United States
Environmental Protection
Agency
Procedures for the Derivation of
Equilibrium Partitioning
Sediment Benchmarks
(ESBs) for the Protection of
Benthic Organisms:
PAH Mixtures
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United States Office of Research and Development EPA-600-R-02-013
Environmental Protection Washington, DC 20460 www.epa.gov
Agency
Procedures for the Derivation
of Equilibrium Partitioning
Sediment Benchmarks (ESBs)
for the Protection of Benthic
Organisms: PAH Mixtures
,.. -
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EPA/600/R-02/013
November 2003
Procedures for the Derivation of
Equilibrium Partitioning Sediment Benchmarks (ESBs)
for the Protection of Benthic Organisms:
PAH Mixtures
David J. Hansen
(formerly with U.S. EPA)
Dominic M. DiToro
Univ. Delaware, Newark, DE; HydroQual, Inc.,
Mahwah, NJ
Joy A. McGrath
HydroQual, Inc., Mahwah, NJ
Richard C. Swartz
(formerly with U.S. EPA)
David R. Mount
Robert L. Spehar
National Health and Environmental Effects Research Laboratory
Mid-Continent Ecology Division
Duluth, MN
Robert M. Burgess
National Health and Environmental Effects Research Laboratory
Atlantic Ecology Division
Narragansett, RI
Robert J. Ozretich
National Health and Environmental Effects Research Laboratory
Western Ecology Division
Newport, OR
Heidi E. Bell
Mary C. Reiley
Office of Water, Washington, DC
Tyler K. Linton
Great Lakes Environmental Center
Columbus, OH
U.S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Atlantic Ecology Division, Narragansett, RI
Mid-Continent Ecology Division, Duluth, MN
Western Ecology Division, Newport, OR
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Notice
The Office of Research and Development (ORD) has produced this document to provide procedures for
the derivation of equilibrium partitioning sediment benchmarks (ESBs) for mixtures of polycyclic aromatic
hydrocarbons (PAHs). ESBs may be useful as a complement to existing sediment assessment tools.
This document should be cited as:
U.S. EPA. 2003. Procedures for the Derivation of Equilibrium Partitioning Sediment Benchmarks (ESBs)
for the Protection of Benthic Organisms: PAH Mixtures. EPA-600-R-02-013. Office of Research and
Development. Washington, DC 20460
The information in this document has been funded wholly by the U.S. Environmental Protection Agency.
It has been subject to the Agency's peer and administrative review, and it has been approved for
publication as an EPA document.
Mention of trade names or commercial products does not constitute endorsement or recommendation
for use.
Abstract
This equilibrium partitioning sediment benchmark (ESB) document describes procedures to derive
concentrations of PAH mixtures in sediment which are protective of the presence of benthic organisms.
The equilibrium partitioning (EqP) approach was chosen because it accounts for the varying biological
availability of chemicals in different sediments and allows for the incorporation of the appropriate
biological effects concentration. This provides for the derivation of benchmarks that are causally linked
to the specific chemical, applicable across sediments, and appropriately protective of benthic organisms.
EqP can be used to calculate ESBs for any toxicity endpoint for which there are water-only toxicity
data; it is not limited to any specific effect endpoint. In this document, the Final Chronic Value (FCV)
for PAHs derived using the National Water Quality Criteria (WQC) Guidelines was used as the toxicity
endpoint for this ESB. This value is intended to be the concentration of a chemical in water that is
protective of the presence of aquatic life. For this PAH mixtures ESB, narcosis theory was used to (1)
demonstrate that the slope of the acute toxicity-octanol water partition coefficient (Kovf) relationship was
similar across species; (2) normalize the acute toxicity of all PAHs in water to an aquatic species using a
reference KQW of 1.0 (where the concentration in water and lipid of the organism would be essentially the
same); (3) establish an acute sensitivity ranking for individual species at the KQW of 1.0 and to use the
rankings to calculate a Final Acute Value (FAV) following the WQC Guidelines; (4) calculate the final
acute-chronic ratio (ACR) from water-only acute and chronic toxicity tests; (5) calculate the Final
Chronic Value (FCV) at the reference KQW of 1.0 from the quotient of the FAV and ACR; and (6) to
calculate the PAH-specific FCV in |ig/L using the FCV at the reference KQW of 1.0, the PAH-specific
KQW, the slope of the KOW-KOC relationship and the universal narcotic slope of the Kow-acute toxicity
relationship. The EqP approach and the slope of the KOW-KOC relationship was then used to calculate,
from the product of the PAH-specific FCV and KQC the FCV concentration for each specific PAH in
sediment (COCPAHi.FCVi, |ig/g organic carbon). Based on this approach, the recommended ESB for total
PAH should be the sum of the quotients of a minimum of each of the suggested 34 individual PAHs in a
specific sediment divided by the COCPAH.FCV. of that particular PAH. This sum is termed the Equilibrium
Partitioning Sediment Benchmark Toxic Unit (SESBTUFCV). Freshwater or saltwater sediments
containing <1.0 SESBTUFCV of the mixture of the 34 PAHs or more PAHs are acceptable for the
protection of benthic organisms, and if the SESBTUFCV is greater than 1.0, sensitive benthic organisms
may be unacceptably affected.
The ESBs do not consider the antagonistic, additive or synergistic effects of other sediment
contaminants in combination with PAH mixtures or the potential for bioaccumulation and trophic transfer
of PAH mixtures to aquatic life, wildlife or humans.
iii
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Foreword
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 support the scientific and technical foundations of the
programs, EPA's Office of Research and Development has conducted efforts to develop
and publish equilibrium partitioning sediment benchmarks (ESBs) 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.
The ESBs and associated methodology presented in this document provide a means to
estimate the concentrations of a substance that may be present in sediment while still
protecting benthic organisms from the effects of that substance. These benchmarks 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 ESBs
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 ESBs do
not consider the antagonistic, additive or synergistic effects of other sediment
contaminants in combination with PAH mixtues or the potential for bioaccumulation and
trophic transfer of PAH mixtures to aquatic life, wildlife or humans.
ESBs may be useful 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.
This document provides technical information to EPA Regions, States, the regulated
community, and the public. It does not 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 technical information
where appropriate. EPA may change this technical information in the future. This
document has been reviewed by EPA's Office of Research and Development (Mid-
Continent Ecology Division, Duluth, MN; Atlantic Ecology Division, Narragansett, RI),
and approved for publication.
This is contribution AED-02-050 of the Office of Research and Development National
Health and Environmental Effects Research Laboratory's Atlantic Ecology Division.
Front cover image provided by Wayne R. Davis and Virginia Lee.
IV
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
This page is left blank intentionally.
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Contents
Contents
Notice [[[ iii
Abstract [[[ iii
Forward [[[ iv
Acknowledgments [[[ xii
Executive Summary [[[ xiv
Glossary [[[ xvi
Section 1
Introduction [[[ 1-1
1.1 General Information [[[ 1-1
1.2 General Information [[[ 1-3
1.2.1 PAH Chemistry [[[ 1-3
1.2.2 PAHMixtures [[[ 1-5
1.3 Applications of Sediment Benchmarks [[[ 1-5
1.4 Data Quality Assurance [[[ 1-6
1.5 Overview [[[ 1-6
Section 2
Narcosis Theory: Model Development
and Application for PAH Mixtures [[[ 2-1
2.1 Section Overview [[[ 2-1
2.2 Narcosis Model Background [[[ 2-1
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
2.8 Comparison to Observed Body Burdens 2-15
2.9 Mixtures and Additivity 2-17
2.10 Aqueous Solubility Constraint 2-17
Section 3
Toxicity of PAHs in Water Exposures
and Derivation of PAH-Specific FCVs 3-1
3.1 Narcosis Theory, EqP Theory and Guidelines:
Derivation of PAH-specific FCVs for Individual PAHs 3-1
3.2 Acute Toxicity of Individual PAHs: Water Exposures 3-8
3.2.1 Acute Toxicity of PAHs 3-8
3.2.2 Acute Values at a ^ow of 1.0 3-8
3.3 Applicability of the WQC as the Effects
Concentration for Benthic Organisms 3-9
3.4 Derivation of the FAV at aKQW of 1.0 3-11
3.5 Chronic Toxicity of Individual PAHs: Water Exposures 3-13
3.5.1 Acenaphthene 3-13
3.5.2 Anthracene 3-14
3.5.3 Fluoranthene 3-14
3.5.4 Phenanthrene 3-14
3.5.5 Pyrene 3-14
3.5.6 Naphthalene 3-15
3.5.7 Derivation of the Final Acute Chronic Ratio 3-15
3.6 Derivation of FCVs 3-15
3.6.1 Derivation of the FCV at a^ow of 1.0 3-15
3.6.2 Derivation of the PAH-Specific FCVs 3-15
Section 4
Derivation of the PAH £ESBTUFCV 4-1
4.1 Derivation of Potencies for Individual PAHs in Sediments (CocpAH.FCV.) 4-1
4.2 Derivation of the ESBFCV for PAH Mixtures ......'......' 4-1
4.3 Aqueous Solubility Constraint 4-2
4.4 Comparison of the SESBTUFCV for Mixtures of PAHs in Estuarine Sediments 4-2
Section 5
Actual and Predicted Toxicity of PAH Mixtures
in Sediment Exposures 5-1
5.1 Introduction 5-1
5.2 Spiked Sediment Toxicity Tests 5-1
5.2.1 Interstitial Water Concentrations and Sediment Toxicity:
Relevance to Water-Only Toxicity Tests and WQC FCVs 5-1
5.2.2 Sediment Toxicity: Prediction Using
Water-Only Toxicity and^oc 5-2
5.2.3 Toxicity of Individual PAHs 5-2
Vll
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Contents
5.2.4 Comparison of Sediment Toxicity to COCPAH,.rcv,. 5-7
5.2.5 PAHMixtures .'.....'' '. 5-8
5.2.6 Additivity of PAH Mixtures 5-8
5.2.7 PAH Additivity Demonstrated Using the Universal Narcosis Slope 5-9
5.2.8 Additivity of Mixtures of High ^ow PAHs 5-11
5.3 Field Sediments versus ESBFCV for PAH Mixtures 5-17
5.3.1 Toxicity toR. abronius of Field Sediments Containing
PAH Mixtures vs. SPSTUs Derived from Narcosis Theory 5-18
5.3.2 Organism Abundance vs. ESBFCV for PAH Mixtures 5-19
Section 6
Implementation 6-1
6.1 Introduction 6-1
6.2 Defining Total PAH Concentration
in Field Collected Sediments 6-1
6.2.1 Introduction 6-2
6.2.2 Data Collection 6-2
6.2.3 Methodology 64
6.2.4 Uncertainty in Predicting SESBTUFCVTOT 64
6.3 Example Calculation of ESBFCV for PAHs and EqP-based Interpretation 6-7
6.4 Interpreting ESBs in Combination with Toxicity Tests 6-12
6.5 Photo-activation 6-13
6.5.1 Overview 6-13
6.5.2 Implications to Derivation of ESB 6-14
6.6 Teratogenicity and Carcinogenicity 6-14
6.6.1 Calculations 6-16
6.6.2 Critical Sediment Concentrations for Teratogenic and
Carcinogenic Effects versus ESBs for PAH Mixtures 6-17
6.7 Equilibrium and ESBs 6-19
6.8 Other Partitioning Phases 6-19
6.8.1 Overview 6-19
6.8.2 Implications to Derivation of ESB 6-20
6.9 Aqueous Solubility Under Non-standard Conditions 6-21
Section 7
Sediment Benchmark Values: Application
and Interpretation 7-1
7.1 Benchmark Value 7-1
7.2 Special Considerations 7-1
7.2.1 Fewer than 34 PAHs have been measured 7-1
7.2.2 Interaction of PAHs with UV light 7-2
7.2.3. Influence of soot carbon and coal on PAH partitioning 7-2
7.2.4. Unusual composition of organic carbon 7-2
7.2.5 Presence of additional narcotic compounds 7-2
7.2.6 Site-specific temperature and salinity corrections 7-3
7.3 Summary .. ...7-3
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Section 8
References 8-1
Appendix A A-l
Appendix B B-l
Appendix C C-l
Appendix D D-l
Appendix E E-l
Appendix F F-l
Appendix G G-l
Appendix H H-l
Tables
Table 2-1. Regression results: y-intercepts and chemical class corrections.
Table 2-2. Comparison of body burdens observed in aquatic organisms acutely exposed to narcotic
chemicals and body burdens predicted from target lipid narcosis theory.
Table 3-1. Summary of the chronic sensitivity of freshwater and saltwater organisms to PAHs; test-specific data.
Table 3-2. Summary of acute and chronic values, acute-chronic ratios and derivation of the final acute values, final
acute-chronic ratios, and final chronic values.
Table 3-3. Results of the approximate randomization (AR) tests forthe equality of freshwater and saltwater FAV
distributions at a Kow of 1.0 and AR tests for the equality of benthic and combined benthic and water column
FAVs for freshwater and saltwater distributions.
Table 3-4. CQC PAH,FCV, concentrations and properties required for their derivation.
Table 5-1. Water-only and spiked-sediment LC50 values used to test the applicability of narcosis
and EqP theories to the derivation of ESB for PAHs.
Table 5-2. Percent mortality of benthic invertebrates inrelationto the sum of the equilibrium partitioning sediment
benchmark toxic units (SESBTUs) of mixtures of PAHs spiked into sediment.
Table 5-3. Chemicals included in the highKow PAH mixture experiment conducted by Spehar et al., (in preparation).
Table 6-1. Relative distribution of SESBTUFCVTOT to SESBTUFCV13 and SESBTUFCV23 for the combined EMAP dataset.
Table 6-2. PAHs measured in various sediment monitoring programs.
Table 6-3. Example calculations of ESBs for PAH mixtures: three sediments.
Table 6-4. Teratogenic and carcinogenic effects of benzo(a)pyrene (BaP) and anthracene on freshwater and
saltwater fishes.
ix
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Contents
Figures
Figure 1-1. Ring structures of representative polycyclic aromatic hydrocarbons. The numbering and lettering
system for several PAHs is also given. A, naphthalene; B, 2-methylnaphthalene; C, phenanthrene;
D, anthracene; E, benz[a]anthracene; F, pyrene; G, benzo[a]pyrene; H, benzo[e]pyrene; I, fluorene;
J, fluoranthene; K, benz[/']aceanthrylene = cholanthrene; L, 3-methylcholanthrene; M, chrysene; N, 5-
methylchrysene; O, dibenzo[cJjl]pyrene = anthranthrene; P, perylene; Q, benzo[g/z/']perylene; R.
coronene; S, indeno[l,2,3-c
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Figure 5-1. Percent mortality versus predicted interstitial water toxic units for six chemicals and three sediments per
chemical.
Figure 5-2. Percent mortality versus predicted sediment toxic units for seven chemicals and three sediments per
chemical.
Figure 5-3. Percent mortality of Rhepoxynius abronius in sediments spiked with acenaphthene, phenanthrene.
fluoranthene, or pyrene concentrations in sediment normalized to ESBTUrcv..
Figure 5-4. Percentage rank, based on ESBTUrcv,, of the sensitivities of genera of benthic organisms from spiked
sediment toxicity tests.
Figure 5-5. Mortality of the amphipod, Rhepoxynius abronius, from 10-day spiked sediment toxicity tests with four
parent PAHs separately (open symbols) and in combination (closed circles) (A) and in tests with
sediments from the field (B) versus predicted sediment toxic units (PSTUs). PSTUs are the quotients of
the concentration of each PAH measured in sediments from the individual spiked sediment treatments.
or individual sediments from the field, divided by the predicted PAH-specific 10-day sediment LC50
values for R. abronius. The predicted PAH-specific 10-day sediment LC50 values for R. abroniusis
were calculated using the critical body burden of 15.8 ^mol/g octanol and Equation 5-2. PSTUs were
summed to obtain the total toxic unit contribution of the mixture of PAHs in spiked or field sediments.
Figure 5-6. Response ofHyalella azteca exposed for 10 days under flow-through conditions to sediment spiked
with a mixture of high Kow PAH.
Figure 5-7. Response ofHyalella azteca exposed for 28 days under flow-through conditions to sediment spiked
with a mixture of high Kow PAH.
Figure 5-8. Survival (after 28 days) and growth (after 10 days) ofHyalella azteca expressed on the basis of
measured PAH concentrations in tissues (lipid normalized).
Figure 5-9. Response ofHyalella azteca exposed for 10 days (3 renewals) to sediment spiked with a mixture of
high ^ow PAH.
Figure 5-10. Response of Leptocheirus plumulosus exposed for 10 days under static conditions to sediment spiked
with a mixture of high Kow PAH.
Figure 5-11. Amphipod (Ampelisca abdita) abundance versus 2ESBTUFCV.
Figure 6-1. Comparison of observed 2ESBTUFCVTOT to observed 2ESBTUFCV13 from (A) 13 PAHs and 2ESBTUFCV23
from (B) 23 PAHs for the combined dataset including U.S. EPA EMAP Louisianian and Carolinian
Provinces.
Figure 6-2. Probability distribution of the (A) 2ESBTUFCV13 and (B) 2ESBTUFCV23 values for each sediment from
the entire database.
Figure 6-3. BaP concentration of 539 sediment samples from the EMAP and Elliott Bay datasets versus (A) the
2ESBTUFCV values of 34 PAHs and (B) a probability plot of these BaP concentrations at an 2ESBTUFCV
=1.0.
Figure 6-4. Anthracene concentration of 539 sediment samples from the EMAP and Elliott Bay datasets versus (A)
the 2ESBTUFCV values of 34 PAHs and (B) a probability plot of these anthracene concentrations at an
SESBTUFCV=1.0.
Figure 6-5. Computed solubilities of nine PAHs relative to their 25° C solubilities as a function of temperature.
XI
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Acknowledgments
Coauthors
David J. Hansen
Dominic M. Di Toro
Joy A. McGrath
Richard C.Swartz
David R. Mount*
Robert L. Spehar
Robert M. Burgess*'**
Robert J. Ozretich
Heidi E. Bell*
Mary C. Reiley
Tyler K.Linton
Environmental consultant (formerly withU.S. EPA)
Univ. of Delaware, Newark, DE; HydroQual, Inc., Mahwah, NJ
HydroQual, Inc., Mahwah, NJ
Environmental consultant (formerly withU.S. EPA)
U.S. EPA, NHEERL, Mid-continent Ecology Division, Duluth, MN
U.S. EPA, NHEERL, Mid-continent Ecology Division, Duluth, MN
U.S. EPA, NHEERL, Atlantic Ecology Division, Narragansett, RI
U.S. EPA, NHEERL, Western Ecology Division, Newport, OR
U.S. EPA, Office of Water, Washington, DC
U.S. EPA, Office of Water, Washington, DC
Great Lakes Environmental Center, Columbus, OH
Significant Contributors to the Development of the Approach and Supporting Science
David J. Hansen
Dominic M. Di Toro
Joy A. McGrath
Richard C.Swartz
David R. Mount
Robert M. Burgess
Robert J. Ozretich
Robert L. Spehar
Environmental consultant (formerly withU.S. EPA)
Univ. of Delaware, Newark, DE; HydroQual, Inc., Mahwah, NJ
HydroQual, Inc., Mahwah, NJ
Environmental consultant (formerly withU.S. EPA)
U.S. EPA, NHEERL, Mid-continent Ecology Division, Duluth, MN
U.S. EPA, NHEERL, Atlantic Ecology Division, Narragansett, RI
U.S. EPA, NHEERL, Pacific Ecology Division, Newport, OR
U.S. EPA, NHEERL, Mid-continent Ecology Division, Duluth, MN
Technical Support and Document Review
Walter J. Berry U.S. EPA, NHEERL, Atlantic Ecology Division, Naragansett, RI
Lawrence P. Burkhard U.S. EPA, NHEERL, Mid-continent Ecology Division, Duluth, MN
Patricia DeCastro Computer Sciences Corporation, Narragansett, RI
Edward Dettmann U.S. EPA, NHEERL, Atlantic Ecology Division, Naragansett, RI
xn
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
JimDwyer
Phillip M.Gschwend
U.S. F&WS Columbia, MO
Massachusetts Institute of Technology, Cambridge, MA
SusanB. Kane-Driscoll Menzie-Cura & Associates, Inc., Chelmsford, MA
Peter F. Landrum
Richard J.Pruell
Marc Tuchman
NO AA, Great Lakes Research Laboratory, Ann Arbor, MI
U.S. EPA, NHEERL, Atlantic Ecology Division, Narragansett, RI
U.S. EPA, Great Lakes National Program Office, Chicago, IL
*Principal U.S. EPA contacts
** Series editor
Xlll
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Executive Summary
Executive Summary
This equilibrium partitioning sediment benchmark (ESB) document recommends an approach for
summing the toxicological contributions of mixtures of 34 polynuclear aromatic hydrocarbons
(PAHs) in sediments to determine if their concentrations in any specific sediment would be
protective of benthic organisms from their direct toxicity. The combination of the equilibrium
partitioning (EqP), narcosis theory, and additivity provide the technical foundation for this
benchmark. These approaches were required because PAHs occur in sediments in a variety of
proportions as mixtures and can be expected to act jointly under a common mode of action.
Therefore, their combined toxicological contributions must be predicted on a sediment-specific
basis. This overall approach provides for the derivation of this Tier 1 ESB that is causally linked
to the specific mixtures of PAHs in a sediment, yet is applicable across sediments and
appropriately protective of benthic organisms.
EqP theory holds that a nonionic chemical in sediment partitions between sediment organic
carbon, interstitial (pore) water and benthic organisms. At equilibrium, if the concentration in any
one phase is known, then the concentrations in the others can be predicted. The ratio of the
concentration in water to the concentration in sediment organic carbon is termed the organic
carbon partition coefficient (KQC), which is a constant for each chemical. The ESB Technical
Basis Document (U.S. EPA, 2003a) demonstrates that biological responses of benthic organisms
to nonionic organic chemicals in sediments are different across sediments when the sediment
concentrations are expressed on a dry weight basis, but similar when expressed on a jWg chemical/
g organic carbon basis (yWg/goc). Similar responses were also observed across sediments when
interstitial water concentrations were used to normalize biological availability. The Technical
Basis Document (U.S. EPA, 2003a) further demonstrates that if the effect concentration in water
is known, the effect concentration in sediments on a i^-g/goc basis can be accurately predicted by
multiplying the effect concentration in water by the chemical's KQC.
EqP can be used to calculate ESBs for any toxicity endpoint for which there are water-only
toxicity data; it is not limited to any specific effect endpoint. In this document, the Final Chronic
Value (FCV) for PAHs derived using the National Water Quality Criteria (WQC) Guidelines
(Stephan et al., 1985) was used as the toxicity endpoint for this ESB. This value is intended to be
the concentration of a chemical in water that is protective of the presence of aquatic life. For
this PAH mixtures ESB, narcosis theory was used to (1) demonstrate that the slope of the acute
toxicity-octanol water partition coefficient (KQVf) relationship was similar across species; (2)
normalize the acute toxicity of all PAHs in water to an aquatic species using a reference KQW of
1.0 (where the concentration in water and lipid of the organism would be essentially the same);
(3) establish an acute sensitivity ranking for individual species at the KQW of 1.0 and to use the
rankings to calculate a Final Acute Value (FAV) following the WQC Guidelines (Stephan et al.,
1985); (4) calculate the final acute-chronic ratio (ACR) from water-only acute and chronic
toxicity tests; (5) calculate the Final Chronic Value (FCV) at the reference KQW of 1.0 from the
quotient of the FAV and ACR; and (6) to calculate the PAH-specific FCV in /j-g/L using the
FCV at the reference KQW of 1.0, the PAH-specific Kow and the universal narcotic slope of the
acute-KQW toxicity relationship. The EqP approach and the slope of the KOW-KQC relationship
was then used to calculate, from the product of the PAH-specific FCV and KQC the FCV
concentration for each specific PAH in sediment (COCPAHl.FCVl, /j-g/g organic carbon).
xiv
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Importantly, because PAHs occur in sediments as mixtures and their toxicities in water, tissues, or
sediments are additive or nearly additive, their combined toxicities must be considered so that the
benchmark is appropriately protective. For this reason, the combined toxicological contributions of
the PAH mixture must be used. In this document, the 34 PAHs monitored in the EMAP program
are used to derive a concentration of "total PAH." Many monitoring and assessment efforts
measure a smaller group of PAHs, such as 13 or 23 PAHs. While adjustment factors have been
calculated to relate these smaller subsets to the expected concentration of the 34 PAHs, their
imprecision precludes their use in critical sediment assessments. Therefore, this document
recommends that the ESB for total PAH should be the sum of the quotients of the concentrations
of each of the 34 individual PAHs in a specific sediment divided by the COCPAHiFCV. of that
particular PAH. This sum is termed the Equilibrium Partitioning Sediment Benchmark Toxic Unit
(SESBTUFCV), which is based on the FCV. Freshwater or saltwater sediments containing <1.0
SESBTUFCV of the mixture of the 34 PAHs or more PAHs are acceptable for the protection of
benthic organisms, and if the SESBTUFCV is greater than 1.0, sensitive benthic organisms may be
unacceptably affected. This provides for the derivation of a benchmark that is causally linked to
the specific mixtures of PAHs in a sediment, applicable across sediment types, and appropriately
protective of benthic organisms. A sediment-specific site assessment would provide further
information on PAH bioavailability and the expectation of toxicity relative to the SESBTUFCV and
associated uncertainty.
These ESBs do not consider the antagonistic, additive or synergistic effects of other sediment
contaminants in combination with PAHs or the potential for bioaccumulation and trophic transfer
of PAHs to aquatic life, wildlife or humans. Consistent with the recommendations of EPA's
Science Advisory Board, publication of these documents does not imply the use of ESBs as stand-
alone, pass-fail criteria for all applications; rather, ESB exceedances could be used to trigger the
collection of additional assessment data. ESBs apply only to sediments having >0.2% organic
carbon by dry weight.
Tier 1 and Tier 2 ESB values were developed to reflect differing degrees of data availability and
uncertainty. Tier 1 ESBs have been derived for polycyclic aromatic hydrocarbon (PAH) mixtures
in this document, and for the nonionic organic insecticides endrin and dieldrin, and metal mixtures
in U.S. EPA (2003c,d,e). Tier 2 ESBs are reported in U.S. EPA (2003f).
xv
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Glossary
Glossary of Abbreviations
ACR
AR
ASTM
BaP
BCF
C
oc
C
OC.PAHi
C
C,
Org
IW
C
OCfAHifCVi
OC,PAHi,Rhepox,LC50
cv
CWA
DOC
EC50
EMAP
EPA
EqP
ESB
Acute-Chronic Ratio
Approximate Randomization
American Society for Testing and Materials
Benzo[a]pyrene
Bioconcentration factor
Freely-dissolved interstitial water concentration of contaminant
Chemical concentration in target lipid
Critical body burden in the target lipid fraction of the organism
Chemical concentration in sediments on an organic carbon basis
PAH-specific chemical concentration in sediment on an organic carbon basis
Chemical concentration in octanol
Chemical concentration in the organism
Critical body burden in the organism
Total interstitial water concentration of contaminant
Effect concentration of a PAH in sediment on an organic carbon basis
calculated from the product of its FCV and KQC
Sediment LC50 concentration on an organic carbon basis for a specific
PAH for Rhepoxinus calculated from the product of its LC50 value at a KQW
of 1.0 and KQC
Maximum solubility limited PAH concentration in sediment on an organic
carbon basis
Coefficient of Variation
Clean Water Act
Dissolved Organic Carbon
Concentration affecting 50% of the test organisms
Environmental Monitoring and Assessment Program
United States Environmental Protection Agency
Equilibrium partitioning
Equilibrium Partitioning Sediment Benchmark(s)
xvi
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
ESBTUFCV,
ESBTU
Rhepox
SESBTUF
J Lipid
foe
Jsc
FACR
FAV
FCV
GMAV
IWTU
IWTUr
K
DOC
K
LC50
LFER
MV
NA
NAPL
ND
NOAA
Equilibrium Partitioning Sediment Benchmark Toxic Unit for PAH based
on the FCV
Equilibrium Partitioning Sediment Benchmark Toxic Unit for PAH. based
on the LC50 of Rhepoxynius abronius.
Sum of Equilibrium Partitioning Sediment Benchmark Toxic Units, where
the units are based on FCV values
Fraction of lipid in the organism
Fraction of organic carbon in sediment
Fraction of soot carbon in sediment
Final Acute-Chronic Ratio
Final Acute Value
Final Chronic Value
Genus Mean Acute Value
Interstitial Water Toxic Unit
Interstitial water toxic unit calculated by dividing the dissolved interstitial
water concentration by the FCV
Dissolved organic carbon: water partition coefficient
Lipid: water partition coefficient
Organic carbon: water partition coefficient
Octanol: water partition coefficient
Sediment: water partition coefficient
Setschenow constant
Soot carbon: water partition coefficient
Concentration estimated to be lethal to 50 % of the test organisms within
a specified time period
Linear free energy relationship
Molar Volume
Not Applicable, Not Available
Non-aqueous Phase Liquid
Not Determined, Not Detected
National Oceanographic and Atmospheric Administration
xvn
-------
Glossary
NOEC
NTU
OEC
PAH
PCB
POC
PSTU
QSAR
REMAP
S
SAB
SCV
SE
SMAV
SPARC
TOC
TU
WQC
WQCTUFCV,
No Observed Effect Concentration
Narcotic Toxic Units
Observable Effect Concentration
Polycyclic aromatic hydrocarbon
Organic carbon-normalized PAH concentration in sediment
Polychlorinated Biphenyl
Particulate Organic Carbon
Predicted Sediment Toxic Units
Quantitative Structure Activity Relationship
Regional Environmental Monitoring and Assessment Program
Aqueous Solubility
U.S. EPA Science Advisory Board
Secondary Chronic Value
Standard Error
Species Mean Acute Value
SPARC Performs Automated Reasoning in Chemistry
Total Organic Carbon
Toxic Unit
Water Quality Criteria
Water Quality Criteria Toxic Unit based on the FCV
XVlll
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Section 1
Introduction
1.1 General Information
Toxic pollutants in bottom sediments of the
Nation's lakes, rivers, wetlands, estuaries, and
marine coastal waters create the potential for
continued environmental degradation even where
water column concentrations comply with
established WQC. In addition, contaminated
sediments can be a significant pollutant source
that may cause water quality degradation to
persist, even when other pollutant sources are
stopped (Larsson, 1985; Salomons etal., 1987;
Burgess and Scott, 1992). The absence of
defensible equilibrium partitioning sediment
benchmarks (ESBs) make it difficult to accurately
assess the extent of the ecological risks of
contaminated sediments and to identify, prioritize,
and implement appropriate cleanup activities and
source controls (U.S. EPA 1997a, b, c).
As a result of the need for a procedure to
assist regulatory agencies in making decisions
concerning contaminated sediment problems, the
U.S. Environmental Protection Agency (EPA)
Office of Science and Technology, Health and
Ecological Criteria Division (OST/HECD) and
Office of Research and Development National
Health and Environmental Effects Research
Laboratory (ORD/NHEERL) established a
research team to review alternative approaches
(Chapman, 1987). All of the approaches
reviewed had both strengths and weaknesses, and
no single approach was found to be applicable for
the derivation of benchmarks in all situations (U.S.
EPA, 1989, 1992). The equilibrium partitioning
(EqP) approach was selected for nonionic organic
chemicals because it presented the greatest
promise for generating defensible, national,
numeric chemical-specific benchmarks applicable
across a broad range of sediment types. The
three principal observations that underlie the EqP
approach to establishing sediment benchmarks are
as follows:
1. The concentrations of nonionic organic
chemicals in sediments, expressed on an organic
carbon basis, and in interstitial waters correlate to
observed biological effects on sediment-dwelling
organisms across a range of sediments.
2. Partitioning models can relate sediment
concentrations for nonionic organic chemicals on
an organic carbon basis to freely-dissolved
concentrations in interstitial water.
3. The distribution of sensitivities of benthic
organisms to chemicals is similar to that of water
column organisms; thus, the currently established
water quality criteria (WQC) final chronic values
(FCV) or secondary chronic values (SCV) can be
used to define the acceptable effects concentration
of a chemical freely-dissolved in interstitial water.
The EqP approach, therefore, assumes that (1)
the partitioning of the chemical between sediment
organic carbon and interstitial water is at or near
equilibrium; (2) the concentration in either phase
can be predicted using appropriate partition
coefficients and the measured concentration in the
other phase (assuming the freely-dissolved
interstitial water concentration can be accurately
measured); (3) organisms receive equivalent
exposure from water-only exposures or from any
equilibrated phase: either from interstitial water via
respiration, from sediment via ingestion or other
sediment-integument exchange, or from a mixture
of exposure routes; (4) for nonionic chemicals,
effect concentrations in sediments on an organic
carbon basis can be predicted using the organic
carbon partition coefficient (Koc) and effects
concentrations in water; (5) the FCV or SCV
concentration is an appropriate effects
concentration for freely-dissolved chemical in
interstitial water; and (6) ESBs derived as the
product of the Koc and FCV are protective of
benthic organisms. ESB concentrations presented
in this document are expressed as |ig chemical/g
sediment organic carbon (|ig/goc) and not on an
interstitial water basis because (1) interstitial water
1-1
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is difficult to sample and (2) significant amounts of
the dissolved chemical may be associated with
dissolved organic carbon; thus, total concentrations
in interstitial water may overestimate exposure.
Sediment benchmarks generated using the
EqP approach are suitable for use in providing
technical information to regulatory agencies
because they are:
1. Numeric values
2. Chemical specific
3. Applicable to most sediments
4. Predictive of biological effects
5. Protective of benthic organisms
ESBs are derived using the available scientific
data to assess the likelihood of significant
environmental effects to benthic organisms from
chemicals in sediments in the same way that the
WQC are derived using the available scientific
data to assess the likelihood of significant
environmental effects to organisms in the water
column. As such, ESBs are intended to protect
benthic organisms from the effects of chemicals
associated with sediments and, therefore, only
apply to sediments permanently inundated with
water, to intertidal sediment, and to sediments
inundated periodically for durations sufficient to
permit development of benthic assemblages.
ESBs should not be applied to occasionally
inundated soils containing terrestrial organisms, nor
should they be used to address the question of
possible contamination of upper trophic level
organisms or the synergistic, additive, or
antagonistic effects of multiple chemicals. The
application of ESBs under these conditions may
result in values lower or higher than those
presented in this document.
ESB values presented herein are the
concentrations of PAH mixtures in sediment that
will not adversely affect most benthic organisms.
It is recognized that these ESB values may need to
be adjusted to account for future data. They may
also need to be adjusted because of site-specific
considerations. For example, in spill situations,
where chemical equilibrium between water and
sediments has not yet been reached, sediment
chemical concentrations less than an ESB may
pose risks to benthic organisms. This is because
for spills, disequilibrium concentrations in
interstitial and overlying water may be
proportionally higher relative to sediment
concentrations. In systems where biogenic
organic carbon dominates, research has shown
that the source or "quality" of total organic carbon
(TOC) in natural sediments does not affect
chemical binding when sediment toxicity was
measured as a function of TOC concentration
(DeWitt et al., 1992). Kocs have also been
demonstrated to not vary in gradients of chemicals
across estuarine sediments (Burgess et al., 2000a).
However, in systems where other forms of carbon
are present at elevated levels, the source or
'quality' of TOC may affect chemical binding
despite expressing toxicity as a function of TOC
concentration. At some sites, concentrations in
excess of an ESB may not pose risks to benthic
organisms because the compounds are partitioned
to or a component of a particulate phase such as
soot carbon or coal or exceed solubility such as in
the case of undissolved oil or chemical (e.g.
conditions at a manufactured gas plant site). In
these situations, an ESB would be overly
protective of benthic organisms and should not be
used unless modified using the procedures outlined
in "Procedures for the Derivation of Site-Specific
Equilibrium Partitioning Sediment Benchmarks
(ESBs) for the Protection of Benthic Organisms"
(U.S. EPA, 2003b). If the organic carbon has a
low capacity (e.g., hair, sawdust, hide), an ESB
would be underprotective. An ESB may also be
underprotective where the toxicity of other
chemicals are additive with an ESB chemical or
where species of unusual sensitivity occur at the
site.
This document presents the theoretical basis
and the supporting data relevant to the derivation
of ESBs for PAH mixtures. The data that support
the EqP approach for deriving ESBs for nonionic
organic chemicals are reviewed by Di Toro et al.
(1991) and EPA (U.S. EPA, 2003a). Before
proceeding through the following text, tables, and
calculations, the reader should also consider
reviewing Stephan et al. (1985).
1-2
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
1.2 General Information: PAH Mixtures
The EPA developed ESBs for metal
mixtures (Cd, Cu, Pb, Ni, Ag, Zn) (U.S. EPA
2003c) and the insecticides endrin and dieldrin
(U.S. EPA 2003d,e) and proposed ESBs for the
individual polycyclic aromatic hydrocarbons
(PAHs) acenaphthene, fluoranthene and
phenanthrene (U.S. EPA 1993a,b,c). Because
PAHs occur in the environment as mixtures, rather
than single chemicals, ESBs for individual PAHs
have the potential to be substantially under-
protective because they do not account for other
co-occurring PAHs. This ESB for PAH mixtures
replaces the earlier draft individual PAH
documents.
Numerous efforts have previously sought to
address and estimate the toxicity of PAH mixtures
in sediments (Barrick et al., 1988; Long and
Morgan, 1991; PTI Environmental Services, 1991;
Long et al., 1995; Swartz et al., 1995; Ingersoll et
al., 1996; MacDonaldetal., 1996,2000; Cubbage
et al., 1997; Di Toro and McGrath, 2000; Di Toro
et al., 2000; Ozretich et al., 1997, 2000). The
resultant sediment benchmarks have engendered
considerable controversy over such issues as the
correlative versus causal relations between dry
weight sediment chemistry and biological effects,
the bioavailability of sediment contaminants, the
effects of covarying chemicals and mixtures, and
ecological relevance. Overviews of the various
approaches are useful (Mount et al., 2003; Swartz
et al., 1999). The use of sediment benchmarks
derived in a variety of ways must be linked to the
derivation procedure and specific intent of the
methodology. The U. S. EPA research team has
concluded, based upon additional investigation, that
recommendation of sediment benchmarks for
PAHs based on EqP, narcosis theory and additivity
was necessary to resolve outstanding issues
related to causality. Sediment benchmarks for
mixtures of PAHs that are derived using these
approaches are adequately protective of benthic
organisms, as well as ecologically relevant.
The SPAH model developed by Swartz et al.
(1995) and based upon a combination of the EqP
approach, quantitative structure activity
relationships (QSAR), narcosis theory, and
additivity models provided initial insight into a
technical approach for resolving these
complexities. This EqP-based SPAH model
provides a method to address causality, account
for bioavailability, consider mixtures, and predict
toxicity and ecological effects. The most
significant contribution to the development of the
scientific basis for deriving ESBs for PAH
mixtures is described by Di Toro et al. (2000) and
Di Toro and McGrath (2000). This pioneering
research in developing a methodology for deriving
ESBs for mixtures of narcotic chemicals and
PAHs forms major portions of this document.
1.2.1 PAH Chemistry
Portions of the following overview of PAH
chemistry are directly, or in part from, Neff'sl979
classic book "Polycyclic Aromatic Hydrocarbons
in the Aquatic Environment" and to a lesser extent
Schwarzenbach et al. (1993). PAHs are
composed of two or more fused aromatic or
benzene rings. Two aromatic rings are fused
when a pair of carbon atoms is shared. The
resulting structure is a molecule with all carbon
and hydrogen atoms lying in a single plane.
Naphthalene (C10Hg), which consists of two fused
aromatic rings, is the lowest molecular weight
PAH. The ultimate fused-ring aromatic system is
graphite, an allotropic form of elemental carbon.
Of primary environmental concern are mobile
compounds ranging in molecular weight from
naphthalene (C10Hg, molecular weight 128.17) to
coronene (C24H12, molecular weight 300.36).
Within this range is an extremely large number of
PAHs differing in the number and positions of
aromatic rings and in the number, chemistry, and
position of substituents on the ring system. Figure
1-1 presents a selection of PAH structures.
Physical and chemical characteristics of
PAHs vary in a more or less regular fashion with
molecular weight. Resistance to oxidation and
reduction tends to decrease with increasing
molecular weight. Vapor pressure and aqueous
solubility decrease almost logarithmically with
increasing molecular weight. As a consequence
of these differences, PAHs of different molecular
weights vary substantially in their behavior and
1-3
-------
Figure 1-1. Ring structures of representative polycyclic aromatic hydrocarbons. The numbering and lettering
system for several PAHs is also given. A, naphthalene; B, 2-methylnaphthalene; C, phenanthrene;
D, anthracene; E, benz[a]anthracene; F, pyrene; G, benzo[a]pyrene; H, benzo[e]pyrene; I, fluorene;
J, fluoranthene; K, benz[/]aceanthrylene = cholanthrene; L, 3-methylcholanthrene; M, chrysene; N,
5-methylchrysene; O, dibenzo[c 10,000).
Later in this document, 34 PAH structures
(specific non-alkylated compounds and generic
alkylated forms) are identified as representing a
minimum for 'total PAHs'. It is recognized that
this subset of all possible PAHs is not complete;
however, the 34 PAHs identified are the ones that
are generally most abundant and commonly
measured as part of environmental monitoring
programs. As analytical techniques improve, the
number of PAHs composing 'total PAHs' will
most certainly increase and users of this document
are encouraged to include newly quantified PAHs
in the derivation of benchmark values assuming
good supporting data are available (e.g., Kows,
solubilities).
PAHs found in aquatic environments originate
from three possible sources: pyrogenic, petrogenic
and diagenic. Pyrogenic PAHs result from the
incomplete but high temperature, short-duration
combustion of organic matter including fossil fuels
and biomass (Neff 1979; Meyers and Ishiwatari
1993). These pyrogenic PAHs are believed to
form from the breakdown or 'cracking' of organic
matter to lower molecular weight radicals during
pyrolysis, followed by rapid reassembly into non-
alkylated PAH structures (Neff 1979). Petrogenic
PAHs are created by diagenic processes at
relatively low temperatures over geologic time
scales, leading to the formation of petroleum and
other fossil fuels containing PAHs (Meyers and
1-4
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Ishiwatari 1993; Boehm etal., 2001). PAHs
formed at relatively low temperatures (-150 °C)
over long periods of time will be primarily alkylated
molecules. The alkylated structure of petrogenic
PAHs reflects the ancient plant material from
which the compounds formed (Neff 1979).
Diagenic PAHs refer to PAHs from biogenic
precursors, like plant terpenes, leading to the
formation of compounds such as retene and
derivatives of phenanthrene and chrysene (Kites
et al., 1980; Meyers and Ishiwatari 1993; Silliman
etal., 1998). Perylene is another common
diagenic PAH. Although its exact formation
process remains unclear, an anaerobic process
appears to be involved (Gschwend et al., 1983;
Venkatesan 1988; Silliman etal., 1998). While
diagenic PAHs are frequently found at background
levels in recent sediments (i.e., deposited over the
last 150 years), they often dominate the
assemblage of PAHs present in older sediments
deposited before human industrial activity
(Gschwend et al., 1983). A potential fourth source
of PAHs is biogenic; that is, purely from bacteria,
fungi, plants or animals in sedimentary
environments without any contributions from
diagenic processes. However, attempts to
produce biogenic PAHs have arguably failed,
indicating this source is not significant (Hase and
Kites 1976; Neff 1979).
The majority of PAHs found in aquatic
environments originate from pyrogenic sources
(Blumer 1976; Suess 1976; Kites et al., 1977;
LaFlamme and Kites, 1978; NRC 1985; Wu et al.,
2001). However, petrogenic PAHs do also occur
alone or in combination with pyrogenic PAHs
(Lake et al., 1979; Wakeham et al., 1980; NRC
1985; Gschwend and Kites 1981; Readman et al.,
1992). In general, petrogenic PAHs appear to be
associated with local or point sources, such as
refineries and other petroleum industries, and
adjacent to roads and navigational routes. This
contrasts with the distribution of pyrogenic PAHs,
which occur on a broader geographic scale.
These distribution are also affected by the relative
persistence of pyrogenic and petrogenic PAHs in
the environment. As compared to petrogenic
PAHs, pyrogenic PAHs are found more
extensively in the sediment core record and appear
to be less vulnerable to biotic and abiotic
degradation (Burgess et al., 2003). Finally,
diagenic PAHs occur at background levels
although anthropogenic sources (e.g., perylene)
can contribute to these types of PAHs.
1.2.2 PAH Mixtures
Unlike most other organic chemicals in the
environment, PAHs are not released in a 'pure' or
well-characterized form. Rather, because PAHs
consist of thousands of structures originating from
at least three sources, they always occur in the
environment as complex mixtures (Burgess et al.,
2003). As discussed above, pyrogenic PAHs,
although not generally alkylated, are produced as
mixtures of parent PAHs based on the conditions
of their combustive formation (e.g., temperature,
presence of oxygen, original organic matter).
Similarly, the composition of petrogenic PAHs is a
function of the diagenic conditions under which the
original organic matter was exposed for thousands
of years (e.g., pressure, temperature). Of course,
human industrial practices convert some crude
petrogenic PAH mixtures into more purified forms
(e.g., fuel oils, creosote). These purified forms
also contain complex mixtures of PAH molecules.
As a consequence of these factors, when PAHs
are released into the aquatic environment from the
burning of fossil fuels and biomass, discharge of
industrial chemicals, and transport of petroleum
products they eventually accumulate in the
sediments as complex mixtures (Neff 1979).
1.3 Application of Sediment Benchmarks
ESBs as presented in this document are meant
to be used with direct toxicity testing of sediments
as a method of sediment evaluation, assuming the
toxicity testing species is sensitive to the
chemical(s) of interest. They provide a chemical -
by-chemical specification of sediment
concentrations protective of benthic aquatic life.
The EqP method should be applicable to nonionic
organic chemicals with a KQW above 3.0.
For the toxic chemicals addressed by the ESB
documents Tier 1 (U.S. EPA, 2003c, d, e, and this
document) and Tier 2 (U.S. EPA, 2003f) values
1-5
-------
were developed to reflect the differing degrees of
data availability and uncertainty. Tier 1 ESBs are
more scientifically rigorous and data intensive than
Tier 2 ESBs. The minimum requirements to derive
a Tier 1 ESB include: (1) Each chemical's organic
carbon-water partition coefficient (Koc) is derived
from the octanol-water partition coefficient (Kow)
obtained using the SPARC (SPARC Performs
Automated Reasoning in Chemistry) model
(Karickhoff et al., 1991) and the KOW-KOC
relationship from Di Toro et al. (1991). This Koc
has been demonstrated to predict the toxic
sediment concentration from the toxic water
concentration with less uncertainty than Koc
values derived using other methods. (2) The FCV
is updated using the most recent toxicological
information and is based on the National WQC
Guidelines (Stephan etal., 1985). (3) EqP-
confirmation tests are conducted to demonstrate
the accuracy of the EqP prediction that the KQC
multiplied by the effect concentration from a
water-only toxicity test predicts the effect
concentration from sediment tests (Swartz, 1991a;
DeWitt et al., 1992). Using these specifications.
Tier 1 ESBs have been derived for PAH mixtures
in this document, metals mixtures (U.S. EPA,
2003c) and, the nonionic organic insecticides
endrin and dieldrin (U.S. EPA, 2003d, e). In
comparison, the minimum requirements for a Tier
2 ESB (U.S. EPA, 2003f) are less rigorous: (1)
The Kow for the chemical that is used to derive
the Koc can be from slow-stir, generator column,
shake flask, SPARC or other sources (e.g., Site
2001). (2) FCVs can be from published or draft
WQC documents, the Great Lakes Initiative or
developed from AQUIRE. Secondary chronic
values (SCV) from Suter and Mabrey (1994) or
other effects concentrations from water-only
toxicity tests can be used. (3) EqP confirmation
tests are recommended, but are not required for
the development of Tier 2 ESBs. Because of
these lesser requirements, there is greater
uncertainty in the EqP prediction of the sediment
effect concentration from the water-only effect
concentration, and in the level of protection
afforded by Tier 2 ESBs. Examples of Tier 2
ESBs for nonionic organic chemicals are found in
U.S. EPA (2003f).
1.4 Data Quality Assurance
All data used to derive the FCV used to calculate
the ESB for PAHs from water-only toxicity tests
were obtained from a comprehensive literature
search completed in 1995. Discussions in other
sections of this document utilized literature
obtained up to 2003. Data were evaluated for
acceptability using the procedures in the Stephan
et al. (1985): Guidelines for deriving numerical
national water quality criteria for the
protection of aquatic organisms and their uses.
Data not meeting the criteria for acceptability
were rejected. All calculations were made using
the procedures in Stephan et al. (1985). All data
and intermediate values are presented in tables or
appendices in the document. Four significant
figures were used in intermediate calculations to
limit the effect of rounding error, and are not
intended to indicate the true level of precision.
The document was reviewed as part of a formal
peer review and all original data were made
available as part of the review process. Any
errors of omission or calculation discovered during
the peer review process were corrected. The
document was revised according to the comments
of peer reviewers and additional scientific
literature and significant data identified by
reviewers were incorporated into the document.
Hard copies of peer-review comments and
responses to these comments are available from
the ORD/NHEERL Atlantic Ecology Division -
Narragansett, Rhode Island. Hard copies of all
literature cited in this document reside at ORD/
NHEERL Atlantic Ecology Division -
Narragansett, Rhode Island.
1.5 Overview
This document presents the theoretical basis
and supporting data relevant to the derivation of
ESBs for mixtures of PAHs.
Section 2 of this document "Narcosis Theory:
Model Development and Application for PAH
Mixtures" contains an analysis of the narcosis and
EqP models to demonstrate the scientific basis for
the derivation of WQC and ESBs for mixtures of
narcotic chemicals, including PAHs. Data are
1-6
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
presented that demonstrate that the toxicity of
narcotic chemicals when based on concentration in
water increase with their KQW and that the slope
of the ATow-toxicity relationship is not different
across species. The universal slope of this
relationship (-0.945) is applicable for all narcotic
chemical classes, whereas the intercept is
chemical class-specific. The intercept of this
slope at a Kow of 1.0 predicts the tissue effect
concentration. The toxicities of mixtures of
narcotic chemicals in water are shown to be
approximately additive, thus the toxic unit concept
is applicable to mixtures. The toxicities of narcotic
chemicals are shown to be limited by their
solubilities in water, hence their toxicities in
sediments are limited.
Section 3 of this document "Toxicity of PAHs
in Water Exposure and Derivation of PAH-
specific FCVs" presents an analysis of acute and
chronic water-only toxicity data for freshwater
and saltwater aquatic organisms exposed to
individual PAHs. It examines (1) the relative
sensitivities of freshwater and saltwater organisms
to determine if separate FCVs are required, and
(2) the relative sensitivities of benthic organisms
and organisms used to derive WQC to determine if
the WQC FCV should be based only on benthic
organisms. These data are used with the narcosis
model presented in Section 2, the EqP approach
(U.S. EPA, 2003a), and the U.S. EPA National
WQC (Stephan et al., 1985) to derive the FCV
for individual PAHs (PAH-specific FCV).
Section 4 "Derivation of PAH 2ESBTU "
rL-V
contains the approach used for deriving the
SESBs for mixtures of PAHs. The COCPAHlFCV. is
derived for each individual PAH as the product of
the PAH-specific FCV and the respective KQC
value as recommended by the EqP approach. The
use of the COCPAHlPCVl value for individual PAHs is
inappropriate for use as the ESB because PAHs
occur as mixtures. The toxicities of mixtures of
narcotic chemicals has been shown to be
approximately additive, therefore, combined toxic
contributions of all PAHs in the mixture can be
determined by summing the quotients of the
concentration of each PAH in the sediment divided
by its Q)CFAHiFcvi to determine the sum of these
Equilibrium Partitioning Sediment Benchmark
Toxic Units (2ESBTUFCV). If theSESBTUFCV is
<1.0, the sediment benchmark for the PAH
mixture is not exceeded and the PAH
concentration in the sediment is protective of
benthic organisms. If the SESBTUFCV exceeds
1.0, the sediment benchmark for the PAH mixture
is exceeded and sensitive benthic organisms may
be affected by the PAHs. The 2ESBTUFCV is
derived for PAH mixtures in sediments from
national monitoring programs to reveal the
incidence of sediment benchmark exceedences.
Section 5 "Actual and Predicted Toxicity of
PAH Mixtures in Sediment Exposures" examines
the applicability of the EqP methodology for
COC,PAH,,FCV, ^d ESB derivation. The COQpAHi.FCVi
and ESB are compared to (1) databases of
observed sediment toxicity, and (2) amphipod
abundance in sediments from the field where
PAHs are the probable contaminants of concern.
Section 6 "Implementation" defines the PAHs
to which the ESB apply. An example calculation
is provided to explain the conversion of
concentrations of individual PAHs on a dry weight
basis into the benchmark. The photo-activation of
PAHs in UV sunlight and teratogenicity and
carcinogenicity of certain PAHs in the mixture are
examined. The importance of equilibrium and the
partitioning of PAHs to other organic carbon
phases (e.g., soot and coal) is described. An
approach for calculating PAH solubilities for
temperatures or salinities at specific sites is
provided.
Section 7 "Sediment Benchmark Values:
Application and Interpretation" presents the
sediment benchmark values and lists several
factors to consider when applying and interpreting
these values.
Section 8 "References" lists references cited
in all sections of this document.
Appendices provide supplementary tabulated
information.
1-7
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Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Section 2
Narcosis Theory:
Model Development and
Application for PAH Mixtures
2.1 Section Overview
This section of the ESB document presents a
model of the toxicity of narcotic chemicals to
aquatic organisms that is applicable to the
derivation of WQC and ESBs for mixtures of
narcotic chemicals, including PAHs. Both the
model and this section of the document are largely
excerpted from the publications of Di Toro et al.
(2000) and Di Toro and McGrath (2000) which
should be consulted for information on components
of the overall model that are not included in this
ESB document. The narcosis model includes a
scientific analysis of the toxicities of narcotic
chemicals fundamental to the derivation of WQC
and ESBs for their mixtures. The ESB for PAH
mixtures described in Section 4 of this document is
derived using this model and toxicity data
exclusively for PAHs (see Section 3).
The narcosis model is used to describe the
toxicity of all type I narcotic chemicals. Since
PAHs are expected to be type I narcotic
chemicals (Hermens, 1989; Verhaar et al., 1992),
the toxicological principles that apply to them
should be more accurately characterized by an
analysis of the principles that apply to narcotic
chemicals overall. Model development utilizes a
database of LC50 values comprising 156
chemicals and 33 aquatic species, including fish,
amphibians, arthropods, molluscs, annelids,
coelenterates and echinoderms. The analysis
detailed in this section is used to demonstrate that
(1) the toxicities of narcotic chemicals, and
therefore PAHs, are dependant on the chemical's
Kow; (2) the slope of the KQW-toxicity relationship
is the same for all species of aquatic organisms
and classes of narcotic chemicals with the
intercepts being species and chemical class-
specific; (3) the species-specific LC50 values
normalized to a KQW =1.0 permit ranking of
species sensitivities and are equivalent to the body
burden LC50 on a lipid basis; and (4) the toxicities
of mixtures of narcotic chemicals are additive.
The analysis of narcotic chemical toxicity data
presented in this section shows that the proposed
model accounts for the variations in toxicity due to
differing species sensitivities and chemical
differences. The model is based on the idea that
the target lipid is the site of action in the organism.
Further, it is assumed that target lipid has the same
lipid-octanol linear free energy relationship for all
species. This implies that the log10LC50 vs
log10KQW slope is the same for all species.
However, individual species may have varying
target lipid body burdens of narcotic chemicals that
cause mortality. The target lipid LC50 body
burdens estimated by extrapolations from the
water-only acute toxicity data and KQW values are
compared to measured total lipid LC50 body
burdens for five species. They are essentially
equal, indicating that the extrapolation in the model
is appropriate for estimation of LC50 body
burdens, i.e., that the target lipid concentration is
equal to the total extracted lipid concentration.
The precise relationship between target lipid and
octanol is established.
2.2 Narcosis Model Background
A comprehensive model of type I narcosis
chemicals which considers multiple species has
been presented by Van Leeuwen et al. (1992).
2-1
-------
They developed QSARs for individual species and
performed species sensitivity analysis. The
analysis and model presented below and in Di Toro
et al. (2000) and that of Van Leeuwen et al.
(1992) are similar. The key differences in the Di
Toro et al. (2000) model are the use of a single
universal slope for the log10LC50 versus log10KQW
QSAR for all the species, the inclusion of
corrections for chemical classes, such as PAHs,
that are slightly more potent than reference
narcotics, and the interpretation of the y-intercepts
as the species-specific critical body burdens for
narcosis mortality.
varies with KQW. For fish, the relationship is
log10BCF-log10Kow-1.3 (2-4)
Therefore, the critical body burden corresponding
to the LC50 for fish narcosis can be computed
using the narcosis LC50 and the BCF
log10C*rg = log10BCF + log10LC50
" lo§ioKow- L3 -lo§ioKow +L7
- 0.4 (2-5)
2.3 Body Burden Model
The initial QSAR models for narcotic toxicity
relied on correlations of log10LC50 and log10KQW
(Konemann, 1981; Veith et al., 1983). An
interesting and important interpretation of this
inverse relationship which relates the toxicity to
chemical body burden has been presented by
McCarty et al. (1991), and proceeds as follows.
The relationship between the LC50 (mmol/L) and
Kow for the narcosis LC50 for fish is
approximately
log10LC50--log10Kow+1.7
(2-1)
For each LC50, a fish body burden, on a wet
weight basis, corresponding to narcosis mortality
can be computed using a bioconcentration factor
BCF (L/kg) which is defined as the ratio of the
chemical concentration in the organism C0r
(mmol/kg wet weight) to the chemical
concentration dissolved in the water Cd (mmol/L)
or
C* - 2.5
Org
wet wt
(2-6)
Thus, McCarty et al. (1991) rationalized the
relationship between LC50 values and KQW by
suggesting that mortality is caused as a result of a
constant body burden of the narcotic chemical.
The reason the critical body burden is a
constant concentration for all the narcotic
chemicals represented by the narcosis LC50 is a
consequence of the unity slopes for log10KQW in
Equations 2-1 and 2-4. For example, if the
fraction of lipid in the fish is ass umed to be 5%
(Aipid = 0-05), then the critical body burden in the
lipid fraction of the fish is
P*
= ^Org =
/ Lipid
= 50 |imol / g lipid
(2-7)
BCF=
(2-2)
which is the estimate of the chemical
concentration in the lipid of these fish that causes
50 % mortality. The model presented below is an
extension of this idea.
Using the BCF, the organism concentration
corresponding to the LC50, which is referred to as
the critical body burden and denoted by C* , can
J J Org'
be computed using
C* =BCFxLC50
Org
(2-3)
The superscript * indicates that it is a critical body
burden corresponding to the LC50. The BCF also
2.4 Target Lipid Model
The body burden model relates the narcosis
concentration to a whole body concentration using
a BCF. If different species are tested, then
species-specific BCFs and lipid concentrations
would be required to convert the LC50
concentration to a body burden for each species.
A more direct approach is to relate narcotic
2-2
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
lethality to the concentration of the chemical in the
target tissue of the organism, rather than to the
concentration in the whole organism. If the
partitioning into the target tissue is independent of
species, then the need for species-specific BCFs is
obviated. The identity of the target tissue is still
being debated (Abernethy et al., 1988; Franks and
Lieb, 1990), but we assume that the target is a lipid
fraction of the organism. Hence the name, target
lipid.
The target lipid model is based on the
assumption that mortality occurs when the
chemical concentration in the target lipid reaches a
threshold concentration. This threshold is assumed
to be species-specific rather than a universal
constant that is applicable to all organisms (e.g., 50
|imol/g lipid, see Equation 2-7). The formulation
follows the body burden model (McCarty et al.,
1991). The target lipid-water partition coefficient
KLW (L/kg lipid) is defined as the ratio of chemical
concentration in target lipid, CL (|imol/g lipid =
mmol/kg lipid), to the freely-dissolved aqueous
concentration Cd, (mmol/L)
K =
LW
(2-8)
This equation can be used to compute the
chemical concentration in the target lipid phase
producing narcotic mortality, i.e., the critical body
burden in the lipid fraction C*, when the chemical
concentration in the water phase is equal to the
LC50
C*L =KLWxLC50
(2-9)
Assuming the narcosis hypothesis is true, i.e., that
50% mortality occurs if any narcotic chemical
reaches the concentration C* then the LC50 for
L=
any chemical can be calculated using the same
critical target lipid concentration C* and the
chemical-specific target lipid-water partition
coefficient
LC50 =
KT
(2-10)
or
log10LC50 = log10C*-log10KLV(
(2-11)
The problem is determining the KLW for narcotic
chemicals. It is commonly observed for many
classes of organic molecules that the logarithms of
the partition coefficient between two liquids are
related by a straight line (Leo, 1972). For target
lipid and octanol, the relationship would be
10§10KLW = aO + ai 10§10K0
(2-12)
Such a relationship is called a linear free energy
relationship (LFER) (Leo etal., 1971; Brezonik,
1994). Combining Equations 2-11 and 2-12 yields
the following linear relationship between
log10LC50 and log10Kow
log10LC50 = log10C*L -a0 - a, log10Kow (2-13)
where log10C* - a0 is the y intercept and -aj is the
slope of the line.
This derivation produces the linear relationship
between log10LC50 and log10KQW which is found
experimentally (see, for example, Table 6 in
Hermens et al., 1984)
log10LC50 = mlog10K
10ow
(2-14)
where m and b are the slope and intercept of the
regression, respectively. In addition, it identifies the
meanings of the parameters of the regression line.
The slope of the line m is the negative of the slope
of the LFER between target lipid and octanol, ar
The intercept of the regression b = log10C* - a0 is
composed of two parameters: C* is the target lipid
concentration at narcosis mortality, and a0 is the
constant in Equation 2-12.
The difference between the target lipid model
and the McCarty et al. (1991) body burden model
is that for the latter, the coefficients a0 and al for
fish are assumed to be known: a0 = - 1.3 and al =
1.0. It is interesting to examine the consequences
of a similar assumption applied to the target lipid
model. If it is assumed that the partitioning of
narcotic chemicals in lipid and octanol are equal,
i.e., that lipid is octanol, a common first
approximation, then al = 1 and aQ = 0 and the y-
intercept becomes
= log10C*
(2-15)
2-3
-------
which is the target-lipid concentration producing
50% narcosis mortality.
This result can be understood by examining
Figure 2-1. The y-intercept b is the LC50 value for
a chemical with a log10KQW = 0 or KQW = 1. The
KQW is the ratio of the chemical's concentration in
octanol to its concentration in water. Hence, for
this hypothetical chemical (an example would be
2-chloroethanol for which log10Kow = -0.0481 0 the
chemical's concentration in water is equal to its
concentration in octanol. However, if the KLW
equals the Kow, i.e., lipid is octanol, then its
concentration in water must be equal to its
concentration in the target lipid of the organism.
Therefore, the y-intercept is the target lipid phase
concentration at which 50% mortality is observed.
That is
coefficients in the following way
LC50
„ =b = C* =C*
Kow=l octanol L
(2-16)
Note that this interpretation is true only if a0 =0
(see Equation 2-13).
Figure 2-1. Schematic diagram of the log10LC50
versus Iog10#ow relationship. At Iog10#ow
= 0 (KQW = 1), the concentration in water
equals the concentration in octanol.
Thus, the target lipid narcosis model
differentiates between the chemical and biological
parameters of the log10LC50 - log10KQW regression
chemical
-a.
chemical biological
(2-17)
The chemical parameters a0 and a: are associated
with the LFER between octanol and target lipid
(Equation 2-12). The biological parameter is the
critical target lipid concentration C*. This result is
important because it suggests that the slope m = -
a: of the log1QLC50- log10KQW relationship should
be the same regardless of the species tested since
it is a chemical property of the target lipid - the
slope of the LFER. Of course this assumes that
the target lipid of all species have the same LFER
relative to octanol. This seems to be a reasonable
expectation since the mechanism of narcosis is
presumed to involve the phospholipids in the cell
membrane and it appears to be a ubiquitous mode
of action. However, the biological component of
the intercept C* (Equations 2-13 and 2-17) should
vary with species sensitivity to narcosis since it is
commonly found that different species have
varying sensitivity to the effects of exposure to the
same chemical. The expectations that follow from
the target lipid model - that the slope should be
constant among species and that the intercepts
should vary among species - is the basis for the
data analysis presented below.
2.5 Acute Lethality Database Compilation
An acute lethality (LC50) database for type I
narcotics from water-only toxicity tests was
compiled from available literature sources. The
principal criterion for acceptance was that a
number of chemicals were tested using the same
species so that the slope and intercept of the
log10LC50 - log10KQW relationship could be
estimated. The data were restricted to acute
exposures and a mortality end point to limit the
sources of variability. A total of 33 aquatic species
including amphibians, fishes, arthropods (insects
2-4
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
and crustaceans), molluscs, annelids, coelenterates
and protozoans were represented. Seventy-four
individual datasets were selected for inclusion in
the database which provided a total of 796
individual data points. Details are provided in
Appendix A. The individual chemicals which
comprise the database are listed in Appendix B.
There are 156 different chemicals including
halogenated and non-halogenated aliphatic and
aromatic hydrocarbons, PAHs, alcohols, ethers,
furans, and ketones.
The log10Kow values and aqueous solubilities
of these chemicals were determined using SPARC
(SPARC Performs Automated Reasoning in
Chemistry) (Karickhoff et al., 1991), which utilizes
the chemical's structure to estimate various
properties. The reliability of SPARC was tested
using log10Kow values measured using the slow
stir flask technique (de Bruijnetal., 1989). Fifty
three compounds such as phenols, anilines,
chlorinated monobenzenes, PAHs, PCBs and
pesticides were employed. A comparison of the
log10KQW values measured using the slow stir flask
technique to the SPARC estimates demonstrates
that SPARC can be used to reliably estimate
measured log10KQW values over nearly a seven
order of magnitude range of log10KQW (Figure 2-
2A). Note that this comparison tests both SPARC
and the slow stir measurements, since SPARC is
not parameterized using octanol-water partition
coefficients (Hilal et al., 1994).
2.5. / Aqueous Solubility
The toxicity data were screened by
comparing the LC50 value to the aqueous
solubility, S, of the chemical (Figure 2-2B). (Note:
For this and other figures in this document where a
large number of data points are available, the
plotting procedure limits the actual number of data
plotted.) Individual LC50 values were eliminated
from the database if the LC50 > S, which
indicated the presence of a separate chemical
phase in the experiment. For these cases,
mortality must have occurred for reasons other
than narcosis - for example, the effect of the pure
liquid on respiratory surfaces - since the target
lipid concentration cannot increase above that
achieved at the water solubility concentration. A
total of 55 data points were eliminated, decreasing
I
2468
Slow Stir Log10A"ow
1
0
-1
-3
U
H, -4
-6
-7
-8
B
-8 -7 -6 -5 -4 -3-2-1012
Log Aqueous Solubility (mol/L)
Figure 2-2. Comparisons of (A) Iog10^ow predicted by SPARC versus measured Iog10^ow using slow stir method
and (B) reported log^LCSO values versus the aqueous solubility estimated by SPARC. The diagonal
line represents equality.
2-5
-------
the number to 736 and the number of individual
chemicals to 145 (Appendix B).
2.5.2 Exposure Duration
The duration of exposure varied in the dataset
from 24 to 96 hours (Appendix A). Before the
data could be combined for ana ysis, the individual
datasets should be adjusted to account for this
difference. The required equilibration time may
vary with both organism and chemical. An
increase in either organism body size or chemical
hyodrphobicity may increase the time to reach
equilibrium.
To determine if acute lethality for narcotic
chemicals varied with exposure time, cata were
selected where toxicity was reported at multiple
exposure times for the same organism and the
same chemical. For seven fish species, data were
available for 96 hours and either 24, 48 or both 24
and 48 hours of exposure. Arithmetic ratios of the
LC50 values for 48 to 96 hours and for the 24 and
96 hours exposure are compared to log 10 Kow.
The 48 to 96 hour ratio is 1.0 for essentially all the
data (Figure 2-3A). The 24 to 96 hour ratio is
larger, approaching 1.4 for the higher Kow
chemicals (Figure 2-3B). A linear regression is
used to fit the relationship in Figure 2-3B.
LC50(24)/LC50% = 0.0988 Iog10 KQW + 0.9807 (2-18)
where LC5024 and LC50% are the LC50 values
for 24 and 96 hour exposures. Since
approximately 46% of the data points in the overall
database represent narcosis mortality after
exposure offish to a chemical for 24 hours, these
data were converted to 96 hour LC50 values using
Equation 2-18 for chemicals having log10KQW
values of >1. No correction factor is applied to 24
hour toxicity data for invertebrates and fishes
exposed to chemicals having log10KQW values of
<1 (DiToroetal.,2000).
2.6 Data Analysis
The analysis of the toxicity data is based on
the target lipid model assumption that the slope of
the log10KQW is the same for all species. This
assumption was tested using a linear regression
model to estimate the species-specific body
burdens and the universal narcosis slope.
1
o
VJ ^
U '
-J
-=
vo
°^ -,
o 2
iyj
u
-j
1
0
; A ;
O Oncorhynchm
^ Cyprinodon
V Pimephales
n Carassim
A Lepomis
A Jordanella
• Poecilia
—
O
A -^^frl^^ A
. 5HleT -
-
4
O
V) 3
t,j
nJ
^
VO
ON
^ 2
iy^
u
^
JS
^f *
f-^ i
n
i i i i i i i i i i i i i
; B
.
•
A
-
rn
^
A ^ii?&t&
A -a^r^
•
-10123456 -1012345*
Figure 2-3. Ratios of (A) 48- to 96-hour LC50 values and (B) 24- to 96-hour LC50 values versus log^
line in (B) is the regression used to correct the 24-hour LC50 to 96-hour LC50.
. The
2-6
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
2.6.1 Regression Model
Consider a species k and a chemical j. The
k
for that species-chemical pair is
log10LC50kj = log10C*(k) - a0 - a, log10Kow(j) (2-19)
= bk-a1log10Kow(j) (2-20)
where
an (2-21)
is the y-intercept. The problem to be solved is:
how to include all the bk, k = 1,...,NS corresponding
to the Ns = 33 species and a single slope a: in one
multiple linear regression model equation.
The solution is to use a set of indicator
variables *ki that are either zero or one depending
on the species associated with the observation
being considered. The definition is
6=1
la
6 =0
la
k = i
(2-22)
which is the Kronecker delta (Kreyszig, 1972).
The regression equation can be formulated using
6, as follows
ki
log^LCSCX. = a.log^CJ) + *A- (2-23)
y 49
o
s
o
•r,
U
Orconectes immunisU
M
2
u
O
21
01
-21
Artemia salina naupliiD •
21
01
-21
-4E ' '
41
2[
01
-21
-41
Hydra oligactisU
Daphnia cucullattaO
I |_,—|—i—|—,—|—,—1_
-2D OD 2D 4D 6[
Alburnus alburnusU •
Tetrahymena elliottiO •
Lymnaea stagnalisU
_ Leptocheirus plumulosusO
Daphnia pulexO
Tanytarsus dissimilisU •
Aedes aegyptiO
Leuciscus idus melanotus[l
Daphnia magnaO
IctaluruspunctatusO •
CulexpipensO
Gambusia affinisO
Portunus pelagicusO
-2D OD 2D 4D 6D 8D-2D OD 2D 4D 6D 8D-2D OD 2D 4D 6D 8D-2D OD 2D 4D 6D
'oWD Log10AOWD ^°§10-*OWD ^°§10-*OWD
Figure 2-4. Log10LC50 versus Iog10^ow for the indicated species. The line has a constant slope of - 0.945.
The y-intercepts vary for each species. Outliers are denoted by a plus symbol (+).
2-7
-------
Equation 2-23 is now a linear equation with Ns+l
independent variables: log10Kow(j) and 5ki, k =
l,...,Ns. There are Ns+l coefficients to be fit: a:
and bj, k = l,...,Ng. For each LC50..
corresponding to species i and chemical j, one of
the bk corresponding to the appropriate species k =
i has a unity coefficient 5;i = 1 while the others
are zero. The way to visualize this situation is to
realize that each row of data consists of the LC50
and these Ns+l independent variables, for example
forj = 1 andi = 3
Iog10 (LCSOp Iog10 (Kow(j))
0.788 1.175
6u 62,
0 0
»* ... SNSI
1 0 0
(2-24)
which is actually the first of the 736 records in the
database. The result is that b3 is entered into the
regression equation as the intercept term
associated with species i = 3 because that 5ki is
one for that record. By contrast, the slope term
ajlogjgK^d) is always included in the regression
because there is always an entry in the
log10Kow(j) column (Equation 2-24). Hence, the
multiple linear regression estimates the common
slope a: and the species-specific intercepts bk, k =
A graphical comparison of the results of fitting
Equation 2-23 to the full dataset are shown in
Figure 2-4 for each of the 33 species. The
regression coefficients are tabulated and discussed
subsequently after a further refinement is made to
the model. The lines appear to be representative
of the data as a whole. There appear to be no
significant deviations from the common slope. A
few outliers, which are plotted as +, were not
included in the regression analysis. An outlier is
identified if the difference between predicted and
observed LC50 values are greater than one log
unit when they are included in the regression. This
decreases the total number of data points from 736
to 722.
loooa
BD
•S -0.201 h
I
|g -0.
5
8. -o.
_0
33
V
w
AaeUNaUAnUAalOLmaUIpU OiO CpO CvD DcODmODpO TeU PprOJJD CaUPpuUPrU HoUXiU LpU LsU AmtTjaUMbaOisO LmeWlU PpeORcU MbeUdU OmO
Figure 2-5 Statistical comparison of slopes fitted to individual species to the universal slope of - 0.945 showing
(A) the probability that the difference occurred by chance (filled bars) and number of data points in
the comparison (hatched bars) for each species in the database, and (B) the deviations of the indi-
vidual estimates from the universal slope.
2-8
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
2.6.2 Testing Model Assumptions
The adequacy of the regression model is tested by
answering three questions:
1. Are the data consistent with the assumption that
the slope is the same for each species tested?
2. Does the volume fraction hypothesis
(Abernethy et al., 1988) provide a better fit?
3. Are there systematic variations for particular
chemical classes?
The first assumption, that the slope estimated
for a particular species is statistically
indistinguishable from the universal slope al
= -0.97 without chemical class correction (see
Section 2.5.4), can be tested using conventional
statistical tests for linear regression analysis
(Wilkinson, 1990). The method is to fit the data
for each species individually to determine a
species-specific slope. Then, that slope is tested
against the universal slope a: = -0.97 without
chemical class correction to determine the
probability that this difference could have occurred
by chance alone. The probability and the number
of data points for each species are shown in
Figure 2-5A. The slope deviations are shown in
Figure 2-5B. Some of the slope deviations are
quite large. However, only three species equal or
exceed the conventional significance level of 5%
for rejecting the equal slope hypothesis.
Testing at the 5% level of significance is
misleading, however, because there is a one in
twenty chance of rejecting one species falsely
when 33 species are being tested simultaneously.
The reason is that the expected number of
rejections for a 5% level of significance would be
33 x 0.05 = 1.65, i.e., more than one species on
average would be rejected due to statistical
fluctuations even though all the slopes are actually
equal. In fact, only 20 tests at 5% would, on
average, yield one slope that would be incorrectly
judged as different. The correct level of
significance is (1/33)(1/20) = 0.152% so that the
expected number of rejections is 33 x 0.00152 =
0.05 or 5% (Wilkinson, 1990). This level of
significance is displayed together with the slope
data presented in Figure 2-5A. As can be seen,
there is no statistical evidence for rejecting the
claim of equal slopes for the tested species. As
would be expected, when 5% was used as the level
of significance two species were identified as
having unique slopes. When the current level of
significance (0.00152) was used for the 33 samples
none were significantly different.
2.6.3 Volume Fraction Hypothesis
The volume fraction hypothesis asserts that
narcotic mortality occurs at a constant volume
fraction of chemical at the target site of the
organism (Abernethy etal., 1988). Basically, this
involves expressing the LC50 as a volume fraction
of chemical rather than a molar concentration.
This is done using the molar volume of the
chemicals (see column MV in Appendix B).
The LC50 on a molar volume basis is
LC50(cm3 /L) =LC50 (mmol/L) xMV(cm3/mmol)
(2-25)
The question is: does using molar volume as the
concentration unit improve the regression analysis?
The results are shown below
Molar concentrations
(mmol/L)
Molar volumes
(cm3/L)
Slope -0.97 ±0.012
-0.90 ±0.012
0.94
0.96
The coefficient of determination (R2 value) for the
volume fraction analysis (0.96) is slightly greater
than that for the molar concentration (0.94).
Because they are essentially the same, this
document uses the molar concentration rather than
those based on the volume fraction. Importantly,
the slope for both volume and weight units of
concentration is not unity.
2.6.4 Chemical Classes
The analysis presented above assumes that all
of the 145 chemicals listed in Appendix B are
narcotic chemicals. That is, the only distinguishing
chemical property that affects their toxicity is KQW.
2-9
-------
A criteria has been suggested that can be used to
determine whether a chemical is a narcotic
(Bradbury et al., 1989), namely that it
demonstrates additive toxicity with a reference
narcotic. However, it is not practical to test each
possible chemical. The more practical test is
whether the toxicity can be predicted solely from
the log10LC50 - log10KQW regression. In fact, this
is used in methods that attempt to discriminate
reference narcotics from other classes of organic
chemicals (Verhaar et al., 1992).
Using this approach, differences in toxicity
among chemical classes would be difficult to
detect if differing species were aggregated or
different slopes were allowed in the regression
analysis. However, with the large dataset
employed above, these differences can be seen by
analyzing the residuals grouped by chemical class.
The criteria for choosing the relevant classes
are not obvious without a detailed understanding of
the mechanism of narcotic toxicity. Hence, the
conventional organic chemical classes based on
structural similarities, e.g. ethers, alcohols, ketones,
etc., are used. The results are shown in Figure 2-
6A. The means ±2 standard error (SE) of the
means are shown for each class. Although not a
rigorous test, the ±2 SE range does not encompass
zero for certain classes. Thus, it is likely that there
are statistically significant chemical class effects.
2.6.4.1 Statistical Analysis of Kow-Toxicity
Relationships
A rigorous test is conducted by including
correction constants for each of the chemical
classes in a manner that is analogous to Equation
2-23. The model equation is formulated using Nc -
1 corrections, Ac(, corresponding to the H = 1,...,NC
- 1 chemical classes. These are interpreted as
corrections relative to the reference class which is
chosen to be aliphatic non-halogenated
hydrocarbons. The regression equation is
formulated as before with a variable £(j that is one
if chemical j is in chemical class H and zero
otherwise
E, = 1 if chemical j is in class £
£(j = 0 otherwise (2-26)
The regression equation that results is
Nc-l
log10LC50i = a1log10Kowa)
(2-27)
Each data record now contains t he dependent
variable log10LC50i , the independent variables
log10Kow(j), and the 8ki, k = 1,...,N and ^ H =
1,...,NC - 1 indicator variables which are 0 or 1
depending on which species and which chemical
class is represented by the LC50i .
Only Nc - 1 chemical class corrections are
required because including Nc class corrections
under-determines the equation set with one too
many unknowns. The reason is that every
equation would have one K and one Ac{ for
species i and chemical j in chemical class H . Since
this condition would occur in every equation there
is no unique solution for the bk and the Ac{ values.
One of these constants could be adjusted by an
arbitrary amount and the rest could then be
adjusted to compensate while still achieving the
same fit of the data. Thus, a reference chemical
class is chosen: non-halogenated aliphatic
hydrocarbons for which Ac{ = 0. The remaining
regression constants Ac(, H = l,...,Nc - 1 are then
the differential toxicity of chemical class H relative
to the reference chemical class. This is the reason
for the Ac notation.
The requirement for a chemical class
correction is decided using a statistical test that
compares the Ac{ values that result from the
regression to the hypothesis Ac{ = 0. For the
classes which are not statistically different, they
are included in the reference class and the
parameters are re-estimated. This is continued
until all the remaining Ac{ values are statistically
different from zero. After a number of trials, it
was found that treating halogen substitutions as a
separate additive correction gave the least number
of statistically significant class corrections. Thus,
chemical class corrections are applied to the base
structure, if necessary, and an additional correction
is made if any substitute is a halogen. Therefore,
2-10
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
U.3
0.4
0.3
0.2
"3
•-
(S °-°
M -0.1
o
-0.2
-0.3
-0.4
-0.5
- A
.
T
•
•
.
T
*
n ' I 1 -
-
-
-
// // // // // ^"/
1 ^ £ 1 1 ^
1 | 1 1 ] $
& ^ | V
U.3
0.4
0.3
0.2
"«
s 01
3
| 0.0
O
M -0.1
0
hJ
-0.2
-0.3
-0.4
_n s
- B
I
T
1
'
.
T I I
{ I I I
•
-
-
, j , , , ,
1
^
1
|
Figure 2-6. Chemical class comparisons of residuals from the regression grouped by class with (A) mean ± 2
standard errors and (B) chemical class corrections included in the regression.
for halogenated chemicals it is possible that two
^ = 1 in Equation 2-27. The chemical classes are
listed in Appendix B.
The results of the final regression analysis are
listed in Table 2-1. Both the logarithmic b: and
arithmetic 10bi values of the intercepts are
included together with their standard errors.
Chemical classes which demonstrate higher
potency than the reference class are ketones and
PAHs. Halogenation increases the potency as
well. After accounting for different potencies in
the chemical classes, the mean residuals are
statistically indistinguishable from zero
(Figure 2-6B).
2.6.4.2 Standard Errors and Residuals
The standard errors of the body burdens
SE^) found from the regression (Equation 2-27)
are in an almost one-to-one correspondence with
the number of data points for that species. Thus.
the b; for Pimephales promelas (fathead minnow)
with 182 data points has a 10% coefficient of
variation, CV(bi) = SE (b;) /b., while the b; for
Neanthes arenaceodentata (polychaete worm)
with 4 data points has a 50% coefficient of
variation (Table 2-1). The relationship of the
sample size (N) to the coefficient of variation of
the estimated critical body burden, C'VXK), is
shown in Figure 2-7A.
The residuals are log normally distributed
(Figure 2-7B) and exhibit no trend with respect to
Kow (Figure 2-7C) which confirms the assumption
underlying the use of regression analysis. The
reason they are restricted to ±1 order of
magnitude is that 14 data points outside that range
were originally excluded as outliers (for some
values previously less than ± one order of
magnitude, chemical class corrections produced
values slightly greater than one order of magnitude
as shown in Figure 2-1C}.
2.6.4.3'Chemical Class Corrections
The corrections due to chemical classes
reduce the critical body burden by a factor of
approximately one-half for ketones and PAHs.
Correction for halogenation reduces it further by
2-11
-------
0.6 q
n
o
c
U
en
o
-J
0.4:
0.2:
AD
o.o d •-
ID 10D 100D 1000
Number of Data Points D
2.oq
n i.o:
"3
3
J o.o:
-i.o:
0.1D ID 10D20D SOD 80D90D 99D 99.91
Probability D
2p
i:
o:
-i:
CD
-2D -ID OD ID 2D 3D 4D 5D 6D 7D
Figure 2-7. The coefficient of variation of the
estimated species-specific body
burdens versus (A) the number of data
points for that species (B), the log
probability plot of the residuals, and
(C) the residuals versus logloKaw.
0.570 (Table 2-1). Thus, a chlorinated PAH would
exhibit a critical body burden of approximately
one-third of a reference narcotic. The coefficients
of variation for these corrections are
approximately 10%.
The chemical class differences among the
type I narcotics affect the LC50-KOW relationship.
The model no longer predicts a single straight line
for the log10LC50-log10Kow relationship for all
narcotic chemicals. What is happening is that the
y-intercepts are changing due to the changing )CR
values. The model (Equation 2-27) when applied
to a single species k is
Nc-l
log10LC50 =
CJ) +bk
(2-28)
This is a straight line if only reference narcotics
are considered Ac(, = 0 or if only one chemical
class correction is involved, e.g., all halogenated
reference narcotics. Otherwise, more than one
Ac{, enters into Equation 2-28 and the line is
jagged. Figure 2-8 presents three examples. The
deviations from the reference narcosis straight line
are caused by the different chemical class
potencies.
2.7 Universal Narcosis Slope
The universal narcosis slope: m = -0.945±0.014
which results from the final analysis that includes
chemical class corrections (Table 2-1) is smaller
than that determined above without chemical class
corrections (-0.97±0.012). It is close to unity, a
value commonly found (Hansch and Leo, 1995),
and larger than the average of individual slopes
(-0.86±0.14) reported by Van Leeuwen et al.
(1992), but comparable with a recent estimate for
fathead minnows of-0.94 (Di Toro et al., 2000).
The fact that the slope is not exactly one
suggests that octanol is not quite lipid. However, it
is also possible that for the more hydrophobic
chemicals in the database, the exposure time may
not have been long enough for complete
equilibration of water and lipid to have occurred.
To test this hypothesis, the regression analysis is
2-12
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
restricted to successively smaller upper limits of
log10KQW. The results are listed below
Maximum log1(1Kow
3.5
4.0
4.5
5.0
5.5
Slope
-0.959 -0.970 -0.958 -0.950 -0.945
Standard Error
0.018 0.015 0.015 0.014 0.015
The variation is within the standard errors of
estimation, indicating that there is no statistically
significant difference if the higher log10KQW data
are removed from the regression. This suggests
that the universal narcosis slope is not minus one
but is actually -0.945 ± 0.014.
One consequence of the use of a universal
narcosis slope is that the species sensitivity ranking
derived from comparing either the water-only
LC50 values or the critical body burdens of
various species are the same. This occurs
because the critical body burden is calculated from
the LC50 value and the universal slope (Equations
2-14 and 2-15)
log10C*=log10LC50 + 0.9451og10K0
(2-29)
If this were not the case, then the species
sensitivity order could be reversed if LC50 values
or C* were considered.
Equation 2-29 is important because it can be
used to compute the critical body burden of any
type I narcotic chemical. Thus it predicts what the
critical body burden should be for a particular
species at its LC50 value. This would be the
concentration that would be compared to a directly
measured critical body burden. It can be thought
of as a normalization procedure that corrects type
I narcotics for the varying KQW and places them
on a common footing, namely, the critical body
burden.
The motivation for the development of the
target lipid model was to apply it to mixtures of
PAHs and other persistent narcotic chemicals in
sediments. The narcosis database used to
determine the universal narcosis slope and the
critical body burdens consists of 145 chemicals, of
which 10 are un-substituted and substituted PAHs
(Di Toro et al., 2000). A comparison of the LC50
data for just these chemicals and the target lipid
o
Bt
O
Bt
O
4p
2C
oc
-2L
-4D
A: Lepomis ntacrochirusO
2C
OC
-2C
B: DaphniapulexD
-4d
4Q
2C
OC
-2C
C: Gambusia
-2D
on
2D
4D
Figure 2-8. Log10LC50 versus Iog10^ow for (A) Lepomis
macrochirus, (B) Daphniapulex, and (C)
Gambusia affinis. The line connects the
individual estimates of the log10LC50 values,
including the chemical class correction.
model is shown in Figure 2-9. The solid
log10LC50 - log10KQW lines are computed using the
universal narcosis slope and the appropriate body
burdens for PAHs for each organism listed. The
dotted lines apply to the chloronaphthalenes which
have a slightly lower critical body burden due to
the halogen substitution. The lines are an
adequate fit of the data, although the scatter in the
D. magna data is larger than some of the other
2-13
-------
Table 2-1. Regression results:
Species i
Americamysis bahia
Portunus pelagicus
Leptocheirus plwnulosus
Palaemonetespugio
Oncorhynchus mykiss
Jordanellafloridae
Ictalurus punctatus
Pirn ephales prom elas
Lepomis macrochirus
Daphnia magna
Cyprinodon variegatus
Oryzias latipes
Carassius auratus
Rana catesbian
Tanytarsus dissimilis
Orconectes immunis
Alburnus albwnus
Nitocra spinipes
Gambusia qffinis
Leucisus idus melanotus
Neanthes arenaceodentata
Artemia salina nauplii
Lymnaea stagnalis
Xenopus laevis
Hydra oligactis
Culexpipiens
Poecilia reticulata
Menidia beryllina
Daphnia pulex
y-intercepts and chemical class corrections1
N
30
4
4
8
44
18
7
182
70
113
33
4
43
5
9
6
7
6
8
26
4
32
5
5
5
5
14
8
6
bi
1.54
1.56
1.56
1.68
1.79
1.82
1.87
2.02
2.03
2.04
2.05
2.05
2.13
2.13
2.14
2.14
2.16
2.17
2.17
2.18
2.23
2.26
2.29
2.33
2.33
2.34
2.36
2.37
2.38
SE(b,)
0.082
0.19
0.191
0.137
0.065
0.096
0.139
0.044
0.0056
0.049
0.078
0.182
0.065
0.162
0.125
0.149
0.137
0.148
0.13
0.075
0.19
0.077
0.163
0.163
0.163
0.163
0.101
0.134
0.15
(Table from Di Toro et al., 2000).
10"
(jin
34.3
36.1
36.2
48.2
61.7
66.1
74.8
105
108
111
111
112
134
135
137
139
144
147
149
152
168
181
195
213
214
216
228
233
240
SE(10")
lol/g octanol)
6.7
18.2
18.4
16.4
9.4
15.2
25.9
10.8
14.1
12.6
20.5
53.9
20.5
55.9
42
52.3
49.1
54.7
47.9
26.8
85
32.8
81.5
88.9
89.5
90.4
55.2
77.3
91
2-14
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Table 2-1. Continued
Species i
N
SE(bQ
10b-
SE(10b-)
(jimol/g octanol)
Ambystoma mexicanim 5 2.39 0.163
Daphnia cucullata 5 2.4 0.163
Aedesaegypti 5 2.42 0.163
Tetrahymena elliotti 10 2.46 0.121
245
249
261
286
103
104
109
85
Chemical Class
N
Ac.
SE(Ac()
1QAc
SE(10Ac()
Aliphatics
Ethers
Alcohols
Aromatics
Halogenated
Ketones
PAHs
Slope
215
13
134
241
319
49
84
0
0
0
0
-0.244 0.033
-0.245 0.059
-0.263 0.057
-0.945 0.014
1
1
1
1
0.57
0.569
0.546
-
-
-
-
0.044
0.078
0.073
* See Equation (2-27).
N = Number of data points.
tv = y-intercept.
SECbv^Standard error of tv.
Ac, =chemical class correction to the y-intercept.
SE(Ac,)=standard error of Ac,.
t=Standard errors of 10bi and 10Ac( are based on the assumption that the estimation errors for bt and
Ac, are gaussian. The formulas follow from the standard error of a log normally distributed random
variable (Aitchison and Brown, 1957). Forx=b or Ac,, (j,,=2.303x, o,=2.303 SE(x), and
SE(10*)=
species with multiple sources of data and there is a
clear outlier for Americamysis bahia. It is for this
reason that the slope representing all data for
narcosis chemicals is used to derive the target lipid
concentration from water-only toxicity data for
PAHs in Section 3 of this document.
2.8 Comparison to Obsered Body Burdens
The target lipid model predicts the
concentration in octanol (the y-intercept) that
causes 50% mortality in 96 hours. The question is:
how do these compare to measured critical body
burdens? The species-specific y-intercepts, b;, are
related to the target lipid concentration by the
relationship
y-intercept = \>. = log10C*(i) - a0 (2-30)
or, with chemical class corrections.
y-intercept = b; + Ac, = log10C*(i) - a0 (2-31)
for species i and chemical class H, where a0 is the
parameter in the LFER between octanol and
target lipid (Equation 2-12).
2-15
-------
LJ UL
j"
^ -1C
S
1 -2:
o
>r>
o
2P -4C
O
J
-s:
R on
-J
O 1 r
S
s
X -2C
o
o
^H
W)
0 -4C
-
-s:
Dfi i —
UL
iB -ic
s
& -2L
O
W -3:
-i
o
M -4C
O
J
c r
-3 L
R on
i
S
X -2C
O
i/^
u
o
M
0 -4C
J
_«r
i i i i i i i
• Artemia salina naupliiD •
*
Vs.
^v
Nfc
_
_
-
.1.1.1.
i I i I i I i
Ictalurus punctatus D
_ _
_ •
^v
" "
•
.
.1.1.1.
1 1 1 1 1 1 1
Oncorhynchm mykissO '
-
_
-
-
>
-
-
-
i i i i i i i
i i i i i i i
Daphnia magnaO
N-
«K
>s«
M
I X .
*
1 1 1 1 1 1 1
Mysidopsis bahiaU
-
_
I
- °*:'Nv -
<>j
+ :
-
i i i i i i i
• Neanthes arenaceodentataO
_ _
s.
* >S.
- >.
x^
~ ~
_
.
I.I.I.
Palaemonetes pugio D
-
_
-
-
-
*
-
-
i i i i i i i
i i i i i i i
Menidia beryllina D
-
>
_
1 1 1 1 1 1 1
• Leptocheirus plumulosusO '
-
_
-
'_
.
_
-
i i i i i i i
Pimephales promelas D
_ _
"
-
.
.
_
+
i.i.i.
Cyprinodon variegatusO '
-
Q|
<>
_
-
-
2D 3D 4D 5D i
Log1(AowD
ID 3D 4D 5D 6D2D 3D 4D 5D 6D
Log10A'own
Figure 2-9. Comparison of target lipid model, line-of-fit and observed LC50 data for individual PAHs, by species.
2-16
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
The relationship between the predicted
concentration in octanol, b; + Ac(, to the
concentration measured in extracted lipid, log10C*.
is examined in Table 2-2 which lists observed
LC50 body burdens (|imol/g lipid) and predicted
critical body burdens (|imol/g octanol) for
organisms in the database for which measured
lipid-normalized critical body burdens were
available. Three fish species: Gambusia affmis
(mosquito fish), Poecilia reitculata (guppy) and P.
promelas, and a crustacean: Portunus pelagicus
(crab) are compared in Figure 2-10. The
predicted and measured body burdens differ by
less than a factor of 1.6. The fish were observed
to have higher critical body burdens than the
crustacean, which the model reproduces.
The apparent near equality between the
estimated and measured critical body burdens,
which come from two independent sets of data,
strongly suggest that in fact
a0=0
so that
^i) = b; + Ac, = y-intercept
(2-32)
(2-33)
This relationship implies that the target lipid is
the lipid measured by the extraction technique
used in the body burden datasets. This is an
important practical result since it suggests that
body burdens normalized to extracted lipid are
expressed relative to the appropriate phase for
narcotic toxicity. Since the intercepts appear to be
the organism's lipid concentration, the y-intercepts
(bj + Ac{) in the discussion presented below are
referred to as body burden lipid concentrations
although the units (|imol/g octanol) are retained
since these are, in fact, the actual units of the
intercepts.
2.9 Mixtures and Additivity
Narcotic chemicals, including PAHs, occur in
the environment as mixtures, therefore, their
mixture effects need to be appropriately resolved.
If the toxicity of mixtures is additive, mixture
effects can be assessed using the concept of toxic
units. A toxic unit (TU) is defined as the ratio of
the concentration in a medium to the effect
concentration in that medium.
The additivity of the toxicity of narcotic
chemicals in water has been demonstrated by a
number of investigators. The results of mixture
experiments which employed a large enough
number of narcotic chemicals so that non-additive
behavior would be detected is presented in Figure
2-11 as adopted from Hermens (1989). Three of
the four experiments demonstrated essentially
additive behavior and the fourth, a chronic
exposure, was almost additive.
2.10 Aqueous Solubility Constraint
The existence of the need for a solubility cut-
off for toxicity was suggested by Veith et al.
(1983) based on data from fathead minnows (P.
promelas) and guppies (P. reticulata). The
highest dissolved concentration in water that can
be achieved by a chemical is its aqueous solubility
(S). Therefore, the maximum lipid concentration
that can be achieved is limited as well. It is for
this reason that the LC50 database is limited to
chemicals with log10Kow <5.3. This is also the
reason that the LC50 database that was used to
generate the FCVs for specific PAHs in Section 3
of this document, was screened initially for LC50
values < S, using the solubilities from Mackay et
al. (1992), rather than log1QKow <5.3 used by Di
Toroetal. (2000).
For sediments, a solubility constraint should be
applied as well. This is readily calculated using the
relationship between interstitial water and the
organic carbon-normalized sediment concentration.
Since the interstitial water concentration is limited
by S, the sediment concentration should be limited
by the concentration in sediment organic carbon
that is in equilibrium with the interstitial water at
the aqueous solubility. Therefore, observed
sediment concentrations are limited by the
condition
C
-------
Narcosis Theory
E
0)
•o
250
200
150
PQ
>,
-a
pa 100
"3
u
50
1 1
0 Observed ±SD(wr
• Predicted ±SE (wn
:
i
i
1
<
nol/goc)
lol/g lipid)
< '
\ •
\ '-
<>
I ;
/ / 1
Figure 2-10. Predicted and observed body burdens for four species.
14-day LC50
Guppy
4-day LC50
Fathead Minnow
2-day LC50
D. magna
16-day NOEC
D. magna
0.1 1 10 100
•Toxic Units Q No. of Chemicals Tested
Figure 2-11. Additivity of type I narcosis toxicity. Comparison of the observed TU concentrations calculated from
four studies to the predicted TU of 1.0.
2-18
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Table 2-2. Comparison of body burdens observed in aquatic organisms acutely exposed to
narcotic chemicals and body burdens predicted from target lipid narcosis theory
(Table from Di Toro et al., 2000).
C'Cv
log time
Organism Chemical Kow (hr)
Mosquito fish,
Gambusia affinis
Guppy,
Poecilia reticulata
Fathead minnow,
Pirn ephales prom elas
Crab,
Portunuspelagicus
1 ,4-dibromonbenzene
1,2,3-trichlorobenzene
1,2,4-trichlorobenzene
pentachlorobenzene
1 ,4-difluorobenzene
1,2-dichlorobenzene
1,4-dichlorobenzene
1,2-dibromobenzene
1,4-dibromobenzene
1,2-dichlorobenzene
1,4-dichlorobenzene
1,2-dibromobenzene
1,4-dibromobenzene
1,2,4-trichlorobenzene
1 , 1 ,2,2-tetrachlorobenzene
dichlorobenzene
dichlorobenzene
1,2-dichlorobenzene
1,2-dichlorobenzene
1,4-dichlorobenzene
1,4-dichlorobenzene
1,2-1,4-dichlorobenzene
1,2-1,4-dichlorobenzene
1,2-1,4-dichlorobenzene
1,2-1,4-dichlorobenzene
naphthalene
1,2,4-trichlorobenzene
1,4-dichlorobenzene
1,2,3-trichlorobenzene
1 ,2,3,4-tetrachlorobenzene
pentachlorobenzene
3.55
3.98
4.00
5.32
2.11
3.31
3.24
3.56
3.55
3.31
3.24
3.56
3.55
4.00
2.31
3.27
3.27
3.31
3.31
3.24
3.24
3.36
4.00
3.24
3.98
4.64
5.32
96
"
"
"
1.5
91
41
4
6
18
10
7
10
50.2
57.2
75.5
129
62.3
96
96
96
96
Obs Mean
(umol/
g lipid)
85.0
140.0
92.0
69.0 93.2
444.0
34.0
400.0
24.0
120.0 110
78.0
68.0
60.0
54.0
98.9
173
121
107
110
138
150
123
215 95
9.6
45.0
119
111 49.9
Pred.
(jimol/g
octanol) References
Chaisuksant and
Cornell, 1997
85.3
Sijm etal., 1993
130
Sijm etal., 1993
van Wezel etal.,
1995
van Wezel et al.,
1996
deMaagd et al.,
59.9 1996
Mortimer and
Cornell, 1994
20.6
2-19
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Section 3
Toxicity of PAHs in Water
Exposures and Derivation
of PAH-Specific FCVs
3.1 Narcosis Theory, EqP Theory and WQC
Guidelines: Derivation of PAH-Specific
FCVs for Individual PAHs
Polycyclic aromatic hydrocarbons occur in the
environment as mixtures. Therefore, in order to
adequately protect aquatic life the approach used
to derive a WQC FCV or sediment benchmark for
PAHs must account for their interactions as a
mixture. In this section, we present an approach
for deriving FCVs for individual PAHs which can
be used to derive the ESB for mixtures of PAHs.
Concepts developed by Di Toro et al. (2000)
and presented in Section 2 of this document
provide the technical framework for screening
and analyzing aquatic toxicity data on PAHs
(Tables 3-1, 3-2). In particular, Section 2
demonstrated that: (1) the universal slope of the
Kow-toxicity relationship for narcotic chemicals is
the same for all aquatic species; and (2) the
intercept of the slope at a Kow of 1.0 for each
species provides the LC50/EC50 in |jmol/g octanol
that indicates the critical body burden in and
relative sensitivities of each species.
These concepts permit the use of the U.S.
EPA National WQC Guidelines (Stephan et al.,
1985) to derive WQC FCVs for individual PAHs
and PAH mixtures. The universal slope is used
with PAH-specific LC50/EC50 values to derive
test-specific KQW normalized reference acute
values at a KQW of 1.0. This normalization was
performed to put the data on the toxicities of
narcotic chemicals on an internally consistent
scale. This was also performed using hardness
when WQC were derived for metals. These KQW
normalized reference acute values are used to
calculate species mean acute values (SMAVs) and
genus mean acute values (GMAVs): (1) because
only acute and chronic toxicity data from water-
only tests with freshwater and saltwater species
exposed to individual PAHs are used, a PAH
chemical class correction is not needed; (2) the
data are screened for acceptability following the
requirements for use of species resident to North
America, test durations, test quality, etc. of the
U.S. EPA National WQC Guidelines (Stephan et
al., 1985); (3) the PAH-specific species mean
acute values (PAH-specific SMAVs) from
Appendix C are adjusted using the universal slope
of the KQW-toxicity relationship from the narcotic
chemical analysis that was shown to apply to all
aquatic species in Section 2 (Equation 2-29) to
derive the acute value for that species at a KQW of
1.0 (Kow normalized PAH-specific SMAV)
(Appendix C); (4) the intercept of the slope at a
Kow of 1.0 provides the LC50/EC50 in ^mol/g
octanol that indicates the relative sensitivity of
each tested species and PAH, which was used to
calculate SMAVs and GMAVs in |jmol/g octanol,
which are indicative of critical tissue
concentrations in organisms on a |jmol/g lipid basis.
The GMAVs are used to calculate the final acute
value (FAV) applicable to PAHs at a KQW of 1.0
(Stephan et al., 1985). This FAV at a KQW of 1.0,
when divided by the Final Acute-Chronic Ratio
(FACR), becomes the FCV at a Kpw of 1.0.
Importantly, the FCV for any specific PAH can
then be derived by back calculating using FAV at a
Knw of 1.0, the Knw of the specific PAH and the
ow ow
universal narcosis slope. When the PAH-specific
FCV exceeds the known solubility of that PAH,
the maximum contribution of that PAH to the
toxicity of the mixture is set at the KQC multiplied
by the solubility of that PAH.
3-1
-------
Toxicity of PAHs in Water Exposures
Table 3-1. Summary of the chronic sensitivity of freshwater and saltwater organisms to PAHs;
test specific data.
Observed
Effects Chronic
(Relative to Value
Controls) (M£/L) Reference
Common
Name.
Species
NOECC OECD
TestA Habitat5 PAH Duration (^g/L) (^g/L)
Cladoceran, LC W Anthracene 2 Id
Daphn ia magn a
2.1
4
8.2
Cladoceran, LC W Fluoranthene 2 Id 6.9-17 35
Daphn ia magn a
73
148
Cladoceran, LC W Phenanthrene 2 Id 46-57 163
Daphn ia magn a
Midge, LC B Acenaphthene 26d 32-295 575
P'aratanytarsus sp.
5.3% fewer <2.1 Hoist and
broods Giesy, 1989
8.0% fewer
broods
13.8% fewer
broods
17% reduction 24.5 SpeharetaL
in length 1999
25% reduction
in length, 37%
fewer
young/adult
No survival
Survival 96.39 Calletal.,
reduced 83%, 1986
98% fewer
broods
Survival
reduced
-90%, -60%
reduction in
growth, no
reproduction
411.8 Northwestern
Aquatic
Sciences,
1982
Midge, LC B Acenaphthene 26d 27-164 315
P aratanytarsus sp.
Fathead minnow, ELS W Acenaphthene 32d
Pimephales
promelas
50
676
109
410
630
Survival 227.3 Northwestern
reduced Aquatic
-20%, -30% Sciences,
reduction in 1982;
growth Thursby,
1991a
Survival
reduced -60%
5% reduction 73.82 Academy of
in growth Natural
Sciences,
1981;
Thursby,
1991a
26% reduction
in growth,
Survival
reduced 45%
No survival
3-2
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Table 3-1. Continued
Common
Name,
Species TestA Habitat13
PAH
NOECC
Duration (|J,g/L)
Observed
Effects Chrome
OECD (Relative to Value
Controls) (M£/L) Reference
Fathead minnow, ELS
Pimephales
promelas
W
Acenaphthene 32d
50-109 410 20% reduction in 211.4 Academy of
growth, Survival Natural Sciences,
reduced 66% 1981; Thursby,
1991a
630 No survival
Fathead minnow, ELS
Pimephales
promelas
W Acenaphthene 32-35d 67-332 495 54% reduction in 405.4 Cairns and
growth Nebeker, 1982
Fathead minnow, ELS
Pimephales
promelas
W
Acenaphthene 32-35d 197-345 509 30% reduction in 419 Cairns and
growth
682 52% reduction in
growth, Survival
reduced 45%
1153 87% reduction in
growth, Survival
reduced 97%
Nebeker, 1982
Fathead minnow, ELS
Pimephales
promelas
W
Fathead minnow, ELS
Pimephales
promelas
W
Acenaphthene 32d
64
Acenaphthene 32d 50-91
98 Survival reduced 79.2 ERCO, 1981
24%
149 Survival reduced
65%
271 Survival reduced
75%
441 Survival reduced
80%
139 Survival reduced 112.5 ERCO, 1981
20%
290 Survival reduced
50%
426 Survival reduced
52%
Fathead minnow, ELS
Pimephales
promelas
W Fluoranthene 32d 3.7-10.4 21.7 Survival reduced 15.02 Speharetal.,
67%, 50% 1999
reduction in
growth
Rainbow trout, ELS
Oncorhynchus
my kiss
B/W
Phenanthrene
90d
8 Survival reduced 6.325 Call et al., 1986
41%, 33%
reduced growth
14 Survival reduced
48%, 44%
reduced growth
32 Survival reduced
52%, 75%
reduced growth
66 No survival
3-3
-------
TO
Toxicity of PAHs in Water Exposures
Table 3-1. Continued
Common
Name.
Species
TestA Habitat13 PAH
Observed
Effects Chrome
NOECC OECD (Relative to Value
Duration (|J,g/L) (|J,g/L) Controls) (Hg/L) Reference
Mysid,
Americamysis
bahia
Mysid,
Americamysis
bahia
LC
B/W Acenaphthene 35d 100-240 340 93% reduction 285.7 HorneetaL
in young 1983
LC
B/W Acenaphthene 25d 20.5-
44.6
510 No survival
91.8 91%reduction 63.99 ThursbyetaL
in young 1989b
168 No
reproduction,
34% reduction
ingrowth
354 Survival
reduced 96%,
no reproduction
Mysid,
Americamysis
bahia
LC B/W Fluoranthene 28d 35926 21
43
Survival 15.87 U.S.EPA, 1978
reduced 26.7%,
91.7%
reduction in
young
No survival
Mysid,
Americamysis
bahia
LC B/W Fluoranthene 3 Id 0.41- 18.8 Survival 14.44 Speharetal.,
11.1 reduced 2 3%, 1999
no reproduction
Mysid, LC B/W Phenanthrene 32d 1.5-5.5 11.9 No survival 8.129 Kuhnand
Americamysis Lussier, 1987
bahia
Mysid,
Americamysis
bahia
LC B/W Pyrene 28d 3.82 5.37 46% reduction
in young
4.53
Champlinand
Poucher, 1992b
6.97
9.82
15.8
20.9
38.2
47% reduction
in young
73% reduction
in young
85% reduction
in young
90% reduction
in young,
Survival
reduced 3 7%
No survival
3-4
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Table 3-1. Continued
Common
Name, NOECC OECD
Species TestA Habitat13 PAH Duration (^g/L) (]ig/L)
Sheepshead ELS B/W Acenaphthene 28d 240-520 970
minnow,
Cyprinodon
variegatus
2000
2800
A Test: LC = life-cycle, PLC = partial life-cycle, ELS = early life-stage
B Habitat: I = infauna, E = epibenthic, W = water column
CNOEC = Concentrations where no significant effects were detected.
D OEC = Concentrations where significant effects were detected on survival.
Observed
Effects Chronic
(Relative to Value
Controls) (M£/L) Reference
Survival 710.2 Wardetal.,
reduced 70% 1981
No survival
No survival
growth, or reproduction.
3-5
-------
Toxicity of PAHs in Water Exposures
Table 3-2. Summary of acute and chronic values, acute-chronic ratios and derivation of the final acute values,
final acute-chronic values and final chronic values.
Species
Mean
Common PAH-Specific Acute-
Name, PAH Value Value Chronic Mean Acute- Chronic
Species Tested (M£/L) (|^g/L) Ratio Chronic Ratio Ratio Reference
FRESHWATER SPECIES
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Midge,
Paratany tarsus sp.
Midge,
Paratany tarsus sp.
Fathead Minnow,
Pimephales
promelas
Fathead Minnow,
Pimephales
promelas
Fathead Minnow,
Pimephales
promelas
Fathead Minnow,
Pimephales
promelas
Fathead Minnow,
Pimephales
promelas
Fathead Minnow,
Pimephales
promelas
Fathead Minnow,
Pimephales
promelas
Rainbow trout,
Oncorhynchus
mykiss
Anthracene - <2. 1 -
Fluoranthene 117 24.5 4.78 4.78
Phenanthrene 117 96.4 1.21 1.21
Acenaphthene 2,040A 411 4.96
Acenaphthene 2,040A 227 9 6.68
Acenaphthene 608 405 1.5
Acenaphthene 608 419 1.45 1.48
Acenaphthene - 73.82
Acenaphthene - 21 1
Acenaphthene - 79.2
Acenaphthene - 112
Fluoranthene 69C 15 4.6 4.6
Phenanthrene 50° 6.32 7.9 7.9
Hoist and Giesy,
1989
Speharetal., 1999
2.41 Call et al., 1986
Northwestern
Aquatic Sciences,
1982
6.68 Northwestern
Aquatic Sciences,
1982;
Thursby,1991a
Caims andNebeker,
1982; Thursby,
1991 a
Caims andNebeker,
1982
Academy of Natural
Sciences, 1981
Academy of Natural
Sciences, 1981
ERCO, 1981
ERCO, 1981
2.61 Speharetal., 1999
7.9 Call et al., 1986
3-6
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Table 3-2. Continued
Common
Name.
Species
PAH
Tested
Value
(|J,g/L)
Value
(|J,g/L)
Species
Mean
PAH-Specific Acute-
Chronic Mean Acute- Chronic
Ratio Chronic Ratio Ratio
Reference
SALTWATER SPECIES
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Sheepshead minnow,
Cyprinodon
variegatus
Acenaphthene
Acenaphthene
Fluoranthene
Fluoranthene
Phenanthrene
Pyrene
Acenaphthene
466
460
40
31
27.1
28.3
3,100B
286
64
15.9
14.4
8.13
4.53
710
1.63
7.19 3.42
2.52
2.15 2.33
3.33 3.33
6.24 6.24 3.59
4.36 4.36 4.36
A Geometric mean of two flow-through measured tests from the same laboratory as conducted the
Home et al., 1983
Thursby etal.,
1989b
U.S. EPA, 1978
Speharetal.,
1999
Kuhn and Lussier,
1987
Champlin and
Poucher, 1992b
Ward etal., 1981
life-cycle tests.
B LC50 concentration slightly greater than acenaphthene's water solubility.
c EC50 based on immobilization used as the acute value instead of the LC50.
Final Acute Value = 9.
3 1 ^mol/g octanol
Final Acute-chronic Ratio = 4.16
Final Chronic Value =
2.24 i^mol/g octanol
3-7
-------
T0
Toxicity of PAHs in Water Exposures
The FCV at a K_ of 1.0 for PAHs derived in
ow
this section of the document differs slightly from
that which would be derived for other narcotic
chemicals according to Di Toro et al. (2000) in that
it: (1) is derived using only acute and chronic
toxicity data from water-only tests with freshwater
and saltwater species exposed to individual PAHs.
therefore, the data do not require the PAH
chemical class correction; (2) the data are
rigorously screened for acceptability following the
requirements for the use of species resident to
North America, test durations, test quality, etc. of
the U.S. EPA National WQC Guidelines (Stephan
et al., 1985). All other steps in the derivation of
FCVs are the same as those used by Di Toro et al.
(2000).
3.2 Acute Toxicity of Individual PAHS:
Water Exposures
3.2.1 Acute Toxicity of PAHs
One hundred and four acute water-only
toxicity tests with 12 different PAHs have been
conducted on 24 freshwater species from 20
genera that meet the requirements of the U.S.
EPA National WQC Guidelines (Stephan et al.,
1985, see Appendix C). The tested life-stages of
15 of the genera were benthic (infaunal or
epibenthic). The most commonly tested
freshwater species were the cladocerans
(Daphnia magna and D. pulex), rainbow trout
(O. mykiss), fathead minnow (P. promelas) and
bluegill (Lepomis macrochirus). The most
commonly tested PAHs with freshwater
organisms were acenaphthene, fluoranthene,
fluorene, naphthalene, phenanthrene and pyrene.
Seventy-seven acute water-only toxicity tests
with 8 different PAHs have been conducted on 30
saltwater species from 29 genera (Appendix C).
The tested life-stages of 21 of the genera were
benthic (infaunal or epibenthic). The most
commonly tested saltwater species were the
annelid worm (N. arenaceodentata), mysid
(Americamysis bahid), grass shrimp
(Palaemonetes pugio), pink salmon
(Oncorhynchus gorbuscha), and sheepshead
minnow (Cyprinodon variegatus}. The most
commonly tested PAHs with saltwater organisms
were acenaphthene, fluoranthene, naphthalene,
phenanthrene and pyrene.
3.2.2 Acute Values at aKQWofl.O
The rules for test acceptability of the National
WQC Guidelines (Stephan et al., 1985) were used
to identify the LC50 values or EC50 (|Jg/L) values
from individual acute aquatic toxicity tests
(Appendix C) and these values were used to
derive the Kow normalized GMAV (|jmol/g
octanol) in the following manner. The goal of this
process was to convert individual LC50 or EC50
values that vary for a species across PAHs into a
PAH-specific GMAV normalized to a Kow of 1.0.
The use of normalizing factors in FCV derivation
is not unique to this ESB document. The use of
Kow to normalize the toxicity of PAHs to put the
toxicity data on an internally consistent scale is
analogous to the hardness normalization applied to
the freshwater WQC for cadmium, copper, lead,
nickel and zinc and the pH and temperature
normalization applied to the freshwater WQC for
ammonia. For multiple PAHs tested against one
species, the KQW normalization should result in
similar PAH-specific SMAVs. The first step in
the analysis of published LC50 or EC50 values
was to compare them to the known solubility in
water of the PAH tested. If the LC50 or EC50
concentration exceeded the solubility of the tested
PAH, the published LC50/EC50 is in parentheses
in Appendix C, the solubility is listed in bold in
Appendix C as a "greater than" acute value to
indicate that the actual toxicity of the dissolved
PAH was unknown. For these tests, this greater
than solubility value, and not the published LC50 or
EC50 value, was used in further calculations only
when there were no acute values for that species
at concentrations less than the solubility. Next, the
LC50, EC50 or greater than solubility value was
converted to mmol of the tested PAH/L. When
the same PAH was tested more than once against
a species, the geometric mean of all LC50 or
EC50 values was calculated to determine the
PAH-specific SMAV using the rules in Stephan et
3-8
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
al. (1985). The -0.945 universal slope of the
toxicity/KQW relationship (Equation 2-29) was
applied to the PAH-Specific SMAVs (|jmol/L) to
calculate the PAH-specific SMAV (|jmol/g
octanol) at a Kow=1.0. The SMAV for all tested
PAHs is the geometric mean of the PAH-Specific
SMAVs at a Kow of 1.0. The GMAV (nmol/g
octanol) at a KQW of 1.0 is the geometric mean of
the SMAVs at a Kow of 1.0.
The SMAVs at a Kow of 1.0 were similar for
multiple PAHs (Appendix C). For 18 freshwater
and saltwater species, two to nine different PAHs
were tested. The ratios of the highest to lowest
acute values for multiple PAHs tested against an
individual species before normalization was 1.37 to
1170; an average ratio of 105. In contrast, the
range in the ratios of the highest to lowest PAH-
specific SMAVs at a Kow of 1.0 was 1.4 to 12.2;
average ratio of 4.27. For 10 of the 18 (56%)
species tested against multiple PAHs, the ratio of
high to low SMAVs at a Kow of 1.0 was 4.0 or
less. This compares favorably with the factor of
four or less difference in the acute values for 12 of
19 (63%) of the same species in multiple tests with
the same PAH. Therefore, the variability of
SMAVs at a Kow of 1.0 across PAHs is similar to
the variability inherent for these data in acute
toxicity testing with only one PAH. This suggests
that the GMAVs provide data across PAHs that
indicate the relative sensitivity of that species that
can be used to describe species at risk and to
calculate the FAV
The Kow-normalized GMAVs (not including
values greater than the solubility of the tested
PAH) range from 7.63 |jmol/g octanol for
Americamysis to 187 |jmol/g octanol for
Tanytarsus, a factor of only 24. Saltwater genera
constitute four of the five genera with GMAVs at
a KQW of 1.0 within a factor of two of the most
sensitive genus (Americamysis). Of the 49
genera, the most sensitive one-third include a
freshwater hydra, two amphipods, an insect,
saltwater fish, a crab, two mysids, two shrimp, and
three saltwater amphipods. All of these 16 genera
have GMAVs at a KQW of 1.0 that are within a
factor of three, and 14 of the genera are benthic.
Benthic and water column genera are distributed
throughout the sensitivity distributions indicating
that they have similar sensitivities. Genera that
are benthic have been tested more frequently than
water column genera.
3.3 Applicability of the WQC as the Effects
Concentration for Benthic Organisms
The use of the FAV or FCV as the effects
concentration for calculation of ESBs assumes
that benthic (infaunal and epibenthic) species,
taken as a group, have sensitivities similar to all
aquatic (benthic and water column) species used
to derive the WQC FCV. The data supporting the
reasonableness of this assumption over all
chemicals for which there were published or draft
WQC documents were presented in Di Toro et al.
(1991) and U.S. EPA (2003a). The conclusion of
similarity of sensitivity was supported by
comparisons between (1) acute values for the
most sensitive benthic species and acute values for
the most sensitive water column species for all
chemicals; (2) acute values for all benthic species
and acute values for all species in the WQC
documents across all chemicals after normalizing
the LC50 values; (3) FAVs calculated for benthic
species alone and FAVs in the WQC documents;
and (4) individual chemical comparisons of benthic
species versus all species. The following analysis
examines the data on the similarity of sensitivity of
benthic and all aquatic species for PAHs.
For PAHs, benthic life-stages were tested
for 15 of 20 freshwater genera and 21 out of 29
saltwater genera (Appendix C). An initial test
of the difference between the freshwater and
saltwater FAVs for all species (water column and
benthic) exposed to PAHs was performed using
the Approximate Randomization (AR) Method
(Noreen, 1989). The AR Method tests the
significance level of a test statistic when compared
to a distribution of statistics generated from many
random sub-samples. The test statistic in this case
was the difference between the freshwater FAV
(computed from the GMAVs at a Kow of 1.0 for
combined water column and benthic organisms)
and the saltwater FAV (computed from the
GMAVs at a
r of 1.0 for combined water
3-9
-------
T0
Toxicity of PAHs in Water Exposures
column and benthic organisms) (Appendix C). In
the AR Method, the freshwater and the saltwater
GMAVs at a KQW of 1.0 were combined into one
dataset. The dataset was shuffled, then separated
back so that randomly generated "freshwater" and
"saltwater" FAVs could be computed. The LC50
values were re-separated such that the number of
GMAVs at a KQW of 1.0 used to calculate the
sample FAVs were the same as the number used
to calculate the original FAVs. These two FAVs
were subtracted and the difference used as the
sample statistic. This was done iteratively so that
the sample statistics formed a probability
distribution representative of the population of FAV
differences (Figure 3-1A). The test statistic was
compared to this distribution to determine its level
of significance. The null hypothesis was that the
GMAVs at a KQW of 1.0 that comprise the
freshwater and saltwater data bases were not
different. If this was true, the difference between
the actual freshwater and saltwater FAVs should
be common to the majority of randomly generated
FAV differences. For PAHs, the test-statistic
occurred at the 93.5 percentile of the generated
FAV differences (Table 3-3). This percentile
suggests that saltwater genera may be somewhat
o
ft
-*J
u
o
M
O
II
3
I
I
et
1)
3
1)
O
O Water column life stages
A Benthic life stages
Crepidula
Attaci..
^
Mudalia
American Lobster.
Aplexa
Cyprinodon
Neanthes^ Paratanvtarsus
Pimephales
Winter Flounder ?"~* Chironomus
Ophiogomplius
LumbriculusX^^Physella
Eutytemora
Oncorhynchus
FAV = 9.32 |amol/g
ncomyncnus
Crangon
Grandidierella
Mysidopsis
40D 60D
D D
Percentage Rank of Genera
Figure 3-1 Probability distributions of FAV difference statistics to compare water-only toxicity data
from (A) freshwater versus saltwater genera and (B) benthic versus WQC.
3-10
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Table 3-3.Results of approximate randomization (AR) test for the equality of the freshwater and
saltwater FAV distributions at a Kow of 1.0 and AR test for the equality of benthic and
combined benthic and water column FAVs for freshwater and saltwater distributions.
Comparison
Fresh vs Salt
Freshwater: Benthic vs
WQCD
Habitat or Water TypeA
Fresh (20) Salt (29)
WQC (49) Benthic (33)
AR Statistic13
5.746
0.862
Probability0
93.5
82.8
A Values in parantheses are the number of GMAVS at a Kow of 1.0 used in the comparison.
B AR statistic = FAV difference between original compared groups.
c Probability that the theoretical AR statistic < the observed AR statistic given that all samples came
from the same population.
D Combined freshwater and saltwater.
more sensitive than freshwater genera as
illustrated in Figure 3-2 and Appendix C.
However, since the probability was less than 95%
in the AR analysis, the null hypothesis of no
significant difference in sensitivity for freshwater
and saltwater species was accepted (Table 3-3).
Since freshwater and saltwater species
showed no significant differences in sensitivity, the
AR Method was applied jointly for the analysis of
the difference in sensitivity for benthic and all
aquatic organisms (benthic and water column
species are always combined to derive WQC,
therefore, the complete GMAV dataset is
hereafter referred to as "WQC"). Using the
criteria in U.S. EPA (2003a), each life stage of
each test organism, hence each GMAV at a Kow
of 1.0, was assigned a habitat (Appendix C). The
test statistic in this case was the difference
between the WQC FAV, computed from the WQC
GMAVs at a Kow of 1.0, and the benthic FAV,
computed from the benthic organism GMAVs at a
Kow of 1.0. The approach used to conduct this
analysis was slightly different than that used in the
previous test for freshwater and saltwater
GMAVs. The difference was that freshwater and
saltwater GMAVs in the first test represented two
separate groups. In this test, the GMAVs at a
Kow of 1.0 for benthic organisms were a subset of
the GMAVs at a Kow of 1.0 in the entire WQC
dataset. In the AR analysis for this test, the
number of data points coinciding with the number
of benthic organisms were selected from the
WQC dataset to compute each "benthic" FAV.
The original WQC FAV and the "benthic" FAV
were then used to compute the difference statistic.
This was done iteratively and the distribution that
results was representative of the population of
FAV difference statistics. The test statistic was
compared to this distribution to determine its level
of significance. The probability distributions of the
computed FAV differences are shown in Figure 3-
1B. The test statistic for this analysis occurred at
the 82.8 percentile and the null hypothesis of no
difference in the sensitivities between benthic
species and species used to derive the WQC FCV
was accepted (Table 3-3). This analysis supports
the derivation of the FCV for PAHs based on all
GMAVs at a Kow of 1.0.
3.4 Derivation of the FAV at a KQW of 1.0
The FAV is an estimate of the concentration
corresponding to a cumulative probability of 0.05 in
the GMAVs at a KQW of 1.0. The analysis above
demonstrates that the acute sensitivities of
freshwater and saltwater genera and the
sensitivities of benthic and benthic plus water
column genera do not differ. Therefore, for
calculation of the FAV, the GMAVs at a KQW of
1.0 for all freshwater and saltwater genera can be
grouped together to represent the relative
sensitivities of all benthic organisms (Figure 3-2).
The FAV at a KQW of 1.0 is calculated using the
procedure in Stephan et al. (1985), the GMAVs at
3-11
-------
T0
Toxicity of PAHs in Water Exposures
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i
ion
8[
6[
4C
2C
o:
-2C
-4C
-6C
-8C
-101
BrBenthicvsWQCD
Freshwater and Saltwater D
ffin mn no —
9.1 D
ID
ion 2on son son 9on
Probability D
99 D
99.9
Figure 3-2. GMAVs at a \ogigKow of 1.0 from water-only acute toxicity tests using
freshwater and saltwater genera versus percentage rank of their sensitivity.
3-12
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
a KQW of 1.0 of 7.63 |jmol/g octanol for
Americamysis, 8.51 |jmol/g octanol for
Grandidierella, 9.83 |jmol/g octanol for Crangon,
11.0 |jmol/g octanol for Oncorhynchus and the
total number of genera tested (N = 49). The FAV
at a KQW of 1.0 is 9.31 |jmol/g octanol. This FAV
is greater than the GMAVs of the two most
acutely sensitive genera as would be expected
given the calculation procedure and the presence
of 31 GMAVs.
3.5 Chronic Toxicity of Individual PAHs:
Water Exposures
3.5.1 Acenaphthene
Chronic life-cycle toxicity tests have been
conducted with acenaphthene with the freshwater
midge (Paratanytarsus sp.) and the saltwater
mysid (A. bahia), and early life-stage tests have
been conducted with the fathead minnow
(P. promelas) and sheepshead minnow
(C. variegatus) (Table 3-1). For each of these
species, one or more benthic life-stages were
exposed. Other chronic toxicity tests have been
conducted with the freshwater chironomid
(Paratanytarsus sp.) and P. promelas (Lemke et
al.,1983; Lemke, 1984; Lemke andAnderson,
1984) but insufficient documentation is available to
permit use of these results (Thursby, 1991a).
Two acceptable life-cycle toxicity tests have
been conducted with Paratanytarsus sp. (North-
western Aquatic Sciences, 1982). In the first test,
575 |jg/L reduced survival 90%, reduced growth
60%, and all eggs failed to hatch (Table 3-1). No
adverse effects occurred at acenaphthene
concentrations up to 295 |Jg/L acenaphthene. In
the second test, survival was reduced 20% and
growth 30% at 315|j,g/L. Egg hatchability was not
affected in the highest concentration of 676 |J,g/L;
although survival of hatched larvae was reduced
-60%. No significant effects were observed at
acenaphthene concentrations up to 164 [ig/L.
A total of six early life-stage toxicity tests
have been conducted with P. promelas as part of
a round-robin test series; two each from three
laboratories (Table 3-1) (Academy of Natural
Sciences, 1981; ERCO, 1981; Cairns andNebeker,
1982). The lowest observed effect concentrations
(LOEC) across laboratories and tests ranged from 98
to 509 |J,g/L, a factor of 5 .2. Growth (dry weight),
survival, or both growth and survival were reduced.
Only one of these test pairs had a suitable measured
acute value that allowed calculation of an ACR
(Cairns and Nebeker, 1982). The concentration-
response relationships were similar for the two tests of
Cairns and Nebeker (1982). In the first test, the early
life-stages of this fish were unaffected in
acenaphthene concentrations ranging from 67 to 332
|Jg/L, but 495 |jg/L reduced growth 54% relative to
control fish. In the second test, growth was reduced
30% at 509 |J,g/L, but no effects were detected in fish
exposed to 197 to
345
Data from saltwater chronic toxicity tests with
acenaphthene are available for A. bahia and C.
variegatus. Reproduction of A. bahia was affected
by acenaphthene in two life -cycle tests from two
different laboratories. In the first test (Home et al.,
1983), 340 |jg/L reduced reproduction 93% relative to
controls and all A. bahia died at 5 10 |J,g/L. No effects
were observed on the parental generation at 100 to
240 |Jg/L and second generation juveniles were not
affected at < 340 |J,g/L. In the second test (Thursby
et al., 1989b), no effects were observed at < 44.6
Mg/L, but a concentration of 91.8 |jg/L reduced
reproduction 91%. No reproduction occurred at
higher concentrations, and growth was reduced 34%
at 168 |jg/L and survival 96% at 354 |jg/L.
A test with early life-stages of C. variegatus
showed that 240 to 520 |jg/L had no effects, but that
concentrations of 970, 2,000 and 2,800 |Jg/L reduced
survival of embryos and larvae by >70% (Table 3-1;
Wardetal., 1981).
In general, the above results show that the
difference between acute and chronic toxicity of
acenaphthene is small and differed minimally between
species (Table 3-2). Species mean acute-chronic
ratios for acenaphthene are 6.68 for Paratanytarsus
sp., 1.48 for P. promelas, 3.42 for A. bahia and 4.36
for C. variegatus.
3-13
-------
T0
Toxicity of PAHs in Water Exposures
3.5.2 Anthracene
A single life-cycle toxicity test has been
conducted with D. magna exposed to only three
concentrations of anthracene (Hoist and Geisy.
1989). Minimal decreases were observed on the
number of broods produced in all three of the
concentrations tested: 2.1 mg/L (5.3%), 4.0 Mg/L
(8.0%) and 8.2 Mg/L (13.8%). No acute toxicity
tests were conducted by the authors. Therefore, an
ACR could not be derived for anthracene.
3.5.3 Fluoranthene
Fluoranthene has been tested in life-cycle
toxicity tests with the freshwater cladoceran, D.
magna (Spehar et al., 1999) and the saltwater
mysid, A bahia (U.S. EPA, 1978, Spehar etaL
1999), and early life-stage tests have been
conducted with the fathead minnow (Spehar et al.,
1999) (Table 3-1). No effects were observed with
D. magna at <17 Mg/L, but growth was reduced
17% at 35 Mg/L and 25% at 73 Mg/L. There were
37% fewer young per adult at 73 Mg/L and no
daphnids survived at 148 Mg/L. An early life-stage
toxicity test conducted with the fathead minnow
showed no effects at <10.4 Mg/L, but reduced
survival (67%) and growth (50%) at 21.7 Mg/L.
Saltwater mysids (A. bahia) were tested in two
life-cycle toxicity tests. In the first test, the mysids
were exposed to fluoranthene for 28 days (U.S.
EPA, 1978). There was no effect on survival or
reproduction (growth was not measured) in
concentrations ranging from 5-12 Mg/L. At a
fluoranthene concentration of 21 Mg/L, survival was
reduced 26.7% and reproduction 91.7%, relative to
the controls. At the highest concentration of
fluoranthene, 43 Mg/L, all A. bahia died. In the
second test, A. bahia were exposed to fluoranthene
for 31 days (Spehar et al., 1999). Effect
concentrations were similar to those in the U.S.
EPA (1978) test. A. bahia were not affected at
fluoranthene concentrations from 0.41-11.1 Mg/L.
At the highest concentration tested, 18.8 Mg/L,
survival was reduced 23% relative to controls and
there was no reproduction. Reproduction was
reduced by 77% in 11.1 Mg/L, but this was not
significantly different from controls even at a=0.1.
The difference between acute and chronic
sensitivity to fluoranthene varied minimally between
species (Table 3-2). Three species mean ACRs are
available for fluoranthene: 4.78 for D. magna, 4.60
for P. promelas, and 2.33 for A. bahia.
3.5.4 Phenanthrene
Phenanthrene has been tested in life-cycle
toxicity tests with D. magna and A. bahia and an
early life-stage test has been conducted with
rainbow trout (O. mykiss) (Table 3-1). There were
no effects of phenanthrene on D. magna at <57
Mg/L, but survival was reduced 83% and repro-
duction 98% at 163 Mg/L (Call et al., 1986). In a
test with O. mykiss, no effects were observed at 5
Mg/L. The percentage of abnormal and dead fry at
hatch was significantly increased at the highest
exposure concentration of 66 Mg/L and survival of
hatched fry was reduced with increase in exposure
concentration (Call etal., 1986). Mortality was 41,
48, 52 and 100% at 8, 14, 32, and 66 Mg/L,
respectively. Wet weight was reduced 33, 44, and
75% at 8, 14 and 32 Mg/L, respectively.
A life-cycle toxicity test with A. bahia exposed
to phenanthrene showed that the effect
concentrations were similar to those that affected
O. mykiss (Kuhn and Lussier, 1987) (Table 3-1).
Survival, growth and reproduction were not
affected at <5.5 Mg/L. However, at the highest test
concentration of phenanthrene (11.9 Mg/L), all
mysids died.
The difference between acute and chronic
sensitivity to phenanthrene varied minimally
between D. magna (PAH-specific ACR= 1.21),
O. mykiss (ACR=7.90) and A. bahia (ACR=
3.33). The ACR for O. mykiss (Call et al., 1986)
was derived using the EC50 for immobilization (50
Mg/L) and not the 96-hour LC50 of 375 Mg/L as was
required in Stephan et al. (1985).
3.5.5 Pyrene
A life-cycle toxicity test with A. bahia exposed
to pyrene was conducted by Champlin and Poucher
(1992b). There were no effects at 3.82 Mg/L, but
3-14
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
20.9 |Jg/L reduced survival 37% and no mysids
survived at the next higher concentration of 3 8.2
|jg/L (Table 3-1). Reproduction was significantly
reduced in >5.37 |J,g/L. The ACR from this test was
pyrene is 6.24.
3.5.6 Naphthalene
Fathead minnows were exposed to naphthalene
in an early life-stage toxicity test (DeGraeve et al.,
1982). Hatching of fry was significantly reduced in
4.38 and 8.51 [ig/L and none were alive in these
concentrations at the end of the 30-day test. Weight
and length offish surviving the test were significant-
ly reduced in 0.85 and 1.84 |J,g/L. No significant
effects were detected in concentrations <0.45 [ig/L.
Control survival was only 42%, which does not meet
requirements according to the American Society of
Testing and Materials (ASTM, 1998). Also, the
carrier methanol was absent from the control.
These data are summarized in the text for complete-
ness, but the ACR of 12.7, chronic value of 0.62 [ig/
L, and 96-hour LC50 of 7.9 |jg/L for naphthanlene
are not included in Tables 3-1 and 3-2.
The calanoid copepod (Eurytemora qffinis)
was exposed indvidually to 14.21 jj^g/L naphthalene,
15.03 |^g/L2-methylnaphthalene, 8.16 ^g/L2,6-
dimethylnaphthalene and 9.27 [ig/L 2,3,5-trimethyl-
naphthalene in life-cycle toxicity tests (Ott et al.,
1978). Survival and reproduction were affected by
each of the naphthalenes, but ACRs could not be
derived because the duration of the acute test was
too short (24 hours) according to WQC Guidelines
(Stephan et al., 1985), and no other concentrations
were tested chronically.
3.5.7 Derivation of the Final Acute
Chronic Ratio
The FACR for the six PAHs is 4.16. This
FACR is the geometric mean of all species mean
ACRs for Daphnia (2.41), Paratanytarsus (6.68),
Pimephales (2.61), Oncorhynchus (7.90),
Americamysis (3.59), and Cyprinodon (4.36)
(Table 3-2).
3.6 Derivation Of FCVs
3.6.1 Derivation of the FCV at a KQW of 1.0
The FCV is the value that should protect 95%
of the tested species. The FCV is the quotient of
the FAV and the FACR for the substance. The
FAV at a KQW of 1.0 is 9.31 mmol/g octanol. It is an
estimate of the acute LC50 or EC50 concentration
corresponding to a cumulative probability of 0.05 for
the GMAVs at a Kow of 1.0. The FACR of 4.16 is
the mean ratio of acute to chronic toxicity for six
species exposed exposed both acutely and chronic-
ally to one or more of six individual PAHs in 15
experiments. (For more information on the calcula-
tion of ACRs, FAVs, and FCVs see the U.S. EPA
National WQC Guidelines (Stephan et al., 1985.))
The FAV at a Kow of 1.0 of 9.31 ^mol/g
octanol is divided by the FACR of 4.16 to obtain a
FCV at a Kow of 1.0 of 2.24 ^mol/g octanol (Table
3-3). Because nonionic organic chemicals partition
similarly into octanol and lipid of organisms, the
FCV at a KQW of 1.0 in |jmol/g octanol approxi-
mately equals tissue-based "acceptable"
concentration of about 2.24 |jmol/g lipid.
3.6.2 Derivation of the PAH-Specific FCVs
The PAH-specific FCVs (mg/L) (Table 3-4,
Appendix D) are calculated from the FCV at a KQW
of 1.0 (|jmol/g octanol), the slope of the KOW-KQC
relationship, the universal narcotic slope of the
KQW-acute toxicity relationship, and the PAH-
specific KQW values (Equation 3-1, 3-2, and 3-3).
log10PAH-specific FCV = (slope)log10Kow + Iog10 FCV
ataKowofl.O (3-1)
log10PAH-specific FCV= -0.945 log10KQW +
Iog10(2.24) (3-2)
PAH-specific FCV (mmol/L) = 1000(antilog
(-0.9451og10Kow + 0.3502)) (3-3)
3-15
-------
T0
Toxicity of PAHs in Water Exposures
Table 3-4. CQC PAH Fcv; concentrations and properties required for their derivation^
PAH PAH
FCVj specific specific
SPARCC Omol/g FCVj FCV;
PAHB log10Kow log10Koc octanol) (umol/L) (ug/L)
indan
naphthalene
Cl -naphthalenes
1 -methy Inaphthalene
2-methy Inaphthalene
acenaphthylene
acenaphthene
1 -ethylnaphthalene
2-ethylnaphthalene
C2-naphthalenes
1 ,4-dimethylnaphthalene
1 ,3-dimethylnaphthalene
2,6-dimethylnaphthalene
2 ,3 -dimethy Inaphthalene
1 ,5 -dimethy Inaphthalene
fluorene
C3-naphthalenes
2,3,5-trimethy Inaphthalene
1 ,4,5-trimethy Inaphthalene
anthracene
phenanthrene
Cl-fluorenes
1-methylfluorene
C4-naphthalenes
2-methylanthracene
1 -methylanthracene
9-methylanthracene
2-methylphenanthrene
1 -methy Iphenanthrene
Cl-phenanthrene/anthracenes
9-ethylfluorene
C2-fluorenes
pyrene
fluoranthene
2-ethylanthracene
C2-phenanthrene/anthracenes
9 , 1 0-dimethy lanthracene
3 ,6-dimethy Iphenanthrene
C3-fluorenes
Cl-pyrene/fluoranthenes
2 ,3 -benzo fluorene
3.158
3.356
3.8
3.837
3.857
3.223
4.012
4.221
4.283
4.3
4.3
4.367
4.373
4.374
4.378
4.208
4.8
4.858
4.872
4.534
4.571
4.72
4.739
5.3
4.991
4.998
5.006
5.029
5.037
5.04
4.973
5.2
4.922
5.084
5.357
5.46
5.494
5.515
5.7
5.287
5.539
3.105
3299
3.736
3.772
3.792
3.168
3.944
4.15
4.21
4227
4227
4293
4299
4.3
4.304
4.137
4.719
4.776
4.789
4.457
4.494
4.64
4.659
5.21
4.906
4.913
4.921
4.944
4.952
4.955
4.889
5.112
4.839
4.998
5266
5.367
5.401
5.422
5.603
5.197
5.445
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.24
2.322
1.509
0.5744
0.53
0.5074
2.016
0.3622
0.2298
0.2008
0.1935
0.1935
0.1673
0.1651
0.1647
0.1633
0.2364
0.0652
0.05747
0.05575
0.1163
0.1073
0.0776
0.07445
0.02197
0.04303
0.04238
0.04165
0.03961
0.03893
0.03868
0.04475
0.02731
0.05
0.03515
0.0194
0.01551
0.0144
0.01376
0.009199
0.0226
0.01306
274.5
193.5
81.69
75.37
72.16
306.9
55.85
35.91
31.37
30.24
30.24
26.13
25.79
25.74
25.52
39.3
11.1
9.785
9.488
20.73
19.13
13.99
13.42
4.048
8.273
8.148
8.007
7.616
7.485
7.436
8.693
5.305
10.11
7.109
4.003
3.199
2.971
2.838
1.916
4.887
2.824
n
^OC.PAHi.FCVi
(H-g/goc)
349
385
444
446
447
452
491
507
509
510
510
513
513
513
514
538
581
584
584
594
596
611
612
657
667
667
668
669
670
670
673
686
697
707
739
746
748
749
769
770
787
C D
^OC,PAHi,MAXi
(Hg/goc)
127200
61700
165700
154800
24000
33400
142500
129900
-
192300
157100
33800
49900
62400
26000
-
-
129300
1300
34300
-
49700
-
2420
-
21775
-
24100
-
-
-
9090
23870
-
-
14071
-
-
-
558
3-16
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Table 3-4. Continued
PAHB
benzo(a)fluorene
G-phenanthrene/anthracene s
naphthacene
be nz(a) anthracene
chrysene
triphenylene
C4-phenanthrenes/ anthracenes
Cl-be nz anthracene/chrysenes
C3-pyrene/fluoranthenes
benzo(a)pyrene
perylene
benzo(e)pyrene
benzo(b)fluoranthene
benzo(j )fluoranthene
benzo(k)fluoranthene
C2-be nz anthracene/chrysenes
9,10-dimethylbenz(a)anthracene
7, 12-dimethylbenz(a)anthracene
7-methylbenzo(a)pyrene
benzo(ghi)perylene
G-be nz anthracene/chrysenes
indeno(l,2r3-cd)pyrene
dibenz(a,h)anthracene
dibenz(a,j)anthracene
dibenz(a,c)anthracene
C4-be nz anthracene/chrysenes
Cl-dibenz(a,h)anthracenes
coronene
C2-dibenz(a,h)anthracenes
C3-dibenz(a,h)anthracenes
SPARCC
log^
5.539
5.92
5.633
5.673
5.713
5.752
6.32
6.14
6.284
6.107
6.135
6.135
6.266
6.291
6.291
6.429
6.567
6.575
6.537
6.507
6.94
6.722
6.713
6.713
6.78
7.36
7.113
6.885
7.513
7.913
AFour significant figures are used even when
log10K
5.445
5.82
5.538
5.577
5.616
5.654
6.213
6.036
6.177
6.003
6.031
6.031
6.16
6.184
6.184
6.32
6.456
6.464
6.426
6.397
6.822
6.608
6.599
6.599
6.665
7.235
6.992
6.768
7.386
7.779
FCV,
(Hmol/g
oc octanol)
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
PAH
specific
FCVj
((omol/L)
0.01306
0.0057
0.01064
0.009756
0.008943
0.008215
0.002387
0.003531
0.002581
0.003794
0.00357
0.00357
0.002685
0.002542
0.002542
0.001883
0.001395
0.00137
0.001489
0.001589
0.0006194
0.0009953
0.001015
0.001015
0.0008773
0.0002483
0.0004251
0.0006981
0.000178
0.0000746
PAH
specific
FCV C C D
1 *" v i ^OC.PAHi.FCVi ^OC,PAHi,MAXi
(Hg/L)
2.824
1256
2.43
2227
2.042
1.875
0.5594
0.8557
0.6307
0.9573
0.9008
0.9008
0.6774
0.6415
0.6415
0.4827
0.3575
0.3513
0.3965
0.4391
0.1675
0275
0.2825
0.2825
0.2442
0.07062
0.1243
0.2097
0.05454
0.02389
fewer are appropriate for the parameter to limit the
error when calculating SESBTUFCV which has two sig
B See Appendix E for solubilities.
cFor C#-PAHs, reported log10Kow
D Cocj.AHi.Maxi iS baSed O11 Solubility
units (see Section 6).
values are
> H ^OC.PAH
nificant figures.
Og/goc) (Hg/goc)
787
829
838
841
844
846
913
929
949
965
967
967
979
981
981
1008
1021
1021
1058
1095
1112
1115
1123
1123
1129
1214
1221
1230
1325
1435
12500
-
207
4153
826
19400
-
-
-
3840
431
4300
2169
3820
1220
-
124200
145300
-
648
-
-
2389
47680
7400
-
-
821
-
-
effects of rounding
the average log10Kow values of all structures.
,,FCV, is >
'-'oc.pAHi.Maxi' tner
CocpAHiMaxi maY be usgd to calculate ESB
toxic
3-17
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Section 4
Derivation of PAH ESB
4.1 Derivation of Potencies for Individual
PAHs in Sediments (Coc PAH1FCV1)
The critical concentration of a PAH in sediment
(COCPAHlFCVl) that is related to the FCV is derived
following the EqP method (U.S. EPA, 2003a; Di
Toro et al., 1991) because the interstitial water-
sediment partitioning of PAHs follows that of other
nonionic organic chemicals. Therefore, a sediment
effects concentration for any measure of effect
can be derived from the product of the water-only
effects concentration for that effect and the Koc
for that particular PAH. The use of Koc to derive
a sediment effects concentration for PAHs is
applicable because partitioning for these chemicals
is primarily determined by the organic carbon
concentration of the sediment.
The partitioning equation between the organic
carbon-normalized sediment concentration, Coc
(|imol/goc = |imol/kgoc), and the free interstitial
water concentration, Cd (mmol/L), is given by the
equation
Coc ~ Koc Cd
(4-1)
where Koc (L/kgoc), defined above, can be
calculated from a K™, obtained from SPARC
ow
(Hilal et al., 1994) using the following equation
from Di Toro (1985)
log10K0= 0.00028+0.983 log10Ko
(4-2)
C
OQPAHLFCV
fl for individual PAHs are then
calculated using Equation 4-1 with the FCV as the
water concentration
-'OC.PAHi.FCV
, = K__FCV.
A OC i
(4-3)
Since Koc is presumed to be independent of
sediment type for nonionic organic chemicals, so
also is Cc
-'OQPAHi.FCVi-
Table 3-4 contains the C
OQPAHL.FCVi
for 74 PAHs found in sediments, including the 34
PAHs (in bold) analyzed by the U.S. EPA in their
FCVS
EMAP program (U.S. EPA, 1996a,b; 1998).
CocpAHiFcvi vames f°r PAHs not in Table 3-4 can
be calculated in a similar manner (see Section 7.2
for discussion on the PAHs to which the ESB
applies). The range in the C
OQPAHLFCV
,, values for
the 74 PAHs listed in Table 3-4, which were
derived using only data for PAHs, is from 349 to
1435 |ig/goc. In contrast, the range of the same
value, termed the Csoc by Di Toro and McGrath
(2000), was about the same (655 to 1940 |ig/goc)
for the 23 PAHs commonly measured when
derived using the database for narcotic chemicals
with a PAH correction.
4.2 Derivation of the ESBFCV for PAH
Mixtures
The correct derivation of the ESB for a mixture
of PAHs is based on the approximate additivity of
narcotic chemicals in water and tissue (Di Toro et
al., 2000; Section 2.8 of this document) and in
sediment (Section 5.2). Because WQC and ESBs
are based on FCVs they are not intended to cause
toxicity in water or sediments to most species, the
term toxic unit could be misleading. Therefore, we
refer to the quotient of the concentration of a
specific chemical in water and its WQC FCV as
water quality criteria toxic units (WQCTUFCVi).
Similarly, the quotient of the sediment
concentration for a specific PAH (COCPAHl) and
theC
OQPAHL.FCV
fi in sediments should be termed
equilibrium partitioning sediment benchmark toxic
unit (ESBTUFCVi). Thus, the ESB for the mixture
of PAHs is the sum of the ESBTU for all of
FCVi
the PAHs in the particular sediment termed the
ZESBTU,
2ESBTUFCV =
C
OQPAHL
•'OQPAHL.FCVi
(4-4)
4-1
-------
D,
Derivation of PAH ESB
For a particular sediment, if the SESBTUFCV
for "total PAHs" is less than or equal to 1.0, the
concentration of the mixture of PAHs in the
sediment is acceptable for the protection of
benthic organisms (see Section 7.2 for the
technical basis for defining total PAH as the
ZESBTU v for the 34 PAHs monitored in the
rL-V
U.S. EPAEMAP). The equilibrium partitioning
sediment benchmark is given by the equation
= ZESBTUFCV<1.0
(4-5)
For a particular sediment, if the EESBTUFCV is >
1.0, the concentration of the mixture of PAHs in
the sediment may not acceptable for the protection
of benthic organisms
ESB = ZESBTU > 1.0 (4-6)
4.3 Aqueous Solubility Constraint
A solubility constraint is applied to sediment
concentrations when computing their individual
contributions to the effect of the PAH mixture
because the COCPAHlFCVl derived for each PAH is
solubility limited, i.e., the interstitial water
concentration of the PAH is limited by the
solubility S. Therefore, COCPAHlFCVl is limited by
the concentration in sediment organic carbon that
is in equilibrium with the interstitial water at the
aqueous solubility (Equation 4-7). This is termed
the maximum C
OC,PAHi,Max
(Table 3-4)
(4-7)
Thus, only the contribution up to the maximum
C is c
PAH mixture.
vi is counted in the SESBTU™,, for the
•^ rL-V
Narcosis theory suggests that highly insoluble
PAHs should contribute fractional toxic units and
ESBTUFCVi, limited by the solubility constraint, to
the sum of the effects of the mixture when these
PAHs are present in mixtures. If so, then this
points out the importance of knowing the aqueous
solubility of these PAHS so that Equations 4-4 and
4-5 can be applied correctly.
The question of whether highly insoluble
chemicals that are not by themselves acutely or
chronically toxic, e.g., high molecular weight
PAHs, contribute fractional toxic units to the total
toxicity when present as mixtures is discussed in
Section 5.2.8 of this document and in Spehar et al.
(In preparation). Spehar et al. (In preparation)
demonstrate that high KQW PAHs do contribute to
the total toxicity of the PAH mixture.
4.4 Comparison of the SESBTUFCV
Mixtures of PAHS in Estuarine
Sediments
for
Coastal and estuarine monitoring data were
compiled from eight sources to obtain a
preliminary assessment of the SESBTUFCV values
for PAHs in the sediments of the Nation's water
bodies (NOAA, 1991; Adams et al., 1996;
Anderson et al., 1996; Fairey et al., 1996; U.S.
EPA, 1996a,b, 1998; Hunt etal., 1998). Data
sources which were identified had measured
concentrations for the 23 PAHs (18 parent and 5
alkylated groups) (see Table 6-2) as well as the
corresponding sediment organic carbon
measurements. Sediments analyzed were from
randomly selected and specifically targeted
locations, samples of surficial grabs and vertical
profiles, and studies where the relative frequency
and intensities of sampling varied. This analysis is
presented as an aid in assessing the range of
reported PAH concentrations, and the extent to
which they may exceed 1.0 SESBTUFCV. The
sediments analyzed were not randomly selected
from the entire United States. Therefore, this
analysis is not intended to reflect expected
occurrence nationwide or at any specific site of
concern. Sediments where 23 PAHs were
analyzed will underestimate the SESBTUFCV if 34
PAHs had been analyzed. SESBTUFCV values
were computed by summing the ESBTUFCVi for
each PAH measured in the sediment sample. For
insoluble PAHs, the COCPAHlMaxi (Table 3-4) was
used to calculate SESBTU,
The probability distribution for the
SESBTUFCV data are shown on Figure 4-1. The
number of data points used to generate each
distribution is provided in the lower right hand
corner of each graph. For visual effect, only non-
4-2
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
100
o
IJH
H
PQ
GO
o.oooi
100
10
o
[JH
H
PQ
GO
r o
10
o
IJH
£
H
pq
GO
10
o
[JH
H
pq
GO
San Diego
,00
00
N=182
SFEI
N=137
li bi
Southern California
Less Than or Equal To
: NOAA
r
: NY/NJR-EMAP
r V
N=153
1 \ ....... hi
Virginian EMAP
N=318
I L
Carolinian EMAP
.-6°
N=229
1.1 1 10 20 50 80 90 99 99.9
% Less Than or Equal To
Figure 4-1. Probability distribution of the SESBTUFCV for PAH mixtures in sediments
from individual coastal and estuarine locations in the United States.
4-3
-------
D,
Derivation of PAH ESB
overlapping data are shown. For comparison
purposes, a line indicating 1.0 ZESBTUFCV is also
shown. Data presented are from sediments with
0.201 to 15.2% organic carbon. With the
exception of the Louisianian and Carolinian
Province EMAP datasets, all of the datasets had
only 23 PAHs measured. The Louisianian and
Carolinian Province EMAP datasets had a total of
34 measured PAHs (18 parent and 16 alkylated
groups). The PAHs in addition to the 23 were the
Cl through C4 alklyated forms of some of the
parent PAHs. To assess the total number of PAHs.
a Cl-PAH series was considered as one PAH.
Computed 2ESBTUFCV values are based on the
total number of PAHs measured. The distributions
across the different locations are relatively similar.
With the exception of the Southern Californian data.
all of the datasets had 2ESBTUFCV values greater
than 1.0 at the 95thpercentile. Although the
ZESBTUFCV from the Lousianian and Carolinian
Province EMAP data are computed from 34 PAHs.
these sediments do not contain greater ZESBTUFCV
values than sediments from the other studies which
measured only 23 PAHs.
A single probability distribution using all of the
data is shown in Figure 4-2. The total number of
sediments is 1979. ZESBTUFCV values computed
from 23 PAHs are denoted by open circles, and for
the 34 PAHs, by open squares. The median
2ESBTUFCV was about 0.06. Approximately 6% of
the samples (109 sediments) had 2ESBTUFCV
values greater than 1.0.
Although the EqP-based ESBs for nonionic
organic chemicals are not intended for use with
largely sandy sediments having <0.2% TOC, the
EMAP Lousianian and Carolinian Provinces (34
PAHs) and the Elliot Bay (31 PAHs) monitoring
databases were examined to determine the
frequency of ESB exceedences. A total of 115 of
the 654 sediments in these databases had <0.2%
TOC. Only two of these sediments (1.7 percent)
exceeded the ESB of > 1.0 ZESBTIJ
H
PQ
VI
W
1X1
0.01 f
0.001 -
0.0001
0.01 0.1
10 20 50 80 90
% Less Than or Equal To
99 99.9 99.99
Figure 4-2. Probability distribution of the 2ESBTUFCV for PAH mixtures in sediments from all of the
coastal and estuarine locations in the United States from Figure 4-1.
4-4
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Section 5
Actual and Predicted Toxicity
of PAH Mixtures in Sediment
Exposures
5.1 Introduction
The COCPAHlFCVl for individual PAHs and ESBs
for their mixtures were derived using water-only
toxicity data (Appendix C) and both equilibrium
partitioning (U.S. EPA, 2003a; Di Toro etal., 1991)
and narcosis theory (Di Toro et al., 2000; Di Toro
and McGrath, 2000). This section examines data
from toxicity tests with spiked and field sediments
contaminated with individual PAHs and their
mixtures to demonstrate the strength of the technical
approach used to derive ESBs and the applicability
of ESBs to sediments from the field.
5.2 Spiked Sediment Toxicity Tests
5.2.1 Interstitial Water Concentrations and
Sediment Toxicity: Relevance to Water-
Only Toxicity Tests and WQC FCVs
The key hypothesis in the derivation of ESBs
from EqP and narcosis theory is that effects
concentrations from water-only aquatic toxicity
tests data using benthic species are similar to
effects concentrations in sediment toxicity tests
based on interstitial water concentrations or
sediment concentrations predicted to be toxic
using EqP. This hypothesis has been tested in two
ways: 1) by comparing LC50 values determined in
water-only experiments to interstitial water LC50
values determined in spiked-sediment exposures.
and 2) by comparing organic carbon-normalized
sediment LC50 values observed in spiked-
sediment exposures with those predicted from
water-only LC50 values multiplied by the KQC
using the equilibrium partitioning model (Di Toro
etal, 1991).
The interstitial water and water-only LC50
values for 28 experiments with a variety of PAHs
and several freshwater and marine species are
listed in Appendix F (Swartz et al., 1990; Swartz,
1991a; DeWitt et al., 1992; Suedel et al., 1993;
Driscoll et al., 1997a,b, 1998). The mean ratio of
the water-only LC50 to interstitial water LC50
from 20 experiments with definitive LC50 values
was 1.60, indicating agreement generally within
less than a factor of two. Interstitial water LC50
values almost always slightly exceeded water-only
LC50 values. Three factors may contribute to that
result: 1) some test species, especially epibenthic
or tube-dwelling organisms, frequently encounter
unspiked, overlying water and, thus, are not
exclusively exposed to interstitial water;
2) interstitial water near the sediment surface may
be slowly diluted by overlying water because of
bioturbation and other transport processes; and
3) chemical analyses of interstitial water may
include a portion of the non-bioavailable PAH
fraction that is bound to dissolved organic matter.
Despite these limitations, the interstitial water and
water-only LC50 values are remarkably close,
especially for sensitive, free-burrowing, infaunal
species like R. abronius. These data support the
evaluation of the risks of sediment-associated
chemicals by comparisons between dissolved
concentrations in interstitial water and water
concentrations of concern from water-only
toxicity tests.
A more comprehensive evaluation of the
degree to which the response of benthic organisms
can be predicted from contaminant concentrations
in interstitial water can be made utilizing
organism responses in each treatment from
toxicity tests with sediments spiked with various
5-1
-------
Ac
Actual and Predicted Toxicity
chemicals, including acenaphthene (Swartz,
199la), phenanthrene (Swartz, 199la),
fluoranthene (Swartz et al., 1990; DeWitt et al,
1992), endrin (Nebeker et al., 1989; Schuytema et
al., 1989), dieldrin (Hoke, 1992), DDT (Nebeker
et al., 1989; Schuytema et al., 1989) or kepone
(Adams et al., 1985) (Figure 5-1). Interstitial
Water Toxic Units (IWTU) are calculated by
dividing the concentration of a chemical in the
interstitial water (|Jg/L) of a treatment by the
water-only LC50 (|Jg/L). Theoretically, 50%
mortality should occur at 1.0 IWTU. Mortality
should be <50% at interstitial water
concentrations < 1.0 IWTU, and > 50% at
concentrations > 1.0 IWTU. Figure 5-1 presents
the percent mortality in individual treatments for
each chemical versus the IWTUs. Mortality was
generally low at concentrations <1.0 IWTU, and
increased sharply at > 1.0 IWTU as would be
expected if interstitial water concentrations
account for the bioavailability of nonionic organic
chemicals across sediments and water-only LC50
values are surrogates for interstitial water LC50
values.
5.2.2 Sediment Toxicity: Prediction Using
Water-Only Toxicity and Koc
The equilibrium partitioning model predicts
the organic carbon-normalized sediment PAH
concen-tration (PAHQC) as the product of the PAH-
specific partition coefficient between organic
carbon and water (Koc) and the water-only effect
concentration for the PAH in water (example, 10-
day LC50 or FCV)(Di Toro et al., 1991).
Predicted LC50 (|lg/goc)=water-only LC50 (|lg/L)x KQC (L/kgoc)
(5-1)
Equation 5-1 was used with the water-only LC50
values in table 5-1 and the Kocs in table 3-4 to
predict the sediment LC50s (ng/goc) for 22
combinations of a variety of PAHs and test
species (Table 5-1). Corresponding LC50 values
were also determined for each combination in
standard sediment toxicity tests. The mean ratio
of observed/predicted LC50 values was 2.07,
indicating that Equation 5-1 predicts PAH LC50
values M-g/goc in sediment with an accuracy within
a factor of two (Table 5-1). This result is
essentially equal to the ratio of the interstitial
water and water-only LC50 values and may be the
result of the same factors listed previously.
As in the case of IWTU, predicted sediment
toxic units (PSTU) can be estimated by dividing
the measured PAH concentration in sediments
from individual treatments of spiked-sediment
toxicity tests (|j,g/goc) by the predicted LC50
(Hg/goc). This standardization allows a compre-
hensive analysis of the efficacy of the EqP
prediction of a sediment effect concentration from
the product of the Koc and water-only effects data
for that chemical and duration of exposure.
Figure 5-2 combines PSTU-response data for
diverse chemicals including acenaphthene
(Swartz, 199la), phenanthrene (Swartz, 1991a),
fluoranthene (Swartz et al., 1990; DeWitt et al.,
1992), endrin (Nebeker et al., 1989; Schuytema et
al., 1989), dieldrin (Hoke, 1992) or kepone
(Adams et al., 1985) (Figure 5-2). As with the
IWTU plot, 50% mortality should occur at about
1.0 PSTU. Figure 5-2 shows that mortality was
generally low at PSTU < 1, increased rapidly at
PSTU « 1, and was high for most samples with
PSTU> 1.
These analyses support the concept that
water-only LC50 values and Ivs can be used to
^ UL-
predict the sediment concentrations on an organic
carbon basis that are toxic to benthic organisms.
It seems probable that this EqP prediction of
sediment effect concentrations from water-only
effect data is applicable to other measures of
aquatic toxicity, including WQC final chronic
values. Therefore, an FCV for a specific PAH
multiplied by its KQC value should be applicable to
the derivation of a value analogous to the FCV,
but based on a sediment concentration. This
concentration is the ESB.
5.2.3 Toxicity of Individual PAHs
Spiked-sediment toxicity tests have provided
an important tool for investigating the effects of
sediment-associated PAHs and the applicability of
the EqP approach for the derivation of sediment
benchmark concentrations. The toxicity test
5-2
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
100
DIELDRIN
• KEPONE
PHENANTHRENE
ENDRIN
FLUORANTHENE
ACENAPHTHENE
AAA DDT
0.01
0.1 1 10
Predicted Interstitial Water Toxic Units
100
Figure 5-1. Percent mortality versus predicted interstitial water toxic units for seven chemicals and
three sediments per chemical (each sediment represented by unique symbol).
method involves: 1) addition and thorough mixing
of the PAH into a reference sediment that contains
little or no background contamination and is not
toxic, by itself, to the test species; 2) storage of
the spiked sediment for up to 28d to allow the
PAH to reach an equilibrium of the partitioning of
the PAH between interstitial water and dissolved
and particulate sedimentary materials; 3) conduct
of a sediment toxicity test following standard U.S.
EPA (1994) or ASTM (1993) procedures; and 4)
analytical measurements, typically of the
sediment/interstitial water concentration of the
PAH, organic carbon, and other sediment
variables. The method yields a dataset on the
relation between the measured PAH concentration
and the toxicity response, from which a LC50,
PvVTU, PSTU, and other statistical parameters can
be calculated.
Sediment contaminant concentrations of
nonionic organic chemicals are typically
normalized to either the dry weight or organic
carbon content of the sediment. To facilitate
comparisons among the four PAHs from spiked
sediment toxicity tests with R. abronius, PAH
concentrations in sediments from each treatment
in each spiked sediment toxicity test are
normalized in this section to the PAH-specific
C
fl (see Table 3-4). This ratio is termed
OC,PAHi,FCV
the ESBTUFCVi, which is the ratio of the measured
PAH concentration in sediments from the toxicity
tests (|j,g/goc) to the COCPAHlFCVl concentration (|j.g/
goc) for that PAH, i.e., the fraction of ESBTUFCV
represented by the observed PAH concentration in
sediment. The COCPAHlFCVl normalization does not
alter the original variability in concentration-
response but allows comparison of PAH effects
among species, compounds, and response criteria.
For example, the COCPAHlFCVl-normalized raw data
for effects of individual PAHs on the amphipod,
R. abronius, indicates similar patterns of
concentration-response for acenaphthene,
phenanthrene, fluoranthene, and pyrene (Figure 5-
3). The individual LC50 values for the four PAHs
ranged from 3.3 to 4.5 ESBTUFCVi (mean = 3.8)
5-3
-------
Ac
Actual and Predicted Toxicity
Table 5-1. Water-only and spiked-sediment LC50 values used to test the applicability of narcosis
and equilibrium partitioning theories to the derivation of ESBs for PAHs. See
Appendix F for water-only and interstitial water LCSOs (jlg/L).
Ratio:
Interstitial Water Organic Carbon-Normalized LC50 ((ig/goc)
Chemical LC50/Water-only LC50 Ratio
Test Species Method LC50 Observed Predicted13 Obs/Pred Reference
Freshwater
Fluoranthene
Diporeiasp.
Hyalella azteca
Hyalella azteca
Hyalella azteca
Hyalella azteca
Chironomus tentans
Chironomus tentans
Chironomus tentans
Saltwater
Acenaphthene
Eohaustorius estuarius
Eohaustorius estuarius
Eohaustorius estuarius
Leptocheirus plumulosus
Leptocheirus plumulosus
Leptocheirus plumulosus
Fluoranthene
Leptocheirus plumulosus
Phenanthrene
Eohaustorius estuarius
Eohaustorius estuarius
Eohaustorius estuarius
Leptocheirus plumulosus
Leptocheirus plumulosus
Leptocheirus plumulosus
2,6 -dimethy Inaphthlene
Rhepoxynius abronius
2,3 ,5 -trimethy Inaphthlene
Rhepoxynius abronius
1-methylfluorene
Rhepoxynius abronius
2-methylphenanthrene
Rhepoxynius abronius
9-methylanthracene
Rhepoxynius abronius
Acenaphthene
Rhepoxynius abronius
Rhepoxynius abronius
FT,M/10
FT,M/10
S,M/10
S,M/10
S,M/10
S,M/10
S,M/10
S,M/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
S/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
S,M/10
S,M/10
S,M/10
S,M/10
S,M/10
S,M/10
S,M/10
>0.58
1.02C
5.27C
2.17C
2.86C
7.87C
2.37C
2.14
1.63
1.45
>2.54
2.08
2.2
-
1.05
1.06
1.11
2.09
1.65
1.95
-
-
-
-
-
-
-
-
500
1480
1250
1587
1740
682
4330
1920
1630
>23,500
7730
11200
>21,200
4050
3920
3820
8200
6490
8200
8120
3190
1950
2270
6840
2110
2310
-
4490
4490
4490
3190
3190
3190
2152
2152
2152
3900
3900
3900
3900
3778
3778
3778
5335
5335
5335
-
-
-
-
-
-
-
-
0.1 lc
0.33C
0.28C
0.50C
0.55C
0.21C
2.01
0.89
0.76
>6.02
1.98
2.87
>5.44
1.07
1.04
1.01
1.54
1.22
1.54
-
-
-
-
-
-
-
Driscolletal.,1997a,b
DriscoUet al., 1997a,b
Suedeletal., 1993
Suedeletal., 1993
Suedeletal., 1993
Suedeletal., 1993
Suedeletal., 1993
Suedeletal., 1993
Swartz, 1991a
Swartz, 1991a
Swartz, 1991a
Swartz, 1991a
Swartz, 1991a
Swartz, 1991a
DriscoUetal.,1998
Swartz, 1991a
Swartz, 1991a
Swartz, 1991a
Swartz, 1991a
Swartz, 1991a
Swartz, 1991a
Ozretichetal., 2000a
Ozretichetal., 2000a
Ozretichetal., 2000a
Ozretichetal., 2000a
Ozretichetal., 2000a
Swartz etal., 1997
Swartz etal., 1997
5-4
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Table 5-1. Continued
Test Species
Ratio:
Interstitial Water
LC50/Water-only
MethodA LC50
Organic Carbon-Normalized LC50 (ug/goc)
LC50 Ratio
Observed Predicted5 Obs/Pred Reference
Phenanthrene
Rhepoxynius abronius SJV1/10
Rhepoxyrtius abronius SJV1/10
Pyrene
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
Fluoranthene
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
Rhepoxynius abronius SJV1/10
1.63
2.12
1.74
22.66D
1.01
1.91
1.38
0.67
3080
2220
1610
1220
2810
2320
3310
1890
2100
2230
>4360
4410
3080
3150
2790
Swartzetal., 19 97
Swartzetal., 1997
Ozretichetal., 2000a
Swartzetal., 1997
Swartzetal., 1997
1390
1390
1390
1390
1390
1390
1390
1390
1390
1390
1.66
2.38
1.36
1.51
1.6
4.04D
3.17
2 22
2.26
2.01
Swartz et al.,
Swartz et al.,
Swartz et al.,
Swartz et al.,
Swartz et al.,
DeWitt et al.,
DeWitt et al.,
DeWitt et al.,
DeWitt et al.,
DeWitt et al.,
1997
1997
1990
1990
1990
1992
1992
1992
1992
1992
MeanLCSO ratio =
1.6
Mean LC50 ratio =
2.07
ATest conditions for water-only toxicity tests: S = static, FT = flow-through, M = measured, 10 = 10-d duration.
Predicted LC50 (^g/goc) = water-only LC50 fag/L) KQC (L/kgoc) 1 kgoc/1000goc.
c Sediments spiked with fluoranthene by Suedel et al. (1993) were not at equilibrium, therefore, are not included
in the mean.
D Source of organic carbon was fresh plant material, not naturally aged organic matter, therefore, value was not
included in the mean.
E 10-day LC50 value from R. Swartz, Environmental Consultant (personal communication).
5-5
-------
*
Actual and Predicted Toxicity
100
g
i
I
80
60
40
20
0©« KEPONE
DOB PHENANTHRENE
ENDRIN
FLUORANTHENE
ACENAPHTHENE
AAA DDT
0.01
0.1 1 10
Predicted Sediment Toxic Units
100
Figure 5-2. Percent mortality versus predicted sediment toxic units for six chemicals
and three sediments per chemical (each sediment represented by unique symbol).
120
100
80
£ 60
"e3
I 40
20
0
-20
0.1
1 D D | D 1 D 1 [ D 1
° Fluoranthene
* Acenaphthene
_
D Phenanthrene
B Pyrene
=
~ n
o
- D 1
<•• o
O •<£]
•30 » D0OD
O O
— • • o •<• n
.
, ..!.,.,! , ,
1 1 1 1 1 1 1 1 111111
.
OB BB»D(D1BEI «D -
0
oo -
o
r
-
=
-
Geometric Mean = 3.78
. 1 ... .1 , .,1.,,,
10
100
ESBTU
FCV
Figure 5-3. Percent mortality of Rhepoxynius abronius in sediments spiked with acenaphthene,
phenanthrene, fluoranthene, or pyrene concentrations in sediment normalized
toESBTUFCV/.
5-6
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
indicating that sediment concentrations would
have to exceed the CocpAHiFCVi by about a factor of
four to cause 50% mortality in this amphipod
during a 10-day exposure. The presence of
mortality only at PAH concentrations in excess of
theC
OC,PAHi,FCV
7i would be expected.
5.2.4 Comparison of Sediment Toxicity
toC,
OC,PAHi,FCVi
The degree to which ESBs derived from
narcosis and EqP theory and FCVs derived from
water-only toxicity databases are appropriately
protective of benthic organisms can be
independently tested using data from spiked-
sediment toxicity tests. The individual PAH
concentrations in sediment (Coc) affecting benthic
organisms in toxicity tests were divided by the
COCPAHIFCVI to determine the ESBTUFCVi. If most
benthic organisms are sensitive at the ESBTUFCVi
greater than 1.0 then the ESB for the PAH mixture
may be appropriately protective of benthic
organisms (see Section 4.2).
A review of the literature on spiked-sediment
toxicity tests yielded 54 estimates of LC50, EC50
or EC25 (concentration affecting 25% of the test
organisms) values for four individual PAHs
(acenaphthene, phenanthrene, fluoranthene.
pyrene; Appendix F). The duration of most of the
tests was 10 days, but a few were longer-term
tests that measured sublethal effects on
reproduction or emergence (sediment avoidance).
Over all the data, there was a substantial range
(500 to 147,000 Mg/goc) in the estimates of the
median response concentrations. For example, the
relative sensitivity of marine amphipods in this
dataset was Rhepoxynius abronius > Eohaustorius
estuarius > Leptocheirus plumulosus. This range
in median response concentrations reflects
differences in species sensitivity, PAH
bioavailability and probably, most importantly,
specific experimental conditions.
The data from some of the toxicity tests with
individual PAHs spiked into sediments needed to
be modified or not included in further analyses.
Some tests with Diporeia sp., Lumbriculus
variegatus, Limnodrilus hoffmeisteri and Hyalella
azteca were conducted at concentra-tions in the
sediment that could not have been at equilibrium
with the concentration of the PAH at solubility in
interstitial water (Kukkonen and Landrum, 1994;
Landrum et al., 1994; Lotufo and Fleeger, 1996;
Driscoll et al., 1997a,b). The reported median
effect concentration is in parenthesis and
maximum sediment concentration at water
solubility (given in Table 3-4) for each PAH is
indicated in bold in Appendix D. To facilitate
comparisons of species sensitivity and to account
for bioavailability, median response concen-
trations were divided by the COCPAHlFCVl values to
obtain the test-specific ESBTUFCVi values. Then
PAH-specific SMAVs and GMAVS across PAHs
were calculated only for 10-day lethality tests.
The maximum solubility-limited sediment
concentration was used to calculate the test-
specific ESBTUFCVi and PAH-specific SMAVs and
GMAVs only if there insufficient no data from
tests that lacked this solubility constraint. Some
tests were conducted with newly spiked
sediments where time was likely insufficient to
permit equilibrium to be achieved between the
interstitial water and organic carbon and other
sediment partitioning phases (Suedel et al., 1993).
Data from these tests were not used because the
median effect concentration in sediments would
be lower than that expected if sediments and
interstitial water were at equilibrium.
For the seven species tested acceptably
against one or more PAH, the 43 test-specific
ESBTUFCVi ranged from 1.47 to 57.8, a factor of
39.3, with no values below 1.0 ESBTUFCVi (Figure
5-4; Appendix D). Within each individual
species, the range of test-specific ESBTUFCVi
across multiple tests with one or more PAH, based
on 10-day LC50 values, was within only a factor
of 1.5 to 4.1 (mean 3.0). For the three saltwater
amphipods tested against multiple PAHs the range
of PAH-specific SMAVs was within a factor of
1.4 to 2.0 (mean 1.7). These observations indicate
that the species tested differed in their sensitivities
to PAHs, but that within a species there was a
similarity of response across tests with the same
or multiple PAHs. The range and frequency
5-7
-------
Ac
Actual and Predicted Toxicity
100
u
tu
P
H
M
10
Diporeia
Leptocheirus
Eohaustorius
Corophium
Rhepoxynius
10 20 30 40 50 60 70 80
Percentage Rank of Genera
90 100
Figure 5-4. Percentage rank, based on ESBTUFCV;, of the sensitivities of genera of benthic organisms
from spiked sediment toxicity tests.
distribution of contaminant sensitivity among
aquatic species is comparable to that of benthic
species in water-only tests (see Section 3.4).
This analysis of data from spiked-sediment
toxicity tests with individual PAHs supports the
conclusion that the COCPAHlFCVl derived from water-
only toxicity tests, narcosis theory and national
WQC are appropriately protective of benthic
organisms. These comparisons between
sediments spiked with individual PAHs and their
respective COCPAHlFCVl have value in suggesting the
validity of the EqP and narcosis approaches.
However, PAHs occur in nature not as individual
compounds, but as mixtures.
5.2.5 PAH Mixtures
Sediments spiked with PAH mixtures have
been used to resolve two issues that are relevant to
the validation of the ESB for PAHs (Swartz et al.,
1997; Landrum et al., 1991; Boese et al., 1999;
Burgess et al., 2000b; Spehar et al., In
preparation). The first concerns the toxicological
additivity of the effects of the individual
components of the mixture. If effects are additive,
relatively simple models can be used to predict the
effects of mixtures. The second issue concerns
the low solubility of PAHs with high octanol-
water partitioning coefficients (i.e., PAHs with
Kow > 5.5). The predicted LC50 of many high
KQW compounds exceeds their solubility limit.
Accordingly, experimental attempts to establish
the LC50 for individual high KQW PAHs spiked
into sediment have observed little or no acute or
chronic toxicity. High KQW PAH mixtures have
been recently tested to see if individual high Kow
PAHs contribute fractional toxic units that are
additive with effects of other PAHs (Spehar et al.,
In preparation).
5.2.6 Additivity of PAH Mixtures
There is a wealth of aquatic toxicological data
that supports the additivity of PAHs and other
narcotic chemicals in water (Konemann, 1980;
5-8
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Hermans et al., 1984; Broderius and Kahl, 1985;
Fig. 2-11). The additivity of sediment-associated
contaminants is less well documented, although
several publications indicate that PAHs in
sediment are either additive or slightly less than
additive (Swartz et al., 1995, 1997; Landrum et
al., 1991, 1994). Landrum et al. (1991) found that
the effects of a mixture of 11 sediment-associated
PAHs on the freshwater amphipod, Diporeia sp.
were "approximately additive with no overt
evidence of synergism or antagonism." Landrum
et al. (1991) also noted that additivity is further
supported by the fact that LD50 values, expressed
as PAH molar concentration in amphipod tissue,
were the same for a single compound (pyrene) and
the mixture of 11 compounds.
The results from some of the above 10-day
studies were analyzed by dividing the
concentrations of each of the PAHs in the
sediments by the COCPAHlFCVl and summing the
quotients to derive the SESBTUFCV for the
mixture (Table 5-2). No acute toxicity was
observed with Diporeia exposed to a SESBTUFCV
for all PAHs up to 3.08 (Landrum et al., 1991),
but none would be expected given the 10-day
LC50 value of >34.0 SESBTUFCV for this species
(Table 5-2). Toxicity to R. abronius was absent in
several tests with mixtures of PAHs in treatments
from 1.42 to 27.8 SESBTUFCV and occurred in
treatments with 5.80 and 10.3 SESBTUFCV
(Swartz et al., 1997; Boese etal., 1999). Fortf.
abronius, the GMAV from 10-day spiked
sediment tests with individual PAHs was 3.67
SESBTUFCV (Table 5-2). This suggests a less than
additive toxicity of the PAH mixtures tested. The
amphipod A. abdita was exposed to a total of 2.58
and 6.05 SESBTUFCV by Burgess et al. (2000b).
Toxicity was absent from both treatments, and
none probably should have been expected given
the 4-day LC50 at 13.8 SESBTUFCV (Table 5-2).
Additivity of mixtures of 13 PAHs was
assumed in the development of the SPAH model
that was used to accurately classify PAH-
contaminated, field-collected sediment as toxic or
not toxic (Swartz et al., 1995). Swartz et al.
(1997) concluded that sediment spiked with a
mixture of acenaphthene, phenanthrene,
fluoranthene and pyrene caused effects on R.
abronius that were slightly less than additive.
Di Toro and McGrath (2000) reanalyzed these
data and concluded that the mixture was additive
(also see Section 5.2.7). Even if PAH interactions
are slightly less than additive, the potential error
introduced by the assumption of additivity in the
derivation of an ESB for PAH mixtures would be
relatively small and would be environmentally
protective (i.e., the toxicity of mixtures would be
slightly over-estimated).
5.2.7 PAH Additivity Demonstrated Using
the Universal Narcosis Slope
The additivity of mixtures of PAHs spiked
into sediments was tested using narcosis theory to
calculate PAH-specific 10-day LC50 values in
sediments for R. abronius. The experimental data
from Swartz et al., (1997) was reexamined using
predicted PAH-specific 10-day sediment LC50
values for R. abronius. The narcosis methodology
was used to test additivity, rather than the actual
sediment LC50 values as was presented above and
by Swartz et al., (1997). This is because the
predicted sediment LC50 values were derived
using data from many tests with a variety of
PAHs. Also, because sediment LC50 values could
be predicted for the 31 or 34 PAHs analyzed from
field sediments used in 10-day toxicity tests with
data from toxicity tests with R. abronius to test
narcosis and EqP predictions (See Section 5.3.1).
Interstitial water concentrations were used in
place of water-only LC50 values in this process
because water-only toxicity data were not
available. This is justified because interstitial
water and water-only LC50 values have been
shown to be nearly the same (see Section 5.2.1).
The 10-day interstitial water LC50 values were
for eight PAHs (fluoranthene, naphthalene,
pyrene, 1 -methylfluorene, 2-methylphenanthrene,
9-methylanthracene, 2,6-dimethylnaphthlene, and
2,3,5-trimethylnaphthlene) tested in separate
experiments. The interstitial water LC50 values
for fluoranthene were from seven separate
experiments (mean LC50 = 19.5 |Jg/L) (Swartz et
al., 1990; DeWitt et al., 1992), whereas the LC50
5-9
-------
Ac
Actual and Predicted Toxicity
Table 5-2. Percent mortality of benthic invertebrates in relation to the SESBTUFCV values of
mixtures of polycyclic aromatic hydrocarbons spiked into sediment.
Species*
SESBTUpcv SESBTUpcv
PAHKow PAHKow SESBTUpcv Percent
<5.5 >5.5 AllPAHs Mortality
PAH Mixture13
Reference
Diporeia sp. 0.01 0.02 0.03 3 fluor, phen, anthr, flu, pyr, chry,
b(b)flu,b(e)pyr, b(a)pyr, pery,
b(ghi)pery
Diporeia sp. 0.21 0.36 0.57 10 fluor, phen, anthr, flu, pyr, chry,
b(b)flu,b(e)pyr, b(a)pyr, pery,
b(ghi)pery
Diporeia sp. 0.49 0.6 1.1 0 fluor, phen, anthr, flu, pyr, chry,
b(b)flu,b(e)pyr, b(a)pyr, pery,
b(ghi)pery
Diporeia sp. 1.37 1.71 3.08 12 fluor, phen, anthr, flu, pyr, chry,
b(b)flu,b(e)pyr, b(a)pyr, pery,
b(ghi)pery
R. abronius 10.32 0 10.3 100 ace; phen; flu; pyr
R. abronius 5.8 0 5.8 38 ace; phen; flu; pyr
R. abronius 5.12 0 5.12 8 ace; phen; flu; pyr
R. abronius 3.25 0 3.25 11 ace; phen; flu; pyr
R. abronius 2.5 0 2.5 4 ace; phen; flu; pyr
R. abronius 1.8 0 1.8 2 ace; phen; flu; pyr
R. abronius 1.42 0 1.42 3 ace; phen; flu; pyr
R. abronius 2.77 0 2.77 5 anthr; flu
R. abronius 4.91 5.02 9.93 3 b(a)anthr; flu
R. abronius 5.88 0 5.88 5 2-methylanthr; flu
R. abronius 5.71 0 5.71 2 9,10-dimethylanth; flu
R. abronius 2.71 2.23 4.94 3 b(b)flu; flu
R. abronius 2.06 0.79 2.84 2 chr; flu
R. abronius 0.63 1.57 2.2 1 3,6-dimethylphen; flu
R. abronius 1.91 25.89 27.8 4 anthr; b(a)anthr; 2-methylanthr;
b(b)flu; chr; 3,6-dimethylphen
R. abronius 0.58 8.03 8.61 5 anthr; b(a)anthr; 2-methylanthr;
b(b)flu; chr; 3,6-dimethylphen
R. abronius 1.55 8.03 9.58 9 anthr;b(a)anthr; 2-methylanthr;
b(b)flu; chry; 3,6-dimethylphen; flu
R. abronius 0.9 3.4 4.3 0 anthr;b(a)anthr; 2-methylanthr;
b(b)flu; chry; 3,6-dimethylphen; flu
A. abdita 5.41 0.64 6.05 7 9,10-dimethylanthr; chry
A. abdita 0 2.58 2.58 7 b(a)pyr; cor
A. abdita 5.41 3.22 8.63 10 9,10-dimethylanthr; chry; b(a)pyr; cor
A. bahia 5.41 0.64 6.05 3 9,10-dimethylanthr; chry
A. bahia 0 2.58 2.58 7 b(a)pyr; cor
A. bahia 5.41 3.22 8.63 7 9,10-dimethylanthr; chry; b(a)pyr; cor
Landrumetal., 1991
Landrumetal., 1991
Landrumetal., 1991
Landrumetal., 1991
Swartz et al.
Swartz et al.
Swartz et al.
Swartz et al.
Swartz et al.
Swartz et al.
Swartz et al.
Boese et al.,
Boese et al.,
Boese et al.,
Boese et al.,
Boese et al.,
Boese et al.,
Boese et al.,
Boese et al.,
,1997
, 1997
,1997
,1997
,1997
, 1997
, 1997
1999
1999
1999
1999
1999
1999
1999
1999
Boese etal., 1999
Boese etal., 1999
Boese etal., 1999
Burg ess etal.,2000b
Burg ess etal.,2000b
Burg ess etal.,2000b
Burg ess etal.,2000b
Burg ess etal.,2000b
Burg ess etal.,2000b
ATest Species: amphipods: Diporeia sp., Rhepoxynius abronius, Ampelisca abdita, mysids: Americamysis bahia
BPAH Code: ace - acenaphthene; anthr - anthracene; b(a)anthr - benz(a)anthracene; b(a)pry - benzo(a)pyrene;
b(ghi)pery - benzo(ghi)perylene; b(b)flu - benzo(b)fluoranthene; chry - chrysene; cor - coronene; 9,10-
dimethylanth - 9,10-dimethylanthracene; 3,6-dimethylphen - 3,6dimethylphenanthrene; flu - fluoranthene;
fluor-fluorene; 2-methylanthr - 2-methylanthracene; pery - perylene; phen - phenanthrene; pyr - pyrene.
5-10
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
log10PAH-specific LC50
Iog1(1( 15. 8 |jmol/g octanol)
= -0.945 log10Kow
(5-2)
The PAH-specific LC50R abmnius is used to calculate
the PAH-specific sediment LC50 (|ig/oc) for R.
abronius (equation 5-3).
PAH-specific sediment LC50R abm^as = Koc x PAH-specific
1C50 C5-31
R. abmnius \ I
values for the remaining seven PAHs are from
single experiments (Ozretich et al., 1997) (Table
5-1). The individual LC50 values, and mean
value for fluoranthene, were normalized to a Kow
of 1.0 using the universal narcosis slope (Equation
2-29). The geometric mean of these LC50 values
at a Kow of 1.0 is the critical body burden of 15.8
|jmol/g octanol (octanol serves as a surrogate for
lipid). The critical body burden is used to
calculate the PAH-specific 10-day LC50 values
(|jg/L) for R. abronius (Equation 5-2). This
equation is analogous to Equation 3.2 which is
used to calculate the PAH-specific WQC.
The mortality of R. abronius in the standard
10-day sediment tests where the sediments were
spiked individually (acenaphthene, fluoranthene,
phenanthrene or pyrene (open symbols)), or a
mixture of these four PAHs (solid circles), is
compared to the predicted sediment toxic units
(PSTU) to test the utility of this approach to
normalize the toxicity of individual PAHs and,
most importantly, to test the additivity of the PAH
mixture experiment of Swartz et al. (1997) (Figure
5-5 A). PSTUs are the quotients of the
concentration of each PAHs measured in the
individual spiked sediment treatments divided by
the predicted PAH-specific 10-day sediment LC50
values for R. abronius. For the mixture, PSTUs
were summed to obtain the total toxic unit
contribution (in Section 5.3.1, sediments from the
field are similarly analyzed; Figure 5-5B). The
percent mortality-PSTU relationship is similar for
the individual PAHs and the mixture. Apparent
LC50 values are approximately within a factor of
two of 1.0 PSTU. This analysis based on the
universal narcosis slope and a similar analysis for
narcotic chemicals in water-only experiments
(Section 2.10), suggests that the assumption of
near additivity of mixtures of PAHs is a
reasonable approximation.
5.2.8 Additivity of Mixtures of High Kow
PAHs
The solubility of PAHs in water generally
decreases with increasing Kow, while the water
column toxicity of PAH increases with increasing
KQW. Although the solubility of individual PAHs
are a function of their structure and polarity rather
than just KQW, the general relationship between
solubility and KQW is such that solubility
decreases with increasing KQW slightly faster than
toxicity increases. The net result of this
relationship is that PAHs with high KQW (roughly
log10KQW of 5.5 and higher) have solubilities
below their predicted LC50. This has led to the
conventional wisdom that high KQW PAHs are not
toxic (at least on an acute basis) because they are
insufficiently soluble to cause toxicity. For
example, high KQW PAHs are generally not toxic
in water-only toxicity tests (Appendix C).
This argument is founded, however, on the
basis of single chemicals. PAHs do not occur as
single chemicals in the environment, and available
experimental evidence indicates that their
toxicities are additive, or slightly less than
additive, when present in mixtures. This has
special significance for the higher KQW PAHs;
although they may be too insoluble to cause
toxicity individually, they could still contribute
fractional toxic units to the overall toxicity of
PAH mixtures.
Historically, toxicity experiments with
mixtures have been conducted by testing the
toxicity of individual chemicals to determine their
potency, then testing mixtures of these chemicals
to determine the potency of the mixture.
Comparing the toxicity of the mixture to the toxic
units contributed by each chemical allows
evaluation of the interactive toxicity of the
mixture. In the case of high Kow PAHs, this
experimental approach cannot be used, because
the toxicity of the individual chemicals cannot be
measured. Use of the narcosis model, however,
allows prediction of toxicity for the mixture
components and can be used to evaluate the
overall toxicity of the mixture.
Spehar et al. (In preparation) conducted a
5-11
-------
Ac
Actual and Predicted Toxicity
1.4U
100
80
£> 60
• p«
o 40
20
0
in
\
Q Acenaphthene AjtH1^
o Fluoranthene
D Phenanthrene A
A QD |
A Pyrene |
• Mixture 1
01
1
A*
D O
3s?9
AO
1.4U
100
80
60
40
20
0
. B
ODD
0
O
o Predicted 34 PAH (Swartz) Q
d Measured 33 PAH Elliott Bay
o
0 or
O
_. 0
o n°
o o o
o oo E
&j o ooO gpipn
- O O O<§> ODD^tmi
•
III) 1 1
1
1
o ID o o o
^°
Bi
i D
i n
i •
i
0|
n
ID
no
o
rpn
i
i
,,,i
o
_
-
-
_
_
-
•
0.001
0.01
0.1
1
PSTU
10
100
1000
Figure 5-5. Mortality of the amphipod, Rhepoxynius abronius, from 10-day spiked sediment toxicity tests
with four parent PAHs separately (open symbols) and in combination (closed circles) (A) and in
tests with sediments from the field (B) versus predicted sediment toxic units (PSTUs). PSTUs
are the quotients of the concentration of each PAH measured in sediments from the individual
spiked sediment treatments, or individual sediments from the field, divided by the predicted
PAH-specific 10-day sediment LC50 values for R. abronius. The predicted PAH-specific 10-day
sediment LC50 values for R. abroniusis were calculated using the critical body burden of 15.8
Fmol/g octanol and Equation 5-2. PSTUs were summed to obtain the total toxic unit
contribution of the mixture of PAHs in spiked or field sediments.
5-12
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
series of sediment toxicity tests using a mixture of
13 PAHs with log10KQW ranging from 5.36 to 6.76
(Table 5-3). Potency of each chemical was
predicted using an earlier version of the narcosis
model, and the concentration for each chemical in
the highest concentration of the mixture was
established at an estimated 0.5 TU for Hyalella
azteca (re-analysis using current models and the
H. azteca GMAV from Appendix C predicts more
than 0.5 TU for most PAHs). For some of these
chemicals, solubility would be expected to limit
their TU contribution (Table 5-3). The PAH
mixture was spiked into a clean freshwater
sediment at several concentrations, and into a
clean marine sediment at the highest concentration
only.
Several toxicity tests were conducted. A 42-
day survival, growth, and reproduction study with
H. azteca (Spehar et al., In preparation) was
conducted in a flow-through system (2x daily
renewal of overlying water) using four
concentrations of the PAH mixture. In this study,
chemical analysis of the bulk sediment showed
that about 80% of the nominal PAH spike was
measured in the sediment at the start of the
exposure, and concentrations of PAH in the
interstitial water were generally within a factor of
2 of the concentrations predicted from KQC and
solubility. After 10 days of exposure, significant
effects on the dry weight of the amphipods were
observed in the three highest concentrations of the
PAH mixture (Figure 5-6), but there were no
effects on survival. After 28 days of exposure,
survival was significantly reduced in the two
highest treatments, although the growth effects
observed at day 10 were no longer present (Figure
5-7). As per the test protocol, organisms were
removed from the sediment at day 28 and held for
14 more days in clean water to assess
reproduction. No further effects on survival,
growth, or reproduction were observed between
days 28 and 42.
Toxicity of the PAH mixture was lower than
would have been predicted based on narcosis
Table 5-3. Chemicals included in the high KQW PAH mixture experiment (Spehar et al.,
In preparation).
Estimated porewater
. . . . „, t. QN°mmalt concentration (|^g/L)
Molecular Estimated Sediment
Chemical Name
2-Ethylanthracene
3,6 Dimethylphenanthrene
2,3 Benzofluorene
Benzo(a)anthracene
Triphenylene
2-(tert-butyl)anthracene
Benzo(a)pyrene
Benzo(b )fluoranthene
Benzo(k)fluoranthene
9-Phenylanthracene
7-Methylbenzo(a)pyrene
7, 1 2Dimethylbenz(a)anthracen
3-Methylcholanthrene
TOTAL PAH
A Predicted by SPARC.
Weight
(g/mol)
206.29
206.29
216.28
228.29
228.3
234.34
252.31
252 32
252.32
254.33
266.35
256.35
268.38
logic Kow*
5.36
5.52
5.54
5.67
5.75
5.88
6.11
6.27
6.29
6.31
6.54
6.58
6.76
Solubility0
log 10 KocB (Hg/L)
5.27
5.42
5.44
5.58
5.65
5.78
6.00
6.16
6.18
6.2
6.43
6.46
6.64
59.62
77.98
25.30
12.28
5.11
33.04
2.88
8.28
8.35
3.64
1.46
13.41
3.11
Concentration
(umol/goc)
39.32
42.38
42.88
45.80
47.66
50.91
57.46
62.75
63.64
64.22
73.37
75.04
83.92
749.4
NominaP
(Sed. ConcVKoc)
43.94
33.12
33.27
27.70
24.11
19.78
14.38
10.96
10.50
10.30
7.32
6.62
5.1
247.1
Limited by
Solubility
43.94
33.12
25.30
12.28
5.11
19.78
2.88
8.28
8.35
3.64
1.46
6.62
3.11
173.9
B Predicted from Di Toro et al. (1991).
c Predicted by SPARC in distilled water at 25 °C.
D Nominal concentration predicted by K
oc, regardless of solubility
limits; hij
;hest concentration only.
5-13
-------
Ac
Actual and Predicted Toxicity
theory. However, concentrations of PAH
measured in the tissue of exposed Hyalella were
considerably lower than would be in equilibrium
with interstitial water, suggesting that the Hyalella
may have avoided the test sediment, thereby
reducing their exposure. Avoidance of toxic
sediments by Hyalella has been reported
previously (e.g., Whiteman et al, 1996). When
10-day growth and 28-day survival responses are
compared on the basis of measured tissue burden,
the thresholds for response fall in the same range
as is predicted by narcosis theory (Figure 5-8).
Thus, although Hyalella had lower uptake of these
PAHs, they did show a response to the high KQW
PAHs suggesting that these chemicals can cause
toxicity to benthic organisms. Moreover, the
relationship of measured tissue concentrations to
biological responses was consistent with that
expected from a narcotic mode of action and
additivity among PAHs in the mixture. It should
be noted that because the toxicity of the individual
mixture components was predicted rather than
measured (which would not be possible if they are
not individually toxic at solubility), we can only
conclude that these results are consistent with the
additivity, or approximate additivity, hypothesis,
but they are not, by themselves, proof of
additivity.
Because of concerns that Hyalella may have
avoided exposure to PAH in the flow-through test
by spending more time in the overlying water
which was being replaced 2x daily, an additional
test was conducted using the same PAH-spiked
sediments, but conducting the test with renewal of
overlying water-only three times during the entire
10-day test. This reduced frequency of renewal
should have increased the concentrations of PAH
in the overlying water (not measured), thereby
increasing exposure of Hyalella to the PAH
mixture. While the flow-through test showed
effects only on growth after 10 days of exposure,
results of the second test showed a concentration-
dependent response of both survival and growth
(Figure 5-9). When expressed on the basis of total
PAH molar concentration in the sediment
0.12
100-
0.00
10
Acute TU in Sediment
Figure 5-6. Response of Hyalella azteca exposed for 10 days underflow-through conditions to sediment
spiked with a mixture of high KQW PAH.
5-14
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
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Acute TU in Sediment
Figure 5-7. Response of Hyalella azteca exposed for 28 days under flow-through conditions to sediment
spiked with a mixture of high J5TOW PAH.
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Acute TU in Tissue
10
Figure 5-8. Survival (after 28 days) and growth (after 10 days) of Hyalella azteca expressed on the basis
of measured PAH concentrations in tissues (lipid normalized).
5-15
-------
Ac
Actual and Predicted Toxicity
100
0.10
-0.08
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-0.06 £
-0.04
-0.02
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Acute TU in Sediment
Figure 5-9. Response of Hyalella azteca exposed for 10 days (3 renewals) to sediment spiked with
a mixture of high KQW PAH.
(normalized to organic carbon), the threshold for
survival and growth effects were close to the
sediment concentration predicted to cause acute
effects based on the narcosis model. Similarly.
the tissue concentration of the mixture in the
amphipods compared favorably with the critical
body burden predicted to cause effects based on
the narcosis model.
In addition to the freshwater experiments
described above, additional experiments were
conducted using marine organisms (Spehar et al.,
In preparation). Two marine organisms, amysid
(Americamysis bahia) and a marine amphipod
(Ampelisca abdita), were exposed to a marine
sediment spiked with the highest concentration of
the PAH mixture. After 10 days of exposure to
the sediment in a static system, both species
showed marked mortality, with 85% mortality of
mysids and 95% mortality of the amphipods.
Because this sediment would be predicted to
contain a large number of acute TU based on the
GMAVs for these species (41 acute TU for
mysids; 10 acute TU for A. abdita), these results
cannot be used to evaluate accuracy of the
narcosis model rigorously; however, they provide
further support to emphasize that mixtures of high
Kow PAHs can cause toxicity.
In a separate test, another species of marine
amphipod (Leptocheirus plumulosus) was exposed
for 10 days to a series of concentrations of the
PAH mixture spiked into the freshwater sediment
used in the freshwater studies (L. plumulosus is
tolerant of the lower salinity in the freshwater
sediment, while A. bahia and A. abdita are not)
(Spehar et al., In preparation). After 10 days
under static conditions, L. plumulosus showed
reduced survival in the four highest PAH
concentrations (Figure 5-10). The observed toxic
unit threshold for mortality was within a factor of
2 of that predicted using narcosis theory and the
GMAVs in sediment from Appendix C for L.
plumulosus.
Taken together, the results of these
experiments with high KQW PAHs clearly
demonstrate that they can cause toxicity to benthic
organisms when present in mixtures. Thresholds
for toxicity in several experiments were slightly
5-16
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
higher than would be predicted directly from the
narcosis model, though this may reflect
uncertainties in the GMAV values as well as
exposure-related factors (e.g., avoidance).
Measured tissue concentrations in freshwater
amphipods from treatments where toxicity was
observed were consistent with those shown to be
toxic for lower KQW PAHs. Therefore,
SESBTUFCV for mixtures must include the partial
contributions of high KQW PAH in the mixture to
insure that the ESB is not under protective.
5.3 Field Sediments Versus ESBFCV
PAH Mixtures
for
The ultimate test of validity of sediment
benchmarks is their predictive ability. That is, can
they be used to predict effects seen in field
collected samples. Unfortunately, the problem of
validation using field collected samples has no
straightforward solution. It is extremely difficult
to separate actual cause and effect from simple
correlation. The primary reason is the presence of
covariation of many chemical contaminants in
field collected sediments, some of which may be
unmeasured. Therefore, it cannot be presumed
that the response observed is due to only the
chemical(s) being investigated.
However, if the PAH benchmark predicts an
effect at a certain SESBTIJ , for a mixture of
rL-V
PAHs (e.g., 50% mortality of a test organism), and
the organism survives exposures significantly
above the SESBTUFCV value, then the benchmark
may not be valid. No other comparison is more
definitive. Of course, mortality at SESBTUFCV
values below those predicted to cause effects may
be due to other causes, and provide no evidence
for the validity or invalidity of the prediction.
100
0.01
0.1 1
Acute TU in Sediment
Figure 5-10. Response of Leptocheirus plumulosus exposed for 10 days under static conditions to sediment
spiked with a mixture of high KQW PAH.
5-17
-------
Ac
Actual and Predicted Toxicity
10000
g 1000
1)
u
=
•o
3 100
t
10
0.1
*
+ ++ j
+ 4+
++ ++ +• ++4-
+ + + + +
+ -H+ -m-H-nn- + +
+ + + + -HHf -H- +
0.0001 0.001
0.01
0.1
1
10
100
1000
2ESBTUFCV
Figure 5-11. Amphipod (Ampelisca abditd) abundance versus 2ESBTUFCV.
5.3.1 Toxicity to R. abronius of Field
Sediments Containing PAH Mixtures
vs. EPSTUs Derived from Narcosis
Theory
A set of 10-day toxicity data using R. abronius
exposed to sediments from locations where 13
PAHs were measured and PAHs are suspected to
be the primary cause of toxicity has been
assembled by Swartz et al. (1995). A similar set
of data from Elliott Bay where 32 PAHs (18
parent and 14 alkylated groups) were measured
(Ozretich et al., 2000) is also available (See Table
3-4 for the list of PAHs). As explained in Section
5.2.1, predicted PAH-specific 10-day sediment
LC50 values for R. abronius were derived using
narcosis theory and 10-day LC50 values based on
interstitial water concentrations of eight PAHs for
R. abronius. The mortality of R. abronius in the
standard 10-day sediment tests in each of these
sediments from the field is compared to the sum
of the PSTUs for that sediment (Figure 5-5B).
PSTUs are the quotients of the concentration of
each PAHs measured in the individual field
sediments divided by the predicted PAH-specific
10-day sediment LC50 values for R. abronius.
The sum of the PSTUs for the sediments where
only 13 PAHs were analyzed were multiplied by
the uncertainty factor of 2.75 (the mean ratio of
the toxic contribution of the 34 PAHs analyzed by
the U.S. EPA EMAP program to the 13 PAHs (see
Table 6-1)). The uncertainty factor of 2.15, rather
than the 95th percent uncertainty factor, was used
to adjust for fewer than 34 PAHs because the goal
was to use the best estimate of the sum of the
toxic units to compare to the observed amphipod
mortality in a specific sediment.
Consider, first, the data for which the sum of
the PSTUs of the 13 PAHs (termed "predicted 34
PAH" in the figure as represented by the open
circles in Figure 5-5B). There is only one
5-18
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
sediment where the sum of the PSTUs exceeds
two where mortality was less than 50%. The
important point here is that all, except one, of the
sediments exceeding this concentration exhibited
>50% mortality consistent with the prediction.
There could be several explanations why the
exception might occur in that one sediment. For
example, the 13 PAHs multiplied by the mean
uncertainty factor may have under-represented the
true total PAH concentration.
For the remaining data, the total PAH
concentrations are from field sediments where 31
of the 34 PAHs were measured (open squares).
For all of these data there appears to be a
concentration-response relationship with an
apparent LC50 approximately at the predicted
LC50 ± a factor of two, and only one sediment
with less than 50% mortality had >2.0 PSTUs.
This suggests that the assumption of near
additivity of mixtures of PAHs is a reasonable
approximation for predicting the toxicity of
sediments from the field and for deriving ESBs
for PAH mixtures.
It is tempting to conclude from the
coincidence of 2ESBTUFCV values >1.0 and the
drop in amphipod abundance, that in fact, these
data support the validity of the ESB. However, it
should be pointed out again that these data can
only be used to demonstrate that the ESB is not in
conflict with observations. They cannot be used
to validate the ESB. However, these data, and
those in Figure 5-5A and B, might have cast doubt
on the ESB if effects were predicted and none
were observed.
The validation procedure requires sediments
for which the nature of all the bioavailable
chemicals are known and quantified. This is
usually only satisfied with laboratory spiked
sediments. This is why the experimental validity
of the narcosis mixture theory as is demonstrated
in Section 5.2 and illustrated in Figure 5-5A is so
important.
5.3.2 Organism Abundance vs. ESBFCVfor
PAH Mixtures
Another test of this sediment benchmark is the
observations of the abundance of sensitive
amphipods versus the total PAH concentrations in
field collected sediments. Figure 5-11 presents
the observed A. abdita abundance versus
ZESBTUFCV when 34 PAHs were measured or
estimated using the 50% uncertainty factor of 1.64
(see Table 6-1) when 23 PAHs were measured.
The data are from sediments collected as part of
the Virginian and Louisianian province EMAP
(U.S. EPA, 1996a,b) and the New York/New
Jersey Harbor REMAP (Adams et al, 1996)
sediment sampling programs. The vertical line is
at the ESB of 1.0 2ESBTUFCV. The results are
very encouraging. The absence of sediments
having high abundances of A. abdita at slightly
above 1.0 ZESBTUFCV and the decrease in
amphipod abundance as the 2ESBTUFCV increases
above 1.0 is consistent with that predicted by this
ESB for PAH mixtures.
5-19
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Section 6
Implementation
6.1 Introduction
This section on implementation defines "total
PAHs" for use with this ESB for PAH mixtures.
presents an example ESB calculation, provides
guidance on the interpretation of the ESB relative
to sediment toxicity tests, describes the role of
photo-activation of PAH toxicity by ultraviolet light
and the relative importance of teratogenicity and
carcinogenicity as a mode of toxic action for
PAHs, and critically examines equilibrium of
PAHs in sediments, including the presence of soot
carbon, coal and similar materials as sediment
binding phases other than natural organic carbon.
The section ends with an approach for calculating
PAH solubilities for temperatures or salinities at a
specific site. This information is needed to apply
this ESB and assess the risks of mixtures of
sediment-associated PAHs based on the EqP
methodology.
6.2 Defining Total PAH Concentration in
Field Collected Sediments
"Total PAHs" required for deriving the ESB
for PAH mixtures is defined in this subsection as
the sum of the ESBTUFCV values for a minimum
of the 34 PAHs (18 parents and 16 alkylated
groups) measured in the U.S. EPA EMAP (U.S.
EPA, 1996b, 1998) (Table 6-1). This pragmatic
definition is required because databases from
sediment monitoring programs that have measured
a greater number of PAHs are rare, methodologies
for quantification of greater than the 34 PAHs are
not standard, and the use of fewer than 34 PAHs
may greatly underestimate the total toxicological
contribution of the PAH mixtures. We recommend
that the uncertainty factors developed in this
section for the 13 or 23 commonly quantified
PAHs NOT be used to estimate the ESB for the
34 PAHs when important decisions are to be made
based on the ESB. However, uncertainty values
may be useful in specific non-ESB related
decisions. The recommendation to not use the
uncertainty factors for derivation of ESBs is
intended to prevent the under- or over-estimation
of an ESB acceptable for the protection of benthic
organisms and to encourage the analysis of a
minimum of the 34 PAHs using readily available
analytical methodologies for new monitoring
programs (NOAA, 1998)
It is expected that many sediment assessors
may be in the position where available data are
limited to only certain PAHs (e.g., 13 un-
substituted compounds) and it is impractical to re-
analyze all samples for the full suite of PAHs, but
Table 6-1. Relative distribution of SESBTUFCVTOT to SESBTUFCV13 and SESBTUFCV23 for the combined EMAP
dataset (N=488).
Percentile
50
80
90
95
99
SESBTUFcv,Tor/2ESBTUFcv,i3 2E<
2.75
6.78
8.45
11.5
16.9
SBTUpcvjoT /2ESBTUFcv,23
1.64
2.8
3.37
4.14
6.57
6-1
-------
also undesirable to accept uncertainties stemming
from the incomplete PAH characterization. In this
instance, an intermediate approach may be to
analyze a subset of sediment samples for the full
suite of PAHs and use these data to develop a
site-specific correction factor. This approach
requires the assumption that this correction factor
is consistent across the site, but it seems likely that
the uncertainty with this assumption will be less
than the uncertainty involved in using the generic
correction factors from Table 6-1.
The following subsection presents the analysis
that led to the adoption of the 34 PAHs as total
PAH. Hereafter, mention of "total PAHs" in this
document refers to use of a minimum of the 34
PAHs to derive the SESBTIL
6.2.1 Introduction
PAHs are present in sediments as mixtures
rather than as single compounds. It has been
shown that the toxicity of sediment associated
PAHs is approximately additive, and that PAHs
with both low and high KQW values contribute to
the total toxicity (Section 5). Therefore,
assessment of the toxicological contribution from
the total PAH concentration present in sediments
would theoretically require the measurement in
every sediment of all PAHs. If the compounds
formed by the alkylation of parent PAHs are
included, there are more than several hundred
possible structures, and quantifying all of them is
impractical and costly.
As an alternative to measuring all PAHs, it
may be possible to estimate the total PAH
concentration in sediments using a subset of the
commonly measured PAHs. This is desirable
because the number of individual PAHs measured
in field sediment monitoring programs varies and if
too few PAHs are measured, the toxicity of
sediment-associated PAHs will be underestimated.
For some historical sediment monitoring data, only
13 PAHs identified by the U.S. EPA as
parameters of concern were measured (Table 6-
2). The National Oceanic and Atmospheric
Administration (NOAA, 1991) began to quantify
10 additional PAHs in sediments, bringing the total
number of PAHs measured to 23. Since then, the
majority of sediment monitoring programs have
measured these 23 PAHs (Table 6-2). More
recently, the U.S. EPA EMAP has increased the
number of PAHs measured from 23 to 34 by
quantifying the Cl through C4 alkylated series for
some patent PAHs where the C# indicates an
alkyl group substitution (Table 6-2). The Cl
represents one methyl substitution at any location
on the PAH. The C2 represents either two methyl
substitutions at any two locations or one ethyl
substitution at any one location. The C3
represents either three methyl groups, one methyl
and one ethyl group or one propyl group
substitution. Similarly, the C4 represents any
combination of methyl, ethyl, propyl and butyl
groups so that the total number of carbons added
to the parent PAH is four (Table 6-2). Although a
C# PAH series by itself represents several
different structures, for simplicity a C# PAH
series was considered as one PAH. In total, this
C# PAH alkylated series represents 16 groups of
compounds as listed in Table 6-2.
In this section, the uncertainty limits are
derived for estimating the total PAH toxicological
contribution of the 34 PAHs from the 13 or 23
commonly measured PAHs. Data are presented
using ESBTUFCVi to sum the contributions of the
individual PAHs and determine the total PAH
toxicity of the mixture as represented by the
SESBTTT
6.2.2 Data Collection
Coastal and estuarine sediment data from the
Nation's water bodies were compiled from nine
sources (NOAA, 1991; Adams et al., 1996;
Anderson et al., 1996; Fairey et al., 1996; U.S.
EPA, 1996a,b,1998; Ozretich et al., 2000; Hunt et
al., 1998). With the exception of the Elliott Bay
data (Ozretich et al., 2000), all of the data sources
were from state and/or government funded
sediment monitoring programs. In Elliott Bay, the
PAHs were suspected to be causing the toxicity
due to their elevated levels. Data sources that
were identified had measured concentrations for at
least the 23 PAHs identified by NOAA and
6-2
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Table 6-2. PAH measured in
various sediment monitoring programs. See
Di Toro and McGrath (2000) for data sources.
San Southern NY/NJ
Parameter NOAA SFEI Diego California REMAPA
Acenaphthene
Acenaphthylene
Anthracene
Chrysene
Fluoranthene
Fluorene
naphthalene
phenanthrene
pyrene
Benzo(k)fluoranthene
Benzo(b)fluoranthene
Benzo(a)pyrene
Benzo(a)anthracene
Benzo(e)pyrene
Benzo(gji ,i) perylene
Dibenz(a,h)anthracene
2,6-dimethylnaphthalene
Indeno(l,2,3-cd)pyrene
1-methylnaphthalene
2-methylnaphthalene
perylene
1-methylphenanthrene
2,3,5-trimethylnaphthalene
2-methylanthracene
2-methylphenanthrene
3,6-dimethylphenanthrene
9-methylanthracene
9, 10-dimethylanthracene
Cl-benzo(a)anthracenes /chrysenes
C2-benzo(a)anthracenes /chrysenes
C3-benzo(a)anthracenes /chrysenes
C4-benzo(a)anthracenes /chrysenes
Cl- fluoranth ene s/pyrenes
C2- fluoranth ene s/pyrenes
Cl-fluorenes
C2-fluorenes
C3-fluorenes
Cl-naphthalenes
C2-naphthalenes
C3-naphthalenes
C4-naphthalenes
C 1 -phenanthrenes/anthracenes
C2-phenanthrenes/anthracenes
C3-phenanthrenes/anthracenes
C4-phenanthrenes/anthracenes
Total Number of PAHsB
Number of data points
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X X X X X
X
X
23 25 23 23 23
640 137 182 40 153
Virginian
EMAPB
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
23
318
Elliott
Bay
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
32
30
Carolinian
EMAP
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
34
280
Louisianian
EMAP
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
34
229
A Benzo(b)fluoranthene and benzo(k)flouranthene were measured together.
B A specific Cl-PAH was not included
in the total if the Cl alkylated PAH series was measured.
For example, 1-methylnaphthalene was not included in the total if the Cl-naphthalenes were measured.
6-3
-------
corresponding sediment organic carbon
measurements. Three sources, Elliott Bay, EMAP
Louisianian Province and EMAP Carolinian
Province, had measurements for some of the
alkylated PAH series. The two EMAP sources
analyzed for the same alkylated PAH series. The
Elliott Bay dataset had some alkylated PAHs that
were similar to the EMAP sources and some
alkylated PAHs that were not included in the
EMAP sources. A listing of the PAHs measured
in each dataset is provided in Table 6-2. The first
13 PAHs in the list are the initial 13 PAHs
identified by the U.S. EPA as PAHs of concern.
The first 23 PAHs in the list include the additional
PAHs monitored by NOAA. The total number of
PAHs measured in each dataset is also provided.
To prevent duplicate counting, a specific Cl, C2,
C3 or C4 PAH was not included in the total
number of PAHs if the alkylated PAH series was
measured. As an example, for Carolinian EMAP,
1 -methylnaphthalene was not included in the total,
because the Cl-naphthalenes were measured.
To screen for insoluble PAHs, interstitial water
concentrations were computed from measured
solid phase concentrations using EqP theory (Di
Toro et al., 1991). If the resulting interstitial water
concentrations were greater than the
corresponding solubilities, insoluble PAHs were
assumed to be present in the sediment. For these
cases, the measured solid phase concentrations
were replaced by solid phase concentrations based
on the aqueous solubility of each compound
(CoC.PAHi.Maxi)'
The data were converted to ESBTUFCVi for
individual PAHs by dividing the concentration of
the specific PAH in the sediment (Coc,|j,g/goc) by
the COCPAHlFCVl. SESBTUFCV for each sediment
sample were computed by summing the
ESBTUFCVi for each PAH measured. For
purposes of this section, SESBTUFCV for the 34
PAHs is denoted by SESBTUFCVJOT. Equation 6-1
was used to compute SESBTUFCVTOT
6.2.3 Methodology
ESBTUj/cnror
CoC,PAHi,FCVi
(6-1)
The objective was to determine the
uncertainty of using the 13 PAHs or the 23 PAHs
to predict the SESBTUFCVJOT. Only the
monitoring databases containing 34 PAHs were
used in this analysis. The 13 PAHs were selected
since the majority of the existing sediment
monitoring data include these PAHs. The
uncertainty values for estimating total PAHs from
datasets where 13 or 23 PAHs were measured
were developed from a database of ratios of
2ESBTUFCVJOT to SESBTUFCVJ3 or
SESBTUFCV23. In addition, regression analyses of
SESBTUFCVJOT to SESBTUFCVJ3 or to
SESBTUFCVJOT to SESBTUFCV23 on a log-log
linear basis were conducted to demonstrate the
utility of the ratio approach across the range of
SESBTUFCV values.
6.2.4 Uncertainty in Predicting
ZESBTUFCVTOT
For use in determining the uncertainty in
predicting SESBTUFCVTOT from datasets consisting
of the 13 or 23 PAHs, the two EMAP data
sources that measured the 34 PAHs were
combined and treated as a single data source. In
doing this, a larger dataset that represents both
alkylated and parent PAHs, and therefore,
inherently has the correlative relationships of both
types of PAHs, was generated (N=488). The
relative distributions of the SESBTUFCVTOT to the
SESBTIL_. for the 13 and 23 PAHs for this
rL-V
dataset are provided in Table 6-1. Based on the
observed ratios, the measured SESBTUFCV13 for
the 13 PAHs must be multiplied by 11.5 to obtain
an accurate estimation of the SESBTUFCVTOT with
95 % confidence. Similarly, the measured
SESBTUFCV23 for the 23 PAHs must be multiplied
by 4.14 to obtain an estimate of the
SESBTUFCVTOT with 95% confidence. High
adjustment factors needed to estimate
SESBTUFCVTOT, particularly from 13 PAHs,
indicate the importance of having real
measurements of the 34 PAHs from sediments
where the PAH concentrations are of likely
toxicological significance. In contrast, for
6-4
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
10
o
o.i
PQ
ffi 0.01
OJ
o.ooi
0.0001
0.00001
10
Observed ESBTU
FCV,13
- B
o
OJ
/• '
/
/
/
o.i
0.01
0.001
0.0001
0000011 1 1 i i i null i iininl i iiiiinl i i
/
0.00001 0.0001 0.001 0.01 0.1 1 10
Observed ESBTUFCV23
Figure 6-1. Comparison of observed SESBTUFCVTOT to observed (A) SESBTUFCV13 froml3 PAHs and
(B) 2ESBTUFCX23 from 23 PAHs for the combined dataset including U.S. EPAEMAP
Louisianian and Carolinian Provinces.
6-5
-------
sediments where the SESBTIJ ,, or
r L- V, 13
SESBTUFCV23 times the uncertainty factors does
exceed the ESB, additional measurements
including the 34 PAHs would not be warranted.
The SESBTUFCVTOT can be plotted against the
SESBTUFCV13 for the 13 PAHs and regression
analysis conducted to show that the ratios can be
used fairly well to estimate the SESBTUFCV for
the 34 PAHs from the sum of the 13 PAHs across
a wide range of SESBTUFCVTOT because the slope
is nearly 1.0 (0.9595) (Figure 6-1 A). The solid line
is the mean (50%) ratio of 2.75 from Table 6-1
and the dashed line is the line representing 95% of
the data with a ratio of 11.5. The resulting linear
regression equation from a log-log relationship is
Iog10 2ESBTUFCVTOT = 0.9595 Iog10 2ESBTUF
0.4251(R2 = 0.8236)
(6-2)
A similar analysis using the SESBTUFCVTOT
plotted against the sum SESBTUFCV23 of 23'PAHs
with a regression analysis conducted to show that
the slope of the regression is also nearly 1.0
(1.038) (Figure 6-IB). The solid line is the mean
(50%) ratio of 1.64 from Table 6-1 and the dashed
line represents 95% of the data with a ratio of
4.14. The resulting linear regression equation from
a log-log relationship is
Iog10 SESBTUFCVJOT =1.038 Iog10
0.3576(R2 = 0.9272)
(6-3)
The regression approach has been used to
derive uncertainty factors for estimating the
SESBTUFCVTOT for the 34 PAHs using
combinations of as few as three to as many as 13
or 23 PAHs (McGrath and Di Toro, 2000)
The probability distributions of the
SESBTIJ ,. and SESBTIL„. ,. values for each
rL- V,l 3 rL- V,2j
sediment from the databases in Table 6-2 were
plotted in Figure 6-2 (A and B, respectively). The
actual SESBTUFCV13 values (triangles) exceeded
1.0 for 5.22% of the 1992 sediment samples
(Figure 6-2A) and the SESBTUFCV23 values
(triangles) exceeded 1.0 for 6.55% of the 2001
1000
100
10
1
nrmnpTTTTimi| | I I | I I p
A O Total 34 with 95% Confidence limits
f O T°tal 34 predicted
V Total 13 me;
1000
100
10
1
O 0.01
^
O
w 0.001
nrmpTTTiimi| | I I | r^
B O Total 34 with 95% Confidence limits
i O Total 34 predicted
V Total 23 measured
o I
O
0.01 0.1 1 10 20 50
99 99.9 99.99
% Less Than or Equal To
Figure 6-2. Probability distribution of the (A)
SESBTUFCX13 and (B) ZESBTUFCV23 values
for each sediment from the entire database.
sediment samples (Figure 6-2B). To estimate the
50% uncertainty of SESBTUFCVJOT (plotted as
diamonds in Figure 6-2), the mean ratio of the
SESBTUFCVTOT to SESBTUFCV13 (2.75; see Table
6-1) was applied to the sediment-specific
SESBTUFCV13 values and the mean ratio of
SESBTUFCVJOT to 2ESBTU (1.64) was
applied to the sediment-specific SESBTUFCV23
values. The SESBTUFCVTOT estimated from the
SESBTUFCV13 exceeded 1.0 for 12.9% of the
1992 sediment samples and the SESBTUFCVTOT
estimated from the SESBTUFCV23 exceeded 1.0
for 9.85% of the 2001 sediment samples. The
95% uncertainty estimates of the SESBTUFCVTOT
for each sediment (plotted as circles in Figure 6-2)
was determined by multiplying the sediment-
specific SESBTUFCV13 values by 11.5 and by
multiplying the sediment-specific SESBTU
4.14 (Table 6-1). The 95% limits on the
FCV23 by
SESBTUFCVTOT estimated from the SESBTUFCV13
exceeded 1.6 for 35.5% of the 1992 sediment
samples and the 95% limits on the SESBTUFCVTOT
6-6
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
estimated from the SESBTUFCV23 exceeded 1.0
for 23.7% of the 2001 sediment samples.
Therefore, if the 95% uncertainty ratios are
applied to the SESBTUFCV13 or the SESBTUFCV23
the predicted SESBTUFCVTOT for about one-third
of the sediments are in excess of the ESB for
PAH mixtures of 1.0 SESBTUFCV. This strongly
suggests that new monitoring programs should
quantify a minimum of the 34 PAHs monitored by
the U.S. EPA EMAP program. In addition, field
sediments containing PAHs of principally
petrogenic origin will contain a greater proportion
of alkalyated PAHs and PAHs not quantified in
the 34 "total PAHs"(Bence et al., 1996; Means,
1998; Ho et al., 1999; Page et al., 2002).
Therefore, the uncertainty factors derived above
from sediments containing mostly pyrogenic
PAHs, will underestimate the total PAH toxic unit
contribution of the PAH mixture in sediments
contaminated with mostly petrogenic PAHs. It is
important to repeat that at present, the uncertainty
of using the 34 PAHs to estimate the total
toxicological contributions of the unmeasured
PAHs is unknown and needs additional research.
For existing databases, individuals may wish to
utilize uncertainty factors for sediment assessment
applications other than the derivation of an ESB
for PAH mixtures. An example, the use of the
50% uncertainty factors from Table 6-1 to provide
the "best estimate" of the SESBTUFCVJOT for the
34 "total PAHs" in field sediments to compare
with amphipod abundance (Section 5.3.1, Figure 5-
11). If the number and kinds of PAHs are of
similar proportions from a database from a specific
site, uncertainty factors for adjusting the
concentrations of the PAHs at that site may be
derived using the approach detailed above.
Research to determine the toxicological
contributions of PAHs in sediments that are not
included in the 34 PAHs is encouraged so that the
uncertainty of this definition of "total PAHs" can
be estimated.
6.3 Example Calculation of ESBFCV for
PAHs and EqP-based Interpretation
To assist the users of this ESB for mixtures of
PAHs, example calculations for deriving the ESB
are provided in Table 6-3. For each of three
sediments, the calculations began with measured
concentrations (in bold) of individual PAHs (|J.g/g
dry wt.) and TOC (%) in each sediment. All other
values were calculated. The specific
concentrations in each sediment were selected to
provide examples of how the chemical
measurements are used with the ESB to determine
the acceptability of the mixture of PAHs in a
specific sediment and how the risks of sediment-
associated PAHs can be evaluated within the
technical framework of the EqP and narcosis
approaches. The 34 PAHs constituting what is
defined as "total PAH" in Section 6 are listed.
Also listed are the critical concentrations in
sediment of each of the 34 individual PAHs
Sediment A is provided as an example to
demonstrate how to calculate the SESBTU when
less than the required 34 PAHs have been
chemically analyzed. It is important to remember
that because of the uncertainty in such calculations
the resultant SESBTU must not be considered as
an ESB nor used in important sediment
management decisions. Uncertainty factors
applied to the SESBTU have value, for example,
in determining if additional chemical analyses are
required and prioritizing which sediments require
the additional analyses.
Sediment A is from a historical monitoring
database, it contains concentrations of 13 PAHs
measured as [ig PAH/g dry sediment and has
0.81% TOC. First, the dry weight concentrations
for each PAH were converted to pg PAH/g
organic carbon (CQC, M-g/goc) by dividing by the
fraction organic carbon (foc= 0.0081), where foc
= %TOC/100. Second, the organic carbon-
normalized PAH concentrations in the sediment
were divided by the PAH-specific sediment
concentration of concern (COCPAHlFCVl) to derive
the toxic unit-like ESBTUFCVi for each individual
PAH. In this sediment, none of the measured COCl
exceed the corresponding COCPAHlMm so solubility
constraints do not affect the calculation of
ESBTUFCVi for this sediment. The ESBTUFCVi for
the 13 PAHs were added to derive the SESBTU
for the 13 PAHs (SESBTUFCV13) which is 0.348
6-7
-------
Table 6-3A. Example
PAHA
naphthalene
Cl naphthalenes
acenaphthy 1 ene
acenaphthene
C2 naphthalenes
fluorene
C3 naphthalenes
anthracene
phenanthrene
Cl flourenes
C4 naphthalenes
Cl phenanthrenes
C2 flourenes
pyrene
flouranthene
C2 phenanthrenes
C3 flourenes
Cl fluoranthenes
C3 phenanthrenes
benz(a)anthracene
chrysene
C4 phenanthrenes
Cl chrysenes
benzo(a)pyrene
perylene
benzo(e)pyrene
benzo(b)fluoranthene
benzo(k)fluoranthene
C2 chrysenes
benzo(g,h,i)peryl ene
C3 chrysenes
indeno(l,2,3-cd)pyrene
dibenzo(a,h)anthracene
C4 chrysenes
Sum total of ESBTUFcvi
A PAHs and correspondin
calculations
Coc.PAH.FCVi
(UR/Roc)
385
444
452
491
510
538
581
594
596
611
657
670
686
697
707
746
769
770
829
841
844
913
929
965
967
967
979
981
1008
1095
1112
1115
1123
1214
3 ^OC.PAHi.FCVi ^
of ESBs for PAH mixtures: three sediments.
Sediment A
(TOC=0.81%;foc=0.0081)
COG, pAHi, Mas Cone. CQC
(UR/ROC) (uR/Rdrywt.) (UR/ROC) ESBTUFCvi
61700 0.0894 11 0.0287
-
24000 00348 4.29 0.0095
33400 000
-
26000 0.0722 8.91 0.0166
-
1300 0628 77.6 0.1306
34300 0139 17.1 0.0287
-
-
-
-
9090 0171 21.1 0.0303
23870 0.0806 9.96 0.0141
-
-
-
-
4153 0.0709 8.75 0.0104
826 0157 19.4 0.023
-
-
3840 0164 20.3 0.021
431
4300
2169 0139 17.2 0.0175
1220 0139 17.2 0.0175
-
648
-
-
2389
-
2ESBTUFCv.i3 = 0.3479
id Coc PAHl Mm values are from Table 3-4 (bold).
6-8
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Table 6-3B. Continued.
Sediment B
(TOC=0.886%; foc=0. 00886)
C
PAHA
naphthalene
Cl naphthalenes
acenaphthy 1 ene
acenaphthene
C2 naphthalenes
fluorene
C3 naphthalenes
anthracene
phenanthrene
Cl flourenes
C4 naphthalenes
Cl phenanthrenes
C2 flourenes
pyrene
flouranthene
C2 phenanthrenes
C3 flourenes
Cl fluoranthenes
C3 phenanthrenes
benz(a)anthracene
chrysene
C4 phenanthrenes
Cl chrysenes
benzo(a)pyrene
perylene
benzo(e)pyrene
benzo(b)fluoranthene
benzo(k)fluoranthene
C2 chrysenes
benzo(g,h,i)peryl ene
C3 chrysenes
indeno(l,2,3-cd)pyrene
dibenzo(a,h)anthracene
C4 chrysenes
Sum total of ESBTUrcvi
OQ PAHl, FCVl
(|lg/goc)
385
444
452
491
510
538
581
594
596
611
657
670
686
697
707
746
769
770
829
841
844
913
929
965
967
967
979
981
1008
1095
1112
1115
1123
1214
Coc, PAH, Mixi Cone.
(Mg/goc) (pg/gdrywt.)
61700 0.2703
1.2084
24000 0.0165
33400 0.0401
3.2691
26000 0.3702
5.1079
1300 0.0507
34300 0.5679
-
-
-
-
9090
23870
-
-
-
-
4153
826
-
-
3840
431
4300
2169
1220
-
648
-
-
2389
0.9362
3.3088
0.9267
1.2384
0.408
0.3244
1.0645
1.2664
0.3824
0.81
0.2011
0.2574
0.5644
0.2987
0.1817
0.3511
0.1673
0.1708
0.1962
0.2242
0.1504
0.0279
0.1473
0.0423
0.1196
Coc
((Ig/gcc)
30.51
136.39
4.53
368.98
41.78
576.51
5.72
64.09
105.67
373.46
104.6
139.77
46.05
36.62
120.15
142.94
43.16
91.43
22.69
29.05
63.71
33.72
20.51
ESBTUrcv
0.07925
0.30719
0.00412
0.00922
0.72348
0.07766
0.99227
0.00962
0.1075
0.17294
0.56843
0.15611
0.20375
0.06606
0.0518
0.16106
0.18587
0.05605
0.11028
0.02698
0.03442
0.06978
0.03629
0.02125
39.63 0.04098
18.89 0.01953
19.28 0.01969
22.15
25.3
16.97
3.15
16.63
4.77
13.5
0.02258
0.0251
0.0155
0.00283
0.01491
0.00425
0.01112
SESBTUFcv,TOT = 4.408
A PAHs and corresponding Coc PAHl FCVl and
Coc PAHl Mm values are from Table 3
-4 (bold).
6-9
-------
Table 6-3C. Continued.
Sediment C
(TOC=6.384%;foc=0
COG PAHi FCVi Coc.PAHi.Maxi CCttlC.
PAHA
naphthalene
Cl naphthalenes
acenaphthylene
acenaphthene
C2 naphthalenes
fluorene
C3 naphthalenes
anthracene
phenanthrene
Cl flourenes
C4 naphthalenes
Cl phenanthrenes
C2 flourenes
pyrene
flouranthene
C2 phenanthrenes
C3 flourenes
Cl fluoranthenes
C3 phenanthrenes
benz(a)anthracene
chrysene
C4 phenanthrenes
Cl chrysenes
benzo (a)py rene
perylene
benzo (e)py rene
benzo (b)fluoranthene
benzo (k) fluoranthene
C2 chrysenes
benzo (g,h,i)pery lene
C3 chrysenes
indeno (1 ,2,3 -cd)pyrene
dibenzo (a,h)anthracene
C4 chrysenes
Sum total of ESBTUpcvi
A PAHs and corresponding
(Mg/goc)
385
444
452
491
510
538
581
594
596
611
657
670
686
697
707
746
769
770
829
841
844
913
929
965
967
967
979
981
1008
1095
1112
1115
1123
1214
(Mg/gcc)
61700
-
24000
33400
-
26000
-
1300
34300
-
-
-
-
9090
23870
-
-
-
-
4153
826
-
-
3840
431
4300
2169
1220
-
648
-
-
2389
-
(Mg/g dry wt.)
2.193
1.37
2.04
0.806
1.448
1.387
1.979
3.695
4.208
1.03
2.009
4.559
1.928
20.14
2.519
4.789
3.419
11.73
5.378
8.293
9.197
4.674
5.24
10.97
28.23
8.92
18.14
5.5
4.753
5.583
0.398
10.8
2.499
1.581
Coc
(Mg/goc)
34.4
21.9
32
12.6
22.7
21.7
31
57.9
65.9
16.1
31.5
71.4
30.2
315.5
39.5
75
53.6
183.7
84.2
129.9
144.1
73.2
82.1
171.8
442.2
139.7
284.1
86.2
74.5
87.5
6.2
169.2
39.1
24.8
06384)
ESBTUFcvi
0.0892
0.0493
0.0707
0.0257
0.0445
0.0404
0.0533
0.0974
0.1106
0.0264
0.0479
0.1066
0.0440
0.4526
0.0558
0.1006
0.0696
0.2386
0.1016
0.1545
0.1707
0.0802
0.0884
0.1781
0.4457B
0.1445
0.2902
0.0878
0.0739
0.0799
0.0056
0.1517
0.0349
0.0204
SESBTUpcv.Tor = 3.831
Coc.PAHi.FCVi
B Because Coc exceeds COC)PAHl)Mm , C0
anfl l"oC,PAHi,Mari
values are from Table 3-4
:PAHlMmis substituted for Cocto calculate
(bold).
ESBTUFCVi
(see text).
6-10
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
(Table 6-3). Importantly, only 13 of the 34
individual PAHs defined as total PAH were
measured. Because the toxicological contributions
of all 34 PAHs must be considered if the ESB is to
be protective of benthic organisms, some
assumption must be made regarding the
contribution of the unmeasured PAHs. For a
confidence level of 95%, the uncertainty factor
from Table 6-1 is 11.5, which is then multiplied by
the calculated SESBTUFCV13 of 0.348 for an
estimated value of SESBTUFCV34 of 4.00. Since
this value is greater than one, it suggests the
potential for adverse effects from PAHs.
However, one must realize that this finding is, in
part, a function of the correction factor selected to
relate the data for 13 PAHs to an estimated
SESBTU for 34 PAHs. If the value for 50%
confidence was selected from Table 6-1 (2.75),
the estimated SESBTUFCV34 drops to 0.957, which
is much lower than the value predicted for the
95% confidence interval. This difference
illustrates the importance of measuring all 34 PAH
compounds in order to eliminate unnecessary
uncertainty in applying the PAH ESB.
Sediment B is a PAH-contaminated sediment
from one of the U.S. EPA EMAP monitoring
programs where all 34 of the PAHs in the
sediment and TOC (0.886%) were measured.
The concentrations of each PAH on a yWg PAH/g
organic carbon (Coc, ng/goc) basis were derived
by dividing the dry weight concentrations by the
fraction organic carbon (foc= 0.00886), where foc
= %TOC/100. The organic carbon-normalized
PAH concentrations in sediment were divided by
the PAH-specific sediment concentration of
concern (COCPAHiFCVi) to derive the ESBTUFCVi for
each individual PAH. As was the case for
Sediment A, none of the measured Coc exceeded
Cor Piw ,,, , so solubility constraints did not factor
LJL-,rArll,lVLaxi. •*
into the calculation of ESBTUFCVi. The
ESBTUFCVi values for the 34 PAHs were summed
to determine the SESBTUFCV which was 4.41,
which exceeds the ESB (2ESBTUFCV >1.0) for
PAH mixtures. Further examination of this
sediment suggested that it is contaminated with
primarily petrogenic PAHs; i.e., the ratio of
SESBTUFCV13 (which contains no alkylated
PAHs) to 2ESBTUFCV for the 34 PAHs is low
(approximately 0.1). Chemical analysis of the
PAHs in interstitial water indicated that this
sediment may be unacceptably contaminated by
the mixture of PAHs because it contained 5.6
interstitial water toxic units (IWTUFCV). Ten day
toxicity tests, which were part of the monitoring
project, showed 64% mortality of R. abronius
which is consistent with the IWTUFCV and the 10-
day spiked sediment LC50 for R. abronius at 3.68
SESBTUFCV values (Appendix D). This suggests
the EqP- and narcosis-based ESB is appropriate to
the sediment. The sediment is unacceptable for
the protection of benthic organisms due to the
PAH mixture present and additional studies to
quantify the spatial extent of contamination are
desirable.
Sediment C is also a PAH-contaminated
sediment from an U.S. EPA EMAP monitoring
program where the 34 PAHs and TOC of 6.38%
were measured. The concentrations of each PAH
on a |jg PAH/g organic carbon (Coc, M-g/goc) basis
were derived by dividing the dry weight
concentrations by the fraction organic carbon (foc
= 0.0638), where foc = %TOC/100. Except for
perylene, the organic carbon-normalized PAH
concentrations in sediment were divided by the
PAH-specific sediment concentration of concern
(COCPAHIFCVI) to derive the ESBTUFCVi for each
individual PAH. The concentration of perylene
442.2 M-g/goc exceeded the solubility-constrained
solid phase concentration (COCPAHiMaxi). Thus, the
ESBTUFCVi for perylene was calculated as the
quotient of the solubility-constrained solid phase
concentration over the perylene-specific solid
phase concentration equivalent to the FCV
(ESB 1 UFCV)Perylene — COC)perylene)Maxl/COC)peiylene)FCV).
The ESBTUFCVi values for the 34 PAHs were
summed to determine the SESBTUFCV which was
3.83, a similar value as in sediment B. The PAH
mixture in sediment C exceeds the ESB
(SESBTUFCV >1.0) for PAH mixtures. In contrast
to sediment B, sediment C was not toxic to R.
abronius in 10-day sediment toxicity tests. This
sediment is contaminated with primarily pyrogenic
PAHs; i.e., the ratio of SESBTUFCV13 (which
contains no alkylated PAHs) to SESBTUFCV for
the 34 PAHs is high (approximately 0.5).
6-11
-------
Because this PAH mixture appears to be
combustion related, it suggests the potential for the
presence of soot carbon, coal, or other carbon
forms that show unusual partitioning behavior
relative to normal diagenetic carbon (see Section
6.8). Indeed, chemical analysis of interstitial water
from this sediment showed <0.12 IWTIJ , of
rL-V
PAHs. If normal partitioning behavior was
occuring, one would expect the IWTUFCV to be
very close to the calculated SESBTUFCV (in this
case, 3.89) is indicative of this unusual partitioning
behavior. Physical examination of the sediment
showed the presents of soot-like particles. The
presence of soot and associated differences in
chemical partitioning make the directly calculated
SESBTUFCV overly protective for this sediment.
However, one could apply the general PAH ESB
approach to the interstitial water using IWTUFCV,
or develop site-specific partition coefficients and
recalculate SESBTUFCV using site-specific
CL., ... values calculated from the site-
UC,rArll,r L- Vl
specific partition coefficients, as described in U.S.
EPA (2003b).
6.4 Interpreting ESBs in Combination
with Toxicity Tests
Sediment toxicity tests provide an important
complement to ESBs in interpreting overall risk
from contaminated sediments. Toxicity tests have
different strengths and weaknesses compared to
chemical-specific benchmarks, and the most
powerful inferences can be drawn when both are
used together.
Unlike chemical-specific benchmarks, toxicity
tests are capable of detecting any toxic chemical,
if it is present in toxic amounts; one does not need
to know what the chemicals of concern are to
monitor the sediment. Toxicity tests are also
useful for detecting the combined effect of
chemical mixtures, if those effects are not
considered in the formulation of the applicable
chemical-specific benchmark. However, if the
sediment requirements of the test species are not
met, observed mortality may not be due to
chemical contaminants in the sediment.
On the other hand, toxicity tests have
weaknesses also; they provide information only for
the species tested, and also only for the endpoints
measured. This is particularly critical given that
most sediment toxicity tests conducted at the time
of this writing measure primarily short-term
lethality; chronic test procedures have been
developed and published for some species, but
these procedures are more resource-intensive and
have not yet seen widespread use. In contrast,
chemical-specific benchmarks are intended to
protect most species against both acute and
chronic effects.
Many assessments may involve comparison of
sediment chemistry (e.g., using ESB values) and
toxicity test results. In cases where results using
these two methods agree (either both positive or
both negative), the interpretation is clear. In cases
where the two disagree, the interpretation is more
complex; some investigators may go so far as to
conclude that one or the other is "wrong," which is
not necessarily the case.
Individual ESBs consider only the effects of
the chemical or group of chemicals for which they
are derived. For this reason, if a sediment shows
toxicity but does not exceed the ESB for a
chemical of interest, it is likely that the cause of
toxicity is a different chemical or group of
chemicals.
In other instances, it may be that an ESB is
exceeded but the sediment is not toxic. As
explained above, these findings are not mutually
exclusive, because the inherent sensitivity of the
two measures is different. ESBs are intended to
protect relatively sensitive species against both
acute and chronic effects, whereas toxicity tests
are performed with specific species that may or
may not be sensitive to chemicals of concern, and
often do not encompass the most sensitive
endpoints (e.g., chronic survival, growth or
reproduction). It is also possible for a sediment
above the ESB to be non-toxic if there are site-
specific partitioning conditions that run counter to
the equilibrium partitioning model and its
assumptions (see Section 7.2).
The first step in interpreting this situation is to
consider the magnitude of the ESB exceedance
6-12
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
and the sensitivity of the test organism and
endpoint to the suspect chemical. For example.
the acute-chronic ratio used for the PAH mixtures
ESB is 4.16 (Section 3.3.7); as such, if
SESBTUFCV = 4, one would anticipate lethal
effects only for highly sensitive species. Between
SESBTUFCV of 1 and 4, one would expect only
chronic effects, unless the test species was
unusually sensitive. If SESBTIJ , for PAHs was
^ rL-V
2, for example, one would not generally expect to
see lethality from PAHs in short term sediment
lethality tests.
A more precise method for evaluating the
results of toxicity tests is to calculate effect
concentrations in sediment that are species
specific. For species contained in the toxicity data
for the PAH mixtures ESB (Section 3.2.1), effect
concentrations in sediment can be calculated that
are specific for that organism (using procedures in
Section 4). These values could then be used to
directly judge whether the absence of toxicity in
the toxicity test would be expected from the
corresponding level of sediment contamination.
If the exceedance of the PAH ESB is
sufficient that one would expect effects in a
toxicity test but they were not observed, it is
prudent to initially evaluate the partitioning
behavior of the chemical in the sediment based on
sediment organic carbon content. Later
evaluations may require evaluating partitioning
based on other partitioning phases as described in
Section 6.8. This is performed by isolation of
interstitial water from the sediment and analyzing it
for the same PAHs measured in the solid phase.
Predicted concentrations of chemicals in the
interstitial water can be calculated from the
measured concentrations in the solid phase
(normalized to organic carbon)
Cd=Coc/Koc
(6-4)
For chemicals with log10KQW greater than 5.5,
corrections for DOC binding in the interstitial
water will be necessary
Cd ~ COC/KDOC
(6-5)
more), it suggests that the organic carbon in that
sediment may not partition similarly to more typical
organic carbon, and derivation of site-specific
ESBs based on interstitial water may be warranted
(U.S.EPA, 2003b).
6.5 Photo-Activation
6.5.1 Overview
Research over the last decade has shown that
the presence of ultraviolet (UV) light can greatly
enhance the toxicity of many PAHs. This "photo-
activated" toxicity has been shown to cause rapid,
acute toxicity to several freshwater and marine
species including fish, amphibians, invertebrates,
plants and phytoplankton (Bowling et al., 1983;
Cody et al., 1984; Kagan et al., 1984; Landrum et
al., 1984a,b;Orisetal., 1984;AllredandGiesy,
1985; Kagan etal., 1985; Oris and Giesy, 1985,
1986, 1987; Gala and Giesy, 1992; Huang etal.,
1993; Gala and Giesy, 1994; Ren etal., 1994;
Arfsten et al., 1996; Boese et al., 1997; Huang et
al., 1997; McConkey et al., 1997; Pelletier et al.,
1997; Diamond and Mount, 1998; Hatch and
Burton, 1998; Boese et al.,1999; Monson et al.,
1999; Speharetal, 1999; Pelletier etal., 2000;
Barren et al., 2003). Depending on the organism
and exposure regime, photo-activation can
increase toxicity of certain PAHs by one to four
orders of magnitude over that caused by narcosis.
The mechanism for phototoxicity has been
related to the absorption of ultraviolet radiation
(UV) by the conjugated bonds of selected PAH
molecules
PAH + UV - PAH* + O - PAH + O *
(6-6)
If the measured chemical in the interstitial
water is substantially less (e.g., 2-3 fold lower or
This excites the PAH molecules to a triplet
state (PAH*) which rapidly transfers the absorbed
energy to ground state molecular oxygen (O2)
forming excited singlet oxygen intermediaries
(O2*) (Newsted and Giesy, 1987). Although
extremely short-lived (2 to 700 \is), oxygen
intermediaries are highly oxidizing and can cause
severe tissue damage upon contact. Despite the
many different parent PAHs and related alkylated
6-13
-------
forms, not all PAHs induce photo-activated
toxicity. Those PAHs that are photo-activated can
be predicted using various molecular physical-
chemical variables (Newsted and Giesy, 1987;
Oris and Giesy, 1987); however, the Highest
Occupied Molecular Orbital - Lowest Unoccupied
Molecular Orbital gap model (HOMO-LUMO)
has been the most successful (Mekenyan et al.,
1994a,b;Veithetal., 1995a,b;Ankleyetal., 1996;
Ankley et al., 1997). As research on the nature of
photo-activated toxicity has evolved, certain key
elements of this phenomena have been better
defined including interactions of UV and PAH
dose, effects of temperature, humic substances,
organism behavior, turbidity, dissolved oxygen,
mixtures, photoperiod and additivity (Oris et al.,
1990; McCloskey and Oris, 1991; Ankley etal.,
1995, 1997; Ireland et al., 1996; Hatch and
Burton,1998, 1999; Erickson etal., 1999;Nikkilaet
al., 1999; Weinstein and Oris 1999, Weinstein
2002).
Several studies have been performed with
sediments contaminated with PAHs to assess the
importance of photo-activated toxicity in the
benthos (Davenport and Spacie, 1991; Ankley et
al., 1994; Monsonetal., 1995; Sibleyetal., 1997;
Swartz et al., 1997; Boese et al., 1998; Kosian et
al., 1998; Boese etal., 1999; Speharetal, 1999;
Wernersson et al., 1999; Pelletier et al., 2000).
These studies conclude that photo-activated
toxicity may occur in shallow water environments;
however, the magnitude of these effects are not as
well characterized as in water-only exposures and
are probably not as dramatic as those observed in
the water column. Comparisons by Swartz et al.
(1995) suggest that responses of benthic
communities in PAH-contaminated sites correlate
well with the toxicity that is predicted based on
narcosis, suggesting that photo-activation was not
a major confounding factor for those environ-
ments. However, Boese et al., (1997) and
Pelletier et al. (In prepartion) show that life history
of benthic organisms is critical to assessing
whether or not photo-activated toxicity will occur.
For example, several marine species which
frequently encounter ultraviolet radiation during
low tide are not vulnerable to photo-activated
toxicity due to light protective adaptation (e.g.,
shells, pigments, borrowing). Additionally, there is
evidence that maternal transfer of PAHs from
benthic adult bivalves to pelagic embryos does
occur (Pelletier et al., 2000).
6.5.2 Implications to Derivation of ESB
Because the PAH mixture ESB derived here
is based on narcosis, if there is additional toxicity
caused by photo-activation it may cause the ESB
to be underprotective. At present, the magnitude
of potential errors can not be specifically
quantified, and are the subject of scientific debate
(Swartz et al., 1997; Boese et al., 1999; Diamond
and Mount, 1998; McDonald and Chapman, 2002).
If photoactivation of PAHs is ecologically relevant,
it is probably most significant primarily for
organisms that inhabit very shallow or very clear
water. This is because of the rapid attenuation of
ultraviolet radiation in the water column (Pickard
and Emery 1982;Wetzel, 1983). For example,
<25% of incident UV penetrates below the first
meter of water in productive aquatic systems. In
areas where PAH-contaminated sediments are
present in shallow environments the risk of photo-
activated toxicity is greater and a site-specific
ESB may need to be generated that considers this
potential risk (U.S. EPA, 2003b).
6.6 Teratogenicity and Carcinogenicity
This subsection presents an analysis intended
to determine if the ESB for PAH mixtures of <1.0
SESBTUFCV is protective for non-narcosis modes
of toxic action of individual PAHs. Published
articles were screened for applicable data on
teratogenic (Appendix G) and carcinogenic
(Appendix H) effects of individual PAHs and
their mixtures. Five laboratory studies with
benzo(a)pyrene (BaP), predominantly water
exposures, and one with anthracene were selected
for analysis of teratogenic effects; two laboratory
studies with BaP were selected for analysis of
carcinogenic effects (Table 6-4). In the teratogen
studies, typically radio-labeled BaP was used to
quantify the accumulation of the PAH and its
metabolites in fish ranging in lifestage from
6-14
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Table 6-4. Teratogenic and carcinogenic effects of benzo(a)pyrene (BaP) and anthracene on freshwater and
saltwater fishes. Measured concentrations of exposure are converted to sediment concentrations
(Coc) likely to result in the equivalent effect using EqP and SAR methodology.
Organism/
Chemical
Measured Cd-derived Measured
CdA Coc CORGB
(ug/L) (ng/goc) (ng/g)
CLB CL-derived
(ng/g Lipid) Coc (^g/goc)
References
FRESHWATER
Fathead minnow eggs
Anthracene
Topminnows
BaP
Rainbow trout eggs
BaP
SALTWATER
English sole eggs
BaP
Sand sole eggs
BaP
Calif, grunioneggs
Calif, grunioneggs
>3.81C
(1,000)
0.21
0.1
>3.81
(5)
>3.81
(24)
Calif, grunioneggs >3.81C
(869)
TERATOGENIC EFFECTS
0.06 147
>3810 9 0.06 150
210 1.9 0.05 38.6
157 0.03 5233D
100 2.1 0.03 70
>3810 1 0.03 33.3
>3810 10.5 0.03 350
>3810 20 0.03 666
CARCINOGENIC EFFECTS
219 Hall and Oris, 1991
256 Goddard etal., 1987
66 Hannah etal., 1982
Hose etal., 1984
8,937D Hose etal., 1981
120 Hose etal., 1982
57 Winkler etal., 1983
598 Winkler etal., 1983
1137 Winkler etal., 1983
FRESHWATER
Japanese medaka
Guppy
>3.81C
(261)
>3.81C
(209)
>3840
>3840
Hawkins et al., 1988,
1990
Hawkins et al., 1988,
1990
A If the concentration of BaP exceeded its solubility of 3.81 /^ig/L, the published concentration in water is listed in parenthesis
with the solubility of 3.81 ,ug/L listed above as the concentration of exposure. Therefore the maximum Coc value for these
exposures is 3840,ug BaP/goc.
B Concentrations in eggs on a wet weight basis are converted to concentrations on a lipid basis using lipid concentrations
(fLipid) from Table 1 in Kamler (1992).
c Water concentrations of BaP were not stable throughout the duration of the experiment.
D The solubility of BaP in water theoretically limits the maximum concentration in eggs to - 3,840 Mg/g lipid and in sediments
to - 3,840 Mg/goc= but metabolites of BaP will likely be included in radio-labeled quantification of total BaP equivalents.
6-15
-------
embryo to adults. The water PAH concentrations
associated with teratogenic and carcinogenic
effects were generally high and steady-state was
not always achieved. The solubility limit in water
for BaP of 3.81 |Jg/L was exceeded in 6 of 8
experiments (Table 6-4). In contrast, for seven of
the experiments, the BaP concentration in eggs or
fish tissue was also listed as an observed effect
concentration. The theoretical solubility-limited
maximum of 3840 |jg BaP/g lip id was exceeded
only in one of the experiments. For these reasons,
when the concentration of BaP plus metabolites
was measured in the eggs or tissue of the
organism, this concentration was considered the
most valid representation of the true observed
exposure concentration and the water
concentration was not used in further analysis.
Elutriates from crude oil contained non-PAH
compounds and the relationship of total PAH
concentrations in the study vs total PAH as
defined in this document were difficult to
determine in the Carls et al. (1999) study;
therefore, these data were also excluded from this
analysis. Although metabolism of PAHs is known
to occur in invertebrates such as polychaetes,
mollusks and crustaceans (McElroy et al., 2000),
data on the potential carcinogenic effects of the
metabolites is unknown.
As indicated in Table 6-4 and Appendix H, the
database for carcinogenic effects of PAHs on
aquatic (fish) species from laboratory studies is
limited. Most of the available data are from
studies of epizootic outbreaks of neoplasia
(tumors) from highly contaminated field sites such
as the Black River, Ohio (see Baumann and
Harshbarger 1998 for a review) or Puget Sound,
WA (Malins et al., 1987, Myers et al., 1990), to
mention only a notable few. The applicability of
these field studies to a causal relationship between
carcinogenic effects observed and PAH
concentrations is limited by the possible interactive
effects of the PAHs with PCBs and other
simultaneously occurring chemicals. The bulk of
laboratory experimental evidence for carcinogenic
effects of PAHs is based on the distribution of
neoplasms in fish species exposed to PAH-
enriched sediment extracts (Black, 1983; Metcalfe
etal., 1988;Fabacheretal., 1991), dietary
exposures or inter-peritoneal injection (Hendricks
et al., 1985), or intermittent water exposures of
7,12-dimethylbenzanthracene (Schultz and Schultz,
1982). These studies are listed in Appendix H for
completeness, but were not included in Table 6-4
for further analysis. This is because the exposure
regime or concentrations of individual or mixtures
of PAHs were not provided in sufficient detail to
permit critical measured sediment concentrations,
or sediment concentrations derived from
concentrations in water or tissue, to be compared
to the observed carcinogenic effects. The study
with 7,12-dimethylbenzanthracene (Schultz and
Schultz, 1982) was not considered for analysis
because this PAH is not commonly measured as
part of environmental monitoring programs (see
Table 6-2).
A far more extensive database exists on the
influence of PAHs on various aspects of tumor
biology, such as PAH-DNA adduct formation and
phase I (oxidation, reduction, and hydrolysis
reactions) and phase II (glucuronidation and
glutathione conjugation) metabolism of individual
compounds. However, as indicative of cytotoxicity
as these biomarkers may or may not be, they have
been excluded from the analysis for the explicit
purposes of this subsection. The methods of PAH
exposure that were useful for this analysis were
aqueous (Hannah et al., 1982; Hose et al., 1982,
1984; Winkler etal., 1983; Goddard etal., 1987;
Hawkins etal., 1988, 1990), maternal (Hall and
Oris, 1991), or inter-peritoneal injection of adult
English sole (Parophrys vetulus) followed by
measurement of concentrations in embryos (Hose
etal., 1981).
6.6.1 Calculations
When the measured concentration of the PAH
dissolved in water (Cd; |^g/L) associated with a
teratogenic or carcinogenic effect was available it
was multiplied by its KQC (L/kgoc) x 10~3 to derive
an equivalent effect concentration in sediment (Cd-
derived Coc; |j,g/goc), as per the EqP methodology
(Table 6-4; Appendix G and H). When the
measured concentration of the PAH in eggs or
tissue (CL; /j-g PAH/g lipid) associated with an
6-16
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
§
•-C
0.001
0.0001 0.001
0.01
10
100
1000
100
10
0.1
0.01
: B
0.001
0ocf*P
OMXJXXDCOCCOOOOO
oooo ol
N=539
0.1 1
10 20
50
80 90
99
99.9
% Less Than or Equal To
Figure 6-3. BaP concentration of 539 sediment samples from the EMAP and Elliott
Bay datasets versus (A) the 2ESBTUFCVvalues of 34 PAHs and (B) a
probability plot of these BaP concentrations at an SESBTUFCV= 1.0.
effect was available, its equivalent effect
concentration in sediment (CL-derived Coc; /j-g/
goc) was calculated using the equation.
log10Coc = 0.00028 + log10CL + 0.038 log10Kow (6-7)
6.6.2 Critical Sediment Concentrations for
Teratogenic and Carcinogenic Effects
versus ESBs for PAH Mixtures
The critical sediment concentrations (i.e..
Cd- or CL-derived Coc) that would be expected to
cause teratogenic or carcinogenic effects on the
five freshwater and three saltwater fishes exposed
to BaP ranged from 57 to 8,937 ug/goc; the only
C for anthracene was 219 ug/gor (Table 6-4).
The majority of Coc values were derived using
concentrations measured in fish eggs. Six of the
nine Coc concentrations for BaP were less than
the solubility-limited maximum concentration of
3,840 ug/goc. The Coc value of 8,937 ug /goc is
retained because the concentrations in the eggs
probably included metabolites of BaP that are
quantified as total BaP equivalents in the radio-
label analysis. The Coc values for individual
PAHs in sediments were then compared to PAH
concentrations in monitored field sediments to
determine if teratogenic or carcinogenic effects
might occur in sediments having <1.0 SESBTUFCV.
This analysis was used to determine if the ESB
derived from the narcosis mode of action was
protective of teratogenic or carcinogenic effects.
6-17
-------
O
CO
0.0001
1000
100
00°'
,oco'
N=539
0.1
10 20
SO
80 90
99 99.9
% Less Than or Equal To
Figure 6-4. Anthracene concentration of 539 sediment samples from the EMAP and
Elliott Bay datasets versus (A) the SESBTUFCV values of 34 PAHs and (B) a
probability plot of these anthracene concentrations at an SESBTUFCV 1.0.
The database from the U.S. EPA EMAP
(U.S. EPA 1996b, 1998) and Elliot Bay (Ozretich
et al., 2000) sediment monitoring programs were
used to compare the BaP (Figure 6-3 A) or
anthracene (Figure 6-4A) concentration of 539
sediment samples where 34 PAHs, or 33 of 34
PAHs for Elliott Bay, were measured versus the
SESBTUFCV for all PAHs measured in those
sediments. The lowest critical sediment
concentration for teratogentic or carcinogenic
effects is indicated with a solid line at 57 |j,g/gocfor
BaP and at 219 v-g/goc for anthracene. None of
the sediments having <1.0 SESBTUFCV contained
BaP or anthracene at concentrations likely to
cause the teratogenetic or carcinogenic effects
reported in Table 6-4. The same database of PAH
concentrations in field sediments was used to
calculate the sediment-specific BaP:SESBTUFCV
ratio and the sediment-specific anthracene:
SESBTUFCV ratio. The total PAH concentration
in each of the 539 sediments was multiplied by its
sediment-specific ratio to determine the BaP or
anthracene concentration for the sediment if the
SESBTUFCV was equal to 1.0. Probability plots of
the calculated concentrations for BaP and
anthracene at 1.0 SESBTUFCV are in Figures 6-3B
and 6-4B, respectively. The dashed lines
represent the critical sediment concentration of 57
|j,g/gocfor BaP and 219 |J.g/goc for anthracene.
Based on this analysis, none of the sediments for
anthracene and only 3.53% of the sediments for
BaP would be expected to produce teratogenic or
carcinogenic effects if the proportions of BaP or
anthracene in these sediments were maintained
and the concentrations of each of the other PAHs
were increased so that all sediments contained 1.0
6-18
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
SESBTUFCV. The approach of examining these
relationships individually with BaP or anthracene
may be flawed because it may under-represent the
teratogenic of carcinogenic contributions of other
PAHs with the same mode of action in the PAH
mixture. However, at present insufficient data are
available to appropriately sum the contributions of
multiple teratogenic or carcinogenic PAHs.
6.7 Equilibrium and ESBs
Care must be used in application of ESBs in
disequilibrium conditions. In some instances site-
specific ESBs may be required to address this
condition (U.S. EPA, 2003b). Benchmarks based
on EqP theory assume that nonionic organic
chemicals are in equilibrium with the sediment and
interstitial water, and that they are associated with
the sediment primarily through absorption into
sediment organic carbon. In order for these
assumptions to be valid, the chemical must be
dissolved in interstitial water and partitioned into
sediment organic carbon. The chemical must,
therefore, be associated with the sediment for a
sufficient length of time for equilibrium to be
reached. With PAHs, the absence of toxicity
when the ESB is exceeded may be because of the
presence of less available PAHs associated with
soot, coal or similar materials in sediments (see
discussion in Section 6.8). Alternatively,
disequilibrium exists, and ESB may be over-
protective, when PAHs occur in sediments as
undissolved liquids or solids; although the use of
solubility limited acceptable sediment
concentrations should adequately account for this.
In very dynamic locations, with highly
erosional or depositional sediments, the partitioning
of nonionic organic chemicals between sediment
organic carbon and interstitial water may only
attain a state of near equilibrium. Likewise,
nonionic organic chemicals with high log10KQW
values may come to equilibrium in clean sediment
only after a period of weeks or months.
Equilibrium times are shorter for chemicals with
low log10KQW values and for mixtures of two
sediments with similar organic carbon-normalized
concentrations, each previously at equilibrium.
This is particularly relevant in tidal situations
where large volumes of sediments are continually
eroded and deposited, yet near equilibrium
conditions between sediment and interstitial water
may predominate over large spatial areas. For
locations where times are sufficient for equilibrium
to occur, near equilibrium is likely the rule and
disequilibrium uncommon. In many environments,
disequilibrium may occur intermittently, but in those
cases ESBs would be expected to apply when the
disturbance abates. In instances where long-term
disequilibrium is suspected, application of site-
specific methodologies may be desirable (U.S.
EPA, 2003b).
6.8 Other Partioning Phases
6.8.1 Overview
In general, laboratory studies with PAHs have
shown the same partitioning behavior
demonstrated by many classes of nonpolar organic
contaminants (Chiou et al., 1979,1983; Karickhoff
et al., 1979; Means et al., 1980; Di Toro et al.,
1991). However, there are some data indicating
that PAHs do not always follow equilibrium
partitioning behavior in the environment.
Specifically, some studies have reported larger
partitioning coefficients for PAHs in field-collected
sediments than is predicted based on laboratory or
theoretically-generated log10Kow/Koc values
(Prahl and Carpenter, 1983; Socha and Carpenter,
1987; Broman et al., 1990; McGroddy and
Farrington, 1995; Maruyaetal., 1996; McGroddy
et al., 1996). The observed differences in
partitioning of PAHs may relate to differences in
PAH sources with the speculation that PAHs from
pyrogenic sources (e.g., soot carbon, coal or
similar materials) may be more strongly associated
with the particulate phase than PAHs from some
petrogenic sources (Readman et al., 1984; Socha
and Carpenter, 1987; McGroddy and Farrington,
1995; Meador et al., 1995; Naes et al., 1995;
Chapman et al., 1996; Maruya et al., 1996;
McGroddy et al., 1996; Gustafsson and Gschwend,
1997; Gustafsson et al., 1997; Naes and Oug,
1997; de Maagd et al. 1998; Naes and Oug 1998;
6-19
-------
Naes et al., 1998; Bucheli and Gschwend 2000;
Jonker and Smedes 2000; Ozretich et al., 2000;
Jonker and Koelmans 2001, 2002a,b; Accardi-Dey
and Gschwend 2002, 2003). The result is that
PAH concentrations in interstitial water are
significantly lower compared to the organic
carbon-based sediment concentration from
laboratory or theoretically-predicted KQC values
and, presumably, exhibit correspondingly lower
bioavailability. Several studies have proposed that
the lack of observable biological effects from
sediments (and other samples) containing high
concentrations of presumably bioavailable PAHs is
related to this phenomena (Farrington et al., 1983;
Varanasi et al., 1985; Bender et al., 1987; Rickey
etal., 1995; Knutzen, 1995; Chapman etal., 1996;
Paine et al., 1996; Maruya et al., 1997; Oug et al.,
1998; Lamoureux and Brownawell 1999; Naes et
al., 1999; West et al., 2001; Talley et al., 2002).
The mechanisms causing these field
observations of unusual PAH partitioning are not
well understood. One explanation proposes that
PAHs condense into the soot matrix during particle
formation, and are thereby sterically inhibited from
partitioning to interstitial water as would be
expected under equilibrium conditions. A second
perspective assumes that the soot fraction
represents a second partitioning phase in addition
to normal organic carbon. The partitioning of
PAHs from this phase approximates the
equilibrium behavior assumed for normal organic
carbon, but have a much higher partition
coefficient than biologically-derived organic carbon
(represented by Koc) (Gustafsson and Gschwend,
1997, 1999). Methods for measuring the soot
carbon fraction in sediments (fsc) continue to be
developed and evaluated (Verardo 1997;
Gustafsson et al., 1997; Karapanagioti et al., 2000;
Gelinas et al., 2001; Currie et al., 2002; Gustafsson
et al., 2001; Song et al., 2002) but no one method
is recognized as most accurate, although those
based on Gustaffson et al. (1997) are probably
used most frequently.
Once partition coeffifients are available and
fsc can be measured, the soot phase can then be
incorporated into an expanded partitioning equation
with two partitioning terms
= foc KOC
fsc Ksc
(6-8)
where, K is the partition coefficient for the
expanded partitioning equation, foc and fsc are the
fraction organic carbon and fraction soot carbon,
respectively, and Koc and Ksc are the organic
carbon and soot carbon partition coefficients.
Recently, Bucheli and Gustaffson (2000) and
Accardi-Dey and Gschwend (2002; 2003)
proposed a new version of Equation 6-8 which
includes a non-linear term for the soot carbon
contribution to partitioning
. — !„„ K. + i K. C ,n
P oc oc sc sc a
(6-9)
where, the exponential 'n' is the Freundlich term
used to fit the non-linear relationship between
particulate and dissolved PAH. This description of
the interaction of PAHs and soot carbon is more
accurate but is currently limited in practicality by
the lack of values for Ksc and n for many PAHs.
Another phase for which there is less data
available as compared to soot carbon but which
may also alter the partitioning and bioavailability of
PAHs is non-aqueous phase liquids (NAPLs) like
coal tar found at manufactured gas plant sites
(Lane and Loehr 1992; Luthy et al., 1994;
Mahjoub et al., 2000). The significance of these
liquids relative to the benthic toxicity of PAHs is
not yet understood fully.
6.8.2 Implications to Derivation ofESB
Irrespective of the mechanisms, these issues
have the potential to affect the predictive power
and accuracy of the PAH mixtures ESB. For
soot, coal and similar materials, their presence are
associated with reduced concentrations of PAH in
interstitial water, one would presume that this
results in decreased bioavailability of PAHs, a
phenomenon demonstrated by West et al. (2001).
This, in turn, would make the PAH mixtures ESB
derived here overprotective, because the Koc-
based partitioning model would overpredict
chemical activity and, therefore, concentrations of
PAH in interstitial water and in organisms.
Importantly, most sediments are expected to
6-20
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
contain insufficient concentrations of PAHs to
exceed the ESB. Therefore, even if partitioning to
soot, coal and similar materials reduces the
interstitial water concentration and biological
availability of the PAHs, the partitioning effect is
not important because PAH concentrations in the
sediment are judged by the ESB as acceptable
without invoking complex measurements of
partitioning to soot, coal and similar materials.
Further, most sediments where empirical data on
partitioning that demonstrates soot, coal and
similar materials are important are sediments that
relative to the ESB are uncontaminated. Also, for
sediments that have concentrations of PAHs in
excess of the ESB, data suggest minimal error in
ignoring partitioning to soot, coal and similar
materials and ascribing partitioning to only organic
carbon. Most applications of the PAH mixture
narcosis model to toxicity data for field-collected
sediments show good predictive ability for the ESB
(see Section 5.3). This may be because most
sediments that are sufficiently contaminated to
cause narcosis are contaminated by PAH sources
that exhibit normal partitioning behavior, such as
creosote and other petrogenic sources. In their
study of PAH-contaminated sediments, Ozretich et
al. (2000) found that discrepancies between
measured and predicted partitioning behavior
predominated in sediments with lower PAH
concentrations, while those with higher PAH
concentrations showed partitioning behavior closer
to that predicted from published KOW/KQC
relationships. This differential behavior was
attributed to the presence of two PAH sources,
with creosote being the source causing the highest
levels of contamination and toxicity.
In cases where it is suspected that soot, coal,
or other materials including coal tars and other
NAPLs may be causing unusual partitioning, direct
measurement of PAH concentrations in interstitial
water may be used to evaluate this possibility and,
where necessary, derive site-specific sediment
benchmarks which account for local differences in
partitioning behavior (see U.S. EPA2003b).
6.9 Aqueous Solubility Under Non-
Standard Conditions
It has been long established that organic
compounds are generally less soluble in aqueous
solutions at colder temperatures than at warmer,
and in salt solutions such as seawater, than in
freshwater, a phenomenon termed the salting-out
effect (May, 1980; Schwarzenbach et al., 1993;
Xie et al., 1997). Setschenow (1889) derived an
empirical relationship for the magnitude of the
salting-out effect
log10('S0 / 4S%o) = Ks Csalt (6-9)
where 'S0 and'S^ are the aqueous solubilities of
the solute in fresh and saltwater (mol/L) at a given
temperature (t in the units °C), respectively, Ks is
1.6
o
U
1.4.
1.2-
1.0.
0.8.
0.6.
0.4.
0.2.
0.0
0
10 15 20
Temperature (DC)
25
30
35
Figure 6-5. Computed solubilities of nine PAHs relative to their 25 ° C solubilities as a function of temperature.
6-21
-------
the Setschenow constant (L/mol) for the salt
solution and the solute of interest, and C .. is the
salt
molar salt concentration. A one molar salt solution
(NaCl) is approximately equivalent to 48%o sea
water (Owen and Brinkley, 1941), and Kg was
found to be essentially invariant with temperatures
from 1 to 30°C, averaging 0.28 ± 0.02 (mean ±
SE) (May, 1980) for 9 PAHs. Temperature has
been shown to have a non-linear effect on PAHs
solubilities (May, 1980). Concentrations of nine
PAHS (naphthalene, fluorene, phenanthrene, 1-
methylphenanthrene, anthracene, fluoranthene,
pyrene, benz(a)anthracene, and chrysene) were
computed for distilled water at temperatures
between 5 and 30°C using the relationships of
May (1980) and are compared with the
compound's concentrations at 25 °C (Figure 6-5).
The least-squares exponential representation of
the data is as follows
CS0 = 25S0) = 0.261 e°0536t, r2 = 0.959 (6-10)
where 25SQ is the commonly reported solubility of a
compound at 25 °C in freshwater. Although
naphthalene's solubility has the least response to
temperature of PAHs, estimates from Equation 6-
10 are only +8% and -30% inaccurate for
naphthalene at the temperature extremes (Figure
6-5).
The solubility of PAHs under environmental
conditions can be estimated from the following
relationship that is a combination of Equations 6-9
and 6-10 using the average Setschenow constant
'S^ = tS0 I0-aooo583%° (6-11)
where %o is the salinity of the sea water. This
correction for solubility can be used as part of the
procedures to modify this ESB for site-specific
conditions.
6-22
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
Section 7
Sediment Benchmark Values:
Application and Interpretation
7.1
Benchmark Value
The procedures described in this document
and in the "Technical Basis for the Derivation of
Equilibrium Partitioning Sediment Benchmarks
(ESBs) for the Protection of Benthic Organisms:
Nonionic Organics" (U.S. EPA, 2003a) indicate
that, except possibly where a locally important
species is very sensitive or benthic organisms are
exposed to both significant amounts of PAHs and
UV light, benthic organisms should be acceptably
protected from the effects of PAH mixtures in
freshwater and saltwater sediments if the
EESBTUFCV is less than or equal to 1.0:
ESB = EESBTUFCV =
Coci
CoC,PAHi,FCVi
(7-1)
Freshwater or saltwater sediments containing
<1.0 SESBTlLrv of the mixture of the 34 PAHs
'FCV
or more PAHs are acceptable for the protection
of benthic organisms, and if the SESBTUFCV is
greater than 1.0, sensitive benthic organisms may
be unacceptably affected. PAHs.
As indicated, this sediment-specific benchmark
is the sum of the quotients of the concentrations
of individual PAHs in a sediment, on an organic
carbon basis, each divided by its respective
CocpAHiFCVi. At a minimum, the definition of total
PAHs for this ESB requires quantification of the
34 PAHs analyzed by the U.S. EPA as part of the
EMAP and REMAP programs (PAHs are
identified in bold in Table 3-4).
The ESB is intended to protect benthic
organisms from direct toxicity associated with
exposure to PAH-contaminated sediments. The
ESB does not consider the antagonistic, additive or
synergistic effects of other sediment contaminants
in combination with PAHs or the potential for
bioaccumulation and trophic transfer of PAHs to
aquatic life, wildlife or humans.
7.2 Special Considerations
To establish a national benchmark that is
widely applicable, certain issues must be
considered. It is possible that site-specific
conditions may affect the broad applicability of
such a benchmark. These include:
1. Fewer than 34 PAHs have been measured.
Particularly in cases where historical data are
being examined, chemistry data may be available
for fewer than the 34 PAHs recommended for this
benchmark. Calculating SESBTUFCV directly
using fewer PAHs will cause the benchmark to be
underprotective because PAH mixtures found in
the environment typically contain substantial
concentrations of PAHs outside the suites of 13
or 23 PAHs commonly measured in monitoring
programs. The analysis of PAH distributions
across many geographic regions has been used to
develop uncertainty factors that can be used to
adjust SESBTUFCV based on subsets of 13 or 23
PAHs with varying degrees of certainty (see
Section 6.2). In some applications using these
uncertainty factors, it may be important to
minimize the frequency of false negatives
(sediments judged to be acceptable when they are
not). For these cases, the SESBTUFCV calculated
from a subset of 13 PAHs (see Table 6-1 for
listing) can be multiplied by 11.5, or the
SESBTUFCV calculated from a subset of 23 PAHs
7-1
-------
Sediment Benchmark Values: Application and Interpretation
(see Table 6-1 for listing) can be multiplied by 4.14
to achieve 95% confidence that the actual
SESBTUFCV for all 34 PAHs would not be higher
than the calculated value. In this case, the
uncertainty for the 95% confidence level is
applied. This means that most of the sediments
may actually contain fewer SESBTUFCV than
indicated by the calculation. In cases where less
conservative assumptions are appropriate, factors
with lower confidence can be applied, as detailed
in Section 6.2.
Use of the uncertainty factors from Section 6.2
assumes that the relative frequency distributions of
PAHs in sediments used to calculate the factors
are similar to those of the sediments to which the
uncertainty factors are applied. This assumption is
likely significantly violated for sediments containing
predominately petrogenic PAHs. While the
uncertainty factors can be used to derive the
SESBTUFCV, this value should not be considered
as an ESB. In principal, ESBs based on the
SESBTUFCV calculated using a minimum of the 34
specified PAHs and can be used to make
important sediment decisions. In contrast,
important sediment decisions should not be made
using SESBTUFCV values when fewer PAHs, such
as the 13 or 23 PAHs commonly quantified, and
uncertainty factors. To avoid errors introduced by
the use of uncertainty factors, wherever possible,
a more complete PAH chemical analysis should be
undertaken with concentrations for a minimum of
the 34 specified PAHs analyzed.
2. Interaction of PAHs with UV light.
Benchmarks calculated in this document are based
on narcotic toxicity only and do not consider
enhanced toxicity that can occur if PAH-exposed
organisms are simultaneously exposed to UV light.
In environments where significant sunlight
penetrates to the sediment and benthic organisms
are exposed to UV light, the ESB may be
underprotective. Consult Section 6.5 for additional
details.
3. Influence of soot carbon and coal on PAH
partitioning. PAHs may partition less to interstitial
water in sediments that contain soot and/or coal
particles or similar materials that expected with
typical organic carbon partitioning. This could
cause the benchmark to be overprotective. The
influence of these phases can be assessed by
measuring concentrations of PAHs directly in
interstitial water and comparing these measures
with concentrations predicted by EqP or through
quantification of partitioning to these other
sediment phases. See Section 6.8 and the site-
specific ESBs (U.S. EPA, 2003b) for further
discussion. NAPLs are not directly addressed by
this document, but may be expected to result in
reduced interstitial water concentrations of PAHs.
4. Unusual composition of organic carbon.
Partition coefficients used for calculating the
national PAH mixture ESB are based on measured
partitioning from natural organic carbon in typical
field sediments. Some sediments influenced
heavily by industrial activities may contain sources
of organic carbon whose partitioning properties are
not similar, such as rubber, animal processing
wastes (e.g., hair or hide fragments), 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 results in partitioning different
from that of typical organic carbon. Sediments
with large amounts of these materials may show
higher concentrations of chemicals in interstitial
water than would be predicted using generic Koc
values, making the ESB underprotective. Direct
analysis of interstitial water can be used to
evaluate this possibility (see U.S. EPA, 2003a,b).
5. Presence of additional narcotic compounds.
The PAH mixture ESB is based on the additivity
of the narcotic toxicity of PAHs. However, some
sediments may contain additional nonionic narcotic
chemicals that would contribute to narcotic
toxicity, such as chlorobenzenes or PCBs (note:
PCBs may also cause adverse effects through
bioaccumulation and transfer to higher trophic
levels; these bioaccumulative effects are not
addressed by this narcosis-based ESB and should
be evaluated separately). The presence of
additional nonionic narcotic chemicals may make
the PAH mixture ESB underprotective, because
the ESB itself only addresses that part of the
narcotic potency caused by PAHs. Di Toro et al.
7-2
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures
(2000) and Di Toro and McGrath (2000) describe
methods by which the contributions of other
narcotic chemicals can be incorporated into an
ESB-type assessment.
6. Site-specific temperature and salinity
corrections. Temperature and salinity both affect
solubility of PAHs and can therefore affect the
solubility-constrained maximum contribution of
individual PAHs to the overall ESB. Solubilities
used in this document are calculated for 25 °C and
salinities less than l%o. Solubilities can be
recalculated to meet site specific conditions using
procedures described in Section 6.9. Within a
temperature range of 0 to 35°C and salinities from
0 to 35%o, solubility can be expected to decrease
by a factor of about 30 to 40% with decrease in
temperature or increase in salinity. Site-specific
recalculation of solubilities will only affect
SESBTUFCV in cases where the contribution of
one or more PAHs are solubility constrained (see
Section 6.9).
7.3 Summary
Benthic organisms should be acceptably
protected from the narcotic effects of PAH
mixtures in freshwater and saltwater sediments if
the SESBTUFCV is less than or equal to 1.0, and if
the SESBTUFCV is greater than 1.0, sensitive
benthic organisms may be adversely affected.
This ESB is intended to protect benthic organisms
from direct toxicity associated with exposure to
PAH-contaminated sediments. This ESB does not
consider the antagonistic, additive or synergistic
effects of other sediment contaminants in
combination with PAH mixtures or the potential
for bioaccumulation and trophic transfer of PAH
mixtures to aquatic life, wildlife or humans.
7-3
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
Section 8
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Varanasi U, Reichert WL, Stein SE, Brown DW, Sanborn
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hydrocarbons in benthic organisms exposed to
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19:836-841.
Veith GD, Call DJ, Brooke LT. 1983. Structure-toxicity
relationships for the fathead minnow, Pimephales
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Aquat Sci 40:743-748.
Veith GD, Mekenyan OG, Ankley GT, Call DJ. 1995a.
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Sci Technol29:1267-1272.
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8-16
-------
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures
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Classifying environmental pollutants. 1. Structure-
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Estimation of toxicity to marine species with structure-
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8-17
-------
Appendix A
Individual datasets which Comprise
the Acute Lethality Database:
Table from Di Toro et al. (2000).
-------
Test Conditions
Common Name,
Scientific Name
Freshwater
Paramecium,
Tetrahymena elliotti
Hydra,
Hydra oligactis
Snail,
Lymnae stagnalis
Cladoceran,
Daphnia cucullata
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia pulex
Cladoceran,
Daphnia pulex
Brine shrimp,
Artemia salina
Test Duration (hr)
24
48
48
48
24
48
48
48
48
48
24
Method*
S
S
S
S
S
S
S
FT,R
S
S
S
Concentration8
U
U
U
U
U
U
U
M
M
U
N
No. of Data
Points0
10(12)
5
5
5
21(28)
72(78)
19
1(2)
(1)
6
32(34)
References
Rogerson et al, 1983
Slooffetal, 1983
Slooffetal, 1983
Canton and Adema, 1978
LeBlanc, 1980a
Abernethy et al., 1988; U.S. EPA, 1978; Canton and
Adema, 1978 Rogerson et al., 1983; Bringman and
Kuhn, 1959; Eastman et al., 1984; Dill, 1980
EG&G Bionomics, 1982; Thurstonet al., 1985;
Adema, 1978; OrisetaL, 1991; Brooke, 1991;
Millemann et al., 1984; Munkrittrick et al., 1991
EG&G Bionomics, 1982; Brooke, 1994
TruccoetaL, 1983
Canton and Adema, 1978; Passino and Smith,
Abernethy et al., 1988; Abernethy et al., 1986
1987
A-2
-------
Common Name,
Scientific Name
Crayfish,
Orconectes immunis
Mosquito,
Aedes aegypti
Mosquito,
Culexpipiens
Midge,
Tanytarsus dissimilis
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Bleak,
Alburnus alburnus
Test Duration (hr)
96
48
48
48
48
24
24
48
96
96
96
96
Test
Method*
FT
S
S
S
FT
FT
S
S
FT
S
S
S
Conditions
Concentration8
M
U
U
M
M
M
U
U
M
M
U
I
No. of Data
Points0
6
5
5
9
7
6
1(2)
6
22
1
1
7
References
Thurstonetal, 1985; Holcombe et al, 1987
Slooffetal, 1983
SlooffetaL, 1983
Thurston et al., 1985; Can et al., 1983
Holcombe et al., 1987; Call et al., 1983
Calletal., 1983
Bentlyetal, 1975
SlooffetaL, 1983; Bentlyetal., 1975
Thurston et al., 1985; Call et al., 1983; Holcombe et
al., 1987; Call et al., 1986; DeGraeve et al., 1982;
HodsonetaL, 1988
HorneetaL, 1983
Bentlyetal., 1975
Bengtssonet al., 1984
A-3
-------
Test Conditions
Common Name,
Scientific Name
Goldfish,
Carasius auratus
Goldfish,
Carasius auratus
Goldfish,
Carasius auratus
Goldfish,
Carasius auratus
Goldfish,
Carasius auratus
Goldfish,
Carasius auratus
Goldfish,
Carasius auratus
Golden orfe,
Leuciscus idus melanotus
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Test Duration (hr)
24
24
24
96
96
48
48
24
24
24
48
48
Method*
S
S
FT
S
FT
S
FT
S
S
FT
S
FT
Concentration8
M
U
M
U
M
U
M
i(ns)
U
M
U
M
No. of Data
Points0
26(28)
5(6)
1(2)
4
1(2)
5(6)
1(2)
26
6
8
11
8
References
Bridie et al., 1979
Pickering and Henderson,
Brenniman et al., 1976
Pickering and Henderson,
Brenniman et al., 1976
Pickering and Henderson,
Brenniman et al., 1976
1966
1966
1966
Juhnke and Ludemann, 1978
Pickering and Henderson,
Ahmad et al., 1984
Pickering and Henderson,
Ahmad et al., 1984
1966
1966
Pimephales promelas
A-4
-------
Common Name,
Scientific Name
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Channel catfish,
Ictalurus punctatus
Medaka,
Oryzias latipes
American flagfish, Jordanella
floridae
American flagfish, Jordanella
floridae
American flagfish, Jordanella
floridae
Mosquitofish,
Gambusia affinis
Mosquitofish,
Gambusia affinis
Test Duration (hr)
96
96
96
96
96
48
24
48
96
24
48
Test Conditions
Method* Concentration8
FT M
S M
R U
S U
FT,S M
S U
FT M
FT M
FT M
S U
S U
No. of Data
Points0
141(146)
3(4)
1
4
7
4(5)
6
6
6
(3)
(3)
References
Veith et al, 1983; Thurston et al, 1985; Holcombe et
al, 1987; Ahmad et al., 1984; Dill,1980; DeGraeve et
al., 1982; Alexander et al., 1978; Broderius and Kahl,
1985; Cairns and Nebeker, 1982; Hall et al., 1989;
Hall et al., 1984; Call et al., 1985; CLSES, 1984;
CLSES, 1985; CLSES, 1986; CLSES, 1988; CLSES,
1990;Kimball, 1978
Bridie et al., 1979; EG&G Bionomics, 1982;
Gendussa, 1990; Horneetal, 1983
Academy Natural Sci., 1981
Pickering and Henderson, 1966
Thurston et al., 1985; Holcombe et al., 1983;
Gendussa, 1990
SlooffetaL, 1983
Smith etal, 1991
Smith etal, 1991
Smith etal., 1991
Thurston etal., 1985
Thurston et al., 1985
A-5
-------
Common Name,
Scientific Name
Mosquitofish,
Gambusia affinis
Mosquitofish,
Gambusia affinis
Guppy,
Poecilia reticulata
Guppy,
Poecilia reticulata
Guppy,
Poecilia reticulata
Bluegill,
Lepomis macrochirus
Bluegill,
Lepomis macrochirus
Bluegill,
Lepomis macrochirus
Bluegill,
Lepomis macrochirus
Bluegill,
Lepomis macrochirus
Bluegill,
Lepomis macrochirus
Tadpole,
Rana catesbeiana
Test Duration (hr)
96
96
24
48
96
24
24
48
48
96
96
96
Test Conditions
Method* Concentration8
FT M
S U
S U
s u
s u
s u
FT M
FT M
S U
FT M
S U
FT M
No. of Data
Points0
5(6)
3
(1)
10(11)
4
18(19)
1
1
6(7)
8
36(40)
5
References
Thurston etal., 1985;
WallenetaL, 1957
WallenetaL, 1957
Pickering and Henderson, 1966
Slooff et al., 1983; Pickering and Henderson, 1966
SlooffetaL, 1983
Pickering and Henderson, 1966;
1981; Bently etal., 1975
Call etal., 1983
Call etal., 1983
Pickering and Henderson, 1966;
Buccafusco et al.,
Bently etal., 1975
Thurston et al., 1985; Bently et al., 1975; Call et al.,
1 983 ;Holcombe etal., 1987
Pickering and Henderson, 1966;
LeBlanc, 1980b; ; Buccafusco et
al., 1975; Dawson et al., 1977.
Thurston et al., 1985
U.S. EPA, 1978;
al., 1 981; Bently et
A-6
-------
Common Name,
Scientific Name
Clawed toad,
Xenopus laevis
Mexican axolotl,
Ambystoma mexicanum
Saltwater
Annelid worm,
Neanthes arenaceodentata
Annelid worm,
Neanthes arenaceodentata
Copepod,
Nitocra spinipes
Amphipod,
Leptocheirus plumulosus
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Test Duration (hr)
48
48
96
96
96
96
96
96
96
96
Test Conditions
Method* Concentration8
S U
s u
S U
R U
S I
FT M
S U
S M
R U
FT M
No. of Data
Points0
5
5
4(5)
(1)
6
4
20(23)
1
1
8(9)
8(9)
References
SlooffandBaerselman, 1980
SlooffandBaerselman, 1980
Home et al., 1983; Rossi and Neff, 1978
Thursbyetal., 1989a
Bengtsson et al, 1984
Swartz, 1991a; Champlin and Poucher, 1992a; Boese
etal, 1997
U.S. EPA, 1978; Champlin and Poucher, 1992a;
Zaroogian et al., 1985
EG&G Bionomics, 1982
Thursbyetal., 1989b
Battelle, 1987; Champlin and Poucher, 1992a; Home
Americamysis bahia
Grass shrimp,
Palaemonetes pugio
96
U
et al., 1983; EG&G Bionomics, 1978; U.S. EPA,
1978; Kuhn and Lussier, 1987; Thursby, 1991b
Battelle, 1987; Thursby et al., 1989a
A-7
-------
Common Name,
Scientific Name
Grass shrimp,
Palaemonetes pugio
Grass shrimp,
Palaemonetes pugio
Grass shrimp,
Palaemonetes pugio
Crab,
Portunus pelagicus
Inland silverside,
Menidia beryllina
Inland silverside,
Menidia beryllina
Sheepshead minnow,
Cyprinodon variegatus
Sheepshead minnow,
Cyprinodon variegatus
Sheepshead minnow,
Cyprinodon variegatus
Sheepshead minnow,
Cyprinodon variegatus
Total Data Points
Test Duration (hr)
96
96
96
96
96
96
24
48
96
96
Test Conditions
Method* Concentration8
S U
FT M
S M
S M
R U
S U
S U
S U
S U
FT M
No. of Data
Points0
4
1
1
4
1
7(8)
7(8)
11(12)
13(15)
2
736 (796)
References
Champlin and Poucher, 1992a; Home et al, 1983;
Thursby, 1991b; Tatem et al., 1978
Battelle, 1987
Tatem, 1977
Mortimer and Connell, 1 994
Thursby et al., 1989a
Champlin and Poucher, 1992a; Dawson et al., 1977;
HorneetaL, 1983
HeitmuUer et al., 1981
HeitmuUer et al., 1981
HeitmuUer et al., 1981:
U.S. EPA, 1978
WardetaL, 1981; Battelle, 1987
AMethod: S=static, FT=flow-through, R=renewal
Concentration: U=unmeasured (nominal), M=chemical measured, I=initial
GNumber of data points used; ()=number of data before screening for concentration>solubility and outliers.
-------
Appendix B
Chemicals which Comprise the
Acute Toxicity Database for Narcosis
Chemicals in Section 2 of this Document:
Table from Di Toro et al. ( 2000).
-------
Chemical
triethylene glycol
methanol
2,4-pentanedione*
ethanol
acetone
2-chloroethanol*
2-(2-ethoxyethoxy)ethanol
1 -chloro-2-propanol*
1 ,3-dichloro-2-propanol*
2-methyl-2,4-pentanediol
2-butanone
2-propanol
3 -chloro- 1 -propanol*
1 -propanol
cyclopentanone
2-methyl-2-propanol
methyl chloride
2-butanol
methyl bromide*
3-methyl-2-butanone
2,3-dibromopropanol*
cyclohexanone
cyclopentanol
2-methyl- 1 -propanol
4-methyl-3-pente-2-one
2-pentanone
1 -butanol
3-pentanone
2-methyl-2-butanol
2-n-butoxyethanol
diethyleneglycolmono-n-butylether
3,3-dimethyl-2-butanone
CASA
112276
67561
123546
64175
67641
107073
111900
127004
96231
107415
78933
67630
627305
71238
120923
75650
74873
78922
74839
563804
96139
108941
96413
78831
141797
107879
71363
96220
75854
111762
112345
75978
Class8
ao
ao
k
ao
k
ao
ao
ao
ao
ao
k
ao
ao
ao
k
ao
al,ha
ao
al,ha
k
ao
k
ao
ao
k
k
ao
k
ao
ao
et
k
v c
-"-ow
-1.48
-0.715
-0.509
-0.234
-0.157
-0.048
0.011
0.156
0.165
0.246
0.316
0.341
0.363
0.399
0.453
0.663
0.677
0.717
0.791
0.792
0.819
0.827
0.849
0.858
0.867
0.877
0.946
0.954
1.03
1.05
1.09
1.09
MWD
150.17
32.04
100.12
46.07
58.08
80.51
134.17
94.54
128.99
118.17
72.11
60.10
94.54
60.10
84.12
74.12
50.49
74.12
94.94
86.13
217.90
98.14
86.13
74.12
98.14
86.13
74.12
86.13
88.15
118.17
162.23
100.16
MVE
131
41.0
100
59.0
74.0
65.0
111
84.0
91.0
120
90.0
77.0
82.0
75.0
89.0
95.0
56.0
93.0
57.0
108
96.0
103
89.0
93.0
118
107
92.0
108
110
131
170
125
SF
-
13.5
7.87
11.9
13.71
9.09
-
44.8
6.30
43.0
2.81
13.6
2.00
11.2
1.11
16.5
0.0666
14.9
0.154
1.32
5.97
0.445
5.19
10.6
2.68
1.03
3.03
0.849
1.62
8.78
40.0
0.954
B-2
-------
Chemical
CASA
Class8
MWD
MVE
diethyl ether
4 -methoxy-4 -methyl-2 -pentane
4-methyl-2-pentanone
dichloromethane
t-butylmethyl ether
cyclohexanol
2-hexanone
1 ,2-dichloroethane
1 -pentanol
3 -methyl-3 -pentanol
2-phenoxyethanol
2,2,2-trichloroethanol
4-methyl-2 -pentanol
3-hexanol
2-heptanone
5-methyl-2-hexanone
2,4-dimethyl-3-pentanol
6-methyl-5-heptene-2-one
2-hexanol
1 , 3 -dichloropropane
1 ,2-dichloropropane
diisopropyl ether
chloroform
1 , 1 ,2-trichloroethane
1 ,4-dimethoxybenzene
2 , 6 -dimethoxytolunene
benzene
1 -hexanol
2-octanone
1 -chloro-3-bromopropane
5 -methyl-3 -heptanone
anisole
60297
107700
108101
75092
1634044
108930
591786
107062
71410
77747
122996
115208
108112
623370
110430
110123
600362
110930
626937
142289
78875
108203
67663
79005
150787
5673074
71432
111273
111137
109706
541855
100663
et
k
k
al,ha
et
ao
k
al,ha
ao
ao
ao
ao
ao
ao
ke
ke
ao
ke
ao
al,ha
al,ha
et
al,ha
al,ha
ar
ar
ar
ao
ke
al,ha
ke
ar
1.15
1.17
1.17
1.18
1.20
1.29
1.29
1.40
1.49
1.49
1.50
1.61
1.66
1.66
1.67
1.68
1.78
1.82
1.83
1.84
1.86
1.87
1.91
1.91
1.95
1.99
2.00
2.02
2.02
2.04
2.05
2.06
74.122
130.19
100.16
84.93
88.149
100.16
100.16
98.96
88.15
102.18
138.17
149.4
102.18
102.18
114.19
114.19
116.2
126.2
102.18
112.99
112.99
102.18
119.38
133.4
138.165
152.19
78.11
102.18
128.21
157.44
128.21
108.14
105
143
124
65.0
122
103
124
79.0
109
125
122
93.0
126
125
141
141
140
151
126
97.0
99.0
138
81.0
94.0
132
147
89.0
125
157
100
156
111
1.16
41.5
0.862
0.211
9.04
1.61
0.598
0.114
0.581
3.79
0.173
48.4
2.25
2.18
0.312
0.271
3.05
0.487
1.13
0.0363
0.0342
0.0918
0.0319
0.0369
0.0250
0.0283
0.0260
0.159
0.111
0.0184
0.111
0.0148
B-3
-------
Chemical
2 , 6 -dimethyl-2 , 5 -heptadiene
t-1 ,2-dichloroethylene
1 ,2,3-trichloroepropane
1 , 1 -dichloroethylene
1 ,3-dibromopropane*
bromofonn
1 , 1 ,2,2-tetrachloroethane
1 ,4-dichlorobutane
1 , 1 -dichloropropane
2-nonanone
1,1,1 -trichloroethane
1,1,1 ,2-tetrachloroethane
5-nonanone
1 -heptanol
chlorobenzene
2-ethyl-l -hexanol
bicyclo(2,2,l)hepta-2,5-diene
toluene
styrene
tetrachloromethane
2-decanone
bromobenzene
cyclopentane
1 , 5 -dichloropentane
1 ,3,5-cycloheptatriene
trichloroethylene
di-n-butyl ether
t-1 ,2-dichlorocyclohexane
pentachloroethane
2,4-hexadiene
butylphenyl ether
benzophenone
CASA
504201
156605
96184
75354
109648
75252
79345
110565
78999
821556
71556
630206
502567
111706
108907
104767
121460
108883
100425
56235
693549
108861
278923
628762
544252
79016
142961
822866
76017
592461
1126790
119619
Class8
ke
al,ha
al,ha
al,ha
al,ha
al,ha
al,ha
al,ha
al,ha
ke
al,ha
al,ha
ke
ao
ar,ha
ao
al
ar
ar
al,ha
ke
ar,ha
al
al,ha
al
al,ha
et
al,ha
al,ha
al
et
ke
v c
-"-ow
2.07
2.10
2.13
2.19
2.24
2.25
2.31
2.33
2.36
2.38
2.38
2.43
2.44
2.57
2.58
2.58
2.60
2.62
2.72
2.73
2.73
2.75
2.76
2.76
2.77
2.81
2.89
2.90
2.95
2.98
3.00
3.05
MWD
138.21
96.94
147.43
96.94
201.9
252.73
167.85
127.01
112.99
142.24
133.4
167.85
142.24
116.2
112.56
130.23
92.14
92.14
104.15
153.82
156.27
157.01
70.134
141.04
92.14
131.39
130.23
153.05
202.29
82.145
150.22
182.22
MVE
164
81.0
107
81.0
103
88.0
106
113
101
174
101
110
174
142
102
155
102
107
116
97.0
190
106
95.0
130
104
90.0
170
128
121
115
160
163
SF
0.0171
0.0202
0.0177
0.0141
0.00930
0.00650
0.0181
0.00990
0.00790
0.0801
0.00662
0.0050
0.0740
0.0487
0.00320
0.132
0.00490
0.00600
0.00550
0.00248
0.0599
0.00196
0.00260
0.00286
0.00377
0.00360
0.00614
0.00162
0.00111
0.00237
0.000790
0.000480
B-4
-------
Chemical
ethylbenzene
2,3-dimethyl-l ,3-butadiene
2-undecanone
1 -octanol
3-chlorotoluene
4-chlorotoluene
o-xylene
m-xylene
p-xylene
1 ,4-dichlorobenzene
3,5,5 -trimethyl- 1 -hexanol
1 ,2-dichlorobenzene
1 ,3-dichlorobenzene
napthalene
cyclohexane
tetrachloroethylene
2-dodecanone
cumene
pentane
1 ,2-dibromobenzene
1 , 5 -cyclooctadiene
1 -nonanol
1 ,2,4-trimethylbenzene
n-propylbenzene
dipentyl ether
1,3,5 -trimethylbenzene
hexachloroethane
2,4-dichlorotoluene
1 -methylnaphthalene
2 -methylnaphthalene
2-chloronaphthalene
1 -chloronaphthalene
3,4-dichlorotoluene
biphenyl
CASA
100414
513815
112129
118875
108418
106434
95476
108383
106423
106467
3452979
95501
541731
91203
110827
127184
6175491
98828
109660
585539
111784
143088
95636
103651
693652
108678
67721
95738
90120
91576
91587
90131
95750
92524
Class8
ar
al
ke
ao
ar,ha
ar,ha
ar
ar
ar
ar,ha
ao
ar,ha
ar,ha
pah
al
al,ha
ke
ar
al
ar,ha
al
ao
ar
ar
et
ar
al,ha
ar,ha
pah
pah
pah,ha
pah,ha
ar,ha
ar
If c
-^ow
3.06
3.06
3.08
3.10
3.12
3.13
3.13
3.19
3.21
3.24
3.29
3.31
3.31
3.36
3.38
3.38
3.43
3.49
3.50
3.56
3.61
3.63
3.65
3.67
3.69
3.69
3.73
3.79
3.84
3.86
3.88
3.88
3.88
3.91
MWD
106.17
82.145
170.29
130.23
126.59
126.59
106.17
106.17
106.17
147.00
144.26
147.00
147.00
128.17
84.16
165.83
184.32
120.19
72.15
235.92
108.18
144.26
120.19
120.19
158.28
120.19
236.74
161.03
142.20
142.20
162.62
162.62
161.03
154.21
MVE
123
121
207
158
118
118
121
124
124
113
172
113
115
125
109
99.0
223
140
116
119
130
175
138
140
202
140
132
129
140
141
136
136
129
150
SF
0.00219
0.00162
0.0459
0.0161
0.000834
0.000817
0.00191
0.00154
0.00146
0.000581
0.0117
0.000507
0.000524
0.00110
0.000919
0.000710
0.0357
0.000762
0.000592
0.000196
0.000386
0.00552
0.000487
0.000467
0.000757
0.000414
0.0000936
0.000457
0.000280
0.000270
0.000100
0.000100
0.000120
0.000216
B-5
-------
Chemical
1 ,3,5-trichlorobenzene
1 ,2,3-trichlorobenzene
1 ,2,4-trichlorobenzene
acenaphthene
2,5-dimethyl-2,4-hexadiene
methyl cyclohexane
1 ,2,4,5-tetramethylbenzene
hexane
1 , 3 -diethylbenzene
1 -decanol
p-tert-butyltoluene
diphenylether
amylbenzene
phenanthrene
1 ,2,4,5-tetrachlorobenzene
1 ,2,3,4-tetrachlorobenzene
1 ,2,3,5-tetrachlorobenzene
1 -undecanol
pyrene
9-methylanthracene
fluoranthene
1 -dodecanol
pentachlorobenzene
octane*
1 -tridecanol*
decane*
CASA
108703
87616
120821
83329
764136
108872
95932
110543
141935
112301
98511
101848
538681
85018
95943
634662
634902
112425
129000
779022
206440
112538
608935
111659
112709
124185
Class8
ar,ha
ar,ha
ar,ha
pah
al
al
ar
al
ar
ao
ar
et
ar
pah
ar,ha
ar,ha
ar,ha
ao
pah
pah
pah
ao
ar,ha
al
ao
al
v c
-"-ow
3.97
3.98
4.00
4.01
4.10
4.10
4.11
4.12
4.17
4.19
4.26
4.36
4.52
4.57
4.64
4.64
4.64
4.70
4.92
5.01
5.08
5.20
5.32
5.34
5.75
6.56
MWD
181.45
181.45
181.45
154.21
110.20
98.19
134.22
86.18
134.22
158.28
148.25
170.21
148.25
178.23
215.89
215.89
215.89
172.31
202.26
192.26
202.26
186.34
250.34
114.23
200.36
142.28
MVE
125
124
126
140
146
128
152
132
156
192
173
152
173
161
136
136
136
207
182
175
197
223
147
164
224
229
SF
0.0000933
0.0000870
0.0000886
0.000100
0.000133
0.000155
0.000159
0.000131
0.000135
0.00181
0.0000995
0.0000595
0.0000502
0.0000340
0.0000151
0.0000145
0.0000148
0.000640
0.0000120
0.00000980
0.0000102
0.000238
0.00000218
0.00000625
0.0000793
0.000000300
*Chemical is not included: LC50>S.
ACAS=Chemical abstract number
BClass: ao=alcohol, ar=aromatic, ha=halogenated, et=ether, al=aliphatic, ke=ketone, pah=PAH
GKow=log10(Kow);
DMW=molecular weight (gm/mol);
EV=molar volume (cmVmol);
FS=aqueous solubility(mol/L)
B-6
-------
Appendix C
Summary of Data on the Acute Toxicity
of PAHs to Freshwater and Saltwater Species
and the Derivation of Genus Mean Acute Values.
-------
Life-
Common/scientific c<. A
aiage
FRESHWATER
Hydra, J
Hydra americana
Hydra, X
Hydra sp.
Annelid, X
Lumbriculus variegatus
Annelid, A
Lumbriculus variegatus
Snail, X
Mudalia potosensis
Snail, X
Aplexa hypnorum
Snail, X
Physa heterostropha
Snail, A
Physella virgata
Cladoceran, X
Daphnia magna
Cladoceran, J
Daphnia magna
Cladoceran, X
Daphnia magna
Cladoceran, J
Daphnia magna
Cladoceran, J
Daphnia magna
LC50/
PAH Tested Log Concen- EC50F
Habitat8 (CAS #) Kowc Method13 trationE (ng/1)
W,E fluoranthene 5.084 FT M 70
(206-44-0)
W,E phenanthrene 4.571 FT M 96
(85-01-8)
I phenanthrene 4.571 FT M >419
(85-01-8)
I fluoranthene 5.084 FT M >178
(206-44-0)
E fluorene 4.208 S U >1900G
(86-73-7) (5600)
E acenaphthene 4.012 FT M >2040
(83-32-9)
E fluoranthene 5.084 S U 137
(206-44-0)
E fluoranthene 5.084 FT M >178
(206-44-0)
W naphthalene 3.356 S U 8570
(91-20-3)
W naphthalene 3.356 S U 4723
(91-20-3)
W naphthalene 3.356 S M 2160
(91-20-3)
W 1-methyl 3.837 S U 1420
naphthalene
(90-12-0)
W 2-methyl 3.857 S U 1491
naphthalene
(91-57-6)
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(^imol/1) (i^mol/1) (nmol/g^) (nmol/g.,0) (nmol/g^) References
0.3461 0.3461 22.06 22.06 _ Speharet al., 1999
0.5386 0.5386 11.24 11.24 15.7 Call et al., 1986
>2.351 >2.351 >49.07 _ _ Call et al., 1986
>0.8801 >0.8801 >56.09 >52.46 >52.5 Speharet al., 1999
>11.42 >11.42 >108.2 >108.2 >108.2 Finger et al., 1985
>13.23 >13.23 >81.82 >81.82 >81.8 Holcombe et al., 1983
0.6773 0.6773 43.17 43.17 43.2 Home and Oblad, 1983
>0.8801 >0.8801 >56.09 >56.09 >56.1 Speharet al., 1999
66.86 _ _ U.S. EPA, 1978
36.85 _ _ Abemethy et al., 1986
16.85 34.63 51.39 _ _ Millemann et al., 1984
9.986 9.986 42.20 _ _ Abemethy et al., 1986
10.49 10.49 46.29 _ _ Abemethy et al., 1986
C-2
-------
Common/scientific
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Life-
Stage* Habitat8
X W
X W
X W
X W
X W
X W
J W
X W
Neonate W
Neonate W
Neonate W
X W
J W
LC50/
PAH Tested Log Concen- EC50F
(CAS#) Kowc Method13 trationE (ng/1)
acenaphthene 4.012 S U 3450
(83-32-9)
acenaphthene 4.012 S U >3800
(83-32-9) (41000)
acenaphthene 4.012 S M 320
(83-32-9)
acenaphthene 4.012 S M 1300
(83-32-9)
acenaphthene 4.012 FT M 120
(83-32-9)
fluorene 4.208 S U 430
(86-73-7)
phenanthrene 4.571 S U 207
(85-01-8)
phenanthrene 4.571 S U 843
(85-01-8)
phenanthrene 4.571 S M 700
(85-01-8)
phenanthrene 4.571 S,R M 212
(85-01-8)
phenanthrene 4.571 FT M 230
(85-01-8)
phenanthrene 4.571 FT M 117
(85-01-8)
pyrene 4.922 S U 90.9
(129-00-0)
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(l^mol/1) (i^mol/1) (nmol/g^) (nmol/g.,0) (nmol/gj References
22.37 _ _ Randall and Knopp, 1980
>24.64 _ _ LeBlanc, 1980a
2.075 EG&G Bionomics, 1982
8.430 _ _ EG&G Bionomics, 1982
0.7782 0.7782 4.813 _ _ EG&G Bionomics, 1982
2.585 2.585 24.49 _ _ Finger et al., 1985
1.160 Abemethy et al., 1986
4.730 Eastmond et al., 1984
3.928 _ _ Millemann et al., 1984
1.189 _ _ Brooke, 1994
1.290 _ _ Brooke, 1993
0.6565 0.9204 19.21 _ _ Call et al., 1986
0.4494 0.4494 20.13 _ _ Abemethy et al., 1986
C-3
-------
Common/scientific
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia pulex
Cladoceran,
Daphnia pulex
Cladoceran,
Daphnia pulex
Cladoceran,
Daphnia pulex
Cladoceran,
Daphnia pulex
Cladoceran,
Daphnia pulex
Cladoceran,
Daphnia pulex
Life-
Stage* Habitat8
J W
J W
J W
J W
X W
X W
X W
X W
X W
X W
Neonate W
X W
LC50/
PAH Tested Log Concen- EC50F
(CAS#) Kowc Method13 trationE (ng/1)
9-methyl 5.006 S U 124.8
anthracene
(779-02-2)
fluoranthene 5.084 S U >260
(206-44-0) (320000)
fluoranthene 5.084 S M 45
(206-44-0)
fluoranthene 5.084 R M 117
(206-44-0)
fluoranthene 5.084 S M 105.7
(206-44-0)
naphthalene 3.356 S U 4663
(91-20-3)
fluorene 4.208 S U 212
(86-73-7)
1,3 -dim ethyl 4.367 S U 767
naphthalene
(575-41-7)
2,6-dimethyl 4.373 S U 193
naphthalene
(581-42-0)
anthracene 4.534 S U >45
(120-12-7) (754)
phenanthrene 4.571 S U 734
(85-01-8)
phenanthrene 4.571 S U >1100
(85-01-8) (>1150)
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(^imol/1) (^imol/1) (nmol/g^) (nmol/g.,0) (nmol/gj References
0.6491 0.6491 34.91 _ _ Abemethy et aL, 1986
>1.285 _ _ LeBlanc, 1980a
0.2225 _ _ Orisetal., 1991
0.5785 _ _ Speharetal., 1999
0.5226 0.4067 25.92 25.23 Suedel ad Rodgers, 1996
36.38 36.38 53.99 _ _ Smith et al., 1988
1.275 1.275 12.08 _ _ Smith et al., 1988
4.917 4.917 65.84 _ _ Smith et al., 1988
1.237 1.237 16.78 _ _ Smith et al., 1988
>0.2528 >0.2528 >4.869L _ _ Smith et al., 1988
4.118 _ _ Passino and Smith, 1987
>6.172 Geiger and Buikema, 1981, 1982
C-4
-------
Common/scientific
Cladoceran,
Daphniapulex
Cladoceran,
Daphniapulex
Cladoceran,
Daphnia pulex
Amphipod,
Gammarus minus
Amphipod,
Gammarus minus
Amphipod,
Gammarus
pseudolimnaeus
Amphipod,
Gammarus
pseudolimnaeus
Amphipod,
Gammarus
pseudolimnaeus
Amphipod,
Hyalella azteca
Dragonfly,
Ophiogomphus sp.
Stonefly,
Peltoperla maria
Stonefly,
Peltoperla maria
LC50/
Life- PAH Tested Log Concen- EC50F
Stage* Habitat8 (CAS #) Kowc Method13 trationE (ng/1)
X W phenanthrene 4.571 S U 350
(85-01-8)
X W phenanthrene 4.571 S M 100
(85-01-8)
X W 2-methyl 4.991 S U >30
anthracene (96)
(613-12-7)
X E acenaphthene 4.012 S U 460
(83-32-9)
A E fluoranthene 5.084 S U 32
(206-44-0)
X E fluorene 4.208 S U 600
(86-73-7)
X E phenanthrene 4.571 FT M 126
(85-01-8)
A E fluoranthene 5.084 FT M 43
(206-44-0)
J E fluoranthene 5.084 FT M 44
(206-44-0)
N E fluoranthene 5.084 FT M >178
(206-44-0)
X E acenaphthene 4.012 S U 240
(83-32-9)
X E fluoranthene 5.084 S U 135
(206-44-0)
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(l^mol/1) (i^mol/1) (nmol/g^) (nmol/g.,0) (nmol/g^) References
1.964 _ _ Smith et al., 1988
0.5611 1.656 34.56 _ _ Truccoet al., 1983
>0.1563 >0.1563 >8.134L 30.15 27.6 Smith et al., 1988
2.983 2.983 18.45 _ _ Home et al., 1983
0.1582 0.1582 10.08 13.64 _ Home and Oblad, 1983
3.607 3.607 34.18 _ _ Finger et al., 1985
0.7070 0.7070 14.76 _ _ Call et al., 1986
0.2126 0.2126 13.55 18.98 16.1 Speharet al., 1999
0.2175 0.2175 13.87 13.87 13.9 Speharet al., 1999
>0.8801 >0.8801 >56.09 >56.09 >56.1 Speharet al., 1999
1.556 1.556 9.626 _ _ Home et al., 1983
0.6675 0.6675 42.54 20.24 20.2 Home and Oblad, 1983
C-5
-------
Common/scientific
Midge,
Chironomus tentans
Midge,
Chironomus tentans
Midge,
Chironomus tentans
Midge,
Chironomus riparius
Midge,
Paratanytarsus sp.
Midge,
Paratanytarsus sp.
Midge,
Tanytarsus dissimilis
Midge,
Tanytarsus dissimilis
Coho salmon,
Oncorhynchus kisutch
Coho salmon,
Oncorhynchus kisutch
LC50/
Life- PAH Tested Log Concen- EC50F
Stage* Habitat8 (CAS #) Kowc Method13 trationE (ng/1)
L I naphthalene 3.356 S M 2810
(91-20-3)
L I phenanthrene 4.571 S M 490
(85-01-8)
L I fluoranthene 5.084 S M >250
(206-44-0)
L I fluorene 4.208 S U >1900
(86-73-7) (2350)
X E acenaphthene 4.012 S M 2000
(83-32-9)
X E acenaphthene 4.012 S M 2090
(83-32-9)
L I naphthalene 3.356 S U 20700
(91-20-3)
L I naphthalene 3.356 S U 12600
(91-20-3)
E I naphthalene 3.356 R M >11800
(91-20-3)
F W naphthalene 3.356 R M 5600
(91-20-3)
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(^imol/1) (^imol/1) (nmol/g^) (nmol/g.,0) (nmol/gj References
21.92 21.92 32.53 _ _ Millemann et al., 1984
2.749 2.749 57.39 _ _ Millemann et al., 1984
>1.236 >1.236 >78.78L 43.21 _ Suedel ad Rodgers, 1996
>11.42 >11.42 >108.2 >108.2 >68.4 Finger et al., 1985
12.97 Northwestern Aquatic Science
Inc., 1982
13.55 13.26 82.00 82.00 82.0 Northwestern Aquatic Science
Inc., 1982
161.5 _ _ Darville and Wilhm, 1984
98.31 126.0 187.0 187.0 187 Darville and Wilhm, 1984
>92.07 _ _ Kom and Rice, 1981
43.69 43.69 64.84 64.84 _ Korn and Rice, 1981
Rainbow trout, pre SU I naphthalene 3.356 S U
Oncorhynchus mykiss (91-20-3)
1800 14.04
Edsall, C.C., 1991
Rainbow trout, pre SU I naphthalene 3.356 S U
Oncorhynchus mykiss (91-20-3)
6100 47.59
Edsall, C.C., 1991
Rainbow trout, pre SU
Oncorhynchus mykiss
I naphthalene 3.356
(91-20-3)
U
2600 20.29
Edsall, C.C., 1991
C-6
-------
Common/scientific
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Rainbow trout,
Oncorhynchus mykiss
Brown trout,
Salmo trutta
Fathead minnow,
Pimephales promelas
Life-
Stage* Habitat8
pre SU I
pre SU I
J W
X W
J W
J W
pre SU I
L W
J W
X W
J W
J W
J W
LC50/
PAH Tested Log Concen- EC50F
(CAS#) Kowc Method13 trationE (ng/1)
naphthalene 3.356 S U 4400
(91-20-3)
naphthalene 3.356 S U 5500
(91-20-3)
naphthalene 3.356 FT M 1600
(91-20-3)
naphthalene 3.356 FT M 2300
(91-20-3)
acenaphthene 4.012 FT M 670
(83-32-9)
fluorene 4.208 S U 820
(86-73-7)
1,3-dimethyl 4.367 S U 1700
naphthalene
(575-41-7)
phenanthrene 4.571 S U >1100
(85-01-8) (3200)
phenanthrene 4.571 FT M 375
(85-01-8)
fluoranthene 5.084 S M 187
(206-44-0)
fluoranthene 5.084 FT M 26.0
(206-44-0)
acenaphthene 4.012 FT M 580
(83-32-9)
naphthalene 3.356 S M 1990
(91-20-3)
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(^imol/1) (^imol/1) (nmol/g^) (nmol/g.,0) (nmol/gj References
34.33 _ _ Edsall, C.C., 1991
42.91 _ _ Edsall, C.C., 1991
12.48 _ _ DeGraeve et al., 1982
17.94 14.97 22.21 _ _ DeGraeve et al., 1980
4.345 4.345 26.87 _ _ Holcombe et al., 1983
4.930 4.930 46.71 _ _ Finger et al., 1985
10.88 14.04 188.1L _ _ Edsall, C.C., 1991
>6.172 _ _ Edsall, C.C., 1991
2.104 2.104 43.92 _ _ Call et al., 1986
0.9246 _ _ Home and Oblad, 1983
0.1285 0.1285 8.193 25.13 40.4 Speharet al., 1999
3.761 3.761 23.26 23.26 23.3 Holcombe et al., 1983
15.53 _ _ Millemann et al., 1984
C-7
-------
Common/scientific
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
LC50/
Life- PAH Tested Log Concen- EC50F
Stage* Habitat8 (CAS #) Kowc Method13 trationE (ng/1)
J W naphthalene 3.356 FT M 7900
(91-20-3)
X W naphthalene 3.356 FT M 4900
(91-20-3)
J W naphthalene 3.356 FT M 6140
(91-20-3)
J W naphthalene 3.356 FT M 8900
(91-20-3)
J W naphthalene 3.356 FT M 6080
(91-20-3)
J W 1-methyl 3.837 S U 9000
naphthalene
(90-12-0)
J W acenaphthene 4.012 S M 3100
(83-32-9)
J W acenaphthene 4.012 S M 1500
(83-32-9)
A W acenaphthene 4.012 R U 3700
(83-32-9)
J W acenaphthene 4.012 FT M 1730
(83-32-9)
J W acenaphthene 4.012 FT M 608
(83-32-9)
J W acenaphthene 4.012 FT M >1400
(83-32-9)
J W acenaphthene 4.012 FT M 1600
(83-32-9)
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(l^mol/1) (i^mol/1) (nmol/g^) (nmol/g.,0) (nmol/gj References
61.64 DeGraeve et al., 1982
38.23 _ _ DeGraeve et al., 1980
47.91 _ _ Geiger et al., 1985
69.44 _ _ DeGraeve et al., 1980
47.44 51.77 76.82 _ _ Holcombe et al., 1984
63.38 63.38 267.9 _ _ Mattson et al., 1976
20.10 _ _ Marine Bioassay Lab., 1981
9.727 EG&G Bionomics, 1982
23.99 _ _ Academy of Natural Sci., 1981
11.22 _ _ Geiger et al., 1985
3.943 _ _ Cairns and Nebeker, 1982
>9.079 EG&G Bionomics, 1982
10.38 7.713 47.71 _ _ Holcombe et al., 1983
-------
Common/scientific
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Fathead minnow,
Pimephales promelas
Channel catfish,
Ictalurus punctatus
Channel catfish,
Ictalurus punctatus
Bluegill,
Lepomis macrochirus
Bluegill,
Lepomis macrochirus
Bluegill,
Lepomis macrochirus
Bluegill,
Lepomis macrochirus
Life- PAH Tested Log Concen-
StageA Habitat8 (CAS #) Kowc Method13 trationE
X W fluorene 4.208 S U
(86-73-7)
J W phenanthrene 4.571 S M
(85-01-8)
J W fluoranthene 5.084 S M
(206-44-0)
J W fluoranthene 5.084 S M
(206-44-0)
A W fluoranthene 5.084 FT U
(206-44-0)
J W fluoranthene 5.084 FT M
(206-44-0)
J E acenaphthene 4.012 FT M
(83-32-9)
J E fluoranthene 5.084 S M
(206-44-0)
J W acenaphthene 4.012 S U
(83-32-9)
X W fluorene 4.208 S U
(86-73-7)
J W phenanthrene 4.571 FT M
(85-01-8)
J W fluoranthene 5.084 S U
(206-44-0)
LC50/
EC50F
(ng/i)
>1900
(100000)
>1100
(>1150)
95
7.71
>260
(>1000)
69
1720
37.40
1700
910
234
>260
(4000)
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(l^mol/1) (i^mol/1) (nmol/g^) (nmol/g.,0) (nmol/gj References
>11.42 >11.42 >108.2L _ _ Finger et al., 1985
>6.172 >6.172 >128.8L _ _ U.S. EPA, 1978
0.4697 _ _ Home and Oblad, 1983
0.0381 _ _ Gendusa, 1990
>1.285 _ _ Birgeetal., 1982
0.3411 0.3411 21.74 67.97 68.0 Speharet al., 1999
11.15 11.15 68.99 _ _ Holcombe et al., 1983
0.1849 0.1849 11.79 28.51 28.5 Gendusa, 1990
11.02 11.02 68.18 _ _ Buccafusco et al., 1981
5.471 5.471 51.84 _ _ Finger et aL, 1985
1.313 1.313 27.41 _ _ Call et al., 1986
>1.285 Buccafiisco et al., 198 1; EPA,
1978
Bluegill, J
Lepomis macrochirus
W fluoranthene 5.084
(206-44-0)
FT
M
44
0.2175 0.2175 13.87 34.04 34.0 Speharet al., 1999
C-9
-------
Life-
Common/scientific c<. A
aiage
South african clawed frog L
Xenopus laevis
South african clawed frog L
Xenopus laevis
SALTWATER
Annelid worm, J
Neanthes
arenaceodentata
Annelid worm, X
Neanthes
arenaceodentata
Annelid worm, J
Neanthes
arenaceodentata
Annelid worm,
Neanthes A
arenaceodentata
Annelid worm, J
Neanthes
arenaceodentata
Annelid worm, J
Neanthes
arenaceodentata
Archiannelid, J
Dinophilus gyrociliatus
Mud snail, A
Nassarius obsoletus
Blue mussel, A
Mytilus edulis
LC50/
PAH Tested Log Concen- EC50F
Habitat8 (CAS #) Kowc Method13 trationE (ng/1)
W naphthalene 3.356 FT M 2100
(91-20-3)
W naphthalene 3.356 FT M 2100
(91-20-3)
I naphthalene 3.356 S U 3800
(91-20-3)
I acenaphthene 4.012 S U 3600
(83-32-9)
I acenaphthene 4.012 R U >3800
(83-32-9) (16440)
I phenanthrene S U 600
(85-01-8) 4.571
I fluoranthene 5.084 S U >260
(206-44-0) (500)
I fluoranthene 5.084 S U >260
(206-44-0) (20000)
I phenanthrene 4.571 R U 185.40
(85-01-8)
I,E phenanthrene 4.571 R M >245
(85-01-8)
E,W phenanthrene 4.571 R M >245
(85-01-8)
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(^imol/1) (^imol/1) (nmol/g^) (nmol/g.,0) (nmol/gj References
16.38 Edmisten and Bantle, 1982
16.38 16.38 24.31 24.31 24.3 Edmisten and Bantle, 1982
29.65 29.65 44.00 _ _ Rossi and Neff, 1978
23.34 _ _ Home et al., 1983
>24.64 23.34 144.4 _ _ Thursby etal., 1989a
3.366 3.366 70.27 _ _ Rossi and Neff, 1978
>1.285 _ _ Rossi and Neff, 1978
> 1.285 >1.285 >81.93L 76.43 76.4 Speharet al., 1999
1.040 1.040 21.71 21.71 21.7 Battelle Ocean Sciences,
>1.375 >1.375 >28.69 >28.69 >28.7 Battelle Ocean Sciences,
>1.375 >1.375 >28.69 >28.69 >28.7 Battelle Ocean Sciences,
1987
1987
1987
C-10
-------
Common/scientific
Pacific oyster,
Crassostrea gigas
Coot clam,
Mulinia lateralis
Coot clam,
Mulinia lateralis
Soft-shell clam,
Mya arenaria
Calanoid copepod,
Eurytemora affinis
Calanoid copepod,
Eurytemora affinis
Calanoid copepod,
Eurytemora affinis
Calanoid copepod,
Eurytemora affinis
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Life-
Stage* Habitat8
E/L W
J E
J E
A I
A X
A X
A X
A X
J E
J E
J E
J E
PAH Tested Log Concen-
(CAS#) Kowc Method13 trationE
naphthalene 3.356 S U
(91-20-3)
pyrene 4.922 FT M
(129-00-0)
fluoranthene 5.084 S U
(206-44-0)
phenanthrene 4.571 R M
(85-01-8)
naphthalene 3.356 S U
(91-20-3)
2-methyl 3.857 S U
naphthalene
(91-57-6)
2,6-dimethyl 4.373 S M
naphthalene
(581-42-0)
2,3,5- 4.856 S M
trimethyl
naphthalene
(2245-38-7)
acenaphthene 4.012 S U
(83-32-9)
acenaphthene 4.012 S M
(83-32-9)
acenaphthene 4.012 R U
(83-32-9)
acenaphthene 4.012 FT M
(83-32-9)
LC50/
EC50F
(ng/i)
>31000
(199000)
>132
(>240)
>260
(10710)
>245
3798
1499
852
316
970
160
1190
460
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(l^mol/1) (i^mol/1) (nmol/g^) (nmol/g.,0) (nmol/gj References
>241.9 >241.9 >358.9 >358.9 >359 U.S. EPA, 1980
>0.6526 >0.6526 >29.24 _ _ Champlin and Poucher, 1992a
>1.285 >1.285 >81.93 >48.94 >48.9 Speharet al., 1999
>1.375 >1.375 >28.69 >28.69 >28.7 Battelle Ocean Sciences, 1987
22.58 22.58 33.51 Ott, et al., 1978
7.741 7.741 34.17 _ _ Ott, et al., 1978
3.860 3.860 52.37 _ _ Ott, et al., 1978
1.271 1.271 49.53 41.51 41.5 Ott, et al., 1978
6.290 _ _ U.S. EPA, 1978;Wardetal., 1981
1.038 EG&G Bionomics, 1982
7.717 _ _ _ Thursby et al., 1989a
2.983 _ _ Thursby et al., 1989b
C-ll
-------
Common/scientific
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Americamysis bahia
Mysid,
Neomysis americana
Mysid,
Neomysis americana
Isopod
Excirolana
vancouverensis
LC50/
Life- PAH Tested Log Concen- EC50F
Stage* Habitat8 (CAS #) Kowc Method13 trationE (ng/1)
J E acenaphthene 4.012 FT M 190
(83-32-9)
J E acenaphthene 4.012 FT M 466.1
(83-32-9)
J E acenaphthene 4.012 FT M 271.9
(83-32-9)
J E phenanthrene 4.571 FT M 27.1
(85-01-8)
J E phenanthrene 4.571 FT M 17.7
(85-01-8)
J E pyrene 4.922 FT M 28.28
(129-00-0)
J E fluoranthene 5.084 S U 31
(206-44-0)
J E fluoranthene 5.084 S U 40
(206-44-0)
J E fluoranthene 5.084 FT M 30.53
(206-44-0)
J E fluoranthene 5.084 FT M 87
(206-44-0)
X E naphthalene 3.356 S M 1250
(91-20-3)
X E naphthalene 3.356 S M 1420
(91-20-3)
J I,E fluoranthene 5.084 R M >70
(206-44-0)
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(l^mol/1) (i^mol/1) (nmol/g^) (nmol/g.,0) (nmol/gj References
1.232 _ _ EG&G Bionomics, 1982
3.023 Home et al., 1 983 ;Thursby,
1991a
1.763 2.104 13.01 _ _ Home etal., 1983 ;Thursby,
1991a
0.1521 _ _ Kuhn and Lussier, 1987
0.0993 0.1229 2.565 _ _ Battelle Ocean Sciences, 1987
0.1398 0.1398 6.264 _ _ Champlinand Poucher, 1992a
0.1533 _ _ Speharetal., 1999
0.1978 _ _ U.S. EPA, 1978
0.1509 Speharetal., 1999
0.4301 0.2548 16.24 7.633 7.63 EG&G Bionomics, 1978
9.753 Hargreaves et al., 1982
11.08 10.39 15.43 15.43 15.4 Hargreaves et al., 1982
>0.3461 >0.3461 >22.06 >22.06 >22.1 Boeseet al., 1997
C-12
-------
Life-
Common/scientmc c<. A
aiage
Amphipod, J
Ampelisca abdita
Amphipod, J
Ampelisca abdita
Amphipod, A
Leptocheirus plumulosus
Amphipod, A
Leptocheirus plumulosus
Amphipod, J
Leptocheirus plumulosus
Amphipod, X
Leptocheirus plumulosus
Amphipod, J
Rhepoxynius abronius
Amphipod, J
Eohaustorius estuarius
Amphipod, J
Grandidierella japonica
Amphipod, J
Corophium insidiosum
Amphipod, J
Emerita analoga
Kelp shrimp, X
Eualis suckleyi
Grass shrimp, X
Palaemonetes pugio
LC50/
PAH Tested Log Concen- EC50F
Habitat8 (CAS #) Kowc Method13 trationE (ng/1)
I acenaphthene 4.012 R U 1125
(83-32-9)
I fluoranthene 5.084 S U 67
(206-44-0)
E,I acenaphthene 4.012 FT M 589.4
(83-32-9)
E,I phenanthrene 4.571 FT M 198.4
(85-01-8)
E,I pyrene 4.922 FT M 66.49
(129-00-0)
E,I fluoranthene 5.084 R M 51
(206-44-0)
I fluoranthene 5.084 R M 63
(206-44-0)
I fluoranthene 5.084 R M >70
(206-44-0)
I fluoranthene 5.084 R M 27
(206-44-0)
I fluoranthene 5.084 R M 54
(206-44-0)
I,E fluoranthene 5.084 R M 74
(206-44-0)
W naphthalene 3.356 FT M 1390
(91-20-3)
E,W naphthalene 3.356 S M 2350
(91-20-3)
LC50/
EC50F
(j^mol/1)
7.295
0.3313
3.822
1.113
0.3287
0.2522
0.3115
>0.3461
0.1335
0.2670
0.3659
10.84
18.34
PAH
Specific
SMAVH
(l^mol/1)
7.295
0.3313
3.822
1.113
0.3287
0.2522
0.3115
>0.3461
0.1335
0.2670
0.3659
10.84
18.34
Kow
Normalized
PAH Specific
SMAV1
45.12
21.11
23.64
23.24
14.73
16.07
19.85
>22.06
8.508
17.02
23.32
16.09
27.21
Species
SMAVJ GMAVK
(nmol/g.,0) (nmol/gj References
Thursby et al., 1989a
30.86 30.9 Speharetal., 1999
Swartz, 199 la
Swartz, 199 la
Champlin and Poucher , 1 992a
18.99 19.0 Boeseetal., 1997
19.85 19.9 BoeseetaL, 1997
>22.06 >22.1 Boeseetal., 1997
8.508 8.51 Boeseetal., 1997
17.02 17.0 BoeseetaL, 1997
23.32 23.3 Boeseet aL, 1997
16.09 16.1 Rice and Thomas, 1989
TatemetaL, 1978
C-13
-------
Life-
Common/scientmc c<. A
aiage
Grass shrimp, X
Palaemonetes pugio
Grass shrimp, L
Palaemonetes pugio
Grass shrimp, A
Palaemonetes pugio
Grass shrimp, A
Palaemonetes pugio
Grass shrimp, J
Palaemonetes pugio
Sand shrimp, X
Crangon septemspinosus
American Lobster, L
Homarus americanus
Hermit crab, A
Paqurus longicarpus
Slipper limpet, L
Crepidula fornicata
Sea urchin, E
A rbacia pun ctalata
Sea urchin, E
A rbacia pun ctalata
Pink salmon, Fry
Oncorhynchus gorbuscha
Pink salmon, Fry
Oncorhynchus gorbuscha
PAH Tested Log Concen-
Habitat8 (CAS #) Kowc Method13 trationE
E,W acenaphthene 4.012 S U
(83-32-9)
E,W acenaphthene 4.012 R U
(83-32-9)
E,W phenanthrene 4.571 R U
(85-01-8)
E,W phenanthrene 4.571 FT M
(85-01-8)
E,W fluoranthene 5.084 S U
(206-44-0)
E acenaphthene 4.012 S U
(83-32-9)
fluoranthene 5.084 R U
(206-44-0)
E phenanthrene 4.571 FT M
(85-01-8)
W acenaphthene 4.012 R U
(83-32-9)
W acenaphthene 4.012 S U
(83-32-9)
W fluoranthene 5.084 S U
(206-44-0)
W naphthalene 3.356 FT M
(91-20-3)
W naphthalene 3.356 FT M
(91-20-3)
LC50/
EC50F
(ng/i)
676.8
1697
200.8
145.4
142
245
>260
(317)
163.7
3426
>3800
(8163)
>260
(20000)
960
900
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(l^mol/1) (i^mol/1) (nmol/g^) (nmol/g.,0) (nmol/g^) References
4.389 Home et al., 1 983 ;Thursby,
1991b
11.00 6.950 42.98 _ _ Thursby etal., 1989a
1.127 Battelle Ocean Sciences, 1987
0.8158 0.8158 17.03 _ _ Battelle Ocean Sciences, 1987
0.7021 0.7021 44.75 30.72 30.7 Speharet al., 1999
1.589 1.589 9.826 9.826 9.83 Horneet al., 1983;Thursby ,
1991b
1.285 1.285 81.93 81.93 81.9 Speharet al., 1999
0.9185 0.9185 19.17 19.17 19.2 Battelle Ocean Sciences, 1987
22.28 22.28 137.8 137.8 138 Thursby etal., 1989a
>24.64 >24.64 >152.4 _ _ Thursby etal., 1989a
>1.285 >1.285 >81.93 >117.2 >117 Speharet al., 1999
7.490 _ _ Rice and Thomas, 1989
7.022 _ _ Rice and Thomas, 1989
C-14
-------
Life-
Common/scientmc c<. A
aiage
Pink salmon, Fry
Oncorhynchus gorbuscha
Pink salmon, Fry
Oncorhynchus gorbuscha
Pink salmon, Fry
Oncorhynchus gorbuscha
Sheep shead minnow, J
Cyprinodon variegatus
Sheep shead minnow, J
Cyprinodon variegatus
Sheep shead minnow, A
Cyprinodon variegatus
Sheep shead minnow, J
Cyprinodon variegatus
Sheep shead minnow, J
Cyprinodon variegatus
Sheep shead minnow, J
Cyprinodon variegatus
Sheep shead minnow, J
Cyprinodon variegatus
Sheep shead minnow, J
Cyprinodon variegatus
Inland silverside, X
Menidia beryllina
Inland silverside, J
Menidia beryllina
Habitat8
W
W
W
E,W
E,W
E,W
E,W
E,W
E,W
E,W
E,W
W
W
PAH Tested Log Concen-
(CAS#) Kowc Method13 trationE
naphthalene 3.356 FT M
(91-20-3)
naphthalene 3.356 FT M
(91-20-3)
naphthalene 3.356 FT M
(91-20-3)
acenaphthene 4.012 S U
(83-32-9)
acenaphthene 4.012 R U
(83-32-9)
acenaphthene 4.012 FT M
(83-32-9)
phenanthrene 4.571 R U
(85-01-8)
phenanthrene 4.571 FT M
(85-01-8)
pyrene 4.922 FT M
(129-00-0)
fluoranthene 5.084 S U
(206-44-0)
fluoranthene 5.084 S U
(206-44-0)
acenaphthene 4.012 S U
(83-32-9)
acenaphthene 4.012 R U
(83-32-9)
LC50/
EC50F
(ng/i)
990
1010
890
2200
>3800
(50000)
3100
>245
429.4
>132
(>640)
>260
(>20000)
>260
(>560000)
2300
>3800
(5564)
Kow
PAH Normalized
LC50/ Specific PAH Specific Species
EC50F SMAVH SMAV1 SMAVJ GMAVK
(l^mol/1) (i^mol/1) (nmol/g^) (nmol/g.,0) (nmol/gjj References
7.724 Rice and Thomas, 1989
7.880 _ _ Rice and Thomas, 1989
6.944 7.40 10.99 10.99 11.0 Rice and Thomas, 1989
14.27 _ _ Heitmulleretal., 1981
>25.00 Thursby etal., 1989a
20.10 20.10 124.3 _ _ Ward et al., 1981
>1.375 _ _ Battelle Ocean Sciences, 1987
2.409 2.409 50.29 Battelle Ocean Sciences, 1987
>0.6526 >0.6526 >29.24 _ _ Champlinand Poucher, 1992a
>1.285 _ _ Spehar etal., 1999
>1.285 >1.285 >81.93L 79.07 79.1 Heitmulleret al., 1981 ;U.S EPA,
1978
14.91 _ _ Home etal., 1983
>24.64 >19.17 >118.6 _ _ Thursby etal., 1989a
C-15
-------
Life-
Common/scientmc c<. A
aiage
Inland silverside, J
Menidia beryllina
Inland silverside, J
Menidia beryllina
Atlantic silverside, A
Menidia menidia
Winter flounder, J
Pseudopleuronectes
americanus
PAH Tested Log Concen-
Habitat8 (CAS #) Kowc Method13 trationE
W pyrene 4.922 FT M
(192-00-0)
W fluoranthene 5.084 S U
(206-44-0)
W phenanthrene 4.571 FT M
(85-01-8)
fluoranthene 5.084 S M
(206-44-0)
Kow
PAH Normalized
LC50/ LC50/ Specific PAH Specific Species
EC50F EC50F SMAVH SMAV1 SMAVJ GMAVK
(ug/1) (umol/1) (umol/1) (umol/g^) (umol/g.,0) (umol/gjj References
>132 >0.6526 >0.6526 >29.24 _ _ Champlinand Poucher, 1992a
(>188.17)
>260 >1.285 >1.285 >81.93 >65.73 _ Speharet al., 1999
(>616)
108 0.6060 0.6060 12.65 12.65 28.8 Battelle Ocean Sciences, 1987
>188 >0.9295 >0.9295 >59.24 >59.24 >59.2 Speharet al., 1999
ALife-stage: A = adult, J = juvenile, L = larvae, E = embryo, U = life-stage and habitat unknown, X = life-stage unknown but habitat known.
BHabitat: I = infauna, E = epibenthic, W = water column.
clog^ow: Predicted using SPARC (Karickoff et al, 1991).
DMethod: S= static, R = renewal, FT= flow-through.
E Concentration: U = unmeasured (nominal), M = chemical measured.
F Acute Values: 96 hour LC50 or EC50, except for Daphnia and Tanytarsus which are 48 hours duration.
GBolded acute values are the water solubilities of the PAH (Mackay et al., 1992). For these tests the acute values exceeded solubility. Therefore, solubilities are used instead of the
acute value for further calculations.
HPAH-specific SMAV: Geometric mean of the acute values by PAH and species.
1 PAH-specific SMAVs at a log Kow = \.0; calculated as antilog (log10LC50 + 0.9451og10Kow)/1000 (see Equation 2-33).
1 Species SMAV: Geometric mean of -Kow-normalized SMAVs for a species across PAHs.
KGMAV: Geometric mean of SMAVs for all species within a genus.
LNot used in calculations.
C-16
-------
Appendix D
Comparison of PAH-specific Equilibrium
Partitioning Sediment Benchmarks (ESBs)
Derived from Narcosis Theory and the
Median Response Concentration
of Benthic Species for Individual PAHs
in Spiked-sediment Toxicity Tests.
-------
Common Name,
Scientific Name
Oligochaete,
Lumbriculus variegatus
Oligochaete,
Lumbriculus variegatus
Oligochaete,
Limnodrilus hoffmeisteri
Oligochaete,
Limnodrilus hoffmeisteri
Oligochaete,
Limnodrilus hoffmeisteri
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Cladoceran,
Daphnia magna
Amphipod,
Hyalella azteca
Amphipod,
Hyalella azteca
Amphipod,
Hyalella azteca
Amphipod,
Hyalella azteca
Amphipod,
Hyalella azteca
Amphipod,
Hyalella azteca
Amphipod,
Chemical
pyrene
pyrene
phenanthrene
phenanthrene
pyrene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
Response
7 d LC50
7 d EC50-SA
lOdLCSO
28 d EC25-R
28 d EC25-R
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
Median
Response
Conc.A
(ug/goc)
>9090
(61100)
>9090
(51400)
>34300
(42500)
5790
8440
2380
955
3260
>23900
(37649)
1250
1480
500
22000
5130
2830
Test-
Specific
Cp o r> ^T T B
OCfAHi, FCVi E a O 1 U Fcvi
(ug/goc) (Unitless)
694 >13.1
694 >13.1
593 >57.8
593 9.80
694 12.2
704
704
704
704
704
704
704
704 31.3
704 7.29
704 4.02
PAH-
Specific
SMAVC GMAVD References15
Kukkonen and Landrum, 1994
Kukkonen and Landrum, 1994
>57.8 >57.8 Lotufo and Fleeger, 1996
Lotufo and Fleeger, 1996
Lotufo and Fleeger, 1996
Suedeletal., 1993
Suedeletal., 1993
Suedeletal., 1993
DriscolletaL, 1997a
Suedeletal., 1993
Suedeletal., 1993
Suedeletal., 1993
Harkeyetal, 1997
15.1 15.1 DeWittetaL, 1989
SwartzetaL, 1990
Corophium spinicorne
D-2
-------
Common Name,
Scientific Name
Amphipod,
Corophium spinicorne
Amphipod,
Leptocheirus plumulosus
Amphipod,
Leptocheirus plumulosus
Amphipod,
Leptocheirus plumulosus
Amphipod,
Leptocheirus plumulosus
Amphipod,
Leptocheirus plumulosus
Amphipod,
Leptocheirus plumulosus
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Chemical
fluoranthene
acenapthene
acenapthene
acenapthene
phenanthrene
phenanthrene
phenanthrene
acenapthene
acenapthene
phenanthrene
phenanthrene
pyrene
pyrene
fluoranthene
fluoranthene
Response
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
lOdLCSO
Median
Response
Conc.A
(ug/goc)
4390
10900
23500
8450
6870
8080
8180
2310
2110
3080
2220
1220
2810
>4360
4410
C OCfAHi.FCVi
(ug/goc)
704
489
489
489
593
593
593
489
489
593
593
694
694
704
704
Test-
Specific
ESBTUFCViB
(Unitless)
6.23
22.3
48.1
17.3
11.59
13.63
13.8
4.72
4.31
5.19
3.74
1.76
4.05
>6.19
6.26
PAH-
Specific
SMAVC GMAVD References15
5.01 5.01 SwartzetaL,
SwartzetaL,
SwartzetaL,
26.4 - Swartz et al.,
Swartz et al.,
SwartzetaL,
13.0 18.5 SwartzetaL,
Swartz et al.,
4.51 - SwartzetaL,
SwartzetaL,
4.41 - SwartzetaL,
Swartz et al.,
2.67 - Swartz et al.,
DeWitt et al.,
DeWitt et al.,
1990
1991a
1991a
1991a
1991a
1991a
1991a
1997
1997
1997
1997
1997
1997
1992
1992
Rhepoxynius abronius
D-3
-------
Common Name,
Scientific Name
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Rhepoxynius abronius
Amphipod,
Eohaustorius estuarius
Amphipod,
Eohaustorius estuarius
Amphipod,
Eohaustorius estuarius
Amphipod,
Eohaustorius estuarius
Amphipod,
Chemical
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
acenapthene
acenapthene
acenapthene
phenanthrene
phenanthrene
Response
10dLC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
Median
Response
Cone. Cocj>AHi,Fcn
(ug/goc) (ug/goc)
3080 704
2230 704
3150 704
1890 704
2790 704
2320 704
1700 704
1030 704
2100 704
3310 704
1630 489
4180 489
1920 489
4210 593
3760 593
Test-
Specific PAH-
ESBTUFCViB Specific
(Unitless) SMAVC GMAVD References15
4.38 - - DeWittetaL,
3.17 - - Swartzetal,
4.50 - - DeWittetaL,
2. 68 - - Swartz et al. ,
3.96 - - DeWittetaL
3.30 - - Swartzetal.,
2.41 - - DeWittetaL,
1.47 - - Swartzetal.,
2.98 - - Swartzetal.,
4.70 3.56 3.67 Swartzetal.,
3.33 - - Swartzetal.,
8.55 - - Swartzetal.,
3.93 4.82 - Swartzetal.,
7.10 - - Swartzetal.,
6.34 - - Swartzetal.,
1992
1990
1992
1990
, 1992
1997
1989
1988
1990
1997
1991a
1991a
1991a
1991a
1991a
Eohaustorius estuarius
D-4
-------
Common Name,
Scientific Name
Amphipod,
Eohaustorius estuarius
Amphipod,
Eohaustorius estuarius
Amphipod,
Eohaustorius estuarius
Amphipod,
Eohaustorius estuarius
Midge,
Chironomus tentans
Midge,
Chironomus tentans
Midge,
Chironomus tentans
Amphipod,
Diporeia sp.
Amphipod,
Diporeia sp.
Chemical
phenanthrene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
fluoranthene
pyrene
fluoranthene
Response
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 d LC50
10 dLCSO
10 dLCSO
31 dLCSO
10 d LC50
Median
Response
Conc.A
(ug/goc)
4060
3100
3930
3570
1590
1740
682
>9090
(147000)
>23900
(29300)
C OCfAHi.FCVi
(ug/goc)
593
704
704
704
704
704
704
694
704
Test-
Specific
ESBTUFCViB
(Unitless)
6.85
4.40
5.59
5.07
-
-
-
>13.1
>34.0
PAH-
Specific
SMAVC GMAVD References15
6.75 - Swartzetal,
DeWitt et al. ,
DeWitt et al. ,
5.00 5.46 DeWitt etal,
Suedel et al. ,
Suedel et al. ,
Suedel et al. ,
Landrum et al
>34.0 >34.0 Driscoll et al. ;
1991a
1989
1989
1989
1993
1993
1993
., 1994
, 1997a
A Bolded median response concentration (acute) values are the Coc pAHiMaxi based on the water solubilities of the PAH (Mackay et al., 1992). For these tests the
interstitial water concentration at the median response concentration exceeded solubility. Therefore, solubilities are used instead of the acute value for further calculations.
B Test-specific ESBTUs: Quotient of the median response concentration (ug/goc) and COCpAHi,Fcvi (from Table 3-4).
c PAH-specific SMAV: Geometric mean of the test-specific ESBTUFCV1 values from 10-d LC50 tests by species and PAH. Test-specific ESBTUFCV1 values greater that
solubility included only if they are the sole 10-d LC50 for the species.
D GMAV: Geometric mean of the PAH-specific SMAVs for all species within a genus.
E Spiked sediments from Suedel et al. (1993) were unlikely at equilibrium; i.e., organisms were tested after only 18 to 24 hours after spiking.
D-5
-------
Appendix E
CAS#, Molecular Weight
and Solid Solubility
of Selected PAHs.
-------
PAH
indan
naphthalene
C 1 -naphthalenes
1 -methylnaphthalene
2 -methylnaphthalene
acenaphthylene
acenaphthene
1 -ethylnaphthalene
2 -ethylnaphthalene
C2 -naphthalenes
1 ,4-dimethyhiaphthalene
1 , 3 -dimethylnaphthalene
2 , 6 -dimethylnaphthalene
2 , 3 -dimethylnaphthalene
1 , 5 -dimethylnaphthalene
fluorene
C3 -naphthalenes
2,3,5 -trimethylnaphthalene
1,4,5 -trimethylnaphthalene
anthracene
phenanthrene
Cl-fluorenes
1 -methylfluorene
C4-naphthalenes
2-methylanthracene
1 -methylanthracene
9-methylanthracene
2 -methylphenanthrene
1 -methylphenanthrene
Cl -phenanthrene/anthracenes
9-ethylfluorene
C2-fluorenes
pyrene
fluoranthene
2-ethylanthracene
C2-phenanthrene/anthracenes
9, 1 0-dimethylanthracene
3,6- dimethylphenanthrene
C3-fluorenes
Cl -pyrene/fluoranthenes
2,3-benzofluorene
CAS#A
496117
91203
-
90120
91576
208968
83329
1127760
939275
-
571584
575417
581420
581408
571619
86737
-
2245387
213411
120127
85018
-
1730376
-
613127
610480
779022
2531842
832699
-
2294828
-
129000
206440
52251715
-
781431
1576676
-
-
243174
Molecular
Weight
(Hg/Hmol)
118.18
128.17
142.20
142.20
142.20
152.20
154.21
156.23
156.23
156.23
156.23
156.23
156.23
156.23
156.23
166.22
170.25
170.26
170.20
178.12
178.23
180.25
180.25
184.28
192.26
192.26
192.26
192.26
192.26
192.26
194.28
194.27
202.26
202.26
206.29
206.29
206.29
206.29
208.3
216.29
216.28
Mackay
Solid
Solubility5
(Hg/L)
100000
30995
??
28001
25000
16314
3800
10100
8001
??
11400
8001
1700
2500
3100
1900
??
??
2100
45.00
1100
??
1090
??
29.99
??
261.1
??
269.9
??
??
??
131.9
239.9
??
??
55.90
??
??
?
2.001
E-2
-------
PAH
benzo(a)fluorene
C3 -phenanthrene/anthracenes
naphthacene
benz(a)anthracene
chrysene
triphenylene
C2 -pyrene/fluoranthenes
C4-phenanthrenes/anthracenes
Cl -benzanthracene/chrysenes
C3 -pyrene/fluoranthenes
benzo(a)pyrene
perylene
benzo(e)pyrene
benzo(b)fluoranthene
benzo(j)fluoranthene
benzo(k)fluoranthene
C2 -benzanthracene/chrysenes
9, 1 0-dimethylbenz(a)anthracene
7, 1 2-dimethylbenz(a)anthracene
7 -methylbenzo (a)pyrene
benzo(ghi)perylene
C3 -benzanthracene/chrysenes
indeno(l ,2,3-cd)pyrene
dibenz(a,h)anthracene
dibenz(a,j )anthracene
dibenz(a,c)anthracene
C4-benzanthracene/chrysenes
Cl -dibenz(a,h)anthracenes
coronene
C2-dibenz(a,h)anthracenes
C3 -dibenz(a,h)anthracenes
CAS#A
238843
-
92240
56553
218019
217594
-
-
-
-
50328
198550
192972
205992
205822
207089
-
56564
57976
63041770
191242
-
193395
53703
58703
215587
-
-
191071
-
-
Molecular
Weight
(Hg/Hmol)
216.29
220.32
228.30
228.29
228.29
228.3
230.13
234.23
242.32
244.32
252.31
252.31
252.32
252.32
252.32
252.32
256.23
256.35
256.35
266.35
276.23
270.36
276.23
278.35
278.35
278.35
284.38
292.37
300.36
306.39
320.41
Mackay
Solid
Solubility5
(Hg/L)
45.00
??
0.600
11.00
2.000
43.00
??
??
??
??
3.810
0.4012
4.012
1.501
2.500
0.7999
??
43.50
49.99
??
0.2600
??
??
0.6012
12.00
1.601
??
??
0.1400
??
??
1 For C#-PAHs, a CAS is not available.
'Mackay et al. (1992).
E-3
-------
Appendix F
Water-only and Interstitial Water
LCSOs used in Table 5-1.
-------
Chemical
Test Species
Freshwater
Fluoranthene
Diporeia sp.
Hyalella azteca
Hyalella azteca
Hyalella azteca
Hyalella azteca
Chironomus tentans
Chironomus tentans
Chironomus tentans
Saltwater
Acenaphthene
Eohaustorius estuarius
Eohaustorius estuarius
Eohaustorius estuarius
Leptocheirus plumulosus
Leptocheirus plumulosus
Leptocheirus plumulosus
Fluoranthene
Leptocheirus plumulosus
Phenanthrene
Eohaustorius estuarius
Eohaustorius estuarius
Eohaustorius estuarius
Leptocheirus plumulosus
Leptocheirus plumulosus
Leptocheirus plumulosus
2,6- dimethylnaphthlene
Rhepoxynius abronius
2,3,5 -trimethylnaphthlene
Rhepoxynius abronius
1 -methylfluorene
Rhepoxynius abronius
2 -methylphenanthrene
Rhepoxynius abronius
9-methylanthracene
Rhepoxynius abronius
Acenaphthene
Rhepoxynius abronius
Rhepoxynius abronius
MethodA
FT.M/10
FT.M/10
S,M/10
S,M/10
S,M/10
S,M/10
S,M/10
S,M/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
S/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
FT,M/10
S,M/10
S,M/10
S,M/10
S,M/10
S,M/10
S,M/10
S,M/10
Water-only
LC50
(Mg/L)
>194
130.7
44.9
44.9
44.9
31.9
31.9
31.9
374
374
374
678
678
678
39.2
131
131
131
185
185
185
-
-
-
-
-
-
-
Interstitial Water
LC50
(Mg/L)
>381.3
>75.4
45.9
236.5
97.6
91.2
251
75.7
800
609
542
>1,720
1410
1490
-
138
139
146
387
306
360
200
153
44
70
32
-
-
References
Driscoll et al, 1997a,b
Driscollet al, 1997a,b
Suedeletal., 1993
SuedeletaL, 1993
Suedeletal., 1993
Suedeletal., 1993
Suedeletal., 1993
Suedeletal., 1993
Swartz, 199 la
Swartz, 199 la
Swartz, 199 la
Swartz, 199 la
Swartz, 199 la
Swartz, 199 la
Driscoll et al., 1998
Swartz, 199 la
Swartz, 199 la
Swartz, 199 la
Swartz, 199 la
Swartz, 199 la
Swartz, 199 la
Ozretich et al., 2000a
Ozretich et al., 2000a
Ozretich et al., 2000a
Ozretich et al., 2000a
Ozretich et al., 2000a
Swartz etal, 1997
Swartz etal, 1997
F-2
-------
Chemical
Test Species
Naphthalene
Rhepoxynius abronius
Phenanthrene
Rhepoxynius abronius
Rhepoxynius abronius
Pyrene
Rhepoxynius abronius
Rhepoxynius abronius
Rhepoxynius abronius
Fluoranthene
Rhepoxynius abronius
Rhepoxynius abronius
Rhepoxynius abronius
Rhepoxynius abronius
Rhepoxynius abronius
Rhepoxynius abronius
Rhepoxynius abronius
Rhepoxynius abronius
Rhepoxynius abronius
Rhepoxynius abronius
MethodA
S.M/10
S.M/10
S,M/10
S,M/10
S.M/10
S.M/10
S,M/10
S.M/10
S,M/10
S.M/10
S,M/10
S.M/10
S.M/10
S.M/10
S,M/10
S.M/10
Water-only
LC50
(ug/L)
-
-
-
-
-
-
13.9
13.9
13.9
13.9
13.9
13.9
13.9
13.9
13.9
13.9
Mean LC50 ratio =
Interstitial Water
LC50
(ug/L)
10440
-
-
28.1
-
-
-
-
22.7
29.4
24.2
>315
14.1
26.6
19.2
9.38
1.6
References
Ozretich et al, 2000a
SwartzetaL, 1997
Swartz et al., 1997
Ozretich et al., 2000a
SwartzetaL, 1997
SwartzetaL, 1997
SwartzetaL, 1997
SwartzetaL, 1997
SwartzetaL, 1990
SwartzetaL, 1990
SwartzetaL, 1990
DeWittetaL, 1992
DeWittetaL, 1992
DeWittetaL, 1992
DeWittetaL, 1992
DeWittetaL, 1992
Test conditions for water-only toxicity tests: S = static, FT = flow-through, M = measured,
10 = 10-d duration.
F-3
-------
Appendix G
Teratogenic Effects from Laboratory Exposure to PAHs.
-------
Species
fathead minnow
(embryos),
Pimephales
promelas
freshwater
topminnows,
Poeciliopsis
monacha
Poeciliopsis
lucida
English sole
(embryos),
Parophrys
vetulus
Rainbow trout
(embryos),
Oncorhynchus
mykiss
Sand sole
(embryos),
Psettichthys
melanostichus
Mode
of
Exposure
maternal
via water
water;
acetone carrier
maternal
via oral
aqueous from
BaP spiked to
sediment
water;
static
Method
lab;
flow-
through
lab;
static
renewal
lab;
wild-
caught
lab;
static
renewal
(7-10d)
lab
Exposure
Cone
Associated
PAH with Effect
Anthracene 6.66 ug/L
11. 6 ug/L
BaP 1,000 ug/L
nominal;
1,250 ug/L
was acutely
lethal
BaP 8,000 ug/L
(8 mg/kg
force-fed)
BaP 0.21 ug/L
measured
BaP 0.1 ug/L
measured;
range
(0.08-0.12)
Exposure
Time
6 wks
3 wks
24 h
followed by
6 mo. of
monitoring
-
through to
36 d
post-hatch
through to
yolk-sac
absorption
(7 -10 d)
Toxic Effect(s):
-yolk-sac malformations
-edema
-eye deformities
-increased AHH and
EROD activities
-malformation of tail
regions
-insufficient yolk-sac
-reduced fin-fold size
-reduced hatching
success
-nuclear pycnosis
-lack of body pigment
-insufficient yolk-sac
-abnormalities of eyes
-increased mortality (at
2.40 ug/L in aqueous)
-muscle necrosis
-abnormal mitosis in
eyes and brains
-overgrowth of tissues
-arrested development
-twinning; Effects only
after 48 h, i.e., during
organogenesis
Tissue Cone
8.8a ug/g (eggs)
9.0 ug/g converted
from 35.7 nmol/g
wet wt.
5 1.2 and 263
ug/g (eggs) - avg. =
157; Tissue cone.
from 80 mg/kg i.p.
maternal injection
1.93 ug/g (eggs),
12.34 ug/g (alevins),
from exposure to 2.40
ug/L BaP
2.1 ug/g
wet weight
Comments:
Effects on embryos
incubated with solar
ultraviolet light radiation
Implied effect - increased
AHH and EROD activity
indicative of carcinogenic
and teratogenic metabolites
formed during metabolism of
BaP by MFO-system
-Eggs maintained 1 1
days until yolk-sac
absorbed; static.
-Incidence of effect 4
times greater than controls
(Chai-squaredf=3.81)
Poor control survival
(52% mortality)
effects only exhibited in 5%
of animals; average hatching
success of controls only 57%
versus 28% BaP-treated
References
Hall and Oris,
1991
Goddard et al.,
1987
HoseetaL, 1981
Hannah et al.,
1982;
HoseetaL, 1984
HoseetaL, 1982
G-2
-------
Species
Flathead sole
(embryos),
Hippoglosso ide
selassodon
English sole
(embryos),
Parophrys
vetulus
gizzard shad,
Dorosoma
cepedianum
gizzard shad,
Dorosoma
cepedianum
estuarine clams,
Rangia cuneata
estuarine clams,
Rangia cuneata
Mode
of
Exposure Method
water; lab
static
water lab
water via lab;
treated static
sediment
water and/or lab;
sediment static
ingestion
water; acetone lab;
carrier static
water; acetone lab;
carrier static
Exposure
Cone
Associated
PAH with Effect
BaP 4.2 ug/L
bound to decreasing
bovine to <0.05
serum ug/L (DL)
albumin
BaP 2.1 ug/L
measured
BaP 1.38 ug/g
sediment
(initial);
0.74 ug/g
sediment
(mean of
days 4,8
and 15)
BaP 1.02 ug/g
sediment
(initial);
0.63 ug/g
sediment
(mean of
days 4,8,
and 15)
BaP 30.5 ug/L
BaP 30.5 ug/L
Exposure
Time Toxic Effect(s):
through to -hatching success sig.
yolk-sac decrease
absorption -nuclear pycnosis and
(7 -10 d) general disruption of
neural and ocular tissues
through to none
yolk-sac
absorption
(7 -10 d)
22 d none
22 days none
24 h none
24 h none
Tissue Cone
_
-
BDL in all but 2 fish
on day 4 -
(0.001 and 0.0002
ug/g wet weight)
ligated fish: 0.010
ug/g wet weight (n=4)
non- ligated: 0.012
ug/g wet weight
(n=14)
7.2 ug/g
wet weight
5.7 ug/g
wet weight
Comments:
very low hatching success in
controls and experimentals;
5.5 and 11.5%, respectively
-
-40 ligated shad in 250
LH2Owith4.15kg
sediment
-no sig. decline in
sediment cone, after day 4.
-50 shad, 30 ligated; 20
non-ligated, in 500 L
H2O with 3.15 kg sediment
-no sig. decline in sediment
cone, after day 4
-all other tissue cones.
BDL (n=26 ligated;
n=6 non- ligated)
-majority of BaP
concentrated in the
viscera (-75%)
-n=5
-majority of BaP
concentrated in the
References
HoseetaL, 1982
HoseetaL, 1982
Kolok et al.,
1996
Kolok et al.,
1996
Neffand
Anderson, 1975
Neffand
Anderson, 1975
viscera (-65%)
-n=8
G-3
-------
Species
coho salmon
(24 h Post
fertilization),
Oncorhynchus
kisutch
coho salmon,
(32 d post
fertilization),
Oncorhynchus
kisutch
coho salmon,
(24 h Post
fertilization),
Oncorhynchus
kisutch
coho salmon,
(32 d post
fertilization),
Oncorhynchus
kisutch
Calif, grunion
(embryos),
Leuresthes
tenuis
Mode
of
Exposure Method PAH
water; 0.5% lab; BaP
DMSO static
exposure
then flow-
through
water; 0.5% lab; BaP
DMSO static
exposure
then flow-
through
water; 0.5% lab; BaP
DMSO static
exposure
then flow-
through
water; 0.5% lab; BaP
DMSO static
exposure
then flow-
through
water lab; BaP
static
Exposure
Cone
Associated Exposure
with Effect Time
25,000 ug/L 24 h
25,000 ug/L 24 h
25,000 ug/L 24 h
25,000 ug/L 24 h
measured: 15 days
5 ug/L
(steady-
state);
24 ug/L
(initial)
Toxic Effect(s): Tissue Cone
none
none
none 0.54 decreasing to
0.15 nmol/mg protien
from 2 to 68 d post
fertilization
none 4.47 decreasing to
0.33 nmol/mg protien
from 2 to 68 d post
fertilization
-reduction in % hatch day 15: 0.992 ppm
-lateral folding of tail (wet weight); 6.872
-absence of caudal fin folds ppm (dry weight)
-hemorrhagic lesion or
congested vasculature in
caudal region
Comments: References
Effects on hatching, Ostrander et al.,
orientation, and foraging 1988
only.
Effects on hatching, Ostrander et al.,
orientation, and foraging 1988
only.
Cone, of BaP in tissue are Ostrander et al.,
not converted because wet 1989
weights were not given; only
the mg protein/animal. Can
possibly borrow weights
from earlier paper.
Cone, of BaP in tissue are Ostrander et al.,
not converted because wet 1989
weights were not given; only
the mg protein/animal. Can
possibly borrow weights
from earlier paper.
steady state concentration
reached in 4 to 10 days Winkler et al.,
1983
G-4
-------
Species
Calif, grunion
(embryos),
Leuresthes
tenuis
Calif, grunion
(embryos),
Leuresthes
tenuis
Pacific herring
(embryos),
Clupea pallasi
Pacific herring
(embryos),
Clupea pallasi
Mode
of
Exposure Method
water lab;
static
water lab;
static
seawater lab; static
contaminated
by contact with
oiled gravel -
experiment 1;
less weathered
seawater lab; state
contaminated
by contact with
oiled gravel -
experiment 2;
more weathered
Exposure
Cone
Associated Exposure
PAH with Effect Time
BaP measured: 15 days
5-24 jjg/L
(steady
state); 24-
361 tig/L
(initial)
BaP measured: 15 days
869 ppb
(initial);
steady-state
not reached
Field 9.1 u/L 16 days
Mixture*
Field 0.41ti/Lto 16 days
Mixture* 0.72 Li/L
Toxic Effect(s):
-retarded growth (14d)
-sporadic heart beat
-displaced head relative to
yolk-sac
-absence of melanophores
near lateral lines
-absence of lens formation
-lesions as larvae (above)
-retarded growth (14d)
-lateral curvature mid-body
-absent melanophores
-unused yolk sac
-lesions as larvae (above)
-yolk sac edema
- yolk sac edema
-pericardial edema
- skeletal, spinal, and
craniofacial abnormalities
- anaphase aberration
Tissue Cone
day 15:0.92 to 10.48
lig/g wet weight; 6.87
to 62.80 jjg/g (dry
weight)
day 15 - 19.98 Lig/g
wet weight; 112.03
tig/g dry weight
13.7 ng/g wet weight
0.022 tig/g wet
weight
Comments: References
steady state concentration Winkler et al.,
reached in 4 to 10 days 1983
steady-state concentration
never reached Winkler et al.,
1983
Crude Oil characterized for Carls et al., 1 999
PAHs only; concentrations
of individual PAHs not given
Crude Oil characterized for Carls et al., 1 999
PAHs only; concentrations
of individual PAHs not given
AArtificially weathered Alaska North Slope crude oil.
G-5
-------
Appendix H
Carcinogenic Effects from Laboratory
and Field Exposure to PAHs and PAH Mixtures.
-------
Species
Japanese Medaka,
Oryzia latipes
(6-10 d old)
guppy,
Poecilia reticulata
(6-10 d old)
Rainbow trout
(fingerlings),
Oncorhynchus mykiss
Rainbow trout
(juvenile),
Oncorhynchus mykiss
(10 mo)
Poeciliopsis lucida and
Poeciliopsis monacha
(1-7 months old)
Mode
of
Exposure
Water;
dimethyl-
formamide
carrier.
Water;
dimethyl-
formamide
carrier.
oral
ip injection
water;
acetone
carrier
Exposure Cone
Associated
Method PAH with Effect
Lab; static BaP 261 ug/L
Lab; static BaP 209 ug/L
Lab BaP 1,000 ppm per
feeding
Lab BaP 1 mg B(a)P in
0.4 ml PG
(I/month for 12
months)
Lab: (multiple 7,12- 5 ppm (per
exposures) 3 dimethylbenz(a)- exposure)
to 4 exposure anthracene
periods of 5-
20 hours each
week
Exposure
Time
2 x 6h, 1
week apart
2 x 6h, 1
week apart
12 and 18
months
1 8 months
(6 months
after final
injection)
7-8
months
(from
initial
exposure)
Toxic Effect(s):
Neoplastic lesions in
livers and other
tissues after 36 weeks
36% vs 1% (controls);
20 fish with adenoma,
6 with hepatocellular
carcinoma
Neoplastic lesions in
livers and other
tissues after 52 weeks
23% vs 0% (controls);
1 altered foci, 5
adenoma, 4 with
hepatocellular
carcinoma
Incidence of
neoplasms on liver
15%(1.0/liver)atl2
months
25%(7.7/liver)atl8
months
Incidence of
neoplasms in various
organs = 46% (x = 7.7
tumors/organ)
incidence of hepatic
tumors = 48%
Tissue
Cone Comments:
Exposures carried out at
26°Cinthedark;
concentration exceeds
saturation solubility of BaP
Studies carried out longer
because tumorigenic
response in guppy is
slower than in medaka
MFO info also available
0% at 6 months
0% on other organs
Organs examined =
gonads, swim bladder,
liver, spleen, head and
trunk kidneys, pancreas,
intestines, and stomach
only survivors examined =
(55% mortality in 5 ppm
treatment)
(13% mortality in control)
References
Hawkins et al,
1988;
Hawkins et al.,
1990
Hawkins et al,
1988;
Hawkins et al.,
1990
Hendricks et al.,
1985
Hendricks et al.,
1985
Schultz and
Schultz 1982
H-2
-------
Species
Poeciliopsis lucida and
Poeciliopsis monacha
(1-6 weeks old)
Bullheads
Japanese Medaka,
Poecilia reticulata
(6-10 d old)
Rainbow trout
(embryos),
Oncorhynchus mykiss
Mode
of
Exposure
water;
acetone
carrier
Direct skin
(river
sediment
extract)
Water via
Sediment
extract re-
dissolved in
acetone
injection of
sediment
extract into
yolk sac
Method
Lab: (multiple
exposures) 5
exposures
periods of 6
hours each
week
Lab
Lab
Lab
Exposure Cone
Associated
PAH with Effect
7,12- 5 ppm (per
dimethylbenz(a)- exposure)
anthracene
Field Mixture* 5% RSE painted
once per week
Field Mixture13 182 ppb TPAH
Black River, OH
extract;
254 ppb TPAH
Fox River, WI
extract
Field Mixture0 DosesD:
(Expl) 0.006 g
(Exp II) 0.012 g
0.006 g
0.003 g
Exposure
Time Toxic Effect(s):
6-7 Incidence of hepatic
months tumors =41.8%
18 months 23% of survivors
hyperplastic
9% with multiple
papillomas
24 h hepatocellular
carcinoma - Black
River Ex. (2/1 5 fish);
Pancreatic-duct cell
adenoma - Fox River
Ex. (1/15 fish)
1 year Hepatic carcinomas
(I) 8. 9% (11/123)
(II) 8.1% (12/148)
4.0% (5/148)
3.1% (2/65)
Tissue
Cone Comments: References
22% mortality in treatment Schultz and
1 6% mortality in control Schultz 1 982
Tumor-bearing livers
enlarged, yellow-white to
greenish and granular.
Survival of control and Black, 1983
experimental fish was
31%.
No incidence of
carcinomas in controls up Fabacher et al.,
to 270 days post-exposure; 1991
one incidence of
lymphoma after 360 days
of exposure.
Note; PCBs also present Metcalfe et al
sediment from Hamilton 1988
Harbour
A Buffalo River, NY; total no. PAHs measured =13, total no. of carcinogenic PAHs = 6.
B Black River, OH. And Fox River, WI; full compliment of measured PAHs.
c Hamilton Harbor, ON, Canada; total no. PAHs measured =13, total no. of carcinogenic PAHs = 6.
D Doses are calculated as gram equivalent wet weight of sediment represented by the volume of extract micro-injected into each trout sac-fry.
H-3
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