Procedures for the Derivation

of Equilibrium Partitioning

Sediment Benchmarks (ESBs)

for the Protection of Benthic

Organisms: PAH Mixtures

United States Office of Research and Development EPA-600-R-02-013

Environmental Protection Washington, DC 20460 www.epa.gov

Agency
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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

iii

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 (KOW) relationship was

similar across species; (2) normalize the acute toxicity of all PAHs in water to an aquatic species using a

reference KOW 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 KOW 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 KOW of 1.0 from the quotient of the FAV and ACR; and (6) to

calculate the PAH-specific FCV in μg/L using the FCV at the reference KOW of 1.0, the PAH-specific

KOW, 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 KOC, the FCV concentration for each specific PAH in

sediment (COC,PAHi,FCVi

, μg/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 COC,PAHi,FCVi of that particular PAH. This sum is termed the Equilibrium

Partitioning Sediment Benchmark Toxic Unit (EESBTUFCV). Freshwater or saltwater sediments

containing <1.0 EESBTUFCV of the mixture of the 34 PAHs or more PAHs are acceptable for the

protection of benthic organisms, and if the EESBTUFCV 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.
Foreword

iv

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 (MidContinent 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.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

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Contents

vi

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 PAH Mixtures ......................................................................................................................................... 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

2.3 Body Burden Model ......................................................................................................................................... 2-2

2.4 Target Lipid Model ........................................................................................................................................... 2-2

2.5 Acute Lethality Database Compilation............................................................................................................. 2-4

2.5.1 Aqueous Solubility ................................................................................................................................ 2-5

2.5.2 Exposure Duration ................................................................................................................................ 2-6

2.6 Data Analysis ................................................................................................................................................... 2-6

2.6.1 Regression Model ................................................................................................................................. 2-7

2.6.2 Testing Model Assumptions ................................................................................................................ 2-9

2.6.3 Volume Fraction Hypothesis................................................................................................................. 2-9

2.6.4 Chemical Classes .................................................................................................................................. 2-9

2.6.4.1 Statistical Analysis of KOW-Toxicity Relationships ................................................................. 2-10

2.6.4.2 Standard Errors and Residuals ................................................................................................ 2-11

2.6.4.3 Chemical Class Corrections..................................................................................................... 2-11

2.7 Universal Narcosis Slope................................................................................................................................ 2-12
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

vii

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 KOW 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 a KOW 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 KOW of 1.0 .................................................................................................. 3-15

3.6.2 Derivation of the PAH-Specific FCVs .................................................................................................. 3-15

Section 4

Derivation of the PAH EESBTUFCV ....................................................4-1

4.1 Derivation of Potencies for Individual PAHs in Sediments (COC,PAHi,FCVi) ......................................................... 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 EESBTUFCV 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 KOC ................................................................................................................. 5-2

5.2.3 Toxicity of Individual PAHs ................................................................................................................... 5-2
Contents

viii

5.2.4 Comparison of Sediment Toxicity to COC,PAHi,FCVi ................................................................................... 5-7

5.2.5 PAH Mixtures........................................................................................................................................ 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 KOW PAHs .......................................................................................... 5-11

5.3 Field Sediments versus ESBFCV for PAH Mixtures ......................................................................................... 5-17

5.3.1 Toxicity to R. abronius of Field Sediments Containing

 PAH Mixtures vs. EPSTUs 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 ........................................................................................................................................ 6-4

6.2.4 Uncertainty in Predicting EESBTUFCV,TOT ............................................................................................. 6-4

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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

ix

Section 8

References.............................................................................................................8-1

Appendix A ..........................................................................................................A-1

Appendix B .......................................................................................................... B-1

Appendix C ..........................................................................................................C-1

Appendix D ..........................................................................................................D-1

Appendix E .......................................................................................................... E-1

Appendix F .......................................................................................................... F-1

Appendix G......................................................................................................... G-1

Appendix H ..........................................................................................................H-1

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 for the 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. COC,PAHi,FCVi 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 in relation to the sum of the equilibrium partitioning sediment

benchmark toxic units (EESBTUs) of mixtures of PAHs spiked into sediment.

Table 5-3. Chemicals included in the high KOW PAH mixture experiment conducted by Spehar et al., (in preparation).

Table 6-1. Relative distribution of EESBTUFCV,TOT to EESBTUFCV,13 and EESBTUFCV,23 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.
Contents

x

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[j]aceanthrylene = cholanthrene; L, 3-methylcholanthrene; M, chrysene; N, 5-

methylchrysene; O, dibenzo[cd,jk]pyrene = anthranthrene; P, perylene; Q, benzo[ghi]perylene; R,

coronene; S, indeno[1,2,3-cd]pyrene (from Neff 1979).

Figure 2-1. Schematic diagram of the log10LC50 versus log10KOW relationship. At log10KOW = 0 (KOW = 1), the

concentration in water equals the concentration in octanol.

Figure 2-2. Comparisons of (A) log10KOW predicted by SPARC versus measured log10KOW using slow stir method

and (B) reported log10LC50 values versus the aqueous solubility estimated by SPARC. The diagonal

line represents equality.

Figure 2-3. Ratios of (A) 48- to 96-hour LC50 values and (B) 24- to 96-hour LC50 values versus log10KOW. The line

in (B) is the regression used to correct the 24-hour LC50 to 96-hour LC50.

Figure 2-4. Log10LC50 versus log10KOW 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 (%).

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 individual

estimates from the universal slope.

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.

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

log10KOW.

Figure 2-8. Log10LC50 versus log10KOW for (A) Lepomis macrochirus, (B) Daphnia pulex, and (C) Gambusia

affinis. The line connects the individual estimates of the log10LC50 values, including the chemical class

correction.

Figure 2-9. Comparison of target lipid model, line-of-fit and observed LC50 data for individual PAHs, by species.

Figure 2-10. Predicted and observed body burdens for four species.

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.

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.

Figure 3-2. GMAVs at a log10KOW of 1.0 from water-only acute toxicity tests using freshwater and saltwater genera

versus percentage rank of their sensitivity.

Figure 4-1. Probability distribution of the EESBTUFCV for PAH mixtures in sediments from individual coastal and

estuarine locations in the United States.

Figure 4-2. Probability distribution of the EESBTUFCV for PAH mixtures in sediments from all of the coastal and

estuarine locations in the United States from Figure 4-1.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

xi

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 ESBTUFCVi

.

Figure 5-4. Percentage rank, based on ESBTUFCVi, 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 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.

Figure 5-6. Response of Hyalella azteca exposed for 10 days under flow-through conditions to sediment spiked

with a mixture of high KOW PAH.

Figure 5-7. Response of Hyalella 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) of Hyalella azteca expressed on the basis of

measured PAH concentrations in tissues (lipid normalized).

Figure 5-9. Response of Hyalella azteca exposed for 10 days (3 renewals) to sediment spiked with a mixture of

high KOW 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 EESBTUFCV.

Figure 6-1. Comparison of observed EESBTUFCV,TOT to observed EESBTUFCV,13 from (A) 13 PAHs and EESBTUFCV,23

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) EESBTUFCV,13 and (B) EESBTUFCV,23 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

EESBTUFCV values of 34 PAHs and (B) a probability plot of these BaP concentrations at an EESBTUFCV

=1.0.

Figure 6-4. Anthracene concentration of 539 sediment samples from the EMAP and Elliott Bay datasets versus (A)

the EESBTUFCV values of 34 PAHs and (B) a probability plot of these anthracene concentrations at an

EESBTUFCV =1.0.

Figure 6-5. Computed solubilities of nine PAHs relative to their 25EC solubilities as a function of temperature.
Acknowledgments

xii

Acknowledgments

Coauthors

David J. Hansen Environmental consultant (formerly with U.S. EPA)

Dominic M. Di Toro Univ. of Delaware, Newark, DE; HydroQual, Inc., Mahwah, NJ

Joy A. McGrath HydroQual, Inc., Mahwah, NJ

Richard C. Swartz Environmental consultant (formerly with U.S. EPA)

David R. Mount* U.S. EPA, NHEERL, Mid-continent Ecology Division, Duluth, MN

Robert L. Spehar U.S. EPA, NHEERL, Mid-continent Ecology Division, Duluth, MN

Robert M. Burgess*,

** U.S. EPA, NHEERL, Atlantic Ecology Division, Narragansett, RI

Robert J. Ozretich U.S. EPA, NHEERL, Western Ecology Division, Newport, OR

Heidi E. Bell* U.S. EPA, Office of Water, Washington, DC

Mary C. Reiley U.S. EPA, Office of Water, Washington, DC

Tyler K. Linton Great Lakes Environmental Center, Columbus, OH

Significant Contributors to the Development of the Approach and Supporting Science

David J. Hansen Environmental consultant (formerly with U.S. EPA)

Dominic M. Di Toro Univ. of Delaware, Newark, DE; HydroQual, Inc., Mahwah, NJ

Joy A. McGrath HydroQual, Inc., Mahwah, NJ

Richard C. Swartz Environmental consultant (formerly with U.S. EPA)

David R. Mount U.S. EPA, NHEERL, Mid-continent Ecology Division, Duluth, MN

Robert M. Burgess U.S. EPA, NHEERL, Atlantic Ecology Division, Narragansett, RI

Robert J. Ozretich U.S. EPA, NHEERL, Pacific Ecology Division, Newport, OR

Robert L. Spehar 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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

xiii

Jim Dwyer U.S. F&WS Columbia, MO

Phillip M.Gschwend Massachusetts Institute of Technology, Cambridge, MA

Susan B. Kane-Driscoll Menzie-Cura & Associates, Inc., Chelmsford, MA

Peter F. Landrum NOAA, Great Lakes Research Laboratory, Ann Arbor, MI

Richard J. Pruell U.S. EPA, NHEERL, Atlantic Ecology Division, Narragansett, RI

Marc Tuchman U.S. EPA, Great Lakes National Program Office, Chicago, IL

*Principal U.S. EPA contacts

** Series editor
Executive Summary

xiv

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 (KOC), 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 Fg chemical/

g organic carbon basis (Fg/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 Fg/gOC basis can be accurately predicted by

multiplying the effect concentration in water by the chemical’s KOC.

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 (KOW) relationship was similar across species; (2)

normalize the acute toxicity of all PAHs in water to an aquatic species using a reference KOW 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 KOW 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 KOW of 1.0 from the

quotient of the FAV and ACR; and (6) to calculate the PAH-specific FCV in Fg/L using the

FCV at the reference KOW of 1.0, the PAH-specific KOW and the universal narcotic slope of the

acute-KOW 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 KOC, the FCV

concentration for each specific PAH in sediment (COC,PAHi,FCVi, Fg/g organic carbon).
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

xv

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 COC,PAHi,FCVi of that

particular PAH. This sum is termed the Equilibrium Partitioning Sediment Benchmark Toxic Unit

(EESBTUFCV), which is based on the FCV. Freshwater or saltwater sediments containing #1.0

EESBTUFCV of the mixture of the 34 PAHs or more PAHs are acceptable for the protection of

benthic organisms, and if the EESBTUFCV 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 EESBTUFCV 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 standalone, 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).
Glossary

xvi

Glossary of Abbreviations

ACR Acute-Chronic Ratio

AR Approximate Randomization

ASTM American Society for Testing and Materials

BaP Benzo[a]pyrene

BCF Bioconcentration factor

Cd Freely-dissolved interstitial water concentration of contaminant

CL Chemical concentration in target lipid

C*L Critical body burden in the target lipid fraction of the organism

COC Chemical concentration in sediments on an organic carbon basis

COC,PAHi PAH-specific chemical concentration in sediment on an organic carbon basis

Coctanol Chemical concentration in octanol

COrg Chemical concentration in the organism

C*Org Critical body burden in the organism

CIW Total interstitial water concentration of contaminant

COC,PAHi,FCVi Effect concentration of a PAH in sediment on an organic carbon basis

calculated from the product of its FCV and KOC

COC,PAHi,Rhepox,LC50 Sediment LC50 concentration on an organic carbon basis for a specific

PAH for Rhepoxinus calculated from the product of its LC50 value at a KOW

of 1.0 and KOC

COC,PAHi,Maxi Maximum solubility limited PAH concentration in sediment on an organic

carbon basis

CV Coefficient of Variation

CWA Clean Water Act

DOC Dissolved Organic Carbon

EC50 Concentration affecting 50% of the test organisms

EMAP Environmental Monitoring and Assessment Program

EPA United States Environmental Protection Agency

EqP Equilibrium partitioning

ESB Equilibrium Partitioning Sediment Benchmark(s)
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

xvii

ESBTUFCVi Equilibrium Partitioning Sediment Benchmark Toxic Unit for PAHi based

on the FCV

ESBTURhepox Equilibrium Partitioning Sediment Benchmark Toxic Unit for PAHi

 based

on the LC50 of Rhepoxynius abronius.

EESBTUFCV Sum of Equilibrium Partitioning Sediment Benchmark Toxic Units, where

the units are based on FCV values

f Lipid Fraction of lipid in the organism

f OC Fraction of organic carbon in sediment

fSC Fraction of soot carbon in sediment

FACR Final Acute-Chronic Ratio

FAV Final Acute Value

FCV Final Chronic Value

GMAV Genus Mean Acute Value

IWTU Interstitial Water Toxic Unit

IWTUFCV Interstitial water toxic unit calculated by dividing the dissolved interstitial

water concentration by the FCV

KDOC Dissolved organic carbon: water partition coefficient

KLW Lipid: water partition coefficient

KOC Organic carbon: water partition coefficient

K OW Octanol: water partition coefficient

KP Sediment: water partition coefficient

KS Setschenow constant

KSC Soot carbon: water partition coefficient

LC50 Concentration estimated to be lethal to 50 % of the test organisms within

a specified time period

LFER Linear free energy relationship

MV Molar Volume

NA Not Applicable, Not Available

NAPL Non-aqueous Phase Liquid

ND Not Determined, Not Detected

NOAA National Oceanographic and Atmospheric Administration
Glossary

xviii

NOEC No Observed Effect Concentration

NTU Narcotic Toxic Units

OEC Observable Effect Concentration

PAH Polycyclic aromatic hydrocarbon

PAHOC Organic carbon-normalized PAH concentration in sediment

PCB Polychlorinated Biphenyl

POC Particulate Organic Carbon

PSTU Predicted Sediment Toxic Units

QSAR Quantitative Structure Activity Relationship

REMAP Regional Environmental Monitoring and Assessment Program

S Aqueous Solubility

SAB U.S. EPA Science Advisory Board

SCV Secondary Chronic Value

SE Standard Error

SMAV Species Mean Acute Value

SPARC SPARC Performs Automated Reasoning in Chemistry

TOC Total Organic Carbon

TU Toxic Unit

WQC Water Quality Criteria

WQCTUFCVi Water Quality Criteria Toxic Unit based on the FCV
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

1-1

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 et al., 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 μg chemical/g

sediment organic carbon (μg/gOC) and not on an

interstitial water basis because (1) interstitial water
Introduction

1-2

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).
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

1-3

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 underprotective 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; MacDonald et al., 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 EPAH 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 EPAH 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’s1979

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 (C10H8

), 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 (C10H8

, 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
Introduction

1-4

distribution in the environment and their toxic

effects. PAHs undergo three types of chemical

reactions characteristic of aromatic hydrocarbons -

electrophilic substitution, oxidation, and reduction.

Oxidation and reduction reactions destroy the

aromatic character of the affected benzene ring

but electrophilic substitution does not.

Several systems of nomenclature have been

used to describe PAH ring structures.

Nomenclature used in this document is that

adopted by the International Union of Pure and

applied Chemistry (IUPAC) and described in detail

in The Ring Index (Patterson et al., 1960).

As noted above, there is an extremely large

number of possible PAH structures (>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 nonalkylated 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

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[j]aceanthrylene = cholanthrene; L, 3-methylcholanthrene; M, chrysene; N,

5-methylchrysene; O, dibenzo[cd,jk]pyrene = anthranthrene; P, perylene; Q, benzo[ghi]perylene; R,

coronene; S, indeno[1,2,3-cd]pyrene (from Neff 1979).
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

1-5

Ishiwatari 1993; Boehm et al., 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 (Hites

et al., 1980; Meyers and Ishiwatari 1993; Silliman

et al., 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 et al., 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

Hites 1976; Neff 1979).

The majority of PAHs found in aquatic

environments originate from pyrogenic sources

(Blumer 1976; Suess 1976; Hites et al., 1977;

LaFlamme and Hites, 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 Hites 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 chemicalby-chemical specification of sediment

concentrations protective of benthic aquatic life.

The EqP method should be applicable to nonionic

organic chemicals with a KOW 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
Introduction

1-6

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 et al., 1985). (3) EqPconfirmation tests are conducted to demonstrate

the accuracy of the EqP prediction that the KOC

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 cconfirmation

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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

1-7

presented that demonstrate that the toxicity of

narcotic chemicals when based on concentration in

water increase with their KOW and that the slope

of the KOW-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 PAHspecific 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 EESBTUFCV”

contains the approach used for deriving the

EESBs for mixtures of PAHs. The COC,PAHi,FCVi

 is

derived for each individual PAH as the product of

the PAH-specific FCV and the respective KOC

value as recommended by the EqP approach. The

use of the COC,PAHiFCVi 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 COC,PAHi,FCVi

 to determine the sum of these

Equilibrium Partitioning Sediment Benchmark

Toxic Units (EESBTUFCV). If theEESBTUFCV 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 EESBTUFCV exceeds

1.0, the sediment benchmark for the PAH mixture

is exceeded and sensitive benthic organisms may

be affected by the PAHs. The EESBTUFCV 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,PAHi,FCVi

 and ESB derivation. The COC,PAHi,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.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

2-1

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 KOW-toxicity relationship

is the same for all species of aquatic organisms

and classes of narcotic chemicals with the

intercepts being species and chemical classspecific; (3) the species-specific LC50 values

normalized to a KOW =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

log10KOW 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 KOW 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).
2-2

Narcosis Theory

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 log10KOW

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.

2.3 Body Burden Model

The initial QSAR models for narcotic toxicity

relied on correlations of log10LC50 and log10KOW

(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 COrg

(mmol/kg wet weight) to the chemical

concentration dissolved in the water Cd

 (mmol/L)

BCF = COrg (2-2)

 Cd

Using the BCF, the organism concentration

corresponding to the LC50, which is referred to as

the critical body burden and denoted by C* Org, can

be computed using

C* Org = BCF x LC50 (2-3)

The superscript * indicates that it is a critical body

burden corresponding to the LC50. The BCF also

varies with KOW. 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

log10 C* Org = log10BCF + log10LC50

• log10KOW - 1.3 -log10KOW +1.7

• 0.4 (2-5)

or

C* Org • 2.5 Fmol/g wet wt (2-6)

Thus, McCarty et al. (1991) rationalized the

relationship between LC50 values and KOW 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 log10KOW in

Equations 2-1 and 2-4. For example, if the

fraction of lipid in the fish is ass umed to be 5%

(ƒLipid = 0.05), then the critical body burden in the

lipid fraction of the fish is

 C* L = 

C* Org = j 50 μmol / g lipid (2-7)

 ƒ Lipid

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.

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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

2-3

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

μmol/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 (μmol/g lipid =

mmol/kg lipid), to the freely-dissolved aqueous

concentration Cd

, (mmol/L)

 KLW= 

CL

 Cd (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* L , when the chemical

concentration in the water phase is equal to the

LC50

 C* L = KLW x LC50 (2-9)

Assuming the narcosis hypothesis is true, i.e., that

50% mortality occurs if any narcotic chemical

reaches the concentration C* L, then the LC50 for

any chemical can be calculated using the same

critical target lipid concentration C* L and the

chemical-specific target lipid-water partition

coefficient

 LC50 = C* L

 KLW (2-10)

or

log10LC50 = log10C* L - log10KLW (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

log10KLW = a0

 + a1 log10KOW (2-12)

Such a relationship is called a linear free energy

relationship (LFER) (Leo et al., 1971; Brezonik,

1994). Combining Equations 2-11 and 2-12 yields

the following linear relationship between

log10LC50 and log10KOW

 log10LC50 = log10C* L -a0 - a1 log10KOW (2-13)

where log10C* L - a0

 is the y intercept and -a1 is the

slope of the line.

This derivation produces the linear relationship

between log10LC50 and log10KOW which is found

experimentally (see, for example, Table 6 in

Hermens et al., 1984)

log10LC50 = m log10KOW + b (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, a1

.

The intercept of the regression b = log10C* L - a0 is

composed of two parameters: C* L 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 a1 for

fish are assumed to be known: a0

 = - 1.3 and a1 =

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 a1

 = 1 and a0 = 0 and the yintercept becomes

b = log10C* L (2-15)
2-4

Narcosis Theory

which is the target-lipid concentration producing

50% narcosis mortality.

This result can be understood by examining

Figure 2-l. The y-intercept b is the LC50 value for

a chemical with a log10KOW = 0 or KOW = 1. The

KOW 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 = -0.048l 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

LC50*Kow=1 = b = C* octanol = C* L (2-16)

Note that this interpretation is true only if a0

 = 0

(see Equation 2-13).

Log10KOW

024

Log 10LC50 (mmol/L)

b

m

Figure 2-1. Schematic diagram of the log10LC50

versus log10KOW relationship. At log10KOW

= 0 (KOW = 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 - log10KOW regression

coefficients in the following way

 slope chemical

m = -a1

 intercept chemical biological (2-17)

 b = -a0 % log10C*L

The chemical parameters a0 and a1 are associated

with the LFER between octanol and target lipid

(Equation 2-12). The biological parameter is the

critical target lipid concentration C* L. This result is

important because it suggests that the slope m = -

a1

 of the log10LC50- log10KOW 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* L (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 - log10KOW 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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

2-5

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 Bruijn et al., 1989). Fifty

three compounds such as phenols, anilines,

chlorinated monobenzenes, PAHs, PCBs and

pesticides were employed. A comparison of the

log10KOW values measured using the slow stir flask

technique to the SPARC estimates demonstrates

that SPARC can be used to reliably estimate

measured log10KOW values over nearly a seven

order of magnitude range of log10KOW (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).

2.5.1 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

Figure 2-2. Comparisons of (A) log10KOW predicted by SPARC versus measured log10KOW using slow stir method

and (B) reported log10LC50 values versus the aqueous solubility estimated by SPARC. The diagonal

line represents equality.

0

2

4

6

8

02468

Slow Stir Log10KOW

A

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2

Log Aqueous Solubility (mol/L)

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B

SPARC Log10KOW

Log 10LC50 (mol/L)
2-6

Narcosis Theory

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)/LC5096 = 0.0988 log10 KOW + 0.9807 (2-18)

where LC5024 and LC5096 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 of fish to a chemical for 24 hours, these

data were converted to 96 hour LC50 values using

Equation 2-18 for chemicals having log10KOW

values of >1. No correction factor is applied to 24

hour toxicity data for invertebrates and fishes

exposed to chemicals having log10KOW values of

<1 (Di Toro et al., 2000).

2.6 Data Analysis

The analysis of the toxicity data is based on

the target lipid model assumption that the slope of

the log10KOW 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.

0

1

2

3

4

Oncorhynchus

Cyprinodon

Pimephales

Carassius

Lepomis

Jordanella

Poecilia

A

0

1

2

3

4

-1 0 1 2 3 4 5 6

Log10KOW

-1 0 1 2 3 4 5 6

Log10KOW

B

48-hr LC50/96-hr LC50

24-hr LC50/96-hr LC50

Figure 2-3. Ratios of (A) 48- to 96-hour LC50 values and (B) 24- to 96-hour LC50 values versus log10KOW. The

line in (B) is the regression used to correct the 24-hour LC50 to 96-hour LC50.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

2-7

2.6.1 Regression Model

Consider a species k and a chemical j. The

LC50k, j for that species-chemical pair is

 log10LC50k,j = log10C* L (k) - a0 - a1 log10KOW(j) (2-19)

 = bk - a1 log10KOW(j) (2-20)

where

 bk = log10C* L(k) - a0 (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 a1 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

*ki = 1 k = i

*ki = 0 k... i (2-22)

which is the Kronecker delta (Kreyszig, 1972).

The regression equation can be formulated using

*ki as follows

log10LC50i,j = a1log10KOW(j) + b

N

k ki

k

S

δ

=

∑

1

 (2-23)

Log 10LC50 (mmol/L) Log 10LC50 (mmol/L) Log 10LC50 (mmol/L) Log 10LC50 (mmol/L)

4

2

0

-2

-4

4

2

0

-2

-4

4

2

0

-2

-4

4

2

0

-2

-4

Artemia salina nauplii

Orconectes immunis

Hydra oligactis

Daphnia cucullatta

Alburnus alburnus Tanytarsus dissimilis Ictalurus punctatus

Tetrahymena elliotti Aedes aegypti Culex pipens

Lymnaea stagnalis

Leptocheirus plumulosus

Leuciscus idus melanotus Gambusia affinis

Daphnia pulex Daphnia magna Portunus pelagicus

-2 0 2 4 6 8 -2 0 2 4 6 8 -2 0 2 4 6 8 -2 0 2 4 6 8

Log10KOW Log10KOW Log10KOW Log10KOW

Figure 2-4. Log10LC50 versus log10KOW 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 (%).
2-8

Narcosis Theory

Equation 2-23 is now a linear equation with Ns

+1

independent variables: log10KOW(j) and *ki, k =

1,...,Ns

. There are Ns+1 coefficients to be fit: a1

and bk, k = 1,...,NS. For each LC50ij

corresponding to species i and chemical j, one of

the bk

 corresponding to the appropriate species k =

i has a unity coefficient *ii = 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

+1 independent variables, for example

for j = 1 and i = 3

log10 (LC50ij) log10 (KOW(j)) *1i *2i *3i ... *Nsi

 0.788 1.175 0 0 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 *ki is

one for that record. By contrast, the slope term

a1

log10KOW(j) 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 a1

 and the species-specific intercepts bk, k =

1,...,Ns

.

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.

AaeNaAn AalLmaIp Oi CpCv DcDmDp Te PprJf CaPpuPr HoXi Lp Ls AmGaMbaNs LmeOl PpeRc MbeTd Om

A

B

1000

100

10

1

0.1

0.01

0.001

Probability Number of Points

5.0%

0.15%

Slope Differential from Baseline

0.20

0.10

-0.10

0.00

-0.20

-0.40

-0.30

-0.50

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 individual estimates from the universal slope.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

2-9

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 a1

= -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 a1

 = -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 et al., 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) x MV (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 Molar volumes

 (mmol/L) (cm3/L)

Slope -0.97 ±0.012 -0.90 ± 0.012

R2 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 KOW.
2-10

Narcosis Theory

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 - log10KOW 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, )cR, corresponding to the R = 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 >Rj

 that is one

if chemical j is in chemical class R and zero

otherwise

>Rj = 1 if chemical j is in class R

>Rj

 = 0 otherwise (2-26)

The regression equation that results is

log10LC50i, j = a1log10KOW(j) + b

N N

k k

k

S

j

C

i δ ξ c = =

∑ ∑+

−

1 1

1

∆ l l

l

 (2-27)

Each data record now contains t he dependent

variable log10LC50i,j, the independent variables

log10KOW(j), and the *ki, k = 1,...,Ns

 and >Rj, R =

1,...,NC - 1 indicator variables which are 0 or 1

depending on which species and which chemical

class is represented by the LC50i,j.

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 bi

 and one )cR for

species i and chemical j in chemical class R. Since

this condition would occur in every equation there

is no unique solution for the bk

 and the )cR 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 )cR

 = 0. The remaining

regression constants )cR, R = 1,...,Nc - 1 are then

the differential toxicity of chemical class R relative

to the reference chemical class. This is the reason

for the )c notation.

The requirement for a chemical class

correction is decided using a statistical test that

compares the )cR

 values that result from the

regression to the hypothesis )cR = 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 )cR

 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,
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

2-11

Aliphatics

Alcohols

Ethers

Aromatics

Ketones

PAHs

Aliphatics

Alcohols

Ethers

Aromatics

Ketones

PAHs

A B

Log 10Residuals

Log 10Residuals

0.5

0.4

0.0

0.3

0.2

0.1

-0.1

-0.2

-0.3

-0.4

-0.5

0.5

0.4

0.0

0.3

0.2

0.1

-0.1

-0.2

-0.3

-0.4

-0.5

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

>Rj = 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 bi

 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(bi

) 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 bi

 for Pimephales promelas (fathead minnow)

with 182 data points has a 10% coefficient of

variation, CV(bi

) = SE (bi) /bi, while the bi 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, CV(bi

), 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 2-7C).

2.6.4.3Chemical 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
2-12

Narcosis Theory

0.0

0.2

0.4

0.6

1 10 100 1000

Number of Data Points

A

-2.0

-1.0

0.0

1.0

2.0

0.1 1 1020 50 8090 99 99.9

Probability

B

+ + +++++++++++++++++++++++++++++++ ++++

+++++++++++++++++++++++++++++++++ + +

-2

-1

0

1

2

-2 -1 0 1 2 3 4 5 6 7

Log10KOW

C

+

+

++

+

+

+

+

+

+

+

+

+

+

+

+

+

++

+

+++

+

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+

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+

+ +

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+

+

+

+

+

+

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+

+

+

+

++

++

+

+

+

+

+

+

+

+

++

+

++

+

+

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+

++

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+

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+

+

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+++

+

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+

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+++

+

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++

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+

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++

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+

+

+

++

+

+

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+

+

++

+

+

+

+

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+

+

+

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+

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+

+

+

+

+

+

+

+

+

+

+

++++

+

+

+

+

+

+

+

+

+

++

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+

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+

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+

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++

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+

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+

+

+

+

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+

++

++

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++

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++

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+

+

+

+

+

++

Log 10Residuals Log 10Residuals Body Burden Coefficient of Variation

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

 log10LC50k,j = a1

log10KOW(j) + bk + ∆c

1

j

1

l l

l

ξ

=

−

∑

NC

 (2-28)

This is a straight line if only reference narcotics

are considered )cR

, = 0 or if only one chemical

class correction is involved, e.g., all halogenated

reference narcotics. Otherwise, more than one

)cR

, 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

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 log10KOW.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

2-13

restricted to successively smaller upper limits of

log10KOW. The results are listed below

Maximum log10KOW 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 log10KOW 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* L = log10LC50 + 0.945log10KOW (2-29)

If this were not the case, then the species

sensitivity order could be reversed if LC50 values

or C* L 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 KOW 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

-4

-2

0

2

4

A: Lepomis macrochirus

-4

-2

0

2

4

B: Daphnia pulex

-4

-2

0

2

4

-2 0 2 4 6

Log10KOW

C: Gambusia affinis

Log 10LC50 (mmol/L) Log 10LC50 (mmol/L) Log10 LC50 (mmol/L)

Figure 2-8. Log10LC50 versus log10KOW for (A) Lepomis

macrochirus, (B) Daphnia pulex, 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 - log10KOW 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
2-14

Narcosis Theory

Table 2-1. Regression results: y-intercepts and chemical class corrections‡ (Table from Di Toro et al., 2000).

Species i N bi SE(bi)

10bi SE(10bi

)

 (μmol/g octanol)

Americamysis bahia 30 1.54 0.082 34.3 6.7

Portunus pelagicus 4 1.56 0.19 36.1 18.2

Leptocheirus plumulosus 4 1.56 0.191 36.2 18.4

Palaemonetes pugio 8 1.68 0.137 48.2 16.4

Oncorhynchus mykiss 44 1.79 0.065 61.7 9.4

Jordanella floridae 18 1.82 0.096 66.1 15.2

Ictalurus punctatus 7 1.87 0.139 74.8 25.9

Pimephales promelas 182 2.02 0.044 105 10.8

Lepomis macrochirus 70 2.03 0.0056 108 14.1

Daphnia magna 113 2.04 0.049 111 12.6

Cyprinodon variegatus 33 2.05 0.078 111 20.5

Oryzias latipes 4 2.05 0.182 112 53.9

Carassius auratus 43 2.13 0.065 134 20.5

Rana catesbian 5 2.13 0.162 135 55.9

Tanytarsus dissimilis 9 2.14 0.125 137 42

Orconectes immunis 6 2.14 0.149 139 52.3

Alburnus alburnus 7 2.16 0.137 144 49.1

Nitocra spinipes 6 2.17 0.148 147 54.7

Gambusia affinis 8 2.17 0.13 149 47.9

Leucisus idus melanotus 26 2.18 0.075 152 26.8

Neanthes arenaceodentata 4 2.23 0.19 168 85

Artemia salina nauplii 32 2.26 0.077 181 32.8

Lymnaea stagnalis 5 2.29 0.163 195 81.5

Xenopus laevis 5 2.33 0.163 213 88.9

Hydra oligactis 5 2.33 0.163 214 89.5

Culex pipiens 5 2.34 0.163 216 90.4

Poecilia reticulata 14 2.36 0.101 228 55.2

Menidia beryllina 8 2.37 0.134 233 77.3

Daphnia pulex 6 2.38 0.15 240 91
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

2-15

Ambystoma mexicanum 5 2.39 0.163 245 103

Daphnia cucullata 5 2.4 0.163 249 104

Aedes aegypti 5 2.42 0.163 261 109

Tetrahymena elliotti 10 2.46 0.121 286 85

Chemical Class R N )cR SE()cR) 10)c

R SE(10)c

R)

Aliphatics 215 0 - 1 -

Ethers 13 0 - 1 -

Alcohols 134 0 - 1 -

Aromatics 241 0 - 1 -

Halogenated 319 -0.244 0.033 0.57 0.044

Ketones 49 -0.245 0.059 0.569 0.078

PAHs 84 -0.263 0.057 0.546 0.073

Slope -0.945 0.014

Species i N bi SE(bi)

10bi SE(10bi)

 (μmol/g octanol)

‡

See Equation (2-27).

N = Number of data points.

bi = y-intercept.

SE(bi)=Standard error of bi.

)cR =chemical class correction to the y-intercept.

SE()cR)=standard error of )cR. †

=Standard errors of 10bi and 10)cR are based on the assumption that the estimation errors for bi and

)cR are gaussian. The formulas follow from the standard error of a log normally distributed random

variable (Aitchison and Brown, 1957). For x =bi or )cR, μR=2.303x, σR=2.303 SE(x), and

SE(10x)= SE(e2.303x) = .

ee e μl 2 2 σ σ l l

2 2

−

Table 2-1. Continued

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, bi

, are

related to the target lipid concentration by the

relationship

y-intercept = bi = log10C* L(i) - a0 (2-30)

or, with chemical class corrections,

 y-intercept = bi + )cR= log10C* L(i) - a0 (2-31)

for species i and chemical class R, where a0

 is the

parameter in the LFER between octanol and

target lipid (Equation 2-12).
2-16

Narcosis Theory

Figure 2-9. Comparison of target lipid model, line-of-fit and observed LC50 data for individual PAHs, by species.

-5

-4

-3

-2

-1

0

Artemia salina nauplii

-5

-4

-3

-2

-1

0

Ictalurus punctatus

-5

-4

-3

-2

-1

0

Oncorhynchus mykiss

-5

-4

-3

-2

-1

0

2 3 4 5 6

Log10KOW

+

Daphnia magna

+

Mysidopsis bahia

Neanthes arenaceodentata

Palaemonetes pugio

2 3 4 5 6

Menidia beryllina

Leptocheirus plumulosus

+

Pimephales promelas

2 3 4 5 6

Cyprinodon variegatus

Log 10LC50 (mmol/L) Log 10LC50 (mmol/L) Log10 LC50 (mmol/L) Log 10LC50 (mmol/L)

Log10KOW

Log10KOW
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

2-17

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 cutoff 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 log10KOW #5.3 used by Di

Toro et al. (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

COC # COC,max = KOCS (2-34)

The relationship between the predicted

concentration in octanol, bi

 + )cR, to the

concentration measured in extracted lipid, log10C* L,

is examined in Table 2-2 which lists observed

LC50 body burdens (μmol/g lipid) and predicted

critical body burdens (μmol/g octanol) for

organisms in the database for which measured

lipid-normalized critical body burdens were

available. Three fish species: Gambusia affinis

(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 (2-32)

so that

log10C* L(i) = bi + )cR = y-intercept (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

(bi

 + )cR) in the discussion presented below are

referred to as body burden lipid concentrations

although the units (μmol/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
2-18

Narcosis Theory

Figure 2-10. Predicted and observed body burdens for four species.

 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. Critical Body Burden (

Fmol/g)

G. affinis

P. reticulata

P. promelas

P. pelagicus

Observed "SD (Fmol/gOC)

Predicted "SE (Fmol/g lipid)

250

200

150

100

50

0

14-day LC50

Guppy

4-day LC50

Fathead Minnow

2-day LC50

D. magna

16-day NOEC

D. magna

0.9

50

1.2

1.2

0.6

23

50

25

0.1 1 10 100

Toxic Units No. of Chemicals Tested
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

2-19

Organism Chemical

 log

KOW

 time

(hr)

C*

Org

References

Obs M ean Pred.

(μmol/

g lipid)

(μ mol/g

octanol)

M osquitofish,

Gambusia affinis

1,4-dibromonbenzene

1,2,3-trichlorobenzene

1,2,4-trichlorobenzene

pentachlorobenzene

3.55

3.98

4.00

5.32

96

“

“

“

85.0

140.0

92.0

69.0 93.2 85.3

Chaisuksant and

Connell, 1997

Guppy,

Poecilia reticulata

1,4-difluorobenzene

1,2-dichlorobenzene

1,4-dichlorobenzene

1,2-dibromobenzene

1,4-dibromobenzene

2.11

3.31

3.24

3.56

3.55

1.5

91

41

4

6

444.0

34.0

400.0

24.0

120.0 110 130

Sijm et al., 1993

Fathead minnow,

Pimephales promelas

1,2-dichlorobenzene

1,4-dichlorobenzene

1,2-dibromobenzene

1,4-dibromobenzene 

3.31

3.24

3.56

3.55

18

10

7

10

78.0

68.0

60.0

54.0

Sijm et al., 1993

1,2,4-trichlorobenzene 

1,1,2,2-tetrachlorobenzene

dichlorobenzene

dichlorobenzene

1,2-dichlorobenzene

4.00

2.31

3.27

3.27

3.31

50.2

57.2

75.5

129

62.3

van Wezel et al.,

1995

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

3.31

3.24

3.24

98.9

173

121

107

110

138

150

van Wezel et al.,

1996

naphthalene

1,2,4-trichlorobenzene

3.36

4.00

123

215 95 59.9

de M aagd et al.,

1996

Crab,

Portunus pelagicus

1,4-dichlorobenzene 

1,2,3-trichlorobenzene

1,2,3,4-tetrachlorobenzene

pentachlorobenzene

3.24

3.98

4.64

5.32

96

96

96

96

9.6

45.0

119

111 49.9 20.6

M ortimer and

Connell, 1994

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).
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

3-1

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 μmol/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 KOW normalized reference acute

values at a KOW 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 KOW

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 wateronly 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 KOW-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 KOW 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 μmol/g

octanol that indicates the relative sensitivity of

each tested species and PAH, which was used to

calculate SMAVs and GMAVs in μmol/g octanol,

which are indicative of critical tissue

concentrations in organisms on a μmol/g lipid basis.

The GMAVs are used to calculate the final acute

value (FAV) applicable to PAHs at a KOW of 1.0

(Stephan et al., 1985). This FAV at a KOW of 1.0,

when divided by the Final Acute-Chronic Ratio

(FACR), becomes the FCV at a KOW of 1.0.

Importantly, the FCV for any specific PAH can

then be derived by back calculating using FAV at a

KOW of 1.0, the KOW of the specific PAH and the

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 KOC multiplied

by the solubility of that PAH.

Section 3

Toxicity of PAHs in Water

Exposures and Derivation

of PAH-Specific FCVs
Toxicity of PAHs in Water Exposures

3-2

Table 3-1. Summary of the chronic sensitivity of freshwater and saltwater organisms to PAHs;

test specific data.

Cladoceran,

Daphnia magna

LC W Anthracene 21d 2.1 5.3% fewer

broods

<2.1 Holst and

Giesy, 1989

4 8.0% fewer

broods

8.2 13.8% fewer

broods

Cladoceran, 

Daphnia magna

LC W Fluoranthene 21d 6.9-17 35 17% reduction

in length

24.5 Spehar et al.,

1999

73 25% reduction

in length, 37%

fewer

young/adult

148 No survival

Cladoceran, 

Daphnia magna

LC W Phenanthrene 21d 46-57 163 Survival

reduced 83%,

98% fewer

broods

96.39 Call et al.,

1986

Midge, 

Paratanytarsus sp.

LC B Acenaphthene 26d 32-295 575 Survival

reduced

~90%, -60%

reduction in

growth, no

reproduction

411.8 Northwestern

Aquatic

Sciences,

1982

Midge, 

Paratanytarsus sp.

LC B Acenaphthene 26d 27-164 315 Survival

reduced

-20%, -30%

reduction in

growth

227.3 Northwestern

Aquatic

Sciences,

1982;

Thursby,

1991a

676 Survival

reduced -60%

Fathead minnow, 

Pimephales

promelas

ELS W Acenaphthene 32d 50 109 5% reduction

in growth

73.82 Academy of

Natural

Sciences,

1981;

Thursby,

1991a

410 26% reduction

in growth,

Survival

reduced 45%

630 No survival

 Observed

Common Effects Chronic

Name, NOECC OECD (Relative to Value

Species TestA HabitatB PAH Duration (μg/L) (μg/L) Controls) (μg/L) Reference
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

3-3

 Observed

Common Effects Chronic

Name, NOECC OECD (Relative to Value

Species TestA HabitatB PAH Duration (μg/L) (μg/L) Controls) (μg/L) Reference

Fathead minnow, 

Pimephales

promelas

ELS W Acenaphthene 32d 50-109 410 20% reduction in

growth, Survival

reduced 66% 

211.4 Academy of

Natural Sciences,

1981; Thursby,

1991a

630 No survival

Fathead minnow,

Pimephales

promelas

ELS W Acenaphthene 32-35d 67-332 495 54% reduction in

growth

405.4 Cairns and

Nebeker, 1982

Fathead minnow, 

Pimephales

promelas

ELS W Acenaphthene 32-35d 197-345 509 30% reduction in

growth

419 Cairns and

Nebeker, 1982

682 52% reduction in

growth, Survival

reduced 45%

1153 87% reduction in

growth, Survival

reduced 97%

Fathead minnow, 

Pimephales

promelas

ELS W Acenaphthene 32d 64 98 Survival reduced

24%

79.2 ERCO, 1981

149 Survival reduced

65% 

271 Survival reduced

75% 

441 Survival reduced

80%

Fathead minnow, 

Pimephales

promelas

ELS W Acenaphthene 32d 50-91 139 Survival reduced

20%

112.5 ERCO, 1981

290 Survival reduced

50%

426 Survival reduced

52%

Fathead minnow, 

Pimephales

promelas

ELS W Fluoranthene 32d 3.7-10.4 21.7 Survival reduced

67%, 50%

reduction in

growth

15.02 Spehar et al.,

1999

Rainbow trout,

Oncorhynchus

mykiss

ELS B/W Phenanthrene 90d 5 8 Survival reduced

41%, 33%

reduced growth

6.325 Call et al., 1986

14 Survival reduced

48%, 44%

reduced growth

32 Survival reduced

52%, 75%

reduced growth

66 No survival

Table 3-1. Continued
Toxicity of PAHs in Water Exposures

3-4

Mysid, 

Americamysis

bahia

LC B/W Acenaphthene 35d 100-240 340 93% reduction

in young

285.7 Horne et al.,

1983

510 No survival

Mysid, 

Americamysis

bahia

LC B/W Acenaphthene 25d 20.5-

44.6

91.8 91% reduction

in young

63.99 Thursby et al.,

1989b

168 No

reproduction,

34% reduction

in growth

354 Survival

reduced 96%,

no reproduction

Mysid, 

Americamysis

bahia

LC B/W Fluoranthene 28d 35926 21 Survival

reduced 26.7%,

91.7%

reduction in

young

15.87 U.S. EPA, 1978

43 No survival

Mysid, 

Americamysis

bahia

LC B/W Fluoranthene 31d 0.41-

11.1

18.8 Survival

reduced 23%,

no reproduction 

14.44 Spehar et al.,

1999

Mysid,

Americamysis

bahia

LC B/W Phenanthrene 32d 1.5-5.5 11.9 No survival 8.129 Kuhn and

Lussier, 1987

Mysid, 

Americamysis

bahia

LC B/W Pyrene 28d 3.82 5.37 46% reduction

in young

4.53 Champlin and

Poucher, 1992b

6.97 47% reduction

in young

9.82 73% reduction

in young

15.8 85% reduction

in young

20.9 90% reduction

in young,

Survival

reduced 37%

38.2 No survival

 Observed

Common Effects Chronic

Name, NOECC OECD (Relative to Value

Species TestA HabitatB PAH Duration (μg/L) (μg/L) Controls) (μg/L) Reference

Table 3-1. Continued
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

3-5

A Test: LC = life-cycle, PLC = partial life-cycle, ELS = early life-stage

B Habitat: I = infauna, E = epibenthic, W = water column

C NOEC = Concentrations where no significant effects were detected.

D OEC = Concentrations where significant effects were detected on survival, growth, or reproduction.

 Observed

Common Effects Chronic

Name, NOECC OECD (Relative to Value

Species TestA HabitatB PAH Duration (μg/L) (μg/L) Controls) (μg/L) Reference

Sheepshead

minnow,

Cyprinodon

variegatus

ELS B/W Acenaphthene 28d 240-520 970 Survival

reduced 70%

710.2 Ward et al.,

1981

2000 No survival

2800 No survival

Table 3-1. Continued
Toxicity of PAHs in Water Exposures

3-6

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 AcuteName, PAH Value Value Chronic Mean Acute- Chronic

Species Tested (μg/L) (μg/L) Ratio Chronic Ratio Ratio Reference

FRESHWATER SPECIES

Cladoceran,

Daphnia magna

Anthracene - <2.1 - - - Holst and Giesy,

1989

Cladoceran, 

Daphnia magna

Fluoranthene 117 24.5 4.78 4.78 - Spehar et al., 1999

Cladoceran, 

Daphnia magna

Phenanthrene 117 96.4 1.21 1.21 2.41 Call et al., 1986

Midge, 

Paratanytarsus sp.

Acenaphthene 2,040A 411 4.96 - - Northwestern

Aquatic Sciences,

1982

Midge, 

Paratanytarsus sp.

Acenaphthene 2,040A 227 9 6.68 6.68 Northwestern

Aquatic Sciences,

1982;

Thursby,1991a

Fathead Minnow, 

Pimephales 

promelas

Acenaphthene 608 405 1.5 - - Cairns and Nebeker,

1982; Thursby,

1991a

Fathead Minnow, 

Pimephales 

promelas

Acenaphthene 608 419 1.45 1.48 - Cairns and Nebeker,

1982 

Fathead Minnow, 

Pimephales 

promelas

Acenaphthene - 73.82 - - - Academy of Natural

Sciences, 1981

Fathead Minnow, 

Pimephales 

promelas

Acenaphthene - 211 - - - Academy of Natural

Sciences, 1981

Fathead Minnow, 

Pimephales 

promelas

Acenaphthene - 79.2 - - - ERCO, 1981

Fathead Minnow, 

Pimephales 

promelas

Acenaphthene - 112 - - - ERCO, 1981

Fathead Minnow, 

Pimephales 

promelas

Fluoranthene 69C 15 4.6 4.6 2.61 Spehar et al., 1999

Rainbow trout, 

Oncorhynchus 

mykiss

Phenanthrene 50C 6.32 7.9 7.9 7.9 Call et al., 1986
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

3-7

SALTWATER SPECIES

Mysid, 

Americamysis bahia

Acenaphthene 466 286 1.63 - - Horne et al., 1983

Mysid, 

Americamysis bahia

Acenaphthene 460 64 7.19 3.42 - Thursby et al.,

1989b

Mysid, 

Americamysis bahia

Fluoranthene 40 15.9 2.52 - - U.S. EPA, 1978

Mysid, 

Americamysis bahia

Fluoranthene 31 14.4 2.15 2.33 - Spehar et al.,

1999

Mysid, 

Americamysis bahia

Phenanthrene 27.1 8.13 3.33 3.33 - Kuhn and Lussier,

1987

Mysid, 

Americamysis bahia

Pyrene 28.3 4.53 6.24 6.24 3.59 Champlin and

Poucher, 1992b

Sheepshead minnow,

Cyprinodon

variegatus

Acenaphthene 3,100B 710 4.36 4.36 4.36 Ward et al., 1981

 Species

 Mean

Common PAH-Specific AcuteName, PAH Value Value Chronic Mean Acute- Chronic

Species Tested (μg/L) (μg/L) Ratio Chronic Ratio Ratio Reference

A Geometric mean of two flow-through measured tests from the same laboratory as conducted the 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 = 9.31 :mol/g octanol

Final Acute-chronic Ratio = 4.16

Final Chronic Value = 2.24 :mol/g octanol

Table 3-2. Continued
Toxicity of PAHs in Water Exposures

3-8

The FCV at a KOW of 1.0 for PAHs derived in

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 bahia), 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 a KOW of 1.0

The rules for test acceptability of the National

WQC Guidelines (Stephan et al., 1985) were used

to identify the LC50 values or EC50 (μg/L) values

from individual acute aquatic toxicity tests

(Appendix C) and these values were used to

derive the KOW normalized GMAV (μmol/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 KOW 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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

3-9

al. (1985). The -0.945 universal slope of the

toxicity/KOW relationship (Equation 2-29) was

applied to the PAH-Specific SMAVs (μmol/L) to

calculate the PAH-specific SMAV (μmol/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 (μmol/g

octanol) at a KOW 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 PAHspecific 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 μmol/g octanol for

Americamysis to 187 μmol/g octanol for

Tanytarsus, a factor of only 24. Saltwater genera

constitute four of the five genera with GMAVs at

a KOW 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 KOW 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 KOW of 1.0 for combined water
Toxicity of PAHs in Water Exposures

3-10

column and benthic organisms) (Appendix C). In

the AR Method, the freshwater and the saltwater

GMAVs at a KOW 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 KOW 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 KOW 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

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.

Mysidopsis

Grandidierella

Crangon

Oncorhynchus

Hyalella

Neomysis

Hydra

Shrimp

Corophium

Gammarus Leptocheirus Paqurus

Rhepoxynius Peltoperla

Dinophilus Excirolana

Eohaustorius

Salmo

Emerita Xenopus

Daphnia

Nassarius

Mytililus

Mya

Menidia Ictalurus

Ampelisca

Palaemonetes

Lepomis

Oncorhynchus Eurytemora Physa

Mulinia

Lumbriculus

Ophiogomplius

Physella

Winter Flounder

Pimephales

Chironomus

Neanthes

Cyprinodon

Aplexa

American Lobster

Paratanytarsus

Mudalia

Arbacia

Crepidula

Tanytarsus

Water column life stages Crassostrea

Benthic life stages

Genus Mean Acute Value (

μmol/g octanol)

Percentage Rank of Genera

FAV = 9.32 μmol/g

0



20



40



60



80



100



5



10



100



400


Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

3-11

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 Habitat or Water TypeA AR StatisticB ProbabilityC

Fresh vs Salt Fresh (20) Salt (29) 5.746 93.5

Freshwater: Benthic vs WQCD WQC (49) Benthic (33) 0.862 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 KOW of 1.0

 The FAV is an estimate of the concentration

corresponding to a cumulative probability of 0.05 in

the GMAVs at a KOW 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 KOW 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 KOW of 1.0 is calculated using the

procedure in Stephan et al. (1985), the GMAVs at
Toxicity of PAHs in Water Exposures

3-12

-15

-12

-9

-6

-3

0

3

6

9

12

15

A: Freshwater vs Saltwater

-10

-8

-6

-4

-2

0

2

4

6

8

10

0.1 1 10 20 50 80 90 99 99.9

Probability

B: Benthic vs WQC

Freshwater and Saltwater

FAV Difference (

Fmol/g octanol) FAV Difference (

Fmol/g octanol)

Figure 3-2. GMAVs at a log10KOW of 1.0 from water-only acute toxicity tests using

freshwater and saltwater genera versus percentage rank of their sensitivity.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

3-13

a KOW of 1.0 of 7.63 μmol/g octanol for

Americamysis, 8.51 μmol/g octanol for

Grandidierella, 9.83 μmol/g octanol for Crangon,

11.0 μmol/g octanol for Oncorhynchus and the

total number of genera tested (N = 49). The FAV

at a KOW of 1.0 is 9.31 μmol/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 and Anderson,

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. (Northwestern Aquatic Sciences, 1982). In the first test,

575 μg/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 μg/L acenaphthene. In

the second test, survival was reduced 20% and

growth 30% at 315μg/L. Egg hatchability was not

affected in the highest concentration of 676 μg/L;

although survival of hatched larvae was reduced

~60%. No significant effects were observed at

acenaphthene concentrations up to 164 μg/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 and Nebeker,

1982). The lowest observed effect concentrations

(LOEC) across laboratories and tests ranged from 98

to 509 μg/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 concentrationresponse 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

μg/L, but 495 μg/L reduced growth 54% relative to

control fish. In the second test, growth was reduced

30% at 509 μg/L, but no effects were detected in fish

exposed to 197 to

345 μg/L.

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 (Horne et al.,

1983), 340 μg/L reduced reproduction 93% relative to

controls and all A. bahia died at 510 μg/L. No effects

were observed on the parental generation at 100 to

240 μg/L and second generation juveniles were not

affected at < 340 μg/L. In the second test (Thursby

et al., 1989b), no effects were observed at < 44.6

μg/L, but a concentration of 91.8 μg/L reduced

reproduction 91%. No reproduction occurred at

higher concentrations, and growth was reduced 34%

at 168 μg/L and survival 96% at 354 μg/L.

A test with early life-stages of C. variegatus

showed that 240 to 520 μg/L had no effects, but that

concentrations of 970, 2,000 and 2,800 μg/L reduced

survival of embryos and larvae by >70% (Table 3-1;

Ward et al., 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.
Toxicity of PAHs in Water Exposures

3-14

3.5.2 Anthracene

A single life-cycle toxicity test has been

conducted with D. magna exposed to only three

concentrations of anthracene (Holst 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 μg/L

(8.0%) and 8.2 μg/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 et al.,

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 μg/L, but growth was reduced

17% at 35 μg/L and 25% at 73 μg/L. There were

37% fewer young per adult at 73 μg/L and no

daphnids survived at 148 μg/L. An early life-stage

toxicity test conducted with the fathead minnow

showed no effects at <10.4 μg/L, but reduced

survival (67%) and growth (50%) at 21.7 μg/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 μg/L. At a

fluoranthene concentration of 21 μg/L, survival was

reduced 26.7% and reproduction 91.7%, relative to

the controls. At the highest concentration of

fluoranthene, 43 μg/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 μg/L.

At the highest concentration tested, 18.8 μg/L,

survival was reduced 23% relative to controls and

there was no reproduction. Reproduction was

reduced by 77% in 11.1 μg/L, but this was not

significantly different from controls even at =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

μg/L, but survival was reduced 83% and reproduction 98% at 163 μg/L (Call et al., 1986). In a

test with O. mykiss, no effects were observed at 5

μg/L. The percentage of abnormal and dead fry at

hatch was significantly increased at the highest

exposure concentration of 66 μg/L and survival of

hatched fry was reduced with increase in exposure

concentration (Call et al., 1986). Mortality was 41,

48, 52 and 100% at 8, 14, 32, and 66 μg/L,

respectively. Wet weight was reduced 33, 44, and

75% at 8, 14 and 32 μg/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 μg/L. However, at the highest test

concentration of phenanthrene (11.9 μg/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

μg/L) and not the 96-hour LC50 of 375 μg/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 μg/L, but
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

3-15

20.9 μg/L reduced survival 37% and no mysids

survived at the next higher concentration of 38.2

μg/L (Table 3-1). Reproduction was significantly

reduced in >5.37 μg/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 μg/L and none were alive in these

concentrations at the end of the 30-day test. Weight

and length of fish surviving the test were significantly reduced in 0.85 and 1.84 μg/L. No significant

effects were detected in concentrations <0.45 μg/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 completeness, but the ACR of 12.7, chronic value of 0.62 μg/

L, and 96-hour LC50 of 7.9 μg/L for naphthanlene

are not included in Tables 3-1 and 3-2.

The calanoid copepod (Eurytemora affinis)

was exposed indvidually to 14.21 μg/L naphthalene,

15.03 μg/L 2-methylnaphthalene, 8.16 μg/L 2,6-

dimethylnaphthalene and 9.27 μg/L 2,3,5-trimethylnaphthalene 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 KOW 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 KOW 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 chronically to one or more of six individual PAHs in 15

experiments. (For more information on the calculation 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 μmol/g

octanol is divided by the FACR of 4.16 to obtain a

FCV at a KOW of 1.0 of 2.24 μmol/g octanol (Table

3-3). Because nonionic organic chemicals partition

similarly into octanol and lipid of organisms, the

FCV at a KOW of 1.0 in μmol/g octanol approximately equals tissue-based “acceptable”

concentration of about 2.24 μmol/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 KOW

of 1.0 (μmol/g octanol), the slope of the KOW-KOC

relationship, the universal narcotic slope of the

KOW-acute toxicity relationship, and the PAHspecific KOW values (Equation 3-1, 3-2, and 3-3).

log10PAH-specific FCV = (slope)log10KOW + log10 FCV

at a KOW of 1.0 (3-1)

log10PAH-specific FCV = -0.945 log10KOW +

log10(2.24) (3-2)

PAH-specific FCV (mmol/L) = 1000(antilog

(-0.945log10KOW + 0.3502)) (3-3)
Toxicity of PAHs in Water Exposures

3-16

 PAH PAH

 FCVi

 specific specific

 SPARCC (μmol/g FCVi FCVi

 COC,PAHi,FCVi COC,PAHi,MAXi

D

PAHB log10Kow log10Koc octanol) (μmol/L) (μg/L) (μg/goc) (μg/goc)

indan 3.158 3.105 2.24 2.322 274.5 349 127200

naphthalene 3.356 3.299 2.24 1.509 193.5 385 61700

C1-naphthalenes 3.8 3.736 2.24 0.5744 81.69 444 -

1-methylnaphthalene 3.837 3.772 2.24 0.53 75.37 446 165700

2-methylnaphthalene 3.857 3.792 2.24 0.5074 72.16 447 154800

acenaphthylene 3.223 3.168 2.24 2.016 306.9 452 24000

acenaphthene 4.012 3.944 2.24 0.3622 55.85 491 33400

1-ethylnaphthalene 4.221 4.15 2.24 0.2298 35.91 507 142500

2-ethylnaphthalene 4.283 4.21 2.24 0.2008 31.37 509 129900

C2-naphthalenes 4.3 4.227 2.24 0.1935 30.24 510 -

1,4-dimethylnaphthalene 4.3 4.227 2.24 0.1935 30.24 510 192300

1,3-dimethylnaphthalene 4.367 4.293 2.24 0.1673 26.13 513 157100

2,6-dimethylnaphthalene 4.373 4.299 2.24 0.1651 25.79 513 33800 

2,3-dimethylnaphthalene 4.374 4.3 2.24 0.1647 25.74 513 49900

1,5-dimethylnaphthalene 4.378 4.304 2.24 0.1633 25.52 514 62400

fluorene 4.208 4.137 2.24 0.2364 39.3 538 26000

C3-naphthalenes 4.8 4.719 2.24 0.0652 11.1 581 -

2,3,5-trimethylnaphthalene 4.858 4.776 2.24 0.05747 9.785 584 -

1,4,5-trimethylnaphthalene 4.872 4.789 2.24 0.05575 9.488 584 129300 

anthracene 4.534 4.457 2.24 0.1163 20.73 594 1300

phenanthrene 4.571 4.494 2.24 0.1073 19.13 596 34300

C1-fluorenes 4.72 4.64 2.24 0.0776 13.99 611 -

1-methylfluorene 4.739 4.659 2.24 0.07445 13.42 612 49700

C4-naphthalenes 5.3 5.21 2.24 0.02197 4.048 657 -

2-methylanthracene 4.991 4.906 2.24 0.04303 8.273 667 2420

1-methylanthracene 4.998 4.913 2.24 0.04238 8.148 667 -

9-methylanthracene 5.006 4.921 2.24 0.04165 8.007 668 21775

2-methylphenanthrene 5.029 4.944 2.24 0.03961 7.616 669 -

1-methylphenanthrene 5.037 4.952 2.24 0.03893 7.485 670 24100

C1-phenanthrene/anthracenes 5.04 4.955 2.24 0.03868 7.436 670 -

9-ethylfluorene 4.973 4.889 2.24 0.04475 8.693 673 -

C2-fluorenes 5.2 5.112 2.24 0.02731 5.305 686 -

pyrene 4.922 4.839 2.24 0.05 10.11 697 9090

fluoranthene 5.084 4.998 2.24 0.03515 7.109 707 23870

2-ethylanthracene 5.357 5.266 2.24 0.0194 4.003 739 -

C2-phenanthrene/anthracenes 5.46 5.367 2.24 0.01551 3.199 746 -

9,10-dimethylanthracene 5.494 5.401 2.24 0.0144 2.971 748 14071

3,6-dimethylphenanthrene 5.515 5.422 2.24 0.01376 2.838 749 -

C3-fluorenes 5.7 5.603 2.24 0.009199 1.916 769 -

C1-pyrene/fluoranthenes 5.287 5.197 2.24 0.0226 4.887 770 -

2,3-benzofluorene 5.539 5.445 2.24 0.01306 2.824 787 558

Table 3-4. COC,PAHi,FCVi concentrations and properties required for their derivationA.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

3-17

A Four significant figures are used even when fewer are appropriate for the parameter to limit the effects of rounding

 error when calculating EESBTUFCV which has two significant figures. B See Appendix E for solubilities.

C For C#-PAHs, reported log10KOW values are the average log10KOW values of all structures. D COC,PAHi,Maxi is based on solubility; if COC,PAHi,FCVi is > COC,PAHi,Maxi, then COC,PAHi,Maxi may be used to calculate ESB toxic

 units (see Section 6).

benzo(a)fluorene 5.539 5.445 2.24 0.01306 2.824 787 12500

C3-phenanthrene/anthracenes 5.92 5.82 2.24 0.0057 1.256 829 -

naphthacene 5.633 5.538 2.24 0.01064 2.43 838 207

benz(a)anthracene 5.673 5.577 2.24 0.009756 2.227 841 4153

chrysene 5.713 5.616 2.24 0.008943 2.042 844 826

triphenylene 5.752 5.654 2.24 0.008215 1.875 846 19400

C4-phenanthrenes/anthracenes 6.32 6.213 2.24 0.002387 0.5594 913 -

C1-benzanthracene/chrysenes 6.14 6.036 2.24 0.003531 0.8557 929 -

C3-pyrene/fluoranthenes 6.284 6.177 2.24 0.002581 0.6307 949 -

benzo(a)pyrene 6.107 6.003 2.24 0.003794 0.9573 965 3840

perylene 6.135 6.031 2.24 0.00357 0.9008 967 431

benzo(e)pyrene 6.135 6.031 2.24 0.00357 0.9008 967 4300

benzo(b)fluoranthene 6.266 6.16 2.24 0.002685 0.6774 979 2169

benzo(j)fluoranthene 6.291 6.184 2.24 0.002542 0.6415 981 3820

benzo(k)fluoranthene 6.291 6.184 2.24 0.002542 0.6415 981 1220

C2-benzanthracene/chrysenes 6.429 6.32 2.24 0.001883 0.4827 1008 -

9,10-dimethylbenz(a)anthracene 6.567 6.456 2.24 0.001395 0.3575 1021 124200

7,12-dimethylbenz(a)anthracene 6.575 6.464 2.24 0.00137 0.3513 1021 145300

7-methylbenzo(a)pyrene 6.537 6.426 2.24 0.001489 0.3965 1058 -

benzo(ghi)perylene 6.507 6.397 2.24 0.001589 0.4391 1095 648

C3-benzanthracene/chrysenes 6.94 6.822 2.24 0.0006194 0.1675 1112 -

indeno(1,2,3-cd)pyrene 6.722 6.608 2.24 0.0009953 0.275 1115 -

dibenz(a,h)anthracene 6.713 6.599 2.24 0.001015 0.2825 1123 2389

dibenz(a,j)anthracene 6.713 6.599 2.24 0.001015 0.2825 1123 47680

dibenz(a,c)anthracene 6.78 6.665 2.24 0.0008773 0.2442 1129 7400

C4-benzanthracene/chrysenes 7.36 7.235 2.24 0.0002483 0.07062 1214 -

C1-dibenz(a,h)anthracenes 7.113 6.992 2.24 0.0004251 0.1243 1221 -

coronene 6.885 6.768 2.24 0.0006981 0.2097 1230 821

C2-dibenz(a,h)anthracenes 7.513 7.386 2.24 0.000178 0.05454 1325 -

C3-dibenz(a,h)anthracenes 7.913 7.779 2.24 0.0000746 0.02389 1435 -

 PAH PAH

 FCVi specific specific

 SPARCC (μmol/g FCVi FCVi

 COC,PAHi,FCVi COC,PAHi,MAXi

D

PAHB log10Kow log10Koc octanol) (μmol/L) (μg/L) (μg/goc) (μg/goc)

Table 3-4. Continued
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

4-1

Section 4

4.1 Derivation of Potencies for Individual

PAHs in Sediments (COC,PAHi,FCVi)

The critical concentration of a PAH in sediment

(COC,PAHi,FCVi) that is related to the FCV is derived

following the EqP method (U.S. EPA, 2003a; Di

Toro et al., 1991) because the interstitial watersediment 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

(μmol/gOC = μmol/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 KOW obtained from SPARC

(Hilal et al., 1994) using the following equation

from Di Toro (1985)

 log10KOC = 0.00028+0.983 log10KOW (4-2)

COC,PAHi,FCVi for individual PAHs are then

calculated using Equation 4-1 with the FCV as the

water concentration

 COC,PAHi,FCVi = KOC FCVi (4-3)

Since KOC is presumed to be independent of

sediment type for nonionic organic chemicals, so

also is COC,PAHi,FCVi.

Table 3-4 contains the COC,PAHi,FCVi (μg/gOC)

for 74 PAHs found in sediments, including the 34

PAHs (in bold) analyzed by the U.S. EPA in their

EMAP program (U.S. EPA, 1996a,b; 1998).

COC,PAHi,FCVi values for 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 COC,PAHi,FCVi values for

the 74 PAHs listed in Table 3-4, which were

derived using only data for PAHs, is from 349 to

1435 μg/gOC. In contrast, the range of the same

value, termed the CS,OC by Di Toro and McGrath

(2000), was about the same (655 to 1940 μg/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 (COC,PAHi) and

the COC,PAHi,FCVi 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 ESBTUFCVi for all of

the PAHs in the particular sediment termed the

EESBTUFCV

 COC,PAHi,

 EESBTUFCV = E COC,PAHi,FCVi (4-4)

 i

Derivation of PAH ESBFCVS
 Derivation of PAH ESBFCVS

4-2

For a particular sediment, if the EESBTUFCV

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

EESBTUFCV for the 34 PAHs monitored in the

U.S. EPA EMAP). The equilibrium partitioning

sediment benchmark is given by the equation

ESB = EESBTUFCV #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 = EESBTUFCV > 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 COC,PAHi,FCVi derived for each PAH is

solubility limited, i.e., the interstitial water

concentration of the PAH is limited by the

solubility S. Therefore, COC,PAHi,FCVi 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 COC,PAHi,Max (Table 3-4)

COC,PAHi,FCVi #COC,PAHi,Maxi = KOCS (4-7)

Thus, only the contribution up to the maximum

COC,PAHi,Maxi is counted in the EESBTUFCV for the

PAH mixture.

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 KOW PAHs do contribute to

the total toxicity of the PAH mixture.

4.4 Comparison of the EESBTUFCV for

Mixtures of PAHS in Estuarine

Sediments

Coastal and estuarine monitoring data were

compiled from eight sources to obtain a

preliminary assessment of the EESBTUFCV 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 et al., 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 EESBTUFCV. 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 EESBTUFCV if 34

PAHs had been analyzed. EESBTUFCV values

were computed by summing the ESBTUFCVi for

each PAH measured in the sediment sample. For

insoluble PAHs, the COC,PAHi,Maxi (Table 3-4) was

used to calculate EESBTUFCV.

The probability distribution for the

EESBTUFCV 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-
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

4-3

Louisanian EMAP Carolinian EMAP

Southern California Virginian EMAP

SFEI NY/NJ R-EMAP

San Diego NOAA

0.1 99.9 1 10 20 50 80 90 99 1 10 20 50 80 90 99 0.1 99.9

N=280 N=229

N=40

N=137

N=182 N=640

N=153

N=318

ΣESBTUFCV

ΣESBTUFCV

ΣESBTUFCV

ΣESBTUFCV

0.0001

0.001

0.01

0.1

1

10

100

0.0001

0.001

0.01

0.1

1

10

100

0.0001

0.001

0.01

0.1

1

10

100

0.0001

0.001

0.01

0.1

1

10

100

% Less Than or Equal To % Less Than or Equal To

Figure 4-1. Probability distribution of the EESBTUFCV for PAH mixtures in sediments

 from individual coastal and estuarine locations in the United States.
 Derivation of PAH ESBFCVS

4-4

overlapping data are shown. For comparison

purposes, a line indicating 1.0 EESBTUFCV 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

C1 through C4 alklyated forms of some of the

parent PAHs. To assess the total number of PAHs,

a C1-PAH series was considered as one PAH.

Computed EESBTUFCV 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 EESBTUFCV values greater

than 1.0 at the 95th percentile. Although the

EESBTUFCV from the Lousianian and Carolinian

Province EMAP data are computed from 34 PAHs,

these sediments do not contain greater EESBTUFCV

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. EESBTUFCV values computed

from 23 PAHs are denoted by open circles, and for

the 34 PAHs, by open squares. The median

EESBTUFCV was about 0.06. Approximately 6% of

the samples (109 sediments) had EESBTUFCV

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 EESBTUFCV.

Figure 4-2. Probability distribution of the EESBTUFCV for PAH mixtures in sediments from all of the

 coastal and estuarine locations in the United States from Figure 4-1.

% Less Than or Equal To

ΣESBTUFCV

ƒOC>0.2%

N=1979

0.0001

0.001

0.01

0.1

1

10

100

0.01 99.99 0.1 99.9 1 10 20 50 80 90 99
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

5-1

Section 5

5.1 Introduction

The COC,PAHi,FCVi 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 et al., 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 WaterOnly 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 spikedsediment exposures with those predicted from

water-only LC50 values multiplied by the KOC

using the equilibrium partitioning model (Di Toro

et al, 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

Actual and Predicted Toxicity

of PAH Mixtures in Sediment

Exposures
5-2

Actual and Predicted Toxicity

chemicals, including acenaphthene (Swartz,

1991a), phenanthrene (Swartz, 1991a),

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 (μg/L) of a treatment by the

water-only LC50 (μg/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 (PAHOC) as the product of the PAHspecific 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 (μg/gOC)=water-only LC50 (μg/L)x KOC (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 (μg/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 μg/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 (μg/gOC) by the predicted LC50

(μg/gOC). This standardization allows a comprehensive 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, 1991a), 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 l l, and was high for most samples with

PSTU > 1.

These analyses support the concept that

water-only LC50 values and KOCs can be used to

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 KOC 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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

5-3

% Mortality

Predicted Interstitial Water Toxic Units

0.01 0.1 1 10 100

100

80

60

40

20

0

E

E

E

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,

IWTU, 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

COC,PAHi,FCVi (see Table 3-4). This ratio is termed

the ESBTUFCVi, which is the ratio of the measured

PAH concentration in sediments from the toxicity

tests (μg/gOC) to the COC,PAHi,FCVi concentration (μg/

gOC) for that PAH, i.e., the fraction of ESBTUFCV

represented by the observed PAH concentration in

sediment. The COC,PAHi,FCVi normalization does not

alter the original variability in concentrationresponse but allows comparison of PAH effects

among species, compounds, and response criteria.

For example, the COC,PAHi,FCVi-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)
5-4

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 LC50s (μg/L).

Chemical

Test Species MethodA

Ratio:

Interstitial Water

LC50/Water-only

LC50

Organic Carbon-Normalized LC50 (μg/gOC)

Observed PredictedB

 LC50 Ratio

Obs/Pred Reference

Freshwater

Fluoranthene

Diporeia sp. FT,M/10 - - - - Driscoll et al., 1997a,b 

Hyalella azteca FT,M/10 > 0.58 - - - Driscollet al., 1997a,b 

Hyalella azteca S,M/10 1.02C 500 4490 0.11C Suedel et al., 1993

Hyalella azteca S,M/10 5.27C 1480 4490 0.33C Suedel et al., 1993

Hyalella azteca S,M/10 2.17C 1250 4490 0.28C Suedel et al., 1993

Chironomus tentans S,M/10 2.86C 1587 3190 0.50C Suedel et al., 1993

Chironomus tentans S,M/10 7.87C 1740 3190 0.55C Suedel et al., 1993

Chironomus tentans S,M/10 2.37C 682 3190 0.21C Suedel et al., 1993

Saltwater

Acenaphthene

Eohaustorius estuarius FT,M/10 2.14 4330 2152 2.01 Swartz, 1991a

Eohaustorius estuarius FT,M/10 1.63 1920 2152 0.89 Swartz, 1991a

Eohaustorius estuarius FT,M/10 1.45 1630 2152 0.76 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 > 2.54 >23,500 3900 > 6.02 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 2.08 7730 3900 1.98 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 2.2 11200 3900 2.87 Swartz, 1991a

Fluoranthene

Leptocheirus plumulosus S/10 - >21,200 3900 >5.44 Driscoll et al., 1998

Phenanthrene

Eohaustorius estuarius FT,M/10 1.05 4050 3778 1.07 Swartz, 1991a

Eohaustorius estuarius FT,M/10 1.06 3920 3778 1.04 Swartz, 1991a

Eohaustorius estuarius FT,M/10 1.11 3820 3778 1.01 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 2.09 8200 5335 1.54 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 1.65 6490 5335 1.22 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 1.95 8200 5335 1.54 Swartz, 1991a

2,6-dimethylnaphthlene

Rhepoxynius abronius S,M/10 - 8120 - - Ozretich et al., 2000a

2,3,5-trimethylnaphthlene

Rhepoxynius abronius S,M/10 - 3190 - - Ozretich et al., 2000a

1-methylfluorene

Rhepoxynius abronius S,M/10 - 1950 - - Ozretich et al., 2000a

2-methylphenanthrene

Rhepoxynius abronius S,M/10 - 2270 - - Ozretich et al., 2000a

9-methylanthracene

Rhepoxynius abronius S,M/10 - 6840 - - Ozretich et al., 2000a

Acenaphthene

Rhepoxynius abronius S,M/10 - 2110 - - Swartz et al., 1997

Rhepoxynius abronius S,M/10 - 2310 - - Swartz et al., 1997
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

5-5

A Test conditions for water-only toxicity tests: S = static, FT = flow-through, M = measured, 10 = 10-d duration.

B Predicted LC50 (Fg/gOC) = water-only LC50 (Fg/L) KOC (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).

Phenanthrene

Rhepoxynius abronius S,M/10 - 3080 - - Swartz et al., 1997

Rhepoxynius abronius S,M/10 - 2220 - - Swartz et al., 1997

Pyrene

Rhepoxynius abronius S,M/10 - 1610 - - Ozretich et al., 2000a

Rhepoxynius abronius S,M/10 - 1220 - - Swartz et al., 1997

Rhepoxynius abronius S,M/10 - 2810 - - Swartz et al., 1997

Fluoranthene

Rhepoxynius abronius S,M/10 - 2320 1390 1.66 Swartz et al., 1997

Rhepoxynius abronius S,M/10 - 3310 1390 2.38 Swartz et al., 1997

Rhepoxynius abronius S,M/10 1.63 1890 1390 1.36 Swartz et al., 1990

Rhepoxynius abronius S,M/10 2.12 2100 1390 1.51 Swartz et al., 1990

Rhepoxynius abronius S,M/10 1.74 2230 1390 1.6 Swartz et al., 1990

Rhepoxynius abronius S,M/10 > 22.66D >4360 1390 4.04D DeWitt et al., 1992

Rhepoxynius abronius S,M/10 1.01 4410 1390 3.17 DeWitt et al., 1992

Rhepoxynius abronius S,M/10 1.91 3080 1390 2.22 DeWitt et al., 1992

Rhepoxynius abronius S,M/10 1.38 3150 1390 2.26 DeWitt et al., 1992

Rhepoxynius abronius S,M/10 0.67 2790 1390 2.01 DeWitt et al., 1992

Mean LC50 ratio = 1.6 Mean LC50 ratio = 2.07

Test Species MethodA

Ratio:

Interstitial Water

LC50/Water-only

LC50

Organic Carbon-Normalized LC50 (μg/gOC)

Observed PredictedB

 LC50 Ratio

Obs/Pred Reference

Table 5-1. Continued
5-6

Actual and Predicted Toxicity % Mortality

100

80

60

40

20

0

0.01 0.1 1 10 100

Predicted Sediment Toxic Units

E

E

E

Figure 5-2. Percent mortality versus predicted sediment toxic units for six chemicals

 and three sediments per chemical (each sediment represented by unique symbol).

Figure 5-3. Percent mortality of Rhepoxynius abronius in sediments spiked with acenaphthene,

phenanthrene, fluoranthene, or pyrene concentrations in sediment normalized

to ESBTUFCVi

.

0.1 1 10 100

-20

0

20

40

60

80

100

120

Fluoranthene

Acenaphthene

Phenanthrene

Pyrene

Geometric Mean = 3.78

ESBTUFCV

% Mortality
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

5-7

indicating that sediment concentrations would

have to exceed the COC,PAHi,FCVi 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

the COC,PAHi,FCVi would be expected.

5.2.4 Comparison of Sediment Toxicity

 to COC,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 spikedsediment toxicity tests. The individual PAH

concentrations in sediment (COC) affecting benthic

organisms in toxicity tests were divided by the

COC,PAHi,FCVi 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 μg/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 concentrations were divided by the COC,PAHi,FCVi 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 testspecific 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
5-8

Actual and Predicted Toxicity

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 COC,PAHi,FCVi derived from wateronly toxicity tests, narcosis theory and national

WQC are appropriately protective of benthic

organisms. These comparisons between

sediments spiked with individual PAHs and their

respective COC,PAHi,FCVi 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

Figure 5-4. Percentage rank, based on ESBTUFCVi, of the sensitivities of genera of benthic organisms

 from spiked sediment toxicity tests.

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 octanolwater partitioning coefficients (i.e., PAHs with

KOW > 5.5). The predicted LC50 of many high

KOW compounds exceeds their solubility limit.

Accordingly, experimental attempts to establish

the LC50 for individual high KOW PAHs spiked

into sediment have observed little or no acute or

chronic toxicity. High KOW 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;

0 10 20 30 40 50 60 70 80 90 100

Percentage Rank of Genera

1

10

100

Rhepoxynius

Corophium

Eohaustorius

Hyalella Leptocheirus

Diporeia

Limnodrilus

ESBTUFCVi
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

5-9

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 μg/L) (Swartz et

al., 1990; DeWitt et al., 1992), whereas the LC50

Hermens 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 COC,PAHi,FCVi and summing the

quotients to derive the EESBTUFCV for the

mixture (Table 5-2). No acute toxicity was

observed with Diporeia exposed to a EESBTUFCV

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 EESBTUFCV 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 EESBTUFCV and occurred in

treatments with 5.80 and 10.3 EESBTUFCV

(Swartz et al., 1997; Boese et al., 1999). For R.

abronius, the GMAV from 10-day spiked

sediment tests with individual PAHs was 3.67

EESBTUFCV (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 EESBTUFCV 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 EESBTUFCV (Table 5-2).

Additivity of mixtures of 13 PAHs was

assumed in the development of the EPAH model

that was used to accurately classify PAHcontaminated, 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,
5-10

Actual and Predicted Toxicity

Table 5-2. Percent mortality of benthic invertebrates in relation to the EESBTUFCV values of

mixtures of polycyclic aromatic hydrocarbons spiked into sediment.

SpeciesA

EESBTUFCV

PAH Kow

<5.5

EESBTUFCV

PAH Kow

>5.5

EESBTUFCV

 All PAHs

Percent

Mortality PAH MixtureB 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

Landrum et al., 1991

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

Landrum et al., 1991

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

Landrum et al., 1991

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

Landrum et al., 1991

R. abronius 10.32 0 10.3 100 ace; phen; flu; pyr Swartz et al., 1997

R. abronius 5.8 0 5.8 38 ace; phen; flu; pyr Swartz et al., 1997

R. abronius 5.12 0 5.12 8 ace; phen; flu; pyr Swartz et al., 1997

R. abronius 3.25 0 3.25 11 ace; phen; flu; pyr Swartz et al., 1997

R. abronius 2.5 0 2.5 4 ace; phen; flu; pyr Swartz et al., 1997

R. abronius 1.8 0 1.8 2 ace; phen; flu; pyr Swartz et al., 1997

R. abronius 1.42 0 1.42 3 ace; phen; flu; pyr Swartz et al., 1997

R. abronius 2.77 0 2.77 5 anthr; flu Boese et al., 1999

R. abronius 4.91 5.02 9.93 3 b(a)anthr; flu Boese et al., 1999

R. abronius 5.88 0 5.88 5 2-methylanthr; flu Boese et al., 1999

R. abronius 5.71 0 5.71 2 9,10-dimethylanth; flu Boese et al., 1999

R. abronius 2.71 2.23 4.94 3 b(b)flu; flu Boese et al., 1999

R. abronius 2.06 0.79 2.84 2 chr; flu Boese et al., 1999

R. abronius 0.63 1.57 2.2 1 3,6-dimethylphen; flu Boese et al., 1999

R. abronius 1.91 25.89 27.8 4 anthr; b(a)anthr; 2-methylanthr; 

b(b)flu; chr; 3,6-dimethylphen

Boese et al., 1999

R. abronius 0.58 8.03 8.61 5 anthr; b(a)anthr; 2-methylanthr; 

b(b)flu; chr; 3,6-dimethylphen

Boese et al., 1999

R. abronius 1.55 8.03 9.58 9 anthr; b(a)anthr; 2-methylanthr; 

b(b)flu; chry; 3,6-dimethylphen; flu

Boese et al., 1999

R. abronius 0.9 3.4 4.3 0 anthr; b(a)anthr; 2-methylanthr; 

b(b)flu; chry; 3,6-dimethylphen; flu

Boese et al., 1999

A. abdita 5.41 0.64 6.05 7 9,10-dimethylanthr; chry Burgess et al., 2000b

A. abdita 0 2.58 2.58 7 b(a)pyr; cor Burgess et al., 2000b

A. abdita 5.41 3.22 8.63 10 9,10-dimethylanthr; chry; b(a)pyr; cor Burgess et al., 2000b

A. bahia 5.41 0.64 6.05 3 9,10-dimethylanthr; chry Burgess et al., 2000b

A. bahia 0 2.58 2.58 7 b(a)pyr; cor Burgess et al., 2000b

A. bahia 5.41 3.22 8.63 7 9,10-dimethylanthr; chry; b(a)pyr; cor Burgess et al., 2000b

A Test Species: amphipods: Diporeia sp., Rhepoxynius abronius, Ampelisca abdita; mysids: Americamysis bahia B PAH 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.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

5-11

log10PAH-specific LC50R. abronius = -0.945 log10KOW +

log10(15.8 μmol/g octanol) (5-2)

The PAH-specific LC50R. abronius is used to calculate

the PAH-specific sediment LC50 (μg/OC) for R.

abronius (equation 5-3).

PAH-specific sediment LC50R. abronius = KOC x PAH-specific

LC50 R. abronius (5-3)

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

μmol/g octanol (octanol serves as a surrogate for

lipid). The critical body burden is used to

calculate the PAH-specific 10-day LC50 values

(μg/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

5-5A). 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

KOW. Although the solubility of individual PAHs

are a function of their structure and polarity rather

than just KOW, the general relationship between

solubility and KOW is such that solubility

decreases with increasing KOW slightly faster than

toxicity increases. The net result of this

relationship is that PAHs with high KOW (roughly

log10KOW of 5.5 and higher) have solubilities

below their predicted LC50. This has led to the

conventional wisdom that high KOW PAHs are not

toxic (at least on an acute basis) because they are

insufficiently soluble to cause toxicity. For

example, high KOW 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 KOW 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
5-12

Actual and Predicted Toxicity

A

B

Acenaphthene

Fluoranthene

Phenanthrene

Pyrene

Mixture

Predicted 34 PAH (Swartz)

Measured 33 PAH Elliott Bay

-20

0

20

40

60

80

100

120

-20

0

20

40

60

80

100

120

Mortality (%) Mortality (%)

0.001 0.01 0.1 1 10 100 1000

PSTU

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.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

5-13

series of sediment toxicity tests using a mixture of

13 PAHs with log10KOW 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,

Table 5-3. Chemicals included in the high KOW PAH mixture experiment (Spehar et al.,

In preparation).

Chemical Name

Molecular

Weight

(g/mol) log10 KOWA log10 KOCB

Estimated

SolubilityC

(μg/L)

Nominal

Sediment

Concentration

(μmol/gOC)

NominalD

(Sed. Conc./KOC)

Limited by

Solubility

2-Ethylanthracene 206.29 5.36 5.27 59.62 39.32 43.94 43.94

3,6 Dimethylphenanthrene 206.29 5.52 5.42 77.98 42.38 33.12 33.12

2,3 Benzofluorene 216.28 5.54 5.44 25.30 42.88 33.27 25.30

Benzo(a)anthracene 228.29 5.67 5.58 12.28 45.80 27.70 12.28

Triphenylene 228.3 5.75 5.65 5.11 47.66 24.11 5.11

2-(tert-butyl)anthracene 234.34 5.88 5.78 33.04 50.91 19.78 19.78

Benzo(a)pyrene 252.31 6.11 6.00 2.88 57.46 14.38 2.88

Benzo(b)fluoranthene 252.32 6.27 6.16 8.28 62.75 10.96 8.28

Benzo(k)fluoranthene 252.32 6.29 6.18 8.35 63.64 10.50 8.35

9-Phenylanthracene 254.33 6.31 6.2 3.64 64.22 10.30 3.64

7-Methylbenzo(a)pyrene 266.35 6.54 6.43 1.46 73.37 7.32 1.46

7,12Dimethylbenz(a)anthracen 256.35 6.58 6.46 13.41 75.04 6.62 6.62

3-Methylcholanthrene 268.38 6.76 6.64 3.11 83.92 5.1 3.11

TOTAL PAH 749.4 247.1 173.9

A Predicted by SPARC.

B Predicted from Di Toro et al. (1991).

C Predicted by SPARC in distilled water at 25EC. D Nominal concentration predicted by KOC, regardless of solubility limits; highest concentration only.

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 KOC 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

Estimated porewater

concentration (μg/L)
5-14

Actual and Predicted Toxicity

Figure 5-6. Response of Hyalella azteca exposed for 10 days under flow-through conditions to sediment

spiked with a mixture of high KOW PAH.

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 KOW

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 concentrationdependent response of both survival and growth

(Figure 5-9). When expressed on the basis of total

PAH molar concentration in the sediment

Acute TU in Sediment

0.1 1 10

10-d Survival (%)

0

20

40

60

80

100

10-d Dry Weight (mg)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

* *

*
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

5-15

Figure 5-7. Response of Hyalella 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) of Hyalella azteca expressed on the basis

 of measured PAH concentrations in tissues (lipid normalized).

Acute TU in Sediment

0.1 1 10

28-d Survival (%)

0

20

40

60

80

100

28-d Dry Weight (mg)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

*

*

Acute TU in Tissue

0.01 0.1 1 10

28-d Survival (%)

0

20

40

60

80

100

10-d Dry Weight (mg)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

GMAV

GMAV/ACR
5-16

Actual and Predicted Toxicity

(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, a mysid

(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

Figure 5-9. Response of Hyalella azteca exposed for 10 days (3 renewals) to sediment spiked with

a mixture of high KOW PAH.

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 KOW PAHs clearly

demonstrate that they can cause toxicity to benthic

organisms when present in mixtures. Thresholds

for toxicity in several experiments were slightly

Acute TU in Sediment

0.1 1 10

10-d Survival (%)

0

20

40

60

80

100

10-d Dry Weight (mg)

0.00

0.02

0.04

0.06

0.08

0.10

*

*

*

*

*

*

*
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

5-17

Figure 5-10. Response of Leptocheirus plumulosus exposed for 10 days under static conditions to sediment

spiked with a mixture of high KOW PAH.

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 KOW PAHs. Therefore,

EESBTUFCV for mixtures must include the partial

contributions of high KOW PAH in the mixture to

insure that the ESB is not under protective.

5.3 Field Sediments Versus ESBFCV for

PAH Mixtures

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 EESBTUFCV for a mixture of

PAHs (e.g., 50% mortality of a test organism), and

the organism survives exposures significantly

above the EESBTUFCV value, then the benchmark

may not be valid. No other comparison is more

definitive. Of course, mortality at EESBTUFCV

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.

Acute TU in Sediment

0.01 0.1 1 10

10-d Survival (%)

0

20

40

60

80

100

*

* * *
5-18

Actual and Predicted Toxicity

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

5.2.7, 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

Figure 5-11. Amphipod (Ampelisca abdita) abundance versus EESBTUFCV.

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 2.75, 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

0.1

1

10

100

1000

10000

0.0001 0.001 0.01 0.1 1 10 100 1000

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ΣESBTUFCV

Ampelisca abdita Abundance (#/m2) 
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

5-19

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.

5.3.2 Organism Abundance vs. ESBFCV for

 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

EESBTUFCV 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 EESBTUFCV. The results are

very encouraging. The absence of sediments

having high abundances of A. abdita at slightly

above 1.0 EESBTUFCV and the decrease in

amphipod abundance as the EESBTUFCV increases

above 1.0 is consistent with that predicted by this

ESB for PAH mixtures.

It is tempting to conclude from the

coincidence of EESBTUFCV 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.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-1

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 unsubstituted compounds) and it is impractical to reanalyze all samples for the full suite of PAHs, but

Table 6-1. Relative distribution of EESBTUFCV,TOT to EESBTUFCV,13 and EESBTUFCV,23 for the combined EMAP

dataset (N=488).

Percentile EESBTUFCV,TOT /EESBTUFCV,13 EESBTUFCV,TOT /EESBTUFCV,23 

50 2.75 1.64

80 6.78 2.8

90 8.45 3.37

95 11.5 4.14

99 16.9 6.57
Implementation

6-2

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 EESBTUFCV.

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 KOW 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 C1 through C4 alkylated series for

some patent PAHs where the C# indicates an

alkyl group substitution (Table 6-2). The C1

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

EESBTUFCV.

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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-3

Table 6-2. PAH measured in various sediment monitoring programs. See

Di Toro and McGrath (2000) for data sources.

A Benzo(b)fluoranthene and benzo(k)flouranthene were measured together.

B A specific C1-PAH was not included in the total if the C1 alkylated PAH series was measured.

 For example, 1-methylnaphthalene was not included in the total if the C1-naphthalenes were measured.

Parameter NOAA SFEI

San

Diego

Southern

California

NY/NJ

REMAPA

Virginian

EMAPB

Elliott

Bay

Carolinian

EMAP

Louisianian

EMAP

Acenaphthene x xx x x x x x x

Acenaphthylene x xx x x x x x x

Anthracene x xx x x x x x x

Chrysene x xx x x x x x x

Fluoranthene x xx x x x x x x

Fluorene x xx x x x x x x

naphthalene x xx x x x x x x

phenanthrene x xx x x x x x x

pyrene x xx x x x x x x

Benzo(k)fluoranthene x xx x x x x x x

Benzo(b)fluoranthene x xx x x x x x x

Benzo(a)pyrene x xx x x x x x x

Benzo(a)anthracene x xx x x x x x x

Benzo(e)pyrene x xx x x x x x x

Benzo(g,h,i)perylene x xx x x x x x x

Dibenz(a,h)anthracene x xx x x x x x x

2,6-dimethylnaphthalene x xx x x x x x x

Indeno(1,2,3-cd)pyrene x xx x x x x x x

1-methylnaphthalene x xx x x x x x x

2-methylnaphthalene x xx x x x x x x

perylene x xx x x x x x x

1-methylphenanthrene x xx x x x x x x

2,3,5-trimethylnaphthalene x xx x x x x x x

2-methylanthracene x

2-methylphenanthrene x x

3,6-dimethylphenanthrene x

9-methylanthracene x x

9,10-dimethylanthracene x

C1-benzo(a)anthracenes /chrysenes x x x

C2-benzo(a)anthracenes /chrysenes x x x

C3-benzo(a)anthracenes /chrysenes x x

C4-benzo(a)anthracenes /chrysenes x x

C1-fluoranthenes/pyrenes xx x

C2-fluoranthenes/pyrenes x

C1-fluorenes xx x

C2-fluorenes xx x

C3-fluorenes xx x

C1-naphthalenes xx x

C2-naphthalenes xx x

C3-naphthalenes xx x

C4-naphthalenes xx x

C1-phenanthrenes/anthracenes x x x

C2-phenanthrenes/anthracenes x x x

C3-phenanthrenes/anthracenes x x x

C4-phenanthrenes/anthracenes x x

Total Number of PAHsB 23 25 23 23 23 23 32 34 34

Number of data points 640 137 182 40 153 318 30 280 229
Implementation

6-4

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 C1, 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 C1-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,μg/gOC) by

the COC,PAHi,FCVi. EESBTUFCV for each sediment

sample were computed by summing the

ESBTUFCVi for each PAH measured. For

purposes of this section, EESBTUFCV for the 34

PAHs is denoted by EESBTUFCV,TOT. Equation 6-1

was used to compute EESBTUFCV,TOT

ESBTUFCV TOT , ESBTU 1.0 OCi

OC,PAHi,FCVi i

== ≤ Σ ∑ C

C (6-1)

6.2.3 Methodology

The objective was to determine the

uncertainty of using the 13 PAHs or the 23 PAHs

to predict the EESBTUFCV,TOT. 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

EESBTUFCV,TOT to EESBTUFCV,13 or

EESBTUFCV,23. In addition, regression analyses of

EESBTUFCV,TOT to EESBTUFCV,13 or to

EESBTUFCV,TOT to EESBTUFCV,23 on a log-log

linear basis were conducted to demonstrate the

utility of the ratio approach across the range of

EESBTUFCV values.

6.2.4 Uncertainty in Predicting

EESBTUFCV,TOT

For use in determining the uncertainty in

predicting EESBTUFCV,TOT 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 EESBTUFCV,TOT to the

EESBTUFCV for the 13 and 23 PAHs for this

dataset are provided in Table 6-1. Based on the

observed ratios, the measured EESBTUFCV,13 for

the 13 PAHs must be multiplied by 11.5 to obtain

an accurate estimation of the EESBTUFCV,TOT with

95 % confidence. Similarly, the measured

EESBTUFCV,23 for the 23 PAHs must be multiplied

by 4.14 to obtain an estimate of the

EESBTUFCV,TOT with 95% confidence. High

adjustment factors needed to estimate

EESBTUFCV,TOT, 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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-5

Figure 6-1. Comparison of observed EESBTUFCV,TOT to observed (A) EESBTUFCV,13 from13 PAHs and

(B) EESBTUFCV,23 from 23 PAHs for the combined dataset including U.S. EPA EMAP

Louisianian and Carolinian Provinces.

A

Observed ESBTUFCV,23

B

Observed ESBTUFCV,TOT Observed ESBTUFCV,TOT

0.00001

0.0001

0.001

0.01

0.1

1

0.00001 0.0001 0.001 0.01 0.1 1 10

10

0.00001

0.0001

0.001

0.01

0.1

1

10

Observed ESBTUFCV,13
Implementation

6-6

sediments where the EESBTUFCV,13 or

EESBTUFCV,23 times the uncertainty factors does

exceed the ESB, additional measurements

including the 34 PAHs would not be warranted.

The EESBTUFCV,TOT can be plotted against the

EESBTUFCV,13 for the 13 PAHs and regression

analysis conducted to show that the ratios can be

used fairly well to estimate the EESBTUFCV for

the 34 PAHs from the sum of the 13 PAHs across

a wide range of EESBTUFCV,TOT because the slope

is nearly 1.0 (0.9595) (Figure 6-1A). 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

log10 EESBTUFCV,TOT = 0.9595 log10 EESBTUFCV,13 +

0.4251 (R2

 = 0.8236) (6-2)

A similar analysis using the EESBTUFCV,TOT

plotted against the sum EESBTUFCV,23 of 23 PAHs

with a regression analysis conducted to show that

the slope of the regression is also nearly 1.0

(1.038) (Figure 6-1B). 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

log10 EESBTUFCV,TOT = 1.038 log10 EESBTUFCV,23 +

0.3576(R2

 = 0.9272) (6-3)

The regression approach has been used to

derive uncertainty factors for estimating the

EESBTUFCV,TOT 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

EESBTUFCV,13 and EESBTUFCV,23 values for each

sediment from the databases in Table 6-2 were

plotted in Figure 6-2 (A and B, respectively). The

actual EESBTUFCV,13 values (triangles) exceeded

1.0 for 5.22% of the 1992 sediment samples

(Figure 6-2A) and the EESBTUFCV,23 values

(triangles) exceeded 1.0 for 6.55% of the 2001

sediment samples (Figure 6-2B). To estimate the

50% uncertainty of EESBTUFCV,TOT (plotted as

diamonds in Figure 6-2), the mean ratio of the

EESBTUFCV,TOT to EESBTUFCV,13 (2.75; see Table

6-1) was applied to the sediment-specific

EESBTUFCV,13 values and the mean ratio of

EESBTUFCV,TOT to EESBTUFCV,23 (1.64) was

applied to the sediment-specific EESBTUFCV,23

values. The EESBTUFCV,TOT estimated from the

EESBTUFCV,13 exceeded 1.0 for 12.9% of the

1992 sediment samples and the EESBTUFCV,TOT

estimated from the EESBTUFCV,23 exceeded 1.0

for 9.85% of the 2001 sediment samples. The

95% uncertainty estimates of the EESBTUFCV,TOT

for each sediment (plotted as circles in Figure 6-2)

was determined by multiplying the sedimentspecific EESBTUFCV,13 values by 11.5 and by

multiplying the sediment-specific EESBTUFCV,23 by

4.14 (Table 6-1). The 95% limits on the

EESBTUFCV,TOT estimated from the EESBTUFCV,13

exceeded 1.0 for 35.5% of the 1992 sediment

samples and the 95% limits on the EESBTUFCV,TOT

Total 34 with 95% Confidence limits

Total 34 predicted

Total 23 measured

Total 34 with 95% Confidence limits

Total 34 predicted

Total 13 measured

N=1992

N=2001

ESGTUFCV,TOT Predicted from 23 PAHs ESGTUFCV,TOT Predicted from 13 PAHs

% Less Than or Equal To

0.01 0.1 99.9 1 10 20 50 80 90 99 99.99

0.0001

0.001

0.01

0.1

1

10

100

1000

0.0001

0.001

0.01

0.1

1

10

100

1000

A

B

Figure 6-2. Probability distribution of the (A)

EESBTUFCV,13 and (B) EESBTUFCV,23 values

for each sediment from the entire database.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-7

estimated from the EESBTUFCV,23 exceeded 1.0

for 23.7% of the 2001 sediment samples.

Therefore, if the 95% uncertainty ratios are

applied to the EESBTUFCV,13 or the EESBTUFCV,23,

the predicted EESBTUFCV,TOT for about one-third

of the sediments are in excess of the ESB for

PAH mixtures of 1.0 EESBTUFCV. 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 EESBTUFCV,TOT 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 (μg/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 sedimentassociated 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

(COC,PAHi,FCVi) from Table 3-4.

Sediment A is provided as an example to

demonstrate how to calculate the EESBTU 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 EESBTU must not be considered as

an ESB nor used in important sediment

management decisions. Uncertainty factors

applied to the EESBTU 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 μg PAH/g dry sediment and has

0.81% TOC. First, the dry weight concentrations

for each PAH were converted to μg PAH/g

organic carbon (COC, μg/gOC) by dividing by the

fraction organic carbon (fOC = 0.0081), where fOC

= %TOC/100. Second, the organic carbonnormalized PAH concentrations in the sediment

were divided by the PAH-specific sediment

concentration of concern (COC,PAHi,FCVi) to derive

the toxic unit-like ESBTUFCVi for each individual

PAH. In this sediment, none of the measured COCi

exceed the corresponding COC,PAHi,Maxi so solubility

constraints do not affect the calculation of

ESBTUFCVi for this sediment. The ESBTUFCVi for

the 13 PAHs were added to derive the EESBTU

for the 13 PAHs (EESBTUFCV,13) which is 0.348
Implementation

6-8

Table 6-3A. Example calculations of ESBs for PAH mixtures: three sediments.

A PAHs and corresponding COC,PAHi,FCVi and COC,PAHi,Maxi values are from Table 3-4 (bold).

Sediment A

(TOC=0.81%; fOC=0.0081)

PAHA

COC, PAHi, FCVi

(μg/gOC)

COC, PAHi, Maxi

(μg/gOC) 

Conc.

(μg/g dry wt.)

COC

(μg/gOC) ESBTUFCVi

naphthalene 385 61700 0.0894 11 0.0287

C1 naphthalenes 444 -

acenaphthylene 452 24000 0.0348 4.29 0.0095

acenaphthene 491 33400 0 0 0

C2 naphthalenes 510 -

fluorene 538 26000 0.0722 8.91 0.0166

C3 naphthalenes 581 -

anthracene 594 1300 0.628 77.6 0.1306

phenanthrene 596 34300 0.139 17.1 0.0287

C1 flourenes 611 -

C4 naphthalenes 657 -

C1 phenanthrenes 670 -

C2 flourenes 686 -

pyrene 697 9090 0.171 21.1 0.0303

flouranthene 707 23870 0.0806 9.96 0.0141

C2 phenanthrenes 746 -

C3 flourenes 769 -

C1 fluoranthenes 770 -

C3 phenanthrenes 829 -

benz(a)anthracene 841 4153 0.0709 8.75 0.0104

chrysene 844 826 0.157 19.4 0.023

C4 phenanthrenes 913 -

C1 chrysenes 929 -

benzo(a)pyrene 965 3840 0.164 20.3 0.021

perylene 967 431

benzo(e)pyrene 967 4300

benzo(b)fluoranthene 979 2169 0.139 17.2 0.0175

benzo(k)fluoranthene 981 1220 0.139 17.2 0.0175

C2 chrysenes 1008 -

benzo(g,h,i)perylene 1095 648

C3 chrysenes 1112 -

indeno(1,2,3-cd)pyrene 1115 -

dibenzo(a,h)anthracene 1123 2389

C4 chrysenes 1214 -

Sum total of ESBTUFCVi GESBTUFCV,13 = 0.3479
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-9

Table 6-3B. Continued.

Sediment B

(TOC=0.886%; fOC=0.00886)

PAHA

COC, PAHi, FCVi

(μg/gOC)

COC, PAHi, Maxi

(μg/gOC) 

Conc.

(μg/g dry wt.)

COC

(μg/gOC) ESBTUFCVi

naphthalene 385 61700 0.2703 30.51 0.07925

C1 naphthalenes 444 - 1.2084 136.39 0.30719

acenaphthylene 452 24000 0.0165 1.86 0.00412

acenaphthene 491 33400 0.0401 4.53 0.00922

C2 naphthalenes 510 - 3.2691 368.98 0.72348

fluorene 538 26000 0.3702 41.78 0.07766

C3 naphthalenes 581 - 5.1079 576.51 0.99227

anthracene 594 1300 0.0507 5.72 0.00962

phenanthrene 596 34300 0.5679 64.09 0.1075

C1 flourenes 611 - 0.9362 105.67 0.17294

C4 naphthalenes 657 - 3.3088 373.46 0.56843

C1 phenanthrenes 670 - 0.9267 104.6 0.15611

C2 flourenes 686 - 1.2384 139.77 0.20375

pyrene 697 9090 0.408 46.05 0.06606

flouranthene 707 23870 0.3244 36.62 0.0518

C2 phenanthrenes 746 - 1.0645 120.15 0.16106

C3 flourenes 769 - 1.2664 142.94 0.18587

C1 fluoranthenes 770 - 0.3824 43.16 0.05605

C3 phenanthrenes 829 - 0.81 91.43 0.11028

benz(a)anthracene 841 4153 0.2011 22.69 0.02698

chrysene 844 826 0.2574 29.05 0.03442

C4 phenanthrenes 913 - 0.5644 63.71 0.06978

C1 chrysenes 929 - 0.2987 33.72 0.03629

benzo(a)pyrene 965 3840 0.1817 20.51 0.02125

perylene 967 431 0.3511 39.63 0.04098

benzo(e)pyrene 967 4300 0.1673 18.89 0.01953

benzo(b)fluoranthene 979 2169 0.1708 19.28 0.01969

benzo(k)fluoranthene 981 1220 0.1962 22.15 0.02258

C2 chrysenes 1008 - 0.2242 25.3 0.0251

benzo(g,h,i)perylene 1095 648 0.1504 16.97 0.0155

C3 chrysenes 1112 - 0.0279 3.15 0.00283

indeno(1,2,3-cd)pyrene 1115 - 0.1473 16.63 0.01491

dibenzo(a,h)anthracene 1123 2389 0.0423 4.77 0.00425

C4 chrysenes 1214 - 0.1196 13.5 0.01112

Sum total of ESBTUFCVi GESBTUFCV,TOT = 4.408

A PAHs and corresponding COC,PAHi,FCVi and COC,PAHi,Maxi values are from Table 3-4 (bold).
Implementation

6-10

Table 6-3C. Continued.

Sediment C

(TOC=6.384%; fOC=0.06384)

PAHA

COC, PAHi, FCVi

(μg/gOC)

COC, PAHi, Maxi

(μg/gOC) 

Conc.

(μg/g dry wt.)

COC

(μg/gOC) ESBTUFCVi

naphthalene 385 61700 2.193 34.4 0.0892

C1 naphthalenes 444 - 1.37 21.9 0.0493

acenaphthylene 452 24000 2.04 32 0.0707

acenaphthene 491 33400 0.806 12.6 0.0257

C2 naphthalenes 510 - 1.448 22.7 0.0445

fluorene 538 26000 1.387 21.7 0.0404

C3 naphthalenes 581 - 1.979 31 0.0533

anthracene 594 1300 3.695 57.9 0.0974

phenanthrene 596 34300 4.208 65.9 0.1106

C1 flourenes 611 - 1.03 16.1 0.0264

C4 naphthalenes 657 - 2.009 31.5 0.0479

C1 phenanthrenes 670 - 4.559 71.4 0.1066

C2 flourenes 686 - 1.928 30.2 0.0440

pyrene 697 9090 20.14 315.5 0.4526

flouranthene 707 23870 2.519 39.5 0.0558

C2 phenanthrenes 746 - 4.789 75 0.1006

C3 flourenes 769 - 3.419 53.6 0.0696

C1 fluoranthenes 770 - 11.73 183.7 0.2386

C3 phenanthrenes 829 - 5.378 84.2 0.1016

benz(a)anthracene 841 4153 8.293 129.9 0.1545

chrysene 844 826 9.197 144.1 0.1707

C4 phenanthrenes 913 - 4.674 73.2 0.0802

C1 chrysenes 929 - 5.24 82.1 0.0884

benzo(a)pyrene 965 3840 10.97 171.8 0.1781

perylene 967 431 28.23 442.2 0.4457B

benzo(e)pyrene 967 4300 8.92 139.7 0.1445

benzo(b)fluoranthene 979 2169 18.14 284.1 0.2902

benzo(k)fluoranthene 981 1220 5.5 86.2 0.0878

C2 chrysenes 1008 - 4.753 74.5 0.0739

benzo(g,h,i)perylene 1095 648 5.583 87.5 0.0799

C3 chrysenes 1112 - 0.398 6.2 0.0056

indeno(1,2,3-cd)pyrene 1115 - 10.8 169.2 0.1517

dibenzo(a,h)anthracene 1123 2389 2.499 39.1 0.0349

C4 chrysenes 1214 - 1.581 24.8 0.0204

Sum total of ESBTUFCVi GESBTUFCV,TOT = 3.831

A PAHs and corresponding COC,PAHi,FCVi and COC,PAHi,Maxi values are from Table 3-4 (bold). B Because COC exceeds COC,PAHi,Maxi , COC,PAHi,Maxi is substituted for COC to calculate ESBTUFCVi (see text).
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-11

(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 EESBTUFCV,13 of 0.348 for an

estimated value of EESBTUFCV,34 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

EESBTU for 34 PAHs. If the value for 50%

confidence was selected from Table 6-1 (2.75),

the estimated EESBTUFCV,34 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 Fg PAH/g

organic carbon (COC, μg/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 (COC,PAHi,FCVi) to derive the ESBTUFCVi for

each individual PAH. As was the case for

Sediment A, none of the measured COC exceeded

COC,PAHi,Maxi., so solubility constraints did not factor

into the calculation of ESBTUFCVi. The

ESBTUFCVi values for the 34 PAHs were summed

to determine the EESBTUFCV which was 4.41,

which exceeds the ESB (EESBTUFCV >1.0) for

PAH mixtures. Further examination of this

sediment suggested that it is contaminated with

primarily petrogenic PAHs; i.e., the ratio of

EESBTUFCV,13 (which contains no alkylated

PAHs) to EESBTUFCV 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

EESBTUFCV 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 μg PAH/g organic carbon (COC, μg/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

(COC,PAHi,FCVi) to derive the ESBTUFCVi for each

individual PAH. The concentration of perylene

442.2 μg/gOC exceeded the solubility-constrained

solid phase concentration (COC,PAHi,Maxi). 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

(ESBTUFCV,Perylene = COC,Perylene,Maxi /COC,Perylene,FCV).

The ESBTUFCVi values for the 34 PAHs were

summed to determine the EESBTUFCV which was

3.83, a similar value as in sediment B. The PAH

mixture in sediment C exceeds the ESB

(EESBTUFCV >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 EESBTUFCV,13 (which

contains no alkylated PAHs) to EESBTUFCV for

the 34 PAHs is high (approximately 0.5).
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6-12

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 IWTUFCV of

PAHs. If normal partitioning behavior was

occuring, one would expect the IWTUFCV to be

very close to the calculated EESBTUFCV (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

EESBTUFCV 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 EESBTUFCV using site-specific

COC,PAHi,FCVi values calculated from the sitespecific 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 sitespecific 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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-13

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

EESBTUFCV = 4, one would anticipate lethal

effects only for highly sensitive species. Between

EESBTUFCV of 1 and 4, one would expect only

chronic effects, unless the test species was

unusually sensitive. If EESBTUFCV for PAHs was

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 log10KOW greater than 5.5,

corrections for DOC binding in the interstitial

water will be necessary

 Cd

 = COC/KDOC (6-5)

If the measured chemical in the interstitial

water is substantially less (e.g., 2-3 fold lower or

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 “photoactivated” 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; Oris et al., 1984; Allred and Giesy,

1985; Kagan et al., 1985; Oris and Giesy, 1985,

1986, 1987; Gala and Giesy, 1992; Huang et al.,

1993; Gala and Giesy, 1994; Ren et al., 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; Spehar et al., 1999; Pelletier et al., 2000;

Barron 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 6 PAH* + O2 6 PAH + O2* (6-6)

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 μs), oxygen

intermediaries are highly oxidizing and can cause

severe tissue damage upon contact. Despite the

many different parent PAHs and related alkylated
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6-14

forms, not all PAHs induce photo-activated

toxicity. Those PAHs that are photo-activated can

be predicted using various molecular physicalchemical 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; Veith et al., 1995a,b; Ankley et al., 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 et al.,

1995, 1997; Ireland et al., 1996; Hatch and

Burton,1998, 1999; Erickson et al., 1999; Nikkilä et

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; Monson et al., 1995; Sibley et al., 1997;

Swartz et al., 1997; Boese et al., 1998; Kosian et

al., 1998; Boese et al., 1999; Spehar et al., 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 environments. 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 photoactivated 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

EESBTUFCV 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
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-15

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

A

(μg/L)

Cd-derived

COC

(μg/gOC)

Measured

CORGB

(μg/g) fLipid

CLB

(μg/g Lipid)

CL-derived

COC (μg/gOC) References

TERATOGENIC EFFECTS

FRESHWATER

Fathead minnow eggs

Anthracene

- - 8.8 0.06 147 219 Hall and Oris, 1991

Topminnows

BaP

>3.81C

(1,000)

>3810 9 0.06 150 256 Goddard et al., 1987

Rainbow trout eggs

BaP

0.21 210 1.9 0.05 38.6 66 Hannah et al., 1982

Hose et al., 1984

SALTWATER

English sole eggs

BaP

- - 157 0.03 5233D 8,937D Hose et al., 1981

Sand sole eggs

BaP

0.1 100 2.1 0.03 70 120 Hose et al., 1982

Calif. grunion eggs >3.81

(5)

>3810 1 0.03 33.3 57 Winkler et al., 1983

Calif. grunion eggs >3.81

(24)

>3810 10.5 0.03 350 598 Winkler et al., 1983

 Calif. grunion eggs >3.81C

(869)

>3810 20 0.03 666 1137 Winkler et al., 1983

CARCINOGENIC EFFECTS

FRESHWATER

 Japanese medaka >3.81C

(261)

>3840 - - - - Hawkins et al., 1988,

1990

Guppy >3.81C

(209)

>3840 - - - - Hawkins et al., 1988, 

1990

A If the concentration of BaP exceeded its solubility of 3.81 Fg/L, the published concentration in water is listed in parenthesis

with the solubility of 3.81 Fg/L listed above as the concentration of exposure. Therefore the maximum COC value for these

exposures is 3840Fg 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 Fg/g lipid and in sediments

to -3,840 Fg/gOC, but metabolites of BaP will likely be included in radio-labeled quantification of total BaP equivalents.
Implementation

6-16

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 μg/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 μg BaP/g lipid 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 PAHenriched sediment extracts (Black, 1983; Metcalfe

et al., 1988; Fabacher et al., 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 et al., 1983; Goddard et al., 1987;

Hawkins et al., 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

et al., 1981).

6.6.1 Calculations

When the measured concentration of the PAH

dissolved in water (Cd; μg/L) associated with a

teratogenic or carcinogenic effect was available it

was multiplied by its KOC (L/kgOC) x 10-3 to derive

an equivalent effect concentration in sediment (Cd

-

derived COC; μg/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; Fg PAH/g lipid) associated with an
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-17

effect was available, its equivalent effect

concentration in sediment (CL-derived COC; Fg/

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 μg/gOC; the only

COC for anthracene was 219 μg/gOC (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 μg/gOC. The COC value of 8,937 μg /gOC is

retained because the concentrations in the eggs

probably included metabolites of BaP that are

quantified as total BaP equivalents in the radiolabel 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 EESBTUFCV.

This analysis was used to determine if the ESB

derived from the narcosis mode of action was

protective of teratogenic or carcinogenic effects.

Elliott Bay

EMAP

BaP at an ESBTU = 1.0 (μmol/gOC) BaP Concentration (μmol/gOC)

N=539

0.1 1 10 20 50 80 90 99 99.9

% Less Than or Equal To

B

A

0.0001 0.001 0.01 0.1 1 10 100

ΣESBTUFCV

0.1

1

10

100

1000

10000

0.01

0.001

0.1

1

10

100

1000

0.01

0.001

Figure 6-3. BaP concentration of 539 sediment samples from the EMAP and Elliott

Bay datasets versus (A) the EESBTUFCV values of 34 PAHs and (B) a

probability plot of these BaP concentrations at an EESBTUFCV = 1.0.
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6-18

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 6-3A) 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

EESBTUFCV 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 μg/gOC for

BaP and at 219 Fg/gOC for anthracene. None of

the sediments having <1.0 EESBTUFCV 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:EESBTUFCV

ratio and the sediment-specific anthracene:

EESBTUFCV 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

EESBTUFCV was equal to 1.0. Probability plots of

the calculated concentrations for BaP and

anthracene at 1.0 EESBTUFCV are in Figures 6-3B

and 6-4B, respectively. The dashed lines

represent the critical sediment concentration of 57

μg/gOC for BaP and 219 μg/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

Elliott Bay

EMAP

A

0.0001 0.001 0.01 0.1 1 10 100

ΣESGTUFCV

Anthracene at an ESGTU = 1.0 (μmol/gOC) Anthracene Concentration (μmol/gOC)

0.1

1

10

100

1000

10000

0.01

0.001

0.1

1

10

100

1000

0.01

0.001

N=539

0.1 1 10 20 50 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 EESBTUFCV values of 34 PAHs and (B) a

probability plot of these anthracene concentrations at an EESBTUFCV 1.0.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-19

EESBTUFCV. 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 sitespecific 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 overprotective, 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 log10KOW

values may come to equilibrium in clean sediment

only after a period of weeks or months.

Equilibrium times are shorter for chemicals with

low log10KOW 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 sitespecific 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; Maruya et al., 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;
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6-20

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 KOC 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; Hickey

et al., 1995; Knutzen, 1995; Chapman et al., 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

 Kp

 = fOC KOC + fSC KSC (6-8)

where, Kp

 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

 Kp

 = fOC KOC + fSC KSC Cd

n-1 (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 of ESB

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 KOCbased partitioning model would overpredict

chemical activity and, therefore, concentrations of

PAH in interstitial water and in organisms.

Importantly, most sediments are expected to
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

6-21

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/KOC

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. EPA 2003b).

6.9 Aqueous Solubility Under NonStandard 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(tS0 / tS‰) = KS Csalt (6-9)

where t

S0 and tS‰ are the aqueous solubilities of

the solute in fresh and saltwater (mol/L) at a given

temperature (t in the units EC), respectively, KS

 is

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 5 10 15 20 25 30 35

Computed Concentration Divided

by Concentration at 25C

Temperature (C)

Figure 6-5. Computed solubilities of nine PAHs relative to their 25EC solubilities as a function of temperature.
Implementation

6-22

the Setschenow constant (L/mol) for the salt

solution and the solute of interest, and Csalt is the

molar salt concentration. A one molar salt solution

(NaCl) is approximately equivalent to 48‰ sea

water (Owen and Brinkley, 1941), and KS

 was

found to be essentially invariant with temperatures

from 1 to 30EC, 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 30EC using the relationships of

May (1980) and are compared with the

compound’s concentrations at 25EC (Figure 6-5).

The least-squares exponential representation of

the data is as follows

 (t

S0 = 25S0) = 0.261 e0.0536t, r2 = 0.959 (6-10)

where 25S0

 is the commonly reported solubility of a

compound at 25EC 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

t

S‰ = tS0 10-0.000583‰ (6-11)

where ‰ 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.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

7-1

Section 7

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 ESBTU 1.0 FCV

OCi

OC,PAHi,FCVi i

== ≤ Σ ∑ C

C

(7-1)

Freshwater or saltwater sediments containing

#1.0 EESBTUFCV of the mixture of the 34 PAHs

or more PAHs are acceptable for the protection

of benthic organisms, and if the EESBTUFCV 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

COC,PAHi,FCVi. 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 EESBTUFCV 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 EESBTUFCV 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 EESBTUFCV calculated

from a subset of 13 PAHs (see Table 6-1 for

listing) can be multiplied by 11.5, or the

EESBTUFCV calculated from a subset of 23 PAHs

Sediment Benchmark Values:

Application and Interpretation
Sediment Benchmark Values: Application and Interpretation

7-2

(see Table 6-1 for listing) can be multiplied by 4.14

to achieve 95% confidence that the actual

EESBTUFCV 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 EESBTUFCV 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

EESBTUFCV, this value should not be considered

as an ESB. In principal, ESBs based on the

EESBTUFCV 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 EESBTUFCV 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 sitespecific 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.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAHs Mixtures

7-3

(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 25EC and

salinities less than 1‰. Solubilities can be

recalculated to meet site specific conditions using

procedures described in Section 6.9. Within a

temperature range of 0 to 35EC and salinities from

0 to 35‰, 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

EESBTUFCV 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 EESBTUFCV is less than or equal to 1.0, and if

the EESBTUFCV 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.
Equilibrium Partitioning Sediment Benchmarks (ESBs): PAH Mixtures

8-1

Section 8

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Appendix A

Individual datasets which Comprise 

the Acute Lethality Database: 

Table from Di Toro et al. (2000).
A-2

Common Name,

Scientific Name

Test Conditions

References Test Duration (hr) MethodA ConcentrationB

No. of Data

PointsC

Freshwater

Paramecium,

Tetrahymena elliotti 24 S U 10(12) Rogerson et al., 1983

Hydra,

Hydra oligactis

48 S U 5 Slooff et al., 1983

Snail,

Lymnae stagnalis 48 S U 5 Slooff et al., 1983

Cladoceran,

Daphnia cucullata

48 S U 5 Canton and Adema, 1978

Cladoceran,

Daphnia magna

24 S U 21(28) LeBlanc, 1980a

Cladoceran,

Daphnia magna

48 S U 72(78) 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

Cladoceran,

Daphnia magna

48 S U 19 EG&G Bionomics, 1982; Thurston et al., 1985;

Adema, 1978; Oris et al., 1991; Brooke, 1991;

Millemann et al., 1984; Munkrittrick et al., 1991

Cladoceran,

Daphnia magna

48 FT,R M 1(2) EG&G Bionomics, 1982; Brooke, 1994

Cladoceran,

Daphnia pulex

48 S M (1) Trucco et al., 1983

Cladoceran,

Daphnia pulex

48 S U 6 Canton and Adema, 1978; Passino and Smith, 1987

Brine shrimp,

Artemia salina

24 S N 32(34) Abernethy et al., 1988; Abernethy et al., 1986
Common Name,

Scientific Name

Test Conditions

References Test Duration (hr) MethodA ConcentrationB

No. of Data

PointsC

A-3

Crayfish,

Orconectes immunis

96 FT M 6 Thurston et al., 1985; Holcombe et al., 1987

Mosquito,

Aedes aegypti

48 S U 5 Slooff et al., 1983

Mosquito,

Culex pipiens

48 S U 5 Slooff et al., 1983

Midge,

Tanytarsus dissimilis 48 S M 9 Thurston et al., 1985; Call et al., 1983

Rainbow trout,

Oncorhynchus mykiss

48 FT M 7 Holcombe et al., 1987; Call et al., 1983

Rainbow trout,

Oncorhynchus mykiss

24 FT M 6 Call et al., 1983

Rainbow trout,

Oncorhynchus mykiss

24 S U 1(2) Bently et al., 1975

Rainbow trout,

Oncorhynchus mykiss

48 S U 6 Slooff et al., 1983; Bently et al., 1975

Rainbow trout,

Oncorhynchus mykiss

96 FT M 22 Thurston et al., 1985; Call et al., 1983; Holcombe et

al., 1987; Call et al., 1986; DeGraeve et al., 1982;

Hodson et al., 1988

Rainbow trout,

Oncorhynchus mykiss

96 S M 1 Horne et al., 1983

Rainbow trout,

Oncorhynchus mykiss

96 S U 1 Bently et al., 1975

Bleak,

Alburnus alburnus

96 S I 7 Bengtsson et al., 1984
Common Name,

Scientific Name

Test Conditions

References Test Duration (hr) MethodA ConcentrationB

No. of Data

PointsC

A-4

Goldfish,

Carasius auratus

24 S M 26(28) Bridie et al., 1979

Goldfish,

Carasius auratus

24 S U 5(6) Pickering and Henderson, 1966

Goldfish,

Carasius auratus

24 FT M 1(2) Brenniman et al., 1976

Goldfish,

Carasius auratus

96 S U 4 Pickering and Henderson, 1966

Goldfish,

Carasius auratus

96 FT M 1(2) Brenniman et al., 1976

Goldfish,

Carasius auratus

48 S U 5(6) Pickering and Henderson, 1966

Goldfish,

Carasius auratus

48 FT M 1(2) Brenniman et al., 1976

Golden orfe,

Leuciscus idus melanotus

24 S i(ns) 26 Juhnke and Ludemann, 1978

Fathead minnow, 

Pimephales promelas

24 S U 6 Pickering and Henderson, 1966

Fathead minnow, 

Pimephales promelas

24 FT M 8 Ahmad et al., 1984

Fathead minnow, 

Pimephales promelas

48 S U 11 Pickering and Henderson, 1966 

Fathead minnow, 

Pimephales promelas

48 FT M 8 Ahmad et al., 1984
Common Name,

Scientific Name

Test Conditions

References Test Duration (hr) MethodA ConcentrationB

No. of Data

PointsC

A-5

Fathead minnow, 

Pimephales promelas

96 FT M 141(146) 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

Fathead minnow, 

Pimephales promelas

96 S M 3(4) Bridie et al., 1979; EG&G Bionomics, 1982;

Gendussa, 1990; Horne et al., 1983

Fathead minnow, 

Pimephales promelas

96 R U 1 Academy Natural Sci., 1981

Fathead minnow, 

Pimephales promelas

96 S U 4 Pickering and Henderson, 1966

Channel catfish,

Ictalurus punctatus

96 FT,S M 7 Thurston et al., 1985; Holcombe et al., 1983;

Gendussa, 1990

Medaka,

Oryzias latipes

48 S U 4(5) Slooff et al., 1983

American flagfish, Jordanella

floridae

24 FT M 6 Smith et al., 1991

American flagfish, Jordanella

floridae

48 FT M 6 Smith et al., 1991

American flagfish, Jordanella

floridae

96 FT M 6 Smith et al., 1991

Mosquitofish,

Gambusia affinis

24 S U (3) Thurston et al., 1985

Mosquitofish,

Gambusia affinis

48 S U (3) Thurston et al., 1985
Common Name,

Scientific Name

Test Conditions

References Test Duration (hr) MethodA ConcentrationB

No. of Data

PointsC

A-6

Mosquitofish,

Gambusia affinis

96 FT M 5(6) Thurston et al., 1985; 

Wallen et al., 1957

Mosquitofish,

Gambusia affinis

96 S U 3 Wallen et al., 1957

Guppy,

Poecilia reticulata

24 S U (1) Pickering and Henderson, 1966

Guppy,

Poecilia reticulata

48 S U 10(11) Slooff et al., 1983; Pickering and Henderson, 1966

Guppy,

Poecilia reticulata

96 S U 4 Slooff et al., 1983

Bluegill,

Lepomis macrochirus

24 S U 18(19) Pickering and Henderson, 1966; Buccafusco et al.,

1981; Bently et al., 1975

Bluegill,

Lepomis macrochirus

24 FT M 1 Call et al., 1983

Bluegill,

Lepomis macrochirus

48 FT M 1 Call et al., 1983

Bluegill,

Lepomis macrochirus

48 S U 6(7) Pickering and Henderson, 1966; Bently et al., 1975

Bluegill,

Lepomis macrochirus

96 FT M 8 Thurston et al., 1985; Bently et al., 1975; Call et al.,

1983; Holcombe et al., 1987

Bluegill,

Lepomis macrochirus

96 S U 36(40) Pickering and Henderson, 1966; U.S. EPA, 1978;

LeBlanc, 1980b; ; Buccafusco et al., 1981; Bently et

al., 1975; Dawson et al., 1977.

Tadpole, 

Rana catesbeiana 96 FT M 5 Thurston et al., 1985
Common Name,

Scientific Name

Test Conditions

References Test Duration (hr) MethodA ConcentrationB

No. of Data

PointsC

A-7

Clawed toad, 

Xenopus laevis 48 S U 5 Slooff and Baerselman, 1980

Mexican axolotl,

Ambystoma mexicanum

48 S U 5 Slooff and Baerselman, 1980

Saltwater

Annelid worm,

Neanthes arenaceodentata

96 S U 4(5) Horne et al., 1983; Rossi and Neff, 1978

Annelid worm,

Neanthes arenaceodentata

96 R U (1) Thursby et al., 1989a

Copepod,

Nitocra spinipes

96 S I 6 Bengtsson et al., 1984

Amphipod,

Leptocheirus plumulosus

96 FT M 4 Swartz, 1991a; Champlin and Poucher, 1992a; Boese

et al., 1997

Mysid,

Americamysis bahia

96 S U 20(23) U.S. EPA, 1978; Champlin and Poucher, 1992a;

Zaroogian et al., 1985

Mysid,

Americamysis bahia

96 S M 1 EG&G Bionomics, 1982

Mysid,

Americamysis bahia

96 R U 1

8(9)

Thursby et al., 1989b

Mysid,

Americamysis bahia

96 FT M 8(9) Battelle, 1987; Champlin and Poucher, 1992a; Horne

et al., 1983; EG&G Bionomics, 1978; U.S. EPA,

1978; Kuhn and Lussier, 1987; Thursby, 1991b

Grass shrimp,

Palaemonetes pugio

96 R U 2 Battelle, 1987; Thursby et al., 1989a
Common Name,

Scientific Name

Test Conditions

References Test Duration (hr) MethodA ConcentrationB

No. of Data

PointsC

A-8

Grass shrimp,

Palaemonetes pugio

96 S U 4 Champlin and Poucher, 1992a; Horne et al., 1983;

Thursby, 1991b; Tatem et al., 1978

Grass shrimp,

Palaemonetes pugio

96 FT M 1 Battelle, 1987

Grass shrimp,

Palaemonetes pugio

96 S M 1 Tatem, 1977

Crab,

Portunus pelagicus 96 S M 4 Mortimer and Connell, 1994

Inland silverside,

Menidia beryllina

96 R U 1 Thursby et al., 1989a

Inland silverside,

Menidia beryllina

96 S U 7(8) Champlin and Poucher, 1992a; Dawson et al., 1977;

Horne et al., 1983

Sheepshead minnow,

Cyprinodon variegatus

24 S U 7(8) Heitmuller et al., 1981

Sheepshead minnow,

Cyprinodon variegatus

48 S U 11(12) Heitmuller et al., 1981

Sheepshead minnow,

Cyprinodon variegatus

96 S U 13(15) Heitmuller et al., 1981:

U.S. EPA, 1978

Sheepshead minnow,

Cyprinodon variegatus

96 FT M 2 Ward et al., 1981; Battelle, 1987

Total Data Points 736 (796)

AMethod: S=static, FT=flow-through, R=renewal

BConcentration: U=unmeasured (nominal), M=chemical measured, I=initial

CNumber 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).
B-2

Chemical CASA ClassB KOW

C MWD MVE SF

triethylene glycol 112276 ao !1.48 150.17 131 !

methanol 67561 ao !0.715 32.04 41.0 13.5

2,4-pentanedione* 123546 k !0.509 100.12 100 7.87

ethanol 64175 ao !0.234 46.07 59.0 11.9

acetone 67641 k !0.157 58.08 74.0 13.71

2-chloroethanol* 107073 ao !0.048 80.51 65.0 9.09

2-(2-ethoxyethoxy)ethanol 111900 ao 0.011 134.17 111 !

1-chloro-2-propanol* 127004 ao 0.156 94.54 84.0 44.8

1,3-dichloro-2-propanol* 96231 ao 0.165 128.99 91.0 6.30

2-methyl-2,4-pentanediol 107415 ao 0.246 118.17 120 43.0

2-butanone 78933 k 0.316 72.11 90.0 2.81

2-propanol 67630 ao 0.341 60.10 77.0 13.6

3-chloro-1-propanol* 627305 ao 0.363 94.54 82.0 2.00

1-propanol 71238 ao 0.399 60.10 75.0 11.2

cyclopentanone 120923 k 0.453 84.12 89.0 1.11

2-methyl-2-propanol 75650 ao 0.663 74.12 95.0 16.5

methyl chloride 74873 al,ha 0.677 50.49 56.0 0.0666

2-butanol 78922 ao 0.717 74.12 93.0 14.9

methyl bromide* 74839 al,ha 0.791 94.94 57.0 0.154

3-methyl-2-butanone 563804 k 0.792 86.13 108 1.32

2,3-dibromopropanol* 96139 ao 0.819 217.90 96.0 5.97

cyclohexanone 108941 k 0.827 98.14 103 0.445

cyclopentanol 96413 ao 0.849 86.13 89.0 5.19

2-methyl-1-propanol 78831 ao 0.858 74.12 93.0 10.6

4-methyl-3-pente-2-one 141797 k 0.867 98.14 118 2.68

2-pentanone 107879 k 0.877 86.13 107 1.03

1-butanol 71363 ao 0.946 74.12 92.0 3.03

3-pentanone 96220 k 0.954 86.13 108 0.849

2-methyl-2-butanol 75854 ao 1.03 88.15 110 1.62

2-n-butoxyethanol 111762 ao 1.05 118.17 131 8.78

diethyleneglycolmono-n-butylether 112345 et 1.09 162.23 170 40.0

3,3-dimethyl-2-butanone 75978 k 1.09 100.16 125 0.954
Chemical CASA ClassB KOW

C MWD MVE SF

B-3

diethyl ether 60297 et 1.15 74.122 105 1.16

4-methoxy-4-methyl-2-pentane 107700 k 1.17 130.19 143 41.5

4-methyl-2-pentanone 108101 k 1.17 100.16 124 0.862

dichloromethane 75092 al,ha 1.18 84.93 65.0 0.211

t-butylmethyl ether 1634044 et 1.20 88.149 122 9.04

cyclohexanol 108930 ao 1.29 100.16 103 1.61

2-hexanone 591786 k 1.29 100.16 124 0.598

1,2-dichloroethane 107062 al,ha 1.40 98.96 79.0 0.114

1-pentanol 71410 ao 1.49 88.15 109 0.581

3-methyl-3-pentanol 77747 ao 1.49 102.18 125 3.79

2-phenoxyethanol 122996 ao 1.50 138.17 122 0.173

2,2,2-trichloroethanol 115208 ao 1.61 149.4 93.0 48.4

4-methyl-2-pentanol 108112 ao 1.66 102.18 126 2.25

3-hexanol 623370 ao 1.66 102.18 125 2.18

2-heptanone 110430 ke 1.67 114.19 141 0.312

5-methyl-2-hexanone 110123 ke 1.68 114.19 141 0.271

2,4-dimethyl-3-pentanol 600362 ao 1.78 116.2 140 3.05

6-methyl-5-heptene-2-one 110930 ke 1.82 126.2 151 0.487

2-hexanol 626937 ao 1.83 102.18 126 1.13

1,3-dichloropropane 142289 al,ha 1.84 112.99 97.0 0.0363

1,2-dichloropropane 78875 al,ha 1.86 112.99 99.0 0.0342

diisopropyl ether 108203 et 1.87 102.18 138 0.0918

chloroform 67663 al,ha 1.91 119.38 81.0 0.0319

1,1,2-trichloroethane 79005 al,ha 1.91 133.4 94.0 0.0369

1,4-dimethoxybenzene 150787 ar 1.95 138.165 132 0.0250

2,6-dimethoxytolunene 5673074 ar 1.99 152.19 147 0.0283

benzene 71432 ar 2.00 78.11 89.0 0.0260

1-hexanol 111273 ao 2.02 102.18 125 0.159

2-octanone 111137 ke 2.02 128.21 157 0.111

1-chloro-3-bromopropane 109706 al,ha 2.04 157.44 100 0.0184

5-methyl-3-heptanone 541855 ke 2.05 128.21 156 0.111

anisole 100663 ar 2.06 108.14 111 0.0148
Chemical CASA ClassB KOW

C MWD MVE SF

B-4

2,6-dimethyl-2,5-heptadiene 504201 ke 2.07 138.21 164 0.0171

t-1,2-dichloroethylene 156605 al,ha 2.10 96.94 81.0 0.0202

1,2,3-trichloroepropane 96184 al,ha 2.13 147.43 107 0.0177

1,1-dichloroethylene 75354 al,ha 2.19 96.94 81.0 0.0141

1,3-dibromopropane* 109648 al,ha 2.24 201.9 103 0.00930

bromoform 75252 al,ha 2.25 252.73 88.0 0.00650

1,1,2,2-tetrachloroethane 79345 al,ha 2.31 167.85 106 0.0181

1,4-dichlorobutane 110565 al,ha 2.33 127.01 113 0.00990

1,1-dichloropropane 78999 al,ha 2.36 112.99 101 0.00790

2-nonanone 821556 ke 2.38 142.24 174 0.0801

1,1,1-trichloroethane 71556 al,ha 2.38 133.4 101 0.00662

1,1,1,2-tetrachloroethane 630206 al,ha 2.43 167.85 110 0.0050

5-nonanone 502567 ke 2.44 142.24 174 0.0740

1-heptanol 111706 ao 2.57 116.2 142 0.0487

chlorobenzene 108907 ar,ha 2.58 112.56 102 0.00320

2-ethyl-1-hexanol 104767 ao 2.58 130.23 155 0.132

bicyclo(2,2,1)hepta-2,5-diene 121460 al 2.60 92.14 102 0.00490

toluene 108883 ar 2.62 92.14 107 0.00600

styrene 100425 ar 2.72 104.15 116 0.00550

tetrachloromethane 56235 al,ha 2.73 153.82 97.0 0.00248

2-decanone 693549 ke 2.73 156.27 190 0.0599

bromobenzene 108861 ar,ha 2.75 157.01 106 0.00196

cyclopentane 278923 al 2.76 70.134 95.0 0.00260

1,5-dichloropentane 628762 al,ha 2.76 141.04 130 0.00286

1,3,5-cycloheptatriene 544252 al 2.77 92.14 104 0.00377

trichloroethylene 79016 al,ha 2.81 131.39 90.0 0.00360

di-n-butyl ether 142961 et 2.89 130.23 170 0.00614

t-1,2-dichlorocyclohexane 822866 al,ha 2.90 153.05 128 0.00162

pentachloroethane 76017 al,ha 2.95 202.29 121 0.00111

2,4-hexadiene 592461 al 2.98 82.145 115 0.00237

butylphenyl ether 1126790 et 3.00 150.22 160 0.000790

benzophenone 119619 ke 3.05 182.22 163 0.000480
Chemical CASA ClassB KOW

C MWD MVE SF

B-5

ethylbenzene 100414 ar 3.06 106.17 123 0.00219

2,3-dimethyl-1,3-butadiene 513815 al 3.06 82.145 121 0.00162

2-undecanone 112129 ke 3.08 170.29 207 0.0459

1-octanol 118875 ao 3.10 130.23 158 0.0161

3-chlorotoluene 108418 ar,ha 3.12 126.59 118 0.000834

4-chlorotoluene 106434 ar,ha 3.13 126.59 118 0.000817

o-xylene 95476 ar 3.13 106.17 121 0.00191

m-xylene 108383 ar 3.19 106.17 124 0.00154

p-xylene 106423 ar 3.21 106.17 124 0.00146

1,4-dichlorobenzene 106467 ar,ha 3.24 147.00 113 0.000581

3,5,5-trimethyl-1-hexanol 3452979 ao 3.29 144.26 172 0.0117

1,2-dichlorobenzene 95501 ar,ha 3.31 147.00 113 0.000507

1,3-dichlorobenzene 541731 ar,ha 3.31 147.00 115 0.000524

napthalene 91203 pah 3.36 128.17 125 0.00110

cyclohexane 110827 al 3.38 84.16 109 0.000919

tetrachloroethylene 127184 al,ha 3.38 165.83 99.0 0.000710

2-dodecanone 6175491 ke 3.43 184.32 223 0.0357

cumene 98828 ar 3.49 120.19 140 0.000762

pentane 109660 al 3.50 72.15 116 0.000592

1,2-dibromobenzene 585539 ar,ha 3.56 235.92 119 0.000196

1,5-cyclooctadiene 111784 al 3.61 108.18 130 0.000386

1-nonanol 143088 ao 3.63 144.26 175 0.00552

1,2,4-trimethylbenzene 95636 ar 3.65 120.19 138 0.000487

n-propylbenzene 103651 ar 3.67 120.19 140 0.000467

dipentyl ether 693652 et 3.69 158.28 202 0.000757

1,3,5-trimethylbenzene 108678 ar 3.69 120.19 140 0.000414

hexachloroethane 67721 al,ha 3.73 236.74 132 0.0000936

2,4-dichlorotoluene 95738 ar,ha 3.79 161.03 129 0.000457

1-methylnaphthalene 90120 pah 3.84 142.20 140 0.000280

2-methylnaphthalene 91576 pah 3.86 142.20 141 0.000270

2-chloronaphthalene 91587 pah,ha 3.88 162.62 136 0.000100

1-chloronaphthalene 90131 pah,ha 3.88 162.62 136 0.000100

3,4-dichlorotoluene 95750 ar,ha 3.88 161.03 129 0.000120

biphenyl 92524 ar 3.91 154.21 150 0.000216
Chemical CASA ClassB KOW

C MWD MVE SF

B-6

1,3,5-trichlorobenzene 108703 ar,ha 3.97 181.45 125 0.0000933

1,2,3-trichlorobenzene 87616 ar,ha 3.98 181.45 124 0.0000870

1,2,4-trichlorobenzene 120821 ar,ha 4.00 181.45 126 0.0000886

acenaphthene 83329 pah 4.01 154.21 140 0.000100

2,5-dimethyl-2,4-hexadiene 764136 al 4.10 110.20 146 0.000133

methyl cyclohexane 108872 al 4.10 98.19 128 0.000155

1,2,4,5-tetramethylbenzene 95932 ar 4.11 134.22 152 0.000159

hexane 110543 al 4.12 86.18 132 0.000131

1,3-diethylbenzene 141935 ar 4.17 134.22 156 0.000135

1-decanol 112301 ao 4.19 158.28 192 0.00181

p-tert-butyltoluene 98511 ar 4.26 148.25 173 0.0000995

diphenylether 101848 et 4.36 170.21 152 0.0000595

amylbenzene 538681 ar 4.52 148.25 173 0.0000502

phenanthrene 85018 pah 4.57 178.23 161 0.0000340

1,2,4,5-tetrachlorobenzene 95943 ar,ha 4.64 215.89 136 0.0000151

1,2,3,4-tetrachlorobenzene 634662 ar,ha 4.64 215.89 136 0.0000145

1,2,3,5-tetrachlorobenzene 634902 ar,ha 4.64 215.89 136 0.0000148

1-undecanol 112425 ao 4.70 172.31 207 0.000640

pyrene 129000 pah 4.92 202.26 182 0.0000120

9-methylanthracene 779022 pah 5.01 192.26 175 0.00000980

fluoranthene 206440 pah 5.08 202.26 197 0.0000102

1-dodecanol 112538 ao 5.20 186.34 223 0.000238

pentachlorobenzene 608935 ar,ha 5.32 250.34 147 0.00000218

octane* 111659 al 5.34 114.23 164 0.00000625

1-tridecanol* 112709 ao 5.75 200.36 224 0.0000793

decane* 124185 al 6.56 142.28 229 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

CKOW=log10(KOW);

DMW=molecular weight (gm/mol);

EV=molar volume (cm3

/mol);

FS=aqueous solubility(mol/L)
Appendix C

Summary of Data on the Acute Toxicity 

of PAHs to Freshwater and Saltwater Species 

and the Derivation of Genus Mean Acute Values.
C-2

Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

FRESHWATER

Hydra,

Hydra americana

J W,E fluoranthene

(206-44-0)

5.084 FT M 70 0.3461 0.3461 22.06 22.06 _ Spehar et al., 1999

Hydra,

Hydra sp.

X W,E phenanthrene 

(85-01-8)

4.571 FT M 96 0.5386 0.5386 11.24 11.24 15.7 Call et al., 1986

Annelid,

Lumbriculus variegatus

X I phenanthrene 

(85-01-8)

4.571 FT M >419 >2.351 >2.351 >49.07 _ _ Call et al., 1986

Annelid,

Lumbriculus variegatus

A I fluoranthene

(206-44-0)

5.084 FT M >178 >0.8801 >0.8801 >56.09 >52.46 >52.5 Spehar et al., 1999

Snail,

Mudalia potosensis

X E fluorene

 (86-73-7)

4.208 S U >1900G

(5600)

>11.42 >11.42 >108.2 >108.2 >108.2 Finger et al., 1985

Snail,

Aplexa hypnorum

X E acenaphthene

(83-32-9)

4.012 FT M >2040 >13.23 >13.23 >81.82 >81.82 >81.8 Holcombe et al., 1983

Snail,

Physa heterostropha

X E fluoranthene

(206-44-0)

5.084 S U 137 0.6773 0.6773 43.17 43.17 43.2 Horne and Oblad, 1983

Snail,

Physella virgata

A E fluoranthene

(206-44-0)

5.084 FT M >178 >0.8801 >0.8801 >56.09 >56.09 >56.1 Spehar et al., 1999

Cladoceran,

Daphnia magna

X W naphthalene

 (91-20-3)

3.356 S U 8570 66.86 _ _ _ _ U.S. EPA, 1978

Cladoceran,

Daphnia magna

J W naphthalene

 (91-20-3)

3.356 S U 4723 36.85 _ _ _ _ Abernethy et al., 1986

Cladoceran,

Daphnia magna

X W naphthalene 

(91-20-3)

3.356 S M 2160 16.85 34.63 51.39 _ _ Millemann et al., 1984

Cladoceran,

Daphnia magna

J W 1-methyl

naphthalene 

(90-12-0)

3.837 S U 1420 9.986 9.986 42.20 _ _ Abernethy et al., 1986

Cladoceran,

Daphnia magna

J W 2-methyl

naphthalene

 (91-57-6)

3.857 S U 1491 10.49 10.49 46.29 _ _ Abernethy et al., 1986
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-3

Cladoceran,

Daphnia magna

X W acenaphthene

(83-32-9)

4.012 S U 3450 22.37 _ _ _ _ Randall and Knopp, 1980

Cladoceran,

Daphnia magna

X W acenaphthene

(83-32-9)

4.012 S U >3800

(41000)

>24.64 _ _ _ _ LeBlanc, 1980a

Cladoceran,

Daphnia magna

X W acenaphthene

(83-32-9)

4.012 S M 320 2.075 _ _ _ _ EG&G Bionomics, 1982

Cladoceran,

Daphnia magna

X W acenaphthene

(83-32-9)

4.012 S M 1300 8.430 _ _ _ _ EG&G Bionomics, 1982

Cladoceran,

Daphnia magna

X W acenaphthene

(83-32-9)

4.012 FT M 120 0.7782 0.7782 4.813 _ _ EG&G Bionomics, 1982

Cladoceran,

Daphnia magna

X W fluorene 

(86-73-7)

4.208 S U 430 2.585 2.585 24.49 _ _ Finger et al., 1985

Cladoceran,

Daphnia magna

J W phenanthrene

(85-01-8)

4.571 S U 207 1.160 _ _ _ _ Abernethy et al., 1986

Cladoceran,

Daphnia magna

X W phenanthrene

(85-01-8)

4.571 S U 843 4.730 _ _ _ _ Eastmond et al., 1984

Cladoceran,

Daphnia magna

Neonate W phenanthrene

(85-01-8)

4.571 S M 700 3.928 _ _ _ _ Millemann et al., 1984

Cladoceran,

Daphnia magna

Neonate W phenanthrene

(85-01-8)

4.571 S,R M 212 1.189 _ _ _ _ Brooke, 1994

Cladoceran,

Daphnia magna

Neonate W phenanthrene

(85-01-8)

4.571 FT M 230 1.290 _ _ _ _ Brooke, 1993

Cladoceran,

Daphnia magna

X W phenanthrene

(85-01-8)

4.571 FT M 117 0.6565 0.9204 19.21 _ _ Call et al., 1986

Cladoceran,

Daphnia magna

J W pyrene 

(129-00-0)

4.922 S U 90.9 0.4494 0.4494 20.13 _ _ Abernethy et al., 1986
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-4

Cladoceran,

Daphnia magna

J W 9-methyl

anthracene 

(779-02-2)

5.006 S U 124.8 0.6491 0.6491 34.91 _ _ Abernethy et al., 1986

Cladoceran,

Daphnia magna

J W fluoranthene

(206-44-0)

5.084 S U >260

(320000)

>1.285 _ _ _ _ LeBlanc, 1980a

Cladoceran,

Daphnia magna

J W fluoranthene

(206-44-0)

5.084 S M 45 0.2225 _ _ _ _ Oris et al., 1991

Cladoceran,

Daphnia magna

J W fluoranthene

(206-44-0)

5.084 R M 117 0.5785 _ _ _ _ Spehar et al., 1999

Cladoceran,

Daphnia magna

X W fluoranthene

(206-44-0)

5.084 S M 105.7 0.5226 0.4067 25.92 25.23 Suedel ad Rodgers, 1996

Cladoceran,

Daphnia pulex

X W naphthalene 

(91-20-3)

3.356 S U 4663 36.38 36.38 53.99 _ _ Smith et al., 1988

Cladoceran,

Daphnia pulex

X W fluorene 

(86-73-7)

4.208 S U 212 1.275 1.275 12.08 _ _ Smith et al., 1988

Cladoceran,

Daphnia pulex

X W 1,3-dimethyl

naphthalene

(575-41-7)

4.367 S U 767 4.917 4.917 65.84 _ _ Smith et al., 1988

Cladoceran,

Daphnia pulex

X W 2,6-dimethyl

naphthalene

(581-42-0)

4.373 S U 193 1.237 1.237 16.78 _ _ Smith et al., 1988

Cladoceran,

Daphnia pulex

X W anthracene

 (120-12-7)

4.534 S U >45

(754)

>0.2528 >0.2528 >4.869L_ _ Smith et al., 1988

Cladoceran,

Daphnia pulex

Neonate W phenanthrene

(85-01-8)

4.571 S U 734 4.118 _ _ _ _ Passino and Smith, 1987

Cladoceran,

Daphnia pulex

X W phenanthrene

(85-01-8)

4.571 S U >1100

(>1150)

>6.172 _ _ _ _ Geiger and Buikema, 1981, 1982
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-5

Cladoceran,

Daphnia pulex

X W phenanthrene

(85-01-8)

4.571 S U 350 1.964 _ _ _ _ Smith et al., 1988

Cladoceran,

Daphnia pulex

X W phenanthrene

(85-01-8)

4.571 S M 100 0.5611 1.656 34.56 _ _ Trucco et al., 1983

Cladoceran,

Daphnia pulex

X W 2-methyl

anthracene 

(613-12-7)

4.991 S U >30

(96)

>0.1563 >0.1563 >8.134L30.15 27.6 Smith et al., 1988

Amphipod,

Gammarus minus

X E acenaphthene

(83-32-9)

4.012 S U 460 2.983 2.983 18.45 _ _ Horne et al., 1983

Amphipod,

Gammarus minus

A E fluoranthene

(206-44-0)

5.084 S U 32 0.1582 0.1582 10.08 13.64 _ Horne and Oblad, 1983

Amphipod,

Gammarus 

pseudolimnaeus

X E fluorene 

(86-73-7)

4.208 S U 600 3.607 3.607 34.18 _ _ Finger et al., 1985

Amphipod,

Gammarus 

pseudolimnaeus

X E phenanthrene

(85-01-8)

4.571 FT M 126 0.7070 0.7070 14.76 _ _ Call et al., 1986

Amphipod,

Gammarus 

pseudolimnaeus

A E fluoranthene

(206-44-0)

5.084 FT M 43 0.2126 0.2126 13.55 18.98 16.1 Spehar et al., 1999

Amphipod,

Hyalella azteca

J E fluoranthene

(206-44-0)

5.084 FT M 44 0.2175 0.2175 13.87 13.87 13.9 Spehar et al., 1999

Dragonfly,

Ophiogomphus sp.

N E fluoranthene

(206-44-0)

5.084 FT M >178 >0.8801 >0.8801 >56.09 >56.09 >56.1 Spehar et al., 1999

Stonefly,

Peltoperla maria

X E acenaphthene

(83-32-9)

4.012 S U 240 1.556 1.556 9.626 _ _ Horne et al., 1983

Stonefly,

Peltoperla maria

X E fluoranthene

(206-44-0)

5.084 S U 135 0.6675 0.6675 42.54 20.24 20.2 Horne and Oblad, 1983
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-6

Midge,

Chironomus tentans

L I naphthalene

 (91-20-3)

3.356 S M 2810 21.92 21.92 32.53 _ _ Millemann et al., 1984

Midge,

Chironomus tentans

L I phenanthrene

(85-01-8)

4.571 S M 490 2.749 2.749 57.39 _ _ Millemann et al., 1984

Midge,

Chironomus tentans

L I fluoranthene

(206-44-0)

5.084 S M >250 >1.236 >1.236 >78.78L43.21 _ Suedel ad Rodgers, 1996

Midge,

Chironomus riparius

L I fluorene

 (86-73-7)

4.208 S U >1900

(2350)

>11.42 >11.42 >108.2 >108.2 >68.4 Finger et al., 1985

Midge,

Paratanytarsus sp.

X E acenaphthene

(83-32-9)

4.012 S M 2000 12.97 _ _ _ _ Northwestern Aquatic Science

Inc., 1982

Midge,

Paratanytarsus sp.

X E acenaphthene

(83-32-9)

4.012 S M 2090 13.55 13.26 82.00 82.00 82.0 Northwestern Aquatic Science

Inc., 1982

Midge,

Tanytarsus dissimilis

L I naphthalene

 (91-20-3)

3.356 S U 20700 161.5 _ _ _ _ Darville and Wilhm, 1984

Midge,

Tanytarsus dissimilis

L I naphthalene 

(91-20-3)

3.356 S U 12600 98.31 126.0 187.0 187.0 187 Darville and Wilhm, 1984

Coho salmon,

Oncorhynchus kisutch

E I naphthalene 

(91-20-3)

3.356 R M >11800 >92.07 _ _ _ _ Korn and Rice, 1981

Coho salmon,

Oncorhynchus kisutch

F W naphthalene 

(91-20-3)

3.356 R M 5600 43.69 43.69 64.84 64.84 _ Korn and Rice, 1981

Rainbow trout,

Oncorhynchus mykiss

pre SU I naphthalene 

(91-20-3)

3.356 S U 1800 14.04 _ _ _ _ Edsall, C.C., 1991

Rainbow trout,

Oncorhynchus mykiss

pre SU I naphthalene

 (91-20-3)

3.356 S U 6100 47.59 _ _ _ _ Edsall, C.C., 1991

Rainbow trout,

Oncorhynchus mykiss

pre SU I naphthalene 

(91-20-3)

3.356 S U 2600 20.29 _ _ _ _ Edsall, C.C., 1991
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-7

Rainbow trout,

Oncorhynchus mykiss

pre SU I naphthalene

 (91-20-3)

3.356 S U 4400 34.33 _ _ _ _ Edsall, C.C., 1991

Rainbow trout,

Oncorhynchus mykiss

pre SU I naphthalene

 (91-20-3)

3.356 S U 5500 42.91 _ _ _ _ Edsall, C.C., 1991

Rainbow trout,

Oncorhynchus mykiss

J W naphthalene 

(91-20-3)

3.356 FT M 1600 12.48 _ _ _ _ DeGraeve et al., 1982

Rainbow trout,

Oncorhynchus mykiss

X W naphthalene 

(91-20-3)

3.356 FT M 2300 17.94 14.97 22.21 _ _ DeGraeve et al., 1980

Rainbow trout,

Oncorhynchus mykiss

J W acenaphthene

(83-32-9)

4.012 FT M 670 4.345 4.345 26.87 _ _ Holcombe et al., 1983

Rainbow trout,

Oncorhynchus mykiss

J W fluorene 

(86-73-7)

4.208 S U 820 4.930 4.930 46.71 _ _ Finger et al., 1985

Rainbow trout,

Oncorhynchus mykiss

pre SU I 1,3-dimethyl

naphthalene

(575-41-7)

4.367 S U 1700 10.88 14.04 188.1L

_ _ Edsall, C.C., 1991

Rainbow trout,

Oncorhynchus mykiss

L W phenanthrene

(85-01-8)

4.571 S U >1100

(3200)

>6.172 _ _ _ _ Edsall, C.C., 1991

Rainbow trout,

Oncorhynchus mykiss

J W phenanthrene

(85-01-8)

4.571 FT M 375 2.104 2.104 43.92 _ _ Call et al., 1986

Rainbow trout,

Oncorhynchus mykiss

X W fluoranthene

(206-44-0)

5.084 S M 187 0.9246 _ _ _ _ Horne and Oblad, 1983

Rainbow trout,

Oncorhynchus mykiss

J W fluoranthene

(206-44-0)

5.084 FT M 26.0 0.1285 0.1285 8.193 25.13 40.4 Spehar et al., 1999

Brown trout,

Salmo trutta

J W acenaphthene

(83-32-9)

4.012 FT M 580 3.761 3.761 23.26 23.26 23.3 Holcombe et al., 1983

Fathead minnow,

Pimephales promelas

J W naphthalene 

(91-20-3)

3.356 S M 1990 15.53 _ _ _ _ Millemann et al., 1984
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-8

Fathead minnow,

Pimephales promelas

J W naphthalene

 (91-20-3)

3.356 FT M 7900 61.64 _ _ _ _ DeGraeve et al., 1982

Fathead minnow,

Pimephales promelas

X W naphthalene 

(91-20-3)

3.356 FT M 4900 38.23 _ _ _ _ DeGraeve et al., 1980

Fathead minnow,

Pimephales promelas

J W naphthalene 

(91-20-3)

3.356 FT M 6140 47.91 _ _ _ _ Geiger et al., 1985

Fathead minnow,

Pimephales promelas

J W naphthalene 

(91-20-3)

3.356 FT M 8900 69.44 _ _ _ _ DeGraeve et al., 1980

Fathead minnow,

Pimephales promelas

J W naphthalene 

(91-20-3)

3.356 FT M 6080 47.44 51.77 76.82 _ _ Holcombe et al., 1984

Fathead minnow,

Pimephales promelas

J W 1-methyl

naphthalene 

(90-12-0)

3.837 S U 9000 63.38 63.38 267.9 _ _ Mattson et al., 1976

Fathead minnow,

Pimephales promelas

J W acenaphthene

(83-32-9)

4.012 S M 3100 20.10 _ _ _ _ Marine Bioassay Lab., 1981

Fathead minnow,

Pimephales promelas

J W acenaphthene

(83-32-9)

4.012 S M 1500 9.727 _ _ _ _ EG&G Bionomics, 1982

Fathead minnow,

Pimephales promelas

A W acenaphthene

(83-32-9)

4.012 R U 3700 23.99 _ _ _ _ Academy of Natural Sci., 1981

Fathead minnow,

Pimephales promelas

J W acenaphthene

(83-32-9)

4.012 FT M 1730 11.22 _ _ _ _ Geiger et al., 1985

Fathead minnow,

Pimephales promelas

J W acenaphthene

(83-32-9)

4.012 FT M 608 3.943 _ _ _ _ Cairns and Nebeker, 1982

Fathead minnow,

Pimephales promelas

J W acenaphthene

(83-32-9)

4.012 FT M >1400 >9.079 _ _ _ _ EG&G Bionomics, 1982

Fathead minnow,

Pimephales promelas

J W acenaphthene

(83-32-9)

4.012 FT M 1600 10.38 7.713 47.71 _ _ Holcombe et al., 1983
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-9

Fathead minnow,

Pimephales promelas

X W fluorene 

(86-73-7)

4.208 S U >1900

(100000)

>11.42 >11.42 >108.2L_ _ Finger et al., 1985

Fathead minnow,

Pimephales promelas

J W phenanthrene

(85-01-8)

4.571 S M >1100

(>1150)

>6.172 >6.172 >128.8L_ _ U.S. EPA, 1978

Fathead minnow,

Pimephales promelas

J W fluoranthene

(206-44-0)

5.084 S M 95 0.4697 _ _ _ _ Horne and Oblad, 1983

Fathead minnow,

Pimephales promelas

J W fluoranthene

(206-44-0)

5.084 S M 7.71 0.0381 _ _ _ _ Gendusa, 1990

Fathead minnow,

Pimephales promelas

A W fluoranthene

(206-44-0)

5.084 FT U >260

(>1000)

>1.285 _ _ _ _ Birge et al., 1982

Fathead minnow,

Pimephales promelas

J W fluoranthene

(206-44-0)

5.084 FT M 69 0.3411 0.3411 21.74 67.97 68.0 Spehar et al., 1999

Channel catfish,

Ictalurus punctatus

J E acenaphthene

(83-32-9)

4.012 FT M 1720 11.15 11.15 68.99 _ _ Holcombe et al., 1983

Channel catfish,

Ictalurus punctatus

J E fluoranthene

(206-44-0)

5.084 S M 37.40 0.1849 0.1849 11.79 28.51 28.5 Gendusa, 1990

Bluegill,

Lepomis macrochirus

J W acenaphthene

(83-32-9)

4.012 S U 1700 11.02 11.02 68.18 _ _ Buccafusco et al., 1981

Bluegill,

Lepomis macrochirus

X W fluorene 

(86-73-7)

4.208 S U 910 5.471 5.471 51.84 _ _ Finger et al., 1985

Bluegill,

Lepomis macrochirus

J W phenanthrene

(85-01-8)

4.571 FT M 234 1.313 1.313 27.41 _ _ Call et al., 1986

Bluegill,

Lepomis macrochirus

J W fluoranthene

(206-44-0)

5.084 S U >260

(4000)

>1.285 _ _ _ _ Buccafusco et al., 1981; EPA,

1978

Bluegill,

Lepomis macrochirus

J W fluoranthene

(206-44-0)

5.084 FT M 44 0.2175 0.2175 13.87 34.04 34.0 Spehar et al., 1999
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-10

South african clawed frog

Xenopus laevis

L W naphthalene 

(91-20-3)

3.356 FT M 2100 16.38 _ _ _ _ Edmisten and Bantle, 1982

South african clawed frog

Xenopus laevis

L W naphthalene 

(91-20-3)

3.356 FT M 2100 16.38 16.38 24.31 24.31 24.3 Edmisten and Bantle, 1982

SALTWATER

Annelid worm, 

Neanthes 

arenaceodentata

J I naphthalene 

(91-20-3)

3.356 S U 3800 29.65 29.65 44.00 _ _ Rossi and Neff, 1978

Annelid worm,

Neanthes 

arenaceodentata

X I acenaphthene

(83-32-9)

4.012 S U 3600 23.34 _ _ _ _ Horne et al., 1983

Annelid worm,

Neanthes 

arenaceodentata

J I acenaphthene

(83-32-9)

4.012 R U >3800

(16440)

>24.64 23.34 144.4 _ _ Thursby et al., 1989a

Annelid worm,

Neanthes 

arenaceodentata

A I phenanthrene

(85-01-8) 4.571

S U 600 3.366 3.366 70.27 _ _ Rossi and Neff, 1978

Annelid worm,

Neanthes 

arenaceodentata

J I fluoranthene

(206-44-0)

5.084 S U >260

(500)

>1.285 _ _ _ _ Rossi and Neff, 1978

Annelid worm,

Neanthes 

arenaceodentata

J I fluoranthene

(206-44-0)

5.084 S U >260

(20000)

> 1.285 >1.285 >81.93L

76.43 76.4 Spehar et al., 1999

Archiannelid,

Dinophilus gyrociliatus

J I phenanthrene

(85-01-8)

4.571 R U 185.40 1.040 1.040 21.71 21.71 21.7 Battelle Ocean Sciences, 1987

Mud snail,

Nassarius obsoletus

A I,E phenanthrene

(85-01-8)

4.571 R M >245 >1.375 >1.375 >28.69 >28.69 >28.7 Battelle Ocean Sciences, 1987

Blue mussel,

Mytilus edulis

A E,W phenanthrene

(85-01-8)

4.571 R M >245 >1.375 >1.375 >28.69 >28.69 >28.7 Battelle Ocean Sciences, 1987
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-11

Pacific oyster,

Crassostrea gigas

E/L W naphthalene 

(91-20-3)

3.356 S U >31000

(199000)

>241.9 >241.9 >358.9 >358.9 >359 U.S. EPA, 1980

Coot clam,

Mulinia lateralis

J E pyrene 

(129-00-0)

4.922 FT M >132

(>240)

>0.6526 >0.6526 >29.24 _ _ Champlin and Poucher, 1992a

Coot clam,

Mulinia lateralis

J E fluoranthene

(206-44-0)

5.084 S U >260

(10710)

>1.285 >1.285 >81.93 >48.94 >48.9 Spehar et al., 1999

Soft-shell clam,

Mya arenaria

A I phenanthrene

(85-01-8)

4.571 R M >245 >1.375 >1.375 >28.69 >28.69 >28.7 Battelle Ocean Sciences, 1987

Calanoid copepod,

Eurytemora affinis

A X naphthalene 

(91-20-3)

3.356 S U 3798 22.58 22.58 33.51 Ott, et al., 1978

Calanoid copepod,

Eurytemora affinis

A X 2-methyl

naphthalene 

(91-57-6)

3.857 S U 1499 7.741 7.741 34.17 _ _ Ott, et al., 1978

Calanoid copepod,

Eurytemora affinis

A X 2,6-dimethyl

naphthalene

(581-42-0)

4.373 S M 852 3.860 3.860 52.37 _ _ Ott, et al., 1978

Calanoid copepod,

Eurytemora affinis

A X 2,3,5-

trimethyl

naphthalene

(2245-38-7)

4.856 S M 316 1.271 1.271 49.53 41.51 41.5 Ott, et al., 1978

Mysid,

Americamysis bahia

J E acenaphthene

(83-32-9)

4.012 S U 970 6.290 _ _ _ _ U.S. EPA, 1978;Ward et al., 1981

Mysid,

Americamysis bahia

J E acenaphthene

(83-32-9)

4.012 S M 160 1.038 _ _ _ _ EG&G Bionomics, 1982

Mysid,

Americamysis bahia

J E acenaphthene

(83-32-9)

4.012 R U 1190 7.717 _ _ _ _ Thursby et al., 1989a

Mysid,

Americamysis bahia

J E acenaphthene

(83-32-9)

4.012 FT M 460 2.983 _ _ _ _ Thursby et al., 1989b
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-12

Mysid,

Americamysis bahia

J E acenaphthene

(83-32-9)

4.012 FT M 190 1.232 _ _ _ _ EG&G Bionomics, 1982

Mysid,

Americamysis bahia

J E acenaphthene

(83-32-9)

4.012 FT M 466.1 3.023 _ _ _ _ Horne et al., 1983;Thursby,

1991a

Mysid,

Americamysis bahia

J E acenaphthene

(83-32-9)

4.012 FT M 271.9 1.763 2.104 13.01 _ _ Horne et al., 1983;Thursby,

1991a

Mysid,

Americamysis bahia

J E phenanthrene

(85-01-8)

4.571 FT M 27.1 0.1521 _ _ _ _ Kuhn and Lussier, 1987

Mysid,

Americamysis bahia

J E phenanthrene

(85-01-8)

4.571 FT M 17.7 0.0993 0.1229 2.565 _ _ Battelle Ocean Sciences, 1987

Mysid,

Americamysis bahia

J E pyrene

 (129-00-0)

4.922 FT M 28.28 0.1398 0.1398 6.264 _ _ Champlin and Poucher, 1992a

Mysid,

Americamysis bahia

J E fluoranthene

(206-44-0)

5.084 S U 31 0.1533 _ _ _ _ Spehar et al., 1999

Mysid,

Americamysis bahia

J E fluoranthene

(206-44-0)

5.084 S U 40 0.1978 _ _ _ _ U.S. EPA, 1978

Mysid,

Americamysis bahia

J E fluoranthene

(206-44-0)

5.084 FT M 30.53 0.1509 _ _ _ _ Spehar et al., 1999

Mysid,

Americamysis bahia

J E fluoranthene

(206-44-0)

5.084 FT M 87 0.4301 0.2548 16.24 7.633 7.63 EG&G Bionomics, 1978

Mysid,

Neomysis americana

X E naphthalene 

(91-20-3)

3.356 S M 1250 9.753 _ _ _ _ Hargreaves et al., 1982

Mysid,

Neomysis americana

X E naphthalene 

(91-20-3)

3.356 S M 1420 11.08 10.39 15.43 15.43 15.4 Hargreaves et al., 1982

Isopod

Excirolana

vancouverensis

J I,E fluoranthene

(206-44-0)

5.084 R M >70 >0.3461 >0.3461 >22.06 >22.06 >22.1 Boese et al., 1997
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-13

Amphipod,

Ampelisca abdita

J I acenaphthene

(83-32-9)

4.012 R U 1125 7.295 7.295 45.12 _ _ Thursby et al., 1989a

Amphipod,

Ampelisca abdita

J I fluoranthene

(206-44-0)

5.084 S U 67 0.3313 0.3313 21.11 30.86 30.9 Spehar et al., 1999

Amphipod,

Leptocheirus plumulosus

A E,I acenaphthene

(83-32-9)

4.012 FT M 589.4 3.822 3.822 23.64 _ _ Swartz, 1991a

Amphipod,

Leptocheirus plumulosus

A E,I phenanthrene

(85-01-8)

4.571 FT M 198.4 1.113 1.113 23.24 _ _ Swartz, 1991a

Amphipod,

Leptocheirus plumulosus

J E,I pyrene 

(129-00-0)

4.922 FT M 66.49 0.3287 0.3287 14.73 _ _ Champlin and Poucher , 1992a

Amphipod,

Leptocheirus plumulosus

X E,I fluoranthene

(206-44-0)

5.084 R M 51 0.2522 0.2522 16.07 18.99 19.0 Boese et al., 1997

Amphipod,

Rhepoxynius abronius

J I fluoranthene

(206-44-0)

5.084 R M 63 0.3115 0.3115 19.85 19.85 19.9 Boese et al., 1997

Amphipod,

Eohaustorius estuarius

J I fluoranthene

(206-44-0)

5.084 R M >70 >0.3461 >0.3461 >22.06 >22.06 >22.1 Boese et al., 1997

Amphipod,

Grandidierella japonica

J I fluoranthene

(206-44-0)

5.084 R M 27 0.1335 0.1335 8.508 8.508 8.51 Boese et al., 1997

Amphipod,

Corophium insidiosum

J I fluoranthene

(206-44-0)

5.084 R M 54 0.2670 0.2670 17.02 17.02 17.0 Boese et al., 1997

Amphipod,

Emerita analoga

J I,E fluoranthene

(206-44-0)

5.084 R M 74 0.3659 0.3659 23.32 23.32 23.3 Boese et al., 1997

Kelp shrimp,

Eualis suckleyi

X W naphthalene 

(91-20-3)

3.356 FT M 1390 10.84 10.84 16.09 16.09 16.1 Rice and Thomas, 1989

Grass shrimp,

Palaemonetes pugio

X E,W naphthalene 

(91-20-3)

3.356 S M 2350 18.34 18.34 27.21 _ _ Tatem et al., 1978
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-14

Grass shrimp,

Palaemonetes pugio

X E,W acenaphthene

(83-32-9)

4.012 S U 676.8 4.389 _ _ _ _ Horne et al., 1983;Thursby,

1991b

Grass shrimp,

Palaemonetes pugio

L E,W acenaphthene

(83-32-9)

4.012 R U 1697 11.00 6.950 42.98 _ _ Thursby et al., 1989a

Grass shrimp,

Palaemonetes pugio

A E,W phenanthrene

(85-01-8)

4.571 R U 200.8 1.127 _ _ _ _ Battelle Ocean Sciences, 1987

Grass shrimp,

Palaemonetes pugio

A E,W phenanthrene

(85-01-8)

4.571 FT M 145.4 0.8158 0.8158 17.03 _ _ Battelle Ocean Sciences, 1987

Grass shrimp,

Palaemonetes pugio

J E,W fluoranthene

(206-44-0)

5.084 S U 142 0.7021 0.7021 44.75 30.72 30.7 Spehar et al., 1999

Sand shrimp,

Crangon septemspinosus

X E acenaphthene

(83-32-9)

4.012 S U 245 1.589 1.589 9.826 9.826 9.83 Horne et al., 1983;Thursby ,

1991b

American Lobster,

Homarus americanus

L _ fluoranthene

(206-44-0)

5.084 R U >260

(317)

1.285 1.285 81.93 81.93 81.9 Spehar et al., 1999

Hermit crab,

Paqurus longicarpus

A E phenanthrene

(85-01-8)

4.571 FT M 163.7 0.9185 0.9185 19.17 19.17 19.2 Battelle Ocean Sciences, 1987

Slipper limpet,

Crepidula fornicata

L W acenaphthene

(83-32-9)

4.012 R U 3426 22.28 22.28 137.8 137.8 138 Thursby et al., 1989a

Sea urchin,

Arbacia punctalata

E W acenaphthene

(83-32-9)

4.012 S U >3800

(8163)

>24.64 >24.64 >152.4 _ _ Thursby et al., 1989a

Sea urchin,

Arbacia punctalata

E W fluoranthene

(206-44-0)

5.084 S U >260

(20000)

>1.285 >1.285 >81.93 >117.2 >117 Spehar et al., 1999

Pink salmon,

Oncorhynchus gorbuscha

Fry W naphthalene 

(91-20-3)

3.356 FT M 960 7.490 _ _ _ _ Rice and Thomas, 1989

Pink salmon,

Oncorhynchus gorbuscha

Fry W naphthalene 

(91-20-3)

3.356 FT M 900 7.022 _ _ _ _ Rice and Thomas, 1989
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-15

Pink salmon,

Oncorhynchus gorbuscha

Fry W naphthalene 

(91-20-3)

3.356 FT M 990 7.724 _ _ _ _ Rice and Thomas, 1989

Pink salmon,

Oncorhynchus gorbuscha

Fry W naphthalene 

(91-20-3)

3.356 FT M 1010 7.880 _ _ _ _ Rice and Thomas, 1989

Pink salmon,

Oncorhynchus gorbuscha

Fry W naphthalene 

(91-20-3)

3.356 FT M 890 6.944 7.40 10.99 10.99 11.0 Rice and Thomas, 1989

Sheepshead minnow,

Cyprinodon variegatus

J E,W acenaphthene

(83-32-9)

4.012 S U 2200 14.27 _ _ _ _ Heitmuller et al., 1981

Sheepshead minnow,

Cyprinodon variegatus

J E,W acenaphthene

(83-32-9)

4.012 R U >3800

(50000)

>25.00 _ _ _ _ Thursby et al., 1989a

Sheepshead minnow,

Cyprinodon variegatus

A E,W acenaphthene

(83-32-9)

4.012 FT M 3100 20.10 20.10 124.3 _ _ Ward et al., 1981

Sheepshead minnow,

Cyprinodon variegatus

J E,W phenanthrene

(85-01-8)

4.571 R U >245 >1.375 _ _ _ _ Battelle Ocean Sciences, 1987

Sheepshead minnow,

Cyprinodon variegatus

J E,W phenanthrene

(85-01-8)

4.571 FT M 429.4 2.409 2.409 50.29 _ _ Battelle Ocean Sciences, 1987

Sheepshead minnow,

Cyprinodon variegatus

J E,W pyrene 

(129-00-0)

4.922 FT M >132

(>640)

>0.6526 >0.6526 >29.24 _ _ Champlin and Poucher, 1992a

Sheepshead minnow,

Cyprinodon variegatus

J E,W fluoranthene

(206-44-0)

5.084 S U >260

(>20000)

>1.285 _ _ _ _ Spehar et al., 1999

Sheepshead minnow,

Cyprinodon variegatus

J E,W fluoranthene

(206-44-0)

5.084 S U >260

(>560000)

>1.285 >1.285 >81.93L

79.07 79.1 Heitmuller et al., 1981;U.S EPA,

1978

Inland silverside,

Menidia beryllina

X W acenaphthene

(83-32-9)

4.012 S U 2300 14.91 _ _ _ _ Horne et al., 1983

Inland silverside,

Menidia beryllina

J W acenaphthene

(83-32-9)

4.012 R U >3800

(5564)

>24.64 >19.17 >118.6 _ _ Thursby et al., 1989a
Common/scientific

LifeStageA HabitatBPAH Tested

(CAS #)

Log

Kow

C MethodD

ConcentrationE

LC50/

EC50F

(μg/l)

LC50/

EC50F

(μmol/l)

PAH 

Specific

 SMAVH

(μmol/l)

Kow

Normalized

PAH Specific

SMAVI

 (μmol/goc)

Species

 SMAVJ

(μmol/goc)

GMAVK

(μmol/goc) References

C-16

Inland silverside,

Menidia beryllina

J W pyrene 

(192-00-0)

4.922 FT M >132

(>188.17)

>0.6526 >0.6526 >29.24 _ _ Champlin and Poucher, 1992a

Inland silverside,

Menidia beryllina

J W fluoranthene

(206-44-0)

5.084 S U >260

(>616)

>1.285 >1.285 >81.93 >65.73 _ Spehar et al., 1999

Atlantic silverside,

Menidia menidia

A W phenanthrene

(85-01-8)

4.571 FT M 108 0.6060 0.6060 12.65 12.65 28.8 Battelle Ocean Sciences, 1987

Winter flounder,

Pseudopleu r onecte s

americanus

J _ fluoranthene

(206-44-0)

5.084 S M >188 >0.9295 >0.9295 >59.24 >59.24 >59.2 Spehar et 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.

C

log KOW: 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.

G Bolded 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.

H PAH-specific SMAV: Geometric mean of the acute values by PAH and species.

I

 PAH-specific SMAVs at a log KOW =1.0; calculated as antilog (log10LC50 + 0.945log10KOW)/1000 (see Equation 2-33).

J

 Species SM AV: Geometric mean of KOW-normalized SMAVs for a species across PAHs.

K GMAV: Geometric mean of SMAVs for all species within a genus.

L Not used in calculations.
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.
D-2

Common Name,

Scientific Name Chemical Response

Median

Response

Conc.A

(μg/goc)

COC,PAHi,FCVi

(μg/goc)

TestSpecific

ESBTUFCVi

B

(Unitless)

PAHSpecific

SMAVC GMAVD ReferencesE

Oligochaete,

Lumbriculus variegatus

pyrene 7 d LC50 >9090

(61100)

694 >13.1 - - Kukkonen and Landrum, 1994

Oligochaete,

Lumbriculus variegatus

pyrene 7 d EC50-SA >9090

(51400)

694 >13.1 - - Kukkonen and Landrum, 1994

Oligochaete,

Limnodrilus hoffmeisteri

phenanthrene 10 d LC50 >34300

(42500)

593 >57.8 >57.8 >57.8 Lotufo and Fleeger, 1996

Oligochaete,

Limnodrilus hoffmeisteri

phenanthrene 28 d EC25-R 5790 593 9.80 - - Lotufo and Fleeger, 1996

Oligochaete,

Limnodrilus hoffmeisteri

pyrene 28 d EC25-R 8440 694 12.2 - - Lotufo and Fleeger, 1996

Cladoceran,

Daphnia magna

fluoranthene 10 d LC50 2380 704 - - - Suedel et al., 1993

Cladoceran,

Daphnia magna

fluoranthene 10 d LC50 955 704 - - - Suedel et al., 1993

Cladoceran,

Daphnia magna

fluoranthene 10 d LC50 3260 704 - - - Suedel et al., 1993

Amphipod,

Hyalella azteca

fluoranthene 10 d LC50 >23900

(37649)

704 - - - Driscoll et al., 1997a

Amphipod,

Hyalella azteca

fluoranthene 10 d LC50 1250 704 - - - Suedel et al., 1993

Amphipod,

Hyalella azteca

fluoranthene 10 d LC50 1480 704 - - - Suedel et al., 1993

Amphipod,

Hyalella azteca

fluoranthene 10 d LC50 500 704 - - - Suedel et al., 1993

Amphipod,

Hyalella azteca

fluoranthene 10 d LC50 22000 704 31.3 - - Harkey et al., 1997

Amphipod,

Hyalella azteca

fluoranthene 10 d LC50 5130 704 7.29 15.1 15.1 DeWitt et al., 1989

Amphipod,

Corophium spinicorne

fluoranthene 10 d LC50 2830 704 4.02 - - Swartz et al., 1990
Common Name,

Scientific Name Chemical Response

Median

Response

Conc.A

(μg/goc)

COC,PAHi,FCVi

(μg/goc)

TestSpecific

ESBTUFCVi

B

(Unitless)

PAHSpecific

SMAVC GMAVD ReferencesE

D-3

Amphipod,

Corophium spinicorne

fluoranthene 10 d LC50 4390 704 6.23 5.01 5.01 Swartz et al., 1990

Amphipod,

Leptocheirus plumulosus

acenapthene 10 d LC50 10900 489 22.3 - - Swartz et al., 1991a

Amphipod,

Leptocheirus plumulosus

acenapthene 10 d LC50 23500 489 48.1 - - Swartz et al., 1991a

Amphipod,

Leptocheirus plumulosus

acenapthene 10 d LC50 8450 489 17.3 26.4 - Swartz et al., 1991a

Amphipod,

Leptocheirus plumulosus

phenanthrene 10 d LC50 6870 593 11.59 - - Swartz et al., 1991a

Amphipod,

Leptocheirus plumulosus

phenanthrene 10 d LC50 8080 593 13.63 - - Swartz et al., 1991a

Amphipod,

Leptocheirus plumulosus

phenanthrene 10 d LC50 8180 593 13.8 13.0 18.5 Swartz et al., 1991a

Amphipod,

Rhepoxynius abronius

acenapthene 10 d LC50 2310 489 4.72 - - Swartz et al., 1997

Amphipod,

Rhepoxynius abronius

acenapthene 10 d LC50 2110 489 4.31 4.51 - Swartz et al., 1997

Amphipod,

Rhepoxynius abronius

phenanthrene 10 d LC50 3080 593 5.19 - - Swartz et al., 1997

Amphipod,

Rhepoxynius abronius

phenanthrene 10 d LC50 2220 593 3.74 4.41 - Swartz et al., 1997

Amphipod,

Rhepoxynius abronius

pyrene 10 d LC50 1220 694 1.76 - - Swartz et al., 1997

Amphipod,

Rhepoxynius abronius

pyrene 10 d LC50 2810 694 4.05 2.67 - Swartz et al., 1997

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 >4360 704 >6.19 - - DeWitt et al., 1992

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 4410 704 6.26 - - DeWitt et al., 1992
Common Name,

Scientific Name Chemical Response

Median

Response

Conc.A

(μg/goc)

COC,PAHi,FCVi

(μg/goc)

TestSpecific

ESBTUFCVi

B

(Unitless)

PAHSpecific

SMAVC GMAVD ReferencesE

D-4

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 3080 704 4.38 - - DeWitt et al., 1992

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 2230 704 3.17 - - Swartz et al., 1990

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 3150 704 4.50 - - DeWitt et al., 1992

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 1890 704 2.68 - - Swartz et al., 1990

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 2790 704 3.96 - - De Witt et al., 1992

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 2320 704 3.30 - - Swartz et al., 1997

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 1700 704 2.41 - - DeWitt et al., 1989

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 1030 704 1.47 - - Swartz et al., 1988

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 2100 704 2.98 - - Swartz et al., 1990

Amphipod,

Rhepoxynius abronius

fluoranthene 10 d LC50 3310 704 4.70 3.56 3.67 Swartz et al., 1997

Amphipod,

Eohaustorius estuarius

acenapthene 10 d LC50 1630 489 3.33 - - Swartz et al., 1991a

Amphipod,

Eohaustorius estuarius

acenapthene 10 d LC50 4180 489 8.55 - - Swartz et al., 1991a

Amphipod,

Eohaustorius estuarius

acenapthene 10 d LC50 1920 489 3.93 4.82 - Swartz et al., 1991a

Amphipod,

Eohaustorius estuarius

phenanthrene 10 d LC50 4210 593 7.10 - - Swartz et al., 1991a

Amphipod,

Eohaustorius estuarius

phenanthrene 10 d LC50 3760 593 6.34 - - Swartz et al., 1991a
Common Name,

Scientific Name Chemical Response

Median

Response

Conc.A

(μg/goc)

COC,PAHi,FCVi

(μg/goc)

TestSpecific

ESBTUFCVi

B

(Unitless)

PAHSpecific

SMAVC GMAVD ReferencesE

D-5

Amphipod,

Eohaustorius estuarius

phenanthrene 10 d LC50 4060 593 6.85 6.75 - Swartz et al., 1991a

Amphipod,

Eohaustorius estuarius

fluoranthene 10 d LC50 3100 704 4.40 - - DeWitt et al., 1989

Amphipod,

Eohaustorius estuarius

fluoranthene 10 d LC50 3930 704 5.59 - - DeWitt et al., 1989

Amphipod,

Eohaustorius estuarius

fluoranthene 10 d LC50 3570 704 5.07 5.00 5.46 DeWitt et al., 1989

Midge,

Chironomus tentans

fluoranthene 10 d LC50 1590 704 - - - Suedel et al., 1993

Midge,

Chironomus tentans

fluoranthene 10 d LC50 1740 704 - - - Suedel et al., 1993

Midge,

Chironomus tentans

fluoranthene 10 d LC50 682 704 - - - Suedel et al., 1993

Amphipod,

Diporeia sp.

pyrene 31 d LC50 >9090 694 >13.1 - - Landrum et al., 1994

(147000)

Amphipod,

Diporeia sp.

fluoranthene 10 d LC50 >23900

(29300)

704 >34.0 >34.0 >34.0 Driscoll et al. , 1997a

A Bolded median response concentration (acute) values are the COC,PAHi,Maxi 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 (μg/goc) and COC,PAHi,FCVi (from Table 3-4).

C PAH-specific SMAV: Geometric mean of the test-specific ESBTUFCVi values from 10-d LC50 tests by species and PAH. Test-specific ESBTUFCVi 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.
Appendix E

CAS#, Molecular Weight 

and Solid Solubility 

of Selected PAHs.
E-2

PAH CAS #A

Molecular

Weight

(:g/:mol)

 Mackay

Solid

SolubilityB

(:g/L)

indan 496117 118.18 100000

naphthalene 91203 128.17 30995

C1-naphthalenes - 142.20 ??

1-methylnaphthalene 90120 142.20 28001

2-methylnaphthalene 91576 142.20 25000

acenaphthylene 208968 152.20 16314

acenaphthene 83329 154.21 3800

1-ethylnaphthalene 1127760 156.23 10100

2-ethylnaphthalene 939275 156.23 8001

C2-naphthalenes - 156.23 ??

1,4-dimethylnaphthalene 571584 156.23 11400

1,3-dimethylnaphthalene 575417 156.23 8001

2,6-dimethylnaphthalene 581420 156.23 1700

2,3-dimethylnaphthalene 581408 156.23 2500

1,5-dimethylnaphthalene 571619 156.23 3100

fluorene 86737 166.22 1900

C3-naphthalenes - 170.25 ??

2,3,5-trimethylnaphthalene 2245387 170.26 ??

1,4,5-trimethylnaphthalene 213411 170.20 2100

anthracene 120127 178.12 45.00

phenanthrene 85018 178.23 1100

C1-fluorenes - 180.25 ??

1-methylfluorene 1730376 180.25 1090

C4-naphthalenes - 184.28 ??

2-methylanthracene 613127 192.26 29.99

1-methylanthracene 610480 192.26 ??

9-methylanthracene 779022 192.26 261.1

2-methylphenanthrene 2531842 192.26 ??

1-methylphenanthrene 832699 192.26 269.9

C1-phenanthrene/anthracenes - 192.26 ??

9-ethylfluorene 2294828 194.28 ??

C2-fluorenes - 194.27 ??

pyrene 129000 202.26 131.9

fluoranthene 206440 202.26 239.9

2-ethylanthracene 52251715 206.29 ??

C2-phenanthrene/anthracenes - 206.29 ??

9,10-dimethylanthracene 781431 206.29 55.90

3,6-dimethylphenanthrene 1576676 206.29 ??

C3-fluorenes - 208.3 ??

C1-pyrene/fluoranthenes - 216.29 ?

2,3-benzofluorene 243174 216.28 2.001
PAH CAS #A

Molecular

Weight

(:g/:mol)

 Mackay

Solid

SolubilityB

(:g/L)

E-3

benzo(a)fluorene 238843 216.29 45.00

C3-phenanthrene/anthracenes - 220.32 ??

naphthacene 92240 228.30 0.600

benz(a)anthracene 56553 228.29 11.00

chrysene 218019 228.29 2.000

triphenylene 217594 228.3 43.00

C2-pyrene/fluoranthenes - 230.13 ??

C4-phenanthrenes/anthracenes - 234.23 ??

C1-benzanthracene/chrysenes - 242.32 ??

C3-pyrene/fluoranthenes - 244.32 ??

benzo(a)pyrene 50328 252.31 3.810

perylene 198550 252.31 0.4012

benzo(e)pyrene 192972 252.32 4.012

benzo(b)fluoranthene 205992 252.32 1.501

benzo(j)fluoranthene 205822 252.32 2.500

benzo(k)fluoranthene 207089 252.32 0.7999

C2-benzanthracene/chrysenes - 256.23 ??

9,10-dimethylbenz(a)anthracene 56564 256.35 43.50

7,12-dimethylbenz(a)anthracene 57976 256.35 49.99

7-methylbenzo(a)pyrene 63041770 266.35 ??

benzo(ghi)perylene 191242 276.23 0.2600

C3-benzanthracene/chrysenes - 270.36 ??

indeno(1,2,3-cd)pyrene 193395 276.23 ??

dibenz(a,h)anthracene 53703 278.35 0.6012

dibenz(a,j)anthracene 58703 278.35 12.00

dibenz(a,c)anthracene 215587 278.35 1.601

C4-benzanthracene/chrysenes - 284.38 ??

C1-dibenz(a,h)anthracenes - 292.37 ??

coronene 191071 300.36 0.1400

C2-dibenz(a,h)anthracenes - 306.39 ??

C3-dibenz(a,h)anthracenes - 320.41 ??

A

 For C#-PAHs, a CAS is not available.

B

 Mackay et al. (1992).
Appendix F 

Water-only and Interstitial Water 

LC50s used in Table 5-1.
F-2

Chemical

Test Species MethodA

Water-only

LC50

(μg/L)

Interstitial Water

LC50

(μg/L) References

Freshwater

Fluoranthene

Diporeia sp. FT,M/10 >194 >381.3 Driscoll et al., 1997a,b 

Hyalella azteca FT,M/10 130.7 >75.4 Driscollet al., 1997a,b 

Hyalella azteca S,M/10 44.9 45.9 Suedel et al., 1993

Hyalella azteca S,M/10 44.9 236.5 Suedel et al., 1993

Hyalella azteca S,M/10 44.9 97.6 Suedel et al., 1993

Chironomus tentans S,M/10 31.9 91.2 Suedel et al., 1993

Chironomus tentans S,M/10 31.9 251 Suedel et al., 1993

Chironomus tentans S,M/10 31.9 75.7 Suedel et al., 1993

Saltwater

Acenaphthene

Eohaustorius estuarius FT,M/10 374 800 Swartz, 1991a

Eohaustorius estuarius FT,M/10 374 609 Swartz, 1991a

Eohaustorius estuarius FT,M/10 374 542 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 678 >1,720 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 678 1410 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 678 1490 Swartz, 1991a

Fluoranthene

Leptocheirus plumulosus S/10 39.2 - Driscoll et al., 1998

Phenanthrene

Eohaustorius estuarius FT,M/10 131 138 Swartz, 1991a

Eohaustorius estuarius FT,M/10 131 139 Swartz, 1991a

Eohaustorius estuarius FT,M/10 131 146 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 185 387 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 185 306 Swartz, 1991a

Leptocheirus plumulosus FT,M/10 185 360 Swartz, 1991a

2,6-dimethylnaphthlene

Rhepoxynius abronius S,M/10 - 200 Ozretich et al., 2000a

2,3,5-trimethylnaphthlene

Rhepoxynius abronius S,M/10 - 153 Ozretich et al., 2000a

1-methylfluorene

Rhepoxynius abronius S,M/10 - 44 Ozretich et al., 2000a

2-methylphenanthrene

Rhepoxynius abronius S,M/10 - 70 Ozretich et al., 2000a

9-methylanthracene

Rhepoxynius abronius S,M/10 - 32 Ozretich et al., 2000a

Acenaphthene

Rhepoxynius abronius S,M/10 - - Swartz et al., 1997

Rhepoxynius abronius S,M/10 - - Swartz et al., 1997
F-3

Chemical

Test Species MethodA

Water-only

LC50

(μg/L)

Interstitial Water

LC50

(μg/L) References

Naphthalene

Rhepoxynius abronius S,M/10 - 10440 Ozretich et al., 2000a

Phenanthrene

Rhepoxynius abronius S,M/10 - - Swartz et al., 1997

Rhepoxynius abronius S,M/10 - - Swartz et al., 1997

Pyrene

Rhepoxynius abronius S,M/10 - 28.1 Ozretich et al., 2000a

Rhepoxynius abronius S,M/10 - - Swartz et al., 1997

Rhepoxynius abronius S,M/10 - - Swartz et al., 1997

Fluoranthene

Rhepoxynius abronius S,M/10 13.9 - Swartz et al., 1997

Rhepoxynius abronius S,M/10 13.9 - Swartz et al., 1997

Rhepoxynius abronius S,M/10 13.9 22.7 Swartz et al., 1990

Rhepoxynius abronius S,M/10 13.9 29.4 Swartz et al., 1990

Rhepoxynius abronius S,M/10 13.9 24.2 Swartz et al., 1990

Rhepoxynius abronius S,M/10 13.9 > 315 DeWitt et al., 1992

Rhepoxynius abronius S,M/10 13.9 14.1 DeWitt et al., 1992

Rhepoxynius abronius S,M/10 13.9 26.6 DeWitt et al., 1992

Rhepoxynius abronius S,M/10 13.9 19.2 DeWitt et al., 1992

Rhepoxynius abronius S,M/10 13.9 9.38 DeWitt et al., 1992

Mean LC50 ratio = 1.6

A Test conditions for water-only toxicity tests: S = static, FT = flow-through, M = measured, 

10 = 10-d duration.
Appendix G 

Teratogenic Effects from Laboratory Exposure to PAHs.
G-2

Species

Mode

of

Exposure Method PAH

Exposure

Conc

Associated

with Effect

Exposure

Time Toxic Effect(s): Tissue Conc Comments: References

fathead minnow

(embryos), 

Pimephales

promelas

maternal 

via water

lab; 

flowthrough

Anthracene 6.66 μg/L

11.6 μg/L

6 wks

3 wks

-yolk-sac malformations

-edema

-eye deformities

8.8a μg/g (eggs) Effects on embryos

incubated with solar

ultraviolet light radiation

Hall and Oris,

1991

freshwater

topminnows,

Poeciliopsis

monacha

Poeciliopsis

lucida

water;

acetone carrier

lab; 

static

renewal

BaP 1,000 μg/L

nominal;

1,250 μg/L

was acutely

lethal

24 h

followed by

6 mo. of

monitoring

-increased AHH and 

 EROD activities

9.0 μg/g converted

from 35.7 nmol/g

wet wt.

Implied effect - increased

AHH and EROD activity

indicative of carcinogenic

and teratogenic metabolites

formed during metabolism of

BaP by MFO-system

Goddard et al.,

1987

English sole

(embryos),

Parophrys

vetulus

maternal 

via oral

lab;

wildcaught

BaP 8,000 μg/L

(8 mg/kg 

force-fed)

- -malformation of tail 

 regions

-insufficient yolk-sac

-reduced fin-fold size

-reduced hatching 

 success

51.2 and 263

μg/g (eggs) - avg. =

157; Tissue conc.

from 80 mg/kg i.p.

maternal injection

-Eggs maintained 11 

 days until yolk-sac 

 absorbed; static.

-Incidence of effect 4 

 times greater than controls 

(Chai-square df=3.81)

Hose et al., 1981

Rainbow trout

(embryos),

Oncorhynchus

mykiss

aqueous from

BaP spiked to

sediment

lab;

static

renewal

(7-10d)

BaP 0.21 μg/L

measured

through to

36 d 

post-hatch

-nuclear pycnosis

-lack of body pigment

-insufficient yolk-sac

-abnormalities of eyes

-increased mortality (at 

 2.40 μg/L in aqueous)

-muscle necrosis

-abnormal mitosis in 

 eyes and brains

1.93 μg/g (eggs),

12.34 μg/g (alevins),

from exposure to 2.40

μg/L BaP

Poor control survival 

(52% mortality)

Hannah et al.,

1982;

Hose et al., 1984

Sand sole

(embryos),

Psettichthys

melanostichus

water;

static

lab BaP 0.1 μg/L

measured;

range

(0.08 - 0.12)

through to

yolk-sac

absorption 

(7 -10 d)

-overgrowth of tissues

-arrested development

-twinning; Effects only 

 after 48 h, i.e., during 

 organogenesis

2.1 μg/g

wet weight

effects only exhibited in 5%

of animals; average hatching

success of controls only 57%

versus 28% BaP-treated

Hose et al., 1982
Species

Mode

of

Exposure Method PAH

Exposure

Conc

Associated

with Effect

Exposure

Time Toxic Effect(s): Tissue Conc Comments: References

G-3

Flathead sole

(embryos),

Hippoglossoide

selassodon

water;

static

lab BaP

bound to

bovine

serum

albumin

4.2 μg/L

decreasing

to <0.05

μg/L (DL)

through to

yolk-sac

absorption 

(7 -10 d)

-hatching success sig. 

 decrease

-nuclear pycnosis and 

 general disruption of 

 neural and ocular tissues

- very low hatching success in

controls and experimentals;

5.5 and 11.5%, respectively

Hose et al., 1982

English sole 

(embryos), 

Parophrys

vetulus

water lab BaP 2.1 μg/L

measured

through to

yolk-sac

absorption 

(7 -10 d)

none - - Hose et al., 1982

gizzard shad,

Dorosoma

cepedianum

water via

treated

sediment

lab; 

static

BaP 1.38 μg/g

sediment

(initial);

 0.74 μg/g

sediment 

(mean of

days 4,8

and 15)

22 d none BDL in all but 2 fish

on day 4 -

(0.001 and 0.0002

μg/g wet weight)

-40 ligated shad in 250 

 L H2O with 4.15 kg

 sediment

-no sig. decline in 

 sediment conc. after day 4.

Kolok et al.,

1996

gizzard shad,

Dorosoma

cepedianum

water and/or

sediment

ingestion

lab; 

static

BaP 1.02 μg/g

sediment

(initial); 

0.63 μg/g

sediment 

(mean of

days 4,8,

and 15)

22 days none ligated fish: 0.010

μg/g wet weight (n=4) 

non- ligated: 0.012

μg/g wet weight

(n=14)

-50 shad, 30 ligated; 20 

 non-ligated, in 500 L 

 H2O with 3.15 kg sediment

-no sig. decline in sediment

 conc. after day 4

-all other tissue concs.

 BDL (n=26 ligated;

 n=6 non- ligated)

Kolok et al.,

1996

estuarine clams,

Rangia cuneata

water; acetone

carrier

lab; 

static

BaP 30.5 μg/L 24 h none 7.2 μg/g 

wet weight

-majority of BaP 

 concentrated in the 

 viscera (~75%)

-n=5

Neff and

Anderson, 1975

estuarine clams,

Rangia cuneata

water; acetone

carrier

lab; 

static

BaP 30.5 μg/L 24 h none 5.7 μg/g

wet weight

-majority of BaP 

 concentrated in the

 viscera (~65%)

-n=8

Neff and

Anderson, 1975
Species

Mode

of

Exposure Method PAH

Exposure

Conc

Associated

with Effect

Exposure

Time Toxic Effect(s): Tissue Conc Comments: References

G-4

coho salmon

(24 h Post

fertilization),

Oncorhynchus

kisutch

water; 0.5%

DMSO

lab; 

static

exposure

then flowthrough

BaP 25,000 μg/L 24 h none - Effects on hatching,

orientation, and foraging

only.

Ostrander et al.,

1988

coho salmon,

(32 d post

fertilization),

Oncorhynchus

kisutch

water; 0.5%

DMSO

lab; 

static

exposure

then flowthrough

BaP 25,000 μg/L 24 h none - Effects on hatching,

orientation, and foraging

only.

Ostrander et al.,

1988

coho salmon,

(24 h Post

fertilization),

Oncorhynchus

kisutch

water; 0.5%

DMSO

lab; 

static

exposure

then flowthrough

BaP 25,000 μg/L 24 h none 0.54 decreasing to

0.15 nmol/mg protien

from 2 to 68 d post

fertilization

Conc. of BaP in tissue are

not converted because wet

weights were not given; only

the mg protein/animal. Can

possibly borrow weights

from earlier paper.

Ostrander et al.,

1989

coho salmon,

(32 d post

fertilization),

Oncorhynchus

kisutch

water; 0.5%

DMSO

lab; 

static

exposure

then flowthrough

BaP 25,000 μg/L 24 h none 4.47 decreasing to

0.33 nmol/mg protien

from 2 to 68 d post

fertilization

Conc. of BaP in tissue are

not converted because wet

weights were not given; only

the mg protein/animal. Can

possibly borrow weights

from earlier paper.

Ostrander et al.,

1989

Calif. grunion

(embryos),

Leuresthes

tenuis

water lab;

 static

BaP measured: 

5 μg/L 

(steadystate);

 24 μg/L

(initial)

15 days -reduction in % hatch

-lateral folding of tail

-absence of caudal fin folds

-hemorrhagic lesion or 

 congested vasculature in 

 caudal region

day 15: 0.992 ppm

(wet weight); 6.872

ppm (dry weight)

steady state concentration

reached in 4 to 10 days Winkler et al.,

1983
Species

Mode

of

Exposure Method PAH

Exposure

Conc

Associated

with Effect

Exposure

Time Toxic Effect(s): Tissue Conc Comments: References

G-5

Calif. grunion

(embryos), 

Leuresthes

tenuis

water lab;

 static

BaP measured: 

5-24 μg/L 

(steady

state); 24-

361 μg/L 

(initial)

15 days -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)

day 15:0.92 to 10.48

μg/g wet weight; 6.87

to 62.80 μg/g (dry

weight)

steady state concentration

reached in 4 to 10 days

Winkler et al.,

1983

Calif. grunion

(embryos), 

Leuresthes

tenuis

water lab;

 static

BaP measured:

869 ppb

(initial);

steady-state

not reached

15 days -retarded growth (14d)

-lateral curvature mid-body

-absent melanophores

-unused yolk sac

-lesions as larvae (above)

day 15 - 19.98 μg/g

wet weight; 112.03

μg/g dry weight

steady-state concentration

never reached Winkler et al.,

1983

Pacific herring 

(embryos),

Clupea pallasi

seawater

contaminated

by contact with

oiled gravel -

experiment 1;

less weathered

lab; static Field

MixtureA

9.1 μ/L 16 days -yolk sac edema 13.7 μg/g wet weight Crude Oil characterized for

PAHs only; concentrations

of individual PAHs not given

Carls et al., 1999

Pacific herring 

(embryos),

Clupea pallasi

seawater

contaminated

by contact with

oiled gravel -

experiment 2;

more weathered

lab; state Field

MixtureA

0.41 μ/L to

0.72 μ/L

16 days - yolk sac edema

-pericardial edema

- skeletal, spinal, and 

craniofacial abnormalities

- anaphase aberration

0.022 μg/g wet 

weight

Crude Oil characterized for

PAHs only; concentrations

of individual PAHs not given

Carls et al., 1999

AArtificially weathered Alaska North Slope crude oil. 
Appendix H 

Carcinogenic Effects from Laboratory 

and Field Exposure to PAHs and PAH Mixtures.
H-2

Species

Mode

of

Exposure Method PAH

Exposure Conc 

 Associated

 with Effect

Exposure

Time Toxic Effect(s):

Tissue

Conc Comments: References

Japanese Medaka,

Oryzia latipes

(6-10 d old)

Water;

dimethylformamide

carrier.

Lab; static BaP 261 μg/L 2 x 6h, 1

week apart

Neoplastic lesions in

livers and other

tissues after 36 weeks

36% vs 1% (controls);

20 fish with adenoma,

6 with hepatocellular

carcinoma

- Exposures carried out at

26"C in the dark;

concentration exceeds

saturation solubility of BaP

Hawkins et al,

1988;

Hawkins et al., 

1990

guppy,

Poecilia reticulata

(6-10 d old)

Water;

dimethylformamide

carrier.

Lab; static BaP 209 μg/L 2 x 6h, 1

week apart

Neoplastic lesions in

livers and other

tissues after 52 weeks

23% vs 0% (controls);

1 altered foci, 5

adenoma, 4 with

hepatocellular

carcinoma

- Studies carried out longer

because tumorigenic

response in guppy is

slower than in medaka

Hawkins et al,

1988;

Hawkins et al., 

1990

Rainbow trout

(fingerlings),

Oncorhynchus mykiss

oral Lab BaP 1,000 ppm per

feeding

12 and 18

months

Incidence of

neoplasms on liver

15% (1.0/liver) at 12

months

25% (7.7/liver) at 18

months

- MFO info also available

0% at 6 months

0% on other organs

Hendricks et al.,

1985

Rainbow trout

(juvenile),

Oncorhynchus mykiss

(10 mo)

ip injection Lab BaP 1 mg B(a)P in

0.4 ml PG

(1/month for 12

months)

18 months

(6 months

after final

injection)

Incidence of

neoplasms in various

organs = 46% (x = 7.7

tumors/organ)

- Organs examined =

gonads, swim bladder,

liver, spleen, head and

trunk kidneys, pancreas,

intestines, and stomach

Hendricks et al.,

1985

Poeciliopsis lucida and

Poeciliopsis monacha

(1-7 months old)

water;

acetone

carrier

Lab: (multiple

exposures) 3

to 4 exposure

periods of 5-

20 hours each

week

7,12-

dimethylbenz(a)-

anthracene

5 ppm (per

exposure)

7 - 8

months

(from

initial

exposure)

incidence of hepatic

tumors = 48%

- only survivors examined =

(55% mortality in 5 ppm

treatment)

(13% mortality in control)

Schultz and

Schultz 1982
Species

Mode

of

Exposure Method PAH

Exposure Conc 

 Associated

 with Effect

Exposure

Time Toxic Effect(s):

Tissue

Conc Comments: References

H-3

Poeciliopsis lucida and

Poeciliopsis monacha

(1-6 weeks old)

water;

acetone

carrier

Lab: (multiple

exposures) 5

exposures

periods of 6

hours each

week

7,12-

dimethylbenz(a)-

anthracene

5 ppm (per

exposure)

6 - 7

months

Incidence of hepatic

tumors = 41.8%

- 22% mortality in treatment

16% mortality in control

Tumor-bearing livers

enlarged, yellow-white to

greenish and granular. 

Schultz and

Schultz 1982

Bullheads Direct skin

(river

sediment

extract)

Lab Field Mixture

A

5% RSE painted

once per week

18 months 23% of survivors

hyperplastic

9% with multiple

papillomas

- Survival of control and

experimental fish was

31%.

Black, 1983

Japanese Medaka,

Poecilia reticulata

(6-10 d old)

Water via

Sediment

extract redissolved in

acetone

Lab Field Mixture

B

182 ppb TPAH

Black River, OH

extract;

254 ppb TPAH

Fox River, WI

extract

24 h hepatocellular

carcinoma - Black

River Ex. (2/15 fish);

Pancreatic-duct cell

adenoma - Fox River

Ex. (1/15 fish)

- No incidence of

carcinomas in controls up

to 270 days post-exposure;

one incidence of

lymphoma after 360 days

of exposure.

Fabacher et al.,

1991

Rainbow trout

(embryos),

Oncorhynchus mykiss

injection of

sediment

extract into

yolk sac

Lab Field Mixture

C DosesD

:

(Exp I) 0.006 g

(Exp II) 0.012 g

 0.006 g

 0.003 g 

1 year Hepatic carcinomas

(I) 8.9% (11/123)

(II) 8.1% (12/148)

 4.0% (5/148)

 3.1% (2/65)

- Note; PCBs also present

sediment from Hamilton

Harbour

Metcalfe et al

1988

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.