COM
BIOACCUMULATION:
ASSESSMENT AND REGULATION
Camp Dresser & McKee
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U.S. Environmental Protection Agency
Contract No. 68-01-6403
BIOACCUMULATION:
ASSESSMENT AND REGULATION
Gary T. Hickman
Ming P. Wang
Laura L. Bowers
Camp Dresser & McKee
7630 Little River Turnpike, Suite 500
Annandale, Virginia 22003
September 1984
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CONTENTS
Section Page
1 INTRODUCTION 1-1
2 BIOACCUMULATION THEORY AND MODELING 2-1
Background 2-1
Empirical Steady-State Method 2-5
Kinetic Methods 2-7
Uptake from Water 2-7
Uptake from Sediment 2-15
Thermodynamic Equilibrium Methods 2-17
Uptake from Water 2-17
Uptake from Sediment 2-25
Other Bioaccumulation Methods 2-32
Discussion 2-34
3 REGULATORY APPLICATION OF BIOACCUMULATION DATA 3-1
Implementation Manual 3-1
Decision Guidelines 3-4
Background 3-4
Application 3-5
Discussion 3-5
Review of Decision Guideline Levels 3-10
Sediment Quality Criteria 3-12
Background Levels 3-13
Water Quality Criteria 3-13
Biological Response 3-14
Equilibrium Partitioning 3-15
REFERENCES
APPENDIX A. ANALYSIS OF MEAN CONTAMINANT RESIDUES IN AQUATIC
INVERTEBRATES OF THE NEW YORK BIGHT
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ACKNOWLEDGEMENT
The authors wish to thank Richard Petti cord and Victor McFarland of the
U.S. Army Waterways Experiment Station and Norm Rubinstein of the U.S.
Environmental Protection Agency for their valuable input.
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SECTION 1
INTRODUCTION
Aquatic organisms are capable of accumulating concentrations of toxic
chemicals in their tissues that may be detrimental to themselves or to
animals which feed upon them, including man. Sources of contaminants for
biological uptake include water, sediment, and biota. A variety of methods
have been developed for estimating the extent of biological uptake. These
include empirical steady-state exposure, kinetic modeling, and methods
based on thermodynamic equilibrium partitioning.
Predicting biological uptake from contaminated sediment is complicated
because only a fraction of the total (bulk) concentration of contaminants
in sediment may be bioavailable. The bioavailability of a sediment-
associated contaminant can be defined as the degree to which the
contaminant is biologically active or accumulated by aquatic biota (Adams,
1984). Sediment contamination has become a matter of concern in part
because negative biological effects have been observed in aquatic
ecosystems where water column contaminant concentrations do not exceed EPA
water quality criteria. It has been suggested that the occurrence of
adverse effects in the absence of water quality criteria violations may be
due to sediment contamination, but that is difficult to prove.
An example of such a situation is the New York Bight Apex. Average water
column concentrations of toxic chemicals in Apex waters are substantially
lower than the average value saltwater quality criteria, while the benthic
community is severely degraded in some areas of the Bight (Wang et al.,
1984). Pavlou and Weston (1983) have suggested a similar scenario for
Puget Sound. Dredged material and sewage sludge are discharged into the
New York Bight Apex, representing two sources of contaminated sediment
whose biological consequences need to be evaluated. The regulatory process
requires that bioavailability be evaluated for dredged material proposed
for discharge into ocean waters.
1-1
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The objectives of this report are:
(1) to review the currently available methods for assessing biological
uptake, with specific emphasis on uptake from sediment, and
(2) to review the regulatory application of bioaccumulation data, with
specific emphasis on dredged material disposal in the marine
environment.
1-2
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SECTION 2
BIOACCUMULATION THEORY AND MODELING
BACKGROUND
Aquatic organisms can accumulate chemicals from their environment via at
least three different pathways (Swartz and Lee, 1980):
o direct adsorption to the body wall or exoskeleton;
o direct absorption through the Integument, gills, or other
respiratory surfaces; or
o ingestion of contaminated sediment, food, or water followed by
absorption through the gut.
Three terms are frequently used in describing biological uptake of
chemicals: bioconcentration, bioaccumulation and biomagnification. These
terms are used inconsistently in the literature. Their meaning in this
paper is consistent with the terminology of Brungs and Mount (1978):
o Bioconcentration is the process by which toxic substances present in
solution enter aquatic organisms directly through the gills or
epithelial tissue.
o Bioaccumulation includes bioconcentration and the uptake of toxic
substances from dietary sources such as contaminated particles or
prey organisms.
o Biomagnification is the process by which the tissue concentration of
a bioaccumulated toxic substance Increases as the material passes up
through two or more trophic levels.
In general, aqueous-phase contaminants are bioavailable and are readily
bioconcentrated. For most chemicals, direct uptake from water is the
primary route of exposure, while dietary uptake is relatively insignificant
(Macek et al., 1979; Bruggeman et al., 1981). However, uptake from dietary
sources may be an important route of exposure for very lipophilic chem-
icals. Macek et al. (1979) reported that dietary uptake by fish was
2-1
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insignificant compared to bioconcentration for seven of eight chemicals
studied of varying lipophilicity, but that dietary uptake of DDT con-
tributed significantly to the total body burden (Jarvinen et al., 1977).
DDT was the most lipophilic chemical in the data reviewed by Macek et al.
and, consequently, exhibited the slowest depuration rates of residues from
fish. Likewise, the models of Weininger (1978) and Thomann and Connolly
(1984) have predicted that food chain transfer of PCBs in Lake Michigan
accounts for nearly 100% of the total PCB residues in adult lake trout.
Bruggeman et al. (1981) stated that blomagnification factors (ratio between
concentration in fish and in food) greater than one can be expected only
for very lipophilic chemicals having octanol/water partition coefficients
greater than 5, and that under natural conditions a biomagnification factor
of one or greater may indicate a significant contribution of dietary uptake
to the total body burden.
Chapman (1984) presented a simple model of the relative importance of food
and water as the route of contaminant uptake by fish. Respiratory exposure
was computed as the product of ventilation rate and water concentration,
and dietary exposure was computed as the product of feeding rate and
contaminant concentration in food. Assuming that the relative efficiency
of uptake via the two routes of exposure is equivalent, the relative
Importance of each route was estimated as a function of relative con-
taminant concentrations in food and water as shown in Table 1. Chapman's
(1984) calculations (Table 1) indicate that dietary uptake is insignificant
until contaminant concentrations in food exceed concentrations in water by
more than 1,000-fold, and that dietary uptake is the dominant route of
exposure when food concentrations are greater than 10,000 times aqueous
concentrations.
It is clear that contaminated sediments can be an important source of
contaminants for biological uptake. Uptake from sediment can occur via two
potential avenues: desorption to interstitial and interfacial water
followed by bioconcentration, or bioaccumulatlon through ingestion of
contaminated sediment particles. The relative contribution of those two
mechanisms has not been elucidated, but it will depend upon the feeding and
respiratory strategies of the organism. Deposit-feeding Infauna and
epibenthos are of primary interest with respect to uptake from sediment.
2-2
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TABLE 1
RELATIVE IMPORTANCE OF FOOD AND WATER AS THE ROUTE
OF UPTAKE FOR FISH AS A FUNCTION OF THE RATIO OF
CONTAMINATION BETWEEN FOOD AND WATER
Contaminant in Percent Uptake3
Food (mg/kg):Water (mg/1) from Food
1:1 < 1
10:1 < 1
100:1 1
1,000:1 9
10,000:1 50
100,000:1 91
1,000,000:1 99
Calculations based on respiratory volume of 200 1/kg/day, food consumption
of 0.02 kg/kg/day, and uptake/depuration rates independent of route of
exposure.
Source: Chapman, 1984.
2-3
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Hydrophobia organic compounds and some toxic metals have a strong affinity
for participate matter; consequently, those contaminants are commonly
associated with the bottom sediments in aquatic systems. Toxic metals
associated with sediments generally are not readily bioavaiTable. Thus,
the water column appears to be the predominant source for biological uptake
of many toxic metals (Fowler et al., 1978). In contrast, contaminated
sediments can be an important source of hydrophobic organic compounds for
biological uptake (Halter and Johnson, 1977; Fowler et al., 1978; Courtney
and Langston, 1978; Rubinstein et al., 1983; Rubinstein et al., 1984),
Although hydrophobic organic substances are readily taken up from water,
they have low solubilities and are highly associated with particulate
material. Consequently, relatively little of those substances is available
for aqueous uptake compared to the amount associated with sediments. For
example, Fowler et al. (1978) measured concentration factors for PCBs in a
polychaete worm (Nereis diversicolor) of 800-fold from water and 3.5-fold
from sediment, but because sediment concentrations of PCBs are typically so
much greater than aqueous concentrations, the authors concluded that 85-99
percent of the worm's body burden could be attributed to uptake from
sediment.
A number of recent studies have shown dietary uptake to be a major source
of biological residues of hydrophobic organic compounds (Thomann, 1981;
Jensen et al., 1982; Pizza and O'Connor, 1983; Thomann and Connolly, 1984;
Rubinstein et al., 1984). Those substances are predominantly associated
with particulate organic material in sediments, which can serve as a food
source for infaunal and epibenthic organisms. Such species can accumulate
synthetic organic compounds from contaminated sediments (Roesijadi et al.,
1978; Fowler et al., 1978; Courtney and Langston, 1978; McLeese et al.,
1980; Wyman and O'Connors, 1980; Lynch and Johnson, 1982; Rubinstein et
al., 1983; Adams et al., 1983; Rubinstein et al., 1984), and many of those
organisms are important prey species for organisms at higher trophic
levels. Rubinstein et al. (1984) demonstrated that contaminated sediment
can serve as a source of PCBs for uptake and trophic transfer in marine
systems. Fish (Leiostomus xanthurus) exposed to PCB-contaminated sediments
and fed a daily diet of polychaetes (Nereis virens) from the same sediment
accumulated more than twice the PCB whole-body residues than fish exposed
2-4
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to PCB-contaminated sediment but fed uncontaminated polychaetes (Rubinstein
et al., 1984).
EMPIRICAL STEADY-STATE EXPOSURE METHOD
Chemicals can be taken up from the environment by aquatic organisms. In
turn, animals may be capable of eliminating contaminants from their bodies.
When the environmental concentration of a contaminant is constant, a
theoretical equilibrium level exists where the amount of chemical entering
an animal is equal to the amount leaving the animal. The condition under
which a contaminant concentration is an animal is constant is defined as
steady state, and is often recognized as a plateau in the observed tissue
concentration (Figure 1). In laboratory tests aquatic organisms commonly
require days to months of exposure to acquire steady state tissue con-
centrations of toxic organic compounds or toxic metals.
Equilibrium residue concentrations can be determined directly by exposing a
large number of animals to a constant concentration of a chemical,
periodically sampling the chemical concentration in the animals, until
steady-state conditions are reached. This method can be used to measure
uptake from water, sediment, food, or combinations of these sources.
The aqueous exposure system permits the most accurate measurement of
biconcentration factor (BCF), which is defined as the ratio of the
equilibrium concentration of a chemical in aquatic organisms to the
concentration in water:
C ss
BCF = Lb (1)
w
where
Cbss = steady-state concentration of the chemical in biota
Cw = concentration of the chemical in water (which is kept
constant during the test)
2-5
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p
c
p
n
y
T
U
E
e
H
e.64-1
6.63-
6.6i-
a 6.
66-
ae
n > [—
46 60
DAYS
se
e.s-
e.4-
e.a-
....
i • i
46 66
DAVS
86
MS-*
§
i »*>
166
6.01
10 10 90 40 SO AO 70 10
DAVI EXPOSURE
35 42 |.j .1 .10 -14 -Jl -28
I*
O*r
Figure 1. Typical uptake curves. Uptake of PCBs from contaminated sediments
by (a) grass shrimp, (b) polychaete worm, and (c) Asiatic clam.
Uptake from contaminated water of (d) mercury by eastern oyster
and (e) isopropalin by bluegill. Sources: Rubinstein et al.,
1983b; McFarland et al., 1984; Kopfler, 1974; Hamelink, 1977.
2-6
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Uptake from the water column has been measured for many chemicals and many
species. For example, in the Ambient Water Quality Criteria Documents
(EPA, 1980, 1983), BCFs are reported for a variety of freshwater and
saltwater organisms for cadmium, mercury, chlordane, DDT, dieldrin,
heptachlor, PCBs, toxaphene, and other toxic chemicals.
Since BCFs consider only uptake from water and ingnore ingestion of
contaminated food and/or sediment, the term bioaccumulation factor (BAF)
has been used to describe uptake from sediment or uptake from all sources.
The empirical steady-state exposure approach has the advantages of
conceptual simplicity and a high degree of biological certainty (Hamelink,
1977), but the disadvantages of long test durations and the associated
difficulty of maintaining constant test conditions. For example,
Rubinstein et al. (1983) found that 30 to 40 days of exposure were required
for Nereis worms to accumulate steady-state PCB concentrations. Another
disadvantage is the inherent variability of tissue concentrations about the
mean which makes it inappropriate to represent the equilibrium concentra-
tion as the observed body burden at the end of the test period. To resolve
that problem, Rubinstein et al. (1983) used a nonlinear regression program
to fit their data to the 3-parameter uptake model of Banner and Oglesby
(1981) to produce smooth uptake curves and yield an estimate of the
steady-state concentration (Figure 2).
KINETIC METHODS
UPTAKE FROM WATER
The process of bioconcentration frequently has been described as the
balance between two competing kinetic processes: uptake and depuration
(e.g., Branson et al., 1975; Hamelink, 1977; Ernst, 1977; Veith et al.,
1979; McLeese et al., 1980; Mackay, 1982). The simpliest kinetic model of
biological uptake consists of a medium (water) compartment (CJ and a
n
biotic compartment (Cb>:
2-7
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6-
*~* 5"
•o
O
CO
CL
*-* 3-
c
z 2-
r (
z
UJ
CJ
S 6-
LU
oo 5-
*— i
l—
CQ
0
•
J* i • 1
f '
I , 5°~
1 y= 5 16/11+ 601"7)
i **
3 20 40 eb ab iob c
I • 60-
^ ' •
i 55-
1
I y=600/(l*52'"6) :
o 45-
20 40 60 80 100 C
DAYS
Bahner and Oglesby
Y = PI
(t-P3)
1 + P2
(£) • ! •
• V^fl 8
• CF
x i.i m
y* 6I6/(I» 91 l l0')
> 20 40 60 80 100
(p) s . •
"x^ ^*
• f
«y • o jO/ 1 1 ^ 92 )
* ^™
20 40 60 80 100
Model
Where Y = In Cg at time t
PI = In Cn» maximum
P2 = rising slope
P3 = t when Y = Pl/2
Figure 2. Bioaccumulation of PCBs to steady-state by N. Virens exposed to
four different sediments. The uptake curves were constructed
using the non-linear regression model of Bahner and Oglesby
(1981). Source: Rubinstein et al., 1983.
2-8
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"l
where kj and k2 are the rate constants for movement of the chemical into
(uptake) and out of (depuration) the animal, respectively. Assuming first
order kinetics for uptake and depuration, the following relationship has
been derived:
dCb/dt = kLCw - k2Cb (3)
Integration of this differential equation, when Cw is constant and for
initial conditions of t and C. equal to zero, gives:
Cb = (k^kg) Cw (l-e'V) (4)
As time approaches infinity, equilibrium Is reached, i.e., dCu/dt a 0, and
the above equation becomes:
CbSS = (kl/k2) Cwss (5)
or
CbSS/cw" = kl/k2 = BCF
Thus, BCF can be estimated as the ratio between the uptake and depuration
rate constants, which can be determined experimentally. Test conditions
employed to determine the uptake rate constant are essentially the same as
those used in the empirical steady-state exposure approach except that
animals are sampled more frequently during a brief exposure period. After
the uptake period, animals are transferred to aquaria containing clean
water (and sediment) and periodically sampled until the depuration rate
is established. Then, k can be calculated. Branson et al. (1975)
2-9
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used this kinetic model to calculate a BCF for 2,2',4,4'-tetrachlorobi-
phenyl in rainbow trout muscle, and found that a 5-day uptake period plus a
clearance time of one half-life (i.e., the time required to eliminate 50
percent of the accumulated contaminant residue) was sufficient to
characterize uptake and depuration. The validity of the method was
demonstrated when the BCF thus obtained compared favorably to that deter-
mined experimentally during 42 days of exposure.
McFarland et al. (1984) described an alternative method for estimating
steady-state body burdens of hydrophobic organic compounds from residues
measured after a short exposure period in which steady-state was not
reached. Combining Equations 4 and 5 and rearranging the resulting
expression yields:
ss
=
Equation 7 implies that under conditions of constant exposure a steady-
state tissue concentration (Cbss) can be estimated from a tissue concen-
tration (Cb) at any time (t), provided the elimination rate constant (k2)
is known or can be estimated for that chemical. The actual exposure
concentration need not be known, but must be assumed to be constant
(McFarland et al., 1984).
Spacie and Hamelink (1982) demonstrated that the kinetic elimination rate
constant, k2» for a particular chemical can be predicted from its
octanol-water partition coefficient (KQW). Using the data of Meely et al.
(1974) and Konemann and van Leeuwen (1980), Spacie and Hamelink derived the
regression:
log k2 = 1.47 - 0.414 log KQW (8)
r = 0.95
McFarland et al . (1984) used k? values estimated in this way along with
residues of di- and tri-chlorob1phenyl in asiatic clams and fathead minnows
2-10
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measured after seven days of exposure to estimate steady-state body burdens
using Equation 7. Steady-state residues predicted from single-time-point
observations did not differ significantly from tissue concentrations
measured at a time corresponding to 99 percent of steady-state (McFarland
et al., 1984).
A number of more complex kinetic models have been proposed for evaluating
residue accumulation. Figure 3A schematically represents the simple two-
compartment model described above. Figure 3B and 3C are three-compartment
models (one water compartment and two biotic compartments) presented by
Blau et al. (1975); the only difference being that Figure 3C includes a
rate constant for elimination from compartment 3 back to the water. Blau
and co-workers suggested that a three-compartment model may be appropriate
in cases where the chemical is preferentially accumulated in a particular
tissue or where the metabolization rate constant (k*?) is substantially
different than the rate of uptake from the medium (k12K Notice that in
the model shown as Figure 3B when k32 is much greater than k23 the model
reduces to the two-compartment model shown as Figure 3A. Krzeminski et al.
(1977) presented the model shown diagramatically in Figure 3D and
mathematically below, which includes a rapid exchange compartment (viscera)
and a slow exchange compartment (tissue) for both the parent compound and
metabolites.
• kxW + k4T - (k2 + k3 + ks) V (9)
dVM
dT ' k5V + k7TM - (k6 + V VM
£ = k3V - k4T
dTM
dF = k6VM " k7TM
where
W = the nominal exposure level during the exposure phase,
and zero during the withdrawal phase
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a
Water
Compartment
Fish
Compartment
Constant
Chemical Addition
Constant Rate
Of
Chemical Removal
Fiih
Compartments
water
water
k' .
KI
* k'
Viscera
(parent
compound)
k,
\
f
Viscera
(metabolites
+ natural
products)
* ^
•x
m
k.
" H,
Tissue
(parent
compound)
Tissue
(metabolites
+ natural
products)
Level of
Complexity
Figure 3. Kinetic compartment models of biological uptake.
Blau et al., 1975; Krzeminski et al., 1977.
Sources:
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V = the concentration of the parent compound in the
viscera at time t
VM = the concentration of the metabolites in the viscera at
" time t
T = the concentration of the parent compound in the tissue
at time t
TM = the concentration of the metabolites in the tissue at
time t
k = the rate constants for the reactions shown in Figure
3D above
For refractory substances that resist metabollzation such as PCBs, DDT, or
toxic metals, this model would reduce to level one complexity, that is, the
three-compartment model shown as Figure 3B.
Although first order kinetics are usually assumed in kinetic studies of
bioaccumulation, they are rarely verified. While simple first order
kinetics are apparently appropriate for describing initial uptake rates
regardless of the mechanism or number of compartments used to model
subsequent distribution in the animal, Spacie and Hamelink (1982) suggest
that models with biphasic, second order, or Michaelis-Menten kinetics may
better describe depuration, depending on exposure level and mode of
elimination. For example, often an initial rapid loss phase is followed by
a slower elimination rate, forming a biphasic pattern that can be resolved
into two linear components (Figure 4). The initial rapid phase represents
elimination from a fast or central compartment, while the slower phase
represents redistribution from a peripheral compartment within the animal
to the central one before elimination occurs. Figure 3B represents this
type of model, and the rate constants can be determined using graphical
constants as shown in Figure 4.
The major advantage of using kinetic models for assessing bioaccumulation
is that shorter test durations are required for some chemicals compared to
the empirical steady-state exposure method. Kinetic models can also
provide insight into the routes and rates functioning in the bioaccumu-
lation process. Use of kinetic models has the disadvantage of requiring
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A + B
Time
'32
= Ab + Ba
A + B
'23 = a + b - k,, - k
= k
K32
32
21
Figure 4. Graphical representation of results obtained from
a depuration experiment for which the first-order,
three-compartment model (e.g., Figure 3b) would
apply. Source: Spacie and Hamelink, 1982.
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nonlinear regression parameter estimation techniques to estimate rate
constants, and multi compartment models may require separate analysis of
multiple anatomical compartments and/or analysis of parent compounds and
metabolites.
UPTAKE FROM SEDIMENT
McLeese et al. (1980) used the first order, two compartment kinetic model
described by Equation 4 to estimate steady-state PCB concentrations
accumulated by Neveri s virens exposed to contaminated sediment. The
authors modified Equation 4 by substituting the ambient sediment
concentration, GS, for the aqueous concentration term, Cw:
C = (kk) C (l-e') (4)
b = g s
where: Cs = concentration of PCB in sediment (ug/g dry weight)
The uptake rate constant (kj) was determined from whole-body residue data
measured over 32 days of exposure to contaminated sediment during which
steady-state was not approached, and the depuration rate constant was
determined from residue measurements after the worms were transferred to
aquaria containing clean sand. Bioaccumulation factors, defined as the
ratio of wet weight tissue concentration to the dry weight sediment
concentration at steady state, were calculated mathematically as k1/k2. A
schematic representation of the bioaccumulation model used by McLeese et
al. (1980) is shown as Figure 5a.
McFarland et al . (1984) also applied the first order kinetic model to
biological uptake of PCBs from contaminated sediment, but their exposure
system was designed to ensure that the aqueous phase was an intermediate
step between sediment and biota (Figure 5b). Indeed, it has been suggested
that water is the probable medium of exchange for all pathways of bioaccum-
ulation (Rubinstein et al . 1984). Fathead minnows (Pimephales promelas)
and Asiatic clams (Corbicula fluminea) were, except for one series of
exposures in which clams were allowed to burrow into sediment, restrained
from contacting the contaminated sediments in the exposure tanks. Equation
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a
SEDIMENT
uptake
depuration
BIOTA
SEDIMENT
deaorptlon
sorptlon
WATER
uptake
depuration
BIOTA
Figure 5. Schematic of kinetic uptake models used by: (a) McLeese et al
(1980) and (b) McFarland et al. (1984).
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4 (without modification) was used to predict steady-state body burdens and
BCFs.
THERHOPYMAMIC EQUILIBRIUM METHODS
The thermodynamic equilibrium approach allows the prediction of equilibrium
tissue concentrations of contaminants in biota based on physico-chemical
equilibrium properties of the contaminant. The method is invalid for
chemicals that are significantly transformed in the biotic phase {Mackay,
1982). However, many toxic compounds which are of greater environmental
concern are fairly persistent, and this method shows great promise in
predicting equilibrium contaminant concentrations in biota.
The theoretical basis behind the thermodynamic equilibrium approach was
discussed by Mackay (1979). Mackay introduced the concept of fugacity by
considering the environment as several compartments—including atmosphere,
water, sediment, soil, and biota—some of which are contiguous (e.g., water
and sediment) while others are not (e.g., atmosphere and aquatic sediment).
If it is assumed that each compartment is completely mixed and at thermo-
dynamic equilibrium, then a contaminant in the environment will be
distributed such that the chemical potential (or fugacity) of the substance
in each phase is equal (Mackay, 1979). The distribution of a substance
between source and sink at equilibrium is independent of pathways of
chemical exchange and can be described by a distribution or partition
coefficient (K):
sink/'' source = ^source, sink
where Ce is the concentration of a substance in a particular phase at
equilibrium (equal chemical potential).
UPTAKE FROM WATER
Simple relationships have been established between equilibrium properties
of organic chemicals and their ability to accumulate In aquatic biota.
Several structure-activity correlations have been presented by which
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bioconcentration factors of hydrophobia organic chemicals can be predicted
from n-octanol/water partition coefficient (KQW) or aqueous solubility (S).
BCF - K0n Relationship
Bioconcentration can be viewed simply as the partitioning of a chemical
between biota and water. n-Octanol is a good medium for simulating natural
fatty tissues of plants and animals; therefore, the octanol/water partition
coefficient,
Ce
K = n-octanol (11)
ow ~e
water
is a good predictor of the partitioning of lipophilic organic chemicals
between biota and water (BCF). Table 2 lists BCF-KQW regression equations
published in the literature. Those regressions were developed from fish
{rainbow trout, fathead minnow, bluegill, guppy, mosquitofish) biocon-
centration data.
Even though all researchers obtained a good linear relationship between log
BCF and log KQW, the values of the coefficients in the regression equations
vary a great deal. Figure 6 is a comparison of the nine regressions listed
in Table 2. The difference between the lowest and highest BCF predicted
from a given KQW by the lines in Figure 6 ranges between 30 to 103. The
disparity among the regression equations may be due to at least three
sources of variation: the chemical compounds used in developing the
regression, the test species (and/or their lipid content) used in deriving
BCFs, and the method used to measure or estimate K '$. The variation
ow
among the regression lines calls for a closer examination of the
biological- chemical meaning of the parameter values.
Applying the fugacity approach and assuming that the chemicals are not
appreciably transformed, Mackay (1982) proposed that BCF is proportional to
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TABLE 2
PUBLISHED REGRESSION EQUATIONS BETWEEN BCF AND K
ow
1
2
3
4
5
6
7
8
9
log
log
log
log
log
log
log
log
log
BCF
BCF
BCF
BCF
BCF
BCF
BCF
BCF
BCF
Number
of
Equation Chemicals
(n)
= 0.124 + 0.542 log KQW 8
= -0.7504 + 1.1587 log KQW 9
= 0.7285 + 0.635 log KQW 11
= -1.495 + 0.935 log KQW 26
= -0.70 + 0.85 log KntaJ 55
uw
= -0.23 + 0.76 log KQW 84
= -1.32 + log KQW 44
= -0.063 + 0.980 log KQW 6
= -0.075 + 0.74 log KQW 36
Correlation
Coefficient Reference
(r)
0.97 Neely et
1974
al.,
0.98 Metcalf et al.,
1975
0.79 Kenaga &
1980
0.87 Kenaga &
1980
0.95 Veith et
1979
0.91 Veith et
1980
Goring,
Goring,
al.,
al.,
0.97 Mackay 1982
0.99 Konemann
Leeuwen,
0.88 Pavlou &
i noi
& van
1980
Vleston,
2-19
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-n
_j.
c
T
en
o
«-«•
o
CO
o
n
ro
i
ro
o
-s
ro
-s
(D
O
3
(D
o
ID
O)
in
r+
a.
cu
cr
n>
2 -
1 -
8
logK
ow
-------
BCF/KOW = A (12)
where A is a constant. In other words, in the general linear relationship
between log BCF and log KQW:
log BCF = a + b log K
QW
the coefficient b should be 1 and the value of a is a function of lipid
content (yL) and the relative activity. coefficient (TJ) and molar volume
( "j) in lipfd (L) or octanol (0):
a - log A = log Lv ° "° (13)
Mackay (1982) tested his hypothesis using the data of Veith et al. (1979).
Some of those data were eliminated because of suspected error in measure-
ment or discrepancies between KQW values used by Veith et al. and those
reported elsewhere in the literature. Data for chemicals with log Kow
greater than 6 and for compounds which are surfactants or may ionize were
also discarded. The equation that resulted is:
log BCF = - 1.32 + log KQW (14)
r = 0.97
This approach makes fuller use of the available theory and provides insight
Into the variables that affect the slope and Intercept of BCF-KOW rela-
tionships. For example, the Hp1d content of the test organisms is an
important variable which is often neglected by researchers.
Mackay (1982) suggested that other BCF-KQW correlations have often
exhibited b values less than unity primarily due to the tendency to
overestimate KQW for high molecular weight compounds. Mackay further
cautioned the use of the correlations when membrane permeability limits
bioconcentration and for chemicals that bind to proteins causing an
underestimation of the biotic concentration.
2-21
-------
Published BCF-KQW regressions have been developed from BCF data for a very
limited group of organisms, primarily nektonic fish. For example, 90
percent of the data points included in Mackay's (1982) analysis (Equation
14) incorporated BCF values obtained using fathead minnows. The ability of
such a regression to accurately predict bioconcentration in other species
is uncertain. Veith et al. (1979) investigated the effect of species
difference on bioconcentration and found that fathead minnow and green
sunfish accumulated three times more PCB residues than rainbow trout.
It is possible that variability in lipid content is the primary cause of
the different bioconcentration potential among species. McFarland et al.
(1984) reported that bioconcentration is activity specific rather than
species specific. McFarland and co-workers found that, when normalized by
the lipid content of the test species, BCFs of PCBs were similar for
Asiatic clams (2.03 percent lipid) and fathead minnnows (5.03 percent
lipid). Based on their results, McFarland et al. (1984) suggested that
expression of residue data on a lipid basis may enable comparisons among
unrelated organisms. If this is true, it could greatly increase the
utility of BCF-KQW regression equations, and, therefore, further
investigation is warranted to verify that suggestion.
BCF - Water Solubility Relationship
The aqueous solubility of organic chemicals can be used to predict
bioconcentration in a manner similar to KOW. Several authors have shown
that solubility is related to KQW (Hansch et al., 1967; Chiou et al., 1977;
Tulp and Hutzinger, 1978; Kenaga and Goring, 1980; Mackay et al., 1980).
Consequently, solubility correlates satisfactorily with BCF, as shown by
the regression equations listed in Table 3 and plotted in Figure 7. Figure
7 shows that, for a given solubility, a BCF predicted by the plotted
equations will be within a difference of 10 to 50. This variation is
considerably less than that associated with BCF-KQW regressions.
The BCF-Kftb. and BCF-S correlations are useful because they allow prediction
UVf
of contaminant residues from aqueous concentrations and physio-chemical
properties that are more readily available or more easily measureable than
2-22
-------
TABLE 3
PUBLISHED REGRESSION EQUATIONS BETWEEN BCF AND WATER SOLUBILITY(S)
Number
Units of Correlation
Equation of S Chemicals Coefficient Reference
(n) (r)
1 log BCF = 3.9950 - 0.3891 log S ppb 11 0.92 Lu & Metcalf,
1975
2 log BCF = 3.41 - 0.508 log S umole/1 8 0.96 Chiou et al.,
1977
3 log BCF = 2.791 - 0.564 log S ppm 36 0.72 Kenaga & Goring,
1980
4 log BCF = 2.183 - 0.629 log S ppm 50 0.66 Kenaga & Goring,
1980
5 log BCF = 3.71 - 0.316 log S ppb 25 0.57 Davies & Dobbs,
1984
(Recalculated
from Veith et
al., 1980)
6 log BCF = 5.09 - 0.85 log S ppb 11 0.87 Metcalf et al.,
1973
7 log BCF = 2.83 - 0.55 log S ppm 42 - Davies & Dobbs,
1984
(Extrapolation
from Kobayashi,
1981)
2-23
-------
ro
n>
-I -o
01 — •
o- o
— • c+
n>
o
ro -h
it i
T3 Q>
T3 r+
o
c
o-
ro
ro
CO
o
3
n>
o>
o
10
rt-
0)
O.
6
u_
O
m
o>
o 3
-5
-4
-3
-2 -1 0
log S
-------
BCFs. However, they must be used discriminately. They may not accurately
predict BCF for: compounds with log KQW outside the range of 1 to 6;
compounds with short half-Hfes {high k2); compounds that differ in
chemical nature from those used in the regression development; or compounds
with steric hinderances to accumulation (Tulp and Hutzinger, 1978; Mackay,
1982). Furthermore, these bioconcentration models do not account for
changes in chemical forms resulting from the presence of suspended solids
or sediments or for the effect of ingestion of contaminated food organisms
or particles on total body burdens.
Bruggeman et al. (1981) developed an equilibrium partitioning model for
directly estimating bioconcentration of lipophilic chemicals. Since
lipophilic chemicals accumulate primarily in the lipid tissue of biota, the
BCF can be estimated as:
kl "Vw
DfT — •*— " V — V V
" F" "" T l " wl 1
where
T = activity coefficient of the chemical in water (w) or
lipid (1)
X] = lipid content of the organism (g lipid/g organism
weight)
Kwl = Hpid/water partition coefficient
Bruggeman et al. (1981) reported that Kwl is relatively organism
independent.
UPTAKE FROM SEDIMENT
Recently, biological uptake from contaminated sediments has generated much
concern, especially since negative biological effects, including bio-
accumulation, have been observed in areas where water column contaminant
concentrations do not exceed ambient water quality criteria. Thermodynamic
2-25
-------
methods have been proposed for predicting residue accumulation by benthic
organisms from contaminated sediments.
Karlckhoff et al. (1979), Means et al. (1980), and KaricJchoff (1981)
studied the equilibrium sorption behavior of hydrophobic organic chemicals
and found that at low pollutant concentrations (Cw < 0.5 S) sorption
isotherms were linear, reversible, and characterized by a partition
coefficient,
C% = Kws Cew (16)
where Ces and Cew are the equilibrium concentrations of the substance in
the sediment and water, respectively, and KWS is the sediment/water
partition coefficient. Kws is directly related to the organic carbon
content of sediments; consequently, KW$ fs frequently normalfzed for
organic carbon:
CX
Kws/Xoc=
where XQC is the fractional mass of organic carbon in the sediment and KQC
is the sediment-organic-carbon/water partition coefficient, commonly called
the soil sorption coefficient. KOC'S are relatively consistent over a wide
variety of sediments (Means et al., 1980; Karickhoff, 1981). Regression
equations have been developed for estimating KQC from octanol/water par-
titioning or aqueous solubility, and are presented in Tables 4 and 5 and
Figures 8 and 9.
Once the partition coefficients between sediment and water (Kws) and
between biota and water (BCF) have been determined or estimated by methods
described above, the contaminant residues that are expected to occur in
benthic biota exposed to interstitial or interfaclal (water at the
sedient/water interface) waters can be approximated as follows:
2-26
-------
TABLE 4
PUBLISHED REGRESSION EQUATIONS BETWEEN K._ AND Kft
OC On
'
2
3
4
5
6
7
8
9
Equation
log K._ = 0.885 + 0.524 log Kftu
\J\f \Jrl
log KQC = - 0.21 + 1.00 log KQW
K-_ = 0.63 K-., .
oc ow
logKoc = 1.377 + 0.544 log KQW
log Koc B - °'317 + 1Q9 Kow
log KA_ = - 0.346 + 0.989 log KniJ
oc ow
Kor = 0.411 K
oc ow
log KQC = -0.006 + 0.937 log KQW
log KQC = 0.02 + 0.94 log KQW
log K.. = - 0.18 + 1.029 1ogKou
\f\f wW
log K_ = 0.158 + 0.843 log KrtlJ
Number
of
Chemicals
(n)
30
10
10
45
22
5
5
19
9
13
19
Correlation
Coefficient Reference
(r)
0.96
1.00
0.98
0.86
0.99
1.00
1.00
0.97
-
0.95
0.96
Briggs, 1973
Karickhoff
et al., 1979
Kenaga &
Goring, 1980
Means et al . ,
1980
Karickhoff,
1981
Pavlou &
Weston, 1984
(From Brown
et al . , in
prep.)
Lyman et al.,
1982
Rao &
Davidson, 1980
Pavlou &
Weston, 1984
2-27
-------
TABLE 5
PUBLISHED REGRESSION EQUATIONS BETWEEN K AND WATER SOLUBILITY(S)
oc
Equation
Units
of S
Number
of
Chemicals
(n)
Correlation
Coefficient
(r)
Reference
1 log Kor = 0.44 - 0.54 log S mole
fraction
xoc
2 log Koc = 4.040 - 0.557 log S umole/1
3 log Kor = 3.64 - 0.55 log S
oc
5 log Kor = 4.070 - 0.82 log S
'oc
ppm
4 log K • 4.273 - 0.686 log S ppm
ppm
6 log Koc = 3.933 - 0.642 log S ppm
10
15
106
22
4
5
0.97 Karickhoff et
al., 1979
0.99 Chiou et al.,
1977
0.84 Kenaga & Goring,
1980
0.97 Hassett et al.,
1980
1.00 Means et al.,
1980
0.97 Calculated from
data given in
Karickhoff, 1981
2-28
-------
-s
fD
00
O
c-t-
ro
i
ro
O
O
O
fD
Id
n>
O)
-Q
C
Q)
O
3
fD
O.
OJ
cr
fD
OJ
6 -
5 -
4 -
o
O
O)
o
o 3 -
2 -
1 r.
log
-------
n>
-HT3
O> — '
o- o
— ' r+
n>
I/O
r>
ii i
ro
u>
O
o
£Z
CT
n>
n>
O
3
0>
c
Ol
o
O>
0.
6
o
O
O)
O 3
0
-4
-3
-2
-1
0 1
log S
-------
= K u (18)
„; Nsb l '
and
C% ' Ksb c% (19)
where Ksb is the biota/sediment partition coefficient. Using this
relationship, Connor (1984) derived an equation for estimating K$b by
combining two regression equations of Kenaga and Goring (1980) along with
the relationship between KQC and Kws (Equation 17):
log BCF = - 1.495 + 0.935 log Kow
log K = 1.377 + 0.544 log K"
K = K /X
NQC WS7 PC
log Ksb = log (BCF/KWS) = - 2.872 + 0.391 log KQW - log XQC (20)
Karickhoff (1984) has proposed a more direct method of determining the
potential for bioaccumulation of hydrophibic organics from contaminated
sediments. This method is based on the assumption that the predominant
pools for hydrophobic organic chemicals are lipid tissue in biota and
organic carbon in sediments. At a state of equl-chemical potential, the
distribution of a substance should be equal to the ratio of the chemical's
affinity for lipid and organic carbon, which should be highly chemical
independent. Thus, the thermodynamic potential for biological uptake of
hydrophobic organics from a sediment source should depend only on the
organic carbon content of the sediment, the lipid content of the organism,
and the lipld/organic carbon partition coefficient (KQC ^):
The value of KQC ^ must be determined experimentally but should be on the
order of unity, I.e.,
2-31
-------
x,
{22)
The pollutant concentration in an aquatic organism at a state of equi-
chemlcal potential with the source defines the maximal chemical uptake from
purely thermodynanric considerations (Karlckhoff, 1984). Unfortunately, no
analogous method currently exists for determining the potential for
bloaccumulation of metals or water soluble organics.
OTHER BIOACCUMULATION MODELS
Several other models capable of predicting bioaccumulation have been
developed which do not fit precisely into the above sections. These are
discussed briefly below.
Norstrom et al. (1976) developed a kinetic bioconcentration model based on
fish bioenergetics. In general, their model can be described by (Neely,
1979):
EC R
- (23)
where
E = efficiency of chemical transfer across the gill
membrane
C = chemical concentration in fish (f) or in water (w)
Rv = volume of water flowing past the gills per unit time
(ventilation rate)
F = weight of Hsh
Bruggeman et al. (1981) also used bioenergetics terms in a kinetic model of
bioaccumulation from contaminated food. Their model of uptake due to
constant dietary exposure is given by:
2-32
-------
Cf(t) = f^ Cfd (Ue ] (24)
where
C = chemical concentration in fish (f) and in food (fd)
e = adsorption efficiency for ingested chemical
f = feeding rate (food weight per fish weight per time)
k2 = depuration rate
t = time
It follows from Equation 24 that the steady-state fish/food partition
coefficient (Kfd f), termed the biomagnification factor by the authors, can
be determined by:
css
Vf=7I? = £ (25)
cfd z
The above equation was further modified by substituting an approximation
for the depuration rate constant
efFK ,X,
K
fd,f
where all variables are as previously defined. The above expression for
uptake from contaminated food incorporates a combination of kinetic-
bioenergetics terms and an equilibrium term, Kwl. Total bioaccumulation
(uptake from both food and water) can be determined by:
Cf ' Kfd,f cfd + BCF cw
More complex food chain models have been developed (Weininger, 1978;
Thomann, 1981; Thomann and Connolly, 1984). Although these models are more
complex due to the number of auxiliary terms, Lake et al. (1984) reported
2-33
-------
that, In general, these models predict contaminant residues in fish by
applying the following differential equation:
-^- = klCw + kfdCfd efd " k2Cf (28)
dt
where
kfd = rate constant for uptake from food
f = uptake efficiency term from food
Fugacity models have been developed to predict the distribution of
chemicals in idealized environments (Mackay, 1979; Mackay and Peterson,
1981, 1982). At thermodynamic equilibrium, the fugacity (f), or escaping
tendency of a chemical substance from a phase, in all environmental
compartments is equal, e.g.:
fair = fwater = fsediment = fbiota (29)
Model assumptions are that: environmental compartments are completely
mixed (i.e., homogeneous); there is no inflow or outflow from the system;
and sufficient time has elapsed so that all compartments are in
equilibrium.
DISCUSSION
Two basic types of models have been proposed to describe the concentration
in the biota: kinetic and equilibrium. The first model considers the
biota concentration as a balance between the kinetic processes of uptake
and depuration. The second model considers the biotic phase as an in-
animate volume of material that is approaching thermodynamic equilibrium.
When first order kinetics are used to describe the uptake and depuration
rates of the first model and the rate to approach equilibrium of the second
model, the biota concentration (Cb) at any time (t) can be expressed
as (Mackay, 1982):
2-34
-------
Model I: Cb = (kj/kg) Cw [l-exp(-k2t)] (30)
Model II: Cb = (BCF) Cw [1-exp [- _ t)] (31)
BCF
It Is apparent that Equations 30 and 31 are of the same form, only their
coefficients are different. Thus, uptake measurements are inherently
Incapable of differentiating between the two models. However, an Important
conceptual difference Is that Model I Is dependent on the kinetic rates and
therefore the steady state concentration may not be the thermodynamic
equilibrium concentration, while Model II Is based on thermodynamic
equilibrium partitioning and does not consider kinetic processes (routes
and rates).
Thermodynamic equilibrium concentrations in the biotic phase will seldom be
reached by steady- state concentrations in the environment (Peddicord and
McFarland, personal coiranunciation).. However, steady-state concentrations
estimated by kinetic modeling generally have not been compared in the
literature to thermodynamic equilibrium concentrations. Comparisons
between steady state concentrations (estimated from kinetic rates) and
equilibrium concentrations (estimated from structure-activity relation-
ships) might be misleading due to the large variation in the published
thermodynamic equilibrium regression equations (e.g., see Figures 6 and 7).
BCFs predicted using those relationships may vary as much as 103.; conse-
quently, the outcome of such a comparison greatly depends on the equation
chosen to estimate the thermodynamic equilibrium concentration.
In the absence of biological transformation of bioaccumulated contaminants,
the steady state concentration in an organism should be equal to the
thermodynamic equilibrium concentration for the biotic phase. If chemical
transformation occurs, however, the steady-state body burden will deviate
from the thermodynamic equilibrium concentration. An important objective
of a kinetic study should be to investigate and quantify this rate of
biotransformation. However, depuration rates are commonly estimated by
transferring test organisms to clean water. This measured depuration rate
may be the cumulative result of exchange processes at the gill, excretion
2-35
-------
via the kidney or bile, and biotransformation. If exchange and excretion
processes are solely responsible for movement of a chemical from biological
tissue to water and the chemical is not metabolized, the kinetic steady-
state residue should be equivalent to the thermodynamic equilibrium
concentration. Thus, the net depuration rate, k2, provides little
information on the deviation from equilibrium concentrations. In some
instances, it is suspected that the metabolism rate is much smaller than
the rate of elimination due to exchange or excretion, or there is a
significant lag period for the metabolism rate; in such cases, the
cumulative depuration rate is truly representative of the rate of chemical
exchange between the biotic compartment and the environment. For example,
the ko rates reported by Neely et al. (1974) have been found to correlate
well with the thermodynamic equilibrium property KQW (Spade and Hamelink,
1982).
2-36
-------
SECTION 3
REGULATORY APPLICATION OF BIOACCUMULATION DATA
IMPLEMENTATION MANUAL
The "Implementation Manual" (EPA and COE, 1977) specifies evaluations to be
performed on dredged material proposed for discharge into marine waters.
The bioavailability of sediment-associated contaminants is assessed via a
10-day solid phase bioassay followed by bioaccumulation analyses (whole-
body residues) on surviving organisms. The procedure for assessing
bioaccumulation potential described in the Implementation Manual is
summarized in Figure 10 and the accompanying text below.
1. The Manual (EPA and COE, 1977) expresses a preference for assessing
bioaccumulation potential in the field, when possible, rather than
using laboratory bioassays, because the field method integrates
influencing factors such as mixing zone, sediment transport, and
long exposure times. Caged animals may also be used, although for
those animals with no history of exposure long periods of time jji
situ will be necessary.
2. Conditions required for field assessment. The use of the field
approach for assessing bioaccumulation potential is "technically
valid only where there exists a true historical precedent for the
proposed operation being evaluated. That is, it can be used only
in the case of maintenance dredging where the quality of the sedi-
ment to be dredged is considered not to have deteriorated or become
more contaminated since the last dredging and disposal operation.
In addition, the disposal must be proposed for the site at which
the dredged material in question has been previously disposed or
for a site of similar sediment type supporting a similar biological
community."
3a. Species selection for field assessment. Organisms must occur in
sufficient numbers at all stations for collection of an adequate
sample (several grams of tissue) to permit measurement of chemical
concentrations. It is desirable to collect large organisms but
they must be relatively immobile--bivalves, some gastropods, and
large polychaetes are recommended.
3b. Species selection for laboratory assessment. Section 227.27(d) of
the Federal Register (42 PR 2481) defines "appropriate sensitive
benthic marine organisms" as at least three species consisting of a
filter-feeding, a deposit-feeding, and a burrowing species. It is
3-1
-------
1. Goal: to evaluate the
bioaccumulation potential
of organisms exposed to
dredged material compared
to organisms not exposed
to dredged material.
2a. Conditions necessary
for field assessment of
bioaccumulation satisfied.
I
J3a. Select species
I
4a. Collect organisms
exposed and not exposed
to dredged material
I
I
1
2b. Conditions necessary
for field assessment of
bioaccumulation
not satisfied.
3b. Select Species
4b. Conduct 10-day solid
phase bioassay
1
5. Select chemical
constituents of concern.
J
6. Analyze surviving
organisms for
tissue concentrations
7. Statistically analyze
data to determine if
organisms exposed to
dredged material
accumulated higher tissue
levels than those not
exposed to dredged material
8. Data interpretation
Figure 10. Flow chart of bioaccumulation potential assessment prescribed
in the Implementation Manual.
3-2
-------
recommended to include a crustacean, an infaunal bivalve, and an
infaunal polychaete. The Manual lists recommended species for use
in solid phase bioassays in Table Fl. The guidance manual for the
New York District (COE and EPA, 1982) specifies the use of
Palaemonetes sp., Mercenaria mercenaria, and Nereis sp. or Neanthes
sp. In bioaccumulation tests conducted on dredged materials pro-
posed for disposal in the New York Bight.
4a. Field method design and conduct. Organisms are collected from at
least three stations within disposal site boundaries and from at
least six stations outside the disposal site with substrate that is
sedimentologically similar to that within the disposal site. Of
the six outside-site stations, three should be "downstream" from
the site (in the direction of net bottom transport) and three or
more must be located in an uncontamlnated sediment in a direction
opposite that of the net bottom transport (data from the latter
will provide a reference level of tissue concentrations). After
collection, organisms are held in clean water in the lab to allow
voiding of digestive tracts (2-3 days). Also, shells are not
included in the chemical analyses.
4b. Lab method design and conduct. Organisms are collected (preferably
from the reference stations) and used in the 10-day solid phase
bioassay described in the Manual (Appendix F). Reference aquaria
contain a 45 mm layer of reference sediment. The New York District
COE has identified specific locations for the collection of
reference and control sediments for use in laboratory solid phase
bioaccumulation tests (COE and EPA, 1982). Test organisms are
allowed to establish themselves in a 30 mm layer of reference
sediment in exposure aquaria and then a 15 mm layer of dredged
material is added. Seawater may be added as a continuous flow-thru
system or as a static system with periodic replacement. After ten
days of exposure, the surviving organisms are removed and held in
clean seawater to void digestive tracts as in the field method.
5. Selection of chemical constituents for analysis. Chemical
constituents to be assessed in the tissues of test organisms are
those deemed critical by the District Engineer and Regional
Administrator after consideration of known inputs to the dredged
material. Section 227.6 of the Federal Register cites the
following constituents of particular concern:
a. Organohalogen compounds,
b. Mercury and its compounds,
c. Cadmium and its compounds,
d. Petroleum hydrocarbons,
e. Known or suspected carcinogens, mutagens, or teratogens.
In the New York District, bioaccumulation analyses are to be per-
formed for cadmium, mercury, PCBs, and petroleum hydrocarbons, or
other material as determined necessary under the above categories
(COE and EPA, 1982).
3-3
-------
6. Analysis of tissue concentrations. Tissue concentrations of the
selected constituents are analyzed according to standard methods
described in references cited in the Manual (Table Gl).
7. Statistical analysis of data. Data is tabulated for each con-
stituent and species.Tissue concentrations in exposed animals are
compared to those in reference animals to determine if the former
show statistically significant higher levels—indicating bio-
accumulation potential of the dredged material. Statistical
methods include:
• Cochrane test—to see if variances of the data sets are
homogeneous and if data transformation is necessary.
t Analysis of variance
• F-test—to see if there is any statistical difference between
exposed and reference tissue concentrations.
• Student-Newman-Keuls Multiple range test—to determine which
exposed tissue concentration means are significantly different
(higher) than reference tissue concentration means.
8. Data Interpretation. The Implementation Manual recommends the
environmentally protective approach of assuming that any statis-
tically significant differences in tissue concentrations between
reference and exposed organisms are a potential cause for concern.
The "Decision Guidelines" are an attempt at interpreting laboratory
bioaccumulation data for regulatory purposes.
DECISION GUIDELINES
BACKGROUND
The Decision Guideline documents (COE and EPA, 1980, undated a-c) provide a
method for interpreting contaminant residues acquired by organisms exposed
to dredged material in accordance with the Implementation Manual (EPA and
COE, 1977) laboratory procedure. In developing the Decision Guidelines,
average tissue levels in biota of the New York Bight were estimated from
the available data for PCBs, cadmium, mercury, total naphthalenes, and DDT
and its metabolites. Those levels were adopted as maximum values for each
of three test species which, if not exceeded, should prevent significant
undesirable effects from occurring in areas contiguous with the dredged
material disposal site in the New York Bight Apex. The Decision Guidelines
3-4
-------
were developed on an interim basis under the philosophy of maintaining the
status quo and thereby preventing further degradation of the Bight.
The Decision Guidelines are regulatory tools, based on the available
bioaccumulation data and the short-term administrative goal of no-further-
degradation; they are not scientifically defensible because they do not
consider the environmental significance or biological effects of con-
taminant residues. The long-range goals of bioaccumulation interpretation
are effects-based regulation and mitigation of the environmental stress on
the New York Bight due to dredged material disposal.
APPLICATION
The Decision Guidelines for the New York Bight and their method of
derivation are summarized in Table 6. Figure 11 illustrates the use of the
Decision Guidelines to interpret bioaccumulation data generated by the
10-day solid phase bioaccumulation test (EPA and COE, 1977). Figure 11 is
actually a continuation of Figure 10 and together they represent the
overall procedure for assessing the bioavailability of contaminants in
dredged material proposed for disposal in the New York Bight Apex.
DISCUSSION
A major criticism of the Decision Guidelines interpretive approach is that,
for some chemicals, non-steady-state residues acquired over ten days of
laboratory exposure are compared to steady-state residues obtained from
field data. The House Committee on Merchant Marine and Fisheries (1980)
voiced that criticism in a question posed to the Corps of Engineers at a
Congressional hearing on dredge spoil disposal and PCB contamination (p.
685):
QUESTION 5; The Corps stated at the May 21 hearings that "it is
equally clear that different species have different uptake rates
for different contaminants and that within this ten-day exposure
period, it is not possible to predict right now what the ultimate
level in a particular organism might be." If this is the case,
what validity do the matrix values have if the test data do not
3-5
-------
TABLE 6
DECISION GUIDELINES FOR THE NEW YORK BIGHT
Chemical
Polychlorinated
blphenyls
Cadmium
Mercury
Total naphthalenes
DDT + metabolites
Decision Guideline Level
Wet Weight Tissue Derivation
Test Organism Concentration (mg/kg) Method
Mercenaria
mercenaria
Palaemonetes sp.
Nereis sp.
all 3 species*
all 3 species
all 3 species
all 3 species
0.1
0.1
0.4
0.3
0.2
0.02
0.04
1
1
2
3,2
3,2
3
2
1. Red Book (EPA, 1976; AFS, 1979) criteria level in fish for protection
of freshwater and marine life and consumers thereof including fish-
eating birds and mammals.
2. (NYB representative water column concentration) x (selected BCF) =
(estimated average tissue concentration in NYB biota)
3. Grand mean of available tissue concentration data for invertebrates
from the NYB.
aMercenaria, Palaemonetes, and Nereis
3-6
-------
9. Goal: Interpretation
of laboratory derived
levels of bioaccumulation,
to prevent significant
undesirable effects from
occuring in areas
contiguous with the Dredged
Material Disposal Site in
the New York Bight Apex
10. Applicant submits
bioaccumulation test data
for review by the
permitting authority.
1
1
lla. Animals exposed to
test sediment show no
significant bioaccumulation
compared to animals
exposed to
reference sediment.
1
lib. Animals exposed to
test sediment show
statistically significant
bioaccumulation compared
to reference animals.
12a. Material may be
ocean dumped
without containment
±
12b. Bioaccumulated levels
are compared to
Decision Guideline Limits.
13a. Statistically
significant bioaccumulation
in 1 or 2 species; level(s)
-------
represent the ultimate concentration level to be reached in the
organism?
The Corps' response, while answering the question, did not fully address
the issue of data incomparability.
RESPONSE 5: The matrix considers the variation in uptake by
different species by considering the uptake of three species of
very different ecological and taxonomic groupings. It also is
based on actual uptake demonstrated upon exposure to the intact
mixture of all contaminants in the dredged material.
The matrix is clearly viewed by all involved parties as nothing
more than an interpretive tool which will be revised on a regular
basis as Interpretive abilities improve. The use of such Interim
tools is necessary since Congress mandated that bioaccumulation be
evaluated at a time when the state-of-the-art for the environ-
mental interpretation of such data is far from fully developed.
Without the use of some sort of interim tool, the only two
approaches to interpretation are to consider any indication of
potential bioaccumulation unacceptable or to consider all bio-
accumulation acceptable, unless it is at a grossly high level
where damage is obvious. Neither of these extremes represents a
socially or environmentally acceptable view. The Ocean Dumping
Implementation Manual gives preference to bioaccumulation studies
done in the field over laboratory studies. In this manner, it is
posible to assess bioaccumulation by animals that have spent major
portions of their lives in and on sediments very similar to the
dredged material in question. These organisms exist under the
physical and chemical conditions actually occurring at the dis-
posal site. The tissue concentrations in such animals are in
equilibrium with their environment and can be compared directly to
tissue levels in organisms from areas unaffected by the dumping.
This approach is the most desirable, but until appropriate field
studies designed specifically for the New York Bight have devel-
oped an adequate data base for intrepretation, laboratory studies
must be used. However, the interpretive difficulties posed by
laboratory bioaccumulation investigations require some sort of
interpretive tool and the matrix is the best currently available
attempt in devising such a tool. ...
The 10-day bioaccumulation test (EPA and COE, 1977) was designed to
indicate whether there is a potential for contaminant uptake from dredged
material (Peddicord, 1980, p. 587). Rubinstein et al. (1983) found that
ten days of exposure is sufficient to demonstrate the potential for PCB
bioaccumulation, but that there is no clear relationship between 10-day
whole-body concentrations and steady-state concentrations. Steady-state
3-8
-------
concentrations are clearly more meaningful than levels reported without
reference to kinetic processes (McFarland et al., 1984). From their
experimental data, Rubinstein et al. (1983) concluded that since 10-day
residues do not reflect steady-state concentrations they cannot be compared
directly to a value intended to represent concentrations in indigenous
biota at the disposal site (i.e., the Decision Guideline values).
If a 10-day exposure period is to have predictive value, its relationship
to long-term exposure must be determined. McFarland et al. (1984) have
described a method by which a steady-state contaminant residue can be
estimated from a single time-point measurement of the contaminant con-
centration in tissue and an elimination rate constant (k2) estimated from
the octanol/water partition coefficient of the chemical. This procedure,
which is outlined in the Section 2, allows estimation of steady-state body
burdens from 10-day bioaccumulation data. Conceptually, the Decision
Guideline levels are more appropriately compared to steady-state residues
than to 10-day, non-steady-state concentrations.
The Decision Guidelines can also be criticized over the inconsistency of
methods used in their development. In spite of that inconsistency,
however, the concentrations adopted to represent average contaminant
residues in New York Bight macroinvertebrates appear to be fairly well
supported by the currently-available data (as illustrated in the next
section). Some arguable points pertaining to the derivation of the
Guideline values are outlined below:
• The EPA Red Book value of 0.1 mg/kg adopted as the PCB Decision
Guideline limit in Mercenaria and Palaemonetes is not based on
recent or site-specific data.
• The approach wherein a water column concentration is multiplied by a
BCF to estimate an average tissue concentration (Method 2 in Table
6) assumes that bioaccumulation of contaminants is via the water
column. That assumption may not be valid for infaunal worms or
deposit-feeding species which may accumulate contaminants primarily
from sediment.
• Using the method described above in the derivation of the PCB
Decision level for worms, the highest value of the range of reported
water column concentrations was selected (44 ng/L), representing the
least conservative choice.
3-9
-------
• The Decision Guideline levels for cadmium and mercury were
established based on the grand mean of body burdens measured in
invertebrates from the New York Bight. The accuracy of those values
was "tested" using Method 2 (Table 6). However, the range of BCFs
reported for cadmium and mercury are wide, and whether or not the
grand mean value fell within the range of values calculated by
Method 2 depended on the arbitrary selection of BCF. For example,
"the lowest bivalue BCF" (750 reported for quahog) was selected as
the most conservative BCF for cadmium, whereas for mercury a
conservative BCF of 1,000 was selected which is the value
"intermediate between that of lobsters and oysters exposed to
inorganic mercury"; while for PCBs the BCF chosen for setting the
Guideline limit for worms was an approximate median value (104
selected from a range of 10 -10s).
REVIEW OF DECISION GUIDELINE LEVELS
The Decision Guidelines were viewed by their authors as "a dynamic tool
which will be frequently reviewed and modified as additional data and more
detailed analyses become available" (COE and EPA, 1980). According to
Murphy (1980, p. 46), EPA expected the PCB Interim Decision Matrix to be
usable for only about a year. Nevertheless, the Decision Guideline values
have not been revised since their development, and the New York District
Corps of Engineers continues to use the Decision Guidelines in their
original form.
The currently-available data on residues of cadmium, mercury, PCBs, and DDT
and its metabolites (quantitative data for total naphthalenes were scarce)
in New York Bight biota were assembled for the purpose of comparing the
Decision Guideline levels to our best estimate of actual average con-
taminant body burdens (Appendix A). To be consistent with the Decision
Guidelines, only invertebrate species were included in the analysis. The
grand mean of existing invertebrate body burdens was calculated for each
contaminant, and the mean values are recorded in Table 7 along with the
Decision Guideline levels. Separate mean PCBs concentrations were cal-
culated for polychaete worms and for all other invertebrates for comparison
to the two corresponding Decision Guideline concentrations. Also, since
DDT residues measured in blue mussels (Mytil us edulis) were anomolously
high compared to levels reported in other species, a mean DDTs concen-
tration was calculated for all invertebrates except blue mussel.
3-10
-------
TABLE 7
COMPARISON OF DECISION GUIDELINE LEVELS
WITH AVERAGE CONTAMINANT RESIDUES
IN NEW YORK BIGHT INVERTEBRATES
Chemical
Decision Guideline
Concentration
(mg/kg, wet)
Mean Concentration
in all NYB
Invertebrates
(mg/kg, wet)
Mean Concentration
in other NYB
Invertebrate Groups
(mg/kg, wet)
Total PCBs
O.la
0.4b
0.193 (n=325)
0.17d (n=304)
0.528e'c (n=21)
Cadmium
0.3
0.337C (n=281)
Mercury
0.2
0.099 (n=124)
Total Naphthalenes
0.02
DDT + Metabolites
0.04
0.073 (n=106)
0.033r (n=87)
Decision Guideline level for PCBs in Palaemonetes sp. or Mercenaria mercenaria.
Decision Guideline level for PCBs in Nereis sp. or Neanthes sp. The Decision
Guideline concentrations for contaminants other than PCBs are applicable for all
test species.
£
No statistical difference («= 0.05) between the mean residue and the Decision
Guideline level. All other computed mean residues are statistically different
from the Decision Guideline concentrations.
Mean concentration of PCBs in NYB invertebrates other than polychaete worms.
P
Mean concentration of PCBs in NYB polychaete worms.
fMean concentration of DDTs in NYB invertebrates excluding blue mussel.
3-11
-------
The computed mean contaminant concentrations in New York Bight biota were
statistically compared to the Decision Guideline levels using the t-test.
At the 5 percent significance level, the residue means were statistically
different from the Decision Guideline concentrations in every case except
two: the mean cadmium concentration for all invertebrates and the mean
PCBs concentration in polychaete worms only. However, the differences
between calculated mean tissue concentrations of contaminants in New York
Bight biota and the Decision Guideline levels are not large, and, in
general, the Decision Guidelines are well supported by the present analysis
of the literature data.
SEDIMENT QUALITY CRITERIA
The Decision Guidelines were developed with the interim goal of preventing
further degradation of the New York Bight, but the ultimate goal of
regulation is to effect improvement of the Bight ecosystem by mitigating
the environmental stress caused by dredged material disposal. Preferable
to the Decision Guidelines approach is to base regulation of contaminated
sediments on biological effects.
EPA has published water quality criteria for many toxic chemicals, based on
an extensive data base that relates biological effects to aqueous con-
taminant concentrations (EPA, 1980, 1983). Analogous sediment quality
criteria have not been established.
A number of problems hinder the development of sediment quality criteria.
Because a significant portion of sediment-associated contaminants may not
be bioavailable and, therefore, not contribute to biological effects, it is
inappropriate to base sediment quality criteria on total (bulk) concentra-
tions of contaminants in sediments. Consequently, the bioavailability of
sediment-associated contaminants needs to be evaluated. Bioaccumulation is
currently the only reliable measure of the bioavailabillty of contaminants
in sediments. Unfortunately, at the present, scientists do not know the
precise relationship between tissue concentrations of contaminants and
their biological consequences, although the relationships between contam-
inant body burdens and biological effects are beginning to be investigated.
3-12
-------
The lack of residue/effects data makes It difficult to identify threshold
concentrations which discriminate between acceptable and unacceptable
levels of sediment contamination.
A number of alternatives for establishing sediment quality criteria have
been proposed. Pavlou and Weston (1983) summarized several methods for the
establishment of sediment criteria, including options based on: (1) back-
ground levels, (2) water quality criteria, (3) biological response, and (4)
equilibrium partitioning.
BACKGROUND LEVELS
This approach considers any increase in contaminant concentrations above
background levels undesirable. Background levels are determined from
surficial sediments from areas isolated from known pollutional sources or
from pre-anthropogenically contaminated strata of deep sediment cores.
Implementation of sediment criteria based on background levels would
require either (1) prohibiting any increase in contaminant concentrations
over background levels or (2) allowing some increment of contamination
above background levels. The former alternative would be unnecessarily
restrictive for some chemicals and the latter alternative would require
identification of threshold levels of sediment contamination—i.e.,
require the existence of some other type of sediment criterion, making the
use of background levels superfluous (Chapman, 1984).
WATER QUALITY CRITERIA
This approach applies EPA ambient water quality criteria to interstitial
and elutriate waters. The approach 1s appealing because it makes use of
the extensive data base on biological effects underlying the water quality
criteria. The application of water quality criteria to interstitial/
interfaclal waters is supported by the findings of Adams et al. (1983) who
concluded that "[t]oxic effects can be expected to occur in benthic
invertebrates only if the chemical concentration is high enough in the
sediments such that the equilibrium interstitial water concentration
3-13
-------
reached by desorption is equal to or higher than the concentration
demonstrated to cause an effect in a water exposure test."
However, this approach assumes that the predominant route of exposure is
via the interstitial water and water at the sediment/water interface, and
neglects exposure following ingestion of sediment particles as an important
exposure route. Consequently, direct application of water quality criteria
to interstitial/interfacial waters is not appropriate for any chemical for
which dietary uptake from sediment is a significant avenue of exposure.
Chapman (1984) suggested that a simple model similar to the one described
in Section 2 (Table 1) can be developed for sediment-dwelling organisms.
Such a model could qualitatively indicate the relative significance of
uptake from ingested sediment particles.
In addition, the water quality criteria approach is hampered by the
difficulties of sampling and analyzing contaminants in interstitial water
and by the unavailability of water quality criteria for a myriad of organic
chemicals.
BIOLOGICAL RESPONSE
This approach involves the use of laboratory bioassays and biological field
surveys to assess the biological response to contaminated sediments. Solid
phase bioassays (e.g., EPA and COE, 1977) are valuable in assessing the
toxicity of a particular sediment, but they do not indicate the contami-
nants) responsible for toxicity and, therefore, provide no guidance for
setting regulatory criteria. Bioassays conducted with otherwise clean
sediments spiked with a known contaminant concentration could be used to
develop sediment quality criteria just as aqueous bioassay data were used
to develop water quality criteria. However, because of the general paucity
of this type of data and the wide variety of possible sediment character-
istics, development of sediment criteria using this approach is unlikely to
be possible in the near future. The utility of biological field surveys in
sediment criteria development is limited because: they are site specific,
natural biological variability can obscure pollutional effects, and
negative environmental effects are observable only after the fact.
3-14
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EQUILIBRIUM PARTITIONING
The equilibrium approaches discussed by Pavlou and Weston (1963) use
equilibrium partitioning constants, which can be derived theoretically or
empirically, to determine contaminant concentrations in various environ-
mental compartments (water, sediment, and biota). Water quality criteria
or FDA action levels are used to identify threshold levels of contaminants
1n those environmental compartments. Pavlou and Weston (1983) discussed
four potential approaches for establishing sediment criteria based on
equilibrium partitioning (Figure 12):
Approach #1. uses KD and the existing EPA water quality criteria
(applied to interfacial water) to compute a sediment threshold
concentration. This calculated value is then compared to the
actual measured contaminant concentration in the ambient sediment
of a designated site to estimate the extent of violation. Con-
versely, using the ambient sediment burden and KD the interfacial
water concentration can be computed and compared directly to the
appropriate EPA water criteria value.
Approach #2, is based on the application of water quality criteria
to Interfacial waters and the use of the BCF to compute a biota
burden. The sediment threshold value can then be calculated by
using the ARS constant. Indirectly, the computed biota burden may
be compared to an existing body burden level known to induce a
toxic effect.
Approach #3, uses the ARS quantity with either a measured body
burden which induces a toxic effect (e.g., pathologic, behavioral
or metabolic effect) or a federally established tissue concentra-
tion (if public health risks are the prime consideration) to
compute a sediment threshold level.
Approach #4, is a combination of the above. It establishes a
biological threshold concentration and determines a corresponding
sediment threshold value via contaminant transfer through the
aqueous phase.
Pavlou and Weston (1983) noted several limitations to the establishment of
sediment criteria based on the equilibrium partitioning approaches
described:
Threshold levels based on water quality criteria do not consider
direct transfer of contaminants from sediment to biota (e.g.,
following ingestion of sediment particles);
3-15
-------
wactual
Wcrlterla
I
Sactual
Scrlterla
B
threshold
W
criteria
BCF
I
B
criteria
ARS
'criteria
OJ
I
(ft
B
actual
APPROACH
B
criteria
I
B
threshold
AR
'threshold
APPROACH +3
APPROACH +2
B
actual
B
threshold
BCF
'threshold
Kr^W"
'threshold
APPROACH
KD <=KW,S) = Sediment/Water Partition Coefficient
ARS (=K s'b) = Accumulation Relative to Sediment
BCF (=Kw',b) = Bioconcentration Factor
Figure 12. Schematic representation of sediment criteria development using equilibrium partitioning
constants. Source: Pavlou and Weston, 1983.
-------
• The use of multiple partition coefficients increases the potential
for error;
• The sediment/water partition coefficients (KQ) determined empirical-
ly from partitioning between suspended participate matter and water
(and tabulated by Pavlou and Weston, 1983) are probably not applic-
able to partitioning between sediment and interstitial water because
of the different physico-chemical conditions in sediments; and
• The biota/sediment partition coefficient (ARS) does not address the
bioaccumulation mechanism.
Also, appropriate biota criteria are generally unavailable. Insufficient
data exists to relate toxic effects to residue concentrations. FDA action
levels are one type of biota criteria, but they have been established for
only a few chemicals and they are designed to protect only human consumers
of fish and shellfish, not the health of the aquatic community.
Pavlou and Weston (1984) used sediment/water partitioning (Approach #1) to
develop sediment criteria for toxic contaminants in marine waters (Puget
Sound). A sediment criterion was defined as the concentration of a con-
taminant in a sediment which insures that its concentration in interstitial
water does not exceed the EPA water quality criterion. The expression for
the sediment/water partition coefficient normalized for organic carbon
(32)
where
= contaminant concentration expressed in units of mass
of contaminant/mass of organic carbon,
CjW - contaminant concentration in interstitial water,
KD = sediment/water partition coefficient (Kws).
TOC - total organic carbon content of the sediment
expressed as fractional mass on a dry weight basis.
3-17
-------
was rearranged and modified by substituting the EPA water quality criterion
(Cw/cr) for CIM. Sediment criteria (Cs/cr/oc) were derived by the result-
ing equation:
Cs/cr/oc = Koc cw/cr (33)
The general sediment criteria are applied to a specific site by multiplying
Cs/cr/oc ky tne organic carbon content of the sediment of concern, to
obtain criteria that can be directly compared to contaminant concentrations
in the sediment (Cs/cr):
Cs/cr ' cs/cr/oc TOC <34>
The method described above can be used to develop sediment criteria for
both organic and inorganic contaminants, providing that a water quality
criterion is available. Pavlou and Weston (1984) estimated KQC values for
organic contaminants theoretically using a KOC-KOW regression equation
developed for organic contaminants of concern in Puget Sound. KQC values
for trace metals were derived empirically by taking the mean of KQC values
calculated for a variety of sediments from measurements of trace metal
concentrations measured in bulk sediments and interstitial water reported
in the literature (Brannon et al., 1980).
The use of KQC instead of the unnormalized sediment/water partition
coefficient is based on the assumption that the organic carbon content of
sediment is a major environmental variable affecting the sediment/water
partitioning of contaminants. While that is clearly the case for synthetic
organic chemicals, for trace metals it is not so evident. Pavlou and
Weston (1984) evaluated the relationship between the sediment/water
partition coefficient (KD) and sediment organic carbon content for six
trace metals. Three of the metals evaluated (Cu, Cd, Pb) exhibited
statistically significant (albeit weakly so) correlations between KD and
sediment organic carbon, while three metals (Zn, As, Hg) did not show
significant relationships.
3-18
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Pavlou and Weston (1984) derived sediment criteria from both acute and
chronic water quality criteria, where available. However, acute sediment
criteria are generally considered to be inappropriate because of the
reasons cited by the authors:
• Sediment contaminant concentrations reflect long-term conditions and
do not demonstrate the extreme temporal variability of water column
concentrations.
• Benthic organisms often lack the mobility required to escape a
contaminated environment and therefore are susceptible to impacts
resulting from long-term chronic exposure.
Thermodynamic estimation of the movement of hydrophobic orgam'cs from
sediment to water may create an unnecessarily high potential for error due
to environmental variability (Peddicord and McFarland, personal communi-
cation). Hydrophobic organic compounds are predominantly associated with
organic carbon in sediments. The solubility of hydrophobic orgam'cs in
sediment organic carbon is much greater (orders of magnitude) than their
solubility in water. Chemical solubility in any environmental compartment
is affected by a variety of environmental factors, including pH, redox
potential, temperature, salinity, suspended particulates, organic matter
content, etc. Consequently, the effect of environmental variability on the
solubility of hydrophobic orgam'cs is low compared to their solubility in
organic carbon, but is very high compared to their aqueous solubility.
Thus, there is a relatively high probability of significant error
propagation associated with partitioning between sediment and water.
In contrast, the solubility of hydrophobic organic compounds in organic
carbon is comparable to their solubility in lipid tissue. Hence, the
probability of error associated with thermodynamic estimation of the
movement of chemicals between sediment and tissue is relatively low.
Karickhoff (1984) described a method by which the potential for bioaccum-
ulation of hydrophobic orgam'cs can be estimated directly from sediment
concentrations without using the aqueous phase as an intermediate step (see
Section 2). Since hydrophobic organics in the sediment are primarily
associated with organic carbon and those chemicals in biota are primarily
3-19
-------
associated with lipid tissue, the thermodynamic potential for bioaccumu-
lation can be estimated from the organic carbon content of the sediment
(XQC) the lipid content of the animal (X-j) and the lipid/organic carbon
partition coefficient (Kocj):
EPA and the Corps of Engineers are working toward the development of sedi-
ment quality criteria, using the theory behind Equation 21.
Workers at the U.S. EPA Environmental Research Laboratory— Narragansett are
developing a thermodynamic equilibrium model for predicting maximum
residues of organic compounds accumulated by aquatic organisms exposed to
dissolved and suspended parti culate-bound contaminants (Lake et al., 1984).
Based on the fugacity model described by Mackay (1979), Lake et al. (1984)
suggest that the bioaccumulation factor (BAF), at thermodynamic equilib-
rium, may be defined as:
BAF = C/(C + Cc) (35)
where
Ce = equilibrium contaminant concentration in lipid (1), water
(w), or organic carbon (oc)
Or, since the fugacity (escaping tendency) of a chemical in any phase is
given by:
fphase = "phase Yphase fR (36)
3-20
-------
where
f = fugacity
x = mole fraction
y = activity coefficient
fR = reference fugacity
the concentration terms in Equation 35 can be converted to mole fractions
and the expression for BAF can be rewritten as:
BAF = (y + O/Y-I (37)
W OC I
For conditions in which the aqueous phase is not an important storage
compartment, as with hydrophobic oranic chemicals, the BAF will depend only
on the contaminant concentration in the organic carbon of suspended
particulate material and in the Hpids of biota. Thus, the thermodynamic
maximum whole-body tissue concentration can be predicted by (Lake et al.,
1984):
re Xl re (38)
b " 1T~ LSPM
or, if the lipid/organic carbon partition coefficient (KQCj) were deter-
mined rather than being assumed approximately equal to one (Karickhoff,
1984):
re - J. K ce (39)
b *oc oc>1 SPM
where
cj = contaminant concentration in the organism at equilibrium
CSPM = contaminant concentration in the SPM at equilibrium
X1 = lipid weight of the organism
3-21
-------
X = organic carbon weight of the suspended solids
K = lipid/organic carbon partition coefficient
OC 9 I
Karickhoff (1984) suggested that this model can be referenced to the
sediment, for hydrophobic organics, by:
« r®
re - -1 K . .s (40)
Cb - XocKoc,l 1
where
C* = contaminant concentration in sediment at equilibrium
aSPM = susPended solids concentration
KOC = sediment-organic-carbon/water partition coefficient
The simple partitioning model described by Lake et al. (Equation 38) is
designed to estimate maximum concentrations of organic chemicals in biota.
After the model is tested, the authors propose to use the model as a first
level screen to assess the advisability of ocean disposal of a given waste
(e.g., dredged material, sewage sludge). If the theory is correct, pre-
dicted maximum concentrations should be greater than, or at least equal to,
residues accumulated in the environment, thereby providing a conservative
estimate for environmental protection regulatory purposes.
Workers at the U.S. Army Engineer Waterways Experiment Station similarly
are developing a method for establishing sediment quality criteria for
hydrophobic and neutral organic compounds based on thermodynamic equi-
librium (maximum) residues (Peddicord, 1984). Thermodynamic equilibrium
concentrations are limited by the basic laws of thermodynamics and
therefore cannot be exceeded in the environment. Thus, sediment quality
criteria set as concentrations of contaminants in sediment which, at
thermodynamic equilibirum, will not result in unacceptable residues in
biota will insure that any sediment with chemical concentrations below the
criteria is environmentally acceptable. Sediments with chemical concen-
trations above such criteria are potentially unacceptable, but because
3-22
-------
thermodynamic equilibrium concentrations are seldom reached in the envi-
ronment, more detailed Investigation of bioaccumulatlon Is necessary to
assess their impact potential. Sediment quality criteria based on
equilibrium partitioning can be expressed mathematically as (Peddicord,
1984):
log SQC = (log CT - 2.28) + log TOC (41)
where
SQC = sediment quality criterion for the particular compound on
total sediment, dry weight basis, in same units
as CT,
Cj = acceptable tissue concentration for the particular
compound, expressed on a lipid basis,
TOC = percent (not decimal fraction) total organic carbon in
the sediment in question,
2.28 = factor accounting for the relative activities of
hydrophobia or neutral compounds in TOC and in lipid, and
for expressing sediment concentrations on a % TQC basis.
As noted earlier, appropriate, acceptable tissue concentrations have not
been established, and insufficient data are presently available to relate
biological effects to contaminant residues. In the absense of an adequate
data base on residue/effects, the COE has elected to use the product of an
EPA chronic water quality criterion and an estimated BCF to calculate an
acceptable tissue concentration (CT) for a given chemical. Peddicord
(1984) used the BCF - K-w regression equation of Konemann and van Leeuwen
(1980) to estimate BCF:
log BCF = 0,980 log KQW - Q.063 (42)
Thus, CT can be estimated as:
log Cy = 0.980 log KQW - 0.063 + log WQC - 3.0 (43)
3-23
-------
where
Cy = acceptable tissue concentration for the compound
expressed on a lipid basis in units of ug/kg,
KOW = octanol-water partition coefficient for the compound of
concern,
WQC = chronic water quality criterion for the compound in
units of ug/1,
3.0 = factor to convert the equation to units of ug/1.
Combining Equations 41 and 43, a sediment quality criterion for a contami-
nant in a particular sediment can be calculated from the knowledge of the
chronic water quality criterion and the octanol/water partition coefficient
for the given chemical and the organic carbon content of the given sediment
as follows:
log SQC = (log WQC + 0.980 log KQW - 5.346) + log TOC (44)
where
SQC = sediment quality criterion for the compound of concern,
dry weight basis, units of ug/kg,
WQC = water quality criterion for the compound of concern,
units of ug/1,
KOW = octanol/water partition coefficient for the compound of
concern
TOC = total organic carbon content of the sediment, dry weight
basis, units of % (not decimal fraction).
This procedure is part of a tiered approach to sediment assessment recom-
mended by the COE (Peddicord and McFarland, personal communication) in
which the thermodynamic equilibrium method expressed in Equation 44 is
applied as the first tier screening level. If the contaminant concentra-
tion in the sediment of interest is lower than the concentration computed
by Equation 44, the sediment is considered environmentally acceptable; if
the ambient concentration is higher than the computed level, the sediment
may or may not be acceptable. When the latter case exists, the sediment
3-24
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must undergo tier 2 testing in which bioaccumulation potential is assessed
via empirical steady-state exposure or kinetic modeling.
Peddicord (1984) noted that the weakest scientific point of the COE
approach is the present need to use water quality criteria to estimate Cp
based on the assumption that chronic water quality criteria are comprehen-
sively protective of all aspects of aquatic ecology. In addition, chronic
criteria have only been established for a few hydrophobic or neutral
organic chemicals.
It is important to note that the COE thermodynamic equilibrium approach can
be used to develop sediment quality criteria only for hydrophobic or
neutral organic compounds and cannot be used for inorganics, water soluble
organics, or compounds which associate with sediment primarily via elec-
trostatic interactions.
3-25
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REFERENCES
Adams, W.J. 1984. Bloava11 ability and Safety Assessment of Lipophilic
Organics Sorbed to Sediments. Draft paper presented at the Workshop on the
Role of Suspended and Settled Sediments in Regulating the Fate and Effects
of Chemicals in the Aquatic Environment, 13-17 August 1984, Florissant, CO.
Adams, W.J., R.A. Kimerle, and R.G. Mosher. 1983. Aquatic Safety
Assessment of Chemicals Sorbed to Sediments. Paper presented at ASTM
Seventh Annual Aquatic Toxicology Symposium, Milwaukee, MI, 17-19 April
1983.
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-------
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-------
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8
-------
APPENDIX A
ANALYSIS OF MEAN CONTAMINANT RESIDUES
IN AQUATIC INVERTEBRATES OF THE
NEW YORK BIGHT
-------
TABLE A-l
CONTAMINANT RESIDUES IN NEW YORK BIGHT BIOTA
SPECIES
Clan, Butter (Saxidonus gigantea)
Clan, Hard [Quahog] (Mercenana mercenana)
Clan, Surf (Spisula solidissina)
Crab (Cancer sp.)
Crab, Blue ( Call i nee tes sapidus)
Crab, Dungeness (Cancer nagister)
Crab, King (Paralithodes camschatica)
Crab, Rock (Cancer irroratus)
REF
5
5
11
4
7
7
7
7
7
7
7
7
7
7
7
7
12
12
12
11
3
3
3
3
3
3
3
4
4
9
9
13
13
13
2
11
5
5
5
5
5
7
7
7
7
7
7
7
CADMIUM
mean n
0.178
0.567
0.800
0.130
0.130
0.150
0.100
0.130
0.040
0.050
0.060
0.175
0.242
0.140
0.210
0.550
6
6
1
6
11
11
1
1
1
1
1
2
9
1
1
1
MERCURY PCBs
mean n mean
0.036 6
0.052 6
0.230
0.020
0.010
0.010
0.020
0.040
0.020
0.040
0.040
0.020
0.030
0.070
0.070
0.360
0.004
0.033
0.030
0.025
0.049
0.030
0.070
0.002
0.320
0.375 2
0.128 9
0.330 1
0.240 1
0.140 1
0.030
0.070
0.060
0.040
0.040
0.060
0.040
n
36
1
1
1
1
1
1
1
1
1
1
1
1
6
1
3
2
2
1
6
2
4
14
1
1
1
1
1
1
1
DOTs
mean
0.018
0.008
0.015
0.026
0.055
0.069
0.090
0.015
0.025
0.018
0.053
0.036
0.012
0.004
0.000
0.094
0.051
0.051
0.047
0.039
0.073
0.039
n
1
1
1
1
1
1
1
1
1
1
1
1
1
1
4
1
1
1
1
1
1
1
-1-
-------
TABLE A-l (Continued)
SPECIES REF
7
7
2
6
6
6
6
6
10
10
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
13
13
13
Lobster, American (Homarus amencanus) 7
7
7
7
7
7
2
2
11
11
8
8
8
8
8
8
8
8
8
8
CADMIUM
mean n
0.120
0.130
0.080
0.130
0.010
0.640
0.080
0.100
0.080
0.014
0.024
0.018
0.015
0.017
0.015
0.023
0.024
0.011
0.017
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
MERCURY
mean
0.100
0.190
0.190
0.030
0.190
0.120
0.180
0.130
0.080
0.080
0.170
0.310
0.130
0.140
n
1
1
1
1
1
1
1
1
1
1
1
1
1
1
PCBs
mean
0.020
0.000
0.043
0.203
0.229
0.385
0.287
0.546
0.410
0.500
0.340
0.003
1.813
0.793
0.050
0.418
0.041
0.030
0.460
0.003
0.019
0.047
0.040
G.Q40
O.G40
0.040
0.001
0.020
0.095
0.150
0.130
0.150
0.070
0.120
0.200
0.230
0.120
0.160
0.130
0.070
0.094
0.083
n
1
1
12
1
1
1
1
1
1
1
1
1
1
1
2
4
1
1
1
1
1
1
1
1
A
1
1
1
1
6
3
1
1
1
1
DOTs
mean
0.017
0.009
0.025
0.041
0.047
0.050
0.100
0.060
0.035
0.002
0.044
0.024
0.050
n
1
1
12
1
1
1
1
1
1
1
1
6
3
-2-
-------
TABLE A-l (Continued)
SPECIES REF
8
8
8
8
8
8
8
8
8
8
3
3
3
4
4
4
4
9
9
13
13
13
13
13
13
13
13
13
13
.obster, Spiny [Alt.] (Panulirus argus) 5
lussel, Blue (Mytilus edulis) 7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
CAOMIIM
wan n
0.018
0.017
0.018
0.021
0.023
0.016
0.017
0.012
0.012
0.020
0.140
1.080
0.160
0.140
0.070
0.070
0.120
0.090
0.080
0.150
0.080
0.070
0.058
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
MERCURY PCBs
mean n mean n
0.200 1 0.100
0.130 1 0.079
0.340 1 0.220
0.160 1 0.095
0.150 1 0.260
0.260 1 0.210
0.230 1 0.140
0.260 1 0.150
0.150 1 0.062
0.370 1 0.190
0.040
0.151
0.145
0.034 1
0.320 1
0.030
0.061 10
0.050
0.200
0.200
0.090
0.200
0.400
0.030
0.200
0.100
0.070
0.800
0.300
0.400
0.400
0.400
0.100
0.200
0.200
1
1
1
1
1
1
1
1
1
1
1
10
10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
DDTs
mean n
0.007
0.232
0.240
0.510
0.150
0.000
0.070
0.170
0.260
0.290
0.270
0.600
0.230
0.340
0.370
0.310
0.211
0.190
0.110
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
-3-
-------
TABLE A-l (Continued)
SPECIES
Oyster, American (Crassostrea uirginica)
Oyster, Giant [Pac.] (Crassostrea 91935)
Polychaete worn
Polychaete worn (Ceriantheopsis aoencanus)
Polychaete worn (Glycera dibranchiata)
Polychaete worn (Haplosoloplos robustus)
Polychaete worn (Lunfannereis fragilis)
Polychaete worn (Lunbnnereis tenuis)
Polychaete worn (Nepthys bucera)
Polychaete worn (Nepthys incisa)
Polychaete worn (Nepthys picta)
Polychaete worn (Nereis virens)
Polychaete worm (Ninoe nigripes)
Polychaete worn (Ophioglycera gigantea)
Polychaete worn (Pherusa affinis)
Polvchaete worm (Travisia carnea)
Polychaete worn(Neothys sp.+Pherusa affinis)
REF
7
11
3
3
3
3
3
3
3
11
4
5
7
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
1
1
1
1
1
CAOrlllH
nean n
2.900
2.812
0.260
0.070
0.130
0.290
0.100
0.290
0.180
0.130
0.100
0.710
0.060
0.060
0.190
0.060
0.250
0.180
0.380
0.300
0.070
1
10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
MERCURY
nean n
0.038
0.142
0.077
0.073
0.052
0.073
0.058
0.052
0.021
0.660
0.032
0.086
0.154
0.070
10
1
1
1
1
1
1
1
1
1
1
1
1
1
PCBs
nean n
0.320
0.000
0.130
0.210
0.050
0.210
0.140
0.450
0.447
0.340
0.200
0.300
0.201
0.710
0.188
0.188
0.830
0.760
1.970
1.020
1.230
1.270
0.099
0.214
0.134
0.216
0.185
1
36
1
1
2
1
2
1
3
36
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
DDTs
nean
0.246
0.032
0.041
0.029
0.083
0.007
0.007
0.093
0.024
0.074
0.112
0.143
0.039
0.018
0.019
0.005
0.009
0.009
n
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
-------
TABLE A-l (Continued)
SPECIES REF
1
1
1
1
Quahogs, Ocean (Artica islandica) 12
12
12
12
4
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
CADMIUM
mean
0.540
n
8
MERCURY PCBs DDTs
•ean n mean n mean n
0.254 1 0.016 1
0.263 1 0.013 1
0.712 1 0.016 1
0.145 1
0.420 IS
0.420
0.390
0.540
0.250
0.1(0
0.470
li.220
0.230
0.170
0.320
0.210
0.220
0.160
0.210
0.300
0.220
0.240
0.160
0.200
0.090
0.040
0.380
0.240
0.400
0.230
0.140
0.580
0.080
0.060
0.470
0.820
0.340
0.350
0.490
0.370
0.460
0.450
0.480
0.280
0.610
0.290
0.800
9
9
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
-5-
-------
TABLE A-l (Continued)
SPECIES
Scallop, Sea (Palctopecten nagillanicus)
Shrimp, Alaskan (Pandalopsis dispar)
Shrimp, Grass (Palaenonetes pugio)
Squid, Longfinned [Alt.] (Loligo pealu)
Squid, Shortfinned (Illex illecebrosus)
REF
13
13
13
13
13
13
13
13
13
13
13
7
7
7
7
2
13
13
13
13
13
13
13
13
13
5
7
5
S
5
5
5
5
CADMIUM
aean n
0.500
0.260
0.710
0.620
0.620
0.450
0.220
0.180
0.440
0.330
0.310
0.320
0.360
0.070
0.180
0.130
0.090
0.210
0.190
0.080
0.085
0.145
0.155
0.158
0.168
0.150
0.179
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
2
3
13
5
2
4
MERCURY PCBs DDTs
mean n mean n nean n
0.030 1 0.060 1
0.020 1 0.050 1
0.010 1 0.004 1
0.020 1 0.050 1
0.001 5 0.000 5
0.040 10
0.190 1 0.094 1
0.000 2
0.030 3
0.048 13
0.023 5
0.120 2
0.135 4
Average Concentration 0.337 0.099 0.193 0.073
-------
REFERENCES
1 Boehm, 1982
2 Boehm and Hurtzer. 1982
3 COE and EPA. 1980
4 COE and EPA, undated a
5 Hall et al., 1978
6 Lee and Jones. 1977
7 MacLeod et al., 1981
8 O'Brien and Gere, 1979
9 O'Connor et al., 1982
10 Raltech Scientific Services, 1981
11 Spagnoli and Skinner, 1977
12 Wenzloff et al., 1979
13 Zdanowicz, 1984
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