United States 

Environmental Protection 

Agency

Predicting Toxicity to 

Amphipods From Sediment 

Chemistry 
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National Center for 

 Environmental Assessment 

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$300 

EPA/600/R-04/030 

March 2005 
 

 

EPA/600/R-04/030 

March 2005 

Predicting Toxicity to Amphipods 

From Sediment Chemistry 

National Center for Environmental Assessment 

Office of Research and Development 

U.S. Environmental Protection Agency 

Washington, DC 20460 
ii

DISCLAIMER 

This document has been reviewed in accordance with U.S. Environmental Protection 

Agency policy and approved for publication. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 

ABSTRACT 

The contribution of contaminated sediments to effects on sediment-dwelling organisms 

(including plants and invertebrates), aquatic-dependent wildlife (amphibians, reptiles, fish, birds, 

and mammals), and human health has become more apparent in recent years. Sediments can 

serve both as reservoirs and as potential sources of contaminants to the water column and can 

adversely affect sediment-dwelling organisms by causing direct toxicity or altering benthic 

invertebrate community structure. Although the results of sediment toxicity tests and benthic 

invertebrate community assessments can be used directly to evaluate or infer effects on resident 

sediment-dwelling organisms, effective interpretation of sediment chemistry data requires tools 

that link chemical concentrations to the potential for observing adverse biological effects. 

This report describes the development of logistic regression models that quantify 

relationships between the concentrations of contaminants in field-collected sediments and the 

classification of samples as toxic on the basis of tests using two species of marine amphipods, 

Rhepoxynius abronius and Ampelisca abdita. Individual chemical logistic regression models 

were developed for 37 chemicals of potential concern in contaminated sediments to predict the 

probability that a sample would be classified as toxic. These models were derived from a large 

database of matching sediment chemistry and toxicity data that includes contaminant gradients 

from a variety of habitats in coastal North America. Chemical concentrations corresponding to a 

20, 50, and 80% probability of observing sediment toxicity (T20, T50, and T80 values) were 

calculated to illustrate the potential for deriving application-specific sediment effect 

concentrations and to provide probability ranges for evaluating the reliability of the models. 

The individual chemical regression models were combined into a single model to 

estimate the probability of toxicity on the basis of the mixture of chemicals present in a sample. 

The average predicted probability of toxicity closely matched the observed proportion of toxic 

samples within the same ranges, demonstrating the overall reliability of the P_Max model for the 

database that was used to derive the model. The magnitude of the toxic effect (decreased 

survival) in the amphipod test increased as the predicted probability of toxicity increased. 

The logistic models have a number of applications, including estimating the probability 

of observing acute toxicity in estuarine and marine amphipods in 10-day toxicity tests based on 

sediment chemistry. The models can also be used to estimate the chemical concentrations that 

correspond to specific probabilities of observing sediment toxicity. Most importantly, the 

models provide a framework for site-specific and regional assessments and for evaluating other 

saltwater and freshwater endpoints. 

Preferred citation: 

U.S. EPA (Environmental Protection Agency). (2005) Predicting toxicity to amphipods from sediment chemistry. 

National Center for Environmental Assessment, Washington, DC; EPA/600/R-04/030. Available from: National 

Technical Information Service, Springfield, VA, and online at http://www.epa.gov/ncea. 
 iii 

CONTENTS 

LIST OF TABLES......................................................................................................................... vi 

LIST OF FIGURES ....................................................................................................................... ix 

LIST OF ABBREVIATIONS AND ACRONYMS .................................................................... xiv 

PREFACE......................................................................................................................................xv 

AUTHORS, CONTRIBUTORS, AND REVIEWERS ............................................................... xvi 

1. EXECUTIVE SUMMARY .......................................................................................................1 

 1.1. SUMMARY......................................................................................................................1 

1.1.1. Individual Chemical Models.................................................................................2 

1.1.2. Multiple-Chemical Models ...................................................................................3 

 1.2. RECOMMENDATIONS..................................................................................................5 

 1.3. APPLICATIONS ..............................................................................................................6 

2. INTRODUCTION .....................................................................................................................8 

3. DATABASE DEVELOPMENT..............................................................................................11 

 3.1. INTRODUCTION ..........................................................................................................11 

 3.2. COMPILATION OF MATCHING SEDIMENT CHEMISTRY AND TOXICITY 

 DATA .............................................................................................................................11 

 3.3. DATA AUDITING.........................................................................................................13 

 3.4. DATA TREATMENT TO SUPPORT MODEL DEVELOPMENT..............................14 

 3.4.1. Calculation of Total PCBs ..................................................................................14 

 3.4.2. Classification of Toxic Samples .........................................................................14 

 3.4.3. Data Screening for Model Development ............................................................15 

 3.5. DATABASE CONTENTS .............................................................................................16 

4. LOGISTIC REGRESSION MODELS FOR INDIVIDUAL CHEMICALS...........................19 

 4.1. INTRODUCTION ..........................................................................................................19 

 4.2. METHODS .....................................................................................................................20 

 4.2.1. Logistic Regression Modeling ............................................................................20 

 4.2.2. Concentration Interval Plots ...............................................................................22 

 4.3. INDIVIDUAL CHEMICAL LRM RESULTS...............................................................22 

 4.3.1. Model Results .....................................................................................................22 

 4.3.1.1. Models Based on Sig Only Toxicity Classification.............................22 

 4.3.1.2. Models Based on MSD Toxicity Classification...................................24 

 4.3.1.3. Models Based on Organic Carbon-Normalized Chemical 

Concentrations .....................................................................................25 

 4.3.2. Model Comparisons............................................................................................26 

 4.3.2.1. Goodness-of-Fit Comparisons .............................................................26 

 4.3.2.2. Reliability Comparisons.......................................................................27 

 4.3.3. Summary of Toxicity Classification Evaluations ...............................................27 

 4.4. EVALUATION OF ALTERNATIVE SCREENING APPROACHES .........................28 

 4.4.1. Unscreened Versus 1X Screening.......................................................................29 

 4.4.2. 2X Screening Versus 1X Screening....................................................................30 

 4.4.3. Summary of Screening Approach Evaluations...................................................30 
CONTENTS (continued) 

iv

 4.5. COMPARISON OF TOXICITY TEST ENDPOINTS ..................................................31 

 4.5.1. Statistical Comparisons of Species-Specific LRMs ...........................................31 

 4.5.2. Comparison of Tp values for Separate Amphipod Models ................................32 

 4.5.3. Reliability Comparisons......................................................................................32 

 4.5.4. Summary of Species Comparisons .....................................................................32 

 4.6. SENSITIVITY OF MODELS TO ERRORS IN UNDERLYING DATA .....................33 

4.7 INDIVIDUAL CHEMICAL MODELS AND SPIKED-SEDIMENT BIOASSAY 

 MEDIAN LETHAL CONCENTRATION (LC50) VALUES .......................................33 

 4.8. SUMMARY AND CONCLUSIONS .............................................................................34 

5. MULTIPLE-CHEMICAL MODELS ......................................................................................37 

 5.1. INTRODUCTION ..........................................................................................................37 

 5.2. MODEL DEVELOPMENT............................................................................................37 

 5.3. MODEL RESULTS........................................................................................................40 

5.4 USING THE MULTIPLE-CHEMICAL MODEL AS AN ANALYTICAL 

 FRAMEWORK...............................................................................................................41 

5.4.1. Relationship Between Probability of Observing a Toxic Effect and 

 Magnitude of Toxicity ........................................................................................41 

 5.4.2. Chemicals that Serve as the Most Effective Surrogates for Toxicity .................42 

 5.4.3. Effect of High Levels of Several Chemicals.......................................................42 

 5.4.4. Sensitivity of P_Max Model to Individual Chemical Models ............................43 

 5.4.4.1. Effect of PCB Model Correction .........................................................43 

 5.4.5. Predictions of P_Max Model for Individual Species..........................................43 

 5.4.6. Predictions of P_Max Model for Individual Studies ..........................................44 

 5.5. APPLICATION OF THE MODELS TO AN INDEPENDENT DATA SET................45 

 5.6. SUMMARY AND CONCLUSIONS .............................................................................46 

6. APPLICATIONS OF MODELS..............................................................................................49 

 6.1. INTRODUCTION ..........................................................................................................49 

 6.2. APPLICATION OF THE P_MAX MODEL TO DATA FOR OTHER 

 ENDPOINTS ..................................................................................................................49

 6.3. USING LRMs TO EVALUATE EXISTING EMPIRICAL GUIDELINES..................50 

6.4. APPLICATION OF MODELS TO EVALUATIONS OF SITE-SPECIFIC OR 

 REGIONAL DATA........................................................................................................53 

 6.5. SUMMARY AND CONCLUSIONS .............................................................................55 

7. CONCLUSIONS AND RECOMMENDATIONS ..................................................................56 

 7.1. CONCLUSIONS.............................................................................................................56 

 7.2. RECOMMENDATIONS................................................................................................59 

 7.3. APPLICATIONS ............................................................................................................60 

 7.4. FUTURE DIRECTIONS ................................................................................................61 
CONTENTS (continued) 

v

REFERENCES ............................................................................................................................155 

APPENDIX A1: REFERENCES FOR MARINE SEDTOX02 DATABASE...........................158 

APPENDIX A2: REFERENCES FOR FRESHWATER SEDTOX02 DATABASE ................162 

APPENDIX B: SEDIMENT TOXICITY DATABASE (SEDTOX02) STRUCTURE.............168 

APPENDIX C: DATA ACQUISITION SCREENING METHODS .........................................173 

APPENDIX D: DATA EVALUATION METHODS ................................................................178 

APPENDIX E: SENSITIVITY OF P_MAX AND P_AVG MODELS TO DIFFERENT 

 BINNING SCENARIOS ...................................................................................182 
 vi 

LIST OF TABLES 

Table 1. Number of samples and percent toxic samples summarized by marine amphipod 

 species and data source...............................................................................................63 

Table 2. Distribution of chemical concentrations in sediment samples with matching 

 toxicity data for marine amphipods............................................................................64 

Table 3. Number of samples and percent toxic samples summarized by sea urchin 

 species and test endpoint ............................................................................................65 

Table 4. Distribution of chemical concentrations in sediment samples with matching 

 sea urchin fertilization toxicity data ...........................................................................66 

Table 5. Distribution of chemical concentrations in sediment samples with matching 

 sea urchin developmental toxicity data ......................................................................67 

Table 6. Endpoint, number of samples, and percent toxic samples for Chironomus spp. 

 (C. tentans and C. riparius) and Hyalella azteca.......................................................68 

Table 7. Distribution of chemical concentrations in sediment samples with matching 

 toxicity data for the H. azteca 10–14-day survival test..............................................69 

Table 8. Distribution of chemical concentrations in sediment samples with matching 

 toxicity data for the C. tentans or C. riparius 10–14-day survival test......................70 

Table 9. Distribution of chemical concentrations in sediment samples with matching 

 toxicity data for the H. azteca 28-day growth and survival test.................................71 

Table 10. Normalized chi-square values and number of samples for individual chemicals 

 based on Sig Only classification of toxic samples for the screened marine amphipod 

 database (logistic regression model parameters are shown for models having 

 normalized chi-square values greater than 0.15)........................................................72 

Table 11. Logistic model point estimates of T20, T50, and T80 values (95% confidence 

 interval) for individual chemicals based on Sig Only classification of toxic 

 samples for the screened marine amphipod database.................................................73 

Table 12. Percent of toxic samples within ranges defined by Sig Only logistic model 

 T20, T50, and T80 values and number of samples used to derive the logistic 

 model for each chemical for the marine amphipod database .....................................74 

Table 13. Normalized chi-square values and number of samples for individual chemicals 

 based on MSD classification of toxic samples for the screened marine amphipod 

 database (logistic regression model parameters are shown for models having 

 normalized ch-square values greater than 0.15).........................................................75 
LIST OF TABLES (continued) 

 vii 

Table 14. Logistic model point estimates of T20, T50, and T80 values (95% confidence 

 interval) for individual chemicals based on MSD classification of toxic 

 samples for the marine amphipod database................................................................76 

Table 15. Percent of toxic samples within ranges defined by logistic model T20, 

 T50, and T80 values and number of samples in the database used to derive the 

 logistic model for each chemical based on MSD classification of toxic samples 

 for the marine amphipod database..............................................................................77 

Table 16. Normalized chi-square values and number of samples for individual chemicals 

 for organic carbon-normalized concentrations for the screened marine amphipod 

 database (logistic regression model parameters are shown for models having 

 normalized chi-square values greater than 0.15)........................................................78 

Table 17. Logistic model point estimates of T20, T50, and T80 organic carbon-normalized 

concentrations (95% confidence interval) for individual chemicals based on Sig 

 Only classification of toxic samples in the screened marine amphipod database......79 

Table 18. Percent of toxic samples within ranges defined by logistic model T20, T50, 

 and T80 values for organic carbon-normalized concentrations and number of 

 samples in the database used to derive the logistic model for each chemical based 

 on Sig Only classification of toxic samples for the marine amphipod database........80 

Table 19. Differences between percent predicted toxic samples and percent observed toxic 

samples (observed minus predicted) within ranges defined by logistic 

 model T20, T50, and T80 values based on Sig Only classification of toxic 

 samples for the marine amphipod database................................................................81 

Table 20. Differences between percent predicted toxic samples and percent observed toxic 

samples (observed minus predicted) within ranges defined by logistic model T20, 

 T50, and T80 values based on MSD classification of toxic samples for the marine 

 amphipod database .....................................................................................................82 

Table 21. Differences between mean percent predicted toxic samples and percent observed 

toxic samples (observed minus predicted) within ranges defined by logistic model 

T20, T50, and T80 values for organic carbon-normalized concentrations 

 based on Sig Only classification of toxic samples for the marine amphipod 

 database ......................................................................................................................83 

Table 22. Differences between mean percent predicted toxic samples and percent observed 

toxic samples (observed minus predicted) within ranges defined by logistic 

 model T20, T50, and T80 values for each chemical using a screening factor of 2X 

 the mean of nontoxic samples and Sig Only classification of toxic samples for the 

 marine amphipod database .........................................................................................84 
LIST OF TABLES (continued) 

 viii 

Table 23. Statistical comparisons of the logistic regression models for A. abdita and 

 R. abronius using the chi-square statistic (−2 log likelihood)....................................85 

Table 24. Ratio of Tp values from species-specific A. abdita models to corresponding 

 Tp values from the combined amphipod models (only models with normalized 

 chi-square >0.15 are included) ...................................................................................86 

Table 25. Ratio of Tp values from the combined amphipod models to corresponding 

 Tp values from the species-specific R. abronius models (only models with 

 normalized chi-square >0.15 are included) ................................................................87 

Table 26. Differences between mean percent predicted toxic samples and percent observed 

toxic samples (observed minus predicted) for A. abdita and R. abronius within 

 ranges defined by T20, T50, and T80 values .............................................................88 

Table 27. Changes in logistic model point estimates of T20, T50, and T80 concentrations for 

 PCBs based on Sig Only classification of toxic samples based on corrected PCB 

model for the screened marine amphipod database....................................................89 

Table 28. Estimated probability of toxicity from marine amphipod chemical-specific 

 logistic regression models for LC50 values (dry wt.) reported from 10-day spiked 

sediment amphipod toxicity tests ...............................................................................90 

Table 29. Number and percent of samples by chemical class that represented the maximum 

 probability of toxicity used in the P_Max model derived from the marine 

 amphipod database .....................................................................................................91 

Table 30. Differences between mean percent predicted toxic samples and percent observed 

toxic samples (observed minus predicted) by probability quartile for individual 

 studies with at least 20 samples..................................................................................92 

Table 31. List of studies (primary data source) combined for analysis of broader geographic 

 areas............................................................................................................................93 

Table 32. Percent of samples predicted to be toxic to amphipods at the chemical 

 concentrations defined by sediment quality guidelines..............................................94 

Table 33. Percent of samples predicted to be toxic to amphipods at the chemical 

 concentrations defined by the Final Chronic Value for individual polycyclic 

 aromatic hydrocarbons ...............................................................................................95 

Table 34. Percent of samples predicted and observed to be toxic to amphipods within 

 ranges defined by toxic units of polycyclic aromatic hydrocarbons (PAHs).............96 
 ix 

LIST OF FIGURES 

Figure 1. Box plots (plotted as per Tukey, 1977) summarizing the distribution of total 

organic carbon (TOC) (log 10) values for each study with greater than 

20 samples from the marine amphipod database........................................................97 

Figure 2. Logistic regression models and proportion of toxic samples in concentration 

 intervals in screened marine amphipod database for 37 chemicals based on 

 Sig Only classification of toxic samples ....................................................................98 

Figure 3. Logistic regression models and proportion of toxic samples in concentration 

 intervals in the screened marine amphipod database for 33 chemicals based on 

 MSD classification of toxic samples ........................................................................105 

Figure 4. Logistic regression models and proportion of toxic samples in organic carbon- 

 normalized concentration intervals in the screened marine amphipod database 

 for 25 chemicals based on Sig Only classification of toxic samples........................111 

Figure 5. Comparison of logistic model goodness of fit for the marine amphipod survival 

 endpoint with different toxicity classifications: Sig Only versus MSD...................116 

Figure 6. Comparison of logistic model goodness of fit for the marine amphipod endpoint 

 survival with different approaches to the expression of chemical concentrations: 

 dry weight (DW) versus organic carbon-normalized (OC) concentrations .............117 

Figure 7. Comparison of logistic regression model Tp values (T20, T50, T80) for Sig Only 

 and MSD toxicity classification approaches for the marine amphipod database.....118 

Figure 8. Comparison of 1X mean screened (left) and unscreened (right) logistic regression 

models and proportion of samples toxic in concentration intervals for lead and 

 phenanthrene for the marine amphipod database .....................................................119 

Figure 9. Comparison of logistic model goodness of fit for the marine amphipod survival 

endpoint using different screening methods (1X screening vs. no screening) and the 

Sig Only classification of toxic samples ..................................................................120 

Figure 10. Logistic regression models and proportion of samples toxic in concentration 

 intervals for the standard screening approach (1X mean) (left) and the 2X mean 

 screening approach (right) for lead and fluoranthene using the marine amphipod 

 database ....................................................................................................................121 

Figure 11. Comparison of logistic model goodness of fit for the marine amphipod survival 

endpoint using different screening methods (1X mean vs. 2X mean) and Sig 

 Only classification of toxic samples.........................................................................122
LIST OF FIGURES (continued) 

 x 

Figure 12. Ratio of logistic regression model Tp values (T20, T50, T80) for the standard 

 screening approach (1X mean) to the 2X mean screening approach for the 

 marine amphipod database .......................................................................................123 

Figure 13. Logistic regression models and concentration interval plots showing the effect 

 of correction in PCB units for 15 samples in the marine amphipod database..........124 

Figure 14. Mean percent control-adjusted marine amphipod survival for toxic samples 

 within intervals defined by the Tp values for all individual chemical models.........125 

Figure 15. Probability density functions for P_Avg, P_Max, and P_Prod................................126 

Figure 16. Proportion of observed toxic samples versus the P_Max and P_Avg value 

probability intervals based on Sig Only classification of toxic samples in the 

 marine amphipod database .......................................................................................127 

Figure 17. Mean predicted and observed proportion of toxic samples within probability 

quartiles for P_Max and P_Avg models derived from the marine 

 amphipod database ...................................................................................................128 

Figure 18. Observed proportion of toxic samples versus the probability of toxicity 

 predicted using the P_Max and P_Avg models derived from the marine 

 amphipod database ...................................................................................................129 

Figure 19. Mean percent control-adjusted survival for toxic samples within probability 

 quartile intervals for P_Max model derived from the marine amphipod 

 database ....................................................................................................................130 

Figure 20. Mean control-adjusted percent survival for samples within probability intervals 

 for P_Max model (R2

 = 0.94) derived from the marine amphipod database ...........131 

Figure 21. Comparison of the original marine amphipod P_Max model with the model 

 derived from data excluding polycyclic aromatic hydrocarbon chemistry 

 (R2

 = 0.88) ................................................................................................................132 

Figure 22. Comparison of the original marine amphipod P_Max model with model 

 derived from data excluding metals chemistry (R2

 = 0.93)......................................133 

Figure 23. Comparison of the original marine amphipod P_Max model with model 

 derived from data excluding pesticides and PCB chemistry (R2

 = 0.92).................134 
LIST OF FIGURES (continued) 

 xi 

Figure 24. Mean predicted and observed proportion of toxic samples for samples with a 

predicted probability of >0.75 from the P_Max model in the marine amphipod 

database ....................................................................................................................135 

Figure 25. Observed proportion of toxic samples versus P_Max values including only 

 those chemicals with normalized chi-square values exceeding 0.27 .......................136 

Figure 26. Median predicted probability of toxicity within probability intervals for the 

 marine amphipod P_Max model compared with data for A. abdita: proportion 

 of observed toxicity based on Sig Only classification (left), control-adjusted 

 survival (center), and proportion of observed toxicity based on the MSD 

 classification (right)..................................................................................................137 

Figure 27. Median predicted probability of toxicity within probability intervals for the 

 marine amphipod P_Max model compared with data for R. abronius: proportion 

 of observed toxicity based on Sig Only classification (left), control-adjusted 

 survival (center), and proportion of observed toxicity based on the MSD 

 classification (right)..................................................................................................138 

Figure 28. Median predicted probability of toxicity within probability intervals for the 

 marine amphipod P_Max model compared with data from Hudson-Raritan/Long 

Island NSTP and Regional EMAP (A. abdita): proportion of observed toxicity 

 based on Sig Only classification (left), control-adjusted survival (center), and 

 proportion of observed toxicity based on the MSD classification (right) ................139 

Figure 29. Median predicted probability of toxicity within probability intervals for the 

 marine amphipod P_Max model compared with data from Virginian Province 

EMAP (A. abdita): proportion of observed toxicity based on Sig Only 

 classification (left), control-adjusted survival (center), and proportion of 

 observed toxicity based on the MSD classification (right) ......................................140 

Figure 30. Median predicted probability of toxicity within probability intervals for the 

 marine amphipod P_Max model compared with data from NSTP and Carolinian 

 EMAP from the southeastern U.S. (A. abdita): proportion of observed toxicity 

 based on Sig Only classification (left), control-adjusted survival (center), and 

proportion of observed toxicity based on the MSD classification (right) ................141 

Figure 31. Median predicted probability of toxicity within probability intervals for the 

 marine amphipod P_Max model compared with data from Puget Sound, WA 

 (R. abronius): proportion of observed toxicity based on Sig Only classification 

 (left), control-adjusted survival (center), and proportion of observed toxicity 

 based on the MSD classification (right)...................................................................142 
LIST OF FIGURES (continued) 

 xii 

Figure 32. Median predicted probability of toxicity within probability intervals for the 

 marine amphipod P_Max model compared with data from California (R. 

 abronius and A. abdita): proportion of observed toxicity based on Sig Only 

classification (left), control-adjusted survival (center), and proportion of 

 observed toxicity based on the MSD classification (right) ......................................143 

Figure 33. Median predicted probability of toxicity within probability intervals for the 

 marine amphipod P_Max model compared with proportion of observed toxicity 

 based on the Sig Only classification from individual studies: Elliot Bay, Puget 

Sound, WA (R. abronius, n = 97) (left) and Biscayne Bay, FL (A. abdita, 

 n = 120) (right) .........................................................................................................144 

Figure 34. Median predicted probability of toxicity within probability intervals for the 

 marine amphipod P_Max model compared with proportion of observed toxicity 

 from an individual study from San Diego Bay, CA (R. abronius, n = 93); 

 toxicity based on Sig Only classification and MSD classification...........................145 

Figure 35. Median predicted probability of toxicity within probability intervals for 

 the marine amphipod P_Max model compared with data from the Calcasieu 

 Estuary (A. abdita): proportion of observed toxicity based on Sig Only 

 classification (left), control-adjusted survival (center), and proportion of 

 observed toxicity based on the MSD classification (right) ......................................146 

Figure 36. Average predicted probability and proportion toxic within probability intervals 

 for the marine amphipod P_Max model applied to the sea urchin development 

 and fertilization endpoints ........................................................................................147 

Figure 37. Median predicted probability and proportion toxic within probability intervals 

 for the marine amphipod P_Max model applied to sea urchin (A. punctulata) 

 development endpoint: proportion of observed toxicity based on Sig Only 

classification (left), control-adjusted response (center), and proportion of 

 observed toxicity based on the MSD classification (right) ......................................148 

Figure 38. Median predicted probability and proportion toxic within probability intervals 

 for the marine amphipod P_Max model applied to sea urchin (A. punctulata) 

 fertilization endpoint: proportion of observed toxicity based on Sig Only 

 classification (left), control-adjusted response (center), and proportion of 

 observed toxicity based on the MSD classification (right) ......................................149 
LIST OF FIGURES (continued) 

 xiii 

Figure 39. Median predicted probability and proportion toxic within probability intervals 

 for the marine amphipod P_Max model applied to sea urchin (S. purpuratus) 

 development endpoint: proportion of observed toxicity based on Sig Only 

 classification (left), control-adjusted response (center), and proportion of 

 observed toxicity based on the MSD classification (right) ......................................150 

Figure 40. Median predicted probability and proportion toxic within probability intervals 

 for the marine amphipod P_Max model applied to sea urchin (S. purpuratus) 

fertilization endpoint: proportion of observed toxicity based on Sig Only 

 classification (left), control-adjusted response (center), and proportion of 

 observed toxicity based on the MSD classification (right) ......................................151 

Figure 41. Proportion of samples that were toxic based on the H. azteca 10–14-day 

 survival endpoint versus median probability of toxicity predicted using the 

 marine amphipod P_Max model ..............................................................................152 

Figure 42. Proportion of samples that were toxic based on the C. tentans or C. riparius 

 10–14-day survival endpoint versus median probability of toxicity predicted 

 using the marine amphipod P_Max model...............................................................153 

Figure 43. Proportion of samples that were toxic based on the H. azteca 28-day growth 

 and survival endpoint versus the median probability of toxicity predicted using 

 the marine amphipod P_Max model ........................................................................154 
 xiv 

LIST OF ABBREVIATIONS AND ACRONYMS 

AET Apparent effect threshold 

ASTM American Society for Testing Materials 

BEDS Biological Effects Database for Sediments (MacDonald Environmental Sciences) 

DDD 5-dichlorodiphenyldichloroethane 

DDE Dichlorodiphenyldichloroethylene 

DDT Dichlorodiphenyltrichloroethane 

EMAP Environmental Monitoring and Assessment Program (U.S. EPA) 

ERL Effect range low 

ERM Effect range median 

LC50 Median lethal concentration 

LRM Logistic regression model 

MLML Moss Landing Marine Laboratory (California) 

MSD Minimum significant difference (classification of samples as toxic) 

NSTP National Status and Trends Program (of the National Oceanic and Atmospheric 

Administration) 

P_Max The maximum probability of observing toxicity, taken from the set of 

probabilities calculated for each individual chemical in a sample 

P_Max model A regression model that predicts the probability that a sample will be toxic to 

amphipods on the basis of the maximum probability of observing toxicity 

calculated for all individual chemicals in a sample 

P_Avg The mean probability of observing toxicity based on the set of probabilities 

calculated for each individual chemical in a sample 

P_Avg model A regression model that predicts the probability that a sample will be toxic to 

amphipods based on the probability of observing toxicity averaged over all of 

the individual chemicals in a sample 

P_Prod The product of the probabilities of surviving exposure to all individual 

chemicals in the sample 

PAH(s) Polycyclic aromatic hydrocarbon(s) 

PCB(s) Polychlorinated biphenyl(s) 

PEL Probable effect level 

SEDQUAL Sediment Quality Information System (State of Washington Department of 

Ecology’s Puget Sound database) 

Sig Only Significance only (classification of samples as toxic) 

SQGs Sediment quality guidelines 

TEL Threshold effect level 

TOC Total organic carbon 

Tp The concentration that corresponds to a toxic response of “p” percent according 

to the single chemical logistic models; for example, the T50 is the concentration 

of a chemical that corresponds to the probability that 50% of the samples would 

be toxic 
 xv 

PREFACE 

 The U.S. EPA’s National Center for Environmental Assessment developed this report 

jointly with the National Oceanic and Atmospheric Administration, Coastal Protection and 

Restoration Division, with substantial contributions from the U.S. Geological Survey. The report 

is intended for risk assessors, field biologists, and research scientists interested in the 

development and application of methods for evaluating the ecological risks associated with 

chemicals in sediments. 

 Effective interpretation of sediment chemistry data requires tools that link chemical 

concentrations to the potential for observing adverse biological effects. This report describes the 

development of logistic regression models that quantify relationships between the concentrations 

of sediment-associated contaminants and toxicity in two species of marine amphipods. The 

models were developed using a large database of matching whole-sediment chemistry and 

toxicity data that contains data published up until 2000. Amphipod toxicity tests were used as a 

surrogate for valued ecological attributes that are more difficult to test and measure, including 

structure and function of benthic communities, population viability of wildlife that depend on 

benthos, and ecosystem processes such as organic matter decomposition and water filtration. 

Because amphipod sediment toxicity tests are conducted using documented, standardized 

methods, they are particularly amenable to analyses that combine results across studies, such as 

those conducted in this project. 

 The logistic regression model (LRM) approach described in this report is similar to other 

empirical approaches for deriving sediment quality guidelines in its reliance on matching fieldcollected sediment chemistry and biological effects data. In contrast to other approaches to 

developing sediment quality guidelines, however, the LRM approach does not identify threshold 

values. Instead, it develops models that enable users to select the probability of observing 

toxicity that corresponds to the users’ specific objectives or to estimate the probability of 

observing effects at a particular chemical concentration. The models provide a nationwide 

framework that can be used to evaluate site-specific data, guide data collection efforts, and 

compare ecological risks across sites and regions. 
 xvi 

AUTHORS, CONTRIBUTORS, AND REVIEWERS 

 The National Center for Environmental Assessment (NCEA) of the U.S. Environmental 

Protection Agency’s (EPA’s) Office of Research and Development was responsible for the 

publication of this document. It was prepared by NCEA and the National Oceanic and 

Atmospheric Administration, Coastal Protection and Restoration Division, under Interagency 

Agreements DW13937819-01-2 and DW1393980401-0. 

 

Authors 

L. Jay Field 

National Oceanic and Atmospheric Administration 

Office of Response and Restoration 

Coastal Protection and Restoration Division 

7600 Sand Point Way, NE 

Seattle, WA 98115 

Susan B. Norton 

U.S. Environmental Protection Agency 

National Center for Environmental Assessment 

1200 Pennsylvania Ave. NW (8623-N) 

Washington, DC 20460 

Donald D. MacDonald 

MacDonald Environmental Sciences Ltd. 

4800 Island Highway, N. 

Nanaimo, British Columbia V9T 1W6 

Corinne G. Severn 

Premier Environmental Services 

10999 Pumpkin Ridge Avenue 

Las Vegas, Nevada 89135 

Christopher G. Ingersoll 

U.S. Geological Survey 

Columbia Environmental Research Center 

4200 New Haven Road 

Columbia, MO 65201 
 xvii 

Contributors 

Dawn Smorong, MacDonald Environmental Sciences Ltd., Nanaimo, British Columbia 

Rebekka Lindskoog, MacDonald Environmental Sciences Ltd., Nanaimo, British Columbia 

Lorraine Read, TerraStat Consulting Group, Seattle, WA 

Carolyn Hong, EVS Consultants, Seattle, WA 

Reviewers 

David Mount 

U.S. Environmental Protection Agency 

National Health and Ecological Effects Research Laboratory 

6201 Congdon Boulevard 

Duluth, MN 55804 

Keith Sappington 

U.S. Environmental Protection Agency 

National Center for Environmental Assessment (8623-N) 

1200 Pennsylvania Ave. NW 

Washington, DC 20460 

Scott Ireland 

U.S. Environmental Protection Agency 

USEPA REGION 5 

77 West Jackson Boulevard (G-17J) 

Chicago, IL 60604-3507 

Donald M. MacDonald 

National Oceanic and Atmospheric Administration 

Office of Response and Restoration 

Coastal Protection and Restoration Division 

7600 Sand Point Way, NE 

Seattle, WA 98115 

John H. Gentile 

Harwell, Gentile & Associates, LC 

98 Moss Lane 

Brewster, MA 02631 

Susan Kane Driscoll 

Menzie-Cura and Associates 

Winchester, MA 01890 

James Shine 

Harvard School of Public Health 

Department of Environmental Health 

Boston, MA 02215 
 xviii 

Acknowledgments 

The authors gratefully acknowledge the intellectual contributions and support from Ed Long, 

Rusty Fairey, Dave Mount, Scott Ireland, Jim Keating, Jon Harcum, Rick Swartz, Scott Carr, 

Pam Haverland, Judy Crane, Sherri Smith, Linda Porebski, Gail Sloane, Steve Bay, Tom Gries, 

Ron Gouguet, Peter Landrum, Eric Smith, Paul Pinsky, and Pat Cirone. 

This project would not be possible without contributions of data from many individuals and 

programs. Data providers: NSTP from Ed Long, California database from Rusty Fairey, Tom 

Gries (special thanks to Tom for providing the replicate data for the Puget Sound studies), and 

Phil Crocker. EMAP data were provided by D. Keith and M. Hughes (Virginian Province), J. 

Macauley and L. Harwell (Lousianian Province), J. Hyland and T. Snoots (Carolinian Province), 

and D. Adams (NY/NJ REMAP). 

Supplementary funding for data compilation was provided by EPA’s Office of Water and the 

National Research Council of Canada. 
 1 

1. EXECUTIVE SUMMARY 

1.1. SUMMARY 

 The contribution of contaminated sediments to effects on sediment-dwelling organisms 

(including plants and invertebrates), aquatic-dependent wildlife (amphibians, reptiles, fish, birds, 

and mammals), and human health has become more apparent in recent years. Sediments can 

serve both as reservoirs and as potential sources of contaminants to the water column and can 

adversely affect sediment-dwelling organisms by causing direct toxicity or altering benthic 

invertebrate community structure. Although the results of sediment toxicity tests and benthic 

invertebrate community assessments can be used directly to evaluate or infer effects on resident 

sediment-dwelling organisms, effective interpretation of sediment chemistry data requires tools 

that link chemical concentrations to the potential for observing adverse biological effects. 

This report describes the development of logistic regression models that quantify 

relationships between the concentrations of sediment-associated contaminants and toxicity to two 

commonly tested species of marine amphipods, Rhepoxynius abronius and Ampelisca abdita. 

Amphipod toxicity tests are used as a surrogate for valued ecological attributes that are 

more difficult to test and measure, including the structure and function of benthic communities, 

population viability of wildlife that depends on benthos, and ecosystem functions such as organic 

matter decomposition and water filtration. Because amphipod sediment toxicity tests are 

conducted using documented, standardized methods (ASTM, 2002a, b), they are particularly 

amenable to analyses that combine results across studies, such as those conducted in this project. 

This report describes logistic regression models for 37 individual chemicals. The results 

of these individual models are then combined into a single explanatory variable for estimating 

the proportion of toxic samples expected in field-collected sediment samples. In addition, the 

report illustrates the applications of the individual logistic models for evaluating sediment quality 

guidelines and the use of the multiple-chemical models to predict toxicity for other locations and 

endpoints. The report is intended for risk assessors, field biologists, and research scientists 

interested in the development and application of methods for evaluating the ecological risks 

associated with chemicals in sediments. 
 2 

1.1.1. Individual Chemical Models 

Logistic regression models for 37 chemicals were developed using a large database of 

matching whole-sediment chemistry and toxicity data that encompass many different 

contaminant gradients from a wide variety of habitats in coastal North America. Logistic 

regression uses a categorical (e.g., yes/no) variable as the dependent variable. Each sample was 

designated as toxic or not toxic on the basis of a statistical comparison of the number of 

amphipods that survived in the test sample relative to the negative control sample. The toxicity 

classification was the dependent variable for the models, and the chemical concentration in the 

field-collected sample was the explanatory variable. The models combined results from tests 

that used either marine amphipod, R. abronius or A. abdita. 

The chemical-specific models provide a basis for estimating the proportion of samples 

expected to be toxic at different chemical concentrations. In contrast to other approaches to 

evaluating the potential for observing toxicity on the basis of sediment chemistry (e.g., Long et 

al., 1995; MacDonald et al., 1996, 2000), the logistic regression modeling approach does not rely 

on specific effects thresholds. Instead, users can use the models to select sediment 

concentrations (Tp values) that most directly meet the needs of their specific application. For 

example, the models can be used to estimate concentrations for individual contaminants that are 

likely to be associated with a relatively low incidence of sediment toxicity (e.g., 10, 15, or 20%). 

Such point estimates of minimal-effect concentrations might be used in a screening assessment 

to identify sediments that are relatively uncontaminated and have a low probability of sediment 

toxicity. Similarly, contaminant concentrations for which there is a high probability of observing 

adverse effects could be estimated. These higher point estimates could be used to identify 

sediments that are highly likely to be toxic to amphipods and have a greater magnitude of effect 

(i.e., higher percent mortality). 

 The Tp values can be used in much the same way as other sediment guidelines, except 

that the Tp value provides a specific probability of observing toxicity and is associated with an 

estimate of variance based on the fit of the model. The logistic regression models do not 

represent dose-response relationships for individual chemicals; rather, they should be considered 

to be indicators of toxicity based on field-collected sediment chemical mixtures. 

 The logistic regression approach was used to evaluate several issues that form the basis 

for our recommendations for using the models. We used the single-chemical models to evaluate 

two approaches for designating samples as toxic: (1) less than 90% survival that was 
 3 

significantly different from negative control samples (Sig Only), and (2) control-normalized 

survival less than 80% that was significantly different from negative control samples (minimum 

significant difference [MSD]) (based on analyses by Thursby et al., 1997). The Sig Only 

approach had a greater tendency to underestimate the toxicity observed at low concentrations; 

however, this discrepancy may be explained by the presence of other chemicals in the sample. 

The MSD approach had a greater tendency to overestimate the toxicity observed at higher 

concentrations. We selected the Sig Only approach for further exploration and development. 

 We also evaluated two approaches for normalizing sediment chemistry: dry weight and 

organic carbon. We selected the dry weight normalization approach because the models had 

higher goodness-of-fit statistics than the organic carbon-normalized sediment chemistry models, 

and they had smaller differences between observed and predicted toxicity. 

 The presence of multiple contaminants, many of which may be present at very low 

concentrations, complicates the evaluation of relationships between individual contaminants and 

toxicity in field-collected samples. A data screening procedure was used to exclude samples for 

which the selected chemical would not serve as a good indicator of observed toxicity. We used 

the single-chemical models to evaluate three alternative screening criteria: (1) include all 

samples in the model data set for an individual chemical (unscreened), (2) exclude toxic samples 

that were less than or equal to the mean of nontoxic samples from the same study (1X screening), 

and (3) exclude toxic samples that were less than or equal to two times the mean of nontoxic 

samples from the same study (2X screening). 

 We selected the 1X screening approach for further exploration and development. The 

models from the unscreened alternative had much lower goodness-of-fit statistics and appeared 

to show a weaker relationship between chemistry and toxicity than was observed with the other 

screening alternatives. The 2X screening approach yielded models with slightly higher 

goodness-of-fit statistics, but the 1X screening approach performed slightly better at 

concentrations above the T80 value. The 1X approach screened out fewer samples in the model 

derivation, which may prove important in the future development of models for less frequently 

measured chemicals. 

1.1.2. Multiple-Chemical Models 

 Because the individual chemical models were derived from field-collected sediments that 

included mixtures of contaminants, to some extent each individual chemical model represents the 
 4 

overall toxicity of the mixtures. However, an individual model would be expected to 

underestimate the probability of observing toxicity in sediments contaminated with multiple 

chemicals. The results of the individual models were combined to better estimate the probability 

that a sediment sample would be toxic, based on the mixture of chemicals present in the sample. 

Two approaches for combining the individual chemical model results into a single explanatory 

variable representing the chemical mixture—the P_Max model and the P_Avg model—

accurately predicted the frequency of toxicity to amphipods observed in the database: 

• P_Max is the maximum probability of observing toxicity, taken from the set of 

probabilities calculated for each individual chemical in the sample, and 

• P_Avg is the mean probability of observing toxicity, based on the set of probabilities 

calculated for each individual chemical in the sample. 

 The multiple-chemical models were used to evaluate several additional issues, including 

the relationship between the probability of observing a toxic effect and the magnitude of toxicity, 

the identification of chemicals most influential in model performance, the performance of the 

models in predicting toxicity of the two amphipod species, and the performance of the models in 

predicting toxicity observed in regional data sets or in individual studies. 

 The magnitude of the effect (decreased survival) in the amphipod test increased as the 

probability of toxicity increased, demonstrating that samples that are estimated to have the 

highest probability of toxicity are also likely to be associated with high mortality. 

 For approximately 70% of the samples, individual chemical regression models for metals 

produced the maximum probability used in the P_Max model. This should not be construed to 

imply that metals were causing toxicity in these samples, only that metals appear to be a good 

indicator of toxicity in field-collected samples. Indeed, removing metals (or other entire 

chemical classes) from the suite of individual chemical models used to generate the P_Max 

model resulted in only minor changes in the model and model fit. 

 Models were developed by combining data from tests that used R. abronius or A. abdita

in order to encompass more areas of the country and a broader range of sediment chemistry. The 

P_Max model was used to examine differences in model performance for the two species. The 

observed toxicity was frequently less than predicted for A. abdita and greater than predicted for 

R. abronius. Nevertheless, the observed proportion of toxicity in the data for both species was 
 5 

strongly related to the nationwide model, affording confidence that the combined-species P_Max 

model provides a common framework that is applicable to both species. 

 We examined the performance of models in predicting observed toxicity for individual 

studies within the database. On a study-by-study basis, there was mixed agreement between the 

frequency of observed toxicity with that predicted by the P_Max model. The mixed performance 

suggests that the nationwide models should not be applied to individual studies without first 

evaluating their performance with matching site-specific toxicity and chemistry data. However, 

the nationwide P_Max model provided a useful, common basis for evaluating toxicity test results 

for individual sites included in the database. Application of the model to regional subsets of the 

database used to derive the model demonstrated significant relationships between the P_Max 

model predictions and both observed proportion toxicity and percent control-adjusted survival. 

There was also a strong relationship between predicted toxicity and observed toxicity in the 

Calcasieu Estuary, an independent data set not included in the original derivation of the models. 

1.2. RECOMMENDATIONS 

 As a starting point for most evaluations, we recommend using the P_Max model, which 

uses the highest predicted probability from any of the individual chemical models as the 

explanatory variable. We recommend using the P_Max model based on data from all studies 

(i.e., the nationwide model), on both marine amphipod species, and on 37 chemical-specific 

models. For the chemical-specific models, we recommend the models that classified samples as 

toxic on the basis of less than 90% survival that was significantly different from negative control 

samples (Sig Only), and that screened the data set by excluding toxic samples that were less than 

or equal to the mean of nontoxic samples from the same study. The bases for these 

recommendations are summarized briefly below. 

 We recommend using the P_Max model, which is based on the highest predicted 

probability from any of the individual chemical models, because it explained slightly more 

variation in the data set than did the P_Avg model. However, the two models provide slightly 

different insights into sediment toxicity. P_Avg may better reflect the overall degree of 

contamination and is less susceptible to overestimating the probability of toxicity at sites with 

high concentrations of one chemical. In some cases, it may be valuable to use both models to 

take advantage of the different perspectives that they provide. 
 6 

 We recommend using the nationwide model that combines data for both species of 

marine amphipods. Combining data across studies and species represents the fullest range of 

chemical concentrations and environmental conditions. In addition, the nationwide combined 

model provides a common basis for comparing site- and species-specific results. 

 We recommend basing the models on the Sig Only classification so that more subtle 

changes can be retained, particularly at lower concentrations. It may be valuable to compare the 

Sig Only nationwide model with site-specific data classified using the MSD approach when test 

variability obscures the relationship between chemistry and response at lower concentrations. 

 Finally, the chemical-specific models were greatly improved by using a screened data set 

that excluded toxic samples that were less than or equal to the mean of nontoxic samples from 

the same study. Using a more stringent criterion, such as excluding toxic samples that were less 

than or equal to two times the mean of nontoxic samples from the same study, resulted in 

improved goodness of fit in the models for most chemicals. However, the more stringent 

criterion excluded an average of 70% of the toxic samples, which may limit future development 

of models for other endpoints, regions, or chemicals that have fewer total samples. We 

concluded that the small improvements in model fit did not outweigh the associated reduction in 

sample size. 

1.3. APPLICATIONS 

 The chemical-specific models provide a basis for estimating the probability that a sample 

will be toxic for 37 individual contaminants over a wide range of contaminant concentrations. In 

addition, they are useful for evaluating the degree of risk associated with commonly used 

sediment quality guidelines (SQGs). The probabilities of toxicity associated with SQG threshold 

values are generally consistent with their narrative intent. However, logistic regression models 

have several advantages over current guideline approaches: (a) they present risk on a continuous 

quantitative scale rather than by defining discrete categories based on threshold values, (b) the 

continuous estimates of risk allow users to match the degree of risk with their objectives, and (c) 

they express risk on a common scale of 0 to 1 across all chemicals. The individual chemicals 

models would be expected to underestimate the probability of observing toxicity in samples that 

are contaminated with many chemicals. For this purpose, we recommend using the multiplechemical models that combine the individual model results into a single explanatory value for 

estimating the probability that a sample will be toxic. 
 7 

 The multiple-chemical models provide a useful basis for conducting screening-level 

assessments that require classifying or prioritizing samples on the basis of sediment chemistry. 

Because the models do not consider potential differences in bioavailability or exposure, the 

probability of toxicity may be over- or underestimated for some locations. Before applying the 

models to a particular site, we recommend first evaluating how well the models fit the local 

situation by collecting a test set of matching sediment chemistry and toxicity test data. The 

logistic regression models can be used to design effective test sampling programs, and they can 

also suggest issues that require further investigation (e.g., bioavailability). They can be very 

useful for classifying samples into broad categories of concern on the basis of sediment 

chemistry. The models should not be considered a complete substitute for direct effects 

assessment (e.g., toxicity tests). 

 We evaluated the relationship between model predictions and the results of other toxicity 

endpoints, including those commonly used in freshwater systems. The P_Max model predictions 

appear to be useful for predicting sea urchin response for Arbacia punctulata, based on 

development or fertilization tests, but not for Strongylocentrotus purpuratus. The models may 

also be useful for predicting the response of freshwater amphipods, particularly the 28-day 

Hyallela azteca growth and survival endpoint. There is the potential for developing endpointspecific models as more data are acquired. 
 8 

2. INTRODUCTION 

 The contribution of contaminated sediments to effects on sediment-dwelling organisms 

(including plants and invertebrates), aquatic-dependent wildlife (amphibians, reptiles, fish, birds, 

and mammals), and human health has become more apparent in recent years (Long and Morgan, 

1991; U.S. EPA, 1997). Many toxic contaminants, such as metals, polycyclic aromatic 

hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), chlorophenols, and pesticides, are 

found in only trace amounts in water, but they can accumulate to elevated levels in sediments 

(Ingersoll et al., 1997). Therefore, sediments can serve both as reservoirs and as potential 

sources of contaminants to the water column. 

 Contaminants associated with sediments can adversely affect resident sediment-dwelling 

organisms by causing direct toxicity or by altering benthic invertebrate community structure 

(Chapman, 1989). Furthermore, contaminated sediments can adversely affect fish and wildlife 

species, either through direct exposure or through bioaccumulation in the food web. 

 A variety of approaches are used to evaluate the hazard posed by contaminated sediments 

to ecological receptors (Ingersoll et al., 1997). These approaches include sediment chemistry 

measurements, ex situ toxicity tests, benthic invertebrate community surveys, sediment toxicity 

identification and evaluation procedures, and bioaccumulation assessments. 

 The results of sediment toxicity tests and benthic invertebrate community assessments 

can be used directly to evaluate or infer effects on resident sediment-dwelling organisms. 

However, effective interpretation of sediment chemistry data requires tools that link chemical 

concentrations to the potential for observing adverse biological effects. Sediment chemistry 

values that have been linked to biological effects can provide an efficient means for evaluating 

the risks of sediment contamination when biological tests or surveys are unavailable. 

 The equilibrium partitioning approach links sediment chemistry values with biological 

effects by combining the results of controlled laboratory tests using manipulated concentrations 

of chemicals with theory on the factors controlling bioavailability (Di Toro et al., 1991, 2000). 

Numerical sediment quality guidelines (SQGs) empirically link biological effects with sediment 

chemistry by combining the results of toxicity tests using field-collected samples with the 

concentrations of chemicals in those same samples (Long et al., 1995; MacDonald et al., 1996, 

2000). 
 9 

 The logistic regression model (LRM) approach described in this report is similar to other 

empirical approaches for deriving SQGs because it relies on matching field-collected sediment 

chemistry and biological effects data (e.g., sediment toxicity or benthic invertebrate community 

structure effects). In contrast to other approaches to developing SQGs, however, the LRM 

approach does not develop threshold values. Instead, it develops models that capture the 

relationship between sediment chemistry and the probability of observing a toxic response. By 

representing the relationship in continuous form, users can select the probability of observing 

sediment toxicity that corresponds to their specific objectives. The relationships can also be used 

to estimate the probability of observing effects, given the mixture of chemicals at a particular 

location (Field et al., 1999, 2002). 

 The primary objectives of this report are to describe the development of individual 

chemical LRMs, based on the standard marine and estuarine amphipod 10-day lethality toxicity 

test endpoint (Chapter 4), and to combine these individual models into a single model for 

predicting toxicity in field-collected sediment samples (Chapter 5). In addition, the report 

illustrates the applications of the individual logistic models for evaluating SQGs and the use of 

the combined models to predict toxicity for other sites and endpoints (Chapter 6). 

 The development of LRMs requires a large database of matching sediment chemistry and 

toxicity data that includes a broad range of concentrations. Chapter 3 describes the development 

of the SEDTOX02 database, which contains more than 3200 samples with matching sediment 

chemistry and toxicity test results. 

 This report contains detailed descriptions of the database and model development. It is 

intended for risk assessors, field biologists, and research scientists interested in a deeper 

understanding of the different data analysis and treatment options considered during model 

development and the strengths, limitations, and recommended application of the final models. 

Our major findings have been published in two journal articles (Field et al., 1999, 2002). We 

also refer readers to a companion effort (Smith et al., 2003) that fit multiple regression models 

using many chemical concentrations measured in the sample as explanatory variables 

simultaneously. These models require chemistry results for all of the chemicals used in each 

model. The LRMs presented here were developed for application to a greater variety of 

sediment chemistry results. This flexibility was used in the U.S. Environmental Protection 

Agency’s (EPA’s) National Sediment Quality Survey (U.S. EPA, 2004), which used the 
 10 

approach described in this report to classify sediments into three tiers of probability of adverse 

effects on the basis of a wide variety of sediment chemistry results. 
 11 

3. DATABASE DEVELOPMENT 

3.1. INTRODUCTION 

 This investigation compiled synoptically collected sediment chemistry and sediment 

toxicity data from throughout North America into the SEDTOX02 database (NOAA, 2004), 

http://www.response.restoration.noaa.gov/cpr/sediment/sed_tox.html. The database is divided 

into separate marine and freshwater databases of identical structure. The primary sources of the 

estuarine and marine data included the National Oceanic and Atmospheric Administration’s 

National Status and Trends Program (NSTP), EPA’s Environmental Monitoring and Assessment 

Program (EMAP), Moss Landing Marine Laboratory (MLML) (which compiled data for the 

state of California), the State of Washington Department of Ecology’s Puget Sound Database 

(SEDQUAL), and MacDonald Environmental Sciences’ Biological Effects Database for 

Sediments (BEDS). Appendices A1 and A2 contain the references for the marine and the 

freshwater SEDTOX02 databases, respectively. 

 Many geographic areas along the Atlantic, Gulf, and Pacific coasts are represented in the 

database, and it includes information on several marine and freshwater toxicity endpoints. 

However, this report focuses on analyses using data from the EPA and American Society for 

Testing Materials standard 10-day amphipod survival toxicity tests with Ampelisca abdita and 

Rhepoxynius abronius (U.S. EPA, 1994a, b; ASTM, 2002a). The database for this project was 

developed in Microsoft FoxPro; the relational database structure is provided in Appendix B. 

3.2. COMPILATION OF MATCHING SEDIMENT CHEMISTRY AND TOXICITY 

DATA 

 All of the candidate data sets considered for inclusion in the database were critically 

evaluated. Application of acceptance criteria (see appendices C and D) to individual studies 

provided a basis for determining whether experimental designs and measurement endpoints, 

sample collection and handling procedures, toxicity testing protocols and environmental 

conditions, control responses, and analytical methods were consistent with established 

procedures (Long et al., 1995; MacDonald et al., 1996; Field et al., 1999; ASTM, 2002a, b). In 

the case of the data sets from NSTP, EMAP, SEDQUAL, and MLML sources, the standard 

protocols established under each program were evaluated, and individual studies were generally 

examined to identify possible deviations from these protocols. All of the data that met the 
 12 

acceptance criteria were incorporated into the project database. Samples were excluded from the 

database if the survival in the associated negative control sample was less than 85% (expressed 

as a mean of the negative control replicates). 

 Data sets that met the screening criteria were compiled in spreadsheets. To facilitate data 

entry, a template was designed to standardize the format of the matching sediment chemistry and 

toxicity data. Each data file included the following fields: location of the investigation (country, 

area, and site); date of sediment collection; sampling and sample handling protocols or 

procedures; species and life stage tested; test type (e.g., static porewater); type of test water (e.g., 

saltwater, freshwater); source of control and reference sediments; endpoint measured; method 

used to determine whether the sample was toxic or nontoxic; study citation; and additional 

explanatory comments. 

 The results of the toxicity tests conducted and the concentrations of all chemical analytes 

measured were compiled in the spreadsheet that was created for each study. These latter data 

were compiled on a sample-by-sample basis, including the control, reference, and test samples. 

All chemical concentrations were entered as normalized to dry weight, with concentrations 

below analytical detection limits reported at the detection limit value and a below-detection-limit 

data qualifier added. Toxic or nontoxic descriptors were also assigned to each endpoint for each 

sediment sample in the database as the first step in toxicity classification. The objective was to 

standardize the toxicity classification within the database wherever possible. For the marine 

amphipods, statistical significance derived from a comparison of test samples to the negative 

control was preferred over other methods for determining significance (e.g., comparison to field 

reference). 

 Data originating from NSTP, EMAP, and MLML were received with statistical 

significance already determined. For Puget Sound data obtained from SEDQUAL, statistical 

comparisons (one tailed t-test, α = 0.05) between the appropriate negative control and the test 

samples were conducted on the replicate data. Other studies (received from BEDS) were 

evaluated on an individual study basis. For these studies, a sediment sample was considered 

toxic if the original investigator conducted suitable statistical analyses and reported that the 

sample was significantly toxic when compared with the negative control or appropriate reference 

site. If statistical significance was not determined by the investigator but sufficient information 

on the replicate sample results or on the standard deviations of the results was provided, then a 
 13 

modified Student’s T-test was conducted to determine the statistical significance of the results 

for each sample. 

 If no statistical analysis could be performed, then a sediment sample was considered to be 

toxic if the measured response was substantially different from the negative control or 

appropriate reference response. A 20% or greater difference was considered to be substantial in 

this context, generally reflecting the results of power analyses conducted on the results of 

numerous toxicity tests (Thursby et al., 1997; Long et al., 1998; Carr and Biedenbach, 1999). 

3.3. DATA AUDITING 

 To ensure the overall integrity of the database, a data verification and auditing plan was 

developed and implemented. This plan consisted of three main elements. 

 First, the candidate data set was reviewed to identify potentially erroneous data. 

Specifically, individual data sets were reviewed to identify improbable or impossible results 

(e.g., extremely high or low chemical concentrations, dissolved oxygen concentrations exceeding 

saturation levels, survival of >100%). If anomalous data were identified in this initial review (an 

infrequent event), the principal investigator on the study was contacted to either verify the 

reported results or provide the correct data. The primary data source was subsequently corrected 

to reflect the input provided by the principal investigator. 

 The second phase of the data auditing process was data verification. For data that were 

acquired electronically (the majority of the data), a minimum of 10% were compared with the 

electronic source files. The few candidate data sets obtained in hard copy format (i.e., not in 

electronic data files), had substantial potential for data transcription errors. For this reason, all of 

the hand-entered data that were compiled in spreadsheets were fully verified against the original 

data source prior to importing the data into the database. Two individuals working cooperatively 

conducted data verification. Any errors or omissions identified were corrected, and the data 

corrections were subsequently verified in a similar manner. 

 The third phase of the data auditing process was designed to determine whether any data 

had been corrupted during the data translation process (i.e., transferring the data from the Excel 

spreadsheets into the electronic database). To confirm that the data translation subroutines were 

functioning appropriately, the data for several studies were exported into a spreadsheet format 

that resembled the spreadsheets that had been constructed initially. The information contained in 

these recompiled spreadsheets was then verified against the original data source. After the entire 
 14 

database had been compiled in the relational database format, data screening procedures (e.g., 

identification of orphan records, general confirmation of the relational database structure, and 

extreme value checks) were applied to identify potential errors and further ensure the internal 

consistency of the data in the database. 

3.4. DATA TREATMENT TO SUPPORT MODEL DEVELOPMENT 

3.4.1. Calculation of Total PCBs 

 The total concentration of PCBs was calculated for each sediment sample represented in 

the database. The procedure used to calculate total PCBs depended on how the data were 

reported in the original study. If only total PCBs were reported, these values were used directly. 

If the concentrations of PCBs were reported as individual Aroclors (e.g., Aroclor 1242, Aroclor 

1248), then the concentrations of the individual Aroclors were summed to determine the 

concentration of total PCBs. When the concentrations of individual congeners were reported, 

these values were summed to determine the total PCB concentration. If fewer than 20 congeners 

were reported, the sum of the congeners was multiplied by 2, following the approach used by 

NSTP (NOAA, 1989). If both Aroclors and congeners were measured, total PCBs were based on 

the congener concentrations. 

 In calculating the total PCB concentration, below-detection-limit values were treated as 

zero values. If all of the individual chemicals to be summed were below detection or if the 

detection limit of any one nondetected chemical exceeded the sum of detected values, the highest 

detection limit of the chemical constituents for the sample was used as the total value and 

qualified as a below-detection-limit value. 

3.4.2. Classification of Toxic Samples 

 Standardizing the classification of toxic samples in the database was an important step, 

because the studies included in the database used several methods to designate individual 

sediment samples as toxic or nontoxic. We used two approaches for identifying a consistent 

response level across studies and applied them to all studies that used R. abronius and A. abdita. 

The first approach, referred to as the “significance only” (Sig Only) approach, classified samples 

as toxic if the sample was statistically different (p≤0.05) when compared with the negative 

control and absolute survival was less than 90%. The criterion of 90% was used to preclude 
 15 

classifying samples as toxic because of low variability in the negative control and was based on 

the minimum acceptable mean survival for negative control response in 10-day marine amphipod 

toxicity tests (ASTM, 2002a). The application of this criterion changed the classification of 119 

samples from toxic to nontoxic. The second approach, referred to as the minimum significant 

difference (MSD) approach, classified samples as toxic if the sample was significantly different 

(p≤0.05) when compared with the negative control and the difference in survival between the test 

sample and control was at least 20%, that is, the test sample had a control-adjusted survival of 

less than 80%. The difference of 20% corresponded to a power (1 − β) of 0.9, based on analysis 

of A. abdita data; in this study 90% of the tests could distinguish a difference of 20% at a 

statistical significance level (α) of 0.05 (Thursby et al., 1997). 

3.4.3. Data Screening for Model Development 

 The presence of multiple contaminants, many of which may be present at very low 

concentrations, complicates the evaluation of relationships between individual contaminants and 

toxicity in field-collected sediments. Consequently, the data for samples that were identified as 

toxic in this investigation were further screened before they were used to develop the logistic 

models for each individual contaminant (Field et al., 1999). The objective of the screening 

process was to exclude toxic samples for which the chemical under consideration would not 

serve as a good indicator of observed toxicity. 

 Although there are many possible approaches to screening, for simplicity, we evaluated 

two approaches (1X and 2X) that followed the general screening approach used by Ingersoll et 

al. (1996) and were similar to approaches used by others (Long and Morgan, 1991; Long and 

MacDonald, 1992; MacDonald et al., 1996). In both approaches, the concentration of the 

selected chemical in each toxic sample was compared with the mean of the concentration of that 

substance in the nontoxic samples collected in the same study and geographic area. 

 For the 1X screening approach, if the concentration of a chemical in an individual toxic 

sample was less than or equal to the mean concentration of that chemical in the nontoxic samples 

from that study area, it was considered unlikely that the observed toxicity could be attributed to 

that chemical. Therefore, these toxic samples were not included in the “screened” data set used 

for developing the logistic model for that chemical. For the 2X screening approach, the 

“screened” data set used to develop the logistic model for a particular chemical excluded toxic 
 16 

samples less than or equal to two times the mean concentration of that chemical in the nontoxic 

samples from that study area. For the development of the organic-carbon normalized models, we 

applied the same procedures to the organic-carbon normalized chemical concentrations. Because 

all of the screening approaches are based on the chemical concentrations in the nontoxic 

samples, an important underlying assumption is that the factors influencing bioavailability are 

similar for both nontoxic and toxic samples. 

 All nontoxic samples were included in the screened data sets produced using both 

approaches. Samples from reference stations were treated the same as other samples and 

included in the analysis. The data for chemical concentrations that were less than the reported 

detection limit were not used to develop the logistic models. 

3.5. DATABASE CONTENTS 

 The final SEDTOX02 database includes matching sediment chemistry and toxicity data 

for both marine and freshwater systems. For convenience, in the data verification and analysis 

steps, the database was separated into marine and freshwater databases with identical structures. 

 The marine database includes matching sediment chemistry and toxicity data from the 

Atlantic, Gulf, and Pacific coasts of North America. Data from 10-day toxicity tests with two 

species of amphipods (Rhepoxynius abronius and Ampelisca abdita), for which survival is the 

measured endpoint, represent the largest component of the database (Table 1). The use of 

species differed by location; in general, R. abronius dominated the West Coast studies, whereas 

the studies conducted in the East used A. abdita. Most of the data originated from large 

programs (EMAP, NSTP, SEDQUAL, MLML), which used standardized methods of chemical 

analyses and toxicity tests. Overall, 1257 (39%) of the 3223 sediment samples in the database 

that had matching chemistry and toxicity were toxic to amphipods (i.e., survival was <90% and 

significantly different from that of the negative control). For A. abdita, 24% of the 2012 samples 

were toxic in 10-day tests (Table 1). A higher proportion of the samples tested with R. abronius

(i.e., 64% of 1211 samples) were identified as toxic (Table 1). Using the MSD approach to 

classifying samples as toxic (i.e., control-adjusted survival was <80% and significantly different 

from that of the negative control), 12.6% of the A. abdita samples and 40.8% of the R. abronius

samples were classified as toxic. 

 The database includes information on the concentrations of more than 300 chemicals of 

potential concern at contaminated sediment sites. More than 90% of the samples were analyzed 
 17 

for at least 10 of the 37 chemicals for which we developed models (Chapter 4). More than 70% 

of the samples were analyzed for at least 20 of the modeled chemicals. For many of these 

chemicals, the assembled data span a broad range of chemical concentrations. Table 2 presents 

the distributions of the chemistry data (10th, 50th, and 90th percentiles) for samples with matching 

amphipod toxicity data for metals, PAHs, PCBs, and several organochlorine pesticides. These 

data show that the 10th to 90th percentile concentrations of the individual contaminants typically 

span two to three orders of magnitude, with ranges often spanning four to six orders of 

magnitude. 

 The covariation among chemical concentrations was found to be substantial in a 

companion analysis (Smith et al., 2003). Principal component analysis was conducted on a 

reduced data set (n = 2219) that contained samples with complete data on 22 metals and PAHs. 

Two principal components explained 83% of the variation in the chemical data. When rotated 

(with varimax rotation), the first factor explained 50% of the variation and was highly correlated 

with the PAHs. The second factor explained 33% of the variation and was highly correlated with 

the metals. 

 The percent total organic carbon (TOC) in test sediments averaged 1.92% (standard 

deviation = 2.05, n = 3117) and ranged from 0.01 to 29.4%. Based on visual inspection, there 

was no clear pattern in TOC differences among the different studies (Figure 1). Because only 

629 samples had results for acid volatile sulfides (AVS) and simultaneously extracted metals, 

AVS normalization methods (Hansen et al., 1996) were not used. 

 The marine database also includes data on 100% porewater embryological development 

and fertilization endpoints for two species of sea urchin (Arbacia punctulata and 

Strongylocentrotus purpuratus) (Table 3). The MSD values for S. purpuratus 96-hour 

embryological development and 1-hour fertilization endpoints were 78% and 88%, respectively, 

corresponding to a statistical significance (α) level of 0.05 and a power (1 − β) of 0.9 (Phillips et 

al., 2001). We used the MSD values derived by Carr and Biedenbach (1999) for A. punctulata

48-hour embryological development and 1-hour fertilization endpoints of 83.6% and 84.5%, 

respectively. These values corresponded to a statistical significance (α) level of 0.05 and a 

slightly more stringent power (1 − β) of 0.95. Using either the Sig Only or the MSD approach, a 

high percentage (>60%) of the 782 samples were toxic for the embryological development 

endpoint for both species combined, and there was little difference between species in percent of 
 18 

toxic samples. For the fertilization endpoint, about 40% of the 612 A. punctulata samples and 

60% of the 212 S. purpuratus samples were toxic. For both species and endpoints, the results 

showed little difference between the Sig Only and MSD classifications in percent of toxic 

samples. 

 The distribution of the bulk sediment chemistry data for the urchin tests (Tables 4 and 5) 

show 10th to 90th percentile concentrations of the individual contaminants ranging from one to 

three orders of magnitude for most chemicals. The ranges were typically larger for the organic 

compounds than for metals. Percent TOC in test sediments averaged 2.1% (2.4% for A. 

punctulata and 1.8% for S. purpuratus) and ranged from 0.02 to 15.8% for the embryological 

development test endpoint. Percent TOC values were similar for the fertilization endpoint. 

 The freshwater database included data from several frequently tested toxicity test 

endpoints. Samples classified as toxic by the original investigator were taken at face value 

because less information was available to independently evaluate statistical significance (e.g., 

replicate data were lacking) and no analyses had been conducted to identify MSD values for the 

freshwater endpoints. The growth endpoints were treated as growth and survival, so if either 

growth or survival had a toxic result, the sample was classified as toxic. Approximately 20% of 

the 585 short-term survival test samples (10–14-day tests were grouped together for analysis) 

were toxic for the freshwater midge species (Chironomus tentans and Chironomus riparius), and 

almost 40% of the 10-day growth samples were toxic (Table 6). For the freshwater amphipod 

Hyalella azteca, 24% of 567 samples were toxic in the 10–14-day survival test, and almost 40% 

of the 125 samples were toxic in the 28-day growth and survival test. 

 The chemistry associated with the freshwater toxicity endpoints exhibited ranges of one 

to three orders of magnitude (Tables 7, 8, and 9). In general, the chemical concentrations in 

samples from the freshwater database were higher than those in the samples from the marine 

database. The freshwater short-term toxicity tests also had higher TOC concentrations, with 

average concentrations >4% and ranges from <0.1 to >50%. The H. azteca 28-day growth and 

survival test samples averaged 3% TOC (standard deviation = 2.1) and ranged from 0.1 to 

11.6%. 
 19 

4. LOGISTIC REGRESSION MODELS FOR INDIVIDUAL CHEMICALS 

4.1. INTRODUCTION 

 This chapter presents logistic regression models (LRMs) that predict the probability of 

toxicity on the basis of individual chemical concentrations. The toxicity endpoint modeled was 

the 10-day survival test conducted using two species of marine amphipods (A. abdita and R. 

abronius). Our objective for this analysis was to develop single-chemical models that could 

serve as screening-level concentration-response relationships for individual chemicals. These 

relationships could then be used to identify concentrations of individual chemicals that represent 

different degrees of risk. The relationships could also be combined into multiple-chemical 

models (Chapter 5). 

 The individual chemical models provide an opportunity to explore and compare different 

methods of toxicity classification and chemistry normalization. Specifically, we evaluated two 

approaches for designating samples as toxic: (1) less than 90% survival that was significantly 

different from negative control samples (Sig Only), and (2) control-adjusted survival less than 

80% that was significantly different from negative control samples (MSD) (based on the analysis 

by Thursby et al., 1997). We evaluated two approaches for normalizing sediment chemistry: 

dry-weight normalized and organic carbon normalized. Models were evaluated using several 

approaches: goodness-of-fit statistics, visual examination of plots of the model and the 

underlying data, and comparison of the predicted probabilities with the proportion of toxic 

samples observed within ranges of predicted probability for the entire data set. 

 We also used the models to evaluate the implications of several data treatment options. 

We evaluated three alternative screening criteria used to decide whether a given sample was 

included in the model data set for an individual chemical: (1) include all samples, (2) exclude 

toxic samples with concentrations that were less than or equal to the mean concentration of 

nontoxic samples from the same study (1X screening), and (3) exclude toxic samples with 

concentrations that were less than or equal to two times the mean concentration of nontoxic 

samples from the same study (2X screening). Finally, we evaluated the implications of 

developing models that combined data for both amphipod species. 
4.2. METHODS 

4.2.1. Logistic Regression Modeling

Statistical models fit to the marine amphipod data in SEDTOX02 describe relationships 

between the probability of a toxic outcome in the amphipod tests and concentrations of the 

chemicals of interest. Exploratory plots of the data generated distributions that resembled typical 

sigmoidal dose-response curves. The shape of these curves indicated that it might be appropriate 

to model these relationships using an LRM. Logistic regression is typically applied to doseresponse data, such as that generated by spiked-sediment bioassays, or laboratory tests with a 

binary outcome (Morgan, 1992). 

The individual chemical LRMs were developed from the screened data set for each 

chemical. The data screening procedures used in this study were intended to identify the 

chemicals that serve as useful indicators of the toxic response observed in individual sediment 

samples. The screening procedures also transformed the underlying data into a form that is more 

consistent with the sigmoidal form of LRMs. 

The individual chemical LRMs used the dichotomous toxicity test result (toxic or 

nontoxic) as the dependent variable and the chemical concentration as the explanatory variable. 

The model parameters (slope, intercept) define the shape of relationship between the chemical 

concentration (Log10) and the probability of a toxic result. In its simplest form, the logistic 

model can be described using the following equation: 

exp[B0 + B1(x)]

 1 + exp[B0 + B1(x)] p = 

where: 

p = probability of observing a toxic effect, 

B0 = intercept parameter, 

B1 = slope parameter, and 

x = chemical concentration or log chemical concentration. 

 20 

This logistic model was applied to the complete screened data for a number of substances 

to develop relationships between the sediment chemistry and the toxicity test results. For each 

substance modeled, the intercept (B0), slope (B1), and chi-square statistic (–2 log likelihood) 
 21 

were determined. The data for each chemical were modeled independently. Thus, there was 

only a single concentration variable (x) in each individual chemical model. However, for each 

model, it is possible to consider the addition of various covariates, such as test species or 

endpoints. If such a covariate is considered, then a separate intercept term, a separate slope term, 

or both a distinct intercept and a distinct slope term can be fit for each level of the covariate. All 

of the logistic regression analyses were conducted using the SAS Institute’s logistic procedure 

(SAS Institute, 1990). The slope and intercept parameters for the model were estimated using 

the maximum-likelihood approach. 

 The chi-square statistic provides useful information for interpreting the results of the 

logistic modeling. Specifically, the chi-square statistic was used to determine whether the slope 

parameter, B1, was significantly different from zero. For all of the models generated, the 

probability (p value) associated with the slope parameter was less than 0.0001; therefore, the null 

hypothesis (slope = 0) can be rejected. Additionally, the chi-square statistic can be used to assess 

how well the model fits the data. For data sets with similar sample sizes, a larger chi-square 

statistic indicates a better fit of the model to the data. Note, however, that for a similar fit, the 

chi-square statistic increases with sample size and thus cannot be used to compare the fit of data 

sets that are not roughly the same size. Normalizing the chi-square statistic to the sample size 

(N) provides a goodness-of-fit measure that could be applied across all the data sets. There are 

no established criteria for considering a normalized chi-square statistic to be good. For the 

purposes of this report, models that had a normalized chi-square value of greater than 0.15 were 

considered to be a good fit. (The use of a stronger criterion is explored in Chapter 6.) 

 After the parameters are estimated, the model can be inverted to estimate the 

concentrations that yield a certain response probability. The notation Tp (e.g., T50) is used to 

denote the concentration that would give a toxic response of “p” percent according to the model 

(e.g., the probability that 50% of the samples would be toxic). Confidence intervals for these 

effect concentrations that describe the uncertainty associated with fitting the model were derived 

using the delta method. The delta method is based on a truncated Taylor series expansion that 

uses the variance-covariance matrix derived from the maximum-likelihood fit and the derivative 

of the function of interest (in this report, T20, T50, and T80 were used as examples) with respect 

to each parameter (Morgan, 1992). 
 22 

4.2.2. Concentration Interval Plots 

 Concentration interval plots were used to visualize the relationship between the matching 

sediment chemistry and toxicity data for individual contaminants. The plots were prepared by 

calculating the proportion of toxic samples within discrete concentration intervals. The 

individual points represent the median of the chemical concentrations in the samples within the 

interval and the proportion of the samples classified as toxic within the interval. Each point on 

the plots represents a minimum of 15 individual samples (a greater number of samples was 

included in the interval if more than one sample had the same concentration). The range 

represented by each concentration interval was determined from an ascending list of unique 

sample concentrations for each contaminant, with the number of intervals determined by the total 

number of unique sample concentrations for the selected contaminant. The purpose of the plots 

was only to help visualize the general relationship between chemical concentrations and the 

probability of observing toxicity. The goodness of fit of each model was evaluated using the 

normalized chi-square statistics discussed above. 

4.3. INDIVIDUAL CHEMICAL LRM RESULTS 

4.3.1. Model Results 

 This section presents LRM results for individual chemicals. We describe models that 

used the two toxicity classification approaches and the two chemistry normalization methods. 

All of these models combined data for the two amphipod species and used a screening criterion 

that excluded toxic samples with concentrations less than or equal to the mean of nontoxic 

samples from the same study (1X screening approach). 

4.3.1.1. Models Based on Sig Only Toxicity Classification

 Acceptable logistic models were generated for 37 substances for the Sig Only approach, 

including 10 trace metals, 22 individual PAHs, total PCBs, and 4 organochlorine pesticides 

(Table 10). All of these models used dry-weight chemical concentrations. The slopes for all 

models were positive, indicating that increased chemical concentrations were associated with 

increased probability of toxicity. Although the models for all 37 substances had normalized chisquare statistics exceeding our criterion of 0.15, the models for arsenic, nickel, and p,p’-DDE 

had normalized chi-square values of 0.17, 0.18, and 0.16, respectively, indicating relatively 

poorer fits. 
 23 

 Concentration interval plots provide additional information for evaluating the 

relationships between chemical concentration and the probability of observing sediment toxicity 

in the screened data set used to derive the model (Figure 2). For example, the plots for lead, 

mercury, and zinc confirm that logistic models provide good fits of the underlying amphipod 

toxicity data. Importantly, the range of concentrations represented in the database appears to 

span the effects range, as demonstrated by the low proportion of toxic samples (0%) observed at 

the lowest concentrations and the high proportion of toxic samples (90 to 100%) observed at the 

highest chemical concentrations. Similar results were obtained for many of the organic 

compounds (e.g., fluoranthene and phenanthrene); however, the observed proportion of toxic 

samples tended to be somewhat lower (roughly 90%) at the highest concentrations of these 

substances. The plot for p,p’-DDE shows both high variability and the presence of several 

outliers, consistent with its relatively low normalized chi-square value. 

 Although the logistic models provide effective tools for estimating the probability of 

observing sediment toxicity at various chemical concentrations, point estimates of sediment 

effect concentrations are also useful for assessing sediment quality conditions. As an example, 

the chemical concentrations that correspond to the 20, 50, and 80% proportion of toxic samples 

for amphipod survival were determined and designated as Tp values: T20, T50, and T80, 

respectively (Table 11). 

 The reliability of the chemical-specific logistic models was evaluated by comparing the 

probability of toxicity predicted by the models to the proportion of samples actually observed to 

be toxic. This comparison differs from the concentration interval plots in that the data screened 

out of the logistic model development process were included in the reliability evaluation. 

Predicted versus observed values were compared for four ranges of chemical concentrations 

defined by the Tp values (i.e., <T20, >T20–T50, >T50–T80, and >T80). The percent of samples 

within each concentration range that were toxic was determined (Table 12). The logistic models 

and associated point estimates were considered reliable if the observed proportion of toxic 

samples was consistent with the predicted probability of toxicity. 

 The results of this evaluation indicate that the logistic models and associated point 

estimates of sediment effect concentrations generally provide a reliable basis for estimating the 

observed proportion of toxic samples in the project database. The models underestimated the 

proportion of toxic samples at concentrations below the T20 value for all 37 chemicals, although 

35 of the 37 chemicals were within 10% of the top of the range. The underestimation of toxicity 
 24 

below the T20 value may be a consequence of the screening procedure, and is discussed further 

in Section 4.4.1. Between the T20 and T50 values, the proportion of toxic samples observed for 

most of the chemicals (30 of 37) was within the predicted range of 20 to 50%. The proportion of 

toxic samples observed between the T50 and T80 values was within the predicted range of 50 to 

80% toxicity for all 37 chemicals. Above the T80 value, the proportion of toxicity was equal to 

or exceeded 80% for 22 chemicals. Arsenic and p,p’-DDE had a substantially lower proportion 

of toxic samples than predicted. The models for these chemicals would be expected to 

overestimate toxicity for high concentrations. 

 Among the logistic models for the various classes of contaminants, those for PAHs were 

the most reliable. For 16 of 22 PAHs, the actual proportion of toxic samples was correctly 

predicted within three of the four concentration ranges defined by the Tp values; however, a 

higher-than-predicted proportion of toxic samples was observed above the T20 values for all 

PAHs (Table 12). Among the logistic models for the trace metals, those for chromium, copper, 

lead, mercury, and zinc were the most reliable, as indicated by the level of agreement between 

the predicted and observed proportion of toxic samples to amphipods. Likewise, the logistic 

model for total PCBs provided an accurate basis for predicting toxicity to amphipods in the 

database. A somewhat lower level of reliability was observed for the organochlorine pesticide 

models. 

 

4.3.1.2. Models Based on MSD Toxicity Classification

 Acceptable logistic models were generated for 33 substances for the MSD approach, 

including 7 trace metals, 22 individual PAHs, total PCBs, and 3 organochlorine pesticides (Table 

13). All of these models used dry-weight chemical concentrations. The slopes for all models 

were positive, indicating that increased chemical concentrations were associated with increased 

probability of toxicity. The models for eight chemicals—chromium, silver, 

1-methylnaphthalene, 2-methylnaphthalene, 2-6 dimethylnapthalene, biphenyl, naphthalene, and 

perylene—had among the lowest normalized chi-square values, indicating relatively poorer fits. 

Models for antimony, arsenic, nickel, and p,p’-DDE had normalized chi-square values of less 

than 0.15. 

 Concentration interval plots for the MSD toxicity classification approach are shown in 

Figure 3. The proportion of toxic samples is often less than 80% at the highest concentrations in 
 25 

the database (e.g., zinc and 1-methylphenanthrene). This truncation at the higher concentrations 

reflects the more stringent criteria for classifying samples as toxic. 

 The T20, T50, and T80 values are shown in Table 14. The percent of toxic samples 

within the ranges defined by these Tp values is shown in Table 15. The agreement between the 

models and the observed proportion of toxic samples was very good below the T80 value. At 

concentrations below the T20 value, the observed proportion of toxic samples was within the 

predicted range for all of the chemicals except p,p’-DDT. The observed proportion of toxic 

samples was within the predicted ranges at concentrations between the T20 and T50 values for 

all of the 33 models. The proportion of toxic samples observed between the T50 and T80 values 

was within the predicted range of 50 to 80% toxicity for 31 of the 33 chemicals. However, 

above the T80 value, the proportion of toxic samples exceeded 80% for only 10 chemicals. 

Above the T80 value, the models overestimated the proportion of toxic samples observed for 16 

chemicals, and insufficient data were available to evaluate seven models. 

4.3.1.3. Models Based on Organic Carbon-Normalized Chemical Concentrations

 Acceptable logistic models based on organic carbon-normalized chemical concentrations 

were developed for 25 organic chemicals, including 21 individual PAHs, total PCBs, and 3 

organochlorine pesticides (Table 16). The number of samples available for developing these 

models was slightly reduced from the total because a small percent of the samples was not 

analyzed for organic carbon. (Models based on acid volatile sulfides and simultaneously 

extracted metals were not pursued because of the low number of samples available with these 

measurements). All organic carbon-normalized models used the Sig Only classification of toxic 

samples. The models for four chemicals—1-methylnaphthalene, 2-methylnaphthalene, biphenyl, 

and naphthalene—had the lowest acceptable normalized chi-square values, indicating relatively 

poorer fits. 

 Concentration interval plots for the organic carbon-normalization approach are shown in 

Figure 4. Contrary to the expectation that normalizing sediment chemistry for nonpolar organic 

chemicals would reduce the variability in the concentration response relationships, the 

concentration interval plots do not show less variability than the Sig Only dry-weight 

normalization plots shown in Figure 2. 

 The T20, T50, and T80 values based on the organic-carbon normalized models are shown 

in Table 17. The percent of toxic samples within the ranges defined by these Tp values is shown 
 26 

in Table 18. The models underestimated the incidence of amphipod toxicity at concentrations 

below the T20 value for all 25 chemicals, although 21 of the 25 were within 10% of the top of 

the range. Between the T20 and T50 values, the proportion of toxic samples observed for 8 of 

the chemicals was within the predicted range of 20 to 50%, and 18 were slightly above the 

predicted range. The proportion of toxic samples observed between the T50 and T80 values was 

within the predicted range of 50 to 80% toxicity for all chemicals except biphenyl. Above the 

T80 value, the proportion of toxicity exceeded 80% for fluorene only, and insufficient data were 

available to evaluate 7 chemicals. The models for the other 17 chemicals would be expected to 

overestimate toxicity for high concentrations. 

 

4.3.2. Model Comparisons 

 We compared the performance of the models to provide additional insights into the 

strengths and limitations of the different options and to explore the implications of those 

differences. The models were compared on the basis of goodness of fit and the degree of 

agreement between the predicted proportion of toxic samples and the proportion observed in the 

database. The implications of different modeling options were further evaluated by comparing 

the resulting Tp values. 

4.3.2.1. Goodness-of-Fit Comparisons

 We compared normalized chi-square statistics across all the different chemical models 

for the different modeling alternatives. We also evaluated the number of chemicals whose 

models exceeded the 0.15 normalized chi-square criterion. 

 The Sig Only approach generated acceptable models for 4 more chemicals than did the 

MSD approach. Models for antimony, arsenic, nickel, and p,p’-DDE had normalized chi-square 

values of less than 0.15 for the MSD toxicity classification. Regression models based on toxicity 

classification using the Sig Only approach consistently had higher normalized chi-square values 

than did those using the MSD approach, with the exception of benzo(b)fluoranthene (Figure 5). 

The organic carbon-normalized approach generated acceptable models for 25 of the 27 organic 

chemicals that had acceptable models using dry-weight normalization. Regression models based 

on dry-weight-normalized concentrations had higher normalized chi-square values than did those 

using organic carbon-normalized concentrations for 25 of the 27 chemicals (Figure 6). 
 27 

4.3.2.2. Reliability Comparisons

 Tables 19, 20, and 21 show the difference between the observed proportion of toxic 

samples and the average predicted proportion of toxic samples within the four ranges defined by 

the Tp values for the Sig Only, MSD, and organic carbon-normalization approaches, 

respectively. Using antimony as an example, of the 1041 samples a with predicted probability of 

toxicity of <T20, 315 were classified as toxic, corresponding to an observed proportion toxic of 

30.3%. The 1041 samples had an average predicted probability of 11%. The difference between 

the observed and predicted values, 19.2%, is shown in the upper left cell of Table 19. Large 

differences between observed and predicted values indicate poor model performance. Positive 

numbers indicate that the observed toxicity is greater than that predicted by the models, whereas 

negative numbers mean that the observed toxicity is less than that predicted by the models. 

Differences greater than 20% in either direction are highlighted to facilitate comparisons among 

the three tables. 

 Comparing the observed and predicted values for the Sig Only and MSD approaches 

(Tables 19 and 20, respectively), the MSD differences are smaller for the predictions less than 

the T50 values. Differences are comparable for the T50–T80 range. For concentrations above 

the T80, the Sig Only models had fewer large differences between observed and predicted than 

did the MSD models. On average, the magnitude of the differences at concentrations above the 

T80 was smaller for the Sig Only models than for the MSD models (absolute average differences 

of 9.1 and 15.3%, respectively). The Tp values for the Sig Only and MSD approaches show a 

high degree of correlation (Figure 7). The difference between the Tp values is largest for the 

T20 values (factor of 3.8) and smallest for the T80 values (factor of 1.7). The organic carbonnormalized models showed many large differences between observed and predicted toxicity for 

concentrations less than the T50 values and greater than the T80 values (Table 21). 

4.3.3. Summary of Toxicity Classification Evaluations 

 We fit LRMs to marine amphipod data in the SEDTOX02 database using two approaches 

for classifying samples as toxic and two approaches for normalizing sediment chemistry. The 

models for nonpolar organic chemicals based on organic carbon-normalized sediment chemistry 

had lower goodness-of-fit statistics than did the dry-weight-normalized models and larger 

differences between observed and predicted toxicity. 
 28 

 The two approaches for classifying toxicity had different strengths and limitations. A 

greater number of models had acceptable goodness-of-fit statistics with the Sig Only approach 

than with the MSD approach. Models fit using the Sig Only classification approach had slightly 

better goodness-of-fit statistics than did those fit using the MSD approach. The differences 

between observed and predicted proportions of toxic samples were smaller for the MSD 

approach at concentrations less than the T50 value. The Sig Only approach had a greater 

tendency to underestimate observed toxicity at low concentrations. In contrast, at concentrations 

greater than the T80 value, differences between observed and predicted proportions of toxic 

samples were smaller for the Sig Only approach. The MSD approach had a greater tendency to 

overestimate toxicity at these higher concentrations. 

 It is not unexpected that individual chemical models would predict a proportion of toxic 

samples lower than that observed at low concentrations. Chemicals in the sample other than the 

one being modeled may be responsible for the observed toxicity. Predictions at high 

concentrations have greater significance for evaluating risk at contaminated sites. For these 

reasons, we selected the Sig Only approach for further evaluation and development. 

4.4. EVALUATION OF ALTERNATIVE SCREENING APPROACHES 

 As discussed in Section 3.4.3, the data for each contaminant were screened prior to 

applying the logistic model or plotting the data. The data screening procedure was designed to 

exclude samples where the chemical under consideration would not serve as a good indicator of 

observed toxicity. The standard screening approach (1X screening) that we used as the basis for 

comparisons was described in the methods section (Section 4.2). In this approach, the 

concentration of the selected chemical in each toxic sample was compared with the mean of the 

concentration of that substance in the nontoxic samples collected in the same study and 

geographic area. If the concentration of a chemical in an individual toxic sample was less than 

or equal to the mean concentration of that chemical in the nontoxic samples from that study area, 

it was considered unlikely that the observed toxicity could be attributed to that chemical. 

Therefore, these toxic samples were not included in the screened data set used for developing the 

logistic model for that chemical. 

 Although there are many possible ways to identify samples for screening (e.g., various 

statistical criteria), we confined our evaluation to simple approaches similar to those used 

previously (Ingersoll et al., 1996; Long and Morgan, 1991; Long and MacDonald, 1992; 
 29 

MacDonald et al., 1996). This section compares the standard method with two alternatives: (1) 

using all of the data in developing single-chemical models (unscreened), and (2) eliminating 

toxic samples having concentrations less than or equal to twice the mean concentration of that 

chemical in the nontoxic samples from that study area (2X screening). 

 The models developed using the two alternative screening approaches were first 

compared with our standard approach on the basis of concentration interval plots and goodness 

of fit. The 2X screening alternative was further evaluated by comparing the degree of agreement 

between the predicted proportion of toxic samples with the proportion observed in the database 

and the resulting Tp values. 

4.4.1. Unscreened Versus 1X Screening 

 The unscreened data set includes all of the data for a chemical, whereas the 1X screening 

removed 41.3 to 59.5 % (average of 48.5%) of the toxic samples from the derivation of the 

models. The effects of including all data for a chemical are illustrated in Figure 8 using lead and 

phenanthrene as representative examples. Each plot shows the LRMs and the concentration 

interval data for the respective screening approach. 

 Visual inspection of these plots revealed that inclusion of the data for those toxic samples 

in which the chemical of concern is a poor indicator of the observed response tended to scatter 

and skew the data distributions, particularly at lower concentrations. The plots of the unscreened 

data for lead and phenanthrene show very few intervals with <20% effects. Importantly, the 

unscreened data for phenanthrene showed only a weak relationship between chemistry and 

toxicity for concentrations <1000 mg/kg dry weight, and the incidence of effects is 

approximately 20 to 60% below that concentration. However, there were few differences 

between the distributions of the screened and unscreened data at higher concentrations. 

 Models fit with the unscreened data had much lower normalized chi-square values 

(Figure 9). The models for only four chemicals—copper, fluorene, dieldrin, and DDT—

exceeded the normalized chi-square criterion of 0.15 that we used to identify models with fits 

sufficient to support the calculation of the Tp values. The 1X screening approach yielded a 

greater number of acceptable models describing the relationship between contaminant 

concentrations and biological effects. 
 30 

4.4.2. 2X Screening Versus 1X Screening 

 The 2X screening approach removed 63.8 to 84.2% (average of 70.4%) of the toxic 

samples from the derivation of the model. The effects of the 2X screening alternative are 

illustrated in Figure 10 using lead and fluoranthrene as representative examples. Each plot 

shows the LRM and the concentration interval data for the respective screening approach. 

 The concentration interval plots show that the 2X screening approach reduced variability 

somewhat. The reduced variability was also reflected in slightly higher normalized chi-square 

values for the models fit using the 2X approach for most chemicals (Figure 11). In addition, the 

concentration interval plots indicate that the logistic regression curves for the 2X screening 

approach were shifted to the right at lower concentrations. 

 Another way to examine the differences between the models developed from the two 

screening approaches is to compare the Tp values. Figure 12 shows the relationship between the 

Tp value concentrations calculated using the different screening approaches. The greatest 

difference was seen in the T20 values, which is consistent with the patterns observed in 

concentration interval plots. The difference in Tp values decreased with concentration; the T80 

values were almost identical. 

 Comparing differences between the observed and predicted proportions of toxic samples 

in the 1X and the 2X screening approaches (Tables 19 and 22, respectively), the differences in 

the 2X screening approach were smaller for predictions below the T50 values. Differences were 

comparable for the T50–T80 value range. For concentrations above the T80 value, the 

differences in the 1X screening approach were slightly smaller than those in the 2X approach 

(absolute average 9.1% difference compared to 10.1%). 

4.4.3. Summary of Screening Approach Evaluations 

 We evaluated alternative screening approaches used to exclude samples where the 

chemical under consideration would not serve as a good indicator of observed toxicity. The 

models from the unscreened alternative showed a weaker relationship between chemistry and 

toxicity than was observed with the other screening alternatives. The models generated using the 

2X screening approach had better goodness-of-fit statistics. The 2X approach had lower 

differences between observed and predicted toxicity at concentrations below the T50 value; 

however, it had a slightly greater tendency to overestimate toxicity at concentrations above the 
 31 

T80 value. In addition, the 2X approach excluded approximately 22% more toxic samples from 

the model derivation. 

 The 2X screening approach has some advantages over the 1X screening approach, 

particularly at concentrations less than the T20 value. However, the 1X screening approach 

performed slightly better at concentrations above the T80 value and screened out considerably 

fewer toxic samples in the model derivation. We selected one modeling approach to manage the 

many permutations associated with further model development and exploration. We retained the 

1X model because of the better performance at high Tp values and retention of more samples. 

4.5. COMPARISON OF TOXICITY TEST ENDPOINTS 

 We used the LRM approach to investigate the implications of using a model that 

combines the response of the two amphipod species. We explored more complex models that 

included different slopes or intercepts for the two species. We compared the Tp values that 

would result from individual species models with those of the combined models. In addition, we 

compared the toxicity observed for each species with predictions based on the combined model. 

4.5.1. Statistical Comparisons of Species-Specific LRMs 

 Using the LRM approach, it is possible to fit a separate slope, an intercept, or both for 

each of the two amphipod species. If the two species responded with different sensitivities to the 

chemicals, a separate slope or intercept would be statistically significant. We tested the 

significance of separate slope, intercept terms using a sequential chi-square comparison (Neter et 

al., 1996). The first test compared a common slope, common intercept model (Model A) with a 

common slope, different intercept model (Model B). The results of this analysis indicated that 

an additional intercept term was statistically significant (α = 0.05, 1 degree of freedom) for all of 

the chemicals considered (Table 23). The second test compared Model B with a different slope, 

different intercept model (Model C). The distinct slope term was not significant (α = 0.05, 1 

degree of freedom) for 20 of the 37 chemicals. For these chemicals, parallel models with a 

different intercept for each species were preferred. 
 32 

4.5.2. Comparison of Tp values for Separate Amphipod Models 

 We developed separate logistic models for each amphipod species and calculated Tp 

values for comparison with the combined amphipod models (Tables 24 and 25). The results 

showed that the T20, T50, and T80 values for A. abdita were higher than those of the combined 

model by a factor of approximately 2, indicating that A. abdita is slightly less responsive than the 

combined model would predict. In contrast, the T20, T50, and T80 values for R. abronius were 

lower than those of the combined model by factors of 3.9 for the T20 value and 3.1 for the T80 

value, indicating that R. abronius is more responsive than the combined models would predict. 

4.5.3. Reliability Comparisons 

 We compared the observed proportion of toxic samples for the two species in the 

unscreened data set with predicted values from the combined model within the four ranges 

defined by the combined model Tp values (i.e., <T20, T20–T50, T50–T80, and >T80). On 

average, the difference between observed and predicted proportion of toxic samples for A. abdita

was less than 10% for all concentration ranges (Table 26). The observed proportion of toxic 

samples using R. abronius was much greater than predicted below the T50 value. Above the T50 

value, the difference between observed and predicted proportions of toxic samples using R. 

abronius averaged 11%. 

4.5.4. Summary of Species Comparisons 

 The results of the species-specific analyses suggest that there are substantial differences 

in the chemical-specific models for the two species. The model comparison indicated that 

parallel models with different intercepts was the preferred model for more than half of the 

chemicals. Comparing the Tp values derived from separate species models with those derived 

from the combined model suggests that R. abronius has a greater response than A. abdita at 

similar concentrations. Still, on average, the T50 values differed by approximately a factor of 2 

for A. abdita and a factor of 3 for R. abronius. The combined model consistently and 

substantially underpredicted the observed proportion of toxic samples for R. abronius below the 

T50 value. Above the T50 value, differences between the observed proportion of toxic samples 

and that predicted using the combined individual chemicals models were minimal for most of the 

A. abdita data and for R. abronius data. 
 33 

 The observed species differences have several possible explanations: unmeasured 

chemicals or factors other than chemistry may have influenced R. abronius results at low 

chemical concentrations, or there may be inherent differences in sensitivity between the two 

species. However, the greatest difference between the species-specific observations and the 

combined models are at lower concentrations for R. abronius. As discussed above, individual 

chemical models may underestimate observed toxicity at low concentrations because chemicals 

in the sample other than the one being modeled may be responsible for the observed toxicity. 

Therefore, the species-specific differences could also be explained if the database for R. abronius

contains a disproportionate amount of data from areas with a high degree of contamination from 

multiple chemicals. This possibility is difficult to investigate with single-chemical models, but 

the issue is revisited in Chapter 5. 

4.6. SENSITIVITY OF MODELS TO ERRORS IN UNDERLYING DATA 

 We did not conduct a formal analysis to evaluate the sensitivity of the models to potential 

errors in the underlying database. However, after initial model development and evaluation, we 

discovered an error in PCB concentration units for 15 samples. These samples had erroneously 

high concentrations, and, in addition, a relatively small number of other samples in the database 

had similarly high PCB concentrations. This situation provided an opportunity to evaluate the 

degree of change in the models resulting from errors in a small number of highly influential 

values. 

 The correction in concentrations for these 15 samples changed the LRM for PCBs, 

particularly at high concentrations (Figure 13). It also improved the model fit: the normalized 

chi-square value changed from 0.24 to 0.27. As expected, the Tp values changed most at high 

concentrations; the T80 was reduced by 42% (Table 27). 

4.7. INDIVIDUAL CHEMICAL MODELS AND SPIKED-SEDIMENT BIOASSAY 

MEDIAN LETHAL CONCENTRATION (LC50) VALUES 

 Dose-response data from laboratory spiked-sediment bioassays provide additional 

perspective on the concentrations of individual chemicals that can be considered to cause 

toxicity. However, results from spiked-sediment bioassays are not immediately comparable with 

predictions from the LRMs. Toxicity from spiked-sediment bioassays is more confidently 

attributed to the chemical added to the sample. In contrast, the response to any individual 
 34 

chemical measured in field-collected sediments may be confounded by the presence of other 

chemicals. 

 In addition, the magnitude of response reported in spiked-sediment bioassays is much 

greater than the magnitude of response required to classify a sample as toxic in this study. Most 

of the studies in the literature on spiked-sediment toxicity report LC50s. An LC50 value 

represents the concentration corresponding to 50% survival of test organisms. In this study, 

many samples with much higher test survival were classified as toxic. The control-normalized 

survival averaged across all samples and chemicals decreases with increasing probability of 

toxicity defined by the model Tp values (Figure 14). The relationship is such that an average 

survival of 50% (LC50) corresponds to concentrations exceeding T50 values. 

 Reported LC50 values for 10-day, spiked-sediment toxicity tests conducted with marine 

amphipods were compared with the probability of toxicity estimated from the individual 

chemical models (Table 28). Using the logistic models, the probability of toxicity at the reported 

LC50 values ranged from 0.54 for zinc to 0.97 for mercury, with most estimates falling within 

the 0.8 to 0.9 range. This is consistent with the average percent survival observed at high 

probability of toxicity, as shown in Figure 14. 

4.8. SUMMARY AND CONCLUSIONS 

 This section presents LRM results for models that predict the probability of toxicity on 

the basis of individual chemical concentrations. The toxicity endpoint modeled was the 10-day 

survival test conducted using two species of marine amphipods (A. abdita and R. abronius). 

 The LRM approach was used to explore and compare different methods of toxicity 

classification and chemistry normalization. We evaluated two approaches for designating 

samples as toxic: (1) less than 90% survival that was significantly different from negative 

control samples (Sig Only), and (2) control-normalized survival less than 80% that was 

significantly different from negative control samples (MSD) (based on the analysis by Thursby 

et al., 1997). We evaluated two approaches for normalizing sediment chemistry: dry-weight 

normalized and organic carbon normalized. 

 Models were evaluated using several approaches: goodness-of-fit statistics, visual 

examination of plots of the model and the underlying data, and reliability (predicted vs. observed 

proportion of toxic samples within ranges of probability) for the entire data set. The MSD 

approach had a greater tendency to overestimate the proportion of toxic samples observed at 
 35 

higher concentrations. Although the Sig Only approach had a greater tendency to underpredict 

observed toxicity at low concentrations, this discrepancy may be explained by the presence of 

other chemicals in the sample. The models based on organic carbon-normalized sediment 

chemistry had lower goodness-of-fit statistics than did the dry-weight-normalized models, and 

they also had larger differences between observed and predicted toxicity. We selected the Sig 

Only approach with dry-weight normalization for further evaluation and development. 

 We also used the LRM approach to evaluate the implications of several data treatment 

options. We evaluated three screening criteria used to decide whether a given sample was 

included in the model data set for an individual chemical: (1) include all samples (unscreened), 

(2) exclude toxic samples with concentrations that were less than or equal to the mean 

concentration of nontoxic samples from the same study (1X screening), and (3) exclude toxic 

samples with concentrations that were less than or equal to two times the mean concentration of 

nontoxic samples from the same study (2X screening). The models from the unscreened 

alternative showed a weaker relationship between chemistry and toxicity than was observed with 

the other screening alternatives. The 2X screening approach has some advantages over the 1X 

screening approach, particularly at concentrations less than the T20 value. However, the 1X 

screening approach performed slightly better at concentrations above the T80 value and screened 

out fewer samples in the model derivation, which may prove important in the future development 

of models for less-frequently measured chemicals. We concluded that the small improvements 

in model fit did not outweigh the associated reduction in sample size, and retained the 1X 

approach for further development and exploration. 

 Finally, we evaluated the implications of developing models that combined data for the 

two amphipod species. Agreement between the observed proportion of toxic samples and that 

predicted using the combined individual chemicals models was good for both species above the 

T50 values. Below the T50 value, the combined models performed well for A. abdita but 

consistently and substantially underpredicted the observed proportion of toxic samples for R. 

abronius. The discrepancies may be explained by inherent differences in sensitivity between the 

two species, by a greater responsiveness of R. abronius to nonchemical factors, or by the 

database for R. abronius containing a disproportionate amount of data from areas with a high 

degree of contamination from multiple chemicals. These issues will be investigated further using 

multiple-chemical models. 
 36 

 The single-chemical models presented in this chapter can be used to develop screeninglevel concentration-response relationships for individual chemicals. These relationships could 

then be used to identify concentrations of individual chemicals that correspond to different 

degrees of risk, depending on the objectives of the user. This application is similar to the current 

use of SQGs; the use of the single-chemical models in evaluating SQGs is discussed in Chapter 

7. The risks of a toxic response posed by the mixture of chemicals present in a particular sample 

are best evaluated using a multiple-chemical approach, which is discussed in the next chapter. 
 37 

5. MULTIPLE-CHEMICAL MODELS 

5.1. INTRODUCTION 

 One of the major challenges in assessing the ecological risk associated with exposure to 

contaminated sediments is the presence of chemical mixtures. Field-collected sediments, as a 

rule, contain complex mixtures of chemicals and other factors that influence toxic response. 

Because the individual models described in Chapter 4 were derived from field-collected 

sediments rather than from laboratory dose-response studies, to some extent the individual 

models incorporate the overall toxicity of the mixture of chemicals in the samples. We sought to 

improve the estimates of response by combining the information contained in the individual 

chemical models into a single estimate of toxic response. The multiple-chemical model could 

then be applied to predict whether new samples with known sediment chemistry would be 

expected to produce a toxic response in an amphipod test. 

 This chapter describes the approach used to develop and evaluate multiple-chemical 

models. Sections 5.2 and 5.3 discuss model development and results, respectively. The models 

were used to evaluate several issues (Section 5.4): the relationship between the probability of 

observing a toxic effect and the magnitude of toxicity, the identification of chemicals that most 

influence model performance, the effect of reducing the number of individual chemical models 

used in developing the multiple-chemical models, the performance of the models in predicting 

toxicity of the two amphipod species, and the performance of the models in predicting toxicity 

observed in individual studies. Finally, the performance of the models was evaluated using an 

independent data set. 

5.2. MODEL DEVELOPMENT 

 Most evaluations of the effects of mixtures on aquatic toxicity endpoints such as survival 

and growth have focused on two empirical models of noninteractive joint action: concentration 

addition and response addition (Broderius, 1991). Concentration addition, which is also referred 

to as “simple similar action,” assumes that contaminants act independently but by a similar mode 

of action. Toxic unit models, which are a specialized case of concentration addition, have been 

applied to the assessment of the toxicity of PAH mixtures in sediment (Swartz et al., 1995; Di 

Toro et al., 2000; Lee et al., 2001), but they are unlikely to be applicable to complex mixtures of 

contaminants commonly found in the environment that have different modes of toxic action. In 
 38 

response addition, or independent action, which is expected to apply to cases where 

contaminants have a different mode of action, toxicity would be predicted only when one or 

more contaminants exceeds its toxicity threshold. 

 We explored three alternative approaches for combining the individual chemical models 

into a single explanatory variable that could be used to estimate the probability of observing 

toxicity in a given sample: 

• the maximum probability of observing toxicity for a sample, taken from the set of 

probabilities calculated for each individual chemical in the sample (P_Max), 

• the mean probability of observing toxicity for a sample, based on the set of 

probabilities calculated for each individual chemical in the sample (P_Avg), and 

• the product of the probabilities of surviving exposure to all individual chemicals in 

the sample (P_Prod), calculated by multiplying the values of one minus the 

probability of observing toxicity for each chemical in the sample and then subtracting 

the resulting product from one. 

 All three approaches can be considered similar to response-addition models. A model 

based on P_Max would predict toxicity on the basis of the individual chemical model with the 

highest probability of toxicity. This approach would be expected to be most effective in 

predicting toxicity when the degree of correlation between responses to individual chemicals is 

high. The P_Prod and P_Avg approaches include all chemicals in the estimate of toxicity. 

Models using these explanatory variables would be expected to be most effective when the 

degree of correlation between responses to individual chemicals is intermediate and low, 

respectively (U.S. EPA, 2000). 

 A possible disadvantage of P_Avg as an explanatory variable is that it gives more weight 

to chemical classes that have many individual chemicals that tend to co-occur. For example, 

P_Avg incorporates the output from models for 22 individual PAHs. Because individual PAHs 

are likely to co-occur in environmental samples, P_Avg may be influenced more by the 

concentrations of PAHs than by the concentrations of other chemicals. In addition, the P_Avg 

approach may appear to reduce the influence of chemicals associated with high probabilities of 

toxicity by averaging them with chemicals having low probabilities of toxicity. 

 P_Max, P_Avg, and P_Prod were all developed using the simple chemical parameter 

estimates shown in Table 10, except for PCBs. As discussed in Section 4.5, we discovered an 
 39 

error in PCB units for 15 samples after the models had been fit and evaluated. This change 

resulted in a minor change in the PCB model parameter estimates. However, because the effects 

of the correction on the multiple-chemical models were extremely small (as discussed further in 

Section 5.4.4), the multiple-chemical models were not changed. 

 Preliminary plots of P_Avg and P_Max versus the percent of samples that were toxic in 

sequential intervals of increasing contamination appeared nearly linear, so a multiple linear 

regression modeling approach was pursued. The dependent variable for the models was the 

frequency of toxicity observed within the sequential intervals of increasing contamination (i.e., 

probability intervals). The probability intervals were defined by first sorting the samples by the 

predicted probability (i.e., P_Avg and P_Max) and then combining samples into groups 

containing 50 unique predicted probability values. The median predicted probability associated 

with each interval was used as the explanatory variable. Because the dependent and explanatory 

variables both depend on how samples are binned, we evaluated the sensitivity of the models to 

different binning approaches (Appendix E). The results of this analysis indicated that, above a 

minimum number of regression samples (i.e., the number of bins) and samples within the bins, 

the regression model coefficients showed little change with variations in binning approach. 

 We initially considered a fourth alternative, the sum of the probabilities of observing 

toxicity for each individual chemical in the sample (P_Sum model). This approach was 

eliminated from further consideration because the resulting range of predicted probabilities had 

no upper bound, making the results very sensitive to the number of chemicals measured in a 

particular sample. 

 We developed the multiple-chemical models using all samples with matching chemistry 

and toxicity (i.e., no additional data screening procedures were employed). To minimize the 

potential impact of samples with partial chemistry, only samples with measured values for at 

least 10 chemicals were included in the data set used to derive the multiple-chemical models. 

We evaluated the alternative approaches by examining goodness of fit (as indicated by Rsquared values), concentration interval plots (described in Section 4.2.2), and the agreement 

between the observed proportion of toxic samples and that predicted using the multiple-chemical 

models (described in Section 4.3.2.2). 

 A multivariate approach to the problem uses all chemical data simultaneously to predict 

the probability of a toxic test result was explored in a companion effort (Smith et al., 2003). The 

multivariate approach requires complete data for all chemicals included in the model. The single 
 40 

explanatory variable approach developed in this chapter is potentially applicable to a wider array 

of chemistry data. 

5.3. MODEL RESULTS 

 Probability density functions of P_Avg, P_Max, and P_Prod values are shown in Figure 

15. The probabilities generated using the P_Avg and P_Max approaches were fairly evenly 

distributed across the range of probabilities. The distribution of P_Prod values was so heavily 

skewed toward higher values that P_Prod would have limited ability to discriminate among 

moderately to highly contaminated samples. We eliminated this model from further 

consideration. 

 The regression models generated using P_Avg and P_Max are shown in Figure 16. A 

quadratic term was significant for the P_Max model, indicating that the relationship is 

curvilinear. Both models explained a large amount of the variation in the observed frequency of 

toxic samples in the probability intervals (R-squared values were 0.89 and 0.93 for P_Avg and 

P_Max models, respectively). Regression diagnostic plots (e.g., Cooks distances, residual 

distributions [not shown]) revealed no overly influential values or severely nonnormal residual 

distributions. 

 We compared the differences between the frequency of observed toxicity with that 

predicted by the P_Avg and P_Max models within quartiles of the predicted probability (Figure 

17). All samples in the database were used in the comparison (including samples with fewer 

than 10 chemicals measured). When applied to individual samples, the P_Avg model could yield 

predictions of probability of toxicity greater than 1, so the predicted probability was capped at 1. 

The mean predicted probability of toxicity within probability quartiles closely matched the 

observed proportion of toxic samples within the same probability quartiles, demonstrating the 

overall reliability of both the P_Max and the P_Avg models within the database that was used to 

derive the model. 

 The P_Max and P_Avg models explained 92% and 88%, respectively, of the variation in 

the frequencies of toxicity observed in the probability intervals derived from the entire database 

(Figure 18). The P_Max model adjusts for the difference between the maximum probability 

from the individual chemical models and the observed proportion of toxic samples within the 

same probability interval. For example, for a maximum probability of 1 from the individual 

chemical models (x-axis), the observed proportion of toxic samples (and the predicted 
 41 

probability from the P_Max model) is 0.84 (Figure 16). The data used to derive the P_Avg 

model show the opposite situation, where the mean probability value from all of the individual 

chemical models is somewhat lower than the corresponding observed proportion of toxic 

samples. Thus, mean probabilities of 0.5 and 0.75 correspond to proportions of toxic samples of 

0.7 and 0.9, respectively; the P_Avg adjusts the final estimate of toxicity accordingly (Figure 

16). 

5.4. USING THE MULTIPLE-CHEMICAL MODEL AS AN ANALYTICAL 

FRAMEWORK 

 The multiple-chemical models provide a consistent analytical framework that can be used 

to evaluate several issues that are relevant to evaluating sediment toxicity. In the following 

sections, we demonstrate how the models can be used to 

• evaluate the relationship between the probability of observing a toxic effect and the 

magnitude of toxicity, 

• identify the chemicals that serve as the most (or least) effective surrogates for 

toxicity, 

• evaluate the effect of reducing the number of individual chemical models combined 

into the multiple-chemical model, 

• evaluate the performance of the models in predicting toxicity of the two amphipod 

species, and 

• evaluate the performance of the models in predicting toxicity observed in specific 

studies. 

 We used the P_Max model in the evaluations discussed in the remainder of this report 

because it explained a slightly higher amount of variation, it is less influenced by the number of 

chemicals analyzed in a sample, and we could reduce the number of analytical permutations. 

5.4.1. Relationship Between Probability of Observing a Toxic Effect and Magnitude 

of Toxicity 

 The magnitude of the effect (decreased survival) in the amphipod test increased as the 

probability of toxicity increased (Figures 19 and 20). Toxic samples with a probability of 
 42 

toxicity less than or equal to 0.25 had an average control-adjusted survival of greater than 75%, 

whereas samples with a probability of toxicity greater than 0.75 had an average control-adjusted 

survival of less than 50%. Figure 20 shows the strong relationship between the probability of 

toxicity predicted by the P_Max model and control-adjusted survival. This demonstrates that 

samples that are estimated to have the highest probability of toxicity are also likely to cause a 

high magnitude of mortality. 

5.4.2. Chemicals that Serve as the Most Effective Surrogates for Toxicity 

 The P_Max model is based on the individual chemical that has the highest probability of 

toxicity. For approximately 70% of the samples, individual chemical regression models for 

metals produced the maximum probability used in the P_Max model (Table 29). This should not 

be construed to imply that metals were causing toxicity in these samples. It does indicate, 

however, that metals appear to be a good predictor of toxicity in field-collected samples where 

mixtures of contaminants are likely to be present. 

 The influence of different chemical classes on the model was explored by excluding the 

individual chemical models for PAHs, metals, and pesticides and PCBs. The models produced 

by excluding the different chemical classes are shown in Figures 21, 22, and 23, (PAHs, metals, 

and pesticides and PCBs, respectively). The difference between the models was most evident 

when metals were excluded, which is not surprising, as 70% of the samples had a metal 

associated with the maximum probability for that sample. Still, there were only minor changes 

in the models. This is consistent with the interpretation that the chemicals associated with the 

P_Max values are serving as surrogates for the overall degree of contamination present in a 

sample. 

5.4.3. Effect of High Levels of Several Chemicals 

 The number of chemicals in a sample that have a high probability of toxicity according to 

the individual chemical models (e.g., p>0.75) makes a difference in how well the model 

predictions match the observed proportion of the samples that are toxic (Figure 24). As shown, 

when only one chemical in a sample has a probability of toxicity greater than 0.75, the P_Max 

model tends to overestimate the incidence of observed toxicity. The P_Max model slightly 

underestimated the frequency of toxicity observed in samples that contained two or more 

chemicals with a probability of toxicity greater than 0.75. The degree of underestimation, 
 43 

however, remained about the same even for samples containing many chemicals with 

probabilities greater than 0.75. 

5.4.4. Sensitivity of P_Max Model to Individual Chemical Models 

 The P_Max model described above was developed by including all 37 chemicals with 

chemical-specific models with normalized chi-square values greater than 0.15. We investigated 

model sensitivity to the inclusion criterion. In particular, we hypothesized that a more stringent 

inclusion criterion (i.e., excluding chemicals with poorer fits) would improve the multiplechemical models. The P_Max model fit using a normalized chi-square criterion of 0.27, which 

represented the average normalized chi-square value for acceptable models, included 19 

chemicals, contained only the linear term, and explained slightly more variation (R2

 = 0.94, 

Figure 25) than the original P_Max models (Figure 16). 

5.4.4.1. Effect of PCB Model Correction

 We examined the influence of the updated PCB model on the P_Max model. As 

discussed in Section 4.6, the correction in PCB units for 15 samples resulted in a model 

sufficiently different to cause us to update the PCB model parameters reported in Table 1. 

However, the effect of updating the PCB model on the P_Max model results was extremely 

small. The model parameters changed slightly, to 0.11 (intercept), 0.34 (linear term), and 0.39 

(quadratic term) from 0.11, 0.33, and 0.4, respectively. The maximum difference in predicted 

probability of toxicity for an individual sample was 0.0025. Because these differences were so 

small, we elected to continue using the original P_Max model in the evaluations discussed in this 

report. 

5.4.5. Predictions of P_Max Model for Individual Species 

 The P_Max models also provide a framework for re-examining the species-specific 

differences in model performance. Figures 26 and 27 plot the probability of toxicity predicted 

from the nationwide P_Max model against three species-specific variables: (1) the proportion of 

observed toxicity based on the Sig Only classification, (2) control-adjusted survival, and (3) the 

proportion of toxicity based on MSD classification. There was a strong relationship between the 

predicted probability of toxicity and the three variables. The relationship between predicted and 

observed proportion toxicity was slightly stronger using the MSD classification for both species. 
 44 

 The relationship between predicted and observed toxicity was very different for the two 

species. In general, R. abronius showed a higher response rate than did A. abdita at similar 

predicted probabilities. This difference is consistent with that discussed for the individual 

chemical models in Section 4.5 and may be explained by inherent differences in sensitivity 

between the two species or a greater sensitivity of R. abronius to nonchemical factors, or the 

database for R. abronius may contain a disproportionate amount of data from areas with a high 

degree of contamination from multiple chemicals. 

 As shown in Figures 26 and 27, most of the A. abdita data fall between predicted 

probabilities of 0 and 0.5, whereas the R. abronius data fall mostly between 0.25 and 0.75. 

Although we cannot completely resolve the cause of differences in species responses, differences 

in sediment chemistry cannot be discounted as a contributing factor. Although the variability in 

the modeled response of R. abronius was higher than that in the A. abdita response, the model 

still served as an effective indicator of the observed proportion of toxic samples. Combining 

data from tests that used either species enables the development of models that encompass more 

areas of the country and a broader range of sediment chemistry. The strong relationships of the 

individual species’ responses with the nationwide model affords confidence that the combinedspecies model provides a common framework that is applicable to both species. 

 

5.4.6. Predictions of P_Max Model for Individual Studies 

 The multiple-chemical models are a function of the covariation among chemicals on a 

nationwide basis. Accordingly, the nationwide model may not accurately predict observed 

toxicity at a particular site if the chemical mixture is very different from the average across the 

nation or if site-specific factors greatly influence the degree of toxicity (e.g., by increasing or 

decreasing chemical bioavailability). 

 To examine the application of the nationwide model to individual studies, we first 

examined the performance of models in predicting observed toxicity for individual studies within 

the database that are represented by more than 20 samples (Table 30). We compared the 

differences between the frequency of observed toxicity with that predicted by the P_Max models 

within quartiles of the predicted probability. Of the 39 studies with sufficient data to evaluate, 

14 had observed frequency of toxicity within 20% of that predicted in all quartiles having data. 

Nine studies had observed frequency of toxicity that differed by more than 20% in all quartiles 

having data. The remainder of the studies had mixed performances. 
 45 

 We conducted a more in-depth evaluation of the performance of the models for 

predicting toxicity in specific studies by comparing predicted and observed toxicity within 

smaller intervals. To have sufficient data for this evaluation, we combined data from several 

studies within a common geographic region (Table 31). Figures 28 through 32 plot the 

probability of toxicity predicted from the nationwide P_Max model against three site-specific 

variables: (1) the proportion of observed toxicity based on the Sig Only classification, (2) 

control-adjusted survival, and (3) the proportion of toxicity based on MSD classification. 

 Figures 28 through 32 show that the nationwide P_Max model provides a useful basis for 

evaluating toxicity test results for individual regions included in the database. There were 

significant relationships between the P_Max model predictions and observed proportion of 

toxicity, but the relationships were not one-to-one, indicating that regions differed from the 

nationwide model by intercept or slope or both (left-hand graphs in Figures 28 through 32). The 

differences were not consistent across the regions. The probability of toxicity was strongly 

related to percent control-adjusted survival for all regions except California (center graphs in 

Figures 28 through 32). For the California data (right-hand graph in Figure 32), comparing the 

P_Max model (which was derived using the Sig Only toxicity classification) with observations 

classified using the MSD approach greatly improved the strength of the relationship. For the 

other regions, using the MSD approach only slightly clarified the relationship, mostly at low 

probability of toxicity. 

 In theory, model predictions also could be compared with observations from studies with 

fewer samples (e.g., from a smaller area). Comparisons are most useful if the observed data 

represent a large range of predicted probabilities of toxicity (left-hand graph in Figure 33). 

Studies with observations from a small range of predicted probabilities may show no apparent 

relationship (right-hand graph in Figure 33). A consistently high proportion of toxic samples 

(left-hand graph in Figure 34) may show a stronger relationship with the nationwide model by 

classifying toxic samples using an MSD approach (right-hand graph in Figure 34). 

 

5.5. APPLICATION OF THE MODELS TO AN INDEPENDENT DATA SET 

 Application of the models to independent data (data not used in model derivation) was an 

important step in evaluating the models. The P_Max model was applied to a small independent 

data set consisting of three studies from the Calcasieu Estuary (Louisiana) that had matching 

sediment chemistry and toxicity data for A. abdita (Redmond et al., 1996; unpublished data set 
 46 

provided electronically by P. Crocker, U.S. EPA, Region 6, Dallas, TX; MacDonald et al., 

2001). The Calcasieu Estuary is a highly contaminated industrial waterbody that is included on 

the National Priorities List of hazardous waste sites. The Calcasieu Estuary data were not 

included in the database used to derive the models. The data set contains 170 matched chemistry 

and toxicity test results using A. abdita and represents a wide range of contaminant 

concentrations. 

 There was a strong linear relationship between the predicted probability of toxicity and 

the proportion of toxicity observed, based on both the Sig Only and the MSD classifications 

(left-hand and right-hand graphs in Figure 35). In general, the frequency of toxicity observed in 

the Calcasieu Estuary samples was greater than that predicted by the P_Max model. The 

predicted probability of toxicity showed a strong relationship with control-adjusted survival 

(center graph in Figure 35). 

5.6. SUMMARY AND CONCLUSIONS 

 We developed multiple-chemical models to combine the individual chemical models into 

a single prediction of the toxicity of sediment samples with known sediment chemistry. Two 

approaches for combining the individual chemical models (the P_Max and P_Avg) produced 

models that accurately predicted the frequency of toxicity to amphipods observed in the 

database: 

• P_Max is the maximum probability of observing toxicity for a sample, taken from the 

set of probabilities calculated for each individual chemical in the sample, and 

• P_Avg is the mean probability of observing toxicity for a sample, based on the set of 

probabilities calculated for each individual chemical in the sample. 

 The P_Max model explained 92% of the variation in the frequency of toxic samples 

observed in probability intervals constructed using the entire data set. The R-squared value for 

models using the P_Avg value was slightly lower. 

 We used the P_Max model to evaluate several issues: the relationship between the 

probability of observing a toxic effect and the magnitude of toxicity, the identification of 

chemicals most influential in model performance, the effect of reducing the number of individual 

chemicals models used in developing the multiple-chemical models, the performance of the 
 47 

models in predicting toxicity of the two amphipod species, and the performance of the models in 

predicting toxicity observed in individual studies and regional areas. 

 The magnitude of the effect (decreased survival) in the amphipod test increased as the 

probability of toxicity increased, demonstrating that samples that are estimated to have the 

highest probability of toxicity are also likely to be extremely toxic. 

 For approximately 70% of the samples, individual chemical regression models for metals 

produced the maximum probability used in the P_Max model. This should not be construed to 

imply that metals were causing toxicity in these samples, only that metals appear to be a good 

predictor of toxicity in field-collected samples. Indeed, removing metals (or other entire 

chemical classes) from the suite of individual chemical models used to generate the P_Max 

model resulted in only minor changes in the model and model fit. 

 The P_Max model also provides a useful basis for comparing the species-specific 

differences in model performance. As observed with the individual chemical models, the 

observed toxicity was almost always less than predicted for A. abdita and greater than predicted 

for R. abronius. However, the distribution of the data differs substantially for the two species 

across the probability of toxicity predicted by the P_Max model. Nevertheless, the observed 

proportion of toxicity observed in both species was strongly related to the nationwide model, 

affording confidence that the combined-species P_Max model provides a common framework 

applicable to both species. 

 Although the P_Max model reliably predicted toxicity for the entire database used to 

derive the models, it may not accurately predict observed toxicity at a particular site if the 

chemical mixture is very different from the distribution of mixtures in the nationwide database or 

if site-specific factors influence the degree of toxicity (e.g., bioavailability). To address this 

issue, we examined the performance of models in predicting observed toxicity for individual 

studies within the database. On a study-by-study basis, there was mixed agreement between the 

frequency of observed toxicity and that predicted by the P_Max model. The mixed performance 

emphasizes the importance of calibrating the national models with site-specific data (discussed 

further in Section 6.4). Still, the nationwide P_Max model provided a useful, common basis for 

evaluating toxicity test results for individual sites included in the database. 

 Application of the model to regional subsets of the database used to derive the model 

demonstrated significant relationships between the P_Max model predictions and both observed 

proportion toxicity and percent control-adjusted survival. There was also a strong relationship 
 48 

between predicted toxicity and observed toxicity in the Calcasieu Estuary, a data set not included 

in the original derivation of the models. 
 49 

6. APPLICATIONS OF MODELS 

6.1. INTRODUCTION 

 This chapter discusses the application of the single-chemical and the multiple-chemical 

LRMs to problems that are frequently encountered by risk assessors. Section 6.2 discusses the 

relevance of the models to evaluating other toxicity endpoints and test systems, including those 

commonly used in freshwater systems. Section 6.3 discusses the models in the context of other 

empirical approaches for evaluating risks associated with individual chemicals found in 

sediments. Finally, in section 6.4, we discuss how these models might best be used to evaluate 

the risks of sediment contamination at specific sites or regions. 

6.2. APPLICATION OF THE P_MAX MODEL TO DATA FOR OTHER ENDPOINTS 

 The SEDTOX02 database contains matched sediment chemistry and toxicity test data for 

endpoints other than the two marine amphipods. The data available for two sea urchins 

(Strongylocentrotus purpuratus and Arbacia punctulata) are presented in Tables 3 through 5. 

Data for the freshwater amphipod (Hyalella azteca) and midges (Chironomus tentans and 

Chironomus riparius) are shown in Tables 6 through 9. These data proved to be insufficient to 

support models for many chemicals of concern for these test endpoints; instead, they were used 

to evaluate the application of the marine amphipod models to these other endpoints. 

 Models for the marine amphipods were applied to the sea urchin embryological 

development and fertilization data sets. The data for the two urchin species were combined for 

each of the development (A. punctulata 48h and S. purpuratus 96h) and fertilization (1h) 

endpoints. The relationship between the average predicted probability of toxicity from the 

P_Max model and the proportion of toxicity observed within probability intervals is shown in 

Figure 36. Note that there is a very weak relationship between the model and both urchin 

response variables. However, if the data for the two urchin species are evaluated separately, A. 

punctulata shows a strong relationship for the development endpoint (Figure 37) and a weaker 

relationship for the fertilization endpoint (Figure 38). The results for S. purpuratus demonstrated 

no relationship between the predicted probability of toxicity and either the development or the 

fertilization endpoint (Figures 39 and 40). 

 Models for the marine amphipods were applied to freshwater data sets for three 

freshwater endpoints, H. azteca 10–14-day survival, Chironomus spp. 10–14-day survival, and 
 50 

H. azteca 28-day growth and survival. The proportion of toxicity observed using H. azteca 

(Figure 41) and Chironomus (Figure 42) 10–14-day survival tests was less than that predicted by 

the P_Max model. Below the mean predicted probability of 0.75, observed proportion of toxic 

samples was consistently low (<25%). This relationship suggested a threshold, so we used a 

spline model to describe the relationships (General Additive Model with 2 degrees of freedom 

[Insightful Corp., 2001]). The spline model explained 51% and 64% of the variability seen in 

the H. azteca and Chironomus data, respectively. One possible explanation for this type of 

threshold relationship is that the marine amphipod toxicity tests respond at lower concentrations 

of chemicals in sediments than do the freshwater short-term toxicity tests. 

 The H. azteca 28-day growth and survival endpoint showed a much stronger relationship 

between the predicted probability based on the P_Max model and the observed proportion toxic 

in a relatively small data set (n = 126) (Figure 43). There is a strong relationship (R2 = 0.95) 

between the probability of toxicity based on the P_Max model versus the H. azteca 28-day 

growth and survival endpoint (Figure 43). 

 In summary, the results of comparing other endpoints with the P_Max model predictions 

suggest that the marine amphipod models are useful for predicting sea urchin response on the 

basis of embryological development or fertilization tests for A. punctulata but not for S. 

purpuratus. The marine amphipod models also have utility for predicting the response of 

freshwater amphipods, particularly the 28-day H. azteca growth and survival endpoint. There is 

the potential for developing endpoint-specific models as more data are acquired. 

 

6.3. USING LRMs TO EVALUATE EXISTING EMPIRICAL GUIDELINES 

 Hazardous waste site evaluations often involve the collection of substantial quantities of 

sediment chemistry data, and these data are frequently used to support screening-level ecological 

risk assessments. To evaluate such data, sediment assessors often use numerical sediment 

quality guidelines (SQGs) such as threshold effect levels (TELs) and probable effect levels 

(PELs), effect range low (ERL) and effect range median (ERM), and apparent effect thresholds 

(AETs) (Gries and Waldow, 1996; Long and Morgan, 1991; Long and MacDonald, 1992; Smith 

et al., 1996; Ingersoll et al., 1996, 2001, 2002; MacDonald et al., 1996, 2001). 

 Although derivation methods for the different SQGs are well described and are consistent 

for all the chemicals within a given type of SQG, there is no straightforward method that enables 

the user to either evaluate the degree to which individual SQGs meet their objectives or compare 
 51 

the risk levels associated with different SQGs. The logistic model approach provides a way to 

put the individual SQG values into perspective by estimating the probability of observing 

toxicity to amphipods at the chemical concentrations defined by the SQGs. Examples are shown 

in Table 32 for three commonly used sets of SQGs that represent a range of threshold values: 

TELs and PELs, ERLs and ERMs, and AETs. ERLs and TELs represent chemical 

concentrations below which toxicity would be expected to occur infrequently (<25%) (Long and 

Morgan, 1991; MacDonald et al., 1996), whereas effects are expected to be frequently observed 

at concentrations exceeding PEL and ERM concentrations. In contrast, endpoint-specific AET 

values represent concentrations above which toxicity is always expected for that endpoint. 

 The results are generally consistent with the narrative intent of the SQGs for most of the 

chemicals for which SQGs had been derived. The highest probability of observing toxicity to 

amphipods was noted for the amphipod AETs, with an estimated proportion of toxic samples 

ranging from 45 to 99% and a median value of 90%. The predicted probability of observing 

toxicity was lower for the PEL and ERM values, with median values of 55% and 72%, 

respectively. At concentrations corresponding to the TELs, predicted probabilities of observing 

sediment toxicity ranged from 10 to 41% (depending on the chemical under consideration), with 

the probability of toxicity below 25% for 24 of the 27 chemicals considered (Table 32). The 

probability of observing sediment toxicity was a little higher at the ERL concentrations (ranging 

from 11 to 47%), with a median value of 33%. The probabilities may be higher than expected 

for ERLs and TELs because they are calculated for individual chemicals. In practice, these 

guidelines are most appropriately applied jointly; that is, a sediment sample has a low probability 

of causing toxicity if all chemicals are below the ERL or TEL. 

 The LRMs help users select the sediment effect concentrations that most directly meet 

the needs of their specific application. T10, T15, or T20 values could be calculated and used to 

identify concentrations for individual contaminants that are likely to be associated with a 

relatively low incidence of sediment toxicity (10, 15, or 20%, respectively). Such point 

estimates of minimal-effect concentrations might be used in a screening assessment to identify 

sediments that are relatively uncontaminated and have a low probability of sediment toxicity. As 

discussed above, this evaluation would best be conducted by evaluating all chemicals 

simultaneously. For example, of the samples having all chemicals at concentrations below their 

respective T20 value, 19% were toxic, based on the Sig Only classification. 
 52 

 Similarly, contaminant concentrations for which there is a high probability of observing 

adverse effects could be estimated by calculating T70, T80, or T90 values. These higher point 

estimates could be used to identify sediments that are highly likely to be toxic to amphipods and 

to have a greater magnitude of effect (i.e., higher percent mortality). The Tp values can be used 

in much the same way as other sediment guidelines, with the difference that the Tp value is 

associated with a specific probability of observing toxicity and can include confidence bounds on 

the sediment concentrations associated with a given Tp value. 

 Individual chemical SQGs are useful for identifying thresholds below which sediment 

toxicity is unlikely to be observed and above which sediment toxicity is likely to occur. 

However, it is difficult to determine the extent to which risk increases with the magnitude of 

exceedance of an SQG. In addition, individual SQGs must be combined to address risks 

associated with mixtures. To address both of these issues, practitioners have applied hazard 

quotient approaches, which sum the ratios of the measured concentration of each contaminant to 

its corresponding toxicity threshold. Several investigators have applied mean SQG quotients to 

evaluate mixtures of contaminants in field-collected sediment samples (Long et al., 1998; 

MacDonald et al., 2000; Fairey et al., 2001, Ingersoll et al., 2001). Such evaluations are based 

on an assumption that concentration-response relationships for each chemical are similar. The 

logistic regression modeling approach avoids this assumption by fitting concentration-response 

relationships separately for each chemical and then standardizing the response variables to values 

between 0 and 1. 

 Another approach to evaluating risks associated with sediment chemistry is the 

equilibrium partitioning (EqP) approach. EqP links sediment chemistry values with biological 

effects by combining the results of controlled laboratory tests using manipulated concentrations 

of chemicals with theory on the factors controlling bioavailability (Di Toro et al., 1991, 2000). 

EqP values represent concentrations of individual chemicals below which effects would not be 

expected to occur. Individual EqP values cannot be directly compared with SQGs derived using 

field-collected data (including Tp values) because the EqP values reflect the toxicity attributable 

to individual chemicals, whereas the SQGs reflect the toxicity of the mixture. For example, the 

Tp values associated with EqP values for individual PAHs are very high (Table 33), a result that 

is consistent with a high degree of covariation among PAHs in sediments. 

 The EqP approach can be used to estimate the toxicity associated with a mixture of PAHs 

using a toxic unit approach (Di Toro et al., 2000). Again, any comparison with empirical 
 53 

approaches is imperfect because the latter reflect the contribution of chemicals other than PAHs 

to toxicity. The PAH toxic units were calculated on the basis of EPA’s Final Chronic Value 

following the methods described in EPA’s National Sediment Quality Survey (U.S. EPA, 2004). 

Most of the samples in the database had PAH toxic units less than 1. Both the P_Max values and 

the proportion of toxic samples in the database increased with increasing PAH toxic units (Table 

34). Below a PAH toxic unit of 1, 37% of the samples were toxic. At PAH toxic units of 10 and 

greater, 81% of the samples were toxic. 

 In summary, the LRMs provide a useful framework for evaluating the degree of risk 

associated with commonly used SQGs. Logistic models have several advantages over current 

SQGs approaches: (a) they present risk on a continuous quantitative scale rather than by 

defining discrete categories based on threshold values, (b) the continuous estimates of risk allow 

users to match the degree of risk with their objectives, (c) they express risk on a common scale 

of 0 to 1 across all chemicals, and (d) they provide a more direct avenue for assessing risk of 

multiple chemicals. 

6.4. APPLICATION OF MODELS TO EVALUATIONS OF SITE-SPECIFIC OR 

REGIONAL DATA 

 The models described in this report were derived from a large database of matching 

sediment chemistry and toxicity that included data from many different coastal areas of North 

America and many different chemical gradients. The models make predictions based on the 

central tendency of the relationship between sediment chemistry and a toxic outcome across this 

broad gradient; therefore, the models can be used to support screening-level assessments that 

roughly rank or prioritize samples on the basis of sediment chemistry, particularly when 

concentrations of chemicals vary over a wide range. For example, the National Sediment 

Quality Survey (U.S. EPA, 2004) used these models to help classify locations into three tiers 

reflecting the probability of adverse effects. 

 However, because the models do not consider potential differences in bioavailability, test 

species, or site-specific mixtures of chemicals, the probability of toxicity may be over- or 

underestimated for some locations. As discussed in Section 5.4.6, the differences between 

quantitative model predictions and site- or region-specific observations can be substantial. 

Although the derivation of site-specific models may be desirable, data from an individual site are 

rarely sufficient to support model derivation. Rather than deriving site- or regional-specific 
 54 

models with a small data set, we recommend using site-specific data to determine how well the 

nationwide models fit the local situation. 

 The evaluation of the independent data set from the Calcasieu Estuary provides an 

example. By comparing the proportion of toxic samples or the mean control-adjusted survival 

with the mean predicted probability of toxicity within discrete probability ranges (e.g., 

probability quartiles, as shown in Table 30, or probability intervals if sufficient data are available 

[Figure 35]), the performance of the models with data from the site can be evaluated. In the best 

case, the observed proportion of toxic samples will closely match that predicted by the model. In 

this case, the models can be applied with confidence to samples having only sediment chemistry. 

If the relationship between observed and predicted proportions of toxic samples is strong but 

different, the relationship can still be used to predict site-specific toxicity. In addition, a strong 

relationship between the percent survival and the predicted probability of toxicity provides 

important information on the magnitude of the response. If the relationship between predicted 

and observed proportions of toxic samples or percent survival is weak, then the models are likely 

to be less useful for the site. 

 Although there are no universal criteria for determining whether a model fit is acceptable, 

standard regression techniques and diagnostic plots can be used to identify whether specific 

values are overly influencing the relationship or the assumptions of linear regression are not met 

(Neter et al., 1996). 

 To best compare model predictions with site-specific observations, we recommend 

collecting matching chemistry and toxicity testing results from samples that have a wide range of 

chemical gradients and predicted probabilities of toxicity. It is particularly important to collect 

sufficient data from areas of high and low probability of toxicity to better define the 

relationships. Therefore, LRMs have great utility in helping to design sampling programs. 

 The models can also suggest issues that require further investigation. If the models 

predict a higher proportion of toxic samples than the proportion observed (false positives), then 

issues related to bioavailability may be investigated further. The individual chemical models 

could be used to determine whether specific chemical models are associated with the high false 

positive rate. If toxicity occurs at a much higher frequency than predicted (false negatives), then 

it may be important to consider chemicals not accounted for (e.g., no models available) or issues 

related to the sediment matrix (e.g., grain size effects). 

 
 55 

6.5. SUMMARY AND CONCLUSIONS 

 In this chapter we discussed the application of the single-chemical and multiple-chemical 

LRMs to problems that are frequently encountered by risk assessors. We evaluated the 

relationship between model predictions and the results of other toxicity endpoints, including 

those commonly used in freshwater systems. The P_Max model predictions appear to be useful 

for predicting sea urchin response for A. punctulata, based on development or fertilization tests, 

but not S. purpuratus. The models are also useful for predicting the response of freshwater 

amphipods, particularly the 28-day H. azteca growth and survival endpoint. There is the 

potential for developing endpoint-specific models as more data are acquired. 

 The LRMs provide a useful framework for evaluating the degree of risk associated with 

commonly used SQGs. The probabilities of toxicity associated with SQG threshold values are 

generally consistent with their narrative intent. However, LRMs have several advantages over 

current guideline approaches. They present risk on a continuous quantitative scale rather than by 

defining discrete categories based on threshold values. The continuous estimates of risk allow 

users to match the degree of risk with their objectives, and risk is expressed on a standardized 

scale of 0 to 1 across all chemicals. The logistic models provide a direct yet flexible avenue for 

assessing risk of multiple chemicals and provide the basis for more quantitatively evaluating the 

reliability and fit of these models to observations. 

 Finally, we discussed how these models might best be used to evaluate the risks of 

sediment contamination at specific sites or regions. The LRM approach can be used to conduct 

screening-level assessments that roughly classify or prioritize samples on the basis of sediment 

chemistry. Because the models do not consider potential differences in bioavailability or 

exposure, the probability of toxicity may be over- or underestimated for some locations. For 

applications that require a greater degree of accuracy (e.g., remediation decisions), we 

recommend first evaluating how well the models fit the local situation by collecting a test set of 

matching sediment chemistry and toxicity test data. The LRMs can be used to design effective 

test sampling programs, and they can also suggest issues requiring further investigation (e.g., 

bioavailability). The LRMs may be most useful for classifying samples into broad categories of 

concern on the basis of sediment chemistry. 
 56 

7. CONCLUSIONS AND RECOMMENDATIONS 

7.1. CONCLUSIONS 

 A large database of matching sediment chemistry and toxicity data was carefully 

evaluated and assembled. The database encompasses many different contaminant gradients from 

a wide variety of habitats in coastal North America. Using this database, LRMs were developed 

for 37 individual chemicals that describe relationships between the concentrations of sedimentassociated contaminants and acute toxicity to two commonly tested species of marine 

amphipods: Rhepoxynius abronius and Ampelisca abdita. 

 The chemical-specific models that were derived in this investigation provide a basis for 

estimating the proportion of samples expected to be toxic over a wide range of contaminant 

concentrations for 37 individual contaminants. As such, these models help users select the 

sediment effect concentrations that most directly meet the needs of their specific application. 

For example, T10, T15, or T20 values could be calculated and used to identify concentrations for 

individual contaminants that are likely to be associated with a relatively low incidence of 

sediment toxicity (10, 15, or 20%, respectively). Such point estimates of minimal-effect 

concentrations might be used in a screening assessment to identify sediments that are relatively 

uncontaminated and have a low probability of sediment toxicity. Similarly, contaminant 

concentrations for which there is a high probability of observing adverse effects could be 

estimated by calculating T70, T80, or T90 values. These higher point estimates could be used to 

identify sediments that are highly likely to be toxic to amphipods and have a greater magnitude 

of effect (i.e., higher percent mortality). 

 The Tp values can be used in much the same way as other sediment guidelines, with the 

difference that the Tp value is associated with a specific probability of observing toxicity and an 

estimate of variance based on the fit of the model. Although the LRMs do not represent doseresponse relationships for individual chemicals, they can be considered indicators of toxicity 

based on field-collected sediment chemical mixtures. 

 Because the individual models were derived from field-collected sediments that include 

mixtures of contaminants rather than from individual dose-response relationships, to some extent 

they incorporate the overall toxicity of the mixtures. Combining the individual model results 

into a single explanatory variable provided a way to estimate the probability that a particular 
 57 

sample will be toxic. Two combined explanatory values (P_Max and P_Avg) accurately 

predicted the frequency of toxicity to amphipods observed in the database: 

• P_Max is the maximum probability of observing toxicity, taken from the set of 

probabilities calculated for each individual chemical in the sample, and 

• P_Avg is the mean probability of observing toxicity, based on the set of probabilities 

calculated for each individual chemical in the sample. 

 The logistic regression approach served as a framework for evaluating several issues that 

form the basis for our recommendations for using the models. We used the single-chemical 

models to evaluate two approaches for designating samples as toxic: (1) less than 90% survival 

that was significantly different from negative control samples (Sig Only), and (2) controlnormalized survival less than 80% that was significantly different from negative control samples 

(MSD) (based on analyses by Thursby et al., 1997). Although the Sig Only approach had a 

greater tendency to underestimate the toxicity observed at low concentrations, this discrepancy 

may be explained by the presence of other chemicals in the sample. The MSD approach had a 

greater tendency to overestimate the toxicity observed at higher concentrations. 

 We also evaluated two approaches for normalizing sediment chemistry: dry-weight 

normalized and organic carbon normalized. The models based on organic carbon-normalized 

sediment chemistry had lower goodness-of-fit statistics than the dry-weight-normalized models 

and larger differences between observed and predicted proportions of toxic samples. 

 We used the single-chemical models to evaluate three screening criteria used to decide 

whether a given sample was included in the model data set for an individual chemical: (1) 

include all samples (unscreened), (2) exclude toxic samples that were less than or equal to the 

mean of nontoxic samples from the same study (1X screening), and (3) exclude toxic samples 

that were less than or equal to two times the mean of nontoxic samples from the same study (2X 

screening). The models developed using 1X screening showed much stronger relationships 

between chemistry and toxicity than was observed when all samples were included. The 1X 

screening approach performed slightly better than the 2X screening approach at concentrations 

above the T80 value. In addition, the 1X approach screened out considerably fewer toxic 

samples in the model derivation than did the 2X approach, which may prove important in the 

future development of models for less frequently measured chemicals. We selected the 1X 
 58 

screening approach to limit the many permutations with further model development and 

exploration. 

 The multiple-chemical models were used as a framework for evaluating several 

additional issues, including the relationship between the probability of observing a toxic effect 

and the magnitude of toxicity, the identification of chemicals most influential in model 

performance, the performance of the models in predicting toxicity of the two amphipod species, 

and the performance of the models in predicting toxicity observed in regional data sets or in 

individual studies. 

 The magnitude of the effect (decreased survival) in the amphipod test increased as the 

probability of toxicity increased, demonstrating that samples that are estimated to have the 

highest probability of toxicity are also likely to be extremely toxic. 

 For approximately 70% of the samples, individual chemical regression models for metals 

produced the maximum probability used in the P_Max model. This should not be construed to 

imply that metals were causing toxicity in these samples, only that metals appear to be a good 

predictor of toxicity in field-collected samples. Indeed, removing metals (or other entire 

chemical classes) from the suite of individual chemical models used to generate the P_Max 

model resulted in only minor changes in the model and model fit. 

 Models were developed by combining data from tests that used two species of marine 

amphipod (R. abronius or A. abdita) in order to encompass more areas of the country and a 

broader range of sediment chemistry. The P_Max model provided a useful basis for examining 

differences in model performance for the two species. The observed toxicity was frequently less 

than predicted for A. abdita and greater than predicted for R. abronius. Nevertheless, the 

observed proportion of toxicity in the data for both species was strongly related to the nationwide 

model, affording confidence that the combined-species P_Max model provides a common 

framework applicable to both species. 

 We examined the performance of models in predicting observed toxicity for individual 

studies within the database. On a study-by-study basis, there was mixed agreement between the 

frequency of observed toxicity and that predicted by the P_Max model. The mixed performance 

suggests that the national models should not be applied to individual studies without first 

evaluating their performance with matching site-specific toxicity and chemistry data. However, 

the nationwide P_Max model provided a useful common basis for evaluating toxicity test results 

for individual sites included in the database. Application of the model to regional subsets of the 
 59 

database used to derive the model demonstrated significant relationships between the P_Max 

model predictions and both the observed proportion of toxic samples and percent controladjusted survival. There was also a strong relationship between the predicted and observed 

proportion of toxic samples in the Calcasieu Estuary, a data set not included in the original 

derivation of the models. 

 

7.2. RECOMMENDATIONS 

 Our analyses and model comparisons resulted in several recommendations. As a starting 

point for most evaluations, we recommend using the P_Max model, which is derived from the 

highest predicted probability from any of the individual chemical models. We recommend using 

the model based on data from all studies (i.e., the nationwide model), on both marine amphipod 

species, and on 37 chemical-specific models. For the chemical-specific models, we recommend 

the models that classified samples as toxic on the basis of less than 90% survival that was 

significantly different from negative control samples (Sig Only) and that screened the data set by 

excluding toxic samples that were less than or equal to the mean of nontoxic samples from the 

same study (1X screening). The bases for these recommendations are summarized briefly below. 

 The P_Max model, which is based on the highest predicted probability from any of the 

individual chemical models, explained slightly more variation in the data set than did the P_Avg 

model. The two models provide slightly different insights into sediment toxicity. P_Avg may 

better reflect the overall degree of contamination, and it is less susceptible to overestimating the 

probability of toxicity at sites with high concentrations of one chemical. P_Max more accurately 

predicted toxicity at sites having high concentrations of more than one chemical. 

 We recommend using the nationwide model that combines data for both species of 

marine amphipods. Combining data across studies and species represents the fullest range of 

chemical concentrations and environmental conditions. In addition, the nationwide combined 

model provides a common framework for comparing site- and species-specific results. 

 We recommend basing the models on the Sig Only classification so that more subtle 

changes can be retained, particularly at lower concentrations. It may be valuable to compare the 

Sig Only nationwide model to site-specific data classified using the MSD approach when test 

variability obscures the relationship between chemistry and response at lower concentrations. 

 Finally, the chemical-specific models were greatly improved by using a screened data set 

that excluded toxic samples that were less than or equal to the mean of nontoxic samples from 
 60 

the same study. Using a more stringent criterion, such as excluding toxic samples that were less 

than or equal to two times the mean of nontoxic samples from the same study, resulted in 

additional improvements for most chemicals. However, the more stringent criterion excluded an 

average of 70% of the toxic samples, which may limit future development of models for other 

endpoints, regions, or chemicals that have fewer total samples. We concluded that the small 

improvements in model fit did not outweigh the associated reduction in sample size. 

 

7.3. APPLICATIONS 

 The chemical-specific models provide a basis for estimating the probability that a sample 

will be toxic for 37 individual contaminants over a wide range of contaminant concentrations. In 

addition, they provide a useful framework for evaluating the degree of risk associated with 

commonly used SQGs. The probabilities of toxicity associated with SQG threshold values are 

generally consistent with their narrative intent. However, LRMs have several advantages over 

current guideline approaches: they present risk on a continuous quantitative scale rather than by 

defining discrete categories based on threshold values, the continuous estimates of risk allow 

users to match the degree of risk with their objectives, and they express risk on a common scale 

of 0 to 1 across all chemicals. The individual chemical models would be expected to 

underestimate the probability of observing toxicity in samples that are contaminated with many 

chemicals. For this purpose, we recommend using the multiple-chemical models that combine 

the individual model results into a single explanatory value for estimating the probability that a 

sample will be toxic. 

 The multiple-chemical models provide a useful framework for conducting screeninglevel assessments that require classifying or prioritizing samples on the basis of sediment 

chemistry. Because the models do not consider potential differences in bioavailability or 

exposure, the probability of toxicity may be over- or underestimated for some locations. Before 

applying the models to a particular site, we recommend first evaluating how well the models fit 

the local situation by collecting a test set of matching sediment chemistry and toxicity test data. 

The LRMs can be used to design effective test sampling programs, and they can also suggest 

issues that require further investigation (e.g., bioavailability). The LRMs should not be 

considered a complete substitute for direct-effects assessment (e.g., toxicity tests). 

 We evaluated the relationship between model predictions and the results of other toxicity 

endpoints, including those commonly used in freshwater systems. The P_Max model predictions 
 61 

appear to be useful for predicting sea urchin response for A. punctulata, based on development or 

fertilization tests, but not for S. purpuratus. The models may also be useful for predicting the 

response of freshwater amphipods, particularly the 28-day H. azteca growth and survival 

endpoint. There is the potential for developing endpoint-specific models as more data are 

acquired. 

7.4. FUTURE DIRECTIONS 

 The results of this study suggest many promising avenues for future work. First and 

foremost, this research provides evidence of the value of combining and standardizing 

information from many different studies. Future efforts directed at encouraging investigators to 

add data to the SEDTOX02 database will enable the investigation of additional species, test 

endpoints, and chemicals. Providing funds and mechanisms to share the database with the 

scientific community will advance our knowledge of the effects of sediment chemicals on 

aquatic organisms. 

 We hope that the release of the SEDTOX02 database will prompt continuing refinement 

and exploration of modeling approaches linking toxicity test results with sediment chemistry. 

Possibilities include optimizing the screening approach, evaluating the minimum number of 

chemicals that need to be included for acceptable performance of the multiple-chemical models, 

exploring alternative multivariate and logistic modeling approaches, and investigating the 

reasons for variable model performance in individual studies. 

 Additional guidance on applying these models to site-specific assessments would 

increase their use and foster consistent application. Guidance development would be aided by 

testing the models with additional independent data sets and developing case studies that 

illustrate their application. 

 We hope that this study will prompt the collection of more data sets that match toxicity 

test results with sediment chemistry. Additional data from freshwater systems are especially 

needed. The most useful data would be broad-scale surveys that include contaminated sites. 

Sediments should be analyzed for the full range of chemical classes and tested using highquality, consistent toxicity test methods. 

 The results of this study suggest that additional work is needed to characterize the 

bioavailability of chemicals to sediment-dwelling organisms. Contrary to expectation, 

normalizing chemical concentrations to TOC did not improve model fits. As additional data are 
 62 

acquired, we may be able to evaluate the effectiveness of acid volatile sulfides as a normalizing 

factor for metal concentrations. 

 Finally, the toxicity endpoint modeled in this project serves as a surrogate for valued 

ecological attributes that are more difficult to test and measure. These include the structure and 

function of benthic communities, population viability of wildlife that depends on the benthos, 

and ecosystem functions such as organic matter decomposition and water filtration. Additional 

research is needed to improve the linkages between these valued endpoints, toxicity test results, 

and sediment chemistry. 
 63 

Table 1. Number of samples and percent toxic samples summarized by 

marine amphipod species and data sourcea

Ampelisca abdita Rhepoxynius abronius

Data Number of Percent toxic Number of Percent toxic 

source samples Sig Only MSD samples Sig Only MSD 

EMAP 1203 22.2 9.5 NA NA NA 

NSTP 649 23.7 15.6 NA NA NA 

MLML 43 11.6 7 465 72.7 52.3 

SEDQUAL NA NA NA 594 63.5 34 

BEDS 117 41 30.8 152 36.8 32.2 

Total 2012 23.6 12.6 1211 63.7 40.8 

a

Samples were classified as toxic if significantly different from control and less than 90% survival (Sig 

 Only) and if significantly different from control and less than 80% control-adjusted survival (MSD). 

BEDS = Biological Effects Database for Sediments, MacDonald Environmental Sciences 

EMAP = U.S. Environmental Protection Agency Estuarine Monitoring and Assessment Project 

MLML = Moss Landing Marine Laboratory (California) 

NSTP = National Oceanic and Atmospheric Administration Status and Trends Program 

SEDQUAL = Sediment Quality Information System, Washington State Department of Ecology 

NA = no data 
 64 

Table 2. Distribution of chemical concentrations in sediment samples with 

matching toxicity data for marine amphipods 

Percentile 

Chemical 

Number of 

samples 10th 50th 90th

Metals (mg/kg dry wt.) 

 Antimony 2173 0.2 0.7 2.9 

 Arsenic 2844 2.2 7.4 19 

 Cadmium 2958 0.05 0.3 1.9 

 Chromium 2827 9.1 50 130 

 Copper 3091 2.6 26 160 

 Lead 3010 5.5 23 130 

 Mercury 2788 0.02 0.1 0.8 

 Nickel 2916 2.4 19 44 

 Silver 2552 0.03 0.2 1.9 

 Zinc 3013 16 89 300 

Polycyclic aromatic hydrocarbons (μg/kg dry wt.)

 1-Methylnaphthalene 1677 0.5 6.5 60 

 1-Methylphenanthrene 1697 0.3 11 130 

 2,6-Dimethylnaphthalene 1505 0.4 6.3 67 

 2-Methylnaphthalene 2077 0.8 12 130 

 Acenaphthene 1795 0.2 7 130 

 Acenaphthylene 1747 0.2 7 120 

 Anthracene 2268 0.5 20 410 

 Benz(a)anthracene 2574 1.2 40 760 

 Benzo(a)pyrene 2526 1.2 53 910 

 Benzo(b)fluoranthene 1645 0.9 48 1000 

 Benzo(g,h,I)perylene 2210 1.1 46 550 

 Benzo(k)fluoranthene 1691 0.5 26 620 

 Biphenyl 1507 0.5 6.8 54 

 Chrysene 2650 1.7 53 1000 

 Dibenz(a,h)anthracene 1886 0.2 14 170 

 Fluoranthene 2734 3 81 1400 

 Fluorene 2011 0.4 11 160 

 Indeno(1,2,3-c,d)pyrene 2212 0.9 47 600 

 Naphthalene 2201 1.8 16 220 

 Perylene 2174 1.5 36 370 

 Phenanthrene 2688 1.7 44 660 

 Pyrene 2768 3.2 87 1500 

Polychlorinated biphenyls (μg/kg dry wt.)

 PCBs, total 1989 2 39 640 

Organochlorine pesticides (μg/kg dry wt.)

 Dieldrin 770 0.04 0.8 5.1 

 p,p'-DDD 1672 0.08 1.8 20 

 p,p'-DDE 1899 0.08 2.2 59 

 p,p'-DDT 1176 0.06 1.2 18 
 65 

Table 3. Number of samples and percent toxic samples summarized by sea 

urchin species and test endpoint 

Development Fertilization 

Number of Percent toxic Number of Percent toxic 

Urchin species samples Sig Only MSD samples Sig Only MSD 

A. punctulata 472 65 64.6 612 40.5 37.1 

S. purpuratus 310 61.6 58.1 212 62.3 61.8 

Total 782 63.7 62 824 46.1 43.5 
 66 

Table 4. Distribution of chemical concentrations in sediment samples with 

matching sea urchin fertilization toxicity data 

Percentile 

Chemical 

Number of 

samples 10th 50th 90th

Metals (mg/kg dry wt.) 

 Arsenic 786 2.1 7.6 18 

 Cadmium 724 0.03 0.2 1.5 

 Chromium, total 788 5.9 43 110 

 Copper 788 2 21 150 

 Lead 781 3.4 23 120 

 Mercury 709 0.02 0.1 0.6 

 Nickel 768 1.3 14 28 

 Silver 622 0.03 0.3 1.5 

 Zinc 789 9.4 82 290 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.)

 2-Methylnaphthalene 572 0.9 9.3 64 

 Acenaphthene 492 0.3 5.7 110 

 Acenaphthylene 533 0.3 6.5 69 

 Anthracene 642 0.7 16 340 

 Benz(a)anthracene 718 1.5 33 840 

 Benzo(a)pyrene 723 1.5 51 1030 

 Chrysene 749 2 46 1160 

 Dibenz(a,h)anthracene 595 0.3 12 150 

 Fluoranthene 770 3.9 81 1660 

 Fluorene 574 0.5 5.1 110 

 Naphthalene 604 2 9.8 84 

 Phenanthrene 736 2 27 600 

 Pyrene 754 4.7 86 1660 

 

Polychlorinated biphenyls (μg/kg dry wt.)

 PCBs, total 622 2 36 290 

Organochlorine pesticides (μg/kg dry wt.)

 DDT, total of six isomers 590 0.6 6.9 82 

 p,p'- DDE 566 0.2 3.2 40 
 67 

Table 5. Distribution of chemical concentrations in sediment samples with 

matching sea urchin developmental toxicity data 

Percentile 

Chemical 

Number of 

samples 10th 50th 90th

Metals (mg/kg dry wt.) 

 Arsenic 680 2.5 8.4 18 

 Cadmium 635 0.04 0.2 1.3 

 Chromium, total 689 6.1 46 140 

 Copper 689 2.2 25 150 

 Lead 682 3.2 23 120 

 Mercury 703 0.02 0.2 0.7 

 Nickel 671 1.2 15 31 

 Silver 573 0.03 0.3 1.6 

 Zinc 694 9.6 94 280 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.)

 2-Methylnaphthalene 533 0.9 9.5 79 

 Acenaphthene 478 0.3 5.3 91 

 Acenaphthylene 525 0.3 6.7 60 

 Anthracene 588 0.6 15 320 

 Benz(a)anthracene 653 1.3 36 840 

 Benzo(a)pyrene 655 1.2 54 980 

 Chrysene 668 1.7 50 1100 

 Dibenz(a,h)anthracene 578 0.3 13 180 

 Fluoranthene 683 3.1 81 1450 

 Fluorene 537 0.4 5.3 100 

 Naphthalene 576 1.9 9.5 84 

 Phenanthrene 664 1.6 30 530 

 Pyrene 662 3 86 1460 

 

Polychlorinated biphenyls (μg/kg dry wt.)

 PCBs, total 637 2.2 33 300 

Organochlorine pesticides (μg/kg dry wt.)

 DDTS, total of six isomers 528 0.8 7.8 79 

 p,p'-DDE 520 0.2 3.4 43 
 68 

Table 6. Endpoint, number of samples, and percent toxic samples for 

Chironomus spp. (C. tentans and C. riparius) and Hyalella aztecaa

Test species Endpoint 

Number of 

samples Percent toxic 

Chironomus spp. 10–14-day survival 585 19.8 

10-day growth and survival 286 37.8 

H. azteca 10–14-day survival 567 24.2 

28-day survival 125 19.2 

28-day growth and survival 125 38.4 

a

Samples were classified as toxic by the original investigator. 
 69 

Table 7. Distribution of chemical concentrations in sediment samples with 

matching toxicity data for the H. azteca 10–14-day survival test 

Percentile 

Chemical 

Number of 

samples 10th 50th 90th

Metals (mg/kg dry wt.) 

 Arsenic 352 1.7 5.3 27.3 

 Cadmium 279 0.1 0.8 8.2 

 Chromium, total 350 9.5 31 111 

 Copper 396 8 32.7 207 

 Lead 325 8.4 35.8 362 

 Mercury 390 0.04 0.28 74.5 

 Nickel 299 6.1 15 50 

 Silver 146 0.04 0.2 4.2 

 Zinc 361 29.5 140 661 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.)

 2-Methylnaphthalene 125 2.7 66.2 21,015 

 Acenaphthene 248 2.7 80 18,000 

 Acenaphthylene 201 2 53 1,828 

 Anthracene 309 8 172 12,000 

 Benz(a)anthracene 357 16 440 10,000 

 Benzo(a)pyrene 359 19 467 7,200 

 Chrysene 376 24 565 11,000 

 Dibenz(a,h)anthracene 199 3.7 101 1,156 

 Fluoranthene 406 37.7 950 16,802 

 Fluorene 276 6.65 111 14,000 

 Naphthalene 292 9.8 167 12,000 

 Phenanthrene 378 18 520 15,000 

 Pyrene 403 40 840 19,691 

 

Polychlorinated biphenyls (μg/kg dry wt.)

 PCBs, total 171 8.5 109 1,490 

 

Organochlorine pesticides (μg/kg dry wt.)

 DDT, total of six isomers 110 2.2 17 143 

 p,p'-DDE 117 2 10 56 
 70 

Table 8. Distribution of chemical concentrations in sediment samples with 

matching toxicity data for the C. tentans or C. riparius 10–14-day survival 

test 

Percentile 

Chemical 

Number of 

samples 10th 50th 90th

Metals (mg/kg dry wt.) 

 Arsenic 392 2.2 7.5 28 

 Cadmium 266 0.3 1.4 5.9 

 Chromium, total 399 9 26 140 

 Copper 413 7 28 130 

 Lead 406 12 32 110 

 Mercury 388 0.04 0.2 1 

 Nickel 380 7.1 16 61 

 Silver 44 0.05 0.4 1.3 

 Zinc 403 33 93 540 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.)

 2-Methylnaphthalene 50 9.6 140 16,500 

 Acenaphthene 162 3 41 1,100 

 Acenaphthylene 155 4 49 1,230 

 Anthracene 205 14 120 2,460 

 Benz(a)anthracene 231 23 350 4,940 

 Benzo(a)pyrene 251 36 360 4,950 

 Chrysene 266 35 440 4,100 

 Dibenz(a,h)anthracene 157 6.9 120 920 

 Fluoranthene 275 59 640 7,510 

 Fluorene 185 8.4 69 1,800 

 Naphthalene 184 10 130 10,000 

 Phenanthrene 251 35 390 4,600 

 Pyrene 275 48 620 5,800 

 

Polychlorinated biphenyls (μg/kg dry wt.)

 PCBs, total 108 42 390 4,000 

Organochlorine pesticides (μg/kg dry wt.)

 DDT, total of six isomers 10 18 76 3,570 

 p,p'-DDE 21 0.9 25 84 
 71 

Table 9. Distribution of chemical concentrations in sediment samples with 

matching toxicity data for the H. azteca 28-day growth and survival test 

Percentile 

Chemical 

Number of 

samples 10th 50th 90th

Metals (mg/kg dry wt.) 

 Arsenic 57 3.9 24 93 

 Cadmium 71 0.5 2.4 12 

 Chromium, total 72 27 61 150 

 Copper 72 22 86 410 

 Lead 72 19 91 200 

 Mercury 68 0.1 0.3 0.8 

 Nickel 56 8.3 18 50 

 Silver 39 0.1 1 1 

 Zinc 72 94 270 940 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.)

 2-Methylnaphthalene 54 16 150 1280 

 Acenaphthene 26 29 56 250 

 Acenaphthylene 44 10 46 280 

 Anthracene 48 10 120 1640 

 Benz(a)anthracene 88 12 99 690 

 Benzo(a)pyrene 75 20 190 1230 

 Chrysene 102 20 120 690 

 Dibenz(a,h)anthracene 22 10 48 110 

 Fluoranthene 103 26 190 1450 

 Fluorene 41 38 77 1530 

 Naphthalene 69 15 44 1080 

 Phenanthrene 92 16 95 1400 

 Pyrene 103 21 170 1480 

 

Polychlorinated biphenyls (μg/kg dry wt.)

 PCBs, total 45 250 2740 7700 

Organochlorine pesticides (μg/kg dry wt.)

 DDT, total of six isomers 41 0.3 1.9 88 

 p,p'-DDE 45 0.1 1 84 
 72 

Table 10. Normalized chi-square values and number of samples for 

individual chemicals based on Sig Only classification of toxic samples for the 

screened marine amphipod database (logistic regression model parameters 

are shown for models having normalized chi-square values greater than 0.15) 

Chemical 

Number of 

samples Intercept (B0) Slope (B1) 

Chi-square 

value/N 

Metals (mg/kg dry wt.)

 Antimony 1718 –0.9 2.41 0.25 

 Arsenic 2336 –4.14 3.17 0.17 

 Cadmium 2413 –0.34 2.51 0.31 

 Chromium 2399 –6.44 3 0.2 

 Copper 2580 –5.79 2.93 0.38 

 Lead 2481 –5.45 2.77 0.27 

 Mercury 2296 0.8 2.55 0.32 

 Nickel 2450 –4.61 2.77 0.18 

 Selenium 1655 0.07 

 Silver 2103 –0.11 1.97 0.25 

 Zinc 2516 –7.98 3.34 0.28 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.)

 1-Methylnaphthalene 1368 –4.14 2.1 0.24 

 1-Methylphenanthrene 1401 –3.59 1.75 0.28 

 2,6-Dimethylnaphthalene 1249 –4.05 1.9 0.2 

 2-Methylnaphthalene 1704 –3.76 1.78 0.25 

 Acenaphthene 1424 –3.62 1.75 0.33 

 Acenaphthylene 1447 –2.96 1.38 0.23 

 Anthracene 1823 –3.66 1.49 0.29 

 Benz(a)anthracene 2099 –4.2 1.58 0.3 

 Benzo(a)pyrene 2053 –4.3 1.58 0.3 

 Benzo(b)fluoranthene 1348 –4.54 1.49 0.27 

 Benzo(g,h,i)perylene 1818 –4.28 1.59 0.25 

 Benzo(k)fluoranthene 1376 –4.28 1.57 0.29 

 Biphenyl 1226 –4.11 2.21 0.26 

 Chrysene 2126 –4.32 1.54 0.29 

 Dibenz(a,h)anthracene 1546 –3.63 1.77 0.33 

 Fluoranthene 2189 –4.46 1.48 0.26 

 Fluorene 1668 –3.71 1.81 0.32 

 Indeno(1,2,3-c,d)pyrene 1837 –4.37 1.62 0.27 

 Naphthalene 1816 –3.78 1.62 0.24 

 Perylene 1823 –4.68 1.76 0.22 

 Phenanthrene 2173 –4.46 1.68 0.3 

 Pyrene 2240 –4.71 1.59 0.29 

 

Polychlorinated biphenyls (μg/kg dry wt.)

 PCBs, total 1617 –3.46 1.35 0.27 

Organochlorine pesticides (μg/kg dry wt.)

 Dieldrin 

 Gamma-hexachlorocyclohexane 

633 

534 

–1.17 2.56 0.35 

0.098 

 p,p'-DDD 1360 –1.9 1.49 0.27 

 p,p'-DDE 1552 –1.84 0.91 0.16 

 p,p'-DDT 931 –1.77 1.68 0.34 
 73 

Table 11. Logistic model point estimates of T20, T50, and T80 values (95% 

confidence interval) for individual chemicals based on Sig Only classification 

of toxic samples for the screened marine amphipod databasea

Chemical T20 T50 T80 

Metals (mg/kg dry wt.)

 Antimony 0.63 (0.55–0.72) 2.4 (2–2.8) 8.9 (6.6–12) 

 Arsenic 7.4 (6.8–8.1) 20 (18–23) 56 (45–69) 

 Cadmium 0.38 (0.34–0.43) 1.4 (1.2–1.5) 4.9 (4–6) 

 Chromium, total 49 (44–53) 140 (130–160) 410 (330–510) 

 Copper 32 (29–35) 94 (86–100) 280 (240–330) 

 Lead 30 (27–33) 94 (84–100) 300 (240–360) 

 Mercury 0.14 (0.12–0.15) 0.48 (0.43–0.54) 1.7 (1.4–2.1) 

 Nickel 15 (13–16) 47 (42–52) 150 (120–190) 

 Silver 0.23 (0.19–0.26) 1.1 (0.98–1.3) 5.8 (4.4–7.6) 

 Zinc 94 (87–100) 240 (220–270) 640 (540–750) 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.)

 1-Methylnaphthalene 21 (17–25) 94 (73–120) 430 (280–670) 

 1-Methylphenanthrene 18 (15–23) 110 (88–140) 700 (450–1070) 

 2,6-Dimethylnaphthalene 25 (20–31) 130 (96–180) 710 (410–1230) 

 2-Methylnaphthalene 21 (18–26) 130 (100–160) 770 (510–1140) 

 Acenaphthene 19 (15–24) 120 (90–150) 710 (470–1090) 

 Acenaphthylene 14 (11–18) 140 (100–190) 1420 (800–2520) 

 Anthracene 34 (27–42) 290 (230–370) 2490 (1630–3790) 

 Benz(a)anthracene 61 (50–75) 470 (380–570) 3530 (2490–5020) 

 Benzo(a)pyrene 69 (57–85) 520 (430–630) 3910 (2750–5550) 

 Benzo(b)fluoranthene 130 (100–170) 1110 (810–1510) 9410 (5530–16000) 

 Benzo(g,h,i)perylene 67 (54–82) 500 (390–630) 3710 (2440–5630) 

 Benzo(k)fluoranthene 70 (55–90) 540 (410–710) 4120 (2540–6680) 

 Biphenyl 17 (14–21) 73 (57–93) 310 (210–470) 

 Chrysene 82 (67–99) 650 (530–800) 5190 (3600–7480) 

 Dibenz(a,h)anthracene 19 (15–23) 110 (92–140) 690 (480–990) 

 Fluoranthene 120 (98–150) 1030 (830–1280) 8950 (6070–13200) 

 Fluorene 19 (16–24) 110 (92–140) 660 (460–950) 

 Indeno(1,2,3-c,d)pyrene 68 (56–84) 490 (390–610) 3480 (2350–5160) 

 Naphthalene 30 (25–37) 220 (170–280) 1570 (1020–2410) 

 Perylene 74 (62–89) 450 (360–570) 2770 (1820–4210) 

 Phenanthrene 68 (57–81) 460 (380–550) 3060 (2190–4260) 

 Pyrene 120 (100–150) 930 (770–1130) 6980 (4940–9860) 

 

Polychlorinated biphenyls 

(μg/kg dry wt.)

 PCBs, total 35 (27–44) 370 (280–480) 3930 (2410–6390) 

Organochlorine pesticides 

(μg/kg dry wt.)

 Dieldrin 0.83 (0.65–1) 2.9 (2.3–3.6) 10 (6.9–15) 

 p,p'-DDD 2.2 (1.7–2.8) 19 (14–25) 160 (95–270) 

 p,p'-DDE 3.1 (2.2–4.4) 100 (61–180) 3410 (1280–9120) 

 p,p'-DDT 1.7 (1.3–2.2) 11 (8.3–15) 76 (45–130) 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). 
 74 

Table 12. Percent of toxic samples within ranges defined by Sig Only logistic 

model T20, T50, and T80 values and number of samples used to derive the 

logistic model for each chemical for the marine amphipod databasea 

Chemical <T20 T20–T50 T50–T80 >T80 

Number of 

samples 

Metals (mg/kg dry wt.)

 Antimony 30.3 48.5 67.9 82.5 2173 

 Arsenic 30 43.8 56.3 69.7 2844 

 Cadmium 27.6 50.9 62.7 78.7 2958 

 Chromium 24.5 42.7 55.7 80 2827 

 Copper 22.1 50.6 64.9 85 3091 

 Lead 28.5 45 60.6 90 3010 

 Mercury 25.5 49 66.1 79.4 2788 

 Nickel 25.3 44.7 60.4 NA 2916 

 Silver 25.6 52.5 60.7 73.7 2552 

 Zinc 23.6 47.3 67.8 71.3 3013 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.)

 1-Methylnaphthalene 23.7 48.3 60.3 75 1677 

 1-Methylphenanthrene 24.7 45.8 65.5 80 1697 

 2,6-Dimethylnaphthalene 23.5 42.2 57.4 NA 1505 

 2-Methylnaphthalene 25.4 47.5 61.1 88 2077 

 Acenaphthene 25.2 50.3 67.7 91.4 1795 

 Acenaphthylene 24 44.7 67.6 NA 1747 

 Anthracene 26.5 48.8 66.9 77.1 2268 

 Benz(a)anthracene 28.5 45.3 65 82.9 2574 

 Benzo(a)pyrene 27.7 48.5 64.2 83.8 2526 

 Benzo(b)fluoranthene 24 46.3 67.4 NA 1645 

 Benzo(g,h,i)perylene 25.3 46.6 63.6 86.7 2210 

 Benzo(k)fluoranthene 25.5 44.2 68.3 93.3 1691 

 Biphenyl 25.2 46.3 56.7 83.3 1507 

 Chrysene 28.7 47.8 64.8 86.1 2650 

 Dibenz(a,h)anthracene 23.9 49 65.3 85.7 1886 

 Fluoranthene 28.4 47.1 64.9 87.9 2734 

 Fluorene 22.1 48.4 68.2 87.2 2011 

 Indeno(1,2,3-c,d)pyrene 25.6 44.5 64.9 90.5 2212 

 Naphthalene 26.4 43.8 64.1 89.7 2201 

 Perylene 26.9 39.4 59.8 NA 2174 

 Phenanthrene 28.2 47.3 64.1 85.7 2688 

 Pyrene 28.3 46.2 65 87.2 2768 

 

Polychlorinated biphenyls 

(μg/kg dry wt.)

 PCBs, total 26.8 46.3 72.7 81.5 1989 

Organochlorine pesticides 

(μg/kg dry wt.)

 Dieldrin 20.2 53.8 66.7 78.8 770 

 p,p'-DDD 25.9 49.4 64.7 80.5 1672 

 p,p'-DDE 22.5 53.4 54.9 57.6 1899 

 p,p'-DDT 25.6 56.5 66.7 76.7 1176 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). 

NA = fewer than 10 samples 
 75 

Table 13. Normalized chi-square values and number of samples for 

individual chemicals based on MSD classification of toxic samples for the 

screened marine amphipod database (logistic regression model parameters 

are shown for models having normalized chi-square values greater than 0.15) 

Chemical 

Number of 

samples Intercept (B0) 

Slope 

(B1) 

Chi-square 

value/N 

Metals (mg/kg dry wt.) 

 Antimony 1905 0.14 

 Arsenic 2551 0.11 

 Cadmium 2641 –1.17 2.27 0.2 

 Chromium, total 2609 –7.47 3.21 0.16 

 Copper 2803 –6.98 3.06 0.29 

 Lead 2718 –6.29 2.79 0.21 

 Mercury 2472 –0.06 2.68 0.25 

 Nickel 2653 0.13 

 Selenium 1822 0.05 

 Silver 2294 –0.85 2.03 0.19 

 Zinc 2742 –9.26 3.54 0.22 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.) 

 1-Methylnaphthalene 1509 –4.94 2.24 0.19 

 1-Methylphenanthrene 1523 –4.89 2.02 0.23 

 2,6-Dimethylnaphthalene 1366 –5 2.15 0.17 

 2-Methylnaphthalene 1857 –4.37 1.72 0.17 

 Acenaphthene 1596 –4.29 1.7 0.23 

 Acenaphthylene 1570 –4.4 1.74 0.2 

 Anthracene 2000 –4.8 1.61 0.22 

 Benz(a)anthracene 2264 –5.96 1.88 0.23 

 Benzo(a)pyrene 2212 –6.15 1.94 0.24 

 Benzo(b)fluoranthene 1451 –7.5 2.3 0.28 

 Benzo(g,h,i)perylene 1939 –6.55 2.12 0.22 

 Benzo(k)fluoranthene 1490 –6.63 2.18 0.27 

 Biphenyl 1353 –4.53 2.1 0.18 

 Chrysene 2307 –5.97 1.78 0.22 

 Dibenz(a,h)anthracene 1677 –5.23 2.19 0.28 

 Fluoranthene 2373 –6.65 1.91 0.23 

 Fluorene 1817 –4.76 1.89 0.23 

 Indeno(1,2,3-c,d)pyrene 1962 –6.59 2.15 0.23 

 Naphthalene 1971 –4.82 1.72 0.18 

 Perylene 1955 –6.29 2.15 0.19 

 Phenanthrene 2335 –6.13 1.92 0.22 

 Pyrene 2404 –7.01 2.03 0.24 

Polychlorinated biphenyls (μg/kg dry wt.) 

 PCBs, total 1766 –4.41 1.48 0.24 

 

Organochlorine pesticides (μg/kg dry wt.) 

 Dieldrin 682 –1.83 2.59 0.28 

 Gamma-Hexachlorocyclohexane 573 0.08 

 p,p'-DDD 1480 –2.59 1.6 0.24 

 p,p'-DDE 1682 0.13 

 p,p'-DDT 1009 –2.51 1.64 0.27 
 76 

Table 14. Logistic model point estimates of T20, T50, and T80 values (95% 

confidence interval) for individual chemicals based on MSD classification of 

toxic samples for the screened marine amphipod databasea

Chemical T20 T50 T80 

Metals (mg/kg dry wt.) 

 Cadmium 0.8 (0.71–0.91) 3.3 (2.7–4) 13 (9.8–18) 

 Chromium, total 78 (72–85) 210 (180–240) 570 (440–730) 

 Copper 67 (61–74) 190 (170–210) 540 (440–660) 

 Lead 57 (52–63) 180 (160–210) 560 (440–720) 

 Mercury 0.32 (0.29–0.36) 1.1 (0.91–1.2) 3.5 (2.7–4.4) 

 Silver 0.55 (0.47–0.63) 2.6 (2.1–3.2) 13 (8.9–18) 

 Zinc 170 (150–180) 410 (370–460) 1,020 (840–1,230) 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.) 

 1-Methylnaphthalene 39 (32–47) 160 (120–220) 670 (410–1,090) 

 1-Methylphenanthrene 54 (44–66) 260 (200–350) 1,270 (790–2,040) 

 2,6-Dimethylnaphthalene 48 (39–61) 210 (150–310) 950 (530–1,700) 

 2-Methylnaphthalene 55 (45–67) 350 (250–490) 2,260 (1,300–3,930) 

 Acenaphthene 51 (40–64) 330 (230–460) 2,150 (1,220–3,770) 

 Acenaphthylene 54 (43–68) 330 (230–480) 2,080 (1,160–3,740) 

 Anthracene 130 (100–160) 940 (690–1280) 6,770 (4,040–11,300) 

 Benz(a)anthracene 270 (230–320) 1,460 (1,150–1,860) 7,980 (5,350–11,900) 

 Benzo(a)pyrene 290 (240–340) 1,510 (1,200–1,890) 7,840 (5,350–11,500) 

 Benzo(b)fluoranthene 460 (370–560) 1,820 (1,420–2,340) 7,300 (4,860–11,000) 

 Benzo(g,h,i)perylene 270 (230–330) 1,240 (960–1,590) 5,580 (3,670–8,480) 

 Benzo(k)fluoranthene 250 (200–310) 1,080 (830–1,410) 4,660 (3,060–7,120) 

 Biphenyl 31 (25–38) 140 (100–200) 650 (380–1,130) 

 Chrysene 370 (310–440) 2,200 (1,690–2,860) 13,200 (8,530–20,300) 

 Dibenz(a,h)anthracene 57 (48–68) 250 (200–310) 1,070 (730–1,550) 

 Fluoranthene 570 (480–680) 3,050 (2,390–3,900) 16,200 (10,900–24,300)

 Fluorene 61 (50–74) 330 (250–430) 1,770 (1,110–2,810) 

 Indeno(1,2,3-c,d)pyrene 260 (220–310) 1,170 (920–1,480) 5,150 (3,480–7,630) 

 Naphthalene 100 (81–120) 640 (460–890) 4,130 (2,430–7,020) 

 Perylene 190 (160–230) 840 (650–1,090) 3,710 (2,420–5,680) 

 Phenanthrene 290 (250–350) 1,540 (1,200–1,980) 8,100 (5,370–12,200) 

 Pyrene 590 (500–700) 2,850 (2,270–3,570) 13,800 (9,480–19,900) 

Polychlorinated biphenyls 

(μg/kg dry wt.) 

 PCBs, total 110 (88–140) 940 (690–1,290) 8,120 (4,770–13,800) 

Organochlorine pesticides 

(μg/kg dry wt.) 

 Dieldrin 1.5 (1.2–1.9) 5.1 (3.9–6.6) 18 (11–27) 

 p,p'-DDD 5.6 (4.5–7.1) 41 (29–58) 300 (170–530) 

 p,p'-DDT 4.9 (3.6–6.5) 34 (23–52) 240 (120–470) 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). 
 77 

Table 15. Percent of toxic samples within ranges defined by logistic model 

T20, T50, and T80 values and number of samples in the database used to 

derive the logistic model for each chemical based on MSD classification of 

toxic samples for the marine amphipod databasea

Chemical <T20 T20–T50 T50–T80 >T80 

Number of 

samples 

Metals (mg/kg dry wt.) 

 Cadmium 17.4 39.9 61.5 69.2 2958 

 Chromium, total 15.4 37.7 54.1 75.9 2827 

 Copper 14.2 40.1 66.5 76.6 3091 

 Lead 16.6 37.8 63.9 61.5 3010 

 Mercury 16.7 44.4 56.4 80.8 2788 

 Silver 16.8 43.5 46.5 NA 2552 

 Zinc 14.6 39.9 65.8 58.3 3013 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.) 

 1-Methylnaphthalene 14.8 39.6 50 NA 1677 

 1-Methylphenanthrene 15.5 40.7 63.6 50 1697 

 2,6-Dimethylnaphthalene 14.5 33.3 51.7 NA 1505 

 2-Methylnaphthalene 17.1 37 55.6 90 2077 

 Acenaphthene 15.9 40.1 56.3 75 1795 

 Acenaphthylene 15.2 37.7 63.8 NA 1747 

 Anthracene 16.9 39.5 62.2 78.6 2268 

 Benz(a)anthracene 17.5 39.4 62.6 73.3 2574 

 Benzo(a)pyrene 18 40.4 63 81.8 2526 

 Benzo(b)fluoranthene 15.9 41.7 61.9 86.7 1645 

 Benzo(g,h,i)perylene 17.4 37.5 64.9 NA 2210 

 Benzo(k)fluoranthene 15.8 42 62.1 84.6 1691 

 Benzofluoranthenes, total 23.3 37.9 63.6 NA 617 

 Biphenyl 16.3 36.5 50 NA 1507 

 Chrysene 18.3 40.3 58.2 72.7 2650 

 Dibenz(a,h)anthracene 16.5 38.4 69.7 68.4 1886 

 Fluoranthene 18.1 41.8 62.9 64.7 2734 

 Fluorene 14.8 38.4 63.2 69.6 2011 

 Indeno(1,2,3-c,d)pyrene 16.5 36.9 65.7 83.3 2212 

 Naphthalene 16.5 37.5 52.5 92.9 2201 

 Perylene 16.5 35.8 48.6 NA 2174 

 Phenanthrene 18.4 40 63.4 69.2 2688 

 Pyrene 18.1 42.6 57.5 72.7 2768 

Polychlorinated biphenyls 

(μg/kg dry wt.) 

 PCBs, total 18.6 44.2 56.2 69.2 1989 

 

Organochlorine pesticides 

(μg/kg dry wt.) 

 Dieldrin 17 45.5 60.9 72.7 770 

 p,p'-DDD 16.9 44.5 58 74.3 1672 

 p,p'-DDT 20.2 48.5 50.9 83.9 1176 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). 

NA = fewer than 10 samples 
 78 

Table 16. Normalized chi-square values and number of samples for 

individual chemicals for organic carbon-normalized concentrations for the 

screened marine amphipod database (logistic regression model parameters 

are shown for models having normalized chi-square values greater than 0.15) 

 Chemical 

(:g/kg organic carbon) 

Number of 

samples 

Intercept 

(B0) 

Slope 

(B1) 

Chi-square 

value/N 

Polycyclic aromatic hydrocarbons 

 1-Methylnaphthalene 1291 –7 1.79 0.17 

 1-Methylphenanthrene 1308 –7.36 1.88 0.27 

 2,6-Dimethylnaphthalene 1185 0.15 

 2-Methylnaphthalene 1573 –6.82 1.67 0.19 

 Acenaphthene 1329 –6.3 1.58 0.25 

 Acenaphthylene 1361 –5.55 1.4 0.2 

 Anthracene 1676 –6.86 1.57 0.26 

 Benz(a)anthracene 1921 –8.25 1.81 0.29 

 Benzo(a)pyrene 1897 –8.16 1.79 0.29 

 Benzo(b)fluoranthene 1245 –8.66 1.81 0.27 

 Benzo(g,h,i)perylene 1664 –7.73 1.68 0.22 

 Benzo(k)fluoranthene 1274 –7.92 1.75 0.27 

 Biphenyl 1157 –6.54 1.71 0.17 

 Chrysene 1974 –8.2 1.76 0.28 

 Dibenz(a,h)anthracene 1411 –7.3 1.86 0.3 

 Fluoranthene 2021 –8.15 1.67 0.25 

 Fluorene 1519 –7.31 1.84 0.28 

 Indeno(1,2,3-c,d)pyrene 1685 –8.07 1.77 0.24 

 Naphthalene 1684 –6.27 1.44 0.16 

 Perylene 1709 –8.9 1.99 0.21 

 Phenanthrene 2005 –8.3 1.82 0.27 

 Pyrene 2092 –8.48 1.76 0.27 

 

Polychlorinated biphenyls 

 PCBs, total 1530 –6.03 1.38 0.22 

Organochlorine pesticides 

 Dieldrin 569 –4.33 1.85 0.24 

 Gamma-Hexachlorocyclohexane 523 0.07 

 p,p'-DDD 

 p,p'-DDE 

1261 

1425 

–5.34 1.72 0.28 

0.15 

 p,p'-DDT 854 –4.51 1.52 0.28 
 79 

Table 17. Logistic model point estimates of T20, T50, and T80 organic 

carbon-normalized concentrations (95% confidence interval) for individual 

chemicals based on Sig Only classification of toxic samples in the screened 

marine amphipod databasea

Chemical 

(:g/kg organic carbon) T20 T50 T80 

Polycyclic aromatic hydrocarbons 

 1-Methylnaphthalene 1,390 (1,120–1,730) 8,310 (5,870–11,800) 49,600 (27500–89,600) 

 1-Methylphenanthrene 1,510 (1,220–1,850) 8,230 (6,430–10,500) 45,000 (29,200–69,200) 

 2-Methylnaphthalene 1,800 (1,480–2,200) 12,200 (9,110–16,400) 82,600 (49,500–138,000) 

 Acenaphthene 1,260 (990–1,610) 9,470 (6,900–13,000) 71,100 (41,600–122,000) 

 Acenaphthylene 920 (710–1,190) 8,960 (6,320–12,700) 87,100 (46,900–162,000) 

 Anthracene 3,010 (2,430–3,740) 22,900 (17,600–29,700) 174,000 (111,000–273,000) 

 Benz(a)anthracene 6,060 (5,070–7,250) 35,200 (29,000–42,700) 204,000 (146,000–287,000) 

 Benzo(a)pyrene 6,230 (5,190–7,480) 37,200 (30,700–45,200) 223,000 (159,000–313,000) 

 Benzo(b)fluoranthene 10,200 (8,150–12,900) 59,500 (45,200–78,300) 346,000 (217,000–550,000) 

 Benzo(g,h,i)perylene 6,000 (4,920–7,320) 40,200 (31,100–5,1900) 269,000 (171,000–424,000) 

 Benzo(k)fluoranthene 5,490 (4,360–6,910) 34,100 (25,800–45,000) 212,000 (132,000–338,000) 

 Biphenyl 1,020 (810–1,290) 6,580 (4,570–9,470) 42,400 (22,500–79,800) 

 Chrysene 7,380 (6,160–8,830) 45,200 (37,100–55,000) 277,000 (196,000–390,000) 

 Dibenz(a,h)anthracene 1,490 (1,210–1,830) 8,250 (6,610–10,300) 45,700 (31,000–67,400) 

 Fluoranthene 11,200 (9,370–13,500) 76,000 (61,100–94,500) 514,000 (350,000–753,000) 

 Fluorene 1,640 (1,350–2,000) 9,300 (7,320–11,800) 52,700 (35,000–79,200) 

 Indeno(1,2,3-c,d)pyrene 6,050 (5,000–7,330) 36,900 (29,300–46,500) 225,000 (150,000–338,000) 

 Naphthalene 2,470 (1,990–3,070) 22,700 (16,200–31,700) 208,000 (114,000–378,000) 

 Perylene 6,000 (5,080–7,070) 29,800 (23,800–37,400) 149,000 (99,400–222,000) 

 Phenanthrene 6,220 (5,250–7,360) 35,800 (29,400–43,800) 207,000 (146,000–293,000) 

 Pyrene 10,500 (8,870–12,500) 64,300 (53,000–77,900) 392,000 (280,000–551,000) 

Polychlorinated biphenyls 

 PCBs, total 2,290 (1,790–2,930) 23,100 (17,500–30,600) 233,000 (138,000–394,000) 

Organochlorine pesticides

 Dieldrin 39 (29–53) 220 (150–310) 1,230 (650–2,320) 

 p,p'-DDD 200 (160–240) 1240 (950–1,630) 7,910 (4,930–12,700) 

 p,p'-DDT 110 (85–160) 940 (650–1,380) 7,740 (4,040–14,900) 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). 
 80 

Table 18. Percent of toxic samples within ranges defined by logistic model 

T20, T50, and T80 values for organic carbon-normalized concentrations and 

number of samples in the database used to derive the logistic model for each 

chemical based on Sig Only classification of toxic samples for the marine 

amphipod databasea

 Chemical 

(:g/kg organic carbon) <T20 T20–T50 T50–T80 >T80 

Number of

samples 

Polycyclic aromatic hydrocarbons 

 1-Methylnaphthalene 26.3 47 55.3 NA 1612 

 1-Methylphenanthrene 27 48.7 65.5 60 1641 

 2-Methylnaphthalene 29.8 48.2 57.8 62.5 2008 

 Acenaphthene 27.2 54.7 68.2 76.2 1743 

 Acenaphthylene 24.7 51.1 58.3 NA 1684 

 Anthracene 29.8 52.6 64.2 72 2189 

 Benz(a)anthracene 31 51.8 61.4 75 2489 

 Benzo(a)pyrene 29.3 53.7 61.2 78.3 2443 

 Benzo(b)fluoranthene 24.9 52.7 65 NA 1571 

 Benzo(g,h,i)perylene 27.4 52.1 61.4 NA 2135 

 Benzo(k)fluoranthene 26.4 50.9 67.1 78.6 1621 

 Biphenyl 28 47.1 43.6 NA 1453 

 Chrysene 30.2 53.4 61.2 50 2564 

 Dibenz(a,h)anthracene 24.8 54.7 68.3 77.4 1815 

 Fluoranthene 30.1 53.2 61.9 78.3 2644 

 Fluorene 26 52.2 66.9 82.1 1936 

 Indeno(1,2,3-c,d)pyrene 27.8 49.7 62.2 78.6 2135 

 Naphthalene 29.5 48 56.9 NA 2123 

 Perylene 28.4 45 50 72.7 2100 

 Phenanthrene 30.5 51.3 63.6 NA 2606 

 Pyrene 29.5 51.9 61.5 74.4 2678 

Polychlorinated biphenyls 

 PCBs, total 27.2 51.5 64.3 77.8 1940 

Organochlorine pesticides 

 Dieldrin 21.8 58.3 74.7 46.2 729 

 p,p'-DDD 27.8 54.9 61.2 70.5 1623 

 p,p'-DDT 28.6 59.2 61.4 78.8 1131 a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). 

NA = fewer than 10 samples 
 81 

Table 19. Differences between percent predicted toxic samples and percent 

observed toxic samples (observed minus predicted) within ranges defined by 

logistic model T20, T50, and T80 values based on Sig Only classification of 

toxic samples for the marine amphipod database a,b

Chemical <T20 T20–T50 T50–T80 >T80 

Metals (mg/kg dry wt.) 

 Antimony 19.2 17.4 6.1 –9.9 

 Arsenic 19.6 12.5 –3.6 –19.7 

 Cadmium 19.2 17.3 0.9 –12 

 Chromium, total 15.7 11.8 –3.4 –7.7 

 Copper 15.3 17.7 2 –2.7 

 Lead 19.1 14.2 –3.3 3.9 

 Mercury 17.5 16.2 2.4 –7.2 

 Nickel 16.5 11.4 –1.7 –34.2 

 Silver 15.7 19.4 –2.7 –11.6 

 Zinc 15.1 14.7 5.8 –16.2 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.)

 1–Methylnaphthalene 17.6 16.3 –2.7 –11.5 

 1–Methylphenanthrene 17.5 12.8 4.3 –7.8 

 2,6–Dimethylnaphthalene 17.3 10.9 –2 –999.0 

 2–Methylnaphthalene 17.4 15.3 –0.3 –0.5 

 Acenaphthene 19.6 16.8 5 2.1 

 Acenaphthylene 16.4 11.5 7.5 –12 

 Anthracene 19.1 15.7 5.3 –10.1 

 Benz(a)anthracene 21 12 2.7 –3.2 

 Benzo(a)pyrene 20.1 14.5 1.1 –1.5 

 Benzo(b)fluoranthene 17.6 13.4 4.2 11 

 Benzo(g,h,i)perylene 17.7 13.4 1.6 0.8 

 Benzo(k)fluoranthene 18.9 11.7 5.8 5.2 

 Biphenyl 19.2 14.3 –4.3 –7 

 Chrysene 20.9 14.1 1.9 –0.2 

 Dibenz(a,h)anthracene 17.4 15.9 2.7 –0.9 

 Fluoranthene 20.5 14.3 3.1 0.9 

 Fluorene 15.7 14.5 5.7 –2.3 

 Indeno(1,2,3–c,d)pyrene 18.2 11.2 2.8 4 

 Naphthalene 17.9 11.6 2.3 0.9 

 Perylene 19.2 7.5 –0.3 –42.9 

 Phenanthrene 20.5 13.5 2.3 –2.8 

 Pyrene 20.6 13.2 2.7 –0.2 

Polychlorinated biphenyls (μg/kg dry wt.)

 PCBs, total 17.1 14.1 11.9 –9.5 

Organochlorine pesticides (μg/kg dry wt.)

 Dieldrin 12.9 21.7 4.2 –7.8 

 p,p'–DDD 16.9 16.3 2.7 –11.2 

 p,p'–DDE 11.3 22 –5.1 –29.8 

 p,p'–DDT 17.3 25.1 2 –16.4 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). b

Differences greater than 20% are shaded. Blank cell indicates fewer than six samples. 
 82 

Table 20. Differences between percent predicted toxic samples and percent 

observed toxic samples (observed minus predicted) within ranges defined by 

logistic model T20, T50, and T80 values based on MSD classification of toxic 

samples for the marine amphipod databasea,b

Chemical <T20 T20–T50 T50–T80 >T80 

Metals (mg/kg dry wt.)

 Cadmium 10.5 8.8 0.3 –21.2 

 Chromium, total 7 7.9 –8.6 –11.2 

 Copper 8.8 7.5 3.4 –11.2 

 Lead 9.8 5.8 2.4 –28.1 

 Mercury 10.8 13.2 –5.9 –6.3 

 Silver 9.6 11.4 –12.2 –999.0 

 Zinc 8 9.1 2.9 –30.9 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.)

 1–Methylnaphthalene 9.9 9.5 –11.2 –999.0 

 1–Methylphenanthrene 10.1 9.5 2.7 –38.6 

 2,6–Dimethylnaphthalene 9.9 1.4 –7 –999.0 

 2–Methylnaphthalene 10.5 6 –4.3 2.5 

 Acenaphthene 10.7 10.1 –3.7 –14.3 

 Acenaphthylene 10 6.3 3.4 –20.4 

 Anthracene 11.6 8.5 1.9 –9.5 

 Benz(a)anthracene 12.6 7.3 1.3 –14.1 

 Benzo(a)pyrene 12.8 8.4 0.8 –4.5 

 Benzo(b)fluoranthene 12.2 9 –2.6 0 

 Benzo(g,h,i)perylene 12.5 6.8 5.2 32 

 Benzo(k)fluoranthene 12 9.4 0.1 –5.7 

 Biphenyl 10.6 5.5 –9.9 –13.1 

 Chrysene 13.1 9.3 –1.4 –15.3 

 Dibenz(a,h)anthracene 11.5 6.6 6.4 –19.8 

 Fluoranthene 13.1 10.5 2.1 –24.4 

 Fluorene 9.4 7.8 2 –20.8 

 Indeno(1,2,3–c,d)pyrene 11.7 6 4.2 –4.8 

 Naphthalene 10.6 6 –8.5 3.2 

 Perylene 11.5 3.9 –8.5 –999.0 

 Phenanthrene 13.3 9.2 2.6 –19.7 

 Pyrene 13.3 10.4 –3.4 –15 

Polychlorinated biphenyls (μg/kg dry wt.)

 PCBs, total 10.1 13 –6.6 –19.4 

Organochlorine pesticides (μg/kg dry wt.)

 Dieldrin 10.3 12.5 –1.9 –13 

 p,p'–DDD 9.7 14.4 –3.3 –16.6 

 p,p'–DDT 13.3 18 –9.2 –8.2 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). b

Differences greater than 20% are shaded. Blank cell indicates fewer than six samples. 
 83 

Table 21. Differences between mean percent predicted toxic samples and 

percent observed toxic samples (observed minus predicted) within ranges 

defined by logistic model T20, T50, and T80 values for organic carbonnormalized concentrations based on Sig Only classification of toxic samples 

for the marine amphipod databasea,b

 Chemical 

(:g/kg organic carbon) <T20 T20–T50 T50–T80 >T80 

Polycyclic aromatic hydrocarbons

 1–Methylnaphthalene 18.7 16.4 –2.8 –43.5 

 1–Methylphenanthrene 19.7 17 2.7 –27.1 

 2–Methylnaphthalene 20.9 16.6 –1 –24.5 

 Acenaphthene 20.2 22.6 7 –12.5 

 Acenaphthylene 16.2 19.2 –1.2 –34.4 

 Anthracene 22.2 20 2.1 –14.9 

 Benz(a)anthracene 23.9 18.9 –1 –11.5 

 Benzo(a)pyrene 22.2 20.8 –1.6 –6.7 

 Benzo(b)fluoranthene 18.9 20.1 1.1 –3.7 

 Benzo(g,h,i)perylene 19.8 20.3 1 –999.0 

 Benzo(k)fluoranthene 19.8 19.1 5.2 –7.3 

 Biphenyl 19.6 15.7 –15.3 –35.4 

 Chrysene 22.9 20.8 –0.9 –8.8 

 Dibenz(a,h)anthracene 18.2 22.8 5.8 –6.1 

 Fluoranthene 22.1 21.3 0.4 –4.9 

 Fluorene 19.1 19.8 5.4 –11.6 

 Indeno(1,2,3–c,d)pyrene 20.5 18 1.4 –20.2 

 Naphthalene 19.4 16.1 –2.9 –13.3 

 Perylene 20.5 12.5 –8.9 –999.0 

 Phenanthrene 22.7 19 1.6 –14.6 

 Pyrene 21.7 19.5 –0.9 –14 

Polychlorinated biphenyls

 PCBs, total 17.1 19.1 4.5 –10.9 

 

Organochlorine pesticides

 Dieldrin 11.7 25.3 13.5 –41.5 

 p,p'–DDD 19.7 22.6 0.3 –20.4 

 p,p'–DDT 19.6 28.6 –1.6 –12.1 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). b

Differences greater than 20% are shaded. Blank cell indicates fewer than six samples. 
 84 

Table 22. Differences between mean percent predicted toxic samples and 

percent observed toxic samples (observed minus predicted) within ranges 

defined by logistic model T20, T50, and T80 values for each chemical using a 

screening factor of 2X the mean of nontoxic samples and Sig Only 

classification of toxic samples for the marine amphipod databasea,b

Chemical <T20 T20–T50 T50–T80 >T80 

Metals (mg/kg dry wt.) 

 Antimony 15.7 14.6 6.1 –11.8 

 Arsenic 16.3 9.9 –5.1 –22.7 

 Cadmium 16.3 14.7 –2.6 –13.1 

 Chromium, total 12 9.5 –4 –7.7 

 Copper 12.1 14.6 0.8 –4.1 

 Lead 16 11.4 –4.8 2.6 

 Mercury 14.2 13.4 0.5 –7.2 

 Nickel 12.7 9 –3.4 –34.2 

 Silver 12.2 17.4 –4.5 –11.6 

 Zinc 11.9 11.8 4.1 –16.2 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.) 

 1–Methylnaphthalene 14.4 14.1 –2.7 –19.8 

 1–Methylphenanthrene 14 10 3.7 –7.8 

 2,6–Dimethylnaphthalene 14.4 9.8 –5 –999.0 

 2–Methylnaphthalene 14.5 12.6 –3 –4.5 

 Acenaphthene 16.5 13.5 3.2 2.1 

 Acenaphthylene 13.3 8.3 6.2 –12 

 Anthracene 16.4 12.2 4.5 –10.1 

 Benz(a)anthracene 18 9 1.6 –3.2 

 Benzo(a)pyrene 17.1 11.7 –0.3 –1.5 

 Benzo(b)fluoranthene 14.5 12.1 4.2 11 

 Benzo(g,h,I)perylene 14.5 10.9 0.8 0.8 

 Benzo(k)fluoranthene 15.6 10.3 5.8 5.2 

 Biphenyl 16.1 12.3 –7.4 –7 

 Chrysene 18 10.9 1.3 –0.2 

 Dibenz(a,h)anthracene 14.6 12.9 1.5 –0.9 

 Fluoranthene 17.4 11.5 1.9 0.9 

 Fluorene 12.9 10.5 4.8 –2.3 

 Indeno(1,2,3–c,d)pyrene 15.1 8.6 2.3 4 

 Naphthalene 15.1 8.6 –0.7 –2.5 

 Perylene 16.4 5.4 –2.1 –42.9 

 Phenanthrene 17.6 10.6 0.5 –2.8 

 Pyrene 17.7 10.1 1.9 –0.2 

Polychlorinated biphenyls (μg/kg dry wt.)

 PCBs, total 14.2 11.9 10.6 –21.3 

Organochlorine pesticides (μg/kg dry wt.)

 Dieldrin 10.6 19.4 1.7 –10.8 

 p,p'–DDD 14.2 14.3 2.7 –11.2 

 p,p'–DDE 8.1 20.5 –6.3 –29.8 

 p,p'–DDT 13.2 22.7 1.1 –16.4 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). b

Differences greater than 20% are shaded. Blank cell indicates fewer than six samples. 
 85 

Table 23. Statistical comparisons of the logistic regression models for 

A. abdita and R. abronius using the chi-square statistic (–2 log likelihood) 

Chemical 

Common slope, 

common 

intercept 

Common slope, 

separate 

intercept 

Separate slope, 

separate 

intercept 

Preferred 

model 

Metals (mg/kg dry wt.) 

 Antimony 548.39 506.3 430.25 a 

 Arsenic 582.09 579.8 404.54 b 

 Cadmium 899.71 872.55 754.76 a 

 Chromium, total 645.99 618.91 468.43 a 

 Copper 1062.54 1061.94 987.62 b 

 Lead 928.02 911.34 679.52 a 

 Mercury 890.64 890.64 734.98 b 

 Nickel 614.01 586.86 441.04 a 

 Silver 753.84 753.56 530.67 b 

 Zinc 913.32 894.37 702.21 a 

Polycyclic aromatic hydrocarbons 

(μg/kg dry wt.) 

 1–Methylnaphthalene 357.46 350.53 327.48 a 

 1–Methylphenanthrene 487.05 486.26 397.74 b 

 2,6–Dimethylnaphthalene 291.27 290.06 251.51 b 

 2–Methylnaphthalene 559.79 559.72 426.77 b 

 Acenaphthene 496.32 487.75 475.79 a 

 Acenaphthylene 381.16 380.26 332.24 b 

 Anthracene 599.92 599.88 526.43 b 

 Benz(a)anthracene 800.12 799.74 624.63 b 

 Benzo(a)pyrene 779.46 779.43 614.03 b 

 Benzo(b)fluoranthene 415.05 410.87 358.83 a 

 Benzo(g,h,i)perylene 614.51 611.4 454.16 b 

 Benzo(k)fluoranthene 438.18 428.92 393.22 a 

 Biphenyl 352.24 346.47 322.79 a 

 Chrysene 775.03 775 608.87 b 

 Dibenz(a,h)anthracene 550.44 550.43 503.33 b 

 Fluoranthene 760.93 760.37 576.24 b 

 Fluorene 588.33 586.25 538.94 b 

 Indeno(1,2,3–c,d)pyrene 619.06 617.96 494.56 b 

 Naphthalene 553.13 547.8 426.97 a 

 Perylene 535.85 526.83 398.32 a 

 Phenanthrene 830.04 824.66 647.82 a 

 Pyrene 842.54 841.84 642.07 b 

Polychlorinated biphenyls 

(μg/kg dry wt.) 

 PCBs, Total 457.11 448.19 389.03 a 

Organochlorine pesticides 

(μg/kg dry wt.) 

 Dieldrin 230.52 229.97 224.24 b 

 p,p'–DDD 443.41 412.79 364.34 a 

 p,p'–DDE 385.15 272.17 251.13 a 

 p,p'–DDT 340.44 323.15 312.15 a 

a = Separate slope, separate intercept 

b = Common slope, separate intercept 
 86 

Table 24. Ratio of Tp values from species-specific A. abdita models to 

corresponding Tp values from the combined amphipod models (only models 

with normalized chi-square >0.15 are included)a

 

Chemical T20 T50 T80 

Metals

 Antimony 1.46 1.04 0.74 

 Cadmium 1.71 1.4 1.15 

 Chromium, total 1.48 1.19 0.96 

 Copper 1.42 1.49 1.56 

 Lead 1.82 1.49 1.21 

 Mercury 1.77 1.92 2.09 

 Silver 2.63 2.66 2.68 

 Zinc 1.59 1.37 1.18 

Polycyclic aromatic hydrocarbons

 1–Methylnaphthalene 1.21 1.29 1.38 

 1–Methylphenanthrene 1.96 2.09 2.23 

 2,6–Dimethylnaphthalene 1.32 1.26 1.21 

 2–Methylnaphthalene 2.11 2.09 2.07 

 Acenaphthene 1.24 1.72 2.4 

 Acenaphthylene 1.74 2.25 2.9 

 Anthracene 2.06 2.6 3.28 

 Benz(a)anthracene 2.82 2.8 2.78 

 Benzo(a)pyrene 2.71 2.91 3.13 

 Benzo(b)fluoranthene 1.92 2.01 2.11 

 Benzo(g,h,i)perylene 2.48 2.46 2.44 

 Benzo(k)fluoranthene 1.58 1.42 1.27 

 Biphenyl 1.21 1.44 1.72 

 Chrysene 2.85 3.25 3.71 

 Dibenz(a,h)anthracene 1.64 1.97 2.37 

 Fluoranthene 3.13 3.21 3.29 

 Fluorene 1.53 2.05 2.74 

 Indeno(1,2,3–c,d)pyrene 2.13 2.16 2.19 

 Naphthalene 2.05 1.83 1.63 

 Perylene 1.86 1.42 1.08 

 Phenanthrene 2.75 2.44 2.17 

 Pyrene 3.03 3.04 3.05 

Polychlorinated biphenyls 

 PCBs, total 1.9 2.6 3.56 

Organochlorine pesticides 

 Dieldrin 1.12 1.12 1.12 

 p,p'–DDD 1.83 1.3 0.92 

 p,p'–DDE 1.46 0.33 0.08 

 p,p'–DDT 1.36 1.07 0.85 

MEAN 1.91 1.91 1.98 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). 
 87 

T50 

Table 25. Ratio of Tp values from the combined amphipod models to 

corresponding Tp values from the species-specific R. abronius models (only 

models with normalized chi–square >0.15 are included)a

Chemical T20 T80 

Metals 

 Cadmium 3.42 1.82 0.97 

 Copper 1.73 1.5 1.3 

 Lead 3.2 2.34 1.71 

 Mercury 2.32 2.13 1.97 

 Silver 3.37 3 2.67 

 Zinc 2.67 1.85 1.29 

Polycyclic aromatic hydrocarbons 

 1–Methylnaphthalene 1.68 3.39 6.82 

 1–Methylphenanthrene 6.14 4.12 2.77 

 2,6–Dimethylnaphthalene 3.5 5.88 9.89 

 2–Methylnaphthalene 3.97 4.3 4.66 

 Acenaphthene 1.05 1.69 2.73 

 Acenaphthylene 3.18 3.72 4.36 

 Anthracene 3.44 2.89 2.43 

 Benz(a)anthracene 4.48 3.88 3.36 

 Benzo(a)pyrene 4.24 3.77 3.35 

 Biphenyl 1.58 2.63 4.36 

 Chrysene 4.54 3.79 3.17 

 Dibenz(a,h)anthracene 2.72 2.2 1.78 

 Fluoranthene 5.1 4.06 3.22 

 Fluorene 2.07 2.27 2.49 

 Indeno(1,2,3–c,d)pyrene 5.83 4.19 3.01 

 Naphthalene 8.52 4.57 2.45 

 Perylene 8.93 4.9 2.69 

 Phenanthrene 5.48 3.49 2.22 

 Pyrene 4.77 3.89 3.17 

Organochlorine pesticides 

 Dieldrin 2.24 1.68 1.26 

MEAN 3.85 3.23 3.08 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). 
 88 

Table 26. Differences between mean percent predicted toxic samples and 

percent observed toxic samples (observed minus predicted) for A. abdita and 

R. abronius within ranges defined by T20, T50, and T80 valuesa,b

A. abdita R. abronius 

Chemical <T20 T20–T50 T50–T80 >T80 <T20 T20–T50 T50–T80 >T80 

Metals (mg/kg dry wt.)

 Antimony 9 –1 –3 –19 54 34 8 –6 

 Arsenic 12 –10 –30 41 37 17 –29 

 Cadmium 8 –9 –7 1 47 35 6 –14 

 Chromium, total 7 –5 –7 3 41 34 5 –17 

 Copper 10 –9 –11 –7 37 31 15 –2 

 Lead 7 –6 –15 6 47 31 13 4 

 Mercury 9 –3 –17 –14 43 35 19 4 

 Nickel 9 –5 –8 –28 46 28 4 –14 

 Silver 7 –9 –16 –10 40 40 15 –5 

 Zinc 8 –7 –7 –13 39 34 20 –6 

Polycyclic aromatic hydrocarbons 

(:g/kg dry wt.)

 1–Methylnaphthalene 12 10 –6 –1 56 41 

 1–Methylphenanthrene 10 1 –11 –12 51 34 21 9 

 2,6–Dimethylnaphthalene 12 1 –1 57 44 

 2–Methylnaphthalene 10 –3 –13 –3 45 40 25 0 

 Acenaphthene 15 2 –7 –8 45 35 14 13 

 Acenaphthylene 11 3 2 –33 42 32 20 

 Anthracene 11 –1 –4 –18 46 35 13 2 

 Benz(a)anthracene 9 –9 –9 –6 47 34 15 6 

 Benzo(a)pyrene 10 –8 –11 –1 47 39 13 1 

 Benzo(b)fluoranthene 9 –1 2 –1 53 38 7 8 

 Benzo(g,h,i)perylene 9 –6 –3 –7 52 40 7 9 

 Benzo(k)fluoranthene 10 3 0 10 55 34 12 3 

 Biphenyl 13 6 –17 –17 59 46 22 15 

 Chrysene 8 –7 –7 –9 49 37 11 6 

 Dibenz(a,h)anthracene 11 –4 –2 –1 48 36 6 2 

 Fluoranthene 9 –6 –7 –17 48 35 13 12 

 Fluorene 11 0 –4 –13 44 34 15 4 

 Indeno(1,2,3–c,d)pyrene 9 –8 –2 0 49 40 9 4 

 Naphthalene 8 0 –3 –8 50 33 11 6 

 Perylene 8 0 –8 –15 53 32 11 

 Phenanthrene 9 –2 –15 –8 48 31 18 –1 

 Pyrene 9 –7 –11 –7 47 35 14 6 

Polychlorinated biphenyls 

(:g/kg dry wt.)

 PCBs, total 8 –3 1 –16 52 29 24 4 

Organochlorine pesticides 

 (:g/kg dry wt.)

 Dieldrin 9 14 4 –26 48 44 15 6 

 p,p'–DDD 8 0 2 –1 55 34 8 –16 

 p,p'–DDE 6 8 34 59 35 –2 –35 

 p,p'–DDT 13 5 –5 1 50 45 12 –21 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent according to 

 the model (e.g., the probability that 50% of the samples would be toxic). b

Percent predicted toxicity and Tp values were calculated using the combined species logistic model using the Sig 

 Only classification of toxicity and the screened marine amphipod database. Differences greater than 20% are shaded. 

 Blank cell indicates fewer than six samples. 
 89 

Table 27. Changes in logistic model point estimates of T20, T50, and T80 

concentrations for PCBs based on Sig Only classification of toxic samples 

based on corrected PCB model for the screened marine amphipod databasea

Estimated T20 T50 T80 

Original 32.5 468 6750 

Corrected 34.5 368 3930 

a

The notation Tp (e.g., T50) is used to denote the concentration that would give a response of “p” percent 

 according to the model (e.g., the probability that 50% of the samples would be toxic). 
 90 

Table 28. Estimated probability of toxicity from marine amphipod chemicalspecific logistic regression models for LC50 values (dry wt.) reported from 

10–day spiked sediment amphipod toxicity tests 

Chemical LC50 

Probability of 

toxicity Source 

Cadmium (mg/kg) 9.81 0.9 Mearns et al., 1986 

 8.8–10 0.88–0.9 Kemp et al., 1986 

 8.2–11.5 0.88–0.91 Robinson et al., 1988 

 6.9 0.85 Swartz et al., 1985 

Mercury (mg/kg) 13.1 0.97 Swartz et al., 1988 

Zinc (mg/kg) 276 0.54 Swartz et al., 1988 

Fluoranthene (mg/kg) 4.2 0.71 Swartz et al., 1988 

 3.3–10.5 0.68–0.82 Swartz et al., 1987 

Phenanthrene (mg/kg) 3.68 0.82 Swartz et al., 1989 

 

Total PCBs (:g/kg) 8.8 0.87 Swartz et al., 1988 

p,p'–DDT (:g/g) 11.2–125 0.5–0.85 Word et al., 1987 

PCBs = polychlorinated biphenyls 
 91 

Chemical class Percent 

Table 29. Number and percent of samples by chemical class that represented 

the maximum probability of toxicity used in the P_Max model derived from 

the marine amphipod database 

Number 

Metals 2234 69.3 

PAHs 596 18.5 

Pesticides–PCBs 393 12.2 

PAHs = polycyclic aromatic hydrocarbons 

PCBs = polychlorinated biphenyls 
 92 

Table 30. Differences between mean percent predicted toxic samples and 

percent observed toxic samples (observed minus predicted) by probability 

quartile for individual studies with at least 20 samplesa

Probability quartile 

Study 

Number of 

samples <25 25–50 50–75 >75 

A. abdita 

 New Bedford Harbor Monitoring, 1993 77 9.99 –20 –18 19 

 NSTP Hudson–Raritan Phase I, 1991 34 9.99 9.99 –10 4 

 NSTP Long Island Sound, 1994 63 9.99 35 30 9.99 

 NSTP Boston Harbor, 1994 30 9.99 –14 –25 9.99 

 REMAP–Hudson/Raritan Bay, 1993 41 9.99 –34 –20 9.99 

 REMAP–Long Island Sound, 1993 43 –7 –13 9.99 9.99 

 REMAP–Hudson/Raritan Bay, 1994 42 –6 –15 –4 9.99 

 REMAP–Long Island Sound, 1994 42 –14 –25 –48 9.99 

 EMAP–Delaware Bay, 1990 42 18 –5 9.99 9.99 

 EMAP–Chesapeake Bay, 1990 61 3 –19 –6 9.99 

 EMAP–Chesapeake Bay, 1991 62 9 12 9.99 9.99 

 EMAP–Chesapeake Bay, 1992 59 –17 –16 –56 9.99 

 EMAP–Chesapeake Bay, 1993 62 –9 –31 9.99 9.99 

 NSTP Charleston Harbor, 1993 79 –18 –36 –55 9.99 

 NSTP Savannah River, 1994 60 –6 –30 –27 9.99 

 NSTP Biscayne Bay, Phase I, 1995 105 8 –1 18 9.99 

 NSTP Biscayne Bay, Phase II, 1996 120 –15 –33 9.99 9.99 

 EMAP Virginia and N. Carolina, 1994 50 –11 –31 9.99 9.99 

 EMAP Virginia and N. Carolina, 1995 50 –16 –32 9.99 9.99 

 NSTP Choctawhatchee, 1994 21 –16 –27 9.99 9.99 

 NSTP St. Andrews Bay, 1993 31 –19 –40 9.99 9.99 

 NSTP Tampa Bay Phase II, 1992 45 –23 –35 –58 9.99 

R. abronius

 Port of Tacoma, Blair Waterway 21 14 37 9.99 9.99 

 Commencement Bay Remedial Invest. 50 9.99 16 9 9.99 

 Elliott Bay sediment survey, 1985 97 9.99 –15 –5 7 

 Puget Sound Eight–Bay survey, 1985 48 9.99 31 22 9.99 

 Everett Harbor, 1985 29 9.99 –10 7 9.99 

 Port of Tacoma Remedial Investigation 79 9.99 53 28 21 

 Puget Sound Ambient Monitoring, 1989 50 –8 –6 9.99 9.99 

 Puget Sound Ambient Monitoring, 1990 50 49 64 9.99 9.99 

 Puget Sound Ambient Monitoring, 1991 47 –5 –29 9.99 9.99 

 BPTCP, 1992 Q3, LA 58 9.99 3 2 9.99 

 BPTCP, Screening, 1992 Q4, San Diego 23 9.99 61 36 9.99 

 BPTCP, Screening, 1993 Q2–3, San Diego 78 9.99 15 –8 9.99 

 BPTCP, Screening, 1994 Q1, LA 45 9.99 60 35 9.99 

 BPTCP, Screening, 1994 Q1, Santa Ana 24 9.99 43 9.99 9.99 

 BPTCP, Screening, 1994 Q1, San Diego 93 67 48 23 9.99 

 EMAP So.CA, 1994 Q3, San Diego 25 33 41 9.99 9.99 

 Palos Verdes shelf and Santa Monica Bay 

 (Swartz et al., 1991) 

31 9.99 –39 9.99 8 

a

Differences greater than 20% are shaded. Blank cell indicates fewer than six samples. 
 93 

Table 31. List of studies (primary data source) combined for analysis of 

broader geographic areas 

Geographic area Number of samples 

Hudson-Raritan/Long Island Sound (NSTP and Regional EMAP) 280 

Virginian Province (EMAP) 489 

Southeastern US (EMAP Carolinian and NSTP) 636 

Puget Sound (SEDQUAL) 594 

California (MLML) 508 
 94 

Table 32. Percent of samples predicted to be toxic to amphipods at the 

chemical concentrations defined by sediment quality guidelines 

Chemical ERL ERM TEL PEL AET 

Metals

 Antimony NA NA NA NA 99 

Arsenic 22 85 20 73 99 

Cadmium 46 89 32 77 93 

 Chromium (total) 33 78 22 54 94 

Copper 21 79 11 54 97 

Lead 30 73 20 55 96 

Mercury 22 60 19 60 85 

Nickel 28 53 22 48 92 

Silver 47 73 41 59 81 

Zinc 33 68 27 54 98 

Polycyclic aromatic hydrocarbons

2–Methylnaphthalene 39 78 19 59 89 

Acenaphthene 18 75 10 45 90 

Acenaphthylene 33 71 13 49 79 

Anthracene 31 70 24 47 92 

Benz(a)anthracene 40 70 22 57 84 

Benzo(a)pyrene 47 68 23 57 79 

 Benzo(g,h,I)perylene NA NA NA NA 78 

Chrysene 41 73 23 54 91 

 Dibenz(a,h)anthracene 39 66 10 53 90 

Fluoranthene 41 74 19 56 90 

Fluorene 20 77 21 55 94 

 Indeno(1,2,3–c,d)pyrene NA NA NA NA 83 

Naphthalene 45 83 22 60 84 

Phenanthrene 39 70 23 53 94 

Pyrene 44 67 22 57 88 

 

Polychlorinated biphenyls

 PCBs, total 16 40 16 40 78 

 

Organochlorine pesticides

Dieldrin NA NA 18 61 55 

p,p'–DDD NA NA 15 36 69 

p,p'–DDE 18 37 17 62 45 

p,p'–DDT NA NA 16 35 91 

AET = Apparent effect threshold for amphipod survival (Gries and Waldo, 1996) 

ERL = Effect–range low (Long and MacDonald, 1992) 

ERM = Effect–range median (Long and MacDonald, 1992) 

PEL = Probable effect level (MacDonald et al., 1996) 

TEL = Threshold effect level (MacDonald et al., 1996) 

NA = No guideline value available 
 95 

Table 33. Percent of samples predicted to be toxic to amphipods at the 

chemical concentrations defined by the Final Chronic Value for individual 

polycyclic aromatic hydrocarbons 

Final chronic value 

Chemical 

ppb dry weight, 

assuming 1% OC Percent predicted toxic 

Acenaphthene 4,910 95 

Acenaphthylene 4,520 89 

Anthracene 5,940 88 

Benz(a)anthracene 8,410 88 

Benzo(a)pyrene 9,650 88 

Benzo(b)fluoranthene 9,790 80 

Benzo(k)fluoranthene 9,810 88 

Benzo(g,h,i)perylene 10,950 89 

Chrysene 8,440 85 

Dibenz(a,h)anthracene 11,230 97 

Fluoranthene 7,070 77 

Fluorene 5,380 95 

Indeno(1,2,3-c,d)pyrene 11,150 90 

Naphthalene 3,850 88 

Perylene 9,670 91 

Phenanthrene 5,960 87 

Pyrene 6,970 80 
 96 

Table 34. Percent of samples predicted and observed to be toxic to 

amphipods within ranges defined by toxic units for polycyclic aromatic 

hydrocarbons (PAHs) 

PAH toxic unit Percent toxic Number of 

Range Average Mean predicted Observed samples 

 <1 0.09 36 37.8 2823 

 1–2 1.41 57 54.4 182 

 2–3 2.49 62 58.1 86 

 3–5 3.86 63 61.7 60 

 5–10 6.61 68 66.7 42 

 10–100 20.41 77 81 21 
-2

-1

0

1

log(TO

C)

Study

Figure 1. Box plots (plotted as per Tukey, 1977) summarizing the 

distribution of total organic carbon (TOC) (log 10) values for each study with 

greater than 20 samples from the marine amphipod database. Studies are 

ordered from left to right by mean TOC. The top and bottom of each rectangular 

box correspond to the upper and lower quartiles of the data, respectively; the dot 

within each box corresponds to the median of the data. 

 97 
10-2 10-1 100 101 102

0

0.2

0.4

0.6

0.8

1

Antimony

Concentration (mg/kg)

Proportion Toxic

 98 

100 101 102 103

0

0.2

0.4

0.6

0.8

1

Arsenic

Concentration (mg/kg)

Proportion Toxic

10-2 10-1 100 101 102

0

0.2

0.4

0.6

0.8

1

Cadmium

Concentration (mg/kg)

Proportion Toxic

100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

Chromium

Concentration (mg/kg)

Proportion Toxic

100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

Copper

Concentration (mg/kg)

Proportion Toxic

100 101 102 103 104 0

0.2

0.4

0.6

Proportion Toxic 0.8

1

Lead

Concentration (mg/kg)

Figure 2. Logistic regression models and proportion of toxic samples in 

concentration intervals in screened marine amphipod database for 37

chemicals based on Sig Only classification of toxic samples. The individual 

points correspond to the median of the sample concentrations within the interval 

and the proportion of the samples that are toxic within the interval. 
10-2 10-1 100 101 102 0

0.2

0.4

0.6

0.8

1

Mercury

Concentration (mg/kg)

Proportion Toxic

 99 

100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

Nickel

Concentration (mg/kg)

Proportion Toxic

10-2 10-1 100 101 102 103 0

0.2

0.4

0.6

0.8

1

Silver

Concentration (mg/kg)

Proportion Toxic

101 102 103 104 0

0.2

0.4

0.6

0.8

1

Zinc

Concentration (mg/kg)

Proportion Toxic

10-1 100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

1-Methylnaphthalene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

1-Methylphenanthrene

Concentration (ng/g)

Proportion Toxic

Figure 2. (continued)
10-1 100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

2-Methylnaphthalene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

2,6-Dimethylnaphthalene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

Acenaphthene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Acenaphthylene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Anthracene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Benz(a)anthracene

Concentration (ng/g)

Proportion Toxic

Figure 2. (continued)

 100 
100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Benzo(a)pyrene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Benzo(b)fluoranthene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Benzo(k)fluoranthene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Benzo(g,h,i)perylene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Biphenyl

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Chrysene

Concentration (ng/g)

Proportion Toxic

Figure 2. (continued)

 101 
10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Dibenz(a,h)anthracene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Fluoranthene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Fluorene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Indeno(1,2,3-c,d)pyrene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Naphthalene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Perylene

Concentration (ng/g)

Proportion Toxic

Figure 2. (continued)

 102 
10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Phenanthrene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Pyrene

Concentration (ng/g)

Proportion Toxic

10-2 10-1 100 101 102 103 0

0.2

0.4

0.6

0.8

1

Dieldrin

Concentration (ng/g)

Proportion Toxic

10-2 10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

p,p'-DDD

Concentration (ng/g)

Proportion Toxic

10-2 10-1 100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

p,p'-DDE

Concentration (ng/g)

Proportion Toxic

10-2 10-1 100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

p,p'-DDT

Concentration (ng/g)

Proportion Toxic

Figure 2. (continued)

 103 
10-1 100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Total PCBs

Concentration (ng/g)

Proportion Toxic

Figure 2. (continued).

 104 
10-2 10-1 100 101 102 103

0

0.2

0.4

0.6

0.8

1

Cadmium

Concentration (mg/kg)

Proportion Toxic

101 102 103 104

0

0.2

0.4

0.6

0.8

1

Chromium

Concentration (mg/kg)

Proportion Toxic

10-2 10-1 100 101 102

0

0.2

0.4

0.6

0.8

1

Mercury

Concentration (mg/kg)

Proportion Toxic

10-2 10-1 100 101 102 103

0

0.2

0.4

0.6

0.8

1

Silver

Concentration (mg/kg)

Proportion Toxic

100 101 102 103 104

0

0.2

0.4

0.6

0.8

1

Copper

Concentration (mg/kg)

Proportion Toxic

100 101 102 103 104

0

0.2

0.4

0.6

Proportion Toxic 0.8

1

Lead

Concentration (mg/kg)

Figure 3. Logistic regression models and proportion of toxic samples in 

concentration intervals in the screened marine amphipod database for 33 

chemicals based on MSD classification of toxic samples. The individual points 

correspond to the median of the sample concentrations within the interval and the 

proportion of the samples that are toxic within the interval.

 105 
101 102 103 104

0

0.2

0.4

0.6

0.8

1

Zinc

Concentration (mg/kg)

Proportion Toxic

100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

1-Methylnaphthalene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

1-Methylphenanthrene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

2-Methylnaphthalene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

2,6-Dimethylnaphthalene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Anthracene

Concentration (ng/g)

Proportion Toxic

Figure 3. (continued)

 106 
10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Acenaphthene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Acenaphthylene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Benz(a)anthracene

Concentration (ng/g)

Proportion Toxi

 107 

c

100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Benzo(a)pyrene

Concentration (ng/g)

Proportion Toxic

101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Benzo(b)fluoranthene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 0

0.2

0.4

0.6

0.8

1

Biphenyl

Concentration (ng/g)

Proportion Toxic

Figure 3. (continued)
100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Benzo(k)fluoranthene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Benzo(g,h,i)perylene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Chrysene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Dibenz(a,h)anthracene

Concentration (ng/g)

Proportion Toxic

101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Fluoranthene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Naphthalene

Concentration (ng/g)

Proportion Toxic

Figure 3. (continued)

 108 
100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Fluorene

Concentration (ng/g)

Proportion Toxic

101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Indeno(1,2,3-c,d)pyrene

 109 

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Perylene

Concentration (ng/g)

Proportion Toxic

100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Phenanthrene

Concentration (ng/g)

Proportion Toxic

101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Pyrene

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

p,p'-DDT

Concentration (ng/g)

Proportion Toxic

Figure 3. (continued)
10-2 10-1 100 101 102 103 0

0.2

0.4

0.6

0.8

1

Dieldrin

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

p,p'-DDD

Concentration (ng/g)

Proportion Toxic

10-1 100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Total PCBs

Concentration (ng/g)

Proportion Toxic

Figure 3. (continued).

 110 
101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

1-Methylnaphthalene

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

1-Methylphenanthrene

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

2-Methylnaphthalene

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

2,6-Dimethylnaphthalene

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Acenaphthene

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Acenaphthylene

Concentration (ng/g OC)

Proportion Toxic

Figure 4. Logistic regression models and proportion of toxic samples in 

organic carbon-normalized concentration intervals in the screened marine 

amphipod database for 25 chemicals based on Sig Only classification of toxic 

samples. The individual points correspond to the median of the sample 

concentrations within the interval and the proportion of the samples toxic within 

the interval.

 111 
101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Anthracene

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Benz(a)anthracene

Concentration (ng/g OC)

Proportion Toxic

102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Benzo(a)pyrene

Concentration (ng/g OC)

Proportion Toxic

102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Benzo(b)fluoranthene

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Benzo(k)fluoranthene

Concentration (ng/g OC)

Proportion Toxic

102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Benzo(g,h,i)perylene

Concentration (ng/g OC)

Proportion Toxic

Figure 4. (continued)

 112 
101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Biphenyl

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Chrysene

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Dibenzo(a,h)anthracene

Concentration (ng/g OC)

Proportion Toxic

102 103 104 105 106 107 108 0

0.2

0.4

0.6

0.8

1

Fluoranthene

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Fluorene

Concentration (ng/g OC)

Proportion Toxic

101 102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Fluorene

Concentration (ng/g OC)

Proportion Toxic

102 103 104 105 106 107 108 0

0.2

0.4

0.6

0.8

1

Indeno(1,2,3-c,d)pyrene

Concentration (ng/g OC)

Proportion Toxic

Figure 4. (continued) 

 113 
101 102 103 104 105 106 107 108 0

0.2

0.4

0.6

0.8

1

Naphthalene

Concentration (ng/g OC)

Proportion Toxic

 114 

102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Perylene

Concentration (ng/g OC)

Proportion Toxic

102 103 104 105 106 107 0

0.2

0.4

0.6

0.8

1

Phenanthrene

Concentration (ng/g OC)

Proportion Toxic

102 103 104 105 106 107 108 0

0.2

0.4

0.6

0.8

1

Pyrene

Concentration (ng/g OC)

Proportion Toxic

100 101 102 103 104 105 0

0.2

0.4

0.6

0.8

1

Dieldrin

Concentration (ng/g OC)

Proportion Toxic

100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

p,p'-DDD

Concentration (ng/g OC)

Proportion Toxic

Figure 4. (continued) 
100 101 102 103 104 105 106 107 108 0

0.2

0.4

0.6

0.8

1

p,p'-DDE

Concentration (ng/g OC)

Proportion Toxic

100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

p,p'-DDT

Concentration (ng/g OC)

Proportion Toxic

100 101 102 103 104 105 106 107 108 0

0.2

0.4

0.6

0.8

1

Total PCBs

Concentration (ng/g OC)

Proportion Toxic

Figure 4. (continued). 

 115 
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45

Cadmium

Chromium, total

Copper

Lead

Mercury

Silver

Zinc

1-Methylnaphthalene

1-Methylphenanthrene

2,6-Dimethylnaphthalene

2-Methylnaphthalene

Acenaphthene

Acenaphthylene

Anthracene

Benz(a)anthracene

Benzo(a)pyrene

Benzo(b)fluoranthene

Benzo(g,h,i)perylene

Benzo(k)fluoranthene

Biphenyl

Chrysene

Dibenz(a,h)anthracene

Fluoranthene

Fluorene

Indeno(1,2,3-c,d)pyrene

Naphthalene

Perylene

Phenanthrene

Pyrene

Dieldrin

PCBS, total

p,p'-DDD

p,p'-DDT

Normalized Chi-square

Sig Only MSD

ig Only

Figure 5. Comparison of logistic model goodness of fit for the marine 

amphipod survival endpoint with different toxicity classifications: S

versus MSD. 

 116 
0.00 0.05 0.10 0.15 0.20

zed Chi0.25 0.30 0.35 0.40 0.45

1-Methylnaphthalene

1-Methylphenanthrene

2,6-Dimethylnaphthalene

2-Methylnaphthalene

Acenaphthene

Acenaphthylene

Anthracene

Benz(a)anthracene

Benzo(a)pyrene

Benzo(b)fluoranthene

Benzo(g,h,i)perylene

Benzo(k)fluoranthene

Biphenyl

Chrysene

Dibenz(a,h)anthracene

Fluoranthene

Fluorene

Indeno(1,2,3-c,d)pyrene

Naphthalene

Perylene

Phenanthrene

Pyrene

Dieldrin

PCBS, total

p,p'-DDD

p,p'-DDE

p,p'-DDT

Normali square

DW OC

oodne

proach

Figure 6. Comparison of logistic model g ss of fit for the marine

amphipod endpoint survival with different approaches to the expression of 

chemical concentrations: dry w ht (DW) versus organic carbonnormalized (OC) concentration Both ap es use the Sig Only

classification of toxic samples. 

eig

s.

 117 
0.1

1

10

100

1000

0.1 1 10 100 1000

MSD T20

SIG Only T20

0.1

1

10

100

1000

0.1 1 10 100 1000 100

50

0.1

1

10

100

1000

1 10 100 

MSD T80

SIG Only T8

y = 1.65x 

R2 = 0.89 R2 = 0.9 R2 = 0.79 

Figure 7. Comparison of logistic regression model Tp values (T20, T50, T80) for Sig Only and MSD classification approaches for the marine amphipod database

00

MSD T50

SIG Only T

7x

0

.

y = 3.77x y = 2.5

118
1 10 100 1000 10000

0.0

0.2

0.4

0.6

0.8

1.0

Lead

Concentration (mg/kg)

Proportion T

o

xic

 119 

100 101 102 103 104 105 106

0.0

0.2

0.4

0.6

0.8

1.0

Phenanthrene

Concentration (mg/kg)

Proportion T

o

xic

1 10 100 1000 10000

0.0

0.2

0.4

0.6

0.8

1.0

Lead

Concentration (mg/kg)

Proportion Toxic

100 101 102 103 104 105 106

0.0

0.2

0.4

0.6

0.8

1.0

Phenanthrene

Concentration (mg/kg)

Proportion T

o

xic

Figure 8. Comparison of 1X mean screened (left) and unscreened (right) 

logistic regression models and proportion of samples toxic in concentration 

intervals for lead and phenanthrene using the marine amphipod database. 
 120 

Figure 9. Comparison of logistic model goodness of fit for the marine 

amphipod survival endpoint using different screening methods (1X screening 

vs. no screening) and the Sig Only classification of toxic samples. 

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45

Antimony

Arsenic

Cadmium

Chromium, total

Copper

Lead

Mercury

Nickel

Silver

Zinc

1-Methylnaphthalene

1-Methylphenanthrene

2,6-Dimethylnaphthalene

2-Methylnaphthalene

Acenaphthene

Acenaphthylene

Anthracene

Benz(a)anthracene

Benzo(a)pyrene

Benzo(b)fluoranthene

Benzo(g,h,i)perylene

Benzo(k)fluoranthene

Biphenyl

Chrysene

Dibenz(a,h)anthracene

Fluoranthene

Fluorene

Indeno(1,2,3-c,d)pyrene

Naphthalene

Perylene

Phenanthrene

Pyrene

Dieldrin

PCBS, total

p,p'-DDD

p,p'-DDE

p,p'-DDT

Normalized Chi-square

Screened Unscreened
1 10 100 1000 10000

0.0

0.2

0.4

0.6

0.8

1.0

Lead

Concentration (mg/kg)

Proportion T

o

xic

100 101 102 103 104 105 106

0.0

0.2

0.4

0.6

0.8

1.0

Fluoranthene

Concentration (ng/g)

Proportion T

o

xic

1 10 100 1000 10000

0.0

0.2

0.4

0.6

0.8

1.0

Lead

Concentration (mg/kg)

Proportion T

o

xic

100 101 102 103 104 105 106

0.0

0.2

0.4

0.6

0.8

1.0

Fluoranthene

Concentration (ng/g)

Prop

ortion T

o

xic

Figure 10. Logistic regression models and proportion of samples toxic in 

concentration intervals for the standard screening approach (1X mean) (left) 

and the 2X mean screening approach (right) for lead and fluoranthene using 

the marine amphipod database.

 121 
0.00 0.05 0.10 0.15 0.20

alized C

0.25 0.30 0.35 0.40 0.45

Antimony

Arsenic

Cadmium

Chromium, total

Copper

Lead

Mercury

Nickel

Silver

Zinc

1-Methylnaphthalene

1-Methylphenanthrene

2,6-Dimethylnaphthalene

2-Methylnaphthalene

Acenaphthene

Acenaphthylene

Anthracene

Benz(a)anthracene

Benzo(a)pyrene

Benzo(b)fluoranthene

Benzo(g,h,i)perylene

Benzo(k)fluoranthene

Biphenyl

Chrysene

Dibenz(a,h)anthracene

Fluoranthene

Fluorene

Indeno(1,2,3-c,d)pyrene

Naphthalene

Perylene

Phenanthrene

Pyrene

Dieldrin

p,p'-DDD

p,p'-DDE

p,p'-DDT

PCBS, total

Norm hi-square

1X Mean 2X M

model

differe

ean

Figure 11. Comparison of logistic goodness of fit for the marine

amphipod survival endpoint g nt screening methods (1X mean vs.

2X mean) and Sig Only classification of toxic samples. 

usin

 122 
123

0.1

1

10

100

1000

0.1 1 10 100 1000

Screening 2X T20 

Screening 1X T20

0.1

1

10

100

1000

10000

0.1 1 10 100 1000 10000

1

10

1

Screening 2X T8

base.

Screening 2X T50 

Screening 1X T50

00

1000

10000

1 10 100 

0

Screening 1X 

Figure 12. Ratio of logistic regression model Tp values (T20, T50, T80) for the standard screening mean) to the 2X mean screening approach for the marine amphipod data

y = 2.39x y = 1.57x y = 1.06

R2 = 0.99 R2 = 0.98 R2 = 0.8
 124 

Original Corrected

10-1 100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Concentration (ng/g)

Proportion T

oxic

10-1 100 101 102 103 104 105 106 0

0.2

0.4

0.6

0.8

1

Concentration (ng/g)

Proportion T

oxic

Figure 13. Logistic regression models and concentration interval plots 

showing the effect of correction in PCB units for 15 samples in the marine 

amphipod database. 
 125 

0

25

50

75

100

<T20 T20-T50 T50-T80 >T80

Probability Range

Figure 14

P

erc

e

nt C

ontrol-adju

ste

d Survival . Mean percent control-adjusted marine amphipod survival for 

toxic samples within intervals defined by the Tp values for all individual 

chemical models. 
 126 

0.0 0.2 0.4 0.6 0.8 1.0

0

2

4

6 P_Prod

P_Avg

P_Max

Probability 

Figure 15. Probability density functions for P_Avg, P_Max, and P_Prod. 

The probability that a variable will have a value with a small interval around x 

can be approximated by multiplying the value of y at x by the width of the

interval. 
0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Proportion T

oxic

P_Max Value

2 y = 0.11+0.33x+0.40x

 R2 = 0.93 

0

0.2

0.4

0.6

portion T

o

xic

0.8

1

0 0.2 0.4 0.6 0.8 1

Pro

P_Avg Value

 y = 0.11+1.42x–0.48x2 

 R2 = 0.89 

Figure 16. Proportion of observed toxic samples versus the P_Max and 

P_Avg value probability intervals based on Sig Only classification of toxic 

samples in the marine amphipod database. The regression lines are the P_Max 

and P_Avg models. Each point represents the median sample probability of a 

minimum of 50 individual samples within the interval and the proportion of the 

samples that were toxic within the interval. Only samples having measurements 

for 10 more modeled chemicals were used (n = 2856). 

 127 
0

0.25

0.5

0.75

1

<0.25 0.25-0.5 0.5-0.75 >0.75

 128 

Predicted

Observed

Pr

o

p

ortio

n T

oxi

c

Probability Range

N=990 N=1323 N=713 N=197

 P_Max Model

0

0.25

0.5

0.75

1

<0.25 0.25-0.5 0.5-0.75 >0.75

Predicted

Observed

Pr

o

p

ortio

n T

oxi

c

Probability Range

N=943 N=1448 N=598 N=234

 P_Avg Model

Figure 17. Mean predicted and observed proportion of toxic samples within 

probability quartiles for P_Max and P_Avg models derived from the marine 

amphipod database.
 129 

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Observed Proportion

Toxic

Predicted Probability of Toxicity

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75

Observed Proportion Toxic

Predicted Probability of Toxicity

1

y = 0.99x y = 0.96x 

R2 = 0.92 R2 = 0.88 

f

g P_Max and P_Avg models derived from the marine 

amphipod database. Each point represents the median sample probability of 

minimum of 50 individual samples within the interval and the mean percent 

survival within the interval (n = 3223). 

 

P_Max Model P_Avg Model 

Figure 18. Observed proportion of toxic samples versus the probability o

toxicity predicted usin
0

25

50

75

100

<= 0.25 >0.25 - 0.50 >0.5 - 0.75 >0.75

Percent Control-adjusted Survival

Probability Range

Figure 19. Mean percent control–adjusted survival for toxic samples within

probability quartile intervals for P_Max model derived from the marine

amphipod database. 

 130 
0

25

50

75

Percent Survival

100

0 0.25 0.5 0.75 1

Probability of Toxicity

Figure 20. Mean control-adjusted percent survival for samples within 

probability intervals for P_Max model (R2 = 0.94) derived from the marine 

amphipod database. Each point represents the median sample probability of a 

minimum of 50 individual samples within the interval and the mean percent 

control-adjusted survival within the interval (n = 3223). 

 131 
 Proportion T

oxic 

0.00

0.25

0.50

0.75

1.00

0 0.25 0.5 0.75 1

Maximum Probability

lid line is the

original P_Max model. 

Figure 21. Comparison of the original marine amphipod P_Max model with 

the model derived from data excluding polycyclic aromatic hydrocarbon 

chemistry (R2

 = 0.88). Data points represent the proportion of toxic samples

within unique probability intervals (minimum of 50 samples per interval) and the

dotted line is the model derived from the interval plot. The so

 132 
0.00

0.25

0.50

0.75

1.00

0 0.25 0.5 0.75 1

 P_Max Value

Proportion T

o

xic

om

Figure 22. Comparison of the original marine amphipod P_Max model with 

model derived from data excluding metals chemistry (R2 = 0.93). Data points 

represent the proportion of toxic samples within unique probability intervals 

(minimum of 50 samples per interval) and the dotted line is model derived fr

the interval plot. The solid line is the original P_Max model. 

 133 
0.00

0.25

0.50

0.75

1.00

0 0.25 0.5 0.75 1

 P_Max Value 

Proportion T

o

xic

Figure 23. Comparison of the original marine amphipod P_Max model with 

model derived from data excluding pesticides and PCB chemistry (R2

 = 0.92). 

Data points represent the proportion of toxic samples within unique probability 

intervals (minimum of 50 samples per interval) and the dotted line is model 

derived from the interval plot. The solid line is the original P_Max model. 

 134 
0

0.25

0.5

0.75

1

1 2 3 - 4 5 - 9 >9

Predicted

Observed

Prop

orti

o

n T

o

xic

Number of chemicals per sample with p>0.75

Figure 24. Mean predicted and observed proportion of toxic samples for samples 

with a predicted probability of >0.75 from the P_Max model in the marine 

amphipod database.

135
 136 

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 1

Obs

erved Proportio

n T

o

xic

P_Max

y= 0.09 + 0.7

 R2

Figure 25. Observed proportion of toxic samples versus P_Max values 

including only those chemica ith normaliz

0.27. Data points represent the proportion 

intervals (minimum of 50 sam model 

derived from the interval plot. 

0.6 0.8

 Value

3x 

 = 0.94 

ed chi-square values exceeding 

toxic within unique probability 

terval) and the line is the P_Max

ls w

ples per in
137

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1

Percent Control-adjusted Survival

Probability of Toxicity

0

0.25

0.5

0.75

1

0 0.25 0.5 

Proportion Toxic

Probability o

2 y = 0. 

R2 = 0

y = 0.29–1.10x+2.18x2 y = 85.27+77.69x–143.7x 17–

 R2 = 0.94 R2 = 0.93 .97

Figure 26. Median predicted probability of toxicity within probability intervals for the marine ampP_Max model compared with data for A. abdita: proportion of observed toxicity based on Sig Only (left), control-adjusted survival (center), and proportion of observed toxicity based on MSD classifi

Each point represents the median sample probability of a minimum of 12 individual samples within the in2022). 
1

38

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1

Percent Control-adjusted Survival

Probability of Toxicity

0

0.25

0.5

0.75

1

0 0.25 0.5 

Proportion Toxic

Probability o

y = 0.22+1.19x–0.59x2 y = 84.29+9.75x–47.76x2 y = 

 R2 = 0.68 R2 0.79 R2 = 

probability of toxici probability intervals for the marine amp

P

_

Max model compared with data for R. abronius: proportion of observed toxicity based on Sig On

classification (left), control-adjusted survival (center), and proportion of observed toxicity based on 

=

Figure 27. Median predicted ty within

classification (right). Each point represents the median sample probability of a minimum of 12 individu

within the interval (n = 1211). 
1

39

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1

Percent Control-adjusted Survival

Probability of Toxicity

0

0.25

0.5

0.75

1

0 0

Proportion Toxic

P

y = –0.29+2.30x–1.31x2 y = 89.92+36.31x–107.47x2 y = –

 R2 = 0.80 R2 = 0.80 R2 = 

d on MSD classification (right). Each point represents the media

probability of a minimum of 25 individual samples within the interval (n = 280). 

Figure 28. Median predicted probability of toxicity within probability intervals for the marine ampP_Max model compared with data from Hudson-Raritan/Long Island NSTP and Regional EMAP (proportion of observed toxicity based on Sig Only classification (left), control-adjusted survival (cenproportion of observed toxicity base
0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1 Pe

rcent Control-adjusted Survival

Probability of Toxicity

0

0.25

0.5

0.75

1

0 0.25 0.5 

Proportion Toxic

Probability o

y = 0.35–1.17x+2.05x2 y = 88.34+56.00x–105.09x2 y = 0.12

 R2 = 0.81 R2 = 0.91 R2 = 0.8

Figure 29. Median predicted probability of toxicity within probability intervals for the marine ampP_Max model compared with data from Virginian Province EMAP (A. abdita): proportion of obser

based on Sig Only classification (left), control-adjusted survival (center), and proportion of observebased on MSD classification (right). Each point represents the median sample probability of a minimum 

individual samples within the interval (n = 489).

140
0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1 Pe

rcent Control-adjusted Survival

Probability of Toxicity

0

0.25

0.5

0.75

1

0 0.25 0.5 

Proportion Toxic

Probability o

2

y = 0.19–1.08x+1.93x2 y = 86.68+85.67x–154.11x y = 0.15

R2 = 0.66 R2 = 0.84 R2 = 0.7

Figure 30. Median predicted probability of toxicity within probability intervals for the marine ampP_Max model compared with data from NSTP and Carolinian EMAP from the southeastern U.S. (Aproportion of observed toxicity based on Sig Only classification (left), control-adjusted survival (cenproportion of observed toxicity based on MSD classification (right). Each point represents the media

probability of a minimum of 25 individual samples within the interval (n = 636). 

141
0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1

Percent Control-adjustedSurvival

Probability of Toxicity

0

0.25

0.5

0.75

1

0 0.25 0.5 

Proportion Toxic

Probability o

2 y = –0.07+1.64x–0.82x y = 93.70–12.30x–29.20x2 y = –0

 R 

2 = 0.83 R2 = 0.96 R2 = 0

Figure 31. Median predicted probability of toxicity within probability intervals for the marine ampP_Max model compared with data from Puget Sound, WA (R. abronius): proportion of observed to

on Sig Only classification (left), control-adjusted survival (center), and proportion of observed toxicMSD classification (right). Each point represents the median sample probability of a minimum of 25 in

samples within the interval (n = 594). 

142
143

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1

ty of Toxicity

0

0.2

Percent Control-adjusted Survival

Probabili

5

0.5

0.75

1

0 0.25 0.5 

ProportionToxic

Probability o

y = 0.61–0.14x+0.57x2 y = 68 y = 0.31

 R2 = 0.32 R2 = 0 R2 = 0.7

Figure 32. Median predicted probability of toxicity wi ls for the marine ampP_Max model compared with data from California (R. a dita): proportion of obser

based on Sig Only classification (left), control-adjusted s nd proportion of observebased on MSD classification (right). Each point represent le probability of a minimum 

individual samples within the interval (n = 508).

.65+46.47x–70.63x2

.46

thin probability interva

bronius and A. ab

urvival (center), a

s the median samp
 144 

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Prop

ortion T

oxi

c

Probability of Toxicity

0

0.25

0.5

0.75

0 0.25 0.5 0.75 1

Prop

ortion T

oxi

c

Probability of Toxicity

0.07–0.20x

Figure 33. Median predicted probability of toxicity within probability

1

y = –0.42+1.61x y =

R2 = 0.92 R2 = 0.28

intervals for the marine amphipod P_Max model compared with proportion 

of observed toxicity based on Sig Only classification from individual studies: 

Elliott Bay, Puget Sound, WA (R. abronius, n = 97) (left) and Biscayne Bay, 

FL (A. abdita, n = 120) (right). Each point represents the median sample

probability of a minimum of 12 individual samples within the interval.
 145 

Sig Only MSD

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Prop

ortion T

o

xic

Probability of Toxicity

0

0.25

0.5

0.

Prop

ortion T

o

xic

ty of toxicity

median samp

the interval.

75

1

0 0.25 0.5 0.75 1

Probability of Toxicity

y = 0.77+0.20x y = 0.22+0.98x

 R2 = 0.17 R2 = 0.76 

Figure 34. Median predicted probabili within probability 

intervals for the marine amphipod P_Max model compared with proportion 

of observed toxicity from an i vidual study from San Diego Bay, CA (R. 

abronius, n = 93); toxicity based on Sig Only classification and MSD 

classification. Each point repr ts the le probability of a 

minimum of 12 individual sam ithin

ndi

esen

ples w
0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75

bability of Toxicity

146

1

Percent Controladjusted Survival

Pro

0

0.25

0.5

0.75

1

0 0.25 0.5 

Proportion Toxic

terv

ility

Probability o

y = 0.45+0.63x y = x2 y = 0.43+0.

 R2 = 0.82 R2 R2 = 0.82 

Figure 35. Median predicted probability of toxicity y in als for the marine ampP_Max model compared with data from the Calcasi dita): proportion of observed ton Sig Only classification (left), control-adjusted su d proportion of observed toxicMSD classification (right). Each point represents the bab of a minimum of 12 in

samples within the interval (n = 170). 

63.78–3.63x–85.80

= 0.97 

within probabilit

eu Estuary (A. ab

rvival (center), an

median sample pro
0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Urchin Development

Proportion T

o

xic

Probability of Toxicity

0

0.25

0.5

0.75

0

Proportion T

o

xic

y and proporti

endpoints. Ea

ples within the

ectively).

1

0.25 0.5 0.75 1

Urchin Fertilization

Probability of Toxicity

y = 0.55+0.23x y = 0.15+1.52x–1.52x2

 R2=0.18 R2 = 0.39 

Figure 36. Average predicted probabilit on toxic within 

probability intervals for the m amphipod P_Max model applied to the 

sea urchin development and fe zation ch point represents the

median sample probability of a m m of 25 individual samples within the 

interval and the proportion of the toxic sam interval (n = 782 and

824 for development and fertiliz resp

arine 

rtili

in

ati

imu

on,

 147 
0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1

% Control-adjusted Response

Probability of Toxicity

0

0.25

0.5

0.75

1

0 0

Proportion Toxic

P

x y =

R2 y = 0.40 + 0.68 y = 65.07 – 67.46x 0

R2 = 0.74 R2 = 0.79 = 

Figure 37. Median predicted probability and proportion toxic within probability intervals for the mamphipod P_Max model applied to sea urchin (A. punctulata) development endpoint: proportion of 

toxicity based on Sig Only classification (left), control-adjusted response (center), and proportion otoxicity based on MSD classification (right). Each point represents the median sample probability of a 

25 individual samples within the interval and the proportion of the toxic samples within the interval (n = 

148

____ ____ ____
0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1

% Control-adjustedResponse

Probability of Toxicity

0

0.25

0.5

0.75

1

0 0

Proportion Toxic

P

y = 

 R2 = 0.49 R2 = 0.68 R

0.25 + 0.60x y = 96.08 – 79.44x y 

Figure 38. Median predicted probability and proportion toxic within probability intervals for the mamphipod P_Max model applied to sea urchin (A. punctulata) fertilization endpoint: proportion of 

toxicity based on Sig Only classification (left), control-adjusted response (center), and proportion otoxicity based on MSD classification (right). Each point represents the median sample probability of a 

25 individual samples within the interval and the proportion of the toxic samples within the interval (n = 

149
0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1

% Control-adjusted Response

Probability of Toxicity

0

0.25

0.5

0.75

1

0 0.

Proportion Toxic

Pr

y = 0.56 + 0.33x y = 37.57 – 12.70x y = 

igure 39. Median predicted probability and proportion toxic within probability intervals for the m

R2 = 0.12 R2 = 0.02 R2 = 

Famphipod P_Max model applied to sea urchin (S. purpuratus) development endpoint: proportion otoxicity based on Sig Only classification (left), control-adjusted response (center), and proportion otoxicity based on MSD classification (right). Each point represents the median sample probability of a 

25 individual samples within the interval and the proportion of the toxic samples within the interval (n = 

150
151

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1

Proportion Toxic

Probability of Toxicity

0

255075

100

0 0.25 0.5 0.75 1

% Control-adjusted Response

Probability of Toxicity

0

0.25

0.5

0.75

1

0 0.25 0.5 

Proportion Toxic

Probability o

R2 = 0.03 R2 = 0.04 R2 

Figure 40. Median predicted probability and proportion toxic within probability intervals for the mamphipod P_Max model applied to sea urchin (S. purpuratus) fertilization endpoint: proportion of toxicity based on Sig Only classification (left), control-adjusted response (center), and proportion otoxicity based on MSD classification (right). Each point represents the median sample probability of a 

25 individual samples within the interval and the proportion of the toxic samples within the interval (n = 

y = 0.59 + 0.09x y = 55.95 – 12.72x y = 
 152 

0.00 0.25 0.50 0.75 1.00

Probability of Toxicity

0.00

0.25

0.50

0.75

1.00

Proportion To

xic

Figure 41. Proportion of samples that were toxic based o the H. azteca 10–

14-day survival endpoint versus media probability of toxicity predicted

n

n

using the marine amphipod P_Max model. Each point represents the median

sample probability of a minimum of 25 individual samples within the interval and

the proportion of toxic samples within the same interval (n = 567). 
 153 

0.00 0.25 0.50 0.75 1.00

Probability of Toxicity

0.00

0.25

0.50

0.75

1.00

Proportion To

xic

pp

Figure 42. Proportion of samples that were toxic based on the C. tentans or 

C. riparius 10–14-day survival endpoint versus median probability of toxicity 

predicted using the marine amphipod P_Max model. Each point represents 

the median sam le probability of a minimum of 25 individual samples within the 

interval and the proportion of toxic samples observed within the same interval (n 

= 585).

p
 154 

y = 0.18 – 0.08x – 1.12x

ty of toxicity

predicted usi nt represents

erved within the same interval

2

R2 = 0.95

Figure 43. Proportion of samples that were toxic based on the H. azteca 28-

day growth and survival endpoint versus the median probabili

ng the marine amphipod P_Max model. Each poi

the median sample probability of a minimum of 15 individual samples within the 

interval and the proportion of the toxic samples obs

(n = 126).

0

0 0.25 0.5 0.75 1

P

0.25 ro

p

0.5

rtio

n T

o

0.75 ic

o

x

1

Probability of Toxicity
 155 

ASTM. (2002b) Standard test method for measuring the toxicity of sediment-associated contaminants with 

freshwater invertebrates. E1706-00B. In: Annual book of standards. Vol. 11.05. E 1367-99; pp. 1125–1241. 

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ute toxicity to amphipods by chemical mixtures. Environ 

valuating sediment chemistry and toxicity data using 

gistic regression modeling. Environ Toxicol Chem 18:1311–1322. 

 sediment chemistry using 

 Sound apparent effects thresholds (AETs). Vol. I. 

994 amphipod and echinoderm larval AETs. Puget Sound dredged disposal analysis (PSDDA). U.S. Army Corps 

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d 

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APPENDIX A1: REFERENCES FOR MARINE SEDTOX02 DATABASE 

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a State Water Resources Control Board, Sacramento, CA. 

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in 

ter Resources Control Board, 

rn California Coastal Water Resources Project, Westminster CA. Prepared for Santa 

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 final addendum report. California State Water Resources Control Board, 

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U. S. Army Engineer District, Chicago. (1994) Clean Water Act Section 404 (b) (1) Contaminant Determination 

for Burns Waterway Harbor. U. S. Army Engineer District, Chicago. 

U.S. EPA (Environmental Protection Agency). (1996) Assessment of sediment in the Indiana Harbor area of 

concern. 

U.S. EPA. (1997) Field sampling methodologies: Clinton River, MaComb County, Michigan (Stations CL9716-

CL9727). U.S. EPA, Great Lakes National Program Office. 

Wade, TL; Velinsky, DJ; Reinharz, E; et al. (1994) Tidal River sediments in the Washington, D.C. area. II. 

Distribution and sources of organic contaminants. Estuarine Research Federation. 

Smith, JA; Glowacky, RS; Crerar, PJ; et al. (1984) Analysis of sediment, Oak Orchard Harbor, Oak Orchard, New

York. Aqua Tech Environmental Consultants, Inc. Prepa

E

Smith, JA; Glow

P
 167 

Webster, CD; McLaughlin, JC; Wan, L; et al. (1995) A report of the whole sediment toxicity of 12 sites in the 

Maum

NCK Associates, Inc. (1995) Harbor sediment sampling documentation report. Prepared for Lakehead Pipe 

ee area of concern to Hyalella azteca. Ohio EPA. 

WE

Line Company. 
 168 

T TOXICITY DATABASE (SEDTOX02) STRUCTURE 

e management system 

d e name of the 

d nd a esc tion of the pur e is a 

“ ibes niq rds in the ase is 

divided into separate databases w data. 

in tion 

APPENDIX B: SEDIMEN

The following tables describe the structure for the databas

eveloped for the SEDTOX02 database (NOAA, 2004). Above each table is th

atabase table a

key” that descr

 brief d rip pose. Following the database table nam

 how u ue reco table are defined. The SEDTOX02 datab

ith identical structure for marine/estuarine and freshwater

Site: Table to def e general loca

Key: Siteid 

FIELD NAME TYPE WIDTH, DEC DESCRIPTION 

SITEID Char 4 Site identifier 

SITENAME Char 40 Descriptive name for site 

EPAREGION Num 2, 0 Region for site location; 11 for Canada 

S s ba rmation regarding th e, contact, etc.). 

K udyi

 

tudy: Provide

t

sic info e study (e.g., nam

ey: Siteid+S

ME

d 

FIELD NA TYPE WIDTH, DEC DESCRIPTION 

SITEID Char 4 Site identifier 

STUDYID Char 2 Study identifier 

STUDYNAME Char 40 Short name of study 

CONTACT Char 40 Contact source for data 

SEDCHEM l Logica 1 Sediment chemistry data, Y or N? 

SEDTOX Logical 1 Sediment toxicity data, Y or N? 

S arily e fo riptive note

recording data in the database. Information ma

c s calc

K tudyi

 

tudynot: Prim a tabl r desc s regarding the study design and method for 

y include how replicates are recorded and any 

hemical sum ulated. 

ey: Siteid+S

FIELD NAME

d 

TYPE WIDTH, DEC DESCRIPTION 

SITEID Char 4 Site identifier 

STUDYID Char 2 Study identifier 

NOTES Memo 10 Memo field data processing notes 

Studyref: Contains information regarding the document that describes the study and data. 

Key: Siteid+Studyid 

FIELD NAME TYPE WIDTH, DEC DESCRIPTION 

SITEID Char 4 Site identifier 

STUDYID Char 2 Study identifier 

YEAR Char 4 Year of publication 

AUTHORS Char 160 Authors, if published 

TITLE Char 160 Title, if published 

SOURCE Char 160 Citation or agency, if published 
 169 

collections. 

Key: Siteid+Studyid+Stationid 

AME DTH, DEC

Station: Listing of stations for sediment chemistry, tissue chemistry, and bioassay sample 

FIELD N TYPE WI DESCRIPTION

SITEID Char 4 Site identifier 

STUDYID Char 2 Study identifier 

STATIONID Char 6 Station identifier 

LATITUDE Num 12, 8 Latitude in decimal degrees, NAD83 

LONGITUDE Num 13, 8 Longitude in decimal degrees, NAD83 

EST_STN Char 8 How coordinates were established 

S nt sa ati ple 

d centimeter ld s ple lab replica

K dyi

ample: Sedime mple collection inform on, including station id, sample date, sam

epth (in s). Fie am tes are treated as separate samples. 

ey: Siteid+Stu d+Stationid+Sampleid+Labrep 

FIELD NAME TYPE WIDTH, DEC DESCRIPTION 

SITEID Char 4 Site identifier 

STUDYID Char 2 Study identifier 

STATIONID Char 6 Station identifier 

SAMPLEID Char 2 Sample identifier 

LABREP Char 2 Lab replicate number 

SAMPDATE Char 8 Date sample collected as YYYYMMDD 

SAMPTIME Char 5 Time sample collected 

UDEPTH Num 8, 2 Top depth of sample from sed/water 

interface in cm 

LDEPTH Num 8, 2 Bottom depth of sample from sed/water 

interface in cm 

TOC Num 6, 2 Total organic carbon as percent 

PCTFINES Num 6, 2 Percent fines 

UAN_PW Num 10, 4 Unionized ammonia in porewater 

H2S_PW Num 10, 4 Hydrogen sulfide in porewater 

EXSAMPID Char 12 Investigator’s sample identifier 
 170 

d+Sampleid+Labrep+Chemcode 

Chem: Chemistry data associated with surface sediment samples. 

Key: Siteid+Studyid+Stationi

FIELD NAME TYPE WIDTH, DEC DESCRIPTION 

SITEID Char 4 Site identifier 

STUDYID Char 2 Study identifier 

STATIONID Char 6 Station identifier 

SAMPLEID Char 2 Sample identifier 

FIELDREP Char 2 Field replicate number 

LABREP Char 2 Lab replicate number 

CHEMCODE Char 10 Code for parameter name

CONC Num 12, 5 Measured concentration 

QUALCODE Char 5 Assigned qualifier for concentration 

UNITS Char 6 Units of concentration for parameter 

MEASBASIS Char 2 Wet (WW) or dry weight (DW) indication 

MISSINGVAL Logical 1 Data missing, Y or N? 

B ean re f re

K dyi oni Sampleid+Tes

FIELD NAME 

iosumm: M sults (o plicate data) for sediment bioassay.

ey: Siteid+Stu d+Stati d+ tid 

TYPE WIDTH, DEC DESCRIPTION 

SITEID Char 4 Site identifier 

STUDYID Char 2 Study identifier 

STATIONID Char 6 Station identifier 

SAMPLEID Char 2 Sample identifier 

TESTID Char 12 Bioassay test code 

FIELDREP Char 2 Field replicate number 

GROUP Char 2 Sample grouping 

SERIES Char 2 Bioassay test series number 

EFFECTVAL Num 7, 2 Measured effect value 

SIGEFFECT Logical 1 Was effect significant, Y or N? 

NEG Logical 1 Negative control sample, Y or N? 

REF Logical 1 Reference sample, Y or N? 

STAT Logical 1 Used for statistical comparison, Y or N? 

CTRLADJ Num 6, 2 Control-adjusted effect value 

SIG_ORIGIN Char 2 Code for toxic sample used by original study
 171 

FIELD NAME TYPE WIDTH, DEC DESCRIPTION 

Bmaster: Descriptive notes associated with bioassay tests where available. 

Key: Siteid+Studyid+Testid 

SITEID Char 4 Site identifier 

STUDYID Char 2 Study identifier 

GROUP Char 2 Sample grouping based on spatial or 

temporal 

TESTID Char 12 Bioassay test code 

SPIKED Logical 1 Sediment spiked with contaminant, Y or N? 

TESTCOMM Memo 10 Bioassay test comments 

Q s qual d w chemical data 

K udyi lcod

 TH, DEC

ualify: Define ifiers use ith

ey: Siteid+St d+Qua e 

FIELD NAME TYPE WID DESCRIPTION 

SITEID Char 4 Site identifier 

STUDYID Char 2 Study identifier 

QUALCODE Char 5 Assigned qualifier code for concentration 

QUALIFIERS Char 30 Qualifiers used in original study 

DESCRIPT Char 80 Description of qualifiers used in study 

Chemdict: Provides a unique list of chemical names and associated chemical codes that are used 

in the Chem table. 

Key: Chemcode 

FIELD NAME TYPE WIDTH, DEC DESCRIPTION 

CHEMCODE Char 10 Code for parameter name 

CHEMNAME Char 40 Full chemical name 

CHEMCLASS Char 8 Chemical classification 

CATEGORY Char 8 Alternate chemical classification 

SUBCATGY Char 10 Subclassification for alternate chemical 

class 

CHEMTOTAL Char 10 Classification used for totaling chemicals 

MOLWT Num 7, 3 Molecular weight of chemical 

CASNUM Char 24 CAS number 

UNITS Char 6 Units of concentration for chemical 

(sed/tiss) 

WA_UNITS Char 6 Units of concentration for chemical in water 

media 
 172 

Testdict: Provides a unique list of bioassay tests and associated test codes that are used in the 

Biosumm an

FIELD NAME 

d Bmaster tables. 

Key: Testid 

TYPE WIDTH, DEC DESCRIPTION 

TESTID Char 12 Bioassay test code 

MEDIUM Char 15 Medium tested 

MEDCODE Char 2 Code for medium tested 

GROUP Char 20 Bioassay species grouping 

ALTGROUP Char 20 Alternate bioassay species grouping 

SPECIES Char 40 Bioassay organism 

SPPCODE Char 3 Code for bioassay organism 

LHS Char 10 Life history stage of bioassay organism 

LHSCODE Char 1 Code for life history stage 

ENDPOINT Char 30 Bioassay test endpoint 

ENDCODE Char 2 Code for test endpoint 

DURATION Char 10 Duration of test 

DURCODE Char 4 Code for test duration 
 173 

upport the development and assessment of 

numerical sediment quality guidelines. The database is composed of matching (i.e., synoptically 

collected) sediment chemistry and laboratory toxicity data from freshwater, estuarine, and 

arine sites. The following screening criteria provided a means of evaluating candidate data sets 

ria

Howev

intende

1. ontain synoptically collected sediment chemistry and biological effects 

ples for biological and chemical testing must have been 

2. contain any number of sediment samples, provided that there is at least one 

nontoxic sample. Preference should be given to data sets that contain ≥5 samples. 

considered to be acceptable provided that the sediment chemistry and toxicity data are 

. posite sediment 

a sets 

5. 

 the 

100% dilution are acceptable). 

6. It is not essential that data sets include the coordinates (i.e., longitude and latitude) of the 

sampling site along with the chemical and biological data. However, these data will be 

included as available. 

. It is not essential that data sets have a minimum range of chemical concentrations in the 

APPENDIX C: DATA ACQUISITION SCREENING METHODS

The SEDTOX02 database was developed to s

m

and ensuring general consistency in the information included in the database. The evaluation 

crite were based on ASTM (2002a, b), Environment Canada (1998a, b), and U.S. EPA (2000). 

er, the screening criteria are not necessarily recommended for applications beyond their 

d purpose. Data from spiked-sediment bioassays were not included in the database. 

A. Approach for Evaluating Data Set Acceptability

Data sets must c

data. That is, the sediment sam

collected from the same location and at the same time. 

Data sets may 

3. Data sets that include toxicity and chemistry data generated on sediment samples from 

any sediment horizon (i.e., surficial sediments, cored sediments, etc.) should be 

matching (i.e., for the same sediment samples). 

4 Data sets that include toxicity and chemistry data generated on com

samples should be considered to be acceptable. Preference should be given to dat

that composite sediments over limited geographic areas. 

Data sets that include data generated from dilution series of bulk sediments and/or 

porewater are not acceptable for incorporation into the database (however, data from

7

sediment samples (i.e., the 10-fold criteria that was used previously to assess data 

acceptability is no longer required). 
 174 

8. ted 

CBs, and pesticides). Data sets with incomplete sediment chemistry 

may also be included, particularly if prior knowledge indicates that certain chemicals are 

unlikely to occur at the site. 

. Analytical detection limits should be below the respective ERLs or TELs for each 

chemical analyte. 

10. The chemical analytical methods used in the study must be reported and should meet 

minimum data quality requirements/objectives (i.e., the precision, accuracy, and detection 

limits m various protocols and best 

professional judgement; and the rationale for decisions regarding data acceptability must 

be docu

11. Concentratio d in the database, 

provided that data on the concentrations of total metals (i.e., strong acid digestion) are 

also av

3. Chemistry data that were generated using atypical methods (e.g., X-ray fluorescence for 

4. Data from elutriate tests must not be included in the database because there is too little 

connec n

15. Data se g

Microt

data are not being targeted for inclusion in the database because the linkage between the 

6. Acceptable environmental conditions must be maintained throughout the toxicity test (as 

O of the overlying water should have been 

measured during the test. If these variables have been measured but not reported, it is 

reasonable to assume that the conditions during the test were not acceptable and 

gators. 

 and 

ld 

 of measured 

contaminants should not exceed their respective TELs or ERLs; and the levels of particle 

Data sets should be preferentially included if full sediment chemistry has been conduc

(i.e., metals, PAHs, P

9

ust be reported; data quality may be evaluated using

mented). 

ns of SEM metals (e.g., Cd, Cu, Pb, Ni, Zn) may be include

ailable. 

12. Information on the environmental conditions in the bioassay chambers should be 

captured in the database, including data on DO, pH, salinity, water hardness, temperature, 

NH3, and H2S. 

1

metals, screening methods for PAHs, etc.) are not acceptable for inclusion in the 

database. 

1

tio between the chemistry and the laboratory toxicity data. 

ts enerated using organic extracts may be included in the database (e.g., 

ox), provided they are available with other toxicity data. Microtox and Mutatox 

toxicity data and effects on sediment-dwelling organisms is tenuous. 

1

defined in the protocol for the toxicity test). Consequently, the temperature, pH, 

hardness, conductivity, salinity, and D

additional information should be obtained from the investi

17. The responses of the test organisms exposed to negative controls must be reported

must be within acceptable limits (i.e., as defined in ASTM standard methods). For 

toxicity tests for which a negative control sediment is not available, the selected fie

reference sediment must be shown to be functionally equivalent to a negative control 

sediment, as indicated by nontoxicity (as defined above); concentrations
 175 

B. Considerations for Prioritizing Data Sets

1. The procedures used for collecting, handling, and storing sediments should be consistent 

with the protocols that have been established by ASTM. Generally, higher priority for 

inclusion in SEDTOX should be assigned if 

(i) surficial sediments were collected and tested; 

 in 

e been 

 biological testing. 

cted 

y 

ods are 

• marine amphipod (Ampelisca and Rhepoxynius) survival; 

The data on other species and associated endpoints should be captured in the database 

. Priority should be given to data sets that test surficial sediments and do not composite 

C. Consid ti

1. Sedime t samples from the same study for which inconsistent chemistry data are 

availab e

should be gro

chemistry should be preferentially included in the database. The portion of the data set 

with in

size distribution, pH, Eh, salinity, and TOC must be similar to those in the basin area 

under investigation. 

(ii) the sediments were tested within 8 weeks of collection (some flexibility

applying this criterion is warranted, as similar bioassay responses hav

observed up to 1 to 2 years after sediment collection; and, 

(iii) the sediments were not frozen prior to

2. Data sets should be preferentially included if full sediment chemistry has been condu

(i.e., metals, PAHs, PCBs, and pesticides). Data sets with incomplete sediment chemistr

may also be included, particularly if prior knowledge indicates that certain chemicals are

unlikely to occur at the site. 

3. Data on all test organisms and endpoints for which standard toxicity testing meth

available should be captured in the database, as available. However, higher priority 

should be given to data sets that include one or more of the following tests/endpoints: 

• marine sea urchin (Arbacia and Strongylocentrotus) fertilization; 

• freshwater amphipod (Hyalella) survival, growth, and reproduction; and, 

• freshwater midge (Chironomus riparius and C. tentans) survival and growth. 

when available, along with data on the high-priority toxicity tests. 

4

samples over large geographic areas. 

era ons Related to Preparing Data for Import

n

le (i. ., metals only for some samples and complete chemistry for other samples) 

uped separately. The portion of the data set with complete sediment 

complete chemistry should be considered as a separate data set. 
 176 

2. Detection lim es 

for which the ow-DL 

values must b g data if the DL has not been reported. 

. 

 

e. 

stances: 

z(a)anthracene, chrysene, benzo(a)pyrene, benzofluoranthene, 

dibenzo (a,h)anthracene, and benzo(g,h,i)perylene. 

4. 

ed using an appropriate method (e.g., sum of 

detected Aroclors or sum of detected congeners, etc.). The method that was used in the 

5. of 

, 

asured. The method that was used in the determination should 

be recorded in the database. 

 concentrations of tPCDDs/PCDFs should be treated as equivalent, 

regardless of which method was used to determine the levels, provided that the most 

. The data on the levels of H2S and NH3 in the replicate bioassay chambers will be 

s, all 

ods 

c 

. Sediment samples should be grouped in a consistent manner to facilitate data analyses, 

ame year should 

be grouped together. 

 be 

d on the year that the samples were collected. 

(iii) In general, samples that were collected within a single study that was 

conducted during one year should be grouped together; however, it may be 

its (DLs) should be reported for all measured chemicals (i.e., for all analyt

 concentration in one or more samples is reported to be < DL). Bel

e treated as missin

3. Calculations of tPAH, tHMW-PAH, or tLMW-PAH will be conducted using subroutines 

in the database. Previously calculated values will not be incorporated into the database

LMW-PAHs are considered to include the following two- and three-ringed substances:

naphthalene, acenaphthene, acenaphthylene, fluorene, phenanthrene, and anthracen

HMW-PAHs are considered to include the following four- and five-ringed sub

fluoranthene, pyrene, ben

The reported concentrations of tPCBs should be treated as equivalent, regardless of 

which method was used to determine the levels. If tPCB concentrations were not 

reported, the value should be calculat

determination should be recorded in the database. 

The reported concentrations of tDDTs should be treated as equivalent, regardless 

which method was used to determine the levels, provided that the p,p'-isomers of DDT

DDE, and DDD were me

6. The reported

toxic substances were measured (i.e., TCDD, HCDD, TCDF, HCDF). The method that 

was used in the determination should be recorded in the database. 

7

summarized on a per-sample basis and included in the database. For these variable

of the measurements should be treated as equivalent, regardless of the analytical meth

that were used. 

8. Data from tests conducted with different types of media (i.e., porewater vs. organi

extracts for Microtox) should be treated separately in the data analyses. 

9

specifically: 

(i) Samples that were collected from the same area within the s

(ii) Samples that were collected from the same area in different years should

separated into groups, base
 177 

 basin area. For 

 maps of 

appro

10. ent samples from the basin area under investigation should be 

ol. For bulk 

11. c or nontoxic using the results of statistical 

egative control data should also be provided for each batch of samples to 

s 

12. ts that are designed to evaluate effects on growth and/or reproduction, samples will 

ugh to point if a sig rvival was 

 it was not possible to m

tion directly or even if it was possible to measure effects on survivors and no 

ere observed). 

ASTM (American Society for T d Ma ndard guide for conducting 

10-day, static sedimen 7-99. In: 

Annual book of standards. Vol. 11.05. West Conshohocken, PA, pp. 693–719. 

ASTM. (2002b) Standard test method for measuring the toxicity of sediment-associated 

contam al book of standards. Vol. 

11.05. West Conshohocken, PA, pp. 1125–1241. 

sedime fre phi onment Canada, Ottawa, 

Ontario l re 33. 

nvironment Canada. (1998b) Biological Test Method: Test for growth and survival in 

ime or Chironomus riparius). 

Enviro i

U.S. EPA (Environmental Protection Agency). (2000) Methods for measuring the toxicity and 

ants with freshwater invertebrates, second 

edition

necessary to create separate groupings for the samples by

example, EMAP data will be grouped by basin area using

priate scale. 

The toxicity of sedim

determined on the basis of statistical comparisons with the negative contr

sediments and porewater, negative control sediments may be obtained from a suitable 

reference site(s), as specified in the ASTM (2002b) standard methods (e.g., >80% 

survival and full chemistry) and other relevant information (i.e., contaminant 

concentrations < ERLs). 

S

analyses. N

ediment samples must be designated as toxi

facilitate the determination of toxicity on the basis of minimum significant difference

from the negative control responses. 

In tes

be treated as tho xic for these end s nificant effect on su

determined for that sample (i.e., even if easure growth or 

reproduc

effects w

References Cited

esting an terials). (2002a) Sta

t toxicity tests with marine and estuarine amphipods. E 136

inants with freshwater invertebrates. E1706–00B. In Annu

Environment Canada. (1998a) Biological Test Method: Test for growth and survival in 

nt using the shwater am pod Hyalella azteca. Envir

. Technica port number EPS1/RM/

E

sed nt using larvae of freshwater midges (Chironomus tentans

nment Canada, Ottawa, Ontar o, Technical report number EPS1/RM/32. 

bioaccumulation of sediment-associated contamin

. EPA 600/R-99/064, Washington, DC. 
 178 

SCREENING CRITERIA FOR BEDS/SEDTOX

CURRENCE DATA e 

Referen 

APPENDIX D: DATA EVALUATION METHODS

CO-OC

*Must b Present

ce: Reference Number: 

1.* iological 

nd chemical data collected from the same location at the same time)? 

NO 

Does data set contain matching sediment chemistry and biological effects (i.e., b

a

 UNACCEPTABLE 

YES Page Reference(s): 

2. ate? Page reference for site 

escription? 

What is the location of sampling site(s)? Collection D

d

3. Freshwater Estuarine Marine 

Salinity 

I. SEDIMENT CHEMISTRY 

4.* Is there at least one nontoxic sample? 

NO ___ UNACCEPTABLE 

YES ___ Number of nontoxic: Number of toxic: 

5. as bioassay conducted on unique W or composite samples. 

Number of replicates? Size of composite area? 

6. What chemistry data have been collected? (i.e., metals, PAHs, pesticides, pH, DO, TOC) 

Metals PCBs pH TOC __ 

PAHs Pesticides DO AVS __

7. Are detection limits below the respective ERLs or TELs? 

NO UNACCEPTABLE YES 

NO 

8. Are total metal concentrations measured? 

 SEM metals may not 

YES 

be included. 

 SEM metals may be included. 
 179 

9. Collection instrument Sediment depth . 

10.* What type of sediment was used: 

Bulk Sediment Porewater Organic Extract Elutriate 

(Sediment with pore water) (Extract porewater from (Sediment extracted with 

d expose 

id form) 

(Sediments with water, mixed, 

settled and exposed water 

column species) 

sediments and expose 

water column species) 

organic solvent an

liqu

OTHER TOXICITY DATA ALSO NEEDED UNACCEPTABLE 

11. est? 

Static 

What type of toxicity test was conducted? Length of t

 (Water, sed-no change) 

Static Renewal (Water, sed-some water change) 

Flow-Through (Water, sed-water flowing through) 

f the 

hat m hod(s)

12.* Are appropriate analytical procedures used to determine total concentrations o

analytes in bulk sediment samples? W et was used? 

(Metals: partial digestion, analysis of elutriates or extracts are unacceptable.) 

3. Is a dilution series used? 

NO 

1

 YES UNACCEPTABLE 

14.* eight contaminant concentrations reported? Conversion from wet 

weight to dry weight concentration may occur ONLY if data on moisture or TOC are 

Are measured dry w

provided. Nominal concentrations are unacceptable. 

NO UNACCEPTABLE YES Page reference(s): 

I. BIOEFFECTS 

15.* 

a o toxicity tests employ appropriate laboratory procedures? (ASTM: E1367, E1611, 

E1706) 

I

D

NO UNACCEPTABLE YES 

b Have the following been recorded during testing? 

Temperature ; pH ; Hardness ; Conductivity ; 

Salinity ; DO ; Alkalinity ; Ammonia . 

c Does DO Remain above 60% Needed for Marine 

40% Needed for Fresh Water 

NO UNACCEPTABLE 

d emperature T
 180 

e Is temperature within natural range, fluctuate less then 3C, and have a time-weighted 

average within 1C of selected temp? 

YES NO UNACCEPTABLE Range 

f Do hardness, alkalinity vary by more than 50% (for freshwater 

NO 

, pH, or ammonia

samples)? 

 YES UNACCEPTABLE 

Range: D O ; Alk ; pH ; NH3 

g Have salinity levels in porewater been adjusted (for marine samples)? 

NO YES UNACCEPTABLE Range 

h List procedure reference(s) or bri et ef d ails: 

16.* ere biological responses compared to the control W or reference sites? 

List the Control and Reference sites? Positive Control . 

reference = uncontaminated site within the same waterbody or watershed; control = 

uncontaminated site outside the tested water body 

17.* 

a Have sediment samples used for biological testing been frozen? 

NO YES If yes, both biological and chemical testing must be performed after 

thawing sediments. 

b ave sediment samples been stored for more than eight (8) weeks prior to biological 

testing? 

H

NO YES UNACCEPTABLE 

What was the holding time? . 

c nd storage of sediments? 

NO 

Are appropriate procedures used for collecting, handling, a

 YES List procedures reference(s) or brief details: 

18. Identify species used in toxicity testing. Identify organism sources. 

9. What life stage were the test species at the start of the test? 

(Hyalella azteca 7–14 day old; Chironomus tentans third-instar larvae; Chironomus 

riparius second instar or younger; Daphnia magna 5 days old; Ceriodaphnia dubia <24h 

old; Hexagenia spp. 3–4 months old; Tubifex tubifex adult; Diporeia spp. juveniles) 

1

20. Organism acclimation time . 
 181 

1. What percentage

Mean range 

2 of the control survived? 

 70% for (Chironomus riparius, Chironomus tentans ) 

80% for (Hexagenia spp., Daphnia magna, Ceriodaphnia dubia, 

Hyalella azteca) 

90% for (Diporeia spp., Tubifex tubifex, Polychaetous annelids, marine amphipods, others) 

NO UNACCEPTABLE 

22. Reference Samples 

Survival % 

Conc. less than TEL and ERLs? YES NO 

Grain size % sand % silt % clay 

23. 

a 

Benthic Community Analysis 

Is there a benthic community abundance analysis? 

NO YES List taxa (e.g., amphipod, sponges,...) upon which the analysis 

focuses: 

e 

 water, etc)? 

NO 

b* Do all of the sites within a sampling area have the same general characteristics (i.e., sam

depth of overlying water, same salinity in overlying

 UNACCEPTABLE YES Briefly list details: 

III. 

24. 

STATISTICAL ANALYSIS 

Are appropriate statistical procedures reported? 

NO YES List procedure reference(s): 

Additional Notes/Comments: 
 182 

 E: SENSITIVITY OF P_MAX AND P_AVG MODELS TO 

DIFFERENT BINNING SCENARIOS 

E.1 Introduction

APPENDIX

Chapter 5 in the main text discusses the development of multiple chemicals to predict the 

frequency of toxicity observed in the SEDTOX02 marine amphipod database. The P_Max and 

r 

ta were 

variable and the corresponding proportion of toxic samples was used as the dependent variable. 

Each bin was selected to contain 50 unique P_Max or P_Avg values. 

.2 Methods

P_Avg models are used to calibrate the probability of toxicity calculated from the individual 

chemical models with the observed incidence of toxicity. This approach uses a nonlinear 

regression equation fit to data summarized by binning individual data points according to thei

P_Max or P_Avg values calculated from the individual chemical models. After the da

placed into bins, the median P_Max or P_Avg value of each bin was used as the explanatory 

This sensitivity analysis evaluated two questions: 

1. How does bin size and binning approach affect the regression coefficients and R-squared 

value for the P_Max and P_Avg models 

2. What is the effect of the median (vs. the minimum, mean, or maximum) of the P_Max

value in the bin for the x-axis values? This latter analysis was conducted only for the 

P_Max model. Results for the P_Avg model would be expected to be similar. 

E

Scenario A

Three binning scenarios were identified as reasonable approaches for setting the bin endpoints. 

Within each binning scenario, variable bin widths were targeted. The complete set of binning

scenarios plus bin widths or sample sizes are as follows: 

This binning approach takes the set of ordered unique P_Max or P_Avg values in the data 

set and sorts them so that there are n unique P_Max or P_Avg values in each bin. The 

number of samples in each bin will be greater than or equal to n, as there may be samples 

with duplicate P_Max or P_Avg values. This scenario was investigated for n = 5, 10, 15

25, 50, 75, 85, and 100. (Note: this method with n = 50 was used in Chapter 5). 

Scenario B

, 

This binning approach takes the set of ordered P_Max or P_Avg values (duplicates 

included) in the data set and sorts them so that there are at least n P_Max or P_Avg 

ould start with 0.34. This scenario was investigated for n = 5, 10, 15, 25, 50, 75, 

85, and 100. 

values in each bin. If the value for the last sample of the bin is the same as the next 

value, then those duplicate values are included in the first bin. For example, if we are 

using a target bin size of n = 5 and the ordered P_Max values start 0.2, 0.233, 0.234, 

0.236, 0.239, 0.239, 0.239, 0.34, then the first bin would start with 0.2 and would end 

with 0.239, including all three of the 0.239 values for a sample size of seven. The next 

bin w
 183 

Scenario C

This binn k

included) and divides them into bins of equal width on the P_Max or P_Avg probability 

scale. In this scenario, the number of samples per bin may be highly variable, and some 

bins may contain no samples. This scenario was investigated for bin widths of 0.002, 

0.004, 0.01, and 0.02. 

The three binning scenarios described above were run on the complete data set consisting of 

samples that had detected values for 10 or m odeled chemicals (n = 2856). All 

analyses were conducted using th e Sig Only classification of 

toxicity. For h sample size within a binning scenario, the data set was binned and 

summarized, and the nonlinear least squares regression equation was fit using S-PLUS 2000. 

E.3. Results

ing approach ta es the set of ordered P_Max or P_Avg values (duplicates 

ore of the m

e 1X screening approach and th

 eac

The regression co i n R u e a es E-1 through E-3 for 

P_Max and E-4 through E-6 for the P_Avg models. Each scenario exhibited the same patterns in 

the data and goodness of fit as bin size decreased and the regression sample size increased: 

variability around the best fit line increased (a smaller R-squared) with increasing regression 

sample size (i.e., the mor r the number of data points summarized per bin). 

For example, Figure E-1 shows increasing variability with increasing regression sample size for 

the Scenario bi ppro n _ a alues. 

All iteration wit i ry similar regression coefficients. The models 

tend to dev o a o _

predicted probabilities of toxicity from using a different binning scenario/method will be below 

P_Max va of 0.2 or above 0.8 (Figure E-2). 

The same ng scenarios and bin sizes, regardless of 

whether the median or the maximum P_Max values were used on the x-axis (Tables E-1 and E-2, 

Figure E-3). However, the differenc etw th st-fit lines for the different bin sizes varied 

more n e maximum P_Max values were use an when the median P_Max values were 

used on the x-axis. Th edian and maximum values in a 

bin in s the bin sizes increase, ore different across bin 

regression lines were very similar. 

eff cie ts and -sq ar d v lue are shown in the Tabl

e bi

ach

ns a

usi

nd t

g th

he f

e P

ewe

 A M

s 

iate

lues

n a x v

hin 

war

each

ds t

 scenar

he t

o re

f th

sult

e P

ed i

Ma

n ve

 t ils x or P_Avg range; the biggest differences in the 

 patterns were apparent across binni

es b een e be

whe th d th

crease a

is is because the differences between m

 making the regression data set m

size scenarios. In the end, however, all 

Conclusions

dels. The R-squared valu

The binning scenario and bin sam

P_Avg m

(as would an

predictions. 

slig

scenarios. 

ple size have a sm

iderab

t a re

all effect on the outco

liable measure of the accuracy of toxicity 

me of the P_Max and 

o es vary cons ly as a function of regression sample size 

 goodness-of-fit metric) and is no

hen com d odel produced 

htly hi - d lues than did the P_Avg model for most sample sizes and binning 

y

W

ghe

p

are

are

 va

across the same binning approach, the P_Max m

r R squ
Table E-1. Sensitivity analysis results for P_Max models; Scenario A: unique P_Max values, var

minimum unique sample size

Unique

sample Number of er

Maximum 

sample 

count per Coefficients for x = median (P_Max) Coefficients for 

Minimum

sample

count p

count b pt x x2 2 bins interce

a binb bin interce R 

5 440 5 20 0.09 0.394 0.356 0.5417 0.08

10 220 10 25 0.098 0.371 0.372 0.6977 0.0915 146 15 33 0.096 0.379 0.369 0.763 0.0925 88 25 46 0.108 0.339 0.398 0.8479 0.10

50c 44 52 79 0.112 0.331 0.405 0.9299 0.10

75 29 80 117 0.112 0.329 0.406 0.9344 0.1085 25 98 181 0.109 0.349 0.382 0.9382 0.09

100 22 108 147 0.111 0.33 0.41 0.9481 0.09

aNumber of data points used in the regression. bNumber of samples summarized within each data point.cApproach used in Chapter 5.

184 
Table E-2. Sensitivity analysis results for P_Max models; Scenario B: nonunique P_Max values

minimum sample size

Unique

sample Number of

Minimum 

sample 

count 

Maximum 

sample 

count Coefficients for x = median (P_Max) Coefficients f

count binsa per binb per binb intercept x x2 R2 pt x 

interce 

5 533 5 13 0.097 0.386 0.362 0.464 0.096 0.3

10 274 10 19 0.107 0.346 0.394 0.609 0.106 0.315 185 15 25 0.111 0.335 0.401 0.6909 0.11 0.325 113 25 29 0.108 0.343 0.397 0.8011 0.106 0.350 56 50 79 0.111 0.332 0.405 0.8987 0.106 0.375 37 75 145 0.108 0.347 0.39 0.9106 0.096 0.385 33 85 116 0.105 0.359 0.38 0.9255 0.093 0.3

100 28 100 149 0.105 0.364 0.373 0.9173 0.092 0.4

aNumber of data points used in the regression. bNumber of samples summarized within each data point.

185 
 186 

of bin 

_Max r of 

m

ple 

unt 

m 

ple 

ount ax) 

Table E-3. Sensitivity analysis results for P_Max models; Scenario C: 

equal widths on P_Max scale 

Width 

on P Numbe

Minimu

sam

co

 Maximu

sam

c Coefficients for x = median (P_M

scale binsa binb binb cept x x2 R2 per per inter

0.002 491 0 21 0.105 374 2 586 0. 0.34 0.4

0.004 246 1 28 0.096 0.383 8 827 0.36 0.6

0.01 99 1 47 0.101 349 4 0.8462 0. 0.40

0.02 50 1 92 0.117 276 7 198 0. 0.47 0.9

a

Number of da oints used e regression b

Number of samples summarized within eac in

ta p in th . 

h data po t. 
 187 

Tabl e P_Avg 

value

e E-4. Sensitivity analysis results for P_Avg models; Scenario A: uniqu

s, vary the minimum unique sample size 

Unique 

sample Number of 

Minimum 

sample 

count 

Maximum 

sample 

count Coefficients for x = median (P_Avg) 

count binsa per binb per binb intercept x x2 R2

5 430 5 12 0.11 1.428 -0.525 0.5368 

10 215 10 21 0.112 1.421 -0.519 0.7096 

15 143 15 32 0.11 1.443 -0.551 0.7765 

25 86 25 51 0.109 1.439 -0.538 0.8245 

50c 43 52 90 0.109 1.417 -0.488 0.8922

75 28 79 136 0.101 1.517 -0.69 0.9068 

85 25 91 150 0.105 1.468 -0.59 0.9086 

100 21 107 174 0.103 1.496 -0.644 0.912 

a

Number of data points used in the regression. 

b

Number of samples summarized within each data point. 

c

Approach used in Chapter 5 
 188 

s, vary the minimum sample size 

Minimum 

sample 

count 

Table E-5. Sensitivity analysis results for P_Avg models; Scenario B: 

nonunique P_Avg value

Target 

sample Number of 

Maximum 

sample 

count Coefficients for x = median (P_Avg) 

count per binb bins intercept a per binb x x2 R2

5 541 5 9 0.104 1.444 -0.533 0.5017 

10 276 10 19 0.104 1.457 -0.558 0.6607 

15 186 15 28 0.103 1.466 -0.563 0.7231 

25 112 25 47 0.103 1.466 -0.571 0.7951 

50 56 50 92 0.1 1.513 -0.672 0.854 

75 37 75 144 0.098 1.54 -0.723 0.8796 

85 33 85 124 0.099 1.515 -0.662 0.9019 

100 28 100 147 0.1 1.513 -0.661 0.9114 

a

Number of data points used in the regression.

b

Number of samples summarized within each data point.
 189 

Table E-6. Sensitivity analysis results for P_Avg models. Scenario C: equal 

widths on P_Avg scale 

Width of bin 

on P_Max Number of 

Minimum 

sample 

count 

Maximum 

sample 

count Coefficients for x = median (P_Avg) 

scale binsa per binb per binb intercept x x2 R2

0.002 455 0 26 0.125 1.266 -0.2 0.5358 73 

0.004 228 0 45 0.122 1.274 -0.2 0.7724 35 

0.01 91 0 104 0.117 1.338 -0.3 0.8477 51 

0.02 46 0 204 0.116 1.343 -0.3 0.8993 78 

a

Number of data points used in the regression.

b

Number of samples summarized within each data point.
bin size = 5

Median P_Max value 

l

 Proportion toxic

0.0 0.2 0.4 0.6 0.8 1.0

0.00.40.8

bin size = 10

Median P_Max value 

l

 Proportion toxic

0.0 0.2 0.4 0.6 0.8 1.0

0.00.40.8

bin size = 15

Median P

_

Max valu

l

 Proportion toxic

0.0 0.2 0.4 0.6 

0.00.40.8

bin size = 25

Median P_Max value 

l

 Proportion toxic 

0.0 0.2 0.4 0.6 0.8 1.0

0.20.6

bin size = 50

Median P_Max value 

l

 Proportion toxic 

0.2 0.4 0.6 0.8 1.0

0.20.6

bin size = 75

Median P

_

Max valu

l

 Proportion toxic 

0.2 0.4 0.6 

0.20.40.60.8

bin size = 85

Median P_Max value 

l

 Proportion toxic 

0.2 0.4 0.6 0.8

0.20.40.60.8 bin size = 100

Median P_Max value 

l

 Proportion toxic 

0.2 0.4 0.6 0.8 1.0

0.20.40.60.8

Figure E-1. Proportion of samples observed to be toxic versus the median P_Max value 

190 

various bin sizes, where the bin size is the number of unique P_Max values in each bin (Scenario A). 
Figure E-2. Best-fit regression lines for all P_Max models. Points shown are for bin size o

50 unique P_Max values (Scenario A). 

191 

Median P_Max value

 Proportion toxic

0.2 0.4 0.6 0.8 

0.20.40.60.8
Figure E-3. Comparison of maximum and median P_Max values and resulting models. Points s

are for bin sizes of 50 unique P_Max values (Scenario A). 

192 Median P_Max value

 Proportion toxic

0.2 0.4 0.6 0.8 1.0

0.20.40.60.8

Median-based modelMax-based model

P_Max value

 Proportion toxic

0.2 0.4 0.6 

0.20.40.60.8

Median P_Max Maximum P_Max