r/EPA
United States Office of Scienco ard Tecnrciogy EPAxxx'x-xx-wx
Envircnmental Protection Agency Health and Ecological Criteria Division August 1991
Office of Water Washington. DC 20460
Proposed Technical Basis
for Establishing Sediment
Quality Criteria for
Nonionic Organic Chemicals
Using Equilibrium Partitioning
-------
PROPOSED TECHNICAL BASIS
FOR ESTABLISHING SEDIMENT
QUALITY CRITERIA FOR
NONIONIC ORGANIC CHEMICALS
USING EQUILIBRIUM' PARTITIONING
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CONTENTS
Section
Water quality criteria (WQC) concentrations versus
colonization experiments 85
Conclusions •. 87
GENERATION OF SQC 89
Parameter Values 89
Example Calculations 92
Sediment quality criteria (SQC) uncertainty 94
SITE SPECIFIC SEDIMENT CRITERIA MODIFICATIONS 99
Site-specific criteria modification based on species
sensitivity 99
CONCLUSIONS , 102
Research needs 103
REFERENCES 106
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CONTENTS
Section
FIGURE iii
TABLES vii
INTRODUCTION 1
OVERVIEW 1
Toxicity and bioavailability of chemicals in sediments 1
Partitioning of nonionic organic chemicals 9
Effects concentration 12
BACKGROUND 16
Rationale for selecting the EqP method 17
Relationship to WQC methodology 18
Applications of Sediment Quality Criteria 20
TOXICITY AND BIOAVAILABILITY OF CHEMICALS IN SEDIMENTS 22
Toxicity experiments 22
Bioaccumulation 29
Conclusion 30
SORPTION OF NONIONIC ORGANIC CHEMICALS 34
Partitioning in particle suspensions 34
Particle concentration effect 35
Organic carbon fraction 40
Dissolved organic carbon (DOC) complexing 43
Phase distribution in sediments 44
Bioavailability of DOC complexed chemicals 48
Field observations of partitioning in sediments 51
Organic carbon normalization 51
Sediment/pore water partitioning 55
Organic carbon normalization of biological responses 58
Toxicity and bioaccumuLation experiments 60
Bioaccumulation and organic carbon normalization 64
Determination of the route of exposure 71
APPLICABILITY OF WQC AS THE EFFECTS LEVELS FOR BENTHIC ORGANISMS 73
Method - relative acute ser--. : : ivi :y 73
Comparison of the sensici.•;• : benthic and water'
column species 77
Most Sensitive Species • 77
All species 80
Benthic community coloniza: . . --r: Tents 82
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FIGURES
Page
7 Comparison of percent mortality of H. azteca to DDT (left) and
endrln (right) concentrations in bulk sediment (top) and pore
water (bottom) for sediments with varying organic carbon
concentrations [21,22] 25
8 Comparison of percent mortality of A. abdita [24] (left) and
R. abronius [23] (right) to concentrations of cadmium in bulk
sediment (top) and pore water (bottom). Also presented is
water-only exposure data, identified with open circles 26
9 Comparison of C_. tentans body burden of cypermethrin (left)
and permethrin (right) versus concentration in bulk sediment
(top) and pore water (bottom) for sediments with varying
organic carbon concentrations [26] 31
10 Comparison of observed reversible component partition
coefficient to calculated partition coefficient using
Equation 10 [31] '....' 37
11 Comparison of the adsorption (left) and reversible component
(right) organic carbon normalized partition coefficient, Koc,
to the octanol/water partition coefficient, Kow, for
experiments with low solids concentrations: m foc Kow < 1.
The line represents equality [31] 38
12 Comparison of the normalized partition coefficients for
adsorption (left) and reversible component sorption (right) to
sediment organic carbon. The data are restricted so that
particle effects are not expected to be significant: ra foc Kow
< 1. The line represents perfect agreement [31] 42
13 Partition coefficients of chemicals to particulate organic
carbon (POC), Aldrich humic acid, and natural DOC.
Benzo[a]pyrene (BaP); 2,2',414',5,5' hexachlorobiphenyl
(HCBP); DDT; 2,2',5,5' tetrachlorobiphenyl (TCBP); pyrene
(PYR); 4 monochlorobiphenyl (MCBP) . (Data: [43]) 45
14 Phase distribution of a chemical in the three-phase system:
water, sediment, and DOC (Eqns. 18, 19, and 20). Koc - KDQC ~
KOW - 106 L/kg, foc - 2.0%, and m - 0.5 kg/L 47
15 - Average uptake rate of chemicals by Pontoporeia hovi with
(filled) and without (hatched) DOC present. Benzo[a]pyrene
(BaP); 2,2',4,4' tetrachlorobiphenyl (TCBP); Pyrene;
Phenanthrene. Data: [46].. ^9
16 Comparison of the DOC partition coefficient calculated from
the suppression of chemical uptake versus the GI% reverse.
phase HPLC column estimate. Circles are Aldrich humic acid;
triangles are interstitial water DOC. Chemicals are listed in
Figure 15 caption (also anthracene and benzo[a]anthracene)....
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FIGURES
Figure Page
1 Diagram of the organism exposure routes for a water-only
exposure (left) and a sediment exposure (right). Equilibrium
partitioning refers to the assumption that an equilibrium
exists between the chemical sorbed to the particulate sediment
organic carbon and the pore water. The partition coefficient
is Koc 4
2 Mortality versus predicted pore water toxic units for five
chemicals and three sediments per chemical. Sediment types
are indicated by the single hatching (lowest organic carbon
content), cross hatching (intermediate organic carbon
content), and filled symbols (highest organic carbon content).
See Tables 1 and 2 for data sources. Predicted pore water
toxic units are the ratio of the pore water concentration to
the water-only LC50 (Eqn. 1) 5
3 Mortality versus predicted sediment toxic units. Predicted
sediment toxic units are the ratio of the organic carbon
normalized sediment chemical concentration to the predicted
sediment LC50 (Eqn. 8). Sediment types are indicated by the
single hatching (lowest organic carbon content), cross-
hatching (intermediate organic carbon content), and filled
symbols (highest organic carbon content). See Tables 1 and 2
for data sources. Koc values are computed from Kow for DDT
(5.84), endrin (4.80), and fluoranthene (5.30) with Equation
11. These are log averages of the reported values in the Log
P data base [71]. The Kepone Koc is the log mean of the ratio
of organic carbon normalized Kepone concentration to pore
water Kepone concentration from the toxicity data set 13
4 A comparison of the minimum LC50 for water column versus
benthic organisms. Each data point represents a particular
chemical in either a freshwater or a saltwater exposure. The
data are from the WQC or draft criteria documents. See Table
4 for data sources 15
5 Comparison of percent mortality (left) and growth rate
reduction (right) of C. tentans to Kepone concentration in
bulk sediment (top) and pore water (bottom) for three
sediments with varying organic carbon concentrations [17] 23
6 Comparison of percent mortality of R. abronius to fluoranthene
[19] (left) and cadmium [20] (right) concentration in bulk
sediment (top) and pore water (bottom) for sediments with
varying organic carbon concentrations J-
i i. i
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FIGURES
Figure
25 Plots of the BSF (ratio of organism-to-sediment concentration)
for three sediments for a series of PCB congeners versus the
logio Kow for that congener. The dry weight normalization for
both organism and sediment (left panels); organic carbon
normalization for the sediment (middle panels); and organic
carbon and lipid normalization (right panels) as indicated.
The organisms are Yoldia (top) and Macoma (bottom). Data from
[58] 68
26 Plots of the BSF (ratio of organism lipid to sediment organic
carbon concentration) for a series of PCB congeners and other
chemicals versus logio Kow. Data for oligachaetes [53] and
polychaetes [58 ] 69
27 Comparison of LC50 or EC50 acute values for the most sensitive
benthic (abscissa) and water column (ordinate) species for
chemicals listed in Table 5. Benthic species are defined as
infaunal species (habitat types 1 and 2, left panel) or
infaunal and epibenthic species (habitat types 1 - 4); see
Table 6 78
28 LCSOs versus rank for nickel in seawater. Infaunal organisms
(left) and infaunal and epibenthic (right) are identified by
the solid symbols. The plot illustrates the distribution of
benthic organisms in the overall species sensitivity
distribution 81
29 Histograms of the proportion of saltwater and freshwater
benthic organisms in 10 percentile groups of all normalized
LCSOs. If benthic organisms were as equally sensitive as
water column organisms, the histograms should be of uniform
height as indicated by the dashed line, the overall percentage
of benthic species in the data set. Top panels include only
infaunal organisms as benthic. The bottom panel includes
infaunal and epibenthic as benthic organisms 83
30 Comparison of the dieldrin LC50 probability distributions for
water column and benthic freshwater species. Lognormal
probability plot (left panel) and '.he empirical cumulative
distribution functions (right panel) with the maximum
difference used in the Kolmogorov - Smirnov test indicated.... 91
31 Logio SQC versus logio Kow- The diagonal lines indicate the
FCV values. The criteria are computed from Equation 34. Koc
is obtained from Kow with Equation 11. The symbols indicate
SQCOC for Cne freshwater (filled) and saltwater (hatched)
criteria for the listed chemicals. The vertical line connects
symbols for the same chemical. The FCVs are from the WQC or
draft criteria documents (Table 4). The octanol/water
partition coefficients are the log mean of the values reported
in the Log P data base [71]
vi
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FIGURES
Figure
17 The organic carbon fractions (X dry weight) in the low-density
fraction 53
18 Comparison of PAH concentrations of the sand-sized and low-
density-fraction sediment particles (ordinate) to the
clay/silt fraction (abscissa) (Stations 4, 5, 7). Top panels
are for dry weight normalization; bottom panels are for
organic carbon normalization. Data from Prahl [48] 54
19 Observed partition coefficient versus the product of organic
carbon fraction and octanol/water partition coefficient. The
line represents equality. The partition coefficients are
computed by using total dissolved PCB (squares), and free PCB
(circles) which is computed Equation 26 with KDOC " Kow.
(Data from [50]) 57
20 Observed apparent partition coefficient to organic carbon
versus the 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 [53] for PCB
congeners and other chemicals and from [52] for phenanthrene,
fluoranthene, and perylene 59
21 Comparison of percent survival (left) and growth rate
reduction (right) of C. 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 [ 17 ] 61
22 Comparison of percent survival of H. azteca 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 [21,22]. 62
23 Comparison of percent survival of R. abronius to fluoranthene
concentration in pore water (cop) and bulk sediment, using
organic carbon normalization (bottom) for sediments with
varying organic carbon concentrations [19] 63
24 Plots of the BSF (ratio of organism-to-sediment concentration)
for three sediments for a series of PCB congeners versus the
log^Q Kow for that congener. The dry weight normalization for
both organism and sediment (left panels); organic carbon
normalization for the sediment (middle panels); and organic
carbon and lipid normalization (right panels) as indicated.
The organisms are Nereis (top) and Nephtvs (bottom). Data from
[58] 67
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TABLE
Table Page
1 SEDIMENT TOXICITY DATA AND BIOACCULUMATION DATA 6
2 LC50 AND EC50 FOR SEDIMENT DRY WEIGHT AND SEDIMENT ORGANIC
CARBON NORMALIZATION AND FOR PORE WATER AND WATER ONLY
EXPOSURES 7
3 BIOACCUMULATION FACTORS 32
4 DRAFT OR PUBLISHED WQC DOCUMENTS AND NUMBER OF INFAUNAL
(HABITAT 1 AND 2) EPIBENTHIC (HABITATS 3 AND 4), AND WATER
COLUMN (HABITATS 5 TO 8) SPECIES TESTED ACUTELY FOR EACH
SUBSTANCE 74
5 HABITAT CLASSIFICATION SYSTEM FOR LIFE SAGES OR ORGANISMS 76
6 ANALYSIS OF VARIANCE FOR DERIVATION OF SEDIMENT QUALITY
CRITERIA CONFIDENCE LIMITS 86
7 COMPARISON OF WQC, FCVs AND CONCENTRATION AFFECTING (LOEC) AND
NOT AFFECTING (NOEC) BENTHIC COLONIZATION 104
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OVERVIEW
This report presents the technical basis for establishing sediment quality
criteria (SQC) for nonionic organic chemicals using the equilibrium
partitioning (EqP) method. An overview is presented first that summarizes the
evidence and the major lines of reasoning. The references are cited in the
body of the report. Sediment quality criteria, as used herein, refers to
numerical concentrations for individual chemicals that are applicable across
the range of sediments encountered in practice. Sediment quality criteria are
intended to be predictive of biological effects. As a consequence they could
be used in much the same way as the final chronic value water quality criteria
- as the concentration of a chemical that is protective of benthic aquatic
life.
The specific regulatory uses of SQC have not been established. However,
the range of potential applications is quite large as the need for the
evaluation of potentially contaminated sediments arises in many contexts.
Sediment quality criteria are meant to be used with direct toxicity testing of
sediments as a method of evaluation. They provide a chemical by chemical
specification of what sediment concentrations would be protective of benthic
aquatic life.
Toxicity and bioavailability of chemicals in sediments
Establishing SQC requires a detenu i: ..- ion of the extent of the
bioavailability of sediment associated • • r. :>• .Is It has frequently been
observed that similar concentrations o: . :. nral., in units of mass of
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INTRODUCTION
This report presents the technical basis for establishing sediment quality
criteria using equilibrium partitioning. This methodology was selected because
it addresses the two principal technical issues that must be resolved: the
varying bioavailability of chemicals in sediments and the appropriate
biological effects concentration. Equilibrium partitioning assumes that the
partitioning of the chemical between sediment organic carbon and pore water is
at equilibrium
Sediment quality criteria are numerical concentrations for individual
chemicals - in this case, nonionic organic chemicals - that can be applied
across the range of sediments encountered. Intended to predict biological
effects, sediment quality criteria can be used much as final chronic value
water quality criteria are used: as the concentration of a chemical that
protects benthic aquatic life.
Sediment quality criteria for nonionic organic chemicals are based on the
chemical concentrations in sediment organic carbon; these criteria are designed
for use with direct toxicity testing of sediments as an evaluation method.
The final validation of sediment quality criteria will come from field
studies that are designed to evaluate the extent to which biological effects
can be predicted from these criteria.
A review paper based on this report is published by the Society for
Environmental Toxicity and Chemistry in volume 10 (1991) of their journal,
Environmental Toxicicy and Chemistry.
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Page 3
effects concentration found for the pore water is essentially equal to that
found in water-only exposures. Organism mortality, growth rate, and
bioaccumulation data are used to demonstrate this correlation, which is a
critical part of the logic behind the EqP approach to developing SQC. For
nonionic organic chemicals, it is shown that the concentration - response
curves correlate equally well with the sediment chemical concentration on a
sediment organic carbon basis.
These observations can be rationalized by assuming that the pore water and
sediment carbon, are in equilibrium and that the concentrations are related by
a partition coefficient, Koc, as shown in Figure 1 (right). The name
equilibrium partitioning (EqP) describes this assumption of partitioning
equilibrium. The rationalization for the equality of water-only and sediment-
exposure-effects concentrations on a pore water basis is that the sediment -
pore water equilibrium system (right) provides the same exposure as a water-
only exposure (left). The reason is that the chemical activity is the same in
each system at equilibrium. It should be pointed out that the EqP assumptions
are only approximately true, and, therefore, the predictions from the model
have an inherent uncertainty. The data presented below illustrate the degree
to which EqP can rationalize the observations.
Figure 2 presents mortality data for various chemicals and sediments
compared to pore water concentrations when normalized on a toxic unit basis.
Three different sediments are tested for each chemical as indicated. Predicted
pore water toxic units are the racio of the measured pore water concentration
to the LC50 from water only toxic icy :escs. The EqP model predicts that the
pore water LC50 will equal the wacer only LC50 which is obtained from a
separate water only exposure toxicicy ;cs;
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Page 2
chemical per mass of sediment dry weight (e.g. micrograms chemical per gram
sediment), can exhibit a range in toxicity in different sediments. If the
purpose of SQC is to establish chemical concentrations that apply to sediments
of differing types, it is essential that the reasons for this varying
bioavailability be understood and 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 overemphasized. For example, if 1
Mg/g of kepone is the LC50 for an organism in one sediment and 35 ^g/g 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 would be the LC50 of a third sediment without performing a toxicity test.
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 - for example, Lake Superior versus well water. Until the source
of the differences was understood, ic would be fruitless to attempt to
establish water quality criteria (WQC). It is for this reason that the issue
of bioavailability is a principal focus of this report.
The observations that provided :he key insight to the problem of
quantifying the bioavailability of chemicals in sediments were that the
concentration-response curve for the biological effect of concern could be
correlated not to the total sediment • • r'icaL concentration (micrograms
chemical per gram sediment), but to : • .: -erscitial water (i.e., pore water)
concentration (micrograms chemical p. : : ;<•:'<.' water). In addition, the
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Pore Water Normalization
o
5
100
BO
60
40
20
0
1 i i i i i 1 1 1 i i i i i i i i
a - ENDRIN
A - ACENAPHTHENE c
v - PHENANTHRENE °
0 - FLUORANTHENE D
o - KEPONE
+ - CADMIUM
o
D I
° D c
0 ^
o <*"• „ °
n a QxA
.„ V V V^ V
v «j^°X
D"^ O do«naxxxnB o a
§ V*
a -
0 A
vv
V
O v v
+ A
) V V
O^ A
°v
A
i ^V -
A
V —
VA
-
1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1 1
0.01
0.1
10
100
Predicted Pore Water Toxic Units
l-'ij-urt; ?. Mortality versus predicted pore water toxic units for five chemicals and three sediments per
(h.-mio.il. Sediment types are indicated by the single hatching (lowest organic carbon content), cross hatching
( iiitcmicdiiiti- organic carbon content), and filled symbols (highest organic carbon content). See Tables 1 and
2 ioi data sources. Predicted pore water toxic units are the ratio of the pore water concentration to the
water-only LC50 (Eqn. 1).
-------
Water Only
Exposure
Sediment - Pore Water
Exposure
Biota
Biota
Water
Sediment
Carbon
K
oc
Pore
Water
Equilibrium Partitioning
iguri- 1. Diagram of the organism exposure routes for a water-only exposure (left) and a sediment exposure
i if,hi ) Kijui 1 ibriuin partitioning refers to the assumption that, an equilibrium exists between the chemical
.»
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Table 2. LC50 and EC50 for sediment dry weight and sediment organic carbon normalization
and for pore water and water only exposures
LC50 and
Chemical
End point
Kepone
(mortality)
Kepone
(growth)
Fluoranthene
(mortal ity)
DDT
(mortal i ly )
DDT
(mortali ty)
Endrin
(mortality)
Endrin
(mortality)
Cadmium
(mo it al ity)
foe
%
0
1
12
0
1
12
0
0
0
3
7
10
3
3
11
3
6
11
3
11
11
0
0
1
.09
.50
.0
.09
.50
.0
.2
.3
.5
.0
.2
.5
.0
.0
.0
.0
.1
.2
. 0
.0
.0
.0
.25
.0
Total Sediment
USL/K.
0
6
35
0
9
37
3
6
10
10
17
44
1
4
10
3
5
5
4
18
10
22
20
10
.90 (0.73
.9 (5.85
.2 (30.6
.46 (0.42
.93 (7.74
.3 (31.5
.2 (2.85
.4 (5.56
.7 (8.34
.3 (8.74
.5 (12.5
.9 (36.7
.54 (1.18
.16 (3.91
.95 (9.34
.39 (2.61
.07 (4.05
.91 (4.73
.76 (3.70
.9 (13.6
.5 (8.29
.5 (18.7
.8 (16.7
.2 (7.02
- 1.10)
- 8.12)
- 40.5)
- 0.51)
- 12.8)
- 44.2)
- 3.59)
- 7.27)
- 13.7)
- 12.2)
- 24.3)
- 55.0)
- 2.00)
- 4.42)
- 12.9)
- 4.41)
- 6.36)
- 7.37)
- 6.13)
- 26.3)
- 12.7)
- 27.1)
- 26.0)
- 14.7)
29
31
18
17
48
20
21
30
22
0
1
0
1
1
1
2
3
2
2
1
Pore water
ue./L
.9 (25.3
.3 (25.7
.6 (15.7
.1 (15.7
.5 (34.6
.1 (16.7
.9 (19.6
.9 (27.0
.2 (17.5
.74 (0.67
.45 (1.20
.77 (0.67
.80 (1.44
.92 (1.55.
.74 (1.37
.26 (1.67
.75 (2.72
.81 (2.44
.50 (2.19
.76 (1.48
- 35.6)
- 38.1)
- 21.9)
- 18.7)
- 67.8)
- 24.1)
- 24.4)
- 35.4)
- 29.3)
- 0.82)
- 1.75)
- 0.89)
- 2.24)
- 2.36)
- 2.20)
- 3.05)
- 5.19)
- 3.23)
- 2.87)
- 2.09)
EC50
Organic Carbon Water Only
ug/E OC ue./L References
1000.
460.
293.
511.
662.
311.
1600.
2130.
2140.
344.
243.
428.
51.3
139.
99.6
113.
83.1
52.8
159.
172.
95.8
(811.
(390.
(255.
(467.
(516.
(262.
(1430.
(1850.
(1670.
(291.
(174.
(350.
(39.3
(130.
(84.9
(87.0
(66.4
(42.2
(123.
(124.
(75.4
- 1220.) 26.4 (22.7 - 30.6) (17|
-541.)
- 337.)
- 567.) 16.2 (15.0 - 17.5) [17]
- 1050.)
- 368.)
- 1800.) (19)
- 2420.)
- 2740.)
- 405.) 0.45 (0.38 - 0.53) [211
- 338.) 0.48 (0.42 - 0.55)
- 524.) 0.52 (0.45 - 0.60)
- 66.7) [22]
- 147.)
- 117.)
- 147.) 4.81 (4.46 - 5.20) [21]
-104.) 3.39 (3.10 - 4.98)
- 65.8) 3.71 (3.11 - 4.44)
-204.) [22]
- 239.)
- 115.)
1.6 (1.4 - 1.8) [23]
. .mil KOUs and the
ay I
confidence limits in parentheses are computed using the modified Spearman - Karber
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Table 1. Sediment toxicity data and bioaccuroulation data
Chemical
Kepone
Kepone
Cadmium
Fluoranthene
DDT
Endr in
Cadmium
Cadmium
Cypermethrin
Permethrin
Kepone
Organism
Chironomus tentans
Chironomus tentans
Rhepoxvnius abronius
Rhepoxvnius abronius
Hvalella azteca
Hval lei la azteca
Rhepoxvnius abronius
Ampelisca abdita
Chironomus tentans
Chironomus tentans
Chironomus tentans
Exposure
Sediment duration
source (davs)
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
14
14
4
10
10
10
4
10
1
1
14
Biological
end point
Mortality
Growth
Mortality
Mortality
Mortality
Mortality
Mortality
Mortality
Body burden
Body burden
Body Burden
Reference
[17]
(17)
(20)
[19]
121.22]
[21,22]
[23]
[24]
[26]
[26]
[17.28]
Figure
5
5
6
6
7
7
8
8
9
9
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Page 9
In addition, if it were true that benthic organisms are as sensitive as,
water column organisms - and the evidence to be presented appears to support
this supposition - then SQC could be established using the final chronic value
(FCV) from WQC documents as the effects concentration for benthic organisms.
The apparent equality between the effects concentration as measured in pore
water and in water-only exposures (Fig. 2) supports using an effects
concentration derived from water only exposures.
The calculation procedure for establishing SQC is as follows. If FCV
is the final chronic WQC for the chemical of interest, then the SQC
sediment) are computed using the partition coefficient Kp (L/kg
sediment) between sediment and pore water:
SQC - Kp FCV
This is the fundamental equation from which SQC are generated. Its utility
depends on the existence of a methodology for quantifying partition
coefficients.
Partitioning of nonionic organic chemicals
The partitioning of nonionic organic chemicals to soil and sediment
particles is reasonably well understood, and a standard model exists for
describing the process. The hydrophobia icy of the chemical is quantified by
using the octanol/water partition coefficient, Kow. The sorption capacity of
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Page 8
To examine this prediction
.. . . (pore water concentration) .,.
predicted pore water toxic unit - : : T_InN (1)
^ * (water only LC50) v '
Therefore, a toxic unit of one occurs when the pore water concentration
equals the water-only LC50, at which point it would be predicted that SOX
mortality would be observed. The correlation of observed mortality to
predicted pore water toxic units in Figure 2 demonstrates (a) the efficacy of
using pore water concentrations to remove sediment to sediment differences and
(d) the applicability of the water-only effects concentration and, by
implication, the validity of the EqP model. By contrast, as shown below, the
mortality versus sediment chemical concentration on a dry weight basis varies
dramatically from sediment to sediment.
The equality of the effects concentration on a pore water basis suggests
that the route of exposure is via pore water. However, the equality of the
effects concentration on a sediment organic carbon basis, which is demonstrated
below, suggests that the ingestion of sediment organic carbon is the primary
route of exposure. It is important to realize that if the sediment and pore
water are in equilibrium, then the effective exposure concentration is the same
regardless of exposure route. Therefore, it is not possible to determine the
primary route of exposure from equilibrated experiments.
Whatever the route of exposure, the correlation to pore water suggests chat
if it were possible to either measure the pore water chemical concentration, or
predict it from the total sediment concentration and the relevant sediment
properties such as the sediment organic carbon concentration, then that
concentration could be used to quantify che exposure concentration for an
organism. Thus, the partitioning of chemicals between the solid and the li-j'.iivl
phase in a sediment becomes a necessary component for establishing SQC.
-------
Page 11
If we define
SQC
SQCoc -— (6)
oc
as the organic carbon normalized SQC concentration (microgram chemical per
kilogram organic carbon, then
SQC - K FCV
oc oc (7)
Hence we arrive at the following important conclusion: For a specific chemical
having a specific Koc> the organic carbon normalized sediment concentration,
SQCOC, is independent of sediment properties.
Hydrophobic chemicals also tend to partition to colloidal-sized organic
carbon particles that are commonly referred to as dissolved organic carbon, or
DOC. Although DOC affects the apparent pore water concentrations of highly
hydrophobic chemicals, the DOC-bound fraction of the chemical appears not to be
bioavailable and Equation 7 for SQCOC still applies.
Therefore, we expect that toxicity in sediment can be predicted from the
water-only effects concentration and the Koc of the chemical. The utility of
these ideas can be tested with the same mortality data as these in Figure 2 but
restricted to nonionic organic chemicals for which organic carbon normalization
applies. The concept of sediment coxic units is useful in this regard. These
are computed as the ratio of the organic carbon-normalized sediment
concentrations, Cs/foc, and the predic-.-.i sediment LC50 using Koc and the
water-only LC50. That is:
-------
Page 10
the sediment is determined by the mass fraction of organic carbon for the
sediment, foc. For sediments with foc > 0.2Z by weight, the organic carbon
appears to be the predominant phase for chemical sorption. The partition
coefficient, Kn, the ratio of sediment concentration, Cs, to pore water
concentration, C^, is given by
K -~ -f K (3)
p C. oc oc
where Koc is the partition coefficient.for sediment organic carbon.
The only other environmental variable that has a dramatic effect on
partitioning appears to be the particle concentration in the suspension in
which Kp is measured. 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 Koc =• Kow for
sediments. A detailed review of the arguments is presented below.
Using Equations 2 and 3, an SQC is calculated from
SQC - f K FCV
oc oc (4)
This equation is linear in the organic carbon fraction, foc. As a consequence,
the relationship can be expressed as
SQC
- K FCV ,,.
f oc (5)
oc
-------
Page 12
C /f
s oc
predicted sediment toxic unit - (8)
Koc (water only LC50)
Figure 3 presents the percent mortality versus predicted sediment toxic
units. The correlation is similar to that obtained using the pore water
concentrations in Figure 2. The cadmium data are not included because
partitioning is not determined by sediment organic carbon. The predicted
sediment toxic units for each chemical follow a similar concentration-response
curve independent of sediment type. The data demonstrate that 50% mortality
occurs at about one sediment toxic unit, independent of chemical, species or
organism or sediment type, as expected if the EqP assumptions are correct.
If the assumptions of EqP were exactly true and there were no experimental
variability or measurement error, then the data in Figures 2 and 3 should all
predict SOX mortality at one toxic unit. There is an uncertainty of
approximately a factor of two in the results (also see Table 2 below). This
variation reflects inherent variability in these experiments as well as
phenomena that have not been accounted for in the EqP model. This appears to
be the limit of the accuracy and precision to be expected.
Effects concentration
The development of SQC requires an effects concentration for benthic
organisms. Because many of the organisms used to establish the WQC are
benthic, perhaps the WQC are adequate estimates of the effects concentrations
for benthic organisms. To examine this possibility, the acute toxicity data
-------
Organic Carbon Normalization
co
h.
o
5
100
BO
60
40
20
0
I i I I I I 1 1 1 l l I l l l 1 1
a - ENDRIN
A - ACENAPHTHENE a c
v - PHENANTHRENE °
o - DIELDRIN
- 0 ~ FLUORANTHENE
o
A
a
oQ0 o
I A 1
av « AV
AR ofc v VX
Q " V W V
i i i i 1 1 1 ii i i i i 1 1 1 1
i i i i i 1 1 1 1 i i i i i 1 1 1
^A ^ ^d!D3 B fT flD OO •"
a A ^
o
o v v -
3 V V °
A 0
V
A
V -
A
1 1 | 1 1 1 1 1 1 1 1 1 1 1 | 1 1
0.01
0.1
10
100
Predicted Sediment Toxic Units
Figure 3. Mortality versus predicted sediment toxic units. Predicted sediment toxic units are the ratio of
the organic carbon normalized sediment chemical concentration to the predicted sediment LC50 (Eqn. 8).
Sediment types are indicated by the single hatching (lowest organic carbon content), cross-hatching
(intermediate organic carbon content), and filled symbols (highest organic carbon content). See Tables 1 and
2 for data sources. Koc values are computed from Kow for DDT (5.84), endrin (4.80), and fluoranthene (5.30)
with Equation 11. These are log averages of the reported values in the Log P data base [71]. The Kepone Koc
the log mean of the ratio of organic carbojM^ormalized Kepone concentration to pore
from the toxicity data set.
-------
Page 14
base, which is used to establish the WQC is segregated into benthic and water
column species, and the relative sensitivities of each group are compared.
Figure 4 compares the acute values for the most sensitive benthic (epibenthic
and infaunal) species to the most sensitive water column species. The data are
from the 40 freshwater and 30 saltwater U.S. Environmental Protection Agency
(EPA) criteria documents. Although there is considerable scatter, these
results, a more detailed analysis of all the acute toxicity data and the
results of benthic colonization experiments, presented below, support the
contention of equal sensitivity.
-------
Comparison of Most Sensitive Species
O
10
O
c
E
3
"5
O
*.
9
+*
CO
0>
o
-1
-3
Freshwater
Saltwater
-3
-1
Log 1O Benthic LC5O (ug/L)
Figure 4. A comparison of the minimum LC50 for water column versus benthic organisms. Each data point
represents a particular chemical in either a freshwater or a saltwater exposure. The data are from the WQC or
di.iIt criteria documents. See Table 4 for data sources.
-------
Page 16
BACKGROUND
Under the Clean Water Act (CWA), the EPA is responsible for protecting the
chemical, physical, and biological integrity of the nation's waters. In
keeping with this responsibility, EPA published 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 and new chemicals have been published since 1980. These WQC are
numerical concentration limits that are protective of human health and aquatic
life. Although 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,
wetlands, and coastal waters create the potential for continued environmental
degradation even where water column contaminant levels comply with established
WQC. The absence of defensible SQC makes it difficult to accurately assess the
extent of sediment contamination.
As a result of the need to assist regulatory agencies in making decisions
concerning contaminated sediment, the EPA's Office of Water Regulations and
Standards, Criteria and Standards Division, established a research team to
review alternative approaches to assess sediment contamination. These and
related problems were the subject of a conference [1]. Alternative approaches
to establishing SQC [2] and their merits and deficiencies were discussed [3].
Additional efforts to identify the scope of national sediment contamination [4]
-------
Page 17
and to review proposed approaches for addressing contaminated sediments [5,6]
were undertaken. The EqP method was selected because it appeared to provide
the most practical, scientifically defensible, and effective regulatory tool
for addressing individual chemicals associated with contaminated sediments on a
national basis [7].
Rationale for selecting the EqP method
The principal reasons for the selection of the EqP method were:
1. It was likely that the EqP method would yield sediment criteria that were
predictive of biological effects in the field and would be defensible when used
in a regulatory context. These criteria directly address the issue of
bioavailability and are founded on the extensive biological effects data base
used to establish national WQC.
2. Sediment criteria could be readily incorporated into existing regulatory
operations because a unique numerical sediment specific criterion can be
established for any chemical and compared to field measurements to assess the
likelihood of significant adverse effects.
3. Sediment 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.
-------
Page 18
4. The method takes advantage of the large amount of data and expertise that
went into the development of the national WQC.
5. The methodology could be used as a regulatory predictive tool to ensure
uncontaminated sites would, be protected from attaining unacceptable levels of
contamination.
Relationship to WQC methodology
Perhaps the first question to be answered is: Why not use the already
existing procedure for the development of WQC to develop SQC? 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 criteria values [8]. Furthermore, WQC
developed with this methodology are routinely used in the regulation of
effluent discharges. A natural extension would be to apply these methods
directly to sediments.
The WQC are based on 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 WQC has demonstrated that the water-effect ratio,
the ratio of chemical concentrations in site water to laboratory water that
produces the same effect has averaged 3.5 [9,10]. The implication is that
s-
differences of this magnitude due to variations in site-specific water
chemistry are not an overwhelming impediment to nationally applicable numerical
WQC. .
-------
Page 19
The WQC are based on using the total chemical concentration as a measure of
bioavailable chemical concentration. However, the use of total sediment
chemical concentration as a measure of bioavailable - or even potentially
bioavailable - chemical concentration is not supported by the available data
[11]. A summary of recent experiments is presented in the two sections that
follow. The results of these experiments indicate that different sediments can
differ in toxicity by factors of 100 or more for the same total chemical
concentration. This is a significant obstacle. Without a quantitative
estimate of the bioavailable chemical concentration in a sediment it is
impossible to predict a sediment's toxicity on the basis of 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 [12,13,14,15].
Without a unique relationship between chemical measurements and biological
end points that 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 100 times more toxic in one sediment than it is
in another, how does one set universal SQC that depend only on the total
sediment chemical concentration? Any SQC that are based on total sediment
concentration have a potential uncertainty of this order of magnitude. Thus,
it appears that bioavailability must be explicitly considered for any sediment
evaluation methodology that depends on chemical measurements and, in
particular, in establishing defensible SQC.
-------
Page 20
Applications of Sediment Quality Criteria
Sediment quality criteria are designed to protect benthic infauna. These
criteria are designed to supplement Ambient Water Quality Criteria in assessing
the hazards to water bodies. For example, discharges of low concentrations of
a hazardous waste over many years may not indicate hazard to aquatic organisms
(as ambient water concentrations of chemicals do not exceed AWQC) but may
result in adverse effects to benthic infauna in the resulting contaminated
sediments. Because sediment criteria identify additional impacts to aquatic
systems they are expected to address an important void. As such, it is
expected that these criteria may be used in identification and monitoring of
contaminated sediments and determining the potential for adverse effects to
benthic infauna.
EPA recommends that SQC be applied only in the following situations:
• the organic carbon concentration of the sediment is greater than or
equal to 0.2% on a dry weight basis
• the sediments are bedded and not suspended
to sediments that are either permanently inundated with water, or the
sediments are in an intertidal zone which is inundated periodically
for durations sufficient to permit development of benthic assemblages.
They do not apply to occasionally inundated soils containing
terrestrial organisms.
-------
Page 21
In spills where chemical equilibrium between water and sediment has
not been reached, sediment chemical concentrations in excess of the
SQC indicate benthic organisms may be at risk. This is because for
spills the concentrations in interstitial and overlying water may be
higher relative to the equilibrium in sediment concentrations. In
spills, sediments having concentrations less than SQC may also pose
risks to benthic organisms.
-------
Page 22
TOXICITY AND BIOAVAILABILITY OF CHEMICALS IN SEDIMENTS
The observation chat provided a key insight into the problem of quantifying
the bioavailabilicy 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 (raicrograms chemical per gram dry
sediment) but to the pore water concentration (micrograms chemical per liter
pore water) [17]. In retrospect ic 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 (see Fig. 1) and the route of exposure cannot be determined.
Nevertheless, this observation was the critical first step in understanding
bioavailability of chemicals in sediments.
Toxicity experiments
A substantial amount of data has been assembled that addresses the
relationship between toxicity and pore water concentration. Table 1 lists the
sources and characteristics of these experiments. The data are presented in a
uniform fashion on Figures 5 to 8. The biological response - mortality or
growth rate suppression - is plotted versus the total sediment concentration in
the top panel, and versus the measured pore water concentration in the bottom
panel. Table 2 summarizes the LC50 and EC50 estimates and 95X confidence
limits for these data on a total sediment and pore water basis, as well as the
water-only values.
-------
Dry Weight Normalization
Kepone - Mortality
Kepone - Growth
100
S? 80
* eo
I 40
| 20
0
foe (X)
• O.O9
• 1.6
• 12
I
0.1 1.O 1O.O 100.O
Dry Weight Concentration (ug/g)
O.I 1.O 1O.O 100.O
Dry Weight Concentration (ug/g)
100
a •«
x
**
•<>
4°
20
0
Pore Water Normalization
Kepone - Mortality
1OO
X BO
\ 6O
5
4O
2O
O
1 1O 10O 100O
Pore Water Concentration (ug/L)
Kepone - Growth
1 1O 10O 1OOO
Pore Water Concentration (ug/L)
Figure 5. Comparison of percent mortality (left) and growth rate reduction (right) of C. tentans to Kepone
concentration in bulk sediment (top) and pore water (bottom) for three sediments with varying organic carbon
concentrations |17).
-------
Dry Weight Normalization
Fluoranthene
Cadmium
foe C/.)
• 0.2
• 0.3 1
• 0.5
foe (X)
• o.o
• 0.25
• 1.0
O 5 1O 15 2O
Dry Weight Concentration (ug/g)
0 2O 40 6O 80
Dry Weight Concentration (ug/g)
Pore Water Normalization
Fluoranthene
Cadmium
^
x
ss
3
5
o 20 4O oo so
Pore Water Concentration (ug/L)
O 2OOO 4000 6000
Pore Water Concentration (ug/L)
. Comparison of percent mortality of R. abronius to fluoranthene [19] (left) and cadmium [20] (right)
\it ion in bulk sediment (top) and pore water (bottom) for sediments with varying organic carbon
at i ons .
-------
Dry Weight Normalization
DDT
Endrln
foe (X)
• 3.0
e 10.5
3
"5
o
100
8O
80
40
2O
o1
foe (X) _^__
• 3.0 J§ ^w^^^^
• 11.2
• * *
••" • *
' feSf ••
SO 1OO
ISO
20O
Dry Weight Concentration (ug/g)
0.1 1.O .1O.O 1OO.O
Dry Weight Concentration (ug/g)
Pore Water Normalization
DDT
Endrln
~
^
3
i
100
80
80
40
20
o1
/ **— > •
•
•
4> •
•• *
:*••*•"•
012345
Pore Water Concentration (ug/L)
O.I 1.O 1O.O 1OO.O
Pore Water Concentration (ug/L)
l-'i^ure 7
hulk i
Comparison of percent mortality of H. azteca to DDT (left) and endrin (right) concentrations, in
MI (top) and pore water (bottom) for sediments with varying organic carbon concentrations [21,22].
-------
Dry Weight Normalization
o
Cadmium - Ampelisca
10 10J 10 10 10 10
Dry Weight Concentration (ug/g)
o
2
Cadmium - Rhepoxynius
i mm
i MIIII
11 mrn—i 11 nnii
10° IO1 102 IO3 IO4 105
Dry Weight Concentration (ug/g)
Pore Water Normalization
100
BO
60
40
20
0
Cadmium - Ampelisca
I 1
HATER ONLY o
SEDIMENT EXPOSURE •
I I HIM • Illl
io~4 io"3 io"2 io~* 10° 10* io2 io3 io4
Pore Water Concentration (mg/L)
100
BO
60
40
20
0
Cadmium - Rhepoxynius
HIM I Him
MATER ONLY O
SEDIMENT EXPOSURE •
10~4 10"310"210~* 10° 101 IO2 103 IO4
Pore Water Concentration (mg/L)
Figure 8. Comparison of percent mortality of A. abdita [24] (left) and R. abronius [23) (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 27
The results from Kepone experiments (Fig. 5) are particularly dramatic
[17,18]. For the low organic carbon sediment (foc - 0.09X), the 50th
percentile total Kepone concentration for both Chironomus tentans mortality
(LC50) and growth rate reduction from a life cycle test (EC50) are <1 /*g/g. By
contrast, the 1.52 organic carbon sediment EC50 and LC50 are approximately 7
and 10 Mg/gi respectively. The high organic carbon sediment (12X) exhibits
still higher LC50 and EC50 values on a total sediment kepone concentration
basis (35 and 37 Mg/g, respectively). However, as shown in the bottom panels,
essentially all the mortality data collapse into a single Curve and the
variation in growth rate data is significantly reduced when the pore water
concentrations are used as the correlating concentrations. On a pore water
basis, the biological responses as measured by LC50 or EC50 vary approximately
less than a factor of two, whereas when they are evaluated on a total sediment
Kepone basis they exhibit an almost 40-fold range in Kepone toxicity. The
comparison between the pore water effects concentrations and the water-only
results indicates that they are similar. The pore water LC50s are 19 to 30
Mg/L, and the water-only exposure LC50 is 26 Mg/L- The pore water EC50s are 17
to 49 Mg/L. and the water-only EC50 is 16 Mg/L (Table 2).
Laboratory experiments have also been performed to characterize the
toxicity of fluoranthene [19] and cadmium [20] to the sediment-dwelling marine
amphipod Rhepoxynius abronius. Figure 6 presents the R. abronius 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 content (0.2%) exhibits the lowest LC50 on a total
-------
Page 28
sediment concentration basis (3.2 Mg/g) and as the organic carbon concentration
increases (0.3 and 0.5%) the LC50 increases (6.4 and 10.7 Mg/g)• On a pore
water basis, the data again collapse to a single concentration-response curve
and the LC50s differ by less than 50%.
The cadmium experiments [20] were done with constant pore water
concentrations and a sediment amended with varying quantities of organic
carbon. The unamended and 0.25% additional organic carbon exhibit essentially
similar responses. However, the IX amended sediment had a much different LC50
based on the total sediment: concentration. Using the pore water concentrations
as the correlating variable again collapses the data into one concentration-
response curve.
Figure 7 presents mortality data for DDT and endrin using the freshwater
amphipod Hvalella azteca [21,22]. The responses for DDT [21] are similar to
those observed for Kepone, cadmium, and fluoranthene. On a total sediment
concentration basis the organism responses differ for the various sediments
(LC50s are 10.3 to 45 /*g/L), but on a pore water basis the responses are again
similar (LCSOs are 0.74 to 1.4 Mg/L) and comparable to the water-only LCSOs of
approximately 0.5 Mg/L- The DDT data in [22] is more variable. By contrast,
the organism survival for endrin exposures varies by a factor of almost six
among the six sediments. The LCSOs are 3.4 to 18.9 Mg/g- The pore water LCSOs
were less variable, 1.7 to 3.8 Mg/L and comparable to than the water-only
exposure LC50 of approximately 4 ^g/L (Table 2).
-------
Page 29
Additional cadmium toxicity data are compared on Figure 8. The responses
of R. abronius [23] and Ampelisca abdita [24] to cadmium in seawater exposures
without sediment and to the measured pore water concentrations in sediment
exposures (lower panels) demonstrate again that the survival responses are
similar with or without the sediment. The concentration response curves using
total cadmium concentrations are also shown (top panels). It is interesting to
note that these two organisms exhibit similar sensitivity to cadmium in water-
only exposures (0.34 mg/L for A. abdita and 1.6 mg/L for R. abronius - 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 SQC. It has been demonstrated that the variation in
bioavailability of cadmium and nickel in various freshwater and marine
sediments can be related to the acid-volatile sulfide concentration of the
sediment [24,25].
Bioaccumulation
A direct measure of chemical bioavailability is the amount of chemical
retained in organism tissues. Hence, tissue bioaccumulation data can be used
to examine the extent of chemical bioavailability. Chironomus tentans was
exposed to two synthetic pyrethroids cypermechrin and permethrin that were
-------
Page 30
added to three sediments, one of which was laboratory grade sand [26]. The
bioaccumulation from the sand was approximately an order of magnitude higher
than it was from the organic carbon-containing sediments for both cypermethrin
and permethrin (Fig. 9, top panels). On a pore water basis, the
bioaccumulation appears to be approximately linear and independent of sediment
type (bottom panels). The mean bioaccumulation factor (BAF) for cypermethrin
(and permethrin) varies from 6.2 to 0.6 (4.0 to 0.23) (^g/g organism//ig/g
sediment) as sediment foc Increases (Table 3). By contrast the mean BAFs on a
pore water basis vary by less than a factor of two.
Bioaccumulation was also measured by Adams et al. [17,27,28] in the £_,.
tentans - Kepone experiments presented previously (Fig. 3). The body burden
variation on a total sediment basis is over two orders of magnitude (BAF - 600
to 3.3 Mg/g organism//jg/g sediment), whereas the pore water bioaccumulation
factor is within a factor of four (5,200 to 17,600 MgAg organism/^g/L), with
the very low organic carbon sediment exhibiting the largest deviation (Table
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 concentration that quantifies exposure
- can be interpreted in a number of ways. However, it has become clear that
these results do not necessarily imply chat pore water is the primary route of
-------
Dry Weight Normalization
Cypermethrln
1 1O 1OO 1OOO 100OO
Dry Weight Concentration (ng/g)
Permethrln
1WWVW
'o>
\ 1OOO
01
| 100
a
£ 10
o
a
1
foe C/.}
• « 0.1 • m
• « 2.9 /~
//^ \
-•'
*_"— 4
*
1 UUU.U
"ai
•^ 100.0
| 10.0
I
$ 1.0
&
01
• • •
_
•- " *
^
^'x^
tx
^ :
1 1O 10O 10OO
Dry Weight Concentration (ng/g)
Pore Water Normalization
Cypermethrln
Permethrin
1UWUW
*^
\ 10OO
01
j ,00
$ 10
2
0.4
foe {'/.) \
• - 0.1 j^m
* 2.9 s^ I
•£
.-^ 1
e*-*'
y jt. -*
•^^
91 O.1O 1.OO 1O.OO IOC
1 UWW.U
-5
N 1OO.O
01
S
I 10.0
£ 1.0
2
LOO CM
foe (%)
• * 0.1 /*<-.
:" F+*S
r~ i
/
-^
^r^ •
)1 O.1O 1.OO 1O.
Pore Water Concentration (ug/L)
Pore Water Concentration (ug/L)
Figure 9. Comparison of C. tentans body burden of cypermethrin (left) and permethrin V^B--/ vCl;,Ui,
concentration in bulk sediment (top) and pore water (bottom) for sediments with varying organic carbon
concentrations [26].
-------
Table 3. Bioaccumulation factors
Bioaccumulation
foe
Chemical
Cypermethrin
Permethrin
Kepone
Total Sediment
(ug/g organism)
Factors3
Pore water
(ug/kg organism)
(X) (uz/e. sediment)
<0
2
3
<0
2
3
1
12
.1
.3
.7
.1
.3
.7
.09
.50
•
6.
0.
0.
4.
0.
0.
600
20
3.
21
50
60
04
38
23
3
(4.41
(0.30
(0.37
(2.89
(0.17
(0.18
( 308
( 4.8
( 0.3
- 8.01)
- 0.71)
- 0.83)
- 5.20)
- 0.59)
- 0.28)
- 892)
- 35.2)
- 6.3)
80.
51.
92.
39.
52.
29.
17,
5,
5,
1
3
9
7
5
7
600
180
790
(ug/L)
(73
(43
(87
(25
(22
(15
(6,
(1.
(2,
.5 -
.8 -
.0 -
.0 -
.6 -
.6 -
540
970
890
Reference
86.7) [26]
58.8)
98.8)
54.3) [26]
82.4)
43.7)
- 28,600) [17,27,28]
- 8,390)
- 8,700)
a 95X confidence limits shown in parentheses
-------
Page 33
i
exposure. This is because all exposure pathways are at equal chemical activity^
in an equilibrium experiment. Hence the route of exposure cannot be
determined. This can be seen by comparing the concentration-response
correlation's to pore water and organic carbon normalized sediment
concentrations. As shown below, both are equally successful at correlating the
data. This suggests that neither the pore water nor the sediment exposure
pathway can be implicated as the primary exposure route.
However, in order to relate pore water exposure to sediment carbon
exposure, it is necessary that the relationship between these two
concentrations be established. Thus, an examination of the state of the art of
predicting the partitioning of chemicals between the solid and the liquid phase
is required. This is examined in the following section.
-------
Page 34
SORFTION OF NONIONIC ORGANIC CHEMICALS
Partitioning in particle suspensions
For nonionic hydrophobic organic chemicals sorbing to natural soils and
sediment particles, a number of empirical models have been suggested (see
Karickhoff [29] for an excellent review). The chemical property that indexes
hydrophobicity is che octanol/water partition coefficient, Kow. The important
particle property is the weight fraction of organic carbon, foc. Another
important environmental variable appears to be the particle concentration
itself [30].
In many experiments using particle suspensions, the partition coefficients
have been observed to decrease as the particle concentration used in the
experiment is increased [30]. Unfortunately very few experiments have been
done on settled or undisturbed 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 would result in lower sediment chemical concentrations
for SQC. However, if this phenomenon is an artifact or is due to a 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.
-------
Page 35
Particle concentration effect. For the reversible (or readily desorbable)
component of sorption, a particle interaction model (PIM) has been proposed
that accounts for the particle concentration effect and predicts the partition
coefficient of nonionic hydrophobia chemicals over a range of nearly seven
orders of magnitude with a logio prediction standard error of 0.38 [31]. The
reversible component partition coefficient, Kp, is the ratio of reversibly
bound chemical concentration, Cs Og/kg dry weight), to the dissolved chemical
concentration, C^
C - K* C.. (9)
s p d
The PIM model for Kp is:
f K
K* - oc oc (10)
P 1 + mf K /v
OC OC X
where:
Kp - reversible component partition coefficient (L/kg dry weight)
Koc - particle organic carbon partition coefficient (L/kg organic carbon)
foc - particle organic carbon weight fraction (kg organic carbon/kg dry
weight)
m - particle concentration in the suspension (kg dry weight/L)
i/x - 1.4, an empirical constant (unitless) .
-------
Page 36
The regression of Koc to the octanol/water coefficient, KOWl yields
lo*10Koc ' °-00028 + °'983 lo6lOKow
which is essentially Koc approximately equals Kow. Figure 10 presents the
observed versus predicted reversible component partition coefficients using
this model [31]. 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 t/x. The low particle
concentration data (m foc Kow < 1) are presented on Figure 11 for the
conventional adsorption (left) and reversible component (right) partition
coefficient, Kp, normalized by foc, that is Koc - Kp/foc. The relationship K
-------
Reversible Component Partition Coefficient
6
O)
J*
O)
o
-2
-2
6
Predicted Log 10 Kp (L/kg)
Figure 10. Comparison of observed reversible component partition coefficient to calculated partition
coefficient using Equation 10 (31).
-------
Partition Coefficient - m foe Kow < 1
Adsorption
Reversible Component
o
o
o> s
.*
\
8 3
O) 1
o
-1
AMtearb
CarfoofurMi
Unuron
Fkiomctron
Carbaryl
Dluron
Itotteyt ParatMen
ParalMon
HCH
ppDOT
O
O
O) 5
X
g 3
-1
1 35
Log 1O Kow
-1
-i
135
Log10 Kow
Figure 11
partition coefficient
concentrations:
Comparison of the adsorption (left) and reversible component (right) organic carbon normalized
efficient, Koc, to th.e octanol/water partition coefficient, Kow, for experiments with low solids
Loc
arption (lertj and reversible component vngnt; organic caroon normalized
.... octanol/water partition coefficient, Kow, for experiments with low solids
< 1. The line represents equality [31].
-------
Page 39
Sorpcion by nonseparated 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 truly
dissolved or 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 that is a ubiquitous feature of aqueous-phase particle
sorption. A number of experiments have been designed to explicitly exclude
possible third-phase interferences. Both the resuspension experiment for
polychlorinated biphynels (PCBs) [39] and metals [40,41] in which particles are
resuspended into a reduced volume of supernatant and the dilution experiment
[39] in which the particle suspension is diluted with supernatant from a
parallel vessel display particle concentration effects. It is difficult to see
how third-phase models can account for these results because the concentration
of the colloidal particles is constant while the concentration of the sediment
particles varies substantially.
The model (Eqn. 10) is based on the hypothesis that particle concentration
effects are due to an additional desorption reaction induced by particle-
particle interactions [31]. It has been suggested that actual particle
collisions are responsible [42]. This interpretation relates i/x to the
collision efficiency for desorption and demonstrates that it is independent of
the chemical and particle properties, a face that has been experimentally
observed [31,40].
-------
Page 40
It is not necessary to decide which of these mechanisms is responsible for
the effect if all the possible interpretations yield the same result for
sediment/pore water partitioning. Particle interaction models would predict
that Koc =« Kow because the particles are stationary in sediments. Third-phase
models would also relate the 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 Koc =• Kow. 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, Koc <* Kow.
Organic carbon fraction. The unifying parameter that permits the
development of SQC for nbnionic hydrophobic organic chemicals that are
applicable to a. broad range of sediment types is the organic carbon content of
the sediments. This can be shown as follows: The sediment/pore water
partition coefficient, Kp, is given by
K - f K - f K (12)
p oc oc oc ow
and the solid phase concentration is given by
C - f K C. (13)
s oc oc d
where Cs is the concentration on sediment particles. An important observation
can be made that leads to the idea of organic carbon normalization. Equation
12 indicates that the partition coefficient for any nonionic organic chemical
is linear in the organic carbon fraction, foc. The partitioning data examined
-------
Page 4L
in Figure 11 can be used to examine the linearity of Kp to foc. Figure 12
compares Kp/KOw co foe f°r boch the adsorption and the reversible component
partition coefficients. The data are restricted to m foc KQW < 1 to suppress
particle effects. The line indicates the expected linear relationship in
Equation 12. These data and an analysis presented below appear to support the
linearity of partitioning to a value of foc - 0.2X. This result and the
toxicity experiments examined below suggest that for foc > 0.2X, organic carbon
normalization is valid.
As a consequence of the linear relationship of Cs and foc, the relationship
between sediment concentration, Cs, and free dissolved concentration, Cd, can
be expressed as
oc
If we define
C
C - — (15)
s,oc f
oc
as the organic carbon normalized sediment concentration (/ig chemical/kg organic
carbon), then from Equation (14):
C - K C , (16)
s,oc oc d
-------
Partition Coefficient - m foe Kow < 1
Adsorption
Reversible Component
o
*
a
O)
o
-1
-2
-3
-4
Aldtearb
Carbofuran
LJnuron
FluofiMtron
Carbaryl
Oluron
Itothyl ParatMon
PcratNon
HCH
O
a
X
o
T-
0>
o
O.O1 O.1O 1.0O 1O.OO 1OO.OO
foe (%)
O.1O 1.00 1O.OO 10O.OO
foe (%)
i.ui. 1^. Comparison of the normalized partition coefficients for adsorption (left) and reversible component
•"I'1 ion
-------
Page 43
Therefore, for a specific chemical with a specific Koc, the organic carbon
normalized tatal sediment concentration, Cs,oc, is proportional to the
dissolved free concentration, C^, for any sediment with foc > 0.2X. This
latter qualification is judged to be necessary because at foc < 0.2X the other
factors that influence partitioning (e.g., particle size and sorption to
nonorganic mineral fractions) become relatively more important [29]. Using the
proportional relationship given by Equation 16, the concentration of free
dissolved chemical can be predicted from the normalized sediment concentration
and Koc. The free concentration is of concern as it is the form that is
bioavailable. The evidence is discussed in the next section.
Dissolved organic carbon (DOC) complexing
In addition to partitioning to particulate organic carbon (POC) associated
with sediment particles, hydrophobic chemicals can also partition to the
organic carbon in colloidal sized particles. Because these particles are too
small to be removed by conventional filtration or centrifugation they are
operationally defined as DOC. Because sediment interstitial waters frequently
contain significant levels of DOC, it must be considered in evaluating the
phase distribution of chemicals.
A distinction is made between che free dissolved chemical concentration,
Cd, and the DOC-complexed chemical. CDOc The partition coefficient for
DOC, KDOC> *-s analogous to Koc as i.: ;...r.:Lfies the ratio of DOC-bound
chemical, CDOC• to cne f^6® dissolved : .-entration, C^:
CDOC " mDOC KDOC Cd
-------
Page 44
where mDOC ^s c^e DOC concentration. The magnitude of KDQC aru* c^e DOC
concentration determine the extent of DOC complexation that takes place. Hence
it is important to have estimates of these quantities when calculating the
level of free dissolved chemicals in sediment pore waters.
A recent compilation of KDQC together with additional experimental
determinations is available [43]. A summary that compares the partitioning of
six chemicals to POC, natural DOC, and Aldrich humic acid (HA) is shown on
Figure 13. The magnitude of the partition coefficients follow the order: POC
> AHA > natural DOC. The upper bound on KDQC would appear to be KDQC " Koc,
the POC partition coefficient.
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 mass balance for total
concentration C^:
C - *C + mf K C, H- 0m KC (18)
T d oc oc d DOC DOC d
where ^ 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 18 the free dissolved
concentration can be expressed as
-------
o
o
8
2
"o
6
0.
o
CD
O
POC
Humlc Acid
Pore Water DOC
BaP DDT HCBP MCBP PYR TCBP
Chemicals
Figure 1 '3. Partition coefficients of chemicals to particulate organic carbon (POC), Aldrich humic acid, and
iKituial UOC . Benzo[alpyrene (BaP); 2 , 2 ' , 4,4' .5,5' hexachlorobipheny1 (HCBP); DDT; 2,2',5,5'
ii-t r.u l.lorobiphcMiyl (TCBP); pyrene (PYR); 4 monochlorobiphenyl (MCBP). (Data: [43]).
-------
Page 46
CA ' TT—T~Z——; r~ '
d * + mfocKoc * *mDOCKDOC
The concentration associated with the particle carbon (Eqn. 16) and DOC (Eqn.
17) can then be calculated. The total pore water concentration is the sum of
the free and DOC complexed chemical, so that
Cpore " Cd + CDOC " Cd(L + mDOCKDOC} (20)
Figure 14 illustrates the phase partitioning behavior of a system for a
unit concentration of a chemical with the following properties: Koc - KDOC ™
10*> L/kg, foc -2.OX, m - 0.5 kg solids/L sediment, and mooc varies from 0 to
50 mg/L, a reasonable range for pore waters [44]. 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^ (Eqn. 19) and a proportional decrease in Cs (Eqn. 16).
It is important to realize that the free chemical concentration, C^, can be
estimated directly from Cs,oc, the organic carbon normalized sediment
concentration, using Equation 16, and chat the estimate is independent of the
DOC concentration. However, to estimate Cj from the pore water concentration
requires that the DOC concentration and KDOC be known. The assumption Cpore -
Cd is clearly not warranted for very hvdrophobic chemicals. For these cases
CS.QC gives a more direct estimate of rht- free dissolved bioavailable
concentration, C^, than does the port- •-.'• r . .uicentration.
-------
1.0000
o
a
»- o.iooo -
o
*3
<0
i.
4*
c
o
o
c
o
o
10
20
30
4O
50
6O
DOC Concentration (mg/L)
Figure 14. Phase distribution of a chemical in the three-phase system: water, sediment, and DOC (Eqns. 18
19, and 20). Koc - KnQC - Kow - 106 L/kg, foc - 2.OX, and m - 0.5 kg/L.
-------
Page 48
Bioavailability of DOC complexed chemicals
The proportion of a chemical in pore water that is complexed to DOC can be
substantial (Fig. 14). Hence, the question of bioavailability of DOC-complexed
chemical can be important in assessing toxicity directly from measured pore
water concentrations. A significant quantity of data indicates that DOC-
complexed chemical is not bioavailable. Fish [45] and amphipod [46] uptake of
polycyclic aromatic hydrocarbons (PAHs) are significantly reduced by adding
DOC. An example is shown in Figure 15 for a freshwater amphipod [46]. For a
highly hydrophobic chemical such as benzo[a]pyrene (BaP) the effect is
substantial, whereas for less hydrophobic chemicals (e.g., phenanthrene) the
reduction in uptake rate is insignificant. This is the expected result
because, for a fixed amount of DOC, the quantity of DOC-complexed chemical
decreases with decreasing KHQC (Eqn. 17).
The quantitative demonstration that DOC-complexed chemicals are not
bioavailable requires an independent determination of the concentration of
complexed chemical. Landrum et al. [46] have developed a C^g reverse-phase
HPLC column technique that 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 che uptake experiments, assuming that all the
complexed chemical is not bioavailable [46,47]. As shown on Figure 16,
although the KnQC inferred from uptake suppression is larger than that inferred
from the reverse phase separation for HA, these data support the assumption
that the DOC-complexed fraction, CQQC • >-s noc bioavailable. Hence the
bioavailable form of dissolved chemical is Cj, the free uncomplexed component.
This is an important observation because ic is C^ that is in equilibrium
with CSjOC, the organic carbon normalized sediment concentration (Eqn. 15).
-------
300
I
O)
£
"a
DC
-------
DOC Partition Coefficient
o
o
O)
o
'55
CO
Q)
h.
a
5
a>
.*
CO
4-»
a
3
E
o
o
o
1000O
1OOO
100
10
• Humic Acid
A Pore Water
10
10O
1OOO
1OOOO
Koc from Reverse Phase (L/g oc)
Figure 16. Comparison of the DOC partition coefficient calculated from the suppression of chemical uptake
versus the C\Q reverse phase HPLC column estimate. Circles are Aldrich humic acid; triangles are interstitial
water DOC. Chemicals are listed in Figure 15 caption (also anthracene and benzo[ajanthracene).
-------
Page 51
Field observations of partitioning in sediments
There exists an enormous quantity of laboratory data for partitioning in
particle suspensions. However, pore water and sediment data from field samples
are scarce. Two types of data from field samples are examined. The first is a
direct test of the partitioning equation CSiOC - Koc C^, which is independent
of the DOC concentration. The second examines the sediment and pore water
concentrations and accounts for the DOC that is present.
Organic carbon normalization. Consider a sediment sample that is
segregated into various size classes after collection. The particles in each
class were in contact with the pore water. If sorption equilibrium has been
attained for each class, then, letting Cs(j) be the particle chemical
concentration of the jc^ size class, it is true that
Cs(J> -foc(J) Koc Cd
where f0c(J) i-s c^e organic carbon fraction for each size class j. On an
organic carbon normalized basis this equation becomes
C (j) - K C.
s,oc J oc d
(22)
where CSiOC(j) - Cs(j)/foc(j). This result indicates that the organic carbon
normalized sediment concentration of a chemical should be equal in each size
class because Koc and C^ are the same for each size class. Thus a direct test
of the validity of both organic carbon normalization and EqP would be to
examine whether Cs,oc(j) is constant across size classes in a sediment sample.
-------
Page 52
Data from Prahl [48] can be used to test this prediction. Sediment cores
were collected at three stations near the Washington State coast (Stations 4, 5
and 7). These were sieved into a silt-and-clay sized fraction (<64 pm), and a
sand sized fraction (>64 pm). This latter fraction was further separated into
a low density fraction (<1.9 g/cra^) and the remaining higher density sand-sized
particles. The concentrations of 13 individual PAHs were measured in each size
fraction.
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, shown on Figure 17, range from
0.2X for the high-density sand-sized fraction to greater than 30X for the low
density fraction. This exceeds two orders of magnitude and essentially spans
the range usually found in practice. For example, 90X of the estuarine and
coastal sediments sampled for the National Status and Trends program exceed
0.2X organic carbon [49].
Figure 18 (top) compares the dry weight normalized clay/silt sized fraction
sediment PAH concentrations, Cs(j), to the sand-sized high and low density PAH
concentrations on a dry weight basis. The dry weight normalized data have
distinctly different concentrations - the low density high organic carbon
fraction is highly enriched, whereas the sand-sized fraction is substantially
below the clay/silt fraction concentrations. Figure 18 (bottom) presents the
same data but on an organic carbon normalized basis, Cs Oc(J)- *n contrast to
dry weight normalization, the PAH concentrations are essentially the same in
each size class, as predicted by Equation 22.
-------
Organic Carbon Fractions
100.0
10.0 |r
o
o
1.0 |r
0.1
LOW SAND SILT/CLAY
Sediment Fraction
Figure 17. The organic carbon fractions (% dry weight) In the low-density fraction 64 ^m <1 9 g/cm3- the
sand sized fration >64 Mn, > 1.9 gm/cra3; the silt/clay sized fraction <64 Mm. Numbered stations as
indicated. Data from Prahl [48].
-------
Dry Weight Normalization
1000
too
*
•x
TJ
in
n
3 10
x
a.
Sand vs Clay/Silt
Low Density vs Clay/Silt
10000
1000
3 too
a.
10 100
PAH (ug/g dry wt)
10OO
1O
^^^^P^^^T^^^PH
10 too
PAH (ug/g dry wt)
100O
Organic Carbon Normalization
Sand vs Clay/Silt
100000
o 10000
a
3
x
1OOO
100
Low Density vs Clay/Silt
100000
o 10000
1OOO
100
100
1OOOOO
1OOO 1OOOO 1OOOOO 1OO 10OO 1OOOO
PAH (ug/g oc) PAH (ug/g oc)
l-'i j'.uri; 18. Comparison of PAH concentrations of the sand-sized and low-density-fraction sediment particles
(01 (1 i ii.-i i i-) t.o the clay/si.lt fraction (abscissa) (Stations 4, 5, 7). Top panels are for dry weight
normalization; bottom panels are for organic carbon normalization. Data from Prahl [48].
-------
Page 55
Ic is concluded from these data that the organic carbon normalized PAH
concentrations are relatively independent of particle size class and that
organic carbon is the predominant controlling factor in determining the
partition coefficient of the different sediment size particles in a sediment
sample. The organic carbon concentration of the high-density sand-sized
fraction (0.2 to 0.3X) suggests that organic carbon normalization is
appropriate at these low levels.
Sediment/pore water partitioning. . Normally when measurements of sediment
chemical concentration, Cs , and total pore water chemical concentrations,
Cpore* are made, the value of the apparent partition coefficient is calculated
directly from the ratio of these quantities. As a consequence of DOC
complexation, the apparent partition coefficient, Kp, defined as
Kp -^-J- (23)
pore
is given by
K f K
B - - 2C_0£ - 2
l + "DOC^OC l + mDOCKDOC
As mj)oc increases, the quantity of DOC-coraplexed chemical increases and the
apparent partition coefficient approaches
f K
0C (25)
DOCDOC
-------
Page 56
which is just the ratio of sorbed to complexed chemical. Because the solid-
phase chemical concentration is proportional to the free dissolved portion of
the pore water concentration, C
-------
00
X
-------
Page 58
and to compute the actual partition coefficient: Kp - Cs/Cd. The data indicate
that if KDOC " Kow i-s used, the results, shown on Figure 19, agree with the
expected partition equation, namely that Kp - foc Kow. A similar three-phase
model has been presented by Brownawell and Farrington [51).
Other data with sediment/pore water partition coefficients for which the
DOC concentrations have not been reported [52,53] are available to assess the
significance of OOC partitioning on the apparent sediment partition
coefficient. Figure 20 presents thes.e apparent organic carbon normalized
partition coefficients, that is Koc - Kp/foc versus Kow. The expected
relationship for DOC concentrations of 0, 1, 10, and 100 mg/L is also shown.
Although there is substantial scatter in these data, reflecting the difficulty
of measuring pore water concentrations, the data conform to DOC levels of 1.0
to 10 mg/L, which is well within the observed range for pore waters [44,50].
Thus these results do not refute the hypothesis that Koc =• Kow in sediments but
show the need to account for DOC complexing in the analysis of pore water
chemical concentrations.
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 parcicioning formula Cs oc - Koc CQ< (Eqn.
16), which relates the free dissolved concentration to the organic carbon
-------
o
o
O>
X
\
o
o
0>
o
8
7
6
5 -
4
3
A Ollvar (Various)
Socha (PAHa)
100.0
6
8
Log1O Kow
if.uif '20. Observed apparent partition coefficient to organic carbon versus the octanol/water partition
in-1 t if iriit . The lines represent the expected relationship for DOC concentrations of 0, 1, 10, and 100 mg/L
itl KIJOC - Kow. Dai;* 1 rom (53) for PCB congeners and other chemicals and from [52] for phenanrtirene,
praiiiheue , and perylene. -^*^^
' i
-------
Page 60
normalized particle concentration. This applies only to nonionic hydrophobic
organic checricals because the rationale is based on a partitioning theory for
this class of chemicals.
Toxicity and bioaccumulation experiments. To demonstrate this
relationship, concentration-response curves for the data presented in Figures 5
and 7 are used to compare results on a pore water-normalized and organic
carbon-normalized chemical concentration basis. Figures 21 to 23 present these
comparisons for Kepone, DDT, endrin, and fluoranthene. The mean and 95Z
confidence limits of the LC50 and EC50 values for each set of data are listed
in Table 2. The top panels repeat the response - pore water concentration
plots shown previously in Figures 5 to-7, while the lower panels present the
response versus the sediment concentration, which is organic carbon normalized
(micrograra chemical per gram organic carbon). The general impression of these
data is that there is no reason to prefer pore water normalization over
sediment organic carbon normalization. In some cases, pore water normalization
is superior to organic carbon normalization, for example, Kepone - mortality
data (Figure 21) whereas the converse sometimes occurs, for example Kepone -
growth rate (Fig. 21). A more quantitative comparison can be made with the
LC50s and EC50s in Table 2. The variation of organic carbon normalized LC50s
and EC50s between sediments is less than a factor of two to three and is
comparable to the variation in pore water LC50s and EC50s. A more
comprehensive comparison has baen presented in Figures 2 and 3, which also
examine the use of the water-only LC50 to predict the pore water and sediment
organic carbon LC50s.
-------
Pore Water Normalization
Kepone - Mortality
Kepone - Growth
N«
-\
v^
.«•
2
100
8O
60
40
20
O
foe ('/.)
~ • 0.09
. • 1.6
• 12
-
A ^^. ""^
r
1
T.| " -------I
•^*~~*
// '
/ / /
f //
^l/
' /
3*Srf
J?
c
_o
««
o
1
1C
10 10O 1000
Pore Water Concentration (ug/L)
100
80
60
40
20
0
""• • ••••••-,
—^*e
/ ''
/ /
/ '' x*
/'•'/
F f
* ' /
"dW*^"^*> ^^^— — *V
. .......i . •
1 1O 1OO 100O
Pore Water Concentration (ug/L)
Organic Carbon Normalization
Kepone - Mortality
^
7[
|
o
100
80
60
40
20
0
foe ('/.}
• 0.09
. • 1.6
• 12
•
,
«__ ^
e-
10
ff^— •
•' / / •
• 1 1
?! 1
* i
/ * 1
• * »
A/ /
- *^»»^
•• ^
t
•••.
>^
^
3
O
e
100 100O 1000O
Organic Carbon Normalized (ug/g oc)
21
Comparison
Kepone - Growth
100
80
60
40
20
0
• •**-•• • --»•»-•!
fa^*
f W
Is
.**'
, /
' » '* '
• ' /
v*^*
% -v
10 10O 100O 1OOO<
Organic Carbon Normalized (ug/g oc)
of percent survival (left) and growth rate reduction (rieht) of C. tentans
concentration in pore water (top) and in bulk sediment, using organic carbon normalization (bottom) for three
sediments with varying organic carbon concentrations [17].
-------
Page 66
performed by Rubinstein and co-workers [58]. The uptake of various PCB
congeners was moni^red until steady-state body burdens were reached. Sediment
organic carbon and organism lipid content were measured. Figures 24 and 25
present the log mean of the replicates for the ratio of organism-to-sediment
concentration for all measured congeners versus KOW for each organism. Dry
weight normalization for both organism and sediment (left panels), organic
carbon normalization for the sediment (center panels), and both organic carbon
and lipid normalization (right panels) are shown. The results for each
sediment are connected by lines and separately identified.
The BSFs basc.1 on dry weight normalization are quite different for each of
the sediments with the low carbon sediment exhibiting the largest values.
Organic carbon normalization markedly reduces the variability in the BSFs from
sediment to sediment (center panels). Lipid normalization usually further
reduces the variability. Note that the BSFs are reasonably constant for the
polychaetes, although some suppression at logio Kow > 7 is evident. The clams,
however, exhibit a marked declining relationship.
Results of a similar though less extensive experiment using one sediment
and oligochaete worms have been reported [53]. A plot of the organic carbon
and lipid-normalized BSF versus Kow from this experiment is shown on Figure 26,
together with the averaged polychaece data (Fig. 24). There appears to be a
systematic variation with respect co Kow, which suggests that the simple lipid
equilibration model with a constant lipid-octanoi solubility ratio is not
descriptive for all chemicals. This suggests that a more detailed model of
benthic organism uptake is required co describe chemical body burdens for all
nonionic chemicals as a function of Kow [55]. However, for a specific chemical
-------
Dry Weight
§
O
o
3.0
2.0
1.0
0.0
-1.0
5.5
Ner/es
Organic Carbon
|
U
3
1.O
0.0
-1.0
-2.0
«.6 7.5
LoglO Kow
8.6
-3
"*6.5 6.5 7*
Log10 Kow
C
5
o
Organic Carbon, Llpld
3.0
1.0
0.0
8.5
8.5 7*
Log 10 Kow
8.5
Dry Weight
Nephtys
Organic Carbon
u
a
O
O
3.0
2.0
QJQ
-1
foe (Y.)
•°6
o
o
!
o
1.O
O.O
-1.0
-2.0
8.5 7.6
LoglO Kow
8.6
-3.0
Organic Carbon, Llpld
5.5
3.0
|
U
3
o
5
1.0
8.5 7.6
Log 10 Kow
8.5
-1.0
66
O5 7.6
Log 10 Kow
8.5
Figure 24. Plots of the BSF (ratio of organism-to-sediment concentration) for three sediments for a series of
PCB congeners versus the loglo Kow for that congener. The dry weight normali2ation for both organism and
^ediment (left panels); organic carbon normalization for the sediment (middle panels); and organic ca^n and
id normalization (right; panels) as indicated. ^^ organisms are Nereis (top) and Nephtvs
m [58]. \ "
-------
Page 64
Bioaccumulation factors calculated on the basis of organic carbon
normalized chemical concentrations are listed in Table 3, for permethrin,
cypermethrin, and kepone. Again, the variation of organic carbon normalized
BAFs between sediments is less than a factor of two to three and is comparable
to the variation in pore water BAFs.
Bioaccumulation and organic carbon normalization. Laboratory and field
data also exist for which no pore water or DOC measurements are available but
for which sediment concentration, organic carbon fraction, and organism body
burden have K°.en determined. These data can be used to test organic carbon
normalization for sediments and to examine organism normalization as well. The
use of organism lipid fraction for this normalization has become conventional
(see references in Chiou [54]). If Cfc is the chemical concentration per unit
wet weight of the organism, then the partitioning equation is
S-KL'L'CI (27)
where:
KL - lipid/water partition coefficient (L/kg lipid)
fL - weight fraction of lipid (kg lipid/kg organism)
C(j - free dissolved chemical concentration (/ig/L)
The lipid-normalized organism concentration, CD>L, is
C
h T - ~T~ " KT CH
b , L E L d
-------
Page 65
The lipid-normalized body burden and the organic carbon normalized sediment
concentration can be used to compute a bioaccuraulation ratio, which can ue
termed the BSF [55]:
C K K
BSF-T^ -IT -? <29>
s,oc oc ow
The second equality results from using the partitioning Equations 16 and 28 and
the third from the approximation that Koc = Kow. The BSF is the partition
coefficient betwr .rganism lipid and sediment organic carbon. If ..lie
equilibrium assumptions are valid for both organisms and sediment particles,
the BSF should be independent of both particle and 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 BSF
should be a constant, independent of particles, organisms, and chemical
properties [54,56,57]. This result can be tested directly.
The representation of benthic organisms as passive encapsulations of lipid
that equilibrate with external chemical concentrations is clearly only a first-
order approximation. Biomagnification effects, which can occur via ingestion
of contaminated food and the dynamics of internal organic carbon metabolism,
can be included in a more comprehensive analysis [55]. Nevertheless it is an
appropriate initial assumption because deviations from the first-order
representation will point to necessary refinements, and for many purposes this
approximation may suffice.
A comprehensive experiment involving four benthic organisms two species of
deposit-feeding marine polychaetes, Nere is and Nephtys. and two species of
deposit-feeding marine clams, Yolda and Macoma and five sediments has been
-------
Pore Water Normalization
Fluoranthene
0 20 40 60 80
Pore Water Concentration (ug/L)
Organic Carbon Normalization
Fluoranthene
0 2OOO 4OOO 6000 800O
Organic Carfaon Normalized (ug/g oc)
Figure 23. Comparison of percent survival of R. abronius to fluoranthene concentration in pore water (top)
and bulk sediment, using organic carbon normalization (bottom) for sediments with varying organic carbon
concentrations [19].
-------
Pore Water Normalization
DDT
Endrin
,_.
X
J
*•
0
100
80
6O
40
20
0
**»•
a
•
•
•
foe M
• 3.0 -
f •" * * 7 2 .
• 10.5
1OO
X 80
£ 60
1 40
I 20
O
foe (y.)
• 3.0
• 11.2
0.01 O.1O 1.OO 1O.OO 100.OO
Por« Water Concentration (ug/L)
O.1 1.0 1O.O 10O.O
Pore Water Concentration (ug/L)
Organic Carbon Normalization
DDT
Endrin
1OO
8O
60
40
20
0
• • • • • •• • w * • * ' *
• .-«*.«
I*
• *
.*••%'*
1OO
7 8O
^ 6O
1 40
| 20
0
rt «•»—
•
M
mf
. " |ef* •*•
. . i i i
•
-
-
-
-
1 10 10O 100O 1000O
Organic Carbon Normalized (ug/g oc)
1 1O 1OO 1OOO 10OOO
Organic Carbon Normalized (ug/g oc)
F'i^un 17. Comparison of percent survival of H. azteca to DDT (left) and endrin (right) concentration in pore
w.itri (top) and in bulk sediment using organic carbon normalization (bottom) for three sediments with varying
organic carbon concent; rat ions [ 21,22).
-------
Dry Weight
Yo/d/a
Organic Carbon
Organic Carbon, Llpid
a
O
3.0
2.0
1.0
0.0
-1.0
foe (X)
* 8.2
• 3.0
• 1.0
o
2-
S
1.0
0.0
-1.0
-2.0
*JS
0.5 7.5
LoglO KOW
M
-3J
tt
3.0
2.0
1.0
0.0
0.5 7.5
LoglO Kow
8.5
-1.0
5.5
0.5 7.5
Log10 Kow
8.5
2.O
0.0
S -,
Dry Weight
Macoma
Organic Carbon
Organic Carbon, Upld
1.0
0.0
-1.0
-2.0
U^
S^u
1—*
8.6 7.5 8.6
LoglO KOW
2.0
s
8.6 7.5 8.6
LoglO Kow
-2.0.
T^^V*
8^ 7^ 8.6
LoglO Kow
Figure '5. Plots of the BSF (ratio of organism-to-sediment concentration) for three sediments for a series of
PCB congeners versus the logio Kow f°r that congener. The dry weight normalization for both organism and
sedime u. (left panels); organic carbon normalization for the sediment (middle panels); and organic carbon and
lipid normalization (right panels) as indicated. The organisms are Yoldia (top) and Hacoma (bottom). Data
from IJ8].
-------
Oligochaete - Polychaete BSF
100.0
£ 10.0
CD
o
o
o
o
vt
O
a
o
0.1
• Ollgocha«t«
• Polychaate
3.0 4.0
11 i
5.0 6.0 7.3
Log 10 Kow
8.0 9.0
i/.m'f 26. Plots of the BSF (ratio of organism lipid to sediment organic carbon concentration) for a series
>l l'i H n.uj'.eners and other chemicals versus logjo Kow. Data for oligachaetes [531 a"d polychaetes (58].
-------
Page 70
and a specific organism, for example Nereis and any PCB congener (Fig. 24)
organic carbon normalization reduces the effect of the varyirg sediments. This
demonstrates the utility of orjanic carbon normalization and supports its use
in generating SQC.
A further conclusion can be reached from these results. It has been
pointed out by Bierman [59] that the fact that the lipid- and carbon-normalized
BSF is in the range of 0.1 to 10 (Fig. 24 to 26) supports the contention that
the partition coefficient for sedimetxts is Koc - Kow and that the particle
concentration effect does not: appear to be affecting the free concentration in
sediment pore water. The reason is that the lipid- and carbon-normalized BSF
B
is the ratio of the solubilities of the chemical in lipid and in particle
carbon (Eqn. 29). Because the solubility of nonionic organic chemicals in
various nonpolar solvents is similar [60], it would be expected that the lipid-
organic carbon solubility ratio should be of order one. If this ratio is taken
to be approximately one, then the conclusion from the BSF data is that, indeed,
Koc is approximately equal to Kow for sediments [59].
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
polychaetes, oligochaetes and clams. The data presented in previous sections
are for amphipods and midges. Hence these data provide important additional
support for organic carbon normalization as a determinant of bioavailability
for different classes of organisms.
-------
Page 71
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. [61]). It might be
supposed that the toxicity and bioaccuraulation 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 with the organic carbon normalized sediment concentration (see Fig. 2 and
3). 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 or, as it is sometimes
called, the fugacity [62], of a chemical controls its biological activity. The
chemical potential, /i
-------
Page 72
where CS(OC is che weight fraction of chemical in organic carbon. If the pore
water is in equilibrium with the sediment organic carbon then
"d-"oc- (32)
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.
The data analysis presented above, which normalizes biological response to
either pore water or organic carbon normalized sediment concentration, suggests
that biological effects are proportional to chemical potential or fugacity.
The issue with respect to bioavailability becomes: In which phase is p most
easily and reliably measured? Pore water concentration is one option.
However, it is necessary that the chemical complexed to DOC be a small fraction
of the total measured concentration or that the free concentration be directly
measured, perhaps by the C]_g column technique [46]. 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 that
contains significant amounts of the chemical. This appears to be a reasonable
assumption for most aquatic sediments. Hence, sediment quality criteria are
based on organic carbon normalization because pore water normalization is
complicated by DOC complexing for highly hydrophobic chemicals
-------
Page 73
APPLICABILITY OF WQC AS THE
EFFECTS LEVELS FOR BENTHIC ORGANISMS
The EqP method for deriving SQC utilizes partitioning theory to relate the
sediment concentration to the equivalent free chemical concentration in pore
water and in sediment organic carbon. The pore water concentration for SQC
should be the effects concentration for benthic species. This section examines
the validity of using the EPA WQC concentrations to define the effects
concentration f~r benthic organisms. This use of WQC assumes cha (a) the
sensitivities of benthic species and species tested to derive WQC predominantly
water column species, are similar and (b) the levels of protection afforded by
WQC are appropriate for benthic organisms. This section examines the
assumption of similarity of sensitivity in two ways. First, a comparative
toxicological examination of the acute sensitivities of benthic and water
column species, using data compiled from the published EPA WQC for nonionic
organic chemicals as well as metals and ionic organic chemicals, is presented.
Then a comparison of the FCVs and the chronic sensitivities of benthic
saltwater species in a series of sediment colonization experiments is made.
Method - relative acute sensitivity
The relative acute sensitivities of benthic ana water column species are
examined by using LC50s for freshwater and saltwater species from draft or
published WQC documents that contain minimum data base requirements for
calculation of final acute values (Table 4). These data sets are selected
-------
Table 4. Draft or published (<3C documents and number of infaunal (habitats 1 and 2),
epibenthic (habitats 3 and *). and water column (habitats 5 to 8) species tested acutely for each substance
Number of saltwater species
Chemical
Acenaphthene
Acrolein
Aldrin
Aluminum
Ammonia
Antimony III
Arsenic III
Cadmium
Chlordane
Chloride
Chlorine
Chlorpyrifos
Chromium III
Chromium VI
Copper
Cyanide
DDT
Dieldnn
2. 4-dimethylphenol
Endosulfan
Endrin
Heptachlor
Hexachlorocyclohexane
Lead
Mercury
Nickel
Parathion
Parathion - Methyl
Pentachlorophenol
Phenanthrene
Phenol
Selenium IV
Selenium VI
Silver
Thallium
Toxaphene
"r:butyltin
1.2, 4 -Trichlorobenzene
2 . <• , 5-Trichlorophenol
iir.c
Date of
Publication
9/37b
9/87b
1980
1986
1985; 1989
9/870
1985
1985
1980
1988
1985
1986
1385
1985
1985
1985
1980
1980
6/do~
1980
1980
1980
1980
1985
1985
1966
1986
10/880
1986
9/870
5/88°
1987
1987
9/870
11/88°
1986
9/87
9/88°
9/870
1987
Total'
.
-
16
-
20
11
12
38
8
-
23
15
-
23
25
9
17
21
9
12
21
19
19
13
33
23
-
19
10
-
16
-
21
.
15
19
15
11
33
Infaunal
.
-
0
-
2
3
2
10
1
-
2
2
•
3
6
1
1
1
2
2
1
1
2
2
10
7
-
7
4
-
1
.
1
-
2
1
7
4
10
Epibenthic
.
-
11
-
7
6
3
18
7
-
9
8
-
9
5
4
11
15
2
3
1*
14
14
3
7
10
-
7
6
-
5
-
6
-
9
3
7
5
9
Water
Column
.
-
12
-
16
5
8
18
7
-
15
10
-
9
18
5
12
15
6
a
16
13
12
10
18
9
-
11
4
-
13
-
16 .
-
11
15
4
5
17
Number of freshwater species
Total*
10
12
21
IS
48
9
16
56
1*
IS
33
18
17
33
57
17
42
19
12
10
28
18
22
14
30
21
37
36
9
32
23
12
19
8
37
9
14
10
45
Infaunal
.'
1
2
-
2
1
1
13
1
3
1
2
3
1
8
1
3
1
1
1
3
2
1
-
11
2
7
1
9
2
6
2
1
1
1
5
1
2
1
5
Epibenthic
3
5
10
5
17
2
6
16
4
6
9
8
8
10
15
6
15
9
3
4
12
3
4
4
8
7
14
9
•11
1
9
6
4
9
3
13
1
5
2
12
Hater
Co Luinn
7
7
15
11
33
6
13
31
10
3
26
i *
12
21
36
12
29
12
7
7
17
12
13
i 1
12
13
-^
4
2:
1 3
• -\
13
3
22
5
;
3
23
a The total numbers of tested species may not be the same as the sum of the number of species from each habitat -.ype
because a species may occupy more than one habitat.
- Draft aquatic life criteria document. U.S. Environmental Protection Agency, Office of Water Regulator.! i-.i
Standards. Criteria and Standards Division, Washington, D.C.
-------
Page 75
because exposures were via water, durations were similar, and data and test
conditions have been scrutinized by reviewing the original references. For
each of the 2,887 tests conducted in fresh water, using 208 species with 40
chemicals, and the 1,046 tests conducted in salt water, using 118 species with
30 chemicals, the chemical, species, life stage, salinity, hardness,
temperature, pH, acute value, and test condition (i.e., static, renewal, flow-
through, nominal, or measured) were entered into a data base. If necessary,
original references were consulted to determine the tested life stage and any
other missing information. Each life stage of the tested species was classified
according to habitat (Table 5). Habitats were based on degree of association
with sediment. A life stage that occupied more than one habitat was assigned
to both of the appropriate habitats.
For each chemical, if 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
testing methodology and the most sensitive life stages. First, if a life stage
for a soecies was tested more than once, flow-through tests with measured
concentrations had precedence, and data from other tests were omitted. When
there were no flow-through tests with measured concentrations, all acute values
for that life stage were given equal weight. If the remaining acute values for
that life stage differed by greater :han a factor of four, the higher values
were omitted and the geometric mean of nhe lower acute values was calculated co
derive the acute value for that life s:age. Second, life stages were
classified as either "benthic" (infaunal species [habitats 1 and 2] or infaunal
and epibenthic species [habitats 1. 2 i. and 4j), or "water column" (habitats
-------
TABLE 5. Habitat classification system for life stages of organisms
Habitat
Type Description
1 Life stages that usually live in the sediment and whose food
consists mostly of sediment or organisms living in the sediment:
infaunal nonfilter feeders.
2 Life stages that usually live in the sediment and whose food
consists mostly of plankton and/or suspended organic matter filtered
from the water column: infaunal filter feeders.
2 Li e stages that usually live on the surface of sediment and whose
food consists mostly of organic matter in sediments and/or organisms
living in or on the sediment: epibenthic bottom feeders.
4 Life stages that usually live on the surface of sediment and whose
food is mostly from the water column, including suspended detritus,
plankton, and larger prey: epibenthic water column feeders.
5 Life stages that usually live in the water column and whose food
consists mostly of organisms that live on or in the sediment.
6 Life stages that usually live in. and obtain their food from, the
water column but have slight interaction with sediment because they
occasionally rest or sit on the sediment and/or occasionally
consume organisms that live in or on the sediment.
7 Life stages that live in or on such inorganic substrates as sand,
rock, and gravel, but have negligible contact with sediment
containing organic carbon.
8 Life stages that have negligible interactions with sediment because
they spend essentially all cheir time in the water column and rarely
consume organisms in direct contact with the sediment; that is
fouling organisms on pilings, ships, and so on, and zooplankton,
pelagic fish, and so on.
-------
Page 77
5 to 8). Third, if two or more life stages were classified as either benthic
or water column and their acute values differed by a factor of four, the higher
values were omitted and the geometric mean of the lower acute values was
calculated to derive the acute value for that life stage of the benthic or
water column species. This procedure is similar to that used for WQC [8].
Comparison of the sensitivity of benthic and water column species
Most Sensitive Species. The relative acute sensitivities of the most
sensitive benthi" and water column species were examined by comparing the
lowest acute LC50 concentration for the benthic and water column organisms,
using acute values from the 40 freshwater and the 30 saltwater WQC documents.
When benthic species were defined as only infaunal organisms (habitat types 1
and 2) and water column species were defined as all others (habitat types 3 :o
8), the water column species were typically the most sensitive. The results
are cross-plotted on Figure 27 (left). The line represents perfect agreement.
In most instances where acute values for saltwater benthic and water column
species are identical, it is because penaeid shrimp are most sensitive to
insecticides and are classified as both infaunal (benthic) and epibenthic
(water column).
Unfortunately data on the sensitivities of benthic infaunal species are
limited. Of the 40 chemicals for which WQC for freshwater organisms are
available, two or fewer infaunal species were tested with 28 (70X) of the
chemicals, and five or fewer species were tested with 34 (85X) of the
chemicals. Of the 30 chemicals for which WQC for saltwater organisms are
available, 2 or fewer infaunal species were cesced with 19 (63Z) of the
-------
Comparison of Most Sensitive Species
Infaunal
Infaunal & Eplbenthic
o
m
o
o
-1
-3
Fraahwatar
Saltwater
O>
3
O
IO
o
o
*-
o»
o
-1
-3-1 1 3 5
Log 10 LC50 (ug/L)
-3
Fraahwatar
Saltwatar
-3-11 3
Log 10 LC50 (ug/L)
Figure- 27. Comparison of I.CM) or EC'iO acute values tor the most sensitive benthic (abscissa) and water column
(ordi naie) species for i-lu-inir.i Is listed in Table '» . Hentbic species are defined as infaunal species (habitat
^•^•s I and 2, li-ft p.nu-l) or iiit.iunal and i-p ibeni Iijg^tpec ies (habitat types 1 - lt) , see Table 6.
-------
Page 79
chemicals, and 5 or fewer species were nested with 23 (77X) of the chemicals.
Of these chemicals only zinc in salt water has been tested using infaunal
species from, three or more phyla and eight or more families, the minimum acute
toxicity data base required for criteria derivation. Therefore, it is probably
premature to conclude from the existing data that infaunal species are more
tolerant than water column species.
A similar examination of the most sensitive benthic and water column
species, where the definition of benthic includes both infaunal and epibenthic
species (ha': icat types: 1 to 4) , is based on more data and suggests a
similarity in sensitivity (Fig. 27, right). In this comparison, the number of
acute values for freshwater benthic species for each chemical averaged nine,
with a range of 2 to 27; the number of acute values for saltwater benthic
species for each chemical substance averaged 11, with a range of 4 to 26. The
variability of these data is high, suggesting that for some chemicals, 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 in which 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, may reflect an absence of appropriate data. Data that
are available suggest that, on the average, benthic and water column species
are similarly sensitive and support che use of WQC to derive SQC for the
protection of infaunal and epibenthic species.
-------
Page 80
All species. A more general comparison of the species sensitivities can be
made if all the LC50 data are used. One approach examines the relative
location of benthic species in the overall species sensitivity distribution.
For each chemical in either fresh or salt water, one can examine the
distribution of benthic species in a rank-ordering of all the species' LC50s.
If benthic species were relatively insensitive, then they would predominate in
ranking among the larger LC50 concentrations. Equal sensitivity would be
indicated by a uniform distribution of species within the overall ranking.
Figure 28 presen' -.he results for te.'ts of nickel in salt water. .; LCSOs
are plotted in rank order, and the benthic species are indicated. Infaunal
species are among the most tolerant (left panel), whereas infaunal and
epibenthic species are uniformly distributed among the species (right panel).
This comparison can be done chemical by chemical. However, in order to
make the analysis more robust, the LC50 data for each chemical - water type can
be normalized to zero log mean and unit log variance as follows:
log(LC50 ) u.
LC50 .. - U-: k (33)
n.ij OL
where i indexes the chemical - wacer type, m is the log mean and a^ is che log
standard deviation, j indexes the LC50s within the ic^ class, and LC50n jj is
the normalized LC50. This places all rhe LC50s from each set of chemical -
water type on the same footing. Thus rhe data can now be combined and che
uniformity of representation of ben:hic species can be examined in th~ combined
data set.
-------
Species Sensitivity for Ni in Seawater
Infaunal
Infaunal & Epibenthic
^ 10000O
o
IO
1OOOO
1000
100
Water Column
Banthlc
•e
10OOOOO
1OOOOO
1000O
100O
O.O 0.2
O.4 O.6
Rank
0.8
i.o
10O
O Wafer Column
• Banthic
•©
o.o 0.2
0.4 0.6
Rank
0.8
1.0
Kip.mr ?H l.i:')l)s vt-rsus I'.ink for nickt-l in soawater. Infaunal organisms (left) and infaunal and epibenthic
(it(,',lii) .u«- ulriii i t i oil by i Ju- solid symbols Tin- plot illustrates the distribution of benthic organisms in
ibt- twt-i.ill ;,()»-ri«-s sriib > i i v i i y d;i b t r i but i on
-------
Page 82
The comparison is made in Figure 29. If che sensitivity of benthic species
is noc unique, then a constant percentage of benthic species normalized LC50s,
indicated by the dashed line, should be represented in each 10-percentile
(decile) interval of data for all species. That is, the 10 rectangles in each
histogram should be identical in height. The infaunal species (top panel)
display a tendency to be underrepresented in the lowest deciles. However, the
infaunal and epibenthic species (bottom panels) more closely follow this
idealized distribution. Infaunal and epibenthic freshwater species are nearly
uniformly distr'v -.ed, whereas che saltwater benthic species are s"?.ewhat
underrepresented in the lowest ranks.
Given the limitations of these data, they appear to indicate that, except
for possibly freshwater infaunal.species, benthic species are not uniquely
sensitive or insensitive and that SQC derived by using the FCV should protect
benthic species.
Benthic community colonization experiments
Toxicity tests that determine the effects of chemicals on the colonization
of communities of benthic saltwater species [64-70] appear to be particularly
sensitive at measuring the impaccs of chemicals on benthic organisms. This is
probably because the experiment exposes the most sensitive life stages of a
wide variety of benthic saltwater species, and they are exposed for a
sufficient duration to maximize response. The test typically includes three
concentrations of a chemical and a control, each with 6 to 10 replicates. The
test chemical is added to inflowing ambient seawater containing planktonic
-------
Infaunal
Saltwater
0 16 26 *6 46 66 66 76 86 98
Itom of Parcanttta Rang*
50
S 40
•5 3O
a
o 2O
5
• 1O
o
Freshwater
m
n
6 15 26 35 45 55 65 75 85 95
Maan of Parcantlla Range (%)
Infaunal & Epibenthic
too
Saltwater
8 16 26 86 46 68 66 76 66 98
Maan of Parcantlto Rang* (%)
1OO
80
80
o -«°
S 2O
Freshwater
6 1.. 26 36 46 68 65 75 65 95
Maan of Parcantlla Rang* (%)
Figure ^9. Histograms of the proportion of saltwater and freshwater benthic organisms in 10 percentile groups
oi all normalized LC50s. If benthic organisms were as equally sensitive as water column organisms, the
histogiams should be of uniform height as indicated by the dashed line, the overall percentage of benthic
specie: in the data set. Top panels include only infaunal organisms as benthic. The bottom panel includes
intaunal arid epibenthic as benthic organisms.
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Page 84
larvae and other life stages of marine organisms chat can settle onto clean
sand in each replicate aquarium. The test typically lasts from two to four
months, and the number of species and individuals in aquaria receiving the
chemical are enumerated and compared to controls.
If this test is extremely sensitive and if concentrations in interstitial
water, overlying water, and the sediment particles reach equilibrium, then the
effect and no-effect concentrations from this test can be compared with the FCV
from the saltwater WQC documents to examine the applicability of WQC to protect
benthic organisms. *.n FCV is the concentration, derived from acute and chronic
toxicity data, that is predicted to protect organisms from chronic effects of a
chemical [8]. In addition, similarities in sensitivities of taxa tested as
individual species and in the colonization experiment can indicate whether the
conclusion of similarity of sensitivities of benthic and water column species
is reasonable.
The benthic colonization experiment is consistent with the assumptions used
to derive SQC. The initially clean sandy sediment will rapidly equilibrate
with the inflowing overlying water chemical concentration as ~he pore water
concentrations reach the overlying water concentration. The production of
sedimentary organic matter should be slow enough to permit its equilibration as
well. As a consequence the organisms will be exposed to an equilibrium system
with a unique chemical potential. Thus the assumption of the EqP is met by
this design. In addition, the experimental design guarantees that the
interstitial water - sediment - overlying water is at the chemical potential of
the overlying water. Hence there is a direct correspondence between the
exposure in the colonization experiment and the water-only exposures from which
WQC are derived, namely the overlying water chemical concentration. This
allows a direct comparison.
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Page 85
Water quality criteria (WQC) concentrations versus colonization experiments
Comparison of the concentrations of six chemicals that had the lowest-
observable-effect concentration (LOEC) and the no-observable-effect
concentration (NOEC) on benthic colonization with the FCVs either published in
saltwater portions of WQC documents or estimated from available toxicity data
(Table 6) suggests that the level of protection afforded by WQC to benthic
organisms is appropriate. The FCV should be lower than the LOEC and larger
than the NOEC.
The FCV from the WQC document for pentachlorophenol of 7.9 ng/L is less
than the LOEC for colonization of 16.0 ng/L. The NOEC of 7.0 Mg/L is less than
the FCV. Although no FCV is available for Aroclor 1254, the lowest
concentration causing no effects on the sheepshead minnow (Cvorinodon
variegatus) and pink shrimp (Penaeus duorarum) as cited in the WQC document is
about 0.1 A»g/L- This concentration is less than the LOEC of 0.6 Mg/L and is
similar to the NOEC of 0.1 Mg/L based on a nominal concentration in a
colonization experiment. The lowest concentration tested with chlorpyrifos
(0.1 Mg/L) and fenvalerate (0.01 Mg/L) affected colonization of benthic
species. Both values are greater chan either the FCV estimated for
chlorpyrifos (0.005 ^g/L) or the FCV estimated from acute and chronic effects
data for fenvalerate (0.002 jig/L) . The draft water quality criteria document
for 1,2,4-trichlorobenzene suggests that the FCV should be 50.0 A»g/L. This
v?.lue is slightly above the LOEC from j colonization experiment (40.0 Mg/L)
suggesting that the criterion might be somewhat underprotective for benthic
species. Finally, a colonization sxperiment with toxaphene provides the only
evidence from these tests that the FCV iu§ht be overprotective for benthic
species; the FCV is 0.2 A*g/L versus :.-.e NCEC for colonization of 0.8
-------
TABLE 6. ANALYSIS OF VARIANCE FOR DERIVATION OF
SEDIMENT QUALITY CRITERIA CONFIDENCE LIMITS
Source of Uncertainty Parameter Value
Exposure media aa 0.39
Replication a£ 0.21
Sediment Quality Criteria <7SQCa 0.39
- on
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Page 87
The taxa most sensitive to chemicals, as indicated by their LCSOs and the
results of colonization experiments, are generally similar, although, as might
be expected, differences occur. Both the WQC documents and the colonization
experiments suggest that Crustacea are most sensitive to Aroclor 1254,
chlorpyrifos, fenvalerate, and toxaphene. Colonization experiments indicated
that molluscs are particularly sensitive to three chemicals, an observation
noted only for pentachlorophenol in WQC documents. Fish, which are not tested
in colonization experiments, are particularly sensitive to four of the six
chemicals.
Conclusions
Comparative toxicological data on the acute and chronic sensitivities of
freshwater and saltwater benthic species in the ambient WQC documents are
limited. Acute values are available for only 34 freshwater infaunal species
from four phyla and only 28 saltwater infaunal species from five phyla. Only
seven freshwater infaunal species and 24 freshwater epibenruic species have
been tested with five or more of the 40 WQC chemicals. Similarly, nine
saltwater infaunal species and 20 epibenthic species have been tested with five
or more of the 30 substances for which saltwater criteria are available.
In spite of the paucity of acute toxicity data on benthic species,
available data suggest that: benthic species are not uniquely sensitive and chat
SQC can be derived from WQC. The data suggest that the most sensitive infaur.jL
species are typically less sensitive than the most sensitive water column
(epibenthio and water column) species. When both infaunal and epibenthic
species are classed as benthic, the sensitivities of benthic and water col-.m
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Page 88
species are similar, on average. Frequency distributions of the sensitivities
of all species to all chemicals indicate that infaunal species may be
relatively insensitive but that infaunal and epibenthic species appear almost
evenly distributed among both sensitive and insensitive species overall.
Finally, in experiments to determine the effects of chemicals on
colonization of benthic saltwater organisms, concentrations affecting
colonization were generally greater, and concentrations not affecting
colonization were generally lower, than estimated or actual saltwater WQC FCVs.
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Page 89
GENERATION OF SQC
Parameter Values
The equation from which SQC are calculated is
SQCOC - Koc FCV (34)
(see Eqns. 2 ~" and associated text). Hence, the SQC concsntr ion depends
only on these two parameters. The Koc of the chemical is calculated from
the Kow of the chemical via the regression Equation 11. The reliability
of SQCOC depends directly on the reliability of Kow. For most chemicals of
interest, the available Kows (e.g. [71]) are highly variable - a range of two
orders of magnitude is not unusual. Therefore the measurement methods and/or
estimation methodologies used to obtain each estimate must be critically
evaluated to ensure their validity. The technology for measuring Kow has
improved in recent years. For example, the generator column [72] and the slow
stirring [73] method appear to give comparable results, whereas earlier methods
produced more variable results. Hence, it is recommended that literature
values for KOWS not be used unless they have been measured using these newer
techniques.
The FCV is used as the appropriate end point for the protection of benthic
organisms. Similarly, its applicability to benthic species for each chemical
should be verified. The analysis presented in the previous section indicated
that this is not an unreasonable assumption across all the criteria chemicals.
To test this assumption for a particular chemical, the Kolmogorov - Smirnov
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Page 90
test [74], which tests whether two samples came from the same population, can
be applied to the distribution of LCSCs for the water column and benthic
species.
The Kolmogorov-Smirnov test is based on the maximum difference between the
two empirical cumulative probability distributions. The test will reject the
hypothesis that the samples come from the same probability distribution if the
difference is so large, given the number of samples in each of the two
distributions, that chance alone cannot account for the difference. An example
for dieldrin is shown in Figure 30, which presents the probability
distributions oT :he freshwater species' LC50s for the water, column and benthic
species. The left panel is a log probability plot of the two distributions.
It presents the LC50s on a log scale versus the rank order on a normal
probability scale. The natural way to judge the equality of these
distributions is to compare the LC50s at a particular probability, for example
at SOX probability, which is a comparison of the medians.
The Kolmogorov-Smirnov test compares another difference. This is
illustrated in the right panel, which presents the same data but in a slightly
different way. The rank order, as a percentage, is plotted versus the LC50s
The points are connected with straight lines to form the empirical cumulative
distribution functions for the two daca sets. The Kolmogorov-Smirnov test is
based on the maximum difference in probability between these two distributions.
as indicated in the figure. Note that this difference is the horizontal
distance on the log probability ploc in Figure 30 if the probability scale were
linear. The test depends on the number of LC50s in each distribution (12. 9)
and the maximum difference in probability (0.333). The probability that a
value of this magnitude or less can occur, given that these two samples came
from the same distribution (0.573), can be calculated [74]. Because this
-------
Test of Equality of Specie t Sensitivity
Lognormal Probability Plot
Cumulative Distribution
10000
1000
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mmm
\
0» 100
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0.11 10 50 90 99 99 . 9 0.1 1
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LC50 (ug/L)
30 Comparison ol tin- dit-ldriii l.CbO probability distributions for water column and benthic freshwater
;>pt.-i irs l.of,norinal probability plot ( 11-1 t paiu 1 ) and t be empirical cumulative distribution functions (rigbt
p.nn-1) with tin- maximum il i t 11-1 i-nri- ust:d in tin- Ko 1 uio£urov - Smiriiov test, indicated.
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Page 92
probability is less than 0.95, the hypothesis that the samples came from the
same distribution is accepted at a 95X confidence level. A similar test for
the saltwater species yields a probability of 0.0617 a value that is much less
than 0.95, which would cause the hypothesis of equality to be rejected.
The conclusion from this analysis is that the benthic and water column
species that have been tested with dieldrin come from the same probability
distribution of LCSOs for both freshwater and saltwater organisms. Therefore
they have the same distribution of acute sensitivity. This suggests that the
freshwater ..d saltwater FVCs for dieldrin are appropriate effects
concentrations for benthic species and should provide a similar level of
protection for benthic organisms and water column organisms. This analysis
should be performed for any chemical for which SQC are developed.
Example Calculations
Equation 34 can be used to compute SQCOC for a range of Kows and FCVs. The
results for several chemicals are shown in Figure 31 in the form of a
nomograph. The diagonal lines are for constant FCVs as indicated. The
abscissa is logio Kow. For example, if a chemical has an FCV of 1.0 Mg/L and a
loS10 Kow of 4, so that KQW - 104, the logic SQCOC is approximately 1 and the
SQC - IQl - 10.0 Mg chemical/g organic carbon.
As can b« seen, the relationships between SQCOC and the parameters chat
determine its magnitude, KQW and FCV, are essentially linear on a log-log
basis. For a constant FCV, a 10-fold increase in KQW (one log unit) increases
the SQCOC by approximately 10-fold (one Log unit) because Koc also increases
approximately 10-fold. Thus, chemicals with similar FCVs will have
larger SQCocs if their Kows are larger
-------
Sediment Quality Criteria
o
o
0)
o
x
4*
"5
O
4-*
C
0
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TI
CO
o
6
5 -
4 -
3
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FCV (ug/L)
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- 1
- 0.1
0.01
O.O01
• M«thyl Parathlon
A Toxaph«n«
T Chtordan*
<4 Parathlon
^ EndoMiltan
* Endrln
• Ph«iMnthr«n«
* Chlorpyrifos
• DUMrin
Figure 31. Log10 SQC versus loglo Kow. The diagonal lines indicate the FCV values. The criteria are computed
from Equation 3A. Koc is obtained from Kow with Equation 11. The symbols indicate SQCOC for the freshwater
(filled) and saltwater (hatched) criteria for the listed chemicals. The vertical line connects symbols for
the same chemical. The FCVs are from the WQC or draft criteria documents (Table A) The octanol/water
partition coefficients are the log mean of the values reported in the Log P data base [71|.
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Page 94
The chemicals listed in Figure 31 have been chosen to illustrate the SQCOC
concentrations that result from applying the EqP method. The water quality
concentrations used are the FCVs (not the final residue values).from draft or
published EPA WQC documents (see Table 4). The Kows are the log averages of
the values reported in the Log P data base [71]. These values are used for
illustrative purposes only because final SQC when published, should reflect the
best current information for both FCV and Kow, as discussed above.
The FCVs that are available for nonionic organic insecticides range from
approximately 0.01 ^g/L to 0.3 Mg/L, a factor of 30. The SQCocs range from
approximately 0.01 Mg/g organic carbon to in excess of 10 Mg/g organic carbon,
a factor of over 1,000. This increased range in values occurs because the K<,ws
of these chemicals span over two orders of magnitude. Hence the most stringent
SQCOC in this example is for chlordane, a chemical with the lowest Kow among
the chemicals with an FCV of approximately 0.01 Mg/L.
By contrast, the PAHs included in this example have a range of FCVs and
Kows of approximately one-half order of magnitude. But these values vary
inversely: The chemical with the larger FCV has a smaller Kow. The result is
chat the SQCocs are approximately the same, 200 Mg/g organic carbon. Classes
of chemicals for which the effects concentrations decrease logarithmically with
increasing KOW*> for example, chemicals that are narcotics [75], will have SQC
that are mor* nearly constant.
Sediment quality criteria (SQC) uncertainty
The SQC methodology relies on an empirical partitioning model lo relate the
pore water exposure concentration (actually the chemical potential) to the
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Page 95
equivalent sediment organic carbon exposure concentration. As a consequence
there is an uncertainty associated with the use of the model.
Some of the uncertainty in the calculation of the sediment quality criteria
can be estimated from the degree to which the equilibrium partitioning model,
which is the basis for the criteria, can rationalize the available sediment
toxicity data. The EqP model asserts that (1) the bioavailability of non-ionic
organic chemicals from sediments is equal on an organic carbon basis, and (2)
that the effects concentration (Mg/goc) can be estimated from the product of
the effects .oncentration from water only exposures (Mg/L) and the partition
coefficient Koc (L/kg). The uncertainty associated with the sediment quality
criteria can be obtained from a quantitative estimate of the degree to which
the available data support these assertions.
The data used in the uncertainty analysis are the water-only and sediment
toxicity tests that have been conducted in support of the sediment criteria
development effort. These freshwater and saltwater tests span a range of
chemicals and organisms; they include both water-only and sediment exposures
and they are replicated within each chemical-organisms-exposure media
treatment. These data are analyzed using an analysis of variance (ANOVA) to
estimate the uncertainty (i.e. the variance) associated with varying the
exposure media and that associated with experimental error. If the EqP model
were perfect, then there would be only experimental error. Therefore, the
uncertainty associated with the use of EqP is the variance associated with
varying exposure media.
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Page 96
The data used in the uncertainty analysis are the LCSOs from the water-only
and sediment exposures for endrin [21,22], dieldrin [76], acenaphthene [77],
phenanchrene [7/j and fluoranthene [19] as used in the individual chemical
sediment quality criteria documents in preparation for these chemicals
[78,79,80,81,82]. The EqP model can be used to normalize the data in order to
put it on a common basis. The LC50 for sediment on an organic carbon basis,
LC50S|OC, is related to the LC50 obtained from a water only exposure, LC50V via
the partitioning equation:
LC50S>OC - KOCLC50W . (35)
Therefore, Koc can be used to define the equivalent sediment toxicity based on
free concentration in pore water:
LC50
LC50 - „ S|0
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Page 97
In order Co perform an analysis of variance, a model of the random
variations is required. As discussed above, experiments that seek to validate
equation 37 are subject to various sources of random variations. A number of
chemicals and organisms have been tested. Each chemical • organism pair was
tested in water-only exposures and in different sediments. Let a represent the
random variation due to this source. Also, each experiment is replicated. Let
f represent the random variation due to this source. If the model were
perfect, there would be no random variations other than that due to
experimental er'-or which is reflected in the replications. Hence a represents
the uncertainty due to the approximations inherent in the model and «
represents the experimental error. Let (<7Q)2 and (a€)2 be the variances of
these random variables. Let i index a specific chemical - organism pair. Let
j index the exposure media: water-only, or the individual sediments. Let k
index the replication of the experiment. Then the equation that describes this
relationship is:
ln(LC50i_j )jc) - Mi + <*j + «j,k (38>
where ln(LC50j_tjtk) are either ln(LC50w) or ln(LC50SiOC) corresponding to a
water-only or sediment exposure; Mi are the population ln(LC50) for chemical -
organism pair i. The error structure is assumed to be lognormal which
corresponds to assuming that the errors are proportional to the means, eg a
20X error, rather than absolute quantities, e.g. 1 mg/L. The statistical
problem is: estimate ^i and the variances of the model error, (aQ)' and :he
measurement error, (c/£)2. The maximum likelihood method is used to make :he-,f
estimates [83]. The results are shown Ln Table 6.
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Page 98
The last line of the Table 6 Ls Che uncertainty associated with the
sediment quality criteria: i.e. the variance associated with the exposure media
variability.
The confidence limits for the sediment quality criteria are computed using
this estimate of uncertainty for sediment quality criteria. For the 95X
confidence interval limits, the significance level is 1.96 for normally
distributed errors.
ln(SQCoc)UPPER - i.n(SQCoc) + 1.96aSQC (39)
In(SQCOC)LOWER - ln(SQCoc) + 1.96aSQC (40)
These are the 95% confidence limits for the sediment quality criteria.
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Page 99
SITE SPECIFIC SEDIMENT CRITERIA MODIFICATIONS
Modifications of SQC to improve their applicability to specific sites may
be desirable. As with site-specific modification of WQC, the SQC modification
procedure should consider site-specific differences in species sensitivity and
the biological availability of specific nonionic chemicals. The following
discussion serves only to highlight issues associated with derivation of site-
specific SQC. It must not be used as guidance to modify national SQC at this
time. EPA intends to provide specific guidance, including testing -°thods for
public comment following a workshop on this topic.
Site-specific criteria modification based on species sensitivity.
Site-specific modification of SQC for nonionic organic chemicals may be
possible by a modification of the WQC using the "Recalculation Option" (U.S.
EPA, 1982). In the derivation of water quality criteria, minimum database
requirements ensure that species from several families and a variety of phyla
are tested. Therefore, the range of acute sensitivities to a chemical of a
diverse group of organisms is determined. For some chemicals for which WQC are
are developed, one of the species tested nay be significantly more sensitive
than other tested species. The most sensitive species may not be benthic or
the sensitive species may not be resident at a specific site of concern. Any
or all of these three factors may result in WQC that may not be applicable to
benthic species or to the site of concern. In these situations, the site-
specific guidelines (U.S. EPA, 1982) may be appropriate for recalculation o:
the WQC following deletion of inappropriate data.
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Page 100
Ic can be argued chac this approach has merit because daca on non-resident
or non-benthic species are not relevant to derivation of SQC for sp~~ific
sites. Alternatively, it can be argued that acute toxicity data on tested
species are surrogates for the acute sensitivities of untested, but equally
sensitive, phylogenetically related species. If true, deletion of data will
result in underprotective site-specific SQC. One compromise position might be
to require tests of resident benthic species phylogenetically related to, and
the same life-stage as, the species deleted from the database.
Given these arguments, the technical defensibility and"specific guidance
for site-specific SQC modifications need to be developed. Three ----.lens which
should be considered are:
Option I:
Allow data from non-benthic and non-resident species to be deleted.
Require no additional testing as long as the minimum data requirements are .T.e:
If minimum data requirements are not met following deletion, resting should be
required to meet data needs.
Option II:
Allow no modification because deletion excludes data from species which T.J .
be surrogates for similarly sensitive un:ested species. The national SQC does
not change and, from a species sensi: :VL:Y standpoint, it is appropriate to t'.'.
sites.
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Page'101
Option III:
Allow data from non-resident and non-benthic species Co be deleted from the
criteria database, but require replacement with other acute toxicity data. The
required acute toxicity tests should emphasize selection of species or life-
stage most likely to be sensitive to that specific chemical and local benthic
taxa not represented in the database. EPA guidance needs to be developed on
the quality and quantity of additional data required for this approach.
Site-specific criteria modification based on bioavailability.
Modification of SQC because of site-specific differences in bioavailability
are probably not necessary for sediments having > 0.2X organic carbon. This is
because almost all toxicological and bioaccumulation data demonstrate that
organic carbon is the dominant sediment phase controlling bioavailability of
non-ionic organic chemicals. However, site-specific modification will be
considered on a case-by-case basis if data demonstrate thrt biological
availability of a specific nonionic organic chemical in sediments from the
specific site differs from the EqP prediction. Experiments should be designed
Co test the precision and accuracy of the EqP prediction. The acceptabilicy of
various differences in biological availability need to be formulated. It is
anticipated that designs similar to, or as robust as, those described in SQC
document Section 4.3 will be appropriate for providing positive control
sediments, appropriate test species, statistical analyses, and other factors co
be included in the experiment design.
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Page L02
CONCLUSIONS
The cechnical basis and data chat support the use of the EqP method to
generate SQC have been presented for nonionic organic chemicals. The use of
organic carbon normalization is equivalent to using pore water normalization as
a means of accounting for varying bioavailability (Figs. 2, 3, 5 - 9, 21 - 23).
The variation in organism body burden across sediments can also be
significantly reduced if organic carbon and lipid normalization are used (Figs.
24 - 26). For naturally contaminated sediments, particle si c' effects are
removed if organic carbon normalized concentrations are compared (Fig. 18).
The reason is that organic carbon is the proper normalization for partitioning
between free dissolved chemical and sediment bound chemical (Fig. 12).
Using pore water normalization for highly hydrophobic chemicals is
complicated by chemical complexing to DOC (Fig. 14). Partitioning between pore
water and sediment organic carbon from field-collected sediments can be
rationalized if DOC complexing is taken into account (Figs. 19 and 20).
However, the complexed chemical appears not to be bioavailable (Fig. 16).
These observations are consistent with the EqP model, which assumes the
equivalence of water-only exposure and the exposure from pore water and/or
sediment organic carbon. Sediment quality criteria are based on organic carbon
normalization because pore water normalization is complicated by DOC complexing
for highly hydrophobic chemicals.
The justification for using the FCV from the WQC to define the effects
level for benthic organisms has also been discussed. Water column and benthic
organisms appear to have similar sensicivicies for both the most sensitive
species tested (Fig. 27) and all tested species (Fig. 29). Benthic
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Page 103
colonization experiments also demonstrate that WQC can be used to predict
effects concentrations for benthic organisms. A direct statistic^ test of the
equality of the distributions can be used to confirm or refute this assumption
for individual chemicals (Fig. 30).
Equilibrium partitioning cannot remove all of the observed variation from
sediment to sediment. It does reduce the much larger sediment-to-sediment
variation that exists if no corrections for bioavailability are made (Figs. 5 -
9). A variation factor of approximately a factor of two to three remains
(Figs. 2 and 3) which includes measurement and other sources of variability.
This is not unexpected as EqP is an idealization of the actual aquation.
Other factors that are not considered in the model play roles in determining
biological effects. Hence, it is recognized that a quantification of the
uncertainty will accompany the SQC that reflect these additional sources of
variation.
Research needs
The final validation of SQC will come from field studies that are designed
to evaluate the extent to which biological effects can be predicted from SQC.
The colonization experiments (Table 7) are a laboratory simulation of a field
validation. Sediment quality criteria can possibility be validated more easily
than WQC because determining the organism exposure is more straightforward.
The benthic population exposure is quantified by the organic carbon normalized
sediment 'concentration.
-------
Table 7. Comparison of WQC FCVs
and concentrations affecting (LOEC) and not affecting (NOEC) benthic colonization
Substance
Pentachlorophenol
Aroclor 1254
Chlorpyrifos
h • nv.i Lerate
1,2,4-
Trichlorobenzene
Toxaphene
Colonization Cone.
versus FCVa ng/L
Colonization LOEC 16.0
FCV 7.9
Colonization NOEC 7.0
Colonization LOEC 0.6
Estimated FCV -0.1
Colonization NOEC <0.1
Colonization LOEC 0.1
FCV 0.005
Colonization NOEC
Colonization LOEC 0.01
Estimated FCV -0.002
Colonization NOEC
Estimated FCV 50.
Colonization LOEC 40.
Colonization NOEC
Colonization LOEC 11.0
Colonization NOEC 0.8
FCV 0.2
Sensitive taxa
Molluscs, Abundance
Molluscs, Crustacea, Fish
Crustacea
Crustacea, Fish
Crustacea, Molluscs,
Species Richness
Crustaceans
Crustacea, Chordates
Crustacfa
Crustacea, Fish
Molluscs, Abundance
Crustacea, Species Richness
Crustacea, Fish
Reference
165.66]
[67]
[64]
[68]
[69]
70[
:64[
aSix day exposure to established benthic community
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Ic has been suggested chat the kinetics of PAH desorption from sediments
control the chemical body burden of a benthic amphipod [84], The extent to
which kinetics can be important in field situations is unknown at present, and
field studies would be an important component in examining this question. In
addition, more laboratory sediment toxicity tests, particularly chronic tests
involving multiple sediments, would also be helpful. In a typical practical
application of SQC mixtures of chemicals are involved. The extension of EqP
methodology to mixtures would be of great practical value. Initial experiments
indicate that it should be possible [85].
The EqP method is presently restricted to computing effects-based criteria
for the protection of benthic organisms. The direct extension of this
methodology for computing sediment criteria that are protective of human
health, wildlife, and marketability of fish and shellfish requires that the
equilibrium assumption be extended to the water column and to water column
organisms. This is, in general, an untenable assumption. Water column
concentrations can be much lower than pore water concentrations if sufficient
dilution flow is present. Conversely, upper-trophic-level organisms are at
concentrations well above equilibrium values [86]. Hence, the application of
the final residue values from the WQC for the computation of SQC, as was done
for certain interim criteria [87], is not technically justifiable. At present,
organism lipid-to-sediment: organic carbon ratios, that is BSFs (Eqn. 29), might
be useful in estimating the concentration of contaminants in benthic species,
for which the assumption of equilibrium is reasonable. However, a site-
specific investigation (e.g. [88]) appears to be the only available method for
performing an evaluation of the effect of contaminated sediments on the body
burdens of upper-trophic-level organisms
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Page 106.
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