JJ905R93100
8
GREAT LAKES WATER QUALITY INITIATIVE
TECHNICAL SUPPORT DOCUMENT FOR
THE PROCEDURE TO DETERMINE BIOACCUMULATION FACTORS
(March 1993 Draft)
U.S. Environmental Protection Agency
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GREAT LAKES WATER QUALITY INITIATIVE
TECHNICAL SUPPORT DOCUMENT FOR
THE PROCEDURE TO DETERMINE BIOACCUMULATION FACTORS
I. INTRODUCTION
A. Purpose and Scope
The purpose of this document is to provide the technical
information and rationale in support of the proposed procedures
to determine bioaccumulation factors. Bioaccumulation factors/
together with the quantity of aquatic organisms eaten, determine
the extent to which people and wildlife are exposed to chemicals
through the consumption of aquatic organisms. The more
bioaccumulative a pollutant is, the more important the
consumption of aquatic organisms becomes as a potential source of
contaminants to humans and wildlife.
Bioaccumulation factors are needed to determine both human health
and wildlife tier I water quality criteria and tier II values.
Also, they are used to define Bioaccumulative Chemicals of
Concern among the the Great Lakes Initiative universe of
pollutants. Bioaccumulation factors range from less than one to
several million.
B. Overview of Bioaccumulation and Bioconcentration ,
Aquatic organisms in nature absorb and retain some water-borne
chemicals in their tissues at levels greater than the
concentrations of these chemicals in the surrounding water. This
process is bioaccumulation. Bioaccumulation can be viewed simply
as the result of competing rates of chemical uptake and
depuration. However, bioaccumulation is a very dynamic process,
affected by the physical and chemical properties of the chemical,
the physiology and biology of the organism, environmental
conditions, and the amount and source of the chemical. When
uptake and depuration are equal, the ratio of the concentration
of the chemical in the organism's tissue to the concentration of
the chemical in the water is the steady state bioaccumulation
factor (BAF). Thus:
Cff
BAF - (1)
Cwf
Where: Cff = concentration of chemical in the fish in the
field
Cwf = concentration of chemical in the water in the
field
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The Cff is expressed on a mass per mass basis and the Cwf is
expressed in a mass per volume basis. For example, the Cff and
Cwf may be in ag/kg and mg/L respectively; the BAF is expressed
in L/kg. Most Cwf values available in the current literature are
total concentrations. BAFs would be more accurate if the Cwf is
limited to that portion of the total concentration that is
available to the organism for uptake. For example, the
bioavailable fraction can be estimated by measuring the
concentration in a filtered sample (dissolved analysis).
Bioaccumulation refers to uptake by aquatic organisms of a
chemical from all sources such as diet and bottom sediments as
well as the water. Measured BAFs are based on field measurements
of tissue and water concentrations. /
Bioconcentration refers to uptake of a chemical by aquatic
organisms exposed only from the water. A bioconcentration factor
(BCF) is, as is the BAF, the ratio between the concentration of
the chemical in the organism's tissues and the concentration in
the water. BCFs are measured in laboratory experiments and have
the same units as BAFs. They are determined as follows:
Cfl-
BCF = (2)
Cwl
Where: Cfl = concentration of chemical in the fish in the
laboratory
Cwl = concentration of chemical in the water in the
laboratory
BCFs, measured in the laboratory, are not always determined under
steady state conditions; i.e., conditions under which the tissue
and the surrounding water concentrations,.and therefore the BCF,
are stable over a period of time. Only steady state BCFs, either
measured directly or projected based on the data, are useful for
the determination of BAFs. Steady state conditions are implied
for the BAFs and BCFs referenced throughout this document.
c. Outline of the BAF Procedure
BAFs are determined in three ways listed below from most
preferred to least preferred.
1. A BAF measured in the field, preferably on fish
in the Great Lakes living at or near the top of
the food chain.
2. A BCF measured in the laboratory, preferably on
a fish species, times the appropriate Food Chain
Multiplier.
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3. A BCF predicted from the log of the
octanol-water partition coefficient times the
appropriate Food Chain Multiplier.
Field measured BAFs, preferred because they reflect
bioaccumulation in nature, are available for relatively few
chemicals. BCFs have been measured for many more pollutants but
a BCF may underestimate bioaccumulation. The BCF data base for
organic chemicals can be utilized to derive a BAF through the
application of a Food Chain Multiplier. When neither a measured
BAF nor BCF is available for an organic chemical, a BCF can be :
predicted from the chemical's hydrophobicity. BAFs for inorganic
chemicals must be based on measured BAFs or BCFs.
II. DATA REQUIREMENTS AND EVALUATION
BAFs and BCFs are obtained from EPA criteria documents, published
papers, the AQUIRE data base, and other reliable sources. Data
should be screened for acceptability using the criteria in The
U.S. Environmental Protection Agency (EPA) guidelines for
deriving aquatic life criteria (Stephan et al. 1985), and
American Society for Testing and Materials guidance (practice E
1022-84) detailing methods for conducting a flow-through
bioconcentration test (ASTM 1990).
In general, the Great Lakes Initiative (GLI) BAF procedures
follow closely the EPA guidance (Stephan et al. 1985) with the
addition of the Food Chain Multiplier. The EPA recently ''±
published draft guidance on the control of bioaccumulative
pollutants in surface waters which recommends the use of food
chain multipliers (USEPA 1991A).
No guidance can cover all the variations of experimental design
and data presentation found in the literature concerning BAFs and
BCFs. Professional judgment is needed throughout the BAF
development process to select the best available information.
III. DETERMINATION OF BAFs FOR ORGANIC CHEMICALS
A. Bioaccumulation-Lipid Relationship
A fundamental assumption made in the determination of BAFs for
organic chemicals is that bioaccumulation can be defined by the
partitioning of the chemical between the water and lipid phase of
the aquatic animal. Making this assumption means, 1) BCFs can be
predicted from the partitioning of an organic chemical between
octanol and water phases, and 2) BAFs can be derived from BAF or
BCF data from a variety of species and tissues by normalizing the
BAFs or BCFs on a lipid basis. This assumption has been
extensively evaluated in the literature (e.g. Mackay 1982,
Connell 1988, Barren, 1990), and is generally accepted. It is
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part of the EPA guidance on bioaccumulation (Stephan et al. 1985,
USEPA 1991A), and is included in the GLI BAF procedure.
It is important to note, however, that some researchers report
little basis for expressing contaminant concentrations on a lipid
basis (Schmitt et al. 1990, Borgmann and Whittle 1991). Schmitt
et al. (1990) and Randall et al. (1991) suggest that solvent
extracted lipid material represents a very complex mixture of
compounds, and organic chemicals may not be distributed uniformly
among the extractable lipids. Consistent with these
observations, it has been shown that the analytical method used .-
to determine percent lipid can affect lipid values because
different solvent systems extract different fractions of total ;-'•
lipids (Randall et al. 1991). Percent lipid is determined f
gravimetrically. The tissue sample is extracted with an organic
solvent; the extract is placed in a tared beaker, allowed to air
dry, and then heated to 85 to 100 degrees C for one hour. The
sample is reweighed and the percent lipid calculated. Resulting
percent lipid values can vary by as much a factor of four
depending on the solvent system used (Randall et al. 1991).
Specifically, the chloroform-methanol method (Bligh and Dyer
1959) results in lipid values about two times larger than methods
using some other solvent systems (Randall et al. 1991).
Lipid content of fish tissue is affected by the age, sex and diet
of the fish, and by the season the fish are sampled, and
differing environmental conditions. Therefore, it is generally
necessary to determine an average percent lipid value for the
test organisms. '*-
The GLI proposes to normalize BAFs and BCFs reported in the
literature to one percent lipid, and adjust them to the percent
lipid selected to represent the Great Lakes community to be
protected. Since BAFs are used to calculate both human health
and wildlife criteria, a standard percent lipid value was needed
for each. GLI criteria are applicable to both the Great Lakes
and the inland waters of the Great Lakes basin. To assure
protection of the Great Lakes the lipid values proposed are based
on the lipid content of Great Lakes fish.
Percent lipid data from the fish contaminant monitoring programs
in Michigan, Wisconsin, Ohio, Indiana, New York and Minnesota
provided lipid data for edible tissues (e.g. muscle) of fish from
each of the Great Lakes (Appendix A). Most lipid data are for
skin-on fillets. Skin-on fillets are the accepted tissue sample
used by most of the Great Lakes fish consumption advisory
programs. These data were used to determine the proposed
standardized lipid value of 5.0 percent for human health BAFs.
Whole fish lipid data from the the U.S. Fish and Wildlife Service
national contaminant biomonitoring program and the Canada
Department of Fisheries and Oceans were used to determine the
proposed standardized lipid value of 7.9 percent for wildlife
BAFs. (Appendix B).
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A variety of solvents were used by the programs providing lipid
data as shown in Table 1. However, the methods used all measure
a subset of total lipids. None used the more exhaustive
chloroform-methanol method, and the resulting variability in
lipid measurements should be within an acceptable range.
(As previously mentioned, the exhaustive chloroform-methanol
method resulted in lipid values two times larger than those
results from the other solvent systems.
Table 1
» „
Organic Solvents Used to Extract Lipids -
from Fish Tissue By State and Federal
Contaminant Programs /
Program Solvent
Edible Tissue Indiana Hexane
Samples Michigan Ethyl ether
Petroleum ether
Minnesota Hexane
New York Hexane
Ohio Petroleum ether
Wisconsin Dichloromethane
Whole Fish U.S. Fish &
Samples Wildlife Service
Canada Dept. of Hexane
Fisheries and
Oceans
The GLI Technical Work Group also reviewed the edible portion
percent lipid data weighted by human fish consumption patterns on
the Great Lakes to determine if this would significantly change
the proposed lipid value. Creel survey and game fish harvest
data from the sources listed below were used in this analysis.
The harvest data in percent of total catch by species was
combined with data for the typical weights of game fish species
(from the same sources), to determine a consumption weighting
"factor". This factor was applied to the edible portion species
mean lipid data discussed above to calculate a consumption
weighted lipid value (Appendix A, Table A4). The overall mean of
the consumption weighted lipid values for the Great Lakes is 4.7
percent. It was felt by the Technical Work Group that this value
was not substantially different from the non-weighted mean of
5.0, and elected to retain the proposed value of 5.0 percent.
Creel Survey Program Lakes Represented
Data Michigan Superior, Huron, Michigan
and Erie
Minnesota Superior
New York Erie and Ontario
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Wisconsin Superior and Michigan
A standardized lipid value for wildlife BAFs was determined using
the whole fish lipid data from the two federal programs mentioned
above, plus some additional whole fish values from the Hew York
Department of nvironmental Conservation. Species mean lipid
values for all fish species, both game and non-game, were
calculated. The mean of these values is 7.9 percent lipid
(Appendix B). The proposed value of 9 percent for wildlife BAFs
is based on an erroneous mean value of 8.9 from an earlier
calculation.
B. Bioconcentration and Octanol-Water Partitioning ;
The widely used surrogate for the lipid-water system in fish is
the partitioning of organic chemicals between octanol and water.
The log of the octanol-water partition coefficient (log K,,) has
been shown empirically to be related to the bioconcentration of
organic chemicals, with certain limitations.
A relationship between bioconcentration and the lipid content of
fish was suggested by Hamelink et al. (1971) in. their
investigation of the increase in DDT bioaccumulation in
successive trophic levels. Subsequently, Neely et al. (1974)
with eight chemicals and Veith et al. (1979) with 55 chemicals
demonstrated a linear correlation between the log BCF and the log
KOW'
The relationship of Veith et al. (1979) can be expressed as
follows:
log BCF = 0.85 log K^ - 0.70 (3)
N « 55
r2 - 0.897
Where: log K^, = log,0 of the octanol-water partition
coefficient
Equation 3 was used by EPA to predict BCFs in the absence of
measured BCFs, for the calculation of the 1980 human health
criteria. Veith and Kosian (1983) expanded the number of
chemicals upon which the relationship is based to 122 by
including data for 12 species of freshwater and saltwater fish in
addition to the fathead minnow data used to determine the
relationship expressed in equation 3. The correlation from the
larger data set is expressed as follows:
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log BCF - 0.79 log Kw - 0.40 (4)
N - 122
r2 = 0.86
This equation has been adopted by EPA to predict BCFs in the
absence of measured values (USEPA 199 1A) , and is the model used
in the computerized Quantitative Structure-Activity Relationships
(QSAR) database to predict BCFs. Equation 4 is
proposed for the GLI procedures for the estimation of BCFs.
The ability to predict the bioconcentration potential of a wide
range of organic chemicals is very useful in toxicology, and the
1°9 Kow model has been widely used for this purpose. However, as
with any model, it is important to understand its limitations.
Some of these are discussed below and in section III.E.
Veith and Kosian (1983) indicate that the BCFs estimated with
equation 4 have 95 percent confidence limits of about one order
of magnitude. For example, a predicted BCF of 100 would have
confidence limits ranging from about 10 to 1000. Also, the
accuracy of BCF prediction is likely to be even less for super
lipophilic chemicals; i.e., chemicals with log K^, values greater
than 6.5. Veith and Kosian (1983) caution the use of their model
for chemicals with molecular weights greater than 600. As
organic molecules increase in size and molecular weight, membrane
permeability apparently is inhibited which limits bioaccumulation
(Veith and Kosian 1983, Oliver and Niimi 1985). A ceiling of
100,000 is used for QSAR estimated BCFs for super lipophilic
organic chemicals. Equation 4 equates a BCF of 100,000 to a log
Kw value of about 6.8 at 7.6 percent lipid. The GLI procedure
proposes a cap of 100,000 (at 7.6 % lipid) for predicted BCFs.
Bioconcentration models based on other factors such as water
solubility (Metcalf et al. 1975), other physicochemical factors
(Schuuman and Klein 1988) , or both biological and physicochemical
factors (Barber et al. 1988, Barber et al. 1991) have been
proposed, but so far none has gained the wide acceptance of the
log K^ model.
C. Food Chain Biomagnif ication
The importance of uptake of chemicals through the diet and the
potential for a stepwise increase in bioaccumulation from one
trophic level to the next in natural systems has been recognized
for many years (Hamelink et al. 1971). This pathway, involving
transfer of a chemical in food through successive trophic levels,
is called biomagnif ication. Many researchers have noted that the
bioaccumulation factors of some chemicals in nature exceed the
bioconcentration factors measured in the laboratory or estimated
by log K,,. models (e.g. Oliver and Niimi 1983, Oliver and Niimi
1988, Niimi 1985, Swackhammer and Hites 1988). Chemicals
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exhibiting this phenomenon are typically highly lipophilic, have
low water solubilities, and are resistant to being metabolized by
aquatic organisms (Metcalf et al. 1975).
Some researchers have modeled bioaccumulation and uptake through
the food chain. Oliver and Niimi (1988) correlated BAFs for PCBs
and other chlorinated organics with log K^ values similar to
what others have done with BCFs and log K,.. The resulting
equation is:
log BAF = 1.07 log K^ - 0.21 (5) :
n = 18 f
r2 = 0.86
BAFs calculated with equation 5 in a range of log K^ values of 4
to 6.5 are about 15 to 70 times larger than BCFs calculated using
equation 4 as shown in Table 2. The factor in Table 2 represents
the predicted ratio of uptake through water plus food to uptake
through water only (a food chain multiplier). Consideration of
uptake only from water, or use of unadjusted BCFs, could
substantially underestimate bioaccumulation for highly lipophilic
chemicals.
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Table 2
Comparison of BAFs from Equation 5 (Oliver and Niimi 1988)
to BCFs from equation 4 (Veith and Kosian 1983)
Normalized to 1 Percent Lipid
Log Row
4.0
4.5
5.0
5.5
6.0
6.5
BAF
Equation 6
1,068
3,661
12,549
43,014
147,437
505,368
BCF Factor
Equation 4 (BAF/BCF)
76
188
467
1,159
2,879
7,148
14
19
27
37
51
71
Connolly and Pedersen (1988) examined the transfer gradients
(fugacity) of chemicals between water and biota. Fugacity ratios
between water and fish increase with log K^ from one at log K^.
of 4 to three or four at log KO. of 6. This basic food chain -
model indicates that for chemicals with log K^ values less than
4, uptake of the chemical from food is not important. At higher
log Kw values and fugacity ratios greater than one, uptake
through food becomes increasingly important because the animal
becomes less able to depurate the assimilated chemical (Connolly
and Pedersen 1988). Thomann and Connolly (1984) modeled the
uptake of PCBs through the food chain using concentrations
measured in Lake Michigan alewife and lake trout to calibrate the
model. The model predicts order of magnitude greater PCB
concentration in juvenile lake trout when food uptake is included
over uptake from water only. The ratio increases to two orders
of magnitude for older trout, which is probably partially
explained by the greater lipid content of older trout. The
predicted BCF for PCBs using a log K^ of 6.72 and equation 4 is
four to five times lower than the measured and modeled BAFs
(Thomann and Connolly 1984).
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D. Food Chain Multipliers
Food chain multipliers (FCM) for organic chemicals were derived
using the model of Thomann (1989). Thomann's model is a four
trophic level pelagic food chain as follows:
Trophic level 1 phytoplankton
Trophic level 2 zooplankton
Trophic level 3 small fish
Trophic level 4 top predator fish
The model predicts tissue residue concentrations at each trophic
level as a result of chemical uptake from water and contaminated
food in the food chain.
Thomann's model was programmed in Fortran on a VAX computer at
the EPA Environmental Research Laboratory in Duluth. The
required input data for the model was taken from Table II in
Thomann's paper. The required input data consists of: a)
weights of organisms for trophic levels 2, 3, and 4, b)
respiration parameters, c) growth parameters, d) lipid fraction
of trophic levels 2, 3, and 4, and e) food assimilation
efficiencies.
Thomann (1989) evaluated four different sets of model -
assumptions, and all four provide similar predictions for
chemicals with log K^ values less than approximately 6.5. Model
set C was selected to derive the food chain multipliers. Model C
assumes that the phytoplankton BCF equals the K^ of the chemical
and that the assimilation efficiency of the chemical is a
function of the chemical's K^,.
Using the data from Table II of Thomann and the assumptions of
model C, the computer model was run using log K^ values of 3.5,
3.6, 3.7, ..., 6.3, 6.4, and 6.5; and BCFs and BAFs were
calculated for each trophic level for each log K^, value. Food
chain multipliers were calculated using the following equations:
For trophic level 2
For trophic level 3
For trophic level 4
FCM « BAF2/BCF2
FCM - BAF3/BCF2
FCM = BAF4/BCF2
where BCF2 is the bioconcentration factor for trophic level 2
organisms and BAF2, BAF3, and BAF4 are the bioaccumulation factors
for trophic levels 2, 3, and 4, respectively.
In calculating the FCMs for each trophic level, the BCF of
trophic level 2 was used since, in many cases, measured BCFs have
10
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been determined using smaller organisms such as guppies, fathead
minnows, and Daphnia. The resulting FCMs for trophic levels 2, 3
and 4 are shown in Table 3. FCMs for trophic level 4 increase
above 1 starting at log K^ equals 4 and reach a maximum of 100
at log Kg,, equals 6.5. Thomann compared predicted BAFs for
trophic level 4 with measured BAFs from the Great Lakes and
concluded that, within an order of magnitude, model predicted
BAFs were a reasonable representation of the observed data for
chemicals with log Kw values in the range of 3.5 to 6.5.
For chemicals with log K^, values greater than 6.5, Thomann's
model is very sensitive to the input parameters and model
assumptions. In addition, other factors not accounted for in the
model such as metabolism of the chemical can affect •
bioaccumulation of these highly lipophilic chemicals, and the
risk of over estimating the BAF is great. Therefore, FCMs for
log Km values greater than 6.5 are given as a range; 0.1 to 19,
0.1 to 45, and 0.1 to 100 for trophic levels 2, 3 and 4,
respectively. USEPA (1991A and 1991B) indicates that the FCM may
be as low as 0.1 at log K^ values greater than 6.5. Super
lipophilic chemicals will be evaluated individually to determine
the appropriate FCM to use within the range of 0.1 to 100. If
chemical-specific data are not available, the GLI Steering
Committee decided that a FCM of 1 should be used. In conclusion,
the FCM model works best for lipophilic chemicals with log K^
values in the range of 4.5 to 6.5 that are poorly metabolized by
aquatic organisms.
In application, FCMs for trophic level 4 are used to determine .
BAFs for calculating human health criteria because most game fish
consumed by people are top, or near top, carnivore fish. FCMs
for trophic levels 3 and 4 are used to determine BAFs for
calculating wildlife criteria because wildlife consume aquatic
organisms over a range of trophic levels. The FCMs in Table 3
are the same FCMs included in EPA's draft guidance on the control
of bioconcentratable pollutants (USEPA 1991A), and the technical
support document for setting water quality-based effluent
limitations (USEPA 199IB).
The bioaccumulation work of several researchers indicates that
FCMs up to 100 are consistent with the differences between
measured BAFs in the Great Lakes compared to their respective
BCFs for highly lipophilic and persistent chemicals (Oliver and
Niimi 1988 and Table 2). Oliver and Miimi (1985) reported field
BAFs up to 220 times larger that laboratory BCFs for some
chlorinated hydrocarbons.
Rasmussen et al. 1990 reported a 3.5 factor increase in
biomagnification of PCBs with each trophic level in lake trout in
Ontario lakes. When corrected for the 1.5 percent increase in
trout lipid content with each additional trophic level below the
trout, the factor becomes 2.3. This factor agrees well with the
ratios of trophic level 4 to 3 FCMs in Table 3, which range from
11
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about 2 to 3.2, for chemicals with log K^, values between 5.5 to
6.5.
12
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Table 3
Food Chain Multipliers
Trophic Levels*
Log Kow 2 3
<3.9 1.0 1.0 1.0
4.0 1.1 1.0 1.0
4.1 1.1 1.1 1.1
4.2 1.1 1.1 1.1
4.3 1.1 1.1 1.1
4.4 1.2 1.1 1.1
4.5 1.2 1.2 1.2
4.6 1.2 1.3 1.3
4.7 1.3 1.4 1.4
4.8 1.4 1.5 1.6
4.9 1.5 1.8 2.0
5.0 1.6 2.1 2.6
5.1 1.7 2.5 3.2
5.2 - 1.9 3.0 4.3
5.3 2.2 3.7 5.8
5.4 2.4 4.6 8.0
5.5 2.8 5.9 11
5.6 3.3 7.5 16
5.7 3.9 9.8 23
5.8 4.6 13 33
5.9 5.6 17 47
6.0 6.8 21 67
6.1 8.2 25 75
6.2 10 29 84
6.3 13 34 92
6.4 15 39 98
6.5 19 45 100
>6.5 ** ** **
*Trophic level: 2 is zooplankton
3 is small fish
4 is piscivorous fish including top predators
**For chemicals with log K^, values greater than 6.5 a FCM can
range from 0.1 to 100. Such chemicals should be evaluated .
individually to determine the appropriate FCM. In the absence of
chemical-specific information, a FCM of 1 should be used.
13
-------
Measured "food chain multipliers" were recently reported for a
plankton, "flysis/Pontoporeia". sculpin food chain in Lakes
Michigan and Ontario (Evans et al. 1991). It is useful to
compare the measured increase in bioaccumulation through the
trophic levels of this food chain to FCMs calculated from the
Thomann model. The measured and predicted increase in
biomagnification show good agreement between trophic levels 3 and
4 for the three organic pollutants studied (Table 4).
14
-------
Table 4
Comparison of Measured to Predicted Ratios
Of Trophic Level 3 to Trophic Level 2 Tissue Residues
Trophic level 3/2
Pollutant
Total DDT
Total PCBs
Toxaphene -
Log- K,,.*
6.4
6.3
5.0
Obser
L. Michigan
2.8
2.5
3.7
•ved**
L. Ontario
2.1
3.5
^
Predicted***
,
2.6
2.6
1.3
* Log KM values are those used by the GLI to estimate BAFs
Log K,,. for DDE used for DDT because DDE accounted for over 75%
of total DDT in Lake Michigan.
Log Kw for total PCBs from the following aroclor specific
values:
Aroclor 1016 5.58
1242 5.58
1248 6.11
1254 6.72
** Observed ratios: Sculpin to mysid/amphipod from Evans et al.
1991; adjusted for lipid content: Sculpin, 8 % and
mysid/amphipod, 3 % from Oliver and Niimi, 1988.
*** Predicted values based on food chain biomagnification model
in Thomann 1989.
15
-------
E. Factors Affecting Bioaccumulation of Organic Chemicals
The steady state BAF for an organic chemical is the result of
very complex and dynamic chemical, physical and biological
interactions. Whereas some factors enhance bioaccumulation,
others can inhibit or reduce bioaccumulation below levels
predicted by log K^ based BCF and FCM models. Some of these
factors were mentioned previously in Section III B.
Low chemical absorption efficiencies from water to the gill and =
the ability of organisms to rapidly metabolize chemicals can
effectively lower bioaccumulation. Niimi et al. (1989) reported
that BCFs for chloronitrobenzenes (mono to penta) ranged from 69
to 1362, but the measured BCFs did not significantly increase as
1°9 K 6.5). Very low water solubility
and large molecular size can limit molecular transport (HcKim et
al. 1985, Oliver and Niimi 1985). Ellgehausen et al. (1980)
found that depuration rate and half-life, which were correlated
with log KM values, were important factors related to
bioaccumulation. Gobas et al. (1989) examined the importance of
reduced bioavailability and slow chemical uptake rates of super
lipophilic chemicals in the inhibition of bioaccumulation in
nature. As discussed under food chain multipliers, the ability
to predict food chain bioaccumulation is poor for super lipophlic
chemicals (Thomann 1989).
16
-------
IV. DETERMINATION OF BAFs FOR INORGANIC CHEMICALS
The lipid-BAF relationship does not apply to the determination of
BAFs for inorganic chemicals. BAF and BCF data for inorganics
are not as transferable from one species, or one tissue, to
another as organic data. Bioaccumulation of some trace metals is
substantially greater in internal organs than muscle tissue. For
example, BCFs for rainbow trout liver, kidney, gut and skin, and
muscle exposed to cadmium for 178 days were-about 325, 75, 7, and
1 respectively (Giles 1988). Merlini and Pozzi (1977) reported
that lead bioconcentrated 30 times more in bluegill liver than in
bluegill muscle tissue after eight days. They reported a BCF for
muscle tissue of 0.46.
5
Because bioaccumulation can differ dramatically between tissues,
BAFs or BCFs for edible tissue should be used for BAFs to
calculate human health criteria. Similarly, BAFs or BCFs for
whole body fish should be used for the BAFs used to calculate
wildlife criteria.
BAFs or BCFs for inorganic chemicals measured in plants or
invertebrate animals might be one or more orders of magnitude
greater than BAFs or BCFs for the edible tissue of fish (see
Table 5 in the EPA criteria documents for cadmium, copper, lead
and nickel; USEPA 1985A, USEPA 1985B, USEPA 1985C, and USEPA
1986). For this reason plant or invertebrate BAFs and BCFs
should not be used to calculate GLI human health criteria and
values. If site-specific conditions warrant, and the resulting
criteria are more stringent, plant or invertebrate BAFs or BCFS-,.
could be used to calculate wildlife criteria.
Mercury and certain other metals are subject to methylation
through microbial action in nature. The organo-metalic form of
the metal, especially methyl mercury, is highly bioaccumulative
in the muscle tissue of fish (Grieb et al. 1990).
V. LITERATURE CITED
ASTM. 1990. Standard practice for conducting bioconcentration
tests with fishes and saltwater bivalve molluscs. Designation
E 1022 - 84. Pages 606-622 In Annual book of ASTM standards.
Section 11, Water and Environmental Technology, Volume 11.04.
American Society for Testing and Materials.
Barber, M.G., L.A. Suarez and R.R. Lassiter. 1988. Modeling
bioconcentration of nonpolar organic pollutants by fish.
Environ. Toxicol. Chem. 7: 545-558.
Barber, M.G., L.A. Sufirez and R.R. Lassiter. 1991. Modeling
bioaccumulation of organic pollutants in fish with an application
to PCBs in Lake Ontario salmonids. Can. J. Fish. Aguat. Sci. 48:
318-337.
17
-------
Barren, M.G. 1990. Bioconcentration Will water-borne organic
chemicals accumulate in aquatic animals? Environ. Sci. Technol.
24: 1612-1618.
Bligh, E.G. and W.J. Dyer. 1959. A rapid method of total lipid
extraction and purification. Can. J. Biochem. Physiol. 37:
911-917.
Borgmann, U..and D.M. Whittle. 1991. Contaminant concentration
trends in Lake Ontario lake trout (Salvelinus namavcush): 1977 to
1988. J. Great Lakes Res. 17: 368-381.
Connell, D.W. 1988. Bioaccumulation behavior of persistent
organic chemicals with aquatic organisms. Pages 117-159 In '
Review of Environmental Contamination and Toxicology, Volume 101.
Connolly/ J.P. and C.J. Pedersen. 1988. A thermodynamic-based
evaluation of organic chemical accumulation in aquatic organisms.
Environ. Sci. Technol. 22: 99-103.
Ellgehausen, H., J.A. Guth and H.O. Esser. 1980. Factors
determining the bioaccumulation potential of pesticides in the
individual compartments of aquatic food chains. Ecotox. Environ.
Safety 4: 134-157.
Evans, M.S., G.E. Noguchi and C.P. Rice. 1991. The
biomagnification of polychlorinated biphenyls, toxaphene, and DDT
compounds in a Lake Michigan offshore food web. Arch. Environ.
Contain. Toxicol. 20: 87-93. '"-
Giles, M.A. 1988. Accumulation of cadmium by rainbow trout,
Salmo crairdneri. during extended exposure. Can. J. Aquat. Sci.
45: 1045-1053.
Grieb, T.M., C.T. Driscoll, S.P. Gloss, C.L. Schofield, G.L.
Bowie and D.B. Porcella. 1990. Factors affecting mercury
accumulation in fish in the upper Michigan Peninsula. Environ.
Toxicol. Chem. 9: 919-930.
Gobas, F.A.P.C., K.E. Clark, W.Y. shiu and D. Mackay. 1989.
Bioconcentration of polybrominated benzenes and biphenyls and
related superhydrophobic chemicals in fish: role of
bioavailability and elimination into the feces. Environ. Toxicol
Chem. 8: 231-245.
Hamelink, J.L., R.C. Waybrant,and R.C. Ball. 1971. A proposal:
exchange equilibria control the degree chlorinated hydrocarbons
are biologically magnified in lentic environments. Trans. Amer.
Fish. Soc. 100: 207-214.
Mackay, D. 1982. Correlation of bioconcentration factors.
Environ. Sci. Technol. 16: 274-278.
18
-------
McKim, J., P. Schmieder and G. Veith. 1985. Absorption dynamics
of organic chemical transport across trout gills and related to
octanol-water partition coefficient. Toxicol. Appl. Pharmaco!.
77: 1-10.
Merlini, M. and G. Pozzi. 1977. Lead and freshwater fishes: part
I - lead accumulation and water pH. Environ Pollut. 12: 167-172.
Metcalf, R.L., J.R. Sanborn, P.Y. Lu and D. Nye. 1975.
Laboratory model ecosystem studies of the degradation and fate of
radiolabeled tri-tetra-, and pentachlorobiphenyl compared with :
DDE. Arch. Environ. Contain. Toxicol. 3: 151-165. :
Neely, W.G., D.R. Branson and G.E. Blau. 1974. The use of the
partition coefficient to measure the bioconcentration potential
of organic chemicals in fish. Environ. Sci. Technol. 8:
1113-1115.
Niimi, A.J. 1985. Use of laboratory studies in assessing the
behavior of contaminants in fish inhabiting natural ecosystems.
Water Poll. Res. J. Canada 20: 79-88.
Niimi, A.J. and G.P. Dookhran. 1989. Dietary absorption
efficiencies and elimination rates of polyeyelie aromatic
hydrocarbons (PAHs) by rainbow trout(Salmo gairdneri). Environ.
Toxicol. Chem. 8: 719-722.
Niimi, A.J., H.B. Lee and G.P. Kissoon. 1989. Octanol/Water
partition coefficients and bioconcentration factors of ''~ •-
chloronitrobenzenes in rainbow trout (Salmo gairdneri). Environ.
Toxicol. Chem. 8: 817-823.
Oliver, B.C. and A.J. Niimi. 1983. Bioconcentration of
chlorobenzenes from water by rainbow trout: correlations with
partition coefficients and environmental residues. Environ. Sci
Technol. 17: 287-291.
Oliver, B.G. and A.J. Niimi. 1985. Bioconcentration factors of
some halogenated organics for rainbow trout: limitations in
their use for prediction of environmental residues. Environ.
Sci. Technol. 19: 842-849.
Oliver, B.G. and A.J. Niimi. 1988. Trophodynamic analysis of
polychlorinated biphenyl congeners and other chlorinated
hydrocarbons in the lake Ontario ecosystem. Environ. Sci.
Technol. 22: 388-397.
19
-------
Randall, R.C., H. Lee II, R.J. Ozretich, J.L. Lake and R.J.
Pruell. 1991. Evaluation of selected lipid methods for
normalizing pollutant bioaccumulation. Environ. Toxicol. Chem.
10: 1431-1436.
Rasmussen, J.B., D.J. Brown, D.R.S. Lean and J.H. Carey. 1990.
Food chain structure in Ontario lakes determines PCB levels in
lake trout (Salvelinus namaycush) and other pelagic fish. Can.
J. Fish. Aguat. Sci. 47: 2030-2038.
Schmitt, C.J., J.L. Zajicek and P.H. Peterman. 1990. National
contaminant biomonitoring program: residues of organochlorine
chemicals in U.S. freshwater fish, 1976-1984. Arch. Environ. ?
Contam. Toxicol. 19: 748-781.
Schuuman, G. and W. Klein. 1988. Advances in bioconcentration
prediction. Chemosphere 17: 1551-1574.
Stephen, C.R., D.I. Mount, D.J. Hansen, J.H. Gentile, G.A.
Chapman and W.A. Brungs. 1985. Guidelines for deriving numerical
national water quality criteria for the protection of aquatic
organisms and their uses. U.S. Environmental Protection Agency,
Office of Research and Development, Environmental Research Labs,
Duluth, MN; Narragansett, RI; Corvallis, OR.
Swackhamer, D.L. and R.A. Kites. 1988. Occurrence and
bioaccumulation of organochlorine compounds in fishes from
Siskiwit Lake, Isle Royale, Lake Superior. Environ. Sci. '''--.
Technol. 22: 543-548.
Thomann, R.V. 1989. Bioaccumulation model of organic chemical
distribution in aquatic food chains. Environ. Sci. Technol. 23:
699-707.
Thomann, R.V. and J.P. Connolly. 1984. Model of PCB in the Lake
Michigan lake trout food chain. Environ. Sci. Technol. 18:
65-71.
U.S.EPA. 1985A. Ambient water quality criteria for cadmium -
1984. EPA 440/5-84-032. U.S.EPA, Office of Water, Washington,
D.C.
U.S.EPA. 1985B. Ambient water quality criteria for copper -
1984. EPA 440/5-84-031. U.S.EPA, Office of Water, Washington,
D.C.
U.S.EPA. 1985C. Ambient water quality criteria for lead - 1984.
EPA 440/5-84-027. U.S.EPA, Office of Water, Washington, D.C.
U.S.EPA. 1986. Ambient water quality criteria for nickel - 1986.
EPA 440/5-86-004. U.S.EPA, Office of Water, Washington, D.C.
20
-------
U.S.EPA. 1991A. Assessment and control of bioconcentratable
contaminants in surface waters. Draft. U.S.EPA, Office of
Water, Washington, D.C.
U.S.EPA. 1991B. Technical support document for water
quality-based toxics control. EPA/505/2-90-001 U.S.EPA, Office
of Water, Washington, D.C.
Veith, G.D., D.L. DeFoe and B.V. Bergstedt. 1979. Measuring and
estimating the bioconcentration factor of chemicals in fish. J.
Fish. Res. Bd. Canada 36: 1040-1048.
Veith, G.D. and P. Kosian. 1983. Estimating bioconcentration
potential from octanol/water partition coefficients. Chapter 15
In PCBs in the Great Lakes. Mackay, D., R. Patterson, S.
Eisenreich, and M. Simmons (eds). Ann Arbor Science.
21
-------
APPENDIX A
TABLE Al
LIPID CONTENT OF EDIBLE PORTIONS OF GREAT LAKES FISH
Species Mean Values from Each Source
LAKE
SUPERIOR
SPECIES
Bloater Chub
Brown Trout
Carp
Chinook
Chinook
Chinook
Chinook
Coho
Coho
Coho
Herring
Herring
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Rainbow Smelt
Rainbow Trout
Rainbow Trout
Walleye
Whitefish
Whitefish
Yellow Perch
PERCENT LIPID
Xg Xa
N PORTION SOURCE
11.34
7.85
10.27
6.40
7.84
3.35
2.95
2.96
2.68
7.50
1.39
1.56
9.20
4.58
11.42
10.46
9.21
0.90
2.13
1.24
1.91
7.15
0.92
3
11
9
10
4
5
14
3
8
5
1
6
44
71
28
71
3
3
8
33
10
2
8
F
F
F
Fs
F
F
F
F
F
F
F
D
F
F
F
F
D
F
F
F
F
F
F
WDNR
WDNR
WDNR
MDNR
WDNR
MPCA
MPCA
WDNR
MPCA
MPCA
WDNR
MPCA
WDNR
MPCA
MPCA
MDNR
MPCA
WDNR
MPCA
WDNR
MDNR
MPCA,
WDNR
MICHIGAN
Black Bullhead
Bloater Chub
Brook Trout
Brovn Trout
Brown Trout
Brown Trout
Brown Trout
Brown Trout
Brown Trout
Carp
Carp
Carp
Channel Catfish
Chinook
Chinook
Chinook
Chinook
5.68
6.82
1.80
14.75
4.33
11.96
11.19
11.22
3.88
6.70
20.43
10.68
8.92
4.20
4.92
2.60
1.45
1
92
68
170
46
21
6
5
9
2
16
47
11
275
30
4
5
Fs
F
F
F
F
A
D
FS
F
F
Fs
F
FS
F
A
D
Fs
WDNR
WDNR
WDNR
WDNR
MDNR
IDEM
IDEM
IDEM
IDEM
IDEM
MDNR
WDNR
WDNR
WDNR
IDEM
IDEM
IDEM
22
-------
Chinook
Chinook
Chinook-trim
Coho
Coho
1.79
0.99
2.46
5.96
6.51
28
71
10
19
8
F
F
0
A
D
IDEM
MDNR
HDNR
IDEM
IDEM
23
-------
TABLE AI (continued)
LAKE
SPECIES
MICHIGAN CohO
(continued) Coho
Coho
Coho
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout
Lake Trout-trim
Longnose Sucker
Longnose Sucker
Longnose Sucker
Northern Pike
Northern Pike
Rainbow Trout
Steelhead
Steelhead
Steelhead
Steelhead
Walleye
Walleye
Walleye
Whitefish
White Sucker
White Sucker
Yellow Perch
Yellow Perch
Yellow Perch
Yellow Perch
Yellow Perch
MICHIGAN Black Bullhead
(Green Bay) Brook Trout
Brown Trout
Carp
Channel Catfish
Chinook
Coho
Lake Trout
Rainbow Trout
Smallmouth Bass
Walleye
White Bass
Yellow Perch
PERCENT LIPID
Xg Xa
2.42
N PORTION SOURCE
16.67
9.19
5.59
0.57
3.76
1.63
1.61
0.82
1.95
2.80
3.82
17.25
16.58
8.81
12.01
12.71
5.45
4.95
3.00
11.09
7.10
2.77
5.62
1.45
2.19
9.00
2.45
3.00
1.55
1.06
0.95
1.10
4.97
9.44
8.17
4.75
4.63
7.70
11.88
6.39
1.34
2.71
3.76
0.76
2
18
36
164
156
13
3
9
60
311
10
2
3
10
2
10
25
17
3
2
6
11
9
9
1
2
10
1
6
9
1O
24
8
9
106
48
15
46
1
28
45
10
67
18
26
Ts
F
F
F
A
D
FS
F
F
F
0
A
F
F
A
Fs
F
A
D
FS
F
F
Fs
F
A
A
F
A
D
F
F
F
FS
F
F
F
Fs
F
F
F
F
F
F
F
F
IDEM
IDEM
MDNR
WDNR
IDEM
IDEM
IDEM
IDEM
MDNR
WDNR
MDNR
IDEM
IDEM
MDNR
IDEM
MDNR
MDNR
IDEM
IDEM
IDEM
IDEM
MDNR
MDNR
WDNR
IDEM
IDEM
.MDNR
IDEM
IDEM
IDEM
MDNR
WDNR
WDNR
WDNR
WDNR
WDNR
WDNR
WDNR
WDNR
WDNR
WDNR
WDNR
WDNR
WDNR
WDNR
24
-------
HURON
Brown Trout 7.54
Carp 11.37
Channel Catfish 10.69
Chinook 1.72
Coho 3.96
Lake Trout 14.12
Walleye 1.62
20
9
1
44
8
80
10
F
FS
Fs
F
F
F
F
MDNR
MDNR
MDNR
MDNR
MDNR
MDNR
MDNR
25
-------
TABLE Al (continued)
LAKE
ERIE
SPECIES
Carp
Chinook
Channel Catfish
Coho
Lake Trout
Smallmouth Bass
Walleye
Walleye
White Bass
Whitefish
PERCENT LIPID
N PORTION SOURCE
Xg
3.44
7.11
2.56
Xa
3.88
4.50
13.00
1.99
1.98
4.42
8.75
8
21
10
22
5
19
40
9
8
4
Fs
F
Fs
F
F
F
F
FS
Fs
FS
MDNR
NYDEC
MDNR
NYDEC
NYDEC
NYDEC
MDNR
OEPA
OEPA
OEPA
ONTARIO Brown Trout
Channel Catfish
Chinook
Coho
Lake Trout
Rainbow Trout
Smallmouth Bass
White Perch
10.40
12.80
2.75
3.38
14.53
9.04
1.85
5.64
91
47
45
98
120
57
161
33
F
Fs
F
F
F
F
F
F
NYDEC
NYDEC
NYDEC
NYDEC
NYDEC
NYDEC
NYDEC
NYDEC
Key to Abbreviations
Percent Lipid:
Xg = geometric mean, contributing program (source) used geometric
means to summarize data
Xa = arithmetic mean, contributing program (source) used arithmetic
means to summarize data
N = Number of fish sampled
Portion:
F = filet, skin on
Fs = filet, skin off
A = Anterior section through fish
D = dressed (gutted, head removed)
0 = filet, skin off, visible fat removed (trimmed)
Source:
MDNR
MPCA
IDEM
Michigan Department of Natural Resources. Fish Contaminant
Monitoring Program, Data for Lakes Erie, Huron, Michigan and
Superior 1986-1989.
Minnesota Pollution Contol Agency. Minnesota Fish Consumption
Advisory Program, Data for Lake Superior.
Indiana Department of Environmental Management, OWM-Biological
Studies, Data for Lake Michigan.
26
-------
OEPA = Ohio Environmental Protection Agency. Ohio Dept. of Natural
Resources, Data for Lake Erie.
WDHR = Wisconsin Department of Natural Resources. Data for Lakes
Michigan and Superior.
NYDEC = New York Department of Environmental conservation. Data for
Lakes Erie and Ontario.
27
-------
TABLE A2
GREAT LAKES INITIATIVE
LIPID CONTENT OF FISH EDIBLE PORTIONS, SPECIES MEAN VALUES BY LAKE
Lake/Species Percent Lipid
Mean n*
LAKE SUPERIOR
Salmonids (excluding Siscowet) x = 5.65 n = 7
lake trout 10.61 4
herring 6.89 2
whitefish 7.50 2
brown trout 6.40 1
Chinook 2.99 4
coho 3.48 3
rainbow trout 1.69 2
Nonsalmonids x = 1.42 n = 2
walleye 1.91 1
yellow perch 0.92 1
Nongame fish x - 6.34 n = 3
bloater chub 10.27 1
carp 7.84 1
rainbow smelt . 0.90 1
All fish x - 5.12 n * 12
LAKE HURON
Salmonids x = 6.84 n = 4
lake trout 14.12 1
brown trout 7.54 1
Chinook 1.72 1
coho 3.96 1
Nonsalmonid fish x = 6.16 n = 2
walleye 1.62 1
channel catfish 10.69 1
All nongame fish (carp) 11.37 1
All fish x = 7.29 n = 7
28
-------
LAKE MICHIGAN (including Green Bay)
Salmonids x = 7.09 n = 7
brook trout 4.65 2
brown trout 8.58 7
rainbow trout (steelhead) 6.12 6
Chinook 3.15 7
coho 4.45 7
lake trout 13.70 7
whitefish 9.00 1
* Number of state programs reporting data for a species.
29
-------
TABLE A2 (continued)
Lake/Species Percent Lipid
Mean n*
LAKE MICHIGAN (including Green Bay) (continued)
Nonsalmonid x = 2.65 n = 7
black bullhead 1.45 2
northern pike 1..79 2
walleye 2.00 4
yellow perch 1.36 6
channel catfish 6.84 2
smallmouth bass 1.34 1
white bass 3.76 1
All nongame fish x = 8.41 n = 4
bloater chub 14.75 1
carp 11.53 4
longnose sucker 5.33 3
white sucker 2.03 2
All fish x = 5.61 n = 18
LAKES ST. CLAIR AND ERIE
Salmonids x = 7.53 n = 4
lake trout 13.00 1
whitefish 8.75 1
Chinook 3.88 1
coho 4.50 l
Nonsalmonid fish x * 3.95 n = 4
walleye 2.27 2
channel catfish 7.11 1
smallmouth bass 1.99 1
white bass 4.42 1
Nongame fish (carp) 3.44 1
All fish x - 5.48 n = 9
LAKE ONTARIO
Salmonids x = 8.02 n = 5
Lake trout 14.53 1
brown trout 10.40 1
coho 3.38 1
Chinook 2.75 1
rainbow trout 9.04 1
Nonsalmonid fish x = 7.33 n=2
smallmouth bass 1.85 1
30
-------
channel catfish 12.80
Nongame fish (excluding american eel)
white perch 5.64
All fish x = 7.55 n = 8
31
-------
SPECIES MEAN LIPID VALUES, POOLED FOR ALL GREAT LAKES
SALMONIDS MEAN(n*) NONSALMONID MEAN(n*) NONGAME FISH MEAN(n*)
Brook trout
Brown trout
Chinook
Coho
Herring
Lake trout
Rainbow trout
Whitefish
OVER ALL MEANS
Std. Dev.
N
OVER ALL MEANS
Std.Dev.
N
2
3
6
13
5
4.65(1)
8.23(4)
90(5)
95(5)
89(1)
19(5)
62(3)
8.42(3)
6.73
3.27
8
NONSALMONID MEAN(n*)
GAME FISH
Black bullhead
Channel catfish
Northern pike
Smallmouth bass
Walleye
White bass
Yellow perch
1.45(1)
9.36(4)
79(1)
73(3)
95(4)
09(2)
1.14(2)
3.07
2.93
7
Bloater chub
Carp
Longnose sucker
Rainbow smelt
White perch
White sucker
ALL GAME FISH
5.02
3.55
15
ALL FISH
5.25
3.68
21
12.51(2)
8.55(4)
33(1)
90(1)
64(1)
5,
0,
5.
2.03(1)
5.83
4.27
6
* Number of lakes for which data are available
APPENDIX B
TABLE BI
LIPID CONTENT OF WHOLE FISH FROM THE GREAT LAKES
Species Mean Values By Lake
SPECIES
Sup.
Mich.
LAKE*
Hur. St.c
Erie Ont.
CDF&O** MEAN
Salmonids
Bloater
Brown trout
Coho salmon
Lake herring
Lake trout
Lake Whitefish
Pink salmon
Rainbow trout
13.1 22.3
16.6
10.5
17.0
20.5
10.0
12.2 15.44
8.45
6.0
15.3# 17.25
1.78
7.59
17.7
13.8
8.5
17.3
10.3
1.8
7.6
32
-------
Skipjack herring 9.8 9.8
Spake 10.12 10.1
SALMONID MEAN 10.28
Nonsalmonid Game Fish
Brown bullhead 6.1 3.58 4.8
Channel catfish 18.7 11.7 15.2
Northern pike 2.17 2.2
Rock bass 4.8 = 4.8
Walleye 8.1 11.4 8.01 : 1.2
White bass 9.6 9.8 10.16 9.9
Yellow perch 7.4 4.1 4.2 5.6 5.95; '5^5
•3
NONSALMONID GAME FISH MEAN 7.35
Nongame Fish
Alewife 9.73 9.7
Bluntnose minnow 1.5# 1.5
Common carp 10.5 9.5 11.0 5.8 8.59 9.1
Emerald shiner 1.6# 2.7# 2.2
Freshwater drum 8.4 8.4
Rainbow smelt 4.78 4.8
Redhorse 6.4 6.4
Slimy sculpin 6.95 7.0
Spottail shiner 2.0# 1.8# ' . 1.9
White perch . 10.2# 10.2
White sucker 6.8 6.0 4.9 5.15 5.7
NONGAME FISH MEAN . 6.07
MEAN, ALL FISH 7.90
Std. Dev. 4.43
N 28
33
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TABLE Bl (continued)
Footnotes
* Data for the individual lakes from U.S. Fish and Wildlife Service
National Contaminant Biomonitoring Program 1976-1984.
** CDF&O = Canada Department of Fisheries and Oceans. Percent lipid
data for unspecified Great Lakes. These data are averaged
together with the lake-specific data from the U.S. Fish and
Wildlife Service.
# Value includes data from the New York State Department of
Environmental Conservation.
Data Sources:
Canada Department of Fisheries and Oceans, Great Lakes Contaminant
Surveillance Program, 1977-1985.
New York Department of Environmental Conservation
Schmitt, C.J., J.L. Zajicek and P.H. Peterman. 1990. National
contaminant biomonitoring program: residues of organochlorine
chemicals in U.S. freshwater fish, 1976-1984. Arch. Environ.
Contam. Toxicol. 19: 748-781.
34
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