United States Office of Water
Environmental Protection Regulations and Standard* Jan' ~ ry 1986
Aeancy Criteria and Standards Oivisior. SCiJ# 4
Washington OC 20480
W>tf
oEPA
ELABORATION OF SEDIMENT NORMALIZATION
THEORY FOR NONPOLAR ORGANIC CONTAMINANTS
ALL COMPOUNDS
-J 1 4 I - ,
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ฃ/?/) OSJ-1
SEDIMENT CRITERIA METHODOLOGY VALIDATION
WORK ASSIGNMENT 37 TASK II
ELABORATION OF SEDIMENT NORMALIZATION THEORY FOR
NONPOLAR HYDROPHOBIC ORGANIC CHEMICALS
FINAL REPORT
Prepared for
BATTELLE PACIFIC NORTHWEST LABORATORIES
Environmental Chemistry Division
P.O. Box 999
Battel1e Boulevard
Richland, Washington 99352
U.S. ENVIRONMENTAL PROTECTION AGENCY
Criteria and Standards Division
Washington, DC
Prepared by
R.D. Kadeg, S.P. Pavlou, A.S. Duxbury
ENVIROSPHERE COMPANY
10900 N.E. 8th Street
Bellevue, Washington 98004
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This document was submitted to Battelle by the preparer, ENVIROSPHERE Company
as a report to the Criteria and Standards Division of the U.S. Environmental
Protection Agency under Work Assignment 37, Task 2. Battelle has thoroughly
reviewed the document's technical content and comments have been incorporated
where necessary.
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ABSTRACT
The U.S. Environmental Protection Agency, under the criteria and
Standards Division, is developing and evaluating sediment criteria
methodologies. This study investigates aspects of the equilibrium
partitipning methodology related to specific nonpolar, hydrophobic
organic contaminants. Included for this class of compounds are: 1) an
update of pertinent partitioning literature, 2) a refinement and
analysis of empirical Koc/Kow regression equations, 3) an
evaluation of environmental variables influencing partitioning and
4) estimated permissible sediment contamination concentrations (PCCs)
based on results from refined equations.
Results from the investigation indicate that 1) percent organic content
of the sediment is a significant normalization parameter, 2) other
parameters, Including salinity, temperature, dissolved organic carbon,
sediment particle size and suspended particulate matter influence
partitioning to a varying degree, 3) there Is insufficient data to
quantify these effects, 4) use of chemical class-specific regression
equations 1s preferred for the nonpolar, hydrophobic organlcs, 5) these
relationships represent a usable simplification of actual theoretical
sorption mechanisms, 6) the calculated PCCs are higher (less, stringent)
than those previously estimated, based on the refined equations.
The study shows the need for additional uncertainty analysis on the
empirical equations and for the development of additional laboratory
and field data to verify and further refine the predictive equations.
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TABLE OF CONTENTS
Page
LIST OF TABLES ii
ABSTRACT
IV
1.0 INTRODUCTION - ]
2.0 LITERATURE SEARCH AND COMPILATION OF INFORMATION 5
3.0 DATA ENTRY AND REDUCTION 8
4.0 RESULTS AND DISCUSSION 9
4.1 DESCRIPTIVE STATISTICS 9
4.2 DEVELOPMENT OF K^/K^ RELATIONSHIPS 9
4.2.1 Approach Q
4.2.2 Detailed Analyses 14
4.3 INFLUENCE OF OTHER PHYSICAL/CHEMICAL PARAMETERS
ON 23
4.3.1 Additional Parameters Influencing Partitionina 23
4.3.2 Current Considerations 30
4.4 RECOMPUTED PERMISSIBLE SEDIMENT CONTAMINATION
CONCENTRATIONS 33
5.0 SUMMARY AND CONCLUSIONS 37
6.0 REFERENCES 3g
APPENDIX I - SUMMARY OF ALL LOG K0Wf LOG KflC,
AND RELATED DATA
APPENDIX II - PLOTS OF Koc/Kw REGRESSION ANALYSES
APPENDIX III - BIBLIOGRAPHY
APPENDIX IV - NAMES AND ADDRESSES OF INDIVIDUALS RESPONDING TO
REQUEST FOR INFORMATION
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LIST OF TABLtS
Table No. Page
1 REPRESENTATIVE HYDROPHOBIC ORGANIC
CHEMICAL COMPOUNDS 6
2 DESCRIPTIVE STATISTICS 10
3 REGRESSION RELATIONSHIPS OF Kqw AND K
DEVELOPED FROM COLLECTED DATA i6
4 SELECTED REGRESSION EQUATIONS FROM THE
LITERATURE FOR THE ESTIMATION OF K
oc
FROM Kqw 20
5 SUMMARY OF REVISED PERMISSIBLE SEDIMENT
CONTAMINATION CONCENTRATIONS BASED UPON
REVISED K^/K^ RELATIONSHIPS 34
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SEDIMENT CRITERIA METHODOLOGY VALIDATION
WORK-ASSIGNMENT 37 TASK II
ELABORATION OF SEDIMENT NORMALIZATION THEORY FOR
NON-POLAR HYDROPHOBIC ORGANIC CHEMICALS
1.0 INTRODUCTION
The U. S. Environmental Protection Agency (EPA) 1s currently
Investigating the feasibility of developing sediment-based criteria for
the protection of aquatic life 1n a manner that parallels the existing
water quality criteria. Sediment quality criteria are needed because
national water quality criteria alone are not sufficient to ensure
protection of aquatic ecosystems consistent with provisions of the
Clean Water Act and subsequent amendments.
To meet this goal, EPA's Criteria and Standards Division 1s funding a
program to develop and validate sediment criteria methodologies. Under
EPA guidance, Battelle Memorial Institute 1s currently coordinating
some components of this program. The program 1s divided Into two major
components: one addresses trace metals and the other nonpolar
hydrophobic organic chemicals. This study, the elaboration of sediment
normalization theory, was Initiated under the second component and will
be Integrated with other tasks currently being performed by Battelle
and subcontractors. These other tasks Include the development of
screening level concentration (SLC) and sediment bloassays. Results
from all Investigations will lead to the selection of an optimum
methodology for the development of criteria.
Specifically, sediment normalization theory as applied to this work
refers to the normalization of equilibrium sediment/water partition
coefficients (Kd) to the organic content of the sediment. The use of
organic carbon-normalized partition coefficients to compute "first cut"
safe levels of sediment contamination tias been discussed to some extent
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in previous publications (Pavlou, 1984; Pavlou and Weston, 1984).
Screening-level concentrations are contaminant threshold concentrations
(at which adverse effects are observed) in sediments estimated from
synoptic benthic and contaminant field data. They provide an interim
measure of a probable no-effect level. These values can be compared to
no-effe.ct levels determined by laboratory bioassays and to "first cut"
safe levels predicted by equilibrium partitioning theory- These three
approaches comprise an integrated methodology for estimating sediment
criteria. In order to better understand the rationale for performing
the calculations presented in this report, the b^sic concept of
equilibrium partitioning and the need for proper normalization of
sediment contaminant concentrations 1s summarized below.
In the equilibrium partitioning approach, sediment/water equilibrium
partition (distribution) coefficients are estimated for selected
chemical contaminants. Corresponding acceptable contaminant
concentrations for interstitial water are equated to the existing EPA
water quality criteria. This approach permits the use of the large
toxlcological data base developed for the water quality criteria. The
equilibrium partition coefficient 1s then multiplied by the water
quality criteria to yield the permissible bulk sediment concentration
value for the specific contaminant in question.
To obtain permissible sediment contamination concentrations (PCCs) that
approximate the contaminant accumulation mechanism as well as account
for site- (or area-) specific changes in ambient environmental
conditions, the physical /chemical variables that Influence the
partitioning process must be determined and used to normalize the bulk
sediment concentrations. In this manner parity and transferability of
PCCs to a variety of sites can be established. Pavlou and Weston
(1984), reviewed the literature and found that sediment organic carbon
content 1s the primary environmental variable influencing
partitioning. They recommended that partition coefficients be
normalized to organic content. They also developed a preliminary
predictive equation that relates organic carbon-normal 1 zed
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sediment/water partition coefficients (Krtr) to octanol/water
oc
partition coefficients (KQW). These equations, which depicted a
linear relationship, estimate the PCC values for groups of nonpolar
hydrophobic organic compounds. The predictive relationships are used
to estimate partition coefficients for contaminants for which K
oc
data was not available.
The objectives of this study are 1) to update the pertinent literature
containing partitioning data for nonpolar, hydrophobic chemicals; 2) to
refine the empirical Koc/Kow equations presented by Pavlou and
Weston (1984), 3) to evaluate the effects of environmental variables
which may Influence equilibrium partitioning; and 4) to compute PCC
values and compare with Uf1rst-cut" safe level values presented by
Pavlou and Weston (1984) based on the updated information and new
K /K relationships. The PCC values were not compared to the SLC
oc ow
and bioassay-detennlned safe levels because the latter data were not
available at the time this report was prepared.
Following 1s a synopsis of the report contents:
Chapter 1, Introduction - Includes Introductory material, a brief
description of methodology, and study objectives.
Chapter 2, Literature Search and Compilation of Information - contains
the scope and review of the scientific literature together with a
preliminary compilation of data. The chapter Includes a 11st of
specific nonpolar, hydrophobic organic chemicals investigated.
Chapter 3, Data Entry and Reduction - discusses the approach used to
summarize the specific numerical partitioning data Into a common format.
Chapter 4, Results and Discussion - describes the analysis of the data
and the results of the Investigation. It Includes summary statistics,
the Koc/Kow correlation approach, statistical analyses (predictive
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regression relationships), a qualitative discussion of other factors
Influencing partitioning, and a discussion of current theoretical
concepts regarding sorption phenomena. The computed PCC values and a
comparison with previous results are also- included.
Chapter 5, Summary and Conclusions - consists of a brief summary and
conclusions. Areas for future Investigation are also recommended.
Chapter 6, References - contains the literature cited.
The Appendices Include a listing of researchers who provided
information, the updated K0C/KQW partitioning data, plots of the
predictive regression relationships by chemical classes, and the
Bibliography.
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2.0 LITERATURE SEARCH AND COMPILATION OF INFORMATION
To perform an effective review of the published and unpublished
literature covering the partitioning of nonpolar, hydrophobic organics,
1t was necessary to restrict the data collection effort to a
representative subset of compounds. The compounds chosen are listed in
Table 1. This 11st also includes representative chemical classes such
as low and high molecular weight polynuclear aromatic hydrocarbons
(PAHs), pesticides, and polychlorinated biphenyls (PCBs).
A computerized literature search was performed through the Dialog
Information Retrieval Service. Data bases-searched Included Water
Resources Abstracts, Pollution Abstracts, Aquallne, Environmental
Bibliography, Oceanic Abstracts, Sclsearch, CA Search (Chemical
Abstracts), and Dissertation Abstracts. Input to these data base
searches Included Individual compound and class names from Table 1, and
additional key words and symbols such as sediment, partitioning,
octanol, Kn , K , hydrophobic, and solubility.
OW Ov
The computer search Identified approximately 700 to 1,000 references.
These citations were carefully reviewed and approximately 140 articles
were identified as potentially useful for this study. Copies of these
articles, with a few exceptions, were obtained. The exceptions were
obscure articles In a foreign language (eg., Japanese or Russian), or
dissertations of limited distribution which duplicated later published
work by the same author.
Concurrent with the computer literature search, key researchers known
to be working on contaminant sediment partitioning were contacted to
obtain additional unpublished Information. Approximately 30
Individuals, 1n the United States and other countries, were contacted.
Nine responses and two unpublished manuscripts were obtained; the
balance of material received had already been Identified from the
computer search. The extensive collection of the University of
Washington libraries was also manually reviewed. One or two additional
articles were identified and obtained.
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TABLE 1
REPRESENTATIVE HYDROPHOBIC ORGANIC CHEMICAL COMPOUNDS
Polycycllc Aromatic Hydrocarbons (PAHs)
Low Molecular Weight PAHs (2 and 3 rings)
Fluorene
Naphthalene
Acenaphthene
Acenaphthylene
Anthracene
Phenanthrene
High Molecular Weight or "Combustion" PAHs (3 to 6 rings)
Fluoranthene
Pyrene
Chrysene
Be n zo (a) a n th race ne
Benzo(a)pyrene
Benzo(b)f1ouranthene
Benzo(k)f'louranthene
Indeno(l,2,3-cd)pyrene
D1benzo(a,h)anthracene
Benzo (gh1 Jperylene
Pesticides
DDD
DDE
DDT
Acrolein
Aldrin
Chlordane
Dieldrln
"-Endosulfan
6-Endosulfan
Endosulfan Sulfate
Endrln
Endrin Aldehyde
Heptachlor
Heptachlor Epoxide
a-Hexachiorocyc1ohexane
6-Hexachlorocyclohexane
r-Hexachlorocyclohexane
6-Hexachlorocyclohexane
Isophorone
2,3,7,8-Tetrachlo rodlbenzo-p-d1oxln
(TCDD)
Toxaphene
Polychlorlnated B1 phenyls (PCBs)
Aroclor 1016
Arocior 1221
Aroclor 1232
Aroclor 1242
Aroclor 1248
Aroclor 1254
Aroclor 1260
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All of the data used In this study were derived from the citations
listed in the collected Bibliography. Emphasis was placed on obtaining
publications that contained quantitative data: for example, values for
K and K , percent carbon content of sediments, or discussions
oc ow
especially relevant to the measurement of sorption parameters and
coefficients.
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3.0 DATA ENTRY AND REDUCTION
The articles collected were reviewed to determine the level of
quantification. Values for octanol/water partition coefficients
(Kow), dry weight-normalized sediment/water partition coefficients
(Kj), organic carbon-normalized sediment/water partition coefficients
(Koc)ป and organic carbon content of sediments (percent 0C) were
compiled. Data for solubility, salinity, temperature, dissolved
organic matter, particle size, particulate matter, Eh, and pH were also
sought. The data were reduced into a uniform format to enable a quick
review and provide Input for statistical analysis. Only a few
citations fn the Bibliography contained matched data sets (KQC and
Kqw); none were extensive. Organic carbon content was rarely
reported with individual KQC values. Other supporting data for the
parameters noted above were not adequate to perform any statistical
analysis of their Influence on partitioning.
Appendix I contains 1n tabular form the results of the data
collection. Included are the Kow, Koc, percent 0C, Kd, and
solubility data contained 1n the references for the compounds listed 1n
Table 1. When available, the error ranges or uncertainties (one
standard deviation) were Included. No attempt was made to Interpolate
data points from graphical presentations of results; only reported
numerical values were used. Not all compounds listed 1n Table 1 are
presented in Appendix I because some compounds lacked matched data sets.
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4.0 RESULTS AND DISCUSSION
This section presents the results of the descriptive statistics, the
update and refinement of chemical class-specific relationships between
K and K , and a discussion of additional physical/chemical
wC UW
factors affecting partitioning. Current concepts regarding
physical/chemical aspects of sorption processes as related to sediment
normalization theory are also discussed. Finally, the permissible
contaminant concentration values calculated using the updated
Koc/Kow relationships are presented.
4.1 DESCRIPTIVE STATISTICS
The data presented 1n Appendix I were used to refine the KQC/K0W
relationships and estimate uncertainties. First, simple descriptive
statistics were performed on the data. Arithmetic means, standard
deviations, and geometric means were determined for the and KQW
values. In most cases, no estimates of uncertainty were reported.
Therefore these values were taken as absolute when determining the
standard deviation for a given compound set. In those Instances where
uncertainty was reported, mean upper and lower ranges were determined.
The results of the descriptive statistics are shown 1n Table 2.
Certain.values were not Incorporated Into these statistics due to
theoretical Inconsistency and Incompatibility with other reported
data. The regression analyses discussed below were performed using
paired (matched) data sets.
4.2 DEVELOPMENT OF Koc/*ow RELATIONSHIPS
4.2.1 Approach
The data (Appendix I), together with the descriptive statistics
summarized in Table 2, were examined to determine the appropriate
method for refining the relationships between KQC and KQW developed
by Pavlou and Weston (1984).
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TABLE 2
DESCRIPTIVE'STATISTICS
iVrfMUNIk COMPOUND
lL<6b IMC
HlbM III.
lUU Ul.
MH
KUiOUUMIHRtfElf
KMOMUMfK*
tEMIOItlFLUMMMNE
KM0I6,H, llttiYLEIC
KMOWIFLUMMItOt
chrvseic
llKMOia,HIซNIMMCEIC
FLUORMItflE
IMEMMI,ฃ,3,Cltlf>VlENE
woe
Dcawtnicii
St OF 6EWETBIC MEM
NO. OF ; MIIIMEIIC: MIIMEIICi KM : EMป
VALUES s KM i MEAN : (LOG)
i-.au
2538775
1*457421
Minu
snast
492M72
SI4512S
Iซ3I63
Stl11724
ldM%
I2MM
13*771
17332
17351
l/j/i
3*7*51
7433%
2*39711
2772*1
HMM
mtti
mini
mail
6M3437
14931
447*4
442*3
44151
9431
Ml
Ute
5.74
6.*7
fc.32
7.N
LU
5.71
Lit
4.42
4.45
5.25
7.7*
5.M
5. *9
5.1*
4.11
4.11
4.1b
101
MUkE
HIGH
IflU
HIMU
HIGH
LOU
MIDDLE
Hlui
Klฃ
St OF GEOKIRIC HE AM
ML OF ; AtllHNEIIC: MlllMCIICi HEM i EMM
VALUES : tfiM HEAM IlOEI i MME
I
t
t
I
IS
I
*
31
i
I737MI t
ikMl
i
6*31375
1*133251
I5l3~-7t2
i;3l354
11597916
14254147
51M44
1957*53
2*4174
11197
311726
31516
6.24
6.27
6.31
4.74
6.19
7.M
5.77
6.23
5. 31
4.17
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TABLE 2 (cont.)
DESCRIPTIVE STATISTICS
ซ
MM
HOC
CMttM CM4UM
cmss mc
ซ
ML V
ft WLIfca
ttllMEIIC
KM
S*OF
MilHCIIC
ION
GEOMETRIC
Km
aaป
ION
EMI
mm.
ML Of
MUCS
miihmeiic
icm
St OF GHKTMC
ttiiwEiiCi mm
Km 1 ILOGI
MEM *
; ERROR
: RANGE
MXWMIHVlEIC
2
1*367
2M3
4.*l
ฆ
WtHMCBC
7
27134
ISM
4.4*
1
M5M4
2I39M 1 4.7>
: Wlซ
ฆ
FU1MQC
7
S*4tt
M62
4.3*
1
1*233
s
; KM
i ฆ
1 PAIR
1
MWMTHMiME
ฃ
2M7
24lป
2&U
tu
311
4S3
13I
3.3*
3.4*
IfaU
IMBUE
HIU
1
tlLl
;
1 3.55
1
1 PAIS
;
t *
HCNRHTHRENE
t &
29M
I2N*
4.43
2
Ohtt
7&9A : t.32
: ฆ PAIR
ซ
KtS
MQCLM 1*16
i 1
19953
3*|9%
-
4.31
5. a
LOU
HIGH
j
1
1
ft
1
ft
t
MQCLOR 1221
1
s
t*3
INN
-
2.7ป
4.N
LM
HIGH
:
t
j a
Moan iฃ32
1
1514
KM
-
111
4.4*
LOU
HICH
t
j
1
j 1
Moam 1242
1
t
INM
39ปl*7
-
4.N
5.E*
ION
HIGH
j
:
a
MQOOR 12a
1
INNN
&.N
ft
j ง
MOO.M 1254
2
l*357ป
51672
6.*i
14
31M651
SfcHU t fc.tf
1 PAIR
j t
Maaai i.-it
1
INMN
-
ฃ.M
*
ft
(tSIICIKS
ACROLEIN
i
4.345
5. *56
.4*
1
5.(1
;
t i.ป
ft
PAIR
m
ftDRIN
i
: ft9l*2fc
14313414
1.12
4
S&3II
ฆ
MIR
-------
TABLE 2 (cont.)
DESCRIPTIVE STATISTICS
COMPOUND COMPOUND
CIASS NAME
QlOMMNE
DbD
DDE
DDT
D1ELDRIN
ENDOSULFAN
ENDNIN
HEPTACHLOR
HEXACYCLODLORQHE1ANE
lSQPHORONE
TCDO
TOXAPHENE
KQU
SO OF GEOMETRIC MEAN
NO. OF : ARITHMETICS ARITHMETICS MEAN s ERROR
VALUES s MEAN
>
MEAN
3 ; 1*1562 : 173581 s
5 s 63%S9 s 515715 s
: i s
7 : *77837 : 2118*7 s
: s s
ia s 31 U7i6 : 698*374 .
: t
2 > 7948% s 1117225
:
3901 i
1
6
3
1 s
3
1
113984
74634
58.197
7943282
1847
161546 :
973*1 :
>
3.466
ii*
(L06)
3.86
5.56
5.6*
6.87
4.95 s
3.6*
4.44
4.54
4.88
1.7*
6.98
3.:-7
RANGE
NO. OF
VALUES
1
17
2
*
2
1
*
1
KOC
SD OF
ARITHMETICS ARITHMETIC
MEAN MEAN
ป
141254
239883
147911 s
67*284 : 968288
GEOMETRIC MEAN
8*97
4642
I MM :
6768
>
s
:
4163 :
:
s
MEAN
(LD6)
5.15
5.38
5.17
5.48
3.82
3.56
. 4.**
1M*
ERROR
RANGE
3.<
-------
Kolomogorov-Smirnov (K-S) goodness-of-fit tests were performed to
determine whether the data were normally distributed and whether
transformations were required. The K-S tests were performed on
untransformed and log-transformed data. These tests Indicated that
only the log-transformed data were normally distributed. The
transformed geometric mean data best fit the normal curve and were
selected for the regressions.
Mean values for each compound rather than individual data points were
used 1n the regression analyses. Mean data values were used because of
the limitations of the data reported 1n the literature. A few
researchers presented all data collected, some provided summary data,
and some single values. Sample sizes were not always reported, and In
certain cases 1t was evident that single values were based upon more
extensive data sets, but no reference was given In the publication. It
was therefore not possible to perform weighted regressions. If one
compound has more Individual data points than others 1n a
chemical-class regression, the resulting regression would be skewed
toward the compound with the most data points. Use of a representative
value (eg. mean) for each compound, results 1n a one-to-one
correspondence between compounds and data points. Also, the use of
mean values permits the Incorporatatlon of reported summarized data.
The use of median values versus mean values was also examined.
Recomputatlon of the statistics using median values was not justified
because the differences between the mean and median values were
negligible (typically 0.01 log unit or less), primarily because of the
small data sets.
In this stu
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The resulting regression agrees well with several Koc/Kow
regression equations previously reported 1n the literature (Kenaga and
Goring, 1980; Lyman et al., 1982). The resulting equation, however,
contains the Inherent weaknesses associated with biasing and skewing,
as noted previously.
4.2.2 Detailed Analyses
A review of the descriptive statistics summarized 1n Table 2 yields
some additional Information to that determined from examining the raw
data presented 1n Appendix I.
o Variation of reported KQC values 1s quite high; of the 11
compounds for which more than one KQC value 1s reported,
five exhibited a standard deviation about the mean exceeding
the arithmetic mean.
o For sample sets of four or more KQC values, the geometric
mean 1s from approximately 10 to 50 percent lower than the
arithmetic mean, which Indicates high data scatter. This
variation In Koc 1s not surprising, and can be attributed to
a number of factors, Including sorbent concentration effects
(Voice and Weber, 1985), particle effects (01 Toro, 1n press;
01 Toro and Horzempa, 1982), physical/chemical effects
(Versar, 1984) or differing experimental and laboratory
techniques.
o The variability 1n KQW for a given compound 1s also large In
many cases. For example, the standard deviation of the mean
compound value 1s greater than the arithmetic mean compound
value for 3 out of 27 compounds. This variability could be
due 1n part to the lack of standardized methodologies for
determining KQW (Karfckhoff and Brown, 1979), as well as to
some of the factors noted above for KQC variability, I.e.,
particle effects and dissolved organic matter.
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The results of the K0CA0W regression analyses are summarized in
Table 3 and are discussed below. These relationships and the
Individual data points are also presented graphically 1n the figures
Included In Appendix II.
All of the regression equations, except the low-weight PAH equation,
are significant at the 0.05 (95 percent confidence) level, based on
analysis of variance (ANOVA) statistics. The relationship Implies that
a unique regression line of non-zero slope may be determined for the
given data set for a high degree of probability (.> 0.95). In the case
of the lowrwelght PAHs, there are too few data points (four), and with
the data scatter, the regression equation 1s not statistically
significant and should not be used.
In all cases, the correlation coefficients (r) are greater than 0.8
Indicating strong correlations between the two variables. The
coefficient of variation, r2, Is typically greater than 0.7,
Indicating that varying KQW explains over 70 percent of the
corresponding change 1n Koc. The coefficient of variation 1s not
Ideal (r* * 1.0), but 1s acceptable for using K values to predict
Koc 1n the absence of empirical data.
The standard error of estimate (SE) 1s a measure of deviation from the
value given by the regression line. For the regression equations 1n
Table 3, SE ranges from about 0.4 to 0.7 log units and averages about
half a log unit. The standard error of estimate defines the
characteristic uncertainty of the regression. By definition, about
two-thirds of the data will lie within one standard error of the
regression line, while about 95 percent of the points will 11e within
two standard errors of the line. Given the small sample size of the
database, however, these boundaries cannot be strictly Imposed. Thus,
for any point predicted by the equations 1n Table 3, the 95 percent
confidence Interval ranges from about 0.8 to 1.4 log units, depending
upon the SE for the specific equation. It is Important to note that
this error measurement Is only for the regression equations, and does
not Incorporate the Intrinsic uncertainty of the individual data
points, which are representative mean compound values.
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TABLE 3
REGRESSION RELATIONSHIPS OF K AND K DEVELOPED FROM COLLECTED DATA
ON OC
Coapound Ftally
Data Type
Regression Equation
correlation
r r2
Standard
Error
(SE) of
Estimate
F
Significance
no.
of
pts
All Compounds
Geometric Mean
log - 0.881 log
~ 0.419
0.874
0.764
0.689
0.0000
21
All PAHs
Geoaetrlc Mean
log Kqc = 1.10 log ^
ฆ 0.376
0.945
0.893
0.379
0.0000
10
Low Height
PAHs
Geoaetrlc Mean
Iฐ9 ^ ซ 0.818 log Kw
+ 0.763
0.804
0.647
0.371
0.1959
4
High Weight
PAHs
Geoaetrlc Mean
log Koc * i23
1.13
0.837
0.701
0.445
0.0376
6
Pesticides
Geoaetrlc Mean
log K^. = 0.717 log
~ 0.802
0.869
0.755
0.742
0.0011
10
All Compounds
Raw Data Points log K = 0.988 log K - 0.087
OC ow
0.818 U.670 0.583
0.0000 83
-------
To generally characterize the Intrinsic uncertainty 1n individual
compound data points, reported error ranges for the Individual compound
data, where available, were determined by calculating the mean high and
low values from the literature. These results are given in Table 2.
Regression equations were determined with these high and low values,
using the same methods as above. An analysis of covarfance test, which
compares lines of regression, Indicated no statistically significant
difference between these regression equations and the ones presented In
Table 3. These results suggest that, given the limited size of the
data sets, the characteristic uncertainty of the regression lines may
overshadow the uncertainty associated with the data measurement. This
uncertainty may be a point for further Investigation In supporting
studies, and underlines a need for additional data.
Residuals are defined as the differences between the observed values
{1n this case, the log-transformed geometric mean KQC data) and the
values predicted by the regression line. They are a measure of the
amount of variability not explained by the model, and when plotted can
be used to evaluate the suitability of the underlying model
assumptions. Ideally, the variance should be constant and the plot of
residuals against Koc will leave the Impression of a horizontal
band. Deviations from this pattern (e.g. expanding, sloping, or curved
bands) give Identifiable Inadequacies In the model assumptions (Draper
and Smith, 1966).
For the three regression equations with the largest data sets (all
compounds, all PAHs, and pesticides), the residuals were determined and
plotted against the corresponding KQC value predicted by the
respective regression model. These plots are presented in Appendix II.
The residuals plot for the "all compounds" regression has an Increasing
variance (expanding band) with Increasing magnitude of KQC. This
Increase in variance Indicates the need for a,weighted regression (I.e.
a weighting of the data before regression analysis). If a weighted
analysis Is called for, but an ordinary (linear) analysis is performed
8906A
17
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as in this case, the estimates obtained are still unbiased but do not
have minimum variance (Draper and Smith, 1966). Therefore, it is not
desirable to use this predictive relationship because it violates one
of the implicit model assumptions, constant variance. This statistical
result supports the Intuitive physical/chemical weakness of combining
data frpm several chemical families to derive a single regression
equation. Further evidence supporting segregation into specific
compound classes comes from a closer examination of the Individual
residuals from this plot. There is a clear separation of the PAH and
pesticide data; most of the PAH residuals are,positive, while most of
pesticide residuals are negative.
In contrast, the residuals plot of the PAH regression indicates a more
nearly constant variance. In addition, the magnitude of the variance
is smaller than that for the "all compounds" regression. Given the
limited number of data points, this plot Indicates that underlying
model assumptions have not been violated, and there 1s a noticeable
improvement by providing a separate regression for this class of
compounds.
The results of the residuals plot for the pesticides regression are not
as clear. The magnitude of the variances 1s greater than that of the
PAH residuals, and one or two points Influence the perception of the
plot. Data are not sufficient, particularly for log Koc values between
1.5 and 3.S, to draw conclusions on the appropriateness of the
underlying regression assumptions. However, given the great
physical/chemical differences of some of these pesticides, one would
intuitively expect the variance to be high when combining them 1n a
single pesticide regression.
From a physical/chemical perspective and as supported by the above
analysis, 1t 1s preferable to employ compound or family-spedf1c
regression equations for K estimation, where possible. Effects on
00
partitioning of chemical characteristics unique to each chemical class
8906A
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will be masked if equations developed from data for all compounds are
used. This need for specific equations has been acknowledged by the
Investigators Identified In Table 4, who have noted the compound
classes of the data bases from which regression equations were
developed.
Selected regression equations reported In previous Investigations are
presented 1n Table 4. Although Insufficient information Is given in
these studies to perform an analysis of covariance, some general
comparisons can be made. The slopes from the previous studies are in
the same general range (0.8 - 1.0) as the slopes determined in this
study. The slope of the pesticide regression Is lower than for the
other regressions in this study; this observation follows the same
pattern as the previous studies. The Intercepts tend to be higher than
those previously reported, perhaps an indication of cumulative error
effect from combining results from numerous studies. The correlation
coefficients are comparable, and in some cases are slightly higher than
those previously reported for corresponding chemical classes. Since
this study 1s an analysis and compilation of data from numerous
researchers, the slightly Improved correlation coefficients were
unexpected, and thus support the approach used to summarize and analyze
the data.
Insufficient data existed to perform a statistical analysis for
nonllnearity 1n the relationship between and sediment organic
content. One would expect, for example, that 1n sediments of low
organic carbon content (e.g., < 1 percent), departures from the linear
relationship between Koc and KQW would occur. These departures can
be attributed to a number of other physical/chemical factors
Influencing the partitioning process. On the other hand, for sediments
of organic content In excess of 25 to 30 percent, one would expect
different kinetics, surface/surface interactions, and affinity effects
to cause departures from the linear relationship.
8906A
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TABLE 4
SELECTED REGRESSION EQUATIONS FROM THE LITERATURE FOR THE ESTIMATION OF FROM Kw
Regression Equation
Nuaber of
Coapounds Correlation
Considered Coefficient
Chemical Class
Reference
log ฆ 0.544 log Kw ~ 1.377
log Koc M 0.937 log KM - 0.006
1ฐ9 Koc " J-000 log Km - 0.210
log Kqc " 0-9*0 log Kqw ~ 0-020
log Koc - 1-029 log Kg* - 0.180
log Koc " 0.524 log Kow + 0.855
log Kqc - 0.989 log Kw - 0.346
log Koc - 0.843 log Kw ~ 0.158
45
19
10
9
13
30
5
19
0.86
0.97
1.00
0.95
0.92
1.00
0.96
Pesticides
Aroaatlcs, polynuclear
aroaatlcs, trlazlnes,
dlnltroanlllne herbicides
Mostly aroaatlc and
polynuclear aroaatlcs
Trlazlnes and dlnltroanlllne
herbicides
Variety of herbicides.
Insecticides, and fungicides
Substituted phenyl ureas and
alkyl N-phenylcarbamates
Aroaatlc and polynuclear
aroaatlc hydrocarbons
Priority pollutants
1984
Kenaga and Goring, 1980
Brown and Flagg, 1981
Karlckhoff et al.. 1979
Lyaan et al., 1982
Rao and Davidson,
1980
Brlggs, 1973
Karlckhoff, 1981
Pavlou and Heston,
Source: Pavlou and Heston, 1984
-------
To further support the above findings, some alternate nonparametric
statistics were run on a small subset of the summary data. For six
high-weight PAH compounds (benzo(a)anthracene, benzo(a)pyrene,
chrysene, d1benzo(a,h)anthracene, fluoranthene, and pyrene), with
matched KQC and KQW data, Efron's Bootstrap Technique (Efron and
T1bsh1ran1, 1985) was employed. This method consists of running all
possible regressions between data points, and determining the
asymptotic value for the Intercept and slope. This method Is
theoretically desirable because no assumptions concerning the
distribution of the data or coefficients are made; rather, the
distribution 1s determined on the basis of the observations. For the
six compounds, using the mean and mean range data sets, use of this
8
method would require nearly 4 x 10 regressions. However, a small
representative subset of these combinations typically 1s chosen, and In
this case nine regressions were selected. Although too limited to be
definitive, the analysis revealed several things. There was not a
great deal of stability 1n slopes and intercepts between data sets.
This lack of stability accounts for the variability associated with the
observed values. Additionally, there were some nonllnearity problems
(non-normal distribution) and residuals were not random across almost
all variables. All of these observations suggest that there are other
factors in the data that are not accounted for by the regression
equations. In other words, while normalization of the partition
coefficients to organic carbon content of the sediment produces usable
regression equations, there are some other factors which Influence
partitioning. Other Influencing factors are also possible based on the
correlation coefficients of the regression equations from the
2
parametric statistics which yield r values of approximately 70
percent, leaving 30 percent of the variation unexplained. Recent
findings, which are discussed In the following sections, may explain
the variation not accounted for by normalization to organic carbon
content.
8906A
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General Inspection of the regression plots in Appendix III also
Indicates another Important factor. There are several points of high
leverage, I.e., data which disproportionately influence the location of
the regression line. Also, 1n some cases only one or two points define
a broad range (two or three log units) on the plot. Thus, the
regressions are sensitive to the deletion (or addition) of data
points. The small data sets do not warrant the statistical rejection
of outlying data points, however. In addition, there may be a basis
for such data because an individual compound within a chemical class
may partition differently than other compounds within the class as a
result of compound-sped f 1c physical/chemical characteristics.
From the above discussion of the detailed analyses, the following key
points are noted:
~
The reported partitioning data vary noticeably. Such
variation Implies the need for care 1n and standardization of
measurements.
Chemical-class specific regressions are preferred over a
single regression combining all data.
The regression equations presented in this study for all PAHs
and pesticides are suitable for predictive use. KQw
accounts for approximately 70 percent of the variability when
predicting KQC.
Other physical/chemical parameters as well as pure error
account for the balance of variability 1n the regression
equations. Additional detailed analyses are needed to
segregate these factors and further define uncertainties.
Additional data are needed to verify and/or refine the
regressions. The additional data would also serve to better
define uncertainties.
8906A
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4.3 INFLUENCE OF OTHER PHYSICAL/CHEMICAL PARAMETERS ON K
oc
It was Immediately evident after the review of the collected literature
that the Information was Insufficient to quantitatively or
statistically evaluate the effects of parameters other than sediment
organic content. Some of these parameters have been discussed 1n the
literature, however, together with other factors relating to sediment
normalization theory- These discussions are characteristically
qualitative, although quantitative values are occasionally presented.
In some Instances researchers either did not report the supporting data
or are employing professional judgment to derive qualitative
relationships. Although a thorough review of the literature 1s beyond
the scope of this study, a brief analysis of some of the key points
that would support the statistical analyses are presented.
4.3.1 Additional Parameters Influencing Partitioning
Parameters such as pH, salinity, temperature, dissolved organic matter
concentration, and redox potential, as well as sediment composition and
particle size may have some effect on the partitioning relationship.
The literature discussing these parameters was recently reviewed by
Versar (1984) 1n a draft study of selected contaminants at a large
marine Superfund site. Their findings, together with additional
information, are summarized below.
pH - The pH may significantly affect sediment adsorption of
some organic chemicals (for example, addle compounds such as
phenols and basic organic compounds like amines). Some of the
organic acids also form strong associations with clays,
particularly when the pH of the water is about 1.0 unit above
the pK value of the acid (Lyman et al., 1982). The effect
of pH on highly hydrophobic neutral organlcs, however, appears
minimal. The pH 1s not expected to have an effect on the
environmental fate of aldrfn or the PAHs and should have no
effect on the fate of PCBs because they are highly lipophilic
molecules with little tendency toward ionization (Versar,
1984).
8906A
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Salinity - The salinity of a solution may affect the
solubility of organic compounds, and thus the adsorption of
these compounds on sediments. As the salinity of a solution
increases, the solubility of hydrophobic compounds can either
decrease or increase. Increased salinity also affects the
adsorption of organic cations by increasing the rate at which
inorganic cations replace organic cations om the sediments
(Lyman et al., 1982).
The solubility of aromatic hydrocarbons is typically reduced
by the presence of most inorganic salts 1n solution (Sutton
and Calder, 1975; Eganhouse and Calder, 1976). These studies
suggest that the relationship for the effect of salinity on
hydrocarbon solubility be expressed as
log S0/Ss . KSCS
where SQ and S$ are the solute solubilities 1n fresh and
salt water, respectively, C$ is the salt molarity, and K$
is the salting constant. values for some compounds are
available from May (1980). The effect of inorganic salts on
the solubilities of the aromatic hydrocarbons appears to be
the same whether they exist 1n pure or multicomponent
solutions (Eganhouse and Calder, 1976); hence, this effect
could easily be accounted for if the KQw values were
determined 1n a saltwater/octanol system.
The presence of electrolytes (salts) in solution also
Increases the sorption of PCB by sediments, a "salting out"
effect; the solubility of PCB in water decreases as salinity
Increases (Sayler and Colwell, 1976; Dexter and Pavlou, 1978;
W1ld1sh et al., 1980). Dexter and Pavlou (1978) experimentally
determined that the solubilities of PCB Isomers at a salinity
of 30 parts per thousand are up to 5 times lower than those in
fresh water.
8906A
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A similar sorption effect Is seen with DDT. Plcer et al.
(1977) found an 11 to 53 percent Increase 1n adsorption of DDT
on different sediments when salinity was Increased from 3.7 to
37 percent. Although there are numerous studies of DDT
distribution and behavior in the freshwater environment, no
data on the solubility of DDT 1n the marine environment have
been published (Versar, 1984).
Normally, the presence of electrolytes 1n water reduces the
solubility of hydrophobic compounds. However, Yaron et al.
(1967) found that varying the salinity over a range of 3 to 63
ppt did not affect the quantity of aldrln adsorbed from
aqueous-solution onto soils. Therefore, salinity 1s not
considered an Important parameter 1n the adsorptlon/desorptlon
determinations for aldrln.
Temperature - Solubility of some organic compounds, and
therefore their adsorption to sediments, may be affected by
the ambient temperature. An 18 percent increase in the
adsorption coefficient was measured with a temperature drop
from 20-C to 5!c (Lyman et al., 1982). The effect of
temperature on absorption varies with the compound under
consideration, and for some compounds no appreciable effect is
seen. Biggar and Rlggs, (1974) observed a strong nonlinear
relationship between temperature and aldrln solubility.
Aldrln solubility decreased from 13.S ppb to 5.5 ppb with a
drop In temperature from 25"!c to 15tC.
Studies have determined that temperature is an Important
parameter In determining PAH solubility. May (1980) used a
dynamic, coupled-column liquid chromatographic procedure to
obtain solubility data on 12 RAHs over a 30"C temperature
range and developed quadratic equations describing the
relationship between temperature and solubility for each
8906A
25
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compound. The solubility data of May (1980) at typical water
temperatures indicated that the decreased solubility at lower
temperatures results 1n an increased affinity of compounds for
the solid organic phase (Versar, 1984). Herbes (1977)
observed a threefold Increase In adsorption of anthracene on
autoclaved yeast cells when the temperature was decreased from
42!c to 4.5*C. Meyers and Qulnn (1973) reported the same
effect for anthracene adsorbing on bentonlte in sea water.
LittTe Information was available on the effect of temperature
on solubility and partitioning behavior of PCBs. However, the
effect 1s not expected to be significant for the 20"C range of
temperatures found in most aquatic environments (Versar, 1984).
Changes 1n temperature affect the solubilities and thus the
partitioning between water and sediment of DDT, DDD, and DDE.
Biggar and R1ggs (1974) found that a temperature decrease from
25!c to IStC results 1n an approximately two-fold lowering of
DDD and DOE solubilities; with DDT, a 1.5-fold lowering
occurs. They noted that the relationship between pesticide
solubilities and temperature 1s not linear. From adsorption
studies of DDT on humlc acid, Well et al. (1973) calculated
that Its heat of adsorption is 2.5 kcal/mol. Using the
Van't Hoff equation, Versar (1984) estimated that the
adsorption coefficient at lo!c is approximately 3 orders of
magnitude greater than that at 25!c. However, this effect was
only for DOT adsorption on humlc acid. The temperature effect
for adsorption on less organic sediments should be similar,
but perhaps not as large. Unfortunately, the reliable
solubility and octanol/water partitioning data for DDT, DDD,
and DOE were determined solely at room temperature (Biggar and
R1ggs, 1974; O'Brien, 1974).
8906A
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Dissolved organic matter - Equilibrium partitioning of organic
contaminants between sediment and water 1s affected by the
concentration of organic matter dissolved 1n the water, a
greater concentration of dissolved organic matter favors a
greater equilibrium concentration of contaminant 1n the
aqueous phase through two mechanisms: first through the
Increased solubility of organic contaminants in water of high
dissolved organic material concentration; and second through
Increased competition for sediment adsorption sites among the
organic material present (Hassett and Anderson, 1978). Low
concentrations of dissolved organic carbon {1 to 2 mg C/1) are
not considered sufficient to significantly shift the
sediment/water partitioning equilibrium towards a higher
pollutant concentration 1n the water (Versar, 1984). Boehm
and Qulnn (1973) discovered that the removal of dissolved
organic matter from Nanragansett Bay water samples had no
effect on the solubility of aromatic.hydrocarbons; however,
aliphatic hydrocarbon solubilities were significantly
affected. It therefore appears from these experiments that
the presence of dissolved organic matter has no effect on the
solubilities or the partitioning behavior of PAHs.
Partitioning of PCBs between water and the dissolved organic
phase can decrease the quantity of PCB available for
partitioning between water and the sediment organic phase.
Sayler and Colwell (1976) demonstrated partitioning of PCBs
between water and crude oil as an organic phase as well as
sediment. Plcer et al. (1977) performed DOT adsorption
experiments In seawater contaminated with crude oil and
detergent. The presence of these organic compounds 1n the
water column seemed to produce a decrease 1 n the rate of DOT
adsorption, but a shift In the final equilibrium toward the
sediment phase (Increasing K). The effect should not be
noticed at low dissolved organic carbon concentrations
(Versar, 1984).
8906A
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Sediment particle size - Size fractionation may be important
as a secondary factor in the determination of sediment/water
partitioning because it indicates sediment composition and the
amount of surface area available for sorption. Large
variations in sediment size fractions at different sites
within an embayment may result in geographical variability in
adsorption of organic compounds. The adsorption coefficient
has been reported to increase by a factor of ten with a 100 um
decrease 1n sediment particle size {Pavlou and Dexter, 1979).
Sorption of organlcs to Inorganic clays can be significant
when the particle size is small and the surface area is large
(Lyman et al.f 1982). The total surface of the sediments
significantly affects the amount of contaminant being
adsorbed. Karickhoff and Brown (1978) demonstrated the
variation in adsorption coefficients for pesticides on coarse
and fine sediment, and recommended that KQC be determined on
finer sediment particles, I.e., <_ 50 tim 1n diameter (the
size/class of s111 and clay). Karickhoff et al. (1979)
measured the sorption of anthracene, 9-methylanthracene,
naphthalene, 2-methyl-naphthalene, phenanthrene, and pyrene on
sediments with different size compositions. Their results
showed that the K values of the compounds sorbed on the
oc
sand fraction (>_ 50 um) were considerably less than those on
smaller size fractions (ฃ 50 um). Similarly, May (1980) found
that phenanthrene adsorption on silica gel 1s an inverse
function of particle surface area. Working with samples from
the continental shelf off Washington State, Prahl and
Carpenter (1983) discovered disproportionately high
concentrations of PAHs associated with low-density,
carbon-rich sediment particles composed mostly of plant
detritus and charcoal. These low-density particles made up
only about 1 percent of bulk dry sediments, but accounted for
10 to 35 percent of the total sediment PAH. The rest of the
PAHs (50 to 78 percent) remained in the silt/clay fraction
(Versar, 1984).
8906A
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The particle size and resulting surface area of the sediments
may be an Important factor 1n the adsorption of PCBs to
sediments (Haque et al., 1974; Steen et al., 1978; Hiraizumi
et al., 1979). H1ra1zum1 et al. (1979) demonstrated a
positive linear relationship between PCB concentration factors
and specific surface area of adsorbing agents such as sand,
mud, clay, and plankton. The effects of sediment surface area
and organic content on the extent of PCB adsorption, however,
are difficult to separate. Choi and Chin (1976) showed that
smaller particle size fractions of marine sediments tend to
have a higher organic content.
Dissolved Inorganic compounds - The presence or absence of
dissolved Inorganic compounds appears to have little effect on
the partitioning of hydrophobic organic compounds. There are,
perhaps, one or two exceptions. There 1s no evidence that
dissolved Inorganic compounds Influence the fate of aldrln,
the PAHs, or PCBs. However, DDT sorption can be reduced 1f
metals are present (Callahan et al., 1979) because the
presence of Fe II 1n soil of high organic content can
facilitate reduction of DDT to DDE. Porphyrins also can
reducing DDT to DDD under anaerobic conditions (Yersar,
1984). These reactions in sediments are difficult to quantify
because they occur only under anaerobic conditions and depend
on the concentrations of metals or porphyrin reducing agents
in the water and sediment. In most cases, dissolved inorganic
compounds are not expected to affect sorption significantly.
Redox potential (Eh) - The redox potential, a measure of the
oxidative state of the system, 1s a much more significant
factor affecting inorganic sediment chemistry than 11 1s for
hydrophobic organlcs. The Eh does not affect the partitioning
of PAHs between sediment and water, and because PCBs are not
oxidized, no effect of Eh on these compounds is observed
(Versar, 1984). It 1s not known whether the chemical
8906A
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oxidation of aldrin to dieldrin 1s a major decomposition
pathway. There 1s empirical evidence of aldrin oxidation
under laboratory conditions (Callahan et al., 1979), but the
rate of oxidation, either in the laboratory or in nature, is
unknown (Versar, 1984). The effect of Eh is significant in
the case of the DDT family, because of the effect described in
the previous section. However, the magnitude is difficult to
quantify.
Based on the above discussion and the general Information contained in
the literature, the unnormallzed partition coefficient (K^) may. be
conceptually expressed as a function of sediment organic carbon (OC),
pH (H), salinity (S), temperature (T), dissolved organic matter (D),
sediment particle size (P), Eh (E), and suspended particulate matter
(M) for most hydrophobic organic compounds. One possible form of this
relationship is:
Kd ป aOC ~ bS ~ cTn + eD ~ fPx + gM,
where a, b, c, e, f, g, n, and x are compound specific proportionality
constants. This expression 1s conceptual, will vary between compounds
and will likely require significant modification as additional
information is obtained. Furthermore, field data will be required to
refine the expression and to determine values for the environmentally
based proportionality constants.
4.3.2 Current Considerations
The recent Interest 1n Hqu1d-sol1d phase sorption or partitioning,
resulting at least 1n part from the concern over the fate and effects
of toxicants released to the environment, has led to studies containing
some contradictory experimental evidence and 1n reconsideration of
theoretical aspects of partitioning of hydrophobic compounds. Voice
and Weber (1985) state that:
8906A
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A notable apparent contradiction of experimental evidence with
sorption theory has been cited in several reports that have
described a 'solids effect' in laboratory isotherm
measurements" (O'Connor and Connolly, 1980; Voice et al. 1983;
DIToro and Horzempa 1982). "These works document a dependence
of partition coefficients (ratios of Individual phase
concentrations) on the mass of the solid phase utilized in the
experimental system, whereas simple phase equilibrium
relationships predict no such dependence.
They go on to note that several explanations have been suggested for
these observations.
DIToro and Horzempa (unpublished) have performed laboratory experiments
in which, after a chemical constituent has come to equilibrium in the
two phases, a portion of the aqueous phase is removed and the sorbent
1s resuspended and allowed to re-equllibrlate. After phase separation,
1t was found that aqueous concentrations had increased in all cases.
They also found similar effects when only local solids concentration
(amount of material) was changed. They explain their results in the
context of a reversible and resistant component equilibrium model and
the role of particle-particle interactions on desorptlon equlibria. In
other words, the sorption of the contaminants Is not completely
reversible and is Influenced by suspended particulate concentration
through particle collision kinetics. DIToro (1n press) has developed a
theoretical model which describes these observations. This model
requires additional Input data and results 1n the experimental values
presented 1n the Appendix II table which do not fit the conventional
regression models. Voice and Weber (1985) note that these results are
not Incompatible with a simpler theory, which 1s also explored in the
work of Gschwend and Wu (1985). They suggest that these observations
could be the result of a dynamic equilibrium between the distribution
of organic material 1n the solid and 1n the liquid phase. When the
sol Ids concentration is Increased, even locally, a driving force may be
set up to transfer organic matter from the solid to the liquid phase.
This additional organic carbon may act as a competing sorbant or carry
sorbed contaminant as It leaves the solid phase. Voice and Weber
8906A
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(1985) thus propose a model that relates the solid phase or particle
phenomenon to the complexatlon (or binding) of the solute by
nonseparable organic matter in the liquid phase, and the subsequent
sorption of both free and bound solute. They proceed to show that
linear partitioning 1n the resulting bisolute system can produce the
nonlinear sorption anomalies that have been observed in previous
Isotherm studies.
Uncertainties in KQW values are also a factor in the current
considerations of partitioning theory. These uncertainties are
important, because in the standard linear regression analyses of KQw
andKQC, KQW is chosen as the Independent variable, I.e., the
variable measured without error. Halfon (1985) touches on this Issue
in his recent article discussing regression methods 1n ecotoxlcology.
He argues that a modified geometric mean rather than linear functional
regression method should be used to compute slope and intercept
coefficients to account for large variations within the Independent
variable for many predictive environmental regression relationships.
However, unlike the examples selected by Halfon, the theoretical
partitioning regression slope does not have to be exactly unity (see
Lyman, 1982), and, as he admits, the correlation coefficient, r, does
account for variability (error) 1n both variables. This discrepancy
may become a point of contention between statisticians and researchers.
It 1s clear from the above discussion that the determination of Kqc
from KQW using a linear Isotherm does not explicitly Incorporate some
of the more subtle factors that may Influence partitioning. It Is also
apparent, however, that the above concepts are uncertain and
researchers are not in agreement as to the theoretical considerations
and mechanisms involved. Until such time as more complete data sets
are available and there 1s a better understanding of physical/chemical
mechanisms, the regression equations presented here provide a useful,
predictive tool. These equations, incorporating one of the most
8906A
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important variables affecting partitioning, organic carbon (Chiou et
al., 1983; Lyman et.al., 1982; Pavlou and Weston, 1984; Versar, 1984),
are suitable to estimate preliminary permissible sediment contamination
concentrations.
4.4 RECOMPUTED PERMISSIBLE SEDIMENT CONTAMINATION CONCENTRATIONS
The compound-class regression relationships given In Table 3, using
geometric mean KQW values reported 1n Table 2, were employed to
calculate revised KQC values for the compounds listed in Table 2.
These revised KQC values were then combined with existing (or
projected) water quality criteria to determine permissible sediment
contamination concentrations. Results of these analyses, together with
the results previously reported by Pavlou and Weston (1984), are
presented 1n Table 5.
Comparing the previous results with the results of this study,
significant differences 1n the projected permissible sediment
contamination concentrations (PCCs) are observed. These differences
cannot be attributed to the relatively minor differences in Kow,
which are typically within *0.2 log units between the two studies.
Rather, the differences are attributed to the large variations In
projected KQC values, which differ by as much as 2 to 3 orders of
magnitude. These variations result from the use of the refined
regression equations. The relationships developed In this study and
the corresponding revised PCC values represent a better estimation
because the present study makes a distinction between compound
classes. Thus several regression relationships apply rather than a
single "universal" relationship, as employed by Pavlou and Weston
(1984). In addition, this study draws on a broader data base in the
development of the regression equations. The projected PCC values are
generally less stringent than previously reported. The relatively
minor differences In Kqw values between these two studies and the
8906A
33
-------
TABLE 5
SUMMARY OF REVISED PERMISSIBLE SEDIMENT CONTAMINATION CONCENTRATIONS BASED UPON REVISED Koc/K0m RELATIONSHIPS
Water Quality .
Criteria a/ b/ Pavlou and Weston PCC's PCC's This Study
(ng/1) Pavlou and Weston This Study (tซg/90C) (og/9oc>
a?/ riiwinlr^ Inn K if v lflฎ Inn K ^ v
Compound Acutet' Chronic-' log K K x 10 log K K x 10 Acute Chronic Acute Chronic
ow oc ow oc
High Wt. PAHs
Benzo(a) anthracene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(g,h,1) perylene
Benzo(k)f1uoranthene
Chrysene
Dibenzo(a,h)anthracene i->u --z.-
Fluoranthene 20 y
Indeno(l,2,3,c,d)pyrene 150 ป/*/
Pyrene 150 ฃ/*/
150 d/e/
150
150 V*!
150 ฃ/ซ/
150 d/e/
150 J/e/
150 f/f/
Low Wt. PAHs
Acenaphthene
Acenaphthylene
Anthracene
Fluorene
Naphthalene
Phenanthrene
PCBs
Aroclor 1016
Aroclor 1221
Aroclor 1232
Aroclor 1242
Aroclor 1248
Aroclor 1254
Aroclor 1260
475
150 2/t!
150 *J%!
150 %(*!
1,175 \r
150 ?/ฃ/
0.014 l!
0.014 *!
0.014 $!,
0.014 ฃ/
0.014 ฃ/
0.014 e/
0.014 ฃ/
5.61
0.370
5.74
8.51
5,500
128,000
6.31
3.00
6.07
21.6
45,000
325,000
6.57
5.00
6.32
44.0
75,000
660,000
7.23
18.00
7.00
302
270,000
4,530,000
6.B4
8.40
6.45
63.6
125,000
954,000
..
5.61
0.770
5.71
7.82
11,500
117,000
..
6.50
1.60
6.42
58.4
24,000
876,000
5.33
0.450
5.25
2.1
900
4,250
7.66
41.0
7.70
2,193
600,000
32,900,000
5.18
0.330
5.09
1.35
4,950
20,000
--
4.17
0.047
4.18
0.167
2,300
7,940
4.07
0.039
4.01
0.108
600
1,620
--
4.40
0.074
4.40
0.291
1,100
4,370
__
4.18
0.048
4.38
0.277
700
4,160
3.31
0.009
3.38
0.022
1,050
2,590
4.52
0.093
4.43
0.314
1,400
4,710
4.89 V
0;203
0.285
T-
3.39
0.017
--
0.0239
3.83 yt
0.035
-
0.0494
4.80 V
0.175
0.245
--
--
6.00
1.27
--
1.78
--
6.02
1.31
1.64
6.00
1.27
1.76
-------
TABLE 5 (Continued)
SUMMARY OF REVISED PERMISSIBLE SEDIMENT CONTAMINATION CONCENTRATIONS BASED UPON REVISED KOC/KOM RELATIONSHIPS
Compound
Hater Quality
Criteria
Cm9/D
Pavlou and Weston*^
This Studyb/
Pavlou and Ueston PCC's*'
(Mg/goc)
PCC's This Study
(n.g/goc>
Acute^ Chronic-'
log kow
*oc x lฐ5
1ฐ9 *ow
Kqc x 105
Acute Chronic
Acute
Chronic
Pesticides
28 d/
Acrolein
0.40
0.00012
0.343
Aldrin
1.3
--
0.004
. 6.12
1.55
0.52
201
Chlordane
0.09
0.004
--
3.86
0.037
--
0.334
0.0148
ODD
1.8 ฑ1
6.03
1.80
5.56
0.614
325
111
--
DOE
7 1/
..
5.74
0.990
5.60
0.656
700
460
__
DDT
0.13
5.98
1.60
6.07
1.42
21
18.5
Dieldrin
2.5
0.0019
4.95
0.224
56
0.0426
Endosulfan
0.034
0.0087
3.60
0.024
0.0822
0.0210
Endri n
0.037
0.0023
--
4.44
0.097
0.358
0.0222
Heptachlor
0.053
0.0036
4.54
0.114
0.604
0.0411
Hexacyclochloro-
0.08 4/
0.047
hexane
4.00
--
0.374
I sophorone
6,450 d/
1.67
0.00037
1.70
0.001
240
677
--
TCOD
--
6.90
5.61
_ _
Toxaptene
0.035 d/ฃ/
-
3.27
0.014
0.0491
a/ As reported in Pavlou and Ueston (1984), Table 7.
![/ Log Kqw based upon geometric Mean values (Table 2). Koc determined from presented Kow values and appropriate compound class regression
from Table 3. The equation for all PAHs was used to determine the low weight PAH values,
c/ Marine (saltwater) criteria as presented in Federal Register, Vol. 45, No. 231 (1980).
7/ Mot nationally adopted water quality criteria, but rather one-half the lowest concentration at which toxic effects have been noted as reported
~ in Federal Register, Vol. 45, No. 231 (1980). Actual criteria, when established, are likely to be different,
e/ Based on class criteria for polynuclear aromatic hydrocarbons (300 ug/1) or polychlorinated biphenyls (0.014 |ig/D
T/ Based on median value of reported range.
ฃ/ Freshwater criteria, no saltwater data available.
-------
resulting large differences in KQC values indicate the need to verify
the regression equations and develop a broader base of measured K
wv
values for specific compounds. A field verification program is
recommended, together with a probabilistic uncertainty analysis, to
ensure that the revised PCC values are appropriate.
8906A
36
-------
5.0 SUMMARY AND CONCLUSIONS
A review of the scientific literature identified approximately 140
articles containing Information related to the partitioning of selected
nonpolar hydrophobic organic chemicals. Analysis of these data
disclosed predictive, chemical class-specific Koc/Kow regression
equations. Insufficient quantitative Information was found to permit
any additional physical/chemical parameters to be incorporated in these
relationships. However, analysis of these equations together with a
review of qualitative information from the literature indicates that
the percent organic content of the sediment 1s a significant
normalization parameter, and that the effects of other parameters, in
general, are minor and are compound-specif1c. Other parameters which
may have an effect on partitioning include salinity, temperature,
dissolved organic carbon, sediment particle size, and suspended
particulate matter. The use of class-specific.Koc/Kow equations is
preferred over the use of a single equation incorporating data from all
compounds where possible.
The review verifies that the derived relationships represent a usable
simplification of actual theoretical sorption mechanisms, that to date
have not been totally resolved for the nonpolar hydrophobic organics in
a sediment/water system. The chemical class-specific regression
relationships are used to recalculate previous "first-cut" safe levels
presented by Pavlou and Weston (1984). The resulting permissible
sediment contamination concentrations are less stringent than those
previously presented. This difference appears to be the direct result
of applying the new refined, chemical class-specific regression
relationships, rather than a result of other factors such as variation
in reported KQW values.
Based on the literature review and subsequent analyses and development
of predictive relationships, two areas for future investigation are
evident. Additional field and laboratory data are required to validate
and possibly refine the predictive partitioning (Koc/Kow) relation-
ships. Also, an uncertainty analysis of the predicted criteria needs
8906A
37
-------
to be performed to a greater level of detail than presented here.
Sufficient data need to be collected to develop additional
class-specific relationships and to narrow the ranges of uncertainty.
This latter effort will permit comparison with SLC values developed in
the other tasks.
8906A
38
-------
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it Boehm, Paul 0. and James G. Quinn. "Solubi lization of Hydrocarbons by
the Dissolved Organic Matter 1n Sea Water", Geochim Cosmochim Acta,
(37), pp. 2459-2477, 1973.
Briggs, G.G. "A Simple Relationship Between Soil Absorption of Organic
Chemicals and Their Octanol/Water Partition Coefficients", Proc. 7th
British Insecticide and Fungicide Conf. Vol I., The Books Co., Ltd.,
Nottingham, GB, 1973.
Brown, D.S. and E.W. Flagg. "Empirical Prediction of Organic Pollutant
Sorption 1n Natural Sediments", J. Environ. Qua!.. 10(3),
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Callahan, M.A., SUmak, M.W., Gabel, N.W. et al. Water-related Fate of
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Chlou, Cary T., Paul E. Porter and David W. Schmeddlng "Partition
Equilibria of Non1on1c Organic Compounds between Soil Organic
Matter and .Water". Environ. Sci. Techno!., 17(4), pp. 227-231, 1983.
Choi, W.W. and K.Y. Chin "Associations of Chlorinated Hydrocarbons with
Fine Particles and Hunris Substances in Surffcial Sediments",
Environ. Sc1. Techno., 10, pp. 782-786, 1976.
Dexter, R.N. and S.P. Pavlou. "Distribution of Stable Organic
Molecules 1n the Marine Environment: Physical Chemical Aspects;
Chlorinated Hydrocarbons. Mar. Chem., pp. 67-84, 1978.
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DiToro, Dominic M. "A Particle Interaction Model of Reversible Organic
Chemical Sorption", Chemosphere, In press.
DiToro, Dominic M. and Lewis M. Horzempa. "Reversible and Resistant
Components of PCB Adsorption-Desorption: Isotherms", Environ. Sci.
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DiToro, Dominic M. and Lewis M. Horzempa. "Exchangeable and
Nonexchangeable Components of PCB Absorptlon-Desorption:
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Draper, N.R. and H. Smith. Applied Regression Analysis. John Wiley and
Sons, Inc. New York, New York. 1966.
Efron, B. and R. Tibshirani "The Bootstrap Method for Assessing
Statistical Accuracy" Tech. Rept. 101, D1v. Blostat., Standford
University, Stanford, California. 1985.
Eganhouse, Robert P. and John A Calder. "The Solubility of Medium
Molecular Weight Aromatic Hydrocarbons and the Effects of
Hydrocarbon Co-Solutes and Salinity", Geochim. Cosmochim. Acta, 40,
pp. 555-561, 1976.
Gschwend, Phi Hp M. and Shian-Chee Wu. "On the Constancy of
Sediment-Water Partition Coefficients of Hydrophobic Organic
Pollutants", Environ. Sci. Techno!., 19(1), pp. 90-96, 1985.
Halfon, Efraim. "Regression Method 1n Ecotoxlcology: A Better
Formulation Using the Geometric Mean Functional Regression",
Environ. Sc1. Techno!., 19(8), pp. 797-749, 1985.
Haque, Rizwanul, D.W. Schmeddlng, and V.H. Freed. "Aqueous
Solubility, Adsorption, and Vapor Behavior of PCB Aroclor 1254."
Environ. Sc1. Techno!. 8, pp. 139-142, 1974.
8906A
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Hassett, J.P., and M.A. Anderson. "Association of Sterols and
PCBs with Dissolved Organic Matter and Effects on Their Sovent
Extraction and Adsorption." 176th National Meeting, Miami.
American Chemical Society, Environmental Chemistry Division, 1978.
Herbes, S.E. "Partitioning of Polycycllc Aromatic Hydrocarbons between
Dissolved and Particulate Phases 1n Natural Waters", Water Res. 11,
pp. 493-496, 1977.
Hiraizuml, Yasushi, M1eko Takahashl and Hajime Nlshimura. "Adsorption
of Polychlorinated B1 phenyl onto Sea Bed Sediment, Marine Plankton
and Other Adsorbing Agents", Environ. Sci. Techno!., 13(5), pp.
580-584, 1979.
Karickhoff, Samuel W. "Semi-Empirical Estimation of Sorption of
Hydrophobic Pollutants on Natural Sediments and Soils", Chemosphere
10(8), pp. 833-846, 1981.
Karickhoff, Samuel W. and David S. Brown. Determination of
Octano!/Water Distribution Coefficients. Water Solubilities, and
Sediment/Water Partition Coefficients for Hydrophobic Organic
Pollutants, EPA-600/4-79-032 NTIS, Springfield, VA, 1979.
Karickhoff, Samuel W., David S. Brown, and Trudy A. Scott. "Sorption
of Hydrophobic Pollutants on Natural Sediments", Water Res., 13,
pp. 241-248, 1979.
Karickhoff, Samuel W. and David S. Brown. "Paraquat Sorption as a
Function of Particle Size 1n Natural Sediments", J. Environ.
Qua!. 7, pp. 246-52, 1978.
8906A
41
-------
Kenaga, E.E. and C.A.I. Goring "Relationship Between Water Solubility,
Soil Sorption, Octanol-Water Partitioning, and Concentration of
Chemicals 1n Biota" 1n Aquatic Toxicology, ASTM STP 707, J.G.
Eaton, P.R. Parrish and A.C. Hendricks, Eds., American Society for
Testing and Materials, 1980, pp. 78-115.
Lyman, W. J. "Adsorption Coefficients for Soils and" Sediments" in
Handbook of Chemical Property Estimation Methods edited by W. J.
Lyman, W. F. Reehl and D. H. Rosenblatt. McGraw H111 Book Company,
New York, 1982.
Lyman, W.J., W.F. Reehl, D.H. Rosenblatt, eds. Handbook of Chemical
Property Estimation Methods - Environmental Behavior of Organic
Compounds. McGraw-Hill Book Company, New York, 960 pp., 1982.
May, W.G. The Development of an Aqueous Trace Organic Standard Reference
Material for Energy-Re la ted Applications. Investigation of the
Aqueous Solubility Behavior of Polycyclic Aromatic Hydrocarbons.
U.S. Environmental Protection Agency, Washington DC, EPA 600
7-80-031, 1980.
Meyers, P.A. and L. Quinn, "Association of Hydrocarbons and Mineral
Particles in Saline Solution", Nature, 244, pp. 23-24, 1973.
O'Brien, R.D. "Nonen^ymic Effects of Pesticides as Membranes'' in
R. Hague and V.H. Freed (eds). Environmental Dynamics of
Pesticides, New York, NY, Plenum Press, pp. 331-342, 1974.
O'Connor, Donald J. and John P. Connolly. "The Effect of Concentration
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14(10), pp. 1517-1523, 1980.
8906A
42
-------
Pavlou, S.P. "The Use of the Equilibrium Partitioning Approach In
Determining Safe Levels of Contaminants in Marine Sediments,"
Paper, 6th Pellston Conference on the Role of Sediments in
Regulating the Fate and Effects of Chemicals in Aquatic
Environments. Florissant, Colorado. August 12-17, 1984.
Pavlou, S.P. and R.N. Dexter, "Thermodynamic Aspects of Equilibrium
Sorption of Persistent Organic Molecules at the Sedlment-Seawater
Interface", 177th National Meeting, Honolulu, American Chemical
Society, Environmental Chemistry Division, 1979.
Pavlou, S.P. and D.P. Weston, Initial Evaluation of Alternatives
for Development of Sediment Related Criteria for Toxic Contaminants
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of the Sediment Water Equilibium Partitioning Approach. JRB
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Suspended Particles in Sea Water", Water Air and Soil Poll.. 8, pp.
429-440, 1977.
Prahl, F.G. and R. Carpenter "Polycyclic Aromatic Hydrocarbon Phase
Associations 1n Washington Coastal Sediment. Geochim Cosmochim
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Transformation Parameters Required 1n Non point Source Pollution
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Impact of Non Point Source Pollution, Ann Arbor Science Publishers
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43
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Steen, W.C., B.F. Parris, G.L. Baughman. "Partitioning of Selected
PCBs to Natural Sediments "Water Res., 12, pp. 655-657, 1978.
Sutton, Chris and John A. Calder "Solubility of Alkylbenzenes 1n
Distilled Water and Seawater at 25.0!c\ J. of Chem. Eng. Data,
20.(3), pp. 320-322, 1975.
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of Priority Contamlnants In Nearshore Udeflats and Deepwater of
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Kohlenassertoffen an Organlschen Was'sertrutsyoffen und anboden",
Wasser und Abwasser Fprchumj 4, pp. 107-112, 1973,
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Aroclor 1254 between Estuarlne Sediments and Water", Bull. Environ.
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by Soils and Clays", J. Agr. Food Chem.. 15(4), pp. 671-675, 1967.
8906A
44
-------
APPENDIX I
SUMMARY OF ALL LOG KQWป LOG K AND RELATED DATA
-------
SUMMARY OF ALL LOG KOU, LOG KOC AND RELATED OATA
COMPOUND YEAR AUTHOR CITATION
BEN20(A)ANTHRACENE
1985 DI TORO
SMITH ET.AL.
1984 LANDRUM ET. AL
1977 mm t SHiii
1984 MACKAY El. AL.
1984 MILLER ET. AL
1985 MILLER ET. AL.
1982 MILLS ET. AL
1975 RADDIN6 ET. AL.
1979 REMOLD ET. AL.
1964 HALTERS I LUTHY
HANSCH
NAY ET. AL.
1CRSAR
MAUCHOPE I GETZEN
YALM0U6Y I UALVANI
BEN20(A)PYROC
1985 DI TOW
SMITH ET. AL
1984 UMMM ET. AL.
1977 MACKAY I SHIU
1988 MACKAY ET. AL.
1984 MALUM I HARRISON
MISC.
L06
KOU
REP.
ERROR.
ADSORB
OESORB
5.38
6.18
ป
5.91
5.61
5.61
*
5.91
i
ADSORB
DESORB
'
5.95
6.57
6.12
5.93
5.96
6.88
5.85
5.91
6.85
6.86
6.85
5.92
i 6.19
6.15
> 5.89
5.97
6.22
L06 REP.
KOC ERROR
5.92 a
5.38 a
SOLD- REP.
B1LITY ERROR
8.8838 8.88831
.8838
3.8 a
t
18288
1.25 a
48 a
31188
1.21 a
3.6 a
18288
1.25 a
4# a
31188
1.21 a
-------
SUMIARV OF all log koh, log koc and related data
aWOJND YEAR AUTHOR CITATION MISC.
KNZOIMPVREME CONT. 19M MALUM I HARRISON
1985 KILLER ET. AL
1982 NUIS ET. AL.
ISM RmVORT I EISENRICH
LITERATURE
1975 RAMUS ET. AL. HANSCH
IMS HEADMAN ET. AL
1979 REINBOLD ET. AL. SMITH ET. AL.
BENZO(B)FLUORAN!HENE 1977 NACKAV t SH1U
1962 MILLS ET. AL.
1982 HEADMAN ET. AL. EPA
BENZOIC, H, ItPERVLENE 1977 NACKAV I SHIU
19N NACKAV ET. AL.
19SS MILLER ET. AL.
1982 MILLS ET. AL.
ISM RAPAPORT I EISENRICH
LITERATURE
GOUO(IUaUORMiTiNE 1977 NACKAV I SHIU
1982 NILLS ET. AL.
1982 READNAN ET. AL. EPA
CHRVSENE ISA! BRIGGS
NAV ET. AL.
1977 MACKAV I SHIU
19RA NACKAV ET. AL.
I964 MILLER ET. AL.
1985 MILLER ET. AL.
1982 NILLS ET. AL.
197S RA0DIN6 ET. AL. HANSCH .
1985 STAPLES ET. AL. MABEV ET. AL.
i - DATA POINTS NOT USED IN CALCULATIONS DUE TO INTERNAL INCONSISTENCIES
LOG REP. L06 REP. SOLU- REP. t OC REP.
KOU ERROR KOC ERROR BILITY ERROR OR ON ERROR
KD/KP
REP.
ERROR
6.84
5.88
5.99
6.88
6.42
6.58
6.84
6.68
6.84
6.77
7.18
7.88
7.85
7.18
6.85
6.84
6.11
5.79
5.68
5.61
7.45 8.11
6.77 t.25
6.73 8.18
6.59 8.86
8.88378
8.88172
8.845 8.8812
8.88826 8.88881
8.88826
8.8828 8.88883
8.8815
8.8118
8.882
8.882
8.8812
8.818
8.8881
8.8882
8.86
8.6
1.4
3.8
17888 5888
35888 27888
76888 24888
-------
SUMMARY OF ALL LOG KOU, LOG HOC AND RELATED DATA
COMPOUND YEAR AUTHOR CITATION
NISC
CHRYSENE CONT. 1964 VERSAR
1964 URLTERS I LUTHY
VERSAR
UAUCHOPE I GETZEN
YALKOUSKY I UALUANI
DIBEN20 (A, H) ANTHRACENE 1966 NEONS ET. AL
-p.
1964 MILLER I MASIK
1962 MILLS ET. AL.
1964 VERSAR
1964 HALTERS I LUTHY
FLUORANTHEiC 1961 BRIGGS
NAY ET. AL
1977 MACKAY I SHIU
1966 MACKAY ET. AL.
1965 MILLER ET. AL.
1962 MILLS ET. AL.
1962 HEADMAN ET. AL. EPA
1964 VERSAR
1964 URLTERS I LUTHY
VERSAR
UAUCHOPE i GETZEN
YALKOUSKY I VALUANI
LOG
KOU
REP.
ERROR
LOG
KOC
REP.
ERROR
SOLU- REP.
B1LITY ERROR
t OC REP.
OR ON ERROR
KD/KP
REP.
ERROR
5.32
5.77
t
t
6.66327
6.66643
6.662
6.66b
5.91
6.56
6.14 *6.23
t 6.66249
6.6661
1.21
26461
ฃ.23
2.67
34929
5.91
2.26
I 16361
6.42
6.72
16662
6.67
6.15
1759
i 6.36
6.11
2566
6.46
t
6.46
14497
6.43
6.95
25362
6.49
6.66
26192
t 5.75
1.31
7345
6.47
1.66
55697
6.38
1.67
39669
5.91
2.36
19254
i 6.43
1.46
39646
6.6665
6.66
i
5.97
5.92
7.19
t 6.66249
6.29
t
t 6.266
6.662
i 6.266
6.626
t
5.29
6.26
5.22
t
ง
5.53
4.96
5.33
5.31
6.199
6.611
6.26
*
t
* 6.265
5.22
i
t
ซ
-------
SUMMARY OF ALL LOG KOM, LOG KOC AND RELATED DATA
COraJND YEAR AUTHOR CITATION-
IWEND(1,2,31C,dIpYRENE~ 1962 MILLS ET.~AL.
PYREIE 1981 BRIGGS
MAY ET. AL.
1985 II TORO KARICKHOFF ET. AL.
1981 KARICKHOFF
1985 KARICKHOFF ft MORRIS
1979 KARICKHOFF ET. AL
1977 NACKAY I SHIU
1969 NACKAY ET. AL.
LITERATURE
I9M MEANS ET. AL.
i - DATA POINTS NOT USED IN CALCULATIONS DUE TO INTERNAL INCONSISTENCIES
MISC. L06 REP.
ADSORB
OESORB
7.7B
5. IS
5.12
4.88
5.19 iM
L06 REP.
SOLU-
REP.
tOC REP.
KD/KP REP.
koc__error_
BILITY
ERROR
OR ON ERROR
ERROR
8.12
8.132
8.881
5.88 *
2.34 a
2828 a 1.87
4.54 a
2.34 a
816 a 1.53
4.83
4.98
3.84
2988
4.94
1.48
1388
4.84
8.88
9.4
4.88
2.78
2188
S.I1
2.34
3888
5.88
2.89
3688
5.88
3.29
3688
4.51
8.13
42
4.%
3.27
3888
5.11
1.98
2588
5.85
1.34
1588
5.88
1.28
1488
5.88
8.57
66
5.85
2.92
3288
5.88
1.99
2388
5.85
8.135
Q. 135
8.885
2.26
2588
4.68
8.135
8.813
1.21
768
4.71
2.87
1865
4.78
2.28
1155
4.93
8.72
614
4.83
8.15
181
4.81
8.11
71
4.76
8.48
277
4.92
.95
763
4.88
8.66
584
4.78
1.38
723
4.77
1.68
1119
4.66
1.67
686
-------
SUMMARY OF ALL LOG KOU, L06 KOC AND RELATED DATA
GONPOUM) YEAR AUTHOR CITATION
NISC.
PYRENE CONT. 1966 MEANS ET. AL.
1962 MILLS ET. AL
1964 RAPAPORT I EISENR1GH
1964 WALTERS I LUTHV
SCHUARZ
VERSAR
UAUCHOPE I GET ZEN
tHIIIttlMllllltlltlllllllMMIHHIIIIimillllltlltllllllllllllllllHIIIHMHItlH
ACENAPHDQE ISM BANERJEE ET. AL.
1977 NACKAV I SH1U
1966 MCKAY RUBRA I 5HIU
1962 MILLS ET. AL.
1965 STAPLES ET. AL. HABEY ET. AL.
1961 VE1TH ET. AL.
1964 HALTERS I LUTHV
ARNOLD ET. AL.
VERSAR
UAUCHOPE I GETZEM
VALMOUSKY ft UALVAN!
ACENAPHTHVLENE 1962 MILLS ET. AL.
1964 HALTERS ft LUTHV
VERSAR
YALKOUSKY ft VALVANI
ANTMMBE 1961 BRIGGS
NAY ET. AL.
1961 KAR1CHHDFF
1979 HARICKHOFF ET. AL.
19M UMDRUM ET. AL.
BRUEGGEHAN ET. AL.'
1977 NACKAY ft SHIU
19B6 NACKAV BOBNA ft SHIU
LITERATURE
L06 REP. L06 REP. SQLlh REP. % OC REP. KD/KP REP.
KOU ERROR KOC ERROR BIL1TV ERROR OR OM ERROR ERROR
4.64 ft
2.36 1643
4.63
1.46 , 994
5.36
ป
4.%
6.133
6.633
6.129
6.14
6.146
TtTTTTTTtTTTI
ฆ
IIHIIIIItltl IIIIIIIHHHH
3.92 6.66
ft
7.37
6.3
ft
ft
3.93
6.614
ft
4.15
ft
4.32
ft
ป
3.42
ft
4.49
ft
4.45
ft
3.92
ft
4.16
6.57
ft
^3.47
ft
3.47
ft
c
166
ft
4.63
ft
4.66
ft
ซ
16.1
6.45
ft
t
3.93
ft
3.94
ft
6.67
ft
6.6446
6.6662
ft
4.54
4.26
ft
4.34
4.42
ft
4.15
ft
6.6724
ft
*
6.673
6.6665
ft
4.73
ft
4.15
ft
-------
SUMMARY OF PLL L06 ROW, LOG HOC MD RELATED DATA
COMPOUND YEAR AUTHOR CITATION MISC.
ANTHRMBC CONT. 1962 MILLS ET. AL.
19M AflPAPORT I EISENRICH
1913 SHRNN ET. flL.
136% IMLTEK t LUTHY
ISHOU
1913 HAS1K ET. AL.
19M UIJAYARATNE ft IONS
SCHUARZ
VERSAR
HAUCHOPE ft GETZEN
FLUORENE
19A1 BRIGGS
MM ET. AL.
1977 MACKAY 1 SHIU
19N MACKAY ET. tL.
LITERATURE
1902 MILLS ET. AL.
19M RflPAPORT 1 EISENRICH
LITERATURE
19M VERSAR
19M HALTERS ft LUTHY
VERSAR
HAUCHOPE ft GETZEN
YALKQUSKY ft VALVANl
1913 UASIK ET. AL.
NAPHTHALENE 19B1 BRIG68
MAY ET. AL.
1977 CHIOU ET. AL CRC
HRNSCH ft FUJITA
ISM GARST ft HILSON
t - DATA POINTS NOT USED IN CALCULATIONS DUE TO INTERNAL INCONSISTENCIES
LOG
KOU
REP.
ERROR
L06
KOC
REP.
ERROR
SOLU-
BILITY
REP.
ERROR
t OC REP.
OR OM ERROR
KD/KP
REP.
.ERROR
5.71
4.M
2.62
.ae
i.ฎ3
iK
l.ซ3
8.12
0.12
.51
.63ft .M75
-75
.Ml
.7S
.75
.M6 ซ.MK
1.5
l.fifi
1.9R
1.56
28.4
31.69
3ป
.M5
I.M4
1.9* 1.157
1.9A
1.9*
.M
-------
SUMMARY OF ALL LOG KOU, LOG HOC AND RELATED DATA
CONUM) YEAR AUTHOR CITATION
NAPHTHALENE CQNT. 1984 6ARST I HlLSON~
1981 KARICKHOFF
1979 KARICXHOFF Ef. AL
1978 KRISHNANURTHY I UASIK
1977 NACKAY I SHIU
19M MCKAY ET. AL.
1984 MILLER ET. AL
1912 MILLS ET. AL.
1984 IMMMRT < EISENRICH
1985 SALEH
1984 VERSA!
1984 HALTERS I LUTHV
ARNOLD ET. AL.
EGANHOUSE I CALOER
ISHOH
CfliiAfll
uUVSffi
VERSAR
HAUCHOPE ft GETZEN
1983 UASIK ET. AL.
1984 NIJHVARATNE ft (CANS
PICNANTHRENE 1981 NIGGS
NAY ET. AL
1981 KARICKHOFF
1979 KARICKHOFF ET. AL
1984 LANDRUM ET. AL.
1977 NACKflY ft SHIU
1988 MCKAY ET. AL
LITERATURE
1982 MILLS ET. AL
1984 HALTERS I LUTHV
EGAMOJSE ft CALOER
ISHOH
SCHUARZ
VERSAR
NISC.
REP. L06
ERROR KOC
8.82
-------
SUMMARY OF ALL LOG KOU, LOG HOC AND RELATED DATA
COMOJND
YEAR AUTHOR
PHEHRNTMRENE CONT.
AROCLOR 1816
AROCLOR 1221
AROGLOR 1232
ARQCLQR 1242
AROCLOR 1248
AROQJM 1254
CITATION
HI SC.
L06 REP. LOB REP. SOLU- REP. t OC REP.
KOU ERROR KOC ERROR B1L1TY ERROR OR OH ERROR
KD/KP
REP.
ERROR
1984 HALTERS I LUTHV
1963 HASIK ET. flL.
1912 KILLS ET. RL
UMJCHOPE I GETZEN
1912 HILLS ET. OL.
1982 HILLS ET. RL.
1962 HOLS ET. RL
1962 HILLS ET. RL
1965 DI TWO
EPA
UILS1SH ET. RL.
1963 HORZEMPA I DI TOW
ADSORB
DESORB
ADSORB
DESORB
1962 HILLS ET. AL.
1963 UEBER ET. AL.
1.166
* 1.66 8.81
ItltttllH)
TTTtTTtTTTTT
f ffffTIIfTtTTTt
4.31 - 5.46a*
6.42
2.76 - 4.88a*
15
3.16 - 4.46a*
1.4S
4.61 - 3.66a*
1.1-6.3
6.66
6.654
6.63
ป 4.96 a
6.6 a
578 a
4.93 k
i
3.66 a
2558a
161 a
6.6 a
5.97 a
* 1.69 a
3.68 a
1.48 a
3.83 a
6.4 a(0H)
15.7 a
* 2.65 a
5.4 a (OH)
14.1 a
2.51 a
4.9 a (OH)
9.3 a
2.63 a
4.6 a (OH)
11.3 a
* 3.26 a
1.2 a(0H)
12.6 a
2.57 a
4.9 a((MI
18.6 a
4.65 a
6.4 a(0H)
26.6 a
3.66 a
5.4 a (OH)
35.2 a
2.69 a
4.9 <(ON)
22.86 a
3.64 a
4.6 a (ON)
29.3 a
* 3.29 a
1.2 a (OH)
13.6 a
2.71 a
4.9 a (OH)
14.6 a
6.66 *
6.61 - 6.66
6.17
6.11
1626
8.68
1654
6.16
1.82
2648A
5.89
6.47
3626
5.84
ซ
3.45
23768
* 5.64
1.85
1152
a - DATA POINTS NOT USED IN CflLCULPTlONS DUE TO INTERNAL INCONSISTENCIES
-------
SUMMARY OF ALL L06 KOU, LOG KOC AW DELATED DATA
COMPOUND YEAR AUTHOR CITATION
AROCLOR 1254 CONT.
1943 UEBER ET. AL.
MISC.
AROCLOR 1261
1962 NILLS ET. AL.
ACROLEIN
1968 KENAGA
1962 MILLS ET. AL.
1961 VEITH ET. flL
ALDRIN
1974 BIGGAR 6 RIGGS
19B1 BRIGG6
1964 GEVER ET. AL.
1966 KENAGA 1 GORING
1962 NILLS ET. AL
1965 STAPLES ET. AL.
1964 VERSAR
HANAKER 1 THONPSON
NABEY ET. AL.
CHLORDANE
1961 KENAGA 1 G0RIN6
1962 NILLS ET. AL
1965 STAPLES ET. AL.
SANBORN ET. AL
RAO 1 DAVIDSON
NABEY ET. AL.
ODD
1974 BIGGAR 1 RIG6S
1961 NCDUFFIE
1962 NILLS ET. AL.
1966 VEITH ET. AL.
1964 VERSAR
RAO ซ DAVIDSON
DOE 1974 DIGGAR I RIGGS
LOG REP. L06 REP. SOLD- REP. t OC REP. KD/KP REP.
KOW ERROR HOC ERROR B1LITY ERROR OR ON ERROR WR
6.31
2.61
53543
6.66 ป
1.96
156666
6.29 i
1.64
35734
7.19 ป
6.67
162641
6.34
ซ 3.51
76759
7.21 t
6.67
i 11143
6.16
9.25
116336
5.97 ง
# 9.46
66961
6.15
ซ 8.36
117693
6.66
6.6627
TTtTTTTTTTTT
~~~~~~+~+~~ซ
6.71
i
6.96
162666
ป
6.613
t
7.46
4.69
6.187
5.66
2.61
6.617 - 6.18
5.36
5.66 ป
6.166
4.66 *
5.5
1.656
t
2.76
6.666 - 1.65
3.32
5.46
5.15
6.656
*
6.665
4.73
6.66
6.62 - 6.1
5.66
ซ
6.62
5.99
5.38 *
6.665
i
t
6.116
1
-------
SUMMARY OF ALL LOG KQU, L06 KQC AND RELATED DATA
coraw
DOE CDNT.
YEAR AUTHOR
1977 CHIOU ET. AL.
1961 MCDUFFIE
1982 MIUS ET. AL
1963 SWIM ET. AL
1966 VEI1H ET. AL
1964 VERSAR
CITATION
0*DRIEN
RAO I DAVIDSON
DOT
1974 6IGGAR I R1GGS
1981 BRIGGS
19DS CHIOU
1961 CHIOU ET. AL.
1977 CHIOU ET. AL.
196S DI TORO
1961 ELLENGHAUGEN ET. AL.
1964 6ERSTL ft HINGEL6RIN
DIGGAR ET. AL
DOUNANET. AL
PICER ET. AL
SHARON ET. AL.
1964 GEVER ET. AL
1966 KENAGA
1966 KENA6A ft G0RIN6
1964 LANDRUM ET. AL.
I9B6 NACKAY ET. AL.
1962 MILLS ET. AL.
1976 PLATFORD
1964 RAPAPORT ft EISENRICH
HAMMER ft THOMPSON
RAO ft DAVIDSON
LITERATURE
a - DATA POINTS NOT USED IN CALCULATIONS DUE TO INTERNAL INCONSISTENCIES
MISC.
L06
REP.
L06 DEP.
SOLO- REP.
* OC REP.
KD/KP
KQU
ERROR.
HOC ERROR
_BIUTY ERROR _.OR ON ERROR
6.946
5.69
5.89
5.76
6.6612 - 6.14
4.66
5.63
6.666
6.6611
5.77
5.69
5.17
6.61
6.6617
5.96
5.36
6.6639
ป.
6.36
6.19
5.14
6.6634
6.6631
6.67 a
6.64 a
9986 a
4.62 a
6.64 a
351 a
4.15 a
1.45 a
263 a
6.19
5.94
5.44
6.24
6.6645
6.45 (OH)
4546
5.95
1.25 (DM)
6468
5.76
2.82 (ON)
8121
5.46
5.82 (ON)
9637
5.67
7.95 (ON)
34564
5.77
4.35 ((H)
15612
6.26
ป
6.36
5.57
5.36
5.44
c
7.48
6.6631
4.60
- 6.61
6.662 - 6.665
5.57
5.38
ซ
5.1
2
6.16
6.11
ป
REP.
ERROR
-------
SUMMARY OF ALL L06 KOU, L06 KOC AND RELATED DATA
COMPOUND
YEAR AUTHOR
CITATION
DOT CQNT.
DIELDRIN
ENDOGULFAN
ENDRIN
rv>
KPTADLOR
1965 STAPLES ET. flL.
1963 9MANN ET. flL.
19M VEITH ET. AL
1964 VERSA*
NABEY ET. AL.
1974 BIGGAR I RIGGS
1961 MIGGS
1965 B1 TORO
1962 MILLS ET. AL
196S STAPLES ET. AL
SHARON ET. AL.
RAO ซ DAVIDSON
MAGEY ET. AL.
1962 MILLS ET. AL
1974 BIGGAR I RIGGS
I96S DI TORO
1962 MILLS ET. AL
1964 RAPAPORT I EISENRICH
1965 STAPLES ET. AL
196t VEITH ET. AL.
SHARON ET. AL.
RAO I DAVIDSON
LITERATURE
NABEY ET. AL.
1974 BIGGAR I RIGGS
1961 KOUFFIE
1962 MILLS ET. AL.
1965 STAPLES ET. AL.
RAO I DAVIDSON
MABEY ET. AL
tEPTACHLOR EPOXIDE
1962 MILLS ET. AL.
HEXACVDLOOLOflOHEXANE 1962 RILLS ET. AL.
ISOPMXOC
1962 MILLS ET. AL.
1961 VEITH ET. AL.
MISC. L06 REP.
KOU ERROR
t 6.91
5.98
6.19
ซ
t
6.26
ป
3.69
I
3.66
5.66
3.26
4.46
4.56
3.54
5.34
5.27
3.87
4.46
ซ
>
4.66
1.76
1.67
1.73
i
L06 REP.
KOC ERROR
6.66
4.64
5.16
5.58
4.11
3.52
3.86
3.23
4.66
SOLU- REP.
BILITY ERROR
6.6655
6.62
6.6623
6.6617
6.622
6.6271
6.186 - 6.266
6.62
6.166 - 6.266
6.624
6.226
6.25
6.636
6.656 - 6.18
6.18
6.266 - 6.356
6.76 - 21.3
12666
14566
t OC REP.
OR ON ERROR
KD/KP REP.
ERROR
1.45
1.45
48.3
169
-------
SUMMARY OF ALL L06 KOU, LOG KOC AND RELATED DATA
COMPOUM) YEAR AUTHOR CITATION MISC. L06 REP. LOG REP. SOLD- REP. t OC REP. KD/KP REP.
KOU ERROR KOC ERROR BILITY ERflOR OR ON ERROR ERROR
TCDO
1961 DRIG6S
6.91
i ป
1962 MILLS ET. AL.
a. Mi?
TOXAPHENE
ISM KENAGA 1 GORING
SANBORN ET. AL.
ซ
1962 MILLS ET. AL.
ซ
ซ 6.7 - 3.1 ง
RAO 1 DAVIDSON
3.23
* ป
1965 STAPLES ET. AL.
HADEY ET. AL.
3.36
t 3.M
t 6.5#
-
* ป ป
-------
APPENDIX IV (Continued)
Dr. Far!da Saleh
Associate Professor
Institute of Applied Sciences
North Texas State University
P.O* Box 13078
Denton, Texas 76203
8906A
3
-------
APPENDIX II
PLOTS OF K /K REGRESSION ANALYSES
oc ow
-------
ALL COMPOUNDS
I
7t Tt
A ' 5.1
1.2
ฃ.4 3.6 4.8
GEO. MEAN LOG HOW
21 ciw p 1 ottvd* RvQr#ssion iit At ift ics of GKOC or, GROW:
Correlation .87403 R Squared .76393 S.E. of E*t .68913 Sig. .OO
Intercept . 41891 ( .56402) Slopซ . 88086 ( ".11234
FIGURE 1 PLOT OF GEOMETRIC MEAN LOG K VS. GEOMETRIC MEAN LOG
ow
K FOR ALL COMPOUNDS
oc
2
-------
ALL PAHS
7.2+
6.64-
5. 44-
4.84-
4. ฃ4-
3.64-
3.25
3.9
5. 32^
5. ฃ 5. 85
' 6.17^
4. 55
GEO. MEAN LOG KOW
6. 5
10 cum plotted. Regression statistics of GKOG on 6K0W:
Correlation .94519 R Squared .89339 S. E. of Est .37906 Sig. .0000
Intercept
-------
LOW WEIGHT PAHS
5. 254-
4.754-
4. 54-
4. 254-
3. 754-
3.25
GEO. MEAN LOG KOW
Co^ITat?intt#a0415QS"SmlOn t*timtie* of **OC on GKOWs
CoKTVlst ion .S0413 R SouarBd tict* e c _ป ,
Intซpcปot (S. E. > .7sl=?T , r*? -f7'46 S' = - -'9?
S1odซ(S. E. ) .81027 ( . 42771 >J
FIGURE 3 PLOT OF GEOMETRIC MEAN LOG Knw VS. GEOMETRIC MEAN LOG K
uw oc
LOW WEIGHT PAHS
4
-------
HIGH WEIGHT PAHS
6. 69+
5. 95+
5. 25+
5. 25
5.6 5.95 6.3
GEO. MEAN LOG KOU
6. 65
6 case* plotted. Regression statistics of 6K0C on GKOW:
Correlation .83711 R Squared .70076 S.E. of Est .44535 Sig. .0376
Intercept (S. E. ) -1. 13018 ( 2.30094) Slooe(S.E. > 1. 22874 ( .40147)
FIGURE 4 PLOT OF GEOMETRIC MEAN LOG KQW VS. GEOMETRIC MEAN LOG
K FOR HIGH WEIGHT PAHS
5
-------
PESTICIDES
6
E
0
M
E
A
N
L
~
G
K
0
C
5.6-
4. 8--
3.2--
1.6--
ฃ. 4--
3.6 4.8
GEO. MEAN LOG KOU
10 cuts plottsd. Regression statistics of GKOC on GKOWj
Correlation .86868 R Squared .73461 S.E. of Est .74ฃ35 Sig. .001 i
Intercept (S. E. ) .80243< .68913) Slop*(S. E. ) . 71715 ( .14453)
FIGURE 5 PLOT OF GEOMETRIC MEAN LOG K VS. GEOMETRIC MEAN LOG K
ow oc
FOR PESTICIDES
6
-------
PLOT OF KQC WITH KOW
ฃ3 C5i5ss ,olc,jt"tecป ^sjressiori งt At ist ics ฉ^ KGC oki K3ws
Cwr"re-*tior, . 81SฃS R Scuarec .ฃฃ252 S.E. cf Est . SSฃฃฃ Si*.
I
-------
-o
O
TJ
p>
-o
O
0)
CO *
i.
4-ป
o.
3= oo
<
r-
*
3 _
CT A
LU W
C
ฐ ft
s-
ซ
o>
-o
c
3
O
a.
ง
CM
o
p-
Id
~ป
3
-a
U)
0)
cc
<4
o
+->
PI
1.0
0.5
0
.05
ฆ1.0
I I
ฃ oo^
3
CD
U_
8
-------
-------
to
10
-------
APPENDIX III
BIBLIOGRAPHY
-------
APPENDIX III - BIBLIOGRAPHY
Arbuckle, William Brian. "Estimating Activity Coefficients for Use
in Calculating Environmental Parameters", Environ. Sri
17(9), pp. 537-542, 1983. ฐU-'
Bagg, John, J. Oavid Smith and William A. Maher. "Distribution of
Polycyclic Aromatic Hydrocarbons in Sediments from Estuaries of
Southeastern Australia", Australia J. Mar. Freshwater p.. 32
pp. 65-73, 1981. *
Bailey, George W. and Joe L. White. "Factors Influencing the
Adsorption, Desorptlon and Movement of Pesticides in Soil" Residue
Rev. 32, pp. 29-92. '
Baker, Robert A. (ed). Contaminants and Sediments: yoltmpc t and 2
Ann Arbor Science Publishers, Inc., Ann Arbor, 1980.
Banerjee, Sujlt, Samuel H. VaJkowsfcy and Shrl c. Valvanl. "Water
Solubility and Octanol/Water Partition Coefficients of Organlcs
Limitations of the Solubility-Partition Coefficient Correlation"
Environ. Sci. Technol., 14(10), pp. 1227-1229, 1980.
Bartell, S.M., R.H. Gardner and R.V. O'Neill. "The Fates of Aroroatics
Model (Foam): Description, Application, and Analysis" Eeoinn^i
Modelling, 22, pp. 109-121, 1984. '
Bates, Timothy S., Susan E. Hamilton and Joel D. CI 1ne. "Vertical
Transport and Sedimentation of Hydrocarbons in the Central Main
Basin of Puget Sound, Washington", Environ. Sc1. Techno l i8{5)
pp. 299-30S, 1984. "
Blggar, J.U. and R.L. Rlggs. "Apparent Solubility of Organochlorlne
Insecticides In Water at Various Temperatures", HUgordla. 4?(in)
pp. 383-391, 1974.
8906A
1
-------
Bjorseth, Alf, Jon Knutzen and Jens Skei. "Determination of Polycyclic
Aromatic Hydrocarbons in Sediments and Mussels from Saudafjord, W.
Norway, by Glass Capillary Gas Chromatography", The Science of the
Total Environment, 13, pp. 71-86, 1979.
Boehm,. Paul D. and James G. Quinn. "Solubilization of Hydrocarbons by
the Dissolved Organic Matter in Sea Water", Geochim Cosmochim Acta,
(37), pp. 2459-2477, 1973.
Boucher, Francis R. and G. Fred Lee. "Adsorption of Lindane and
Dleldrin Pesticides on Unconsolidated Aquifer Sands", Environ. Sci.
Techno!., 6(6), pp. 538-543, 1972.
Bowman, J.T. and W.W. Sans. "Determination of Octanol-Water
Partitioning Coefficients of 61 Organophosphorous and Carbamate
Insecticides and their Relationship to Respective Water Solubility
Values", J. Environ. Sc1. Health, B18(6), pp. 667-683, 1983.
Brlggs, G.G. "A Simple Relationship.Between Soil Absorption of Organic
Chemicals and Their Octanol/Water Partition Coefficients", Proc. 7th
British Insecticide and Fungicide Conf. Vol I., The Books Co., Ltd.,
Nottingham, GB, .1973.
Brlggs, G.G. "Molecular Structure of Herbicides and their Sorption by
Soils", Nature, 223, p. 1288, 1969.
Brlggs, G.G. "Theoretical and Experimental Relationships
between Soil Adsorption, Octanol-Water Partition Coefficients,
Water Solubilities, B1oconcentrat1on Factors and the Parachor", J.
Agrlc. Food Chem., 29(5), pp. 1050-1059, 1981.
Brown, D.S. and E.W. Flagg. "Empirical Prediction of Organic Pollutant
Sorption in Natural Sediments", J. Environ. Qua!.. 10(3),
pp. 382-386, 1981.
8906A
2
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8906A
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APPENDIX IV
NAMES AND ADDRESSES OF INDIVIDUALS
RESPONDING TO REQUEST FOR INFORMATION
-------
APPENDIX IV
NAMES ANO ADDRESSES OF
TO REQUEST Ft
Dr. David S. Brown
Soil Scientist
U.S. Environmental Protection
Agency
Environmental Research Laboratory
Athens, Georgia 30613
Dr. Gary T. Chiou
Research Hydrologlst
U.S. Department of the Interior
Geological Survey
Box 25046 M.S. 407
Denver Federal Center
Denver, Colorado 80225
Dr. Dominic M. DIToro
Professor
Environmental Engineering and
Science Program
Manhattan College
Manhattan College Parkway
Bronx, New York 10471
Dr. Richard W. Gossett
Professor
Southern California Coastal
Water Research Project
646 West Pacific Coast Highway
Long Beach, California 90806
INDIVIDUALS RESPONDING
I INFORMATION
Dr. Lewis Horzempa
Consulting Engineer
Envlrosphere Company
2 World Trade Center
New York, New York 10048
Dr. Donald MacKay
Professor
Institute for Environmental
Studies
University of Toronto
Toronto, Canada M5S 1A4
Dr. Jay C. Means
Professor
Center for Environmental and
Estuarine Studies
Chesapeake Biological Laboratory
University of Maryland
Solomons, Maryland 20688
Dr. R.F. Platford
Environment Canada
Canada Center for Inland Waters
P.O. Box 5050
Burlington, Ontario L7R 4A6
8906A
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