Chemodynamic behavior of complex mixtures

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Title:
Chemodynamic behavior of complex mixtures liquid-liquid partitioning and sorption of organic contaminants from mixed solvents
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xiv, 183 leaves : ill. ; 29 cm.
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Lee, L.S ( Linda Shahrabani ), 1959-
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Soil and Water Science thesis Ph. D
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Thesis:
Thesis (Ph. D.)--University of Florida, 1993.
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Includes bibliographical references (leaves 164-182).
Statement of Responsibility:
by Linda S. Lee.
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Typescript.
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Vita.

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CHEMODYNAMIC BEHAVIOR OF COMPLEX MIXTURES:
LIQUID-LIQUID PARTITIONING AND SORPTION OF
ORGANIC CONTAMINANTS FROM MIXED SOLVENTS









by

LINDA S. LEE


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


1993





























Copyright 1993

by

Linda S. Lee














ACKNOWLEDGEMENTS


I want to thank my committee members Professor Suresh Rao, Dean Rhue,

Joe Delfino, Kirk Hatfield and John Zachara for support and helpful guidance

leading to the successful completion of this project. I especially want to thank Dr.

Rao for his continual contribution to both my personal and professional growth. The

exceptional role Dr. Rao has played as my chairman can be best summarized by his

most recently awarded title of Graduate Research Professor.

I thank my colleagues Dr. Arthur Hornsby, Ron Jessup, Lynn Wood, Dr. Mark

Brusseau, Dr. Ken Van Reese, Dr. Sam Traina, Cheryl Bellin, Denie Augustijn, Dong

Ping Dai, Itaru Okuda, and Dr. Peter Nkedi-Kizza for their assistance, support, and

friendship. Special thanks go to Cheryl Bellin for her assistance in the acid/base

titrations, Itaru Okuda for the UNIFAC simulations, Dr. Mary Collins and Dr. Ron

Kuehl for providing numerous subsamples of Webster soil from Iowa, Vicki Neary

for her technical assistance in the completion of the laboratory experiments, and

Candace Biggerstaff for her help in the preparation and submission of my final

draft.

It has been a pleasure to be affiliated with the Soil and Water Science

Department at the University of Florida through both employment and education,








and I would like to acknowledge both staff and faculty for their continual support

throughout the past fourteen years. I also thank my family, as well as two dear

friends, Donna English and Dagne Hartman, whose long-suffering and support have

not gone unnoticed, and God for His unfailing grace, love, and guidance. I would

also like to acknowledge the unique inspiration I've received from Dr. Jim Davidson

and Dr. George Bailey.

The financial support I received from Dr. Rao as my major professor and

supervisor, as well as the Subsurface Science Program, United States Department of

Energy through a contract (DE-AC06-76RLO) to Battelle PNL; United States

Environmental Protection Agency through a cooperative agreement (CR-814512);

and the Electric Power Research Institute (contract #RP-2879-7) is gratefully

acknowledged.















TABLE OF CONTENTS


ACKNOWLEDGEMENTS .......................

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


. iii

. ix


LIST OF FIGURES ............................................. x


ABSTRACT ..................................

CHAPTERS
1 INTRODUCTION .........................


xiii


................ 1


Partitioning from Multi-phasic Liquids .............
Sorption from Aqueous Solutions .................
Hydrophobic Organic Compounds (HOCs) ......
Hydrophobic ionogenic organic compounds (HIOCs)
Cosolvency ..................................
Solubility in Mixed Solvents ..................
Equilibrium Sorption from Mixed Solvents ......
Hydrophobic Organic Chemicals (HOCs) ....
Hydrophobic Ionizable Organic Chemicals
(HIOCs) .........................


. . . .. 16
. . . .. 16

........ 17


2 EQUILIBRIUM PARTITIONING OF POLYAROMATIC
HYDROCARBONS FROM ORGANIC IMMISCIBLE LIQUIDS INTO
W ATER ............................................... 20


Introduction ..........................
Theory ..........................
Application of Raoult's Law for Gasoline,
Motor Oil, and Diesel Fuel .......
Materials and Methods ..................
Chemicals ........................
Batch Equilibration Technique ........
Chromatographic Analysis ............










CHAPTER
2


Results and Discussion .........................
Coal Tar Composition ......................
Tar-Water Partitioning ...............
Analysis of Laboratory Data ..............
Analysis of Literature Data ..............
Predicting Aqueous-Phase PAH Concentrations ...
Coal Tars ...........................
Diesel Fuels .........................
Assessment of Deviations from Ideal Behavior for
for Equilibrium Conditions ...............
Sum mary ...................................


........ 32
........ 32
........ 35
........ 35
........ 41
........ 43
........ 43
........ 47

........ 49
........ 56


3 COSOLVENT EFFECTS ON SORPTION OF ORGANIC ACIDS BY SOILS


FROM METHANOL/WATER SOLUTIONS .....

Introduction ...........................
Theory ...............................
Materials and methods ...................
Sorbents ..........................
Chemicals .........................
Determination of Ionization Constants ....
pH of Soil Suspensions in Mixed Solvents .
Solubility Experiments ................
Miscible Displacement Experiments ......
Equilibrium Sorption Isotherms .........
Results ...............................
pK,' Measurements ..................
Solubility .........................
Miscible Displacement Studies .........
Batch Equilibration Studies ............
Effect of Solvent Addition .............
Discussion ...........................
Solute-solvent Interactions .............
Desorption Characterisics .............
Estimation of pH by pHx"P ............
Summary .............................


.............. 58










4 IMPACT OF pH ON SORPTION OF BENZOIC ACID FROM
METHANOL/WATER SOLUTIONS ......................... 95


Introduction ...................................
Materials and Methods ...........................
Sorbents ..................................
Chemicals .................................
Equilibrium Sorption Isotherms .................
Results and Discussion ...........................
Effects of pH on Benzoic Acid Sorption at f,<0.5 ...
Effects of pH'ap on Benzoic Acid Sorption at f,>0.75 .
Effects of pH"PP on PCP Sorption at fc>0.75 .......
Sorption of Neutral Benzoic Acid Relative to Benzoate
Soil-Solution pH'PP ..........................
Effect of pH Treatments ......................
Sorption Domains ...........................
Sum mary .....................................


. 95
. 98
. 98
. 99
101
103
106
107
108
110
113
113
114
119


5 IMPACT OF SOLUTE STRUCTURE AND ORGANIC COSOLVENT ON
THE SORPTION OF CARBOXYLIC ACIDS BY SOILS FROM MIXED
SOLVENTS .......................................... 122


Introduction ........
Materials and Methods
Sorbents .......


................................ 122
................................ 123
................................ 123


Chemicals .................................
Equilibrium Sorption Isotherms .................
Determination of Octanol-Water Partition Coefficients
Results and Discussion ...........................
Sorption of Benzoic Acid in Several Solvent-
W ater Solutions .........................
Sorption of Several Substituted Carboxylic Acids
in Methanol/Water Solutions ..............
Sum mary .....................................


..... 123
. . 125
.... 126
..... 127

..... 133

..... 143
..... 147


6 SUMMARY AND CONCLUSIONS
Complex Mixtures ..........
Liquid -Liquid Partitioning ....
Sorption of Organic Acids .....
Conclusions ...............


149
149
150
151
155


i:-








APPENDICES

A SUPERCOOLED LIQUID SOLUBILITIES ................ 156

B SAMPLE pKa DETERMINATION ...................... 159

REFERENCES ............................................ 164

BIOGRAPHICAL SKETCH .................................. 183








































viii













LIST OF TABLES


2-1. Selected physico-chemical properties for the PAHs investigated ...... 28

2-2. Range of properties observed for eight coal tars (EPRI, 1993) .. .... 33

2-3. Maximum Cw values for several PAHs based on the data compiled for eight
coal tars .............................................. 45

3-1. Selected Solute Properties .................................. 73

3-2. Retardation factors for several organic acids in aqueous and methanol
solutions from Eustis Soil ................................... 84

4-1. Cation exchange capacity (CEC) in cmol(+)/kg and elemental analysis in
mg/kg of pH treated Webster soils .......................... 100

4-2. Chemical characteristics of benzoic acid and
pentachlorophenol (PCP) .................................. 101

4-3. Parameters for linear and Freundlich fits to the isotherm data for
benzoic acid sorption as a function of pH and f. ................ 103

5-1. List of various chemical and physical solvent properties ........... 124

5-2. List of various chemical and physical solute properties ............ 125

5-3. Parameters for linear and Freundlich fits to the isotherm data for
benzoic acid in several solvent/water solutions ................ 128

5-4. Parameters for linear and Freundlich fits to the isotherm data for
substituted benzoic acids in methanol/water solutions ............. 129

5-5. The logarithms of the octanol/water partition coefficients
(log Kow) for both the neutral subscriptt n) and ionized
subscriptt i) species of several substituted carboxylic acids ......... 145













LIST OF FIGURES


1-1. Comparison of measured and calculated (Raoult's law) aqueous solubilities
in binary mixtures of benzene-toluene (A) and benzene-octanol (B). Data
from: Sanemesa et al. (1987) ................................. 6

1-2. Measured and predicted sorption of flumetsulam by several soils normalized
to organic carbon content plotted as a function of pH. (Data form Fontaine
et al., 1991) ............................................. 11

1-3. Normalized sorption coefficients for several organic acids plotted as a function
of pH-pKa. [Data from Kukowski (1989) and Jafvert (1990)] ........ 13


2-1. log Kdw values plotted versus log S, for eight PAHs along with the ideal line
(solid line) calculated form Eq. (2-6) for each diesel fuel ........... 29

2-2. Comparison of measured tar-water partition coefficients (Kt) and predictions
based on Raoult's law for ID# 1(A) and ID# 2 (B) coal tars ........ 37

2-3. Comparison of measured tar-water partition coefficients (K.) and predictions
based on Raoult's law, for ID# 3(A) and ID# 4(B) coal tars ......... 38

2-4. Comparison of measured tar-water partition coefficients (K,) and predictions
based on Raoult's law, for ID# 5(A) and ID# 7(B) coal tars ........ 39

2-5 Comparison of measured tar-water partition coefficients (K,) and predictions
based on Raoult's law, for ID# 7N(A) and ID# 9(B) coal tars collected by
EPRI ......................................... ............ 40

2-6. Comparison of measured tar-water partition coefficients (K) reported in the
literature and predictions based on Raoult's law. Literature source as
indicated ............................................... 42

2-7. Comparison of laboratory-measured aqueous-phase concentrations (C,) with
those predicted on the basis of Raoult's law for eight coal tars ........ 44

2-8. Comparison of laboratory-measured aqueous-phase concentrations (C., jg/L)
with those predicted on the basis of Raoult's law for four diesel fuel. .. 48








2-9. Schematic representation of the ideal behavior (Raoult's law) and nonideality
in liquid-liquid partitioning. ................................. 51

2-10. log K, values for several aromatic hydrocarbons resulting from UNIFAC
model calculations and the average log, values experimentally determined by
Cline et al. (1991) plotted against log S, values along with the ideal line based
on Raoult's law .......................................... 52

2-11. log Kdw values for several aromatic hydrocarbons resulting from UNIFAC
model calculations plotted against log S, values along with the ideal line based
on Raoult's law ........ ........... ........................ 54

2-12. Comparison of measured and predicted tar-water partition coefficients for
several PAHs: Raoult's law (solid line) and UNIFAC model (solid
triangle). .............................................. 55

3-1. Schematic representation of cosolvency plots for solutes with a range of log
Kow values ............................ ................... 60

3-2. Example cosolvency curves that may be predicted by the use of various
parameters in Eq. (3-6). ...................................... 68

3-3. Effect of methanol content on the pK,' of benzoic acid and
pentachlorophenol. ....................................... 82

3-4. Solubility (Sb) of benzoic acid in methanol/water solutions ......... .82

3-5. Representative sorption isotherms for (A) pentachlorophenol, (B) benzoic
acid, and (C) dicamba, on Webster soil in various methanol/water
solutions. .............................................. 87

3-6. Measured and predicted sorption by Webster soil of (A) pentachlorophenol,
and (B) benzoic acid as a function of volume fraction methanol (f). . 88

4-1. Retention data for benzoic acid as a function of pHaPP at different methanol
fraction (v/v) by RPLC. .................................... 96

4-2. Representative isotherms for benzoic acid in (A) aqueous solutions; (B)
f,=0.1;and (C) f,=0.9buffered at several pH values .............. 105

4-3. Sorption of benzoic acid by Webster soil buffered at different pH values in
methanol/water solutions of fc<0.5 .................... ..... .107








4-4. Sorption of benzoic acid by Webster soil buffered at different pH values in
methanol/water solutions of f,=0.75,0.8, and 0.9. ............... 108

4-5. Sorption of PCP by Webster soil buffered at different pH values in
methanol/water solutions of fo =0.75 and 1.0 ................... 109

4-6. Sorption data obtained as a function of pH and methanol content, for neutral
benzoic acid and benzoate. ................................ 112

4-7. Isotherm data for benzoic acid on (A) A1203, AI(OH)3, and SAz-1 (pH=8);
and (B) Pahokee muck (pH=7) along with linear and Freundlich fits 117

5-1. Representative isotherms for benzoic acid in (A) acetone/water;
(B) acetonitrile/water; (C) DMSO/water; and (D) 1,4-dioxane/water
solutions. .............................................. 131

5-2. Representative isotherms for (A) anthroic acid; (B) 2-chlorobenzoic acid (C)
2,4-dichlorobenzoic acid; and (D) 2,4,6-trichlorobenzoic acid in various
methanol/water solutions. ................................. 132

5-3. (A) Benzoic acid solubility data where Sb and S, are solubilities in the binary
solution and water, respectively; and (B) benzoic sorption data with Webster
soil in binary mixtures of water and several organic cosolvents as a function
of volume fraction cosolvent (f)). ............................ 134

5-4. Trends in pH'PP of soil-suspensions in binary mixtures of water and several
organic cosolvents. ....................................... 135

5-5. Measured and predicted (Eq. 3-6) sorption of benzoic acid by Webster soil
from (A) acetone/water; (B) acetonitrile/water; (C) DMSO/water; and (D)
1,4-dioxane/water solutions as a function of volume fraction cosolvent (f338

5-6. Normalized sorption coefficients, log (Kb/K,), for the sorption of selected
substituted carboxylic acids by Webster soil as a function of volume fraction
methanol (f,). ....................................... 145

5-7. Correlation between benzoic acid sorption in neat methanol (log KMoH) and
the log Kow values for both the ionized (i) and neutral (n) forms of the
substituted carboxylic acids. ................................ 146

A-1. Schematic representation of the steps involved in the thermodynamic cycle for
producing a hypothetical supercooled liquid from a crystal solute. .... 157














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


CHEMODYNAMIC BEHAVIOR OF COMPLEX MIXTURES:
LIQUID-LIQUID PARTITIONING AND SORPTION OF
ORGANIC CONTAMINANTS FROM MIXED SOLVENTS

By

Linda S. Lee

August 1993


Chairman: Dr. P.S.C.Rao
Major Department: Soil and Water Science

Contamination of soils and water at waste disposal sites commonly involves

various combinations of nonpolar or hydrophobic organic chemicals (HOCs) and

hydrophobic ionogenic organic chemicals (HIOCs), as well as mixtures of water and

one or more organic cosolvents (either completely or partially miscible in water).

Emphasis of this work was on understanding the chemodynamics of such complex

mixtures, specifically solubility and sorption. Experimental and theoretical analysis

presented has focused on: (1) liquid-liquid partitioning behavior of aromatic

hydrocarbons between environmentally relevant organic immiscible liquids (OILs)

and water; and (2) the solubility and sorption of HIOCs by soils from completely

miscible organic solvent/water mixtures.








Partition coefficients for several HOCs were either measured or compiled from

the literature for a wide range of OILs (e.g.,gasoline, diesel fuel, motor oil, and coal

tar). The use of the UNIFAC (UNIQUAC Functional Group Activity Coefficient)

model to estimate the likely nonidealities resulting from interactions between

components in these complex OILs is also presented. Both the UNIFAC simulations

and the observed OIL-water partition coefficients suggest that nonideality is

sufficiently small. Thus, the use of Raoult's law convention for activity coefficients

in conjunction with super-cooled liquid solubilities was considered adequate in

assessing the partitioning of HOCs between several OILs and water.

The role of solute hydrophobicity and acidity, solvent type, and pH on the

sorption of organic acids by a surface soil from mixed solvents was investigated.

Predictions of a model that incorporated effects of cosolvent-enhanced solubility and

cosolvent-suppressed speciation were compared to measured data. Sorption of

neutral benzoic acid was observed to decrease with increasing methanol content,

while benzoate sorption increased. Effects of specific solvent and solute properties

were investigated by measuring (1) benzoic acid sorption from additional binary

mixtures of water and cosolvents with a wide range in solvent properties and (2)

sorption of several substituted carboxylic acids from methanol/water solutions. Of the

different solute-solvent combinations investigated, enhanced sorption by soils was only

observed with carboxylic acids in the presence of methanol or dimethylsulfoxide

(DMSO). It was postulated that enhanced sorption resulted from hydrogen-bonding

interactions combined with the formation of heterogeneous solvation shells about the

solute and the sorbent.













CHAPTER 1
INTRODUCTION


Environmental contamination problems at most industrial waste disposal sites

or spill sites commonly involve wastes consisting of complex mixtures of organic and

inorganic chemicals. Complex mixtures are defined here as those systems comprising

multiple organic solutes and multiple solvents. The solute mixtures of interest might

consist of various combinations of nonpolar or hydrophobic organic chemicals

(HOCs) and hydrophobic ionogenic organic chemicals (HIOCs). The solvent may

be a mixture of water and one or more organic cosolvents (either completely or

partially miscible in water). Solvent mixtures of interest may consist of water and

cosolvents in a single, homogeneous liquid phase, or multi-phases that form at least

two distinct liquid phases. The behavior of such mixtures is not well understood

because the primary chemodynamic properties have usually been characterized in

aqueous solutions which are simple in composition relative to many waste mixtures

found at or near disposal/spill sites. Several researchers have made considerable

efforts during the past decade to investigate the primary processes (e.g., solubility,

sorption, transport) governing the environmental dynamics of organic chemicals in

complex mixtures.

The release and migration of organic constituents from a waste disposal/spill

source will produce a contaminant plume, either in the vadose zone or in the

saturated zone or both. The contaminant plume composition will vary with time and








2
distance as the plume size increases. For discussing solubility and sorption processes

within the plume, three separate regions may be considered: a near-field region, a

transition zone, and a far-field region. The basis for such a distinction is not the

distance from the contaminant source. Rather, the criterion employed to designate

these regions is the chemistry of the contaminant mixture within the plume as

contrasted to the waste.

In the near-field region, corresponding to the source itself and its immediate

vicinity, the composition and concentrations of most waste constituents are similar

to that in the waste. There are usually two, possibly three, liquid phases in this

region. This would be the case, for example in the vadose zone, at waste disposal

sites where we may find both "dense" and "light" organic immiscible liquids (OILs)

and an aqueous phase as well as a vapor phase. In the transition zone, if it occurs

in the saturated zone, the solution phase is likely to be predominantly a single-phase,

homogeneous liquid made up of water and varying amounts of cosolvents (if they

were present in the near-field region). The concentration of one or more waste

constituents may be so high that approximations based on expected behavior in dilute

aqueous solutions are often found to be inadequate. Finally, the far-field region

corresponds to that region of the plume in which the waste constituents are present

in an aqueous solution. Most of these chemicals will be at concentrations well below

their aqueous solubility limits. During migration of the contaminant plume through

the vadose zone and the saturated zone, chromatographic separation of the waste

constituents occurs due to their different mobilities. Furthermore, dilution resulting

from hydrodynamic dispersion and attenuation resulting from abiotic/biotic








3
transformations could decrease contaminant concentrations. Thus, high

concentrations of multiple contaminants are less likely to be found as the distance

from the source increases. Nevertheless, it is possible that these contaminant

concentrations may be higher than the standards set by regulatory agencies.



Partitioning from Multi-phasic Liquids

An understanding of solubility (or partitioning) of HOCs from complex OILs

is essential for predicting organic contaminant release from mixtures such as fuels

(e.g., gasoline, diesel, kerosene) and industrial wastes (coal tar, creosote). The

properties of an organic mixture complex only in composition are determined by the

properties of its pure components and their concentrations in the mixture. This

implies that the chemicals of interest behave ideally in the matrix containing them.

Under these conditions, the concentration in the aqueous phase of a chemical is

proportional to the mole fraction of the chemical in the organic phase corresponding

to Raoult's law. With the stated assumptions, the concentrations of a chemical in the

aqueous phase in contact with a complex mixture can be predicted using the

following simplified expression based on Raoult's law:


C, x S (1-1)



where CW is the chemical's concentration (moles/L) in the aqueous phase in

equilibrium with the organic phase, S, is the aqueous solubility (moles/L) of the pure

liquid chemical, and xo is the mole fraction of the chemical in the organic phase. The

derivation of Eq. (1-1) was based on the pure liquid chemical as the standard state.








4

Many components of interest are solid in their pure form at standard state; however,

Eq. (1-1) can be extended to solid solutes by employing hypothetical super-cooled

liquid solubilities (S, ).

Raoult's law is applicable to a vast number of mixtures of organic chemicals

and its use in predicting aqueous phase concentrations in contact with a complex

organic mixture is invaluable. These mixtures may be considered complex based on

the number of chemicals that constitute the mixture. On the other hand, complexity

of a mixture can be defined by considering how the properties of the mixture deviate

from some "ideal" behavior, regardless of the number of components. The former

view corresponds to a mixture being complex in composition, whereas the latter

implies complexity in behavior. The important point is that a mixture can be complex

in composition without being complex in behavior and vice versa.

In general terms, structurally similar chemicals are likely to form "ideal"

mixtures, and solubility of such mixtures can then be estimated using Raoult's law.

A simple example of the application of Raoult's law is shown in Figure 1-1A for a

mixture of two structurally similar compounds, benzene and toluene. The pure

aqueous compound solubilities of benzene and toluene are 23.1 and 5.60 mmol/L,

respectively. Note that the pure compound solubilities are observed only in the

absence of the second component (i.e., only when xo= 0 or 1). The concentration

of either compound in the mixture is attenuated by the presence of the other. The

excellent agreement between the measured results and those predicted from Raoult's

law (lines) clearly exemplifies the role of mole-fraction on solubility.








5
In contrast, a mixture of benzene and n-octanol illustrates a system simple in

composition, yet nonideal in behavior. Deviations from Raoult's law assuming ideal

behavior are evident in Figure 1-1B. Such deviations, however, are not surprising

when we consider the dissimilarity in the chemical nature of these two components.

Benzene is a hydrophobic aromatic compound while octanol is an alkane with a polar

functional group (-OH). The two illustrations given in Figure 1-1 were for

compositionally simple mixtures. However, in most environmental scenarios,

mixtures with a much larger number of constituents are of interest.

Deviations from ideal behavior can arise if the activity coefficient of the solute

in the organic phase is not unity and/or if the solute activity in the aqueous phase

is significantly impacted by the presence of other components. A number of

computational schemes are available to estimate various activity coefficients such that

liquid-liquid partitioning for nonideal mixtures can be evaluated. One of the most

frequently used models for this purpose is the UNIFAC (UNIQUAC Functional-

Group Activity Coefficient) model proposed by Prausnitz et al. 1980). This model

is based on the UNIQUAC model (Abrams and Prausnitz, 1975) and the solution-of-

group concept (Wilson and Deal, 1962). In this model, a mixture of different

chemicals is treated as a mixture of functional groups constituting the components

in the mixture. The interactions between functional groups in the mixture and the

likely nonidealities, resulting from such interactions, are calculated in order to

estimate the activity coefficient of a chemical for a specified phase. Calculations

based on the UNIFAC model require the values for group interaction parameters as

well as the mole fraction of each component in the mixture. The interaction











parameters required in the UNIFAC model have been continuously reviewed and

updated since the model was first introduced (Skjold-Jorgensen et al., 1979;

Magnussen et al., 1981; Gmehling et al., 1982; Alameida-Macedo et al., 1983; Hansen

et al., 1991).



25
A Raoult's Law Prediction

S 20- (Ideal Behavior)
I' *

015
E-
0
c 10 Benzene
.3 A Toluene
m CO 5 -





0
0 0.2 0.4 0.6 0.8 1

25 ,5



E -

15 -a 3


10- -2-
a *3
N A (D
5 1
CJr,


0.2 0.4 0.6 0.8
Benzene Mole Fraction in the Organic Phase


Figure 1-1.


Comparison of measured and calculated (Raoult's law) aqueous
solubilities in binary mixtures of benzene-toluene (A) and benzene-
octanol (B). Data from Sanemesa et al. (1987).










Sorption from Aqueous Solutions

Most of the available data and theories for predicting sorption and transport

of organic chemicals may be successfully applied to predict contaminant behavior in

the far-field region (i.e., dilute aqueous solutions). The following section will

highlight the information available on equilibrium sorption of organic chemicals

relevant to this dissertation work.

Sorption is one of the dominant processes affecting the mobility of organic

contaminants in soils and groundwater. This process can be conceptualized either

as binding at a two-dimensional interface of the sorbent or as a partitioning into the

three-dimensional bulk of the sorbent. Several methods for estimating the magnitude

of sorption for organic contaminants have been developed based on the chemical and

physical properties of the sorbate, the sorbent, and the solvent.

Hydrophobic Organic Compounds (HOCs)

Equilibrium sorption of hydrophobic organic compounds (HOCs) by soils and

sediments has been successfully predicted in many cases by the "solvophobic theory"

and the use of linear free energy relationships (LFER). Excellent log-log, linear

relationships have been reported between K,, the sorption coefficient normalized to

the fraction of organic carbon (OC) of the sorbent, and Kow, the octanol-water

partition coefficient for several HOCs (c.f.,Dzombak and Luthy, 1984; Karickhoff,

1981; 1984; Kenega and Goring, 1980). Linear relationships have also been found

between log Ko and solute hydrophobic surface area (HSA) (Dzombak and Luthy,

1984; Rao et al., 1985) and solute molecular connectivity (Sabljic, 1984; 1987). The








8
different slopes and intercepts found in these regression equations are predominantly

determined by the characteristics of a group of compounds (i.e., class, degree of

hydrophobicity, and structure), while the sorbent properties other than OC appear

to have only minor impact in most cases (Karickhoff, 1981, 1984; Schwarzenbach and

Westall, 1985). The equations derived from LFER and experimental data obtained

for only a few sorbents provide reasonable predictions of HOC distribution in diverse

soil-water and sediment-water systems. However, the limitations of the K, concept

have been pointed out by a number of authors (e.g.,Mingelgrin and Gerstl, 1983;

Green and Karickhoff, 1991; Gerstl, 1990). The two main concerns involve the

contribution of adsorption on mineral constituents and the possibility of site-specific

interactions between functional moieties of the solute and the sorbent.

Hydrophobic ionogenic organic compounds (HIOCs)

For hydrophobic, ionogenic organic compounds (HIOCs), several factors (e.g.,

speciation, soil-solution pH, sorbent-surface pH, charge, ionic strength, ionic

composition, multiple solutes) make predicting sorption from a single parameter

difficult due to additional mechanisms that must be considered. Several mechanisms

proposed in the literature for sorption of organic solutes from aqueous solutions

include: hydrophobic interactions; London-van der Waals or dispersion forces;

hydrogen bonding; cation and water bridging; cation and anion exchange; ligand

exchange; protonation; covalent bonding or chemisorption; and interlayer adsorption

(Koskinen and Harper, 1990). Hydrophobic interactions are driven by weak solute-

solvent interactions and the preference of an organic molecule to be near an organic








9
surface; thus, strong inverse correlations are observed between K, and solubility of

HOCs. London-van der Waals forces result from correlations in the electron

movement between molecules that produce a small net electrostatic attraction.

Although small in magnitude (2-4 kJ/mol), these interactions are additive and have

been found to be significant for the sorption of large neutral polymeric solutes.

Hydrogen bonding interactions involve the electrostatic interaction between

protons and electronegative atoms, and can be stronger than dispersion forces (2-60

kJ/mol) (Kohl and Taylor, 1961; Stumm et al., 1980). Hydrogen-bonding interactions

may occur with both inorganic and organic surfaces, but for soils interactions with

organic matter are more important due to the abundance ofcarbonyl-type functional

groups (Sposito, 1984).

Cation bridging results if a polar organic functional group displaces a water

molecule from the primary hydration shell of an exchangeable cation (i.e.,formation

of an inner-sphere complex), whereas water bridging results when interaction occurs

without displacement of the hydrating water molecules (i.e., outer-sphere

complexation) (Farmer and Russell, 1967). The occurrence of cation bridging versus

water bridging will be a function of the heat of cation hydration, which varies with

cation size and charge (i.e., charge density). For example, water bridging would be

preferred in a Ca+2-saturated sorbent due to its large negative heat of hydration

(AH=-377 kcal/mol) compared to a saturation with K' (AH=-75 kcal/mol) (Bailey

et al., 1968).

Ion exchange involves the exchange of a cation or an anion for another ion

of similar charge at specific binding sites. Cation exchange is of much greater








10
importance for most soils due to the predominance of negatively charged surfaces.

Similar to cation bridging, but a much stronger interaction, is ligand exchange which

involves the formation of an inner-sphere complex with a structural cation of a soil

mineral (i.e., displacement of either water or hydroxyl molecules from iron or

aluminum oxides)(Stumm et al., 1980; Kummert and Stumm, 1980). Ligand exchange

is commonly believed to be the mechanism responsible for the adsorption of

oxyanions. Likewise, protonation involves the formation of charge-transfer complexes

with protons on mineral surfaces and organic functional groups such as amino and

carbonyl groups. Interlayer adsorption involves the sorption and entrapment of

solute molecules within clay interlayers. From infrared spectroscopic data, Farmer

and Russell (1967) infer that benzoic acid enters the interlayer space as an unionized

monomer, and then the oxygens from both the hydroxyl and carbonyl groups become

coordinated to the interlayer cation.

In many cases, it is difficult to definitively conclude what particular mechanism

is responsible for the observed sorption; however, frequently we can predict the

magnitude of sorption by incorporation a few parameters. For example, on the basis

of an analysis of a large data set for pentachlorophenol (PCP) sorption from aqueous

solutions by several sorbents over a broad pH range, Lee et al. (1990) showed that

equilibrium sorption could be predicted with a knowledge of pH, organic carbon

(OC) content of the soil, and the acid dissociation constant (pK.) for PCP. Their

model for predicting sorption coefficient is:


Koc Koc,An + Koc,i (1 4>)


(1-2)











where


(1-3)


n (1 + 10PH-PKa)-1


and K is the measured distribution ratio for the sorbed- and solution-phase

concentrations; Ko =(K/OC); OC is the soil organic carbon content (mass fraction);

0, is the fraction of the neutral HIOC; and the subscripts n and i refer to neutral and

ionized species, respectively.

Sorption data compiled from the literature for several other organic acids

could be, in most cases, adequately described by Eq. (1-2). Shown in Figure 1-2 for

example, is reasonable predictions by Eq. (1-2) of OC normalized sorption of the

herbicide flumetsulam compiled from Fontaine et al. (1991) for several soils.


Figure 1-2.


0 1 2 3 4 5 6 7 8 9 10 11 12
pH
Measured and predicted sorption of flumetsulam by several soils
normalized to organic carbon content plotted as a function of pH.
(Data form Fontaine et al., 1991)


Flumetsulam Sorption by Soils








7F4P46


S Measured
Predicted


91


N-SO/ N CH,
F H
(Data from Fontaine et al.; 1991)
I i i ; i


'"'''








12

Data compiled from Kukowski (1989) and Jafvert (1990) for sorption of a variety

of organic acids by soils from aqueous solutions are shown in Figure 1-3. To

facilitate viewing of sorption data from different solute-sorbent combinations

simultaneously, the pH scale is referenced to the solute's pK, (i.e., pH-pK,) and

sorption is scaled to the solute's Kn and Ki values as follows: (Kobs Ki)/(Kn Ki).

Values for K, and Ki were estimated in the sorption experiments where pH-pK, was

less than or greater than one (i.e., acid was predominately neutral or ionic,

respectively). Agreement of Eq. (1-2) with the measured data suggests that the

measured bulk soil-solution pH is representative of the pH seen by the solute, and

that K. and K, are additive. Note that this does not infer a particular sorption

mechanism or that the mechanisms for the neutral and ionized species are the same.

For organic bases, sorption is affected by similar factors as for organic acids.

However, ion-exchange has been shown to be the controlling sorption mechanism for

organic bases even at pH values as much as two units greater than the solute pK.

(Zachara et al., 1987, 1990; Ainsworth et al., 1987; Bellin, 1993). Competitive

sorption between compounds has also been observed for organic cations (Zachara

et al., 1987; Felice et al., 1985) In contrast, for HOCs and neutral HIOCs

competition is minimal (Zachara et al., 1987; Karickhoff et al., 1979; Schwarzenbach

and Westall, 1981; Chiou et al., 1983; Maclntyre and deFur, 1985; Rao et al., 1986).

The predominance of ion-exchange in the sorption of organic bases suggests the use

of a sorption coefficient normalized to the cation exchange capacity of the sorbent

as a first approximation, analogous to the use of Ko for describing sorption of HOCs.






































-4 -3 -2 -1 0
pH pKa


0.8


0.2

ft


1 2 3 4 5 -


-4 -3 -2 -1 0
pH pKa


1 2 3


Figure 1-3.


Normalized sorption coefficients for several organic acids plotted as a function of pH-pK,. [Data from
Kukowski (1989) and Jafvert (1990)]


e 0.8
C
S0.6


. 0.4
0

0.2


0
-5
5


A Muck Soil
__O O 2-4-D
A O 2,4,6-trichlorophenol
A 4-nltrophenol
S0 Predicted




0
00


(Data from Kukowski, 1989) C3
_ I 1


Sediment
o pentachlorophenol
0 dinitro-o-cresol
Sdichlorobutyric acid
SIIvex
Predicted




0

0
(Data from Jafvert, 1990) oo
I_ I I 1 1 0 0%0o o '


4 5


i


i










Cosolvency

The effects on solubility and sorption (hence, on transport) of organic

chemicals upon addition of one or more organic cosolvents to an aqueous solution

are defined here as cosolvency. This section will focus on the most significant

interactions affecting solubility and sorption of both HOCs and HIOCS. Such

interactions include solute-cosolvent, cosolvent-cosolvent, and cosolvent-water

interactions for solubility; for sorption, solvent-sorbent interactions must also be

considered.

Solubility in Mixed Solvents

The log-linear cosolvency model and the UNIFAC model are among the

theoretical approaches that have been used to examine cosolvent effects on solubility

(Fu and Luthy, 1986a; Pinal et al., 1990). The log-linear cosolvency model

(Yalkowsky and Roseman, 1981) is based on the central assumption that the

logarithm of the solute solubility in a mixed solvent is given by the weighted-average

of the logarithms of solubilities in the component solvents in the mixture; the

weighting coefficient is taken to be the volume fraction of each solvent component.

Thus,

log Sm- fi log S (1-4)

where S is solubility (mg/L), f is volume fraction of the solvent, and the subscript m

denotes mixed solvent and i the i-th cosolvent. Note that averaging the logarithms

of solubilities is equivalent to averaging the free energies of solution in different

solvents in the mixture.








15
In many cases the UNIFAC model may be preferred over the log-linear model

because (i) it has a more sound theoretical basis, (ii) activity coefficients in mixtures

can be calculated given only pure component data, and (iii) all possible interactions

among the components in the mixture are explicitly considered. A limitation of the

UNIFAC model, however, is that although the group interaction parameters required

to estimate the solute activity coefficients are continuously reviewed and updated,

their values are not available for a number of systems of interest here. Also, there

are both experimentally-based (Banerjee, 1985; Arbuckle, 1986) and theoretically

based (Pinal, 1988) reasons that limit the applicability of UNIFAC to aqueous

systems.

A convenient measure of the impact of a cosolvent on the solubility of an

organic chemical is the cosolvency power (a), which is defined as


a log (1-5)




where the subscripts c and w refer to neat cosolvent and pure water, respectively.

HOC solubility in organic solvents is larger than that in water, thus a > 0. Larger

values of a indicate a greater solubilizing power of the solvent for a specific solute.

Rubino and Yalkowsky (1987a) and Pinal et al. (1990) have shown that a

values can be viewed as being equivalent to hypothetical partition coefficients for the

HOC between a cosolvent and water. Morris et al. (1988) have shown that a values

can be correlated to HOC octanol-water partition coefficient (K,) as follows:










o a log Ko + b (1-6)



where a and b are empirical constants unique for a given cosolvent. Other cosolvent

and solute properties may also be used to estimate a values (Rubino and Yalkowsky,

1987a,b; Morris et al., 1988).

Although both Eq. (1-5) and (1-6) provide useful first-order approximations

of the cosolvency power of a solvent for a solute, measured HOC solubility profiles

in solvent mixtures often exhibit deviations from the expected log-linear behavior

primarily due to solvent-cosolvent interactions. The observed cosolvency in a binary

mixed solvent can be more generally defined as,


log Sb log S, + ac fc (1-7)

where Sb is the solubility in the binary mixture.



Equilibrium Sorption from Mixed Solvents

Hydrophobic Organic Chemicals (HOCs)

A log-linear cosolvency model describing the decrease in sorption of HOCs

with increasing f, in a binary solvent is given by (Rao et al., 1985; Fu and Luthy,

1986b):


log Kb log K, -a oc f,


(1-8)








17
where K is the equilibrium sorption coefficient (mL/g), a is an empirical constant

for describing solvent-sorbent interactions, and the subscript b stands for binary

mixed solvent.

An extensive amount of data has shown that in binary mixed solvents, HOC

solubility increases and sorption decreases in a log-linear manner as the volume

fraction of the organic cosolvent increases (Rao et al., 1985, 1986, 1989, 1990; Nkedi-

Kizza et al., 1985, 1987, 1989; Woodburn et al., 1986; Fu and Luthy, 1986a,b;

Yalkowsky 1985, 1987; Rubino and Yalkowsky, 1985, 1987a,b,c; Walters and

Guiseppi-Ellie, 1988). These experimental findings are consistent with the predictions

of both the UNIFAC model and the log-linear cosolvency model. Also, for the

sorption of HOCs, solvent-solute interactions as described by solubility are found to

predominate such that the impact of solvent-sorbent interactions has been considered

minor. However, for solutes containing specific functional groups, the impact of the

cosolvent on the sorbent may have considerable impact.

Hydrophobic Ionizable Organic Chemicals (HIOCs)

For hydrophobic ionogenic compounds (HIOCs) of environmental interest,

data on solubility, sorption, and transport in mixed solvents are limited. However,

pharmaceutical literature contains solubility data for several drugs spanning a wide

polarity range. Yalkowsky and Roseman (1981) observed that as solute polarity

increases relative to the solvent, the solubilization curves become increasingly more

parabolic in shape until an inverse relationship occurs (i.e.,decreased solubility with

cosolvent additions). Such behavior is explained on the basis of the solute-solute and

solute-cosolvent interactions.

The sorption of HIOCs from mixed solvents has received little research

attention to date. For several HIOCs of environmental relevance (log Ko > 1.0),








18
solubility does increase with increasing f,; thus, a decrease in sorption is expected.

Fu and Luthy (1986b) observed an inverse log-linear behavior in the sorption by

three different soils of naphthol, quinoline, and dichloroaniline in methanol/water

and acetone/water solutions up to 50% by volume. Similar behavior was observed

by Zachara et al. (1986) for quinoline sorption by a natural clay isolate and

montmorillonite in the same binary mixtures. However, for the sorption of an

ionizable fluorescent dye (Rhodamine WT) from binary mixtures of methanol/water

and acetone/water, Soerens and Sabatini (1992) observed adherence to the log-linear

model only for cosolvent fractions less than 30%, while at higher fractions sorption

increased.

For hydrophobic, ionogenic organic compounds (HIOCs), several factors (e.g.,

speciation, soil-solution pH, sorbent-surface pH, charge, ionic strength, ionic

composition, multiple solutes) make predicting sorption from a single parameter

difficult due to additional mechanisms that must be considered. As discussed

previously, prediction of HIOC sorption by soils from aqueous solutions is already

complicated due to the potential for a variety of different sorption mechanisms.

Prediction of HIOC sorption from mixed solvents is further confounded by a number

of indirect effects resulting from cosolvent-induced phenomena occurring either in

the solution phase or on the sorbent. For example, for an organic acid in solvents

of low dielectric constants (e.g.,methanol, acetone, dimethylsulfoxide) an alkaline

shift in the solute pKa results in an increase in the fraction of neutral species.

Similar impacts on the ionization of sorbent functional groups and subsequent solute-

sorbent interactions must also be considered. Also, the impact of cosolvent-water

interactions that have been considered negligible in predicting the chemodynamic

behavior of HOCs may become important when assessing the behavior of HIOCs.








19
In addition, the different propensities of the cosolvent and water to solvate both the

solute and the sorbent will be important in understanding the sorption of HIOCs.

The existence of codisposal sites, implementation of cosolvents in remediation

schemes, and the development of alcohol-based fuels further warrants a better

understanding of the behavior of HIOCs in complex solvent mixtures.

Emphasis of this work was on understanding the solubility and sorption of

HOCs in multi-phasic mixtures, and of HIOCs in complex miscible-solvent/water

mixtures. The liquid-liquid partitioning behavior of aromatic hydrocarbons between

environmentally relevant organic immiscible liquids (OILs) and water was

investigated. The applicability of Raoult's law was assessed by measuring and

compiling partitioning data from several multi-component OILs, and the UNIFAC

model was utilized to estimate the likely nonidealities resulting from interactions

between components in these complex OILs. These results are discussed in Chapter

2. For the partitioning of HIOCs from binary miscible-cosolvent/water mixtures, the

role of solute hydrophobicity and acidity, solvent type, and pH on the sorption of

organic acids by a surface soil from mixed solvents was investigated. These studies

included (1) sorption of several organic acids from methanol/water solutions

(Chapter 3), (2) sorption of benzoic acid and PCP as a function of pH at several

fixed methanol/water compositions (Chapter 4), and (3) benzoic acid sorption from

additional binary mixtures of water and cosolvents with a wide range in solvent

properties, as well as, sorption of several substituted carboxylic acids from

methanol/water solutions (Chapter 5). The observed sorption of these HIOCs was

assessed in terms of cosolvent-enhanced solubility, cosolvent-induced speciation, as

well as specific and nonspecific solvent association mechanisms.













CHAPTER 2
EQUILIBRIUM PARTITIONING OF POLYAROMATIC HYDROCARBONS
FROM ORGANIC IMMISCIBLE LIQUIDS INTO WATER


Introduction

Background

Environmental contamination problems at most industrial waste disposal sites

or spill sites commonly involve the presence of an immiscible organic phase

constituting a multi-phasic waste with multiple components. Of great concern is the

transport of organic constituents from these wastes resulting in contamination of soil

and water. Near the source of contamination where a separate organic phase is

present, solubility is the primary process controlling the release of organic chemicals

to the aqueous phase. Therefore, an understanding of the solubility (or partitioning)

of polyaromatic hydrocarbons (PAHs) from a complex liquid such as those suggested

is essential in predicting contaminant release.

Over the last few years efforts have been made to measure the partitioning

of PAHs from environmentally relevant organic liquid wastes such as gasoline, motor

oil, diesel fuel, and coal tar. Coal tars are among the most complex organic liquid

wastes and comprise a large number of hydrocarbons spanning a broad spectrum of

molecular weights. The concentrations of individual constituents in coal tars vary

significantly from one manufacturing gas plant (MGP) site to another. The








21

manufacturing of gas from coal and oil for residential, commercial, and industrial use

in the late 1800s and early 1900s resulted in the production of large amounts of coal

tar wastes. Eng and Menzies (1985) reported that more than 11 billion gallons of

coal tar were generated in the U.S. during the period 1816-1947, but the disposition

of several billion gallons is unknown and remains unaccounted. In many cases, the

wastes were left on-site in pits or containers, placed in near by ponds or lagoons, or

taken to off-site areas for land disposal. Such practices resulted in contamination of

soils and groundwater at most former MGP sites. Hydrophobic organic chemicals

(HOCs) have been detected at former MGP sites, and are of particular concern due

to their potential carcinogenic nature (Guerin, 1978). Several of these compounds

have already been included on the U.S. EPA list of priority pollutants.

In the past, it has often been assumed that concentrations of organic

contaminant in the aqueous phase leaving a coal tar source would be equal to their

corresponding pure-compound aqueous solubilities. This may be a reasonable

estimate if the source of interest was composed of a single contaminant (e.g.,

trichloroethylene, tetrachloroethylene). However, most complex wastes (e.g.,coal tar,

diesel, gasoline) consist of mixtures of contaminants. These mixtures may be

considered complex based on the number of chemicals that constitute the mixture.

On the other hand, complexity of a mixture can be defined by considering how the

properties of the mixture deviate from some "ideal" behavior, regardless of the

number of components. The former view corresponds to a mixture being complex

in composition, whereas the latter implies complexity in behavior. The important








22
point is that a mixture can be complex in composition without being complex in

behavior and vice versa.

To assess the extent of groundwater contamination and the long-term

environmental impacts from land disposal or spill sites containing multi-phasic

wastes, it is necessary to characterize the total amounts released and the release rates

of HOCs from the waste matrix. The properties of an organic mixture complex only

in composition are determined by the properties of its pure components and their

concentrations in the mixture. This implies that the chemicals of interest behave

ideally in the matrix containing them. Under these conditions Raoult's law would

suggest that the concentration in the aqueous phase of a chemical is proportional to

the mole fraction of the chemical in the organic phase.

This chapter will focus on the use of equilibrium theory to characterize the

total amounts of PAHs released from organic liquid wastes. Coal-tar/water partition

coefficients for several PAHs were measured from several coal tars spanning a wide

range in physical and chemical properties. To estimate aqueous-phase concentrations

of PAHs in equilibrium with coal tar, the utility of applying Raoult's law convention

for activity coefficients in conjunction with supercooled liquid solubilities for PAHs

that are crystalline in their pure form will be assessed. Although the majority of this

chapter is on coal tar wastes, a reassessment of diesel fuel/water and gasoline/water

partitioning data will also be presented including the use of the UNIFAC

(UNIQUAC functional group activity coefficient) model to estimate the likely

nonidealities resulting from interactions between components in these complex

organic liquids.










Theory

The release of a chemical from an organic liquid phase can be estimated from

a liquid-liquid partition coefficient (Kd) which is defined as


Kd o (2-1)
C,

where Co and C, are the molar concentrations (mol/L) of the chemical of interest

in the organic and aqueous phases at equilibrium, respectively. The partition

coefficients (K) for coal tar, diesel fuel, and gasoline will be designated using

subscripts tw, dw, and gw, respectively.

For liquid-liquid partitioning, thermodynamic equilibrium is defined by the

equality of the chemical potentials in the aqueous and organic phases. This equality,

in conjunction with the choice of pure (liquid) solute as the standard state and the

Raoult's law convention for activity coefficients, results in the following expression

at equilibrium

(2-2)
SYo Xw Y (2-2)

where subscripts o and w denote organic and aqueous phases, respectively; xo and

x, are the respective mole fractions of the chemical in the organic and aqueous

phase; Yo* is the activity coefficient of the chemical in the organic phase in

equilibrium with the aqueous phase; and y, is the activity coefficient of the chemical

in the aqueous phase in equilibrium with the organic phase.

From Eq. (2-2), molar concentration of a solute in the aqueous phase (Cw) can

be approximated with the following assumptions: (1) the presence of other








24
components in the aqueous phase is ignored, i.e., y,' is set equal to the aqueous

phase activity coefficient of the solute in equilibrium with the pure solute (y,); (2)

the solute behaves ideally in the organic phase, i.e.,Yo* is unity; (3) the aqueous mole

fraction solubility (Sx,) of the pure liquid solute is equal to 1/ y; and (4) the

solution is sufficiently dilute (i.e., moles of the solute are small relative to the total

moles of solvent; C = x V and S/ V, = S,w where S, is the aqueous solubility of the

pure liquid solute in moles/L) and V, is the molar volume of water. Application of

these four assumptions yields


Cw xo St (2-3)

Therefore, the partition coefficient (Eq. 2-1) for a solute can be approximated

as follows,


C
Kd (2-3)
xo S1
For mixtures comprising a large number of constituents, each contributing a

small fraction to the total, xo/Co can be replaced by the molar volume (Vo, L/mole)

of the organic phase. The molar volume can then be approximated by the ratio of

the average molecular weight (MW,, g/mole) and density (po, g/L). The resulting

expression for Kd is:


1 (P. / MW.)
Kd (2-5)
VSt St








25
Taking logarithms of both sides of Eq. (2-5), it is evident that the inverse relationship

between log Kd and log St results in a unit negative slope and an intercept that is

dependent upon the molar volume of the organic phase (i.e.,MW,/ p):


log Kd -log S log M(2-6)

Derivation of Eq. (2-6) was based on a choice of the pure liquid solute as the

standard state. Most of the PAHs investigated in this study are solids in their pure

form; therefore, the hypothetical supercooled liquid solubilities of the solid solutes

must be employed. The supercooled liquid solubility (S) of a solute at a given

temperature can be calculated directly from the solute's measured heat of fusion

(AHf) and melting point (T) (Yalkowsky, 1980), or alternately can be estimated by

assuming a constant entropy of fusion (ASf=AH/T.) for the PAHs of interest

(Yalkowsky, 1979; Martin et al., 1979) (see Appendix A).

Application of Raoult's Law for Gasoline. Motor Oil, and Diesel Fuel

The utility of the relationship defined by Eq. (2-6) was successfully

demonstrated for several gasolines by Cline et al. (Cline et al., 1991) for several

monocyclic aromatic hydrocarbons (MAHs). Gasoline is composed of several

branched-chain paraffins, cycloparaffins, alkanes, aromatic compounds, and small

amounts of various additives. Results presented by Cline et al. (1991) revealed that

although gasoline is complex in composition, MAH partitioning into water behavior

was essentially ideal. None of these MAHs exhibit crystalline structure in their pure

form which is common to most PAHs. Chen (1993) investigated the applicability of








26
Raoult's law for the partitioning of MAHs as well as some PAHs from new and used

motor oil. Given the absence of experimental artifacts, nonideality was noted for the

partitioning of MAHs from the new motor oils, whereas, the one PAH investigated

(phenanthrene) partitioning was successfully predicted using Raoult's law and

supercooled liquid solubilities. However, Raoult's law appeared applicable within

a factor-of-four for the partitioning of both MAHs and several PAHs from used

motor oil.

Hagwall (1992) measured the partitioning of several PAHs from diesel fuel

into water and concluded that the use of supercooled liquid solubilities (S.) in

applying Raoult's law was not successful. However, Hagwall (1992) used an

inaccurate estimation of S., resulting in a wrong conclusion regarding the

applicability of Raoult's law. Using the crystal solubilities (Sw) given in Table 2-1

and assuming a constant ASf of 13.5 eu, a much better relationship was observed

between log Kd, and log S,. In Figure 2-1, the measured log Kdw values are plotted

against their log S, for the eight PAHs investigated along with the ideal line (solid

line) calculated from Eq. (2-6) for each diesel fuel using the MW, and p, given by

Hagwall (1992). For most PAHs in all four diesel fuels, the log Kdw values lie near

the ideal line suggesting that the assumption of ideal behavior may be adequate for

describing the partitioning of PAHs from diesel fuels to water. The confidence

intervals (bars) shown in Figure 2-1 were estimated using an error propagation

method (Shoemaker et al., 1980) which incorporates the errors incurred in the

analysis of both the neat fuel and aqueous phase concentrations. Arrowheads reflect








27
the few cases where the propagated error was larger than the average KdW value as

was the case for anthracene and fluoranthene. Note that both compounds were

present in small quantities in the neat fuel and or analytical problems were

encountered in detecting small aqueous phase concentrations. Several factors other

than nonideal behavior could result in apparent deviations such as analytical

uncertainty in Kw, as well as, errors incurred in the estimations of S, (i.e., reported

S, values and the use of a constant ASf value).

The success in applying Raoult's law for gasolines, diesel fuels, and motor oils

leads to the question of whether ideal behavior can also be assumed for coal tars.

Compared to gasolines, diesel fuels, and motor oils coal tars are even more complex

in composition, especially because over 60% of their constituents are not known.

Gasolines, diesel fuels, and coal tars collected from different sites vary greatly in

their composition, but only a small variance exists in their molecular weights (Cline

et al., 1991; Hagwall, 1992). In contrast, different coal tars exhibit a wide range in

composition, MWo and po (EPRI, 1993). The applicability of Raoult's law to

tar/water partitioning will be assessed as well as the potential for nonideal behavior.










Table 2-1. Selected physico-chemical properties for the PAHs investigated.


Melting' Molecular
Point Weight" S,b
Compound (Co) (g/mole) (mg/L) log SId


Naphthalene 80.2 128.2 32 -3.05

1-methylnaphthalene -22 142.2 27" -3.72e

2-methylnaphthalene 34 142.2 26' -3.62

Acenaphthylene 82 152.2 3.93 -4.02

Acenaphthene 93 154.2 3.42 -3.98

Fluorene 116.5 166.2 1.9 -4.03

Phenanthrene 100 178.2 1.0 -4.5

Anthracene 216.3 178.2 0.07 -4.49

Fluoranthene 107 202 0.27 -5.19

Pyrene 150 202 0.16 -4.85

Chrysene 254 228.2 0.006 -5.29

Benzo(a)anthracene 156 228.2 0.0057 -6.29

Benzo(a)pyrene 179 252 0.0038 -6.28



"Verschuren (1983); b Crystal solubility at 250C (Little, 1981) unless stated otherwise;
' Miller et al. (1985); d Supercooled liquid solubility (moles/L) calculated assuming
a constant ASf for PAHs; e liquid solute at standard state.










5.5 h


4.5 F


3.5 F


- 3 3L
-3 -6


-3.5 -3 -6 -5.5
log [Sscl, moles/L]


Figure 2-1.


log Kdw values plotted versus log S, for eight PAHs along with the ideal line (solid line)
Eq. 2-6 for each diesel fuel.


calculated from


DF #2
6



80 7-
84 2
3


0


-5.5 -5 -4.5 -4 -3.5


-5.5 -5 -4.5 -4 -3.5 -3










Materials and Methods

Chemicals

For all the PAHs investigated (see Table 2-1) standards were purchased from

Aldrich Chemical Co. at > 98% purity except for acenaphthene, which was available

only at 85% purity. Methylene chloride, the solvent used for the aqueous phase

extractions, was purchased from Fisher Scientific at Fisher grade Optima.

Batch Equilibration Technique

Approximately 0.3-0.5 g of coal tar were added to a glass centrifuge tube

(nominal volume 40 mL); enough electrolyte solution (0.01 N CaC2) was added such

that no headspace remained; and tubes were closed with phenolic caps fitted with

Teflon-lined septa. Prior to sampling the coal tar for equilibration with an aqueous

phase, coal tars were rotated end-over-end at room temperature (23 + 2"C) for 12-18

hours. The coal tar/water (0.01 N CaCl) mixtures were then equilibrated for 3-7

days in the dark. Preliminary studies where samples were equilibrated for 1, 3, 5,

and 7 days showed no measurable differences in PAH concentrations after 3 days.

Following centrifugation (300 RCF for 30 minutes) of the equilibrated coal tar/water

mixtures, a portion of the aqueous phase (z25 mL) was quantitatively removed for

extraction with methylene chloride and subsequent concentration prior to analysis.

Due to the large masses of the compounds of interest present in the coal tar phase,

experimental artifacts from PAH sorption to the equilibration vessels were

considered negligible. To avoid volatilization losses and contamination of the

aqueous phase aliquot with the coal tar phase, the aqueous aliquot was removed

through the septa using a 50-mL Teflon-backed gas/liquid syringe equipped with a








31
3-inch needle. The equilibration vessel was vented during sampling by piercing the

septa with a second needle.

Following aqueous phase transfers, as much residual water as possible was

removed from the equilibration vessel without loss of the coal tar. The coal tar in

the equilibration vessel and the cap were rinsed with methylene chloride into a 100-

mL volumetric flask and brought to volume. Dissolved coal tar samples were filtered

(0.45 pm) prior to analysis. For the coal tar samples from which it was difficult to

remove residual water without loss (i.e., thin liquid coal tars), an aliquot of the neat

coal tar was sampled for analysis as well.

Chromatographic Analysis

PAH concentrations in the coal tar and aqueous phases were determined using

a gas chromatograph (GC) equipped with an ion trap detector (ITD). The GC/ITD

method included an HP Ultra 2 column (95% methyl, 5% phenyl polysiloxane, 0.5

micron thickness; 30 cm x 0.32 mm ID); helium as a carrier gas at a flow rate of

approximately 1.0 Ml/min; temperature gradient program, and an ion trap detector.

The temperature gradient program consisted of a 1 minute hold at 50"C; a ramp to

1300C at 30*C/min followed by a 3 minute hold; a ramp to 1800C at 12*C/min

followed by a 1 minute hold; a ramp to 240 C at 7C/min; and a ramp to 300 C at

12 C/min followed by a 15 minute hold. The ITD was set at an electron energy of

70 eV and scanned from 45 to 450 amu at 2 scans/sec. The electron multiplier

voltage was 1650 volts and the transfer temperature from the GC was 2800C. Prior

to GC analysis, samples were usually spiked with an internal standard consisting of

naphthalene-d8 and anthracene-dg.










Results and Discussion

Coal Tar Composition

The coal tars used in this study were received from META Environmental, Inc.

Various physical and chemical properties of these coal tars had been characterized

(EPRI, 1993), including density, viscosity, water and ash content, average molecular

weight, elemental and organic analysis. The ranges observed for these properties in

terms of percentages or concentrations are summarized in Table 2-2.

The viscosity of the coal tars ranged from approximately 34 cps to 6600 cps

(40C), with the coal tar consistency varying from thin liquids (ID# 1,4, and 5) to

thick liquids (ID# 7) and from soft (ID# 3 and 9) to sticky (ID# 2) "taffy-like"

materials. Coal tar viscosity will generally increase with aging and decrease with

temperature. Some coal tars had high ash contents, suggesting the presence of other

solids. For example, coal tar ID# 7N had a high content (37%) of what appeared

to be sand and silt. The PAH concentrations for this coal tar were corrected to

represent the mass of PAH present per actual mass of coal tar. For the remaining

coal tars an occasional rock or pellet was found, which was easily removed prior to

experimentation.

Water content of the thin liquid coal tars was small (<1% mass basis). For

the more viscous coal tars, reported water contents were as high as 30% (mass basis);

however, high molecular weights and densities for these coal tars strongly suggests

that these high water contents were in actuality a sampling artifact. It appears that

water may have been trapped as a separate liquid phase within the taffy-like matrix

of the coal tar.












Table 2-2. Range of properties observed for eight coal tars (EPRI, 1993).


Physical Properties:
Ash
Water Content
TOC "
Viscosityb
Density
MW d




Organic Compounds
monocyclics
polycyclic:
2 & 3 rings
> 3 rings
NPAHsf
SPAHsg
Pitch


Range
0-50%
0-30%
40-90%
34-6,600 cps (400C)
1.06-1.43 g/mL (24C)
230-780e g/mole




Range (mg/kg)
13-25,300


6,800-218,000
12,000-110,000
70-1,000
0-4,000


Elemental Analysis:
Carbon
Hydrogen
Nitrogen
Oxygen
Sulfur
Cyanide




Metals Analysis
Arsenic
Beryllium
Cadmium
Lead
Nickel
Selenium
Vanadium
Chromium


Range(%)
43-90
2-7
<0.5-1
1-33%
0.4-4%
< 1-580 mg/kgh
< 1-150 mg/kg'


Range (mg/kg)
3-23
<1
<1-4
1-930
2-74
< 1-5
6-230
< 1-230


" Total Organic Carbon; b Test Methods ASTM D445 and D88; c Test Methods
ASTM D70, D369, or D1429; d Average molecular weight determined using vapor
pressure osmometry; e Exception: asphaltene-like tar 1600 g/mole; f Nitrogen
polyaromatic hydrocarbons; g Sulfur polyaromatic hydrocarbons; h Determined using
EPA Method 4500;' Determined using EPA Method 9010.








34

Similar compounds were found in all of the tars, but individual hydrocarbon

concentrations varied significantly from one MGP site to another. PAH

concentrations ranged from 7,000mg/kg to 220,000mg/kg, with various naphthalenes

as the dominant components. Several monocyclic aromatic hydrocarbons (e.g.,

benzene, toluene, ethylbenzene, and xylenes (BTEX), and styrene) were also present

in concentrations ranging from 13 to 25,300 mg/kg. Much smaller amounts of

nitrogen- and sulfur-containing aromatic hydrocarbons (e.g., carbazole and

dibenzothiophene) were also found.

It is important to recognize that less than 40% (on a mass basis) of the coal

tar constituents can be quantified (see Table 2-2) using common extraction and

chromatographic techniques. The unidentified tar fraction is often referred to as the

"pitch" for operational purposes. Current sophisticated analytical techniques still lack

the capability needed to identify most of the pitch constituents; however, their

general nature may be surmised based on coal composition (e.g.,Whitehurst et al.,

1980) or oil composition. A majority of the pitch constituents are aromatic

compounds with high molecular weights and low aqueous solubilities; thus, they may

not be of direct concern in terms of groundwater contamination. However, the

physical and chemical characteristics of the pitch may exert a strong influence on the

rates of release and the equilibrium partitioning of the more-soluble tar constituents

(e.g., BTEX, naphthalenes) that are of greater environmental concern. Also,

nitrogen- and sulfur-containing aromatic hydrocarbons present in coal tars may

impart nonideal behavior.










Tar-Water Partitioning

The relative success in applying a model based on Raoult's law convention for

gasolines (Cline et al., 1991), diesel fuels, and motor oil prompted the investigation

of whether ideal behavior could also be assumed for coal tars. Compared to

gasolines and diesel fuels, coal tars are compositionally more complex; thus, greater

deviations from ideal behavior might be expected. The assumption of ideal behavior

for coal tar is postulated here for practical expediency, since it reduces the number

of parameters needed to estimate PAH concentrations in groundwater. Ideal

behavior is not necessarily expected for such materials, but it is hoped that the

assumption will be adequate within a specified acceptance factor; a factor-of-two has

been chosen here to be adequate for field-scale applications. Experimental

measurements of tar-water partition coefficients are difficult, and are subject to

significant errors. Thus, experimental artifacts as a possible cause must be

eliminated before attributing nonideal behavior to a given coal tar or even to one or

more constituents within a coal tar. It is with this pragmatic perspective that we will

interpret tar-water partitioning data. The investigations of tar-water partitioning

involved analysis of data collected in this study for eight tars, analysis of published

data, and theoretical analysis of solute-solute interactions that might lead to nonideal

behavior.

Analysis of Laboratory Data

The tar-water partitioning data for the eight tars examined in this study are

presented in Figures 2-2 through 2-5. The logarithm of the average K, value and

the calculated standard deviations are shown along with the prediction based on Eq.








36
(2-6) (solid line) and the factor-of-two tolerance intervals. For most coal tars, the

data points are scattered about the ideal line within the factor-of-two bounds

suggesting that the assumption of ideal behavior suffices (again, within a factor-of-two

error) in predicting KI for the PAHs. For the one exception (ID# 1), measured

data points lie consistently above the ideal line (Figure 2-2A) indicative of an error

in the estimate of the molar volume. Specific causes for the systematic deviation

observed with coal tar ID# 1 need to be further explored.

Benzo(a)anthracene is the only PAH that consistently lies substantially below

the ideal line for most of the coal tars. Uncertainties arising from both analysis and

parameter estimation may have resulted in the observed negative deviations.

Analysis of benzo(a)anthracene in the aqueous phase approached detection limits,

thus contributing to uncertainties. A greater source of error was probably incurred

in the estimation of the supercooled liquid solubility for benzo(a)anthracene. The

S, values (given in Table 2-1) used in plotting log K, values in Figures 2-2 through

2-5 were estimated assuming a constant entropy of fusion (ASE)(Yalkowsky, 1979).

For most compounds, this method may be preferred over attempts to find reliable

measured AHf values needed for a direct calculation. However, in the case of

benzo(a)anthracene the S, values estimated using the average ASf value was about

one order of magnitude higher than that calculated using the AHf value reported by

Chio et al. (1985). Thus, the reasons for the observed deviation of

benzo(a)anthracene data points from the ideal line are indeterminate.





























-5.5 -5 -4.5


-3.5


-5.5 -5 -4.5 -4 -3.5 -3
log [S, moles/L]


Figure 2-2.


Comparison of measured tar-water partition coefficients
predictions based on Raoult's law for ID# 1(A) and ID#
tars.


5
'3=
o

2=n
o


36
-6


(K.) and
2(B) coal














6-


5.5


.5-


4.5


4


3.5


i35
7
0)
0 1

6-



5



4


3L
-6


Figure 2-3.


-4.5


-3.5


-5.5 -5 -4.5 -4 -3.5
log [S, ,moles/L]

Comparison of measured tar-water partition coefficients
predictions based on Raoult's law, for ID# 3(A) and ID#
tars.


-3



(IK) and
4(B) coal



























5
~
o

ai,
o


3-
-6


Figure 2-4.


-5.5


-4.5


-3.5


-5.5 -5 -4.5 -4 -3.5 -3
log [S, moles/L]


Comparison of measured tar-water partition coefficients (K,) and
predictions based on Raoult's law, for ID# 5(A) and ID# 7(B) coal
tars.










7



6



5



4


SA
S-6
6

5.5
be
5

4.5

4

3.5

B
-6



Figure 2-5.


-5.5 -5


-4.5 -4 -3.5


-5.5 -5 -4.5 -4 -3.5 -3
log [S, moles/L]


Comparison of measured tar-water partition coefficients
predictions based on Raoult's law, for ID# 7N(A) and ID#
tars collected by EPRI.


O(K) and
9(B) coal









Analysis of Literature Data

The tar-water partition coefficients (K,) for several PAHs compiled from the

literature (Rostad, 1985; Groher, 1990; Picel, 1988) for three different coal tars, are

plotted in Figure 2-6 in a manner similar to Figures 2-2 through 2-5. For each coal

tar, the ideal line (solid line) shown was calculated from Eq. (2-6) using the best

estimates available for MW, and p,,. For the coal tar investigated by Rostad et al.

(1985) (Figure 2-6A), the ideal line was calculated using the Pet reported and a MWVt

value estimated from a weighted average of the mole fraction and molecular weight

of each known component. For the unknown fraction, an average molecular weight

of 300 g/mole was assumed. Picel et al. (1988) reported values for both pt and

MW,. Groher (1990) did not report values fore MW, and pc; therefore, data for a

coal tar, similar in composition, obtained from the same site a few years later was

used to estimate the ideal line (Figure 2-6B).

For most of the PAHs, the measured KI values are within a factor-of-two

from the ideal line, with the best agreement observed for the Picel et al. (1988) data

(Figure 2-6C). Observed deviations from the ideal line could be the result of

considerable nonideality in the tar-water system or a consequence of various

experimental artifacts including inadequate time for equilibration and poor recovery

of the PAH from the aqueous phase. The probability of such experimental artifacts

increases for the larger PAHs where a greater difficulty is often encountered in

accurately measuring the solubility of rather insoluble compounds.








42


6.5
Rostad et al., 198
S- dibenz(a,h)anthr cRostad et al., 1985
5.5 benzo(a)anthracene '-..
5 -... ".--.-methylnaphthalen
Ideal Line anthracen~e
4.5 -
MWt = 265 g/mole fluorene-..hthalen
4 p = 1.03 g/mL --
A p acenaphthene
3.5
-6.5 -6 -5.5 -5 -4.5 -4 -3.5 -3
7 -... chrysene Groher, 1990
6.5 --- fluoranthene
Sp-yrene
60 --. anthracene
5.5 .phenanthrene
0) 5~ i 'e U--. -fluorene
O 5 Ideal Line ----.....
4.5 MWct = 230 g/mole acenaphthelne--.
4 P ct = 1.064 g/mL 2-methylnaphtihaen.
B naphthatene
-6 -5.5 -5 -4.5 -4 -3.5 -3

6.5 -.. pyrene Picel et al., 1988
..fluor.nthene
6 --- -
s. phenanthrene biphenyl
I -l --... fluorene
5 Ideal Line 1.... __ ... .-- -methylnaphthalene
MWct = 150 g/mole acenaPt e h phtha
4.5 -.. aphthalene
p C = 0.99 g/mL ---.-
4 naP. ne'
3 .5 -3

log [S, moles/L]

Figure 2-6. Comparison of measured tar-water partition coefficients (K,) reported
in the literature and predictions based on Raoult's law. Literature
source as indicated.








43
A decrease in the measured K, values would be anticipated for deviations

resulting from sufficient nonideality as observed in Figure 2-6A for the Rostad et al.

(1985) data. The expectation of the presence of nonideality resulting in negative

deviations for PAHs is based on work by Chiou and Schmedding (1982) and Chio et

al. (1985) where the activity coefficients of several PAHs were measured in water-

saturated octanol and mixtures of benzene and cyclohexane. In both cases, the

activity coefficient of a given PAH in the organic phase (Yo') were found to be

greater than unity. Values of y., greater than unity will result in log K, values

smaller than those estimated assuming ideal behavior.

Predicting Aqueous-Phase PAH Concentrations

Coal Tars

The log Kt, versus log S, relationship observed for several coal tars (Figures

2-2 through 2-5) suggests that the application of Raoult's law and the assumption of

ideal behavior may be adequate to predict the concentration of PAHs in groundwater

(C,) in contact with a coal-tar source. Equation (2-3) was used to estimate the

concentrations of several PAHs expected to be present in a groundwater in

equilibrium with a coal tar were estimated using Eq. (2-3) for the coal tars

investigated. The mole fraction of the PAH in the organic phase (xo) needed in Eq.

(2-3) was approximated by the product of the mass fraction (mg/g) in the coal tar

and MWt, (i.e., C, = Mi MW, St). A log-log plot comparing predicted aqueous

concentrations (converted to commonly reported units of mg/L) and those measured

during the laboratory partitioning studies is shown in Figure 2-7. The error bars









44

shown in Figure 2-7 for the laboratory-measured concentrations represent the

standard errors calculated from replicate averages. An arrowhead on an error bar

indicates that the lower bound approached the limit of detection. For the predicted

concentrations, the error bars shown in Figure 2-7 were estimated from the standard

errors calculated from the replicate average of Mi. Also given in Figure 2-7 is the

ideal line (i.e., 1:1 correlation) with the corresponding factor-of-two tolerance

intervals.


a 0

E

?-1
0)
o
-0 -2

a,
a. -3


-3 -2 -1 0 1 2
Measured [log (C, mg/L)]


Figure 2-7. Comparison of laboratory-measured aqueous-phase concentrations
(C,) with those predicted on the basis of Raoult's law for eight coal
tars.








45

Confidence in the C, values predicted using Eq. (2-3) is dependent on several

factors other than the premise of ideal behavior, including uncertainty about the

input parameters (e.g., Mi, 1MWt, and S). Both Mi and MW, can be determined

experimentally; therefore, errors associated with these parameters can be obtained

from replicate analysis information. Sampling and chromatographic analysis of this

heterogeneous liquid waste is prone to considerable errors; therefore, the deviation

associated with Mi is probably the greatest source of error in estimating C, values.

A majority of the data presented in Figure 2-7 lie within the factor-of-two

intervals given about the 1:1 correlation. The data that lie outside the factor-of-two

intervals result in predicted concentrations greater than those measured, with the

exception of the data points below the 1:1 correlation which correspond to

benzo(a)anthracene. Benzo(a)anthracene is present in small amounts in coal tar, thus

often approaching the limits of analytical detection. In addition, aqueous solubility

measurements for compounds with small values (i.e., <10-2 mg/L) become

increasingly less reliable. Good agreement for a majority of PAHs within a factor-of-

two suggests that the use of Eq. (2-3), based on Raoult's law, may be adequate for

estimating PAH concentrations. At the very least, aqueous concentrations estimated

using this approach should be considered more appropriate and definitive than

merely assuming crystal solubilities for aqueous-phase concentrations.

Given the variations that may exist in (1) the different coal tar deposits at a

given site, and (2) the extent of weathering at that site, it would be advantageous to

estimate maximum PAH concentrations that might be found at any site. In order to








46

estimate maximum CW values, the eight coal tars investigated were assumed to be

representative of coal tars that might be found at any site in the United States. The

maximum concentrations of the PAHs investigated based on the data compiled for

the eight coal tars, are given in Table 2-3 along with the ratios of C, to S,. Note

that the maximum Cw expected is the crystal aqueous solubility for anthracene,

chrysene, and benzo(a)anthracene.



Table 2-3. Maximum CW values for several PAHs based on the data compiled for
eight coal tars.



Compound SW Maximum
(mg/L) CWa CJ/S

Naphthalene 32 14b 0.44
1-methylnaphthalene 27 2 0.05
2-methylnaphthalene 26 1.4 0.05
Acenapthylene 3.93 0.5 0.13
Acenapthene 3.42 0.3 0.1
Fluorene 1.9 0.3 0.16
Phenanthrene 1.0 0.4 0.3
Anthracene 0.07 SW 1.0
Fluoranthene 0.27 0.01 0.4
Pyrene 0.16 0.1 0.5
Benzo(a)anthracene 0.0057 SW 1.0
Chrysene 0.006 SW 1.0
Benzo(a)pyrene 0.0038 0.001 0.3


T=25oC
Result from data compiled for seven
resulted in a prediction of 26 mg/L.


of the eight coal tars; data for one tar








47
In the absence of cosolvents and other solubility-enhancing adjuvants (e.g.,

dissolved organic carbon, surfactants, etc.), the maximum aqueous-phase

concentration (C,) is limited by the crystal solubility (Sw). Although the hypothetical

supercooled liquid solubility is used to obtain best estimates for C,, mixing of the

chemical with the aqueous phase is ultimately governed by interactions with the

solvent. These are expressed through the crystal solubility (S,) (Pinal, 1988). For

a PAH that has a low aqueous solubility, high melting point, and is present in high

concentration in the coal tar, the concentration predicted in the aqueous phase

assuming ideal behavior would be the crystal aqueous solubility (S,).

Diesel Fuels

Reasonable agreement shown previously in the predicted and measured log

Kdw versus log S, relationships for most PAHs (Figure 2-1) also supports the use of

Raoult's law in predicting maximum PAH concentrations that may be present in the

aqueous leachate leaving a diesel-fuel contaminated area. Using Raoult's law and

assuming ideal behavior, the concentration of a constituent in the aqueous phase in

equilibrium with the organic phase is proportional to the mole fraction of that

constituent in the organic phase (see Eq. 2-3). Substituting Eq. (2-5) into Eq. (2-1)

gives the following equation for the equilibrium aqueous-phase concentrations:


C C, MW, St (2-7)
P f








48

where the subscripts df and w refer to diesel fuel and water, respectively. In Figure

2-8, PAH concentrations predicted using eq 2-7 were converted to commonly

reported units (ig/L) and plotted against concentrations measured in the laboratory

partitioning studies with the four diesel fuels.








3
ID*
i 1:1
'i2- A 2 A
03
0 4 naphthalene
1 1-methylnaphthalene
S2-methylnaphthalene




C acenaphthene fluorene
)- 1 a- phenanthrene
anthracene
fluoranthene
-2


-3 I
-1 0 1 2 3
log [Measured C, ug/L]


Figure 2-8.


Comparison of laboratory-measured aqueous-phase
concentrations (C,, /g/L) with those predicted on the basis of
Raoult's law for four diesel fuels.








49
Also included in Figure 2-8 are the confidence intervals for both the measured and

predicted concentrations. Measured concentration errors were estimated from the

standard deviations observed in triplicate analyses of the aqueous phase; confidence

intervals with arrows reflect limits of detection. Similarly, the errors associated with

the predicted values were estimated from the standard deviations obtained from

triplicate analyses of the neat diesel fuel, i.e., the determination of Cdf. The

confidence intervals given for the predicted C, in Figure 2-8 did not include errors

incurred in estimating MWdf or pdf. Overall, the correspondence between measured

and predicted equilibrium aqueous phase concentrations shown in Figure 2-8 is to

be very good.

Assessment of Deviations from Ideal Behavior for Equilibrium Conditions

The relationship between Kd and S, assumed previously (el 2-6) was based on

the simplifying assumption of ideal behavior (i.e., yo = 1 and yw = yw). Several

factors may cause deviations from the assumed ideal behavior for diesel-water

partitioning of PAHs. For example, negative deviations from the ideal line could

result from the presence of surfactants or emulsions or sufficient nonideality, while

positive deviations can be expected if equilibrium has not been reached, and

apparent deviations (positive or negative) can result from uncertainty in parameter

estimation.

For a mixture which is complex in composition and behaves in a "nonideal"

fashion, the partition coefficient (Kd) between an organic liquid and an aqueous

phase can be related to the aqueous solubility of the pure liquid (S) in the following









manner (Chiou and Schmedding, 1982):


log Kd -log S, logf MW
SP" ) (2-8)

log Yo + log



Comparison of Eqs. (2-6) and (2-8) suggests that any deviations due to nonideal

behavior will arise from the last two terms on the right hand side of Eq. (2-8).

Banerjee (1984) observed that the presence of other components in the aqueous

phase had a minimal effect on solute activity; therefore, it was assumed that y,'*/y,

= 1, thus requiring only estimates of Yo'. The UNIFAC model UNIFAC

(UNIQUAC Functional-Group Activity Coefficient) model proposed by Prausnitz et

al. (1980) for estimating activity coefficients in liquid-liquid equilibria was employed

to estimate yo values needed in Eq. (2-8). In this model, a mixture of different

chemicals is treated as a mixture of functional groups constituting the components

of the mixture. Interactions between functional groups in the mixture, and the likely

nonidealities resulting from such interactions, are calculated in order to estimate the

activity coefficient of a chemical for a specified phase. Interaction parameters

required in the UNIFAC model were obtained from the most current update

(Hansen et al., 1991).

A schematic representation of Eqs. (2-6) and (2-8) is shown in Figure 2-9 as

a plot of log KD versus log S1. Note that the expected relationship for an ideal

mixture is depicted by the solid line, with a unit slope and the intercept given as the








51
log Vo (see Eq. 2-8). The single data point represents a possible value for a solute

partitioning between a hypothetical nonideal mixture and water. Note that the

magnitude of deviation from the ideal line is given by the last two terms on the right

hand side of Eq. (2-8) plus an error term, E, representing experimental uncertainty.


Log SI (moles/I) 0

Pure lquid aqueous solubility


Figure 2-9. Schematic representation of the ideal behavior (Raoult's law) and
nonideality in liquid-liquid partitioning.

Application of the UNIFAC model for assessing the potential for nonideality

is presented for a gasoline, diesel fuel, and coal tar. Using the UNIFAC model,

activity coefficients (Yo') of several aromatic compounds were estimated for an

unleaded gasoline simulated to represent the relative compositions (see inset in

Figure 2-10) reported in Cline et al. (1991).


















7
6-
A

0 A
.-J 5 10

Gasoline composition
Alkanes mole fraction
pentane 0.20
octane 0.20
decane 0.15
dodecane 0.15
Aromatic Hydrocarbons
1 MTBE -
2 benzene 0.07
3 toluene 0.07
4 xylene 0.07
5 ethylbenzene 0.07
6 1,2,3-trimethylbenzene -
7 n-propylbenzene -
8 3,4-ethyltoluene -
9 naphthalene 0.014
10 anthracene 5E-3
11 pyrene 1E-6
3,4-benzopyrene 1 E-7
Others
H20 1E-4


A UNIFAC
0 Cline et al. (6)







3 2
2


-3 -2
Log S (moles/L)


Figure 2-10. log K, values for several aromatic hydrocarbons resulting from
UNIFAC model calculations and the average log, values
experimentally determined by Cline et al. (1991) plotted against log S,
values along with the ideal line based on Raoult's law.




The estimated y* values were then used to predict log K, values (shown as solid

triangles in Figure 2-10) according to Eq. (2-8). UNIFAC model calculations for the

monocyclic aromatic compounds represented in Figure 2-10 (compounds 2-5) confirm

the experimental observations of Cline et al. (1991) that gasoline-water partition

coefficients of several liquid hydrocarbons can be approximated by assuming ideal


t f


--








53

behavior. However, for compounds with increasingly more aromaticity and are solids

in their standard state (PAH compounds 9-11 in Figure 2-10), the UNIFAC model

predicted some negative deviation from ideal behavior. Partition coefficients for

these compounds were not measured by Cline et al. (1991) as they are present only

in small quantities in gasoline. Compared to gasolines, diesel fuels contain a larger

fraction of low-solubility PAHs. Therefore, it was of interest to see if the UNIFAC

model estimations of Yo* for these PAHs resulted in deviations from ideality.

The composition of the diesel fuel assumed in the UNIFAC model

calculations is shown in Figure 2-11. The concentrations of the eight PAHs chosen

were comparable to those found in the diesel fuels used in this investigation; the

concentrations of monocyclic aromatic hydrocarbons used were based on analyses

reported by Thomas and Delfino (1991); and the mole fraction of water was selected

based on the maximum ASTM limiting requirement for diesel fuel (Kirk-Othmer,

1980). To simulate the alkane fraction of the diesel fuel, a representative compound

for each alkane (n-, iso-, and cyclo-alkane) was selected (see Figure 2-11) in

proportion to those reported by Mackay et al. (1985). The UNIFAC model

calculations for the yo* values of the PAHs ranged between 0.99 for toluene to 1.16

for fluoranthene. The close proximity of the calculated log Kdw values (solid triangles

in Figure 2-6) to the ideal line based on Raoult's law for the simulated diesel fuel

suggest that deviations from ideal behavior for PAHs smaller than fluoranthene may

be negligible. These calculations suggest that deviations from the ideal line for the

larger PAHs noted in Figure 2-1 cannot be attributed to solute-solute interactions,








54
lending support to analytical sources of error for the observed deviations.

Independently assessing the potential for nonideal behavior emphasizes the need to

account for experimental and analytical sources of errors when judging whether the

deviation noted from the ideal line is indeed the result of nonideal behavior.


Figure 2-11. log Kd, values for several aromatic hydrocarbons resulting from
UNIFAC model calculations plotted against log S, values along with
the ideal line based on Raoult's law.



Based on the success for gasoline and diesel fuel, an attempt was made to use

the UNIFAC model to assess the likelihood of nonideality for coal tar ID#4. Since

less than 40% of the composition of this coal tar was unknown (as usually is the

case), it was represented by a single compound indicated in Figure 2-12. The

UNIFAC model simulations suggested that nonidealities are indeed small, and that








55
Raoult's law approximation was justified (Figure 2-3). A note of caution is in order,

however, the UNIFAC model results depend heavily on the presumed composition

of the pitch (62% mole fraction in our example with coal tar ID#4), and on the

presence of polar constituents in coal tar (none were present in significant

quantities in this example).


-5 -4.5 -4
log [S, moles/L]


Figure 2-12. Comparison of measured and predicted tar-water partition coefficients
for several PAHs: Raoult's law (solid line) and UNIFAC model (solid
triangle).










Summary



Release of aromatic hydrocarbons from an immiscible organic liquid waste is

governed primarily by solubility phenomena. In assessing the likelihood of soil and

water contamination from complex organic wastes (e.g.,gasoline, diesel fuel, and coal

tar), it is incorrect to assume that PAH concentrations in groundwater would be

equal to the corresponding aqueous solubilities of the pure compounds. Such an

assumption usually leads to considerable over-predictions of the PAH concentrations

likely to be found in groundwater.

According to the model based on Raoult's law, the concentration of an

organic constituent in the aqueous phase in equilibrium with an "ideal" organic

mixture is proportional to the mole fraction of that constituent in the organic phase.

An experimental evaluation of a model based on ideal behavior was presented for

the partitioning of aromatic hydrocarbons from diesel fuel and coal tar into water,

and the results compared to data reported earlier for gasoline/water and motor

oil/water partitioning. The diesel fuel/water and tar/water partitioning of several

PAHs, all solids in their standard state, was well described within a factor of four for

diesel fuels, and within a factor of two for coal tars by employing supercooled liquid

solubilities and assuming ideal behavior. Good agreement between the observed

partitioning of several PAHs and UNIFAC model calculations for a simulated

gasoline, diesel fuel, and coal tar further suggests that the extent of deviations from

ideal behavior may be relatively small.

Agreement between the model predictions based on Raoult's law and
measured liquid-liquid partitioning data for several aromatic hydrocarbons is not to

be taken as evidence that such compositionally-complex organic liquid wastes are








57
indeed ideal mixtures. Rather, the assumption of ideal behavior might suffice for

practical considerations in providing first-order estimates for maximum PAH

concentrations likely to be found in groundwater leaving an area contaminated with

residual OILs. Several site-specific hydrogeologic factors might lead to significant

mass transfer constraints for solute partitioning. Such factors include: random

spatial variability in aquifer hydraulic properties, the patterns of residual fuel

entrapment, and the source of fuel contamination (e.g.,surface spill versus subsurface

leaks). Under nonequilibrium mass transfer conditions, the concentrations of organic

constituents detected in groundwater are likely to be smaller than those estimated

using the equilibrium approach presented here. In contrast, larger concentrations

might be observed in the presence of surfactants, emulsifiers, or cosolvents.














CHAPTER 3
COSOLVENT EFFECTS ON SORPTION OF ORGANIC ACIDS
BY SOILS FROM METHANOL/WATER SOLUTIONS


Introduction

The codisposal of contaminants, as well as the potential use of alternative

fuels and mixing of contaminant plumes from different sources, will result in

environmental contamination problems consisting of a complex mixture of chemicals

including both polar and nonpolar organic in miscible and immiscible solvent

mixtures. Solubility, sorption, and transport of hydrophobic organic compounds

(HOCs) are well characterized in aqueous solutions and various complex mixtures.

Solubility of HOCs increases with increasing volume fraction cosolvent of an organic

cosolvent (Yalkowsky and Roseman, 1981; Yalkowsky, 1985; 1987; Rubino and

Yalkowsky, 1987a; 1987b; Fu and Luthy, 1986; Pinal et al., 1990; 1991). Sorption of

HOCs is inversely related to solubility and as a result, an increase in solubility from

the addition of a cosolvent leads to a proportional decrease in sorption (Rao et al.,

1985; 1990; Nkedi-Kizza et al., 1985; 1987; Rao and Lee, 1988; Woodburn et al.,

1986; Fu and Luthy, 1986).

For hydrophobic ionizable compounds (HIOCs) of environmental interest,

data on solubility, sorption, and transport in mixed solvents are limited. Some

research investigating the impact of multiple solutes on HIOC sorption (i.e.,








59
competitive sorption) by soils from aqueous solutions has been documented (Felice

et al., 1985; Zachara et al., 1987; Rao and Lee, 1987); however, little attention has

been given to the behavior of HIOCs in solvent mixtures.

Pharmaceutical literature contains solubility data for several drugs spanning

a wide polarity range. As shown in Figure 3-1, Yalkowsky and Roseman (1981)

observed that as solute polarity increases relative to the solvent, cosolvency curves

become increasingly more parabolic in shape until an inverse relationship occurs (i.e.,

decreased solubility with cosolvent additions). Such behavior is explained on the

basis of the solute-solute and solute-cosolvent interactions. Therefore, for

compounds that exhibit a decrease in solubility with addition of a cosolvent (log Ko,

< 1), sorption may increase with increasing cosolvent composition.

For the sorption of naphthol, quinoline, and dichloroaniline by three different

soils from methanol/water and acetone/water solutions up to 50% by volume, Fu

and Luthy (1986b) observed log-linear behavior inversely proportional to

corresponding solubility data (Fu and Luthy, 1986a) as observed with HOCs. Similar

behavior was observed by Zachara et al. (1986) for quinoline sorption by a natural

clay isolate and montmorillonite in binary mixtures of methanol or acetone and water

regardless if the protonated or neutral species predominated in solution. For these

HIOCs it appears that the cosolvent effect on sorption is dominated by solvation

forces (i.e., solubility) similar to that observed with HOCs even though sorption

mechanisms for HIOCs and HOCs are different (electrostatic and ion exchange

versus hydrophobic partitioning).


1








60

log KOW


5.0 2.0 1.5 0.0 -1.0



_o




Volume Fraction Cosolvent, f





Figure 3-1. Schematic representation of cosolvency plots for solutes with a range
of log Kow values.



For an acidic fluorescent dye (Rhodamine WT) in binary mixtures of

methanol/water and acetone/water at cosolvent fractions above 30%, sorption was

observed to increase even though at lower cosolvent fractions (< 30%) sorption

appeared to follow an inverse log-linear relationship (Soerens and Sabatini, 1992).

Previous use of Rhodamine WT as a surface and groundwater tracer prompted an

investigation on the potential use of this dye as a tracer in alternative fuel research

(i.e.,alcohol-based fuels). In soil thin-layer chromatography (TLC) studies (Hassett

et al., 1981), the herbicide dicamba (3,6-dichloro-2-methoxybenzoic acid) moved with

the solvent front in both aqueous and 50/50 (v/v) ethanol/water solutions, but was

strongly retained by soil with neat ethanol as the mobile phase.








61
For analysis of various carboxylic acids and substituted phenols by reversed-

phase chromatography using an acidified mobile phase (i.e., when neutral species

dominate), retention is inversely proportional to cosolvent content as observed for

HOCs. Lewis and Wilson (1984) observed similar behavior for several carboxylic

acids in non-acidified methanol/water solutions (0 to 80%) using reversed-phase TLC

plates impregnated with an ion-pairing agent tetra-n-butylammonium bromide (TBA).

However, in the absence of an acidified mobile phase or an ion-pairing reagent,

cosolvent effects were minor. These data suggest that the retention behavior of

organic acids by a hydrophobic surface is similar to HOCs only when the charge is

compensated (e.g., neutral by protonation or paired with a counterion).

In considering the impact of cosolvents on sorption, the following interactions

must be considered: solute-solute; solvent-solvent; solvent-solute, solvent-sorbent,

and solute-solvent-sorbent interactions. The first three pairs of interactions can be

assessed from solubility studies. For HOCs, solute-solute interactions are ignored

due to the absence of both specific functional groups and high solute concentrations.

For most miscible solvents (e.g., alcohols), irregular behavior due to specific

complexation of solvent and water molecules doesn't appear to be of concern. For

sorption of HOCs, solvent-solute interactions, as described by solubility, are found

to predominate such that the impact of solvent-sorbent interactions have been

considered minor. However, for solutes containing specific functional groups, the

impact of the cosolvent on the sorbent may have considerable impact.








62
For HIOCs, the impact of adding a cosolvent to aqueous solutions on the

conditional ionization constant of a HIOC must be considered. Likewise, similar

impacts on the ionization of sorbent functional groups and subsequent solute-sorbent

interactions must also be considered. Also, the impact of solvent-water interactions

that were considered negligible in predicting HOC behavior may be of importance

in understanding the chemodynamic behavior of HIOCs, as well as the different

propensities of the cosolvent and water to hydrate both the solute and the sorbent.

Cosolvent-induced interactions involving the sorbent surface include:

speciation of organic matter functional groups, clay surface acidity, and ion-

association with the surface. Both acidic and basic groups tend to become neutral

with increasing cosolvent content as a result of shifts in the pKa' (Perrin et al., 1981),

leading to a net increase in hydrophobicity of soil organic matter. This phenomenon

may explain why decreases in HOC sorption with increasing f, are smaller in

magnitude than would be predicted from solubility profiles in mixed solvents (Rao

et al., 1990; Nkedi-Kizza et al., 1985; 1987; Rao and Lee, 1988). Parallel to changes

in pK,', Kan and Tomson (1990) observed a decrease in naphthalene sorption by

Lincoln fine sand from aqueous solutions by increasing pH (pK, fixed, but pH

varied). However, the increase in sorption resulting from such changes on surface

hydrophobicity are likely to be more than compensated by cosolvency effects.

The presence of cosolvents may also alter the surface acidity of the clay

fraction. Loeppert et al. (1977, 1979) found that the amount of base required to

titrate the pH-dependent sites of kaolinite varied in the following manner: methanol








63
< water < DMSO < acetonitrile. The fact that larger titers in DMSO and

acetonitrile were required was attributed to (1) pH-dependent sites for which a

quantitative endpoint was not obtained in aqueous media due to the acidic properties

of water, and (2) increased surface acidity in organic solvents. Loeppert et al. (1986)

also observed an increase acidity of montmorillonite in acetonitrile and

dimethylformamide. No apparent changes were observed in surface acidity with neat

methanol (Loeppert et al., 1979). Similar conclusions were made by Hesleitner et

al. (1991), who noted that addition of methanol (f <5 0.5) caused no apparent change

in the surface charge density of a hematite surface (iron oxide) or in the point of

zero charge which coincided with the isoelectric point.

Cosolvent-enhanced formation of ion-pairs with positive charges on the

sorbent surface may cause an increase in sorption of organic acids with addition of

an organic cosolvent even if solubility increases. As previously mentioned, Hesleitner

et al. (1991) observed no changes in the total surface charge of hematite in the f,

range investigated (f, < 0.5), but noted a pronounced decrease in electrokinetic

potentials with increasing methanol fractions (i.e., effective surface charge was

lowered). They attributed the decrease in electrokinetic permittivity to an

enhancement of counterion association with the surface charged groups. This

counterion association could include both the formation of outer-sphere complexes

by bridging of the carbonyl to the solvent (water and/or cosolvent) coordinated on

the exchange cation (Farmer and Russell, 1967) and inner-sphere complexation by

hydrogen bonding of the carbonyl group with protonated hydroxyls on the surface








64
(Kohl and Taylor, 1961; Stumm et al., 1980). These mechanisms have been included

among those proposed in the literature for sorption of organic acids in aqueous

solutions (Farmer and Russell, 1967; Kohl and Taylor, 1961; Stumm et al., 1980;

Davis, 1982; Kummert and Stumm, 1980); however, the impact of cosolvents on such

interactions has yet to be investigated.

In this chapter, the overall impact of methanol additions on (1) the

enhancement of solute-solvent interactions as described by solubility; and (2)

speciation changes due to cosolvent induced changes in the solute's pK,' will be

assessed for the sorption of several organic acids by soils. Subsequent chapters will

assess (1) speciation changes due to changing pH at several fixed methanol/water

compositions for benzoic acid and PCP sorption; (2) the overall impact of several

solvents with a wide range in solvent properties on the sorption of benzoic acid; and

(3) the relationship between solute properties, such as acidity and hydrophobicity, on

the shapes of the sorption curves observed in methanol/water solutions.



Theory

The following log-linear model successfully describes (Yalkowsky and

Roseman, 1981; Fu and Luthy,1986; Pinal et al., 1990; Rao et al., 1985; Nkedi-Kizza

et al., 1985, 1987; Rao and Lee, 1988; Woodburn et al., 1986) solubility and sorption

of HOCs in miscible solvent-water systems,


log Sb log S, + of, (3-1)








65

log(S,/Sw ) a (3-2)


log Kb log K, aof, (3-3)



where S is solubility (mg/L), K is sorption coefficient (mL/g) with subscripts b, c,

and w referring to binary mixtures, pure cosolvent, and water, respectively; f is

volume fraction cosolvent; a describes the cosolvency power of a solvent for a solute;

and a accounts for solvent-sorbent interactions.

Sorption of HIOCs is dependent on the formation of neutral and ionized

species, as determined by pH and the solute acid dissociation constant (pK,). For

many organic acids, the neutral species is sorbed more than its dissociated (anionic)

species, and the differences in the sorption coefficient values can be rather large.

Thus, the measured sorption coefficient for HIOCs is a strong function of pH and

conditional dissociation constants (pKa') of the solute in the solvent system of

interest. Lee et al. (1990) showed that the pH-dependence of pentachlorophenol

(PCP) sorption from aqueous solutions can be described by,


K Kw,nF + Kwi(1-#) (3-4)

where


c" (1 + 1O3-P')-1


(3-5)








66
and subscripts n and i refer to neutral and ionized species, respectively. Similar

findings for the sorption of several other organic acids by various sorbents have been

reported in the literature (Jafvert, 1990; Kukowski, 1989; Fontaine et al., 1991).

If solubility of a solute increases with addition of a cosolvent to an aqueous

solution (see Figure 3-1), a decrease in sorption is expected. Also, the addition of

a solvent with a low dielectric constant will result in an alkaline shift in the pK,' of

an organic acid (Perrin et al., 1981), leading to an increase in the fraction of neutral

species. In the absence of specific adsorption reactions, the neutral species will be

sorbed to a greater extent. Therefore, the addition of a cosolvent brings about two

opposing effects. To incorporate both speciation and cosolvent effects, Eq. (3-1) and

Eqs. (3-3, 3-4, and 3-5) were combined,


Kb KwA, n n + K,,(1-n) (3-6)

where


3, 10-l-"'" ; Pi 10-a,'a (3-7)

The cosolvency power for the neutral species (as) will increase relative to

hydrophobicity. The cosolvency power for the ionized species (ai) will be a function

of the relative hydrophobicity of the anion and the potential for ion-pairing. For

example, Lee et al. (1990) observed a log-linear decrease in sorption of PCP by

Webster soil in methanol/water (0.01 N CaC12) solutions (f,= 0 to 40%) for both the

neutral species (pH < 3) and ionized species (pH < 9) with resulting values for aiai and

ana, were 2.56 and 3.88, respectively. The decrease in sorption observed with








67

increasing f, for pentachlorophenolate was attributed to the relatively large

hydrophobicity of the anion and the formation of neutral ion-pairs.

Figure 3-2 illustrates the types of cosolvency curves for the sorption of organic

acids that might be predicted using Eq. (3-6). Using parameter set #3 results in the

presence of primarily the neutral species of the HIOC (pH-pK,' <-1) thus yielding

cosolvency curves similar to that observed for HOCs (Eq. 3-3). In the absence of

specific interactions, a reduction in solubility with increasing cosolvent content might

be expected for a solute existing as an anion in solution, thus potentially increasing

sorption (parameter set #6). Similar results are predicted using Eq. (3-6) for a

solute with relatively small hydrophobicity (a=l ) and assuming no impact of

cosolvency on the anionic species (ai=0) (parameter set #2 and #5). Note how the

magnitude of the increased sorption predicted by Eq. (3-6) is a function of the inter-

relationship between initial soil-solution pH (i.e.,pH-pK,,) and the a values. The

a values used in sets #1 and #3 are larger than those used for sets #2 and #5

changing the impact of pH variations. For sets #1 and #3, enhanced linearity and

an upward shift is observed with decreasing pH; whereas, for sets #2 and #5, the

shape of the sorption curve changes from a convex to a concave shape as pH

decreased. Therefore, the overall magnitude and direction of the sorption observed

will vary as a function of cosolvency power (a), soil-solution pH, and cosolvent

induced shifts in the observed pK,'.









-0.6-
K, = 0.1
a=a=1

-0.8 .--



-1 -1

0
-1.2



-1.4 --- 1
2

--- 34
7-77- 4
-1.6 .................. 5
-------- 6


0



Figure 3-2. Example


0.2 0.4 0.6 0.8 1

Volume Fraction Methanol

cosolvency curves that may be predicted by the use of various parameters in Eq. (3-6).








69
The success of Eq. (3-6) in describing sorption of organic acids is predicated

on the ability to measure (or define) the ionization constant (pK,') and pH in the

solutions of interest. Defining pK,' and pH is fairly straightforward for aqueous

systems; however, various complications must be considered for mixed solvent

systems. The pH of an aqueous solution is thermodynamically defined as the

negative logarithm of the hydrogen ion activity (aH+)


pH -log aR. -log H[H'] (3-8)


where YH+ and [H+] are the hydrogen activity coefficient and concentration,

respectively. Experimentally, an electrometric method is usually employed (e.g.,pH

meter) where the determination of pH is based on the measurement of the

electromotive forces (e.m.f.)of standard aqueous buffer solutions. Therefore, the pH

of an unknown solution (pHx) can be determined by


E-E
pH, pH, + x E (3-9)
(RT In 10/F)

or at T=298,


pH H pH, + E- E (3-10)
0.06

where Ex and E, are the e.m.f. values of the solutions, R and F are the gas and

Faraday's constants, respectively, and T is absolute temperature. Not shown in Eq.

(3-9) are the potentials that arise from the liquid junction and the standard potential

of the glass electrode. The difference in these potentials between the standard and








70
unknown solutions are assumed to be the same when the solution matrix is similar,

thus cancelling out in the (E,-E) term.

Likewise, in mixed solvents (denoted by *), pH is thermodynamically defined

as


pH* -log y.t[H] (3-11)



If standard mixed solvent buffers are employed, pH can be operationally defined as

follows:



pH* pH + E -E (3-12)
0.06


It is usually expedient to employ readily available standard aqueous buffers in which

case Eq. (3-12) must be modified to estimate the pH of a solution in mixed solvents,



pH pH Es (3-13)
0.06


where pHaPP is the measured pH of a mixed solvent solution relative to a standard

aqueous buffer solution. The differences in the liquid junction potential and the

standard potential of the glass electrode between mixed solvents and aqueous

solutions cannot be assumed to be the same and must be considered. However,

Gelsema et al. (1967) have shown that differences in the standard potentials of the

glass electrodes between mixed solvents and aqueous solutions are negligible. The








71
operational definition of pH for a mixed solvent solution (pHx*) referenced to an

aqueous standard can then be written as


pH E, E, E-E (3-14)
pH. pH, + -1
0.06 0.06


Therefore, differences in the apparent pH and the actual pH arise from the

difference in the liquid junction potentials:


8 pHxa pH* jE'- (3-15)
0.06


Van Uitert and Haas (1953) achieved a practical standardization of pH

measurements in dioxane-water solutions by measuring the pH of a series of HCI

solutions of known concentration in the mixed solvents. The difference between the

measured pH and the known hydrogen concentration was assumed to be a

reasonable estimate for 6. This approach yields estimates for 6 that encompass all

differences (i.e.,liquid junction potential, standard potential, activity, solvent medium

effects) observed between a measurement in aqueous versus solvent/water solutions

irrespective of the source. The magnitude of 6 increases with increasing amounts of

an organic solvent. For methanol/water solutions at fc<0.8, 6 values are relatively

small, but 6 values may become greater than 2 as the neat organic solvent is

approached (Van Uitert and Haas, 1953; De Ligny and Rehbach, 1960). Although

there are obvious shortcomings to this simplified approach, it appears adequate in

many cases for estimating pH in several mixed solvent solutions.












Materials and Methods

Sorbents

The primary sorbents used in this study were Eustis fine sand (Psammentic

Paleudult) from Florida containing 96.4%, 1.8%, 1.8%,and 0.39% sand, silt, clay, and

organic carbon (OC), respectively; and Webster silty clay loam (Typic Haplaquoll)

from Iowa (5 miles north and 3 miles east of Ames) containing 30.7%,42.8%, 27%,

3.0% sand, silt, clay (predominately montmorillonite), and OC, respectively. Specific

surface measurements by N2-BET of approximately 4 m2/g was obtained for a similar

Webster soil subsample used in previous studies (Rao et al., 1988). Both the Eustis

and Webster soils were collected from the surface horizon (0-30 cm). The soil OC

contents were determined using the Walkley-Black method (Nelson and Sommers,

1982). The soil-solution pH in 0.01 N CaC12 was 5.0 and 6.9 for Eustis and Webster

soils, respectively. Soils were air-dried and passed through a 2 mm sieve prior to use.

Chemicals

The organic acids used in this study are listed in Table 3-1 along with selected

physical and chemical properties. All crystalline compounds had a chemical purity

of >98%. All solvents were purchased from J.T. Baker (high purity, HPLC grade)

and used without further preparation. For sorption experiments with

pentachlorophenol (PCP), 14C uniformly ring-labeled compound was purchased from

Sigma Chemical Co. with a specific activity of 12 mCi/mmol and a reported

radiochemical purity of >98%.







Table 3-1. Selected Solute Properties


Aqueous
Melting Molecular pK, Solubility' Log
Solute Point' (oC) Weight' Aqueous Methanol2 (mg/L) Kow

Pentachlorophenol 190 266.3 4.744 8.6 14 5.01
2,4-Dichlorophenol 42 163.0 7.854 11.9 4,500 3.23
Picric Acid (2,4,6-trinitrophenol) 121 229.1 0.419' 4.1 14,000 2.03
Gentisic Acid 205 154.1 2.97' 7.6 21,5002 NA9
(2,5-dihydroxybenzoic acid)
2,4,5-trichlorophenoxy acetic acid 156 255.5 2.85 7.4 278 NA
2,4-dichlorophenoxy acetic acid 138 221.0 2.64' 7.6 890 NA
Benzoic Acid 122 122.1 4.201 9.0 2,900 1.87
Pentafluorobenzoic acid 101 212.1 1.496 5.8 NA NA
Dicamba (3,6-dichloro-o-anisic acid) 115 221.9 1.947 6.9 7,900 2.468


'From Dean (1985); 2 This study; 3 From Verschueren (1983); 4 From Callahan et al.(1979); S Koskinen and O'Connor
(1979); 6 From Walters (1982); From Kearney and Kaufman (1976); a EPA Environmental Fate One-Line Data Base,
Version 3.04; 9 Not available.










Determination of Ionization Constants

The conditional ionization constants (pKa') for benzoic acid, gentisic acid, 2,4-

dichlorophenoxyacetic acid, PCP, 2,4-dichlorophenol, and dicamba were determined

in methanol/water solutions by measuring pH as a function of NaOH additions

(Albert and Sergeant, 1984). Solvent mixtures were prepared with 0 to 100%

methanol and degassed prior to use. For all solutes except for PCP and dicamba,

0.01 M solutions were titrated with 0.1 M NaOH. For PCP and dicamba, 0.001 M

solutions were titrated with 0.01 M NaOH. A Metrohm 686 Titroprocessor,

employing a combined pH glass electrode (6.0202.100)and a resistance thermometer

(6.1103.000), continuously measured pH and temperature, respectively. The pH

meter was calibrated using aqueous buffers. The temperature of the solutions was

24 0.5 C. Titrations were performed in duplicate in 50 mL beakers placed on a

stirring plate to mix the solutions. The ionization constants determined in this study

are mixed ionization constants (Albert and Sergeant, 1987) rather than true

thermodynamic ionization constants. A brief discussion on the difference between

the various ionization constants are given in Appendix B along with sample sets of

titration data from this study and corresponding calculations.

In calculating pK.' values, adjustments for the impact of methanol on pH

measurements were made using a method similar to that employed by Van Uitert

and Haas (1953) as described above for the measurement of pH in mixed solvents.

This method consisted of measuring the pH of 0.001 M hydrochloric acid in the

mixed solvent. The difference (6) between the pH measured in water and in the








75
mixed solvent was added to the pKa estimated from the titration curve (i.e.,

pK,'=pK. + 6). From 0 to 70% methanol, 6 values were negligible. At higher

methanol fractions, 6 values were approximately 0.1,0.4, and 2.3 for f,values of 0.8,

0.9 and 1.0, respectively. Similar results were obtained by De Ligny and Rehbach

(1960) for methanol/water solutions by comparing pH measured in aqueous standard

buffers (KC1 saturated solutions) and standard buffers prepared in the appropriate

mixed solvent using the method proposed by the National Bureau of Standards

(Bates et al., 1963). Therefore, the corrections needed to adjust the pK, determined

in mixed solvents relative to the use of aqueous standard buffers are only significant

at f,>0.9.

pH of Soil Suspensions in Mixed Solvents

When considering the measurement of pH in mixed solvent soil-suspensions,

problems in addition to those previously discussed for pH measurements in mixed

solvents arise. It has long been recognized that the ambiguity of measuring the pH

in aqueous soil solutions, and even more so in soil suspensions, is due to the inability

to accurately determine liquid junction potential differences between standard buffer

solutions (Ej,) and soil-solutions (Ej,x). Even with this ambiguity, the error in the

measured pH resulting from differences in the liquid junction potentials (EFj, E,,

is usually assumed to be within 0.2 pH units for an aqueous soil-solution or dilute

soil-suspension given a background electrolyte concentration of approximately 0.01

N (Sposito, 1989).

In these studies, pH of the supernatant and/or the resuspended soil sample








76
were measured using a Coming Model 130 pH meter and a Fisher Scientific or

Orion combination micro-electrode (AgCl saturated 3 M KC1 filling solution)

following equilibration and analysis of the sample. For suspensions of Webster silty

clay loam in methanol/water solutions with a background electrolyte of 0.01N CaCl2,

changes in the measured pH (pH"xPP) of less than 0.5 pH units were observed going

from aqueous to methanol solutions. Recall that a change (6) of over 2 pH units

were previously noted for solutions going from aqueous to methanol solutions. This

prompts questions regarding (1) the interactions between the liquid junction

potentials arising from the solvent and soil medium; and (2) the effect of methanol

on the activity of hydronium ions on the soil surface. Given the difficulty of

answering such questions at this time, pH/,"P will be used in combination with pK,'

to estimate solute speciation.

Solubility Experiments

Experimental techniques described by Pinal et al. (1990) were employed to

measure benzoic acid solubility in methanol/water solutions that were either acidified

with 0.01 M HC1 or made basic with 0.3 N NaOH. These data were compared to

solubilities obtained by Yalkowsky (1985) without additions of an acid or a base.

Solute concentrations were analyzed using reversed-phase liquid chromatography

(RPLC) techniques. The RPLC system consisted of a ternary solvent pump (LDC

Milton Roy Model CM4000, Eldex Model 9600, or Gilson Model 302), a Waters

Radial Compression Column with a C-18 cartridge, a UV detector (Gilson Model

115 or Waters Model 490), and a Waters Intelligent Sample Processor (Model 710B

or 715). The composition of the mobile phase (acetonitrile/methanol/water; pH2








77
w/HCl) and the UV wavelength were optimized for analysis of each solute. When

necessary, samples were diluted to within an optimal concentration range of the

specific analytical method.

Miscible Displacement Experiments

Miscible displacement techniques described by Brusseau et al. (1990) and Lee

et al. (1991), were used to estimate retardation factors with water and neat methanol

as the eluent for the solutes listed in Table 3-1. The column was packed with air-

dried Eustis soil and 0.01 N CaCl2 solution was pumped through the column until

steady-state, water-saturated conditions were established. All solutions were filtered

(0.45 pm) and degassed with helium before use. The physical properties of the

Eustis soil column were as follows: 5 cm length, 0.4 mL/cm3 volumetric water

content (0), 1.69g/cm3 bulk density (p), and a column pore volume of 9.64 mL. A

pore-water velocity (v) of about 90 cm/hr was used for all experiments.

Solute concentrations in the influent solutions were approximately 100 pg/mL,

except for PCP which was 3 4g/mL. Solute concentrations in the column effluent

were monitored continuously as described by Brusseau et al. (1990) using a flow-

through, variable-wavelength UV detector (Gilson Holochrome, Waters 450 or LDC

UV) connected to a linear chart recorder (Fisher Recordall 5000). Retardation

factors (R) were obtained by calculating the area above the measured breakthrough

curves (Nkedi-Kizza et al., 1987). Periodic measurements were made of the column

effluent pH with a combination glass electrode using a Coming 130 pH meter or a

Brinkman 686 unit.










Equilibrium Sorption Isotherms

Equilibrium sorption isotherms were measured using the batch-equilibration

method (Rao et al., 1990). The vials used for this study were 5 mL (1 dram) screw

cap borosilicate glass autosampler vials with teflon-lined septa inserts. Amber vials

were employed to minimize photolysis. Soil mass to solution volume ratios ranged

from 1:2 to 2:3 to achieve sorption of 50% ( 20%) of the chemical added. All

solutions used had a 0.01 N CaC1, matrix unless noted otherwise. Initial solution

concentrations added to the soils ranged from 5 to 45 jig/mL for all solutes with the

exception of PCP. A concentration range of 0.25 to 3 gg/mL was used in the PCP

equilibration studies. All sorption isotherms were measured at room temperature

(T=22-25" C). Following equilibration, the solution and solid phases were separated

by centrifuging the soil samples at approximately 300 RCF (relative centrifugal force)

using a Sorvall RT6000 centrifuge.

Each isotherm consisted of sorption measured in duplicate at four or more

concentrations and at least 30% of the isotherms were replicated. Also for each

isotherm, blanks containing the solvent with and without soil were run to check for

coelution of any peaks from the soil. Samples were usually equilibrated by rotating

for 16-24 hours. For f. <0.2,degradation was noted after 4 hours in control studies

where the soil-solution matrix was removed from the soil and spiked with the

appropriate solute concentrations. Therefore, equilibration of samples in solutions

of f,<0.2was reduced to a maximum of 2 hours with no differences observed in

sorption coefficients measured after 1 and 2 hours.








79

Nonradiolabeled samples were analyzed by RPLC techniques as described

previously for the solubility studies. The use of autosampler vials in conjunction with

the Waters Intelligent Sample Processor (WISP 715) enabled direct analysis of the

samples by RPLC techniques without further sample transfer. The WISP 715 has the

capability of varying sampling depths within a vial allowing sampling of the

supernatant without removal of the soil. The higher mass to volume ratios (2:3),

however, necessitated transfer of the supernatant to a new vial. For 4C-labeled

solutes, 0.5 mL aliquots of the supernatant were taken from each sample and mixed

with 20 mL of Scinti-Verse II for analysis. Solute concentrations were then assayed

using liquid scintillation counting (LSC) methods employing a Searle Delta 300 liquid

scintillation counter.

Sorption coefficients, K (mL/g), were estimated by fitting the sorption data

to a linear isotherm: S. = K Ce, where Se and C. are sorbed (mg/g) and solution

(Ag/mL) concentrations, respectively, at equilibrium. The solution concentrations

were directly determined, whereas S. values were determined by difference: S. =

(Ci C)(V/M), where Ci is the initial solution concentration (Ajg/mL) of the solute;

V is the solution volume (mL); and M is the soil mass (g).


Results
pK Measurements

The pK,' values measured as a function of volume fraction (fQ) methanol

increased linearly up to approximately f,=0.6,and then increased markedly at higher

cosolvent contents. Representative data for benzoic acid and pentachlorophenol








80
(PCP) are presented in Figure 3-3. Similar results were observed for the other

compounds. For organic bases, a decrease in pK,' (an acid shift) occurs upon

addition of a cosolvent (shift towards neutral species); however, the overall shift from

aqueous to neat solvents is usually much less than a single pH unit. Also shown in

Figure 3-3 are pK,' values for benzoic acid determined conductometrically by Pal et

al. (1983) up to 80% methanol, and the pK,' value reported by Bacarella et al. (1955)

in neat methanol using a different type of potentiometric method with an electrode

system void of a liquid junction. Good agreement between our data and the

published data suggests that the procedure used in this study was adequate. The

lower pKa' value obtained in this study for benzoic acid in neat methanol is most

likely due to the use of hydrated methanol (0.05%); residual water was removed from

the methanol used in the cited studies. Also the constants determined in this study

are mixed ionization constants, whereas thermodynamic ionization constants were

reported by Bacarella et al. (1955).

Solubility

Solubility data reported by Yalkowsky (1985) for benzoic acid in

methanol/water solutions are shown in Figure 3-4. Solubility increased with

increasing volume fraction methanol. For the solubility of an organic acid in an

unbuffered solution, the pH at saturation will be less than the solute pKa. For

example, the pH of an aqueous solution saturated with benzoic acid is approximately

2.8 (Bates, 1973). Thus, the neutral species dominates over the solubility profile,

with over 90% existing in the neutral form at f_>0.3. Also shown in Figure 3-4 are








81
the solubilities of benzoic acid in acidified methanol/water solutions (0.01 M HC1)

measured in this study. At saturation, the acidified samples remained near a pH of

2. Minimal differences were observed between the solubility of benzoic acid in

acidified and nonacidified methanol/water solutions. Solubility curves were not

measured for other solutes in this study, but benzoic acid is believed to be

representative of the general behavior of carboxylic acids in methanol/water

solutions. For example, the solubility reported for dicamba in ethanol is over a 100

times greater than its aqueous solubility (Humberg et al., 1989). The observed

solubility of benzoic acid in methanol/water solutions is similar to the curve shown

in Figure 3-1 for a log Ko of 2.

To further investigate the effect of speciation on solubility, benzoic acid

solubility in solutions containing approximately 0.3 M NaOH was measured for 0 to

40% volume fraction methanol. In the presence of a base, the solubility of benzoic

acid was greater than that observed in the unbuffered or acidified solutions. At the

solubility limits, the saturated solution pH was 5.0. Given the pK,' and measured pH

(pH"P), speciation of benzoic acid in saturated solutions was estimated to range from

approximately 90% to 60% ionized going from aqueous solutions to f,=0.4. The

increase observed in solubility with increasing f, parallels the increase in the neutral

species suggesting that cosolvent effects on benzoate solubility are negligible in the

range investigated (i.e., aari = 0).


1









---e--* Benzoic Acid (This study)
Benzoic Acid (Pal et al., 1983)
A Benzoic Acid (Bacarella et al., 1955)
...... Pentachlorophenol




........... .......'

.......................-- -'
.----


) 0.2 0.4 0.6 0.8
Volume Fraction Methanol, fo
a


Figure 3-3.






6-


Effect of methanol
pentachlorophenol.


content on the pKa' of benzoic acid and


5.5 k


3.5#


0.2
Volume


Benzoic Acid (0.01 M HCI)
A Benzoic Acid (Yalkowsky, 1985)
O Benzoic Acid (0.3 M NaOH)

0.4 0.6 0.8
Fraction Cosolvent, fo
C


Solubility (Sb) of benzoic acid in methanol/water solutions.


-2
CL


Figure 3-4.










Miscible Displacement Studies

The retardation factors (R) estimated from the miscible displacement studies

with Eustis soil column are shown in Table 3-2. The column effluent pH ranged

between approximately 4 and 4.8 for the different solute/solvent combinations.

Comparisons of influent pulse sizes with zero-th moments showed greater than 97%

mass recovery for all solute pulses. Loss of soil organic matter from the soil column

during elution with methanol was considered negligible as shown previously by Lee

et al. (1991). For the substituted phenols, R values determined in methanol were

smaller than those water (f,=0). For both chlorophenols in neat methanol,

retardation factors decreased to one corresponding to no sorption (R= 1). A decrease

in R with increasing f is expected from the log-linear cosolvency model in the

absence of any specific interactions. The opposite trend, however, was observed for

all of the substituted benzoic acids. Benzoic acid and dicamba were chosen for

further investigation in several methanol/water solutions using batch techniques with

PCP included as a control. For the batch studies, Webster soil with a higher organic

carbon content was used to better differentiate sorption in the various

methanol/water solutions. Although the greatest increase in retardation was

observed with gentisic acid, preliminary batch isotherm data exhibited extreme

nonlinearity (data not shown). Since problems associated with isotherm nonlinearity

may confound assessment of the proposed model, further investigation of gentisic

acid was not pursued.











Table 3-2. Retardation factors for several organic acids in aqueous and methanol
solutions from Eustis Soil.


Retardation Factors

Solute Aqueous Methanol


Substituted Phenols

Pentachlorophenol 4.7 1.0

2,4-Dichlorophenol 3.6 1.0

Picric Acid 1.9 1.4
(2,4,6-trinitrophenol)




Substituted Benzoic Acids


Gentisic Acid 1.9 3.1
(2,5-dihydroxy acetic acid)

2,4,5 Trichlorophenoxy Acetic Acid 1.7 2.1

2,4-Dichlorophenoxy Acetic Acid 1.4 2.7

Benzoic Acid 1.2 2.2

Pentafluorobenzoic Acid 1.0 1.6

Dicamba 1.0 2.0










Batch Equilibration Studies

Sorption of benzoic acid, dicamba, and PCP by Webster soil was measured

from several methanol/water solutions. Representative isotherms are shown in

Figure 3-5. Sorption isotherms were linear for PCP and dicamba in both aqueous

and mixed-solvent systems over the concentration range investigated. Sorption

isotherms for benzoic acid were slightly nonlinear, but a linear approximation of the

sorption coefficients (K) adequately described the data. The correlation coefficients

(r2) ranged between 0.95 and 1.0.

Effect of Solvent Addition

As noted previously, addition of an organic cosolvent to an aqueous solution

results in an increase in the pKa' for organic acids. Changes in speciation become

significant at fe >0.5 as marked changes occur in the pK,' values. In neat methanol,

the measured soil-solution pH for Webster soil ranged between 6.2 and 6.5;

therefore, essentially all the benzoic acid and PCP existed in the neutral form, while

20% to 30% of dicamba remained ionized.

The sorption coefficients estimated from batch equilibration studies of PCP

and benzoic acid are plotted in Figure 3-6 as a function of volume fraction methanol

(fo). Sorption of PCP in methanol/water systems was well described by the log-linear

model with speciation given by Eq. (3-6) (Figure 3-6A) except in neat methanol. Of

the required model parameters, bulk pH and pK, 'were measured; pn was estimated

using Eq. (3-5); and K,,K,, ,,a,, and a.on were taken from Lee et al. (1990) where

sorption of PCP was measured as a function of f, while pH was maintained such that








86
PCP was either completely ionized or completely neutral. The values for KE,i and

Kw,n were adjusted for differences in the OC content of the Webster soil used in the

two studies (i.e., K=Ko OC).

Benzoic acid sorption decreased with the addition of methanol up to fc0.2,

but then increased with f, thereafter (Figure 3-6B). Eq. (3-6) was applied to the

benzoic acid data using four reasonable parameter sets to investigate if this behavior

was mostly due to changes in speciation with methanol additions. For all cases, the

sorption coefficient for benzoate (K,,i) was measured at pH = 6.9; K,, was

estimated by measuring the K, at pH = 3.0 and applying Eq. (3-4); and ao was

estimated by regressing benzoic acid solubility data in methanol/water solutions (data

in f,=0 to 0.8; Yalkowsky, 1985). Two values for ai were used. In one case, ai was

set equal to zero as suggested by the solubility data (Figure 3-4), and in the second

case, ai was set equal to 0.65 as estimated from the initial portion of the log K.

versus f, curve (i.e., fe 0.2) where benzoic acid remained 2 99% ionized. For two

parameter sets, solvent-sorbent interactions were ignored (a,=ai=l) while in the

remaining two parameter sets an average a value of 0.5 observed by Fu and Luthy

(1986b) for several solute, soil, and solvent combinations was used as an initial

estimate of solvent-sorbent interactions. In all cases, Eq. (3-6) failed to adequately

predict the magnitude of sorption observed for benzoic acid at higher methanol

contents (Figure 3-6B). Similar sorption data were observed for dicamba (data not

shown). Model parameters were estimated for the dicamba sorption data in a

manner analogous to the calculations for benzoic acid with similar results.




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