
(65)
0.05
(135)
12/01/86
1.87
(50)
3.35
(34)
0.31
(146)
12/08/86
0.90
(41)
3.28
(58)
1.48
(54)
12/15/86
0.39
(41)
3.07
(71)
2.10
(34)
12/22/86
0.25
(45)
1.42
(53)
1.58
(23)
12/29/86
0.22
(63)
1.15
(58)
1.37
(65)
01/05/87
0.08
(225)
0.85
(70)
0.91
(59)
01/12/87
0.11
(96)
0.75
(71)
0.75
(56)
01/19/87
0.04
(65)
0.68
(89)
0.68
(73)
01/26/87
0.04
(44)
0.65
(58)
0.29
(156)
02/02/87
0.09
(156)
0.52
(78)
0.51
(60)
02/09/87
0.12
(191)
0.45
(65)
0.67
(81)
--Bromide applied 11/17/86 2Mean
-^Coefficient of variation (%) 4One saxtple


139
storage would nearly always be substantially (order of magnitude) higher
than the observed change.
There are a number of possible reasons why the mass balances of
water did not agree. One reason is that both PRZM and GLEAMS assume that
all percolation through the soil profile occurs within one day. The
actual movement of percolating water through the profile may be much
slower than this and thus, may be sustained over significantly longer
periods. Therefore, water which was predicted to have been added to the
profile during the interval between sampling dates may actually have
slowly entered the groundwater over several sampling intervals. It is
possible that PRZM was overestimating the volume of percolation. GLEAMS,
however, predicted nearly identical percolation volumes using an entirely
different calculation procedure. This would suggest that the predicted
percolation volumes are reasonable.
Another source of error may be the assumption that the conductivity
is constant over the depth of the groundwater. The conductivity may
increase as the groundwater depth increases. There may also be large
pores or conduits near the restricting layer which can move water much
more rapidly than would be predicted using the methods described above.
There is also significant error associated with the actual location of
the restricting layer. The bottom of the wells were assumed to be placed
on top of this layer. Curing placement of the wells, the augered holes
did not step exactly on top of the restricting layer. The auger had to
cut into the restricting layer in order visually determine (due to color
and texture change) that the restricting layer had been reached. There
could easily be variations in the depth of penetration in the restricting


161
flew at a location will improve calculations of water flux. An estimate
of the seepage of water through the restricting layer may also improve
the water budget for this site. The current system adequately defines
the elevation of the water table surface from which gradients are
calculated.
Model validation will require (in addition to the soil properties
listed above) that the partition coefficient and degradation rate for
the chemical of interest be measured from on-site soil samples. The
accuracy of model predictions of mass fluxes should improve significantly
by using site-specific data. Careful measurement of application rates
should be made. Measured application rates were lower than the intended
rates. The models should also be tested using the observed range of
sensitive soil and chemical parameters.
A second year study on this same site should be done to try to
quantify seme of the characteristics or properties described above. In
addition, the study should monitor the movement of atrazine, one other
pesticide, bromide, and nitrate. The usefulness of the second year data
would reflect and be enhanced by the lessons learned from the work done
to complete this dissertation.


50
The intended application rate of the herbicides was 4.5 kg Al/ha (4
lb/ac). One liter of herbicide solution was prepared by mixing 313 mL of
each of the chemicals with 374 mL of deionized water. The sprayer was
operated at a velocity of 3.05 m/min moving from south to north along the
edge of the application area. The chemical solution was injected into
the water stream of the sprayer using an injection pump calibrated to
deliver 83 mL/rain* The water pressure at the inlet of the sprayer was
207 kPa. The pressure at the spray nozzles was maintained at 138 kPa.
Despite the calibration of the injection pump and measurement of sprayer
ground speed, the chemical solution ran out as the sprayer reached well
10-09 which is 6.1 m short of the intended end of the application area.
The application of the herbicides was considered complete at this point,
and the remaining portion of the application area was left untreated.
Fourteen 0.35 1 plastic cups were randomly placed within the
application area. The cups were supported upright in holders at a
height of approximately 35 cm above the soil surface. The cups were
used to collect samples of the application solution for analysis of
chemical concentrations, determination of the depth of water applied to
the area during application, and uniformity of chemical and water
application. The use of these cups and holders has been tested and
found to accurately reflect water application amounts (Stansell et al.,
1982).
Immediately after completing the application of the herbicides, the
sprinkler irrigation system was started and 5.1 cm of water was applied
over the entire study site. This irrigation was intended to wash the


106
Table 6.7. Mean concentration of brcmide (ing/L) at each sampling depth
following second application1.
Sample Date
61
Sample Depth (cm)
122
183
04/27/87
0.06z
(32)J
0.25
(66)
0.31
(*4)
04/28/87
2.06
(186)
0.40
(32)
0.11
(*)
04/29/87
6.28
(133)
0.29
(84)
0.56
(*)
04/30/87
7.81
(112)
0.54
(143)
0.95
(93)
05/01/87
7.92
(82)
0.99
(152)
1.47
(127)
05/02/87
5.56
(116)
4.35
(89)
2.02
(114)
05/03/87
5.54
(75)
9.02
(77)
2.96
(112)
05/05/87
3.47
(68)
6.79
(51)
4.62
(100)
05/08/87
1.58
(104)
5.44
(49)
6.97
(128)
05/11/87
1.14
(96)
7.78
(76)
4.99
(82)
05/13/87
0.79
(99)
6.11
(*)
1.91
(56)
05/25/87
0.51
(158)
1.32
(56)
3.04
(113)
06/01/87
0.10
(72)
1.05
(63)
2.20
(130)
--Bromide applied 04/27/87 2Mean
3Coefficient of variation (%) 4One sample
application. The maximum concentration of nitrate in these samplers was
5.6 mg/L on 5/03/87. Three samplers located at a depth of 183 cm
responded to the application of fertilizer. The maximum concentration of
nitrate in these samplers was 8.1 mg/L on 5/08/87. Concentrations of
nitrate in the soil solution were not as high as observed concentrations
within the groundwater over much of the study site (see Section 6.5.3 for
nitrate concentrations in the saturated zone). This is due to the fact
that the tractor-mounted broadcast spreader was unable to get close to


34
at depths of 1.5, 2.1, and 2.7 m. The samplers were made using stainless
steel for the body, a high-flcw ceramic cup, and teflon tubing for sample
collection and applying the vacuum. A silica flour slurry was poured
around each sampler to insure good contact with the surrounding soil.
The vacuum and sample lines were buried to avoid interference during
field operations. Vacuum was applied to the samplers for a period of 24
hours prior to collection of the sample.
At 15 of the monitoring sites, permanent stainless steel monitoring
wells were drilled to a depth of approximately 4.6 m. A stainless steel
screen 0.6 m in length was placed at the bottom of each well. A gravel
pack was placed around the screen and a cement grout was placed from the
gravel to the top of the finished well. The wells were finished so that
the top of the well was approximately 0.6 m below the soil surface so as
not to interfere with cropping operations. After field operations are
complete, the wells are extended above the soil surface. Water samples
are collected using a dedicated teflon tube (one per well) and a
peristaltic pump. The 250 mL glass sample containers were connected in
line between the well and the pump to prevent cross contamination. The
wells were typically pumped for 5-7 minutes prior to collecting a sample.
Soil samples were collected using a hand auger in 15 cm increments
to a depth of 120 cm. After each sample was collected, gravel and plant
material was removed and then the sample was sealed in a metal container.
The references which were obtained for this review did not report
any results of pesticide monitoring from the field site. The detailed
monitoring network and experimental design used at this site should
produce a valuable data set for model validation and testing.


110
09-1 1
Date: 01/12/87
Figure 6.22. Bromide concentration in the groundwater on 1/12/87.
Vertical bars indicate sampling locations.
Date: 01/19/87
09-11
Figure 6.23. Bromide concentration in the groundwater on 1/19/87.
Vertical bars indicate sampling locations.


138
Prediction of mass transport across boundaries was performed as
described above for water. The mass storage of chemical calculated for
each subarea was ccsnpared to the predicted storage based on mass inflows
and outflows to a subarea.
6.5.6.4 Results
The conductivity of the soil in the saturated zone was not measured.
As an initial estimate, a conductivity of 14.4 m/day (60 cm/hr) was used
(Carlisle et al., 1978). For periods when the water table was falling
and no percolation was added, the predicted and observed change in water
storage on 4 of the subareas agreed reasonably well. Subareas 3 and 6,
however, would accumulate water when such accumulation was not observed.
In order to avoid accumulation of water within subareas 4 and 6, a
conductivity value on the order of 40 m/day was required along the
boundary between subareas 4 and 5, and the boundary at the bottom
(western edge) of area 6. This would suggest that the cross-sectional
area of flew across these boundaries was not adequately characterized.
With the higher conductivity values at the boundaries described
above, the predicted and observed changes in water balances for the site
were in reasonable agreement when no percolation was added. When
percolation was predicted during the interval between sampling dates,
however, the mass balances were not acceptable. The calculated change
in storage in a subarea suggested that water accumulated within the
subarea when measurements indicated that there was a net loss of water in
that area. Whenever percolation was added, the predicted change in


CHAPTER 8
RECDMMENE&.TTONS POR IMPROVEMENTS AND FURIHER STUDY
Additional information must be collected in order to quantify the
mass flux of chemicals and water within the vadose zone and the
groundwater.
The water content, or tension, of the soil must be observed on a
more frequent basis. Soil moisture blocks or tensiometers with pressure
transducers could be read on frequent intervals (5 min 1 hr) using a
datalogger. If there is sufficient sensitivity to the small changes in
water content on this soil (0.13 0.02 cm3/cm3) then the total flux of
water moving by the tensiometers could be estimated.
Further characterization of the restricting layer is needed. Core
samples should be taken from throughout the soil profile and presumed
restricting layer. Measurements of hydraulic conductivity, bulk density,
particle size distribution, and organic matter content should be made on
the cores. These data would refine estimates of soil properties
throughout the soil profile and further define the location of the
restricting layer. A detailed mapping of the surface of the restricting
layer using ground penetrating radar (GPR) would help to define the
locations and extent of small channels or irregularities.
Knowing where the bottoms of the observation wells are located
relative to the restricting layer will improve the estimates of flow
depth. Improved values for conductivity and the cross-sectional area of
160


73
the measured rates. The dates and application rates used as model inputs
are shown in Table 5.3. The applications were assumed to reach the soil
surface with no chemical residues remaining on plant surfaces. The
amount applied was further assumed to be uniformly incorporated into the
top 1 cm of the soil.
5.1.4 Length of simulations
Both models simulated the two year time period from 1/1/86 through
12/31/87. This was done to allow the models to overcome any effects of
initial conditions prior to beginning simulation of chemical
applications. Continuing simulations for six months beyond the end of
field data collection would show if any significant leaching would likely
have occurred after saupling was terminated.
Table 5.3 Chemical applications summary.
Date
Chemical Applied
Application Rate
(kg/ha)
11/12/86
Atrazine
4.9
Alachlor
4.9
11/17/86
Bromide
10.0
4/16/87
Nitrate
18.6
4/27/87
Bromide
17.0
5.2 Parameters Unique to FRZM
5.2.1 Evapotranspiration prediction.
PRZM uses either measured daily pan evaporation or daily mean
temperature to determine evaporation from the soil and plant surfaces,
and transpiration by the crop. In these simulations, daily pan


152
DAYS SINCE 11/12/86
Figure 6.69. Measured and H?ZM predicted bromide concentrations in
the soil solution at a 61 cm depth following the first
application.
Figure 6.70. Measured and PRZM predicted bromide concentrations in
the soil solution at a 122 cm depth following the
first application.


155
would perform when run with minimal site-specific inputs. A
governmental regulator would certainly not have detailed site specific
data available for all the combinations of crop/soil/chemical that may be
of interest. Such a user would be unlikely to have site measured
application rates for use as input. If the normal application rates for
atrazine and alachlor are 4.9 kg/ha on the cropping system which is being
simulated, the model user would certainly use those values as the input
to the model. They would not knew that the actual average application
rates may only be 3.4 kg/ha.
A small number of soil solution samples from samplers were extracted
for analysis of atrazine residues. The volume of these samples was
between 30 and 40 mL. Figure 6.35 shews the measured and FRZM simulated
concentrations of atrazine in the soil solution at a depth of 61 cm. PRZM
was run in the free drainage mode using the calculated plant uptake
coefficient of 0.65.
Figure 6.75. Measured and PRZM predicted atrazine concentrations in
the soil-water at a depth of 61 cm.


Figure C.l Continued.
WMASS(I,J) = WTD(I,J) FORD DELX DELY / 100.0
CWMASS(I,J) = WMASS(I,J) CONC(I,J)
CSMASS(I,J) = CWMASS(I,J) KD BULKDEN / FORD
CMASS(I,J) = CWMASS(I,J) + CSMASS(I,J)
CCCCCC
C CALCULATE MAX AND MIN VALDES OF SELECTED PARAMETERS
CCCCCC
IF(I.EQ.2.AND.J.EQ.2) TOEN
WTCMAX = WTD(2,2)
WTCMIN = WID(2,2)
SLPXMAX = SLPX(2,2)
SLPXMIN = SLPX(2,2)
SLPYMAX = SLPY (2,2)
SLPYMIN = SLPY(2,2)
MAQ4AX = MAGNITUDE (2,2)
MAGMIN = MAGNITUDE (2,2)
FLCWXMAX = FLOWX(2,2)
FLOWXMIN = FLOWX(2,2)
FICWYMAX = FLOWY(2,2)
FLOWYMIN = FLCWY(2,2)
CFLOWXMAX = CFLOWX(2,2)
CFLOWXMIN = CFLOWX(2,2)
CFLDWYMAX = CFLOWY(2,2)
CFLCWYMIN = CFLCWY (2,2)
CMASSMAX = CMASS(2,2)
CMASSMIN = CMASS(2,2)
ENDIF
IF(WTD(I, J) .GT.WTCMAX) WTCMAX = WTD(I,J)
IF(WTD(I,J) .IT.WTCMIN) WTCMIN = WTD(I,J)
IF(SLPX(I,J) .LT.SLFXMIN) SLPXMIN = SLPX(I,J)
IF(SLPX(I,J) .GT.SLPXMAX) SLPXMAX = SLPX(I,J)
IF(SLPY(I,J) .IT.SLPYMIN) SLPYMIN = SLPY(I,J)
IF (SLPY (I, J) .CT. SLPYMAX) SLPYMAX = SLPY (I,J)
IF (MAGNITUDE (I, J) .GT.MAG4AX) MAGMAX = MAGNITUDE (I, J)
IF (MAGNITUDE (I, J) .IT.MAGLLN) MAGMIN = MAGNITUDE (I, J)
IF(FICWX(I,J) .G7T.FLCWXMAX) FLCWXMAX = FICWX(I,J)
IF(FLCWX(I,J) .LT.FLCWXMIN) FLOWXMIN = FICWX(I,J)
IF(FLCWY(I,J) .GT.FICWYMAX) FICWYMAX = FICWY(I,J)
IF(FIOWY(I,J) .LT.FLCWYMIN) FICWYMIN = FICWY(I,J)
IF(CFI£WX(I,J) .GT.CFLDWXMAX) CFLCWXMAX = CFICWX(I,J)
IF(CFIOWX(I, J) .IT.CFLOWXMIN) CFLOWXMIN = CFIOWX(I,J)
IF(CFLOWY(I, J) .GT.CFIOWYMAX) CFIOWYMAX = CFICWY(I,J)
IF (CFLCWY (I ,J) .IT.CFLCWYMIN) CFICWYMIN = CFICWY(I,J)
IF(CMASS(I,J) .GT.CMASSMAX) CMASSMAX = CMASS(IfJ)
IF(CMASS(I,J) .IT.CMASSMIN) CMASSMIN = CMASS(I,J)
CCCCCC
C END NODAL CALCULATION LOOPS
CCCCCC
50 CONTINUE


132
The results of these calculations for the two methods of gridding
are presented in Table 6.8. Note that the mass of chloride within the
groundwater greatly exceeds the amount applied. Significant
concentrations of chloride were apparently contributed frcxn other sources
such as fertilizer and irrigation water. There are notable differences
in the total mass of chemicals stored as calculated from the two methods
of gridding the observed data. There is no evidence to suggest that one
method is more correct than the other, therefore the values calculated
based on the two methods can be considered as the extremes of the
possible mass storage in the groundwater with the actual value probably
lying somewhere between the extremes. Figure 6.54 shows the mass of
atrazine in the saturated zone (based on the second gridding method)
during the entire period of observation. What is not shewn by Figure
6.54 is the mass of atrazine which may have been transported off-site
during the period of observation. In order to estimate the transport of
atrazine from the site, it is necessary to first calculate the flux of
water from the site. The next section describes the methods used to try
to estimate the water and chemical fluxes on the experimental site.


67
many parameters such as rainfall, pesticide properties, and soil
characteristics which are common to both models. Thus, the selection of
common parameter values will be discussed first, followed by selection of
values for parameters which are unique to each model.
5.1 Selection of Common Input Parameter Values
5.1.1 Rainfall and irrigation
Both models use daily rainfall data. The formats of the rainfall
files for the models are different and PRZM reads in values in
centimeters while GLEAMS reads in values in hundredths of an inch.
The data used to create the precipitation files came from a
combination of two rain gages. As stated previously, one weighing rain
gage is centrally located within the experimental site. This rain gage
was installed in June of 1986. A second rain gage is located
approximately 300 m north-northeast of the experimental site. This gage
is a tipping bucket type rain gage that is connected to a data logger.
All rainfall data used in the simulations came from this off-site gage
since the data from it was already on a computer and available. The
rainfall amounts recorded in both gages agreed well. The gage located on
the site was used to supplement the rainfall record with amounts of
irrigation applied to the site. This rainfall plus irrigation file was
then manipulated into the units and formats required by each model, and
checked to insure that the two formats contained identical information.


143
soil properties such as the organic carton content over larger depths
relative to the smaller layers utilized by FRZM. The increased number of
layers in FRZM allows the calculated layer properties to more nearly
match the profile description input into the model. Another reason for
the differences in model predictions may be the method by which pesticide
transport is calculated in each model. In ERZM, a form of the
advection-dispersion equation is solved (after water velocities have been
determined separately) using finite-difference techniques to calculate
pesticide transport. In GLEAMS, pesticide transport is calculated by
sequentially moving the chemicals between layers based on water flux and
the concentration of the chemical in each layer. After the chemicals
have been added to or removed from a layer, the mass of the chemical is
redistributed between the sorbed and solution phases based upon the
partition coefficient in that layer.
The predicted leaching of bromide by the two models was similar.
Closer examination indicates that GLEAMS predicted higher fluxes of
bromide for the first application, and PRZM predicted higher fluxes for
the second application. The overall effect was to predict nearly
identical bromide fluxes. The nitrate leaching predicted by PRZM was
higher than the GLEAMS prediction due to FRZM's higher predicted
percolation volumes after the nitrate was applied as shown in Figure
6.56. Overall, the percolation predicted by the models is very similar.
A small adjustment in the leaf area index (LAI) in GLEAMS could result in
closer agreement between the percolation predicted by the models.
Figures 6.57-6.62 shew the measured concentrations of atrazine in
the soil profile and the corresponding predictions by PRZM. Figures


Figure C.2 Continued.
WRITE(5,154) (CERROR(I,J) ,J=1,7)
WRITE(5,178) (CERRDRP(I, J) ,J=1,7)
WRITE(7,135)DAY(I),DATE(I-1),PERC(I)
WRITE(7,141) (AVGWFLCW(I, J) ,J=1,7)
WRrrE(7,142)(DEIWMASS(I,J),J=1,7)
WRITE(7,143) (PDEIWMASS(I,J) ,J=1,7)
ENDIF
25 CONTINUE
WRITE(5,436) (TOIWERROR(J) ,J=1,7)
WRITE(5,437) (TOTCERRDR(J) ,J=1,7)
WRITE(7,438) (TOIWERROR(J) ,J=1,7)
CCCCCC
C STOP AND END PROGRAM
CCCCCC
STOP END OF FROCCESSING !!!!!'
CCCCCC
C FORMAT STATEMENTS
CCCCCC
110
FORMAT (13,3X, 2 (FIO. 4))
115
FORMAT(16X,7(F10.5,2X),A8)
125
FORMAT('
SUMMARY OF FTJUX DATA',/)
130
FORMAT(/,
1 CHANGE BETWEEN ,A8, AND ,A8)
135
FORMAT('
DAYS SINCE APPLICATION: ,13,', PERCOLATION SINCE '
# A8,' = '
,F5.2,' cm')
136
FORMAT('
DAYS SINCE APPLICATION: ,13,', CHEMICAL FLUX SINCE
# A8,' = '
,F5.2,' mg/m**2 )
137
FORMAT('
AREA OF SUBAREA (nf2j
: ',8(F10.4,2X))
140
FORMAT('
FORMAT('
NEIWFIDW CXJ ,A8, '
',8(F10.4,2X))
141
AVGWFDDW OVER PERIOD
: ',8(F10.4,2X))
142
FORMAT('
DELIA WSTORAGE (nf 3)
: ',8(F10.4,2X))
247
FORMAT('
DELIA MEAN WTD (cm)
: ',8(F10.4,2X))
143
FORMAT('
FRED DELIA WSTORAGE
: ',8(F10.4,2X))
144
FORMAT('
ERROR IN WSTORAGE(nT3) : ,8(F10.4,2X))
145
FORMAT('
SAT. STORAGE ',A8,'
',8(F10.4,2X))
242
PORMATC
FORMAT ('
BRAINED VOL ,A8, '
',8(F10.4,2X))
241
MAX STORAGE ',A8,'
',8(F10.4,2X))
243
FORMAT('
MEAN WTDEPIH ,A8, '
',8(F10.4,2X))
155
FORMAT('
C STORAGE CW ',A8,'
',8(F10.4,2X))
150
FOFMAT('
NETCFLCW ,A8, '
' ,8(F10.4,2X))
151
PORMATC
AVGCFLOW OVER PERIOD
: ',8(F10.4,2X))
152
PORMATC
DELIA CSTORAGE (rrg)
: ',8(F10.4,2X))
153
PORMATC
FRED DELIA CSTORAGE
: ',8(F10.4,2X))
154
PORMATC
ERROR IN CSTORAGE
: ',8(F10.4,2X))
177
PORMATC
ERROR IN WSTORAGE (%) : ',8(F10.4,2X))
178
PORMATC
ERROR IN CSTORAGE (%) : ',8(F10.4,2X))
436
FORMAT(/,
' TOT ERROR IN WSTORAGE : ,8 (F10.4,2X))
437
PORMATC
TOT ERROR IN CSTORAGE : ,8(F10.4,2X))
438
PORMATC
TOT ERROR IN WSTORAGE : ',8(F10.4,2X) ,/)
721
FORMAT(6A12)
END


127
07-09
Date: 05/01/87
a
u
c
o
Figure 6.46. Chloride concentration in the groundwater on 5/01/87.
Vertical bars indicate sampling locations.
Figure 6.47. Chloride concentration in the groundwater on 5/03/87.
Vertical bars indicate sampling locations.


Figure C.l Listing of program to calculate water and chemical fluxes and
storages.
CCCCCCC
C PROGRAM TO CALCULATE WATER AND CHEMICAL FLUXES AND STORAGES FROM
C GRIDDED WATER TABLE ELEVATION DATA
C WRITTEN: 11/22/87 BY: MATT C. SMITH
C IAST UPDATE: 4/12/88
CCCCCC
EROGRAM ANALYZE
REAL WT(23,45) ,WIMIN,WIMAX/MEANWrD(7) ,KD,BUIKDEN
REAL MAGNITUDE(23,45) ,DEIX,DELY,ANGIE(23,45) ,MAGMIN,MAGMAX
REAL SLPX(23,45) ,SLPY(23,45) DIRECTION (23,45) ,SURFMAX
REAL SURF(23,45) ,SURPMIN,ROW/OOL,MAXSTOR(23,45) ,DRVOL(23,46)
REAL IMP(23,45) ,XMIN,XMAX,YMIN,YMAX,IMFMAX,IMIMIN,XPOS, YPOS
REAL WTD(23,45) ,WTEMIN,WIIMAX,SLPXMAX,SLPYMAX,SLPXMIN,SLPYMIN
REAL VEIX(23,45) ,VELY(23,45) ,FLOWXMAX,FLOWYMAX,FI£WXMIN,FICWYMIN
REAL FLCWX(23,45) ,FIOWY(23,45) ,BFICWX1(7) ,BFI£WX2(7) ,BFICWY(7)
REAL NETFI0W(7) ,AREA(7) ,C0ND(45) ,PORQ,NODECOND(7)
REAL WMASS(23,45) ,CMASS(23,45) ,C0NC(23,45) ,TCMASS(7) ,CFIOWYMIN
REAL BCFD0WX1(7) ,BCFIOWX2(7) ,BCFI£WY(7) ,NETCFTOW(7) ,TWMASS(7)
REAL CXNCMIN,CONCMAX,CMASSMAX,CMASSMIN,CWMASS(23,45) ,CSMASS(23,45)
REAL CFLCWX(23,45) ,CFIOWY(23,45) ,CTTOWXMAX,CFLOWXMIN,CFL3W!MAX
REAL DRVOLMAX,DRVOIMIN,DRVOL5UM(7) ,MST0RSUM(7) ,TEMP
INTEGER*2 NX,NY,I, J, INC,II, JJ,KK,IL,IM,NN,YY(7) ,XX(2) ,STRT,STP
INTEGER*2 IYR, DON, IDAY,IHR, IMIN,ISEC, I100TH, IOUT
CHARACTER*2 MONTH,DAY,YEAR
CHARACTER*8 DATE,CHEMFTIE
CHARACTER* 12 INFILE1, INFTLE2, INFILE3, INFILE4, INETLE6, OUTFTLE
CHARACTER* 12 FIUXFILE,COMMENT(14)
CCCCCC
C INITIALIZE VARIABLES
C PI = PI
C OOND(J) = SAT. HYDRAULIC CONDUCTIVITY (m/day) AT NODE J
C NODECOND(J) = SAT. HYDRUALIC COND. AT SPECIFIED NODES J
C PORO = SOIL POROSITY (cm/cm)
C KD = PESTICIDE DISTRIBUTION COEFF. (cm**3/g)
C BUIKDEN = SOIL BULK DENSITY (g/cm**3)
CCCCCC
PI = 3.141592654
PORO = 0.36
KD = 0.0489
BULKDEN = 1.59
3NFIIE1 = 'IMPERMBA.GRD'
INFILE3 = 'SURFACEA.GRD'
INFIIE2 = 'INFILE .DAT'
CCCCCC
C FILE INFILE.DAT CONTAINS MONTH, DAY, YEAR VALUES FOR GENERATING
C FILENAMES AND DATE
C FILE IMPERMBA.GRD CONTAINS GRIDDED ELEVATIONS OF IMPERMEABLE LAYER
C FILE SURFACEA.GRD CONTAINS GRIDDED ELEVATIONS OF SOIL SURFACE
CCCCCC
177


63
capture detector was used. This detector had a high sensitivity to
alachlor and a lower sensitivity to atrazine. However, when field
samples were analyzed, as opposed to DI water spiked with pesticides, it
was observed that the EC detector was sensitive to many naturally-
occurring compounds (the EC detector is sensitive to chlorine molecules)
which had been extracted along with the pesticides. Thus, the output was
so crowded with peaks that the quantities of atrazine and alachlor
present could no longer be reliably determined. The NP detector is very
sensitive to molecules of nitrogen and phosphorus and insensitive to
almost anything else. When the samples were analyzed on this detector,
the chromatograms were much cleaner and atrazine and alachlor were often
the only visible peaks. The NP detector is somewhere between 5 and 10
times more sensitive to atrazine than it is to alachlor due to the
greater number of nitrogen atoms in the structure of atrazine. The NP
detector was operated with detector gas flowrates of air at 50 mL/min and
hydrogen at 3 mL/min. The carrier gas was helium with a flowrate of 30
mL/roin*
The column used in the GC was 1.83 m long with a 4 mm ID. The
column was packed with 3% OV-17 (50% phenyl, methyl silicone) on 100/120
mesh Gas Chrcm Q. The operating conditions were: injector port
temperature set at 240C, column temperature at 200C, and detector
temperature at 300C.
The sample protocol used with the auto sampler was usually of the
form: 4 standards, 1 solvent blank, 10 samples, 4 standards, 1 solvent
blank, 10 samples, 4 standards, 1 solvent blank. The solvent blank was
used to help prevent carryover from the last (and highest) standard into


Table A.l Continued
WELL /
STATION
INSTALLED
BY
DATE
INSTALLED
DISTANCE FRCM RISER
CASING
LENGTH
[MEIERS]
ELEVAT
ION (METERS) *
X Y
TOP OF
CASING
SOIL
SURFACE
RESTR.
LAYER
07-14
UF
5/20/86
12.19
158.50
4.45
28.62
28.09
24.17
08-14
UF
5/20/86
24.38
158.50
4.01
28.35
27.85
24.34
09-14
UF
5/20/86
36.58
158.50
3.22
28.11
27.63
24.89
10-14
USDA
2/7/85
48.77
158.50
3.54
27.97
27.55
24.43
06-15
USDA
2/7/85
0.00
170.69
4.18
28.14
27.72
23.96
08-15
USDA
2/7/85
25.15
172.83
4.86
27.31
26.85
22.45
IOW
USDA
2/7/85
39.62
200.18
1.67
22.38
22.11
20.71
1 ORIGIN @ SPRINKLER RISER 6-1 WITH X POSITIVE TO NORTH AND Y POSITIVE TO WEST
2 ELEVATION OF TOP OF SPRINKLER RISER 09-09 SET EQUAL TO 30.48 MEIERS
3 ELEVATION OF BOTTOM OF WELL


129
07-11
Date: 05/13/87
-J
o
\

Oi
UJ)
o
-
'J-
o
-
u
c
,
0
O
o
~
DISI*v(
. /( (r)
Figure 6.50. Chloride concentration in the groundwater on 5/13/87.
Vertical bars indicate sampling locations.
08-11
Figure 6.51. Chloride concentration in the groundwater on 5/18/87.
Vertical bars indicate sampling locations.


BIOGRAPHICAL SKETCH
Matthew Clay Smith was bom on September 27, 1957, in DeLand,
Florida. He attended Deland Senior High School during 1973 and 1974. In
1974 he attended Brevard College in Brevard, North Carolina, under the
early admissions program. In 1977 he received the Associate of Science
degree from Abraham Baldwin Agricultural College in Tifton, Georgia. In
1980 he received the Bachelor of Science degree in agricultural
engineering from the University of Georgia in Athens, Georgia. While
attending the University of Georgia he participated in the cooperative
education program by alternating quarters between academic courses and
employment with the U. S. Environmental Protection Agency in Athens,
Georgia.
He attended graduate school at North Carolina State University in
Raleigh, North Carolina, where he worked as a research assistant in the
Department of Biological and Agricultural Engineering. He received the
Master of Science degree in 1983. His thesis topic was a study of the
water and energy use efficiency of drainage/subirrigation systems.
He worked as a research agricultural engineer in the Department of
Agricultural Engineering, University of Georgia, Coastal Plain Experiment
Station in Tifton, Georgia, from 1982 to 1984.
He attended the University of Florida in Gainesville, Florida, where
he worked as a research assistant in the Agricultural Engineering
Department from May, 1984, through September, 1987.
213


CHAPTER 4
EXPERIMENTAL METHODS
4.1 Field Site Description
The study site is located near Tifton, Georgia, in the coastal plain
physiographic region of the southeastern U.S. The geology of this area
has been described by Asmussen et al. (1986). The site is part of a farm
rented by the University of Georgia Coastal Plain Experiment Station
which is referred to as Gopher Ridge in honor of the many gopher
tortoises which make their home on the edges of the fields and in the
woods nearby.
The study site covers 0.7 ha in the northwest comer of a 2.3-ha
field. The field was developed for irrigated agricultural research.
Irrigation sprinkler risers are located on a 12 by 12 m grid. Supply
lines for irrigation water are buried approximately 1 m below the soil
surface. The field was removed from active crop research in 1982. At
that time the field was deep tilled in both north-south and east-west
directions to remove possible residual tillage effects. A permanent
bahia grass cover was established and fertilized and irrigated as needed.
No pesticides were applied after the grass cover was established. The
field was maintained in this condition until the spring of 1986 when
instrumentation for this research was installed. The site is bounded on
the north and west by trees. Just beyond the tree line on the western
edge of the field, the surface elevation drops rapidly into a seepage
39


44
widened to approximately 10 on in diameter and was filled with a
bentonite slurry to form a seal around the sampler and prevent direct
flew from the surface dewn the side of the PVC pipe to the ceramic cup.
Figure 4.1 shews the location of the application area, which is the
strip to which the herbicides and bromide tracer were applied. The
samplers and three monitoring wells (08-09, 09-09, and 10-09) are within
this area. The samplers were installed in groups of three at a distance
of 3.05 m on either side of each well along a north-south axis. The
samplers were assigned identification labels that indicated their
position with respect to the wells and the depth to which they were
installed, eg. the group of samplers located 3.05 m south of well 09-09
were assigned labels of 09S-2, 09S-4, and 09S-6 for depths of 61, 122,
and 183 cm (2, 4, and 6 ft), respectively. A cross-section of the
application area shewing the locations of solution samplers and
monitoring wells is shewn in Figure 4.2.
A small instrument trailer was located approximately 4 m east of the
application area. A vacuum pump located in the trailer was used to
apply a vacuum to all samplers simultaneously through a manifold. The
pump was capable of creating a vacuum of approximately 28 cm of Hg. A
0.64 cm diameter polypropylene sample tube was connected to the sample
fitting on each sampler and routed back to the trailer where it was
connected to a 50 mL polycarbonate sample container. All sample tubing
was enclosed in a protective FVC conduit which was located 1.5 m from the
samplers to avoid interference with chemical application in the vicinity
of the samplers. Between the samplers and the PVC conduit, the stiff
polypropylene vacuum and sample lines were fastened together and held


CHAPTER 6
RESUITS AND DISCUSSION
There are many areas in this study for which results can be
reported. The approach used in this chapter is to report significant
results relative to each area such as sample collection, sample analysis,
simulation results, etc., and then relate the individual results where
appropriate. A summary of activities at the field site is presented in
Table 6.1 for reference.
6.1 Data Collection
The site was visited on at least a weekly basis throughout the
study. At each visit, samples were collected from the monitoring wells
and soil solution samplers. The water table elevation was also recorded
for every monitoring well on the site and for several wells located
adjacent to the study area. Soil samples were collected on an irregular
basis during the study.
The procedure used for collecting samples from the groundwater
worked well in general. Sand intrusion into a few of the wells, however,
was a consistent problem. It was possible in seme instances to fill the
250 mL sampling bottle with sand. This problem was overcame by slowly
raising the bottom of the sampling tube in order to stay above the sand.
These wells were pumped out using a 1.6 cm diameter suction line to
remove accumulated sand as needed. The problem could have been avoided
79


REFERENCES
Agrichemical Handbook, The. 1983. Royal Society of Chemistry. The
University, Nottingham, England.
Alexander, W J. and S. K. Little. 1986. Ground water vulnerability
assessment in support of the first stage of the national pesticide
survey, in Agricultural Impacts On Groundwater QualityA
conference. Omaha, NE. National Water Well Association, Water Well
Publishing Co., Dublin, OH. pp. 77-86.
Aller, L., T. Bennett, J. H. Lehr, and R. J. Petty. 1985. DRASTIC: A
standardized system for evaluating ground water pollution
potential using hydrogeologic settings. U. S. Environmental
Protection Agency. Washington, DC. EPA 600/2-85/018.
Asmussen, L. E., H. F. Perkins, and H. D. Allison. 1986. Subsurface
descriptions by ground penetrating radar for watershed
delineation. Georgia Agricultural Experiment Stations Research
Bulletin No. 340. Athens, GA. 15 pp.
Berteau, P. E. and D. P. Spath. 1986. The toxicological and
epidemiological effects of pesticide contamination in California
ground water, in Evaluation of Pesticides in Groundwater. W. Y.
Gamer, R. C. Honeycut, and H. N. Nigg, Eds. ACS Symposium Series
No. 315, American Chemical Society, Washington D.C. pp. 423-435.
Brinsfield, R. B., K. W. Staver, and W. L. Magette. 1987. Impact of
tillage practices on pesticide leaching in Coastal Plain soils.
ASAE Paper No. 87-2631. ASAE, St. Joseph, MI. 21 pp.
Brinsfield, R. B., K. W. Staver, and W. L. Magette. 1988. The role
of cover crops in reducing nitrate leaching to groundwater, in
Proceedings of the Agricultural Impacts On Groundwater QualityA
conference. Des Moines, IA. National Water Well Association. Water
Well Publishing Co., Dublin, OH. pp. 127-146.
Carlisle, V. W., R. E. Caldwell, F. Sodek, III., L. C. Hammond, F.
G. Calhoun, M. A. Granger, and H. L. Breland. 1978.
Characterization data for selected Florida soils. University of
Florida, Institute of Food and Agricultural Sciences, Soil
Science Department Research Report No. 78-1. Gainesville, FL.
335 pp.
162


D.2. Atrazine concentration in the groundwater on 1/26/87 201
D.3. Atrazine concentration in the groundwater on 2/02/87 202
D.4. Atrazine concentration in the groundwater on 2/09/87 202
D.5. Atrazine concentration in the groundwater on 2/16/87 203
D.6. Atrazine concentration in the groundwater on 2/23/87 203
D.7. Atrazine concentration in the groundwater on 3/02/87 204
D.8. Atrazine concentration in the groundwater on 3/09/87 204
D.9. Atrazine concentration in the groundwater on 3/16/87 205
D. 10. Atrazine concentration in the groundwater on 3/23/87 205
D.ll. Atrazine concentration in the groundwater on 3/31/87 206
D. 12. Atrazine concentration in the groundwater on 4/06/87 206
D. 13. Atrazine concentration in the groundwater on 4/13/87 207
D.14. Atrazine concentration in the groundwater on 4/20/87 207
D. 15. Atrazine concentration in the groundwater on 4/30/87 208
D.16. Atrazine concentration in the groundwater on 5/01/87 208
D. 17. Atrazine concentration in the groundwater on 5/03/87 209
D. 18. Atrazine concentration in the groundwater on 5/05/87 209
D. 19. Atrazine concentration in the groundwater on 5/08/87 210
D.20. Atrazine concentration in the groundwater on 5/13/87 210
D.21. Atrazine concentration in the groundwater on 5/18/87 211
D.22. Atrazine concentration in the groundwater on 5/25/87 211
D.23. Atrazine concentration in the groundwater on 6/01/87 212
xiii


Table C.l
Continued
INPUT FILES
UNIT 1 = IMPERMBA.GRD
UNIT 2 = INFILE .DAT
UNIT 3 = SURFACEA.GRD
INPUT FILES
OUTPUT FILE
UNIT 5 = 5-05WTD.SUM
OUTPUT FILE
UNIT 4 = 5-05ELV.GRD
UNIT 6 = 5-05ATRS.GRD
HYD COND. : 14.00
POROSITY : .36
PART ION ING COEFF
BULK DENSITY
X-NODE BOUNDARIES
Y-NODE BOUNDARIES
COMMENT : CONSTANT HYD. CONDUCTIVITY = 14 m/day
COMMENT : USING ATRAZINE DATA W/O FORCED ZERO CONCENTRATIONS,
PROGRAM RUN ON 06/06/1988 AT 21.52.27.50
14.00 14.00 14.00 14.00 14.00 14.00
.04890
1.590
1 23
1 8 14 20 26 30 34
WHOLE SITE
193


Nicholas and Sarah Smith, my children, for their love and for
providing meaning and purpose to my life and education.
Dr. G. W. Isaacs, for financial support in the form of a research
assistantship.
Dr. D. L. Thomas, for his friendship and support. He shielded me
from many distractions at work while I completed this document.
Dr. W. B. Wheeler and Ms. S. J. Scherer, at the UF Pesticide
Residue Laboratory, for allowing me to use their equipment and guiding
me through the intricacies of pesticide residue analysis.
Mr. L. A. Asmussen, Dr. R. A. Leonard, Dr. W. G. Knisel, Ms. L. R.
Marti, and others at the USDA Southeast Watershed Research Laboratory for
their advice, encouragement, and support.
USDA, Southern Region Pesticide Impact Assessment Program, for
financial support in the form of a grant.
Many others, too numerous to list here, in both Gainesville and
Tifton who contributed in many ways to the success of the project.
iv


178
Figure C.l Continued.
OPEN (UNIT=1, FILE=INFILE1, STATOS= OLD')
OPEN (UN3T=3, FIIE=INFHE3, STA1US=' OLD')
OPEN (UNIT=2, FILE=INFIL£2, STAIUS=' OLD')
CCCCCC
C READ IN IMPERMEABLE LAYER ELEVATION DATA
C NX,NY ARE # OF X AND Y VALUES IN GRID
C XMLN, YMIN AND XMAX, YMAX ARE MLN AND MAX VALUES ON RESPECTIVE AXIS
CCCCCC
READ(1,*)
READ(1,*) NX,NY
READ(1,*) XMIN,XMAX
READ(1,*) YMIN,YMAX
READ(1, *) IMFMIN,IMFMAX
DO 5, J= 1,NY
READ(1,*) (IMP(I,J), 1=1,NX)
READ(1,*)
5 CONTINUE
CCCCCCC
C CALCULATE SPACING OF GRID POINTS IN X AND Y DIRECTIONS
C DELX = DISTANCE BETWEEN NODES O X-AXIS
C DELY = DISTANCE BETWEEN NODES ON Y-AXIS
CCCCCCC
DELX = (XMAX-XMIN) / (NX-1)
DELY = (YMAX-YMIN) / (NY-1)
CCCCCC
C READ IN SOIL SURFACE ELEVATION DATA
CCCCCC
READ(3,*)
READ(3,*)
READ(3,*)
READ(3,*)
READ(3,*) SURFMIN,SURFMAX
DO 10, J= 1,NY
READ(3,*) (SURF(I,J), 1=1,NX)
READ(3,*)
10 CONTINUE
CCCCCC
C READ IN :
C OUTFUT INCREMENT VALUE, PRINT EVERY INC NODES IN X AND Y
C CONDUCTIVITY (m/day) AT Y-AXIS BOUNDARY NODES
C BOUNDARY NODE NUMBERS ON X-AXIS FOR SUBAREA BOUNDARIES, 2 VALUES
C BOUNDARY NODE NUMBERS ON Y-AXIS FOR SUBAREA BOUNDARIES, 5 VALUES
C SUMMARY FILE NAME
C .AND FOR 28 FILES READ TOE FOLLOWING...
C MONTH, DAY, YEAR AND CHEMICAL FILENAME
CCCCCC
C CREATE INFUT AND OUTFUT FILENAMES BY ADDING APPROPRIATE EXTENSIONS
CCCCCC
READ(2,102)INC,IOUT
READ(2,104)(NODECOND(I),1=1,7)


114
6.5.2 Atrazine
Atrazine first appeared in the groundwater beneath the application
area in the samples of Jan. 21. By this date, approximately 45 cm of
water had been applied to the site as rainfall and irrigation since
application of the herbicides.
Groundwater samples from Jan. 21 through June 6 show what appear to
be distinct pulses of atrazine reaching the saturated zone and moving
downslope with the saturated flow. These pulses are characterized by low
initial concentrations in the groundwater followed by the appearance of
atrazine beneath the application area and movement within the groundwater
as the water flows downgradient to the west. Figures 6.30 through 6.35
show the concentration of atrazine (in /ug/L) in the groundwater at
weekly intervals for a five week period beginning on Feb. 23, 1987. A
vertical bar in these figures indicates that a well sample was collected
and analyzed from that location. The transport of atrazine with the
saturated flow is clearly visible and atrazine moved a distance of
approximately 85 m in a period of 21 days.
Figure 6.36 shows the general directions of flow based upon the
slope of the water table on May 8, 1987. The slope of the water table on
other sampling dates shows that flow would occur in the same general
directions. In Figures 6.30 through 6.32 it is observed that atrazine
has moved downslope from well 09-11 to 09-14. The average gradient of
the water table between these wells during this period was approximately
0.05 m/m. The hydraulic conductivity of the soil was not measured during
this study. Assuming a hydraulic conductivity of 14.4 ny'day (60 cm/hr)
at a depth of approximately 3 m (Carlisle et al., 1978) and an effective


81
Table 6.1 Continued.
Date
Event
3/23/87
Collect soil solution and well samples.
3/31/87
Collect soil solution and well samples.
4/06/87
Collect soil solution and well samples.
4/13/87
Collect soil solution and well samples.
4/16/87
Apply 560 kg/ha of 5-10-15 to entire study site.
4/20/87
Collect soil solution and well samples, Very few wells
have water.
4/27/87
Collect soil solution and well samples, Apply 2.5 cm of
irrigation, apply approximately 17 kg/ha Br, apply 2.5
cm of irrigation.
4/28/87
Collect soil solution and well samples, apply 4.2 cm of
irrigation.
4/29/87
Collect soil solution and well samples, apply 3 cm of
irrigation.
4/30/87
Collect soil solution and well samples, apply 3 cm of
irrigation.
5/01/87
Collect soil solution and well samples, pour 10 1 of
solution containing 500 g KC1 into well 07-09, apply 2.7
cm of irrigation.
5/02/87
Collect soil solution and well samples, apply 3.2 cm of
irrigation.
5/03/87
Collect soil solution and well samples, apply 1.9 cm of
irrigation.


90
significantly lower than for atrazine although they were applied at the
same rate (4.9 kg/ha). Figure 6.4 shows the relationship between
measured concentrations of alachlor and atrazine in the sample collection
containers. The concentration of alachlor in a container is strongly
correlated to the concentration of atrazine. The alachlor concentrations
were, in general, only about 24% of the atrazine concentrations.
Soil sample data taken six days after application (Table 6.3) suggests
that more alachlor reached the soil than indicated by the application
rate shewn in Table 6.2. Possible explanations for the concentrations of
alachlor in the application samples include errors in formulating the
application solution, adsorption to the plastic cup, and volatilization.
APPLICATION SOLUTION CONCENTRATIONS
Figure 6.4. Comparison of alachlor and atrazine concentrations in
application samples.


120
In Figure 6.37, the concentration of atrazine in one well is plotted
along with the water table elevation in the well. There appears to be an
inverse relationship between concentration and water table elevation or
depth of the saturated zone. This well is 20 m downslope from the
application area. Based on this figure, it is hypothesized that as the
water table rises, the water around the well is primarily coming from
percolation through the untreated soil above it which would cause the
concentration of atrazine in the vicinity to decrease. During the period
that the water table is receding, water which has infiltrated through the
application area is flowing past the well, thus increasing the
concentration.
o o
a
>
w
w
w
_)
m
<
E-*
35
W
E-
<
Figure 6.37. Concentration of atrazine and water table elevation in
well 09-11.


23
aldicarb residues within a shallow water table aquifer. Dean and Carsel
(1988) reported on progress in linking FRZM to a two-dimensional
saturated transport model. The linked model will lose FRZM for simulating
the root zone, a one-dimensional transport model for the vadose zone, and
a two-dimensional saturated zone model which can simulate confined,
unconfined, and leaky confined aquifers. The saturated zone model will
also simulate pumping from the aquifer(s). The linked model is expected
to be released for testing during the fall of 1988.
3.3.4 Model testing and uncertainty
Methods (such as described above) to predict the transport of the
chemicals used by agriculture are needed by regulators in order to assess
the impacts on surface water and groundwater quality from the use of
these chemicals. Field testing of the contamination potential of all
chemicals registered for use by agriculture would be infeasible due to
both cost and time constraints. Since decisions regarding chemical use
will be made based upon the results of transport models, it is important
to understand the errors and uncertainty associated with the models.
Each model or method will have errors associated with the assumptions
relating to the description of the system, choice of equations to
represent the system, and input parameters required by the models. There
are some general points that can be made regarding the uncertainties
inherent in deterministic, physically based models such as FRZM and
GLEAMS which were described previously. An excellent discussion of the
uncertainty in transport models is presented by Leonard and Knisel
(1988).


89
Figure 6.3. Uniformity of bromide application on 4/27/87,
a) concentration in application solution (vertical bars
shew sampling locations), b) application rate (vertical
bars show location of monitoring wells)


40
area where subsurface flows reemerge as surface water that flews to the
Little River.
The soil on the study site is classified as a lakeland sand (Typic
Quartzipsarnmants, thermic, coated). The soil profile depth on this site
ranges frem 1.9 to 4.4 meters. Underlying the soil is a restricting
layer consisting of tight clays. This restricting layer is the top of
the Hawthorne formation which forms the confining layer over much of the
aquifer system (Floridan) of southern Georgia and Florida. The presence
of this restricting layer causes percolating rainfall or irrigation to
saturate the soil above the layer and form a transient water table.
Previous work by Asmussen et al. (1986) had shown, through the use
of ground penetrating radar (GPR), that the restricting layer below the
study site would cause saturated flow above it to converge into channels.
The presence of these channels made the site look premising for saturated
zone monitoring because samples from wells located in these channels
would be very likely to show the presence of any chemicals which were
moving with the saturated flow. The maps drawn frem the (GPR)
descriptions also indicated that the slope of the restricting layer near
the study area boundaries was generally away from the study area. This
would minimize influences of saturated flows coming into the site from
other areas.
Figure 4.1 shews the elevations of both the soil surface and
restricting layer for the 0.7-ha study site. The elevations given are
relative to a local bench mark which was assigned an elevation of 30.48
m. The soil surface has a relatively uniform slope of approximately 4%
towards the west. The restricting layer shews a more complicated


137
the subarea on that date. The calculations described above were
performed for all nodes and subareas for the 28 sampling dates in this
study.
6.5.6.3 Mass balances between observation dates
The net flcwrate between sampling dates for each subarea was
calculated by averaging the net flowrates calculated on the two dates.
The average net flowrate multiplied by the interval between the sampling
dates resulted in an estimate of the total volume of water which had
moved into or out of a subarea by saturated flew between the dates. The
percolation volume during the interval between dates predicted by PRZM
was used to calculate the volume of water added to each subarea due to
rainfall or irrigation. The sum of the volume of water added or removed
from a subarea by saturated flow and the volume added by percolation gave
an estimate of the predicted change in water volume in the subarea
between the sampling dates.
The change in drained volume in a subarea between sampling dates was
assumed to represent the actual change in water volume in that subarea.
Thus, the predicted change in water volume could be compared to a
"measured" value.
Mass balances of chemicals within the saturated zone were done in a
similar manner. Chemicals were assumed to be added to the saturated zone
only in subarea number 2 which contained the application area. The
of chemical leaching past the bottom of the soil profile (2.62 m)
predicted by PRZM was multiplied by the area of the application area to
calculate the total mass of chemical added to subarea number 2.


82
Table 6.1 Continued.
Date
5/05/87
5/08/87
5/11/87
5/13/87
5/18/87
5/25/87
6/01/87
Event
Collect soil solution and well samples, apply 0.9 cm of
irrigation.
Collect soil solution and well samples, apply 1.9 cm of
irrigation.
Collect soil solution and well samples, apply 5.2 cm of
irrigation.
Collect soil solution and well samples.
Collect soil solution and well samples.
Collect soil solution, well, and soil samples.
Collect soil solution and well samples.
by using commercially available PVC well screening in the monitoring
wells.
No problems were observed with the operation of the soil solution
samplers. The volumes of water collected during sampling varied from 0
to greater than 50 mL. Even though the samples were pulled back to the
instrument trailer through as much as 35 m of 0.64 cm ID polypropelene
tubing, the vacuum applied to the sample lines was sufficient to pull
very small droplets of water along the wall of the tube and into the
containers. No carryover of the tracers between sampling periods was
observed.
The tensiometers were difficult to maintain properly on the weekly
sampling schedule. Even when the tensiometers were working, their
usefulness was limited by the short length of time during which they


142
6.6 Model Results and Comparisons
The PRZM and GLEAMS models were run to simulate the time period from
1/1/86 through 12/31/87. Simulation of the system for the ten months in
1986 prior to application was done to minimize the effects of initial
conditions. The simulated total mass flux of pesticides, bromide, and
nitrates below the root zone during the sampling period are shewn in
Table 6.9. The mass flux of the chemicals to the bottom of the profile
as simulated by PRZM is also included in Table 6.9. The models
predicted that no additional movement of the applied chemicals below the
root zone occurred after 6/1/87.
Table 6.9 Simulated mass flux of chemicals.
Chemical
Simulated Mass Flux
(Q/ha)
GLEAMS1
PRZM1
FRZM2
Atrazine4
2970
2314
1154
(60.6)3
(47.2)
(23.6)
Alachlor5
516
165
10
(10.5)
(3.4)
(0.2)
Bromide6
23604
23718
17116
(87.4)
(87.8)
(63.4)
Nitrate6
10657
14551
9376
(57.3)
(78.23)
4tv u
(50.4)
1Bottom of root zone 4Plant uptake coef. =0.65
2Bottom of profile 5Plant uptake coef. =0.52
3Percent of application 6Plant uptake coef. = 1.00
GLEAMS predicted a higher percentage of herbicide leaching than did
PRZM. One reason for the difference may be that GLEAMS defines 6 major
soil layers (there is a 1 cm layer at the soil surface) and thus averages


62
was then added to the soil sample. The sample was agitated for 30
minutes and the acetone was carefully decanted into a separate labeled
glass container. Another 100 mL portion of acetone was added to the soil
and the agitation was repeated for another 30 minutes. The acetone was
poured into the container with the previous portion. Several rinses of
acetone were poured through the extracted soil to remove any acetone
left iron the extractions. The acetone rinses were added to the acetone
from the extractions and the entire volume was vacuum filtered through a
47 mm diameter filter with a 0.45 micron pore size. The filtrate was
then reduced to near dryness on the rotary evaporator and brought to a
final volume of 10 mL in acetone. Soil samples taken from outside the
study area were extracted as field blanks and were spiked with known
quantities of the herbicides and extracted as a check on the extraction
and analysis procedures.
A subsample of approximately 10 g of each soil sample was taken to
determine the soil-water content so that the concentrations of the
herbicides in the soil could be reported on dry weight basis. Soil
samples for soil-water content determinations were dried for 24 hours in
an oven at 100C.
4.5.3. Herbicide residue analysis
All herbicide residue samples were analyzed on a Hewlett Packard
model 5840A gas chromatograph (GC). The GC was equipped with a 35
position autosampler and a model 5840 GC terminal (integrator). The GC
had both electron capture (EC) and nitrogen-ptfiosjphorus (NP) detectors.
Curing the early phases of the study (methods development), the electron


CHAPTER 3
REVIEW OF THE LITERATURE
3.1 Evidence of Pesticide Residues in Groundwater
The U. S. Environmental Protection Agency estimates that at least 19
pesticides have been detected in groundwater in 24 states as a result of
agricultural practices (U.S. ERA, 1987a). The total number of
pesticides detected in groundwater is greater than nineteen. These
additional detections are the result of manufacturing, storage, and
loading activities which are not included in the above figures. The
number of reported cases of pesticides in groundwater is increasing,
echen et al. (1986) suggest that the increase is due to improvements in
the quality and quantity of studies instead of an increase in the
problem.
The insecticide/nematicide aldicarb was detected in the shallow
groundwater on Long Island, New York in 1979 (Wartenburg, 1988).
Aldicarb was used as an insecticide on potatoes. Aldicarb was detected
in 20 of 31 wells tested in 1978 and the presence was confirmed through a
repeat sampling of the wells in June, 1979. According to the U.S. EPA
(1987a), aldicarb residues have been detected in almost 2000 wells on
Long Island. Concentrations of aldicarb exceeded the state health
guideline of 7 /g/L (ppb) in nearly 50% of those wells. Concentrations
as high as 515 ¡ig/L were recorded in the Long Island wells as reported by
Ritter (1986). Aldicarb residues have been found in groundwater in 15
states (Cohen et al., 1986) with typical concentrations reported in the
range of 0.3 3 g/L.
5


8
3.2.1 Transport
The chemicals can be transported in at least three different phases:
adsorbed to solid materials, in solution, and as a vapor. Surface runoff
can transport chemicals in both the adsorbed and solution phases.
Erosion and transport of soil particles during rainfall and irrigation
can result in significant transport of highly-adsorbed, lew-solubility
pesticides (Wauchope, 1978). For most pesticides, the majority of
transport in runoff is in the solution phase (Wauchope, 1978; Rao and
Davidson, 1980). Chemicals in the solution phase can be transported
within the soil profile by saturated and unsaturated water flews.
Water percolating through the soil profile can transport chemicals
in the solution phase beneath the root zone and possibly into
groundwater. The most common methods used to describe the transport of
water and solutes in porous media are based on the assumptions that
Darcian flew conditions exist, and that solute movement is controlled by
advective and dispersive processes (Freeze and Cherry, 1979; Jury, 1986a;
Wagenet, 1986; Wang and Anderson, 1982). Darcian flew conditions imply
that the flew equations are representative of seme volume of soil. All
soil properties such as pore size distribution and volumetric water
content are assumed to be uniform throughout the volume of soil (Jury,
1986b).
The advective component of transport is usually based upon Richards'
equation and describes the average rate of water flow. The average rate
of solute flew is the product of the solute concentration and the average
pore-water velocity. Additional transport of solute can occur du^ to
mechanical mixing of water in adjoining pores during advective transport


57
A single injection could reveal the presence and concentrations (if
standards were prepared) of the following anions (in order of increasing
retention times): fluoride (F-), chloride (Cl-), bromide (Br), nitrate
(N03-), phosphate (P043-), and sulfate (S042-).
In ion chromatography, the retention times of the various anions are
dependent upon total ionic strength of the sample, ionic strength of the
individual ions, eluant flowrate, and temperature of the sample and
eluant. Thus, the retention times could, and did, vary. When the
integrator was set to calculate the areas under the peaks, it was found
that the automatic selection of the beginning and ending points of peaks
was not consistent. A small change in retention time could
significantly alter the reported area for a given ion even when using
duplicate injections. Better results were achieved by setting the
integrator to report maximum peak height. This value was not sensitive
to variations in the starting and stopping times of a peak as long the
baseline was relatively stable.
Initial injections of water table samples from the experimental
site revealed the presence Cl- and S042- in all samples, and relatively
high concentrations of NO3- in samples from well I£W. Standards were
prepared using DI H20 and oven-dried quantities of certified or primary
standard grades of potassium chloride, potassium brcmide, potassium
nitrate, and potassium sulfate. The standards initially included Cl-
and S042- because of their presence in the samples. They were not used
to quantify the concentrations of either species during initial sample
analysis. When Cl- was introduced into the water table as a tracer late
in the experiment, sample concentrations of Cl- were quantified. It was


112
08-09
Figure 6.26. Bromide concentration in the groundwater on 5/13/87.
Vertical bars indicate sampling locations.
Date: 05/18/87
08-13
Figure 6.27. Bromide concentration in the groundwater on 5/18/87.
Vertical bars indicate sampling locations.


59
the species can generate a noticeable positive response and yet be
calculated to have a negative concentration. It is expected that a
positive intercept would be a normal result, i.e. there is seme threshold
concentration of the species required before the detector indicates a
response. The piecewise fit insured that any positive response would
result in a positive calculated concentration. Conversations with an
organic chemist at the Pesticide Residue Laboratory of the USDA-ARS
Southeast Watershed Research Laboratory indicated that the piecewise fit
would be an acceptable method of generating a standard curve (Marti,
1987).
The standard curves generated on either end of the analysis of 10
samples were used to compute the concentrations of the species in the
samples. The resulting concentrations were then linearly weighted based
on sample position with respect to either set of standards, i.e. the
first sample following the standards would be weighted towards the curve
generated by the preceding standards and only slightly effected by the
standard curve generated 9 samples later. The sample midway between
standards would be evenly weighted between the two standard curves. In
general, there was little drift in the instrument and the standard
curves did not vary much over a period of hours.
4.5.2 Herbicide residue extraction
Analysis of samples for residues of atrazine and alachlor required
different extraction procedures for soil and water samples. Once the
extractions were completed, all samples were analyzed on a common gas
chromatograph with identical operating conditions.


BROMIDE CONC. (mg/L) i BROMIDE CONC. (mg/L)
DAYS SINCE 11/12/86
ire 6.10. Bromide concentration for six sampling locations
following the first application at a 61 cm depth.
Figure 6.11. Bromide concentration for six sampling locations
following the first application at a 122 on depth.


Table A.l Monitoring Well Statistics
WELL / INSTALLED DATE DISTANCE FROM RISER CASING ELEVATION (METERS)2
STATION BY INSTALLED 6-1 [METERS]1 LENGTH
X
Y
[METERS]
TOP OF
CASING
SOIL
SURFACE
RESTR
LAYER
06-07
USDA
2/7/85
0.00
73.15
3.14
30.85
30.42
27.71
08-07
USDA
2/7/85
24.38
73.15
2.84
30.84
30.40
28.00
11-07
USDA
2/7/85
60.96
73.15
2.59
30.70
30.24
28.11
09-08
UF
3/23/87
36.58
85.34
2.91
30.93
30.41
28.02
07-09
USDA
2/7/85
12.19
97.54
3.58
30.86
30.30
27.28
08-09
UF
5/22/86
24.38
100.58
3.40
30.76
30.26
27.36
09-09
UF
5/22/86
36.58
100.58
2.83
30.76
30.25
27.93
10-09
UF
5/22/86
48.77
100.58
3.17
30.75
30.22
27.58
07-10
UF
5/22/86
12.19
109.73
3.18
30.64
30.13
27.46
08-10
USDA
2/7/85
24.38
109.73
3.83
30.56
30.04
26.73
09-10
UF
5/22/86
36.58
109.73
2.53
30.52
30.03
27.99
10-10
UF
5/22/86
48.77
109.73
2.45
30.56
30.02
28.11
11-10
USDA
2/7/85
60.96
109.73
2.47
30.64
30.13
28.17
06-11
UF
3/11/88
0.00
121.92
3.07
30.69
30.08
27.62
07-11
USDA
2/7/85
12.19
121.92
3.26
30.29
29.74
27.03
08-11
UF
5/21/86
24.38
121.92
3.34
30.16
29.64
26.82
09-11
UF
5/21/86
36.58
121.92
3.62
30.09
29.55
26.47
10-11
UF
5/21/86
48.77
121.92
2.68
30.09
29.57
27.41
07-12
UF
5/21/86
12.19
134.11
3.56
29.77
29.28
26.21
08-12
USDA
2/7/85
24.38
134.11
3.60
29.57
29.09
25.97
09-12
UF
5/21/86
36.58
134.11
3.66
29.54
29.02
25.88
10-12
USDA
2/7/85
48.77
134.11
3.38
29.53
29.07
26.15
11-12
USDA
2/7/85
60.96
134.11
2.67
29.68
29.19
27.01
07-13
USDA
2/7/85
12.19
146.30
3.88
29.21
28.70
25.33
08-13
UF
5/21/86
24.38
146.30
3.67
29.05
28.51
25.38
DEEP
UF
11/10/86
30.48
146.30
10.25
28.92
28.46
18.67-
09-13
UF
5/21/86
36.58
146.30
3.93
28.93
28.42
25.00
10-13
UF
5/21/86
48.77
146.30
3.80
28.86
28.43
25.06
171


117
09-11 Date: 03/23/87
Figure 6.34. Atrazine concentration in the groundwater on 3/23/87.
Vertical bars indicate sampling locations.
Date: 03/31/87
Figure 6.35. Atrazine concentration in the groundwater on 3/31/87.
Vertical bars indicate sampling locations.


08-09
210
Figure D.19. Atrazine concentration in the groundwater on 5/08/87.
Vertical bars indicate sampling locations.
08-09
Date: 05/13/87
'I, (m)
5dis^C
Figure D.20. Atrazine concentration in the groundwater on 5/13/87.
Vertical bars indicate sampling locations.


13
between the vapor and solution phases. A larger % implies increased
volatilization. Increasing concentrations of the pesticide will increase
the volatilization as long as the vapor density is not saturated.
Adsorption of the pesticide will reduce volatilization. Volatilization
increases as temperature increases. As the soil water content decreases,
the rate of volatilization increases. Increasing wind speed can increase
the volatilization of pesticides, particularly those with low % values.
Evaporation of water from the soil surface can transport pesticides from
within the soil profile to the surface where temperature and wind
effects can increase the volatilization. Each of these factors is
discussed in more detail by Jury (1986d).
3.2.4 Transformation/decrradation
Transformation of a pesticide is the change in structure or
composition of the original compound and degradation is the breakdown of
the compound into smaller fragments with eventual inorganic endproducts
such as H20 and 002 (Cheng and Koskinen, 1986). The transformation and
degradation processes represent a loss of the original (or parent)
compound, thus reducing the amount remaining in the soil for transport by
surface runoff and percolation.
In general, transformation and degradation processes occur at faster
rates on plant surfaces and in the top few centimeters of soil than in
the deeper soil zones (Donigian and Rao, 1986a). Thus, a chemical that
is foliar or surface-applied will likely degrade more rapidly than if it
were incorporated. For non-persistent pesticides (half-lives less than
15-20 days), the timing of rainfall events or irrigation is important in


Cone. (mg/L) Cone. (mg/L)
108
08-09
Date: 12/15/86
re 6.18.
Bromide concentration in the groundwater on 12/15/86.
Vertical bars indicate sampling locations.
10-09
Date: 12/22/86
o
Figure 6.19. Bromide concentration in the groundwater on 12/22/86.
Vertical bars indicate sampling locations.


141
and is presentad in Figure C.2 in Appendix C along with a sample output
(Table C.2).
6.5.7 Summary of observations of chemical transport
The discussion of the observed concentrations of atrazine and the
tracers has been descriptive in nature. This is due to the fact that
additional data are required in order to quantitatively assess the
results.
Each of the applied chemicals (except for alachlor) was observed to
move through the soil profile and enter the groundwater. However,
insufficient data were collected from which the quantity or velocity of
water percolating through the soil profile could be directly determined.
Similarly, movement of chemicals within the groundwater was observed.
There were insufficient data, however, to quantitatively assess the
volume and velocity of flow within the saturated zone to acceptable
levels. The tracer data were inconclusive due to the transient nature of
the groundwater. Seme of the wells would dry rapidly. The wells along
the boundary between subareas 4 and 5 (Figure 55) would often be dry when
wells on both sides of this boundary would have water in them. This
would indicate that some saturated flew was occurring across this
boundary in small channels in the restricting layer which did not
coincide with the established well network ie. the wells did not
intercept these channels.


10
specified depths which is not based on physical processes. Rather, the
transfer function theory uses measured values to develop a probability
density function of travel times and uses it to predict solute travel
times to depths greater than those that were measured. Gish (1987)
combined the convective-diffusive transport theory with a stochastic
representation of water velocities to describe field-scale (average)
concentrations of bromide at various depths in the soil profile.
Comparisons between the average measured bromide concentrations and
predicted values shewed good agreement.
Volatilization from soil and plant surfaces transports the chemicals
into the atmosphere and reduces the amount remaining for transport with
runoff or percolation. Diffusion of chemicals in the vapor phase can
also transport the chemical within the soil profile. Most transport
models do not consider vapor diffusion.
Many of the factors which are assumed to influence the transport of
chemicals through porous media are summarized by Jury (1986b). Some of
the soil properties influencing transport are water content, bulk
density, permeability, clay content, organic matter content, and water
retention (field capacity). Helling and Gish (1986) present results of a
simple modeling exercise to demonstrate the effect of several soil
properties on pesticide transport. Same of the environmental factors
which have been shewn to influence transport are precipitation,
evapotranspiration, and temperature (Jury, 1986b).
3.2.2 Sorption
Ihe partitioning of solutes between the liquid and solid phases
(dissolved and adsorbed) is a major factor determining the mass of


MEASUREMENT AND PREDICTION OF HERBICIDE
TRANSPORT INTO SHALLOW GROUNDWATER
By
MATTHEW CLAY SMITH
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF FHILOSOFHY
UNIVERSITY OF FLORIDA
1988
|j QF F LIBRAS?

DEDICATION
To ray mother,
Corrine L. Smith
She was taken from her family by illness during this research. Her
wisdom, courage, and strength continue to inspire me. Without that inner
strength, I would never have made it this far.
I love you Mem.

ACKNOWLEDGMENTS
I would like to express my sincere appreciation to the following:
Dr. A. B. (Del) Bottcher, chairman of my advisory committee, for
his friendship, patience, and guidance. He provided valuable advice and
philosophy during the highs and lews encountered in my Fh.D. program.
Dr. K. L. Campbell, cochairman of my advisory committee, for his
guidance and support. His calm and reasoned approach to problem solving
is a model which I need to emulate.
Dr. E. D. Threadgill, for serving on my advisory committee, for
making available the field site on which this research was conducted,
and for being the best engineer/scientist/administrator that I could
ever hopo to be associated with.
Dr. W. C. Huber, for serving on my advisory committee, and being
one of the very best instructors that I have encountered during many
years of college. If all instructors were that good, my GPAs would have
been higher.
Dr. P. S. C. Rao, for serving on my advisory committee, for showing
. 1 .
a sincere interest in my research, and for teaching me several very
valuable lessons during the qualifying exam.
Ms. M. W. Smith, for being a wonderful, laving, and supportive
wife. She assumed a greatly disproportionate share of child care and
household duties so that I could devote time to this research.
iii

Nicholas and Sarah Smith, my children, for their love and for
providing meaning and purpose to my life and education.
Dr. G. W. Isaacs, for financial support in the form of a research
assistantship.
Dr. D. L. Thomas, for his friendship and support. He shielded me
from many distractions at work while I completed this document.
Dr. W. B. Wheeler and Ms. S. J. Scherer, at the UF Pesticide
Residue Laboratory, for allowing me to use their equipment and guiding
me through the intricacies of pesticide residue analysis.
Mr. L. A. Asmussen, Dr. R. A. Leonard, Dr. W. G. Knisel, Ms. L. R.
Marti, and others at the USDA Southeast Watershed Research Laboratory for
their advice, encouragement, and support.
USDA, Southern Region Pesticide Impact Assessment Program, for
financial support in the form of a grant.
Many others, too numerous to list here, in both Gainesville and
Tifton who contributed in many ways to the success of the project.
iv

TABLE OF CONTENT'S
Page
ACKNOWLEDGEMENTS iii
LIST OF TABLES V
LIST OF FIGURES viii
ABSTRACT xiv
CHAPTERS
1 INTRODUCTION 1
2 OBJECTIVES 4
3 REVIEW OF THE LITERATURE 5
3.1 Evidence of Pesticide Residues in Groundwater 5
3.2 Factors Which Influence Pesticide Transport to
to Groundwater 7
3.3 Predicting Pesticide Transport 15
3.4 Field Studies of Pesticide Transport 27
3.5 Summary 36
4 EXPERIMENTAL METHODS 39
4.1 Field Site Description 39
4.2 Site Instrumentation 42
4.3 Chemical Applications 49
4.4 Sample Collection and Storage 52
4.5 Sample Analysis 55
5 MODELING THE EXPERIMENTAL SITE 66
5.1 Selection of Common Input Parameter Values 67
5.2 Parameters Unique to PRZM 73
5.3 Parameters Unique to GLEAMS 77
v

6 RESULTS AND DISCUSSION 79
6.1 Data Collection 79
6.2 Sample Analysis 83
6.3 Chemical Applications 85
6.4 Chemicals in the Unsaturated Zone 91
6.5 Chemicals in the Saturated Zone 107
6.6 Model Results and Comparisons 142
7 SUMMARY AND CONCLUSIONS 157
8 RECOMMENDATIONS FOR IMPROVEMENTS AND FURTHER STUDY 160
REFERENCES 162
APPENDICES
A. MONITORING WELL STATISTICS 170
B. HOW TO GET COMPLETE DATA SET 173
C. WATER BALANCE PROGRAMS 176
D. SURFACE PLOTS OF ATRAZINE CONCENTRATION IN GROUNDWATER.... 200
BIOGRAPHICAL SKETCH 213
vi

LIST OF TABLES
Page
5.1. Soil properties used in simulations 69
5.2. Chemical properties used in simulations 70
5.3. Chemical applications summary 73
6.1. Chronological summary of field site activities 80
6.2. Chemical application results 88
6.3. Measured application rates and soil surface concentrations
of atrazine and alachlor 91
6.4. Mean concentrations of atrazine (mg/kg) in soil samples 96
6.5. Mean concentrations of alachlor (mg/kg) in soil samples 97
6.6. Mean concentration of bromide (mg/L) at each sampling
following first application 105
6.7. Mean concentration of bromide (mg/L) at each sampling
following second application 106
6.8. Total mass of chemicals in the saturated zone 133
6.9. Simulated mass flux of chemicals 142
A. 1. Monitoring well statistics 171
B.l. Example listing of the water sample data set 174
B.2. Example listing of the soil sample data set 175
C.l. Sample output from program ANALYZE 191
C.2. Sample output from program FLUX 198
vii

LIST OF FIGURES
Page
4.1. Contour maps of soil surface and restricting layer
showing locations and ID labels of monitoring wells 41
4.2. Cross-section of soil profile through application area
showing locations of monitoring wells and soil solution
samplers 45
6.1. Uniformity of atrazine application 86
6.2. Uniformity of alachlor application 87
6.3. Uniformity of bromide application on 4/27/87 89
6.4. Comparison of alachlor and atrazine concentrations in
application samples 90
6.5. Correlation between atrazine concentrations in the top 5 cm
of soil and application solution concentrations 93
6.6. Correlation between atrazine concentrations in the top 5 cm
of soil and application rate 93
6.7. Correlation between alachlor concentrations in the top 5 cm
of soil and application solution concentrations 94
6.8. Correlation between alachlor concentrations in the top 5 cm
of soil and application rate 94
6.9. Bromide and atrazine concentrations in solution sampler
09N-2 as a function of total water applied since
application 98
6.10. Bromide concentration for six sampling locations following
the first application at a 61 cm depth 101
viii

6.11. Bromide concentration for six sampling locations following
the first application at a 122 cm depth 101
6.12. Bromide concentration for six sampling locations following
the first application at a 183 cm depth 102
6.13. Average bromide concentration for the three sampling depths
following the first application 102
6.14. Bromide concentration for six sampling locations following
the second application at a 61 cm depth 103
6.15. Bromide concentration for six sampling locations following
the second application at a 122 cm depth 103
6.16. Bromide concentration for six sampling locations following
the second application at a 183 cm depth 104
6.17. Average bromide concentration for the three sampling depths
following the second application 104
6.18. Bromide concentration in the groundwater on 12/15/86 108
6.19. Bromide concentration in the groundwater on 12/22/86 108
6.20. Bromide concentration in the groundwater on 12/29/86 109
6.21. Bromide concentration in the groundwater on 01/05/87 109
6.22. Bromide concentration in the groundwater on 1/12/87 110
6.23. Bromide concentration in the groundwater on 1/19/87 110
6.24. Bromide concentration in the groundwater on 5/05/87 Ill
6.25. Bromide concentration in the groundwater on 5/08/87 Ill
6.26. Bromide concentration in the groundwater on 5/13/87 112
6.27. Bromide concentration in the groundwater on 5/18/87 112
6.28. Bromide concentration in the groundwater on 5/25/87 113
6.29. Bromide concentration in the groundwater on 6/01/87 113
ix

6.30. Atrazine concentration in the groundwater on 2/23/87 115
6.31. Atrazine concentration in the groundwater on 3/02/87 115
6.32. Atrazine concentration in the groundwater on 3/09/87 116
6.33. Atrazine concentration in the groundwater on 3/16/87 116
6.34. Atrazine concentration in the groundwater on 3/23/87 117
6.35. Atrazine concentration in the groundwater on 3/31/87 117
6.36. Contour plot of water table elevation on 5/08/87 showing
direction of flew 118
6.37. Concentration of atrazine and water table elevation in
well 09-11 120
6.38. Nitrate concentration in groundwater on 5/01/87 122
6.39. Nitrate concentration in groundwater on 5/03/87 122
6.40. Nitrate concentration in groundwater on 5/05/87 123
6.41. Nitrate concentration in groundwater on 5/08/87 123
6.42. Nitrate concentration in groundwater on 5/13/87 124
6.43. Nitrate concentration in groundwater on 5/18/87 124
6.44. Nitrate concentration in groundwater on 5/25/87 125
6.45. Nitrate concentration in groundwater on 6/01/87 125
6.46. Chloride concentration in groundwater on 5/01/87 127
6.47. Chloride concentration in groundwater on 5/03/87 127
6.48. Chloride concentration in groundwater on 5/05/87 128
6.49. Chloride concentration in groundwater on 5/08/87 128
6.50. Chloride concentration in groundwater on 5/13/87 129
6.51. Chloride concentration in groundwater on 5/18/87 129
6.52. Chloride concentration in groundwater on 5/25/87 130
6.53. Chloride concentration in groundwater on 6/01/87 130
x

6.54. Total mass of atrazine stored in the saturated zone 133
6.55. Subareas used in water balance 135
6.56. Comparison of percolation volumes predicted by GLEAMS and
FRZM 144
6.57. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 11/18/86 145
6.58. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 11/24/86 145
6.59. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 12/22/86 146
6.60. Comparison of measured and FPZM predicted atrazine
concentrations in the soil on 2/09/87 146
6.61. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 3/16/87 147
6.62. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 5/25/87 147
6.63. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 11/18/86 148
6.64. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 11/24/86 148
6.65. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 12/22/86 149
6.66. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 2/09/87 149
6.67. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 3/16/87 150
xi

6.68. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 5/25/87 150
6.69. Measured and FRZM predicted bromide concentrations in the
soil solution at a 61 cm depth following the first
application 152
6.70. Measured and FRZM predicted bromide concentrations in the
soil solution at a 122 cm depth following the first
application 152
6.71. Measured and PRZM predicted bromide concentrations in the
soil solution at a 183 cm depth following the first
application 153
6.72. Measured and PRZM predicted bromide concentrations in the
soil solution at a 61 cm depth following the second
application 153
6.73. Measured and PRZM predicted bromide concentrations in the
soil solution at a 122 cm depth following the second
application 154
6.74. Measured and PRZM predicted bromide concentrations in the
soil solution at a 183 cm depth following the second
application 154
6.75. Measured and PRZM predicted concentrations of atrazine in
the soil solution at a 61 cm depth 155
C.l. Listing of program to calculate water and chemical
fluxes and storages 177
C.2. Program to calculate mass balance between sampling periods.. 194
D.l. Atrazine concentration in the groundwater on 1/19/87 201
xii

D.2. Atrazine concentration in the groundwater on 1/26/87 201
D.3. Atrazine concentration in the groundwater on 2/02/87 202
D.4. Atrazine concentration in the groundwater on 2/09/87 202
D.5. Atrazine concentration in the groundwater on 2/16/87 203
D.6. Atrazine concentration in the groundwater on 2/23/87 203
D.7. Atrazine concentration in the groundwater on 3/02/87 204
D.8. Atrazine concentration in the groundwater on 3/09/87 204
D.9. Atrazine concentration in the groundwater on 3/16/87 205
D. 10. Atrazine concentration in the groundwater on 3/23/87 205
D.ll. Atrazine concentration in the groundwater on 3/31/87 206
D. 12. Atrazine concentration in the groundwater on 4/06/87 206
D. 13. Atrazine concentration in the groundwater on 4/13/87 207
D.14. Atrazine concentration in the groundwater on 4/20/87 207
D. 15. Atrazine concentration in the groundwater on 4/30/87 208
D.16. Atrazine concentration in the groundwater on 5/01/87 208
D. 17. Atrazine concentration in the groundwater on 5/03/87 209
D. 18. Atrazine concentration in the groundwater on 5/05/87 209
D. 19. Atrazine concentration in the groundwater on 5/08/87 210
D.20. Atrazine concentration in the groundwater on 5/13/87 210
D.21. Atrazine concentration in the groundwater on 5/18/87 211
D.22. Atrazine concentration in the groundwater on 5/25/87 211
D.23. Atrazine concentration in the groundwater on 6/01/87 212
xiii

Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
MEASUREMENT AND PREDICTION OF HERBICIDE
TRANSPORT INTO SHALLOW GROUNDWATER
By
Matthew Clay Smith
December 1988
Chairman: Dr. A. B. Bottcher
Major Department: Agricultural Engineering
A field study was conducted to observe the movement of the
herbicides atrazine and alachlor within the soil profile and a shallow
water table aquifer following a surface application. Surface-applied
bromide was used as a nonadsorbed tracer of water movement. The movement
of nitrate from a fertilizer application to the site was also monitored.
Measurements of chemical concentration were made using soil core samples,
samples of soil water from the unsaturated zone, and water samples from
monitoring wells belcw the water table.
Atrazine was observed to move rapidly with both saturated and
unsaturated flews. Concentrations of atrazine exceeded 350 g/L in soil
water samples at a depth of 61 cm. Samples of shallow groundwater
contained atrazine residues as high as 90 jug/L. Measurable
concentrations of alachlor did not move below a depth of 45 cm in the
soil.
xiv

Bromide and nitrate concentrations in soil water demonstrated large
variability between sampling locations. Nitrate concentrations in the
groundwater exceeded 40 mg/L after fertilization of the field.
Two root/vadose zone pesticide transport models, GLEAMS and
PRZM, were used to simulate the conditions on the site. Comparisons were
made between model simulation results and observed data. The
uncalibrated models predicted concentrations that were generally within
an order of magnitude, and often were within a factor of 2 to 3, of
observed values. Differences between the predictions of the models
appear to be due to the relative detail by which each model describes
soil properties as a function of depth.
xv

CHAPTER 1
INTRODUCTION
Agricultural production systems have undergone many changes since
the 1940s. The capability of post-war industry to synthesize almost
unlimited varieties of chemicals in great quantities has provided
farmers with an arsenal with which to battle insects, weeds, and
disease. These chemicals can also provide plants with required
nutrients and regulate their growth. The result of using these chemical
tools along with improved machines for planting, cultivating, and
harvesting has been a dramatic increase in the productivity of the
American farmer.
However, some serious problems associated with the use of agricul
tural chemicals have been identified in recent years. Many of these
chemicals are toxic to non-target organisms such as birds, fish, and the
person who is applying them. Rachel Carson (1962) brought to the
attention of the public the potential harmful effects of using these
chemicals in large quantities over large land areas. Since the
publication of her book 'Silent Spring' there has been much research
conducted to evaluate the fate of agricultural chemicals in the
environment. Daring the 1960s and 1970s, great effort was initiated to
determine the quantities of soil, fertilizers and pesticides which were
entering our lakes and rivers. Through these efforts methods have been
1

2
developed (though only a few have been implemented) to minimize or
eliminate agricultural impacts on surface waters.
The discovery of abandoned waste sites such as the famous love
Canal and the discovery of the pesticide aldicarb in the shallow
groundwater on long Island in New York generated concern for the
protection of groundwater. These concerns led to analyzing samples of
groundwater for toxic wastes and pesticides.
As more groundwater samples were analyzed, it became apparent that
agricultural chemicals had somehow entered groundwater aquifers in
several areas of the country. These results were publicized, and
citizens throughout the nation expressed alarm that they were drinking
potentially toxic chemicals. The federal and state governments
responded to these concerns by intensifying monitoring efforts and
reviewing data on the many agricultural chemicals to determine potential
for leaching to groundwater and the extent of the current problem.
Several federal agencies, along with university and private industry
scientists, began developing computer models to help explain why these
chemicals were moving beyond their target locations and to screen
chemicals for their relative mobilities in soil. A limiting factor in
the development and utilization of these models was a lack of the
detailed data needed to test and validate model predictions.
To date, data on the presence of agricultural chemicals in
groundwater have been limited primarily to sampling of municipal and
domestic wells, providing only a large scale picture of contamination.
There have been only a few intensive monitoring studies completed which
provide sufficient data for model parameter estimation. Currently, there

3
are increased efforts at specific sites to provide more detailed
monitoring of chemical movement through the soil profile and within
groundwater aquifers.
The study described here is an effort to develop data for use in
the development and testing of models which describe the fate and
transport of pesticides and nutrients used in agriculture.

CHAPTER 2
OBJECTIVES
The overall objective of this research is to develop data on the
movement of two widely used herbicides through the crop root zone,
through the vadose zone (unsaturated zone between the crop root zone and
the top of the unconfined water table aquifer), and within the shallow
water table aquifer. These data will then be compared with the
predictions of two root/vadose zone pesticide transport models.
The specific objectives are to perform the following:
1. Instrument a field site for monitoring the movement of water and
chemicals through the soil profile and a shallow, water table
aquifer.
2. Observe the fate and transport of surface-applied herbicides
(atrazine and alachlor) over time.
3. Use surface-applied bromide as a nonadsorbed tracer of water
movement.
4. Observe transport of the nitrate component of fertilizers
applied to field.
5. Compile the observations of chemical movement into a data base
for use in testing pesticide transport models.
6. Simulate the field conditions present during the monitored
period with two pesticide transport models and compare
to observed results.
4

CHAPTER 3
REVIEW OF THE LITERATURE
3.1 Evidence of Pesticide Residues in Groundwater
The U. S. Environmental Protection Agency estimates that at least 19
pesticides have been detected in groundwater in 24 states as a result of
agricultural practices (U.S. ERA, 1987a). The total number of
pesticides detected in groundwater is greater than nineteen. These
additional detections are the result of manufacturing, storage, and
loading activities which are not included in the above figures. The
number of reported cases of pesticides in groundwater is increasing,
echen et al. (1986) suggest that the increase is due to improvements in
the quality and quantity of studies instead of an increase in the
problem.
The insecticide/nematicide aldicarb was detected in the shallow
groundwater on Long Island, New York in 1979 (Wartenburg, 1988).
Aldicarb was used as an insecticide on potatoes. Aldicarb was detected
in 20 of 31 wells tested in 1978 and the presence was confirmed through a
repeat sampling of the wells in June, 1979. According to the U.S. EPA
(1987a), aldicarb residues have been detected in almost 2000 wells on
Long Island. Concentrations of aldicarb exceeded the state health
guideline of 7 /g/L (ppb) in nearly 50% of those wells. Concentrations
as high as 515 ¡ig/L were recorded in the Long Island wells as reported by
Ritter (1986). Aldicarb residues have been found in groundwater in 15
states (Cohen et al., 1986) with typical concentrations reported in the
range of 0.3 3 g/L.
5

6
In the state of Florida, residues of the nematicide EDB have been
detected in over 10% of the public and private drinking water wells which
have been sampled (U.S. EPA, 1987a). Approximately 1,200 wells require
treatment or have been closed as sources of drinking water. Marti et al.
(1984) detected EDB in the groundwater in southwest Georgia. EDB
residues have been detected in groundwater in at least 6 other states.
In California, residues of approximately 57 pesticides have been
detected in groundwater (U.S. EPA, 1987a). Most of these detections
were related to factors other than routine use on agricultural fields.
The nematicide DBCP has been detected at levels exceeding 1 /xg/L in
approximately 60% of the 2500 drinking water wells tested.
Residues of the widely used herbicide atrazine are commonly
identified in groundwater. Ritter (1986) reports that approximately
36,000,000 kg of atrazine were used in the United States in 1982.
Atrazine has been detected in PA, IA, NE, WI, MN, and MD (U.S. EPA,
1987; Cohen et al., 1986; Ritter, 1986). According to Berteau and Spath
(1986), atrazine has also been detected in concentrations of 15) to 2 /xg/L
in California groundwater. Pionke et al. (1988) reported atrazine
concentrations in groundwater at levels 15) to 1.1 /xg/L in an agricultural
watershed in Pennsylvania. The wells sampled by Pionke et al. (1988)
were all located in unconfined aquifers with depths to the water table
ranging from 2 to 21 m. Atrazine concentrations of 10 /xg/L have been
reported in a karst aquifer in Iowa (Libra et al., 1986; Kelley et al.,
1986).
Residues of the herbicide alachlor have been found in groundwater in
the states listed for atrazine, excluding WI. According to Ritter

7
(1986), alachlor and atrazine together accounted for 25% of all
pesticides sold in the U. S. in 1982. Ritter (1986) reported that in
1982, approximately 38,600,000 kg of alachlor were applied to cropland in
the United States. Kelley et al. (1986) reported alachlor concentrations
in groundwater as high as 16 jug/L in Iowa.
There are many individual cases of pesticide residue detection in
groundwater that could be cited. The reader is referred to the
proceedings of the Agricultural Impacts on Groundwater Conferences
(National Water Well Assoc., 1986, 1988) for a variety of reports
concerning the detection of pesticide residues in groundwater. A book
edited by Gamer et al. (1986) is also a good source of information
related to this subject.
3.2 Factors Which Influence Pesticide Transport To Groundwater
Donigian and Rao (1986a) list five processes which affect the fate
and movement of chemicals within the soil. These processes are
transport, sorption, transformation/degradation, volatilization, and
plant uptake. Other authors, e.g., Cheng and Koskinen (1986), use
slightly varied groupings which include the processes named above. The
interactions of these processes over time and space determine the fate of
chemicals in the soil. The spatial variability of soil properties and
other factors is an important consideration when interpreting field data
and model predictions (Donigian and Rao, 1986a). This section will
describe each of the processes and same of the factors which -influence or
regulate them.

8
3.2.1 Transport
The chemicals can be transported in at least three different phases:
adsorbed to solid materials, in solution, and as a vapor. Surface runoff
can transport chemicals in both the adsorbed and solution phases.
Erosion and transport of soil particles during rainfall and irrigation
can result in significant transport of highly-adsorbed, lew-solubility
pesticides (Wauchope, 1978). For most pesticides, the majority of
transport in runoff is in the solution phase (Wauchope, 1978; Rao and
Davidson, 1980). Chemicals in the solution phase can be transported
within the soil profile by saturated and unsaturated water flews.
Water percolating through the soil profile can transport chemicals
in the solution phase beneath the root zone and possibly into
groundwater. The most common methods used to describe the transport of
water and solutes in porous media are based on the assumptions that
Darcian flew conditions exist, and that solute movement is controlled by
advective and dispersive processes (Freeze and Cherry, 1979; Jury, 1986a;
Wagenet, 1986; Wang and Anderson, 1982). Darcian flew conditions imply
that the flew equations are representative of seme volume of soil. All
soil properties such as pore size distribution and volumetric water
content are assumed to be uniform throughout the volume of soil (Jury,
1986b).
The advective component of transport is usually based upon Richards'
equation and describes the average rate of water flow. The average rate
of solute flew is the product of the solute concentration and the average
pore-water velocity. Additional transport of solute can occur du^ to
mechanical mixing of water in adjoining pores during advective transport

9
and molecular diffusion of solute from pores with high concentrations to
adjoining pores with lower concentrations (Freeze and Cherry, 1979).
Molecular diffusion can be significant when average pore-water
velocities are lew; otherwise, the processes involved in mechanical
dispersion usually dominate. Differences in pore-water velocities in the
direction of bulk flew cause a spreading out, and consequently a lowering
of peak concentrations, of the solute plume. Seme of the solute will
arrive at a reference point earlier, and seme will arrive later, than
would be predicted based upon the average linear flew velocity. This is
referred to as longitudinal dispersion. The tortuosity and
interconnection of pores in the soil will also cause the solute to move
perpendicularly to the bulk flew direction. This is referred to as
transverse dispersion. Freeze and Cherry (1979) and Rao et al. (1988)
provide detailed discussions of the dispersive-diffusive processes and
their mathematical formulations.
Transport predictions based upon the methods described above have
not compared well with field observations, e.g. Jury et al. (1986).
Numerous modifications to the general theory have been proposed in order
to help explain and reduce the differences between field observations and
theoretical predictions. Rao et al. (1980) proposed separating pore-
water in soils into two regions: inter-aggregate and intra-aggregate.
Convective-dispersive transport was assumed to be limited to the water in
the inter-aggregate region. Ihe intra-aggregate region was assumed to
act as a source/sink of solute. Diffusion between the regions could
remove or add solute to the water in the inter-aggregate region. Jury
(1982) proposed a method of predicting the transport times of solutes to

10
specified depths which is not based on physical processes. Rather, the
transfer function theory uses measured values to develop a probability
density function of travel times and uses it to predict solute travel
times to depths greater than those that were measured. Gish (1987)
combined the convective-diffusive transport theory with a stochastic
representation of water velocities to describe field-scale (average)
concentrations of bromide at various depths in the soil profile.
Comparisons between the average measured bromide concentrations and
predicted values shewed good agreement.
Volatilization from soil and plant surfaces transports the chemicals
into the atmosphere and reduces the amount remaining for transport with
runoff or percolation. Diffusion of chemicals in the vapor phase can
also transport the chemical within the soil profile. Most transport
models do not consider vapor diffusion.
Many of the factors which are assumed to influence the transport of
chemicals through porous media are summarized by Jury (1986b). Some of
the soil properties influencing transport are water content, bulk
density, permeability, clay content, organic matter content, and water
retention (field capacity). Helling and Gish (1986) present results of a
simple modeling exercise to demonstrate the effect of several soil
properties on pesticide transport. Same of the environmental factors
which have been shewn to influence transport are precipitation,
evapotranspiration, and temperature (Jury, 1986b).
3.2.2 Sorption
Ihe partitioning of solutes between the liquid and solid phases
(dissolved and adsorbed) is a major factor determining the mass of

11
solute available for advective-dispersive transport through the soil
profile. If the chemical of interest is strongly adsorbed, then only a
small fraction will exist in the solution phase at a given time and be
available for transport. Non-adsorbed chemicals, however, will exist
entirely in the solution phase and are available for transport with
percolating water.
Many pesticides are nonpolar and adsorption occurs primarily on
organic matter surfaces. Pesticides and chemicals which cure polar will
adsorb primarily to clay surfaces (Jury, 1986c). Since the majority of
pesticides are nonpolar, the adsorption onto soils has been primarily
related to the organic matter or organic carbon content of the soil. A
detailed discussion of the factors influencing pesticide adsorption onto
soils is given by Jury (1986c). The relationship between concentrations
of a pesticide in the dissolved and sorbed phases is often represented by
the Freundlich equation:
S = KC11 3.1
where S = adsorbed concentration (/ig/g of soil), C = solution
concentration (/xg/mL), and K and n are empirical constants for the soil-
pesticide system. The exponent n in equation 3.1 is often assumed to be
equal to 1.0 which results in a linear relationship of the form:
S = KdC 3.2
where = partition coefficient (mL/g of soil).
For most uses of equation 3.2 in transport modeling, the
partitioning between the solution and adsorbed phases is assumed to be
instantaneous and reversible. Errors associated with the assumptions of

12
linearity, instantaneous equilibrium, and reversibility are discussed by
Rao and Davidson (1980).
Hie partition coefficient, K, is unique to a given pesticide-soil
combination. However, Rao and Davidson (1980) report that investigators
have shewn that when is normalized for the organic carbon content of
the soil, the resulting value is independent of the soil type and can be
considered a property of the pesticide. The normalized partition
coefficient is represented as Kq,-. and is defined by:
Kqc = % 100 / %OC 3.3
where %OC is the percent organic carbon content of the soil and is the
measured partition coefficient. Methods for measurement of the
partition coefficient are reviewed by Rao and Davidson (1980).
3.2.3 Volatilization
Volatilization of pesticides from the soil and plant surfaces
reduces the amount of the pesticide available to be leached or
transported with runoff. Volatilization also determines the quantity of
the pesticide which exists in the vapor phase within the soil and thus
the amount available for diffusive vapor transport. Thus, as noted by
Donigian and Rao (1986a), volatilization affects both the fate and
transport of a pesticide.
Seme of the factors which influence volatilization are summarized by
Jury and Valentine (1986). They are Henry's constant Kjp chemical
concentration, adsorption site density, temperature, water content, wind
speed, and water evaporation. Henry's constant, % is the ratio of
saturated vapor density to solubility and is an index of the partitioning

13
between the vapor and solution phases. A larger % implies increased
volatilization. Increasing concentrations of the pesticide will increase
the volatilization as long as the vapor density is not saturated.
Adsorption of the pesticide will reduce volatilization. Volatilization
increases as temperature increases. As the soil water content decreases,
the rate of volatilization increases. Increasing wind speed can increase
the volatilization of pesticides, particularly those with low % values.
Evaporation of water from the soil surface can transport pesticides from
within the soil profile to the surface where temperature and wind
effects can increase the volatilization. Each of these factors is
discussed in more detail by Jury (1986d).
3.2.4 Transformation/decrradation
Transformation of a pesticide is the change in structure or
composition of the original compound and degradation is the breakdown of
the compound into smaller fragments with eventual inorganic endproducts
such as H20 and 002 (Cheng and Koskinen, 1986). The transformation and
degradation processes represent a loss of the original (or parent)
compound, thus reducing the amount remaining in the soil for transport by
surface runoff and percolation.
In general, transformation and degradation processes occur at faster
rates on plant surfaces and in the top few centimeters of soil than in
the deeper soil zones (Donigian and Rao, 1986a). Thus, a chemical that
is foliar or surface-applied will likely degrade more rapidly than if it
were incorporated. For non-persistent pesticides (half-lives less than
15-20 days), the timing of rainfall events or irrigation is important in

14
determining the fraction of the applied mass that will be available for
transport. Events occurring shortly after application will likely result
in the largest concentrations in runoff and percolation water (Wauchope,
1979; Donigian and Rao, 1986a). The timing of rainfall and irrigation
events is less critical for persistent (half-lives in excess of 100 days)
pesticides since they will reside in the soil for longer periods
(Donigian and Rao, 1986a).
The major processes involved in transformation and degradation
include biotransformation, chemical hydrolysis, photolysis, and
oxidation-reduction (Donigian and Rao, 1986a). Factors influencing
transformation and degradation of pesticides, as summarized by Jury and
Valentine (1986), include: microbial populations, chemical concentration,
temperature, oxygen, pH, soil water content, and light. Detailed
descriptions of the many factors influencing these processes are
presented by Rao and Davidson (1980), Valentine and Schnoor (1986), and
Valentine (1986).
3.2.5 Plant processes
Plant processes relating to pesticides are very complex. The
uptake, translocation, accumulation, and transformation of pesticides by
plants affect the availability of pesticides for transport processes and
the potential exposure to pesticide residues by the consumers of the
vegetation, fruits, etc. (Donigian and Rao, 1986a). Plant processes can
serve as both a sink and a source of pesticide residues available for
transport.

15
Pesticides applied to plant foliage will likely be transformed or
degraded more rapidly than if the pesticide were applied to the soil or
incorporated. Pesticides on the foliage may also be absorbed into the
plant. Pesticides in the solution phase may be taken up by plant roots
and translocated to various parts of the plant. These processes serve as
a loss mechanism reducing the availability of the pesticide for
transport.
Pesticide residues on plant foliage can be dislodged by rainfall and
irrigation and washed onto the soil surface, resulting in an increased
mass of pesticide available for transport in runoff and percolation
(Smith et al., 1981; Donigian and Rao, 1986a). Plant residues which are
left on the soil surface or incorporated can also serve as a source of
pesticide which may be available for transport.
3.3 Predicting Pesticide Transport
There are many methods that can be used to predict the nobility of a
given pesticide. These range from simple indices to complex research
models. Each method has value when used for the purpose for which it was
designed and with recognition of the assumptions and limitations
associated with it.
3.3.1 Indices of contamination potential
Rao et al. (1985) compared several methods for computing indices of
the contamination potential of pesticides. Most of the indices are based
on one or more chemical properties such as and solubility. Three
indices also included distance to groundwater and recharge rate. The

16
attenuation factor (AF) preposed by Rao et al. (1985) can also be used to
estimate the mass of pesticide which will leach below the root zone or
out of the vadose zone and into groundwater. The AF index is the only
one of the indices compared by Rao et al. (1985) which incorporated mass
loadings.
Dean et al. (1984) developed a methodology in which soil
characteristics, crop type, chemical properties, and management practices
(e.g. tillage type) can be combined to find a cumulative frequency
distribution of the percentage of applied pesticide mass leaching below
the crop root zone. These frequency distributions were derived from
hundreds of 25-year simulations of pesticide leaching using the PRZM
model (Carsel et al., 1984). The methodology was applied to four crop
types (com, soybean, cotton, and wheat) and 19 representative growing
regions. The results are somewhat unique in that the user is presented
with a statistical probability of leaching based on a 25-year
meteorologic record.
Another index which is currently receiving much attention is the
DRASTIC index developed by Aller et al. (1985). This index does not
consider chemical properties; instead, it is an index of the
vulnerability of groundwater at a given location to contamination from
surface-applied chemicals. The DRASTIC index assigns numerical scores or
weights to seven factors which could influence pollution potential. The
factors are depth to water, net recharge, aquifer media, soil media,
topography, inpact of vadose zone, and conductivity of the aquifer. The
final DRASTIC score is used to describe an area as having high, medium,
or lew susceptibility to groundwater pollution.

17
DRASTIC has been used to map every county in the United States
(Alexander and Little, 1986) as part of the first stage of a national
survey of pesticide contamination of drinking water (U.S. EPA, 1987b).
In the first county-level assessment the Southeast Coastal Plain, which
covers all of Florida and southern Georgia, was shewn to be the
groundwater region of highest vulnerability.
Indices like those mentioned above are beneficial to regulatory
agencies as a method to screen great numbers of compounds and locate
vulnerable areas. This information can aid in allocating funding for
further studies concentrating on the chemicals of interest in regions
which may be most vulnerable. These studies may involve detailed
modeling of pesticide transport to groundwater, modeling of surface and
groundwater hydrology, and collection of data from test locations.
3.3.2 Transport models
There are many models of pesticide transport reported in the
literature. These range from steady-state screening models, e.g. PESTAN
(Enfield et al., 1982), to very complex process-oriented research models,
e.g. IEACHMP (Wagenet and Hutson, 1986). Donigian and Rao (1986a), and
Shoemaker and Magette (1987) review a number of the models which are
available. Most models which are capable of simulating pesticide fate
and transport do not explicitly represent agricultural management and
cropping practice effects on runoff and leaching. Modifications of
management practices will be one method of reducing pesticide leaching to
groundwater. The models used for regulatory purposes should reflect the
trade-offs between surface water and groundwater quality. There is seme

18
evidence that no-till farming will increase pesticide transport to
groundwater while reducing surface runoff and thereby reduce pollution
of surface waters (Dick et al., 1986). Thus, solutions to one problem
may exacerbate another problem. The models discussed belcw are capable
of simulating, to seme degree, agricultural management practice effects
on pesticide fate and transport.
According to Donigian and Rao (1986a), SESOIL (The Seasonal Soil
Compartment model) is designed for long-term simulation of pesticide fate
in the soil environment. It was developed for the EPA and is used as a
screening model. SESOIL can simulate many components of the hydrologic
cycle including precipitation, evapotranspiration (ET) and surface
runoff. The model considers transport within the unsaturated zone
extending iron the soil surface to the top of the saturated zone. The
hydrologic responses are determined losing physically based equations in
which uncertainty has been included. The water balance used in the
model is a statistical representation of the hydrologic components over a
"season." A season is the time step of the model, e.g., month or year.
Erosion is simulated using the Universal Soil Loss Equation (Wischmeier
and Smith, 1978). Hetrick and Travis (1988) coupled SESOIL with EROS (a
submodel of the CREAMS (Knisel, 1980) watershed model) to predict surface
runoff and sediment yield from small watersheds. The pesticide component
of the model considers most of the processes and factors influencing
pesticide transport which were described in previous sections.
SESOIL requires calibration and represents long-term averages. Same
of the results of use and testing of SESOIL reported by Donigian and Rao
(1986a) indicate that it should not be used for short-term predictions.

19
The combined SESOII/EROS model of Hetrick and Travis (1988) adequately
predicted long-term (several months) average runoff and sediment yields
when tested against data from three small watersheds. However, monthly
predictions were not good, especially when most of the runoff occurred
due to large single storm events.
M3USE (Method Of Underground Solute Evaluation) was developed at
Cornell University as a management model and a training tool for students
and professionals (Steenhuis et al., 1987). M3JSE is an interactive,
menu-driven program which can run on a IEM-PC or compatible computer.
The model can read daily historical weather files or generate synthetic
rainfall, air, and soil temperature patterns. Surface runoff is computed
using the SCS curve number method. Erosion is not considered. Solute
movement in the vadose zone considers advective and dispersive flux, as
well as degradation and adsorption. MDUSE will also simulate the
movement of a pesticide in a two-dimensional unconfined aquifer.
Extensive graphics illustrate movement as it is being simulated.
PRZM (Pesticide Root Zone Model) is a field-scale hydrology and
transport model developed by the EPA (Carsel et al., 1984). PRZM is a
continuous model capable of simulating water and chemical fluxes over
many years of historical daily weather records. Runoff is predicted
based on the SCS curve number equation, and erosion is simulated using a
modification of the Universal Soil Loss Equation for daily time steps.
The model can simulate the entire vadose zone (soil surface to
groundwater). The vadose zone can be characterized by several layers
with varying properties. For calculations, the vadose zone is divided
into many compartments of equal depth. The model simulates crop growth

20
(leaf area and rooting depth). Water in the root zone can be removed by
percolation, evaporation, or transpiration by the plant. Percolation is
calculated based on the water-holding capacity of the soil. When the
water content in a layer exceeds field capacity (1/10 1/3 bar tension)
the excess water drains into the next lcwer compartment. There is an
option in PRZM that allows the draining of the profile to occur over a
longer period than one day.
Pesticide processes represented include advective and dispersive
flux, sorption, degradation in soil and on plant foliage, and plant
uptake. Volatilization and transport in the vapor phase are not
considered. Application of pesticides can be partitioned between the
soil surface and plant foliage. Applications to the soil can be
incorporated by tillage. Different degradation rates can be specified
for soil and foliar pesticide residues. The degradation rate within the
soil can also be varied by soil layer.
PRZM will simulate multiple applications of one pesticide each year
for many years of continuous climatic record. Thus it can predict
temporal variations in leaching and runoff. Effects of agricultural
management and cropping practices can be simulated. FRZM allows the user
to request time series results for many variables in the model. This
feature is useful for observing processes at intermediate locations
within the vadose zone over time.
GLEAMS (Groundwater Loading Effects of Agricultural Management
Systems) is also a field-scale hydrology and transport model (Leonard et
al., 1987). It is based upon the extensively documented and applied
CREAMS model (Knisel, 1980). CREAMS is a nonpoint source model for

21
predicting sediment, nutrient, and pesticide losses with surface runoff
from agricultural management systems. GLEAMS builds upon the foundations
in CREAMS by adding components to simulate movement of water and
chemicals within the crop root zone. Like CREAMS, GIEAMS is a
continuous, daily simulation model.
GIEAMS predicts runoff using the SCS curve number method. Erosion
is predicted based upon modifications to the Universal Soil Loss
Equation. Surface runoff and eroded sediment, with chemicals in both the
dissolved and sorbed filases, can be routed overland, in channels, and
through impoundments.
GIEAMS divides the crop root zone into seven layers. The vadose
zone between the root zone and the water table is not considered. The
first layer has a thickness of 1 cm. This layer is assumed to be the
portion of the soil that determines the mass of pesticide available for
extraction into surface runoff. The thickness of the second layer is
1/6 of the root zone depth minus 1 cm for the first layer. The remaining
5 layers are each 1/6 of the root zone in thickness. This layering
structure is fixed by the program. A strong correlation has been
observed between pesticide concentrations in runoff and the concentration
of pesticides in the top 1 cm of the soil profile (Leonard, 1988). Thus
the authors of CREAMS and GIEAMS believe that a 1 cm active zone at the
soil surface is required to maintain sensitivity of runoff concentrations
to soil concentrations (Leonard and Knisel, 1987). Soil profile
properties can vary with depth. The model weights the input values to
establish average properties for each layer in the model. Percolation
through the profile is based on the water-holding capacity of the soil as

22
in FRZM. Water in excess of field capacity drains to the next lower
layer.
Pesticide transport within the root zone is by advection. No
dispersive flux components are included. Volatilization is not
considered. Pesticide applications can be partitioned between plant
foliage and the soil surface. Different degradation rates can be
specified for chemicals on the foliage and within the soil. Soil-applied
chemicals can be incorporated to a specified depth.
The model will simulate up to ten chemicals simultaneously. This
feature makes possible the observation of the effect of changes in
chemical properties, e.g. partition coefficient, on resulting leaching
losses with one model run. The model can also simulate the formation,
fate, and transport of degradation products of the parent chemicals.
3.3.3 Coupled saturated/unsaturated zone transport models
The root/vadose zone models described above are important tools for
assessing the mobility of pesticides within the unsaturated zone. The
concern expressed by the public and governmental officials relate to
pesticide residues in groundwater supplies used for drinking water. Thus
the unsaturated zone models need to be linked to saturated zone transport
models so that the concentrations and transport of pesticides within
aquifers can be calculated.
As described above, MOUSE (Steenhuis et al., 1987) can predict the
transport of pesticides in a vertical, two-dimensional cross section of
a water table aquifer. Jones et al. (1987) coupled the PRZM model to a
two-dimensional saturated zone transport model to predict the movement of

23
aldicarb residues within a shallow water table aquifer. Dean and Carsel
(1988) reported on progress in linking FRZM to a two-dimensional
saturated transport model. The linked model will lose FRZM for simulating
the root zone, a one-dimensional transport model for the vadose zone, and
a two-dimensional saturated zone model which can simulate confined,
unconfined, and leaky confined aquifers. The saturated zone model will
also simulate pumping from the aquifer(s). The linked model is expected
to be released for testing during the fall of 1988.
3.3.4 Model testing and uncertainty
Methods (such as described above) to predict the transport of the
chemicals used by agriculture are needed by regulators in order to assess
the impacts on surface water and groundwater quality from the use of
these chemicals. Field testing of the contamination potential of all
chemicals registered for use by agriculture would be infeasible due to
both cost and time constraints. Since decisions regarding chemical use
will be made based upon the results of transport models, it is important
to understand the errors and uncertainty associated with the models.
Each model or method will have errors associated with the assumptions
relating to the description of the system, choice of equations to
represent the system, and input parameters required by the models. There
are some general points that can be made regarding the uncertainties
inherent in deterministic, physically based models such as FRZM and
GLEAMS which were described previously. An excellent discussion of the
uncertainty in transport models is presented by Leonard and Knisel
(1988).

24
Limitations of the conceptual formulation of the system that is
described by a model lead to uncertainty in the results. Most
deterministic models assume that seme of the physical properties of the
system are uniform with respect to location. That is, soil properties
such as porosity, water-holding capacity, and organic matter content are
constant across the field, although most models will allow these types of
properties to vary with depth in the soil profile. Furthermore,
variations in pore sizes within the soil, which can cause uneven water
velocity distributions, are usually not considered. Each model will
represent a system (ie. the crop root zone) in varying levels of detail.
The equations which are used to represent the fate and transport
processes have limitations and uncertainties. For example, PRZM and
GLEAMS both use the SCS curve number method for partitioning rainfall
between runoff and infiltration. This method utilizes daily rainfall
records and does not consider the effects of rainfall intensity on the
runoff process. Testing of the CREAMS model (Knisel, 1980), from which
GLEAMS was derived, shewed that the daily hydrology option represented
average annual runoff volumes well, but did not do as well with daily and
monthly runoff volumes (Smith and Williams, 1980). PRZM and GLEAMS both
utilize modified versions of the USLE (Wischmeier and Smith, 1978) for
prediction of daily soil erosion. The USIE was developed for long-term
(20-yr) average annual erosion rates. Although these modifications have
been tested, the basis of these methods is the USIE which is an empirical
equation derived for long-term average predictions. Many transport
models utilize the linear form of the Freundlich equation (Equation 3.1)
to describe the partitioning of a chemical between the dissolved and

25
adsorbed phases. Rao and Davidson (1980) indicate that the assumption of
linearity can lead to errors on the order of a factor of two to three.
The previous examples illustrate that the mathematical representations of
processes used in models often have inherent inaccuracies and
uncertainties.
The parameters which are required by the models to solve the
equations which describe the system also have inherent uncertainties
associated with them. Jury, (1985), in a review of the literature,
reported coefficients of variation (CV) from field measurements of
several soil physical properties. Soil porosity was found to have a mean
CV of 10%. Bulk density measurement had a CV of 9%. Determinations of
particle size fractions had an average CV of 28%, with a range of 3 to
55%. The water-holding capacity of the soil at matric potentials of 0.1
and 15 bars had a CV of 15 and 25%, respectively. Measurements of
saturated hydraulic conductivity and infiltration rates showed a CV of
124 and 72%, respectively. In a study by Jury et al. (1986), the
partition coefficient, Kj, of napropamide measured on 36 samples from a
1.44-ha field had a CV of 31%. Rao and Davidson (1980) reported that the
normalized partition coefficient, K^, reported from individual studies
generally had a CV of 40-60%. The variability of the properties reported
above are not necessarily due to the fact the samples were taken over
large areas. Jury (1985) reported that one author had reported that up
to 50% of the variation in a parameter over a field occurred within a 1
m2 area.
Given the uncertainties associated with models and the parameters
required by the models as outlined briefly above, how closely must a

26
model match field observations in order to be considered validated
(determined to accurately represent the system)? Leonard and Knisel
(1988) indicate that there are no standard criteria for model validation.
Since the spatial distribution of variations in soil properties is
usually unknown, it would be unreasonable to expect any model to exactly
match point measurements from a field study. Hedden (1986) reported that
participants of the Predictive Esposare Assessment Workshop sponsored by
the U. S. EPA in Atlanta, GA, on April 27-29, 1982, agreed on two
criteria for model acceptance. For screening applications of the model
(limited site-specific data and not calibrated to previous data from the
site), the model should be able to replicate measured field data within
an order of magnitude. For site-specific applications (parameters
measured on-site and with the model calibrated to the site), the model
should be able to match the field observations within a factor of two.
The screening level criteria seems to be quite reasonable when all of the
sources of error and uncertainty are considered. The site-specific
criteria, however, may be difficult to meet by even the best models using
carefully measured site-specific parameters.
The fact that models can not be expected to exactly replicate field
measured values does not mean that they have no value. Models that have
been verified (ie. their response to changes in parameters reflect what
would be expected based on knowledge of the system) can be used with same
confidence to evaluate the differences in predicted leaching of different
chemicals or, perhaps, differences in leaching of a single chemical due
to different climatic or management conditions.

27
3.4 Field Studies of Pesticide Transport
Field-scale studies of pesticide runoff and leaching have, until
recently, been very limited. Studies of sufficient detail to be used for
the validation and testing of transport models have been limited due to
three factors, according to Donigian and Rao (1986b):
1. Most field studies have focused on either chemical leaching or
surface runoff iron watersheds; few studies have examined both.
2. Since sampling and analysis costs associated with pesticide
leaching are high, most studies are of short duration.
3. Most studies were designed for purposes other than developing
data sets for model testing and therefore are incomplete with
regard to measurement of all the required model input parameters.
In this section, four field studies are reviewed. Methods of
sampling and monitoring will be emphasized. Same of the studies were
conducted specifically for model validation and testing purposes. A
review of the Big Spring Basin study is included due to the unique
conditions in the study area.
3.4.1 Aldicarb in Florida citrus groves
A three-year field study of the movement and degradation of
aldicarb in both the unsaturated zone and a shallow water table was
conducted at two locations in Florida (Hornsby et al., 1983; Jones et
al., 1987). The two locations are referred to as the Oviedo site and the
Lake Hamilton site. At both locations, aldicarb was applied to bedded
citrus trees located on coarse-textured soils. Scare of the soils at the
Oviedo site had a thick organic layer overlying the coarse textured

28
subsoil. The Oviedo site is in the flatwoods area and is poorly drained.
The Lake Hamilton site was located on a sand ridge. Drainage of the
upper soil layers was very rapid. The Oviedo site encompassed 3.6 ha of
treated area and the other site encompassed approximately 1.7 ha.
Aldicarb was banded along each side of the trees and incorporated to
a depth of 5 cm. At the Oviedo site, soil samples were collected in 30
cm increments to a depth of 150 cm. At the Lake Hamilton site, soil
samples were collected in 30 cm increments to a depth of 60 cm, and then
in 60 cm increments to a depth of 300 cm. Soil samples were collected
using a bucket auger and were transferred into plastic bags for storage
at -20C until analyzed. The soil samples were collected monthly for six
months following the first application of aldicarb in 1983. Undisturbed
soil cores and bulk soil samples were also collected from untreated areas
at each site for laboratory determination of soil and chemical
properties.
At each site, two clusters of observation wells were installed
within the treated area, and additional well clusters were located
upgradient and downgradient of the treated area. The wells were screened
at depths of 2, 3, 5, and 6 m at the Oviedo site, and at depths ranging
from 5 to 15 m, depending on location, at the Lake Hamilton site. Water
samples were usually collected with a peristaltic pump. At least 5 well
volumes were pumped prior to collecting a sample for analysis. When
flowrates into the well were too slow to permit pumping of 5 well
volumes, the wells were evacuated and the water entering the wells was
pumped for an additional 5 minutes prior to collection of the sample.
The groundwater temperature, pH, and conductivity were measured during

29
sampling. Water samples were collected monthly throughout the study.
The storage conditions prior to analysis were not specified.
Results from the first 6 months of the study were reported by
Hornsby et al. (1983). Results are reported in terms of total toxic
residues (TTR), which is the sum of residues of aldicarb, aldicarb
sulfoxide, and aldicarb sulfone. Fifteen days after application, TTR
were not detected belcw 30 cm at the Oviedo site, but were detected in
all replicates at the Lake Hamilton site to a depth of 120 cm. After 120
days, TTR were detected at the deepest sampling depths (150 cm at Oviedo,
and 300 cm at Lake Hamilton) at both sites. No TTR were observed at the
Oviedo site in any of the observation wells during the first six months.
Two wells within the treated area at the Lake Hamilton site showed
measurable levels of TTR. In one well, TTR levels as high as 1.2 mg/L
were measured approximately 130 days after application. Data relating to
rainfall or irrigation during the study were not presented.
Jones et al. (1983) used the data from the aldicarb study above to
evaluate three pesticide transport models. One of the models tested was
PRZM (Carsel et al., 1984). Agreement between PRZM predicted movement of
aldicarb and the observed data was good. A sensitivity analysis was
performed to demonstrate the response of PRZM to variations in several
soil and pesticide properties. Jones et al. (1987) presented results
from the Lake Hamilton site in which water samples from the shallow water
table were collected from a network of 174 wells over a period of three
years. These data were used to evaluate predictions from a linkage of
the FRZM model to a one- or two-dimensional saturated transport model.
The linked model was developed to allow predictions of the extent of

30
lateral transport of TTR which have entered a shallow water table. PRZM
was used to predict daily mass loadings of aldicarb to the top of the
water table. Results of the saturated zone modeling were in reasonably
good agreement with the observed concentrations in the wells and the rate
of lateral movement.
3.4.2 Comparison of tillage effects in Maryland
Paired watersheds were established in 1984 on the Eastern Shore of
Maryland to observe the impacts of tillage practices on agricultural
chemical leaching and runoff. The experimental design and early results
have been reported by Brinsfield et al. (1987, 1988). One of the
objectives of the experiments was to generate a data set for testing and
validation of pesticide transport models. One watershed was used to grow
com using conventional tillage (CT) practices, and on the other
watershed com was grown using no-till (NT) methods. The CT watershed is
approximately 6 ha in size, and the NT watershed cavers approximately 8.9
ha. The soils in both watersheds are silty, well-drained, and nearly
level.
Soil samples were collected at 5 locations in each watershed 4 times
per year. The samples were collected using a hand auger in 15 cm
increments to a depth of 120 m.
Monitoring wells made of 6.35 cm diameter FVC were installed to
depths of 3-4 m on 30 m centers in each field. The wells were arranged
to divide each field into four quadrants. The wells were installed so
that only the top of the water table would be sampled. A bentonite-soil
mixture was used to seal the top 0.5 m of each well. Well samples were

31
collected monthly. The wells were pumped for 5 minutes prior to
collecting a sample for analysis.
To monitor surface runoff, H-flumes with automatic samplers and
recorders were installed in the outlets of the watersheds. All samples
were frozen for transport and storage prior to analysis. Details of the
analytical procedures used for pesticide residue analysis are presented
by Brinsfield et al. (1987).
Gravity-fed lysimeters were installed horizontally through the walls
of a reinforced pit to collect samples of percolating water. The
lysimeters were installed at a depth of 1 m below the soil surface.
Three lysimeters were installed in each pit. These lysimeters collect
samples of saturated flow percolating through the root zone. Samples
were collected from these lysimeters after each rainfall event.
Herbicide treatments were the same for both watersheds. Atrazine,
metolachlor, and simazine were applied pre-emergence at the rate of 1.68
kg AI (active ingredient)/ha. Cyanazine was applied at 2.24 kg Al/ha,
and carbofuran at 1.12 kg Al/ha, both pre-emergence. Dicamba was
applied post-emergence at a rate of 0.55 kg Al/ha.
Leachate collected in the lysimeters during January, 1985, had
atrazine concentrations as high as 2 /ug/L. After planting in 1985, no
leachate samples were collected in the CT watershed until September.
Atrazine and metolachlor concentrations in those samples ranged from 1-2
/ug/L. Simazine was detected in only one sample, and cyanazine was not
detected. In the NT watershed, samples collected in July had
concentrations of atrazine at 8-10 /ug/L, simazine at 7-10 /ug/L, cyanazine
at less than 1 /ug/L, and metolachlor at less than 2 /ug/L. Leachate from

32
the NT watershed in the fall of 1985 had similar concentrations to those
collected from the CT watershed.
Groundwater samples collected 15 days after application in 1984
showed levels of atrazine, simazine, and cyanazine exceeding 1 /jg/L. The
concentration of atrazine in one well reached 7 /jg/L on this date.
During late summer and fall, atrazine was the only pesticide detected in
the wells. Pesticide concentrations in the groundwater were similar for
the two watersheds, although the concentrations were somewhat higher
during the growing season in the NT watershed. In April, 1986, (prior to
application) atrazine concentrations of 4 /ig/L were observed in the water
table on both watersheds. This indicates that atrazine may be persistent
in the lower soil zones and in the water table.
3.4.3 Aldicarb movement in the Dougherty Plain of Georgia
The U. S. Environmental Protection Agency and the U. S. Geological
Survey began a study in 1982 to observe the movement of aldicarb and its
residues in the unsaturated and saturated zones at a field site in Lee
County, in southwestern Georgia. Aldicarb is used as an insecticide and
nematicide on peanuts grown on the site. One of the primary objectives
was to develop an extensive database of soil, land use, geologic, and
pesticide data for use in the validation and testing of the PRZM (Carsel
et al., 1984) model. Different aspects of this study have been reported
by a number of investigators (e.g. Cooper, 1986; Rao et al., 1986; Hook,
1987; Hedden, 1986; Smith and Carsel, 1986).
The study site covers an area of approximately 4.5 ha. Four soils
are mapped in the field. All have a fine-loamy texture. Below a depth

33
of approximately 1 m there are layers of clay separating zones of coarser
sand and gravel. The conductivity of the clay layers is very lew, which
suggests that perched water tables may form on these layers and cause
lateral transport of leached pesticides. There is a shallow water table
at the site which can vary in position by as much a 6 m during a year.
Soil samples were collected from the site to characterize soil
properties and pesticide sorption and decay characteristics. Geologic
information was collected from holes bored to the top of the Ocala
Limestone. The residuum was approximately 13 m thick. The Ocala
Limestone is a part of the Floridan aquifer system. Four wells were
cased into the Floridan aquifer for measurement of water levels.
A weather station was installed at the site to collect meteorological
data.
Statistical procedures were used to determine that 20 monitoring
sites would accurately reflect the fate and transport of aldicarb at this
site. The locations of the monitoring sites were randomly selected and
distributed between the three major soil series based upon the relative
area in each series.
Each monitoring site was equipped with an impressive array of
instruments for monitoring of conditions and collecting samples. Five
tensiometers were installed to a depth of 150 cm for monitoring soil
water content. The tensiometers were monitored and serviced three times
per week. Five thermistors were installed to a depth of 115 cm for
measurement of soil temperatures. The thermistors were monitored three
times per week during the middle of the afternoon in order to estimate
maximum soil temperatures. Three soil solution samplers were installed

34
at depths of 1.5, 2.1, and 2.7 m. The samplers were made using stainless
steel for the body, a high-flcw ceramic cup, and teflon tubing for sample
collection and applying the vacuum. A silica flour slurry was poured
around each sampler to insure good contact with the surrounding soil.
The vacuum and sample lines were buried to avoid interference during
field operations. Vacuum was applied to the samplers for a period of 24
hours prior to collection of the sample.
At 15 of the monitoring sites, permanent stainless steel monitoring
wells were drilled to a depth of approximately 4.6 m. A stainless steel
screen 0.6 m in length was placed at the bottom of each well. A gravel
pack was placed around the screen and a cement grout was placed from the
gravel to the top of the finished well. The wells were finished so that
the top of the well was approximately 0.6 m below the soil surface so as
not to interfere with cropping operations. After field operations are
complete, the wells are extended above the soil surface. Water samples
are collected using a dedicated teflon tube (one per well) and a
peristaltic pump. The 250 mL glass sample containers were connected in
line between the well and the pump to prevent cross contamination. The
wells were typically pumped for 5-7 minutes prior to collecting a sample.
Soil samples were collected using a hand auger in 15 cm increments
to a depth of 120 cm. After each sample was collected, gravel and plant
material was removed and then the sample was sealed in a metal container.
The references which were obtained for this review did not report
any results of pesticide monitoring from the field site. The detailed
monitoring network and experimental design used at this site should
produce a valuable data set for model validation and testing.

35
3.4.4 Big Spring Basin in Icwa.
The Big Spring Basin is a 267 km2 groundwater basin located in
northeastern Iowa. The basin is entirely agricultural with
approximately 60% of the land area in rcw crops. The basin has been
extensively monitored and characterized, and results from these studies
have been presented by a number of investigators (Libra et al., 1986;
Kelley et al., 1986; Hallberg, 1986; Libra et al., 1987). Over 85% of
the basin's groundwater is discharged through Big Spring. Thus,
investigators have an easily accessible point for monitoring average
groundwater quality in the basin as affected by agricultural activities.
Big Spring is a karst spring with an estimated 10% of flow contributed by
directed recharge through sinkholes. By losing hydrograph separation
techniques, the investigators can determine the water quality effects of
both normal infiltration and direct recharge through the sinkholes.
Four years of monitoring have shown flow-weighted mean
concentrations of N03-N ranging from 7-11 mg/L. Combined losses of N03-N
in both surface water and groundwater amount to 33-55% of the average
annual fertilizer nitrogen applied in the basin. Historic records
indicate that the magnitude of N03-N concentrations have increased by
200-300% over the last 20 years. This corresponds to a 200-300% increase
in the application of nitrogen fertilizers during this period.
The herbicide atrazine is the only pesticide that is always present
in the discharge from Big Spring. Flow-weighted mean concentrations of
atrazine are less than 1 /ug/L, but the average concentrations have
steadily increased during four years of monitoring. Total losses of

36
atrazine in the groundwater amount to less than 0.1% of the annual
application. Pesticide concentrations in surface waters are generally an
order of magnitude higher than observed in the discharge from Big Spring.
Peak concentrations of pesticides occur following recharge events in the
spring after crops have been planted.
3.5 Summary
There is a growing body of evidence that pesticide residues are
being leached to groundwater in some areas of the country. The U. S. EPA
recognizes that at least 19 pesticides have been found in groundwater in
24 states as a result of agricultural practices (U. S. EPA, 1987a). Many
of the pesticide detections in groundwater have been from the use of
nematic ides, which in general need to be very soluble in order to be
effective. The other major class of compounds found in groundwater from
agricultural practices is herbicides. The herbicides atrazine and
alachlor are used over large areas in many regions of the country, and
have been frequently detected in groundwater. Nitrates from
agriculturally applied fertilizers have also been found in groundwater.
The health risks associated with the low concentrations (typically less
than 10 /xg/L) of pesticides in drinking water are still unknown. There
are documented health risks, however, to infants drinking water with high
levels of nitrates. The agricultural production systems in this country
will continue to require large inputs of chemical fertilizers and
pesticides. Scientists and governmental regulators must identify ways to
protect groundwater supplies from contamination while allowing farmers to
use the chemical inputs required to maintain yields. Simulation models

37
will play an important role in evaluating the contamination potential
from applications of pesticides and fertilizers and assess the benefits
of modifications to current agricultural management practices
(application methods, timing, chemical formulations, tillage practices,
etc.).
There are many factors which influence the mobility and persistence
of pesticides in the environment. Soil physical properties, chemical
properties, crop characteristics, environmental factors, and the
interactions among them determine the fate of agriculturally applied
chemicals. Many of the processes involved in the transport and
degradation of pesticides in the environment are poorly understood, and
the mathematical equations used to represent these processes are
simplifications based on experimental observations. The soil properties
and environmental factors which strongly influence the fate of a chemical
can vary significantly within small areas.
Methods to predict the fate of agriculturally applied chemicals
range from simple indices to complex mathematical research models.
Models which can represent the effects of agricultural management
practices on the transport of chemicals are being used by governmental
regulators as part of the chemical registration process. Most of the
management models developed to date are designed to represent the
processes on the soil and plant surfaces, within the crop root zone and
possibly the unsaturated zone between the root zone and the water table.
New model developments will incorporate groundwater transport models to
assess how far and at what rate a chemical that leaches into groundwater
will move. There are many sources of uncertainty and error associated

38
with model predictions. Thus models should not be expected to provide
exact predictions of transport and fate within a field. There have been
a limited number of studies however, which provide data of sufficient
detail to assess hew well the models do represent average transport
within a field. In order to gain confidence in the use of models for
pesticide fate and transport predictions, many new studies in various
regions throughout the country are needed. To be of real value in model
testing and validation, these studies should last for several years. For
models which require calibration, a year or more of data may be needed to
calibrate the models and then several additional years of data from that
site will be needed to test the predictions of the calibrated models.

CHAPTER 4
EXPERIMENTAL METHODS
4.1 Field Site Description
The study site is located near Tifton, Georgia, in the coastal plain
physiographic region of the southeastern U.S. The geology of this area
has been described by Asmussen et al. (1986). The site is part of a farm
rented by the University of Georgia Coastal Plain Experiment Station
which is referred to as Gopher Ridge in honor of the many gopher
tortoises which make their home on the edges of the fields and in the
woods nearby.
The study site covers 0.7 ha in the northwest comer of a 2.3-ha
field. The field was developed for irrigated agricultural research.
Irrigation sprinkler risers are located on a 12 by 12 m grid. Supply
lines for irrigation water are buried approximately 1 m below the soil
surface. The field was removed from active crop research in 1982. At
that time the field was deep tilled in both north-south and east-west
directions to remove possible residual tillage effects. A permanent
bahia grass cover was established and fertilized and irrigated as needed.
No pesticides were applied after the grass cover was established. The
field was maintained in this condition until the spring of 1986 when
instrumentation for this research was installed. The site is bounded on
the north and west by trees. Just beyond the tree line on the western
edge of the field, the surface elevation drops rapidly into a seepage
39

40
area where subsurface flows reemerge as surface water that flews to the
Little River.
The soil on the study site is classified as a lakeland sand (Typic
Quartzipsarnmants, thermic, coated). The soil profile depth on this site
ranges frem 1.9 to 4.4 meters. Underlying the soil is a restricting
layer consisting of tight clays. This restricting layer is the top of
the Hawthorne formation which forms the confining layer over much of the
aquifer system (Floridan) of southern Georgia and Florida. The presence
of this restricting layer causes percolating rainfall or irrigation to
saturate the soil above the layer and form a transient water table.
Previous work by Asmussen et al. (1986) had shown, through the use
of ground penetrating radar (GPR), that the restricting layer below the
study site would cause saturated flow above it to converge into channels.
The presence of these channels made the site look premising for saturated
zone monitoring because samples from wells located in these channels
would be very likely to show the presence of any chemicals which were
moving with the saturated flow. The maps drawn frem the (GPR)
descriptions also indicated that the slope of the restricting layer near
the study area boundaries was generally away from the study area. This
would minimize influences of saturated flows coming into the site from
other areas.
Figure 4.1 shews the elevations of both the soil surface and
restricting layer for the 0.7-ha study site. The elevations given are
relative to a local bench mark which was assigned an elevation of 30.48
m. The soil surface has a relatively uniform slope of approximately 4%
towards the west. The restricting layer shews a more complicated

Distance (m) Distance
41
Soil Surface Elevation (m)
Distance (m)
Restricting Layer Elevation (m)
Distance (m)
Figure 4.1.
Contour maps of soil surface and restricting layer
shewing locations and ID labels of monitoring wells.

42
topography. Slopes of the restricting layer range from 1 to 15 percent,
with the general direction of the slope also being towards the west. The
fact that the soil is a coarse sand also was considered to be
advantageous for monitoring pesticide movement. The high hydraulic
conductivities and lew clay and organic matter contents should result in
increased leaching and movement of applied chemicals as compared with
tighter soils. Another advantage of this site was the availability of
irrigation which could be used to supplement the natural rainfall if
required to maintain a dynamic flew regime.
This study was not intended to represent typical agricultural
chemical usage and practices in the coastal plain of Georgia. The
primary objective was to observe chemical movement within the unsaturated
soil and in a shallow water table aquifer (shallow groundwater). The
properties of the soil and restricting layer described above were suited
for this objective.
4.2 Site Instrumentation
The movement of the applied chemicals was monitored using a
combination of soil samples, soil-water (soil solution) samples from the
unsaturated zone, and saturated zone samples. These samples were
collected using augers, soil solution samplers, and monitoring wells,
respectively. Soil sample collection required no permanent
instrumentation, and the collection procedure will be described in a
later section.
Samples of the water from unsaturated soil were collected using
soil solution samplers. An excellent review of soil solution samplers is

43
presented by Litaor (1988). Smith and Carsel (1988) presented a design
for a stainless steel solution sampler that do not react with pesticides
during sampling. Samplers constructed of stainless steel were not
economically feasible in this research.
The samplers used in this study were constructed of 4.2 cm OD,
schedule 40 FVC and a 1 bar, high flew ceramic exp, 3.99 cm diameter by
19.05 cm long attached to the FVC pipe with epoxy. A 0.64 cm
polypropylene tube was extended from the inside bottom of the ceramic cip
to a bulkhead fitting on the top of the sampler. A second fitting was
placed on the side of sampler, near the top, for attaching a vacuum line.
The samplers were constructed in two lengths, 1.08 and 2.09 m. The
polypropylene tubing used in the samplers was not tested for adsorption
of atrazine and alachlor. This was not done since the solution samplers
were to be used for collection of samples to be analyzed for inorganic
chemicals such as bromide and nitrate. The volumes of samples collected
from the solution samples were in general too small (less than 50 mL) to
permit reliable quantification of pesticide residues.
The samplers were placed such that the center of the ceramic cup
was located at depths of 61, 122, and 183 cm. The samplers were
installed by augering a 5 cm hole to a depth that was approximately 10
cm less than the desired sampling depth. A piece of thin wall aluminum
tubing with ID slightly smaller than the ceramic exp was then driven 20
cm beyond the augered hole, removing a core of soil. The sampler was
pushed into the hole until seated on the bottom. A slurry of water and
soil taken from a borrow pit adjacent to the site was then poured into
the annulus between the FVC and soil. The top 12 cm of the hole was

44
widened to approximately 10 on in diameter and was filled with a
bentonite slurry to form a seal around the sampler and prevent direct
flew from the surface dewn the side of the PVC pipe to the ceramic cup.
Figure 4.1 shews the location of the application area, which is the
strip to which the herbicides and bromide tracer were applied. The
samplers and three monitoring wells (08-09, 09-09, and 10-09) are within
this area. The samplers were installed in groups of three at a distance
of 3.05 m on either side of each well along a north-south axis. The
samplers were assigned identification labels that indicated their
position with respect to the wells and the depth to which they were
installed, eg. the group of samplers located 3.05 m south of well 09-09
were assigned labels of 09S-2, 09S-4, and 09S-6 for depths of 61, 122,
and 183 cm (2, 4, and 6 ft), respectively. A cross-section of the
application area shewing the locations of solution samplers and
monitoring wells is shewn in Figure 4.2.
A small instrument trailer was located approximately 4 m east of the
application area. A vacuum pump located in the trailer was used to
apply a vacuum to all samplers simultaneously through a manifold. The
pump was capable of creating a vacuum of approximately 28 cm of Hg. A
0.64 cm diameter polypropylene sample tube was connected to the sample
fitting on each sampler and routed back to the trailer where it was
connected to a 50 mL polycarbonate sample container. All sample tubing
was enclosed in a protective FVC conduit which was located 1.5 m from the
samplers to avoid interference with chemical application in the vicinity
of the samplers. Between the samplers and the PVC conduit, the stiff
polypropylene vacuum and sample lines were fastened together and held

45
Figure 4.2. Cross-section of soil profile through application area
shewing locations of monitoring wells and soil solution
samplers.
about 0.6 m above the soil surface to minimize interference around the
samplers. When it was time to collect the samples from the samplers,
the vacuum was switched to the sample collection side and the vacuum side
was opened to atmospheric pressure. Any water that had accumulated in
the samplers would then be pulled into the sample containers.
Monitoring wells for collection of samples from the saturated zone
and measurement of the water table elevation were installed during May
of 1986. The wells were constructed of 6.3 cm diameter, schedule 40 PVC
and were slotted using a veneer blade on a radial arm saw. Three rows of
slots were cut along the circumference of the well. Each slot was
approximately one-sixth as long as the pipe circumference. The slots
were spaced approximately 2.5 cm apart over the bottom 1.22 m of the
well. Although several manufacturers offer slotted PVC well screens,

46
these were beyond the budgetary constraints of this project. Seme
existing wells, installed by the USDA-ARS Southeast Watershed Research
Laboratory in conjunction with the GPR mapping by Asmussen et al. (1986)
were also used. Conversations with the technicians involved with the
installation revealed that the wells were constructed of the same
materials as described above but were perforated with tiny drill holes
instead of being slotted. They were unable to recall the diameter of the
holes or the extent of the perforation above the bottom of the well.
Appendix A lists statistics associated with each well and indicates which
wells were installed by USDA.
The wells were installed by hand augering a 10 cm diameter hole
down to the top of the restricting layer. The restricting layer was
identified by a sudden change from yellcwish-white sand to red clay
mixed with small rocks. The change was very abrupt and the effort
required to auger through the clay was significantly greater than to
auger through the coarse sand. A PVC end cap was slipped onto the end
of the well and held in place by friction during installation of the
well. Fine gravel was placed around the wells in the zone of the slots
in an attempt to minimize sand migration into the wells through the
relatively coarse slots. Soil from a nearby borrow pit was used to fill
the hole around the well to within 12 cm of the surface. Ten centimeters
of a water and bentonite slurry was then added to form a seal around the
well. The final 2 cm was filled with soil. The USDA augered down to
the restricting layer with a hollow stem auger on a small drill rig.
The well was slipped into the center of the auger and the auger was

47
withdrawn. No details on backfill procedures used by the USDA are
available.
The wells were continuous from the restricting layer to a height of
approximately 50 cm above the soil surface. No solvent weld joints or
connections were used. Each well was fitted with a 0.64 cm diameter
polypropylene tube which extended from approximately 2.5 cm above the
bottom of the well through a #2 rubber stepper placed in a vented PVC
cap. The polypropylene tubes were not tested for adsorption of atrazine
and alachlor. The caps were vented by milling small channels into
opposite sides of the inside of the cap. This was done to prevent a
vacuum from forming in the well during sampling.
The wells were installed on a 12 by 12 m grid corresponding to the
locations of existing sprinkler risers. The wells were offset by
approximately 50 cm to the west of the sprinkler risers to avoid hitting
supply lines beneath the soil surface. In the application area the
wells and solution samplers were offset from the sprinkler risers by
about 3.05 m so that the risers and supply lines would not interfere
with the downward movement of chemicals within the vicinity of the wells
and samplers. The monitoring wells were assigned ID labels to indicate
their position relative to the sprinkler riser in the SE comer of the
2.7 ha field on which the experimental site is located. Figure 4.1 shows
the locations of the wells and the associated ID labels. Well 08-15 is
located at the edge of the tree line bordering this side of the study
site. One well is located in the woods approximately 30 m west northwest
of well 08-15. This area is a seepage zone where subsurface flows

48
reemerge as surface water and flew to the Little River. This well is
referred to as "LCW".
Additional instrumentation installed at the site consists of a
weighing rain gage which was located near well 09-12, a water table
recorder which was located near well 08-12, tensiometers for measuring
the water content of the unsaturated soil, and a "deep" well located
between wells 08-13 and 09-13.
The rain gage is a US Weather Bureau standard weighing bucket rain
gage with a seven day chart and 30.5 cm capacity. The rain gage was
calibrated using standard calibration weights after installation.
The water table recorder was installed to provide a continuous
record of the water table elevation to supplement the weekly
measurements taken on all wells. The recorder was equipped with a seven
day clock and a drum type chart recorder.
Eight tensiometers were installed around each set of solution
samplers. The tensiometers were water filled and connected with very
small tubing to a mercury manometer board. The tensiometers were
installed at depths of 30, 60, 90, 110, 122, 140, and 183 cm. There were
two tensiometers located at the 60 cm depth. The tubing connecting the
tensiometers to the manometer boards was routed above the soil surface
with the vacuum and sample lines as noted above.
The "deep" well was installed by drilling a 10 cm diameter hole
inside of a 11.4 cm diameter PVC casing. The casing was pushed down as
the auger advanced. The casing was installed in this manner using 1.5 m
sections to a depth of approximately 12.2 m below soil surface. The
last 3 m of the well was located in a saturated formation that liquified

49
during drilling. Hie observation well was made of a 1.5 m section of
6.3 cm diameter commercial PVC well screening solvent-welded to two 6.1 m
sections of schedule 40 PVC. Hiis was jetted down inside of the casing
until it bottomed out at the bottom of the previously augered hole.
After placement of the well, a mixture of bentonite and water was poured
into the area between the casing and well up to the top of the casing.
Hie purpose of this well was to observe the piezcmetric head difference
across the restricting layer.
4.3 Chemical Applications
Hie first application of chemicals to the experimental site
occurred on 11/12/86. On the day preceding this first application, the
entire field site was rotary mowed. Hie grass within the application
area was cut to a height of approximately 5 cm. All dead grass and
clippings were raked up and removed from the area prior to application.
Hie application area consists of a strip 36.6 m long by 9.14 m wide as
shewn in Figure 4.1.
Atrazine and alachlor were applied simultaneously on November 12.
Atrazine was applied in the form of AAtrex (Ciba-Geigy Corp.) which is
an emulsifiable concentrate containing 0.479 kg AI (active ingredient)/L
(4 lb/gal). Alachlor was applied in the form of Lasso (Monsanto
Agricultural Products Co.) which is also an emulsifiable concentrate
containing 0.479 kg AI/L. Hie herbicides were applied using a self-
propelled boom sprayer with provisions for injecting chemicals directly
into the water stream (Sumner et al., 1987). Hie boom length on the
sprayer is 9.14 m.

50
The intended application rate of the herbicides was 4.5 kg Al/ha (4
lb/ac). One liter of herbicide solution was prepared by mixing 313 mL of
each of the chemicals with 374 mL of deionized water. The sprayer was
operated at a velocity of 3.05 m/min moving from south to north along the
edge of the application area. The chemical solution was injected into
the water stream of the sprayer using an injection pump calibrated to
deliver 83 mL/rain* The water pressure at the inlet of the sprayer was
207 kPa. The pressure at the spray nozzles was maintained at 138 kPa.
Despite the calibration of the injection pump and measurement of sprayer
ground speed, the chemical solution ran out as the sprayer reached well
10-09 which is 6.1 m short of the intended end of the application area.
The application of the herbicides was considered complete at this point,
and the remaining portion of the application area was left untreated.
Fourteen 0.35 1 plastic cups were randomly placed within the
application area. The cups were supported upright in holders at a
height of approximately 35 cm above the soil surface. The cups were
used to collect samples of the application solution for analysis of
chemical concentrations, determination of the depth of water applied to
the area during application, and uniformity of chemical and water
application. The use of these cups and holders has been tested and
found to accurately reflect water application amounts (Stansell et al.,
1982).
Immediately after completing the application of the herbicides, the
sprinkler irrigation system was started and 5.1 cm of water was applied
over the entire study site. This irrigation was intended to wash the

51
herbicides off of the grass foliage and move them into the soil profile
to minimize volatilization losses from the soil and plant surfaces.
Bromide in the form of potassium bromide (KBr) solution was applied
to the application area using the same sprayer on 11/17/86. There was a
light drizzle of rain during application. Five hundred grains of KBr was
dissolved in 1050 mL of deionized water and injected into the sprayer as
described above. This was equivalent to an application rate of 10 kg/ha
of bromide. The sprayer again was operated at a nozzle pressure of 138
kPa, and a ground speed of 3.05 m/min. Fourteen collectors in the same
locations as for the herbicide application were used to collect
application samples.
No further chemical applications were made prior to fertilization
of the grass on 4/16/87. The fertilizer was applied at a rate of 560
kg/ha of 5-10-15 using a tractor-mounted broadcast spreader. Fifteen
percent of the nitrogen in the fertilizer was in the form of nitrate
nitrogen (N03-N). This is equivalent to applying 18.6 kg/ha of nitrate
(N03).
In an effort to further characterize bromide movement within the
soil profile and determine flow velocities within the groundwater, an
additional bromide application and a separate chloride application were
made. On 4/27/87, KBr was applied to the application area using the
sprayer utilized during the previous application. The application
solution was made by dissolving 1.2 kg of KBr in 2.3 1 of deionized
water. The sprayer made two passes across the application area during
which it applied approximately 1.6 1 of the KBr solution. This was
equivalent to an application rate of 17 kg/ha of bromide. Fifteen

52
plastic cups were used to catch application solution samples in the same
manner as previously described. Immediately following application the
field was irrigated. Irrigations continued daily for a week in order to
raise the water table and provide plenty of infiltrating water to
transport the bromide through the soil.
Prior to the beginning of irrigations on April 27, most of the wells
on the site were dry. The water table began to rise on May 1 at which
time 10 1 of solution containing 500 g of potassium chloride (KC1) was
poured directly into well 07-09 which is near the top of the site. It
was anticipated that the high concentration of chloride in the solution
could be followed dcwnslcpe with the saturated flew and allcw for
determination of water table flew velocities.
4.4 Sample Collection and Storage
Samples were collected weekly (Monday) throughout the study. There
were occasional periods of more frequent sampling (immediately after
initial applications and during the last week of April through the
middle of May).
The flint glass containers used for well sample collection and
storage throughout this study did not have teflon lined caps. Therefore,
a small piece of parafilm (a common laboratory film used for sealing
beakers and containers to prevent evaporation and contamination) was
stretched over the mouth of the bottle prior to screwing on the caps.
Samples of the application solutions of the herbicides and bromide
were transferred to glass containers and placed in a cooler with ice

53
packs immediately after the sprayer had passed completely by the sample
collector.
Soil samples were collected in one of two ways: taking 2.5 cm
diameter by 5 cm long cores using a soil sampling probe, or using a 5 cm
diameter stainless steel bucket auger. The soil sampling probe was
generally used for collection of samples from the top 5 cm of the soil
profile. When this method was used, 5 to 8 samples would be collected
from a small area and composited into a plastic sample bag. This would
represent one final sample for the soil surface at that location. For
deeper samples the auger was used. The auger was used to remove soil to
within approximately 5 cm of the desired sample depth. The auger was
then cleaned to remove traces of soil, and a sample was collected which
represented soil from a depth of 5 cm above to 5 cm below the target
depth. The sample was carefully poured from the top of bucket into a
plastic sample bag and sealed. The number of locations within the
application area sampled depended on the number of depths to be sampled
and ranged from 5 to 14. After collection, the soil samples were placed
into coolers with ice packs for transport to Gainesville. Soil samples
from outside of the application area were collected several times during
the study to use as blanks and spikes during soil residue analysis.
Vacuum was applied to the solution samplers immediately upon
arrival at the study site (7:00 8:00 am). The vacuum would typically
be left on for 8 to 10 hours prior to collecting the soil solution
samples. The vacuum applied to the samplers was approximately 28 cm of
Hg. Near the end of the day the vacuum would be released and the samples
would be drawn directly into individual containers. The containers are

54
made of polycarbonate with a capacity of 50 mL. The containers had
polycarbonate screw caps for sealing. Immediately after all soil
solution samples were collected, the sample containers were placed in a
cooler for transport.
The elevation of the water table was measured using a well depth
indicator with a stainless steel probe. The depth indicator would sound
an audible alarm when the probe contacted water in the wells. The probe
was attached to a flat cable with markings in feet and inches, with the
smallest division being 1/4 in. Water depths were recorded early in the
morning after arrival.
After the water table elevations in all wells were measured, the
wells were pumped out to insure that the water to be sampled was
representative of the surrounding water in the saturated zone at that
time. When possible, the well was pumped dry. When the depth of water
in the well exceeded approximately 0.4 to 0.5 m, the pumping rate was
less than the rate of flew into the well. When this occurred, the well
was pumped long enough to insure that 2-3 well volumes had been pulled
from the well. A small, 12v battery powered, peristaltic pump was used
to pump the wells and collect samples.
Samples were collected after all wells had been pumped as described
above. A rubber stopper was attached to the intake side of the pump
through aim length of 1.25 cm OD Tygon tubing. The stepper also had a
piece of Tygon tubing approximately 10 cm in length connected to it for
attachment to the sampling tube in each well. The stopper was placed in
a sample bottle and the short piece of tygon tubing was attached to the
well sample tube. Since the pump was downstream from the sample con-

55
tainer, the only components common to each sample collection were the
rubber stepper and the short piece of Tygon tubing. When a sample was
collected, the initial water from the well was swirled around the bottle,
and the bottle was then inverted to remove this rinse water. This served
to rinse the Tygon tubing, stepper, and bottle. Thus, cross
contamination between wells was minimized. The sample bottles used for
the collection and storage of well samples had a capacity of 250 mL.
Well samples were placed in coolers with ice packs for storage until they
could be refrigerated.
All soil samples were frozen as soon as possible after collection
and maintained at -18C until preparation for analysis. All water
samples were refrigerated after collection and maintained at 4C until
prepared for analysis.
4.5 Sample Analysis
4.5.1 Inorganic tracer analysis
The concentrations of the three inorganic chemicals of interest
which were applied during this study were quantified using ion
chromatography. A DIONEX QIC ion chromatograph (IC) was used for all
analyses.
A DIONEX AS4A anion separation column was used with an AG4A guard
column in place to remove organic contaminants prior to reaching the
separation column. The IC was equipped with a conductivity detector
connected to an external integrator/recorder. The IC was also equipped
with an anion micromembrane suppressor to reduce the background

56
conductivity of the eluant and thus allcw for greater sensitivity to the
conductivity changes caused by the presence of anions in the sample.
There was no automatic sampling device with this instrument, so
each sample was injected by hand. Approximately 1 mL of sample was
loaded onto a sample loop prior to injection onto the column. The
sample loop retained approximately 0.25 mL of the sample with the rest
being discarded to a waste line. Between injections, the sample loop
was flushed with 1 mL of deionized (DI) water. Prior to the guard
column, the sample was passed through two filters rated at 30 and 5
microns, respectively, to remove solids from the sample. These filters
were an integral part of the IC and the filter elements were changed
whenever the system pressure was observed to be increasing above nominal
levels.
The eluant used in these analyses was 2.2 millimolar (mM) sodium
carbonate (0.933 g Na2OC>3 in 4 1 DI H20) and 0.75 mM sodium bicarbonate
(0.25 g NaH003 in the same 4 1 of DI water). This solution was made
fresh as needed. All references herein to deionized (DI) water refer to
water that had been passed through carbon filters and ion exchange resins
to produce DI water with a resistivity of at least 1 megohm. The
regenerant solution was 0.025 N sulfuric acid (2.8 mL of concentrated
H2S04 in 4 1 of DI water). The regenerant solution was passed through
the micrcmembrane suppressor to reduce background conductivity.
In normal operation the eluant flowrate was approximately 2.0-2.5
mlyiriin, and regenerant flowrate was approximately 4 ml/min. The
pressure at the discharge of the pump at the stated eluant flowrate was
approximately 8,274 kPa (1200 psi).

57
A single injection could reveal the presence and concentrations (if
standards were prepared) of the following anions (in order of increasing
retention times): fluoride (F-), chloride (Cl-), bromide (Br), nitrate
(N03-), phosphate (P043-), and sulfate (S042-).
In ion chromatography, the retention times of the various anions are
dependent upon total ionic strength of the sample, ionic strength of the
individual ions, eluant flowrate, and temperature of the sample and
eluant. Thus, the retention times could, and did, vary. When the
integrator was set to calculate the areas under the peaks, it was found
that the automatic selection of the beginning and ending points of peaks
was not consistent. A small change in retention time could
significantly alter the reported area for a given ion even when using
duplicate injections. Better results were achieved by setting the
integrator to report maximum peak height. This value was not sensitive
to variations in the starting and stopping times of a peak as long the
baseline was relatively stable.
Initial injections of water table samples from the experimental
site revealed the presence Cl- and S042- in all samples, and relatively
high concentrations of NO3- in samples from well I£W. Standards were
prepared using DI H20 and oven-dried quantities of certified or primary
standard grades of potassium chloride, potassium brcmide, potassium
nitrate, and potassium sulfate. The standards initially included Cl-
and S042- because of their presence in the samples. They were not used
to quantify the concentrations of either species during initial sample
analysis. When Cl- was introduced into the water table as a tracer late
in the experiment, sample concentrations of Cl- were quantified. It was

58
observed that the response of the chromatogram to Cl- was approximately
twice the response to the other anions of interest. Thus, standards were
prepared with Cl at half of the concentrations of the other species so
that the responses of all species would be similar and on scale
simultaneously. Standards were prepared with concentrations of the main
species of interest ranging from 0.01 to 100.0 mg/L. A typical standard
would contain 0.5 mg/L Cl and 1.0 mg/L of Br, NC^, and S042. It
should be noted here that all references to nitrate (NC>3) indicate
concentrations of N03 and not N03-N (nitrate-nitrogen).
Samples were taken from the refrigerator and allowed to come to
room temperature prior to injection. No sample preparation was
performed. The analysis protocol typically followed the pattern of 3
standards, 10 samples, 3 standards, etc. The standards used were
selected based upon the concentrations of the analytes being observed in
the samples. Thus, standards were selected to bracket the observed
values as closely as possible. Occasionally a sample would be reinjected
to observe the repeatability of the analysis procedures. Samples from
wells located away from the experimental site, including the deep well
which supplies irrigation water to the farm, were regularly analyzed as
field blanks (no Br would be expected; however, Cl, N03, and S042
could be present).
A piecewise linear fit to each sequence of injected standards was
used to generate a function of the form: concentration = f(peak height).
The piecewise fit was selected instead of linear regression because for
some species (particularly high concentrations of Cl) a linear
regression resulted in a negative y-axis intercept. This implies that

59
the species can generate a noticeable positive response and yet be
calculated to have a negative concentration. It is expected that a
positive intercept would be a normal result, i.e. there is seme threshold
concentration of the species required before the detector indicates a
response. The piecewise fit insured that any positive response would
result in a positive calculated concentration. Conversations with an
organic chemist at the Pesticide Residue Laboratory of the USDA-ARS
Southeast Watershed Research Laboratory indicated that the piecewise fit
would be an acceptable method of generating a standard curve (Marti,
1987).
The standard curves generated on either end of the analysis of 10
samples were used to compute the concentrations of the species in the
samples. The resulting concentrations were then linearly weighted based
on sample position with respect to either set of standards, i.e. the
first sample following the standards would be weighted towards the curve
generated by the preceding standards and only slightly effected by the
standard curve generated 9 samples later. The sample midway between
standards would be evenly weighted between the two standard curves. In
general, there was little drift in the instrument and the standard
curves did not vary much over a period of hours.
4.5.2 Herbicide residue extraction
Analysis of samples for residues of atrazine and alachlor required
different extraction procedures for soil and water samples. Once the
extractions were completed, all samples were analyzed on a common gas
chromatograph with identical operating conditions.

60
There are many published methods for preparing samples for
determination of atrazine and alachlor residues (e.g., Rohde et al.,
1981; Voznakova and Tatar, 1983). In general, the procedures are rather
detailed and costly in terms of time and supplies. Dr. Willis Wheeler,
Director of the Pesticide Residue laboratory in the Food Science and
Human Nutrition Department at the University of Florida, Gainesville,
indicated that a simpler method of sample preparation might be developed
which would be satisfactory in terms of the recovery of the pesticide of
interest while being more economical in both the time required for sample
preparation and expense (Wheeler, 1987). The more steps involved in the
extraction and preparation of a sample, the more likely it is that
something can go wrong and the residues lost. With these factors in
mind, methods for extraction of the two herbicides from soil and water
samples were developed which were in keeping with the budgetary
constraints of the project and the time constraints on the analyst.
Extraction of the herbicide residues from water samples was done
using a liquid-liquid extraction procedure. One hundred fifty
milliliters of the sample water were placed in a quart mason jar along
with 150 mL of pesticide-grade hexane. The jar was sealed with a
standard mason jar lid which had been lined with a self-adhesive teflon
film. The jar was agitated on a shaker table for thirty minutes after
which the contents of the jar were poured into a labeled 500 mL
separatory funnel. The sample water was drained through the separatory
funnel back into the jar, leaving the hexane in the funnel. Another 150
mL of hexane was added to the jar and it was again agitated for 30
minutes. The contents of the jar were again added to the funnel. The

61
water was then drained and discarded. The hexane was filtered into a 500
inL boiling flask through sodium sulfate to remove any remaining water.
Next, the boiling flask was placed on a rotary evaporator and the
reduced in volume to near dryness. A few mL of fresh hexane was then
added to the flask and swirled to wash the sides of the flask. This
hexane was placed in a 10 mL volumetric flask. A few mL of hexane was
again added to the boiling flask and the swirling repeated. After two
rinses of the flask, the hexane in the volumetric flask was brought to a
final volume of 10 mL. The finished sample was stored under
refrigeration in test tubes with teflon lined screw closures. The
initial sample volume of 150 mL and the final extracted volume of 10 mL
indicate that this procedure concentrated the residues by a factor of 15.
Blanks taken from remote wells as well as DI water were extracted as a
check on the procedure. Spiked samples of well water and DI water were
extracted to determine the recovery levels of the herbicides using the
procedure described above.
Soil samples were removed from the freezer and allowed to air dry
for one day prior to extraction. This was done to allow excess moisture
to evaporate since the acetone used in the extraction process can absorb
water which will not be removed by filtration through sodium sulfate as
was done for the hexane extractions. Acetone was chosen as the solvent
for soil extractions because it was observed that the water in the soil
samples, or seme other factor, tended to repel hexane. It did not
appear that the hexane was adequately dispersing the soil particles.
A fifty gram portion of the air-dried soil sample was weighed and
placed into the mason jar. One hundred mL of pesticide grade acetone

62
was then added to the soil sample. The sample was agitated for 30
minutes and the acetone was carefully decanted into a separate labeled
glass container. Another 100 mL portion of acetone was added to the soil
and the agitation was repeated for another 30 minutes. The acetone was
poured into the container with the previous portion. Several rinses of
acetone were poured through the extracted soil to remove any acetone
left iron the extractions. The acetone rinses were added to the acetone
from the extractions and the entire volume was vacuum filtered through a
47 mm diameter filter with a 0.45 micron pore size. The filtrate was
then reduced to near dryness on the rotary evaporator and brought to a
final volume of 10 mL in acetone. Soil samples taken from outside the
study area were extracted as field blanks and were spiked with known
quantities of the herbicides and extracted as a check on the extraction
and analysis procedures.
A subsample of approximately 10 g of each soil sample was taken to
determine the soil-water content so that the concentrations of the
herbicides in the soil could be reported on dry weight basis. Soil
samples for soil-water content determinations were dried for 24 hours in
an oven at 100C.
4.5.3. Herbicide residue analysis
All herbicide residue samples were analyzed on a Hewlett Packard
model 5840A gas chromatograph (GC). The GC was equipped with a 35
position autosampler and a model 5840 GC terminal (integrator). The GC
had both electron capture (EC) and nitrogen-ptfiosjphorus (NP) detectors.
Curing the early phases of the study (methods development), the electron

63
capture detector was used. This detector had a high sensitivity to
alachlor and a lower sensitivity to atrazine. However, when field
samples were analyzed, as opposed to DI water spiked with pesticides, it
was observed that the EC detector was sensitive to many naturally-
occurring compounds (the EC detector is sensitive to chlorine molecules)
which had been extracted along with the pesticides. Thus, the output was
so crowded with peaks that the quantities of atrazine and alachlor
present could no longer be reliably determined. The NP detector is very
sensitive to molecules of nitrogen and phosphorus and insensitive to
almost anything else. When the samples were analyzed on this detector,
the chromatograms were much cleaner and atrazine and alachlor were often
the only visible peaks. The NP detector is somewhere between 5 and 10
times more sensitive to atrazine than it is to alachlor due to the
greater number of nitrogen atoms in the structure of atrazine. The NP
detector was operated with detector gas flowrates of air at 50 mL/min and
hydrogen at 3 mL/min. The carrier gas was helium with a flowrate of 30
mL/roin*
The column used in the GC was 1.83 m long with a 4 mm ID. The
column was packed with 3% OV-17 (50% phenyl, methyl silicone) on 100/120
mesh Gas Chrcm Q. The operating conditions were: injector port
temperature set at 240C, column temperature at 200C, and detector
temperature at 300C.
The sample protocol used with the auto sampler was usually of the
form: 4 standards, 1 solvent blank, 10 samples, 4 standards, 1 solvent
blank, 10 samples, 4 standards, 1 solvent blank. The solvent blank was
used to help prevent carryover from the last (and highest) standard into

64
the following sample. Carryover was normally not a problem, but the
solvent blank would often shew some trace of the herbicides.
Working standards of the two herbicides were prepared using
reference standards provided by the EPA in Research Triangle Park, North
Carolina. The initial standard was made by dissolving 10 mg of the
reference into 10 mL of solvent yielding a concentration of 1 mg/mL
(1000 mg/L). This standard was then used as the basis for subsequent
working standards. Ihe standards were prepared with the concentration
of alachlor being 5 times greater than the concentration of atrazine so
that the detector response to the chemicals would be nearly equal.
Working standards of atrazine ranged from 0.01-500.0 mg/L with the
concentrations of alachlor generally being 5 times higher as noted above
(very high atrazine standards did not include alachlor). The 0.01 mg/L
standard of atrazine represented the smallest concentration which was
detectable without pushing the limits of machine and detector. Ihe
autosampler injected a volume of 4.8 fiL. So an injection of 4.8 /L of
0.01 mg/L atrazine represented a lowest detectable mass of 10 nanograms
of atrazine.
Whenever new standards were prepared, they were compared with the
previous standards to assure similar response and that no mistakes had
been made during the dilutions. Two sets of standards were made. One
set was made in acetone for use in analyzing the soil sample extracts.
The other set was made in hexane for use in analyzing the water sample
extracts. Comparisons between the two sets of standards revealed no
differences in response, but recommended procedures (Wheeler, 1987)
dictate that the standards be made up in the same solvent as the samples.

65
The integrator available with the GC did not work properly. In
order to quantify the amount of herbicides present in the samples, all
peak heights were hand measured (a peak height mode, as used with the IC
was not available on this integrator).
A linear regression was performed from the standards to give a
relationship of the form: concentration = f(peak height). There were no
problems with negative intercepts in these regressions as discussed in
the section on ion chromatography. Sample concentrations were computed
with the regression equations calculated from standards on either side of
the samples. In order to account for drift in the detector response, the
final concentrations were calculated by linearly weighting the
concentrations computed from each set of standards as discussed
previously. The NP detector had a tendency to drift and this correction
for drift was usually needed.

CHAPTER 5
MODELING THE EXPERIMENTAL SITE
Two models were selected for the purpose of simulating the movement
of the chemicals applied to the experimental site. The models selected
for use were the Pesticide Root Zone Model, PRZM (Carsel et al., 1984),
version 2, and Groundwater Loading Effects of Agricultural Management
Systems, GLEAMS (Leonard et al., 1987), version 1.8.53. These models
were selected because they were designed specifically to simulate
pesticide transport through the root zone of a crop and reflect
influences of agricultural management practices such as tillage.
Reference to the manual for GIEAMS includes the documentation for the
CREAMS model (Knisel, 1980) from which GIEAMS was derived as well as the
supplementary GIEAMS user manual which is provided with the model code
and describes the differences in input data sets between CREAMS and
GIEAMS.
This chapter will discuss the selection of input values used in the
models. Only the parameters relevant to the results will be discussed.
There are parameters in both models for which values were selected, but
which had no bearing on the results to be presented. For example the
models utilize the Universal Soil Loss Equation, USIE, (Wischmeier and
Smith, 1978) for prediction of soil erosion, however, in the simulations
surface runoff was never predicted and therefore the parameters chosen
for use with the USIE did not affect the reported results. There are
66

67
many parameters such as rainfall, pesticide properties, and soil
characteristics which are common to both models. Thus, the selection of
common parameter values will be discussed first, followed by selection of
values for parameters which are unique to each model.
5.1 Selection of Common Input Parameter Values
5.1.1 Rainfall and irrigation
Both models use daily rainfall data. The formats of the rainfall
files for the models are different and PRZM reads in values in
centimeters while GLEAMS reads in values in hundredths of an inch.
The data used to create the precipitation files came from a
combination of two rain gages. As stated previously, one weighing rain
gage is centrally located within the experimental site. This rain gage
was installed in June of 1986. A second rain gage is located
approximately 300 m north-northeast of the experimental site. This gage
is a tipping bucket type rain gage that is connected to a data logger.
All rainfall data used in the simulations came from this off-site gage
since the data from it was already on a computer and available. The
rainfall amounts recorded in both gages agreed well. The gage located on
the site was used to supplement the rainfall record with amounts of
irrigation applied to the site. This rainfall plus irrigation file was
then manipulated into the units and formats required by each model, and
checked to insure that the two formats contained identical information.

68
5.1.2 Soil properties
Both models use the Soil Conservation Service (SCS) curve number
method (USDA-SCS, 1972) to partition rainfall between runoff and
infiltration. Ihe runoff curve number was selected from Table 9 in the
PRZM manual and was chosen based on the assumption that the bahia grass
cover could be assumed to be similar to a pasture in good condition
without contouring (Lakeland sand is classified as being in hydrologic
soil group A).
Physical descriptions of the soil profile were obtained from two
sources. Hook (1985) measured properties of the soil in another section
of the farm on which the experimental site is located. The data
collected by Hook (1985) were measured within the top 120 cm of the soil
profile. The soils on the farm are uniformly classified as either a
Lakeland sand or Bonifay sand. The primary differentiation between the
soils is the depth to the restricting layer with the lakeland soil being
deeper than the Bonifay. Soil physical properties such as water
retention and hydraulic conductivities are considered to be the same
between these soils on this farm. The second source of data used to
determine soil properties is a soil characterization report for Florida
soils prepared by the Soil Science Department at the University of
Florida (Carlisle et al., 1978). This report contains characterization
data for a lakeland sand.
The properties that were selected to represent the soil profile of
the experimental site are summarized in Table 5.1. The average depth to
the restricting layer for the three wells within the application area is
2.62 m and this was chosen to represent the profile depth.

69
Table 5.]
L Soil properties used in simulations.1
Depth
(cm)
Organic
Carbon
(%)
Bulk
Density
(g/can3)
Hydraulic
Conductivity
(cm/hr)
Water Content (%)
Effective Field Wilting
Saturation Capacity2 Point3
0- 13
0.55
1.45
13.3
33.0
10.0
2.1
13- 20
0.28
1.60
13.3
33.0
10.0
2.1
20- 51
0.08
1.58
22.9
33.3
10.0
2.1
51-102
0.04
1.59
36.5
33.8
9.0
2.1
102-262
0.03
1.59
45.0
34.0
9.0
2.1
3Data from Hook (1985) and Carlisle, et al., (1978).
2Field gravimetric water content following 48 hours of drainage under
plastic following 16 hours of flooding.
3Water content at 15 bars tension.
5.1.3 Pesticide chemical properties and applications
The pesticide chemical properties required by the models are
solubility, partition coefficient, degradation rates on foliage and in
the soil, and a coefficient of plant uptake. The models may require
these parameters in slightly different forms, but the basic information
required is the same. In addition to the model manuals, the manual for
LEACH (Dean et al., 1984) was a good source of estimates of the required
parameters. No exhaustive search of possible values of parameters for
each chemical was undertaken. At this point in the study, the models
were run using values that a user could obtain from the manuals. This
may result in less than optimum performance of the models, but probably
reflects the way in which a typical user would apply them.
Reported properties of pesticides are often widely variable. For
properties such as partition coefficient and degradation rates, the

70
variability is understandable and expected. However, even water
solubility was found to vary by a factor of two. The solubility of
atrazine is not available from the PRZM manual. The CREAMS manual lists
a value of 33 mg/L. However, the Agrichemical Handbook (1983) gives a
value of 70 mg/L at 20C. The solubility of alachlor is given in the
PRZM manual as 220 mg/L at 20-25C. The CREAMS manual reports a value of
242 mg/L at an unspecified temperature. The Farm Chemical Handbook
(1984) does not report a value for alachlor and the Agrichemical Handbook
(1983) gives the solubility of alachlor in water as 148 mg/L at room
temperature. The final values selected for all chemical properties are
shown in Table 5.2.
The solubilities of bromide, nitrate, and chloride listed in Table
5.2 are considerably less than the actual solubilities. However,
solubilities of ionic species are seldom listed for reference. The
values in Table 5.2, while less than the actual solubility values, are
sufficiently high such that solubility was not a limiting factor in the
mobility of the chemicals.
Table 5.2 Chemical properties used in simulations.
Property
Atrazine
Alachlor
Bromide
Nitrate
Chloride
Solubility
33
148
800
800
800
(mg/L)
Partition coef.,
163
268
0
0
0
Kqc (cair/g)
Half-life
78
18
999
999
999
(days)
Plant Uptake Coef.
1.0
0.65
1.0
0.52
1.0
1.0
1.0
0.0
0.0
0.0
0.0
0.0

71
The user manuals for the models contain tables drawn from many
sources which provide values of the partition coefficient for many
pesticides. The LEACH manual also contains a number of tables. The
tables in CREAMS and LEACH are more extensive than those in FRZM.
Different tables within the same manual may give differing values. Table
values often list coefficients of variation (CV) on the order of 50-130
percent. FRZM presents equations by which the organic carbon partition
coefficient, K^, can be calculated if solubility or the octanol^water
partition coefficient, K^, is known.
The partition coefficients of alachlor and atrazine were not
measured on this soil and thus values were chosen from the sources
described above. A value for atrazine of 163 cm3/g with a CV of 49%
was found in the LEACH manual. No listing of a was found for
alachlor. FRZM did list a value for logiK^) which was 2.78. FRZM
presents a relationship between logfK^) and which is
log Kqc = 1.00 (log K^) 0.21 5.1
Using this equation a Kq,-, of 371 was calculated for alachlor. LEACH
lists a value for alachlor as 434, using this and equation 5.1, a
value of 268 was calculated. Since alachlor has been reported as one of
the pesticides commonly found in groundwater, it was decided to choose
the lower Kqq value for use in the model as this would tend to cause
higher predicted leaching losses.
The inorganic tracers bromide, chloride, and nitrate were assumed to
act as non-adsorbed pesticides. Thus they were assigned a value of
zero.

72
The degradation rate constant of atrazine in soil was given in the
LEACH manual as ranging iron 0.0149 to 0.0063 days-1, corresponding to a
half-life of between 46 and 110 days respectively. The midpoint value
of 78 days was chosen for use in these simulations. A degradation rate
constant of 0.0384 days-1, which corresponds to a half-life of 18 days,
was given for alachlor in both the CREAMS and LEACH manuals.
The coefficient of plant uptake of pesticides with transpiration was
determined using the relationship given in the FKZM manual in which the
uptake factor is a function of and is given as
UPTKF = 0.784 exp [(log % 1.78)2/2.44] 5.2
where UPTKF = plant uptake efficiency factor.
Using the for alachlor of 434 as discussed above, the uptake factor
was calculated to be 0.52. Back calculating a for atrazine, based
upon the chosen of 163 using equation 5.1, yields a of 264.
Using this value in equation 5.2 gives a plant uptake factor of 0.65.
The models were also run using uptake factors for both atrazine and
alachlor of 0.0 and 1.0 in order to assess the sensitivity to these
factors. GLEAMS currently recommends that in the absence of well defined
uptake coefficients, a value of 1.0 should be used. Uptake factors for
the inorganic tracers were assigned values of both 0.0 and 1.0 to observe
the sensitivity of these non-adsorbed chemicals to plant uptake.
The dates and amounts of the chemicals applied to the soil surface
of the experimental site were entered into the models in a direct manner,
ie. there were no calculations or transformations required to convert the
actual values into model parameters. The application rates used in the
simulations correspond to the intended application rates as opposed to

73
the measured rates. The dates and application rates used as model inputs
are shown in Table 5.3. The applications were assumed to reach the soil
surface with no chemical residues remaining on plant surfaces. The
amount applied was further assumed to be uniformly incorporated into the
top 1 cm of the soil.
5.1.4 Length of simulations
Both models simulated the two year time period from 1/1/86 through
12/31/87. This was done to allow the models to overcome any effects of
initial conditions prior to beginning simulation of chemical
applications. Continuing simulations for six months beyond the end of
field data collection would show if any significant leaching would likely
have occurred after saupling was terminated.
Table 5.3 Chemical applications summary.
Date
Chemical Applied
Application Rate
(kg/ha)
11/12/86
Atrazine
4.9
Alachlor
4.9
11/17/86
Bromide
10.0
4/16/87
Nitrate
18.6
4/27/87
Bromide
17.0
5.2 Parameters Unique to FRZM
5.2.1 Evapotranspiration prediction.
PRZM uses either measured daily pan evaporation or daily mean
temperature to determine evaporation from the soil and plant surfaces,
and transpiration by the crop. In these simulations, daily pan

74
evaporation values recorded at a weather station located on the
University of Georgia Coastal Plain Experiment Station carpas were used.
This weather station is located approximately 8 km from the research
site. PRZM requires a pan coefficient which is a multiplier used to
adjust pan readings to represent total daily potential evapotranspiration
(PET). A pan factor of 0.75 was selected from a figure provided in the
manual showing pan factors as related to geographic location.
Two additional parameters required for evapotranspiration (ET)
prediction are the depth in the soil profile from which evaporation can
occur, and the depth of rainfall and irrigation that can be stored on the
plant canopy (interception storage). A figure in the manual indicates
that for southern Georgia, the depth of soil contributing to evaporation
is approximately 25 cm. Data pertaining to the interception storage
capacity of bahia grass was not included in the manual. Values presented
in the manual for various crops ranged from 0.0-0.3 cm. Assuming that
the storage capacity of bahia grass is relatively small, a value of 0.05
cm was assumed.
5.2.2 Croo related parameters
Characteristics of the crop(s) grown during the simulation period
are defined by parameters related to rooting depth, maximum canopy
coverage, and dates for emergence, maturation, and harvest. A rooting
depth for bahia grass was difficult to obtain. Most references to bahia
grass simply stated that it is a deep or very deep rooted plant. Based
on such generalizations and without more specific information, an active
rooting depth (depth containing 90% of root mass) of 91 cm (3 ft) was

75
selected. Based on observations at the research site, a maximum areal
coverage of the bahia grass foliage was estimated as 98 percent.
Dates of crop emergence, maturation, and harvest are used in the
model to define the development and growth of roots. Roots are assumed
to begin growing at crop emergence and reach the maximum rooting depth at
plant maturation. The roots stay at maximum depth until harvest when
they are set back to zero. These parameters are somewhat difficult to
define in relation to a perennial crop such as bahia grass. In view of
the use made of the selected dates, same artificial dates for emergence
and harvest could be selected. Since the emergence date really defines
the beginning of plant transpiration as a component of the water balance,
an emergence date of March 1 was selected to correspond to the time when
the grass begins to "green up" in the spring. Since the majority of the
roots already exist when the grass begins active growth in the spring, a
maturation date of April 1 was selected to reflect water extraction from
the full maximum rooting depth shortly after growth for the new year
begins. The harvest date was chosen to be December 1 which reflects the
approximate date when the grass turns brown in the winter and active
growth ceases.
5.2.3 Soil related parameters
PRZM will allow simulation of a larger section of the profile than
just the root zone. A soil core depth of 2.62 m was chosen to represent
the average profile depth beneath the application area. The model will
allow the user to select the number of compartments used to represent the
profile. Too many compartments will increase simulation times and too

76
few will lead to increased numerical errors in the solution of the
equations used within the model. The manual suggests that no fewer than
30 compartments be used. Thirty-five compartments were selected because
this gives a compartment depth that is one-half the compartment depth in
GLEAMS for a root zone depth of 91 cm (see section 5.3.3).
FRZM and GLEAMS both normally operate on the assumption that any
soil water in excess of field capacity will drain into the next lower
compartment. This drainage will continue until any excess water drains
from the bottcmi of the simulated profile. This drainage is assumed to
occur within one day. In the PRZM model, this type of drainage is called
' free drainage'. IRZM has a feature to allow selection of a drainage
rate parameter that serves to slow down the percolation process. This is
an empirical constant and requires calibration. A figure is provided in
the manual whereby the user can make a first cut selection of the
drainage parameter based upon the soil type (sand, clay loam, etc.) and
the number of simulation compartments selected as described above.
Observations at the experimental site clearly shew that there is a delay
of 2-4 days from the time excess water is applied to the time that the
water table responds. Therefore a few simulations were made using the
restricted drainage option to compare with the experimental data. The
drainage rate parameter for this case was selected fremi the grafa in the
manual for a sand soil with 35 compartments to be 2.63 day-1.

77
5.3 Parameters Unique to GTFAMS
5.3.1 Evapotranspiration prediction
Potential evapotranspiration (PET) is computed in GLEAMS based on
mean monthly maximum and minimum temperatures and mean monthly solar
radiation. The mean monthly temperatures and solar radiation data were
obtained from the same weather station that was used for the pan
evaporation data input to PRZM (section 5.2.1). GLEAMS also
differentiates between soil evaporation and plant transpiration. A soil
evaporation parameter is required as a model input and the manual gives a
suggested value of 3.3 (no units) for this parameter for sands.
5.3.2 Crop related parameters
Crop related parameters which are different from PRZM are leaf area
index (LAI), and winter cover factor. The LAI data are used to reflect
changes in plant transpiration with stage of growth. In keeping with the
observed behavior of bahia grass discussed in section 5.2.2, the LAI of
bahia grass was assumed to be zero until March 1 of each year and to
return to zero on December 1st. The maximum LAI for bahia grass was
assumed to be similar to the LAI for pasture as presented in the CREAMS
manual which had a maximum LAI of 3.0. The LAI for the bahia grass was
assumed to reach 2.0 on April 1 and to reach 3.0 May 1 where it remained
until it began to decline again to a value of 2.0 on Nov. 1.
The winter cover factor is used to moderate evaporation from the
soil surface during the winter due to presence of a bare surface or cover
crops. Since the soil surface at the site was not bare during winter,
the recommended value of the winter cover factor of 0.5 was used.

78
5.3.3 Soil related parameters
GLEAMS was designed to simulate only the root zone of a crop. The
vadose zone below the crop is ignored. Thus, the soil profile
description input to GLEAMS covers only the 91 cm root zone as opposed to
the 2.62 m profile input to PRZM. There are no soil parameters which are
significant to the results presented in this work which differ from those
discussed in section 5.1.2. In order to highlight one of the major
differences between the two models, a short discussion of the
computational layering of the soil profile in GLEAMS will be presented
here. GLEAMS divides the crop root zone into seven computational layers.
The first layer is defined to be 1 cm in depth and the other 6 layers are
equal in thickness (layer 2 is 1 cm less than layers 3-7) and together
account for the rest of the root zone. The 1 cm upper layer is
considered an important feature of CREAMS and GLEAMS because it increases
the sensitivity of erosion and runoff to chemical concentrations near the
soil surface. PRZM divides the entire profile into a specified number of
compartments of equal depth. For the case presented here, GLEAMS layers
would be 15 cm thick after the surface layer (91 cm / 6 layers). For
PRZM with a 2.62 m profile and 35 compartments, each compartment will be
7.5 cm thick. Thus, PRZM may be better able to describe effects of
actual soil layering since properties of adjoining layers would not be
averaged over as large a layer as in GLEAMS. However the surface layer
in PRZM will also be 7.5 cm thick and pesticide storages will be averaged
over this whole layer effectively lowering the mass at the surface which
is available for transport with eroded sediment and runoff water.

CHAPTER 6
RESUITS AND DISCUSSION
There are many areas in this study for which results can be
reported. The approach used in this chapter is to report significant
results relative to each area such as sample collection, sample analysis,
simulation results, etc., and then relate the individual results where
appropriate. A summary of activities at the field site is presented in
Table 6.1 for reference.
6.1 Data Collection
The site was visited on at least a weekly basis throughout the
study. At each visit, samples were collected from the monitoring wells
and soil solution samplers. The water table elevation was also recorded
for every monitoring well on the site and for several wells located
adjacent to the study area. Soil samples were collected on an irregular
basis during the study.
The procedure used for collecting samples from the groundwater
worked well in general. Sand intrusion into a few of the wells, however,
was a consistent problem. It was possible in seme instances to fill the
250 mL sampling bottle with sand. This problem was overcame by slowly
raising the bottom of the sampling tube in order to stay above the sand.
These wells were pumped out using a 1.6 cm diameter suction line to
remove accumulated sand as needed. The problem could have been avoided
79

80
Table 6.1 Chronological summary of field site activities.
Date
Event
11/12/86
Apply 4.9 kg/ha of atrazine and alachlor, apply 5.1 cm
of irrigation after herbicide application.
11/17/86
Apply 500 g of KBr (10 kg/ha Br-), collect soil solution
samples.
11/18/86
Collect soil solution samples. First soil samples taken
from top 5 cm in application area.
11/24/86
Collect soil solution and soil samples.
12/01/86
Collect soil solution samples.
12/08/86
Collect soil solution samples, apply 5.1 cm of
irrigation.
12/15/86
Collect soil solution and well samples.
12/22/86
Collect soil solution, well, and soil samples.
12/29/86
Collect soil solution and well samples.
1/05/87
Collect soil solution and well samples.
1/12/87
Collect soil solution and well samples.
1/19/87
Collect soil solution and well samples.
1/26/87
Collect soil solution and well samples.
2/02/87
Collect soil solution and well samples.
2/09/87
Collect soil solution, well, and soil samples.
2/16/87
Collect soil solution and well samples.
2/23/87
Collect soil solution and well samples.
3/02/87
Collect soil solution and well samples.
3/09/87
Collect soil solution and well samples.
3/16/87
Collect soil solution, well, and soil samples.

81
Table 6.1 Continued.
Date
Event
3/23/87
Collect soil solution and well samples.
3/31/87
Collect soil solution and well samples.
4/06/87
Collect soil solution and well samples.
4/13/87
Collect soil solution and well samples.
4/16/87
Apply 560 kg/ha of 5-10-15 to entire study site.
4/20/87
Collect soil solution and well samples, Very few wells
have water.
4/27/87
Collect soil solution and well samples, Apply 2.5 cm of
irrigation, apply approximately 17 kg/ha Br, apply 2.5
cm of irrigation.
4/28/87
Collect soil solution and well samples, apply 4.2 cm of
irrigation.
4/29/87
Collect soil solution and well samples, apply 3 cm of
irrigation.
4/30/87
Collect soil solution and well samples, apply 3 cm of
irrigation.
5/01/87
Collect soil solution and well samples, pour 10 1 of
solution containing 500 g KC1 into well 07-09, apply 2.7
cm of irrigation.
5/02/87
Collect soil solution and well samples, apply 3.2 cm of
irrigation.
5/03/87
Collect soil solution and well samples, apply 1.9 cm of
irrigation.

82
Table 6.1 Continued.
Date
5/05/87
5/08/87
5/11/87
5/13/87
5/18/87
5/25/87
6/01/87
Event
Collect soil solution and well samples, apply 0.9 cm of
irrigation.
Collect soil solution and well samples, apply 1.9 cm of
irrigation.
Collect soil solution and well samples, apply 5.2 cm of
irrigation.
Collect soil solution and well samples.
Collect soil solution and well samples.
Collect soil solution, well, and soil samples.
Collect soil solution and well samples.
by using commercially available PVC well screening in the monitoring
wells.
No problems were observed with the operation of the soil solution
samplers. The volumes of water collected during sampling varied from 0
to greater than 50 mL. Even though the samples were pulled back to the
instrument trailer through as much as 35 m of 0.64 cm ID polypropelene
tubing, the vacuum applied to the sample lines was sufficient to pull
very small droplets of water along the wall of the tube and into the
containers. No carryover of the tracers between sampling periods was
observed.
The tensiometers were difficult to maintain properly on the weekly
sampling schedule. Even when the tensiometers were working, their
usefulness was limited by the short length of time during which they

83
could be observed. If an irrigation was initiated early in the morning,
a few of the 30 cm tensiometers might have responded by the end of the
day. Weekly observations of soil-water tension were insufficient for the
purpose of calculating the flux of water through the unsaturated zone.
6.2 Sample Analysis
Water samples collected from the monitoring wells were analyzed for
residues of atrazine and alachlor and concentrations of the inorganic
tracers utilized in this research (Br-, NO3-, and Cl-). The volume of
samples from the solution samplers ranged from 0 to more than 50 xnL, with
the average sample volume being approximately 10 mL. Water samples from
the soil solution samplers were generally analyzed for the Inorganic
tracers only. Most of the solution samples were not extracted for
residues of the herbicides due to the small sample volumes.
Approximately 50 of the soil solution samples were extracted and analyzed
for residues of the herbicides. Soil samples were extracted and analyzed
for residues of atrazine and alachlor. The presence of alachlor and
atrazine residues in the soil samples was confirmed in a composite of
soil sample extracts that was analyzed using gas chromatography/mass
spectrometry (GC/MS).
During extraction of water samples with hexane, an emulsion of
hexane and water sometimes formed. This would usually separate if
allowed to sit undisturbed for a few minutes. If the emulsion did not
separate, herbicide residues contained within the emulsion could have
been discarded. There was an apparent contamination of the glassware
used for water sample collection or extraction. Samples of deionized

84
water were extracted and they shewed concentrations of atrazine of
approximately 1 /xg/L. All glassware was routinely washed with a
commercial glassware detergent, triple rinsed with deionized water, and
rinsed with acetone. The contamination levels appeared to be consistent
in that water samples, in which atrazine was not expected, shewed a
concentration of approximately 1 xg/L. The source of the contamination
was not determined. Several attempts were made to isolate and identify
the source of the contamination, however these attempts were
unsuccessful. To account for the contamination, the measured
concentrations were reduced by 1 /xg/L. Thus no concentrations of less
than 1 /xg/L are reported even though the sensitivity of the GC would
allow detection down to the range of tenths of a /xg/L.
The gas chromatograph performed well except that the N-P detector
response had a tendency to drift. This was accounted for by running
standards frequently and adjusting calculated concentrations as described
in the methods section.
The recoveries of atrazine and alachlor from spiked water samples
were nearly identical and ranged from 68 to 104% with a mean of 79% and a
standard deviation of 12 percent. The standard deviation would probably
be reduced if these analyses had been performed by an experienced
analyst. Experience contributes significantly to the ability to produce
accurate and consistent results.
The recoveries of atrazine and alachlor from spiked soil samples
could not be determined from the data collected. Moisture contents of
the soils used for spiking were not recorded. The spike levels were
based on the moist weight of the soil. After spiking, the soil was air-

85
dried for 1 day in order to allow the solvent from the herbicide
standards to evaporate prior to taking subsamples for analysis. The loss
of water from the sairples during this time resulted in an increased mass
of pesticide per unit weight of wet soil. Since the initial moisture
contents were unknown, it was not possible to calculate the expected
concentrations in the air dried sairples. Calculations based on the
spiking levels of the moist soil resulted in apparent recoveries that
exceeded 100 percent. It can be concluded from the data that recoveries
from the soil were high. When the emissions in the soil recovery study
were discovered, the facilities used to perform extractions and analyses
were no longer available.
All concentrations (water and soil) reported in the following
sections are the analytically determined concentrations and have not been
adjusted for expected recovery rates.
6.3 Chemical Applications
Applications of the herbicides and bromide tracer were monitored to
determine the concentration of the chemicals in the application water and
the volume of water applied. Concentrations of the chemicals were
multiplied by the volume of water collected and divided by the opening
area of the collectors in order to determine the application rate on a
mass per unit area basis. Table 6.2 summarizes the measured chemical
application data. The intended application rates were presented in Table
5.3.
The application of atrazine and alachlor to the designated
application area was not uniform as can be seen in Figures 6.1 and 6.2.

86
a) Solution Concentration
08-09
Figure 6.1. Uniformity of atrazine application, a) concentration in
application solution (vertical bars shew sampling
locations), b) application rate (vertical bars show
location of monitoring wells)

87
08-09
b) Application Rate
Figure 6.2. Uniformity of alachlor application, a) concentration in
application solution (vertical bars shew sampling
locations), b) application rate (vertical bars shew
location of monitoring wells)

88
The application solution was used up just as the sprayer crossed over
well 10-09 (6.1 m short of intended end of application) causing the rapid
decrease in application rate at that point. Overall, the application
showed considerable variability that is probably representative of
chemical applications in general. Similar nonuniformity was observed
during the second application of the bromide tracer as shewn in Figure
6.3. The samples taken during the first application of bromide were lost
due to inadvertent freezing of the samples and subsequent breakage of the
glass sample containers.
Table 6.2 Chemical application results.
Date of
Application
Chemical
Depth of
Water (cm)1
Concentration
(mg/L)1
Application
Rate (kg/ha)1
11/12/86
Atrazine
0.70
51.5
3.41
(31)2
(28)
(39)
11/12/86
Alachlor
0.70
11.1
0.72
(31)
(34)
(36)
11/17/86
Bromide
0.86
_3
_3
(from KBr)
(20)
4/27/87
Bromide
0.74
111.0
7.47
(from KBr)
(20)
(87)
(75)
1Mean of observations
2Coefficient of variation (%)
3Samples lost prior to analysis.
The nonuniformity of application demonstrates one of the problems
associated with prediction of chemical transport within the soil profile.
In addition to recognized variability of soil properties, there is the
canpounding influence of variability in application rates over a field.
The concentrations of alachlor measured in the application samples were

89
Figure 6.3. Uniformity of bromide application on 4/27/87,
a) concentration in application solution (vertical bars
shew sampling locations), b) application rate (vertical
bars show location of monitoring wells)

90
significantly lower than for atrazine although they were applied at the
same rate (4.9 kg/ha). Figure 6.4 shows the relationship between
measured concentrations of alachlor and atrazine in the sample collection
containers. The concentration of alachlor in a container is strongly
correlated to the concentration of atrazine. The alachlor concentrations
were, in general, only about 24% of the atrazine concentrations.
Soil sample data taken six days after application (Table 6.3) suggests
that more alachlor reached the soil than indicated by the application
rate shewn in Table 6.2. Possible explanations for the concentrations of
alachlor in the application samples include errors in formulating the
application solution, adsorption to the plastic cup, and volatilization.
APPLICATION SOLUTION CONCENTRATIONS
Figure 6.4. Comparison of alachlor and atrazine concentrations in
application samples.

91
Alachlor is more volatile than atrazine. However, these samples were
exposed for less than 10 minutes after the application (while being
collected, sealed in glass containers, and placed on ice).
6.4 Chemicals in the Unsaturated Zone
6.4.1 Atrazine and Alachlor
Samples of the top 5 cm of soil were taken from 14 locations within
the application area on 11/18/86, which was six days after application.
Five cm of irrigation water had been applied prior to this sampling.
Table 6.3 compares the measured application rates of atrazine and
alachlor shown in Table 6.2 with the average measured soil
concentrations on this date. A simple calculation reveals that if the
actual application rate of alachlor was 0.72 kg/ha and was entirely
contained within the top 5 cm of soil, the maximum possible concentration
in the soil would be approximately 1 mg/kg (assuming that the soil bulk
density equals 1.45 g/cm3). The 3.41 mg/kg measured average value could
Table 6.3 Measured application rates and
soil surface concentrations
of atrazine and alachlor.
Application
rate (kg/ha)
Atrazine
3.411
(39)2
Alachlor
0.72
(36)
Soil 0.29 3.26
Concentration (68) (58)
(mg/kg)
3Mean ^Coefficient of variation (%)

92
only be achieved if the actual application rate were at least 2.5 kg/ha.
If the planned application rate of 4.9 kg/ha were contained within the
top 5 cm of soil, the expected concentration in the soil would be 6.75
mg/kg. The observed soil concentrations suggest that the alachlor in the
application samples either volatilized rapidly or adsorbed to the
collection containers prior to transfer into glass containers. If
volatilization from the sample containers was the reason for the lew
concentrations, it would be expected that there would also have been
significant volatilization fran the grass foliage and soil surface prior
to irrigating the site and moving the chemical off of the foliage and
into the soil profile.
The low concentration of atrazine within the top 5 cm of soil is
probably due to movement below this depth with percolating water before
collection of these samples. Soil samples collected six days later
(11/24/86) showed considerably more atrazine in the zone from 5 to 15 cm
than frem 0 to 5 cm. Samples from the 5-15 cm depth range were ret
collected on November 18.
Figures 6.5 and 6.6 show the relationship between the measured soil
concentration of atrazine in the top 5 cm of soil on 11/18/86 and the
concentration of the application solution and the application rate,
respectively. Figures 6.7 and 6.8 show the same relationships for
alachlor. Figure 6.5 shews that there was a poor correlation between the
concentration of the application solution and the concentration in the
upper 5 cm of soil measured at the same locations 6 days after
application. Figure 6.6 shews that there was a higher correlation
between application rate and the soil concentration than there was for

SOIL CONC. VS. APPLICATION CONC.
93
Figure 6.5. Correlation between atrazine concentrations in the top
5 cm of soil and application solution concentrations.
SOIL CONC. VS. APPLICATION RATE
Figure 6.6. Correlation between atrazine concentrations in the top
5 cm of soil and application rate.

94
SOIL CONC. VS. APPLICATION CONC.
Figure 6.7. Correlation between alachlor concentrations in the top
5 cm of soil and application solution concentrations.
SOIL CONC. VS. APPLICATION RATE
Figure 6.8. Correlation between alachlor concentrations in the top
5 cm of soil and application rate.

95
application solution concentration and soil concentration. Figures 6.7
and 6.8 shew that there was essentially no correlation between either
alachlor concentration in the application solution or application rate
and the concentration of alachlor in the top 5 cm of soil.
Additional soil samples were collected four more times during the
study. In order to reduce the total number of samples collected, the
number of depths sampled was increased and the number of sampling
locations was decreased with each successive sampling date. Alachlor was
not observed to move belcw a depth of 36 cm in the soil. No trace of
alachlor was detected in the shallow groundwater samples. Consequently,
most of the results discussed in subsequent sections will be limited to
atrazine. Results of soil sample analyses for atrazine and alachlor
residues are presented in Tables 6.4 and 6.5, respectively. Table 6.4
shows that the sampling strategy to increase the depth of sampling with
time since application resulted in missing the passage of the initial
atrazine front past any point. Samples from a depth of 178-188 cm on
12/22/87 already showed atrazine residues. Tables 6.4 and 6.5 include a
calculation of the total mass of atrazine and alachlor in the soil
profile beneath the application area, respectively. For these
calculations, the concentration was assumed to vary linearly between
sample points. The concentration of atrazine within the profile appears
to increase with time for the first 4 sampling dates due to the way in
which the samples were collected ( increasing depth of sampling over
time. The maximum mass of atrazine within the soil profile was
calculated to be 70 g on 2/9/87 which is approximately 47% of the
intended application of 150 g, or 62% of the measured application

96
Table 6.4. Mean concentrations of atrazine (mg/kg) in soil samples.
Sample Depth
(cm)
11/18/86
11/24/86
Sample Date
12/22/86
2/9/87
3/16/87
5/25/87
0-5
0.291
0.23
CM
£
NS
0.06
NS
(68)3
(61)
(42)
0-15
NS
NS
NS
0.16
0.15
0.04
(53)
(29)
(15)
5-15
NS
0.55
NS
NS
NS
NS
(47)
25-36
NS
NS
0.11
0.23
0.13
0.07
(45)
(61)
(51)
(20)
41-51
NS
NS
NS
0.15
0.08
NS
(30)
(71)
56-66
NS
NS
0.02
0.09
0.05
0.04
(45)
(49)
(65)
(78)
71-81
NS
NS
NS
0.05
NS
NS
(53)
86-97
NS
NS
0.02
0.04
0.03
0.03
(63)
(40)
(94)
(61)
117-127
NS
NS
NS
0.03
0.03
NS
(58)
(125)
132-142
NS
NS
0.02
NS
NS
0.02
(49)
(31)
147-157
NS
NS
0.01
NS
0.02
NS
(77)
(38)
178-188
NS
NS
0.01
NS
0.04
0.01
(64)
(67)
(38)
Calculated
7
32
40
70
58
32
Mass in
Profile (g)
No Sample
^Coefficient of variation (%)
^Mean

97
Table 6.5. Mean concentrations of alachlor (mg/kg) in soil samples.
Sample Depth
(can)
11/18/86
11/24/86
Sample Date
12/22/86
2/9/87
3/16/87
5/25/87
0-5
3.26
3.11
NS
NS
1.67
NS
(58)
(59)
(55)
0-15
NS
NS
NS
0.73
0.68
0.12
(104)
(23)
(65)
5-15
NS
0.17
NS
NS
NS
NS
(96)
25-36
NS
NS
0.00
0.00
0.02
0.01
(224)
(141)
Calculated
Mass in
Profile (g)
79
84
0
53
69
13
rate of 3.41 kg/ha over an application area of 334 m2 (114 g). The
maximum mass of alachlor within the soil profile was calculated to be 79
g on 11/18/86. This represents 53% of the intended application rate, and
in excess of 300% of the calculated application rate based on the
collection of the application solution as described above.
The complete data set of soil sample concentrations can be provided
upon request. Refer to Appendix B for information on how to request the
data and a sample of the data set.
Approximately 30 samples from the soil solution samplers were
extracted and analyzed for residues of atrazine and alachlor. The
samples from sampler 09N-2 showed a pulse of atrazine moving past the 61
cm depth. The peak concentration of atrazine in samples from this
sampler was approximately 0.35 mg/L and this peak occurred on Nov. 24,

98
1986, which was 16 days after application. A plot of the atrazine and
bromide concentrations, from these samples, as a function of the total
depth of water applied since application of each chemical is presented in
Figure 6.9. Since the bromide was applied 5 days after the atrazine, the
concentrations were plotted as a function of the total water applied
since each chemical was applied to provide a common base for comparison.
From this figure it can be seen that approximately 2.4 times as much
water had to be applied to move the peak concentration of atrazine past
the 61 on depth as was required to move the bromide peak past the same
point. This can be interpreted as an approximation of the retardation
factor of atrazine in this soil (assuming that the bromide
Figure 6.9. Bromide and atrazine concentrations in solution
sampler 09N-2 as a function of total water applied
since application.

99
tracer is nonadsorbed). Hie retardation factor can be written as
R = 1 + (Kd) (Pb)
fc
where R = retardation factor, dimensionless, = partition coefficient,
Pb = soil bulk density, and 9fc = soil-water content at field capacity
(Dean et. al, 1984). Hie retardation factor indicates the velocity of
water or a nonadsorbed chemical relative to the velocity of an adsorbed
chemical. Thus a retardation factor of 2 would indicate that the
adsorbed chemical would move at 1/2 of the velocity of a nonadsorbed
species.
Using the soil properties listed in Table 5.1 and a normalized
partition coefficient for atrazine of 163 cm3/g), a weighted average
retardation factor for the top 61 cm of the soil profile was calculated
to be approximately 6. Hie properties listed for the top 13 cm yield a
retardation factor of 14 and the properties of the layer from 51-62 cm
give a retardation factor of 2.
Alachlor was not detected in any of the extracted soil solution
samples.
6.4.2 Bromide
Hie concentrations of bromide (Br) in the 61, 122, and 183 cm deep
solution samplers following the first application of bromide to the site
are shown in Figures 6.10-6.12, respectively. Hie first application of
brcsid.de was made 5 days after the application of atrazine and alachlor.
Hie date of the application of the herbicides to the application site
(11/12/86) is used as the benchmark time to which all observations and

100
results are referenced. These figures demonstrate the observed
variability between sampling locations. Figure 6.13 shews the average
concentration, from the 6 sampling locations, of bromide at each sample
depth following the first bromide application. Figures 6.14-6.16 show
the concentrations of bromide following the second application in the
61, 123, and 183 cm solution samplers, respectively. Again, the
variability between sampling locations is evident. Figure 6.17 shows the
average concentration of bromide at each sample depth following the
second bromide application.
Tables 6.6 and 6.7 present the mean concentrations and coefficients
of variation between sampling locations of bromide at each sampling depth
on selected sampling dates following the first and second applications of
bromide, respectively. These tables shew that the coefficient of
variation between sampling locations ranges from 23 to over 200%.
The results of all analyses for tracers and herbicides in both soil
and water samples can be provided on magnetic media. Refer to Appendix B
for information on how to request this data and a sample of the data
sets.
6.4.3 Nitrate
The entire field was fertilized on 4/16/87 at a rate of 560 kg/ha of
5-10-15. Concentrations of nitrate (as NO3) moving through the vadose
zone following the fertilizer application exhibited extreme variability.
There were virtually no detections of nitrate due to the fertilizer
application in the samplers located at a 61 cm depth. Two samplers
located at a depth of 122 cm shewed a response to the fertilizer

BROMIDE CONC. (mg/L) i BROMIDE CONC. (mg/L)
DAYS SINCE 11/12/86
ire 6.10. Bromide concentration for six sampling locations
following the first application at a 61 cm depth.
Figure 6.11. Bromide concentration for six sampling locations
following the first application at a 122 on depth.

BROMIDE CONC. (mg/L) S BROMIDE CONC. (mg/L)
102
DAYS SINCE 11/12/86
ire 6.12.
Bromide concentration for six sampling locations
following the first application at a 183 cm depth.
AVERAGE CONC. AT EACH SAMPLING DEPTH
DAYS SINCE 11/12/86
Figure 6.13. Average bromide concentration for the three sampling
depths following the first application.

BROMIDE CONC. (tng/L) § BROMIDE CONC. (mg/L)
103
DAYS SINCE 11/12/86
are 6.14. Brani.de concentration for six sampling locations
following the second application at a 61 cm depth.
Figure 6.15. Bronide concentration for six sampling locations
following the second application at a 122 cm depth.

BROMIDE CONC. (mg/L) f BROMIDE CONC. (mg/L)
104
DAYS SINCE 11/12/86
6.16. Bromide concentration for six sampling locations
following the second application at a 183 cm depth.
DAYS SINCE 11/12/86
Figure 6.17. Average bromide concentration for the three sampling
depths following the second application.

105
Table 6.6. Mean concentration of bromide (mg/L) at each sampling depth
following first application1.
Sample Date
61
Sample Depth (cm)
122
183
11/17/86
0.03z
: (115)J
0.00
(0)
0.00
"FT-
11/18/86
0.05
(114)
0.10
(82)
0.10
(*)
11/24/86
1.92
(106)
0.72
(65)
0.05
(135)
12/01/86
1.87
(50)
3.35
(34)
0.31
(146)
12/08/86
0.90
(41)
3.28
(58)
1.48
(54)
12/15/86
0.39
(41)
3.07
(71)
2.10
(34)
12/22/86
0.25
(45)
1.42
(53)
1.58
(23)
12/29/86
0.22
(63)
1.15
(58)
1.37
(65)
01/05/87
0.08
(225)
0.85
(70)
0.91
(59)
01/12/87
0.11
(96)
0.75
(71)
0.75
(56)
01/19/87
0.04
(65)
0.68
(89)
0.68
(73)
01/26/87
0.04
(44)
0.65
(58)
0.29
(156)
02/02/87
0.09
(156)
0.52
(78)
0.51
(60)
02/09/87
0.12
(191)
0.45
(65)
0.67
(81)
--Bromide applied 11/17/86 2Mean
-^Coefficient of variation (%) 4One saxtple

106
Table 6.7. Mean concentration of brcmide (ing/L) at each sampling depth
following second application1.
Sample Date
61
Sample Depth (cm)
122
183
04/27/87
0.06z
(32)J
0.25
(66)
0.31
(*4)
04/28/87
2.06
(186)
0.40
(32)
0.11
(*)
04/29/87
6.28
(133)
0.29
(84)
0.56
(*)
04/30/87
7.81
(112)
0.54
(143)
0.95
(93)
05/01/87
7.92
(82)
0.99
(152)
1.47
(127)
05/02/87
5.56
(116)
4.35
(89)
2.02
(114)
05/03/87
5.54
(75)
9.02
(77)
2.96
(112)
05/05/87
3.47
(68)
6.79
(51)
4.62
(100)
05/08/87
1.58
(104)
5.44
(49)
6.97
(128)
05/11/87
1.14
(96)
7.78
(76)
4.99
(82)
05/13/87
0.79
(99)
6.11
(*)
1.91
(56)
05/25/87
0.51
(158)
1.32
(56)
3.04
(113)
06/01/87
0.10
(72)
1.05
(63)
2.20
(130)
--Bromide applied 04/27/87 2Mean
3Coefficient of variation (%) 4One sample
application. The maximum concentration of nitrate in these samplers was
5.6 mg/L on 5/03/87. Three samplers located at a depth of 183 cm
responded to the application of fertilizer. The maximum concentration of
nitrate in these samplers was 8.1 mg/L on 5/08/87. Concentrations of
nitrate in the soil solution were not as high as observed concentrations
within the groundwater over much of the study site (see Section 6.5.3 for
nitrate concentrations in the saturated zone). This is due to the fact
that the tractor-mounted broadcast spreader was unable to get close to

107
the instrumented application area due to the presence of the vacuum and
sample tubing and the instrument trailer. As a result, the application
rate of fertilizer in this area was reduced. Refer to Appendix B for
information on the availability of the data set containing results of all
nitrate analyses.
6.5 Chemicals in the Saturated Zone
6.5.1 Bromide
There was no shallow groundwater present on the site between the
date of herbicide application (11/12/1986) and Dec. 15, 1986. Bromide
was observed in the groundwater on this first well sampling date at a
concentration of 0.8 mg/L in well 08-09 (see Figure 4.1 for well
locations) which is within the application area. The continued detection
of bromide in the groundwater following the first application was
sporadic, and the maximum concentration of 3.9 mg/L was detected in well
09-11 on January 12, 1987. The maximum concentration of bromide in the
groundwater following the second application (4/27/87) was 5.2 mg/L on
May 13th, in well 08-09. Figures 6.18-6.23 shew the concentration of
bromide within the groundwater over six sampling periods following the
first application of bromide. Figures 6.24-6.29 show the concentration
of bromide within the groundwater for six sampling periods following the
second application of bromide.

Cone. (mg/L) Cone. (mg/L)
108
08-09
Date: 12/15/86
re 6.18.
Bromide concentration in the groundwater on 12/15/86.
Vertical bars indicate sampling locations.
10-09
Date: 12/22/86
o
Figure 6.19. Bromide concentration in the groundwater on 12/22/86.
Vertical bars indicate sampling locations.

Cone. (mg/L) £ Cone. (mg/L)
109
09-11 Date: 12/29/86
o
ire 6.20.
Bromide concentration in the groundwater on 12/29/86.
Vertical bars indicate sampling locations.
Date: 01/05/87
Figure 6.21. Bromide concentration in the groundwater on 1/05/87.
Vertical bars indicate sampling locations.

110
09-1 1
Date: 01/12/87
Figure 6.22. Bromide concentration in the groundwater on 1/12/87.
Vertical bars indicate sampling locations.
Date: 01/19/87
09-11
Figure 6.23. Bromide concentration in the groundwater on 1/19/87.
Vertical bars indicate sampling locations.

Cone. (mg/L) 'g Cone. (mg/L)
111
Date: 05/05/87
re 6.24.
Bromide concentration in the groundwater on 5/05/87.
Vertical bars indicate sampling locations.
Date: 05/08/87
08-09
Figure 6.25. Bromide concentration in the groundwater on 5/08/87.
Vertical bars indicate sampling locations.

112
08-09
Figure 6.26. Bromide concentration in the groundwater on 5/13/87.
Vertical bars indicate sampling locations.
Date: 05/18/87
08-13
Figure 6.27. Bromide concentration in the groundwater on 5/18/87.
Vertical bars indicate sampling locations.

113
10-09
Date: 05/25/87
Figure 6.28. Bromide concentration in the groundwater on 5/25/87.
Vertical bars indicate sampling locations.
09-11
Figure 6.29. Bromide concentration in the groundwater on 6/01/87.
Vertical bars indicate sampling locations.

114
6.5.2 Atrazine
Atrazine first appeared in the groundwater beneath the application
area in the samples of Jan. 21. By this date, approximately 45 cm of
water had been applied to the site as rainfall and irrigation since
application of the herbicides.
Groundwater samples from Jan. 21 through June 6 show what appear to
be distinct pulses of atrazine reaching the saturated zone and moving
downslope with the saturated flow. These pulses are characterized by low
initial concentrations in the groundwater followed by the appearance of
atrazine beneath the application area and movement within the groundwater
as the water flows downgradient to the west. Figures 6.30 through 6.35
show the concentration of atrazine (in /ug/L) in the groundwater at
weekly intervals for a five week period beginning on Feb. 23, 1987. A
vertical bar in these figures indicates that a well sample was collected
and analyzed from that location. The transport of atrazine with the
saturated flow is clearly visible and atrazine moved a distance of
approximately 85 m in a period of 21 days.
Figure 6.36 shows the general directions of flow based upon the
slope of the water table on May 8, 1987. The slope of the water table on
other sampling dates shows that flow would occur in the same general
directions. In Figures 6.30 through 6.32 it is observed that atrazine
has moved downslope from well 09-11 to 09-14. The average gradient of
the water table between these wells during this period was approximately
0.05 m/m. The hydraulic conductivity of the soil was not measured during
this study. Assuming a hydraulic conductivity of 14.4 ny'day (60 cm/hr)
at a depth of approximately 3 m (Carlisle et al., 1978) and an effective

115
09-11
Date: 02/23/87
Figure 6.30. Atrazine concentration in the groundwater on 2/23/87.
Vertical bars indicate sampling locations.
Date: 03/02/87
Figure 6.31. Atrazine concentration in the groundwater on 3/02/87.
Vertical bars indicate sampling locations.

Cone. (ug/L)
116
09-11
08-09
Date: 03/09/87
09-14
\T
A30 ASO 170
Figure 6.32. Atrazine concentration in the groundwater on 3/09/87.
Vertical bars indicate sampling locations.
09-11
Date: 03/16/87
\T
A30 no A70
WSTAUCE. {xx\)
*
Figure 6.33. Atrazine concentration in the groundwater on 3/16/87.
Vertical bars indicate sampling locations.

117
09-11 Date: 03/23/87
Figure 6.34. Atrazine concentration in the groundwater on 3/23/87.
Vertical bars indicate sampling locations.
Date: 03/31/87
Figure 6.35. Atrazine concentration in the groundwater on 3/31/87.
Vertical bars indicate sampling locations.

Water Table Elevation (m) 5/08/87
118
Figure 6.36. Contour plot of water table elevation on 5/08/87
shewing direction of flew.
saturated porosity of 0.36 (Table 5.1), it is possible to calculate an
estimated water velocity between the wells. The average pore water
velocity is calculated as
v = k s / pe 6.1
where v = average pore water velocity, m/day
k = saturated hydraulic conductivity, m/day
s = hydraulic gradient, m/m
pe = effective saturated porosity, cm3/cm3
Using Equation 6.1 and the values of the variables described above,
a pore-water velocity of 2 m/day was calculated. The distance between
wells 09-11 and 09-14 is 36 m. Based on the calculated pore-water
velocity, water should move the distance between the wells in 18 days.
Atrazine was observed to have moved the distance between the wells in 14

119
days. This would suggest that atrazine was moving faster than the
velocity of the water. In Figures 6.32 and 6.33 it is observed that
atrazine moved the distance iron well 09-14 to well LCW in a period of 7
days. The water table gradient between these wells was approximately
0.7 rn/m. The average pore water velocity for the area between these
wells was calculated to be approximately 3 m/day. These wells are
located a distance of 42 m apart. Based on the calculated water velocity,
it should take 14 days for water to move the distance between the wells.
Thus, using this method, atrazine would not be expected to have traversed
the distance between these wells in 7 days. These simple calculations
indicate that the conductivity value is probably too lew. It is also
possible that atrazine is moving along the top of the restricting layer
in large pores or conduits which are not represented by the conductivity
value used in the calculations. Without measured values of the saturated
conductivity in the saturated zone, it is difficult to explain the rapid
movement of atrazine within the groundwater.
Since samples were collected on 7-day intervals and the wells are
spaced 12 m apart, there is considerable error associated with estimates
of the distance traveled between successive dates and the time required
for atrazine to have moved a specific distance. There are also
significant errors associated with assuming conductivity values for this
site from general soil characterization data. The above examples
illustrate that additional site specific data (hydraulic conductivity,
effective porosity, etc.) are needed to adequately characterize the
observed atrazine transport.

120
In Figure 6.37, the concentration of atrazine in one well is plotted
along with the water table elevation in the well. There appears to be an
inverse relationship between concentration and water table elevation or
depth of the saturated zone. This well is 20 m downslope from the
application area. Based on this figure, it is hypothesized that as the
water table rises, the water around the well is primarily coming from
percolation through the untreated soil above it which would cause the
concentration of atrazine in the vicinity to decrease. During the period
that the water table is receding, water which has infiltrated through the
application area is flowing past the well, thus increasing the
concentration.
o o
a
>
w
w
w
_)
m
<
E-*
35
W
E-
<
Figure 6.37. Concentration of atrazine and water table elevation in
well 09-11.

121
All concentrations of atrazine in the groundwater were below 0.1
mg/L and in roost cases were below 0.01 mg/L. The highest observed
concentration was 0.09 mg/L and occurred on 5/5/87 in well 08-09 which is
located within the application area. This concentration was observed
after the water table had risen due to the application of approximately
24 cm of irrigation water within a p)eriod of 8 days. There were high
concentrations of atrazine in the groundwater when sampling was
discontinued on 6/1/87. Thus, the data collected do not indicate hew
long atrazine residues persisted on the site. Figures D.1-D.23 in
Appendix D shew the concentration of atrazine in the groundwater on every
sampling date from 1/19/87 through 6/01/87.
6.5.3 Nitrate
Background concentrations of nitrate (as N03) in the groundwater
were generally below 1 mg/L. Well LOW, which may respond to drainage
from other parts of the research farm, often had background
concentrations exceeding 15 mg/L. The study site was fertilized on April
16, 1987, and was heavily irrigated beginning on April 27th. The water
table on the site started to rise on May 1st. Concentrations of nitrate
in the groundwater began increasing on May 3rd. Nitrate concentration
levels in the groundwater were observed to be as high as 35 mg/L.
Figures 6.38-6.45 show the nitrate concentrations in the groundwater on
selected dates.

Cone. (mg/L) $ Cone. (mg/L)
122
Date: 05/01/87
re 6.38. Nitrate concentration in the groundwater on 5/01/87.
Vertical bars indicate sampling locations.
07-10
Figure 6.39. Nitrate concentration in the groundwater on 5/03/87.
Vertical bars indicate sampling locations.

Cone. (mg/L) g Cone. (mg/L)
123
Date: 05/05/87
6.40. Nitrate concentration in the groundwater on 5/05/87.
Vertical bars indicate sampling locations.
Date: 05/08/87
Figure 6.41. Nitrate concentration in the groundwater on 5/08/87.
Vertical bars indicate sampling locations.

Cone. (mg/L) g Cone. (mg/L)
124
09-14 Date: 05/13/87
o
n ~~
6.42. Nitrate concentration in the groundwater on 5/13/87.
Vertical bars indicate sampling locations.
Date: 05/18/87
Figure 6.43. Nitrate concentration in the groundwater on 5/18/87.
Vertical bars indicate sampling locations.

125
Date: 05/25/87
Figure 6.44. Nitrate concentration in the groundwater on 5/25/87.
Vertical bars indicate sampling locations.
Date: 06/01/87
Figure 6.45. Nitrate concentration in the groundwater on 6/01/87.
Vertical bars indicate sampling locations.

126
6.5.4 Chloride
In order to further characterize the flew velocities within the
groundwater, 10 L of solution containing 23.8 g/L of chloride (50 g/L of
KC1) was poured into well 07-09 on 5/1/87. The chloride was added to the
groundwater at a time when nearly continuous percolation was occurring
due to irrigation, and the water table was beginning to rise. Chloride
concentrations in wells downslope frem well 07-09 showed chloride levels
exceeding 80 mg/L following the chloride application.
Figures 6.46-6.53 shew the chloride concentrations within the
saturated zone on selected dates. In Figure 6.46 the background
concentration of chloride on May 1, 1987 can be seen to be on the order
of 1 mg/L or less. Figure 6.47 shews that chloride from other sources
(KC1 in the fertilizer) is also entering the groundwater. This is most
clearly demonstrated by the high concentration of chloride in well 11-10
which is on the opposite side of the study site fretn well 07-09 which is
where the chloride was introduced into the groundwater. Figures 6.46-
6.53 show the general movement of chloride in the groundwater over a
period of 29 days.
Discussions presented in Section 6.5.6 will show that additional
characterization of soil properties near the restricting layer is needed
before the observations of chemical movement within the groundwater can
be used to describe flew patterns and velocities within the groundwater.
No calculations of flew velocities were performed with the chloride data
and no conclusions were drawn from it for presentation here.

127
07-09
Date: 05/01/87
a
u
c
o
Figure 6.46. Chloride concentration in the groundwater on 5/01/87.
Vertical bars indicate sampling locations.
Figure 6.47. Chloride concentration in the groundwater on 5/03/87.
Vertical bars indicate sampling locations.

128
07-10
Date: 05/05/87
^c ^
Figure 6.48. Chloride concentration in the groundwater on 5/05/87.
Vertical bars indicate sampling locations.
07 10 Date: 05/08/87
08-12
07-14
no no no
D\ST\UCE. (/M)
Figure 6.49. Chloride concentration in the groundwater on 5/08/87.
Vertical bars indicate sampling locations.

129
07-11
Date: 05/13/87
-J
o
\

Oi
UJ)
o
-
'J-
o
-
u
c
,
0
O
o
~
DISI*v(
. /( (r)
Figure 6.50. Chloride concentration in the groundwater on 5/13/87.
Vertical bars indicate sampling locations.
08-11
Figure 6.51. Chloride concentration in the groundwater on 5/18/87.
Vertical bars indicate sampling locations.

Cone. (mg/L) | Cone. (mg/L)
0 20 40 ro 0 20 40 60 80
130
08-1 1
Date: 05/25/87
07-14
1,1
6.52. Chloride concentration in the groundwater on 5/25/87.
Vertical bars indicate sampling locations.
Figure 6.53. Chloride concentration in the groundwater on 6/01/87.
Vertical bars indicate sampling locations.

131
6.5.5 Total mass of chemicals within the saturated zone
In addition to observations of the concentrations of the chemicals
within the groundwater, calculations of the total mass of chemicals
within the saturated zone were performed. An inverse distance weighting
method was used for each observation period to generate a square grid of
values over the site. One potential option for gridding data was kriging
which has been shewn to be more accurate than the inverse distance method
(Golden Software, 1987). When kriging was used, hewever, it was observed
that negative concentration values were generated at several intermediate
grid points. With the inverse distance method, negative concentrations
were not a problem. The data from observation wells spaced 12 m by 12 m
apart were gridded to produce data on a 3m by 3m spacing. This was done
initially for illustrative purposes.
The data from the wells were gridded in two ways. First, only the
wells from which samples were obtained and analyzed were included in the
input data set for the gridding process. Second, all wells were included
by assigning the concentration in wells without samples a value of zero.
The second method was used as an estimate of the minimum mass of a
chemical within the saturated zone. The three-dimensional views of the
concentrations were judged to better convey to the viewer the values at
individual wells when the second method was used as compared to the first
method.
Using the concentrations and groundwater depths computed on a 3 m
square grid, calculations of water storage and chemical mass storage
within the groundwater were performed. These calculations are described
in more detail in Section 6.5.6.

132
The results of these calculations for the two methods of gridding
are presented in Table 6.8. Note that the mass of chloride within the
groundwater greatly exceeds the amount applied. Significant
concentrations of chloride were apparently contributed frcxn other sources
such as fertilizer and irrigation water. There are notable differences
in the total mass of chemicals stored as calculated from the two methods
of gridding the observed data. There is no evidence to suggest that one
method is more correct than the other, therefore the values calculated
based on the two methods can be considered as the extremes of the
possible mass storage in the groundwater with the actual value probably
lying somewhere between the extremes. Figure 6.54 shows the mass of
atrazine in the saturated zone (based on the second gridding method)
during the entire period of observation. What is not shewn by Figure
6.54 is the mass of atrazine which may have been transported off-site
during the period of observation. In order to estimate the transport of
atrazine from the site, it is necessary to first calculate the flux of
water from the site. The next section describes the methods used to try
to estimate the water and chemical fluxes on the experimental site.

133
Table 6.8
Total mass of
chemicals in the :
saturated zone
Chemical
Date of
Maximum Mass
in Saturated Zone (g)
Maximum
Method l-*-
Method 21
Atrazine
5/18/87
8
4
(5)
(3)
Bromide
5/13/87
308
198
(54)
(35)
Nitrate
5/05/87
2916
1873
(23)
(15)
Chloride
5/13/87
16367
11812
(6882)
(4967)
--Method 1: using only measured values in gridding procedure
Method 2: assuming that wells without observations had a concentration
of zero
2Numbers in () are percent of total application
DAYS SINCE 11/12/86
id
>
>
2:
+

id
id
>
p
o
B
Figure 6.54. Total mass of atrazine stored in the saturated zone.
Atrazine was applied to the site on 11/12/86.

134
6.5.6 Water Balance Calculations
An attempt was made to calculate a water and chemical budget for the
study site. The primary objective of this exercise was to calculate the
mass flux of chemicals moving off the site. The mass of a chemical
stored in the saturated zone at the end of the monitoring period plus the
mass of chemical transported off-site should be comparable to the total
mass of chemical leached from the root zone as predicted by the models.
6.5.6.1 Nodes and subareas
As noted in section 6.5.5, the data for water table elevation,
restricting layer elevation, and chemical concentrations were gridded on
a 3 m by 3 m spacing. Each grid point was considered to be a node for
the calculations described below. The study area was divided into six
subareas as shown in Figure 6.55. The subareas were numbered from the
top of the slope. The chemical application area was entirely contained
within subarea number 2 as shown in Figure 6.55.
6.5.6.2 Nodal calculations
The slope of the water table in both the x and y directions was
calculated at every node within the site using centered-difference
techniques, ie. along the y-axis the slope at node j is calculated as:
slope(j) = [elev(j-l) elev(j+l)] / [2 <5y] 6.1
where slqpe(j) = slope of the water table in ny/m, elev(j-l) and elev(j+l)
= elevation of the water table at nodes j-1 and j+1, respectively, in m,
and 6y is the spacing between nodes in m. The gradient of the water

135
Distance m)
Figure 6.55. Subareas used in water balance calculations.
table, as used in flux calculations, is the negative of the slope defined
above. The flux of the water at any node was calculated as:
flux(j) = -cond(j) slope(j) 6.2
where flux(j) = the water flux at node j in m/day, cond(j) = the
saturated conductivity at node j in m/day, and slope(j) is as defined
above. The volumetric flowrate of water through the area represented by
node j was calculated as
Q(j) = flux(j) wtd(j) 6y 6.3
where Q(j) = volumetric flowrate past node j in m3/day, wtd(j) =
groundwater depth at node j in m, and flux(j) and Sy are as defined
above. The mass of chemical moving past a node was calculated as the
product of the volumetric flowrate, Q, and the solution concentration of
the chemical at that node.
To compute the change in water storage between sampling periods, it
was necessary to calculate the relationship between drained volume and

136
water table depth. The slope of the drained volume-water table depth
relationship is called the drainable porosity. A program called DVOIWTD
was used to calculate the drainable porosity. The DVOIWTD program is
provided with the water table management model ERAINMOD (Skaggs, 1978).
The program uses the soil-water characteristic data for the profile and
computes the volume of water drained as the water table falls. The soil
water characteristic data input to DVOIWTD was obtained frcm Hook
(1985). Graphs of the drained volume-^water table depth relationship and
the soil-water characteristic curve are presented in Figures C.3 and C.4
in Appendix C. The drained volume was calculated at every node based
upon the depth to the water table at that node.
The mass of chemical stored (sorbed and in solution) in the volume
represented by each node was calculated based upon the groundwater depth
at the node, the concentration of the chemical in solution, and the
partitioning coefficient for the chemical. The for atrazine used in
this study is 163 cm3/g (Table 5.2). The organic carbon content of the
soil above the restricting layer was assumed to be 0.03% (Table 5.1).
Thus the Kq for atrazine in the lower soil zones is 0.049. The drained
volume and chemical storage values calculated as described above were
summed over each subarea.
6.5.6,2 Boundary and subarea flux calculations
The total volumetric flowrate of water was calculated for each
boundary in a subarea. This was done by summing the flowrates calculated
above for all the nodes along a boundary. The sum of the flowrates
calculated for the four boundaries yields the net flowrate into or out of

137
the subarea on that date. The calculations described above were
performed for all nodes and subareas for the 28 sampling dates in this
study.
6.5.6.3 Mass balances between observation dates
The net flcwrate between sampling dates for each subarea was
calculated by averaging the net flowrates calculated on the two dates.
The average net flowrate multiplied by the interval between the sampling
dates resulted in an estimate of the total volume of water which had
moved into or out of a subarea by saturated flew between the dates. The
percolation volume during the interval between dates predicted by PRZM
was used to calculate the volume of water added to each subarea due to
rainfall or irrigation. The sum of the volume of water added or removed
from a subarea by saturated flow and the volume added by percolation gave
an estimate of the predicted change in water volume in the subarea
between the sampling dates.
The change in drained volume in a subarea between sampling dates was
assumed to represent the actual change in water volume in that subarea.
Thus, the predicted change in water volume could be compared to a
"measured" value.
Mass balances of chemicals within the saturated zone were done in a
similar manner. Chemicals were assumed to be added to the saturated zone
only in subarea number 2 which contained the application area. The
of chemical leaching past the bottom of the soil profile (2.62 m)
predicted by PRZM was multiplied by the area of the application area to
calculate the total mass of chemical added to subarea number 2.

138
Prediction of mass transport across boundaries was performed as
described above for water. The mass storage of chemical calculated for
each subarea was ccsnpared to the predicted storage based on mass inflows
and outflows to a subarea.
6.5.6.4 Results
The conductivity of the soil in the saturated zone was not measured.
As an initial estimate, a conductivity of 14.4 m/day (60 cm/hr) was used
(Carlisle et al., 1978). For periods when the water table was falling
and no percolation was added, the predicted and observed change in water
storage on 4 of the subareas agreed reasonably well. Subareas 3 and 6,
however, would accumulate water when such accumulation was not observed.
In order to avoid accumulation of water within subareas 4 and 6, a
conductivity value on the order of 40 m/day was required along the
boundary between subareas 4 and 5, and the boundary at the bottom
(western edge) of area 6. This would suggest that the cross-sectional
area of flew across these boundaries was not adequately characterized.
With the higher conductivity values at the boundaries described
above, the predicted and observed changes in water balances for the site
were in reasonable agreement when no percolation was added. When
percolation was predicted during the interval between sampling dates,
however, the mass balances were not acceptable. The calculated change
in storage in a subarea suggested that water accumulated within the
subarea when measurements indicated that there was a net loss of water in
that area. Whenever percolation was added, the predicted change in

139
storage would nearly always be substantially (order of magnitude) higher
than the observed change.
There are a number of possible reasons why the mass balances of
water did not agree. One reason is that both PRZM and GLEAMS assume that
all percolation through the soil profile occurs within one day. The
actual movement of percolating water through the profile may be much
slower than this and thus, may be sustained over significantly longer
periods. Therefore, water which was predicted to have been added to the
profile during the interval between sampling dates may actually have
slowly entered the groundwater over several sampling intervals. It is
possible that PRZM was overestimating the volume of percolation. GLEAMS,
however, predicted nearly identical percolation volumes using an entirely
different calculation procedure. This would suggest that the predicted
percolation volumes are reasonable.
Another source of error may be the assumption that the conductivity
is constant over the depth of the groundwater. The conductivity may
increase as the groundwater depth increases. There may also be large
pores or conduits near the restricting layer which can move water much
more rapidly than would be predicted using the methods described above.
There is also significant error associated with the actual location of
the restricting layer. The bottom of the wells were assumed to be placed
on top of this layer. Curing placement of the wells, the augered holes
did not step exactly on top of the restricting layer. The auger had to
cut into the restricting layer in order visually determine (due to color
and texture change) that the restricting layer had been reached. There
could easily be variations in the depth of penetration in the restricting

140
layer of 5 cm. Since the depth of flew on this site was often less than
10 cm, an error of +/- 5 cm in the location of the restricting layer
would significantly affect the calculated depth of flow. As noted above,
the restricting layer was determined by visual observation of color and
texture changes. The depth at which the vertical hydraulic conductivity
of this layer becomes small enough to caase saturation may not correspond
to the visually determined top of the layer. There may also be seme
small channels on the surface of the restricting layer in which
significant flow could occur that are not shewn by the wells. This is
demonstrated by the fact that conductivities along the two boundaries
mentioned above had to be increased significantly above the values used
elsewhere in order to move sufficient quantities of water across those
boundaries. The potential water movement into the restricting layer was
also not accounted for.
There is much additional work to be done before an adequate water
balance can be performed on this site. Additional characterization of
soil properties in the saturated zone and improved estimates of the
location of the restricting layer are needed.
Since the water balance attempts were generally unsuccessful,
chemical balances, although calculated, were not considered further.
Two FORTRAN programs were written to perform the calculation
described in the previous section. Program ANALYZE performs nodal and
boundary flux calculations for each observation date and is presented in
Figure C.l in Appendix C along with a sample output (Table C.l). Program
FLUX computes the water and chemical balances between sampling periods

141
and is presentad in Figure C.2 in Appendix C along with a sample output
(Table C.2).
6.5.7 Summary of observations of chemical transport
The discussion of the observed concentrations of atrazine and the
tracers has been descriptive in nature. This is due to the fact that
additional data are required in order to quantitatively assess the
results.
Each of the applied chemicals (except for alachlor) was observed to
move through the soil profile and enter the groundwater. However,
insufficient data were collected from which the quantity or velocity of
water percolating through the soil profile could be directly determined.
Similarly, movement of chemicals within the groundwater was observed.
There were insufficient data, however, to quantitatively assess the
volume and velocity of flow within the saturated zone to acceptable
levels. The tracer data were inconclusive due to the transient nature of
the groundwater. Seme of the wells would dry rapidly. The wells along
the boundary between subareas 4 and 5 (Figure 55) would often be dry when
wells on both sides of this boundary would have water in them. This
would indicate that some saturated flew was occurring across this
boundary in small channels in the restricting layer which did not
coincide with the established well network ie. the wells did not
intercept these channels.

142
6.6 Model Results and Comparisons
The PRZM and GLEAMS models were run to simulate the time period from
1/1/86 through 12/31/87. Simulation of the system for the ten months in
1986 prior to application was done to minimize the effects of initial
conditions. The simulated total mass flux of pesticides, bromide, and
nitrates below the root zone during the sampling period are shewn in
Table 6.9. The mass flux of the chemicals to the bottom of the profile
as simulated by PRZM is also included in Table 6.9. The models
predicted that no additional movement of the applied chemicals below the
root zone occurred after 6/1/87.
Table 6.9 Simulated mass flux of chemicals.
Chemical
Simulated Mass Flux
(Q/ha)
GLEAMS1
PRZM1
FRZM2
Atrazine4
2970
2314
1154
(60.6)3
(47.2)
(23.6)
Alachlor5
516
165
10
(10.5)
(3.4)
(0.2)
Bromide6
23604
23718
17116
(87.4)
(87.8)
(63.4)
Nitrate6
10657
14551
9376
(57.3)
(78.23)
4tv u
(50.4)
1Bottom of root zone 4Plant uptake coef. =0.65
2Bottom of profile 5Plant uptake coef. =0.52
3Percent of application 6Plant uptake coef. = 1.00
GLEAMS predicted a higher percentage of herbicide leaching than did
PRZM. One reason for the difference may be that GLEAMS defines 6 major
soil layers (there is a 1 cm layer at the soil surface) and thus averages

143
soil properties such as the organic carton content over larger depths
relative to the smaller layers utilized by FRZM. The increased number of
layers in FRZM allows the calculated layer properties to more nearly
match the profile description input into the model. Another reason for
the differences in model predictions may be the method by which pesticide
transport is calculated in each model. In ERZM, a form of the
advection-dispersion equation is solved (after water velocities have been
determined separately) using finite-difference techniques to calculate
pesticide transport. In GLEAMS, pesticide transport is calculated by
sequentially moving the chemicals between layers based on water flux and
the concentration of the chemical in each layer. After the chemicals
have been added to or removed from a layer, the mass of the chemical is
redistributed between the sorbed and solution phases based upon the
partition coefficient in that layer.
The predicted leaching of bromide by the two models was similar.
Closer examination indicates that GLEAMS predicted higher fluxes of
bromide for the first application, and PRZM predicted higher fluxes for
the second application. The overall effect was to predict nearly
identical bromide fluxes. The nitrate leaching predicted by PRZM was
higher than the GLEAMS prediction due to FRZM's higher predicted
percolation volumes after the nitrate was applied as shown in Figure
6.56. Overall, the percolation predicted by the models is very similar.
A small adjustment in the leaf area index (LAI) in GLEAMS could result in
closer agreement between the percolation predicted by the models.
Figures 6.57-6.62 shew the measured concentrations of atrazine in
the soil profile and the corresponding predictions by PRZM. Figures

144
6.63-6.68 present similar data for alachlor. Results frcsn GLEAMS are not
included in these figures due to the fact that GLEAMS only outputs
results for days on which there was sufficient rainfall to cause
percolation beneath the root zone. There were no GLEAMS results
available for most of the days shewn in Figures 6.57-6.68. The results
in Figures 6.57-6.62 suggest that the selected partition coefficient,
for atrazine may be too lew. The model appears to predict that
atrazine would leach more rapidly than the observed rate of movement. It
also appears that atrazine is persistent in the soil and that the 78 day
half-life used as a model input may also be too lew. Figures 6.63-6.68
clearly show that the half-life selected for alachlor was too low. It is
difficult to assess the influence of the Kgy used for alachlor since the
lew half-life degraded the alachlor so rapidly that very little was
available to be leached.
Figure 6.56. Comparison of percolation volumes predicted by GLEAMS
and FRZM.

145
-
Oh
W
Q
0.0
o-t-1-^
25j /'
50i '
4
100t
1 25 T
150 ^
175 -E
200-
ATRAZINE CONC. (mg/kg)
0.2 0.4 0.6 0.8 1.0 1.2 1.4
i i f i I i i i i i i i I i i i I i i i iiiij
Sample Date: 11/18/86
MEASURED
PRZM
Notes:
No Samples Below 5 cm.
Measured Value is Avg.
of 14 Samples
Figure 6.57. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 11/18/86.
Jh
E-
CU
H
Q
ATRAZINE CONC. (mg/kg)
o.o
0
25£
50 ~
75
100
0.2
1 L l 1 | 1
1 25 ^
1 50 ;
0.4 0.6 0.8 1.0 1.2 1.4
J 1 1 I I I I I I I I L, I L_J I lilil
i.
Sample Date: 11/24/86
MEASURED
PRZM
Notes:
No Samples Below 20 cm.
Measured Values are Avg.
of 8 Samples/Depth
1 75 t
200 d
Figure 6.58. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on n/24/86.

DEPTH cm) M DEPTH
146
ATRAZINE CONC. (mg/kg)
0.0 0.1
Q J I I I I I L
a
100^
125
150
175
200 d
0.2 0.3 0.4 0.5
J 1 I I I L J I I 1 I I 1 1 1 i I
Sample Date : 12/22/86
MEASURED
PRZM
Notes:
No Samples Above 25 cm.
Measured Values are Avg.
of 6 Samples/Depth
rigure 6.59.
Comparison of measured and FRZM predicted atrazine
concentrations in the soil on 12/22/86.
ATRAZINE CONC. (mg/kg)
0.0 0.1 0.2 0.3 0.4 0.5
Figure 6.60. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 02/09/87.

ATRAZINE CONC. (mg/kg)
0.00
0.05
0.10
0.15
50-
75 -
100 7
a
o
K
£ ,25i
g 1 50 !
175 -i
200 J
J I 1 i
-i 1 1 1 I I I I 1 J I L 1
0.20
Sample Date: 3/16/87
MEASURED
PRZM
Notes:
Measured Values are Avg.
of 5 Samples/Depth
147
Figure 6.61. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 03/16/87.
a
o
K
E-1
CU
W
Q
0.
0
25
50
75
100
125
150-
175 -
200-
00
ATRAZINE CONC. (mg/kg)
0.05 0.10 0.15
Jiiii l i till
0.20
j;l i
Sample Date: 5/25/87
MEASURED
PRZM
Notes:
Measured Values are Avg.
of 7 Samples/Depth
Figure 6.62. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 05/25/87.

DEPTH (cm) $ DEPTH (
148
a
0.0
o-=
25 -j
50!
-
75i
WOi
125 t
150 t
1 75 t
200-
ALACHLOR CONC. (mg/kg)
0.5 1.0 1.5 2.0 2.5 5.0 5.5
l.i l i i i i,i.l i i | i
Sample Date: 11/18/86
MEASURED
PRZM
Notes:
No Samples Below 5 cm.
Measured Value is Avg.
of 14 Samples
gure 6.63.
Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 11/18/86.
0.0
75-,
WOi
1 25 i
150i
175i
200-
ALACHLOR CONC.
0.5 1.0 1.5 2.0
l i I i i i i
(mg/kg)
2.5 5.0
l i i
5.5
l
Sample Date: 1 1 /24/86
MEASURED
PRZM
Notes:
No Samples Below 20 cm.
Measured Values are Avg.
of 8 Samples/Depth
Figure 6.64. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 11/24/86.

149
0.0
04-
25 "i
i
100t
E
H 125 4
CU
g .50]
1 75 4
200-
ALACHLOR CONC. (mg/kg)
0.5 1.0 1.5 2.0
11 i i i i i i i i i i i i i iiii
\
>
Sample Date : 12/22/86
MEASURED
PRZM
Notes:
No Samples Above 25 cm.
Measured Values are Avg.
of 6 Samples/Depth
Alachlor not Detected in
These Samples
Figure 6.65.
Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 12/22/86.
0.0
0
25 z'
E
E-*
CU
H
Q
50
100 4
_
1 25 z
150^
1 75 z
200 J
ALACHLOR CONC. (mg/kg)
0.5 1.0 1.5 2.0
J 1 1 1 U I I I I I I I I I I l i I
Sample Date: 2/09/87
MEASURED
PRZM
Notes:
No Samples Below 135 cm.
Measured Values are Avg.
of 6 Samples/Depth
Figure 6.66. Comparison of measured and FRZM predicted alachlor
concentrations in the soil on 02/09/87.

150
a
o
K
E-
CU
0.0
0-
ALACHLOR CONC. (mg/kg)
0.5 1.0 1.5
2.0
i 1 i L 1 i i i t I i i i i 1 i i 1
in
Sample Date: 3/16/87
MEASURED
PRZM
Notes:
li.ii il i uU-inilii
Measured Values are Avg.
of 5 Samples/Depth
Figure 6.67.
Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 03/16/87.
a
o
K
E-
cu
w
Q
0.0
odS1
25^]
50 £
75 ~
100^
125-E
ALACHLOR CONC. (mg/kg)
0.5 1.0 1.5 2.0
jili i i i l i i i i iiiiii
Sample Date: 5/25/87
MEASURED
PRZM
Notes:
Measured Values are Avg.
of 7 Samples/Depth
150
175 i.
200
Figure 6.68. Comparison of measured and PRZM predicted alachlor
concentrations in the soil on 05/25/87.

151
Figures 6.69-6.71 shew the measured and predicted concentrations of
bromide in the soil solution at depths of 61, 122, and 183 cm following
the first application of bremide. Figures 6.72-6.74 shew the bromide
concentrations at the same depths follcwing the second bremide
application. PRZM predicted concentrations are shewn for both free
drainage and restricted drainage options (see Section 5.2.3 for
discussion of drainage options in FRZM). In almost every case shewn in
Figures 6.69-6.74, the time to peak concentration of the measured data
falls between the predicted time to peak concentration for the two
drainage options. Thus it would appear that a drainage rate parameter
could be selected which would match the measured time to peak
concentration more closely. This would be part of a calibration
procedure. Calibration of the models was not attempted since there is
only one year of data from this study and if this data is used for
calibration, there would be no independent data to test the calibrated
models against.
It should also be noted in Figures 6.69-6.74 that in all cases the
measured and predicted peak concentrations agreed to within an order of
magnitude, and that in most cases they agreed to within a factor of 2 to
3. Thus for the case of the bromide applications, PRZM would meet the
criteria for acceptance suggested by Hedden (1986). In the work
presented here PRZM and GLEAMS were essentially run in a screening mode.
Many of the model parameters were selected from tables and other
information contained the model's users manuals. Parameters were not
optimized or calibrated to produce the best fit. At this stage of data
collection and model use, it was decided to investigate how the models

152
DAYS SINCE 11/12/86
Figure 6.69. Measured and H?ZM predicted bromide concentrations in
the soil solution at a 61 cm depth following the first
application.
Figure 6.70. Measured and PRZM predicted bromide concentrations in
the soil solution at a 122 cm depth following the
first application.

153
DAYS SINCE 11/12/86
Figure 6.71. Measured and PRZM predicted brcsnide concentrations in
the soil solution at a 183 cm depth following the
first application.
Figure 6.72. Measured and PRZM predicted brcsnide concentrations in
the soil solution at a 61 cm depth following the
second application.

BROMIDE CONC. (mg/L) & BROMIDE CONC. (mg/L)
154
DAYS SINCE 11/12/86
6.73. Measured and PRZM predicted bromide concentrations in
the soil solution at a 122 cm depth following the
second application.
DAYS SINCE 11/12/86
Figure 6.74. Measured and PRZM predicted bromide concentrations in
the soil solution at a 183 cm depth following the
second application.

155
would perform when run with minimal site-specific inputs. A
governmental regulator would certainly not have detailed site specific
data available for all the combinations of crop/soil/chemical that may be
of interest. Such a user would be unlikely to have site measured
application rates for use as input. If the normal application rates for
atrazine and alachlor are 4.9 kg/ha on the cropping system which is being
simulated, the model user would certainly use those values as the input
to the model. They would not knew that the actual average application
rates may only be 3.4 kg/ha.
A small number of soil solution samples from samplers were extracted
for analysis of atrazine residues. The volume of these samples was
between 30 and 40 mL. Figure 6.35 shews the measured and FRZM simulated
concentrations of atrazine in the soil solution at a depth of 61 cm. PRZM
was run in the free drainage mode using the calculated plant uptake
coefficient of 0.65.
Figure 6.75. Measured and PRZM predicted atrazine concentrations in
the soil-water at a depth of 61 cm.

156
The maximum observed concentration of atrazine at the 61 cm depth
was approximately 0.35 mg/L and the maximum concentration predicted by
PRZM was 0.57 mg/L. The predicted and observed maximum concentrations
are again within a factor of 2. The 0.35 mg/1 maximum observed value
however was from one of three samplers from which seme samples were
extracted. The data from the other two samplers do not shew any distinct
peak concentration, however, the concentrations are within an order of
magnitude of the predicted values. The models were run with the intended
application rate of 4.9 kg/ha, but the actual application samples suggest
the true application rate was 3.4 kg/ha. The time to peak concentration
between predicted and observed was reasonably close; however FRZM
simulated a much broader peak than was observed.
The simulated transport of applied chemicals by the two models were
in reasonable agreement with each other. The differences are probably
due to the detail of discretization of the root zone and the method of
calculated chemical transport as discussed previously. FRZM can more
nearly match the observed soil layering than can GLEAMS due to the fact
that GLEAMS describes the entire root zone with only seven layers. PRZM
and GLEAMS both simulated large losses of atrazine from the root zone (40
and 62% of application, respectively). The FRZM simulated transport of
atrazine to a depth of 2.6 m amounted to 24% of the mass applied. As
discussed previously, less than 4% of the applied atrazine could be
accounted for on a given day within the saturated zone. This figure
does not account for storage in the vadose zone or mass transported off
site.

CHAPTER 7
SUMMARY AND CONCLUSIONS
A field experiment was conducted to observe the movement of two
surface applied herbicides (atrazine and alachlor) through the soil
profile and within a shallow water table aquifer. Bromide was applied to
the soil surface to act as a non-adsorbed tracer of water movement. The
nitrate component of a surface applied fertilizer was also monitored
through the soil profile and within the water table.
Instrumentation was installed on a 0.7 ha field for collection of
soil water samples from the unsaturated zone and water samples from the
saturated zone. Soil water samples were collected with soil solution
samplers installed in six group of three samplers each at depths of 61,
122, and 183 cm. Shallcw groundwater samples were collected frcm 5 cm
diameter FVC monitoring wells. The wells were installed on a 12 by 12 m
grid over the study site. Soil samples were collected several times
during the study from various depths beneath the application area.
Methods were developed to extract the herbicide residues from soil
and water samples. Herbicide residue samples were analyzed on a gas
chromatograph using a nitrogen-phosphorus detector. Concentrations of
the inorganic tracers were analyzed using an ion chrcmatograph.
The uniformities of application of atrazine, alachlor, arri brcmide
were measured. There was considerable variability observed in the
application rates (kg/ha) of the three chemicals. The chemicals were
157

158
applied using a toon sprayer developed for vise in chemigation research.
The results indicate that chemical application rates from conventional
farm equipment are likely to be highly variable. The variability of
application, in addition to the variability of soil properties, makes
prediction of field measured concentrations difficult.
Concentrations of bromide and nitrate at all three monitored depths
showed a high degree of variability. Ihe peak concentration and the time
required to reach the peak concentration varied significantly between
sampling locations. The effect of this variability may be to reduce
maximum concentrations and increase the duration of loading reaching a
water table.
Atrazine moved rapidly through the sandy soil on the study site.
Concentrations of atrazine in the soil water at a depth of 61 cm reached
0.35 mg/L 19 days after application. Detectable levels of atrazine
reached the water table 2 months after application. Atrazine
concentrations as high as 0.09 mg/L were observed in the groundwater
nearly 6 months after application.
Alachlor was not detected in the soil belcw a depth of 45 cm. No
trace of alachlor was detected in the groundwater samples.
As much as 20 percent of the nitrate from the fertilization of the
study site was observed to be in the groundwater on a given date. Ihe
decreasing concentrations of nitrate in the groundwater with continued
percolation of water suggests that most of the nitrate was leached from
the soil profile.
The PRZM and GLEAMS models were found to be easy to use. Sufficient
information is provided in the user manuals for estimation of required

159
parameters. When the models were run using pesticide properties obtained
from the manuals, simulated leaching and degradation of the herbicides
exceeded field observations. The models were not calibrated to the
observed data.
The data collected during this study provide a picture of the
concentrations of various chemicals within the soil profile and
groundwater on specific dates. Data to accurately quantify the movement
of water on the site, however, are missing. The models predict the mass
flux of chemicals past a given point (usually the bottom of the root
zone). The observed data from this first year study are insufficient to
determine mass fluxes and therefore can not be used to adequately test
the mass flux predictions of the models. PRZM simulation results were
used to compare observed and predicted concentrations of the chemicals.
This is because PRZM will report the simulated concentrations at any
depth on a daily basis. GLEAMS will only output soil concentration data
(/ig/g) on days with a storm event that causes leaching below the root
zone. This makes it difficult to compare GLEAMS predictions with samples
taken between storm events.
The results presented in this dissertation represent data from the
first year of a field study and application of two pesticide transport
models to the conditions present at the field site during the study.
Much has been learned about sample collection, sample analysis, and the
limitations of the data which have been collected, for both describing
the movement of the chemicals and testing model predictions.

CHAPTER 8
RECDMMENE&.TTONS POR IMPROVEMENTS AND FURIHER STUDY
Additional information must be collected in order to quantify the
mass flux of chemicals and water within the vadose zone and the
groundwater.
The water content, or tension, of the soil must be observed on a
more frequent basis. Soil moisture blocks or tensiometers with pressure
transducers could be read on frequent intervals (5 min 1 hr) using a
datalogger. If there is sufficient sensitivity to the small changes in
water content on this soil (0.13 0.02 cm3/cm3) then the total flux of
water moving by the tensiometers could be estimated.
Further characterization of the restricting layer is needed. Core
samples should be taken from throughout the soil profile and presumed
restricting layer. Measurements of hydraulic conductivity, bulk density,
particle size distribution, and organic matter content should be made on
the cores. These data would refine estimates of soil properties
throughout the soil profile and further define the location of the
restricting layer. A detailed mapping of the surface of the restricting
layer using ground penetrating radar (GPR) would help to define the
locations and extent of small channels or irregularities.
Knowing where the bottoms of the observation wells are located
relative to the restricting layer will improve the estimates of flow
depth. Improved values for conductivity and the cross-sectional area of
160

161
flew at a location will improve calculations of water flux. An estimate
of the seepage of water through the restricting layer may also improve
the water budget for this site. The current system adequately defines
the elevation of the water table surface from which gradients are
calculated.
Model validation will require (in addition to the soil properties
listed above) that the partition coefficient and degradation rate for
the chemical of interest be measured from on-site soil samples. The
accuracy of model predictions of mass fluxes should improve significantly
by using site-specific data. Careful measurement of application rates
should be made. Measured application rates were lower than the intended
rates. The models should also be tested using the observed range of
sensitive soil and chemical parameters.
A second year study on this same site should be done to try to
quantify seme of the characteristics or properties described above. In
addition, the study should monitor the movement of atrazine, one other
pesticide, bromide, and nitrate. The usefulness of the second year data
would reflect and be enhanced by the lessons learned from the work done
to complete this dissertation.

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168
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Handbook No. 537. Washington, DC. 58 pp.

APPENDIX A
MONITORING WELL STATISTICS

Table A.l Monitoring Well Statistics
WELL / INSTALLED DATE DISTANCE FROM RISER CASING ELEVATION (METERS)2
STATION BY INSTALLED 6-1 [METERS]1 LENGTH
X
Y
[METERS]
TOP OF
CASING
SOIL
SURFACE
RESTR
LAYER
06-07
USDA
2/7/85
0.00
73.15
3.14
30.85
30.42
27.71
08-07
USDA
2/7/85
24.38
73.15
2.84
30.84
30.40
28.00
11-07
USDA
2/7/85
60.96
73.15
2.59
30.70
30.24
28.11
09-08
UF
3/23/87
36.58
85.34
2.91
30.93
30.41
28.02
07-09
USDA
2/7/85
12.19
97.54
3.58
30.86
30.30
27.28
08-09
UF
5/22/86
24.38
100.58
3.40
30.76
30.26
27.36
09-09
UF
5/22/86
36.58
100.58
2.83
30.76
30.25
27.93
10-09
UF
5/22/86
48.77
100.58
3.17
30.75
30.22
27.58
07-10
UF
5/22/86
12.19
109.73
3.18
30.64
30.13
27.46
08-10
USDA
2/7/85
24.38
109.73
3.83
30.56
30.04
26.73
09-10
UF
5/22/86
36.58
109.73
2.53
30.52
30.03
27.99
10-10
UF
5/22/86
48.77
109.73
2.45
30.56
30.02
28.11
11-10
USDA
2/7/85
60.96
109.73
2.47
30.64
30.13
28.17
06-11
UF
3/11/88
0.00
121.92
3.07
30.69
30.08
27.62
07-11
USDA
2/7/85
12.19
121.92
3.26
30.29
29.74
27.03
08-11
UF
5/21/86
24.38
121.92
3.34
30.16
29.64
26.82
09-11
UF
5/21/86
36.58
121.92
3.62
30.09
29.55
26.47
10-11
UF
5/21/86
48.77
121.92
2.68
30.09
29.57
27.41
07-12
UF
5/21/86
12.19
134.11
3.56
29.77
29.28
26.21
08-12
USDA
2/7/85
24.38
134.11
3.60
29.57
29.09
25.97
09-12
UF
5/21/86
36.58
134.11
3.66
29.54
29.02
25.88
10-12
USDA
2/7/85
48.77
134.11
3.38
29.53
29.07
26.15
11-12
USDA
2/7/85
60.96
134.11
2.67
29.68
29.19
27.01
07-13
USDA
2/7/85
12.19
146.30
3.88
29.21
28.70
25.33
08-13
UF
5/21/86
24.38
146.30
3.67
29.05
28.51
25.38
DEEP
UF
11/10/86
30.48
146.30
10.25
28.92
28.46
18.67-
09-13
UF
5/21/86
36.58
146.30
3.93
28.93
28.42
25.00
10-13
UF
5/21/86
48.77
146.30
3.80
28.86
28.43
25.06
171

Table A.l Continued
WELL /
STATION
INSTALLED
BY
DATE
INSTALLED
DISTANCE FRCM RISER
CASING
LENGTH
[MEIERS]
ELEVAT
ION (METERS) *
X Y
TOP OF
CASING
SOIL
SURFACE
RESTR.
LAYER
07-14
UF
5/20/86
12.19
158.50
4.45
28.62
28.09
24.17
08-14
UF
5/20/86
24.38
158.50
4.01
28.35
27.85
24.34
09-14
UF
5/20/86
36.58
158.50
3.22
28.11
27.63
24.89
10-14
USDA
2/7/85
48.77
158.50
3.54
27.97
27.55
24.43
06-15
USDA
2/7/85
0.00
170.69
4.18
28.14
27.72
23.96
08-15
USDA
2/7/85
25.15
172.83
4.86
27.31
26.85
22.45
IOW
USDA
2/7/85
39.62
200.18
1.67
22.38
22.11
20.71
1 ORIGIN @ SPRINKLER RISER 6-1 WITH X POSITIVE TO NORTH AND Y POSITIVE TO WEST
2 ELEVATION OF TOP OF SPRINKLER RISER 09-09 SET EQUAL TO 30.48 MEIERS
3 ELEVATION OF BOTTOM OF WELL

APPENDIX B
HCW TO GET COMPLETE DATA SET

Complete data sets shewing the concentrations of all chemicals in
both water and soil samples can be obtained by writing to:
Matt C. Smith
Agricultural Engineering Department
University of Georgia
P.0. Box 748
Tifton, Georgia 31793
The data sets can be made available on tape, or floppy disk (5-1/4
or 3-1/2 inch). Data sets will be in ASCII format.
An example of the water sample data set is given in Table B.l The
water sample data set also contains data on the elevation of the water
table at each well that was sampled. This data set contains over 900
records. An example of the soil sample data set is presented in Table
B.2. This data set contains approximately 240 records.
Table B.l Example listing of the water sample data set.
Sample Station Water Table Chemical Concentration (mg/1)
Date
ID
Elevation
(m)
Atrazine
Bromide
Nitrate
Chloride
5/25/87
08-09
27.46
0.006
0.01
0.47
13.3
5/25/87
08-10
27.26
0.005
0.04
3.52
41.5
5/25/87
08-11
26.92
0.013
0.00
0.00
82.3
5/25/87
08-12
25.97
#1



5/25/87
08-13
25.60
0.011
0.02
0.11
22.87
^No sample
174

175
Table B.2 Example listing of the soil sample data set.
Sample
Date
Sample
Location
Depth
(cm)
Concentration
Atrazine
(mg/kg)
Alachlor
5/25/87
1
0-6
0.04
0.02
5/25/87
2
0-6
0.05
0.12
5/25/87
3
0-6
0.03
0.27
5/25/87
4
0-6
0.04
0.17
5/25/87
5
0-6
0.04
0.06
5/25/87
6
0-6
0.04
0.11
5/25/87
7
0-6
0.04
0.12

APPENDIX C
WATER BALANCE PROGRAMS

Figure C.l Listing of program to calculate water and chemical fluxes and
storages.
CCCCCCC
C PROGRAM TO CALCULATE WATER AND CHEMICAL FLUXES AND STORAGES FROM
C GRIDDED WATER TABLE ELEVATION DATA
C WRITTEN: 11/22/87 BY: MATT C. SMITH
C IAST UPDATE: 4/12/88
CCCCCC
EROGRAM ANALYZE
REAL WT(23,45) ,WIMIN,WIMAX/MEANWrD(7) ,KD,BUIKDEN
REAL MAGNITUDE(23,45) ,DEIX,DELY,ANGIE(23,45) ,MAGMIN,MAGMAX
REAL SLPX(23,45) ,SLPY(23,45) DIRECTION (23,45) ,SURFMAX
REAL SURF(23,45) ,SURPMIN,ROW/OOL,MAXSTOR(23,45) ,DRVOL(23,46)
REAL IMP(23,45) ,XMIN,XMAX,YMIN,YMAX,IMFMAX,IMIMIN,XPOS, YPOS
REAL WTD(23,45) ,WTEMIN,WIIMAX,SLPXMAX,SLPYMAX,SLPXMIN,SLPYMIN
REAL VEIX(23,45) ,VELY(23,45) ,FLOWXMAX,FLOWYMAX,FI£WXMIN,FICWYMIN
REAL FLCWX(23,45) ,FIOWY(23,45) ,BFICWX1(7) ,BFI£WX2(7) ,BFICWY(7)
REAL NETFI0W(7) ,AREA(7) ,C0ND(45) ,PORQ,NODECOND(7)
REAL WMASS(23,45) ,CMASS(23,45) ,C0NC(23,45) ,TCMASS(7) ,CFIOWYMIN
REAL BCFD0WX1(7) ,BCFIOWX2(7) ,BCFI£WY(7) ,NETCFTOW(7) ,TWMASS(7)
REAL CXNCMIN,CONCMAX,CMASSMAX,CMASSMIN,CWMASS(23,45) ,CSMASS(23,45)
REAL CFLCWX(23,45) ,CFIOWY(23,45) ,CTTOWXMAX,CFLOWXMIN,CFL3W!MAX
REAL DRVOLMAX,DRVOIMIN,DRVOL5UM(7) ,MST0RSUM(7) ,TEMP
INTEGER*2 NX,NY,I, J, INC,II, JJ,KK,IL,IM,NN,YY(7) ,XX(2) ,STRT,STP
INTEGER*2 IYR, DON, IDAY,IHR, IMIN,ISEC, I100TH, IOUT
CHARACTER*2 MONTH,DAY,YEAR
CHARACTER*8 DATE,CHEMFTIE
CHARACTER* 12 INFILE1, INFTLE2, INFILE3, INFILE4, INETLE6, OUTFTLE
CHARACTER* 12 FIUXFILE,COMMENT(14)
CCCCCC
C INITIALIZE VARIABLES
C PI = PI
C OOND(J) = SAT. HYDRAULIC CONDUCTIVITY (m/day) AT NODE J
C NODECOND(J) = SAT. HYDRUALIC COND. AT SPECIFIED NODES J
C PORO = SOIL POROSITY (cm/cm)
C KD = PESTICIDE DISTRIBUTION COEFF. (cm**3/g)
C BUIKDEN = SOIL BULK DENSITY (g/cm**3)
CCCCCC
PI = 3.141592654
PORO = 0.36
KD = 0.0489
BULKDEN = 1.59
3NFIIE1 = 'IMPERMBA.GRD'
INFILE3 = 'SURFACEA.GRD'
INFIIE2 = 'INFILE .DAT'
CCCCCC
C FILE INFILE.DAT CONTAINS MONTH, DAY, YEAR VALUES FOR GENERATING
C FILENAMES AND DATE
C FILE IMPERMBA.GRD CONTAINS GRIDDED ELEVATIONS OF IMPERMEABLE LAYER
C FILE SURFACEA.GRD CONTAINS GRIDDED ELEVATIONS OF SOIL SURFACE
CCCCCC
177

178
Figure C.l Continued.
OPEN (UNIT=1, FILE=INFILE1, STATOS= OLD')
OPEN (UN3T=3, FIIE=INFHE3, STA1US=' OLD')
OPEN (UNIT=2, FILE=INFIL£2, STAIUS=' OLD')
CCCCCC
C READ IN IMPERMEABLE LAYER ELEVATION DATA
C NX,NY ARE # OF X AND Y VALUES IN GRID
C XMLN, YMIN AND XMAX, YMAX ARE MLN AND MAX VALUES ON RESPECTIVE AXIS
CCCCCC
READ(1,*)
READ(1,*) NX,NY
READ(1,*) XMIN,XMAX
READ(1,*) YMIN,YMAX
READ(1, *) IMFMIN,IMFMAX
DO 5, J= 1,NY
READ(1,*) (IMP(I,J), 1=1,NX)
READ(1,*)
5 CONTINUE
CCCCCCC
C CALCULATE SPACING OF GRID POINTS IN X AND Y DIRECTIONS
C DELX = DISTANCE BETWEEN NODES O X-AXIS
C DELY = DISTANCE BETWEEN NODES ON Y-AXIS
CCCCCCC
DELX = (XMAX-XMIN) / (NX-1)
DELY = (YMAX-YMIN) / (NY-1)
CCCCCC
C READ IN SOIL SURFACE ELEVATION DATA
CCCCCC
READ(3,*)
READ(3,*)
READ(3,*)
READ(3,*)
READ(3,*) SURFMIN,SURFMAX
DO 10, J= 1,NY
READ(3,*) (SURF(I,J), 1=1,NX)
READ(3,*)
10 CONTINUE
CCCCCC
C READ IN :
C OUTFUT INCREMENT VALUE, PRINT EVERY INC NODES IN X AND Y
C CONDUCTIVITY (m/day) AT Y-AXIS BOUNDARY NODES
C BOUNDARY NODE NUMBERS ON X-AXIS FOR SUBAREA BOUNDARIES, 2 VALUES
C BOUNDARY NODE NUMBERS ON Y-AXIS FOR SUBAREA BOUNDARIES, 5 VALUES
C SUMMARY FILE NAME
C .AND FOR 28 FILES READ TOE FOLLOWING...
C MONTH, DAY, YEAR AND CHEMICAL FILENAME
CCCCCC
C CREATE INFUT AND OUTFUT FILENAMES BY ADDING APPROPRIATE EXTENSIONS
CCCCCC
READ(2,102)INC,IOUT
READ(2,104)(NODECOND(I),1=1,7)

179
Figure C.l Continued.
READ(2,102)XX(1),XX(2)
READ(2,102)(YY(I),1=1,7)
READ(2,108)FLDXFIIE
READ(2,933)(CCMMENT(I),1=1,14)
CCCCCC
C INTERPOLATE CONDUCTIVITLES AT EACH Y NODE
CCCCCC
DO 621, I = 1,45
DO 621, J = 1,6
IF(I.GE.YY(J) .AND.I.LE.YY(J+1)) THEN
COND(I) = NODEQOND(J) + FLOAT(I-YY (J) )* (NODEOOND(J+1) -
# NODECOND(J)) / (YY (J+l) YY(J))
EI£>EIF(I.LE. YY(1)) THEN
OOND(I) = NODECOND(l)
ELSEIF(I.GE.YY(7)) THEN
COND(I) = NODECOND(7)
ENDIF
621 CONTINUE
CCCCCC
C OPEN OOTRJT FILE POR TRANSFER OF DATA TO WEEKLY FIDX CALCULATION
C PROGRAM
CCCCCC
OPEN (UNIT=7, FILE=FLUXFILE, STATUS=' UNKNOWN')
WRTTE(7,934)(OCMMENT(I),1=1,14)
CALL GEITTM(IHR,IMIN,ISEC, I100TH)
CALL GETDAT (IYR, IMON, IDAY)
WRITE(7,171) IMON,IDAY,IYR,IHR,IMIN,ISEC,I100TH
WRITE(7,195)(NODECOND(I),1=1,7)
WRITE(7,196)PORO
WRITE(7,921)KD
WRITE(7,922)BULKDEN
WRITE(7,731)XX(1),XX(2)
WRITE(7,732)(YY(I),1=1,7)
CCCCCC
C BEGIN MAIN LOOP TO PERFORM CALCULATIONS FOR EACH OF 28 OBSERVATION
C PERIODS
CCCCCC
30 DO 15, K=l,50
READ(2,100,END=20) MONTH,DAY,YEAR,CHEMFILE
DATE = MONTH // '/ // DAY // '/' // YEAR
INFILE4 = MONIH // // DAY // 'ELV // .GRD'
INFILE6 = CHEMFILE // '.GRD'
OOTFTLE = MONTH // '-' // DAY // 'WTD* // '.SUM'
WRITE(*,*) INFILE4,INFILE6,OOTFILE
CCCCCCC
C OPEN INRJT AND OUTHJT FIIES
CCCCCCC
OPEN (UNIT=4, FILE=INFILE4, STATUS^' OLD')
OPEN (UNIT=5, FILE=OUTFILE, STATUS=' UNKNOWN')
OPEN (UNIT=6, FILE=INFILE6, STATUS=' OLD')

180
Figure C.l Continued.
CCCCOOC
C READ IN VALUES FROM INHJT FIIES
C WIMIN AND WTMAX ARE MIN AND MAX VALUES OF WT ELEVATION
C CONCMIN AND CONCMAX ARE MIN AND MAX CHEM CONCENTRATION VALUES
ccccccc
READ(4,*)
READ(4,*)
READ(4,*)
READ(4,*)
READ(4,*) WIMIN, WIMAX
CCCCCC
READ(6,*)
READ(6,*)
READ(6,*)
READ(6,*)
READ(6,*) OONCMIN, CONCMAX
CCCCCC
C ZERO OUT OLD VALUES AT THE NODES AND READ IN NEW VALUES OF WT(I,J)
C AND C0NC(I,J)
CCCCCC
DO 35 1=1,23
DO 35 J=l,45
WT(I,J) = 0.0
WTD(I,J) = 0.0
cmc(i,j) =o.o
SLPX(I,J) = 0.0
SLPY(I,J) = 0.0
VELX(I,J) = 0.0
VELY (I,J) = 0.0
FLCWX(I, J) = 0.0
FLCWY (I, J) = 0.0
CFD0WX(I, J) = 0.0
CFICWY (I, J) = 0.0
MAGNITUDE (I, J) = 0.0
DIRECTION (I, J) = 0.0
DRVOL(I, J) =0.0
MAXST0R(I, J) = 0.0
35 CONTINUE
DO 45, J= 1,NY
READ(4,*) (WT(I,J), 1=1,NX)
READ(4,*)
READ(6, *) (CmC(I,J), 1=1,NX)
READ(6,*)
45 CONTINUE
CCCCCC
C NOW COMPUTE SLOPE AND MAGNITUDE AT INTERIOR NODES USING CENTERED
C DIFFERENCE TECHNIQUES
C FIRST COMPUTE SLOPE INDIVIDUALLY IN X AND Y DIRECTIONS
C MINUS SIGN GIVES ACTUAL SLOPE AND NOT GRADIENT
CCCCCC

181
Figure C.l Continued.
DO 50, J= 2, (NY-1)
DO 50, 1= 2, (NX-1)
SLPX(I,J) = -( WT(I+1,J) WT(I-1,J) ) / ( 2.0*DELX )
SLPY(I,J) = -( WT(I,J+l) WT(I,J-l) ) / ( 2.0*DELY )
CCCCCCC
C GOMRJTE MAGNITUDE OF RESULTANT SLOPE
CCCCCCC
MAGNITUDE (I, J) = SQRT ( SLPX(I,J)**2 + SLPY(I,J)**2 )
CCCCCCC
C GCMEUTE DIRECTTCN OF RESULTANT SLOPE
C ANGLE IS MEASURED wrt +X AXIS, ie ANGIE = 90 = +Y AXIS
CCCCCCC
ANGLE(I,J) = ATAN2 ( SLPY(I,J) SLPX(I,J) ) 180.0 / PI
IF (ANGIE(I,J) .LT.0.0) THEN
DIEECnON(I,J) = 360.0 + ANGLE(I, J)
ELSE
DIRECnCN(I,J) = ANGLE (I, J)
ENDIF
CCCCCC
C CALCULATE WATER TABLE DEPIH (cm) AT EACH NODE
CCCCCC
WTD(I,J) = 100.0 (WT(I, J) IMP(I, J))
CCCCCC
C CALCULATE DRAINED VOLME (m**3) AT EACH NODE
CCCCCC
WTDEPIH = SURF(I,J) WT(I,J)
DKVOL(I,J) = VOLDRAIN(WTDEPIH) DELX DELY / 100.0
CCCCCC
C CALCULATE MAXIMUM STORAGE VOLUME (m**3) AT EACH NODE
CCCCCC
MAXSTOR(I,J) = (SURF(I,J) IMP(I,J)) PORO DELX DELY
CCCCCC
C CALCULATE VELOCITY, AND FLOWRATE IN X AND Y DIRECTIONS
C AT EACH NODE.
C VELX(I,J) = VELOCITY IN X-DIEECITON (m/day)
C FIOWX(I,J) = VOLUMETRIC FLOWRATE IN X-DIRECITON (m**3/day)
CCCCCC
VELX(I,J) = OCND(J) SLPX(I,J) / PORO
VELY(I,J) = OQND(J) SLPY(I,J) / PORO
FLCWX(I,J) = COND(J) SLPX(I,J) DELX WTD(I,J) / 100.0
FLCWY(I, J) = OOND(J) SLPY (I, J) DELY WTD(I,J) / 100.0
CFI£WX(I,J) = aiC(I,J) FIOWX(I,J)
CFT£WY(I,J) = OONC(I,J) FLCWY(I,J)
CCCCCC
C CALCULATE MASS OF WATER AND CHEMICAL AT EACH NODE
C WMASS = WATER STORAGE IN SATURATED ZONE (m**3) IN NODAL AREA
C CWMASS = MASS OF CHEMICAL IN WATER (mg) IN NODAL AREA
C CSMASS = MASS OF CHEMICAL ON SOIL (irg) IN NODAL AREA
C CMASS = TOTAL MASS OF CHEMICAL (mg) " "
CCCCCC

Figure C.l Continued.
WMASS(I,J) = WTD(I,J) FORD DELX DELY / 100.0
CWMASS(I,J) = WMASS(I,J) CONC(I,J)
CSMASS(I,J) = CWMASS(I,J) KD BULKDEN / FORD
CMASS(I,J) = CWMASS(I,J) + CSMASS(I,J)
CCCCCC
C CALCULATE MAX AND MIN VALDES OF SELECTED PARAMETERS
CCCCCC
IF(I.EQ.2.AND.J.EQ.2) TOEN
WTCMAX = WTD(2,2)
WTCMIN = WID(2,2)
SLPXMAX = SLPX(2,2)
SLPXMIN = SLPX(2,2)
SLPYMAX = SLPY (2,2)
SLPYMIN = SLPY(2,2)
MAQ4AX = MAGNITUDE (2,2)
MAGMIN = MAGNITUDE (2,2)
FLCWXMAX = FLOWX(2,2)
FLOWXMIN = FLOWX(2,2)
FICWYMAX = FLOWY(2,2)
FLOWYMIN = FLCWY(2,2)
CFLOWXMAX = CFLOWX(2,2)
CFLOWXMIN = CFLOWX(2,2)
CFLDWYMAX = CFLOWY(2,2)
CFLCWYMIN = CFLCWY (2,2)
CMASSMAX = CMASS(2,2)
CMASSMIN = CMASS(2,2)
ENDIF
IF(WTD(I, J) .GT.WTCMAX) WTCMAX = WTD(I,J)
IF(WTD(I,J) .IT.WTCMIN) WTCMIN = WTD(I,J)
IF(SLPX(I,J) .LT.SLFXMIN) SLPXMIN = SLPX(I,J)
IF(SLPX(I,J) .GT.SLPXMAX) SLPXMAX = SLPX(I,J)
IF(SLPY(I,J) .IT.SLPYMIN) SLPYMIN = SLPY(I,J)
IF (SLPY (I, J) .CT. SLPYMAX) SLPYMAX = SLPY (I,J)
IF (MAGNITUDE (I, J) .GT.MAG4AX) MAGMAX = MAGNITUDE (I, J)
IF (MAGNITUDE (I, J) .IT.MAGLLN) MAGMIN = MAGNITUDE (I, J)
IF(FICWX(I,J) .G7T.FLCWXMAX) FLCWXMAX = FICWX(I,J)
IF(FLCWX(I,J) .LT.FLCWXMIN) FLOWXMIN = FICWX(I,J)
IF(FLCWY(I,J) .GT.FICWYMAX) FICWYMAX = FICWY(I,J)
IF(FIOWY(I,J) .LT.FLCWYMIN) FICWYMIN = FICWY(I,J)
IF(CFI£WX(I,J) .GT.CFLDWXMAX) CFLCWXMAX = CFICWX(I,J)
IF(CFIOWX(I, J) .IT.CFLOWXMIN) CFLOWXMIN = CFIOWX(I,J)
IF(CFLOWY(I, J) .GT.CFIOWYMAX) CFIOWYMAX = CFICWY(I,J)
IF (CFLCWY (I ,J) .IT.CFLCWYMIN) CFICWYMIN = CFICWY(I,J)
IF(CMASS(I,J) .GT.CMASSMAX) CMASSMAX = CMASS(IfJ)
IF(CMASS(I,J) .IT.CMASSMIN) CMASSMIN = CMASS(I,J)
CCCCCC
C END NODAL CALCULATION LOOPS
CCCCCC
50 CONTINUE

183
Figure C.l Continued.
CCCCCC
C BEGIN CALOUIATIONS FOR SUB-AREAS AND TOTAL CONTROL AREA
CCCCCC
C CALCULATE MASS FLOWS ACCROSS BOUNDARIES OF THE SIX AREAS
C BFLOWXl(I) = FLOW ACCROSS PLANES WITH X = XX(1), Y = YY(I) TO YY(I+1)
C BFLCWX2(I) = FIOW ACCROSS PLANES WITH X = XX(2), Y = YY(I) TO YY(I+1)
C BFDOWY(I) = FLOW ACCROSS PLANES WITH Y = YY(I) X = XX(1) TO XX(2)
CCCCCC
C FIRST FLOWS ACCROSS Y PLANES
CCCCCC
DO 200, KK = 1,7
BFLOWY(KK) = 0.0
BCFLOWY(KK) = 0.0
DO 200, LL = XX(1),XX(2)
IF(LL.EQ.XX(1) .OR.LL.EQ.XX(2)) THEN
C **HALF AREA CONTRIBUTING O BOUNDARIES**
BFIOWY(KK) = BFLOWY(KK) + FLCWY(LL,YY(KK) )/2.0
BCFLOWY(KK) = BCFLOWY(KK) + CFLCWY (LL, YY (KK)) /2.0
ELSE
BFLOWY (KK) = BFLOWY (KK) + FLOWY (LL, YY (KK))
BCFLCWY (KK) = BCFLOWY(KK) + CFLCWY (LL, YY (KK))
ENDIF
200 CONTINUE
CCCCCC
C NOW FLOWS ACCROSS X-PLANES
CCCCCC
DO 210, KK = 1,7
BFLOWXl(KK) = 0.0
BFL0WX2 (KK) = 0.0
BCFLOWX1(KK) =0.0
BCFL0WX2 (KK) = 0.0
STRT = YY (KK)
STP = YY (KK+1)
IF(KK.EQ.7) THEN
STRT = YY(1)
STP = YY(7)
ENDIF
DO 210 LL = STRT,STP
IF(LL.EQ.STRT.OR.LL.EQ.STP) THEN
C **HALF AREA CONTRIBUTING ON BOUNDARIES**
BFLOWXl(KK) = BFLOWXl(KK) + FL0WX(XX(1) ,LL)/2.0
BFL3WX2 (KK) = BFL0WX2(KK) + FL0WX(XX(2) ,LL)/2.0
BCFL0WX1 (KK) = BCFLOWXl(KK) + CFL0WX(XX(1) ,LL)/2.0
BCFLCWX2(KK) = BCFLCWX2(KK) + CFL0WX(XX(2) ,LL)/2.0
ELSE
BFLOWXl(KK) = BFL0WX1 (KK) + FL0WX(XX(1) ,LL)
BFLGWX2 (KK) = BFL0WX2(KK) + FL0WX(XX(2) ,LL)
BCFL0WX1 (KK) = BCFLOWXl(KK) + CFL0WX(XX(1) LL)
BCFL0WX2 (KK) = BCFL0WX2(KK) + CFL0WX(XX(2) ,LL)
ENDIF

184
Figure C.l Continued.
210 CONTINUE
ccecee
C CAICUIATE NET MASS FLUXES IN EACH OF THE SIX SUB-AREAS AND FOR TOTAL
C AREA.
CCCCCC
DO 220, MM = 1,6
NETFIOW (MM) =BFI£WY (MM) + BFICWXl(MM) BFT£WX2(MM) BFLCWY (MMfl)
NETCFIOW (MM) =BCFL£WY (MM) + BCFTCWXl(MM) BCETCWX2(MM) -
# BCFICWY(MMfl)
220 CONTINUE
NETFIOW(7) = BFLOWY(l) + BFI0WX1(7) BFI£WX2(7) BFIOWY(7)
NETCFIOW(7) = BCFIOWY(l) + BCFI0WX1(7) BCFIOWX2(7) BCFLCWY(7)
CCCCCCC
C COMEUIE MEAN WTD, TOTAL WATER AND CHEMICAL MASSES IN EACH AREA
CCCCCCC
DO 310, KK = 1,7
TWMASS(KK) = 0.0
TCMASS(KK) = 0.0
DRVOISUM(KK) =0.0
MSTORSUM(KK) =0.0
MEANWTD(KK) = 0.0
AREA(KK)=0.0
STRT = YY(KK)
STP = YY(KK+1)
IF(KK.EQ.7) THEN
STRT = YY(1)
STP = YY(7)
ENDIF
DO 300, J = STRT,STP
DO 300, I = XX(1),XX(2)
IF(I.EQ.XX(1).OR.I.EQ.XX(2)) THEN
IF(J.EQ.STRT.OR.J.EQ.STP) THEN
C ** QUARTER AREA CONTRIBUTION IN CORNERS **
TWMASS(KK) = TWMASS(KK) + WMASS(I,J)/4.0
TCMASS(KK) = TCMASS(KK) + CMASS(I, J)/4.0
MSTORSUM(KK) = MSTORSUM(KK) + MAXSTOR(I,J)/4.0
DRVOISUM (KK) = DKVOLSUM (KK) + ERVOL(I, J)/4.0
AREA(KK) = AREA(KK) + DELX DELY / 4.0
MEANWTD(KK) = MEANWTD(KK) + WTD (I, J) *DELX*DELY/4.0
C WRITE(5,951)KK,I,J,TWMASS(KK) ,WMASS(I,J)
C951 F0EMAT(1X,3I3,2(4X,F10.5) QUARTER AREA')
ELSE
C **HALF AREA CONTRIBUTING ON BOUNDARIES**
TWMASS(KK) = TWMASS(KK) + WMASS(I, J)/2.0
TCMASS(KK) = TCMASS(KK) + CMASS(I,J)/2.0
MSTORSUM(KK) = MSTORSUM(KK) + MAXSTOR(I, J)/2.0
DRVOISUM (KK) = DRVOISUM (KK) + DRVOL(I,J)/2.0
AREA(KK) = AREA(KK) + DELX DELY / 2.0
MEANWTD(KK) = MEANWTD(KK) + WTD(I, J) *DELX*DELY/2.0
C WRITE(5,952)KK,I,J,TWMASS(KK) ,WMASS(I,J)

185
Figure C.l Continued.
C952 F0RMAT(1X,3I3,2(4X,F10.5),' HALF AREA')
ENDIF
ELSEIF(J.EQ.STRT.OR.J.EQ.STP) THEN
IF(I.EQ.XX(1).OR.I.EQ.XX(2)) THEN
GO TO 300
ELSE
TWMASS(KK) = TWMASS(KK) + WMASS(I, J)/2.0
TCMASS(KK) = TCMASS(KK) + CMASS(I,J)/2.0
MSTORSUM(KK) = MSTORSUM(KK) + MAXSTOR(I, J)/2.0
DRVOLSUM(KK) = DRVOLSUM(KK) + CKVOL(I, J)/2.0
AREA(KK) = AREA(KK) + DELX DELY / 2.0
MEANWTD(KK) = MEANWID(KK) + WTD(I, J) *DELX*DELY/2.0
C WRITE(5,952)KK,I,J,TWMASS(KK) ,WMASS(I,J)
ENDIF
ELSE
TWMASS(KK) = TVJMASS (KK) + WMASS(I,J)
TCMASS(KK) = TCMASS(KK) + CMASS(I,J)
MSTORSUM(KK) = MSTORSUM(KK) + MAXSTOR(I,J)
DRVOISUM (KK) = DRVOISUM(KK) + HVOL(I,J)
AREA(KK) = AREA(KK) + DELX DELY
MEANWTD(KK) = MEANWTD(KK) + WTD (I, J) *DELX*DELY
C WRITE(5,953)KK,I,J,TWMASS(KK) ,WMASS(I,J)
C953 F0RMAT(1X,3I3,2(4X,F10.5),' FULL AREA')
ENDIF
300 CONTINUE
MEANWTD(KK) = MEANWTD(KK) / AREA(KK)
310 CONTINUE
CCCCCC
C WRITE NODAL VALUES TO OUTR7T FILE
C XPOS AND YPOS ARE THE X AND Y POSITIONS IN THE FIELD IN METERS
C ROW AND COL CORRESPOND TO THE STATION ID LOCATIONS
CCCCCC
IF(IOUT.GT.O) THEN
WRITE(5,110) DATE
C WRITE(5,623)(OQND(I),1=1,45)
C623 FORMAT(10F7.2)
WRTIE(5,120)
DO 60, J= 2, (NY-1), INC
DO 60, 1= 2, (NX-1), INC
IF(J.EQ.14.AND.INC.GT.1.AND.I.EQ.2) THEN
JJ = 11
DO 80 II = 10,18,4
XPOS = FLOAT(II-l) DELX + XMIN
YPOS = FLOAT (JJ-1) DELY + YMIN
ROW = 6.0 + FLOAT (II-2) / 4.0
COL = 7.0 + FLOAT (JJ-2) / 4.0
WRITE(5,130)II,JJ,ROW,COL,XPOS,YPOS,SURF(II,JJ) ,IMP(II,JJ) ,
# WT(II,JJ) ,WTD(II,JJ) ,SLPX(II,JJ) ,SLPY(II,JJ),MAGNITUDE(II,JJ) ,
# DIRECTION (II, JJ)
80 CONTINUE

186
Figure C.l Continued.
ENDIF
XPOS = FLOAT(I-l) DELX + XMIN
YPOS = FLOAT(J-l) DELY + YMIN
ROW = 6.0 + FLOAT (1-2) / 4.0
COL = 7.0 + FLOAT (J-2) / 4.0
WRITE(5,130)I,J,ROW,COL,XPOS,YPOS,SURF(I,J) ,IMP(I,J) ,
# WT(I,J) ,WTD(I,J) ,SLPX(I,J) ,SLPY(I,J) ,MAGNITUDE^, J) ,
# DIRECTION (I,J)
60 CONTINUE
WRITE(5,140)
WRITE(5,431)
WRITE(5,432)
WRITE(5,433)
DO 320, J= 2, (NY-1), INC
DO 320, 1= 2, (NX-1), INC
IF(J.EQ.14.AND.INC.Gr.l.AND.I.EQ.2) THEN
JJ = 11
DO 380 II = 10,18,4
WRITE(5,142) II,JJ,MAXSTOR(II,JJ) ,EKVOL(II, JJ) ,WMASS(II,JJ) ,
# OONC(II,JJ),CWMASS(II,JJ),CSMASS(II,JJ),CMASS(II,JJ),
# F!OWX(II,JJ) ,VELX(II,JJ) ,CFI£WX(II,JJ) ,FLOWY(II, JJ) ,
# VELY(II,JJ) ,CFLCWY(II,JJ)
380 CONTINUE
ENDIF
WRITE(5,142)I,J,MAXSTOR(I,J) ,DRVOL(I,J) ,WMASS(I,J) ,C0NC(I,J) ,
# CWMASS(I,J) ,CSMASS(I,J) ,CMASS(I,J) ,FD0WX(I,J) ,VELX(I,J),
# CFLCWX(I,J) ,FIOWY(I,J) ,VELY(I,J) ,CFLCWY(I,J)
320 cmriNUE
cccccc
C WRITE BOUNDARY FLUX AND STORAGE VALUES
CCCCCC
WRITE(5,140)
WRITE(5,180)
WRITE(5,448)(AREA(I),1=1,7)
WRITE(5,181)(BFIOWXl(I),1=1,7)
WRITE(5,182)(BFIOWX2(I),1=1,7)
WRITE(5,183)(BFLCWY(I),1=1,6),BFLOWY(l)
WRITE(5,421)(BFLCWY(I),1=2,7),BFLOWY(7)
WRITE(5,187) (NETFICW(I) ,1=1,7)
WRITE(5,412)(MSTORSUM(I),1=1,7)
WRITE(5,411) (DfM)LSUM(I) ,1=1,7)
WRTIE(5,191)(TWMASS(I),1=1,7)
WRITE(5,447) (MEANWTD(I) ,1=1,7)
WRITE(5,184)(BCFLOWXl(I),1=1,7)
WRTIE(5,185)(BCFIOWX2(I),1=1,7)
WRITE(5,186)(BCFLCWY(I),1=1,6),BCFLOWY(l)
WRITE(5,422)(BCFIOWY(I),1=2,7),BCFLOWY(7)
WRITE(5,188) (NETCFLOW(I) ,1=1,7)
WRITE(5,192)(TCMASS(I),1=1,7)

187
Figure C.l Continued.
CCCCCC
C WRITE MAXIMUMS AND MINIMUMS
CCCCCC
WRITE(5,149)
WRirE(5,150) SURFMIN,SURFMAX
WRITE (5,151) IMEMIN, IMEMAX
WRITE(5,152)WTMIN,WIMAX
WRITE(5,153)WTCMIN/WrCMAX
WRITE (5,154) SLPXMIN, SLPXMAX
WRITE (5,155) SLPYMIN, SLPYMAX
WRITE (5,15 6) MAQGN, MAGMAX
WRITE (5,157) FLUWXMIN, FIDWXMAX
WRITE (5,158) FL3WYMIN,FI£WYMAX
WRITE (5,163) CFICWXMIN, CFICWXMAX
WRITE (5,164) CFD3WYMIN, CFICWYMAX
WRITE (5,165) CMASSMIN, CMASSMAX
WRITE (5,166) CmCMIN, CONCMAX
CCCCCC
C POST FILENAMES USED IN THIS ANALYSIS
CCCCCC
WRITE(5,160)
WRITE (5,161) INFILE1, CXJTFILE
WRITE (5,162) INFILE2, INFILE3, INFIIE4, INFIIE6
WRITE(5,195)(NODECOND(I),1=1,7)
WRITE(5,196)PORO
WRITE(5,921)KD
WRITE(5,922)BUIEDEN
WRITE(5,731)XX(1),XX(2)
WRITE(5,732)(YY(I),1=1,7)
WRITE(5,934) (COyiMENT(I) ,1=1,14)
CCCCCC
C CALL DATE AND TIME TO MARK OUTPUT FOR LATER REFERENCE
CCCCCC
CALL GETTTM(IHR, IMIN, ISEC, I100TH)
CALL GETDAT (IYR, IMON, IDAY)
WRITE(5,170) IM3N,IDAY,IYR,IHR,IMIN,ISEC,I100TH
ENDIF
CCCCCC
C WRITE TO FLUX SUMMARY OUTPUT FILE
CCCCCC
WRITE(7,452) (AREA(I) ,1=1,7) ,DATE
WRITE(7,401) (NETFLCW(I) ,1=1,7) ,DATE
WRITE(7,402) (TWMASS(I) ,1=1,7) ,DATE
WRITE(7,405) (DRVOISUM(I) ,1=1,7) ,DATE
WRITE(7,406)(MSTORSUM(I),1=1,7),DATE
WRITE(7,451) (MEANWTD(I) ,1=1,7) ,DATE
WRITE(7,403) (NETCFICW(I) ,1=1,7) ,DATE
WRTIE(7,404)(TCMASS(I),1=1,7), DATE

188
Figure C.l Continued.
CCCCCC
C CLOSE FILES
CCCCCC
CLOSE(UNIT=4,STATUS='KEEP')
CLOSE (UNIT=5,STATUS='KEEP')
CLOSE (UNTP=6, STA1US=' KEEP')
CCCCCC
C IOOP BACK FOR NEXT FILE
CCCCCC
15 CONTINUE
CCCCCC
C STOP FOR NORMAL TERMINATION
CCCCCC
20 STOP END OF FILE ON INFILE.DAT1
CCCCCC
C FORMAT STATEMENTS
CCCCCC
100 FORMAT (3 (A2, IX) ,A8)
102 FORMAT(1013)
104 FORMAT(7F10.2)
106 FORMAT(II)
108 FORMAT(A12)
110 FORMAT (' ',/,45X, 'GOPHER RIDGE WATER TABLE DATA',/,52X, 'DATE = ',
# A8,//,2X,118(* *))
120 FORMAT(' ,2X, 'NODE STATION ID LOCATION (m) ELEVATION '
#'(m) WT DEPTH WATER TABLE SLOPE (m/m) ',
#'DIRECTION',/,4X, 'I J',17X, 'X',7X, 'Y',5X, 'SURF',4X, 'IMP'f6Xf 'WT',
# 6X, (cm)',10X, 'X',9X, 'Y',8X, 'MAGNITUDE' ,2X,' (degrees) ',/,
# 2X,118('-'))
130 FORMAT(' ',1X,I2,1X,I2,2X,F5.2,'-',F5.2,2X,F5.2,2X,F6.2,
# 3(2X,F6.3),3X,F5.1,4X,3(E10.4,2X),3X,F4.0,4(F7.3,1X))
140 FORMAT (' ',118 ('-'))
431 FORMAT(IX, 126('-') ,/,3X, 'I J MAXIMUM TRAINED SATURATED ',
#'CHEMICAL CHEMICAL STORAGE ',2X,8('-'),'X DIRECTION',7('-') ,
#3X,6('-'),'Y DIRECTION',8('-'))
432 FORMAT(10X,'STORAGE VOLUME STORAGE OONC.',6X,
#'WATER SOIL TOTAL FLOWRATE VELOCITY CHEMICAL FLOWRATE',
#' VELOCITY CHEMICAL')
433 FORMAT(9X, (m**3) (m**3) (m**3) (ppb) ',3(' (mg) ')
#,' (m**3/day) ,1X,' (m/day) (mg/day) (m**3/day) (m/day) ',
# '(mg/day)',/,IX,126('-'))
142 P0RMAT(2X,I2,2X,I2,3(F9.4),F9.2,9(F9.3))
149 FORMAT(' ,3X,53 ('-') ,/,5X,'PARAMETER', 18X,
# 'MINIMUM', 7X, 'MAXIMUM',/,5X,53('-'))
150 FORMAT(' ',4X,'SOIL SURFACE ELEVATION',5X,F5.2,9X,F5.2)
151 FORMAT(' ',4X,'IMP. LAYER ELEVATION',5X,F5.2,9X,F5.2)
152 P0RMAT(' ',4X, 'WATER TABLE ELEVATION' ,5X,F5.2,9X,F5.2)
153 FORMAT(' ,4X,'WATER TABIE DEPTH',9X,F5.1,9X,F5.1)
154 FORMAT (' ',4X,'SIOPE IN X-DIRECTICT',3X,E10.4,5X,E10.4)
155 FORMAT (' ',4X,'SLOPE IN Y-DIRECTIW ,3X,E10.4,5X,E10.4)

189
Figure C.l Continued.
156
157
158
160
161
162
163
164
165
166
170
171
180
448
181
182
183
421
184
185
186
422
187
188
191
447
192
411
412
195
196
401
452
402
403
404
405
406
451
731
732
921
922
933
934
FORMAT(' ,4X, 'SIDPE MAGNITUDE' ,9X,E10.4,5X,E10.4)
FORMAT(' \4X, 'FLOW IN X-DIRECITCN',3X,E10.4,5X,E10.4)
FORMAT(1 1, 4X, 'FLOW IN Y-DIRECnON' ,3X,E10.4,5X,E10.4)
FORMATC ,4X,53() ,//,5X, 'INHJT FIIES'f14X, 'OUTEUT FIIE',/,
# 5X,11(1-'),14X,11('-'))
FORMATC
FORMATC
# 'UNIT 4
FORMATC
FORMATC
FORMATC
FORMATC
I I
/
I I
/
I I
/
I I
',4X, 'UNIT 1 = ',A12,4X, 'UNIT 5 = ',A12)
' 4X, 'UNIT 2 = \A12,//5X, 'UNIT 3 = ',A12,/,5X,
= ',A12,/,5X,'UNIT 6 = ',A12)
' 4X, 'CHEM FLOW IN X-DIR',3X,E10.4,5X,E10.4)
',4X,'CHEM FLOW IN Y-DIR' ,3X,E10.4,5X,E10.4)
',4X, 'CHEMICAL MASS STORAGE' ,3X,E10.4,5X,E10.4)
',4X, 'CHEMICAL CONCENTRATION' ,3X,E10.4,5X,E10.4)
FORMAT(/,4X, 'FROGRAM RUN ON ,12.2,'/' ,12.2,'/' ,14,' AT
# 12.2,'.',12.2,'.',12.2,'.'12.2)
FORMAT(4X, 'PROGRAM PUN O ,12.2,'/',12.2,'/',14, AT ',
# 12.2,'.',12.2,'.',12.2,'.'12.2)
'2X, BOUNDARY FLUXES',/,2X, 110('-'))
AREA(I) ',7(F10.5,2X))
BF1GWX1 (I) ,7 (F10.5,2X))
BFLCWX2 (I) ,7 (F10.5,2X))
BFICWYl(I) ',7(F10.5,2X))
',' BFTCWY2(I) ',7(F10.5,2X))
',/,' BCFLCWXl(I) ,7 (F10.5,2X))
',' BCETOWX2(I)',7(F10.5,2X))
',' BCFLOWYl(I)',7(F10.5,2X))
',' BCFLDWY2(I)',7(F10.5,2X))
',' NETFIGW(I) ,7(F10.5,2X))
',' NETCFLOW(I) ,7(F10.5,2X))
TVJMASS(I) ,7 (F10.5,2X))
',7(F10.5,2X))
',7(F10.5,2X) ,/,2X,110('-'))
',7(F10.5,2X))
',7(F10.5,2X))
',7(F5.2,2X))
',F5.2)
',7(F10.5,2X),A8)
: ',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
',7(F10.5,2X),A8)
X-NODE BOUNDARIES : ',213)
Y-NODE BOUNDARIES : ',713)
PARTIONING COEFF : ,F7.5)
BUIK DENSITY : ,F7.3)
FORMATC
FORMAT('
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMAT(/,'
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMATC
FORMAT(7A12)
FORMATC
/
i i
MEANWTD(I)
TCMASS(I)
DRVOL(I)
MAXSTOR(I)
HYD COND.
POROSITY
NETFIOW(I) :
AREA(I)
IWMASS(I)
NETCFIOW(I)
TCMASS(I)
ERVOL(I)
MAXSTOR(I)
MEANWID(I)
CXM1ENT : ', 7A12)

190
Figure C.l Continued.
CCCCCC
C THE END !!!!!
CCCCCC
END
CCCCCC
C FUNCTION TO CALCULATE THE VOLUME DRAINED IN CM FOR A GIVEN WATER TABLE
C DEPIH. FUNCTION CREATED BY USING LINEAR REGRESSION ON THE VOLUME
C DRAINED WATER TABLE DEPTH CURVE POR WT DEPIH VALUES IN THE RANGE
C OF 1.5 3.5 METERS
CCCCCC
FUNCTION VOLDRAIN (WTDEPIH)
VOLERAIN = -12.5887 + 29.048 WTDEPIH
RETURN
END

Table C.l. Sample ouput from program ANALYZE
GOPHER RIDGE WATER TABLE DATA
DATE = 5/05/87
NODE
I J
STATION ID
LOCATION (m)
X Y
----ELEVATION
SURF IMP
(m)
WT
WT DEPTH
(cm)
WATER
X
TABLE SLOPE
Y
(m/m)
MAGNITUDE
DIRECTION
(degrees)
2
2
6.00- 7.00
.00
73.15
30.420
27.710
28.037
32.7
-.5500E-02
-. 1263E-02
.5643E-02
193.
6
2
7.00- 7.00
12.19
73.15
30.405
27.770
27.997
22.7
.5601E-02
-.6131E-02
.8304E-02
312.
10
2
8.00- 7.00
24.38
73.15
30.400
28.000
28.000
.0
-.7234E-02
-.8363E-02
.1106E-01
229.
14
2
9.00- 7.00
36.58
73.15
30.334
27.945
28.094
14.9
- .2259E-02
-.1603E-01
.1618E-01
262.
18
2
10.00- 7.00
48.77
73.15
30.270
27.951
28.112
16.1
-.4843E-02
-.7401E-02
.8845E-02
237.
22
2
11.00- 7.00
60.96
73.15
30.240
28.110
28.230
12.0
-.5080E-02
-.7938E- 02
.9424E-02
237.
2
6
6.00- 8.00
.00
85.34
30.368
27.593
27.937
34.3
.2883E- 02
.1717E-02
.3356E-02
31.
6
6
7.00- 8.00
12.19
85.34
30.376
27.606
27.933
32.7
-.1626E-02
.1012E-01
.1025E-01
99.
10
6
8.00- 8.00
24.38
85.34
30.383
27.789
28.024
23.5
-.1604E-01
.7099E-02
.1754E-01
156.
14
6
9.00- 8.00
36.58
85.34
30.410
28.020
28.218
19.9
-.2878E-02
.6295E-02
.6921E-02
115.
18
6
10.00- 8.00
48.77
85.34
30.320
27.926
28.142
21.6
.3308E-02
.5636E-02
.6535E-02
60.
22
6
11.00- 8.00
60.96
85.34
30.261
28.022
28.189
16.7
-.7888E-02
-.3624E-02
.8681E-02
205.
I J MAXIMUM DRAINED SATURATED CHEMICAL CHEMICAL STORAGE X DIRECTION Y DIRECTION
STORAGE VOLUME STORAGE CONC. WATER SOIL TOTAL FLOWRATE VELOCITY CHEMICAL FLOWRATE VELOCITY CHEMICAL
(m**3) (m**3) (m**3) (ppb) (mg) (mg) (mg) (m**3/day) (m/day) (mg/day) (m**3/day) (m/day) (mg/day)
2
2
9.0636
5.2613
1.0937
17.38
19.010
4.106
23.116
-.077
-.214
-1.334
-.018
-.049
-.306
6
2
8.8152
5.3305
.7595
20.64
15.676
3.386
19.062
.054
.218
1.120
-.059
-.238
-1.226
10
2
8.0269
5.3073
.0000
22.03
.000
.000
.000
.000
-.281
.000
.000
-.325
.000
14
2
7.9896
4.8762
.4970
19.05
9.466
2.044
11.510
-.014
-.088
-.273
-.102
-.623
-1.936
18
2
7.7569
4.6551
.5383
14.72
7.922
1.711
9.633
-.033
-.188
-.490
-.051
-.288
-.748
22
2
7.1239
4.2553
.4007
11.89
4.762
1.029
5.791
-.026
-.198
-.309
-.041
-.309
-.482
2
6
9.2798
5.3920
1.1479
17.50
20.083
4.337
24.421
.042
.112
.739
.025
.067
.440
6
6
9.2629
5.4230
1.0926
25.47
27.830
6.011
33.841
-.023
-.063
-.577
.141
.394
3.595
10
6
8.6776
5.1975
.7867
32.77
25.785
5.569
31.354
-.161
-.624
-5.276
.071
.276
2.335
14
6
7.9938
4.7446
.6642
22.91
15.215
3.286
18.500
-.024
-.112
-.559
.053
.245
1.222
18
6
8.0052
4.7072
.7220
12.93
9.334
2.016
11.350
.030
.129
.394
.052
.219
.671
22
6
7.4898
4.4228
.5591
9.74
5.448
1.177
6.625
-.056
-.307
-.548
-.026
-.141
-.252

Table C.l. Continued
BOUNDARY FLUXES
AREA(I)
K30.70500
1226.31900
1226.31900
BFLOWX1(I)
.00000
.00000
.00000
BFL0WX2O )
.00000
.00000
.00000
BFLOWY1(I)
.00000
1.77918
4.17766
BFLOWY2CI)
1.77918
4.17766
17.00191
NET FLOW(I)
-1.77918
-2.39848
-12.82425
MAXSTOR(I)
1142.99300
1089.65600
1074.21200
DRVOL(I)
682.37870
628.29070
628.39540
TWMASS(I)
99.45535
128.36940
112.79580
MEANWTDU )
19.30970
29.07741
25.54977
BCFLOWX1(I)
.00000
.00000
.00000
BCFLOWX2( I )
.00000
.00000
.00000
BCFLOWY1(I )
.00000
69.81870
30.65918
BCFLOWY2(I )
69.81870
30.65918
7.25275
NETCFLOWCI )
-69.81870
39.15952
23.40643
TCMASSJI)
2540.22400
3290.70000
446.42620
1226.31900
817.54620
817.54620
6744.80800
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
17.00191
19.74327
14.53423
.00000
19.74327
14.53423
21.56270
21.56270
-2.74135
5.20904
-7.02847
-21.56270
1269.00300
917.17760
944.13010
6437.17500
780.16060
578.92000
604.75040
3902.89800
119.49960
77.95414
72.89392
610.96830
27.06828
26.48648
24.76717
25.16207
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
7.25275
.00000
.00000
.00000
.00000
.00000
.00000
.00000
7.25275
.00000
.00000
.00000
8.06712
.00000
.00000
6285.41500
PARAMETER MINIMUM MAXIMUM
SOIL SURFACE ELEVATION
IMP. LAYER ELEVATION
WATER TABLE ELEVATION
WATER TABLE DEPTH
SLOPE IN X-DIRECTION
SLOPE IN Y-DIRECTION
SLOPE MAGNITUDE
FLOW IN X-DIRECTION
FLOW IN Y-DIRECTION
CHEM FLOW IN X-DIR
CHEM FLOW IN Y-DIR
CHEMICAL MASS STORAGE
CHEMICAL CONCENTRATION
22.12
30.42
20.72
28.17
21.22
28.28
.0
75.0
.7606E-01
.5374E-01
. 1603E-01
.1070E+00
. 1089E-02
.1186E+00
.1208E+01
.7787E+00
.1665E+00
.2049E+01
.3292E+02
.1587E+02
.2325E+01
.3688E+02
. 0000E + 00
.9983E+02
. 0000E+00
.8900E+02

Table C.l
Continued
INPUT FILES
UNIT 1 = IMPERMBA.GRD
UNIT 2 = INFILE .DAT
UNIT 3 = SURFACEA.GRD
INPUT FILES
OUTPUT FILE
UNIT 5 = 5-05WTD.SUM
OUTPUT FILE
UNIT 4 = 5-05ELV.GRD
UNIT 6 = 5-05ATRS.GRD
HYD COND. : 14.00
POROSITY : .36
PART ION ING COEFF
BULK DENSITY
X-NODE BOUNDARIES
Y-NODE BOUNDARIES
COMMENT : CONSTANT HYD. CONDUCTIVITY = 14 m/day
COMMENT : USING ATRAZINE DATA W/O FORCED ZERO CONCENTRATIONS,
PROGRAM RUN ON 06/06/1988 AT 21.52.27.50
14.00 14.00 14.00 14.00 14.00 14.00
.04890
1.590
1 23
1 8 14 20 26 30 34
WHOLE SITE
193

194
Figure C.2 Program to calculate mass balances between sampling periods.
CCCCCC
C PROGRAM TO ANALYZE MASS BALANCES BETWEEN SUCCESSIVE SAMPLING PERIODS
C CREATED: 1/3/88 BY: MATT C. SMITH
C LAST UPDATE: 4/6/88
CCCCCC
REAL NEIWFD3W(28,7) ,NETCFI£W(28i7) ,TWMASS(28,7) ,TCMASS(28,7)
REAL CERRDR(28,7) ,WERROR(28,7) ,AVGWFI£W(28,7) ,AVGCFIOW(28,7)
REAL DELWMASS(28,7),DELCMASS(28,7),AREA(7),PERC(28)
REAL PDEIWMASS(28,7) ,PDELCMASS(28,7) ,DRVOISUM(28,7) ,MSTORSUM(28,7)
REAL MEANWTD(28,7) ,DELEAWTD(28,7) ,WERRORP(28,7) ,CERRORP(28,7)
REAL TOIWERROR(7) ,TOTCERROR(7) ,CFILIX(28) ,APPAREA
INTEGER I,J,DAY(28) ,NFILE
CHARACTER*8 DATE(28)
CHARACTER*12 INFILE,INFILE2,OUTFILE,COMMENTS(100)
CCCCCC
C APPAREA = APPLICATION AREA SIZE (ha)
CCCCCC
APPAREA = 0.02787
CCCCCC
C READ IN INHJT AND OUTHJT FILENAMES
CCCCCC
OPEN (UNIT^l, FILE=' INFLUX. DATSTATUS=' OID')
READ(1,102) INFIIE
READ(1,108)NFILE
READ(1,102) INFILE2
READ(1,102)OUTFIIE
102 FORMAT (A12)
108 FORMAT(12)
CCCCCC
C OPEN FILES
CCCCCC
OPEN (UNIT=3, FILE=INFILE, STATUS=' OID')
OPEN (UNIT=4, FILE=INFILE2, STATUS=' OID')
OPEN (UNIT=5, FILE=OUTFILE, STATUS=' UNKNOWN')
OPEN (UNIT=7, FILE=' SUMMARY. SUMSTATUS=' UNKNOWN')
CCCCCC
C READ IN DATA FROM INPUT FILES
C DAY (I) = DAYS SINCE APPLICATION
C PERC(I) = PERCOLATION DURING PERIOD (can)
C CFLUX(I) = CHEMICAL FLUX DURING PERIOD (g/ha)
C AREA(I) = AREA OF SUBAREAS (m**2)
C NETFLCW(I) = NET WATER FLUX INTO (+) OR OUT OF (-) SUBAREAS (m**3/day)
C TWMASS(I) = TOTAL WATER IN SUBAREA (m**3)
C DRVOLSUM(I) = DRAINED VOLUME IN SUBAREA (cm)
C MSTORSUM(I) = WATER IN SATURATED ZONE IN SUBAREA (m**3)
C MEANWTD(I) = MEAN WATER TABLE DEPTH IN SUBAREA (CM)
CCCCCC
READ(3,721)(COMMENTS(I),1=1,54)
DO 10, I = 1,NFILE
READ(3,*)

Figure C.2 Continued.
READ(3,115) (NEIWFI£W(I,J) ,J=1,7) ,DAIE(I)
READ(3,115) (TWMASS(I,J) ,J=1,7) ,DATE(I)
READ(3,115) (DRVOI£!UM(I, J) ,J=1,7) ,DATE(I)
READ(3,115) (MSTORSUM(I, J) ,J=1,7) ,DAIE(I)
READ(3,115) (MEANWTD(I,J) ,J=1,7) ,DAIE(I)
READ(3,115) (NETCFLOW(I, J) ,J=1,7) ,DATE(I)
READ(3,115) (TCMASS(I,J) ,J=1,7) ,DATE(I)
10 OCmTNUE
CCCCCC
C BEGIN CALCULATIONS
CCCCCC
WRITE(*,94)
94 FORMAT (' BEGINING CAIOJIATTONS')
DO 15, I = 2,NFILE
DO 15, J = 1,7
CCCCCC
C CAUJIATE AVERAGE WATER AND CHEMICAL FUJX IN EACH SUB-AREA BETWEEN
C SAMPLING DATES
CCCCCCC
AVGWFL0W(I,J) = (NETWFICW(I-1,J) + NEIWFICW(I,J)) / 2.0
AVGCFLCW(I,J) = (NETCFLDW(I-1,J) + NETCFTCW(I, J)) / 2.0
CCCCCC
C CALCULATE CHANGES IN OBSERVED PARAMETERS BETWEEN DATES
CCCCCC
C DRAINED VOL @ I GT DRVOL @ 1-1 => WATER LOSS
DELWMASS(I,J) = CRVOLSUM(I-l,J) DRVOLSUM(I,J)
DELCMASS(I,J) = TCMASS(I,J) TCMASS(I-1,J)
DELTAWTD(I,J) = MEANWTD(I,J) MEANWTD(I-1,J)
CCCCCC
C CALMATE PREDICTED CHANGE IN WATER AND CHEMICAL MASS BASED
C ON AVERAGE FLCWRATES BETWEEN PERIODS
CCCCCC
PDELWMASS (I, J) = AVGWFLDW(I,J) FLOAT (DAY (I)-DAY (1-1)) +
# (PERC(I)/100.0)*AREA(J)
IF(J.EQ.2) THEN
PDELCMASS(I, J) = AVGCFL3W(I,J) FLOAT (DAY (I)-DAY (1-1)) +
# CFLUX(I) APPAREA 1000.0
ELSE
PDELCMASS(I,J) = AVGCFLOW (I, J) FLOAT (DAY (I)-DAY (1-1))
ENDIF
711 FORMAT (A8,413,8 (F8.3, IX))
CCCCCC
C CALCULATE DIFFERENCE BETWEEN PREDICTED AND OBSERVED VALUES
CCCCCC
WERROR(I, J) = PDELWMASS (I, J) DELWMASS (I, J)
CERROR(I,J) = PDELCMASS(I,J) DELCMASS(I,J)
TOIWERROR(J) = TOTWERROR(J) + WERROR(I,J)
TOTCEEROR(J) = TOTCERROR(J) + CERROR(I,J)

196
Figure C.2 Continued.
CCCCCC
C CALCULATE THE PERCENTAGE ERROR FROM OBSERVED VALUE
CCCCCC
IF(DELWMASS(I,J).NE.O.O) THEN
WERRORP(I, J) = 100.0 WERROR(I,J) / ABS(DEIWMASS(I, J))
ENDIF
IF(DEI£MASS(I,J) .NE. 0.0) THEN
CERRORP(I, J) = 100.0 CERROR(I,J) / ABS(DELCMASS(I, J))
ENDIF
15 CONTINUE
CCCCCC
C WRITE RESULTS TO OOTEUT FIIES
CCCCCC
WRTIE(5,125)
WRITE(5,721)(COMMENTS(I),1=1,54)
WRITE(7,721)(OOMMENTS(I),1=1,54)
DO 25,I=1,NFIIE
IF(I.EQ.l) THEN
C FOR 1ST DATA PERIOD THE ARE NO COMPARISONS TO BE FEINTED
WRITE(5,130)DATE(I),'11/12/86'
WRITE(5,135)DAY(I),'11/12/86',PERC(I)
WRITE(5,137) (AREA(J) ,J=1,7)
WRITE (5,241)DATE(I),(MSTORSUM(I,J),J=1,7)
WRITE (5,242) DATE (I), (DRVOLSUM(I, J) ,J=1,7)
WRITE(5,145)DATE(I),(TWMASS(I,J),J=1,7)
WRITE (5,243) DATE (I) (MEANWTD(I, J) ,J=1,7)
WRITE (5,140) DATE (I) (NETWFLOW(I, J) ,J=1,7)
WRITE (5,136) DAY (I) '11/12/86',CFLUX(I)
WRITE (5,150) DATE (I), (NETCFLCW(I, J) ,J=1,7)
WRITE(5,155)DATE(I),(TCMASS(I,J),J=1,7)
ELBE
WRITE(5,130)DATE(I),DAIE(I-1)
WRITE(5,135)DAY(I),DATE(I-1),PERC(I)
WRITE(5,241)DATE(I),(MSTORSUM(I,J),J=1,7)
WRITE (5,242) DATE (I) (DRVOISUM(I, J) ,J=1,7)
WRITE(5,145)DATE(I),(TWMASS(I,J),J=1,7)
WRITE (5,243) DATE (I) (MEANWTD(I, J) ,J=1,7)
WRITE(5,140)DATE(I) (NETWFIOW(I, J) ,J=1,7)
WRITE(5,141)(AVGWFIOW(I,J),J=1,7)
WRITE(5,247)(DELTAWTD(I,J),J=1,7)
WRITE(5,142)(DEIWMASS(I,J),J=1,7)
WRITE(5,143)(FDEIWMASS(I,J),J=1,7)
WRITE(5,144) (WERROR(I,J) ,J=1,7)
WRITE(5,177) (WERRORP(I,J) ,J=1,7)
WRITE(5,136)DAY(I),DATE(I-1),CFLUX(I)
WRITE (5,150) DATE (I) (NETCFIOW(I, J) ,J=1,7)
WRITE (5,155) DATE (I) (TCMASS(I,J) ,J=1,7)
WRITE(5,151)(AVGCFLOW(I,J),J=1,7)
WRTIE(5,152) (DELCMASS(I,J) ,J=1,7)
WRITE(5,153)(FDEICMASS(I,J),J=1,7)

Figure C.2 Continued.
WRITE(5,154) (CERROR(I,J) ,J=1,7)
WRITE(5,178) (CERRDRP(I, J) ,J=1,7)
WRITE(7,135)DAY(I),DATE(I-1),PERC(I)
WRITE(7,141) (AVGWFLCW(I, J) ,J=1,7)
WRrrE(7,142)(DEIWMASS(I,J),J=1,7)
WRITE(7,143) (PDEIWMASS(I,J) ,J=1,7)
ENDIF
25 CONTINUE
WRITE(5,436) (TOIWERROR(J) ,J=1,7)
WRITE(5,437) (TOTCERRDR(J) ,J=1,7)
WRITE(7,438) (TOIWERROR(J) ,J=1,7)
CCCCCC
C STOP AND END PROGRAM
CCCCCC
STOP END OF FROCCESSING !!!!!'
CCCCCC
C FORMAT STATEMENTS
CCCCCC
110
FORMAT (13,3X, 2 (FIO. 4))
115
FORMAT(16X,7(F10.5,2X),A8)
125
FORMAT('
SUMMARY OF FTJUX DATA',/)
130
FORMAT(/,
1 CHANGE BETWEEN ,A8, AND ,A8)
135
FORMAT('
DAYS SINCE APPLICATION: ,13,', PERCOLATION SINCE '
# A8,' = '
,F5.2,' cm')
136
FORMAT('
DAYS SINCE APPLICATION: ,13,', CHEMICAL FLUX SINCE
# A8,' = '
,F5.2,' mg/m**2 )
137
FORMAT('
AREA OF SUBAREA (nf2j
: ',8(F10.4,2X))
140
FORMAT('
FORMAT('
NEIWFIDW CXJ ,A8, '
',8(F10.4,2X))
141
AVGWFDDW OVER PERIOD
: ',8(F10.4,2X))
142
FORMAT('
DELIA WSTORAGE (nf 3)
: ',8(F10.4,2X))
247
FORMAT('
DELIA MEAN WTD (cm)
: ',8(F10.4,2X))
143
FORMAT('
FRED DELIA WSTORAGE
: ',8(F10.4,2X))
144
FORMAT('
ERROR IN WSTORAGE(nT3) : ,8(F10.4,2X))
145
FORMAT('
SAT. STORAGE ',A8,'
',8(F10.4,2X))
242
PORMATC
FORMAT ('
BRAINED VOL ,A8, '
',8(F10.4,2X))
241
MAX STORAGE ',A8,'
',8(F10.4,2X))
243
FORMAT('
MEAN WTDEPIH ,A8, '
',8(F10.4,2X))
155
FORMAT('
C STORAGE CW ',A8,'
',8(F10.4,2X))
150
FOFMAT('
NETCFLCW ,A8, '
' ,8(F10.4,2X))
151
PORMATC
AVGCFLOW OVER PERIOD
: ',8(F10.4,2X))
152
PORMATC
DELIA CSTORAGE (rrg)
: ',8(F10.4,2X))
153
PORMATC
FRED DELIA CSTORAGE
: ',8(F10.4,2X))
154
PORMATC
ERROR IN CSTORAGE
: ',8(F10.4,2X))
177
PORMATC
ERROR IN WSTORAGE (%) : ',8(F10.4,2X))
178
PORMATC
ERROR IN CSTORAGE (%) : ',8(F10.4,2X))
436
FORMAT(/,
' TOT ERROR IN WSTORAGE : ,8 (F10.4,2X))
437
PORMATC
TOT ERROR IN CSTORAGE : ,8(F10.4,2X))
438
PORMATC
TOT ERROR IN WSTORAGE : ',8(F10.4,2X) ,/)
721
FORMAT(6A12)
END

Table C.2. Sample output from program FLUX
COMMENT : OPTIMIZING CONDUCTIVITY IN SUBAREA #1
COMMENT : USING PRZM PERCOLATION FLUX DATA
PROGRAM RUN ON 04/22/1988 AT 10.43.20.59
HYD COND. : 12.00 12.00 12.00 12.00 12.00 12.00 12.00
POROSITY : .36
X-NODE BOUNDARIES : 6 18
Y-NODE BOUNDARIES : 2 8 14 18 22 26 30
DAYS SINCE APPLICATION
40, PERCOLATION
SINCE
12/15/86 = 1.32
cm
DELTA WSTORAGE (m3)
-13.3543
12.5565
-10.1853
15.0013
-12.6839
-5.3586
-69.1410
PRED DELTA WSTORAGE
-3.3449
7.2061
-7.6461
21.0692
-15.0421
25.8956
28.1378
DAYS SINCE APPLICATION
47, PERCOLATION
SINCE
12/22/86 = 3.04
cm
DELTA WSTORAGE (m3)
-.6429
-.4067
1.9388
.0304
-3.4596
-4.6639
-7.2030
PRED DELTA WSTORAGE
10.8180
16.7252
3.9850
33.0902
-13.0865
29.6292
81.1611
DAYS SINCE APPLICATION
54, PERCOLATION
SINCE
12/29/86 = 2.62
cm
DELTA WSTORAGE (m3)
-8.7798
12.0053
-6.6799
-5.3921
-5.2577
-5.5686
-43.6831
PRED DELTA WSTORAGE
10.1124
13.0013
3.2031
30.8369
-12.3547
26.2670
71.0661
DAYS SINCE APPLICATION
61, PERCOLATION
SINCE
1/05/87 = 2.75
cm
DELTA WSTORAGE (m3)
.1695
.1741
.4625
.1102
-3.9066
-5.5415
-8.5320
PRED DELTA WSTORAGE
12.8376
13.5732
6.0237
27.4354
-4.5690
21.0263
76.3270
DAYS SINCE APPLICATION
68, PERCOLATION
SINCE
1/12/87 = 5.28
cm
DELTA WSTORAGE (m3)
4.3478
5.5865
4.6180
3.6360
2.4435
2.5201
23.1521
PRED DELTA WSTORAGE
28.8188
31.1019
13.3882
44.5662
10.9241
31.1496
159.9487

Table C.2. Continued
DAYS SINCE APPLICATION
DELTA WSTORAGE (m3)
PRED DELTA WSTORAGE
DAYS SINCE APPLICATION
DELTA WSTORAGE (m3)
PRED DELTA WSTORAGE
DAYS SINCE APPLICATION
DELTA WSTORAGE (m'3)
PRED DELTA WSTORAGE
DAYS SINCE APPLICATION
DELTA WSTORAGE (m3)
PRED DELTA WSTORAGE
DAYS SINCE APPLICATION
DELTA WSTORAGE (m3)
PRED DELTA WSTORAGE
DAYS SINCE APPLICATION
DELTA WSTORAGE (m3)
PRED DELTA WSTORAGE
TOT ERROR IN WSTORAGE
174, PERCOLATION
SINCE
5/03/87 = 1
.41
cm
10.9312
29.1446
7.8003
12.2948
16.2689
15.6362
92.0759
5.4211
10.4245
-2.4286
13.6757
7.6882
15.4218
50.2027
177, PERCOLATION
SINCE
5/05/87 =
.00
cm
-7.2057
-3.5609
-.2990
.2405
9.0190
16.4747
14.6681
-5.7125
-2.0040
-12.0272
8.0393
.3808
11.0193
-.3043
182, PERCOLATION
SINCE
5/08/87 = 7
.54
cm
15.6433
16.1147
4.6156
-.0760
-7.6467
-9.7187
18.9330
40.1281
46.0137
13.9722
50.9695
28.3841
51.0628
230.5303
187, PERCOLATION
SINCE
5/13/87 =
.64
cm
-21.3326
20.2321
-12.0260
-9.1887
.8297
1.8801
-60.0701
-4.7329
1.1261
-11.7398
12.8980
-4.4958
23.3991
16.4547
194, PERCOLATION
SINCE
5/18/87 =
.73
cm
-14.5931
23.8722
-15.4708
-22.5782
-24.5092
-15.6859
-116.7080
-2.6033
-1.1742
-6.8849
13.6701
-22.8199
29.7160
9.9038
201, PERCOLATION
SINCE
5/25/87 =
o
o
cm
-5.4107
10.3354
-4.0324
-3.4908
-9.9932
-14.0623
-47.3250
-5.2955
-5.4908
-4.8896
14.2750
-22.6561
13.5992
-10.4578
271.1864 338.8987
71.6449
620.3365
-97.0587
568.4956
1773.5030

APPENDIX D
SURFACE PLOTS OF ATRAZINE CONCENTRATION IN GROUNDWATER

Cone. (jug/L) E Cone. (jug/O
08-09
Date: 01/19/87
o
D.l. Atrazine concentration in the groundwater on 1/19/87.
Vertical bars indicate sampling locations.
08-09
o
M-
o
n
o
CN
O
O
Figure D.2. Atrazine concentration in the groundwater on 1/26/87.
Vertical bars indicate sampling locations.
201

Cone. (/ug/L) g Cone. (/ug/L)
202
Date: 02/02/87
08-14
10-09
re D.3.
Atrazine concentration in the groundwater on 2/02/87.
Vertical bars indicate sampling locations.
07-11
Date: 02/09/87
Figure D.4. Atrazine concentration in the groundwater on 2/09/87.
Vertical bars indicate sampling locations.

Cone, (jug/L) £ Cone. (jxg/L)
203
Date: 02/16/87
o
CN
o
T
o
re D.5.
Atrazine concentration in the groundwater on 2/16/87.
Vertical bars indicate sampling locations.
09-11
Date: 02/23/87
Figure D.6. Atrazine concentration in the groundwater on 2/23/87.
Vertical bars indicate sampling locations.

204
Date: 03/02/87
'STANce: (m) 170 iso SjJ¡C ^
Figure D.7. Atrazine concentration in the groundwater on 3/02/87.
Vertical bars indicate sampling locations.
09-11
08-09
09-14
0!s-rANCE. -- ,7o
Date: 03/09/87
190 5?,cfAN^
Figure D.8. Atrazine concentration in the groundwater on 3/09/87.
Vertical bars indicate sampling locations.

Cone. (ug/L) ^3 Cone. (jj.g/L)
205
nntp- n^/iK/7
D.9. Atrazine concentration in the groundwater on 3/16/87.
Vertical bars indicate sampling locations.
Date: 03/23/87
o
cn
o
T-*
o
Figure D.10. Atrazine concentration in the groundwater on 3/23/87.
Vertical bars indicate sampling locations.

206
Date: 03/31/87
Figure D.ll. Atrazine concentration in the groundwater on
Vertical bars indicate sampling locations.
3/31/87.
09-11
o H
Figure D.12. Atrazine concentration in the groundwater on 4/06/87.
Vertical bars indicate sampling locations.

207
10-13
Figure D.13. Atrazine ¡concentration in the groundwater on 4/13/87.
Vertical bars indicate sampling locations.
=1
O
u
c
o
O
o
08-10
Date: 04/20/87
Figure D.14. Atrazine concentration in the groundwater on 4/20/87.
Vertical bars indicate sampling locations.

208
Date: 04/30/87
Figure D.15. Atrazine concentration in the groundwater on 4/30/87.
Vertical bars indicate sampling locations.
Date: 05/01/87
Figure D.16. Atrazine concentration in the groundwater on 5/01/87.
Vertical bars indicate sampling locations.

Cone. (/j.g/L) g Cone. (jug/L)
209
Date: 05/01/R7
:e D.17. Atrazine concentration in the groundwater on 5/03/87.
Vertical bars indicate sampling locations.
08-09
Figure D.18. Atrazine concentration in the groundwater on 5/05/87.
Vertical bars indicate sampling locations.

08-09
210
Figure D.19. Atrazine concentration in the groundwater on 5/08/87.
Vertical bars indicate sampling locations.
08-09
Date: 05/13/87
'I, (m)
5dis^C
Figure D.20. Atrazine concentration in the groundwater on 5/13/87.
Vertical bars indicate sampling locations.

211
10-09
Figure D.21. Atrazine concentration in the groundwater on 5/18/87.
Vertical bars indicate sampling locations.
09-11 07-14
Date: 05/25/87
Figure D.22. Atrazine concentration in the groundwater on 5/25/87.
Vertical bars indicate sampling locations.

09-11
212
Figure D.23. Atrazine concentration in the groundwater on 6/01/87.
Vertical bars indicate sampling locations.

BIOGRAPHICAL SKETCH
Matthew Clay Smith was bom on September 27, 1957, in DeLand,
Florida. He attended Deland Senior High School during 1973 and 1974. In
1974 he attended Brevard College in Brevard, North Carolina, under the
early admissions program. In 1977 he received the Associate of Science
degree from Abraham Baldwin Agricultural College in Tifton, Georgia. In
1980 he received the Bachelor of Science degree in agricultural
engineering from the University of Georgia in Athens, Georgia. While
attending the University of Georgia he participated in the cooperative
education program by alternating quarters between academic courses and
employment with the U. S. Environmental Protection Agency in Athens,
Georgia.
He attended graduate school at North Carolina State University in
Raleigh, North Carolina, where he worked as a research assistant in the
Department of Biological and Agricultural Engineering. He received the
Master of Science degree in 1983. His thesis topic was a study of the
water and energy use efficiency of drainage/subirrigation systems.
He worked as a research agricultural engineer in the Department of
Agricultural Engineering, University of Georgia, Coastal Plain Experiment
Station in Tifton, Georgia, from 1982 to 1984.
He attended the University of Florida in Gainesville, Florida, where
he worked as a research assistant in the Agricultural Engineering
Department from May, 1984, through September, 1987.
213

214
In October, 1987, he began employment with the University of
Georgia, Coastal Plain Experiment Station in Tifton, Georgia, where he
holds the title of Assistant Professor in the Department of Agricultural
Engineering.
!t>S3

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
A. B. (Del) Bottcher, Chairman
Professor of Agricultural Engineering
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
7,
/
y,,
Kenneth L.
Campbell, Codhairman
Associate Professor of Agricultural
Engineering
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy. ^
^^ /tz^Aa-
Huber
>r of Environmental Engineering
Wayne
Profi
Sciences
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Fhilosophy.
P.
Professor of Soil Science
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
E. Dale Ihreadgill /
Professor and Division Chairman
Agricultural Engineering, University
of Georgia

This dissertation was submitted to the Graduate Faculty of the
College of Engineering and to the Graduate School and was accepted as
partial fulfillment of the requirements for the degree of Doctor of
Philosophy.
December, 1988
Dean, College of
Dean, Graduate School



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58
observed that the response of the chromatogram to Cl- was approximately
twice the response to the other anions of interest. Thus, standards were
prepared with Cl at half of the concentrations of the other species so
that the responses of all species would be similar and on scale
simultaneously. Standards were prepared with concentrations of the main
species of interest ranging from 0.01 to 100.0 mg/L. A typical standard
would contain 0.5 mg/L Cl and 1.0 mg/L of Br, NC^, and S042. It
should be noted here that all references to nitrate (NC>3) indicate
concentrations of N03 and not N03-N (nitrate-nitrogen).
Samples were taken from the refrigerator and allowed to come to
room temperature prior to injection. No sample preparation was
performed. The analysis protocol typically followed the pattern of 3
standards, 10 samples, 3 standards, etc. The standards used were
selected based upon the concentrations of the analytes being observed in
the samples. Thus, standards were selected to bracket the observed
values as closely as possible. Occasionally a sample would be reinjected
to observe the repeatability of the analysis procedures. Samples from
wells located away from the experimental site, including the deep well
which supplies irrigation water to the farm, were regularly analyzed as
field blanks (no Br would be expected; however, Cl, N03, and S042
could be present).
A piecewise linear fit to each sequence of injected standards was
used to generate a function of the form: concentration = f(peak height).
The piecewise fit was selected instead of linear regression because for
some species (particularly high concentrations of Cl) a linear
regression resulted in a negative y-axis intercept. This implies that


78
5.3.3 Soil related parameters
GLEAMS was designed to simulate only the root zone of a crop. The
vadose zone below the crop is ignored. Thus, the soil profile
description input to GLEAMS covers only the 91 cm root zone as opposed to
the 2.62 m profile input to PRZM. There are no soil parameters which are
significant to the results presented in this work which differ from those
discussed in section 5.1.2. In order to highlight one of the major
differences between the two models, a short discussion of the
computational layering of the soil profile in GLEAMS will be presented
here. GLEAMS divides the crop root zone into seven computational layers.
The first layer is defined to be 1 cm in depth and the other 6 layers are
equal in thickness (layer 2 is 1 cm less than layers 3-7) and together
account for the rest of the root zone. The 1 cm upper layer is
considered an important feature of CREAMS and GLEAMS because it increases
the sensitivity of erosion and runoff to chemical concentrations near the
soil surface. PRZM divides the entire profile into a specified number of
compartments of equal depth. For the case presented here, GLEAMS layers
would be 15 cm thick after the surface layer (91 cm / 6 layers). For
PRZM with a 2.62 m profile and 35 compartments, each compartment will be
7.5 cm thick. Thus, PRZM may be better able to describe effects of
actual soil layering since properties of adjoining layers would not be
averaged over as large a layer as in GLEAMS. However the surface layer
in PRZM will also be 7.5 cm thick and pesticide storages will be averaged
over this whole layer effectively lowering the mass at the surface which
is available for transport with eroded sediment and runoff water.


6.30. Atrazine concentration in the groundwater on 2/23/87 115
6.31. Atrazine concentration in the groundwater on 3/02/87 115
6.32. Atrazine concentration in the groundwater on 3/09/87 116
6.33. Atrazine concentration in the groundwater on 3/16/87 116
6.34. Atrazine concentration in the groundwater on 3/23/87 117
6.35. Atrazine concentration in the groundwater on 3/31/87 117
6.36. Contour plot of water table elevation on 5/08/87 showing
direction of flew 118
6.37. Concentration of atrazine and water table elevation in
well 09-11 120
6.38. Nitrate concentration in groundwater on 5/01/87 122
6.39. Nitrate concentration in groundwater on 5/03/87 122
6.40. Nitrate concentration in groundwater on 5/05/87 123
6.41. Nitrate concentration in groundwater on 5/08/87 123
6.42. Nitrate concentration in groundwater on 5/13/87 124
6.43. Nitrate concentration in groundwater on 5/18/87 124
6.44. Nitrate concentration in groundwater on 5/25/87 125
6.45. Nitrate concentration in groundwater on 6/01/87 125
6.46. Chloride concentration in groundwater on 5/01/87 127
6.47. Chloride concentration in groundwater on 5/03/87 127
6.48. Chloride concentration in groundwater on 5/05/87 128
6.49. Chloride concentration in groundwater on 5/08/87 128
6.50. Chloride concentration in groundwater on 5/13/87 129
6.51. Chloride concentration in groundwater on 5/18/87 129
6.52. Chloride concentration in groundwater on 5/25/87 130
6.53. Chloride concentration in groundwater on 6/01/87 130
x


09-11
212
Figure D.23. Atrazine concentration in the groundwater on 6/01/87.
Vertical bars indicate sampling locations.


125
Date: 05/25/87
Figure 6.44. Nitrate concentration in the groundwater on 5/25/87.
Vertical bars indicate sampling locations.
Date: 06/01/87
Figure 6.45. Nitrate concentration in the groundwater on 6/01/87.
Vertical bars indicate sampling locations.


175
Table B.2 Example listing of the soil sample data set.
Sample
Date
Sample
Location
Depth
(cm)
Concentration
Atrazine
(mg/kg)
Alachlor
5/25/87
1
0-6
0.04
0.02
5/25/87
2
0-6
0.05
0.12
5/25/87
3
0-6
0.03
0.27
5/25/87
4
0-6
0.04
0.17
5/25/87
5
0-6
0.04
0.06
5/25/87
6
0-6
0.04
0.11
5/25/87
7
0-6
0.04
0.12


TABLE OF CONTENT'S
Page
ACKNOWLEDGEMENTS iii
LIST OF TABLES V
LIST OF FIGURES viii
ABSTRACT xiv
CHAPTERS
1 INTRODUCTION 1
2 OBJECTIVES 4
3 REVIEW OF THE LITERATURE 5
3.1 Evidence of Pesticide Residues in Groundwater 5
3.2 Factors Which Influence Pesticide Transport to
to Groundwater 7
3.3 Predicting Pesticide Transport 15
3.4 Field Studies of Pesticide Transport 27
3.5 Summary 36
4 EXPERIMENTAL METHODS 39
4.1 Field Site Description 39
4.2 Site Instrumentation 42
4.3 Chemical Applications 49
4.4 Sample Collection and Storage 52
4.5 Sample Analysis 55
5 MODELING THE EXPERIMENTAL SITE 66
5.1 Selection of Common Input Parameter Values 67
5.2 Parameters Unique to PRZM 73
5.3 Parameters Unique to GLEAMS 77
v


188
Figure C.l Continued.
CCCCCC
C CLOSE FILES
CCCCCC
CLOSE(UNIT=4,STATUS='KEEP')
CLOSE (UNIT=5,STATUS='KEEP')
CLOSE (UNTP=6, STA1US=' KEEP')
CCCCCC
C IOOP BACK FOR NEXT FILE
CCCCCC
15 CONTINUE
CCCCCC
C STOP FOR NORMAL TERMINATION
CCCCCC
20 STOP END OF FILE ON INFILE.DAT1
CCCCCC
C FORMAT STATEMENTS
CCCCCC
100 FORMAT (3 (A2, IX) ,A8)
102 FORMAT(1013)
104 FORMAT(7F10.2)
106 FORMAT(II)
108 FORMAT(A12)
110 FORMAT (' ',/,45X, 'GOPHER RIDGE WATER TABLE DATA',/,52X, 'DATE = ',
# A8,//,2X,118(* *))
120 FORMAT(' ,2X, 'NODE STATION ID LOCATION (m) ELEVATION '
#'(m) WT DEPTH WATER TABLE SLOPE (m/m) ',
#'DIRECTION',/,4X, 'I J',17X, 'X',7X, 'Y',5X, 'SURF',4X, 'IMP'f6Xf 'WT',
# 6X, (cm)',10X, 'X',9X, 'Y',8X, 'MAGNITUDE' ,2X,' (degrees) ',/,
# 2X,118('-'))
130 FORMAT(' ',1X,I2,1X,I2,2X,F5.2,'-',F5.2,2X,F5.2,2X,F6.2,
# 3(2X,F6.3),3X,F5.1,4X,3(E10.4,2X),3X,F4.0,4(F7.3,1X))
140 FORMAT (' ',118 ('-'))
431 FORMAT(IX, 126('-') ,/,3X, 'I J MAXIMUM TRAINED SATURATED ',
#'CHEMICAL CHEMICAL STORAGE ',2X,8('-'),'X DIRECTION',7('-') ,
#3X,6('-'),'Y DIRECTION',8('-'))
432 FORMAT(10X,'STORAGE VOLUME STORAGE OONC.',6X,
#'WATER SOIL TOTAL FLOWRATE VELOCITY CHEMICAL FLOWRATE',
#' VELOCITY CHEMICAL')
433 FORMAT(9X, (m**3) (m**3) (m**3) (ppb) ',3(' (mg) ')
#,' (m**3/day) ,1X,' (m/day) (mg/day) (m**3/day) (m/day) ',
# '(mg/day)',/,IX,126('-'))
142 P0RMAT(2X,I2,2X,I2,3(F9.4),F9.2,9(F9.3))
149 FORMAT(' ,3X,53 ('-') ,/,5X,'PARAMETER', 18X,
# 'MINIMUM', 7X, 'MAXIMUM',/,5X,53('-'))
150 FORMAT(' ',4X,'SOIL SURFACE ELEVATION',5X,F5.2,9X,F5.2)
151 FORMAT(' ',4X,'IMP. LAYER ELEVATION',5X,F5.2,9X,F5.2)
152 P0RMAT(' ',4X, 'WATER TABLE ELEVATION' ,5X,F5.2,9X,F5.2)
153 FORMAT(' ,4X,'WATER TABIE DEPTH',9X,F5.1,9X,F5.1)
154 FORMAT (' ',4X,'SIOPE IN X-DIRECTICT',3X,E10.4,5X,E10.4)
155 FORMAT (' ',4X,'SLOPE IN Y-DIRECTIW ,3X,E10.4,5X,E10.4)


6 RESULTS AND DISCUSSION 79
6.1 Data Collection 79
6.2 Sample Analysis 83
6.3 Chemical Applications 85
6.4 Chemicals in the Unsaturated Zone 91
6.5 Chemicals in the Saturated Zone 107
6.6 Model Results and Comparisons 142
7 SUMMARY AND CONCLUSIONS 157
8 RECOMMENDATIONS FOR IMPROVEMENTS AND FURTHER STUDY 160
REFERENCES 162
APPENDICES
A. MONITORING WELL STATISTICS 170
B. HOW TO GET COMPLETE DATA SET 173
C. WATER BALANCE PROGRAMS 176
D. SURFACE PLOTS OF ATRAZINE CONCENTRATION IN GROUNDWATER.... 200
BIOGRAPHICAL SKETCH 213
vi


Table C.l. Continued
BOUNDARY FLUXES
AREA(I)
K30.70500
1226.31900
1226.31900
BFLOWX1(I)
.00000
.00000
.00000
BFL0WX2O )
.00000
.00000
.00000
BFLOWY1(I)
.00000
1.77918
4.17766
BFLOWY2CI)
1.77918
4.17766
17.00191
NET FLOW(I)
-1.77918
-2.39848
-12.82425
MAXSTOR(I)
1142.99300
1089.65600
1074.21200
DRVOL(I)
682.37870
628.29070
628.39540
TWMASS(I)
99.45535
128.36940
112.79580
MEANWTDU )
19.30970
29.07741
25.54977
BCFLOWX1(I)
.00000
.00000
.00000
BCFLOWX2( I )
.00000
.00000
.00000
BCFLOWY1(I )
.00000
69.81870
30.65918
BCFLOWY2(I )
69.81870
30.65918
7.25275
NETCFLOWCI )
-69.81870
39.15952
23.40643
TCMASSJI)
2540.22400
3290.70000
446.42620
1226.31900
817.54620
817.54620
6744.80800
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
17.00191
19.74327
14.53423
.00000
19.74327
14.53423
21.56270
21.56270
-2.74135
5.20904
-7.02847
-21.56270
1269.00300
917.17760
944.13010
6437.17500
780.16060
578.92000
604.75040
3902.89800
119.49960
77.95414
72.89392
610.96830
27.06828
26.48648
24.76717
25.16207
.00000
.00000
.00000
.00000
.00000
.00000
.00000
.00000
7.25275
.00000
.00000
.00000
.00000
.00000
.00000
.00000
7.25275
.00000
.00000
.00000
8.06712
.00000
.00000
6285.41500
PARAMETER MINIMUM MAXIMUM
SOIL SURFACE ELEVATION
IMP. LAYER ELEVATION
WATER TABLE ELEVATION
WATER TABLE DEPTH
SLOPE IN X-DIRECTION
SLOPE IN Y-DIRECTION
SLOPE MAGNITUDE
FLOW IN X-DIRECTION
FLOW IN Y-DIRECTION
CHEM FLOW IN X-DIR
CHEM FLOW IN Y-DIR
CHEMICAL MASS STORAGE
CHEMICAL CONCENTRATION
22.12
30.42
20.72
28.17
21.22
28.28
.0
75.0
.7606E-01
.5374E-01
. 1603E-01
.1070E+00
. 1089E-02
.1186E+00
.1208E+01
.7787E+00
.1665E+00
.2049E+01
.3292E+02
.1587E+02
.2325E+01
.3688E+02
. 0000E + 00
.9983E+02
. 0000E+00
.8900E+02


56
conductivity of the eluant and thus allcw for greater sensitivity to the
conductivity changes caused by the presence of anions in the sample.
There was no automatic sampling device with this instrument, so
each sample was injected by hand. Approximately 1 mL of sample was
loaded onto a sample loop prior to injection onto the column. The
sample loop retained approximately 0.25 mL of the sample with the rest
being discarded to a waste line. Between injections, the sample loop
was flushed with 1 mL of deionized (DI) water. Prior to the guard
column, the sample was passed through two filters rated at 30 and 5
microns, respectively, to remove solids from the sample. These filters
were an integral part of the IC and the filter elements were changed
whenever the system pressure was observed to be increasing above nominal
levels.
The eluant used in these analyses was 2.2 millimolar (mM) sodium
carbonate (0.933 g Na2OC>3 in 4 1 DI H20) and 0.75 mM sodium bicarbonate
(0.25 g NaH003 in the same 4 1 of DI water). This solution was made
fresh as needed. All references herein to deionized (DI) water refer to
water that had been passed through carbon filters and ion exchange resins
to produce DI water with a resistivity of at least 1 megohm. The
regenerant solution was 0.025 N sulfuric acid (2.8 mL of concentrated
H2S04 in 4 1 of DI water). The regenerant solution was passed through
the micrcmembrane suppressor to reduce background conductivity.
In normal operation the eluant flowrate was approximately 2.0-2.5
mlyiriin, and regenerant flowrate was approximately 4 ml/min. The
pressure at the discharge of the pump at the stated eluant flowrate was
approximately 8,274 kPa (1200 psi).


3
are increased efforts at specific sites to provide more detailed
monitoring of chemical movement through the soil profile and within
groundwater aquifers.
The study described here is an effort to develop data for use in
the development and testing of models which describe the fate and
transport of pesticides and nutrients used in agriculture.


69
Table 5.]
L Soil properties used in simulations.1
Depth
(cm)
Organic
Carbon
(%)
Bulk
Density
(g/can3)
Hydraulic
Conductivity
(cm/hr)
Water Content (%)
Effective Field Wilting
Saturation Capacity2 Point3
0- 13
0.55
1.45
13.3
33.0
10.0
2.1
13- 20
0.28
1.60
13.3
33.0
10.0
2.1
20- 51
0.08
1.58
22.9
33.3
10.0
2.1
51-102
0.04
1.59
36.5
33.8
9.0
2.1
102-262
0.03
1.59
45.0
34.0
9.0
2.1
3Data from Hook (1985) and Carlisle, et al., (1978).
2Field gravimetric water content following 48 hours of drainage under
plastic following 16 hours of flooding.
3Water content at 15 bars tension.
5.1.3 Pesticide chemical properties and applications
The pesticide chemical properties required by the models are
solubility, partition coefficient, degradation rates on foliage and in
the soil, and a coefficient of plant uptake. The models may require
these parameters in slightly different forms, but the basic information
required is the same. In addition to the model manuals, the manual for
LEACH (Dean et al., 1984) was a good source of estimates of the required
parameters. No exhaustive search of possible values of parameters for
each chemical was undertaken. At this point in the study, the models
were run using values that a user could obtain from the manuals. This
may result in less than optimum performance of the models, but probably
reflects the way in which a typical user would apply them.
Reported properties of pesticides are often widely variable. For
properties such as partition coefficient and degradation rates, the


53
packs immediately after the sprayer had passed completely by the sample
collector.
Soil samples were collected in one of two ways: taking 2.5 cm
diameter by 5 cm long cores using a soil sampling probe, or using a 5 cm
diameter stainless steel bucket auger. The soil sampling probe was
generally used for collection of samples from the top 5 cm of the soil
profile. When this method was used, 5 to 8 samples would be collected
from a small area and composited into a plastic sample bag. This would
represent one final sample for the soil surface at that location. For
deeper samples the auger was used. The auger was used to remove soil to
within approximately 5 cm of the desired sample depth. The auger was
then cleaned to remove traces of soil, and a sample was collected which
represented soil from a depth of 5 cm above to 5 cm below the target
depth. The sample was carefully poured from the top of bucket into a
plastic sample bag and sealed. The number of locations within the
application area sampled depended on the number of depths to be sampled
and ranged from 5 to 14. After collection, the soil samples were placed
into coolers with ice packs for transport to Gainesville. Soil samples
from outside of the application area were collected several times during
the study to use as blanks and spikes during soil residue analysis.
Vacuum was applied to the solution samplers immediately upon
arrival at the study site (7:00 8:00 am). The vacuum would typically
be left on for 8 to 10 hours prior to collecting the soil solution
samples. The vacuum applied to the samplers was approximately 28 cm of
Hg. Near the end of the day the vacuum would be released and the samples
would be drawn directly into individual containers. The containers are


Figure C.2 Continued.
READ(3,115) (NEIWFI£W(I,J) ,J=1,7) ,DAIE(I)
READ(3,115) (TWMASS(I,J) ,J=1,7) ,DATE(I)
READ(3,115) (DRVOI£!UM(I, J) ,J=1,7) ,DATE(I)
READ(3,115) (MSTORSUM(I, J) ,J=1,7) ,DAIE(I)
READ(3,115) (MEANWTD(I,J) ,J=1,7) ,DAIE(I)
READ(3,115) (NETCFLOW(I, J) ,J=1,7) ,DATE(I)
READ(3,115) (TCMASS(I,J) ,J=1,7) ,DATE(I)
10 OCmTNUE
CCCCCC
C BEGIN CALCULATIONS
CCCCCC
WRITE(*,94)
94 FORMAT (' BEGINING CAIOJIATTONS')
DO 15, I = 2,NFILE
DO 15, J = 1,7
CCCCCC
C CAUJIATE AVERAGE WATER AND CHEMICAL FUJX IN EACH SUB-AREA BETWEEN
C SAMPLING DATES
CCCCCCC
AVGWFL0W(I,J) = (NETWFICW(I-1,J) + NEIWFICW(I,J)) / 2.0
AVGCFLCW(I,J) = (NETCFLDW(I-1,J) + NETCFTCW(I, J)) / 2.0
CCCCCC
C CALCULATE CHANGES IN OBSERVED PARAMETERS BETWEEN DATES
CCCCCC
C DRAINED VOL @ I GT DRVOL @ 1-1 => WATER LOSS
DELWMASS(I,J) = CRVOLSUM(I-l,J) DRVOLSUM(I,J)
DELCMASS(I,J) = TCMASS(I,J) TCMASS(I-1,J)
DELTAWTD(I,J) = MEANWTD(I,J) MEANWTD(I-1,J)
CCCCCC
C CALMATE PREDICTED CHANGE IN WATER AND CHEMICAL MASS BASED
C ON AVERAGE FLCWRATES BETWEEN PERIODS
CCCCCC
PDELWMASS (I, J) = AVGWFLDW(I,J) FLOAT (DAY (I)-DAY (1-1)) +
# (PERC(I)/100.0)*AREA(J)
IF(J.EQ.2) THEN
PDELCMASS(I, J) = AVGCFL3W(I,J) FLOAT (DAY (I)-DAY (1-1)) +
# CFLUX(I) APPAREA 1000.0
ELSE
PDELCMASS(I,J) = AVGCFLOW (I, J) FLOAT (DAY (I)-DAY (1-1))
ENDIF
711 FORMAT (A8,413,8 (F8.3, IX))
CCCCCC
C CALCULATE DIFFERENCE BETWEEN PREDICTED AND OBSERVED VALUES
CCCCCC
WERROR(I, J) = PDELWMASS (I, J) DELWMASS (I, J)
CERROR(I,J) = PDELCMASS(I,J) DELCMASS(I,J)
TOIWERROR(J) = TOTWERROR(J) + WERROR(I,J)
TOTCEEROR(J) = TOTCERROR(J) + CERROR(I,J)


196
Figure C.2 Continued.
CCCCCC
C CALCULATE THE PERCENTAGE ERROR FROM OBSERVED VALUE
CCCCCC
IF(DELWMASS(I,J).NE.O.O) THEN
WERRORP(I, J) = 100.0 WERROR(I,J) / ABS(DEIWMASS(I, J))
ENDIF
IF(DEI£MASS(I,J) .NE. 0.0) THEN
CERRORP(I, J) = 100.0 CERROR(I,J) / ABS(DELCMASS(I, J))
ENDIF
15 CONTINUE
CCCCCC
C WRITE RESULTS TO OOTEUT FIIES
CCCCCC
WRTIE(5,125)
WRITE(5,721)(COMMENTS(I),1=1,54)
WRITE(7,721)(OOMMENTS(I),1=1,54)
DO 25,I=1,NFIIE
IF(I.EQ.l) THEN
C FOR 1ST DATA PERIOD THE ARE NO COMPARISONS TO BE FEINTED
WRITE(5,130)DATE(I),'11/12/86'
WRITE(5,135)DAY(I),'11/12/86',PERC(I)
WRITE(5,137) (AREA(J) ,J=1,7)
WRITE (5,241)DATE(I),(MSTORSUM(I,J),J=1,7)
WRITE (5,242) DATE (I), (DRVOLSUM(I, J) ,J=1,7)
WRITE(5,145)DATE(I),(TWMASS(I,J),J=1,7)
WRITE (5,243) DATE (I) (MEANWTD(I, J) ,J=1,7)
WRITE (5,140) DATE (I) (NETWFLOW(I, J) ,J=1,7)
WRITE (5,136) DAY (I) '11/12/86',CFLUX(I)
WRITE (5,150) DATE (I), (NETCFLCW(I, J) ,J=1,7)
WRITE(5,155)DATE(I),(TCMASS(I,J),J=1,7)
ELBE
WRITE(5,130)DATE(I),DAIE(I-1)
WRITE(5,135)DAY(I),DATE(I-1),PERC(I)
WRITE(5,241)DATE(I),(MSTORSUM(I,J),J=1,7)
WRITE (5,242) DATE (I) (DRVOISUM(I, J) ,J=1,7)
WRITE(5,145)DATE(I),(TWMASS(I,J),J=1,7)
WRITE (5,243) DATE (I) (MEANWTD(I, J) ,J=1,7)
WRITE(5,140)DATE(I) (NETWFIOW(I, J) ,J=1,7)
WRITE(5,141)(AVGWFIOW(I,J),J=1,7)
WRITE(5,247)(DELTAWTD(I,J),J=1,7)
WRITE(5,142)(DEIWMASS(I,J),J=1,7)
WRITE(5,143)(FDEIWMASS(I,J),J=1,7)
WRITE(5,144) (WERROR(I,J) ,J=1,7)
WRITE(5,177) (WERRORP(I,J) ,J=1,7)
WRITE(5,136)DAY(I),DATE(I-1),CFLUX(I)
WRITE (5,150) DATE (I) (NETCFIOW(I, J) ,J=1,7)
WRITE (5,155) DATE (I) (TCMASS(I,J) ,J=1,7)
WRITE(5,151)(AVGCFLOW(I,J),J=1,7)
WRTIE(5,152) (DELCMASS(I,J) ,J=1,7)
WRITE(5,153)(FDEICMASS(I,J),J=1,7)


91
Alachlor is more volatile than atrazine. However, these samples were
exposed for less than 10 minutes after the application (while being
collected, sealed in glass containers, and placed on ice).
6.4 Chemicals in the Unsaturated Zone
6.4.1 Atrazine and Alachlor
Samples of the top 5 cm of soil were taken from 14 locations within
the application area on 11/18/86, which was six days after application.
Five cm of irrigation water had been applied prior to this sampling.
Table 6.3 compares the measured application rates of atrazine and
alachlor shown in Table 6.2 with the average measured soil
concentrations on this date. A simple calculation reveals that if the
actual application rate of alachlor was 0.72 kg/ha and was entirely
contained within the top 5 cm of soil, the maximum possible concentration
in the soil would be approximately 1 mg/kg (assuming that the soil bulk
density equals 1.45 g/cm3). The 3.41 mg/kg measured average value could
Table 6.3 Measured application rates and
soil surface concentrations
of atrazine and alachlor.
Application
rate (kg/ha)
Atrazine
3.411
(39)2
Alachlor
0.72
(36)
Soil 0.29 3.26
Concentration (68) (58)
(mg/kg)
3Mean ^Coefficient of variation (%)


2
developed (though only a few have been implemented) to minimize or
eliminate agricultural impacts on surface waters.
The discovery of abandoned waste sites such as the famous love
Canal and the discovery of the pesticide aldicarb in the shallow
groundwater on long Island in New York generated concern for the
protection of groundwater. These concerns led to analyzing samples of
groundwater for toxic wastes and pesticides.
As more groundwater samples were analyzed, it became apparent that
agricultural chemicals had somehow entered groundwater aquifers in
several areas of the country. These results were publicized, and
citizens throughout the nation expressed alarm that they were drinking
potentially toxic chemicals. The federal and state governments
responded to these concerns by intensifying monitoring efforts and
reviewing data on the many agricultural chemicals to determine potential
for leaching to groundwater and the extent of the current problem.
Several federal agencies, along with university and private industry
scientists, began developing computer models to help explain why these
chemicals were moving beyond their target locations and to screen
chemicals for their relative mobilities in soil. A limiting factor in
the development and utilization of these models was a lack of the
detailed data needed to test and validate model predictions.
To date, data on the presence of agricultural chemicals in
groundwater have been limited primarily to sampling of municipal and
domestic wells, providing only a large scale picture of contamination.
There have been only a few intensive monitoring studies completed which
provide sufficient data for model parameter estimation. Currently, there


Cone. (mg/L) $ Cone. (mg/L)
122
Date: 05/01/87
re 6.38. Nitrate concentration in the groundwater on 5/01/87.
Vertical bars indicate sampling locations.
07-10
Figure 6.39. Nitrate concentration in the groundwater on 5/03/87.
Vertical bars indicate sampling locations.


166
Jury, W. A., H. Elabd, and M. Resketo. 1986. Field study of
naprcpamide movement through unsaturated soil. Water Resources
Research. 22(5)749-755.
Jury, W. A. and R. L. Valentine. 1986. Transport mechanisms and
loss pathways for chemicals in soils, in Vadose Zone Modeling of
Organic Pollutants. S. C. Hem, and S. M. Mel ancon, Eds. Lewis
Publishers, Inc. Chelsea, MI. pp. 37-60.
Kelley, R., G. R. Hallberg, L. G. Johnson, R. D. Libra, C. A.
Thompson, R. C. Splinter, and M. G. DeTroy. 1986. Pesticides in
ground water in Icwa. in Agricultural Impacts On Groundwater
QualityA conference. Omaha, NE. National Water Well
Association. Water Well Publishing Co., Dublin, OH. pp. 662-647.
Knisel, W. G. (Ed). 1980. CREAMS: A Field-Scale Model for
Chemicals, Runoff, and Erosion from Agricultural Management
Systems. U.S. Department of Agriculture, Conservation Research
Report No. 26, Washington, DC. 640 pp.
Leonard, R. A. 1988. Herbicides in surface waters, in Environmental
Chemistry of Herbicides, Volume 1. R. Grover, Ed. CRC Press, Boca
Raton, FL. pp. 45-87.
Leonard, R. A. and W. G. Knisel. 1987. Personal Communication. Soil
Scientist and Hydraulic Engineer, respectively. USDA, Southeast
Watershed Research Laboratory, Tifton, GA.
Leonard, R. A. and W. G. Knisel. 1988. Can pesticide transport models be
validated using field data: new and in the future? J. Env. Sci. and
Health, Part B: Pesticides, Food Contaminants and Agricultural Wastes.
In Press.
Leonard, R. A., W. G. Knisel, and D. A. Still. 1987. GIEAMS:
Groundwater loading effects of agricultural management systems.
Transactions of the ASAE. 30(5):1403-1418.
Libra, R. D., G. R. Hallberg, and B. E. Hoyer. 1987. Impacts of
agricultural chemicals on ground water quality in Iowa, in Ground
Water Quality and Agricultural Practices. D. M. Fairchild, Ed.
Lewis Publishers, Chelsea, MI. pp. 185-216
Libra, R. D., G. R. Hallberg, B. E. Hoyer, and L. G. Johnson.
1986. Agricultural impacts on groundwater quality: the Big
Spring Basin study, in Agricultural Impacts On Groundwater
QualityA conference. Omaha, NE. National Water Well
Association. Water Well Publishing Co., Dublin, OH. pp. 253-273.
Litaor, M. I. 1988. Review of soil solution samplers. Water
Resources Research. 24(5):727-733.
Marti, L. R. 1987. Personal communication. Chemist, USDA, Southeast
Watershed Research Laboratory, Tifton, GA.


DEDICATION
To ray mother,
Corrine L. Smith
She was taken from her family by illness during this research. Her
wisdom, courage, and strength continue to inspire me. Without that inner
strength, I would never have made it this far.
I love you Mem.


38
with model predictions. Thus models should not be expected to provide
exact predictions of transport and fate within a field. There have been
a limited number of studies however, which provide data of sufficient
detail to assess hew well the models do represent average transport
within a field. In order to gain confidence in the use of models for
pesticide fate and transport predictions, many new studies in various
regions throughout the country are needed. To be of real value in model
testing and validation, these studies should last for several years. For
models which require calibration, a year or more of data may be needed to
calibrate the models and then several additional years of data from that
site will be needed to test the predictions of the calibrated models.


ATRAZINE CONC. (mg/kg)
0.00
0.05
0.10
0.15
50-
75 -
100 7
a
o
K
£ ,25i
g 1 50 !
175 -i
200 J
J I 1 i
-i 1 1 1 I I I I 1 J I L 1
0.20
Sample Date: 3/16/87
MEASURED
PRZM
Notes:
Measured Values are Avg.
of 5 Samples/Depth
147
Figure 6.61. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 03/16/87.
a
o
K
E-1
CU
W
Q
0.
0
25
50
75
100
125
150-
175 -
200-
00
ATRAZINE CONC. (mg/kg)
0.05 0.10 0.15
Jiiii l i till
0.20
j;l i
Sample Date: 5/25/87
MEASURED
PRZM
Notes:
Measured Values are Avg.
of 7 Samples/Depth
Figure 6.62. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 05/25/87.


28
subsoil. The Oviedo site is in the flatwoods area and is poorly drained.
The Lake Hamilton site was located on a sand ridge. Drainage of the
upper soil layers was very rapid. The Oviedo site encompassed 3.6 ha of
treated area and the other site encompassed approximately 1.7 ha.
Aldicarb was banded along each side of the trees and incorporated to
a depth of 5 cm. At the Oviedo site, soil samples were collected in 30
cm increments to a depth of 150 cm. At the Lake Hamilton site, soil
samples were collected in 30 cm increments to a depth of 60 cm, and then
in 60 cm increments to a depth of 300 cm. Soil samples were collected
using a bucket auger and were transferred into plastic bags for storage
at -20C until analyzed. The soil samples were collected monthly for six
months following the first application of aldicarb in 1983. Undisturbed
soil cores and bulk soil samples were also collected from untreated areas
at each site for laboratory determination of soil and chemical
properties.
At each site, two clusters of observation wells were installed
within the treated area, and additional well clusters were located
upgradient and downgradient of the treated area. The wells were screened
at depths of 2, 3, 5, and 6 m at the Oviedo site, and at depths ranging
from 5 to 15 m, depending on location, at the Lake Hamilton site. Water
samples were usually collected with a peristaltic pump. At least 5 well
volumes were pumped prior to collecting a sample for analysis. When
flowrates into the well were too slow to permit pumping of 5 well
volumes, the wells were evacuated and the water entering the wells was
pumped for an additional 5 minutes prior to collection of the sample.
The groundwater temperature, pH, and conductivity were measured during


183
Figure C.l Continued.
CCCCCC
C BEGIN CALOUIATIONS FOR SUB-AREAS AND TOTAL CONTROL AREA
CCCCCC
C CALCULATE MASS FLOWS ACCROSS BOUNDARIES OF THE SIX AREAS
C BFLOWXl(I) = FLOW ACCROSS PLANES WITH X = XX(1), Y = YY(I) TO YY(I+1)
C BFLCWX2(I) = FIOW ACCROSS PLANES WITH X = XX(2), Y = YY(I) TO YY(I+1)
C BFDOWY(I) = FLOW ACCROSS PLANES WITH Y = YY(I) X = XX(1) TO XX(2)
CCCCCC
C FIRST FLOWS ACCROSS Y PLANES
CCCCCC
DO 200, KK = 1,7
BFLOWY(KK) = 0.0
BCFLOWY(KK) = 0.0
DO 200, LL = XX(1),XX(2)
IF(LL.EQ.XX(1) .OR.LL.EQ.XX(2)) THEN
C **HALF AREA CONTRIBUTING O BOUNDARIES**
BFIOWY(KK) = BFLOWY(KK) + FLCWY(LL,YY(KK) )/2.0
BCFLOWY(KK) = BCFLOWY(KK) + CFLCWY (LL, YY (KK)) /2.0
ELSE
BFLOWY (KK) = BFLOWY (KK) + FLOWY (LL, YY (KK))
BCFLCWY (KK) = BCFLOWY(KK) + CFLCWY (LL, YY (KK))
ENDIF
200 CONTINUE
CCCCCC
C NOW FLOWS ACCROSS X-PLANES
CCCCCC
DO 210, KK = 1,7
BFLOWXl(KK) = 0.0
BFL0WX2 (KK) = 0.0
BCFLOWX1(KK) =0.0
BCFL0WX2 (KK) = 0.0
STRT = YY (KK)
STP = YY (KK+1)
IF(KK.EQ.7) THEN
STRT = YY(1)
STP = YY(7)
ENDIF
DO 210 LL = STRT,STP
IF(LL.EQ.STRT.OR.LL.EQ.STP) THEN
C **HALF AREA CONTRIBUTING ON BOUNDARIES**
BFLOWXl(KK) = BFLOWXl(KK) + FL0WX(XX(1) ,LL)/2.0
BFL3WX2 (KK) = BFL0WX2(KK) + FL0WX(XX(2) ,LL)/2.0
BCFL0WX1 (KK) = BCFLOWXl(KK) + CFL0WX(XX(1) ,LL)/2.0
BCFLCWX2(KK) = BCFLCWX2(KK) + CFL0WX(XX(2) ,LL)/2.0
ELSE
BFLOWXl(KK) = BFL0WX1 (KK) + FL0WX(XX(1) ,LL)
BFLGWX2 (KK) = BFL0WX2(KK) + FL0WX(XX(2) ,LL)
BCFL0WX1 (KK) = BCFLOWXl(KK) + CFL0WX(XX(1) LL)
BCFL0WX2 (KK) = BCFL0WX2(KK) + CFL0WX(XX(2) ,LL)
ENDIF


11
solute available for advective-dispersive transport through the soil
profile. If the chemical of interest is strongly adsorbed, then only a
small fraction will exist in the solution phase at a given time and be
available for transport. Non-adsorbed chemicals, however, will exist
entirely in the solution phase and are available for transport with
percolating water.
Many pesticides are nonpolar and adsorption occurs primarily on
organic matter surfaces. Pesticides and chemicals which cure polar will
adsorb primarily to clay surfaces (Jury, 1986c). Since the majority of
pesticides are nonpolar, the adsorption onto soils has been primarily
related to the organic matter or organic carbon content of the soil. A
detailed discussion of the factors influencing pesticide adsorption onto
soils is given by Jury (1986c). The relationship between concentrations
of a pesticide in the dissolved and sorbed phases is often represented by
the Freundlich equation:
S = KC11 3.1
where S = adsorbed concentration (/ig/g of soil), C = solution
concentration (/xg/mL), and K and n are empirical constants for the soil-
pesticide system. The exponent n in equation 3.1 is often assumed to be
equal to 1.0 which results in a linear relationship of the form:
S = KdC 3.2
where = partition coefficient (mL/g of soil).
For most uses of equation 3.2 in transport modeling, the
partitioning between the solution and adsorbed phases is assumed to be
instantaneous and reversible. Errors associated with the assumptions of


I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
A. B. (Del) Bottcher, Chairman
Professor of Agricultural Engineering
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
7,
/
y,,
Kenneth L.
Campbell, Codhairman
Associate Professor of Agricultural
Engineering
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy. ^
^^ /tz^Aa-
Huber
>r of Environmental Engineering
Wayne
Profi
Sciences
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Fhilosophy.
P.
Professor of Soil Science
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
E. Dale Ihreadgill /
Professor and Division Chairman
Agricultural Engineering, University
of Georgia


214
In October, 1987, he began employment with the University of
Georgia, Coastal Plain Experiment Station in Tifton, Georgia, where he
holds the title of Assistant Professor in the Department of Agricultural
Engineering.
!t>S3


CHAPTER 1
INTRODUCTION
Agricultural production systems have undergone many changes since
the 1940s. The capability of post-war industry to synthesize almost
unlimited varieties of chemicals in great quantities has provided
farmers with an arsenal with which to battle insects, weeds, and
disease. These chemicals can also provide plants with required
nutrients and regulate their growth. The result of using these chemical
tools along with improved machines for planting, cultivating, and
harvesting has been a dramatic increase in the productivity of the
American farmer.
However, some serious problems associated with the use of agricul
tural chemicals have been identified in recent years. Many of these
chemicals are toxic to non-target organisms such as birds, fish, and the
person who is applying them. Rachel Carson (1962) brought to the
attention of the public the potential harmful effects of using these
chemicals in large quantities over large land areas. Since the
publication of her book 'Silent Spring' there has been much research
conducted to evaluate the fate of agricultural chemicals in the
environment. Daring the 1960s and 1970s, great effort was initiated to
determine the quantities of soil, fertilizers and pesticides which were
entering our lakes and rivers. Through these efforts methods have been
1


208
Date: 04/30/87
Figure D.15. Atrazine concentration in the groundwater on 4/30/87.
Vertical bars indicate sampling locations.
Date: 05/01/87
Figure D.16. Atrazine concentration in the groundwater on 5/01/87.
Vertical bars indicate sampling locations.


88
The application solution was used up just as the sprayer crossed over
well 10-09 (6.1 m short of intended end of application) causing the rapid
decrease in application rate at that point. Overall, the application
showed considerable variability that is probably representative of
chemical applications in general. Similar nonuniformity was observed
during the second application of the bromide tracer as shewn in Figure
6.3. The samples taken during the first application of bromide were lost
due to inadvertent freezing of the samples and subsequent breakage of the
glass sample containers.
Table 6.2 Chemical application results.
Date of
Application
Chemical
Depth of
Water (cm)1
Concentration
(mg/L)1
Application
Rate (kg/ha)1
11/12/86
Atrazine
0.70
51.5
3.41
(31)2
(28)
(39)
11/12/86
Alachlor
0.70
11.1
0.72
(31)
(34)
(36)
11/17/86
Bromide
0.86
_3
_3
(from KBr)
(20)
4/27/87
Bromide
0.74
111.0
7.47
(from KBr)
(20)
(87)
(75)
1Mean of observations
2Coefficient of variation (%)
3Samples lost prior to analysis.
The nonuniformity of application demonstrates one of the problems
associated with prediction of chemical transport within the soil profile.
In addition to recognized variability of soil properties, there is the
canpounding influence of variability in application rates over a field.
The concentrations of alachlor measured in the application samples were


194
Figure C.2 Program to calculate mass balances between sampling periods.
CCCCCC
C PROGRAM TO ANALYZE MASS BALANCES BETWEEN SUCCESSIVE SAMPLING PERIODS
C CREATED: 1/3/88 BY: MATT C. SMITH
C LAST UPDATE: 4/6/88
CCCCCC
REAL NEIWFD3W(28,7) ,NETCFI£W(28i7) ,TWMASS(28,7) ,TCMASS(28,7)
REAL CERRDR(28,7) ,WERROR(28,7) ,AVGWFI£W(28,7) ,AVGCFIOW(28,7)
REAL DELWMASS(28,7),DELCMASS(28,7),AREA(7),PERC(28)
REAL PDEIWMASS(28,7) ,PDELCMASS(28,7) ,DRVOISUM(28,7) ,MSTORSUM(28,7)
REAL MEANWTD(28,7) ,DELEAWTD(28,7) ,WERRORP(28,7) ,CERRORP(28,7)
REAL TOIWERROR(7) ,TOTCERROR(7) ,CFILIX(28) ,APPAREA
INTEGER I,J,DAY(28) ,NFILE
CHARACTER*8 DATE(28)
CHARACTER*12 INFILE,INFILE2,OUTFILE,COMMENTS(100)
CCCCCC
C APPAREA = APPLICATION AREA SIZE (ha)
CCCCCC
APPAREA = 0.02787
CCCCCC
C READ IN INHJT AND OUTHJT FILENAMES
CCCCCC
OPEN (UNIT^l, FILE=' INFLUX. DATSTATUS=' OID')
READ(1,102) INFIIE
READ(1,108)NFILE
READ(1,102) INFILE2
READ(1,102)OUTFIIE
102 FORMAT (A12)
108 FORMAT(12)
CCCCCC
C OPEN FILES
CCCCCC
OPEN (UNIT=3, FILE=INFILE, STATUS=' OID')
OPEN (UNIT=4, FILE=INFILE2, STATUS=' OID')
OPEN (UNIT=5, FILE=OUTFILE, STATUS=' UNKNOWN')
OPEN (UNIT=7, FILE=' SUMMARY. SUMSTATUS=' UNKNOWN')
CCCCCC
C READ IN DATA FROM INPUT FILES
C DAY (I) = DAYS SINCE APPLICATION
C PERC(I) = PERCOLATION DURING PERIOD (can)
C CFLUX(I) = CHEMICAL FLUX DURING PERIOD (g/ha)
C AREA(I) = AREA OF SUBAREAS (m**2)
C NETFLCW(I) = NET WATER FLUX INTO (+) OR OUT OF (-) SUBAREAS (m**3/day)
C TWMASS(I) = TOTAL WATER IN SUBAREA (m**3)
C DRVOLSUM(I) = DRAINED VOLUME IN SUBAREA (cm)
C MSTORSUM(I) = WATER IN SATURATED ZONE IN SUBAREA (m**3)
C MEANWTD(I) = MEAN WATER TABLE DEPTH IN SUBAREA (CM)
CCCCCC
READ(3,721)(COMMENTS(I),1=1,54)
DO 10, I = 1,NFILE
READ(3,*)


185
Figure C.l Continued.
C952 F0RMAT(1X,3I3,2(4X,F10.5),' HALF AREA')
ENDIF
ELSEIF(J.EQ.STRT.OR.J.EQ.STP) THEN
IF(I.EQ.XX(1).OR.I.EQ.XX(2)) THEN
GO TO 300
ELSE
TWMASS(KK) = TWMASS(KK) + WMASS(I, J)/2.0
TCMASS(KK) = TCMASS(KK) + CMASS(I,J)/2.0
MSTORSUM(KK) = MSTORSUM(KK) + MAXSTOR(I, J)/2.0
DRVOLSUM(KK) = DRVOLSUM(KK) + CKVOL(I, J)/2.0
AREA(KK) = AREA(KK) + DELX DELY / 2.0
MEANWTD(KK) = MEANWID(KK) + WTD(I, J) *DELX*DELY/2.0
C WRITE(5,952)KK,I,J,TWMASS(KK) ,WMASS(I,J)
ENDIF
ELSE
TWMASS(KK) = TVJMASS (KK) + WMASS(I,J)
TCMASS(KK) = TCMASS(KK) + CMASS(I,J)
MSTORSUM(KK) = MSTORSUM(KK) + MAXSTOR(I,J)
DRVOISUM (KK) = DRVOISUM(KK) + HVOL(I,J)
AREA(KK) = AREA(KK) + DELX DELY
MEANWTD(KK) = MEANWTD(KK) + WTD (I, J) *DELX*DELY
C WRITE(5,953)KK,I,J,TWMASS(KK) ,WMASS(I,J)
C953 F0RMAT(1X,3I3,2(4X,F10.5),' FULL AREA')
ENDIF
300 CONTINUE
MEANWTD(KK) = MEANWTD(KK) / AREA(KK)
310 CONTINUE
CCCCCC
C WRITE NODAL VALUES TO OUTR7T FILE
C XPOS AND YPOS ARE THE X AND Y POSITIONS IN THE FIELD IN METERS
C ROW AND COL CORRESPOND TO THE STATION ID LOCATIONS
CCCCCC
IF(IOUT.GT.O) THEN
WRITE(5,110) DATE
C WRITE(5,623)(OQND(I),1=1,45)
C623 FORMAT(10F7.2)
WRTIE(5,120)
DO 60, J= 2, (NY-1), INC
DO 60, 1= 2, (NX-1), INC
IF(J.EQ.14.AND.INC.GT.1.AND.I.EQ.2) THEN
JJ = 11
DO 80 II = 10,18,4
XPOS = FLOAT(II-l) DELX + XMIN
YPOS = FLOAT (JJ-1) DELY + YMIN
ROW = 6.0 + FLOAT (II-2) / 4.0
COL = 7.0 + FLOAT (JJ-2) / 4.0
WRITE(5,130)II,JJ,ROW,COL,XPOS,YPOS,SURF(II,JJ) ,IMP(II,JJ) ,
# WT(II,JJ) ,WTD(II,JJ) ,SLPX(II,JJ) ,SLPY(II,JJ),MAGNITUDE(II,JJ) ,
# DIRECTION (II, JJ)
80 CONTINUE


71
The user manuals for the models contain tables drawn from many
sources which provide values of the partition coefficient for many
pesticides. The LEACH manual also contains a number of tables. The
tables in CREAMS and LEACH are more extensive than those in FRZM.
Different tables within the same manual may give differing values. Table
values often list coefficients of variation (CV) on the order of 50-130
percent. FRZM presents equations by which the organic carbon partition
coefficient, K^, can be calculated if solubility or the octanol^water
partition coefficient, K^, is known.
The partition coefficients of alachlor and atrazine were not
measured on this soil and thus values were chosen from the sources
described above. A value for atrazine of 163 cm3/g with a CV of 49%
was found in the LEACH manual. No listing of a was found for
alachlor. FRZM did list a value for logiK^) which was 2.78. FRZM
presents a relationship between logfK^) and which is
log Kqc = 1.00 (log K^) 0.21 5.1
Using this equation a Kq,-, of 371 was calculated for alachlor. LEACH
lists a value for alachlor as 434, using this and equation 5.1, a
value of 268 was calculated. Since alachlor has been reported as one of
the pesticides commonly found in groundwater, it was decided to choose
the lower Kqq value for use in the model as this would tend to cause
higher predicted leaching losses.
The inorganic tracers bromide, chloride, and nitrate were assumed to
act as non-adsorbed pesticides. Thus they were assigned a value of
zero.


145
-
Oh
W
Q
0.0
o-t-1-^
25j /'
50i '
4
100t
1 25 T
150 ^
175 -E
200-
ATRAZINE CONC. (mg/kg)
0.2 0.4 0.6 0.8 1.0 1.2 1.4
i i f i I i i i i i i i I i i i I i i i iiiij
Sample Date: 11/18/86
MEASURED
PRZM
Notes:
No Samples Below 5 cm.
Measured Value is Avg.
of 14 Samples
Figure 6.57. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on 11/18/86.
Jh
E-
CU
H
Q
ATRAZINE CONC. (mg/kg)
o.o
0
25£
50 ~
75
100
0.2
1 L l 1 | 1
1 25 ^
1 50 ;
0.4 0.6 0.8 1.0 1.2 1.4
J 1 1 I I I I I I I I L, I L_J I lilil
i.
Sample Date: 11/24/86
MEASURED
PRZM
Notes:
No Samples Below 20 cm.
Measured Values are Avg.
of 8 Samples/Depth
1 75 t
200 d
Figure 6.58. Comparison of measured and PRZM predicted atrazine
concentrations in the soil on n/24/86.


65
The integrator available with the GC did not work properly. In
order to quantify the amount of herbicides present in the samples, all
peak heights were hand measured (a peak height mode, as used with the IC
was not available on this integrator).
A linear regression was performed from the standards to give a
relationship of the form: concentration = f(peak height). There were no
problems with negative intercepts in these regressions as discussed in
the section on ion chromatography. Sample concentrations were computed
with the regression equations calculated from standards on either side of
the samples. In order to account for drift in the detector response, the
final concentrations were calculated by linearly weighting the
concentrations computed from each set of standards as discussed
previously. The NP detector had a tendency to drift and this correction
for drift was usually needed.


Table C.2. Sample output from program FLUX
COMMENT : OPTIMIZING CONDUCTIVITY IN SUBAREA #1
COMMENT : USING PRZM PERCOLATION FLUX DATA
PROGRAM RUN ON 04/22/1988 AT 10.43.20.59
HYD COND. : 12.00 12.00 12.00 12.00 12.00 12.00 12.00
POROSITY : .36
X-NODE BOUNDARIES : 6 18
Y-NODE BOUNDARIES : 2 8 14 18 22 26 30
DAYS SINCE APPLICATION
40, PERCOLATION
SINCE
12/15/86 = 1.32
cm
DELTA WSTORAGE (m3)
-13.3543
12.5565
-10.1853
15.0013
-12.6839
-5.3586
-69.1410
PRED DELTA WSTORAGE
-3.3449
7.2061
-7.6461
21.0692
-15.0421
25.8956
28.1378
DAYS SINCE APPLICATION
47, PERCOLATION
SINCE
12/22/86 = 3.04
cm
DELTA WSTORAGE (m3)
-.6429
-.4067
1.9388
.0304
-3.4596
-4.6639
-7.2030
PRED DELTA WSTORAGE
10.8180
16.7252
3.9850
33.0902
-13.0865
29.6292
81.1611
DAYS SINCE APPLICATION
54, PERCOLATION
SINCE
12/29/86 = 2.62
cm
DELTA WSTORAGE (m3)
-8.7798
12.0053
-6.6799
-5.3921
-5.2577
-5.5686
-43.6831
PRED DELTA WSTORAGE
10.1124
13.0013
3.2031
30.8369
-12.3547
26.2670
71.0661
DAYS SINCE APPLICATION
61, PERCOLATION
SINCE
1/05/87 = 2.75
cm
DELTA WSTORAGE (m3)
.1695
.1741
.4625
.1102
-3.9066
-5.5415
-8.5320
PRED DELTA WSTORAGE
12.8376
13.5732
6.0237
27.4354
-4.5690
21.0263
76.3270
DAYS SINCE APPLICATION
68, PERCOLATION
SINCE
1/12/87 = 5.28
cm
DELTA WSTORAGE (m3)
4.3478
5.5865
4.6180
3.6360
2.4435
2.5201
23.1521
PRED DELTA WSTORAGE
28.8188
31.1019
13.3882
44.5662
10.9241
31.1496
159.9487


20
(leaf area and rooting depth). Water in the root zone can be removed by
percolation, evaporation, or transpiration by the plant. Percolation is
calculated based on the water-holding capacity of the soil. When the
water content in a layer exceeds field capacity (1/10 1/3 bar tension)
the excess water drains into the next lcwer compartment. There is an
option in PRZM that allows the draining of the profile to occur over a
longer period than one day.
Pesticide processes represented include advective and dispersive
flux, sorption, degradation in soil and on plant foliage, and plant
uptake. Volatilization and transport in the vapor phase are not
considered. Application of pesticides can be partitioned between the
soil surface and plant foliage. Applications to the soil can be
incorporated by tillage. Different degradation rates can be specified
for soil and foliar pesticide residues. The degradation rate within the
soil can also be varied by soil layer.
PRZM will simulate multiple applications of one pesticide each year
for many years of continuous climatic record. Thus it can predict
temporal variations in leaching and runoff. Effects of agricultural
management and cropping practices can be simulated. FRZM allows the user
to request time series results for many variables in the model. This
feature is useful for observing processes at intermediate locations
within the vadose zone over time.
GLEAMS (Groundwater Loading Effects of Agricultural Management
Systems) is also a field-scale hydrology and transport model (Leonard et
al., 1987). It is based upon the extensively documented and applied
CREAMS model (Knisel, 1980). CREAMS is a nonpoint source model for


25
adsorbed phases. Rao and Davidson (1980) indicate that the assumption of
linearity can lead to errors on the order of a factor of two to three.
The previous examples illustrate that the mathematical representations of
processes used in models often have inherent inaccuracies and
uncertainties.
The parameters which are required by the models to solve the
equations which describe the system also have inherent uncertainties
associated with them. Jury, (1985), in a review of the literature,
reported coefficients of variation (CV) from field measurements of
several soil physical properties. Soil porosity was found to have a mean
CV of 10%. Bulk density measurement had a CV of 9%. Determinations of
particle size fractions had an average CV of 28%, with a range of 3 to
55%. The water-holding capacity of the soil at matric potentials of 0.1
and 15 bars had a CV of 15 and 25%, respectively. Measurements of
saturated hydraulic conductivity and infiltration rates showed a CV of
124 and 72%, respectively. In a study by Jury et al. (1986), the
partition coefficient, Kj, of napropamide measured on 36 samples from a
1.44-ha field had a CV of 31%. Rao and Davidson (1980) reported that the
normalized partition coefficient, K^, reported from individual studies
generally had a CV of 40-60%. The variability of the properties reported
above are not necessarily due to the fact the samples were taken over
large areas. Jury (1985) reported that one author had reported that up
to 50% of the variation in a parameter over a field occurred within a 1
m2 area.
Given the uncertainties associated with models and the parameters
required by the models as outlined briefly above, how closely must a


94
SOIL CONC. VS. APPLICATION CONC.
Figure 6.7. Correlation between alachlor concentrations in the top
5 cm of soil and application solution concentrations.
SOIL CONC. VS. APPLICATION RATE
Figure 6.8. Correlation between alachlor concentrations in the top
5 cm of soil and application rate.