United States
Environmental Protection
Agency
Office of Science and Technology and
Office of Research and Development
Washington, DC 20460
Technical Basis for the Derivation
of Equilibrium Partitioning
Sediment Guidelines (ESGs) for
the Protection of Benthic
Organisms: Nonionic Organics
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..;:'"" .'-':•' • Technical. Basis.ifor Derivation of ESGs: Nonibnic Organics i
Foreword
Under the Clean Water Act (CWA), the U.S. Environmental Protection Agency (EPA) and the
States develop programs for protecting the chemical, physical, and biological integrity of the
nation's waters. To meet the objectives of the CWA, EPA has periodically issued ambient water
quality criteria (WQC) beginning with the publication of "Water Quality Criteria, 1972" (NAS,
1973). The development of WQC is authorized by Section 304(a)(l) of the CWA, which directs the
Administrator to develop and publish "criteria" reflecting the latest scientific knowledge on (1)
the kind and extent of effects on human health and welfare, including effects on plankton, fish,
shellfish, and wildlife, that may be expected from the presence of pollutants in any body of water,
including ground water; and (2) the concentration and dispersal of pollutants on biological
community diversity, productivity, and stability. All criteria guidance through late 1986 was
summarized in an EPA document entitled "Quality Criteria for Water, 1986" (U.S. EPA, 1987).
Updates on WQC documents for selected chemicals and new criteria recommendations for other
pollutants have been more recently published as "National Recommended Water Quality Criteria-
Correction" (U.S. EPA, 1999). EPA will continue to update the nationally recommended WQC as
needed in the future.
In addition to the developmentofWOC:andjorontinue,jojjneeyhe objectives of the CWA, EPA
has conducted efforts to develop ancTpi^ish equilibrium partitioning sediment guidelines (ESGs)
for some of the 65 toxic pollutants or toxic pollutant categories. Toxic contaminants in bottom
sediments of the nation's lakes, rivers, wetlands, and coastal waters create the potential for
continued environmental degradation even where water column contaminant levels meet
applicable water quality standards. In addition, contaminated sediments can lead to water quality
impacts, even when direct discharges to the receiving water have ceased. These guidelines are
authorized under Section 304(a)(2) of the CWA, which directs the Administrator to develop and
publish information on, among other things, the factors necessary to restore and maintain the
chemical, physical, and biological integrity of all navigable waters.
The ESGs and associated methodology presented in this document are EPA's best
recommendation as to the concentrations of a substance that may be present in sediment while
still protecting benthic organisms from the effects of that substance. These guidelines are
applicable to a variety of freshwater and marine sediments because they are based on the
biologically available concentration of the substance in the sediments. These ESGs are intended
to provide protection to benthic organisms from direct toxicity due to this substance. In some
cases, the additive toxicity for specific classes of toxicants (e.g., metal mixtures or polycyclic
aromatic hydrocarbon mixtures) is addressed. The ESGs do not protect against synergistic or
antagonistic effects of contaminants or bioaccumulative effects to benthos. They are not
protective of wildlife or human health endpoints.
EPA recommends that ESGs be used as a complement to existing sediment assessment tools, to
help assess the extent of sediment contamination, to help identify chemicals causing toxicity. and
to serve as targets for pollutant loading control measures. EPA is developing guidance to assist
in the application of these guidelines in water-related programs of the States and this Agency.
This document provides guidance to EPA Regions. States, the regulated community, and the
public. It is designed to implement national policy concerning the matters addressed. It does not.
however, substitute for the CWA or EPA's regulations, nor is it a regulation itself. Thus, it cannot
impose legally binding requirements on EPA, States, or the regulated community. EPA and State
decisionmakers retain the discretion to adopt approaches on a case-by-case basis that differ from
this guidance where appropriate. EPA may change this guidance in the future.
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This document has been reviewed by EPA's Office of Science and Technology (Health and
Ecological Criteria Division, Washington, DC) and Office of Research and Development (Mid-
Continent Ecology Division, Duluth, MN; Atlantic Ecology Division, Narragansett, RI), and
approved for publication.
Mention of trade names or commercial products does not constitute endorsement or
recommendation of use.
Front cover image provided by Wayne R. Davis and Virginia Lee.
iv
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Contents
Foreword ffi
Acknowledgments »
Executive Summary ™
Glossary w
Section 1
Introduction M
1.1 General Information |"J
12 Rationale for Selecting the EqP Method 1-1
13 Relationship to WQC Methodology |-2
\A Applications of Sediment Guidelines |-2
15 Overview
Section 2
Partitioning of Nonionics in Sediments 2-1
2.1 Toxicity and Bioavailability of Chemicals in Sediments 2-1
22 Partitioning of Nonionic Organic Chemicals 2-3
23 Effects Concentration
Section 3
Toxicity and Bioavailability of Chemicals in Sediments 3-1
3.1 Toxicity Experiments
32 Bioaccumulation
33 Conclusions
Section 4
Sorption of Nonionic Organic Chemicals 4-1
4.1 Partitioning in Particle Suspensions
4.1.1 Particle Concentration Effect J*
4.12 Organic Carbon Fraction 4-4
4.13 Organic Carbon Composition 4-6
42 Dissolved Organic Carbon Complexing _•
43 Phase Distribution in Sediments
4.4 Bioavailability of DOC-Complexed Chemicals J*
45 Field Observations of Partitioning in Sediments 4-*
45.1 Organic Carbon Normalization
452 Verification of Field Organic Carbon Normalization
4.5.2.1 Sediment Equilibrium
45.2.2 Summary
453 Sediment/Interstitial Water Partitioning
45.4 Laboratory Toxicity Tests
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,' Contents
4.6 Organic Carbon Normalization of Biological Responses .......................................... 4-27
4.6.1 Toxicity and Bioaccumulation Experiments ................................................ 4-27
4.62 Bioaccumulation and Organic Carbon Normalization ................................. 4-28
4.7 Determination of Route of Exposure [[[ 4-32
Section 5
-Applicability of WQC as Effects Levels for
Benthic Organisms [[[ 5-1
5.1 Relative Acute Sensitivity of Benthic and Water Column Species ............................ 5-1
52 Comparison of Sensitivity of Benthic and Water Column Species ............................ 5-1
52.1 Most Sensitive Species [[[ 5-1
522 All Species [[[ 5-4
53 Relating Acute to Chronic Sensitivities for Benthic Organisms ................................ 5-6
5.4 Benthic Community Colonization Experiments [[[ 5-6
55 WQC Concentrations Versus Colonization Experiments ............................................ 5-8
5.6 Conclusions [[[ 5-10
Section 6
Generation of ESG [[[ 6-1
6.1 Parameter Values [[[ frl
62 Selection of Kow [[[ frl
62.1 KQC Determination [[[ 6-1
63 Species Sensitivity .............. .. [[[ fr2
6.4 Quantification of Uncertainty Associated with ESGs ................................................ 6-2
65 Minimum Requirements to Compute an ESG [[[ 6-7
6.5.1 Octanol-Water Partition Coefficient [[[ 6-8
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Tables
Table 3-1. Sediment toxicity and bioaccumulation data [[[ 3-2
Table 3-2. Comparison of LC50 and EC50 values on a sediment dry weight- and sediment organic
carbon-normalized basis to values calculated from interstitial water and water-only exposures ......... 3-6
Table 3-3. Bioaccumulation factors for C. tentans [[[ 3-6
Table4- 1. Summary information for studies used to verify field organic carbon normalization .......................... 4-11
Table 4-2. Comparison of average coefficient of variation using dry weight- or organic carbon-
normalized concentrations [[[ 4-22
Table 5-1. Draft or published WQC documents and number of infaunal (habitats 1 and 2), epibenthic
(habitats 3 and 4), and water column (habitats 5 to 8) species tested acutely for each substance ....... 5-2
Table5-2. Habitat classification system for life-stages of organisms [[[ 5-3
Table 5-3. Comparison of WQC FCVs and concentrations affecting (LOEC) and not affecting (NOEC)
benthic colonization [[[ 5-9
Table6-l. Recommended log,0Kow and their corresponding log,,,^ values for five chemicals ....................... 6-1
Table 6-2. Results of approximate randomization (AR) test for the equality of freshwater and saltwater
FAV distributions for endrin and dieldrin [[[ 64
Table 6-3. Results of approximate randomization (AR) test for benthic and combined benthic
and water column (WQC) FAV distributions for endrin and dieldrin [[[ 64
Table6-4. Data used in the equilibrium partitioning uncertainty analysis [[[ 6-5
Table6-5. ANOVA for derivation of confidence limits of ESG values [[[ 6-7
Table6-6. Comparison of individual and combined error estimates [[[ 6-7
Table6-7. Confidence limits of the ESGs for endrin and dieldrin [[[ 6-9
Table 6-8. San Francisco Bay sediment samples [[[ 6-17
Figures
Figure 2- 1 . Diagram of the organism exposure routes for a water-only exposure and a sediment
2-2
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p
[Contents -..-'• .. \. ,-. ;'•/•' ' ^- '- . Y : ..•'•_"_:£ ".. J'l/.-l'-/'- - 1-..:1.-1_ .".'
Figure 3-2. Comparison of percent mortality of A?, abronius to fluoranthene concentrations in bulk sediment
and interstitial water for sediments with varying organic carbon concentrations 3-3
Figure3-3. Comparisonof percent mortality of ff. azteca with DDT and endrin concentrations in
bulk sediment and interstitial water for three sediments
Figure 3-4. Comparison of body burden in C. tentans of cypermethrin and permethrin versus
concentration in bulk sediment and interstitial water for sediments >5
Figure4-l. Observed versus predicted reversible component partition coefficient for nonionic
organic chemicals using Equation 4-2
Figure4-2 Comparison of the conventional adsorption and reversible component organic carbon-
normalized partition coefficient, Kx, to the octanol-water partition coefficient, Kow, for
experiments with low solids concentrations
Figure4-3. Comparison of the normalized partition coefficients for adsorption and reversible
component sorption to sediment organic carbon
Figured. Partition coefficients of chemicals to paniculate organic carbon (POC), Aldrich® humic ^
acid, and natural DOC
Figure4-5. Phase distribution of a chemical in the three-phase system: sediment, interstitial water, ^
and freely-dissolved
Figured Average uptake rate of chemicals by Pontoporeia hoyi with and without DOC present 4-9
Rgure4-7. Comparison of the DOC partition coefficient calculated from the suppression of chemical
uptake versus the CIg reverse-phase HPLC column estimate
Figure4-8. Organic carbon fractions (percent dry weight) by sediment size fraction 4-12
Figure4-9. Comparison of PAH concentrations of the sand-sized and low-density sediment
particles to the clay/silt fraction
Figure4-10. Comparison of PAH concentrations of the sand-sized and coarse sand-sized
sediment particles indicated by symbols to the clay/silt fraction
Figured! 1 Organic carbon fractions for two sediment size classes from seven sampling stations, five
sediment size classes from two sampling locations, and three sediment size classes
Figure4-12. Comparison of eightPCB congener concentrations of >63 urn sized particles
with <63 um sized particles
Figure 4-13. Comparison of 20 PCB congener concentrations on the <15 um size particles
with four larger size fractions
Figure4-14. Comparison of six chlorinated organic chemical concentrations on three sediment
size fractions
Figure4-15. Comparison of the average coefficient of variation for dry weight-normalized
and organic carbon-normalized concentrations and the percent reduction
in the average coefficient of variation due to organic carbon normalization for each study «i
Vlll
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Figure 4- 16. Apparent partition coefficient versus the product of the organic carbon fraction
andKow [[[ 4-23
Figure4-17. Apparent organic carbon-normalized partition coefficient (K^O versus ^Tow ................................. 4-24
Figure 4- 18. Comparison of K^ observed in toxicity tests to K^ calculated using Equation
4-3 and £ow values [[[ 4-25
Figure4-19. Comparison of K^ observed in toxicity tests to K^ calculated using Equation 4-3
and ffow values [[[ 4-26
Figure 4-20. Correlation of log,,,^ from sediment toxicity tests to log,,,/^ estimated from
EPA-recommended Kow values for five chemicals [[[ 4-27
Figure4-21. Comparison of percent mortality and growth rate reduction of C. tentans to kepone
concentrations in interstitial water and in bulk sediment using organic carbon normalization
for three sediments [[[ 4-28
Figure 4-22. Comparison of percent mortality of H. azteca with DDT and endrin concentrations in
interstitial water and in bulk sediment using organic carbon normalization for three sediments ........ 4-29
Figure 4-23. Comparison of percent mortality of R. abronius to fluoranthene concentrations in
interstitial water and bulk sediment using organic carbon normalization
for sediments with varying organic carbon concentrations [[[ 4-30
Figure 4-24. Plots of the BSAFs for Nereis and Nephtys for three sediments for a series of
PCB congeners versus the log,,,*^ for that congener [[[ 4-31
Figure 4-25. Plots of the BSAFs for Yoldia and Macoma for three sediments for a series
of PCB congeners versus the Iog,0ffow for that congener [[[ 4-32
Figure4-26. Plots of the BSAFs for a series of PCB congeners and other chemicals versus log,0/ifow ................ 4-33
Figure 5-1 . Comparison of the FAVs for water column versus benthic organisms
for chemicals listed on Table 5-1 [[[ ^
Figure 5-2. LC50 values versus percentage rank sensitivities for nickel in saltwater species ................................. S6
Figure 5-3. Proportion of saltwater and freshwater benthic organisms in 10 percentile groups of
all normalized LC50 values for infaunal and infaunal and epibenthic organisms as benthic ................ 5-7
Figure 5-4. Distribution of acute-chronic ratios showing all species and benthic species only
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Figure 6-5 Probability distribution of concentrations of dieldrin in sediments from streams, lakes, and
estuaries in the United States from 1986 to 1990 from the STORET database compared to
the dieldrin ESG values
Figure 6-6. Probability distribution of concentrations of dieldrin in sediments from coastal and
estuarine sites from 1984 to 1989 as measured by the National Status and Trends Program ............. 6-16
Figure 6-7. Probability distribution of organic carbon-normalized sediment endrin and dieldrin concen-
trations from the U.S. Army Corps of Engineers (1991) monitoring program of San Francisco Bay ... 6-18
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Acknowledgments
Coauthors
Dominic M.DiToro
David I. Hansen
Laurie D.De Rosa
Walter J. Berry*
Heidi E. Bell*
MaryC.Reiley
Christopher S.Zarba
Manhattan College, Riverdale, NY; HydroQual, Inc., Mahwah, NJ
HydroQual, Inc., Mahwah, NJ; Great Lakes Environmental
Center, Traverse City, MI (formerly with U.S. EPA)
HydroQual, Inc., Mahwah, NJ
U.S. EPA, NHEERL, Atlantic Ecology Division, Narragansett, RI
U.S. EPA, Office of Water, Washington, DC
U.S. EPA, Office of Water, Washington, DC
U.S. EPA, Office of Research and Development, Washington, DC
Significant Contributors to the Development of the Approach and Supporting Science
Herbert E. Allen
Gerald T.Ankley
Christina E. Cowan
Dominic M.DiToro
David J. Hansen
Samuel W.Karickhoff
Paul R. Paquin
Spyros P. Pavlou
Richard C.Swartz
Nelson A. Thomas
Christopher S.Zarba
University of Delaware, Newark, DE
U.S. EPA, NHEERL, Mid-Continent Ecology Division, Duluth, MN
The Procter & Gamble Company, Cincinnati, OH
Manhattan College, Riverdale, NY; HydroQual, Inc., Mahwah, NJ
HydroQual, Inc., Mahwah, NJ; Great Lakes Environmental
Center, Traverse City, MI (formerly with U.S. EPA)
U.S. EPA, NERL, Athens, GA
HydroQual, Inc., Mahwah, NJ
Ebasco Environmental, Bellevue, WA
Environmental consultant (formerly with U.S. EPA)
U.S. EPA, NHEERL, Mid-Continent Ecology Division, Duluth, MN
(retired)
U.S. EPA, Office of Research and Development, Washington, DC
Technical Support and Document Review
Robert A. Hoke
D.Scott Ireland
Tyler K.Linton
David R. Mount*
Robert L.Spehar
E.I. DuPont deNemours and Company, Newark, DE
U.S. EPA, Office of Water, Washington, DC
Great Lakes Environmental Center, Columbus, OH
U.S. EPA. NHEERL, Mid-Continent Ecology Division, Duluth, MN
U.S. EPA, NHEERL, Mid-Continent Ecology Division, Duluth, MN
*Principal U.S. EPA contact
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Executive Summary
The purpose of this document is to present the technical basis for establishing sediment
guidelines for nonionic organic chemicals using equilibrium partitioning (EqP). This approach is
chosen because it addresses the two principal technical issues that must be resolved in deriving
nationally applicable, scientifically defensible sediment guidelines: varying bioavailability of
chemicals in sediment and selection of an appropriate biological effects concentration.
The data 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 demonstrated that if the different sediments in each experiment are
compared, there are only weak relationships between sediment chemical concentrations on a dry
weight basis and biological effects. However, if the chemical concentrations in the interstitial
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 for the different sediments (typically within a factor of two). Most
importantly, the effects concentrations are the same as, or they can be predicted from, the effects
concentration determined in water-only exposures.
The EqP methodology assumes that the partitioning of a chemical between sediment organic
carbon and interstitial water is at or near equilibrium. For both of these phases, the fugacity or
activity of the chemical is the same at equilibrium. As a result, the principal assumption is that the
organism receives an equivalent exposure from the water-only phase or from any equilibrated
phase: either from interstitial water via respiration, or from sediment carbon via ingestion, or from
a mixture of exposure routes. Therefore, the pathway of exposure is not significant. For the data
presented-herein, the observed effects concentration for a chemical on an organic carbon basis
can be predicted to within the uncertainty of the model.
Equilibrium partitioning sediment guidelines (ESGs) for nonionic organic chemicals are based on
the organic carbon-normalized chemical concentration. The interstitial 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 dissolved organic carbon, which is not
bioavailable. For highly hydrophobic chemicals (K^ > 5.5), this is necessary because the
interstitial water concentration is, for these chemicals, not necessarily a good estimate of the
chemical activity; that is, the proportion of chemical bound to dissolved organic carbon is high.
Use of the chemical concentration in sediment organic carbon eliminates this ambiguity.
ESGs also require that a chemical concentration be selected that is appropriately protective of
benthic organisms. The final chronic value (FCV) from the U.S. Environmental Protection Agency
(EPA) aquatic life water quality criteria (WQC) has been chosen. Analysis of the data compiled in
the published or draft WQC documents demonstrates that benthic species, defined as either
epibenthic or infaunal species, have a similar sensitivity to all water quality criteria species
combined. 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 use of the FCV.
Therefore, if the effects concentrations in sediments can be accurately predicted using the
partitioning of the chemical in organic carbon and data from water-only tests, the ESGs protecting
benthic species can be predicted using the organic carbon partition coefficient, Kx, and FCV.
EqP cannot remove all the variation in the experimentally observed sediment-effects concentration
and the concentration predicted from water-only exposures. Thus, a quantification of this
uncertainty should accompany the ESG.
xiii
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EPA has developed both Tier 1 and Tier 2 ESGs to reflect the differing degrees of data availability
and uncertainty. The minimum requirements to derive a Tier 1ESG include (1) an octanol-water
partitioning coefficient (K^) of the chemical, measured with current experimental techniques.; (2)
derivation of the FCV which should also be updated to include the most recent toxicological
information; and (3) sediment toxicity "check" tests to verify EqP predictions. As such, the ESGs
derived for dieldrin, endrin, metal mixtures (Cd, Cu, Pb, Ni, Ag, Zn), and polycyclic aromatic
hydrocarbon (PAH) mixtures would represent Tier 1 ESGs (U.S. EPA, 2000b,c,d,f). In comparison,
the minimum requirements for a Tier 2 ESG include a K^, for the chemical (as described above)
and the use of either a FCV or secondary chronic value (SCV). The performance of sediment
toxicity tests is recommended, but not required for the development of Tier 2 ESGs. Therefore, in
comparison to Tier 1 ESGs, the level of protection provided by the Tier 2 ESGs would be
associated with more uncertainty due to the use of the SCV and absence of sediment check tests.
Examples of Tier 2 ESGs for nonionics are found in U.S. EPA (2000g).
XIV
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Glossary of Abbreviations
ACR Acute-chronic ratio
ANOVA Analysis of variance
AR Approximate randomization
ASTM American Society for Testing and Materials
BAF Bioaccumulation factor; partition coefficient between organism lipid and water
BSAF Biota sediment accumulation factor; partition coefficient between organism lipid and
sediment organic carbon
C Chemical concentration per unit wet weight of the organism
C Lipid-normalized organism concentration
C Freely-dissolved interstitial water chemical concentration
DOC-complexed chemical concentration in interstitial water
Total interstitial water chemical concentration (includes freely-dissolved and DOC-
complexed)
Chemical concentration in octanol
Chemical concentration on sediment particles
Chemical concentration on sediment particles normalized to organic carbon
Total chemical concentration in sediment (includes solid and interstitial water
phases)
C™ Chemical concentration in water
Yf
COE U.S. Army Corps of Engineers
CV Coefficient of variation
CWA Clean Water Act
DOC Dissolved organic carbon
EC50 Concentration estimated to cause effects to 50% of test organisms within a specified
time period
EPA United States Environmental Protection Agency
EqP Equilibrium partitioning
ESGXs) Equilibrium partitioning sediment guidelines)
ESO- Organic carbon-normalized equilibrium partitioning sediment guideline
f Fraction of organic carbon in sediment
/L Weight fraction of lipid
FACR Final acute-chronic ratio
FAV Final acute value
PCV Final chronic value
HA Humic acid
HBCD U.S. EPA, Health and Ecological Criteria Division
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K *
Ap
tf i
Kf
LC50
LC50SiOC
LOBC
HPLC High performance liquid chromatography
IUPAC International Union of Pure and Applied Chemistry
IWTU Interstitial water toxic unit
^DOC Dissolved organic carbon-water partition coefficient
KL Lipid-water partition coefficient
KQC Organic carbon-interstitial water partition coefficient
Apparent organic carbon-normalized partition coefficient
Octanol-water partition coefficient
Sediment-water partition coefficient
Reversible component partition coefficient
Apparent partition coefficient
Concentration estimated to be lethal to 50% of test organisms within a specified
time period
Organic carbon-normalized LC50 from sediment exposures
LC50 from water-only exposures
Lowest observed effect concentration
Measured DOC concentration
NAS National Academy of Sciences
NERL U.S. EPA, National Exposure Research Laboratory
NHEERL U.S. EPA, National Health and Environmental Effects Research Laboratory
NOAA National Oceanographic and Atmospheric Administration
NOBC No observed effect concentration
NTIS National Technical Information Service
OST U.S. EPA, Office of Science and Technology
PAH Polycyclic aromatic hydrocarbon
PCB Polychlorinated biphenyl
PIM Particle interaction model
POC Paniculate organic carbon
PSTU Predicted sediment toxic unit
SPARC SPARC performs automated reasoning in chemistry
STORET EPA's computerized database for STOrage and RETrieval of water-related data
TOC Total organic carbon
TU Toxic unit
WQC Water quality criteria
xvi
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Bfcsis£oiii)e>ivStidn of JESGs: Nonioiiic Orgaiiics
Section 1
Introduction
1.1 General Information
Under the Clean Water Act (CWA), the U.S.
Environmental Protection Agency (EPA) is responsible
for protecting the chemical, physical, and biological
integrity of the nation's waters. In keeping with this
responsibility, EPA published water quality criteria
(WQC) in 1980 for 64 of the 65 priority pollutants and
pollutant categories listed as toxic in the CWA.
Additional WQC documents that update criteria for
selected consent decree and new chemicals have been
published since 1980. These WQC are numerical
concentration limits that are protective of aquatic
organisms and their uses. Although these criteria play
an important role in ensuring a healthy aquatic
environment, they alone are not sufficient to ensure
appropriate levels of environmental and human health
protection.
Toxic contaminants in bottom sediments of the
nation's lakes, rivers, wetlands, and coastal waters
create potential for continued environmental
degradation even where water column contaminant
levels comply with established WQC. The absence of
defensible sediment guidelines makes it difficult to
assess the extent of sediment contamination, implement
measures to limit or prevent additional contamination
from occurring, or identify and implement appropriate
remediation as needed.
As a result of the need to assist regulatory
agencies in making decisions concerning contaminated
sediment, the EPA Office of Science and Technology
(OST), Health and Ecological Criteria Division
(HECD), established a research team to review
alternative approaches to assess sediment
contamination. Sediment contamination and related
problems were the subject of a conference (Dickson et
al., 1987). Alternative approaches to establishing
sediment guidelines (Pavlou and Weston, 1983a) and
their merits and deficiencies were discussed
(Chapman, 1987). Additional efforts were undertaken
to identify the scope of national sediment
contamination (Bolton et al., 1985) and to review
proposed approaches for addressing contaminated
sediments (Pavlou and Weston, 1983b; JRB Associates,
1984). The equilibrium partitioning (EqP) method was
selected because it provides the most practical,
scientifically defensible, and effective regulatory tool
for addressing individual nonionic chemicals associated
with contaminated sediments on a national basis
(Battelle, 1984).
1.2 Rationale for Selecting the EqP Method
The principal reasons for selection of the EqP
method include the following:
1. The EqP method was most likely to yield sediment
guidelines predictive of biological effects in the
field and defensible when used in a regulatory
context. These guidelines address the issue of
bioavailability and are founded on the extensive
biological effects database used to establish
national WQC.
2. Sediment guidelines can be readily incorporated
into existing regulatory operations because a unique
numerical sediment-specific guideline can be
established for any chemical and compared with
field measurements to assess the likelihood of
significant adverse effects.
3. Sediment guidelines provide a simple and cost-
effective means of screening measured sediment
contaminant concentrations to identify areas of
concern and provide information to regulators in a
short period of time.
4. The method takes advantage of the data and
expertise 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
and can assist in restoring the beneficial uses at
contaminated sites.
1-1
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1.3 Relationship to WQC Methodology
At first glance, it might seem logical to use the
WQC procedure for development of sediment
guidelines. A detailed methodology has already been
developed that presents the supporting logic, establishes
the required minimum lexicological dataset, and
specifies the numerical procedures to be used to
calculate the WQC values (Stephan et al., 1985).
Furthermore, WQC developed through this methodology
are routinely used in regulation of effluent discharges.
Therefore, it would seem natural to extend these
methods directly to sediments.
The WQC are based on using the total chemical
concentration as a measure of bioavailable chemical
concentration. However, use of total sediment
chemical concentration as a measure of the
bioavailable—or even potentially bioavailable—
chemical concentration is not supported by existing
data (Luoma and Bryan, 1981). The results of recent
experiments indicate that sediments can differ in
toxicity by factors of 100 or more for the same total
chemical concentration. This difference is a
significant obstacle. Without a quantitative estimate of
the bioavailable chemical concentration in a sediment,
it is impossible to predict a sediment's toxicity on the
basis of chemical measurements, regardless of the
method used to assess biological impact—be it
laboratory toxicity experiments or field datasets
comprising benthic biological and chemical sampling
(Chapman and Long, 1983; Long and Chapman, 1985;
Barricketal., 1985; Long and Morgan, 1990).
Similarly, without a unique relationship between
chemical measurements and biological endpoints that
can be applied across the range of sediment properties
and affect bioavailability, the cause and effect linkage
is not supportable. For example, if the same total
chemical concentration is 100 times more toxic in one
sediment than it is in another, how can universal
guidelines be set that depend only on the total sediment
chemical concentration? Any sediment guideline based
on total sediment concentration would reflect a
potential uncertainty of at least this magnitude. Thus,
bioavailability must be explicitly considered for any
sediment evaluation methodology that depends on
chemical measurements to establish defensible
sediment guidelines.
1.4 Applications of Sediment Guidelines
Sediment guidelines that are reasonably accurate in
their ability to predict the potential for biological
impacts are useful for other applications (Cowan and
Zarba, 1987). Sediment guidelines can play an
important role in identification, monitoring, and cleanup
of contaminated sediment sites on a national basis and
also provide a basis to ensure that sites that are
uncontaminated will remain so. They are particularly
useful when used in conjunction with biological
sampling and testing.
In many ways, sediment guidelines 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. Contaminated
sediments have often been in place for long periods of
time, and controlling the source of the pollution (if the
source still exists) may not be sufficient to alleviate the
problem. The difficulty is compounded because safe
removal and treatment or disposal of contaminated
sediment can be laborious and expensive.
Application of equilibrium partitioning sediment
guidelines (ESGs) in specific EPA regulatory programs
is detailed in the "Implementation Framework for Use of
Equilibrium Partitioning Sediment Guidelines" (U.S.
EPA, 2000a). The range of potential uses is quite large
because the need to evaluate potentially contaminated
sediment arises in many contexts. ESGs are intended to
complement existing sediment assessment tools such
as whole sediment toxicity tests and benthic community
analyses. They provide a chemical-by-chemical
specification of what sediment concentrations are
protective of benthic aquatic life. Therefore, they can
be useful in identifying causative chemicals when
toxicity is indicated by biological assessment tools.
They can also be used to help prioritize sites for
biological testing, and to serve as targets for load
controls to help assure attainment of designated uses
of water bodies. ESGs can also be useful screening
tools in assessing dredged material and contaminated
hazardous waste sites. The utility of EqP for a
particular chemical will be verified in subsequent
sections.
Sediment guidelines can be used as a means for
predicting or identifying the degree and extent of
contaminated areas such that more informed regulatory
decisions can be made. Sediment guidelines will be
particularly valuable in monitoring applications in
which sediment contaminant concentrations are
gradually approaching the guidelines. Comparison of
field measurements with sediment guidelines will
1-2
-------
Technical Basis for Derivation of ESGs: Nbnionk.Ofgames "•,
provide reliable warning of potential problems. Such an
early warning provides an opportunity to take
corrective action before adverse impacts occur.
For the purposes of this and other related
documents, EPA has developed both Tier 1 and Tier 2
ESGs to reflect the differing degrees of data availability
and uncertainty. Examples of chemicals to which this
methodology applies include dieldrin, endrin, metal
mixtures (Cd, Cu, Pb, Ni, Ag, Zn), and polycyclic
aromatic hydrocarbon (PAH) mixtures. The minimum
requirements to derive a Tier 1ESG include (1) an
octanol-water partitioning coefficient (Kow) of the
chemical, measured with current experimental
techniques, which appears to remove the large
variation in reported values; (2) derivation of the FCV,
which should also be updated to include the most
recent toxicological information; and (3) sediment
toxicity "check" tests to verify EqP predictions. Check
experiments can be used to verify the utility of EqP for
a particular chemical. As such, the ESGs derived for
nonionic organics, such as dieldrin and endrin, metal
mixtures, and PAH mixtures represent Tier 1 ESGs (U.S.
EPA 2000b,c,d,f)- In comparison, the minimum
requirements for a Tier 2 ESG include a K^ for the
chemical (as described above) and the use of either a
FCV or secondary chronic value (SCV). The
performance of sediment toxicity tests is recommended,
but not required for the development of Tier 2 ESGs.
Therefore, in comparison to Tier 1 ESGs, the level of
protection provided by the Tier 2 ESGs would be
associated with more uncertainty due to the use of the
SCV and absence of sediment toxicity tests. Examples
of Tier 2 ESGs for nonionics are found in U.S. EPA
(2000g). The minimum requirements to compute Tier 1
and 2 ESGs are further discussed in Section 6.5.
1.5 Overview
This document presents the technical basis for
developing ESGs for nonionic organic chemicals. The
term ESG, as used herein, refers to numerical
concentrations for individual chemicals or groups of
chemicals mat are applicable across the range of
sediments encountered in practice. ESGs are intended
to be predictive of biological effects. As a
consequence, they can be used in much the same way as
final chronic values (FCVs) are used in WQC as the
concentration of a chemical that is protective of
benthic aquatic life.
Section 1 of this document reviews the background
that led to the need for ESGs and also the selection of
the EqP methodology. Section 2 summarizes the
evidence and the major lines of reasoning of the EqP
methodology, with supporting references cited in the
body of the document. Section 3 reviews the
development of concentration-response curves for
interstitial water concentrations and sediment organic
carbon-normalized concentrations to determine toxicity
and bioavailability in contaminated sediments. It also
presents analyses of sediment toxicity and
bioaccumulation experiments. Section 4 reviews the
partitioning 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 WQC
documents to show the applicability of WQC as effects
levels for benthic organisms. Section 6 reviews the
computation of an ESG and presents an analysis for
quantifying uncertainty associated with the ESG. This
section also presents minimum data requirements and
example calculations and compares the ESG computed
for two chemicals, endrin and dieldrin, with field data.
The description of Tier 1 and Tier 2 ESGs is also
provided. Section 7 presents conclusions and further
research needs. Section 8 lists the references cited in
this document.
1-3
-------
Organics '<
Section 2
Partitioning of Nonionics
in Sediments
2.1 ToxicityandBioavailabilityof
Chemicals in Sediments
Establishing ESGs requires determination of the
extent of the bioavailability of chemicals associated
with sediment. It has frequently been observed that
similar concentrations of a chemical, in units of mass of
chemical per mass of sediment dry weight (e.g., Aig/g
sediment), can exhibit a range in toxicity in different
sediments. If the purpose of ESGs is to establish
chemical concentrations that apply to sediments of
differing types, it is essential that the reasons for this
varying bioavailability be understood and explicitly
included in the guidelines. Otherwise the guidelines
cannot be presumed applicable across sediments of
differing properties (Di Toro et al., 1991).
The importance of this issue cannot be
overemphasized. For example, if 1 ^g/g of kepone is
the LC50 for an organism in one sediment and 35 jzg/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 determine
what the LCSO of a third sediment would be without
performing a toxicity test. The results of toxicity tests
used to establish the toxicity of chemicals in sediments
could not be generalized to other sediments. Imagine
the situation if the results of toxicity tests in water
depended strongly on the particular water source, for
example, Lake Superior versus well water. Until the
source of the difference was understood, it would be
fruitless to attempt to establish WQC. For instance,
one source of uncertainty that has been identified in
the WQC for metals in freshwater is hardness. Organic
carbon may also effect toxicity in the water column.
For this reason bioavailability is a principal focus of
this document.
The key insight into the problem of quantifying
the bioavailability of chemicals in sediments was that
the concentration-response curve for the biological
effect of concern can be correlated not to the total
sediment chemical concentration G/g/g sediment), but
to the interstitial water chemical concentration (jug
chemical/L interstitial water) (Adams et al., 1985). In
addition, the effects concentration found for the
chemical in interstitial water is essentially equal to that
found in water-only exposures. Organism mortality,
growth rate, and bioaccumulation data are used to
demonstrate this correlation, which is a critical part of
the logic behind the EqP approach to developing ESGs.
For nonionic organic chemicals, the concentration-
response curves correlate equally well with the
sediment chemical concentration on a sediment organic
carbon basis.
These observations can be explained by assuming
that the chemical concentration in interstitial water and
sediment organic carbon are in equilibrium and that
these two concentrations are related by the organic
carbon partition coefficient, K^ as shown in Figure
2-1. The term equilibrium partitioning describes this
assumption. The assumption for the equality of water-
only and sediment exposure effects concentrations on
an interstitial water basis is that the sediment-
interstitial water equilibrium system (Figure 2-1. right)
provides the same exposure as a water-only exposure
(Figure 2-1, left). The chemical activity is the same in
each system at equilibrium. It should be pointed out
that the EqP assumptions are only approximately true;
for example, not all organic carbon has exactly the same
partitioning properties, and not all sediments are at
complete equilibrium. Therefore, predictions from the
model have an inherent uncertainty. The data
presented below illustrate the degree to which EqP can
explain the observations. For the data presented herein,
the observed effects concentration for a chemical on an
organic carbon basis can be predicted to within the
uncertainty of the model.
Figure 2-2 presents mortality data for various
chemicals and sediments compared with interstitial water
concentrations when normalized on a toxic unit (TU)
basis. Interstitial water TUs (IWTUs) are the ratio of the
measured interstitial water concentration to the LCSO
from water-only toxicity tests (Equation 2-1). Three
2-1
-------
Partitioning of Noiuonics in'Sediments ?•
.«•.
Water-Only
Exposure
Sediment - Interstitial Water
Exposure
Biota
Biota
I
Water
Sediment
Carbon
Loc
Interstitial
Water
Equilibrium Partitioning
Figure 2-1. Diagram of the organism exposure routes for a water-only exposure (left) and a sediment
exposure (right). Equilibrium partitioning refers to the assumption that an equilibrium exists
between the chemical sorbed to the particulate sediment organic carbon and the interstitial
water. K^. is the organic carbon partition coefficient
different sediments are tested for each chemical as
indicated. The EqP model predicts that the interstitial
water LCSO will equal the water-only LC50, which is
obtained from a separate water-only exposure toxicity
test
IWTU =
(interstitial water concentration)
(water-only LCSO)
(2-1)
Therefore, an IWTU of 1 occurs when the interstitial
water concentration equals the water-only LCSO, at
which point it would be predicted that 50% mortality
would be observed. The correlation of observed
mortality to predicted IWTUs in Figure 2-2
demonstrates (1) the efficacy of using interstitial water
concentrations to remove sediment-to-sediment
differences and (2) the applicability of the water-only
effects concentration and, by implication, the validity
of the EqP model. By contrast, the mortality 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 an
interstitial water basis could be taken to suggest that
the route of exposure is via interstitial water. However,
the equality of the effects concentration on a sediment
organic carbon basis, which is demonstrated below,
could similarly be taken to suggest that ingestion of
sediment organic carbon is the primary route of
exposure. It is important to realize that if the
concentration of chemical in sediment and interstitial
water are in equilibrium, then the effective exposure
concentration is the same regardless of exposure route.
Therefore, it is not possible or necessary to determine
the primary route of exposure from equilibrated
experiments.
Whatever the route of exposure, the correlation to
interstitial water suggests that if it were possible to
either measure the interstitial water chemical
concentration or predict it from the total sediment
concentration and the relevant sediment properties
such as the sediment organic carbon concentration,
then that concentration could be used to quantify the
exposure concentration for an organism. Thus,
partitioning of chemicals between the solid and the
2-2
-------
-...-:•
. '•;-. .•,.'-,,-• •' •_- '•'•.,-ff-'". - : •": ' ••-:.'--.'. •--•• :-.* "• • ,- • «••* • :-.:•• •/. ..-..< . . ;•.<--".-•. "~
Technical Basis for Derivation of ESGs: Npnionic Organics
a
•c
o
§
20
0.01
100
Predicted Interstitial Water Toxic Units
Figure 2-2. Percent mortality versus predicted interstitial water toxic units for six chemicals and three sediments
per chemical. Sediment types are indicated by the open symbols (lowest organic carbon content),
double symbols (intermediate organic carbon content), and filled symbols (highest organic carbon
content). See Tables 3-1 and 3-2 for data sources.
liquid phase in a sediment becomes a necessary
component for establishing ESGs.
In addition, if it is true that benthic organisms are
as sensitive as water column organisms, and the
evidence to be presented appears to support this
supposition, then ESGs could be established using the
FCV from WQC documents as the effects concentration
for benthic organisms. The apparent equality between
the effects concentration as measured in interstitial
water and in water-only exposures (Figure 2-2)
supports using an effects concentration derived from
water-only exposures.
The calculation procedure for establishing ESGs is
as follows. If the FCV (^g/L) is the final chronic value
from the WQC for the chemical of interest, then the ESG
(/zg/g sediment) is computed as the product of the FCV
and the partition coefficient, K (L/kg sediment),
defined as the ratio of the chemical concentration in the
sediment and in the interstitial water at equilibrium
This is the fundamental equation from which the ESGs
are generated.
2.2 Partitioning of Nonionic Organic
Chemicals
Partitioning of nonionic organic chemicals to soil
and sediment particles is reasonably well understood,
and a standard model exists for describing the process.
The hydrophobicity of the chemical is quantified by
using the octanol-water partition coefficient, ^ow- The
sorption capacity of the sediment is determined by the
mass fraction of organic carbon for the sediment,/^.
For sediments with/^ ^ 0.2% by weight, the organic
carbon appears to be the predominant phase for
chemical sorption. The partition coefficient, Kf, the
ratio of sediment concentration, Cs, to the
concentration freely-dissolved in interstitial water, Cd,
is given by
(2-3)
ESG = KFCV
(2-2)
2-3
-------
Partitioning of Nfonionicsjn Sediments v-;
ing
where KQC is the partition coefficient for sediment
organic carbon.
The only other environmental variable that has a
dramatic effect on partitioning appears to be the
particle concentration in the suspension in which K is
measured. There is considerable controversy regardinj
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-interstitial water
partitioning, namely that K^ - Kow for sediments.
Using Equations 2-2 and 2-3, an ESG is calculated from
ESG=rjrJFCV (2-4)
This equation is linear in the organic carbon fraction,
/,„.. As a consequence, the relationship can be
expressed as
(2-5)
/oc
If we define
™r _ESG
ESGoc=-7—
as the organic carbon-normalized ESG concentration
Oug chemical/g organic carbon), then
ESGOC=/^OCFCV
(2-7)
Thus, we arrive at the following important conclusion:
for a specific chemical having a specific KQ^., the
organic carbon-normalized sediment concentration,
c, 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
(DOC) (Gobas and Zhang, 1994). Although DOC
affects the apparent interstitial water concentrations of
highly hydrophobic chemicals, the DOC-bound fraction
of the chemical appears not to be bioavailable and
Equation 2-7 for ESG^ still applies.
Therefore, we expect that toxicity in sediment can
be predicted from the water-only effects concentration
and the K^ of the chemical. The utility of these ideas
can be tested with the same mortality data as those in
Figure 2-2. The concept of predicted sediment TUs
(PSTUs) is useful in this regard. These units are
computed as the ratio of the organic carbon-normalized
sediment concentrations, C^f^, and the predicted
sediment LC50 using K^ and the water-only LC50
(PSTU). That is,
PSTU=-
CS//QC
water-only LC50)
(2-8)
Figure 2-3 presents the percent mortality versus
PSTU for seven chemicals. The correlation is similar to
that obtained using the interstitial water concentrations
in Figure 2-2. The PSTUs for each chemical follow a
similar concentration-response curve independent of
sediment type. The data demonstrate that 50%
mortality occurs at about 1 PSTU, 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 measurement
error, then all data in Figures 2-2 and 2-3 should predict
50% mortality at 1 PSTU. There is an uncertainty of
approximately ±2 in the results. This uncertainty is
calculated in Section 6 of this document. The error bars
computed from the uncertainty are shown in Figure 2-3
as ± 1.96 x (ESG uncertainty). The value 1.96 is the
t statistic. It is used to provide a 95% confidence
interval around the ESG. 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 with this
model.
2.3 Effects Concentration
Development of an ESG requires an effects
concentration for benthic organisms. Because many of
the organisms used to establish the WQC are benthic,
perhaps the WQC are adequate estimates of the effects
concentrations for benthic organisms. To examine this
possibility, the acute toxicity database, which is used to
establish the WQC, is segregated into benthic and
water column species, and the relative sensitivities of
each group are compared. Figure 2-4 compares the final
acute values (FAVs) for the most sensitive benthic
(epibenthic and infaunal) species with the most
sensitive water column species. The data are from
EPA's proposed and published water quality criteria,
and are described further in Di Toro et al. (1991).
Despite the scatter, these results, a more detailed
analysis of all the acute toxicity data, and the results of
benthic colonization experiments support the
contention of equal sensitivity.
2-4
-------
5,,, :,-...<-.., . .. - ..... i AT- • -•/-* ' •• •
Technical Basis for Derivation of ESGs: Nomomc Qrgamcs
100
so
-
1
a 60
r
1
S? 40
20
0
r 1 1 — i i i i i r ill
* Dieldrin
• Kepont
• Phenanthreut
• Kndria
• Fluoranthene
T Aceuphthene
A DDT
4
w
^ _A_ ^/
t+ 1
<^>
O ^1
^2>
^.
•*
"
ffiu •
@t<5^%f^r "
> $ «&*«**
Ff •
r°T
i
@
^!§ °'
D 86s,
i
'
0
T
1
-
gjCO£0^te»»A A A
tf
P
V
A
b
. & - -
00,
Predicted Sediment Toxic Units
Fieure 2-3 Percent mortality versus predicted sediment toxic units for seven chemicals and three
sediments per chemical. Sediment types are indicated by the open symbols (lowest organic
carbon content), double symbols (intermediate organic carbon content), and Filled symbols
(highest organic carbon content). See Tables 3-1 and 3-2 for data sources. Uncertainty error
bars are represented by solid vertical lines.
1
C3
W)
6
U
O>
WD
O
-J
-2
A Freshwater
• Saltwater
_L
_L
-2 -1 0 1 2 3
Log,, Benthic Organism FAV (^g/L)
Figure 2-4. Comparison of the 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 5-1 for data sources.
2-5
-------
Section 3
Toxicity and Bioavailability of
Chemicals in Sediments
3.1 Toxicity Experiments
As mentioned above, 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 sediment chemical
concentration (jig chemical/g dry sediment), but to the
interstitial water concentration (pgchemical/L
interstitial water) (Adams et al., 1985). However, these
results do not necessarily imply that interstitial water is
the primary route of exposure, because all exposure
pathways are at equal chemical activity in an
equilibrium experiment (see Figure 2-1), and the route
of exposure cannot be determined. Nevertheless, this
observation is the critical first step in understanding
bioavailability of chemicals in sediments.
A substantial amount of data has been assembled
addressing the relationship between toxicity and
interstitial water chemical concentrations. Table 3-1
lists the sources and characteristics of these
experiments. Chemicals listed in Table 3-1 for which
sediment toxicity experiments have been performed at
EPA research laboratories as part of the development
of ESGs include acenaphthene, dieldrin, fluoranthene,
and phenanthrene. The remaining chemicals listed in
Table 3-1 represent experiments in the literature that
can be used to demonstrate EqP theory. Individual
ESGs have been derived for endrin and dieldrin (U.S.
EPA, 2000c, d). These derivations will be presented
later in this document. The derivation of a polycyclic
aromatic hydrocarbon (PAH) mixtures ESG will be
presented in a separate document (U.S. EPA, 2000b).
However, the experiments performed for the three
individual PAHs will be used in this document along
with the endrin and dieldrin data to demonstrate the
applicability of EqP theory.
Some of the data listed in Table 3-1 are presented
in Figures 3-1 through 3-4. The remaining data listed in
Table 3-1 are presented elsewhere in this document. In
Figures 3-1 through 3-3, the biological response—
mortality or growth rate suppression—is plotted
versus the total sediment concentration in the top
panels, and versus the measured interstitial water
concentration in the bottom panels. Table 3-2
summarizes the LC50 and ECSO estimates and 95%
confidence limits for these data on a total sediment,
interstitial water, and organic carbon basis, as well as
the water-only values. The results from kepone
experiments (Figure 3-1) illustrate the general trends in
these data (Adams etal., 1985). For the low organic
carbon sediment (fx=0.09%). the 50th percentile total
kepone concentration for both Chironomus tentans
mortality (LC50) and growth rate reduction from a life-
cycle test (ECSO) are <1 /zg/g sediment dry weight. By
contrast, the 1.5% organic carbon sediment LC50 and
EC50 are approximately 7 and 10 A*g/g sediment dry
weight, respectively. The high organic carbon sediment
(12%) exhibits still higher LC50 and ECSO values on a
total sediment kepone concentration basis (35.2 and
37.3 A*g/g sediment dry weight, respectively). However,
as shown in the bottom panels of Figure 3-1, essentially
all the mortality data collapse into a single curve and
the variation in growth rate is significantly reduced
when the interstitial water concentrations are used as
the correlating concentrations. On an interstitial water
basis the biological responses, as measured by LC50 or
ECSO, vary by approximately a factor of two, whereas
when they are evaluated on a total sediment kepone
basis, they exhibit a 40-fold range in kepone toxicity.
The comparison between the interstitial water
effects concentrations and the water-only results for
kepone indicates that they are similar. The interstitial
water LC50 values are 18.6 to 31.3 Mg/L. and the water-
only exposure LC50 is 26.4 /zg/L. The interstitial water
ECSO values are 17.1 to 48.5 //g/L, and the water-only
ECSO is 16.2 /zg/L (Table 3-2).
Laboratory experiments have also been performed
to characterize the toxicity of fluoranthene (Swartz et al.,
1990) to the sediment-dwelling marine amphipod
Rhepoxynius abronius. Figure 3-2 presents the R.
abronius mortality data for the fluoranthene experiment.
The results of the fluoranthene experiments parallel
3-1
-------
those for kepone. The sediment with the lowest
organic carbon content (0.2%) exhibits the lowest LC50
on a total sediment concentration basis (3.2 Mg/g
sediment dry weight), and as the organic carbon
concentration increases (0.3% and 0.5%), the LC50
increases (6.4 and 10.7 ^g/g sediment dry weight,
respectively). On an interstitial water basis, the data
again collapse to a single concentration-response
curve and the LCSO values differ by less than 50%.
Tfeble 3-1. Sediment toxicity and bioaccumulation data
Chemical
Acenaphthene
Acenaphthene
Cypermethrin
DDT
Dieldrin
Dieldrin
Dieldrin
Endrin
Endrin
Fluoranthene
Fluoranthene
Kepone
Kepone
Kepone
Permethhn
Phenanthrene
Phenanthrene
Eohaustorius
estuarius
Leptocheirus
plumulosus
Chironomus
tentans
HyaUUa
azteca
Chironomus
tentans
Hyalella
azteca
Hyalella
azteca
Diporeia sp.
Hyalella
azteca
Rhepoxynius
abronius
Rhepoxynius
abronius
Chironomus
tentans
Chironomus
tentans
Chironomus
tentans
Chironomus
tentans
Eohaustorius
estaurius
Leptocheirus
plumulosus
Exposure
Duration
Sediment Source (days)
South Beach, OR
South Beach, OR
River and pond
Soap Creek,
Mercer Lake
Airport Pond, MN
West Bearskin and
Pequaywan
Lakes, 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
South Beach, OR
10
10
1
10
10
10
10
10
10
10
10
14
14
14
1
10
10
Biological
Endpoiitt
Mortality
Mortality
Body
burden
Mortality
Mortality
Mortality
Mortality
Mortality
Mortality
Mortality
Mortality
Body
burden
Growth
Mortality
Body
burden
Mortality
Mortality
Reference
Swartz, 1991
Swartz, 1991
Muiretal., 1985
Nebekeretal., 1989;
Schuytemaetal.,
1989
Hokeetal., 1995
Hoke et al., 1995
Hoke and Ankley,
1992
Stehly. 1991
Nebekeretal., 1989;
Schuytema et al.,
1989
DeWittetal., 1992
Swartz et al., 1990
Adams et al., 1985;
Adams, 1987
Adams et al., 1985
Adams etal., 1985
Muiretal., 1985
Swartz, 1991
Swartz, 1991
Figure
4-18
4-18
3-4
3-3
4-18
4-19
4-18
3-3
4-18
3-2,4-18
™
3-1,4-21
3-1,4-21
3-4
4-18
4-18
3-2
-------
'
: Nbmo
0.1 1 10 100
Sediment Concentration fcg/g dry wt)
100
80
CO
40
20
0
i MI i|
" • "I
• ""l
1 10 100 1000
Interstitial Water Concentration G*g/L)
1.5
12
0.1 i 10 too
Sediment Concentration 0/g/g dry wt)
£
too
80
CO
2
1 10 100 10W
Interstitial Water Concentration fcg/L)
Figure 3-1. Comparison of percent mortality (left) and growth rate reduction (right) of C. teutons to kepone
concentrations in bulk sediment (top) and interstitial water (bottom) for three sediments with
varying organic carbon concentrations (data from Adams et al., 1985).
0 5 10 IS 20
Sediment Concentration C*g/g dry wt)
0 20 40 60 80
Interstitial Water Concentration fcg/L)
Figure 3-2. Comparison of percent mortality of R. abromus to fluoranthene concentrations in bulk sediment
(left) and interstitial water (right) for sediments with varying organic carbon concentrations
(data from Swartz et al., 1990).
3-3
-------
100
•" f *
0 SO 100 ISO 200
Sediment Concentration (pg/g dry wt)
0.1 i 10 too
Sediment Concentration fttg/g dry wt)
too
e -
if 60
*„'
••*•:
012345
Interstitial Water Concentration fctg/L)
0.1 i 10 100
Interstitial Water Concentration (pg/L)
Figure 3-3. Comparison of percent mortality of H. azteca with DDT (left) and endrin (right) concentrations
in bulk sediment (top) and interstitial water (bottom) for three sediments with varying organic
carbon concentrations (data from Nebeker et al., 1989; Schuytema et ah, 1989).
Figure 3-3 presents mortality data for DDT and
endrin using the freshwater amphipod Hyalella azteca
(Nebeker etal., 1989; Schuytema etal., 1989). The
responses for DDT (Nebeker et al., 1989) are similar to
those observed for kepone and fluoranthene. On a
total sediment concentration basis, the organism
responses differ for the various sediments (LCSO values
are 10.3 to 44.9 Mg/g sediment dry weight), but on an
interstitial water basis the responses are again similar
(LCSO values are 0.74 to 1.45 ng/L) and comparable to
the water-only LCSO value of approximately 0.5 jzg/L.
The total sediment LCSO data for DDT reported by
Schuytema et al. (1989) are more variable. Similarly,
organism survival for endrin exposures on a sediment
dry weight basis varies by a factor of almost six among
the six sediment tests. The LCSO values are 3.39 to 18.9
A*g/g sediment dry weight. The interstitial water LCSO
values were less variable (1.74 to 3.75 Atg/L) and
comparable to the water-only exposure LCSO of
approximately 4 yug/L (Table 3-2).
3.2 Bioaccumulation
One measure of bioavailability is the amount of
chemical retained in organism tissues. Hence, tissue
bioaccumulation data can be used to examine the extent
of chemical bioavailability. Chironomus tertians was
exposed to two synthetic pyrethroids, cypermethrin
and permethrin, spiked into three sediments, one of
which was laboratory-grade sand (Muir et al., 1985).
The bioaccumulation from the sand was approximately
an order of magnitude higher than it was from the
organic carbon-containing sediments for both
cypermethrin and permethrin (Figure 3-4A and B). On
an interstitial water basis, the bioaccumulation appears
to be approximately linear and independent of sediment
type (Figure 3-4C and D). The mean bioaccumulation
factor (BAF) for cypermethrin (and permethrin) varies
from 6.21 to 0.50 (4.04 to 0.23) /zg/g organism per ng/g
sediment as sediment/^ increases (Table 3-3). By
contrast, the mean BAFs on an interstitial water basis
vary by less than a factor of two.
3-4
-------
-v'-'^^~<'':~-L-:^:^^
•;»:,:*-. '- i! :ltv_ ..••_Vj±rl-^/-i—- -.-:.*. 1*-.-^_^jJ-.-:^_^^—^^—--~- =*-*_-—±_j.
10000
1000
fl
"5 100
a
•8" 10
CO
1
!
10000
•a
g> 1000
«•*
fl
"2 too
CQ
•g 10
1
€
1
A:'^ ' ' !
• *0j' ^P * _,
" * " JV//^
r t / * 1
r ' «P:* 1
:
1 10 100 1000 19
Sediment Concentration (ng/g dry wt)
i i — "i — '"i • i
C _^ :
m^
^Es^
r V5^
' Ax x
s^r *^f -i
:
Al 0.1 1 >0 1
Interstitial Water Concentration (pg/L)
1000
"ek inn
s
S
"2 10
t i
000
1000
1 "•
fl
"2 10
CQ
1 *
01
w «
B: /„.(%) =
' * " ^-^-*^4
' »
^X^x»
- r '
' 1 i i i mil 1 i ' in"
1 10 100 11
Sediment Concentration (ng/g dry wt)
D • :
/ "*"^^^^r -
_ ^ ™
/ "=
X
" *^'* 1
' 1 i i mill i ' ' '""
.01 0.1 1
Interstitial Water Concentration fcg/L
NO
10
')
Figure 3-4. Comparison of body burden in C. fcntow of cypermethrin (left) and permethrin (right) versus
concentration in bulk sediment (top) and interstitial water (bottom) for sediments with varying
organic carbon concentrations (data from Muir et al., 1985).
Bioaccumulation was also measured by Adams et
al. (1983 and 1985) and Adams (1987) in the C. tentans
kepone experiments presented previously (Table 3-3).
The body burden variation on a total sediment basis is
over two orders of magnitude (B AF = 3.3 to 600 ^g/g
organism per Atg/g sediment), whereas the interstitial
waterBAF is within a factor of four (5,180 to 17,600
jig/kg organism per /ug/L), with the very low organic
carbon sediment exhibiting the largest deviation
(Table 3-3).
3.3 Conclusions
There is more than one way to interpret the
observation that organism concentration-response and
bioaccumulation from different sediments can be
reduced to one curve if interstitial water is considered
as the concentration that quantifies exposure. First,
these results do not necessarily imply that interstitial
water is the primary route of exposure, because all
exposure pathways are at equal chemical activity in an
equilibrium experiment. Second, the route of exposure
cannot be determined, as we can see by comparing the
concentration-response correlations with interstitial
water and organic carbon-normalized sediment
concentrations. That both interstitial water and organic
carbon-normalized sediment concentration are equally
successful at correlating the data suggests that neither
the interstitial water nor the sediment exposure
pathway can be implicated as the primary exposure
route.
In order to relate interstitial water exposure to
sediment carbon exposure, it is necessary to establish
the relationship between these two concentrations.
Therefore, a method for predicting the partitioning of
chemicals between the solid and the liquid phase is
required. This method is described in the following
section.
3-5
-------
' *• - * i
^^^^^SiS?sl^MS:l^^fej
Table 3-2 Comparison of LC50 and ECSO values on a sediment dry weight- and sediment organic carbon-
normalized basis to values calculated from interstitial water and water-only exposures
LC50 and EC5P Values3
Chemical
(Endpoint)
Kepone
(Mortality)
Kepone
(Growth)
Fluoranthene
(Mortality)
DDT
(Mortality)
DDT
(Mortality)
Endrin
(Mortality)
Endrin
(Mortality)
/oc
(%)
0.09"
l.SO
12.0
0.09
l.SO
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
fcg/g)
0.90(0.73-1.10)
6.9(5.85-8.12)
35.2 (30.6-40.5)
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(5.56-727)
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)
Interstitial Water
(A*g/L)
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
G*g/g)
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
fcg/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
Adams et
ah, 1985
Adams et
al., 1985
Swartzet
al.. 1990
Nebekeret
al.. 1989
Schuytema
etal., 1989
Nebekeret
al., 1989
Schuytema
etal., 1989
(Hamilton et al.. 1977).
Bioaccumulation factors for C. tentans
Chemical
Kepone
Cypermethrin
Permethrin
f
foe
(%)
0.09
1.50
12
<0.1
2.3
3.7
<0.1
2.3
3.7
Total Sediment1*
600 (308-892)
20 (4.8-35.2)
3.3 (0.3-6.3)
6.21 (4.41-8.01)
0.50(0.30-0.71)
0.60(0.37-0.83)
4.04 (2.89-5.20)
0.38 (0.17-0.59)
0.23 (0.18-0.28)
BAF"
Interstitial Water0
17.600(6.540-28.600)
5,180(1,970-8,390)
5,790(2,890-8,700)
80.1 (73.5-86.7)
51.3 (43.8-58.8)
92.9 (87.0-98.8)
39.7 (25.0-54.3)
52.5 (22.6-82.4)
29.7 (15.6-43.7)
Organic
Carbon-Normalized
Sediment*1
0.54(0.277-0.803)
0.30(0.072-0.528)
0.40(0.036-0.756)
<0.006 (0.004-0.008)
0.012 (0.008-0.016)
0.022 (0.012-0.032)
<0.004 (0.002-0.006)
0.009(0.005-0.013)
0.008 (0.006-0.010)
Reference
Adams, 1987; Adams
etal., 1983. 1985
Muir etal., 1985
Muir et al., 1985
. • —
"The 95% confidence limits are shown in parentheses.
bValues are in ^g/g organism per ng/g sediment.
cValues are in //g/kg organism per ng/L.
dValues are in jig/kg organism per Mg/goc sediment.
3-6
-------
Section 4
Sorption of Nonionic
Organic Chemicals
4.1 Partitioning in Particle Suspensions
A number of empirical models have been
suggested to explain the sorption of nonionic
hydrophobia organic chemicals to natural soils and
sediment particles (see Karickhoff, 1984). The chemical
property that indexes hydrophobicity is the octanol-
water partition coefficient, Kov. The important particle
property is the weight fraction of organic carbon./^.
Another important environmental variable appears to be
the particle concentration itself.
In many experiments using particle suspensions,
the partition coefficients have been observed to
decrease as the particle concentration used in the
experiment is increased (O'Connor and Connolly, 1980).
Very few experiments have been done on settled or
undisturbed sediments; therefore, the correct
interpretation of particle suspension experiments is of
critical importance. It is not uncommon for the partition
coefficient to decrease by two to three orders of
magnitude at high particle concentrations. If this
partitioning behavior is characteristic of bedded
sediments, then quite low partition coefficients would
be appropriate, which would result in lower sediment
chemical concentrations for ESGs. If, however, this
phenomenon is an artifact 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 particle concentration effect.
4.1.1 Particle Concentration Effect
For the reversible (or readily desorbable)
component of sorption, a particle interaction model
(PIM) has been proposed that accounts for the particle
concentration effect and predicts the partition
coefficient of nonionic hydrophobic chemicals over a
range of nearly seven orders of magnitude with a log,0
standard error of the estimate of 0.38 (Di Toro, 1985).
The reversible component partition coefficient, Kf*, is
the ratio of reversibly bound sediment chemical
concentration, Cs (Mg/kg dry weight), to the freely-
dissolved chemical concentration, Cd (
cs=Vc-
The PIM model for Kf* is
(4-1)
_
=
(4-2)
where
K *= reversible component partition coefficient (L/kg
dry weight)
KQC= particle organic carbon partition coefficient (L/kg
organic carbon)
/„ = particle organic carbon weight fraction (kg
organic carbon/kg dry weight)
m = particle concentration in the suspension (kg dry
weight/L)
ux = 1 .4, an empirical constant (unitless)
The regression of K^ to the octanol-water coefficient,
Kov, yields
Iog10/i:oc=0.00028+0.983 log10Kow (4-3)
or essentially that K^ approximately equals *TOW (Di
Toro, 1985). Figure 4-1 presents the observed versus
predicted reversible component partition coefficients
using this model (Di Toro, 1985). A substantial fraction
of the data in the regression is at high particle
concentrations (mfocK0^ > 10). where the partitioning
is determined only by the solids concentration and vx.
The low particle concentration data (mfofJC^, < 1) are
presented in Figure 4-2 for the conventional adsorption
(Figure 4-2 A) and reversible component (Figure 4-2B)
partition coefficient, Kf, normalized by/^, that is:
KQC = KJfoc. The relationship KQC-KOW is
demonstrated from the agreement between the line of
perfect equality and the data. It is important to note
that although Equation 4-2 applies only to the
4-1
-------
-2
0 2 4
Predicted LogJT,* (L/kg)
Figure 4-1. Observed versus predicted reversible component partition coefficient for nonionic organic chemicals
using Equation 4-2 (figure from Di Toro, 1985).
reversible component partition coefficient, Kf*, the
equation Kf- f^ Kov applies to the conventional
adsorption partition coefficient as well (Figure 4-2A).
A number of explanations have been offered for
the particle concentration effect. The most popular is
the existence of an additional third sorbing phase or
complexing component that is associated 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 (Benes and Majer,
1980; Gschwend and Wu, 1985) and dissolved ligands
or macromolecules that desorb from the particles and
remain in solution (Carter and Suffett, 1983; Voice et al.,
1983;CurlandKeolelan, 1984;Nelsonetal., 1985) 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 (Karickhoff and
Morris, 1985). The effect has also been attributed to
kinetic effects (Karickhoff, 1984).
Sorption by nonseparated particles or complexing
by DOC can produce an apparent decrease in partition
coefficient with increasing particle concentration if the
operational method of measuring dissolved chemical
concentration does not properly discriminate the truly
dissolved or free chemical concentration from the
complexed or colloidally sorbed portion. However, the
question is not whether improperly measured dissolved
concentrations can lead to an apparent decrease in
partition coefficient with increasing particle
concentrations, but whether these third-phase models
explain all (or most) of the observed partition
coefficient-particle concentration relationships.
An alternative possibility is that the particle
concentration effect is a distinct phenomenon that is a
ubiquitous feature of aqueous-phase particle sorption.
A number of experiments have been designed explicitly
to exclude possible third-phase interferences. Particle
concentration effects are displayed in the resuspension
experiment for polychlorinated biphenyls (PCBs) and
metals, in which particles are resuspended into a
4-2
-------
, •-- ' •"-»" . f -«-» : -.*•.. .t* ;'--' -'TV" "
echmcal Basis for I)e
Jrganics
-l
Aldicarb
Carbofuran
Linuron
Fluometron
Carbarjl
Diuron
Methyl Parathion
Parathion
Gamma HCH
Kepone
pp-DDT
-1
1 3 5
Log,,Kow(L/kgoc)
-1
B
-l
Figure 4-2. Comparison of the conventional adsorption (A) and reversible component (B) organic carbon-
normalized partition coefficient, KQC, to the octanol-water partition coefficient, tfow, for experiments
with low solids concentrations: mfocKQVf
-------
reduced volume of supernatant (Nelson et al., 1985;
Karickoff and Morris, 1985;Di Toro and Horzempa,
1983), and in the dilution experiment in which the
particle suspension is diluted with supernatant from a
parallel vessel (Nelson et al., 1985). It is difficult to see
how third-phase models can account for these results
because the concentration of the colloidal particles is
constant whereas the concentration of the sediment
particles varies substantially.
The model (Equation 4-2) is based on the
hypothesis that particle concentration effects result
from an additional desorption reaction induced by
particle-particle interactions (Di Toro, 1985). It has
been suggested that actual particle collisions are
responsible (Mackay and Powers, 1987). This
interpretation relates ux to the collision efficiency for
desorption and demonstrates that it is independent of
the chemical and particle properties, a fact that has
been experimentally observed (Di Toro, 1985 ; Di Toro
etal.,1986).
It is not necessary to decide which of these
mechanisms is responsible for the effect if all the
possible interpretations yield the same result for
sediment-interstitial water partitioning. Particle
interaction models would predict that KQJ. - KQW
because the particles are stationary in sediments.
Third-phase models would also relate the freely-
dissolved (i.e., uncomplexed) chemical concentration to
paniculate concentration via the same equation. As for
kinetic effects, the equilibrium concentration is again
given by the relationship K^ - KQV. Thus, there is
unanimity on the proper partition coefficient to be used
in order to relate the freely-dissolved chemical con-
centration to the sediment concentration, KQ^. - Kow.
4.1.2 Organic Carbon Fraction
The unifying parameter that permits development
of ESGs for nonionic hydrophobic organic chemicals
applicable to a broad range of sediment types is the
organic carbon content of the sediments. This
development can be shown as follows. The sediment-
interstitial water partition coefficient. Kf, is given by
where Cs is the chemical concentration on sediment
particles. An important observation can be made that
leads to the idea of organic carbon normalization.
Equation 4-4 indicates that the partition coefficient for
any nonionic organic chemical is linear in the organic
carbon fraction./^. The partitioning data examined in
Figure 4-2 can be used to examine the linearity of Kp to
fa. Figure 4-3 compares K_/KOW to fa for both the
adsorption and the reversible component partition
coefficients. The data are restricted to mfa Kow < 1
to suppress particle effects. The line indicates the
expected linear relationship in Equation 4-4. These data
and an analysis presented below appear to support the
linearity of partitioning to a value of fa = 0.2%. This
result, and the toxicity experiments examined below,
suggest that for fa > 0.2%, organic carbon
normalization is valid.
As a consequence of the linear relationship of Cs
and fa, the relationship between sediment
concentration, Cs, and freely-dissolved concentration,
Cd, can be expressed as
If we define
cs.oc=-rr-
(4-7)
as the organic carbon-normalized sediment
concentration fag chemical/kg organic carbon), then
from Equation 4-6
and the solid phase concentration is given by
Cs=focKocCi (4-5)
(4-8)
Therefore, for a specific chemical with a specific
KQT, the organic carbon-normalized total sediment
concentration, CSJK, is proportional to the freely-
dissolved concentration, Cd, for any sediment with/j^
> 0.2%. This latter qualification is judged necessary
because at fa < 0.2%, other factors that influence
partitioning (e.g., particle size and sorption to
nonorganic mineral fractions) become relatively more
important (Karickhoff, 1984). Using the proportional
relationship given by Equation 4-8, the concentration
of freely-dissolved chemical can be predicted from the
normalized sediment concentration and K^. The
freely-dissolved concentration is of concern because it
is the form that is bioavailable. The evidence is
discussed in the next section.
4-4
-------
dnipnic ;Qrganic
-1
a -2
ex
o
-3
T r
@ Aldicarb
A Carbofuran
V Linuron
^ Fluometron
^> Carbaryl
il Diuron
V A,
Methyl Parathion
%: Paratbion
•fa Gamma HCH
^ Kepone
^ pp-DDT
J J
-3 -2 -1 0
to
be significant: m/oc*ow
-------
4.1.3 Organic Carbon Composition
The KQC values used in the EqP calculations
described in this document assume that the organic
carbon in sediments is similar in partitioning properties
to "natural" organic carbon found in most sediments.
While this has proven true for most sediments EPA has
studied, it is possible that some sites may have
components of sediment organic carbon with different
properties. This might be associated with sediments
whose composition has been highly modified by
industrial activity, resulting in high percentages of
atypical organic carbon such as rubber, animal
processing waste (e.g., hair or hide fragments), coal
particles, or wood processing wastes (bark, wood fiber,
or chips). Relatively undegraded woody debris or plant
matter (e.g., roots, leaves) may also contribute organic
carbon that partitions differently from typical organic
carbon (e.g., Iglesias-Jimenez et al., 1997; Grathwohl,
1990; Xing etal., 1994). Sediments with substantial
amounts of these materials may exhibit higher
concentrations of chemicals in interstitial water than
would be predicted using generic K^ values, thereby
making the ESG underprotective. If such a situation is
encountered, the applicability of literature KQ^. values
can be evaluated by analyzing for the chemical of
interest in both sediment and interstitial water. If the
measured concentration in interstitial water is markedly
greater (e.g., more than twofold) than that predicted
using the K^ values recommended herein (after
accounting for DOC binding in the interstitial water),
then the national ESGs would be underprotective and
calculation of a site-specific ESG should be considered
(seeU.S.EPA,2000e).
The presence of organic carbon in large particles
may also influence the apparent partitioning. Large
particles may artificially inflate the effect of the organic
carbon because of their large mass, but comparatively
small surface area; they also may increase variability in
n 1 1
• POC
dJHumicAcid
E53 Interstitial Water DOC
BaP DDT HCBP MCBP
Chemicals
PYR TCBP
Figure 4-4. Partition coefficients of chemicals to particulate organic carbon (POC), Aldrich® hunuc add, and
natural DOC. Benzo[a]pyrene (BaP); DDT; W'A^&S'-heMchlorobiphenyl (HCBP);
4-monochlorobiphenyl (MCBP); pyrene (PYR); 2,2',5,5>-trtrachIorobiPhenyl (TCBP) (data from Eadie et
al., 1990).
4-6
-------
TOC measurements by causing sample heterogeneity.
The effect of these particles on partitioning can be
evaluated by analysis of interstitial water as described
above, and site-specific ESGs may be used if required.
It may be possible to screen large particles from
sediment prior to analysis to reduce their influence on
the interpretation of sediment chemistry relative to
ESGs.
4.2 Dissolved Organic Carbon Completing
In addition to the partitioning of a chemical to the
paniculate organic carbon (POC) associated with
sediment particles, hydrophobic chemicals can also
partition to the organic carbon in colloidal-sized
particles. These particles are too small to be removed
by conventional filtration or centrifugation and are
therefore operationally defined as dissolved organic
carbon, or DOC. Sediment interstitial waters frequently
contain significant levels of DOC and must be
considered in evaluating the phase distribution of
chemicals.
A distinction is made between the freely-dissolved
chemical concentration, Cd, and the DOC-complexed
chemical, Cp^.. The partition coefficient for DOC,
KPQJ., is analogous to K^. as it quantifies the ratio of
DOC-bound chemical, C^, to the freely-dissolved
concentration, Cd
= '"
DOC
(4-9)
where ffipoc is the measured DOC concentration. The
magnitude of both Kj^ and DOC determine the extent
of DOC complexation that takes place. Thus, it is
important to have estimates of these quantities when
calculating the level of freely-dissolved chemical in
sediment interstitial waters.
A recent compilation of Kp^ together with
additional experimental determinations is available
(Eadieetal., 1990). A summary that compares the
partitioning of six chemicals with POC. natural DOC,
and Aldrich humic acid (HA) is shown on Figure 4-4.
The magnitude of the partition coefficients follows the
order: POC > HA > natural DOC. The upper bound on
^DOC WOUW appear to be where Kp^. = K^, the POC
partition coefficient.
4.3 Phase Distribution in Sediments
Chemicals in sediments are partitioned into three
phases: freely-dissolved chemical, chemical sorbed to
POC, and chemical sorbed to DOC. To evaluate the
partitioning among these three phases, consider the
mass balance for the total chemical concentration in
sediment, CT:
(4-10)
where 0 is the sediment porosity (volume of water/
volume of water plus solids) and m is the sediment
solids concentration (mass of solids/volume of water
plus solids). The three terms on the right side of the
equation are the concentration of freely-dissolved
chemical in the interstitial water, and that sorbed to the
POC and DOC, respectively. Hence, from Equation
4-10, the freely-dissolved chemical concentration can
be expressed as
Cr (4-11)
The concentration associated with the particle carbon
(Equation 4-8) and DOC (Equation 4-9) can then be
calculated. The total interstitial water concentration,
Cm, is the sum of the freely-dissolved chemical and
DOC-complexed chemical, so that
iw
(4-12)
Figure 4-5 illustrates the phase partitioning
behavior of a system for a unit concentration of a
chemical with the following properties: K^, = K^^. =
10* L/kg./oc=2.0%, m=0.5 kg soIids/L sediment, and
"DOC varies from °to 50 m^'a reasonable ranse for
interstitial waters (Thurman, 1985). With no DOC
present, the interstitial water concentration equals the
freely-dissolved concentration. As DOC increases, the
interstitial water concentration increases because of the
increase in complexed chemical, CQ^. Accompanying
this increase in C^^ is a slight—in fact, insignificant—
decrease in Cd (Equation 4-11) and a proportional
decrease in Cs (Equation 4-8).
It is important to realize that the freely-dissolved
chemical concentration, Cd, can be estimated directly
from Cs QC, the organic carbon-normalized sediment
concentration, using Equation 4-8. and that the
estimate is independent of the DOC concentration.
However, to estimate Cd from the interstitial water
concentration requires that the DOC concentration and
KDOC be known. The assumption Cw = Cd is clearly
not warranted for very hydrophobic chemicals. For
these cases. Cs QC gives a more direct estimate of the
freely-dissolved bioavailable concentration, Cd, than
does the interstitial water concentration.
4-7
-------
4.4 BioavailabilityofDOC-Complexed
Chemicals
The proportion of a chemical in interstitial water
that is complexed to DOC can be substantial (Figure
4-5). Hence, the question of bioavailability of DOC-
complexed chemical can be important in assessing
.toxicity directly fronuneasured interstitial water
concentrations. Data indicate that DOC-complexed
chemical is not bioavailable. Fish (McCarthy and
Jimenez, 1985) and amphipod(Landnunetal., 1987)
uptake of PAHs are significantly reduced by adding
DOC. An example is shown in Figure 4-6 for a
freshwater amphipod, Pontoporeia hoyi (Landrum et
al., 1987). 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-complexed chemical decreases with
decreasing K^X. (Equation 4-9).
The quantitative demonstration that DOC-
complexed chemicals are not bioavailable requires an
independent determination of the concentration of
complexed chemical. Landrum et al. (1987) have
developed a C,g reverse-phase HPLC column technique
that separates the complexed and freely-dissolved
chemical. Thus it is possible to compare the measured
DOC-complexed chemical with the quantity of
complexed chemical inferred from the uptake experi-
ments, assuming that all the complexed chemical is not
bioavailable (Landrum etal., 1985,1987). As shown in
Figure 4-7, although the Kp^ 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, CQ^,
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 Csoc, the organic
carbon-normalized sediment concentration (Equation
4-7).
0.001
0.0001
DOC Concentration (mg/L)
Figure 4-5. Phase distribution of a chemical in the three-phase system: sediment, interstitial water '
and freely-dissolved (Cd) (Equations 4-10, 4-11, and 4-12). K^ = ffow = » «* Lfc= z'
andm = 0.5kg/L.
«* Lfcg./oc
4-8
-------
100 -
Chemical
Figure 4-6. Average uptake rate of chemicals by Pontoportio hoyi with (filled) and without (open) DOC present
BenzoMpyrene (BaP); phenanthrene; pyrene; 2,2',4<41-tetrachlorobiphenyl (TCBP) (data from
Landrum et al., 1987).
4.5 Field Observations of Partitioning in
Sediments
An enormous quantity of laboratory data exists for
partitioning in particle suspensions. However,
interstitial water and sediment data from field samples
are scarce. Two types of data from field samples are
examined. The first is a direct test of the partitioning
equation Csoc = K^ Cd, which is independent of the
DOC concentration. The second examines the sediment
and interstitial water concentrations and accounts for
the DOC that is present.
4.5.1 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 interstitial water. If
sorption equilibrium has been attained for each class,
then, letting Cs(j) be the particle chemical concentration
of the 7th size class, it is true that
Cs(j)=/oc(i)KocCd
(4-13)
4-9
-------
10000
1
1000 -
1
O HumicAcid
A Interstitial Water
1000
10000
from Reverse Phase (L/goc)
Figure 4-7. Comparison of the DOC partition coefficient calculated from the suppression of chemical uptake versus
the CIS reverse-phase HPLC column estimate. Circles are Aldrich® humic acid; triangles are
interstitial water DOC. Chemicals are listed in Figure 4-6 caption (also anthracene and
benz[a]anthracene). The line represents equality.
I is the organic carbon fraction for each size
class j. On an organic carbon-normalized basis this
equation becomes
(4-14)
where Cs pcG) = CsO)//oc(J)- This resultindicates that
the organic carbon-normalized sediment concentration
of a chemical should be equal in each size class
because K^ 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
s
is constant across size classes in a sediment
sample.
A literature review was performed to identify
studies where sediment chemical concentrations were
reported along with sediment organic carbon for
various size classes within a sediment sample. Five
studies were identified that can be used to verify
organic carbon normalization under field conditions.
These studies represent data collected in estuaries,
canals, and coastal areas. These studies were not
specifically designed to test organic carbon
normalization; nonetheless they provide useful data.
Table 4-1 lists the five study areas, sediment sample
depth, and chemicals analyzed in each study. Also
included in Table 4-1 is a brief description of the
sedimentation characteristics of the study area if they
were given by the respective authors. These study
areas represent areas of both deposition and
resuspension.
4-10
-------
Table 4-1. Summary information for studies used to verify field organic carbon normalization
Study Area
Washington
Coastal Basin
Author's
Sedimentation
Description
Resuspension,
deposition
Sediment
Sample
Depth
4-8 cm
Chemicals
Phenanthrene
Fluoranthene
Reference
Prahl, 1982
Columbia River
Basin
No information given
Anthracene
Pyrene
Retene
Benz(a)anthracene
Chrysene
Benzofluoranthenes
Benzo(e)pyrene
Benzo(a)pyrene
Perylene
Indeno(c,d)pyrene
Benzo(ghi)perylene
River Derwent
Derbyshire, U.K.
Predominantly
eroding at stations C
& Da; predominantly
depositing at stations
G, H, & K
0-10 cm Total PAH
Evans et al.. 1990
Belgian
Continental Shelf
(coastal region) &
Scheldt Estuary
High turbidity, high
sedimentation rates
0-1 cm 8 PCB congeners
(IUPAC #s 28.52,101.
118,138,153.170,
180)
Delbekeetal., 1990
Lazaret Bay near
Toulon Harbor and
Porquerolles Is.,
French
Mediterranean
Coast
No information given 0-5 cm
20 PCB congeners
(IUPAC #s 8.18,28,
44,52,66,87,101,
105.118,128,138.
153,170,180,187.
195.200.206.209)
Pierardetal., 1996
Bayou d'Inde.
tributary of
Calcasiew River
near Lake Charles,
Louisiana
No information given 0-15 cm
Hexachlorobenzene
Hexachlorobutadiene
Bentachlorobenzene
Tetrachlorobenzene
Trichlorobenzene
Dichlorobenzene
Prytula and
Pavlostathis, 1996
In Prahl (1982), sediment cores were collected at
three stations near the Washington State coast
(Stations 4,5, and 7). These were sieved into a silt-and-
clay-sized fraction (<64 /im) and a sand-sized fraction
(>64 mm). This latter fraction was further separated
into a low-density(<1.9 g/cm3) and high-density
fraction of sand-sized particles. The concentrations of
13 individual PAHs were measured in each size fraction.
It is important to realize that the size fractions reported
in this study, and in each of the following studies, are
not pure clay, silt, or sand, but natural particles in the
size classes denoted by clay, silt, and sand. The
organic carbon fractions, shown in Figure 4-8A, range
from 0.2% for the high-density sand-sized fraction to
greater than 30% for the low-density fraction. This
exceeds two orders of magnitude and essentially spans
the range usually found in practice. For example, 90%
of the estuarine and coastal sediments sampled for the
National Status and Trends Program exceed 0.2%
organic carbon (NO AA, 1991).
4-11
-------
Low-Density, Sand-Sized High-Density, Sand-Sized
Sediment Fraction
Silt/Clay
100
- B
10
1— —1—
| | Station C Y/A Station H
H| Station Da \/ J Station K
^^ Station G
I
<63 63-125 125-250 250-500
Sediment Fraction (a
500-1000 1000-2000
Figure 4-8. Organic carbon fractions (percent dry weight) as indicated on the y-axis. The top panel shows the
low-density sand-sized fraction (>64 urn, <1.9 g/cm3), the high-density sand-sized fraction (>64 nm,
>1.9 g/cmj), and the silt/clay-sized fraction (<64 //m) (data from Prahl, 1982). The bottom panel shows
the organic carbon fraction in the size classes as indicated (data from Evans et al., 1990).
Figures 4-9A and B compare the dry weight-
normalized clay/silt-sized fraction sediment PAH
concentrations, Cs(j) (y-xis), with the sand-sized high-
and low-density PAH concentrations on a dry weight
basis. The dry weight-normalized data have distinctly
different concentrations—the low-density high-
organic carbon fraction is highly enriched, whereas the
sand-sized fraction is substantially below the clay/silt
fraction concentrations. Figures 4-9C and D present
the same data but on an organic carbon-normalized
basis, Cs ocQ. In contrast to dry weight normalization,
the PAH'concentrations are essentially the same in
each size class, as predicted by Equation 4-14. The
lines in Figure 4-9 represent equality and have been
4-12
-------
1000
100
10
10000
10 100 1000
PAH Clay/Silt (^g/g dry wt)
100
10
B
10 100 1000
PAH Clay/Silt (pg/g dry wt)
1000 10000 100000
PAH Clay/Silt (pig/goc)
1000 10000 100000
PAH Clay/Silt (pg/goJ
Figure 4-9. Comparison of PAH concentrations of the sand-sized (left) and low-density (right) sediment particles to
the clay/silt fraction (x-axis). PAH sediment concentrations are dry weight- (top) and organic carbon-
normalized (bottom) for Stations 4,5, and 7. The line represents equality (data from Prahl, 1982).
added as a visual aid. These lines have also been used
in Figures 4-10,4-12,4-13,and 4-14.
Evans et al. (1990) collected sediments at five sites
along the River Derwent, Derbyshire, United Kingdom.
Sediments were separated into six sediment size classes
representative of clay and silt (<63 ^m) to coarse sand
(1.0 to 2.0 mm). Organic carbon content and total PAH
were measured in each sediment size class. Figure 4-8B
presents the different size classes and associated
organic carbon contents. Evans et al. (1990) attributed
the bimodal distribution of/^ to two types of organic
matter. Organic matter in the 1.0 to 2.0 mm size class
may result from fragmentary plant material from organic
carbon, whereas the size class less than 500 urn is the
result of aging humic material. The organic content in
this study ranged from 2.0% to 40%.
Figure 4-10 presents a comparison of PAH
concentration for different sediment classes for dry
weight normalization and organic carbon normalization.
Figure 4-10A compares PAH concentrations on the
sand fraction (63 to 500 /un) and the clay/silt fraction
(<63 /zm) on a dry weight basis. Figure 4- 10B compares
PAH concentrations on the coarse sand fraction (0.5 to
2.0 mm) and the clay/silt fraction (<63 //m) on a dry
weight basis. The data indicate that the PAH
concentration is higher in the coarse sand fraction of
sediment. Recall from Figure 4-8A that the clay/silt and
low-density sand fractions contain a higher fraction
organic carbon content. The bottom panels of Figure
4-10 (C and D) present the organic carbon-normalized
comparison of PAH concentrations by sediment class.
For both panels, the organic carbon-normalized PAH
concentrations are similar regardless of the sediment
size class as predicted by Equation 4-14.
Delbeke et al. (1990) 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 /urn) sediment fraction. In
4-13
-------
1000
I ill mil I ill iui|
A: Size Clan:
O 63-175 #im
1000
1 10 100 1000
PAH day/Silt frzgfe dry wt)
0.1 1 10 100 1000
PAH Cby/SiK (Mg/g dry wt)
10000
1000
100
10
10 100 1000
PAH Clay/Silt (Mg/&>c)
10000
10 100 1000
PAH Clay/Silt (Acg/goc)
10000
Figure 4-10. Comparison of PAH concentrations of the sand-sized (left) and coarse sand-sized (right) sediment
particles indicated by symbols to the clay/silt fraction (x-axis). PAH sediment concentrations are dry
weight- (top) and organic carbon-normalized (bottom) for Stations C, Da, G, H, and K. The line
represents equality (data from Evans et aL, 1990).
addition, analyses of the samples were done to
determine the percentage of size fractions ranging from
3 to 500 ion that made up the sample. The PCB
congeners measured in this study were IUPAC
numbers 28,52,101,118,138,153,170,and 180 (Table
4-1). Using concentrations reported for bulk sediment
samples and for clay/silt samples, and measurements of
percent size fractions of each sample, calculations were
done to estimate concentrations on the greater than (>)
63 Aim portion of the sample. Similar calculations were
done to determine organic carbon content on the less
than (<) 63 fan portion of the sample. Organic content
varied from 0.01% to 10% inclusive of both <63 ion. and
>63 Aim portions of the sediment.
Figure 4-11A presents the percent organic carbon
on the <63 nm portion of the sample (filled bar) and on
the >63 fun portion of the sample (hatched bar). PCB
congener concentrations on a dry weight basis (Figure
4-12A) and on an organic carbon basis (Figure 4-12B)
are shown for comparison. Organic carbon content in
the >63 fan class size at stations 2 and 4 is 0.01 % and
0.06%, respectively, as indicated in Figure 4-11. The
data for these stations are shown in Figure 4-12 using
filled symbols. Although an/^ > 0.2% has been
presented as the value for which organic carbon
normalization applies, normalization at these/og values
seems appropriate for this dataset. Figure 4- 12A
indicates no evident relationship between PCBs in the
<63 nm sample and PCBs in the >63 jum sample on a dry
weight basis. When concentrations in either class size
are normalized to organic carbon content, then
concentrations are similar for both class sizes as shown
in Figure 4- 12B. This indicates that PCB
concentrations are similar across sediment class sizes.
Pierard et al. (1996) collected sediment samples
from three locations in the French Mediterranean
Coast: Lazaret Bay near Toulon Harbor, Roquebrune
Bay near Monaco, and Porquerolles Island, which is a
marine natural park. Only data from the Lazaret Bay
and Porquerolles Island areas are discussed here
4-14
-------
; , ,
Technical Basis fp
100
10
1
0.1
0.01
0.001
100
10
= A:
<63
345
Station
0.1
\ B:
BH Lazaret Bay
\/A Porquerolles Island
1
<15 15-63 63-300 300-800
Sediment Fraction (//m)
>800
<53
53-106 106-1400
Sediment Fraction (/zm)
Figure 4-11. Organic carbon fractions (percent dry weight) for (A) two sediment size classes
(<63 nm and >63 /^m) from seven sampling stations (data from Delbeke et al.,
1990), (B) five sediment size classes (x-axis) from two sampling locations (data
from Pierard et al., 1996), and (C) three sediment size classes as indicated on the
x-axis (data from Prytula and Pavlostathis, 1996).
4-15
-------
CO
.s
100
10
w>
§
0.1
0.01
0.001
I I I HUH I I I Him
A
A Station 1
• Station 2
V Station 3 D
• Station 4
O Station 5
O Station 6
O Station 7
nun
0.001 0.01
0.1
10 100
PCB on <63 f^m grain size
(ng/gdrywt)
•P*f
10000
1000
100
^ 10
C
O
11HIM i 11 in
i B
1 iniiiii i mi i M i mi i 11 mill
0.1 1 10 100 1000 10000
PCB on <63 Mm grain size
(ng/goc)
Figure 4-12. Comparison of eight PCB congener concentrations of >63 ion sized particles with <63 faa sized
particles. Sediment concentrations plotted as dry weight- (A) and organic carbon-normalized (B) for
Stations 1-7. The line represents equality (data from Delbeke et al., 1990).
4-16
-------
because sediment organic carbon was reported for
these two areas. Sediment samples were taken at 0 to
5 cm depths. Pierard et al. separated the sediment
samples into five grain-size fractions defined as
follows: fine silts/clays (<1S Aim), medium silts (IS to 63
Aim), medium to fine sands (63 to 300 Aim), medium to
coarse sands (300 to 800 Aim), and very coarse sands
(>800 Aim). Fractions here are defined by sieve size, and
no attempt to determine the amount of silt or sand in
each defined class is made.
After sample separation, organic carbon content
and PCB concentrations for 20 congeners (see Table
4-1) were measured for each size class. Figure 4-11B
compares the organic carbon content within each of the
size classes for the Lazaret and Porquerolles samples.
Organic carbon ranges from 0.44% to 16.1%. Three
samples for which both locations report organic carbon
indicate that the organic carbon at the two locations is
comparable. The 16.1 % value for the 63 to 300 Aim
Lazaret sample appears high. However, the
Porquerolles 63 to 300 Aim organic carbon value of
3.24% is also applied to the Lazaret 63 to 300urn size
class sample for normalization. Organic carbon for the
Porquerolles 300 to 800 tan size fraction was not
reported. In order to use the data, and because three of
the fractions show comparable organic carbon content
between the two locations, the Lazaret 300 to 800 Aim
organic carbon content was used for the Porquerolles
300 to 800 Aim sample for normalizing. Comparisons of
bulk dry weight-normalized chemical (top panel) and
organic carbon-normalized chemical (bottom panel)
concentrations for the <1S Aim size class with each of
the other size classes are made in Figure 4-13.
Comparisons of size classes with similar organic carbon
contents do not change with organic carbon
normalization (<15 Aim vs. 15 to 63 Aim and vs. 63 to 300
Aim, panels A and B). Panels C and D indicate a
somewhat improved relationship with normalization.
Additional comparisons for each of the size classes
versus the remaining size classes were done with
similar results. These are not presented here.
Prytula and Pavlostathis (1996) reported sediment
fractionation data for a sediment site in the Bayou
d'Inde, a tributary of the Calcasieu River near Lake
Charles, Louisiana. This site is part of an ongoing
investigation and is located at the intersection of an
industrial canal and Bayou d'Inde. Sediments were
sieved into three particle sizes;
-------
00
1000
A: Size Class:
<15 urn vs 15-63 urn
0.1
0.01
B: Size Class:
<15 urn vs 63-300 urn
C: Size Class:
<15 ion vs 300-800 ia*
D: Size Class:
<15 ion Vs >800 ion
0.01 0.1
10 100 1000 0.01 0.1
1 10 100 1000 0.01 0.1 1 10 100 1000 0.01 -0.1
PCB in Fine Clay/Silt (ng/g dry wt)
10 100 1000
Wfl
10000
A: Size Class:
1000 b- <15 AMU vs 15-63 ;«n
B: Size Class:
<15 urn vs 63-
300 um
C: Size Class:
. <15*tmvs300-
800 Aim
•q-n*
CO
D: Size Class:
<15 um vs >800 Aim
10 100 1000 10000 0.1
10 100 1000 10000 0.1 1 10 100 1000 10000 0.1
PCB in Fine Clay/Silt (ng/g^)
10 100 1000 10000
Figure 4-13. Comparison of 20 PG3 congener concentrations on the <15 yum size particles (x-aris) with four larger size fractions (y-axis). Sediment
concentrations plotted as dry weight- (top) and organic carbon-normalized (bottom) for grain size classes (A) <15 /*m vs 15-63 ^m,
(B) <15 um vs 63-300 um, (C) <15 faa vs 300-800 urn, and (D) <1S i/m vs >800 um (data from Pierard et ah, 1996).
-------
— 1000
*
»1
L.
H> 100
0.1
0.1
10
100 1000
10000
1000
10
10
100
1000 10000
0.1 1 10 100 1000
Chlorinated Organic (^g/g dry wt)
B
10 100 1000 10000
Chlorinated Organic (yug/goc)
o.i
Size Class:
53-106 Mm vs 106-1400 urn
10
100
1000
53-106 urn vs 106-1400 urn
10
100
1000
10000
' '^V
;|
yjt.'
a
r SIM
[.. B-. ^
It
'' S?1'-
ft-f
I?
•-A
Figure 4-14. Comparison of six chlorinated organic chemical concentrations on three sediment size fractions. Sediment concentration plotted as dry
weight- (top) and organic carbon-normalized (bottom) for grain size classes (A) <53 yum vs 53-106 /4n, (B) <53 pm vs 106-1400 jjm, and
(C) 53-106 /^m vs 106-1400 fan (data from Prytula and Pavlostathis, 1996).
-------
The variance (a) for a chemical at each station due to
particle size effects can then be computed
k=l
The variance for each chemical-station pair can then
be normalized by the average for each chemical-
station pair
(4-18)
The CV over all chemicals and stations for each dataset
is then computed by summing the normalized variances
and dividing by the number of chemical station pairs
(4-19)
The resulting CV for each study for both dry weight-
and organic carbon-normalized concentrations is
shown in Figure 4- ISA. Table 4-2 presents CV values
for both dry weight- and organic carbon-normalized
concentrations for each study. Percent changes in CV
due to organic carbon normalization were calculated.
Percent reduction in CV due to organic carbon
normalization is tabulated in Table 4-2 and shown
graphically in Figure 4-15B. Reductions in variation
range from 69.1 % to 96.4%. These results indicate that
large reductions in variation occur when sediments of
varying class sizes are carbon-normalized.
The average organic carbon-normalized CV for the
five datasets is 18.3%. The results of this statistical
analysis verify the utility of organic carbon
normalization for field-collected sediments.
4.S.2.1 Sediment Equilibrium
An important assumption in the ESG methodology
is the assumption of sediment equilibrium. It is difficult
to prove equilibrium in the field. A more realistic
assumption is that sediments exist in near-equilibrium
conditions. Information on specific sedimentation rates
was not collected for the five studies; however,
information given in Table 4-1 indicates that surface
sediments are in flux. A reasonable generalization of
sedimentation for coastal areas and rivers is 1.0 cm/year
(Sadler, 1981). Therefore, bedded sediment contact
times are long, and desorption would have to be very
slow for nonequilibrium conditions to exist for the
sample depths of 1 cm or greater. Sediment samples at
the depths listed in Table 4-1 would be in place long
enough for sediment equilibrium to be established for
four of the five studies even though surface sediments
may not be in equilibrium. Less confidence in this
assumption should be given to Delbeke et al. (1990)
where samples of 0-1 cm in depth were obtained.
Verification of organic carbon normalization as
presented in Figure 4-IS validates the assumption of
sediment equilibrium. The above datasets indicate that
organic carbon normalization is valid and the
assumption of sediment equilibrium, as applied to
ESGs, is also valid in areas under near-equilibrium
conditions.
During resuspension of sediments or similar events
(e.g., dredging and disposal), the equilibrium of
sediments will likely be disturbed. In essence, this
disequilibrium manifests itself as decreased
concentrations of chemicals in the interstitial water
caused by mixing of the sediment with additional water
from the water column (assuming that concentrations of
the chemical are comparatively low in the water
column). Immediately folio wing resettling of the
sediment and allowing time for any particle interactions
to subside, interstitial water may show lower chemical
concentrations than would be predicted through EqP
and, accordingly, EqP might predict greater toxicity
than would be expected based on that instantaneous
condition. However, in evaluating such sediments, it
should be remembered that absent further disturbance,
near-equilibrium conditions can be expected to be
reestablished in the sediment within one to a few
weeks, depending on the chemical (Schwarzenbach et
al, 1993; Wu and Gschwend, 1986). For this reason,
when near-equilibrium conditions are reestablished, the
long-term toxicity of the sediment can be expected to be
close to that predicted by EqP, even if the sediment is
not in equilibrium when the solid-phase chemistry is
measured. Based on these considerations, ESGs may
be useful for predicting the long-term risk from
nonpolar organic contaminants (with log KQVf > 3) in
sediment even in cases where short-term disturbances
occur. When equilibrium is lost due to disturbance, the
resulting errors should be toward overpredicting
toxicity (i.e., the guideline would be overprotective
rather than underprotective), errors that may still be
acceptable for certain assessment scenarios.
4.5.2.2 Summary
Field data from five studies were analyzed to
confirm the utility of organic carbon normalization. For
each of the five studies, both bulk concentrations and
organic carbon-normalized concentrations were
4-20
-------
.. _ . .
Technical Basis for Derivation
DC
rt
u
o»
100
10
-~I— -1 I - —--,•-
A: |H - Dry Weight-Normalized Concentrations
| | . Organic Carbon-Normalized Concentrations
100
e
o
1
10
- B: |///1 - Organic Carbon-Normalized Concentrations
—
i
I
\
\
1
i
Prahl
Evans et al.
1990
Delbeke et al.
1990
Pierard et al.
1996
Prytula et al.
1996
Figure 4-15. Comparison of the average coefficient of variation for dry weight-normalized (A) and organic carbon-
normalized (B) concentrations and the percent reduction in the average coefficient of variation due to
organic carbon normalization for each study.
4-21
-------
Table 4-2. Comparison of average coefficient of variation using dry weight- or organic carbon-normalized
concentrations
Average Coefficient of Variation (CV)
Dry Weight-
Normalized Concentrations
Organic Carbon-
Normalized Concentrations
Percent Reduction in
CV due to OC
Normalization
Reference
65.1
37.5
540
787
110
5.62
11.6
21.6
28.1
24.6
91.4
69.1
96.0
96.4
77.7
Prahl, 1982
Evans et al., 1990
Delbekeetal., 1990
Pierardetal., 1996
Prytulaetal., 1996
compared across particle size classes of a sample. In
almost all cases, organic carbon normalization resulted
in more comparable concentrations. The average CV
was computed for each dataset for both bulk
concentrations and organic carbon-normalized
concentrations. In all cases, the CV was reduced with
organic carbon normalization. Studies used here
represent areas where surface sediments (upper cm)
may not be in equilibrium due to hydrodynamic
conditions. However, sample depths provide sediment
samples that are in equilibrium or near equilibrium. Two
important conclusions can be drawn from this analysis:
(1) organic carbon normalization has been shown to be
valid under field conditions and (2) the assumption of
sediment equilibrium for the purpose of organic carbon
normalization is valid in areas where typical
sedimentation and resuspension may be occurring.
4.5.3 Sediment/Interstitial Water Partitioning
Normally, when measurements of sediment
chemical concentration, Cs, and total interstitial water
chemical concentrations, Cw, are made, the value of
the apparent partition coefficient is calculated directly
from the ratio of these quantities. As a consequence of
DOC complexation, the apparent partition coefficient,
K' defined as
(4-20)
(4-21)
is given by
P ~l+m
As wiuoc (measured DOC) increases, the quantity
of DOC-complexed chemical increases and the apparent
partition coefficient approaches
v, fac^ac
"•DOC^DOC
(4-22)
which is just the ratio of sorbed to complexed chemical.
Because the solid-phase chemical concentration is
proportional to the freely-dissolved portion of the
interstitial water concentration, Cd, die actual partition
coefficient, K , should be calculated using the freely-
dissolved concentration. The freely-dissolved
concentration will typically be lower than the total
dissolved interstitial water chemical concentration in
the presence of significant levels of interstitial water
DOC (e.g., Figure 4-5). As a result, the actual partition
coefficient calculated with the freely-dissolved
concentration is higher than the apparent partition
coefficient calculated with the total dissolved
interstitial water concentration Cw.
Direct observations of interstitial water partition
coefficients are restricted to the apparent partition
coefficient, Kf' (Equation 4-20), because (1) total
concentrations in the interstitial water are typically
reported and (2) DOC complexing is expected to be
significant at the DOC concentrations found in
interstitial waters. Data reported by Brownawell and
Farrington (1986) demonstrate the importance of DOC
complexing in interstitial water. Figure4-16presents
the apparent partition coefficient, measured for 10 PCB
congeners at various depths in a sediment core, versus
/oc*owthe calculated partition coefficient The
partition coefficients are computed for total (squares)
and freely-dissolved PCB (circles) using Equation 4-23
and KPQC = Kov/. The line corresponds to the
relationship K^ = ATOW, which is the expected result if
4-22
-------
I
o
U
I
I
«
I
o
do
o
8.0
- 7.0
6.0
5.0
4.0
3.0
2.0
4.0
I I I
D Based on measured PCB in interstitial water
O Based on predicted freely-dissolved
(JSTOW
5.0
6.0
7.0
ow
8.0
Figure 4-16. Apparent partition coefficient versus the product of the organic carbon fraction and Kow. The line
represents equality (data from Brownawell and Farrington, 1986).
DOC complexing were not significant. Because DOC
concentrations were measured for these data, it is
possible to estimate Cd with Equation 4-12 in the form
'IW
(4-23)
and to compute the actual partition coefficient: K =
CJCf The data indicate that if *TDOC = Kow is used,
the results, shown on Figure 4-16, agree with the
expected partition equation, namely that Kp =foc
A similar three-phase model was presented by
Brownawell and Farrington (1985).
Other data with sediment-interstitial water partition
coefficients for which the DOC concentrations have not
been reported (Socha and Carpenter, 1987;Oliver, 1987)
are available to assess the significance of DOC
partitioning on the apparent sediment partition
coefficient. Figure 4-17 presents these apparent
organic carbon-normalized partition coefficients, that
4-23
-------
A Oliver (Various)
Socha and Carpenter (PAHs)
DOC
(mg/L)
1.0
10.0
100.0
Figure 4-17. Apparent organic carbon-normalized partition coefficient (AT^.') versus Xow. The lines represent the
expected relationship for DOC concentrations of 0,1,10, and 100 mg/L K^^. = Kow (data from Oliver
(1987) for PCB congeners and other chemicals, and from Socha and Carpenter (1987) for
phenanthrene, fluoranthene, and perylene).
is, KQC' = K'l/oc versus ^ow- The expected
relationship for DOC concentrations of 0,1,10, and 100
mg/L is also shown. Although substantial scatter in
these data reflect the difficulty of measuring interstitial
water concentrations, the data conform to DOC levels
of 1.0 to 10 mg/L, which is well within the observed
range for interstitial waters (Thurman, 1985; Brownawell
and Farrington, 1986). Thus, these results do not refute
the hypothesis that JC^. « Kow in sediments but show
the need to account for DOC complexing in the analysis
of interstitial water chemical concentrations.
4.5.4 Laboratory Toxicity Tests
Another way to verify Equation 4-14 is from data
collected during sediment toxicity tests in the
laboratory. These tests yield sediment (Cs>oc) and
freely-dissolved interstitial water (Cd) chemical
concentrations at several dosages bounding an
experimentally 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 when sediment and interstitial
water are in equilibrium. The results of these tests can
be used to compute the organic carbon partition
coefficient, K^. To verify Equation 4-14, estimates of
KQC computed from Equation 4-3 and EPA-
recommended ffow values are compared with
partitioning in sediment toxicity tests. Sediment
toxicity tests and KQV/ measurements are available for
five chemicals: endrin (Nebekeretal, 1989; Schuytema
etal., 1989;Stehly, 1991), dieldrin(Hoke and Ankley,
1992), acenaphthene (Swartz, 1991), phenanthrene
4-24
-------
for Derivation of ESGs: ,Noi
(Swartz, 1991),andfluoranthene(Swartzetal., 1990;
DeWitt et al., 1992). Sediment toxicity tests for these
chemicals were performed as part of the development of
ESGs. Mortality results for these tests were presented
in Figures 2-2 and 2-3. A discussion of ATOW selection
follows.
Figure 4-18 shows organic carbon-normalized
sorption isotherms for acenaphthene, endrin,
phenanthrene, and fluoranthene, where the sediment
concentration O/g/g^) is plotted versus interstitial
water concentration (A(g/L). These tests represent
freshwater and marine sediments exhibiting a range of
organic carbon content of 0.07% to 11 %. In each panel,
the line corresponds to Equation 4-8, where K^ is
derived from EPA-recommended £ow values. In each
of the panels, the toxicity test data are in agreement
with the line computed from the calculated K^. For
these chemicals, DOC measurements are unavai lable,
and partitioning to DOC in the interstitial water has not
been considered. The figure indicates, however, that
DOC complexing in these experiments appears to be
negligible.
Partitioning in the dieldrin experiment indicates that
DOC complexation may be significant. The partitioning
isotherm for dieldrin represents organic carbon-
normalized sediment concentrations versus total
(Figure 4- 19A) and computed dissolved (Figure 4- 19B)
interstitial water concentrations. Dissolved interstitial
water concentrations are computed using Equation
4-23, DOC measurements, and an estimated log10Ki
=5.28. Log Kpoc is estimated from log|0 K^ = 5.28 for
|0
dieldrin. Figure 4-19 represents data from Hoke and
Ankley (1992) because Hoke et al. (1995) did not
measure interstitial water. Adjusting for partitioning to
DOC (Figure 4- 19B) indicates better agreement with the
recommended K^. These data represent one sediment
e
o
CJ
B
O»
e
100000
10000
1000
100
10
1
10
1000°
1000
100
10
0.1
0.01
oSwartictal.,1991
100000
10000
1000
100
O Swarti etal., 1991-
100
1000
10000
100000 101
100000
III I Mill I I I I I
10
100
1000
10000
o Nebeker cl al., 1989
D Sehojtemaetal., 1989]
v Stehly, 1991
mill
10000
1000
100
10
nil i i il
O Swarti elal., 1990 '
d De Witt et aL, 1992
i i timl iii iiiiH i i i inn
o.i i 10 100 looe
Interstitial Water Concentration (/zg/L)
Vl 1 10 100 1000 10000
Interstitial Water Concentration
Figure 4-18. Comparison of KQC observed in toxicity tests (symbols) to K^. calculated using Equation 4-3 and £ow
values recommended by Karickhoff and Long (1995; solid line). Symbols are sediment concentration
versus interstitial water concentration. Solid line is CSioc=KocCd» where lo8uAocis 3"85 for
acenaphthene, 4.97 for endrin, 4.47 for phenanthrene, and 5.03 for fluoranthene.
4-25
-------
1
I
i
100000
10000
1000
100
10
0.1
I I I Mill I III Illl I III Illll
I HIM
• Hoke and Ankley, 1992 |
i i i mill i i i mill
mill
I
Mill
i i inn
0.01
0.1 1 10 100
Total Interstitial Water Concentration
1000
100000
10000
1000
100
10
0.1
I 11 Illlj
B
I 1 I
I 111]
mill
I Illll
ITTH
mill
mil
i null i i i mill
inn
0.01 0.1 1 10 100
Freely-Dissolved in Interstitial Water 0/g/L)
1000
Figure 4-19. Comparison of K^ observed in toririty tests (symbols) to K^. calculated using Equation 4-3 a
values recommended by Karickhoff and Long (1995; solid line). Symbols are sediment concentration
versus total (top) and freely-dissolved (bottom) interstitial water concentrations. Solid line is
CS|OC=KocCd, where Iog10*:oc is 5.28 for dieldrin.
4-26
-------
Technical Basis; for Derivation oJf^Gs: N<>ni6iSic't)i
. _c* *_..*•',.-• ~-v' p. ^ • . -• j-' *--,'»',- _^• 'J*i_-. r-\, ^ \ •*'- .•' • ~*/- *'^^
with an organic carbon content of 1.6%. It is important
to note that dieldrin has the highest K^ of the five
chemicals (log,,,^^ dieldrin = 5.28, acenaphthene =
3.85, endrin = 4.97, phenanthrene = 4.47, fluoranthene =
5.03). DOC complexing increases with an increasing
partition coefficient, which explains why DOC
complexing is significant for dieldrin.
A correlation of Iog,0/iroc from sediment toxicity
tests to log^oc calculated from selected £ow is
presented in Figure 4-20. The data yield a correlation
coefficient of 0.843. Therefore, the calculated K^ and
the KQC from sediment toxicity tests are in agreement.
4.6 Organic Carbon Normalization of
Biological Responses
The results discussed above suggest that if a
concentration-response curve correlates to interstitial
water concentration, it should correlate equally well to
organic carbon-normalized total chemical
concentration, independent of sediment properties.
This is based on the partitioning formula Csoc = K^C^
(Equation 4-8), which relates the freely-dissolved
concentration to the organic carbon-normalized
particle concentration. This applies only to nonionic
hydrophobic organic chemicals because the rationale is
based on a partitioning theory for this class of
chemicals.
4.6.1 Toxicity and Bioaccumulation
Experiments
To demonstrate the relationship between organic
carbon normalization and biological response
concentration, concentration-response curves for the
data presented in Figures 3-1 and 3-3 are used to
compare results on a interstitial water-normalized and
organic carbon-normalized chemical concentration
basis. Figures 4-21 through 4-23 present these
comparisons for kepone, DDT, endrin, and
fluoranthene. The mean and 95% confidence limits of
6.0
5.5
5.0
4.5
4.0
3.5
3.0
O Dieldrin
D Phenanthrene
O Endrin
A Fluoranthene
V Acenaphthene
r = 0.843
3.0 3.5 4.0 4.5 5.0 5.5
6.0
from Recommended JTOW (L/kg)
Figure 4-20. Correlation of log^^ from sediment toxicity tests to log,/^ estimated from EPA-recommended
KOYl values for five chemicals.
4-27
-------
100
80
60
20
0
A:/oc(%)
• A AA
• 0.09
•--,**/
100
80
60
40
20
0
1 10 100 1000
Interstitial Water Concentration 0/g/L)
l 10 100 1000
Interstitial Water Concentration fcg/L)
100
80
60
20
0
g 100
g 80
| W
f 20
rl 0
D
10 100 1000 10000
Sediment Concentration (^g/goc)
10 100 1000 10000
Sediment Concentration O^g/goc)
Figure 4-21. Comparison of percent mortality (left) and growth rate reduction (right) of C. feiriiuu to kepone
concentrations in interstitial water (top) and in bulk sediment using organic carbon normalization
(bottom) for three sediments with varying organic carbon concentrations (data from Adams et al.,
1985).
the LCSO and ECSO values for each set of data were
listed in Table 3-2. The top panels repeat the response-
interstitial water concentration plots shown previously
in Figures 3-1 through 3-3, and the lower panels present
the response versus the sediment concentration, which
is organic carbon-normalized (^g chemical/g^).
The general impression of these data is that there
is no reason to prefer interstitial water normalization
over sediment organic carbon normalization. In some
cases, interstitial water normalization is superior to
organic carbon normalization, e.g., kepone-mortality
data (Figure 4-21; left), whereas the converse
sometimes occurs, e.g., kepone-growth rate (Figure
4-21; right). A more quantitative comparison can be
made with the LCSO and ECSO values in Table 3-2. The
variation of organic carbon-normalized LCSO and ECSO
values between sediments is less than a factor of two
to three and is comparable to the variation in interstitial
water LCSO and ECSO values. A more comprehensive
comparison was presented in Figures 2-2 and 2-3, which
also examine the use of the water-only LCSO to predict
the interstitial water and sediment organic carbon LCSO
values.
Bioaccumulation factors calculated on the basis of
organic carbon-normalized chemical concentrations are
listed in Table 3-3, for permethrin, cypermethrin, and
kepone. Again, the variation of organic carbon-
normalized BAFs between sediments is less than a
factor of two to three and is comparable to the variation
in interstitial water BAFs.
4.6.2 Bioaccumulation and Organic Carbon
Normalization
Laboratory and field data also exist for which no
interstitial water or DOC measurements are available
4-28
-------
—r—^—.'; . --. "|1- ^7—**~.—;—'^i
i:? '' '• *''. Technical.^asfe fpr Derivation.<>f ESGs:'N6pi6nic?Organics [
100
~ 80
* *°
1 40
I 2°
0
0.1
]
100
g "
f60
40
0
. A:/oc(%) ,«.<.
. :s
• 10.5 •
.
f •• * DDT -
11 0.1 1 10 10
Interstitial Water Concentration 0/g/L)
h
•
.* • • % * % DDT -
10 100 1000 101
Sediment Concentration (Mg/goc)
100
-7> 80
1 W
1 40
1 -
0
0 0.
]
100
1 "
V, *°
0
MO 1
. B:/oc(%) »*»_» .-
• 3.0 . •
- » 6.1 •
• 11J
i * •«* ••""• Endrin -
1 1 10 10
Interstitial Water Concentration (pg/L]
D rc ««..
: :
*j
.* • |JH •*• Endrin .
[ 10 100 1000 101
Sediment Concentration (pg/goJ
«
1
100
Figure 4-22. Comparison of percent mortality of H. azteca with DDT (left) and endrin (right) concentrations in
interstitial water (top) and in bulk sediment using organic carbon normalization (bottom) for three
sediments with varying organic carbon concentrations (data from Nebeker et al., 1989; Schuytema et
al., 1989).
but for which sediment concentration, organic 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 conventional to
use organism lipid fraction for this normalization (see
references in Chiou, 1985). If Cb is the chemical
concentration per unit wet weight of the organism, then
the partitioning equation is
Cb=KL/LCd (4-24)
where KL=lipid/water partition coefficient (L/kg lipid);
/L=weight fraction of lipid (kg lipid/kg organism); and
Cd=freely-dissolved chemical concentration (/ig/L).
The lipid-normalized organism concentration, Cb L,
is
C±
L
The lipid-normalized body burden and the organic
carbon-normalized sediment concentration can be used
to compute a biota-sediment accumulation factor, BS AF
(Thomannetal., 1992)
(4-26)
The second equality results from using partitioning
Equations 4-8 and 4-25 and the third from the
approximation that K^ - J^ow- The BS AF is the
partition coefficient between organism lipid and
sediment organic carbon. If the equilibrium
assumptions are valid for both organisms and sediment
particles, the BSAF should be independent of both
particle and organism properties. In addition, if lipid
4-29
-------
I
0 20 40 60 80
Interstitial Water Concentration C"g/L)
2000 4000 6000 8000
Sediment Concentration (^g/goc)
Figure 4-23. Comparison of percent mortality of R. abronius to fluoranthene concentrations in interstitial water
(top) and bulk sediment using organic carbon normalization (bottom) for sediments with varying
organic carbon concentrations (data from Swartz et ah, 1990).
solubility of a chemical is proportional to its octanol
solubility, KL - KQV, then the lipid-normalized, organic
carbon-normalized BSAF should be a constant,
independent of particles, organisms, and chemical
properties (Thomannetal., 1992;McFarland, 1984;
Lakeetal., 1987). This result can be tested directly.
Representation of benthic organisms as passive
encapsulations of lipid that equilibrate with external
chemical concentrations is clearly only a first-order
approximation. Biomagnification effects, which can
occur via ingestion of contaminated food, and the
dynamics of internal organic carbon metabolism can be
included in a more comprehensive analysis (Thomann
et al., 1992). It is, nevertheless, an appropriate initial
assumption because deviations from the first-order
representation will point to necessary refinements, and
for many purposes this approximation may suffice.
A comprehensive experiment involving four
benthic organisms (two species of deposit-feeding
marine polychaetes. Nereis and Nephtys, and two
species of deposit-feeding marine clams, Yoldia and
Macoma) and five sediments was performed by
4-30
-------
Technical Basis for Derivation, of ESGs: Nonionic" Organics
Rubinstein et al. (1988). The uptake of various PCB
congeners was monitored until steady-state body
burdens were reached. Sediment organic carbon and
organism lipid content were measured. Figures 4-24
and 4-25 present the log mean of the replicates for the
ratio of organism-to-sediment concentration for all
measured congeners_versus £QW for each organism.
Dry weight normalization for both organism and
sediment (left panels), organic carbon normalization for
the sediment (center panels), and both organic carbon
and lipid normalization (right panels) are shown. The
results for each sediment are connected by lines and
separately identified. The BSAFs based on dry weight
normalization (Figures 4-24A and 4-25 A) are quite
different for each of the sediments, with the low carbon
sediment exhibiting the largest values. Organic carbon
normalization (Figures 4-24B and 4-25B) markedly
reduces the variability in the BSAFs from sediment to
sediment. Lipid normalization usually further reduces
the variability (Figures 4-24C and 4-25C). Note that the
BSAFs are reasonably constant for the polychaetes
(Figure 4-24), although some suppression is evident at
logjoAT^, >7. The clams, however, exhibit a marked
declining relationship (Figure 4-25).
Results of a similar although less extensive
experiment using one sediment and oligochaete worms
have been reported (Oliver, 1987). A plot of the organic
carbon- and lipid-normalized BSAF versus £ow from
this experiment is shown on Figure 4-26, together with
the averaged polychaete data (Figure 4-24). There
appears to be a systematic variation with respect to
Afow, 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 KQV/ (Thomann et
al., 1992). However, for a specific chemical and a
specific organism, for example. Nereis and any PCB
congener (Figure4-24), organic carbon normalization
reduces the effect of the varying sediments. This
demonstrates the utility of organic carbon
normalization and supports its use in generating ESGs.
A further conclusion can be reached from these
results. Bierman (1990) pointed out that the lipid- and
carbon-normalized BSAF is in the range of 0.1 to 10
3
3.0
2.0
1.0
0.0
A: Dry Weight {•c£*>
U
U '
Nereis
65
IS
U
3.0
2.0
i ..»
0.0
VJ
Hi
J
-1.0
A: Dry Weight /«<*>
A U
• 33 .
• 13
Nephtys
1.0
0.0
-1.0
-2.0
-3.0
1.0
0.0
B: Dry Wt; Orguilc Cirbon
3.0
2-0
0.0
7.5 M
-1.0
C: Lipid; Organic Carbon
5JS 6S 7.5 15
J
r:
B: Dry Wt; Organic Carbon
3.0
2.0
0.0
5S
&S
73
8J
5.5
6.5
7.5
8J
-1.0
C: Lipid; Organic Carbon
S.S
7.S
IS
Figure 4-24. Plots of the BSAFs for Nereis (top) and Nephtys (bottom) for three sediments for a series of PCB
congeners versus the log10Kow for that congener: (A) dry weight-normalized for both organism and
sediment, (B) organic carbon-normalized for the sediment, and (C) lipid- and organic carbon-
normalized as indicated (data from Rubinstein et al., 1988).
4-31
-------
3.0
2.0
j-
0.0
A: Dry Weight
A U
• 3.9
• 13
-1.0
2.0
1.0
0.0
-1.0
-2.01—
S3
1.0
6J
7.5
A: Dry Weight
Afacome
-2.0
•3.0
1.0
-1.0
-2.0
B: Dry Wt; Orgaofc Cuboo
3.0
2.0
'••
0.0
•1.0
C:Llpld;OrgaiifcC«rboe
63 IS
LO&.KOW
13 S3
-3.0
B: Dry Wt.; Organk Carbon
1.0
M
-1.0
-2.0
C: Lipid; Orguk CarboD
63
73
S3
63
63 13
Figure 4-25. Plots of the BSAFs for fr&fta (top) and Macoma (bottom) for three sediments for a series of PCB
congeners versus the Iog10/irow for that congener: (A) dry weight-normalized for both organism and
sediment; (B) organic carbon-normalized for the sediment; and (C) lipid and organic carbon-
normalized as indicated (data from Rubinstein et al., 1988).
(Figures 4-24 through 4-26), which supports the
contention that the partition coefficient for sediments is
KQC = ATOW and that the particle concentration effect
does not appear to affect the free concentration in
sediment interstitial water. The reason for this
observation is that the lipid- and organic carbon-
normalized BS AF is the ratio of the solubilities of the
chemical in lipid and in particle carbon (Equation 4-26).
Because the solubility of nonionic organic chemicals in
various nonpolar solvents is similar (Leo, 1972), it
would be expected that the lipid-organic carbon
solubility ratio should be on the order of one. If this
ratio is taken to be approximately one, then the
conclusion from the BS AF data is that K^ is approx-
imately equal to KOW for sediments (Bierman, 1990).
A final observation can be made. The data
analyzed in this section demonstrate that organic
carbon normalization accounts for much of the reported
differences in bioavailability of chemicals in sediments
for deposit-feeding polychaetes, oligochaetes, and
clams. The data presented in previous sections are for
amphipods and midges. Hence these data provide
important additional support for organic carbon
normalization as a determinant of bioavailability for
different classes of organisms.
4.7 Determination of 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 Connolly,
1984). It might be supposed that the toxicity and
bioaccumulation data presented above can be used to
examine the question of the route of exposure. The
initial observations were that biological effects appear
to correlate to the interstitial water concentration,
independent of sediment type. This has been
interpreted to mean that exposure is primarily via
interstitial water. However, the data correlate equally
well with the organic carbon-normalized sediment
concentration (see Figures 2-2 and 2-3). This
observation could be taken to suggest that sediment
organic carbon is the route of exposure. In fact, neither
conclusion follows necessarily from these data because
4-32
-------
£y"'f' " ' 'J\^-^ ^ ^r.VTechirical" Basis' for DerivaUon of ES6s: Noi^o^c Oi^anics
100
~ 10
o
M)
•o
s
<
en
pa
1
0.1
3.
: O Oligochaete
• Polychaete
" " 0 O
0 c*P
: o o
'
1
1
6.0
gj
8 °
.ffi
T mf
r if
1
fo.
"f
7.0
-
1
m
1 II
1
_
"
1
-
—
\
8.0 9.0
Figure 4-26. Plots of the BSAFs for a series of PCB congeners and other chemicals versus log10Kow Data for
oligochaetes from Oliver (1987). Data for polychaetes from Rubinstein et al. (1988).
an alternative explanation is available that is
independent of the exposure pathway.
Consider the hypothesis that the chemical
potential, sometimes called fugacity (Mackay, 1979), of
a chemical controls its biological activity. The chemical
potential, A
-------
Section 5
Applicability of WQC as Effects
Levels for Benthic Organisms
The EqP method for deriving ESGs utilizes
partitioning theory to relate die sediment concentration
to the equivalent freely-dissolved chemical
concentration in interstitial water and in sediment
organic carbon. The interstitial water concentration
for ESGs should be the effects concentration for
benthic species.
This section examines the validity of using EPA
WQC concentrations to define the effects concentration
for benthic organisms. -This use of WQC assumes that
(1) the sensitivities of benthic species and species
tested to derive WQC, predominantly water column
species, are similar; (2) the levels of protection
afforded by WQC are appropriate for benthic
organisms; and (3) exposures are similar regardless of
feeding type or habitat. This section examines the
assumption of similarity of sensitivity in two ways.
First, a comparative lexicological examination is
presented of the acute sensitivities of benthic and
water column species using data compiled from the
published and draft EPA WQC for nonionic organic
chemicals as well as metals and ionic organic
chemicals. Then a comparison of the FCVs and the
chronic sensitivities of benthic saltwater species in a
series of sediment colonization experiments is made.
5.1 Relative Acute Sensitivity of Benthic
and Water Column Species
Relative acute sensitivities of benthic and water
column species are examined by using LCSO values for
freshwater and saltwater species from draft or
published WQC documents that contain minimum
database requirements for calculation of final acute
values (FAVs) (Table 5-1). These datasets 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 freshwater, using
208 species with 40 chemicals, and the 1,046 tests
conducted in saltwater, using 118 species with 30
chemicals, the chemical, species, life-stage, salinity,
hardness, temperature, pH, acute value, and test
condition (i.e., static, renewal, flow-through, nominal,
or measured) were entered into a 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 5-2). Habitats
were based on degree of association with sediment. A
life-stage that occupied more than one habitat was
assigned to both of the appropriate habitats.
For each chemical, if a life-stage was tested more
than once or more than one life-stage was tested, data
were systematically sorted in a three-step process to
'arrive at the acute value based on the most
experimentally sound testing methodology and the most
sensitive life-stages. First, if a life-stage for a 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 concentrations, all acute
values for that life-stage were given equal weight. If
the remaining acute values for that life-stage differed
by a factor greater than 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 [habitats 1 and 2] or
infaunal and epibenthic species [habitats 1,2,3, and 4])
or "water column" (habitats 5 to 8) (Table 5-2). 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 (Stephan et al., 1985).
5.2 Comparison of Sensitivity of Benthic
and Water Column Species
5.2.1 Most Sensitive Species
The relative acute sensitivities of the most
sensitive benthic and water column species were
5-1
-------
m
s?iS5.(4S
MM
«:
isfesslfisi!
Hi
ffigsEzSi
mm
Table 5-1. Draft or published WQC documents and number of infaunal (habitats 1 and 2), epibenthic (habitats 3
and 4), and water column (habitats 5 to 8) species tested acutely for each substance
Number of Saltwater Species
Chemical
Acenaphlhene
Acrolein
Aldrin
Aluminum
Ammonia
Antimony 0D)
Arsenic (III)
Cadmium
Chlordane
Chloride
Chlorine
Chloipyrifos
Chromium (III)
Chromium (VI]
Copper
Cyanide
DDT
Dieldrin
Date of
Publication
9/87b
9/87b
1980
1988
1985; 1989
9/87b
1985
198S
1980
1988
198S
1986
1985
1985
1985
1985
1980
1980
2,4-Dimethylphenol 6/88b
Endosulfan
Endrin
Heptachlor
1980
1980
1980
Hexachlorocy-clohexane 1980
Lead
Mercury
Nickel
Parathion
1985
198S
1986
1986
Total
—
—
16
—
20
11
12
38
8
—
23
IS
—
23
25
9
17
21
9
12
21
19
19
13
33
23
—
a Infaunal
—
—
0
—
2
3
2
10
1
—
2
2
—
8
6
1
. 1
1
2
2
1
1
2
2
10
7
—
Epibenthic
—
—
11
—
7
6
3
18
7
—
9
8
—
9
5
4
11
IS
2
8
14
14
14
3
7
10
—
Water
Column
—
—
12
—
16
5
8
18
7
—
IS
10
—
9
18
5
12
IS
6
8
16
13
12
10
18
9
—
Parathion, Methyl 10/88b — — — —
Pentachlorophenol 1986
Phenanthrene
9/87b
19
10
7
4
7
6
11
4
Total8
10
12
21
IS
48
9
16
56
14
IS
33
18
17
33
57
17
42
19
12
10
28
18
22
14
30
21
37
36
41
9
Number of Freshwater Species
Infaunal Epibenthic
—
1
2
—
2
1
1
13
1
3
1
2
3
1
8
1
3
1
1
1
3
2
1
—
11
2
7
1
9
2
3
5
10
5
17
2
6
16
4
6
9
8
8
10
IS
6
IS
9
3
4
12
8
4
4
8
7
14
9
11
1
Water
Column
7
7
IS
11
33
6
13
31
10
8
26
11
12
21
36
12
29
12
7
7
17
12
18
11
12
13
23
25
23
6
5-2
-------
Table 5-1. Draft or published WQC documents and number of infaunal (habitats 1 and 2), epibenthic (habitats 3
and 4), and water column (habitats 5 to 8) species tested acutely for each substance (continued)
Number of Saltwater Species
Number of Freshwater Species
Chemical
Date of Water Water
.Publication Total3 Infaunal Epibenthic Column Totala Infaunal Epibenthic Column
Phenol
Selenium (IV)
Selenium (VI)
Silver
Thallium
Toxaphene
Tributyltin
1,2.4-
Trichlorobenzene
2.4,5-
Trichlorophenol
Zinc
5/88b
1987
1987
9/87b
ll/88b
1986
9/87
9/88b
9/87b
1987
—
16
—
21
—
IS
19
15
11
33
—
1
—
1
—
2
1
7
4
10
—
5
—
6
—
9
8
7
5
9
—
13
—
16
—
11
15
4
5
17
32
23
12
19
8
37
9
14
10
45
6
2
1
1
1
5
1
2
1
5
9
6
4
9
3
13
1
5
2
12
20
19
10
13
3
23
6
7
8
30
*The total numbers of tested species may not be the same as the sum of the number of species from each habitat type because a
species may occupy more than one habitat.
bAquatic life criteria document. U.S. Environmental Protection Agency. Office of Science & Technology, Health and Ecological
Criteria Division. Washington. DC.
Table 5-2. Habitat classification system for life-stages of organisms
Habitat
Type
Description
1
Life-stages that usually live in the sediment and whose food consists mostly of sediment or organisms living in
the sediment: infaunal nonfiltcr 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 rood 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
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; e.g., fouling organisms on
pilings or ships and zooplankton, pelagic fish.
5-3
-------
examined by comparing the FAVs for benthic and water
column organisms from databases having eight or more
acute values. These FAVs were derived from the 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 5-1 A. The line represents
perfect agreement.
Data on the sensitivities of benthic infaunal species
are limited. Of the 40 chemicals for which published
and draft WQC for freshwater organisms are available,
2 or fewer infaunal species were tested with 28 (70%)
of the chemicals, and 5 or fewer species were tested
with 34 (85 %) 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 %)
of the chemicals, and 5 or fewer species were tested
with 23 (77%) of the chemicals. Of these chemicals
only zinc in saltwater has been tested using infaunal
species from three or more phyla and eight or more
families, the minimum acute toxicity database required
for criteria derivation. As a result, FAVs could not be
computed for several of the chemicals. Therefore, it is
probably premature to conclude from the existing data
that infaunal species are more tolerant than water
column species.
A similar examination of the FAVs calculated for
benthic and water column species, where the definition
of benthic includes both infaunal and epibentbic species
(habitat types 1 to 4), is based on more data and
suggests a similarity in sensitivity (Figure S-1B). In
this comparison, the number of acute values for
freshwater benthic species for each chemical averaged
9, 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 examining species
sensitivity is not sufficiently rigorous.
Examination of individual criteria documents in
which benthic species were markedly less sensitive
than water column species suggests that the major
factor for this difference is that benthic species
phylogenetically related to sensitive water column
species have not been tested. Apparent differences in
sensitivity, therefore, may reflect an absence of
appropriate data. Data that are available suggest that,
on the average, benthic and water column species are
similarly sensitive and support the use of WQC to
derive ESGs for the protection of infaunal and
epibenthic species.
5.2.2 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 location of benthic
species in the overall species sensitivity distribution.
For each chemical in either fresh- or saltwater, one
can examine the distribution of benthic species in a
rank-ordering of all the species' LCSO values. If
benthic species were relatively insensitive, then they
would predominate in ranking among the higher LCSO
concentrations. Equal sensitivity would be indicated by
a uniform distribution of species within the overall
ranking. Figure 5-2 presents the results for tests of
nickel in saltwater. The LCSO values are plotted in
rank order, and the benthic species are indicated.
Infaunal species are among the most tolerant (Figure
5-2A), whereas infaunal and epibentbic species are
uniformly distributed among the species (Figure 5-2B).
This comparison can be done chemical by
chemical. However, to make the analysis more robust,
the data for each chemical-water type can be
normalized to zero log mean and unit log variance as
follows
(5-1)
where i indexes the chemical-water type, ^ is the log
mean, and Oj is the log standard deviation; j indexes the
LCSO values within the ith class; and LC50n ^ is the
normalized LCSO. This places all the LC50'values
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 dataset.
The comparison is made in Figure 5-3. If the
sensitivity of benthic species is not unique, then a
constant percentage of benthic species-normalized
LCSO values, indicated by the dashed line, should be
represented in each 10-percentile (decile) interval of
data for all species. That is, the 10 rectangles hi each
histogram should be identical in height. The infaunal
species (Figure S-3A and B) display a tendency to be
5-4
-------
i
I
8
O
u
I
s
O
u
4
3
2
1
0
-1
-2
-3
5
4
3
2
1
0
-1
-2
-3
A: Infaunal Only
A Freshwater
• Saltwater
B: Infaunal and Epibenthic
-2-101234
Loglt Benthic Organism FAY 0/g/L)
Figure 5-1. Comparison of the FAVs for water column versus benthic organisms for chemicals listed on Table
5-1. Benthic species are defined as (A) infaunal species (habitat types 1 and 2) or (B) infaunal and
epibenthic species (habitat types 1-4). See Table 5-2 for habitat definitions.
5-5
-------
Applicability;
1000000
100000
i
o 1000°
3
1000
III 1 -
A: Infaunal -
•C
o»**0*°° ^
•0*°° ^
o :
O
0° j
O
0
I 20 40 CO 80 11
Percentage Rank of Saltwater Species
10
B: Infaunal & Epibenthic :
0**°~
.•o»° :
o*° 1
o :
:
:o •
* i i i i
0 20 40 60 SO 11
Percentage Rank of Saltwater Species
10
Figure 5-2. LC50 values versus percentage rank sensitivities for nickel in saltwater species. The closed
symbols identify (A) infaunal and (B) infaunal and epibenthk organisms (figure from Di Toro et
al., 1991). The open symbols identify water column organisms.
underrepresented in the lowest deciles. However, the
infaunal and epibenthic species (Figure 5-3C and D)
more closely follow this idealized distribution.
Infaunal and epibenthic freshwater species are nearly
uniformly distributed, whereas the saltwater benthic
species are somewhat underrepresented in the lowest
ranks.
Given the limitations of these data, they appear to
indicate that, except for possibly freshwater infaunal
species, benthic species are not uniquely sensitive or
insensitive and that ESGs derived by using the FC V
should protect benthic species.
5.3 Relating Acute to Chronic Sensitivities
for Benthic Organisms
Thus far, comparisons of overall species
sensitivities and benthic species sensitivities have used
acute test results. Acute-chronic ratios (ACRs)
extracted from draft or published EPA criteria
documents (see Table 5-1) will be used to relate acute
to chronic sensitivities for benthic species. The dataset
of ACRs is made up of 295 data points of which 83
represent benthic organisms. A test of applying the
idea of similar sensitivities, as indicated by analyses of
acute data, to chronic sensitivities would be to
determine if the distributions of ACRs for all species
and for benthic species are similar.
If ACRs for benthic species were dissimilar, either
much higher or much lower, then this would indicate
that the relationship of acute to chronic toxicity is
anomalous for benthic organisms. That is, the benthic
organisms could be construed to be either more or less
sensitive than the overall set of test species. However,
similar distributions would indicate that sensitivity for
benthic species is similar to overall species sensitivity.
Rank distributions of ACRs for all species (open
symbols) and ACRs for benthic species (closed
symbols) are shown in Figure 5-4. Similarity of the
distributions indicates that overall species sensitivity
and benthic species sensitivity are the same for chronic
data. This supports the use of the FCV in computing
ESG.
5.4 Benthic Community Colonization
Experiments
Toxicity tests that determine the effects of
chemicals on the colonization of communities of
benthic saltwater species (Hansen, 1974; Tagatz, 1977;
Hansen and Tagatz, 1980; Tagatz and Ivey, 1981;
Tagatz etal., 1982,1983) appear particularly sensitive
for measuring the impacts of chemicals on benthic
organisms. This is probably because the experiment
exposes the most sensitive life-stages of a wide variety
of benthic saltwater species, and they are exposed for a
5-6
-------
50
40
30
20
10
B: Freshwater
Tnfannal
5 15 2S 35 45 55 <5 75 85 95
5 15 25 35 45 55 C5 75 85 95
100
I "
I 60
« 40
20
0
C: Saltwater
Infannal&Epibentfaic
D: Freshwater
Infaunal&Epibenthlc
Illlllll
5 15X535455565758595
Mean of Percentile Range (%)
5 IS 25 35 45 55 65 75 85 »S
Mean of Percentile Range (%)
Figure 5-3. Proportion of saltwater (left) and freshwater (right) benthlc organisms in 10 percentile groups of
aU normalized LCSO values for infaunal (top) and infaunal and epibenthic (bottom) organisms as
benthic. The dashed lines indicate the average sensitivities of the overall percentage of bentbic
species in the dataset.
sufficient duration to maximize response. The test
typically includes 3 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 2 to 4
months, and the numbers of species and individuals in
aquaria receiving the chemical are enumerated and
compared with controls.
If this test is extremely sensitive and if
concentrations hi interstitial water, overlying water,
and the sediment particles reach equilibrium, then the
effect and no-effect concentrations from this test can be
compared with the FCV from the saltwater WQC
documents to examine the applicability of WQC to
protect benthic organisms. An FCV is the
concentration, derived from acute and chronic toxicity
data, that is predicted to protect 95 % of the tested
organisms from chronic effects of a chemical (Stephan
et al., 1985). In addition, similarities in sensitivities of
taxa tested as individual species and in the colonization
experiment can indicate whether the conclusion of
similarity of sensitivities of benthic and water column
species is reasonable.
The benthic colonization experiment is consistent
with the assumptions used to derive ESGs. The
initially clean sandy sediment will rapidly equilibrate
with the inflowing overlying water chemical
concentration as the interstitial water concentrations
reach the overlying water concentration. The
production of sedimentary organic matter should be
slow enough to permit its equilibration as well. As a
consequence, the organisms will be exposed to an
equilibrium system with a unique chemical potential.
Thus, the assumption of the EqP is met by this design.
5-7
-------
10000
1000
100
10
0.1
o All Species
• Benthic Species
J_
1
J L
*•
0.01 0.1 1
10 20 50 80 90
Probability
99
99.9 99.99
Figure 5-4. Distribution of acute-chronic ratios showing all species (O) and bentbic species only (•) (species
from Table 5-1).
In addition, the experimental design guarantees that the
interstitial water-sediment-overlying water is at the
chemical potential of the overlying water. Hence there
is a direct correspondence between the exposure in the
colonization experiment and the water-only exposures
from which WQC are derived, namely, the overlying
water chemical concentration. This allows a direct
comparison.
5.5 WQC Concentrations Versus
Colonization Experiments
Comparison of the concentrations of six chemicals
that had the lowest-observable-effect concentration
(LOEQ and the no-observable-effect concentration
(NOEC) on bentbic colonization with the FC Vs either
published in saltwater portions of WQC documents or
estimated from available toxicity data (Table 5-3)
suggests that the level of protection afforded by WQC
to benthic organisms is appropriate. The FCV should
be lower than the LOEC and larger than the NOEC.
The FCV from the WQC document for
pentachlorophenol of 7.9 ng/L is less than the LOEC
for colonization of 16.0 Atg/L. The NOEC of 7.0 ^g/L
is less than the FCV. Although no FCV is available
for Aroclor 1254, the lowest concentration causing no
effects on the sheepshead minnow (Cyprinodon
variegatus) and pink shrimp (Penaeus duorarum) as
cited in the WQC document is about 0.1 jzg/L. This
concentration is less than the LOEC of 0.6 /jg/L and is
the same as the NOEC of 0.1 //g/L based on a nominal
concentration in a colonization experiment. The lowest
concentration tested with chlorpyrifos (0.1 ^g/L) and
fenvalerate (0.01 i*g/L) affected colonization of benthic
species. Both values are greater than either the FCV
estimated for chlorpyrifos (0.005 uglL) or the FCV
estimated from acute and chronic effects data for
fenvalerate (0.002 Mg/L). The draft WQC document for
1,2,4-trichlorobenzene suggests that the FCV should be
50.0 Mg/L. This value is slightly above the LOEC from
a colonization experiment (40.0 jtg/L) suggesting that
the criterion might be somewhat underprotective for
benthic species. Finally, a colonization experiment
with toxaphene provides the only evidence from these
5-8
-------
Table 5-3. Comparison of WQC FCVs and concentrations affecting (LOEQ and not affecting (NOEQ benthic
colonization
Substance
Pentachlorophenol
Aroclor 1254
Chlorpyrifos
Fenvalerate
1.2.4-
Trichlorobenzene
Toxaphene
Colonization vs.
FCV"
Colonization
"LOEC*
FCV
Colonization
NOEC
Colonization
LOEC
Estimated FCV
Colonization
NOEC
Colonization
LOEC
Estimated FCV
Colonization
NOEC
Colonization
LOEC
Estimated FCV
Colonization
NOEC
Estimated FCV
Colonization
LOEC
Colonization
NOEC
Colonization
LOEC
Colonization
NOEC
FCV
Concentration
(Mg/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 References
Molluscs, abundance Tagatz, 1977; Tagatz et al.. 1983
Molluscs, Crustacea,
fish
—
Crustacea Hansen, 1974
Crustacea, fish Hansen and Tagatz, 1980
—
Crustacea, molluscs, Tagatz et al., 1982
species richness
Crustacea
—
Crustacea, chordates Tagatz and Ivey, 1981
Crustacea
—
Crustacea, fish Tagatz et al.. 1985
Molluscs, abundance
—
Crustacea, species Hansen and Tagatz, 1980
richness
—
Crustacea, fish
aSix-day exposure to established benthic community.
S-9
-------
tests that the FCV might be overprotective for benthic
species; the FCV is 0.2 uglL versus the NOEC for
colonization of 0.8 ng/L. The taxa most sensitive to
chemicals, as indicated by their LCSOs and the results
of colonization experiments, are generally similar,
although, as might be expected, differences occur.
Both the WQC documents and the colonization
experiments suggest that Crustacea are most sensitive
to Aroclor 1254, chlorpyrifos, fenvalerate, and
toxaphene. Colonization experiments indicated that
molluscs are particularly sensitive to three chemicals,
an observation noted only for pentachlorophenol in
WQC documents. Fish, which are not tested in
colonization experiments, are particularly sensitive to
four of the six chemicals.
5.6 Conclusions
Comparative lexicological data on the acute and
chronic sensitivities of freshwater and saltwater
benthic species in the ambient WQC documents are
limited. Acute values are available for only 34
freshwater infaunal species from 4 phyla and only 28
saltwater infaunal species from 5 phyla. Only 7
freshwater infaunal species and 24 freshwater
epibenthic species have been tested with 5 or more of
the 40 WQC chemicals. Similarly. 9 saltwater
infaunal species and 20 epibenthic species have been
tested with 5 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 ESGs can be
derived from WQC. The data suggest that the most
sensitive infaunal species are typically less sensitive
than the most sensitive water column (epibenthic and
water column) species. When both infaunal and
epibenthic species are classed as benthic, the
sensitivities of 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 almost evenly
distributed among both sensitive and insensitive species
overall. Distributions of ACRs indicate that chronic
sensitivities of benthic organisms are similar to chronic
sensitivities of all species.
Finally, in experiments to determine the effects of
chemicals on colonization of benthic saltwater
organisms, concentrations affecting colonization were
generally greater, and concentrations not affecting
colonization were generally lower than estimated or
actual saltwater WQC FCVs.
5-10
-------
Section 6
Generation of ESG
6.1 Parameter Values
The equation from which ESGs are calculated is
ESGOC=«OCPCV
(6-1)
(see Equations 2-2 to 2-7 and associated text). Hence,
the ESG concentration depends only on these two
parameters. The K^ of the chemical is calculated from
the ATOW of the chemical via the regression Equation 4-
3. Reliability of ESG^, depends directly on reliability of
£ow. For most chemicals of interest, the available K.
vow-
ow
values (e.g., Leo and Hansch, 1986) are highly variable;
a range of two orders of magnitude is not unusual.
Therefore, the measurement methods and/or estimation
methodologies used to obtain each estimate must be
critically evaluated to ensure their validity. The
technology for measuring Afow has improved in recent
years. For example, the generator column method
(Woodburn et al., 1984) and the slow stirring method
(Tagatz and Ivey, 1981) appear to give comparable
results, whereas earlier methods produced more
variable results. Hence, it is recommended that
literature values for KQV/ not be used unless they have
been measured using the newer techniques.
6.2 Selection of K.
ow
The KQW 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
dependent only on temperature and pressure
(6-2)
where CQ^. is the concentration of the substance in n-
octanol and Cw is the concentration of the substance in
water. The Kov is frequently reported in the form of its
logarithm to base 10 as log P.
The EPA Ecosystems Research Division at Athens,
GA, has established a protocol for recommending the
best K0y value (U.S. EPA, 1996). The protocol includes
assembling and evaluating all experimental and
calculated data. Using this protocol, Karickoff and
Long (1995) recommend log10ATow values for several
chemicals. These recommended log10ATow values were
used to compute log,,,/^ for five chemicals (Table 6-
1).
6.2.1
Determination
The previous section discusses selecting the
method for measuring £ow for use in computing ESGs.
It is widely accepted that AT^, can be estimated from
Kow. The Koc used to calculate the ESGs is based on
the regression of log,,^^ to Iog10#ow, Equation 4-3.
Computed log^K^ values are given in Table 6-1. This
equation is based on an analysis of an extensive body
of experimental data for a wide range of compound
types and experimental conditions, thus encompassing
a wide range of Afow and/^ values (Di Toro, 1985).
Sediment toxicity tests provide a favorable
environment for measuring ATQW. Figures 4-18 and 4-19
presented plots of the organic carbon-normalized
sorption isotherm from sediment toxicity tests for
the five chemicals where the sediment concentration
(A
-------
Generation of ESG
concentration (yug/L). Also included in each panel is
the line to the partition, Equation 4-8, where K^ is
computed from the recommended KQV values. These
plots can be used to compare the K^ computed from
the recommended KQV and the regression equation
with the partitioning behavior of the chemical in the
sediment toxicity tests. For each of the chemicals, the
KQC line is in agreement with the data demonstrating
the validity of the use of the recommended /fow in the
ESG computation.
6.3 Species Sensitivity
The FCV is used as the endpoint for the protection
of benthic organisms. Although previous work has
indicated that the FCV is applicable for all criteria
chemicals, this assumption should be verified for each
chemical. To test this assumption for a particular
chemical, a statistical method known as the approximate
randomization (AR) method (Noreen, 1989) can be used.
The idea is to test whether the difference between the
FAV derived from considering only benthic organisms
is statistically different from the FAV contained in the
WQC document.
The AR method tests the significance level of the
test statistic by comparing it with the distribution of
statistics generated from many random reorderings of
the LCSO values from WQC documents. For example,
the test statistic in this case is the difference between
the 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 AR method
for this test, the number of data points coinciding with
the number of benthic organisms is selected from the
WQCdataset. A "benthic" FAV is computed. The
original WQC FAV and the "benthic" FAV are then used
to compute the difference statistics. This is done many
times and the resulting distribution is representative of
the population of FAV difference statistics. The test
statistic is compared with this distribution to determine
its level of significance.
For each chemical, an initial test of the difference
between the freshwater and saltwater FAVs for all
species (benthic and water column species combined,
hereafter referred to as "WQC") is performed. The
probability distribution of the FAV differences for
dieldrin is shown in Figure 6-1 A. The horizontal line
that crosses the distribution is the test statistic
computed from the original WQC and benthic FAVs.
For dieldrin, the test statistic falls at the 16th
percentile. Since the probability is less than 95%, the
hypothesis of no significant difference in sensitivity is
accepted.
Because freshwater and saltwater species show
similar sensitivity, a test of difference in sensitivity for
benthic and WQC organisms combining freshwater and
saltwater species can be made. Figure 6-IB represents
the AR analysis to test the hypothesis of no difference
in sensitivity between benthic and WQC organisms for
dieldrin. The test statistic for this analysis falls at the
68th percentile and the hypothesis of no difference in
sensitivity is accepted.
Table 6-2 presents the AR analysis for endrin and
dieldrin for which ESG documents have been
developed. Dieldrin results indicate no difference in
sensitivity for freshwater and saltwater species. The
test for endrin fails at the 99th percentile, which
indicates that FAVs for freshwater and saltwater are
different. Therefore, separate analyses for the
freshwater and saltwater organisms are performed.
Table 6-3 presents the results of the statistical
analysis for each chemical for benthic organisms and
WQC organisms. In all cases the hypothesis of no
difference in sensitivity is accepted. Therefore, for
each individual chemical the WQC are accepted as the
appropriate effects concentrations for benthic
organisms.
6.4 Quantification of Uncertainty
Associated with ESGs
The uncertainty in the ESGs can be estimated from
the degree to which the EqP model (which is the basis
for the guidelines) predicts toxicity in sediment tests
using the water-only LCSO data, K^, and the organic
carbon-normalized sediment concentration. The EqP
model asserts that (1) bioavailability of nonionic
organic chemicals from sediments is equal on an
organic carbon basis and (2) the effects concentration
in sediment can be estimated from the product of the
effects concentration from water-only exposures and
the partition coefficient K^. The uncertainty
associated with the ESGs can be obtained from a
quantitative estimate of the degree to which the
available data support these assertions.
The data used in the uncertainty analysis are from
the water-only and sediment toxicity tests that were
conducted in support of the sediment guidelines
development effort. A listing of the data sources used
6-2
-------
I
s
A
i
1
I
5
4
3
2
1
0
-1
-2
-3
-4
-5
5
4
3
2
1
0
-1
-2
-3
-4
-5
limn i i i nun i i i i i i
A: Freshwater vs Saltwater
i iiinii
mill
mini
mrn i
B: Benthic vs WQC
_o
MIIII
iiiiiii i
o -
oo
nun i i i iiinii
INI I I
IIIIIII
mini i i i mm
j i
i nun i i i iiinii i
0.1
10 20 50 80 90
Probability
99
99.9
Figure 6-1. Probability distributions of randomly generated differences between freshwater FAVs and
saltwater FAVs (A) and randomly generated differences between benthic FAVs and WQC FAVs
(B) using the approximate randomization method for dieldrin. The horizontal lines in each panel
indicate the test statistics, which are the FAV differences from the original LCSO datasets.
6-3
-------
Generation of ESG.
in the EqP uncertainty analysis is presented in Table
6-4. These freshwater and saltwater tests span a range
of chemicals and organisms; they include both water-
only and sediment exposures, and with the exception of
the dieldrin water-only exposure test, they are
replicated within each chemical-organism-exposure
media treatment. These data are analyzed using an
analysis of variance (ANOVA) to estimate the
uncertainty (i.e., the variance) associated with varying
the exposure media and the uncertainty associated with
experimental error. If the EqP model were perfect, then
there would be only experimental error. Therefore, the
uncertainty associated with the use of EqP is the
variance associated with varying exposure media.
Sources of variation present in the EqP methodology
are represented as one overall uncertainty calculated as
the variance associated with varying exposure media.
Sediment and water-only LCSOs are computed from
the sediment and water-only toxicity tests. An LCSO is
computed for each replicate using the Spearman-Karber
calculation. 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, LC50W (Atg/L), is
related to the LCSO for sediment on an organic carbon
basis, LC50S oc (^g/goc) via the partitioning equation
LC50soc=/rocLC50w
(6-3)
The EqP model asserts that the toxicity of sediments
expressed on an organic carbon basis equals toxicity in
water-only tests multiplied by the K^. Therefore,
either LC50S ^ (^g/goc), from sediment toxicity
experiments, or K^ x LC50W (^g/L) are estimates of the
true LCSO for this chemical-organism pair. In this
analysis, the accuracy of K^ is not treated separately.
Any error associated with AT^ will be reflected in the
uncertainty attributed to varying the exposure media.
A model of the random variations is required to
perform an ANOVA. As discussed above, experiments
that seek to validate Equation 5-1 are subject to various
sources of random variations. A number of chemicals
and organisms have been tested. Each chemical-
organism pair was tested in water-only exposures and
in different sediments. Let a represent the random
variation due to 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 resulting from the experimental error 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 ot2 be
the variances of these random variables. Let i index a
specific chemical-organism pair. Let j index the
exposure media, water-only, or the individual
Table 6-2. Results of approximate randomization (AR) test for the equality of the freshwater and saltwater FAV
distributions for endrin and dieldrin
Number1
Final Acute Value (FAV)
Chemical
Endrin
Dieldrin
Saltwater
19
19
Freshwater
32
21
Saltwater
0.033
0.621
Freshwater
0.182
0.287
Difference
0.149
-0.334
%
99
16
aNote that greater than (>) values were omitted. This resulted in one dieldrin saltwater benthic organism and two endrin freshwater
benthic organisms being omitted from the AR analysis. Hence, these FAVs are slightly different from those reported in the original
ESG. See individual guidelines documents for data listings (U.S. EPA, 2000c.d).
Table 6-3. Results of approximate randomization (AR) test for benthic and combined benthic and water column
(WQC) FAV distributions for endrin and dieldrin
Chemical
Water Type
Number8
WQC Benthic
Final Acute Value (FAV)
WQC Benthic Difference
Endrin
Endrin
Dieldrin
Freshwater
Saltwater
Combined
32
19
40
21
11
26
0.182
0.033
0.543
0.224
0.021
0.491
-0.042
0.012
O.OS2
7
68
68
aNote that greater than (>) values were omitted. This resulted in one dieldrin saltwater benthic organism and two endrin freshwater
benthic organisms being omitted from the AR analysis. Hence, these FAVs are slightly different from those reported in the original
ESG See individual guidelines documents for data listings (U.S. EPA, 2000c,d).
6-4
-------
L^^^S
JV"*-T. vc--**- _*«.- ^ -
.-. ,./ __-. .^J. JL*j3a*"_s.ki
iS^^Ji
*•! -". ''._-. -|r%$.''
Table 6-4. Data used in the
Chemical
Endrin
Endrin
Endrin
Endrin
Endrin
Endrin
Endrin
Endrin
Dieldrin
Dieldrin
Dieldrin
Dieldrin
Dieldrin
Fluoranthene
Fluoranthene
Fluoranthene
Fluoranthene
Fluoranthene
Fluoranthene
Acenaphthene
Organism
Hyalella
azteca
Hyalella
azteca
Hyalella
azteca
Hyalella
azteca
Hyalella
azteca
Hyalella
azteca
Hyalella
azteca
Hyalella
Hyalella
azteca
Chironomus
tentans
Chironomus
tentans
Hyalella
azteca
Hyalella
azteca
Rhepoxynius
abronius
Rhepoxynius
abronius
Rhepoxynius
abronius
Rhepoxynius
abronius
Rhepoxynius
abronius
Rhepoxynius
abronius
Eohaustorius
estuarius
8^3$S
V?» '"( ''.' ' -{"i^hvv
fctJmMSl
yP^^.^f-
iJ^jDm^tiofi'bf'liBsi
fei-^-VV ";•£*• «slsj43
N&o^SSlS2i*i
ijr^4''53£W'^!"'^'"s?^"f{
equilibrium partitioning uncertainty analysis
Test Type
Sediment
Sediment
Sediment
Sediment
Sediment
Water-only
Water-only
Water-only
Water-only
Sediment
Sediment
Sediment
Sediment
Sediment
Sediment
Sediment
Sediment
Sediment
Sediment
Sediment
No. of
Replicates
2
4
3
3
3
3
3
3
1
4
4
4
4
2
2
2
3
2
3
2
Sediment (OC%)
Soap Creek, soluble-cold exp.
(3%)
Mercer Lake, soluble-cold exp.
(11%)
Soap Creek (3%)
Soap Creek & Mercer Lake
(6.1%)
Mercer Lake (11.1%)
Airport Pond (1.82-2.14%)
Airport Pond (1.42-1.69%)
West Bearskin (2.52-3.36%)
Pequaywan (6.68-10.9%)
Yaquina Bay (0.1 8%)
Yaquina Bay (0.31%)
Yaquina Bay (0.48%)
Suspended Solids (0.34%)
McKinney Mud (0.40%)
Shrimp feces (0.31%)
South Beach (0.82%, 1.23%)
Reference
Schuytema et al..
1989
Schuytema etal..
1989
NebekeretaL, 1989
Nebekeretai.. 1989
Nebekeretai., 1989
NebekeretaL, 1989
Nebekeretai., 1989
Nebekeretai., 1989
Hoke etal., 1995
Hoke etal., 1995
Hoke etal., 1995
Hoke etal., 1995
Hoke etal.. 1995
Swartz etal., 1990
Swartz etaL, 1990
Swartz etal., 1990
DeWitt etal., 1992
DeWitt etal., 1992
DeWitt etaL, 1992
Swartz, 1991
6-5
-------
•r , tt v ''.'*'• ". *- i ' - "1 "•' "*"" 'j * •**".. "*"'"."* ***\ • 11 *•!••'' .1 ;>" * '"-i"" *" '" f" •/'"I • ? 1* T '* " * * * . **%£?^'jjr^*rt •*?
Table 6-4. Data used in the equilibrium partitioning uncertainty analysis (continued)
Chemical
Acenaphthene
Acenaphthene
Acenaphthene
Acenaphthene
Acenaphthene
Acenaphthene
Acenaphthene
Phenanthrene
Phenanthrene
Phenanthrene
Phenanthrene
Phenanthrene
Phenanthrene
Phenanthrene
Phenanthrene
Organism
Eohaustorius
estuarius
Eohaustorius
estuarius
Leptocheirus
pUmulosus
Leptocheirus
pUmulosus
Leptocheirus
pUmulosus
Leptocheirus
pUmulosus
Leptocheirus
pUmulosus
Eohaustorius
estuarius
Eohaustorius
estuarius
Eohaustorius
estuarius
Leptocheirus
pUmulosus
Leptocheirus
pUmulosus
Leptocheirus
pUmulosus
Leptocheirus
pUmulosus
Eohaustorius
estuarius
No. of
Test Type Replicates Sediment (OC%)
Sediment
Sediment
Sediment
Sediment
Sediment
Water-only
Water-only
Sediment
Sediment
Sediment
Sediment
Sediment
Sediment
Water-only
Water-only
2
2
2
2
2
4
4
2
2
2
2
2
2
4
4
McKinney (2.49%)
Eckman (4.21)
South Beach (0.82%, 1.62%)
McKinney (2.36%, 252%)
Eckman (2.97%, 3.66%)
South Beach (0.82%, 1.02%)
McKinney (2.36%, 247%)
Eckman (2.97%, 3.66%)
South Beach (0.82%, 1.96%)
McKinney (2.36%, 250%)
Eckman (2.97%, 3.60%)
Reference
Swartz, 1991
Swartz, 1991
Swartz, 1991
Swartz.1991
Swartz, 1991
Swartz, 1991
Swartz, 1991
Swartz.1991
Swartz.1991
Swartz. 1991
Swartz, 1991
Swartz, 1991
Swartz.1991
Swartz, 1991
Swartz, 1991
sediments. Let k index the replication of the experiment.
Then the equation that describes this relationship is
(64)
where InOLCSO^) are either ln(LC50w) or ln(LC50s „,)
corresponding to a water-only or sediment exposure
and ft, are the population of ln(LC50) for chemical-
organism pair i. The error structure is assumed to be
lognormal, which corresponds to assuming that the
errors are proportional to the means (e.g., 20%) rather
than absolute quantities (e.g., 1 mg/L). The statistical
problem is to estimate ^ and the variances of the model
error, oa2, and the measurement error, oc2. The
maximum likelihood method is used to make these
estimates (El-Shaarawi and Dolan, 1989). In summary,
LCSO values from water-only exposures and sediment
toxicity tests were used to compute the variances
resulting from varying the exposure media and those
due to experimental error. The results are shown in
Table 6-5.
6-6
-------
<$i *i^:M^^M^
Table 6-5. ANOVA for derivation of confidence limits of ESG values
Value
Source of Uncertainty
Parameter
Exposure media
Replication
ESG
DO
0.41
0.29
0.41
This ANOVA was computed using combined
datasets shown in Table 6-4. Combining the datasets
provides a more robust analysis with a wider range of
organic carbon. The ANOVA were computed for
individual chemicals for comparative purposes. The
individual and combined error estimates for exposure
media and replication along with the number of data
points are tabulated in Table 6-6. Errors due to
exposure media are shown in Figure 6-2. The solid line
represents the combined error for comparison to the
individual error. The differences between the mean
errors of the individual chemicals and the error of the
combined chemicals are minimal. Combining the
datasets provides the best estimate of the uncertainty
of the overall model. This uncertainty will be
recomputed as additional ESGs are formulated.
The last line of Table 6-5 is the uncertainty
associated with the ESG, that is, the variance
associated with the exposure media variability. The
confidence limits for the ESG are computed using this
uncertainty. For the 95% confidence interval limits, the
significance level is 1.96 for normally distributed errors.
Thus
(6-5)
- 1.960
^
The ESG and 95% confidence limits for endrin and
dieldrin are given in Table 6-7. Figure 2-3 reflects this
uncertainty using solid vertical bars around 1TU.
6.5 Minimum Requirements To Compute
an ESG
It has been demonstrated that the computation of
an ESG for a particular chemical requires 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 ESGs within the limits of
uncertainty set forth in this document This section
outlines the minimum data requirements to compute
ESGs and the necessary guidance for deriving them.
This step is critical to the development of reliable
parameters that are to be used to compute ESGs. The
minimum requirements to compute an ESG are as
follows:
• Octanol-water partition coefficient (Kov)
• Final chronic value (FCV)
• Sediment toxicity tests
Procedures to ensure that these data meet assumptions
of the EqP approach will also be addressed.
Table 6-6. Comparison of individual and combined error estimates
Chemical
Acenaphthene
Phenanthrene
Dieldrin
Endrin
Fluoranthene
Mean of Individual Chemicals
Number of Data Points
20
20
17
24
14
Variance Due to Exposure
Media
0.38
0.12
0.31
0.65
0.34
0.36
Variance Due to Replication
0.15
0.14
0.54
0.29
0.056
0.24
6-7
-------
totieratibn dfpS(i;" . V'.'^' V • '
0.80
0.70
3 -
& 0.60
1 °'50
e
^ 0.40
S
v 0.30
a
Q
2 0.20
w 0.10
0.00
1 1 1 1
_ —
-
-
~
—
-
-
-
-
Combined
•
• I
• •
• I
1 1
1 1 1
J^ 9. j» 9.
x/x/x
Figure 6-2. Comparison of individual error due to exposure media (bars) and combined error due to
exposure media (solid line).
6.5.1 Octanol-Water Partition Coefficient
A £ow value is required to compute the K^. EPA
recommends using £ow values from Karickhoff and
Long (1995; Long and Karickhoff, 1996) when available.
6.5.2 Final Chronic Value
The FCV is computed as part of the derivation of
the WQC for a compound, and is defined as the
quotient of the FAV and the final acute-chronic ratio
(FACR)(Stephanetal., 1985). The data required to
compute the FCV are water-only toxicity tests for a
variety of organisms meeting minimum database
requirements. The FCV computation and minimum
database requirements are presented in Stephan et al.
(1985).
WQC are based on an assessment of a
compound's acute and chronic toxicity for organisms
representing a range of sensitivities, particularly the
most sensitive organisms. This is appropriate because
the objective of WQC is to set limits based on the best
estimate of organism sensitivity. The toxicity database
should therefore include all available data that meet
requirements. That is, a complete search, retrieval, and
review for any applicable data must be conducted, to
locate all preexisting toxicity data. For some
compounds, a WQC FCV may exist that would provide
a significant amount of toxicity data. Literature
searches are recommended to locate other sources of
toxicity data.
Reevaluation of an already existing FCV is
warranted because data postdating publication of the
WQC can be incorporated into the FCV value. Also,
minimum database requirements have changed since
some WQC were published. For those compounds for
which FCVs do not exist, compiled toxicity data are
evaluated to see if minimum data requirements, as put
forth by Stephan et al. (1985), are met. If so, an FCV
could then be computed. If there are not enough water-
only toxicity data to compute an FCV, additional water-
6-8
-------
' ''•X/^V.vHr&d^
Table 6-7. Confidence limits of the ESGs for endrin and dieldrin
Chemical
TJrpe of Water Body
ESGoc
95% Confidence Limits 0/g/goc)
Lower
Upper
Endrin
Dieldrin
Freshwater
Saltwater
Freshwater
Saltwater
5.4
0.99
12
28
2.4
0.44
5.4
12
12
2.2
27
62
only tests should be conducted so that sufficient data
are available to satisfy minimum database requirements.
6.5.3 Sediment Toxicity Tests
Verification of the applicability 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 concentration is direct
confirmation of the EqP approach. Validity of EqP is
confirmed when the toxicity test results fall within the
limits of uncertainty determined in this document.
Guidelines for conducting sediment toxicity tests
ensure that the tests are uniform and are designed to
incorporate the assumptions of EqP. These tests must
represent a range of organic carbon content and
include organisms that exhibit sensitivity to the
chemical in question. The range of organic carbon
should be at least a factor of 3. Minimum organic
carbon content for test sediments should be 0.2%.
Replicated toxicity tests for at least two sediments are
required. Organisms to be used in the sediment toxicity
tests are benthic animals, which are most sensitive to
the compound in question. Guidelines on appropriate
selection of benthic organisms is given in the American
Society for Testing and Materials (ASTM) annual
handbook (ASTM, 1992).
Several studies are required as part of the sediment
toxicity testing. A water-only flow-through test is
required to establish the water-only effect
concentrations for the chemical and organism to be
used in the sediment toxicity test. Water-only tests are
run for multiple concentrations (e.g., five) of the
compound in question and a control. The endpoint of
interest is the 10-day mortality of the test species, if
that matches the duration of the corresponding
sediment test. Test concentrations should be selected
to provide a range in organism mortality (e.g., 0% to
100%) and to characterize the range of partial mortality
as effectively as possible. This value will be compared
with the interstitial water and sediment mortality from
the sediment spiking tests discussed next. Guidelines
for conducting water-only tests are given by ASTM
(1996).
Once water-only toxicity data have been collected,
sediment toxicity tests with spiked sediments are
conducted. In early sediment check tests, two
sediment-spiking tests were often employed. In the
first test, sediment concentrations were selected using
the water-only LCSO fcg/L) and K^ so that the
interstitial water concentrations bracketed the LCSO
from the water-only test. Three spiking treatments were
typically used in the first test: low, medium, and high
concentrations. The amount of compound to add to
each treatment was calculated using the initial chemical
weight, the % total organic carbon (TOC), the % dry
weight, and the total volume of spiked sediment.
Samples for chemical analyses in bulk sediment and
interstitial water were collected at various time
intervals. Nominal sediment-spiking concentrations,
measured sediment TOC, and measured and EqP-
predicted compound concentrations in sediments and
interstitial waters were obtained for each sample period
to establish time-to-equilibrium and to verify that
spiking produced the appropriate concentrations in the
interstitial water. Sorption equilibrium, an assumption
of EqP theory, is essential for valid interstitial water and
sediment concentrations. Guidelines for conducting
sediment toxicity tests are given by ASTM (1992,
1994a,b), and EPA (U.S. EPA, 1994).
The second test was a definitive experiment that
utilized three sediments with differing organic carbon
concentrations that were spiked with a series of
concentrations such that the chemical concentrations
were similar on an organic carbon-normalized basis.
Experience with these check-tests has shown that
range-finding tests above are not required for selection
of spiking concentrations in the definitive tests so long
as the selected water-only LCSO value and K^ values
(derived from Karickhoff and Long, 1995; Long and
Karickhoff, 1996) are within a factor of about two of the
6-9
-------
Generation of :ESG' £ .•-".- •'',-' ' .. ;',< ..' '".\ ->->Vh
_• :.;Jp; .'. ., '/tt-. V- - ".••••'^/- **• ..... = V.i ">, ^T J '-' ..--L;_
"
sediment LC50 (Figure 2-3). Prior to testing, spiked
sediments must be held for an appropriate time to
ensure equilibrium is established. This can be
demonstrated by measuring the interstitial water and
sediment concentrations. The other requirement is that
the benthic species selected for these toxicity tests
must be sensitive at less than the chemical's solubility
in water and preferably one of the species should be
the most sensitive to the chemical. Although five
sediment concentrations with dilution factors of about
0.5 have been used successfully to bracket the
predicted sediment LCSO, more treatments or different
dilution factors may be useful. Organisms are placed in
replicated beakers and 10-day sediment toxicity tests
with the equilibrated spiked sediments are conducted.
Biological and chemical replicates for each treatment
are required. Chemical replicates should be sampled at
the beginning and end of the tests for interstitial water
and sediment analyses. Biological replicates are
sampled at the end of the 10-day test
The overall approach for conducting EqP check
experiments is given by Hoke et al. (1995). These
experiments provide data to compute IWTUs and
PSTUs (Equations 2-1 and 2-8). The results of these
equations serve as direct comparisons of the predicted
toxicity (Equation 2-1 and 2-8 numerator) to the
observed toxicity (Equation 2-1 and 2-8 denominator).
That is, validity of EqP for a chemical is confirmed
when the IWTUs and PSTUs fail within the limits of
uncertainty determined in this document.
6.5.4 Test of the Applicability to ESG
Derivation
The purpose of these procedures is to verify that
• The WQC FCV applies to benthic organisms
• The^ocfromtheEPA-recommended^owisan
accurate estimate of K,
ow
A test for the WQC FCV, which is applicable to the
most sensitive water column organisms and to the most
sensitive benthic organisms, is needed for each
chemical. In computing ESGs for endrin and dieldrin,
the AR method was applied. This is a statistical test to
compare the WQC toxicity database with benthic
organism toxicity. The methodology is presented in
Section 6-3. 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 ESG. If benthic
organisms exhibit a greater sensitivity than the WQC
organisms, then toxicity experiments for benthic
organisms are required.
A check on the computed K^ from the
recommended KQV must be done by comparing it with
the KQC computed from sediment toxicity tests.
Interstitial water and sediment concentrations from the
sediment toxicity test provide data necessary to com-
pute the Kfjf.. For chemicals with log£ow substantially
greater than 5.0, DOC binding must be considered
when evaluating interstitial water data (see Section 4.3).
Finally, when a site's sediments are being studied,
a check to show that the ESG applies to the site is
needed. National ESGs may be under- or over-
protective if (1) the species at the site are more or less
sensitive than those included in the dataset used to
derive the ESG or (2) the sediment quality
characteristics of the site alter the bioavailability
predicted by EqP and, ultimately, the predicted toxicity
of the sediment bound chemical. Therefore, it is
appropriate that site-specific guidelines procedures
address each of these conditions separately, as well as
jointly. Methods to determine the applicability of
national ESGs to a site and to determine site-specific
ESGs if needed are presented in "Methods for the
Derivation of Site-Specific Equilibrium Partitioning
Sediment Guidelines (ESGs) for the Protection of
Benthic Organisms: Nonionic Organics" (U.S. EPA, 2000e).
6.5.5 Tier 1 and Tier 2 ESGs
Minimum database and analytical requirements
must be set when deriving national sediment
guidelines. The reasons for this are twofold. First, the
requirements establish a level of protection intended by
the guidelines. Second, they establish that the
behavior and toxicity of the chemical in sediment
adheres to the assumptions of EqP theory. The
required parameters include a Kov value (used to
compute the K^), a FCV, and sediment toxicity tests.
Procedures to verify that these values are appropriate
to use in the ESG computation are also required. It
must be shown that the FCV is protective of benthic
organisms. Confidence in the K^. must also be
established by comparing the Kx with the observed
Kx in sediment toxicity tests. Individual sites may
exhibit greater or lesser toxicity of a chemical than that
predicted by ESGs at an individual site. EPA
procedures to test this assumption, as well as to
compute site-specific ESGs, are available.
EPA has developed both Tier 1 and Tier 2 ESGs to
reflect the differing degrees of data availability and
6-10
-------
uncertainty. A Tier 1 ESG would meet all of the above
requirements as previously described; these include
KQVf and FCV values as well as sediment toxicity tests
to verify EqP assumptions. As such, the ESGs derived
for nonionic organics, such as dieldrin and endrin,
metal mixtures, and PAH mixtures would represent Tier
1 ESGs (U.S. EPA, 2000b,c,d,f).
Comparatively, the derivation of a Tier 2 ESG
requires a KQW value and either a FCV or a secondary
chronic value (SCV). The minimum requirement for
deriving a SCV is toxicity data from a single taxonomic
family (Daphnidae), provided the data are acceptable.
The EPA methodology for deriving WQC SCVs is
described further in "Water Quality Guidance for the
Great Lakes System: Supplementary Information
Document" (U.S. EPA, 1995). Performance of sediment
toxicity "check" tests is recommended, but not required
for a Tier 2 ESG. Therefore, in comparison to Tier 1
ESGs, the level of protection provided by the Tier 2
ESGs would be associated with more uncertainty due to
the use of the SCV and absence of sediment check
tests. Examples of Tier 2 ESGs for nonionics are found
inU.S.EPA(2000g). Information on how EPA
recommends ESGs be applied in specific regulatory
programs is described in "Implementation Framework
for the Use of Equilibrium Partitioning Sediment
Guidelines (ESGs)" (EPA, 2000a).
6.5.6 Summary
Computation of ESGs for individual chemicals
requires a number of key parameters. Minimum
requirements for these parameters are warranted to
provide the level of protection intended by ESGs within
the limits of uncertainty set forth in this document. The
following minimum data are required to compute Tier 1
and Tier 2 ESGs:
Tierl
ESGs
Tier 2
ESGs
Partitioning
Coefficient
Octanol-
water
partitioning
coefficient
Octanol-
water
partitioning
coefficient
(Kow)
Aquatic
Life
Protection
Level
WQC final
chronic
value
(FCV)
WQC FCV
or
secondary
chronic
value
(SCV)
Verification
Requirements
Sediment
toxicity test
6.6 Example Calculations
Equation 2-7 can be used to compute an ESG^ for
a range of KOVI and FCV values. The results for several
chemicals are shown in Figure 6-3 in the form of a
nomograph. The diagonal lines are for constant FCV as
indicated. Thex-axisislog|0£ow. For example, for
endrin the saltwater FCV is 0.01 Mg/L and the log,0^ow
is 5.06, so that the log10ESG is approximately 10 to the
zero=0.99 yug endrin/g^..
As can be seen, the relationships between ESG^,
and the parameters that determine its magnitude, KQV/
and FCV, are essentially linear on a log-log basis. For a
constant FCV, a 10-fold increase in £ow (1 log unit)
increases the ESG^, by approximately 10-fold (1 log
unit) because KQ^, also increases approximately 10-fold.
Thus, chemicals with similar FCVs will have larger
ESGgc values if their £ow values are larger.
The chemicals listed in Figure 6-3 have been
chosen to illustrate the ESGOC concentrations that
result from applying the EqP method. The WQCs are
the FCVs (not the final residue values) computed as
part of the development of ESGs for endrin and dieldrin
or from published and draft EPA WQC documents (see
Table 5-1 for the remaining chemicals plotted). The
£ow values for endrin and dieldrin are from the EPA-
recommended values in Table 6-1. The £ow values for
the remaining chemicals are the log averages of the
values reported in the Log P database (Leo and
Hansch, 1986). Although the ESGs for endrin and
dieldrin meet the minimum database requirements
presented in the previous section, ESGs for the
remaining chemicals are illustrative only and should not
be considered final ESG values. Final ESGs, when
published, should reflect the best current information
for both FCV and Kow
FCVs available for nonionic organic insecticides
range from approximately 0.01 to 0.3 Mg/L, a factor of 30
(Dave Hansen, Great Lakes Environmental Center,
Traverse City, MI, personal communication). The
ESGQg values range from approximately 0.01 Mg/gocto
in excess of 10 Mg/goc*a factor of over 1,000. This
increased range in values occurs because the KQV
values of these chemicals span over two orders of
magnitude. The most stringent ESG^, in this example is
for pesticide C, a chemical with one of the lowest KQW
values among the chemicals with an FCV of
approximately 0.01 //g/L.
6-11
-------
Generation of ESG ., '
1
-2
iooo ,x'oo
,'•10
0.001
Pesticide A
PesticideB
T Pesticide C
• Pesticide D
A Pesticide E
* Pesticide F
• Endrin
• Dieldrin
Figure 6-3. Log10ESG versus Iog10ffow. Diagonal lines indicate the FCV values. ESG values are computed from
Equation 6-1. K^ is obtained from £ow with Equation 4-3. The symbols indicate ESG^ for the
freshwater (filled) and saltwater (open) criteria for the listed chemicals. Vertical lines connect symbols
for the same chemical.
6-12
-------
6.7 Field Data
6.7.1 STORE! Data
Information on the actual levels of the guidelines
chemicals in the environment was assembled to provide
an indication of the relationship between ESG
concentrations and the concentration levels observed
in the sediments of U.S. surface water bodies. Three
separate databases were examined:
• EPA's STORET database (U.S. EPA, 1989)
• NOAA's National Status and Trends database,
which focused on water bodies in coastal areas
(O'Connor, 1991)
• Corps of Engineers (COE) database for San
Francisco Bay (U.S. Army Corps of Engineers,
1991)
Several of the guidelines chemicals are frequently
measured when samples are taken to measure sediment
contamination, and these values are frequently
reported in databases on sediment contamination. This
means that it is possible that many of the sediments
from the nation's waterways might exceed the
guidelines for these chemicals. In order to investigate
this possibility, the guidelines for dieldrin and endrin
were compared with data from several available
databases of sediment chemistry.
The data that were retrieved have been summarized
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 provided
by the plot overlay showing the ESG concentration
developed by this research. In the case of the STORET
data, the ESG is shown as a band because the/^ is not
reported. The lines represent the ESG for/^ between
1% and 10%. The other two databases provide the
necessary information on sediment organic carbon
levels, and the results have been properly normalized.
Very few of the measured values from either of the
databases for endrin and dieldrin exceeded the ESGs.
A STORET data retrieval was performed to obtain a
preliminary assessment of the concentrations of the
guidelines chemicals in the sediments of the nation's
water bodies. The data retrieved were restricted to
samples measured in the period 1986 to 1990. Selection
of this period eliminated many older data with higher
detection limits to more accurately indicate current
conditions. Log probability plots concentrations are
shown in Figure 6-4. Concentrations are shown on a
dry weight basis, because sediment organic carbon is
not reported. The ESGs are computed on the basis of a
sediment organic carbon content (^oc) of 1% and 10%,
which is the typical range for inland sediments. The
STORET data distinguish between type of water body,
and separate displays are provided for stations in
streams, lakes, and estuaries.
The data for endrin and dieldrin are shown in
Figures 6-4 and 6-5, respectively. The total 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, exceed the ESG for
/oc=l%,andfewerexceedtheESGfor/oc= 10%. 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 plotting positions of the detected data are
uncertain, because the nondetected data may occupy
plotting positions further to the left, at lower
probabilities. Thus the exceedence probabilities for the
detected data are at least as large as indicated on the
plots. Less than 3% of the detected dieldrin and endrin
samples exceed the lower ESG.
6.7.2 National Status and Trends Program
Data
NOAA's National Status and Trends Program
developed 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 (O'Connor, 1991). Figure 6-6 displays
the distribution of sediment concentrations from the
National Status and Trends Program sites for dieldrin
(endrin concentrations were not measured). Sediment
organic carbon concentrations were measured in this
program and ranged from less than 0.20% to 16.2%.
The availability of/^ permits the plots to display both
observed concentrations and the ESG value using
organic carbon normalization.
6-13
-------
I
Generation of ESG
...... ;; .. •• . ... ,- ,:;: •... . ,:./,..- ...';•:;.:•..,,. ... . -. v: ;.,,,-, ^,,; ;. v, .'•• .,.;.
wo
3
-t->
a
1
'•3
a>
V)
•d
E
W
10'
10*
10'
102
Iff3
I—i i miii| 1—i i 111 ii| 1 1—i—|—i—i 1 pi 11 M i—i |iimi i i ]
A: Stream
r «
Total Samples: 2677
Measured Samples: 87
1 I
WD
•6h
3
•a
-!
Total Samples: 478
Measured Samples: 12
1 1 1 1 1 1 1 lllllllJ 1 lullll_l_l
OD
"bio
3
•4-1
C
-------
Technical Basis for Derivation of ESGs: ^Nonioiiic Organics
t*
3
+•>
c
01
•3
o>
IH
-a
M
I
"5
I
«
o
S
102
10'
10°
10'
102
103
10"
10s
10"
107
B: Lake
j£x
Total Samples: 457
Measured Samples: 124
i I I —i him i i i
3
c
o>
B
G,
ja
13
ill
10'
10'
10'
102
10J
10"
10s
in7
? ' ' """I I 1 1 1 1 1 ll| !
r C: Estuary
r
r
r
-XK<5SSSSJSSSS
r •*
•-
<
r < <
| i i | |ii i i |ini i ;
T
I
-^*W^** 4* 4*
^^^^^^^^^«5s.V.V» V. v^
«K<^S«K««S;<< ^
1
"
Total Samples: 160
Measured Samples: 3
i i i i in 1 1 1 1 i i ii
0.1
10 20 50 80 90
Probability
99
99.9
Figure 6-5. Probability distribution of concentrations of dieldrin in sediments from streams
(A), lakes (B), and estuaries (C) in the United States from 1986 to 1990 from the
STORE! database compared to the dieldrin ESG values. Sediment dieldrin
concentrations below the detection limits are shown as less than symbols (<);
measured concentrations are shown as solid circles (•). The upper dashed line on
each figure represents the ESG value when TOC=10%, the lower dashed line
represents the ESG when TOC=1 % (data from U.S. EPA, 1989).
6-15
-------
.Generation of ESG
100
10
DO
3
.5
i*
2
"3
5
0.1
0.01
0.001
0.0001
I I I I I II
O»
(N=539)
/oc>0.2% (N=408)
In 11
0.1
10 20
50
Probability
80 90
99
99.9
Figure 6-6. Probability distribution of concentrations of dieldrin in sediments from coastal and estuarine sites
from 1984 to 1989 as measured by the National Status and Trends Program. The horizontal dashed
line is the saltwater ESG value of 28 Mg/g^. Samples with organic carbon greater than 0.2% (•) and
samples for all organic carbon contents (o) are shown. Data from NOAA (O'Connor, 1991).
Results are displayed in the plots for all samples
(open symbols) and for the subset that contained
organic carbon fractions greater than 0.2%, the limit of
applicability for the EqP-derived ESG (filled symbols).
The ESG is applicable to the 75% to 85% of all sediment
samples that have/oc values greater than 0.2%.
However, there is only a nominal effect on estimates of
the percentage of the samples that exceed the ESG,
depending on whether the full set, or the subset, of
samples is considered. The dieldrin ESG is not
exceeded.
6.7.3 Corps of Engineers Data
A set of data from the U.S. Army Corps of
Engineers monitoring program for a number of locations
in various parts of San Francisco Bay has been
analyzed. Table 6-8 identifies the locations sampled,
the number of observations at each site, and the period
during which the results were obtained. These data
were collected to examine the quality of dredged
sediments in order to determine 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
occurrence (in individual samples, not dredge sites)
with the ESG developed using the EqP methodology.
The major portion (93%) of the samples analyzed had
organic carbon fractions greater than 0.2%, for which
the ESG concentrations are applicable. The
concentrations of each chemical measured in these
sediments was normalized by the organic carbon
content and the results are displayed (Figure 6-7) as
probability plots to illustrate the frequency at which
different levels are observed. Results are presented for
the two guidelines chemicals: endrin and dieldrin. A
horizontal line at the concentration value of the ESG
provides a reference that indicates the relationship
6-16
-------
;.-.,".~<\ •• •.:T;' "<~;~.~ : "» --I-TT^~"°^-"'~'^^¥j^.^ r-Z~~
••it".!
Table 6-8. San Francisco Bay sediment samples
Location
Port of San Francisco: Piers 27-29, 35, 38, 48, 70, 80, and 94
Fisherman'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, 25. 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
Analyzed3
23
2
6
11
44
10
6
43
6
6
41
8
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
3Samples were analyzed for endrin and dieldrin.
between observed range of quality and the ESG for
each chemical. 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 ESG. An estimate of the possible frequency
distribution of sediment concentrations of dieldrin and
endrin was developed by the application of an analysis
technique that accounts for the varying detection limits
and the presence of nondetected observations (U.S.
EPA, 1989). The results are illustrated by the straight
line, which suggests that no appreciable number of
exceedences is expected. However, the virtual absence
of detected concentrations makes the distribution
estimates unreliable. They are presented only to
suggest the probable relationship between the levels of
these two pesticides in relation to sediment guidelines.
6-17
-------
Generation of ESG'
3
w
104
103
101
10'
10°
10-
10^
10°
I I III TT
1 I
I 1111 111 I II I I I II
J h
0.1
10 20 50 80 90
Probability
99 99.9
Figure 6-7. Probability distribution of organic carbon-normalized sediment endrin (A) and dieldrin
(B) concentrations from the U^. Army Corps of Engineers (1991) monitoring program of
San Francisco Bay. Sediment concentrations less than the detection limits are shown as open
triangles (V); measured concentrations are shown as solid circles (•). The solid lines in each are
an estimate of the distribution developed by accounting for nondetected observations. The dashed
line represents the organic carbon-normalized ESG.
6-18
-------
Section 7
Conclusions
The technical basis and data that support the use
of the ESGs have been presented for nonionic organic
chemicals. The use of organic carbon normalization is
•equivalent to using interstitial water normalization as a
means of accounting for varying bioavailability
(Figures 2-2,2-3,3-1 through 3-4, and 4-21 through
4-23). The variation in organism body burden across
sediments is significantly reduced if organic carbon
and lipid normalization are used (Figures 4-24 through
4-26). For contaminated sediments, particle size effects
are removed if organic carbon-normalized
concentrations are compared (Figures 4-9,4-10, and
4-12 through 4-14). These data establish that organic
carbon is an appropriate normalization for partitioning
ibetween freely-dissolved chemical and sediment-
bound chemical (Figure 4-3).
Interpretation of interstitial water chemistry data
for highly hydrophobic chemicals is complicated by
chemical complexing to DOC (Figure
4-5). Partitioning between interstitial water and
sediment organic carbon from field-collected sediments
can be clarified if DOC complexing is taken into
account (Figures 4-16 and 4-17). However, the
complexed chemical appears not to be bioavailable
(Figure 4-7).
These observations are consistent with the EqP
model, which assumes the equivalence of water-only
exposure and the exposure from interstitial water and/
or sediment organic carbon. ESGs are based on
organic carbon normalization because interstitial 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 when
both the FAVs are compared for individual chemicals
(Figure 5-1) and when data from all chemicals are
pooled (Figure 5-2). Benthic colonization experiments
also demonstrate that WQC FCVs can be used to
predict effects concentrations for benthic organisms.
A direct statistical test of the equality of the
distributions can be used to confirm or refute this
assumption for individual chemicals (Figure 6-1).
Equilibrium partitioning cannot remove all of the
observed variation from sediment-to- sediment. It does,
however, reduce the much larger sediment-to-sediment
variation that exists if no corrections for bioavailability
are made (Figures 3-1 to 3-4). A variation factor of
approximately two to three remains (Figures 2-2 and
2-3), which includes measurement and other sources of
variability. This is not unexpected, because EqP is an
idealization of the actual situation. Other factors that
are not considered in the model have roles in
determining biological effects. Hence, it is recognized
that a quantification of the uncertainty should
accompany the ESG that reflects these additional
sources of variation.
7.1 Research Needs
Final validation of ESG will come from field studies
designed to evaluate the extent to which biological
effects can be predicted from ESG. The colonization
experiments (Table 5-3) are a laboratory simulation of a
field validation. ESGs may be easier to validate than
WQC because determining the organism exposure is
more straightforward (chemical concentrations are
relatively stable in bedded sediments and many benthic
organisms show relatively small home ranges, unlike
many pelagic organisms). Benthic population exposure
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 (Landrum, 1989). The
extent to which kinetics can be important in field
situations is unknown at present, and field studies
would be an important component in examining this
question. In addition, more laboratory sediment toxicity
tests, particularly chronic tests involving multiple
sediments, would also be helpful. In a typical practical
application of an ESG, mixtures of chemicals are
involved. The extension of EqP methodology to
mixtures would be of great practical value, and for these
reasons EPA has also developed ESGs for mixtures of
metals (cadmium, copper, lead, nickel, silver, and zinc)
(U.S. EPA, 2000f ), as well as for mixtures of PAHs (U.S.
EPA,2000b).
7-1
-------
The EqP method is presently restricted to
computing effects-based guidelines for the protection
of benthic organisms. Direct extension of this
methodology for computing sediment guidelines that
are protective of human health, wildlife, and
marketability offish and shellfish requires that the
equilibrium assumption be extended to the water
column and to watefcolumn organisms. This
assumption is, in general, untenable. Water column
concentrations can be much lower than interstitial
water concentrations if sufficient dilution flow is
present. Conversely, upper-trophic-level organisms are
at concentrations well above equilibrium values
(Connolly and Pederson, 1988). Hence, the application
of the final residue values from the WQC for the
computation of ESGs, as was done for certain interim
criteria (Cowan and Di Toro, 1988), is not technically
justifiable. At present, organism lipid-to-sediment
organic carbon ratios, that is, BSAFs (Equation 4-26),
might be useful in estimating the concentration of
contaminants in benthic species, for which the
assumption of equilibrium is reasonable. EPA is
currently refining methods for predicting chemical
concentrations in tissues of organisms in upper trophic
levels; see U.S. EPA (1998) for details. However, at this
time, the ESGs do not protect against synergistic
effects, or other interactions with non-ESG chemicals.
Therefore, they do not address effects mediated
through bioaccumulation and food-chain transfer, and
thus are not protective of wildlife or human health
endpoints.
7-2
-------
Section 8
References
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Adams WJ.KimerleRA.MosherRG. 1985. Aquatic
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Di Toro DM, Mahony JD, KirchgraberPR, O'Byrne AL,
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