&EPA
United States
Environmental Protection
Agency
Office Of Water (WH-553)
Washington, IDC 20460
EPA-822-R-93-011
September 1993
Technical Basis for Deriving
Sediment Quality Criteria for
Nonionic Organic Contaminants
for the Protection of Benthic
Organisms by Using Equilibrium
Partitioning
Printed on Recycled Paper
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Sediment Quality Criteria Using Equilibrium Partitioning
This document presents the technical basis EPA has used in establishing the proposed method-
ology for deriving sediment quality criteria for the protection of benthic organisms from non-
ionic organic chemicals. It was issued in support of EPA regulations and policy initiatives
involving the application of biological and chemical assessment techniques to control toxic pol-
lution to surface waters and sediments. This document does not establish or affect legal rights
or obligations. It does not establish a binding norm and is not finally determinative of the issues
addressed. Agency decisions in any particular case will be made applying the law and regula-
tions on the basis of specific facts when permits are issued or regulations promulgated. This
document is expected to be revised periodically to reflect advances in this rapidly evolving area.
This report has been reviewed by the Health and Ecological Criteria Division, Office of
Science and Technology, U.S. Environmental Protection Agency, as well as other pertinent and
interested offices in the Agency, and approved for publication. Mention of trade names or com-
mercial products does not constitute endorsement or recommendations for use.
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Sediment JQuality Criteria Using Equilibrium Partitioning
CONTENTS
ABSTRACT
OVERVIEW ........... ...... • . . ..... 3
Toxidty and Bioavailabfility of Chemicals in Sediments ............. 3
Partitioning of Nonionic Organic Chemicals . . ....... . ..... . . 5
Effects Concentration ....................... .... 7
BACKGROUND ................... .... 7
Rationale for Selecting the EqP Method ............ ....... 8
Relationship to WQC Methodology ...... ........... ..... 8
Applications of SQC ..................... ....... 8
TOXICITY AND BlOAVAILABILITY OF CHEMICALS IN SEDIMENTS ....... 9
Toxicity Experiments ... ..... .......... ....... ... 9
Bioaccumulation ..... ' ........................ 12
Conclusion ...... . ........................ 12
SORPTION OF NONIONIC ORGANIC CHEMICALS ... ..... ..... 14
Partitioning in Particle Suspensions .................... 14
Particle concentration effect ............... ; ...... 14
Organic carbon fraction ........................ 16
Dissolved Organic Carbon (DOC) Complexing ............... 17
Phase Distribution in Sediments ....... ............... 17
Unavailability of DOC Complexed Chemicals ............... 18
Field Observations of Partitioning in Sediments ............... 19
Organic carbon normalization ..................... 19
Sediment /pore water partitioning ................... 23
Laboratory toxicity tests ............... . ........ 24
Organic Carbon Normalization of Biological Responses ........... 26
Toxicity and bioaccumulation experiments . ........... . ....... 26
Bioaccumulation and organic carbon normalization ..... ....... 28
Determination of the Route of Exposure ..... . ............ 31
APPLICABILITY OF WQC AS THE EFFECTS LEVELS FOR BENTHIC ORGANISMS ... 31
Method-Relative Acute Sensitivity .................... . 32
Comparison of the Sensitivity of Benthic and Water Column Species ..... 32
Most sensitive species ...................... ... 32
All species ..... ...... . ............... ... 32
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Sediment Quality Criteria Using Equilibrium P
BentWc Community Colonization Experiments 36
Water Quality Criteria (WQC) Concentration Versus
Colonization Experiments ..... t 36
Conclusions. . . . , 37
GENERATION OF SQC . 37
Parameter Values '. 37
Measurement of JCow 38
Literature Kow 40
Estimated K0w 40
KOW selection 42
Koc determination 42
Species Sensitivity 43
Quantification of Uncertainty Associated with SQC 44
Minimum Requirements to Compute SQC 46
Laboratory octanol-water partition coefficient 46
Final chronic value 47
Sediment toxicity test 47
Analytical procedures 48
Conclusion 48
Example Calculations 48
Field Data 49
STORE! data 50
National Status and Trends Program data 50
Corps of Engineers data 51
CONCLUSIONS 55
Research Needs 57
REFERENCES
57
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Sediment Quality Criteria Using Equilibrium Partitioning
Abstract
The purpose of this report is to present the technical basis for es-
tablishing sediment quality criteria for nonionic organic chemicals, us-
ing equilibrium partitioning (EqP). Equilibrium partitioning is chosen
because it addresses the two principal technical issues that must be re-
solved: the varying bioavailability of chemicals in sediments and the
choice of the appropriate biological effects concentration.
The data that are used to examine the question of varying
bioavailability across sediments are from toxicity and bioaccumulation
experiments using the same chemical and test organism but different
sediments. It has been found that if the different sediments in each ex-
periment are compared, there is essentially no relationship between
sediment chemical concentrations on a dry weight basis and biological
.effects. However, if the chemical concentrations in the pore water of the
sediment are used (for chemicals that are not highly hydrophobic) or if
the sediment chemical concentrations on an organic carbon basis are
used, then the biological effects occur at similar concentrations (typi-
cally within a factor of two) for the different sediments. Most impor-
tantly, the effects concentrations are the same as, or they can be
predicted from, the effects concentration determined in water-only
exposures.
The EqP methodology rationalizes these results by assuming
that the partitioning of the chemical between sediment-organic carbon
and pore water is at equilibrium. In each of these phases, the fugacity
or activity of the chemical is the same at equilibrium. As a consequence,
it is assumed that the organism receives an equivalent exposure from a
water only-exposure or from any equilibrated phase: either from pore
water via respiration; or from sediment carbon, via ingestion; or from a
mixture of the routes. Thus, the pathway of exposure is not significant.
The biological effect is produced by the chemical activity of the single
phase or the equilibrated system.
Sediment quality criteria (SQC) for nonionic organic chemicals
are based on the chemical concentration in sediment organic carbon.
For highly hydrophobic chemicals this is necessary because the pore
water concentration is, for those chemicals, no longer a good estimate
of the chemical activity. The pore water concentration is the sum of the
free chemical concentration, which is bioavailable and represents the
chemical activity, and the concentration of chemical complexed to dis-
solved organic carbon, which is not bioavailable. Using the chemical
concentration in sediment organic carbon eliminates this ambiguity.
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Sediment Quality Criteria Using Equilibrium pltttitioning
SQC also require that a chemical concentration be chosen that is
sufficiently protective of benthic organisms. The final chronic value
(FCV) from the U.S. Environmental Protection Agency (EPA) water
quality criteria is proposed._An analysis of the data compiled in the
water quality criteria documents demonstrates that benthic species, de-
fined as either.epibenthic or infaunal species, have a similar sensitivity
to water column species. This similarity is the case if the most sensitive
species are compared and if all species are compared. The results of
benthic colonization experiments also support the use of the FCV. Thus,
if effects concentrations in sediments can be accurately predicted using
the KOC and data from water-only tests, the SQC protecting benthic spe-
cies can be predicted using the KOC and FCV/
Equilibrium partitioning cannot remove all .the variation in the
experimentally observed sediment-effects concentration and the con-
centration predicted from water-only exposures. A variation factor of
approximately four to five remains. Thus, a quantification of this uncer-
tainty should accompany the SQC.
The derivation of SQC requires that a minimum database be
available. This includes: (1) the octanol/water partition coefficient of
the chemical, which should be measured with modern experimental
techniques, which appear to remove the large variation in reported val-
ues, (2) the derivation of the final chronic value, which should also be
updated to include the most recent toxicological information, and (3) an
SQC check test to establish variation of the EqP prediction. The SQC is
then the FCV x Koc with confidence limits based on SQC check tests.
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Sediment Quality Criteria Using Equilibrium Partitioning
OVERVIEW
This report presents the technical basis for estab-
lishing sediment quality criteria (S.QC) for nonionic
.organic chemicals using the equilibrium partitioning
(EqP) method. The term sediment quality criteria, as
used herein, refers to numerical concentrations for in-
dividual chemicals that are applicable across the
range of sediments encountered in practice. Sediment
quality criteria are intended to be predictive of bio-
logical effects. As a consequence, they can be used in
much the same way as final chronic values (FCV) are
used in water quality criteria—as the concentration of
a chemical that is protective ofbenthic aquatic life.
The specific regulatory uses of SQC have not
been established. However, the range of potential ap-
plications is quite large because the need for the
evaluation of potentially contaminated sediments
arises in many contexts. SQC 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 are
protective of benthic aquatic life.
This1 overview (Section 1) summarizes the evi-
dence and the major lines of reasoning of the EqP
methodology, with supporting references cited in the
body of the report. Section 2 reviews the background
that led to the need for SQC and also the selection of
the EqP methodology. Section 3 reviews the develop-
ment of concentration-response curves for pore-
water concentrations and sediment oirganic-carbon
normalized concentrations to determine toxicity and
bioavailability in contaminated sediments. It also
presents analyses of sediment toxicity and bioaccu-
mulation experiments. Section 4 reviews the parti-
tioning of nonionic organic chemicals to sediments
using laboratory and field studies. Section 5 reviews
a comparison of benthic and 'water column species
using aquatic toxicity data contained in EPA's Water
Quality Criteria (WQC) documents to show the ap-
plicability of WQC as the effects levels for benthic or-
ganisms. Section 6 reviews the computation of an
SQC and presents an analysis for quantifying the un-
certainty associated with SQC. This section also pre-
sents minimum data requirements eind example
calculations and compares the SQC computed for
five chemicals to field data.-Section 7 presents con-
clusions and further research needs. Section 8 lists
the references used in this document.
Toxicity and Bioavailabiiity of
Chemicals in Sediments
Establishing SQC requires a determination of the ex-
tent of the bioavailability of sediment associated
chemicals. It has frequently been observed that simi-
lar concentrations of a chemical, in units of mass of
chemical per mass of sediment dry weight (e.g., mi-
crograms chemical per gram sediment [ug/g]) can
exhibit a range in toxicity in different sediments. If the
purpose of SQC is to establish chemical concentra-
tions that apply to sediments of differing types, it is
essential that the reasons for this varying bioavailabil-
ity be understood and explicitly included in the crite-
ria. Otherwise the criteria cannot be presumed to be
applicable across sediments of differing properties.
The importance of this issue cannot be over-
emphasized. For example, if 1 ug/g of Kepone is the
LCso for an organism in one sediment and 35 ug/g is
the LCso 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 LCso of a third sediment without per-
forming a toxicity test. The results of toxicity tests
used to establish the toxicity of chemicals in sedi-
ments would not be generalizable to other sedi-
ments. 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 under-
stood, it would be fruitless to attempt to establish
WQC. For this reason, bioavailability is a principal
focus of this report.
The key insight into the problem of quantify-
ing the bioavailability of chemicals in sediments was
that the concentration-response curve for the biologi-
cal effect of concern can be correlated not to the total
sediment-chemical concentration (micrograms chemi-
cal per gram sediment), but to the interstitial water or
pore water concentration (micrograms chemical per
liter pore water). In addition, the effects concentra-
tion found for the pore water is essentially equal to
that found in water-only exposures. Organism mor-
tality, growth rate, and bioaccumulation data are
used to demonstrate this correlation, which is a criti-
cal part of the logic behind the EqP approach to de-
veloping SQC. For nonionic organic chemicals, the
concentration-response curves correlate equally well
with the sediment-chemical concentration on a sedi-
ment-organic carbon basis.
These observations can be rationalized by as-
suming 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.
The name equilibrium partitioning (EqP) describes this
assumption. The..rationalization for the equality of
water-only and sediment-exposure-effects concentra-
tions on a pore water basis is that the sediment-pore
water equilibrium system (Fig. 1, right) provides the
same exposure as a water-only exposure (Fig. 1, left).
The chemical activity is the same in each system at
equilibrium. It should be pointed out that the EqP as-
sumptions are only approximately true; therefore,
predictions from the model have an inherent uncer-
tainty. The data presented below illustrate the degree
to which EqP can rationalize the observations.
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Sediment Quality Criteria Using Equilibrium Partitioning
Water Only
Exposure
Sediment - Pore Water
Exposure
Biota
Equilibrium Partitioning
Figure 1.—Diagram of the organism exposure routes for a water-only exposure (left and a sediment exposure (right). Equilib-
rium partitioning refers to the assumption that an equilibrium exists between the chemical sorted to the particulate sedi-
ment organic carbon and the pore water. K^ is the organic carbon partition coefficient.
Figure 2 presents mortality data for various
chemicals and sediments compared to pore water
concentrations when normalized on a toxic unit ba-
sis. Pore water toxic units are the ratio of the meas-
ured pore water concentration to the LCso from
water-only toxicity tests. Three different sediments
are tested for each chemical as indicated. The EqP
model predicts that the pore water LCso will equal
the water-only LCso which is obtained from a sepa-
rate water-only exposure toxicity test. Define:
pore water toxic unit
_ (pore water concentration)
(water-only LCso)
(1)
Therefore, a toxic unit of one occurs when the
pore water concentration equals the water-only LCso,
at which point it would be predicted that 50 percent
mortality would be observed. The correlation of ob-
served 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 (b) the applicability of the
water-only effects concentration and, by implication,
the validity of the EqP model. By contrast, the mor-
.tality versus sediment chemical concentration on a
dry weight basis varies dramatically from sediment
to sediment. This will be presented subsequently.
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 con-
centration on a sediment-organic carbon basis, which is
demonstrated below, suggests that the ingestion of
sediment-organic carbon is the primary route of expo-
sure. It is important to realize that if the sediment and
pore water are in equilibrium, then the effective expo-
sure concentration is the same regardless of exposure
route. Therefore, it is not possible to determine the pri-
mary route of exposure from equilibrated experiments.
Whatever the route of exposure, the correla-
tion to pore water suggests that if it were possible to
either measure the pore water chemical concentra-
tion, or predict it from the total sediment concentra-
tion and the relevant sediment properties such as the
sediment organic carbon concentration, then that
concentration could be used to quantify the exposure
concentration for an organism. Thus, the partitioning
of chemicals between the solid and the liquid phase
in a sediment becomes a necessary component for es-
tablishing SQC.
In addition, if it were true that benthic organ-
isms are as sensitive as water column organisms—
and the evidence to be presented appears to support
this supposition—then SQC could be established us-
ing the FCV from WQC documents as the effects con-
centration for benthic organisms. The apparent
equality between the effects concentration as meas-
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Sediment Quality Criteria Using Equilibrium Partitioning
Poire Wator Normalization
.-AW
100
•S 80
>• 6O
5 40
O
20
0
• Kapono
• PlMnanthrmm
* Endrln
• FkraranthaiM
v Ae«naphth«n«
^ DOT
0.01
0.10
1.OO
10.00
100.00
Porn Water Toxic Units
Figure 2.—Mortality versus predicted pore water toxic units for five chemicals and three sediments per chemical
Sf ^fiSr^h?^g'e,hatChing {'°WeSt °rganiC Carb°n C0ntent)' «•**•«*« (intermedia^ organic co
SS'i?t2i-^"JS8 (h'gheSt>°rgani° Carb0n C0ntent)- See Tables 1 and 2 for data s°"«*s- Predicted pore water toxfc
units are the ratio of the pore water concentration to the water-only LC50 (Eqn. 1).
ured 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 (ug/L) is the final chronic
WQC for the chemical of interest, then the SQC
(u,g/g sediment) are computed using the partition
coefficient Kp (L/kg sediment) defined as the ratio of
chemical concentration in the sediment and in the
pore water at equilibrium.
cent by weight, the organic carbon appears to be the
predominant phase for chemical sorption. The parti-
tion coefficient, Kp, the ratio of sediment concentra-
tion, Cs, to pore water concentration, Cd, is given by
(3)
SQC-KpFCVxO.001
(2)
g
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 under-
stood, and a standard model exists for describing the
process. The hydrophobicity of the chemical is quan-
tified by using the octanol/water partition coeffi-
cient, Kow. The sorption capacity of the sediment is
determined by the mass fraction of organic carbon
for the sediment,/oc. For sediments with/oc a 02 per-
where Koc is the partition coefficient for sediment or-
ganic 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 re-
garding 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 parti-
tioning, namely that Koc » Kow for sediments.
Using Equations 2 and 3, a SQC is calculated
from
SQC./ocKocFCV.
(4)
This equation is linear in the organic carbon fraction,
foe- As a consequence, the relationship can be ex-
pressed as
SQC
/oc
-KocFCV.
(5)
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Sediment Quality Criteria Using Equilibrium Partitioning
If we define
SQCoc-
SQG
foe
(6)
'as the organic carbon-normalized SQC concentration
(mioogram chemical per gram organic carbon), then
SQCoc-KocFCV.
(7)
Thus, we arrive at the following important conclu-
sion: For a specific chemical having a specific Koc, the
organic carbon-normalized sediment concentration,
SQCoo 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 concen-
tration and the KOC of the chemical. The utility of
these ideas can be tested with the same mortality
data as those in Figure 2 but restricted to nonionic or-
ganic chemicals for which organic carbon normaliza-
tion applies. The concept of sediment toxic units is!
useful in this regard. These units are computed as the
ratio of the organic carbon-normalized sediment con-
centrations, Cs//oo and the predicted sediment LCso.
using Koc and the water-only LCso. That is,
/predicted\ • ^
sediment ^ .
toxic unit Koc (water-only LCso)
(8)
Figure 3 presents the percent mortality versus
predicted sediment toxic units. The correlation is
similar to that obtained using the pore water concen-
trations in Figure 2. The cadmium data are not in-
cluded because its partitioning is not determined by
Organic Carbon Normalization
100
~ 80
X 60
"5
I
n
•: 40
20
0
&>
• v
«
o
1
^ A A Dicldrin
O © • Kapon*
a D • Ph*nanthr*n*
. -> * Endrln
O O * Ruoranfh*n*
T v T Ae*naphth*n*
[' > * DDT
0.01
O.10 1.OO 10.OO
Predicted Sediment Toxic Units
100.00
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 LCso (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), en-
drin (4.84), fluoranthene (5.00), dieldrin (5.25). phenanthrene (4.46), and acenaphthene (3.76) with Equation 11. Km, for
DDT is the log average of the reported values in the Log P database [75]. The kepone Koc is the log mean of the ratio of or-
ganic carbon-normalized kepone concentration to pore water-kepone concentration from the toxicity data set. ffcws for the re-
maining compounds were computed by the U.S. EPA, Environmental Research Laboratory, Athens, Georgia. Methods are
presented later in this document.
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Sediment Quality Criteria Using Equilibrium Partitioning
sediment organic carbon. The predicted sediment
toxic units for each chemical follow a similar concen-
tration-response curve independent: of sediment
type. The data demonstrate that 50-percent mortality
occurs at about one sediment toxic, unit, independent
. of chemical, species of 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 meas-
urement error, then all data in Figures 2 and 3 should
predict 50 percent mortality at one toxic unit. There is
an uncertainty factor of approximately four to five in
the results. This variation reflects inherent variability
in these experiments and phenomena that have not
been accounted for in the EqP model. It also appears
to be the limit of the accuracy and precision that can
be expected.
Effects Concentration
The development of SQC requires an effects concen-
tration for benthic organisms. Because many of the
organisms used to establish the WQC are benthic,
perhaps the WQC are adequate estimates of the ef-
fects concentrations for benthic organisms. To exam-
ine this possibility, the acute toxiciry database, which
is used to establish the WQC, is segregated into ben-
thic and water column species, and the relative sensi-
tivities of each group are compared. Figure 4
compares the acute values for the most sensitive ben-
BENTWC
i i
A FRESH WATER
• SALT WATER
-1012346
BENTHC ORQAMSM FAV (ug/l)
Figure 4.-—A comparison of the final acute values (FAV) for
water column versus benthic organisms. Each data point
represents an FAV for 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."
thic (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. Al-
though there is considerable scatter, these results, a
more detailed analysis of all the acute toxiciry data,
and the results of benthic colonization experiments,
support the contention of equal sensitivity.
BACKGROUND
Under the Clean Water Act (CWA), the EPA is respon-
sible for protecting the chemical, physical, and bio-
logical 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 cate-
gories listed as toxic in the CWA. Additional water
quality documents that update criteria for selected
consent decree and new chemicals havexbeen pub-
lished since 1980. These WQC are numerical concen-
tration limits that are protective of human health and
aquatic life. Although these criteria play an impor-
tant role in assuring a healthy aquatic environment,
they 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, wa-
ters 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 assess the extent
of sediment contamination, implement measures to
limit or prevent additional contamination from oc-
curring, or to identify and implement appropriate re-
mediation as needed.
As a result of the need to assist regulatory
agencies in making decisions concerning contami-
nated sediment, the EPA's Office of Science and Tech-
nology, Health and Ecological Criteria Division,
established a research team to review alternative ap-
proaches to assess sediment contamination. Sedi-
ment contamination and related problems were the
subject of a conference [I]. Alternative approaches to
establishing SQC [2] and their merits and deficiencies
were discussed [3]. Additional efforts were under-
taken to identify the scope of national sediment con-
tamination [4] and to review proposed approaches
for addressing contaminated sediments [5,6]. The
EqP method was selected because it provides the
most practical, scientifically defensible, and effective
regulatory tool for addressing individual honionic
chemicals associated with contaminated sediments
on a national basis [7].
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Sediment Quality Criteria Using Equilibrium Partitioning
Rationale for Selecting
the EqP Method
The principal reasons for the selection of the-EqP
(method include the following:
1. The EqP method was most likely to yield sedi-
ment criteria that are predictive'of biological ef-
fects in the field and defensible when used in a
regulatory context These criteria address the is-
sue of bioavailability and are founded on the ex-
tensive biological effects database used to
establish national WQC.
*
2. Sediment criteria can be readily incorporated into
existing regulatory operations because a unique
numerical sediment-specific criterion can be es-
tablished for any chemical and compared to field
measurements to assess the likelihood of signifi-
cant adverse effects.
3. Sediment criteria provide a simple and cost-effec-
tive means of screening sediment measurements
to identify areas of concern and provide informa-
tion to regulators in a short period of time.
4. The method takes advantage of the data and exper-
tise that led to the development of national WQC.
5. The methodology can be used as a regulatory tool to
ensure that uncontaminated sites are protected from
attaining unacceptable levels of contamination.
Relationship to WQC Methodology
The first question to be answered is this: Why not use
the WQC procedure for the development of SQC? A
detailed methodology has been developed that pre-
sents the supporting logic, establishes the required
minimum lexicological data set, and specifies the nu-
merical procedures to be used to calculate the criteria
values 18]. Further, WQC developed through this
methodology are routinely used in the regulation of
effluent discharges. Therefore, it is only natural to ex-
tend these methods directly to sediments.
The WQC are based on total chemical concen-
tration, so the transition to using dissolved chemical
concentration for those chemicals that partition to a
significant extent would not be difficult. The experi-
ence 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 35 [9,10]. The implication is that differences
of this magnitude result from variations in site-specific
water chemistry and are not an overwhelming impedi-
ment to nationally applicable numerical WQC.
The WQC are based on using the total chemi-
cal"concentration as a measure of bioavailable chemi-
cal concentration. However, the use of total sediment
chemical concentration as a measure of the bioavail-
able—or even potentially bioavailable—chemical
concentration is not supported by the available data
[11]. The results of recent experiments indicate that
sediments can differ irutoxidty by factors of 100 or
more for the same total chemical concentration. This
difference is a significant obstacle. Without a quanti-
tative estimate of the bioavailable chemical concen-
tration in a sediment, it is impossible to predict a
sediment's toxicdry on the basis of chemical measure-
ments, regardless of the method used to assess bio-
logical impact—be it laboratory toxicity experiments
or field data sets comprising benthic biological and
chemical sampling [12-15].
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 concen-
tration is 100 times more toxic in one sediment than it
is in another, how can we set universal SQC that de-
pend only on the total sediment chemical concentra-
tion? Any SQC that are based on total sediment
concentration have a potential uncertainty of this
magnitude. Thus, bioavailability must be explicitly
considered for any sediment evaluation methodol-
ogy that depends on chemical measurements to es-
tablish defensible SQC.
Applications of SQC
SQC that are reasonably accurate in their ability to
predict the potential for biological impacts are useful
in many activities [16]. Sediment quality criteria can
play a significant role in the identification, monitor-
ing, and cleanup of contaminated sediment sites on a
national basis and provide a basis to ensure that sites
that are uncontaminated will remain so. In some
cases, sediment criteria alone are sufficient to iden-
tify and establish cleanup levels for contaminated
sediments. In other cases, they must be supple-
mented with biological sampling and testing before
decisions are made.
In many ways, sediment criteria developed
using the EqP methodology are similar to WQC.
. However, their application may be quite different. In
most cases, contaminants exceeding WQC in the
water column need only be controlled at the source
to eliminate unacceptable adverse impacts. Contami-
nated sediments have often been in place for quite
some time, and controlling the source of that pollu-
tion (if the source still exists) may not be sufficient to
alleviate the problem. The difficulty is compounded
because the safe removal and treatment or disposal
of contaminated sediments can be laborious and
expensive.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Sediment criteria can be used as a means for
predicting or identifying the degree and extent of
contaminated areas such that more informed regula-
tory decisions can be made. Sediment criteria will be
particularly valuable in monitoring applications in
. which sediment contaminant concentrations are
gradually approaching the criteria. The comparison
of field measurements to sediment criteria will pro-
vide reliable warning of potential problems. Such an
early warning provides an opportunity to take cor-
rective action before adverse impacts occur.
TOXICITY AND BlOAVAILABILITY
OF CHEMICALS IN SEDIMENTS
A key insight into the problem of quantifying the
bioavailability of chemicals in sediments was that the
concentration-response curve for the biological effect
of concern could be correlated, not to the total sedi-
ment chemical concentration (micrograms chemical
per gram dry sediment) but to the pore water con-
centration (micrograms chemical per liter pore
water) [17]. However, these results do not necessarily
imply that pore water is the primary route of expo-
sure because all exposure pathways are at equal
chemical activity in an equilibrium experiment (see
Fig. 1) and the route of exposure cannot be deter-
mined. Nevertheless, this observation was the critical
first step in understanding bioavailability of chemi-
cals in sediments.
Toxicity Experiments
A substantial amount of data has been assembled
that addresses the relationship between toxicity and
pore water chemical concentrations. Table 1 lists the
sources and characteristics of these experiments.
Some of these data are presented in Figures 5 to 8.
The remaining data are presented elewhere in this
document. In Figures 5 to 8 the biological response—
mortality or growth rate suppression—is plotted ver-
sus the total sediment concentration in the top panel,
and versus the measured pore water concentration in
the bottom panel. Table 2 .summarizes the LCso and
ECso estimates and 95 percent confidence limits for
these data on a total sediment and pore water basis,
as well as the water-only values.
The results from kepone experiments (Fig. 5)
are illustrative of the general trends in these data [17,
18]. For the low organic carbon sediment (foe = 0.09
percent), the 50th percentile total kepone concentra-
Table 1. — Sediment toxicity data and bioaccumulation data.
CHEMICAL
ACENAPHTHENE
ACENAPHTHENE
CYPERMETHRIN
DDT
DIELDRIN
DIELDRIN
DIELDRIN
ENDRIN
ENDRIN
FLUORANTHENE
FLUORANTHENE
KEPONE
KEPONE
KEPONE
PERMETHRIN
PHENANTHRENE
PHENANTHRENE
ORGANISM
^ i^^-^— —
Eohaustorius estuarius
Leptocherius plumulosus
Chironomus ientans
Hyalella azteca
Chironomus tentans
Hyalella azteca
Hyalella azteca
Diporeia sp.
Hyalella azteca
Rhepoxynius abronius
Rhepoxynius abronius
Chironomus tentans
Chironomus tentans
Chironomus tentans
Eohaustorius estuarius
Leptocherius plumulosus '
SEDIMENT SOURCE
South Beach. OR
McKinney Slough, OR
Eckman Slough, OR
South Beach, OR
McKinney Slough, OR
Eckman Slough, OR
River and pond
Soap Creek, Mercer Lake
Airport Pond, MN
West Bearskin Lake. MN
Pequaywan Lake, MN
Airport Pond, MN
Lake Michigan
Soap Creek, Mercer Lake
Amended Ona Beach, OR
Yaquina Bay, OR
Soil
Soil
Soil
River and pond
South Beach, OR
MeKinney Slouth; OR
Eckman Slough, OR
South Beach, OR
McKinney Slough, OR
EXPOSURE
DURATION
(DAYS)
10
10
1
10
10
10
10
10
10
10
10 .
14
14
14
1
10
10
BIOLOGICAL
END POINT
Mortality
Mortality
Body burden
Mortality
Mortality
Mortality
Mortality
Mortality
Mortality
Mortality
Mortality
Body burden
Growth
Mortality
Body burden
Mortality
Mortality
REFERENCE
[56]
[56]
[22]
[20,21]
[55]
[53]
[54]
[52]
[20,21]
[57]
[19]
[17,24] '
t!7]
[17]
[22]
[56]
[56]
FIGURE
23
23
8
26
24
23
7
23
6
5
8
23
23
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Dry Weight Normalization
Kapona - Mortality
K«pon*-- Growth
o.i uo IDA 100.0
Dry Weight Concentration (ug/g)
O.1 1.0 1O.O 1OO.O
•, Dry Weight Concentration (ug/e)
Pore Water Normalization
Kapona - Mortality
1OO
80
eo
4O
80
O
Kapona - Growth
100
H 80
J 80
3 4O
« 20
0
* -
1 1O 1OO 1OOO
Pore Water Concentration (ug/U
1 10 100 1000
fan W«t«r Conewrtntton (ug/U
Rgure 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
Fkioranth«n«
Cadmium
100
2 *
*. •<>
5 40
i 20
O
ao
*
e
5 1O 18 2O
Dry Weight Concentration (ug/g)
0 2O 4O 6O 8O
Dry Weight Concentration (i»«/g)
Pore Water Normalization
Fkioranthana Cadmium
100
3 «o
80
! 40
2O
0
20 4O 60 80
Pore Water Concentration (ug/U
0 2OOO 4OOO 6OOO
Pore Water Concentration (iKi/L)
Figure 6.r-Comparison of percent mortality of R. abronium to fluoranthene [19] concentrations in bulk sediment (top) and
pore water (bottom) for sediments with varying organic carbon concentrations..
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Tabte 2.—LCso and ECso for sediment dry weight and sediment-organic carbon normalization and for pore-water and
water-only exposures. <™awn anu
LCSO AND ECSO
CHEMICAL
(END POINT)
KEPONE
(MORTALITY)
KEPONE
(GROWTH)
FLUORANTHENE
(MORTALITY)
DOT
(MORTALITY)
DOT
(MORTALITY)
ENDRIN
• (MORTALITY)
ENDRIN
(MORTALITY)
fce(%)
0.90
1.50
12.0
0.09
1.5
12.0
0.2
0.3
0.5
3.0
7.2
10.5
3.0
3.0
11.0
3.0
6.1
11.2
3.0
11.0
11.0
TOTAL SEDIMENT («/«)
0.90 (0.73-1.10)
6.9(5.85-8.12)
35.2 (30.6-40.3)
0.46 (0.42-0.51)
9.93 (7.74-12.8)
37.3 (31.5-44.2)
3.2 (2.85-3.59)
6.4 (5.56-7.27)
10.7 (8.34-13.7)
10.3 (8.74-12.2)
17.5 (12.5-24.3)
44.9 (36.7-55.0)
1.54 (1.18-2.00)
4.16 (3.91-4.42)
10.95 (9.34-12.9)
3.39 (2.61-4.41)
5.07 (4.05-6.36)
5.91 (4.73-7.37)
4.76 (3.70-6.13)
18.9 (13.6-26.3)
10.5 (8.29-12.7)
PORE WATER (u*/D
29.9(25.3-35.6)
31.3 (25.7-38.1)
18.6 (15.7-21.9)
17.1 (15.7-18.7)
48.5 (34.6-67.8)
20.1 (16.7-24.1)
21.9 (19.6-24.4)
30.9 (27.0-35.4)
22.2 (17.5-29.3)
0.74 (0.67-0.82)
1.45 (1.20-1.75)
0.77 (0.67-0.89)
1.80 (1.44-2.24)
1.92 (1.55-2.36)
1.74 (1.37-2.20)
2.26 (1.67-3.05)
3.75 (2.72-5.19)
2.81 (2.44-3.23)
ORGANIC CARBON (pt/c)
1,000 (811-1.220)
460 (390-541)
293 (255-337)
511 (467-567)
662 (516-1,050)
"• 311 (262-368)
1,600 (1,430^1.800)
2,130 (1.850-2,420)
2.140 (1.670-2,740)
344 (291-405)
243 (174-338)
428 (350-524)
51.3 (39.3-66.7)
139 (130-147)
99.6 (84.9-117)
113 (87.0147)
83.1 (66.4-104)
52.8 (42.2-65.8)
159 (123-204)
172 (124-239)
95.8 (75.4-115)
WATER ONLY (pS/L)
26.4 (22.7-30.6)
16.2 (15.0-17.5)
0.45 (0.38-0.53)
0.48 (0.42-0.55)
0.52 (0.45-0.60)
4.81 (4.46-5.20)
3.39 (3.10-4.98)
3.71 (3.11-4.44)
REFERENCE
[17]
[171
• [19]
[20]
[21J
[20]
[21]
The LCsos and ECsos and the 95% confidence limits in parentheses are computed by the modified Spearman-Karber method I123J
tion for both Chironomus tenians mortality (LCso) and
growth rate reduction from a life cycle test (ECso) are
<1 ug/g. By contrast, the 1.5 percent organic carbon
sediment ECso and LCso are approximately 7 and 10
ug/g, respectively. The high organic Cetrbon sediment
(12 percent) exhibits still higher LCso and ECso val-
ues on a total sediment kepone concentration basis
(35 and 37 ug/g, respectively).
However, as shown in the bottom panels, es-
sentially all the mortality data collapse into a single
curve and the variation in growth rate 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
LCso or ECso vary by approximately 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 ef-
fects concentrations and the water-only results indi-
cates that they are similar. The pore water LCsos are
19 to 30 ug/L, and the water-only exposure LCso is
26 ug/L. The pore water ECsos are 17 to 49 ug/L, and
the water-only ECso is 16 ug/L (Table 2).
Laboratory experiments have also been per-
formed to characterize the toxicity of fluoranthene
[19] to the sediment-dwelling marine amphipod Rhe-
poxynius abronius. Figure 6 presents the R. abronius
mortality data for the fluoranthene experiment. The
results of the fluoranthene experiments parallel those
for kepone. The sediment with the lowest organic
carbon content (02 percent) exhibits the lowest LCso
on a total sediment concentration basis (3.2 ug/g)
and as the organic carbon concentration increases
(0.3 and 0.5 percent) the LCso increases (6.4 and 10.7
ug/g). On a pore water basis, the data again collapse
to a single concentration-response curve and the
LCsos differ by less than 50 percent
Figure 7 presents mortality data for DDT and
- endrin using the freshwater amphipod Hyalella azteca
[20,21]. The responses for DDT [20] are similar to
those observed for kepone, cadmium, and fluoran-
thene. On a total sediment concentration basis the or-
ganism responses differ for the various sediments
(LCsos are 10.3 to 45 ug/L), but on a pore water basis
the responses are again similar (LCsos are 0.74 to 1.4
Ug/L) and comparable to the water-only LCsos of ap-
proximately 0.5 ug/L. The DDT data reported by
Schuytema et al. [21] are more variable. By contrast,
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Dry Weight Normalization
DOT
Endrln
»OC (5-.)
* *•*
• 10*
80 10O ISO 200
Dry Weight Concentration (ug/g)
„
£
I
100
80
80
4O
2O
O1
fee IX)
' • M _ II**
K
e 11JI
.*"•
I -I*'"5.'1
am-
.
•
•
0.1 . 1.0
Dry Weight
1O.O . 100.0
ntratton
• *
• •
• •
•>^/
8" •
• •
•
.
•
•
0.1 1.O 1O.O 10O.O
Pom Water Concentration (ug/U
Figure 7.—Comparison of percent mortality of H. azteca to DDT (left) and endrin (right) concentrations in bulk sediment (top)
and pore water (bottom) for sediments with varying organic carbon concentrations [20, 21].
the organism survival for endrin exposures on a dry
weight basis varies by a factor of almost six among
the six sediments. The LCsos are 3.4 to 18.9 ng/g. The
pore water LCsos were less variable, 1.7 to 3.8 u.g/L
and comparable to the water-only exposure LCso of
approximately 4 ug/L (Table 2).
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. Chiro-
nomus tentans was exposed to two synthetic pyre-
throids, cypermethrin and permethrin, spiked into
three sediments, one of which was laboratory-grade
sand [22]. 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. 8, top
panels).
On a pore water basis, the bioaccumulation
appears to be approximately linear and independent
of sediment type (bottom panels). The mean bioaccu-
mulation factor (BAF) for cypermethrin (and per-
methrin) varies from 6.2 to 0.6 (4.0 to 023) (ug/g or-
ganism/ug/g sediment) as sediment foe 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. [17,23] and Adams [24] in the C. ten-
tons-kepone experiments presented previously (Fig-
ure 3). The body burden variation on a total sediment
basis is over two orders of magnitude (BAF = 600 to
3.3 ng/g organism /ng/g sediment), whereas the
pore water bioaccumulation factor is within a factor
of four (5,200 to 17,600 ng/kg organism/ng/L), with
the very low organic carbon sediment exhibiting the
largest deviation (Table 3).
Conclusion
These observations—that organism concentration re-
sponse and bioaccumulation from different sedi-
ments can be reduced to one curve if pore water is
considered as the concentration that quantifies expo-
sure—can be interpreted in a number of ways. How-
ever, these results do not necessarily imply that pore
water is the primary route of exposure because all ex-
posure pathways are at equal chemical activity in an
-------
Sediment [Qujality Criteria Using Equilibrium Partitioning
Dry Weight Normalization
10000
£ 1000
i 100
•
I .
Cyparmathrin
1000.0
2 100.0
I 10.0
1.O
10 100 1000 toooo
Dry Weight CMcentnrttoa hg/g)
O.T
Parmethrln
Dry Weight
100 1000
<"0/g)
Pore Water Normalization
10000
N 1000
100
10
Cyparmatfirin
«»0j»
1OOO.O
9
8
0.01 0.10 1.00 10.00 10000
Weter Concentration (ug/L)
9
-
-
o
Parmathrin
foc(J«|
• "0.1
*"
<"»» 0.10 1.00 10.00
Weter Concentration (ug/L)
versus concentration in bulk
122].
Table 3.—Bioaccumulation factors for C. tentans.
BIOACCUMULATION FACTORS*
'TOTAL SEDIMEN
IK/K ORGANISM
M/g SEDIMENT
PORE WATER
Hi/kg ORGANISM
6.21 (4.41-8.01)
80.1 (73.5-86.7
0.50 (0.30-0.71)
51.3 (43.8-58.8)
0.60 (0.37-0.83)
92.9 (87.0-98.8)
4.04 (2.89-5.20)
39.7 (25.0-54.3]
0.38 (0.17-0.59)
52.5 (22.6-82.4)
0.23 (0.18-0.28)
29.7 (15.6-43.7)
17.600 (6,540-28.600)
5,180 (1,970-8,3901
5,790 (2.890-8.7001
95% confidence limits shown in parentheses.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
equilibrium experiment. The route of exposure can-
not be determined, as we can see by comparing the
concentration-response correlations to pore water
and organic carbon-normalized sediment concentra-
tions. That both are equally successful at correlating
•the data suggests that neither the pore water nor the
sediment exposure pathway can be implicated as the
primary exposure route.
In order to relate pore water exposure to sedi-
ment carbon exposure, it is necessary to establish the
relationship between these two concentrations.
Therefore, an examination of the state of the art of
predicting the partitioning of chemicals between the
solid and the liquid phase is required. This examina-
tion is described in the following section.
SORPTION OF NONIONIC
ORGANIC CHEMICALS
Partitioning In Particle Suspensions
A number of empirical models have been suggested
to explain the sorption of nonionic hydrophobic or-
ganic chemicals to natural soils and sediment parti-
cles (see Karickhoff [25] for an excellent review). The
chemical property that indexes hydrophobicity is the
octanol/water partition coefficient, Kow- The impor-
tant particle property is the weight fraction of or-
ganic carbon, /oc. Another important environmental
variable appears to be the particle concentration
itself.
In many experiments using particle suspen-
sions, the partition coefficients have been observed to
decrease as the particle concentration used in the ex-
periment is increased [26]. Very few experiments
have been done on settled or undisturbed sediments;
therefore, the correct interpretation of particle sus-
pension experiments is of critical importance. It is
not uncommon for the partition coefficient to de-
crease 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, which
would result in lower sediment chemical concentra-
tions for SQC If, however, this phenomenon is an ar-
tifact or a result of 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 par-
ticle concentration effect.
Particle concentration effect. For the revers-
ible (or readily desorbable) component of sorption, a
particle interaction model (PIM) has teen proposed
that accounts for the particle concentration effect and
predicts the partition coefficient of nonionic hydro-
phobic chemicals over a range of nearly seven orders
of magnitude with a logio prediction standard error
of 038 [27]. The reversible component partition coef-
ficient, .Kp, is the ratio of reversibly bound chemical
concentration, Cs (ug/kg dry weight), to the dis-
solved chemical concentration, Cd (ug/L):
Cs-Kp-Cd.
The PIM model for K p1, is
~ „ /ocKoc
(9)
(10)
1 + m/oc
where
% * = reversible component partition coefficient
P (L/kg dry weight)
Koc = particle organic caxbon partition
coefficient (L/kg organic carbon)
/oc = particle organic caxfoon weight fraction (kg
organic carbon/kg dry weight)
m = particle concentration in the suspension
(kg dry weight/L)
vx = 1.4, an empirical constant (unitless).
The regression of Koc to the octanol/water coeffi-
cient, Kow, yields
logioKoc = 0.00028 + 0.983 logioKow (11)
which is that essentially K0c approximately equals
Kow. Figure 9 presents the observed versus predicted
reversible component partition coefficients using this
model [27]. A substantial fraction of the data in the
regression is at high particle concentrations (m/ocKow
> 10), where the partitioning is determined only by
the solids concentration and vx. The low particle con-
centration data (m/ocKow < 1) are presented in Figure
10 for the conventional adsorption (left) and revers-
ible component (right) partition coefficient, Kp, nor-
malized by /oc, that is Koc = Kp/foc. The relationship
Koc - Kow is demonstrated from the agreement be-
tween the line of perfect equality and the data. It is
important to note that while Equation 10 applies only
to the reversible component partition coefficient, Kp,
the equation: Kp -foe Kow applies to the conventional
adsorption partition coefficient as well (Fig. 10, left).
A number of explanations have been offered
for the particle concentration effect. The most popu-
lar is the existence of an additional third sorbing
phase or complexing component that is associated
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Reversible Component Partition Coefficient
O)
JC
o
o>
-------
Sediment Quality Criteria Using Equilibrium Partitioning
with the particles but is inadvertently measured as
part of the dissolved chemical concentration because
of experimental limitations. Colloidal particles that
remain in solution after particle*separation [28,29]
and dissolved ligands or macromolecules that desorb
.from the particles and remain in solution [30-33]
have been suggested. It has also been suggested that
increasing particle concentration increases the degree
of particle aggregation, decreasing the surface area
and hence the partition coefficient [34]. The effect has
also been attributed to kinetic effects [25].
Sorption by nonseparated particles or com-
plexing by dissolved organic carbon can produce an
apparent decrease in partition coefficient with in-
creasing 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 col-
loidally sorbed portion. However, the question is not
whether improperly measured dissolved concentra-
tions can lead to an apparent decrease in partition coef-
ficient with increasing particle concentrations. The
question is whether these third-phase models explain
all (or most) of the observed partition coefficient-parti-
cle concentration relationships.
An alternate possibility is that the particle
concentration effect is a distinct phenomena that is a
ubiquitous feature of aqueous-phase particle sorp-
tion. A number of experiments have been designed
explicitly to exclude possible third-phase interfer-
ences. Particle concentration effects are displayed in
the resuspension experiment for polychlorinated
biphenyls (PCBs) and metals [35-37], in which parti-
cles are resuspended into a reduced volume of super-
natant, and in the dilution experiment in which the
particle suspension is diluted with supernatant from
a parallel vessel [35]. 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 var-
ies substantially.
The model (Eqn. 10) is based on the hypothe-
sis that particle concentration effects result from an
additional desorption reaction induced by particle-
particle interactions [27]. It has been suggested that
actual particle collisions are responsible [38]. This in-
terpretation relates vx to the collision efficiency for
desorption and demonstrates that it is independent
of the chemical and particle properties, a fact that has
been experimentally observed [27,36].
It is not necessary to decide which of these
mechanisms is responsible for the effect if all the pos-
sible interpretations yield the same result for sedi-
ment/pore water partitioning. Particle interaction
models would predict that Koe = Kow because the par-
tides are stationary in sediments. Third-phase mod-
els would also relate the free (i.e., uncomplexed)
dissolved chemical concentration to participate con-
centration via the same equation. As for kinetic ef-
fects, 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: Koc s Kow.
Organic carbon fraction. The unifying para-
meter that permits the development of SQC for non-
ionic hydrophobic organic chemicals that are applic-
able to a broad range of sediment types is the. organic
carbon content of the sediments. This development
can be shown as follows: The sediment/pore water
partition coefficient, Kp, is given by
(12)
and the solid phase concentration is given by
Cs=/ocKocCd (13)
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 non-
ionic organic chemical is linear in the organic carbon
fraction, foe. The partitioning data examined in Fig-
ure 10 can be used to examine the linearity of Kp to
foe- Figure 11 compares Kp/Kow to/oc for both the ad-
sorption and the reversible component partition coef-
ficients. The data are restricted to m/0c Kow < 1 to
suppress particle effects. The line indicates the ex-
pected linear relationship in Equation 12. These data
and an analysis presented below appear to support
the linearity of partitioning to a value of foe = 0.2 per-
cent. This result and the toxicity experiments exam-
. ined below suggest that for foe > 0.2 percent, organic
carbon-normalization is valid.
As a consequence of the linear relationship of
Cs and foe, the relationship between sediment con-
centration, Cs, and free dissolved concentration, Cd,
can be expressed as
Joe
If we define
(14)
(15)
as the organic carbon normalized sediment concen-
tration (jig chemical/kg organic carbon), then from
Equation 14: -
Cs,oc ~
(16)
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Partition Coefficient - m foe Kow < 1
;
Adsorption Reversible Component
I
o
^»
-i
-2
-3
• AMIetct
A CwkofwM
T Lbwraii
.<<
t> Cvkvyl
Methyl PwatMm .
•$• PmtMon
•A- QMMHCH
HP COT
O.O1 O.1O 1.0O 1IO.OO 1OO.OO
foe C%)
O.10 1.OO .1O.OO 100.0O
foe (X)
•Figure 11.—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: m/bcKow
< 1. The line represents perfect agreement [27].
Therefore, for a specific chemical with a specific KOCl
the organic carbon-normalized total sediment con-
centration, Cs,oc, is proportional to the dissolved free
concentration, Cd, for any sediment with/oc > 0.2 per-
cent. This latter qualification is judged to be neces-
sary because at/oc < 0.2 percent the other factors that
influence partitioning (e.g., particle size and sorption
to nonorganic mineral fractions) become relatively
more important [25]. Using the proportional relation-
ship given by Equation 16, the concentration of free
dissolved chemical can be predicted from the nor-
malized 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, hy-
drophobic chemicals can also partition to the organic
carbon in colloidal sized particles. Because these par-
ticles are too small to be removed by conventional fil-
tration 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 the free dis-
solved chemical concentration, Cd, and the DOC-
complexed chemical, CDOC- The partition coefficient
for DOC, KDOO is analogous to Koc as it quantifies
the ratio of DOC-bound chemical, CDOO to the free
dissolved concentration, Cd:
CDOC =
(17)
where mooc is the DOC concentration. The magni-
tude of KDOC and the DOC concentration determine
the extent of DOC complexation that takes place.
Thus, it is important to have estimates of these quan-
tities when calculating the level of free dissolved
chemicals in sediment pore waters.
A recent compilation of KDOC together with
additional experimental determinations is available
[39]. A summary that compares the partitioning of six
chemicals to POC, natural DOC, and Aldrich humic
acid (HA) is shown on Figure 12. The magnitude of
the partition coefficients follow the order: POC > HA
> natural DOC. The upper bound on KDOC would ap-
pear to be KDOC = 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 CT:
CT = 0Cd +
+ 0mDocKDocCd (18)
-------
Sediment Quality Criteria Using Equilibrium Partitioning
o
O '
o
o
POC ~ ''
Hunle AaM
Pen W«t«r DOC
BaP DDT HCBP MCBP PYR TCBP
Chemicals
12.—Partition coefficients of chemicals to particu-
late organic carbon (POC), Aldrich humic acid, and natural
DOC. Benzo[a]pyrene (BaP); 2,2',4,4',5,5' hexachloro-
biphenyl (HCBP); DDT; 2,2'.5.5'-tetrachlorobiphenyl (TCBP);
pyreoe (PYR); 4-monochlorobipheny! (MCBP). Data from
Eadie et al. [39].
I
1.0000
o
O.1000
s 0.0100
5
O
U
O.OO10
O.0001
S«dlm*nt
Por» Waur
0 10 20 30 40 50 60
DOC Concentration (mg/LJ
Figure 13.—Partition coefficients of chemicals to particu-
late organic carbon (POC), Aldrich humic acid, and natural
DOC. Benzo[a]pyrene (BaP); 2,2',4,4',5,5' hexachlorc-
.biphenyl (HCBP); DDT; 2,2',5,5'-tetrachlorobiphenyl (TCBP);
pyrene (PYR); 4-monochlorobiphenyl (MCBP). Data from
Eadie et al. [39].
where 0 is the sediment porosity (volume of water/ vol-
ume 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 equa-
tion are the concentration of free chemical in the in-
terstitial water, and that sorbed to the POC and DOC,
respectively. Hence, from Equation 18 the free dis-
solved concentration can be expressed as
Cd--
CT
0 + m;
(19)
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 + mDoc^DOc)- (20)
Figure 13 illustrates the phase partitioning be-
havior of a system for a unit concentration of a
chemical with the following properties: Koc = KDOC =
106 L/kg,/oc = 2.0 percent, m = 0.5 kg solids/L sedi-
ment, and TTJDOC varies from 0 to 50 mg/L, a reason-
able range for pore waters [40]. Witi^ no DOC
present, the pore water concentration equals the free
concentration. As DOC increases, the pore water con-
centration increases because of the increase in com-
plexed chemical, CDOO Accompanying this increase
in CDOC is a slight—in fact, insignificant—decrease in
Cd (Eqn. 19) and a proportional decrease in Cs
(Eqn. 16).
It is important to realize that the free chemical con-
centration, Cd, can be estimated directly from Cs,oc, the or-
ganic carbon-normalized sediment concentration, using
Equation 16, and that the estimate is independent of the
DOC concentration. However, to estimate Cdfrom the pore
water concentration requires that the DOC concentration
and KDOC be known. The assumption Cpore = Cd is clearly
not warranted for very hydrophobic chemicals. For these
cases Cs,oc gives a more direct estimate of the free dissolved
bioavailable concentration, Cd, than does the pore water
concentration.
Bioavailability of DOC-Complexed
Chemicals
The proportion of a chemical in pore water that is
complexed to DOC can be substantial (Fig. 13).
Hence, the question of bioavailability of DOC-com-
plexed chemical can be important in assessing toxic-
ity directly from measured pore water concentra-
ions. A significant quantity of data indicates that
DOC-complexed chemical is not bioavailable. Fish
[41]-and amphipod [42] uptake of polycydic aromatic
hydrocarbons (PAHs) are significantly reduced by
adding DOC. An example is shown in Figure 14 for a
freshwater amphipod [42]. 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 result was expected because, for a
fixed amount of DOC, the quantity of DOC-com-
plexed chemical decreases with decreasing KDOC
(Eqn. 17).
-------
Sediment Quality Criteria Using Equilibrium Partitioning
I
o»
-i
a
oc
0
1C
CO
a
300
200 -
100
DOC Partition Coefficient
>***
Chemical
Figure 14.—Average uptake rate of chemicals by Pontopo-
reia hoyi with (filled) and without (hatched) DOC present.
Benzo[a]pyrene (BaP); 2,2',4.4'-tetrachlorobiphenyl (TCBP);
pyrene, phenanthrene. Data from Landrum et al. [42].
o
o
I
I
(0
o
o
o
10000
1000 r
100 r
• Humlc Add
A Por* Water
tOO
1OOO
10OOO
Koc from Reverse Phase (L/g oc)
Figure 15.—Comparison of the DOC partition coefficient
calculated from the suppression of chemical uptake versus
the Cis reversed-phase HPLC column estimate. Circles are
Atdrich humic acid; triangles are interstitial water DOC.
Chemicals are listed in Rgure 14 caption (also anthracene
and benzo[a]anthracene).
The quantitative demonstration that DOC-
complexed chemicals are not bioavailable requires an
independent determination of the concentration of
complexed chemical. Landrum et al. [42] have devel-
oped a Cis reverse-phase HPLC column technique
that separates the complexed and free chemical. Thus
it is possible to compare the measured DOC-com-
plexed chemical to the quantity of complexed chemi-
cal inferred from the uptake experiments, assuming
that all the complexed chemical is not bioavailable
[42,43]. As shown on Figure 15, although the KDOC
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, CDOO is not bioavailable. Hence
the bioavailable form of dissolved chemical is Cd, the
free uncomplexed component. This is an important
observation because it is Cd that is in equilibrium
with Cs,oo the organic carbon-normalized sediment
concentration (Eqn. 15).
Field Observations of Partitioning in
Sediments
An enormous quantity of laboratory data exists for
partitioning in particle suspensions. However, pore
water and sediment data from field samples are
scarce: Two types of data from field samples are ex-
amined. The first is a direct test of the partitioning
equation Cs,oc = Koc Cd, which is independent of the
DOC concentration. The second examines the sedi-
ment 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 equi-
librium has been attained for each class, then, letting
Cs(;) be the particle chemical concentration of the;th
size class, it is true that
Cs(;) =/oc(;)KocCd
(21)
where/oc(;) is the organic carbon fraction for each
size class /'. On an organic carbon-normalized basis
this equation becomes
Cs,oc(;) =
(22)
where Cs/oc(;) = Cs(;)//oc(;). This result indicates
that the organic carbon-normalized sediment concen-
tration of a chemical should be equal in each size
class because K0c and Cd 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(;) is constant across size classes in a
sediment sample.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Data from three field studies, Prahl [44],
Evans et al. [45], and Delbeke et al. [46], can be used
to test this prediction. In Prahl's study, sediment
cores were collected at three stations near the Wash-
ington State coast (Stations 4, 5 and 7). These were
•sieved into a silt-and-day sized fraction (<64 um),
and a sand-sized fraction (>64 mm). This latter frac-
tion was further separated into a low density fraction
(<1.9 g/cm3) 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 frac-
tions are not pure day, silt, or sand but are natural
particles in the size classes denoted by clay, silt, arid
sand. The organic carbon fractions, shown on Figure
16, range from 0.2 percent for the high-density sand-
sized fraction to greater than 30 percent for the low-
density fraction. This exceeds two orders of
magnitude and essentially spans the range usually
found in practice. For example, 90 percent of the es-
tuarine and coastal sediments sampled for the Na-
tional Status and Trends program exceed 0.2 percent
organic carbon [47].
Organic Carbon Fractions
100.0
10.0
1.0
0.1
• st«. 7
m st«. s
• SU. 4
LOW SAND SILT/CLAY
S*dlm«nt Fraction
Figure 16.—-The organic carbon fractions (% dry weight) in
the low-density fraction .64 um, <1.9 g/cm3; the sand-
sized fraction >64 um, >1.9 g/cm3; the silt/clay-sized frac-
tion <64 urn. Numbered stations as indicated. Data from
[44].
Figure 17 (top) compares the dry weight-nor-
malized day/silt sized fraction sediment PAH con-
centrations, Cs(j), to the sand-sized high- and
low-density PAH concentrations on a dry weight ba-
sis. The dry weight-normalized data have distinctly
different concentrations—the low-density high-or-
ganic carbon fraction is highly enriched, whereas the-
sand-sized fraction is substantially below the clay/silt
fraction concentrations. Figure 17 (bottom) presents
the same data but on an organic carbon-normalized
basis, Cs/oc(;). In contrast to dry weight normaliza-
tion, the PAH concentrations are essentially the same
in each size dass, as predicted by Equation 22.
In the field data from Evans et al. [45] sedi-
ments were collected at five sites along the River
Derwent, Derbyshire, United Kingdom, and sepa-
rated into six sediment size classes. The size dasses
were representative of day and silt (<63 u,m). to
course sand (1.0 to 2.0 mm). Organic carbon content
and total PAH were measured in each sediment size
class. Figure 18 presents the size classes and associ-
ated organic carbon content. Evans et aL attribute the
bimodal distribution of foe to two types of organic
matter. Organic matter in the 1.0 to 2.0 mm size dass
may be from fragmentary plant material while the
size classes less then 500 u,m organic carbon content
is the result of aging humic material. The organic
content in this study ranges from 2.0 to 40 percent.
Figure 19 presents a comparison of PAH con-
centration for different sediment classes for dry
weight normalization and organic carbon normaliza-
tion. The top left panel compares PAH concentrations
on sand (63 urn - 500 urn) and clay/silt (< 63 um) on a
dry weight basis. The top right panel compares PAH
concentrations on course sand (05 um - 2.0 um) and
day/silt (< 63 um) on a dry weight basis. The data in-
dicates that the PAH concentration is higher in the
course sand fraction of sediment. Recall from Figure
18 that the day/silt and course sand fractions contain
higher fraction organic carbon content. The bottom
panels of Figure 19 present the organic carbon nor-
malized comparison of PAH concentrations by sedi-
ment class. For both panels, the organic carbon
normalized PAH concentrations are similar regard-
less of the sediment size class as predicted by
Equation 22.
Lastly, Delbeke et al. [46] collected sediments
from seven sites in the Belgian continental shelf and
the Scheldt estuary. These sites were analyzed for
eight PCB congeners and organic carbon in the bulk
sediment and clay/silt (< 63 um) sediment fraction.
In addition, analyses of .the samples were done to de-
termine the percent of size fractions ranging from
500 um to 3 um which made up the sample. The PCB
congeners tested for in this study were IUPAC num-
bers 28,52,101,118,153,138,170 and 180.
Using concentrations reported for bulk sam-
ples, concentrations for clay/silt samples, and per-
cent size fractions of each sample, calculations were
done to estimate concentrations on the greater than
63 um portion of the sample. Similar calculations
were done to determine organic carbon content on
the >63 um portion of the sample. Organic content
varied from 0.01 percent to 10 percent inclusive of
both <63 uin and >63 um portions of the sediment.
-------
• ,'f* '.. '- -j -S>*
1OOO
a
» too
100000
8 1OOOO
X 1OOO
100
100
Sediment Quality Criteria Using Equilibrium Partitioning
Dry Weight Normalization
Send vs Cliy/SHt
Low Density vs Clay/Silt
10000
f
1OOO
10 100 1000
PAH
c
Dz
e
t
ii
^
'/
i>
ii
/
*
El
• U
Sta
Sta
H
K
-
/
.
1
r
t
t
t
j
t
t 1
/ 1
fl
1
"
Hx
P
wfc
mfc
-
x
/
^
/
^
^
^
f
7
^
/
/
/
/
/
/
/
/
/
/
7
^
/
^
1
a
F.
^
2
/
?
j;
^
7
'
/
/
'
,
5
_
-
—
-
—
63 urn
Il3-126um t2S-250um 26O-600W1 O6-1mm
SIZE CLASS
Figure 18.—The organic carbon fractions (% dry weight) in five sediment size classes ranging from clay and silt (<63 um) to
course sand (1.0 to 2.0 mm). Stations are indicated by hatch type. Data from [45].
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Dry Weight Normalization
1000
p 100
* «
I,
Sand va Clay/Sit
i
0.1
1000
a wo
* »
i
110 wo
PAH (uo/Q dry wt)
woo
0.1
Course Sand va Clay/Sit
0.1 1 w wo
PAH (ua/g dry wt)
WOO
WOOD
^ WOO
wo
I
Organic Carbon Normalization
Sand va Clay/Sit Court* Sand v« day/Sit
O63-125un
A125-250um
V250-500um
WOO
g
I «°
I"
I O0.5-1.0mm
OlQ-2.0mm
w wo woo
PAH (ue/g OC)
W WO WOO
PAH Cuo/g OC)
WOOD
Figure 19.—Comparison of PAH concentrations of the sand-sized and course sand-sized fraction sediment particles.indi-
cated by symbols, ordinate, to the clay/silt fraction, abscissa (Stations C, Da, G, H, K). Top panels are for dry weight normali-
zation; bottom panels are for organic carbon normalization. Data from [45].
Figure 20 presents the percent organic carbon on the
<63 um portion of the sample (filled bar) and on the
>63 um portion of the sample (shaded bar). A com-
parison of the PCB congener concentrations on a dry
weight basis (top) and on an organic carbon basis
(bottom) is shown in Figure 21. Organic carbon con-
tent in the >63 um class size at stations 2 and 4 is 0.01
percent and 0.06 percent respectively, as indicated in
Figure 20. The data for these stations are shown on
Figure 21 using filled symbols. Though an foc > 02
percent has been presented as the value.for which or-
ganic carbon normalization applies, normalization at
these/oc values seems appropriate for this data set.
The top panel of Figure 21 indicates no evi-
dent relationship between PCBs in the <63 um sam-
pl -id PCBs in the >63 um sample on a dry weight
b. When concentrations in either class size are
nc, alized to organic carbon content then the con-
centrations are similar for both class sizes as shown
in the bottom panel. This indicates that PCB concen-
trations are similar across sediment class sizes which
supports organic carbon normalization.
Organic Carbon Fractions
wo
O001
STATION
Figure 20.—The organic carbon fractions (% dry weight) in
two sediment size classes, <63 urn and >63 umi Seven
stations are indicated. Data from [46].
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Dry Weight Normalized
O001
OOO1 O01 0.1 1 10
PCB (ng/g}<63 urn
100
Organic Carbon Normalized
tcooo
A
•
V
•
D
O
i O -
Sta 1
Sta 2
Sta 3
Sta 4
Sta 5
Sta 6
Sta 7
WOO 10000
PCB (ng/o OCX 63 urn
Figure 21.—Comparison of eight PCB congener concentra-
tions of >63 urn sized particles, ordinate, to <63 urn sized
particles,. abscissa (Stations 1-7). Top panel is for dry
weight normalization; bottom panel is for organic carbon
normalization. PCB congeners are IUPAC Nos 28, 52,101,
118,138,. 153,179 and 180. Data from [47].
It can be concluded from the data of Prahl,
Evans et al., and Delbeke et al., that the organic car-
bon-normalized PAH and PCB concentrations are
relatively independent of particle size class and that
organic carbon is the predominant controlling factor
in determining the partition coefficient of the differ-
ent sediment size particles in a sediment sample. The
organic carbon concentration of the high-density
sand-sized fraction in Prahl's data (02 to 0.3 percent)
suggests that organic carbon normalization is appro-
priate at these low levels. The data from Evans et al.
suggests that EqP can be applied to organic carbon
originating from more than one source, that is, frag-
mentary plant matter and aging humic: material.
Sediment/pore water partitioning. Normally
when measurements of sediment chemical concen-
tration, Cs, and total pore water chemical concentra-
tions, Cpore, are made, the value of the apparent
partition coefficient is calculated directly from the ra-
tio of these quantities. As a consequence of DOC
complexation, the apparent partition coefficient, Kp,
defined as
(23)
is given by
/ocKoc
(24)
1 •*• W
As mooc increases, the quantity of DOC-complexed
chemical increases and the apparent partition coeffi-
cient approaches
/ooKoc
(25)
which is just the ratio of sorbed to complexed chemi-
cal. Because the solid-phase chemical concentration
is proportional to the free dissolved portion of the
pore water concentration, Cd, the actual partition co-
efficient, Kp, should be calculated using the free dis-
solved concentration. The free dissolved concentration
will typically be lower than the total dissolved pore
water chemical concentration in the presence of signifi-
cant levels of pore water DOC (e.g., Fig. 13). As a result,
the actual partition coefficient calculated with the free
dissolved concentration is higher than the apparent
partition coefficient calculated with the total dissolved
pore water concentration.
Direct observations of pore water partition
coefficients are restricted to the apparent partition co-
efficient, Kp (Eqn. 23), because total concentrations
in the pore water are reported and DOC complexing
is expected to be significant at the DOC concentra-
tions found in pore waters. Data reported by
Brownawell and Farrington in 1986 [48] demonstrate
the importance of DOC complexing in pore water.
Figure 22 presents the apparent partition coefficient,
measured for 10 PCB congeners at various depths in
a sediment core, versus/oc Kow, the calculated parti-
tion coefficient. The line corresponds to the relation-
ship, Koc = Kow, which is the expected result if DOC
complexing were not significant. Because DOC con-
centrations were measured for these data, it is possi-
ble to estimate Cd with Equation 20 in the form:
Cd-
-pore
(26)
1 +
-------
Sediment Quality Criteria Using Equilibrium Partitioning
O
\
I
c
O
O
O
CL
IB
O
• Apparent K'p
e Actual Kp
4.0
5.O 6.0 7.0
Log 10 foe Kow
Figure 22.—Observed partition coefficient versus the prod-
uct of organic carbon fraction and octanol/water partition
coefficient The line represents equality. The partition coef-
ficients are computed by using total dissolved PCB
(squares), and free PCB (circles) which is computed with
Equation 26 with Kooc - Kow. Data from [48].
and to compute the actual partition coefficient: Kp =
Cs/Cd. The data indicate that if KDOC =? Kow is used,
the results, shown on Figure 22, agree with the ex-
pected partition equation, namely mat Kp = foe Kow.
A similar three-phase model was presented by
Brownawell and Farrington in 1984 [49].
Other data with sediment/pore water parti-
tion coefficients for which the DOC concentrations
have not been reported [50,51] are available to assess
the significance of DOC partitioning on the apparent.
sediment partition coefficient. Figure 23 presents
these.apparent organic carbon-normalized partition
coefficients, that is KOC = Kp /-foe versus KOW The ex-
pected 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 diffi-
culty 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 [40,48].
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.
Laboratory toxicity tests. Another way to
verify Equation 22 is from data collected during sedi-
ment toxicity tests in the laboratory. These tests yield
sediment (Cs/oc) and pore water (Cd) chemical 'con-
centrations at several dosages bounding an experi-
mentally estimated toxic concentration for the test
organism. The organic content of the sediment must be
measured also. Sediment toxicity tests are done under
quiescent conditions and sediment and pore water are
in equilibrium. The results of these tests can be used to
o
o
O»
X
O
O
o
I
I 1
A diver (V«tetis)
B Spch. (PAH*)
100.0
8
Log 10 Kow
Rgufe 23.—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 KDOC = AW Data from [51] for
PCB congeners and other chemicals and from [50] for phenanthrene, fluoranthene, and perylene.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
compute the organic carbon partition coefficient Koc.
To verify Equation 22, estimates of Koc computed
from Equation 11 using laboratory measurements of
Kow are then compared to partitioning in the sedi-
ment toxicity test. Sediment toxicity tests and Kow
' measurements are available for five chemicals: en-
drin [20,21,52], dieldrin {54,55], acemaphthene [56],
phenanthrene [56], and fluoranthene [19,57]. Sediment
toxicity tests for these chemicals were performed as
part of the development of SQC. Mortality results for
these tests were presented in Figures 2 and 3..
Figure 24 shows organic carbon normalized
sorption isotherm for acenaphthene, endrin, phenan-
threne and fluoranthene, where the sediment concen-
tration (ug/g OC) is plotted versus pore water
concentration (ug/L). These tests represent fresh-
water and marine sediments having a range of or-
ganic carbon content of 0.07 to 11.0 percent. In each
ACENAPHTHENE
10000
100
«
1
O - Swortz et oL. 1991 -
panel, the line corresponds to Equation 16 where Koc
is derived from Kow measurements in the laboratory.
A full discussion of laboratory K0w measurements is
presented subsequently. In each of the panels the toxic-
ity test data are in agreement with the line computed
from experimentally determined Koc. For these chemi-
cals DOC measurements are unavailable and partition-
ing to DOC in the pore water has not been considered.
The figure indicates, however, that DOC complexing in
these experiments appears to be negligible.
Partitioning in the dieldrin experiment indi-
cated-that DOC complexation may have been signifi-
cant. The partitioning isotherm for dieldrin, Figure
25), represents organic carbon normalized sediment
concentration, versus total (top panel) and computed
dissolved (bottom panel) pore water concentrations.
Dissolved pore water concentrations are computed
100000
PHENANTHRENE
100 ^000 10000 100000
O - Swortz et ol. 1991 =
1QOOO
10000
1000
100
ENDRIN
FLUORANTHENE
8f «
0.1
O- Nebeker et oL, 1989
P- Schuytemo et d., 1989 :
V— SteMy,-1991 I
i 11 mil ill tiitil i i i inn! i i i nmil ' i i i tun
001 0.1
DOO 1000
O- Swortz et ol., 1990
O - De Witt et ol., 1992
t i i mid i t i unit lit tnn i i i Hud til 11 in
PORE WATER CONCENTRATION
(ug/L)
100 1000 1OOOO
PORE WATER CONCENTRATION
(ug/L)
Figure 24.—Comparison of organic carbon partition coefficient (Koc) observed in toxicity tests (symbols) to Koc derived from
laboratory Kow and Equation 11 (solid line). Symbols are sediment concentration, ordinate, versus pore water concentration,
abscissa. Solid line is Cs.oc = Koc * Ca, where Logio Koc is 3.76 for acenaphthene, 4.84 for endrin, 4.46 for phenanthrene,
and"5.00 for fluoranthene. These Logio Koc values are estimated from Logic KJW values measured at the U.S. EPA Environ-
mental Research Laboratory at Athens, Georgia. Data sources as indicated.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
DIELDRIN
t I Hfltl I I I IIIMt I I I HUM f I Illlltl I I I Illl
TOTAL PORE WATER CONCENTRATION (uQ/D
10000
"
_< «
at
It I 1 1 1111(1
111 Illl I I rii I ill I ii 11 mi I iLJ*nii
I III ttllt I III Mil
am
at
CONFUTED PORE WATER CONCENTRATION (ug/U
Figure 25. — Comparison of organic carbon partition coeffi-
cient (Koc) observed in toxicity tests (symbols) to Koc de-
rived from laboratory Kow and Equation 11 (solid line).
Symbols are sediment concentration, ordinate, versus total
(top panel) and free (bottom panel) pore water concentra-
tions, abscissa. Solid line is Cs,oe ~ Koc* Ca, where Logio
Koc is 5.25 for dieldrin. The Logio Koc value is estimated
from Logio KOM value measured at the U.S. EPA Environ-
mental Research Laboratory at Athens, Georgia. Data
source as indicated.
using Equation 26, DOC measurements and an esti-
mated log KDOC s 5.25. Log KDOC is estimated from
log Koc - 525 for dieldrin. Figure 25 represents data
from Hoke and Aukley [55] since Hoke [56] did not
measure pore water. Adjusting for partitioning on to
the DOC in the bottom panel results in better agree-
ment with the experimentally determined Koc. These
data represent one sediment with an organic carbon
content of 1.6 percent. It is important to note that
dieldrin has the highest Koc of the five chemicals
(LogioKoc dieldrin - 525, acenaphthene = 3.76, en-
drin = 4.84, phenanthrene = 4.46, fluoranthene = 5.00.
DOC complexing increases with an increasing parti-
tion coefficient which explains why DOC complexing
is significant for dieldrin.
Organic Carbon Normalization of
Biological Responses
The results discussed above suggest that if a concen-
tration-response curve correlates to pore water con-
centration, it should correlate equally well to organic
carbon-normalized total chemical concentration, in-
dependent of sediment properties. This is based on
the partitioning formula Cs,oc = Ko<£d (Eqn.16),
which relates the free dissolved concentration to the
organic carbon-normalized particle concentration.
This applies only to nonionic hydrophobic organic
chemicals because the rationale is based on a parti-
tioning theory for this class of chemicals.
Toxicity andbioaccumulation experiments. To
demonstrate this relationship, concentration-re-
sponse curves for the data presented in Figures 5 and
7 are used to compare results on a pore water-nor-
malized and organic carbon-normalized chemical
concentration basis. Figures 26 to 28 present these
comparisons for kepone, DDT, endrin, and fluoran-
thene. The mean and 95 percent confidence limits of
the LCso and ECso 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 re-
sponse versus the sediment concentration, which is
organic carbon-normalized (microgram 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 (Fig. 26) whereas the converse some-
times occurs, for example kepone-growth rate (Fig.
26). A more quantitative comparison can be made
with the LCsos and ECsos in Table 2. The variation of
organic carbon-normalized LCsos and ECsos between
sediments is less than a factor of two to three and is
comparable to the variation in pore water LCsos and
ECsos. A more comprehensive comparison has been
presented in Figures 2 and 3, which also examine the
use of the water-only LCso to predict the pore water
and sediment organic carbon LCsos.
Bioaccumulation factors calculated on the basis
of organic carbon-normalized chemical concentrations
are listed in Table 3, for permethrin, cypermethrin, and
-------
Sediment Quality Criteria Using Equilibrium Partitioning
100
S 80
]f •«>
5 40
i 20
0
Pore Water Normalization
Kapone - Mortality Kopon. - Growth
100
80
eo
40
20
0
10 10O 1000
Water Concentration (ug/L)
1 10 100 1OOO
•Per* Water Concentration (ug/U
Organic Carbon Normalization
Kcpoiw - Mortality Kvpom - Growth
_
1
100
80
80
40
20
0
«oc(X)
' a O.O8 - _ A ./• — • •
• » 1.6 ' ' 1
e 12 ' ' 1
*
**
I
I
1O 100 1OOO 100OO
Organic Carbon Normalized (ug/g oc)
100
80
eo
40
20
0
M m m •
ff^
.'"''/
10 100 1000 10000
Organic Carbon Normalized (ug/g oc)
Figure 26.—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
carbon concentrations [17]. -e
Pore Water Normalization
DDT
Endrln
1OO
- 80
>. eo
I 4°
I .20
0
n<
f
*
•oc |SS)
t-* :«;
* 1OJ
100
~ 8O
1 80
1 40
| 20
o'
tec (X)
. • *.1 • "
e mi
m» *"
Pora Water Concentration (UJ/L)
O.1 1.O 1O.O 100.O
Pora Water Concentration (ug/L)
Organic Carbon Normalization
DDT
^
k,
••
i
a
100
80
80
4O
20
0
OB- «*• «•
•ll
O
*
• •
•
.* . • * •* »
Endrln
1 1O 1OO 10OO 1000O
Organic Carbon Normalized {ug/g oc)
2
~
s
i
100
8O
eo
20
o
l>«
I
• •
jj
.*•!->• •-
•»••
.
•
1 1O 1OO 1OOO 10000
Organic Carbon Normalized (ug/g oc)
Rgure 27.—Comparison of percent survivalof 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 concentra-
iions j
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Por« Wafer Normalization
FkK>r*nth«n« >
= free dissolved chemical concentration
o 20 40 ao ao
Per* Wafer ConoMrtnUon (•o/U
Organic Carbon Normalization
FluoranUwn*
0 2000 4000 6000 8OOO
Orjinlc Carbon Normalized (nf/fl oe)
Figure 28.—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].
kepone. Again, the variation of organic carbon nor-
malized 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 nor-
malization. Laboratory and field data also exist for
which no pore water or -DOC measurements are
available but for which sediment concentration, or-
ganic carbon fraction, and organism body burden
have been determined. These data can be used to test
organic carbon normalization for sediments and to
examine organism normalization as well. It is con-
ventional to use organism lipid fraction for this nor-
malization (see references in Chiou [58]). If Cb is the
chemical concentration per unit wet weight of the or-
ganism, then the partitioning equation is
Cb =
(27)
where
KL s lipid/water partition coefficient (L/kg
lipid)
ft '= weight fraction of lipid (kg lipid /kg
organism)
The lipid-normalized organism concentration, Cb,L, is
r Cb (28)
Q>,L - -z- - Kl-Cd.
fi,
The lipid-normalized body burden and the organic
carbon-normalized sediment concentration can be
used to compute a bioaccumulation ratio, which can
be termed the BSF [59]:
BSF-
Q>,L KL KL
s,oc
KOW.
(29)
The second equality results from using the partition-
ing Equations 16 and 28 and the third from the ap-
proximation that Koc = KOW. The BSF is the partition
coefficient between organism lipid and sediment or-
ganic carbon. If the equilibrium assumptions are
valid for both organisms and sediment particles, the
BSF should be independent of both particle and or-
ganism properties. In addition, if lipid solubility of a
chemical is proportional to its octanol solubility, KL «
KOW, then the lipid normalized-organic carbon nor-
malized BSF should be a constant, independent of
particles, organisms, and chemical properties [59,60,
61]. This result can be tested directly.
The representation of benthic organisms as
passive encapsulations of lipid that equilibrate with
external chemical concentrations is dearly 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 [59]. It
is, nevertheless, an appropriate initialassumption be-
cause 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, Nereis and Nephtys, and two
species of deposit-feeding marine clams, Yoldia and
Macoma—and five sediments has been performed by
Rubinstein et al. [62]. The uptake of various PCB con-
geners was monitored until steady-state body bur-
dens were reached. Sediment organic carbon and
organism lipid content were measured. Figures 29
and 30 present the log mean of the replicates for the
ratio of organism-to-sediment concentration for all
measured congeners versus K0w for each organism.
Dry weight normalization for both organism and
sediment (left panels), organic carbon normalization
for the sediment (center panels), and both organic
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Dry Weight
Ner/os
Organic Carbon
o
i
u
3JO
3.O
1.0
o
D>
2 o.o
foe*(K)
4 «£
» 3.9 ,
6.5 7.5
LoglO Kow
1.0
0.0
Organic Carbon, Lipid
g -1.0
O
jj -2.0
3.0
2.0
I-
O
? 0.0
8.S
-i.<
Logic Kow
8.5 7.6
LogtO Kow
8.S
Dry Weight
Nepfltys
Organic Carbon
3.0
24
1.0
OJ>
-1.0
1.0
Organic .Carbon, Lipld
8 0.0
5 -1.0
o"
-2.O
•.6 7.8 a8
LoglO Kow
•JS 7.8
LoglO Kow
2.0
0.0
U 7.8
LoglO Kow
8.S
Figure 29.—Plots of the BSF (ratio of organism-to-sediment concentration) for three sediments for a series of PCB congeners
versus the logioKow for that congener. The dry weight normalization for both organism and sediment (left panels); organic
carbon normalization for the sediment (middle panels); and organic and lipid normalization (right panels) as indicated The or-
ganisms are Nereis (top) and Nephtys (bottom). Data from [62].
carbon and lipid normalization (right panels) are
shown. The results for each sediment eire connected
by lines and separately identified.
The BSFs based 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 reason-
ably constant for the polychaetes, although some
suppression is evident at logioKow > 7. The clams,
however, exhibit a marked declining relationship.
Results of a similar though less extensive ex-
periment using one sediment and oligochaete worms
have been reported [52]. A plot of the organic carbon-
and lipid-normalized BSF versus JCOW from this ex-
periment is shown on Figure 31, together with the
averaged polychaete data (Fig. 29). There appears to
be a systematic variation with respect to Kow, which
suggests that the simple lipid equilibration model
with a constant lipid-octanol solubility ratio is not
descriptive for all chemicals. This suggests that a
more detailed model of benthic organism uptake is
required to describe chemical body burdens for all
nonionic chemicals as a function of KOW [59]. How-
ever, for a specific chemical and a specific organism,
for example Nereis and any PCB congener (Fig. 29)
organic carbon normalization reduces the effect of
the varying sediments. This demonstrates the utility
of organic 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 [63]
that the lipid- and carbon-normalized BSF is in the
range of 0.1 to 10 (Figs. 29 to 31) supports the conten-
tion that the partition coefficient for sediments 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 is the ratio of the solubilities
of the chemical in lipid and in particle carbon (Eqn.
29). Because the solubility of nonionic organic chemi-
cals in various nonpolar solvents is similar [64], it
would be expected that the lipid-organic carbon solu-
bility ratio should be on the order of one. If this ratio
is taken to be approximately one, then the conclusion
from the BSF data is that Koc is approximately equal
to Kow for sediments [63].
-------
1 30
9.0
' ! •"
o
«•
3 ox>
Sediment Quality Criteria Using Equilibrium Partitioning \ 1
Dry Weight .
' locifc)
* 82
• 3X1 .
• 1X1
1 6.6 6.3 7.6 ft,
LoglO Kow
Dry Weight
8 "
3 -ix>
-W,
feo(X)
1.0
o O.O
£
8 -ix)
o
S -2.0
s *•%.
1X>
8 O.O
f ,
g -ix)
e
^ -2.O
M»S ft* 7JS &£ ""s.
LoglO Kow
K^A//»
Organic Carbon
^;
* 9JS 7JS 8
LoglO Kow
Macoma
Organic Carbon
•Un
fc^.
Logic Kow
3.O
o
9. 2.0
0
* 1-%
(
2X>
I "
g 0.0
e
| -IX,
i -*°.
Organic Carbon, Llpld
•
.^^.
a ex* us 84
Logic Kow
Organic Carbon, Llpld
Tfe^Y-
1 U 7.B •.
LoglO Kow
t
Rgure 30. — Plots of the BSF (ratio of organism-to-sediment concentration) for three sediments for a series of PCB congeners
versus the logioKiw for that congener. The dry weight normalization for both organism and sediment (left panels); organic
carbon normalization for the sediment (middle panels); and organic and lipid normalization (right panels) as indicated. The or-
ganisms are Yoldia (top) and Macoma (bottom). Data from [62].
Oligochaete - Polychaete BSF
100.O
^ 10.0
o
3
e 1>0
O
ja
u
0.1
• CHIgoehaoto
• Polychwt*
3.0 4.0 5.0 6.0 7.0 8.0
LoglO Kow
9.0
Figure 31.—Plots of the BSF (ratio of organism lipid to sediment organic carbon concentration) for a series of PCB congeners
and other chemicals versus logio/W Data for oligochaetes [51] and polychaetes [62].
-------
Sediment Quality Criteria Using Equilibrium Partitioning
.. A final observation can be made. The data
analyzed in-this section demonstrate that organic
carbon, normalization accounts 'for much of the re-
ported differences in bioavailability of chemicals in
sediments for deposit-feeding polychaetes, oligo-
' chaetes, and clams. The data presented in previous
sections are for amphipods and midges. Hence these
data provide important additional siupport for or-
ganic carbon normalization as a determinant of
bioavailability for different classes of organisms.
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., by Thomann and Con-
nolly [65]). It might be supposed that the toxicity and
bioaccumulation data presented above can be used to
examine the question of the route of exposure. The in-
itial observations were that biological effects appear
to correlate to the interstitial water concentration, in-
dependent of sediment type. This has been inter-
preted to mean that exposure is primarily via pore
water. However, the data correlate equally well with
the organic carbon-normalized sediment concentra-
tion (see Figs. 2 and 3). This observation suggests that
sediment organic carbon is the route of exposure. In
fact, neither conclusion follows necessarily from these
data because an alternate explanation is available that
is independent of the exposure pathwciy.
Consider the hypothesis that the chemical po-
tential or, as it is sometimes called, the fugacity [66]
of a chemical controls its biological activity. The
chemical potential, ud, of the free concentration of
chemical in pore water, Cd, is
(30)
where u,o is the standard state chemical potential and
RT is the product of the universal gas constant and
absolute temperature [67]. For a chemical dissolved
in organic carbon—assuming that particle organic
carbon can be characterized as a homogeneous
phase—its chemical potential is
-Ho + RTln(Cs,oc)
(31)
where Cs,oc is the weight fraction of chemical in or-
ganic carbon. If the pore water is in equilibrium with
the sediment organic carbon then
ud-Hoc.
(32)
The chemical potential that the organism experiences
from either route of exposure (pore water or sedi-
ment) is the same. Hence, so long as the sediment is
in equilibrium with the pore water, the route of expo-
sure 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 concentra-
tion, suggests that biological effects are proportional
to chemical potential or fugacity.
The issue with~respect to bioavailability is
this: In which phase is u, 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 concentra-
tion be directly measured, perhaps by the cis column
technique [42]. Total sediment concentration normal-
ized 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 con-
tains significant amounts of the chemical. This ap-
pears to be a reasonable assumption for most aquatic
sediments. Hence, SQC are based on organic carbon
normalization because pore water normalization is
complicated by DOC complexing for highly hydro-
phobic chemicals.
APPLICABILITY OF WQC AS
THE EFFECTS LEVELS FOR
BENTHIC ORGANISMS
The EqP method for deriving SQC utilizes partition-
ing theory to relate the sediment concentration to the
equivalent free chemical concentration in pore water
and in sediment organic carbon. The pore water con-
centration for SQC should be the effects concentra-
tion for benthic species.
This section examines the validity of using
the EPA WQC concentrations to define the effects
concentration for benthic organisms. This use of
WQC assumes that (a) the sensitivities of benthic
species and species tested to derive WQC, predomi-
nantly water column species, are similar, (b) the lev-
els of protection afforded by WQC are appropriate
for benthic organisms, and c) exposures are similar
regardless of feeding type or habitat. This section ex-
amines the assumption of similarity of sensitivity in
two ways. First, a comparative toxicological exami-
nation of the acute sensitivities of benthic and water
column species, using data compiled from the pub-
lished EPA WQC for nonionic organic chemicals as
well as metals and ionic organic chemicals, is pre-
sented. Then a comparison of the FCVs and the
chronic sensitivities of benthic saltwater species in a
series of sediment colonization experiments is made.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
•J!'
Method-Relative Acute Sensitivity
The relative acute sensitivities of benthic and water
column species are examined by using LCsos for
freshwater and saltwater species from draft or pub-
• lished WQC documents that contain minimum data-
base requirements for calculation of final acute
values (Table 4). These data sets are selected 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 spe-
cies 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, tem-
perature, pH, acute value, and test condition (i.e.,
static, renewal, flow-through, nominal, or measured)
were entered into a database. 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 ap-
propriate 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
species 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 concen-
trations, all acute values for that life stage were given
equal weight. If the remaining acute values for that
life stage differed by greater than 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. Second, life stages were
classified as either "benthic" (infaunal species [habi-
tats 1 and 2] or infaunal and epibenthic species [habi-
tats 1,2,3, and 4]), or "water column" (habitats 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 sensitivi-
ties of the most sensitive benthic and water column
species were examined by comparing the final acute
values (FAV) for benthic and water column organ-
isms, using acute LCso concentrations 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 to
8), the water column species were typically the most
sensitive. The results are cross-plotted on Figure 32
(top). The line represents perfect agreement.
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 (70 per-
cent) of the chemicals, and five or fewer species were
tested with 34 (85 percent) of the chemicals. Of the 30
chemicals for which WQC for saltwater organisms
are available, 2 or fewer infaunal species were tested
with 19 (63 percent) of the chemicals, and 5 or fewer
species were tested with 23 (77 percent) of the chemi-
cals. 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
toxitity database required for criteria derivation. As a
result, FAVs could not be computed for several of the
chemicals. Therefore, it is probably premature to con-
clude 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 defini-
tion of benthic includes both infaunal and epibenthic
species (habitat types 1 to 4), is based on more data
and suggests a similarity in sensitivity (Fig. 32, bot-
tom). In this comparison, the number of acute values
for freshwater benthic species for each chemical aver-
aged 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 and that additional
testing is desirable, or that this approach to examin-
ing species sensitivity is not sufficiently rigorous.
Examination of individual criteria documents
in which benthic species were markedly less sensi-
tive than water column species suggests that the ma-
jor 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 ap-
propriate data. Data that are available suggest that,
on the average, benthic and water column species are
similarly sensitive and support the use of WQC to
derive SQCfor the protection of infaunal and epiben-
thic species.
All species. A more general comparison of the
species sensitivities can be made if all the LCso data
are used. One approach examines the relative loca-
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Table 4.—Draft of published WQC documents and number of infaunal (habitats 1 and 2) eoibenthic (habitat-? i ann A\
and water column (habitats 5-8) species tested acutely for each substance ep'bentn,c (habitats 3 and 4),
CHEMICAL
ACENAPTHENE
ACROLEIN
ADRIN
ALUMINUM
AMMONIA
ANTIMONY (III)
ARSENIC (III)
CADMIUM
CHLORDANE
CHLORINE
CHLORPYRIFOS
CHROMIUM (III)
CHROMIUM (VI)
COPPER
CYANIDE
DDT
DIELDRIN
2,4,-DIMETHYLPHENOL
ENDOSULFAN
ENDRIN
HEPTACHLOR
HEXACHLOROCYCLOHEXANE
.LEAD
MERCURY
NICKEL
PARATHION
PARATHION, METHYL-
PENTACHLOROPHENOL
PHENANTHRENE
PHENOL
SELENIUM (IV)
SELENIUM (VI)
SILVER
THALLIUM
TOXAPHENE
TRIBUTYLTIN
1,2,4-TRICHLOROBENZENE
2,4,5-TRlCHLOROPHENOL
ZINC
DATE OF
PUBUCATIO
9/87"
9/87"
1980
1988
1985:1989
9/87"
1985
1985
1980
1985
1986
1984
1985
1985
1985
1980
1980
6/88b
1980
1980
1980
1980
1985
1985
1986
1986
10/88"
-1986
9/87b
5/88"
1987
1987
9/87*
11/88"
1986
9/87"
9/88"
9/87"
1987
NO.OFS ITWATEH SPECIES NO. OF FRESHWATPP «Fr F*
TOTAL"
mH^H
10
. -
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
| INFAUNA
^^••••H
-
0
-
2
3
2
10
1
2
2
-
8
6
1
1
1
2
2
1
1 •
2
2
10
7
-
-
7
4
-
1
-
1
-
2
1
7
4
10
EPIBENTHIC
™^™^^^— ^
3
-
11
-
- 7
6
3
18
7
9
8
-
9
5
4
11
15
2
8
14
14
14
3
7
10
-
-
7
6
-
5
—
6
-
9
8
7
5
9
WATER
COLUM
^••i^MH
7
_
12
-
16
5
8
18
7
15
10
-
9
18
5
12
15
6
8
16
13
12
10
18
9
_
11
4
-
13
_
16
_
11
15
4
5
17
TOTAL
^^••Mi
12
21
15
48
9
16
56
14
33
18
17
33
57
17
42
19
12
10
28
18
22
14
30
21
37
36
41
9
32
23
12
19
8
37
9
14
10
45
" The total numbers of tested species may not be the same as the sum of the number of spec
cies may occupy more than one habitat.
b Draft aquatic life criteria document, U.S. Environmental Protection Agency. Office of Water Re
Standards division, Washington, D.C.
INFAUNAL
1
2
2
1
1
13
1
1
2
3
1
8
1
3
1
1
1
3
1
11
7
1
9
2
6
2
1
1
1
5
1
2
1
5
^"^"•^••i^™™^
ies from each habitat
gulations and Standa
EPIBENTHI
10
17
6
16
4
9
8
10
15
6
15
9
3
4
12
8
4
4
8
14
9
11
1
9
6
4
9
1
13
1
12
i^^n^K^^
type becaus
rds. Criteria
WATER
COLUMN
7
15
11
33
13
31
10
19
36
29
7
17
12
18
11
12
13
23
23
6
20
19
10
13
3
23
6
8
30
•MH^HM
e a spe-
and
-------
Sediment Quality Criteria Using Equilibrium Partitioning
INFAUNAL
FRESH WATER
SALT WATER
-2-1012346
LOQ-K) MFAUHAL ORQAMSM FAV (ug/1)
BENTHIC
A FRESH WATER
• SALT WATER
J3-3-2 -101 2346
LOG*) BENTHC ORQAMSM FAV (ug/0
figure 32.—Comparison of LCso or ECso 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 (habi-
tat types 1-4); see Table 6.
tion of benthic species in the overall species sensitiv-
ity distribution. For each chemical in either fresh or
salt water, one can examine the distribution of ben-
thic species in a rank-ordering of all the species'
LCsos. If benthic species were relatively insensitive,
then they would predominate in ranking among the
larger LCso concentrations. Equal sensitivity would
be indicated by a uniform distribution of species
within the overall ranking. Figure 33 presents the re-
sults for tests of nickel in salt water. The LCsos are
plotted in rank order, and the benthic species are in-
dicated. Infaunal species are among the most tolerant
(left panel), whereas infaunal and epibenthic species
are uniformly distributed among the species (right
panel).
Table 5.—Habitat classification system for life stages of
organisms.
HABITAT
TYPE
DESCRIPTION
Life stages that usually live in the sediment and whose
food consists mostly of sediment or organisms living in
the sediment: infaunal nonfilter feeders.
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.
Life 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.
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
6
7
8
Life stages that usually live in the water column and
whose food consists mostly of organisms that live on or
in the sediment.
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.
Life stages that live in or on such inorganic substrates as
sand, rock, and gravel, but have negligible contact with
sediment containing organic carbon.
Life stages that have negligible interactions with
sediment because they spend essentially all their 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.
This comparison can be done chemical by
chemical. However, to make the analysis more ro-
bust, the LCso data for each chemical-water type can
be normalized to zero log mean and unit log variance
as follows:
LCson,ij -
log(LC50ij) - u.
(33)
Oi
where i indexes the chemical-water type, u,j is the log
mean and si is the log standard deviation, j indexes
the LCsos within the ith class, and LCson,ij is the nor-
malized LCso. This places all the LCsos from each set
of chemical-water type on the same footing. Thus,
the data can now be combined and the uniformity of
representation of benthic species can be examined in
the combined data set.
The comparison is made in Figure 34. If the
sensitivity of benthic species is not unique, then a
constant percentage of benthic species-normalized
LCsos, indicated by the dashed line, should be repre-
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Species Sensitivity for Ni in Sea water
Infaunal
Infaunal & Eplbenthlc
Q 100000
\
^ 10000
in
2
1000
100
o.
i — i • — i 1
; © Watar Column
• • Banthlc © .
®«@
©
A
@®®
0
®
®
o :
3
j 1 1 1
0 O.2 0.4 O.6 O.8 1.
1000000
100000
10000
1000
100
0 O.
© Watar Column
- e Banthic •©*'
e****
' .."• ' ]
©
. ©
•• i
©
•
0 0,2 O.4 0.6 0.8 1.0
Rank Rank
Figure 33.—L.CSOS 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.
Infaunal
Saltwater
i
so
30
20
1O
Freshwater
8 16 28 38 48 88 OS 78 88 OS
MMH of Parcantlla Rang* {•/.)
8 IS 28 38 48 88 88 78 88 SB
Mean of Parcantlla Rang* (5{)
H 80
£ 60
£
5>
o «0
1 20
ffl
O
•
•
1
1
I
%
1
s
•',.
j-
1
I
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i
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n
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£
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i
!
•
i
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i
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_ .
-
-
Infaunal & Epibenthic
Freshwater
8 18 28 38 48 65
-------
Sediment Quality Criteria Using Equilibrium Partitioning
sented in each 10-percentile (decile) interval of data
for all species. That is, the 10 rectangles in each histo-
gram should be identical in 'height. The infaunal
species (top panel) display a tendency to be under-
represented in the lowest deciles. However, the in-
* faunal and epibenthic species (bottom panels) more
closely follow this idealized distribution. Infaunal
and epibenthic freshwater species are nearly uni-
formly distributed, whereas the saltwater benthic
species are somewhat underrepresented in the
lowest ranks.
Given the limitations of these data, they ap-
pear to indicate that, except for possibly freshwater
infaunal species, benthic species are not uniquely
sensitive or insensitive and that SQC derived by us-
ing 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 salt-
water species [68-74] appear to be particularly sensi-
tive at measuring the impacts of chemicals on benthic
organisms. This is probably because the experiment
exposes the most sensitive life stages of a wide vari-
ety 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 larvae and other life
stages of marine organisms that can settle on 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 con-
centrations in interstitial water, overlying water, and
the sediment particles reach equilibrium, then the ef-
fect 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. An FCV is the concentra-
tion, 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 con-
sistent with the assumptions used to derive SQC. The
initially clean sandy sediment will rapidly equili-
brate with the inflowing overlying water chemical
concentration as the 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 sys-
tem with a unique chemical potential. Thus, the as-
sumption 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 expo-
sure 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.
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 pen-
tachlorophenol of 7.9 ng/L is less than the LOEC for
colonization of 16.0 ug/L. The NOEC of 7.0 ug/L is
less than the FCV. Although no FCV is available for
Aroclor 1254, the lowest concentration causing no ef-
fects on the sheepshead minnow (Cyprinodon vari-
egatus) and pink shrimp (Penaeus duorarum) as
cited in the WQC document is about 0.1 ug/L. This
concentration is less than the LOEC of 0.6 ug/L and
is similar to the NOEC of 0.1 ug/L based on a nomi-
nal concentration in a colonization experiment. The
lowest concentration tested with chlorpyrifos (0.1
ug/L) and fenvalerate (0.01 ug/L) affected coloniza-
tion of benthic species. Both values are greater than
either the FCV estimated for chlorpyrifos (0.005
ug/L) or the FCV estimated from acute and chronic
effects data for fenvalerate (0.002 ug/L). The draft
water quality criteria document for 1,2,4-trichlo-
robenzene suggests that the FCV should be 50.0
ug/L. This value is slightly above the LOEC from a
colonization experiment (40.0 ug/L) suggesting that
the criterion might be somewhat underprotective for
benthic species. Finally, a colonization experiment
with toxaphene provides the only evidence from
these tests that the FCV might be overprotective for
benthic species; the FCV is 02 ug/L versus the
NOEC for colonization of 0.8 ug/L.
The taxa most sensitive to chemicals, as indi-
cated by their LCsos and the results of colonization
experiments, are generally similar, although, as
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Table ,6.—Comparison of WQC FCVs and concentrations affecting (LOEC) and not affecting (NOEC)
benthic colonization, i '-•>•
SUBSTANCE
PENTACHLOROPHENOL
AROCLOR1254
CHLORPYRIFOS
FENVALERATE
1,2,4-TRICHLOROBENZENEa
TOXAPHENE
COLONIZATION
VERSUS FCVa "'"
Colonization LOEC
FCV
Colonization NOEC
Colonization LOEC
Estimated FCV
Colonization NOEC
Colonization LOEC
FCV
Colonization NOEC
Colonization LOEC
Estimated FCV
Colonization NOEC
Estimated FCV
Colonization LOEC
Colonization NOEC
Colonization LOEC
Colonization NOEC
FCV
"CONC. M8/L
16.0
7.9
7.0
0.6
-0.1
0.1
0.1
0.005
-
0.01
-0.002
-
50.
40.
-
11.0
0.8
0.2
SENSITIVE TAXA
Molluscs, Abundance
Molluscs, Crustacea, Fish
-
Crustacea
Crustacea, Fish
-
Crustacea, Molluscs, species richness
Crustacea
-
Crustacea, Chordates
Crustacea
-
Crustacea, Fish
Molluscs, abundance
-
Crustacea, species richness
-
Crustacea, fish
REFERENCE
[69,70]
[71]
[68]
[72]
[73]
[74]
[68]
"Six day exposure to established benthic community '
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 toxaphcne. Coloniza-
tion experiments indicated that molluscs are particu-
larly sensitive to three chemicals, an observation
noted only for pentachlorophenol in WQC docu-
ments. Fish, which are not tested in colonization ex-
periments, are particularly sensitive to four of the six
chemicals.
Conclusions
Comparative toxicological data on the acute and
chronic sensitivities of freshwater and saltwater ben-
thic species in the ambient WQC documents are lim-
ited. 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
epibenthic 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 that SQC can
be derived from WQC. The data suggest that the
most sensitive infaunal species are typically less sen-
sitive than the most sensitive water column (epiben-
thic and water column) species. When both infaunal
and epibenthic species are classed as benthic, the sen-
sitivities of benthic and water column 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 al-
most evenly distributed among both sensitive and in-
sensitive species overall.
Finally, in experiments to determine the ef-
fects of chemicals on colonization of benthic saltwa-
ter organisms, concentrations affecting colonization
were generally greater, and concentrations not affect-
ing colonization were generally lower, than esti-
mated or actual saltwater WQC FCVs.
GENERATION OF SQC
Parameter Values
The equation from which SQC are calculated is
(34)
(see Eqns. 2 to 7 and associated text). Hence, the SQC
concentration depends only on these two parame-
ters. The Koc of the chemical is calculated from the
Kow of the chemical via the regression Equation .11.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
The reliability of SQCoc depends directly on the reli-
ability of Kow. For most chemicals of interest, the
available KowS (e.g., [75]) are highly variable—a
range of two orders of magnitude is not unusual.
Therefore the measurement methods and/or estima-
• tion methodologies used to obtain each estimate
must be critically evaluated to ensure their validity.
The technology for measuring K0w has improved in
recent years. For example, the generator column [76]
and the slow stirring [73] method appear to give
comparable results, whereas earlier methods pro-
duced more variable results. Hence, it is recom-
mended that literature values for KowS not be used
unless they have been measured using the newer
techniques.
Measurement of Kbw
The Kow is defined as the ratio of the equilibrium
concentrations of a dissolved substance in a system
consisting of n-octanol and water and is ideally de-
pendent only on temperature and pressure:
(35)
where COCT is the concentration of the substance in
n-octanol and Cw is the concentration of the sub-
stance in water. The Kow is frequently reported in the
form of its logarithm to base ten as log P.
At the EPA Environmental Research Labora-
tory (ERL) at Athens, Georgia, three methods were
selected for measurement and one for estimation of
Kow for the five chemicals for which SQC are being
developed. The measurement methods were shake-
centrifugation (SQ, generator column (GC), and
slow-stir-flask (SSF). The estimations were made us-
ing the computer expert system SPARC. The discus-
sion of these methods is adapted from ERL at Athens
research protocols.
The SC method [78] is routinely used to meas-
ure the partitioning of compounds with Kow values
on the order of 102 to 10. The method involves add-
ing a layer of octanol containing the compound of in-
terest onto the surface of the water contained in a
centrifuge tube. Both phases were mutually presatu-
rated before beginning the measurements. Equilibra-
tion is established by gentle agitation and any
emulsions formed are broken by centrifugation. The
concentration in each phase is determined, usually
by a chromatographic method, and the Kow value
calculated using Equation 35.
The original GC method, limited to com-
pounds with Kovf values of less than 106, was modi-
fied [76] and used to determine K0w values up to 108.
Briefly, the method requires the packing of a 24-cm
length of tubing with silanized Chromosorb W. Oc-
tanol, containing the chemical in a known concentra-
tion, is then pulled through the dry support by gentle
suction until the octanol appears at the exit of the col-
umn. Water is then pumped through the column at a
rate of less than 2 mL per minute to allow equilibra-
tion of the chemical between the octanol and water.
The first 100 mL are discarded followed by collection
of an amount of water sufficient to determine ths
chemical concentration. The Kow is calculated using
Equation 35.
The SSF method [77] achieves equilibrium of
the compound between octanol and water by a gen-
tle stirring of the phases contained in a six-liter flask.
One liter aliquots of the aqueous phase are with-
drawn at two-day intervals and the concentration of
the chemical determined. Equilibrium is considered
to be established when the concentration of the
chemical is constant in successive samples (usually
after two to six days). The procedure is to set up three
six-liter flasks in a constant temperature room. Five
liters of water are added to each flask and the water
is stirred with teflon-coated magnetic stir bars over-
night to achieve temperature equilibration. The tem-
perature equilibrated octanol is added very gently
along the side wall to avoid mixing of the two
phases. At the time of sampling, a one-liter aqueous
sample is drained from a sampling port at the base of
the flask without disturbing the octanol layer. The
concentration in each phase is determined, usually
by a chromatographic method, and the K0w value
calculated using Equation 35.
When repetitive measures were made in the
Athens laboratory, a protocol was established to as-
sure compatibility with future experiments. These
protocols described the entire experimental scheme
including planning, sample requirements, experi-
mental set up and chemical analysis, handling of
data, and quality assurance. Only established ana-
lytical methods for solute concentration measure-
ment were applied and the purity and identity of the
chemical was determined by spectroscopic means.
The name on the label of the chemical's container
was not proof of identity.
Standard reference compounds (SRCs) were
tested with each experiment. SRCs are compounds
that are used as quality assurance standards and as
references in inter-laboratory generation of data. The
value of the process constant(s) has been established
by repetitive measurements for an SRC and serves as
baseline information for evaluating all experimental
techniques and all aspects of quality assurance. Be-
cause the SRC is taken through the entire experimen-
tal scheme, its acceptable result assures the
experimenter that equipment and measurement
methods are functioning satisfactorily. Table 7 shows
the logioKow values for endrin, dieldrin, acenap-
thene, phenantherene, and fluoranthene and the
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Table 7.—LogioKow values measured by shake-centrifugation (SC), Generator column (GC), and
slbw-stir-flask (SSF) for Endrin, Dieldrin, Acenapthene, Phenanthrene, Fluoranthene and
Concurrently analyzed standard reference compounds.
CHEMICAL
ENDRIN
DIELDRIN
ACENAPHTHENE
PHENANTHRENE
FLUORANTHENE
BIPHENYL
PYRENE
SC
4.65
4.91
4.79
4.76
4.84
4.83
4.84
4.83
5.04
5.00
5.04
5.03
5.04
4.88
4.99
5.04
3.82
3.84
3.88
3.84
4.29
4.25
4.33
4.33
4.99
5.00
5.01
4.06
5.17
GC
4.67
5.01
4.73
4.62
5.09
5.28
4.89
4.88
5.18
5.15
5.26
5.38
5.67
4.18
4.17
4.16
4.17
4.47
4.41
4.46
4.24
5.19
5.35
s 5.47
5.48
SSF
4.86
4.59
4.97
4.95
5.02
4.82
5.04
4.91
5.07
4.93
4.96
4.78
" 5.33
5.43
5.38
5.33
5.43
5.08
5.28
3.81
3.84
3.84
4.57
4.53
4.50
4.98
5.02
5.02
5.10
5.14
5.23
Source: U.S. EPA Environmental Research Laboratory, Athens Georgia.
SRCs, biphenyl and pyrene, measured at the Athens
laboratory by the SC methods. The SRCs were not
measured by the GC or SSF methods.
The logio of the average of eight previous
measurements of K0w by the shake-centrifugation
method for biphenyl is 4.06. The logio of the average
of 13 previous measurements of K0w by the shake-
centrifugation method for pyrene is 5.17. These aver-
age KowS are in good agreement with the SQC shake-
centrifugation measurements for biphenyl and
pyrene made concurrently with the measurements
for the five chemicals providing quality assurance for
the experimental techniques (see Table 7).
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Literature Kow An extensive literature search
was performed for the five compounds and two
standard reference compounds, biphenyl and py-
rene. Generally, problems encountered in compiling
and reporting fate constants from published data and
• from databases during a several years have ranged
from retrieval of misquoted numbers to solution of
nested citations [79]. Some citations were three or
more authors removed from the original work or
contained data referenced as unpublished data or as
personal communication. The same problems were
experienced during a ERL, Athens literature search.
The largest difference in misquoting numbers was six
orders of magnitude. For these reasons, ERL ob-
tained data from the primary sources and released
values only from these primary sources. Unpub-
lished data or data that originated through personal
communication were rejected as well as data that
were insufficiently documented to determine their
credibility and applicability or reliability.
Tables 8 and 9 show the measured and esti-
mated logioKow values, respectively, retrieved by this
literature search. Each of the measured values was
experimentally determined by the researcher using
one of several laboratory methods. The individual
experimental methods are not identified here. The es-
timated literature values were computed by the re-
searchers by one of several published techniques.
The individual computational techniques also are not
identified here.
Estimated Kow A promising new computa-
tional method for predicting reactivity is the com-
puter expert system SPARC (SPARC Performs
Automated Reasoning in Chemistry) being devel-
oped by ERL's Samuel W. Karickhoff and scientists at
the University of Georgia [106]. The system has the
capability of crossing chemical boundaries to cover
all organic chemicals and uses algorithms based on
mdamental chemical structural theory to estimate
"parameters. Organic chemists have in the past estab-
lished the types of structural groups or atomic arrays
that impart certain types of reactivity and have de-
scribed, in "mechanistic" terms, the effects on reac-
tivity of the structural constituents appended to the
site of reaction.
To encode this knowledge base, Karickhoff
and his associates developed a classification scheme
that defines the role of structural constituents in af-
fecting or modifying reactivity. SPARC quantifies re-
activity by classifying molecular structures and
selecting appropriate "mechanistic" models. It uses
an approach that combines principles of quantitative
structure-activity relationships, linear free energy the-
ory (LFET), and perturbed molecular orbital (PMO)
or quantum chemistry theory. In general, SPARC uses
LFET to compute thermal properties and PMO theory
to describe quantum effects such as delocalization en-
ergies or polarizabilities of pi electrons.
Table 8.—Measured logioKow values found in the
literature.
CHEMICAL
ENDRIN
DIELDRIN
ACENAPTHENE
PHENANTHRENE
FLUORANTHENE
BIPHENYL
PYRENE
LOGIOKOW VALUE
4.40
4.92
5.01
5.195
4.09
4.54
4.65
5.401
6.2
3.92
4.28
4.46
. 4.562
4.57
4.63
5.155
3.16
3.63
3.75
3.76
3.79
3.89
4.008
4.01
4.04
4.09
4.10
4.96
5.05
5.09
5.18
5.22
5.52
REFERENCE
[80]
[81]
[82]
[77]
[81]
[83]
[84]
[77]
[85]
[86]
[87]
[88]
[77]
[78]
[89]
[77]
[90]
[84]
[91] -
[92]
[80]
[76]
[77]
[82]
[86]
[84]
[89]
[80]
[84]
[93]
[78]
[89]
[94]
SPARC computes K0w values from activity co-
efficients in the octanol (~lo) and water (~lw) phases
using Equation 36:
LogioKow = logio(-lw/~lo) + logio(Mo/Mw) (36)
where Mo and Mw are solvent molecularities of oc-
tanol and water, respectively. SPARC computes activ-
ity coefficients for any solvent/solute pair for which
the structure parser can process the structure codes.
Ultimately, any solvent/solute combination can be
addressed. NJew solvents can be added as easily as
solutes by simply providing a Simplified Molecular
-------
Sedimetrt Quality Criteria Using Equilibrium Partitioning
Table 9.—Estimated logioKow values found in the
literature.
' CHEMICAL
ENDRIN
DIELDRIN
ACENAPTHENE
PHENANTHRENE
FLUORANTHENE
BIPHENYL
PYRENE
LOGlOKOW VALUE
3.54
5.6
3.54
3.70 -
3.92
3.98
4.03
4.15
4.22
4.33
4.43
4.44
4.45
4.63
4.64
4.90
4.95
5.22
5.29
5.33
3.79
3.95
3.98
4.14
4.25
4.42
4.50
4.85
4.88
4.'90
5.12
5.22
5.32
REFERENCE
[95]
[96]
[95]
[96]
[97]
[95]
[98]
[99]
[100]
[101]
[102]
[100]
[95]
[99]
[96]
[95]
U.S. EPA, Graphical
Exposure Modeling '
System [GEMS]*
[96]
[99]
[101]
[96]
[97]
[100]
[99]
[102]
[103]
[104]
[100]
[105]
[95]
[99]
[96]
[101]
The Graphical Exposure Modeling System (GEMS) is an inter-
active computer system located on the VAX Cluster in the Na-
tional Computer Center in Research Triangle Park, North
Carolina, under management of EPA's Office of Toxic Sub-
stances. PC GEMS is the version for personal computers.
Interactive Linear Entry System (SMILES) string [107,
108]. Activity coefficients for either solvent or solute
are computed by solvation models that are built from
structural constituents requiring no data besides the
structures. —
A goal for SPARC is to compute a value that is
as accurate as a value obtained experimentally for a
fraction of the cost required to measure it. Because
SPARC does not depend .on laboratory operations
conducted on compounds with structures closely re-
lated to that of the solute of interest, it does not have
the inherent problems of phase separation encoun-
tered in measuring highly hydrophobic compounds
(logioKow > 5). For these compounds, SPARCs com-
puted value should, therefore, be more reliable than
a measured one. Reliable experimental data with
good documentation are still necessary, however, for
further testing and validation of SPARC.
CLOGP [109] is a computerized program that
estimates the logioKow, based on Leo's Fragment
Constant Method [105]. CLOGP provides an estimate
of logioKow using fragment constants (ft) and struc-
tural factors (Fj) that have been empirically derived
for many molecular groups. The estimated logioKow
is obtained from the sum of constants and factors for
each of the molecular subgroups comprising the
molecule using Equation 37.
n
(37)
The method assumes that logioKow is a linear addi-
tive function of the structure of the solute and its con-
stituent parts and that the most important structural
effects are described by available factors. The struc-
ture of the compound is specified using the SMILES
notation. The CLOGP algorithm is included in the
database QSAR (see Table 10) located at EPA's Envi-
ronmental Research Laboratory at Duluth, Minne-
sota. All CLOGP values reported here were obtained
through QSAR. Table 10 shows the estimated
logioKow values that were computed with SPARC
and CLOGP.
Table 10.—QSAR -"obtained LogioKow values estimated
by SPARC and CLOGP
CHEMICAL
ENDRIN
DIELDRIN
ACENAPTHENE
PHENANTHRENE
FLUORANTHENE gan
BIPHENYL
PYRENE
SPARC
5.40
5.40
3.88
4.58
5.21
4.25
5.13
CLOGP
-
-
4.07
4.49
4.95
4.03
4.95
•Quantitative Structure^Activity Relationships (QSAR) is an in-
teractive chemical database and hazard assessment system
designed to provide basic information for the evaluation of
the fate and effects of chemicals in the environment. QSAR
was developed jointly by the U.S. EPA Environmental Re-
search Laboratory, Duluth, Minnesota, Montana State Univer-
sity Center for Data System and Analysis, and the Pomona
College Medicinal Chemistry Project.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Selection. Investigators selected SSF de-
rived Kow values to derive the KoC to calculate SQC
concentrations because SSF is tiie superior method
for chemicals with low and high Kow values, has the
least statL ,cal bias, and is highly.reproducible. Use
• of values from one method provides consistency
across chemicals. This choice was made after an
analysis of the Kow values generated by the three
measurement methods discussed above (GC, SC, and
SSF) and the SPARC estimation method for the five
chemicals for which SQC are currently being devel-
oped (acenaphthene, dieldrin, endrin, fluoranthene,
and phenanthrene). Kows were measured with repeat
experiments and a mean Kow was computed for each
method, for each chemical (Table 11).
The mean measured Kows and the SPARC esti-
mation method provide similar Kow estimates. To se-
lect a final Kow for computing the SQC, the four
methods were compared and the bias of each method
were quantified. (Bias is defined as the mean differ-
ence between the best-fit estimate of Kow using all
four methods and the estimates from each method.)
Figure 35 presents the mean measured K0ws for each
chemical and the range of values. SGtends to estimate
lower values while the GC method estimates higher
values. GC values exhibit greater variability than the
SC and SSF values. SSF estimates of Kows were gener-
ally within the range of the SC and GC methods.
A .stistical analysis of the three measure-
ment me;. ;ds and SPARC method was performed.
The following linear model was used to compute es-
timates of Kow for each chemical (represented by El,
E2, E3, E4, E5) and the biases contributed by each of
the estimation methods (represented by Bl, B2, B3,
B4). The regression model is as follows:
logioKow - El * Endrin +
E2 * Dieldrin +
E3 * Acenaphthene +
E5 * Fiuoranthene +
Bl * Shake Centrifugation +
B2 * Generator Column +
B3 * Slow-Stir-Rask
B4 * SPARC
To compute logirjKow the variables, ENDRIN,
... SPARC, are set to either 0 or 1 and the appropriate
coefficient for the chemical and method that corre-
sponds to the particular Kow measurement or esti-
mate is selected. Table 12 presents the model Kow
results and bias contributed by each method. Shake
centrifugation and SPARC estimates provide the
greatest bias, followed by the generator column
method. Slow-stir-flask provides the least bias. Slow-
stir-flask was chosen as the method to use to deter-
mine Kow for use in computing SQC since it appears
to have the least bias. In addition, it exhibits similar
variability to the shake centrifugation method and
less variability than the generator column method.
Table 12.—Model results to determine method bias
COEFFICIENT
El
E2
E3
E4
E5
Bl
B2
B3
B4
ESTIMATE OF Kow OR BIAS FOR
Endrin
Dieldrin
Acenaphthene
Phenanthrene
Fluoranthene
Shake Centrifugation
Generator Column
Slow-stir Flask
SPARC
VALUE
4.88
5.17
3.94
4.40
5.13"
•0.115
0.091
0.040
0.193
Determination. The previous section dis-
cusses selecting the method for measuring Kows for
' use in computing SQC. It is widely accepted that KocS
can be estimated from K0w. The K0c used to calculate
the sediment quality criteria is based on the regres-
sion of logioKoc to logioKow, Equation 11.
Table ±L—Kg* as measured by the EPA Environmental Laboratory at Athens, GA.
LogioKow (NUMBER OF DETERMINATIONS)
CHEMICAL
ENDRIN
DIELDRIN
ACENAPHTHENE
PHENANTHRENE
FLUORANTHENE
SHAKE CENTRIFICATION
4.80 (8)
5.01 (8)
3.85 (4)
4.30 (4)
5.00 (3)
GENERATOR COLUMN
4.97 (6)
5.16 (7)
4.17 (4)
4.40 (4)
5.39 (4)
SLOW STIR FLASK
4.92 (12)
5.34(7)
3.83 (3)
4.54 (3)
5.09 (6)
-------
Sediment Quality Criteria Using Equilibrium Partitioning
o
§
ACENAPHTHENE ErCWi
PtdANTHRENE
FLUORANTHENE
Figure 35.—Laboratory Kbw values for five chemicals using three experimental methods with replication. Ka* values were
measured at the Environmental Research Laboratory, Athens, GA. For each chemical the average of the methods is indicated
by 0 for shake centrifugation, D for slow stir flask and O for generator column. Ranges are indicated by I.
This equation is based on any analysis of an
extensive body of experimental data for a wide range
of compound types and experimental conditions,
thus encompassing a wide range of Kows and/ocs.
Sediment toxicity tests provide a favorable
environment for measuring Kow. Figures 24 and 25
presented plots of the organic carbon-normalized sorp-
tion isotherm from sediment toxicity tests for the five
chemicals where the sediment concentration (ug/g oc)
is plotted versus pore water concentration (ug/L).
Also included in each panel is the line to the parti-
tion, Equation 16, where Koc is computed from the
slow-stir flask Kow values. These plots can be used to
compare the Koc computed from laboratory deter-
mined Kow and the regression equation with the par-
titioning behavior of the chemical in the sediment
toxicity tests. For each of the chemicals the Koc line is
in agreement with the data demonstrating the valid-
ity of the use of the slow-stir flask Kow in the -SQC
computation.
Species Sensitivity
The FCV is used as the appropriate end point for the
protection of benthic organisms. Therefore, its appli-
cability to benthic species for each chemical should
be verified. The previous work has indicated that this
is a reasonable assumption across all criteria chemi-
cals. To test this assumption for a particular chemical
a statistical method known as Approximate Ran-
domization [110] is applied to each chemical. The
idea is to test whether the difference between the fi-
nal acute value (FAV) derived from considering only
benthic organisms is statistically different from the
FAV contained in the Water Quality Criteria (WQ'C).
The Approximate Randomization method
tests the significance level of the test statistic by com-
paring it to the distribution of statistics generated
from many random reorderings of the LCso values.
For example, the test statistic in this case is the differ-
ence between the WQC FAV, computed from the
WQC LCso values, and the benthic FAV, computed
from the benthic organism LCso values. Note that the
benthic organism LCso values are a subset of the
WQC LCso values. In the Approximate Randomiza-
tion method for this test, the number of data points
coinciding with the number of benthic organisms are
selected from the WQC data set. A "benthic" FAV is
computed. The original WQC FAV and the "benthic"
FAV are then used to compute the difference statis-
tics. This is done many times and the distribution
that results is representative of the population of FAV
difference statistics. The test statistic is compared to
this distribution to determine its level of significance.
For each chemical, an initial test of the differ-
ence between the freshwater and saltwater FAVs for
all species (water column and benthic) is performed.
The probability distribution of the FAV differences
for fluoranthene are shown in the top panel of Figure
37. The horizontal line that crosses the distribution is
the test statistic computed from the original WQC
and benthic FAVS. For fluoranthene, the test statistic
falls at the 78th percentile. Since the probability is
less than 95 percent, the hypothesis of no significant
difference in sensitivity is accepted.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Since freshwater and saltwater species show
similar sensitivity, a test of difference in sensitivity
for benthic and WQC organism^ combining freshwa-
ter and saltwater species can be made. The bottom
t panel of Figure 36 represents the bootstrap analysis
to test the hypothesis of no difference in sensitivity
between benthic and WQC organisms for fluoran-
thene. The test statistic for this analysis falls at the
74th percentile and the hypothesis of no difference in
sensitivity is accepted.
Table 13 presents the Approximate Randomi-
zation analysis for five chemicals for which SQC
documents are being developed. Four chemicals,
(acenaphthene, phenanthrene, fluoranthene and
dieldrin) indicate that there is no difference in sensi-
tivity for freshwater and saltwater species. The test
for endrin fails at the 99 percentile which indicates
that FAVs for freshwater and saltwater are different.
Therefore, separate analyses for the freshwater and
saltwater organisms are performed.
Table 14 presents the results of the statistical
analysis for each chemical for benthic organisms and
WQC organisms. In all cases the hypothesis of no differ-
ence in sensitivity is accepted. Therefore, for each indi-
vidual chemical the WQC is accepted as the appropriate
effects concentrations for benthic organisms.
Quantification of Uncertainty
Associated with SQC
The uncertainty in the SQC can be estimated from
the degree to which the equilibrium partitioning
model, which is the basis for the criteria, can rational-
ize the available sediment toxicity data. The EqP
model asserts that (1) the bioavailability of nonionic
organic chemicals from sediments is equal on an or-
ganic carbon basis; and (2) that the effects concentra-
tion in sediment can be estimated from the product
of the effects concentration from water-only expo-
sures and the partition coefficient K0c- The uncer-
tainty associated with the sediment quality criteria
can be obtained from a quantitative estimate of the
degree to which the available data support these
assertions.
FRESHWATER V8 SALTWATER
11limn i I limn 1—i i i i i—i mmi. i ..|.|m ,
PROHABOTY
Figure 36.—Probability distributions of randomly generated
differences between saltwater FAVs and freshwater FAVs
(top panel) and randomly generated differences between
WQC FAVs and benthic FAVs (bottom panel) using the Ap-
proximate Randomization method. Horizontal line in both
panels indicates the test statistic which is the FAV differ-
ence from original LCso data sets.
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. A listing of the data sources used
in the EqP uncertainty analysis is presented in Table
15. 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-organism-exposure media
Table 13.—Approximate randomization analysis freshwater versus saltwater organisms.
NUMBER
CHEMICAL
ENDRIN
DIELDRIN
ACENAPHTHENE
PHENANTHRENE
FLUORANTHENE
SALTWATER
19
21
10
11
8
FRESH WATER
35
19
10
8
12
FINAL ACUTE VALUE (FAV)
SALT WATER
0.033
0.662
139.05
16.6
16.13
FRESH WATER
0.189
0.377
80.01
59.63
33.58
DIFF
0.156
-0.305 :
-59.04
43.02
17.45
%
99
31
33
73
78
-------
Sediment Quality Criteria Using Equilibrium Partitioning
-Table 14.—Approximate randomization analysis benthic versus WQC organisms.
-: •' -• '
CHEMICAL
ENDRIN
ENDRIN
DIELDRIN
ACENAPHTHENE
PHENANTHRENE
FLUORANTHENE
NUMBER ...
WATER TYPE
Fresh
Salt
Combined
Combined
Combined
Combined
WATER COL +
BENTHIC
,- 35 ,
19
40
20
19
20
BENTHIC
21
12
26
10
13
14
FINAL ACUTE VALUE (FAV)
WATER COL +
BENTHIC
0.189
0.033
0.621
132.6
26.62
37.91
BENTHIC
.234
0.023
0.532
173.9
19.27
34.27
DIFF
•O.045
0.010
0.090
-38.34
7.35
3.64
%
7
66
72
31
80
74
Table 15.—Data for uncertainty analysis number of replicates//bc.
ENDRIN
Hy
3(3/-)
2/11%
4/11%
3/3.0%
3/6.1%
3/11.%
DIELD
Hy
V-
2/1.7%
4/2.9%
4/8.7%
FLUOR
Re
l/-
I2/.18%
2/.30X
!2/.48% '
ACENAP
Le
L 4/--
2/1.6%
2/2.5%
2/3.6%
ACENAP
Eo
4/-
2/1.2%
2/2.5%
2/3.6%
PHEN
Le
4/-
2/1.9%
2/2.5%
2/3.6%
PHEN
Eo
4/-
2/1.0% '
2/2.5%
2/3.3%
Hy B Hyalella Re = Rhapoxynlut Le = Loptoehelru* Eo = Eohaustorius
Data sources: endrin [20, 21, 52], dieldrin [53] fluoranthene [19], acenaphthene [56] phenanthrene [56].
treatment. These data are analyzed using an analysis
of variance (ANOVA) to estimate the uncertainty
(i.e., the variance) associated with varying the expo-
sure media and the uncertainty associated with ex-
perimental 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.
Sediment and water only LCsos are computed
from the sediment and water-only toxicity tests. The
EqP model can be used to normalize the data in order
to put it on a common basis. The LCso from water-
only exposures, LCsow (ug/L), is related to the LCso
for sediment on an organic carbon basis, LCsos,oc
(ug/goc) via the partitioning equation:
LCsOs,oc = Koc LCsOw
(38)
The EqP model asserts that the toxicity of sedi-
ments expressed on an organic carbon basis equals toxic-
ity in water-only tests multiplied by the Koc. Therefore,
either LCsos,oc (ug/goc), from sediment toxicity ex-
periments or Koc x LCsow.(ug/goc), are estimates of
the true LCso for this chemical-organism pair.
In this analysis, the accuracy of Koc is not
treated separately. Any error associated with Koc will
be reflected in the uncertainty attributed to varying
the exposure media.
In order to perform an analysis of variance, a
model of the random variations is required. As dis-
cussed above, experiments that seek to validate
Equation 38 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 sedi-
ments. Let a represent the random variation due to
the varying exposure media. Also, each experiment
was replicated. Let e represent the random variation
due to replication. If the model were perfect,,there
would be no random variation other than that result-
ing from the experimental error which is reflected in
the replications. Thus, a represents the uncertainty
due to the approximations inherent in the model, and
e represents the experimental error. Let oa2 and oe2
be the variances of these random variables. Let i in-
dex a specific chemical-organism pair. Let j index the
-------
Sediment Quality Criteria Using Equilibrium Partitioning
exposure media, water-only, or the individual sedi-
ments. Let k-index the replication of the experiment.
Then the equation that describes this relationship is
Ln(LCsoi,j,k) - Hi + ay + £i,j/k
(39)
where Ln(LCso)i,j,k, are either In(LCsow) or
In(LCsopw) corresponding to a water-only or sedi-
ment exposure; urs are the population of In(LCso) for
chemical-organism pair i. The error structure is as-
sumed to be lognormal which corresponds to assum-
ing that the errors are proportional to the means, for
example, 20 percent; rather than absolute quantities,
for example, 1 mg/L. The statistical problem is to es-
timate us and the variances of the model error, aa2,
and the measurement error, aE2. The maximum likeli-
hood method is used to make these estimates [109].
The results are shown in Table 16.
Tablo 16.—Analysis of variance for derivation of
criteria confidence limits.
SOURCE OF UNCERTAINTY
Exposure media
Replication
Sediment Quality Criteria
PARAMETER
Oa
Ot
OSQC
VALUE
(M/Coc)
" 0.39
0.21
0.39
Note that OSQC • oa the variability due to EqP
The last line of Table 16 is the uncertainty as-
sociated with the SQC, that is, the variance associ-
ated with the exposure media variability. The
confidence limits for the SQC are computed using
this uncertainty for SQC. For the 95 percent confi-
dence interval limits, the significance level is 1.96 for
normally distributed errors. Thus
ln(SQCoc)upper - ln(SQCoC) + 1.96oSQc (40)
ln(SQCoc)lower - In(SQCoc) - 1.96osQC (41)
The confidence limits are given in Table 17.
Minimum Requirements to
Compute SQC
It has been demonstrated that the computation of
sediment quality criteria for a particular chemical re-
quires key parameter values as well as evidence that
EqP is applicable for a particular chemical. Minimum
requirements for these parameters are warranted so
that they provide the level-of protection intended by
SQC and that are within the limits of uncertainty set
forth in this document. This section outlines mini-
mum data requirements and guidance for deriving
them. This is a necessary step to develop reliable pa-
rameters to be used in computing SQC. The mini-
mum requirements for an EqP based SC are as
follows.
• Octanol-Water Partition Coefficient (Kow)
• Final chronic value (FCV)
• Sediment Toxicity Tests
Procedures to ensure that these data meet assump-
tions of the EqP approach will also be addressed.
Laboratory octanol-water partition coefficient
Kow data developed by the slow stir flask measure-
ment technique is required. This method has been
shown to provide the least amount of variability and
TaWe 17.—Sediment quality criteria confidence limits for five chemicals.
SEDIMENT QUALITY CRITERIA 95% CONFIDENCE LIMITS ((lg/goc)
CHEMICAL
ACENAPTHENE
FLUORANTHENE
PHENANTHRENE
ENDRIN
DIELDRIN
TYPE OF WATER BODY
Fresh Water
Salt Water
Fresh Water
Salt Water
Fresh Water
Saltwater
Fresh Water
Saltwater
Fresh Water
Salt Water
SQCoe
ug/goc
132
232
616
296
182
238
4.22
0.76
11.1
20.4
LOWER LIMIT
61.5
103
290
140
85
111
1.96
0.354
5.17
9.50
UPPER LIMIT
283
498
1,300
640
391
511
9.06
1.63
23.8
43.8
-------
Sediment Quality Criteria Using Equilibrium Partitioning
the least bias when comparisons of Kow estimation
techniques were done for Kows derived for the five
chemicals for which SQC have been developed. A
minimum of three K0w values are required. These
values may be taken from the literature provided
» that methods followed yield a degree of confidence
similar to that provided by the methodology used by
EPA to derive Kow values.
If slow stirk flask Kow values do not exist then
laboratory experiments must be conducted. Meas-
urements of Kow done at the EPA ERL, Athens, Geor-
gia were presented. At a minimum, these procedures
are recommended. EPA laboratory procedures in-
clude a quality assurance and control plan. The plan
includes testing the compound by spectroscopic
means to ensure its identity and purity as well as
running concurrent Kow measurements of reference
compounds which have KowS that have been
verified.
Final chronic value. The FCV is computed as
part of the derivation of the water quality criteria for
.a compound, and is defined as the quotient of the Fi-
nal Acute Value (FAV) and the Final Acute-Chronic
Ratio (8). The data required to compute the WQC
FCV are water-only toxicity tests for a variety of or-
ganisms meeting minimum data base requirements.
The FCV computation and minimum database re-
quirements are presented in the EPA document
which describes methods to be used in deriving na-
tional ambient WQC (8).
^WQC are based on an assessment of a com-
pound's acute and chronic toxiciry for organisms rep-
resenting a range of sensitivities, most importantly
most sensitive organisms. This is appropriate since
the objective of WQC is to set limits based on the best
estimate of organism sensitivity. The toxiciry data
base should therefore include all available data that
meets requirements. That is, a complete search, re-
trieval and review for any applicable data must be
conducted, to locate all preexisting toxicity data. For
some compounds a WQC FCV may exist which
would provide a significant amount of toxicity data.
Literature searches are recommended to locate other
sources of toxiciry data.
A reevaluation of an already existing FCV is
warranted because data post dating publication of
the national FCV can be incorporated into the FCV
value. Also minimum database requirements have
changed since some WQC have been published. For
those compounds for which WQC FCVs do not exist,
compiled toxicity data are evaluated to see if mini-
mum data requirements as put forth by EPA (8) are
met. If so an FCV could then be computed. If there is
not enough water only toxicity data to compute an
FCV additional water only tests will be conducted so
that there is enough data to satisfy minimum data-
base requirements.
Sediment toxicity test. Verification of applica-
bility of EqP theory is required for each compound.
Sediment toxicity tests can be used for this. These
tests provide a sediment based LCso. Comparison of
the EqP predicted LCso with the sediment LCso con-
centration is direct confirmation of the EqP approach.
The validity of EqP is confirmed when the toxiciry
test results fall within the limits of uncertainty deter-
mined in this document.
Guidelines for conducting sediment toxicity
tests ensure that the tests are uniform and are de-
signed to incorporae the assumptions of EqP. These
tests must represent a range of organic carbon con-
tent and include organisms that exhibit sensitivity to
the chemical in question. The range of organic carbon
must be no less than a factor of 3 and a factor of 10 is
recommended. Organic carbon content shuld be no
less than 0.2 percent. Replicated toxicity tests for at
least two sediments are required. Organisms to be
used in the sediment toxicity tests are benthic ani-
mals which are most sensitive to the compound in
question. Guidelines on appropriare selection of ben-
thic organisms is given in the American Society for
Testing and Materials annual handbook [111].
Several studies are required as part of sedi-
ment toxicity testing. A water-only flow through test
is required. Water-only tests are run for five concen-
trations of the compound in question and a control.
The endpoint of interest is the 10-day mortality of the
test species. This value will be compared to the pore
water and sediment mortality from the sediment
spiking tests discussed next.
Two sediment spiking tests are required. The
first test is for the purpose of identifying sediment
spiking concentrations so that pore water concentra-
tions in spiked sediments bracket the LCso deter-
mined in the water-only test. In addition, this test is
done to determine the time-to-equilibrium of the
compound between the pore water and sediment.
Sorption equilibrium, and assumption of EqP theory,
is essential for valid porewater and sediment
concentrations.
Three spiking treatments are recommended
for this first test: low, medium and high concentra-
tion. The amount of compound to add to each treat-
ment is calculated using the initial chemical weight,
the % total organic carbon (TOC), % dry weight and
total volume of spiked sediment. The results are sedi-
ment concentrations that bracket the predicted LCsos
estimated from the water-only LCso (ug/L) andKoc.
Samples for chemical analyses in bulk sediment and
pore water are collected at various time intervals.
Nominal sediment spike concentrations, measured
sediment TOC and measured and EqP-predicted
compound concentrations in sediments and pore wa-
ters are obtained for each sample period to establish
time-to-equilibrium and to verify that spiking pro-
duces the appropriate concentrations in the pore
water.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
In the second sediment spiking tests three
sediments representing a range of organic carbon
content are spiked to yield five estimated sediment
concentrations to bracket the predicted sediment
LCso. The amount of compound for spiking is based
* on similar computations as in the first sediment spik-
ing experiment. Each treatment (sediment by concen-
tration) is held for the appropriate time based on
time to equilibrium established in the first spiking
test. Day 0 samples are taken for sediment and pore
water analyses. Then organisms are placed in repli-
cated beakers and 10-day sediment toxicity tests with
the equilibrated spiked sediments are conducted.
Eight replicates for each treatment are required. Four
replicates are used for day 10 sediment and pore
water analyses while the remaining four replicates
are used to assess organism mortality.
These experiments provide data to compute
pore water toxic units and sediment toxic units
(Equations 1 and 8). The results of these equations
serve as direct comparisons of the predicted toxicity
(Equation 1 and 8 numerator) to the observed toxic-
ity (Equation 1 and 8-denominator). That is, the va-
lidity of EqP for a chemical is confirmed when the
pore water and sediment toxic units fall within the
limits of uncertainty determined in this document.
Analytical procedures. The purpose of these
procedures is to verify that:
• the WQC FCV applies to benthic organisms
• the Koc from the slow stir flask Kow is an
accurate estimate of Kow
A test that the WQC FCV, which is applicable
to the most sensitive water column organisms, is ap-
plicable to the most sensitive benthic orgnaisms is
needed for each chemical. In computing SQC for en-
drin/ dieldrin, acenapthene, phenanthrene and
fluoranthene, the Approximate Randomization test
was applied. This is a statistical test to compare the
WQC toxicity database to benthic organism toxicity.
The methodology is presented previously. If it is
found that benthic organisms exhibit similar or less
sensitivity to a chemical than those organisms used
to compute WQC, then the WQC FCV can be applied
in computing an SQC. If benthic organisms exhibit a
greater sensitivity than the WQC orgnaisms then tox-
icity experiments for benthic organisms are required.
A check on the laboratory Koc must be done
by comparing it to the Koc computed from sediment
toxicity tests. Pore water and sediment concentra-
tions from the sediment toxicity test provide data
necessary to compute Koc- This Koc is then compared
to the Koc from the slow stir flask K0w.
Lastly, when a site's sediments are being stud-
ied, a check to show that the SQC applies to the site is
needed. National SQC may be under or over protec-
tive if 1) the species at the site are more or less sensi-
tive than those included in the data set used to derive
SQC or 2) the sediment quality characteristics of the
site alter the bioavailability predicted by EqP and, ul-
timately, the predicted toxicity of the sediment
bound chemical. Therefore, it is appropriare that site-
specific guidelines procedures address each of these.
conditions separately, as well as jointly. Methods to
determine the applicability of national SQC to a site
and to determine site specific SQC if needed are pre-
sented in the EPA guidelines document for deriving
site specific sediment criteria [112].
Conclusion. Minimum database and analyti-
cal requirements must be set when deriving national
sediment criteria. The reasons for this is twofold.
First, the requirements provide that a level of protec-
tion intended by the criteria are met. Secondly, the re-
quirements provide that parameters used to compute
the criteria satisfy assumptions underlying the EqP
theory. The key required parameters are Kow using
the slow stir flask measurement method, the WQC
FCV and sediment toxicity tests. Procedures to verify
that these values are appropriate to use in the SQC
computation are also required. It must be shown that
the FCV is protective of benthic organisms. Confi-
dence in the Koc must also be established by compar-
ing the Kow to the observed K0c in sediment toxicity
tests. Individual sites may exhibit greater or lesser
toxicity to a chemical than that predicted by SQC to
an individual site. EPA procedures to test this as well
as to compute site specific SQC are available.
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 38 in the form, of a nomograph. The di-
agonal lines are for constant FCVs as indicated. The ab-
scissa is iogioKow- For example, if a chemical has an
FCV of 1.0 ug/L and a IogioKow of 4, so that K0w =
104, the logic SQCoc is approximately 1 and the SQC
= 10 = 10.0 ng chemical/g organic carbon.
As can be seen, the relationships between
SQCoc and the parameters that determine its magni-
tude, Kow and FCV, are essentially linear on a log-log
basis. For a constant FCV, a 10-fold increase in Kow
(one log unit) increases the SQCoc by approximately
10-fold (one log unit) because Koc also increases ap-
proximately 10-fold. Thus, chemicals with similar
FCVs will have larger SQCocS if their KowS are larger.
The chemicals listed in Figure 37 have been
chosen to ilustrate the SQCoc concentrations that re-
sult from applying the EqP method. The water qual-
ity concentrations are the FCVs (not the final residue
values) computed as part of the development of SQC
for acenaphthene, endrin, phenanthrene, dieldrin
and fluoranthene or from draft or published EPA
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Sediment Quality Criteria
o
o
o»
JO
9
O
X
0
E
•a
o
u>
D)
O
6
5
4
3
1000 100
-1
-2
-3
FCV (ug/L)
10
1
O.I *"
O.O1
0.001
M»thyl Parathion
Toxaphan*
Chlordana
Parathion
EndoaOlfan
Acanaphthwi*
Endrln
Ph«nanthr«n*
Chlorpyrifoa
DMdrln
Fluoranth*n»
3 . 4
6
Log10 Kow
Figure 37.—Logio SQC versus logio Kow. The diagonal lines indicate the FCV values. The criteria are computed from Equa-
tion 34. KM 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 for methyl
parathion, toxaphene, chlordane, parathion are from WQC or draft documents, see Table 4. The FCVs for acenapthene, en-
drin, phenanthrene, fluoranthene and dieldrin are computed as part of the development of SQC. The octanol/water partition
coefficients for methyl parathion, toxahene, chlordane, parathion, endosulfan and chlorpyrifos are the log mean of the values
reported in the Log P database [75]. The KS>WS for acenaphthene, endrin, phenanthrene and dieldrin are those measured
from the slow stir flask method.
WQC documents (see Table 4) for the remaining
chemicals plotted. Measurement of Kows for ace-
naphthene, endrin, phenanthrene, dieldrin and
fluoranthene are from the slow stir flask method as
previously presented. The Kows for the remaining
chemicals are the log averages of the values reported
in the Log P database [75]. While the SQCs for ace-
napthene, endrin, phenanthrene, fluoranthene and
dieldrin meet the minimum database requirements
presented in the previous section, SQCs for the re-
maining chemicals are for illustrative purposes only
and should not be considered final SQC values. Final
SQC, when published, should reflect the best current
information for both FCV and Kow.
The FCVs that are available for nonionic or-
ganic insecticides range from approximately 0.01
Ug/L to 0.3 ug/L, a factor of 30. The SQCocs range
from approximately 0.01 ug/g organic carbon to in
excess of 10 ug/g organic carbon, a factor of over
1,000. This increased range in values occurs because
the KowS of these chemicals span over two orders of
magnitude. The most stringent SQCoc in this exam-
ple is for chlordane, a chemical with one of the low-
est KowS among the chemicals with an FCV of
approximately 0.01 ug/L.
By contrast, the PAHs included in this exam-
ple have a range of FCVs and Kows of approximately
an order of magnitude. But these values vary in-
versely: The chemical with the larger FCV has a
smaller K0w. The result is that the SQCocs are ap-
proximately the same, 240 ug/g organic carbon.
Classes of chemicals for which the effects concentra-
tions decrease logarithmically with increasing KowS,
for example, chemicals that are narcotics [113], will
have SQC that are more nearly constant.
Field Data
Information on actual levels of the criteria chemicals
in the environment was assembled in order to pro-
vide an indication of the relationship between the
-------
Sediment Quality Criteria Using Equilibrium Partitioning
SQC concentrations and the actual concentration lev-
els observed in the sediments of U.S. surface water
bodies. Three separate databases were examined:
• EPA's STORE! database .[114],
• NOAA's National Status and Trends data-
base, which focused on water bodies in
coastal areas [47], and the
• Corps of Engineers database for San Fran-
cisco Bay [116].
The data that were retrieved have been sum-
marized on probability graphs that are presented in
the subsections that follow for each of the data
sources. A large proportion of the observations are
below detection limit values and indicate only that
the actual concentration is unknown, but less than
the concentration plotted. These data are plotted
with a "less than" symbol. As a result, the probability
plots should not be interpreted as representations of
.the actual probability distribution of the monitored
samples. They do, however, provide a useful visual
indication of the range of concentration levels of the
study chemicals in natural sediments.
A suggestion of the probable extent to which
problem sediments might be encountered is pro-
vided by the plot overlay showing the SQC concen-
tration developed by this research. In the case of the
STORET data, the SQC is shown as a band because
the foe is not reported. The lines represent the SQC
for between an foe = 1 to 10 percent. The other two
data bases provide the necessary information on
sediment organic carbon levels, and the results have
been properly normalized.
Some salient features of the available field
data displayed by the plots are summarized in Table
18. The SQC concentration is listed for each of the
five criteria chemicals, together with the number of
samples and the approximate percentage of the sam-
ples that exceed the SQC. The table also lists the sedi-
ment concentrations that are exceeded by 10,5, and 1
percent of the measurements.
We recognize that the tabulated information
represents only approximate estimates, because of
the presence of large numbers of detection limit val-
ues. Nevertheless, it provides what we consider to be
a reliable expectation that only a small percentage of
sediment sites in the databases, less than 5 percent,
will have concentrations that exceed the SQC levels.
We did not attempt a more rigorous analysis
to provide a more definitive characterization of the
spatial and temporal features of the database. Some
of the recorded data dearly represent multiple sam-
ples at a particular site. The very high observed con-
centrations are relatively few in absolute number and
may reflect multiple samples at one or a few particu-
larly contaminated sites. Some of the probability
plots also show a discontinuity at the .high end. This
is particularly true of the Corps of Engineers data
that pool results from a limited number of stations in
San Francisco Bay. Until a more detailed analysis is
performed, the results of the preliminary screening
should be considered approximate, upper bound es-
timates of the probable prevalence of sediment sites
that may exceed the SQC.
STORET data. A STORET data retrieval was
performed to obtain a preliminary assessment of the
concentrations of the criteria chemicals in the sedi-
ments of the nation's waterbodies. The data retrieved
was restricted to samples measured in the period
1986 to 1990. The selection of this recent period elimi-
nated much of the older data with the higher detec-
tion limits to provide a more accurate indication of
current conditions. Log probability plots concentra-
tions are shown in Figures.38 and 39. Concentrations
are shown on a dry weight basis, because sediment
organic carbon is not reported. The SQCs are com-
puted on the basis of a sediment organic carbon con-
- tent (foe) of 1 percent and 10 percent, which is the
typical range for inland sediments. The STORET data
distinguishes between the type of waterbody, and
separate displays are provided for stations on
streams, lakes, and estuaries.
The PAH data are shown in Figure 38. The to-
tal number of samples, and the number of detected
samples are indicated on the figures. The plotted
points are restricted to a subset of the total number of
samples, so that the plots are legible. A few samples
with detected concentrations, the solid symbols, ex-
ceed the SQC for foc = 1 percent, and fewer exceed
the SQC for/oc = 10 percent. The nondetected data,
plotted at the detection limit with "<", are below the
value indicated on the plot. In fact with nondetected
data included in the probability plot, the actual plot-
ting positions of the detected data is uncertain, since
the nondetected data may in fact occupy plotting po-
sitions further to the left, at lower probabilities. Thus
the exceedence probabilities for the detected data are
at least as large as is indicated on the plots. Approxi-
mately 5 percent or less of the detected samples ex-
ceed the/oc = 1 percent SQC.
The data for endrin and dieldrin are shown in
Figure 39. Similar results are obtained. Less than 3
percent of the detected dieldrin and endrin samples
exceed the lower SQC.
National Status and Trends Program data.
NOAA's National Status and Trends Program devel-
oped a database on the quality of marine sediments
focusing on estuarine and coastal sites that are not in
close proximity to known sources of contamination
[116]. Figure 40 displays the distribution of sediment
concentrations from the National Status and Trends
Program sites for four of the five criteria chemicals
-------
Sediment Quality Criteria Using Equilibrium Partitioning
Table 18.—Observed quality of natural sediments.
SALTWATER
CORPS OF
ENGINEERS
NOAA
NATIONAL STATUS
AND TRENDS
PROGRAM
EPA STORE!
ESTUARIES
FRESH WATER
EPA STORET
STREAMS
EPA STORET LAKES
SQC (|ig/g oc)
NO. OF SAMPLES
% that exceed SQC
10% exceed - conoc
5% exceed - conoc
1% exceed - cone
NO. OF SAMPLES
% THAT EXCEED SQC
10% exceed - cone
5% exceed - cone
1% exceed - cone
SQC(ng/g)for
sediment OC: 1-10%
NO. OF SAMPLES
% THAT EXCEED SQC
10% exceed - cone
5% exceed - cone
1% exceed - cone
SQC (ng/g) for
sediment OC: 1-10%
NO. OF SAMPLES
% THAT EXCEED SQC
10% exceed - cone
5% exceed - cone
1% exceed - cone
NO. OF SAMPLES
% THAT EXCEED SQC
10% exceed - cone
5 exceed - cone
1% exceed - cone
FIJUORANTHENE
293
231
3%
40
300
40,000
797
0.2%
4
7
40
3.0-30
88
1%
4
7
40
6.2-62
786
2%
4
7
40
57
5%
4
7
40
PHENANTHRENE
238
231
4%
60
300
25,000
736
0.1%
25
40
90
2.4-24
87
<1%
0.1
0.8
1.0
1.8-18
584
7%
1
2
? .
50
<0%
K?)
2(?)
10(?)
ACENAPTHENE
232
130
5%
30
200
3,000
245
0%
4
7
40
2.3-23
74
0%
0.3
0.3
0.5
1.3-13
681
<4%
1
1.5
40
56
2%
0.7
1
3
ENDRIN
0.744
260
0%
60
150
700
-
-
-
-
-
.007-0.07
^
150
<10%
all
data are
-------
52 Sediment Quality Criteria Using Equilibrium Partitioning i 1
f&
h
m***
*o5
!§
IS
••S'
»
n
10
10
V
*>
»
n
»
•n
«
X
•i
:
-i
o.
4
I
I
1
0
.<
J
-1
-4
*^t ^
STREAM
r~
r
r
i 1
5 LWE ^
r
r
r" '
f— '
r
r
r
TOTAL SAJbFLES: 881
I£ASU£D SA1FLES: 53
' lf~
r*~~
TO 2O SO SO SO M
TOTAL' SAiFLES: 58 '
MEASURED SAVPLES: 5
_•*««*<
««««*
•
SO
PROBABLJTY
- ESTUART TOTAL SAllpUS: 8?'
f MEASURED SMFLES: 28
L-l -- -- -1
r
r ^««««
\
r
r. _«
- 3
-•
2
3
-- «r< .-- *
1
i
1
20 fo »o eo
PROBABLTTY
n
10
n
«
10
10
n
•n
' STREAM
r
r
r
!"«««««
L1 1
' LAKE
r
r
r
r
r
TOTAL SAMPLES: 7B£-
fcCASURED SAUUS: V.»
' ^Pk«««^*<«^«<<^^^
.-/
10 20 00 MM
TOTAL SAMPLES: 5?'
MEASURED SAVPLES: 28
^f^
--r-.ir... .^
•"1
.^ T
i
i
i
i
1
M M.
' ' ' ' !
I
:
1
n
1
V)
n
V
n
•n
10
«
n
4
a
t
.1
-4
J
- ESTUARY TOTAL SAMR£S: 88
T MEASURED SAIFLES: 32
c
r .. ..
r
r ^f*""™
r , ««««<«,«<^^
r
r
i
j
• -\
1
i
i
, ' T
PROBABLTTY
Figure 38.—Comparison of sediment quality criteria for sediments containing 1 percent and 10 percent organic carbon to
the distributions of three sediment PAH concentrations from the U.S. EPA STORE! database from 1986 to 1990. Samples
above the detection limit (filled symbols) and samples less than the detection limit (less than symbols) are shown. Data from
U.S. EPA [114].
-------
10'
10°
io-'
io •'
10 ":
10 "!
10"'
•«#
0.1
10 '
10° •
10"'[
10'2 'r
10
10
10
0.1
'3
Sediment Quality Criteria Using Equilibrium Partitioning
TOTAL SAMPLES: 2677
. MEASURED SAMPLES: 67
10 20
60 90
99
99,9
LAKE TOTAL SAMPLES: 478
r MEASURED SAMPLES: 12
. "
i <^
o ._
10-'
10"
10"
10"
10
TOTAL SAMPLES: ISO
MEASURED SAMPLES: 0
<
,<«»;
10 20 SO 80
PROBABILITY
JO
10"
ESTUARY
0.1
TOTAL SAMPLES: 160
MEASURED SAMPLES: 3
PROBABILITY
« < i
Figure 39.—Comparison of sediment quality criteria for sediments containing 1 percent and 10 percent organic carbon to
the distributions of two sediment pesticide concentrations from the U.S. EPA STORE! Database. Data are from 1986 to
1990. Samples above the detection limit (filled symbols) and samples less than the detection limit (less than symbols) are
shown. Data from U.S. EPA [114].
Table 19.—San Francisco Bay sediment samples.
LOCATION
Port of San Francisco: Piers 27-29, 35, 38. 48, 70, 80 and 94
Rsherman's Wharf and Islais Creek
Suisun Channel
West Richmond
Pinole Shoal
Carquinez Strait
Mare Island Strait
Richmond Harbor Channel
Santa Fe Channel
Outer and Inner Richmond Harbor Channel
Port of Oakland Tier II: Berths 20-23, 2!>, 26-30, 31, 35-38, 60-63 and 82-84
Port of Oakland Outer and Inner Harbor
Treasure Island . -
San Leandro Bay
San Pablo Bay
NO. OF SAMPLES
PAHt ft PESTICIDES
21
2
6
11
44
10
6
48
6
6
40
27
5 composites
1 composite
6
YEARS
1988 and 1990
1990
1991
1990
1990
1990
1990
1990
1990
1991
1989-1990
1990-1991
1990
1990
1989-1990
Note: PAHs = Fluoranthene, Phenanthrene, Acenapthene; Pesticides = Dieldrin, Endrin.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
10000
1000
100
=8 «
if 1
J3 ai
aoi
aooi
i IIHIIIJ - 1 — i i | i i — | - |iinn i i
SQCoc
O - all f o,. (N=288)
•- foe > °-2« (N=245) I
I lltl«l I I Illllll
100
10
0.1
aoi
aooi
10 20 60 8080
ftOOOl
oo oao
0.1
10 20 SO 8090
00 OOA
10000
10 20 60 8OOO
PROBABUTY
10 20 60 8000
PROBABUTY
00 OOJO
Figure 40.—Comparison of sediment quality criteria to the distributions of acenaphthene, fluoranthene, dieldrin, and phen-
anthrene organic carbon normalized sediment concentrations from NOAA's National Status and Trends Program. Data are
from 1984 to 1989. Samples with organic carbon greater than 0.2 percent (filled symbols) and samples for all organic carb-
on contents (open symbols) are shown. Data from NOAA [47].
the qualify of dredged sediments in order to deter-
mine their suitability for open water disposal. The
database did not indicate what determinations were
made concerning their acceptability for this purpose.
Investigators compared the frequency of occur-
rence (in individual samples, not dredge sites) with the
SQC criteria developed using the EqP methodology.
The major portion (93 percent) of the samples analyzed
had organic carbon fractions greater than 02 percent,
for which the SQC concentrations are applicable. The
concentrations of each chemical measured in these
sediments was normalized by the organic carbon con-
tent and the results are displayed below as probability
plots to illustrate the frequency at which different levels
are observed. Results are presented for the five criteria
chemicals. A horizontal line at the concentration value
of the SQC provides a reference that indicates the rela-
tionship between observed range of quality and the
SQC for each chemical.
PAH results are summarized in Figure 41.
Less than 5 percent of the individual samples con-
tained concentrations in excess of the sediment
quality. It is informative to note that the small set of
very high concentrations are nearly all from one sam-
ple site (Treasure Island). These samples are respon-
sible for the discontinuous pattern of the frequency
distribution.
Figure 42 presents the monitoring program
results for the two pesticides in the same format. In
this case, virtually all of the samples were less than
the varying detection limits of the analytical tests.
Each of the samples for which actual measurements
were obtained were at least an order of magnitude
lower than the SQC. An estimate of the possible fre-
quency distribution of sediment concentrations of
dieldrin and endrin was developed by the applica-
tion of an analysis technique that accounts for the
varying detection limits and the presence of nonde-
tected observations [117]. The results are illustrated
by the straight line, which suggests that no apprecia-
ble number,of exceedences is expected. However, the
virtual absence of detected concentrations makes the
-------
Sediment Quality Criteria Using Equilibrium Partitioning
if
10'
10 "
10 3
10 '
10 t
10°
10"
10"
1 '
F
r SQCOC
r
r
r •
r "
• 1 1 ,,i, ni i.i. i mi
1 ' ' i
,. «•• • ^
• 1
1
• ° •*°° 1
• all foc (N-112) :
0 foc > 0.2X (N-105) 1
i , , i , , i i, , |MI •
0.1
10 20
50
80 90
99
99.9
^^
§
if
10'
10
10-
10'
102
10l
10 J
10 C
10"
10"
all foc (N-207)
foc > 0.2X (N-190) 1
10 20
50
80 90
99
99.9
all foc (N-207)
foc > 0.2X (N-190)
0.1
10 20
SO
80 90
99
99.9
PROBABUTY
Rgure 41.—Comparison of sediment quality criteria to the distributions of acenapthene, fluoranthene, and phenantnrene or-
ganic carbon normalized sediment concentrations from the U.S. Army Corps of Engineers monitoring program of San Fran-
cisco Bay. Samples with organic carbon greater than 0.2 percent (filled symbols) and samples for all organic carbon contents
(open symbols) are shown. See Table 19 for description of location, number of samples, and sample period. Data from U.S.
Army COE [115].
distribution estimates unreliable. They are presented
only to suggest the probable relationship between
the levels of these two pesticides in relation to sedi-
ment quality criteria.
CONCLUSIONS
The technical basis and data that support the use of
the EqP method to generate SQC have been pre-
sented for nonionic organic chemicals. The use of or-
ganic carbon normalization is equivalent to using
pore water normalization as a means of accounting
for varying bioavailability (Figs. 2,3, 5-8,26-28). The
variation in organism body burden across sediments
can also be significantly reduced if organic carbon
and lipid normalization are used (Figs. 29-31). For
contaminated sediments, particle size effects are re-
moved if organic carbon-normalized concentrations
are compared (Figs. 17,19,21). The reason is that or-
ganic carbon is the proper normalization for parti-
tioning between free dissolved chemical and
sediment-bound chemical (Fig. 11).
Using pore water normalization for highly
hydrophobic chemicals is complicated by chemical
complexing to DOC (Fig. 13). Partitioning between
-------
Sediment Quality Criteria Using Equilibrium Partitioning
10
0.1
01*09
5s
0.1
10 20 60 80 00
PROBABILITY
09 99*
Figure 42.—Comparison of sediment quality criteria to the distributions of endrin and dieldrin organic carbon normalized
sediment concentrations from the U.S. Army Corps of Engineers monitoring program of San Francisco Bay. See Table 19 for
description of location, number of samples and sample period. Samples with organic carbon greater than 0.2 percent (filled
symbols) and samples less than the detection limit (less than symbols) are shown. Also shown is an estimate of the distribu-
tion developed by accounting for nondetected observations (solid line). Data from U.S. Army COE [115].
pore water and sediment organic carbon from field-
collected sediments can be rationalized if DOC com-
plexing is taken into account (Figs. 22 and 23).
However, the complexed chemical appears not to be
bioavailable (Fig. 15).
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 sensitivities for
both the most sensitive species tested (Fig. 32) and all
tested species (Fig. 34). Benthic colonization experi-
ments also demonstrate that WQC can be used to
predict effects concentrations for benthic organisms.
A direct statistical test of the equality of the distribu-
tions can be used to confirm or refute this assump-
tion for individual chemicals (Fig. 36).
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 bioavailabil-
ity are made (Figs. 5-8). A variation factor of approxi-
mately two to three remains (Figs. 2 and 3), which
includes measurement and other sources of variabil-
ity. This is not unexpected as EqP is an idealization of
the actual situation. Other factors that are not
considered in the model play roles in determining
biological effects. Hence, it is recognized that a quan-
tification of the uncertainty should accompany the
SQC that reflect these additional sources of variation.
-------
Sediment Quality Criteria Using Equilibrium Partitioning
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 6) are a labora-
tory simulation of a field validation. Sediment qual-
ity criteria can possibly be validated more easily than
WQC because determining the organism exposure is
more straightforward. The benthic population expo-
sure is quantified by the organic carbon-normalized
sediment concentration.
It has been suggested that the kinetics of PAH
desorption from sediments control the chemical body
burden of a benthic amphipod [118]. The extent to
which kinetics can be important in fit'ld 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 sedi-
ments, would also be helpful. In a typical practical
application of SQC, mixtures of chemicals are in-
volved. The extension of EqP methodology to mix-
tures would be of great practical value. Initial
experiments indicate that it should be possible [119].
The EqP method is presently restricted to •
computing effects-based-criteria for the protection of
benthic organisms. The direct extension of this meth-
odology for computing sediment criteria that are
protective of human health, wildlife, and marketabil-
ity of fish and shellfish requires that the equilibrium
assumption be extended to the water column and to
water column organisms. This assumption is, in gen-
eral, untenable. Water column concentrations can be
much lower than pore water concentrations if suffi-
cient dilution flow is present. Conversely, upper-tro-
phic-level organisms are at concentrations well
above equilibrium values [120]. Hence, the applica-
tion of the final residue values from the WQC for the
computation of SQC, as was done for certain interim
criteria [121], 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 reason-
able. However, a site-specific investigation (e.g., Con-
nolly [122]) appears to be the only available method
for performing an evaluation of the effect of contami-
nated sediments on the body burdens of upper-tro-
phic-level organisms. 3
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