|Table of Contents|
Table of Contents
List of Tables
List of Figures
Chapter 1. Introduction
Chapter 2. Objectives
Chapter 3. Literature review
Chapter 4. Materials and methods
Chapter 5. Results and discussion
Chapter 6. Summary and conclusions
Appendix A. Chromatographic conditions and quality control parameters for the analysis of aromatic hydrocarbons
Appendix B. Field sampling procedures
Appendix C. Isotherm data for the sorption of study compounds to aquifer materials
Appendix D. Breakthrough curve data for sorption of study compounds to aquifer materials
Appendix E. Batch biodegradation data
Appendix F. Column breakthrough data for biodegradation columns
Appendix G. Hydrocarbon concentrations in monitoring wells at the Lake Alfred Citrus Research and Education Center
AN EVALUATION OF THE ATTENUATION MECHANISMS
FOR DISSOLVED AROMATIC HYDROCARBONS FROM GASOLINE
SOURCES IN A SANDY SURFICIAL FLORIDA AQUIFER
JOSEPH TIMOTHY ANGLEY
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
Joseph T. Angley
My sincere thanks are extended to my commitee chairman, Dr. Lamar Miller, for his insight and support during this research. Special thanks are also extended to my cochairman, Dr. Joseph J. Delfino, for his guidance and thoughtful criticism. I would also like to thank Dr. Paul Chadik, Dr. Peter Nkedi-Kizza and Dr. Daniel Spangler for their generous assistance in the design and interpretation of these experiments. My thanks also go to Dr. Suresh Rao for his probing questions and criticisms and for the kind use of his laboratory.
The work presented in this dissertation could not have been accomplished without the support and assistance of many of my colleagues. my sincere thanks are extended to Mr. Norman Cabrera for his valuable assistance with several laboratory experiments, and to Mr. Gene Killan, Ms. Vicki Card and Mr. Ben Horenstein for their help in the collection of field samples and maintenance of the field site. I would also like to thank Ms. Robin Mitchell for her work on the microbial analyses, Mr. Jimmy Yeh for his work on GC analyses, Ms. Linda Lee for her help in setting up the column studies, and Mr. Bill Davis for his expert assistance with gas chromatography and quality assurance procedures.
Finally, this work would not have been possible without the love, support and friendship provided by my wife Beth. I also thank my family for their financial support during my many years of schooling.
This research was funded through a research grant from the Institute for Food and Agricultural Sciences.
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ................................... iii
LIST OF TABLES .................. o .................. vii
LIST OF FIGURES ............................... x
ABSTRACT ........................................... xii
I INTRODUCTION, ................................ 1
II OBJECTIVES ................................... 6
III LITERATURE REVIEW ............................ 7
3.1 Introduction ............................ 7
3.2 Environmental Effects of Gasoline
Contamination ....................... 7
3.3 Convective-Dispersive Models ............ 10
3.4 Sorption of Aromatic Compounds .......... 13
3.5 Biodegradation of Aromatic Compounds .... 24 3.6 Summary ................................. 33
IV MATERIALS AND METHODS ........................ 34
4.1 Introduction ......................... :*: 34
4.2 Site Description .................... 34
4 3 Aquifer Material ........................ 35
4 4 Choice of Solutes ....................... 38
4 5 Hydrocarbon Analyses .................... 40
4 6 Hydrolysis Studies ...................... 42
4.7 Batch Sorption Studies .................. 43
4.8 Column Sorption Studies ................. 45
4.9 Hydrogen Peroxide Evaluation ............ 47
4.10 Batch Biodegradation Studies ............ 48
4.11 Column Biodegradation Studies ........... 53
4.12 Field Studies ........................... 56
V RESULTS AND DISCUSSION ....................... 59
5.1 Introduction ............................ 59
5.2 Hydrolysis of Aromatic Compounds ........ 59 5.3 Characterization of Aquifer Materials ... 61 5.4 Batch Sorption Studies .................. 61
5.5 Breakthrough Curves for Aromatic Solutes 79
5.6 Evaluation of Sorption models ........... 93
5.7 Comparison of Mixed Solute and Single
Solute Retardation .................. 106
5.8 Evaluation of Hydrogen Peroxide
Reactivity .......................... 108
5.9 Batch Biodegradation Experiment #1 ...... ill 5.10 Batch Biodegradation Experiment #2 ...... 128 5.11 Column Biodegradation Experiments ....... 146 5.12 Comparison of Field and Laboratory data. 156
VI SUMMARY AND CONCLUSIONS ...................... 168
6.1 Summary ................................. 168
6.2 Conclusions ............................. 178
A CHROMATOGRAPHIC CONDITIONS AND QUALITY
CONTROL PARAMETERS FOR THE ANALYSIS OF
AROMATIC HYDROCARBONS ........................ 179
B FIELD SAMPLING PROCEDURES .................... 182
C ISOTHERM DATA FOR THE SORPTION OF
STUDY COMPOUNDS TO AQUIFER MATERIALS ......... 186
D BREAKTHROUGH CURVE DATA FOR SORPTION OF
STUDY COMPOUNDS TO AQUIFER MATERIALS ......... 214
E BATCH BIODEGRADATION DATA .................... 229
F COLUMN BREAKTHROUGH DATA FOR
BIODEGRADATION COLUMNS ....................... 272
G HYDROCARBON CONCENTRATIONS IN MONITORING
WELLS AT THE LAKE ALFRED CITRUS RESEARCH
AND EDUCATION CENTER ......................... 291
REFERENCES ............ o ........ o ................... 306
BIOGRAPHICAL SKETCH ................................ 318
LIST OF TABLES
3-1 Selected physical properties of study compounds 9
3-2 Summary of adsorption data for aromatic
hydrocarbons ................................... 23
3-3 Sorption coefficients of selected aromatic
hydrocarbons on low organic soil ............... 25
4-1 Experimental design for batch biodegradation
experiment #1 .................................. 49
4-2 Experimental design for batch biodegradation
experiment #2 .................................. 51
5-1 Selected physical and chemical properties of the
Lake Alfred aquifer material ................... 62
5-2 Regression parameters for the analysis of
average values of equilibrium batch isotherm
sorption data with the linear model ............ 65
5-3 Regression parameters for the analysis of
average values of equilibrium batch isotherm
sorption data with the linear model suppressedd
intercept) ..................................... 67
5-4 Regression parameters for the analysis of
average values of equilibrium batch isotherm
data with the Freundlich model ................. 68
5-5 Ratios of sorbed concentrations calculated from
Freundlich and linear equilibrium models ....... 71
5-6 Regression parameters for the analysis of
average values of equilibrium batch
desorption data with the linear model .......... 76
5-7 Regression parameters for the analysis of
average values of equilibrium batch desorption data with the linear model
suppressedd intercept) .......................... 77
5-8 Regression parameters for the analysis of
average values of equilibrium batch
desorption data with the Freundlich model .........78
5-9 Values of Dispersion calculated from the
breakthrough curves of unretained solutes in
laboratory columns................................. 81
5-10 Calculated values of R, K and K from
analysis of solute breakthrough curves........... 87
5-11 Retardation factors calculated from leaching
column and equilibrium batch isotherm data ........88
5-12 An empirical index of sorption nonequilibrium
(ISNE) for 12 selected aromatic solutes leaching
through Lake Alfred aquifer material............. 90
5-13 Regression coefficients for plots of log Koc
VS. log Kow and log Koc vs log WS................ 98
5-14 Comparison of relationships to predict Koc
from Kow values.................................... 99
5-15 Regression coefficients for the relationship
between log Koc and X............................ 104
5-16 Total average hydrocarbon values (ug/L) in the
microcosms of batch biodegradation
experiment #1...................................... 113
5-17 Biodegradation rate constants, half lives and
correlation coefficients for the fit of
biodegradation experiment #1 data to a first
order rate equation................................ 116
5-18 Biodegradation rate constants, half lives
and correlation coefficients for the fit of
biodegradation experiment #1 data to the
Thomas-slope rate equation........................ 118
5-19 Total average hydrocarbon values (ug/L) in
the microcosms of batch biodegradation
experiment #2...................................... 129
5-20 Biodegradation rate constants, half lives and
correlation coefficients for the fit of
biodegradation experiment #2 data to a first
order rate equation................................ 133
5-21 Biodegradation rate constants, half lives and
correlation coefficients for the fit of
biodegradation experiment #2 data to the
Thomas-slope rate equation........................ 135
5-22 First order biological rate constants and
half-lives of aromatic hydrocarbons for the
biodegradation column with flow at 0.90 mL/hr.. 149
5-23 First order biological rate constants and
half-lives of aromatic hydrocarbons for the
biodegradation column with flow at lmL/min........151
5-24 Microbial populations in a soil core taken
south of the paint shop (bldg 54), June, 1986.. 163
5-25 Microbial populations in a soil core taken
in the spray field June, 1986..................... 163
5-26 Microbial populations in a soil core taken
south of the pump house (bldg 12) july, 1986 ... 164
5-27 Microbial populations from samples collected
during instasllation of monitoring wells
RAP-5 amd RAP-6, September, 1986.................. 165
5-28 Water chemistry parameters from selected
monitoring wells at Lake Alfred CREC, 1986 ........166
LIST OF FIGURES
4-1 Site plan of the field research site at
the Citrus Research and Education Center,
Lake Alfred, Fl.................................... 36
4-2 Extent of the hydrocarbon pl1ume at the field
research site as of October, 1986................. 37
5-1 Approach to equilibrium for several aromatic
solutes on Lake Alfred aquifer material.......... 63
5-2 Freundlich sorption isotherm
for benzene at equilibrium........................ 73
5-3 Freundlich sorption isotherm for
toluene at equilibrium............................ 74
5-4 Breakthrough curve for chloride for a 5 cm
sorption column.................................... 80
5-5. Breakthrough curve for benzene from
Lake Alfred water (Co = 4700 ug/L)................ 83
5-6 Breakthrough curve for toluene from
Lake Alfred water (Co 2600 ug/L)................ 84
5-7 Breakthrough curve for n-propylbenzene from
Lake Alfred water (C. = 1000 ug/L)................ 85
5-8 Log K o vs. log K o for study compounds.......... 95
5-9 Log K H(from column data) vs. log WS
for s udy compounds................................ 97
5-10 Regression equations for several models
describing the relationship between K and K :(a) Curtis et al., 1985
(8 Schwaorzenbach and Westall, 1981 (c) this
study (d) Briggs, 1981 (e) Chiou et al., 1983.. 101
5-11 Log K ocVS. l X for aromatic solutes (a) in this
study and (b) from Sabljic (1987)................. 103
5-12 Breakthrough curve for benzene (single solute)
spiked into RAP-2 well water (Co = 4000 ug/L).. 107
5-13 Reaction of OHM-4 well water to the
addition of 50% hydrogen peroxide and aquifer
5-14 Relative concentration vs. time for five aromatic
compounds in biodegradation treatment 1A ..........121
* 5-15 Relative concentration vs. time for~ four C9H1
compounds in biodegradation treatment 1A ..........122
5-16 Concentration vs. time for dissolved oxygen
in biodegradation treatments 1A, 1B and 1C ........124
5-17 Concentration vs. time for dissolved oxygen
in biodegradation treatments 1D, 1E, 1F
and 1G.............................................. 126
5-18 Relative concentrations of C C aromatic
hydrocarbons vs. time in biogegradation
treatment 2B....................................... 137
5-19 Relative concentrations of C -Caromatic
hydrocarbons vs. time in biogegradation
treatment 2D....................................... 139
5-20 Concentration vs. time for dissolved oxygen
in biodegradation treatments 2D, 2E and 2F ........140
5-21 Electron transport activity in biodegradation
treatments 2D, 2E and 2F.......................... 141
5-22 Electron transport activity in biodegradation
treatments 2A, 2B and 2C.......................... 145
5-23 Breakthrough curves for aromatic compounds in
column biodegradation experiments performed
at a flow rate of 0.90 mL/hr..................... 148
5-24 Breakthrough curve for benzene in column
biodegradation experiment performed at a flow
rate ofl1mL/min.................................. 152
5-25 Breakthrough curve for toluene in column
biodegradation experiment performed at a flow
rate of 1 mL/min.................................. 153
5-26 Breakthrough curve for 1,2,4-trimethylbenzene
in column biodegradation experiment performed
at a flow rate of 1 mL/min....................... 154
5-27 Breakthrough curve for field tracer (NH4Cl)
experiment measured at RAP-10 ..................... 158
5-28 Distribution of benzene (ug/L) at the
Lake Alfred field site ........................ 160
5-29 Distribution of o-xylene (ug/L) at the
Lake Alfred field site ........................ 162
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
AN EVALUATION OF THE ATTENUATION MECHANISMS
FOR DISSOLVED AROMATIC HYDROCARBONS FROM GASOLINE
SOURCES IN A SANDY SURFICIAL FLORIDA AQUIFER
JOSEPH TIMOTHY ANGLEY
Chairman: Wesley Lamar MIller Cochairman: Joseph J. Delfino Major Department: Environmental Engineering Sciences
Gasoline is a significant source of groundwater
contamination in Florida. This results from the large numbers of gasoline storage tanks, high rainfall, reliance on groundwater-based potable water supplies and the hydrogeology of Florida. Sorption, biodegradation and hydrolysis of dissolved aromatic hydrocarbons (all isomers of C 6H 6- C9 H12 ) were determined in multicomponent experiments with natural aquifer materials under saturated conditions. Hydrogen peroxide, air, oxygen gas and ammonium chloride treatments were evaluated as methods to enhance microbial degradation of aromatic hydrocarbons. The solutes and sorbents were from a gasoline contaminated aquifer in central Florida. This site was typical of sandy surficial Xiii
aquifers in Florida with a low organic carbon content (0.015%). The aquifer was composed primarily of fine to medium grained sands.
Hydrolysis was not a significant removal mechanism for the selected aromatic solutes. Equilibrium batch isotherms and column studies determined sorption coefficients for aromatic solutes ranging between 0.045 and 0.1 with retardation values between 1.36 and 2.40. Column breakthrough curves exhibited minimal effects of adsorption non-equilibrium. Sorption isotherms were linear through the concentration range tested and no significant hysteresis was noted. Partitioning and surface dependent adsorption were evaluated by regression of column K oc data with literature values of K ow water solubility and first order connectivity indices. No single model fully described the sorption process, and the sorption mechanism appeared to be a combination of several processes. Competitive solute interactions were not shown to be significant.
Column biodegradation experiments with acclimated
microorganisms were performed at flow velocities close to those from the contaminated aquifer. Half lives ranged from
0.940 hr for benzene to 0.086 hr for n-propylbenzene at
0.680 cm/min. Branched aromatic solutes were more easily degraded in column studies.
Batch studies demonstrated the ability of field
microbes to degrade aromatic hydrocarbons to less than 0.5 ug/L given sufficient oxygen. Microbes were not phosphorus Xiv
or nitrogen limited. Hydrogen peroxide increased dissolved oxygen, but did not lead to increased hydrocarbon removal in lab studies. Ammonium chloride produced nitrifying conditions. Oxygen augmentation with air and oxygen gas was shown to enhance biological removal of aromatic hydrocarbons.
Groundwater contamination is a topic of great
scientific interest and public concern. Groundwater provides approximately 100 million people with potable water in the United States (Hoag and Marley, 1986) and nearly every state contains some number of contaminated wells (Barbash and Roberts, 1986). The sources of groundwater contamination are numerous. These include seepage from lagoons and impoundments, landfills, agricultural and silvicultural practices, accidental spills and leaking storage tanks and transfer equipment.
Gasoline and petroleum products are some of the most common groundwater pollutants. The potential magnitude of this problem is evident from the volume of petroleum used in the United States. Approximately 110 billion gallons of motor fuels are stored underground each year, in an estimated 1.4 million underground storage tanks, 85% of which are unprotected steel tanks with finite lifetimes (Hoag and Marley, 1986). It is expected that 10 to 30% of these tanks may leak (Dowd, 1984).
Gasoline contamination of groundwater in Florida is a particularly serious problem. This results from the confluence of three factors: the large number of petroleum storage tanks in the state, the reliance on groundwater
based potable water supplies, and the hydrogeology of Florida.
The major sources of petroleum contamination in Florida are leaking storage tanks and pipes. The high water table in the'state leads to conditions favorable for corrosion. As of February 1986 there were 455 known storage tank incidents resulting in 368 cases of groundwater contamination. The total volume of spilled gasoline exceeds
4.2 million gallons (FLDER, 1986). The remaining 60,000 petroleum storage tanks in the state provide potential sources for future groundwater pollution.
These sources of contamination are particularly significant owing to the importance of groundwater in Florida. Groundwater withdrawal for potable water use is approximately 1400 Mgal/d, comprising 87% of public water and 94% of rural water supplies. It is noteworthy that nearly 2 million residents drink untreated water from shallow private wells which are particularly prone to contamination from underground storage tanks (Fernald and Patton, 1984).
Hydrogeology is the third factor which contributes to the sensitivity of Florida's water supplies to gasoline contamination. Most of the potable water aquifers are surficial or intermediate in depth, and are susceptible to contamination. In addition, the generally porous nature of top soil in the state enhances pollutant transport to the underlying aquifers. Most soils in Florida are sandy loam,
sandy clay and sandy clay loams, all of which are noted for their relatively high permeabilities (Fernald and Patton, 1984). The sandy deposits of the Pliocene and Pleistocene ages common to Florida are also marked by low organic carbon and clay content (Fetter, 1980), resulting in high permeability and low sorptive capacity.
Given the magnitude of this problem, the transport and environmental reactions of dissolved gasoline components in shallow sandy aquifers is an important area of study. This is particularly true of the aromatic constituents of gasoline, owing to their toxicity, concentrations in gasoline and their high aqueous solubility. Gasoline products which are released into the vadose zone travel downward under the influence of capillary and gravitational forces. When the sorptive capacity of the soil is exceeded, gasoline moves onto the groundwater table, where it spreads laterally across the top of the saturated zone. This is a result of the density of gasoline (0.7-0.75 g/cm 3 ). Gasoline components partition into the water to the extent of their water solubility, and move in the direction of the water table gradient.
Physical, chemical and biological factors must all be
considered in the determination of the fate and transport of dissolved gasoline hydrocarbons in groundwater. The interaction of these factors may be conveniently examined in the context of a generalized mass transport equation. A one-dimensional form of this equation is
@C3 D h ( C/9x2) V v( 3C/3x) -/ E/( @S/ 3t) Qi [1.1]
where C = solution phase concentration of solute (ug/L)
S = adsorbed phase concentration of 3solute (ng/g)
a = volumetric water content (mL/cm
t = time (min)
x = horizontal distance (cm)
p = bulk density (g/cm ) 2
D h= hydrodynamic dispersion coefficient (cm /min)
v = average pore water velocity (cm/min)
Q.i = degradation rate (minm
The components of this equation are convection,
dispersion, sorption and degradation terms. Convection describes the movement of a dissolved contaminant with the groundwater. Dispersion describes the spreading of the solutes during flow through the aquifer material. Sorption terms account for the retardation of the dissolved solutes by interaction with the aquifer matrix, and degradation terms evaluate the removal or transformation of the contaminants. Research has shown that sorption and biological degradation are the major attenuation mechanisms for organic solutes in soils and groundwater (Woodburn, 1985).
Mathematical models based on such equations are
important tools for the prediction of contaminant movement (Pinder, 1984). However, the adequacy of these predictions is directly related to a knowledgeable and accurate quantification of the processes involved (MacKay et al., 1985). The remediation of groundwater contamination also
requires detailed and usually site specific data for these processes.
This dissertation presents a detailed investigation of the attenuation of selected dissolved aromatic gasoline hydrocarbons in a typical sandy surficial aquifer in Florida, using a variety of batch and column techniques. Sorption coefficients and biological and abiotic degradation rates from laboratory studies are presented. These studies simulated conditions at a contaminated field site and included experiments to assess various treatment alternatives. Actual field data are summarized, and compared to the laboratory data.
The improved understanding obtained from the collection and analysis of these data should aid in the formation and improved use of predictive models describing the movement and reaction rates of water soluble components of gasoline. in shallow groundwater systems and aid in the selection of appropriate groundwater reclamation technologies.
The main objectives of this study were:
(1) To evaluate the sorption coefficients for 12
selected aromatic hydrocarbons found in water at the Lake Alfred research site, employing batch isotherms and soil columns;
(2) To determine the rates of hydrolysis of the 12 selected aromatic hydrocarbons;
(3) To determine the rates of biodegradation of the 12 selected aromatic hydrocarbons under simulated field conditions, and after treatment with hydrogen peroxide, oxygen gas and ammonium chloride;
(4) To determine the most appropriate predictive model for sorption of the 12 selected aromatic hydrocarbons in a sandy surficial aquifer in Florida;
(5) To correlate molecular properties of the selected aromatic hydrocarbons with sorptive and biological parameters;
(6) To evaluate field data based on the laboratory measurements of sorption and biodegradation and
(7) To extrapolate the laboratory data for application of aquifer remediation practices.
This chapter presents a review of the pertinent
literature for the reactions of gasoline derived aromatic hydrocarbons in groundwater. The major areas of discussion are the environmental effects of gasoline contamination, the use of advection-dispersion transport models, the sorption of aromatic compounds to aquifer materials, and the biodegradation of aromatic compounds in groundwater systems.
3.2 Environmental Effects of Gasoline Contamination
Gasoline is a complex mixture of many hydrocarbon compounds. A typical gasoline contains between 150-250 identifiable hydrocarbon components (Sanders and Maynard, 1968) consisting of alkane, alkene, aromatic and napthene hydrocarbons. Automobile gasolines are comprised of C 5- C12 hydrocarbons with boiling points in the range 32-210 C. Unleaded gasolines contain greater~ concentrations of aromatic hydrocarbons to provide for anti-knock protection and branched hydrocarbons to increase octane ratings (Moore and Moore, 1976).
Despite the large number of hydrocarbons comprising
gasoline, much of the environmental concern focuses on the water soluble components of gasoline, particularly the single ring aromatic compounds. These compounds are of concern based on their toxicity, aqueous solubility and concentration in gasoline (Barker and Patrick, 1985). Acute toxicity is associated with the water soluble fraction of oils (Blumer et al., 1973) and the major components of the water soluble fraction are aromatic (Coleman et al., 1984). Data from the work of Coleman et al. (1984) showed that although aromatic components made up only 50% of the unleaded gasoline product in their study, 87-95% of the components in the water soluble fraction were aromatic. Thus in a spill situation a significant amount of the contaminants in the water phase will be aromatic. Selected physical properties of the compounds used in this study are listed in Table 3-1.
The health effects from the use of gasoline
contaminated water may be significant. Benzene is a carcinogen in rats and mice and exposure is linked with leukemia (USPHS, 1981). The maximum contaminant level for benzene in community drinking water supplies is 1 ppb in Florida. Toluene, ethylbenzene and m-xylene affect the central nervous system (Windholtz, 1976). Unleaded gasoline induces renal and hepatocellular carcinomas in rats and the use of petroleum contaminated water can produce elevated levels of indoor air pollutants allowing chronic exposure to
Table 3-1. Selected physical properties of study compounds.
Moleculara Waterb Boilinga
Weight, Solubility, Point, Density, log d
Compound AMU mg/L OC g/ml Kow X
Benzene 78.11 1740-1791 80.1 0.8675 1.56-2.28 3.000
Toluene 92.13 515- 724 110.6 0.8669 2.11-2.73 3.394
Ethylbenzene 106.2 131- 208 136.2 0.8670 3.15 3.932
m,p-Xylene 106.7 134- 196 139.1 0.8642 3.18 3.788
o-Xylene 106.7 142- 213 144.4 0.8802 2.77-3.13 3.805
3,4-Ethyltoluene 120.2 40 161.3 0.8645 4.326
1,3,5-Trimethylbenzene 120.2 48- 92 164.7 0.8652 3.42-3.60 4.182
2-Ethyltoluene 120.2 40 165.2 0.8807 4.343
1,2,4-Trimethylbenzene 120.2 52- 59 169.3 0.8758 4.198
1,2,3-Trimethylbenzene 120.2 75 176.1 0.8944 3.60 4.215
Isopropylbenzene 120.2 48- 73 165.4 0.9106 3.60 4.305
n-Propylbenzene 120.2 55 159.2 0.8620 3.57-3.68 4.432
aCRC Handbook of Chemistry and Physics, 1980.
Brookman et al., 1985.
cLeo et al., 1971.
hydrocarbons (Shehata, 1985). Dermal absorption of volatile organic contaminants from gasoline may also be a significant exposure (Brown et al., 1984).
Fire and explosion hazards are also a risk factor in
the release of gasoline to the environment. Volatilization and subsequent gas phase transport of hydrocarbons in the unsaturated zone have destroyed buildings (Hoag and Marley, 1986).
3.3 Convective-Dispersive models
The cogent evaluation of contaminant plumes, remedial action alternatives, and risk assessment for organic compounds in groundwater requires a thorough understanding of the behavior of these contaminants in groundwater systems. This includes an assessment and quantification of the relevant processes which influence their fate and transport (Miller and Weber, 1984).
The interaction of these processes may be examined in the context of convective-dispersive models. These models have been reviewed (Anderson, 1979, Freeze and Cherry, 1979) and are marked by their computational simplicity, reasonable data requirements and sufficiently accurate output (Roberts et al., 1985). Although the adequacy of convective-dispersive models for describing solute transport has been questioned (Anderson, 1979, Smith and Schwartz, 1980), particularly with regard to dispersivity
approximations, these models provide a convenient framework for understanding the transport of dissolved solutes in groundwater.
The general form of the solute transport equation under saturated flow conditions is given by Bear (1979). A onedimensional form of this equation for conservative contaminants under steady flow conditions is
aC/at = D h (a 2C/ax2 )- v( ac/ax) [3.1]
where C = solution phase concentration of solute (ug/L)
S = sorbed phase concentration of solute (n2/g)
D h = hydrodymanic dispersion coefficient (cm /min)
t = time (min)
x = horizontal distance (cm)
V = average pore water velocity (cm/mmn)
The major components of this equation are convection (bulk flow) and dispersion (deviation from bulk flow). A brief discussion of dispersion follows, with reference to extrapolation of laboratory data to field scale applications.
The hydrodynamic dispersion coefficient describes the spreading of a solute as it moves through porous media. Hydrodynamic dispersion (D h) is the sum of mechanical dispersion, caused by differences in water velocity through sinuous and tortuous pores, and molecular diffusion (Biggar and Nielsen, 1962). Dispersion values reflect the heterogeneity of the aquifer material. Dispersion is usually determined by measuring the breakthrough of a conservative tracer such as chloride or tritiated water.
The physical and mathematical relationships of water and solute transport were reviewed by Davidson et al. (1983). Solute dispersion was noted to occur because of macroscale spatial changes in the direction and magnitude of water flow. The continuum approach to mathematically describe water and solute transport in laboratory soil columns was shown to be reasonably successful.
In practice, laboratory measurements and theory may be of little value in predicting dispersion in natural aquifers. Laboratory columns give dispersivity estimates on the order of centimeters, whereas field scale dispersion is usually in meters (Bedient et al., 1985). This is a result of the greater heterogeneity of a field site versus a small homogeneous laboratory column. A solution for equation [3.1] for a finite column using dimensionless variables was presented by Brenner (1962). The dimensionless Peclet number (P e) was used as a measure of dispersion: P= vL/4D h [3.2]
where v is pore water velocity (cm/mmn), L is the length
(cm) of the soil column and D h is the hydrodynamic dispersion coefficient (cm 2 min). For values of P e> 100 dispersion is assumed negligible. Values of P e< 10 generally indicate complete mixing. Boundary conditions for displacement experiments through short laboratory columns were reviewed by van Genuchten and Parker (1984). The solution of Brenner (1962) was shown to correctly conserve mass in finite laboratory soil columns, based on mass
balance considerations. For a flux type inlet boundary condition (flowing concentrations), Brenner's solution was applicable provided the column Peclet number was not much less than five. The solution of Lapidus and Amundson (1952) was recommended to evaluate flux averaged concentrations in finite laboratory columns or semi infinite field profiles.
3.4 Sorption of Aromatic Compounds
Sorption is a major mechanism in the attenuation of organic solutes in the saturated zone. Solutes differentially sorb onto aquifer materials and thus are retarded in their movement through the subsurface, resulting in a chromatographic like separation of the soluble constituents of a plume, with groundwater as the mobile phase.
Sorption describes the transfer of solutes from a
liquid phase to a solid phase (Miller and Weber, 1984). In this literature review the liquid phase is assumed to be water, containing solubilized organic solutes and the solid phase is the aquifer material under saturated, steady flow conditions. Sorption is influenced by the physical and chemical characteristics of the aquifer (ie., soil type, fraction of organic carbon), and the solute (ie., solubility, volatility, density).
Although sorption is a major component in the
attenuation of solutes in the subsurface, the fundamental
processes of solute-soil interaction and the thermodynamics of this process are not completely characterized. Therefore, sorption is used in this study as a generic term to describe solute retention (ie. uptake of solute), regardless of whether the process is one of adsorption, absorption or partitioning (Woodburn, 1985). Desorption is used here to describe solute removal from the solid phase.
3.4.1 Sorption Processes
The attractive forces acting to effect sorption of
hydrophobic compounds onto natural sorbents were reviewed by voice and Weber (1983). The major theory is discussed below.
Bonding forces in sorption may be both physical and
chemical, though both are basically electrostatic in nature. Physical sorption results from Van der Waals forces. The strength of these interactions is generally on the order of 1-2 Kcal/mole. These energies may be augmented by a thermodynamic gradient driving hydrophobic molecules out of solution. This is based on entropic considerations (solvophobic theory).
Chemical sorption is the interaction between specific sites of the sorbent and individual solute molecules. This approximates a true chemical bond with heats of adsorption between 15-50 Kcal/mole. Voice and Weber (1983) point out that it is difficult to assess the importance of each type of bonding. The heterogeneous nature of natural sorbent materials is largely unknown, and sorption processes probably involve all types of interactions.
3.4.2 Sorption Equilibria
Two experimental techniques are widely used to evaluate the @S1 @t term in equation [1.1]. These are batch equilibrium and soil column methods. Batch studies allow the evaluation of the linearity of the sorption isotherm and their use is well documented (Schwarzenbach and Westall, 1981, Chiou et al., 1979). The most widely used models to describe sorption equilibria in groundwater systems are the linear [3.3] and Freundlich models [3.4] (Miller and Weber, 1984):
S =K d* C [3.3]
S= K f Cn (n < 1) [3.4]
where S (ug/g) and C (ug/L) are the adsorbed phase and solution phase concentrations respectively at equilibrium, K d (L/g) is the linear sorption coefficient, K f (L/g) is the Freundlich sorption coefficient (both K dand K indicating sorption capacity) and n is an empirical constant (indicating sorption intensity). The linear model is in effect, a special case of the Freundlich model where n=l. The Freundlich equation is often linearized (log transformed) to facilitate calculation of variables K f and n in batch studies:
log S =n *log C +Log K f [3.5]
In column studies K dis evaluated through the retardation factor (R). The mass transport equation for reactive solutes under steady flow is described by equation [3.6]:
SC/3t + p/0 3 S/Dt Dh C /3x v [3.6]
where p is the bulk density, 0 is the volumetric water content and S is the sorbed phase concentration. Note that equation [3.6] is equivalent to equation [3.1] with the addition of the sorption term ;S/ ;t. Assuming linear, reversible sorption, the sorbed concentration of a solute is related to the aqueous concentration of the solute by the relationship:
S/3t = Kd Dc/at [3.7]
Substitution for ;S/ 3t in equation [3.6] with equation [3.7] yields the relationship:
aC/at + Kd aC/at (p/G) = Dh a2C/ x2 v aC/ax [3.8]
After separation of variables equation [3.8] becomes aC/at [ 1 + p Kd/e] = Dh 2C/x2 v aC/ax [3.9]
and by defining the retardation factor (R) as R = 1 + p Kd/0 [3.10]
sQbstitution of equation [3.10] into [3.9] results in the incorporation of the retardation factor (R) into the mass transport equation for solute transport under saturated steady flow conditions:
R ac/ t = Dh a2C/ax2 -v aC/ax [3.11]
Analysis of equation [3.10] indicates that the value of R is largely dependent on Kd for a homogeneous aquifer system or laboratory column. Determination of R from soil
column studies leads to the evaluation of Kd from equation [3.10]. Nkedi-Kizza et al. (1987) compared techniques for the calculation of R from soil column leaching experiments and from batch isotherm experiments. Values of R calculated by determining the area above the breakthrough curve were shown to be equivalent to R values calculated by using equation [3.10].
3.4.3 Sorption Estimators
Recently, approximation methods based on the assumption of partitioning as the dominant method of solute interaction have become common (Karickhoff et al., 1979, Chiou et al., 1979, Kenaga and Goring, 1980, Chiou et al., 1983). Their use is largely a result of the time and difficulty in the accurate measurement of sorption coefficients (Kd), and the general lack of data on hydrocarbon sorption to environmental sorbents. These authors note a correlation between the fractional organic carbon content of the sorbent material (f oc) and Kd. The Kd normalized to foc of the sorbent is described as K where: oc
Koc = Kd /foc [3.12]
Values of Koc is well correlated with aqueous solubility
(WS) (Chiou et al., 1979) and the octanol-water partition coefficient (K ow) (Karickhoff et al., 1979). These authors suggest that the solute-sorbent interaction is a partitioning process rather than an interaction between solute and the mineral surface. Evidence for partitioning is partially supported by the hydrophobic character of soil
organic matter, and by solvophobic theory (Rao et al., 1985). The general relationship between K ocand. K oand WS takes the form (Curtis et al., 1986):
Log K = a Log K ow+ Log if oc+ b [3.13]
Log K = c Log WS + Log fo + d [3.14]
where a,b,c, and d result from regression analysis of laboratory isotherm data and depend on the solute-sorbent system.
However, there are significant limitations on the use of these estimators, and the basis of partitioning as a sorption mechanism is questionable (Milgelgrin and Gerstl, 1983). In a strict sense, these relationships hold only for those compounds and sorbents used in the original studies (i.e., these are empirical relationships). This is reflected in orders of magnitude variation in estimates of sorption using these relationships. Application of the partitioning models may not be appropriate in experimental systems with solutes and sorbents which are different from those used to develop these models. In addition, these equations may not apply at organic carbon fractions less than 0.1% (Curtis et al., 1986). Rao and Jessup (1983) noted that the use of K octo estimate sorption can lead to significant errors with soils with very low (less than 0.1%) organic carbon contents.
Milgelgrin and Gerstl (1983) reviewed the evidence for partitioning and noted that a correlation between the organic carbon content of the soil and sorption was not
universally significant. These authors cited several studies where removal of organic carbon from a soil actually increased the amou nt of sorption, or had no negative effect on the sorption values. These authors suggested that molecular structure of the solute may be a better predictor of sorption to sediments than water solubility or octanol/water partition coefficients. This results from the observation that with a relatively rigid adsorbing surface, the conformation of the solute molecule will greatly affect its adsorption (i.e., steric effects), but not its partitioning between an organic phase and water.
Recently, first order molecular connectivity indexes
X) were shown to be well correlated with K ocvalues
(Sabljic, 1984, Sabljic, 1987). Molecular connectivity is described as a quantitative measure of the area occupied by the projection of the non-hydrogen skeleton of a molecule. The correlation between K ocand the first order molecular connectivity index supports the contention that the process of soil sorption may be viewed as an attractive interaction between two planes, with the magnitude of the interaction directly proportional to the surface area of the molecule. This suggests that the soil sorption and partitioning process reflect different mechanisms. An accurate model of sorption may include both partitioning and surface area dependent affects.
The relationship between K ocand 1X is (Sabljic, 1987):
Log K = 0.53 *X + 0.54 [3.15]
This relationship is based on literature values of K from oc
laboratory experiments with 72 compounds covering a broad range of polarities and classes, and a variety of sorbent systems. The correlation coefficient is 0.976 which explains 95.2% of the variance.
In most cases sorption is considered to be completely
reversible, that is, the adsorption-desorption isotherms are reversible and single valued. However, several investigators report desorptions which display hysteresis in batch studies (Bailey and White, 1970, Boucher and Lee, 1972, Carringer et al., 1975, DiToro and Horzempa, 1982). Van Genuchten et al. (1974) found that the exponent for desorption is concentration dependent, and described the hysteretic behavior by using separate isotherm equations for sorption and desorption:
Ss = Kds C ns [3.16]
Sd = K dd Cd nd [3.17]
where subscripts s and d indicate sorption and desorption respectively. Hysteresis in column studies was noted by Schwarzenbach and Westall (1981), although the reaction was termed reversible, since all the solute was eventually eluted from the column.
3.4.5 Sorption Kinetics
Hysteretical behavior may actually be a manifestation of sorption-desorption kinetics. Rao and Jessup (1983)
noted that the influence of non-singular isotherms (ie., isotherms which display hysteresis) on solute movement may be less significant than the effects of sorption nonequilibria. In a study of the transport of pesticides at high concentrations, Rao and Davidson (1979) noted that the position of an adsorbed solute in a breakthrough curve was governed by the nature of the equilibrium adsorption isotherm equation, whereas the shape of the curve was defined by the kinetics of the sorption-desorption process.
Sorption reactions between hydrophobic pollutants and
sediments are generally rapid and not rate limited (Weber et al., 1983). Rao and Davidson (1980) concluded that many sorption reactions are complete within one minute in batch slurry experiments, although longer times to equilibrium were noted in several studies (Karickhoff et al., 1979, Miller and Weber, 1984). Schwarzenbach and Westall (1981), in a comparison of solute breakthrough at various flow velocities, concluded that K d values -from column experiments where velocity was less than 10- cm/second were similar to K values from 18 hour equilibrium batch studies.
3.4.6 Aromatic SorptionValues From The Literature
There are few data in the literature addressing
sorption of dissolved gasoline components in the subsurface. Much of the research involved aromatic compounds in single
solute experiments, simple mixtures, or data from crude oil studies.
Houzim (1978) observed a decrease in sorption in the order alkenes > aromatics > cycloalkanes > alkanes. Nathawani and Phillips (1977) in a study of hexadecane, oxylene, toluene and benzene in crude oil on soils of varying organic matter presented sorption coefficients based on Freundlich isotherms. Rodgers et al. (1980) reported the adsorption and desorption of benzene on several soils and clays at 25 C. The aqueous phase concentration range was 10 to 1000 ug/L. Sorption of benzene was minimal, except on aluminum saturated clay. These data are summarized in Table 3-2.
Wilson et al. (1981) evaluated the sorption of toluene on a fine sand in a column study. A retardation factor less than 2 for the concentration range of 200-900 ug/L was reported. This indicates the relatively low retardation potential of sandy aquifers. The retardation factor describes the extent of solute transport relative to water. The retardation factor for water is defined as unity. Solutes with large retardation factors are less mobile and and their movement is retarded, relative to that of water.
Schwarzenbach and Westall (1981) presented data for the sorption of several chlorinated and alkyl benzenes on twelve natural aquifer materials with varying amounts of organic carbon. The initial concentrations of the alkylbenzene components were 20 ug/L. Sorption coefficients from batch
Table 3-2. Summary of adsorption data for aromatic hydrocarbons.
Percent Benzene Toluene o-Xylene
Soil Content 1/n K 1/n K 1/n K
Silty Clay 16.2 1.272 3.23 1.008 3.52 0.947 11.03
Sandy Loam 10.8 1.298 0.583 1.002 2.69 0.707 4.77
Silty Clay 1.7 1.366 0.003
Silt Loam 1.0 1.51 0.028 0.996 0.931 1.098 0.62
Silty Clay Loam 2.6 0.89 2.4
Silty Clay Loam 1.8 0.94 1.8
Montmorillonite 0 1.08 30.9
Montmorillonite 0 0.99 4.4
Adapted from Brookman et al., 1985.
studies of a soil with low organic carbon (0.0015 g o/g soil) are shown in Table 3-3.
As may be noted from this short review, most of the above studies involve data from individual components or from oil based products. Given the differences in composition among these petroleum products and gasoline, extrapolation may be insufficient to provide accurate data (Brookman et al., 1985).
3.5 Biodegradation of Aromatic Hydrocarbons in Groundwater
Biological activity is an important process in the attenuation of gasoline hydrocarbons in the subsurface environment. This realization is only recent. Early techniques for the enumeration of microbes in the subsurface (Waksman, 1916) underrepresented the numbers of microbes in the subsurface, showing a decline in population with depth. These data resulted from the use of nutrient rich growth media, inappropriate for the enumeration of groundwater bacteria (Wilson and McNabb, 1983).
Recent work shows that more substantial populations of heterotrophic organisms exist in shallow water table aquifers than were previously thought. Wilson et al. (1983a) demonstrated that the numbers of organisms were relatively constant to a depth of six meters in a shallow water table aquifer. The populations of heterotrophic bacteria were estimated to be approximately 10 6 organisms/gram dry weight soil (Ghiorse and Balkwill, 1985).
Table 3-3. Sorption coefficients of selected aromatic
hydrocarbons on low organic carbon soil.
Compound average standard deviation
Toluene 0.37 0.12
p-Xylene 0.50 0.10
1,3,5-Trimethylbenzene 1.00 0.16
1,2,3-Trimethylbenzene 0.95 0.11
Source: Schwarzenbach and Westall, 1981.
A review of the techniques for the enumeration and estimation of microbial biomass were presented by Atlas (1982) and Webster et al. (1985). Bouwer and McCarty (1884) noted that the majority of bacterial activity was associated with bacteria attached to surfaces. This results in the formation of biofilms, which are favored in low substratehigh surface area conditions. The biofilm may also present an active surface with solutes sorbing to the surfaces of microbial cells. In terms of the advection-dispersion models, the rates of biological degradation are incorporated into the model through the sink term Qi' which describes the microbial degradation of solutes from the aqueous phase. Qi is defined as:
Qi = -k E C [3.18]
where k is the rate of biological degradation (T-), 0 is the volumetric water content (ml/cm 3)and C is the solution phase concentration of a solute (ug/L).
3.5.1 Environmental Factors Affecting Biodegradation
Many factors can affect the transformation of organic
contaminants in the subsurface. McCarty (1984) included low substrate concentrations, toxic conditions, molecular structure of the substrate, inaccessibility of the substrate, and absence of essential arowth fact-rs Biological activity is often limited bv certain met c li requirements of the cell, supplied from the environment. Important geochemical properties include OH- redox potential, nitrogen and phosphorus concentrations and the
availability of an appropriate electron acceptor. Oxygen is used as the ultimate electron acceptor for aerobic degradation processes and is often a limiting factor in the degradation of hydrocarbons. Molecular oxygen is also essential to the aerobic metabolism of aromatic compounds, because it is incorporated into the structure of the metabolic products (Evans, 1977). The biochemistry of the aerobic metabolism of aromatic compounds is well established (Dagley, 1975). The first step in this metabolic pathway
is the removal of side chains, followed by the enzyme (oxygenases) mediated hydroxylation of the aromatic ring. Assuming 50% conversion of carbon to biomass and incomplete oxidation of the hydrocarbon molecules, two parts of oxygen are required for the degradation of each part hydrocarbon (Wilson et al., 1986). The complete oxidation of hydrocarbon molecules to CO 2and H 20 mayrqieteeo four parts of oxygen per part hydrocarbon.
There is some evidence for the anaerobic biodegradation of aromatic compounds in the environment. In the absence of oxygen, nitrates, sulfates and CO 2 become electron acceptors. Bouwer and McCarty (1984) presented a review of these processes. Nitrate respiration (Psuedomonas and Moraxella .22.*) and methanogenic fermentation processes can reduce the benzene nucleus followed by hydrolysis to yield aliphatic acids (Evans, 1977). Wilson and Rees (1985) showed the anaerobic degradation of benzene, toluene, xylenes and alkylbenzenes under methanogenic conditions.
Over a six week period only toluene showed substantial degradation, but after 40 weeks, benzene was reduced by 72%, toluene by 99%, ethylbenzene by 74% and o-xylene by 78%. Nutrient addition decreased the rate of hydrocarbon removal. The metabolic products from the anaerobic degradation of the aromatic molecules were not investigated. Nitrate respiration of xylene in a river alluvium was demonstrated by Kuhn et al. (1985). However, anaerobic biotransformations occur extremely slowly (months), relative to aerobic processes which may be completed in a matter of
hours (Wilson, 1985).
Physical properties of the aquifer also play an important role in determining the extent of microbial degradation. Porosity and hydraulic conductivity are significant parameters since the resupply of oxygen, substrate and nutrients to the microbial cells must come via the groundwater.
The concentration of the contaminant substrates is an important factor in the extent of biodegradation. High concentrations may result in incomplete degradation resulting from rapid depletion of oxygen and high substrate concentrations may also lead to increased acclimation times. Jensen et al. (1985) demonstrated an increase in the time required for acclimation (lag time) of bacterial cultures with increasing concentrations of napthelene. Lag times prior to substantial microbial degradation of a solute or nutrient reflects the time required by the indigenous
microflora to adapt to the added substance. Adaption is a phenomenon rather than a mechanism or process, and the term refers to an increase in the rate of biotransformation of a
substance resulting from exposure to that substance (Wilson et al., l983b). Low solute concentrations may result in the occurrence of a threshold limit, below which the microflora are unable to utilize the solute with out a cosolute (Wilson and McNabb, 1983). Jensen et al. (1985) demonstrated the degradation of aromatic molecules to less than 1 ug/L, implying that there was a very low threshold limit for the aromatic hydrocarbons. The relationship between concentration and biodegradation was reviewed by Alexander (1985). He stressed the importance of studying contaminant levels that exist in the environment.
3.5.2 Aromatic Biodegradation Values From the Literature
McKee et al. (1972) reported the oxidation of gasoline by Pseudomonas and Arthrobacter under aerobic but not anaerobic conditions. Degradation of gasoline by Pseudomonas was reported by Williams and Wilder (1971), and Litchfield and Clark (1973) showed significant numbers (10 4 cells/mL) of hydrocarbon degrading bacteria in groundwater contaminated with petroleum hydrocarbons from twelve sites. Bacterial populations appeared to be related to the concentrations of hydrocarbons. These data indicate the adaptation of microbial communities to the changing nutrient source (i.e., gasoline). The two major mechanisms of adaptation are induction of metabolic pathways, or the
activation or transfer of plasmids (Litchfield, 1986). This ability of microorganisms to adapt to the presence of contaminants forms the basis of in-situ biodegradation.
Several researchers have reported the biodegradation of aromatic compounds in groundwater. One shortcoming of most of this research is the lack of degradation rate coefficient data, required for use in groundwater transport models, and insufficient data on solute concentrations.
Jamison et al. (1976) reported the use of benzene as a sole carbon source. No rate coefficient data were given. McKenna and Heath (1976) noted the slow oxidation of benzene by P. putida. Delfino and Miles (1985) showed the degradation of benzene in 16 days under aerobic conditions in Floridan groundwater with an eight day lag phase. Ethylbenzene was degraded as a sole carbon source (Gibson and Yeh, 1973), but no rate data were given. Schwarzenbach et al. (1983) found toluene rapidly degrades within several meters in a study of river water infiltration to groundwater but rate and initial concentration data were not specified.
Kappeler and Wuhrmann (1978b) in a study of gas oil degradation reported that nitrogen and oxygen were the. limiting factors in hydrocarbon degradation. Addition of NH 4Cl resulted in further microbial degradation, and cell densities were on the order of 10 6/mL. Lag times of 5-6 days were noted in the batch experiments. Kappeler and Wuhrmann (1978a) showed that microbes from uncontaminated groundwater can attack gas oil components. Lag times of 1
day at 25 C and 5 days at 10 C were reported in column studies with mixed autochthonous flora from clean groundwater. The meta and para isomers of xylene and 1,2,4trimethylbenzene were degraded more rapidly than o-xylene, 1,2,3-trimethylbenzene or 1,3,5-trimethylbenzene. These studies by Kappeler and Wuhrmann (1978a,b) make up the bulk of the work on degradation of alkyl substituted benzenes.
3.5.3 In-situ Biodegradation
Application of in-situ bioremediation technology for
the renovation of hydrocarbon contaminated aquifers is based primarily on the work of Raymond et al. (1975a,b) and Raymond et al. (1977) at Suntech. Nutrients and oxygen were introduced with injection wells, and circulated through the aquifer with pumping wells. This technique and other bioremediation methods were reviewed by Wilson et al. (1986). These authors noted that studies are needed to investigate the effectiveness of natural biorestoration and to evaluate whether enhancement of natural processes is possible or desirable.
Transport of sufficient oxygen to subsurface microbes is a major technical problem. Oxygen is only slightly soluble in water and is quickly depleted during aerobic biodegradation. Oxygen addition by air sparging, oxygen sparging and the use of hydrogen peroxide are documented in the literature (Lee and Ward, 1984). The use of hydrogen peroxide appears particularly advantageous (TRI, 1982). Hydrogen peroxide is relatively inexpensive, nonpersistent
and is more soluble in water than air or molecular oxygen. However, it is also cytotoxic and may be chemically reduced, especially in the presence of iron salts. The biological decomposition of hydrogen peroxide is enzymatic:
2H2 02 --------- > 2H 20 + 02 [3.18]
H202 + XH2 -----------> 2H 20 + X [3.19]
where X is a biological reducing agent. Non-enzymatic decomposition occurs most frequently in the presence of iron salts:
Fe++ + H202 ------> Fe+++ + OH- + OH' [3.20]
OH* + H202 ------> H20 + H+ + 02 [3.21]
Britton (1985) reported that hydrogen peroxide was
relatively stable in combination with phosphates, even in the presence of moderate iron concentrations, and that bacterial populations can tolerate H202 concentrations up to 500 mg/L. Hydrogen peroxide was shown (Britton, 1985) to increase microbial counts by 102, but there was no reported increase in hydrocarbon removal.
3.5.4 Measurement of Microbial Activity
The reduction of INT (2-p-iodophenol-3-p-nitrophenyl-5phenyl tetrazolium chloride) to INT-formazan by the electron transport system is a function of cell respiration, and is widely used as a general measure of microbial activity.
This technique is recommended as an index of general microbial activity of soil microorganisms (Klein et al., 1971).
Reduction of INT to INT-formazan is a sensitive assay for dehydrogenase activity. The INT-formazan is easily extracted from sediments and soils by methanol, and the INTformazan complex is stable. Trevors et al. (1982) found a high correlation between electron transport system activity and oxygen consumption. Klein et al. (1971) presented a rapid and simple procedure for the determination of dehydrogenase activity using INT in soils with low organic carbon.
This literature review has presented some of the basic principles required as a basis for the discussion of the experimental work reported in this dissertation, and has highlighted some of the important findings relative to the dispersion, sorption and biodegradation of aromatic compounds in groundwater systems.
MATERIALS AND METHODS
This chapter discusses the materials and experimental
methods employed during this study. The field site, and the solutes and sorbets are described followed by a description of the chromatographic systems. Laboratory experiments for the determination of hydrolysis, sorption and biodegradation parameters are discussed. Finally, the field procedures and experiments are discussed.
4.2 Site Description
The field research site used for a portion of this study was located at the Citrus Research and Education Center (CREC) at Lake Alfred, Fl. The site was located in the Trail-Ridge Lake Wales Ridge system of hills containing deep internally drained lake basins. Unconsolidated deposits in the area consisted of sand and sandy clays up to 150 ft thick above the limestone bedrock. The geology was marked by many sinkholes formed through subsidence of the unconsolidated deposits into solution cavities in the limestone (Spangler, 1984).
The research site was located on the rim of an ancient sinkhole. The surficial aquifer was composed of sand and clayey sands. An continuous clayey confining layer of uneven depth was present between 7 to 12 ft below land surface. This layer supported a saturated zone between 3 to 6 ft in thickness. Local relief was from 156 ft (above mean sea level) at the top of the hill at the eastern boundary of the site, to 131 ft in the wetland area at the west edge of the site. A site map is shown in Figure 4-1. The surficial aquifer was comprised of medium angular grained sands and fill material. The hydrology of the site was discussed by Killan (1987).
The surficial aquifer was contaminated during the
spring of 1983 by the loss of 7500-8000 gallons of leaded gasoline from a storage tank. Free floating gasoline was removed by surface skimming as of May 1985. The outline of the contaminated area as of October, 1986 is shown in Figure 4-2. The plume was defined by determination of explosive gas concentrations in bore holes throughout the site. These data were confirmed by GC analysis of soil cores and the use of ground penetrating radar. These techniques were described in detail by Killan (1987).
4.3 Aquifer Material
Aquifer materials used in this research were obtained
from the field research site at the IFAS-Citrus Research and Education Center at Lake Alfred Florida. A site map is
AIDE 2L. BLDG 22N .
EL~GPAP- ~ aIM-IFENCELINE
PREVIOUS SITE OF
oNI'9 GASO L INE TANK
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REETA AC C PROJECT
BLDG 24 1 N
0 20 40
BLDG *,i oHM-1
14 RAP-8 PREV!'US SITE OF
I j CAS2LIt; ,z
o RAP- 10
RAP-7 o RAP-6 a S
R A R AUP 5 P Eu
a o .. .:_ P----5'
o RAP-1 / \
RAP-2 O / *UF-2M 00,-2
BLDG 31 LD 5I P-3 e o a
GROUNDWATER POLLUTION REMEDIAL ACTION PROJECT
OCTOBER 1986 '4 ET L A D
Figure 4-2. Extent of the hydrocarbon plume at the
field research site as of October, 1986.
shown in Figure 4-1. All experiments were carried out with subsamples of the same aquifer material. The sample was collected with a stainless steel auger just below the water table at a depth of about 4 feet, approximately 10 feet east of Well RAP-1. Care was taken to avoid contamination with surface materials by removing one foot of top soil, and through careful handling of the auger. The aquifer material was oven dried at 105 C for 24 hours, sieved through 2 mm standard sieve and stored, covered, at room temperature. Prior to use, the aquifer material was autoclaved for 90 minutes on each of three consecutive days to sterilize the materials.
Prior to sterilization and drying, selected physical
and chemical properties of the aquifer materials used in the laboratory studies were characterized (pH, particle density, particle size analysis, percent organic carbon, bulk density, hydraulic conductivity, and water content) using standard methods of soil analysis (Black, 1965).
4.4 Choice of Solutes
Gasoline contaminated well water from the Lake Alfred research site was used as the source of dissolved solutes for the majority of experiments in this study. With the exception of a single solute column sorption experiment with benzene spiked in to RAP-2 water, all experiments were performed with mixtures of dissolved hydrocarbons at
concentrations occurring in the field. These concentrations are the result of the solubilization and subsequent weathering of gasoline hydrocarbons into groundwater. Well OHM-4 was used as the source of water for these experiments. This well was chosen based on consistently high levels of dissolved aromatic hydrocarbons. Hydrocarbon free groundwater was obtained from a non-contaminated portion of the aquifer (Well RAP-2). Hydrocarbon concentrations in these waters were monitored monthly. Well RAP-2 remained free of aromatic hydrocarbons throughout the course of these experiments. Concentrations of aromatic hydrocarbons varied in Well OHM-4 but remained high enough to provide samples for laboratory experiments.
Water samples were collected with a 5.1 cm (2") id poly vinyl chloride (PVC) bailer, following removal of five well volumes to allow collection of a representative sample. Well volumes were calculated based on the diameter of the well, and the depth of water in the well. These water samples were collected in four liter brown glass bottles transported on ice, and stored at 4 C upon arrival at the laboratory. The pH of these well waters ranged from 6 to 7. The conductivity was approximately 300 umhos. Total phosphate was 0.4 mg/L for RAP-2 and was 0.65 mg/L for well 0HM-4. Nitrate was 0.29 mg/L in well RAP-2 and 0.20 in well
The single solute column experiment with benzene used RAP-2 water spiked with benzene (Aldrich, gold label 99.9%) to yield a solution of 4000 ug/L benzene.
4.5 Hydrocarbon Analyses
Gas chromatographic analyses of hydrocarbons for this study were performed on two systems. These are described below.
4.5.1 GC/MS Analyses
Field samples collected before September, 1986, and the initial hydrolysis vials were analyzed for volatile aromatic constituents using a Hewlett Packard model 5985B GC/MS/COMP system equipped with a 10 port Tekmar Automatic Liquid Sampler (ALS) and Liquid Sample Concentrator (LSC) purge and trap system. EPA method 624 was used. Separation of analytes was achieved with a 0.32 mm i.d., 30 meter long, DB-5 (1 um film thickness) fused silica capillary column (J & W Scientific), with manual liquid nitrogen cryofocusing. The 1,4 isomer of dichlorobenzene was used as an internal standard. Response factors for benzene, toluene, ethylbenzene and o-xylene were determined relative to the internal standard and used for quantitation. Response factors for meta and para xylene were assumed to be the same as the ortho isomer. A response factor of 1 was assumed for the C9H12 hydrocarbons. Chromatographic conditions were as follows:
mass range 45-450 amu
Temp 1 30 C
Temp 2 280 C
Rate 5 C/min
Hold time 10 minutes
Cryofocus time 5 minutes Pre-cool 2 minutes
4.5.2 GC Analyses
Hydrocarbon analyses were performed on a Perkin Elmer model 8410 gas chromatograph with a flame ionization detector and microprocessor data system. Samples were concentrated by purge and trap with a Tekmar LSC/ALS system employing a modified version of EPA method 602. Analytical separation was achieved with a 0.53 mm i.d., 30 meter long, fused silica Megabore DB-l (100% methylpolysiloxane) column (J & W Scientific) with a 3 um film thickness.
Benzene, toluene, ethylbenzene, o-xylene and m,p-xylene were quantified using the internal standard method (1,4 dichlorobenzene) during August and September 1986 for monthly analysis of field samples and for day = 0 hydrolysis ampules. After this date, eight isomers of C9H12 were identified and confirmed by analysis of individual standards and were quantified in all chromatograms along with BTEX (benzene + toluene + ethylbenzene + m,p,o-xylene) compounds. The internal standard was changed from 1,4-dichlorobenzene to chlorobenzene to avoid co-elution problems. A complete description of this analytical method and a summary of the quality control parameters for this method are in Appendix A.
The meta and Para isomers of xylene were not resolved on either chromatographic system employed in this research. The combination of these analytes was reported as m,pxylene. Likewise, 3-ethyltoluene and 4-ethyltoluene were not resolved with the analytical system employed in this study, and the combined concentrations of these analytes were reported in this study with the abbreviation 3,4ethyltoluene.
4.6 Hydrolysis Studies
Hydrolysis studies were performed in 5 mL glass ampules (Fisher Scientific). Ampules were rinsed with methanol and oven dried at 105 C .Ten microliters of hydrocarbon contaminated groundwater were spiked into ampules containing
5 mL of buffer solution. Buffer solutions were prepared with non contaminated well water, and the pH was adjusted to PH = 2.0, 7.0, 9.2, and 12.0 with 0.01 M phosphate buffers. The ampules were sealed with an ampule sealer (Oceanographic International, College Station, TX), and autoclaved (1 hour at 120 C). One set of ampules was analyzed at time zero. Another set of ampules was stored in the dark at 20, 40 and 60 C and analyzed by gas chromatography (GC) after 60 days.
4.7 Batch Sorption Studies
Sorption batch studies were performed in 40 mL VOA
vials with Teflon coated septa (Fisher Scientific). Vials were first filled with 60 g of aquifer material, and then autoclaved at 120 C for 1 hour on each of three consecutive days.
Water from well OHM-4, containing a mixture of
dissolved aromatic hydrocarbons, was used in the batch sorption experiments. As a result, all these experiments are multisolute, at concentrations representative of those found across the aquifer at Lake Alfred.,
4.7.1 Sorption Experiments
Water used in the sorption experiments was filter
sterilized through 0.2 um membrane filters (Gelman Metricel) and then added to each vial. The range of solute concentrations was achieved by dilution of Well OHM-4 water with Well RAP-2 water at ratios between 1:1 to 1:1000. Each dilution was performed in triplicate. Non-soil controls (solute water with no soil) were also set up in triplicate. To minimize headspace, the vials were premixed on a rotary tumbler for approximately 1 hour to remove interstitial air and to disperse the foam that formed during mixing. The vials were then opened, completely filled with sample and recapped. A high solids to solution ratio (2.9 g/ 9 ) was used to maximize the fractional decrease in solution
concentration owing to sorption, and to more closely simulate natural aquifer conditions.
vials used in sorption experiments were equilibrated at room temperature (20 + 2 C) on a rotary tumbler at approximately 20 rpm for 18 hours, and then centrifuged at 800 G for 30 minutes. Samples were analyzed by purge and trap/gas chromatography. Vials used in the batch sorption kinetic rate study were sampled at 1, 2, 4, 8, 16, 24, 36 and 48 hours.
4.7.2 Desorption experiments
Desorption experiments were conducted subsequent to a sorption experiment. Following centrifugation and sampling for sorption losses, approximately 10 mL of supernatant were removed and replaced with hydrocarbon free water (Well RAP2). The vials were re-equilibrated for 24 hours on the rotary tumbler, centrifuged and sampled. Each vial was only desorbed one time. These experiments were not designed to calculate desorption isotherms or test isotherm nonsingularity.
4.7.3 Calculation of Sorption Coefficients
The amount of solute sorbed to the aquifer material (ng solute/gram soil) was calculated by determining the difference between the solution concentration of the nonsoil blanks and the soil containing vials. The amount of solute lost was divided by the solution to soil ratio to normalize the data to a ng/gram basis. Sorption coefficients were calculated by fitting isotherm data to three models; linear, linear with intercept forced through
zero, and the log normalized (Freundlich) models (Miller and Weber, 1984).
4.8 Column Sorption Studies
4.8.1 Experimental Procedures
Leaching column experiments were performed with a 25 x 250 mm glass preparative chromatography column (Altex cat. no. 252-18) with a Teflon coated adjustable plunger (NkediKizza et al., 1987). Aquifer material was dry packed into the column which was then autoclaved at 120 C for 1 hour. The solutes were pumped from 2.6 L Teflon gas sampling bags (Alltech Associates, Deerfield, IL) with a Gilson model 302 HPLC pump fitted with a model 5s pump head (Gilson Medical Electronics, Middleton-, WI). The flow range of this system was 0.005 5.00 mL per minute. All transfer lines and connections were Teflon or stainless steel to minimize interaction of solutes with reactive surfaces. Column length was adjusted to 5.0 cm. Flow rates through the column were set at 1 ml/min (0.204 cm/mmn) for sorption studies. Column effluent breakthrough curves (BTCs) were measured under steady saturated water flow conditions with continuous application of solute containing water.
Effluents from the sorption columns were collected
manually in 1 ml crimp seal vials. These column effluents were either analyzed immediately or stored at 4 C in 1 mL
crimp seal vials with Teflon coated septa for later analysis. All samples were analyzed within 48 hours.
The breakthrough of an unretained solute was determined for each column using calcium chloride (1 ml/min columns). Breakthrough curves were determined by spiking hydrocarbon free groundwater from Lake Alfred (RAP-2) with chloride (600 mg/L CaCL 2). Chloride analyses were performed with a chloridometer automatic titrator (Buchler-Cotlove). Chloride ion was not expected to be adsorbed owing to the low cation exchange capacity of the Lake Alfred soil.
well water used in the sorption experiments was
filtered through 0.2 um membrane filters (Gelman Metricel) directly into the Teflon bags. The bags were autoclaved prior to each use. Columns were saturated with filter sterilized water from well RAP-2 prior to the input of solute containing water.
4.8.2 Estimation of Retardation Factor (R) in Columns
Three methods were used to estimate the value of R from the column data. In method 1, retardation factors (R b) were calculated by fitting the solution of Brenner (1962) to the column effluent curves. Peclet numbers used for these calculations were determined from the breakthrough of the non-retained solutes having retardation factors equal to unity. Method 2 was based on the conservation of mass principle. This method calculated retardation factors (Ra by evaluating the area above the breakthrough curve using Simpson's Rule (Swokowski, 1975). The R value was assumed
equal to the area above the BTC when the effluent concentration (C) divided by the influent concentration (C 0) was plotted vs pore volume as described by equation [4.1] pvma
R = f [ 1-C/C 0] dpv [4.1]
where pvma is the total number of pore volumes displaced through the column, and pv is pore volumes (Nkedi-Kizza et al., 1987). This method assumed a mass balance existed in the soil columns. The third method set the retardation factor (R pv) to equal the number of pore volumes required for the effluent concentration of each analyte to reach 0.5 of the influent concentration. The use of this method assumes that the breakthrough curve is symmetrical and sigmoidal, and that equilibrium conditions exist between the solution and sorbed concentrations during leaching through the column (Nkedi-Kizza et al., 1987). The value of K d was calculated from the various R values with equation [3.10].
4.9 Hydrogen Peroxide Evaluation
The reaction rate of hydrogen peroxide in the aquifer
environment was simulated by monitoring the dissolved oxygen
(DO) (YSI model 5739 probe and YSI model 54A DO meter), redox potential (platinum redox electrode, Fisher Scientific) and pH (gel membrane electrode, Fisher Scientific) of well water and aquifer material in a 3 arm 500 mL reaction flask. Contaminated well water was
equilibrated at room temperature (20 :L 2 C) in the sealed flask. Hydrogen peroxide (50%) was added undiluted in microliter quantities and at various dilutions. Aquifer material was then added to assess the ability of the material to catalyze the reaction. The 50% hydrogen peroxide stock was titrated with 0.01N potassium permanganate (Dupont, 1984) to check the strength of the stock solution. The standardized stock was then used to make the appropriate dilutions without further calibration.
4.10 Batch Biodegradation Studies
4.10.1 Experimental Procedure
Batch biodegradation experiments were performed in 40 mL VOA vials as described for the batch sorption experiments.
Well water from OHM-4 was used as the source of both dissolved aromatic hydrocarbons and bacteria in these studies. The water was not filtered prior to use. This experiment was designed to evaluate the ability of adapted groundwater bacteria to degrade mixtures of dissolved aromatic solutes at field scale concentrations. The experimental design for batch biodegradation experiment number 1 is shown in Table 4-1. Seven treatments were set up, with 15 replicate vials per treatment. water from Well OHM-4 was added (350 mL) to a 500 mL erlenmeyer flask, and then amended with hydrogen peroxide (50%), ammonium chloride
Table 4-1. Experimental design for batch biodegradation
-- ------------------------------------------------------Hydrogen Sodium
Peroxide NH4 C1 Azide
Treatment (mg/L) (mg/L) (mg/L)
-- ------------------------------------------------------IA none none none
IB 17 none none
IC 68 none none
ID none 18 none
IE 17 18 none
IF 68 18 none
1G none none 1.25
(Reagent grade, Fisher Scientific) and 10% (w/v) aqueous solution of sodium azide (Fisher Scientific) as outlined in Table 4-1. Triplicate samples were analyzed for each treatment at 0, 3, 7, 15, and 31 days. Treatment number 1G was a sterile control. Hydrogen peroxide was added based on data from the hydrogen peroxide evaluation experiment and on data from Britton (1985), who demonstrated that cytotoxicity was minimal at hydrogen peroxide concentrations less than 100 mg/L. Ammonium chloride was added based on data from Mitchell (1974) who found that ammonia nitrogen is assimilated quickly during microbial growth. Ammonia (as NH 4 CL) was added to achieve quantities calculated to meet nitrogen requirements of the bacteria.
Biodegradation experiment number 2 was designed to
evaluate the efficacy of oxygen gas in addition to hydrogen peroxide (Table 4-2). Sterile controls were maintained in treatments 2C, 2F and 21. Ammonium chloride and hydrogen peroxide were added as in biodegradation experiment #1. oxygen was added by bubbling 0 2 gas into a closed 3 liter flask filled with 1200 mL of contaminated well water. A valve allowed for pressure relief. Water was released through a glass tube at the bottom of the flask, fitted with a teflon stopcock. Vials were filled as described in the sorption experiments. The vials used in both batch biodegradation experiments were placed in an incubator (20 1
1 C) and inverted once every two days to provide mixing. Samples were taken at 0, 2, 7, 14, 21 and 35 days.
Table 4-2. Experimental design for batch biodegradation
Oxygen NH4Cl Azide
Treatment # addition (rng/L) (mg/L)
2A air none none
2B air 18 none
2C air none 1.25
2D 60 mg/L H 20 none none
2E 60 mg/L H 20 218 none
2F 60 mg/L H 2 02 none 1.25
2G 0 2 saturation none none
2H 0 2 saturation 18 none
21 0 2 saturation none 1.25
Following sample removal for GC analysis, dissolved oxygen was measured in each batch biodegradation vial (batch experiments 1 and 2) with a YSI model 5739 DO probe and YSI model 54A Do meter (Yellow Springs Instruments Co.).
Microbial activity was assessed through the measurement of INT reduction to INT-formazan (Klein et al., 1971). Ten grams of soil from each vial were placed in sterile 50 mL Erlenmeyer flasks. Each flask was amended with 1 mL distilled water and 1.5 mL of 0.4% (w/v) aqueous solution of filter sterilized (0.2 um Gelman Metricel membrane filters) INT (Eastman-Kodak Co.). The soil was mixed with a sterile glass rod, capped with aluminum foil and incubated at 20 C for 72 hours. Sterile controls were prepared by autoclaving several flasks for 3 consecutive days for 90 minutes. Approximately 3 grams (dry weight) of soil were removed from each flask following incubation and placed in a test tube. Ten mL of methanol were added to each tube and the contents were mixed on a vortex mixer for 1 minute, then centrifuged at 800G for 20 minutes. The INT- formazan in the methanolic extract was measured spectrophotometrically at 480 nm against a methanol extract of soil containing no INT. The INT-formazan concentration was derived from a standard curve of INT-formazan in methanol.
4.10.2 Calculation of Biological Rate Constants
Aqueous concentration data from the batch
biodegradation vials were used with the regression equations from the Freundlich fit of the batch desorption data to
calculate the amount of solute lost to sorption in each batch vial. The extent of the sorption loss correction varied with the concentration of the analyte and the sorption parameters K fdand n. This correction factor added as much as 20% to the measured concentration values. These predicted losses resulting from sorption were added to the aqueous concentration for each analyte in each vial to calculate the total concentration of each solute in the vial (C t). These C t values were employed to obviate the need for simultaneous calculation of biodegradation on both sorbed and aqueous concentrations and any calculation of rates of sorption.-desorption during biodegradation. The Ctvalues were used to model the biological rate coefficients. The rate data were fitted to zero order, first order, second order and mixed order rate equations (Levenspiel, 1972), and to the Thomas slope method (Thomas, 1950).
4.11 Column Biodegradation Studies
4.11.1 Experimental Procedure
Column biological degradation experiments were
performed with the same column system described in section 4.8. Two flow rates were used in these experiments. These were 1 ml/min (0.680 cm/mmn) and 0.90 ml/hr (0.010 cm/mmn). Columns operated at 0.90 ml/hr were fitted with a low dead volume in-line septa. Effluent was withdrawn with a 50 ul syringe (Hamilton Co.) and analyzed immediately. Effluents
from the 1 mL/min columns were collected and analyzed as described in section 4.8. Breakthrough curves for a non retained solute were obtained with tritiated water (0.01 uci 3H for 0.90 ml/hr columns) or CaCI2 (1 ml/min columns). Analyses of 3H 20 effluents were performed on a Delta 300 model 6890 Liquid Scintillation Counter (Cearle Analytical) sing Scintiverse II scintillation cocktail (Fisher Scientific). Columns operated for biodegradation experiments were set up in the same manner as the sorption columns. Solute containing water was filtered through 0.45 um membrane filters (Gelman Metricel) to remove particulates. A standing microbial population was developed in the columns operated at 1 ml/min by inoculation with water from well OHM-4.
4.11.2 Calculation of Rate Constants
Rate constants for the biological degradation of aromatic solutes from column breakthrough curves were determined by application of the first order rate equation to the breakthrough curve data at steady state. Microbial degradation processes are often assumed to be first order (Bossert and Bartha, 1984). Substitution of equation [3.18] into equation [3.11] incorporates the degradation term into the one dimensional mass transport equation:
RC/ Dt = Dh C/ax v 3C/3x k C [4.2]
Dividing all terms of equation [4.2] through by R and defining D. = D/R, v. = v/R and k* = k/R, equation [4.2] becomes
C/ a t = Dh* C/2 v.- aC/ax k. C [4.3]
at steady state conditions where aC/at = 0, equation 4.3 reduces to:
Dh 2 C/ 2 v @C/ a x + k C = 0 [4.4]
For steady state conditions there is no sorption effect since the R term cancels out and the rate of biodegradation
(k) may then be calculated by application of the first order rate equation to the portion of the BTC which is at steady state. The first order rate equation for this system is:
C/C = e -kt [4.5]
For a column of length x, and with a pore water velocity of v, time may be expressed as
t = x/v [4.6]
and substitution of [4.6] into [4.5] and rearrangement allows calculation of the rate constant for biodegradation:
k = ( ln C/C0 ) v/x [4.7]
Average C/C 0 values were calculated from the regions of the solute breakthrough curves where C/ t = 0. This derivation assumes that microbial degradation occurs only from the aqueous phase, and that dispersion is negligible.
4.12 Field Studies
4.12.1 Aquifer Characterization
A tracer experiment was conducted to measure seepage
velocities and obtain better estimates of aquifer hydraulic conductivity and field scale dispersion. RAP-9 was used as the dosing well. The following steps outline the experimental procedure:
1. A tracer solution was prepared by dissolving 50 lb (23 kg) of technical grade ammonium chloride in 55 gal (208 L) of tap water. The resulting concentration was 109,000 mg/L ammonium chloride.
2. The ammonium chloride solution was injected into the dosing well and simultaneously diluted with tap water at a metered rate of 1 gallon per minute (gpm). Dosing continued for 15.8 hours, resulting in a total dose volume of 1,035 gallons of tracer solution.
3. Detection of the tracer was monitored in
wells RAP-10 and RAP-11 using a conductivity meter with a field probe. Measurements were obtained at one half- to one-hour intervals for the first 24-hour period. Wells P-
6, P-7, UF-lE, RAP-4, OHM-4, EJF-2M and UF-3W were also monitored periodically for the following two weeks.
The breakthrough of the tracer was calculated in pore volumes using the equation
pv = vt/L [4.8]
where pv is pore volumes, t is time (hours), L is the distance between RAP-9 and RAP-10 (5 ft) and v is the seepage velocity (ft/hour)from work by Killan (1987).
4.12.2 Water Quality Monitoring
Samples for hydrocarbon analysis were taken from
selected monitoring wells monthly from February 1, 1986 to June 1987. Sampling procedures are detailed in Appendix B. Hydrocarbon analyses were as described previously using the three chromatographic methods as they became available. The pH, temperature, dissolved oxygen, and conductivity were measured periodically in selected wells. Sampling procedures are detailed in Appendix B.
Total phosphorus concentrations were determined in all monitoring wells (EPA method 365.1). All phosphorus forms were converted to orthophosphate by autoclaving with potassium permanganate in an acidic medium. Orthophosphate was determined spectrophotometrically at 880 nm with a Perkin Elmer model 552 spectrophotometer.
Chloride was determined colorometrically in each
monitoring well over several months to determine background levels of chloride via EPA method 325.1. Average background concentrations were 20 + 8 mg/L. Nitrate was measured with
an Orion nitrate electrode via standard method 418b (APHA, 1980).
4.12.3 Microbial analyses
Viable microbial cells were enumerated by plate count
technique using dilute soil extract agar (DSEA) media. This technique was developed based on work by Ghiorse and Balkwill (1983) and Wilson et al. (J 983).
DSEA was prepared by autoclaving 100 g of surface soil in 100 mL of distilled water for one hour at 120 C. The supernatant was filtered (Whatman glass fiber filters) to remove particulates and diluted ten fold with distilled water and amended 1.5% (w/v) with agar (Fisher scientific).
Ten grams of subsurface material were suspended
aseptically in 100 mL 0.1% sodium pyrophosphate (Fisher Scientific) then appropriate dilutions were plated in triplicate on DSEA media. All plates were incubated aerobically at 27-30 C for ten days.
RESULTS AND DISCUSSION
This chapter will review the results of all experiments performed as part of this dissertation research. The presentation of results and the interpretation of the data for each major experimental section are grouped together to avoid loss of continuity. Hydrolysis of aromatics in groundwater is discussed first, followed by the results and discussion of the batch and column sorption experiments. Batch and column biodegradation experiments are addressed next*t followed by the presentation of field data, and the correlation of field data with laboratory experiments.
5.2 Hydrolysis of Aromatic Hydrocarbons
The results of initial measurements (time = 0) of the hydrolysis ampules were inconclusive, resulting from overdilution of the samples. Several analyses were below the limit of detection of the GCMS analytical system, therefore no conclusive statements may be made relative to the rates
of aromatic hydrolysis.
Statistical analysis of data from ampules after 60 days of temperature controlled storage indicated that for a given temperature, there was no significant (student's t-test,
0.05 level) change in concentration of analytes over the pH range tested (pH 2,7,9,12).
only one compound, l,2,3-trimethylbenzene, demonstrated a significant (student's t-test, 0.01 level) temperature effect at pH values of 7,9 and 12. This change in concentration occurs only at 40 C, and the concentration values at 20 and 60 C are equivalent for all pH values. These data are not consistent with data from other aromatic compounds in this study, which showed no change in concentration with varying temperatures. The apparent loss of solute seen for l,2,3-trimethylbenzene was likely the result of working near the detection limit of the analytical system, or the result of experimental error.
The absence of concentration differences across a wide range of pH and temperature for the 60 day ampules implies that hydrolysis was not a significant mechanism for removal of aromatic hydrocarbons. This was expected, owing to the resistance of aromatic structures to nucleophilic attack by water. This results from the electronegativity associated with the delocalization of electrons in the pi bonds of the aromatic nucleous. McCarty (1984) has noted that chemical
hydrolysis may occur, but that for most compounds this process was slow relative to biological removal rates. In addition, hydrolysis results in simple changes in the molecular structure, whereas biological transformations often result in the mineralization of organic compounds to carbon dioxide and water.
5.3 Characterization of Aquifer Materials
An analysis of a sub-sample of the aquifer materials
used in the laboratory experiments is presented in Table 51. All experiments with aquifer material were performed with subsamples of well-mixed aquifer material. Single size fractions of aquifer material were not used since extrapolation from one size fraction to another has been shown to lead to errors in the estimation of sorption values (Abdul et al., 1986). Unwashed, natural sorbent material from the Lake Alfred site was used in this study to more closely approximate field conditions. The organic carbon content of this material was low, and particle size analysis indicated the dominance of fine to medium grained sands. The pH of the aquifer material was in the range suitable for biological degradation, and was consistent with the pH values in the well water.
5.4 Batch Sorption Studies
5.4.1 Sorption Rate Studies
Rate studies were conducted to determine the sorption kinetics of the selected aromatic solutes with Lake Alfred aquifer material. These experiments established the equilibration time for the sorption isotherms. The approach to equilibrium is shown in Figure 5-1. These curves are
Table 5-1. Selected physical and chemical Properties
of the Lake Alfred aquifer material.
--- -----------------------------------------------------Parameter Value
pH 7.4 (0.01M CaCl )
Particle Density 2.6 g/mL
Water Content (by weight) 24%
Organic Carbon 0.015%
Bulk Density 1.4 g/mL
Particle Size Analysis
very fine sand 3.0%
fine sand 38.2%
medium sand 47.0%
coarse sand 8.2%
very coarse sand 0.2%
4J S 3 33 2
0 20 40 60 80 100 120
TIME (HOURS) o BNZ + TOL o EBZ A mp-X"/L x o-XYL
Figure 5-1. Approach to equilibrium for several aromatic solutes on
Lake Alfred aquifer material.
marked by an initial rapid sorption, and equilibrium conditions are established within several (4 to 8) hours. These data are in agreement with Weber et al. (1983) who stated that sorption reactions with natural sorbets were generally rapid and not rate limited. Based on these data an equilibration time of 18 hours was chosen. Eighteen hours was chosen to maximize the time for sorption yet minimize the time for losses from the system (ie., via diffusion of solutes through the Teflon septum). This was equivalent to time scales used in previous studies (Chiou et al., 1979, 1983; Schwarzenbach and Westall, 1981). Longer equilibration times were not possible using this experimental technique since losses in non-soil blanks after
3 days made it difficult to differentiate between sorption and loss from the system. The equilibration time used in this study did not guarantee that the sorption process was complete, but that it was complete to the extent that it could be accurately measured.
5.4.2 Batch Sorption Isotherm Data
Data for the equilibrium batch sorption isotherms are presented in Appendix C. Solution concentrations are in ug/Lj and sorbed concentrations are in ng/g. Three models were fitted to these data using the method of least squares regression analysis. These models were the linear, linear with suppressed fit (forced through the origin), and the Freundlich (log-log transformed). The results of these analyses for the linear models are presented in Table 5-2
Table 5-2. Regression parameters for the analysis of
average values of equilibrium batch isotherm
sorption data with the linear model.
Compound Na (ug ) Kd stdc y-intd, std r
Benzene 14 950 0.066, 0.005 1.5, 4.28 0.914
Toluene 14 4200 0.049, 0.010 10.9, 65.6 0.694
m,p-Xylene 16 4300 0.095, 0.005 3.4, 28.7 0.961 o-Xylene 16 2500 0.097, 0.004 3.1, 10.8 0.979
3 or 4 ETe 11 935 0.087, 0.012 3.8, 12.6 0.861
1,3,5-TMBf 11 460 0.142, 0.008 1.6, 3.92 0.973
2-ET9 11 373 0.106, 0.011 0.81, 4.66 0.910
1,2,4-TMBh 10 1600 0.131, 0.006 4.6, 10.6 0.981 1,2,3-TMBi 10 558 0.124, 0.010 1.7, 6.10 0.951
anumber of data points
maximum concentration c standard deviation dy-intercept
3 or 4 Ethyltoluene f1,3,5-Trimethylbenzene 92-Ethyltoluene h,2,4-Trimethylbenzene i1,2,3-Trimethylbenzene
and Table 5-3. The Freundlich regression parameters are presented in Table 5-4.
There is no significant difference (student's t-test,
0.05 level) between sorption coefficients predicted with the linear model and those predicted with the linear model with suppressed intercept. The sorption coefficients are determined form the slope of the linear isotherms. In addition, statistical determination of the confidence intervals of the y-intercepts indicate that there is no significant difference between the predicted value of the yintercept in the linear model and zero at the 0.05 significance level, confirming that these two models are analogous. Equivalence between these two models is expected since the sorbed concentration should equal zero when no solute is added to the system. A non zero intercept is an indication of nonlinearity in the isotherm. In most cases both linear models fit the data well as evidenced by the relatively high coefficients of determination (r 2 ). Based on these analyses, the isotherms for the sorption of aromatic solutes from Lake Alfred water onto Lake Alfred aquifer material were concluded to be linear. The coefficients of determination for toluene in both linear models were substantially lower than for other compounds in this study. The linear model with suppressed intercept accounted for only 63.6% of the total sum of squares deviations about the means for the 14 values in the toluene isotherm. This suggested that this model was not
Table 5-3. Regression parameters for the analysis of
average values of equilibrium batch isotherm
sorption data with the linear model
a ma c d 2
Compound N (ugL) Kd, std y-intd r
Benzene 14 950 0.069, 0.005 0 0.904
Toluene 14 4200 0.051, 0.009 0 0.636
m,p-Xylene 16 4300 0.096, 0.004 0 0.960
o-Xylene 16 2500 0.099, 0.003 0 0.978
3 or 4 ETe 11 935 0.093, 0.010 0 0.850
1,3,5-TMB 11 460 0.146, 0.007 0 0.969
2-ET9 11 373 0.108, 0.009 0 0.908
1,2,4-TMBh 10 1600 0.135, 0.005 0 0.978
1,2,3-TMB 9 558 0.128, 0.008 0 0.948
anumber of data points
e3 or 4 Ethyltoluene f1,3,5-Trimethylbenzene 92-Ethyltoluene h,2,4-Trimethylbenzene i1,2,3-Trimethylbenzene 1,2,3-Trimethylbenzene
Table 5-4. Regression parameters for the analysis of
average values of equilibrium batch isotherm
data with the Freundlich model.
C log loge d e 2
Compound Na (ug L) Kf, std n std r
Benzene 14 950 -0.857, 0.217 Q.901, 0.071 0.952
Toluene 14 4200 -0.783, 0.260 0.876, 0.076 0.917
m,p-Xylene 16 4300 -0.758, 0.110 0.916, 0.031 0.984 o-Xylene 16 2500 -0.873, 0.098 0.966, 0.026 0.990
3 or 4 ETf 11 935 -0.702, 0.158 0.904, 0.054 0.958 1,3,5-TMB9 11 460 -0.605, 0.081 0.921, 0.029 0.991 2-ETh 11 373 -0.951, 0.255 0.993, 0.088 0.934
1,2,4-TMBi 10 1600 -0.641, 0.099 0.937, 0.035 0.989 1,2,3-TMBj 9 558 -0.623, 0.078 0.918, 0.028 0.995
--- ------------------------------------------------------anumber of data points maximum concentration clog standard deviation of Kf values dFreundlich exponent
e standard deviation of Freundlich exponent f3 or 4 Ethyltoluene
appropriate for estimation of sorption. However, analysis of variance with a global F-test indicated that the model was useful for predicting sorption at the 0.01 significance level. Therefore, the linearity of all isotherms was confirmed. Linear isotherms have been noted by several authors (Schwarzenbach and Westall, 1981, Chiou et al., 1979, Karickhoff et al., 1979) and the data presented in this study are in agreement with these studies.
Curtis et al. (1986) noted that the use of the linear regression technique was not statistically rigorous since variance in the dependent variable was not distributed uniformly across the observed concentration range. These authors suggested that a least squares fit on the log transformed data (Freundlich model) gives a better approximation by providing a more uniform distribution of variance. The Freundlich model provided a good fit to the data in this study (Table 5-4) as evidenced by the high coefficients of determination for the Freundlich model. The Freundlich isotherm explained between 91.7 to 99.5% of the variance in the data, and provided a slightly improved fit to the isotherm data relative to the linear models. Ther2
value for toluene was 0.917, which was much improved over the coefficient of determination for toluene in the linear model. The log K f values for the study compounds are also presented in Table 5-4. Values for several components (ethylbenzene, and the propylbenzenes) were not included in the table owing to their low concentrations in Well OHM-4
water on the day samples were collected for this study. The linearity of these isotherms was confirmed by the values of the regression coefficients and the Freundlich exponents
(n), both of which were close to unity. As n approaches unity the models should converge since the linear model is in effect a special case of the Freundlich model.
A comparison of the Freundlich and linear models is presented in Table 5-5. The deviation between predicted amounts of sorption for the linear model with suppressed intercept and Freundlich models are expressed as ratios between the calculated sorbed concentrations. This method was used to evaluate the predictive equivalence of both models over the concentration ranges encountered in this study. This method was chosen, since direct comparison of K d and K f values may be misleading owing to the log transformation of the data in the Freundlich isotherm model. The largest deviations occurred for toluene at 1 ug/L. In general the ratios approached unity as the concentrations increased, but diverged in the range between 1 to 50 ug/L. This comparison indicates that the models, were essentially similar, as is predicted from the values of the Freundlich exponent (n). As n approaches unity, the Freundlich isotherm approaches the linear isotherm. The convergence of these models is confirmed by an examination of 2ethyltoluene in Table 5-5. This compound has the highest Freundlich constant (n = 0.993) and the ratios of predicted
Table 5-5. Ratio of sorbed concentrations calculated
from Freundlich and linear equilibrium models
Solute 1 50 100 500 1000
Benzene 2.01a 1.73 1.27 1.09 1.01
Toluene 3.23 1.97 1.81 1.48 1.36
m,p-Xylene 1.81 1.30 1.23 1.08 1.01
o-Xylene 1.35 1.18 1.16 1.09 1.07
3 or 4 ETb 2.14 1.48 1.38 1.18 1.10
1,3,5-TMBc 1.70 1.25 1.18 1.04 0.99
2-ETd 1.04 1.01 1.00 0.99 0.98
1,2,4-TMBe 1.69 1.34 1.28 1.15 1.10
1,2,3-TMB 1.88 1.35 1.27 1.12 1.05
athe ratio of the amount sorbed as calculated from the Freundlich model to the amount sorbed predicted from the linear model with suppressed intercept, at the same solution concentration.
3 or 4 Ethyltoluene
sorption from the two models are consistently close to one over the entire concentration range tested.
Freundlich isotherms for benzene and toluene are shown in Figures 5-2 and 5-3. Graphs of Freundlich isotherms for the remaining solutes are presented in Appendix C. Average values are plotted in Figures 5-2 and 5-3 and error bars showing one standard deviation in the experimental determination of the sorbed concentrations are presented to give an indication of the variance in these data. Standard deviations for all compounds are shown in Appendix C. The influence of dissolved organic carbon was not assessed during this study. However, based on the work of Curtis et al., (1986) with a sandy aquifer material (0.02% organic carbon), organic carbon in this study was not expected to decrease the values of K d by more than 5%. Water from the Lake Alfred aquifer was used in these experiments, and the organic carbon in solution was assumed to be in equilibrium with the organic carbon on the aquifer material. Therefore, dissolution of additional organic carbon into solution should have been minimal, and K d should not be greatly affected. This hypothesis was confirmed by evaluation of the partitioning model as a predictive technique for sorption of aromatic solutes to the Lake Alfred aquifer material in section 5.6. The interaction between organic carbon and the solutes was shown to be low.
F0 0. 1 1 2242 LOO SO-O OCNRTO
Fiur 5-2Fenlc sopiniohr fo bezn ateu irum
0 12 3 4
100 SOLUTION CONCENTRATION 1)SORPTION > DESORPTION
Figure 5-3. Freundlich sorption isotherm for toluene at equilibrium.
5.4.3 Batch Desorption Experiments
Desorption data are also presented in Figures 5-2 and 5-3t fit with the Freundlich type model. Visual inspection of the desorption data suggests some degree of irreversibility or some difference in desorption kinetics based on the upward displacement of the desorption regression lines. However, the calculated values of the partition coefficient for desorption with the linear type model (Table 5-6) and for the linear type model with suppressed intercept (Table 5-7) were not significantly different from sorption values (Kd) at the 0.05 probability level. Desorption coefficients from the Freundlich type model (K fd ) were also not significantly different from K f values at the 0.05 level (Table 5-8). Statistical analyses of the models used to evaluate the desorption coefficients indicated that all three models gave excellent fit to the data, as evidenced by the high coefficients of determination.
These data suggested the reversibility of the sorption process, and demonstrated that the hysteretical behavior of the desorption data were not significant. This was consistent with a majority of the published literature on sorption of organic compounds to natural sorbets (Miller and Weber, 1984).
For purposes of discussion, the Kd values from the linear model with suppressed intercept are used in the following sections. As discussed earlier, these data were
Table 5-6. Regression parameters for the analysis of
average values of equilibrium batch
desorption data with the linear model.
--- -----------------------------------------------------a C btcd 2
Compound Na ( bW) Kdd, std y-intd std r
Benzene 5 950 0.248, 0.006 2.2, 4.18 0.998
Toluene 8 4200 0.303, 0.011 4.13, 55.8 0.992
m,p-Xylene 9 4300 0.186, 0.006 13.3, 30.1 0.993
o-Xylene 9 2500 0.152, 0.012 11.0, 31.1 0.955
3 or 4 ETe 9 935 0.250, 0.018 -2.9, 18.4 0.968
1,3,5-TMBf 8 460 0.194, 0.004 1.5, 1.62 0.998
2-ET9 7 373 0.566, 0.010 0.78, 2.64 0.999
1,2,4-TMBh 10 1600 0.199, 0.005 3.3, 7.62 0.996
1,2,3-TMB 9 558 0.219, 0.032 1.1, 20.3 0.871
-- -------------------------------------------------------anumber of data points maximum concentration Standard deviation
e3 or 4 Ethyltoluene
Table 5-7. Regression parameters for the analysis of
average values of equilibrium batch
desorption data with the linear model
C2 a ax c d 2
Compound Na ug/L) Kdd, std y-int r
Benzene 5 950 0.251, 0.004 0 0.998
Toluene 8 4200 0.304, 0.008 0 0.992
m,p-Xylene 9 4300 0.190, 0.005 0 0.992
o-Xylene 9 2500 0.159, 0.010 0 0.951
3 or 4 ETe 9 935 0.256, 0.014 0 0.967
1,3,5-TMB 8 460 0.1968 0.003 0 0.997
2-ET 7 373 0.570, 0.007 0 0.999
1,2,4-TMBh 10 1600 0.202, 0.004 0 0.996
1,2,3-TMB 9 558 0.221, 0.022 0 0.870
anumber of data points maximum concentration standard deviation d.
y-intercept e3 or 4 Ethyltoluene f1,3,5-Trimethylbenzene 92-Ethyltoluene h1,2,4-Trimethylbenzene i1,2,4-Trimethylbenzene 11,2,3-Trimethylbenzene
Table 5-8. Regression parameters for the analysis of
average values of equilibrium batch
desorption data with the Freundlich model.
C b log log
Compound Na (m L) Kf, std n stde r
Benzene 5 950 -0.635, 0.168 1.02, 0.079 0.982
Toluene 8 4200 -0.478, 0.039 0.992, 0.019 0.998
m,p-Xylene 9 4300 -0.561, 0.096 0.963, 0.043 0.986
o-Xylene 9 2500 -0.159, 0.263 0.774, 0.099 0.891
3 or 4 ETf 9 935 -0.692, 0.068 1.029, 0.030 0.994
1,3,5-TMB9 8 460 -0.569, 0.051 0.956, 0.025 0.996
2-ETh 7 373 -0.657, 0.079 0.983, 0.046 0.989
1,2,4-TMBi 10 1600 -0.658, 0.122 0.997, 0.056 0.981 1,2,3-TMBj 9 558 -0.624, 0.092 0.988, 0.038 0.990
anumber of data points
maximum concentration clog standard deviation of Kf values dFreundlich exponent e standard deviation of Freundlich exponent
3 or 4 Ethyltoluene 91,3,5-Trimethylbenzene
or linear models, and this model is more convenient for the application of equation [3.10].
5.5 Breakthrough Curves for Aromatic Solutes
5.5.1 Measurement of Column Dispersion
Breakthrough curves (BTCs) for a non-retained solute were determined for each column used in these experiments. Analysis of these data allowed the determination of the Peclet number used to model the breakthrough of the aromatic solutes. Evaluation of these data also allowed the calculation of dispersion in the column. Chloride and tritiated water were used in these experiments.
Dispersion (D h) was calculated from the slope of a plot of C/Co vs pore volumes (pv) at pv = 1. according to the equation (Rao, 1985):
D h v L/ 4 pi B 2 [5.1]
where D h is the hydrodynamic dispersion coefficient (cm 2 min), v is the flow velocity (cm/mi, L is the length of the column (cm) and B is the slope of the BTC at C/Co =1. This assumes a sigmoidal shaped curve, and this assumption was valid for these breakthrough curves. A typical
breakthrough curve is shown in Figure 5-4. Some values of D hare presented in Table 5-9. Columns with flow rates of 1 mi/mmn exhibited higher values of D h since dispersion
0 2 4
El CHLORIDE Figure 5-4. Breakthrough curve for chloride for a 5 cm sorption column.
Table 5-9. Values of dispersion coefficients calculated
from the breakthrough curves of unretained
solutes in laboratory columns.
---- ----------------------------------------------------Flow Velocity Dispersion (D h) a st
Tracer (mL/min) (cm/mmn) (cm /min) avgst
3 H 20 0.015 0.003 0.00053
3 H 20 0.015 0.003 0.00066
3H2 0 0.015 0.003 0.00040
Cadl 1.0 0.204 0.051
Cadl 1.0 0.204 0.013
Cadl 1.0 0.204 0.069
a - - - - - - - - - - - - - -
aaverage values of dispersion measurements b standard deviation of dispersion measurements
mi/mmn exhibited higher values of D h since dispersion increases with increasing pore water velocity (Roberts et al., 1985). It may be noted that the pore water velocity of 0.680 cm/mmn was equivalent to the seepage velocity in some portions of the aquifer at the Lake Alfred field site. These data are compared to field dispersion data in section
5.5.2 Aromatic Solute Breakthrough Curves
Breakthrough curves for selected, dissolved aromatic
solutes in the column effluent (Well 0HM-4 water) are shown in Figure 5-5 (benzene) Figure 5-6 (toluene) and Figure 5-7 (n-propylbenzene). Breakthrough curves for these solutes are presented because they show the the breakthrough of the least retained compounds (benzene and toluene) and the most retained (n-propylbenzene). These solutes are presented separately to avoid overlap on a single plot, but are part of the multi-component mixture resulting from the solubilization of gasoline into groundwater at the Lake Alfred site. Graphical representations of the remaining solutes in the column effluent are shown in Appendix D. The changes in effluent concentration near the end of each breakthrough curve was consistent for each solute, reflecting the same relative variability. These deviations may be explained by heterogeneities in flow paths in the porous media, or by analytical error.
Calculated values of R, K d' and Ko based on the
analyses of the column data by curve fitting to Brenner
0 2 4
PORE VOLUMES u CHLORIDE +- BENZENE
Figure 5-5. Breakthrough curve for benzene from Lake Alfred water (C = 4700 ug/L)
0.7 / ''
PORE VOLUMES o CHLORIDE + TOLUENE
Figure 5-6. Breakthrough curve for toluene from Lake Alfred
water (C = 2600 ug/L) .
o 0 -- ------,
0 2 4
PORE VOLUMES O CHLORIDE + N-PROP'/LBENZENE
Figure 5-7 Breakthrough curve for n-propylbenzene from Lake Alfred
water (Co = 1000 ug/L).