The Incidence and Severity of 

Sediment Contamination in 

Surface Waters of the 

United States, National Sediment 

Quality Survey: Second Edition

EPA-823-R-04-007

The Incidence and Severity of Sediment

Contamination in Surface Waters of the

United States

National Sediment Quality Survey

Second Edition

November 2004

United States Environmental Protection Agency

Office of Science and Technology

Standards and Health Protection Division

1200 Pennsylvania Avenue, NW

Washington, DC 20460
ii

DISCLAIMER

The National Sediment Quality Survey is a screening-level assessment of sediment quality that

compiles and evaluates sediment chemistry and related biological data taken from existing

databases. This document has no immediate or direct regulatory consequence. It does not in

itself establish any legally binding requirements on the U.S. Environmental Protection Agency,

states, tribes, other regulatory authorities, or the regulated community. It does not establish or

affect legal rights or obligations or represent a determination of any party’s liability. The data

and information contained in this document, however, could be used in various EPA regulatory

programs for priority setting or other purposes after further evaluation for program-specific

criteria. Any future policies and/or actions to address contaminated sediments will have to be

considered in the context of the budget process and competing demands for funding.
National Sediment Quality Survey

iii

CONTENTS

Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxix

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxxii

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxxv

Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

What Is the National Sediment Quality Survey? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

Why Is Contaminated Sediment an Important National Issue? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3

How Significant Is the Problem? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

Chapter 2. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

Description of NSI Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

NSI Data Evaluation Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-10

Strengths of the NSI Data Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-17

Limitations of the NSI Data Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-18

Chapter 3. Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

National Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

Watershed Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9

Contaminated Sediment CERCLA Sites and Their Relationship to Report Findings . . . . . . . . . . . 3-13

Wildlife Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-20

Regional and State Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-21

Evaluation of Data from the 1997 National Sediment Quality Survey with Current 

Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-60

Chapter 4. Assessment of Trends in Sediment Contamination Throughout the United States . . . . . . . . . 4-1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

Database Trend Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5

Sediment Core Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-11

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15
National Sediment Quality Survey

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Chapter 5. Conclusions and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

Extent of Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

Sources of Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4

Other Studies Evaluating the Extent of Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . 5-6

Other Indications of Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-7

Continuing Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8

Observation 1: Further Assessment of the Extent and Severity of Sediment Contamination in

the 96 Targeted Watersheds Would Improve Contaminated Sediment Management 

Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8

Observation 2: Watershed Management Activities Would Create Multidisciplinary and

Multijurisdictional Partnerships Focusing on Sediment Contamination . . . . . . . . . . . . 5-10

Observation 3: Better Coordination of Contaminated Sediment Management and Research

Activities Would Promote Application of Sound Science in Managing Contaminated

Sediments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-11

Observation 4: Better Monitoring and Assessment Tools Would Improve Contaminated

Sediment Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-12

Observation 5: A Weight-of-Evidence Approach and Measures of Chemical Bioavailability

in Sediment Monitoring Programs Would Improve the Assessment of Contaminated

Sediment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14

Observation 6: Increased Geographic Coverage in the NSI Database Would Refine a National

Assessment of the Extent and Severity of Contaminated Sediment . . . . . . . . . . . . . . . 5-15

Observation 7: Assessment of Atmospheric Deposition of Sediment Contaminants Would

Improve Contaminated Sediment Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-17

Observation 8: Prevention of Continuing Sources of Sediment Contamination is Important

in Contaminated Sediment Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-17

Observation 9: Better Coordination and Communication with External Stakeholders and Other

Federal Agencies Would Improve Contaminated Sediment Management Process . . . 5-18

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

Appendix A. National Sediment Inventory Field Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

Appendix B. Description of Evaluation Parameters Used in the NSI Data Evaluation . . . . . . . . . . . . . B-1

Aquatic Life Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

Equilibrium Partitioning Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-2

Human Health Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-17

Wildlife Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-21

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-23

Appendix C. Values Used for Chemicals Evaluated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

Sediment Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1
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Fish Tissue Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

Biota-Sediment Accumulation Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

Methodology for Combining Chemical Data Using a Risk-Based Approach . . . . . . . . . . . . . . C-1

Frequency of Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-2

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-2

Appendix D. Species Characteristics Related to NSI Bioaccumulation Data . . . . . . . . . . . . . . . . . . . . D-1

Appendix E. Trend Analysis Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1

Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1

Mercury Loading to Lake Pepin from the Upper Mississippi River . . . . . . . . . . . . . . . E-1

Total Hg Concentrations in Lake Pepin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-3

Historical Trends in Organochlorine Compounds from Four Georgia Lakes . . . . . . . . E-3

Trends in PCBs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-4

Trends in Total DDT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-4

Trends in Chlordane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-5

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-5

Accumulation of Chemicals in Puget Sound Introduction . . . . . . . . . . . . . . . . . . . . . . E-6

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-9

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-10

Appendix F. Comparison of Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-1
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TABLES

Table 1. USGS Cataloging Unit Numbers and Names for Watersheds

Containing APCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiii

Table 2-1. Number of Stations Evaluated in the NSI by State . . . . . . . . . . . . . . . . . . . . . . . . 2-6

Table 2-2. NSI Data Evaluation Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11

Table 3-1. National Assessment: Evaluation Results for Sampling

Stations and River Reaches by EPA Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3

Table 3-2. Regions 1–10: River Reach and Watershed Evaluation

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5

Table 3-3. Tier Classification Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7

Table 3-4. USGS Cataloging Unit Numbers and Names for Watersheds

Containing APCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-11

Table 3-5. River Reaches with 10 or More Tier 1 Sampling Stations

Located in Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-14

Table 3-6. Contaminated Sediment CERCLA Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-16

Table 3-7. Region 1: River Reach and Watershed Evaluation Summary . . . . . . . . . . . . . . . 3-22

Table 3-8. Region 1: Evaluation Results for Sampling Stations and

River Reaches by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-22

Table 3-9. Region 1: Watersheds Containing Areas of Probable

Concern for Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-24

Table 3-10. Region 1: Number of Tier 1 Stations in Region 1 That Are

Located in Watersheds Containing APCs by Waterbody

Name. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-24

Table 3-11. Region 2: River Reach and Watershed Evaluation Summary. . . . . . . . . . . . . . . 3-25

Table 3-12. Region 2: Evaluation Results for Sampling Stations and

River Reaches by State. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-26

Table 3-13. Region 2: Watersheds Containing Areas of Probable

Concern for Sediment Contamination. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-28

Table 3-14. Region 2: Number of Tier 1 Stations in Region 2 That Are

Located in Watersheds Containing APCs by Waterbody

Name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-28

Table 3-15. Region 3: River Reach and Watershed Evaluation Summary . . . . . . . . . . . . . . . 3-30

Table 3-16. Region 3: Evaluation Results for Sampling Stations and

River Reaches by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30

Table 3-17. Region 3: Watersheds Containing Areas of Probable

Concern for Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-33
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Table 3-18. Region 3: Number of Tier 1 Stations in Region 3 That Are

Located in Watersheds Containing APCs by Waterbody

Name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-33

Table 3-19. Region 4: River Reach and Watershed Evaluation Summary . . . . . . . . . . . . . . . 3-35

Table 3-20. Region 4: Evaluation Results for Sampling Stations and

River Reaches by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-36

Table 3-21. Region 4: Watersheds Containing Areas of Probable

Concern for Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-36

Table 3-22. Region 4: Number of Tier 1 Stations in Region 4 That Are

Located in Watersheds Containing APCs by Waterbody

Name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-38

Table 3-23. Region 5: River Reach and Watershed Evaluation Summary . . . . . . . . . . . . . . . 3-39

Table 3-24. Region 5: Evaluation Results for Sampling Stations and

River Reaches by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-39

Table 3-25. Region 5: Watersheds Containing Areas of Probable

Concern for Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-41

Table 3-26. Region 5: Number of Tier 1 Stations in Region 5 That Are

Located in Watersheds Containing APCs by Waterbody

Name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-42

Table 3-27. Region 6: River Reach and Watershed Evaluation Summary . . . . . . . . . . . . . . . 3-43

Table 3-28. Region 6: Evaluation Results for Sampling Stations and

River Reaches by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-44

Table 3-29. Region 6: Watersheds Containing Areas of Probable

Concern for Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-44

Table 3-30. Region 6: Number of Tier 1 Stations in Region 6 That Are

Located in Watersheds Containing APCs by Waterbody

Name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-46

Table 3-31. Region 7: River Reach and Watershed Evaluation Summary . . . . . . . . . . . . . . . 3-47

Table 3-32. Region 7: Evaluation Results for Sampling Stations and

River Reaches by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-47

Table 3-33. Region 7: Watersheds Containing Areas of Probable

Concern for Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-49

Table 3-34. Region 7: Number of Tier 1 Stations in Region 7 That Are

Located in Watersheds Containing APCs by Waterbody

Name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-49

Table 3-35. Region 8: River Reach and Watershed Evaluation Summary . . . . . . . . . . . . . . . 3-50

Table 3-36. Region 8: Evaluation Results for Sampling Stations and

River Reaches by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-50

Table 3-37. Region 8: Watersheds Containing Areas of Probable

Concern for Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-52
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Table 3-38. Region 8: Number of Tier 1 Stations in Region 8 That Are

Located in Watersheds Containing APCs by Waterbody

Name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-52

Table 3-39. Region 9: River Reach and Watershed Evaluation Summary . . . . . . . . . . . . . . . 3-53

Table 3-40. Region 9: Evaluation Results for Sampling Stations and

River Reaches by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-53

Table 3-41. Region 9: Watersheds Containing Areas of Probable

Concern for Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-55

Table 3-42. Region 9: Number of Tier 1 Stations in Region 9 That Are

Located in Watersheds Containing APCs by Waterbody

Name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-55

Table 3-43. Region 10: River Reach and Watershed Evaluation

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-57

Table 3-44. Region 10: Evaluation Results for Sampling Stations and

River Reaches by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-57

Table 3-45. Region 10: Watersheds Containing Areas of Probable

Concern for Sediment Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-59

Table 3-46. Region 10: Number of Tier 1 Stations in Region 10 That Are

Located in Watersheds Containing APCs by Waterbody

Name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-59

Table 3-47. Summary of Tier Classification Using Previous and Current

Evaluation Methodologies With the NSI Data Evaluated in

the 1997 National Sediment Quality Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-60

Table 3-48. Transition in Tier Classification Using Previous and Current

Evaluation Methodologies With the NSI Data Evaluated in

the 1997 National Sediment Quality Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-61

Table 4-1. Number of Predicted Proportion Toxic Observations

Available for Trend Analysis After Data Preparation Step . . . . . . . . . . . . . . . . . . 4-4

Table 4-2. Number of Predicted Proportion Toxic Observations

Available for Trend Analysis After Data Preparation Step

From Concentrated Data Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6

Table 4-3. Number of Observations Classified by Tier and Percentage

of Observations Classified as Tier 1 or Tier 2 by Time Period

and Hydrologic Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7

Table 4-4. Summary of Statistical Tests Used to Compare Predicted

Proportion Toxic Within Hydrologic Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9

Table 4-5. Sediment Core Locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-11

Table 5-1. Example Estimated Mercury Emission Reductions

Attributable to MACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6

Table 5-2. Example Estimated Air Toxics Emission Reductions

Attributable to MACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6
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Table A-1. Number of Sampling Stations With Data Included in the NSI

Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-2

Table A-2. Data Tables Available in the NSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-2

Table B-1. EPA Aquatic Life Secondary Acute/Chronic Values

(SAV/SCV), Final Acute/Chronic Values (FAV/FCV), Draft

Equilibrium Partitioning Sediment Guideline (ESG),

Log Kow, and Log Koc Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-5

Table B-2. EPA Aquatic Life Final Acute/Chronic Values (FAV/FCV),

and Effect Concentration of PAH in Sediment (Coc),

Log Kow, and Log Koc for PAH Mixtures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-7

Table B-3. Relative Distribution of EESGTUFCV,TOT to EESGTUFCV,13 for

the Combined EMAP Data Set (N = 488) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-7

Table B-4. Logistic Regression Model Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-12

Table B-5. Species Used in Bulk Sediment Toxicity Tests . . . . . . . . . . . . . . . . . . . . . . . . B-15

Table B-6. Minimum Detectable Differences (MDDs) Calculated from

Round Robin Test Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-16

Table C-1. Screening Values for Chemicals Evaluated . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-4

Table C-2. Toxic Equivalency Factors for Dioxins, Furans, and DioxinLike PCBs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-10

Table C-3. Toxic Equivalency Factors for Various PAHs . . . . . . . . . . . . . . . . . . . . . . . . . C-11

Table C-4. Frequency of Detection of Chemicals in Sediment and

Tissue Residue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-12

Table C-5. Number of Detected Sediment Observations in Watersheds

Containing APCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-16

Table D-1. Species Characteristics Related to Tissue Residue Data . . . . . . . . . . . . . . . . . . . D-2

Table E-1. Total Hg Concentrations in Lake Pepin Sediment Cores . . . . . . . . . . . . . . . . . . E-2

Table E-2. PCB Concentrations in Lake Sediments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-4

Table E-3. DDT Concentrations in Lake Sediments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-5

Table E-4. Chlordane Concentrations in Lake Sediments . . . . . . . . . . . . . . . . . . . . . . . . . . E-5

Table E-5. Maximum and Surface Concentrations of Selected Metals for

Three Core Locations Collected During 1991 . . . . . . . . . . . . . . . . . . . . . . . . . . E-7

Table E-6. Maximum and Surface Concentrations of Selected Organic

Contaminants for Three Core Locations Collected During

1991 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-9

Table F-1. Watersheds Containing APCs: Comparison of Previous

Report to Congress and Current Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-1

Table F-2. Detailed Comparison of Watersheds Containing APCs:

Previous Report to Congress and Current Analysis . . . . . . . . . . . . . . . . . . . . . . . F-2
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FIGURES

Figure 1. Location of All Evaluated Sampling Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . xx

Figure 2. National Assessment: Percent of River Reaches That Include

Tier 1, Tier 2, and Tier 3 Sampling Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi

Figure 3. Watersheds Identified as Containing APCs . . . . . . . . . . . . . . . . . . . . . . . . . . . xxii

Figure 1-1. Areas of Concern in the Great Lakes–St. Lawrence River Basin . . . . . . . . . . . . 1-6

Figure 2-1. NSI Sediment Sampling Stations Evaluated . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7

Figure 2-2. NSI Tissue Residue Sampling Stations Evaluated . . . . . . . . . . . . . . . . . . . . . . . 2-8

Figure 2-3. NSI Toxicity Test Sampling Stations Evaluated . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

Figure 3-1. Location of All Evaluated Sampling Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

Figure 3-2. Sampling Stations Classified as Tier 1 (Associated Adverse 

Effects Are Probable) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4

Figure 3-3. National Assessment: Percent of River Reaches that

Include Tier 1, Tier 2, and Tier 3 Sampling Stations . . . . . . . . . . . . . . . . . . . . . 3-6

Figure 3-4. National Assessment: Watershed Classification . . . . . . . . . . . . . . . . . . . . . . . . 3-10

Figure 3-5. Watersheds Identified as Containing APCs . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-12

Figure 3-6. Contaminated Sediment CERCLA Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-18

Figure 3-7. Contaminated Sediment CERCLA Sites for New

England/Mid-Atlantic and Washington . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19

Figure 3-8. Region 1: Location of Sampling Stations Classified as

Tier 1 or Tier 2 and Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . 3-23

Figure 3-9. Region 2: Location of Sampling Stations Classified as

Tier 1 or Tier 2 and Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . 3-27

Figure 3-10A. Region 3: Location of Sampling Stations Classified as

Tier 1 or Tier 2 and Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . 3-32

Figure 3-10B. Status of Chemical Contaminant Effects on Living

Resources in the Chesapeake Bay’s Tidal Rivers . . . . . . . . . . . . . . . . . . . . . . . 3-34

Figure 3-11. Region 4: Location of Sampling Stations Classified as

Tier 1 or Tier 2 and Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . 3-37

Figure 3-12. Region 5: Location of Sampling Stations Classified as

Tier 1 or Tier 2 and Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . 3-40

Figure 3-13. Region 6: Location of Sampling Stations Classified as

Tier 1 or Tier 2 and Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . 3-45

Figure 3-14. Region 7: Location of Sampling Stations Classified as

Tier 1 or Tier 2 and Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . 3-48
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Figure 3-15. Region 8: Location of Sampling Stations Classified as Tier 1

or Tier 2 and Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-51

Figure 3-16. Region 9: Location of Sampling Stations Classified as Tier 1

or Tier 2 and Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-54

Figure 3-17. Region 10: Location of Sampling Stations Classified as

Tier 1 or Tier 2 and Watersheds Containing APCs . . . . . . . . . . . . . . . . . . . . . . 3-58

Figure 4-1. Locations of Data Clusters Used for Temporal Trend

Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

Figure 4-2. USGS Hydrologic Regions in Contiguous United States . . . . . . . . . . . . . . . . . . . 4-4

Figure 4-3. Locations of Concentrated Data Clusters Used for Temporal

Trend Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5

Figure 4-4. Box Plot of Predicted Proportion Toxic as a Function of

Hydrologic Region for Data Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8

Figure 4-5. Box Plot of Predicted Proportion Toxic as a Function of

Hydrologic Region for Concentrated Data Clusters . . . . . . . . . . . . . . . . . . . . . . . 4-8

Figure 4-6. Box Plot of Predicted Proportion Toxic as a Function of

Region for EMAP Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10

Figure 4-7. PAH Trends Throughout the United States Using Sediment

Core Data from 1970 to Top of Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12

Figure 4-8. White Rock Lake PAH Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13

Figure 4-9. DDT Trends Throughout the United States Using Sediment

Core Data from 1970 to Top of Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13

Figure 4-10. White Rock Lake DDT Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-14

Figure 4-11. Lead Trends Throughout the United States Using Sediment

Core Data from 1975 to Top of Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-14

Figure 4-12. White Rock Lake Lead Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15

Figure A-1. Relationship Between the Station, Sediment Chemistry,

Tissue Residue, and Toxicity Tables and the Related

Look-up Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-3

Figure B-1. Application of the Logistic Model to Freshwater Data for

Hyalella azteca 10- to 14-Day Survival Endpoint . . . . . . . . . . . . . . . . . . . . . . B-14

Figure B-2. Application of the Logistic Model to Freshwater Data for

Hyalella azeteca 28-Day Growth and Survival Endpoint . . . . . . . . . . . . . . . . . B-14

Figure E-1. Lake Pepin Location Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1

Figure E-2. Historical Hg Loading Rates in the Upper Mississippi River

Reconstructed from Sediments of Lake Pepin . . . . . . . . . . . . . . . . . . . . . . . . . . E-2

Figure E-3. Location Map of Upper Chattahoochee Basin . . . . . . . . . . . . . . . . . . . . . . . . . . E-3

Figure E-4. Location Map of Puget Sound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-6

Figure F-1. Comparison of Watersheds Containing APCs: Previous

Report to Congress and Current Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-9
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EXECUTIVE SUMMARY

This report, The Incidence and Severity of Sediment Contamination in Surface Waters of the United

States: National Sediment Quality Survey, Second Edition, describes the accumulation of chemical

contaminants in river, lake, ocean, and estuary bottoms and includes a screening-level assessment of the

potential for associated adverse effects on human and/or environmental health. The United States

Environmental Protection Agency (EPA) prepared this report to Congress in response to requirements set

forth in the Water Resources Development Act (WRDA) of 1992. WRDA directed EPA, in consultation

with the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Army Corps of

Engineers (USACE), to conduct a comprehensive national survey of data regarding the quality of aquatic

sediments in the United States. Section 503(a) of WRDA required EPA to “compile all existing

information on the quantity, chemical and physical composition, and geographic location of pollutants in

aquatic sediment, including the probable source of such pollutants and identification of those sediments

which are contaminated....” It further required EPA to “report to the Congress the findings, conclusions,

and recommendations of such survey, including recommendations for actions necessary to prevent

contamination of aquatic sediments and to control sources of contamination.” In addition, Section 503(b)

of WRDA requires EPA to conduct a comprehensive and continuing program to assess aquatic sediment

quality. This program must establish methods and protocols for monitoring the physical, chemical, and

biological effects of pollutants in aquatic sediment and of contaminated sediment. EPA submitted the first

Report to Congress (EPA-823-R-97-006) on January 7, 1997.

To comply with Section 503(b), EPA’s Office of Science and Technology (OST) (1) initiated the

National Sediment Inventory (NSI), which is designed to compile sediment quality information from

available electronic databases into one centralized, easily accessible location, and (2) developed the

National Sediment Quality Survey report.

Description of the NSI

The NSI database includes approximately 4.6 million records of sediment chemistry, tissue residue, and

toxicity data for more than 50,000 monitoring stations across the country. To efficiently collect usable

information for inclusion in the NSI database, EPA sought data that were available in electronic format,

represented broad geographic coverage, and represented specific sampling locations identified by latitude

and longitude coordinates. Although EPA elected to evaluate in this report only data collected since 1990

(i.e., 1990 through 1999), data from before 1990 are maintained in the NSI database for comparison

purposes. The initial National Sediment Quality Survey evaluated data from 1980 through 1993. At a

minimum, EPA required that electronically available data include monitoring program, sampling date,

latitude and longitude coordinates, and measured units for inclusion in the data evaluation. The NSI

database includes data from the following data storage systems and monitoring programs:

• Selected data sets from EPA’s Storage and Retrieval System (STORET)

• NOAA’s Query Manager Data System

• State of Washington Department of Ecology’s Sediment Quality Information System (SEDQUAL)

• Selected data sets from the U.S. Geological Survey’s (USGS’s) WATSTORE

• EPA’s Environmental Monitoring and Assessment Program (EMAP)

• Data compiled for the previous report to Congress, 1990 through 1993

• Chesapeake Bay Program

• Upper Mississippi River System data compilation prepared by USGS 
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• Indiana Department of Environmental Management Sediment Sampling Program

• Oklahoma Reservoir Fish Tissue Monitoring Program, 1990 through 1998

• Houston Ship Channel Toxicity Study

National Sediment Quality Survey Report Objective

The objective of the National Sediment Quality Survey report is to develop screening-level assessment

protocols to allow the identification of potentially contaminated sediment. The report is to be produced

biennially for Congress, as well as the regions, states, and tribes, on the incidence and severity of

sediment contamination nationwide. One objective of the original report, as well as this first update to

that report, is to depict and characterize the incidence and severity of sediment contamination based on

the probability of adverse effects to human health and/or the environment. As used in this report, the

probability or potential for adverse effects reflects a range of situations where the analysis of a station's

data might indicate adverse effects on aquatic life and/or human health. To accomplish this objective,

EPA applied assessment protocols to existing available data in a uniform fashion. EPA intended to

accurately depict and characterize the incidence and severity of sediment contamination based on the

probability of adverse effects to human health and/or the environment. This was done through the use of a

number of different measures of sediment quality (i.e., multiple lines of evidence). Information contained

in this report may be used to further investigate sediment contamination on a national, regional, or

site-specific scale. Further studies might involve toxicological investigations, risk assessment, analyses of

temporal and spatial trends, feasibility of natural recovery, and source control.

The initial report presented a national baseline screening-level assessment of contaminated sediments

from sediment quality data collected from 1980 through 1993 using a weight-of-evidence approach. This

report presents the results of the screening-level assessment of the NSI data from 1990 through 1999. One

major advantage of screening out older data (data collected prior to January 1, 1990) for this report is that

it prevents the results from being unduly influenced by historical data when more recent data are

available. However, this would not account for any decrease in sediment contaminant levels due to

scouring, re-burial, natural attenuation, or active sediment remediation that have occurred since that

sample was collected.

This report identifies locations where available data indicate that direct or indirect exposure to the

sediment could be associated with adverse effects to aquatic life and/or human health. Further, even

though this report focuses on data collected from 1990 through 1999, conditions might have improved or

worsened since the sediment was sampled. This report does not and cannot provide a definitive

assessment of the national condition or relative health of sediments across the country because the data

were generally not collected in a randomized sampling approach. While this report does not provide an

assessment of the “national condition” of contaminated sediments, it does evaluate data collected from

1980 through 1999 in the NSI database to assess changes in the extent and severity of sediment

contamination over time for specific areas in the United States where sufficient data exist.

As mentioned above, this report provides a screening-level assessment outlining stations throughout the

United States where the probability of adverse effects to human health and/or the environment exist.

Because the data compiled for this report consist largely of non-random sampling events and do not

provide complete national coverage, EPA has not developed a “national estimate” of the areal extent of

contaminated sediments. Because the limitations of the data do not allow for a national estimate of the

percentage of contaminated sediments, the report should not be used to estimate the national cost of

potential sediment remediation or to prioritize sites for sediment remediation or risk management

decisions based solely on the results of this report. Such decisions should be based on all available

information, including the data reported in this report.
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A screening-level assessment typically identifies many potential problems that subsequently

prove not to be significant upon further analysis (i.e., more conservative).

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Evaluation Approach

Section 503 of WRDA 1992 defines contaminated sediment as “aquatic sediment which contains

chemical substances in excess of appropriate geochemical, toxicological, or sediment quality criteria or

measures; or is otherwise considered by the Administrator [of EPA] to pose a threat to human health or

the environment....” The approach used to evaluate the NSI data focuses on the risk to benthic organisms

exposed directly to contaminated sediments and the risk to human consumers of organisms exposed to

sediment contaminants. EPA evaluated sediment chemistry data, chemical residue levels in edible tissue

of aquatic organisms, and sediment toxicity data taken at the same sampling station (where available)

using a variety of assessment methods.

The following measurement parameters and techniques were used alone or in combination to perform a

screening-level assessment of the probability of adverse effects.1

Aquatic Life

• Comparison of sediment chemistry measurements to draft equilibrium partitioning sediment

guidelines (ESGs) derived from final or secondary acute values and final or secondary chronic

values.

• Comparison of the molar concentration of acid-volatile sulfides ([AVS]) in sediment to the molar

concentration of simultaneously extracted metals ([SEM]) in sediment. (Under equilibrium

conditions, sediment with [AVS] greater than [SEM] does not demonstrate toxicity from metals.)

• Estimation of the predicted proportion toxic from sediment chemistry observations using a logistic

regression model.

• Comparison of the total ESG toxic unit for polycyclic aromatic hydrocarbons (PAHs) to final

chronic or acute values.

• Toxicity based on acute or chronic solid-phase sediment toxicity data.

Human Health

• Comparison of theoretical bioaccumulation potential (TBP) values derived from sediment

chemistry to

– EPA cancer and noncancer risk levels or

– Food and Drug Administration (FDA) tolerance, action, or guidance values in the absence of,

or if more stringent than, EPA levels.

• Comparison of fish tissue contaminant levels to

– EPA cancer and noncancer risk levels or

– FDA tolerance, action, or guidance values in the absence of, or if more stringent than, EPA

levels.

The sediment chemistry screening values used as the basis for comparison in this report are not regulatory

criteria, site-specific cleanup standards, or remediation goals. Sediment chemistry screening values are

reference values above which a sediment ecotoxicological assessment might indicate a potential threat to

aquatic life. The sediment chemistry screening values include both theoretically and empirically derived

values. The theoretically derived screening values (e.g., ESG, [SEM]![AVS]) rely on the
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physical/chemical properties of sediment and chemicals derived to protect aquatic benthic organisms from

direct toxicity due to that chemical or chemicals in the case of metals mixtures and PAH mixtures. The

empirically derived or correlative approaches (e.g., predicted proportion toxic) rely on paired field and

laboratory data to relate incidence of observed biological effects to the dry-weight sediment

concentrations. Correlative screening values can relate measured concentration to a probability of

association with adverse effects, but they do not definitively establish cause and effect for a specific

chemical. Toxicity data were used to classify sediment sampling stations based on their demonstrated

toxicity to aquatic life in laboratory bioassays.

Under an assumed exposure scenario, TBP and tissue residue data can indicate potential adverse effects

on humans from the consumption of fish that become contaminated through exposure to contaminated

sediment. TBP is an estimate of the equilibrium concentration (concentration that does not change with

time) of a contaminant in tissues of aquatic organisms if the sediment in question were the only source of

contamination to the organism. At present, the TBP calculation can be performed only for nonpolar

organic chemicals. The TBP is estimated from the concentration of contaminant in the sediment, the

organic carbon content of the sediment, the lipid content of the organism, and the relative affinity of the

chemical for sediment organic carbon and animal lipid content. This relative affinity is measured in the

field and is called a biota-sediment accumulation factor (BSAF, as discussed in detail in Appendix B). In

practice, field-measured BSAFs can vary by an order of magnitude or greater for individual compounds

depending on location and time of measurement. For this evaluation, EPA selected BSAFs that represent

the central tendency, suggesting an approximate 50 percent chance that an associated tissue residue level

would exceed a screening risk value.

The reader should exercise caution in evaluating the data in this report for a number of reasons.

Uncertainty is associated with site-specific measures, assessment techniques, exposure scenarios, and

default parameter selections. Many mitigating biological, chemical, hydrological, and habitat factors can

affect whether sediment poses a threat to aquatic life or human health. Because of the limitations of the

available sediment quality measures and assessment methods, EPA characterizes this evaluation as a

screening-level analysis. A screening-level analysis typically identifies many potential problems that

prove not to be significant upon further analysis. Thus, classification of sampling stations in this analysis

is not meant to be definitive, but is intended to be inclusive of potential problems arising from persistent

metal and/or organic chemical contaminants. For this reason, EPA elected to evaluate data collected from

1990 through 1999 and to evaluate each chemical or biological measurement taken at a given sampling

station individually. The reader should keep in mind that a single measurement of a chemical at a

sampling station, taken at any point in time over the past 10 years, might have been sufficient to

categorize the sampling station as having an increased probability of association with adverse effects on

aquatic life or human health.

In this report, EPA associates sampling stations with their “probability of adverse effects.” Each sampling

station falls into one of three categories, or tiers:

Tier 1: Associated adverse effects on aquatic life or human health are probable.

Tier 2: Associated adverse effects on aquatic life or human health are possible.

Tier 3: No indication of associated adverse effects (any sampling station not classified as Tier 1 or Tier

2; includes sampling stations for which substantial data were available, as well as sampling

stations for which limited data were available).

The potential risk of adverse effects on aquatic life and/or human health is greatest in areas with a

multitude of contaminated locations. The assessment of individual sampling stations is useful for

estimating the number and distribution of contaminated spots and the overall magnitude of sediment

contamination in monitored waterbodies of the United States. However, a “hot spot” might not pose a

significant threat to either the benthic community at large or consumers of resident fish because the
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spatial extent of exposure could be small. On the other hand, if many contaminated spots are located in

close proximity, the spatial extent and probability of exposure are much greater. EPA examined sampling

station classifications within watersheds to identify areas of probable concern (APCs) for sediment

contamination, where the exposure of benthic organisms and resident fish to contaminated sediment

might be more frequent. In this report, EPA defines watersheds by 8-digit USGS hydrologic unit codes,

which are roughly the size of a county. Watersheds containing APCs are those in which 10 or more

sampling stations were classified as Tier 1 and in which at least 75 percent of all sampling stations were

categorized as either Tier 1 or Tier 2.

The definition of “area of probable concern” was developed for this report to identify watersheds for

which further study of the effects and sources of sediment contamination, and possible risk reduction

needs, would be warranted. Where data have been generated through intensive sampling in areas of

known or suspected contamination within a watershed, the APC definition should identify watersheds that

contain even relatively small areas that are considerably contaminated. This designation does not imply,

however, that sediment throughout the entire watershed, which is typically very large compared to the

extent of available sampling data, is contaminated. On the other hand, where data have been generated

through comprehensive sampling, or where sampling stations were selected randomly or evenly

distributed throughout a sampling grid, the APC definition might not identify watersheds that contain

small or sporadically contaminated areas. A comprehensively surveyed watershed of the size typically

delineated by a USGS cataloging unit might contain small but significant areas that are considerably

contaminated, but the watershed might be too large in total area for 75 percent of all sampling stations to

be classified as Tier 1 or Tier 2. Limited random or evenly distributed sampling within such a watershed

also might not yield 10 Tier 1 sampling stations. Thus, the process used to identify watersheds containing

APCs might both include some watersheds with limited areas of contamination and omit some watersheds

with significant contamination. However, given the available data, EPA has concluded that the process

represents a reasonable screening analysis to identify watersheds where further study is warranted.

Because the Tier 1, Tier 2, and Tier 3 evaluation benchmarks established in this report represent recent

advances in sediment assessment techniques, they have been used in this report as a way to relate all the

different data from all the different sources around the United States using common benchmarks. These

benchmarks and interpretations used in this report, however, are not currently appropriate for use in EPA

regulatory programs that have developed their own frameworks and regulatory requirements. They were

not designed to be a substitute for the various EPA program regulatory frameworks and/or authorities.

EPA’s regulatory programs (e.g., Office of Solid Waste and Emergency Response, OSWER) have

developed their own scientifically defensible approaches to sediment evaluation based on the needs of

their programs, and they will continue to use their current regulatory frameworks when making decisions

regarding potentially contaminated sediments (e.g., sediment remediation, sediment disposal).

Strengths and Limitations

For this report to Congress, EPA has compiled the most extensive database of sediment quality

information currently available in electronic format. To evaluate these data, EPA has applied sediment

assessment techniques using a multiple-lines-of-evidence approach recommended by national experts

(Ingersoll et al., 1997). The evaluation approach uses sediment chemistry, tissue residue, and toxicity test

results. The assessment tools employed in this analysis have been applied in North America, and results

of these applications have been published in peer-reviewed literature. Toxicity test data were generated

using established standard methods employed by multiple federal agencies. The evaluation approach

addresses potential impacts on both aquatic life and human health. Some chemicals pose a greater risk to

human health than to aquatic life; for others, the reverse is true. By evaluating both potential human

health and aquatic life impacts, EPA has ensured that the most sensitive endpoint is used to assess

environmental impacts.
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There are two general types of limitations the reader should keep in mind in interpreting the results in this

report to Congress—limitations of the compiled data and limitations of the evaluation approach.

Limitations of the compiled data include the mixture of data sets derived from different sampling

strategies, incomplete sampling coverage, the age and quality of data, and the lack of measurements of

important assessment parameters. Limitations of the evaluation approach include uncertainties in the

interpretive tools used to assess sediment quality (e.g., the propensity of certain chemicals to

bioaccumulate and move through the aquatic food chain), use of assumed exposure potential in screeninglevel quantitative risk assessment (e.g., fish consumption rates for human health risk), and the subsequent

difficulties in interpreting assessment results. These limitations and uncertainties are discussed in detail in

Chapter 2 of this report under “Limitations of the NSI Data Evaluation.”

Data compiled for this report were generated using a number of different sampling strategies. Component

sources contain data derived from different spatial sampling plans, sampling methods, and analytical

methods. Most of the NSI data were compiled from nonrandom monitoring programs. Such monitoring

programs focus their sampling efforts on areas where contamination is known or suspected to occur.

Reliance on these data is consistent with the stated objective of this survey: to identify those sediments

which are contaminated. However, one cannot accurately make inferences regarding the overall condition

of the Nation's sediment, or characterize the “percent contamination,” using the data in the NSI database

because of the incomplete national sampling coverage and because, in EPA’s view, uncontaminated areas

are most likely substantially underrepresented.

Because this analysis is based only on readily available electronically formatted data, contamination

problems exist at some locations where data are lacking. Conversely, older data might not accurately

represent current sediment contamination conditions. The reliance on readily available electronic data has

undoubtedly excluded a vast amount of information available from sources such as local and state

governments and published academic studies. In addition, some data in the NSI database were not

evaluated because of questions concerning data quality or because no locational information (latitude and

longitude) was available. NSI data do not evenly represent all geographic regions in the United States, nor

do the data represent a consistent set of monitored chemicals. More than two-thirds of all stations

evaluated in the NSI database are in Washington, Virginia, California, Illinois, Florida, Wisconsin, New

York, Texas, Oregon, and South Carolina. Each of these states has more than 500 monitoring stations.

Other states of similar or larger size (e.g., Georgia, Pennsylvania) have far fewer sampling stations with

data for evaluation. Individual stations may vary considerably in terms of the number of chemicals

monitored. Some stations have data that represent a large number of organic and inorganic contaminants,

whereas others have measured values for only a few chemicals. Thus, the inventory should not be

construed as comprehensive even for locations with sampling data.

EPA recognizes that sediment is dynamic and that great temporal and spatial variability in sediment

quality exists. Movement of sediment is highly temporal and dependent upon the physical and biological

processes at work in the watershed. Some deposits redistribute, whereas others remain static unless

disturbed by extreme events. Because the data analyzed in this report were collected over a relatively long

period of time, conditions might have improved or worsened since the sediment was sampled.

Consequently, this report does not provide a definitive assessment of the current condition of sediments

but serves as a baseline for future assessments.

The lack of data required to apply some important assessment parameters hampered EPA’s efforts to

determine the incidence and severity of sediment contamination. For example, the component databases

contain a dearth of total organic carbon (TOC) and acid-volatile sulfide (AVS) measurements relative to

the abundance of contaminant concentration measurements in bulk sediment. TOC and AVS are essential

pieces of information for interpreting the bioavailability, and subsequent toxicity, of nonpolar organic and

metal contaminants, respectively. In addition, matched sediment chemistry with toxicity tests and

matched sediment chemistry with tissue residue data, were typically lacking. Also, because the evaluation
National Sediment Quality Survey

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approach outlined in this report needs to be applicable across the entire United States, various

assumptions were made (e.g., assuming 1 percent organic carbon when none was reported, assuming the

average individual consumes on average 17.5 grams of fish per day). Generally, the exposure assumptions

and safety factors incorporated into toxicity assessments are intended to be protective of the majority of

the general population associated with sediment contamination. However, these assumptions and factors

might underestimate risks to populations of subsistence fishers and sensitive subpopulations (such as

pregnant women, nursing mothers, and children).

It is important to understand both the strengths and limitations of this analysis to appropriately interpret

and use the information in this report. The limitations do not prevent intended uses, and future reports to

Congress on sediment quality will contain less uncertainty. To ensure that future reports to Congress

accurately reflect current knowledge concerning the conditions of the Nation's sediment as our knowledge

and application of science evolve, the NSI will develop into a periodically updated, centralized

assemblage of sediment quality measurements and state-of-the-art assessment techniques.

Findings

EPA evaluated 19,398 sampling stations nationwide as part of the NSI data evaluation (Figure 1). Of the

sampling stations evaluated, EPA classified 8,348 stations (43.0 percent) as Tier 1, 5,846 (30.1 percent)

as Tier 2, and 5,204 (26.8 percent) as Tier 3. EPA has concluded that these results in all likelihood are not

representative of the overall condition of sediment across the country. It could be that the overall extent of

contaminated sediments and the corresponding adverse effects are much less. This is the case primarily

because most of the NSI data were obtained from monitoring programs targeted toward areas of known or

suspected contamination (i.e., sampling stations were not randomly selected).

The NSI sampling stations were located in 5,695 individual river reaches (or waterbody segments) across

the contiguous United States, or approximately 8.8 percent of all river reaches in the country (based on

EPA's River Reach File 1). A river reach can be part of a coastal shoreline, a lake, or a length of stream

between two major tributaries ranging from approximately 1 to 10 miles long. As depicted in Figure 2,

approximately 3.6 percent of all river reaches in the contiguous United States had at least one station

categorized as Tier 1, almost 3 percent (2.9 percent) of reaches had at least one station categorized as Tier

2 (but none as Tier 1), and all of the sampling stations were classified as Tier 3 in about 2.3 percent of

reaches. Looking at only the river reaches where sampling stations were evaluated, approximately 40

percent of the 5,695 river reaches evaluated had at least one sampling station categorized as Tier 1,

approximately 33 percent of the river reaches evaluated had at least one station categorized as Tier 2 (but

none as Tier 1), and all of the sampling stations in river reaches evaluated as Tier 3 in about 26 percent of

the reaches had all sampling stations categorized as Tier 3.

Watersheds containing areas of probable concern for sediment contamination (APCs) are those that

include at least 10 Tier 1 sampling stations and in which at least 75 percent of all sampling stations were

classified as Tier 1 or Tier 2. The NSI data evaluation identified 96 watersheds throughout the United

States as containing APCs (Figure 3 and Table 1; The map numbers listed on Table 1 correspond to the

numbered watersheds identified in Figure 3). About 26 percent of the 370 eligible watersheds (96)

contained an APC, or 4.2 percent of all the 2,264 watersheds in the United States. APC designation could

result from expansive sampling throughout a watershed or from intensive sampling at a single

contaminated location or a few contaminated locations. In comparison to the overall results presented in

Figure 2, 23.9 percent of reaches in watersheds containing APCs have at least one Tier 1 sampling station

and 18.3 percent have no Tier 1 sampling station but at least one Tier 2 sampling station. In many of these

watersheds, contaminated areas may be concentrated in specific river reaches in a watershed. 
xx
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Figure 2. National Assessment: Percent of River Reaches that

Include Tier 1, Tier 2, and Tier 3 Sampling Stations.

Within the 96 watersheds containing APCs across the country, 97 individual river reaches or waterbody

segments have 10 or more Tier 1 sampling stations.

The evaluation results indicate that sediment contamination associated with probable or possible adverse

effects on both aquatic life and human health exists. Overall, fewer stations were classified as Tier 1 using

aquatic life evaluation parameters (5,006 stations) than were classified using human health evaluation

parameters (6,385 stations). Of the stations classified as Tier 2, 4,439 stations were so classified using

aquatic life evaluation parameters and 3,131 stations were so classified using human health evaluation

parameters.

Recognizing the imprecise nature of some assessment parameters used in this report, Tier 1 sampling

stations are distinguished from Tier 2 sampling stations based on the magnitude of a contaminant

concentration in sediment or the degree of corroboration among the different types of sediment quality

measures. In response to uncertainty in both biological and chemical measures of sediment

contamination, environmental managers must balance Type I errors (false positives, i.e., sediment

classified as posing a threat when in fact it does not) with Type II errors (false negatives, i.e., sediment

that poses a threat but was not so classified). In screening analyses, the environmentally protective

approach is to minimize Type II errors, which leave toxic sediment unidentified. To achieve a balance and

to direct attention to areas most likely to be associated with adverse effects, Tier 1 sampling stations are

intended to have a higher probability of posing an adverse effect (e.g., sediment posing a threat) and a

balance between Type I and Type II errors. On the other hand, to retain a sufficient degree of

environmental conservatism in screening, Tier 2 sampling stations are intended to have a very low

number of false negatives in exchange for a large number of false positives.
xxii
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Table 1. USGS Cataloging Unit Numbers and Names for Watersheds Containing APCs.

Map

No.

Cataloging

Unit

Number Cataloging Unit Name

Map

No.

Cataloging

Unit

Number Cataloging Unit Name

1 01080205 Lower Connecticut 49 07120001 Kankakee

2 01090001 Charles 50 07120002 Iroquois

3 01090004 Narragansett 51 07120003 Chicago

4 01100004 Quinnipiac 52 07120004 Des Plaines

5 01100005 Housatonic 53 07120005 Upper Illinois

6 01100006 Saugatuck 54 07120006 Upper Fox

7 01100007 Long Island Sound 55 07120007 Lower Fox

8 02020003 Hudson-Hoosic 56 07130001 Lower Illinois-Senachwine Lake

9 02020004 Mohawk 57 07130003 Lower Illinois-Lake Chautauqua

10 02020006 Middle Hudson 58 07130007 South Fork Sangamon

11 02020008 Hudson-Wappinger 59 07130011 Lower Illinois

12 02030101 Lower Hudson 60 07130012 Macoupin

13 02030102 Bronx 61 08030207 Big Sunflower

14 02030103 Hackensack-Passaic 62 08030209 Deer-Steele

15 02030104 Sandy Hook-Staten Island 63 08090100 Lower Mississippi-New Orleans

16 02030105 Raritan 64 11070209 Lower Neosho

17 02030201 Northern Long Island 65 12030102 Lower West Fork Trinity

18 02030202 Southern Long Island 66 12090205 Austin-Travis Lakes

19 02040202 Lower Delaware 67 14010002 Blue

20 02040205 Brandywine-Christina 68 15060106 Lower Salt

21 02060003 Gunpowder-Patapsco 69 16050203 Carson Desert

22 02060004 Severn 70 17020001 Franklin D. Roosevelt Lake

23 02080107 York 71 17080001 Lower Columbia-Sandy

24 03050201 Cooper 72 17090012 Lower Willamette

25 03050202 South Carolina Coastal 73 17100102 Queets-Quinault

26 03060109 Lower Savannah 74 17100105 Grays Harbor

27 03070203 Cumberland-St. Simons 75 17110002 Strait Of Georgia

28 03100206 Tampa Bay 76 17110012 Lake Washington

29 03130002 Middle Chattahoochee-Lake Harding 77 17110013 Duwamish

30 03140105 Pensacola Bay 78 17110019 Puget Sound

31 03160205 Mobile Bay 79 18010102 Mad-Redwood

32 04030108 Menominee 80 18020112 Sacramento-Upper Clear

33 04030204 Lower Fox 81 18040005 Lower Cosumnes-Lower Mokelumne

34 04040001 Little Calumet-Galien 82 18050001 Suisun Bay

35 04040002 Pike-Root 83 18050002 San Pablo Bay

36 04120101 Chautauqua-Conneaut 84 18050003 Coyote

37 04140201 Seneca 85 18050004 San Francisco Bay

38 05060001 Upper Scioto 86 18060006 Central Coastal

39 05120106 Tippecanoe 87 18060011 Alisal-Elkhorn Sloughs

40 05120201 Upper White 88 18070103 Calleguas

41 05120208 Lower East Fork White 89 18070104 Santa Monica Bay

42 06010201 Watts Bar Lake 90 18070106 San Gabriel

43 06010205 Upper Clinch 91 18070201 Seal Beach

44 06020001 Middle Tennessee-Chickamauga 92 18070203 Santa Ana

45 07040001 Rush-Vermillion 93 18070204 Newport Bay

46 07080101 Copperas-Duck 94 18070301 Aliso-San Onofre

47 07090005 Lower Rock 95 18070304 San Diego

48 07090007 Green 96 19020201 Eastern Prince William Sound
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Section 503 of the WRDA of 1992 required EPA, as part of its program to assess sediment quality, to

provide an assessment of aquatic sediment quality trends. The first Report to Congress suggested EPA

“consider whether to design future evaluations of NSI data to determine the temporal trends of

contamination.” In response, EPA evaluated surficial sediment data from the entire NSI database (data

from 1980 through 1999). The evaluation of historical surficial sediment data is limited because of the

heterogeneous nature of monitoring programs and available data. Nevertheless, the evaluation tended to

show decreased or no change in sediment contamination in most regions where data were available.

The USGS National Water-Quality Assessment (NAWQA) program also examined trends in sediment

contamination for a number of contaminants by reconstructing water-quality histories using lake and

reservoir sediment cores from 22 locations nationally. Statistically significant increasing trends in total

PAH concentrations occur at nine lakes, and significant decreasing trends were detected at two lakes. The

analysis of the organochlorine compounds (pesticides and PCBs) showed that only a few locations had

significant trends since 1975. Since 1965, however, significant decreasing trends in total DDT have

occurred at 12 of the 22 lakes. Among the organochlorine compounds, dieldrin and chlordane have

increased in almost as many lakes as they have decreased since 1975. The most consistent trend since the

mid-1970s for any of the constituents tested is that all 22 lakes had statistically significant decreasing

trends in lead concentrations. Two other trace elements had somewhat consistent trends; chromium and

nickel each increased in only one lake and decreased in nine and eight lakes, respectively. Three other

elements, arsenic, copper, and mercury, had significant trends in 10 or more lakes, all with more

decreasing trends than increasing. The only trace element with more increasing trends than decreasing

trends was zinc. Nine of the 19 urban lakes had increasing trends in zinc, and 4 lakes had decreasing

trends.

Conclusions

The characteristics of the NSI data, as well as the degree of certainty afforded by available assessment

tools, allow neither an absolute determination of adverse effects on human health or the environment at

any location nor a definitive determination of the areal extent of contamination on a national scale.

However, the evaluation results suggest that sediment contamination may be significant enough to pose

potential risks to aquatic life and/or human health in some locations. EPA designed its evaluation

methodology for this effort to develop a screening-level assessment of sediment quality. Further

evaluation will be required to confirm that sediment contamination poses actual risks to aquatic life or

human health for any given sampling station or watershed.

The results of the NSI data evaluation must be interpreted in the context of data availability. Many states

and EPA regions appear to have a much greater incidence of sediment contamination than others. To

some degree, this appearance reflects the relative abundance of readily available electronic data, not

necessarily the relative incidence of sediment contamination.

Although the APCs were selected by means of a screening exercise, it is EPA’s view that they represent

the highest priority for further ecotoxicological assessments, risk analysis, temporal and spatial trend

assessment, and contaminant source evaluation because of the preponderance of evidence in these areas.

Although the procedure for classifying APCs using multiple sampling stations was intended to minimize

the probability of making an erroneous classification, further evaluation of conditions in watersheds

containing APCs is necessary because the same mitigating factors that might reduce the probability of

associated adverse effects at one sampling station might also affect neighboring sampling stations.

EPA chose the watershed as the unit of spatial analysis because many states and federal water and

sediment quality management programs, as well as data acquisition efforts, are centered on this unit. This

choice reflects the growing recognition that activities taking place in one part of a watershed can greatly

affect other parts of the watershed, and that management efficiencies are achieved when viewing the
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watershed holistically. At the same time, EPA recognizes that contamination in some reaches in a

watershed does not necessarily indicate that the entire watershed is affected. Further analysis should be

conducted within APC watersheds to delineate sediment contamination. This will allow sediment

management activities determined to be necessary be performed in the most cost effective and

environmentally sound manner.

Watershed management is a critical component of community-based environmental protection using

watershed or hydrologic boundaries to define the problem area. Many public and private organizations are

joining forces and creating multidisciplinary and multijurisdictional partnerships to focus on water quality

problems community by community and watershed by watershed. These watershed approaches are likely

to result in significant restoration, maintenance, and protection of water resources throughout the United

States. As was reported in the initial National Sediment Quality Survey in 1997, various programs across

the United States as part of the National Estuary Program have used a watershed approach that has led to

specific actions to address contaminated sediment problems. These include the Chesapeake Bay,

Narragansett (RI) Bay, Long Island Sound, Puget Sound, New York/New Jersey Harbor, and San

Francisco Bay Estuary programs. These specific programs have all recommended actions to reduce

sources of toxic contaminants to sediment.

Continuing Challenges

The following are observations on continuing challenges to improve sediment quality assessment and

management in the United States.

• Further Assessment of the Extent and Severity of Sediment Contamination in the 96 Targeted

Watersheds Would Improve Contaminated Sediment Management Decisions. States and tribes, in

cooperation with EPA and other federal agencies, should further evaluate the 96 watersheds

containing APCs. In many cases, it is likely that much additional investigation and assessment has

already occurred, especially in well-known areas at risk for contamination, and that some areas

have been remediated. If active watershed management programs are in place, states and tribes may

coordinate these evaluations within the context of current or planned actions. Future assessment

efforts should focus on areas such as the waterbody segments located in the 96 watersheds

containing APCs that had 10 or more sampling stations classified as Tier 1. The purpose of these

efforts should be to gather additional sediment chemistry and related biological data, and to

conduct further evaluation of data to determine human health and/or ecological risk, to determine

temporal and spatial trends, to identify potential sources of sediment contamination and determine

whether the appropriate source controls are being applied. Any future policies and/or actions to

address contaminated sediments will have to be considered in the context of the budget process and

competing demands for funding.

• Watershed Management Activities Would Create Multidisciplinary and Multijurisdictional

Partnerships Focusing on Sediment Contamination. Addressing water issues within a given

watershed or hydrologic boundaries—known as watershed management—is a critical component

of community-based environmental protection. A watershed management framework requires a

high level of inter-program coordination to consider all factors contributing to water and sediment

quality problems and to develop integrated, science-based, cost-effective solutions that involve all

the stakeholders. It is within the watershed framework, therefore, that federal, state, tribal, and local

government agencies and industrial and citizens’ groups can pool their common resources and

coordinate their efforts to address their common sediment contamination issues. These watershed

activities will support efforts such as monitoring and regulatory actions.

• Better Coordination of Contaminated Sediment Management and Research Activities Would

Promote Application of Sound Science in Managing Contaminated Sediments. EPA developed
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the Contaminated Sediment Management Strategy (USEPA, 1998a). Building on the Strategy,

EPA’s Contaminated Sediment Management Committee (CSMC) has developed the Contaminated

Sediment Action Plan. This plan outlines the next steps for the Agency in the management of

contaminated sediments. The multimedia, cross-program plan describes the commitments from the

EPA program offices to develop and apply sound science in managing contaminated sediments. A

key component of future coordination within EPA in addressing sediment contamination is the

contaminated sediment assessment pilots. The Office of Solid Waste and Emergency Response

(OSWER), the Office of Water (OW), and EPA’s regional offices will initiate pilot projects to

facilitate cross-program coordination on contaminated sediments. The pilot projects will bring a

cross-Agency focus to identifying and assessing waters that are impaired by sediment

contamination. The pilots will use the legal authorities and techniques available to satisfy the needs

of both the Remedial Investigation/Feasibility Study (RI/FS) evaluations and Total Maximum

Daily Load (TMDL) modeling. EPA is also developing an Agency-wide Contaminated Sediment

Science Plan to identify and prioritize the Agency’s contaminated sediment science needs.

• Better Monitoring and Assessment Tools Would Improve Contaminated Sediment Management.

The sediment quality evaluation tools used and outlined in this report should be used as the basis

for future contaminated sediment assessment methods. As sediment quality data become more

available and the state of the science for sediment assessment continues to evolve, better

assessment methods will also evolve. As new and better sediment screening values and biological

assessment techniques become available and are proven to be reliable, EPA will incorporate these

techniques into future NSI data evaluations.

• A Weight-of-Evidence Approach and Measures of Chemical Bioavailability in Sediment

Monitoring Programs Would Improve the Assessment of Contaminated Sediment. The ideal

assessment methodology would be based on matched data sets of multiple types of sediment

quality measures to take advantage of the strengths of each measurement type and to minimize

their collective weaknesses. As the state of science is constantly evolving, future sediment

monitoring programs should collect tissue residue, biological effects (i.e., toxicity, histopathology),

and biological community (e.g., benthic abundance and diversity) measurements whenever possible

along with sediment chemistry data. Collection of data to measure chemical bioavailability is

critical to the success of weight-of-evidence assessments. Where metals are expected to be a

concern, sediment monitoring programs should collect AVS and SEM measurements. More

accurate assessments will be possible if future monitoring programs include TOC measurements

wherever organic chemicals are a concern.

• Increased Geographic Coverage in the NSI Database Would Refine a National Assessment of

the Extent and Severity of Contaminated Sediment. The NSI database is currently limited in terms

of the number of data sets it includes and the national coverage it provides. The focus of additional

data additions will be (1) to obtain a greater breadth of coverage across the United States and (2) to

increase the number of waterbodies evaluated. These types of data will be extremely useful in

future analyses to assess changes in the extent and severity of sediment contamination over time.

Upon completion of this report, EPA will make a concerted effort to accumulate more data for

inclusion in the NSI database and for future National Sediment Quality Survey reports to Congress.

This effort will begin its focus on areas (river reaches and watersheds) with minimal or no coverage

outlined in this report. As part of this effort, EPA will broadly advertise its need for information on

contaminated sediments. EPA also encourages third parties to send their information to STORET

(www.epa.gov/STORET) so that it can be reflected in the next National Sediment Quality Survey.

As part of the initial National Sediment Quality Survey, EPA included the data used for that report

in its comprehensive GIS/modeling system, Better Assessment Science Integrating Point and

Nonpoint Sources (BASINS). EPA is working on getting the additional data in the NSI database

into BASINS. In addition to this effort, EPA is also working with NOAA to incorporate the NSI
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database into Query Manager, which is a database program that can be used to access sediment

data (chemistry, toxicity, and tissue residue data) for individual watersheds and allow the data to be

queried and analyzed.

• Assessment of Atmospheric Deposition of Sediment Contaminants Would Improve

Contaminated Sediment Management. The relative contribution of contaminants to the sediment

from air deposition has been virtually unknown on a national scale, but could be significant. Under

Section 112(m) of the Clean Air Act, the EPA in cooperation with NOAA has been conducting a

program to assess the contributions and effects of hazardous air pollutants on the Great Lakes,

Lake Champlain, the Chesapeake Bay, and near-coastal waters. The findings and conclusions from

this program and others described in the third Great Waters Report to Congress will be

incorporated into future iterations of the National Sediment Quality Survey.

• Prevention of Continuing Sources of Sediment Contamination is Important in Contaminated

Sediment Management. Although sediment contamination is frequently the result of historical

discharges of pollutants before the National Pollutant Discharge Elimination System (NPDES)

regulatory program was established, there are still continuing sources of sediment contamination.

Therefore, source control and pollution prevention are crucial items in preventing contaminated

sediments. As outlined in EPA’s Contaminated Sediment Management Strategy, EPA OW and

other EPA program offices are working with non-governmental organizations and the States to

prevent point and nonpoint source contamination from accumulating in sediments. Pollution

prevention is a key element in reducing the sources of contaminants that can end up in the

sediments, potentially resulting in adverse effects to aquatic life or human health. Pollution

prevention has been shown to reduce costs, as well as pollution risks, through source reduction and

recycling/reuse techniques. Additionally, EPA has developed and is implementing a national

multimedia strategy (under the cross-agency PBT Program) for the reduction of persistent,

bioaccumulative, toxic chemicals (PBTs), which generally accumulate in sediments. EPA is

forging a new approach to reducing risks from and exposures to priority PBT pollutants. This

approach, focused on increased coordination among EPA and regional programs also requires the

significant involvement of stakeholders, including international, state, local, and tribal

organizations, the regulated community, environmental groups, and private citizens.

• Better Coordination and Communication with External Stakeholders and Other Federal

Agencies Would Improve the Contaminated Sediment Management Process. Sediment

contamination is a concern to stakeholders throughout the United States. EPA will work closely

with other Federal Agencies (e.g., USACE, NOAA, USGS) to compile and evaluate data in the NSI

database as well as the development of future reports. Additionally, EPA will reach out to the

public as we compile additional sediment quality data in the NSI database and develops the next

report to Congress. During the next year, EPA anticipates setting up “listening sessions” to gather

information that can be used for future reports to Congress. During these sessions, EPA will be

searching for additional data for the NSI database and subsequent reports, taking recommendations

on how to improve the report, and establishing better and more effective ways to keep the public

and interested stakeholders informed.

This report and future National Sediment Quality Survey reports will provide environmental managers at

the federal, state, tribal, and local levels with valuable information. The NSI database and this report can

assist local watershed managers by providing data and by demonstrating the application of a multiplelines-of-evidence approach for identifying and screening contaminated sediment locations. It also allows

researchers to draw on a large data set of sediment information to conduct new analyses that will continue

to advance the science of contaminated sediment assessments, which ultimately can be applied at the local

level to assist environmental managers in making sediment management decisions.
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ACRONYMS

AET apparent effects threshold

ANOVA analysis of variance

AOC area of concern

APC area of probable concern for sediment contamination

AQUIRE Aquatic Toxicity Information Retrieval database

AVS acid-volatile sulfide

BASINS Better Assessment Science Integrating Point and Nonpoint Sources (EPA modeling tool)

BHC benzene hexachloride

BSAF biota-sediment accumulation factor

CAA Clean Air Act

CAS Chemical Abstracts Service

CDI chronic daily intake

CERCLA Comprehensive Environmental Response, Compensation, and Liability Act

CSMC Contaminated Sediment Management Committee 

CWA Clean Water Act

DDD p,pN-dichlorodiphenyldichloroethane

DDE p,pN-dichlorodiphenyldichloroethylene

DDT p,pN-dichlorodiphenyltrichloroethane

EMAP Environmental Monitoring and Assessment Program

EPA U. S. Environmental Protection Agency

EqP equilibrium partitioning

ERDC Engineer Research and Development Center

ERL effects range-low value

ERM effects range-median value

ESG equilibrium sediment partitioning guideline

FAV final acute value

FCV final chronic value

FDA Food and Drug Administration

FDEP Florida Department of Environmental Protection

FFDCA Federal Food, Drug and Cosmetic Act

FIELDS fully integrated environmental location decision support system

FIFRA Federal Insecticide, Fungicide, and Rodenticide Act

GCR/IHSC Grand Calumet River, Indiana Harbor Ship Canal

GIS geographic information systems

GLI Great Lakes Initiative

GLNPO Great Lakes National Program Office (U.S. Environmental Protection Agency)

GLWC Great Lakes Water Quality Wildlife Criterion

GMAV genus mean acute value

HAP hazardous air pollutants

HQ hazard quotient

HUC hydrologic unit code

LOE lines of evidence

LRM logistic regression model

MACT Maximum Available Control Technology 

MARPLOT Mapping Applications for Response, Planning, and Local Operational Tasks

MDD minimum detectable difference
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MDR minimum data requirements

MOU Memorandum of Understanding

MPRSA Marine Protection, Research, and Sanctuaries Act

NAWQA National Water-Quality Assessment Program (U.S. Geological Survey) 

NLFWA National Listing of Fish and Wildlife Advisories

NOAA National Oceanic and Atmospheric Administration

NODC National Oceanographic Data Center

NPDES National Pollutant Discharge Elimination System

NPL National Priorities List

NSI National Sediment Inventory database

NS&T National Status and Trends Program (National Oceanic and Atmospheric Administration)

OECA Office of Enforcement and Compliance Assurance (U.S. Environmental Protection

Agency)

ORD Office of Research and Development (U.S. Environmental Protection Agency)

OST Office of Science and Technology (U.S. Environmental Protection Agency)

OSWER Office of Solid Waste and Emergency Response (U.S. Environmental Protection Agency)

OW Office of Water (U.S. Environmental Protection Agency)

PAH polycyclic aromatic hydrocarbon

PCB polychlorinated biphenyl

PEL probable effects level

PSWQA Puget Sound Water Quality Authority

PBT persistent, bioaccumulative, and toxic

QA/QC quality assurance/quality control

QSAR quantitative structure-activity relationship

RAP Remedial Action Plan

RCRA Resource Conservation and Recovery Act

REMAP Regional Environmental Monitoring and Assessment Program

RF1 River Reach File 1

RI/FS Remedial Investigation/Feasability Study

ROD Record of Decision

SAB Science Advisory Board

SAV secondary acute value

SCV secondary chronic value

SEDQUAL Sediment Quality Information System

SEM simultaneously extracted metals

SETAC Society of Environmental Toxicology and Chemistry

SMAV species mean acute value

SQAL sediment quality advisory level

STORET Storage and Retrieval System

TBP theoretical bioaccumulation potential

TCDD tetrachlorodibenzo-p-dioxin

TEF toxic equivalency factor

TEL threshold effects level

TIE toxicity identification evaluation

TMDL Total Maximum Daily Load

TOC total organic carbon

TSCA Toxic Substances Control Act

USACE U.S. Army Corps of Engineers

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey
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WQC water quality criteria

WRDA Water Resources Development Act
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GLOSSARY

Acid-volatile sulfide (AVS): Reactive solid-phase sulfide fraction that can be extracted by cold

hydrochloric acid. Appears to control the bioavailability of most divalent metal ions because of the

sulfide ions’ high affinity for divalent metals, resulting in the formation of insoluble metal sulfides in

anaerobic (anoxic) sediments.

Acute toxicity: Immediate or short-term response of an organism to a chemical substance. Refers to

generalized toxic response with lethality usually being the observed endpoint.

Benthic organisms: Species living in or on the bottom of streams, rivers, or oceans.

Bioaccumulation: The net accumulation of a chemical substance by an organism as a result of uptake

from all environmental sources.

Bioavailability: The fraction of chemical present that is available for uptake by aquatic organisms.

Biological community: An assemblage of organisms that are associated in a common environment and

interact with each other in a self-sustaining and self-regulating relationship.

Biological effects correlation approach: A method for relating the incidence of adverse biological

effects to the dry-weight sediment concentration of a specific chemical at a particular site based on the

evaluation of paired field and laboratory data. Exceedance of the identified level of concern concentration

is associated with a likelihood of adverse organism response, but it does not demonstrate that a particular

chemical is solely responsible.

Cataloging unit: Sometimes referred to as a hydrologic unit; corresponds to a watershed that was

delineated by the U.S. Geological Survey. A watershed is an area that drains ultimately to a particular

watercourse or body of water. Each cataloging unit, as used in this report, is uniquely identified with an

8-digit hydrologic unit code (HUC). There are approximately 2,100 such 8-digit cataloging units in the

contiguous United States, which are, on average, somewhat larger than counties. 

Chronic toxicity: Response of an organism to repeated, long-term exposure to a chemical substance.

Typical observed endpoints include growth expressed as length and weight.

Combined sewer overflow: A discharge of a mixture of storm water and untreated domestic wastewater

that occurs when the flow capacity of a sewer system is exceeded during a rainstorm. 

Contaminated sediment: Sediment that contains chemical substances at concentrations that pose a

known or suspected threat to aquatic life, wildlife, or human health.

Demersal species: Swimming organisms that prefer to spend the majority of their time on or near the

bottom of a waterbody.

Divalent metals: Metals that are available for reaction in a valence state of two (i.e., carrying a positive

electric charge of two units).

Ecosystem: An ecological unit consisting of both the biotic communities and the nonliving (abiotic)

environment, which interact to produce a system that can be defined by its functionality and structure.

Equilibrium concentration: The concentration at which a system is in balance due to equal action by

opposing forces within the system. When the partitioning of a nonionic organic chemical between organic

carbon and pore water and the partitioning of a divalent metal between solid and solution phases are

assumed to be at equilibrium, an organism in the sediment is assumed to receive an equivalent exposure

to the contaminant from water only or from any equilibrated phase. The pathway of exposure might
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include pore water (respiration), sediment carbon (ingestion), sediment organism (ingestion), or a

combination of routes.

Equilibrium partitioning (EqP) approach: Approach used to relate the dry-weight sediment

concentration of a particular chemical that causes an adverse biological effect to the equivalent free

chemical concentration in pore water and to that concentration sorbed to sediment organic carbon or

bound to sulfide. Based on the theory that the partitioning of a nonionic organic chemical between

organic carbon and pore water and the partitioning of a divalent metal between the solid and solution

phases are at equilibrium.

Hydrology: A science dealing with the properties, distribution, and circulation of water on the surface of

the land, in the soil, and in the atmosphere.

Logistic regression model: An empirically derived model that relies on matching field-collected

sediment chemistry and biological effects (e.g., sediment toxicity) data. Unlike other empirical methods

(which result in sediment quality guidelines), the logistic regression model yields an estimate of the

probability of observing sediment toxicity for a given sediment chemistry observation.

Molar concentration: The ratio of the number of moles (chemical unit referring to the amount of an

element having a mass in grams numerically equal to its atomic weight) of solute (the substance being

dissolved or that present in the smaller proportion) in a solution divided by the volume of the solution

expressed in liters.

Monotonic trend: Trend that exists between two variables, such as concentration and time, when

concentration generally increases (i.e., does not decrease) or decreases (i.e., does not increase) with time.

The relationship between the two variables may be linear or nonlinear.

National Sediment Inventory (NSI): A national compilation of sediment quality data and related

biological data. Results of the evaluation of data from the NSI serve as the basis for the report to

Congress on the incidence and severity of sediment contamination across the country (i.e., the National

Sediment Quality Survey).

Nonionic organic chemicals: Compounds that do not form ionic bonds (bonds in which the electrical

charge between bonded atoms in the compound is unequally shared). Nonionic compounds do not break

into ions when dissolved in water and therefore are more likely to remain in contact with and interact with

sediment compounds or other compounds in water.

Nonpoint source pollution: Pollution from diffuse sources without a single point of origin or pollution

not introduced into a receiving stream from a specific outlet. Such pollutants are generally carried off the

land by storm water runoff. Sources of nonpoint source pollution include atmospheric deposition,

agriculture, silviculture, urban runoff, mining, construction, dams and channels, inappropriate land

disposal of waste, and saltwater intrusion.

Nonpolar organic chemicals: Compounds that do not exhibit a strong dipole moment (there is little

difference between the electrostatic forces holding the chemical together). Nonpolar compounds tend to

be less soluble in water. In aquatic systems, nonpolar chemicals are more likely to be associated with

sediments or other nonpolar compounds than with the surrounding water.

Pelagic species: Species living in the open water or in the open ocean away from the shore or coastline.

Point source pollution: Pollution contributed by any discernible, confined, and discrete conveyance,

including but not limited to any pipe, ditch, channel, tunnel, conduit, well, discrete fissure, container,

rolling stock, concentrated animal feeding operation, or vessel or other floating craft from which

pollutants are or may be discharged.
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Quantitative structure-activity relationship (QSAR) technique: A tool based on the premise that a

relationship exists between the molecular structure and physical, chemical, or biological activity, such as

molar volume, boiling point, or toxicity.

River Reach: A stream segment between the consecutive confluences of a stream. Most river reaches

represent simple streams and rivers, while some river reaches represent the shoreline of wide rivers, lakes,

and coastlines. EPA's River Reach File 1 (RF1) was completed for the contiguous United States in the

mid-1980s and includes approximately 68,000 river reaches. The average length of a river reach is 10

miles. Neither the more detailed version of the Reach File (RF3) nor the National Hydrography Dataset

(NHD) was used for the National Sediment Inventory.

Sampling station: A specific location associated with latitude and longitude coordinates where data have

been collected. Multiple sampling stations can have the same latitude and longitude coordinates if labeled

with a different station identification code for sampling performed on different dates or by different

sponsoring agencies.

Simultaneously extracted metals (SEM): Metal concentrations that are extracted during the same

analysis in which the acid-volatile sulfide (AVS) content of the sediment is determined.

Solid-phase toxicity test: A toxicity test in which test organisms are exposed directly to sediments.

Sediments are carefully placed in the exposure chamber and the chamber is then filled with clean water.

Resuspended particles are allowed to settle before initiation of exposure. Solid-phase toxicity tests

integrate multiple exposure routes, including chemical intake from dermal contact with sediment particles

as well as ingestion of sediment particles, interstitial water, and food organisms.

Theoretical bioaccumulation potential (TBP): An estimate of the equilibrium concentration of a

contaminant in tissues if the sediment in question were the only source of contamination to the organism.

TBP is estimated from the organic carbon content of the sediment, the lipid content of the organism, and

the relative affinities of the chemical for sediment organic carbon and animal lipid content.

Total organic carbon (TOC): A measure of the organic carbon content of sediment expressed as a

percentage. Used to normalize the dry-weight sediment concentration of a chemical to the organic carbon

content of the sediment.

U.S. Environmental Protection Agency (EPA) risk levels: Levels of contaminant concentrations in an

exposure medium that pose a potential carcinogenic risk (e.g., 10-5, or a 1 in 100,000 extra chance of

cancer over a lifetime) and/or noncancer hazard (i.e., exceeds a reference dose). Used in this document to

estimate human health risk associated with the consumption of chemically contaminated fish tissue.

U.S. Food and Drug Administration (FDA) tolerance/action or guideline levels: FDA has prescribed

levels of contaminants that will render a food “adulterated.” The establishment of action levels (levels of

food contaminants to which consumers can be safely exposed) or tolerances (regulations having the force

of law) is the regulatory procedure that FDA uses to control environmental contaminants in the

commercial food supply.
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ACKNOWLEDGMENTS

The United States Environmental Protection Agency’s Office of Science and Technology (OST)

produced this report with technical support from Tetra Tech, Inc., under EPA Contract 68-C-01-041. Staff

from EPA regional offices and other headquarters program offices participated in this project by

providing technical guidance and reviewing previous drafts. We greatly appreciate their efforts and

helpful comments, which made this a better document. In particular, we wish to acknowledge the

following: D. Scott Ireland, who served as the principal investigator for this report; Jim Keating, who

served as the principal investigator on the initial report released in 1997 and provided technical assistance

for this report; Elizabeth Southerland, Jim Pendergast, Thomas Armitage and Richard Healy of EPA for

their assistance in completing this report; and Peter Van Metre, Edward Callender, Barbara J. Mahler, and

Jennifer T. Wilson of U.S. Geological Survey for the sediment core analysis included in Chapter 4.

We wish to offer a special expression of gratitude to several scientists who provided technical

information, guidance, and expert counsel to OST during the development of this report. These scientists

were: Walter Berry, EPA, Office of Research and Development; Jay Field, National Oceanic and

Atmospheric Administration, Office of Response and Restoration; Chris Ingersoll, U.S. Geological

Survey, Columbia Environmental Research Center; and Dave Mount, EPA, Office of Research and

Development.

We also wish to acknowledge the following persons who provided external peer review of the

evaluation methodology (Chapter 2 and Appendix B) and its application to the data compiled for this

report (Chapter 3): William Adams of Kennecott Utah Copper Corporation in Magna, Utah; Allen Burton

of Wright State University in Dayton, Ohio; and William Stubblefield of ENSR Technology in Fort

Collins, Colorado. These persons reviewed the soundness of proposed evaluation methods for intended

purposes and provided meaningful recommendations. Participation in the review process does not imply

concurrence by these individuals with all observations contained in this report.

We greatly appreciate the comments received from various stakeholders during the review of this

report. Our thanks to all state government officials, tribal representatives, trade association

representatives, environmental advocacy professionals, and members of the scientific community who

provided valuable insights. We also wish to acknowledge consultation with the National Oceanic and

Atmospheric Administration and the U.S. Army Corps of Engineers. 

Finally, we wish to recognize Jon Harcum, Regno Arulgnanendran, Alex Trounov, Mark Sievers,

Susan Adair, and all other participating staff and management at Tetra Tech, Inc., for their efforts and

professionalism in providing technical support and data management.
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CHAPTER 1

INTRODUCTION

What Is the National Sediment Quality Survey?

The Water Resources Development Act (WRDA) of 1992 directed the U.S. Environmental Protection

Agency (EPA), in consultation with the National Oceanic and Atmospheric Administration (NOAA) and

the U.S. Army Corps of Engineers (USACE), to conduct a comprehensive national survey of data

regarding the quality of sediments in the United States. Section 503 of WRDA 1992 required EPA to

“compile all existing information on the quantity, chemical and physical composition, and geographic

location of pollutants in aquatic sediments, including the probable sources of such pollutants and

identification of those sediments which are contaminated....” Section 501(b)(4) of WRDA 1992 defines

contaminated sediment as “aquatic sediment which contains chemical substances in excess of appropriate

geochemical, toxicological or sediment quality criteria or measures; or is otherwise considered by the

Administrator [of EPA] to pose a threat to human health or the environment....” Section 503 further

required EPA to “report to Congress the findings, conclusions, and recommendations of such survey,

including recommendations for actions necessary to prevent contamination of aquatic sediments and to

control sources of contamination.” In addition, Section 503(b) requires EPA to conduct a comprehensive

and continuing program to assess aquatic sediment quality. This program must establish methods and

protocols for monitoring the physical, chemical, and biological effects of pollutants in aquatic sediment

and of contaminated sediment. EPA must submit a report to Congress every 2 years on the finding of the

monitoring required under the Act.

To comply with this mandate, EPA’s Office of Science and Technology (OST) initiated the National

Sediment Inventory (NSI) database. The goals of the NSI are to compile sediment quality information

from available electronic databases, develop screening-level assessment protocols to identify potentially

contaminated sediment, and produce biennial reports to Congress as well as the EPA regions, states, and

tribes on the incidence and severity of sediment contamination nationwide. To ensure that future reports

to Congress accurately reflect the latest conditions of the Nation’s sediment as science evolves, the NSI

database will develop into a regularly updated, centralized aggregation of sediment quality measurements.

In 1997 EPA published the first report, titled The Incidence and Severity of Sediment Contamination in

Surface Waters of the United States, volumes 1 through 3. The first volume, The National Sediment

Quality Survey (USEPA, 1997), presented a national baseline screening-level assessment of contaminated

sediments based on sediment quality data collected from 1980 through 1993 using a weight-of-evidence

approach. The purpose of the initial National Sediment Quality Survey, as well as this first update to that

report, is to depict and characterize the incidence and severity of sediment contamination based on the

probability of adverse effects to human health and/or the environment, and the information provided

could be used to further investigate sediment contamination on a national, regional, or site-specific scale.

Volume 2 of the first report presented data summaries for watersheds that had been identified as

containing areas of probable concern for sediment contamination, and Volume 3 presented a screening

analysis to identify probable point source contributors of sediment pollutants.

For this current National Sediment Quality Survey, OST added to the data compiled in the initial NSI

database additional data from across the country currently stored in large electronic databases and

covering the years up through 1999. This effort required a substantial synthesis of multiple formats and

the coordinated efforts of many federal and state environmental information programs that maintain

relevant data. Data from many sampling and testing studies have not yet been incorporated into the NSI

database and therefore are not evaluated in this National Sediment Quality Survey. Thus, it is highly likely
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that additional locations ranging from relatively pristine to extensive sediment contamination do not

appear in the NSI database and are therefore not evaluated in the National Sediment Quality Survey. As

data management systems and access capabilities continue to improve, EPA anticipates that a greater

amount of data will be incorporated into the NSI database and subsequent National Sediment Quality

Survey reports to Congress.

This report presents the results of the screening-level assessment of the NSI data from 1990 through 1999.

For this assessment, OST examined sediment chemistry data, tissue residue data, and sediment toxicity

test results. The purpose of this assessment was to determine whether potential adverse effects from

sediment contamination exist currently or existed over the past 10 years at distinct monitoring locations

throughout the United States. The initial National Sediment Quality Survey report to Congress used all the

data available from 1980 through 1993 to develop a baseline assessment. Because of the biennial

reporting requirements associated with this report, EPA wanted to “window in” on a regular time frame

for including data. One major advantage of screening out older data (data collected prior to January 1,

1990) is that it prevents the results presented in this report from being unduly influenced by historical

data when more recent data are available. However, this would not allow for the results of any decrease in

sediment contaminant levels due to scouring, re-burial, natural attenuation, or active sediment

remediation that have occurred since that sample was collected.

This report identifies locations where available data indicate that direct or indirect exposure to the

sediment could be associated with adverse effects to aquatic life and/or human health. Even though this

report focuses on data collected from 1990 through 1999, conditions might have improved or worsened

since the sediment was sampled. Additionally, the data were generally not collected in a randomized

sampling approach. Consequently, this report does not and cannot provide a definitive assessment of the

national condition or relative health of sediments across the country. Even though this report does not

provide an assessment of the “national condition” of contaminated sediments it does,. however, evaluate

data from 1980 through 1999 in the NSI database to assess changes in the extent and severity of sediment

contamination over time for specific areas in the United States where sufficient data exist.

This National Sediment Quality Survey and future iterations of this report will provide environmental

managers at the federal, state, tribal and local levels with valuable information. The NSI database and this

report can assist local watershed managers by providing additional data that they might not have,

demonstrating the application of a multiple-lines-of-evidence approach for identifying and screening

contaminated sediment locations. It also allows researchers to draw on a large data set of sediment

information to conduct new analyses that will continue to advance the science of contaminated sediment

assessments and ultimately can be applied at the local level to assist environmental managers in making

sediment management decisions. Any future policies and/or actions to address contaminated sediments

will have to be considered in the context of the budget process and competing demands for funding.

This National Sediment Quality Survey provides a screening-level assessment of data collected from 1990

through 1999 and contained in the NSI database. Chapter 1 provides the intent of this report and

background information on sediment quality issues. Chapter 2 is an overview of the assessment

methodology used to evaluate the NSI data (from 1990 through 1999) to identify potentially contaminated

sediment locations. Chapter 3 contains the results of the evaluation on the national, regional, and state

levels. Chapter 4 presents the methods used and the results of a temporal trend analysis of sediment

contamination over time. Chapter 5 provides a discussion of the results and observations on continuing

challenges to improve sediment quality assessment and management in the United States. Several

appendices present detailed descriptions of both the data in the NSI database and the approaches used to

evaluate the data:

Appendix A: National Sediment Inventory Field Description

Appendix B: Description of Evaluation Parameters Used in the NSI database Evaluation
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Appendix C: Values Used for Chemicals Evaluated

Appendix D: Species Characteristics Related to NSI Bioaccumulation Data

Appendix E: Trend Analysis Case Studies

Appendix F: Comparison of Watersheds Containing APCs

Why Is Contaminated Sediment an Important National Issue?

In response to the need for national guidance on addressing contaminated sediments, EPA released its

Contaminated Sediment Management Strategy in 1998. This document establishes four goals to manage

the problem of contaminated sediment and it describes actions the Agency intends to take to accomplish

those goals. The goals are as follows: (1) prevent the volume of contaminated sediment from increasing;

(2) reduce the volume of existing contaminated sediments; (3) ensure that sediment dredging and dredged

material disposal are managed in an environmentally sound manner; and (4) develop scientifically sound

sediment management tools for use in pollution prevention, source control, remediation, and dredged

material management.

Contaminated sediments may be directly toxic to aquatic life or can be a source of contaminants for

bioaccumulation in the food chain. A wide range of physical, chemical, and biological factors have the

potential to influence the bioavailability of sediment contaminants. The bioavailability of contaminants in

sediment is a function of the type of chemical and the chemical speciation, as well as the behavior and

physiology of the organism. The two basic routes of exposure for organisms are (1) transport of dissolved

contaminants in pore water across biological membranes and (2) ingestion of contaminated food or

sediment particles with subsequent transport across the gut. For upper-trophic-level species, ingestion of

contaminated prey is the predominant route of exposure, especially to hydrophobic chemicals. Uptake

through ingestion of or direct exposure to water or sediment can also be important depending on the

trophic level of the organism and the physical-chemical characteristics of the contaminant (USEPA,

2000a).

Contaminated sediment poses ecological and/or human health risks in many watersheds throughout the

United States. Even in areas where EPA water quality criteria (WQC) are not exceeded, adverse effects

have been observed in organisms in or near the sediments (Chapman, 1989). Because many chemicals of

anthropogenic origin (e.g., pesticides, polycyclic aromatic hydrocarbons [PAHs], and chlorinated

hydrocarbons) tend to sorb to sediments and organic materials, these chemicals also end up concentrating

in the sediment, which acts as a reservoir. Although concentrations of chemicals in sediment may be

several orders of magnitude higher than those in the overlying water, bulk sediment concentrations have

not been strongly correlated to bioavailability (Burton, 1991). Nevertheless, sediment contamination can

have many detrimental effects on an ecosystem, some of which are evident and others more discrete or

unknown. For example, benthic invertebrate communities can be totally lost or converted from sensitive

to pollution-tolerant species. These tolerant species process a variety of materials, and their metabolic

products can also be different. These differences mean that ecosystem functions such as energy flow,

productivity, and decomposition processes might be significantly altered (Griffiths, 1983). Loss of any

biological community in the ecosystem can indirectly affect other components of the system. For

example, if the benthic community is significantly changed, nitrogen cycling might be altered such that

forms of nitrogen necessary for key phytoplankton species are lost and the phytoplankton are replaced

with blue-green algae (cyanobacteria) capable of nitrogen fixation. Other effects from sediment

contamination are direct, as observed in the Great Lakes where top predator fish have become highly

contaminated from consuming bottom-feeding fish and benthic invertebrates that are laden with sedimentassociated pollutants, such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls

(PCBs), mercury, and pesticides. Documented adverse ecological effects from contaminated sediments
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include fin rot, increased tumor frequency, and reproductive toxicity in fish, as well as a decrease in

aquatic ecosystem biodiversity (USEPA, 1998a). Effects on ecosystem processes have been very dramatic

in areas affected by acid precipitation and acid mine drainage, which contribute to pollutant loadings to

waterbodies. In most areas receiving pollutant loadings, however, the effects are difficult to observe and

require use of various assessment tools, such as benthic macroinvertebrate community analysis, chemical

testing, quantification of habitat characteristics, and toxicity testing (Burton and Scott, 1992).

As described above, sediment contamination can adversely affect the health of organisms and provide a

source of contaminants to the aquatic food chain (Lyman et al., 1987). For example, fin rot and a variety

of tumors have been found in fish living above sediments contaminated by PAHs located near a creosote

plant on the Elizabeth River in Virginia. These impacts have been correlated with the extent of sediment

contamination in the river (Van Veld et al., 1990). Human and wildlife consumption of finfish and

shellfish that have accumulated contaminants in their tissue (bioaccumulation) is an important human

health and wildlife concern. In fact, fish consumption represents the most significant route of aquatic

exposure of humans to many metals and organic compounds (USEPA, 1992). Most sediment-related

human exposure to contaminants is through indirect routes that involve the transfer of pollutants out of

the sediments and into the water column or aquatic organisms. Many surface waters have fish

consumption advisories or fishing bans in place mostly due to mercury, PCBs, chlordane, dioxins, and

DDT and its metabolites (DDD and DDE), which are commonly found in sediments. Based on EPA’s

2002 National Listing of Fish and Wildlife Advisories database (NLFWA) there are 2,800 fish advisories

in the United States for the types of contaminants often found in contaminated sediments. These

advisories affect more than 544,000 river miles, 71 percent of the Nation’s coastal waters, and more than

95,000 lakes, including 100 percent of the Great Lakes.

Additional examples of direct impacts of contaminated sediment on wildlife and humans have been noted.

Bishop et al. (1995, 1999) found good correlations between a variety of chlorinated hydrocarbons in the

sediment and concentrations in bird eggs. These researchers found that this relationship indicated that the

female contaminant body burden was obtained locally, just before egg laying. Other studies by Bishop et

al. indicated a link between exposure of snapping turtle (Chelydra s. serpentina) eggs to contaminants

(including sediment exposure) and developmental success (Bishop et al., 1991, 1998). Other

investigations of environmentally occurring persistent organics have shown bioaccumulation and a range

of effects in the mudpuppy, Necturus maculosus (Bonin et al., 1995; Gendron et al., 1997). In the case of

humans there is only anecdotal evidence from cases like Monguagon Creek, a small tributary to the

Detroit River, where incidental human contact with the sediment resulted in a skin rash (Zarull et al.,

1999).

In addition to human health and ecological impacts, contaminated sediments can cause severe economic

impacts. Economic risk might be felt by the transportation, tourism, and fishing industries. In one Great

Lakes harbor (Indiana Harbor Ship Canal), navigational dredging has not been conducted since 1972 “due

to the lack of an approved economically feasible and environmentally acceptable disposal facility for

dredged materials” from the Indiana Harbor Ship Canal (USACE, 1995). The accumulation of sediment

in this canal has increased costs for industry. Ships carrying raw materials have difficulty navigating in

the harbor and canal. In addition, ships come into the harbor loaded at less than optimum vessel drafts.

There is also restricted use of various docks, requiring unloading at alternative docks and double handling

of bulk commodities to the preferred dock. These problems are causing increased transportation costs for

waterborne commerce in this Canal, estimated in 1995 at $12.4 million annually (USACE, 1995).

How Significant Is the Problem?

In the first Report to Congress, EPA found that every state in the country had at least one sampling

location that was classified as having probable adverse effects to aquatic life or human health, indicating a
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geographically diverse problem. Other more geographically targeted studies have attempted to quantify

the extensiveness of sediment contamination. For example, studies conducted on sediment from the Great

Lakes area have demonstrated that contaminated sediments are of great concern to humans and wildlife

that live in the Great Lakes Basin. Years of industrial and municipal discharges, combined sewer

overflows, and urban and agricultural nonpoint source runoff have contributed to the creation of vast

amounts of highly polluted sediments that pose serious human and ecological health risks (USEPA,

2000b). Sediments have been collecting on the bottoms of the Great Lakes since they were formed by

glacial scouring and melting. Even after cleanup efforts began in the late 1960s, little attention was paid

to the toxics that accumulated in the bottom sediments. The first priority was to stop the discharge of new

contaminants into waterways, and little concern was paid to sediments (USEPA, 2000b). It was not until

the early 1980s that environmental problems caused by sediment contamination began to generate interest

in the Great Lakes.

EPA’s Great Lakes National Program Office (GLNPO) has reported that polluted sediment is the largest

major source of contaminants in Great Lakes rivers and harbors entering the food chain, including the

current 42 Areas of Concern (AOC) designated by the United States and Canada, the Parties to the Great

Lakes Water Quality Agreement (Figure 1-1). Over the past several years, Great Lakes stakeholders have

moved forward in the pursuit of sediment remediation. In the years 1997-2002, almost 2.3 million cubic

yards of contaminated sediment has been removed from the U.S. Great Lakes Basin (EPA/GLNPO,

March, 2004).

Numerous statutes, including the Clean Water Act (CWA), the Marine Protection, Research, and

Sanctuaries Act (MPRSA or Ocean Dumping Act), and the Comprehensive Environmental Response,

Compensation, and Liability Act (CERCLA), authorize programs that address contaminated sediments

(USEPA, 1998a). First, the disposal of material resulting from navigational dredging in the Nation’s

waters is regulated under either the CWA (Section 404) or the MPRSA, depending on the location of the

disposal site. Although it is estimated that only 5 to 10 percent of the material dredged each year is not

suitable for open water disposal due to contamination, there are widespread concerns among the public

regarding the effect of contaminated dredged material disposal. The difficulty associated with finding

alternative disposal options for contaminated dredged material often results in project delays, additional

costs, and significant controversy.

CERCLA provides one of the most comprehensive authorities available to EPA to obtain sediment

cleanup, reimbursement of EPA cleanup costs, and compensation to natural resource trustees for damages

to natural resources affected by contaminated sediments. Removal actions and enforcement actions can be

brought at both National Priorities List (NPL) and non-NPL sites. To date, about 300 sites (approximately

20 percent) on the Superfund NPL appear to have some kind of contaminated sediment. EPA has made

decisions at almost 50 percent of these sites to address that contamination. Most of the sites are small, but

a few sites are quite large.

In addition, under CWA Section 303(d), EPA and the states may address contaminated sediments in

developing Total Maximum Daily Loads (TMDLs). TMDLs identify the loading capacity of waters not

meeting water quality standards. TMDLs allocate the receiving waters’ pollutant loading capacity among

point and nonpoint sources of pollutants of concern. Based on the states’1998 lists of impaired waters,

about 36,000 TMDLs will need to be developed for about 20,000 impaired waterbodies throughout the

United States. Based on the TMDL tracking system with the 1998 data, only 32 impaired waterbodies

were specifically identified as impaired by contaminated sediments. However, about 21 percent of the

TMDLs are for pollutants that are also often found in contaminated sediments (e.g., PCBs, mercury,

pesticides). It is very likely that these TMDLs will require some analysis for the contribution of pollutants

from contaminated sediments.
1-6

Figure 1-1. Areas of Concern in the Great Lakes–St. Lawrence River Basin. Source: http://www.on.ec.gc.ca/water/raps/map_e.html; Environment Canada
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In 1994 the National Oceanic and Atmospheric Administration (NOAA) released its Inventory of

Chemical Concentrations in Coastal and Estuarine Sediments (NOAA, 1994). This study characterized

2,800 coastal sites as either “high” or “hot,” based on the contaminant concentrations found at the

sampling locations. NOAA did not use risk-based screening values for its analysis. Using the National

Status and Trends Mussel Watch data set, “high” values were defined as the mean concentration for a

specific chemical plus one standard deviation. NOAA’s “high” values correspond to about the 85th

percentile of contaminant concentration. “Hot” concentrations were defined as those exceeding five times

the “high” values. Most of the “hot” sites were in locations with high ship traffic, industrial activity, and

relatively poor flushing, such as harbors, canals, and intercoastal waterways (NOAA, 1994). Mercury and

cadmium exceeded the NOAA “hot” thresholds at a greater percentage of sites where they were measured

(about 7 percent each) than other sediment contaminants (USEPA, 1998a).

In selected areas throughout 25 estuaries and marine bays along the Atlantic, Gulf of Mexico, and Pacific

coasts, NOAA performed toxicity tests on 1,543 surficial sediment samples collected from 1991 through

1997. The toxicity of each sample was determined by exposing amphipods to bulk sediments for 10 days

and measuring their survival. These 1,543 samples collectively represented a total area of approximately

7,300 square kilometers. Toxicity was observed in samples that represented approximately 6 percent of

the combined area (Long, 2000). Using similar tests conducted on samples collected in different, but

overlapping study areas, EPA estimated that about 7 percent of the combined estuarine area sampled was

toxic. The northeastern and southwestern estuaries displayed the most severe toxicity generally, and

toxicity was observed the least in southeastern and northwestern areas. However, extensive portions of the

Pacific coast have not been tested using the same methods. Toxicity was considerably much more

widespread (25 to 39 percent), however, when the results of two sublethal sediment toxicity tests were

evaluated (Long, 2000).

As part of EPA’s Environmental Monitoring and Assessment Program (EMAP), sediment samples were

collected to assess toxicity on a regional scale in streams and rivers in the Mid-Atlantic United States in

1994, 1997, and 1998 and in the Colorado Rocky Mountains in 1994 and 1995. Sample sites were

selected randomly using a probability design so that the results could be extrapolated for the entire region.

Toxicity was evaluated on these samples by exposing an amphipod (Hyalella azteca) to bulk sediment

and measuring lethality and growth. In 1994 approximately 5.7 percent of the Mid-Atlantic stream length

(10,700 kilometers out of 188,700 kilometers) was found to have toxic sediments. In 1997 and 1998

sediments from about 8.7 percent (21,830 kilometers out of 250,500 kilometers) of Mid-Atlantic stream

length were found to be toxic. In the Southern Colorado Rockies, an estimated 422 kilometers (6.4

percent) of the 6,600 kilometers of target stream length had toxic sediments (Lazorchak et al., 1999).
National Sediment Quality Survey

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CHAPTER 2

METHODOLOGY

In the first report to Congress, The Incidence and Severity of Sediment Contamination in Surface Waters

of the United States: National Sediment Quality Survey (USEPA, 1997), EPA noted that it faced two

primary challenges in achieving the short-term goals of the National Sediment Quality Survey and

fulfilling the mandate of WRDA 1992, as described in the introduction to this report. Those two

challenges remain in this first update to the National Sediment Quality Survey. The first challenge is to

compile a database of consistent sediment quality measures suitable for all regions of the country. The

second is to identify scientifically sound methods to determine whether a particular sediment is

“contaminated” based on the definition set forth in the statute.

In many known areas of contamination, visible and relatively easy-to-recognize evidence of harmful

effects on resident biota is concurrent with elevated concentrations of contaminants in sediment. In most

cases, however, less obvious effects on biological communities and ecosystems are far more difficult to

identify and are frequently associated with varying concentrations of sediment contaminants. In other

words, bulk sediment chemistry measures are not always indicative of toxic effect levels. Similar

concentrations of a chemical can produce widely different biological effects in different sediments. This

discrepancy occurs because toxicity is influenced by the extent to which chemical contaminants bind to

other constituents in sediment. These other sediment constituents, such as organic ligands and inorganic

oxides and sulfides, are said to control the bioavailability of accumulated contaminants (Di Toro et al.,

1990). Toxicant binding, or sorption, to sediment particles suspends the toxic mode of action in biological

systems (Swartz et al., 1995). Because the binding capacity of sediment varies, the degree of toxicity

exhibited also varies for the same total quantity of toxicant.

The five general categories of sediment quality measurements are sediment chemistry, sediment toxicity,

community structure, tissue chemistry, and pathology (Power and Chapman, 1992). Each category has

strengths and limitations for a national-scale sediment quality assessment. To be efficient in collecting

usable data of similar types, EPA sought data that were available in electronic format, represented broad

geographic coverage, and represented specific sampling locations identified by latitude and longitude

coordinates.

As described previously, sediment chemistry measures alone might not accurately reflect risk to the

environment. However, EPA has developed assessment methods that combine contaminant

concentrations with measures of the primary binding phase to address bioavailability for certain chemical

classes, under assumed conditions of thermodynamic equilibrium (USEPA, 2000c). Other methods,

which rely on statistical correlations of contaminant concentrations with incidence of adverse biological

effects, also exist (Barrick et al., 1988; FDEP, 1994; Field et al., 1999; 2002; Ingersoll et al., 2001; Long

et al., 1995; MacDonald et al., 1996). In addition, fish tissue levels can be predicted by using sediment

contaminant concentrations along with independent field measures of chemical partitioning behavior and

other known or assigned fish tissue and sediment characteristics. EPA can evaluate risk to consumers

from predicted and field-measured tissue chemistry data using established dose-response relationships

and standard consumption patterns (USEPA and USACE, 1998). Evaluations based on tissue chemistry

circumvent the bioavailability issue while also accounting for other mitigating factors such as

metabolism. The primary difficulty in using field-measured tissue chemistry is relating chemical residue

levels to a specific sediment, especially for those fish species which typically forage across great

distances.
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1

A screening-level assessment typically identifies many potential problems that prove not to be

significant upon further analysis (i.e., more conservative).

2-2

Sediment toxicity, community structure, and pathology measures are less widely available than sediment

chemistry and fish tissue data in the broad-scale electronic format EPA sought for the NSI database.

Traditionally sediment toxicity data have been expressed as percent survival in comparison to a control

for indicator organisms exposed to the field-sampled sediment in laboratory bioassays (ASTM, 2002a,

2002b, 2002c; USEPA, 1994a, 1994b, 2000d). More recently, as indicated in the data collected for this

report, sublethal measurements (e.g., reduction in survival, growth, and reproduction) are being used

(Ingersoll et al., 2001). These sublethal endpoints are more prevalent in this update to the first National

Sediment Quality Survey report. Although these measures account for bioavailability and the antagonistic

and synergistic effects of pollutant mixtures, they do not identify specific contaminants responsible for

observed toxicity. Indicator organisms also might not represent the most sensitive species. Community

structure measures, such as fish abundance and benthic diversity, and pathology measures are potentially

indicative of long-term adverse effects, yet there are a multitude of mitigating physical, hydrologic, and

biological factors that might not relate in any way to chemical contamination.

Studies have been conducted evaluating the ecological relevancy between response endpoints (i.e.,

reduction in growth of Hyalella azteca) and the ecological resources to be protected (i.e., the indigenous

benthic community). Burton et al. (1996) compared results from laboratory sediment toxicity tests to

colonization of artificial substrates exposed in situ to contaminated sediments. Survival and growth of

Hyalella azteca and Chironomus tentans in laboratory exposures negatively correlated to percent

chironomids and percent tolerant taxa colonizing artificial substrates in the field. Schlekat et al. (1994;

Canfield et al., 1994, 1996) also reported general good agreement between sediment toxicity tests with

Hyalella azteca and benthic community responses.

An important goal of this report is to evaluate data collected throughout the United States in an attempt to

describe the ecological integrity of sediments in the Nation’s waterways. Ideally, the assessment

methodology used to accomplish this task would be based on matched data sets of all five types of

sediment quality measures described above to take advantage of the strengths of each measurement type

and to minimize their collective weaknesses in a weight-of-evidence approach. Unfortunately, such a

database does not exist on a national scale, nor is it typically available on a smaller scale. The statutory

definition of contaminated sediments in WRDA 1992 enables EPA to identify locations where sediment

chemistry measures exceed “appropriate geochemical, toxicological, or sediment quality criteria or

measures.” By the same statutory definition, based on screening values (e.g., EPA risk levels for fish

tissue consumption) or availability of control samples for purposes of comparison, EPA can also use

tissue chemistry and sediment toxicity measures to identify aquatic sediments that “otherwise pose a

threat to human health or the environment.” Without appropriate comparable reference conditions, EPA

believes that it cannot accurately evaluate community structure or pathology measures to identify

contaminated sediments based purely on the statutory definition.

For the first report to Congress, the following measurement parameters and techniques were used alone or

in combination to perform a screening-level assessment of the probability of adverse effects:1

Aquatic Life

• Comparison of sediment chemistry measurements to sediment chemistry screening values.

– Draft sediment quality criteria (SQCs)

– Sediment quality advisory levels (SQALs)
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– Effects range-median (ERM) and effects range-low (ERL) values 

– Probable effects levels (PELs) and threshold effects levels (TELs)

– Apparent effects thresholds (AETs)

• Comparison of the molar concentration of acid-volatile sulfides ([AVS]) in sediment to the molar

concentration of simultaneously extracted metals ([SEM]) in sediment. (Under equilibrium

conditions, sediment with [AVS] greater than [SEM] will not demonstrate toxicity from metals.)

• Lethality based on sediment toxicity data

Human Health

• Comparison of theoretical bioaccumulation potential (TBP) values derived from sediment

chemistry to:

– EPA cancer and noncancer risk levels or

– Food and Drug Administration (FDA) tolerance, action, or guidance values in the absence of,

or if more stringent than, EPA levels.

• Comparison of fish tissue contaminant levels to

– EPA cancer and noncancer risk levels or

– FDA tolerance, action, or guidance values in the absence of, or if more stringent than, EPA

levels.

For the first report to Congress, EPA evaluated more than 21,000 sampling stations nationwide as part of

the NSI data evaluation. Of the sampling stations evaluated, 5,521 stations (26 percent) were classified as

Tier 1; 10,401 (49 percent) were classified as Tier 2; and 5,174 (25 percent) were classified as Tier 3.

For the current analysis in this update, EPA evaluated sediment chemistry, tissue chemistry, and sediment

toxicity data, taken at the same sampling station, individually and in combination using a variety of

assessment methods. Because of the limitations of the available sediment quality measures and

assessment methods, EPA characterizes this identification of contaminated sediment locations as a

screening-level analysis. A screening-level analysis typically identifies many potential problems that

prove not to be significant upon further analysis. Thus, classification of sampling stations in this analysis

is not meant to be definitive, but rather is intended to indicate potential problems arising from persistent

metal and organic chemical contaminants.

The first report to Congress used all data available from 1980 through 1993 for developing a baseline

assessment. Because of the regular reporting requirements associated with this report, EPA wished to

“window in” on a regular time frame for including sediment chemistry, tissue residue, and sediment

toxicity data. The principal advantage of screening out older data (data collected before January 1, 1990)

is to prevent the results presented in this report from being unduly influenced by historical data when

more recent data are available. EPA recognizes, however, that this “time windowing” will result in

locations that have no evaluation provided in this document even though data are available in the NSI

database. For the current analysis, EPA elected to evaluate data collected from 1990 through 1999 and to

evaluate each chemical or biological measurement taken at a given sampling station individually. The

methodology used for the current analysis has been modified to take advantage of scientific advances

since the release of the first National Sediment Quality Survey. Similar to the previous analysis, sampling

data obtained at a sampling station during the past 10 years for an individual chemical might result in the

sampling station’s being associated with adverse effects on aquatic life or human health. The final section
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in Chapter 3 presents a comparison based on applying the methodology presented in this chapter to the

data used for the first National Sediment Quality Survey.

EPA recognizes that sediment is dynamic and that great temporal and spatial variability in sediment

quality exists. This variability can be a function of sampling (e.g., a contaminated area might be sampled

one year, but not the next) or a function of natural events (e.g., floods can move contaminated sediment

from one area to another, bury it, or deposit it on the floodplain). Movement of sediment is highly

temporal and depends on the physical and biological processes at work in the watershed. Some deposits

redistribute, whereas others remain static unless disturbed by extreme events.

In this report EPA associates sampling stations with their “probability of adverse effects on aquatic life or

human health.” Each sampling station falls into one of three categories (tiers): associated adverse effects

on aquatic life or human health are probable (Tier 1); associated adverse effects on aquatic life or human

health are possible (Tier 2); or no indication of associated adverse effects (Tier 3). A Tier 3 sampling

station classification does not necessarily imply a zero or minimal probability of adverse effects; it

implies only that available data (which might be substantial or limited) do not indicate an increased

probability of adverse effects. Recognizing the imprecise nature of the numerical assessment parameters,

Tier 1 sampling stations are distinguished from Tier 2 sampling stations based on the magnitude of a

sediment chemistry measure or the degree of corroboration among the different types of sediment quality

measures.

The remainder of this chapter presents a description of the NSI data, an explanation of the data evaluation

approach, and the strengths and limitations of the data evaluation used for this National Sediment Quality

Survey.

Description of NSI Data

The NSI database includes data from numerous data storage systems and monitoring programs. These

systems and programs are listed below along with the percentage of stations that make up the NSI

database. 

• Selected data sets from EPA’s Storage and Retrieval System (STORET) (35 percent of sampling

stations)

– U.S. Army Corps of Engineers (USACE)

– EPA

– States

• NOAA’s Query Manager Data System (18.5 percent of sampling stations)

– Including NOAA’s National Status and Trends Program

• State of Washington Department of Ecology’s Sediment Quality Information System (SEDQUAL)

(16.5 percent of sampling stations)

• Selected data sets from the U.S. Geological Survey’s (USGS’s) WATSTORE (13.5 percent of

sampling stations)

• EPA’s Environmental Monitoring and Assessment Program (EMAP) (6.5 percent of sampling

stations)

• Data compiled for the previous report to Congress (4.8 percent of sampling stations)

• Chesapeake Bay Program (2.4 percent of sampling stations)
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• Upper Mississippi River System data compilation prepared by USGS (1.1 percent of sampling

stations)

• Other sampling programs (1.7 percent of sampling stations)

– Indiana Department of Environmental Management Sediment Sampling Program

– Oklahoma Reservoir Fish Tissue Monitoring Program, 1990 through 1998

– Houston Ship Channel Toxicity Study

Although EPA elected to evaluate data collected since 1990 (i.e., 1990 through 1999), data from before

1990 are maintained in the NSI database. At a minimum, EPA required that electronically available data

include monitoring program, sampling date, latitude and longitude coordinates, and measured units for

inclusion in the data evaluation. Additional information about available data fields is presented in

Appendix A of this report.

The types of data contained in the NSI database include the following:

• Sediment chemistry: Measurement of the chemical composition of sediment-associated

contaminants.

• Tissue residue: Measurement of chemical contaminants in the tissues of organisms.

• Toxicity: Measurement of the lethal or sublethal effects of contaminants in environmental media on

various test organisms.

The NSI database represents a compilation of environmental monitoring data from a variety of sources.

Most of the component databases are maintained under known and documented quality assurance and

quality control procedures. EPA’s STORET database, however, is intended to be a broad-based repository

of data. Consequently, the quality of the data in STORET, in terms of both database entry and analytical

instrument error, is unknown and probably varies a great deal depending on the quality assurance

management associated with specific data submissions.

Inherent in the diversity of data sources are contrasting monitoring objectives and scope. Component

sources contain data derived from different spatial sampling plans, sampling methods, and analytical

methods. For example, most data from EPA’s EMAP program represent sampling stations that lie on a

standardized grid over a given geographic area, whereas data in EPA’s STORET most likely represent

state monitoring data sampled from locations near known discharges or thought to have elevated

contaminant levels. In contrast, many of NOAA’s National Status and Trends Program data represent

sampling stations purposely selected because they are removed from known discharges.

From an assessment point of view, STORET data might be useful for developing a list of contaminated

sediment locations but might overstate the general extent of contaminated sediment in the Nation by

focusing largely on areas most likely to be problematic. On the other hand, analysis of EMAP data might

result in a more balanced assessment in terms of the mix of contaminated sampling stations and

uncontaminated sampling stations. Approximately one-third of the sampling stations in the NSI database

are from the STORET database. Reliance on these data is consistent with the stated objective of this

survey—to identify those sediments which are contaminated. Realizing that uncontaminated areas are

most likely substantially underrepresented and that the data in the NSI database do not provide a complete

national coverage, EPA does not believe it is appropriate to make inferences regarding the overall

condition of the Nation’s sediment or characterizing the “percent contamination” using the data in the

NSI database.

NSI data do not evenly represent all geographic regions in the United States, as mentioned above; nor do

the data represent a consistent set of monitored chemicals. For example, several of the databases are
National Sediment Quality Survey

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targeted toward marine environments or other geographically focused areas. Table 2-1 presents the

number of stations evaluated per state (including District of Columbia and Puerto Rico). More than twothirds of all stations evaluated in the NSI database are in Washington, Virginia, California, Illinois,

Florida, Wisconsin, New York, Texas, Oregon, and South Carolina. Each of these states has more than

500 monitoring stations. Other states of similar or larger size (e.g., Georgia, Pennsylvania) have far fewer

sampling stations with data for evaluation. Figures 2-1, 2-2, and 2-3 depict the location of monitoring

stations with data collected from 1990 through 1999 for sediment chemistry, tissue residue, and toxicity

data, respectively. Individual stations may vary considerably in terms of the number of chemicals

monitored. Some stations have data that represent a large number of organic and inorganic contaminants,

whereas others have measured values for only a few chemicals. Thus, the inventory should not be

construed as comprehensive even for locations with sampling data. The reliance on readily available

electronic data has undoubtedly led to exclusions of a vast amount of information available from sources

such as local and state governments and published reports. Other limitations, including data quality

issues, are included in the Conclusions and Discussion chapter of this report.

Table 2-1. Number of Stations Evaluated in the NSI by State.

Region 1 Connecticut

Maine

Massachusetts

New Hampshire

Rhode Island

Vermont

121

0

127

4

18

5

Region 6 Arkansas

Louisiana

New Mexico

Oklahoma

Texas

34

396

167

292

600

Region 2 New Jersey

New York

Puerto Rico

492

753

10

Region 7 Iowa

Kansas

Missouri

Nebraska

113

119

194

157

Region 3 Delaware

District of Columbia

Maryland

Pennsylvania

Virginia

West Virginia

234

6

290

216

1,577

105

Region 8 Colorado

Montana

North Dakota

South Dakota

Utah

Wyoming

133

11

33

32

56

29

Region 4 Alabama

Florida

Georgia

Kentucky

Mississippi

North Carolina

South Carolina

Tennessee

173

1,157

263

63

187

291

576

164

Region 9 Arizona

California

Hawaii

Nevada

123

1,535

18

76

Region 5 Illinois

Indiana

Michigan

Minnesota

Ohio

Wisconsin

1,370

233

30

339

441

772

Region 10 Alaska

Idaho

Oregon

Washington

290

38

599

4,336
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2-8
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NSI Data Evaluation Approach

The methodology developed for this report for classifying sampling stations according to the probability

of adverse effects on aquatic life and/or human health from sediment contamination relies on

measurements of sediment chemistry (surficial), sediment toxicity, and contaminant residue in tissue. The

approach used to evaluate the NSI data focuses on the protection of benthic organisms from exposure to

contaminated sediments and the protection of humans from the consumption of fish that bioaccumulate

contaminants from sediment. Table 2-2 presents the classification scheme used in the evaluation of the

NSI data. Each component, or evaluation benchmark, of the classification scheme is numbered on Table

2-2. Each evaluation benchmark is discussed under a section heading cross-referenced to these numbers.

EPA analyzed the NSI data by evaluating each benchmark in Table 2-2 measurement by measurement

and sampling station by sampling station. Each sampling station was associated with a “probability of

adverse effects” by combining benchmarks as shown in Table 2-2. Because each individual measurement

was considered independently except for divalent metals, PCBs, and DDT, whose concentrations were

summed, and PAHs, whose effect was analyzed as a mixture, a single observation of elevated

concentration could place a sampling station in Tier 1 (associated with probable adverse effects). Any

sampling station not meeting the requirements to be classified as Tier 1 or Tier 2 was classified as Tier 3.

Sampling stations classified as Tier 3 include those for which substantial data were available without

evidence of adverse effects, as well as sampling stations for which limited data were available to

determine the potential for adverse effects.

Applying individual evaluation benchmarks to various measurements independently could lead to

different site classifications. If one evaluation benchmark indicated Tier 1 but another evaluation

benchmark indicated Tier 2 or Tier 3, a Tier 1 classification was assigned to the sampling station. For

example, if a sampling station was categorized as Tier 2 based on all sediment chemistry data but was

categorized as Tier 1 based on toxicity data, the station was placed in Tier 1. This principle also applies to

evaluating multiple contaminants within the same evaluation benchmark. For example, if the evaluation

of sediment chemistry data placed a sampling station in Tier 1 for PCBs and in Tier 2 for metals, the

station was placed in Tier 1.

Recognizing the imprecise nature of some assessment benchmarks used in this report, Tier 1 sampling

stations are distinguished from Tier 2 sampling stations based on the magnitude of a contaminant

concentration in sediment or based on the degree of corroboration among the different types of sediment

quality measures. This approach of integrating several assessment methods has been described as the

most desirable approach for assessing the effects of contaminants associated with sediments (Ingersoll et

al., 1996; 1997; 2001; Long and Morgan, 1990; MacDonald et al., 1996; USEPA, 2000d). In response to

uncertainty in both biological and chemical measures of sediment contamination, environmental

managers must balance Type I errors (false positives: sediment classified as posing a threat when in fact it

does not) with Type II errors (false negatives: sediment that poses a threat but was not classified as such).

In screening analyses, the environmentally protective approach is to minimize Type II errors, which

would leave toxic sediment unidentified. To achieve a balance and to direct attention to areas most likely

to be associated with adverse effects, Tier 1 sampling stations are intended to have a higher probability of

posing an adverse effect (e.g., sediment posing a threat) and a balance between Type I and Type II errors.

On the other hand, to retain a sufficient degree of environmental conservatism in screening, Tier 2

sampling stations are intended to have a very low number of false negatives in exchange for a large

number of false positives.
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Table 2-2. NSI Data Evaluation Approach.

 Sampling Station

Classification

Data Used to Determine Classifications

Sediment Chemistry Tissue Residue Toxicity

Tier 1:

Associated

Adverse Effects on

Aquatic Life or

Human Health Are

Probable

Sediment chemistry value exceeds a

draft equilibrium partitioning sediment

guideline (ESG) derived from a final

or secondary acute value (FAV or

SAV)a 1

OR

Tissue levels of chemicals with a

log Kow$ 5.5 in samplesd that

exceed EPA’s human health

cancer risk of 10-5, a noncancer

HQ of 1, or FDA’s

tolerance/action/guidance 

levels 12

OR

Toxicity demonstrated by one

solid-phase sediment test resulting

in (1) < 75% control-adjusted

survival, (2) freshwater

invertebrate (Hyalella azteca)

sublethal toxicity < 90% controladjusted length, or (3) freshwater

invertebrate (Hyalella azteca,

Chironomus tentans, and

Chironomus riparius) sublethal

toxicity < 70% control-adjusted

weight 15

OR

[SEM]![AVS] > 5 for the sum of

molar concentrations of Cd, Cu, Ni,

Pb, Zn, and 1⁄2 x Agb 2

OR

Any sample with a predicted

proportion toxic $0.5 using a logistic

regression model 3

OR

Sum PAH ESG toxicity unit (draft)

derived from FAV > 1a,c 4

OR

Any sample meeting the

benchmark described under Tier 2

for toxicity using at least two

different species 16

OR

Sediment chemistry TBP exceeds

EPA’s human health cancer risk of

10-4 or a noncancer hazard quotient

(HQ) of 10a 5

OR

For chemicals with log Kow < 5.5,

sediment chemistry TBP exceeds

EPA’s human health cancer risk of 

10-5, a noncancer HQ of 1, or FDA’s

tolerance/action/guidance levelsa 6

AND

Tissue levels of chemicals with a

log Kow < 5.5 in samplesd

 that

exceed EPA’s human health

cancer risk of 10-5, a noncancer

HQ of 1, or FDA’s tolerance/

action/guidance levels 13

Tier 2:

Associated

Adverse Effects on

Aquatic Life or

Human Health Are

Possible

Sediment chemistry value exceeds a

draft ESG derived from a final or

secondary chronic value (FCV or

SCV)a 7

OR

Tissue levels of chemicals with a

log Kow < 5.5 in samplesd

 that

exceed EPA’s human health

cancer risk of 10-5, a noncancer

HQ of 1, or FDA’s

tolerance/action/guidance 

levels 14

OR Toxicity demonstrated by one

solid-phase sediment test resulting

in (1) < 90% control-adjusted

survival (but $ 75% controladjusted survival), 

(2) freshwater invertebrate

(Hyalella azteca) sublethal

toxicity < 95% control-adjusted

length (but $ 90% controladjusted length), or (3) freshwater

invertebrate (Hyalella azteca,

Chironomus tentans, and

Chironomus riparius) sublethal

toxicity < 90% control-adjusted

weight (but $70% controladjusted weight) 17

OR

[SEM]![AVS] = 0–5 for the sum of

molar concentrations of Cd, Cu, Ni,

Pb, Zn, and 1⁄2 x Agb 8

OR

Any sample with a predicted

proportion toxic$ 0.25 but < 0.5 using

a logistic regression model 9

OR

Sum PAH ESG toxicity unit (draft)

derived from FCV > 1a,c 10

OR

Sediment chemistry TBP exceeds

EPA’s human health cancer risk of 

10-5, a noncancer HQ of 1, or FDA’s

tolerance/action/guidance levelsa 11

Tier 3:

No Indication of

Associated

Adverse Effects

Any sampling station not categorized as Tier 1 or Tier 2. Available data (which may be very limited or quite extensive) do

not indicate a likelihood of adverse effects on aquatic life or human health.

a If total organic carbon (TOC) is not reported, a default value of 1% was assumed. For ESG-based methods if the reported TOC is less than 0.2%, a default TOC 

value of 0.2% was used. b Metals: Cd = cadmium, Cu = copper, Ni = nickel, Pb = lead, Zn = zinc, Ag = silver. c Acenaphthene, acenaphthylene, anthracene, benzo(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(k)fluoranthene, chrysene, fluoranthene, fluorene, naphthalene, phenanthrene, and pyrene used to compute ESG toxicity unit. d Only those species considered benthic (demersal), nonmigratory (resident), and edible by human populations are included in human health assessments.
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For this NSI data evaluation EPA opted to analyze data collected since 1990 with valid latitude and

longitude coordinates. The numbered evaluation benchmarks used in the NSI data evaluation are briefly

described below. A detailed description of the evaluation benchmarks is presented in Appendix B.

As noted in the first footnote to Table 2-2, if the total organic carbon (TOC) was not reported, a default of

1 percent was assumed. This assumption was based on a literature review performed during the

preparation of the first report to Congress. TOC values can range from 0.1 percent in sandy sediments to

1 to 4 percent in silty harbor sediments and 10 to 20 percent in navigation channel sediments (Clarke and

McFarland, 1991). Long et al. (1995) reported an overall mean TOC concentration of 1.2 percent from

data compiled from 350 publications for their biological effects database for marine and estuarine

sediments. Ingersoll et al. (1996) reported a mean TOC concentration of 2.7 percent for inland freshwater

samples. Based on this review of TOC data, EPA selected a default TOC value of 1 percent for this

evaluation. Consistent with the screening-level application, this value should not lead to an underestimate

of the bioavailability of associated contaminants in most cases.

Sediment Chemistry Data

The sediment chemistry screening values used as the basis for comparison in this report are not regulatory

criteria, site-specific clean up standards, or remediation goals. Sediment chemistry screening values are

reference values above which a sediment ecotoxicological assessment might indicate a potential threat to

aquatic life. The sediment chemistry screening values used to evaluate the NSI data for potential adverse

effects of sediment contamination on aquatic life include values based on theoretical calculations and

empirically/statistically derived values. The theoretically based values rely on the physical/chemical

properties of sediment and chemicals to predict the level of contamination that would not cause an

adverse effect on aquatic life. The empirically/statistically derived screening values are based on

estimating the probability that a sediment toxicity test would indicate significant toxicity using multiple

chemical measures of 37 target chemicals.

The theoretically based screening values used in the evaluation of NSI data include draft ESGs developed

by EPA. These include: dieldrin, endrin, 32 nonionic organics, mixtures of PAHs, and metal mixtures.

The use of each of these screening values in the evaluation of the NSI data is described below. Another

theoretically based evaluation benchmark, the theoretical bioaccumulation potential (the TBP, which was

used for human health assessments), is also described below.

Sediment Chemistry Values Exceed EPA Draft Equilibrium Partitioning Sediment Guideline (ESG) [1, 7]

EPA developed draft ESGs using the equilibrium partitioning (EqP) approach (described in detail in

Appendix B) for linking bioavailability to toxicity. This approach accounts for the varying biological

availability of chemicals in different sediments and permits the incorporation of the relevant biological

effects concentration. The approach enables the derivation of a guideline that is causally linked to the

specific chemical, is applicable across sediments, and is protective of benthic organisms. The EqP theory

asserts that a nonionic chemical in sediment partitions between sediment organic carbon, interstitial (pore)

water, and benthic organisms. At equilibrium, if the concentration in any one phase is known, the

concentration in the others can be predicted. EPA has developed different draft ESGs based on final or

secondary acute or chronic values to reflect the differing degrees of data availability and uncertainty.

These draft ESGs are expressed as a concentration of a chemical in sediment and are derived to protect

aquatic benthic organisms from direct toxicity due to that chemical (or chemicals in the case of metals

mixtures and PAH mixtures). The draft ESG for nonionic organics applies only to sediments that have at

least 0.2 percent organic carbon. For samples with TOC less than 0.2 percent, a default TOC value of 0.2

percent was used.
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Comparison of AVS to SEM Molar Concentrations [2, 8]

The use of the total concentration of a trace metal in sediment as a measure of its toxicity and its ability to

bioaccumulate is problematic because different sediments exhibit different degrees of bioavailability for

the same total quantity of metal (Di Toro et al., 1990; Luoma, 1983). These differences have been

reconciled by relating organism toxic response (mortality) to the contaminant concentration in the

sediment interstitial water (Adams et al., 1985; Di Toro et al., 1990). AVS is one of the major chemical

components that control the activities and availability of metals in interstitial waters of anoxic (lacking

oxygen) sediments (Meyer et al., 1994).

A large reservoir of sulfide exists as iron sulfide in anoxic sediment. Sulfide reacts with several divalent

transition metal cations (cadmium, copper, mercury, nickel, lead, and zinc) and predominantly

monovalent silver to form highly insoluble compounds that are not bioavailable (Allen et al., 1993,

Ankley et al., 1991, Berry et al., 1999, Carlson et al. 1991). It follows in theory, and with verification

(Di Toro et al., 1990), that divalent transition metals do not begin to cause toxicity in anoxic sediment

until the reservoir of sulfide is used up (i.e., the molar concentration of metals exceeds the molar

concentration of sulfide), typically at relatively high dry-weight metal concentrations. This observation

has led to a laboratory measurement technique for calculating the difference between simultaneously

extracted metal (SEM) concentration and acid-volatile sulfide (AVS) concentration from field samples to

determine potential toxicity (Ankley et al., 1991, Carlson et al. 1991).

To evaluate the potential effects of metals on benthic species, the molar concentration of AVS ([AVS])

was compared to the sum of SEM molar concentrations ([SEM]) for six metals: cadmium, copper, nickel,

lead, zinc, and silver. Molar concentrations of cadmium, copper, nickel, lead, and zinc are comparable

with AVS on a one-to-one basis. Because silver exists predominantly as a monovalent metal, half the

molar concentration of silver is compared with the molar AVS concentration. Mercury was excluded from

AVS comparison because other important factors play a major role in determining the bioaccumulation

potential of mercury in sediment. Specifically, under certain conditions mercury binds to an organic

methyl group and is readily taken up by living organisms.

Sediment with measured [SEM] in excess of [AVS] does not necessarily exhibit toxicity. This is because

other binding phases can tie up metals. However, research indicates that sediment with [AVS] in excess

of [SEM] will not be toxic from metals, and the greater the [SEM]![AVS] difference, the greater the

likelihood of toxicity from metals. Analysis of toxicity data for freshwater and saltwater sediment

amphipods (crustaceans) from EPA's Environmental Research Laboratory in Narragansett, Rhode Island,

revealed that 80 to 90 percent of the sediments were toxic at [SEM]![AVS] > 5 (Hansen, 1995; see also

Hansen et al., 1996a; 1996b). Thus, EPA selected [SEM]![AVS] = 5 as the demarcation line between

Tier 1 and Tier 2. For the purpose of this evaluation, where [SEM]![AVS] was greater than 5, the

sampling station was classified as Tier 1. If [SEM]![AVS] was between zero and 5, the sampling station

was classified as Tier 2. If [SEM]![AVS] was less than zero, or if AVS or the six AVS metals were not

measured at the sampling station, the sampling station was classified as Tier 3 unless otherwise classified

by another benchmark.

There are several important factors to consider in interpreting the [SEM]![AVS] difference. First, all

toxic SEMs present in amounts that contribute significantly to the [SEM] sum should be measured.

Because mercury presents special problems, however, it is not included in the current SEM analysis.

Second, if the AVS content of sediment is low, as in fully oxidized sediments, the metal-binding capacity

of the sediment decreases and the method will not work (Adams et al., 1992; Zhuang et al., 1994). Most

benthic macroorganisms, including those used in toxicity tests, survive in sediments that have a thin

oxidized surface layer and then an anoxic layer. The anoxic layer can have significant AVS

concentrations that would reduce the metal activity to which these organisms are exposed (Di Toro et al.,

1992). Third, AVS varies spatially in sediment—vertically with depth and horizontally where patches of
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an appropriate carbon source occur under low-oxygen conditions for the sulfate-reducing bacteria.

Finally, AVS can vary when sediments are oxygenated during physical disturbance and seasonally as

changes in the productivity of the aquatic ecosystem alter the oxidation state of sediment and oxidize

metal sulfides; therefore, the toxicity of the metals present in the sediment also changes over time

(Howard and Evans, 1993).

Selection of an [SEM]![AVS] difference sufficiently high to place a sediment in the Tier 1 classification

requires careful consideration because the relationship between organism response and the [SEM]![AVS]

difference of sediment depends on the amount and kinds of other binding phases present. Using

freshwater and saltwater sediment amphipod toxicity data, researchers at EPA’s Environmental Research

Laboratory in Narragansett, Rhode Island, plotted [SEM]![AVS] versus the percentage of sediments with

a higher [SEM]![AVS] value that were toxic. For this analysis, the researchers defined toxicity as greater

than 24 percent mortality. Analysis of these data reveals that between 80 percent and 90 percent of the

sediments were toxic at [SEM]![AVS] = 5. The running average mortality at this level was between 44

percent and 62 percent (Hansen, 1995). EPA’s Office of Science and Technology selected [SEM]![AVS]

= 5 as the demarcation line between the higher (Tier 1) and intermediate (Tier 2) probability categories.

Predicted Proportion Toxic from Sediment Chemistry [3, 9]

The empirically based or correlative screening values used in the previous NSI data evaluation rely on

paired field and laboratory data to relate the incidence of observed biological effects to the dry-weight

sediment contamination of a specific chemical. The empirically based or correlative screening values

include the effects range-median (ERM)/effects range-low (ERL) values, probable effects level

(PEL)/threshold effects level (TEL), and apparent effects thresholds (AET) (Barrick et al., 1988; Long et

al., 1995; MacDonald et al., 1996). Field et al. (1999, 2002) developed an alternative method for the

evaluation of sediment quality by using a logistic regression model in place of the correlative screening

values used in the first National Sediment Quality Survey. The logistic regression model approach is

similar to other empirical approaches for deriving screening values because it relies on matching fieldcollected sediment chemistry and biological effects (e.g., sediment toxicity or benthic invertebrate

community structure effects) data. In contrast to other approaches to developing screening values,

however, the logistic regression model approach does not develop threshold values. Instead, it develops

models that enable users to select the probability of observing sediment toxicity that corresponds to their

specific objectives or to estimate the probability of observing effects at a particular chemical

concentration (Field et al., 1999). This model (described in detail in Appendix B) is used to predict the

probability of observing specific toxic effects—for selected toxicity test endpoints and a wide range of

concentrations—for individual contaminants. Using the sediment chemistry and toxicity data, individual

logistic models were developed for each contaminant, and the slope and intercept values were calculated

using the maximum likelihood approach.

A total of 37 chemicals are included in the logistic regression model. For the NSI data evaluation, the

probability of toxic effects was computed for the various contaminants from individual logistic regression

equations. The predicted proportion toxic was then estimated from the maximum probability of toxic

effects using a regression equation. When the maximum predicted proportion toxic for any sample was $

0.5, the sampling station was assigned to Tier 1. When the maximum predicted proportion toxic was $

0.25 but < 0.5, the sampling station was classified as Tier 2. Other sampling stations with available data

for chemicals included in the logistic regression model were classified as Tier 3 unless otherwise

classified by another benchmark.

PAH-Based ESG Toxicity Unit Exceed Screening Benchmark [4, 10]

The EESGTUPAH model estimates the probability of toxic effects in PAH-contaminated sediments by

using equilibrium partitioning, the quantitative structure-activity relationship (QSAR) technique, toxic
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unit, additivity, and concentration-response models (Swartz, 1999; Swartz et al.,1995). The model

predicts the probability of acute sediment toxicity to marine and estuarine amphipods caused by a

combination of PAHs. EPA’s draft ESG recommends an approach for summing the toxicological

contributions of mixtures of 34 PAHs in sediments to determine whether their concentrations in any

specific sediment are acceptable for the protection of benthic organisms from PAH toxicity. Because

PAHs occur in sediments as mixtures and their toxicities in water, tissues, and sediments are additive or

nearly additive (Di Toro and McGrath, 2000), considering their toxicities on an individual basis would

result in guidelines that are underprotective. For this reason, EPA recommends the use of combined

toxicological contributions of the PAH mixture in evaluating sediments.

Because many monitoring and assessment efforts measure a smaller group of PAHs, such as 13 or 23

PAHs, EPA has recommended adjustment factors to relate these smaller subsets to the expected

concentration of the 34 PAHs. The total Equilibrium Partitioning Sediment Guideline Toxic Unit

(EESGTU)—based on the final chronic or acute value—is used to classify sampling stations as Tier 1 or

Tier 2 (described in detail in Appendix B). For use in determining the uncertainty in predicting

EESGTUFCV,TOT from data sets consisting of 13 or 23 PAHs, EPA combined two data sources that

measured the 34 PAHs and treated the data set as a single data source. In doing this data combination, a

data set containing both alkylated and parent PAHs with their correlative relationships was generated.

Based on the relative distributions of the EESGTUFCV,TOT to theEESGTUFCV for the 13 PAHs, EPA

recommended various multiplication factors to achieve various degree-of-confidence levels. Table B-3 in

Appendix B presents the relative distribution of the multiplication factors. The NSI data evaluation

targeted 13 PAHs and used the EPA-recommended multiplication factor of 2.75 to obtain an accurate

estimation of the EESGTU. However, for this data evaluation not all 13 PAHs were required to be

measured at any one station for that station to be considered for tier classification. Based on the

sensitivity analysis done, it was observed that this variation from the EPA-recommended practice did not

dramatically change the total number of station tier classifications. This analysis applies only to sediments

that have at least 0.2 percent organic carbon. For samples with TOC less than 0.2 percent, a default TOC

value of 0.2 percent was used.

Sediment Chemistry TBPs Exceed Screening Benchmark [5, 6, 11]

This evaluation benchmark addresses the risk to human consumers of organisms exposed to sediment

contaminants. The TBP is an estimate of the equilibrium concentration (concentration that does not

change with time) of a contaminant in tissues if the sediment in question were the only source of

contamination to the organism. At present, the TBP calculation can be performed only for nonpolar

organic chemicals. The TBP is estimated from the concentration of contaminant in the sediment, the

organic carbon content of the sediment, the lipid content of the organism, and the relative affinity of the

chemical for sediment organic carbon and animal lipid content. This relative affinity is measured in the

field and is called a biota-sediment accumulation factor (BSAF). In practice, field-measured BSAFs can

vary by an order of magnitude or greater for individual compounds depending on location and time of

measurement. For this evaluation, EPA selected BSAFs that represent the central tendency, suggesting an

approximate 50 percent chance that an associated tissue residue level would exceed a screening risk

value.

In the evaluation of NSI data, if a calculated sediment chemistry TBP value exceeded a screening value

derived using the EPA risk assessment methodology (i.e., EPA’s human health cancer risk of 10-4 or a

noncancer hazard quotient [HQ] of 10, evaluation benchmark 5), the station was classified as Tier 1.

Individual chemical risk levels were considered separately; that is, risks from multiple contaminants were

not added.

For chemicals with an octanol-water partition coefficient (log Kow) < 5.5, the following benchmark was

used: if a calculated sediment chemistry TBP value exceeded a screening value derived using EPA’s
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human health cancer risk of 10-5 or a noncancer HQ of 1 or the Food and Drug Administration’s (FDA’s)

tolerance/action/guidance level (evaluation benchmark 6, Table 2-2), and if a corresponding tissue residue

level for the same chemical in demersal, resident, and edible species at the same sampling station also

exceeded one of those screening values (evaluation benchmark 13, Table 2-2), the station was classified

as Tier 1. Individual chemical risk levels were considered separately; that is, risks from multiple

contaminants were not added. In this assessment, both sediment chemistry and tissue residue samples

must have been taken from the same sampling station. If tissue residue levels for the same chemical for a

demersal, resident, and edible species at the same sampling station did not exceed standard EPA risk

levels or FDA levels or there were no corresponding tissue data, the sampling station was classified as

Tier 2.

In addition, for all chemicals irrespective of their octanol-water partition coefficient, when the sediment

chemistry TBP exceeded stated EPA risks or FDA guidelines shown in Table 2-2, the sample stations

were classified as Tier 2. If neither TBP values nor fish tissue residue levels exceeded the appropriate

EPA risk levels given in Table 2-2 or the FDA guidance levels, or if no chemicals with TBP values, EPA

risk levels, or FDA levels were measured, the sampling station was classified as Tier 3 unless otherwise

classified by another benchmark. A detailed description of the methods used to develop TBP values and

to determine the EPA risk levels used in this comparison is presented in Appendix B.

Tissue Residue Data [12, 13, 14]

Tissue residue data were used to assess potential adverse effects on humans from the consumption of fish

that become contaminated through exposure to contaminated sediment. Only those species considered

benthic, nonmigratory (resident), and edible by human populations were included in human health

assessments. A list of species included in the NSI database and their characteristics is presented in

Appendix D.

For chemicals with a log Kow $ 5.5, if the tissue residue levels in demersal, resident, and edible species

exceeded EPA risk screening values (i.e., EPA’s human health cancer risk of 10-5 or a noncancer HQ of 1

or the FDA tolerance/action/guidance level), the station was classified as Tier 1.

For chemicals with a log Kow < 5.5, both a tissue residue level exceeding an FDA tolerance/action/guidance level or stated EPA risk level and a sediment chemistry TBP value exceeding that risk/tolerance

level for the same chemical were required to classify a sampling station as Tier 1. If tissue residue levels

exceeded FDA levels or EPA risk levels but corresponding TBP values were not exceeded at the same

station (or there were no sediment chemistry data from that station), the sampling station was classified as

Tier 2. If neither fish tissue levels nor TBP values exceeded EPA risk levels or FDA levels, or if no

chemicals with TBP values, EPA risk levels, or FDA levels were measured, the sampling station was

classified as Tier 3 unless otherwise classified by another benchmark.

Toxicity Data [15, 16, 17]

Toxicity data were used to classify sediment sampling stations based on short- or long-term sediment

toxicity tests. Nonmicrobial sediment toxicity tests based on survival and on variation in length or weight

were evaluated. For all of the endpoints (i.e., survival and variations in length or weight), the test results

were “adjusted” to compare against a control test for the same species (described in more detail in

Appendix B). Toxicity test results that lacked control data were excluded. EPA has standardized testing

protocols for marine and freshwater toxicity tests (USEPA, 1994a, 1994b, 2000d, 2001a).

For the NSI data evaluation, only solid-phase bulk sediment toxicity tests, with test durations of 7 or more

days, were considered. Calculated values of the percentage of species surviving were reported by

individual databases. These percentages were based on values adjusted for a control sample. Sampling

stations with tests resulting in less than 75 percent of the control-adjusted survival in marine and
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freshwater species were classified as Tier 1. Similar to the results reported for percent survival, calculated

values of the percentage variation in length and weight were reported in various studies. These percentage

values were also reported as adjusted for a control test. Sample stations with freshwater invertebrates

(Hyalella azteca) that indicated sublethal toxicity by lengths of less than 90 percent of the controladjusted length or with freshwater invertebrates (Hyalella azteca, Chironomus tentans, and Chironomus

riparius) that indicated sublethal toxicity by weights of less than 70 percent of the control-adjusted weight

were classified as Tier 1.

Stations were classified as Tier 2 based on benchmarks similar to those established for Tier 1

classification, but with lower threshold values. Toxicity tests resulting in less than 90 (but $ 75) percent

of the control-adjusted survival for both marine and freshwater species were classified as Tier 2.

Sampling stations with freshwater invertebrates (Hyalella azteca) that indicated sublethal toxicity by

lengths of less than 95 (but $ 90) percent of the control-adjusted length or with freshwater invertebrates

(Hyalella azteca, Chironomus tentans, and Chironomus riparius) that indicated sublethal toxicity by

weights of less than 90 (but $ 70) percent of the control-adjusted weight were classified as Tier 2.

A station could be classified as Tier 2 by the benchmark stated above based on more than one test species.

When a station was classified as Tier 2 based on results from two or more species from that station, the

tier classification for that station was upgraded to Tier 1.

Evaluation Using EPA Wildlife Criteria

In addition to the evaluation parameters described above and presented in Table 2-2, EPA conducted an

assessment of NSI data based on a comparison of sediment chemistry TBP values and fish tissue values to

EPA wildlife criteria developed for the Great Lakes. Wildlife criteria based solely on fish tissue

concentrations were derived for EPA wildlife criteria for water that are presented in the Great Lakes

Water Quality Initiative Criteria Documents for the Protection of Wildlife: DDT; Mercury; 2,3,7,8-

TCDD; PCBs (USEPA, 1995). EPA has developed wildlife criteria for four contaminants: DDT, mercury,

2,3,7,8-TCDD, and PCBs. The method used to adjust these wildlife criteria for the NSI data evaluation is

explained in detail in Appendix B. This sediment evaluation was comparable to the sediment chemistry

TBP (evaluation benchmarks 6 and 11, Table 2-2), and the tissue evaluation was comparable to

benchmarks 13 and 14 from Table 2-2. This evaluation was not included with the results of evaluating the

NSI data based on the other parameters. The results of evaluating NSI data based on wildlife criteria are

presented in Chapter 3.

Strengths of the NSI Data Evaluation

For this report to Congress, EPA has compiled the most extensive database of sediment quality

information currently available in electronic format. To evaluate these data, EPA has applied sediment

assessment techniques in a multiple-lines-of-evidence approach recommended by national experts

(Ingersoll et al., 1997). The evaluation approach uses sediment chemistry, tissue residue, and toxicity test

results. The assessment tools employed in this analysis have been applied in North America, and results

have been published in peer-reviewed literature. Toxicity test data were generated using established

standard methods employed by multiple federal and state agencies. The evaluation approach addresses

potential impacts on both aquatic life and human health.

Because of the complex nature of the reactions among different chemicals in different sediment types, in

water, and in tissues, no single sediment assessment technique can be used to adequately evaluate

potential adverse effects from exposure to all contaminants. Uncertainties and limitations are associated

with all sediment quality evaluation techniques. To compensate for those limitations, EPA has used

multiple assessment techniques, singularly and in combination, to evaluate the NSI data. For example,

EPA applied draft equilibrium partitioning sediment guidelines for nonionic organics, for mixtures of
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PAHs, and for five divalent metals. The screening values used to evaluate the NSI data include both

theoretical and correlative approaches. The theoretical approaches (e.g., draft ESGs and TBPs) are based

on the best information available concerning how chemicals react in sediments and organisms and how

organisms react to those chemicals. The correlative approach (i.e., logistic model) is based on matched

sediment and biological data gathered in the field and in the laboratory, and it provides substantial

evidence of actual biological effects from sediments.

As stated above, the NSI data evaluation approach includes assessments of potential impacts on both

human health and aquatic life. Some chemicals pose a greater risk to human health than to aquatic life; for

others, the reverse is true. By evaluating both potential human health and aquatic life impacts, EPA has

ensured that the most sensitive endpoint is used to assess environmental impacts.

Because sediment chemistry data are not the only indicators of potential environmental degradation due

to sediment contamination, the NSI data evaluation approach also includes evaluations of fish tissue

residue and toxicity data. If high levels of PCBs, dioxins, or other highly hydrophobic organic chemicals

(commonly found associated with sediments) were measured in fish tissue at a given sampling station, the

station could be categorized as Tier 1 with no corroborating sediment chemistry data. For other

chemicals, high concentrations in tissues alone were not sufficient to categorize a sampling station as

Tier 1; corroborating sediment chemistry data were also required. For a sampling station to be categorized

as Tier 1 based on toxicity data alone, only solid-phase tests were analyzed.

Limitations of the NSI Data Evaluation

This methodology was designed for the purpose of a screening-level assessment of sediment quality. A

considerable amount of uncertainty is associated with the site-specific measures, assessment techniques,

exposure scenarios, and default parameter selections. Therefore, the results of evaluating particular

sampling stations based on this methodology should be followed up with more intensive assessment

efforts, when appropriate (e.g., for waterbodies with multiple Tier 1 sampling stations located in APCs).

Two types of limitations are associated with the evaluation of the NSI data: limitations associated with the

data themselves and limitations associated with the evaluation of the data.

Limitations of Data

The NSI database is a multimedia compilation of environmental monitoring data obtained from a variety

of sources, including state and federal government offices. Inherent in the diversity of data sources are

contrasting monitoring objectives and scopes, which make comparison of data from different data sets

difficult. For example, several of the databases contain only information from marine environments or

other geographically focused areas. The potential for inconsistencies in measured concentrations of

contaminants at different stations exists for samples taken from different monitoring programs. For

example, sampling different age profiles in sediments, applying different sampling and analysis methods,

and sampling for different objectives can affect the results of the NSI data evaluation. The surficial

samples analyzed in this report vary because many different sampling devices are used depending on

water depth and study objectives. For this report, samples were included when the reported lower depth

was no greater than 30 cm and the reported upper depth was less than 2 cm, not reported, or left blank. It

is important to note that it is relatively common for monitoring programs that focus on surficial sediment

samples to not report sample depth. Therefore, because unreported or blank sample depths are relatively

common, they were assumed to be surficial samples for this report. Although some monitoring programs

identified sampling and laboratory methods, this information is rarely provided with the data. In addition,

some data sets included in the NSI database were not peer-reviewed (e.g., some data sets from EPA’s

STORET). Furthermore, each monitoring program used unique sampling and analysis protocols. For
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example, PCBs are measured by nearly all of the monitoring programs but were analyzed and reported as

aroclor-specific data, congener-specific data, total PCBs, or a combination of these.

The only quality assurance/quality control (QA/QC) information required for data to be included in the

NSI database was information on the source of the data and the location of the sampling station.

Available information on several types of QA/QC procedures that can influence the quality of the data

and can be used to check the quality of data was included in the NSI database. None of this information

was required before a data set could be included in the NSI database; however, most of the component

databases are maintained under known and documented QA/QC procedures. For the 19,470 stations

evaluated in this report, approximately 97 percent contain sufficient information in the database to allow

the user to contact an agency, contact an investigator, or reference a report to obtain the available QA/QC

information. Data reporting was also inconsistent among the different data sources. Inconsistencies that

required resolution included the lack or inconsistent use of Chemical Abstract Service (CAS) numbers,

analyte names, species names, and other coding conventions, as well as the lack of detection limits and

associated data qualifiers (remark codes). The evaluation of toxicity data required the presence of control

data. Depending on the data source, control data were not regularly reported with the data and could not

be evaluated.

Some of the data analyzed for the tier classification were compiled as early as 1990 (the analyzed data

cover the period of 1990 through 1999) and might not reflect current conditions. Emissions of many

prominent contaminants have declined, and significant remediation efforts have taken place at many

locations since that time. In addition, dredging, burial, natural attenuation, and scouring might have

removed contaminants from some sampling stations. Unlike the first report to Congress, this analysis did

include a temporal assessment of trends in sediment contaminant levels using data from 1980 through

1999, but it cannot be considered comprehensive and is applicable to only the locations where data were

collected and evaluated.

Some data parameters are consistently absent throughout the NSI database. (Refer to Appendix A, Table

A-1, for information on the number of NSI database stations at which the various types of data were

compiled.) For example, only 10 percent of the stations with sediment chemistry data had associated

toxicity data. For many of the fish tissue data included in the NSI database, the species was not identified.

Also, assessment parameters other than sediment chemistry, sediment toxicity, and tissue residue, such as

benthic macroinvertebrate data, are not included in the NSI database and therefore not used in the

evaluation process.

The unavailability of matching sediment chemistry and tissue residue data also limited the NSI data

evaluation. In several instances, fish tissue was not analyzed for the same suite of chemicals for which

sediment was analyzed. Spatial and temporal limitations of the data might have directly affected the

analysis. Although some sediment chemistry and tissue residue data might have been collected in the

same or very similar sampling stations, if the station names were not identical, the data could not be

treated as if they were collected from the same location. This very likely resulted in an underestimate of

the number of Tier 1 stations identified based on potential human health effects. The underestimate

occurred because exceedances of sediment TBP and tissue levels (EPA risk levels and FDA levels) at the

same sampling station were required to categorize stations as Tier 1.

The lack of consistency among the different monitoring programs in the suite of chemicals analyzed also

represents an area of uncertainty in the NSI data evaluation. Certain databases contain primarily

information describing concentrations of metals or pesticides, whereas others contain data describing

concentrations of nearly every chemical monitored in all of the NSI data. Many monitoring programs use

a screening list of chemicals that are indicator pollutants for contaminated sediments. Thus, many of the

specific chemicals assessed in the NSI data evaluation are not measured in every sample. In addition,
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certain classes of in-place sediment contaminants might not be recognized as causing significant impacts

and thus are not routinely measured.

Information describing local background levels of sediment contaminants was usually not presented with

the data included in the NSI database and thus was not considered when the significance of elevated

contaminant concentrations in sediment was evaluated. Background conditions can be important in an

evaluation of potential adverse effects on aquatic life because ecosystems can adapt to their ambient

environmental conditions. For example, high metals concentrations in samples collected from a particular

station might occur from natural geological conditions at that location, as opposed to the effects of human

activities.

Most data are associated with a specific location and collected from a nonrandom sampling design. As a

result, establishing the extent of contaminated sediment within a waterbody is not possible because it is

difficult to assess the extent to which a monitoring station represents a larger segment of a waterbody.

Furthermore, the NSI data are geographically biased. More than two-thirds of all stations evaluated in the

NSI database are in Washington, Virginia, California, Illinois, Florida, Wisconsin, New York, Texas,

Oregon, and South Carolina. Each of these states has more than 500 monitoring stations. Finally, EPA did

not verify reported latitude and longitude coordinates for each sampling station.

During the development of this report, several reviewers highlighted locations or areas throughout the

United States with contaminated sediments either not included in this report or having limited coverage.

These comments indicated that sediment chemistry, sediment toxicity, or tissue residue data (or various

combinations of these) are available from the following areas: tribal waters (e.g., Minnesota Chippewa

Tribal lakes); the Chesapeake Bay; the State of Ohio; the New England area; the State of New York (e.g.,

data from the New York State Department of Environmental Conservation); the State of Washington

(e.g., Commencement Bay, Spokane River); the Great Lakes and its tributaries; and Superfund sites

where risks to human health and/or the environment have been linked to sediment contamination. As

pointed out in the Executive Summary and Chapter 5 of this report, EPA will make a concerted effort to

accumulate more data for inclusion in the NSI database and for future National Sediment Quality Survey

reports to Congress. The areas and locations mentioned above will be a high priority in this effort.

Limitations of Approach

Sediment Chemistry Screening Values

As indicated in the first National Sediment Quality Survey, there are gaps in our knowledge concerning

sediment-pollutant chemistry (especially bioavailability) and direct and indirect effects on aquatic biota.

The certainty with which sediment toxicity can be predicted for each chemical using the various screening

values included in the NSI database evaluation can vary significantly based on the quality of the available

data and the appropriateness of exposure assumptions. For example, the draft ESGs are based on either

secondary or final acute/chronic values, which are not equivalent even though they were developed using

the same methodology. Draft ESGs based on final acute/chronic values are based on the highest-quality

toxicity and octanol/water partitioning data, which have been reviewed extensively. Some draft ESGs

based on secondary acute/chronic values have also undergone extensive field validation experiments.

However, other draft ESGs based on secondary acute/chronic values are in many cases based on a less

extensive toxicity data set and have not been field-validated.

The bioavailability of metals in sediment is addressed by the comparison of the molar concentration of

sulfide anions (i.e., acid-volatile sulfide [AVS]) to the molar concentration of metals (i.e., simultaneously

extracted metals [SEM]). To apply the [SEM]![AVS] difference to indicate positive bioavailability and

toxicity for this evaluation, EPA used laboratory data that indicated the probability of observed toxic

effects at various [SEM]![AVS] levels. Based on these data, EPA defined the Tier1 level as

[SEM]![AVS] > 5. Thus, this use of [SEM]![AVS] represents a hybrid of a theoretical approach and a
National Sediment Quality Survey

2-21

correlative approach. Currently, the [SEM]![AVS] difference is most usually considered an indicator of

when metals are not bioavailable; however, some data have shown that metal bioaccumulation occurs

where the [SEM]![AVS] predicts no adverse effect. Differences in dietary exposures, applicability of

equilibrium partitioning theory to sediment assessments, and varying redox conditions in some anaerobic

sediment might limit the general applicability of the [SEM]![AVS] method. Despite these limitations,

EPA’s Science Advisory Board (SAB, 2000) indicated that the [SEM]![AVS] method “may be

particularly useful to prioritize sites requiring attention ....”

Only those chemicals for which sediment chemistry screening values (i.e., draft ESGs) are available were

evaluated in the analysis of NSI data. Therefore, the methodology could not identify contamination

associated with chemical classes such as ionic organic compounds (e.g., alkyl phenols) and

organometallic complexes (e.g., tributyl tin).

Biological effects correlation approaches like the logistic model are based on the evaluation of paired

field and laboratory data that relate adverse biological effects to the dry-weight chemical concentrations

for a particular sample. Although the predicted proportion toxic is computed from individual or multiple

chemical observations, it does not demonstrate that a particular chemical is solely responsible. In fact, a

given sample typically contains a mixture of chemicals that contribute to observed adverse effects to

some degree. For this reason, these correlative approaches are better at predicting toxicity in complex

mixtures of contaminants in sediment.

Another concern is the application of screening values based on freshwater data (draft ESGs) and those

based on saltwater data alone (logistic model) to evaluate sediment contaminant concentrations in the NSI

database from both freshwater and saltwater habitats. Freshwater organisms exhibit tolerance to toxic

chemicals similar to that of saltwater species when tested in their respective water; however, estuarine

organisms might be less tolerant if osmotically stressed (Rand, 1995). Thus, the relative toxicity of a

chemical in water (i.e., its chronic threshold water concentration) is usually within an order of magnitude

for saltwater and freshwater species, although final chronic values and proposed sediment quality

guidelines values are usually slightly higher for saltwater species. Ingersoll et al. (1996) reported similar

reliability and predictive ability between marine and freshwater guidelines. The logistic model, as used in

this assessment, was developed using only saltwater acute toxicity data.

Additional false positive and false negative classifications of risk to aquatic life from sediment

contaminant concentrations could occur when a default value for organic carbon content is applied. Draft

ESGs are based on the partitioning of a chemical between organic carbon in the sediment and pore water

at equilibrium. Because the organic carbon content of most sediment samples in the NSI database is

unknown, these sediment samples were assumed to contain 1 percent organic carbon. TOC can range

from 0.1 percent in sandy sediments to 1 to 4 percent in silty harbor sediments and from 10 to 20 percent

in navigation channel sediments (Clarke and McFarland, 1991). Long et al. (1995) reported an overall

mean TOC concentration of 1.2 percent from data compiled from 350 publications for their biological

effects database for sediments. Ingersoll et al. (1996) reported a mean TOC concentration of 2.7 percent

with a 95 percent confidence interval of only 0.65 percent. In contrast, the concentration ranges of

contaminants normalized to dry-weight typically varied by several orders of magnitude. Therefore,

normalizing dry-weight concentrations to a relatively narrow range of TOC concentrations had little

influence on relative concentrations of contaminants among samples.

Uncertainty associated with the equilibrium partitioning theory for developing draft ESGs includes the

degree to which the equilibrium partitioning model explains the available sediment toxicity data (USEPA,

1993b). An analysis of variance using freshwater and saltwater organisms in water-only and sediment

toxicity tests (using different sediments) was conducted to support development of the draft sediment

guidelines. This analysis indicated that varying the exposure medium (i.e., water or sediment) resulted in

an estimate of variability that should be used for computing confidence limits for the draft ESGs. The
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2-22

methodology used to derive the octanol/water partitioning coefficient and the final chronic value can also

influence the degree of uncertainty associated with the draft ESGs. Differences in the response of water

column and benthic organisms, as well as limitations in understanding the relationship of individual and

population effects to community-level effects, have also been noted (Mancini and Plummer, 1994).

Site-specific modifications to screening values derived using the equilibrium partitioning model have

been recommended to better address chemical bioavailability and species sensitivities (USEPA, 1993a).

Sediment chemistry screening values developed using the equilibrium partitioning approach also do not

address possible synergistic, antagonistic, or additive (except in the case of PAHs and metals, as outlined

in this chapter and Appendix B) effects of contaminants.

Toxicity Data

Differences in toxicity responses between tube building and burrowing sediment species have been

reported in the literature and stem from differences in the degree and type of exposure to sediment

contaminants. However, the overall assessment should not be affected to a significant extent by this

issue. Both types of species are prevalent members of the benthos across the country and therefore,

responses of both types of species is useful in the assessment process. Also, in many of the test methods

used, tube-building organisms will be exposed to sediment for some length of time prior to being able to

make a tube and so, at least initially, would be exposed to sediment pollutants in a similar manner as

burrowing species.

Fish Tissue Screening Values

The approach used to assess sediment chemistry data for the potential for contaminants to accumulate in

fish tissue also represents a theoretical approach with field-measured components. In addition to applying

a site-specific or default organic carbon content, the TBP calculation includes a field-measured biotasediment accumulation factor (BSAF) to account for the relative affinity of a chemical for fish tissue

lipids or sediment organic carbon. The BSAF will account for the effects of metabolism and

biomagnification in the organism in which it is measured. The primary limitation of this approach is the

applicability of a field-measured BSAF, or a percentile from a distribution of values, at a variety of sites

where the conditions might vary.

TBPs were assumed to be equivalent to levels detectable in fish tissue. However, this approach might not

completely account for biomagnification in the food chain, especially when using a BSAF derived from a

benthic organism. In addition, it is assumed that sediment does not move, that contaminant sources other

than sediment are negligible, that fish migration does not occur, and that exposure is consistent. The TBP

calculation assumes that various lipids in different organisms and organic carbon in different sediments

are similar and have distributional properties similar to the field-measured values used to derive BSAFs.

Other simplifying assumptions are that chemicals are similarly exchanged between the sediments and

tissues and that compounds behave alike, independent of site conditions other than organic carbon

content. In reality, physical-chemical processes (e.g., diffusion through porous media and sediment

mixing) can vary and limit the rate at which chemicals can exchange with bottom sediments. Uptake of

contaminants by aquatic organisms is also a kinetic (rate-controlled) process that can vary and be slowed,

for example, by awkward passage of a bulky molecule across biological membranes. Also, a BSAF of 1

(thermodynamic equilibrium) was used to estimate TBPs for many nonpolar organics. This BSAF might

overestimate or underestimate the bioaccumulative potential for certain nonpolar organic chemicals

because it is assumed that there is no metabolic degradation or biotransformation of such chemicals.

Site-specific organic carbon content was often not available, leading to additional uncertainty concerning

the comparability of BSAFs among different locations. Because of these factors, actual residue levels in

fish resulting from direct and/or indirect exposure to contaminated sediment might be higher or lower.
National Sediment Quality Survey

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There is therefore uncertainty regarding sampling station classifications based on comparison of estimated

TBPs with FDA tolerance/action/guideline levels and EPA risk levels.

TBPs could not be calculated for polar organic compounds or heavy metals. Therefore, sampling stations

could not be classified using FDA levels or EPA risk levels for those chemicals using a TBP approach

(although fish tissue monitoring data are often available for many stations).

Uncertainties and numerous assumptions are associated with exposure parameters and toxicity data used

to derive EPA risk levels and FDA tolerance/action/guideline levels. For example, the derivation of EPA

risk levels is based on the assumption that an individual consumes on average 17.5 g/day of fish caught

from the same site over a 70-year period. A consumption rate of 17.5 g/d is chosen to be protective of the

majority (i.e., 90 percent) of the population (Appendix B). Also, the TBP calculation for human health

assessments assumes that fish tissue contains 3 percent lipid. This value is intended to be indicative of the

fillet rather than the whole body. Generally, the exposure assumptions and safety factors incorporated into

toxicity assessments might overestimate risks to the general population associated with sediment

contamination but might underestimate risks to populations of subsistence or recreational fishers and

sensitive subpopulations (such as pregnant women, nursing mothers, and children).

Whereas the Tier 1, Tier 2, and Tier 3 evaluation benchmarks established in this report represent recent

advances in sediment assessment techniques, they have been used in this report as a way to relate all the

different data from all the different sources around the United States using common benchmarks.

Therefore, the Tier 1, Tier 2, and Tier 3 benchmarks and interpretations used in this report are not

currently appropriate for use in EPA regulatory programs that have developed their own frameworks and

regulatory requirements, and they were not designed to be a substitute for the various EPA program

regulatory frameworks and/or authorities. EPA’s regulatory programs (e.g., Office of Solid Waste and

Emergency Response - OSWER) have developed their own scientifically defensible approaches to

sediment evaluation based on the needs of their programs, and they will continue to use their current

regulatory frameworks when making decisions regarding potentially contaminated sediments (e.g.,

sediment remediation, sediment disposal).

Other Limitations

Because a numerical score was not assigned to each sampling station to indicate the level of

contamination associated with that station, it is not possible to determine which of the stations in Tier 1

should be considered the “most” contaminated. Such a numerical ranking system was intentionally not

used for the NSI data evaluation because EPA does not believe that such ranking is appropriate for a

screening-level analysis such as this, given the level of uncertainty.
National Sediment Quality Survey

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National Sediment Quality Survey

3-1

CHAPTER 3

FINDINGS

This chapter presents the results of the evaluation of NSI data based on the methodology described in

Chapter 2. This discussion includes a summary of the results of national and regional assessments. These

summary results do not include locations with contaminated sediment not identified in the NSI database.

The data compiled for the NSI database are primarily from large national electronic databases. Data from

many sampling and testing studies have not yet been incorporated into the NSI database. Thus, there are

additional locations with sediment contamination that do not appear in this summary. The final section in

this chapter evaluates the data used for the first report to Congress by applying the methodology

presented in Chapter 2.

National Assessment

EPA evaluated a total of 19,398 sampling stations nationwide as part of the NSI data evaluation

(Figure 3-1). The evaluation included data collected from 1990 through 1999. Of the sampling stations

evaluated, 8,348 stations (43.0 percent) were classified as Tier 1; 5,846 (30.1 percent) were classified as

Tier 2; and 5,204 (26.8 percent) were classified as Tier 3 (Table 3-1). As described in more detail later,

the frequency of Tier 1 classification based on the evaluation of all NSI data is greater than that based on

the evaluation of data sets derived from purely random sampling. This suggests that state monitoring

programs (accounting for most of the NSI data) have tended to focus their sampling efforts on areas

where contamination is known or suspected to occur.

The national distribution of Tier 1 sampling stations is illustrated in Figure 3-2. The distribution of Tier 1

stations depicted in Figure 3-2 must be viewed in the context of the distribution of all sampling stations

depicted in Figure 3-1. Table 3-1 presents the number of sampling stations in each tier by EPA region.

The greater number of Tier 1 and Tier 2 sampling stations in some regions is to some degree a function of

a larger set of available data. Although there are 10 times more Tier 1 stations in EPA Region 4

(southeastern states) than in EPA Region 8 (mountain states), there are also 8 times more Tier 3 stations

in Region 4.

The NSI database sampling stations were located in 5,695 individual river reaches (Table 3-1) throughout

the contiguous United States (based on EPA’s River Reach File 1; Bondelid and Hanson, 1990). In the

contiguous United States, there are 64,591 reaches representing approximately 1 million miles of coastal

shorelines, lake shorelines, or lengths of stream between two major tributaries. NSI database sampling

stations were located in about 8.8 percent of all river reaches identified in the contiguous United States

(Tables 3-1 and 3-2). Approximately 77.6 percent of the 5,695 reaches had one or two NSI database

sampling stations. Less than 4 percent of the 5,695 reaches had more than 10 NSI database sampling

stations. About 3.6 percent of all river reaches in the United States contained at least one sampling station

classified as Tier 1 (Figure 3-3). Around 2.9 percent of all reaches contained at least one sampling station

classified as Tier 2 (but none as Tier 1). In 2.3 percent of reaches in the contiguous United States, all of

the sampling stations were classified as Tier 3. EPA has not cataloged river reaches (at the River Reach 1

level) outside the contiguous United States (e.g., Alaska, Hawaii, Puerto Rico), and some sampling

stations in the ocean were not linked to a specific reach. Sampling bias toward areas of known or

suspected contamination might be more pronounced in some regions than in others and could be related

to the relative extent of sampling.
3-2
3-3

Table 3-1. National Assessment: Evaluation Results for Sampling Stations and River Reaches by EPA Region.EPA Region (State)

Station Evaluation River Reach Evaluationa

Total Number of

Stations

Evaluated

Tier 1 Tier 2 Tier 3

No. of

Stations

Not

Identified

by an

RF1

Reachc

Reaches

With at

Least

One

Station

in Tier 1

Reaches

With at

Least

One

Station

in Tier 2d

Reaches

With All

Stations

in Tier 3

No. of

Reaches

With at

Least One

Station

Evaluated

Total

Reaches

in

Region

No. % 

b No. %b No. %b

Region 1(CT, ME, MA, NH, RI, VT)

 275 182 66.2 64 23.3 29 10.5 28 97 23 5 125 2,764 

Region 2 (NJ, NY, PR)

 1,255 901 71.8 228 18.2 126 10.0 13 217 102 45 364 1,845 

Region 3 (DE, DC, MD, PA, VA, WV)

 2,428 714 29.4 809 33.3 905 37.3 103 385 313 301 999 3,388 

Region 4 (AL, FL, GA, KY, MS, NC, SC, TN)

 2,874 841 29.3 1,022 35.6 1,011 35.2 15 444 461 301 1,206 10,078 

Region 5 (IL, IN, MI, MN, OH, WI)

 3,185 1,146 36.0 1,095 34.4 944 29.6 — 532 401 316 1,249 6,151 

Region 6 (AR, LA, NM, OK, TX)

 1,489 425 28.5 392 26.3 672 45.1 — 226 222 289 737 7,577 

Region 7 (IA, KS, MO, NE)

 583 134 23.0 239 41.0 210 36.0 — 94 161 136 391 4,915 

Region 8 (CO, MT, ND, SD, UT, WY)

 294 79 26.9 95 32.3 120 40.8 — 59 77 68 204 13,860 

Region 9 (AZ, CA, HI, NV)

 1,752 1,040 59.4 429 24.5 283 16.2 18 156 63 40 259 4,686 

Region 10 (AK, ID, OR, WA)

 5,263 2,886 54.8 1,473 28.0 904 17.2 290 177 121 49 347 10,462 

Total for United Statese 19,398 8,348 43.0 5,846 30.1 5,204 26.8 467 2,298 1,891 1,506 5,695 64,591 a River reaches based on EPA River Reach File (RF1). RF1 does not include data outside the contiguous United States.b Percent of all NSI stations evaluated in the region.c Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed.d No stations in these reaches were included in Tier 1.e Because some reaches occur in more than one region, the total number of reaches in each category for the country might not equal the sum of reaches in the regions.
3-4
3-5

Table 3-2. Regions 1–10: River Reach and Watershed Evaluation Summary.EPA Region (State)

River Reach Evaluationa Watershed Evaluation

Total

Number of

River

Reaches

River

Reaches

With at

Least One

Tier 1

Station

River

Reaches

With at

Least One

Tier 2

Station and

Zero Tier 1

Stations

River

Reaches

With All

Tier 3

Stations

River

Reaches

With No

Data

Total

Number of

Watersheds

Watersheds

Containing

APCs

Watersheds

With at

Least One

Tier 1

Station

Watersheds

With at

Least One

Tier 2

Station and

Zero Tier 1

Stations 

W

Region 1 (CT, ME, MA, NH, RI, VT) 

2,764 97

 (3.5%)

23 

(0.8%)

5 

(0.2%)

2,639 

(95.5%)

62 9 

 (14.5%)

13 

 (21.0%)

6 

 (9.7%)

Region 2 (NJ, NY, PR) 

1,845 217 

(11.8%)

102 

(5.5%)

45 

(2.4%)

1,481 

(80.3%)

71 17 

 (23.9%)

35 

 (49.3%)

3 

 (4.2%)

Region 3 (DE, DC, MD, PA, VA, WV) 

3,388 385 

(11.4%)

313 

(9.2%)

301 

(8.9%)

2,389 

(70.5%)

126 7 

 (5.6%)

96 

(76.2%)

11

 (8.7%)

Region 4 (AL, FL, GA, KY, MS, NC, SC, TN)

10,078 444

 (4.4%)

461 

(4.6%)

301

 (3.0%)

8,872

 (88.0%)

307 13 

 (4.2%)

142 

(46.3%)

57 

(18.6%)

Region 5 (IL, IN, MI, MN, OH, WI) 

6,151 532

 (8.6%)

401 

(6.5%)

316 

(5.1%)

4,902 

(79.7%)

278 25 

 (9.0%)

144 

(51.8%)

31 

(11.2%)

Region 6 (AR, LA, NM, OK, TX) 

7,577 226

 (3.0%)

222 

(2.9%)

289 

(3.8%)

6,840 

(90.3%)

402 4 

(1.0%)

117 

(29.1%)

69 

(17.2%)

Region 7 (IA, KS, MO, NE) 

4,915 94 

(1.9%)

161 

(3.3%)

136 

(2.8%)

4,524

 (92.0%)

238 1 

(0.4%)

60 

(25.2%)

72 

(30.3%)

Region 8 (CO, MT, ND, SD, UT, WY) 

13,860 59 

(0.4%)

77 

(0.6%)

68 

(0.5%)

13,656

 (98.5%)

385 1 

(0.3%)

34 

(8.8%)

41 

(10.6%)

Region 9 (AZ, CA, HI, NV) 

4,686 156 

(3.3%)

63 

(1.3%)

40 

(0.9%)

4,427

 (94.5%)

288 19 

(6.6%)

41 

(14.2%)

19 

 (6.6%)

Region 10 (AK, ID, OR, WA) 

10,462 177 

(1.7%)

121 

(1.2%)

49 

(0.5%)

10,115

 (96.7%)

355 10 

(2.8%)

48 

(13.5%)

29 

(8.2%)

Total for United Statesb 64,591 2,298 

(3.6%)

1,891 

(2.9%)

1,506 

(2.3%)

58,896 

(91.2%)

 2,264 96 

(4.2%)

658 

(29.1%)

302 

(13.3%)

a River reaches based on EPA River Reach File (RF1). RF1 does not include data outside the contiguous United States.b Because some reaches and watersheds occur in more than one region, the total number of reaches and watersheds in each category for the country might not equal the su

in the regions.
National Sediment Quality Survey

3-6

Figure 3-3. National Assessment: Percent of River Reaches

that Include Tier 1, Tier 2, and Tier 3 Sampling Stations.

Not all sampling programs target only sites of known or suspected contamination. The NSI database

includes data from EPA’s Environmental Monitoring and Assessment Program (EMAP), which uses a

probabilistic sampling design; that is, the sampling locations are randomly selected. The percentage of

sampling stations placed in each tier based on these data alone differs considerably from the percentage of

sampling stations in each tier based on an evaluation of all the data in the NSI database. Smaller

percentages of EMAP sampling stations are categorized as Tier 1 (33.4 percent for EMAP compared to

43.0 percent for all NSI database sampling stations), greater percentages are categorized as Tier 2 (41.9

percent for EMAP compared to 30.1 percent for all NSI database stations), and comparable percentages

are categorized as Tier 3 (24.8 percent for EMAP and 26.8 overall). For comparison, the NSI database

also contains data from the National Oceanic and Atmospheric Administration’s National Status and

Trends Program (NS&T). The NS&T does not target known or suspected contaminated sites. Greater

percentages of NS&T sampling stations are categorized as Tier 1 (55.5 percent for NS&T compared to

43.0 percent for all NSI database sampling stations), similar percentages are categorized as Tier 2 (32.9

percent for NS&T compared to 30.1 percent for all NSI database stations), and smaller percentages are

categorized as Tier 3 (11.6 percent for NS&T compared to 26.8 percent for all NSI database stations).

These differences might also reflect the lower detection limits of more sensitive analytical chemistry

techniques, the sensitivity of Tier 2 evaluation parameters, and the nearly ubiquitous presence of low to

intermediate levels of contamination in the areas sampled by these programs.

Table 3-3 presents the number of sampling stations categorized by tier for the different evaluation

parameters described in Table 2-2 and organized by aquatic life and human health effects. Most stations

(87.9 percent) are evaluated using the logistic regression model. Nearly 75 percent of the stations are

evaluated using the sediment-based human health assessment. The draft ESG and draft PAH toxicity unit

analyses are applied to 65.2 and 48.7 percent of the stations, respectively. The reduced percentages of

NSI database stations evaluated with the draft ESG and draft PAH toxicity unit analyses can typically be

tied to the absence of analytical results for the appropriate organic chemicals (i.e., PAHs), which might be

typical of monitoring programs that targeted metals or PCBs. Only 17.8 percent of the stations were

evaluated using sediment toxicity analysis.
National Sediment Quality Survey

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Table 3-3. Tier Classification Summary.

Tier Evaluation Parameter

Table 2-2

Evaluation

Parameter

Reference

Number of Stations

Total Tier 1 Tier 2 Tier 3

Aquatic Life Assessment

Draft ESG analysis 1, 7 12,649 69 228 12,352

SEM analysis 2, 8 739 10 205 524

Logistic regression model analysis 3, 9 17,056 4,513 6,415 6,128

Draft PAH toxicity unit analysis 4, 10 9,442 545 1,144 7,753

Toxicity analysis 15, 17 3,446 745 858 1843

Toxicity demonstrated in two or more species classified

as Tier 2 16 n/a 54 n/a n/a

Human Health Assessment

Sediment chemistry TBP exceeds EPA’s human health

cancer risk of 10-4 or a noncancer hazard quotient (HQ)

of 10

5

14,484

5,372 n/a n/a

Sediment chemistry TBP exceeds EPA’s human health

cancer risk of 10-5 or a noncancer HQ of 1, or FDA’s

tolerance/action/guidance levels

11 n/a 3,236 5,876

Tissue levels of chemicals with a log Kow $ 5.5 in

samples that exceed EPA’s human health cancer risk of

10-5, a noncancer HQ of 1, or FDA’s

tolerance/action/guidance levels

12

2,364

1,133 n/a n/a

Tissue levels of chemicals with a log Kow < 5.5 in

samples that exceed EPA’s human health cancer risk of

10-5, a noncancer HQ of 1, or FDA’s

tolerance/action/guidance levels

14 n/a 563 668

Tissue levels and sediment chemistry TBP of chemicals

with a log Kow < 5.5 in samples that exceed EPA’s human

health cancer risk of 10-5, a noncancer HQ of 1, or FDA’s

tolerance/action/guidance levels

6 and 13 n/a 46 n/a n/a

Totala 19,398 8,348 5,846 5,204

a

 Because stations might be evaluated by more than one criterion, the sum of the number of stations evaluated under each criterion might not be equal

to the total number of stations.

Many of the 19,398 evaluated stations were assessed using more than one of the evaluation parameters.

About 38 percent of the stations classified as Tier 1 (3,171 stations) were classified as Tier 1 based on

more than one of the evaluation parameters. About 31 percent of the stations classified as Tier 2 (1,817

stations) were classified as Tier 2 based on more than one of the evaluation parameters. Of the remaining

5,177 stations classified as Tier 1 based on only one evaluation parameter, 1,597 stations were classified

as Tier 1 based on the logistic regression model, 2,286 stations were classified as Tier 1 based on the

sediment chemistry TBP’s exceeding risk levels, and 980 stations were classified as Tier 1 based on tissue

risk levels. Of the remaining 4,029 stations classified as Tier 2 based on only one evaluation parameter,

2,389 were classified as Tier 2 based on the logistic regression model and 1,042 were classified as Tier 2

based on the sediment chemistry TBP’s exceeding risk levels. About 62 of the stations classified as Tier 3

were classified as Tier 3 based on more than one evaluation parameter. Of the remaining 1,970 stations

classified as Tier 3 based on only one evaluation parameter, 1,161 were classified as Tier 3 based on the

logistic regression model and 530 stations were classified as Tier 3 based on the sediment chemistry

TBP’s not exceeding risk levels. Overall, fewer stations were classified as Tier 1 using aquatic life

evaluation parameters (5,006 stations) than were classified using human health evaluation parameters
National Sediment Quality Survey

3-8

(6,385 stations). Of the stations classified as Tier 2, 4,439 stations were classified as Tier 2 using aquatic

life evaluation parameters and 3,131 stations were classified as Tier 2 using human health evaluation

parameters. Additionally, the chemicals most often associated with Tier 1 designation using both

sediment chemistry TBP and tissue residue evaluation parameters were determined. Based on tissue

residue data, PCBs, DDTs, chlordane, dioxins, and hexachlorobenzene are the leading chemicals

associated with Tier 1 designation looking at probable adverse effects to human health in this report.

Looking at sediment chemistry TBP evaluation parameters also for probable adverse effects to human

health, PAHs, PCBs, DDTs, dieldrin, and dioxins are the leading chemicals associated with Tier 1

designation in this report. It is important to note that this report, as stated earlier, provides a screeninglevel assessment that identifies potential problems for further study. A screening-level analysis typically

identifies problems that prove not to be significant upon further analysis. Therefore, further studies (e.g.,

risk assessment, toxicological evaluations) would need to be conducted to determine whether sediment

contaminants are resulting in adverse effects to aquatic life and/or human health as well as the cause(s) of

those adverse effects.

Two important issues in interpreting the results of sampling station classification are naturally occurring

“background” levels of chemicals and the effect of chemical mixtures. Site-specific naturally occurring

(or background) levels of chemicals might be an important risk management consideration in examining

sampling station classification. This is most often an issue for naturally occurring chemicals like metals

and PAHs. In addition, although the sediment chemistry screening levels for individual chemicals are

used as indicators of potential adverse biological effects, other co-occurring chemicals (which might or

might not be measured) can cause or contribute to any observed adverse effect at specific locations.

To help judge the effectiveness of the NSI data evaluation approach, EPA examined the agreement

between sediment chemistry and toxicity test results for the 2,999 NSI database sampling stations where

toxicity data existed so sediment chemistry data for aquatic health could be evaluated. The toxicity test

data indicate whether significant lethality to indicator organisms occurs as a result of exposure to

sediment. About two-thirds (67.9 percent) of the stations classified as Tier 1 based on aquatic life effects

from sediment chemistry data evaluation were classified as Tier 1 or 2 based on toxicity test results.

About 43.6 percent of the stations classified as Tier 2 based on aquatic life effects from sediment

chemistry data were classified as Tier 1 or 2 based on toxicity test results. Less than one-fourth (23.4

percent) of the stations classified as Tier 3 based on aquatic life effects from sediment chemistry data

were classified as Tier 1 or 2 based on toxicity test results. These results are generally consistent with the

range of predicted proportion toxic used to classify a station as Tier 1, 2, or 3. The results also

demonstrate, in part, the differing sensitivities of varying test organisms and endpoints.

During an initial screening of the NSI data, EPA noted data quality problems that might have affected all

or many of the data reported in a given database. The data review process included steps to review the

incoming data for consistency. The steps included confirmation of metadata such as sample date,

qualifying codes, chemicals analyzed, and range checks. Typical problems encountered included the

reporting of multiple results for a single chemical, inconsistent reporting units, the absence of remark

codes, and inconsistencies between tables that reported sample-level information and chemical results.

Databases with obvious quality problems were not included in the NSI data evaluation. Also, if a database

included in the NSI database did not have associated locational information (latitude/longitude), data in

that database were not included in the NSI data evaluation. Other data were organized in a manner that

prevented simple electronic manipulation or were not provided in an electronic format, precluding their

use in this assessment. In general, these data were associated with small geographic areas. It is likely that

this data exclusion led to not identifying certain Tier 1 and 2 stations, and possibly additional APCs. It is

also likely that this data exclusion led to not identifying certain Tier 3 stations as well. However, it is not

likely that this data exclusion would have substantively changed the conclusions and discussion presented

in this report.
National Sediment Quality Survey

3-9

Watershed Assessment

The potential risk of adverse effects to aquatic life and human health is greatest in areas with a multitude

of contaminated locations. The assessment of individual sampling stations is useful for estimating the

number and distribution of contaminated spots and the overall magnitude of sediment contamination in

monitored waterbodies of the United States. However, a single “hot spot” might not pose a great threat to

either the benthic community at large or consumers of resident fish because the spatial extent of exposure

could be small. On the other hand, if many contaminated spots are located in close proximity, the spatial

extent and probability of exposure are much greater. EPA examined sampling station classifications

within watersheds to identify areas of probable concern for sediment contamination (APCs), where the

exposure of benthic organisms and resident fish to contaminated sediment might be likely. In this report,

EPA defines watersheds by 8-digit U.S. Geological Survey (USGS) hydrologic unit codes (cataloging

units), which are roughly the size of a county. In the United States and Puerto Rico, there are 2,264

watersheds.

Watersheds containing APCs are those which include at least 10 Tier 1 sampling stations and in which at

least 75 percent of all sampling stations were classified as either Tier 1 or Tier 2. The definition of an

APC requires that a watershed include at least 10 sampling stations because at least 10 must be classified

as Tier 1. About 16.3 percent of the watersheds (370 of 2,264) met this requirement and thus were eligible

to contain an APC. These dual criteria were based on empirical observation of the data in the first

National Sediment Quality Survey report to Congress and are maintained for this evaluation. The

definition of area of probable concern was developed to identify watersheds for which further study of

the effects and sources of sediment contamination, and possible risk reduction needs, would be warranted.

Where data have been generated through intensive sampling in areas of known or suspected

contamination in a watershed, the APC definition should identify watersheds that contain even relatively

small areas that are considerably contaminated. This designation does not imply, however, that sediment

throughout the entire watershed, which is typically very large compared to the extent of available

sampling data, is contaminated. For example, the Lower Mississippi–New Orleans watershed has been

identified as containing an APC. This designation is due to multiple Tier 1 stations identified in the lower

Mississippi River rather than in the Mississippi River delta and nearshore areas. The delta and the

nearshore areas are reported as one of the largest and documented healthy commercial and recreation

fisheries in North America (Bob Engler, USACE/ERDC, personal communication, March 1, 2002), and

they have only three Tier 1 stations, which by themselves would not qualify as an APC. On the other

hand, where data have been generated through comprehensive sampling, or where sampling stations were

selected randomly or evenly distributed throughout a sampling grid, the APC definition might not identify

watersheds that contain small or sporadically contaminated areas. A comprehensively surveyed watershed

of the size typically delineated by a USGS cataloging unit might contain small but significant areas that

are considerably contaminated but might be too large in total area for 75 percent of all sampling stations

to be classified as Tier 1 or Tier 2. In addition, limited random or evenly distributed sampling within such

a watershed might not yield 10 Tier 1 sampling stations. Thus, the process used to identify watersheds

containing APCs might include some watersheds with limited areas of contamination and omit some

watersheds with significant contamination. Given available data, however, EPA has concluded that the

process represents a reasonable screening analysis to identify watersheds where further study is

warranted. NSI database sampling stations are located in 1,224 of the 2,264 watersheds, or approximately

54.1 percent of the total number of watersheds. The application of the above procedure identified 96

watersheds that contain APCs. These watersheds represent about 4.2 percent of all watersheds (96 of

2,264). The watershed analysis also indicated that 29.1 percent of all watersheds contain at least one Tier

1 sampling station, 13.3 percent contain at least one Tier 2 sampling station but no Tier 1 stations, and 7.4

percent contain all Tier 3 sampling stations (Figure 3-4). About 45.9 percent of all watersheds in the

country did not include a sampling station. Table 3-4 provides a list of all watersheds that contain an
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3-10

Figure 3-4. National Assessment: Watershed Classification.

APC. The location of these watersheds is shown on Figure 3-5. The name and cataloging unit number on

Table 3-4 correspond to the labels on Figure 3-5.

Of the 370 watersheds with enough stations to potentially contain an APC, 25.9 percent (96 of 370)

contained an APC. To some extent, the sampling effort does contribute to the number of Tier 1 stations. A

simple statistical regression analysis of total number of sampling stations versus number of Tier 1

sampling stations for the 370 watersheds eligible to contain an APC (including at least 10 and up to 200

sampling stations) resulted in a statistically significant correlation coefficient (R-square) of 0.67. When a

regression analysis of total number of sampling stations versus percentage of Tier 1 and Tier 2 stations is

performed, however, the resulting correlation coefficient is 0.02, which indicates no correlation. Because

of these dual criteria, the sampling effort does not overly contribute to APC designation. Of the 96

watersheds, 55 watersheds would have been identified as containing an APC if only aquatic life

evaluation parameters had been evaluated. Sixty of the 96 watersheds containing an APC would have

been identified if only human health evaluation parameters had been used. Thirty-six of these watersheds

are in common. Seventeen of the 96 watersheds would not have been identified at all.
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Table 3-4. USGS Cataloging Unit Numbers and Names for Watersheds Containing APCs.

Map

No.

Cataloging

Unit

Number Cataloging Unit Name

Map

No.

Cataloging

Unit

Number Cataloging Unit Name

1 01080205 Lower Connecticut 49 07120001 Kankakee

2 01090001 Charles 50 07120002 Iroquois

3 01090004 Narragansett 51 07120003 Chicago

4 01100004 Quinnipiac 52 07120004 Des Plaines

5 01100005 Housatonic 53 07120005 Upper Illinois

6 01100006 Saugatuck 54 07120006 Upper Fox

7 01100007 Long Island Sound 55 07120007 Lower Fox

8 02020003 Hudson-Hoosic 56 07130001 Lower Illinois-Senachwine Lake

9 02020004 Mohawk 57 07130003 Lower Illinois-Lake Chautauqua

10 02020006 Middle Hudson 58 07130007 South Fork Sangamon

11 02020008 Hudson-Wappinger 59 07130011 Lower Illinois

12 02030101 Lower Hudson 60 07130012 Macoupin

13 02030102 Bronx 61 08030207 Big Sunflower

14 02030103 Hackensack-Passaic 62 08030209 Deer-Steele

15 02030104 Sandy Hook-Staten Island 63 08090100 Lower Mississippi-New Orleans

16 02030105 Raritan 64 11070209 Lower Neosho

17 02030201 Northern Long Island 65 12030102 Lower West Fork Trinity

18 02030202 Southern Long Island 66 12090205 Austin-Travis Lakes

19 02040202 Lower Delaware 67 14010002 Blue

20 02040205 Brandywine-Christina 68 15060106 Lower Salt

21 02060003 Gunpowder-Patapsco 69 16050203 Carson Desert

22 02060004 Severn 70 17020001 Franklin D. Roosevelt Lake

23 02080107 York 71 17080001 Lower Columbia-Sandy

24 03050201 Cooper 72 17090012 Lower Willamette

25 03050202 South Carolina Coastal 73 17100102 Queets-Quinault

26 03060109 Lower Savannah 74 17100105 Grays Harbor

27 03070203 Cumberland-St. Simons 75 17110002 Strait Of Georgia

28 03100206 Tampa Bay 76 17110012 Lake Washington

29 03130002 Middle Chattahoochee-Lake Harding 77 17110013 Duwamish

30 03140105 Pensacola Bay 78 17110019 Puget Sound

31 03160205 Mobile Bay 79 18010102 Mad-Redwood

32 04030108 Menominee 80 18020112 Sacramento-Upper Clear

33 04030204 Lower Fox 81 18040005 Lower Cosumnes-Lower Mokelumne

34 04040001 Little Calumet-Galien 82 18050001 Suisun Bay

35 04040002 Pike-Root 83 18050002 San Pablo Bay

36 04120101 Chautauqua-Conneaut 84 18050003 Coyote

37 04140201 Seneca 85 18050004 San Francisco Bay

38 05060001 Upper Scioto 86 18060006 Central Coastal

39 05120106 Tippecanoe 87 18060011 Alisal-Elkhorn Sloughs

40 05120201 Upper White 88 18070103 Calleguas

41 05120208 Lower East Fork White 89 18070104 Santa Monica Bay

42 06010201 Watts Bar Lake 90 18070106 San Gabriel

43 06010205 Upper Clinch 91 18070201 Seal Beach

44 06020001 Middle Tennessee-Chickamauga 92 18070203 Santa Ana

45 07040001 Rush-Vermillion 93 18070204 Newport Bay

46 07080101 Copperas-Duck 94 18070301 Aliso-San Onofre

47 07090005 Lower Rock 95 18070304 San Diego

48 07090007 Green 96 19020201 Eastern Prince William Sound
3-12
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APC designation within a watershed could result from either expansive sampling throughout a watershed

or intensive sampling at a single contaminated location or a few such locations. In comparison to the

overall results presented in Figure 3-3, sampling stations are located on an average of 34.1 percent of the

reaches in watersheds containing APCs. On the average, 23.9 percent of reaches in watersheds containing

APCs have at least one Tier 1 sampling station and 7.9 percent have no Tier 1 sampling station but at

least one Tier 2 sampling station. In many of these watersheds, contaminated areas may be concentrated

in specific river reaches in a watershed. Within the 96 watersheds containing APCs across the country, 97

individual river reaches or waterbody segments have 10 or more Tier 1 sampling stations (Table 3-5).

These are localized areas in the watershed for which an abundance of evidence indicates potentially

severe contamination. Because EPA's Reach File 1 was used to index the location of NSI database

sampling stations, some sampling stations might not actually occur on the identified Reach File 1 stream,

but rather on a smaller stream that is hydrologically linked or is relatively close to the Reach File1 stream.

The first report to Congress (USEPA, 1997) identified 96 watersheds with areas of probable concern

(APCs) based on data collected from 1980 through 1993. Using the updated methodology described in

Chapter 2 and the same APC definition, this second report identified 96 watersheds containing an APC

based on data collected from 1990 through 1999. Appendix F compares the watersheds identified in the

reports. Thirty-seven watersheds were identified in both reports as containing an APC because data were

available on these watersheds from both time periods, 1980 through 1993 and 1990 through 1999. Of the

remaining 59 watersheds with an APC in the previous report to Congress, 26 of the watersheds had fewer

than 10 total monitoring stations with data evaluated, 26 watersheds had fewer than 10 Tier 1 stations,

and 7 watersheds had less than 75 percent of the analyzed stations classified as Tier 1 or Tier 2 in the

current report. Of the remaining 59 watersheds with an APC in the current analysis, 19 of the watersheds

had fewer than 10 total monitoring stations with data evaluated, 36 watersheds had fewer than 10 Tier 1

stations, and 4 watersheds had less than 75 percent of the analyzed stations classified as Tier 1 or Tier 2 in

the previous report to Congress. Appendix F also presents a detailed listing and geographical location of

the watersheds summarized in the appendix. As indicated above, this disparity could be due to a lack of

data collected in those watersheds identified as containing an APC in the first report but not containing an

APC in this report. This difference could also be due to different stations’ being evaluated in those

watersheds that resulted in the APC designation in the first report than were evaluated in the same

watersheds in the current report, and not designated as containing an APC in this report. Therefore, it

should not be inferred that there are no ecological or human health impacts due to contaminated

sediments for the stations located in watersheds that were designated as containing APCs in the first

report but are not designated as such in this first update. Additional analysis should be conducted to

determine the degree of impact due to contaminated sediments.

Contaminated Sediment CERCLA Sites and Their Relationship to Report

Findings

Table 3-6 and Figures 3-6 and 3-7 present the name and location of 66 Comprehensive Environmental

Response, Compensation, and Liability Act (CERCLA, also known as Superfund) sites in the United

States where the risks to human health and/or the environment are unacceptable because of sediment

contamination and for which, as of September 30, 2002, EPA has issued a Record of Decision (ROD) or

Action Memo that describes the sediment remedy necessary to mitigate those risks. The table and figures

do not include all sites where a sediment cleanup decision has been made, but only those that the

CERCLA program has classified as Tier 1 sites. These Tier 1 CERCLA sites (not to be confused with the

Tier 1 sampling stations identified in this report) are sites where the sediment action will address at least

10,000 cubic yards of contaminated sediment or an area of at least 5 acres. The CERCLA program will

continue to track the progress at these sites to evaluate the effectiveness of the selected remedy and will
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Table 3-5. River Reaches with 10 or More Tier 1 Sampling Stations Located in Watersheds

Containing APCs.

Cataloging

Unit

Number Cataloging Unit Name RF1 Reach ID RF1 Reach Name

Number

of Tier 1

Stations

Total

Number of

Stations in

Reach

01090001 Charles 01090001022 Boston Bay 16 32

02020003 Hudson-Hoosic 02020003031 Hudson River 16 16

02020003056 Hudson River 16 16

02020003057 Hudson River 29 33

02020003078 Hudson River 67 67

02020008 Hudson-Wappinger 02020008031 Hudson River 12 12

02030101 Lower Hudson 02030101009 Hudson River 10 10

02030101039 Hudson River 11 11

02030102 Bronx 02030102001 Long Island Sound 26 27

02030103 Hackensack-Passaic 02030103001 Hackensack River 17 21

02030103010 Passaic River 105 106

02030103023 Rockaway River 11 19

02030104 Sandy Hook-Staten

Island

02030104001 Upper New York Bay 36 39

02030104002 Newark Bay 62 74

02030104004 Staten Island 24 29

02030201 Northern Long Island 02030201001 Upper Bay 10 11

02030201003 Long Island Sound 17 17

02030201004 Long Island Sound 10 11

02030201005 Long Island Sound 15 18

02030202 Southern Long Island 02030202028 Jamaica Bay 31 41

02040205 Brandywine-Christina 02040205011 Christina River 71 147

02040205013 Red Clay Creek 11 15

02060004 Severn 02060004002 Severn River 15 22

02060004004 South River 15 22

03050201 Cooper 03050201030 Cooper River 18 27

03050201034 Cooper River 11 12

03050202 South Carolina Coastal 03050202010 Ashley River 25 25

03100206 Tampa Bay 03100206009 Hillsborough Bay 28 34

03100206019 Tampa Bay 11 14

03140105 Pensacola Bay 03140105011 Pensacola Bay 17 27

04030108 Menominee 04030108001 Menominee River 12 12

04040002 Pike-Root 04040002002 Lake Michigan 32 46

07080101 Copperas-Duck 07080101007 Mississippi River 12 14

07080101008 Mississippi River 48 58

07080101009 Mississippi River 12 19

07080101020 Duck Creek 16 17

07090007 Green 07090007005 Green River 11 23

07120003 Chicago 07120003001 Chicago Sanitary Ship Canal 17 20

07120004 Des Plaines 07120004011 Des Plains River 13 23

07120004016 Salt Creek 12 16

07120006 Upper Fox 07120006011 Fox River 10 14

08030207 Big Sunflower 08030207005 Big Sunflower River 14 14
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Table 3-5. (Continued)

Cataloging

Unit

Number Cataloging Unit Name RF1 Reach ID RF1 Reach Name

Number

of Tier 1

Stations

Total

Number of

Stations in

Reach

08030209 Deer-Steele 08030209003 Black Bayou 18 19

08090100 Lower Mississippi-New

Orleans

08090100004 Mississippi River 18 18

12030102 Lower West Fork Trinity 12030102049 Mountain Creek Lake 11 12

12090205 Austin-Travis Lakes 12090205004 Colorado River 13 13

15060106 Lower Salt 15060106001 Salt River 20 28

15060106026 Cave Creek 19 24

17080001 Lower Columbia-Sandy 17080001009 Columbia River 11 49

17090012 Lower Willamette 17090012017 Willamette River 54 97

17090012018 Willamette River 42 49

17090012019 Willamette River 125 197

17090012026 Columbia Slough 11 26

17100102 Queets-Quinault 17100102040 Matheny Creek 50 74

17100102042 Sams River 27 34

17100105 Grays Harbor 17100105022 Big Creek 86 86

17100105025 Humptulips River, East Fork 14 14

17110002 Strait Of Georgia 17110002019 Bellingham Bay 71 104

17110002022 Bellingham Bay 58 114

17110002030 Strait Of Georgia 22 61

17110002038 Fidalgo Island 12 43

17110012 Lake Washington 17110012001 Lake Washington Ship Canal 69 74

17110012003 Lake Union 58 59

17110012004 Lake Union 14 14

17110012009 Lake Washington 24 45

17110013 Duwamish 17110013001 Duwamish Waterway 82 130

17110013003 Elliot Bay 498 745

17110013005 Green River 12 15

17110019 Puget Sound 17110019022 Sinclair Inlet 165 191

17110019024 Sinclair Inlet 11 30

17110019068 Budd Inlet 62 130

17110019081 Chambers Creek 19 20

17110019084 Puget Sound 31 45

17110019085 Puget Sound 605 848

17110019086 Puget Sound 198 257

17110019087 Puget Sound 83 230

18010102 Mad-Redwood 18010102010 Arcata Bay 12 15

18040005 Lower Cosumnes-Lower

Mokelumne

18040005005 Comanche Reservoir 15 36

18050002 San Pablo Bay 18050002002 San Pablo Bay 28 29

18050002036 San Pablo Bay 14 20

18050004 San Francisco Bay 18050004001 San Francisco Bay 59 66

18050004038 San Francisco Bay 10 14

18050004049 San Francisco Bay 33 35
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Table 3-5. (Continued)

Cataloging

Unit

Number Cataloging Unit Name RF1 Reach ID RF1 Reach Name

Number

of Tier 1

Stations

Total

Number of

Stations in

Reach

18060006 Central Coastal 18060006015 Charro Creek 19 20

18070103 Calleguas 18070103009 Pacific Ocean 18 18

18070104 Santa Monica Bay 18070104001 Pacific Ocean 51 62

18070104002 Dominguez Channel 13 13

18070104003 Pacific Ocean 35 46

18070104005 Pacific Ocean 10 10

18070106 San Gabriel 18070106021 Pacific Ocean 17 26

18070201 Seal Beach 18070201001 Pacific Ocean 38 59

18070203 Santa Ana 18070203001 Santa Ana River 27 85

18070204 Newport Bay 18070204002 San Diego Creek 15 23

18070204005 Pacific Ocean 10 32

18070304 San Diego 18070304001 Pacific Ocean 41 49

18070304008 San Diego Bay 13 19

18070304014 San Diego Bay 139 169

Table 3-6. Contaminated Sediment CERCLA Sites.

Map ID CERCLA Site EPA Region State

1 GE - Housatonic River 1 MA

2 Hocomonco Pond 1 MA

3 New Bedford 1 MA

4 Nyanza Chemical Waste Dump 1 MA

5 Sullivan’s Ledge 1 MA

6 Loring Air Force Base 1 ME

7 Newport Naval Education & Training Center 1 RI

8 Pine Street Canal 1 VT

9 Burnt Fly Bog 2 NJ

10 Chemical Insecticide Corp. 2 NJ

11 Chemical Leaman Tank Lines, Inc. 2 NJ

12 Lipari Landfill 2 NJ

13 Alcoa Aggregation Site (Grasse River, Massena) 2 NY

14 Batavia Landfill 2 NY

15 FMC Corp. (Dublin Road Landfill) 2 NY

16 General Motors (Central Foundry Division) (Massena) 2 NY

17 Hooker (102nd Street) 2 NY

18 Hudson River PCBs 2 NY

19 Love Canal 2 NY

20 Marathon Battery Corp. 2 NY

21 Onondaga Lake 2 NY

22 Reynolds Metals Co. (Massena) 2 NY

23 Richardson Hill Road Landfill/Pond 2 NY

24 York Oil Co. 2 NY

25 E.I. du Pont de Nemours & Co., Inc. (Newport Pigment Plant Landfill) 3 DE
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3-17

Table 3-6. (Continued)

Map ID CERCLA Site EPA Region State

26 Halby Chemical Co. 3 DE

27 Metal Banks 3 PA

28 Dixie Caverns County Landfill 3 VA

29 Stauffer Chemical Co. (Cold Creek Plant) 4 AL

30 Triana/Tennessee River 4 AL

31 Koppers Co., Inc. (Charleston Plant) 4 SC

32 Sangamo Weston, Inc./Twelve-Mile Creek/Lake Hartwell PCB Contamination 4 SC

33 Ross Metals Inc. 4 TN

34 Outboard Marine Corp. 5 IL

35 Sangamo Electric Dump/Crab Orchard National Wildlife Refuge (USDOI) 5 IL

36 Yeoman Creek Landfill 5 IL

37 Allied Paper, Inc./Portage Creek/Kalamazoo River 5 MI

38 Ford Motor Co. (Monroe-River Raisin-Ford Outfall) 5 MI

39 Manistique River/Harbor 5 MI

40 Velsicol Chemical Corp. (Michigan) 5 MI

41 Fox River 5 WI

42 Sheboygan Harbor & River 5 WI

43 Southern Lakes Trap & Skeet Club 5 WI

44 Bayou Bonfouca 6 LA

45 Cleveland Mill 6 NM

46 Alcoa (Point Comfort)/Lavaca Bay 6 TX

47 Bailey Waste Disposal 6 TX

48 Nahant Marsh 7 IA

49 Eagle Mine 8 CO

50 Rocky Mountain Arsenal (U.S. Army) 8 CO

51 Silver Bow Creek/Butte Area 8 MT

52 Monticello Mill Tailings (U.S. DOE) 8 UT

53 Sharon Steel Corp. (Midvale Tailings) 8 UT

54 Concord Naval Weapons Station 9 CA

55 McCormick & Baxter Creosoting Co. 9 CA

56 Moffett Field Naval Air Station 9 CA

57 United Heckathorn Co. 9 CA

58 Ketchikan Pulp Company 10 AK

59 Bunker Hill (OU3 Coeur d’Alene Basin) 10 ID

60 McCormick & Baxter Creosoting Co. (Portland Plant) 10 OR

61 Commencement Bay, Nearshore/Tide Flats 10 WA

62 Harbor Island (Lead) 10 WA

63 Old Navy Dump/Manchester Laboratory (U.S. EPA/NOAA) 10 WA

64 Pacific Sound Resources 10 WA

65 Puget Sound Naval Shipyard Complex 10 WA

66 Wyckoff Co./Eagle Harbor 10 WA
3-18
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add more sites to the list as more decisions are made on the need to clean up other sites. More information

on these sites can be found on the Internet at www.epa.gov/superfund/resources/sediment/sites.htm.

As outlined previously in the Watershed Assessment section, a total of 96 watersheds containing APCs

have been identified throughout the United States. Twenty-eight of the 66 CERCLA sites are located in

18 of these watersheds containing APCs. There are 48 of the 66 CERCLA sites with no Tier 1 sampling

stations within a 1-mile radius and 31 of the 66 CERCLA sites with no Tier 1 stations within 5 miles.

At the time of this report, the data associated with these CERCLA sites have not been compiled in the

NSI database; however, several sampling stations in the database and evaluated in this report are close to

these 66 CERCLA sites. For example 3,225 Tier 1, 2, and 3 sampling stations, out of the 19,398 stations

evaluated in this report, are within 5 miles of the 66 sites, and 984 are within 1 mile of a CERCLA site.

Of the 3,225 sampling stations, 2,257 are Tier 1 sampling stations (27 percent of all 8,348 Tier 1 stations)

located within 5 miles of a CERCLA site; 719 of these Tier 1 sampling stations are located within 1 mile

of a CERCLA site.

Wildlife Assessment

As described in Chapter 2, EPA conducted a separate analysis of the NSI data to determine the number of

sampling stations where chemical concentrations of DDT, mercury, dioxin, and PCBs exceeded levels set

to be protective of wildlife (i.e., EPA wildlife criteria). The wildlife criteria used in this evaluation were

derived from those presented in the Great Lakes Water Quality Initiative Criteria Documents for the

Protection of Wildlife: DDT; Mercury; 2,3,7,8-TCDD; PCBs (USEPA, 1995) subtracting out exposure

from direct water consumption. The only assumed route of exposure for this evaluation was the

consumption of contaminated fish tissue by wildlife.

Data were available to evaluate a total of 14,420 NSI sampling stations using the wildlife criteria. Based

on wildlife criteria alone, 30 sampling stations would be classified as Tier 1 (matched sediment chemistry

and fish tissue data) and 3,284 sampling stations would be classified as Tier 2 (sediment chemistry TBP

or fish tissue data). If wildlife criteria had been used to complete the national assessment, the number of

Tier 1 stations would have remained at 8,348 stations, the number of Tier 2 stations would have increased

from 5,846 to 6,158, and the number of Tier 3 stations would have decreased to 4,892. The change is

related to 312 sampling stations classified as Tier 3 that would be classified as Tier 2 if the wildlife

criteria were used.

The reason for the increase in Tier 2 stations when using wildlife criteria is twofold: (1) the wildlife

criteria for DDT and mercury are significantly lower than the EPA risk levels used in the corresponding

human health evaluations; (2) the lipid content used in the wildlife TBP analysis (10.31 percent for whole

body) exceeded the lipid content used in the human health TBP analysis (3.0 percent for fillet). No

additional sampling stations would be classified as Tier 1 based on wildlife criteria. For a sampling

station to be classified as Tier 1, both sediment chemistry TBP and measured fish tissue concentrations

taken from that sampling station had to exceed the wildlife criteria. At very few sampling stations in the

NSI were both sediment chemistry and fish tissue levels for the same chemical measured. In those few

cases where contaminants in both media were measured, there were no additional sampling stations

(stations not already classified as Tier 1) where both the sediment chemistry TBP and fish tissue levels

exceeded the wildlife criteria. Moreover, no sampling stations were classified as Tier 1 for exceedance of

the wildlife criteria for mercury because sediment chemistry TBPs cannot be calculated for metals.
National Sediment Quality Survey

3-21

Regional and State Assessment

The remainder of this chapter presents more detailed results from the evaluation of NSI data for sampling

stations in each of the 10 EPA regions and each state. The sections that follow present the number of Tier

1, Tier 2, and Tier 3 sampling stations in each region and state. Tables and figures similar to those

presented in the national assessment of sampling station evaluation results and river reach evaluation

results are included. Regional maps display the location of Tier 1 and Tier 2 sampling stations and APCs.

The presentation format is identical for all regions.

These summary results do not include locations with contaminated sediment not identified in the NSI

database. The data compiled for the NSI database are primarily from large national electronic databases.

Data from many sampling and testing studies have not yet been incorporated into the NSI database. Thus,

there are additional locations with sediment contamination that do not appear in this summary. On the

other hand, data in this evaluation were collected between 1990 and 1999 and any single measurement of

a chemical at a sampling station taken at any point in time during that period could result in classification

of the sampling station in Tier 1 or Tier 2. Because the evaluation is a screening-level analysis, sampling

stations that appear in Tier 1 or Tier 2 might not actually cause unacceptable impacts. In addition,

management programs to address identified sediment contamination might already exist.

It is important to repeat here that some regions and states, as demonstrated in Table 2-1, have

significantly more data compiled and evaluated in this report than do most other regions and states. For

example, more than two-thirds of all stations evaluated in the NSI database are in Washington, Virginia,

California, Illinois, Florida, Wisconsin, New York, Texas, Oregon, and South Carolina. Each of these

states has more than 500 monitoring stations. This situation, to some degree, accounts for the relatively

large number of sampling stations classified as Tier 1 in some regions and states.

EPA Region 1

Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont

EPA evaluated 275 sampling stations in Region 1 as part of the NSI database evaluation. Sediment

contamination associated with probable adverse effects on aquatic life was found at 100 of these sampling

stations, placing them in Tier 1; sediment contamination associated with possible adverse effects was

found at 127 stations, placing them in Tier 2. For human health, data for 150 sampling stations indicated

probable association with adverse effects (Tier 1), and data for 34 sampling stations indicated possible

association with adverse effects (Tier 2). Overall, this evaluation resulted in the classification of 182

sampling stations (66.2 percent) as Tier 1, 64 (23.3 percent) as Tier 2, and 29 (10.5 percent) as Tier 3.

The NSI database sampling stations in Region 1 were located in 125 separate river reaches, or 4.5 percent

of all reaches in the region. About 3.5 percent of all river reaches in Region 1 included at least one Tier 1

station, 0.8 percent included at least one Tier 2 station but no Tier 1 stations, and 0.2 percent had only

Tier 3 stations (Table 3-7). Table 3-8 presents a summary of sampling station classification and

evaluation of river reaches for each state and for the region as a whole.

This evaluation identified 9 watersheds containing APCs out of the 62 watersheds (14.5 percent) in

Region 1 (Table 3-7). In addition, 21.0 percent of all watersheds in the region had at least one Tier 1

sampling station but were not identified as containing APCs, 9.7 percent had at least one Tier 2 station

but no Tier 1 stations, and 0.0 percent had only Tier 3 stations; 54.8 percent of the watersheds did not

include a sampling station. The locations of the watersheds containing APCs and the Tier 1 and Tier 2

sampling stations in Region 1 are illustrated in Figure 3-8.

Within the 9 watersheds in Region 1 identified as containing APCs (Table 3-9), 32 waterbodies have at

least 1 Tier 1 sampling station and 5 waterbodies have 10 or more Tier 1 sampling stations (Table 3-10).

For those watersheds that contain APCs, Table 3-10 presents a list of all waterbodies that contain one or
National Sediment Quality Survey

3-22

more Tier 1 sampling stations. Based on the information in Table 3-10, Boston Bay, Long Island Sound,

the Atlantic Ocean, the Connecticut River, and the Housatonic River appear to have the most significant

sediment contamination in Region 1.

Table 3-7. Region 1: River Reach and Watershed Evaluation Summary.

River Reach Classification Watershed Classification

Total Number of River Reaches 2,764 Total Number of Watersheds 62 

River Reaches With at Least One Tier

1 Station

97 (3.5%) Watersheds Containing APCs 9 (14.5%)

Watersheds With at Least One Tier 1

Station

13 (21.0%)

River Reaches With at Least One Tier

2 Station and Zero Tier 1 Stations

23 (0.8%) Watersheds With at Least One Tier 2

Station and Zero Tier 1 Stations 

6 (9.7%)

River Reaches With All Tier 3

Stations

5 (0.2%) Watersheds With All Tier 3 Stations 0 (0.0%)

River Reaches With No Data 2,639 (95.5%) Watersheds With No Data 34 (54.8%)

Table 3-8. Region 1: Evaluation Results for Sampling Stations and River Reaches by State.

State

Station Evaluation River Reach Evaluationa

 Total Number of  Stations Evaluated

Tier 1 Tier 2 Tier 3

 Number of Stations Not Identified by an RF1 Reachc Reaches With at Least  One Station in Tier 1 Reaches With at Least  One Station in Tier 2d

No. %

 Reaches With All  Stations in Tier 3 Number of Reaches With at  Least One Station Evaluated Total Reaches in Region Percent of All Reaches in  Region With at Least  One Station Evaluated Percent of Reaches With at Least One Tier 1  or Tier 2 Station

b No. %b No. %b

Connecticut 121 103 85.1 15 12.4 3 2.5 28 57 6 1 64 215 29.8 29.3

Maine — — — — — — — — — — — — 1,675 0.0 0.0

Massachusetts 127 64 50.4 38 29.9 25 19.7 — 33 11 3 47 270 17.4 16.3

New Hampshire 4 4 100.0 — 0.0 — 0.0 — 4 2 — 6 283 2.1 2.1

Rhode Island 18 11 61.1 6 33.3 1 5.6 — 10 2 1 13 56 23.2 21.4

Vermont 5 — 0.0 5 100.0 — 0.0 — 5 6 — 11 375 2.9 2.9

Region 1e 275 182 66.2 64 23.3 29 10.5 28 97 23 5 125 2,764 4.5 4.3

a

 River reaches based on EPA River Reach File (RF1).

b

 Percent of all stations evaluated in the NSI in the state. c

 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed. d

 No stations in these reaches were included in Tier 1. e

 Because some reaches occur in more than one state, the total number of reaches in each category for the country might not equal the sum of reaches

in the states.
3-23
National Sediment Quality Survey

3-24

Table 3-9. Region 1: Watersheds Containing Areas of Probable Concern for Sediment

Contamination.

Cataloging

Unit Number Cataloging Unit Name State(s)a

Number of Sampling Stations

Percent of

Sampling

Stations in Tier

Total Tier 1 Tier 2 Tier 3 1 or Tier 2

01080205 Lower Connecticut CT, MA 19 17 2 0 100

01090001 Charles MA 69 38 20 11 84

01090004 Narragansett MA, RI 14 12 1 1 93

01100004 Quinnipiac CT 13 12 1 0 100

01100005 Housatonic CT, MA, NY 24 22 0 2 92

01100006 Saugatuck CT, (NY) 19 18 1 0 100

01100007 Long Island Sound CT, NY 31 23 7 1 97

02020003 Hudson-Hoosic NY, MA, (VT) 163 155 8 0 100

02030202 Southern Long Island NY, CT, NJ 85 47 21 17 80

a

 No data were available for states listed in parentheses.

Table 3-10. Region 1: Number of Tier 1 Stations in Region 1 That Are Located in Watersheds

Containing APCs by Waterbody Name.

Waterbody

Number of

Tier 1

Stations Waterbody

Number of

Tier 1

Stations

Boston Bay 24 Conanicut Island 1

Long Island Sound 15 Green River 1

Atlantic Ocean 13 Hoosic River 1

Connecticut River 11 Ipswich River 1

Housatonic River 11 Konkapot River 1

Quinnipiac River 5 Mattabesset River 1

Boston Harbor And Mystic River Area 4 Muddy River 1

Naugatuck River 4 Neponset River 1

Taunton River 3 Norwalk River 1

Woonasquatucket River 3 Rhode Island 1

Hockanum River 2 Rippowan River 1

Narragansett Bay 2 Saugatuck Reservoir 1

Pawtuxet River 2 Saugus River 1

Scanite River 2 Shepaug River 1

Bantam River 1 Still River 1

Coginchaug River 1 Windsor Brook 1
National Sediment Quality Survey

3-25

EPA Region 2

New Jersey, New York, Puerto Rico

EPA evaluated 1,255 sampling stations in Region 2 as part of the NSI database evaluation. Sediment

contamination associated with probable adverse effects on aquatic life was found at 546 of these sampling

stations, placing them in Tier 1; sediment contamination associated with possible adverse effects was

found at 327 stations, placing them in Tier 2. For human health, data for 834 sampling stations indicated

probable association with adverse effects (Tier 1), and 193 data for sampling stations indicated possible

adverse effects (Tier 2). Overall, this evaluation resulted in the classification of 901 sampling stations

(71.8 percent) as Tier 1, 228 (18.2 percent) as Tier 2, and 126 (10.0 percent) as Tier 3. The NSI database

sampling stations in Region 2 were located in 364 separate river reaches, or 19.7 percent of all reaches in

the region. About 11.8 percent of all river reaches in Region 2 included at least one Tier 1 station, 5.5

percent included at least one Tier 2 station but no Tier 1 stations, and 2.4 percent had only Tier 3 stations

(Table 3-11). Table 3-12 presents a summary of sampling station classification and evaluation of river

reaches for each state and for the region as a whole.

This evaluation identified 17 watersheds containing APCs out of the 71 watersheds (23.9 percent) in

Region 2 (Table 3-11). In addition, 49.3 percent of all watersheds in the region had at least one Tier 1

sampling station but were not identified as containing APCs, 4.2 percent had at least one Tier 2 station

but no Tier 1 stations, and 4.2 percent had only Tier 3 stations; 18.3 percent of the watersheds did not

include a sampling station. The locations of the watersheds containing APCs and the Tier 1 and Tier 2

sampling stations in Region 2 are illustrated in Figure 3-9.

Within the 17 watersheds in Region identified as containing APCs (Table 3-13), 76 waterbodies have at

least 1 Tier 1 sampling station and 13 waterbodies have 10 or more Tier 1 sampling stations (Table 3-14).

For those watersheds that contain APCs, Table 3-14 presents a list of all waterbodies that contain one or

more Tier 1 sampling stations. Based on the information in Table 3-14, Hudson River, Passaic River,

Long Island Sound, Newark Bay, Upper New York Bay, Jamaica Bay, Staten Island, Hackensack River,

Atlantic Ocean, Mohawk River, Sandy Hook Bay, Rockaway River, and Upper Bay appear to have the

most significant sediment contamination in Region 2.

Table 3-11. Region 2: River Reach and Watershed Evaluation Summary.

River Reach Classification Watershed Classification

Total Number of River Reaches 1,845 Total Number of Watersheds 71

River Reaches With at Least One

Tier 1 Station

217 (11.8%) Watersheds Containing APCs 17 (23.9%)

Watersheds With at Least One Tier 1 Station 35 (49.3%)

River Reaches With at Least One

Tier 2 Station and Zero Tier 1

Stations

102 (5.5%) Watersheds With at Least One Tier 2 Station

and Zero Tier 1 Stations 

3 (4.2%)

River Reaches With All Tier 3

Stations

45 (2.4%) Watersheds With All Tier 3 Stations 3 (4.2%)

River Reaches With No Data 1,481 (80.3%) Watersheds With No Data 13 (18.3%)
National Sediment Quality Survey

3-26

Table 3-12. Region 2: Evaluation Results for Sampling Stations and River Reaches by State.

State

Station Evaluation River Reach Evaluationa

 Total Number of  Stations Evaluated

Tier 1 Tier 2 Tier 3

 Number of Stations Not Identified by an RF1 Reachc Reaches With at Least  One Station in Tier 1

 Reaches With at Least  One Station in Tier 2d

No. %

 Reaches With All  Stations in Tier 3

 Number of Reaches With at  Least One Station Evaluated

 Total Reaches in Region Percent of All Reaches in  Region With at Least  One Station Evaluated

 Percent of Reaches With at Least One Tier 1  or Tier 2 Station

b No. %b No. %b

New Jersey 492 341 69.3 102 20.7 49 10.0 — 62 45 21 128 304 42.1 35.2

New York 753 556 73.8 120 15.9 77 10.2 3 166 57 24 247 1,562 15.8 14.3

Puerto Rico 10 4 40.0 6 60.0 — 0.0 10 — — — — — — —

Region 2e 1,255 901 71.8 228 18.2 126 10.0 13 217 102 45 364 1,845 19.7 17.3

a

 River reaches based on EPA River Reach File (RF1).

b

 Percent of all stations evaluated in the NSI in the state. c

 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed. d

 No stations in these reaches were included in Tier 1. e

 Because some reaches occur in more than one state, the total number of reaches in each category for the country might not equal the sum of reaches

in the states.
3-27
National Sediment Quality Survey

3-28

Table 3-13. Region 2: Watersheds Containing Areas of Probable Concern for Sediment

Contamination.

Cataloging

Unit Number Cataloging Unit Name State(s)a

Number of Sampling Stations

Percent of

Sampling

Stations in Tier

Total Tier 1 Tier 2 Tier 3 1 or Tier 2

01100005 Housatonic CT, MA, NY 24 22 0 2 92

01100007 Long Island Sound CT, NY 31 23 7 1 97

02020003 Hudson-Hoosic NY, MA, (VT) 163 155 8 0 100

02020004 Mohawk NY 43 32 7 4 91

02020006 Middle Hudson NY, (MA) 76 55 13 8 89

02020008 Hudson-Wappinger NY 40 34 6 0 100

02030101 Lower Hudson NJ, NY, (CT) 68 60 2 6 91

02030102 Bronx NY 27 26 0 1 96

02030103 Hackensack-Passaic NJ, NY 172 149 20 3 98

02030104 Sandy Hook-Staten Island NJ, NY 194 155 31 8 96

02030105 Raritan NJ 30 15 9 6 80

02030201 Northern Long Island NY 75 62 9 4 95

02030202 Southern Long Island NY, CT, NJ 85 47 21 17 80

02040202 Lower Delaware NJ, PA 26 11 15 0 100

02040205 Brandywine-Christina DE, PA, NJ,

(MD)

220 109 67 44 80

04120101 Chautauqua-Conneaut NY, OH, PA 16 13 2 1 94

04140201 Seneca NY 20 11 4 5 75

a

 No data were available for states listed in parentheses.

Table 3-14. Region 2: Number of Tier 1 Stations in Region 2 That Are Located in Watersheds

Containing APCs by Waterbody Name.

Waterbody

Number of

Tier 1

Stations Waterbody

Number of

Tier 1 Stations

Hudson River 266 Beden Brook 1

Passaic River 111 Big Timber Creek, South Fork 1

Long Island Sound 74 Black Creek 1

Newark Bay 62 Canajoharie Creek 1

Upper New York Bay 36 Canopus Creek 1

Jamaica Bay 31 Cayadutta Creek 1

Staten Island 24 Cincinnati Creek 1

Hackensack River 17 Claverack Creek 1

Atlantic Ocean 16 Clyde River 1

Mohawk River 12 Cranbury Brook 1

Sandy Hook Bay 12 East Bay 1

Rockaway River 11 East Canada Creek 1

Upper Bay 10 Esopus Creek 1
National Sediment Quality Survey

3-29

Table 3-14. (Continued).

Waterbody

Number of

Tier 1

Stations Waterbody

Number of

Tier 1

Stations

East River 9 Fall Creek 1

Sauquoit Creek 9 Flint Creek 1

Valatie Kill 7 Great Peconic Bay 1

Arthur Kill 6 Hohohus Brook 1

Lower Bay 6 Lake Erie, US Shore 1

Ninemile Creek 5 Little Peconic Bay 1

Delaware River 4 Manalapan Brook 1

Rahway River 4 Mud Creek 1

Saddle River 4 Neshanic River 1

Smithtown Bay 4 Onesquethaw Creek 1

Green Brook 3 Oriskany Creek 1

Hoosic River 3 Pennsauken Creek 1

Onondaga Lake 3 Pompton Creek 1

Raritan River 3 Ramapo River 1

Batten Kill 2 Raritan River, North Branch 1

Croton River 2 Raritan River, South Branch 1

Lisha Kill 2 Repaupo Creek 1

Millstone River 2 Seneca River 1

Normans Kill 2 Silver Creek 1

Onondaga Creek 2 Stony Brook 1

Raritan Bay 2 Swamp River 1

Vloman Kill 2 Walnut Creek 1

Walloomsac River 2 Wanaque Reservoir 1

Whippany River 2 Wappinger Creek 1

Amawalk Reservoir 1 Woodbury Creek 1

EPA Region 3

Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia

EPA evaluated 2,428 sampling stations in Region 3 as part of the NSI database evaluation. Sediment

contamination associated with probable adverse effects on aquatic life was found at 405 of these sampling

stations, placing them in Tier 1; sediment contamination associated with possible adverse effects was

found at 836 stations, placing them in Tier 2. For human health, data for 458 sampling stations indicated

probable association with adverse effects (Tier 1), and data for 363 sampling stations indicated possible

association with adverse effects (Tier 2). Overall, this evaluation resulted in the classification of 714

sampling stations (29.4 percent) as Tier 1, 809 (33.3 percent) as Tier 2, and 905 (37.3 percent) as Tier 3.

The NSI database sampling stations in Region 3 were located in 999 separate river reaches, or 29.5

percent of all reaches in the region. About 11.4 percent of all river reaches in Region 3 included at least

one Tier 1 station, 9.2 percent included at least one Tier 2 station but no Tier 1 stations, and 8.9 percent

had only Tier 3 stations (Table 3-15). Table 3-16 presents a summary of sampling station classification

and evaluation of river reaches for each state and for the region as a whole.
National Sediment Quality Survey

3-30

Table 3-15. Region 3: River Reach and Watershed Evaluation Summary.

River Reach Classification Watershed Classification

Total Number of River Reaches 3,388 Total Number of Watersheds 126 

River Reaches With at Least One

Tier 1 Station

385 (11.4%) Watersheds Containing APCs 7 (5.6%)

Watersheds With

at Least One Tier

1 Station

96 (76.2%)

River Reaches With at Least One

Tier 2 Station and Zero Tier 1

Stations

313 (9.2%) Watersheds With at Least One Tier

2 Station and Zero Tier 1 Stations 

11 (8.7%)

River Reaches With All Tier 3

Stations

301 (8.9%) Watersheds With All Tier 3

Stations 

4 (3.2%)

River Reaches With No Data 2,389 (70.5%) Watersheds With No Data 8 (6.3%)

Table 3-16. Region 3: Evaluation Results for Sampling Stations and River Reaches by State.

State

Station Evaluation River Reach Evaluationa

 Total Number of  Stations Evaluated

Tier 1 Tier 2 Tier 3

 Number of Stations Not Identified by an RF1 Reachc Reaches With at Least  One Station in Tier 1 Reaches With at Least  One Station in Tier 2d

No. %

 Reaches With All  Stations in Tier 3 Number of Reaches With at  Least One Station Evaluated Total Reaches in Region Percent of All Reaches in  Region With at Least  One Station Evaluated Percent of Reaches With at Least One Tier 1  or Tier 2 Station

b No. %b No. %b

Delaware 234 112 47.9 74 31.6 48 20.5 — 13 15 4 32 91 35.2 30.8

District of

Columbia

6 2 33.3 3 50.0 1 16.7 — 6 2 — 8 16 50.0 50.0

Maryland 290 125 43.1 106 36.6 59 20.3 44 61 52 15 128 440 29.1 25.7

Pennsylvania 216 121 56.0 43 19.9 52 24.1 — 94 21 25 140 710 19.7 16.2

Virginia 1,577 313 19.8 556 35.3 708 44.9 59 204 227 252 683 1,330 51.4 32.4

West Virginia 105 41 39.0 27 25.7 37 35.2 — 47 27 12 86 1,000 8.6 7.4

Region 3e 2,428 714 29.4 809 33.3 905 37.3 103 385 313 301 999 3,388 29.5 20.6

a

 River reaches based on EPA River Reach File (RF1).

b

 Percent of all stations evaluated in the NSI in the state. c

 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed. d

 No stations in these reaches were included in Tier 1. e

 Because some reaches occur in more than one state, the total number of reaches in each category for the country might not equal the sum of reaches

in the states.
National Sediment Quality Survey

3-31

This evaluation identified 7 watersheds containing areas of APCs out of the 126 watersheds (5.6 percent)

in Region 3 (Table 3-15). In addition, 76.2 percent of all watersheds in the region had at least one Tier 1

sampling station but were not identified as containing APCs, 8.7 percent had at least one Tier 2 station

but no Tier 1 stations, and 3.2 percent had only Tier 3 stations; 6.3 percent of the watersheds did not

include a sampling station. The locations of the watersheds containing APCs and the Tier 1 and Tier 2

sampling stations in Region 3 are illustrated in Figure 3-10A.

Within the 7 watersheds in Region 3 identified as containing APCs (Table 3-17), 25 waterbodies have at

least 1 Tier 1 sampling station; 7 waterbodies have 10 or more Tier 1 sampling stations (Table 3-18). For

those watersheds that contain APCs, Table 3-18 presents a list of all waterbodies that contain one or more

Tier 1 sampling stations. Based on the information in Table 3-18, Christina River, Severn River, South

River, Curtis Bay, Pamunkey River, Red Clay Creek, and Lake Erie shoreline appear to have the most

significant sediment contamination in Region 3.

In 1993, EPA and its state partners in the Chesapeake Bay Program designed three toxics Regions of

Concern in the entire 64,000 square mile Bay watershed, due to contaminated sediment. These areas

were, and still are: the Elizabeth River, Baltimore Harbor and the Anacostia River. These designations

have led to the creation and implementation of Regional Action Plans to address contamination in these

areas. In 1999, the Chesapeake Bay Program published a characterization of the chemical contaminant

effects on living resources in the tidal rivers of the Chesapeake Bay (Figure 3-10B) entitled Targeting

Toxics: A Characterization Report. A Tool for Directing Management and Monitoring Actions in the

Chesapeake Bay’s Tidal Rivers. This characterization offers a more detailed picture of chemical

contaminant problems in individual tidal rivers of the Chesapeake Bay watershed. This report, once again,

confirmed the three Regions of Concern. However, in the National Sediment Quality Survey, the

Anacostia River and the Elizabeth River were not singled out as Areas of Probable Concern because the

larger watersheds containing these rivers did not have a sufficient number of contaminated areas overall

to result in these watersheds being designated as Areas of Probable Concern (i.e., the percent of Tier 1

and 2 stations in the larger watersheds is less than 75%).

An additional 10 tidal areas were identified by the Chesapeake Bay Program as rivers with potential for

adverse effects: Middle River (MD), Back River (MD), Magothy River (MD), Severn River (MD), the

upper and middle Patuxent River (MD), upper and middle Potomac River (MD), Chester River (MD), and

the lower James River (VA). These areas are not as contaminated as the three Regions of Concern, but

deserve management attention. Although the Severn River watershed was designated as containing an

Area of Probable Concern in the National Sediment Quality Survey, the Chesapeake Bay Program has a

finer level of characterization, which distinguishes between highly contaminated sites like the Patapsco

River and areas that have a lesser degree of contamination like the Severn River.

The York watershed (VA) was also classified as containing an Area of Probable Concern in this National

Sediment Quality Survey. This designation is based primarily on a localized area at the mouth of the York

River. The Chesapeake Bay Program also found localized contamination in this area, but, overall

considered the data insufficient to characterize the entire river as having the potential for adverse effects.

The Chesapeake Bay Program is working to fill data gaps in the tidal York River in order to further

characterize it in the future.

The Chesapeake Bay Program Toxics Characterization will be further refined and updated in 2005. All

data generated for this effort will be added to the National Sediment Quality Survey.
3-32

Figure 3-10A. Region 3: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Conta
National Sediment Quality Survey

3-33

Table 3-17. Region 3: Watersheds Containing Areas of Probable Concern for Sediment

Contamination.

Cataloging

Unit Number Cataloging Unit Name State(s)a

Number of Sampling Stations

Percent of

Sampling

Stations in Tier

Total Tier 1 Tier 2 Tier 3 1 or Tier 2

02040202 Lower Delaware NJ, PA 26 11 15 0 100

02040205 Brandywine-Christina DE, (MD), PA, NJ 220 109 67 44 80

02060003 Gunpowder-Patapsco MD, (PA) 32 22 8 2 94

02060004 Severn MD 72 48 20 4 94

02080107 York VA 67 16 35 16 76

04120101 Chautauqua-Conneaut NY, OH, PA 16 13 2 1 94

06010205 Upper Clinch TN, VA 27 10 11 6 78

a

 No data were available for states listed in parentheses.

Table 3-18. Region 3: Number of Tier 1 Stations in Region 3 That Are Located in Watersheds

Containing APCs by Waterbody Name.

Waterbody

Number of

Tier 1

Stations Waterbody

Number of

Tier 1

Stations

Christina River 82 Delaware River 3

Severn River 24 Queen Creek 3

South River 19 Bush River 2

Curtis Bay 13 Chesapeake-Delaware Canal 2

Pamunkey River 11 York River 2

Red Clay Creek 11 Clinch River, Corder Branch 1

Lake Erie, US Shore 10 Clinch River, North Fork 1

White Clay Creek 7 Darby Creek 1

Brandywine Creek 6 Guest River 1

Magothy River 5 Mudlick Creek 1

Black River 4 Stock Creek 1

Chesapeake Bay 3 White Clay Creek, East Branch 1

Clinch River 3
National Sediment Quality Survey

3-34

Figure 3-10B. Status of Chemical Contaminant Effects on Living Resources in the

Chesapeake Bay’s Tidal Rivers. Source: Targeting Toxics: A Characterization Report.

A Tool for Directing Management and Monitoring Actions in the Chesapeake Bay’s

Tidal Rivers.
National Sediment Quality Survey

3-35

EPA Region 4

Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee

EPA evaluated 2,874 sampling stations in Region 4 as part of the NSI database evaluation. Sediment

contamination associated with probable adverse effects on aquatic life was found at 437 of these sampling

stations, placing them in Tier 1; sediment contamination associated with possible adverse effects were

found at 918 stations, placing them in Tier 2. For human health, data for 623 sampling stations indicated

probable association with adverse effects (Tier 1), and data for 762 sampling stations indicated possible

association with adverse effects (Tier 2). Overall, this evaluation resulted in the classification of 841

sampling stations (29.3 percent) as Tier 1, 1,022 (35.6 percent) as Tier 2, and 1,011 (35.2 percent) as

Tier 13. The NSI database sampling stations in Region 4 were located in 1,206 separate river reaches, or

12 percent of all reaches in the region. About 4.4 percent of all river reaches in Region 4 included at least

one Tier 1 station, 4.6 percent included at least one Tier 2 station but no Tier 1 stations, and 3.0 percent

had only Tier 3 stations (Table 3-19). Table 3-20 presents a summary of sampling station classification

and evaluation of river reaches for each state and for the region as a whole.

This evaluation identified 13 watersheds containing APCs out of the 307 watersheds (4.2 percent) in

Region 4 (Table 3-19). In addition, 46.3 percent of all watersheds in the region had at least one Tier 1

sampling station but were not identified as containing APCs, 18.6 percent had at least one Tier 2 station

but no Tier 1 stations, and 8.1 percent had only Tier 3 stations; 22.8 percent of the watersheds did not

include a sampling station. The locations of the watersheds containing APCs and the Tier 1 and Tier 2

sampling stations in Region 4 are illustrated in Figure 3-11.

Within the 13 watersheds in Region 4 identified as containing APCs (Table 3-21), 50 waterbodies have at

least 1 Tier 1 sampling station and 12 waterbodies have 10 or more Tier 1 sampling stations (Table 3-22).

For those watersheds that contain APCs, Table 3-22 presents a list of all waterbodies that contain one or

more Tier 1 sampling stations. Based on the information in Table 3-22, Cooper River, Ashley River,

Hillsborough Bay, Big Sunflower River, Black Bayou, Tennessee River, Pensacola Bay, Savannah River,

Mobile Bay, Chattahoochee River, Turtle River, and Tampa Bay appear to have the most significant

sediment contamination in Region 4.

Table 3-19. Region 4: River Reach and Watershed Evaluation Summary.

River Reach Classification Watershed Classification

Total Number of River Reaches 10,078 Total Number of Watersheds 307 

River Reaches With at Least One Tier

1 Station

444 (4.4%) Watersheds Containing APCs 13 (4.2%)

Watersheds With at Least One Tier 1 Station 142 (46.3%)

River Reaches With at Least One Tier

2 Station and Zero Tier 1 Stations

461 (4.6%) Watersheds With at Least One Tier 2 Station

and Zero Tier 1 Stations 

57 (18.6%)

River Reaches With All Tier 3 Stations 301 (3.0%) Watersheds With All Tier 3 Stations 25 (8.1%)

River Reaches With No Data 8,872 (88.0%) Watersheds With No Data 70 (22.8%)
National Sediment Quality Survey

3-36

Table 3-20. Region 4: Evaluation Results for Sampling Stations and River Reaches by State.

State

Station Evaluation River Reach Evaluationa

 Total Number of  Stations Evaluated

Tier 1 Tier 2 Tier 3

 Number of Stations Not Identified by an RF1 Reachc

 Reaches With at Least  One Station in Tier 1 Reaches With at Least  One Station in Tier 2d

No. %

 Reaches With All  Stations in Tier 3

 Number of Reaches With at  Least One Station Evaluated

 Total Reaches in Region Percent of All Reaches in  Region With at Least  One Station Evaluated

 Percent of Reaches With at Least One Tier 1  or Tier 2 Station

b No. %b No. %b

Alabama 173 56 32.4 55 31.8 62 35.8 — 42 28 36 106 1,592 6.7 4.4

Florida 1,157 270 23.3 346 29.9 541 46.8 15 86 96 75 257 888 28.9 20.5

Georgia 263 115 43.7 117 44.5 31 11.8 — 95 57 27 179 1,707 10.5 8.9

Kentucky 63 24 38.1 27 42.9 12 19.0 — 31 30 18 79 1,276 6.2 4.8

Mississippi 187 97 51.9 52 27.8 38 20.3 — 35 13 13 61 995 6.1 4.8

North Carolina 291 33 11.3 150 51.5 108 37.1 — 35 109 57 201 1,456 13.8 9.9

South Carolina 576 169 29.3 218 37.8 189 32.8 — 100 109 74 283 1,110 25.5 18.8

Tennessee 164 77 47.0 57 34.8 30 18.3 — 59 45 19 123 1,490 8.3 7.0

Region 4e 2,874 841 29.3 1,022 35.6 1,011 35.2 15 444 461 301 1,206 10,078 12.0 9.0

a

 River reaches based on EPA River Reach File (RF1).

b

 Percent of all stations evaluated in the NSI in the state. c

 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed. d

 No stations in these reaches were included in Tier 1. e

 Because some reaches occur in more than one state, the total number of reaches in each category for the country might not equal the sum of reaches

in the states.

Table 3-21. Region 4: Watersheds Containing Areas of Probable Concern for Sediment

Contamination.

Cataloging

Unit Number Cataloging Unit Name State(s)a

Number of Sampling Stations

Percent of

Sampling

Stations in

Tier 1 or

Total Tier 1 Tier 2 Tier 3 Tier 2

03050201 Cooper SC 105 52 30 23 78

03050202 South Carolina Coastal SC 60 33 18 9 85

03060109 Lower Savannah GA, SC 68 20 45 3 96

03070203 Cumberland-St. Simons GA 30 19 8 3 90

03100206 Tampa Bay FL 70 41 18 11 84

03130002 Middle ChattahoocheeLake HardingAL, GA 26 21 4 1 96

03140105 Pensacola Bay FL 59 20 25 14 76

03160205 Mobile Bay AL 31 17 14 0 100

06010201 Watts Bar Lake TN 19 16 3 0 100

06010205 Upper Clinch TN, VA 27 10 11 6 78

06020001 Middle TennesseeChickamaugaGA, TN, (AL) 33 15 12 6 82

08030207 Big Sunflower MS 38 38 0 0 100

08030209 Deer-Steele MS, (LA) 24 23 1 0 100

a

 No data were available for states listed in parentheses.
3-37
National Sediment Quality Survey

3-38

Table 3-22. Region 4: Number of Tier 1 Stations in Region 4 That Are Located in Watersheds

Containing APCs by Waterbody Name.

Waterbody

Number of

Tier 1

Stations Waterbody

Number of

Tier 1

Stations

Cooper River 45 Back River 1

Ashley River 28 Bullfrog Creek 1

Hillsborough Bay 28 Clinch River 1

Big Sunflower River 23 Cooper River, West Branch 1

Black Bayou 18 Cooper River/Charleston Harbor 1

Tennessee River 18 Cumberland River 1

Pensacola Bay 17 Deer Creek 1

Savannah River 17 Dorchester Creek 1

Mobile Bay 15 Fort Loudoun Lake 1

Chattahoochee River 13 Goose Creek 1

Turtle River 13 Hillabatchee Creek 1

Tampa Bay 11 Intracoastal Waterway 1

Watts Bar Lake 8 Jekyll Island 1

Bogue Phalia 7 Lake Harding 1

Little Sunflower River 6 Lake Moultrie 1

Atlantic Ocean 3 Lake Washington 1

Lake Chickamauga 3 Norris Lake 1

Savannah River, South Channel 3 Noses Creek 1

St. Simons Sound 3 Old Tampa Bay 1

Steele Bayou 3 Quiver River 1

Carpenter Creek 2 Santa Rosa Sound 1

Long Cane Creek 2 Silver Creek 1

Muddy Creek 2 St. Simons Island 1

Utoy Creek 2 West Chickamauga Creek 1

Wando River 2 West Pont Lake 1

EPA Region 5

Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin

EPA evaluated 3,185 sampling stations in Region 5 as part of the NSI database evaluation. Sediment

contamination associated with probable adverse effects on aquatic life was found at 606 of these sampling

stations, placing them in Tier 1; sediment contamination associated with possible adverse effects was

found at 1,052 stations, placing them in Tier 2. For human health, data for 888 sampling stations

indicated probable association with adverse effects (Tier 1), and data for 647 sampling stations indicated

possible association with adverse effects (Tier 2). Overall, this evaluation resulted in the classification of

1,146 sampling stations (36.0 percent) as Tier 1, 1,095 (34.4 percent) as Tier 2, and 944 (29.6 percent) as

Tier 3. The NSI database sampling stations in Region 5 were located in 1,249 separate river reaches, or

20.3 percent of all reaches in the region. About 8.6 percent of all river reaches in Region 5 included at

least one Tier 1 station, 6.5 percent included at least one Tier 2 station but no Tier 1 stations, and 5.1

percent had only Tier 3 stations (Table 3-23). Table 3-24 presents a summary of sampling station

classification and evaluation of river reaches for each state and for the region as a whole.
National Sediment Quality Survey

3-39

This evaluation identified 25 watersheds containing APCs out of the 278 watersheds (9.0 percent) in

Region 5 (Table 3-23). In addition, 51.8 percent of all watersheds in the region had at least one Tier 1

sampling station but were not identified as containing APCs, 11.2 percent had at least one Tier 2 station

but no Tier 1 stations, and 6.8 percent had only Tier 3 stations; 21.2 percent of the watersheds did not

include a sampling station. The locations of the watersheds containing APCs and the Tier 1 and Tier 2

sampling stations in Region 5 are illustrated in Figure 3-12.

Within the 25 watersheds in Region 5 identified as containing APCs (Table 3-25), 83 waterbodies have at

least 1 Tier 1 sampling station and 12 waterbodies have 10 or more Tier 1 sampling stations (Table 3-26).

For those watersheds that contain APCs, Table 3-26 presents a list of all waterbodies that contain one or

more Tier 1 sampling stations. Based on the information in Table 3-26, Mississippi River, Fox River,

Lake Michigan, Illinois River, Chicago Sanitary Ship Canal, Des Plains River, Menominee River, Salt

Creek, White River, Duck Creek, Green River, and Kanakee River appear to have the most significant

sediment contamination in Region 5.

Table 3-23. Region 5: River Reach and Watershed Evaluation Summary.

River Reach Classification Watershed Classification

Total Number of River Reaches 6,151 Total Number of Watersheds 278 

River Reaches With at Least One Tier

1 Station

532 (8.6%) Watersheds Containing APCs 25 (9.0%)

Watersheds With at Least One Tier 1

Station

144 (51.8%)

River Reaches With at Least One Tier

2 Station and Zero Tier 1 Stations

401 (6.5%) Watersheds With at Least One Tier 2

Station and Zero Tier 1 Stations 

31 (11.2%)

River Reaches With All Tier 3 Stations 316 (5.1%) Watersheds With All Tier 3 Stations 19 (6.8%)

River Reaches With No Data 4,902 (79.7%) Watersheds With No Data 59 (21.2%)

Table 3-24. Region 5: Evaluation Results for Sampling Stations and River Reaches by State.

State

Station Evaluation River Reach Evaluationa

 Total Number of  Stations Evaluated

Tier 1 Tier 2 Tier 3

 Number of Stations Not Identified by an RF1 Reachc Reaches With at Least  One Station in Tier 1 Reaches With at Least  One Station in Tier 2d

No. %

 Reaches With All  Stations in Tier 3 Number of Reaches With at  Least One Station Evaluated Total Reaches in Region Percent of All Reaches in  Region With at Least  One Station Evaluated Percent of Reaches With at Least One Tier 1  or Tier 2 Station

b No. %b No. %b

Illinois 1,370 490 35.8 577 42.1 303 22.1 — 236 181 73 490 936 52.4 44.6

Indiana 233 130 55.8 74 31.8 29 12.4 — 73 39 15 127 561 22.6 20.0

Michigan 30 14 46.7 10 33.3 6 20.0 — 12 11 5 28 1,178 2.4 2.0

Minnesota 339 118 34.8 38 11.2 183 54.0 — 86 31 105 222 1,392 15.9 8.4

Ohio 441 71 16.1 240 54.4 130 29.5 — 51 99 50 200 1,056 18.9 14.2

Wisconsin 772 323 41.8 156 20.2 293 38.0 — 121 65 76 262 1,210 21.7 15.4

Region 5e 3,185 1,146 36.0 1,095 34.4 944 29.6 — 532 401 316 1,249 6,151 20.3 15.2

a

 River reaches based on EPA River Reach File (RF1).

b

 Percent of all stations evaluated in the NSI in the state. c

 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed. d

 No stations in these reaches were included in Tier 1. e

 Because some reaches occur in more than one state, the total number of reaches in each category for the country might not equal the sum of reaches

in the states.
3-40
National Sediment Quality Survey

3-41

Table 3-25. Region 5: Watersheds Containing Areas of Probable Concern for Sediment

Contamination.

Cataloging

Unit Number Cataloging Unit Name State(s)a

Number of Sampling Stations

Percent of

Sampling

Stations in Tier

Total Tier 1 Tier 2 Tier 3 1 or Tier 2

04030108 Menominee MI, WI 21 18 2 1 95

04030204 Lower Fox WI 26 16 5 5 81

04040001 Little Calumet-Galien IL, IN, (MI) 24 22 1 1 96

04040002 Pike-Root IL, WI 60 39 13 8 87

04120101 Chautauqua-Conneaut NY, OH, PA 16 13 2 1 94

05060001 Upper Scioto OH 50 10 32 8 84

05120106 Tippecanoe IN 25 17 3 5 80

05120201 Upper White IN 42 23 14 5 88

05120208 Lower East Fork White IN 19 10 8 1 95

07040001 Rush-Vermillion MN, WI 19 10 5 4 79

07080101 Copperas-Duck IL, IA 136 99 22 15 89

07090005 Lower Rock IL, (WI) 37 11 20 6 84

07090007 Green IL 47 17 24 6 87

07120001 Kankakee IL, IN, (MI) 34 20 10 4 88

07120002 Iroquois IL, IN 29 10 18 1 97

07120003 Chicago IL, IN 49 34 14 1 98

07120004 Des Plaines IL, WI 81 40 37 4 95

07120005 Upper Illinois IL 24 11 12 1 96

07120006 Upper Fox IL, WI 81 31 37 13 84

07120007 Lower Fox IL 26 10 13 3 88

07130001 Lower IllinoisSenachwine LakeIL 12 11 1 0 100

07130003 Lower Illinois-Lake

Chautauqua

IL 36 16 15 5 86

07130007 South Fork Sangamon IL 16 12 4 0 100

07130011 Lower Illinois IL 36 17 14 5 86

07130012 Macoupin IL 19 10 9 0 100

a

 No data were available for states listed in parentheses.
National Sediment Quality Survey

3-42

Table 3-26. Region 5: Number of Tier 1 Stations in Region 5 That Are Located in Watersheds

Containing APCs by Waterbody Name.

Waterbody

Number of

Tier 1 Stations Waterbody

Number of

Tier 1 Stations

Mississippi River 43 Fall Creek 2

Fox River 38 Honey Creek 2

Lake Michigan 37 Jackson Creek 2

Illinois River 34 Lake Calumet 2

Chicago Sanitary Ship Canal 19 Mckee Creek 2

Des Plains River 18 Olentangy River 2

Menominee River 18 Otter Creek 2

Salt Creek 16 Sandy Creek 2

White River 16 Sangamon River, South Fork 2

Duck Creek 15 Ashwaubenon Creek 1

Green River 15 Blackberry Creek 1

Kanakee River 15 Buck Creek 1

Fox Lake 9 Burns Ditch 1

Wolf Lake 9 Cicero Creek 1

Little Calumet River 7 Du Page River, East Branch 1

Rock River 7 Du Page River, West Branch 1

Tippecanoe River 7 Eagle Creek 1

Deeds Creek 6 Exline Slough 1

Indiana Harbor Canal 5 Kent Creek, North Fork 1

Macoupin Creek 5 Kyte River 1

Mill Creek 5 Lake Muskego 1

Beaver Creek 4 Lake Springfield 1

Chicago River, North Branch 4 Leatherwood Creek 1

Hodges Creek 4 Macoupin Creek, Dry Fork 1

Iroquois River 4 Mauvaise Terre Creek 1

Lake Chautauqua 4 Mauvaise Terre Lake 1

Mazoon River 4 Mazon River, West Fork 1

Root River 4 Mud Creek 1

Sangchris Lake 4 Pewaukee Lake 1

Scioto River 4 Pike River 1

Yellow Creek 4 Pipe Creek 1

Calumet River 3 Portage-Burns Waterway 1

Calumet Sag Channel 3 Prairie Creek 1

Du Page River 3 Rock Creek 1

Horse Creek 3 Somonauk Creek 1

Indian Creek 3 Spring Creek 1

Lake Taylorsville 3 Sugar Run 1

Sugar Creek 3 Trim Creek 1

White River, East Fork 3 Vermillion River 1

Apple Creek 2 West Bureau Creek 1

Chicago Ship Canal 2 White Lick Creek 1

Elkhorn Creek 2
National Sediment Quality Survey

3-43

EPA Region 6

Arkansas, Louisiana, New Mexico, Oklahoma, Texas

EPA evaluated 1,489 sampling stations in Region 6 as part of the NSI database evaluation. Sediment

contamination associated with probable adverse effects on aquatic life was found at 187 of these sampling

stations, placing them in Tier 1; sediment contamination associated with possible adverse effects was

found at 394 stations, placing them in Tier 2. For human health, data for 330 sampling stations indicated

probable association with adverse effects (Tier 1), and data for 209 sampling stations indicated possible

association with adverse effects (Tier 2). Overall, this evaluation resulted in the classification of 425

sampling stations (28.5 percent) as Tier 1, 392 (26.3 percent) as Tier 2, and 672 (45.1 percent) as Tier 3.

The NSI database sampling stations in Region 6 were located in 737 separate river reaches, or 9.7 percent

of all reaches in the region. Three percent of all river reaches in Region 6 included at least one Tier 1

station, 2.9 percent included at least one Tier 2 station but no Tier 1 stations, and 3.8 percent had only

Tier 3 stations (Table 3-27). Table 3-28 presents a summary of sampling station classification and

evaluation of river reaches for each state and for the region as a whole.

This evaluation identified 4 watersheds containing APCs out of the 402 watersheds (1.0 percent) in

Region 6 (Table 3-27). In addition, 29.1 percent of all watersheds in the region had at least one Tier 1

sampling station but were not identified as containing APCs, 17.2 percent had at least one Tier 2 station

but no Tier 1 stations, and 10.9 percent had only Tier 3 stations; 41.8 percent of the watersheds did not

include a sampling station. The locations of the watersheds containing APCs and the Tier 1 and Tier 2

sampling stations in Region 6 are illustrated in Figure 3-13.

Within the 4 watersheds in Region 6 identified as containing APCs (Table 3-29), 17 waterbodies have at

least 1 Tier 1 sampling station and 3 waterbodies have 10 or more Tier 1 sampling stations (Table 3-30).

For those watersheds that contain APCs, Table 3-30 presents a list of all waterbodies that contain one or

more Tier 1 sampling stations. Based on the information in Table 3-30, the Mississippi River, the

Colorado River, and Mountain Creek Lake appear to have the most significant sediment contamination in

Region 6.

Although the Buffalo-San Jacinto watershed was not designated as containing an APC, a study of the

Houston Ship Channel, its tributaries and side bays yielded useful data to characterize sediment quality

(ENSR, 1995). The study found elevated levels of several contaminants and significant toxicity at several

sites, including Patrick Bayou, which recently has been added to the National Priorities List (NPL). Other

unpublished data collected primarily by the Superfund Program in Lavaca Bay, Texas (mercury) and

Calcasieu Estuary, Louisiana (both priority pollutant organics and metals) demonstrates both sediment

contamination and bioaccumulation is occurring in these watersheds.

Table 3-27. Region 6: River Reach and Watershed Evaluation Summary.

River Reach Classification Watershed Classification

Total Number of River Reaches 7,577 Total Number of Watersheds 402 

River Reaches With at Least One

Tier 1 Station

226 (3.0%) Watersheds Containing APCs 4 (1.0%)

Watersheds With at Least One Tier

1 Station

117 (29.1%)

River Reaches With at Least One

Tier 2 Station and Zero Tier 1

Stations

222 (2.9%) Watersheds With at Least One Tier

2 Station and Zero Tier 1 Stations 

69 (17.2%)

River Reaches With All Tier 3

Stations

289 (3.8%) Watersheds With All Tier 3

Stations 

44 (10.9%)

River Reaches With No Data 6,840 (90.3%) Watersheds With No Data 168 (41.8%)
National Sediment Quality Survey

3-44

Table 3-28. Region 6: Evaluation Results for Sampling Stations and River Reaches by State.

State

Station Evaluation River Reach Evaluationa

 Total Number of  Stations Evaluated

Tier 1 Tier 2 Tier 3

 Number of Stations Not Identified by an RF1 Reachc Reaches With at Least  One Station in Tier 1 Reaches With at Least  One Station in Tier 2d

No. %

 Reaches With All  Stations in Tier 3 Number of Reaches With at  Least One Station Evaluated Total Reaches in Region Percent of All Reaches in  Region With at Least  One Station Evaluated Percent of Reaches With at Least One Tier 1  or Tier 2 Station

b No. %b No. %b

Arkansas 34 13 38.2 15 44.1 6 17.6 — 18 15 5 38 883 4.3 3.7

Louisiana 396 128 32.3 97 24.5 171 43.2 — 38 40 40 118 886 13.3 8.8

New Mexico 167 10 6.0 48 28.7 109 65.3 — 11 37 44 92 941 9.8 5.1

Oklahoma 292 69 23.6 47 16.1 176 60.3 — 44 40 110 194 1,363 14.2 6.2

Texas 600 205 34.2 185 30.8 210 35.0 — 128 102 103 333 3,734 8.9 6.2

Region 6e 1,489 425 28.5 392 26.3 672 45.1 — 226 222 289 737 7,577 9.7 5.9

a

 River reaches based on EPA River Reach File (RF1).

b

 Percent of all stations evaluated in the NSI in the state. c

 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed. d

 No stations in these reaches were included in Tier 1. e

 Because some reaches occur in more than one state, the total number of reaches in each category for the country might not equal the sum of reaches

in the states.

Table 3-29. Region 6: Watersheds Containing Areas of Probable Concern for Sediment

Contamination.

Cataloging

Unit Number Cataloging Unit Name State(s)

Number of Sampling Stations

Percent of

Sampling

Stations in

Tier 1 or

Total Tier 1 Tier 2 Tier 3 Tier 2

08090100 Lower Mississippi-New Orleans LA 34 28 6 0 100

11070209 Lower Neosho AR, OK 20 11 5 4 80

12030102 Lower West Fork Trinity TX 31 19 10 2 94

12090205 Austin-Travis Lakes TX 22 16 4 2 91
3-45
National Sediment Quality Survey

3-46

Table 3-30. Region 6: Number of Tier 1 Stations in Region 6 That Are Located in Watersheds

Containing APCs by Waterbody Name.

Waterbody

Number of

Tier 1

Stations Waterbody

Number of

Tier 1

Stations

Mississippi River 22 Barton Creek 1

Colorado River 14 East Bay 1

Mountain Creek Lake 11 Lake Austin 1

Neosho River 5 Lake Hudson 1

Trinity River, West Fork 4 Mississippi River, Pass Loutre 1

Gulf Of Mexico 3 Mississippi River, SW Pass 1

Pryor Creek 3 Mountain Creek 1

Big Fossile Creek 2 Rush Creek 1

Fort Gibson Lake 2

EPA Region 7

Iowa, Kansas, Missouri, Nebraska

EPA evaluated 583 sampling stations in Region 7 as part of the NSI database evaluation. Sediment

contamination associated with probable adverse effects on aquatic life was found at 73 of these sampling

stations, placing them in Tier 1; sediment contamination associated with possible adverse effects was

found at 165 stations, placing them in Tier 2. For human health, data for 106 sampling stations indicated

probable association with adverse effects (Tier 1), and data for 125 sampling stations indicated possible

association with adverse effects (Tier 2). Overall, this evaluation resulted in the classification of 134

sampling stations (23.0 percent) as Tier 1, 239 (41.0 percent) as Tier 2, and 210 (36.0 percent) as Tier 3.

The NSI database sampling stations in Region 7 were located in 391 separate river reaches, or 8 percent

of all reaches in the region. About 1.9 percent of all river reaches in Region 7 included at least one Tier 1

station, 3.3 percent included at least one Tier 2 station but no Tier 1 stations, and 2.8 percent had only

Tier 3 stations (Table 3-31). Table 3-32 presents a summary of sampling station classification and

evaluation of river reaches for each state and for the region as a whole.

This evaluation identified 1 watershed containing APCs out of the 238 watersheds (0.4 percent) in Region

7 (Table 3-31). In addition, 25.2 percent of all watersheds in the region had at least one Tier 1 sampling

station but were not identified as containing APCs, 30.3 percent had at least one Tier 2 station but no Tier

1 stations, and 12.2 percent had only Tier 3 stations; 31.9 percent of the watersheds did not include a

sampling station. The locations of the watersheds containing APCs and the Tier 1 and Tier 2 sampling

stations in Region 7 are illustrated in Figure 3-14.

Within the one watershed in Region 7 identified as containing APCs (Table 3-33), 2 waterbodies have at

least 1 Tier 1 sampling station and 1 waterbody has 10 or more Tier 1 sampling stations (Table 3-34). For

those watersheds that contain APCs, Table 3-34 presents a list of all waterbodies that contain one or more

Tier 1 sampling stations. Based on the information in Table 3-34, the Mississippi River appears to have

the most significant sediment contamination in Region 7.
National Sediment Quality Survey

3-47

Table 3-31. Region 7: River Reach and Watershed Evaluation Summary.

River Reach Classification Watershed Classification

Total Number of River Reaches 4,915 Total Number of Watersheds 238 

River Reaches With at Least One Tier 1

Station

94 (1.9%) Watersheds Containing APCs 1 (0.4%)

Watersheds With at Least One Tier 1 Station 60 (25.2%)

River Reaches With at Least One Tier 2

Station and Zero Tier 1 Stations

161 (3.3%) Watersheds With at Least One Tier 2 Station

and Zero Tier 1 Stations 

72 (30.3%)

River Reaches With All Tier 3 Stations 136 (2.8%) Watersheds With All Tier 3 Stations 29 (12.2%)

River Reaches With No Data 4,524 (92.0%) Watersheds With No Data 76 (31.9%)

Table 3-32. Region 7: Evaluation Results for Sampling Stations and River Reaches by State.

State

Station Evaluation River Reach Evaluationa

 Total Number of  Stations Evaluated

Tier 1 Tier 2 Tier 3

 Number of Stations Not Identified by an RF1 Reachc Reaches With at Least  One Station in Tier 1 Reaches With at Least  One Station in Tier 2d

No. %

 Reaches With All  Stations in Tier 3 Number of Reaches With at  Least One Station Evaluated Total Reaches in Region Percent of All Reaches in  Region With at Least  One Station Evaluated Percent of Reaches With at Least One Tier 1  or Tier 2 Station

b No. %b No. %b

Iowa 113 59 52.2 35 31.0 19 16.8 — 24 24 13 61 1,204 5.1 4.0

Kansas 119 18 15.1 53 44.5 48 40.3 — 16 47 41 104 1,192 8.7 5.3

Missouri 194 40 20.6 89 45.9 65 33.5 — 42 42 29 113 1,403 8.1 6.0

Nebraska 157 17 10.8 62 39.5 78 49.7 — 18 52 54 124 1,270 9.8 5.5

Region 7e 583 134 23.0 239 41.0 210 36.0 — 94 161 136 391 4,915 8.0 5.2

a

 River reaches based on EPA River Reach File (RF1).

b

 Percent of all stations evaluated in the NSI in the state. c

 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed. d

 No stations in these reaches were included in Tier 1. e

 Because some reaches occur in more than one state, the total number of reaches in each category for the country might not equal the sum of reaches

in the states.
3-48
National Sediment Quality Survey

3-49

Table 3-33. Region 7: Watersheds Containing Areas of Probable Concern for Sediment

Contamination.

Cataloging

Unit Number Cataloging Unit Name State(s)

Number of Sampling Stations

Percent of

Sampling

Stations in Tier

Total Tier 1 Tier 2 Tier 3 1 or Tier 2

07080101 Copperas-Duck IL, IA 136 99 22 15 89

Table 3-34. Region 7: Number of Tier 1 Stations in Region 7 That Are Located in Watersheds

Containing APCs by Waterbody Name.

Waterbody

Number of

Tier 1

Stations Waterbody

Number of

Tier 1 Stations

Mississippi River 49 Duck Creek 1

EPA Region 8

Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming

EPA evaluated 294 sampling stations in Region 8 as part of the NSI database evaluation. Sediment

contamination associated with probable adverse effects on aquatic life was found at 59 of these sampling

stations, placing them in Tier 1; sediment contamination associated with possible adverse effects was

found at 105 stations, placing them in Tier 2. For human health, data for 26 sampling stations indicated

probable association with adverse effects (Tier 1), and data for 25 sampling stations indicated possible

association with adverse effects (Tier 2). Overall, this evaluation resulted in the classification of 79

sampling stations (26.9 percent) as Tier 1, 95 (32.3 percent) as Tier 2, and 120 (40.8 percent) as Tier 3.

The NSI database sampling stations in Region 8 were located in 204 separate river reaches, or 1.5 percent

of all reaches in the region. Only 0.4 percent of all river reaches in Region 8 included at least one Tier 1

station, 0.6 percent included at least one Tier 2 station but no Tier 1 stations, and 0.5 percent had only

Tier 3 stations (Table 3-35). Table 3-36 presents a summary of sampling station classification and

evaluation of river reaches for each state and for the region as a whole.

This evaluation identified 1 watershed containing APCs out of the 385 watersheds (0.3 percent) in Region

8 (Table 3-35). In addition, 8.8 percent of all watersheds in the region had at least one Tier 1 sampling

station but were not identified as containing APCs, 10.6 percent had at least one Tier 2 station but no Tier

1 stations, and 8.1 percent had only Tier 3 stations; 72.2 percent of the watersheds did not include a

sampling station. The locations of the watersheds containing APCs and the Tier 1 and Tier 2 sampling

stations in Region 8 are illustrated in Figure 3-15.

Within the 1 watershed in Region 8 identified as containing APCs (Table 3-37), 5 waterbodies have at

least 1 Tier 1 sampling station and no waterbodies have 10 or more Tier 1 sampling stations (Table 3-38).

For those watersheds that contain APCs, Table 3-38 presents a list of all waterbodies that contain one or

more Tier 1 sampling stations. Based on the information in Table 3-38, the Blue River appears to have the

most significant sediment contamination in Region 8. 
National Sediment Quality Survey

3-50

Table 3-35. Region 8: River Reach and Watershed Evaluation Summary.

River Reach Classification Watershed Classification

Total Number of River Reaches 13,860 Total Number of Watersheds 385 

River Reaches With at Least One Tier 1

Station

59 (0.4%) Watersheds Containing APCs 1 (0.3%)

Watersheds With at Least One Tier 1

Station

34 (8.8%)

River Reaches With at Least One Tier 2

Station and Zero Tier 1 Stations

77 (0.6%) Watersheds With at Least One Tier 2

Station and Zero Tier 1 Stations 

41 (10.6%)

River Reaches With All Tier 3 Stations 68 (0.5%) Watersheds With All Tier 3 Stations 31 (8.1%)

River Reaches With No Data 13,656 (98.5%) Watersheds With No Data 278 (72.2%)

Table 3-36. Region 8: Evaluation Results for Sampling Stations and River Reaches by State.

State

Station Evaluation River Reach Evaluationa

 Total Number of  Stations Evaluated

Tier 1 Tier 2 Tier 3

 Number of Stations Not Identified by an RF1 Reachc Reaches With at Least  One Station in Tier 1

 Reaches With at Least  One Station in Tier 2d

No. %

 Reaches With All  Stations in Tier 3

 Number of Reaches With at  Least One Station Evaluated

 Total Reaches in Region Percent of All Reaches in  Region With at Least  One Station Evaluated

 Percent of Reaches With at Least One Tier 1  or Tier 2 Station

b No. %b No. %b

Colorado 133 52 39.1 43 32.3 38 28.6 — 36 33 21 90 2,204 4.1 3.1

Montana 11 — 0.0 3 27.3 8 72.7 — — 3 9 12 5,606 0.2 0.1

North Dakota 33 6 18.2 16 48.5 11 33.3 — 9 14 4 27 1,042 2.6 2.2

South Dakota 32 18 56.3 6 18.8 8 25.0 — 12 4 8 24 1,691 1.4 0.9

Utah 56 2 3.6 17 30.4 37 66.1 — 2 13 18 33 1,080 3.1 1.4

Wyoming 29 1 3.4 10 34.5 18 62.1 — 1 13 14 28 2,474 1.1 0.6

Region 8e 294 79 26.9 95 32.3 120 40.8 — 59 77 68 204 13,860 1.5 1.0

a

 River reaches based on EPA River Reach File (RF1).

b

 Percent of all stations evaluated in the NSI in the state. c

 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed. d

 No stations in these reaches were included in Tier 1. e

 Because some reaches occur in more than one state, the total number of reaches in each category for the country might not equal the sum of reaches

in the states.
3-51
National Sediment Quality Survey

3-52

Table 3-37. Region 8: Watersheds Containing Areas of Probable Concern for Sediment

Contamination.

Cataloging

Unit Number Cataloging Unit Name State(s)

Number of Sampling Stations

Percent of

Sampling

Stations in Tier

Total Tier 1 Tier 2 Tier 3 1 or Tier 2

14010002 Blue CO 15 15 0 0 100

Table 3-38. Region 8: Number of Tier 1 Stations in Region 8 That Are Located in Watersheds

Containing APCs by Waterbody Name.

Waterbody

Number of

Tier 1 Stations Waterbody

Number of Tier

1 Stations

Blue River 8 Dillon Reservoir 1

Swan River 3 Tenmile Creek 1

Snake River 2

EPA Region 9

Arizona, California, Hawaii, Nevada

EPA evaluated 1,752 sampling stations in Region 9 as part of the NSI database evaluation. Sediment

contamination associated with probable adverse effects on aquatic life was found at 790 of these sampling

stations, placing them in Tier 1; sediment contamination associated with possible adverse effects was

found at 577 stations, placing them in Tier 2. For human health, data for 645 sampling stations indicated

probable association with adverse effects (Tier 1), and data for 280 sampling stations indicated possible

association with adverse effects (Tier 2). Overall, this evaluation resulted in the classification of 1,040

sampling stations (59.4 percent) as Tier 1, 429 (24.5 percent) as Tier 2, and 283 (16.2 percent) as Tier 3.

The NSI database sampling stations in Region 9 were located in 259 separate river reaches, or 5.5 percent

of all reaches in the region. About 3.3 percent of all river reaches in Region 9 included at least one Tier 1

station, 1.3 percent included at least one Tier 2 station but no Tier 1 stations, and 0.9 percent had only

Tier 3 stations (Table 3-39). Table 3-40 presents a summary of sampling station classification and

evaluation of river reaches for each state and for the region as a whole.

This evaluation identified 19 watersheds containing APCs out of the 288 watersheds (6.6 percent) in

Region 9 (Table 3-39). In addition, 14.2 percent of all watersheds in the region had at least one Tier 1

sampling station but were not identified as containing APCs, 6.6 percent had at least one Tier 2 station

but no Tier 1 stations, and 5.2 percent had only Tier 3 stations; 67.4 percent of the watersheds did not

include a sampling station. The locations of the watersheds containing APCs and the Tier 1 and Tier 2

sampling stations in Region 9 are illustrated in Figure 3-16.

Within the 19 watersheds in Region 9 identified as containing APCs (Table 3-41), 36 waterbodies have at

least 1 Tier 1 sampling station and 14 waterbodies have 10 or more Tier 1 sampling stations (Table 3-42).

For those watersheds that contain APCs, Table 3-42 presents a list of all waterbodies that contain one or

more Tier 1 sampling stations. Based on the information in Table 3-42, the Pacific Ocean, San Diego

Bay, San Francisco Bay, San Pablo Bay, Santa Ana River, Comanche Reservoir, Salt River, Arcata Bay,

Cave Creek, Charro Creek, San Diego Creek, Sacramento River, Suisun Bay, and Dominguez Channel

appear to have the most significant sediment contamination in Region 9.
National Sediment Quality Survey

3-53

Table 3-39. Region 9: River Reach and Watershed Evaluation Summary.

River Reach Classification Watershed Classification

Total Number of River Reaches 4,686 Total Number of Watersheds 288 

River Reaches With at Least One Tier 1

Station

156 (3.3%) Watersheds Containing APCs 19 (6.6%)

Watersheds With at Least One Tier 1

Station

41 (14.2%)

River Reaches With at Least One Tier 2

Station and Zero Tier 1 Stations

63 (1.3%) Watersheds With at Least One Tier 2

Station and Zero Tier 1 Stations 

19 (6.6%)

River Reaches With All Tier 3 Stations 40 (0.9%) Watersheds With All Tier 3 Stations 15 (5.2%)

River Reaches With No Data 4,427 (94.5%) Watersheds With No Data 194 (67.4%)

Table 3-40. Region 9: Evaluation Results for Sampling Stations and River Reaches by State.

State

Station Evaluation River Reach Evaluationa

 Total Number of  Stations Evaluated

Tier 1 Tier 2 Tier 3

 Number of Stations Not Identified by an RF1 Reachc Reaches With at Least  One Station in Tier 1 Reaches With at Least  One Station in Tier 2d

No. %

 Reaches With All  Stations in Tier 3 Number of Reaches With at  Least One Station Evaluated Total Reaches in Region Percent of All Reaches in  Region With at Least  One Station Evaluated Percent of Reaches With at Least One Tier 1  or Tier 2 Station

b No. %b No. %b

Arizona 123 70 56.9 35 28.5 18 14.6 — 15 15 9 39 1,169 3.3 2.6

California 1,535 935 60.9 353 23.0 247 16.1 — 132 32 32 196 2,655 7.4 6.2

Hawaii 18 10 55.6 1 5.6 7 38.9 18 — — — — — — —

Nevada 76 25 32.9 40 52.6 11 14.5 — 10 19 3 32 935 3.4 3.1

Region 9e 1,752 1,040 59.4 429 24.5 283 16.2 18 156 63 40 259 4,686 5.5 4.7

a

 River reaches based on EPA River Reach File (RF1).

b

 Percent of all stations evaluated in the NSI in the state. c

 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed. d

 No stations in these reaches were included in Tier 1. e

 Because some reaches occur in more than one state, the total number of reaches in each category for the country might not equal the sum of reaches

in the states.
3-54
National Sediment Quality Survey

3-55

Table 3-41. Region 9: Watersheds Containing Areas of Probable Concern for Sediment Contamination.

Cataloging

Unit Number Cataloging Unit Name State(s)

Number of Sampling Stations

Percent of

Sampling

Stations in Tier

Total Tier 1 Tier 2 Tier 3 1 or Tier 2

15060106 Lower Salt AZ 52 39 13 0 100

16050203 Carson Desert NV 19 14 5 0 100

18010102 Mad-Redwood CA 26 20 4 2 92

18020112 Sacramento-Upper Clear CA 25 23 2 0 100

18040005 Lower Cosumnes-Lower

Mokelumne

CA 60 23 23 14 77

18050001 Suisun Bay CA 27 16 8 3 89

18050002 San Pablo Bay CA 101 69 28 4 96

18050003 Coyote CA 32 25 7 0 100

18050004 San Francisco Bay CA 130 113 16 1 99

18060006 Central Coastal CA 54 25 22 7 87

18060011 Alisal-Elkhorn Sloughs CA 34 25 9 0 100

18070103 Calleguas CA 26 26 0 0 100

18070104 Santa Monica Bay CA 132 109 21 2 98

18070106 San Gabriel CA 34 21 11 2 94

18070201 Seal Beach CA 59 38 18 3 95

18070203 Santa Ana CA 98 30 53 15 85

18070204 Newport Bay CA 74 36 20 18 76

18070301 Aliso-San Onofre CA 19 17 1 1 95

18070304 San Diego CA 278 208 47 23 92

Table 3-42. Region 9: Number of Tier 1 Stations in Region 9 That Are Located in Watersheds

Containing APCs by Waterbody Name.

Waterbody

Number of

Tier 1

Stations Waterbody

Number of

Tier 1

Stations

Pacific Ocean 261 Napa River 7

San Diego Bay 156 A Line Canal 5

San Francisco Bay 128 Alisal Slough 5

San Pablo Bay 60 Peters Canyon Wash 4

Santa Ana River 29 San Gabriel River 4

Comanche Reservoir 23 Aliso Creek 3

Salt River 20 Calleguas Creek 3

Arcata Bay 19 Los Penasquitos Canyon 3

Cave Creek 19 San Diego River 3

Charro Creek 19 San Juan Creek 3

San Diego Creek 16 Calero Reservoir 2

Sacramento River 15 Petaluma River 2

Suisun Bay 14 Arroyo Trabusco 1

Dominguez Channel 13 Humboldt Bay 1

Carson River 9 Oso Creek 1
National Sediment Quality Survey

3-56

Table 3-42. (Continued).

Waterbody

Number of

Tier 1

Stations Waterbody

Number of

Tier 1

Stations

Elkhorn Slu 9 San Dieguito River 1

Spring Creek 9 Suisun Creek 1

Alamitos Creek 8 Warm, E. Twin & Strawberry Cr Area 1

EPA Region 10

Alaska, Idaho, Oregon, Washington

EPA evaluated 5,330 sampling stations in Region 10 as part of the NSI database evaluation. Sediment

contamination associated with probable adverse effects on aquatic life was found at 1,803 of these

sampling stations, placing them in Tier 1; sediment contamination associated with possible adverse

effects was found at 2,052 stations, placing them in Tier 2. For human health, data for 2,325 sampling

stations indicated probable association with adverse effects (Tier 1), and data for 988 sampling stations

indicated possible association with adverse effects (Tier 2). Overall, this evaluation resulted in the

classification of 2,886 sampling stations (54.8 percent) as Tier 1, 1,473 (28.0 percent) as Tier 2, and 904

(17.2 percent) as Tier 3. The NSI database sampling stations in Region 10 were located in 347 separate

river reaches, or 3.3 percent of all reaches in the region. About 1.7 percent of all river reaches in Region

10 included at least one Tier 1 station, 1.2 percent included at least one Tier 2 station but no Tier 1

stations, and 0.5 percent had only Tier 3 stations (Table 3-43). Table 3-44 presents a summary of

sampling station classification and evaluation of river reaches for each state and for the region as a whole.

This evaluation identified 10 watersheds containing APCs out of the 355 watersheds (2.8 percent) in

Region 10 (Table 3-43). In addition, 13.5 percent of all watersheds in the region had at least one Tier 1

sampling station but were not identified as containing APCs, 8.2 percent had at least one Tier 2 station

but no Tier 1 stations, and 5.9 percent had only Tier 3 stations; 69.6 percent of the watersheds did not

include a sampling station. The locations of the watersheds containing APCs and the Tier 1 and Tier 2

sampling stations in Region 10 are illustrated in Figure 3-17.

Within the 10 watersheds in Region 10 identified as containing APCs (Table 3-45), 48 waterbodies have

at least 1 Tier 1 sampling station are 21 waterbodies have 10 or more Tier 1 sampling stations

(Table 3-46). For those watersheds that contain APCs, Table 3-46 presents a list of all waterbodies that

contain one or more Tier 1 sampling stations. Based on the information in Table 3-46, Puget Sound, Elliot

Bay, Willamette River, Sinclair Inlet, Bellingham Bay, Big Creek, Duwamish Waterway, Lake Union,

Lake Washington Ship Canal, Budd Inlet, Columbia River, Matheny Creek, Sams River, Lake

Washington, Strait of Georgia, Chambers Creek, Roosevelt Lake, East Fork of Humptulips River, Fidalgo

Island, Green River, and Columbia Slough appear to have the most significant sediment contamination in

Region 10.
National Sediment Quality Survey

3-57

Table 3-43. Region 10: River Reach and Watershed Evaluation Summary.

River Reach Classification Watershed Classification

Total Number of River Reaches 10,462 Total Number of Watersheds 355

River Reaches With at Least One Tier 1

Station

177 (1.7%) Watersheds Containing APCs 10 (2.8%)

Watersheds With at Least One Tier 1

Station

48 (13.5%)

River Reaches With at Least One Tier 2

Station and Zero Tier 1 Stations

121 (1.2%) Watersheds With at Least One Tier 2

Station and Zero Tier 1 Stations 

29 (8.2%)

River Reaches With All Tier 3 Stations 49 (0.5%) Watersheds With All Tier 3 Stations 21 (5.9%)

River Reaches With No Data 10,115 (96.7%) Watersheds With No Data 247 (69.6%)

Table 3-44. Region 10: Evaluation Results for Sampling Stations and River Reaches by State.

State

Station Evaluation River Reach Evaluationa

 Total Number of  Stations Evaluated

Tier 1 Tier 2 Tier 3

 Number of Stations Not Identified by an RF1 Reachc Reaches With at Least  One Station in Tier 1 Reaches With at Least  One Station in Tier 2d

No. %

 Reaches With All  Stations in Tier 3 Number of Reaches With at  Least One Station Evaluated Total Reaches in Region Percent of All Reaches in  Region With at Least  One Station Evaluated Percent of Reaches With at Least One Tier 1  or Tier 2 Station

b No. %b No. %b

Alaska 290 46 15.9 42 14.5 202 69.7 290 — — — — — — —

Idaho 38 17 44.7 12 31.6 9 23.7 — 16 13 7 36 3,298 1.1 0.9

Oregon 599 310 51.8 203 33.9 86 14.4 — 45 52 16 113 4,317 2.6 2.2

Washington 4,336 2,513 58.0 1,216 28.0 607 14.0 — 125 65 30 220 3,056 7.2 6.2

Region 10e 5,263 2,886 54.8 1,473 28.0 904 17.2 290 177 121 49 347 10,462 3.3 2.8

a

 River reaches based on EPA River Reach File (RF1).

b

 Percent of all stations evaluated in the NSI in the state. c

 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed. d

 No stations in these reaches were included in Tier 1. e

 Because some reaches occur in more than one state, the total number of reaches in each category for the country might not equal the sum of reaches

in the states.
3-58
National Sediment Quality Survey

3-59

Table 3-45. Region 10: Watersheds Containing Areas of Probable Concern for Sediment

Contamination.

Cataloging

Unit Number Cataloging Unit Name State(s)

Number of Sampling Stations

Percent of

Sampling

Stations in Tier

Total Tier 1 Tier 2 Tier 3 1 or Tier 2

17020001 Franklin D. Roosevelt Lake WA 66 52 9 5 92

17080001 Lower Columbia-Sandy OR, WA 72 20 39 13 82

17090012 Lower Willamette OR 382 243 96 43 89

17100102 Queets-Quinault WA 108 77 23 8 93

17100105 Grays Harbor WA 139 102 33 4 97

17110002 Strait Of Georgia WA 443 184 179 80 82

17110012 Lake Washington WA 216 179 30 7 97

17110013 Duwamish WA 930 599 267 64 93

17110019 Puget Sound WA 2135 1246 552 337 84

19020201 Eastern Prince William Sound AK 31 10 15 6 81

Table 3-46. Region 10: Number of Tier 1 Stations in Region 10 That Are Located in Watersheds

Containing APCs by Waterbody Name.

Waterbody

Number of

Tier 1

Stations Waterbody

Number of

Tier 1 Stations

Puget Sound 926 Eld Inlet 8

Elliot Bay 498 Portage Bay 8

Willamette River 227 Camano Island 7

Sinclair Inlet 176 Vashon Island 7

Bellingham Bay 139 Whatcom Creek 6

Big Creek 86 Johnson Creek 5

Duwamish Waterway 86 Mercer Island 3

Lake Union 72 Panther Lake Ditch 3

Lake Washington Ship Canal 69 Sequalitchew Creek 3

Budd Inlet 62 Grays Harbor 2

Columbia River 51 Hammersley Inlet 2

Matheny Creek 50 Henderson Inlet 2

Sams River 27 Onion Creek 2

Lake Washington 26 Port Orchard 2

Strait Of Georgia 25 Port Susan 2

Chambers Creek 19 Cedar Creek 1

Roosevelt Lake 17 Chuckanut Creek 1

Humptulips River, East Fork 14 Indian Island 1

Fidalgo Island 12 Morey Creek 1

Green River 12 North Creek 1

Columbia Slough 11 Oakland Bay 1

Dyes Inlet 9 Sammish Bay 1

Whidbey Island 9 Sandy River 1

Bainbridge Island 8 Totten Inlet 1
National Sediment Quality Survey

3-60

Evaluation of Data from the 1997 National Sediment Quality Survey with

Current Methodology

The data evaluation methodology (described in Table 2-2) was revised from the methodology used in the

previous report to Congress to include new and updated analytical approaches. Modifications were made

to the analysis used in determining the tier classification based on sediment chemistry, tissue residue, and

toxicity data to take advantage of scientific advances since the release of the 1997 National Sediment

Quality Survey. Biological effects concentration approaches were replaced with an alternative empirical

method, namely, a logistic regression model that is used to estimate the predicted proportion toxic. EPA’s

draft ESGs derived from final or secondary acute values were also used in evaluating sediment chemistry

data. In addition, EPA risk levels and PAH toxicity units were included to analyze sediment chemistry

data. Moreover, for analyzing tissue residue data, all chemicals with log Kow greater than 5.5 were

evaluated instead of dioxins and PCBs only. Toxicity data were analyzed based on one solid-phase

sediment toxicity test, replacing the requirement of two or more tests using two different species. Controladjusted survival was considered for both marine and freshwater species, whereas control-adjusted length

or weight was considered for selected freshwater species sublethal toxicity tests.

In view of the preceding changes to the evaluation methodology, EPA conducted an analysis of the data

used to evaluate 21,096 stations in the first National Sediment Quality Survey using the current, revised

methodology. This analysis allows comparison of the resulting tier classifications from both evaluation

methodologies. The results of the tier classification using the previous and current methodology are

presented in Table 3-47. 

Table 3-47. Summary of Tier Classification Using Previous and Current Evaluation

Methodologies With the NSI Data Evaluated in the 1997 National Sediment Quality Survey.

Tier

Previous Evaluation

Methodology

Current Evaluation

Methodology

Net Gain/Loss in Number of

Stations 

1 5,521 8,932 3,411 

2 10,401 5,813 (4,588)

3 5,174 6,235 1,061 

Total 21,096 20,980 (116)

There is a net increase of 3,411 Tier 1 stations and a net increase of 1,061 Tier 3 stations. These increases

are the result of 4,588 Tier 2 stations being classified as Tier 1 or Tier 3 by the new methodology. This

decrease in the number of Tier 2 stations (a total of 4,588 stations) equals the increase of 3,411 Tier 1

stations, the increase of 1,061 Tier 3 stations, and the loss of 116 stations previously analyzed and

classified as Tier 3 but not analyzed by the new methodology.

All of the 116 stations not analyzed with the current methodology were previously classified as Tier 3

stations. Certain chemicals (such as phenol and pentachlorophenol) that were evaluated using biological

effects correlation approaches are not analyzed by the new methodology because they do not have any

evaluation criterion for sediment chemistry analysis. Also, in the previous analysis, a sensitivity analysis

related to wildlife criteria was considered although not included in the final methodology. The wildlife

criteria evaluation included species not normally eaten by humans (nonedible species). Rather than

reporting different numbers of stations evaluated in the previous report, those stations that were not

evaluated when the wildlife criteria evaluation was not included were simply classified as Tier 3. In the

current methodology, stations with only tissue data from edible species are included in the analysis or

station count.
National Sediment Quality Survey

3-61

Though there are net increases in the number of Tier 1 and Tier 3 stations, as shown in Table 3-47, a total

of 171 stations previously classified as Tier 1 would be classified as Tier 2 and 12 stations previously

classified as Tier 1 would be classified as Tier 3 (see Table 3-48). Similarly 1,412 stations classified as

Tier 2 by the previous method would be classified as Tier 3. More than 3,500 stations previously

classified as Tier 2 would be classified as Tier 1.

Table 3-48. Transition in Tier Classification Using Previous and Current Evaluation

Methodologies With the NSI Data Evaluated in the 1997 National Sediment Quality Survey.

Tier Classification Using

Previous Methodology

Tier Classification Using Current Methodology

Not Analyzed 1 2 3 Total

1 0 5,338 171 12 5,521

2 0 3,543 5,446 1,412 10,401

3 116 51 196 4,811 5,174

Total 116 8,932 5,813 6,235 21,096

A significant component of the increase in Tier 1 stations is the new classification methodology for

sediment chemistry data, followed by tissue residue data and to a lesser extent by toxicity data. Changes

in the sediment chemistry methodology can be attributed to the contribution of different chemicals, metals

in particular, included in the logistic regression model, as well as the use of an EPA human health cancer

risk of 10-4 or a noncancer hazard quotient (HQ) of 10. Increasing the ingestion rate from 6.5 grams per

day to 17.5 grams per day also increased the proportion of Tier 1 stations. Including all chemicals with

log Kow greater than or equal to 5.5 in evaluating tissue residue, instead of dioxins and PCBs only, also

contributed to the increase in Tier 1 stations. Finally, the previous methodology required two or more

nonmicrobial acute toxicity tests using two different species for Tier 1 designation using toxicity data.

Use of toxicity data in the current evaluation methodology was based on a single solid-phase sediment

test without any restrictions on control data.

Of the 3,594 stations being classified as Tier 1 (3,543 Tier 2 stations and 51 Tier 3 stations; see

Table 3-48), approximately 55.2 percent are so classified based on the logistic regression model, 47.8

percent based on the use of a higher EPA risk criterion (sediment TBP), around 7.5 percent are classified

in a higher tier based on tissue residue analysis, and less than 3 percent are from other evaluation

parameters. Of the 196 Tier 3 stations being classified as Tier 2 by the new methodology, more than 65

percent are so classified because of changes in the human health risk assessment for either tissue or

sediment. Because stations may be evaluated by more than one criterion, the sum of the previous

percentages exceeds 100.
National Sediment Quality Survey

3-62
National Sediment Quality Survey

4-1

CHAPTER 4

ASSESSMENT OF TRENDS IN SEDIMENT

CONTAMINATION THROUGHOUT THE UNITED

STATES

Introduction

The historical reconstruction of sediment contamination is a tool to improve measures for reducing

sediment contamination as well as to provide feedback to estimate the success of pollution control

techniques and regulations. Sediments can provide an invaluable record of transformations due to natural

or anthropogenic processes. These transformations have typically occurred over time periods greater than

what has been monitored and assessed. Within a stable environment, the sediment retains a record of all

contaminant inputs that can be dated on the basis of the decay of naturally occurring radioisotopes

associated with the sediment (Hermanson, 1991). The sediment chronology can be established by

approximate reconstruction of historical events. Some of the common methods used are the use of

radioactive decay products such as 210Pb and 137Cs, mineral magnetism, or correlation of sedimentary

pollen or charcoal with historical records of logging or fires (Gubala et al., 1990). The depth distribution

of the radioactive decay products in sediment cores provides valuable information on the period of

sediment deposition. When the sediment cores are undisturbed, the activity of the radioactive product

(e.g., 137Cs) decreases exponentially toward the sediment-water interface (Bopp et al., 1998). By

establishing a relationship between sediment depth and the time period, the history of the selected

sediment components can be established.

One of the recommendations from the EPA report The Incidence and Severity of Sediment Contamination

in Surface Waters of the United States, Volume 1: National Sediment Quality Survey (USEPA, 1997) was

to “consider whether to design future evaluations of NSI data to determine the temporal trends of

contamination.” To accurately assess potential trends in sediment contamination, it would be necessary

to measure levels of the same contaminants in sediment collected throughout time at the same randomly

selected locations (not concentrating solely on “hot-spots”). This approach could enable identification of

patterns or trends in sediment contaminants over time. Trends identified using this approach would,

however, be applicable only to the areas where data were collected and could not be extrapolated beyond

those areas. Extrapolation is not possible because of variability resulting from factors such as land use

patterns and historical anthropogenic activities. Despite the limitations imposed by the lack of routine

monitoring information described above, EPA has developed an approach to provide the means for

assessing changes in the extent and severity of sediment contamination over time for specific areas where

sufficient data exist. To accomplish this, the data from the entire NSI database (data from 1980 through

1999) were evaluated. EPA’s approach and the results of the Agency’s analysis are discussed below. In

addition to the trend assessment developed using the data from the NSI database, EPA has included

information recently completed from the USGS National Water-Quality Assessment (NAWQA) program.

To further evaluate potential trends in sediment contaminants, EPA has also conducted a literature

review of studies conducted on sediment cores throughout the United States. This information is

presented in Appendix E.
National Sediment Quality Survey

4-2

Database Trend Analysis

The NSI database contains more than 96,000 sediment samples collected from 41,594 sampling stations

located in all 50 states, the District of Columbia, and Puerto Rico. These samples were collected from

1980 through 1999 and are associated with more than 3 million chemical and other sediment-related

observations. Data from just under one-half (19,398) of the stations were collected after January 1, 1990,

and these stations were classified into tiers as described earlier. The purpose of this analysis is to

determine whether the severity of sediment contamination in areas where data were evaluated is changing

with time. For this purpose, all applicable sediment chemistry data from 1980 through 1999 were

considered for analysis. A variety of heterogeneous monitoring programs gathered the available data.

These monitoring programs provide an adequate amount of data for assessing sediment contamination at

single sampling stations and times. These programs do not provide, however, all the information needed

for detecting trends in sediment contamination. Any of the factors described below can influence the

analysis of trends:

• Different/changing sampling and analytical methods over time. During the past 20 years, a

wide variety of sampling and analytical methods have been used to collect and analyze sediment

chemistry data. Different monitoring programs often use different methods. Programs take

advantage of new technology as improved methods become available. Differences in monitoring

program objectives (e.g., screening versus detailed assessment) also lead to different methods of

sampling and analysis.

• Limited quality assurance/quality control (QA/QC) information. Many of the historical data

included in the NSI database are not associated with known data quality information. Although

QA/QC and meta data are more frequently available for many data sets in the recent decade, it is

still difficult, if not impractical, to apply more than a screening-level “use/don’t use” approach for

assessing the quality of available data sets.

• Monitoring strategies tend to focus on a single assessment. To detect temporal trends, it is

usually appropriate to have collected a time series of data (multiple observations over time) at a

station or to have two or more random samples, each of which represents a different time period.

About two-thirds of the sampling stations (27,676) in the NSI database are represented by only one

sediment sample, and only 740 stations are represented by 10 or more observations spread across

at least a 5-year period. Only programs similar to the EMAP/REMAP design provide suitable

random samples. At this time, however, the NSI database includes data from only 1,828 EMAP

stations collected from 1990 through 1995, and the data are limited to the coastal regions of 19

states.

Despite these limitations, this report presents the methodology and findings of a temporal trend

assessment of surficial sediment contamination throughout the United States for areas for which data

were evaluated.

The following steps were used to prepare the data for analysis:

1. Compute the predicted proportion toxic for all sediment chemistry samples collected from

1980 through 1999. Computing the predicted proportion toxic using the logistic model

(described in detail in Appendix B) is a convenient approach for combining measurements from

several different chemicals into one value per sample. For samples with only censored data (e.g.,

less thans and nondetects), the predicted proportion toxic was set to 0.10, which is 0.01 less than

the minimum predicted proportion toxic from the logistic equation.
National Sediment Quality Survey

4-3

Figure 4-1. Locations of Data Clusters Used for Temporal Trend Analysis.

2. Group samples based on latitude and longitude. The latitude and longitude of each sample

were rounded to the nearest 0.0001 degree. All samples from the same rounded latitude and

longitude were then treated as if they had come from the same location. This would roughly

correspond to assuming that stations within about 45 feet of each other are the same station. This

allowed EPA to create data clusters, which have a time series of data at a single location.

3. Average the predicted proportion toxic for a range of time periods (1980–1983, 1984–1987,

1988–1991, 1992–1995, and 1996–1999). For each unique rounded latitude and longitude and

time period, the average predicted proportion toxic was computed. (Each station could have only

one value of predicted proportion toxic per time period.)

4. Eliminate any data clusters where the predicted proportion toxic was not estimated in at

least two of the time periods. Based on a preliminary analysis of data contained in the NSI

database, it was found that there were some major geographical shifts in data collection over the

past 20 years. For example, during the early 1980s, a substantial amount of data was collected by

the USGS in the Appalachian Mountains. In later years, USGS did not collect data in that region.

This step eliminates the geographic diversity of the available data, but it also reduces the

potential to compare geographically different data over time.

Application of this approach resulted in the availability of 4,153 stations for trend analysis. The locations

of these stations in the contiguous United States are presented in Figure 4-1. There are clear geographical

biases in the available data. These biases range from little to no data in some states to wide-scale 

monitoring across an entire state. Because of the data preparation step, each station could only have one

value of predicted proportion toxic per time period. Table 4-1 presents the number of predicted

proportion toxic observations (or stations) by time period and hydrologic region. For example, there are

89 stations with predicted proportion toxic observations in the Great Lakes hydrologic region (04) during

the 1984 through 1987 time period. The location of hydrologic regions in the contiguous United States is

presented in Figure 4-2. Hydrologic regions 19 and 20 represent Alaska and Hawaii, respectively. 
National Sediment Quality Survey

4-4

Figure 4-2. USGS Hydrologic Regions in Contiguous United States.

Table 4-1. Number of Predicted Proportion Toxic Observations Available for Trend Analysis

After Data Preparation Step.

USGS Hydrologic Region 1980–1983 1984–1987 1988–1991 1992–1995 1996–1999

01 7 16 14 11 5

02 379 393 422 493 394

03 530 657 558 512 427

04 87 89 98 74 42

05 109 135 151 176 124

06 55 47 60 66 34

07 170 124 197 174 226

08 87 92 59 23 31

09 2 0 0 2 0

10 24 74 76 16 9

11 96 143 127 69 24

12 275 294 255 124 84

13 23 27 27 34 21

14 11 10 5 3 3

15 5 6 5 6 2

16 23 23 1 7 6

17 83 156 217 185 53

18 75 129 120 111 75

19 2 23 25 2 2

20 31 30 0 0 1
National Sediment Quality Survey

4-5

Figure 4-3. Locations of Concentrated Data Clusters Used for Temporal Trend

Analysis.

Because of the lack of data in hydrologic regions 01, 09, 10, 14, 15, 16, 19, and 20, individual statistics

are not presented, but are included in national data where applicable.

To evaluate the effect of focusing the attention on those stations with more concentrated data sets, the

same data preparation step was performed using only the 740 stations associated with 10 or more

observations spread across at least a 5-year period. Of these 740 stations, 422 stations have data for four

or five time periods and 231 stations have data for three time periods. The locations of these 740 stations

are presented in Figure 4-3. Table 4-2 presents the number of predicted proportion toxic observations (or

stations) by time period and hydrologic region. For example, there are 47 stations or predicted proportion

toxic observations in the Mid-Atlantic hydrologic region (02) during the 1984 through 1987 time period.

Results

The logistic model classification scheme described earlier in this document was used to classify stations

(i.e., predicted proportion toxic greater than 0.5 is Tier 1). Table 4-3 presents the classification of

observations by tier, time period, and hydrologic region for the data clusters. Overall, the percentage of

Tier 1 or Tier 2 stations ranges from 54 percent during the first time period, to 47 to 49 percent during

the middle three time periods, to 40 percent for the last time period. It is also possible to discern the

results in Table 4-3 by examining the box plots in Figure 4-4. Figure 4-5 is a comparable box plot figure

for the concentrated data clusters.
National Sediment Quality Survey

4-6

Table 4-2. Number of Predicted Proportion Toxic Observations Available for Trend Analysis

After Data Preparation Step From Concentrated Data Clusters.

USGS Hydrologic Region 1980–1983 1984–1987 1988–1991 1992–1995 1996–1999

01 6 6 6 6 3

02 51 47 53 57 49

03 213 241 266 241 232

04 0 0 1 1 1

05 30 27 35 31 30

06 25 25 25 20 16

07 10 15 17 9 5

08 1 1 8 7 0

10 1 7 7 0 0

11 40 45 45 31 8

12 115 137 141 65 45

13 18 20 19 24 17

14 1 2 2 2 2

18 39 37 44 32 31

20 17 17 0 0 0

Box plots provide a quick summary view of data. For each hydrologic region and time period, the 25th

and 75th percentile is represented by the bottom and top of the box. The box represents the central 50

percent of the data and is equal to the interquartile range. The median is represented as a horizontal line

in the box. The lines (or whiskers) extending from the bottom or top of the box show how stretched the

distribution tails are. The length of the whiskers can extend to 1.5 times the interquartile range.

Individual points outside the whiskers are plotted as circles and stars depending on whether the point

exceeds three times the interquartile range.

Visual inspection of Figures 4-4 and 4-5 suggests that there might be some change in the predicted

proportion toxic in some hydrologic regions and between some time periods. Two types of statistical

tests are performed to evaluate these potential changes: the paired t-test and the Kolmogorov-Smirnov

goodness of fit test. The paired t-test was selected to compare each combination of time periods for a

shift in mean. Applying a two-sided paired t-test (" = 0.05), it is possible to test for statistical differences

between the different time periods. For example, it is possible to determine whether the mean predicted

proportion toxic for 1984 through 1987 is the same as the mean predicted proportion toxic for 1980

through 1983 in hydrologic region 02. For each hydrologic region, there are 10 combinations of time

periods (1984–1987 vs. 1980–1983, 1988–1991 vs. 1980–1983, ..., 1996–1999 vs. 1992–1995). The

Kolmogorov-Smirnov two-sample (KS) test (" = 0.05) is used to compare an entire distribution, unlike

the t-test, which is a comparison of means. The KS test was used to compare the first and last time

periods for each hydrologic region.
National Sediment Quality Survey

4-7

Table 4-3. Number of Observations Classified by Tier and Percentage of Observations Classified

as Tier 1 or Tier 2 by Time Period and Hydrologic Region.

Tier 1 Hydrologic Region All

YEAR 02 03 04 05 06 07 08 11 12 13 17 18 Data

1980–1983 50 16 34 27 9 57 10 0 37 0 34 9 305

1984–1987 55 38 15 25 8 33 3 4 14 0 69 27 324

1988–1991 48 32 17 27 8 50 16 4 28 0 63 14 320

1992–1995 33 18 19 27 9 45 1 0 13 0 44 32 253

1996–1999 17 3 13 16 1 25 10 0 7 0 6 34 139

Tier 2 Hydrologic Region All

YEAR 02 03 04 05 06 07 08 11 12 13 17 18 Data

1980–1983 177 151 30 54 26 76 35 43 92 3 26 51 811

1984–1987 147 153 42 67 17 71 42 36 121 5 64 41 889

1988–1991 172 120 52 72 34 93 12 26 73 6 104 24 825

1992–1995 175 122 37 83 34 84 6 18 42 6 82 55 761

1996–1999 101 78 17 55 9 116 6 4 22 2 32 32 488

Tier 3 Hydrologic Region All

YEAR 02 03 04 05 06 07 08 11 12 13 17 18 Data

1980–1983 152 363 23 28 20 37 42 53 146 20 23 15 958

1984–1987 191 466 32 43 22 20 47 103 159 22 23 61 1,255

1988–1991 202 406 29 52 18 54 31 97 154 21 50 82 1,272

1992–1995 285 372 18 66 23 45 16 51 69 28 59 24 1,074

1996–1999 276 346 12 53 24 85 15 20 55 19 15 9 936

% Tier 1 or

Tier 2 Hydrologic Region

All

YEAR 02 03 04 05 06 07 08 11 12 13 17 18 Data

1980–1983 60% 32% 74% 74% 64% 78% 52% 45% 47% 13% 72% 80% 54%

1984–1987 51% 29% 64% 68% 53% 84% 49% 28% 46% 19% 85% 53% 49%

1988–1991 52% 27% 70% 66% 70% 73% 47% 24% 40% 22% 77% 32% 47%

1992–1995 42% 27% 76% 63% 65% 74% 30% 26% 44% 18% 68% 78% 49%

1996–1999 30% 19% 71% 57% 29% 62% 52% 17% 35% 10% 72% 88% 40%
National Sediment Quality Survey

4-8

HYDROLOGIC REGION

PREDICTED PROPORTION TOXIC

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2 3 4 5 6 7 8 11 12 13 17 18

1980-83 1992-95

1984-87 1996-99

1988-91

Figure 4-4. Box Plot of Predicted Proportion Toxic as a Function of Hydrologic Region for

Data Clusters.

HYDROLOGIC REGION

PREDICTED PROPORTION TOXIC

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2 3 5 6 11 12 13 18

1980-83 1992-95

1984-87 1996-99

1988-91

Figure 4-5. Box Plot of Predicted Proportion Toxic as a Function of Hydrologic Region for

Concentrated Data Clusters.
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In applying the paired t-test to all paired data, it was determined that of the 10 pairwise time period

combinations, 9 showed a significant downward trend in the predicted proportion toxic for the data

clusters. Eight of the 10 comparisons showed a significant downward trend in the predicted proportion

toxic for the concentrated data clusters. Applying the KS test to all the data from the first and last time

periods (1980–1983 vs. 1996–1999) showed a significant difference in the predicted proportion toxic

between the two time periods for both the data clusters and the concentrated data clusters. The results of

both of these tests suggest that sediment contamination (as measured by the predicted proportion toxic

using the logistic model) is decreasing with time for all of the hydrologic regions evaluated. When

looking at each hydrologic region, with sufficient data for the concentrated data clusters, the results

suggest that there has been a slight decrease (Hydrologic Regions 02, 03, 05, 06, and 11) to no change

(Hydrologic Regions 12, 13, and 18) in the levels of sediment contamination (as measured by the

predicted proportion toxic using the logistic model). The results for individual hydrologic regions are

presented in Table 4-4.

Box plots of the predicted proportion toxic associated with EMAP data are presented in Figure 4-6 for

the four major regions for which EMAP data were available. Using an analysis of variance (ANOVA)

test, it was determined that there were no significant differences in predicted proportion toxic for

different years of data compiled in the Long Island/Hudson (L.I./Hud), New England, and Southeast

regions. There is a significant difference (at the 95 percent confidence level) in predicted proportion

toxic for different years of data collected along the Gulf Coast. Using a two-sample t-test, it was

determined that the mean predicted proportion toxic in 1993 is less than the mean predicted proportion

toxic in either 1991 or 1992, but the mean predicted proportions toxic in 1991 and 1992 are not

significantly different.

Table 4-4. Summary of Statistical Tests Used to Compare Predicted Proportion Toxic Within

Hydrologic Regions.

Hydrologic

Region

Data Cluster Concentrated Data Cluster

Paired t-test results for pairwise

comparison of time periods

KS Test

Paired t-test results for pairwise

comparison of time periods

Increasing Decreasing KS Test

No

change Increasing Decreasing

No

change

02 0 8 2 decrease 0 8 2 decrease

03 0 4 6 decrease 0 6 4 decrease

04 1 4 5 no trend

05 0 2 8 decreasea 0 7 3 decrease

06 0 5 5 decreasea 0 7 3 decrease

07 0 6 4 decrease

08 1 2 7 no trend

11 0 6 4 decreasea 0 3 3 decreaseb

12 0 7 3 decreasea 0 5 5 no trend

13 0 1 9 no trend 0 1 9 no trend

17 0 1 9 decreasea

18 5 1 4 increasea 5 1 4 no trend

Overall 0 9 1 decrease 0 8 2 decrease

a

 There is a significant difference between the distributions from the two time periods; however, the cumulative density function overlaps. The

result presented in the table corresponds to whether the maximum difference indicates a decrease or an increase in concentration.

Conservatively, these results should be considered “inconclusive.”

b Comparison performed between 1980–1983 and 1991–1995 data because of lack of 1996–1999 data.
National Sediment Quality Survey

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REGION

PREDICTED PROPORTION TOXIC

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Gulf Coast L.I./Hud (REMAP) New England Southeast

1990 1993

1991 1994

1992 1995

Figure 4-6. Box Plot of Predicted Proportion Toxic as a Function of Region for EMAP Data.

It is important to note here that this trend analysis does not address the issue of the current sediment

quality in those regions outlined above. In other words, just because the data suggest that there has been a

slight decrease in the levels of sediment contamination (as measured by the predicted proportion toxic

using the logistic regression model) in the regions outlined above it does not mean that there is no risk to

human health or aquatic life from the sediments.

Instead, it means only that the level of contaminants has slightly decreased. Further evaluations should be

conducted to confirm the extent and severity of sediment contamination for any given site or watershed.

Sediment Core Analysis

The USGS NAWQA program is reconstructing water-quality histories using lake and reservoir sediment

cores. The approach used for this assessment is paleolimnology—the use of age-dated sediment cores to

reconstruct water quality histories using radioactive tracers and physical markers in the cores such as the

pre-reservoir soil boundary in the reservoirs. Using this approach, trends in concentrations of numerous

hydrophobic organic compounds (Eisenreich et al., 1989; Hites et al., 1981; Van Metre et al., 1997,

2000) and trace elements (Callender and Van Metre, 1997) have been identified in a variety of settings.

The NAWQA program is using paleolimnological methods to determine trends in metals and

hydrophobic organic compounds in river basins (Callender and Van Metre, 1997; Van Metre et al.,

1997). Sediment cores were collected from 1996 to 2000 in 15 reservoirs and 7 natural lakes in or near

15 U.S. cities (Table 4-5). Sites were chosen based on watershed land use to represent one of three

general land use settings: older urban development (developed before about the 1940s) dominated by

residential and commercial land uses; newer urban development characterized by rapid urbanization

beginning in the 1950s or later; and reference sites with little or no development. Land use in the
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4-11

Table 4-5. Sediment Core Locations.

Name

Location 

(major urban areas)

Lake Anne Washington, DC

Lake Ballinger Seattle, WA

Berkeley Atlanta, GA

R.R. Canyon Lake Los Angeles, CA

Great Salt Lake, Farmington Bay Salt Lake City, UT

Lake Harriet Minneapolis, MN

Lake Hemet Los Angeles, CA

Lake Houston, South Houston, TX

Lake Killarney Orlando, FL

Lowrence Creek Lake San Antonio, TX

Lake Mead, Las Vegas Bay Las Vegas, NV

Newbridge Pond New York City, NY

Orange Reservoir Newark, NJ

Packanack Lake Newark, NJ

Panola Atlanta, GA

Palmer Lake, West Lobe Minneapolis, MN

Sloans Lake Denver, CO

Sand Lake Orlando, FL

Town Lake Austin, TX

Lake Washington Seattle, WA

White Rock Lake Dallas, TX

West Street Basin Los Angeles, CA

watersheds of these reservoirs and lakes ranges

from undeveloped and protected forest to nearly

100 percent urban.

Contaminants analyzed in this program included

major and trace elements (including arsenic,

lead, mercury, and zinc), organochlorine

pesticides (including DDT, chlordane, and

dieldrin), polychlorinated biphenyls (PCBs),

and polycyclic aromatic hydrocarbons (PAHs).

Van Metre et al. (n.d.) describe specific

methods used for sediment collection, age

dating, and the chemical analysis.

In this analysis, trends in concentrations of

individual constituents were evaluated using

Kendall’s tau non-parametric statistical test to

determine whether there was a statistically

significant relationship between concentration

and time from 1975 (trace elements) or 1970

(organic compounds) to the top of the core. The

starting times for these evaluations were chosen

for various reasons, including the passage of

significant environmental legislation and

establishment of new programs (e.g., the

establishment of EPA in 1971). Other reasons

for selecting this time frame include the

promulgation and enforcement of regulations

under the Clean Water Act and Clean Air Act

and other actions such as the banning of DDT in

1972, restrictions of PCBs in 1971 and their

banning in 1976, and the introduction of

unleaded gasoline in the early 1970s.

Trends in concentrations of eight trace elements, nine individual PAHs, total PAH (the sum of 18 two- to

seven-ringed parent PAHs and their alkylated homologues, excluding perylene [Van Metre et al., 2000]),

5 organochlorine pesticides, and total PCBs were tested. These elements and compounds were chosen for

analysis because they all have sediment quality guidelines (McDonald et al., 2000) and include all of the

constituents measured by NAWQA studies that have pronounced temporal trends in multiple sites

(Callender and Van Metre, 1997; Van Metre and Callender, 1997; Van Metre et al., 1997; Van Metre et

al., 2000).

The rank correlation test has at least two limitations as applied here. First, the data sets are small,

averaging 8 and 14 samples for organic compounds and trace elements, respectively. Second, the test is

for only monotonic trend; it does not identify constituents or sites showing nonmonotonic temporal

variations such as peaks or valleys within the time period tested.

Results

Statistically significant increasing trends in total PAH concentrations occur at 10 lakes (at 90 percent

confidence) and significant decreasing trends at two lakes (Figure 4-7). All 10 lakes with increasing
National Sediment Quality Survey

4-12

trends in PAHs are in urban watersheds. Of the 10 lakes that did not have significant trends, 2 are

reference lakes (Sand and Hemet) and 6 of the other 8 are in stable urban watersheds (Lakes Harriet,

West Street Basin, Great Salt Lake, and Packanak), are just beginning to urbanize (Lakes Houston and

R.R. Canyon Lake), or are in high-erosion settings where urban effects are diluted (Lake Mead in

addition to Lakes Houston and R.R. Canyon Lake).

The two lakes that had significant decreasing trends are Lake Washington in Seattle and Panola Reservoir,

a small reference lake near Atlanta, Georgia. Lake Washington has older urban development in its

watersheds, which might be related to the relatively higher concentrations of PAHs in older sediments.

PAHs in some older urban areas have been shown to have decreased since highs in the 1940s through 1960s

(Van Metre et al., 2000). Additionally, sewage inputs to Lake Washington ended in 1967 and the Asarco

copper smelter in nearby Tacoma was closed in 1985. These changes could contribute to the decreasing

trends in PAHs, and the closing of the smelter very likely contributes to the decreasing trends in most trace

elements. The decreasing PAH trend at Panola Reservoir could be the result of improved emissions

control, for example, on power plants; decreases in PAHs have been reported for other lakes in the

eastern U.S. (Heit et al., 1988).

In Figure 4-8 a dramatic increase in PAH concentrations in White Rock Lake can be observed since

urbanization of the watershed began in the 1950s. This pattern is repeated in lakes with urbanizing

watersheds across the United States (Van Metre et al., 2000). PAHs are produced by combustion of fossil

fuels (oil, coal, gasoline, diesel, and wood). They have many urban sources, including industrial and

power plant emissions, car and truck exhaust, tires, asphalt roads, and roofs. These sources are reflected

in the relationship between historical traffic data for greater Dallas and PAHs in the sediment core from

White Rock Lake (Figure 4-8).

Figure 4-7. PAH Trends Throughout the United States Using Sediment Core Data

from 1970 to Top of Core.
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4-13

Figure 4-8. White Rock Lake PAH

Concentrations.

Figure 4-9. DDT Trends Throughout the United States Using Sediment Core Data

from 1970 to Top of Core.

The analysis of the organochlorine compounds

(pesticides and PCBs) showed decreasing trends,

but fewer than expected considering the

regulatory history of these compounds. Total

DDT decreased in 12 of the 22 lakes (Figure 4-9)

and PCBs decreased in 6 lakes and were not

detected in 6 other lakes. In no lake did DDT or

PCBs increase. One factor that could contribute

to the lack of statistically significant trends is the

combination of large variability among samples

and small sample size. Another that is

particularly relevant for PCBs and DDT and its

metabolites is that concentrations have been

decreasing exponentially in the environment

since use peaked and began to decline in the

1960s (Van Metre et al., 1998). The period

chosen here, beginning in 1970, is after peak

concentrations and on the flatter portion of the

exponential decrease, and therefore it might be

less sensitive to detecting trends.

Among the organochlorine compounds it is also

notable that chlordane has increased in more

lakes than is has decreased since 1970. The trend test showed one decreasing trend and four increasing

trends. Chlordane was used as an agricultural pesticide in the United States from 1948 to 1978 and

continued in urban use until 1988 (http://www.epa.gov/pbt/chlordane.htm). These trends, and lack of
National Sediment Quality Survey

4-14

Figure 4-10. White Rock Lake DDT

Concentrations.

Figure 4-11. Lead Trends Throughout the United States Using Sediment Core Data

from 1975 to Top of Core.

trends, suggest that more recent urban use of

chlordane has caused increasing trends in some

lakes or delayed the onset of significant decreases

noted elsewhere for DDT and PCBs (Van Metre et

al., 1998).

DDT use was widespread in the United States in

the 1950s and 1960s, and sediment cores from

many lakes show a large DDT peak in the early

1960s (Van Metre et al., 1998). Trends in total

DDT (the sum of DDT and its breakdown

products, DDD and DDE) closely follow

historical use of DDT, as can be seen from the

sediment core from White Rock Lake in Texas

(Figure 4-10). The initial occurrence of the DDT

in cores is usually in the 1940s, when widespread

use began. Peak concentrations are from the late

1950s to the mid 1960s, when use peaked. DDT

use declined in the 1960s and was eventually

banned in 1972. Levels of DDT have been

decreasing by about half every 10 years since the

1960s, although they are still at detectable levels

in all but the most pristine lakes.

The most consistent trend for any of the

constituents tested is the decreasing trend in lead since the mid-1970s. Twenty-one of the 22 lakes,

including the 3 reference lakes, had statistically significant decreasing trends (Figure 4-11). Decreasing

trends in lead have been reported previously and have been attributed to the large reduction in
National Sediment Quality Survey

4-15

Figure 4-12. White Rock Lake Lead

Concentrations.

anthropogenic lead releases since the 1970s

brought about by the switch to unleaded gasoline

and, to a lesser extent, reductions in industrial and

waste emissions (e.g. Callender and Van Metre,

1997). Significant trends in 21 of the 22 lakes

tested, including the 3 reference lakes, indicates

the strength of the gasoline lead signal in the U.S.

environment. As shown in Figure 4-12, lead

concentrations in the White Rock Lake sediment

core track well with national atmospheric trends

in lead. Two other trace elements had

concentrations of somewhat consistent trends:

chromium and nickel increased in zero and two

lakes and decreased in 10 and 11 lakes,

respectively. The only apparent pattern to these

decreases is that they mostly occurred in the

central and western United States, with the one

exception of a decreasing trend in chromium in

Lake Killarney in Orlando. Two other

elements—copper and mercury—had significant

trends in 10 or more lakes, with about equal

numbers of decreasing and increasing trends. No

clear regional or land use patterns are apparent

for trends in these elements, or for the five sites

with decreasing trends in cadmium.

The only trace element with more increasing trends than decreasing trends was zinc. Nine of the 19 urban

lakes had increasing trends in zinc (at a 90 percent confidence level), and three had decreasing trends.

Increasing levels of zinc in urban and urbanizing watersheds could be a result of the use of zinc in rubber

tires and increasing levels of vehicle use on U.S. roads. The two lakes with decreasing trends in zinc,

Lakes Washington and Great Salt Lake, are both in older urban areas. As noted above, Lake Washington

had large historical inputs of metals from the Asarco smelter in Tacoma.

Additionally, this analysis of trends in sediment contamination using sediment cores does not address the

issue of current sediment quality in the lakes and reservoirs discussed above. As was outlined earlier for

the results of the trend analysis as measured by the logistic regression model using the NSI data (1980

through 1999), any decrease in levels of sediment contamination does not imply that there is no risk to

human health or aquatic life from the sediments. Further evaluations should be conducted to confirm the

extent and severity of sediment contamination for any given site or watershed.

Discussion

An assessment of the contamination of sediments in lakes, rivers, and estuaries using sediment cores is a

useful tool to evaluate the water quality in these waters, as can be seen from the information provided

above. In addition, sediment core analysis can be used as feedback to evaluate the impact of current

legislation on reducing the level of contaminants in different waterbodies of the United States. A major

limitation of the study of historical trend analysis is the post-depositional disturbance of the sediment by

benthic organisms and other physical processes such as wave action and dredging. Researchers have used

different sediment dating techniques to study the existence and extent of such disturbances.
National Sediment Quality Survey

4-16

Some of the most significant sources of persistent and toxic chemicals have been eliminated or reduced

as the result of environmental controls put into place during the past two decades. In addition, effluent

controls on industrial and municipal point source discharges and best management practices for the

control of nonpoint sources have greatly reduced contaminant loadings to many rivers and streams. The

results of better controls over releases of sediment contamination are evident from the case studies

presented.

Metals and persistent organic chemicals are the contaminants most often associated with sediment

contamination. Despite recent progress in controlling sediment contaminant releases to the different

compartments of the environment, active sources of these contaminants still exist. These include

nonpoint source loadings such as surface water runoff and atmospheric deposition, point source loadings,

and resuspension of in-place sediment contaminants from historical sources. To achieve EPA’s

Contaminated Sediment Goals, it is evident from the information presented in this chapter that a

combination of pollution prevention, source control, and continuous monitoring is essential.
National Sediment Quality Survey

5-1

CHAPTER 5

CONCLUSIONS AND DISCUSSION

The NSI database is EPA’s largest compilation of sediment chemistry data (measuring the chemical

concentration of sediment-associated contaminants); tissue residue data (measuring chemical

contaminants in the tissue of organisms); and toxicity data (measuring the lethal and sublethal effects of

contaminants in environmental media on various test organisms). This database contains environmental

monitoring data from a variety of sources (e.g., state and federal monitoring programs) and includes more

than 4.6 million analytical observations and 50,000 stations throughout the United States from 1980

through 1999. For this report, EPA presents the results of the screening-level assessment of the NSI data

from 1990 through 1999. The purpose of this assessment was to determine whether potential adverse

effects to aquatic life and/or human health from sediment contamination exist at present or existed over

the past 10 years at distinct monitoring locations throughout the United States. One major advantage of

screening out older data (data collected before January 1, 1990) is to prevent the results presented in this

report from being unduly influenced by historical data when more recent data are available. This would

not allow the results of any decrease in sediment contaminant levels due to scouring/redeposition, natural

attenuation, navigational dredging, or active sediment remediation that have occurred since that sample

was collected.

EPA evaluated a total of 19,398 sampling stations nationwide as part of the NSI data evaluation. Of these

sampling stations, 8,348 stations (43.0 percent) were classified as Tier 1 (associated adverse effects on

aquatic life or human health are probable); 5,846 stations (30.1 percent) were classified as Tier 2

(associated adverse effects on aquatic life or human health are possible); and 5,204 stations (26.8

percent) were classified as Tier 3 (no indication of associated adverse effects). As pointed out earlier in

the document, many sampling programs target only sites of known or suspected contamination. This

factor could contribute significantly to the high percentage of Tier 1 classifications. To further evaluate

this, EPA conducted an evaluation using the same tiering methodology on data collected from EPA’s

EMAP. This program uses a probabilistic sampling design and selects sampling locations at random. The

analysis revealed that 33.4 percent of EMAP sampling stations were classified as Tier 1, 41.9 percent

were classified as Tier 2, and 24.8 percent were classified as Tier 3. This finding suggests that state

monitoring programs (accounting for most of the NSI data) have tended to focus their sampling efforts on

areas where contamination is known or suspected to occur. Further evaluation of the effects of

nonrandom sampling design on the frequency of detecting contaminated sampling stations can be found

in Swartz et al. (1995). They compared the percent of sediment sampling stations that exceeded PAH

screening levels (ERL, SQC, AET) based on random sampling station selection (Virginian Province

EMAP stations) to the percent of sampling stations that exceeded those levels based on sampling station

selection on the basis of known PAH contamination (such as creosote-contaminated Eagle Harbor,

Washington). The investigators found that the frequency of exceeding a sediment screening value in

sampling stations known to be contaminated was 5 to 10 times greater than that for randomly selected

sampling stations. 

In addition to the baseline screening-level assessment of the extent and severity of sediment

contamination throughout the United States, EPA also used the data in the NSI to evaluate potential

trends in sediment contaminant levels throughout the country. Despite the various limitations imposed by

a lack of routine monitoring information outlined in Chapter 4, EPA developed an approach to provide a

means for assessing changes in the extent and severity of sediment contamination over time for specific

areas where sufficient data exist in the NSI database. When looking at each hydrologic region (defined as
National Sediment Quality Survey

5-2

a major geographic area containing either the drainage area of a major river, such as the Missouri region,

or the combined drainage areas of a series of rivers, such as the Texas-Gulf Region, which includes a

number of rivers draining into the Gulf of Mexico) with sufficient data, focusing on stations with more

concentrated data sets, the results suggest that there has been a slight decrease to no change in the levels

of sediment contamination (as measured by the predicted proportion toxic using the logistic model) from

1980 through 1999. The results for these individual hydrologic regions are presented in Chapter 4. Also

discussed in that chapter are the results from the USGS NAWQA program, which collected and analyzed

sediment cores in lakes and reservoirs throughout the United States. Results from this analysis suggest

that DDT (from 1965 through 1990) and lead (from 1970 through 1990) sediment concentrations

displayed significant decreasing trends in several of the lakes and reservoirs (12 of 22 lakes for DDT and

all 22 lakes for lead) throughout the United States. These decreases appear to be linked to the banning of

DDT in 1972 and the switch to unleaded gasoline in the 1970s. This analysis of sediment cores also

demonstrated a significant increase in sediment PAH levels that appears to be correlated with an increase

in urbanization.

It is important to note here that these analyses of trends in sediment contamination do not address the

issue of the current sediment quality in the areas outlined above. In other words, just because the data

suggest that there has been a slight decrease in the levels of sediment contamination (as measured by the

predicted proportion toxic using the logistic regression model or from the NAWQA study) in the regions

or lakes and reservoirs outlined above it does not mean that there is no risk to human health or aquatic

life from the sediments. Further evaluations should be conducted to confirm the extent and severity of

sediment contamination for any given site or watershed.

The characteristics of the NSI data, as well as the degree of certainty afforded by available assessment

tools, allow neither an absolute determination of adverse effects on human health or aquatic life at any

location nor a determination of the areal extent of contamination on a national scale. The evaluation

results strongly suggest, however, that sediment contamination is significant enough to pose potential

risks to aquatic life and/or human health in various locations throughout the United States. The

evaluation methodology was designed for the purpose of a screening-level assessment of sediment

quality. Further evaluation should be conducted to confirm the extent and severity of sediment

contamination for any given site or watershed.

Based on the number and percentage of sampling stations containing contaminated sediment within

watershed boundaries, EPA identified a number of watersheds that contain areas of probable concern

(APCs) for sediment contamination. About 26 percent of the 370 eligible watersheds (96) contained an

APC, or 4.2 percent of all the 2,264 watersheds in the United States. Although the APCs were selected

by means of a screening assessment, EPA believes that they represent the highest priority for further

ecotoxicological assessment, risk analysis, and contaminant source evaluation because of the increased

weight-of-evidence in these areas. Although the procedure for classifying APCs using multiple sampling

stations was intended to minimize the probability of making an erroneous classification, further

monitoring of conditions in watersheds containing APCs is necessary because the same mitigating factors

that might reduce the probability of associated adverse effects at one sampling station might also affect

neighboring sampling stations.

EPA chose the watershed as the unit of spatial analysis because many states and federal water and

sediment quality management programs, as well as data acquisition efforts, are centered on this unit. This

choice reflects the growing recognition that activities taking place in one part of a watershed can greatly

affect other parts of the watershed, and that management efficiencies are achieved when viewing the

watershed holistically. At the same time, EPA recognizes that contamination in some reaches in a

watershed does not necessarily indicate that the entire watershed is affected. Further analysis should be
National Sediment Quality Survey

5-3

conducted within that watershed to delineate sediment contamination, which would allow any sediment

management activities determined to be necessary to be done in the most cost-effective and

environmentally sound manner.

Watershed management is a critical component of community-based environmental protection using

watershed or hydrologic boundaries to define the problem area. Many public and private organizations

are joining forces and creating multidisciplinary and multijurisdictional partnerships to focus on water

quality problems community by community and watershed by watershed. These watershed approaches

are likely to result in significant restoration, maintenance, and protection of water resources throughout

the United States. As reported in the initial National Sediment Quality Survey in 1997, various programs

across the United States as part of the National Estuary Program have used a watershed approach that has

led to specific actions to address contaminated sediment problems. These include the Chesapeake Bay,

Narragansett Bay (Rhode Island), Long Island Sound, Puget Sound, New York/New Jersey Harbor, and

San Francisco Bay Estuary programs. These specific programs have all recommended actions to reduce

sources of toxic contaminants to sediment.

As part of EPA’s Contaminated Sediment Action Plan, the Office of Solid Waste and Emergency

Response, the Office of Water, and EPA’s regional offices will initiate a pilot project to facilitate

cross-program coordination on contaminated sediments. The pilot project will bring a cross-Agency

focus to identifying and assessing waters that are impaired by sediment contamination. The pilots will

use the legal authorities and techniques available to both programs to satisfy the needs of both the

Remedial Investigation/Feasibility Study (RI/FS) evaluations and Total Maximum Daily Load (TMDL)

modeling. The ultimate goal of the pilots is to develop more watershed-based approaches to identifying,

assessing, and addressing, as necessary, contaminated sediments. EPA will work with other Federal

agencies, States, and interested stakeholders as these pilots are identified and implemented.

Additional pilot projects are outlined under a Memorandum of Understanding (MOU) between EPA and

the USACE. The purpose of the MOU, signed in July 2002, is to facilitate cooperation between the U.S.

Department of the Army and EPA with respect to environmental remediation and restoration of degraded

urban rivers and related resources in the United States. To begin an evaluation of this urban rivers

cooperative approach, it is proposed that eight demonstration pilot projects be announced and undertaken

during the next 12 months. The pilot projects will include, but not limited to, projects for water quality

improvement, contaminated sediment removal and remediation, and riparian habitat restoration.

The remainder of this chapter presents some general conclusions about the extent and severity of

sediment contamination in locations throughout the United States, as well as potential sources of

sediment contaminants. It also looks at conclusions from other studies addressing the regional and

national extent of sediment contamination. Finally, this chapter discusses other indications of sediment

contamination.

Extent of Sediment Contamination

Based on EPA’s evaluation, sediment contamination exists at levels where associated adverse effects are

probable (Tier 1) in some locations in every region of the country. The waterbodies affected include

streams, lakes, harbors, nearshore areas, and oceans. A number of specific areas in the United States had

large numbers of sampling stations where associated adverse effects are probable. Puget Sound, Elliot

Bay, Hudson River, the Pacific Ocean (near Santa Monica and San Diego), Willamette River, Sinclair

Inlet, San Diego Bay, Bellingham Bay, San Francisco Bay, Sheboygan River, Passaic River, Christina

River, Mississippi River, Big Creek (Grays Harbor), and Duwamish Waterway were among those

locations. Based on the above list, several harbors appear to have some of the most severely
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5-4

contaminated sediments in the country. This finding is not surprising because major harbors have been

affected throughout the years by large volumes of boat traffic, contaminant loadings from upstream

sources, and many local point and nonpoint sources.

Thousands of waterbodies in hundreds of watersheds throughout the country contain sampling stations

classified as Tier 1. Many of these sampling stations might represent isolated “hot spots” rather than

widespread sediment contamination, although insufficient data were available in the NSI database to

make such a determination. EPA’s River Reach File 1 (RF1) delineates the Nation’s rivers and

waterways into segments or reaches of approximately 1 to 10 miles in length. Based on RF1,

approximately 8.8 percent of all river reaches in the contiguous United States contained NSI sampling

stations. More than 8,300 sampling stations in almost 2,300 river reaches across the country (3.6 percent

of all reaches) were classified as Tier 1. About 5,850 sampling locations were classified as Tier 2. In

total, almost 4,200 river reaches in the United States—approximately 6.5 percent of all river

reaches—include at least one Tier 1 or Tier 2 station.

EPA cannot determine the areal extent or number of river miles of contaminated sediment in the United

States from the data in the NSI because (1) the NSI database does not provide complete coverage for the

entire nation; (2) sampling locations are largely based on a nonrandom sampling design; and (3) sediment

quality can vary greatly within very short distances.

The results of the NSI data evaluation discussed in this report must be interpreted in the context of data

availability. Many States and Regions appear to have a much greater incidence of sediment

contamination than others. To some degree, this appearance reflects the relative abundance of readily

available electronic data, not necessarily the relative incidence of sediment contamination. For example,

2,886 sampling stations in Region 10 (Alaska, Idaho, Oregon, Washington) are designated as Tier 1,

whereas only 79 sampling stations in Region 8 (Colorado, Montana, North Dakota, South Dakota, Utah,

Wyoming) are designated as Tier 1. However, of the 19,398 sampling stations evaluated from 1990

through 1999 throughout the United States, only 1.5 percent (294) were located in Region 8 while 27.1

percent (5,263) were located in Region10. Therefore, although the absolute number of Tier 1 and Tier 2

stations in each state is important, relative comparisons of the incidence and severity of sediment

contamination between states is not possible because the extent of sampling and data availability vary

widely.

For a number of reasons, some potentially contaminated sediment sites were missed in this evaluation.

The most obvious reason is that the NSI database does not include all the sediment quality data collected

throughout the United States from 1990 through 1999. As pointed out in Chapter 2, several reviewers

highlighted locations or areas throughout the United States with contaminated sediments either not

included in this report or with limited coverage. EPA is continually updating the NSI database for future

evaluations to provide better national coverage, and the areas and locations listed during this review will

be a high priority for addition to the NSI database and subsequent reports. Moreover, some data in the

NSI database were not evaluated because of questions concerning data quality or because information

regarding the location of the samples (i.e., latitude and longitude) was not available.

Sources of Sediment Contamination

Toxic chemicals that accumulate in sediment and are associated with adverse effects to aquatic and

human health enter the environment from a variety of sources. These sources can be separated into point

sources and nonpoint sources. The term point source is defined by the Clean Water Act (CWA) and

generally refers to any specific conveyance, such as a pipe or ditch, from which pollutants are

discharged. In contrast, nonpoint sources do not have a single point of origin and generally include
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diffuse sources, such as urban areas or agricultural fields, that tend to deliver pollutants to surface waters

during and after rainfall events. Some sources, such as landfills (including confined disposal facilities

that contain dredged sediments) and mining sites, are difficult to categorize as point or nonpoint sources.

Although these land areas represent discrete sources, pollution from such areas tends to result from

rainfall runoff and leaching. Likewise, atmospheric deposition of pollutants, generally considered to be a

nonpoint source of water pollution, arises from the emission of chemicals from discrete stationary and

mobile points of origin. The CWA specifies water vessels and other floating craft as point sources

although taken as a whole they function as a diffuse source.

Many point and nonpoint pollutant sources have been the subject of federal and other action over the past

25 years. The direct discharge of pollutants to waterways from municipal sewage treatment and industrial

facilities requires a permit under the CWA. EPA has delegated the authority to issue such permits to

many states. These permits contain technology-based and water quality-based pollutant discharge limits

and monitoring requirements designed for the protection of the water column and are not designed for the

protection of sediment quality. The disposal of sediment dredged to maintain navigation channels is

managed under both the CWA and the Marine Protection, Research, and Sanctuaries Act (MPRSA) to

ensure that unacceptable degradation from chemical pollutants in the dredged material does not occur at

the disposal location. Emission standards and controls on stationary and mobile sources of air pollutants

have also been established in federal regulations promulgated under the authority of the Clean Air Act

(CAA). These actions have reduced emissions of gaseous compounds such as inorganic oxides, as well as

pollutants that eventually enter waterbodies and accumulate in sediment. The Toxic Substances Control

Act (TSCA) and the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) have greatly reduced

the toxic pollutant input to the environment through bans and use restrictions on many pesticides and

industrial-use chemicals.

The combined impact of these actions has yielded improvements in water quality. A lag is evident in the

improvement of sediment quality compared to water quality because of the persistent nature of many

pollutants, especially since sediment acts as a reservoir for many contaminants. Other factors include the

difficulty in monitoring and regulating most toxic bioaccumulative pollutants. As discussed earlier in this

chapter and outlined in detail in Chapter 4, several hydrologic regions across the United States exhibited

a slight decrease in the levels of sediment contamination (as measured by the predicted proportion toxic

using the logistic model). One possible explanation as to why more regions did not exhibit decreasing

trends or why more dramatic trends were not observed could be the underrepresentation of

uncontaminated areas in the NSI database.

The feasibility and long-term success of sediment remediation approaches (natural recovery, dredging, or

capping) depend on effective pollutant source control. For some classes of sediment contaminants, such

as PCBs and organochlorine pesticides, use and manufacture bans or severe restrictions have been in

place for many years. Past disposal of PCBs continues to result in evaporation of these contaminants

from some landfills and leaching from soils, but most active PCB sources have been controlled. The

predominant sources of organochlorine pesticides are runoff and atmospheric deposition from past

applications on agricultural land, and occasional discharge from municipal treatment facilities. For other

classes of sediment contaminants, active sources continue to contribute environmental releases. For

example, the release of inorganic mercury from fuel burning and other incineration operations continues,

as do urban runoff and atmospheric deposition of metals and PAHs. Although these releases to the

environment still exist, great strides have been made in reducing such inputs. This is evident from the

substantial reductions that have been recognized in air emissions resulting from the Maximum Available

Control Technology (MACT) standards for Hazardous Air Pollutants (HAP), established under Section

112 and Section 129 of the Clean Air Act. Overall, the air toxics regulations will reduce air toxic
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emissions by more than 1.5 million tons per year. Tables 5-1 and 5-2 provide examples of emission

reductions expected from some of the MACT standards.

Table 5-1. Example Estimated Mercury Emission Reductions Attributable to MACT.

Mercury Source Categories 1990 Inventory Estimated Reductions

Coal-Fired Utility Boilers 25% uncertain

Medical Waste Incinerators 24% 98%

Municipal Waste Combusters 20% 90%

Chlorine Production 6% 92%

Industrial Boilers 6% 25%

Table 5-2. Example Estimated Air Toxics Emission Reductions Attributable to MACT.

Municipal Waste

Combuster 2001a 2005b

Dioxins/Furans 99% 99+%

Lead 77% 94%

Cadmium 68% 78%

a Large municipal waste combuster compliance date. b Small municipal waste combuster compliance date.

Other Studies Evaluating the Extent of Sediment Contamination

In 2002, EPA completed the first National Coastal Condition Report (USEPA, 2002a). The report is

available at EPA’s Web site at http://www.epa.gov/owow/oceans/nccr/index.html, and copies are also

available by calling 1-800-490-9198. Key contributors to this draft report were the National Oceanic and

Atmospheric Administration (NOAA); the Department of the Interior, U.S. Fish and Wildlife Service;

and several other local, state, and federal agencies.

One of the indicators used in the National Coastal Condition Report to assess the conditions of the

Nation’s coastal waters was sediment contamination. National and regional monitoring programs

conducted by EPA (EMAP for estuaries [EMAP-E]), along with NOAA’s National Status and Trends

Program, provided the data evaluated in the sediment contamination indicator. This indicator was

evaluated using the NOAA effects range-medium (ERM) and effects range-low (ERL) values (Long and

Morgan, 1990), where ERM values are the concentrations of contaminants that will result in ecological

effects 50 percent of the time and ERL values are the concentrations of contaminants that will result in

ecological effects 10 percent of the time. An estuary was determined to be in “poor” condition if it

exceeded one ERM value or five ERL values.

The geographic regions analyzed included the northeast coast, southeast coast, Gulf of Mexico, west

coast, and Great Lakes. Although the objective of the report was to evaluate the condition of coastal

resources (in this case primarily estuaries) on a national level, there is sufficient information to assess

only northeastern, southeastern, and Gulf of Mexico estuaries. Partial assessments are possible for the

west coast estuaries and the Great Lakes, and no assessment is currently possible for the estuarine

systems of Alaska, Hawaii, and the island territories (USEPA, 2002a). Results from this report indicate

that the overall condition of the sediments throughout the estuaries and the Great Lakes of the United

States would generally be classified as poor. Probabilistic surveys conducted by EPA’s EMAP-E,
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outlined in the National Coastal Condition Report, allowed for spatial estimates of ecological condition

for the following regions: Northeast, Southeast, and Gulf of Mexico. This spatial estimate was expressed

as a percent of degradation measured as the percentage of total estuarine surface area in the region (or

nation).

Results from the northeast coast indicate that 41 percent of the estuaries are degraded as a result of

sediment contamination. Results of the percent area degraded from the southeast coast and the Gulf of

Mexico due to contaminated sediments are 13 percent and 43 percent, respectively. The national estimate

of estuarine areas degraded due to contaminated sediments is 35 percent (USEPA, 2002a). Even though

probabilistic surveys that allow for spatial estimates of sediment contamination were not conducted in the

Great Lakes and in the west coast estuaries, existing monitoring data from various programs are available

to assess the condition of the sediments. EPA’s Great Lakes National Program Office (GLNPO) has

determined that polluted sediments remain the largest major source of contaminants to the Great Lakes

food chain and that more than 2,000 miles (20 percent) of shoreline is considered impaired because of

sediment contamination. Studies conducted on the west coast show that sediment contaminant conditions

in the Southern California Bight are poor. Using ERL and ERM values, it was determined that 67 percent

of the sediments in the bight have contaminants that could potentially result in adverse ecological effects.

As highlighted in Chapter 1, NOAA performed toxicity tests on 1,543 surficial sediment samples

collected from 1991 through 1997 from estuaries and bays along the Atlantic, Gulf of Mexico, and

Pacific coasts. These samples encompassed an area of approximately 7,300 square kilometers. Toxicity

was observed in samples that represented approximately 6 percent of the combined area (Long, 2000)

when using amphipod lethality tests. Toxicity was considerably more widespread (25 percent to 39

percent), however, when the results of two sublethal sediment toxicity assays were evaluated (Long,

2000). It has been demonstrated that in some cases long-term sediment toxicity tests in which survival

and growth are measured tend to be more sensitive than short-term tests, with chronic toxicity six times

higher than acute toxicity as indicated for Hyalella azteca (Ingersoll et al., 2001).

Other Indications of Sediment Contamination

EPA’s National Fish and Wildlife Contamination Program provides technical assistance to State, Federal,

and Tribal agencies on matters related to health risks associated with dietary exposure to chemical

contaminants in fish and wildlife. Human and wildlife consumption of finfish and shellfish that have

accumulated contaminants in their tissue (bioaccumulation) is an important human health and wildlife

concern. In fact, fish consumption represents the most significant route of aquatic exposure of humans to

many metals and organic compounds (USEPA, 1992). Most sediment-related human exposure to

contaminants is through indirect routes that involve the transfer of pollutants out of the sediment and into

the water column or aquatic organisms. Many surface waters have fish consumption advisories or fishing

bans in place because of mercury, as well as a group of bioaccumulative contaminants (PCBs, chlordane,

dioxins, and DDT and its metabolites [DDD and DDE]) that are commonly found in sediments based on

the NSI database. Based on EPA’s 2002 National Listing of Fish and Wildlife Advisories (NLFWA)

database there are 2,800 fish advisories in the United States for the types of contaminants (such as those

listed above) often found in contaminated sediments. These advisories affect more than 544,000 river

miles, 71 percent of the Nation’s coastal waters, and approximately 95,000 lakes, including 100 percent

of the Great Lakes. The number of advisories in the United States in 2002 represents a 7 percent increase

over 2001. This increase in advisories issued by the states generally reflects an increase in the number of

assessments of contaminants in fish and wildlife tissues.
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Continuing Challenges

The following discussion presents observations on continuing challenges to improving sediment quality

assessment and management in the United States. Any future policies and/or actions to address

contaminated sediments will have to be considered in the context of the budget process and competing

demands for funding. These observations build on the analyses completed in the first National Sediment

Quality Survey and goals outlined in the EPA’s 1998 Contaminated Sediment Management Strategy.

They are as follows:

1. Further assessment of the extent and severity of sediment contamination in the 96 targeted

watersheds would improve contaminated sediment management decisions.

2. Watershed management activities would create multidisciplinary and multijurisdictional

partnerships focusing on sediment contamination.

3. Better coordination of contaminated sediment management and research activities would

promote application of sound science in managing contaminated sediments.

4. Better monitoring and assessment tools would improve contaminated sediment management.

5. A weight-of-evidence approach and measures of chemical bioavailability in sediment monitoring

programs would improve the assessment of contaminated sediment.

6. Increased geographic coverage in the NSI database would refine a national assessment of the

extent and severity of contaminated sediment.

7. Assessment of atmospheric deposition of sediment contaminants would improve contaminated

sediment management.

8. Prevention of continuing sources of sediment contamination is important in contaminated

sediment management.

9. Better coordination and communication with external stakeholders and other federal agencies

would improve the contaminated sediment management process.

Observation 1: Further Assessment of the Extent and Severity of Sediment

Contamination in the 96 Targeted Watersheds Would Improve Contaminated

Sediment Management Decisions

To characterize the incidence and severity of sediment contamination in the United States, EPA has

developed and performed a screening-level analysis of the information in the NSI from 1990 through

1999, the results of which are presented in Chapter 3. The results of this assessment should not be used

as justification for requiring sediment remediation actions at potentially contaminated sites. This

evaluation of the NSI data was performed as a means of screening and targeting. Additional site-specific

data and information is needed to expand the NSI data evaluation into a comprehensive assessment of the

incidence and severity of sediment contaminant problems in the Nation’s various watersheds.

Further investigation and assessment of contaminated sediment will improve contaminated sediment

management decisions. States and tribes, in cooperation with EPA and other federal agencies, should

proceed with further evaluation of the 96 watersheds containing areas of probable concern (APCs) for

sediment contamination. Because this assessment uses data from the early 1990s through 1999, it is

likely that additional investigation and assessment have already been conducted (especially in wellknown areas of documented sediment contamination), and some areas might have been remediated. If
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active watershed management programs are in place, further evaluations can be coordinated within the

context of current or planned actions (e.g., TMDL development or sediment remediation). Future

monitoring and assessment efforts can focus on areas such as the 97 individual river reaches (or

waterbody segments) located within the 96 watersheds containing APCs that had 10 or more stations

categorized as Tier 1. The purpose of these efforts should be, to gather additional sediment chemistry

data and related biological data (i.e., sediment toxicity, macrobenthic community analysis), as needed,

and to conduct further assessments of the data to determine human health and ecological risk, determine

temporal and spatial trends, and identify potential sources of sediment contamination and determine

whether the appropriate source controls are being applied. Any existing data collected from the area of

interest should be compiled and evaluated before additional assessment efforts begin. A helpful tool for

delineating sediment contamination is a computerized sampling design program called the “Fully

Integrated Environmental Location Decision Support” (FIELDS) system developed by EPA. This system

is a set of software modules designed to simplify sophisticated site and contamination analysis. Each

module is a self-contained unit that can be applied to a variety of scenarios. The modules allow discrete

sampling points to be interpolated into a surface area. Important uses of these interpolated surface areas

include delineating hot spots, calculating average concentrations, estimating contamination mass and

volume, and developing post-remediation scenarios. More information on this system is available on the

Internet at www.epa.gov/region5fields. 

Additional monitoring and analysis of data from watersheds containing APCs (as well as watersheds not

containing APCs) can also be used to track and document the effectiveness (or ineffectiveness) of

sediment management actions that have been applied to address these areas over time. These trends will

be useful in supplementing the results presented in Chapter 4 and could be reported in future reports to

Congress. Comparisons to the 96 watersheds identified as APCs in the first National Sediment Quality

Survey should not be made for potential trends in sediment contamination. Although the methodology

used to designate APCs remained the same from the first report to this one, the methodology used to

categorize sampling stations into the three tiers has been updated from the first National Sediment

Quality Survey. Also, because the first report focused on data collected in the 1980s and this report

focuses on data collected in the 1990s, the sampling stations did not necessarily overlap; that is, a

watershed identified as having an APC in the first report might not have met the minimum data

requirements to be identified as having an APC in this report. Comparisons between watersheds

containing APCs identified in the first report and watersheds containing APCs in the current report are

provided in Appendix F.

The plethora of data compiled in the NSI database indicate that many state and federal government

monitoring programs already do a good job of gathering data at locations with known sediment

contamination problems (including some of the 96 APCs), and additional monitoring at such locations

might not be necessary. For other locations not previously targeted for focused monitoring, however,

additional data might be required to adequately assess potential sediment contamination problems,

especially in areas where significant human health exposures occur. In addition, in some cases it might be

necessary to conduct baseline studies to determine where to focus monitoring activities. If during these

studies it is determined that a biological impairment has occurred, a useful tool in discerning the cause or

causes of that impairment is an EPA publication titled the Stressor Identification Guidance Document

(USEPA, 2000e). That document is intended to identify stressors causing biological impairments in

aquatic ecosystems, and it provides a structure for organizing the scientific evidence that supports the

conclusions.
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Observation 2: Watershed Management Activities Would Create

Multidisciplinary and Multijurisdictional Partnerships Focusing on Sediment

Contamination

As was recommended in the first National Sediment Quality Survey and highlighted in this update,

watershed management is a critical component of community-based environmental protection using

watershed or hydrologic boundaries to define the problem area. Many public and private organizations

are joining forces and creating multidisciplinary and multijurisdictional partnerships to focus on water

quality problems community by community and watershed by watershed. These watershed approaches

are likely to result in significant restoration, maintenance, and protection of water resources throughout

the United States. A watershed management framework (such as the one designed by the Chesapeake

Bay Program) requires a high level of inter-program coordination to consider all factors contributing to

water and sediment quality problems and to develop integrated, science-based, cost-effective solutions

that involve all the stakeholders. It is within the watershed framework, therefore, that federal, state,

tribal, and local government agencies; industry; and citizens’ groups can pool their common resources

and coordinate their efforts to address their common sediment contamination issues. These watershed

activities will support efforts such as monitoring and regulatory actions.

One example of addressing sediment contamination using a watershed management approach is the

Remedial Action Plan (RAP) process used in the Great Lakes areas of concern (AOCs). In 1972 the

Great Lakes Water Quality Agreement was established between the United States and Canada. The

Agreement addresses 43 AOCs recognized in the Great Lakes Basin. These AOCs were identified

because they had impairment to one or more of the 14 beneficial uses recognized for the Great Lakes

Basin. Identifying the AOCs led to the initiation of the RAP. The RAP outlines the activities necessary

for all stakeholders to complete when addressing a known AOC. One RAP, for example, is for the Grand

Calumet River/Indiana Harbor Ship Canal (GCR/IHSC) AOC. For the GCR/IHSC, all 14 beneficial uses

were determined to be impaired, and contaminated sediments were associated with the majority of these

impairments. As part of the RAP process for this AOC, a group of individuals was appointed to oversee

the development of the Plan. This group, composed of representatives from industry, government (local,

state, and federal), citizens’ groups, and academia, also assisted in implementing the Plan. One watershed

approach used by this group was the development of a matrix listing all actions occurring in the

watershed that were associated (directly or indirectly) with the restoration of the impaired uses. This

matrix is being used to assist in prioritizing activities, as well as tracking the success of actions taken to

restore the beneficial uses. The RAP process might also be used to address areas of potential concern

because it sets forth the indicators used to detect environmental degradation and the benchmarks for

measuring progress.

Another example of watershed management/coordination is detailed in a recently signed Memorandum

of Understanding (MOU) between EPA and the U.S. Army Corps of Engineers (USACE). As discussed

earlier in this report, the purpose of this MOU is to facilitate cooperation between the U.S. Department of

the Army and the EPA with respect to environmental remediation and restoration of degraded urban

rivers and related resources in the United States. This MOU seeks to foster environmental quality to

ensure the protection of public health, economic sustainability and community vitality. The MOU was

entered into for the purpose of coordinating remedial, water quality, and environmental restoration

activities under the Clean Water Act (CWA), the Comprehensive Environmental Response,

Compensation, and Liability Act (CERCLA), the Resource Conservation and Recovery Act (RCRA), and

the various Water Resources Development Act (WRDA) authorities. The EPA and the USACE agreed to

enter into watershed-specific agreements to coordinate remedial, water quality and environmental

restoration activities under, but not limited to, the WRDA, CERCLA, RCRA, CWA, and other related
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authorities at locations where the parties agree that such cooperative arrangements are mutually

beneficial. The success of this agreement will be evidenced by the efficient accomplishment of each

agency’s statutory requirements within areas of mutual concern in a timely manner and minimizing

misunderstandings and duplication of effort.

This National Sediment Quality Survey provides an important and essential tool for targeting efforts to

further investigate the 96 watersheds that contain APCs. It is also useful in highlighting areas of concern

where there are known data gaps for additional analysis. As more data become available and the NSI

database expands, the database will provide further information to help environmental managers better

understand which of their watersheds have sediment contamination problems that pose the greatest risk

to aquatic life and human health and will allow them to track progress as they address those problems.

Also, as more data are added to the NSI database, researchers will have more site-specific information to

draw upon to conduct new analyses that could lead to better assessments.

A vital component of watershed management is education and engagement of all stakeholders in

government (federal, state, tribal, and local), industry, citizens’ groups, and the community. As part of

EPA's Contaminated Sediment Action Plan, the Agency will continue to solicit stakeholders’ views on

science and policy issues affecting contaminated sediment management to promote better decisionmaking. In May 2001 EPA sponsored a forum on managing contaminated sediments that brought together

technical experts, stakeholders, and risk managers. EPA plans to hold additional meetings in the future to

discuss the Agency’s efforts and to address technical issues. As a part of the Contaminated Sediment

Action Plan, EPA will continue its efforts to improve community involvement during the investigation

and cleanup of contaminated sites. In addition to providing communities with technical assistance

opportunities, a workshop will be held to identify methods to improve consideration of the societal and

cultural impacts of both baseline contamination and remedial alternatives at contaminated sites.

Observation 3: Better Coordination of Contaminated Sediment Management

and Research Activities Would Promote Application of Sound Science in

Managing Contaminated Sediments

Coordination of contaminated sediment research and management activities would promote the use of

sound science in managing contaminated sediment. One collaborative effort within EPA has been the

establishment of a Contaminated Sediment Management Committee (CSMC). This committee was

established to coordinate all the appropriate programs and their associated regulatory authorities involved

in the management of contaminated sediments. The CSMC includes representation at the Office Director

and Regional Division Director levels from EPA’s Office of Solid Waste and Emergency Response

(OSWER), Office of Water (OW), Office of Research and Development (ORD), and Office of

Enforcement and Compliance Assurance (OECA) and many of the EPA regions. This committee

developed the Contaminated Sediment Action Plan mentioned earlier. This multimedia, cross-program

plan outlined the next steps for the Agency in the management of contaminated sediments. It described

the commitments needed from the EPA program offices to develop and apply sound science in managing

contaminated sediments.

Another collaborative effort to address contaminated sediments, as mentioned above, was the publication

of the Agency’s Contaminated Sediment Management Strategy (USEPA, 1998a). That document

summarized EPA’s understanding at the time of the extent and severity of sediment contamination (as

was described in the 1997 National Sediment Quality Survey and accompanying NSI database); described

the cross-program policy framework in which EPA intends to promote consideration and reduction of

ecological and human health risks posed by sediment contamination; and identified actions that EPA
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believes are needed to bring about consideration and reduction of risks posed by contaminated sediments.

The Contaminated Sediment Action Plan further supports activities outlined in the Contaminated

Sediment Management Strategy, such as preventing sediment contamination as early as possible by

continuing to emphasize identification and control of direct and indirect sources of significant

contamination to waterbodies that can affect sediments.

EPA has also established a mechanism to develop and coordinate Agency office- and region-wide

activities in the contaminated sediment area. This mechanism is the Contaminated Sediment Science

Plan. Currently in draft form, the plan provides an analysis of the current Agency science activities (the

contaminated sediments science activities database), identifies and evaluates the science gaps, and

suggests a strategy for filling some of the highest priority gaps. EPA’s Science Policy Council initiated

the Contaminated Sediment Science Plan because contamination of sediments is a cross-Agency issue

that needs a more comprehensive and higher level of coordination across the Agency. Additionally, this

plan provides key recommendations for future Agency research and science activities in contaminated

sediments. The document has undergone EPA Science Advisory Board peer review, as well as a public

comment period. Currently the Agency is addressing the comments and revising the document. The

Agency anticipates releasing the document in 2004.

A precedent-setting team has been established by EPA’s Region 5 in the Great Lakes area. Contaminated

sediments were designated as a Region 5 Environmental Priority in 1995 because of the extent and

severity of the problem across the region, and therefore a Regional Contaminated Sediment Team was

established. This team, formed with members representing a variety of regional programs and offices,

coordinates all program/office efforts to address contaminated sediment sites and provide technical

expertise to the region, state agencies, and others. Additional activity in the Great Lakes area include

EPA’s Great Lakes National Program Office (GLNPO). From 1993 through 2003, GLNPO awarded

more than $15.7 million to various state and federal agencies, tribes, universities, and non-governmental

organizations for sediment-related work. The Sediment Assessment and Remediation Team under

GLNPO is responsible for providing the funding, technical support, and vessel support to assist

contaminated sediment work in the Great Lakes watershed.

A key component of future coordination within EPA in addressing sediment contamination is initiation of

the contaminated sediment assessment pilot projects described earlier. As part of EPA’s Contaminated

Sediment Action Plan, OSWER, OW, and EPA’s regional offices will initiate pilot projects to facilitate

cross-program coordination on contaminated sediments. The pilot projects will bring a cross-Agency

focus to identifying and assessing waters that are impaired by sediment contamination. The pilots will

use the legal authorities and techniques available to satisfy the needs of both the Remedial

Investigation/Feasibility Study (RI/FS) evaluations and Total Maximum Daily Load (TMDL) modeling.

The ultimate goal of the pilots is to develop more watershed-based approaches to identifying, assessing,

preventing, and remediating contaminated sediments. EPA will work with other federal agencies, States,

and interested stakeholders as these pilots are identified and implemented.

Observation 4: Better Monitoring and Assessment Tools Would Improve

Contaminated Sediment Management

The National Sediment Quality Survey reports (the initial report published in 1997 and this current

version) present analyses of sediment chemistry and biological data from numerous databases in an effort

to identify the national incidence and severity of sediment contamination. Because the data were not

generated by one single monitoring program specifically designed to provide this national picture,

numerous obstacles had to be overcome to analyze these data with as little bias as possible and the most

scientific validity possible.
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To ensure effective quality assurance and quality control (QA/QC) management, monitoring programs

should adopt standard sample collection and storage procedures. To assist in this, EPA has recently

released Methods for Collection, Storage, and Manipulation of Sediments for Chemical and

Toxicological Analyses (USEPA, 2001b). Interference encountered as part of the sediment matrix,

particularly in samples from heavily contaminated areas, can limit the ability of available methods to

detect or quantify some analytes. There is a need for cost-effective methods sensitive enough (i.e., with

low enough detection limits) to detect sediment contaminants and the chemical parameters that control

the bioavailability of contaminants such as PCBs, dioxins, PAHs, metals, and pesticides. QA/QC

management will be improved if databases include documentation of procedures used in collecting

sediment and performing chemical and biological analyses. The modernization of federal and other data

repositories to accommodate the storage of this QA/QC documentation should help facilitate this process.

Developing this report has shown the need for additional “tools” to assist in the assessment of

contaminated sediments. Significant progress has been made in the development of better monitoring and

assessment tools since the first National Sediment Quality Survey report was published in 1997, but more

work still needs to be done in this area. Although EPA has recently released sediment toxicity test

methods designed for evaluating sublethal effects (e.g., reduction in growth and reproduction) for some

freshwater and marine/estuarine benthic species (USEPA, 2000d, 2001a), protocols using new test

species must be developed to provide sensitive tests (with both lethal and sublethal endpoints)

representing a greater range of species and habitat types. Also, when applicable, standardized methods

for measuring sublethal endpoints should be developed for sediment toxicity test methods that currently

provide information on lethal endpoints. In addition, field validation of new and existing sediment

toxicity test methods is needed. Field validation determines the ecological significance of a reduction in

growth or reproduction of organisms evaluated in the laboratory with sediments collected from the field.

EPA is currently evaluating the ecological significance of its recently released freshwater sediment

toxicity test methods by comparing the results of sediment toxicity assays of spiked sediments to results

from benthic colonization trays spiked with the same sediment concentrations and placed in the field.

Results of this comparison should be available in 2004.

One concern about traditional sediment toxicity assays is that the toxicity might be altered by

manipulation of the sediment during its collection in the field and distribution into test vessels in the

laboratory. One method that prevents this alteration is the use of in situ sediment toxicity test methods.

This approach, which places the test organisms in the field instead of placing them in sediment brought

back to the laboratory, has been used extensively in marine bioaccumulation studies using mussels. It is

now being used effectively in sediment and storm water contamination studies using a host of test species

(Bascombe et al., 1990; Ellis et al., 1993; Ireland et al., 1996; McCahon et al., 1991; Sasson-Brickson

and Burton, 1991; Skalski et al., 1990). Further work is necessary to standardize these methods to allow

this approach to be used throughout the Nation in monitoring programs.

Another tool needed for the assessment of contaminated sediments is the further development of

sediment toxicity identification evaluation (TIE) procedures. Because sediment contaminants most

commonly occur in mixtures, there is a need for procedures to determine which contaminant is

responsible for the observed toxicity. Currently, EPA ORD is developing TIE methods capable of

characterizing the toxicity of a sediment by identifying classes of toxic contaminants (e.g., metals,

organics). More work is needed to improve upon these methods so that individual chemical contaminants

can be identified. In addition, work is needed to conduct field validation studies to support the TIE

method development.

One approach used to evaluate the data in the NSI is the use of numeric sediment screening levels or

sediment quality guidelines. These values are based on concentrations of contaminants in sediment that
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are associated with potential adverse effects and have been proposed by a number of investigators around

the world (Chapman, 1989; Field et al., 1999, 2002; Ingersoll et al., 1996, 2001; Long and Morgan, 1990;

MacDonald et al., 1996, 2000; USEPA, 1992, 1997). Sediment quality guidelines are needed by EPA,

states and tribes, and other federal agencies to assist in developing a weight-of-evidence approach in

support of sediment quality assessments. With respect to numeric sediment screening levels, EPA’s SAB

has stated that within the framework of the known uncertainty, it appears that the equilibrium partitioning

(EqP) method provides a useful sediment assessment tool. The SAB concluded that the method is

sufficiently valid to be used in the regulatory process if the uncertainty associated with the method is

considered, described, and incorporated. The SAB concludes that the EqP-derived criteria, if applied

properly, are ready for publication and use (SAB, 1992).

Further field and laboratory studies would help evaluate the accuracy of chemical-specific sediment

quality guidelines in different sediment types. The sediment screening levels and sediment quality

guidelines developed to date have primarily focused on the protection of benthic organisms from direct

toxicity and do not address the potential effects of bioaccumulative sediment pollutants (e.g., DDT and

PCBs) that make their way up the food chain and can cause adverse human health effects. Additional

work is needed to determine whether current screening levels for bioaccumulative contaminants in

sediment are predictive of effects on sediment-dwelling organisms or on organisms that consume

sediment-dwelling organisms. Also needed is the development and validation of additional sediment

quality guidelines for bioaccumulative contaminants. The impact sediments have on wildlife through

dermal contact and ingestion (food web transfers) is another area where more work is needed. Along with

additional work on refining and developing sediment quality guidelines, a framework for the application

of these values needs to be developed. In response to this need, a Society of Environmental Toxicology

and Chemistry (SETAC)-sponsored Pellston workshop was held in August 2002. A description of

approaches that can be used to integrate sediment quality guidelines into various sediment quality

assessment frameworks using multiple chemical and biological lines of evidence is provided in the

workshop summary (Wenning and Ingersoll, 2002).

The sediment quality evaluation tools described and used in this report should be used as the basis for

future contaminated sediment assessment methods. As sediment quality data become more available and

the state of the science for sediment assessment continues to evolve, assessment methods will also

evolve. As new and better sediment screening values and biological assessment techniques become

available and proven to be reliable, EPA will incorporate them into future NSI data evaluations.

Observation 5: A Weight-of-Evidence Approach and Measures of Chemical

Bioavailability in Sediment Monitoring Programs Would Improve the

Assessment of Contaminated Sediment

As stated in Chapter 2 of this report, the ideal assessment methodology would be based on matched data

sets of multiple types of sediment quality measures to take advantage of the strengths of each

measurement type and to minimize their collective weaknesses. For example, sediment chemistry can

indicate the presence of contaminants but cannot definitively indicate an adverse effect. On the other

hand, toxicity tests or benthic community surveys can indicate an adverse effect but cannot definitively

implicate the causative contaminant. Matched sediment chemistry data and sediment toxicity tests,

however, can provide a preponderance of evidence indicating a chemical cause of an adverse biological

effect (Ingersoll et al., 1997). The sediment TIEs mentioned earlier are also an extremely valuable tool in

attributing cause to the observed effect. Studies have shown that overall, an integration of several

methods using the weight of evidence is the most desirable approach for assessing the effects of

contaminants associated with sediment (Ingersoll et al., 1996; 1997; Long and Chapman, 1985; Long and
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Morgan, 1990; MacDonald et al., 1996). The weight-of-evidence approach has been successfully

incorporated into some existing sediment monitoring and management regulatory programs (e.g., Puget

Sound Disposal Analysis and Washington State Sediment Management Standards). In response to this,

monitoring and development of sediment management programs should be planned and implemented to

support weight-of-evidence assessments.

Because the state of the science is constantly evolving, future sediment monitoring programs should

collect tissue residue, biological effects (i.e., toxicity, histopathology), and biological community (e.g.,

benthic abundance and diversity) measurements whenever possible along with sediment chemistry data.

These types of data are necessary to better assess actual adverse effects resulting from exposure to

contaminated sediment. Matched sediment chemistry and tissue residue data should be collected where

human exposures are a concern. In areas where aquatic life effects are a concern, monitoring programs

should collect matched sediment chemistry data, biological effects data, and biological community

measurements. There is a need to evaluate matched sediment chemistry and toxicity data to determine the

predictive ability of sediment screening values to correctly classify sediment toxicity and minimize both

Type I errors (falsely classifying a sample as toxic when it is not toxic) and Type II errors (falsely

classifying a sample as nontoxic when it is toxic). Also, whenever possible, monitoring programs should

use a randomized approach to select sampling stations. As stated in Chapter 5, the frequency of

exceeding a sediment screening value in sampling stations known to be contaminated was 5 to 10 times

greater than that for randomly selected sampling stations.

The collection of data to measure chemical bioavailability is critical to the success of weight-of-evidence

assessments. Appropriate chemical bioavailability measures include acid-volatile sulfide (AVS) and

simultaneously extracted metals (SEM) data and total organic carbon (TOC) data. AVS and SEM provide

essential information for assessing the bioavailability of cationic metals in sediment. Where metals are

expected to be a concern, sediment monitoring programs should collect AVS and SEM measurements.

TOC provides information related to the bioavailability of nonionic organic contaminants. For the

evaluation process used in this report, when TOC values were not reported, a default value was used for

comparing measured sediment chemistry values to screening values.

This approach resulted in the possible overestimation or underestimation of potential impacts. More

accurate assessments will be possible if future monitoring programs include TOC measurements

wherever organic chemicals are a concern.

Observation 6: Increased Geographic Coverage in the NSI Database Would

Refine a National Assessment of the Extent and Severity of Contaminated

Sediment

The NSI database is limited in terms of the number of data sets it includes and the national coverage it

provides. The data in the NSI used for the initial National Sediment Quality Survey published in 1997

consisted of approximately 2 million records for more than 21,000 monitoring stations across the

country. There has been a significant increase in the amount of data compiled in the NSI database since

the first report was published. At present, the expanded NSI database includes more than 4.6 million

analytical observations for approximately 50,000 stations throughout the United States. For this report,

EPA used data from the NSI for 1990 through 1999 and evaluated 19,398 stations that met the minimum

data requirements to be evaluated. Over 50 percent of the monitoring stations evaluated for this report are

in five states (California, Florida, Illinois, Virginia, and Washington). Despite the large number of

sampling stations, only 8.8 percent of all river reaches in the contiguous United States contain one or

more sampling stations that were evaluated for this report.
National Sediment Quality Survey

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For this report, great strides were made in adding to the NSI database. Additional efforts to add data to

the NSI database include the substantial work done by EPA’s Region 1 and GLNPO. They have begun

efforts to centralize sediment assessment data and integrate these data, incorporating the northeastern

United States as well as the Great Lakes into the NSI database. EPA is continuing to compile additional

sediment chemistry data and related biological data for future reports. The focus of the data additions

will be to obtain a greater breadth of coverage across the United States and to increase the number of

waterbodies evaluated. These types of data will be extremely useful in future analyses to assess the

changes in the extent and severity of sediment contamination over time. Upon completion of this report,

EPA will make a concerted effort to accumulate more data for inclusion in the NSI database and for use

in future National Sediment Quality Survey reports to Congress. This effort will begin by focusing on

areas (river reaches and watersheds) with minimal or no coverage in this report. As part of this effort,

EPA will broadly advertise its need for information on contaminated sediments. EPA also encourages

third parties to send their information to STORET (www.epa.gov/STORET) so that it can be reflected in

the next National Sediment Quality Survey.

As part of the initial National Sediment Quality Survey, EPA included the data used for that report in its

comprehensive geographic information system (GIS)/modeling system, Better Assessment Science

Integrating Point and Nonpoint Sources (BASINS). EPA will be putting the new data in the NSI database

into BASINS. In addition to this effort, EPA is also working with the National Oceanic and Atmospheric

Administration (NOAA) to incorporate the NSI database into Query Manager. Query Manager is a

database program developed by NOAA's Office of Response and Restoration. It can be used to access

sediment chemistry (surface and subsurface), sediment toxicity, and tissue chemistry data from the

relational database for individual watersheds. Users can select from a menu of queries that sort and

analyze the data in various ways to produce output tables. The selected data can be immediately

displayed on maps using Mapping Application for Response, Planning, and Local Operational Tasks

(MARPLOT), or the output tables from the queries can be saved in a variety of formats for use with other

mapping software (e.g., ArcView) or other applications (e.g., spreadsheets, statistics packages, word

processors). MARPLOT is a general-purpose desktop mapping program that was jointly developed by

NOAA, the U.S. Coast Guard, and the Census Bureau. It allows the user to create, view, and modify

maps quickly and easily and to link objects on maps to data in other programs.

The NSI database can be a powerful tool for program and water resource managers at the national,

regional, state, watershed, and waterbody levels. It provides in a single location a wealth of information

that could be very useful, especially with improved access and availability. All agencies should have

access to the same data for decision-making in regional, state-level, and watershed-level management.

EPA released the NSI database used in this report in early 2001 to give stakeholders a chance to use the

data for their own purposes. As was discussed in the first report to Congress, increased access to data and

information in the NSI database has many applications. At the national level, the data and associated

information can demonstrate the need and provide the driving force for increased pollution prevention

efforts. It can also demonstrate the need for safer or biodegradable chemicals and determine the relative

risk compared to other problems to assist in prioritizing activities. At the state and watershed levels,

better access to the information contained in the NSI can assist in educating and involving the public,

setting goals and prioritizing activities and expenditures, and evaluating the adequacy and effectiveness

of control actions, sediment remediation activities, and other management activities.
National Sediment Quality Survey

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Observation 7: Assessment of Atmospheric Deposition of Sediment

Contaminants Would Improve Contaminated Sediment Management

The relative contribution of contaminants to the sediment from air deposition is virtually unknown on a

national scale, but is under study. Under Section 112(m) of the CAA, EPA in cooperation with NOAA

has been conducting a program to assess the contribution and effects of hazardous air pollutants on the

Great Lakes, Lake Champlain, the Chesapeake Bay, and near-coastal waters. This program is referred to

as the Great Waters Program. As part of the program, EPA has supported air deposition monitoring, fate

and transport modeling, bioaccumulation assessments, and sediment contamination modeling. Nationalscale deposition assessment modeling is under way. EPA has produced three reports to Congress

documenting current knowledge about deposition of hazardous air pollutants to the Great Waters,

including source identification and effects. The third report, Deposition of Air Pollutants to the Great

Waters: Third Report to Congress (USEPA, 2000f), outlines programs under way to reduce air toxics but

also calls for additional deposition monitoring to more fully assess the contribution to water, sediment,

and fish tissue contamination. Findings and conclusions from these programs will be incorporated into

future iterations of the National Sediment Quality Survey.

Observation 8: Prevention of Continuing Sources of Sediment Contamination

is Important in Contaminated Sediment Management

Although sediment contamination is frequently the result of historical discharges of pollutants before the

National Pollutant Discharge Elimination System (NPDES) regulatory program was established (USEPA,

1998a), there are still continuing sources of sediment contamination (as outlined in Chapter 5 of this

report). Therefore, source control and pollution prevention are crucial items in preventing contaminated

sediments. As outlined in EPA’s Contaminated Sediment Management Strategy, OW and other EPA

program offices are working with non-governmental organizations and the States to prevent point and

nonpoint source contamination from accumulating in sediments.

Pollution prevention is a key element in reducing the sources of contaminants that can end up in the

sediments, potentially resulting in adverse effects to aquatic life or human health. Pollution prevention

has been shown to reduce costs as well as pollution risks through source reduction and recycling/reuse

techniques. Under Section 6602(b) of the Pollution Prevention Act of 1990, Congress established a

national policy for a hierarchy of environmental management (1) pollution should be prevented or

reduced at the source, whenever feasible; (2) pollution that cannot be prevented should be recycled in an

environmentally safe manner, whenever feasible; (3) pollution that cannot be prevented or recycled

should be treated in an environmentally safe manner, whenever feasible; and (4) disposal or other release

into the environment should be employed only as a last resort and should be conducted in an

environmentally safe manner. The Pollution Prevention Act emphasizes that pollution prevention means

source reduction and defines source reduction as any practice that (1) reduces the amount of any

hazardous substance, pollutant, or contaminant entering any waste stream or otherwise released into the

environment (including fugitive emissions) prior to recycling, treatment, or disposal; (2) reduces the

threats to public health and the environment associated with the release of hazardous substances,

pollutants, or contaminants; and (3) increases the efficiency of using raw materials, energy, water, or

other resources, or protects natural resources by conservation.

Additionally, EPA has developed and is implementing a national multimedia strategy (under the crossagency PBT Program) for the reduction of persistent, bioaccumulative toxic chemicals (PBTs), which

typically accumulate in sediments. As is stated in the 1998 PBT strategy (USEPA, 1998b), EPA is

forging a new approach to reducing risks from and exposures to priority PBT pollutants. This approach,
National Sediment Quality Survey

5-18

focused on increased coordination among EPA and regional programs, also requires the significant

involvement of stakeholders, including international, state, local, and tribal organizations, the regulated

community, environmental groups, and private citizens. The four main elements of this approach are

(1) develop and implement national action plans for priority PBT pollutants; (2) continue to screen and

select more priority pollutants for action; (3) prevent the introduction of new PBTs; and (4) measure the

progress of these actions.

If additional investigation reveals the need for actions to reduce the risks posed from the contaminated

sediments, the preferred actions would depend on factors such as protectiveness, regulatory compliance,

effectiveness, implementability, cost, and State and community acceptance. Additional guidance for

addressing risks from contaminated sediments includes the Principles for Managing Contaminated

Sediment Risks at Hazardous Waste Sites (USEPA, 2002b) and the Draft Contaminated Sediment

Remediation Guidance for Hazardous Waste Sites (USEPA, 2002c).

Observation 9: Better Coordination and Communication with External

Stakeholders and Other Federal Agencies Would Improve Contaminated

Sediment Management Process

Sediment contamination is a concern to stakeholders throughout the United States. EPA will work

closely with other federal agencies (e.g., USACE, NOAA, USGS) to compile and evaluate data in the

NSI database as well as the development of future reports. Additionally, EPA will reach out to the public

as EPA compiles additional sediment quality data in the NSI database and develops the next report to

Congress. During the next year, EPA anticipates setting up “listening sessions” to gather information that

can be used for future reports to Congress. During these sessions, EPA will be searching for additional

data for the NSI database and subsequent reports, taking recommendations on how to improve the report,

and establishing better and more effective ways to keep the public and interested stakeholders informed.
National Sediment Quality Survey

References-1

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Long, E.R., D.D. MacDonald, S.L. Smith, and F.D. Calder. 1995. Incidence of adverse biological effects

within ranges of chemical concentrations in marine and estuarine sediments. Environ. Manage.

19(1):81-97.

Long, E.R., and L.G. Morgan. 1990. The potential for biological effects of sediment sorbed contaminants

tested in the National Status and Trends Program. NOAA tech. memo. NOA OMA 52. National

Oceanic and Atmospheric Administration, Seattle, WA.

Luoma, S.N. 1983. Bioavailability of trace metals to aquatic organisms—A review. Sci. Total Environ.

28:1-22.

Lyman, W.J., A.E. Glazer, J.H. Ong, and S.F. Coons. 1987. An overview of sediment quality in the

United States. Prepared for U.S. Environmental Protection Agency, Office of Water Regulations and

Standards, Washington, DC.
National Sediment Quality Survey

References-5

Mancini, J.L., and A.H. Plummer, Jr. 1994. A review of EPA sediment criteria. In Surface Water Quality

and Ecology, Vol. 4, Proceedings of the Water Environment Federation’s 67th Annual Conference

and Exposition, Chicago, IL, October 15-19, 1994, pp. 681-694.

MacDonald, D.D., R.S. Carr, F.D. Calder, E.R. Long, and C.G. Ingersoll. 1996. Development and

evaluation of sediment quality guidelines for Florida coastal waters. Ecotoxicology 5:253-278.

McDonald, D.D., C.G. Ingersoll, and T.A. Berger. 2000. Development and evaluation of consensus-based

sediment quality guidelines for freshwater ecosystems. Arch. Environ. Contam. Toxicol. 39:20-31.

McCahon, C.P., M.J. Poulton, P.C. Thomas, Q. Xu, D. Pascoe, and C. Turner. 1991. Lethal and sublethal

toxicity of field simulated farm waste episodes to several freshwater invertebrate species. Water Res.,

25, 661-671.

Meyer, J.S., W. Davison, B. Sundby, J.T. Ores, D.J. Lauren, U. Forstner, J. Hong, and D.G. Crosby.

1994. Synopsis of discussion session: The effects of variable redox potentials, pH, and light on

bioavailability in dynamic water-sediment environments. In Bioavailability physical, chemical, and

biological interactions, proceedings of the 13th Pellston Workshop, ed. J.L. Hamelink, P.F. Landrum,

H.L. Bergman, and W.H. Benson, pp. 155-170. Lewis Publishers, Boca Raton, FL.

NOAA (National Oceanic and Atmospheric Administration). 1994. Inventory of chemical concentrations

in coastal and estuarine sediments. NOAA tech. memo. NOS ORCA 76. National Oceanic and

Atmospheric Administration, National Ocean Service, Silver Spring, MD.

Power, E.A., and P.M. Chapman. 1992. Assessing sediment quality. In Sediment toxicity assessment, ed.

G.A. Burton, Jr., 1-18. Lewis Publishers, Ann Arbor, MI.

Rand, G.M. 1995. Fundamentals of Aquatic Toxicology. 2nd ed. Taylor & Francis Publishers,

Washington, DC.

Sasson-Brickson, G., and G.A. Burton, Jr. 1991. In situ and laboratory sediment toxicity testing with

Ceriodaphnia dubia. Environ. Toxicol. Chem., 10:201-207.

Schlekat, C.E., B.L. McGee, E. Boward, E. Reinharz, D.J. Velinsky, and T.L. Wade. 1994. Tidal river

sediments in the Washington, D.C. area. III. Biological effects associated with sediment

contamination. Estuaries 17:334-344.

Science Advisory Board. 1992. An SAB report: Review of sediment criteria development methodology

for non-ionic organic contaminants. Prepared by the Sediment Quality Subcommittee of the

Ecological Processes and Effects Committee. EPA-SAB-EPEC-93-002. EPA Science Advisory

Board, Washington, DC.

———. 2000. An SAB Report: Review of an Integrated approach to metals assessment in surface waters

and sediments. EPA-SAB-EPEC-00-005. U.S. Environmental Protection Agency, Science Advisory

Board. EPA Science Advisory Board, Washington, DC.

Skalski, C., R. Fisher, and G.A. Burton, Jr. 1990. An in situ interstitial water toxicity test chamber,

Abstract for the Annual Society of Environmental Toxicology and Chemistry meeting Environ.

Toxicol. Chem., 132, 58.

Swartz, R.C., D.W. Schults, R.J. Ozretich, J.O. Lamberson, F.A. Cole, T.H. DeWitt, M.S. Redmonds,

and S.P. Ferraro. 1995. EPAH: A model to predict the toxicity of polynuclear aromatic hydrocarbon

mixtures in field-collected sediments. Environ. Toxicol. Chem. 14(11):1977-1987.

Swartz, R.C. 1999. Consensus sediment quality guidelines for polycyclic aromatic hydrocarbon mixtures.

Environ. Toxicol. Chem. 18(4):780-787.
National Sediment Quality Survey

References-6

USACE (U.S. Army Corps of Engineers). 1995. Draft environmental impact statement (EIS): Indiana

Harbor and Canal dredging and confined disposal facility, construction and operation,

comprehensive management plan, East Chicago, Lake County, IN. EIS No. 950489. U.S. Army

Corps of Engineers, Chicago, IL.

USEPA (U.S. Environmental Protection Agency). 1992. Proceedings of EPA’s contaminated sediment

management strategy forums, Chicago, IL, April 21-22, 1992; Washington, DC, May 27-28, 1992;

and June 16, 1992. EPA 823/R-92-007. U.S. Environmental Protection Agency, Washington, DC.

———. 1993a. Guidelines for deriving site-specific sediment quality criteria for the protection of

benthic organisms. EPA 822/R-93-017. U.S. Environmental Protection Agency, Office of Science

and Technology, Health and Ecological Criteria Division, Washington, DC.

———. 1993b. Technical basis for establishing sediment quality criteria for nonionic organic

contaminants for the protection of benthic organisms by using equilibrium partitioning. Draft. EPA

822/R-93-011. U.S. Environmental Protection Agency, Office of Science and Technology, Health

and Ecological Criteria Division, Washington, DC.

———. 1994a. Methods for measuring the toxicity and bioaccumulation of sediment-associated

contaminants with estuarine and marine amphipods. EPA 600/R-94-025. U.S. Environmental

Protection Agency, Office of Research and Development, Washington, DC.

———. 1994b. Methods for measuring the toxicity and bioaccumulation of sediment-associated

contaminants with freshwater invertebrates. EPA 600/R-94-024. U.S. Environmental Protection

Agency, Office of Research and Development, Duluth, MN.

———. 1995. Great Lakes Water Quality Initiative criteria documents for the protection of wildlife:

DDT; mercury; 2,3,7,8-TCDD; PCBs. EPA 820/B-95-008. U.S. Environmental Protection Agency,

Office of Science and Technology, Washington, DC.

———. 1997. The incidence and severity of sediment contamination in surface waters of the United

States. Vol. 1. National sediment quality survey. EPA 823/R-97-006. U.S. Environmental Protection

Agency, Office of Science and Technology, Washington, DC.

———. 1998a. EPA’s contaminated sediment management strategy. EPA 823/R-98-001. U.S.

Environmental Protection Agency, Office of Water, Washington, DC.

———. 1998b. A multimedia strategy for priority persistent, bioaccumulative, and toxic (PBT)

pollutants. <http://www.epa.gov/pbt/pbtstrat.htm>.

———. 2000a. Bioaccumulation Testing and Interpretation for the Purpose of Sediment Quality

Assessment. EPA 823/R-00-001. U.S. Environmental Protection Agency, Office of Water, Office of

Solid Waste and Emergency Response, Washington, DC.

———. 2000b. Realizing remediation II: An updated summary of contaminated sediment remediation

activities at Great Lakes Areas of Concern. Great Lakes National Program Office, Chicago, IL.

———. 2000c. Technical basis for the derivation of equilibrium partitioning sediment guidelines (ESGs)

for the protection of benthic organisms: Nonionic organics. U.S. Environmental Protection Agency,

Office of Science and Technology and Office of Research and Development, Washington, DC. (2000

draft).

———. 2000d. Methods for measuring the toxicity and bioaccumulation of sediment-associated

contaminants with freshwater invertebrates. EPA 600/R-99-064. U.S. Environmental Protection

Agency, Office of Water, Office of Research and Development, Washington, DC.
National Sediment Quality Survey

References-7

———. 2000e. Stressor identification guidance document. EPA 822/B-00-025. U.S. Environmental

Protection Agency, Office of Water, Washington, DC.

———. 2000f. Deposition of air pollutants to the Great Waters: Third report to Congress. EPA 453/R00-005, June 2000. U.S. Environmental Protection Agency, Office of Air Quality Planning and

Standards, Research Triangle Park, NC.

———. 2001a. Methods for assessing the chronic toxicity of marine and estuarine sediment-associated

contaminants with the amphipod Leptocheirus plumulosus. EPA 600/R-01-020. U.S. Environmental

Protection Agency, Office of Water, Office of Research and Development, and U.S. Army Corps of

Engineers, Washington, DC.

———. 2001b. Methods for the Collection, Storage and Manipulation of Sediments for Chemical and

Toxicological Analysis. EPA 823/B-01-002. U.S. Environmental Protection Agency, Office of Water,

Office of Research and Development, Washington, DC.

———. 2002a. Clean water action plan: National coastal condition report. EPA 620/R-00-004. U.S.

Environmental Protection Agency, Office of Water, Office of Research and Development,

Washington, DC.

———. 2002b. Principles for managing contaminated sediment risks at hazardous waste sites. U.S.

Environmental Protection Agency, Office of Solid Waste and Emergency Response Directive

9285.6-08, Washington, DC.

———. 2002c. Contaminated Sediment Remediation Guidance for Hazardous Waste Sites. U.S.

Environmental Protection Agency, Office of Solid Waste and Emergency Response Directive

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Evaluation of dredged material proposed for discharge in waters of the U.S.—Testing manual. EPA

823/B-98-004. U.S. Environmental Protection Agency, Office of Water, and U.S. Army Corps of

Engineers, Washington, DC.

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river basins identified using sediment cores from reservoirs. Environ. Sci. Technol. 31(8):2339-2344.

Van Metre, P.C., and E. Callender. 1997. Water-quality trends in White Rock Creek Basin from 1912-94

identified using sediment cores from White Rock Lake reservoir, Dallas, Texas. J.

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Van Metre, P.C., J.T. Wilson, E. Callender, and C.C. Fuller. 1998. Similar rates of decrease of persistent,

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3317.

Van Metre, P.C., B.J. Mahler, and E.T. Furlong. 2000. Urban sprawl leaves its signature. Environ. Sci.

Technol. 34(19):4064-4070.

Van Metre, P.C., J.T. Wilson, C.C. Fuller, E. Callender, and B. J. Mahler. n.d. Methods, Site

Characteristics, and Age Dating of Sediment Cores for 56 U.S. Lakes and Reservoirs Sampled by the

USGS National Water Quality Assessment Program, 1993–2001. U.S. Geological Survey Scientific

Investigations Report. U.S. Geological Survey

Van Veld, P.A., D.J. Westbrook, B.R. Woodin, R.C. Hale, C.L. Smith, R.J. Hugget, and J.J. Stegman.

1990. Induced cytochrome P-450 in intestine and liver of spot (Leiostomus xanthurus) from a

polycyclic aromatic contaminated environment. Aquat. Toxicol. 17:119-132.
National Sediment Quality Survey

References-8

Wenning, R.J. and C.G. Ingersoll. 2002. Summary of the SETAC Pellston Workshop on Use of Sediment

Quality Guidelines and Related Tools for the Assessment of Contaminated Sediments; 17-22 August

2002; Fairmont, Montana, USA. Society of Environmental Toxicology and Chemistry (SETAC).

Pensacola FL, USA.

Zarull, M.A., J.H. Hartug, and L. Maynard. 1999. Ecological benefits of contaminated sediment

remediation in the Great Lakes Basin. Sediment Priority Action Committee, Great Lakes Water

Quality Board, International Joint Commission.

Zhuang, Y., H.E. Allen, and G. Fu. 1994. Effect of aeration of sediment on cadmium binding. Environ.

Toxicol. Chem. 13(5):717-724.
National Sediment Quality Survey

A-1

APPENDIX A

NATIONAL SEDIMENT INVENTORY FIELD

DESCRIPTION

The National Sediment Inventory (NSI) database is a national compilation of readily available data that

has been compiled to evaluate the incidence of sediment contamination throughout the United States.

Data sets with sediment chemistry, tissue residue, and toxicity data collected from 1980 through 1999 are

included in the NSI. In all, data for more than 50,000 stations and 4.6 million analytical observations have

been compiled. The data types available in the NSI are as follows:

• Sediment chemistry. Sediment chemistry data include detailed analytical results, analyte sampled,

sampling date, qualifier for concentration, field and laboratory replication identifier, core depths,

and grain size information. Percent organic carbon and acid-volatile sulfide content of sediments

are also included when available.

• Tissue residue. Tissue residue data include detailed analytical results, analyte sampled, species,

sex, tissue type, length, qualifier for concentration, field and laboratory replication identifier, and

weight.

• Toxicity. Toxicity data include test species, dilution, endpoints (e.g., mortality), measured effect

value, control-adjusted value, and test duration. Solid-phase and elutriate data are provided when

available.

Table A-1 presents the total number of sampling stations at which each of these parameters was measured

and the number of sampling stations for which coordinates (latitude and longitude) were available.

The NSI data are contained in a series of tables (see Table A-2) that correspond to the different data types

described above. This organization is largely derived from NOAA’s Watershed Database Management

System. The purpose of this coordination between the NOAA Watershed Database Management System

and the NSI is to facilitate data sharing. The primary table in the NSI is the station table. Each record in

the table corresponds to a unique sampling station. The records in the station table can be related to tables

for each type of data, such as sediment chemistry data or tissue residue data. These tables can then be

related to additional look-up tables that include ancillary information such as chemical or species names.

Figure A-1 illustrates the relationship between the station, sediment chemistry, tissue residue, and toxicity

tables and the related look-up tables.
National Sediment Quality Survey

A-2

Table A-1. Number of Sampling Stations With Data Included in the NSI Evaluation.

Measurement Parameter

Total Number

of Stations

Stations with Coordinates

Number of Stations

Percent of Total

Number of Stations

with Coordinatesa

Sediment chemistry 40,335 36,684 81

Total organic carbon 16,407 13,936 31

Acid-volatile sulfide 2,714 2,172 5

Tissue residue 11,384 10,632 23

Toxicity 6,238 4,510 10

Elutriate phase 229

Solid phase 10,980

Sediment chemistry and tissue residue 3,110 3,078 7

Sediment chemistry and toxicity 5892 4386 10

Sediment chemistry, tissue residue, and

toxicity

112 112 < 1

a

 Total number of stations with coordinates = 45,353.

Table A-2. Data Tables Available in the NSI.

Table Name Table Description Table Name Table Description

(1)SITE Site (5c)CHEMTISS Tissue residue

(2)STUDY Study (6)BIOSUMM Biotoxicity

(3a)STATION Station (7a)BMASTER Bioassay type

(3b)STUDYNOT Study notes (7b)BIO_INFO Bioassay information

(3c)STUDYREF Study reference (8a)CHEMDICT Dictionary of chemical codes

(4a)SAMPLE Sediment sample (8b)QUALIFY Concentration qualifier

(4b)SMPSEDSB Sediment core sample (8c)SPECIES Dictionary of species

(4c)SMPTISS Tissue sample (8d)TESTDICT Dictionary of bioassays

(5a)CHEM Surface sediment chemistry (8e)TISSTYPE Type of tissue

(5b)CHEMSB Sediment core data
A-3

Figure A-1. Relationship Between the Station, Sediment Chemistry, Tissue Residue, and Toxicity Tables and the RelaTables.
National Sediment Quality Survey

A-4

The remainder of this section contains a listing of the field names and descriptions associated with each

data table in the NSI.

(1)SITE Site

SITEID Site identifier

SITENAME Descriptive name of site

EPAREGION EPA region of site location: 11 for Canada

COUNTY County

STATE State

CERCLIS CERCLIS number for site

REACH USGS 8-digit hydrologic unit code

REACHSEG EPA River Reach (v1) segment number for site

LATITUDE General latitude for site location

LONGITUDE General longitude for site location

WATERSHED Watershed name for site location

CHEMTOX Sediment chemistry and toxicity data for study, T or F?

(2)STUDY Study

SITEID Site identifier

STUDYID Study identifier code

STUDYNAME Short name for study

PRINCINVES Study investigators

REFID Internal reference ID

SEDCHEM Surface sediment chemistry data, T or F?

SUBSURF Subsurface sediment chemistry data, T or F?

SEDTOX Sediment toxicity data, T or F?

TISSCHEM Tissue chemistry for study, T or F?

LABACCUM Tissue data from lab bioaccumulation tests, T or F?

STUDCOMM Study comments

(3a)STATION Station

SITEID Site identifier

STUDYID Study identifier code

STATIONID Station identifier code

LATITUDE Station latitude expressed in decimal degrees

LONGITUDE Station longitude expressed in decimal degrees

MINUS Internal use (to support conversion of longitude)

LATDEG Station location expressed in degrees of latitude

LATMIN Station location expressed in minutes of latitude

LATSEC Station location expressed in seconds of latitude

LONGDEG Station location expressed in degrees of longitude

LONGMIN Station location expressed in minutes of longitude

LONGSEC Station location expressed in seconds of longitude

LOCDESC Station location description

EST_STN Indication of derivation of latitude and longitude; selected from

“reported,” “plotted,” or “assigned”

SECTOR Internal use

AREA Internal use
National Sediment Quality Survey

A-5

SORT Internal use

ORIG_STAT Original station identifier code assigned by source data set

STATE State

COUNTY County

CU USGS 8-digit hydrologic unit code

CUSEG EPA River Reach (v1) segment number

MILEPT EPA River Reach (v1) mile point

LATLONGS Source for reported latitude and longitude

RF3 EPA River Reach (v3) segment

(3b)STUDYNOT Study notes

SITEID Site identifier

STUDYID Study identifier code

SITENAME Descriptive name for site

STUDYNAME Short name for study

NOTES Memo field containing study notes

(3c)STUDYREF Study reference

SITEID Site identifier

STUDYID Study identifier code

YEAR Year of report

AUTHORS Report authors

TITLE Report title

SOURCE Report source

STUDYCOMM Short comment on study

(4a)SAMPLE Sediment sample

SITEID Site identifier

STUDYID Study identifier

STATIONID Station identifier

SAMPLEID Sample identifier

FIELDREP Field replicate number

LABREP Laboratory replicate number

SEDDEPTH Depth of sediment sample

UDEPTH Upper depth of sediment collection in centimeters

LDEPTH Lower depth of sediment collection in centimeters

SAMPDATE Date sample collected

SAMPTIME Time sample collected

TOC Total organic carbon as percent

PCTFINES Percent fines

UAN_PW Un-ionized ammonia in porewater

H2S_PW Hydrogen sulfide in pore water 

EXSAMPID Investigators’ sample identifier

AVS Acid-volatile sulfide in :mol/g sediment
National Sediment Quality Survey

A-6

(4b)SMPSEDSB Sediment core sample

SITEID Site identifier

STUDYID Study identifier

STATIONID Station identifier

SAMPLEID Sample identifier

FIELDREP Field replicate number

LABREP Laboratory replicate number

SEDDEPTH Depth of sediment sample

UDEPTH Upper depth of sediment collection in centimeters

LDEPTH Lower depth of sediment collection in centimeters

SAMPDATE Date sample collected

SAMPTIME Time sample collected

TOC Total organic carbon as percent

PCTFINES Percent fines

EXSAMPID Investigators’ sample identifier

(4c)SMPTISS Tissue sample

SITEID Site identifier

STUDYID Study identifier

STATIONID Station identifier

SAMPLEID Sample identifier

FIELDREP Field replicate number

LABREP Laboratory replicate number

SAMPDATE Date sample collected

SPECIES Code identifying species collected

TISSUE Code identifying tissue collected

NOINCOMP Number of organisms in composite

LENGTH Length of organism in cm

WEIGHT Weight of organism in grams

SEX Sex of organism

PCTLIPID Percent lipid

EXSAMPID Investigators’ sample identifier

(5a)CHEM Surface sediment chemistry

SITEID Site identifier

STUDYID Study identifier

STATIONID Station identifier

SAMPLEID Sample identifier

FIELDREP Field replicate number

LABREP Laboratory replicate number

CHEMCODE Abbreviated chemical name

CONC Measured concentration

QUALCODE Assigned qualifier for concentration

UNITS Units of concentration for chemical

MEASBASIS Wet or dry weight indication

MISSINGVAL Data missing, Y or N?
National Sediment Quality Survey

A-7

(5b)CHEMSB Sediment core data

SITEID Site identifier

STUDYID Study identifier

STATIONID Station identifier

SAMPLEID Sample identifier

FIELDREP Field replicate number

LABREP Laboratory replicate number

CHEMCODE Abbreviated chemical name

CONC Measured concentration

QUALCODE Assigned qualifier for concentration

UNITS Units of concentration for chemical

MEASBASIS Wet or dry weight indication

MISSINGVAL Data missing, Y or N?

(5c)CHEMTISS Tissue residue

SITEID Site identifier

STUDYID Study identifier

STATIONID Station identifier

SAMPLEID Sample identifier

FIELDREP Field replicate number

LABREP Laboratory replicate number

CHEMCODE Abbreviated chemical name

CONC Measured concentration

QUALCODE Assigned qualifier for concentration

UNITS Units of concentration for chemical

MEASBASIS Wet or dry weight indication

MISSINGVAL Data missing, Y or N?

(6)BIOSUMM Biotoxicity

SITEID Site identifier

STUDYID Study identifier

STATIONID Station identifier

SAMPLEID Sample identifier

FIELDREP Field replicate number

LABREP Laboratory replicate number

TESTID Bioassay test code 

GROUP Bioassay test grouping

SERIES Bioassay test series number

EFFECTVAL Measured effect value

DILUTION Dilution value

SIGEFFECT Was effect significant, Y or N?

NEG Negative control, Y or N?

REF Reference sample, Y or N?

STAT Used for statistical comparison, Y or N?

CTRLADJ Control adjusted effect value

SIG_ORIGN Original code from investigator for significant effect

TOXCODE Character designation for toxic effect (T for toxic and N for nontoxic)
National Sediment Quality Survey

A-8

(7a)BMASTER Bioassay type

SITEID Site identifier

STUDYID Study identifier

GROUP Bioassay test grouping

TESTID Bioassay test code

SPIKED Sediment spiked with contaminant, Y or N?

RATING Rating of bioassay test

TESTCOMM Bioassay test comments

(7b)BIO_INFO Bioassay information

SITEID Site identifier

STUDYID Study identifier

STATIONID Station identifier

SAMPLEID Sample identifier

TESTID Bioassay test code

UAN_S Un-ionized ammonia in sediment

UAN_E Un-ionized ammonia in elutriate

UAN_A Un-ionized ammonia

(8a)CHEMDICT Dictionary of chemical codes

CHEMCODE Abbreviated chemical name

CHEMNAME Full chemical name

CASNUM Chemical Abstracts Service (CAS) number

(8b)QUALIFY Concentration qualifier

SITEID Site identifier

QUALCODE Assigned qualifier for concentration

QUALIFIERS Not used

DESCRIPT Description of QUALCODE

(8c)SPECIES Dictionary of species

SPECIES Species code

COMMONNAME Commonly used name for organism

SCIENTIFIC Scientific name for organism

SPECCODE Numeric species code

NODC 10-digit National Oceanographic Data Center (NODC) code

GROUP Species grouping

RESMIG Whether species is resident or migratory

DEMPEL Whether species is demersal or pelagic

EDIBLE Whether species is edible by human populations
National Sediment Quality Survey

A-9

(8d)TESTDICT Dictionary of bioassays

TESTID 12-digit bioassay test code

MEDIUM Medium tested

MEDCODE 2-digit code for medium tested

GROUP Bioassay species grouping

ALTGROUP Alternate bioassay species grouping

SPECIES Bioassay organism

SPPCODE 3-digit code for bioassay organism

LHS Life history stage of organism

LHSCODE Single digit code for life history stage

ENDPOINT Bioassay test endpoint

ENDCODE 2-digit code for test endpoint

DURATION Duration of test

DURCODE 4-digit code for duration of test

HABITAT Habitat of test organism

OLDID Formerly assigned test code

JFID Not used (old identification code)

ANA_GUDE Test included or not in NSI evaluation

(8e)TISSTYPE Type of tissue

TISSUE Description code of tissue collected

TISSUENAME Description of tissue collected
National Sediment Quality Survey

A-10
National Sediment Quality Survey

B-1

APPENDIX B

DESCRIPTION OF EVALUATION PARAMETERS

USED IN THE NSI DATA EVALUATION

Chapter 2 of this document presented an overview of the methodology used in the evaluation of the NSI

data. This appendix describes in greater detail the screening values and other parameters used in the NSI

data evaluation. The actual parameter values used are presented in Appendix C. For the purpose of

discussion, the sediment evaluation parameters have been placed into two groups: (1) those used to

assess potential impacts on aquatic life and (2) those used to assess potential impacts on human health.

Aquatic Life Assessments

To evaluate the potential threat to aquatic life from chemical contaminants detected in sediments,

measured concentrations of contaminants were compared to sediment chemistry screening levels. The

results of toxicity tests to indicate the actual toxicity of sediment samples to species of aquatic organisms

were also evaluated for the National Sediment Quality Survey.

Sediment chemistry screening levels are reference values that provide evidence of sediment contaminant

concentrations that could pose a significant threat to aquatic life based on statistical significance.

Although the quantitative relationship between statistical significance and expected ecological effects is

not fully understood, EPA presumes that these values are related to expected ecological effects, as is the

presumption of other EPA assessment approaches (USEPA, 1985). Several different approaches, based

on causal or empirical/statistical correlative methodologies, have been developed for deriving screening

levels of sediment contaminants. Each of these approaches attempts to predict contaminant concentration

levels to provide protection for benthic species, which are extrapolated to represent the entire aquatic

community for this evaluation. For the purpose of this analysis, the screening tools selected include the

following:

• EPA’s draft equilibrium partitioning sediment guidelines (ESGs) for nonionic organics using an

equilibrium partitioning approach (USEPA, 1992a, 2000a).

• EPA’s draft ESGs for mixtures of polycyclic aromatic hydrocarbons (PAHs) using an equilibrium

partitioning approach (USEPA, 1992a, 2000b).

• The sum of simultaneously extracted divalent transition metals concentrations minus the

acid-volatile sulfide concentration ([SEM]![AVS]), also based on an equilibrium partitioning

approach (USEPA, 2000c).

• Logistic regression model (Field et al., 1999, 2002).

The principles behind the development of each of these sediment chemistry screening values are

discussed below. The sediment toxicity tests are also briefly described in this section.
National Sediment Quality Survey

B-2

Equilibrium Partitioning Approaches

The potential toxicity of sediment-associated nonionic organic chemicals and divalent metals is indicated

by the amount of the contaminant that is uncomplexed or freely available in the interstitial (pore) water.

The bioavailability and toxicity of nonionic organic chemicals and divalent metals in sediments are

mediated by several physical, chemical, and biological factors, including sediment grain size, particulate

and dissolved organic carbon, and sulfide produced by sulfate-reducing bacteria (Di Toro et al., 1991,

1992; Howard and Evans, 1993; USEPA, 2000a). For nonionic organic chemicals, sorption to the organic

carbon dissolved in the interstitial water and bound to sediment particles is the most important factor

affecting bioavailability. Sulfide, specifically the reactive solid-phase sulfide fraction that can be

extracted by cold hydrochloric acid (acid-volatile sulfide, or AVS), appears to control the bioavailability

of most divalent metal ions because of the sulfide ions’ high affinity for divalent metals, resulting in the

formation of insoluble metal sulfides in anaerobic sediments (USEPA, 2000c).

When the concentrations of nonionic organic chemicals and divalent metals were measured in pore water

extracted from spiked sediment and field-collected sediment used in toxicity tests, the biological effects

observed in those tests occurred at similar pore water concentrations, even when different types of

sediments were used, typically within a factor of 2 (Di Toro et al., 1991, 1992). Biological effects also

occurred at similar concentrations in tests with different sediment types containing different amounts of

organic carbon when (1) the dry-weight sediment concentrations of nonionic organic chemicals were

normalized for organic carbon content (i.e., :g chemical/goc) and (2) when the difference between molar

concentrations of simultaneously extracted metals ([SEM]) in the sediment exceeded the molar

concentration of AVS ([AVS]) in the sediments by similar amounts. (The mortality of sensitive species

increases in the range of 1.5 to 12.5 :mol of SEM per :mol of AVS.) Most importantly, the effects

concentrations in the sediment could be predicted from the effects concentrations determined in

water-only exposures to these chemicals. Most measurements of sediment chemical concentrations are

made from whole sediment samples and converted to units of chemical per dry-weight of sediment

because of the difficulties in extracting the pore water. However, when dry-weight concentrations of

nonionic organics and metals were used to plot concentration- response curves of the toxicity of different

sediments, biological effects occurred at different dry-weight concentrations when measured in different

sediments (Luoma, 1983; USEPA, 2000a). To develop advisory levels for comparing the toxicity of

different chemicals in different sediments, it was necessary to examine the role of organic carbon and

other complexing factors in the bioavailability of chemicals in sediment.

In sediment, the partitioning of a nonionic organic chemical between organic carbon and pore water and

the partitioning of a divalent metal between the solid and solution phases are assumed to be at

equilibrium. The fugacity (activity) of the chemical in each of these phases is the same at equilibrium.

Fugacity describes mathematically the rates at which chemicals diffuse or are transported between phases

(Mackay, 1991). Hence, an organism in the sediment is assumed to receive an equivalent exposure from

water only or from any equilibrated phase. The pathway of exposure might include pore water

(respiration), sediment carbon (ingestion), sediment organism (ingestion), or a mixture of routes. The

biological effect is produced by the chemical activity of the single phase or the equilibrated system (Di

Toro et al., 1991). The equilibrium partitioning approach uses this partitioning theory to relate the

dry-weight sediment concentration of a particular chemical that causes an adverse biological effect to the

equivalent free chemical concentration in pore water and to the concentration sorbed to sediment organic

carbon or bound to sulfide.

The processes that govern the partitioning of chemical contaminants among sediments, pore water, and

biota are better understood for some kinds of chemicals than for others. Partitioning of nonionic

hydrophobic organic compounds between sediments and pore water is highly correlated with the organic
National Sediment Quality Survey

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carbon content of sediments, but it does not account for all of the toxicity variation observed between

sediment and water-only experimental exposures. Other factors that can affect biological responses are

not considered in the model. The equilibrium partitioning approach has been tested using only nonionic

organic chemicals with octanol/water partition coefficients (log Kows) between 3.8 and 5.3. However,

because the theory should be applicable to nonionic organic chemicals with log Kows from 2.0 to 5.5

(Dave Hansen, EPA ORD-Narragansett, pers. commun., April 17, 1995), nonionic organic chemicals

with log Kows in this range were evaluated for the analysis of NSI data. For trace metals, concentrations

of sulfides and organic carbon have been identified as important factors that control the phase

associations and, therefore, the bioavailability of trace metals in anoxic sediments. However, models that

can use these factors to predict the bioavailability of trace metals in sediments are not fully developed

(see below). Mechanisms that control the partitioning of nonionic and nonpolar organic compounds with

log Kows of less than 2.0 or greater than 5.5 and polar organic compounds in sediments, and affect their

toxicity to benthic organisms, are less well understood. Models for predicting biological effects from

concentrations of such compounds have not yet been developed; therefore, these chemicals (with log

Kows of less than 2.0 or greater than 5.5 and polar organic compounds) have not been evaluated using

equilibrium partitioning approaches.

Draft Sediment Equilibrium Partitioning Sediment Guidelines (ESGs) for Nonionic Organics

The equilibrium partitioning model was selected for use in this assessment because of its ability to

predict sediment contaminant concentrations that are protective for benthic aquatic life from direct

toxicity due to that contaminant (or contaminants in the case of metal mixtures and PAH mixtures). The

predominant phase for sorption of nonionic organic chemicals to sediment particles appears to be organic

carbon for sediments in which the fraction of organic carbon (foc) is greater than 0.2 percent. When the

fraction of organic carbon is less than 0.2 percent, other factors, such as particle size and sorption to

nonorganic mineral fractions, play a relatively important role (Karickhoff, 1984).

The partitioning of a chemical between the interstitial water and sediment organic carbon is explained by

the sediment/pore water partition coefficient for a chemical, Kp, which is equal to the organic carbon

content of the sediment (foc) multiplied by the sediment particle organic carbon partition coefficient (Koc).

Kp is the ratio of the concentration of the chemical in the sediment to the concentration of the chemical in

the pore water. Normalizing the dry-weight concentration of the chemical in sediment to organic carbon

is as appropriate as using the interstitial water concentration of the chemical because organic carbon in

the sediment can also bind the chemical and affect its bioavailability and toxicity. The particle organic

carbon partition coefficient (Koc) is related to the chemical's octanol-water partition coefficient (Kow) by

the following equation (Di Toro et al., 1991; Di Toro and McGrath, 2000):

log Koc = 0.00028 + 0.983(log Kow)

The octanol/water partition coefficient for each chemical can thus predict the likelihood of the chemical

to complex or sorb to organic carbon, when measured with modern experimental techniques that provide

the most accurate estimate of this parameter. The concentration of the chemical on sediment particles

(Cs) is then equal to the dissolved concentration of chemical (Cd) multiplied by the organic carbon

content of the sediment (foc) and the particle organic carbon partition coefficient (Koc), when foc is greater

than 0.2 percent (USEPA, 2000a), thus normalizing the dry-weight sediment concentration of the

chemical to the organic carbon content of the sediment:

Cs = Cd foc Koc

The value for the dissolved concentration of chemical (Cd) is derived from the chronic or acute value in

EPA's water quality criteria (GLI, 1995; USEPA, 1985). Freshwater and saltwater acute and chronic
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values are based on the results of acceptable laboratory tests conducted to determine the toxicity of a

chemical in water to a variety of species of aquatic organisms, and they represent levels below which

adverse effects are not expected. An evaluation of data from the water quality criteria documents and

benthic colonization experiments demonstrated that benthic species have chemical sensitivities similar to

those of water column species (Di Toro et al., 1991). Therefore, these guidelines can be used to protect

benthic aquatic life from direct toxicity due to the specific contaminant(s).

EPA has developed draft equilibrium partitioning sediment guidelines (ESGs) for the protection of

aquatic life for the 34 specific nonionic contaminants listed in Table B-1. In the NSI data evaluation,

sediment chemistry values exceeding draft ESG guidelines derived from acute values were used to

classify stations as Tier 1. Draft ESG guidelines obtained from chronic values were used for Tier 2

classification.

On a sediment organic carbon basis, the draft ESG,

ESGoc (:g/goc) = Koc (L/kg) x [FCV, SCV] (:g/L) x (10-3 kgoc/goc)

or 

ESGoc (:g/goc) = Koc (L/kg) x [FAV, SAV] (:g/L) x (10-3 kgoc/goc)

where:

ESGoc = draft ESG on a sediment organic carbon basis in :g/goc;

FCV or SCV = EPA aquatic life water quality criterion final or secondary chronic value in :g/L;

FAV or SAV = EPA aquatic life water quality criterion final or secondary acute value in :g/L;

and

Koc = organic carbon-water partitioning coefficient in L/kg.

Koc is presumed to be independent of sediment type for nonionic organic chemicals, so that the draft

ESGoc is also independent of sediment type. Using a site-specific organic carbon fraction, foc (goc/g

sediment), the draft ESGoc can be expressed as a sediment-specific value: ESG = (ESGoc) (foc).

Draft Sediment Equilibrium Partitioning Sediment Guidelines (ESGs) for PAH Mixtures

Similar to the equilibrium partitioning approach used for nonionic organics, EPA has developed draft

ESGs for PAH mixtures (USEPA, 2000b). The draft ESGs developed consider the toxicological

contribution of mixtures of 34 PAHs in sediments to determine whether their concentrations in any

specific sediment are acceptable for the protection of benthic organisms from PAH toxicity. The

equilibrium partitioning theory, the narcosis theory, and the concept of additivity (Swartz, 1999; Swartz

et al., 1995) are the technical foundation for the development of draft ESGs for PAH mixtures. Because

different mixtures of PAHs occur in sediments, the above approach is justified for the derivation of draft

ESGs for PAHs. PAHs are considered type 1 narcotic chemicals, and the toxicities of PAHs in sediment

and tissues are additive or nearly additive (Di Toro et al., 2000). Consequently, consideration of their

toxicities on an individual basis may result in arriving at an underprotective guideline.
National Sediment Quality Survey

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Table B-1. EPA Aquatic Life Secondary Acute/Chronic Values (SAV/SCV), Final Acute/Chronic

Values (FAV/FCV), Draft Equilibrium Partitioning Sediment Guideline (ESG), Log Kow,

and Log Koc Values.

CAS

Number Chemical Name Log Kow

 SAV

(:g/L)

 SCV

(:g/L) Log Koc

Draft ESG for

Tier 1

(:g/goc)

Draft ESG for

Tier 2

(:g/goc)

71432 Benzene 2.13 815.4 45.5 2.094 100 5.7

319868 BHC, delta- 3.78 43.6 2.44 3.716 230 13

58899 BHC, gamma- (Lindane) 3.73 1.903a 0.08b 3.667 8.8 0.37

92524 Biphenyl 3.96 108.7 13.69 3.893 850 110

101553 Bromophenyl phenyl ether, 4- 5 27.69 1.538 4.915 2300 130

85687 Butyl benzyl phthalate 4.84 262.3 18.84 4.758 15000 1100

108907 Chlorobenzene 2.86 2271 127 2.812 1500 82

84742 Di-n-butyl phthalate 4.61 234 32.7 4.532 8000 1100

333415 Diazinon/Spectracide 3.7 0.1687a 0.04329b 3.637 0.73 0.19

132649 Dibenzofuran 4.07 366 20.4 4.001 3700 200

95501 Dichlorobenzene, 1,2- 3.43 259 14.39 3.372 610 34

541731 Dichlorobenzene, 1,3- 3.43 625 71.31 3.372 1500 170

106467 Dichlorobenzene, 1,4- 3.42 183.6 15.11 3.362 420 35

60571 Dieldrin 5.37 0.2874a,c 0.06589b,c 5.279 55 13

84662 Diethyl phthalate 2.5 3947 220 2.458 1100 63

115297 Endosulfan mixed isomers 4.1 0.1277 0.05059 4.031 1.4 0.54

959988 Endosulfan, alpha- 3.83 0.1277 0.05059 3.765 0.74 0.29

33213659 Endosulfan, beta- 4.52 0.1277 0.05059 4.443 3.5 1.4

72208 Endrin 5.06 0.1803a,c 0.05805b,c 4.974 17 5.5

100414 Ethylbenzene 3.14 6971 389 3.087 8500 480

67721 Hexachloroethane 4 211.9 11.77 3.932 1800 100

121755 Malathion 2.89 0.8884a 0.09671 2.841 0.62 0.067

72435 Methoxychlor 5.08 0.0962 0.0188 4.994 9.5 1.9

608935 Pentachlorobenzene 5.26 8.377 0.466 5.171 1200 69

79345 Tetrachloroethane, 1,1,2,2- 2.39 3698 719 2.350 830 160

127184 Tetrachloroethene 2.67 998 125 2.625 420 53

56235 Tetrachloromethane 2.73 4375 243.1 2.684 2100 120

108883 Toluene 2.75 3153 176 2.704 1600 89

8001352 Toxaphene 5.5 1.903a 0.039b 5.407 490 10

75252 Tribromomethane/Bromoform 2.35 2254 316.8 2.310 460 65

120821 Trichlorobenzene, 1,2,4- 4.01 699.5a 105.1 3.942 6100 920

71556 Trichloroethane, 1,1,1- 2.48 617 62.1 2.438 170 17

79016 Trichloroethene 2.71 4350 465 2.664 2000 210

108383 Xylene, m- 3.2 32.29 1.794 3.146 45 2.5

a

 FAV values. b FCV values. c In freshwater.
National Sediment Quality Survey

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Using PAH-specific final chronic values (FCVs) or final acute values (FAVs), the effect concentration of

a PAH in sediment (Coc,PAHi,FCVi or Coc,PAHi,FAVi) on an organic carbon basis is calculated as the product of

its FCV and Koc or FAV and Koc. The quotient of the organic carbon-normalized sediment concentration

for a specific PAH (Coc,PAHi) and the effect concentration of a PAH in sediment for a PAH-specific FCV

(Coc,PAHi,FCVi) or FAV (Coc,PAHi,FAVi) is called the equilibrium partitioning sediment guideline toxic unit

(ESGTUFCVi) or (ESGTUFAVi). The draft ESG for the mixture of PAHs is the sum of the ESGTUFCVi or

ESGTUFAVi for all of the PAHs in the particular sediment. This sum is called EESGTUFCV or EESGTUFAV

and is given by

ESGTU ESGTU

C

C FCV FCVi

oc,PAHi

oc,PAHi,FCVi

∑∑ ∑ = =

or

ESGTU ESGTU

C

C FAV FAVi

oc,PAHi

oc,PAHi,FAVi

∑ = = ∑ ∑

Because the effect concentration of a PAH in sediment (Coc,PAHi,FCVi or Coc,PAHi,FAVi) on an organic carbon

basis is solubility-limited, a solubility constraint is applied to sediment concentrations when computing

their individual contributions. The effect concentration is limited by the concentration in sediment

organic carbon that is in equilibrium with the interstitial water at the aqueous solubility, called the

maximum effect concentration Coc,PAHi,MAX. Thus, only the contribution up to the maximum Coc,PAHi,MAX is

considered in the EESGTUFCV or EESGTUFAV analysis for PAH mixtures.

For a particular sediment, if the EESGTUFCV based on final chronic values for “total PAHs” exceeds 1.0,

the station is classified as Tier 2. Similarly, if the EESGTUFAV based on final acute value exceeds 1.0, the

station is classified as Tier 1. For the NSI data evaluation, most data sets reported results for only 13

PAHs. However, for this data evaluation not all 13 PAHs were required to be measured at any one station

for that station to be considered for tier classification. Based on the sensitivity analysis done, it was

observed that this variation from the EPA-recommended practice did not dramatically change the total

number of station tier classifications. Table B-2 presents the list of 13 PAHs analyzed in this National

Sediment Quality Survey report.

Though EPA recommends the use of 34 PAHs to derive the total draft ESG toxicity unit, some

monitoring programs measure only 13 or 23 PAHs instead of a total of 34 PAHs. To determine the

uncertainty in predicting the total draft ESG toxicity unit from data sets consisting of 13 or 23 PAHs, two

Environmental Monitoring and Assessment Program (EMAP) data sources that measured the 34 PAHs

were evaluated. Using the combined data, EPA determined the factors for the total draft ESG toxicity

unit for 34 PAHs from monitoring programs that measure only 13 or 23 PAHs. The relative distribution

of the equilibrium partitioning sediment guideline toxic unit with 34 PAHs (EESGTUFCV,TOT) to the draft

equilibrium partitioning sediment guideline toxic unit with 13 PAHs (EESGTUFCV,13) is presented in

Table B-3.

Method for Determination of Log Kows. The determination of log Kow values was based on EPA draft

guidelines (USEPA 2000a; 2000b; 2000c; 2000d).
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Table B-2. EPA Aquatic Life Final Acute/Chronic Values (FAV/FCV), and Effect Concentration

of PAH in Sediment (Coc), Log Kow, and Log Koc for PAH Mixtures.

CAS

Number Chemical Name Log Kow

FAV

(:g/L)

FCV

(:g/L) Log Koc

Coc,PAHi,FAVi

(:g/goc)

Coc,PAHi,FCVi

(:g/goc)

Coc,PAHi,MAXa

(:g/goc)

83329 Acenaphthene 4.012 232.3 55.85 3.944 2043 491 33400

208968 Acenaphthylene 3.223 1277 306.9 3.168 1880 452 24000

120127 Anthracene 4.534 86.24 20.73 4.457 2471 594 1300

56553 Benzo(a)anthracene 5.673 9.264 2.227 5.577 3499 841 4153

50328 Benzo(a)pyrene 6.107 3.982 0.9573 6.003 4014 965 3840

205992 Benzo(b)fluoranthene 6.266 2.818 0.6774 6.160 4073 979 2169

207089 Benzo(k)fluoranthene 6.291 2.669 0.6415 6.184 4081 981 1220

218019 Chrysene 5.713 8.495 2.042 5.616 3511 844 826

206440 Fluoranthene 5.084 29.57 7.109 4.998 2941 707 23870

86737 Fluorene 4.208 163.5 39.3 4.137 2238 538 26000

91203 Naphthalene 3.356 805 193.5 3.299 1602 385 61700

85018 Phenanthrene 4.571 79.58 19.13 4.494 2479 596 34300

129000 Pyrene 4.922 42.06 10.11 4.839 2900 697 9090

a When the organic carbon-normalized sediment concentration (Coc,PAHi) is greater than Coc,PAH,MAX, use Coc,PAH,MAX in place of Coc,PAHi.

Table B-3. Relative Distribution of EESGTUFCV,TOT to EESGTUFCV,13 for

the Combined EMAP Data Set (N = 488).

Percentile EESGTUFCV,TOT/EESGTUFCV,13

50 2.75

80 6.78

90 8.45

95 11.5

99 16.9

Selection of Chronic Toxicity Values. EPA developed a hierarchy of sources for chronic toxicity values

for the development of the draft ESGs (USEPA, 2000e). The following sources were identified and

ranked from most to least confidence in the chronic values to be used:

1. Final chronic values from the Great Lakes Initiative (GLI, 1995).

2. Final chronic values from the National Ambient Water Quality Criteria documents.

3. Final chronic values from draft freshwater criteria documents.

4. Final chronic values developed from data in EPA's Aquatic Toxicity Information Retrieval database

(AQUIRE) and other sources.

5. Secondary chronic values developed from data in AQUIRE and other sources.

6. Secondary chronic values from Suter and Mabrey (1994).

Twelve aquatic toxicity values were based on work conducted by Oak Ridge National Laboratories

(Suter and Mabrey, 1994) using the GLI (1995) methodology for obtaining secondary chronic values
National Sediment Quality Survey

B-8

(“Tier II”). This methodology was developed to obtain whole-effluent toxicity screening values based on

all available data, but the methodology could also be used to calculate SCVs with less toxicity data than

the amount required for the criteria methodology outlined in USEPA (1985). The SCVs are generally

more conservative than those which can be produced by the FCV methodology, reflecting greater

uncertainty in the absence of additional toxicity data. The minimum requirement for deriving an SCV is

toxicity data from a single taxonomic family (Daphnidae), provided the data are acceptable. Only those

values from Suter and Mabrey (1994) that included at least one daphnid test result in the calculation of

the SCV were included for the National Sediment Quality Survey. SCVs from Suter and Mabrey (1994)

were used to develop draft ESGs for the following chemicals:

benzene ethylbenzene

chlorobenzene 1,1,2,2-tetrachloroethane

delta-BHC tetrachloroethene

dibenzofuran toluene

diethyl phthalate 1,1,1-trichloroethane

di-n-butyl phthalate trichloroethene

A preliminary search of data records in EPA’s AQUIRE database indicated that the following chemicals

might have sufficient toxicity data for the development of FCVs or SCVs using the GLI (1995)

methodology. Only diazinon had sufficient data for the development of an FCV. The other chemicals

listed below had sufficient data for the development of SCVs.

biphenyl hexachlorethane

4-bromophenyl phenyl ether malathion

butyl benzyl phthalate methoxychlor

diazinon pentachlorobenzene

1,2-dichlorobenzene tetrachloromethane

1,3-dichlorobenzene tribromomethane

1,4-dichlorobenzene 1,2,4-trichlorobenzene

endosulfan mixed isomers trichloromethane

alpha-endosulfan xylene

beta-endosulfan

In addition, EPA has developed FCVs for dieldrin and endrin (USEPA, 2000f, 2000g).

Calculation of Acute Toxicity Values. Acceptable freshwater acute test results were entered in

taxonomic order. If the tests were conducted properly, acute values reported as “greater than” values and

those that were above the solubility of the test material were entered because rejection of such acute

values would unnecessarily lower the FAV by eliminating acute values for resistant species. Reported

results were not rounded off to fewer than four significant digits. To derive freshwater FAVs (USEPA,

1985), it was necessary to have results of acceptable acute toxicity tests with at least one species of

freshwater animal in eight different families, such that all of the following minimum data requirements

(MDRs) were satisfied:

• The family Salmonidae in the class Osteichthyes.

• A second family in the class Osteichthyes, preferably a commercially or recreationally important

warm-water species (e.g., bluegill, channel catfish).
National Sediment Quality Survey

B-9

• A third family in the phylum Chordata (may be in the class Osteichthyes or may be an amphibian,

etc.).

• A planktonic crustacean (e.g., cladoceran, copepod).

• A benthic crustacean (e.g., ostracod, isopod, amphipod, crayfish).

• An insect (e.g., mayfly, dragonfly, damselfly, stonefly, caddisfly, mosquito, midge).

• A family in a phylum other than Arthropoda or Chordata (e.g., Rotifera, Annelida, Mollusca).

• A family in any order of insect or any phylum not already represented.

In the case of a species for which at least one acceptable acute value was available, the species mean

acute value (SMAV) was computed as the geometric mean of the results of all flow-through tests in

which the concentrations of test material were measured. For each genus for which one or more SMAVs

were available, the genus mean acute value (GMAV) was calculated as the geometric mean of the

SMAVs available for the genus. The GMAVs were ranked from the highest to the lowest.

If all eight of the MDRs were satisfied, the FAV was calculated using the procedure outlined by USEPA

(1985), which uses the total number of GMAVs and the four lowest. The calculated value of FAV was

compared with the low SMAVs to determine whether the FAV should be lowered to protect a

commercially or recreationally important species. When all eight of the acute freshwater MDRs were not

satisfied, a freshwater SAV was calculated. It was essential to have at least one acceptable acute toxicity

test with a species in one of the three genera (Daphnia, Ceriodaphnia, or Simocephaus) in the family

Daphnidae.

Acid-Volatile Sulfide Concentration

EPA (USEPA, 2000c) has developed draft ESGs for metal mixtures based on their bioavailability in

sediment. These guidelines are similar to the draft ESGs for nonionic organic chemicals. The draft ESGs

consider cadmium, copper, lead, nickel, silver, and zinc and mixtures thereof. Solid-phase and interstitial

water-phase draft ESGs have been developed. These draft guidelines are intended to protect benthic

organisms from the direct effects of these six metals in sediments that are permanently inundated with

water, are intertidal, or are inundated periodically for durations sufficient to permit development of

benthic assemblages. Moreover, the draft guidelines do not consider the possibility of bioaccumulation

and transfer to organisms at upper trophic levels.

The use of the total concentration of a trace metal in sediment as a measure of its toxicity and its ability

to bioaccumulate is not supported by field and laboratory studies because different sediments exhibit

different degrees of bioavailability for the same total quantity of metal (Di Toro et al., 1990; Luoma,

1983). These differences have been reconciled by relating organism toxic response (mortality) to the

metal concentration in the sediment pore water (Adams et al., 1985; Di Toro et al., 1990). Some metals

form insoluble complexes with the reactive pool of solid-phase sulfides in sediments (iron and

manganese sulfides), restricting their bioavailability. AVS has been used for divalent cationic metals to

predict their bioavailability in sediments. The metals that can bind to these sulfides have sulfide

solubility parameters smaller than those of iron sulfide, and they include nickel, zinc, cadmium, copper,

lead, and mercury. In addition, more recently Berry et al. (1999) used AVS to predict the toxicity of

sediments spiked with silver. However, silver is different from divalent transition metals because it

predominantly exists as monovalent and 2 moles of silver are required to bind to 1 mole of sulfide. In this

NSI data evaluation, silver has been added to other metals (cadmium, copper, lead, nickel, and zinc) in

sediment AVS assessment.
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Acid-volatile sulfide (AVS) is one of the major chemical components that control the activities and

availability of metals in the pore waters of anaerobic sediments (Meyer et al., 1994). Because binding

factors other than AVS dominate the bioavailability, the SEM!AVS methodology for predicting the

bioavailability and toxicity of selected metals is valid only in anaerobic sediments (Berry et al., 1996).

AVS is operationally defined as the sulfide fraction consisting of solid metal sulfide, mainly in the form

of iron monosulfide (Hansen et al., 1996a). The metal concentrations extracted during the same analysis

are called the simultaneously extracted metals (SEM). SEM is operationally defined as those metals that

form less soluble sulfides than do iron or manganese (i.e., the solubility products of these sulfides are

lower than that of iron or manganese sulfide) and that are at least partially soluble under the same test

conditions in which the AVS content of the sediment is determined (Allen et al., 1993; Di Toro et al.,

1992; Meyer et al., 1994).

Laboratory studies using spiked sediments and field-collected metal-contaminated sediments

demonstrated that when the molar ratio of SEM to AVS, [SEM]/[AVS], was less than 1 (excess AVS

remained), no acute toxicity (mortality greater than 50 percent) was observed in any sediment for any

benthic test organism. When [SEM]/[AVS] was greater than 1 (excess metal remained), the mortality of

sensitive species (e.g., amphipods) increased in the range of 1.5 to 2.5 μmol of SEM per μmol AVS

(Casas and Crecelius, 1994; Di Toro et al., 1992).

Experimental studies indicate that the lower limit of applicability for AVS is approximately 1 mmol

AVS/g sediment and possibly lower; other sorption phases, such as organic carbon, probably become

important for sediments with smaller AVS concentrations and for metals with large partition coefficients

and large chronic water quality criteria (Di Toro et al., 1990). In addition, studies indicate that copper, as

well as mercury, might be associated with another phase in sediments, such as organic carbon, and AVS

alone might not be the appropriate partitioning phase for predicting its toxicity. Pore-water

concentrations of metals should also be evaluated (Allen et al., 1993; Ankley et al., 1993; Casas and

Crecelius, 1994). The AVS approach has been traditionally used to predict when a sediment

contaminated with metals is not acutely toxic (Ankley et al., 1993; Di Toro et al., 1992). However,

Hansen et al. (1996b) studied the chronic effect of cadmium in sediments and concluded that the

equilibrium partitioning-based SEM!AVS analysis may be used for chronically exposed benthic

organisms.

Logistic Regression Model Approach

The sediment chemistry screening values used to evaluate the NSI data for potential adverse effects of

sediment contamination on aquatic life include both theoretically and empirically based values. The

theoretically based values rely on physical/chemical properties of sediment and chemicals to derive

concentrations of a substance (or substances in the case of metal mixtures and PAH mixtures) that are

protective to benthic aquatic life. The theoretically based screening values include the draft equilibrium

partitioning sediment guidelines for nonionic organics, metal mixtures, and PAH mixtures. The

empirically based, or correlative, screening values rely on paired field and laboratory data to relate

incidence of observed biological effects to the dry-weight sediment contamination of a specific chemical.

The empirically based, correlative screening values include the effects range-median (ERM)/effects

range-low (ERL) values, probable effects level (PEL)/threshold effects level (TEL), and apparent effects

thresholds (AET). Field et al. (1999, 2002) have proposed an alternative empirical method for evaluating

sediment quality by using logistic regression models using a marine and estuarine database. These

models can be used to predict the probability of observing specific toxic effects.
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The logistic model was originally developed for use in survival analysis, where the dependent variable of

interest has only two outcomes—toxic or nontoxic—and hence can be represented by a binary indicator

variable taking on values of 0 and 1.

For a single independent variable (x), the logistic regression model can be expressed in the following

form:

p = +

+ +

exp[B B (x)]

1 exp[B B (x)]

0 1

0 1

where p = probability of observing a toxic effect, B0 = intercept parameter, B1 = slope parameter, and x =

chemical concentration in log10 units. Metal concentrations are expressed in parts per million (ppm) and

concentrations of organics are in parts per billion (ppb) in the preceding equation.

Field et al. (1999) used matched sediment chemistry and toxicity data obtained from sources spanning

many geographic areas and toxicity endpoints. From the database, separate tables were created for

individual contaminants. The individualized tables contained chemical concentrations for each sample,

with the toxicity results indicating whether the sample was toxic or nontoxic for each toxicity endpoint.

Samples classified as toxic were screened to eliminate the possibility of a selected contaminant’s not

contributing to the reported toxic effect. Within the same study and geographical area, the concentration

of a particular contaminant was compared to the mean concentration of the same contaminant identified

as nontoxic. When the concentration in a toxic sample was less than or equal to the mean concentration

in a nontoxic sample, the samples were excluded from the data set used to develop the logistic model for

the particular chemical. These models were developed using 10-day amphipod survival toxicity tests with

marine and estuarine data. Samples were considered toxic if they were significantly different from a

negative control—as designated by the original investigator—and had less than 90 percent survival.

The screening procedure developed by the authors enabled the data to be transformed into a format

consistent with logistic regression modeling. For preselected concentration intervals—based on the range

of sample concentrations for each contaminant—the proportion of toxic samples was computed. Using

the screened data, individual logistic regression models were developed for each contaminant, and the

slope (B1), intercept (B0), and chi-square statistic values were calculated using the maximum likelihood

approach. Similar to the correlation coefficient (r) in linear regression models, the chi-square statistic

provides information on the slope parameter (B1) of the logistic regression model and the goodness-of-fit

of the model with the data. For data sets with comparable sample sizes,a larger chi-square indicates a

goodness-of-fit between the logistic model and the data used to derive the model. Because the chi-square

statistic increases with sample size, the normalized chi-square statistic value (i.e., chi-square divided by

the sample size) is more applicable when data sets of different magnitude are considered.

Although the logistic model developed gives the probability of observing a toxic effect for a particular

contaminant concentration, the model can also be inverted to determine the concentrations at which a

certain percentage of the samples would be deemed toxic. When the model is used in the inverse form, it

is also possible to calculate the confidence interval for the probability of finding a percentage of the

samples toxic at a particular concentration. The confidence interval reflects the range of concentrations

within which a certain percentage of toxic effect can be expected.

Table B-4 gives the intercept coefficients, the slope, the number of samples used to derive the individual

chemical-specific logistic regression model, and the normalized chi-square value for a list of 37

chemicals representing metals, PAHs, and PCBs. The log chemical concentrations, normalized to either
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dry weight or total organic carbon, are in parts per million (ppm) for metals and are in parts per billion

(ppb) for organics.

Table B-4. Logistic Regression Model Coefficients.

CAS Number Chemical Name

Intercept 

(B0)

Slope

 (B1)

No. of

Samples

Normalized 

P-square Value

83329 Acenaphthene -3.6165 1.7532 1424 0.334

208968 Acenaphthylene -2.962 1.3797 1447 0.23

120127 Anthracene -3.6574 1.4854 1823 0.289

7440360 Antimony -0.9005 2.4111 1718 0.25

7440382 Arsenic -4.1407 3.1674 2336 0.173

56553 Benzo(a)anthracene -4.2013 1.5747 2099 0.298

50328 Benzo(a)pyrene -4.3005 1.5832 2053 0.299

205992 Benzo(b)fluoranthene -4.5409 1.4916 1348 0.266

191242 Benzo(g,h,i)perylene -4.2811 1.5878 1818 0.25

207089 Benzo(k)fluoranthene -4.2781 1.5669 1376 0.286

92524 Biphenyl -4.1144 2.2085 1226 0.263

7440439 Cadmium -0.34 2.5073 2413 0.313

7440473 Chromium, total -6.4395 2.9952 2399 0.195

218019 Chrysene -4.3241 1.5372 2126 0.286

7440508 Copper -5.7878 2.9325 2580 0.383

72548 DDD, p,p'- -1.8983 1.4913 1360 0.268

72559 DDE, p,p'- -1.8392 0.9129 1552 0.162

50293 DDT, p,p'- -1.7705 1.6786 931 0.335

53703 Dibenz(a,h)anthracene -3.6308 1.7692 1546 0.326

60571 Dieldrin -1.1728 2.558 633 0.354

581420 Dimethylnaphthalene, 2,6- -4.0456 1.904 1249 0.201

206440 Fluoranthene -4.4574 1.4787 2189 0.263

86737 Fluorene -3.7146 1.8071 1668 0.323

193395 Indeno(1,2,3-c,d)pyrene -4.3674 1.6245 1837 0.269

7439921 Lead -5.4523 2.7662 2481 0.274

7439976 Mercury 0.8041 2.5461 2296 0.32

90120 Methylnaphthalene, 1- -4.1405 2.0961 1368 0.239

91576 Methylnaphthalene, 2- -3.7579 1.7833 1704 0.25

832699 Methylphenanthrene, 1- -3.5884 1.7501 1401 0.284

91203 Naphthalene -3.7753 1.6152 1816 0.235

7440020 Nickel -4.6119 2.7658 2450 0.18

1336363 Polychlorinated biphenyls -3.4613 1.3488 1617 0.241

198550 Perylene -4.6827 1.7632 1823 0.218

85018 Phenanthrene -4.4576 1.6768 2173 0.298

129000 Pyrene -4.708 1.5854 2240 0.287

7440224 Silver -0.1117 1.9684 2103 0.252

7440666 Zinc -7.9834 3.342 2516 0.279

Source: Field et al., 2002.
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Using the logistic model developed for each contaminant, the probability of observing a toxic effect is

computed for various chemical concentrations (Field et al., 1999, 2002). The logistic regression models

for individual chemicals were combined to estimate the probability of toxicity for the mixture of 

contaminants in a given sample using the maximum (Pmax model) probabilities from the individual

models. The multichemical model (Pmax) was derived from the probability-interval plots, which

summarize all the data in the database (Field et al., 2002). The parameter estimates shown in Table B-4

were used to develop the Pmax model, except for PCBs. A correction in PCB units for 15 samples resulted

in a minor change in the PCB model. However, because the effects of the correction on the Pmax model

was extremely small (the maximum difference in the predicted probability of toxicity was 0.0025 for the

Pmax model), the Pmax model was not changed (Field et al., 2002).

For the unscreened data, the proportion of toxic samples—within different ranges of maximum

probability of toxic effects computed above at discrete concentration intervals—is determined as the ratio

of the number of toxic samples to the total number of samples in the unscreened data. This procedure can

be repeated for different concentrations of the individual contaminants to obtain sufficient data to

generate a regression equation with the proportion of toxic samples as the dependent variable and the

maximum probability of observing toxic effects as the independent variable. The following regression

equation (Field et al., 2002) was used in the NSI data evaluation:

y = 0.11 + 0.33 pmax + 0.4 pmax2

where pmax = maximum probability of observing a toxic effect and y = predicted proportion toxic.

From multiple chemical measures of the 37 target chemicals, the predicted proportion toxic is computed

for each sample using the preceding regression equation. When the maximum value of the predicted

proportion toxic is greater than or equal to 50 percent (0.5), the station is classified as Tier 1. When the

maximum value of the predicted proportion toxic is less than 50 percent but greater than or equal to 25

percent, the station is classified as Tier 2. All other stations with available data are grouped as Tier 3.

To evaluate the applicability of the marine amphipod models to freshwater data, matching sediment

chemistry and toxicity data were compiled for three freshwater toxicity test endpoints: 10- to 14-day

acute lethality tests with Hyalella azteca and Chironomus spp. and a long-term 28-day growth and

survival test with Hyalella azteca. The predicted proportion toxic from the marine models was compared

to the observed acute toxicity for each test endpoint within four probability quartiles. The results of the

evaluations for all three endpoints showed that the increase in probability of toxicity based on the marine

amphipod model was accompanied by an increase in the observed proportion toxic. For the acute

freshwater tests with Hyalella azteca and Chironomus spp., only samples that the model predicted to

have a high probability of toxicity (p > 0.75) showed substantial increase in the proportion of samples

that were toxic (Figure B-1; Chironomus plot not shown). However, the results for the chronic Hyalella

azteca test endpoint (28-day growth and survival) correspond very well to the model predictions (Figure

B-2). In the 28-day database, 61 samples had a predicted proportion toxic greater than 0.5 (with a mean

of 0.68) compared to 0.61 observed proportion toxic. These results indicate that the LRM P_Max model

used in this analysis would tend to overestimate toxicity observed in Hyalella azteca and Chironomus

spp. 10- to 14-day survival tests, but not the Hyalella azteca 28-day growth and survival test. Based on

this evaluation, the difference between model predictions and the acute freshwater toxicity test results

may be more related to differences in endpoint sensitivity than to differences between marine and

freshwater geochemistry. 
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Figure B-2. Application of Logistic Model to Freshwater Data for

Hyalella azteca 28-day Growth and Survival Endpoint.

Figure B-1. Application of the Logistic Model to Freshwater Data

for Hyalella azteca 10- to 14-day Survival Endpoint.
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Sediment Toxicity Approaches

Sediment toxicity tests provide important information on the effects of multiple chemical exposures to

assist in the evaluation of sediment quality. Methods for testing the short- and long-term toxicity of

sediment samples to benthic freshwater and marine organisms have been developed (see reviews in API,

1994; Burton et al., 1992; Lamberson et al., 1992; USEPA, 1994a, 1994b, 2000h) and used primarily for

dredged material evaluation (USEPA and USACE, 1998). The NSI data contain short- and long-term

sediment toxicity results from tests in which organisms were exposed to field-collected sediments and

mortality or other endpoints were recorded.

Data in the NSI database were reviewed, and only bulk sediment nonmicrobial toxicity tests with test

durations of 7 days or more were analyzed. Test results with survival (or mortality) as an endpoint were

considered for all marine and freshwater species with valid control-adjusted results. In addition, for

freshwater species growth-based endpoints—length and weight—were considered for long-term toxicity.

Test results with the freshwater invertebrate Hyalella azteca were analyzed for variation in controladjusted length. Variations in control-adjusted weight were considered for the freshwater invertebrates

Hyalella azteca and Chironomus spp. Test results with either unknown test species or unknown test

duration were not analyzed in this NSI data evaluation. Table B-5 presents a list of species used in

toxicity tests whose results are included in the National Sediment Quality Survey.

Table B-5. Species Used in Bulk Sediment Toxicity Testsa

.

 Survival (or Mortality) Endpoint: Marine and Freshwater Species

Acanthomysis costata Eohaustorius estuarius Neanthes spp.

Ampelisca abdita Grandidierella japonica Nebalia pugettensis

Ampelisca verrilli Hexagenia limbata Nephtys caecoides

Ceriodaphnia dubia Hyalella azteca Nereis virens

Chironomus riparius Macoma nasuta Panaeus duorarum

Chironomus tentans Mysidopsis bahia Rhepoxynius abronius

Crassostrea virginica Neanthes arenaceodentata Rhepoxynius hudsoni

Growth-Based Endpoint (Length): Freshwater Species

Hyalella azteca

Growth-Based Endpoint (Weight): Freshwater Species

Chironomus riparius Chironomus tentans Hyalella azteca

a

 With test durations $ 7 days.

Test Controls. Toxicity data were screened to determine whether control data were reported. Sediment

toxicity test laboratory or performance controls are usually clean sand or sediment tested under the same

conditions in which the test organisms are exposed at the same time as those exposed to the sediment

samples tested. Controls are used to determine whether observed mortality might be the result of the

quality of test organisms used or other factors, and not the result of exposure to possible toxics in the

sediment samples.

The databases were screened to locate control test data for each sediment sample tested. Multiple control

sample test results were reported in some of the databases. These were determined to be replicate test
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results. The percent survival (or mortality) for the reference replicates were averaged for each reference

site to obtain the mean percent survival (or mortality).

Control-corrected survival (percent) was computed using the following formula:

Controlcorrected

survival (%)

= 100 ×

organism survival in test sample (%)

organism survival in control sample (%)

Where necessary, data reported as “percent mortality” were converted to “percent survival” by the

following formula:

Survival (%) = 100 ! mortality (%)

Determination of Thresholds for Tier Classification. Minimum detectable differences (MDDs), based

on sediment toxicity data from round robin tests, were used to determine the thresholds for tier

classification of toxicity data (USEPA, 2000h). Although the quantitative relationship between statistical

significance and expected ecological effects is not fully understood, EPA presumes that these values are

related to expected ecological effects, as is the presumption of other EPA assessment approaches

(USEPA, 1985). Table B-6 shows the MDDs calculated for the different species and test endpoints.

MDD values from a control sediment are compared with contaminated sediments used in round robin 10-

day and 28-day tests. The MDDs were calculated with a one-tailed t-test at a confidence level of 95

percent with four replicates. Based on the values of MDDs presented in Table B-6, samples with a

percent reduction of mean MDD plus 2 standard deviations from control data (selected 25 percent

mortality from a range of 25.0 to 29.8 percent mortality, i.e., < 75 percent control-adjusted survival) were

classified as Tier 1 for survival endpoints. Similarly, when the percent reduction from the control data

was mean MDD less 1 standard deviation (i.e., < 90 percent control-adjusted survival), the samples with

survival endpoints were categorized as Tier 2.

Table B-6. Minimum Detectable Differences (MDDs) Calculated from Round Robin Test Data.

Species/Endpoint

Sediment

Compared

Average MDD

(% reduction

from control)

Standard

Deviation (as

% of control)

Mean + 2 SD

(% reduction

from control)

Mean ! 1 SD

(% reduction

from control)

Chironomus tentans

10-d survival

WBSa

 vs LSb 13.7 7.2 28.1 6.5

WBS vs DCc 13.3 5.9 25 7.4

Hyalella azteca

10-d survival

WBS vs LS 15.8 5.1 26.1 10.7

WBS vs DC 16.6 6.6 29.8 10

Hyalella azteca

28-d length

WBS vs LS 4.9 1.1 7.1 3.9

WBS vs CCd 5.3 1.1 7.5 4.2

Chironomus tentans

10-d weight

WBS vs LS 12.3 5.1 22.5 7.2

WBS vs DC 19.6 5.8 31.2 13.9

Hyalella azteca

28-d weight

WBS vs LS 17.6 7.1 31.8 10.5

WBS vs CC 27.3 11.1 49.5 16.2 a

 WBS: control sediment from West Bearskin Lake, Minnesota

b

 LS: contaminated sediments from Little Scioto River, Ohio.

c

 DC: contaminated sediments from Defoe Creek site, Michigan.

d

 CC: contaminated sediments from Cole Creek, Michigan.
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Using the threshold stated above, for growth-based measurements of length, samples with less than 90

percent control-adjusted length were classified as Tier 1 and samples with less than 70 percent controladjusted weight were classified as Tier 1. Tier 2 classification for length was based on less than 95

percent control-adjusted length and less than 90 percent control-adjusted weight.

Human Health Assessments

In the evaluation of NSI data, two primary evaluation parameters were used to assess potential human

health impacts from sediment contamination: (1) sediment chemistry theoretical bioaccumulation

potential and (2) tissue levels of contaminants in demersal, nonmigratory, and edible species.

Theoretical Bioaccumulation Potential

The theoretical bioaccumulation potential (TBP) is an estimate of the equilibrium concentration of a

contaminant in tissues if the sediment in question were the only source of contamination to the organism

(USEPA and USACE, 1998). The TBP calculation is used as a screening mechanism to represent the

magnitude of bioaccumulation likely to be associated with nonpolar organic contaminants in the

sediment. At present, the TBP calculation can be performed only for nonpolar organic chemicals;

however, methods for TBP calculations for metals and polar organic chemicals are under development

(USEPA and USACE, 1998).

The environmental distribution of nonpolar organic chemicals is controlled largely by their solubility in

various media. Therefore, in sediments they tend to occur primarily in association with organic matter

(Karickhoff, 1981) and in organisms they are found primarily in the body fats or lipids (Bierman, 1990;

Geyer et al., 1982; Konemann and van Leeuwen, 1980; Mackay, 1982). Bioaccumulation of nonpolar

organic compounds from sediment can be estimated from the organic carbon content of the sediment, the

lipid content of the organism, and the relative affinities of the chemical for sediment organic carbon and

animal lipid content (USEPA and USACE, 1998). It is possible to relate the concentration of a chemical

in one phase of a two-phase system to the concentration in the second phase when the system is in

equilibrium. The TBP calculation focuses on the equilibrium distribution of a chemical between the

sediment and the organism. By normalizing nonpolar organic chemical concentration data for lipid in

organisms, and for organic carbon in sediment, it is possible to estimate the preference of a chemical for

one phase or the other (USEPA and USACE, 1998).

The TBP can be calculated relative to the biota-sediment accumulation factor (BSAF), as in the

following equation (USEPA and USACE, 1998):

TBP = BSAF (Cs/foc)fl

where TBP is expressed on a whole-body basis in the same units of concentration as Cs and

TBP = theoretical bioaccumulation potential (ppm);

Cs = concentration of nonpolar organic chemical in sediment (ppm);

BSAF = biota-sediment accumulation factor (ratio of the concentration of a chemical in tissue,

normalized to lipid, to the concentration of the chemical in surface sediment, normalized to

organic carbon (in kg sediment organic carbon/kg lipid));
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foc = total organic carbon (TOC) content of sediment expressed as a decimal fraction (i.e., 1 percent

= 0.01); and

fl = organism lipid content expressed as a decimal fraction (e.g., 3 percent = 0.03) of fillet or

whole-body dry weight.

BSAFs are transfer coefficients that relate concentrations in biota to concentrations in sediment. They are

calculated as the ratio of the concentration of nonpolar organic chemical in fish tissue (normalized by

lipid content) to the concentration of nonpolar organic chemical in sediment (normalized by organic

carbon content). At equilibrium, BSAFs are in theory approximately 1.0. In practice, BSAFs can be

greater than or less than 1.0 depending on the disequilibrium between fish and water, and that between

water and sediment. Although based on a partitioning theory, field-measured BSAFs empirically account

for factors such as metabolism and food chain magnification. BSAFs can vary depending on the biota,

dynamics of chemical loadings to the waterbody, food chain effects, and rate of sediment-water

exchange. Thus, measured BSAF values will depend on many site-specific variables, including hydraulic,

biological, chemical, and ecological factors that affect bioavailability. The accuracy of a BSAF,

measured at one location at a point in time, when applied to another location at another point in time

depends on two factors. The first factor is the degree to which variation from a theoretical BSAF of 1.0 is

controlled by inherent properties of the chemical as opposed to environmental conditions of the locale.

The second is the degree of similarity between environmental conditions at the place of measurement and

at the place of application.

The BSAFs used in the TBP evaluation were obtained from the EPA Office of Research and

Development (EPA ORD) Environmental Research Laboratories at Duluth, Minnesota, and Narragansett,

Rhode Island (USEPA, 1997). The BSAFs developed by EPA ORD-Narragansett were for benthic

organisms and demersal (bottom-dwelling) fishes. The BSAFs developed by EPA ORD-Duluth, on the

other hand, were for benthically coupled pelagic (open-water) fishes (USEPA, 1997). If TOC

measurements were not available at a site, foc was assumed to be 0.01 (1 percent).

For the evaluation of NSI data, EPA selected a 3 percent lipid content in fish fillets for the TBP

calculation for assessing human health effects from the consumption of contaminated fish. Lipid

normalization is now part of the EPA guidance on bioaccumulation, and the current national

methodology uses a 3 percent value for human health assessments. The Great Lakes Water Quality

Initiative Technical Support Document for the Procedure to Determine Bioaccumulation Factors

(USEPA, 1995a) uses a 3.10 percent lipid value for trophic level 4 fish and 1.82 percent for trophic level

3 fish in its human health assessments.

As part of the NSI data TBP evaluation, EPA also evaluated percent lipid measurements included in the

STORET database, the National Study of Chemical Residues in Fish (NSCRF; USEPA, 1992b), and

other published sources and compared those values to the value selected for the NSI data evaluation

(Appendix C of The Incidence and Severity of Sediment Contamination in Surface Waters of the United

States; USEPA, 1997). The mean fillet percent lipid content for various groups of fish species in the

STORET database ranged from 0.753 to 4.49 percent; in the NSCRF, mean fillet values ranged from 1.6

to 4.9 percent. The mean whole-body percent lipid content for various groups of fish species in the

STORET database ranged from 3.757 to 6.33 percent; in the NSCRF, mean whole-body values ranged

from 4.6 to 8.8 percent.

In the NSI data evaluation approach, TBP values were compared to U.S. Food and Drug Administration

(FDA) tolerance/action/guidance levels and EPA risk levels. These parameters are discussed below.
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FDA Tolerance/Action/Guidance Levels

FDA is responsible for the safety of the Nation’s commercial food supply, including fish and shellfish for

human consumption. Under the authority of the Federal Food, Drug and Cosmetic Act (FFDCA), FDA

ensures that regulated products are safe for use by consumers. The FFDCA authorizes FDA to conduct

assessments of the safety of ingredients in foods. The key element of the FFDCA, and the source of

FDA’s main tools for enforcement, is the prohibition of the “adulteration” of foods. FDA can prescribe

the level of contaminant that will render a food adulterated and, therefore, can initiate enforcement action

based on scientific data. The establishment of guidance and action levels (informal judgments about the

level of a food contaminant to which consumers can be safely exposed) or tolerances (regulations having

the force of law) is the regulatory procedure FDA uses to control environmental contaminants in the

commercial food supply.

During the 1970s the available detection limits were considered to demonstrate elevated contamination

and were used as action levels. Since that time FDA has focused on using risk-based standards derived by

individually considering each chemical and the species of fish it is likely to contaminate. FDA also

considered (1) the amount of potentially contaminated fish eaten and (2) the average concentrations of

contaminants consumed. FDA has established action levels in fish for 10 pesticides and methylmercury,

tolerance levels for polychlorinated biphenyls (PCBs), and guidance for 5 metals.

EPA Risk Levels

Potential impacts on humans are evaluated by estimating potential carcinogenic risks and

noncarcinogenic hazards associated with the consumption of chemically contaminated fish tissue. In this

assessment it was assumed that the only source of contamination to fish is contaminated sediment. The

procedures for estimating human health risks due to the consumption of chemically contaminated fish

tissue are based on Risk Assessment Guidance for Superfund (USEPA, 1989) and Guidance for Assessing

Chemical Contamination Data for Use in Fish Advisories, Volume I: Fish Sampling and Analysis

(USEPA, 2000i).

EPA human health risk assessment methods were used in this assessment to determine the levels of

contamination in fish that might result in a 10-5 cancer risk (1 in 100,000 extra chance of cancer over a

lifetime) or a noncancer hazard in humans. A 10-5 risk level exceeds the lower bound (10-6) but is lower

than the upper bound (10-4) of the risk range accepted by EPA (USEPA, 1990).

Human health cancer risks and noncancer hazards are based on the calculation of the chronic daily intake

(CDI) of contaminants of concern:

(BW)(AT)

(EPC)(IR)(EF)(ED) CDI =

where:

CDI = chronic daily intake (mg/kg/day);

EPC = exposure point concentration (contaminant concentration in fish);

IR = ingestion rate (17.5 g/day);

EF = exposure frequency (365 days/year);
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ED = exposure duration (70 years);

BW = body weight (70 kg); and

AT = averaging time (70 years x 365 days/year).

These are the same parameter values EPA used to develop human health water quality criteria. A

consumption rate of 17.5 g/d is chosen to be protective of the majority (i.e., 90 percent) of the population

(USEPA, 2000i). Carcinogenic risks are then quantified using the following equation:

Cancer riski

 = (CDI) (SFi)

where:

Cancer riski = the potential carcinogenic risk associated with exposure to chemical i (unitless);

CDIi = chronic daily intake for chemical i (mg/kg/day); and

SFi = slope factor for chemical i (mg/kg/day)-1.

The hazard quotient, which is used to quantify the potential for an adverse noncarcinogenic effect to

occur, is calculated using the following equation:

HQ

CD

RfD

i

i

i =

where:

HQi = hazard quotient for chemical i (unitless);

CDIi = chronic daily intake for chemical i (mg/kg/day); and 

RfDi = reference dose for chemical i (mg/kg/day).

If the hazard quotient exceeds unity (i.e., 1), an adverse health effect might occur. The higher the hazard

quotient, the more likely that an adverse noncarcinogenic effect will occur as a result of exposure to the

chemical. If the estimated hazard quotient is less than unity, noncarcinogenic effects are unlikely to

occur.

Using these formulas, the fish tissue concentration (EPC) of a contaminant that equates to a cancer risk

of 10-5 or a hazard quotient that exceeds unity can be back-calculated.

Cancer risk:

EPC (10 )(BW)(AT)(C )

(IR)(EF)(ED)(SF )

5

1

1

=

−

Noncancer hazard:

(IR)(EF)(ED)

(BW)(AT)(RfD )(C ) EPC i 1 =
National Sediment Quality Survey

B-21

where:

C1 = conversion factor (103

 g/kg).

The cancer slope factors and noncancer reference doses reported in the Guidance for Assessing Chemical

Contaminant Data for Use in Fish Advisories (USEPA, 2000i) were used to calculate the EPA risk levels

and hazard quotients in this NSI data evaluation. When the risk levels and hazard quotients were not

reported in Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories (USEPA,

2000i), cancer slope factors and noncancer reference doses used in the 1997 National Sediment Quality

Survey report to Congress (Appendix E, Table E-1, USEPA, 1997) were used. In such instances an

average consumption of 17.5 grams per day of uncooked fish and shellfish from estuarine waters and

freshwaters by recreational fishers was used in the computation of risk values and hazard quotients.

Tissue Levels of Contaminants

In addition to sediment chemistry TBP values, measured levels of contaminants in the tissues of resident

aquatic species were used to assess potential human health risk. As was the case with the evaluation of

TBP values, the NSI data evaluation approach compared contaminant tissue levels to FDA

tolerance/action/guidance levels and EPA risk levels. Each of these parameters was discussed in the

previous section. In such a comparison it is assumed that contaminant concentrations in tissue result from

bioaccumulation of contaminants in the sediment.

The draft ESGs used for the NSI data evaluation for nonionic organics, PAH mixtures, and metal

mixtures; model parameters used for the logistic regression models; EPA risk levels; and FDA

tolerance/action/guidance levels are presented in Table C-1 of Appendix C.

Wildlife Assessments

In addition to the evaluation parameters described above for the assessment of potential aquatic life and

human health impacts, EPA also conducted a separate analysis of potential wildlife impacts resulting

from exposure to sediment contaminants.

First, wildlife criteria based on fish tissue concentrations were derived using methods similar to those

employed for deriving EPA wildlife criteria, as presented in the Great Lakes Water Quality Initiative

Criteria Documents for the Protection of Wildlife: DDT; mercury; 2,3,7,8-TCDD; PCBs (USEPA,

1995b). EPA has developed Great Lakes Water Quality Wildlife Criteria for four chemicals: DDT;

mercury; 2,3,7,8-TCDD; and PCBs. A Great Lakes Water Quality Wildlife Criterion (GLWC) is the

concentration in the water of a substance that, if not exceeded, protects avian and mammalian wildlife

populations from adverse effects resulting from the ingestion of surface waters and aquatic prey

(USEPA, 1995b). Wildlife values are calculated using the following equation:

WV (NOAEL SSF) Wt

W (F (BAF)

A

A A

= +

)( ( )

)

where:

WV = wildlife value (mg/L);

NOAEL = no-observed-adverse-effect level, as derived from mammalian or avian studies (mg/kg-d);
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WtA = average weight for the representative species identified for protection (kg);

WA = average daily volume of water consumed by the representative species identified for

protection (L/d);

SSF = species sensitivity factor, an extrapolation factor to account for the difference in toxicity

between species;

FA = average daily amount of food consumed by the representative species identified for

protection (kg/d); and

BAF = bioaccumulation factor (L/kg), the ratio of the concentration of a chemical in tissue,

normalized to lipid, to the concentration in ambient water. Chosen using guidelines for

wildlife presented in appendix B to part 132, Methodology for Development of

Bioaccumulation Factors (Federal Register, Vol. 58, No. 72, April 16, 1993).

In the development of the four GLWCs, wildlife values for five representative Great Lakes basin wildlife

species (bald eagle, herring gull, belted kingfisher, mink, and river otter) were calculated, and the

geometric mean of these values within each taxonomic class (birds and mammals) was determined. The

GLWC is the lower of two class-species means (USEPA, 1995b).

The wildlife values are considered to be generally protective of wildlife species. However, it should be

noted that the approach is not based on the most sensitive wildlife species, but rather a typical class of

either avian or mammalian piscivores. Despite this limitation, this approach is still considered

appropriate and conservative because of the many conservative assumptions used to derive these wildlife

values (e.g., species sensitivity factors, assumption that animals consume only contaminated fish).

Proposed EPA wildlife criteria are based on surface water contaminant levels protective of potential

wildlife exposure. Thus, the proposed EPA wildlife criteria cannot be compared directly to the NSI fish

tissue concentrations (either the calculated TBPs or fish tissue monitoring data). It was necessary,

therefore, to develop an approach for estimating wildlife criteria for fish tissue based on the same toxicity

and exposure parameter assumptions that were used to derive the surface water wildlife criteria. First,

wildlife values (i.e., fish tissue concentrations protective of wildlife) were derived for the most sensitive

mammalian species (i.e., otter and mink) and avian species (i.e., kingfisher, herring gull, and eagle)—the

same species used to derive the proposed EPA wildlife criteria. The equation used to estimate wildlife

values for fish tissue is presented below. (Exposure assumptions used for each species are presented in

USEPA, 1995b.)

WV (NOAEL)(SSF) Wt

F fish

A

A

= ( )

where:

WVfish = wildlife value for fish tissue (mg/kg);

NOAEL = no-observed-adverse-effect level (mg/kg-day);

SSF = species sensitivity factor;

WtA = average weight of animal in kilograms (kg); and

FA = average daily amount of food consumed (kg/day).
National Sediment Quality Survey

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Second, the geometric mean of the wildlife values was calculated for the mammal group, as well as for

the avian group. Finally, the lower of the two geometric mean values was considered the wildlife

criterion for fish tissue for a given chemical.

It should be noted that direct ingestion of surface water was included when developing proposed EPA

wildlife criteria for surface water. This exposure route, however, was not considered when evaluating

NSI data, even though sediment contamination might result in contamination of surface water available

for wildlife consumption. A sensitivity analysis was conducted to evaluate the impact of excluding the

surface water ingestion exposure route. Based on this analysis, ingestion of surface water contributes less

than 0.0001 percent of the total exposure (i.e., ingestion of fish and water). Therefore, excluding the

water ingestion exposure route had no significant impact on the evaluation of NSI data with respect to

potential wildlife impacts.

Wildlife criteria derived for DDT; mercury; 2,3,7,8-TCDD; and PCBs based on fish tissue concentration

are presented below.

Chemical Fish Tissue Criterion (mg/kg)

DDT 3.93E-2

Mercury 5.73E-2

2,3,7,8-TCDD 5.20E-7

PCBs 1.60E-1

The wildlife criteria were compared to measured fish tissue residue data contained in the NSI and to

TBPs calculated for DDT; 2,3,7,8-TCDD; and PCBs. Mercury is not a nonpolar organic chemical, and

thus a TBP for mercury was not calculated. A whole-body lipid value of 10.31 was assumed for the TBP

evaluation of potential wildlife impacts, based on the Great Lakes Water Quality Technical Support

Document for the Procedure to Determine Bioaccumulation Factors (USEPA, 1995a).

References

Adams, W.J., R.A. Kimerle, and R.G. Mosher. 1985. Aquatic safety assessment of chemicals sorbed to

sediments. In Aquatic Toxicology and Hazard Assessment: Seventh Annual Symposium, American

Society for Testing and Materials, Philadelphia, PA, ed. R.D. Cardwell, R. Purdy, and R.C. Bahner,

pp. 429-453.

Allen, H.E., G. Fu, and B. Deng. 1993. Analysis of acid-volatile sulfide (AVS) and simultaneously

extracted metals (SEM) for the estimation of potential toxicity in aquatic sediments. Environ.

Toxicol. Chem. 12:1441-1453.

Ankley, G.T., V.R. Mattson, E.N. Leonard, C.W. West, and J.L. Bennett. 1993. Predicting the toxicity of

copper in freshwater sediments: Evaluation of the role of acid-volatile sulfide. Environ. Toxicol.

Chem. 12:315-320.

API (American Petroleum Institute). 1994. User’s guide and technical resource document: Evaluation of

sediment toxicity tests for biomonitoring programs. API pub. no. 4607. Prepared for American

Petroleum Institute, Health and Environmental Sciences Department, Washington, DC, by PTI

Environmental Services, Bellvue, WA.

Berry, W.J., M.G. Cantwell, P.A. Edwards, J.R. Serbst, and D.J. Hansen. 1999. Predicting toxicity of

sediments spiked with silver. Environ. Toxicol. Chem. 18(1):40-48.
National Sediment Quality Survey

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Berry, W.J., D. Hansen, J.D. Mahony, D.L. Robson, D.M. Di Toro, B.P. Shipley, B. Rogers, J.M. Corbin,

and W.S. Boothman. 1996. Predicting the toxicity of metal-spiked laboratory sediments using acidvolatile sulfide and interstitial water normalizations. Environ. Toxicol. Chem. 15(12):2067-2079.

Bierman, V.J. 1990. Equilibrium partitioning and magnification of organic chemicals in benthic animals.

Environ. Sci. Technol. 24:1407-1412.

Burton, G.A., Jr., J.K. Nelson, and C.G. Ingersoll. 1992. Freshwater benthic toxicity tests. In Sediment

toxicity assessment, ed. G.A. Burton, Jr., pp. 213-240. Lewis Publishers, Chelsea, MI.

Casas, A.M., and E.A. Crecelius. 1994. Relationship between acid-volatile sulfide and the toxicity of

zinc, lead and copper in marine sediments. Environ. Toxicol. Chem. 13(3):529-536.

Di Toro, D.M., J.D. Mahony, D.J. Hansen, K.J. Scott, M.B. Hicks, S.M. Mays, and M.S. Redmond. 1990.

Toxicity of cadmium in sediments: The role of acid-volatile sulfide. Environ. Toxicol. Chem.

9:1487-1502.

Di Toro, D.M., J.D. Mahony, D.J. Hansen, K.J. Scott, A.R. Carlson, and G.T. Ankley. 1992.

Acid-volatile sulfide predicts the acute toxicity of cadmium and nickel in sediments. Environ. Sci.

Technol. 26(1):96-101.

Di Toro, D.M., and J.A. McGrath. 2000. Technical basis for narcotic chemicals and polycyclic aromatic

hydorcarbon criteria. II. Mixtures and sediments. Environ. Toxicol. Chem. 19(8):1971-1982.

Di Toro, D.M., J.A. McGrath, and D. J. Hansen. 2000. Technical basis for narcotic chemicals and

polycyclic aromatic hydorcarbon criteria. I. Water and tissue. Environ. Toxicol. Chem.

19(8):1951-1970.

Di Toro, D.M., C.S. Zarba, D.J. Hansen, W.J. Berry, R.C. Swartz, C.E. Cowan, S.P. Pavlou, H.E. Allen,

N.A. Thomas, and P.R. Paquin. 1991. Technical basis for establishing sediment quality criteria for

nonionic organic chemicals using equilibrium partitioning. Environ. Toxicol. Chem. 10:1541-1583.

Field, L.J., D.D. MacDonald, S.B. Norton, C.G. Severn, and C.G. Ingersoll. 1999. Evaluating sediment

chemistry and toxicity data using logistic regression modeling. Environ. Toxicol. Chem.

18(6):1311-1322.

Field, L.J., D.D. MacDonald, S.B. Norton, C.G. Severn, C.G. Ingersoll, D. Smorong, and R. Linkskoog.

2002. Predicting amphipod toxicity from sediment chemistry using logistic regression models.

Environ. Toxicol. Chem 21(9):1993-2005. 

Geyer, H., P. Sheehan, D. Kotzias, and F. Korte. 1982. Prediction of ecological behavior of chemicals:

Relationship between physico-chemical properties and bioaccumulation of organic chemicals in the

mussel Mytilus edulis. Chemosphere 11:1121-1134.

GLI (Great Lakes Initiative). 1995. Final water quality guidance for the Great Lakes System. Final Rule.

U.S. Environmental Protection Agency. Fed. Regist., March 23, 1995, 60:1500ff.

Hansen, D.J., W.J. Berry, J.D. Mahony, W.S. Boothman, D.M. Di Toro, D.L. Robson, G.T. Ankley,

D. Ma, Q. Yan, and C.E. Pesch. 1996a. Predicting the toxicity of metal-contaminated field sediments

using interstitial concentration of metals and acid-volatile sulfide normalizations. Environ. Toxicol.

Chem. 15(12):2080-2094.

Hansen, D.J., J.D. Mahony, W.J. Berry, S.J. BenyI, J.M. Corbin, S.D. Pratt, D.M. Di Toro, and

M.B. Abel. 1996b. Chronic effect of cadmium in sediments on colonization by benthic marine
National Sediment Quality Survey

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organisms: An evaluation of the role of interstitial cadmium and acid-volatile sulfide in biological

availability. Environ. Toxicol. Chem. 15(12):2126-2137.

Howard, D.E., and R.D. Evans. 1993. Acid-volatile sulfide (AVS) in a seasonally anoxic mesotrophic

lake: Seasonal and spatial changes in sediment AVS. Environ. Toxicol. Chem. 12:1051-1057.

Karickhoff, S. 1981. Semi-empirical estimation of sorption of hydrophobic pollutants on natural

sediments and soils. Chemosphere 9:3-10.

Karickhoff, S.W. 1984. Organic pollutant sorption in aquatic systems. J. Hydraul. Div. ASCE 110:707-

735.

Konemann, H., and K. van Leeuwen. 1980. Toxicokinetics in fish: Accumulation and elimination of six

chlorobenzenes by guppies. Chemosphere 9:3-19.

Lamberson, J.O., T.H. DeWitt, and R.C. Swartz. 1992. Assessment of sediment toxicity to marine

benthos. In Sediment toxicity assessment, ed. G.A. Burton, Jr., pp. 183-211. Lewis Publishers,

Chelsea, MI.

Luoma, S.N. 1983. Bioavailability of trace metals to aquatic organisms—A review. Sci. Tot. Environ.

28:1-22.

Mackay, D. 1982. Correlation of bioconcentration factors. Environ. Sci. Technol. 5:274-278.

———. 1991. Multimedia environmental models: The fugacity approach. Lewis Publishers, Boca Raton,

FL.

Meyer, J.S., W. Davison, B. Sundby, J.T. Ores, D.J. Lauren, U. Forstner, J. Hong, and D.G. Crosby.

1994. Synopsis of discussion session: The effects of variable redox potentials, pH, and light on

bioavailability in dynamic water-sediment environments. In Bioavailability physical, chemical, and

biological interactions, proceedings of the Thirteenth Pellston Workshop, ed. J.L. Hamelink,

P.F. Landrum, H.L. Bergman, and W.H. Benson, pp.155-170. Lewis Publishers, Boca Raton, FL.

Suter, G.W. II, and J.B. Mabrey. 1994. Toxicological benchmarks for screening potenital contaminants

of concern for effects on aquatic biota: 1994 revision. ES/ER/TM-96/R1. Oak Ridge National

Laboratory, Environmental Sciences Division, Oak Ridge, TN.

Swartz, R.C., 1999. Consensus sediment quality guidelines for polycyclic aromatic hydrocarbon

mixtures. Environ. Toxicol. Chem. 18(4):780-787.

Swartz, R.C., D.W. Schults, R.J. Ozretich, J.O. Lamberson, F.A. Cole, T.H. DeWitt, M.S. Redmonds,

and S.P. Ferraro. 1995. EPAH: A model to predict the toxicity of polynuclear aromatic hydrocarbon

mixtures in field-collected sediments. Environ. Toxicol. Chem. 14(11):1977-1987.

USEPA (U.S. Environmental Protection Agency). 1985. Guidelines for deriving numerical national

water quality criteria for the protection of aquatic organisms and their uses. PB85-227049. National

Technical Information Service, Springfield, VA.

———. 1989. Risk assessment guidance for Superfund. Volume I: Human health evaluation manual

(Part A). Interim final. OSWER Directive 9285.7-01a. U.S. Environmental Protection Agency, Office

of Solid Waste and Emergency Response, Washington, DC. December 1989.

———. 1990. National contingency plan. Fed. Regist., March 8, 1990, 55:8666.

———. 1992a. Sediment classification methods compendium. EPA 823-R-92-006. U.S. Environmental

Protection Agency, Office of Water, Washington, DC.
National Sediment Quality Survey

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———. 1992b. National study of chemical residues in fish. 2 vols. EPA 823-R-92-008a,b. U.S.

Environmental Protection Agency, Office of Science and Technology, Washington, DC. 

———. 1994a. Methods for measuring the toxicity and bioaccumulation of sediment-associated

contaminants with estuarine and marine amphipods. EPA 600/R-94/025. U.S. Environmental

Protection Agency, Office of Research and Development, Washington, DC.

———. 1994b. Methods for measuring the toxicity and bioaccumulation of sediment-associated

contaminants with freshwater invertebrates. EPA 600/R-94/024. U.S. Environmental Protection

Agency, Office of Research and Development, Duluth, MN.

———. 1995a. Great Lakes Water Quality Initiative technical support document for the procedure to

determine bioaccumulation factors. EPA-820-B-95-005. U.S. Environmental Protection Agency,

Office of Water, Washington, DC.

———. 1995b. Great Lakes Water Quality Initiative criteria documents for the protection of wildlife.

EPA-820-B-95-008. U.S. Environmental Protection Agency, Office of Science and Technology,

Washington, DC.

———. 1997. The incidence and severity of sediment contamination in surface waters of the United

States. EPA 823-R-97-006. U.S. Environmental Protection Agency, Office of Science and

Technology, Washington, DC. 

———. 2000a. Technical basis for the derivation of equilibrium partitioning sediment guidelines (ESGs)

for the protection of benthic organisms: Nonionic organics. U.S. Environmental Protection Agency,

Office of Science and Technology and Office of Research and Development, Washington, DC. (2000

draft).

———. 2000b. Equilibrium partitioning sediment guidelines (ESGs) for the protection of benthic

organisms: PAH mixtures. U.S. Environmental Protection Agency, Office of Science and

Technology and Office of Research and Development, Washington, DC. (2000 draft)

———. 2000c. Equilibrium partitioning sediment guidelines (ESGs) for the protection of benthic

organisms: Metal mixtures (cadmium, copper, lead, nickel, silver, and zinc). U.S. Environmental

Protection Agency, Office of Science and Technology and Office of Research and Development,

Washington, DC. (2000 draft).

———. 2000d. Methods for the derivation of site-specific equilibrium partitioning sediment guidelines

(ESGs) for the protection of benthic organisms: Nonionic organics. U.S. Environmental Protection

Agency, Office of Science and Technology and Office of Research and Development, Washington,

DC. (2000 draft).

———. 2000e. Equilibrium partitioning sediment guidelines (ESGs) for the protection of benthic

organisms: Nonionics compendium. Environmental Protection Agency, Office of Science and

Technology and Office of Research and Development, Washington, DC. (2000 draft).

———. 2000f. Equilibrium partitioning sediment guidelines (ESGs) for the protection of benthic

organisms: Dieldrin. Environmental Protection Agency, Office of Science and Technology and

Office of Research and Development, Washington, DC. (2000 draft).

———. 2000g. Equilibrium partitioning sediment guidelines (ESGs) for the protection of benthic

organisms: Endrin. Environmental Protection Agency, Office of Science and Technology and Office

of Research and Development, Washington, DC. (2000 draft).
National Sediment Quality Survey

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———. 2000h. Methods for measuring the toxicity and bioaccumulation of sediment-associated

contaminants with freshwater invertebrates. EPA-600-R-99-064. U.S. Environmental Protection

Agency, Office of Water, Office of Research and Development, Washington, DC.

———. 2000i. Guidance for assessing chemical contaminant data for use in fish advisories, Volume 1:

Fish sampling and analysis. EPA-823-B-00-007. U.S. Environmental Protection Agency, Office of

Water, Office of Research and Development, Washington, DC.

USEPA (U.S. Environmental Agency) and USACE (U.S. Army Corps of Engineers). 1998. Evaluation of

dredged material proposed for discharge in waters of the U.S.—Testing manual. EPA-823-B-98-004.

U.S. Environmental Protection Agency, Office of Water, and U.S. Army Corps of Engineers,

Washington, DC.
National Sediment Quality Survey

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National Sediment Quality Survey

C-1

APPENDIX C

VALUES USED FOR CHEMICALS EVALUATED

Sediment Values

Table C-1 (at the end of this appendix) presents the values used in the evaluation of NSI sediment

chemistry data.

Fish Tissue Concentrations

Fish tissue concentrations are presented in Table C-1. EPA risk levels were calculated for a standard

human health cancer risk of 10-5 and a noncancer hazard quotient of 1 and for an elevated cancer risk

level of 10-4 and a noncancer hazard quotient of 10 (USEPA, 1995a; 1995b). Other available EPA sources

were consulted as necessary to obtain risk-based concentrations for use in the screening analysis,

including the Environmental Criteria and Assessment Office (as cited in USEPA, 1995c). FDA tolerance/

action/guidance levels were obtained from the FDA Office of Seafood (Title 40 of the Code of Federal

Regulations, Sections 180.213a and 180.142; DHHS, 1994; USFDA, 1993a, 1993b, 1993c, 1993d,

1993e).

Biota-Sediment Accumulation Factors

The final column in Table C-1 presents the biota-sediment accumulation factors (BSAFs) used in the

analysis. The BSAFs were used in calculating the theoretical bioaccumulation potential (TBP), which

represents the potential concentrations that might occur in tissues of fish exposed to contaminated

sediments.

Methodology for Combining Chemical Data Using a Risk-Based Approach

Several screening values, as provided in the original source documents, refer to groups of chemicals. The

majority of the data included in the NSI database are for specific chemicals. To perform a screening

analysis that accommodates the way the data appear in the NSI database and provides a reasonably

conservative risk-based approach, chemical data were combined in particular cases.

Four of the chemical groups affected by this approach are polychlorinated biphenyls (PCBs); DDT and its

derivatives; polycyclic aromatic hydrocarbons; and dioxins, furans, and dioxin-like PCB compounds. The

maximum value computed from the sum of detected PCB congeners, the sum of detected PCB homologs,

or the maximum detected PCB aroclor was used for analyzing total PCBs in this report. If congener,

homolog or aroclor data were not available, the total PCBs reported in the NSI database were used. If the

above calculations did not result in a detected PCB result, then the largest non-detected result was used.

This value enabled comparisons to the screening values available for total PCBs. DDT and its derivatives

were summed to derive a total DDT value. In addition, the dioxins, furans, and dioxin-like PCB

congeners and the polycyclic aromatic hydrocarbons were evaluated using the toxic equivalency factor

(TEF) approach (Van den Berg et al., 1998; Nisbet and LaGoy, 1992; USEPA, 1989). For dioxins, furans,

and dioxin-like PCB congeners this approach involves summing specific dioxin congeners and dioxinlike compounds based on their relative toxicity as compared to 2,3,7,8-tetrachlorodibenzo-p-dioxin

(TCDD), for which screening values are available. TEFs as recommended by the World Health

Organization for risk assessments for humans and other mammals were used to derive a composite TEF

(Van den Berg et al., 1998). The TEFs used for dioxins, furans, and dioxin-like PCBs in the NSI data
National Sediment Quality Survey

C-2

evaluation are presented in Table C-2. Similarly TEF, factors used for PAHs in the NSI data evaluation

are presented in Table C-3.

Because EPA typically performs risk-based screening by analyzing closely related chemicals with the

same risk-based concentrations, this methodology was applied in the NSI data evaluation. If no screening

values were available for a certain chemical but were available for a closely related chemical or group of

chemicals, the lower or more conservative screening values of the closely related chemicals were used in

analyzing the chemicals without screening values. The following chemicals and chemical groups were

affected by this methodology: benzene hexachlorides (BHCs), chlordanes, cresols, DDT and its

derivatives, dichlorobenzenes, endosulfans, methylmercury, nonachlor, anthracene and phenanthrene,

benzo(a)anthracene/ chrysene, xylenes, and PCBs.

Frequency of Detection

The frequency at which a given chemical or chemical group is responsible for sites in the NSI being

categorized as Tier 1 or Tier 2 is often a reflection of the number of times that chemical is measured and

detected in sediment samples. Thus, chemicals that are measured and detected less frequently might be

identified less frequently as posing a potential risk to aquatic life or human health, even though the

chemicals are highly toxic. Table C-4 lists the number of stations at which each chemical included in the

NSI data evaluation was measured and detected (i.e., a positive result) in sediment and fish tissue. Table

C-5 presents the number of detected sediment observations in watersheds containing areas of probable

concern (APCs).

References

Cook, P.M. 1995. Pelagic BSAFs for NSI methodology. Memorandum from P.M. Cook, Environmental

Research Laboratory Duluth, to C. Fox, U.S. Environmental Protection Agency, Office of Water,

March 29, 1995.

DHHS (Department of Health and Human Services). 1994. Action levels for poisonous or deleterious

substances in human food and animal feed. Department of Health and Human Services, Public Health

Service, U.S. Food and Drug Administration, Washington, DC.

Hansen, D.J. 1995. Assessment tools that can be used for the National Sediment Inventory. Memorandum

from D.J. Hansen, Environmental Research Laboratory, Narragansett, to C. Fox, USEPA Office of

Water, February 28, 1995.

Nisbet, I.C.T., and P.K. LaGoy. 1992. Toxic equivalency factors (TEFs) for polycyclic aromatic

hydrocarbons (PAHs). Reg. Toxicol. Pharmacol. 16:290-300.

USEPA (U.S. Environmental Protection Agency). 1989. Interim procedures for estimating risks

associated with exposures to mixtures of chlorinated dibenzo-p-dioxins and dibenzofurans (CDDs

and CDFs) and 1989 update. EPA/625/3-89/016 U.S. Environmental Protection Agency, Risk

Assessment Forum, Washington, DC.

———. 1995a. Integrated Risk Information System (IRIS). Online. U.S. Environmental Protection

Agency, Office of Health and Environmental Assessment, Environmental Criteria and Assessment

Office, Cincinnati, OH.

———. 1995b. Health effects assessment summary tables FY 1995. EPA/540/R-95/036. U.S.

Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington,

DC.
National Sediment Quality Survey

C-3

———. 1995c. Risk-based concentration table, January–June l995. U.S. Environmental Protection

Agency, Region 3.

USFDA (U.S. Food and Drug Administration). 1993a. Guidance document for arsenic in shellfish. U.S.

Food and Drug Administration, Center for Food Safety and Applied Nutrition, Washington, DC.

———. 1993b. Guidance document for cadmium in shellfish. U.S. Food and Drug Administration,

Center for Food Safety and Applied Nutrition, Washington, DC.

———. 1993c. Guidance document for chromium in shellfish. U.S. Food and Drug Administration,

Center for Food Safety and Applied Nutrition, Washington, DC.

———. 1993d. Guidance document for lead in shellfish. U.S. Food and Drug Administration, Center for

Food Safety and Applied Nutrition, Washington, DC.

———. 1993e. Guidance document for mercury in shellfish. U.S. Food and Drug Administration, Center

for Food Safety and Applied Nutrition, Washington, DC.

Van den Berg, M.V., L. Birnbaum, A.T.C. Bosveld, B. Brunström, P. Cook, M. Feeley, J.P. Giesy, A.

Hanberg, R. Hasegawa, S.W. Kennedy, T. Kubiak, J.C. Larsen, F.X.R. van Leeuwen, A.K.D. Liem,

C. Nolt, R.E. Peterson, L. Poellinger, S. Safe, D. Schrenk, D. Tillitt, M. Tysklind, M. Younes, F.

Wœrn, and T. Zacharewski. 1998. Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for

humans and wildlife. Environ. Health Perspect. 106(12):775-792. 
National Sediment Quality Survey

C-4

Table C-1. Screening Values for Chemicals Evaluated.

GUIDANCE VALUES INTENDED ONLY FOR SCREENING-LEVEL HAZARD COMPARISON AMONG CHEMICALS

May Be Over- or Under-protective of Sediment at a Given Location Depending on Site-specific Conditions

CAS

Number Chemical Name

Code

Sediment Value Fish Tissue Concentration (ppm)

BSAF (Unitless)

ESG 

(:g/goc)

Logistic

Regression

Model 

Coc, PAH (:g/goc)

Concen. = EPARisk 10-5

EPA Noncancer Hazard Quotient = 1

Concen. = EPARisk 10-4

EPA NoncancerHazard Quotient = 10FDA Tolerance/Action/Guidance Level

Tier 1a Tier 2b T50 c

(ppm) T25 c(ppm) Tier 1d Tier 2e Maxf

83329 Acenaphthene 1 0.27 0.04 2,043 491 33,400 0.29g

208968 Acenaphthylene 1 0.42 0.04 1,880 452 24,000

67641 Acetone 1 409 4,090 1h

98862 Acetophenone 1 409 4,090

107028 Acrolein 1 81.7 817

107131 Acrylonitrile 1 0.0743 4.09 0.743 40.9 1h

15972608 Alachlor/Lasso 1 0.483 40.9 4.83 409

116063 Aldicarb/Temik 4.09 40.9

309002 Aldrini 1,3 0.00234 0.119 0.0234 1.19 0.3 1.8g

62533 Aniline 7.06 70.6

120127 Anthracene 1 0.8 0.08 2,471 594 1,300 0.29g

Anthracene and Phenanthrene 1 1,190 11,900 0.29g

7440360 Antimony 4.41 1.09 1.6 16

7440382 Arsenic 2 32.61 11.29 0.026 1.2 0.26 12 68

1912249 Atrazine 0.182 141 1.82 1,410

7440393 Barium 279 2,790

71432 Benzene 1 100 5.7 1.37 13.7 1h

92875 Benzidine 0.000175 11.9 0.00175 119

56553 Benzo(a)anthracenei 1 1.21 0.14 3,499 841 4,153 0.29g

Benzo(a)anthracene/Chrysenei 1 0.0557 0.557 0.29g

50328 Benzo(a)pyrenei 1 1.34 0.16 4,014 965 3,840 0.29g

205992 Benzo(b)fluoranthenei 1 3.03 0.32 4,073 979 2,169 0.29g

191242 Benzo(g,h,i)perylenei 1 1.28 0.15

207089 Benzo(k)fluoranthenei 1 1.4 0.16 4,081 981 1,220 0.29g

65850 Benzoic acid 16,000 160,000

Benzoquinone-p 1

98077 Benzotrichloride 1 0.00308 0.0308

100516 Benzyl alcohol 1,190 11,900

100447 Benzyl chloride 1 0.234 2.34

7440417 Beryllium 0.00929 20.1 0.0929 201

319846 BHC, alpha- 1,3 0.00631 0.0631 0.3 1.8g

319857 BHC, beta- 1,3 0.0223 0.223 0.3 1.8g

319868 BHC, delta- 1,3 230 13 0.0223 0.223 0.3 1.8g

58899 BHC, gamma- (Lindane) 1,3 8.8 0.37 0.0307 1.2 0.307 12 0.3 1.8g

608731 BHC, technical grade 1,3 0.0223 1.19 0.223 11.9 0.3 1.8g

92524 Biphenyl 1 850 110 0.14 0.03 201 2,010 0.29g

111444 Bis(2-chloroethyl)ether 1 0.0364 0.364

108601 Bis(2-chloroisopropyl)ether 1 0.557 160 5.57 1,600

117817 Bis(2-ethylhexyl)phthalatei 1 2.86 81.7 28.6 817 1h

542881 Bis(chloromethyl)ether 0.000182 0.00182

7440428 Boron 360 3,600
National Sediment Quality Survey

C-5

Table C-1. (Continued)

GUIDANCE VALUES INTENDED ONLY FOR SCREENING-LEVEL HAZARD COMPARISON AMONG CHEMICALS

May Be Over- or Under-protective of Sediment at a Given Location Depending on Site-specific Conditions

CAS

Number Chemical Name

Code

Sediment Value Fish Tissue Concentration (ppm)

BSAF (Unitless)

ESG 

(:g/goc)

Logistic

Regression 

Model 

Coc, PAH (:g/goc)

Concen. = EPARisk 10-5EPA Noncancer Hazard Quotient = 1Concen. = EPARisk 10-4EPA NoncancerHazard Quotient = 10FDA Tolerance/Action/Guidance Level

Tier 1a Tier 2b T50 c

(ppm) T25 c(ppm) Tier 1d Tier 2e Maxf

75274 Bromodichloromethane 1 0.631 81.7 6.31 817

74839 Bromomethane 1 5.57 55.7

101553 Bromophenyl phenyl ether, 4- 1 2,300 130 230 2,300 1h

1689845 Bromoxynil 81.7 817

85687 Butyl benzyl phthalate 1 15,000 1,100 817 8,170 1h

7440439 Cadmium 2 2.49 0.65 4 40 3

63252 Carbaryl/Sevin 409 4,090

1563662 Carbofuran/furadan 20.1 201

75150 Carbon disulfide 409 4,090

133904 Chloramben 59.4 594

57749 Chlordanei 1,3 0.114 2 1.14 20 0.3 4.77j

Chlordane (Nonachlor)i 1,3 0.114 2 1.14 20 0.3 4.77j

Chlordane (cis-Nonachlor)i 1,3 0.114 2 1.14 20 0.3 4.77j

3734494 Chlordane (trans-Nonachlor)i 1,3 0.114 2 1.14 20 0.3 4.77j

5103719 Chlordane, alpha (cis)-i 1,3 0.114 2 1.14 20 0.3 4.77j

5103742 Chlordane, beta (trans)-i 1,3 0.114 2 1.14 20 0.3 2j

Chlordane, cisi 1,3 0.114 2 1.14 20 0.3 4.77j

5566347 Chlordane, gamma (trans)-i 1,3 0.114 2 1.14 20 0.3 2.22j

Chlordane, transi 1,3 0.114 2 1.14 20 0.3 2.22j

108907 Chlorobenzene 1 1,500 82 81.7 817 1h

510156 Chlorobenzilate 0.149 81.7 1.49 817

75003 Chloroethane 1 1,600 16,000

75014 Chloroethene 1 0.0212 0.212

110758 Chloroethylvinyl ether, 2- 1 100 1,000

74873 Chloromethane 1 3.08 30.8

91587 Chloronaphthalene, 2- 1 319 3,190

95578 Chlorophenol, 2- 20.1 201

2921882 Chlorpyrifos/Dursban 1 1.2 12 1.8g

7440473 Chromium 2 233.27 76.00 20.1 201 11

218019 Chrysenei 1 1.73 0.19 3,511 844 826 0.29g

7440508 Copper 157.13 49.98 149 1,490

108394 Cresol, m- 201 2,010

95487 Cresol, o- 201 2,010

106445 Cresol, p- 20.1 201

1319773 Cresols 20.1 201

98828 Cumene 1 160 1,600

21725462 Cyanazine 0.0483 8.17 0.483 81.7

57125 Cyanide 81.7 817

1861321 DCPA/Dacthal 1 40.9 409 1.8g

72548 DDD, p, p'-i 1,3 0.05 0.01 5 0.28j

72559 DDE, p, p'-i 1,3 0.54 0.01 5 7.7j

DDT (Total)i 1,3 0.117 2 1.17 20 5 7.7j
National Sediment Quality Survey

Table C-1. (Continued)

GUIDANCE VALUES INTENDED ONLY FOR SCREENING-LEVEL HAZARD COMPARISON AMONG CHEMICALS

May Be Over- or Under-protective of Sediment at a Given Location Depending on Site-specific Conditions

CAS

Number Chemical Name

Code

Sediment Value Fish Tissue Concentration (ppm)

BSAF (Unitless)

ESG 

(:g/goc)

Logistic

Regression 

Model 

Coc, PAH (:g/goc)

Concen. = EPARisk 10-5EPA Noncancer Hazard Quotient = 1Concen. = EPARisk 10-4EPA NoncancerHazard Quotient = 10FDA Tolerance/Action/Guidance Level

Tier 1a Tier 2b T50 c

(ppm) T25 c(ppm) Tier 1d Tier 2e Maxf

C-6

50293 DDT, p, p'-i 1,3 0.03 0.004 5 1.67j

1163195 Decabromodiphenyl oxide 1 40.9 409

84742 Di-n-butyl phthalate 1 8,000 1,100 409 4,090 1h

117840 Di-n-octyl phthalatei 1 81.7 817 1h

333415 Diazinon/Spectracide 1 0.73 0.19 2.8 28 1.8g

53703 Dibenzo(a,h)anthracenei 1 0.26 0.04 0.29g

132649 Dibenzofuran 1 3,700 200 16 160 1h

96128 Dibromo-3-chloropropane, 1,2- 1 0.0286 0.286

124481 Dibromochloromethane 1 0.483 81.7 4.83 817 1

1918009 Dicamba 119 1,190

95501 Dichlorobenzene, 1,2- 1 610 34 360 3,600 1h

541731 Dichlorobenzene, 1,3- 1 1,500 170 357 3,570 1h

106467 Dichlorobenzene, 1,4- 1 420 35 1.67 16.7 1h

25321226 Dichlorobenzenes 1 1.67 357 16.7 3,570 1

91941 Dichlorobenzidine, 3,3'- 0.0891 0.891

75718 Dichlorodifluoromethane 1 817 8,170

75343 Dichloroethane, 1,1- 1 409 4,090 1h

107062 Dichloroethane, 1,2- 1 0.446 4.46 1h

75354 Dichloroethene, 1,1- 1 0.0669 36 0.669 360

156605 Dichloroethene, trans-1,2- 1 81.7 817 1h

156592 Dichloroethylene, cis-1,2- 1 40.9 409

75092 Dichloromethane 1 5.2 241 52 2,410 1h

120832 Dichlorophenol, 2,4- 11.9 119

94757 Dichlorophenoxyacetic acid, 2,4- 5 40.9 409 1

94826 Dichlorophenoxybutanoic acid, 2,4- 31.9 319

78875 Dichloropropane, 1,2- 1 0.594 5.94 1h

542756 Dichloropropene, 1,3- 1 0.23 1.19 2.3 11.9

62737 Dichlorvos 1 0.137 2.01 1.37 20.1

115322 Dicofol/Kelthanei 2.5 1.6 25 16

60571 Dieldrin 1,3 55 13 0.01 0.001 0.0025 0.2 0.025 2 0.3 1.8g

84662 Diethyl phthalate 1 1,100 63 3,190 31,900 1h

119904 Dimethoxybenzidine, 3,3'- 2.86 28.6

131113 Dimethyl phthalate 1 40,900 409,000 1

581420 Dimethylnaphthalene, 2,6- 0.29 0.05

105679 Dimethylphenol, 2,4- 81.7 817

528290 Dinitrobenzene, 1,2- 1.6 16

99650 Dinitrobenzene, 1,3- 0.409 4.09

100254 Dinitrobenzene, 1,4- 1.6 16

51285 Dinitrophenol, 2,4- 8.17 81.7

121142 Dinitrotoluene, 2,4- 8.17 81.7

606202 Dinitrotoluene, 2,6- 4.09 40.9

88857 Dinoseb/DNBP 4.09 40.9
National Sediment Quality Survey

Table C-1. (Continued)

GUIDANCE VALUES INTENDED ONLY FOR SCREENING-LEVEL HAZARD COMPARISON AMONG CHEMICALS

May Be Over- or Under-protective of Sediment at a Given Location Depending on Site-specific Conditions

CAS

Number Chemical Name

Code

Sediment Value Fish Tissue Concentration (ppm)

BSAF (Unitless)

ESG 

(:g/goc)

Logistic

Regression 

Model 

Coc, PAH (:g/goc)

Concen. = EPARisk 10-5EPA Noncancer Hazard Quotient = 1Concen. = EPARisk 10-4EPA NoncancerHazard Quotient = 10FDA Tolerance/Action/Guidance Level

Tier 1a Tier 2b T50 c

(ppm) T25 c(ppm) Tier 1d Tier 2e Maxf

C-7

122667 Diphenylhydrazine, 1,2- 0.0483 0.483

298044 Disulfoton 1 0.16 1.6

115297 Endosulfan mixed isomers 1 1.4 0.54 24 240 1.8g

959988 Endosulfan, alpha- 1 0.74 0.29 24.1 241 1.8g

33213659 Endosulfan, beta- 1 3.5 1.4 24.1 241 1.8g

72208 Endrin 1 17 5.5 1.2 12 1.8g

563122 Ethion/Bladen 1 2 20 1.8g

141786 Ethyl acetate 1 3,600 36,000

100414 Ethylbenzene 1 8,500 480 409 4,090 1h

106934 Ethylene dibromide 1 0.000483 0.00483

206440 Fluoranthene 1 2.86 0.29 2,941 707 23,870 0.29g

86737 Fluorene 1 0.26 0.04 2,238 538 26,000 0.29g

944229 Fonofos 1 8.17 81.7

76448 Heptachlori 1,3 0.00891 2.01 0.0891 20.1 0.3 1.8g

1024573 Heptachlor epoxide 1,3 0.00439 0.052 0.0439 0.52 0.3 1.8g

118741 Hexachlorobenzenei 1 0.025 3.2 0.25 32 0.09j

87683 Hexachlorobutadiene 1 0.52 0.817 5.2 8.17 1h

74474 Hexachlorocyclopentadiene 1 27.9 279

67721 Hexachloroethane 1 1,800 100 2.86 4.09 28.6 40.9 1h

51235042 Hexazinone 1 134 1,340

123319 Hydroquinone 160 1,600

193395 Indeno(1,2,3-cd)pyrenei 1 1.23 0.16 0.29g

78591 Isophorone 1 40.9 817 409 8,170 1h

33820530 Isopropalini 59.4 594

7439921 Lead 2 161.06 47.82 1.3

121755 Malathion 1 0.62 0.067 81.7 817 1.8g

108316 Maleic anhydride 409 4,090

7439965 Manganese 20.1 201

7439976 Mercury 0.87 0.23 0.4 4 1

72435 Methoxychlor 1 9.5 1.9 20.1 201 1.8g

78933 Methyl ethyl ketone 1 2,410 24,100 1h

108101 Methyl isobutyl ketone 1 319 3,190

22967926 Methyl mercury 3 0.4 4 1

90120 Methylnaphthalene, 1- 0.19 0.04

91576 Methylnaphthalene, 2- 1 0.30 0.05

832699 Methylphenanthrene, 1- 0.27 0.04

21087649 Metribuzin 100 1,000

2385855 Mirex/Dechloranei 1,3 0.8 8 0.1 1.31j

7439987 Molybdenum 20.1 201

91203 Naphthalene 1 0.55 0.07 1,602 385 61,700 160 1,600 0.29g

91598 Naphthylamine, 2- 0.000308 0.00308

7440020 Nickel 2 80.07 23.77 81.7 817 70
National Sediment Quality Survey

Table C-1. (Continued)

GUIDANCE VALUES INTENDED ONLY FOR SCREENING-LEVEL HAZARD COMPARISON AMONG CHEMICALS

May Be Over- or Under-protective of Sediment at a Given Location Depending on Site-specific Conditions

CAS

Number Chemical Name

Code

Sediment Value Fish Tissue Concentration (ppm)

BSAF (Unitless)

ESG 

(:g/goc)

Logistic

Regression 

Model 

Coc, PAH (:g/goc)

Concen. = EPARisk 10-5EPA Noncancer Hazard Quotient = 1Concen. = EPARisk 10-4EPA NoncancerHazard Quotient = 10FDA Tolerance/Action/Guidance Level

Tier 1a Tier 2b T50 c

(ppm) T25 c(ppm) Tier 1d Tier 2e Maxf

C-8

98953 Nitrobenzene 2.01 20.1

100027 Nitrophenol, 4- 249 2,490

55185 Nitrosodiethylamine, N- 0.00078 0.0078

924163 Nitrosodi-n-butylamine, N- 0.00743 0.0743

621647 Nitrosodi-n-propylamine, N- 0.00557 0.0557

86306 Nitrosodiphenylamine, N- 8.17 81.7

42874033 Oxyfluorfen 0.546 12 5.46 120

56382 Parathion, ethyl- 24.1 241

608935 Pentachlorobenzene 1 1,200 69 3.19 31.9 0.04j

82688 Pentachloronitrobenzene/Quintozene 0.152 11.9 1.52 119

87865 Pentachlorophenol 0.334 119 3.34 1,190

198550 Perylene 1.06 0.16

85018 Phenanthrene 1 1.12 0.15 2,479 596 34,300

108952 Phenol 2,410 24,100

298022 Phorate/Famophos/Thimet 1 0.817 8.17

85449 Phthalic anhydride 8,170 81,700

1336363 Polychlorinated biphenylsi 1,4 1.12 0.09 0.02 0.08 0.2 0.8 2 1.85j

1610180 Prometon/Pramitol 59.4 594

7287196 Prometym/Caparol 16 160

23950585 Pronamide 301 3,010

1918167 Propachlor 52 520

129000 Pyrene 1 2.41 0.29 2,900 697 9,090 0.29g

91225 Quinoline 1 0.00334 0.0334

7782492 Selenium 20 200

7440224 Silver 2.45 0.44 20.1 201

122349 Simazine 5 0.334 20.1 3.34 201 12

7440246 Strontium 2,410 24,100

100425 Styrene 1 817 8,170

TEF Dioxins, Furans, and Dioxin-like

PCBsi

1 2.56E-07 2.56E-06 0.025j

TEF PAHs 0.00547 0.0547 0.29g

13071799 Terbufos/Counter 1 0.08 0.8

886500 Terbutryn 4.09 40.9

95943 Tetrachlorobenzene, 1,2,4,5- 1 1.19 11.9 1h

79345 Tetrachloroethane, 1,1,2,2- 1 830 160 0.201 2.01 1h

127184 Tetrachloroethene 1 420 53 0.78 40.9 7.8 409 1h

56235 Tetrachloromethane 1 2,100 120 0.308 2.79 3.08 27.9 1h

58902 Tetrachlorophenol, 2,3,4,6- 119 1,190

961115 Tetrachlorvinphos/Gardona/Stirofos 1 1.67 119 16.7 1,190

7440315 Tin 2,410 24,100

108883 Toluene 1 1,600 89 817 8,170 1h

8001352 Toxaphenei 1 490 10 0.0363 1 0.363 10 1.8g
National Sediment Quality Survey

Table C-1. (Continued)

GUIDANCE VALUES INTENDED ONLY FOR SCREENING-LEVEL HAZARD COMPARISON AMONG CHEMICALS

May Be Over- or Under-protective of Sediment at a Given Location Depending on Site-specific Conditions

CAS

Number Chemical Name

Code

Sediment Value Fish Tissue Concentration (ppm)

BSAF (Unitless)

ESG 

(:g/goc)

Logistic

Regression 

Model 

Coc, PAH (:g/goc)

Concen. = EPARisk 10-5EPA Noncancer Hazard Quotient = 1Concen. = EPARisk 10-4EPA NoncancerHazard Quotient = 10FDA Tolerance/Action/Guidance Level

Tier 1a Tier 2b T50 c

(ppm) T25 c(ppm) Tier 1d Tier 2e Maxf

C-9

75252 Tribromomethane/Bromoform 1 460 65 5.2 81.7 52 817 1h

688733 Tributyltin 1.2 12

120821 Trichlorobenzene, 1,2,4- 1 6,100 920 40.9 409 1h

71556 Trichloroethane, 1,1,1- 1 170 17 360 3,600 1h

79005 Trichloroethane, 1,1,2- 1 0.706 16 7.06 160 1h

79016 Trichloroethene 1 2,000 210 3.64 24.1 36.4 241 1h

75694 Trichlorofluoromethane 1 1,190 11,900 1h

67663 Trichloromethane/Chloroform 1 6.69 40.9 66.9 409 1h

95954 Trichlorophenol, 2,4,5- 409 4,090

88062 Trichlorophenol, 2,4,6- 3.64 36.4

93765 Trichlorophenoxyacetic acid, 2,4,5- 40.9 409

93721 Trichlorophenoxypropionic acid, 2,4,5- 31.9 319

1582098 Trifluralin/Treflani 5.2 30.1 52 301

95636 Trimethylbenzene, 1,2,4- 1 2.01 20.1

118967 Trinitrotoluene 1.34 2.01 13.4 20.1

7440622 Vanadium 27.9 279

108054 Vinyl acetate 1 4,090 40,900

108383 Xylene, m- 1 45 2.5 8,170 81,700 1h

95476 Xylene, o- 1 8,170 81,700 1h

106423 Xylene, p- 1 1

1330207 Xylenes (Total) 1 8,170 81,700 1h

7440666 Zinc 383.81 140.48 1,190 11,900

Codes:

1. Chemical is a nonpolar organic.

2. FDA criterion is a guideline.

3. FDA criterion is an action level.

4. FDA criterion is a tolerance level, with the force of law.

5. Fish tissue action level set by USEPA, 40 CFR Part 180.

6. Preliminary ESG developed for this chemical is under technical review.

 

a

 Derived from chemical-specific log Kow, log Koc, and FAV or SAV as indicated in Table B-1. b

 Derived from chemical-specific log Kow, log Koc, and FCV or SCV as indicated in Table B-1. c

 Tp = Effect concentration that would give a response of “p” percent according to the logistic regression model. 

d

 Derived from chemical-specific log Kow, log Koc and PAH-specific FAV as indicated in Table B-2. e

 Derived from chemical-specific log Kow, log Koc and PAH-specific FCV as indicated in Table B-2. f

 When the organic carbon normalized sediment concentration (Coc,PAHi) is greater than Coc,PAH,MAX, use Coc,PAH,MAX in place of Coc, PAHi. g

 Hansen, 1995 (BSAF source).

h

 Default value of 1.

i

 Chemicals with log Kow$ 5.5. j

 Cook, 1995 (BSAF source).
National Sediment Quality Survey

C-10

Table C-2. Toxic Equivalency Factors for Dioxins,

Furans and Dioxin-Like PCBs.a

Chemical Name TEF 

1,2,3,4,7,8-HexaCDD 0.1

1,2,3,6,7,8-HexaCDD 0.1

1,2,3,7,8,9-HexaCDD 0.1

1,2,3,4,7,8-HexaCDF 0.1

1,2,3,6,7,8-HexaCDF 0.1

1,2,3,7,8,9-HexaCDF 0.1

2,3,4,6,7,8-HexaCDF 0.1

1,2,3,4,6,7,8-HeptaCDD 0.01

1,2,3,4,6,7,8-HeptaCDF 0.01

1,2,3,4,7,8,9-HeptaCDF 0.01

1,2,3,4,6,7,8,9-OctaCDD 0.0001

1,2,3,4,6,7,8,9-OctaCDF 0.0001

1,2,3,7,8-PentaCDD 1

1,2,3,7,8-PentaCDF 0.05

2,3,4,7,8-PentaCDF 0.5

2,3,7,8-TetraCDD (dioxin) 1

2,3,7,8-TetraCDF 0.1

3,3',4,4'-TetraCB (77) 0.0001

3,4,4',5-TetraCB (81) 0.0001

2,3,3',4,4'-PentaCB (105) 0.0001

2,3,4,4',5-PentaCB (114) 0.0005

2,3',4,4',5-PentaCB (118) 0.0001

2',3,4,4',5-PentaCB (123) 0.0001

3,3',4,4',5-PentaCB (126) 0.1

2,3,3',4,4',5-HexaCB (156) 0.0005

2,3,3',4,4',5'-HexaCB (157) 0.0005

2,3',4,4',5,5'-HexaCB (167) 0.00001

3,3',4,4',5,5'-HexaCB (169) 0.01

2,3,3',4,4',5,5'-HeptaCB (189) 0.0001

a

 Source: Van den Berg et al., 1998. 
National Sediment Quality Survey

C-11

Table C-3. Toxicity Equivalency Factors for

Various PAHs.a

Compound TEF 

Acenaphthene 0.001

Acenaphthylene 0.001

Anthracene 0.01

Benzo(a)anthracene 0.1

Benzo(a)pyrene 1

Benzo(b)fluoranthene 0.1

Benzo(g,h,i)perylene 0.01

Benzo(k)fluoranthene 0.1

Chrysene 0.01

Dibenz(a,h)anthracene 5

Fluoranthene 0.001

Fluorene 0.001

Indeno(1,2,3-cd)pyrene 0.1

Phenanthrene 0.001

Pyrene 0.001

a

 Source: Nisbet and LaGoy, 1992.
National Sediment Quality Survey

C-12

Table C-4. Frequency of Detection of Chemicals in Sediment and Tissue Residue.a

CAS

Number Chemical Name

Number of Stations

Where Chemical

Was Measured in

Sediment

Number of Stations

Where Chemical

Was Detected in

Sediment

Number of Stations

Where Chemical

Was Measured in

Tissue Residue

Number of Stations

Where Chemical

Was Detected in

Tissue Residue

83329 Acenaphthene 8,259 2,864

208968 Acenaphthylene 7,879 2,298

67641 Acetone 545 197

107028 Acrolein 5

107131 Acrylonitrile 333 37

15972608 Alachlor/Lasso 3

309002 Aldrin 9,624 612 1,324 30

120127 Anthracene 8,062 4,332

Anthracene and Phenanthrene 44 16

7440360 Antimony 5,784 3,746 436 17

7440382 Arsenic 12,526 10,922 804 515

1912249 Atrazine 16

7440393 Barium 464 353

71432 Benzene 1,110 16 63 2

92875 Benzidine 45

56553 Benzo(a)anthracene 7,892 5,347

Benzo(a)anthracene/Chrysene 290 238 4

50328 Benzo(a)pyrene 7,115 4,943

205992 Benzo(b)fluoranthene 6,192 4,273

191242 Benzo(g,h,i)perylene 8,098 4,849

207089 Benzo(k)fluoranthene 6,179 3,981

65850 Benzoic acid 24

100516 Benzyl alcohol 27

7440417 Beryllium 465 4

319846 BHC, alpha- 6,234 309 1,197 24

319857 BHC, beta- 5,070 255 799 14

319868 BHC, delta- 3,394 217 710 2

58899 BHC, gamma- (Lindane) 8,419 684 1,255 57

608731 BHC, technical grade 1,530 707

92524 Biphenyl 2,382 1,656 30 21

111444 Bis(2-chloroethyl)ether 94

108601 Bis(2-chloroisopropyl)ether 93

117817 Bis(2-ethylhexyl)phthalate 4,339 2,609 97 7

7440428 Boron 375 364

75274 Bromodichloromethane 58

74839 Bromomethane 24

101553 Bromophenyl phenyl ether, 4- 3,458 16 104

85687 Butyl benzyl phthalate 4,814 1,280 99

7440439 Cadmium 13,621 8,948 940 592

57749 Chlordane 6,619 1,688 728 332

Chlordane (Nonachlor) 42 150 4

Chlordane (cis-Nonachlor) 2,781 554 864 159

3734494 Chlordane (trans-Nonachlor) 3,628 1,153 923 327

5103719 Chlordane, alpha (cis)- 3,244 862 42 33

Chlordane, cis- 2,789 650 856 247

5566347 Chlordane, gamma (trans)- 2,330 483 19 14

Chlordane, trans- 2,886 645 857 168

108907 Chlorobenzene 1,037 29 60
National Sediment Quality Survey

C-13

Table C-4. (Continued) 

CAS

Number Chemical Name

Number of Stations

Where Chemical

Was Measured in

Sediment

Number of Stations

Where Chemical

Was Detected in

Sediment

Number of Stations

Where Chemical

Was Measured in

Tissue Residue

Number of Stations

Where Chemical

Was Detected in

Tissue Residue

75003 Chloroethane 54 1

75014 Chloroethene 60

110758 Chloroethylvinyl ether, 2- 47

74873 Chloromethane 24

91587 Chloronaphthalene, 2- 111

95578 Chlorophenol, 2- 91

2921882 Chlorpyrifos/Dursban 1,072 248 128 8

7440473 Chromium 12,509 12,246 889 596

218019 Chrysene 8,165 5,921

7440508 Copper 13,896 13,464 842 800

95487 Cresol, o- 10

106445 Cresol, p- 10

57125 Cyanide 21 1

1861321 DCPA/Dacthal 1,175 81 492 31

72548 DDD, p, p'- 7,685 2,479 907 279

72559 DDE, p, p'- 7,907 2,837 949 597

DDT (Total) 11,000 5,099 1,529 1,018

50293 DDT, p, p'- 7,757 1,838 948 204

84742 Di-n-butyl phthalate 4,576 1,218 105 5

117840 Di-n-octyl phthalate 5,014 424 86 1

333415 Diazinon/Spectracide 1,156 95 130 2

53703 Dibenzo(a,h)anthracene 8,149 3,121

132649 Dibenzofuran 3,584 1,174 46 12

96128 Dibromo-3-chloropropane, 1,2- 16

124481 Dibromochloromethane 899 57

95501 Dichlorobenzene, 1,2- 4,972 127 171 7

541731 Dichlorobenzene, 1,3- 4,871 134 171 1

106467 Dichlorobenzene, 1,4- 4,931 249 95 1

91941 Dichlorobenzidine, 3,3'- 82

75718 Dichlorodifluoromethane 16

75343 Dichloroethane, 1,1- 1,082 1 57

107062 Dichloroethane, 1,2- 1,076 3 60

156605 Dichloroethene, trans-1,2- 667 58

75354 Dichloroethene, 1,1- 60 2

75092 Dichloromethane 936 120 55 25

120832 Dichlorophenol, 2,4- 95

78875 Dichloropropane, 1,2- 1,082 58

542756 Dichloropropene, 1,3- 8

115322 Dicofol/Kelthane 7

60571 Dieldrin 9,600 2,158 1,380 486

84662 Diethyl phthalate 4,734 310 101

131113 Dimethyl phthalate 5,201 554 115 11

581420 Dimethylnaphthalene, 2,6- 3,083 1,992

105679 Dimethylphenol, 2,4- 96

51285 Dinitrophenol, 2,4- 70

121142 Dinitrotoluene, 2,4- 95

606202 Dinitrotoluene, 2,6- 95

122667 Diphenylhydrazine, 1,2- 55

298044 Disulfoton 5

115297 Endosulfan mixed isomers 993 27 35 1
National Sediment Quality Survey

Table C-4. (Continued) 

CAS

Number Chemical Name

Number of Stations

Where Chemical

Was Measured in

Sediment

Number of Stations

Where Chemical

Was Detected in

Sediment

Number of Stations

Where Chemical

Was Measured in

Tissue Residue

Number of Stations

Where Chemical

Was Detected in

Tissue Residue

C-14

959988 Endosulfan, alpha- 3,889 82 168 7

33213659 Endosulfan, beta- 3,315 368 167 6

72208 Endrin 8,406 370 1,293 33

563122 Ethion/Bladen 805 25 9

100414 Ethylbenzene 2,118 45 60

206440 Fluoranthene 8,354 6,474

86737 Fluorene 7,262 3,364

76448 Heptachlor 9,444 566 1,328 18

1024573 Heptachlor epoxide 8,579 518 1,247 166

118741 Hexachlorobenzene 8,377 1,068 750 94

87683 Hexachlorobutadiene 4,033 150 176 73

74474 Hexachlorocyclopentadiene 91

67721 Hexachloroethane 3,472 32 169 17

193395 Indeno(1,2,3-cd)pyrene 8,058 4,788

78591 Isophorone 3,676 19 92

7439921 Lead 13,623 12,802 944 456

121755 Malathion 1,018 22 48

7439965 Manganese 443 435

7439976 Mercury 13,236 9,316 1,545 1,299

72435 Methoxychlor 4,265 191 532 2

78933 Methyl ethyl ketone 614 85

90120 Methylnaphthalene, 1- 2,613 1,673

91576 Methylnaphthalene, 2- 6,249 2,704

832699 Methylphenanthrene, 1- 3,353 1,998

2385855 Mirex/Dechlorane 4,224 320 1,052 29

7439987 Molybdenum 378 368

91203 Naphthalene 7,873 3,441 161 24

7440020 Nickel 12,404 11,680 598 340

98953 Nitrobenzene 9

100027 Nitrophenol, 4- 92

621647 Nitrosodi-n-propylamine, N- 94

86306 Nitrosodiphenylamine, N- 102

56382 Parathion, ethyl- 48 1

608935 Pentachlorobenzene 90 2 79 57

87865 Pentachlorophenol 103

198550 Perylene 2,966 2,348

85018 Phenanthrene 8,263 5,889

108952 Phenol 95 1

1336363 Polychlorinated biphenyls 12,305 5,590 1,962 1,020

129000 Pyrene 8,272 6,303

7782492 Selenium 625 555

SEM est 739 739

7440224 Silver 10,504 5,844 488 217

122349 Simazine 16

7440246 Strontium 375 370

100425 Styrene 18

TEF Dioxins, Furans, and Dioxin-like PCBs 3,814 2,662 93 80

TEF PAHs 9,422 7,244 165 31

95943 Tetrachlorobenzene, 1,2,4,5- 108 5 76 13

79345 Tetrachloroethane, 1,1,2,2- 1,059 3 60
National Sediment Quality Survey

Table C-4. (Continued) 

CAS

Number Chemical Name

Number of Stations

Where Chemical

Was Measured in

Sediment

Number of Stations

Where Chemical

Was Detected in

Sediment

Number of Stations

Where Chemical

Was Measured in

Tissue Residue

Number of Stations

Where Chemical

Was Detected in

Tissue Residue

C-15

127184 Tetrachloroethene 1,130 29

56235 Tetrachloromethane 1,083 1 59

58902 Tetrachlorophenol, 2,3,4,6- 6

7440315 Tin 59 28

108883 Toluene 1,078 96 58 1

8001352 Toxaphene 5,894 99 1,200 24

75252 Tribromomethane/Bromoform 1,080 1 58

688733 Tributyltin 16 15

120821 Trichlorobenzene, 1,2,4- 4,904 94 178 21

71556 Trichloroethane, 1,1,1- 1,073 2 58 1

79005 Trichloroethane, 1,1,2- 1,022 1 60

79016 Trichloroethene 1,480 35 3

75694 Trichlorofluoromethane 444 1 37

67663 Trichloromethane/Chloroform 1,091 23 60 1

95954 Trichlorophenol, 2,4,5- 16

88062 Trichlorophenol, 2,4,6- 98

93721 Trichlorophenoxypropionic acid, 2,4,5- 1

1582098 Trifluralin/Treflan 4 2

7440622 Vanadium 401 254

108383 Xylene, m- 20 1

95476 Xylene, o- 198 10

1330207 Xylenes (Total) 1,826 84 36

7440666 Zinc 13,210 13,034 718 718

a

 Only stations with data included in NSI evaluation were included in this assessment.
National Sediment Quality Survey

C-16

Table C-5. Number of Detected Sediment Observations in Watersheds Containing APCs.

Chemical

Lower Connecticut (01080205)Charles (01090001)

Narragansett (01090004)Quinnipiac (01100004)Housatonic (01100005)

Saugatuck (01100006)Long Island Sound (01100007)

Hudson-Hoosic (02020003)

Mohawk (02020004)

Middle Hudson (02020006)Hudson-Wappinger (02020008)

Lower Hudson (02030101)Bronx (02030102)Hackensack-Passaic (02030103)Sandy Hook-Staten Island (02030104)Raritan (02030105)

Acenaphthene 10 2 6 8 8 17 18 4 8 7 18 28 25 43 85 2

Acenaphthylene 16 4 3 9 15 18 16 8 12 10 12 25 25 44 86 4

Acetone 42 1

Aldrin 1 3 2 2 26 11 2

Anthracene 15 16 10 11 14 19 23 12 23 11 17 30 25 86 97 7

Antimony 13 6 4 8 9 17 25 16 22 18 22 31 25 22 104 5

Arsenic 19 35 5 9 21 18 25 22 38 18 22 31 25 140 132 54

Benzene 2

Benzo(a)anthracene 14 4 10 10 14 20 27 22 32 14 20 30 25 102 102 9

Benzo(a)anthracene/Chrysene 12

Benzo(a)pyrene 10 12 6 9 9 16 22 8 16 3 6 15 25 97 105 1

Benzo(b)fluoranthene 15 4 3 9 15 17 7 19 32 10 10 15 14 94 45 8

Benzo(g,h,i)perylene 11 15 8 10 15 20 28 5 28 9 16 26 25 88 93 4

Benzo(k)fluoranthene 19 4 8 10 16 18 12 19 32 12 14 20 15 92 47 9

BHC, alpha- 12 7

BHC, beta- 9 4

BHC, delta- 17 7

BHC, gamma- (Lindane) 2 1 2 13 9 12 7 46

BHC, technical grade 1 2 13 9 12 2 42

Biphenyl 3 6 6 3 14 15 3 10 18 25 16 82 1

Bis(2-ethylhexyl)phthalate 6 3 1 2 8 4 14 14 10 6 2 84 21 4

Bromophenyl phenyl ether, 4-

Butyl benzyl phthalate 4 4 2 4 3 14 14 8 10 10 29 1

Cadmium 15 56 6 11 17 18 33 31 40 29 29 34 26 124 132 13

Chlordane 3 6 16 5 4 14 21 2 5 29 25 43 98 41

Chlordane (cis-Nonachlor) 2 3 1 1 2 4 16 8 1

Chlordane (trans-Nonachlor) 8 4 3 7 2 15 16 2 2 2 4 20 23 25 94 1

Chlordane, alpha (cis)- 3 1 5 4 13 16 1 10 24 57 71

Chlordane, cis- 5 4 1 2 1 3 2 2 11 1 18 29 1

Chlordane, gamma (trans)- 54 6

Chlordane, trans- 5 4 4 3 1 4 1 4 1 6 13 1 19 12 2

Chlorobenzene 23

Chlorpyrifos/Dursban

Chromium 19 62 3 10 24 19 19 36 41 29 19 28 26 146 132 54

Chrysene 17 16 5 10 16 20 15 18 30 10 11 24 25 104 105 8

Copper 20 64 7 13 24 20 33 36 53 41 29 34 26 160 139 60

DCPA/Dacthal 13 7

DDD, p, p'- 9 4 4 6 7 15 15 6 5 10 15 24 84 114 2

DDE, p, p'- 11 3 5 9 7 18 16 6 12 13 14 25 26 90 117 2

DDT (Total) 13 16 22 10 7 18 28 6 12 17 23 46 26 145 133 52
National Sediment Quality Survey

C-17

Table C-5. (Continued)

Chemical

Lower Connecticut (01080205)Charles (01090001)

Narragansett (01090004)Quinnipiac (01100004)Housatonic (01100005)

Saugatuck (01100006)Long Island Sound (01100007)

Hudson-Hoosic (02020003)

Mohawk (02020004)

Middle Hudson (02020006)Hudson-Wappinger (02020008)

Lower Hudson (02030101)Bronx (02030102)Hackensack-Passaic (02030103)Sandy Hook-Staten Island (02030104)Raritan (02030105)

DDT, p, p'- 8 15 2 4 2 13 16 4 7 12 23 17 68 92 1

Di-n-butyl phthalate 7 3 2 5 3 12 14 10 10 12 17 1

Di-n-octyl phthalate 2 3 1 2 2 2 2 2 2 2 37 4 1

Diazinon/Spectracide 4 1

Dibenzo(a,h)anthracene 11 16 7 9 8 17 20 6 16 6 15 25 23 65 70 4

Dibenzofuran 2 4 1 1 1 4 2 3 1 15 3 1

Dichlorobenzene, 1,2- 2 1

Dichlorobenzene, 1,3-

Dichlorobenzene, 1,4- 2 1 2 2 2 16

Dichloroethane, 1,1-

Dichloroethane, 1,2-

Dichloromethane 7 10

Dieldrin 5 3 9 6 4 14 16 4 4 17 23 73 100 24

Diethyl phthalate 2 2 1 2 2 4 2

Dimethyl phthalate 1 2 3 5 2 8 11 9 51

Dimethylnapthalene, 2,6- 10 4 9 9 8 15 13 14 12 13 16 22 14 2 29 1

Endosulfan mixed isomers

Endosulfan, alpha- 1 2 2

Endosulfan, beta- 32 1

Endrin 1 2 3 2 21 16

Ethion/Bladen

Ethylbenzene 3 1

Fluoranthene 20 16 10 13 17 21 28 20 30 14 18 28 25 118 114 9

Fluorene 5 12 7 6 5 15 22 2 3 10 18 25 41 88 1

Heptachlor 1 4 3 9 1 1 3 9 3 19

Heptachlor epoxide 1 2 3 1 1 6 9 26 36 4

Hexachlorobenzene 1 6 1 5 11 10 16 18 89

Hexachlorobutadiene

Hexachloroethane

Indeno(1,2,3-cd)pyrene 13 16 8 12 17 20 27 14 28 13 19 28 25 90 97 6

Isophorone

Lead 17 65 6 13 23 20 31 36 55 40 26 32 26 155 136 45

Malathion

Mercury 20 33 8 13 22 20 32 23 49 20 28 34 26 152 137 31

Methoxychlor 1 3 5

Methyl ethyl ketone 13 2

Methylnaphthalene, 1- 3 4 6 3 13 17 3 10 18 25 19 81 1

Methylnaphthalene, 2- 4 7 7 3 14 21 3 10 17 25 32 85 1

Methylphenanthrene, 1- 8 4 4 7 8 16 12 6 12 8 13 21 20 13 48 3

Mirex/Dechlorane 1 1 2 4 3 11 28 24

Naphthalene 10 2 6 9 8 17 18 4 8 6 11 20 25 32 90
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Lower Connecticut (01080205)Charles (01090001)

Narragansett (01090004)Quinnipiac (01100004)Housatonic (01100005)

Saugatuck (01100006)Long Island Sound (01100007)

Hudson-Hoosic (02020003)

Mohawk (02020004)

Middle Hudson (02020006)Hudson-Wappinger (02020008)

Lower Hudson (02030101)Bronx (02030102)Hackensack-Passaic (02030103)Sandy Hook-Staten Island (02030104)Raritan (02030105)

C-18

Nickel 18 62 8 12 21 19 33 36 56 41 28 34 26 125 137 42

Perylene 4 12 7 8 3 14 26 4 9 18 25 20 93 1

Phenanthrene 18 16 10 13 17 20 29 18 30 14 18 29 25 103 107 9

Polychlorinated biphenyls 6 16 24 9 7 17 29 158 26 58 43 65 26 148 138 45

Pyrene 20 16 10 13 15 21 29 20 32 14 18 28 25 118 115 9

SEM est 3 2 7 3 12 12 2 4 1 12 14 11

Silver 13 7 11 11 18 30 14 22 19 27 33 25 80 121 5

TEF Dioxins, Furans, and Dioxin-like

PCBs 4 12 6 6 4 15 22 127 1 45 30 43 25 126 153 1

TEF PAHs 20 16 11 13 20 21 33 22 32 15 22 32 26 120 119 9

Tetrachloroethene

Toluene 14 2

Toxaphene 2 1

Trichlorobenzene, 1,2,4- 1 1

Trichloroethane, 1,1,1-

Trichloroethene

Trichlorofluoromethane

Trichloromethane/Chloroform

Xylene, oXylenes (Total) 7 1

Zinc 20 63 8 12 24 20 33 36 56 41 29 34 26 160 138 61
National Sediment Quality Survey

C-19

Table C-5. (Continued)

Chemical

Northern Long Island (02030201)Southern Long Island (02030202)Lower Delaware (02040202)Brandywine-Christina (02040205)Gunpowder-Patapsco (02060003)Severn (02060004)York (02080107)Cooper (03050201)South Carolina Coastal (03050202)Lower Savannah (03060109)Cumberland-St_ Simons (03070203)Tampa Bay (03100206)Middle Chattahoochee-Lake Harding (03130002)Pensacola Bay (03140105)Mobile Bay (03160205)Menominee (04030108)

Acenaphthene 56 17 4 10 16 56 6 31 25 32 10 7 10 5

Acenaphthylene 52 20 2 4 11 53 6 38 29 38 17 5 11 2

Acetone 6 7

Aldrin 6 6 4 4 3 15 8 7 3 2 19 7

Anthracene 64 29 6 15 22 65 17 54 31 46 17 14 2 17 1 9

Antimony 62 47 4 16 14 60 5 12 17 10 2 4 12 13 8 5

Arsenic 61 55 29 106 23 68 40 47 38 65 28 59 51 53 31 20

Benzene

Benzo(a)anthracene 68 38 7 15 24 66 39 57 35 47 22 22 5 29 7 1

Benzo(a)anthracene/Chrysene

Benzo(a)pyrene 60 36 4 14 20 58 35 55 35 44 20 25 24 7

Benzo(b)fluoranthene 28 2 9 3 54 7 55 38 52 24 40 6 13 7 11

Benzo(g,h,i)perylene 70 34 6 15 20 60 39 48 33 39 17 38 3 29 7 10

Benzo(k)fluoranthene 38 6 5 15 10 56 6 47 35 48 22 1 5 13 7 11

BHC, alpha- 5 2 36334 1 1

BHC, beta- 4 2 9 4 6 8 1 1

BHC, delta- 4 2 15 3 6 8 1 1

BHC, gamma- (Lindane) 9 30 2 4 4 11 11 10 21 3 44 8 1

BHC, technical grade 12 30 1 2 13 19 20 8 44 5 7

Biphenyl 54 19 2 5 16 63 6 32 22 50 18 9 1

Bis(2-ethylhexyl)phthalate 1 3 3 11 1

Bromophenyl phenyl ether, 4-

Butyl benzyl phthalate 1 1 1 3

Cadmium 78 57 8 106 23 69 23 49 42 65 19 66 18 52 31 14

Chlordane 57 50 26 15 7 4 3 14 22 8 9 54 22 6 7

Chlordane (cis-Nonachlor) 7 12 2 1 1

Chlordane (trans-Nonachlor) 45 43 1 7 36 3 6 15 12 2 49 3 10

Chlordane, alpha (cis)- 37 45 3 11 31 5 13 14 9 4 51 15

Chlordane, cis- 10 1 3 3 7

Chlordane, gamma (trans)- 1 1 6 6 1

Chlordane, trans- 732351 23

Chlorobenzene

Chlorpyrifos/Dursban 4 2

Chromium 54 53 30 122 22 65 48 156 106 78 28 73 123 57 31 14

Chrysene 47 32 3 17 16 63 40 60 37 52 24 30 5 29 7 14

Copper 78 63 35 134 25 70 50 137 97 74 27 72 123 56 31 23

DCPA/Dacthal

DDD, p, p'- 46 45 2 3 14 49 4 15 19 8 6 46 2 19 3

DDE, p, p'- 45 47 2 5 15 59 10 30 22 7 4 37 4 9 7 2

DDT (Total) 70 53 32 31 23 65 13 35 38 16 10 57 23 28 8 2
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Northern Long Island (02030201)Southern Long Island (02030202)Lower Delaware (02040202)Brandywine-Christina (02040205)Gunpowder-Patapsco (02060003)Severn (02060004)York (02080107)Cooper (03050201)South Carolina Coastal (03050202)Lower Savannah (03060109)Cumberland-St_ Simons (03070203)Tampa Bay (03100206)Middle Chattahoochee-Lake Harding (03130002)Pensacola Bay (03140105)Mobile Bay (03160205)Menominee (04030108)

C-20

DDT, p, p'- 36 28 1 5 12 30 2 15 21 9 3 37 1 17

Di-n-butyl phthalate 3 1 3 3 2

Di-n-octyl phthalate 1 1

Diazinon/Spectracide 3

Dibenzo(a,h)anthracene 59 15 3 7 19 56 10 35 30 18 6 2 20 1 2

Dibenzofuran 84344113 6

Dichlorobenzene, 1,2- 1

Dichlorobenzene, 1,3-

Dichlorobenzene, 1,4- 1

Dichloroethane, 1,1-

Dichloroethane, 1,2-

Dichloromethane

Dieldrin 42 37 22 12 11 30 12 9 5 2 2 10 18

Diethyl phthalate 3 1 1

Dimethyl phthalate 15 32 1

Dimethylnapthalene, 2,6- 48 3 5 4 16 54 1 42 25 49 18 2 1 11 3 1

Endosulfan mixed isomers

Endosulfan, alpha- 4 2

Endosulfan, beta- 4 13 5 12 1 1

Endrin 1 7 4 2 6 2 5 3 1 30 10

Ethion/Bladen 3

Ethylbenzene 2 1

Fluoranthene 69 47 8 33 24 70 43 67 40 56 27 36 7 32 7 15

Fluorene 64 21 4 14 21 62 6 42 27 50 17 10 17 1 5

Heptachlor 9 4 3 4 4 15 1 4 1 10 3 21 3 6

Heptachlor epoxide 12 18 8 3 5 9 7 5 1 21 1 7

Hexachlorobenzene 30 41 8 30 2 11 13 28 10 30 1 6

Hexachlorobutadiene

Hexachloroethane

Indeno(1,2,3-cd)pyrene 64 30 5 15 20 63 34 51 34 28 14 38 6 26 7 12

Isophorone 1

Lead 73 61 28 144 25 70 51 123 79 72 28 72 124 57 31 22

Malathion 1

Mercury 77 58 30 85 24 60 32 44 33 63 26 71 16 58 31 20

Methoxychlor 2 4 1 1 1

Methyl ethyl ketone 11 1

Methylnaphthalene, 1- 59 12 5 3 17 53 6 48 25 47 21 13 7

Methylnaphthalene, 2- 62 24 5 5 18 55 6 50 29 46 23 13 7

Methylphenanthrene, 1- 40 7 1 2 14 56 5 30 25 31 7 1 12 7 1

Mirex/Dechlorane 10 11 1 4 31 1 8 6 6 2 9
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Northern Long Island (02030201)Southern Long Island (02030202)Lower Delaware (02040202)Brandywine-Christina (02040205)Gunpowder-Patapsco (02060003)Severn (02060004)York (02080107)Cooper (03050201)South Carolina Coastal (03050202)Lower Savannah (03060109)Cumberland-St_ Simons (03070203)Tampa Bay (03100206)Middle Chattahoochee-Lake Harding (03130002)Pensacola Bay (03140105)Mobile Bay (03160205)Menominee (04030108)

C-21

Naphthalene 56 31 2 6 20 60 15 49 31 40 24 5 15 7 1

Nickel 78 61 8 124 24 70 45 117 85 68 25 66 123 56 8 5

Perylene 66 35 5 5 16 54 23 47 36 56 17 35 24 7 13

Phenanthrene 70 42 6 28 24 63 39 55 36 58 27 24 4 27 7 14

Polychlorinated biphenyls 73 54 27 54 29 66 15 45 36 52 25 55 34 17 7 1

Pyrene 70 45 8 32 24 67 43 63 35 55 26 41 7 13 7 14

SEM est 27 3 2 1 4 57 8 32 16 44 18 7

Silver 70 58 5 18 13 62 15 19 29 34 21 64 14 54 8 4

TEF Dioxins, Furans, and Dioxin-like

PCBs 62 53 4 21 15 41 1 9 14 25 10 44 15

TEF PAHs 72 50 8 38 31 71 44 71 45 58 29 50 7 32 7 15

Tetrachloroethene 2

Toluene 1

Toxaphene 4

Trichlorobenzene, 1,2,4-

Trichloroethane, 1,1,1-

Trichloroethene

Trichlorofluoromethane

Trichloromethane/Chloroform

Xylene, oXylenes (Total) 4 1

Zinc 78 64 37 149 25 70 52 165 103 77 28 70 124 57 31 5
National Sediment Quality Survey

C-22

Table C-5. (Continued)

Chemical

Lower Fox (04030204)Little Calumet-Galien (04040001)

Pike-Root (04040002)Chautauqua-Conneaut (04120101)Seneca (04140201)Upper Scioto (05060001)

Tippecanoe (05120106)Upper White (05120201)Lower East Fork White (05120208)Watts Bar Lake (06010201)

Upper Clinch (06010205)

Middle Tennessee-Chickamauga (06020001)Rush-Vermillion (07040001)Copperas-Duck (07080101)

Lower Rock (07090005)Green (07090007)

Acenaphthene 1 8 1 5 3 50

Acenaphthylene 1 7 1 7

Acetone 4 3 6

Aldrin 5 1 1 3 4 2

Anthracene 3 18 1 2 10 20 9 47

Antimony 2 6 7 9 3 1 7 1 5

Arsenic 21 56 48 6 12 57 26 45 5 3 57 5 5 50 52 33

Benzene 4

Benzo(a)anthracene 1 21 4 12 2 28 20 1 5 1 4 135

Benzo(a)anthracene/Chrysene

Benzo(a)pyrene 20 4 12 2 24 18 165

Benzo(b)fluoranthene 2 22 1 4 12 2 29 14 1 5 1 7 164

Benzo(g,h,i)perylene 6 20 1 3 12 2 26 17 1 5 1 142

Benzo(k)fluoranthene 1 20 1 4 12 2 27 18 1 7 1 5 153

BHC, alpha- 2 1 4

BHC, beta- 5 2 2

BHC, deltaBHC, gamma- (Lindane) 1 4 1 4

BHC, technical grade

Biphenyl 6

Bis(2-ethylhexyl)phthalate 4 1 3 1 20 3 1 1 1

Bromophenyl phenyl ether, 4-

Butyl benzyl phthalate 1 8 3 6 3

Cadmium 6 39 47 4 15 56 11 36 9 2 13 2 28 26 5

Chlordane 2 38 7 4 4

Chlordane (cis-Nonachlor) 2 4 1 4

Chlordane (trans-Nonachlor) 1 6 2 2 4

Chlordane, alpha (cis)- 5 1 6 2 4

Chlordane, cis- 22 5 1

Chlordane, gamma (trans)- 4

Chlordane, trans- 14 4 13 4 2

Chlorobenzene

Chlorpyrifos/Dursban

Chromium 22 58 56 6 12 55 24 62 15 22 108 39 28 99 52 33

Chrysene 6 22 1 4 12 2 30 20 1 5 6 120

Copper 18 57 56 6 29 57 26 62 15 22 112 39 40 101 52 33

DCPA/Dacthal

DDD, p, p'- 2 30 13 4 1 8 6

DDE, p, p'- 35 12 3 4 4 4 3 1

DDT (Total) 2 41 15 5 49 5 5 3 8 6 3

DDT, p, p'- 2 16 13 3 4 1 3 3 2 3
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Lower Fox (04030204)Little Calumet-Galien (04040001)

Pike-Root (04040002)Chautauqua-Conneaut (04120101)Seneca (04140201)Upper Scioto (05060001)

Tippecanoe (05120106)Upper White (05120201)Lower East Fork White (05120208)Watts Bar Lake (06010201)

Upper Clinch (06010205)

Middle Tennessee-Chickamauga (06020001)Rush-Vermillion (07040001)Copperas-Duck (07080101)

Lower Rock (07090005)Green (07090007)

C-23

Di-n-butyl phthalate 1 8 3 1 7 1 7

Di-n-octyl phthalate 2

Diazinon/Spectracide 37

Dibenzo(a,h)anthracene 1 20 2 8 2 17 8 2 2 76

Dibenzofuran 1

Dichlorobenzene, 1,2- 1

Dichlorobenzene, 1,3-

Dichlorobenzene, 1,4- 3

Dichloroethane, 1,1-

Dichloroethane, 1,2-

Dichloromethane 1

Dieldrin 14 3 2 51 2 4 4 5 12 21

Diethyl phthalate 1

Dimethyl phthalate 1

Dimethylnapthalene, 2,6- 1 7 3 7 1 4

Endosulfan mixed isomers 3

Endosulfan, alpha- 1 1 1

Endosulfan, beta- 1

Endrin 2 2 1 4 2

Ethion/Bladen

Ethylbenzene

Fluoranthene 6 23 3 4 12 5 38 20 1 7 1 8 167

Fluorene 3 17 1 2 5 7 36

Heptachlor 2 1 2 4 1

Heptachlor epoxide 6 1 16 4 5

Hexachlorobenzene 3 2 1 4 1

Hexachlorobutadiene

Hexachloroethane

Indeno(1,2,3-cd)pyrene 4 20 1 3 12 2 25 18 1 5 1 4 126

Isophorone

Lead 22 56 56 6 29 43 23 60 15 22 111 39 43 90 48 8

Malathion 9

Mercury 22 38 35 2 22 9 26 55 13 21 15 30 44 5 6

Methoxychlor 2 2

Methyl ethyl ketone

Methylnaphthalene, 1- 1 1

Methylnaphthalene, 2- 9 1 1

Methylphenanthrene, 1- 6 1 5

Mirex/Dechlorane

Naphthalene 1 2 6 1 5 61

Nickel 6 56 54 6 29 44 26 49 13 20 111 39 5 51 52 33
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Lower Fox (04030204)Little Calumet-Galien (04040001)

Pike-Root (04040002)Chautauqua-Conneaut (04120101)Seneca (04140201)Upper Scioto (05060001)

Tippecanoe (05120106)Upper White (05120201)Lower East Fork White (05120208)Watts Bar Lake (06010201)

Upper Clinch (06010205)

Middle Tennessee-Chickamauga (06020001)Rush-Vermillion (07040001)Copperas-Duck (07080101)

Lower Rock (07090005)Green (07090007)

C-24

Perylene 4 1 1 16

Phenanthrene 6 22 1 5 12 2 31 17 1 5 1 4 67

Polychlorinated biphenyls 10 51 31 1 9 32 7 2 8 1 20 82 2

Pyrene 6 22 4 3 12 2 41 21 1 7 1 8 153

SEM est 15 2 10 19

Silver 2 10 3 3 1 8 3 1 7 1 5 4 1

TEF Dioxins, Furans, and Dioxin-like

PCBs

TEF PAHs 6 23 4 5 12 5 42 22 1 7 1 8 170

Tetrachloroethene

Toluene

Toxaphene 4

Trichlorobenzene, 1,2,4-

Trichloroethane, 1,1,1-

Trichloroethene

Trichlorofluoromethane

Trichloromethane/Chloroform

Xylene, oXylenes (Total)

Zinc 2 57 47 6 29 56 26 50 15 22 110 39 22 98 52 33
National Sediment Quality Survey

C-25

Table C-5. (Continued)

Chemical

Kankakee (07120001)Iroquois (07120002)Chicago (07120003)Des Plaines (07120004)Upper Illinois (07120005)Upper Fox (07120006)Lower Fox (07120007)Lower Illinois-Senachwine Lake (07130001)Lower Illinois-Lake Chautauqua (07130003)South Fork Sangamon (07130007)Lower Illinois (07130011)Macoupin (07130012)Big Sunflower (08030207)Deer-Steele (08030209)Lower Mississippi-New Orleans (08090100)Lower Neosho (11070209)

Acenaphthene 1 2 13

Acenaphthylene 1 2 1 9

Acetone 1 2

Aldrin 5 3 3 2 3 2 2 3 4 3 3 1

Anthracene 3 3 2 2 1 26

Antimony 4 1 3 1 2 1 2 1 2 3 5 33

Arsenic 28 27 27 70 21 67 28 8 31 20 44 27 28 12 37 3

Benzene

Benzo(a)anthracene 5 4 2 2 1 1 1 29

Benzo(a)anthracene/Chrysene

Benzo(a)pyrene 4 4 29

Benzo(b)fluoranthene 5 4 2 2 7 1 1 29

Benzo(g,h,i)perylene 4 4 2 2 3 1 1 30

Benzo(k)fluoranthene 5 4 2 2 5 1 1 29

BHC, alpha- 9 6 2 9 1 12

BHC, beta- 1 6 1

BHC, delta- 6 1

BHC, gamma- (Lindane) 1 5 7 2 8

BHC, technical grade 23

Biphenyl 26

Bis(2-ethylhexyl)phthalate 1 1 2 2 4 2 2

Bromophenyl phenyl ether, 4-

Butyl benzyl phthalate 1 1 1

Cadmium 13 7 14 22 10 27 7 8 17 3 11 4 27 4 32 4

Chlordane 1 7 8 1 1 1 5 1 28

Chlordane (cis-Nonachlor) 2 1 7

Chlordane (trans-Nonachlor) 3 1 1 1 1 24

Chlordane, alpha (cis)- 3 1 12

Chlordane, cis- 2 56262 1811

Chlordane, gamma (trans)- 20

Chlordane, trans- 4 2 11 12 2 1 1 3 8 2 3

Chlorobenzene

Chlorpyrifos/Dursban 2

Chromium 32 27 27 70 23 79 28 8 32 20 44 27 28 12 38 8

Chrysene 5 4 2 2 6 1 1 1 31

Copper 32 27 27 70 23 68 28 8 32 20 44 27 28 12 38 8

DCPA/Dacthal

DDD, p, p'- 8 9 27 48 6 33 9 1 3 2 4 2 33 18 22

DDE, p, p'- 18 7 30 49 9 34 13 1 4 1 8 1 34 18 13

DDT (Total) 23 10 32 53 10 41 16 1 4 2 8 3 35 26 35

DDT, p, p'- 20 8 14 31 1 4 6 2 2 1 35 13 16
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Kankakee (07120001)Iroquois (07120002)Chicago (07120003)Des Plaines (07120004)Upper Illinois (07120005)Upper Fox (07120006)Lower Fox (07120007)Lower Illinois-Senachwine Lake (07130001)Lower Illinois-Lake Chautauqua (07130003)South Fork Sangamon (07130007)Lower Illinois (07130011)Macoupin (07130012)Big Sunflower (08030207)Deer-Steele (08030209)Lower Mississippi-New Orleans (08090100)Lower Neosho (11070209)

C-26

Di-n-butyl phthalate 1 1 2 1

Di-n-octyl phthalate 1

Diazinon/Spectracide

Dibenzo(a,h)anthracene 2 4 2 2 1 25

Dibenzofuran

Dichlorobenzene, 1,2-

Dichlorobenzene, 1,3-

Dichlorobenzene, 1,4-

Dichloroethane, 1,1-

Dichloroethane, 1,2-

Dichloromethane

Dieldrin 27 24 17 33 11 16 21 1 9 23 20 19 22 1 1

Diethyl phthalate 1

Dimethyl phthalate

Dimethylnapthalene, 2,6- 1 2 1 29

Endosulfan mixed isomers 2

Endosulfan, alpha- 6 5

Endosulfan, beta- 4 2

Endrin 3 3 5 4 1 6 3 19 4 4

Ethion/Bladen

Ethylbenzene

Fluoranthene 5 1 4 2 2 17 1 2 1 1 32

Fluorene 1 2 27

Heptachlor 1 1 3 1 9 1 27 8 1

Heptachlor epoxide 2 3 2 3 1 8 1 6 5 1 3

Hexachlorobenzene 5 12 8 1 12

Hexachlorobutadiene

Hexachloroethane

Indeno(1,2,3-cd)pyrene 5 4 2 2 3 1 29

Isophorone

Lead 29 27 27 70 19 80 21 8 22 20 43 27 26 11 33 8

Malathion

Mercury 10 27 17 30 4 45 8 7 14 5 12 5 3 7 30 3

Methoxychlor 5 12 6 3 3 2 3 1

Methyl ethyl ketone 1

Methylnaphthalene, 1- 1 32

Methylnaphthalene, 2- 1 32

Methylphenanthrene, 1- 2 2 1 29

Mirex/Dechlorane

Naphthalene 1 2 1 32

Nickel 29 27 27 70 23 66 28 8 32 20 44 27 28 12 33 3
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Kankakee (07120001)Iroquois (07120002)Chicago (07120003)Des Plaines (07120004)Upper Illinois (07120005)Upper Fox (07120006)Lower Fox (07120007)Lower Illinois-Senachwine Lake (07130001)Lower Illinois-Lake Chautauqua (07130003)South Fork Sangamon (07130007)Lower Illinois (07130011)Macoupin (07130012)Big Sunflower (08030207)Deer-Steele (08030209)Lower Mississippi-New Orleans (08090100)Lower Neosho (11070209)

C-27

Perylene 1 32

Phenanthrene 5 4 2 2 6 1 1 32

Polychlorinated biphenyls 5 11 19 2 18 1 3 34 1

Pyrene 5 1 4 2 2 15 1 2 2 1 29

SEM est 4 3

Silver 3 17 1 19 2 5 4 1 3 2 1 32

TEF Dioxins, Furans, and Dioxin-like

PCBs 20

TEF PAHs 5 1 4 2 2 17 1 2 2 1 32

Tetrachloroethene

Toluene 2

Toxaphene

Trichlorobenzene, 1,2,4-

Trichloroethane, 1,1,1-

Trichloroethene

Trichlorofluoromethane

Trichloromethane/Chloroform

Xylene, oXylenes (Total)

Zinc 30 27 27 70 23 57 28 8 32 20 44 27 28 12 38 3
National Sediment Quality Survey

C-28

Table C-5. (Continued)

Chemical

Lower West Fork Trinity (12030102)Austin-Travis Lakes (12090205)Blue (14010002)Lower Salt (15060106)Carson Desert (16050203)Franklin D_ Roosevelt Lake (17020001)Lower Columbia-Sandy (17080001)Lower Willamette (17090012)Queets-Quinault (17100102)Grays Harbor (17100105)Strait Of Georgia (17110002)Lake Washington (17110012)Duwamish (17110013)Puget Sound (17110019)Mad-Redwood (18010102)Sacramento-Upper Clear (18020112)

Acenaphthene 14 7 194 30 37 132 120 251 729 17

Acenaphthylene 14 1 5 114 27 69 103 90 75 438 9

Acetone 13 5 8 5 31

Aldrin 1 16 14 31 12 22 96

Anthracene 26 1 1 1 8 214 44 106 187 116 325 1,110 19

Antimony 15 4 18 2 19 46 4 28 16 66 93 38 87 389 20

Arsenic 94 76 18 58 18 46 67 213 57 144 330 213 589 1,529 10

Benzene 1621

Benzo(a)anthracene 36 2 1 1 15 271 48 112 225 151 396 1,139 20

Benzo(a)anthracene/Chrysene 3 1 33 209

Benzo(a)pyrene 2 12 270 47 108 215 149 382 1,327 20

Benzo(b)fluoranthene 35 1 2 1 1 137 11 104 141 128 450 706 10

Benzo(g,h,i)perylene 32 1 1 1 10 252 44 108 148 136 346 1,052 8

Benzo(k)fluoranthene 33 1 1 2 3 194 11 110 141 92 366 651 8

BHC, alpha- 1

BHC, beta- 1 2 3

BHC, delta- 1 1 3

BHC, gamma- (Lindane) 3 9 7 7 6 132 7

BHC, technical grade 2 7

Biphenyl 59 24 20

Bis(2-ethylhexyl)phthalate 30 2 3 1 16 153 93 81 107 396 970

Bromophenyl phenyl ether, 4- 4

Butyl benzyl phthalate 26 2 2 1 75 42 23 33 299 379

Cadmium 49 75 18 26 18 52 58 258 5 142 250 164 549 1,385 20 62

Chlordane 14 43 49 6 15 9 112

Chlordane (cis-Nonachlor) 4 1 2 1

Chlordane (trans-Nonachlor) 7 2 2 2 1

Chlordane, alpha (cis)- 20 6 1 114

Chlordane, cis- 8 2 22 1

Chlordane, gamma (trans)- 4 2 22 245

Chlordane, trans- 8 1 22 1 1

Chlorobenzene 1

Chlorpyrifos/Dursban 2

Chromium 98 72 18 56 19 46 69 337 10 51 243 166 590 1,570 20

Chrysene 39 1 1 1 17 288 49 114 248 181 511 1,495 20

Copper 95 67 18 63 19 58 28 334 81 128 357 251 659 2,066 20 62

DCPA/Dacthal 3

DDD, p, p'- 6 1 5 48 50 11 35 70 231 4

DDE, p, p'- 12 2 10 48 8 8 17 19 165

DDT (Total) 38 47 51 12 77 53 98 37 102 578 4

DDT, p, p'- 4 1 4 54 4 32 9 10 117

Di-n-butyl phthalate 30 2 2 1 3 69 8 21 25 108 203
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Lower West Fork Trinity (12030102)Austin-Travis Lakes (12090205)Blue (14010002)Lower Salt (15060106)Carson Desert (16050203)Franklin D_ Roosevelt Lake (17020001)Lower Columbia-Sandy (17080001)Lower Willamette (17090012)Queets-Quinault (17100102)Grays Harbor (17100105)Strait Of Georgia (17110002)Lake Washington (17110012)Duwamish (17110013)Puget Sound (17110019)Mad-Redwood (18010102)Sacramento-Upper Clear (18020112)

C-29

Di-n-octyl phthalate 5 1 1 54 11 5 33 133

Diazinon/Spectracide 16

Dibenzo(a,h)anthracene 21 1 6 137 37 52 83 72 267 641 9

Dibenzofuran 4 86 21 144 81 182 545

Dichlorobenzene, 1,2- 1 4 11 4 10 89

Dichlorobenzene, 1,3- 1 1 4 8 103

Dichlorobenzene, 1,4- 1 2 1 4 1 11 7 18 125

Dichloroethane, 1,1- 1

Dichloroethane, 1,2- 2

Dichloromethane 1 7 2 5 1 8

Dieldrin 16 38 45 2 7 3 26 8 15 111 2

Diethyl phthalate 23 1 1 2 4 3 11 8 6 86

Dimethyl phthalate 21 17 4 11 28 120 163

Dimethylnapthalene, 2,6- 32 1 1 1 1 3 64 2 1 38 20

Endosulfan mixed isomers 1 7

Endosulfan, alpha- 1

Endosulfan, beta- 1

Endrin 1 1 1

Ethion/Bladen

Ethylbenzene 1 1 2 25

Fluoranthene 37 2 2 6 17 300 53 126 267 165 450 1,495 20

Fluorene 8 197 36 71 172 104 286 795 20

Heptachlor 5 9 4 11 7 3 97

Heptachlor epoxide 1 23 27 1 6 7 1

Hexachlorobenzene 1 9 53 7 4 249 2

Hexachlorobutadiene 1 2 7 5 136

Hexachloroethane 1 29

Indeno(1,2,3-cd)pyrene 30 1 1 9 232 44 107 150 119 402 1,108 6

Isophorone 2

Lead 96 79 18 59 19 56 68 272 70 123 332 232 629 1,922 20

Malathion 4

Mercury 10 65 18 57 18 44 50 247 139 120 372 184 562 1,473 20

Methoxychlor 3 2 6 9

Methyl ethyl ketone 2 2 14 15 15

Methylnaphthalene, 1- 61 24 30 20

Methylnaphthalene, 2- 8 99 20 40 152 93 85 490 20

Methylphenanthrene, 1- 20 2 3 63 2 1 31 16

Mirex/Dechlorane

Naphthalene 4 8 201 30 79 168 110 95 706 10

Nickel 98 20 18 7 19 28 251 100 230 225 637 1,944 10

Perylene 2 49 47 19

Phenanthrene 25 1 1 1 12 287 52 119 265 154 411 1,405 20
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Lower West Fork Trinity (12030102)Austin-Travis Lakes (12090205)Blue (14010002)Lower Salt (15060106)Carson Desert (16050203)Franklin D_ Roosevelt Lake (17020001)Lower Columbia-Sandy (17080001)Lower Willamette (17090012)Queets-Quinault (17100102)Grays Harbor (17100105)Strait Of Georgia (17110002)Lake Washington (17110012)Duwamish (17110013)Puget Sound (17110019)Mad-Redwood (18010102)Sacramento-Upper Clear (18020112)

C-30

Polychlorinated biphenyls 38 39 40 8 62 80 42 61 901 979 8

Pyrene 36 2 2 6 18 299 53 120 253 166 422 1,471 20

SEM est 2 54

Silver 43 5 18 2 16 11 235 2 102 170 69 472 1,092 20

TEF Dioxins, Furans, and Dioxin-like

PCBs 2 22 4 536 127 5

TEF PAHs 42 4 2 7 18 337 55 126 364 203 566 1,836 20

Tetrachloroethene 4 23

Toluene 6 3 3 7 8

Toxaphene 1 38 1

Trichlorobenzene, 1,2,4- 8 4 3 66

Trichloroethane, 1,1,1- 1

Trichloroethene 2 38

Trichlorofluoromethane 1

Trichloromethane/Chloroform 1 1 1

Xylene, o- 1 62

Xylenes (Total) 1 2 5 4 5 45

Zinc 97 83 18 64 19 58 28 284 79 135 379 251 642 1,738 20 62
National Sediment Quality Survey

C-31

Table C-5. (Continued)

Chemical

Lower Cosumnes-Lower Mokelumne (18040005)Suisun Bay (18050001)San Pablo Bay (18050002)Coyote (18050003)San Francisco Bay (18050004)Central Coastal (18060006)Alisal-Elkhorn Sloughs (18060011)Calleguas (18070103)Santa Monica Bay (18070104)San Gabriel (18070106)Seal Beach (18070201)Santa Ana (18070203)Newport Bay (18070204)Aliso-San Onofre (18070301)San Diego (18070304)Eastern Prince William Sound (19020201)

Acenaphthene 19 73 11 70 1 9 4 79 4 8 3 1 110 8

Acenaphthylene 19 81 11 73 5 4 64 3 1 4 1 133

Acetone

Aldrin 1 6 1 5 7 1 5 1 6 11 2 1

Anthracene 1 25 102 13 76 1 11 7 115 18 23 2 7 1 176 16

Antimony 1 5 29 3 42 24 10 14 127 23 37 20 4 217

Arsenic 1 25 108 32 143 52 10 9 109 24 56 180 55 4 209

Benzene

Benzo(a)anthracene 1 26 106 13 76 3 18 11 126 23 31 4 20 5 219 14

Benzo(a)anthracene/Chrysene

Benzo(a)pyrene 25 105 13 76 3 17 12 130 23 34 3 20 5 225 14

Benzo(b)fluoranthene 1 25 106 13 76 8 11 76 12 24 3 21 7 194

Benzo(g,h,i)perylene 27 104 13 76 8 10 80 12 18 2 20 5 192

Benzo(k)fluoranthene 1 25 104 13 76 8 10 76 12 18 4 19 4 191

BHC, alpha- 4 18 2 7 2 12 1 4 32 2 2

BHC, beta- 1 10 2 9 1 1 4 3 36 6 1 1

BHC, delta- 2 2 1 7 15 5 3

BHC, gamma- (Lindane) 5 18 3 8 1 4 3 9 2 1 2 1 17

BHC, technical grade

Biphenyl 22 88 11 70 9 4 61 7 5 2 83 1

Bis(2-ethylhexyl)phthalate 1 5 2 38 1 1

Bromophenyl phenyl ether, 4-

Butyl benzyl phthalate 1 1 17 4

Cadmium 59 25 103 25 78 10 10 14 127 24 74 206 133 60 217

Chlordane 1 35 6

Chlordane (cis-Nonachlor) 1 7 21 9 36 1 9 49 12 12 1 15 2 117

Chlordane (trans-Nonachlor) 1 8 17 8 29 1 10 11 76 23 26 3 19 4 136

Chlordane, alpha (cis)- 4 10 7 4

Chlordane, cis- 1 3 9 1 26 10 11 80 23 25 1 19 4 142

Chlordane, gamma (trans)- 3 13 3 5

Chlordane, trans- 1 2 9 1 23 8 11 59 12 15 2 20 4 140

Chlorobenzene

Chlorpyrifos/Dursban 4 14 2 27 8 9 38 6 10 3 22

Chromium 1 25 109 41 216 52 10 14 127 24 107 212 253 117 218

Chrysene 1 26 106 13 76 3 18 13 125 23 37 5 21 6 224 34

Copper 60 25 109 41 216 52 10 14 127 24 107 212 235 107 222

DCPA/Dacthal 2 2 8 9 13 11 1 2 1 7

DDD, p, p'- 26 100 13 75 5 18 14 127 23 54 4 75 7 182

DDE, p, p'- 1 25 102 13 72 6 18 14 130 23 69 7 148 26 216

DDT (Total) 1 26 102 13 75 6 202 14 130 23 86 139 192 26 218
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Lower Cosumnes-Lower Mokelumne (18040005)Suisun Bay (18050001)San Pablo Bay (18050002)Coyote (18050003)San Francisco Bay (18050004)Central Coastal (18060006)Alisal-Elkhorn Sloughs (18060011)Calleguas (18070103)Santa Monica Bay (18070104)San Gabriel (18070106)Seal Beach (18070201)Santa Ana (18070203)Newport Bay (18070204)Aliso-San Onofre (18070301)San Diego (18070304)Eastern Prince William Sound (19020201)

C-32

DDT, p, p'- 13 42 6 33 4 15 13 89 17 34 5 71 2 87

Di-n-butyl phthalate 1 2 1 14 3 1

Di-n-octyl phthalate 19 1

Diazinon/Spectracide 1

Dibenzo(a,h)anthracene 23 87 12 67 2 12 9 123 22 23 14 3 185 10

Dibenzofuran 5 6

Dichlorobenzene, 1,2-

Dichlorobenzene, 1,3-

Dichlorobenzene, 1,4-

Dichloroethane, 1,1-

Dichloroethane, 1,2-

Dichloromethane 6

Dieldrin 12 40 10 48 1 14 12 50 9 15 24 12 60

Diethyl phthalate

Dimethyl phthalate 1 5 4

Dimethylnapthalene, 2,6- 1 22 68 11 58 9 4 82 11 7 4 3 76

Endosulfan mixed isomers

Endosulfan, alpha- 1 31 1 6 1 4 2

Endosulfan, beta- 2 4 13 1 41 9 19 4 1 14 4 12

Endrin 4 7 1 5 1 8 2 6 1 1 4 2

Ethion/Bladen 1 2 1

Ethylbenzene

Fluoranthene 1 27 107 13 76 3 18 13 126 23 37 14 20 6 226 45

Fluorene 23 87 11 71 1 10 6 96 9 14 4 1 126 19

Heptachlor 1 2 14 2 2 32 5 2 10

Heptachlor epoxide 4 1 6 2 2 18 4 1 7

Hexachlorobenzene 15 34 2 30 1 10 9 62 7 11 3 2 35

Hexachlorobutadiene

Hexachloroethane

Indeno(1,2,3-cd)pyrene 27 106 13 76 8 11 80 12 14 1 19 4 191

Isophorone

Lead 9 25 109 41 216 48 10 14 127 24 107 208 167 74 222

Malathion

Mercury 6 34 141 49 241 3 10 14 127 23 48 196 60 4 217

Methoxychlor 4 4 1 1 6 16 1 1 22

Methyl ethyl ketone

Methylnaphthalene, 1- 17 76 11 68 13 5 98 12 6 3 1 92 3

Methylnaphthalene, 2- 21 81 11 70 1 16 7 117 22 20 6 1 131 15

Methylphenanthrene, 1- 23 94 12 74 1 11 9 111 20 18 7 2 154 76

Mirex/Dechlorane 1 1 18 2 14 1 4
National Sediment Quality Survey

Table C-5. (Continued)

Chemical

Lower Cosumnes-Lower Mokelumne (18040005)Suisun Bay (18050001)San Pablo Bay (18050002)Coyote (18050003)San Francisco Bay (18050004)Central Coastal (18060006)Alisal-Elkhorn Sloughs (18060011)Calleguas (18070103)Santa Monica Bay (18070104)San Gabriel (18070106)Seal Beach (18070201)Santa Ana (18070203)Newport Bay (18070204)Aliso-San Onofre (18070301)San Diego (18070304)Eastern Prince William Sound (19020201)

C-33

Naphthalene 23 91 11 71 8 11 76 12 16 6 2 162 4

Nickel 1 22 108 41 203 52 10 9 111 24 101 210 208 97 218

Perylene 22 88 11 70 3 17 13 130 23 28 19 4 211 16

Phenanthrene 1 26 105 13 76 3 17 13 125 23 30 6 20 5 214 64

Polychlorinated biphenyls 34 132 21 100 14 14 133 23 41 108 39 6 221

Pyrene 1 27 107 13 76 3 18 13 126 23 37 18 23 10 227 65

SEM est 3 1 12 8 12 26 3 1 8

Silver 1 25 100 25 72 14 10 14 127 22 57 196 48 8 217

TEF Dioxins, Furans, and Dioxin-like

PCBs 12 87 17 91 11 11 132 23 24 17 3 205

TEF PAHs 1 27 107 13 76 3 18 13 130 23 37 22 25 10 227 76

Tetrachloroethene

Toluene

Toxaphene 1 1 2 4

Trichlorobenzene, 1,2,4-

Trichloroethane, 1,1,1-

Trichloroethene

Trichlorofluoromethane

Trichloromethane/Chloroform

Xylene, oXylenes (Total)

Zinc 60 25 109 41 220 52 10 14 127 24 107 212 264 119 222
National Sediment Quality Survey

C-34
National Sediment Quality Survey

D-1

APPENDIX D

SPECIES CHARACTERISTICS RELATED TO NSI

BIOACCUMULATION DATA

Table D-1 presents the species for which tissue residue analyses are included in the NSI database. For

each species listed, Table D-1 identifies the species as resident or migratory (or either) and demersal or

pelagic (or either) and specifies whether the species might be consumed by humans (i.e., recreational or

subsistence anglers). A species is considered either resident or migratory if it stays predominately in one

location as long as food and habitat are available but is capable of traveling long distances to find food

and suitable habitat. A species is considered either demersal or pelagic if it spends much of its time in the

water column but is likely to feed off the bottom. If a species is identified as either resident or migratory,

it is considered resident for the purpose of this analysis. If a species is identified as either demersal or

pelagic, it is considered demersal. 
National Sediment Quality Survey

D-2

Table D-1. Species Characteristics Related to Tissue Residue Data.

Scientific Name Common Name

Resident/

Migratorya

Demersal/

Pelagicb

Potentially

Edible

Acanthomysis macropsis Mysid shrimp E E N

Acartia spp. Copepod (unknown species) M P N

Acipenser brevirostrum Shortnose sturgeon M D Y

Acipenser fulvescens Lake sturgeon R D Y

Acipenser oxyrhynchus Atlantic sturgeon M D Y

Acipenser spp. Sturgeon (unknown species) M D Y

Acipenser transmontanus White sturgeon M D Y

Acrocheilus alutaceus Chiselmouth R P N

Agrionidae Broad-winged damselflies R E N

Acroneuria spp. Stonefly (unknown species) R D N

Allosmerus elongatus Whitebait smelt M P Y

Alosa aestivalis Blueback herring M P Y

Alosa alabamae Alabama shad M P Y

Alosa chrysochloris Skipjack herring M P Y

Alosa mediocris Hickory shad M P Y

Alosa pseudoharengus Alewife M P Y

Alosa sapidissima American shad M P Y

Amblema plicata Three-ridge mussel R D Y

Ambloplites cavifrons Roanoke bass R P Y

Ambloplites constellatus Ozark bass R P Y

Ambloplites rupestris Rock bass R P Y

Amia calva Bowfin R E Y

Amphipoda Amphipod (order) R E N

Amphistichus rhodoterus Redtail surfperch R P Y

Anarhichas denticulatus Northern wolffish R D Y

Anchoa mitchilli Bay anchovy R P Y

Anguilla rostrata American eel M P Y

Anoplopoma fimbria Sablefish M E Y

Apeltes quadracus Fourspine stickleback R E N

Aplodinotus grunniens Freshwater drum M E Y

Archoplites interruptus Sacramento perch R P Y

Archosargus probatocephalus Sheepshead M P Y

Arctica islandica Ocean quahog R D Y

Arctopsyche spp. Caddisfly (unknown species) R D N

Arius felis Hardhead catfish M D Y

Artedius notospilotus Bonehead sculpin R D N

Asellus militaris Aquatic sow bug R D N

Asellus militaris Aquatic sow egg R D N

Astacidae Crayfish (family) R D Y

Astarte spp. Astarte clam (unknown species) R D N

Astarte undata Waved astarte R D N

Astronotus ocellatus Oscar R P Y

Astropecten verrilli Margined seastar R D N

Atherinidae Silversides (family) R P N

Bagre marinus Gafftopsail catfish M E Y

Bairdiella chrysoura Silver perch M P Y

Belostomatidae Giant waterbug R E N

Bidessinae Dytiscid beetle (subfamily) R E N

Bivalvia Bivalvia (class) R D Y

Brachiodontes recurvus Hooked mussel R D Y
National Sediment Quality Survey

D-3

Table D-1. (Continued)

Scientific Name Common Name

Resident/

Migratorya

Demersal/

Pelagicb

Potentially

Edible

Brachycentrus spp. Four-sided case maker caddisfly (unknown species) R D N

Brevoortia patronus Gulf menhaden M P Y

Brevoortia spp. Menhaden (unknown species) M P Y

Brevoortia tyrannus Atlantic menhaden M P Y

Callinectes sapidus Blue crab M D Y

Cambarus bartoni Appalachian brook crayfish R D Y

Cambarus spp. Crayfish (unknown species) R D Y

Campostoma anomalum Central stoneroller R E N

Cancer gracilis Graceful rock crab R D Y

Cancer magister Dungeness crab M D Y

Cancer productus Red rock crab R D Y

Caranx hippos Crevalle jack M P Y

Carassius auratus Goldfish R E N

Carcharhinus obscurus Dusky shark M E Y

Carcharhinus plumbeus Brown shark (sandbar) M E Y

Carpiodes carpio River carpsucker R D Y

Carpiodes cyprinus Quillback R D Y

Carpiodes spp. Carpsucker (unknown species) R D Y

Carpiodes velifer Highfin carpsucker R D Y

Catostomus ardens Utah sucker R D Y

Catostomus catostomus Longnose sucker R D Y

Catostomus columbianus Bridgelip sucker R D Y

Catostomus commersoni White sucker R D Y

Catostomus discobolus Bluehead sucker R D Y

Catostomus latipinnis Flannelmouth sucker R D Y

Catostomus macrocheilus Largescale sucker R D Y

Catostomus occidentalis Sacramento sucker R D Y

Catostomus platyrhynchus Mountain sucker R D Y

Catostomus snyderi Klamath largescale sucker R D Y

Catostomus spp. Sucker (unknown species) R D Y

Catostomus tahoensis Tahoe sucker R D Y

Centrarchidae Sunfish (family) R P Y

Centrarchus macropterus Flier R P Y

Centropomus undecimalis Common snook M P Y

Centropristis striata Black sea bass M P Y

Ceratopsyche spp. Caddisfly (unknown species) R D N

Chaetodipterus spp. Spade fish E E Y

Chelydra serpentina Snapping turtle R E Y

Cheumatopsyche spp. Net-spinning caddisfly (unknown species) R D N

Chironomidae Midge (family) R D N

Chironomus riparius Midge R D N

Cichla ocellaris Peacock cichlid R P Y

Cichlasoma uropthalmus Mayan cichlid R P Y

Citharichthys sordidus Pacific sanddab E D N

Citharichthys xanthostigma Longfin sanddab E D N

Clarius fuscus Chinese catfish R E Y

Clinocardium nuttalli Nuttall cockle R D Y

Clinostomus funduloides Rosyside dace R P N

Clupea harengus harengus Atlantic herring M P Y

Compsomyax subdiaphana Milky venus R D Y

Corbicula spp. Asiatic clam (unknown species) R D Y
National Sediment Quality Survey

Table D-1. (Continued)

Scientific Name Common Name

Resident/

Migratorya

Demersal/

Pelagicb

Potentially

Edible

D-4

Corbicula manilensis Asiatic clam R D Y

Coregonus artedii Cisco (lake herring) M P Y

Coregonus clupeaformis Lake whitefish M P Y

Coregonus hoyi Bloater M P Y

Coregonus spp. Whitefish M P Y

Cottidae Sculpin (family) R D Y

Cottus aleuticus Coastrange sculpin R D N

Cottus bairdi Mottled sculpin R D N

Cottus beldingi Paiute sculpin R D N

Cottus carolinae Banded sculpin R D N

Cottus cognatus Slimy sculpin R D N

Cottus gulosus Riffle sculpin R D N

Cottus leiopomus Wood river sculpin R D N

Cottus spp. Sculpin (unknown species) R D N

Crassostrea gigas Pacific oyster R D Y

Crassostrea spp. Oysters (unknown species) R D Y

Crassostrea virginica Eastern oyster R D Y

Ctenopharyngodon idella Grass carp R E Y

Cycleptus elongatus Blue sucker M D Y

Cynoscion arenarius Sand sea trout R P Y

Cynoscion nebulosus Spotted sea trout R P Y

Cynoscion nothus Silver sea trout M P Y

Cynoscion regalis Weakfish M P Y

Cyprinella lutrensis Red shiner R P N

Cyprinella spiloptera Spotfin shiner R P N

Cyprinid spp. Minnows R E Y

Cyprinidae Carp/goldfish (hybrid) R E Y

Cyprinidae (Family) Minnows (family) R E Y

Cyprinus carpio Common carp R D Y

Dasyatis sabina Atlantic stingray M D Y

Decapoda Lobsters (order) E D Y

Dicentrarchus labrax Bass M P Y

Diptera larva True fly larva R D N

Dorosoma cepedianum Gizzard shad M P N

Dorosoma petenense Threadfin shad M P N

Elliptio complanata Freshwater clam R D Y

Eopsetta exilis Slender sole E D Y

Equetus punctatus Spotted drum R D N

Erimyzon oblongus Creek chubsucker R E N

Erimyzon spp. Chubsucker (unknown species) R E N

Erimyzon sucetta Lake chubsucker R E N

Esocidae Pike (family) R P Y

Esox americanus americanus Redfin pickerel R P Y

Esox americanus vermiculatus Grass pickerel R P Y

Esox lucius Northern pike R P Y

Esox lucius × Esox masquinongy Tiger muskellunge R P Y

Esox masquinongy Muskellunge R P Y

Esox niger Chain pickerel R P Y

Etheostoma olmstedi Tesselated darter R D N

Etheostoma radiosum Orangebelly darter R D N

Etheostoma spectabile Orangethroat darter R D N
National Sediment Quality Survey

Table D-1. (Continued)

Scientific Name Common Name

Resident/

Migratorya

Demersal/

Pelagicb

Potentially

Edible

D-5

Etheostoma stigmaeum Speckled darter R D N

Etheostoma whipplei Redfin darter R D N

Etheostoma zonale Banded darter R D N

Exoglossum maxillingua Cutlips minnow R P N

Fundulus diaphanus Banded killifish R P N

Fundulus heteroclitus Mummichog/killifish R P N

Fundulus olivaceus Blackspotted topminnow R P N

Fundulus spp. Killifish species R P N

Fundulus zebrinus Plains killifish R P N

Gadus macrocephalus Pacific cod M E Y

Galeocerdo cuvier Tiger shark M E Y

Gambusia holbrooki Eastern mosquito fish R P N

Gambusio affinis Mosquito fish R P N

Gastropoda Gastropods (class) R D N

Genyonemus lineatus White croaker R E Y

Gila robusta Roundtail chub R E N

Gila spp. Chub (unknown species) R E N

Girella nigricans Opaleye M P N

Glupeidae Herring family M P Y

Glyptocephalus zachi Rex sole E D N

Gonidea angulata Freshwater mussel R D Y

Haemulon spp. Grunt R P Y

Hexagenia limbata Mayfly R D N

Hexagenia spp. Burrowing mayfly (unknown species) R D N

Hiodon alosoides Goldeye M P Y

Hiodon tergisus Mooneye M P Y

Hippoglossina stomata Bigmouth sole M D Y

Hippoglossoides elas Flathead sole M D Y

Hippoglossoides platessoides American plaice M D Y

Homarus americanus American lobster E D Y

Hyalella azteca Freshwater amphipod R E N

Hybognathus placitus Plains minnow R P N

Hydrolagus colliei Spotted rat fish M D N

Hydropsyche spp. Caddisfly (unknown species) R D N

Hydropsychidae Caddisfly (family) R D N

Hypentelium nigricans Northern hog sucker R D Y

Hypomesus pretiosus Surf smelt M P Y

Hypsopsetta guttulata Diamond turbot M D Y

Ictaluridae Bullhead catfish (family) R D Y

Ictalurus brunneus Snail bullhead R D Y

Ictalurus catus White catfish R D Y

Ictalurus furcatus Blue catfish R D Y

Ictalurus melas Black bullhead R D Y

Ictalurus natalis Yellow bullhead R D Y

Ictalurus nebulosus Brown bullhead R D Y

Ictalurus platycephalus Flat bullhead R D Y

Ictalurus punctatus Channel catfish R D Y

Ictalurus serracanthus Spotted bullhead R D Y

Ictalurus spp. Catfish (unknown species) R D Y

Ictiobus bubalus Smallmouth buffalo R E Y

Ictiobus cyprinellus Bigmouth buffalo R E Y
National Sediment Quality Survey

Table D-1. (Continued)

Scientific Name Common Name

Resident/

Migratorya

Demersal/

Pelagicb

Potentially

Edible

D-6

Ictiobus niger Black buffalo R E Y

Isopoda Isopod (order) R D N

Labidesthes sicculus Brook silverside R P N

Lagodon rhomboides Pinfish E P N

Lamniformes Mackerel shark (order) M P Y

Lampetra spp. Lamprey (unknown species) M E N

Lavinia exilicauda Hitch R P N

Leiostomus xanthurus Spot M P Y

Lepidogobius lepidus Bay goby R P N

Lepidopsetta bilineata Rock sole E D Y

Lepisosteidae Garfish (family) E P Y

Lepisosteus oculatus Spotted gar E P Y

Lepisosteus osseus Longnose gar E P Y

Lepisosteus platostomus Shortnose gar E P Y

Lepisosteus platyrhincus Florida gar E P Y

Lepisosteus spatula Alligator gar E P Y

Lepisosteus spp. Gar (unknown species) E P Y

Lepomis auritus Redbreast sunfish R P Y

Lepomis cyanellus Green sunfish R P Y

Lepomis gibbosus Pumpkinseed R P Y

Lepomis gulosus Warmouth R P Y

Lepomis humilis Orangespotted sunfish R P Y

Lepomis macrochirus Bluegill R P Y

Lepomis marginatus Dollar sunfish R P Y

Lepomis megalotis Longear sunfish R P Y

Lepomis microlophus Redear sunfish R P Y

Lepomis punctatus Spotted sunfish R P Y

Lepomis spp. Common sunfishes R P Y

Loligo opalescens California market squid M P Y

Loligo pealei Longfin squid M P Y

Lota lota Burbot M E Y

Loxorhynchus grandis Sheep crab R D N

Lumbriculus variegatus Aqauatic worm R D N

Lumbricus terrestris Earthworm R D N

Lutjanus campechanus Red snapper M D Y

Lutjanus griseuses Gray snapper M D Y

Luxilus chrysocephalus Striped shiner R P N

Luxilus cornutus Common shiner R P N

Lytechinus anamesus Little gray sea urchin R D N

Macoma inquinata Stained macoma R D Y

Macoma irus Clam (macoma) R D Y

Macoma nasuta Bent-nosed macoma R D N

Macrhybopsis gelida Sturgeon chub R E N

Macromia magnifica Dragon fly R D N

Megalonaias gigantea Washboard mussel R D Y

Menidia menidia Atlantic silverside R P N

Mercenaria mercenaria Quahog R D Y

Mercenaria spp. Hard clam (unknown species) R D Y

Merluccius bilinearis Silver hake M E Y

Merluccius productus Pacific hake M E Y

Microgadus proximus Pacific tomcod M E Y
National Sediment Quality Survey

Table D-1. (Continued)

Scientific Name Common Name

Resident/

Migratorya

Demersal/

Pelagicb

Potentially

Edible

D-7

Microgadus tomcod Atlantic tomcod M E Y

Micropogonias undulats Atlantic croaker M P Y

Micropterus coosae Redeye bass R P Y

Micropterus dolomieu Smallmouth bass R P Y

Micropterus notius Swannee bass R P Y

Micropterus punctulatus Spotted bass R P Y

Micropterus salmoides Largemouth bass R P Y

Micropterus spp. Bass (unknown species) R P Y

Minytrema melanops Spotted sucker E D Y

Modiolus demissus Ribbed mussel R D Y

Modiolus modiolus Northern horse mussel R D Y

Molpadia intermedia Sea cucumber R D Y

Morone americana White perch M P Y

Morone chrysops White bass M P Y

Morone chrysops x saxatilis Hybrid striped bass (white/striped) E P Y

Morone mississippiensis Yellow bass M P Y

Morone saxatilis Striped bass M P Y

Morone spp. Temperate bass (unknown species) E P Y

Moxostoma anisurum Silver redhorse R D Y

Moxostoma carinatum River redhorse R D Y

Moxostoma congestum Gray redhorse R D Y

Moxostoma duquesnei Black redhorse R D Y

Moxostoma erythrurum Golden redhorse R D Y

Moxostoma macrolepidotum Shorthead redhorse R D Y

Moxostoma pappillosum V-lip redhorse R D Y

Moxostoma poecilurum Blacktail redhorse R D Y

Moxostoma rupiscartes Striped jumprock R D Y

Moxostoma spp. Redhorse (unknown species) R D Y

Mugil cephalus Striped mullet M E Y

Mugil curema White mullet M E Y

Mugilidae Mullet (family) M E Y

Mustelus canis Smooth dogfish M E Y

Mya arenaria Soft shell clam R D Y

Mylocheilus caurinus Peamouth R E N

Mylopharodon conocephalus Hardhead R E N

Mytilus californianus California mussel R D Y

Mytilus edulis Blue mussel R D Y

Mytilus spp. Mussel (unknown species) R D Y

Neanthes arenaceodentata Sand worm R D N

Neoamphitrite robusta Terrebellid worm R D N

Nephtys caecoides Sand worm R D N

Nephtys incisa Red-lined worm R D N

Nigronia serricornis Hellgrammite R D N

Nocomis asper Redspot chub R E N

Nocomis biguttatus Hornyhead chub R E N

Nocomis leptocephalus Bluehead chub R E N

Nocomis micropogon River chub R E N

Notemigonus crysoleucas Golden shiner M P N

Notonectidae Backswimmer (family) R P N

Notropis amblops Bigeye chub R E N

Notropis boops Bigeye shiner R P N
National Sediment Quality Survey

Table D-1. (Continued)

Scientific Name Common Name

Resident/

Migratorya

Demersal/

Pelagicb

Potentially

Edible

D-8

Notropis buchanani Ghost shiner R P N

Notropis chrysocephalus Striped shiner R P N

Notropis cornutus Common shiner R P N

Notropis emiliae Pugnose minnow R P N

Notropis hudsonius Spottail shiner R P N

Notropis nubilus Ozark minnow R E N

Notropis spp. Shiner (composite) R E N

Notropis stramineus Sand shiner R P N

Noturus flavus Stone cat R D N

Noturus insignis Margined madtrom R D N

Noturus miurus Brindled madtom R D N

Noturus phaeus Brown madtom R D N

Odonata Dragonfly (order) R E N

Odontaspis taurus Sand tiger M E Y

Oligochaetes Aquatic worms R D N

Oncorhynchus clarkii Cutthroat trout E P Y

Oncorhynchus gorbuscha Pink salmon M P Y

Oncorhynchus kisutch Coho salmon M P Y

Oncorhynchus mykiss Rainbow trout E P Y

Oncorhynchus nerka Sockeye salmon M E Y

Oncorhynchus tshawytscha Chinook salmon M E Y

Ondatra zibethicus Muskrat R E Y

Opsanus spp. Toadfish R D N

Opsanus spp. Toadfish (unknown species) R D N

Orthodon microlepidotus Sacramento blackfish R P N

Orthopristis chrysoptera Pigfish R P Y

Osmeridae Smelt (family) M P Y

Osmerus mordax Rainbow smelt M P Y

Palaemontes pugio Grass shrimp R E N

Pandalus borealis Northern longbeak R D N

Paralabrax nebulifer Barred sand bass E D Y

Paralichthys spp. Flounder (unknown species) M D Y

Paralichthys californicus California halibut M D Y

Paralichthys dentatus Summer flounder (fluke) M D Y

Paralichthys lethostigma Southern flounder M D Y

Parastichopus californicus California sea cucumber R D N

Pectinaria californiensis Sandworm R D N

Penaeus aztecus Brown shrimp R E Y

Penaeus setiferus White shrimp R E Y

Penaeus spp. Shrimp (unknown species) R D Y

Peprilus triacanthus Butterfish M P Y

Perca flavescens Yellow perch R P Y

Percina copelandi Channel darter R D N

Perlidae Common stonefly (family) R D N

Phanerodon furcatus White seaperch R P Y

Phoca vitulinae Harbor seal R P Y

Phoxinus erythrogaster Southern redbelly dace R P N

Pimephales notatus Bluntnose minnow R P N

Pimephales promelas Fathead minnow R P N

Pisaster brevispinus Starfish R D N

Placopecten magellanicus Sea scallop R D N
National Sediment Quality Survey

Table D-1. (Continued)

Scientific Name Common Name

Resident/

Migratorya

Demersal/

Pelagicb

Potentially

Edible

D-9

Platichthys stellatus Starry flounder M D Y

Platygobio gracilis Flathead chub R E N

Plecoptera Stonefly (order) R D N

Pleuronectes americanus Winter flounder M D Y

Pleuronectes bilineatus Rock sole E D Y

Pleuronectes vetulus English sole M D Y

Pleuronectidae Righteye flounder (family) M D Y

Pleuronichthys coenosus C-O sole M D Y

Pleuronichthys decurrens Curlfin sole M D Y

Pleuronichthys verticalis Hornyhead turbot M D Y

Poecilia vittata Cuban limia E P N

Pogonias cromis Black drum M P Y

Polyodon spathula Paddlefish M P Y

Pomatomus saltatrix Bluefish M P Y

Pomoxis annularis White crappie R P Y

Pomoxis nigromaculatus Black crappie R P Y

Pomoxis spp. Crappie (unknown species) R P Y

Potamogeton pectinatus Sago pondweed R D N

Prionotus carolinus Northern searobin R D Y

Prionotus evolans Stripped searobin R D Y

Decapoda Crayfish (order) R D Y

Procambarus clarkii Red crayfish R D Y

Prosopium cylindraceum Round whitefish M P Y

Prosopium williamsoni Mountain whitefish M P Y

Protothaca staminea Clam (Pacific littleneck) R D Y

Psettichthys melanostictus Sand sole M D Y

Psilotreta/Cheumatopsyche Caddisfly (unknown species) R D N

Ptychocheilus oregonensis Northern squawfish R E Y

Ptychocheilus spp. Squawfish R E Y

Pylodictis olivaris Flathead catfish R E Y

Raja binoculata Winter skate M D Y

Rana spp. Common frog (unknown species) R P Y

Rana catesbeiana Bullfrog R P Y

Rana clamitans Green frog R P Y

Rangia cuneata Brackish water clam R D Y

Remora spp. Sucker (unknown species) E P N

Rhinichthys atratulus Blacknose dace R D N

Rhinichthys cataractae Longnose dace R D N

Rhinichthys spp. Dace (unknown species) R D N

Rhithropanopeus harrisii Mud crab R D N

Richardsonius balteatus Redside shiner R P N

Salmo gairdneri Rainbow trout E P Y

Salmo salar Atlantic salmon M P Y

Salmo spp. Trout (unknown species) E P Y

Salmo trutta Brown trout E P Y

Salmonidae Trout (family) E P Y

Salvelinus fontinalis Brook trout E P Y

Salvelinus hybrid Splake (hybrid) E P Y

Salvelinus malma Dolly varden E P Y

Salvelinus namaycush Lake trout E P Y

Saxidomus giganteus Clam (smooth Washington) R D Y
National Sediment Quality Survey

Table D-1. (Continued)

Scientific Name Common Name

Resident/

Migratorya

Demersal/

Pelagicb

Potentially

Edible

D-10

Scaphirhynchus platorynchus Shovelnose sturgeon M D Y

Sciaenidae Drum (family) M E Y

Sciaenops ocellatus Red drum M E Y

Scomber japonicus Chub mackerel M P Y

Scomberomorus cavalla King mackerel M P Y

Scomberomorus maculatus Spanish mackerel M P Y

Scophthalmus aquosus Windowpane M D Y

Scorpaena guttata California scorpionfish R D Y

Scorpaenichthys marmoratus Cabezon R D N

Sebastes auriculatus Brown rockfish M P Y

Sebastes caurinus Copper rockfish M P Y

Sebastes maliger Quillback rockfish M P Y

Sebastes marinus Ocean perch M P Y

Sebastes melanops Black rockfish M P Y

Sebastes norvegicus Golden redfish M P Y

Sebastes paucispinis Bocaccio M P Y

Sebastes proriger Redstripe rockfish M P Y

Sebastes sp. Rockfish (unknown species) M P Y

Semotilus atromaculatus Creek chub R E N

Semotilus corporalis Fallfish R E N

Semotilus lumbee Sandhills crab R E N

Serranidae Sea bass (family) E E Y

Sicyonia ingentis Pacific rock shrimp R D N

Solen sicarius Razor clam R D N

Spisula spp. Surf clam R D Y

Squalus acanthias Spiny dogfish M E Y

Stizostedion canadense Sauger R P Y

Stizostedion vitreum Walleye R P Y

Strongylura marina Atlantic needlefish M P N

Syacium papillosum Dusky flounder M D Y

Symphurus atricauda California tonguefish M D N

Synodus foetens Inshore lizardfish R D N

Tapes philippinarum Japanese littleneck R D Y

Tautoga onitis Tautog R E Y

Tautogolabrus adspersus Cunner (bergall) R E Y

Thalamita crenata Blue pincher crab R D Y

Theragra chalcogramma Walleye pollock M E Y

Thunnus atlanticus Blackfin tuna M P Y

Thymallus arcticus Arctic grayling E P Y

Tilapia mossambica Mozambique tilapia R E Y

Tilapia zillii Redbelly tilapia R E Y

Tipula spp. Cranefly (unknown species) R D N

Trachinotus carolinus Pompano M P Y

Tresus capax Horse clam R D Y

Triakis semifasciata Leopard shark M E Y

Tridentiger trigonocephalus Chameleon goby R D N

Trinectes maculatus Hogchoker M D N

Tylosurus crocodilus Houndfish M E Y

Uca minax Redjointed fiddler R D N

Uca pugnax Atlantic marsh fiddler R D N

Umbra limi Central mudminnow R E N
National Sediment Quality Survey

Table D-1. (Continued)

Scientific Name Common Name

Resident/

Migratorya

Demersal/

Pelagicb

Potentially

Edible

D-11

Urophycis chuss Red (squirrel) hake M E Y

Urophycis regia Spotted hake M E Y

Vaucheria Macroalgae R E N

Sorted benthic sample R D N

Unsorted benthic sample R D N a

 Fish species is considered R-resident, M-migratory, E-either resident or migratory.

b

 Fish species is considered D-demersal, P-pelagic, E-either.
National Sediment Quality Survey

E-1

Figure E-1. Lake Pepin Location Map.

APPENDIX E

TREND ANALYSIS CASE STUDIES

Introduction

This appendix discusses temporal trends in sediment

contamination by using three case studies reported in

literature. These studies are Lake Pepin (downstream

of Minneapolis, Minnesota, on the Mississippi

River), four lakes in the Chattahoochee and Flint

River Basin (Georgia), and Puget Sound

(Washington).

Case Studies

Mercury Loading to Lake Pepin from

the Upper Mississippi River

Introduction

Lake Pepin is a natural lake located on the

Mississippi River, 80 km downstream of

Minneapolis and St. Paul, Minnesota. Upstream of

Lake Pepin, the St. Croix and Minnesota Rivers join

the Mississippi River, creating a catchment of

122,000 km2 (Figure E-1). The major land use in the

Minnesota River Basin is agriculture. Forests and

wetlands make up much of the headwater Mississippi

(despite the twin cities) and St. Croix River Basins.

Because of the diverse nature of the three watersheds—Mississippi, Minnesota, and St. Croix—the flows

from these watersheds are different. The nutrient content, suspended sediment, and mercury (Hg)

concentrations are substantially higher in the Minnesota River than in the headwater Mississippi and St.

Croix Rivers. Lake Pepin has a typical hydraulic residence time of more than 5 days, sufficient to enable

settling of suspended matter. Eighty-five to ninety percent of the sediment load to Lake Pepin originates

from the Minnesota River Basin and consists of fine silt and clays. The location, the hydraulic residence

time, and the waters that flow into Lake Pepin make it a repository for sediment and the attached heavy

metals. The Mississippi River and Lake Pepin received significant inputs of Hg during the 1960s when

the other lakes in the region received no point source loads (Balogh et al., 1997). Balogh et al. (1999)

collected sediment cores from Lake Pepin to reconstruct the history of Hg loading in the Upper

Mississippi River over the past 200 years. Sediment cores were dated and stratigraphically correlated by

using210Pb, 137Cs, 14C, magnetic susceptibility, pollen analysis, and loss-on-ignition. The authors reported

that the Hg stratigraphy in Lake Pepin was undisturbed by postdepositional processes.

Total Hg Concentrations in Lake Pepin

Balogh et al. (1999) reported that Hg concentrations in deep presettlement core samples in Lake Pepin

varied between 33 and 40 nanograms per gram (ng/g). These levels of Hg are similar to those in other

lakes in the region and are comparable to the surface soil Hg concentrations of 18 to 44 ng/g in the
National Sediment Quality Survey

E-2

Figure E-2. Historical Hg Loading Rates in the Upper

Mississippi River Reconstructed from Sediments of Lake

Pepin. Source: Balogh et al, 1999.

Minnesota River Basin. Results from sediment cores indicate increased Hg concentrations after 1800,

when the European settlers established their continuous presence along the Minnesota and Mississippi

Rivers. By 1900 the Hg concentration in sediments was above 200 ng/g, and most cores indicated a

steady or decreasing Hg concentration between 1920 and 1950. However, between 1949 and 1968, Hg

concentrations began to increase, reaching peak values ranging from 452 to 669 ng/g. The concentration

of Hg in sediment cores declined substantially from the late 1960s and continued to decrease to

concentrations between 106 and 161 ng/g, as presented in Table E-1.

Table E-1. Total Hg Concentrations in Lake Pepin Sediment Cores.

Core

Number

Pre-settlement

(ng/g)

Peak

(ng/g)

Modern

(ng/g)

1 33 662 106

2 34 669 120

3 34 528 120

4 33 561 128

5 40 519 121

6 36 503 135

7 38 570 123

8 34 499 128

9 37 452 137

10 39 492 161

Source: Balogh et al., 1999.

Averaging the sediment core data by 10-year intervals, Balogh et al. (1999) estimated a loading rate of

3 kilograms per year (kg/yr) to characterize naturally occurring deposition of Hg under pristine conditions

before European settlement began around 1830. Hg deposition progressively increased during the 19th and

20th centuries; about one-half of the total Hg load was deposited from 1940 to 1970, and the peak

accumulation rate of 357 kg/yr was

identified during the 1960s. As a result of

decreasing the discharges of Hg from

municipal and industrial wastewater

plants, the Hg deposition rate in Lake

Pepin has declined by almost 70 percent

from the maximum loading during the

1960s to 110 kg/yr in 1990 through 1996

as shown in Figure E-2. However, the

current Hg loading rate of 110 kg/yr for

Lake Pepin is substantially higher than the

estimated pre-settlement loading rate of

3 kg/yr. The average Hg loading rate in the

Mississippi River immediately upstream of

Lake Pepin was reported as 102 kg/yr by

Balogh et al. (1999). This indicates that

much of the Hg loading from the Upper

Mississippi River is deposited with the

sediment into Lake Pepin.
National Sediment Quality Survey

E-3

Figure E-3. Location Map of Upper

Chattahoochee Basin.

Summary and Conclusions

The accumulation of Hg to Lake Pepin can be attributed to urban runoff and industrial/domestic users. In

addition to the Hg loading due to atmospheric deposition from coal combustion and industrial emissions,

Balogh et al. (1999) have identified other possible sources of Hg that might have contributed to the

accumulation in Lake Pepin. During the period of initial European settlement, Hg was used extensively

for medicinal purposes, in photography, in dentistry, in the tanning/dyeing industries, and in

thermometers. In the 1900s the medicinal use of Hg decreased, but its industrial use continued to escalate.

Balogh et al. (1999) have suggested that the inability of Metropolitan Wastewater Treatment Plant,

located along the Mississippi River at St. Paul, Minnesota, to cope with the increased population might

have contributed to the increased Hg loading. The capacity of the treatment plant was increased and a

secondary treatment unit was added during the years 1966 through 1968. The enhanced treatment

capacity and the diminished use of Hg after the 1960s are reflected in the substantial decline of Hg

loadings to Lake Pepin, as shown in Figure E-2. Although the investment in water pollution control has

been very successful in reducing Hg in the Upper Mississippi River, ambient levels are still 30 times

greater than the pristine conditions of the early 1800s (MCES, 2000).

Historical Trends in Organochlorine Compounds from Four Georgia Lakes

Introduction

One of the major objectives of the National Water-Quality Assessment (NAWQA) Program of the U.S.

Geological Survey is to study and define historical trends of water quality in the waters of United States.

As a part of the NAWQA program, Van Metre et al. (1997) studied temporal trends of PCBs, total DDT,

and chlordane concentrations using sediment cores in four lakes in Georgia (Figure E-3). The study used

radiochemical dating of sediment cores and measurement of chlorinated organic compounds by standard

extraction techniques. Other results for a lake in Texas and a reservoir in Iowa can be found in Van Metre

et al. (1997).

Lake Harding is located along the Chattahoochee River 184 km downstream of Atlanta and receives

drainage from metropolitan Atlanta. The Chattahoochee River flows from northeast Georgia through

metropolitan Atlanta and is a major water supply

source and receptacle for wastewater disposal for

Atlanta. The drainage to Lake Harding was reduced

when West Point Lake, 25 km upstream of Lake

Harding was constructed in 1974. Lake Walter F.

George is located on the Chattahoochee River, 290

miles downstream of Atlanta and is separated from

metropolitan Atlanta by Lake Harding and West Point

Lake. Lake Blackshear is located 314 km downstream

from Atlanta along the Flint River. Land use in the

drainage area for Lake Blackshear consists of

agriculture and forestry. Lake Seminole is located at

the confluence of the Flint and Chattahoochee Rivers,

forming the Apalachicola River. The drainage area for

Lake Seminole is mainly agricultural.

In all four locations (i.e., Lakes Harding, Walter F.

George, Blackshear and Seminole), Van Metre et al.

(1997) reported uniform, fine-textured sediments with

no evidence of bioturbation, i.e., no major
National Sediment Quality Survey

E-4

displacements within the unconsolidated sediments by benthic organisms. In addition, the authors

reported no postdepositional mixing of the sediment by current or waves in the lakes. This conclusion was

feasible by virtue of pronounced 137Cs peaks with exponential decreases in 137Cs to the sediment surfaces

at the lakes.

Trends in PCBs

Results of the findings by Van Metre et al. (1997) for sediment concentrations of PCBs are presented in

Table E-2. The peak PCB concentrations from 1950 through 1969 in Lakes Harding and Walter F.

George near Atlanta, Georgia, are higher than those in Lakes Blackshear and Seminole in rural Georgia.

Lakes Harding and Walter F. George receive effluent from metropolitan Atlanta, whereas the drainage

areas for Lakes Blackshear and Seminole are mainly agricultural and forested areas.

Table E-2. PCB Concentrations in Lake Sediments.

Period

Lake Harding

(:g/kg)

Lake Walter F. George

(:g/kg)

Lake Blackshear

(:g/kg)

Lake Seminole

(:g/kg)

Before 1950 ~50 NA NA NA

1950–1969 280–380a 220b 8 3–10

1990s 42–46 <14 1 10

% Decrease 85–88 94 88 (70)–0

NA: not available.

a

 Between 1950 and 1966.

b

 In 1968.

Source: Data extracted from Van Metre et al., 1997.

Drainage and effluent from metropolitan Atlanta was directed to Lake Harding until 1974, when West

Point Lake, upstream of Lake Harding, was constructed to intercept a portion of the flow. Van Metre et

al. (1997) have reported that PCB concentrations in seven sediment core samples at West Point Lake

ranged from 110 mg/kg to 32 mg/kg for the period from 1974 through the 1990s. The concentrations of

PCB during the corresponding time period at Lake Harding are similar (Table E-2). This led them to

conclude that the substantial decrease in PCB concentrations in sediments in Lake Harding between the

1970s and the 1990s is not due to West Point Lake’s intercepting a significant PCB load from upstream.

The decreases are due to reduced manufacture and use of PCB, and its regulation in the United States.

The authors have attributed the spatial trend of PCB concentrations in the sediments of Lake Seminole

partly to the location of the coring site.

Trends in Total DDT

Sediment concentrations of DDT and its derivatives (DDD and DDE) found in core samples in the four

lakes are shown in Table E-3. Like the temporal variation of PCBs in the lake sediments, total DDT

concentrations showed steep decline in all lake sediments presented here. This decline is consistent with

the use of DDT in the United States, with peak usage in the early 1960s until the use of DDT was

prohibited nationally in 1973. DDT concentrations in West Point Lake sediments follow a pattern similar

to that of the two downstream lakes, Lakes Harding and Walter F. George, after the early 1970s. As in the

case of PCBs, the decline in DDT concentrations in Lake Harding is not due to West Point Lake’s

intercepting a portion of the DDT load before it reaches Lake Harding. Actual reduction in usage and the

banning of DDT resulted in reduced concentrations of total DDT in lake sediments.
National Sediment Quality Survey

E-5

Table E-3. DDT Concentrations in Lake Sediments.

Period

Lake Harding

(:g/kg)

Lake Walter F.

George (:g/kg) Lake Blackshear (:g/kg)

Lake Seminole

(:g/kg)

1940s–1965 35 74a 57 30

1990s 5.9 5.5 6.5 7.5

% Decrease 83 93 89 75

a

 In 1968

Source: Data extracted from Van Metre et al., 1997.

Trends in Chlordane

Used as a pesticide in agricultural and residential areas, chlordane was introduced for agricultural use in

the United States in the early 1970s and was used extensively until 1974, when its agricultural use was

banned. It continued to be used in termite control, however, until 1988 (Van Metre and Callender, 1997).

The only permitted uses of chlordane after 1988 were in power transformers, for fire ant control, and the

use of existing stocks by homeowners. Sediment concentrations of chlordane from the study by Van

Metre et al. (1997) are presented in Table E-4. Like PCB concentrations, chlordane concentrations are

higher in urban lakes by virtue of their use in such surroundings. Unlike PCBs and total DDT, which

reached peak values during the 1960s, chlordane concentrations peaked during the late 1980s or the

1990s. Chlordane showed a minor decline during sampling in 1994/95. This might be the result of the

more stringent control of the use of chlordane even in urban areas, as permitted under current regulations.

Table E-4. Chlordane Concentrations in Lake Sediments.

Period

Lake Harding

(:g/kg)

Lake Walter F. George

(:g/kg) Lake Blackshear (:g/kg)

Lake Seminole

(:g/kg)

1960s 46–84a 6c ND ND

1990s 6–8b NA ND ND

% Decrease 87–90 NA NA NA

ND: not detected.

NA: not available.

a

 1950–1970.

b

 Early 1970s.

c

 In 1973.

Source: Data extracted from Van Metre et al., 1997.

Van Metre et al. (1997) have reported that chlordane concentrations in West Point Lake ranged between

20 to 55 mg/kg. These concentrations are higher than the chlordane concentrations in Lake Harding prior

to 1974. Hence the substantial decrease in chlordane concentration in Lake Harding is due to West Point

Lake’s serving as a sink for chlordane from urban Atlanta and other enhanced sources of chlordane.

Summary and Conclusions

The temporal decline of PCB and DDT and its derivatives, DDD and DDE, in the lake sediments parallels

the usage and regulation of these chemicals in the United States. Eisenreich et al. (1989) have reported

similar results on sediment cores from the Rochester Basin of eastern Lake Ontario. Steep increases in

PCB concentrations in sediments occurred between the 1940s and 1960s, with peak values between 1966

and 1969. These concentrations decreased substantially by 1980. Sanders et al. (1992) studied sediment

cores from a lake in rural England. Their study found similar declines in PCB and total DDT
National Sediment Quality Survey

E-6

Figure E-4. Location Map of Puget Sound.

concentrations from peak values during the respective peak production and usage periods. However,

Eisenreich et al. (1989) and Sanders et al. (1992) have alluded to the possibility of sediment mixing by

various processes, distorting the historical records of contaminant deposition. In addition, Sanders et al.

(1992) have reported that PCB and DDT in sediment cores were recorded prior to their use in the United

Kingdom. The authors have suggested long-range atmospheric transport from the United States and

mainland Europe as possible sources of such contamination.

Accumulation of Chemicals in Puget Sound Introduction

Puget Sound, an estuary in northwestern Washington State (Figure E-4), was one of the first areas in the

United States to be studied extensively for sediment contamination. Early studies from the 1980s

demonstrated fairly extensive sediment contamination, especially near major industrial embayments

(Dexter et al., 1981; Long, 1982; Malins et al., 1980; Riley et al., 1981). These early assessments

demonstrated that Puget Sound sediments were contaminated by many organic and inorganic chemicals,

including PCBs, polycyclic aromatic hydrocarbons (PAHs), and metals. Although contaminant

concentrations in sediments tended to decrease rapidly with distance from the nearshore sources,

researchers also documented widespread low-level contamination in the deepwater sediments of the main

basin of Puget Sound (Ginn and Pastorok, 1982). Also in the 1980s, several kinds of biological effects,

including cancerous tumors, were reported in organisms from contaminated areas of Puget Sound (Becker

at al., 1987).

Although contaminant levels in some surface sediments have started to decrease since pollution controls

were established in the past few decades, contamination in the deep central Puget Sound Basin are still

significantly higher than estimated preindustrial levels. In urban areas, present levels of contaminants are

up to 100 times the levels in the cleanest rural bays. Sediment samples collected from many locations in

Puget Sound, such as Bellingham Bay, Commencement Bay, Port Gardner Bay, Elliot Bay, and Eagle

Harbor, were observed to be toxic to test organisms. In 1985 the Washington State legislature established

the Puget Sound Water Quality Authority (PSWQA). The PSWQA has exercised greater control of point

source discharges to the sound by mandating industries to reduce the chemical concentrations of their

discharges.

Scientists from the Battelle/Marine Sciences Laboratory (MSL) in Sequim, Washington, collected

sediment cores from six locations in the main basin of Puget Sound during 1991. The location of these

sites was based on minimal disturbances reported due to natural or anthropogenic activity in these areas.

The study analyzed PAHs, PCBs, metals, and pesticides using sediment cores. The field sampling

techniques, the different analytical methods, and the

data analysis used for the various classes of

chemicals listed above are described in the National

Oceanic and Atmospheric Administration Technical

Memorandum NOS ORCA 111 (Lefkovitz et al.,

1997). Recovery rates for the various contaminants

were computed based on the decline from a peak

concentration to the background level over a certain

time period. Sediment ages were calculated by

ignoring the sediment density. The study used both

210Pb and 137Cs dating. By virtue of a distinct

subsurface maximum of 137Cs, the authors have

ruled out the possibility of sediment disturbance by

mixing and migration. A trend analysis was also

carried out for metals, PCBs, and DDT at three core

locations to evaluate the statistical significance of
National Sediment Quality Survey

E-7

the relationship between the concentrations of the various contaminants and the time period from 1970 to

1991. A decrease in concentration of the contaminants during this 20-year period—at a predetermined

confidence level (95 percent, i.e., " = 0.05)—served as empirical evidence of the effectiveness of the

environmental legislation enacted during this period.

Of the 16 metals analyzed, 8 showed reproducible temporal trends of increasing concentrations to a peak

followed by a decline through the present time. The peak and surface (background) concentrations of

metals showing a temporal trend from three locations and the year associated with these concentrations

are listed in Table E-5. The concentrations listed in Table E-5 were extracted from profiles of

concentration versus year at the various core sites.

Table E-5. Maximum and Surface Concentrations of Selected Metals for Three Core Locations

Collected During 1991.

Metal

Silver

(:g/g)

Arsenic

(:g/g)

Copper

(:g/g)

Mercury

(:g/g)

Lead

(:g/g)

Antimony

(:g/g)

Tin

(:g/g)

Zinc

(:g/g)

Location 1

Max Year 1965 1965 1960 1949 1965 1960 1965 1965

Max 0.91 19.5 54.6 0.479 48.9 2.05 4.9 134.6

Surface 0.68 12.5 42.7 0.179 30.3 1.28 3.94 114.7

% Decrease 25.3 35.9 21.8 62.6 38.0 37.6 19.6 14.8

Recovery Rate 0.009 0.280 0.397 0.007 0.744 0.026 0.038 0.796

Location 2

Max Year 1982 1964 1947 1947 1922 1952 1962 1962

Max 0.84 28.3 70 0.505 69.4 3.9 4.85 167.7

Surface 0.69 13.1 49.3 0.213 36.7 1.6 3.96 119.2

% Decrease 17.9 53.7 29.6 57.8 47.1 59 18.4 28.9

Recovery Rate 0.017 0.563 0.470 0.007 0.474 0.059 0.031 1.672

Location 3

Max Year 1965 1950 1963 1950 1954 1963 1963 1954

Max 0.65 23.5 64.7 0.403 62.3 2.43 4.25 128.8

Surface 0.59 17.3 52.7 0.28 44.7 1.52 2.78 115.5

% Decrease 9.2 26.4 18.5 31.3 28.3 37.4 34.6 10.3

Recovery Rate 0.002 0.155 0.444 0.003 0.489 0.034 0.054 0.369

Average Recovery Rate 0.009 0.333 0.437 0.006 0.569 0.039 0.041 0.946

95% CL ± 0.014 0.424 0.076 0.005 0.308 0.035 0.025 1.348

Source: Lefkovitz et al., 1997.

The concentration of lead in all sites peaked during the period from 1920 to 1960, followed by a declining

trend from 1960 to the present. In addition to the recovery rate, lead also showed a decreasing trend with

time for the period from 1970 to 1991. The authors of this study have attributed the effectiveness of the

current environmental regulations for this declining trend. The concentration of arsenic peaked between

the early 1950s and the 1960s. The maximum concentration of arsenic was found on two locations close

to a copper smelter operated from 1889 to the 1980s in Tacoma, Washington. Crecelius et al. (1975)

reported the smelter as the major anthropogenic source of arsenic and antimony, both by-products of the

smelting operation. Concentrations of antimony showed an increasing trend between 1900 and the 1950s

followed by a decline in concentration to the time of sampling. Though both metals showed percentage

decreases greater than 25 percent, only antimony had a statistically significant recovery rate of 0.039 at
National Sediment Quality Survey

E-8

the 95 percent confidence level. Moreover, antimony had a decreasing trend with time, whereas

concentrations of arsenic remained unchanged during this period. Levels of Hg in all three locations

decreased by an average of 50 percent from peak concentrations during late 1940s, and mercury had a

sediment recovery of 0.006 at a 95 percent confidence level. However, from the trend analysis for the

period from 1970 to 1991, the level of Hg remained the same with no statistically significant decrease

with time.

Silver showed an average recovery of 17 percent from peak values in the three locations. The trend

analysis did not show a significant correlation between time period and concentration for silver except at

location 3. Copper and zinc produced a statistically significant decreasing trend with time. The sediment

recovery rate for copper, was significant at a 95 percent confidence level, whereas zinc did not show a

significant recovery rate. Tin showed an average decrease of 24 percent in all three locations and an

average recovery rate of 0.041 (±0.025) :g/g per annum. However, concentrations have not shown a

declining trend since wide fluctuations are still reflected in surface sediments. For most metals the highest

reductions in concentrations were detected at location 2, which is in the vicinity of many sewage

treatment plant outfalls. Because secondary treatment of sewage discharge to Puget Sound was initiated

after the late 1950s, the decrease in metal concentrations can be attributed to these treatment techniques.

In addition to metal concentrations in sediment cores, the study included concentrations of PAHs, PCBs,

and DDT. Details of the levels of the various contaminants are shown in Table E-6. Concentrations of

total PAH varied from 100 (±21.6) :g/g at the lower levels of the sediment to a maximum of 6,788 μg/g

during the early 1940s, subsequently declining to an average of 1,300 μg/g at the time of sampling in

1991. Neither the sediment recovery rate nor the correlation between time period and the concentration of

total PAHs were statistically significant at the preselected 95 percent confidence level. The temporal trend

in PAHs in the sediment is consistent with the use of fossil fuel in two cities, Seattle and Tacoma in

Washington State, during the time period. Barrick (1982) has reported that combined sewage and storm

water effluent and atmospheric deposition are the major sources of PAH contamination of the sediments

of Puget Sound. Combustible PAHs derived from combustion of fossil fuels and other organic-rich

materials such as wood have a high molecular weight and consist of four to six aromatic ring compounds.

Like the total PAHs, the combustible PAHs had neither the sediment recovery rate nor the decreasing

trend between time period and concentration.

Both PCBs and DDT showed trends similar to those of metals and PAHs. PCB concentrations in Puget

Sound sediments appeared from the 1930s to peak values until the mid-1970s when the use of PCBs was

restricted in the United States. In spite of an average reduction of 68 percent from peak values of PCB in

all three locations, in only one location was a statistically significant decrease with time recorded for

sediments deposited after 1970 to the present. Moreover, the estimated sediment recovery rate of 0.649

(±0.653) :g/g was not statistically significant at the 95 percent confidence level. DDT concentrations

were reduced by an average of 42 percent in the three locations. In only one location was there a

decreasing trend in DDT concentrations for sediments deposited after 1970. The average recovery rate for

DDT in all three core locations was 0.103 (± 0.096) :g/g per year.
National Sediment Quality Survey

E-9

Table E-6. Maximum and Surface Concentrations of Selected Organic Contaminants for Three

Core Locations Collected During 1991.

Organic Contaminant

PCB

(:g/g)

DDT 

(:g/g)

Total PAH

(:g/g)

Combustible PAH

(:g/g)

Location 1

Max Year 1960 1960 1943 1943

Max 34.5 4.71 6,788 5,917

Surface 9.00 1.19 1,434 1,162

% Decrease 73.9 74.8 78.9 80.4

Recovery Rate 0.851 0.117 114 101

Location 2

Max Year 1966 1984 1942 1942

Max 25.8 5.76 3,430 2,898

Surface 5.33 4.77 1,303 1,050

% Decrease 79.3 17.2 62 63.8

Recovery Rate 0.819 0.142 43.412 37.878

Location 3

Max Year 1961 1961 1935 1935

Max 15.5 4.25 1,883 1,516

Surface 7.39 2.8 1,212 977

% Decrease 52.2 34.2 35.6 35.5

Recovery Rate 0.278 0.050 12.198 9.793

Average Recovery Rate 0.649 0.103 56.5 49.6

95% CL ± 0.653 0.096 106 95

Source: Lefkovitz et al., 1997.

Summary and Conclusions

The concentrations of copper, lead, antimony, and zinc declined in Puget Sound at statistically significant

levels from 1970 to the time of sampling in 1991. Silver, mercury, arsenic, and tin did not exhibit a

similar declining trend in sediment contamination. Moreover, the average recovery rates (i.e., decrease in

sediment contamination levels) for arsenic, silver, and zinc were not significant between the peak period

and time of sampling. Among the organic chemicals studied, only DDT showed a statistically significant

recovery rate, whereas the recovery rates for PCBs, total PAH, and combustible PAH had wider margins

of variation. The authors of this study have not given significance to the recovery rates reported for Hg

because the analytical error could be greater than the actual concentrations of Hg observed in this study.

Though the declining trend in sediment contamination for certain chemicals is an encouraging sign, it is

essential to note that there are some areas of concern with certain classes of chemicals. These may be

attributed to either previous use and disposal practices or to chemicals discharged currently to Puget

Sound from direct or indirect sources. Puget Sound is surrounded by Seattle and Tacoma, two rapidly

developing and heavily industrialized regions. Hence it is essential to control future discharges to the

area, as well as to implement the best available technology and to monitor the water quality.
National Sediment Quality Survey

E-10

References

Balogh, S.J., M.L. Meyer, and D.K. Johnson. 1997. Mercury and suspended sediment loadings in the

Lower Minnesota River. Environmental Science and Technology 31(1):198-202.

Balogh, S.J., D.R. Engstrom, J.E. Almendinger, M.L. Meyer, and D.K. Johnson. 1999. History of

mercury loading in the upper Mississippi River reconstructed from the sediments of Lake Pepin.

Environmental Science and Technology 33(19):3297-3302.

Barrick, R.C. 1982. Flux of aliphatic and polycyclic aromatic hydrocarbons to Central Puget Sound from

Seattle (Westpoint) primary sewage effluent. Environmental Science and Technology 16(10):682-

692.

Becker, D.S., T.C. Ginn, M.L. Landolt, and D.B. Powell. 1987. Hepatic lesions in English sole

(Parophrys vetulus) from Commencement Bay, Washington (USA). Marine Environmental Research

23:153-173.

Crecelius, E.A., M.H. Bothner, and R. Carpenter. 1975. Geochemistries of arsenic, mercury and related

elements in sediments of Puget Sound. Environmental Science and Technology 9(4):325-333.

Dexter, R.N., D.E. Anderson, E.A. Quinlan, L.S. Goldstein, R.M. Strickland, R.M. Kocan, M. Landolt,

J.P. Pavlou, and V.R. Clayton. 1981. A summary of knowledge of Puget Sound related to chemical

contaminants. NOAA tech. Mem. OMPA-13. National Oceanic and Atmospheric Administration,

Washington, DC.

Eisenreich, S.J., P.D. Capel, J.A. Robbins, and R. Bourbonniere. 1989. Accumulation and diagenesis of

chlorinated hydrocarbons in lacustrine sediments. Environmental Science and Technology

23(9):1116-1126.

Ginn, T.C., and R.A. Pastorok. 1982. Assessment and management of contaminated sediments in Puget

Sound. In Sediment toxicity assessment, ed. G.A. Burton, Jr., pp. 371-397. Lewis Publishers, Chelsea,

MI.

Lefkovitz. L.F., V.I. Cullinan and E.A. Crecelius. 1997. Historical trends in the accumulation of

chemicals in Puget Sound. NOAA tech. mem. NOS ORCA 111. National Oceanic and Atmospheric

Administration, Washington, DC.

Long, E.R. 1982. An assessment of marine pollution in Puget Sound. Marine Pollution Bulletin 13:380-

383.

Malins, D.C., B.B. McCain, D.W. Brown, A.K. Sparks, and H.O. Hodgins. 1980. Chemical contaminants

and biological abnormalities in central and southern Puget Sound. NOAA tech. mem. OMPA-2.

National Oceanic and Atmospheric Administration, Washington, DC.

MCES (Metropolitan Council Environmental Services). 2000. Council Directions (newsletter).

Metropolitan Council Environmental Services, St. Paul, MN. January/February.

Riley, R.G., E.A. Crecelius, M.L. O’Malley, K.H. Abel, and D.C. Mann. 1981. Organic pollutants in

waterways adjacent to Commencement Bay (Puget Sound). NOAA tech. mem. OMPA-12. National

Oceanic and Atmospheric Administration, Rockville, MD.

Sanders, G., K.C. Jones, and J. Hamilton-Taylor. 1992. Historical inputs of polychlorinated biphenyls and

other organochlorines to a dated lacustrine sediment core in rural England. Environmental Science

and Technology 26(9):1815-1821.
National Sediment Quality Survey

E-11

Van Metre, P.C., and E. Callender. 1997. Water-quality trends in White Rock Creek Basin from 1912-

1994 identified using sediment cores from White Rock Lake reservoir, Dallas, Texas. J. Paleolim.

17:239-249.

Van Metre, P.C., E. Callender, and C.C. Fuller. 1997. Historical trends in organochlorine compounds in

river basins identified using sediment cores from reservoirs. Environ. Sci. and Technol. 31(8):2339-

2344.
National Sediment Quality Survey

F-1

APPENDIX F

COMPARISON OF WATERSHEDS CONTAINING

APCS

The 1997 National Sediment Quality Survey identified 96 watersheds with areas of probable concern

(APCs) based on data collected from 1980 through 1993. Using the updated methodology described in

Chapter 2 and the same APC definition, this second report identified 96 watersheds containing an APC

based on data collected from 1990 through 1999. Table F-1 compares the watersheds identified in the

reports. Thirty-seven watersheds were identified in both reports as containing an APC because data were

available on these watersheds from both time periods, 1980 through 1993 and 1990 through 1999. Of the

remaining 59 watersheds with an APC in the previous report to Congress, 26 watersheds had fewer than

10 total monitoring stations with data evaluated, 26 had fewer than 10 Tier 1 stations, and 7 had less than

75 percent of the analyzed stations classified as Tier 1 or Tier 2 in the current report. Of the remaining 59

watersheds with an APC in the current analysis, 19 watersheds had fewer than 10 total monitoring

stations with data evaluated, 36 had fewer than 10 Tier 1 stations, and 4 watersheds had less than 75

percent of the analyzed stations classified as Tier 1 or Tier 2 in the previous report to Congress. Table F-2

and Figure F-1 present a detailed listing and geographical location, respectively, of the watersheds

summarized in Table F-1. As indicated above, this disparity could be due to a lack of data collected in

those watersheds identified as containing an APC in the first report but not containing an APC in this

report. This difference could also be due to different stations being evaluated in those watersheds that

resulted in the APC designation in the first report than were evaluated in the same watersheds in the

current report and not designated as containing an APC in this report. Therefore, it should not be inferred

that there are no ecological or human health impacts due to contaminated sediments for the stations

located in watersheds that were designated as containing APCs in the first report but are not designated as

such in this first update. Additional analysis should be conducted to determine the degree of impact due to

contaminated sediments.

Table F-1. Watersheds Containing APCs: Comparison of Previous Report to Congress and

Current Report.

Characteristics

Watershed Contained an APC

Based on Data Evaluated in the

Previous Report to Congress

Watershed Contains an APC

Based on Data Evaluated in the

Current Report to Congress

Identified as APC 37 37

Had fewer than 10 total monitoring stations 26 19

Had 10 or more total stations, but fewer than 10

stations were classified as Tier 1 26 36

Had 10 or more stations classified as Tier 1, but less

than 75 percent of all stations were classified as Tier

1 or Tier 2

7 4

Total Watersheds Containing an APC 96 96
F-2

Table F-2. Detailed Comparison of Watersheds Containing APCs: Previous Report to Congress and Current An

Cataloging Unit Number Cataloging Unit Name First Report to Congress Results Current Report to Congress Re

APC Status Number of Sampling Stations Percent of Sampling Stations in Tier 1 or Tier 2 APC Status Number of Sampling 

Total Tier 1 Tier 2 Tier 3 Total Tier 1 Tier 2 

Watersheds that contain areas of probable concern in both reports 01090001 Charles APC 708 195 402 111 84 APC 69 38 20 

01090004 Narragansett APC 48 28 20 0 100 APC 14 12 1 02030103 Hackensack-Passaic APC 103 43 58 2 98 APC 172 149 20 02030104 Sandy Hook-Staten Island APC 100 60 21 19 81 APC 194 155 31 02030105 Raritan APC 65 13 37 15 77 APC 30 15 9 02030202 Southern Long Island APC 43 11 24 8 81 APC 85 47 21 02040202 Lower Delaware APC 57 18 29 10 83 APC 26 11 15 02060003 Gunpowder-Patapsco APC 29 17 7 5 83 APC 32 22 8 03130002 Middle Chattahoochee-Lake Harding APC 27 21 4 2 93 APC 26 21 4 03160205 Mobile Bay APC 81 31 43 7 91 APC 31 17 14 04030108 Menominee APC 21 12 6 3 86 APC 21 18 2 04030204 Lower Fox APC 51 49 2 0 100 APC 26 16 5 04040001 Little Calumet-Galien APC 89 45 26 18 80 APC 24 22 1 04040002 Pike-Root APC 72 34 30 8 89 APC 60 39 13 04120101 Chautauqua-Conneaut APC 110 21 86 3 97 APC 16 13 2 06010201 Watts Bar Lake APC 89 63 7 19 79 APC 19 16 3 06020001 Middle Tennessee-Chickamauga APC 94 47 29 18 81 APC 33 15 12 07040001 Rush-Vermillion APC 14 13 1 0 100 APC 19 10 5 07080101 Copperas-Duck APC 27 17 5 5 82 APC 136 99 22 07120003 Chicago APC 103 64 36 3 97 APC 49 34 14 07120004 Des Plaines APC 110 61 43 6 95 APC 81 40 37 07120006 Upper Fox APC 60 15 40 5 92 APC 81 31 37 07130001 Lower Illinois-Senachwine Lake APC 21 11 10 0 100 APC 12 11 1 08030209 Deer-Steele APC 21 11 10 0 100 APC 24 23 1 
F-3

Table F-2. (Continued).

Cataloging

Unit Number Cataloging Unit Name

First Report to Congress Results Current Report to Congress Res

APC

Status

Number of Sampling Stations

Percent of

Sampling

Stations in

Tier 1 or

Tier 2

APC

Status

Number of Sampling S

Total Tier 1 Tier 2 Tier 3 Total Tier 1 Tier 2 

Watersheds that contain areas of probable concern in both reports (continued)

08090100 Lower Mississippi-New Orleans APC 51 16 34 1 98 APC 34 28 6 11070209 Lower Neosho APC 20 13 3 4 80 APC 20 11 5 17090012 Lower Willamette APC 76 21 51 4 95 APC 382 243 96 17110002 Strait Of Georgia APC 263 32 168 63 76 APC 443 184 179 17110013 Duwamish APC 127 48 69 10 92 APC 930 599 267 17110019 Puget Sound APC 1,383 418 851 114 92 APC 2,135 1,246 552 18050003 Coyote APC 24 18 6 0 100 APC 32 25 7 18050004 San Francisco Bay APC 64 19 37 8 88 APC 130 113 16 18070104 Santa Monica Bay APC 132 79 31 22 83 APC 132 109 21 18070201 Seal Beach APC 442 63 339 40 91 APC 59 38 18 18070204 Newport Bay APC 108 24 68 16 85 APC 74 36 20 18070301 Aliso-San Onofre APC 32 10 22 0 100 APC 19 17 1 18070304 San Diego APC 107 53 51 3 97 APC 278 208 47 

Watersheds that contain areas of probable concern in the first report but have fewer than 10 total monitoring stations in the current repor

04060103 Manistee APC 14 11 3 0 100 00004090002 Lake St. Clair APC 19 13 5 1 95 00004090004 Detroit APC 115 85 29 1 99 00004100002 Raisin APC 38 18 19 1 97 1 0 1 04100012 Huron-Vermilion APC 45 10 35 0 100 2 0 2 04110003 Ashtabula-Chagrin APC 31 10 18 3 90 8 0 4 04120103 Buffalo-Eighteenmile APC 101 59 33 9 91 4 4 0 04120104 Niagara APC 41 24 16 1 98 3 2 0 04130001 Oak Orchard-Twelvemile APC 86 39 46 1 99 5 3 0 04150301 Upper St. Lawrence APC 31 21 5 5 84 20005030101 Upper Ohio APC 53 12 29 12 77 6 6 0 
F-4

Table F-2. (Continued). Cataloging Unit Number Cataloging Unit Name First Report to Congress Results Current Report to Congress Res

APC Status Number of Sampling Stations Percent of Sampling Stations in Tier 1 or Tier 2 APC Status Number of Sampling S

Total Tier 1 Tier 2 Tier 3 Total Tier 1 Tier 2 

Watersheds that contain areas of probable concern in the first report but have fewer than 10 total monitoring stations in the current repo

05030102 Shenango APC 15 11 1 3 80 5 5 0 06010104 Holston APC 15 12 2 1 93 5 3 1 06010207 Lower Clinch APC 79 61 14 4 95 6 5 1 06030001 Guntersville Lake APC 92 25 46 21 77 4 1 1 06040001 Lower Tennessee-Beech APC 25 15 6 4 84 3 0 1 

08010100 Lower Mississippi-Memphis APC 20 14 3 3 85 5 0 2 08040207 Lower Ouachita APC 12 12 0 0 100 00010270104 Lower Kansas APC 29 12 15 2 93 8 1 1 11070207 Spring APC 41 10 25 6 85 9 9 0 17010303 Coeur D'Alene Lake APC 23 10 13 0 100 2 2 0 17030003 Lower Yakima APC 47 23 19 5 89 40017110014 Puyallup APC 19 12 6 1 95 9 8 1 18030012 Tulare-Buena Vista Lakes APC 20 10 5 5 75 3 1 2 18070105 Los Angeles APC 37 14 19 4 89 6 6 0 18070107 San Pedro Channel Islands APC 25 14 10 1 96 4 2 1 

Watersheds that contain areas of probable concern in the first report but have fewer than 10 stations classified as Tier 1 in the current rep

01090002 Cape Cod APC 108 15 73 20 82 34 6 15 02040105 Middle Delaware-Musconetcong APC 48 11 26 11 77 18 6 5 02040301 Mullica-Toms APC 42 10 22 10 76 20 6 7 02070004 Conococheague-Opequon APC 29 11 12 6 79 13 2 7 03040201 Lower Pee Dee APC 34 11 20 3 91 29 6 15 03060101 Seneca APC 16 10 3 3 81 12 8 1 03060106 Middle Savannah APC 36 20 11 5 86 21 6 5 03080103 Lower St. Johns APC 188 32 111 45 76 33 9 15 03140102 Choctawhatchee Bay APC 51 19 23 9 82 44 9 14 
F-5

Table F-2. (Continued).

Cataloging

Unit Number Cataloging Unit Name

First Report to Congress Results Current Report to Congress Resu

APC

Status

Number of Sampling Stations

Percent of

Sampling

Stations in

Tier 1 or

Tier 2

APC

Status

Number of Sampling S

Total Tier 1 Tier 2 Tier 3 Total Tier 1 Tier 2 

Watersheds that contain areas of probable concern in the first report but have fewer than 10 stations classified as Tier 1 in the current repor

03140107 Perdido Bay APC 38 10 24 4 90 19 8 9 04030102 Door-Kewaunee APC 20 12 5 3 85 12 6 1 04050001 St. Joseph APC 32 17 9 6 81 11 6 5 04100001 Ottawa-Stony APC 29 13 15 1 97 19 4 10 04100010 Cedar-Portage APC 56 13 39 4 93 32 6 20 04110001 Black-Rocky APC 59 24 31 4 93 22 4 10 05040001 Tuscarawas APC 78 10 53 15 81 16 8 4 05120109 Vermilion APC 28 12 16 0 100 34 6 24 05120111 Middle Wabash-Busseron APC 33 15 17 1 97 18 8 7 06020002 Hiwassee APC 33 13 17 3 91 17 7 9 06030005 Pickwick Lake APC 69 49 9 11 84 20 9 7 06040005 Kentucky Lake APC 30 15 14 1 97 11 1 6 07040003 Buffalo-Whitewater APC 26 17 3 6 77 10 1 5 07070003 Castle Rock APC 22 20 0 2 91 19 5 7 07090006 Kishwaukee APC 34 10 24 0 100 15 6 8 07140201 Upper Kaskaskia APC 55 31 24 0 100 26 9 16 07140202 Middle Kaskaskia APC 38 13 22 3 92 20 5 10 

Watersheds that contain areas of probable concern in the first report but have less than 75 percent of all stations classified as Tier 1 or Tier 2 report

02040203 Schuylkill APC 44 12 23 9 80 56 26 15 04040003 Milwaukee APC 90 60 16 14 84 67 32 10 07010206 Twin Cities APC 35 26 2 7 80 44 25 6 07140101 Cahokia-Joachim APC 56 18 34 4 93 37 10 14 07140106 Big Muddy APC 94 23 65 6 94 100 20 29 08080206 Lower Calcasieu APC 100 26 52 22 78 153 73 11 12040104 Buffalo-San Jacinto APC 36 10 23 3 92 57 21 20 
F-6

Table F-2. (Continued). Cataloging Unit Number Cataloging Unit Name First Report to Congress Results Current Report to Congress Resu

APC Status Number of Sampling Stations Percent of Sampling Stations in Tier 1 or Tier 2 APC Status Number of Sampling St

Total Tier 1 Tier 2 Tier 3 Total Tier 1 Tier 2 

Watersheds that contain areas of probable concern in the current report but have fewer than 10 total monitoring stations in the first report

01100007 Long Island Sound 0 0 0 0 0 APC 31 23 7 02030101 Lower Hudson 8 4 3 1 88 APC 68 60 2 02030102 Bronx 8 4 3 1 88 APC 27 26 0 05120106 Tippecanoe 0 0 0 0 0 APC 25 17 3 05120208 Lower East Fork White 1 1 0 0 100 APC 19 10 8 08030207 Big Sunflower 7 4 3 0 100 APC 38 38 0 12030102 Lower West Fork Trinity 7 5 2 0 100 APC 31 19 10 12090205 Austin-Travis Lakes 8 5 3 0 100 APC 22 16 4 14010002 Blue 1 0 0 1 0 APC 15 15 0 17080001 Lower Columbia-Sandy 5 3 2 0 100 APC 72 20 39 17100102 Queets-Quinault 0 0 0 0 0 APC 108 77 23 17110012 Lake Washington 1 0 1 0 100 APC 216 179 30 18010102 Mad-Redwood 9 4 4 1 89 APC 26 20 4 18020112 Sacramento-Upper Clear 1 0 1 0 100 APC 25 23 2 18040005 Lower Cosumnes-Lower Mokelumne 1 1 0 0 100 APC 60 23 23 18050001 Suisun Bay 3 3 0 0 100 APC 27 16 8 18070103 Calleguas 2 0 0 2 0 APC 26 26 0 18070203 Santa Ana 5 1 4 0 100 APC 98 30 53 19020201 Eastern Prince William Sound 0 0 0 0 0 APC 31 10 15 
F-7

Table F-2. (Continued).

Cataloging

Unit Number Cataloging Unit Name

First Report to Congress Results Current Report to Congress Resul

APC

Status

Number of Sampling Stations

Percent of

Sampling

Stations in

Tier 1 or

Tier 2

APC

Status

Number of Sampling Sta

Total Tier 1 Tier 2 Tier 3 Total Tier 1 Tier 2 T

Watersheds that contain areas of probable concern in the current report but have fewer than 10 stations classified as Tier 1 in the first report

01080205 Lower Connecticut 16 2 8 6 63 APC 19 17 2 01100004 Quinnipiac 21 3 18 0 100 APC 13 12 1 01100005 Housatonic 36 7 26 3 92 APC 24 22 0 01100006 Saugatuck 14 4 10 0 100 APC 19 18 1 02020003 Hudson-Hoosic 23 3 10 10 57 APC 163 155 8 02020004 Mohawk 27 0 23 4 85 APC 43 32 7 02020006 Middle Hudson 14 1 13 0 100 APC 76 55 13 02020008 Hudson-Wappinger 19 3 9 7 63 APC 40 34 6 02030201 Northern Long Island 31 8 20 3 90 APC 75 62 9 02060004 Severn 18 1 14 3 83 APC 72 48 20 02080107 York 22 3 17 2 91 APC 67 16 35 03050201 Cooper 25 9 10 6 76 APC 105 52 30 03060109 Lower Savannah 27 8 12 7 74 APC 68 20 45 03070203 Cumberland-St. Simons 39 9 13 17 56 APC 30 19 8 04140201 Seneca 14 1 11 2 86 APC 20 11 4 05060001 Upper Scioto 57 8 48 1 98 APC 50 10 32 05120201 Upper White 13 7 6 0 100 APC 42 23 14 06010205 Upper Clinch 67 8 30 29 57 APC 27 10 11 07090005 Lower Rock 54 7 33 14 74 APC 37 11 20 07090007 Green 37 6 28 3 92 APC 47 17 24 07120001 Kankakee 16 8 8 0 100 APC 34 20 10 07120002 Iroquois 25 0 25 0 100 APC 29 10 18 07120005 Upper Illinois 14 6 6 2 86 APC 24 11 12 07120007 Lower Fox 49 6 42 1 98 APC 26 10 13 07130003 Lower Illinois-Lake Chautauqua 27 6 21 0 100 APC 36 16 15 
F-8

Table F-2. (Continued). Cataloging Unit Number Cataloging Unit Name First Report to Congress Results Current Report to Congress Results APC Status Number of Sampling Stations Percent of Sampling Stations in Tier 1 or Tier 2 APC Status Number of Sampling Stati

Total Tier 1 Tier 2 Tier 3 Total Tier 1 Tier 2 Ti

Watersheds that contain areas of probable concern in the current report but have fewer than 10 stations classified as Tier 1 in the first repo

07130007 South Fork Sangamon 48 8 37 3 94 APC 16 12 4 07130011 Lower Illinois 44 5 38 1 98 APC 36 17 14 07130012 Macoupin 16 0 15 1 94 APC 19 10 9 15060106 Lower Salt 13 7 4 2 85 APC 52 39 13 16050203 Carson Desert 33 7 10 16 52 APC 19 14 5 17020001 Franklin D. Roosevelt Lake 32 4 24 4 88 APC 66 52 9 17100105 Grays Harbor 20 1 18 1 95 APC 139 102 33 18050002 San Pablo Bay 36 8 21 7 81 APC 101 69 28 18060006 Central Coastal 71 8 62 1 99 APC 54 25 22 18060011 Alisal-Elkhorn Sloughs 18 1 15 2 89 APC 34 25 9 18070106 San Gabriel 15 5 6 4 73 APC 34 21 11 

Watersheds that contain areas of probable concern in the current report but have less than 75 percent of all stations classified as Tier 1 or T

report

02040205 Brandywine-Christina 101 22 21 58 43 APC 220 109 67 403050202 South Carolina Coastal 64 14 29 21 67 APC 60 33 18 03100206 Tampa Bay 196 11 54 131 33 APC 70 41 18 103140105 Pensacola Bay 96 12 47 37 62 APC 59 20 25 1
F-9
National Sediment Quality Survey

F-10