UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
OFFICE OF
RESEARCH AND DEVELOPMENT
January 31, 1983
SUBJECT: ERL-jNarragansett Report on Structure-Activity Models
for Marine Medium
FROM : Rizwanul Haque, Tc
Toxics and Pesticfde^ DiviMon (RD-682)
TO : See Addressees
Attached please find a report describing structure-activity models
related to estimating bioconcentration of chemicals in marine systems.
Further information on this project may be obtained from Dr. William Brungs
at ERL-Narragansett.
Attachment
Addressees:
M. Williams
E. LaPointe
R. Brink
J. Gilford
cc: C. Hendricks
W. Brungs
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DATF- January 11, 1933
uaJc;- ^rj^l-r; ^n=nrac :.c. ...,.•; - -= :^--: -'.on iJ 3 .jcouc^n^rctio
With Structure Activity Models
'^/'^U^v^
FROr' Tudor T. Davis?, Dle
3ffica of Znvironiii-iiil Processes and Effects P-esearca
The si'bjoc" r-a^o-c 1.3 Output 1, Project 54 (Structure-Activity) in
our FY-8 3 Work ?ians. Ic is cae first oi" several outputs which will cesc
^.L'. ^oceta rr.r rr'=o^.:t.in-- Bnv^ronniental fata, bioaccuTQulation and toxicity
of caemical^ co marine organisn.
The resuics of this initial research suggest that freshwater models
can be used to predict the bioconcentration factor (BCF) of a. cheinicai in
xarine species. This inforuiation should greatly simplify the application
of SAR analysis by OTS in their evaluation of new chenicals under the
Premanufacture Notification Program.
Please see that the report receives appropriate distribution. r-/s
would be nappy co discuss these research results in greater def.il. or
wortc with your staff on the application of these results.
R'/iL: 1ml
£nclosure
cc: Gil Veith
=or-n 1320^ (S«v. 3-76)
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Environmental Protection
Agencv
Research and
Development
Estimation of Biocoacentracion in Marine Species
with Structure Activity Models.
Prepared for
Office of Pesticides and Toxic Substances
Prepared by
Environmental Research
Laboratory
Narragansett Rl 02880
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Estimation of Bioconcentration in
Marine Species with Structure Activity Models
Gerald £. Zaroogian
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ABSTRACT
Structure-activity models which were developed to predict bioconcen-
tration of organic chemicals in freshwater fish were tested for use with
several marine species (Cypriaodon variagatus, Lagodon rhomboides, Crassostrea
virginica, Mytilus edulis). Significant linear relationships existed
between bioconcentration factor (BCF) for each marine species tesced and
log ? (octanol/water particion coefficient). The results suggested that
freshwater models can be used to predict the BCF of a chemical in marine
species, since the slopes of the freshwater models were within the 95%
confidence intervals for marine models. Freshwater models were used to
calculate BCF values for each of the marine species. The calculated BCF
values were compared to the measured BCF values for each marine species
and those measured for the fathead minnow (Pimephales promelas). The data
indicaced that the log BCF can be estimated for the marine species with
freshwater models to within an order or magnitude for a minimum of 71% of
the chemicals having a range of 3,000,000 in the partition coefficient.
It appeared chat freshwater models offer the same precision in estimation
of BCF values for marine species as for freshwater fish. Good agreement
existed between measured and calculated BCF values for both _L. rhomboides
and M. edulis; whereas, the data for C_. virginica were more variable and
those for £. variegatus were the most variable. Tests indicated that,
generally, calculated BCF values are overestimates of the measured BCF
values. Significant linear relationships existed between measured BCF
values for £. promelas and each marine species except M. edulis.
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INTRODUCTION
Structure-activity relationship (SAR) analysis is a critical component
of EPA's evaluation of new chemicals under Section 5, of the Toxic Substances
Control Act (TSCA/. 1=. i;79 I?A inic^ziad the premanufacture notification
program (PMN) undar the Office of Toxic Substances. Most PMN's are accompanied
by few if any test daca en health and environmental effaces. As a result,
SAR analyses are employed to set priorities among PMN's in terras of potential
hazard and to justify additional testing of new chemicals. SAR analysis
may also be used to support testing requirements or to guide in the selection
of most appropriate laboratory tests for existing chemicals under Section
4 of TSCA.
The use of the bioconcentration factor (BCF) as an estimate of the
bioaccumulation potential of organic chemicals in aquatic organisms has
become increasingly importanc in hazard evaluation programs since Che
discovery that some chemicals such as DDE and ?C3s posed a greater threat
to consumers of fish and molluscs than Co the populations which accumulated
them (Veith, et_ «a. , 1980).
The BCF is a constant which relates the residue of a chemical in
aquatic species to the concentration of chemical in water to which the
species is exposed (Pringle e_£_ al_. , 1968; Branson e_t_ al_. , 1975), and is
calculated by dividing the mean concentration of the chemical in the aquatic
species by the concentration in the water. The concentration in water
during bioconcentration studies is selected to be less than that which
would adversely affect the test species. Since uptake of chemicals is time-
dependent, BCFs are calculated only when the tissue residue no longer
changes with continued exposure (steady state). Although the laboratory
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2
derived SCF may not account entirely for the residues observed-in some-
molluscs and fish which also accumulate chemicals rrom cne food chain
(Bahner et_ al^ , 1977; Weiniger, 1978), the BCF does provide a conservative
prediction, of residues in fish and molluscs, as veil as a seans for rrr.lxLng
the bioaccumulation potential of organic chemicals in cne environment
(Veith et_ al_. , 1979).
In considering Che problem of evaluating the behavior of industrial
chemicals in the aquatic environment, a rapid means of estimating bio-
accumulation potential through the use of predictive sr.ort-cerm meabure:r.enc3
are required. Neely et_ al^ (197&) suggested that the n-ocranol/water
partition coefficient (P) be used as an estimator of the BCF. The measure-
ment and estimation with (?) of BCF in freshwater fish have been reported
by Veith et_ al^. (1979, 1980). They found that the structure-activity
correlation between the 3C7 and (?) of individual chsaicals is cur^zari^ed
by the equation:
log 3C7 = 0.85 log ? - C.7C.
This_ equation was intended as an estimation of the bioconcentratioa potential
within an order of magnitude for screening purposes. The accuracy of
predictions made with this model is limited by the inherent variances in
the BCF test and the variations in uptake by the species tested.
Chiou et^ al. (1977) and Ernst (1977) have also reported that the BCF
can be correlated with the water solubility of the chemical. Hansch et
al. (1968) were able to demonstrate the quantitative relationships between
the water solubility of chemicals and their n-octanol/water partition
coefficients on the basis of factors which control partitioning of a chemical
between water and lip id. phase.
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No marine studies concerned vith structure-activity relationships
as predictors or bioccucenuracion aave been reported. Therefore, this
study was initiated to determine whether the models established for predict-
ing 3CF in freshwater species can be used with marine species.
MATERIALS AND METHODS
The models used to predict bioconcentration factors (BCF) for fresh-
water fish and tested in this study for use in predicting BCF for marine
species are shown below as equations 1, 2 and 3.
(1) log BCF - 0.85 log P - 0.70 Veith et_ al_. (1979)
Muscle tissue of fathead minnow (Pimephales promelas) was used for
chemical analysis.
(2) log BCF - 0.76 log ? - 0.23 Veith et_ aJ^. (1980)
Whole fish, bluegill (Lapomis inachrochirus) was used for chemical
analysis.
(3) log BCF = 3.41 - 0.58 log S Chiou et_ a^. (1977)
S = water solubility in yM/L
Test fish, rainbow trout (Salaio gairdneri) was used for chemical
analysis.
Table 1 contains the reported values for log BCF, exposure concentra-
tion, time of treatment and their sources for the chemicals and species
used in this study.
Table 2 contains the reported va'lues of log P and their sources for
the chemicals used in this study.
Table 3 contains the reported values for water solubility
and their sources for the chemicals used in this study.
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The data outlined in Tables 1,2,3 were used for comparisons and aocel
developmental not aajusted on the basis of percent iipid. These same
data were used to test the validity of freshwater models for use in estimating
the BCF for marine species. Linear regression models were fit to each
marine species data sec and 95;« confidence intervals about the slopes were
computed. These models and confidence intervals were used to compare
freshwater models witn marine models.
RESULTS AND DISCUSSION
In table 4 are summarized the results obtained when log BCF was re-
gressed linearly on log P or water solubility. Significant (a <0.05)
linear relationships exist between bioconcentration factor (BCF) for each
marine species tested and the log octanol/water partition coefficient (log
P) for the chemicals listed in Table 1. The correlation coefficients (r)
for the linear sodels ranged frca 0.54 for Crassostrea virginlca, the oyster,
to 0.98 for Mytilus edulis, the mussel. However, the linear model for M.
edulis was obtained witn 6 values compared to 17 for C_. virginica. An
excellent relationship (r = 0.88) existed between log BCF and log P for
Lagodon rhomboides, a fish.
Several authors have reported on the use of water solubility in place
of log P in development of linear model's for estimation of BCF in freshwater
species (Chiou et_ a^, 1977; Kenaga, 1980; Kenaga and Goring, 1980). Signifi-
cant linear relationships (cc <0.05) were obtained for each marine species
tested except L. rhomboides when log BCF values were regressed on water
solubilities (Table 4). However, the model obtained for M. edulis using
water solubility data had a lower correlation coefficient (r = 0.84) and
a lower level of significance (a <0.05) than the comparable model using
log P (r = 0.98, a <0.01) (Table 4). Despite the significant relationships
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5
which were obtained with log BCF and log water solubility for the three
marine species and the improvement in aaca fit with 2 species when compared
to the models developed with log P, the fact that the model for L. rhomboides
was not significant (u >0.05) influenced our decision to scudy more
comprehensively the relationships between log BCF and log P. It was decided
to test the freshwater models which were developed with log P in preference
to those developed with water solubility since log P and log water solubility
are directly related (Hansch et_ al_., 1968). The partition/scrption phenomena
upon which bioaccuiaulation is based, depend mainly on the lipophilicity of
the chemicals (Esser and Moser, 1982). Since log P reflects this property
and water solubility does not, log P was considered a more suitable measure
of bioaccumulation.
Statistical tests were performed to determine whether a significant
relationship exists between models for freshwater fish (equations 1,2) and
those models derived here for marine species (Table 4, equations, 4,5,7,9).
Equality of the freshwater and marine models was tested by determining the
95% confidence intervals for the marine models. Since the slopes of both
freshwater models are within the 95% confidence intervals for the marine
models, all lines tested are equal (Table 4). However, the relatively
large confidence intervals which are due to the small sample sizes lessen
the likelihood of rejection and increase the probability of falsely concluding
that no difference exists between the models. These results suggest that
we cannot reject the use of freshwater models to predict the BCF of a
chemical in marine species.
An attempt was made to reduce the variability inherent with multiple
source data (Table 1) by analyzing the data from a single source. Suffi-
cient data from a single source could be extracted from Table 1 for
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C_. virginica and Cypr-:.odo". '/an^gatur , a fish, co perfora regression
analyses, iignii j.ca.ii- rc-ci-ioaonLpd "«ers noc ooc^ineu. V.G />u.uDy D>=;wifci.i
log BCF and log P vich cnese. select data for C_. virgin! ca and C_. variagatus .
Thus, t.'^rB is lass vz.*ir.:.iiC7 i.i all rr.a 3CF dan?, for C. virg-r.lca ar.-d
.. Yo T-cgi _ JS l.id.. - ". ____ : •__..!..=. :_~~>.J d sl..l3_d o«!_-Ji. _.:-.3 "v«ia C-.l-.a.^^ <- 0
^S2ac:ac.ic.'. 1- 'i.. ^^^-did P.OC co ^r.clcie Che outliers ( f =ri vale rate,
ne77i£Ci"rir. ar.c. .-^22-7'Jj) in tlia ~oceis, oecause che bioconcencration
casts indicates ci=i.- chrse cheniicals bioconcentrate very little despite
cr.eir cct3?drit_-7«l. -iiili log ? values. Vr.iie there nay be other checicals
vita a nigh log ? value that do not bioconcentrate significantly, these
data are inconsistent with the relationship between log ? and 3CF as reported
here and by Veith e_t_ al_ (1979). when the linear model for £. variegatus
was developed without incorporating the outliers, a highly significant
(a ^0.01, r = 0.8^) relationship was obtai^d between lo^ BCF ar.d lc^ ?.
The model obtained was:
(II) log BCF = 0.69 -f- 0.61 log ? n = 10
95%CI 0.61 + 0.32
Comparison of this model (equation 11) to that generated with all BCF data
er-
ror C_. variegatus (Table 4; equation 4) clearly shows the improvement,
not only in the level of significance (a = 0.01 to a <0.01) and in the
correlation coefficient (r = 0.65 to r = 0.84) but also in the narrower
95% confidence interval (0.504 + 0.368 to 0.61 +0.32). The freshwater
models (equations 1,2) more closely resemble this model (equation 11)
than do the models generated with all the BCF data. Elimination of the
outliers from the single source data helped to improve the relationship
between log BCF and log P with models for C_. virglnica, but the improvement
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was not sufficient to result in significance (a >0.05).
To demonstrate more clearly the capacity or" freshwater models to
predict BCF in marine species, models 1 and 2 (equations 1 and 2) were
used to calculate BCF values for each marine species tested in this study.
The calculated BCF values were compared zo che measured 3CJ values for
each marine species and chose measured for the fathead minnow (Tables
5,6,7,3). These data indicate that log 3C? can be estimated for marine
species with freshwater models within an order of magnitude for a minimum
of 71% of the chemicals having a range of 3,000,000 in the partition coeffi-
cient. Measured 3CF values for marine species are within an order of
magnitude of those measured for fathead minnow for a minimum of 63% of
these same chemicals. Thus, it appears that freshwater models offer the
same precision in estimation of BCF values for marine species as for fresh-
water fish.
To determine how well the measured BCF values agreed with calculated
BCF values, data from Tables 5,6,7,8 were plotted in Figures 1,2. The
closer these values fall about a 45° line (log measured BCF = log predicted
BCF), the better the agreement between predicted and measured values. It
is evident in Figures 1,2 that good agreement exists between measured and
calculated BCF values for both pinfish, L. rhomboides and mussel, M.
edulis. The data for oyster, C_. virgin!ca is more variable and that for
sheepshead minnow, C. variegatus is the most variable. Also apparent in
Figures 1,2 is the bias downward from the line log Y = log X for each of
the marine species. This is indicative of the calculated BCF values being
over-estimates of the measured BCF values. Conversely, underestimates of
measured BCF values appear above the line.
Linear regression analyses were performed with data on chemicals
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for which log BCF values ware available for both fathead minnow and the
respective marine species (Fig. 3). It is evident in Figure 3 that a
significant linear relationship exists between measured BCF values for
fathead minnow and each marine species except nussel. The highest correla-
cion (r = 0.96, a = O.Oi) was obtained wicn pinfish. Hie coopar-sccs
made with BCF data for both sheepshead minnow and oyster were also signifi-
cant (r = 0.69, a = 0.05, for both species); however, these relationships
were not as highly correlated with fathead minnow as those for pinfish
(Fig. 3). Despite the nonsignificant (a = 0.07) linear relationship
with the mussel, a high correlation exists (r = 0.33) (Fig. 3). The lack
of statistical significance was due to small sample size.
The same precision in estimating log BCF was achieved for the marine
species with freshwater models as has been reported for freshwater fish
using the same models. Veith et_ al_.- (1979) reported that log BCF for P_.
promelas can be estimated with their model to within 60% before iaooratory
testing. They also indicated that their model was intended for screening
purposes, to estimate bioconcentration potential within an order of
magnitude.
The accuracy of predictions made with the various models in this
study is limited by the inherent variances in the BCF test, variation due
to species differences, and variation associated with use of data from
more than one source. Chemicals which have metabolic routes that are not
reflected in the log P may produce BCF values lower than the bioconcentra-
tion potential predicted by log P. In addition, factors such as test
species, lipid content of test species, determination of steady-state,
temperature, metabolism and chemical analyses can influence the evaluation
of the bioconcentration potential. The use of equations 1,2,3 may be invalid
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for compounds that do not penetrate vail through tissues, ara rapidly lost
from water or are rapidly m-iiabolized by organises.
It is evident in this study and that of Veith et_al. (1979) that the
log BCF varies wish the test animal. Esser and Moser (1982) reported that
leg 5CJ variacics. dua co Cisz ani^l-s -can ce -aduced drastically LZ 3.C7 is
related to fish fat ccalant and not to body weight. The fact that freshvate:
aodels can be used to predict BCF in select inarine speciss suggested that
the same mechanisms involved in bioconcentration of organic chemicals are
operating in both freshwater and these select sarins fish and bivalves.
This contention is supported by the fact chat highly water soluble chemicals
do not bioconcentrate significantly in either freshwater or marine species,
as evidenced by the low BCF values for chemicals with low log P values.
To properly determine bioconcentration, steady-state concentrations of the
che*cical in the test species must be realized. Much of the variability in
this study could be due to this single factor. Many of the BCF values used
in this study were reported in conjunction with toxicity tests and tissue
residues were determined at the end of the treatment period without regard
for steady-state. Absence of steady-state residue data is apparent when
different BCF values for different test concentrations were reported in the
same study for the same chemical (Table 1). In addition, some of the higher
concentrations used in toxicity tests were toxic, and in some cases high
BCF values were reported for tests in which 50% or more mortality occurred
(Hansen et_ al^. , 1974; Parrish et_ al^. , 1976; Schimmel et_ al. , 1976; Hansen
et_ al_., 1977; Schimmel et_ al_., 1977a,b). These values were not used inten-
tionally, since the use of moribund test species or those of questionable
physiological condition could lend significant variability to the BCF values.
Temperature is another contributor to variability in bioconcentration
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studies. Ernst (1979) :.sported that, as temperature increased from 5 to
153C trie rate 3; uptake of chemicals in aisrine organisms increased. 7eiuh
et_ al^. (1979) reported that BCF varied directly with temperature; however,
the increases in BCF also varied with the species. In this study, tenpera-
c^re vas no.: coujic=r5u, sines c^apdrac^rs was aoc always reported.
Metabolism and chesical analyses are additional sources of arror in
determining BCF values for aquatic species. Analytical methods that rely
on the physical and chemical characteristics of the parent molecule for
quantification should be used to prevent errors due to analysis. It is
also important that the aolecule of the test chemical remains intact and
is not degraded or metabolized by micro-organisms or by the test organism.
Marine fish have well-developed drug-metabolizing and excreting systems.
Several species of marine fish are capable of metabolizing petroleum derived
arooatic hydrocarbons to polar water soluble metabolites (Bend and James,
1978; Neff, 1979). Mussels do not degrade organohalogen compounds in the
environment; however, they readily fora conjugates of organic compounds
vith sulfate, (Ernst, 1979). Marine bivalves, sussels and oysters in
particular, have been shown to have little or no metabolic capability for
detoxifying xenobiotics. Studies on the accumulation and release of petroleum
hydrocarbons by oysters and mussels strongly suggest that the primary
mechanism for hydrocarbon release is by passive exchange with the external
medium (Lee et_ al_. , 1972; Stegeman and Teal, 1973; Neff et_ al_. , 1976).
Degradation of phthalate esters to metabolites which are not measured
could be responsible for the low BCF reported for sheepshead minnow,
since Wofford et_ al^. (1981) reported that this species has a high capacity
to degrade these estars. Fathead minnows also have this same capacity
(Mayer, 1975). Wofford e_t_ al_. (1981) have reported Chat oysters have a
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li
low capacity for degrading phthalate esters.- Therefore,-the low-SCF values
reported for phthalate esters wich oysters and sheepshead nd.nr.ows nay be
due to different phenomena. Perhaps, as Veith et_ al_. (1980) reported, the
highly water soluble compounds are highly bioconcencra-ed.
CONCLUSION
Despite the aany sources of error inherent in tr.e d^ca used in chii
study, highly significant relationships were obtained. This strongly
suggests that models developed for freshwater acd marine spacies may oa
used interchangeably to predict 3CF of chemicals within ar. or-j^r of cai-^i-
tude for screening purposes.
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i2
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Kanazawa, J. (1980). Prediction of biological concentration potential
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Neff, J.M. (1979). ?oiycyci.ic Aroaacic Hydrocarbons in the Aquatic
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Stageiaau, J.J. and J.H, Teal. (1973). Accvrawlation, release and retentio-n
of petroleum hydrocarbons by oyster Crassostrea virginica. Mar.
Biol. 22:37-44.
Veith, G.D. and R.T. Morris. (1978). A rapid method for estimating Log
? for organic cnernicaiS. U.S. £nvironme£iral Protection Agency,
Ecological Research Series EPA-6CO/3-78-049.
Veith, G.D., D.L. Deroe and 3.V. Bergstedt. (1979), Measuring and
estimating the bioconcentration factor of chemicals in fish. J.
Fish. Res. 3d. Car.. 36:1040-1043.
Veith, G.D. , K.J. J^acek, S.R. Petrocelli and J. Carroll. (1980). An
evaluation of using partition coefficients and water solubility to
estimate bioconcentration factors for organic chemicals in fish.
Aquatic Toxicology, ASTM/STP7Q7, J.G. Eaton, P.2.. Parrish and A.C,
Hendricks, Eds. Am. Soc. for Testing and Materials, pp. 73-115.
Veith, G.D. (1982). Personal Comminication. U.S. Environmental Protec-
tion Agency, Environmental Research Laboratory, Congdon Blvd, Duluth,
Minnesota.
Weiniger, D. (1978). Accumulation of PCBs in the lake trout in Lake
Michigan. Ph.D. Thesis. Univ. or Wisconsin - tMadison, 232 pp.
Wofford, H.W., C.D. Wilsey, G.S. Neff, C.S. Giam and J.M. Neff. (1981).
Bioaccumulation and metabolism of phthalate esters by oysters, brown
shrimp and sheepshead minnows. Ecotoxicology and Environ. Safety.
5:202-210.
-------
TaliU
Reported values for log liCF, exposure conceit
respective chemicals and species.
«:Jon and time of trantmaut for the
Chemical
FenLtrothion
Lindane
DieLdLin
lleptachlorepoxide
EndrLn
001)
Dl( 2-iithylhexyl)Phthalate
Dibit tylphthalate
BIIC
Heptachlorepoxlde
Pernielhriu
Ac 222 705
Aroclor 1016
FenvaLerate
CliLordane
OleLdrJn
Kcpone
Dieldfin
llepfnchlor
Toxaphune
UDT
DUll
Aroclor 1254
Llndauc
11I1C
lleplachloirepoxide
Chlonlane
Toxaphene
Cl i.l or da ne
Htptachlor
ArocLou 1016
Aroclor 1254
Aror.lor 1016
Dlmuthylplithalate
Log UCF
2.11
2.38
3.19
3.23
,28
,96
,04
,32
3.
3.
1,
1.
2.50
2.93
3.27
3.36
3.
3.
3.
3.
3.
3.
3.
4.
4.
4.
5.
64
67
70
70
84
90
93
18
68
68
52
2.46
2.68
3.40
3.46
3.57
3.
3,
4,
4,
.87
.89
,14
.34
4.42
0.77
Exposure
Period
(days)
7.00
SS*
SS
SS
SS
SS
1.00
1.00
28.00
4.
2.
7.
4.
00
50
70
00
15.00
4.00
43.00
28.00
56.00
4.00
84.00
56.00
56.00
56.00
4.00
28.00
4,
4,
.00
.00
32.00
4.00
4.00
28.00
35.00
28.00
1.00
Exposure
Cone.
(ng/L)
13.00
0.02
1.97
1.95
1.78
2.18
100.00
100.00
0.09
0.91
1.00
1.00
7.20
1.00
2.20
1.00
0.03
0.01
0.91
3.10
0.00
0.00
0.00
23.00
36.00
4.40
5.40
0.50
30
40
0.80
,00
,50
Spucics
Mussel
MllKSlil
Mussel
Mussel
Mussel
Oyster
Oyster
Oyater
Oyster
Oyster
Oyster
Oyster
Oyster
Oyoter
Oy uter
Oyster
Oyater
Oy liter
Oys,ter
Oyster
Oyuter
I'inLish
I'luf tsh
1'Liit'isti
1'LnElsh
I'lnfLsh
Pinfish
1'Lufish
Hut Lsli
I'lntlsii
PillfLBll
Reference
100.00
McLeese et al. 1979
lima I 1975
lirnst 1977
Ernst l'.)77
Ernst J'J77
Ernst 1977
Wotford et al. 1981
Wofford et al. 1981
Schiirnnul et al. 1977a
et al. 1976
tit al. In Press
Schiuimc I et al. In Press
llanboa ,:t al. 1974
Schiiiinu.:! et ul. In Pre;:s
Parrisli et al. 1976
Emanuuluen et al. 1978
llahner et al. 1977
Parrlsh 1974
SchiumcL et al. 1976
Schiiumol et al. 1977b
ParrLsli 1974
Parrlfili 1974
Parrlsh 1974
Schluiiiu-J et al. 1977a
SchiiniiiuL et al. 1977a
Schimrncl et al. 1976
Purr lull et al. 1976
SchlnuiHjl et al. 19/71)
Parrlsh et al. 197G
Schlnniitil et al. 19/6
Hanson i>t al. 1974
llanuen or. al. 1971
llansun oL al. 1974
Woffoi I ei: al. 1981
Minnow
*SS:sLeady-state
-------
Tab, 1. (Cont'd)
Chuml cal
Diai'Jn on
AC 2? 2 705
Liiulane
Pi- 1 mutlirin
Ft;iiv«.lerate
HciiUichLorepoxide
Kupoue
Cliloi dane
iindrin
Toi.iphene
HepL.ichlor
Ai-oclor 1254
Aroc.Lor 1254
Log BCF
2.30
2.60
2.67
2.68
2.75
3.65
3.86
4.08
4.11
4.31
4.33
4.64
4.83
Exposure
Period
(days)
4.00
28.00
4.00
28.00
28.00
4.00
28.00
4.00
28.00
28.00
4.00
28.00
28.00
1^. pot! ure
Cone.
( iig/M
6.50
1.00
41.90
1.00
1.00
4.00
0.05
15.00
0.07
1.70
4.00
1. 10
0.14
Spucies
Shcepshead
Mi nnow
Shecpshead
Ml nnow
Siiecpahead
Minnow
Sl-ecp:!he.id
Minnow
fihcepiihead
Ml nnow
IJiictipoliead
Mj nnow
Stioopshead
Minnow
Stiucpiiheud
Minnow
Slituipohead
Minnow
Shi-iipshcad
Minnow
Silmeiiahead
Minnow
Stieupiihuad
Minnow
!iheepiiheud
Mi nnow
JU'-fcrence
Goodman et al. 1979
Srhlioui.-L et al. In Press
ScliluiiiuL et al. 1^77
Sf-.hJiui!,eL et al. In Press
Sr.hlmi:f: 1 et al. In Press
Scliluih,: 1 fct al. 19/6
Bahner eL al. 1977
Prirrlsh et al. 1976
Udnsen et al. 1977
So.liinmi:! et al. 1977
SdiJuimul et al. 1976
liansen et al. 1973
Hansen et al. 197 'J
-------
Table '2. Reported va-luas of log ? for the respective chemicals used
in this study.
Chemical
Log
Reference
DimethyIphthalate
Diazinon
renitrothion
3HC
Lindane
Di(2-£cliylhex7l)Phthal3te
Dieldrin
Endrin
Dieldrin
Toxaphene
Dibutylphthaiate
Haptachlorepoxide
Heptachlor
Aroclor 1016
Chlordane
ODD
Kepone
DDT
AC 222 705
Fenvalerate
Aroclor 1254
Permethrin
1. 61 Veith et al. 1980
3. 14 Xanazawa 1980
3. 38 Chiou et al. 1977
3. 89 Veith (Pers. Conm. 1982)
3. 89 Veith et al. 1979
4. 20 Veich at al. 1979
4. 31 Kanazawa 1980
4. 56 Veith et al. 1979
4. 69 Veith (Pers. Comrn. 1932)
4. 83 Veith (Pers. Comm. 1982)
5. 15 Veith & Morris 1978
5. 40 Veith et al. 1979
5. 44 Veith et al. 1979
5. 88 Veith et al. 1979
6. 00 Veith et al. 1979
6. 02 Veith & Morris 1978
6. 08 Veith (Pers. Comm. 1982)
6. 19 Chiou et al. 1977
6. 20 Schimmel et al. In Press
6. 20 Schimmel et al. In Press
6. 47 Veith et al. 1979
6. 50 Schimmel et al. In Press
-------
Table 3. Values for water solubility C-M/L) cr th
used in this study.
s rss^sctLY2 ch^rii ca Is
Chemical
DDT
LLD
Aroclor 1254
Fenvalerate
Dieldrin
Endrin
Heptachlor
Permethrin
AC 222, 705
Chlordane
Aroclor 1016
Lindane
BSC
Lindane
Heptachlorepoxide
Toxaphene
Toxaphene
Di(2-Ethylhexyl)Phthalate
Kepone
Dibutylphthalate
Fenitrothion
Diazinon
Diazethylphthalata
Water
Solubility
(UM/L)*
0. 005
0. 05
0. 06
0, 06
0. 06
0. 08
0. II
0. 13
0. 14
0. 33
0. 52
0. 52
0. 52
0. 90
0. 96
0. 96
1. 02
6. 11
46. 76
108. 00
132. 00
22165. 00
Reference
Kanaga & Gori-g 1930
Gorins i930
Schinnsel £t al. In ?~ '-
Tlancga i Gcr_-5
15-30
Schisms1 eu al. In
Schiouzxel et al. In
Schinuael et al. 1983
Kanaga & Goring 1980
Kenaga & Goring 1980
Kenaga 1980
Kenaga 1930
Kenaga 1930
Callahan et al. 1979
Kenaga & Goring 1980
Kenaga & Goring 1980
Callahan et al. 1979
Kenaga 1980
Callahan et al. 1979
Kenaga 1980
Kenaga & Goring 1980
Callahan et al. 1979
*Calculat=d i'rom ??a data rcccrr^d in the respective references.
-------
Table 4. Models obtained when log BCF was regressed linearly on log P or water solubility usiuj; data from
Table (1) for the respective marine fish ami molluscs.
M.irine Species
Cypri uodon varlegatua
(slieepbliead minnow
L.tj/rxlon rhoiuboides
(pinflsh)
Cr-t:jsostrea virginica
(oyster)
My 1 1] us edulis
(luiiBuel)
Model
(4) Jog BCK=0. 78+0. 504 log P<»
(5) log BCF=3.33-.49i) log Sb
(6) log BCF=0.14+0.65 log Pa
log BCl'=3.19-0.77 Jog Sb
(7) log BCF=0.41+0.72 log Pa
(8) log BCF=3.06-0.78 log Sb
(9) log BCF=-0.05+0.66 log Pu
(10) log BCF=2.86-0.42 log Sb
Correlation
Coefficient
(r)
0.7L
0.88
0.51
0.54
0.67
0.98
0.84
Level of
Significance
(a)
0.01
<0.01
<0.01
>0.05
<0.05
<0.01
<0.0l
<0,05
Saoip 1 1:
Size
(n)
14
10
10
17
16
6
6
Confidence
Interval
of iiLope
0. 504 -K). 368
-0. 499t0.312
0.65*0.274
-0.77J-1.05
0.72-i0.620
-0.7UT0.490
0.66-1-0.308
-0.4 2+0.377
a f - octanol/water partition coefficient
b S - water bolubillty (HM/L)
-------
Table 5. Estimation of the bioconcentration factor for Cypri.iodon variegatus
(sheepshead minnow) using log P and
fish, Piinephaies promelas (rathead
(bluegill).
models derived for freshwater
minnow) and Lepomis machrochirus
Sheepshead minnow
Chemical
Aroclor 1254
Aroclor 1254
Heptachlor
Toxaphene
Endrin
Chlordane
Kepone
Heptachlorepoxide
7anvalerate
Paraechria
Lindane
Ambush
Diazinon
Diethylhexylphthalate
Measured
4. S3
4.65
4.33
4.31
4.11
4.08
3.37
3.65
2.76
2.68
2.68
2.60
2.30
: 1.04
1.3- 2CF
Calculated
.Model la
4,63
4.63
2. -3
3."
2.24
4.33
4.39
3.87
4.48
4.71
2.78
4.48
2.15
2.96
Model 2°
4.00
-'..30
3.32
3.41
3.18
4.40
4.47
3.89
4.57
4.83
2.61
4.57
1.97
2.87
Fathead minnow
log 3CF
Measured0
5.00
5,OC
4.50
-
3.17
5.90
-
4.16
-
-
2.68
-
-
2.93
a Model 1 = log 3CF = 0.76 log P - 0.23 (whole bluegill)
0 Model 2 = log BCF = 0.85 log P - 0.70 (muscle tissue, fathead minnow)
c Taken from Veith et al. (1979)
-------
Table 6. Estimation of the bioconcencration factor for Lagodon rhoaboides
(pinfish) using log P and models derived for freshwater fish,
Pimephales promelas (facnead minnow) and Lepomis machrochirus
(bluegill).
Pinfish
log BCF
Cheinical
Aroclor 1016
Aroclor 1016
Aroclor 1254
Beptachlor
Chlordane
Toxaphene
Chlordane
Eaptachlorepoxide
BEG
Lindaae
Measured
4.42
4.14
4.34
3.89
3.87
3.58
3.46
3.40
2.28
2.46
Calculated
Model la
4.24
4.24
4.68
3.90
3.90
3.44
4.33
3.87
2.72
2.73
iModei 2C
4.30
4.30
4.80
3.92
3.92
3.41
4.40
3.89
2.61
2.61
Fathead Einr.ow
log SCJ
hsasuredc
4.63
4.63
5.00
4.30
4.58
-
4.58
-
-
2.68
a Model 1 = log 3CF = 0.76 log P - 0.23 (whole bluegill)
b Model 2 = log 3CF = 0.85 log P - 0.70 (muscle tissue, fathead niinnow)
c Taken from Veith et al. (1979)
-------
Taoie 7. Estiaau.::: ;r :ne oiocorn-^ncracion faccor fcr '.-iycilus edola.3 (
using loe ? and models derived for freshwater fish, Pimeshales
;j..-:.s «, i iCiieac oinnowj and i_apoiais oiacnrocnirus (c
^-dssel 7-?. o-.e:i.i cir.nr.ow
Mcdei i" :iodel 2°
aci:rii 3.23 3,24 3.13 3.17
C^=Id:i^ 3,20 3.33 3.29
T_i::dar-a 2.33 2.78 2.51 2.68
Fanitrothion 2.11 2.3^ 2.17
f Model i •= log 3C? = C.76 lag ? - 0.23 (whola biuegiil)
b Model 2 = log 3C7 = 0,65 leg ? - 0.70 (suscle tissue, fa:h=-d uu.r^ov)
c Taken free: 7s:. ir. -31: al. (1979)
-------
Table 8. Estimation of the bioconcencration factor for Crassostrea virginica
(oyster) using log P and models derived for freshwater fish,
Pimephales promelas (fathead minnow) and Lepomis machrochirus
(bluegill).
Chemical
Aroclor 1254
DDT
ODD
Toxaphene
Eeptachlor
Dieldrind
Kepone
Dieldrind
Co lor da ne
Fenvalerate
Aroclor 1016
Permethrin
Heptachorepoxide
BHC
Dibutylphthalate
Diethylhexylphthalate
Measured
5,52
^.68
4.68
4.18
3.93
3.90
3.85
3.71
3,70
3.67
3.64
3.28
2.93
2.51
1.32
1.0*
Oyster
ios 3C7
Fathead minnow
log 3CF
Calculated Measured0
Model la
4.63
4.47
4.34
3.44
3.90
3.33
4.39
3.33
4.33
4.43
4.24
4.71
3.87
2.73
3.68
2.96
Model 2°
4.80 5.00
4.56 4.47
4.42 4.72
3.41
3.92 4.30
3.29
4.46
3.29
4.40 5.90
4.57
4.30 4.63
4.83
3.89 4.16
2.61
3.68
2.87 2.93
a Model 1 = log BCF = 0.76 log P - 0.23 (whole bluegill)
b Model 2 = log BCF = 0.85 log P - 0.70 (muscle tissue, fathead minnnow)
c Taken from Veith et_ al_. (1979)
d Different authors, refer to Table 1
-------
o
c
r:
E
TJ
O
O)
D
nj
n>
-IT
to
o
I'l
0:
u.
O
CT)
c
0)
• -
w
>>
o
O
lii
n:
bl
^
IL
O
m
''' 3
K
liJ
U I
o
01 ?. 3 4 5
LOG BCF CALCULATED (shecpsheod minnow,model I )
o
1) O
I
o I 2345
LOG BCF CALCULATED (pinfish, model I )
_L_ .J_ _L
LOG HCF CALCULATED (oyster, model I )
012 3 4
LOG BCF CALCULATED (mussel, model I )
Figure 1. I'loir, of. measured TH'F-" values against IW values calculated with model 1, showing the fit al>out
Llie line lop; Y - IOR X for each of the mar Inn p
-------
jr ^_^
O .('
C~ r in r-
c; ^
E
o
r-> <1
r'
ex
0.1 3
^
O P
III
£K
D
CO
< 1
UJ
U. ^
f\'
^f
A A y
A >X^
/^A
/
./
A/ A A
A X
,/
,s
/'
.-
1 1
;-- '
C
~~J' y1
n
uj
nc
(/) •'
<
UJ
g
li-
O
O.1
0 '
-'
x
/
A-^A
v**^
A
/VX A
X^ A
.X^
x^
X^
^x^
x^
X^
./^
.x^
>x
X"^ i i I
(i I ? 7, 1 5 0 1 231 f>
" LOG BCF CALCULATED (shoepsheod minnow, model 2) LOG BCF CALCULATED (pinfish, model ?)
o 5
_J
,-, 4
fO
/ A
,x^
^AA A
,x
XA
X
,x^
_ >x
x^
x^
X i i I I
O 0 1 '•' 3 4 5 e> "o 1 2 3 1
3 LOG BCF CALCULATED (oyster, model 2) J LOG BCF CALCULATED (mussel, model 2)
Figure 2. Motr, of mnarnir'Hl iu;i' values ap.ainnl: I'd' valtios calculated with model 2, showing the fit al">ui
i ho 1 in' 1
-------
10
0
O
0
in
o
0
0
Log Y - O.Sfi Log X
r - O 69
o
o
Log Y =O.95 K)62 Log X
r =0 00
/\-
A
^L_
_ I... I .
LOG DCF (falheacl minnow)
Figure T. Hod'-lf! nl>l;;i liifl vll.li dnln fr«im TnMn r),r>,7 or f! whon monsurcd 1 oj; BCF vnltjcs for nn«:!i <>f I hr
HIM rf tie n[i!-r. io.;; i;ni c r^gireorjetl llncnrlv f)ii in nn suited lop, Bf'F v;i1ues for J[^. JgroracljaB (r.-ilrl"^;i
------- |