Unfted States
Environmental Protection
Office of Water
Regulations and Standards
Criteria and Standards
Washington, DC 20460
EPA 440/549-002
April 1989
Water
Briefing Report
to the
EPA Science Advisory Board
on the
Equilibrium Partitioning Approach
to Generating Sediment
Quality Criteria
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BRIEFING REPORT
to the
EPA SCIENCE ADVISORY BOARD
on the
EQUILIBRIUM PARTITIONING APPROACH
TO GENERATING SEDIMENT QUALITY CRITERIA
April 1989
U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF WATER
OFFICE OF WATER REGULATIONS AND STANDARDS
CRITERIA AND STANDARDS DIVISION
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DISCLAIMER
This report has been reviewed by the U.S. Environmental Protection Agency and
approved for publication. Approval does not signify that the contents
necessarily reflect the views and policies of the U.S. Environmental Protection
Agency.
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CONTENTS
Section
FIGURES ill
TABLES x
SUMMARY 1
1 INTRODUCTION 1- 1
2 SELECTION OF A METHODOLOGY 2- 1
2.1 STATUTORY BASIS - CLEAN WATER ACT 2- 1
2.2 AVAILABLE APPROACHES FOR DEVELOPING SEDIMENT QUALITY
CRITERIA 2- 2
2.3 RATIONALE FOR SELECTING THE EQUILIBRIUM PARTITIONING
METHOD 2- 3
2.4 RELATIONSHIP TO WATER QUALITY CRITERIA METHODOLOGY 2-3
2.5 APPLICATIONS OF SEDIMENT CRITERIA 2- 5
2.6 COMPUTING SEDIMENT QUALITY CRITERIA 2-8
3 TOXICITY AND BIOAVAILABILITY OF CHEMICALS IN SEDIMENTS 3-1
3.1 TOXICITY EXPERIMENTS 3- 1
3.2 BIOACCUMULATION 3- 6
3.3 CONCLUSION 3-10
4 NON-IONIC ORGANIC CHEMICALS 4- 1
4.1 PARTITIONING IN PARTICLE SUSPENSIONS 4- 1
4.1.1 Particle Concentration Effect 4- 2
4.2 DISSOLVED ORGANIC CARBON (DOC) COMPLEXINC 4-8
4.3 PHASE DISTRIBUTION IN SEDIMENTS 4- 9
4.4 BIOAVAILABILITY OF DOC COMPLEXED CHEMICALS 4-14
4.5 FIELD OBSERVATIONS OF PARTITIONING IN SEDIMENTS 4-14
4.5.1 Organic Carbon Normalization 4-17
4.5.2 Sediment - Pore Water Partitioning 4-26
4.6 ORGANIC CARBON NORMALIZATION OF BIOLOGICAL RESPONSES 4-28
4.6.1 Toxicity Experiments 4-30
4.6.2 Bioaccumulation and Organic Carbon
Normalization 4-30
4.7 DETERMINATION OF THE ROUTE OF EXPOSURE 4-44
4.8 FIELD VALIDATION 4-45
4.8.1 Screening Level Methodology 4-47
4.8.2 Determining Screening Level Concentrations 4-49
4.9 CONCLUSION 4-53
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CONTENTS
(Continued)
Section " Page
5 APPLICABILITY OF USING WATER QUALITY CRITERIA AS THE EFFECTS
LEVEL FOR BENTHIC ORGANISMS 5- I
5.1 METHOD - RELATIVE ACUTE SENSITIVITY 5-1
5.2 BENTHIC COMMUNITY COLONIZATION EXPERIMENTS 5-5
5.3 COMPARISON OF THE SENSITIVITY OF BENTHIC AND WATER
COLUMN SPECIES 5- 5
5.4 WATER QUALITY CRITERIA CONCENTRATIONS VERSUS COLONIZATION
EXPERIMENTS 5-10
5.5 CONCLUSIONS 5-16
6 APPROACH FOR DEVELOPMENT OF SEDIMENT QUALITY CRITERIA
FOR METALS 6- 1
6.1 THE PROBLEM 6- 1
6.2 TOXICITY CORRELATES TO METAL ACTIVITY 6-2
6.3 METAL SORPTION MODELS 6- 8
6.3.1 Three Phase Metal Sorption Model 6- 8
6.4 EXTRACTION AND PHASE NORMALIZATION 6-11
6.4.1 Bioavallable Fraction 6-11
6.4.2 Partition Coefficients 6-12
6.5 DEVELOPMENT OF SEDIMENT QUALITY CRITERIA FOR METALS 6-14
6.5.1 Extraction Methodology 6-14
6.5.2 Sorption Model 6-16
6.6 ONGOING STUDIES 6-16
6.6.1 Sediment Toxicitv Experiments 6-16
6.6.2 Metal Partitionine 6-17
6.6.3 Sulfide Precipitation 6-17
6.7 CONCLUSION 6-17
7 GENERATION OF SEDIMENT QUALITY CRITERIA 7- 1
7.1 METHOD TO CALCULATE SEDIMENT QUALITY CRITERIA
UNCERTAINTY 7- 1
7.2 PRELIMINARY SEDIMENT QUALITY CRITERIA VALUES FOR
NON-IONIC ORGANIC CHEMICALS 7- 3
7.3 CONCLUSIONS 7- 7
8 REFERENCES 8- 1
ii
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FIGURES
Number
3-1 Comparison of percent survival (left) and growth rate
reduction (right) of Chironomus tentans to kepone
concentration in bulk sediment (top) and pore water (bottom)
for three sediments with varying organic carbon
concentrations 3- 4
3-2 Comparison of percent survival of Rhepoxvnlus abronius to
fluoranthene (left) and cadmium (right) concentration in bulk
sediment (top) and pore water (bottom) for sediments with
varying organic carbon concentrations 3- 6
3-3 Comparison of percent survival of Hyalella to DDT (left) and
endrin (right) concentration in bulk sediment (top) and pore
water (bottom) for sediments with varying organic carbon
concentrations 3- 8
3-4 Comparison of percent survival of Ampelisca (left) and
Rheooxvnius (right) to concentrations of cadmium in bulk
sediment (top) and pore water (bottom). Also presented is
water-only exposure data, identified with open circles 3-9
3-5 Comparison of Chironomus tentans body burden of permethrin
(left) and cypermethrin (right) versus concentration in bulk
sediment (top) and pore water (bottom) for sediments with
varying organic carbon concentrations 3-12
3-6 Comparison of Chironomus tentans body burden of kepone versus
concentration in bulk sediment (top) and pore water (bottom)
for sediments with varying organic carbon concentrations.
(Body Burdens calculated from average bioaccumulation factors.
Data: Adams et al. , 1983) 3-13
4-1 Comparison of observed partition coefficient to calculated
partition coefficient using Equation (4-2) (Di Toro, 1985) 4- 4
4-2 Comparison of the adsorption (top) and reversible component
(bottom) organic carbon normalized partition coefficient, Koc,
to the octonal-water partition coefficient. Kou, for
experiments with low solids concentrations: mfoc Kow < 1.
The line represents equality (Di Toro, 1985) 4- 5
iii
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FIGURES
(Continued)
Number
4-3 Partition coefficients of chemicals to particulate organic
carbon (POC), Aldrich humic acid (AHA), and natural DOC.
Benzo(a)pyrene (BaP); 2,2',4,4',5,5' hexachlorobiphenyl
(HCBP); DDT; 2,2',5,5' tetrachlorobiphenyl (TCBP); pyrene
(PYR); 4 monochlorobiphenyl (MCBP). (Data: Eadie et al.,
1988) 4-10
4-4 Phase distribution of a chemical in the three phase system:
water, sediment, and DOC (Equation .4-11), KQC - KDOC ~ Kow ~
106 L/kg, foc - 2.OX and m - 0.5 kg/L 4-12
4-5 Average uptake rate of chemicals by Pontoporeia hoyi with
(filled) and without (hatched) DOC present. Benzo(a)pyrene
(BaP); 2,2',4,4' tetrachlorobiphenyl (TCBP); Pyrene (Pyr);
Phenanthrene (Fhen) (Data: Landrum et al., 1987) 4-15
4-6 Comparison of logio of the DOC partition coefficient
calculated from the suppression of chemical uptake versus the
C-18 reverse phase HPLC column estimate. Circles are Aldrich
humic acid; triangles are interstitial water DOC. Chemicals
are listed on Figure 4-3 and Figure 4-5 captions (also
anthracene and benzo(a)anthracene) 4-16
4-7 The organic carbon fractions (X dry weight) in the unseparated
sediment (BULK) and separated sediment fractions: the low
density fraction >64 urn, <1.9 gm/cc (LOW); the sand sized
fraction >64um, >1.9 gm/cc (SAND); the silt/clay sized
fraction <64um. In one case (station 4) this fraction was
further separated into the clay and silt sized faction.
Numbered stations as indicated; Wells Dam, (WD); Tongue Point
(TP) (Data: Prahl, 1982) 4-19
4-8A Sediment chemical concentrations for each chemical on a dry
weight (left side) and an organic carbon basis (right side)
for the bulk sediment concentration (filled) and the sediment
fractions for each station. The bars in the plot are ordered
as follows: for Station 4) bulk, low density, clay, silt, and
sand; Stations 5 and 7) bulk, low density, silt/clay, and
sand; Wells Dam and Tongue Point) bulk, and low density (Data:
Prahl, 1982) 4-20
iv
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FIGURES
(Continued)
Number Page
4-8B Sediment chemical concentrations for each chemical on a dry
weight (left side) and an organic carbon basis (right side)
for the bulk sediment concentration (filled) and the sediment
fractions for each station. The bars in the plot are ordered
as follows: for Station 4) bulk, low density, clay, silt, and
sand; Stations 5 and 7) bulk, low density, silt/clay, and
sand; Wells Dam and Tongue Point) bulk, and low density
(Data: Prahl, 1982) 4-21
4-9 Dry weight normalization. Comparison of (top panel) the bulk
sediment concentration for all PAHs (x axis) with the sane
chemical concentration in the individual sediment fractions (y
axis) on a dry weight basis. Bottom panel presents a
probability plot of the ratio of these quantities for the
three size fractions (Data: Prahl, 1982) 4-22
4-10 Organic carbon normalization. Comparison of (top panel) the
bulk sediment concentration for all PAHs (x axis) with the
same chemical concentration in the individual sediment
fractions (y axis) on an organic carbon basis. Bottom panel
presents a probability plot of the ratio of these quantities
for the three size fractions (Data: Prahl, 1982) 4-23
4-11 Dry weight normalization with foc > 0.5Z. Comparison of (top
panel) the bulk sediment concentration for all PAHs (x axis)
with the same chemical concentration in the individual
sediment fractions (y axis) on a dry weight basis. Bottom
panel presents a probability plot of the ratio of these
quantities for the three size fractions (Data: Prahl, 1982). 4-24
4-12 Organic carbon normalization with foc > 0.5Z. Comparison of
(top panel) the bulk sediment concentration for all PAHs (x
axis) with the same chemical concentration in the individual
sediment fractions (y axis) on an organic carbon basis.
Bottom panel presents a probability plot of the ratio of these
quantities for the three size fractions (Data: Prahl, 1982-). 4-25
4-13 Observed partition coefficient versus the product of organic
carbon fraction and octanol-water partition coefficient. The
line represents equality. The partition coefficients are
computed using total dissolved PCB (*) and using free PCB (o)
computed using Equation (4-20) with KDOC ~ Kow 4-27
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FIGURES
(Continued)
Number Page
4-14 Observed partition coefficient versus the product of organic
carbon fraction and octanol-water partition coefficient. The
lines represent the expected relationship for DOC
concentrations of 0, 1, 10 and 100 mg/L and KQQC ~ Kow. Data
from Oliver (1987) for PCB congeners and other chemicals (A),
from Socha and Carpenter (1987) for Phenanthrene (B),
Fluoranthene (C) and Perylene (D) and from Kadeg and Pavlou
(1987) for Naphthalene (E), Phenanthrene (F), Pyrene (G),
Anthracene (H) and Flouranthene .(I) 4-29
4-15 Comparison of percent survival (left) and growth rate
reduction (right) of Chironomus tentans to kepone
concentration in pore water (top) and in bulk sediment using
organic carbon normalization (bottom) for three sediments with
varying organic carbon concentrations 4-31
4-16 Comparison of percent survival of Hyalella to DDT (left) and
endrin (right) concentration in pore water (top) and in bulk
sediment using organic carbon normalization (bottom) for three
sediments with varying organic carbon concentrations 4-32
4-17 Comparison of percent survival of Rhepoxvnius abronius to
fluoranthene concentration in pore water (top) and bulk
sediment using organic carbon normalization (bottom) for
sediments with varying organic carbon concentrations 4-33
4-18 Comparison of body burden of Chironomus tentans to kepone
concentration in pore water (top) and bulk sediment using
organic carbon normalization (bottom) for sediments with
varying organic carbon concentrations. (Body burdens
calculated from average bioaccumulation factors. Data: Adams
et al., 1983.) 4-35
4-19 Probability plots of the bioaccumulation factor (ratio of
organism to sediment concentration) of a 2,2',4,4'tetrachloro
biphenyl using dry weight normalization for both organism and
sediment (top panels); organic carbon normalization for the
sediment (middle panels); and organic carbon and lipid
normalization (bottom panels). Two experiments (A and B)
involving four benthic organisms: Yoldia (A), Nephtys (A),
Nereis (A and B), and Macoma (B) and five sediments (1,2,3 for
A; 1,4,5 for B) are shown 4-39
vi
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FIGURES
(Continued)
Number Page
4-20 Probability plots of the bioaccumulation factor (ratio of
organism to sediment concentration) of a 2,2'3,5,5',6
hexachloro biphenyl using dry weight normalization for both
organism and sediment (top panels); organic carbon
normalization for the sediment (middle panels); and organic
carbon and lipid normalization (bottom panels). Two
experiments (A and B) involving four benthic organisms: Yoldia
(A), Nephtys (A), Nereis (A and B), and Macoma (B) and five
sediments (1,2,3 for A; 1,4,5 for B) are shown 4-40
4-21 Plots of the bioaccumulation factor (ratio of organism to
sediment concentration) of a series of PCB congeners versus
Log Kow for that congener using dry weight normalization for
both organism and sediment (top panels); organic carbon
normalization for the sediment (middle panels); and organic
carbon and lipid normalization (bottom panels). Two
experiments (A and B) involving four benthic organisms: Yoldia
(A), Neohtvs (A), Nereis (A and B), and Macoma (B) and five
sediments (1,2,3 for A; 1,4,5 for B) are shown 4-42
4-22 Plots of the bioaccumulation factor (ratio of organism lipid
to sediment organic carbon concentration) for a series of PCB
congeners versus Log Kow (Data: Oliver, 1987 and Rubenstein
et al., 1988) 4-43
4-23A Probability distribution of organic carbon normalized
benzo(a)pyrene sediment concentration for sediments in which
the indicated species was found to coexist (see SCO 7 for the
species identification) 4-50
4-23B Probability distribution of organic carbon normalized
benzo(a)pyrene sediment concentration for sediments in which
the indicated species was found to coexist (see SCO 7 for the
species identification) 4-51
4-24 Probability distribution of the 90th percentile sediment
..concentrations for benzo(a)pyrene (third panel on left, with
data from Figure 4-23A and 4-23B) and for ten other other
compounds 4-52
5-1 Comparison of LC50 or EC50 acute values for the most sensitive
benthic and water column species from 30 saltwater water
quality criteria documents. Benthic species are defined as
infaunal species (habitat types 1 and 2) and water column
species are defined as those species having a lesser
association with sediments (habitat types: 3 to 8). The line
is the line of equal sensitivity 5- 7
vii
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FIGURES
(Continued)
Number Page
5-2 Comparison of LC50 or EC50 acute values for the most sensitive
benthic and water column species from 36 freshwater and 30
saltwater quality criteria documents. Benthic species are
defined as infaunal species (habitat types 1 and 2) and water
column species are defined as those species having a lesser
association with sediments (habitat types 3 to 8). Only
chemicals for which species from 3 or more infaunal phyla have
been tested are included. The line is the line of equal
sensitivity 5- 8
5-3 Comparison of LC50 or EC50 acute values for the most sensitive
benthic and water column species from 36 freshwater and 30
saltwater water quality criteria documents. Benthic species
are defined as infaunal and epibenthic species (habitat types
1 to 4) and water column species are defined as those species
having a lesser association with sediments (habitat types 5 to
8). The number of freshwater benthic species tested ranged
from 2 to 26. The number of saltwater benthic species tested
ranged from 5 to 26. The line is the line of equal
sensitivity 5- 9
5-4 Comparison of histograms of the relative acute sensitivity
(Equation 5-1) of benthic and water column freshwater species
as derived from the 36 water quality criteria documents.
Histograms show the percentage of benthic and water column
species with acute values within the indicated percentile
ranges of the pooled data. Benthic species are defined as
infaunal species 5-11
5-5 Comparison of histograms of the relative acute sensitivity
(Equation 5-1) of benthic and water column saltwater species
as derived from the 30 water quality criteria documents.
Histograms show the percentage of benthic and water column
species with acute values within the indicated percentile
ranges of the pooled data. Benthic species are defined as
infaunal species 5-12
5-6 Comparison of histograms of the relative acute sensitivity
(Equation 5-1) of benthic and water column freshwater species
as derived from the 36 water quality criteria documents.
Histograms show the percentage of benthic and water column
species with acute values within the indicated percentile
ranges of the pooled data. Benthic species are defined as
infaunal and epibenthic species 5-13
viii
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FIGURES
(Continued)
Number Page
5-7 Comparison of histograms of the relative acute sensitivity
(Equation 5-1) of benthic and water column saltwater species
as derived from the 30 water quality criteria documents.
Histograms show the percentage of benthic and water column
species with acute values within the indicated percentile
ranges of the pooled data. Benthic species are defined as
infaunal and epibenthic species 5-14
6-1 Acute toxicity to Palaemonetes of total cadmium (top) and
cadmium activity (bottom) with different concentrations of the
complexing agents NTA (left) and chloride as salinity (right). 6- 4
6-2 Acute toxicity to a dinoflagellate (left) of total copper
(top) and copper activity (bottom), with and without EDTA.
Chronic toxicity of zinc to Microcystis aeruginosa (right)
showing growth as cells/ml versus time with different levels
of EDTA and NTA (top) and number of cells at five days as a
function of free zinc concentration (bottom) 6- 5
6-3 Specific growth rate of a diatom (left) and Monochrysis
lutheri (right) versus total copper (top) and copper activity
(bottom) for a range of concentrations of the complexing
ligands tris and natural DOC 6- 6
6-4 Body burden of copper in oysters versus total copper (top) and
copper activity (bottom) with different levels of the
complexing ligand NTA 6- 7
6-5 Copper (left) and zinc (right) body burdens in molluscs versus
total sediment metal concentration (top) and extracted
metal/Fe ratio (bottom) 6-13
6-6 Zinc (left) and nickel (right) partition coefficients versus
pH in comparison to a single phase model of sediment sorption
(dashed line) 6-15
ix
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TABLES
Number
2-1 SUMMARY OF POTENTIAL APPLICATIONS OF SEDIMENT CRITERIA IN
IMPLEMENTING KEY SECTIONS OF SOME MAJOR ENVIRONMENTAL LAWS 2-6
3-1 SEDIMENT TOXICITY DATA 3- 2
3-2 DOSE-RESPONSE PARAMETERS 3- 3
3-3 BIOACCUMULATION FACTORS 3-10
4-1 DOSE-RESPONSE PARAMETERS 4-34
4-2 BIOACCUMULATION FACTORS 4-36
5-1 DRAFT OR PUBLISHED WATER QUALITY CRITERIA DOCUMENTS AND
NUMBER OF INFAUNAL (HABITATS 1 AND 2), EPIBENTHIC (HABITATS 3
AND 4), AND WATER COLUMN (HABITATS 5 TO 8) SPECIES TEST FOR
EACH OF THE WATER QUALITY CRITERIA DOCUMENTS 5-2
5-2 HABITAT CLASSIFICATION SYSTEM FOR LIFE-STAGES OF ORGANISMS 5- 3
5-3 COMPARISON OF WATER QUALITY CRITERIA (WQC) FINAL CHRONIC
VALUES (FCV) AND CONCENTRATIONS AFFECTING (OEC) AND NOT
AFFECTING (NOEC) BENTHIC COLONIZATION 5-15
7-1 COMPARISON OF INTERIM SEDIMENT QUALITY CRITERIA WITH
SCREENING LEVEL CRITERIA (SLC) 7-4
7-2 TABULATION OF FLUORANTHENE SEDIMENT TOXICITY RESULTS PORE
WATER CONCENTRATIONS AND EP VALUES 7- 6
7-3 TABULATION OF FLUORANTHENE SEDIMENT TOXICITY RESULTS ORGANIC
CARBON NORMALIZATIONS AND EP VALUES 7- 6
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ACKNOWLEDGMENTS
This document is a summary of- the combined efforts of many persons over a
number of years. The mention of all who have contributed in one way or another
would be a significant if not impossible task. However, there are a number of
individuals who have had significant input into the technical development and
content of this document. These persons are as follows:
Principal author
Dominic M. Di Toro
Contributors (alphabetical order)
Herbert E. Allen
Christina E. Cowan
David J. Hansen
Paul R. Paquin
Spyros P. Pavlou
Alexis E. Steen
Richard C. Swartz
Nelson A. Thomas
Christopher S. Zarba
Manhattan College/HydroQual. Inc.
Drexel University
Battelle
EPA Laboratory Narragansett, RI
HydroQual, Inc.
Envirosphere
Battelle
EPA Laboratory Newport, OR
EPA Laboratory Duluth, MN
EPA Headquarters, Office of
Water Regulation and Standards
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Page 1
SUMMARY
This report has been prepared to assist the EPA Science Advisory Board with
its evaluation of the Equilibrium Partitioning method for generating sediment
quality criteria. Sediment quality criteria as used in this report refer to
numerical values for individual chemicals that are applicable across the range
of sediments encountered in practise. They are intended to be predictive of
biological effects and protective of the presence and uses of benthic
organisms. As a consequence they could be used in much the same way as water
quality criteria - as the concentration of a chemical which is protective of
the intended use.
The specific regulatory uses of sediment quality criteria have not been
established. However, the range of potential applications is quite large since
the need for the evaluation of potentially contaminated sediments arises in
many contexts. Sediment quality criteria are not meant to replace direct
toxicity testing of sediments as a method of evaluation, but rather to provide
a chemical by chemical specification of what sediment concentrations would be
protective of aquatic life and their uses.
TOXICITY AND BIOAVAILABILITY OF CHEMICALS IN SEDIMENTS
The principal technical difficulty that must be overcome in establishing
sediment quality criteria is to determine the extent of bioavailability of
sediment associated chemicals. It has frequently been observed that similar
concentrations of a chemical in units of mass of chemical per mass of sediment
dry weight (e.g. /ig chemical/g sediment) can produce widely different
biological effects in different sediments. If the purpose of sediment quality
criteria is to establish chemical concentrations that apply across sediments of
differing types it is essential that the reasons for this varying
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bioavailability be understood and that they be explicitly included in the
criteria. Otherwise the criteria .cannot be presumed to be applicable across
sediments of differing properties.
The importance of this issue cannot be underestimated. For example, if 1
ppm of kepone is the LC50 for an organism in one sediment and 40 ppm is the
LC50 in another sediment, then unless the cause of this difference can be
associated with some explicit sediment properties it is not possible to decide
what the LC50 would be of a third sediment, without a direct toxicity test.
An additional difficulty is that the results of toxicity tests used to
establish the toxicity of chemicals in sediments would not be generalizable to
other sediments. Imagine the situation if the results of toxicity tests in
water depended strongly on the particular water source - e.g., Lake Superior
versus well water. Until the source of the differences were understood, it
would be fruitless to attempt to establish water quality criteria. It is for
this reason that the issue of bioavailability is a principal focus of this
report.
The observation which provided the key insight to the problem of
quantifying the bioavailability of chemicals in sediments was that the
concentration-response curve for the biological effect of concern could be
correlated not to the total sediment chemical concentration (/*g chemical/g
sediment) but to the interstitial water (i.e., pore water) concentration (pg
chemical/liter pore water). Organism mortality, growth rate, and
bioaccumulation data were used to demonstrate this correlation, which is a
critical part of the logic behind the equilibrium partitioning approach to
developing sediment quality criteria. A substantial amount of data is
presented in the report to illustrate the generality of this finding (Sections
3.1 through 3.3).
This correlation can be interpreted in a number of ways. In particular it
is premature to conclude that the route of exposure for the organism is only
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Page 3
via Che pore water. The reason is that the solid phase is in equilibrium with
the liquid phase and the effective exposure concentration is likely to be the
same via either route. However" from a purely empirical point of view the
correlation suggests that if it were possible to either (1) measure the pore
water chemical concentration or (2) predict it from the total sediment
concentration and the relevant sediment properties, then that concentration
could be used to quantify the exposure concentration for an organism. Thus,
the partitioning of chemicals between the solid and the liquid phase in a
sediment becomes a necessary component of sediment quality criteria. It is for
this reason that the methodology is called the equilibrium partitioning (EF)
method.
In addition, if it were true that benthic organisms are as sensitive as
water column organisms - and as shown in Section 5 the evidence appears to
support this supposition - then a sediment quality criteria could be
established using the water quality criteria, C^QC. as the effects
concentration, and the partition coefficient, Kp, to relate the pore water
concentration to the sediment quality criteria concentration, rgqc via the
partitioning equation. The calculation procedure is as follows. If CUQC
(pg/L) is the water quality criteria for the chemical of interest, then the
sediment quality criteria, rgqc (Mg/kg sediment) is computed using the
partition coefficient, Kp (L/kg sediment) between sediment and water:
rSQC " KpCWQC
This is the fundamental equation from which sediment quality criteria are
generated. Its utility depends upon the existence of a methodology for
quantifying partition coefficients.
PARTITIONING OF NON-IONIC ORGANIC CHEMICALS
The partitioning of non-ionic organic chemicals between particles and water
is reasonably well understood and a standard model exists for describing the
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Page 4
process. The chemical property of importance is the octanol-water partition
coefficient, Kow. The important .particle property is the mass fraction of
organic carbon, foc. For particles with foc > 0.5 percent the organic carbon
appears to be the predominant sorption phase. The partition coefficient, Kp,
the ratio of sediment to pore water concentration is given by:
K - f K
p oc oc
where Koc is the partition coefficient for particle organic carbon.
The only other environmental variable that has a dramatic effect on
partitioning appears to be the particle concentration itself. There is
considerable controversy regarding the mechanism responsible for the particle
concentration effect and a number of explanations have been offered. However,
all the interpretations yield the same result for sediment-pore water
partitioning, namely that KQC - K
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Page 5
as Che organic carbon normalized sediment concentration (fig chemical/kg organic
carbon) then:
rSQC,OC " KowCWQC
Hence we arrive at the following important conclusion: for a specific chemical,
with a specific KOW, the organic carbon normalized total sediment concentration
is proportional to the dissolved free effects concentration, cyqc, for any
sediment for foc > 0.5 percent.
Hydrophobic chemicals tend to partition to colloidal sized organic carbon
particles (DOC) as well. Although DOC affects the apparent pore water
concentrations of highly hydrophobic chemicals the DOC-bound chemical appears
not to be bioavailable and the above equation still applies (Sections 4.2
through 4.4). The available field data for sediment partitioning is reviewed
and related to the model presented above.
The above discussion suggests that toxicity and bioaccumulation data for
sediments should be normalized by the sediment organic carbon concentration. It
is found that responses which are quite variable on a dry weight normalized
basis are either statistically equivalent or the differences are significantly
reduced on an organic carbon basis. The low carbon sediments are seen to
depart from the normalized results as is expected (Section 4.6).
FIELD VALIDATION OF SEDIMENT QUALITY CRITERIA
The most convincing demonstration that sediment quality criteria are sound
would be a demonstration that they can predict the degree of toxicity of
natural sediments. There are three technical difficulties that apply to all
field data based approaches: bioavailability, chemical mixtures and control
sediments.
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Page 6
Bioavailabilitv
Contaminated sediments contain many chemicals. In order to use the
magnitude of the chemical concentration as a measure of its potential to
have biological effects, it is necessary that its bioavailability in that
particular sediment be assessed. For toxic metals and ionic organic
chemicals there is as yet no comprehensive partitioning theory that
identifies the normalization quantities and provides the parameters to
predict free dissolved concentration. Hence bioavailability cannot be
directly assessed.
Chemical Mixtures and Causality
If the bioavailability problem were solved there remains a difficulty with
using naturally contaminated sediments. Just as with water quality criteria it
is always possible that there is present another chemical or chemicals that are
biologically very active but, which have yet to be identified. If this
chemical is the cause of significant toxicity then it would cause a biological
effect that would not be predicted from the application of sediment quality
criteria.
Control Sediments and Non Toxic Variations
Variations in sediment toxicity test results and community structure
can be caused by variations in sediment characteristics other than
chemical contamination. Grain size distribution and organic carbon
content are well known examples. In order to judge the toxicity of a sediment
it is necessary that a comparative control response be obtained. The perfect
control is the same sediment without any ',chemical contamination. Since this is
not available, sediments from an unimpacted site are assumed to approximate the
response of the perfect control. The degree to which this approximation is
inappropriate limits the assessment of comparative toxicity.
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Page 7
These three major difficulties appear to render a direct field validation
of sediment criteria beyond current capabilities. Nevertheless it would be
helpful if some evidence that criteria developed from laboratory toxicological
data are at least reasonable. A methodology is presented that can be used to
establish lower bounds for sediment quality criteria from field observations of
organism presence and sediment chemistry.
EFFECTS CONCENTRATION
The other principal assumption in the development of sediment quality
criteria is that the water quality criteria are adequate estimates of the
effects concentrations for benthic organisms. Two sets of analyses are
presented to examine this question. The acute toxicity data base from the
water quality criteria are segregated into benthic and water column species and
the relative sensitivity of each group are compared for the water quality
criteria chemicals. In addition, saltwater benthic colonization experiments
for six chemicals are examined.
The conclusion from this examination is that the sensitivities of benthic
species are sufficiently similar to those of water column species to
tentatively permit the use of water quality criteria for the derivation of
sediment quality criteria in the equilibrium partitioning approach. The acute
toxicity data base derived from the water quality criteria documents suggests
that the most sensitive infaunal species is typically less sensitive than the
most sensitive water column (epibenthic and water column) species. When both
infauna and epibenchic species are classed as "benthic," the sensitivities of
the most sensitive benthic and water column species are on the average similar
(Section 5).
UNCERTAINTY
The sediment quality criceria methodology employed above relies on an
empirical model to compute the free interstitial water concentration from the
solid phase measurements. As a consequence there is an uncertainty associated
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Page 8
with the use of the model. In addition there is uncertainty with respect to
the KQW associated with the specific chemical since it is an experimentally
determined quantity. Finally the assumption that water column and benthic
organisms have similar sensitivities has a level of uncertainty.
The quantification of the level of uncertainty for sediment quality
criteria has only been accomplished in a preliminary way (Section 7.1). It is
anticipated that a complete uncertainty analysis will accompany a sediment
quality criteria and that, for example, 95 percent confidence limits will be
specified as well as the most probable value.
PRELIMINARY SEDIMENT QUALITY CRITERIA
An initial attempt to compute equilibrium partitioning based sediment
quality criteria for 13 chemicals is presented in Section 7.2. The 95 percent
confidence limits are computed from a method which is known to exaggerate the
uncertainty. For chemicals where either field data derived lower bounds or
sediment toxicity experiments are available the results are reasonable.
TOXIC METALS
The rationale for establishing sediment quality criteria for toxic metals
is similar to that developed for non-ionic organic chemicals. The bioavailable
fraction is identified and a partitioning model will be investigated in order
to predict the bioavailable fraction. Water column experiments point to the
fact that biological effects can be correlated to the divalent metal activity
[Me2+] . The implication to be drawn from these experiments is that the
partitioning model required for establishing sediment quality criteria should
predict [Me2+] in the pore water (Section 6.1 - 6.2).
METAL SORPTION MODELS AND EXTRACTIONS
The state-of-the-art of modeling metal sorption in laboratory systems is
well developed. Models for natural soil and sediment particles are less well
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Page 9
developed. However, recent applications suggest that similar models can be
applied to soil systems. An approach is presented which envisions three
sorption phases in aerobic sediments (Section 6.3).
In addition to the sorption phase concentrations it is necessary to
quantify the fraction of total sediment metal that is chemically interacting
with the pore water. A substantial effort is needed over several years to
determine the bioavailable portion of trace metals in soils and sediments using
chemical extractions. Initial results are reviewed and preliminary directions
are suggested (Section 6.4).
CONCLUSION
Methodologies are being developed to establish sediment quality criteria
using sediment equilibrium partitioning. The importance of bioavailability and
the role of partitioning between sediment and pore water is clarified. The
effects concentration for benthic organisms can be estimated from the water
quality criteria. For non-ionic organic chemicals an adequate partitioning
model exists and is presented in this document. As a result sediment quality
criteria can be computed. For metals, initial studies indicate that the same
rationale can be used. The development of sediment criteria for metal
contaminants using equilibrium partitioning is the focus of future sediment
criteria development activities.
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Page 1-1
SECTION 1.
INTRODUCTION
Under the Clean Water Act (CWA) the Environmental Protection Agency (EPA)
is responsible for protecting the chemical, physical, and biological integrity
of the nation's waters. In keeping with this responsibility, EPA has published
ambient water quality criteria (WQC) in 1980 for 64 of the 65 priority
pollutants and pollutant categories listed as toxic in the CWA. Additional
water quality documents that update criteria for selected consent decree
chemicals and new criteria have also been published since 1980. These water
quality criteria are numerical concentration limits that are protective of
human health and aquatic life. While these criteria play an important role in
assuring a healthy aquatic environment, they alone are not sufficient to ensure
appropriate levels of environmental and human health protection.
Toxic contaminants in bottom sediments of the nation's lakes, rivers, wet
lands, and coastal waters create the potential for continued environmental
degradation even where water-column contaminant levels comply with established
water quality criteria. In addition, contaminated sediments can lead to water
quality degradation, even when pollutant sources are stopped. The absence of
defensible sediment quality criteria makes it difficult to accurately assess
the extent of the contaminated sediment problem and to identify and implement
appropriate remediation activities when needed. As a result of the need for a
procedure to assist regulatory agencies in making decisions concerning
contaminated sediment problems, a EPA Office of Water Regulations and
Standards, Criteria and Standards Division (OWRS/CSD) research team was
established to review alternative approaches. Each approach had both strengths
and weaknesses and no single approach was found to be most applicable in all
situations. The equilibrium partitioning method was selected, because it
appeared to provide the most practical and effective regulatory tool for
addressing contaminated sediments on a national basis. The three principal
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Page 1-2
observations that underlay the equilibrium partitioning method of establishing
sediment quality criteria are:
1. for sediment dwelling organisms, the pore water concentration of a
chemical correlates to observed biological effects across a range of
sediments,
2. the range of sensitivities of benthic organisms to chemicals are
similar to water-column organisms_sQ .jthat. the currently established
water quality criteria can be used to define acceptable pore water
levels; and,
3. partitioning models which relate pore water concentrations to bulk
sediment concentrations either exist (for non-ionic organic chemicals)
or can be developed (for toxic metals and, perhaps, for ionic organic
chemicals).
The data that support these observations will be examined in subsequent
sections of this report.
Sediment quality criteria generated using the equilibrium partitioning
method are suitable for use in providing guidance to regulatory agencies
because they are:
1. numerical values,
2. chemical specific,
3. applicable across sediments.
4. predictive of biological effects, and
5. protective of the presence and uses of benthic organisms.
As is the case with water quality criteria, the sediment quality criteria
reflect the use of available scientific data to: (1) assess the likelihood of
significant environmental effects from contaminants in sediments, and to (2)
derive regulatory requirements which will protect against these effects.
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Page 1-3
Over the past several years research activities have focused on the
evaluation and development of the equilibrium partitioning methodology for
generating sediment quality criteria for use in providing guidance to
regulatory agencies. It is the purpose of this report to describe results that
support the equilibrium partitioning method for establishing sediment quality
criteria. This report is structured in the following way:
The historical framework and statutory basis for developing sediment
quality criteria are discussed in Section 2. Toxicity and bioavailability of
chemicals in sediments are discussed in Section 3 where the importance of pore
water concentration is established. This leads to a discussion of partitioning
behavior of chemicals and their division into two major classes: non-ionic
organic chemicals and metals, for which partitioning models have been proposed.
Non-ionic organic chemicals are discussed in Section 4. Sections 4.1
through 4.5 concentrate on partitioning and the role of particulate and
dissolved organic carbon. The models available to evaluate the partitioning of
chemicals in sediments are presented in Section 4.1 for particle suspensions
and Sections 4.2 through 4.4 for in-place sediments, including a discussion of
the effect of DOC complexing. Field data, related to partitioning in
sediments, are analyzed in Section 4.5. The results of organic carbon
normalization of toxicity and bioaccumulation experiments are presented in
Section 4.6. The issue of pore water versus sediment as the route of exposure
is addressed in Section 4.7. This section concludes with a review of the field
validation of sediment criteria in Section 4.8, where a screening level
methodology is presented.
The applicability of using water, quality criteria for the effects
concentration in sediments is examined 'in Section 5. A discussion of the
overall similarity of the sensitivities of benthic and water column species is
included in this section.
Section 6 reviews the current status of sediment quality criteria
development efforts related to toxic metals. The difficulties in using pore
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Page 1-4
water metal concentration directly are discussed in Section 6.1. This leads to
a discussion of the data demonstrating the correlation of toxicity to divalent
metal activity which is presented in Section 6.2. The state-of-the-art of
metal sorption models is discussed in Section 6.3. The suitability of
extraction methodologies to quantify the bioavailable fraction is examined in
Section 6.4. The remainder of Section 6 describes the initial approaches that
are being pursued in order to establish sediment metal criteria.
Finally, Section 7 describes - -the generation .of interim sediment quality
criteria for non-ionic organic chemicals. The uncertainty associated with the
criteria is discussed (Section 7.1) and preliminary values are presented
(Section 7.2).
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Page 2-1
•SECTION 2.
SELECTION OF A METHODOLOGY
This section presents an overview of the statutory basis for establishing
sediment quality criteria and the historical evolution of the equilibrium
partitioning method. The relationship of this approach to methodologies
adopted for generating national water quality criteria is discussed and areas
of application described. The rationale for selecting this approach and the
procedure for utilizing it are summarized.
2.1 STATUTORY BASIS - CLEAN WATER ACT
The statutory basis for the development of sediment quality criteria is the
Clean Water Act. Section 104 of the Act authorizes the Administrator to
conduct and promote research into the causes, effects, extent, prevention,
reduction and elimination of pollution, and to publish relevant information.
Section 104(n)(l) in particular provides for study of the effects of pollution,
including sedimentation, on aquatic life in estuaries.
Pursuant to the Act the Administrator is required to develop and publish
"criteria for water quality" reflecting the latest scientific knowledge on the
kind and extent of effects on plankton, fish, shellfish and wildlife which may
be expected from the presence of pollutants in any body of water, including
ground water, and on the effects of pollutants on biological community
diversity, productivity and stability (Section 304(a)(l)>.
The Administrator is also directed by the Act to develop and publish
information on the factors necessary for the protection and propagation of
shellfish, fish and wildlife for different classes and categories of receiving
waters (Section 304(a)(2)). Additionally, the Administrator is authorized
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Page 2-2
under Section 303 to provide for states to adopt state sediment standards.
These requirements of the Act provide for the development of a scientifically
defensible methodology or setting sediment quality criteria.
2.2 AVAILABLE APPROACHES FOR DEVELOPING SEDIMENT QUALITY CRITERIA
The sediment criteria development effort was initiated in November 1984 as
a result of concerns expressed by EPA regions and program offices, state and
local government agencies, environmental organizations. and others with regard
to contaminated sediments in the nation's water bodies. Concerns about
contaminated sediments also focused on the fact that no effective regulatory
tool was available that would address the wide variety of contaminated areas.
The concern over these and related problems were the subject of a 1984
conference entitled, "Fate and Effects of Sediment-Bound Chemicals in Aquatic
Systems." The conference proceedings (Dickson et al. , 1987) record the
alternative approaches to establishing sediment quality criteria as they were
perceived at the time (Pavlou and Ueston, 1983) and their merits and
deficiencies (Chapman, 1987). Following this conference an EPA sponsored
workshop was convened that focused on developing a means by which EPA and
others could address this problem. This workshop was supported by reports that
identified the scope of national sediment contamination (SCD 3)1 and that
reviewed approaches investigated by other efforts addressing contaminated
sediments (SCD 0; SCD 1) . The workshop was attended by personnel from EPA
regional offices, headquarters and laboratories, state and local governments,
the environmental community, industry, universities, laboratories and
contractors. A consensus was reached by the participants of the workshop that
the equilibrium partitioning approach was most likely to provide the EPA with
an effective regulatory tool that was technically sound and could be readily
incorporated into a variety of agency regulatory activities (SCD 2).
IThe citation SCD refers to the sediment criteria development reports listed in
Section 9.
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Page 2-3
2.3 RATIONALE FOR SELECTING THE EQUILIBRIUM PARTITIONING METHOD
The equilibrium partitioning method was selected as the most likely method
to provide the EPA with an effective regulatory tool because it reflects the
needs of regulators and because it incorporates the most useful technical
aspects of a variety of approaches. The principal reasons for the selection of
the equilibrium partitioning approach were as follows:
1. It was likely that the equilibrium-partitioning method would yield
sediment criteria that were predictive of biological effects in the
field. They address the issue of bioavailability and are based on the
extensive biological effects data base used to establish national
water quality criteria.
2. The developed criteria could be readily incorporated into existing
regulatory operations since a unique numerical sediment specific
criteria can be established for any chemical and compared to field
measurements to assess the likelihood of significant adverse effects.
3. The developed criteria could provide a simple and cost effective means
of screening sediment measurements to identify areas of concern and
could provide regulators with information in a short period of time.
4. The method took advantage of the large amount of data and expertise
that went into the development of the National Water Quality Criteria.
2.4 RELATIONSHIP TO WATER QUALITY CRITERIA METHODOLOGY
Perhaps the first question to be answered is: why not use the already
existing procedure for the development of water quality criteria to develop
sediment quality criteria? A detailed methodology has been developed that
presents the supporting logic, establishes the required minimum toxicological
data set, and specifies the numerical procedures to be used to calculate the
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Page 2-4
criteria values (Scephan et al., 1985). Furthermore, water quality criteria
developed using this methodology, have found utility in the regulation of
effluent discharges. A natural • extension would be to apply these methods
directly to sediments.
The water quality criteria are based upon total chemical concentration and
the transition to using dissolved chemical concentration for those chemicals
that partition to a significant extent would not be difficult. The experience
with site specific modifications .of the national. water quality criteria has
demonstrated that the water effect ratio - the ratio of chemical concentrations
in site water versus laboratory water that produces the same effect - has
averaged 3.5 (Spehar and Carlson, 1984; Carlson et al., 1986). The implication
is that differences of this magnitude due to variations in site specific water
chemistry are not an overwhelming impediment to nationally applicable numerical
water quality criteria. In addition, application of site specific water
quality criteria guidelines suggest site specific differences in
bioavailability of substances associated with water can be measured and site
specific water quality criteria can be developed.
In contrast to the use of total water column concentration, the use of
total sediment chemical concentration as a measure of bioavailable - or even
potentially bioavailable - concentration is not supported by the available data
(see, for example, the review by Luoma, [1983]). A summary of recent
experiments is presented in Sections 3 and 4. The results of these experiments
indicate that different sediments can differ in toxicity by factors of 10 to
100 for the same total chemical concentration of a toxicant. This is a
significant obstacle since without some quantitative estimate of the
bioavailable chemical concentration in a sediment it is impossible to predict a
sediment's toxicity based on chemical measurements. This is true regardless of
the methodology used to assess biological impact - be it laboratory toxicity
experiments or field data sets comprising benthic biological and chemical
sampling (Chapman and Long, 1983; Long and Chapman, 1985; Barrick et al. ,
1985).
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Page 2-5
Without a unique relationship between the chemical measurements and the
biological end points which -applies across the range of sediment properties
that affect bioavailability, the cause and effect linkage is not supportable.
If the same total chemical concentration is 10 times more toxic in one sediment
than another, how does one set a universal sediment quality criteria that
depends only on the total sediment chemical concentration? Thus, it appears
that bioavailability must be explicitly considered in the establishment of
sediment quality criteria. Further, any sediment quality criteria methodology
that depends on chemical measurements in the sediment must address this issue.
2.5 APPLICATIONS OF SEDIMENT CRITERIA
Any method capable of generating sediment criteria that are reasonably
accurate in their ability to predict the potential for biological impacts are
likely to be useful in many of the activities currently being pursued within
EPA. Table 2-1 (SCD 10) identifies a variety of likely statutes and
applications under which sediment criteria could be used. Sediment quality
criteria are likely to play a significant role in the identification,
monitoring and clean up of contaminated sediment sites on a national basis and
in ensuring that those sites that are uncontaminated will remain so. In some
cases sediment criteria alone would be sufficient to identify and to establish
clean up levels for contaminated sediments. In other cases the sediment
criteria should be supplemented with biological sampling and testing, or other
types of analyses, before decisions are made. Sediment criteria can provide a
basis for determining whether contaminants are accumulating in sediments to the
extent that an unacceptable contaminant level is being approached or has been
exceeded. By monitoring contaminants in the vicinity of a -discharge,
contaminant levels can be compared to sediment criteria to assess the
likelihood of impact. Sediment criteria will be particularly valuable in site
monitoring applications where sediment contaminant concentrations are gradually
approaching the criteria over time. Comparison of field measurements to
sediment criteria will be a reliable method for providing early warning of
-------
TABLE 2-1. SUMMARY OF POTENTIAL APPLICATIONS OF SEDIMENT CRITERIA IN IMPLEMENTING KEY SECTIONS OF SOME MAJOR ENVIRONMENTAL LAWS
Clean-up Clean-up
-------
Page 2-7
potential problems. Such an early warning would provide an opportunity to take
corrective action before adverse impacts occur.
Evaluation of in-place pollutants in aquatic sediment will be one of the
most appropriate and immediate applications of sediment criteria. Sediment
criteria will be useful in evaluating the potential risks posed by in-place
pollutants. For example, under Section 303 of the CWA sediment criteria could
be used to help determine whether an area might benefit from clean up
activities. Sediment criteria will be useful in:
1. assessing the need for clean up,
2. setting numerical goals for clean up, thereby helping to establish the
size of the area to be remediated and the cost of the clean up effort,
and
3. assessing the degree of benefit to be realized by cleaning up an area
to meet the criteria.
In many ways sediment criteria developed using the equilibrium partitioning
methodology are similar to existing water quality criteria. However, in their
application it is likely that they may vary significantly. Contaminants at
levels of concern (exceeding a water quality criteria) in the water column in
most cases need only be controlled at the source to eliminate unacceptable
adverse impacts. Contaminated sediments often have been in place for quite
some time and controlling the source of that pollution (if the source still
exists) will not be sufficient to alleviate the problem. The problem is
compounded due to the fact that the safe removal and treatment or disposal of
contaminated sediments can be difficult and expensive. For this reason it is
not anticipated that sediment criteria will be used as mandatory clean up
levels, but as a means for predicting or identifying the degree and spatial
extent of contaminated areas such that regulatory decisions can be made.
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Page 2-8
Regulatory frameworks for the application of these criteria are currently
being considered by the policy oriented EPA Contaminated Sediment Steering
Committee. Public input is expected prior to the adoption of any formal
regulatory framework for deriving and implementing sediment criteria.
\
2.6 COMPUTING SEDIMENT QUALITY CRITERIA
The sediment quality criteria for a specific chemical is defined as the
solid phase concentration that will result in an uncomplexed.interstitial water
concentration equal to the water quality criteria for that chemical. The
justification for the use of the water quality criteria as the effect
concentration for benthic organisms is that the species sensitivity range for
this subclass appears to be similar to the water column organisms (Section 5).
With remarkable foresight this approach was suggested for establishing sediment
quality criteria by Pavlou and Ueston (1983) before the evidence discussed
herein was available.
The calculation procedure is as follows. If cyqc (pg/L) is the water
quality criteria for the chemical of interest, then the sediment quality
criteria, rgqc (/*g/kg sediment) is computed using the partition coefficient, Kp
(L/kg sediment) between sediment and water:
rsqc ' VWQC (2'1)
Hence, the development of sediment quality criteria is directly dependent on
the availability of a methodology that relates the partition coefficient of the
chemical or class of chemicals to measurable properties of the sediments in
question.
The three principal observations that underlay this method of establishing
sediment quality criteria are:
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Page 2-9
1. For sediment dwelling organisms, the pore water concentration of a
chemical correlates to observed biological effects, and the effects
concentration is the same' as that observed in a water-only exposure.
2. Partitioning models either exist (for non-ionic organic chemicals) or
can be developed (for toxic metals and, perhaps, for ionic organic
chemicals).
3. The range of sensitivities of benthic organisms to chemicals are
similar co water column organisms.
The data supporting each of these observations will be examined in the
following sections of this report.
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Page 3-1
SECTION 3.
TOXICITY AND BIOAVAILABILITY OF CHEMICALS IN SEDIMENTS
The observation that provided the key insight into the problem of
quantifying the bioavailability of chemicals in sediments was that the
concentration-response curve for the biological effect of concern could be
correlated not to the total sediment chemical concentration (jig chemical/g dry
sediment) but to the interstitial water (i.e., pore water) concentration (/ig
chemical/liter pore water). In retrospect it has become clear that these
results do not necessarily imply that pore water is the primary route of
exposure. This is because all exposure pathways are at equal chemical activity
in an equilibrium experiment. Hence the route of exposure cannot be determined
(Section 4.7). The difficulty of establishing the primary route of exposure
does not diminish the importance of the empirical fact that the concentration-
response curve is correlated to pore water concentration. Further, this
observation is a critical part of the development of the equilibrium
partitioning approach to formulating sediment quality criteria.
3.1 TOXICITY EXPERIMENTS
A substantial amount of data has been assembled that addresses the
relationship between toxicity and pore water concentration. Table 3-1 lists
the sources and characteristics of these experiments. The data are presented
in a uniform fashion on Figures 3-1 to 3-4. The biological response - survival
rate or growth rate - is plotted versus the total sediment concentration on the
top panel, and versus the measured pore water concentration on the bottom
panel. Table 3-2 summarizes the LC50 and EC50 estimates and 95 percent
confidence limits for these data on a total sediment and pore water basis.
The results from kepone experiments (Figure 3-1) are particularly dramatic
(Adams et al., 1985; Ziegenfuss et al., 1986). For the low organic carbon sedi-
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Page 3-2
TABLE 3-1. SEDIMENT TOXICITY DATA
Chemical Organism
Kepone
Keponc
Cadmium
Chironomus
Chlronomus
tentans
tentans
Rhepoxvnius abronius
Flouranthene Rhepoxvnius abronius
DDT
Endrin
Cadmium
Cadmium
Hvalella azteca
Hyallella azteca
Rhepoxynius abronius
Amoelisca abdita
Cypermethrin Chironomus
Permethrin Chironomus
Kepone
Chironomus
tentans
tentans
tentans
Sediment
Source
Soil
Soil
Yaquina Bay, OR
Yaquina Bay, OR
Soap Creek,
Mercer Lake
Soap Creek
Mercer Lake
Yaquina Bay, OR
Long Island
Sound
River and Pond
River and Pond
Soil
Exposure
Duration
(days)
14
14
4
10
10
10
4
10
1
1
14
Biological
Endooint
Mortality
Growth
Mortality
Mortality
Mortality
Mortality
Mortality
Mortality
Body Burden
Body Burden
Body Burden
Reference
Adams et al., 1985
Adams et al.. 1985
Kemp and Suartz, 1986
Suartz et al., 1987
Nebeker and Schuytema,
1988
Nebeker and Schuytema ,
1988
Suartz et al., 1985
Di Tore et al.. 1989
Muir et al., 1985
Muir et al., 1985
Adams et al.. 1983
and 1985
Figure
3-1
3-1
3-2
3-2
3-3
3-3
3-4
3-4
3-5
3-5
3-6
TABLE 3-2. DOSE-RESPONSE PARAMETERS*
Chemical
(End Point)
Kepone
1985
(Mortality)
Kepone
1985
(Growth)
Fluoranthene
1987
(Mortality)
DDT
(Mortality)
Endrin
(Mortality)
JSL
0.09
1.50
12.0
0.09
1.50
12.0
0.2
0.3
0.5
3.0
7.2
10.5
3.0
6.1
11.2
LC50 and
EC50
Total Sediment
(Jjq/q)
0.97
7.89
42.0
0.40
9.87
48.9
3.3 ( 3.0 -
6.2 ( 5.4 -
10.5 ( 8.3 -
11.0 (10.1 -
19.6 (15.6 -
49.7 (44.2 -
4.4 ( 3.9 -
4.8 ( 3.7 -
6.0 ( 4.7 -
3.7)
7.1)
13.4)
12.7)
24.3}
56.3)
5.2)
6.3)
7.4)
32
35
18
15
48
21
22
31
23
0
1
0
2
1
1
.0
.4
.7
.0
.8
.9
.0
.1
.6
.9
.5
.8
.1
.9
.8
Pore Water
(ua/L)
(19.8 -
(27.3 -
(18.5 -
( 0.8 -
( 1.3 -
( 0.7 -
( 1.8 -
( 1.6 -
< 1.4 •
Reference
Adams et a I.,
Adams et a I.,
24.5) Suartz et al.,
35.4)
30.2)
1.0) Nebeker and
1.8) Schuytema, 1988
0.9)
2.5) Nebeker and
2.4) Schuytema, 1988
2.2)
•95X confidence limits shown in parentheses
-------
cc
in
ACUTE TOXICITY OF KEPONE TO
CHIRONOMUS TENTANS
100
CC
3
l/l
BO
60
40
% ORGANIC CARBON
.09 •
1.5 •
12.0 *
\
%
«^.
20 O 40.0 60 0
SEDIMENT KEPONE (ug/g)
so 100
PORE WATER KEPONE (ug/L)
DATA: AOAHS. et.al.. 1983
BO 0
ISO
CHRONIC TOXICITY OF KEPONE TO
CHIRONOMUS TENTANS
ISO,
o
cc
o
u
o
DC
ID
-K*
% ORGANIC CARBON
.09 •
1.5 •
12.0 *
75
SO
! \
V
0 0
20.0 40.0 60 0
SEDIMENT KEPONE (ug/g)
ISO.
-, I?!
o
cc
o
u
o
cc
ID
SO 75
PORE WATER KEPONE (ug/L)
DATA- ADAMS, et.al . 1983
BO 0
i o
FIGURE 3-1. Comparison of percent survival (left) and growth rate reduction
(right) of Chtronomus tentans to kepone concentration in bulk sediment (top)
and pore water (bottom) for three sediments with varying organic carbon
concentrations.
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Page 3-4
raent (0.09 percent) the 50th percentile total kepone concentration for both
Chrironomus tentans mortality (LC50) and growth rate reduction from a life
cycle test (EC50) are <1 pg/g. -By contrast, the 1.5 percent organic carbon
sediment EC50 and LC50 are approximately 8 and 10 pg/g respectively. The high
organic carbon sediment (12 percent) exhibits still higher LC50 and EC50 values
on a total sediment kepone concentration basis (42 and 49 Mg/g)• However, as
shown in the bottom panels, essentially all the data collapse into a single
curve when the pore water concentrations are used as the correlating
concentrations. On a pore water basis the biological responses are essentially
the same for the three different sediments: the EC50 - 23 pg/L and LC50 - 28
pg/L, whereas when they are evaluated on a total sediment kepone basis they
exhibit an almost 50-fold range in kepone toxicity.
Laboratory experiments have also been performed to characterize the
toxicity of fluoranthene (Swartz et al., 1987), cadmium (Kemp and Swartz,
1986), and DDT (SCD 7: Word et al., 1987) to the sediment dwelling marine
amphipod Rhepoxvnius abronius. Figure 3-2 presents the Rhepoxynius mortality
data for the fluoranthene and cadmium experiments. The results of the
fluoranthene experiments parallel those for kepone. The sediment with the
lowest organic carbon fraction (0.2 percent) exhibits the lowest LC50 on a
total sediment concentration basis (3.1 pg/g) and as the organic carbon
concentration increases (0.3 and 0.5 percent) the LC50 increases (6.7 and 11.
Mg/g)• On a pore water basis the data again collapse to a single concentration
response curve.
The cadmium experiments (Kemp and Swartz, 1986) were done using constant
pore water concentrations and a sediment amended with varying quantities of
organic carbon. The unamended and 0.25 percent additional organic carbon
exhibit essentially similar responses. However, the one and two percent
amended sediments had much different LC50 concentrations based on the total
sediment concentration. Using the pore water concentrations as the correlating
variable again collapses the data into one dose-response curve.
-------
II
ID
in
tr
tn
ACUTE TOXICITY OF FLUORANTHENE TO
RHEPOXYNIUS ABRONIUS
10Q_
% ORGANIC CARBON
0.2 •
0.3 •
0.5
100^
ACUTE TOXICITY OF CADMIUM TO
RHEPOXYNIUS ABRONIUS
20
0.0
tr
3
i/i
BOL
GoL
40
% ORGANIC CARBON
Unamended •
+0.25 •
+1.00 A
+2.00 -«
50 10 0 IS 0
SEDIMENT FLUORANTHENE (ug/g)
20.0
0 _
0.0
10 0 20.0 30.0 40.0 BO.O 60.0 70.0
SEDIMENT CADMIUM (ug/g)
00 20 0 40 0 bO 0
PORE WATER FLUORANTHENE (ug/L)
DATA SMARTZ. et a) . 19B7
DC
in
BO.O
1000 POOO 3000 4000
PORE WATER CADMIUM (ug/L)
DATA KFMP and SWARTZ. 1986
FIGURE 3-2. Comparison of percent survival of Rhepoxvnlus abronlus to
fluoranthene (left) and cadmium (right) concentration in bulk sediment (top)
" *"
-------
Page 3-6
Figure 3-3 presents survival data for DDT and endrin using the freshwater
amphipod Hvalella (Nebeker and Schuytema, 1988). The responses are similar to
that observed for kepone, cadmium, and fluoranthene. On a total sediment
concentration basis the organism responses differ for the various sediments,
but on a pore water basis the responses are again similar.
Cadmium toxicity data are compared on Figure 3-4 to demonstrate one
additional point. The responses of Rhepoxvnlus (Swartz et al., 1985) and
Ampelisca (Di Toro et al., 1989) to cadmium in seawater exposures without
sediment and to the measured pore water concentrations in sediment exposures
(lower panels). The survival responses are similar with or without the
sediment present. The concentration response curves using total cadmium
concentrations are also shown (top panels). It is interesting to note that two
organisms exhibit similar sensitivity to cadmium in water only exposures (0.3
mg/L for Ampelisca and - 1 mg/L for Rhepoxynius - bottom panels): yet the
total sediment cadmium LC50s differ by almost two orders of magnitude (25 and
2,000 pg/g respectively) for the different sediments. These dramatic
differences demonstrate the need to explicitly consider bioavailability of
sediment cadmium and, by implication, any toxicant of concern, in developing
sediment quality criteria.
3.2 BIOACCUMU1ATION
A direct measure of chemical bioavailability is the amount of chemical
retained in organism tissues. Tissue bioaccumulation data are examined to
address the issue of chemical bioavailability. The results are presented on
Figures 3-5 and 3-6 and the mean and 95 percent confidence limits of the
bioaccumulation factors associated with each set of data are summarized in
Table 3-3.
Chironomus tentans were exposed to two synthetic pyrethroids, cypermethrin
and permethrin that were added to three sediments, one of which was laboratory
grade sand (Muir et al., 1985). The bioaccumulation from the sand was
approximately an order of magnitude higher than the organic carbon-containing
-------
ACUTE TOXICITY OF DDT TO HYALELLA
too*.
QC
in
% ORGANIC CARBON
3.0 •
7.2 •
10.5 *
20.
cc
in
ACUT.E TOXICITY OF ENDRIN TO HYALELLA
100..
X ORGANIC CARBON
3.0 •
6. 1 •
11.2 *
cr
en
100 ISO
SEDIMENT DDT (ug/g)
200
o.o
10.0
20 0
SEDIMENT ENDRIN (ug/g)
100,.
4 00
PORE WATER DDT (ug/L)
DATA. NEBEKER and SCHUYTEMA. 1988
5.00
0.0
2.0 40 6.0 BO
PORE WATER ENDRIN (ug/L)
DATA: NEBEKER and SCHUYTEHA. 1988
0.0
FIGURE 3-3. Comparison of percent survival of Hvalella to DDT (left) and
endrin (right) concentration In bulk sediment (top) and pore water (bottom) for
sediments with varying organic carbon concentrations.
-------
ACUTE TOXICITY OF CADMIUM
TO AMPELISCA
ACUTE TOXICITY OF CADMIUM
TO RHEPOXYNIUS
en
M
cc
in
too
ao
60.
40 .
20.
10"
I01 10* 101 10"
SEDIMENT CADMIUM (ug/g)
10°
10"
10'
10*
10'
10*
10a
SEDIMENT CADMIUM (ug/g)
100
BO
> 60
t-i
CC
In «
MATER ONLY o
SEDIMENT EXPOSURE •
10"
10'* 10** 10"' 10° 101 10* 103
PORE WATER CADMIUM (mg/L)
REFERENCE: DITORO. «t.6l.. 1989
to*
MATER ONLY o
SEDIMENT EXPOSURE •
PORE WATER CADMIUM (mg/L)
REFERENCE: SMARTZ. et.al..1985
FIGURE 3-4. Comparison of percent survival of Ampelisca (left) and
Rhepoxyntus (right) to concentrations of cadmium In bulk sediment (top) and
pore water (bottom). Also presented is water-only exposure data, identified
with open circles.
-------
Page 3-9
sediment for both cypermethrin and permethrin (Figure 3-5 top panels). On a
pore water basis the bioaccumulation appears to be linear (the lines have slope
- 1) and independent of sediment type (bottom panels).
TABLE 3-3. BIOACCUMULATION FACTORS8
Bioaccunulation Factors
Chemical
Kepone
Cypermethrin
Permethrin
B95X confidence
Jn
.09
1.50
12.
2.3
3.7
2.3
3.7
limits
Total Sediment
ug/g organism
ug/g sediment
600
20
3.3
6.21
0.50
0.60
( 308 -
( 4.8 -
( 0.3 -
(0.30 -
(0.37 -
4.04 (2.89 •
0.38 (0.17 -
0.23 (0.18 -
shown in
892}
35.2}
6.3)
8.01)
0.71)
0.83)
5.20)
0.59)
0.28)
Pore Water
ug/kg organism
UQ/L
17,600
5,180
5,790
80.1
51.3
92.9
39.7
52.5
29.7
(6,540
(1,970
(2,890
(73.5
(43.8
(87.0
(25.0
(22.6
(15.6
- 28,600)
- 8,390)
- 8,700)
- 86.7)
- 58.8)
- 98.8)
- 54.3)
- 82.4)
• 43.7)
Reference
Adams. Kimerle
and Mosher,
1983 and 1985
Muir et al.,
1985
Muir et al.,
1985
parentheses
Bioaccumulation was also measured by Adams et al. (1983 and 1985) in the
Chironomid - kepone experiments discussed in Section 3.1. Figure 3-6 presents
the organism body burden (/*g chemical/g organism) versus total sediment
concentration (top panel) and pore water concentration (bottom panel) for a
range of sediment organic carbon levels. The body burdens used on this figure
are not the actual measurements, which were not reported, but are computed from
the reported average bioaccumulation factor for each sediment type. Again, the
variation on a total sediment basis is over two orders of magnitude whereas the
pore water bioaccumulation factor is within a factor of three with the very low
organic carbon sediment exhibiting the deviation.
3.3 CONCLUSION
These observations - that organism concentration response and
bioaccumulation from different sediments can be reduced to essentially one
curve if pore water is considered as the exposure concentration - can be
-------
Page 3-10
interpreted in a number of ways. However, from a purely empirical point of
view, it suggests that if it were possible to either measure the pore water
concentration of a chemical, or to predict it from the total sediment
concentration and the relevant sediment properties, then that concentration
could be used to quantify the exposure concentration for an organism. Thus an
examination of the state-of-the-art with respect to predicting the partitioning
of chemicals between the solid and the liquid phase is required. This is
examined in Section 4.
-------
BIOACCUMULATION OF PERMETHRIN IN
CHIRONOMUS TENTANS
1000
a>
at
c
a
DC
m
a
o
m
too
10
O.I
i inn 1—i i i inn 1—i i i inn 1—i i mil
i i i i mi
i i i inn
% ORGANIC C:
< 0.1 •
2.3 •
3.7 *
i i nil i i i i i in
10 100 1000
SEDIMENT PERMETHRIN (ng/g)
10000
BIOACCULUMATION OF CYPERMETHRIN IN
CHIRONOMUS TENTANS
10000
en
^
CD
1000
£ 100
cr
m
a
o
CD
10
% ORGANIC C:
< 0.1 •
2.3 •
3.7 *
i i mi i i i i i mi
I 10 100 1000
SEDIMENT CYPERMETHRIN (ng/g)
10000
1000
en
at
c
a
cr
m
0
O
m
too
10
o il—
o 01
10000
01 I 10
PORE WATER PERMETHRIN (ug/L)
DATA MUIR. et a) . 1985
100
en
en
c
UJ
0
cr
m
0
o
m '
1000
100
to
i
o 01
o.i i 10
PORE WATER CYPERMETHRIN (ug/L)
DATA: MUIR. et al . 1985
100
FIGURE 3-S. Comparison of Chlronomus tentans body burden of permethrin (left)
and cyperraethrin (right) versus concentration in bulk sediment (top) and pore
water (bottom) for sediments with varying organic carbon concentrations.
-------
BIOACCULUMATION OF KEPONE .IN
CHIRONOMUS TENTANS
10000
Ol
\
01
LU
Q
DC
Z)
CD
O
o
CD
1000
100
10
I I I I I I III I ! I I I I III I I I I I I III I I I I I I III I I I I I ll±
NOTE:
BODY BURDEN ESTIMATED
FROM REPORTED AVERAGE
BAF FOR EACH SEDIMENT TYPE. _
% ORGANIC C:
< 0.09 •
1.50 •
12.00 A
1J I I I I I I III I I I I I I III I I I I I I III I I I I I Illl I I I I I III
0.1
1 10 100 1000
SEDIMENT KEPONE (ug/g)
10000
10000
Q>
1000
LU
D
OC
Z3
CD
O
O
CD
100
10
\ \ i i M 111 I i i i i 11 n I I i i i 11 ii I I i i i i
NOTE:
BODY BURDEN ESTIMATED
FROM REPORTED AVERAGE
BAF FOR EACH SEDIMENT TYPE.
1! i i i i i 1111 i i i i i 11 n i i i i i 11 n i i i i ii 11
1 10 100 1000 10000
PORE WATER KEPONE (ug/L)
FIGURE 3-6. Comparison of Chironomus tentans body burden of kepone versus
concentration in bulk sediment (top) and pore water (bottom) for sediments with
varying organic carbon concentrations. (Body Burdens calculated from average
bioaccumulation factors. Data: Adams et al., 1983.)
-------
Page 4-1
SECTION 4.
NON-IONIC ORGANIC CHEMICALS
A discussion of the state-of-the-art of modeling sorption to particles is
best organized by classes of chemicals. Non-ionic organic chemicals are
discussed in this section and metals and charged organic chemicals are
discussed in Section 6.
4.1 PARTITIONING IN PARTICLE SUSPENSIONS
For non-ionic hydrophobic organic chemicals sorbing to natural soils and
sediment particles, a number of empirical models have been suggested (see
Karickhoff, 1984 for an excellent review). The characteristic that indexes the
hydrophobicity of the chemical is the octanol-water partition coefficient, Kow.
The important particle property is the mass fraction of organic carbon, foc.
For particles with foc > 0.5 percent the organic carbon appears to be the
predominant sorption phase (Karickhoff, 1984). The only other important
environmental variable appears to be the particle concentration itself
(O'Connor and Connolly, 1980).
The particle concentration effect is an observable fact. In many
experiments using particle suspensions the partition coefficients have been
observed to decrease as the particle concentration used in the experiment is
increased. Unfortunately very few experiments have been done using bedded
sediments. Therefore the correct interpretation of particle• suspension
experiments is of critical importance. It is not uncommon for the partition
coefficient to decrease by two to three orders of magnitude at high particle
concentrations. If this partitioning behavior is characteristic of bedded
sediments then quite low partition coefficients would be appropriate. This
-------
Page 4-2
would result In lower sediment concentrations for sediment quality criteria.
However, if this phenomena is an artifact or it is due to a new phenomenon that
does not apply to bedded sediments, then a quite different partition coefficient
would be used. The practical importance of this issue requires a detailed
discussion of the particle concentration effect.
4.1.1 Particle Concentration Effect
For the reversible (or labile) component of sorption, a model has been
proposed which accounts for the particle concentration effect and predicts the
partition coefficient of non-ionic hydrophobic chemicals over a range of nearly
seven orders of magnitude with a logio prediction standard error of 0.38 (Di
Toro, 1985). The partition coefficient, Kp, which is the ratio of particle-
bound chemical concentration, r, and dissolved chemical concentration, c
-------
Page 4-3
The regression of Koc to Kow yields:
lo*10Koc ' °-°°28 + °'983 lo*10Kow <4'3>
For the equations that follow this relationship is approximated by Koc - Kow.
However Equation (4-3) is used for numerical computations of r§qc presented
subsequently. Figure 4-1 presents the predicted versus observed reversible
component partition coefficients using this model (Di Toro, 1985). A
substantial fraction of the data in the regression is at high particle
concentrations (m foc Kow > 10) where the partitioning is determined only by
the solids concentration and i/x. The low particle concentration data (m foc
KQW < 1) is presented on Figure 4-2 for both the reversible component and
conventional adsorption partition coefficients normalized by f0c- The
relationship: Koc = KOW is demonstrated from the agreement between the line of
perfect equality and the data.
A number of explanations have been offered regarding the mechanism
responsible for the particle concentration effect. The most popular is to
posit the existence of an additional third sorbing phase or complexing
component which is associated with the adsorbent, but which is inadvertently
measured as part of the dissolved chemical concentration due to experimental
limitations. Colloidal particles which remain in solution after particle
separation (Benes and Majer, 1980; Gschwend and Uu; 1985), and dissolved
ligands or macromolecules which desorb from the particles and remain in
solution (Carter and Suffet, 1982; Voice et al., 1983; Curl and Keolelan, 1984;
Nelson et al., 1985) have been suggested as the cause of the influence of
particle concentration on the partition coefficient. It has also been
suggested that increasing particle concentration increases the degree of
particle aggregation, decreasing the surface area, and hence the partition
coefficient (Karickhoff and Morris, 1985). The effect has also been attributed
to kinetic effects (Karickhoff, 1984).
-------
REVERSIBLE COMPONENT PARTITION COEFFICIENTS
COMPARISON TO PARTICLE INTERACTION MODEL
NEUTRAL ORGANIC CHEMICALS
1.40 loO|0Koc' 0-00028 +0.983 .
O.
o
O
u
CO
CD
O
6
5
4
3
2
I
0
-I
-2
-2-101 2345
CALCULATED loglo Kp (L/kg)
FIGURE 4-1. Comparison of observed partition coefficient to calculated
partition coefficient using Equation (4-2) (Di Tore, 1985).
-------
RELATIONSHIP OF Koc TO Kow
m focKow<1
ADSORPTION
•
7
e
s
4
S 3
294
109. K«w
REVERSIBLE COMPONENT
12948478
•ofl»Kow
FIGURE 6-2. Comparison of the adsorption (top) and reversible component
(bottom) organic carbon normalized partition coefficient, Koc, to the oc tonal -
water partition coefficient, Kow, for experiments with low solids
concentrations: m f
oc
< 1. The line represents equality (Di Tore, 1985).
-------
Page 4-6
Sorption by non-separated particles or complexing by dissolved organic
carbon can produce an apparent decrease in partition coefficient with
increasing particle concentration if the operational method of measuring
dissolved chemical concentration does not properly discriminate the dissolved,
free chemical concentration from the complexed or colloidally sorbed portion.
However, the question is not whether improperly measured dissolved
concentrations can lead to an apparent decrease in partition coefficient with
increasing particle concentrations. The question is whether these third phase
models explain all (or most) of the observed partition coefficient - particle
concentration relationships.
An alternate possibility is that the particle concentration effect is a
distinct phenomena which is a ubiquitous feature of aqueous phase - particle
sorption. A number of experiments have been designed to explicitly exclude
possible third phase interferences. The resuspension experiment for PCBs (Di
Toro and Horzempa, 1983) and metals (Di Toro et al., 1986; Mcllroy et al.,
1986) in which particles are resuspended into a reduced volume of supernatant,
and the dilution experiment (Di Toro and Horzempa, 1983) in which the particle
suspension is diluted with supernatant from a parallel vessel, both display
particle concentration effects. It is difficult to see how third phase models
can account for these results since the concentration of the colloidal
particles is constant while the concentration of the sediment particles vary
substantially.
The model (Equation 4-2) is based on the hypothesis that particle
concentration effects are due to an additional desorption reaction induced by
particle-particle interactions (Di Toro, 1985). It has been suggested that
actual particle collisions are responsible (HacKay and Powers, 1986). This
interpretation relates i/x to the collision efficiency for desorption and
demonstrates that it is independent of the chemical and particle properties, a
fact that has been experimentally observed (Di Toro, 1985; Di Toro et al.,
1986)
-------
Page 4-7
It is not necessary to decide which of these mechanisms are responsible for
the effect if all the interpretations yield the same result for sediment-pore
water partitioning. Particle interaction models would predict that KQC - KQW
since the particles are stationary in sediments. Third phase models would also
relate free (i.e. uncomplexed) dissolved chemical concentration to particulate
concentration via the same equation. As for kinetic effects, the equilibrium
concentration is again given by the relationship KQC - KQW. Thus there is
unanimity on the proper partition coefficient to be used in order to relate the
free dissolved chemical concentration to the sediment concentration, that is,
As discussed previously, the unifying feature that allows for the
development of sediment quality criteria that are applicable to a broad range
of sediment types is the organic carbon content of the sediments . The
equations that apply are developed below. The sediment-pore water partition
coefficient, K« is given by:
K - f K - f K (4-4)
p oc oc oc ow
and the solid phase concentration is given by:
r - f K c. (4-5)
oc ow d
An important observation can be made which leads to the idea of organic carbon
normalization. Equation (4-5) indicates that the partition coefficient for
non- ionic organic chemicals is linear in the organic carbon fraction, fOc- As
a consequence, the relationship between solid phase concentration, r, and
uncomplexed or free dissolved concentration, c^, can be expressed as:
r
T~ ~ K c . (4-6)
f ow d
oc
-------
Page 4-8
If we define:
V ' £- <4'7)
oc
as the organic carbon normalized sediment concentration (/ig chemical/g organic
carbon) then from Equation (4-6):
r - K c. (4-8)
oc ow d v '
Therefore, for a specific chemical with a specific KQW the organic carbon
normalized total sediment concentration, roc, is proportional to the dissolved
free concentration, cj, for any sediment with foc > 0.5 percent. This latter
qualification is judged to be necessary because at foc < 0.5 percent, the
factors controlling second order effects on partitioning (e.g., particle size
and sorption to non-organic mineral fractions) become relatively more
important. Using the proportional relationship given by Equation (4-8), the
concentration of free dissolved chemical can be predicted from the normalized
sediment concentration and KOW. This concentration is of concern since it is
in this form that contaminants are bioavailable. It is also this concentration
which can be used to quantify sediment quality.
4.2 DISSOLVED ORGANIC CARBON (DOC) COMPLETING
In addition to partitioning to particulate organic carbon (POC) associated
with suspended and sediment particles, hydrophobia chemicals can also partition
to the organic carbon in colloidal sized particles as well. Because these
particles are too small to be removed by conventional filtration or
centrifugation they are operationally defined as dissolved organic carbon
(DOC). Since sediment interstitial waters frequently contain significant
levels of DOC, it oust be considered in evaluating the phase distribution of a
chemical. A distinction must be made between the free dissolved chemical
concentration, cj, and the DOC-complexed chemical, CQQC- The partition
-------
Page 4-9
coefficient for DOC, KDOC« i-s analogous to KOC since it quantifies the ratio of
DOC-bound chemical, cnoc> to c^e frjee dissolved concentration, c AHA > natural DOC. The upper bound on Knoc would
appear to be KQC - KOW, the POC partition coefficient.
4.3 PHASE DISTRIBUTION IN SEDIMENTS
Chemicals in sediments are partitioned into three phases: free chemical,
chemical sorbed to POC, and chemical sorbed to DOC. To evaluate the
partitioning among these three phases consider the total concentration cj. The
mass balance equation for CT is:
c_ • ^c. •+• mf K c. + ^m_r._K_.__c. (4-10)
T O OC OC d DOC ^}OC Q
where 4> is the sediment porosity (volume of water/volume of water plus solids)
and m is the sediment solids concentration (mass of solids/volume of water plus
solids). The three terms on the right side of the equation are the
concentration of free chemical in the interstitial water, and that sorbed to
the POC and DOC respectively. Hence, from Equation (4-10) the free dissolved
concentration can be expressed as:
-------
*£
PARTITIONING OF POC AND DOC
8
6
4 -
BaP HCBP DDT TCBP PYR MCBP
CHEMICALS
FIGURE 4-3. ParciCion coefficients of chemicals to particulate organic carbon
(POC), Aldrich hunic acid (AHA), and natural DOC. Benzo(a)pyrene (BaP);
2,2",4,4',5,5' hexachlorobiphenyl (HCBP); DDT; 2,2',5,5< tetrachlorobiphenyl
(TCBP); pyrene (PYR); 4 monochlorobiphenyl (MCBP). (Data: Eadie et al..
1988).
-------
Page 4-11
CT
mfocKoc
As discussed previously the concentration associated with the particle carbon
and DOC are:
Kowcd
CDOC
The total pore water concentration is the sum of the free and DOC complexed
chemical so that:
Cpore " Cd CDOC
Figure 4-4 illustrates the phase partitioning behavior of a system for a
unit concentration of a chemical with the following properties: Koc -
KOW - 106 LAg. foe ~ 2.0 percent, m - 0.5 kg solids/L sediment and
varying from 0 to 50 mg/L. With no DOC present the pore water concentration
equals the free concentration. As DOC increases the pore water concentration
increases due to the increase in complexed chemical, CDOC- Accompanying this
increase in CDOC *s a slight - in fact insignificant - decrease in c& (Equation
4-11) and a proportional decrease in roc (Equation 4-12).
Normally when field measurements of bulk sediment chemical concentration,
r, and total pore water chemical concentrations, Cp0re, are made, the value of
the "apparent" field partition coefficient is calculated directly from the
ratio of these quantities. As a consequence of DOC complexaclon, the apparent
partition coefficient, Kp, defined as:
-------
PHASE DISTRBUTION
EFFECT OF DOC COMPLEX1NG
1000000
uooooo
Q10000 •
001000 -
000100 -
000010
000001
10 20 30 40 60 60
DOC CONCENTRATION (nx>/U
4-4. Phase distribution of a chemical in the three phase system: water,
sediment, and DOC (Equation 4-11). KOC - KDOC " KOW - 106 L/kg, foe - 2. OX
and m - 0.5 kg/L.
-------
Page 4-13
(4-15)
pore
is given by:
oc ow
"DOC^OC
(4-16)
As mQoc increases, the quantity of DOC complexed chemical increases and the
apparent partition coefficient approaches:
f K
oc ow
mDOCKDOC
which is just the ratio of sorbed to complexed chemical.
It is important to realize that the ratio of organic carbon normalized
chemical concentration, roc, to free pore water concentration, eg, is still
constant (Equation 4-12) despite the presence of DOC. This is a critical
point. The free chemical concentration, eg, can be estimated directly from
roc, the organic carbon normalized sediment concentration, and the estimate is
independent of the DOC concentration. Using CnOre to estimate Cjj requires that
the DOC concentration and KQQC be known. The assumption Cp0re - c
-------
Page 4-14
4.4 BIOAVAILABILITY OF DOC COMPLEXED CHEMICALS
The proportion of a chemical in pore water that is complexed to DOC can be
substantial as shown previously (Figure 4-4). The question of bioavailability
of DOC complexed chemical can be important in assessing toxicity directly from
pore water determinations. A significant quantity of data indicates that DOC-
complexed chemical is not bioavailable. McCarthy and Jimenez (1985) using fish
and Landrum et al. (1987) using an amphipod demonstrate that by adding DOC the
uptake of PAHs are significantly reduced (Figure 4-5). For a highly
hydrophobia chemical such as benzo(a)pyrene the effect is substantial while for
less hydrophobic chemicals, e.g., phenanthrene, the reduction in uptake rate is
insignificant. This is the expected result since for a fixed amount of DOC,
the quantity of DOC complexed chemical decreases with decreasing KQQC (Equation
4-13).
The quantitative demonstration that DOC complexed chemicals are not
bioavailable requires an independent determination of the concentration of
complexed chemical. Landrum et al. (1987) have developed a C-18 reverse-phase
HPLC column technique which separates the complexed and free chemical. Thus,
it is possible to compare the measured DOC-complexed chemical to the quantity
of complexed chemical inferred from the uptake experiments, assuming that all
the complexed chemical is not bioavailable (Figure 4-6). Although the uptake
suppression is larger than that inferred from the reverse phase separation, it
appears that the DOC complexed fraction, cnoc« *-s not bioavailable. Hence the
bioavailable form of dissolved chemical is c^, the free uncomplexed component.
This is an important observation since it is c
-------
800
200
i
i
100
UPTAKE OF CHEMICALS BY
PONTOPOREIA HOYI
WITH DOC
WITHOUT DOC
BeP TCBP Pyr
Phan
DATA LANDRUM ET AL 1987
FIGURE 4-5. Average uptake rate of chemicals by Pontoooreia hovi with (filled)
and without (hatched) DOC present. Benzo(a)pyrene (BaP); 2,2',4,4'
tetrachlorobiphenyl (TCBP); Pyrene (Pyr); Phenanthrene (Phen) (Data: Landrum
et al.f 1987).
-------
DOC PARTITION COEFFICIENT
UPTAKE SUPPRESSION VS REVERSE PHASE DETERMINATION
§ 7
6
6
LOQ10 REVERSE PHASE Krp d/kg OC)
DATA: L*NDRUM ET AL. 1985; LANDRUM ET AL 1987
FIGURE 4-6. Comparison of logic of the DOC partition coefficient calculated
from the suppression of chemical uptake versus the C-18 reverse phase HPLC
column estimate. Circles are Aldrich humic acid; triangles are interstitial
water DOC. Chemicals are listed on Figure 4-3 and Figure 4-5 captions (also
anthracene and benzo(a)anthracene).
-------
Page 4-17
partitioning equation: roc - Kow c64 pm). This latter fraction was further separated into a low
-------
Page 4-18
density fraction (<1.9 g/cm^) and the remaining higher density sand sized
particles. It is important to realize that these size fractions are not pure
clay, silt or sand but are natural particles in the size classes denoted by
clay, silt and sand. The organic carbon fractions of the bulk sediment samples
and each of the fractions are shown on Figure 4-7. The organic carbon content
of the bulk sediments range from foc - 0.3 to 1.6 percent. The fractions range
from less than 0.5 percent (the horizontal line) for the high density sand
fraction to greater than 10 percent for the low density fraction. This is a
substantial range in organic carbon content foc.
Figures 4-8A and 4-8B compare the sediment concentrations for each chemical
on a dry weight (left hand side) and an organic carbon basis (right hand side).
The concentrations across the sediment fractions at each station are nearly
constant on an organic carbon basis. In contrast the concentrations on a dry
weight basis are dramatically different.
Figure 4-9 compares the dry weight normalized bulk sediment concentration
to the individual dry weight normalized class concentrations, rj, (upper panel)
and as a frequency distribution of their ratio (lower panel). The dry weight
normalized data have distinctly different concentrations - the low density high
organic carbon fraction is highly enriched whereas the sand fraction is
substantially below the bulk concentration. Figure 4-10 presents the same data
but on an organic carbon normalized basis, rocj. In contrast to Figure 4-9
essentially all the organic carbon normalized low density and silt/clay data
are within a factor of three of the bulk data (lower panel). A significant
fraction of the sand data exceeds the bulk concentration.
Since the validity of organic carbon normalization appears to be limited to
foc > 0.5 percent, Figures 4-11 and 4-12 present the data restricted to foc >
0.5 percent. Again, the dry weight normalization does not account for the
chemical concentration in the low density fraction, whereas the organic carbon
normalization is reasonably consistent and the consistency of the ratios of the
organic carbon normalized concentrations is apparent. It is concluded from
-------
ORGANIC CARBON FRACTIONS,
10QO
10.0
s
0.1
I
B STA TP
Z3 STA WD
0 STA 7
Q STA 5
• STA 4
FRACTION
^^ the Unseparated
<1.9 gm/cc (LOW); the sand sized fraction >6± £ ?nSity f"Ction >64
silt/clay sized fraction <64um In onl c«e fc,?-' >l,\9 ^/CC (SAND>: the
further separated into the cl^ and silt Sft H £Sta,tlOn A> this fraction was
indicated; Wells Da*,
-------
i"
M
8"
FLUORANTHENE
§"4
LJ
I,.-
tntm *nt« nnMi
4 • »
BENZO FLUORANTHENE
^* w'
J
I..
««m rum
IIMIBI IMIOI tutm nut lawi
4 ( I MM MM
ANTHRACENE
j
BENZO (A) ANTHRACENE
I-
l-
I
nnm nui« rtuai
« • r
•mm maim
DATA: PRAHL. 1982
u ^'8^' Sedlment chemical concentrations for each chemical on a dry
weight (left side) and an organic carbon basis (right side) for the bulk
sediment concentration (filled) and the sediment fractions for each station
The bars in the plot are ordered as follows: for Station 4) bulk, low density'
ft ??' n ' ! Sand: Statlons 5 and 7) bulk, low density, silt/clay, and sand-
Wells Dam and Tongue Point) bulk, and low density (Data- Prahl 1932)
-------
PERYLENE
$,.•
t
i
I
[
!
I
I I
8,.-
^
m m m
s s s
>") 1N3MI03S
! I
| f.
|
n jji
1
1
1
n
t»tia» n»ia»
DM Mint
Hfi« t*u» mm*
» DM nut
BENZO PERYLENE
I
I
I
ntttai HMIM nmai
4 • - I
Kan
INDENOPERYLENE
ntttoN «rm« tutiai mu* TBM
« • 7 am Min
"'
to
W— *
—
1C1
1
luiiw nmai mat TDM
•MTIW nUI« tt»l« «U.t • TOM
• • I OM MIW
itHiM n«i« mil TMM
miiw IMTIM IUIIOM mil TMUI
BENZO (A)PYRENE
!-
10*
IllllM IU1IOH «U« NMUi
• r DM mm
J
•HI1W IMIIM (IMIOH KUt IBM
4 • T DM MINI
BENZO (E)PYRENE
IUTIM nmai ITMIM «u« IOMM
10'
•MUCH ItXIM ITAIIW H1U tO««
« D > OM mn
FIGURE 4-8B. Sediment chemical concentrations for each chemical on a dry
weight (left side) and an organic carbon basis (right side) for the bulk
sediment concentration (filled) and the sediment fractions for each station.
The bars in the plot are ordered as follows: for Station 4) bulk, low
density, clay, silt, and sand; Stations 5 and 7) bulk, low density, silt/clay,
and sand; Wells Dam and Tongue Point) bulk, and low density (Data- Prahl
1982).
-------
01
o
D
<
Q.
cn
cn
<
u
UJ
N
M
cn
LU
O
UJ
cn
10000
lOOOu
100.
DRY WEIGHT NORMALIZATION
I I I II Ml I I I I I I III I I I I I I
LOW DENSITY +
SILT/CLAY o
SAND x
I I I I I III! I I I I I I
III I I I I I I II
10 100 1000
BULK SEDIMENT PAH (ug/g)
10000
<
oc
$£
gen
il
cn
cn
O)
Kl
*-i
CO
1000
100
10
1
0.1
0 01
DRY WEIGHT NORMALIZATION
g i i mini i i iiiiiii
LOW DENSITY +
= SILT/CLAY o
= SAND x
E +**
: + ***
0 0"°°
E o
= x x xxx
X
1 1 Illllll 1 1 1 1 1 1 III
1
hH*"J"*"f++
ooooooooJJ333
<
xxx
1 1 1
1
++H++*"
K*+
g
EtfffOOOOOO <
1 1 1
1 =
ut++ "•"
H 1 " ~
=
xxx
ggg o 0 0 -
"•
=
III 1 1 1 1 1 1 Illllll 1 1
0.1
10 20 50
PROBABII1TY
BO 90
gg gg. g
FIGURE 4-9. Dry weight normalization. Comparison of (top panel) the bulk
sediment concentration for all PAHs (x axis) with the same chemical
concentration in the individual sediment fractions (y axis) on a dry weight
basis. Bottom panel presents a probability plot of the ratio of these
quantities for the three size fractions (Data: Prahl, 1982).
-------
1000000
ORGANIC CARBON NORMALIZATION
8
O)
o>
D
I
LU
M
M
CO
h-
100000
10000
1000
100
i i 11111 i i i i mrr i i i iiini
LOW DENSITY +
SILT/CLAY o
SAND x
\c\\/ i i i 111111 i i i i 11111 i i i 11 mi i i i i i mi i i i i 11 u
10 100 1000 1.0000 100000 1000000
BULK SEDIMENT PAH (ug/g OC)
1000
100
ORGANIC CARBON NORMALIZATION
UJ
o ra
II
iu^
cnr?
w£
u
LJ
o>
v.
O)
en
10
0.1
0.01
= i i mini i i i 1 1 1 in
LOW DENSITY +
E SILT/CLAY 0
= SAND x
-
-
E 0OOC
\***
I +*
I +0
y
1 1 1 1 Mill II 1 1 1 1 1 II
1 1
^«
1 1 1
1 1 1
towSpP00
P+JF^
i i i
III 1 1 1 1 1 1 HUM 1 1 1 E-
xxx
*ooo o o
-
a
-
mi 1 1 i i i nun i i i
0.1
10 20 50 80 90
PROBABILITY
99
99.9
FIGURE 4-10. Organic carbon normalization. Comparison of (top panel) the bulk
sediment concentration for all PAHs (x axis) with the same chemical
concentration in the individual sediment fractions (y axis) on an organic
carbon basis. Bottom panel presents a probability plot of the ratio of these
quantities for the three size fractions (Data: Prahl, 1982).
-------
0>
\
O)
3
X
CL
cn
CO
u
LJ
M
M
10
10000
1000
100
10
\
DRY WEIGHT NORMALIZATION
i nun—i i i mm—i i i mm—r—r
LOW DENSITY +
SILT/CLAY 0
foc > °-5%
. . mm i i i nun - 1 I I Illlll - 1 I I Mllll - 1 I I Mil
i 10 100 1000 10000
BULK SEDIMENT PAH (ug/g)
o
IH
1
5-
IS
fi^
cn
to
o>
S3
a
M
CO
1000
100
10
1
0.1
0.01
DRY WEIGHT NORMALIZATION
E i iiiniii i mini!
•
•
LOW DENSITY +
5 SILT/CLAY 0
= foc > 0.5%
•
•i
+ ++-H'
•i
•
v
•
•
1 1 Illlll
. . * t i T
l*f*l III'1
OOOOOCCD3**1
' ' 1
opxcooooooo
1 1 1
1 =
,000 o o E
=
IIIIIM 1 1 IIIMIII 1
0.1
10 20 50 BO 90
PROBABILITY
99 99.9
FIGURE 4-11. Dry weight normalization with foc > 0.5X. Comparison of (top
panel) the bulk sediment concentration for all PAHs (x axis) with the same
chemical concentration in the individual sediment fractions (y axis) on a dry
weight basis. Bottom panel presents a probability plot of the ratio of these
quantities for the three size fractions (Data: Prahl, 1982).
-------
0>
en
CO
3
0
UJ
M
IH
CO
1000000
100000
10000
1000
100
ORGANIC CARBON NORMALIZATION
I III
I Illllll
I I I I I III I I I I I III!
LOW DENSITY +
SILT/CLAY 0
f0c > °-5x
inL^ i i i i inn
10 100
Minn i i i 111111 i i i 111111 i iiiiin
1000 10000 100000 1000000
BULK SEDIMENT PAH (ug/g OC)
1000
£2 100
ORGANIC CARBON NORMALIZATION
|8
S|
ia
UJ^
cor?
s°
d^»
UJ
t-t
en
O)
10
0.1
0.01
= IM linn i Minn
LOW DENSITY +
E SILT/CLAY o
= foc > 0.5X
=
E o°°C
» ^T »
Q
+0
1 1 Illllll 1 1 1 1 Mill
1 1 1
^^n^n^rn^EG
' 'rvTml
^f™
1 1 1
*•«**
1 1 1
III 1 1 Illllll 1 1 =
iooo o o
a
III 1 1 1 1 1 1 Illllll 1 1
0.1
10 20 50 BO 90
PROBABILITY
99 99.9
^
chemical concentration in the individual sediment fracti^s (y
-------
Page 4-26
these data chat the organic carbon normalized measurements are relatively
independent of particle size class and that organic carbon is a controlling
factor in establishing the level of contamination of sediment particles.
4.5.2 Sediment - Pore Water Partitioning
Since the solid phase chemical concentration is proportional to the free
dissolved portion of the pore water concentration, eg, the actual partition
coefficient, Kp, should be calculated using the free dissolved concentration.
The free dissolved concentration will typically be lower than the total
dissolved pore water chemical concentration in the presence of significant
levels of pore water DOC. So, the actual partition coefficient is higher than
the apparent partition coefficient.
Direct observations of pore water partition coefficients are restricted to
the apparent partitioning coefficient, Kp (Equation 4-15), because total
concentrations in the pore water are reported and DOC complexing is expected to
be significant at the DOC concentrations found in pore waters. Data reported
by Brownawell and Farrington (1986) demonstrate the importance of DOC
complexing in pore water. Figure 4-13 presents the apparent organic carbon
normalized partition coefficient that was measured for 10 PCB congeners at
various depths in the sediment core versus foc KOW, the calculated partition
coefficient (solid symbols). The line corresponds to the relationship, KOC -
KOW. which is the expected result if DOC complexing were not significant.
Since DOC concentrations were measured for these data it is possible to
estimate eg using Equation (4-14) in the form:
°pore
and to compute the actual partition coefficient: Kp - r/C(j. The data indicate
that if KDQC ~ KOW *-s used, the results shown on Figure 4-13 as the open
-------
O)
Q
LU
DC
co
<
LU
- 0 ACTUAL Kg (BASED ON FREE DISSOLVED PCB)
* APPARENT K (BASED ON TOTAL DISSOLVED PCB)
DATA: BRONNANELL AND FARRINOTON. isse
4-13. Observed partition coefficient versus the product of organic
carbon fraction and octanol -water partition coefficient. The line represents
equality. The partition coefficients are computed using total dissolved PCB
(*) and using free PCB (o) computed using Equation (4-20) with KDOc - K<>w.
-------
Page 4-28
symbols, agree with the expected partition equation, namely that Kp - foc KQW.
A similar three phase model has been presented by Brownawell and Farrington
(1985).
Other data with sediment • pore water partition coefficients which are
based on total dissolved concentrations (Kadeg and Pavlou, 1987), or for which
the DOC concentrations have not been reported (Socha and Carpenter, 1987;
Oliver, 1987), are available to assess the significance of DOC partitioning on
the apparent sediment partition coefficient. Figure 4-14 presents these
apparent partition coefficients versus foc Kow using individual ratios of
sediment and pore water concentrations. The expected relationship for DOC
concentrations of 0, 1, 10, and 100 mg/L is shown on Figure 4-14 and the data
roughly conform to DOC levels of 10 to 100 mg/L. These DOC levels are
representative of pore waters (Brownawell and Farrington, 1986; Bricker et al.,
1977). Further, the results do not refute the hypothesis that Koe - KQW in
sediments but show the need to account for DOC complexing.
It is concluded from the results of this section that the effect of DOC
complexing can be significant in aquatic sediments and that it should be
assessed when evaluating the distribution of chemicals in sediments. As shown
above, methods are available to make this assessment.
4.6 ORGANIC CARBON NORMALIZATION OF BIOLOGICAL RESPONSES
The results discussed above suggest that if a concentration-response curve
correlates to pore water concentration, it should correlate equally well to
organic carbon normalized total chemical concentration, independent of sediment
properties. This is based on the partitioning formula roc - Kowcd (Equation
4-12) which relates the free dissolved concentration to the organic carbon
normalized particle concentration. This only applies to non-ionic hydrophobic
organic chemicals since the rationale is based on a partitioning theory for
these chemicals. To demonstrate this relationship, concentration-response
curves for the data presented in Section 3 were used to compare
-------
10'
O)
Q
LU
DC
Z)
CD
<
LU
10'
i i 11 mi I i i 11 mi I i i 11 mi
1 T
I Illl
T I I I III
B C
B
iiiINI
i i i
INI
i i i i inn
i mi
DOC:
(mg/L)
o
10
100
10
10'
,3
10
10'
10'
focKow (L/kg)
FIGURE 4-14. Observed partition coefficient versus the product of organic
carbon fraction and octanol-water partition coefficient. The lines represent
the expected relationship for DOC concentrations of 0, 1, 10 and 100 mg/L and
KDOC - KOW Data from OU-ver (1987) for PCB congeners and other chemicals (A),
from Socha and Carpenter (1987) for Phenanthrene (B), Fluoranthene (C) and
Perylene (D) and from Kadeg and Pavlou (1987) for Naphthalene (E), Phenanthrene
(F), Pyrene (G), Anthracene (H) and Flouranthene (I).
-------
Page 4-30
results on a pore water-normalized and organic carbon-normalized total chemical
concentration basis. These results are described below.
4.6.1 Toxicitv Experiments
The results of a number of laboratory experiments can be used to assess the
correlation of observed data with concentration-response curves based on both
pore water concentration and organic carbon normalized total chemical
concentration. Figures 4-15 to 4-17 present these comparisons for kepone, DDT
and fluoranthene. The mean and 95 percent confidence limits of the LC50 and
EC50 values for each set of data are shown in Table 4-1. The top panels repeat
the response - pore water concentration plots shown previously in Section 3
while the lower panels present the response versus total sediment concentration
data which is organic carbon normalized
-------
ACUTE TOXICITY OF KEPONE TO
CHIRONOMUS TENTANS
100
4
>
t-H
>
a.
in
% ORGANIC CARBON
.09 •
1.5 •
12.0
29 SO 75 1DO 125
PORE WATER KEPONE lug/L)
ISO
too,
cr
in
o
o
CHRONIC TOXICITY OF KEPONE TO
CHIRONOMUS TENTANS
ISO,.
o
cr
i-
o
O
CC
CD
% ORGANIC CARBON
.09 •
1.5 •
12.0 A
25 50 75
PORE WATER KEPONE (ug/L)
ISO,.
o
cr
o
LJ
o
cr
CD
1000 2000
SEDIMENT KEPONE (ug/g OC)
DATA- ADAMS, et a).. 1983
1000
SEDIMENT KEPONE (ug/g OC)
DATA: ADAMS, et.al.. 1983
100
2000
4-1?. Comparison of percent survival (left) and growth rate reduction
(right) of Chironomus tentanq to kepone concentration in pore water (top) and
in bulk sediment using organic carbon normalization (bottom) for three
sediments with varying organic carbon concentrations.
-------
ACUTE TOXICITY OF DDT TO HYALELLA
100
% ORGANIC CARBON
3.0 •
7.2 •
10 5
ACUTE TOXICITY OF ENDRIN TO HYALELLA
I00r
cc
in
o oo
too*
i oo ?oo 3.00 4.00
PORE WATER DDT (ug/L)
5 00
% ORGANIC CARBON
3.0 •
6.1 •
11 2
cc
in
90.
0.0
too,
20 40 60 BO
PORE MATER ENORIN (ug/L)
10 0
soo 1000
SEDIMENT DDT (ug/g OC)
DATA NEBEKEH and SCHUVTEMA. 1988
1500
100 200 3OO 400
SEDIMENT ENDRIN (ug/g OC)
DATA. NEBEKER and SCHUYTEMA. 1988
500
FIGURE 4-16. Comparison of percent survival of Hyalella to DDT (left) and
endrin (right) concentration in pore water (top) and in bulk sediment using
organic carbon normalization (bottom) for three sediments with varying organic
carbon concentrations.
-------
ACUTE TOXICITY OF FLUORANTHENE TO
RHEPOXYNIUS ABRONIUS
cr
t/i
X
% ORGANIC CARBON
0.2 •
0.3 •
0.5 «•
0.0 20.0 40.0 "50.0 60.0
PORE WATER FLUORANTHENE (ug/L)
cc
in
40
20.
.
0 1000 2000 3000 4000 9000 6000 7000
SEDIMENT FLUORANTHENE (ug/g OC)
DATA FROM 5NARTZ. et.al.. 1987
FIGURE 4-17. Comparison of percent survival of Rhepoxvntus abronius to
fluoranthene concentration in pore water (top) and bulk sediment using organic
carbon normalization (bottom) for sediments with varying organic carbon
concentrations.
-------
TABLE 4-1. DOSE-RESPONSE PARAMETERS3
Chemical
(End Point)
Kepone
(Mortality)
Kepone
(Growth)
Fluoranthene
(Mortality)
DDT
(Mortality)
Endrin
(Mortality)
foe
M
0.09
1.50
12.0
0.09
1.50
12.0
0.2
0.3
0.5
3.0
7.2
10.5
3.0
6.1
11.2
Total Sediment
(uz/e.)
0.97
7.89
42.0
0.40
9.87
48.9
3.3
6.2
10.5
11.0
19.6
49.7
4.4
4.8
6.0
( 3.0 -
( 5.4 -
( 8.3 -
(10.1 -
(15.6 -
(44.2 -
( 3.9 -
( 3.7 -
( 4.7 -
3
7
13
12
24
56
5
6
7
.7)
.1)
.4)
.7)
.3)
.3)
.2)
.3)
-4)
LC
-------
BIOACCULUMATION OF KEPONE-IN
CHIRONOMUS TENTANS
10000
01
D
LJJ
Q
CC
Z)
CD
Q
O
CO
1000
100
10
i i i i iT m i i i i MiM i i i i M 111 i i i i iiit
NOTE=
BODY BURDEN ESTIMATED
FROM REPORTED AVERAGE
BAF FOR EACH SEDIMENT TYPE.
% ORGANIC C:
< 0.09 •
1.50 •
12.00 A
n
n
10 100 1000
PORE WATER KEPONE (ug/L)
10000
10000
01
\ 1000
O)
^D
g 100
CC
Z)
CO
D 10
0
CO
1
= 1
-
I
1
FIGURE 4-18.
I 1 I 1 I 1 II 1 1 I 1 1 1 1 II 1 I 1 I I
A
x
1 1 ll l l l l i
•
•
k M
V~
^
A m NOTE:
BODY BURDEN ESTIMATED
FROM REPORTED AVERAGE
BAF FOR EACH SEDIMENT TYPE.
i i i i 1 1 n i i i i i 1 1 ii i i i i i 1 1 M i i i i i
10 100
1000
1 l±
=
1 > '
10000
SEDIMENT KEPONE (ug/g OC)
Comparison of body burden of Chironomus tentans to kepc
concentration in pore water (top) and bulk sediment using organic carbon
normalization (bottom) for sediments with varying organic carbon
concentrations. (Body burdens calculated from average bioaccumulacion factors.
Data: Adams et al., 1983.)
-------
V
00
ID
TABLE 4-2. BIOACCUMULATION FACTORS8
Bioaccumulation Factors
Total Sediment
Chemical
Kepone
Cypermethrin
Permethrin
foe
m
.09
1.50
12.
2.3
3.7
2.3
3.7
iig/g organism
utL/tL sediment
600
20
3.3
6.21
0.50
0.60
4.04
0.38
0.23
( 308
( 4.8
( 0.3
(4.41
(0.30
(0.37
(2.89
(0.17
(0.18
- 892)
- 35.2)
- 6.3)
- 8.01)
- 0.71)
- 0.83)
- 5.20)
- 0.59)
- 0.28)
Pore Water
/ig/kg organism
t»g/L
17,600
5,180
5,790
80.1
51.3
92.9
39.7
52.5
29.7
(6,540
(1,970
(2,890
(73.5
(43.8
(87.0
(25.0
(22.6
(15.6
- 28.600)
- 8.390)
- 8,700)
- 86.7)
- 58.8)
- 98.8)
- 54.3)
- 82.4)
- 43.7)
Organic Carbon
Normalized
utL/ti organism
ue/g sediment OC
.54
.30
.40
<.006
.012
.022
<.004
.009
.008
(.277 -
(.072 -
(.036 -
(.004 -
(.008 -
(.012 -
(.002 -
(.005 -
(.006 -
.803)
.528)
.756)
.008)
.016)
.032)
.006)
.013)
.010)
Reference
Adams, Kimerle
and Mosher,
.1983 and 1985
Muir et al., 1985
Muir et al., 1985
a95Z confidence limits shown in parentheses
-------
Page 4-37
organic carbon normalization for sediments and to examine organism
normalization as well. The use . of organism lipid as the phase which is
analogous to POC has become conventional (see references in Chiou, 1985). If
corg is tne chemical concentration per unit dry weight, then the partitioning
equation is:
c - K_f_c.
org L L d
where:
KL - lipid-water partition coefficient (L/kg lipid)
fL - weight fraction of lipid (kg lipid/kg organism)
eg - free dissolved chemical concentration (pg/L)
The lipid-normalized organism concentration, corg,L, is:
c
org
cwL--r--K.c. (4-22)
org.L f T. d
If the organic carbon normalized sediment concentration is used to compute a
bioaccumulation factor (BAF), then:
(4-23)
oc ""
where the second equality results from using the partitioning Equations (4-12)
and (4-22). The BAF is the partition coefficient between organism lipid and
sediment organic carbon. If the equilibrium assumptions are valid for both
organisms and sediment particles, the BAF should be independent of particle and
-------
Page 4-38
organism properties. In addition if lipid solubility of a chemical is
proportional to its octanol solubility, KL « KOW, then the lipid normalized -
organic carbon normalized BAF should be a constant, independent of particles,
organism, and chemical properties (McFarland, 1984; Lake et al., 1987). This
result can be directly tested.
The representation of benthic organisms as passive encapsulations of lipid
that equilibrate with external chemical concentrations is certainly only a
first order approximation. Biomagnificatlon effects via ingestion of
contaminated food and the dynamics of internal organic carbon metabolism are
ignored. Nevertheless it is an appropriate initial assumption since deviations
from the first order representation will point to necessary refinements, and
for many purposes this approximation may suffice.
A comprehensive two-part experiment (labeled A and B) involving four
benthic organisms: species of Yoldta (A), Nephtvs (A), Nereis (A and B), and
Macoma (B) and five sediments (1, 2, 3 for A; 1, 4, 5 for B) has recently been
performed (Rubinstein et al., 1988). The uptake of various PCB congeners was
monitored until steady state body burdens were reached. Sediment organic
carbon and organism lipid content were measured. The utility of organic carbon
normalization is examined on Figures 4-19 and 4-20 which present probability
plots of the BAF (ratio of organism to sediment concentration) of a tetra-
chloro and hexa-chloro biphenyl congener using dry weight normalization for
both organism and sediment (top panels); organic carbon normalization for the
sediment (middle panels); and both organic carbon and lipid normalization
(bottom panels). The individual sediments are separately identified. The two
experiments are presented separately since there is an unexplained systematic
difference between the results - even when comparing the same organism and
sediment. The BAFs based on dry weight normalization are quite different for
each of the sediments. Organic carbon normalization essentially collapses the
BAFs for each sediment and also reduces the variability somewhat (middle
panel). There is no statistically significant difference between the BAFs of
the organisms in each test. A summary of all data versus Kow, a measure of the
-------
ACCUMULATION FACTORS: 2, 2! A. 4-TETRACHLOROBIPHENYL
3 O)
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DRY WEIGHT NORMALIZATION
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.11 1020 50 8090 99 99.9 0.1 1 1020 50 8090 99 99.9
ORGANIC CARBON NORMALIZATION
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PROBABILITY PROBABILITY
DATA: RUBENSTEIN ET AL.f 1988
FTCURE 4-19 Probability plots of the bioaccumulation factor (ratio of
organism to sediment concentration) of a 2,2',4,4'tetrachloro biphenyl using
dry weight normalization for both organism and sediment (top panels); organic
carbon normalization for the sediment (middle panels); and organic carbon and
lipid normalization (bottom panels). Two experiments (A and B) involving four
benthic organisms: Yoldia (A), Neohtvs (A), Nereis (A and B), and Hacpi^a (B)
and five sediments (1,2,3 for A; 1,4,5 for B) are shown.
-------
ACCUMULATION FACTORS: 2, 2,' 3, 5, 5,' 6-HEXACHLOROBIPHENYL
10*
crn
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ORGANIC CARBON NORMALIZATION
P
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ORGANIC CARBON AND LIPID NORMALIZATION
MIIH II IIIIIH
I I
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PROBABILITY
!'" "
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B
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99 99.9
0.1 1 10 20 50 8090
PROBABILITY
99 99.9
DATA: RUBENSTEIN ET AL., 1988
FIGURE 4-20. Probability plots of the bioaccumulation factor (ratio of
organism to sediment concentration) of a 2,2'3,5,5',6 hexachloro biphenyl using
dry weight normalization for both organism and sediment (top panels); organic
carbon normalization for the sediment (middle panels); and organic carbon and
lipid normalization (bottom panels). Two experiments (A and B) involving four
benthic organisms: ^sl^i& (A), Nephtvs (A), Nereis (A and B), and Macoma (B)
and five sediments (1,2,3 for A; 1,4,5 for B) are shown.
-------
Page 4-41
degree of hydrophobicity of the congeners, are presented on Figure 4-21. The
log means are shown for the same sequence of normalizations. The variability
due to sediment type is significantly reduced with organic carbon
normalization. Also, the BAFs are reasonably constant although some
suppression at the high KQW range is evident.
Results of a similar though less extensive experiment using one sediment
and Oligochaete worms has been reported (Oliver, 1987). A plot of the organic
carbon and lipid normalized BAF versus KQW from this experiment is shown on
Figure 4-22, together with the previous data. There appears to be a systematic
variation with respect to Kow which suggests that the simple lipid
equilibration model with a constant lipid-octanol solubility ratio is not
completely descriptive for all chemicals.
A further conclusion can be reached from these results. It has been
pointed out by Bierman (1988) that the fact that the lipid-carbon normalized
BAF is of order 1 to 10 supports the contention that the partition coefficient
for sediments is Koc and that the particle concentration effect does not appear
xto be affecting the free concentration in sediments. The reason is that the
lipid-carbon normalized BAF is the ratio of the solubilities of the chemical in
lipid and in particle carbon, Equation (4-23). Since the solubility of non-
ionic organic chemicals in various non-polar solvents is similar, it would be
expected that the lipid-organic carbon solubility ratio should be of order one.
If this ratio is taken to be one then the conclusion from the BAF data is that
indeed Koc - Kow for sediments (Bierman, 1988).
A final observation can be made. The data analyzed in this section
demonstrate that organic carbon normalization accounts for much of the reported
differences in bioavailability of chemicals in sediments for deposit feeding
organisms. By contrast, the data presented in previous sections are limited to
amphipods and midges. Hence they provide important additional support for
organic carbon normalization as a determinant of bioavailability for two quite
different classes of organisms.
-------
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ORGANIC CARBON AND LIPID NORMALIZATION
- 10'
O)
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I
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3 10'
10'
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LOG Kow LOG Kow
DATA: RUBENSTEIN ET AL., 1988
FIGURE 4-21. Plots of the bioaccumulation factor (ratio of organism to
sediment concentration) of a series of PCB congeners versus Log KQW for that
congener using dry weight normalization for both organism and sediment (top
panels); organic carbon normalization for the sediment (middle panels); and
organic carbon and lipid normalization (bottom panels). Two experiments (A and
involving four benthic organisms: v^ldia. (A), Neohtvs (A), Nereis (A and B)
and Mflsojjfi (B) and five sediments (1,2,3 for A; 1,4.5 for B) are shown.
-------
10'
JO'
o
o>
v.
O)
10'
[n 10°
if
10
-i
10'
DATA: Log Mean +/- SO
o Oliver. 1987
• Rubenstein et al..
(Study A)
19BB
O M x.
o
8 o
fl
o
o
488
L0610 Kow
FIGURE 4-22. Plots of the bioaccumulation factor (ratio of organism lipid to
sediment organic carbon concentration) for a series of PCS congeners versus Log
Kow (Data: Oliver, 1987 and Rubenstein et al., 1988).
-------
Page 4-44
4.7 DETERMINATION OF THE ROUTE OF EXPOSURE
The exposure route by which organic chemicals are accumulated has been
examined in some detail for water column organisms (e.g., Thomann and Connolly,
1984) . It might be supposed that the toxicity and bioaccumulation data
presented above can be used to examine the question of the route of exposure.
The initial observations were that biological effects appear to correlate to
the interstitial water concentration, independent of sediment type. This has
been interpreted to mean that exposure is primarily via pore water. However,
the data correlate equally well to the organic carbon normalized sediment
concentration. This suggests that the sediment organic carbon is the route of
exposure. In fact neither of these conclusions necessarily follow from these
data. The reason is that an alternate explanation is available that is
independent of the exposure pathway.
Consider the hypothesis that the chemical potential (fugacity) of a
chemical controls its biological activity. The chemical potential, ^4, of the
free concentration of chemical in pore water, c^, is:
Md - /*Q + RT ln(cd) (4-24)
where no is the standard state chemical potential, and RT is the product of the
universal gas constant and absolute temperature (Stumm and Morgan, 1970) . For
a chemical dissolved in organic carbon - assuming that particle organic carbon
can be characterized as a homogeneous phase - its chemical potential is:
p. - n + RT ln(r ) (4-25)
*oc 'o oc
where roc is the weight fraction of chemical in organic carbon. If the pore
water is in equilibrium with the sediment organic carbon then:
' "oc
-------
Page 4-45
The chemical potential that the organism experiences from either route of
exposure (pore water or sediment) is the same. Hence, so long as the sediment
is in equilibrium with the pore water, the route of exposure is immaterial.
Equilibrium experiments cannot distinguish between different routes of
exposure. Furthermore, if chemical potential (or fugacity) is proportional to
biological effects then the issue becomes: in which phase is n most easily and
reliably measured? Pore water concentration is one option. However it is
necessary that chemical complexed to DOC be a small fraction of the total
measured concentration or that the free concentration is measured. Total
sediment concentration normalized by sediment organic carbon fraction is a
second option. This measurement is not affected by DOC complexing. The only
requirement is that sediment organic carbon be the only sediment phase which
contains significant amounts of the chemical. This appears to be a reasonable
assumption for aquatic sediments with foc > 0.5 percent.
4.8 FIELD VALIDATION
The most convincing evidence that sediment quality criteria based on
equilibrium partitioning theory are technically sound would be a demonstration
that the criteria can predict the degree of toxicity of natural sediments. The
measure of sediment toxicity could be either sediment bioassays or benthic
community structure alteration. Sediment data collected following the triad
approach (Long and Chapman, 1985) could provide such information.
There are three technical difficulties that impede this demonstration.
Since they apply to all field data based approaches (e.g. , Barrick et al. ,
1985) they are discussed in some detail.
1. Btoavailability
Contaminated sediment contains measurable concentrations of many chemicals.
In order to apply a sediment quality criteria or to use the magnitude of
the chemical concentration as a measure of its potential to have biological
-------
Page 4-46
effects, it is necessary that the bioavailability of that chemical in that
particular sediment be determined. For non-ionic organic chemicals
sediment organic carbon normalization can be used. However for toxic
metals and ionic organic chemicals there is no currently available
comprehensive partitioning theory that identifies the normalization
quantities and provides the parameters to permit the free dissolved
concentration to be calculated. Hence bioavailability cannot be assessed.
As demonstrated above, bioavailability can vary as much as two orders of
magnitude. Therefore, dry weight normalization cannot be expected to
suffice.
2. Chemical Mixtures and Causality
There is a fundamental difficulty with using naturally contaminated
sediments. Assume that the list of chemicals that are identified and
quantified cover the known range of potentially toxic chemicals. It is
always possible that there is present another chemical, or chemicals, which
are biologically very active but have yet to be identified. If this
chemical is the cause of significant toxicity then it would cause a
biological effect that would not be predicted from the application of
sediment quality criteria. This result might be interpreted as a failure
of the criteria when in fact it is a failure in identification of toxic
chemicals.
3. Control Sediments and Non-toxic Variations
To judge the relative toxicity of a sediment it is necessary that a
comparable control response be obtained. The perfect control is an
identical sediment without any chemical contamination. Since this is not
possible, sediments from an unimpacted site are assumed to approximate the
response of the perfect control. The degree to which this approximation is
correct limits the assessment of comparative toxicity. Variations in
sediment toxicity test results and ecological community structure can be
-------
Page 4-47
caused by variations in sediment characteristics other than chemical
contamination. For example, the effect of grain size distribution and
organic carbon content on habitat are well known.
In view of these three technical difficulties an alternative means to field
validation was used. The section below describes the alternative use of the
screening level methodology.
4.8.1 Screening Level Methodology ...
A straightforward check of the validity of sediment quality criteria
appears to be precluded. However, it would be helpful if some evidence could
be found that criteria developed from laboratory toxicological data are at
least reasonable. The concept of a screening level concentration provides some
help in this regard.
The approach for computing a screening level concentration for chemicals
was developed at the same workshop that selected the Equilibrium Partitioning
Methodology (SCD 2). During the discussion it became apparent that a direct
approach which correlated some measure of biological impairment to increasing
concentrations of various chemicals was subject to the criticisms outlined in
the previous section. Consider, for example, data on the simple presence or
absence of a particular species of benthic organism in a sediment sample.
Assume, also, that the proper normalization to quantify bioavailability were
known. Suppose an organism is present in one sediment sample with a low
concentration of a particular chemical and absent in another with a higher
concentration of the same chemical. It would be tempting to conclude that the
cause of the absence of the organism is that the higher concentration exceeded
the threshold of tolerance of that organism for that chemical. However there
is no way of proving the causal link between that chemical's concentration and
the absence of the organism in that sample. Mere covariation does not prove
causality. The multiple chemical - correlation approach using this same data
-------
Page 4-48
would also determine covariation but not causality. The absence of the
organism cannot unequivocally be. related to the concentration of certain
chemicals.
Consider an alternative possibility. An organism is present in a sediment
sample and co-exists with a suite of chemicals. One can conclude that the
organism can tolerate those concentrations of chemicals - again assuming that
bioavailability has been accounted for by proper normalization. Some care is
still required since the organism may be on the verge of exhibiting an effect
or it may have just come into contact with the sediment and not yet responded.
However these possibilities are likely to be rare. For the majority of cases
the co-occurrence of an organism and a chemical at a specific (bioavailable)
concentration implies that the organism can tolerate that exposure. It is the
co-occurrence of chemical concentration and organism presence - not organism
absence - that is not subject to the criticisms enumerated above.
The utility of this observation can be seen from the following hypothetical
situation. Suppose many species of benthic organisms are found to co-exist
with concentrations of a chemical that are well in excess of the sediment
quality criteria for that chemical. This situation would surely call into
question the criteria for that chemical since the field observations contradict
the presumption of biological effects at concentrations well in excess of the
criteria. On the other hand, if the criteria were well above the concentration
which co-exists with many organisms it only confirms that field concentrations
have not yet reached levels at which effects would be seen.
It is concluded, therefore, that the highest (or nearly the highest)
concentration that co-exists with many benthic species is the lower bound of
the criteria concentration. The criteria can be a higher concentration if only
relatively uncontaminated sediments are examined. It is for this reason that
this concentration is called a Screening Level Concentration or SLC.
Concentrations below the SLC are known to be tolerated and therefore it is a
lower bound on the criteria concentration.
-------
Page 4-49
4.8.2 Determining Screening Level Concentrations
The application of these idea's to available field data sets is presented
in SCD 7. The following discussion is a summary of the two quantitative issues
that need to be addressed. First, a determination must be made of the
concentration level to be used as the highest co-existence concentration. For
a particular species a tabulation of the co-existence concentrations can be
made. Choosing the highest concentration ignores the possibilities of an
incorrectly reported high concentration, incipient biological effects, and
other causes of a statistical outlier. A better procedure is to choose a
specific percentile - in this case the 90tn percentile was chosen - as the
maximum concentration that co-exists with that species. This concentration is
referred to as the Species Screening Level Concentration or SSLC. For each
species for which at least 20 occurrences were recorded in the data base, an
SSLC was computed. Figures 4-23A and 4-23B illustrate the probability
distributions of the concentrations of Benzo(a)Pyrene (BaP) that co-exist with
the various species (see SCD 7 for the species identification). The
distributions occasionally include an outlier but, for the most part, are
lognormally distributed.
The methodology used for calculating the SLC from.the SSLC is similar to
Che methodology for generating water quality criteria (Stephan et al. , 1985).
The level of protection adopted for water quality criteria is 95 percent of the
species represented in the data set used to define the criteria. The sediment
quality criteria analog to this procedure is to choose the 5C^ percentile SSLC
for the SLC. The probability distributions of the SSLCs for 11 chemicals
including BaP are shown on Figure 4-24. The procedure used to estimate the 5Cn
percentile is the same as is recommended for generating water quality criteria.
The resulting organic carbon normalized concentration is the SLC. The
probability distributions for the SSLC values for the other chemicals for which
field data were available are also shown on Figure 4-24. The SSLCs for the
PAHs are remarkably similar with only a factor of two variation in
concentration across the species. The PCBs are much more widely distributed
reflecting, perhaps, their presence in a larger fraction of the data base. The
actual values that result will be discussed in Section 7.
-------
SPECIES 5
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FIGURE 4-23A. Probability distribution of organic carbon normalized
benzo(a)pyrene sediment concentration for sediments in which the indicated
species was found to coexist (see SCD 7 for the species identification).
-------
E
SED
,n3 SPECIES 48
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concentrations for benzo(a)pyrene (third panel on left, with data from Figure
4-23A and 4-23B) and for ten other other compounds.
-------
Page 4-53
4.9 CONCLUSION
This section reviewed the general approach for development of sediment
quality criteria for non-ionic organic chemicals using the equilibrium
partitioning approach. The theory of partitioning of chemicals in sediments
between the pore water and solid phase was described and equations derived for
organic carbon normalization and DOC complexing. The partitioning of chemicals
in sediments into free, sorbed to POC and sorbed to DOC components is also
presented. This information is used to examine the bioavailability of DOC
complexed non-ionic organic chemicals and it is shown that the free dissolved
component is the bioavailable fraction. Similarly, field data confirm that the
free dissolved component is the fraction which controls partitioning to
sediment organic carbon as well. In view of the difficulty with directly
measuring the free dissolved chemical concentration, it is shown that an
equivalent method is to use the organic carbon normalized sediment chemical
concentration. Due to the difficulties of direct field validation of sediment
quality criteria an alternative method is proposed using the screening level
methodology. Preliminary sediment quality criteria are presented in Section 7.
-------
Page 5-1
SECTION 5.
APPLICABILITY OF USING WATER QUALITY CRITERIA AS THE
EFFECTS LEVEL FOR BENTHIC ORGANISMS
The equilibrium partitioning method for derivation of sediment quality
criteria utilizes equilibrium partitioning theory to predict the biologically
reactive chemical concentrations in pore water, and uses published water
quality criteria concentrations to define concentrations of contaminants in
sediments that will protect the presence and uses of benthic organisms. Use of
water quality criteria assumes that: (1) the sensitivity of benthic species
and species tested to derive water quality criteria are similar, and (2) the
levels of protection afforded by water quality criteria are appropriate to
benthic organisms. This section examines the appropriateness of the assumption
of similarity of sensitivity through a preliminary comparative toxicological
examination of selected data bases on the acute and chronic sensitivities of
benthic and water column species.
5.1 METHOD - RELATIVE ACUTE SENSITIVITY
The relative acute sensitivities of benthic and water column species are
examined using the data bases from the freshwater and saltwater sections of
draft or published water quality criteria documents that contain minimum data
base requirements for calculation of "Final Acute Values" (Table 5-1). This
data base was selected because exposures were via water, durations were
similar, and data and test conditions have been scrutinized for quality through
review of all original references. For each of the 2,180 tests on 218 species
in the 36 freshwater criteria documents and the 1,074 tests on 102 species in
the 30 saltwater criteria documents, the substance, species, life-stage,
salinity, hardness, temperature, pH, acute value, and test condition (i.e.,
static, renewal, flow-through, nominal, or measured), were entered into the
data base. If necessary, original references were consulted to determine the
-------
TABLE 5-1. DRAFT OR PUBLISHED HATER QUALITY CRITERIA DOCUMENTS
AND NUMBER OF INFAUNAL (HABITATS 1 AND 2). EPIBENTHIC
(HABITATS 3 AND 4). AND WATER COLUMN (HABITATS 5 TO 8)
SPECIES TESTED FOR EACH OF THE HATER QUALITY CRITERIA DOCUMENTS
Number of Saltwater Species
Chemical
Acenaphthene
Acrolein
Aldrin
Aluminum
Ammonia
Antimony III
Arsenic III
Cadmium
Chlordane
Chloride
Chlorine
Chlorpyrifos
Chromium VI
Copper
Cyanide
DDT
Dleldrin
2,4-dimethylphenol
Endosulfan
Endrin
Heptachlor
Hexachlorocyc lohexane
Lead
Mercury
Nickel
Parathion
Parathlon - Methyl
Pentachlorophenol
Phenanthrene
Phenol
Selenium IV
Selenium VI
Silver
Thallium
Toxaphene
Trlbutyltin
1,2,4-trichlorobenzene
2,4.5-trichlorophenol
Zinc
Date of
Publication
9/87°
9/87°
1980
1988
5/88°
9/87°
1985
1985
1980
1988
1985
1986
1985
1985
1985
1980
1980
6/88°
1980
1980
1980
1980
1985
1985
1986
1986
10/88°
1986
9/87°
5/88°
1987
1987
9/87°
ll/88b
1986
9/87
9/BBb
9/87°
1987
Total*
_
-
16
-
16
11
12
38
8
-
20
IS
23
25
9
17
21
10
12
21
19
19
13
33
23
-
-
18
10
16
-
21
-
15
19
10
11
32
Infaunal
_
-
0
-
1
3
2
10
1
-
1
2
8
6
1
1
1
2
2
1
1
2
2
10
7
-
-
6
4
1
-
1
-
2
1
4
4
9
Epibenthlc
_
-
10
-
4
6
3
16
6
-
7
7
8
4
4
10
13
2
6
12
13
13
3
6
9
-
-
3
4
4
-
6
.
6
8
4
4
7
Hater
Column
_
-
9
.
13
5
a
17
5
-
13
9
9
18
5
10
12
6
7
13
10
9
10
18
8
-
.
11
4
12
-
16
9
14
4
S
17
Number of Freshwater Spades
Total*
10
13
21
16
9
17
57
14
15
38
19
-
-
23
41
19
11
10
26
18
22
14
29
17
39
31
44
g
33
25
12
19
7
43
8
14
10
48
Infaunal
.
1
2
-
_
1
1
13
1
3
1
2
-
.
1
3
1
1
1
3
2
1
-
11
2
7
1
9
2
6
2
1
1
1
S
1
2
1
5
Eplbenthic
3
5
9
S
2
6
IS
4
6
8
8
-
-
6
IS
9
3
4
12
8
4
4
a
6
14
a
11
i
9
5
4
g
3
12
1
5
2
11
Water
Column
7
B
IS
12
6
13
37
9
8
31
12
-
-
18
29
12
7
7
17
12
18
11
12
13
25
26
26
Q
21
21
10
13
3
24
5
8
7
36
•The total numbers of species tested may not be the same as the sum of the number of species from each habitat tyne
because a species may occupy more than one habitat.
°Draft
o>
OQ
in
K>
-------
Page 5-3
life stage tested and any other missing information. Each of the species/life-
stages tested were classified into.one of eight habitat types listed in Table
5-2, each of which has a different-degree of association with bedded sediments.
TABLE 5-2. HABITAT CLASSIFICATION SYSTEM
FOR LIFE-STAGES OF ORGANISMS
Habitat
Type Description
1 Life-stages that usually live in the sediment and obtain their food
by ingesting sediment or organisms living in the sediment, (infaunal
non-filter feeders)
2 Life-stages that usually live in the sediment but obtain their food
from the water column. These infaunal filter feeders may consume
plankton and suspended detritus.
3 Life-stages that usually live on the surface of sediment and obtain
their food from the sediment.
4 Life-stages that usually live on the surface of sediment but obtain
their food from the water column. This food may include plankton
and suspended detritus.
5 Life-stages that usually live in the water column but mostly consume
food that is on or in the sediment.
6 Life-stages that usually live in and obtain their food from the
water column but sometime rest or sit on the sediment. These life-
stages have slight contact with sediment (resting on the surfaces of
plants, rocks, etc.).
7 Life-stages that are usually in or on an inorganic substrate, such
as sand, rocks, and gravel, spend all their time at the bottom of a
body of water, but have negligible contact with sediment.
8 Life-stages that have negligible contact with the bottom of a body
of water. These life-stages spend essentially all their time in the
water column (i.e., pilings, zooplankton and fish), in the air, etc.
If for any chemical a life-stage was tested more than once, or more than
one life-stage was tested, data were systematically sorted in a three-step
process to arrive at the acute value based on the most experimentally sound
-------
Page 5-4
testing methodology and most sensitive life-stages. First, if a life-stage for
species was tested more than once, flow-through tests with measured
concentrations had precedence, and.data from other tests were omitted. If the
remaining acute values for that life-stage differed by greater than a factor of
four, the geometric mean of the lowest acute values was calculated to derive
the acute value for that life-stage. Second, life-stages were classified as
"benthic" (i.e., infaunal species [habitats 1 and 2] or infaunal and epibenthic
species [habitats 1, 2, 3 and 4]) or "water column" (remaining habitats).
Third, if two or more life-stages were classified as benthic or water column
and their acute values differed by a factor of four, the geometric mean of the
lowest acute values was calculated to derive the acute value for that benthic
or water column life-stage/species. The acute value was converted into a
natural logarithm to normalize the value. Finally, the natural logs of the
acute values for any one chemical were transformed into Z-values using the
equation:
log(c . )-p
° acute rc
z - (5-1)
o
c
where /ic and ac are the log mean and standard deviation of the acute
concentrations for that chemical.
This normalization permits the pooling of all data .for all chemicals and
the selective separation of data on the relative sensitivities of benthic and
water column species relative to the entire spectrum of sensitivities for all
species; i.e., benthic plus water column species. This data base was used to
compare the (1) acute sensitivities of the most sensitive benthic and water
column species, and (2) the frequency distributions of the sensitivities of all
tests with benthic and water column species relative to that expected based on
the pooled data from both benthic plus water column species.
-------
Page 5-5
5.2 BENTHIC COMMUNITY COLONIZATION EXPERIMENTS
Toxicicy tests that determine the effects of chemicals on the colonization
of communities of benthic saltwater species (Hansen and Tagatz, 1980) appear to
be particularly sensitive at measuring the impacts of substances on benthic
organisms. This is probably because the most sensitive life-stages of a wide
variety of benthic saltwater species are exposed and the exposure is of
sufficient duration to maximize response. The test typically includes 3
concentrations of a substance and a control, each with 6 to 10 replicates. The
test substance is added to incoming raw seawater containing planktonic larvae
and other life-stages of marine organisms which can settle onto clean sand in
each replicate aquarium. The test typically lasts from two to four months and
the number of species and the abundance of individuals of each species in
aquaria receiving the substance are compared to controls. If this test is
extremely sensitive and concentrations in interstitial and overlying water
rapidly reach equilibrium, then the effect and no effect concentrations from
this test can be compared with the final chronic value (FCV) from saltwater
water quality criteria documents to gain insight into the applicability of
water quality criteria to protect benthic organisms. A FCV is the concentration
derived from acute and chronic toxicity data that is predicted to protect
organisms from chronic effects of a substance (Stephan et al., 1985). In
addition, similarities in sensitivities of taxa tested as individual species
and in the colonization experiment can indicate reasonableness of the
conclusion of similarity of sensitivities of benthic species relative to water
quality criteria data bases.
5.3 COMPARISON OF THE SENSITIVITY OF BENTHIC AND WATER COLUMN SPECIES
The acute sensitivities of the most; sensitive benthic and water column
species were compared using acute values from the 36 freshwater and the 30
saltwater water quality criteria documents. When benthic species are defined
as just infaunal organisms (habitat types: 1 and 2) and water column species
were defined as all others (habitat types: 3 to 8), the acute values for the
water column species indicated that they were typically the most sensitive.
-------
Page 5-6
The results are cross plotted on Figure 5-1. In most instances where acute
values for saltwater benthic and water column species appear identical, it is
because penaeid shrimp were classified as infaunal (benthic) and epibenthic
(water column) and are most sensitive to insecticides. Clearly, data on the
sensitivities of benthic infaunal species are limited. Of the 36 substances
for which water quality criteria for freshwater organisms are available, 2 or
fewer infaunal species were tested against 25 (69 percent) of the substances,
and 5 or fewer species were tested against 30 (83 percent) of the substances.
Of the 30 substances for which water quality criteria for saltwater organisms
are available, 2 or fewer infaunal species were tested against 19 (63 percent)
of the substances, and 5 or fewer species were tested against 23 (77 percent)
of the substances. Of these chemicals only 3 (8 percent) have been tested
against freshwater infaunal species from 3 or more phyla, and 7 (23 percent)
have been tested against saltwater infaunal species from 3 or more phyla
(Figure 5-2). Therefore, it is probably premature to conclude that benthic
species are more tolerant than water column species, that only sensitive or
insensitive benthic species were tested, or that sediment quality criteria
derived from water quality criteria are over-restrictive.
A similar examination of the most sensitive benthic and water column
species, where the definition of benthic includes both infaunal and epibenthic
species (habitat types: 1 to 4), is based on more data and begins to suggest a
similarity in sensitivity on the average (Figure 5-3). In this comparison, the
number of acute values for freshwater benthic species for each substance
averaged 12, with a range of 2 to 33; the number of acute values for saltwater
benthic species for each substance averaged 10, with a range of 4 to 26. The
variability of these data is high suggesting that for some substances, benthic
and water column species may differ in sensitivity, that additional testing
would be desirable, or that this approach to examining species sensitivity is
not sufficiently rigorous. Examination of individual criteria documents where
benthic species were markedly less sensitive than water column species suggests
that the major factor for this difference is that benthic species
phylogenetically related to sensitive water column species have not been
tested. Apparent differences in sensitivity, therefore, are likely not real,
-------
o
c
CD
GO
10*-
10 -
10 -
10 •
1.0 -
0.1
0.01
Benthic=lnfaunal
Lowest Acute Value
• Freshwater
^Saltwater
0.01 0.1 1.0 10 102 103 104 105 106
Water Column
FIGURE 5-1. Comparison of LCSO or EC50 acute values for the most
sensitive benthic and water column species from 30 saltwater water
quality criteria documents. Benthic species are defined as infaunal
species (habitat types 1 and 2) and water column species are defined as
those species having a lesser association with sediments (habitat
types: 3 to 8). The line is the line of equal sensitivity.
-------
4O
-*-•
c
DD
10°1
105J
3
•
102-J
10
1.0
0.1
0.01
Benthic=lnfaunal
Lowest Acute Value
2 Phyla Tests Also Removed
• Freshwater
^Saltwater
0.01 0.1 1.0 10 102 103 104 105 106
Water Column
FIGURE 5-2. Comparison of 'LC50 or ECSO acute values for the most
sensitive benthic and water column 'species from 36 freshwater and 30
saltwater quality criteria documents. Benthic species are defined as
infaunal species (habitat types 1 and 2) and water column species are
defined as those species having a lesser association with sediments
(habitat types 3 to 8). Only chemicals for which species from 3 or
more infaunal phyla have been tested are included. The line is the
line of equal sensitivity.
-------
,2 1CT-I
Q)
CD
10 -
1.0 -
0.1 -
0.01
Benthic=lnfaunal + Epibentftic
Lowest Acute Value
dP
• Freshwater
aSaltwater
0.01 0.1
1.0 10 10^ 10'
Water Column
104 105
FIGURE 5-3. Comparison of LC50 or EC50 acute values for the most
sensitive benthic and water column species from 36 freshwater and 30
saltwater water quality criteria documents. Benthic species are
defined as infaunal and eptbenthic species (habitat types 1 to 4) and
water column species are defined as those species having a lesser
association with sediments (habitat types 5 to 8). The number of
freshwater benthic species tested ranged from 2 to 26. The number of
saltwater benthic species tested ranged from 5 to 26. The line is the
line of equal sensitivity.
-------
Page 5-10
but reflect an absence of sufficient data. Data that are available suggest
that on the average, benthic and water column species are similarly sensitive
and support the initial use of.water quality criteria to derive sediment
quality criteria.
A second method of comparing the relative sensitivities of water column and
benthic species from freshwater and saltwater habitats compares the relative
sensitivities of acute values from toxicity data for all species to that of
benthic or water column species. Histograms of the relative sensitivities of
infaunal species (Figures 5-4 and 5-5; habitat types 1 and 2) and infaunal and
epibenthic species (Figures 5-6 and 5-7; habitat types 1 to 4) were remarkably
similar. Histograms of the relative sensitivities of water column species
(habitat types 3 to 8 or 5 to 8) were also similar (Figures 5-4 through 5-7).
For these two habitat groupings, frequency distributions suggest that water
column species are only slightly more sensitive than benthic species. Overall,
this similarity suggests that sediment quality criteria derived from water
quality criteria would protect benthic species. These data, along with the
comparisons of the most acutely sensitive species (Figures 5-1 and 5-3),
suggest that some over protection may occur.
5.4 WATER QUALITY CRITERIA CONCENTRATIONS VERSUS COLONIZATION EXPERIMENTS
Comparison of the concentrations of six chemicals that affected (OEC) and
did not affect (NOEC) benthic colonization with the FCVs either published in
saltwater portions of water quality criteria documents or estimated from
available toxicity data (Table 5-3) suggests that the level of protection
afforded by water quality criteria to benthic organisms may be appropriate.
The final chronic value from the water quality criteria document for
pentachlorophenol of 7.9 /ig/L is less than the OEC for colonization of 16.0
/ig/L and the NOEC of 7.0 /jg/L is similar to the FCV. Although no FCV is
available for Aroclor 1254, the lowest concentration causing no effects on the
sheepshead minnow and pink shrimp as cited in the water quality criteria
document is about 0.1 Mg/L. This concentration is less than the OEC of 0.6
Mg/L and is similar to the NOEC of <0.1 pg/L in a colonization experiment. The
-------
Water Column Species
(N=692)
Benthic Species = Infaunal
(N=98)
0-5 5-10 10-25 25-50 50-75 75-90 90-95 95-100
Percentile Ranges of Pooled Data
MORE
SENSITIVE
LESS
SENSITIVE
FIGURE 5-4. Comparison of histograms of the relative acute sensitivity
(Equation 5-1) of benthic and water column freshwater species as
derived from the 36 water quality criteria documents. Histograms show
the percentage of benthic and water column species with acute values
within the indicated percentile ranges of the pooled data. Benthic
species are defined as infaunal species.
-------
I I Water Column Species
L—' (N=465)
Benthic Species = Infaunal
(N=102)
0-5 5-10 10-25 25-50 50-75 75-90 90-95 95-100
Percentile Ranges of Pooled Data
MORE
SENSITIVE
LESS
SENSITIVE
FIGURE 5-5. Comparison of histograms of the relative acute sensitivity
(Equation 5-1) of benthic and water column saltwater species as derived
from the 30 water quality criteria documents. Histograms show the
percentage of benthic and water column species with acute values within
the indicated percentile ranges of the pooled data. Benthic species
are defined as infaunal species.
-------
50
45-
40-
35-
30-
25-
20-
15-
10-
5-
Water Column Species
(N=514)
Benthic Species = Infaunal + Epibenthic
(N=300)
0-5 5-10 10-25 25-50 50-75 75-90 90-95 95-100
Percentile Ranges of Pooled Data
MORE
SENSITIVE
LESS
SENSITIVE
FIGURE 5-6. Comparison of histograms of the relative acute sensitivity
(Equation 5-1) of benthic and water column freshwater species as
derived from the 36 water quality criteria documents. Histograms show
the percentage of benthic and water column species with acute values
within the indicated percentile ranges of the pooled data. Benthic
species are defined as infaunal and epibenthic species.
-------
50
45-
40-
35-
30-
25-
20-
15-
10-
5
0
Water Column Species
(N=317)
Benthic Species = Infaunal + Epibenthic
(N=328)
0-5 5-10 10-25 25-50 50-75 75-90 90-95 95-100
Percentile Ranges of Pooled Data
LESS
SENSITIVE
MORE
SENSITIVE
FIGURE 5-7. Comparison of histograms of Che relative acute sensitivity
(Equation 5-1) of benthic and water column saltwater species as derived
from the 30 water quality criteria documents. Histograms show the
percentage of benthic and water column species with acute values within
the indicated percentile ranges of the pooled data. Benthic species
are defined as infaunal and epibenthic species.
-------
Page 5-15
lowest concentration tested with chlorpyrifos of 0.1 /ig/L and fenvalerate of
0.01 /Jg/L affected colonization of benthic species. Both values are greater
than either the FCV for chlorpyrifos of 0.01 ng/L or a FCV of 0.002 pg/L for
fenvalerate estimated from acute and' chronic effects data. Insufficient data
are available in the draft water quality criteria document for 1,2,4 trichloro-
benzene; however, data from single species tests suggest the FCV should be
<73.0 pg/L. This value is consistent with the conclusion from a colonization
experiment that the NOEC is <40.0 pg/L. Finally, a colonization experiment
with toxaphene provides the only evidence from . these tests that a FCV from a
water quality criteria document might be markedly over protective for benthic
species; the FCV is 0.2 MgA versus the NOEC for colonization of 0.8
TABLE 5-3. COMPARISON OF WATER QUALITY CRITERIA (HOC) FINAL CHRONIC VALUES (FCV)
AND CONCENTRATIONS AFFECTING (OEC) AND NOT AFFECTING (NOEC) BENTHIC COLONIZATION
Substance
Pentachlorophenol
Aroclor 1254
Chlorpyrifos
Colonization
versus WOC"
Colonization (OEC)
WOC (FCV)
Colonization (NOEC)
Colonization (OEC)
WOC (Estimated FCV)
Colonization (NOEC)
Colonization (OEC)
WOC (FCV)
Cone.
ua/l
16.0
7.9
7.0
0.6
-0.1
<0.1
0.1
0.01
Sensitive Taxa
Molluscs, Abundance
Molluscs, Crustaceans, Fish
Crustaceans
Crustaceans, Fish
Crustaceans, Molluscs,
Species Richness
Crustaceans
Colonization Reference
Tagatz et a I., 1977, 1983
Hansen, 1974;
Hansen and Tagatz, 1980
Tagatz et al., 1982
Fenvalerate
1,2,4-
Triehlorobenzene
Toxaphene
Colonization (NOEC)
Colonization (OEC)
WOC (Estimated FCV)
Colonization (NOEC)
WOC (Estimated FCV)
Colonization (OEC)
Colonization (NOEC)
Colonization (OEC)
Colonization (NOEC)
WOC (FCV)
0.01 Crustaceans, Chordates
-0.002 Crustaceans
<73.0 Crustaceans, Fish
40.0 Molluscs, Abundance
Tagatz and Ivey, 1981
Tagatz et al., 1985
11.0 Crustaceans, Species Richness Hansen and Tagatz, 1980
0.8
0.2 Crustaceans, Fish
aSix day exposure to established benthic community
Taxa most sensitive to substances as reported in saltwater portions of
water quality criteria documents and from results of colonization experiments
are generally similar although, as might be expected, differences occur. For
example, for both water quality criteria documents and colonization
-------
Page 5-16
experiments, crustaceans were most sensitive to Aroclor 1254, chlorpyrifos,
fenvalerate, and toxaphene. Colonization experiments indicated that molluscs
were particularly sensitive to three substances, an observation noted only for
pentachlorophenol in water quality criteria documents. Fish, which were not
tested in a colonization experiment, were particularly sensitive to four of the
six substances.
5.5 CONCLUSIONS
Comparative toxicological data on the acute and chronic sensitivities of
freshwater and saltwater benthic species as published in the water quality
criteria documents are limited. Acute values are available for only 34
freshwater infaunal species from 4 phyla and only 23 saltwater infaunal species
from 4 phyla. Only 4 freshwater infaunal species and 16 freshwater epibenthic
species and 3 saltwater infaunal species and 5 saltwater epibenthic species
have been tested with 5 or more of the 30 water quality criteria substances.
In spite of the paucity of acute toxicological data on benthic species,
available data suggests their sensitivities are sufficiently similar to those
of water column species and that sediment quality criteria could be derived
from water quality criteria.
Based on the results described in this section it is concluded that the
sensitivities of benthic species are sufficiently similar to those of water
column species to tentatively permit the use of water quality criteria for the
derivation of sediment quality criteria in the equilibrium partitioning
approach. The acute toxicity data base derived from 36 freshwater and 30
saltwater water quality criteria documents suggests that the most sensitive
infaunal species is typically less sensitive than the most sensitive water
column (epibenthic and water column) species. When both infaunal and
epibenthic species are classed as "benthic," the sensitivities of the most
sensitive benthic and water column species are similar, on average. A
comparison of the frequency distributions of the sensitivities of all benthic
versus all water column species indicates that water column species may be only
slightly more sensitive than benthic species. Finally, in experiments to
-------
Page 5-17
determine the effects of substances on colonization of benthic saltwater
organisms, concentrations affecting colonization were always greater than the
five estimated or actual FCVs for saltwater organisms water quality criteria
documents. Concentrations not affecting colonization were similar to final
chronic values for the three substances where data are available.
-------
Page 6-1
SECTION 6.
APPROACH FOR DEVELOPMENT OF SEDIMENT
QUALITY CRITERIA FOR METALS
The rationale for establishing sediment quality criteria for toxic metals
is similar to that developed for non-ionic organic chemicals. The bioavailable
fraction is identified and a partitioning model will be investigated in order
to predict the bioavailable fraction.
6.1 THE PROBLEM
The equilibrium partitioning methodology for establishing sediment quality
criteria requires that the chemical potential of the chemical be determined.
The experimental results presented in Section 3 (Figure 3-4) suggest that pore
water concentrations of metals as well as non-ionic organic chemicals correlate
to biological effects. Based on this observation a direct approach to
establishing sediment quality criteria for metals would be to apply the water
quality criteria to measured pore water concentrations. The validity of this
approach depends on the degree to which pore water concentration represents
free metal activity. Some metals readily bind to DOC, and DOC complexes do not
appear to be bioavailable. Hence, for metals with significant DOC 'complexing,
the direct use of pore water concentration is precluded in the same way as for
non-ionic organics with DOC complexing (Section 4.4).
By inference this effect of complexation on metal bioavailability extends
to any complexing ligand that is present in sufficient quantity. The decay of
sediment organic matter can cause substantial changes in interstitial water
chemistry (e.g., Berner, 1980). In particular, bicarbonate concentration
increases due to sulfate reduction complicate the determination of the
bioavailable specie(s) because it can result in increases in the metal-
carbonate complexes.
-------
Page 6-2
To implement the equilibrium partitioning approach to setting sediment
criteria for metals, pore water concentrations must be measured. However, the
sampling of sediment interstitial water for metals is not a routine procedure.
The least invasive technique employs a diffusion sampler which has cavities
covered with a filter membrane (Hesslein, 1976; Carignan et al., 1984 and
1985). The sampler is inserted into the sediment for a period of time
sufficient to allow the concentrations on either side of the membrane to
equilibrate. when the sampler is removed the cavities contain filtered pore
water samples. Use of this technique requires that the time required for
equilibration be determined.
The alternate technique is to obtain a sediment core, slice it, and filter
or centrifuge the slice to separate the pore water from the bulk sediment. For
anaerobic sediments this must be done in a nitrogen atmosphere to prevent the
precipitation of iron hydroxide which would scavenge the metals and yield
artificially low dissolved concentrations (Troup, 1974).
Although either of these techniques are suitable for research
investigations they require more than the normally available sampling skills
and time. If solid phase chemical measurements were available from which pore
water metal activity could be deduced, it would obviate the need for pore water
sampling and analysis, and it would circumvent the need to deal with complexing
ligands.
6.2 TOXICITY CORRELATES TO METAL ACTIVITY
The results from a substantial number of water column experiments
indicate that biological effects can be correlated to the divalent metal
activity [Me2+]. This is not meant to imply that the only bioavailable form is
Me2* (for example MeOH+ may also be bioavailable), but rather that the DOC or
other ligand complexed fractions are not bioavailable. Data to support this
are discussed below.
-------
Page 6-3
The acute toxicity of cadmium to grass shrimp (Palaemonetes) has been
determined at various concentrations of the complexing agents chloride and NTA,
both of which form cadmium complexes (Sunda et al., 1978). The results are
shown on Figure 6-1. The top panels are concentration-response curves as a
function of total cadmium. The responses differ at different concentrations of
chloride, indexed by salinity, and NT A. When the concentration-response is
evaluated with respect to Cd2+ activity in the solution, then the dose response
curves collapse into a single curve (bottom panels). Comparable results have
been reported for copper-EDTA complexes (Anderson and Morel, 1978) for which
dose response correlates to Cu2+ activity (Figure 6-2, left).
Chronic toxicity, with growth as the endpoint, has also been examined. The
results of an experiment in which the concentration of Cu and Zn is held
constant and the complexing ligand is varied are shown on Figure 6-2 (right)
(Allen et al., 1980). As NTA is added the toxicity of zinc to Microcvstis
decreases: the cell density increases rather than decreases in time and
reaches control levels at the highest NTA concentration (top panel). These
data can be correlated to free zinc activity as shown (bottom panel). Similar
results for copper and the complexing ligand tris are shown on Figure 6-3
(left) (Sunda and Guillard, 1976). Variations in tris concentrations and pH
produce markedly different growth rates (top) which can all be correlated to
the Cu2+ activity (bottom). Similar results have been obtained by Sunda and
Lewis (1978) with DOC as the complexing ligand, Figure 6-3 (right).
Metal bioavailability as measured by organism uptake is also related' to
metal ion activity. Uptake of copper by oysters (Zamuda and Sunda, 1982) is
correlated not to total copper concentration (Figure 6-4, top), bur to copper
activity (bottom).
The conclusion to be drawn from these experiments is that the partitioning
model required for establishing sediment quality criteria should predict [Me?*]
in the pore water. The next section discusses one possible approach to
developing such a partitioning model.
-------
ACUTE TOXICITYOF CADMIUM TO
GRASS SHRIMP ( Polaemonetes )
EFFECT OF NTA COMPLEXATION
(AFTER W.6. SUNOA et al., 1978 )
ACUTE TOXICITYOF CADMIUM TO
GRASS SHRIMP (Palaemonetet )
EFFECT OF SALINITY
(AFTER W.6. SUNDA etol., 1978 )
S
10-
TOTAL CADMIUM ( -toe C«f )
4.0
A A 4.1*0.4
• • I 410 1
» »I4 J^O J
• •lOOtO.l
TOTAL CADMIUM (-LOO C«r I
>
•
3
•-
to
*o •
m 40 •
40
CADMIUM ACTIVITY
7.0
4 *°
2
>
•
2 40
1-
m
u
S 40
&
10
I.I-,fT (X.I
A4J » * « *-* »
• i.4 ^x- "^"
^ ^ ^
• 200 ,• 4>
4>2i.t '
/
"I
/*
• •* • /
" * - ' fc tf)^ ,
CADMIUM ACTIVITY l»[c«*+])
FIGURE 6-1. Acute toxlclty to Palaemonetes of total cadmium (top) arid cadmium
activity (bottom) with different concentrations of the complexing agents NTA
(left) and chloride as salinity (right).
-------
ACUTE TOXICITY OF COPPER
TO A DINOFLAGELLATE
(FROM ANDERSON AND MOREL. 1978)
CHRONIC TOXICITY OF ZINC
ON MICRO'CYSTIS AERU6INOSA
(FROM ALLEN, et.al., 1980)
56 T B 9
TOTAL COPPER (-LOG(CuT) 1
to
o-CMOS
-Builder M
•-Control
10 II 12 13
COPPER ACTIVITY (pCu)
T I—
1.0 2.0 JO 4.0 1.0
Fro* Zinc (molet/lit«r HO7)
FTC Acute toxicity to a dlnoflagellate (left) of total copper (top)
and copper activity (bottom), with and without EDTA. Chronic toxicity of zinc
to Mtcroevstis ^rueinosa (right) showing growth as cells/ml versus time with
different levels of EDTA and NTA (top) and number of cells at five days
function of free zinc concentration (bottom).
-------
CHRONIC TOXICITY OF COPPER
TO A DIATOM
(FROM SUNDA AND 6UILLARD, 1976)
CHRONIC TOXICITY OF COPPER
TO MONOCHRYSIS LUTHEHI
(FROM SUNDA AND LEWIS. 1978)
S
Q-
M
«3U
ss.
2.00
TOTAL COPPER (-L06 CuT)
go 1-00
taut
H
u>
•.a
-T4
0.00
4.00
lOt HIVEH HATCH
301 RIVER MATER
901 RIVER MATER
S.OO 6.00
TOTAL COPPER (-log CuT)
7.00
U
to
0
j, \±
.|,|. -
1
li
1 lrf*T=E:~
-*H4*f*~
1
+
4-
±_
~1
•
e.uu
•H
Ui
si
is '•°°
UIA
M
O>
o
s
tfl
0.00
• 10* RIVER MATER
• Ml RIVER MATER
* MB RIVER MATER
•
• -"^
•
/
/
»s
^^.m^
•
* * * . " 6-<>° 7.00 8.00 9.<
COPPER ACTIVITY
-------
UPTAKE OF COPPER BY OYSTERS
ISOi —_____
.00
>
*•
w v
K 9
U U
IS
U
0--
0.01
0.1 1.0
TOTAL COPPER CONC. { M M )
i
10 11
COPPER ACTIVITY (pCu)
AFTER CD. ZAMUDA AND W.6. SUNDA
USING (Crassostreo virginco), 1962
10.0
200
180
160
140
120
100
60
40
20
0
TOTAL NTA
Winter
A 1.0
610.0
Summer
A 1.0
• 8.1
• 10.0
m
T I
•
I 1
1 *
.
- ii
1
i \,f.
1 *~
(uM)
-\
Body burden of copper in oysters versus total copper (top) and
copper activity (bottom) with different levels of the complexing ligand NTA.
-------
Page 6-8
6.3 METAL SORPTION MODELS
The state-of-the-art of modeling metal sorption to oxides in laboratory
systems is well developed and detailed models are available for cation and
anion sorption (refer to articles in Stumm, 1987, for recent summaries). The
models consider surface complexation reactions as well as electrical
interactions via models of the double layer. Models for natural soil and
sediment particles are less well developed. However, recent applications
suggest that similar models can be applied to soil systems (Goldberg and
Sposito, 1984; Barrow, 1986a,b; Barrow and Ellis, 1986a,b; Sposito et al.,
1988). Since the ability to predict partition coefficients is required if
pore water metal ion concentrations are to be inferred from the total
concentration, a theoretically based model that is relatively easy to apply in
practice is required. An approach is presented which uses the three sorption
phases in aerobic sediments.
6.3.1 Three Phase Metal Sorption Model
The initial difficulty that one confronts in using a metal sorption model
is that the available models are quite complex and many of the parameters are
specific to individual soils or sediments. However, the success of organic
carbon based non-ionic chemical sorption models suggests that a model of
intermediate complexity that is based on an identification of the most
important sorption phases may be generally applicable to metal sorption as
well.
A start in this direction was made recently (Di Toro et al.. 1987; Jenne et
al., 1986). Instead of considering only one sorption phase, as is assumed for
non-ionic hydrophobic chemical sorption, multiple sorption phases are
considered. In oxic soils and freshwater sediments these sorption phases have
been identified as POC and the oxides of iron and manganese (Jenne, 1968;
Jenne, 1977; Luoma and Bryan, 1981; Oakley et al., 1980). They are important
because of their large sorptive capacity. Further they appear as coatings on
-------
Page 6-9
the particles and occlude the other mineral components. These phases provide
the primary sites for sorption of. metals and restrict the importance of the
clay and other mineral components of soils and sediments. For more reducing
sediments, sulfide precipitation may .also be important.
The following discussion is restricted to aerobic sediments. Let [FeOx],
[MnOx] , and [POC] be the concentration of solid phase reactive iron and
manganese oxides, and POC, respectively. Define the mass action sorption
coefficients for the divalent metal cation, Me2+, as:
[Me-FeOx]
KFe - - 2T - (6'1}
[Me ][FeOx]
[Me-MnOx]
KMH * - T+ - <6'2>
[Me* ] [MnOx]
[Me-POC]
K_.r -- 57 - (6-3)
POC [Me*+][POC]
where [Me2+] is the metal activity, and [Me-FeOx] , [Me-MnOx] , [Me-POC] are the
concentrations of metal sorbed to iron and manganese oxide, and POC.
In order to examine the importance of pore water ligand complexation, let
[Li], [L2l,... be the concentration of complexing ligands (e.g., OH* , Cl~ ,
DOC) . The mass balance equation for the total metal in both the sediment and
pore water is:
NeT - (1 - 4)([Me-FeOx] + [Me -MnOx] + [Me-POC])
[MeL1] + [MeLg] + ...) (6-4)
-------
Page 6-10
where Me? is the concentration of total desorbable metal per unit bulk volume
of sediment, and 0 is the porosity of the sediment.
The key observation is that the absolute quantity of metal in the pore
water (the second line of Equation 6-4), is negligible relative to the quantity
of sorbed metal (the first line). The mass balance equation can then be
simplified to:
MeT - (1 - 0)([Me-FeOx] + [Me-MnOx] + [Me-POC]) (6-5)
Using the equilibrium Equations (6-1) to (6-3) yields:
MeT - (1 - *)[Me2+](KFe[FeOx] + ^[MnOx] + K^IPOC]) (6-6)
so that:
2+ f
IMe ] ' KFe[FeOx] + K^tMnOx] + Kp()C[POC] (6'7)
where Hes - Hef/(l-^) is the total sorbed metal per unit dry weight of
sediment.
It is important to realize that Equation (6-7) gives the pore water metal
activity directly in readily measurable quantities: the total desorbable metal
Mes, and the sorption phase concentrations [FeOx], [MnOx], and [POCj. Pore
water complexing ligand concentrations do not affect the partitioning Equation
(6-7) for exactly the same reason that DOC concentrations do not affect the
validity of organic carbon normalization for non-ionic organic chemicals. Of
course the metal sorption constants: Kpe, K^n, and KPQC. are also required.
The practical utility of this three phase sorption model depends upon the
-------
Page 6-11
availability of these coefficients and either their relative independence of
the details of the remaining sediment chemical and physical properties, or the
existence of suitable correlations ' which relate these coefficients to sediment
properties .
It is known that the interstitial water pH will affect the sorption
constants. This is conventionally modeled by including the species MeOH+ as a
sorbed species as well. The result is that the sorption constants are
replaced by expressions of the form:
KFe - e +KFe[OHl (6'8)
The utility of these models will be examined below.
6.4 EXTRACTION AND PHASE NORMALIZATION
In addition to the sorption phase concentrations it is necessary to
quantify the fraction of total sediment metal that is chemically interacting
with the pore water. The modeling of metal partitioning involves not only a
determination of the principal sorption phases, but also. a determination of the
corresponding chemical extraction technique to be applied to the sediment
sample. It would be convenient if the same extraction could be used to
determine both the desorbable metal and the phase concentrations. This
possibility is examined below.
6.4.1 Bioavailable Fraction
A substantial effort has been expended over the years in attempting to
determine the bioavailable portion of trace metals in soils and sediments
using chemical extractions (refer to Jenne, 1987 [SCD 13]), for a review. The
use of a relatively mild reductant (hydroxylamine hydrochloride) which
dissolves the Fe and Mn oxides and liberates the sorbed metals is recommended
(Jenne, 1987).
-------
Page 6-12
The reported results using this procedure have been very encouraging. An
example is shown on Figure 6-5 (Te.ssier et al., 1984). The copper and zinc
body burden in Elliptic complanata. a sediment dwelling mollusc, roughly
correlates with the total metal in the sediment (top panels) . The
relationship greatly improves when the ratio of metal to iron in a
hydroxylamine extract is used (bottom panel). Similar results, using an acid
extraction, have been found for arsenic in Nereis, a deposit feeding
polychaete. and Macoma. a deposit-feeding bivalve (Langston, 1980); and copper
in aquatic plants (Campbell et al., 1985). For mercury body burdens in
various benthic species a strong correlation exists between the sediment
concentration normalized by organic matter content (Langston, 1986).
The success of these extraction-normalization procedures can be
rationalized as follows. Assume that the primary sorption phase for a metal,
He, is amorphous iron. Then the extraction, which only dissolves the amorphous
iron coating, removes only that quantity of Me and Fe in equilibrium with the
interstitial water. Further, it follows from Equation (6-7) that the ratio of
Me/Fe in the extract (i.e., Mes/[FeOx]), would be proportional to the
interstitial water concentration of the metal:
Me
From the data presented in Section 6.2, the interstitial water concentration
appears to correlate to toxicity and bioaccumulation. Thus the fact that body
burden is proportional to the phase-normalized metal concentration supports its
use for predicting interstitial water metal activity.
6.4.2 Partition Coefficients
The most direct evidence in support of the use of the extraction - phase
normalization procedure comes from simultaneous observations of the oxic layer
-------
SEDIMENT BOUND METAL
UPTAKE BY MOLLUSCS
(TESSIERet ol., 1984)
K
UJ
Q.
Q.
O
U
IOO
80
60
40
20
TOTAL SEDIMENT METAL
500
* 0.63
I
I
400
300
o
S 200
IOO
R2= 0.53
I
I
I
I
50 IOO ISO 200
Cu(/tg/g)
IOO 200 300 400 500
Zn ('/i,g /g)
UJ
o
(E
CD
£ loo
o
0 80
£ 60
a
a.
0 40
20
0
EXTRACTE
R2* 0.96 /
/
(D/®
/^
X®
^
1 1 1 1
500
400
300
200
IOO
0
R2= 0.71
I
I
I
I
3 6 9 12
Cu/Fe (mg/g)
IS
10 20 30 40 50 60
Zn/Fe(m /g)
FIGURE 6-S. Copper (left) and zinc (right) body burdens in molluscs versus
total sediment metal concentration (top) and extracted metal/Fe ratio (bottom;.
-------
Page 6-14
interstitial water, and sediment metal concentrations. Initial data of this
type for metals with small DOC complexing capacity (Tessier et al. , 1985) and
more recent results (Tessier, personal communication) demonstrate its utility.
Figure 6-6 presents the relationship of the observed nickel and zinc partition
coefficient, KA, versus pH where:
[Me-FeOx]
KA - JT (6-10)
[FeOx][Me/+]
[Me-FeOx] and [FeOx] are the extracted Me and Fe concentrations, and [Me2+] is
the estimated aqueous nickel and zinc activity. The linear relationship to pH
can be used to predict interstitial water metal concentrations in the oxic
layer of sediments given the extraction data and pH. This is a one-phase model
of sediment sorption. Some of the remaining scatter in the relationship may be
related to the presence of other sorption phases such as amorphous Mn and POC.
Similar results have been obtained for the sorption of copper and zinc to
suspended solids in streams (Johnson, 1986).
6.5 DEVELOPMENT OF SEDIMENT QUALITY CRITERIA FOR METALS
The solution to the bioavailability problem for sediment associated metals
is more complex than for non-ionic organic chemicals. The requirements for a
methodology based on equilibrium partitioning are an extraction methodology and
a multiple phase sorption model. Initial steps have been taken to evaluate the
state-of-the-art of each of these components.
6.5.1 Extraction Methodology
The selection of an appropriate extraction methodology that quantifies the
portion of the total sediment metal that is chemically reactive with respect to
pore water has been made (Jenne, 1987 [SCD 13]). In addition a series of
experiments have been performed that examine the details of the method and make
-------
Zn
lo« *o» -33* l.3pH
Tessier. 1987
60 -
7
SO
4.0-
/•
/
t
3.0
|
./
/
/
Z0\
' /
«0
pH
..7.0
: A -02
MA-OI
MC-OI
:CH- 01
SW-OI
: L8 - Ol
: H-OI
BC-OI
•CC-05
:BC-03
:BR-OI
:8R-04
: J-Oi
it*-02
:TA-OI
CI.-OI
: WI-OI
uc-oi
HE 01
BR 01
: 80-01
:MO-OI
80
7.0
Ni
6.0
S.O
.2 40
SO -
20
log Ko ••2,5+ 1.1 PH
Tessier. 1987
V
x •
.
X
4
4
S.O
6.O
PH
70
: *-OI
. *-02
MA-OI
:ME-OI
:CH-OI
CE-05
:CL-03
:CL-OI
WI-OI
MC-OI
:NC-OI
BR-OI
:MO-OI
a.o
FIQURE
Zinc (left) and nickel (right) partition coefficients versus pH in
comparison to a single phase model of sediment sorption (dashed line).
-------
Page 6-16
final recommendations with respect to experimental protocol (Crecelius et al.,
1987 [SCO 16]).
6.5.2 Sorption Model
The practical utility of the three phase model of metal sorption rests on
the availability of reasonably universal phase-specific sorption constants. As
an initial screening exercise, reviews were prepared of the available
literature information for POC (Allen and Mazzacone, 1987 [SCO 9]) and iron
oxides (Jenne, 1987 [SCO 12]). The tabulations for POC protonation and metal
binding reactions show order of magnitude consistency with occasional outliers.
The major difficulty is the lack of a consistent model with which the data can
be interpreted. As a consequence, the degree of consistency cannot be
established.
The review by Jenne (1987), of sorption constants for iron oxide indicated
some degree of consistency but also some widely variable constants. However,
it has been shown that a universal set of constants can produce a credible fit
of almost all iron oxide protonation and sorption data (Dzombak, 1986).
6.6 ONGOING STUDIES
Additional studies are planned or are currently being conducted to gain an
improved understanding of the fate and toxicity of metals in sediments. These
efforts are described subsequently.
6.6.1 Sediment Toxicitv Experiments
Very few metal toxicity experiments have been conducted for which the pore
water metals concentration has been measured. The two cadmium - seawater
experiments discussed above (Figure 6-1) are the extent of the laboratory
toxicity data that support the contention that pore water metal concentration
(actually metal activity) correlates to toxicity. Ongoing experiments
-------
Page 6-17
utilizing diffusion samplers and specific ion electrode determinations of
cadmium and subsequently copper activity are planned for both freshwater and
marine sediments in order to examine this question.
6.6.2 Metal Partitioning
The development of a three phase partitioning model requires the
generation of a reasonably large sorption data base with both extraction and
pore water sampling. The use of laboratory and field data sets is envisioned
with an emphasis on the latter if possible. Laboratory investigations of
amorphous manganese oxide are ongoing in order to compliment the available
iron oxide data. Detailed base titration data for sediment organic carbon are
being generated and early indications are that a consistent set of model
parameters can describe the results. Titrations of sediment samples are also
proceeding. Field data collection programs are being discussed as well.
6.6.3 Sulfide Precipitation
Recent experimental work has reinforced the importance of sulfide
precipitation as a sink for metals in marine and even freshwater sediments.
The dramatic difference in the toxicity of the two sediments shown on Figure
3-4 has been tentatively attributed to the presence of acid volatile sulfide in
the Long Island Sound sediment and not in the sandy oxic Yaquina Bay sediment.
The sulfide precipitates the cadmium as cadmium sulfide which appears not to be
bioavailable. It is only when the available sulfide is exhausted that cadmium
toxicity is exhibited. This first order effect will be examined in detail for
both marine and freshwater sediments.
6.7 CONCLUSION
This section examines the use of the equilibrium partitioning approach in
the development of sediment quality criteria for metals. It is shown that the
activity of metals must be determined in order to properly quantify the
bioavailable concentration. A three phase metal sorption model was presented
-------
Page 6-18
as a means to determine metal activity in pore waters and was used to estimate
the bioavailable fraction of .metals. Preliminary results suggest that the
extraction partitioning methodology can be used to establish metals criteria in
a way that directly addresses bioavailability. Ongoing work being conducted to
gain an improved understanding of the factors affecting the toxicity of metals
in sediments was described.
-------
Page 7-1
SECTION 7.
GENERATION OF SEDIMENT QUALITY CRITERIA
The approaches for development of sediment quality criteria were developed
in Section 4 for non-ionic organic chemicals and in Section 6 for metals. The
technical evidence indicates that sediment quality criteria can be determined
and in this section preliminary sediment quality criteria for organic chemicals
are presented. The uncertainty associated with calculation of sediment quality
criteria is addressed prior to providing the sediment quality criteria values.
7.1 METHOD TO CALCULATE SEDIMENT QUALITY CRITERIA UNCERTAINTY
The sediment quality criteria methodology relies on an empirical model to
compute the interstitial water concentration (actually the chemical potential)
from the solid phase measurements. As a consequence there is an uncertainty
associated with the use of the model. In addition there is uncertainty with
respect to the Kow associated with the specific chemical since it is an
experimentally determined quantity. Finally, the assumption that water column
and benthic organisms have similar sensitivities has a level of uncertainty.
The equation from which sediment quality criteria are calculated is:
rSQC' KocCBQC (7-L)
where cgqc is the effects concentration for benthic species. A first order
uncertainty analysis of rgqc can be computed from the variance of this
equation:
CBQCJ (7'2)
-------
Page 7-2
where the notation V{z) denotes the variance of z. The variance of KQC can be
obtained from the uncertainty of the regression that relates Koc to Kow. If it
is assumed that Kow is known exactly then the variance of KQC can be estimated
from the nonlinear regression fit. However, since KQW is not known exactly -
and for high Kow chemicals the estimates can vary over an order of magnitude -
the uncertainty of Kow must be explicitly included in the analysis. This
uncertainty analysis has not yet been performed. Further, the variance
associated with the assumption that benthic and water column species
sensitivities are equal: CBQC ~ CWQC. is required.
An initial analysis of the uncertainty has been made for the Koc regression
(Pavlou et al., 1987; SCD 14). However the problem of uncertainty in Kow has
not been included in the regression uncertainty analysis.
For the generation of interim sediment criteria values (Cowan and Di Toro,
1988, SCD 17), the sediment quality criteria formula used is:
rSQC ' KocCWQC
or in logarithmic form:
1°S
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Page 7-3
uncertainty in log Kow itself. The uncertainty of the equality of water column
and benthic species sensitivity was not included in this preliminary
uncertainty analysis. The resulting confidence limits are included in Section
7.2. The range in uncertainty that results is quite large with 95 percent
confidence limits spanning roughly an order of magnitude. This is primarily a
result of the rather large regression standard error: SE (log Kp) - 0.38
reported previously (Di Toro, 1985). However, this regression analysis assumed
that all KQWS were precisely known. That assumption artificially inflated the
regression variance since that lack of fit which may be due to an uncertain Kow
is attributed to regression error. The solution to this problem is to redo the
regression analysis with the uncertainty of KQW explicitly included. Thus the
confidence limits of the interim criteria are conservative and a more refined
analysis will reduce the uncertainty range.
7.2 PRELIMINARY SEDIMENT QUALITY CRITERIA VALUES
FOR NON-IONIC ORGANIC CHEMICALS
An initial attempt to compute sediment quality criteria for 13 non-ionic
organic chemicals is presented. The 95 percent confidence limits are computed
from a method which is known to exaggerate the uncertainty. For these
chemicals, where either field data derived lower bounds or sediment toxicity
experiments are available, the results seem reasonable.
The procedure followed for the computation of interim sediment quality
criteria is presented in SCO 17. The purpose of this section is to present a
summary analysis of the results and to compare them to SLCs where possible.
Table 7-1 presents the sediment quality criteria and SLC values based on the
FCV and final residue value (FRV) water quality criteria. The freshwater and
saltwater sediment quality criteria results are listed together with the 95
percent confidence limits. The quantification of the level of uncertainty has
only been accomplished in a preliminary way. It is anticipated that a complete
uncertainty analysis will accompany the final development of sediment quality
criteria and that, for example, 95 percent confidence limits will be specified
as well as the most probable value.
-------
OJ
OQ
a
TABLE 7-1. COMPARISON OF INTERIM SEDIMENT QUALITY CRITERIA WITH SCREENING LEVEL CRITERIA (SLC) ^
I
*•
Interim Sediment Quality Criteria (lig/R OC)
Freshwater or
Saltwater
F or S
PAHs:
Acenapthene
Anilene
Fhenanthrene
Other
PESTICIDES:
Chlordane
Chlorpyrifoa
DDT
Dieldrln
Endrin
Ethyl Parathlon
Beptachlor
Heptachlor Ep
Llndane
OTHER:
PCB
F
S
F
S
F
S
F
S
F
S
F
S
F
S
F
S
F
S
F
S
F
S
F
S
F
S
F
S
Residue Basis
Median 951 Confidence Limits
-
-
-
-
-
0.828 .183 - 3.80
0.828 .183 - 3.80
0.130 .00976 - 1.79
0.130 .00976 - 1.79
0.0532 .0065* - 0.443
0.0532 .00654 - 0.443
-
0.110 .0148 - 0.840
0.104 .0140 - 0.796
-
-
19.5 3.87 - 99.9
41.8 6.29 - 214.
Chronic Effect Basis SLC (UB/K OC)
Median 9SX Confidence Limits Median
730. 180. - 3030.
4.74
O.OB62 0.0169 - 0.262
0.248 0.0635 - 0.984
139. 32.6 - 605.
102. 23.8 - 442. 36.8
10.1 - 66.5
' .098
3.22 0.831 - 12.7
0.440 0.114 - 1.73
.190
SO. 5
19.9 1.49 - 273. .021
5.77 0.431 - 79.2
1.04 0.128 - 8.68
0.215 0.0264 - 1.79
0.081 0.016 - 0.416
-
.008
0.157 0.0394 - 0.636
.290
3.66
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Page 7-5
The differences between freshwater and saltwater sediment quality criterion
are a direct reflection of the differences in water quality criteria since the
partition coefficient is not significantly affected by the change in ionic
strength. The range in 95 percent confidence limits reflects mainly the
inflated regression uncertainty estimate as discussed above. The comparison to
screening level criteria can be made in only two cases using chronic data
(Table 7-1). The median saltwater chronic sediment quality criteria for
phenanthrene is 102 pg/gOC (23.8 - 442 /ig/gOC) and the SLC is 36.8 ng/g OC.
Since the lower bound of the 95 percent confidence limits (23.8 jig/gOC) is
below the SLC it appears that the confidence limits range perhaps is too
conservative. In contrast, the median freshwater chronic sediment quality
criteria for dieldrin is 19.9 jig/g OC (1.49 - 273 pg/gOC) while the freshwater
SLC is only 0.021 pg/gOC. The low freshwater SLCs are attributed to the
particular data base that was assembled (SCD 7). If the sediments are
relatively uncontaminated then SLCs are bound to be small numbers.
A more detailed comparison of the available information for fluoranthene is
presented in Table 7-2 using interstitial water concentrations. The chronic
lowest observed effect level (LOEL) for saltwater organisms is 16 pg/L. This
can be compared to the range of the pore water concentrations from sediment
exposures associated with chronic responses: 8 to 26 pg/L. The acute LOEL (40
/ig/L) is also comparable to the acute mortality concentrations (41 to 62 pg/L).
A similar presentation for organic carbon normalized sediment
concentrations is presented in Table 7-3. The equilibrium partitioning values
corresponding to the chronic LOEL is 1,330 /tg/gOC. The screening level
concentration (43 /Jg/gOC) is well below that value. The Amphipod AETs (on an
organic carbon normalized basis) are closer (160 and 891 /ig/gOC) as is the
reported LC50 (817 pg/gOC). The equilibrium partitioning acute LOEL (3,330
/jg/gOC) compares well with the available acute sediment toxicity data (2,130 to
6,700 Mg/gOC). It appears, therefore, from this preliminary evaluation that
equilibrium partitioning generated sediment quality criteria are reasonable and
in conformity with the available information.
-------
Page 7-6
TABLE 7-2. TABULATION OF FLUORANTHENE SEDIMENT TOXICITY RESULTS
PORE WATER CONCENTRATIONS AND EP VALUES
Fluoranthene
Pore Water
Concentration
62
42
41
40
26
19
16
12
8
aEagle Harbor
Response
Criterion
96 hr LC100
10 d LC100
10 d LC95
Acute LOEL
10 d LC50
96 hr LC65
Chronic LOEL
10 d LCOS
10 d LCO
Exposure
Medium
Interstitial water9
Sediment8
Sediment spike
Saltwater
Sediment spike
Interstitial watera
Saltwater
Sediment spike
Sedimenta
Reference
Swartz et al., 1989
Barrick et al.. 1986
Swartz et al., in prep.
USEPA, 1980
Swartz et al.
Swartz et al.
USEPA, 1980
Swartz et al.
in prep.
1989
in prep.
Barrick et al., 1986
TABLE 7-3. TABULATION OF FLUORANTHENE SEDIMENT TOXICITY RESULTS
ORGANIC CARBON NORMALIZATIONS AND EP VALUES
Fluoranthene
Concentration
(ae/g PC)
6700
3437
3330
2130
1330
891
817
160
43
Method
LC100, Eagle Harbor Sediment
LC95 Sediment spike
Equilibrium partitioning,
Acute LOEL
LC50 Sediment spike
Equilibrium partitioning,
Chronic LOEL
Araphipod AET II
LCOS Sediment spike
Araphipod AET I
Screening Level Concentration
Reference
Barrick et al., 1986
Swartz et al., in prep.
USEPA, 1980
Swartz et al., in prep.
USEPA, 1980
Barrick et al., 1986
Swartz et al., in prep.
Seller et al., 1986
Neff et al., 1986
-------
Page 7-7
The interim residue based criteria in Table 7-1 should be used with
caution. The problem is whether the water quality criteria concentration is
computed from a bioconcentration factor (BCF) - which is derived from water
only exposure - or from a field BAF which incorporates food chain magnification
effects. The PCB and DDT residue criteria are derived from BCFs and are
therefore underprotective.
7.3 CONCLUSIONS
The technical basis and data which support the use of the equilibrium
partitioning method to generate sediment quality criteria have been previously
presented for both non-ionic organic chemicals (Sections 3 and 4), and for
metals (Section 6). The justification for using water quality criteria to
define the effects level for benthic organisms was also discussed (Section 5).
This section presents preliminary sediment quality criteria for 13 non-ionic
organic chemicals and in cases where comparisons are made with screening level
criteria, the sediment quality criteria appear reasonable. The development of
sediment quality criteria for metals using the equilibrium partitioning
approach also appears to be viable. Additional work is required to develop a
reasonably large sorption data base, with both extraction and pore water data,
prior to generating sediment quality criteria for metals.
-------
Page 8-1
SECTION 8.
REFERENCES
SEDIMENT CRITERIA DOCUMENTS
SCD 0 Pavlou, S. P. and D. P. West on, 1983. "Initial Evaluation of
Alternatives for Development of Sediment Related Criteria for Toxic
Contaminants In Marine Waters (Puget Sound) - Phase I and Phase II."
SCD 1 JRB Associates, 1984. "Background And Review Document On The
Development Of Sediment Criteria."
SCD 2 Battelie, 1984. "Sediment Quality Criteria Development Workshop."
SCD 3 Bolton, S. H., R. J. Breteler, B. W. Vigon. J. A. Scanlon and S. L.
Clark 1985. "National Perspective On Sediment Quality."
SCD 4 Kadeg, R. D. , S. P. Pavlou and A. S. Duxbury, 1986. "Sediment
Criteria Methodology Validation Work Assignment 37 Task II
Elaboration Of Sediment Normalization Theory For Nonpolar
Hydrophobic Organic Chemicals-Final Report."
SCD 5 Poston, T. M. and L. A. Prohammer, 1986. "Sediment Criteria
Methodology Validation Work Assignment 56, Task 1 Protocol For
Sediment Toxicity Testing of Nonpolar Organic Compounds."
SCD 6 Jenne. E. A., D. M. Di Tore, H. E. Allen and C. S. Zarba, June 1986.
"An Activity Based Model for Developing Sediment Criteria for
Metals, Part I: A New Approach."
SCD 7 Neff, J. M., D. J. Bean, B. W. Cornaby, R. M. Vaga, T. C. Gulbransen
and J. A. Scanlon, 1986. "Sediment Quality Criteria Methodology
Validation: Calculation of Screening Level Concentrations From
Field Data."
Neff, J. M. . J. Q. Word and T. C. Gulbransen, 1987. "Recalculation
of Screening Level Concentrations For Nonpolar Organic Contaminants
In Marine Sediments."
SCD 8 Cowan, C. E. and R. G. Riley, 1987. "Guidance For Sampling of And
Analyzing For Organic Contaminants On Sediments."
SCD 9 Allen, H. E. and J. M. Mazzacone, 1987. "Sediment Quality Criteria
For Metals: III. Review of Data on the Complexation of Trace
Metals By Particulate Organic Carbon."
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Page 8-2
SCO 10 Cowan, C. E. and C. S. Zarba, 1987. "Regulatory Applications of
Sediment Quality Criteria - Final Report."
SCO 11 Word, J. Q., J. A. Ward, L. M. Franklin, V. I Cullinan and S. L.
Kiesser, 1987. "Evaluation of the Equilibrium Partitioning Theory
for Estimating the Toxicity of the Nonpolar Organic Compound DOT to
the Sediment Dwelling Amphipod Rhepoxynius Abronius."
SCD 12 Jenne, E. A., 1987. "Sediment Quality Criteria For Metals: IV.
Surface Complexation And Acidity Constants For Modeling Cadmium and
Zinc Adsorption onto Iron Oxides."
SCD 13 Jenne, E. A., 1987. "Sediment Quality Criteria For Metals: II.
Review of Methods For Quantitative Determination of Important
Adsorbents and Sorbed Metals In Sediments."
SCD 14 Pavlou, S., R. Kadeg, A. Turner and M. Marchlik, 1987. "Sediment
Quality Criteria Methodology Validation: Uncertainty Analysis of
Sediment Normalization Theory For Nonpolar Organic Contaminants."
SCD 15 Kadeg, R. D. and S. P. Pavlou, November 1987. "Reconnaissance Field
Study for Verification of Equilibrium Partitioning: Nonpolar
Hydrophobic Organic Chemicals."
SCD 16 Crecelius, E. A., E. A. Jenne and J. S. Anthony, December 1987.
"Sediment Quality Criteria for Metals: V. Optimization of
Extraction Methods for Determining the Quantity of Sorbents and
Adsorbed Metals in Sediments."
SCD 17 Cowan, C. E. and D. M. Di Toro, March 1988. "Interim Sediment
Criteria Values for Nonpolar Hydrophobic Organic Compounds."
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-------
Page 8-3
Allen, H. E., R. H. Hall and T. D. Brisbin. 1980. "Metal Speciation. Effects
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Battelle, February 1984. "Sediment Quality Criteria Development Workshop."
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Beller. H. R. , R. C. Barrick and D. S. Becker. 1986. Development of Sediment
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Bolton, S. H., R. J. Breteler, B. W. Vigon, J. A. Scanlon and S. L. Clark, July
1985. "National Perspective On Sediment Quality." Prepared for USEPA, Office
of Water Regulations and Standards, Criteria and Standards Division. (SCD 3)
Bricker, 0. P., G. Matisoff and G. R. Holdren, Jr. 1977. Interstitial Water
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Carignan, R. 1984. "Interstitial Water Sampling by Dialysis: Methodological
Notes." Limnol. Oceanogr. 29(3): pp. 667-670.
Carignan, R., F. Rapin and A. Tessier. 1985. "Sediment Porewater Sampling
for Metal Analysis: A Comparison of Techniques." Geochimica et Cosmochem.
Acta 49: pp. 2493-2497.
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Validation of Site - Specific Water Quality Criteria for Copper."
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Chapman, G. A. 1987. "Establishing Sediment Criteria for Chemicals -
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in Aquatic Systems, pp. 355-376. 'Editors: K. L. Dickson, A. W. Maki and W. A.
Brungs. Pergamon Press, New York.
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Uater Systems and Correlations with Fish Bioconcentration Factors." Environ.
Sci. Technol. 19: pp. 57-62.
Cowan, C. E. and D. M. Di Toro, March 1988. "Interim Sediment Criteria Values
for Nonpolar Hydrophobic Compounds." Prepared for USEPA, Office of Water
Regulations and Standards, Criteria and Standards Division. (SCD 17).
Cowan, C. E. and R. G. Riley, January 1987. "Guidance For Sampling and
Analyzing For Organic Contaminants On Sediments." Prepared for USEPA, Office
of Water Regulations and Standards, Criteria and Standards Division. (SCD 8)
Cowan, C. E. and C. S. Zarba, June 1987. "Regulatory Applications of Sediment
Quality Criteria - Final Report." Prepared for USEPA, Office of Water
Regulations and Standards, Criteria and Standards Division. (SCD 10)
Crecelius, E.A., E. A. Jenne and J. S. Anthony, Decembe 1987. "Sediment
Quality Criteria for Metals: V. Optimization of Extraction Methods for
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Jenne, E. A. 1987. "Sediment Quality Criteria for Metals: II. Review of
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onto Iron Oxides." Prepared for USEPA, Office of Water Regulations and
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Methods For Quantitative Determination of Important Adsorbents and Sorbed
Metals In Sediments." Prepared for USEPA, Office of Water Regulations and
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•
Johnson, C. A. 1986. "The Regulation of Trace Element Concentrations in River
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