o
The Incidence and Severity of
Sediment Contamination in
Surface Waters of the
United States, National Sediment
Quality Survey: Second Edition
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1.
2.
Cover photos
1. Photo by Jonas Jordan, courtesy of USAGE
2. Photo by Lynn Betts, courtesy of USDA NRCS
3. Photo by Ron Nichols, courtesy of USDA NRCS
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EPA-823-R-04-007
UJ
(9
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
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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.
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National Sediment Quality Survey
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
in
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National Sediment Quality Survey
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-l
Appendix B. Description of Evaluation Parameters Used in the NSI Data Evaluation B-l
Aquatic Life Assessments B-l
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-l
Sediment Values C-l
IV
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Fish Tissue Concentrations C-l
Biota-Sediment Accumulation Factors C-l
Methodology for Combining Chemical Data Using a Risk-Based Approach C-l
Frequency of Detection C-2
References C-2
Appendix D. Species Characteristics Related to NSI Bioaccumulation Data D-l
Appendix E. Trend Analysis Case Studies E-l
Introduction E-l
Case Studies E-l
Mercury Loading to Lake Pepin from the Upper Mississippi River E-l
Total Hg Concentrations in Lake Pepin E-l
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-l
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TABLES
Table 1.
Table 2-1.
Table 2-2.
Table 3-1.
Table 3-2.
Table 3-3.
Table 3-4.
Table 3-5.
Table 3-6.
Table 3-7.
Table 3-8.
Table 3-9.
Table 3-10.
Table 3-11.
Table 3-12.
Table 3-13.
Table 3-14.
Table 3-15.
Table 3-16.
Table 3-17.
USGS Cataloging Unit Numbers and Names for Watersheds
Containing APCs xxiii
Number of Stations Evaluated in the NSI by State 2-6
NSI Data Evaluation Approach 2-11
National Assessment: Evaluation Results for Sampling
Stations and River Reaches by EPA Region 3-3
Regions 1-10: River Reach and Watershed Evaluation
Summary
. 3-5
Tier Classification Summary 3-7
USGS Cataloging Unit Numbers and Names for Watersheds
Containing APCs 3-11
River Reaches with 10 or More Tier 1 Sampling Stations
Located in Watersheds Containing APCs 3-14
Contaminated Sediment CERCLA Sites 3-16
Region 1: River Reach and Watershed Evaluation Summary 3-22
Region 1: Evaluation Results for Sampling Stations and
River Reaches by State 3-22
Region 1: Watersheds Containing Areas of Probable
Concern for Sediment Contamination 3-24
Region 1: Number of Tier 1 Stations in Region 1 That Are
Located in Watersheds Containing APCs by Waterbody
Name 3-24
Region 2: River Reach and Watershed Evaluation Summary 3-25
Region 2: Evaluation Results for Sampling Stations and
River Reaches by State 3-26
Region 2: Watersheds Containing Areas of Probable
Concern for Sediment Contamination 3-28
Region 2: Number of Tier 1 Stations in Region 2 That Are
Located in Watersheds Containing APCs by Waterbody
Name 3-28
Region 3: River Reach and Watershed Evaluation Summary 3-30
Region 3: Evaluation Results for Sampling Stations and
River Reaches by State 3-30
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-l. 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 2ESGTUFCV TOT to 2ESGTUFCV13 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-l5
Table B-6. Minimum Detectable Differences (MDDs) Calculated from
Round Robin Test Data B-16
Table C-l. Screening Values for Chemicals Evaluated C-4
Table C-2. Toxic Equivalency Factors for Dioxins, Furans, and Dioxin-
Like PCBs C-10
Table C-3. Toxic Equivalency Factors for Various PAHs C-l 1
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-l. Species Characteristics Related to Tissue Residue Data D-2
Table E-l. 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-l. Watersheds Containing APCs: Comparison of Previous
Report to Congress and Current Report F-l
Table F-2. Detailed Comparison of Watersheds Containing APCs:
Previous Report to Congress and Current Analysis F-2
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National Sediment Quality Survey
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 3-1 OB. 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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National Sediment Quality Survey
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-l. Relationship Between the Station, Sediment Chemistry,
Tissue Residue, and Toxicity Tables and the Related
Look-up Tables A-3
Figure B-l. 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-l. Lake Pepin Location Map E-l
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-l. 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 (USAGE), to conduct a comprehensive national survey of data regarding the quality of aquatic
sediments in the United States. Section 5 03 (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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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 offish 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
JA 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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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 offish 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 screening-
level 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
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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 offish 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.
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Figure 1. Location of All Evaluated Sampling Stations.
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At Least One
Tier 1 Station
3.6%
No Data
91.2%
At Least One
Tier 2 Station and
Zero Tier 1 Stations
2.9%
All Tier 3 Stations
2.3%
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.
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Figure 3. Watersheds Identified as Containing APCs.
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Table 1. USGS Cataloging Unit Numbers and Names for Watersheds Containing APCs.
Map
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Cataloging
Unit
Number
01080205
01090001
01090004
01100004
01100005
01100006
01100007
02020003
02020004
02020006
02020008
02030101
02030102
02030103
02030104
02030105
02030201
02030202
02040202
02040205
02060003
02060004
02080107
03050201
03050202
03060109
03070203
03100206
03130002
03140105
03160205
04030108
04030204
04040001
04040002
04120101
04140201
05060001
05120106
05120201
05120208
06010201
06010205
06020001
07040001
07080101
07090005
07090007
Cataloging Unit Name
Lower Connecticut
Charles
Narragansett
Quinnipiac
Housatonic
Saugatuck
Long Island Sound
Hudson-Hoosic
Mohawk
Middle Hudson
Hudson- Wappinger
Lower Hudson
Bronx
Hackensack-Passaic
Sandy Hook-Staten Island
Raritan
Northern Long Island
Southern Long Island
Lower Delaware
Brandywine-Christina
Gunpowder-Patapsco
Severn
York
Cooper
South Carolina Coastal
Lower Savannah
Cumberland-St. Simons
Tampa Bay
Middle Chattahoochee-Lake Harding
Pensacola Bay
Mobile Bay
Menominee
Lower Fox
Little Calumet-Galien
Pike-Root
Chautauqua-Conneaut
Seneca
Upper Scioto
Tippecanoe
Upper White
Lower East Fork White
Watts Bar Lake
Upper Clinch
Middle Tennessee-Chickamauga
Rush-Vermillion
Copperas-Duck
Lower Rock
Green
Map
No.
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
Cataloging
Unit
Number
07120001
07120002
07120003
07120004
07120005
07120006
07120007
07130001
07130003
07130007
07130011
07130012
08030207
08030209
08090100
11070209
12030102
12090205
14010002
15060106
16050203
17020001
17080001
17090012
17100102
17100105
17110002
17110012
17110013
17110019
18010102
18020112
18040005
18050001
18050002
18050003
18050004
18060006
18060011
18070103
18070104
18070106
18070201
18070203
18070204
18070301
18070304
19020201
Cataloging Unit Name
Kankakee
Iroquois
Chicago
Des Plaines
Upper Illinois
Upper Fox
Lower Fox
Lower Illinois-Senachwine Lake
Lower Illinois-Lake Chautauqua
South Fork Sangamon
Lower Illinois
Macoupin
Big Sunflower
Deer-Steele
Lower Mississippi-New Orleans
Lower Neosho
Lower West Fork Trinity
Austin-Travis Lakes
Blue
Lower Salt
Carson Desert
Franklin D. Roosevelt Lake
Lower Columbia-Sandy
Lower Willamette
Queets-Quinault
Grays Harbor
Strait Of Georgia
Lake Washington
Duwamish
Puget Sound
Mad-Redwood
Sacramento-Upper Clear
Lower Cosumnes-Lower Mokelumne
Suisun Bay
San Pablo Bay
Coyote
San Francisco Bay
Central Coastal
Alisal-Elkhom Sloughs
Calleguas
Santa Monica Bay
San Gabriel
Seal Beach
Santa Ana
Newport Bay
Aliso-San Onofre
San Diego
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., USAGE, 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 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, 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
GDI chronic daily intake
CERCLA Comprehensive Environmental Response, Compensation, and Liability Act
CSMC Contaminated Sediment Management Committee
CWA Clean Water Act
DDD p,p' -dichlorodiphenyldichloroethane
DDE p,p' -dichlorodiphenyldichloroethylene
DDT p,p' -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
MOD 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-/>-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
USAGE 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 (USAGE), 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 overtime. 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 sediment-
associated 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 (ODD 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 (USAGE, 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 (USAGE, 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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National Sediment Quality Survey
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.
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Areas of Concern in the
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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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National Sediment Quality Survey
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).
1-7
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National Sediment Quality Survey
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National Sediment Quality Survey
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 USAGE, 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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National Sediment Quality Survey
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 offfyalella aztecd) 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)
JA screening-level assessment typically identifies many potential problems that prove not to be
significant upon further analysis (i.e., more conservative).
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National Sediment Quality Survey
- 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 offish 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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National Sediment Quality Survey
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 (USAGE)
- 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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National Sediment Quality Survey
• 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 abroad-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
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National Sediment Quality Survey
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 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. 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
Region 2
Region 3
Region 4
Region 5
Connecticut
Maine
Massachusetts
New Hampshire
Rhode Island
Vermont
New Jersey
New York
Puerto Rico
Delaware
District of Columbia
Maryland
Pennsylvania
Virginia
West Virginia
Alabama
Florida
Georgia
Kentucky
Mississippi
North Carolina
South Carolina
Tennessee
Illinois
Indiana
Michigan
Minnesota
Ohio
Wisconsin
121
0
127
4
18
5
492
753
10
234
6
290
216
1,577
105
173
1,157
263
63
187
291
576
164
1,370
233
30
339
441
772
Region 6
Region 7
Region 8
Region 9
Region 10
Arkansas
Louisiana
New Mexico
Oklahoma
Texas
Iowa
Kansas
Missouri
Nebraska
Colorado
Montana
North Dakota
South Dakota
Utah
Wyoming
Arizona
California
Hawaii
Nevada
Alaska
Idaho
Oregon
Washington
34
396
167
292
600
113
119
194
157
133
11
33
32
56
29
123
1,535
18
76
290
38
599
4,336
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Total Number of Stations: 17,348
Figure 2-1. NSI Sediment Sampling Stations Evaluated.
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Total Number of Stations: 3,446
Figure 2-3. NSI Toxicity Test Sampling Stations Evaluated.
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National Sediment Quality Survey
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 offish 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
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
Data Used to Determine Classifications
Sediment Chemistry
Sediment chemistry value exceeds a
draft equilibrium partitioning sediment
guideline (ESG) derived from a final
or secondary acute value (FAV or
SAV)' 1
OR
[SEM]-[AVS] > 5 for the sum of
molar concentrations of Cd, Cu, Ni,
Pb, Zn, and '/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 > 1"'° 4
OR
Sediment chemistry TBP exceeds
EPA's human health cancer risk of
10"" or a noncancer hazard quotient
(HQ)oflO' 5
OR
For chemicals with log Kow < 5.5,
sediment chemistry TBP exceeds
EPA's human health cancer risk of
W5, a noncancer HQ of 1, or FDA's
tolerance/action/guidance levels" 6
Sediment chemistry value exceeds a
draft ESG derived from a final or
secondary chronic value (FCV or
SCV)' 7
OR
[SEM]-[AVS] = 0-5 for the sum of
molar concentrations of Cd, Cu, Ni,
Pb, Zn, and V2 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 > l'-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 levels" 11
OR
AND
OR
Tissue Residue
Tissue levels of chemicals with a
log Kow> 5.5 in samples'1 that
exceed EPA's human health
cancer risk of 10"5, a noncancer
HQ of 1, or FDA's
tolerance/action/guidance
levels 12
Tissue levels of chemicals with a
log Kow < 5.5 in samples'1 that
exceed EPA's human health
cancer risk of 10"5, a noncancer
HQ of 1, or FDA's tolerance/
action/guidance levels 13
Tissue levels of chemicals with a
log Kow < 5.5 in samples'1 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
OR
Toxicity
Toxicity demonstrated by one
solid-phase sediment test resulting
in (1) < 75% control-adjusted
survival, (2) freshwater
invertebrate (Hyalella azteca)
sublethal toxicity < 90% control-
adjusted length, or (3) freshwater
invertebrate (Hyalella azteca,
Chironomus tentans, and
Chironomus riparius) sublethal
toxicity < 70% control-adjusted
weight 15
OR
Any sample meeting the
benchmark described under Tier 2
for toxicity using at least two
different species 16
Toxicity demonstrated by one
solid-phase sediment test resulting
in (1) < 90% control-adjusted
survival (but > 75% control-
adjusted survival),
(2) freshwater invertebrate
(Hyalella azteca) sublethal
toxicity < 95% control-adjusted
length (but > 90% control-
adjusted length), or (3) freshwater
invertebrate (Hyalella azteca,
Chironomus tentans, and
Chironomus riparius) sublethal
toxicity < 90% control-adjusted
weight (but >70% control-
adjusted weight) 17
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.
If total organic carbon (TOG) 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 TOG
value of 0.2% was used.
Metals: Cd = cadmium, Cu = copper, Ni = nickel, Pb = lead, Zn = zinc, Ag = silver.
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.
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 ofAVS to SEMMolar 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)Ahreshold 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 field-
collected 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 2ESGTUPAH 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
(ZESGTU)—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
2ESGTUFCVTOTfrom 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 2ESGTUFCVTOT to theZESGTUFCV 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 ZESGTU. 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 offish
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/gui-
dance 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.
Toxidty 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 aztecd) that indicated sublethal toxicity by lengths of less than 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 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 aztecd) 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 offish 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-l, 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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National Sediment Quality Survey
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 Tierl level as
[SEM]- [AVS] > 5. Thus, this use of [SEM]- [AVS] represents a hybrid of a theoretical approach and a
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National Sediment Quality Survey
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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National Sediment Quality Survey
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 biota-
sediment 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.
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National Sediment Quality Survey
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 offish 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.
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National Sediment Quality Survey
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National Sediment Quality Survey
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.
3-1
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Puerto Rico
Total Number of Stations: 19,398
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1
Figure 3-1. Location of All Evaluated Sampling Stations.
-------
Table 3-1. National Assessment: Evaluation Results for Sampling Stations and River Reaches by EPA Region.
EPA Region (State)
Region 1
(CT, ME, MA, NH, RI, VT)
Region 2
(NJ, NY, PR)
Region 3
(DE, DC, MD, PA, VA, WV)
Region 4
(AL, FL, GA, KY, MS, NC,
SC, TN)
Region 5
(IL, IN, MI, MN, OH, WI)
Region 6
(AR, LA, NM, OK, TX)
Region 7
(IA, KS, MO, NE)
Region 8
(CO, MT, ND, SD, UT, WY)
Region 9
(AZ, CA, HI, NV)
Region 10
(AK, ID, OR, WA)
TotalforUnited States'
Station Evaluation
Total Number of
Stations
Evaluated
275
1,255
2,428
2,874
3,185
1,489
583
294
1,752
5,263
19,398
Tierl
No.
182
901
714
841
1,146
425
134
79
1,040
2,886
8,348
%"
66.2
71.8
29.4
29.3
36.0
28.5
23.0
26.9
59.4
54.8
43.0
Tier 2
No.
64
228
809
1,022
1,095
392
239
95
429
1,473
5,846
%"
23.3
18.2
33.3
35.6
34.4
26.3
41.0
32.3
24.5
28.0
30.1
Tier 3
No.
29
126
905
1,011
944
672
210
120
283
904
5,204
%"
10.5
10.0
37.3
35.2
29.6
45.1
36.0
40.8
16.2
17.2
26.8
River Reach Evaluation"
No. of
Stations
Not
Identified
by an
RF1
Reach1
28
13
103
15
—
—
—
—
18
290
467
Reaches
With at
Least
One
Station
in Tier 1
97
217
385
444
532
226
94
59
156
177
2,298
Reaches
With at
Least
One
Station
in Tier 2"
23
102
313
461
401
222
161
77
63
121
1,891
Reaches
With All
Stations
in Tier 3
5
45
301
301
316
289
136
68
40
49
1,506
No. of
Reaches
With at
Least One
Station
Evaluated
125
364
999
1,206
1,249
737
391
204
259
347
5,695
Total
Reaches
in
Region
2,764
1,845
3,388
10,078
6,151
7,577
4,915
13,860
4,686
10,462
64,591
Percent
of All
Reaches
in Region
With at
Least One
Station
Evaluated
4.5
19.7
29.5
12.0
20.3
9.7
8.0
1.5
5.5
3.3
8.8
Percent
of
Reaches
With at
Least
One
Tier 1 or
Tier 2
Station
4.3
17.3
20.6
9.0
15.2
5.9
5.2
1.0
4.7
2.8
6.5
9
O.
B
n
rt-
O
" 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.
"Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed.
11 No stations in these reaches were included in Tier 1.
'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.
-------
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Alaska Hawaii
**^
Puerto Rico
Total Number of Stations: 8,348
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Figure 3-2: Sampling Stations Classified as Tier 1 (Associated Adverse Effects Are Probable).
-------
Table 3-2. Regions 1-10: River Reach and Watershed Evaluation Summary.
EPA Region (State)
Region 1
(CT, ME, MA, NH, RI, VT)
Region 2
(NJ, NY, PR)
Region 3
(DE, DC, MD, PA, VA, WV)
Region 4
(AL, FL, GA, KY, MS, NC, SC, TN)
Region 5
(IL, IN, MI, MN, OH, WI)
Region 6
(AR, LA, NM, OK, TX)
Region 7
(IA, KS, MO, NE)
Region 8
(CO, MT, ND, SD, UT, WY)
Region 9
(AZ, CA, HI, NV)
Region 10
(AK, ID, OR, WA)
Total for United States"
River Reach Evaluation"
Total
Number of
River
Reaches
2,764
1,845
3,388
10,078
6,151
7,577
4,915
13,860
4,686
10,462
64,591
River
Reaches
With at
Least One
Tierl
Station
97
(3.5%)
217
(11.8%)
385
(11.4%)
444
(4.4%)
532
(8.6%)
226
(3.0%)
94
(1.9%)
59
(0.4%)
156
(3.3%)
177
(1.7%)
2,298
(3.6%)
River
Reaches
With at
Least One
Tierl
Station and
Zero Tier 1
Stations
23
(0.8%)
102
(5.5%)
313
(9.2%)
461
(4.6%)
401
(6.5%)
222
(2.9%)
161
(3.3%)
77
(0.6%)
63
(1.3%)
121
(1.2%)
1,891
(2.9%)
River
Reaches
With All
Tier3
Stations
5
(0.2%)
45
(2.4%)
301
(8.9%)
301
(3.0%)
316
(5.1%)
289
(3.8%)
136
(2.8%)
68
(0.5%)
40
(0.9%)
49
(0.5%)
1,506
(2.3%)
River
Reaches
With No
Data
2,639
(95.5%)
1,481
(80.3%)
2,389
(70.5%)
8,872
(88.0%)
4,902
(79.7%)
6,840
(90.3%)
4,524
(92.0%)
13,656
(98.5%)
4,427
(94.5%)
10,115
(96.7%)
58,896
(91.2%)
Watershed Evaluation
Total
Number of
Watersheds
62
71
126
307
278
402
238
385
288
355
2,264
Watersheds
Containing
APCs
9
(14.5%)
17
(23.9%)
7
(5.6%)
13
(4.2%)
25
(9.0%)
4
(1.0%)
1
(0.4%)
1
(0.3%)
19
(6.6%)
10
(2.8%)
96
(4.2%)
Watersheds
With at
Least One
Tierl
Station
13
(21.0%)
35
(49.3%)
96
(76.2%)
142
(46.3%)
144
(51.8%)
117
(29. 1%)
60
(25.2%)
34
(8.8%)
41
(14.2%)
48
(13.5%)
658
(29. 1%)
Watersheds
With at
Least One
Tierl
Station and
Zero Tier 1
Stations
6
(9.7%)
3
(4.2%)
11
(8.7%)
57
(18.6%)
31
(11.2%)
69
(17.2%)
72
(30.3%)
41
(10.6%)
19
(6.6%)
29
(8.2%)
302
(13.3%)
Watersheds
With all
Tier 3
Stations
0
(0.0%)
3
(4.2%)
4
(3.2%)
25
(8.1%)
19
(6.8%)
44
(10.9%)
29
(12.2%)
31
(8.1%)
15
(5.2%)
21
(5.9%)
168
(7.4%)
Watersheds
With No
Data
34
(54.8%)
13
(18.3%)
8
(6.3%)
70
(22.8%)
59
(21.2%)
168
(41.8%)
76
(31.9%)
278
(72.2%)
194
(67.4%)
247
(69.6%)
1,040
(45.9%)
9
O.
B
n
rt-
O
"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 sum of reaches or watersheds
in the regions.
-------
National Sediment Quality Survey
At Least One
Tier 1 Station
3.6%
No Data
91.2%
At Least One
Tier 2 Station and
Zero Tier 1 Stations
2.9%
All Tier 3 Stations
2.3%
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.
3-6
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National Sediment Quality Survey
Table 3-3. Tier Classification Summary.
Tier Evaluation Parameter
Table 2-2
Evaluation
Parameter
Reference
Number of Stations
Total
Tierl
Tier 2
Tier 3
Aquatic Life Assessment
Draft ESG analysis
SEM analysis
Logistic regression model analysis
Draft PAH toxicity unit analysis
Toxicity analysis
Toxicity demonstrated in two or more species classified
as Tier 2
1,7
2,8
3,9
4,10
15,17
16
12,649
739
17,056
9,442
3,446
n/a
69
10
4,513
545
745
54
228
205
6,415
1,144
858
n/a
12,352
524
6,128
7,753
1843
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
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
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
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
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
5
11
12
14
6 and 13
Total1
14,484
2,364
n/a
19,398
5,372
n/a
1,133
n/a
46
8,348
n/a
3,236
n/a
563
n/a
5,846
n/a
5,876
n/a
668
n/a
5,204
" 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
3-7
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National Sediment Quality Survey
(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 screening-
level 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
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
3-9
-------
National Sediment Quality Survey
No Data
43.1%
Contain APCs
4.2%
At Least One
Tier 1 Station
29.1%
All Tier 3
Stations
7.4%
At Least One
Tier 2 Station and
Zero Tier 1 Stations
13.3%
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.
3-10
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National Sediment Quality Survey
Table 3-4. USGS Cataloging Unit Numbers and Names for Watersheds Containing APCs.
Map
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Cataloging
Unit
Number
01080205
01090001
01090004
01100004
01100005
01100006
01100007
02020003
02020004
02020006
02020008
02030101
02030102
02030103
02030104
02030105
02030201
02030202
02040202
02040205
02060003
02060004
02080107
03050201
03050202
03060109
03070203
03100206
03130002
03140105
03160205
04030108
04030204
04040001
04040002
04120101
04140201
05060001
05120106
05120201
05120208
06010201
06010205
06020001
07040001
07080101
07090005
07090007
Cataloging Unit Name
Lower Connecticut
Charles
Narragansett
Quinnipiac
Housatonic
Saugatuck
Long Island Sound
Hudson-Hoosic
Mohawk
Middle Hudson
Hudson- Wappinger
Lower Hudson
Bronx
Hackensack-Passaic
Sandy Hook-Staten Island
Raritan
Northern Long Island
Southern Long Island
Lower Delaware
Brandywine-Christina
Gunpowder-Patapsco
Severn
York
Cooper
South Carolina Coastal
Lower Savannah
Cumberland-St. Simons
Tampa Bay
Middle Chattahoochee-Lake Harding
Pensacola Bay
Mobile Bay
Menominee
Lower Fox
Little Calumet-Galien
Pike-Root
Chautauqua-Conneaut
Seneca
Upper Scioto
Tippecanoe
Upper White
Lower East Fork White
Watts Bar Lake
Upper Clinch
Middle Tennessee-Chickamauga
Rush-Vermillion
Copperas-Duck
Lower Rock
Green
Map
No.
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
Cataloging
Unit
Number
07120001
07120002
07120003
07120004
07120005
07120006
07120007
07130001
07130003
07130007
07130011
07130012
08030207
08030209
08090100
11070209
12030102
12090205
14010002
15060106
16050203
17020001
17080001
17090012
17100102
17100105
17110002
17110012
17110013
17110019
18010102
18020112
18040005
18050001
18050002
18050003
18050004
18060006
18060011
18070103
18070104
18070106
18070201
18070203
18070204
18070301
18070304
19020201
Cataloging Unit Name
Kankakee
Iroquois
Chicago
Des Plaines
Upper Illinois
Upper Fox
Lower Fox
Lower Illinois-Senachwine Lake
Lower Illinois-Lake Chautauqua
South Fork Sangamon
Lower Illinois
Macoupin
Big Sunflower
Deer-Steele
Lower Mississippi-New Orleans
Lower Neosho
Lower West Fork Trinity
Austin-Travis Lakes
Blue
Lower Salt
Carson Desert
Franklin D. Roosevelt Lake
Lower Columbia-Sandy
Lower Willamette
Queets-Quinault
Grays Harbor
Strait Of Georgia
Lake Washington
Duwamish
Puget Sound
Mad-Redwood
Sacramento-Upper Clear
Lower Cosumnes-Lower Mokelumne
Suisun Bay
San Pablo Bay
Coyote
San Francisco Bay
Central Coastal
Alisal-Elkhorn Sloughs
Calleguas
Santa Monica Bay
San Gabriel
Seal Beach
Santa Ana
Newport Bay
Aliso-San Onofre
San Diego
Eastern Prince William Sound
5-11
-------
z
88
rt-
5'
9
St
i'
fD
rt-
O
05
1
Figure 3-5. Watersheds Identified as Containing APCs.
-------
National Sediment Quality Survey
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 orwaterbody 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 File 1 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
5-13
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National Sediment Quality Survey
Table 3-5. River Reaches with 10 or More Tier 1 Sampling Stations Located in Watersheds
Containing APCs.
Cataloging
Unit
Number
01090001
02020003
02020008
02030101
02030102
02030103
02030104
02030201
02030202
02040205
02060004
03050201
03050202
03100206
03140105
04030108
04040002
07080101
07090007
07120003
07120004
07120006
08030207
Cataloging Unit Name
Charles
Hudson-Hoosic
Hudson- Wappinger
Lower Hudson
Bronx
Hackensack-Passaic
Sandy Hook-Staten
Island
Northern Long Island
Southern Long Island
Brandywine-Christina
Severn
Cooper
South Carolina Coastal
Tampa Bay
Pensacola Bay
Menominee
Pike-Root
Copperas-Duck
Green
Chicago
Des Plaines
Upper Fox
Big Sunflower
RF1 Reach ID
01090001022
02020003031
02020003056
02020003057
02020003078
02020008031
02030101009
02030101039
02030102001
02030103001
02030103010
02030103023
02030104001
02030104002
02030104004
02030201001
02030201003
02030201004
02030201005
02030202028
02040205011
02040205013
02060004002
02060004004
03050201030
03050201034
03050202010
03100206009
03100206019
03140105011
04030108001
04040002002
07080101007
07080101008
07080101009
07080101020
07090007005
07120003001
07120004011
07120004016
07120006011
08030207005
RF1 Reach Name
Boston Bay
Hudson River
Hudson River
Hudson River
Hudson River
Hudson River
Hudson River
Hudson River
Long Island Sound
Hackensack River
Passaic River
Rockaway River
Upper New York Bay
Newark Bay
Staten Island
Upper Bay
Long Island Sound
Long Island Sound
Long Island Sound
Jamaica Bay
Christina River
Red Clay Creek
Severn River
South River
Cooper River
Cooper River
Ashley River
Hillsborough Bay
Tampa Bay
Pensacola Bay
Menominee River
Lake Michigan
Mississippi River
Mississippi River
Mississippi River
Duck Creek
Green River
Chicago Sanitary Ship Canal
Des Plains River
Salt Creek
Fox River
Big Sunflower River
Number
of Tier 1
Stations
16
16
16
29
67
12
10
11
26
17
105
11
36
62
24
10
17
10
15
31
71
11
15
15
18
11
25
28
11
17
12
32
12
48
12
16
11
17
13
12
10
14
Total
Number of
Stations in
Reach
32
16
16
33
67
12
10
11
27
21
106
19
39
74
29
11
17
11
18
41
147
15
22
22
27
12
25
34
14
27
12
46
14
58
19
17
23
20
23
16
14
14
3-14
-------
National Sediment Quality Survey
Table 3-5. (Continued)
Cataloging
Unit
Number
08030209
08090100
12030102
12090205
15060106
17080001
17090012
17100102
17100105
17110002
17110012
17110013
17110019
18010102
18040005
18050002
18050004
Cataloging Unit Name
Deer-Steele
Lower Mississippi-New
Orleans
Lower West Fork Trinity
Austin- Travis Lakes
Lower Salt
Lower Columbia-Sandy
Lower Willamette
Queets-Quinault
Grays Harbor
Strait Of Georgia
Lake Washington
Duwamish
Puget Sound
Mad-Redwood
Lower Cosumnes-Lower
Mokelumne
San Pablo Bay
San Francisco Bay
RF1 Reach ID
08030209003
08090100004
12030102049
12090205004
15060106001
15060106026
17080001009
17090012017
17090012018
17090012019
17090012026
17100102040
17100102042
17100105022
17100105025
17110002019
17110002022
17110002030
17110002038
17110012001
17110012003
17110012004
17110012009
17110013001
17110013003
17110013005
17110019022
17110019024
17110019068
17110019081
17110019084
17110019085
17110019086
17110019087
18010102010
18040005005
18050002002
18050002036
18050004001
18050004038
18050004049
RF1 Reach Name
Black Bayou
Mississippi River
Mountain Creek Lake
Colorado River
Salt River
Cave Creek
Columbia River
Willamette River
Willamette River
Willamette River
Columbia Slough
Matheny Creek
Sams River
Big Creek
Humptulips River, East Fork
Bellingham Bay
Bellingham Bay
Strait Of Georgia
Fidalgo Island
Lake Washington Ship Canal
Lake Union
Lake Union
Lake Washington
Duwamish Waterway
Elliot Bay
Green River
Sinclair Inlet
Sinclair Inlet
Budd Inlet
Chambers Creek
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Arcata Bay
Comanche Reservoir
San Pablo Bay
San Pablo Bay
San Francisco Bay
San Francisco Bay
San Francisco Bay
Number
of Tier 1
Stations
18
18
11
13
20
19
11
54
42
125
11
50
27
86
14
71
58
22
12
69
58
14
24
82
498
12
165
11
62
19
31
605
198
83
12
15
28
14
59
10
33
Total
Number of
Stations in
Reach
19
18
12
13
28
24
49
97
49
197
26
74
34
86
14
104
114
61
43
74
59
14
45
130
745
15
191
30
130
20
45
848
257
230
15
36
29
20
66
14
35
5-15
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National Sediment Quality Survey
Table 3-5. (Continued)
Cataloging
Unit
Number
18060006
18070103
18070104
18070106
18070201
18070203
18070204
18070304
Cataloging Unit Name
Central Coastal
Calleguas
Santa Monica Bay
San Gabriel
Seal Beach
Santa Ana
Newport Bay
San Diego
RF1 Reach ID
18060006015
18070103009
18070104001
18070104002
18070104003
18070104005
18070106021
18070201001
18070203001
18070204002
18070204005
18070304001
18070304008
18070304014
RF1 Reach Name
Charro Creek
Pacific Ocean
Pacific Ocean
Dominguez Channel
Pacific Ocean
Pacific Ocean
Pacific Ocean
Pacific Ocean
Santa Ana River
San Diego Creek
Pacific Ocean
Pacific Ocean
San Diego Bay
San Diego Bay
Number
of Tier 1
Stations
19
18
51
13
35
10
17
38
27
15
10
41
13
139
Total
Number of
Stations in
Reach
20
18
62
13
46
10
26
59
85
23
32
49
19
169
Table 3-6. Contaminated Sediment CERCLA Sites.
Map ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
CERCLA Site
GE - Housatonic River
Hocomonco Pond
New Bedford
Nyanza Chemical Waste Dump
Sullivan's Ledge
Loring Air Force Base
Newport Naval Education & Training Center
Pine Street Canal
Burnt Fly Bog
Chemical Insecticide Corp.
Chemical Leaman Tank Lines, Inc.
Lipari Landfill
Alcoa Aggregation Site (Grasse River, Massena)
Batavia Landfill
FMC Corp. (Dublin Road Landfill)
General Motors (Central Foundry Division) (Massena)
Hooker (102nd Street)
Hudson River PCBs
Love Canal
Marathon Battery Corp.
Onondaga Lake
Reynolds Metals Co. (Massena)
Richardson Hill Road Landfill/Pond
York Oil Co.
E.I. du Pont de Nemours & Co., Inc. (Newport Pigment Plant Landfill)
EPA Region
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
State
MA
MA
MA
MA
MA
ME
RI
VT
NJ
NJ
NJ
NJ
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
DE
3-16
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National Sediment Quality Survey
Table 3-6. (Continued)
Map ID
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
CERCLA Site
Halby Chemical Co.
Metal Banks
Dixie Caverns County Landfill
Stauffer Chemical Co. (Cold Creek Plant)
Triana/Tennessee River
Koppers Co., Inc. (Charleston Plant)
Sangamo Weston, Inc. /Twelve-Mile Creek/Lake Hartwell PCB Contamination
Ross Metals Inc.
Outboard Marine Corp.
Sangamo Electric Dump/Crab Orchard National Wildlife Refuge (USDOI)
Yeoman Creek Landfill
Allied Paper, Inc. /Portage Creek/Kalamazoo River
Ford Motor Co. (Monroe-River Raisin-Ford Outfall)
Manistique River/Harbor
Velsicol Chemical Corp. (Michigan)
Fox River
Sheboygan Harbor & River
Southern Lakes Trap & Skeet Club
Bayou Bonfouca
Cleveland Mill
Alcoa (Point Comfort )/Lavaca Bay
Bailey Waste Disposal
Nahant Marsh
Eagle Mine
Rocky Mountain Arsenal (U.S. Army)
Silver Bow Creek/Butte Area
Monticello Mill Tailings (U.S. DOE)
Sharon Steel Corp. (Midvale Tailings)
Concord Naval Weapons Station
McCormick & Baxter Creosoting Co.
Moffett Field Naval Air Station
United Heckathorn Co.
Ketchikan Pulp Company
Bunker Hill (OU3 Coeur d'Alene Basin)
McCormick & Baxter Creosoting Co. (Portland Plant)
Commencement Bay, Nearshore/Tide Flats
Harbor Island (Lead)
Old Navy Dump/Manchester Laboratory (U.S. EPA/NOAA)
Pacific Sound Resources
Puget Sound Naval Shipyard Complex
Wyckoff Co. /Eagle Harbor
EPA Region
3
3
3
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
6
6
6
6
7
8
8
8
8
8
9
9
9
9
10
10
10
10
10
10
10
10
10
State
DE
PA
VA
AL
AL
SC
SC
TN
IL
IL
IL
MI
MI
MI
MI
WI
WI
WI
LA
NM
TX
TX
IA
CO
CO
MT
UT
UT
CA
CA
CA
CA
AK
ID
OR
WA
WA
WA
WA
WA
WA
5-17
-------
oo
Z
88
rt-
5'
9
St
i'
fD
rt-
O
05
1
Figure 3-6. Contaminated Sediment CERCLA Sites.
-------
National Sediment Quality Survey
22
16
13
Figure 3-7. Contaminated Sediment CERCLA Sites for New England/Mid-Atlantic and
Washington.
5-19
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National Sediment Quality Survey
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.
5-20
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National Sediment Quality Survey
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
3-21
-------
National Sediment Quality Survey
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
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
2,764
97(3.5%)
23 (0.8%)
5 (0.2%)
2,639 (95.5%)
Watershed Classification
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
Watersheds With All Tier 3 Stations
Watersheds With No Data
62
9 (14.5%)
13(21.0%)
6 (9.7%)
0 (0.0%)
34 (54.8%)
Table 3-8. Region 1: Evaluation Results for Sampling Stations and River Reaches by State.
State
Connecticut
Maine
Massachusetts
New Hampshire
Rhode Island
Vermont
Region le
Station Evaluation
•e
*o "S
i+ 3
pA «
g _%
Z e
11
121
—
127
4
18
5
275
Tierl
No.
103
—
64
4
11
—
182
%b
85.1
—
50.4
100.0
61.1
0.0
66.2
Tier 2
No.
15
—
38
—
6
5
64
%b
12.4
—
29.9
0.0
33.3
100.0
23.3
Tier 3
No.
3
—
25
—
1
—
29
%b
2.5
—
19.7
0.0
5.6
0.0
10.5
River Reach Evaluation"
£
13 S
1/3 fe
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'5 ^
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28
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28
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as _
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57
—
33
4
10
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97
^
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A l/l
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ci 0
6
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11
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1,675
270
283
56
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2,764
.S
u - "S
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^ ™ a
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S § 5O
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29.8
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4.5
«
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29.3
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16.3
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21.4
2.9
4.3
" River reaches based on EPA River Reach File (RF1).
b Percent of all stations evaluated in the NSI in the state.
0 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.
'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.
5-22
-------
+ Tier 1
Tier 2
APC
Figure 3-8. Region 1: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing APCs.
Z
88
p^
5'
9
C/5
n
O.
O
e
£.
•f
05
to
-------
National Sediment Quality Survey
Table 3-9. Region 1: Watersheds Containing Areas of Probable Concern for Sediment
Contamination.
Cataloging
Unit Number
01080205
01090001
01090004
01100004
01100005
01100006
01100007
02020003
02030202
Cataloging Unit Name
Lower Connecticut
Charles
Narragansett
Quinnipiac
Housatonic
Saugatuck
Long Island Sound
Hudson-Hoosic
Southern Long Island
State(s)a
CT,MA
MA
MA,RI
CT
CT, MA, NY
CT, (NY)
CT,NY
NY, MA, (VT)
NY, CT, NJ
Number of Sampling Stations
Total
19
69
14
13
24
19
31
163
85
Tierl
17
38
12
12
22
18
23
155
47
Tier 2
2
20
1
1
0
1
7
8
21
Tier 3
0
11
1
0
2
0
1
0
17
Percent of
Sampling
Stations in Tier
1 or Tier 2
100
84
93
100
92
100
97
100
80
1 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
Boston Bay
Long Island Sound
Atlantic Ocean
Connecticut River
Housatonic River
Quinnipiac River
Boston Harbor And Mystic River Area
Naugatuck River
Taunton River
Woonasquatucket River
Hockanum River
Narragansett Bay
Pawtuxet River
Scanite River
Bantam River
Coginchaug River
Number of
Tierl
Stations
24
15
13
11
11
5
4
4
o
6
o
6
2
2
2
2
I
I
Waterbody
Conanicut Island
Green River
Hoosic River
Ipswich River
Konkapot River
Mattabesset River
Muddy River
Neponset River
Norwalk River
Rhode Island
Rippowan River
Saugatuck Reservoir
Saugus River
Shepaug River
Still River
Windsor Brook
Number of
Tierl
Stations
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5-24
-------
National Sediment Quality Survey
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
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
1,845
217(11.8%)
102 (5.5%)
45 (2.4%)
1,481 (80.3%)
Watershed Classification
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
Watersheds With All Tier 3 Stations
Watersheds With No Data
71
17 (23.9%)
35 (49.3%)
3 (4.2%)
3 (4.2%)
13 (18.3%)
5-25
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National Sediment Quality Survey
Table 3-12. Region 2: Evaluation Results for Sampling Stations and River Reaches by State.
State
New Jersey
New York
Puerto Rico
Region 2e
Station Evaluation
Total Number of
Stations Evaluated
492
753
10
1,255
Tier 1
No.
341
556
4
901
%b
69.3
73.8
40.0
71.8
Tier 2
No.
102
120
6
228
%b
20.7
15.9
60.0
18.2
Tier 3
No.
49
77
—
126
%b
10.0
10.2
0.0
10.0
River Reach Evaluation"
Number of Stations Not
Identified by an RFl Reach1
—
3
10
13
Reaches With at Least
One Station in Tier 1
62
166
—
217
Reaches With at Least
One Station in Tier 2"
45
57
—
102
Reaches With All
Stations in Tier 3
21
24
—
45
Number of Reaches With at
Least One Station Evaluated
128
247
—
364
'So
.3
1/3
Cj
&
"o
304
1,562
—
1,845
Percent of All Reaches in
Region With at Least
One Station Evaluated
42.1
15.8
—
19.7
Percent of Reaches With at
Least One Tier 1
or Tier 2 Station
35.2
14.3
—
17.3
" River reaches based on EPA River Reach File (RF1).
b Percent of all stations evaluated in the NSI in the state.
0 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed.
11 No stations in these reaches were included in Tier 1.
" 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.
5-26
-------
Figure 3-9. Region 2: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing APCs.
to
Z
88
in
o.
1'
9
O
e
IF
05
fB
-------
National Sediment Quality Survey
Table 3-13. Region 2: Watersheds Containing Areas of Probable Concern for Sediment
Contamination.
Cataloging
Unit Number
01100005
01100007
02020003
02020004
02020006
02020008
02030101
02030102
02030103
02030104
02030105
02030201
02030202
02040202
02040205
04120101
04140201
Cataloging Unit Name
Housatonic
Long Island Sound
Hudson-Hoosic
Mohawk
Middle Hudson
Hudson- Wappinger
Lower Hudson
Bronx
Hackensack-Passaic
Sandy Hook-Staten Island
Raritan
Northern Long Island
Southern Long Island
Lower Delaware
Brandywine-Christina
Chautauqua-Conneaut
Seneca
State(s)a
CT, MA, NY
CT,NY
NY, MA, (VT)
NY
NY, (MA)
NY
NJ, NY, (CT)
NY
NJ,NY
NJ,NY
NJ
NY
NY, CT, NJ
NJ, PA
DE, PA, NJ,
(MD)
NY, OH, PA
NY
Number of Sampling Stations
Total
24
31
163
43
76
40
68
27
172
194
30
75
85
26
220
16
20
Tierl
22
23
155
32
55
34
60
26
149
155
15
62
47
11
109
13
11
Tier 2
0
7
8
7
13
6
2
0
20
31
9
9
21
15
67
2
4
Tier 3
2
1
0
4
8
0
6
1
3
8
6
4
17
0
44
1
5
Percent of
Sampling
Stations in Tier
1 or Tier 2
92
97
100
91
89
100
91
96
98
96
80
95
80
100
80
94
75
11 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
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
Upper Bay
Number of
Tierl
Stations
266
111
74
62
36
31
24
17
16
12
12
11
10
Waterbody
Beden Brook
Big Timber Creek, South Fork
Black Creek
Canajoharie Creek
Canopus Creek
Cayadutta Creek
Cincinnati Creek
Claverack Creek
Clyde River
Cranbury Brook
East Bay
East Canada Creek
Esopus Creek
Number of
Tier 1 Stations
1
1
1
1
1
1
1
1
1
1
1
1
1
3-28
-------
National Sediment Quality Survey
Table 3-14. (Continued).
Waterbody
East River
Sauquoit Creek
Valatie Kill
Arthur Kill
Lower Bay
Ninemile Creek
Delaware River
Rahway River
Saddle River
Smithtown Bay
Green Brook
Hoosic River
Onondaga Lake
Raritan River
Batten Kill
Croton River
LishaKill
Millstone River
Normans Kill
Onondaga Creek
Raritan Bay
Vloman Kill
Walloomsac River
Whippany River
Amawalk Reservoir
Number of
Tierl
Stations
9
9
7
6
6
5
4
4
4
4
3
3
3
3
2
2
2
2
2
2
2
2
2
2
1
Waterbody
Fall Creek
Flint Creek
Great Peconic Bay
Hohohus Brook
Lake Erie, US Shore
Little Peconic Bay
Manalapan Brook
Mud Creek
Neshanic River
Onesquethaw Creek
Oriskany Creek
Pennsauken Creek
Pompton Creek
Ramapo River
Raritan River, North Branch
Raritan River, South Branch
Repaupo Creek
Seneca River
Silver Creek
Stony Brook
Swamp River
Walnut Creek
Wanaque Reservoir
Wappinger Creek
Woodbury Creek
Number of
Tierl
Stations
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
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.
5-29
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National Sediment Quality Survey
Table 3-15. Region 3: River Reach and Watershed Evaluation Summary.
River Reach Classification
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
3,388
385(11.4%)
Watersheds With
at Least One Tier
1 Station
313(9.2%)
301 (8.9%)
2,389 (70.5%)
Watershed Classification
Total Number of Watersheds
Watersheds Containing APCs
96 (76.2%)
Watersheds With at Least One Tier
2 Station and Zero Tier 1 Stations
Watersheds With All Tier 3
Stations
Watersheds With No Data
126
7 (5.6%)
11 (8.7%)
4 (3.2%)
8 (6.3%)
Table 3-16. Region 3: Evaluation Results for Sampling Stations and River Reaches by State.
State
Delaware
District of
Columbia
Maryland
Pennsylvania
Virginia
West Virginia
Region 3e
Station Evaluation
Total Number of
Stations Evaluated
234
6
290
216
1,577
105
2,428
Tier 1
No.
112
2
125
121
313
41
714
%b
47.9
33.3
43.1
56.0
19.8
39.0
29.4
Tier 2
No.
74
3
106
43
556
27
809
%b
31.6
50.0
36.6
19.9
35.3
25.7
33.3
Tier 3
No.
48
1
59
52
708
37
905
%b
20.5
16.7
20.3
24.1
44.9
35.2
37.3
River Reach Evaluation"
Number of Stations Not
Identified by an RFl Reach1
—
—
44
—
59
—
103
Reaches With at Least
One Station in Tier 1
13
6
61
94
204
47
385
Reaches With at Least
One Station in Tier 2"
15
2
52
21
227
27
313
Reaches With All
Stations in Tier 3
4
—
15
25
252
12
301
Number of Reaches With at
Least One Station Evaluated
32
8
128
140
683
86
999
'5x
.3
1/3
CJ
|
"o
91
16
440
710
1,330
1,000
3,388
Percent of All Reaches in
Region With at Least
One Station Evaluated
35.2
50.0
29.1
19.7
51.4
8.6
29.5
Percent of Reaches With at
Least One Tier 1
or Tier 2 Station
30.8
50.0
25.7
16.2
32.4
7.4
20.6
" River reaches based on EPA River Reach File (RF1).
b Percent of all stations evaluated in the NSI in the state.
0 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed.
11 No stations in these reaches were included in Tier 1.
'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.
J-J
-------
National Sediment Quality Survey
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 3-1OA.
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 3-1 OB) 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.
> O1
5-31
-------
z
88
rt-
5'
9
St
i'
fD
rt-
O
05
1
Figure 3-10A. Region 3: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing APCs.
-------
National Sediment Quality Survey
Table 3-17. Region 3: Watersheds Containing Areas of Probable Concern for Sediment
Contamination.
Cataloging
Unit Number
02040202
02040205
02060003
02060004
02080107
04120101
06010205
Cataloging Unit Name
Lower Delaware
Brandywine-Christina
Gunpowder-Patapsco
Severn
York
Chautauqua-Conneaut
Upper Clinch
State(s)a
NJ,PA
DE, (MD), PA, NJ
MD, (PA)
MD
VA
NY, OH, PA
TN, VA
Number of Sampling Stations
Total
26
220
32
72
67
16
27
Tierl
11
109
22
48
16
13
10
Tier 2
15
67
8
20
35
2
11
Tier 3
0
44
2
4
16
1
6
Percent of
Sampling
Stations in Tier
1 or Tier 2
100
80
94
94
76
94
78
1 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
Christina River
Severn River
South River
Curtis Bay
Pamunkey River
Red Clay Creek
Lake Erie, US Shore
White Clay Creek
Brandywine Creek
Magothy River
Black River
Chesapeake Bay
Clinch River
Number of
Tierl
Stations
82
24
19
13
11
11
10
7
6
5
4
3
3
Waterbody
Delaware River
Queen Creek
Bush River
Chesapeake-Delaware Canal
York River
Clinch River, Corder Branch
Clinch River, North Fork
Darby Creek
Guest River
Mudlick Creek
Stock Creek
White Clay Creek, East Branch
Number of
Tierl
Stations
3
3
2
2
2
1
1
1
1
1
1
1
-------
National Sediment Quality Survey
Status of Chemical Contaminant Effects on Living Resources
in the Chesapeake Bay's Tidal Rivers
Susquehanna River
Bush River
Gunpowder River
Middle River
Back River
Baltimore Harbor/
Patapsco River
Magothy River
Severn River
Chester River
Anacostia River
— Choptank River
Potomac River
Eastern Bay
Wye River
Miles River
Northeast River
Elk River
Bohemia River
Sassafras River
Rappahannock River
Mattaponi River
Pamunkey River
Nanticoke River
Wicomico River
Manokin River
Big Annemessex River
— Pocomoke River
Tangier Sound
James River
Elizabeth River
ff Region of Concern - Area with probable effects
1 | Area of Emphasis - Area with potential adverse effects
^H Area with Low Probability for Adverse Effects
^H Area with Insufficient or Inconclusive Data
I | Not characterized due to historically low levels of chemical contamination
Watershed ^^| Chesapeake Bay
The Chesapeake Bay Watershed
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.
> O A
3-34
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National Sediment Quality Survey
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
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
10,078
444 (4.4%)
461 (4.6%)
301 (3.0%)
8,872 (88.0%)
Watershed Classification
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
Watersheds With All Tier 3 Stations
Watersheds With No Data
307
13(4.2%)
142 (46.3%)
57(18.6%)
25(8.1%)
70 (22.8%)
-------
National Sediment Quality Survey
Table 3-20. Region 4: Evaluation Results for Sampling Stations and River Reaches by State.
State
Alabama
Florida
Georgia
Kentucky
Mississippi
North Carolina
South Carolina
Tennessee
Region 4e
Station Evaluation
Total Number of
Stations Evaluated
173
1,157
263
63
187
291
576
164
2,874
Tierl
No.
56
270
115
24
97
33
169
77
841
%b
32.4
23.3
43.7
38.1
51.9
11.3
29.3
47.0
29.3
Tier 2
No.
55
346
117
27
52
150
218
57
1,022
%b
31.8
29.9
44.5
42.9
27.8
51.5
37.8
34.8
35.6
Tier 3
No.
62
541
31
12
38
108
189
30
1,011
%b
35.8
46.8
11.8
19.0
20.3
37.1
32.8
18.3
35.2
River Reach Evaluation"
Number of Stations Not
Identified by an RFl Reach1
—
15
—
—
—
—
—
—
15
Reaches With at Least
One Station in Tier 1
42
86
95
31
35
35
100
59
444
Reaches With at Least
One Station in Tier 2"
28
96
57
30
13
109
109
45
461
Reaches With All
Stations in Tier 3
36
75
27
18
13
57
74
19
301
Number of Reaches With at
Least One Station Evaluated
106
257
179
79
61
201
283
123
1,206
Total Reaches in Region
1,592
888
1,707
1,276
995
1,456
1,110
1,490
10,078
Percent of All Reaches in
Region With at Least
One Station Evaluated
6.7
28.9
10.5
6.2
6.1
13.8
25.5
8.3
12.0
Percent of Reaches With at
Least One Tier 1
or Tier 2 Station
4.4
20.5
8.9
4.8
4.8
9.9
18.8
7.0
9.0
" River reaches based on EPA River Reach File (RFl).
b Percent of all stations evaluated in the NSI in the state.
0 Stations not identified by an RFl reach were located in coastal areas, open water areas, or areas where RFl was not developed.
11 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
03050201
03050202
03060109
03070203
03100206
03130002
03140105
03160205
06010201
06010205
06020001
08030207
08030209
Cataloging Unit Name
Cooper
South Carolina Coastal
Lower Savannah
Cumberland-St. Simons
Tampa Bay
Middle Chattahoochee-
Lake Harding
Pensacola Bay
Mobile Bay
Watts Bar Lake
Upper Clinch
Middle Tennessee-
Chickamauga
Big Sunflower
Deer-Steele
State(s)a
SC
sc
GA, SC
GA
FL
AL, GA
FL
AL
TN
TN,VA
GA, TN, (AL)
MS
MS, (LA)
Number of Sampling Stations
Total
105
60
68
30
70
26
59
31
19
27
33
38
24
Tierl
52
33
20
19
41
21
20
17
16
10
15
38
23
Tier 2
30
18
45
8
18
4
25
14
3
11
12
0
1
Tier 3
23
9
3
3
11
1
14
0
0
6
6
0
0
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
78
85
96
90
84
96
76
100
100
78
82
100
100
11 No data were available for states listed in parentheses.
5-36
-------
Z
88
Sf.
O
s.
05
fB
g.
§'
fD
e^-
O
VI
I
Figure 3-11. Region 4: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing AFCs.
-------
National Sediment Quality Survey
Table 3-22. Region 4: Number of Tier 1 Stations in Region 4 That Are Located in Watersheds
Containing APCs by Waterbody Name.
Waterbody
Cooper River
Ashley River
Hillsborough Bay
Big Sunflower River
Black Bayou
Tennessee River
Pensacola Bay
Savannah River
Mobile Bay
Chattahoochee River
Turtle River
Tampa Bay
Watts Bar Lake
Bogue Phalia
Little Sunflower River
Atlantic Ocean
Lake Chickamauga
Savannah River, South Channel
St. Simons Sound
Steele Bayou
Carpenter Creek
Long Cane Creek
Muddy Creek
Utoy Creek
Wando River
Number of
Tierl
Stations
45
28
28
23
18
18
17
17
15
13
13
11
8
7
6
3
3
3
3
3
2
2
2
2
2
Waterbody
Back River
Bullfrog Creek
Clinch River
Cooper River, West Branch
Cooper River/Charleston Harbor
Cumberland River
Deer Creek
Dorchester Creek
Fort Loudoun Lake
Goose Creek
Hillabatchee Creek
Intracoastal Waterway
Jekyll Island
Lake Harding
Lake Moultrie
Lake Washington
Norris Lake
Noses Creek
Old Tampa Bay
Quiver River
Santa Rosa Sound
Silver Creek
St. Simons Island
West Chickamauga Creek
West Pont Lake
Number of
Tierl
Stations
1
1
1
1
1
1
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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
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
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
6,151
532 (8.6%)
401 (6.5%)
316(5.1%)
4,902 (79.7%)
Watershed Classification
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
Watersheds With All Tier 3 Stations
Watersheds With No Data
278
25 (9.0%)
144(51.8%)
31(11.2%)
19(6.8%)
59(21.2%)
Table 3-24. Region 5: Evaluation Results for Sampling Stations and River Reaches by State.
State
Illinois
Indiana
Michigan
Minnesota
Ohio
Wisconsin
Region 5e
Station Evaluation
o "^5
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233
30
339
441
772
3,185
Tierl
No.
490
130
14
118
71
323
1,146
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55.8
46.7
34.8
16.1
41.8
36.0
Tier 2
No.
577
74
10
38
240
156
1,095
%b
42.1
31.8
33.3
11.2
54.4
20.2
34.4
Tier 3
No.
303
29
6
183
130
293
944
%b
22.1
12.4
20.0
54.0
29.5
38.0
29.6
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-------
National Sediment Quality Survey
Table 3-25. Region 5: Watersheds Containing Areas of Probable Concern for Sediment
Contamination.
Cataloging
Unit Number
04030108
04030204
04040001
04040002
04120101
05060001
05120106
05120201
05120208
07040001
07080101
07090005
07090007
07120001
07120002
07120003
07120004
07120005
07120006
07120007
07130001
07130003
07130007
07130011
07130012
Cataloging Unit Name
Menominee
Lower Fox
Little Calumet-Galien
Pike-Root
Chautauqua-Conneaut
Upper Scioto
Tippecanoe
Upper White
Lower East Fork White
Rush-Vermillion
Copperas-Duck
Lower Rock
Green
Kankakee
Iroquois
Chicago
Des Plaines
Upper Illinois
Upper Fox
Lower Fox
Lower Illinois-
Senachwine Lake
Lower Illinois-Lake
Chautauqua
South Fork Sangamon
Lower Illinois
Macoupin
State(s)a
M,WI
WI
IL, IN, (MI)
IL,WI
NY, OH, PA
OH
IN
IN
IN
MN,WI
IL,IA
IL, (WI)
IL
IL, IN, (MI)
IL,IN
IL,IN
IL,WI
IL
IL,WI
IL
IL
IL
IL
IL
IL
Number of Sampling Stations
Total
21
26
24
60
16
50
25
42
19
19
136
37
47
34
29
49
81
24
81
26
12
36
16
36
19
Tierl
18
16
22
39
13
10
17
23
10
10
99
11
17
20
10
34
40
11
31
10
11
16
12
17
10
Tier 2
2
5
1
13
2
32
3
14
8
5
22
20
24
10
18
14
37
12
37
13
1
15
4
14
9
Tier 3
1
5
1
8
1
8
5
5
1
4
15
6
6
4
1
1
4
1
13
3
0
5
0
5
0
Percent of
Sampling
Stations in Tier
1 or Tier 2
95
81
96
87
94
84
80
88
95
79
89
84
87
88
97
98
95
96
84
88
100
86
100
86
100
1 No data were available for states listed in parentheses.
5-41
-------
National Sediment Quality Survey
Table 3-26. Region 5: Number of Tier 1 Stations in Region 5 That Are Located in Watersheds
Containing APCs by Waterbody Name.
Waterbody
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
Kanakee River
Fox Lake
Wolf Lake
Little Calumet River
Rock River
Tippecanoe River
Deeds Creek
Indiana Harbor Canal
Macoupin Creek
Mill Creek
Beaver Creek
Chicago River, North Branch
Hodges Creek
Iroquois River
Lake Chautauqua
Mazoon River
Root River
Sangchris Lake
Scioto River
Yellow Creek
Calumet River
Calumet Sag Channel
Du Page River
Horse Creek
Indian Creek
Lake Taylorsville
Sugar Creek
White River, East Fork
Apple Creek
Chicago Ship Canal
Elkhorn Creek
Number of
Tier 1 Stations
43
38
37
34
19
18
18
16
16
15
15
15
9
9
7
7
7
6
5
5
5
4
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
2
2
2
Waterbody
Fall Creek
Honey Creek
Jackson Creek
Lake Calumet
Mckee Creek
Olentangy River
Otter Creek
Sandy Creek
Sangamon River, South Fork
Ashwaubenon Creek
Blackberry Creek
Buck Creek
Burns Ditch
Cicero Creek
Du Page River, East Branch
Du Page River, West Branch
Eagle Creek
Exline Slough
Kent Creek, North Fork
Kyte River
Lake Muskego
Lake Springfield
Leatherwood Creek
Macoupin Creek, Dry Fork
Mauvaise Terre Creek
Mauvaise Terre Lake
Mazon River, West Fork
Mud Creek
Pewaukee Lake
Pike River
Pipe Creek
Portage-Burns Waterway
Prairie Creek
Rock Creek
Somonauk Creek
Spring Creek
Sugar Run
Trim Creek
Vermillion River
West Bureau Creek
White Lick Creek
Number of
Tier 1 Stations
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3-42
-------
National Sediment Quality Survey
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
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
7,577
226 (3.0%)
222 (2.9%)
289 (3.8%)
6.840 (90.3%)
Watershed Classification
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
Watersheds With All Tier 3
Stations
Watersheds With No Data
402
4 (1.0%)
117(29.1%)
69 (17.2%)
44 (10.9%)
168 (41.8%)
5-43
-------
National Sediment Quality Survey
Table 3-28. Region 6: Evaluation Results for Sampling Stations and River Reaches by State.
State
Arkansas
Louisiana
New Mexico
Oklahoma
Texas
Region 6e
Station Evaluation
•e
o "8
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34
396
167
292
600
1,489
Tierl
No.
13
128
10
69
205
425
%b
38.2
32.3
6.0
23.6
34.2
28.5
Tier 2
No.
15
97
48
47
185
392
%b
44.1
24.5
28.7
16.1
30.8
26.3
Tier 3
No.
6
171
109
176
210
672
%b
17.6
43.2
65.3
60.3
35.0
45.1
River Reach Evaluation"
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11 No stations in these reaches were included in Tier 1.
" 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
08090100
11070209
12030102
12090205
Cataloging Unit Name
Lower Mississippi-New Orleans
Lower Neosho
Lower West Fork Trinity
Austin-Travis Lakes
State(s)
LA
AR,OK
TX
TX
Number of Sampling Stations
Total
34
20
31
22
Tierl
28
11
19
16
Tier 2
6
5
10
4
Tier 3
0
4
2
2
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
100
80
94
91
5-44
-------
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-------
National Sediment Quality Survey
Table 3-30. Region 6: Number of Tier 1 Stations in Region 6 That Are Located in Watersheds
Containing APCs by Waterbody Name.
Waterbody
Mississippi River
Colorado River
Mountain Creek Lake
Neosho River
Trinity River, West Fork
Gulf Of Mexico
Pryor Creek
Big Fossile Creek
Fort Gibson Lake
Number of
Tierl
Stations
22
14
11
5
4
3
3
2
2
Waterbody
Barton Creek
East Bay
Lake Austin
Lake Hudson
Mississippi River, Pass Loutre
Mississippi River, SW Pass
Mountain Creek
Rush Creek
Number of
Tierl
Stations
1
1
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.
5-46
-------
National Sediment Quality Survey
Table 3-31. Region 7: River Reach and Watershed Evaluation Summary.
River Reach Classification
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
4,915
94(1.9%)
161 (3.3%)
136(2.8%)
4,524 (92.0%)
Watershed Classification
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
Watersheds With All Tier 3 Stations
Watersheds With No Data
238
1 (0.4%)
60 (25.2%)
72 (30.3%)
29 (12.2%)
76(31.9%)
Table 3-32. Region 7: Evaluation Results for Sampling Stations and River Reaches by State.
State
Iowa
Kansas
Missouri
Nebraska
Region T
Station Evaluation
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H S
113
119
194
157
583
Tier 1
No.
59
18
40
17
134
%b
52.2
15.1
20.6
10.8
23.0
Tier 2
No.
35
53
89
62
239
%b
31.0
44.5
45.9
39.5
41.0
Tier 3
No.
19
48
65
78
210
%b
16.8
40.3
33.5
49.7
36.0
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11 No stations in these reaches were included in Tier 1.
' 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
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5-47
-------
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-------
National Sediment Quality Survey
Table 3-33. Region 7: Watersheds Containing Areas of Probable Concern for Sediment
Contamination.
Cataloging
Unit Number
07080101
Cataloging Unit Name
Copperas-Duck
State(s)
IL,IA
Number of Sampling Stations
Total
136
Tierl
99
Tier 2
22
Tier 3
15
Percent of
Sampling
Stations in Tier
1 or Tier 2
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
Mississippi River
Number of
Tierl
Stations
49
Waterbody
Duck Creek
Number of
Tier 1 Stations
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.
5-49
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National Sediment Quality Survey
Table 3-35. Region 8: River Reach and Watershed Evaluation Summary.
River Reach Classification
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
13,860
59 (0.4%)
77 (0.6%)
68 (0.5%)
13,656(98.5%)
Watershed Classification
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
Watersheds With All Tier 3 Stations
Watersheds With No Data
385
1 (0.3%)
34 (8.8%)
41 (10.6%)
31 (8.1%)
278 (72.2%)
Table 3-36. Region 8: Evaluation Results for Sampling Stations and River Reaches by State.
State
Colorado
Montana
North Dakota
South Dakota
Utah
Wyoming
Region 8e
Station Evaluation
Total Number of
Stations Evaluated
133
11
33
32
56
29
294
Tier 1
No.
52
—
6
18
2
1
79
%b
39.1
0.0
18.2
56.3
3.6
3.4
26.9
Tier 2
No.
43
3
16
6
17
10
95
%b
32.3
27.3
48.5
18.8
30.4
34.5
32.3
Tier 3
No.
38
8
11
8
37
18
120
%b
28.6
72.7
33.3
25.0
66.1
62.1
40.8
River Reach Evaluation"
Number of Stations Not
Identified by an RFl Reach1
—
—
—
—
—
—
—
Reaches With at Least
One Station in Tier 1
36
—
9
12
2
1
59
Reaches With at Least
One Station in Tier 2"
33
3
14
4
13
13
77
Reaches With All
Stations in Tier 3
21
9
4
8
18
14
68
Number of Reaches With at
Least One Station Evaluated
90
12
27
24
33
28
204
§
•a
QJ
.S
J=
1
2,204
5,606
1,042
1,691
1,080
2,474
13,860
Percent of All Reaches in
Region With at Least
One Station Evaluated
4.1
0.2
2.6
1.4
3.1
1.1
1.5
Percent of Reaches With at
Least One Tier 1
or Tier 2 Station
3.1
0.1
2.2
0.9
1.4
0.6
1.0
" River reaches based on EPA River Reach File (RF1).
b Percent of all stations evaluated in the NSI in the state.
0 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed.
11 No stations in these reaches were included in Tier 1.
'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.
5-50
-------
z
88
p^
5'
9
C/5
CD
g.
§'
CD
o
e
IF
05
CD
Figure 3-15. Region 8: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing APCs.
-------
National Sediment Quality Survey
Table 3-37. Region 8: Watersheds Containing Areas of Probable Concern for Sediment
Contamination.
Cataloging
Unit Number
14010002
Cataloging Unit Name
Blue
State(s)
CO
Number of Sampling Stations
Total
15
Tierl
15
Tier 2
0
Tier 3
0
Percent of
Sampling
Stations in Tier
1 or Tier 2
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
Blue River
Swan River
Snake River
Number of
Tier 1 Stations
8
3
2
Waterbody
Dillon Reservoir
Tenmile Creek
Number of Tier
1 Stations
1
1
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.
5-52
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National Sediment Quality Survey
Table 3-39. Region 9: River Reach and Watershed Evaluation Summary.
River Reach Classification
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
4,686
156(3.3%)
63(1.3%)
40 (0.9%)
4,427 (94.5%)
Watershed Classification
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
Watersheds With All Tier 3 Stations
Watersheds With No Data
288
19(6.6%)
41 (14.2%)
19 (6.6%)
15(5.2%)
194(67.4%)
Table 3-40. Region 9: Evaluation Results for Sampling Stations and River Reaches by State.
State
Arizona
California
Hawaii
Nevada
Region 9e
Station Evaluation
Total Number of
Stations Evaluated
123
1,535
18
76
1,752
Tierl
No.
70
935
10
25
1,040
%b
56.9
60.9
55.6
32.9
59.4
Tier 2
No.
35
353
1
40
429
%b
28.5
23.0
5.6
52.6
24.5
Tier 3
No.
18
247
7
11
283
%b
14.6
16.1
38.9
14.5
16.2
River Reach Evaluation"
iber of Stations Not
tified by an RFl Reach1
II
—
—
18
—
18
:hes With at Least
Station in Tier 1
& O
15
132
—
10
156
:hes With at Least
Station in Tier 2"
& O
15
32
—
19
63
:hes With All
ions in Tier 3
$ a
9
32
—
3
40
Number of Reaches With at
Least One Station Evaluated
39
196
—
32
259
1
.3
j=
u
•s
"o
1,169
2,655
—
935
4,686
Percent of All Reaches in
Region With at Least
One Station Evaluated
3.3
7.4
—
3.4
5.5
Percent of Reaches With at
Least One Tier 1
or Tier 2 Station
2.6
6.2
—
3.1
4.7
" River reaches based on EPA River Reach File (RF1).
b Percent of all stations evaluated in the NSI in the state.
0 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed.
11 No stations in these reaches were included in Tier 1.
' 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.
5-53
-------
z
88
rt-
5'
9
St
i'
fD
P^-
O
05
1
Figure 3-16. Region 9: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing AFCs.
-------
National Sediment Quality Survey
Table 3-41. Region 9: Watersheds Containing Areas of Probable Concern for Sediment Contamination.
Cataloging
Unit Number
15060106
16050203
18010102
18020112
18040005
18050001
18050002
18050003
18050004
18060006
18060011
18070103
18070104
18070106
18070201
18070203
18070204
18070301
18070304
Cataloging Unit Name
Lower Salt
Carson Desert
Mad-Redwood
Sacramento-Upper Clear
Lower Cosumnes-Lower
Mokelumne
Suisun Bay
San Pablo Bay
Coyote
San Francisco Bay
Central Coastal
Alisal-Elkhorn Sloughs
Calleguas
Santa Monica Bay
San Gabriel
Seal Beach
Santa Ana
Newport Bay
Aliso-San Onofre
San Diego
State(s)
AZ
NV
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
Number of Sampling Stations
Total
52
19
26
25
60
27
101
32
130
54
34
26
132
34
59
98
74
19
278
Tierl
39
14
20
23
23
16
69
25
113
25
25
26
109
21
38
30
36
17
208
Tier 2
13
5
4
2
23
8
28
7
16
22
9
0
21
11
18
53
20
1
47
Tier 3
0
0
2
0
14
3
4
0
1
7
0
0
2
2
3
15
18
1
23
Percent of
Sampling
Stations in Tier
1 or Tier 2
100
100
92
100
77
89
96
100
99
87
100
100
98
94
95
85
76
95
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
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
Dominguez Channel
Carson River
Number of
Tierl
Stations
261
156
128
60
29
23
20
19
19
19
16
15
14
13
9
Waterbody
Napa River
A Line Canal
Alisal Slough
Peters Canyon Wash
San Gabriel River
Aliso Creek
Calleguas Creek
Los Penasquitos Canyon
San Diego River
San Juan Creek
Calero Reservoir
Petaluma River
Arroyo Trabusco
Humboldt Bay
Oso Creek
Number of
Tierl
Stations
7
5
5
4
4
3
3
3
3
3
2
2
1
1
1
5-55
-------
National Sediment Quality Survey
Table 3-42. (Continued).
Waterbody
Elkhorn Slu
Spring Creek
Alamitos Creek
Number of
Tierl
Stations
9
9
8
Waterbody
San Dieguito River
Suisun Creek
Warm, E. Twin & Strawberry Cr Area
Number of
Tierl
Stations
1
1
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.
5-56
-------
National Sediment Quality Survey
Table 3-43. Region 10: River Reach and Watershed Evaluation Summary.
River Reach Classification
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
10,462
177(1.7%)
121 (1.2%)
49 (0.5%)
10,115(96.7%)
Watershed Classification
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
Watersheds With All Tier 3 Stations
Watersheds With No Data
355
10 (2.8%)
48(13.5%)
29 (8.2%)
21 (5.9%)
247 (69.6%)
Table 3-44. Region 10: Evaluation Results for Sampling Stations and River Reaches by State.
State
Alaska
Idaho
Oregon
Washington
Region 10e
Station Evaluation
*O ^5
h 3
- ?
1 e
— o
3 "B
H 35
290
38
599
4,336
5,263
Tier 1
No.
46
17
310
2,513
2,886
%b
15.9
44.7
51.8
58.0
54.8
Tier 2
No.
42
12
203
1,216
1,473
%b
14.5
31.6
33.9
28.0
28.0
Tier 3
No.
202
9
86
607
904
%b
69.7
23.7
14.4
14.0
17.2
River Reach Evaluation"
u
u
>3- P^
W ft,
O ^
'= S
S 3
tfi >~*
% 1
1 S
290
—
—
—
290
•8
« ^
« h
"S ^
si .3
P °
S 1
a ™
& 0
—
16
45
125
177
•8 '
ca N
« h
"S ^
* .3
P °
u S
a !«
tf O
—
13
52
65
121
_
•^ s-
- a
S H
^ S
1 e
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rt ^
—
7
16
30
49
•s «
^1
^ ^
» —
^ g
« •*
u ^
^ 55
"8 u
13 (§
S«
1 j
—
36
113
220
347
S"
CH
.3
1/3
-S
(S
•a
—
3,298
4,317
3,056
10,462
.3
a ^ "°
u « «
££ ^ e5
_ C! &
•^ 5 e
S e -
§ 0 50
a •& «
(Sao
—
1.1
2.6
7.2
3.3
«
g
i
u . g
s 33 ••"
tf H S
<« OJ 50
«a-2
" " H
d, J 0
—
0.9
2.2
6.2
2.8
" River reaches based on EPA River Reach File (RF1).
b Percent of all stations evaluated in the NSI in the state.
0 Stations not identified by an RF1 reach were located in coastal areas, open water areas, or areas where RF1 was not developed.
11 No stations in these reaches were included in Tier 1.
'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.
5-57
-------
z
88
rt-
5'
9
St
i'
fD
rt-
O
05
1
Figure 3-17. Region 10: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing APCs.
-------
National Sediment Quality Survey
Table 3-45. Region 10: Watersheds Containing Areas of Probable Concern for Sediment
Contamination.
Cataloging
Unit Number
17020001
17080001
17090012
17100102
17100105
17110002
17110012
17110013
17110019
19020201
Cataloging Unit Name
Franklin D. Roosevelt Lake
Lower Columbia-Sandy
Lower Willamette
Queets-Quinault
Grays Harbor
Strait Of Georgia
Lake Washington
Duwamish
Puget Sound
Eastern Prince William Sound
State(s)
WA
OR,WA
OR
WA
WA
WA
WA
WA
WA
AK
Number of Sampling Stations
Total
66
72
382
108
139
443
216
930
2135
31
Tierl
52
20
243
77
102
184
179
599
1246
10
Tier 2
9
39
96
23
33
179
30
267
552
15
Tier 3
5
13
43
8
4
80
7
64
337
6
Percent of
Sampling
Stations in Tier
1 or Tier 2
92
82
89
93
97
82
97
93
84
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
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
Humptulips River, East Fork
Fidalgo Island
Green River
Columbia Slough
Dyes Inlet
Whidbey Island
Bainbridge Island
Number of
Tierl
Stations
926
498
227
176
139
86
86
72
69
62
51
50
27
26
25
19
17
14
12
12
11
9
9
8
Waterbody
Eld Inlet
Portage Bay
Camano Island
Vashon Island
Whatcom Creek
Johnson Creek
Mercer Island
Panther Lake Ditch
Sequalitchew Creek
Grays Harbor
Hammersley Inlet
Henderson Inlet
Onion Creek
Port Orchard
Port Susan
Cedar Creek
Chuckanut Creek
Indian Island
Morey Creek
North Creek
Oakland Bay
Sammish Bay
Sandy River
Totten Inlet
Number of
Tier 1 Stations
8
8
7
7
6
5
3
3
3
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
5-59
-------
National Sediment Quality Survey
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. Control-
adjusted 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
1
2
3
Total
Previous Evaluation
Methodology
5,521
10,401
5,174
21,096
Current Evaluation
Methodology
8,932
5,813
6,235
20,980
Net Gain/Loss in Number of
Stations
3,411
(4,588)
1,061
(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.
5-60
-------
National Sediment Quality Survey
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
1
2
3
Total
Tier Classification Using Current Methodology
Not Analyzed
0
0
116
116
1
5,338
3,543
51
8,932
2
171
5,446
196
5,813
3
12
1,412
4,811
6,235
Total
5,521
10,401
5,174
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.
5-61
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National Sediment Quality Survey
5-62
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National Sediment Quality Survey
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 overtime 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.
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National Sediment Quality Survey
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 overtime) 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.
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National Sediment Quality Survey
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.
Figure 4-1. Locations of Data Clusters Used for Temporal Trend Analysis.
4-3
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National Sediment Quality Survey
Table 4-1. Number of Predicted Proportion Toxic Observations Available for Trend Analysis
After Data Preparation Step.
USGS Hydrologic Region
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
1980-1983
7
379
530
87
109
55
170
87
2
24
96
275
23
11
5
23
83
75
2
31
1984-1987
16
393
657
89
135
47
124
92
0
74
143
294
27
10
6
23
156
129
23
30
1988-1991
14
422
558
98
151
60
197
59
0
76
127
255
27
5
5
1
217
120
25
0
1992-1995
11
493
512
74
176
66
174
23
2
16
69
124
34
3
6
7
185
111
2
0
1996-1999
5
394
427
42
124
34
226
31
0
9
24
84
21
3
2
6
53
75
2
1
Hydologic Regions
01: New England
02: Mid-Atlantic
03: South Atlantic-Gulf
04: Great Lakes
05: Ohio River
06: Tennessee River
07: Upper Mississippi River
08: Lower Mississippi River
09: Souris-Red-Rainy Rivers
10: Missouri River
11: Arkansas-White-Red Rivera
12: Texas-Gulf
13: Rio Grande
14: Upper Colorado River
15: Lower Colorado River
16: Great Basin
17: Pacific Norlhwest
18: California
Figure 4-2. USGS Hydrologic Regions in Contiguous United States.
4-4
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National Sediment Quality Survey
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.
Figure 4-3. Locations of Concentrated Data Clusters Used for Temporal Trend
Analysis.
4-5
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National Sediment Quality Survey
Table 4-2. Number of Predicted Proportion Toxic Observations Available for Trend Analysis
After Data Preparation Step From Concentrated Data Clusters.
USGS Hydrologic Region
01
02
03
04
05
06
07
08
10
11
12
13
14
18
20
1980-1983
6
51
213
0
30
25
10
1
1
40
115
18
1
39
17
1984-1987
6
47
241
0
27
25
15
1
7
45
137
20
2
37
17
1988-1991
6
53
266
1
35
25
17
8
7
45
141
19
2
44
0
1992-1995
6
57
241
1
31
20
9
7
0
31
65
24
2
32
0
1996-1999
3
49
232
1
30
16
5
0
0
8
45
17
2
31
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 (a = 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 (a = 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.
4-6
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National Sediment Quality Survey
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.
Tierl
YEAR
1980-1983
1984-1987
1988-1991
1992-1995
1996-1999
Tier 2
YEAR
1980-1983
1984-1987
1988-1991
1992-1995
1996-1999
Tier 3
YEAR
1980-1983
1984-1987
1988-1991
1992-1995
1996-1999
% Tier 1 or
Tier 2
YEAR
1980-1983
1984-1987
1988-1991
1992-1995
1996-1999
Hydrologic Region
02
50
55
48
33
17
03
16
38
32
18
3
04
34
15
17
19
13
05
27
25
27
27
16
06
9
8
8
9
1
07
57
33
50
45
25
08
10
3
16
1
10
11
0
4
4
0
0
12
37
14
28
13
7
13
0
0
0
0
0
17
34
69
63
44
6
18
9
27
14
32
34
Hydrologic Region
02
177
147
172
175
101
03
151
153
120
122
78
04
30
42
52
37
17
05
54
67
72
83
55
06
26
17
34
34
9
07
76
71
93
84
116
08
35
42
12
6
6
11
43
36
26
18
4
12
92
121
73
42
22
13
3
5
6
6
2
17
26
64
104
82
32
18
51
41
24
55
32
Hydrologic Region
02
152
191
202
285
276
03
363
466
406
372
346
04
23
32
29
18
12
05
28
43
52
66
53
06
20
22
18
23
24
07
37
20
54
45
85
08
42
47
31
16
15
11
53
103
97
51
20
12
146
159
154
69
55
13
20
22
21
28
19
17
23
23
50
59
15
18
15
61
82
24
9
Hydrologic Region
02
60%
51%
52%
42%
30%
03
32%
29%
27%
27%
19%
04
74%
64%
70%
76%
71%
05
74%
68%
66%
63%
57%
06
64%
53%
70%
65%
29%
07
78%
84%
73%
74%
62%
08
52%
49%
47%
30%
52%
11
45%
28%
24%
26%
17%
12
47%
46%
40%
44%
35%
13
13%
19%
22%
18%
10%
17
72%
85%
77%
68%
72%
18
80%
53%
32%
78%
88%
All
Data
305
324
320
253
139
All
Data
811
889
825
761
488
All
Data
958
1,255
1,272
1,074
936
All
Data
54%
49%
47%
49%
40%
4-7
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National Sediment Quality Survey
CH 1980-83 m 1992-95
1984-87 Enna 1996-99
1988-91
7 8 11 12 13 17 18
HYDROLOGIC REGION
Figure 4-4. Box Plot of Predicted Proportion Toxic as a Function of Hydrologic Region for
Data Clusters.
I I 1980-83^ 1992-95
1984-87 EHUD 1996-99
1988-91
5 6 11
HYDROLOGIC REGION
12
13
18
Figure 4-5. Box Plot of Predicted Proportion Toxic as a Function of Hydrologic Region for
Concentrated Data Clusters.
4-8
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National Sediment Quality Survey
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
02
03
04
05
06
07
08
11
12
13
17
18
Overall
Data Cluster
Paired <-test results for pairwise
comparison of time periods
Increasing
0
0
1
0
0
0
1
0
0
0
0
5
0
Decreasing
8
4
4
2
5
6
2
6
7
1
1
1
9
No
change
2
6
5
8
5
4
7
4
3
9
9
4
1
KSTest
decrease
decrease
no trend
decrease"
decrease*
decrease
no trend
decrease"
decrease"
no trend
decrease"
increase"
decrease
Concentrated Data Cluster
Paired t-test results for pairwise
comparison of time periods
Increasing
0
0
Decreasing
8
6
No
change
2
4
KS Test
decrease
decrease
:=====— ^=^^
0
0
7
7
3
3
decrease
decrease
:======- ^=^^
:=====— ^=^^
0
0
0
3
5
1
3
5
9
decrease11
no trend
no trend
:=====— ^=^^
5
0
1
8
4
2
no trend
decrease
" 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.
4-9
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National Sediment Quality Survey
n Q
OQ
, . -o
X
O n?
H U./
z
o 06
h- u-b
a:
CL o ^
0
or
Q- n4
Q °'4
LU
1 1 n •?
Q
LU
o: n 9
D. U'^
n ~\
n n
1 11990
• 1991
••11992
o
o
0
o
IT
JL
Tp I H
-i-T
| |1994
• 1995
8 °
— «--g-~-
0 ° 0
, §
J>~ o
0
o
0 °
0 -p
JL|i •
ill T»
•
3T
Gulf Coast LI./Hud (REMAP) New England Southeast
REGION
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
4-10
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National Sediment Quality Survey
Table 4-5. Sediment Core Locations.
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 poly cyclic 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
Name
Lake Anne
Lake Ballinger
Berkeley
R.R. Canyon Lake
Great Salt Lake, Farmington Bay
Lake Harriet
Lake Hemet
Lake Houston, South
Lake Killarney
Lowrence Creek Lake
Lake Mead, Las Vegas Bay
Newbridge Pond
Orange Reservoir
Packanack Lake
Panola
Palmer Lake, West Lobe
Sloans Lake
Sand Lake
Town Lake
Lake Washington
White Rock Lake
West Street Basin
Location
(major urban areas)
Washington, DC
Seattle, WA
Atlanta, GA
Los Angeles, CA
Salt Lake City, UT
Minneapolis, MN
Los Angeles, CA
Houston, TX
Orlando, FL
San Antonio, TX
Las Vegas, NV
New York City, NY
Newark, NJ
Newark, NJ
Atlanta, GA
Minneapolis, MN
Denver, CO
Orlando, FL
Austin, TX
Seattle, WA
Dallas, TX
Los Angeles, CA
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Explanation
I Decreasing trend
™ No trend
f Increasing trend
Figure 4-7. PAH Trends Throughout the United States Using Sediment Core Data
from 1970 to Top of Core.
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. (Heitetal., 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).
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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.
TOTAL PAH, IN MICROGRAMS PER KILOGRAM
0 2000 4000
1990
1980
LU
!< 1970
Q
1960
1950
Q"
.-b
0"
.9
4
6
•-O- TOTAL PAH
^ VEHICLE
MILES
TRAVELED
0 10000 20000 30000 40000
VEHICLE MILES TRAVELED IN DALLAS, TX
Figure 4-8. White Rock Lake PAH
Concentrations.
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
Explanation
I Decreasing trend
™ No trend
Figure 4-9. DDT Trends Throughout the United States Using Sediment Core Data
from 1970 to Top of Core.
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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, ODD 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.
2000
1990
1980
'1970
1960
1950
TOTAL DDT, IN MICROGRAMS PER KILOGRAM
0 10 20 30 40 50 60
1940
•O- TOTAL DDT
DDT USE
0 20000 40000
DDT USE IN METRIC TONS PER YEAR
Figure 4-10. White Rock Lake DDT
Concentrations.
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
Explanation
I Decreasing trend
™ No trend
Figure 4-11. Lead Trends Throughout the United States Using Sediment Core Data
from 1975 to Top of Core.
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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.
2000
1990
1980
LEAD, IN MICROGRAMS PER GRAM
0 20 40 60 80 100
LU
1970
1960
1950
1940
O- LEAD
^ ATMOS.
LEAD
SOURCE
FUNCTION
0 1000 2000 3000
ATMOS. LEAD SOURCE FUNCTION,
IN RELATIVE UNITS
Figure 4-12. White Rock Lake Lead
Concentrations.
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.
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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.
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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 overtime for specific
areas where sufficient data exist in the NSI database. When looking at each hydro logic region (defined as
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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
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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 USAGE. 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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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 RegionlO. 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
Coal-Fired Utility Boilers
Medical Waste Incinerators
Municipal Waste Combusters
Chlorine Production
Industrial Boilers
1990 Inventory
25%
24%
20%
6%
6%
Estimated Reductions
uncertain
98%
90%
92%
25%
Table 5-2. Example Estimated Air Toxics Emission Reductions Attributable to MACT.
Municipal Waste
Combuster
Dioxins/Furans
Lead
Cadmium
2001a
99%
77%
68%
2005"
99+%
94%
78%
' Large municipal waste combuster compliance date.
' 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 fortfyalella 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 [ODD 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 well-
known 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 (USAGE). 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 USAGE 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 decision-
making. 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, 200 Ib). 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, 200la), 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
Unavailability 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.
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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.
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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. National-
scale 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 cross-
agency 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,
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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., USAGE, 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.
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. 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.^4 multimedia strategy for priority persistent, bioaccumulative, and toxic (PBT)
pollutants, .
. 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.
References-6
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National Sediment Quality Survey
-. 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/R-
00-005, June 2000. U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, Research Triangle Park, NC.
--- . 200 la. 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.
--- . 200 Ib. 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
9355. 0-85. (2002 draft).
USEPA (U.S. Environmental Protection Agency) and USAGE (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.
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. Technol. 3 1(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.
Paleolimnol. 17:239-249.
Van Metre, P.C., J.T. Wilson, E. Callender, and C.C. Fuller. 1998. Similar rates of decrease of persistent,
hydrophobic and particle-reactive contaminants in riverine systems. Environ. Sci. Technol. 32:3312-
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, RC. 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. 17:1 19-132.
References-7
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National Sediment Quality Survey
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.
References-8
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National Sediment Quality Survey
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-l 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 datatypes
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-l illustrates the relationship between the station, sediment chemistry, tissue residue, and toxicity
tables and the related look-up tables.
A-l
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National Sediment Quality Survey
Table A-l. Number of Sampling Stations With Data Included in the NSI Evaluation.
Measurement Parameter
Sediment chemistry
Total organic carbon
Acid-volatile sulfide
Tissue residue
Toxicity
Elutriate phase
Solid phase
Sediment chemistry and tissue residue
Sediment chemistry and toxicity
Sediment chemistry, tissue residue, and
toxicity
Total Number
of Stations
40,335
16,407
2,714
11,384
6,238
229
10,980
3,110
5892
112
Stations with Coordinates
Number of Stations
36,684
13,936
2,172
10,632
4,510
3,078
4386
112
Percent of Total
Number of Stations
with Coordinates3
81
31
5
23
10
7
10
<1
1 Total number of stations with coordinates = 45,353.
Table A-2. Data Tables Available in the NSI.
Table Name
(l)SITE
(2)STUDY
(3a)STATION
(3b)STUDYNOT
(3c)STUDYREF
(4a)SAMPLE
(4b)SMPSEDSB
(4c)SMPTISS
(5a)CHEM
(5b)CHEMSB
Table Description
Site
Study
Station
Study notes
Study reference
Sediment sample
Sediment core sample
Tissue sample
Surface sediment chemistry
Sediment core data
Table Name
(5c)CHEMTISS
(6)BIOSUMM
(7a)BMASTER
(7b)BIO_INFO
(8a)CHEMDICT
(8b)QUALIFY
(8c)SPEOES
(Sd)TESTDICT
(Se)TISSTYPE
Table Description
Tissue residue
Biotoxicity
Bioassay type
Bioassay information
Dictionary of chemical codes
Concentration qualifier
Dictionary of species
Dictionary of bioassays
Type of tissue
A-2
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Figure A-l. Relationship Between the Station, Sediment Chemistry, Tissue Residue, and Toxicity Tables and the Related Look-up
Tables.
9
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VI
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National Sediment Quality Survey
The remainder of this section contains a listing of the field names and descriptions associated with each
data table in the NSI.
(l)SITE
SITEID
SITENAME
EPAREGION
COUNTY
STATE
CERCLIS
REACH
REACHSEG
LATITUDE
LONGITUDE
WATERSHED
CHEMTOX
Site
Site identifier
Descriptive name of site
EPA region of site location: 11 for Canada
County
State
CERCLIS number for site
USGS 8-digit hydrologic unit code
EPA River Reach (vl) segment number for site
General latitude for site location
General longitude for site location
Watershed name for site location
Sediment chemistry and toxicity data for study, T or F?
(2)STUDY
SITEID
STUDYID
STUDYNAME
PRINCINVES
REFID
SEDCHEM
SUB SURF
SEDTOX
TISSCHEM
LABACCUM
STUDCOMM
Study
Site identifier
Study identifier code
Short name for study
Study investigators
Internal reference ID
Surface sediment chemistry data, T or F?
Subsurface sediment chemistry data, T or F?
Sediment toxicity data, T or F?
Tissue chemistry for study, T or F?
Tissue data from lab bioaccumulation tests, T or F?
Study comments
(3a)STATION
SITEID
STUDYID
STATIONID
LATITUDE
LONGITUDE
MINUS
LATDEG
LATMIN
LATSEC
LONGDEG
LONGMIN
LONGSEC
LOCDESC
EST_STN
SECTOR
AREA
Station
Site identifier
Study identifier code
Station identifier code
Station latitude expressed in decimal degrees
Station longitude expressed in decimal degrees
Internal use (to support conversion of longitude)
Station location expressed in degrees of latitude
Station location expressed in minutes of latitude
Station location expressed in seconds of latitude
Station location expressed in degrees of longitude
Station location expressed in minutes of longitude
Station location expressed in seconds of longitude
Station location description
Indication of derivation of latitude and longitude; selected from
"reported," "plotted," or "assigned"
Internal use
Internal use
A-4
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National Sediment Quality Survey
SORT
ORIG STAT
STATE
COUNTY
CU
CUSEG
MILEPT
LATLONGS
RF3
Internal use
Original station identifier code assigned by source data set
State
County
USGS 8-digit hydrologic unit code
EPA River Reach (vl) segment number
EPA River Reach (vl) mile point
Source for reported latitude and longitude
EPA River Reach (v3) segment
(3b)STUDYNOT
SITEID
STUDYID
SITENAME
STUDYNAME
NOTES
Study notes
Site identifier
Study identifier code
Descriptive name for site
Short name for study
Memo field containing study notes
(3c)STUDYREF
SITEID
STUDYID
YEAR
AUTHORS
TITLE
SOURCE
STUDYCOMM
Study reference
Site identifier
Study identifier code
Year of report
Report authors
Report title
Report source
Short comment on study
(4a)SAMPLE
SITEID
STUDYID
STATIONID
SAMPLEID
FIELDREP
LABREP
SEDDEPTH
UDEPTH
LDEPTH
SAMPDATE
SAMPTIME
TOC
PCTFINES
UAN PW
H2S_PW
EXSAMPID
AVS
Sediment sample
Site identifier
Study identifier
Station identifier
Sample identifier
Field replicate number
Laboratory replicate number
Depth of sediment sample
Upper depth of sediment collection in centimeters
Lower depth of sediment collection in centimeters
Date sample collected
Time sample collected
Total organic carbon as percent
Percent fines
Un-ionized ammonia in porewater
Hydrogen sulfide in pore water
Investigators' sample identifier
Acid-volatile sulfide in ^mol/g sediment
A-5
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National Sediment Quality Survey
(4b)SMPSEDSB
SITEID
STUDYID
STATIONID
SAMPLEID
FIELDREP
LABREP
SEDDEPTH
UDEPTH
LDEPTH
SAMPDATE
SAMPTIME
TOC
PCTFINES
EXSAMPID
Sediment core sample
Site identifier
Study identifier
Station identifier
Sample identifier
Field replicate number
Laboratory replicate number
Depth of sediment sample
Upper depth of sediment collection in centimeters
Lower depth of sediment collection in centimeters
Date sample collected
Time sample collected
Total organic carbon as percent
Percent fines
Investigators' sample identifier
(4c)SMPTISS
SITEID
STUDYID
STATIONID
SAMPLEID
FIELDREP
LABREP
SAMPDATE
SPECIES
TISSUE
NOINCOMP
LENGTH
WEIGHT
SEX
PCTLIPID
EXSAMPID
Tissue sample
Site identifier
Study identifier
Station identifier
Sample identifier
Field replicate number
Laboratory replicate number
Date sample collected
Code identifying species collected
Code identifying tissue collected
Number of organisms in composite
Length of organism in cm
Weight of organism in grams
Sex of organism
Percent lipid
Investigators' sample identifier
(5a)CHEM
SITEID
STUDYID
STATIONID
SAMPLEID
FIELDREP
LABREP
CHEMCODE
CONC
QUALCODE
UNITS
MEASBASIS
MISSINGVAL
Surface sediment chemistry
Site identifier
Study identifier
Station identifier
Sample identifier
Field replicate number
Laboratory replicate number
Abbreviated chemical name
Measured concentration
Assigned qualifier for concentration
Units of concentration for chemical
Wet or dry weight indication
Data missing, Y or N?
A-6
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National Sediment Quality Survey
(Sb)CHEMSB
SITEID
STUDYID
STATIONID
SAMPLEID
FIELDREP
LABREP
CHEMCODE
CONC
QUALCODE
UNITS
MEASBASIS
MISSINGVAL
Sediment core data
Site identifier
Study identifier
Station identifier
Sample identifier
Field replicate number
Laboratory replicate number
Abbreviated chemical name
Measured concentration
Assigned qualifier for concentration
Units of concentration for chemical
Wet or dry weight indication
Data missing, Y or N?
(Sc)CHEMTISS
SITEID
STUDYID
STATIONID
SAMPLEID
FIELDREP
LABREP
CHEMCODE
CONC
QUALCODE
UNITS
MEASBASIS
MISSINGVAL
Tissue residue
Site identifier
Study identifier
Station identifier
Sample identifier
Field replicate number
Laboratory replicate number
Abbreviated chemical name
Measured concentration
Assigned qualifier for concentration
Units of concentration for chemical
Wet or dry weight indication
Data missing, Y or N?
(6)BIOSUMM
SITEID
STUDYID
STATIONID
SAMPLEID
FIELDREP
LABREP
TESTID
GROUP
SERIES
EFFECTVAL
DILUTION
SIGEFFECT
NEG
REF
STAT
CTRLADJ
SIG ORIGN
TOXCODE
Biotoxicity
Site identifier
Study identifier
Station identifier
Sample identifier
Field replicate number
Laboratory replicate number
Bioassay test code
Bioassay test grouping
Bioassay test series number
Measured effect value
Dilution value
Was effect significant, Y or N?
Negative control, Y or N?
Reference sample, Y or N?
Used for statistical comparison, Y or N?
Control adjusted effect value
Original code from investigator for significant effect
Character designation for toxic effect (T for toxic and N for nontoxic)
A-7
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National Sediment Quality Survey
(7a)BMASTER
SITEID
STUDYID
GROUP
TESTID
SPIKED
RATING
TESTCOMM
Bioassay type
Site identifier
Study identifier
Bioassay test grouping
Bioassay test code
Sediment spiked with contaminant, Y or N?
Rating of bioassay test
Bioassay test comments
(7b)BIO_INFO
SITEID
STUDYID
STATIONID
SAMPLEID
TESTID
UAN_S
UAN_E
UAN A
Bioassay information
Site identifier
Study identifier
Station identifier
Sample identifier
Bioassay test code
Un-ionized ammonia in sediment
Un-ionized ammonia in elutriate
Un-ionized ammonia
(Sa)CHEMDICT
CHEMCODE
CHEMNAME
CASNUM
Dictionary of chemical codes
Abbreviated chemical name
Full chemical name
Chemical Abstracts Service (CAS) number
(Sb)QUALIFY
SITEID
QUALCODE
QUALIFIERS
DESCRIPT
Concentration qualifier
Site identifier
Assigned qualifier for concentration
Not used
Description of QUALCODE
(Sc)SPECIES
SPECIES
COMMONNAME
SCIENTIFIC
SPECCODE
NODC
GROUP
RESMIG
DEMPEL
EDIBLE
Dictionary of species
Species code
Commonly used name for organism
Scientific name for organism
Numeric species code
10-digit National Oceanographic Data Center (NODC) code
Species grouping
Whether species is resident or migratory
Whether species is demersal or pelagic
Whether species is edible by human populations
A-8
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National Sediment Quality Survey
(Sd)TESTDICT
TESTID
MEDIUM
MEDCODE
GROUP
ALTGROUP
SPECIES
SPPCODE
LHS
LHSCODE
ENDPOINT
ENDCODE
DURATION
DURCODE
HABITAT
OLDID
JFID
ANA GUDE
Dictionary of bioassays
12-digit bioassay test code
Medium tested
2-digit code for medium tested
Bioassay species grouping
Alternate bioassay species grouping
Bioassay organism
3-digit code for bioassay organism
Life history stage of organism
Single digit code for life history stage
Bioassay test endpoint
2-digit code for test endpoint
Duration of test
4-digit code for duration of test
Habitat of test organism
Formerly assigned test code
Not used (old identification code)
Test included or not in NSI evaluation
(Se)TISSTYPE
TISSUE
TISSUENAME
Type of tissue
Description code of tissue collected
Description of tissue collected
A-9
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National Sediment Quality Survey
A-10
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National Sediment Quality Survey
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.
B-l
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National Sediment Quality Survey
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., |ig 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 |imol of SEM per |imol 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
B-2
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National Sediment Quality Survey
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:
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
B-3
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National Sediment Quality Survey
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-l. 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] (p.g/L) x (10'3 kgoc/goc)
or
ESGOC (^g/goc) = Koc (L/kg) x [FAV, SAV] (p.g/L) x (10'3 kgoc/goc)
where:
ESGOC = draft ESG on a sediment organic carbon basis in M-g/goc;
FCV or SCV = EPA aquatic life water quality criterion final or secondary chronic value in |ig/L;
FAV or SAV = EPA aquatic life water quality criterion final or secondary acute value in |ig/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.
B-4
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National Sediment Quality Survey
Table B-l. 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
71432
319868
58899
92524
101553
85687
108907
84742
333415
132649
95501
541731
106467
60571
84662
115297
959988
33213659
72208
100414
67721
121755
72435
608935
79345
127184
56235
108883
8001352
75252
120821
71556
79016
108383
Chemical Name
Benzene
BHC, delta-
BHC, gamma- (Lindane)
Biphenyl
Bromophenyl phenyl ether, 4-
Butyl benzyl phthalate
Chlorobenzene
Di-n-butyl phthalate
Diazinon/Spectracide
Dibenzofuran
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Dieldrin
Diethyl phthalate
Endosulfan mixed isomers
Endosulfan, alpha-
Endosulfan, beta-
Endrin
Ethylbenzene
Hexachloroethane
Malathion
Methoxychlor
Pentachlorobenzene
Tetrachloroethane, 1,1,2,2-
Tetrachloroethene
Tetrachloromethane
Toluene
Toxaphene
Tribromomethane/Bromoform
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethene
Xylene, m-
Log^
2.13
3.78
3.73
3.96
5
4.84
2.86
4.61
3.7
4.07
3.43
3.43
3.42
5.37
2.5
4.1
3.83
4.52
5.06
3.14
4
2.89
5.08
5.26
2.39
2.67
2.73
2.75
5.5
2.35
4.01
2.48
2.71
3.2
SAV
(Hg/L)
815.4
43.6
1.903s1
108.7
27.69
262.3
2271
234
0.1687s1
366
259
625
183.6
0.2874SI'C
3947
0.1277
0.1277
0.1277
0.1 803"'°
6971
211.9
0.8884s1
0.0962
8.377
3698
998
4375
3153
1.903s1
2254
699.5s1
617
4350
32.29
scv
(Hg/L)
45.5
2.44
0.08"
13.69
1.538
18.84
127
32.7
0.04329"
20.4
14.39
71.31
15.11
0.06589"-1
220
0.05059
0.05059
0.05059
0.05805"-c
389
11.77
0.09671
0.0188
0.466
719
125
243.1
176
0.039"
316.8
105.1
62.1
465
1.794
LogK,,,
2.094
3.716
3.667
3.893
4.915
4.758
2.812
4.532
3.637
4.001
3.372
3.372
3.362
5.279
2.458
4.031
3.765
4.443
4.974
3.087
3.932
2.841
4.994
5.171
2.350
2.625
2.684
2.704
5.407
2.310
3.942
2.438
2.664
3.146
Draft ESG for
Tierl
(Hg/goc)
100
230
8.8
850
2300
15000
1500
8000
0.73
3700
610
1500
420
55
1100
1.4
0.74
3.5
17
8500
1800
0.62
9.5
1200
830
420
2100
1600
490
460
6100
170
2000
45
Draft ESG for
Tier 2
(Hg/goc)
5.7
13
0.37
110
130
1100
82
1100
0.19
200
34
170
35
13
63
0.54
0.29
1.4
5.5
480
100
0.067
1.9
69
160
53
120
89
10
65
920
17
210
2.5
1FAV values.
J FCV values.
= In freshwater.
B-5
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National Sediment Quality Survey
Using PAH-specific final chronic values (FCVs) or final acute values (FAVs), the effect concentration of
a PAH in sediment (CocPAHlFCVl or CocPAHlFAVl) on an organic carbon basis is calculated as the product of
its FCV and Kocor FAV and Koc. The quotient of the organic carbon-normalized sediment concentration
for a specific PAH (CocPAHl) and the effect concentration of a PAH in sediment for a PAH-specific FCV
(Coc pAHiFcvi) or FAV (CocPAHlFAVl) is called the equilibrium partitioning sediment guideline toxic unit
(ESGTUFCVl) or (ESGTUFAVl). The draft ESG for the mixture of PAHs is the sum of the ESGTUFCVl or
ESGTUFAVl for all of the PAHs in the particular sediment. This sum is called SESGTUFCV or SESGTUFAV
and is given by
ESGTUFCV = 1 ESGTUFCV1 = ^
^oc.PAHi.FCVi
or
I ESGTUFAV = I ESGTUFAVI =
^oc.PAHi.FAVi
Because the effect concentration of a PAH in sediment (CocPAF£lFCVlor CocPAHlFAVl) 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 CocPAHlMAX. Thus, only the contribution up to the maximum CocPAHlMAX is
considered in the SESGTUFCV or SESGTUFAV analysis for PAH mixtures.
For a particular sediment, if the SESGTUFCV based on final chronic values for "total PAHs" exceeds 1.0,
the station is classified as Tier 2. Similarly, if the SESGTUFAV 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 (SESGTUFCV TOT) to the draft
equilibrium partitioning sediment guideline toxic unit with 13 PAHs (SESGTUFCV13) 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).
B-6
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National Sediment Quality Survey
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
83329
208968
120127
56553
50328
205992
207089
218019
206440
86737
91203
85018
129000
Chemical Name
Acenaphthene
Acenaphthylene
Anthracene
Benzo(a)anthracene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(k)fluoranthene
Chrysene
Fluoranthene
Fluorene
Naphthalene
Phenanthrene
Pyrene
LogK^
4.012
3.223
4.534
5.673
6.107
6.266
6.291
5.713
5.084
4.208
3.356
4.571
4.922
FAV
(Hg/L)
232.3
1277
86.24
9.264
3.982
2.818
2.669
8.495
29.57
163.5
805
79.58
42.06
FCV
(Hg/L)
55.85
306.9
20.73
2.227
0.9573
0.6774
0.6415
2.042
7.109
39.3
193.5
19.13
10.11
LogK,,,
3.944
3.168
4.457
5.577
6.003
6.160
6.184
5.616
4.998
4.137
3.299
4.494
4.839
r
*"OC,PAH1,FAV1
(Hg/goc)
2043
1880
2471
3499
4014
4073
4081
3511
2941
2238
1602
2479
2900
r
*"OC,PAH1,FCV1
(Hg/goc)
491
452
594
841
965
979
981
844
707
538
385
596
697
C a
*-oc,PAHl,MAX
(Hg/goc)
33400
24000
1300
4153
3840
2169
1220
826
23870
26000
61700
34300
9090
"When the organic carbon-normalized sediment concentration (COCPAHi) is greater than COCPAHMAX, use Cocp,
: in place of C0(
Table B-3. Relative Distribution of SESGTUFCV,TOT to SESGTUFCV,13 for
the Combined EMAP Data Set (N = 488).
Percentile
50
80
90
95
99
SESGTUFCV>TOT/SESGTUFCV>13
2.75
6.78
8.45
11.5
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
B-7
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National Sediment Quality Survey
("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).
B-8
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National Sediment Quality Survey
• 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.
B-9
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National Sediment Quality Survey
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 etal., 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 |omol of SEM per |omol 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.
B-10
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National Sediment Quality Survey
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~
1+exptBo + B^x)]
where p = probability of observing a toxic effect, B0 = intercept parameter, Bj = slope parameter, and x =
chemical concentration in Iog10 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 (Bj), 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 (Bj) 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
83329
208968
120127
7440360
7440382
56553
50328
205992
191242
207089
92524
7440439
7440473
218019
7440508
72548
72559
50293
53703
60571
581420
206440
86737
193395
7439921
7439976
90120
91576
832699
91203
7440020
1336363
198550
85018
129000
7440224
7440666
Chemical Name
Acenaphthene
Acenaphthylene
Anthracene
Antimony
Arsenic
Benzo(a)anthracene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(g,h,i)perylene
Benzo(k)fluoranthene
Biphenyl
Cadmium
Chromium, total
Chrysene
Copper
ODD, p,p'-
DDE, p,p'-
DDT, p,p'-
Dibenz(a,h)anthracene
Dieldrin
Dimethylnaphthalene, 2,6-
Fluoranthene
Fluorene
Indeno( 1 ,2,3 -c,d)pyrene
Lead
Mercury
Methylnaphthalene, 1-
Methylnaphthalene, 2-
Methylphenanthrene, 1-
Naphthalene
Nickel
Polychlorinated biphenyls
Perylene
Phenanthrene
Pyrene
Silver
Zinc
Intercept
(B0)
-3.6165
-2.962
-3.6574
-0.9005
-4.1407
-4.2013
-4.3005
-4.5409
-4.2811
-4.2781
-4.1144
-0.34
-6.4395
-4.3241
-5.7878
-1.8983
-1.8392
-1.7705
-3.6308
-1.1728
-4.0456
-4.4574
-3.7146
-4.3674
-5.4523
0.8041
-4.1405
-3.7579
-3.5884
-3.7753
-4.6119
-3.4613
-4.6827
-4.4576
-4.708
-0.1117
-7.9834
Slope
(BO
1.7532
1.3797
1.4854
2.4111
3.1674
1.5747
1.5832
1.4916
1.5878
1.5669
2.2085
2.5073
2.9952
1.5372
2.9325
1.4913
0.9129
1.6786
1.7692
2.558
1.904
1.4787
1.8071
1.6245
2.7662
2.5461
2.0961
1.7833
1.7501
1.6152
2.7658
1.3488
1.7632
1.6768
1.5854
1.9684
3.342
No. of
Samples
1424
1447
1823
1718
2336
2099
2053
1348
1818
1376
1226
2413
2399
2126
2580
1360
1552
931
1546
633
1249
2189
1668
1837
2481
2296
1368
1704
1401
1816
2450
1617
1823
2173
2240
2103
2516
Normalized
%-square Value
0.334
0.23
0.289
0.25
0.173
0.298
0.299
0.266
0.25
0.286
0.263
0.313
0.195
0.286
0.383
0.268
0.162
0.335
0.326
0.354
0.201
0.263
0.323
0.269
0.274
0.32
0.239
0.25
0.284
0.235
0.18
0.241
0.218
0.298
0.287
0.252
0.279
Source: Field etal., 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-l; 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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P_Max Model Applied to
Hyalella 10-14d Survival
1.00
0.75
Predicted Toxic
Proportion Toxic
I
0.50
ft
0.25
0.00
<0.25
N=81
0.25 to 0.50
N=148
Probability Range
Figure B-l. Application of the Logistic Model to Freshwater Data
for Hyalella azteca 10- to 14-day Survival Endpoint.
P_Max Model Applied to
Hyalella 28d Growth and Survival
1.00
Predicted Toxic
Proportion Toxic
0.75 -
0.00
<0.25 0.25 to 0.50 0.50 to 0.75 >0.75
N=18 N=46 N=41 N=20
Probability Range
Figure B-2. Application of Logistic Model to Freshwater Data for
Hyalella azteca 28-day Growth and 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 USAGE, 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 control-
adjusted 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 Tests3.
Survival (or Mortality) Endpoint: Marine and Freshwater Species
Acanthomysis costata
Ampelisca abdita
Ampelisca verrilli
Ceriodaphnia dubia
Chironomus riparius
Chironomus tentans
Crassostrea virginica
Eohaustorius estuarius
Grandidierella japonica
Hexagenia limbata
Hyalella azteca
Macoma nasuta
Mysidopsis bahia
Neanthes arenaceodentata
Neanthes spp.
Nebalia pugettensis
Nephtys caecoides
Nereis virens
Panaeus duorarum
Rhepoxynius abronius
Rhepoxynius hudsoni
Growth-Based Endpoint (Length): Freshwater Species
Hyalella azteca
Growth-Based Endpoint (Weight): Freshwater Species
Chironomus riparius
Chironomus tentans
Hyalella azteca
1 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:
organism survival in test sample (%)
Control-
corrected = 100 x
survival (%) 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.
MOD 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 MOD 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 MOD 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
Chironomus tentans
10-d survival
Hyalella azteca
10-d survival
Hyalella azteca
28-d length
Chironomus tentans
10-d weight
Hyalella azteca
28-d weight
Sediment
Compared
WBSavsLSb
WBSvsDCc
WBSvsLS
WBSvsDC
WBSvsLS
WBSvsCCd
WBSvsLS
WBSvsDC
WBSvsLS
WBSvsCC
Average MDD
(% reduction
from control)
13.7
13.3
15.8
16.6
4.9
5.3
12.3
19.6
17.6
27.3
Standard
Deviation (as
% of control)
7.2
5.9
5.1
6.6
1.1
1.1
5.1
5.8
7.1
11.1
Mean + 2 SD
(% reduction
from control)
28.1
25
26.1
29.8
7.1
7.5
22.5
31.2
31.8
49.5
Mean - 1 SD
(% reduction
from control)
6.5
7.4
10.7
10
3.9
4.2
7.2
13.9
10.5
16.2
1WBS: 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 control-
adjusted 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 USAGE, 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 USAGE, 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 USAGE, 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 USAGE, 1998).
The TBP can be calculated relative to the biota-sediment accumulation factor (BSAF), as in the
following equation (USEPA and USAGE, 1998):
TBP = BSAF (C/fJf,
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
fj = 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 fortrophic 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 offish 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 offish 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 offish 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
(GDI) of contaminants of concern:
GDI = (EPC)(IR)(EFKED)
(BW)(AT)
where:
GDI = 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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National Sediment Quality Survey
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 risk, = (GDI) (SF,)
where:
Cancer risk, = the potential carcinogenic risk associated with exposure to chemical /' (unitless);
GDI, = chronic daily intake for chemical / (mg/kg/day); and
SF, = slope factor for chemical /' (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:
CD
where:
HQ, = hazard quotient for chemical / (unitless);
GDI, = chronic daily intake for chemical / (mg/kg/day); and
RfD, = reference dose for chemical /' (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:
(IR)(EF)(ED)(SF1)
Noncancer hazard:
(BW)(AT)(RfDi)(C1)
(IR)(EF)(ED)
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National Sediment Quality Survey
where:
Cj = 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-l, 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-l 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:
(NOAEL)(SSF)(WtA)
w v =
WA + (FA)(BAF)
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.)
wvfish =
(NOAEL)(SSF)(WtA)
where:
WVflsh = 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).
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National Sediment Quality Survey
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 offish 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(l):40-48.
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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 acid-
volatile 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. Benyl, 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
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National Sediment Quality Survey
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: Thefugacity 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.P. 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. SPAH: 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-Ola. 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.
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National Sediment Quality Survey
. 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 ofbenthic 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 ofbenthic
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 ofbenthic
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 ofbenthic 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 ofbenthic
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 ofbenthic
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 ofbenthic
organisms: Endrin. Environmental Protection Agency, Office of Science and Technology and Office
of Research and Development, Washington, DC. (2000 draft).
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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 USAGE (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.
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National Sediment Quality Survey
APPENDIX C
VALUES USED FOR CHEMICALS EVALUATED
Sediment Values
Table C-l (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-l. 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 180.213aand 180.142; DHHS, 1994; USFDA, 1993a, 1993b, 1993c, 1993d,
1993e).
Biota-Sediment Accumulation Factors
The final column in Table C-l 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 offish 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 dioxin-
like compounds based on their relative toxicity as compared to 2,3,7,8-tetrachlorodibenzo-/>-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
C-l
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National Sediment Quality Survey
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 FY1995. EPA/540/R-95/036. U.S.
Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington,
DC.
C-2
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National Sediment Quality Survey
. 1995c. Risk-based concentration table, January-June 1995. 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. Brunstrom, 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.
Wcern, and T. Zacharewski. 1998. Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for
humans and wildlife. Environ. Health Per sped. 106(12): 775-792.
C-3
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National Sediment Quality Survey
Table C-l. 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
83329
208968
67641
98862
107028
107131
15972608
116063
309002
62533
120127
7440360
7440382
1912249
7440393
71432
92875
56553
50328
205992
191242
207089
65850
98077
100516
100447
7440417
319846
319857
319868
58899
608731
92524
111444
108601
117817
542881
7440428
Chemical Name
Acenaphthene
Acenaphthylene
Acetone
Acetophenone
Acrolein
Acrylonitrile
Alachlor/Lasso
Aldicarb/Temik
Aldrin'
Aniline
Anthracene
Anthracene and Phenanthrene
Antimony
Arsenic
Atrazine
Barium
Benzene
Benzidine
Benzo(a)anthracene'
Benzo(a)anthracene/Chrysene'
Benzo(a)pyrene'
Benzo(b)fluoranthene'
Benzo(g,h,i)perylene'
Benzo(k)fluoranthene'
Benzoic acid
Benzoquinone-p
Benzotrichloride
Benzyl alcohol
Benzyl chloride
Beryllium
BHC, alpha-
BHC, beta-
BHC, delta-
BHC, gamma- (Lindane)
BHC, technical grade
Biphenyl
Bis(2-chloroethyl)ether
Bis(2-chloroisopropyl)ether
Bis(2-ethylhexyl)phthalate'
Bis(chloromethyl)ether
Boron
-§
s
1
1
1
1
1
1
1
1,3
1
1
2
1
1
1
1
1
1
1
1
1
1
1,3
1,3
1,3
1,3
1,3
1
1
1
1
Sediment Value
ESG
(Mg/g°=)
TieMa
100
230
8.8
850
Tier2b
5.7
13
0.37
110
Logistic
Regression
Model
,750°
(ppm)
0.27
0.42
0.8
4.41
32.61
1.21
1.34
3.03
1.28
1.4
0.14
,725 °
(ppm)
0.04
0.04
0.08
1.09
11.29
0.14
0.16
0.32
0.15
0.16
0.03
Coo, PAH (Mg/goo)
7ier1d
2,043
1,880
2,471
3,499
4,014
4,073
4,081
7ier2e
491
452
594
841
965
979
981
Maxf
33,400
24,000
1,300
4,153
3,840
2,169
1,220
Fish Tissue Concentration (ppm)
2
LJJ
II
d ^
0 "
c -*
5 .i2
o a:
0.0743
0.483
0.00234
7.06
0.026
0.182
1.37
0.000175
0.0557
0.00308
0.234
0.00929
0.00631
0.0223
0.0223
0.0307
0.0223
0.0364
0.557
2.86
0.000182
EPA Noncancer
Hazard Quotient = 1
409
409
81.7
4.09
40.9
4.09
0.119
1,190
1.6
1.2
141
279
11.9
16,000
1,190
20.1
1.2
1.19
201
160
81.7
360
Concen. = EPA
Risk 10 4
0.743
4.83
0.0234
70.6
0.26
1.82
13.7
0.00175
0.557
0.0308
2.34
0.0929
0.0631
0.223
0.223
0.307
0.223
0.364
5.57
28.6
0.00182
EPA Noncancer
Hazard Quotient = 10
4,090
4,090
817
40.9
409
40.9
1.19
11,900
16
12
1,410
2,790
119
160,000
11,900
201
12
11.9
2,010
1,600
817
3,600
FDA Tolerance/Action/
Guidance Level
0.3
68
0.3
0.3
0.3
0.3
0.3
BSAF (Unitless)
0.299
1h
1h
1.89
0.299
0.299
1h
0.29E
0.29E
0.29E
0.299
0.299
1.8s
1.89
1.89
1.8E
1.8E
0.29E
1h
C-4
-------
National Sediment Quality Survey
Table C-l. (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
75274
74839
101553
1689845
85687
7440439
63252
1563662
75150
133904
57749
3734494
5103719
5103742
5566347
108907
510156
75003
75014
110758
74873
91587
95578
2921882
7440473
218019
7440508
108394
95487
106445
1319773
98828
21725462
57125
1861321
72548
72559
Chemical Name
Bromodichloromethane
Bromomethane
Bromophenyl phenyl ether, 4-
Bromoxynil
Butyl benzyl phthalate
Cadmium
Carbaryl/Sevin
Carbofuran/furadan
Carbon disulfide
Chloramben
Chlordane'
Chlordane (Nonachlor)'
Chlordane (c/s-Nonachlor)'
Chlordane (frans-Nonachlor)'
Chlordane, alpha (c/s)-'
Chlordane, beta (frans)-'
Chlordane, c/s-'
Chlordane, gamma (frans)-'
Chlordane, frans-'
Chlorobenzene
Chlorobenzilate
Chloroethane
Chloroethene
Chloroethylvinyl ether, 2-
Chloromethane
Chloronaphthalene, 2-
Chlorophenol, 2-
Chlorpyrifos/Dursban
Chromium
Chrysene'
Copper
Cresol, m-
Cresol, o-
Cresol, p-
Cresols
Cumene
Cyanazine
Cyanide
DCPA/Dacthal
ODD, p, p'-'
DDE, p, p'-'
DDT (Total)'
V
•o
o
o
1
1
1
1
2
1,3
1,3
1,3
1,3
1,3
1,3
1,3
1,3
1,3
1
1
1
1
1
1
1
2
1
1
1
1,3
1,3
1,3
Sediment Value
ESG
(Mg/9oc)
Tier1a
2,300
15,000
1,500
Tier2b
130
1,100
82
Logistic
Regression
Model
T50-;
(ppm)
2.49
233.27
1.73
157.13
0.05
0.54
(ppm)
0.65
76.00
0.19
49.98
0.01
0.01
Coc,PAH(ng/goc)
Tier1d
3,511
Tier2e
844
Max'
826
Fish Tissue Concentration (ppm)
2
UJ
ii
§£
= -*
ii
0.631
0.114
0.114
0.114
0.114
0.114
0.114
0.114
0.114
0.114
0.149
0.0212
3.08
0.0483
0.117
EPA Noncancer
Hazard Quotient = 1
81.7
5.57
230
81.7
817
4
409
20.1
409
59.4
2
2
2
2
2
2
2
2
2
81.7
81.7
1,600
100
319
20.1
1.2
20.1
149
201
201
20.1
20.1
160
8.17
81.7
40.9
2
Concen. = EPA
Risk 10 4
6.31
1.14
1.14
1.14
1.14
1.14
1.14
1.14
1.14
1.14
1.49
0.212
30.8
0.483
1.17
EPA Noncancer
Hazard Quotient = 10
817
55.7
2,300
817
8,170
40
4,090
201
4,090
594
20
20
20
20
20
20
20
20
20
817
817
16,000
1,000
3,190
201
12
201
1,490
2,010
2,010
201
201
1,600
81.7
817
409
20
/Action/
I
FDA Tolerance
Guidance Leve
3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
11
5
5
5
BSAF (Unities:
1h
1h
4.77
4.77
4.77
4.77
4.77
2
4.77
2.22
2.22
1h
1.8fl
0.299
1.89
0.28
7.7
7.7
C-5
-------
National Sediment Quality Survey
Table C-l. (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
50293
1163195
84742
117840
333415
53703
132649
96128
124481
1918009
95501
541731
106467
25321226
91941
75718
75343
107062
75354
156605
156592
75092
120832
94757
94826
78875
542756
62737
115322
60571
84662
119904
131113
581420
105679
528290
99650
100254
51285
121142
606202
88857
Chemical Name
DDT, p, p'-'
Decabromodiphenyl oxide
Di-n-butyl phthalate
Di-n-octyl phthalate1
Diazinon/Spectracide
Dibenzo(a,h)anthracene'
Dibenzofuran
Dibromo-3-chloropropane, 1 ,2-
Dibromochloromethane
Dicamba
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Dichlorobenzenes
Dichlorobenzidine, 3,3'-
Dichlorodifluoromethane
Dichloroethane, 1,1-
Dichloroethane, 1,2-
Dichloroethene, 1,1-
Dichloroethene, frans-1 ,2-
Dichloroethylene, c/s-1 ,2-
Dichloromethane
Dichlorophenol, 2,4-
Dichlorophenoxyacetic acid, 2,4-
Dichlorophenoxybutanoic acid, 2,4-
Dichloropropane, 1 ,2-
Dichloropropene, 1 ,3-
Dichlorvos
Dicofol/Kelthane'
Dieldrin
Diethyl phthalate
Dimethoxybenzidine, 3,3'-
Dimethyl phthalate
Dimethylnaphthalene, 2,6-
Dimethylphenol, 2,4-
Dinitrobenzene, 1,2-
Dinitrobenzene, 1,3-
Dinitrobenzene, 1,4-
Dinitrophenol, 2,4-
Dinitrotoluene, 2,4-
Dinitrotoluene, 2,6-
Dinoseb/DNBP
V
8
1,3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
1
1
1
1,3
1
1
Sediment Value
ESG
(Mg/9oc)
Tierf
8,000
0.73
3,700
610
1,500
420
55
1,100
Tier2b
1,100
0.19
200
34
170
35
13
63
Logistic
Regression
Model
T50-;
(ppm)
0.03
0.26
0.01
0.29
(ppm)
0.004
0.04
0.001
0.05
Coc,PAH(ng/goc)
Tier1d
Tier2e
Max'
Fish Tissue Concentration (ppm)
2
UJ
ii
§£
= -*
ii
0.0286
0.483
1.67
1.67
0.0891
0.446
0.0669
5.2
0.594
0.23
0.137
2.5
0.0025
2.86
EPA Noncancer
Hazard Quotient = 1
40.9
409
81.7
2.8
16
81.7
119
360
357
357
817
409
36
81.7
40.9
241
11.9
40.9
31.9
1.19
2.01
1.6
0.2
3,190
40,900
81.7
1.6
0.409
1.6
8.17
8.17
4.09
4.09
Concen. = EPA
Risk 10 4
0.286
4.83
16.7
16.7
0.891
4.46
0.669
52
5.94
2.3
1.37
25
0.025
28.6
EPA Noncancer
Hazard Quotient = 10
409
4,090
817
28
160
817
1,190
3,600
3,570
3,570
8,170
4,090
360
817
409
2,410
119
409
319
11.9
20.1
16
2
31,900
409,000
817
16
4.09
16
81.7
81.7
40.9
40.9
/Action/
I
FDA Tolerance
Guidance Leve
5
1
0.3
BSAF (Unities:
1.67
1h
1h
1.89
0.299
1h
1
1h
1h
1h
1
1h
1h
1h
1h
1h
1.89
1h
1
C-6
-------
National Sediment Quality Survey
Table C-l. (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
122667
298044
115297
959988
33213659
72208
563122
141786
100414
106934
206440
86737
944229
76448
1024573
118741
87683
74474
67721
51235042
123319
193395
78591
33820530
7439921
121755
108316
7439965
7439976
72435
78933
108101
22967926
90120
91576
832699
21087649
2385855
7439987
91203
91598
7440020
Chemical Name
Diphenylhydrazine, 1,2-
Disulfoton
Endosulfan mixed isomers
Endosulfan, alpha-
Endosulfan.beta-
Endrin
Ethion/Bladen
Ethyl acetate
Ethylbenzene
Ethylene dibromide
Fluoranthene
Fluorene
Fonofos
Heptachlor'
Heptachlor epoxide
Hexachlorobenzene'
Hexachlorobutadiene
Hexachlorocyclopentadiene
Hexachloroethane
Hexazinone
Hydroquinone
lndeno(1 ,2,3-cd)pyrene'
Isophorone
IsopropalirY
Lead
Malathion
Maleic anhydride
Manganese
Mercury
Methoxychlor
Methyl ethyl ketone
Methyl isobutyl ketone
Methyl mercury
Methylnaphthalene, 1-
Methylnaphthalene, 2-
Methylphenanthrene, 1-
Metribuzin
Mirex/Dechlorane'
Molybdenum
Naphthalene
Naphthylamine, 2-
Nickel
V
8
1
1
1
1
1
1
1
1
1
1
1
1
1,3
1,3
1
1
1
1
1
1
1
2
1
1
1
1
3
1
1,3
1
2
Sediment Value
ESG
(Mg/9oc)
Tierf
1.4
0.74
3.5
17
8,500
1,800
0.62
9.5
Tier2b
0.54
0.29
1.4
5.5
480
100
0.067
1.9
Logistic
Regression
Model
T50-;
(ppm)
2.86
0.26
1.23
161.06
0.87
0.19
0.30
0.27
0.55
80.07
(ppm)
0.29
0.04
0.16
47.82
0.23
0.04
0.05
0.04
0.07
23.77
Coc,PAH(ng/goc)
Tier1d
2,941
2,238
1,602
Tier2e
707
538
385
Max'
23,870
26,000
61,700
Fish Tissue Concentration (ppm)
2
UJ
ii
§£
= -*
ii
0.0483
0.000483
0.00891
0.00439
0.025
0.52
2.86
40.9
0.000308
EPA Noncancer
Hazard Quotient = 1
0.16
24
24.1
24.1
1.2
2
3,600
409
8.17
2.01
0.052
3.2
0.817
27.9
4.09
134
160
817
59.4
81.7
409
20.1
0.4
20.1
2,410
319
0.4
100
0.8
20.1
160
81.7
Concen. = EPA
Risk 10 4
0.483
0.00483
0.0891
0.0439
0.25
5.2
28.6
409
0.00308
EPA Noncancer
Hazard Quotient = 10
1.6
240
241
241
12
20
36,000
4,090
81.7
20.1
0.52
32
8.17
279
40.9
1,340
1,600
8,170
594
817
4,090
201
4
201
24,100
3,190
4
1,000
8
201
1,600
817
/Action/
I
FDA Tolerance
Guidance Leve
0.3
0.3
1.3
1
1
0.1
70
BSAF (Unities:
1.89
1.89
1.89
1.89
1.89
1h
0.299
0.299
1.89
1.89
0.09
1h
1h
0.299
1h
1.89
1.89
1h
1.31'
0.299
C-7
-------
National Sediment Quality Survey
Table C-l. (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
98953
100027
55185
924163
621647
86306
42874033
56382
608935
82688
87865
198550
85018
108952
298022
85449
1336363
1610180
7287196
23950585
1918167
129000
91225
7782492
7440224
122349
7440246
100425
13071799
886500
95943
79345
127184
56235
58902
961115
7440315
108883
8001352
Chemical Name
Nitrobenzene
Nitrophenol, 4-
Nitrosodiethylamine, N-
Nitrosodi-n-butylamine, N-
Nitrosodi-n-propylamine, N-
Nitrosodiphenylamine, N-
Oxyfluorfen
Parathion, ethyl-
Pentachlorobenzene
Pentachloronitrobenzene/Quintozene
Pentachlorophenol
Perylene
Phenanthrene
Phenol
Phorate/Famophos/Thimet
Phthalic anhydride
Polychlorinated biphenyls'
Prometon/Pramitol
Prometym/Caparol
Pronamide
Propachlor
Pyrene
Quinoline
Selenium
Silver
Simazine
Strontium
Styrene
TEF Dioxins, Furans, and Dioxin-like
PCBs'
TEF PAHs
Terbufos/Counter
Terbutryn
Tetrachlorobenzene, 1,2,4,5-
Tetrachloroethane, 1,1,2,2-
Tetrachloroethene
Tetrachloromethane
Tetrachlorophenol, 2,3,4,6-
Tetrachlorvinphos/Gardona/Stirofos
Tin
Toluene
Toxaphene'
V
8
1
1
1
1,4
1
1
5
1
1
1
1
1
1
1
1
1
1
Sediment Value
ESG
(Mg/9oc)
Tierf
1,200
830
420
2,100
1,600
490
Tier2b
69
160
53
120
89
10
Logistic
Regression
Model
T50-;
(ppm)
1.06
1.12
1.12
2.41
2.45
(ppm)
0.16
0.15
0.09
0.29
0.44
Coc,PAH(ng/goc)
Tier1d
2,479
2,900
Tier2e
596
697
Max'
34,300
9,090
Fish Tissue Concentration (ppm)
2
UJ
ii
§£
= -*
ii
0.00078
0.00743
0.00557
8.17
0.546
0.152
0.334
0.02
0.00334
0.334
2.56E-07
0.00547
0.201
0.78
0.308
1.67
0.0363
EPA Noncancer
Hazard Quotient = 1
2.01
249
12
24.1
3.19
11.9
119
2,410
0.817
8,170
0.08
59.4
16
301
52
20
20.1
20.1
2,410
817
0.08
4.09
1.19
40.9
2.79
119
119
2,410
817
1
Concen. = EPA
Risk 10 4
0.0078
0.0743
0.0557
81.7
5.46
1.52
3.34
0.2
0.0334
3.34
2.56E-06
0.0547
2.01
7.8
3.08
16.7
0.363
EPA Noncancer
Hazard Quotient = 10
20.1
2,490
120
241
31.9
119
1,190
24,100
8.17
81,700
0.8
594
160
3,010
520
200
201
201
24,100
8,170
0.8
40.9
11.9
409
27.9
1,190
1,190
24,100
8,170
10
/Action/
I
FDA Tolerance
Guidance Leve
2
12
BSAF (Unities:
0.04
1.85
0.299
0.025
0.299
1h
1h
1h
1h
1h
1.89
-------
National Sediment Quality Survey
Table C-l. (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
75252
688733
120821
71556
79005
79016
75694
67663
95954
88062
93765
93721
1582098
95636
118967
7440622
108054
108383
95476
106423
1330207
7440666
Chemical Name
Tribromomethane/Bromoform
Tributyltin
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethane, 1,1,2-
Trichloroethene
Trichlorofluoromethane
Trichloromethane/Chloroform
Trichlorophenol, 2,4,5-
Trichlorophenol, 2,4,6-
Trichlorophenoxyacetic acid, 2,4,5-
Trichlorophenoxypropionic acid, 2,4,5-
Trifluralin/Treflan'
Trimethylbenzene, 1,2,4-
Trinitrotoluene
Vanadium
Vinyl acetate
Xylene, m-
Xylene, o-
Xylene, p-
Xylenes (Total)
Zinc
V
8
1
1
1
1
1
1
1
1
1
1
1
1
1
Sediment Value
ESG
(Mg/9oc)
Tierf
460
6,100
170
2,000
45
Tier2b
65
920
17
210
2.5
Logistic
Regression
Model
T50-;
(ppm)
383.81
(ppm)
140.48
Coc,PAH(ng/goc)
Tier1d
Tier2e
Max'
Fish Tissue Concentration (ppm)
2
UJ
ii
§£
= -*
ii
5.2
0.706
3.64
6.69
3.64
5.2
1.34
EPA Noncancer
Hazard Quotient = 1
81.7
1.2
40.9
360
16
24.1
1,190
40.9
409
40.9
31.9
30.1
2.01
2.01
27.9
4,090
8,170
8,170
8,170
1.190
Concen. = EPA
Risk 10 4
52
7.06
36.4
66.9
36.4
52
13.4
EPA Noncancer
Hazard Quotient = 10
817
12
409
3,600
160
241
11,900
409
4,090
409
319
301
20.1
20.1
279
40,900
81,700
81,700
81,700
11.900
/Action/
I
FDA Tolerance
Guidance Leve
BSAF (Unities:
1h
1h
1h
1h
1h
1h
1h
1h
1h
1
1h
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.
0 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 (Cor,PAHl) is greater than Coc „,„ ufx, use Coc PAH „„ in place of Coc, PAH,.
9 Hansen, 1995 (BSAF source).
'Default value of 1.
Chemicals with log Knw> 5.5.
'Cook, 1995 (BSAF source).
C-9
-------
National Sediment Quality Survey
Table C-2. Toxic Equivalency Factors for Dioxins,
Furans and Dioxin-Like PCBs.a
Chemical Name
1,2,3,4,7,8-HexaCDD
1,2,3,6,7,8-HexaCDD
1,2,3,7,8,9-HexaCDD
1,2,3,4,7,8-HexaCDF
1,2,3,6,7,8-HexaCDF
1,2,3,7,8,9-HexaCDF
2,3,4,6,7,8-HexaCDF
1,2,3,4,6,7,8-HeptaCDD
1,2,3,4,6,7,8-HeptaCDF
1,2,3,4,7,8,9-HeptaCDF
1,2,3,4,6,7,8,9-OctaCDD
1,2,3,4,6,7,8,9-OctaCDF
1,2,3,7,8-PentaCDD
1,2,3,7,8-PentaCDF
2,3,4,7,8-PenteCDF
2,3,7,8-TetraCDD (dioxin)
2,3,7,8-TetraCDF
3,3',4,4'-TetraCB (77)
3,4,4',5-TetraCB(81)
2,3,3',4,4'-PenteCB(105)
2,3,4,4',5-PentaCB(114)
2,3',4,4',5-PenteCB(118)
2',3,4,4',5-PenteCB(123)
3,3',4,4',5-PenteCB(126)
2,3,3',4,4',5-HexaCB(156)
2,3,3',4,4',5'-HexaCB(157)
2,3',4,4',5,5'-HexaCB(167)
3,3',4,4',5,5'-HexaCB(169)
2,3,3',4,4',5,5'-HeptaCB(189)
TEF
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.01
0.01
0.01
0.0001
0.0001
1
0.05
0.5
1
0.1
0.0001
0.0001
0.0001
0.0005
0.0001
0.0001
0.1
0.0005
0.0005
0.00001
0.01
0.0001
" Source: Van den Berg et al., 1998.
C-10
-------
National Sediment Quality Survey
Table C-3. Toxicity Equivalency Factors for
Various PAHs.a
Compound
Acenaphthene
Acenaphthylene
Anthracene
Benzo(a)anthracene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(g,h,i)perylene
Benzo(k)fluoranthene
Chrysene
Dibenz(a,h)anthracene
Fluoranthene
Fluorene
lndeno(1,2,3-cd)pyrene
Phenanthrene
Pyrene
TEF
0.001
0.001
0.01
0.1
1
0.1
0.01
0.1
0.01
5
0.001
0.001
0.1
0.001
0.001
' Source: Nisbet and LaGoy, 1992.
C-ll
-------
National Sediment Quality Survey
Table C-4. Frequency of Detection of Chemicals in Sediment and Tissue Residue."
CAS
Number
83329
208968
67641
107028
107131
15972608
309002
120127
7440360
7440382
1912249
7440393
71432
92875
56553
50328
205992
191242
207089
65850
100516
7440417
319846
319857
319868
58899
608731
92524
111444
108601
117817
7440428
75274
74839
101553
85687
7440439
57749
3734494
5103719
5566347
108907
Chemical Name
Acenaphthene
Acenaphthylene
Acetone
Acrolein
Acrylonitrile
Alachlor/Lasso
Aldrin
Anthracene
Anthracene and Phenanthrene
Antimony
Arsenic
Atrazine
Barium
Benzene
Benzidine
Benzo(a)anthracene
Benzo(a)anthracene/Chrysene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(g,h,i)perylene
Benzo(k)fluoranthene
Benzoic acid
Benzyl alcohol
Beryllium
BHC, alpha-
BHC, beta-
BHC, delta-
BHC, gamma- (Lindane)
BHC, technical grade
Biphenyl
Bis(2-chloroethyl)ether
Bis(2-chloroisopropyl)ether
Bis(2-ethylhexyl)phthalate
Boron
Bromodichloromethane
Bromomethane
Bromophenyl phenyl ether, 4-
Butyl benzyl phthalate
Cadmium
Chlordane
Chlordane (Nonachlor)
Chlordane (c/s-Nonachlor)
Chlordane (frans-Nonachlor)
Chlordane, alpha (c/s)-
Chlordane, c/s-
Chlordane, gamma (frans)-
Chlordane, frans-
Chlorobenzene
Number of Stations
Where Chemical
Was Measured in
Sediment
8,259
7,879
545
333
9,624
8,062
44
5,784
12,526
1,110
7,892
290
7,115
6,192
8,098
6,179
6,234
5,070
3,394
8,419
1,530
2,382
4,339
3,458
4,814
13,621
6,619
42
2,781
3,628
3,244
2,789
2,330
2,886
1,037
Number of Stations
Where Chemical
Was Detected in
Sediment
2,864
2,298
197
612
4,332
3,746
10,922
16
5,347
238
4,943
4,273
4,849
3,981
309
255
217
684
707
1,656
2,609
16
1,280
8,948
1,688
554
1,153
862
650
483
645
29
Number of Stations
Where Chemical
Was Measured in
Tissue Residue
5
37
3
1,324
16
436
804
16
464
63
45
4
24
27
465
1,197
799
710
1,255
30
94
93
97
375
58
24
104
99
940
728
150
864
923
42
856
19
857
60
Number of Stations
Where Chemical
Was Detected in
Tissue Residue
30
17
515
353
2
4
24
14
2
57
21
7
364
592
332
4
159
327
33
247
14
168
C-12
-------
National Sediment Quality Survey
Table C-4. (Continued)
CAS
Number
75003
75014
110758
74873
91587
95578
2921882
7440473
218019
7440508
95487
106445
57125
1861321
72548
72559
50293
84742
117840
333415
53703
132649
96128
124481
95501
541731
106467
91941
75718
75343
107062
156605
75354
75092
120832
78875
542756
115322
60571
84662
131113
581420
105679
51285
121142
606202
122667
298044
115297
Chemical Name
Chloroethane
Chloroethene
Chloroethylvinyl ether, 2-
Chloromethane
Chloronaphthalene, 2-
Chlorophenol, 2-
Chlorpyrifos/Dursban
Chromium
Chrysene
Copper
Cresol, o-
Cresol, p-
Cyanide
DCPA/Dacthal
ODD, p, p1-
DDE, p, p1-
DDT (Total)
DDT, p, p1-
Di-n-butyl phthalate
Di-n-octyl phthalate
Diazinon/Spectracide
Dibenzo(a,h)anthracene
Dibenzofuran
Dibromo-3-chloropropane, 1 ,2-
Dibromochloromethane
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Dichlorobenzidine, 3,3'-
Dichlorodifluoromethane
Dichloroethane, 1,1-
Dichloroethane, 1,2-
Dichloroethene, frans-1 ,2-
Dichloroethene, 1,1-
Dichloromethane
Dichlorophenol, 2,4-
Dichloropropane, 1 ,2-
Dichloropropene, 1 ,3-
Dicofol/Kelthane
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Dimethylnaphthalene, 2,6-
Dimethylphenol, 2,4-
Dinitrophenol, 2,4-
Dinitrotoluene, 2,4-
Dinitrotoluene, 2,6-
Diphenylhydrazine, 1,2-
Disulfoton
Endosulfan mixed isomers
Number of Stations
Where Chemical
Was Measured in
Sediment
1,072
12,509
8,165
13,896
1,175
7,685
7,907
11,000
7,757
4,576
5,014
1,156
8,149
3,584
899
4,972
4,871
4,931
1,082
1,076
667
936
1,082
9,600
4,734
5,201
3,083
993
Number of Stations
Where Chemical
Was Detected in
Sediment
248
12,246
5,921
13,464
81
2,479
2,837
5,099
1,838
1,218
424
95
3,121
1,174
127
134
249
1
3
120
2,158
310
554
1,992
27
Number of Stations
Where Chemical
Was Measured in
Tissue Residue
54
60
47
24
111
91
128
889
842
10
10
21
492
907
949
1,529
948
105
86
130
46
16
57
171
171
95
82
16
57
60
58
60
55
95
58
8
7
1,380
101
115
96
70
95
95
55
5
35
Number of Stations
Where Chemical
Was Detected in
Tissue Residue
1
8
596
800
1
31
279
597
1,018
204
5
1
2
12
7
1
1
2
25
486
11
1
C-13
-------
National Sediment Quality Survey
Table C-4. (Continued)
CAS
Number
959988
33213659
72208
563122
100414
206440
86737
76448
1024573
118741
87683
74474
67721
193395
78591
7439921
121755
7439965
7439976
72435
78933
90120
91576
832699
2385855
7439987
91203
7440020
98953
100027
621647
86306
56382
608935
87865
198550
85018
108952
1336363
129000
7782492
7440224
122349
7440246
100425
95943
79345
Chemical Name
Endosulfan, alpha-
Endosulfan, beta-
Endrin
Ethion/Bladen
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachlorobutadiene
Hexachlorocyclopentadiene
Hexachloroethane
lndeno(1,2,3-cd)pyrene
Isophorone
Lead
Malathion
Manganese
Mercury
Methoxychlor
Methyl ethyl ketone
Methylnaphthalene, 1-
Methylnaphthalene, 2-
Methylphenanthrene, 1-
Mirex/Dechlorane
Molybdenum
Naphthalene
Nickel
Nitrobenzene
Nitrophenol, 4-
Nitrosodi-n-propylamine, N-
Nitrosodiphenylamine, N-
Parathion, ethyl-
Pentachlorobenzene
Pentachlorophenol
Perylene
Phenanthrene
Phenol
Polychlorinated biphenyls
Pyrene
Selenium
SEM est
Silver
Simazine
Strontium
Styrene
TEF Dioxins, Furans, and Dioxin-like PCBs
TEF PAHs
Tetrachlorobenzene, 1,2,4,5-
Tetrachloroethane. 1.1.2.2-
Number of Stations
Where Chemical
Was Measured in
Sediment
3,889
3,315
8,406
805
2,118
8,354
7,262
9,444
8,579
8,377
4,033
3,472
8,058
3,676
13,623
1,018
13,236
4,265
614
2,613
6,249
3,353
4,224
7,873
12,404
90
2,966
8,263
12,305
8,272
739
10,504
3,814
9,422
108
1.059
Number of Stations
Where Chemical
Was Detected in
Sediment
82
368
370
25
45
6,474
3,364
566
518
1,068
150
32
4,788
19
12,802
22
9,316
191
85
1,673
2,704
1,998
320
3,441
11,680
2
2,348
5,889
5,590
6,303
739
5,844
2,662
7,244
5
3
Number of Stations
Where Chemical
Was Measured in
Tissue Residue
168
167
1,293
9
60
1,328
1,247
750
176
91
169
92
944
48
443
1,545
532
1,052
378
161
598
9
92
94
102
48
79
103
95
1,962
625
488
16
375
18
93
165
76
60
Number of Stations
Where Chemical
Was Detected in
Tissue Residue
7
6
33
18
166
94
73
17
456
435
1,299
2
29
368
24
340
1
57
1
1,020
555
217
370
80
31
13
C-14
-------
National Sediment Quality Survey
Table C-4. (Continued)
CAS
Number
127184
56235
58902
7440315
108883
8001352
75252
688733
120821
71556
79005
79016
75694
67663
95954
88062
93721
1582098
7440622
108383
95476
1330207
7440666
Chemical Name
Tetrachloroethene
Tetrachloromethane
Tetrachlorophenol, 2,3,4,6-
Tin
Toluene
Toxaphene
Tribromomethane/Bromoform
Tributyltin
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethane, 1,1,2-
Trichloroethene
Trichlorofluoromethane
Trichloromethane/Chloroform
Trichlorophenol, 2,4,5-
Trichlorophenol, 2,4,6-
Trichlorophenoxypropionic acid, 2,4,5-
Trifluralin/Treflan
Vanadium
Xylene, m-
Xylene, o-
Xylenes (Total)
Zinc
Number of Stations
Where Chemical
Was Measured in
Sediment
1,130
1,083
1,078
5,894
1,080
4,904
1,073
1,022
1,480
444
1,091
20
198
1,826
13,210
Number of Stations
Where Chemical
Was Detected in
Sediment
29
1
96
99
1
94
2
1
35
1
23
1
10
84
13,034
Number of Stations
Where Chemical
Was Measured in
Tissue Residue
59
6
59
58
1,200
58
16
178
58
60
3
37
60
16
98
1
4
401
36
718
Number of Stations
Where Chemical
Was Detected in
Tissue Residue
28
1
24
15
21
1
1
2
254
718
" Only stations with data included in NSI evaluation were included in this assessment.
C-15
-------
National Sediment Quality Survey
Table C-5. Number of Detected Sediment Observations in Watersheds Containing APCs.
Chemical
Acenaphthene
Acenaphthylene
Acetone
Aldrin
Anthracene
Antimony
Arsenic
Benzene
Benzo(a)anthracene
Benzo(a)anthracene/Chrysene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(g,h,i)perylene
Benzo(k)fluoranthene
BHC, alpha-
BHC, beta-
BHC, delta-
BHC, gamma- (Lindane)
BHC, technical grade
Biphenyl
Bis(2-ethylhexyl)phthalate
Bromophenyl phenyl ether, 4-
Butyl benzyl phthalate
Cadmium
Chlordane
Chlordane (c/s-Nonachlor)
Chlordane (frans-Nonachlor)
Chlordane, alpha (c/s)-
Chlordane, c/s-
Chlordane, gamma (frans)-
Chlordane, frans-
Chlorobenzene
Chlorpyrifos/Dursban
Chromium
Chrysene
Copper
DCPA/Dacthal
ODD, p, p1-
DDE, p, p1-
DDT (Total)
Lower Connecticut (01080205)
10
16
15
13
19
14
10
15
11
19
3
6
4
15
3
2
8
3
5
5
19
17
20
9
11
13
o
^H
O,
¥
"3
2
4
16
6
35
4
12
12
4
15
4
3
4
56
6
3
4
4
4
62
16
64
4
3
16
Narragansett (01090004)
6
3
1
10
4
5
10
6
3
8
8
2
6
1
2
6
16
1
3
1
1
4
3
5
7
4
5
22
^
^H
^H
O,
u
.R
•a
a
8
9
11
8
9
10
9
9
10
10
1
1
6
2
11
5
7
5
2
3
10
10
13
6
9
10
0
i-H
i-H
O,
Cj
1
1
1
8
15
14
9
21
14
9
15
15
16
3
8
4
17
4
1
2
4
1
1
24
16
24
7
7
7
Saugatuck (01100006)
17
18
19
17
18
20
16
17
20
18
2
2
14
4
3
18
14
2
15
13
3
4
19
20
20
15
18
18
Long Island Sound (01100007)
18
16
3
23
25
25
27
22
7
28
12
13
13
15
33
21
16
16
1
19
15
33
15
16
28
Hudson-Hoosic (02020003)
4
8
12
16
22
22
8
19
5
19
14
14
31
2
36
18
36
6
6
Mohawk (02020004)
8
12
23
22
38
32
16
32
28
32
14
14
40
2
2
4
41
30
53
6
12
12
Middle Hudson (02020006)
7
10
11
18
18
14
3
10
9
12
3
10
8
29
2
2
2
1
29
10
41
5
13
17
Hudson- Wappinger (02020008)
18
12
2
17
22
22
20
6
10
16
14
10
6
10
29
5
4
1
6
19
11
29
10
14
23
Lower Hudson (02030101)
28
25
2
30
31
31
30
15
15
26
20
9
9
18
2
10
34
29
4
20
10
11
13
28
24
34
15
25
46
Bronx (02030102)
25
25
25
25
25
25
25
14
25
15
12
12
25
26
25
23
24
1
1
26
25
26
24
26
26
Hackensack-Passaic (02030103)
43
44
42
26
86
22
140
2
102
97
94
88
92
12
9
17
7
2
16
84
29
124
43
16
25
57
18
54
19
23
146
104
160
13
84
90
145
Sandy Hook-Staten Island (02030104)
85
86
1
11
97
104
132
102
105
45
93
47
7
4
7
46
42
82
21
1
132
98
8
94
71
29
6
12
132
105
139
7
114
117
133
Raritan (02030105)
2
4
2
7
C
54
c
1
8
4
9
1
4
13
41
1
1
1
2
54
8
60
2
2
52
C-16
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
DDT, p, p1-
Di-n-butyl phthalate
Di-n-octyl phthalate
Diazinon/Spectracide
Dibenzo(a,h)anthracene
Dibenzofuran
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Dichloroethane, 1,1-
Dichloroethane, 1,2-
Dichloromethane
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Dimethylnapthalene, 2,6-
Endosulfan mixed isomers
Endosulfan, alpha-
Endosulfan, beta-
Endrin
Ethion/Bladen
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachlorobutadiene
Hexachloroethane
lndeno(1,2,3-cd)pyrene
Isophorone
Lead
Malathion
Mercury
Methoxychlor
Methyl ethyl ketone
Methylnaphthalene, 1-
Methylnaphthalene, 2-
Methylphenanthrene, 1-
Mirex/Dechlorane
Naphthalene
Lower Connecticut (01080205)
8
7
2
11
2
2
5
2
1
10
1
20
5
13
17
20
3
4
8
1
10
Charles (01090001)
15
3
3
16
3
4
16
12
1
16
65
33
4
2
Narragansett (01090004)
2
1
7
4
9
9
10
7
1
2
1
8
6
8
1
4
7
4
6
^
^H
^H
o,
u
.R
[5.
a
4
2
9
1
6
2
2
9
1
13
6
4
6
12
13
13
6
7
7
9
0
i-H
i-H
O,
Cj
1
1
1
2
5
2
8
1
1
4
8
17
5
1
17
23
22
3
3
8
1
8
Saugatuck (01100006)
13
3
2
17
1
14
1
3
15
2
21
15
3
3
5
20
20
20
13
14
16
2
17
£
^H
o,
•e
&
•a
VI
OD
2i
16
20
4
16
5
13
3
28
22
9
1
11
27
31
32
17
21
12
4
18
Hudson-Hoosic (02020003)
12
2
6
14
2
20
14
36
23
6
4
Mohawk (02020004)
4
14
2
16
2
2
12
30
2
28
55
49
12
8
Middle Hudson (02020006)
7
10
2
6
4
2
2
13
14
3
1
13
40
20
3
3
8
6
Hudson- Wappinger (02020008)
12
10
2
15
2
2
4
16
18
10
1
1
19
26
28
10
10
13
11
Lower Hudson (02030101)
23
12
2
25
3
2
2
17
4
8
22
28
18
3
6
10
28
32
34
18
17
21
3
20
Bronx (02030102)
17
23
1
23
11
14
2
25
25
9
9
16
25
26
26
25
25
20
11
25
Hackensack-Passaic (02030103)
68
17
37
4
65
15
1
16
7
73
2
9
2
32
21
3
118
41
3
26
18
90
155
152
3
13
19
32
13
28
32
Sandy Hook-Staten Island (02030104)
92
1
4
70
3
10
100
51
29
2
1
16
1
114
88
19
36
89
97
136
137
5
2
81
85
48
24
90
Raritan (02030105)
1
1
1
4
1
24
1
9
1
4
6
45
31
1
1
3
C-17
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Nickel
Perylene
Phenanthrene
Polychlorinated biphenyls
Pyrene
SEM est
Silver
TEF Dioxins, Furans, and Dioxin-like
PCBs
TEF PAHs
Tetrachloroethene
Toluene
Toxaphene
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethene
Trichlorofluoromethane
Trichloromethane/Chloroform
Xylene, o-
Xylenes (Total)
Zinc
Lower Connecticut (01080205)
18
4
18
6
20
3
13
4
20
20
Charles (01090001)
62
12
16
16
16
12
16
63
Narragansett (01090004)
8
7
10
24
10
2
7
6
11
2
8
^
^H
^H
o,
u
.R
[5.
a
12
8
13
9
13
7
11
6
13
12
0
i-H
i-H
O,
Cj
1
1
1
21
3
17
7
15
3
11
4
20
1
24
Saugatuck (01100006)
19
14
20
17
21
12
18
15
21
20
£
^H
o,
•e
&
•a
VI
OD
2i
33
26
29
29
29
12
30
22
33
33
Hudson-Hoosic (02020003)
36
18
158
20
14
127
22
36
Mohawk (02020004)
56
30
26
32
22
1
32
56
Middle Hudson (02020006)
41
4
14
58
14
2
19
45
15
41
Hudson- Wappinger (02020008)
28
9
18
43
18
4
27
30
22
29
Lower Hudson (02030101)
34
18
29
65
28
1
33
43
32
34
Bronx (02030102)
26
25
25
26
25
12
25
25
26
26
Hackensack-Passaic (02030103)
125
20
103
148
118
14
80
126
120
14
1
1
7
160
Sandy Hook-Staten Island (02030104)
137
93
107
138
115
11
121
153
119
2
1
138
Raritan (02030105)
42
1
9
45
9
5
1
9
61
C-18
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Acenaphthene
Acenaphthylene
Acetone
Aldrin
Anthracene
Antimony
Arsenic
Benzene
Benzo(a)anthracene
Benzo(a)anthracene/Chrysene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(g,h,i)perylene
Benzo(k)fluoranthene
BHC, alpha-
BHC, beta-
BHC, delta-
BHC, gamma- (Lindane)
BHC, technical grade
Biphenyl
Bis(2-ethylhexyl)phthalate
Bromophenyl phenyl ether, 4-
Butyl benzyl phthalate
Cadmium
Chlordane
Chlordane (c/s-Nonachlor)
Chlordane (frans-Nonachlor)
Chlordane, alpha (c/s)-
Chlordane, c/s-
Chlordane, gamma (frans)-
Chlordane, frans-
Chlorobenzene
Chlorpyrifos/Dursban
Chromium
Chrysene
Copper
DCPA/Dacthal
ODD, p, p1-
DDE, p, p1-
DDT (Total)
Northern Long Island (02030201)
56
52
6
64
62
61
68
60
28
70
38
9
12
54
78
57
45
37
10
7
54
47
78
46
45
70
Southern Long Island (02030202)
17
20
6
29
47
55
38
36
34
6
30
30
19
57
50
43
45
3
53
32
63
45
47
53
Lower Delaware (02040202)
4
2
4
6
4
29
7
4
2
6
5
2
1
2
1
8
26
1
2
30
3
35
2
2
32
Brandywine-Christina (02040205)
10
4
6
4
15
16
106
15
14
9
15
15
5
4
4
4
2
5
106
15
3
1
3
122
17
134
3
5
31
Gunpowder-Patapsco (02060003)
16
11
3
22
14
23
24
20
3
20
10
2
2
2
4
16
23
7
7
11
1
5
22
16
25
14
15
23
Severn (02060004)
56
53
15
65
60
68
66
58
54
60
56
36
9
15
11
63
69
4
36
31
1
1
65
63
70
49
59
65
York (02080107)
6
6
17
5
40
39
35
7
39
6
3
4
3
6
23
3
3
5
48
40
50
4
10
13
Cooper (03050201)
31
38
7
8
54
12
47
57
55
55
48
47
3
6
6
11
13
32
3
1
49
14
7
6
13
6
156
60
137
15
30
35
South Carolina Coastal (03050202)
25
29
7
31
17
38
35
35
38
33
35
4
8
8
10
19
22
1
42
22
12
15
14
6
106
37
97
19
22
38
Lower Savannah (03060109)
32
38
3
46
10
65
47
44
52
39
48
21
20
50
65
8
12
9
78
52
74
8
7
16
Cumberland-St_ Simons (03070203)
10
17
2
17
2
28
22
20
24
17
22
1
1
1
3
8
18
19
9
2
4
28
24
27
6
4
10
Tampa Bay (03100206)
7
5
19
14
4
59
22
25
40
38
1
44
44
3
1
66
54
2
49
51
3
2
73
30
72
46
37
57
Middle Chattahoochee-Lake Harding (03130002)
2
12
51
5
6
3
5
11
3
18
22
1
3
3
3
123
5
123
2
4
23
PensacolaBay(03140105)
10
11
7
17
13
53
29
24
13
29
13
1
1
1
8
5
9
52
6
1
10
15
7
1
4
57
29
56
19
9
28
Mobile Bay (031 60205)
1
8
31
7
7
7
7
7
1
7
1
31
7
2
31
7
31
3
7
8
Menominee (04030108)
C
2
c
c
20
1
11
10
11
1
14
14
14
23
2
2
C-19
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
DDT, p, p1-
Di-n-butyl phthalate
Di-n-octyl phthalate
Diazinon/Spectracide
Dibenzo(a,h)anthracene
Dibenzofuran
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Dichloroethane, 1,1-
Dichloroethane, 1,2-
Dichloromethane
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Dimethylnapthalene, 2,6-
Endosulfan mixed isomers
Endosulfan, alpha-
Endosulfan, beta-
Endrin
Ethion/Bladen
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachlorobutadiene
Hexachloroethane
lndeno(1 ,2,3-cd)pyrene
Isophorone
Lead
Malathion
Mercury
Methoxychlor
Methyl ethyl ketone
Methylnaphthalene, 1-
Methylnaphthalene, 2-
Methylphenanthrene, 1-
Mirex/Dechlorane
Northern Long Island (02030201)
36
59
8
42
15
48
1
69
64
9
12
30
64
73
77
59
62
40
10
Southern Long Island (02030202)
28
15
4
37
32
3
7
47
21
4
18
41
30
61
58
12
24
7
11
Lower Delaware (02040202)
1
3
3
22
5
8
4
3
8
5
28
30
2
5
5
1
Brandywine-Christina (02040205)
5
7
4
12
4
4
4
4
2
33
14
4
3
15
144
85
4
11
3
5
2
1
Gunpowder-Patapsco (02060003)
12
19
4
11
16
2
24
21
4
5
8
20
25
24
1
17
18
14
4
Severn (02060004)
30
56
1
30
54
13
6
70
62
15
9
30
63
70
60
53
55
56
31
York (02080107)
2
10
1
1
2
43
6
1
2
34
51
32
6
6
5
1
Cooper (03050201)
15
3
35
3
12
3
1
42
5
5
1
67
42
4
7
11
51
1
123
44
1
1
48
50
30
8
South Carolina Coastal (03050202)
21
1
30
9
25
12
3
40
27
1
5
13
34
79
1
33
1
25
29
25
6
Lower Savannah (03060109)
9
18
5
49
1
56
50
10
28
28
72
63
47
46
31
6
Cumberland-St_ Simons (03070203)
3
6
2
18
1
1
27
17
3
1
10
14
28
26
21
23
7
Tampa Bay (03100206)
37
3
1
3
2
1
2
2
30
3
36
10
21
21
30
38
72
71
1
2
Middle Chattahoochee-Lake Harding (03130002)
1
3
1
2
10
1
7
3
1
1
6
124
16
PensacolaBay(03140105)
17
20
6
18
11
10
32
17
6
7
6
26
57
58
13
13
12
9
Mobile Bay (031 60205)
1
3
7
1
7
31
31
7
7
7
Menominee (04030108)
2
2
1
1
1
1
15
c
12
22
20
1
C-20
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Naphthalene
Nickel
Perylene
Phenanthrene
Polychlorinated biphenyls
Pyrene
SEM est
Silver
TEF Dioxins, Furans, and Dioxin-like
PCBs
TEF PAHs
Tetrachloroethene
Toluene
Toxaphene
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethene
Trichlorofluoromethane
Trichloromethane/Chloroform
Xylene, o-
Xylenes (Total)
Zinc
Northern Long Island (02030201)
56
78
66
70
73
70
27
70
62
72
78
Southern Long Island (02030202)
31
61
35
42
54
45
3
58
53
50
64
Lower Delaware (02040202)
2
8
5
6
27
8
2
5
4
8
37
Brandywine-Christina (02040205)
6
124
5
28
54
32
1
18
21
38
2
1
4
4
149
Gunpowder-Patapsco (02060003)
20
24
16
24
29
24
4
13
15
31
25
Severn (02060004)
60
70
54
63
66
67
57
62
41
71
70
York (02080107)
15
45
23
39
15
43
8
15
1
44
52
Cooper (03050201)
49
117
47
55
45
63
32
19
9
71
1
165
South Carolina Coastal (03050202)
31
85
36
36
36
35
16
29
14
45
103
Lower Savannah (03060109)
40
68
56
58
52
55
44
34
25
58
77
Cumberland-St_ Simons (03070203)
24
25
17
27
25
26
18
21
10
29
28
Tampa Bay (03100206)
5
66
35
24
55
41
64
44
50
70
Middle Chattahoochee-Lake Harding (03130002)
123
4
34
7
14
7
124
PensacolaBay(03140105)
15
56
24
27
17
13
7
54
15
32
57
Mobile Bay (031 60205)
7
8
7
7
7
7
8
7
31
Menominee (04030108)
1
5
13
14
1
14
4
15
5
C-21
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Acenaphthene
Acenaphthylene
Acetone
Aldrin
Anthracene
Antimony
Arsenic
Benzene
Benzo(a)anthracene
Benzo(a)anthracene/Chrysene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(g,h,i)perylene
Benzo(k)fluoranthene
BHC, alpha-
BHC, beta-
BHC, delta-
BHC, gamma- (Lindane)
BHC, technical grade
Biphenyl
Bis(2-ethylhexyl)phthalate
Bromophenyl phenyl ether, 4-
Butyl benzyl phthalate
Cadmium
Chlordane
Chlordane (c/s-Nonachlor)
Chlordane (frans-Nonachlor)
Chlordane, alpha (c/s)-
Chlordane, c/s-
Chlordane, gamma (frans)-
Chlordane, frans-
Chlorobenzene
Chlorpyrifos/Dursban
Chromium
Chrysene
Copper
DCPA/Dacthal
ODD, p, p1-
DDE, p, p1-
DDT (Total)
DDT. p. p1-
Lower Fox (04030204)
1
1
3
2
21
1
2
6
1
4
1
6
22
6
18
2
2
2
Little Calumet-Galien (04040001)
8
4
5
18
6
56
4
21
20
22
20
20
2
5
1
39
2
2
1
5
2
14
58
22
57
30
35
41
16
Pike-Root (04040002)
1
1
1
48
1
1
1
3
47
2
4
56
1
56
13
12
15
13
Chautauqua-Conneaut (04120101)
2
6
4
4
4
3
4
4
6
4
6
Seneca (04140201)
10
12
12
12
12
12
12
15
12
12
29
Upper Scioto (05060001)
57
56
55
57
Tippecanoe (05120106)
7
26
2
2
2
2
2
1
1
1
11
1
24
2
26
4
3
5
Upper White (05120201)
5
7
3
1
20
9
45
28
24
29
26
27
2
4
20
8
36
38
4
6
6
5
13
62
30
62
1
4
49
3
Lower East Fork White (05120208)
3
1
3
9
3
5
20
18
14
17
18
2
1
3
9
1
2
2
4
15
20
15
4
5
4
S
O
i
0,
V
J£
(5
_l
(5
m
i
1
3
1
1
1
1
2
7
22
1
22
Upper Clinch (06010205)
4
7
57
5
5
5
7
1
3
13
4
2
108
5
112
5
1
Middle Tennessee-Chickamauga (06020001)
1
5
1
1
1
1
1
2
4
39
39
3
3
Rush-Vermillion (07040001)
5
5
4
7
5
4
4
1
6
28
4
4
4
4
28
6
40
8
4
8
3
Copperas-Duck (07080101)
50
7
6
47
50
135
165
164
142
153
6
3
26
99
120
101
Lower Rock (07090005)
2
52
5
52
52
6
3
6
2
Green (07090007)
33
1
2
33
33
1
q
*:
C-22
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Di-n-butyl phthalate
Di-n-octyl phthalate
Diazinon/Spectracide
Dibenzo(a,h)anthracene
Dibenzofuran
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Dichloroethane, 1,1-
Dichloroethane, 1,2-
Dichloromethane
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Dimethylnapthalene, 2,6-
Endosulfan mixed isomers
Endosulfan, alpha-
Endosulfan, beta-
Endrin
Ethion/Bladen
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachlorobutadiene
Hexachloroethane
lndeno(1 ,2,3-cd)pyrene
Isophorone
Lead
Malathion
Mercury
Methoxychlor
Methyl ethyl ketone
Methylnaphthalene, 1-
Methylnaphthalene, 2-
Methylphenanthrene, 1-
Mirex/Dechlorane
Naphthalene
Nickel
Lower Fox (04030204)
1
1
1
1
6
3
4
22
22
1
6
Little Calumet-Galien (04040001)
20
1
14
1
1
2
23
17
2
6
3
20
56
38
2
1
9
2
56
Pike-Root (04040002)
3
1
2
1
56
35
54
Chautauqua-Conneaut (04120101)
2
4
2
3
6
2
6
Seneca (04140201)
8
3
12
12
29
22
29
Upper Scioto (05060001)
43
9
44
Tippecanoe (05120106)
2
1
2
1
5
1
2
23
26
1
26
Upper White (05120201)
8
2
37
17
1
3
51
1
7
3
2
38
5
1
16
1
25
60
9
55
6
6
49
Lower East Fork White (05120208)
3
8
2
3
1
1
20
7
2
18
15
13
1
1
1
1
13
S
O
i
0,
V
J£
(5
_l
(5
m
i
1
1
1
22
21
20
Upper Clinch (06010205)
7
2
4
7
4
7
4
4
5
111
15
5
5
111
Middle Tennessee-Chickamauga (06020001)
1
1
1
1
39
30
39
Rush-Vermillion (07040001)
7
2
4
4
2
8
4
4
43
44
5
Copperas-Duck (07080101)
76
5
167
36
126
90
5
61
51
Lower Rock (07090005)
12
1
48
6
2
52
Green (07090007)
21
1
C
8
33
C-23
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Perylene
Phenanthrene
Polychlorinated biphenyls
Pyrene
SEM est
Silver
TEF Dioxins, Furans, and Dioxin-like
PCBs
TEF PAHs
Tetrachloroethene
Toluene
Toxaphene
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethene
Trichlorofluoromethane
Trichloromethane/Chloroform
Xylene, o-
Xylenes (Total)
Zinc
Lower Fox (04030204)
4
6
10
6
2
6
2
Little Calumet-Galien (04040001)
22
51
22
15
10
23
57
Pike-Root (04040002)
1
1
31
4
3
4
47
Chautauqua-Conneaut (04120101)
5
1
3
5
6
Seneca (04140201)
12
9
12
12
29
Upper Scioto (05060001)
3
56
Tippecanoe (05120106)
2
2
2
1
5
26
Upper White (05120201)
31
32
41
10
8
42
50
Lower East Fork White (05120208)
17
7
21
19
3
22
15
S
O
i
0,
V
J£
(5
_l
TO
m
i
1
2
1
1
1
22
Upper Clinch (06010205)
5
8
7
7
7
4
110
Middle Tennessee-Chickamauga (06020001)
1
1
1
1
1
39
Rush-Vermillion (07040001)
1
4
20
8
5
8
22
Copperas-Duck (07080101)
16
67
82
153
4
170
98
Lower Rock (07090005)
2
1
52
Green (07090007)
33
C-24
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Acenaphthene
Acenaphthylene
Acetone
Aldrin
Anthracene
Antimony
Arsenic
Benzene
Benzo(a)anthracene
Benzo(a)anthracene/Chrysene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(q,h,i)perylene
Benzo(k)fluoranthene
BHC, alpha-
BHC, beta-
BHC, delta-
BHC, gamma- (Lindane)
BHC, technical grade
Biphenyl
Bis(2-ethylhexyl)phthalate
Bromophenyl phenyl ether, 4-
Butyl benzyl phthalate
Cadmium
Chlordane
Chlordane (c/s-Nonachlor)
Chlordane (frans-Nonachlor)
Chlordane, alpha (c/s)-
Chlordane, c/s-
Chlordane, gamma (frans)-
Chlordane, frans-
Chlorobenzene
Chlorpyrifos/Dursban
Chromium
Chrysene
Copper
DCPA/Dacthal
ODD, p, p1-
DDE, p, p1-
DDT (Total)
DDT. p. p1-
kee (07120001)
£
i
1
1
5
3
4
28
5
4
5
4
5
1
13
1
3
3
2
4
32
5
32
8
18
23
20
>is (07120002)
5
jy
o
1
27
1
7
1
2
27
27
9
7
10
8
go (07120003)
8
5
2
3
3
3
27
4
4
4
4
4
9
1
14
7
2
1
1
5
11
27
4
27
27
30
32
14
laines (07120004)
Q.
V>
&
2
1
3
2
70
2
2
2
2
6
1
2
1
22
8
1
1
6
12
70
2
70
48
49
53
31
Illinois (07120005)
§L
0.
^
2
2
2
1
21
2
2
2
2
2
2
10
1
2
2
23
2
23
6
9
10
1
Fox (07120006)
§L
0.
^
3
2
67
1
7
3
5
9
5
4
27
1
6
1
79
6
68
33
34
41
4
r Fox (071 20007)
1
2
28
7
2
1
28
28
9
13
16
6
r Illinois-Senachwine Lake (07130001)
1
1
1
1
8
1
1
1
8
8
1
8
1
1
1
r Illinois-Lake Chautauqua (07130003)
1
2
2
31
1
1
1
1
2
1
17
1
1
3
32
1
32
3
4
4
Fork Sangamon (07130007)
0
tn
3
1
20
3
5
1
8
8
20
20
2
1
2
2
r Illinois (07130011)
1
4
2
44
1
2
1
11
1
2
44
44
4
8
8
2
npin (07130012)
8
I
3
27
4
1
3
27
27
2
1
3
1
Linflower (08030207)
CO
O>
m
3
3
28
6
6
7
27
1
28
1
28
33
34
35
35
Steele (08030209)
1
1
5
12
1
1
2
4
12
12
18
18
26
13
|
O
S
i.
U>
1
6
z
.J.
Q.
Q.
"(/)
.2
(A
(A
fe
1
13
9
26
33
37
29
29
29
30
29
12
1
8
23
26
32
28
7
24
12
20
2
38
31
38
22
13
35
16
rNeosho (11 070209)
1
q
4
8
8
C-25
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Di-n-butyl phthalate
Di-n-octyl phthalate
Diazinon/Spectracide
Dibenzo(a,h)anthracene
Dibenzofuran
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Dichloroethane, 1,1-
Dichloroethane, 1,2-
Dichloromethane
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Dimethylnapthalene, 2,6-
Endosulfan mixed isomers
Endosulfan, alpha-
Endosulfan, beta-
Endrin
Ethion/Bladen
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachlorobutadiene
Hexachloroethane
lndeno(1,2,3-cd)pyrene
Isophorone
Lead
Malathion
Mercury
Methoxychlor
Methyl ethyl ketone
Methylnaphthalene, 1-
Methylnaphthalene, 2-
Methylphenanthrene, 1-
Mirex/Dechlorane
Naphthalene
Nickel
kee (07120001)
£
i
1
2
27
1
3
5
1
1
2
5
29
10
1
1
29
>is (07120002)
5
jy
o
24
1
27
27
27
go (07120003)
8
5
4
17
3
4
2
1
3
5
4
27
17
5
1
27
laines (07120004)
Q.
V>
&
2
33
1
5
2
3
12
2
70
30
12
2
1
70
Illinois (07120005)
8.
0.
^
1
2
11
2
1
2
2
19
4
2
2
23
Fox (07120006)
8.
0.
^
2
1
16
2
4
17
9
3
8
3
80
45
6
66
r Fox (071 20007)
1
21
1
1
21
8
3
28
r Illinois-Senachwine Lake (07130001)
1
1
1
1
1
8
7
1
1
8
r Illinois-Lake Chautauqua (07130003)
1
9
2
1
22
14
3
32
Fork Sangamon (07130007)
0
tn
23
1
8
1
20
5
2
20
r Illinois (07130011)
1
20
6
1
1
43
12
3
44
npin (07130012)
8
I
19
3
6
27
5
27
Linflower (08030207)
CO
O>
m
1
22
6
4
19
1
27
5
26
3
1
28
Steele (08030209)
1
1
2
5
2
4
8
1
11
7
12
|
O
S
i.
U>
1
6
z
.J.
Q.
Q.
"(/)
.2
(A
(A
fe
1
25
1
29
4
32
27
3
12
29
33
30
32
32
29
32
33
rNeosho (11 070209)
1
1
8
3
3
C-26
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Perylene
Phenanthrene
Polychlorinated biphenyls
Pyrene
SEM est
Silver
TEF Dioxins, Furans, and Dioxin-like
PCBs
TEF PAHs
Tetrachloroethene
Toluene
Toxaphene
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethene
Trichlorofluoromethane
Trichloromethane/Chloroform
Xylene, o-
Xylenes (Total)
Zinc
kee (07120001)
£
i
5
5
5
4
5
30
>is (07120002)
5
jy
o
1
1
27
go (07120003)
8
5
4
11
4
3
3
4
2
27
laines (07120004)
Q.
V>
&
2
19
2
17
2
70
Illinois (07120005)
§L
0.
^
2
2
2
1
2
23
Fox (07120006)
§L
0.
^
1
6
18
15
19
17
57
r Fox (071 20007)
1
2
28
r Illinois-Senachwine Lake (07130001)
1
1
1
1
5
1
8
r Illinois-Lake Chautauqua (07130003)
1
1
2
4
2
32
Fork Sangamon (07130007)
0
tn
1
20
r Illinois (07130011)
1
3
2
3
2
44
npin (07130012)
8
I
27
Linflower (08030207)
CO
O>
m
1
2
1
28
Steele (08030209)
1
1
12
|
O
S
i.
U>
1
6
z
.J.
Q.
Q.
"(/)
.2
(A
(A
fe
1
32
32
34
29
32
20
32
38
rNeosho (11 070209)
1
1
3
C-27
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Acenaphthene
Acenaphthylene
Acetone
Aldrin
Anthracene
Antimony
Arsenic
Benzene
Benzo(a)anthracene
Benzo(a)anthracene/Chrysene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(g,h,i)perylene
Benzo(k)fluoranthene
BHC, alpha-
BHC, beta-
BHC, delta-
BHC, gamma- (Lindane)
BHC, technical grade
Biphenyl
Bis(2-ethylhexyl)phthalate
Bromophenyl phenyl ether, 4-
Butyl benzyl phthalate
Cadmium
Chlordane
Chlordane (c/s-Nonachlor)
Chlordane (frans-Nonachlor)
Chlordane, alpha (c/s)-
Chlordane, c/s-
Chlordane, gamma (frans)-
Chlordane, frans-
Chlorobenzene
Chlorpyrifos/Dursban
Chromium
Chrysene
Copper
DCPA/Dacthal
ODD, p, p1-
DDE, p, p1-
DDT (Total)
DDT, p, p1-
Di-n-butvl phthalate
Lower West Fork Trinity (12030102)
14
14
26
15
94
36
35
32
33
30
26
49
14
4
7
8
8
98
39
95
6
12
38
4
30
Austin-Travis Lakes (12090205)
1
1
4
76
2
2
1
1
1
1
1
3
75
43
72
1
67
47
Blue (14010002)
1
18
18
1
2
1
1
2
2
18
18
1
18
2
Lower Salt (150601 06)
1
16
1
2
58
1
1
1
2
9
3
2
26
49
1
2
2
1
56
1
63
1
2
51
1
2
Carson Desert (16050203)
19
18
1
18
19
19
1
Franklin D_ Roosevelt Lake (17020001)
46
46
52
46
58
O
§
8
1*-
>,
•o
c
TO
°?
TO
la
3
O
O
O
_l
7
5
13
8
4
67
15
12
1
10
3
1
16
1
58
2
2
2
2
69
17
28
5
10
12
4
3
^"
i
§
i*-
E
ro
i
o
_i
194
114
5
214
28
213
271
270
137
252
194
1
2
153
75
258
2
2
4
2
1
337
288
334
48
48
77
54
69
Queets-Quinault (17100102)
30
27
44
16
57
48
47
11
44
11
5
10
49
81
Grays Harbor (17100105)
37
69
14
106
66
144
112
108
104
108
110
7
93
42
142
20
2
51
114
128
50
8
53
4
8
Strait Of Georgia (17110002)
132
103
31
187
93
330
1
225
3
215
141
148
141
2
59
81
23
250
6
6
243
248
357
11
8
98
32
21
5"
o
o
?I
1
O>
c
Ic
tft
re
V
J£
TO
_l
120
90
8
12
116
38
213
6
151
1
149
128
136
92
7
107
33
164
15
1
1
1
166
181
251
35
17
37
9
25
m-
o
o
?I
.c
tft
'E
Q
251
75
5
22
325
87
589
2
396
33
382
450
346
366
3
3
6
396
299
549
9
1
22
590
511
659
70
19
102
10
108
Puget Sound (17110019)
729
438
31
96
1,110
389
1,529
1
1,139
209
1,327
706
1,052
651
132
7
24
970
4
379
1,385
112
114
245
1,570
1,495
2,066
231
165
578
117
203
Mad-Redwood (18010102)
17
9
19
20
10
20
20
10
8
8
7
20
20
1
1
2
20
20
20
3
4
4
Sacramento-Upper Clear (18020112)
62
62
C-28
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Di-n-octyl phthalate
Diazinon/Spectracide
Dibenzo(a,h)anthracene
Dibenzofuran
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Dichloroethane, 1,1-
Dichloroethane, 1,2-
Dichloromethane
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Dimethylnapthalene, 2,6-
Endosulfan mixed isomers
Endosulfan, alpha-
Endosulfan, beta-
Endrin
Ethion/Bladen
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachlorobutadiene
Hexachloroethane
lndeno(1 ,2,3-cd)pyrene
Isophorone
Lead
Malathion
Mercury
Methoxychlor
Methyl ethyl ketone
Methylnaphthalene, 1-
Methylnaphthalene, 2-
Methylphenanthrene, 1-
Mirex/Dechlorane
Naphthalene
Nickel
Perylene
Phenanthrene
Lower West Fork Trinity (12030102)
5
21
1
1
1
16
23
21
32
37
1
30
2
96
10
20
4
98
25
Austin-Travis Lakes (12090205)
38
1
2
5
23
1
79
65
20
1
Blue (14010002)
1
1
2
1
18
18
18
1
Lower Salt (150601 06)
16
1
1
2
45
1
1
1
1
6
9
27
59
4
57
3
2
7
1
Carson Desert (16050203)
1
1
1
19
18
19
Franklin D_ Roosevelt Lake (17020001)
56
44
O
§
8
1*-
>,
•o
c
TO
°?
TO
la
3
O
O
O
_l
6
4
1
7
2
2
1
1
17
8
1
1
9
68
50
2
8
8
28
12
^"
i
§
i»-
E
ro
i
o
_i
54
137
86
1
4
2
7
4
17
3
1
1
300
197
1
1
232
272
247
99
3
201
251
2
287
Queets-Quinault (17100102)
37
53
36
44
70
139
20
30
52
Grays Harbor (17100105)
52
21
4
1
3
3
4
126
71
4
9
2
107
123
120
40
79
100
119
Strait Of Georgia (17110002)
11
83
144
11
11
5
26
11
11
64
1
267
172
11
53
7
150
332
372
2
2
61
152
63
168
230
49
265
5"
o
o
?I
1
O>
c
Ic
tft
re
V
J£
(5
_l
5
72
81
4
4
7
2
8
8
28
2
165
104
7
7
5
119
232
184
14
24
93
2
110
225
154
m-
o
o
?I
.c
tft
'E
Q
33
267
182
10
8
18
1
1
15
6
120
1
2
450
286
3
6
4
402
629
562
6
15
85
1
95
637
411
Puget Sound (17110019)
133
641
545
89
103
125
8
111
86
163
38
7
1
25
1,495
795
97
7
249
136
29
1,108
1,922
1,473
15
30
490
31
706
1,944
47
1,405
Mad-Redwood (18010102)
9
2
20
20
20
1
2
6
20
20
9
20
20
16
10
10
19
20
Sacramento-Upper Clear (18020112)
C-29
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Polychlorinated biphenyls
Pyrene
SEM est
Silver
TEF Dioxins, Furans, and Dioxin-like
PCBs
TEF PAHs
Tetrachloroethene
Toluene
Toxaphene
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethene
Trichlorofluoromethane
Trichloromethane/Chloroform
Xylene, o-
Xylenes (Total)
Zinc
rinity (12030102)
Lower West Fork T
38
36
43
42
1
97
s (12090205)
Austin-Travis Lake
39
2
5
4
83
Blue (14010002)
2
18
2
18
S
Lower Salt (150601
40
6
2
7
38
64
8
8
in
Carson Desert (160
16
19
relt Lake (17020001)
Franklin D_ Roosev
58
ndy (17080001)
.5
1
"o
O
O
8
18
2
11
2
18
6
1
1
1
28
i
s
Lower Willamette (
62
299
54
235
22
337
3
1
2
284
i
o
o
Queets-Quinault (1
53
2
55
79
£
Grays Harbor (1710
80
120
102
126
135
8
|
Strait Of Georgia (1
42
253
170
364
8
5
379
O
O
1)
IE
c/>
TO
V
TO
61
166
69
4
203
3
4
6
4
251
o
.c
(A
'E
Q
901
422
472
536
566
4
7
3
1
2
1
1
2
5
642
5T
s
Puget Sound (1711
979
1,471
1,092
127
1,836
23
8
66
38
1
45
1,738
i
o
Mad-Redwood (180
8
20
20
5
20
20
Clear (180201 12)
Sacramento-Upper
62
C-30
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Acenaphthene
Acenaphthylene
Acetone
Aldrin
Anthracene
Antimony
Arsenic
Benzene
Benzo(a)anthracene
Benzo(a)anthracene/Chrysene
Benzo(a)pyrene
Benzo(b)fluoranthene
Benzo(g,h,i)perylene
Benzo(k)fluoranthene
BHC, alpha-
BHC, beta-
BHC, delta-
BHC, gamma- (Lindane)
BHC, technical grade
Biphenyl
Bis(2-ethylhexyl)phthalate
Bromophenyl phenyl ether, 4-
Butyl benzyl phthalate
Cadmium
Chlordane
Chlordane (c/s-Nonachlor)
Chlordane (frans-Nonachlor)
Chlordane, alpha (c/s)-
Chlordane, c/s-
Chlordane, gamma (frans)-
Chlordane, frans-
Chlorobenzene
Chlorpyrifos/Dursban
Chromium
Chrysene
Copper
DCPA/Dacthal
ODD, p, p1-
DDE, p, p1-
DDT (Total)
Lower Cosumnes-Lower Mokelumne (18040005)
1
1
1
1
1
1
1
1
59
1
1
1
1
1
1
60
1
1
Suisun Bay (18050001)
19
19
1
25
5
25
26
25
25
27
25
4
1
5
22
25
7
8
4
3
3
2
4
25
26
25
26
25
26
San Pablo Bay (18050002)
73
81
6
102
29
108
106
105
106
104
104
18
10
2
18
88
103
21
17
10
9
13
9
14
109
106
109
2
100
102
102
Coyote (18050003)
11
11
1
13
3
32
13
13
13
13
13
2
2
3
11
25
9
8
7
1
3
1
2
41
13
41
13
13
13
San Francisco Bay (18050004)
70
73
5
76
42
143
76
76
76
76
76
7
9
2
8
70
78
36
29
4
26
5
23
27
216
76
216
2
75
72
75
Central Coastal (18060006)
1
1
24
52
3
3
1
5
10
1
52
3
52
5
6
6
Alisal-Elkhorn Sloughs (18060011)
9
5
7
11
10
10
18
17
8
8
8
1
4
9
10
1
10
10
8
8
10
18
10
8
18
18
202
FT
o
1
U>
TO
f
8
4
4
1
7
14
9
11
12
11
10
10
2
1
1
3
4
14
9
11
11
11
9
14
13
14
9
14
14
14
f
o
CO
re
m
re
o
'c
o
re
tn
79
64
5
115
127
109
126
130
76
80
76
12
4
7
9
61
127
49
76
80
59
38
127
125
127
13
127
130
130
San Gabriel (18070106)
4
3
1
18
23
24
23
23
12
12
12
1
2
7
24
12
23
23
12
6
24
23
24
23
23
23
Seal Beach (18070201)
8
1
6
23
37
56
31
34
24
18
18
4
3
5
2
1
74
1
12
26
25
15
10
107
37
107
11
54
69
86
Santa Ana (18070203)
11
2
180
4
3
3
2
4
32
36
15
1
38
17
206
35
1
3
1
2
212
5
212
1
4
7
139
Newport Bay (18070204)
3
4
2
7
20
55
20
20
21
20
19
2
6
5
2
2
1
4
133
6
15
19
19
20
3
253
21
235
2
75
148
192
Aliso-San Onofre (18070301)
1
1
1
4
4
5
5
7
5
4
1
1
60
2
4
4
4
117
6
107
1
7
26
26
San Diego (18070304)
110
133
1
176
217
209
219
225
194
192
191
2
1
3
17
83
1
217
117
136
142
140
22
218
224
222
7
182
216
218
Eastern Prince William Sound (19020201)
8
16
14
14
1
34
C-31
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
DDT, p, p1-
Di-n-butyl phthalate
Di-n-octyl phthalate
Diazinon/Spectracide
Dibenzo(a,h)anthracene
Dibenzofuran
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Dichloroethane, 1,1-
Dichloroethane, 1,2-
Dichloromethane
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Dimethylnapthalene, 2,6-
Endosulfan mixed isomers
Endosulfan, alpha-
Endosulfan, beta-
Endrin
Ethion/Bladen
Ethylbenzene
Fluoranthene
Fluorene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Hexachlorobutadiene
Hexachloroethane
lndeno(1,2,3-cd)pyrene
Isophorone
Lead
Malathion
Mercury
Methoxychlor
Methyl ethyl ketone
Methylnaphthalene, 1-
Methylnaphthalene, 2-
Methylphenanthrene, 1-
Mirex/Dechlorane
Lower Cosumnes-Lower Mokelumne (18040005)
1
1
1
9
6
Suisun Bay (18050001)
13
23
12
22
2
4
27
23
1
15
27
25
34
17
21
23
1
San Pablo Bay (18050002)
42
87
5
40
1
68
1
4
7
107
87
2
4
34
106
109
141
4
76
81
94
1
Coyote (18050003)
6
12
10
11
1
13
11
1
2
13
41
49
11
11
12
San Francisco Bay (18050004)
33
67
6
48
5
58
13
5
1
76
71
14
6
30
76
216
241
4
68
70
74
18
Central Coastal (18060006)
4
2
2
1
1
1
3
1
1
48
3
1
1
1
Alisal-Elkhorn Sloughs (18060011)
15
12
14
9
31
41
8
2
18
10
2
2
10
8
10
10
1
13
16
11
2
FT
o
g
i
u>
TO
f
13
9
12
4
1
9
2
13
6
2
2
9
11
14
14
6
5
7
9
Santa Monica Bay (18070104)
89
123
50
82
6
19
6
1
126
96
32
18
62
80
127
127
16
98
117
111
14
San Gabriel (18070106)
17
22
9
11
1
4
1
23
9
5
4
7
12
24
23
1
12
22
20
1
g
i
.c
o
re
"re
V
CO
34
1
23
15
7
1
1
37
14
11
14
107
48
6
20
18
S
g
i
re
c
re
CO
5
14
19
6
24
4
4
4
14
4
14
1
208
196
Newport Bay (18070204)
71
3
14
12
3
4
2
20
4
2
1
3
19
167
60
3
6
7
Aliso-San Onofre (18070301)
2
1
3
2
6
1
2
4
74
4
1
1
1
2
San Diego (18070304)
87
1
1
185
60
76
12
226
126
10
7
35
191
222
217
22
92
131
154
4
Eastern Prince William Sound (19020201)
10
45
19
3
15
76
C-32
-------
National Sediment Quality Survey
Table C-5. (Continued)
Chemical
Naphthalene
Nickel
Perylene
Phenanthrene
Polychlorinated biphenyls
Pyrene
SEM est
Silver
TEF Dioxins, Furans, and Dioxin-like
PCBs
TEF PAHs
Tetrachloroethene
Toluene
Toxaphene
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethene
Trichlorofluoromethane
Trichloromethane/Chloroform
Xylene, o-
Xylenes (Total)
Zinc
Lower Cosumnes-Lower Mokelumne (18040005)
1
1
1
1
1
60
Suisun Bay (18050001)
23
22
22
26
34
27
3
25
12
27
25
San Pablo Bay (18050002)
91
108
88
105
132
107
1
100
87
107
1
109
Coyote (18050003)
11
41
11
13
21
13
25
17
13
41
San Francisco Bay (18050004)
71
203
70
76
100
76
12
72
91
76
220
Central Coastal (18060006)
52
3
3
3
14
3
1
52
Alisal-Elkhorn Sloughs (18060011)
8
10
17
17
14
18
8
10
11
18
10
FT
o
g
i
u>
TO
f
8
11
9
13
13
14
13
12
14
11
13
2
14
f
O
fe
CO
>^
re
m
re
O
'c
o
ra
tn
76
111
130
125
133
126
26
127
132
130
4
127
San Gabriel (18070106)
12
24
23
23
23
23
3
22
23
23
24
Seal Beach (18070201)
16
101
28
30
41
37
57
24
37
107
Santa Ana (18070203)
210
6
108
18
196
22
212
Newport Bay (18070204)
6
208
19
20
39
23
1
48
17
25
264
Aliso-San Onofre (18070301)
2
97
4
5
6
10
8
3
10
119
San Diego (18070304)
162
218
211
214
221
227
8
217
205
227
222
Eastern Prince William Sound (19020201)
4
16
64
65
76
C-33
-------
National Sediment Quality Survey
C-34
-------
National Sediment Quality Survey
APPENDIX D
SPECIES CHARACTERISTICS RELATED TO NSI
BlOACCUMULATION DATA
Table D-l presents the species for which tissue residue analyses are included in the NSI database. For
each species listed, Table D-l 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.
D-l
-------
National Sediment Quality Survey
Table D-l. Species Characteristics Related to Tissue Residue Data.
Scientific Name
Acanthomysis macropsis
Acartia spp.
Acipenser brevirostmm
Acipenser fulvescens
Acipenser oxyrhynchus
Acipenser spp.
Acipenser transmontanus
Acrocheilus alutaceus
Agrionidae
Acroneuria spp.
Allosmerus elongatus
Alosa aestivalis
Alosa alabamae
Alosa chrysochloris
Alosa mediocris
Alosa pseudoharengus
Alosa sapidissima
Amblema plicata
Ambloplites cavifrons
Ambloplites constellatus
Ambloplites rupestris
Amia calva
Amphipoda
Amphistichus rhodoterus
Anarhichas denticulatus
Anchoa mitchilli
Anguilla rostrata
Anoplopoma fimbria
Apeltes quadracus
Aplodinotus grunniens
Archoplites interruptus
Archosargus probatocephalus
Arctica islandica
Arctopsyche spp.
Ariusfelis
Artedius notospilotus
Asellus militaris
Asellus militaris
Astacidae
Astarte spp.
Astarte undata
Astronotus ocellatus
Astropecten verrilli
Atherinidae
Eagre marinus
Bairdiella chrysoura
Belostomatidae
Bidessinae
Bivalvia
Brachiodontes recurvus
Common Name
Mysid shrimp
Copepod (unknown species)
Shortnose sturgeon
Lake sturgeon
Atlantic sturgeon
Sturgeon (unknown species)
White sturgeon
Chiselmouth
Broad-winged damselflies
Stonefly (unknown species)
Whitebait smelt
Blueback herring
Alabama shad
Skipjack herring
Hickory shad
Alewife
American shad
Three-ridge mussel
Roanoke bass
Ozark bass
Rock bass
Bowfm
Amphipod (order)
Redtail surfperch
Northern wolffish
Bay anchovy
American eel
Sablefish
Fourspine stickleback
Freshwater drum
Sacramento perch
Sheepshead
Ocean quahog
Caddisfly (unknown species)
Hardhead catfish
Bonehead sculpin
Aquatic sow bug
Aquatic sow egg
Crayfish (family)
Astarte clam (unknown species)
Waved astarte
Oscar
Margined seastar
Silversides (family)
Gafftopsail catfish
Silver perch
Giant waterbug
Dytiscid beetle (subfamily)
Bivalvia (class)
Hooked mussel
Resident/
Migratory"
E
M
M
R
M
M
M
R
R
R
M
M
M
M
M
M
M
R
R
R
R
R
R
R
R
R
M
M
R
M
R
M
R
R
M
R
R
R
R
R
R
R
R
R
M
M
R
R
R
R
Demersal/
Pelagic"
E
P
D
D
D
D
D
P
E
D
P
P
P
P
P
P
P
D
P
P
P
E
E
P
D
P
P
E
E
E
P
P
D
D
D
D
D
D
D
D
D
P
D
P
E
P
E
E
D
D
Potentially
Edible
N
N
Y
Y
Y
Y
Y
N
N
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
N
Y
N
N
N
Y
N
N
Y
N
N
Y
Y
N
N
Y
Y
D-2
-------
National Sediment Quality Survey
Table D-l. (Continued)
Scientific Name
Brachycentrus spp.
Brevoortia patronus
Brevoortia spp.
Brevoortia tyrannus
Callinectes sapidus
Cambarus bartoni
Cambarus spp.
Campostoma anomalum
Cancer gracilis
Cancer magister
Cancer productus
Caranx hippos
Carassius auratus
Carcharhinus obscurus
Carcharhinus plumbeus
Carpiodes carpio
Carpiodes cyprinus
Carpiodes spp.
Carpiodes velifer
Catostomus ardens
Catostomus catostomus
Catostomus columbianus
Catostomus commersoni
Catostomus discobolus
Catostomus latipinnis
Catostomus macrocheilus
Catostomus occidentalis
Catostomus platyrhynchus
Catostomus snyderi
Catostomus spp.
Catostomus tahoensis
Centrarchidae
Centrarchus macropterus
Centropomus undecimalis
Centropristis striata
Ceratopsyche spp.
Chaetodipterus spp.
Chelydra serpentina
Cheumatopsyche spp.
Chironomidae
Chironomus riparius
Cichla ocellaris
Cichlasoma uropthalmus
Citharichthys sordidus
Citharichthys xanthostigma
Clarius fuscus
Clinocardium nuttalli
Clinostomus funduloides
Clupea harengus harengus
Compsomyax subdiaphana
Corbicula spp.
Common Name
Four-sided case maker caddisfly (unknown species)
Gulf menhaden
Menhaden (unknown species)
Atlantic menhaden
Blue crab
Appalachian brook crayfish
Crayfish (unknown species)
Central stoneroller
Graceful rock crab
Dungeness crab
Red rock crab
Crevalle jack
Goldfish
Dusky shark
Brown shark (sandbar)
River carpsucker
Quillback
Carpsucker (unknown species)
Highfin carpsucker
Utah sucker
Longnose sucker
Bridgelip sucker
White sucker
Bluehead sucker
Flannelmouth sucker
Largescale sucker
Sacramento sucker
Mountain sucker
Klamath largescale sucker
Sucker (unknown species)
Tahoe sucker
Sunfish (family)
Flier
Common snook
Black sea bass
Caddisfly (unknown species)
Spade fish
Snapping turtle
Net-spinning caddisfly (unknown species)
Midge (family)
Midge
Peacock cichlid
Mayan cichlid
Pacific sanddab
Longfin sanddab
Chinese catfish
Nuttall cockle
Rosyside dace
Atlantic herring
Milky venus
Asiatic clam (unknown species)
Resident/
Migratory"
R
M
M
M
M
R
R
R
R
M
R
M
R
M
M
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
M
M
R
E
R
R
R
R
R
R
E
E
R
R
R
M
R
R
Demersal/
Pelagic"
D
P
P
P
D
D
D
E
D
D
D
P
E
E
E
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
P
P
P
P
D
E
E
D
D
D
P
P
D
D
E
D
P
P
D
D
Potentially
Edible
N
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
N
N
N
Y
Y
N
N
Y
Y
N
Y
Y
Y
D-3
-------
National Sediment Quality Survey
Table D-l. (Continued)
Scientific Name
Corbicula manilensis
Coregonus artedii
Coregonus clupeaformis
Coregonus hoyi
Coregonus spp.
Cottidae
Cottus aleuticus
Cottus bairdi
Cottus beldingi
Cottus carolinae
Cottus cognatus
Cottus gulosus
Cottus leiopomus
Cottus spp.
Crassostrea gigas
Crassostrea spp.
Crassostrea virginica
Ctenopharyngodon idella
Cycleptus elongatus
Cynoscion arenarius
Cynoscion nebulosus
Cynoscion nothus
Cynoscion regalis
Cyprinella lutrensis
Cyprinella spiloptera
Cyprinid spp.
Cyprinidae
Cyprinidae (Family)
Cyprinus carpio
Dasyatis sabina
Decapoda
Dicentrarchus labrax
Diptera larva
Dorosoma cepedianum
Dorosoma petenense
Elliptic complanata
Eopsetta exilis
Equetus punctatus
Erimyzon oblongus
Erimyzon spp.
Erimyzon sucetta
Esocidae
Esox americanus americanus
Esox americanus vermiculatus
Esox lucius
Esox lucius x Esox masquinongy
Esox masquinongy
Esox niger
Etheostoma olmstedi
Etheostoma radiosum
Etheostoma spectabile
Common Name
Asiatic clam
Cisco (lake herring)
Lake whitefish
Bloater
Whitefish
Sculpin (family)
Coastrange sculpin
Mottled sculpin
Paiute sculpin
Banded sculpin
Slimy sculpin
Riffle sculpin
Wood river sculpin
Sculpin (unknown species)
Pacific oyster
Oysters (unknown species)
Eastern oyster
Grass carp
Blue sucker
Sand sea trout
Spotted sea trout
Silver sea trout
Weakfish
Red shiner
Spotfm shiner
Minnows
Carp/goldfish (hybrid)
Minnows (family)
Common carp
Atlantic stingray
Lobsters (order)
Bass
True fly larva
Gizzard shad
Threadfm shad
Freshwater clam
Slender sole
Spotted drum
Creek chubsucker
Chubsucker (unknown species)
Lake chubsucker
Pike (family)
Redfm pickerel
Grass pickerel
Northern pike
Tiger muskellunge
Muskellunge
Chain pickerel
Tesselated darter
Orangebelly darter
Orangethroat darter
Resident/
Migratory"
R
M
M
M
M
R
R
R
R
R
R
R
R
R
R
R
R
R
M
R
R
M
M
R
R
R
R
R
R
M
E
M
R
M
M
R
E
R
R
R
R
R
R
R
R
R
R
R
R
R
R
Demersal/
Pelagic"
D
P
P
P
P
D
D
D
D
D
D
D
D
D
D
D
D
E
D
P
P
P
P
P
P
E
E
E
D
D
D
P
D
P
P
D
D
D
E
E
E
P
P
P
P
P
P
P
D
D
D
Potentially
Edible
Y
Y
Y
Y
Y
Y
N
N
N
N
N
N
N
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Y
Y
Y
N
N
N
Y
Y
N
N
N
N
Y
Y
Y
Y
Y
Y
Y
N
N
N
D-4
-------
National Sediment Quality Survey
Table D-l. (Continued)
Scientific Name
Etheostoma stigmaeum
Etheostoma whipplei
Etheostoma zonale
Exoglossum maxillingua
Fundulus diaphanus
Fundulus heteroclitus
Fundulus olivaceus
Fundulus spp.
Fundulus zebrinus
Gadus macrocephalus
Galeocerdo cuvier
Gambusia holbrooki
Gambusio affinis
Gastropoda
Genyonemus lineatus
Gila robusta
Gila spp.
Girella nigricans
Glupeidae
Glyptocephalus zachi
Gonidea angulata
Haemulon spp.
Hexagenia limbata
Hexagenia spp.
Hiodon alosoides
Hiodon tergisus
Hippoglossina stomata
Hippoglossoides elas
Hippoglossoides platessoides
Homarus americanus
Hyalella azteca
Hybognathus placitus
Hydrolagus colliei
Hydropsyche spp.
Hydropsychidae
Hypentelium nigricans
Hypomesus pretiosus
Hypsopsetta guttulata
Ictaluridae
Ictalurus brunneus
Ictalurus catus
Ictalurus furcatus
Ictalurus melas
Ictalurus natalis
Ictalurus nebulosus
Ictalurus platycephalus
Ictalurus punctatus
Ictalurus serracanthus
Ictalurus spp.
Ictiobus bubalus
Ictiobus cyprinellus
Common Name
Speckled darter
Redfm darter
Banded darter
Cutlips minnow
Banded killifish
Mummichog/killifish
Blackspotted topminnow
Killifish species
Plains killifish
Pacific cod
Tiger shark
Eastern mosquito fish
Mosquito fish
Gastropods (class)
White croaker
Roundtail chub
Chub (unknown species)
Opaleye
Herring family
Rex sole
Freshwater mussel
Grunt
Mayfly
Burrowing mayfly (unknown species)
Goldeye
Mooneye
Bigmouth sole
Flathead sole
American plaice
American lobster
Freshwater amphipod
Plains minnow
Spotted rat fish
Caddisfly (unknown species)
Caddisfly (family)
Northern hog sucker
Surf smelt
Diamond turbot
Bullhead catfish (family)
Snail bullhead
White catfish
Blue catfish
Black bullhead
Yellow bullhead
Brown bullhead
Flat bullhead
Channel catfish
Spotted bullhead
Catfish (unknown species)
Smallmouth buffalo
Bigmouth buffalo
Resident/
Migratory"
R
R
R
R
R
R
R
R
R
M
M
R
R
R
R
R
R
M
M
E
R
R
R
R
M
M
M
M
M
E
R
R
M
R
R
R
M
M
R
R
R
R
R
R
R
R
R
R
R
R
R
Demersal/
Pelagic"
D
D
D
P
P
P
P
P
P
E
E
P
P
D
E
E
E
P
P
D
D
P
D
D
P
P
D
D
D
D
E
P
D
D
D
D
P
D
D
D
D
D
D
D
D
D
D
D
D
E
E
Potentially
Edible
N
N
N
N
N
N
N
N
N
Y
Y
N
N
N
Y
N
N
N
Y
N
Y
Y
N
N
Y
Y
Y
Y
Y
Y
N
N
N
N
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
D-5
-------
National Sediment Quality Survey
Table D-l. (Continued)
Scientific Name
Ictiobus niger
Isopoda
Labidesthes sicculus
Lagodon rhomboides
Lamniformes
Lampetra spp.
Lavinia exilicauda
Leiostomus xanthurus
Lepidogobius lepidus
Lepidopsetta bilineata
Lepisosteidae
Lepisosteus oculatus
Lepisosteus osseus
Lepisosteus platostomus
Lepisosteus platyrhincus
Lepisosteus spatula
Lepisosteus spp.
Lepomis auritus
Lepomis cyanellus
Lepomis gibbosus
Lepomis gulosus
Lepomis humilis
Lepomis macrochirus
Lepomis marginatus
Lepomis megalotis
Lepomis microlophus
Lepomis punctatus
Lepomis spp.
Loligo opalescens
Loligo pealei
Lota lota
Loxorhynchus grandis
Lumbriculus variegatus
Lumbricus terrestris
Lutjanus campechanus
Lutjanus griseuses
Luxilus chrysocephalus
Luxilus cornutus
Lytechinus anamesus
Macoma inquinata
Macoma irus
Macoma nasuta
Macrhybopsis gelida
Macromia magnifica
Megalonaias gigantea
Menidia menidia
Mercenaria mercenaria
Mercenaria spp.
Merluccius bilinearis
Merluccius productus
Microgadus proximus
Common Name
Black buffalo
Isopod (order)
Brook silverside
Pinfish
Mackerel shark (order)
Lamprey (unknown species)
Hitch
Spot
Bay goby
Rock sole
Garfish (family)
Spotted gar
Longnose gar
Shortnose gar
Florida gar
Alligator gar
Gar (unknown species)
Redbreast sunfish
Green sunfish
Pumpkinseed
Warmouth
Orangespotted sunfish
Bluegill
Dollar sunfish
Longear sunfish
Redear sunfish
Spotted sunfish
Common sunfishes
California market squid
Longfm squid
Burbot
Sheep crab
Aqauatic worm
Earthworm
Red snapper
Gray snapper
Striped shiner
Common shiner
Little gray sea urchin
Stained macoma
Clam (macoma)
Bent-nosed macoma
Sturgeon chub
Dragon fly
Washboard mussel
Atlantic silverside
Quahog
Hard clam (unknown species)
Silver hake
Pacific hake
Pacific tomcod
Resident/
Migratory"
R
R
R
E
M
M
R
M
R
E
E
E
E
E
E
E
E
R
R
R
R
R
R
R
R
R
R
R
M
M
M
R
R
R
M
M
R
R
R
R
R
R
R
R
R
R
R
R
M
M
M
Demersal/
Pelagic"
E
D
P
P
P
E
P
P
P
D
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
E
D
D
D
D
D
P
P
D
D
D
D
E
D
D
P
D
D
E
E
E
Potentially
Edible
Y
N
N
N
Y
N
N
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
N
Y
Y
N
N
N
Y
Y
N
N
N
Y
N
Y
Y
Y
Y
Y
D-6
-------
National Sediment Quality Survey
Table D-l. (Continued)
Scientific Name
Microgadus tomcod
Micropogonias undulats
Micropterus coosae
Microptems dolomieu
Micropterus notius
Microptems punctulatus
Micropterus salmoides
Micropterus spp.
Minytrema melanops
Modiolus demissus
Modiolus modiolus
Molpadia intermedia
Morone americana
Morone chrysops
Morone chrysops x saxatilis
Morone mississippiensis
Morone saxatilis
Morone spp.
Moxostoma anisurum
Moxostoma carinatum
Moxostoma congestum
Moxostoma duquesnei
Moxostoma erythrurum
Moxostoma macrolepidotum
Moxostoma pappillosum
Moxostoma poecilurum
Moxostoma rupiscartes
Moxostoma spp.
Mugil cephalus
Mugil curema
Mugilidae
Mustelus canis
Mya arenaria
Mylocheilus caurinus
Mylopharodon conocephalus
Mytilus californianus
Mytilus edulis
Mytilus spp.
Neanthes arenaceodentata
Neoamphitrite robusta
Nephtys caecoides
Nephtys incisa
Nigronia serricornis
Nocomis asper
Nocomis biguttatus
Nocomis leptocephalus
Nocomis micropogon
Notemigonus crysoleucas
Notonectidae
Notropis amblops
Notropis hoops
Common Name
Atlantic tomcod
Atlantic croaker
Redeye bass
Smallmouth bass
Swannee bass
Spotted bass
Largemouth bass
Bass (unknown species)
Spotted sucker
Ribbed mussel
Northern horse mussel
Sea cucumber
White perch
White bass
Hybrid striped bass (white/striped)
Yellow bass
Striped bass
Temperate bass (unknown species)
Silver redhorse
River redhorse
Gray redhorse
Black redhorse
Golden redhorse
Shorthead redhorse
V-lip redhorse
Blacktail redhorse
Striped jumprock
Redhorse (unknown species)
Striped mullet
White mullet
Mullet (family)
Smooth dogfish
Soft shell clam
Peamouth
Hardhead
California mussel
Blue mussel
Mussel (unknown species)
Sand worm
Terrebellid worm
Sand worm
Red-lined worm
Hellgrammite
Redspot chub
Hornyhead chub
Bluehead chub
River chub
Golden shiner
Backswimmer (family)
Bigeye chub
Bigeye shiner
Resident/
Migratory"
M
M
R
R
R
R
R
R
E
R
R
R
M
M
E
M
M
E
R
R
R
R
R
R
R
R
R
R
M
M
M
M
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
M
R
R
R
Demersal/
Pelagic"
E
P
P
P
P
P
P
P
D
D
D
D
P
P
P
P
P
P
D
D
D
D
D
D
D
D
D
D
E
E
E
E
D
E
E
D
D
D
D
D
D
D
D
E
E
E
E
P
P
E
P
Potentially
Edible
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
Y
Y
Y
N
N
N
N
N
N
N
N
N
N
N
N
N
D-7
-------
National Sediment Quality Survey
Table D-l. (Continued)
Scientific Name
Notropis buchanani
Notropis chrysocephalus
Notropis cornutus
Notropis emiliae
Notropis hudsonius
Notropis nubilus
Notropis spp.
Notropis stramineus
Noturusflavus
Noturus insignis
Noturus miurus
Noturus phaeus
Odonata
Odontaspis taurus
Oligochaetes
Oncorhynchus clarkii
Oncorhynchus gorbuscha
Oncorhynchus kisutch
Oncorhynchus mykiss
Oncorhynchus nerka
Oncorhynchus tshawytscha
Ondatra zibethicus
Opsanus spp.
Opsanus spp.
Orthodon microlepidotus
Orthopristis chrysoptera
Osmeridae
Osmerus mordax
Palaemontes pugio
Pandalus borealis
Paralabrax nebulifer
Paralichthys spp.
Paralichthys californicus
Paralichthys dentatus
Paralichthys lethostigma
Parastichopus californicus
Pectinaria californiensis
Penaeus aztecus
Penaeus setiferus
Penaeus spp.
Peprilus triacanthus
Perca flavescens
Percina copelandi
Perlidae
Phanerodonfurcatus
Phoca vitulinae
Phoxinus erythrogaster
Pimephales notatus
Pimephales promelas
Pisaster brevispinus
Placopecten magellanicus
Common Name
Ghost shiner
Striped shiner
Common shiner
Pugnose minnow
Spottail shiner
Ozark minnow
Shiner (composite)
Sand shiner
Stone cat
Margined madtrom
Brindled madtom
Brown madtom
Dragonfly (order)
Sand tiger
Aquatic worms
Cutthroat trout
Pink salmon
Coho salmon
Rainbow trout
Sockeye salmon
Chinook salmon
Muskrat
Toadfish
Toadfish (unknown species)
Sacramento blackfish
Pigfish
Smelt (family)
Rainbow smelt
Grass shrimp
Northern longbeak
Barred sand bass
Flounder (unknown species)
California halibut
Summer flounder (fluke)
Southern flounder
California sea cucumber
Sandworm
Brown shrimp
White shrimp
Shrimp (unknown species)
Butterfish
Yellow perch
Channel darter
Common stonefly (family)
White seaperch
Harbor seal
Southern redbelly dace
Bluntnose minnow
Fathead minnow
Starfish
Sea scallop
Resident/
Migratory"
R
R
R
R
R
R
R
R
R
R
R
R
R
M
R
E
M
M
E
M
M
R
R
R
R
R
M
M
R
R
E
M
M
M
M
R
R
R
R
R
M
R
R
R
R
R
R
R
R
R
R
Demersal/
Pelagic"
P
P
P
P
P
E
E
P
D
D
D
D
E
E
D
P
P
P
P
E
E
E
D
D
P
P
P
P
E
D
D
D
D
D
D
D
D
E
E
D
P
P
D
D
P
P
P
P
P
D
D
Potentially
Edible
N
N
N
N
N
N
N
N
N
N
N
N
N
Y
N
Y
Y
Y
Y
Y
Y
Y
N
N
N
Y
Y
Y
N
N
Y
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Y
N
N
Y
Y
N
N
N
N
N
D-8
-------
National Sediment Quality Survey
Table D-l. (Continued)
Scientific Name
Platichthys stellatus
Platygobio gracilis
Plecoptera
Pleuronectes americanus
Pleuronectes bilineatus
Pleuronectes vetulus
Pleuronectidae
Pleuronichthys coenosus
Pleuronichthys decurrens
Pleuronichthys verticalis
Poecilia vittata
Pogonias cromis
Polyodon spathula
Pomatomus saltatrix
Pomoxis annularis
Pomoxis nigromaculatus
Pomoxis spp.
Potamogeton pectinatus
Prionotus carolinus
Prionotus evolans
Decapoda
Procambarus clarkii
Prosopium cylindraceum
Prosopium williamsoni
Protothaca staminea
Psettichthys melanostictus
P silotreta/Cheumatopsyche
Ptychocheilus oregonensis
Ptychocheilus spp.
Pylodictis olivaris
Raja binoculata
Rana spp.
Rana catesbeiana
Rana clamitans
Rangia cuneata
Remora spp.
Rhinichthys atratulus
Rhinichthys cataractae
Rhinichthys spp.
Rhithropanopeus harrisii
Richardsonius balteatus
Salmo gairdneri
Salmo salar
Salmo spp.
Salmo trutta
Salmonidae
Salvelinus fontinalis
Salvelinus hybrid
Salvelinus malma
Salvelinus namaycush
Saxidomus giganteus
Common Name
Starry flounder
Flathead chub
Stonefly (order)
Winter flounder
Rock sole
English sole
Righteye flounder (family)
C-0 sole
Curlfin sole
Hornyhead turbot
Cuban limia
Black drum
Paddlefish
Bluefish
White crappie
Black crappie
Crappie (unknown species)
Sago pondweed
Northern searobin
Stripped searobin
Crayfish (order)
Red crayfish
Round whitefish
Mountain whitefish
Clam (Pacific littleneck)
Sand sole
Caddisfly (unknown species)
Northern squawfish
Squawfish
Flathead catfish
Winter skate
Common frog (unknown species)
Bullfrog
Green frog
Brackish water clam
Sucker (unknown species)
Blacknose dace
Longnose dace
Dace (unknown species)
Mud crab
Redside shiner
Rainbow trout
Atlantic salmon
Trout (unknown species)
Brown trout
Trout (family)
Brook trout
Splake (hybrid)
Dolly varden
Lake trout
Clam (smooth Washington)
Resident/
Migratory"
M
R
R
M
E
M
M
M
M
M
E
M
M
M
R
R
R
R
R
R
R
R
M
M
R
M
R
R
R
R
M
R
R
R
R
E
R
R
R
R
R
E
M
E
E
E
E
E
E
E
R
Demersal/
Pelagic"
D
E
D
D
D
D
D
D
D
D
P
P
P
P
P
P
P
D
D
D
D
D
P
P
D
D
D
E
E
E
D
P
P
P
D
P
D
D
D
D
P
P
P
P
P
P
P
P
P
P
D
Potentially
Edible
Y
N
N
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
N
N
N
N
N
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
D-9
-------
National Sediment Quality Survey
Table D-l. (Continued)
Scientific Name
Scaphirhynchus platorynchus
Sciaenidae
Sciaenops ocellatus
Scomber japonicus
Scomberomorus cavalla
Scomberomoms maculatus
Scophthalmus aquosus
Scorpaena guttata
Scorpaenichthys marmoratus
Sebastes auriculatus
Sebastes caurinus
Sebastes maliger
Sebastes marinus
Sebastes melanops
Sebastes norvegicus
Sebastes paucispinis
Sebastes proriger
Sebastes sp.
Semotilus atromaculatus
Semotilus corporalis
Semotilus lumbee
Serranidae
Sicyonia ingentis
Solen sicarius
Spisula spp.
Squalus acanthias
Stizostedion canadense
Stizostedion vitreum
Strongylura marina
Syacium papillosum
Symphurus atricauda
Synodus foetens
Tapes philippinarum
Tautoga onitis
Tautogolabrus adspersus
Thalamita crenata
Theragra chalcogramma
Thunnus atlanticus
Thymallus arcticus
Tilapia mossambica
Tilapia zillii
Tipula spp.
Trachinotus carolinus
Tresus capax
Triakis semifasciata
Tridentiger trigonocephalus
Trinectes maculatus
Tylosurus crocodilus
Uca minax
Uca pugnax
Umbra limi
Common Name
Shovelnose sturgeon
Drum (family)
Red drum
Chub mackerel
King mackerel
Spanish mackerel
Windowpane
California scorpionfish
Cabezon
Brown rockfish
Copper rockfish
Quillback rockfish
Ocean perch
Black rockfish
Golden redfish
Bocaccio
Redstripe rockfish
Rockfish (unknown species)
Creek chub
Fallfish
Sandhills crab
Sea bass (family)
Pacific rock shrimp
Razor clam
Surf clam
Spiny dogfish
S auger
Walleye
Atlantic needlefish
Dusky flounder
California tonguefish
Inshore lizardfish
Japanese littleneck
Tautog
Gunner (bergall)
Blue pincher crab
Walleye pollock
Blackfin tuna
Arctic grayling
Mozambique tilapia
Redbelly tilapia
Cranefly (unknown species)
Pompano
Horse clam
Leopard shark
Chameleon goby
Hogchoker
Houndfish
Redjointed fiddler
Atlantic marsh fiddler
Central mudminnow
Resident/
Migratory"
M
M
M
M
M
M
M
R
R
M
M
M
M
M
M
M
M
M
R
R
R
E
R
R
R
M
R
R
M
M
M
R
R
R
R
R
M
M
E
R
R
R
M
R
M
R
M
M
R
R
R
Demersal/
Pelagic"
D
E
E
P
P
P
D
D
D
P
P
P
P
P
P
P
P
P
E
E
E
E
D
D
D
E
P
P
P
D
D
D
D
E
E
D
E
P
P
E
E
D
P
D
E
D
D
E
D
D
E
Potentially
Edible
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
N
Y
N
N
Y
Y
Y
Y
N
Y
N
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
N
N
Y
N
N
N
D-10
-------
National Sediment Quality Survey
Table D-l. (Continued)
Scientific Name
Urophycis chuss
Urophycis regia
Vaucheria
Common Name
Red (squirrel) hake
Spotted hake
Macroalgae
Sorted benthic sample
Unsorted benthic sample
Resident/
Migratory"
M
M
R
R
R
Demersal/
Pelagic"
E
E
E
D
D
Potentially
Edible
Y
Y
N
N
N
Fish species is considered R-resident, M-migratory, E-either resident or migratory.
Fish species is considered D-demersal, P-pelagic, E-either.
D-ll
-------
National Sediment Quality Survey
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-l). 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.
Upper Mississippi
River Watershed
Figure E-l. Lake Pepin Location Map.
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
E-l
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National Sediment Quality Survey
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-l.
Table E-l. Total Hg Concentrations in Lake Pepin Sediment Cores.
Core
Number
1
2
o
5
4
5
6
7
8
9
10
Pre-settlement
(ng/g)
33
34
34
33
40
36
38
34
37
39
Peak
(ng/g)
662
669
528
561
519
503
570
499
452
492
Modern
(ng/g)
106
120
120
128
121
135
123
128
137
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.
100
200
Mercury Accumulation (kg/yr)
300
400
Figure E-2. Historical Hg Loading Rates in the Upper
Mississippi River Reconstructed from Sediments of Lake
Pepin. Source: Balogh et al, 1999.
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National Sediment Quality Survey
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
etal. (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
Figure E-3. Location Map of Upper
Chattahoochee Basin.
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National Sediment Quality Survey
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
Before 1950
1950-1969
1990s
% Decrease
Lake Harding
(Mg/kg)
-50
280-380"
42^6
85-88
Lake Walter F. George
(Hg/kg)
NA
220"
<14
94
Lake Blackshear
(Mg/kg)
NA
8
1
88
Lake Seminole
(Hg/kg)
NA
3-10
10
(70M>
NA: not available.
" Between 1950 and 1966.
"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 (ODD 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.
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National Sediment Quality Survey
Table E-3. DDT Concentrations in Lake Sediments.
Period
1940s-1965
1990s
% Decrease
Lake Harding
(Hg/kg)
35
5.9
83
Lake Walter F.
George (ng/kg)
74"
5.5
93
Lake Blackshear (ng/kg)
57
6.5
89
Lake Seminole
(Hg/kg)
30
7.5
75
1 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
1960s
1990s
% Decrease
Lake Harding
(Hg/kg)
46-84"
6-8"
87-90
Lake Walter F. George
(Mg/kg)
6C
NA
NA
Lake Blackshear (ng/kg)
ND
ND
NA
Lake Seminole
(Hg/kg)
ND
ND
NA
ND: not detected.
NA: not available.
' 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, ODD 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
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National Sediment Quality Survey
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
atal., 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 Figure E-4. Location Map of Puget Sound.
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National Sediment Quality Survey
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., a = 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 apeak
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
(Hg/g)
Arsenic
(Hg/g)
Copper
(Hg/g)
Mercury
(Hg/g)
Lead
(Hg/g)
Antimony
(Hg/g)
Tin
(Hg/g)
Zinc
(ng/g)
Location 1
Max Year
Max
Surface
% Decrease
Recovery Rate
1965
0.91
0.68
25.3
0.009
1965
19.5
12.5
35.9
0.280
1960
54.6
42.7
21.8
0.397
1949
0.479
0.179
62.6
0.007
1965
48.9
30.3
38.0
0.744
1960
2.05
1.28
37.6
0.026
1965
4.9
3.94
19.6
0.038
1965
134.6
114.7
14.8
0.796
Location 2
Max Year
Max
Surface
% Decrease
Recovery Rate
1982
0.84
0.69
17.9
0.017
1964
28.3
13.1
53.7
0.563
1947
70
49.3
29.6
0.470
1947
0.505
0.213
57.8
0.007
1922
69.4
36.7
47.1
0.474
1952
3.9
1.6
59
0.059
1962
4.85
3.96
18.4
0.031
1962
167.7
119.2
28.9
1.672
Location 3
Max Year
Max
Surface
% Decrease
Recovery Rate
Average Recovery Rate
95% CL ±
1965
0.65
0.59
9.2
0.002
0.009
0.014
1950
23.5
17.3
26.4
0.155
0.333
0.424
1963
64.7
52.7
18.5
0.444
0.437
0.076
1950
0.403
0.28
31.3
0.003
0.006
0.005
1954
62.3
44.7
28.3
0.489
0.569
0.308
1963
2.43
1.52
37.4
0.034
0.039
0.035
1963
4.25
2.78
34.6
0.054
0.041
0.025
1954
128.8
115.5
10.3
0.369
0.946
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
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National Sediment Quality Survey
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) |ig/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) [ig/g at the lower levels of the sediment to a maximum of 6,788 |o,g/g
during the early 1940s, subsequently declining to an average of 1,300 |o,g/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) [ig/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) [ig/g per year.
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National Sediment Quality Survey
Table E-6. Maximum and Surface Concentrations of Selected Organic Contaminants for Three
Core Locations Collected During 1991.
Organic Contaminant
PCB
(ng/g)
DDT
(Hg/g)
Total PAH
(Hg/g)
Combustible PAH
(Hg/g)
Location 1
Max Year
Max
Surface
% Decrease
Recovery Rate
1960
34.5
9.00
73.9
0.851
1960
4.71
1.19
74.8
0.117
1943
6,788
1,434
78.9
114
1943
5,917
1,162
80.4
101
Location 2
Max Year
Max
Surface
% Decrease
Recovery Rate
1966
25.8
5.33
79.3
0.819
1984
5.76
4.77
17.2
0.142
1942
3,430
1,303
62
43.412
1942
2,898
1,050
63.8
37.878
Location 3
Max Year
Max
Surface
% Decrease
Recovery Rate
Average Recovery Rate
95% CL ±
1961
15.5
7.39
52.2
0.278
0.649
0.653
1961
4.25
2.8
34.2
0.050
0.103
0.096
1935
1,883
1,212
35.6
12.198
56.5
106
1935
1,516
977
35.5
9.793
49.6
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.
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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.O. 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.
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National Sediment Quality Survey
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.
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National Sediment Quality Survey
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-l 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-l present a detailed listing and geographical location, respectively, of the watersheds
summarized in Table F-l. 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-l. Watersheds Containing APCs: Comparison of Previous Report to Congress and
Current Report.
Characteristics
Identified as APC
Had fewer than 10 total monitoring stations
Had 10 or more total stations, but fewer than 10
stations were classified as Tier 1
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
Total Watersheds Containing an APC
Watershed Contained an APC
Based on Data Evaluated in the
Previous Report to Congress
37
26
26
7
96
Watershed Contains an APC
Based on Data Evaluated in the
Current Report to Congress
37
19
36
4
96
F-l
-------
to
Table F-2. Detailed Comparison of Watersheds Containing APCs: Previous Report to Congress and Current Analysis.
Cataloging
Unit Number
Cataloging Unit Name
First Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tier 1
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
Current Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tier 1
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
Watersheds that contain areas of probable concern in both reports
01090001
01090004
02030103
02030104
02030105
02030202
02040202
02060003
03130002
03160205
04030108
04030204
04040001
04040002
04120101
06010201
06020001
07040001
07080101
07120003
07120004
07120006
07130001
08030209
Charles
Narragansett
Hackensack-Passaic
Sandy Hook-Staten Island
Raritan
Southern Long Island
Lower Delaware
Gunpowder-Patapsco
Middle Chattahoochee-Lake Harding
Mobile Bay
Menominee
Lower Fox
Little Calumet-Galien
Pike-Root
Chautauqua-Conneaut
Watts Bar Lake
Middle Tennessee-Chickamauga
Rush-Vermillion
Copperas-Duck
Chicago
Des Plaines
Upper Fox
Lower Illinois-Senachwine Lake
Deer-Steele
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
708
48
103
100
65
43
57
29
27
81
21
51
89
72
110
89
94
14
27
103
110
60
21
21
195
28
43
60
13
11
18
17
21
31
12
49
45
34
21
63
47
13
17
64
61
15
11
11
402
20
58
21
37
24
29
7
4
43
6
2
26
30
86
7
29
1
5
36
43
40
10
10
111
0
2
19
15
8
10
5
2
7
3
0
18
8
3
19
18
0
5
3
6
5
0
0
84
100
98
81
77
81
83
83
93
91
86
100
80
89
97
79
81
100
82
97
95
92
100
100
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
69
14
172
194
30
85
26
32
26
31
21
26
24
60
16
19
33
19
136
49
81
81
12
24
38
12
149
155
15
47
11
22
21
17
18
16
22
39
13
16
15
10
99
34
40
31
11
23
20
1
20
31
9
21
15
8
4
14
2
5
1
13
2
3
12
5
22
14
37
37
1
1
11
1
3
8
6
17
0
2
1
0
1
5
1
8
1
0
6
4
15
1
4
13
0
0
84
93
98
96
80
80
100
94
96
100
95
81
96
87
94
100
82
79
89
98
95
84
100
100
O.
i'
fD
rt-
O
1
-------
Table F-2. (Continued).
Cataloging
Unit Number
Cataloging Unit Name
First Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tierl
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
Current Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tierl
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
Watersheds that contain areas of probable concern in both reports (continued)
08090100
11070209
17090012
17110002
17110013
17110019
18050003
18050004
18070104
18070201
18070204
18070301
18070304
Lower Mississippi-New Orleans
Lower Neosho
Lower Willamette
Strait Of Georgia
Duwamish
Puget Sound
Coyote
San Francisco Bay
Santa Monica Bay
Seal Beach
Newport Bay
Aliso-San Onofre
San Diego
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
51
20
76
263
127
1,383
24
64
132
442
108
32
107
16
13
21
32
48
418
18
19
79
63
24
10
53
34
3
51
168
69
851
6
37
31
339
68
22
51
1
4
4
63
10
114
0
8
22
40
16
0
3
98
80
95
76
92
92
100
88
83
91
85
100
97
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
34
20
382
443
930
2,135
32
130
132
59
74
19
278
28
11
243
184
599
1,246
25
113
109
38
36
17
208
6
5
96
179
267
552
7
16
21
18
20
1
47
0
4
43
80
64
337
0
1
2
3
18
1
23
100
80
89
82
93
84
100
99
98
95
76
95
92
Watersheds that contain areas of probable concern in the first report but have fewer than 10 total monitoring stations in the current report
04060103
04090002
04090004
04100002
04100012
04110003
04120103
04120104
04130001
04150301
05030101
Manistee
Lake St. Clair
Detroit
Raisin
Huron- Vermilion
Ashtabula-Chagrin
Buffalo-Eighteenmile
Niagara
Oak Orchard- Twelvemile
Upper St. Lawrence
Upper Ohio
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
14
19
115
38
45
31
101
41
86
31
53
11
13
85
18
10
10
59
24
39
21
12
3
5
29
19
35
18
33
16
46
5
29
0
1
1
1
0
3
9
1
1
5
12
100
95
99
97
100
90
91
98
99
84
77
0
0
0
1
2
8
4
3
5
2
6
0
0
0
0
0
0
4
2
3
0
6
0
0
0
1
2
4
0
0
0
0
0
0
0
0
0
0
4
0
1
2
2
0
0
0
0
100
100
50
100
67
60
0
100
Z
88
p^
5'
9
C/5
CD
CL
§'
CD
o
05
CD
-------
Table F-2. (Continued).
Z
88
rt-
5'
9
CL
i'
fD
rt-
O
05
1
Cataloging
Unit Number
Cataloging Unit Name
First Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tier 1
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
Current Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tier 1
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
Watersheds that contain areas of probable concern in the first report but have fewer than 10 total monitoring stations in the current report (continued)
05030102
06010104
06010207
06030001
06040001
08010100
08040207
10270104
11070207
17010303
17030003
17110014
18030012
18070105
18070107
Shenango
Holston
Lower Clinch
Guntersville Lake
Lower Tennessee-Beech
Lower Mississippi-Memphis
Lower Ouachita
Lower Kansas
Spring
Coeur D'Alene Lake
Lower Yakima
Puyallup
Tulare-Buena Vista Lakes
Los Angeles
San Pedro Channel Islands
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
15
15
79
92
25
20
12
29
41
23
47
19
20
37
25
11
12
61
25
15
14
12
12
10
10
23
12
10
14
14
1
2
14
46
6
3
0
15
25
13
19
6
5
19
10
3
1
4
21
4
3
0
2
6
0
5
1
5
4
1
80
93
95
77
84
85
100
93
85
100
89
95
75
89
96
5
5
6
4
3
5
0
8
9
2
4
9
3
6
4
5
3
5
1
0
0
0
1
9
2
0
8
1
6
2
0
1
1
1
1
2
0
1
0
0
0
1
2
0
1
0
1
0
2
2
3
0
6
0
0
4
0
0
0
1
100
80
100
50
33
40
0
25
100
100
0
100
100
100
75
Watersheds that contain areas of probable concern in the first report but have fewer than 10 stations classified as Tier 1 in the current report
01090002
02040105
02040301
02070004
03040201
03060101
03060106
03080103
03140102
Cape Cod
Middle Delaware-Musconetcong
Mullica-Toms
Conococheague-Opequon
Lower Pee Dee
Seneca
Middle Savannah
Lower St. Johns
Choctawhatchee Bay
APC
APC
APC
APC
APC
APC
APC
APC
APC
108
48
42
29
34
16
36
188
51
15
11
10
11
11
10
20
32
19
73
26
22
12
20
3
11
111
23
20
11
10
6
3
3
5
45
9
82
77
76
79
91
81
86
76
82
34
18
20
13
29
12
21
33
44
6
6
6
2
6
8
6
9
9
15
5
7
7
15
1
5
15
14
13
7
7
4
8
3
10
9
21
62
61
65
69
72
75
52
73
52
-------
Table F-2. (Continued).
Cataloging
Unit Number
Cataloging Unit Name
First Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tierl
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
Current Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tierl
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
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 report (continued)
03140107
04030102
04050001
04100001
04100010
04110001
05040001
05120109
05120111
06020002
06030005
06040005
07040003
07070003
07090006
07140201
07140202
Perdido Bay
Door-Kewaunee
St. Joseph
Ottawa- Stony
Cedar-Portage
Black-Rocky
Tuscarawas
Vermilion
Middle Wabash-Busseron
Hiwassee
Pickwick Lake
Kentucky Lake
Buffalo-Whitewater
Castle Rock
Kishwaukee
Upper Kaskaskia
Middle Kaskaskia
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
38
20
32
29
56
59
78
28
33
33
69
30
26
22
34
55
38
10
12
17
13
13
24
10
12
15
13
49
15
17
20
10
31
13
24
5
9
15
39
31
53
16
17
17
9
14
3
0
24
24
22
4
3
6
1
4
4
15
0
1
3
11
1
6
2
0
0
3
90
85
81
97
93
93
81
100
97
91
84
97
77
91
100
100
92
19
12
11
19
32
22
16
34
18
17
20
11
10
19
15
26
20
8
6
6
4
6
4
8
6
8
7
9
1
1
5
6
9
5
9
1
5
10
20
10
4
24
7
9
7
6
5
7
8
16
10
2
5
0
5
6
8
4
4
3
1
4
4
4
7
1
1
5
89
58
100
74
81
64
75
88
83
94
80
64
60
63
93
96
75
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 in the current
report
02040203
04040003
07010206
07140101
07140106
08080206
12040104
Schuylkill
Milwaukee
Twin Cities
Cahokia- Joachim
Big Muddy
Lower Calcasieu
Buffalo-San Jacinto
APC
APC
APC
APC
APC
APC
APC
44
90
35
56
94
100
36
12
60
26
18
23
26
10
23
16
2
34
65
52
23
9
14
7
4
6
22
3
80
84
80
93
94
78
92
56
67
44
37
100
153
57
26
32
25
10
20
73
21
15
10
6
14
29
11
20
15
25
13
13
51
69
16
73
63
70
65
49
55
72
z
88
p^
5'
9
C/5
CD
CL
§'
CD
o
e
IF
05
CD
-------
Z
88
rt-
5'
9
Table F-2. (Continued).
Cataloging
Unit Number
Cataloging Unit Name
First Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tierl
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
Current Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tierl
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
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
02030101
02030102
05120106
05120208
08030207
12030102
12090205
14010002
17080001
17100102
17110012
18010102
18020112
18040005
18050001
18070103
18070203
19020201
Long Island Sound
Lower Hudson
Bronx
Tippecanoe
Lower East Fork White
Big Sunflower
Lower West Fork Trinity
Austin- Travis Lakes
Blue
Lower Columbia-Sandy
Queets-Quinault
Lake Washington
Mad-Redwood
Sacramento-Upper Clear
Lower Cosumnes-Lower Mokelumne
Suisun Bay
Calleguas
Santa Ana
Eastern Prince William Sound
0
8
8
0
1
7
7
8
1
5
0
1
9
1
1
3
2
5
0
0
4
4
0
1
4
5
5
0
3
0
0
4
0
1
3
0
1
0
0
3
3
0
0
3
2
3
0
2
0
1
4
1
0
0
0
4
0
0
1
1
0
0
0
0
0
1
0
0
0
1
0
0
0
2
0
0
0
88
88
0
100
100
100
100
0
100
0
100
89
100
100
100
0
100
0
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
31
68
27
25
19
38
31
22
15
72
108
216
26
25
60
27
26
98
31
23
60
26
17
10
38
19
16
15
20
77
179
20
23
23
16
26
30
10
7
2
0
3
8
0
10
4
0
39
23
30
4
2
23
8
0
53
15
1
6
1
5
1
0
2
2
0
13
8
7
2
0
14
3
0
15
6
97
91
96
80
95
100
94
91
100
82
93
97
92
100
77
89
100
85
81
CL
i'
fD
rt-
O
05
1
-------
Table F-2. (Continued).
Cataloging
Unit Number
Cataloging Unit Name
First Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tierl
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
Current Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tierl
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
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
01100004
01100005
01100006
02020003
02020004
02020006
02020008
02030201
02060004
02080107
03050201
03060109
03070203
04140201
05060001
05120201
06010205
07090005
07090007
07120001
07120002
07120005
07120007
07130003
Lower Connecticut
Quinnipiac
Housatonic
Saugatuck
Hudson-Hoosic
Mohawk
Middle Hudson
Hudson- Wappinger
Northern Long Island
Severn
York
Cooper
Lower Savannah
Cumberland-St. Simons
Seneca
Upper Scioto
Upper White
Upper Clinch
Lower Rock
Green
Kankakee
Iroquois
Upper Illinois
Lower Fox
Lower Illinois-Lake Chautauqua
16
21
36
14
23
27
14
19
31
18
22
25
27
39
14
57
13
67
54
37
16
25
14
49
27
2
3
7
4
3
0
1
3
8
1
3
9
8
9
1
8
7
8
7
6
8
0
6
6
6
8
18
26
10
10
23
13
9
20
14
17
10
12
13
11
48
6
30
33
28
8
25
6
42
21
6
0
3
0
10
4
0
7
3
3
2
6
7
17
2
1
0
29
14
3
0
0
2
1
0
63
100
92
100
57
85
100
63
90
83
91
76
74
56
86
98
100
57
74
92
100
100
86
98
100
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
19
13
24
19
163
43
76
40
75
72
67
105
68
30
20
50
42
27
37
47
34
29
24
26
36
17
12
22
18
155
32
55
34
62
48
16
52
20
19
11
10
23
10
11
17
20
10
11
10
16
2
1
0
1
8
7
13
6
9
20
35
30
45
8
4
32
14
11
20
24
10
18
12
13
15
0
0
2
0
0
4
8
0
4
4
16
23
3
3
5
8
5
6
6
6
4
1
1
3
5
100
100
92
100
100
91
89
100
95
94
76
78
96
90
75
84
88
78
84
87
88
97
96
88
86
Z
88
p^
5'
9
C/5
n
o.
O
-------
oo
Z
88
rt-
5'
9
Table F-2. (Continued).
Cataloging
Unit Number
Cataloging Unit Name
First Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tierl
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
Current Report to Congress Results
APC
Status
Number of Sampling Stations
Total
Tierl
Tier 2
Tier 3
Percent of
Sampling
Stations in
Tier 1 or
Tier 2
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 (continued)
07130007
07130011
07130012
15060106
16050203
17020001
17100105
18050002
18060006
18060011
18070106
South Fork Sangamon
Lower Illinois
Macoupin
Lower Salt
Carson Desert
Franklin D. Roosevelt Lake
Grays Harbor
San Pablo Bay
Central Coastal
Alisal-Elkhorn Sloughs
San Gabriel
48
44
16
13
33
32
20
36
71
18
15
8
5
0
7
7
4
1
8
8
1
5
37
38
15
4
10
24
18
21
62
15
6
3
1
1
2
16
4
1
7
1
2
4
94
98
94
85
52
88
95
81
99
89
73
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
APC
16
36
19
52
19
66
139
101
54
34
34
12
17
10
39
14
52
102
69
25
25
21
4
14
9
13
5
9
33
28
22
9
11
0
5
0
0
0
5
4
4
7
0
2
100
86
100
100
100
92
97
96
87
100
94
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 Tier 2 in the first
report
02040205
03050202
03100206
03140105
Brandywine-Christina
South Carolina Coastal
Tampa Bay
Pensacola Bay
101
64
196
96
22
14
11
12
21
29
54
47
58
21
131
37
43
67
33
62
APC
APC
APC
APC
220
60
70
59
109
33
41
20
67
18
18
25
44
9
11
14
80
85
84
76
CL
i'
n
O
1
-------
Legend
AFC in previous report to Congress
•!•:•:•!• APC in current analysis
APC in both
Figure F-l. Comparison of Watersheds Containing APCs: Previous Report to Congress and Current Analysis.
z
88
p^
o'
9
C/5
fB
CL
§'
CD
p^-
O
•*
05
CD
-------
National Sediment Quality Survey
F-10
------- |