United States Science and, Technology EPA 823-R-97-0Q6
Environmental Protection (4305) September 1997
Agency ,
*>EPA The Incidence And Severity
Of Sediment Contamination
In Surface Waters Of The
United States
Volume 1:
National Sediment
Quality Survey
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S! i
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
JAN 7 19:
THE ADMINISTRATOR
The Honorable Albert Gore, Jr.
President of the Senate
Washington, D.C, 20510
Dear Mr. President:
As required by the Water Resources Development Act of 1992 (WRDA), I am pleased to
transmit the Environmental Protection Agency's (EPA) Report to Congress on the Incidence and
Severity of Sediment Contamination in Surface Waters of the United States. This report
describes the accumulation of chemical contaminants in river, lake, ocean, and estuary bottoms
and includes a screening assessment of the potential for associated adverse effects to human and
environmental health. It represents the first comprehensive EPA analysis of sediment chemistry
and related biological data to assess what is known about the national incidence and severity of
sediment contamination. As directed by WRDA, EPA consulted with the U.S. Army Corps of
Engineers and the National Oceanic and Atmospheric Administration in compiling data and
preparing the report.
EPA studied available data from sixty-five percent of the 2,111 watersheds in the
continental United States and identified ninety-six watersheds that contain "areas of probable
concern." In portions of these watersheds, environmental conditions may be unsuitable for
bottom dwelling creatures, and fish that live in these waters may contain chemicals at levels
unsafe for regular consumption. Areas of probable concern are located in regions affected by
urban and agricultural runoff, municipal and industrial waste discharge, and other pollution
sources. EPA recommends that resource managers fully examine the risks to human health and
the environment in these watersheds. Authorities should take steps to ensure that major pollution
sources are effectively controlled and that plans are in place to improve sediment conditions and
to support long-term health goals. EPA's goals for managing the problem of contaminated
sediment are provided as an enclosure to this letter.
Printed on Recycled Paper
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The process to produce EPA's Report to Congress on the Incidence and Severity of
Sediment Contamination in Surface Waters of the United States has been thorough and
extensive, meeting WRDA requirements for Federal agency consultation, as well as EPA's own
standards and policies regarding internal program and regional office review, external scientific
peer review, and external stakeholder review. I would be pleased to further discuss the contents
of this report at your convenience.
Sincerely,
Carol M, Browner
Enclosure
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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
JAN ~7 199T
THE ADMINISTRATOR
The Honorable Newt Gingrich
Speaker of the House of Representatives
Washington, D.C. 20515
Dear Mr. Speaker:
As required by the Water Resources Development Act of 1992 (WRDA), I am pleased to
transmit the Environmental Protection Agency's (EPA) Report to Congress on the Incidence and
Severity of Sediment Contamination in Surface Waters of the United States. This report
describes the accumulation of chemical contaminants in river, lake, ocean, and estuary bottoms
and includes a screening assessment of the potential for associated adverse effects to human and
environmental health. It represents the first comprehensive EPA analysis of sediment chemistry
and related biological data to assess what is known about the national incidence and severity of
sediment contamination. As directed by WRDA, EPA consulted with the U.S. Army Corps of
Engineers and the National Oceanic and Atmospheric Administration in compiling data and
preparing the report.
EPA studied available data from sixty-five percent of the 2,111 watersheds in the
continental United States and identified ninety-six watersheds that contain "areas of probable
concern." In portions of these watersheds, environmental conditions may be unsuitable for
bottom dwelling creatures, and fish that live in these waters may contain chemicals at levels
unsafe for regular consumption. Areas of probable concern are located in regions affected by
urban and agricultural runoff, municipal and industrial waste discharge, and other pollution
sources. EPA recommends that resourcb managers fully examine the risks to human health and
the environment in these watersheds. Authorities should take steps to ensure that major pollution
sources are effectively controlled and that plans are in place to improve sediment conditions and
to support long-term health goals. EPA's goals for managing the problem of contaminated
sediment are provided as an enclosure to this letter.
Printed an Recycled Paper
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2
The process to produce EPA's Report to Congress on the Incidence and Severity of
Sediment Contamination in Surface Waters of the United States has been thorough and
extensive, meeting WRDA requirements for Federal agency consultation, as well as EPA's own
standards and policies regarding internal program and regional office review, external scientific
peer review, and external stakeholder review. I would be pleased to further discuss the contents
of this report at your convenience.
Sincerely,
Carol M. Browner
Enclosure
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Managing Contaminated Sediment in the United States
Issue Background
Many pollutants released to the environment settle and accumulate in the silt and mud
called sediment on the bottoms of rivers, lakes, estuaries, and oceans. Much of the
contaminated sediment in the U.S. was polluted years ago by such chemicals as DDT,
PCBs, and mercury, which have since been banned or restricted. These contaminants are
now found less frequently in overlying surface water than in the past. However, they can
persist for many years in the sediment, where they can cause adverse effects to aquatic
organisms and to human health. Some other chemicals released to surface waters from
industrial and municipal discharges, and polluted runoff from urban and agricultural
areas, continue to accumulate to environmentally harmful levels in sediment.
Costs of Sediment Contamination
Ecological and human health impairment due to contaminated sediment imposes costs
on society. Fish diseases causing tumors and fin rot and loss of species and communities
that cannot tolerate sediment contamination can severely damage aquatic ecosystems.
Contaminants in sediment can also poison the food chain. Fish and shellfish can become
unsafe for human or wildlife consumption. Potential costs to society include lost
recreational enjoyment and revenues or, worse, possible long-term adverse health effects
such as cancer or children's neurological and IQ impairment if fish consumption
warnings are not issued and heeded. The health and ecological risks posed by
contaminated sediment dredged from harbors can lead to increased cost of disposal and
lost opportunities for beneficial uses, such as habitat restoration.
Volume of Contaminated Sediments
The U.S. Environmental Protection Agency estimates that approximately 10 percent of
the sediment underlying our nation's surface water is sufficiently contaminated with toxic
pollutants to pose potential risks to fish and to humans and wildlife who eat fish. This
represents about 1.2 billion cubic yards of contaminated sediment out of the
approximately 12 billion cubic yards of total surface sediments (upper five centimeters)
where many bottom dwelling organisms live, and where the primary exchange processes
between the sediment and overlying surface water occur. Approximately 300 million
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cubic yards of sediments are dredged from harbors and shipping channels annually to
maintain commerce, and about 3-12 million cubic yards of those are sufficiently
contaminated to require special handling and disposal. These amounts are graphically
illustrated in the diagram below.
Volume of U.S.
Sediment by Category /
~ Sadlmant: approx 18 billion cubic yard»\
¦Contaminated 8adlmatrt: approx, 1,2 \
billion cubic yards \
HODradgad Material; 300 million cublo yaida*
¦contaminated Dradgad matarial: 3-12
million cubic yard*'
•Annual ntlmilM •« raportad In EPA'a draft Contamlnatad Sadlmut Managanant Stiatagy
Where Is contaminated sediment a potential concern?
EPA has studied data from 1,372 of the 2,111 watersheds in the continental U.S. Of
these, EPA has identified 96 watersheds that contain "areas of probable concern" where
potential adverse effects of sediment contamination are more likely to be found. These
areas, identified in the figure below, are on the Atlantic, Gulf, Great Lakes, and Pacific
coasts, as well as in inland waterways, in regions affected by urban and agricultural
runoff, municipal and industrial
' waste discharges, and other
pollution sources. Some of
these areas have been studied
extensively, and now have
appropriate management
actions in place. However,
others may require further
evaluation to confirm that
environmental effects are
occurring.
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3
EPA's Contaminated Sediment Goals
EPA's Contaminated Sediment Management Strategy establishes four goals to
manage the problem of contaminated sediment, and describes actions the Agency intends
to take to accomplish those goals. The four goals are:
1. Prevent the volume of contaminated sediment from increasing. To accomplish
this, EPA will employ its pollution prevention and source control programs. Both the
pesticides and toxic substances programs will use new and existing chemical registration
programs to reduce the potential for release of sediment contaminants to surface waters.
The water program will work with States and Tribes to identify waterbodies with
contaminated sediment as impaired and target them for Total Maximum Daily Load
evaluations. EPA will also work with the States and Tribes to enhance the
implementation of point and nonpoint source controls in these watersheds.
2. Reduce the volume of existing contaminated sediment. EPA will consider a range
of risk management alternatives to reduce the volume and effects of existing
contaminated sediment, including in-situ containment and contaminated sediment
removal. In some cases, risk managers may select a combination of practicable
alternatives as the remedy. Where natural attenuation is part of the selected alternative,
EPA will accelerate pollution prevention and source control efforts, where appropriate, to
ensure that clean sediments will bury contaminated ones within an acceptable recovery
period. During the recovery period, EPA will work with the States to improve human
health protection by establishing and maintaining appropriate fish consumption
advisories. In all cases, environmental monitoring will be conducted to ensure that risk
management goals are achieved.
3. Ensure that sediment dredging and dredged material disposal are managed in an
environmentally sound manner. EPA carefully evaluates the potential environmental
effects of proposed dredged material disposal. In addition, EPA is initiating a national
stakeholder review process to help the Agency review the ocean disposal testing
requirements and ensure that any future revisions reflect both sound policy and sound
science. EPA and the Army Corps of Engineers also will provide appropriate guidance to
further encourage and promote beneficial uses of dredged material.
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4. Develop scientifically sound sediment management tools for use in pollution
prevention, source control, remediation, and dredged material management. Such
tools include national inventories of sediment quality and environmental releases of
contaminants, numerical assessment guidelines to evaluate contaminant concentrations,
and standardized bioassay tests to evaluate the bioaccumulation and toxicity potential of
specific sediment samples.
Working with States and Tribes through existing statutory authorities, EPA can
identify impaired waterbodies and watersheds at risk from contaminated sediment,
implement appropriate actions to accomplish the goals described above, and monitor the
effectiveness of actions taken to accomplish the Agency's goals.
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The Incidence And Severity
Of Sediment Contamination
In Surface Waters Of The
United States:
Volume 1:
National Sediment
Quality Survey
September 1997
Office of Science and Technology
United States Environmental Protection Agency
401 M Street, SW
Washington, DC 20460
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The National Sediment Quality Survey is a screening-level assessment of sediment quality
that compiles and evaluates sediment chemistry data and related biological data taken from
existing databases. The data and information contained in this document could be used in
various EPA regulatory programs for priority setting or other purposes after further evaluation for
program-specific criteria. However, this document has no immediate or direct regulatory conse-
quence, It does not in' itself establish any legally binding requirements, establish or affect legal rights
or obligations, or represent a determination of any party's liability.
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Contents
; Page
Tables
Figures ix
Acknowledgments
Executive Summary XV
Introduction 1 -1
What Is the National Sediment Quality Survey? 1-1
Why Is Contaminated Sediment an Important National Issue?.. 1-2
How Significant is the Problem? ........... . 1-3
What Are the Potential Sources of Sediment Contamination? 1-4
Methodology 2—1
Background 2-2
Description of NSI Data , 2-3
NSI Data Evaluation Approach 2-4
Sediment Chemistry Data 2-12
Tissue Residue Data 2-15
Toxicity Data 2-15
Incorporation of Regional Comments on the Preliminary Evaluation of
Sediment Chemistry Data 2-16
Evaluation Using EPA Wildlife Criteria 2-16
.....a3—1
National Assessment , 3-1
Watershed Analysis 3-12
Wildlife Assessment 3-17
Regional and State Assessment 3-17
EPA Region 1 3-22
EPA Region 2 3-27
EPA Region 3 3-32
EPA Region 4 3-37
EPA Region 5 ; 3-43
EPA Region 6 3-49
EPA Region 7 3-54
EPA Region 8 3-59
EPA Region 9 3-63
EPA Region 10 3-68
Potentially Highly Contaminated Sites Not Identified by the NSI Evaluation 3-73
in
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Contents (continued)
Pollutant Sources 4-1
Extent of Sediment Contamination by Chemical Class . 4-2
Major Sediment Contaminant Source Categories „„ 4-3
Land Use Patterns and Sediment Contamination . 4-8
EPA's Point and Nonpoint Source Sediment Contaminant Inventories ...................... 4-14
Conclusions sind Discussion 5-1
Extent of Sediment Contamination . ........... 5-2
Sources of Sediment Contamination 5-4
Comparison of NSI Evaluation Results to Results of Previous Sediment
Contamination Studies 5-4
Comparison of NSI Evaluation Results to Fish Consumption Advisories 5-5
Sensitivity of Selected PCB Evaluation Parameters 5-7
Strengths of the NSI Data Evaluation 5-8
Limitations of the NSI Data Evaluation 5-10
Limitations of Data 5-10
Limitations of Approach 5-12
Recommendations 6-1
Recommendation 1: Further Investigate Conditions in the 96 Targeted Watersheds .. 6-1
Recommendation 2; Coordinate Efforts to Address Sediment Quality Through
Watershed Management Programs 6-2
Recommendation 3; Incorporate a Weight-of-Evidence Approach and
Measures of Chemical Bioavailability into Sediment Monitoring Programs...... 6-2
Recommendation 4; Evaluate the NSI's Coverage and Capabilities and Provide
Better Access to Information in the NSI .... 6-3
Recommendation 5: Develop Better Monitoring and Assessment Tools 6-4
Glossary Glossary-1
Acronyms Acronyms-1
References References-1
6
Appendices
A. Detailed Description of NSI Data A-l
B. Description of Evaluation Parameters Used in the NSI Data Evluation B-l
C. Method for Selecting Biota-Sediment Accumulation Factors and Percent
Lipids in Fish Tissue Used for Deriving Theoretical Bioaccumulation
Potentials C-l
D. Screening Values for Chemicals Evaluated D-l
E. Cancer Slope Factors and Noncancer Reference Doses Used to Develop
EPA Risk Levels E-l
F. Species Characteristics Related to NSI Bi(accumulation Data F-l
G. Notes on the Methodology for Evaluating Sediment Toxicity Tests G-l
H. Additional Analyses for PCBs and Mercury H-l
I. NSI Data Evaluation Approach Recommended at the National Sediment
Inventory Workshop, April 26-27, 1994 1-1
iv
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Tables
Table Page
2-1 Number of Stations Evaluated in the NSI by State 2-5
2-2 NSI Data Evaluation Approach . 2-9
3-1 National Assessment: Evalutaion of Results for Sampling Stations and River
Reaches by EPA Region 3-3
3-2 Chemicals or Chemical Groups Most Often Associated With Tier 1 or Tier 2
Sampling Station Classifications 3-7
3-3 Number of Sampling Stations Classified as Tier 1 and Tier 2 Based on Each
Component of the Evaluation Approach 3-11
3-4 USGS Cataloging Unit Numbers and Names for Watersheds Containing APCs 3-14
3-5 River Reaches With 10 or More Tier 1 Sampling Stations Located in
Watershed Containing APCs 3-17
3-6 Increased Number of Sampling Stations Classified as Her 1 and Her 2 by
Including Wildlife Criteria in the National Assessment 3-20
3-7 Region 1; Evaluation Results for Sampling Stations and River Reaches
by State 3-23
3-8 Region 1: Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-25
3-9 Region 1: Water Bodies With Sampling Stations Classified as Tier 1 Located in
Watersheds Containing APCs 3-25
3-10 Region 1: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling
Station Classifications 3-26
3-11 Region 2: Evaluation Results for Sampling Stations and River Reaches
by State 3-28
3-12 Region 2: Watersheds Containing Areas of Probable Concern for Sediment
Contamination ...3-30
3-13 Region 2: Water Bodies With Sampling Stations Classified as Tier 1 Located in
Watersheds Containing APCs 3-30
3-14 Region 2: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling
Station Classifications 3-31
3-15 Region 3: Evaluation Results for Sampling Stations and River Reaches
by State 3-33
3-16 Region 3; Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-35
3-17 Region 3: Water Bodies With Sampling Stations Classified as Tier 1 Located in
Watersheds Containing APCs 3-35
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Tables
I
Tables (continued)
3-18 Region 3: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling
Station Classifications 3-36
3-19 Region 4; Evaluation Results for Sampling Stations and River Reaches
by State 3-38
3-20 Region 4: Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-40
3-21 Region 4: Water Bodies With Sampling Stations Classified as Tier 1 Located in
Watersheds Containing APCs .....3-41
3-22 Region 4: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling
Station Classifications . ,,.3-42
3-23 Region 5: Evaluation Results for Sampling Stations and River Reaches
by State 3-44
3-24 Region 5: Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-46
3-25 Region 5: Water Bodies With Sampling Stations Classified as Tier 1 Located in
Watersheds Containing APCs 3-47
3-26 Region 5: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling
Station Classifications 3-48
3-27 Region 6: Evaluation Results for Sampling Stations and River Reaches
by State 3-50
3-28 Region 6: Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-52
3-29 Region 6: Water Bodies With Sampling Stations Classified as Tier 1 Located in
Watersheds Containing APCs 3-52
3-30 Region 6: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling
Station Classifications 3-53
3-31 Region 7: Evaluation Results for Sampling Stations and River Reaches
by State 3-55
3-32 Region 7: Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-57
3-33 Region 7; Water Bodies With Sampling Stations Classified as Tier 1 Located in
Watersheds Containing APCs .3-57
3-34 Region 7: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling
Station Classifications ..3-58
3-35 Region 8: Evaluation Results for Sampling Stations and River Reaches
by State 3-60
3-36 Region 8: Chemicals Most Often Associated With Her 1 or Tier 2 Sampling
Station Classifications 3-62
vi
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Tables (continued)
3-37 Region 9: Evaluation Results for Sampling Stations and River Reaches
by State ............. ........ 3-64
3-38 Region 9: Watersheds Containing Areas of Probable Concern for Sediment
Contamination ....3-66
3-39 Region 9: Water Bodies With Sampling Stations Classified as Her 1 Located in
Watersheds Containing APCs » 3-66
3-40 Region 9: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling
Station Classifications 3-67
3-41 Region 10: Evaluation Results for Sampling Stations and River Reaches
by State ....3-69
3-42 Region 10: Watersheds Containing Areas of Probable Concern for Sediment
Contamination ....3-71
3-43 Region 10: Water Bodies With Sampling Stations Classified as Tier 1 Located in
Watersheds Containing APCs ...3-71
3-44 Region 10: Chemicals Most Often Associated With Her 1 or Her 2 Sampling
Station Classifications 3-72
3-45 Potentially Highly Contaminated Sites Not Identified in the NSI Evaluation 3-73
4-1 Correlations of Sources to Chemical Classes of Sediment Contaminants 4-4
4-2 Tier 1 and Her 2 Station Classification by Chemical Class and Land Uses in
Watersheds Containing APCs 4-9
4-3 Comparison of Percent Agricultural Land Use in Watersheds Containing APCs
to Percent of Tier 1 and Tier 2 Stations by Chemical Class 4-13
4-4 Comparison of Percent Urban Land Use in Watersheds Containing APCs
to Percent of Tier 1 and Tier 2 Stations by Chemical Class ..4-14
5-1 Comparison of Contaminants Most Often Associated With Fish Consumption
Advisories and Those Which Most Often Cause Stations To Be Placed in Tier 1 or
Her 2 Based on the NSI Data Evaluation 5-5
5-2 National Sediment Inventory Database: Summary of QA/QC Information 5-11
vii
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viii
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*¦71 • -
Figures
Figure Page
2-1 NSI Sediment Sampling Stations Evaluated 2-6
2-2 NSI Tissue Residue Sampling Stations Evaluated 2-7
2-3 NSI Toxicity Test Stations Evaluated 2-8
2-4 Aquatic Life Assessments: Sediment Chemistry Analysis for Organic Chemicals
and Metals Not Included in the AVS Analysis................ 2-9
2-5 Aquatic Life Assessments: Sediment Chemistry Analysis for Divalent Metals 2-10
2-6 Aquatic Life Assessments: Sediment Toxicity Analysis 2-10
2-7 Human Health Assessments: Sediment Chemistry and Fish Tissue Residue
Analysis (excluding dioxins and PCBs) 2-11
2-8 Human Health Assessments: PCBs and Dioxin in Fish Tissue Analysis 2-11
3-1 Location of All NSI Sampling Stations 3-2
3-2 Sampling Stations Classified as Tier 1 (Associated Adverse Effects Probable) 3-4
3-3 National Assessment: Percent of River Reaches That Include Tier 1, Tier 2,
and Tier 3 Sampling, Stations 3-5
3-4 National Assessment: Percent of NSI Measurements That Indicate
Potential Risk 3-6
3-5 Sampling Stations Classified as Tier 1 or Tier 2 for Potential Risk to Aquatic Life .. 3-9
3-6 Sampling Stations Classified as Tier 1 or Tier 2 for Potential Risk to Human
Health 3-10
3-7 Watersheds Identified as Containing APCs 3-13
3-8 National Assessment: Watershed Classifications : ...,3-16
3-9 Sampling Stations Classified as Tier 1 or Her 2 Based on Wildlife Criteria 3-19
3-10 Region 1: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Sampling Stations 3-22
3-11 Region 1: Watershed Classifications 3-22
3-12 Region 1: Location of Sampling Stations Classified as Tier 1 or Tier 2 and
Watersheds Containing Areas of Probable Concern for Sediment
Contamination ......3-24
3-13 Region 2: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Sampling Stations : 3-27
3-14 Region 2: Watershed Classifications 3-27
3-15 Region 2: Location of Sampling Stations Classified as Her 1 or Her 2 and
Watersheds Containing Areas of Probable Concern for Sediment Contamination... 3-29
3-16 Region 2: Percent of River Reaches That Include Tier 1, and Tier 3
Sampling Stations 3-32
ix
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Figures
Figures (continued)
3-17 Region 3: Watershed Classifications 3-32
3-18 Region 3: Location of Sampling Stations Classified as Tier 1 or Tier 2
and Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-34
3-19 Region 4: Percent of River Reaches That Include Tier 1, Tier 2, and
Her 3 Sampling Stations . 3-37
3-20 Region 4: Watershed Classifications ......3-37
3-21 Region 4; Location of Sampling Stations Classified as Tier 1 or Tier 2
and Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-39
3-22 Region 5: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Sampling Stations 3-43
3-23 Region 5: Watershed Classifications 3-43
3-24 Region 5: Location of Sampling Stations Classified as Her 1 or Tier 2
and Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-45
3-2S Region 6: Percent of River Reaches That Include Tier 1, Tier 2, and
Her 3 Sampling Stations 3-49
3-26 Region 6: Watershed Classifications 3-49
3-27 Region 6: Location of Sampling Stations Classified as Tier 1 or Tier 2
and Watersheds Containing Areas of Probable Concern for Sediment
Contamination ...3-51
3-28 Region 7: Percent of River Reaches That Include Tier 1, Tier 2, and
Her 3 Sampling Stations 3-54
3-29 Region 7: Watershed Classifications 3-54
3-30 Region 7: Location of Sampling Stations Classified as Tier 1 or Tier 2
and Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-56
3-31 Region 8: Percent of River Reaches That Include Tier 1, Tier 2, and
Her 3 Sampling Stations 3-59
3-32 Region 8: Watershed Classifications 3-59
3-33 Region 8: Location of Sampling Stations Classified as Tier 1 or Tier 2 3-61
3-34 Region 9: Percent of River Reaches That Include Tier 1, Tier 2, and
Her 3 Sampling Stations 3-63
3-35 Region 9: Watershed Classifications 3-63
3-36 Region 9: Location of Sampling Stations Classified as Tier 1 or Tier 2
and Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-65
3-37 Region 10: Percent of River Reaches That Include Tier 1, Tier 2, and
Her 3 Sampling Stations 3-68
x
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Nntiomil Sediment Qmilily Survey
Figures (continued)
3-38 Region 10: Watershed Classifications 3-68
3-39 Region 10: Location of Sampling Stations Classified as Her 1 or Tier 2
and Watersheds Containing Areas of Probable Concern for Sediment
Contamination 3-70
3-40 Location of Potentially Highly Contaminated Water Bodies Not Identified in the
NSI Evaluation 3-74
4-1 Average Percent Contamination in Watersheds Containing APCs by
Chemical Class 4-3
4-2 Percent Tier 1 and Tier 2 Stations vs. Agricultural Land Use in APCs 4-13
4-3 Percent Tier 1 and Her 2 Stations vs. Total Urban Land Use in APCs 4-14
5-1 Tier 1 and Tier 2 Sampling Stations for Potential Risk to Human Health Located
Within Water Bodies With Fish Consumption Advisories in Place for the Same
Chemical Responsible for the Tier 1 or Tier 2 Classfieation 5-6
5-2 Sampling Stations Classified as Her 1 or Tier 2 for Potential Risk to Human
Health Excluding PCBs 5-9
xi
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('inures
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Nafioiiiil St'dimciM Quality Siu \ < y
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 Num-
ber 68-C3-0374. Staff from EPA Regional Offices and other headquarters program offices
have participated in this project, provided technical guidance, and reviewed previous reports. We
greatly appreciate their efforts and helpful comments. In particular, we wish to acknowledge Catherine
Fox who served as the initial project officer and oversaw the development of the database and
evaluation methodology. We also wish to express our appreciation to the EPA Regional Office and
State personnel who participated in the review of the preliminary evaluation of sediment chemistry
data, as well as to the persons who participated in national workshops in April 1993 (data compila-
tion) and April 1994 (evaluation methodology).
We wish to offer a special expression of gratitude to several Agency scientists who provided
technical information, guidance, and expert counsel to OST. Gerald Ankley, Phil Cook, David
Hansen, Richard Swartz, and Nelson Thomas provided technical assistance in refining the evalua-
tion methodology. Charles Stephan assisted in the process of reviewing aquatic toxicity literature.
Samual Karickoff and J. Mac Arthur Long reviewed chemical property literature and recommended
organic carbon partitioning coefficients for specific chemicals.
We also wish to acknowledge the following persons who provided external peer review of the
evaluation methodology and its application to the data compiled for this report: William Adams of
Kennecott Utah Copper Corporation in Magna, Utah; Derek Muir of the Freshwater Institute in
Winnipeg, Manitoba; Spyros Pavlou of URS Grenier in Seattle, Washington; Anne Spacie of Purdue
University in Lafayette, Indiana; and William Stubblefield of ENSR Technology in Fort Collins,
Colorado. These persons reviewed the soundness of proposed evaluation methods for intended
purposes, and recommended appropriate and meaningful presentation and interpretation of results.
Chapter 4 and the Executive Summary of this document were not included in prior reviews because
they had not yet been completed. Participation in the review process does not imply concurrence by
these individuals with all observations and recommendations contained in this report.
We greatly appreciate the comments received from various stakeholders during the review of
the July 1996 of this report. Our thanks to all state government officials, trade association represen-
tatives, environmental advocacy professionals, and members of the scientific community who pro-
vided valuable insights. We also wish to acknowledge the consultation with the National Oceanic
and Atmospheric Administration, the United States Army Corps of Engineers, and the United
States Geological Survey.
Finally, we wish to recognize Andrew Zacherle, Jon Harcum, Alex Trounov, Esther Peters, and
all other participating staff and management at Tetra Tech, Inc. for their efforts and professionalism
in providing technical support and data management.
James Keating, Principal Investigator
Thomas Armitage, Acting Chief, Risk Assessment and Management Branch
Elizabeth Southerland, Acting Director, Standards and Applied Science Division
xiii
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Acknow lodgments
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National Sediment Quality Survey
Executive Summary
T|his report, The Incidence and Severity of Sedi-
ment Contamination in Surface Waters of the
United States, describes the accumulation of
chemical contaminants in river, lake, ocean, and estuary
bottoms and includes a screening assessment of the po-
tential for associated adverse effects on human and envi-
ronmental health. The United States Environmental
Protection Agency (EPA) prepared this report to Con-
gress in response to requirements set forth in the Water
Resources Development Act (WRDA) of 1992, which di-
rected EPA, in consultation with the National Oceanic and
Atmospheric Administration (NOAA) and the U.S. Army
Corps of Engineers (USAGE), to conduct a comprehen-
sive national survey of data regarding the quality of aquatic
sediments in the United States. The Act 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. The Act 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. The Act also re-
quires EPA to establish a comprehensive and continu-
ing program to assess aquatic sediment quality. As
part of this continuing program, EPA must submit a
national sediment quality report to Congress every 2
years.
To comply with the WRDA mandate, EPA's Office
of Science and Technology (OST) initiated the National
Sediment Inventory (NSI). The NSI is a compilation of
existing sediment quality data; protocols used to evaluate
the data; and various reports and analyses produced to
present the findings, conclusions, and recommendations
for action. EPA produced this first report to Congress in
four volumes:
• Volume 1: National Sediment Quality Sur-
vey—Screening analysis to qualitatively as-
sess the probability of associated adverse
human or ecological effects based on a weight-
of-evidence evaluation
• Volume 2; Data Summaries for Areas of Prob-
able Concern (APCs)—Sampling station loca-
tion maps and chemical and biological summary
data for watersheds containing APCs
• Volume 3: National Sediment Contaminant
Point Source Inventory—Screening analysis to
identify probable point source contributors of
sediment pollutants
• Volume 4: National Sediment Contaminant
Nonpoint Source Inventory—Screening analy-
sis to identify probable nonpoint source con-
tributors of sediment pollutants (in preparation
for subsequent biennial reports)
EPA prepared Volume I, the National Sediment Qual-
ity Survey, to provide a national baseline screening-level
assessment of contaminated sediment over a time period
of the past 15 years. 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 ef-
fects to human health and the environment. The process
has demonstrated the use of "weight-of-evidence" mea-
sures (including measures of the bioavailability of toxic
chemicals) in sediment quality assessment. Information
contained in this volume may be used to further investi-
gate sediment contamination on a national, regional, and
site-specific scale. Further studies may involve toxico-
logical investigations, risk assessment, analyses of tem-
poral and spatial trends, feasibility of natural recovery,
and source control.
The National Sediment Quality Survey is the first
comprehensive EPA analysis of sediment chemistry and
related biological data to assess what is known about the
national incidence and severity of sediment contamina-
tion. This volume presents a screening-level identifica-
tion of sampling stations in several areas across the
country where sediment is contaminated at levels sug-
gesting an increased probability of adverse effects on
aquatic life and human health. Based on the number and
percentage of sampling stations containing contaminated
xv
-------
I'Aix'utiu'Siitnnuirv
sediment within watershed boundaries, EPA identified a
number of watersheds containing areas of probable con-
cern where additional studies may be needed to draw con-
clusions regarding adverse effects and the need for actions
to reduce risks.
In addition to this and future reports to Congress,
EPA anticipates that products generated through the NSI
will provide managers at the federal, state, and local levels
with information. Many of the NSI data were obtained by
local watershed managers from monitoring programs tar-
geted toward areas of known or suspected contamina-
tion. NSI data and evaluation results can assist local
watershed managers by providing additional data that they
may not have, demonstrating the application of a weight-
of-evidence approach for identifying and screening con-
taminated sediment locations, and allowing researchers
to draw upon a large data set of information to conduct
new analyses that ultimately will be relevant for local as-
sessments.
Description of the NSI Database
The NSI is the largest set of sediment chemistry and
related biological data ever compiled by EPA. It includes
approximately two million records for more than 21,000
monitoring stations across the country. To efficiently
collect usable information for inclusion in the NSI, EPA
sought data that were available in electronic format, repre-
sented broad geographic coverage, and represented spe-
cific sampling locations identified by latitude and longitude
coordinates. The minimum data requirements for inclu-
sion of computerized data in the NSI were monitoring pro-
gram, sampling date, latitude and longitude coordinates,
and measured units. Additional data fields such as sam-
pling method and other quality assurance/quality control
information were retained in the NSI if available, but were
not required for a data set to be included in the NSI.
The NSI includes data from the following data stor-
age systems and monitoring programs:
• Selected data from EPA's Storage and Retrieval
System (STORET)
• EPA Region 10/USACE Seattle District's Sediment
Inventory
• EPA Region 9's Dredged Material Tracking Sys-
tem (DMATS)
• EPA's Great Lakes Sediment Inventory
• EPA's Environmental Monitoring and Assess-
ment Program (EMAP)
• United States Geological Survey (Massachusetts
Bay) Data
In addition to sediment chemistry data, the NSI in-
cludes tissue residue, toxicity, benthic abundance, histo-
pathology, and fish abundance data. The sediment
chemistry, tissue residue, and toxicity data were evalu-
ated for this report to Congress. Data from 1980 to 1993
were used in the NSI data evaluation, but older data also
are maintained in the NSI.
Evaluation Approach
The WRDA defines contaminated sediment as
aquatic sediment that contains chemical substances in
excess of appropriate geochemical, toxicological, or sedi-
ment quality criteria or measures; or is otherwise consid-
ered 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 contami-
nated sediments, and the risk to human consumers of or-
ganisms exposed to sediment contaminants. EPA
evaluated sediment chemistry data, chemical residue lev-
els 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 tech-
niques were used alone or in combination to evaluate the
probability of adverse effects:
Aquatic Life
(1) Comparison of sediment chemistry measurements
to sediment chemistry screening values
• Draft sediment quality criteria (SQCs)
• Sediment quality advisory levels (SQALs)
• Effects range-median (ERM) and effects
range-low (ERL) values
• NOAA's Coastal Sediment Inventory (COSED)
* EPA's Ocean Data Evaluation System (ODES)
• EPA Region 4's Sediment Quality Inventory
* Gulf of Mexico Program's Contaminated Sedi-
ment Inventory
-------
• Probable effects levels (PELs) and
threshold effects levels (TELs)
• Apparent effects thresholds (AETs)
(2) Comparison of the molar concentration of acid
volatile sulfides ([AVS]) in sediment to the molar
concentration of simultaneously extracted met-
als ([SEM]) in sediment (under equilibrium con-
ditions, sediment with [EVS] greater than [SEM]
will not demonstrate toxicity from metals)
(3) Lethality based on sediment toxicity data
Human Health
(4) Comparison of theoretical bioaccumulation
potential (TBP) of measured sediment contami-
nants to:
• EPA cancer and noncancer risk levels
• Food and Drug Administration (FDA)
tolerance, action, or guidance values
(5) Comparison of fish tissue contaminant levels to
• EPA cancer and noncancer risk levels
• FDA tolerance, action, or guidance
values
The sediment chemistry screening values used in this
report are not regulatory criteria, site-specific cleanup stan-
dards; or remediation goals. Sediment chemistry screen-
ing values are reference values above which a sediment
ecotoxicological assessment might indicate a potential
threat to aquatic life. For example, independent analyses
of matching chemistry and bioassay data reveal that ERL/
ERMs and TEL/PELs frequently classify samples correctly
either as nontoxic when chemical concentrations are lower
than all these values or as toxic when concentrations ex-
ceed these values. (See Appendix B.) The sediment chem-
istry screening values include both theoretically and
empirically derived values. The theoretically derived
screening values (e.g., SQC, SQAL, [SEM]-[AVS]) rely on
the physical/chemical properties of sediment and chemi-
cals to predict the level of contamination that would not
cause an adverse effect on aquatic life under equilibrium
conditions in sediment. The empirically derived, or cor-
relative, screening values (e.g.,ERM/ERL, PEL/TEL, AET)
rely on paired field and laboratory data to relate incidence
of observed biological effects to the dry-weight sediment
concentration of a specific chemical. Correlative screen-
ing values can relate measured concentration to a prob-
ability of association with adverse effects, but do not
establish cause and effect for a specific chemical. Toxicity
data were used to classify sediment sampling stations
based on their demonstrated lethality to aquatic life in
laboratory bioassays.
Under an assumed exposure scenario, theoretical
bioaccumulation potential (TBP) and tissue residue data
can indicate potential adverse effects on humans from the
consumption of fish that become contaminated through
exposure to contaminated sediment. TBP is an estimate of
the equilibrium concentration (concentration that does
not change with time) of a contaminant in tissues of aquatic
organisms if the sediment in question were the only source
of contamination to the organism. At present, the TBP
calculation can be performed only for nonpolar organic
chemicals. The TBP is estimated from the concentration
of contaminant in the sediment, the organic carbon con-
tent of the sediment, the lipid content of the organism,
and the relative affinity of the chemical for sediment or-
ganic carbon and animal lipid content This relative affin-
ity is measured in the field and is called a biota-sediment
accumulation factor (BSAF, as discussed in detail in Ap-
pendix C). 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 represents the cen-
tral tendency, suggesting an approximate 50 percent
chance that an associated tissue residue level would ex-
ceed a screening risk value.
Uncertainty is associated with site-specific measures,
assessment techniques, exposure scenarios, and default pa-
rameter selections. Many mitigating biological, chemical,
hydrological, and habitat factors may affect whether sedi-
ment 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. Similar to a potential human
illness screen, a screening-level analysis should pick up
potential problems and note them for further study. A screen-
ing-level analysis will typically identify many potential prob-
lems 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 organic
chemical contaminants For this reason, EPA elected to evalu-
ate data collected from 1980 to 1993 and to evaluate each
chemical or biological measurement taken at a given sam-
pling station individually. A single measurement of a chemi-
cal at a sampling station, taken at any point in time over the
past 15 years, may have been sufficient to categorize the
sampling station as having an increased probability of asso-
ciation with adverse effects on aquatic life or human health.
xvii
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Executive Summary
In this report, EPA associates sampling stations with
their "probability of adverse effects," Each sampling sta-
tion falls into one of three categories, or tiers:
• Tier 1: associated adverse effects are probable
• Tier2: associated adverse effects are possible,
but expected infrequently
• 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
human health is greatest in areas with a multitude of con-
taminated locations. The assessment of individual sam-
pling stations is useful for estimating the number and
distribution of contaminated spots and 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 con-
taminated 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 contami-
nation (APCs), 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
United States Geological Survey (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 I, and in which at least 75
percent of all sampling stations were categorized as either
Tier lor Tier 2.
The definition of "area of probable concern" was de-
veloped for this report to identify watersheds for which fur-
ther study of the effects and sources of sediment
contamination, and possible risk reduction needs, would be
warranted. Where data have been generated through inten-
sive sampling in areas of known or suspected contamina-
tion within a watershed, the APC definition should identify
watersheds which contain even relatively small areas that
are considerably contaminated. However, this designation
does not imply that sediment throughout the entire water-
shed, 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 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 may both in-
clude some watersheds with limited areas of contamination
and omit some watersheds with significant contamination.
However, given available data EPA believes it represents a
reasonable screening analysis to identify watersheds where
further study is warranted.
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 in a weight-
of-evidence approach recommended by national experts.
The process to produce this report to Congress has en-
gaged a broad array of government, industry, academic, and
professional experts and stakeholders in development and
review stages. The evaluation approach uses sediment chem-
istry, tissue residue, and toxicity test results. The assess-
ment tools employed in this analysis have been applied in
North America, with results published in peer-reviewed lit-
erature. 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.
Two general types of limitations are associated with
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. Limi-
tations of the evaluation approach include uncertainties
in the interpretive tools to assess sediment quality, lack of
quantitative risk assessment that consideres exposure
potentials as well as contamination (e.g., fish consump-
tion rates within APCs for human health risk), and the
subsequent difficulties in interpreting assessment results.
xviii
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Nation;)! Sedlineijf Quality Survey
These limitations and uncertainties are discussed in detail
in Chapter 5 of this volume 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 pro-
grams. Such monitoring programs focus their sampling ef-
forts on areas where contamination is known or suspected
to occur. Reliance on these date 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," us-
ing the data in die NSI because uncontaminated areas are
most likely substantially underrepresented.
Because this analysis is based only on readily avail-
able electronically formatted data, contamination prob-
lems exist at some locations where data are lacking.
Conversely, older data might not accurately represent cur-
rent 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 were not evalu-
ated because of questions concerning data quality or be-
cause no locational information (latitude and longitude)
was available. NSI data do not evenly represent all geo-
graphic regions in the United States, nor do the data rep-
resent a consistent set of monitored chemicals.
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 will redistribute
while others will 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 definitively
assess 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 deter-
mine the incidence and severity of sediment contamina-
tion. 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 sedi-
ment. TOC and AVS are essential pieces of information
for interpreting the bioavailability, and subsequent toxic-,
ity, of nonpolar organic and metal contaminants, respec-
tively. In addition, matched sediment chemistry with
toxicity tests, and matched sediment chemistry with tis-
sue residue data, were typically lacking.
It is important to understand both the strengths and
limitations of this analysis to appropriately interpret and
use the information contained in this report. The limita-
tions 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 re-
flect 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 mea-
surements and state-of-the-art assessment techniques.
Findings
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 classi-
fied as Tier 2, and 5,174 (25 percent) were classified as Tier
3. This distribution suggests that state monitoring pro-
grams (accounting for the majority of NSI data) have been
efficient and successful in focusing their sampling efforts
on areas where contamination is known or suspected to
occur. The frequency of Tier 1 classification based on all
NSI data is greater than the frequency of Tier 1 classifica-
tion based on data sets derived from purely random sam-
pling.
The percentage of all NSI sampling stations where
associated effects are "probable" or "possible but expected
infrequently" (i.e„ 26 percent in Tier 1 and 49 percent in
Tier 2) does not represent the overall condition of sedi-
ment across the country: the overall extent of contami-
nated sediment is much less, as is the percentage of
sampling stations where contamination is expected to ac-
tually exert adverse effects. For example, a reasonable
estimate of the national extent of contamination leading to
adverse effects to aquatic life is between 6 and 12 percent
of sediment underlying surface waters (see Chapter 5 for
expanded discussion of "extent of contamination"). This
is 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 6,744 indi-
vidual river reaches (or water body segments) across the
xix
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I \ceuli\i*.Summitry
contiguous United States, or approximately 11 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 shore-
line, a lake, or a length of stream between two major tribu-
taries ranging from approximately 1 to 10 miles long. As
depicted in Figure 1, approximately 4 percent of all river
reaches in the contiguous United States had at least one
station categorized as Tier 1, approximately 5 percent of
reaches had at least one station categorized as Tier 2 (but
none as Tier 1), and all of the sampling stations were clas-
sified as Tier 3 in about 2 percent of reaches.
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 either
Tier 1 or Tier 2. The NSI data evaluation identified 96
watersheds throughout the United States as containing
APCs (Figure 2 and Table 1). (The map numbers listed on
Table 1 correspond to the numbered watersheds identi-
fied in Figure 2.) These watersheds represent about 5
percent of all watersheds in the United States (96 of 2,111).
APC designation could result from extensive sampling
throughout a watershed, or from intensive sampling at a
single contaminated location or a few contaminated loca-
tions. In comparison to the overall results presented on
Figure 1, sampling stations are located on an average of
46 percent of reaches within watersheds containing APCs.
On the average, 30 percent of reaches in watersheds con-
taining APCs have at least one Tier 1 sampling station,
and 13 percent have no Tier 1 sampling
station but at least one Tier 2 sampling
station. In many of these watersheds, the
risk might be concentrated on certain wa-
ter bodies or river reaches. Within the 96
watersheds containing APCs, 57 river
reaches include 10 or more Tier 1 sam-
pling stations. For more detailed informa-
tion concerning individual watersheds
containing APCs, please consult Volume
2 of this report
The evaluation results indicate that
sediment contamination associated with
probable or possible but infrequent ad-
verse effects exists for both aquatic life
and human health. More sampling sta-
tions were categorized as either Tier 1 or
Tier 2 for aquatic life concerns than for
human health concerns. About 41 per-
cent more sampling stations were classi-
fied as Tier 1 for aquatic life (3,287 stations)
than for human health (2,327 stations).
About 60 percent more sampling stations
were categorized as Tier 2 for aquatic life (9,921 stations)
than for human health (6,196 stations).
Recognizing the imprecise nature of some assess-
ment parameters used in this report, Tier 1 sampling sta-
tions are distinguished from Tier 2 sampling stations based
on the magnitude of a contaminant concentration in sedi-
ment, or the degree of corroboration among the different
types of sediment quality measures. In response to un-
certainty in both biological and chemical measures of sedi-
ment contamination, environmental managers must balance
T^pe I errors (false positives: sediment classified as pos-
ing a threat that does not) with Type II errors (false nega-
tives: sediment that poses a threat but was not classified
as such). In screening analyses, the environmentally pro-
tective approach is to minimize Type n 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 high rate of "correct" classification (e.g., sediment
definitely posing or definitely not posing a threat) and a
balance between T^pe I and Type II errors. On the other
hand, to retain a sufficient degree of environmental con-
servatism in screening, Tier 2 sampling stations are in-
tended to have a very low number of false negatives in
exchange for a large number of false positives.
To help judge the effectiveness of the evaluation ap-
proach described previously, EPA examined the agreement
between matched sediment chemistry and toxicity test re-
At Least One
Tier 1 Station
4%
At Least One
Tier 2 Station
5%
Tier 3 Stations
2%
No Data Available
89%
Although 77 percent of reaches with
sampling stations include m least one Tier
i or Tier 2 sampling station, if all reaches
in eluded sampling stations this proportion
would Ukely be much smaller because
most available data are from sampling
targeted toward contaminated areas.
Figure 1. National Assessment: Percent of River Reaches That Include
Tier 1, Tier 2, and Tier 3 Sampling Stations.
xx
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IXmilh c Summary
I
liable 1. USGS Cataloging Unit Number and Name for Watersheds Containing APCs.
Map #
Cataloging Unit
Number
Cataloging Unit Name
1
1090001
Charles
2
1090002
Cape Cod
3
1090004
Narragansett
4
2030103
Hackensack-Passaic
5
2030104
Sandy Hook-Staten Island
6
2030105
Raritan
7
2030202
Southern Long Island
8
2040105
Middle Delaware-Musconetcong
9
2040202
Lower Delaware
10
2040203
Schuylkill
11
2040301
Mullica-Toms
12
2060003
Guhpowdsr-Patapseo
13
2070004
Conococheague-Opequon
14
3040201
Lower Pee Dee
15
3060101
Seneca
16
3060106
Middle Savannah
17
3080103
Lower St. Johns
18
3130002
Middle Chattahoochee-Lake Harding
19
3140102
Choctawhatchee Bay
20
3140107
Perdido Bay
21
3160205
Mobile Bay
22
4030102
Door-Kewaunee
23
4030108
Menominee
24
4030204
Lower Fox
25
4040001
Little Calumet-Galien
26
4040002
Pike-Root
27
4040003
Milwaukee
28
4050001
St Joseph
29
4060103
Manistee
30
4090002
Lake St. Clair
31
4090004
Detroit
32
4100001
Ottawa-Stony
33
4100002
Raisin
34
4100010
Cedar-Portage
35
4100012
Huron-Vermillion
36
4110001
Black-Rocky
37
4110003
Ashtabula-Chagrin
xxii
-------
Thblel. (Continued)
Map #
Cataloging Unit
Number
Cataloging Unit Name
38
4120101
Chautauqu a-Conneaut
39
4120103
Buffalo-Eighteenmile
40
4120104
Niagara
41
4130001
Oak Orchard-Twelvemile
42
4150301
Upper St. Lawrence
43
5030101
Upper Ohio
44
5030102
Shenango
45
5040001
Tuscarawas
46
5120109 .
Vermilion
47
512011!
Middle Wabash-Busseron
48
6010104
Holston
49
6010201
Watts Bar Lake
50
6010207
Lower Clinch
51
6020001
Middle Tennessee-Chickamauga
52
6020002
Hi was see
53
6030001
Guntersville Lake
54
6030005
Pickwick Lake
55
6040001
Lower Tennessee-Beech
56
6040005
Kentucky Lake
57
7010206
Twin Cities
58
7040001
Rush-Vermillion
59
7040003
Buffalo-Whitewater
60
7070003
Castle Rock
61
7080101
Copperas-Duck
62
7090006
Kishwaukee
63
7120003
Chicago
64
7120004
Des Plaines
65
7120006
Upper Fox
66
7130001
Lower llli nois-Senach wi ne Lake
67
71401001
Cahokia-Joachim
68
7140106
Big Muddy
69
7140201
Upper Kaskaskia
70
7140202
Middle Kaskaskia
71
8010100
Lower Mississippi-Memphis
72
8030209
Deer-Steele
73
8040207
Lower Ouachita
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I'Xmit i\ e Siiimiiarv
table 1. (Continued)
Map #
Cataloging Unit
Number
Cataloging Unit Name
74
8080206
Lower Calcasieu
75
8090100
Lower Mississippi-New Orleans
76
10270104
Lower Kansas
77
11070207
Spring
78
11070209
Lower Neosho
79
12040104
Buffalo-San Jacinto
80
17010303
Coeur D'Alene Lake
81
17030003
Lower Yakima
82
17090012
Lower Willamette
83
17110002
Strait of Georgia
84
17110013
Duwamish
85
17110014
Puyallup
86
17110019
Puget Sound
87
18030012
Tuiare-Buena Vista Lakes
88
18050003
Coyote
89
18050004
San Francisco Bay
90
18070104
Santa Monica Bay
91
18070105
Los Angeles
92
18070107
San Pedro Channel Islands
93
18070201
Seal Beach
94
18070204
Newport Bay
95
18070301
Aliso-San Onofre
96
18070304
San Diego
suits for the 805 sampling stations where both data types
are available. The toxicity test data indicate whether signifi-
cant lethality to indicator organisms occurs as a result of
exposure to sediment. Tier 1 classification for aquatic life
effects from sediment chemistry data correctly matched tox-
icity test results for about three-quarters of the sampling
stations, with the remainder balanced between false posi-
tives (12 percent) and false negatives (14 percent). In con-
trast, when Tier 2 classifications from sediment chemistry
data are added in, false negatives drop to less than 1 percent
at the expense of false positives (increases to 68 percent)
and correctly matched sampling stations (drops to 30 per-
cent). This result highlights the fact, already discussed
above, that classification in Tier 2 is very conservative, and
it does not indicate a high probability of adverse effects to
aquatic life. If bioassay test results for chronic toxicity end-
points were included in the NSI evaluation, the rate of false
positives would likely decrease and correctly matched sam-
pling stations would likely increase for both tiers.
Data related to more than 230 different chemicals or
chemical groups were included in the NSI evaluation.
Approximately 40 percent of these chemicals or chemical
groups (97) were present at levels that resulted in classifi-
cation of sampling stations as Tier 1 or Tier 2. The con-
taminants most frequently at levels in fish or sediment
where associated adverse effects are probable include
PCBs (58 percent of the 5,521 Tier 1 sampling stations)
and mercury (20 percent of Tier 1 sampling stations). Pes-
ticides, most notably DDT and metabolites at 15 percent
of Tier 1 sampling stations, and polynuclear aromatic hy-
drocarbons (PAHs) such as pyrene at 8 percent of Tier 1
sampling stations, also were frequently at levels where
associated adverse effects are probable.
xxiv
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National Sediment Quality Survey
Dry weight measures of divalent metals other than
mercury (e.g., copper, cadmium, lead, nickel, and zinc) in
sediment were not used to place a sampling station in Tier
1 without an associated measurement of acid volatile sul-
fide, a primary mediator of bioavailability for which data
are not often available in the database. As a result, metals
other than mercury (which also include arsenic, chromium,
and silver) are solely re^onsible for only 6 percent of Tier
1 sampling stations and overlap with mercury or organic
compounds at an additional 6 percent of Tier 1 sampling
stations. In contrast, metals other than mercury are solely
responsible for about 28 percent of the 15,922 Tier 1 and
Tier 2 sampling stations and overlap with mercury or or-
ganic compounds at an additional 28 percent of Tier 1 and
Tier 2 sampling stations. The remaining 44 percent of Tier
1 and Tier 2 sampling stations are classified solely for
mercury or organic compounds.
Two important issues in interpreting the results of
sampling station classification are naturally occurring
"background" levels of chemicals and the effect of chemi-
cal mixtures. Site-specific naturally occurring (or back-
ground) levels of chemicals may be an important risk
management consideration in examining sampling station
classification. This is most often an issue for naturally
occurring chemicals such as metals and PAHs. In addi-
tion, although the sediment chemistry screening levels
for individual chemicals are used as indicators of poten-
tial adverse biological effects, other co-occurring chemi-
cals (which may or may not be measured) can cause or
contribute to observed adverse effects at specific loca-
tions.
Because PCBs were the contaminants most often re-
sponsible for Tier 1 classifications in the NSI evaluation,
and because EPA took a precautionary approach (de-
scribed in Chapter 2) in evaluating the effects of PCB
exposure, the Agency conducted two separate analyses
of PCB data to determine the impact of the precautionary
approach on the overall classification of NSI sampling
stations. EPA first examined the effect of excluding PCBs
entirely from the NSI evaluation. If PCBs were excluded,
the number of Tier 1 stations would be reduced by 42
percent, from 5,521 to 3,209 stations. The number of Tier
2 stations would be increased by 18 percent, from 10,401
to 11,957 stations. This increase reflects the movement
of stations formerly classified as Tier 1 into Tier 2. In the
second PCB evaluation, EPA evaluated the effect on the
overall results of using a less precautionary noncancer
screening value (rather than the cancer screening value)
for predicting human health risk associated with PCB sedi-
ment contamination. When the noncancer screening value
was used, the number of Tier 1 stations decreased by 12
percent, from 5,521 to 4,844 stations, and the number of
Tier 2 stations increased by 4 percent, from 10,401 to
10,802 stations.
Conclusions and Recommendations
The characteristics of the NSI data, as well as the de-
gree 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
determination of the areal extent of contamination on a na-
tional scale. However, the evaluation results strongly sug-
gest that sedi i nent contamination may be significant enough
to pose potential risks to aquatic life and human health in
some locations. The evaluation methodology was designed
for the purpose of a screening-level assessment of sediment
quality; further evaluation would be required to confirm that
sediment contamination poses actual risks to aquatic life or
human health for any given sampling station or watershed.
EPA's evaluation of the NSI data was the most geo-
graphically extensive investigation of sediment contami-
nation ever performed in the United States. The evaluation
was based on procedures to address the probability of
adverse effects on aquatic life and human health. Based
on the evaluation, sediment contamination exists at lev-
els where associated adverse effects are probable (Tier 1)
in some locations within each region and state of the
country. The water bodies affected include streams, lakes,
harbors, nearshore areas, and oceans. At the Tier 1 level,
PCBs, mere ry, organochlorine pesticides, and PAHs are
the most freijuent chemical indicators of sediment con-
tamination.
The results of the NSI data evaluation must be inter-
preted 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 inci-
dence of sediment contamination.
Although the APCs were selected by means of a
screening exercise, EPA believes that they represent the
highest priority for further ecotoxicological assessments,
risk analysis, temporal and spatial trend assessment, con-
taminant source evaluation, and management action be-
cause of the preponderance of evidence in these areas.
Although the procedure for classifying APCs using mul-
tiple samplin g stations was intended to minimize the prob-
ability of iraking an erroneous classification, further
evaluation of conditions in watersheds containing APCs
is necessary because the same mitigating factors that might
xxv
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Executive Summary
reduce the probability of associated adverse effects at
one sampling station might also affect neighboring sam-
pling stations.
EPA chose the watershed as the unit of spatial analy-
sis because many state and federal water and sediment
quality management programs, as well as data acquisition
efforts, are centered around 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, the Agency recognizes that contamination in some
reaches in a watershed does not necessarily indicate that
the entire watershed is affected.
Watershed management is a vital component of
community-based environmental protection. The Agency
and its state and federal partners can address sediment
contamination problems through watershed management
approaches. Watershed management programs focus on
hydrologically defined drainage basins rather than areas
defined by political boundaries. Local management, stake-
holder involvement, and holistic assessments of water
quality are characteristics of the watershed approach. The
National Estuary Program is one example of the water-
shed approach that has led to specific actions to address
contaminated sediment problems. Specifically, the
Narragansett (Rhode Island) Bay, Long Island Sound,
New York/New Jersey Harbor, and San Francisco Bay
Estuary Programs have all recommended actions to re-
duce sources of toxic contaminants to sediment. Numer-
ous other examples of watershed management programs
are summarized in The Watershed Approach: 1993/94
Activity Report (USEPA, 1994g) and A Phase I Inventory
of Current EPA Efforts to Protect Ecosystems (USEPA,
1995b).
Available options for reducing health and environ-
mental risks from contaminated sediment include physical
removal and land disposal; subaqueous capping; in situ
or ex situ biological, physical/chemical, or thermal treat-
ment to destroy or remove contaminants; or natural re-
covery through continuing deposition of clean sediment.
Assuming further investigation reveals the need for man-
agement attention to reduce risks, the preferred means
depends on factors such as the degree and extent of con-
tamination, the value of the resource, the cost of available
options, likely human and ecological exposure, and the
acceptable time period for recovery. If risk managers an-
ticipate a lengthy period of time prior to recovery of the
system, state and local authorities can consider options
such as placing a fish consumption advisory on water
bodies or portions of water bodies where a significant
human health risk exists.
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 10 to 20 years. For example, the com-
mercial use of PCBs and the pesticides DDT and chlo-
rdane has been restricted or banned in the United
States. In addition, effluent controls on industrial and
municipal point source discharges and best manage-
ment practices for the control of nonpoint sources have
greatly reduced contaminant loadings to many of our
rivers and streams.
The feasibility of natural recovery, as well as the
long-term success of remediation projects, depends on the
effective control of pollutant sources. Although most ac-
tive sources of PCBs are controlled, past disposal and use
continue to result in evaporation from some landfills and
leaching from soils. The predominant continuing sources
of organochlorine pesticides are runoff and atmospheric
deposition from past applications on agricultural land. For
other classes of sediment contaminants, active sources con-
tinue to contribute substantial environmental releases. For
example, liberation of inorganic mercury from fuel burning
and other incineration operations continues, as do urban
runoff and atmospheric deposition of metals and PAHs. In
addition, discharge limits for municipal and industrial point
sources are based on either technology-based limits or
state-adopted standards for protection of the water column,
not necessarily for downstream protection of sediment qual-
ity. Determi ing the local and far-field effects of individual
point and nenpoint sources on sediment quality usually
requires site-specific in-depth study.
The primary recommendation of this report to Con-
gress is to encourage further investigation and assess-
ment of contaminated sediment. States, in cooperation
with EPA and other federal agencies, should proceed with
further evaluations of the 96 watersheds containing APCs.
In many cases, it is likely that much additional investiga-
tion and assessment has already occurred, especially in
well-known areas at risk for contamination, and some ar-
eas have been remediated. If active watershed management
programs are in place, these evaluations should be coordi-
nated within he context of current or planned actions. Fu-
ture assessment efforts should focus on areas such as the
57 water body segments located within 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 biologi-
cal data, and to conduct further evaluation of data to deter-
xx vi
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National Sediment Quality Survey
mine human health and ecological risk, to determine tempo-
ral and spatial trends, to identify potential sources of sedi-
ment contamination and determine whether potential
sources are adequately controlled, and to determine
whether natural recovery is a feasible option for risk re-
duction.
Other recommendations resulting from the NSI evalu-
ation include the following:
• Coordinate efforts to address sediment quality
through watershed management programs.
Federal, state, and local government agencies
should pool their resources and coordinate their
efforts to address their common sediment con-
tamination issues. These activities should sup-
port efforts such as the selection of future moni-
toring sites, the setting of priorities for
reissuance of National Pollutant Discharge Elimi-
nation System (NPDES) permits and permit syn-
chronization, pollutant trading between non-
point and point sources, and total maximum daily
load (TMDL) development.
• Incorporate a weight-of-evidence approach
and measures of chemical bioavailability into
sediment monitoring programs. Future moni-
toring programs should specify collection of AVS
and SEM measurements where metals are a con-
cern and site-specific total organic carbon (TOC)
measurements where organic chemicals are a
concern. Future sediment monitoring programs
should also collect tissue residue, biological ef-
fects, and biological community measurements
as well as sediment chemistry measurements.
• Evaluate the NSI's coverage and capabilities
and provide better access to information in the
NSI. EPA should consider whether to design
future evaluations of NSI data to determine the
temporal trends of contamination and to iden-
tify where and why conditions are improving or
worsening. EPA should consider whether to
expand the NSI to provide more complete na-
tional coverage of sediment quality data. EPA
should also consider increasing the number of
water bodies for evaluation and expanding the
suite of biological and chemical information avail-
able to evaluate each site. EPA should continue
its efforts to make the NSI data and evaluation
results more accessible to other agencies and to
the states.
• Develop better monitoring and assessment
tools. EPA should continue to update the NSI
evaluation methodology as new assessment
tools become available and the state of the sci-
ence evolves. In the context of the budget pro-
cess, EPA and other federal agencies should
evaluate whether to request funding to support
the development of tools to better characterize
the sources, fate, and effects of sediment con-
taminants.
xxvii
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M\wul i\ eSummary
xxviii
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National Sediment Quality Surv<-y
Chapter 1
Introduction
What Is The National Sediment
Quality Survey?
The Water Resources Development Act (WRDA)
of 1992 directed the U.S. Environmental Protec-
tion Agency (EPA), in consultation with the
National Oceanic and Atmospheric Administration
(NOAA) and the U.S. Army Corps of Engineers, to con-
duct a comprehensive national survey of data regarding
the quality of sediments in the United States. The Act
required EPA to compile all existing information on the
quantity, chemical and physical composition, and geo-
graphic location of pollutants in aquatic sediment, includ-
ing the probable sources of such pollutants and
identification of those sediments which are contaminated.
The statute defines contaminated sediment as aquatic sedi-
ment that contains chemical substances in excess of ap-
propriate geochemical, toxicological, or sediment quality
criteria or measures, or is otherwise considered to pose a
threat to human health or the environment The Act fur-
ther required EPA to report to the Congress the find-
ings, conclusions, and recommendations of such
survey, including recommendations for actions neces-
sary to prevent contamination of aquatic sediments
and to control sources of contamination. In addition,
the Act requires EPA to establish a comprehensive and
continuing program to assess aquatic sediment quality.
As part of this continuing program, EPA must report to
Congress every 2 years on the assessment's findings.
To comply with the WRDA mandate, EPA's Office of
Science and Technology (OST) initiated the National Sedi-
ment Inventory (NSI). The goals of the NSI are to compile
sediment quality information from available electronic da-
tabases, gather information from available electronic da-
tabases and published reports on sediment contaminant
sources, develop screening-level assessment protocols
to identify potentially contaminated sediment, and pro-
duce biennial reports to Congress on the incidence and
severity of sediment contamination nationwide. The Inci-
dence and Severity of Sediment Contamination in Sur-
face Waters of the United States is the first of these reports
to Congress. To ensure that future reports to Congress
accurately reflect contemporary conditions of the Nation's
sediment as science evolves, the NSI will develop into a
regularly updated, centralized assemblage of sediment qual-
ity measurements and assessment techniques.
The Incidence and Severity of Sediment Contamina-
tion in Surface Waters of the United States is presented
as a four-volume series. This volume, Volume 1: The
National Sediment Quality Survey, presents a national
baseline screening-level assessment of contaminated sedi-
ment over a time period of the past 15 years using a weight-
of-evidence approach. The purpose of The National
Sediment Quality Survey is to depict and characterize the
incidence and severity of sediment contamination based
on the probability of adverse effects to human health and
the environment. Information contained in this volume
may be used to further investigate sediment contamina-
tion on a national, regional, and site-specific scale. Vol-
ume 2 of this series presents data summaries for
watersheds that have been identified in this volume as
containing areas of probable concern for sediment con-
tamination. Volume 3 presents a screening analysis to
identify probable point source contributors of sediment
pollutants. Volume 4 presents a screening analysis to
identify probable nonpoint contributors of sediment pol-
lutants (in preparation for subsequent biannual reports).
For The National Sediment Quality Survey, OST
compiled and analyzed historical data that were collected
from 1980 to 1993 from across the country and are cur-
rently stored in large electronic databases. This effort
required a substantial synthesis of multiple formats and
the coordinated efforts of many federal and state environ-
mental information programs that maintain relevant data.
Published data that have not been entered into databases,
or are not readily available to EPA, are not included in the
NSI at this time and thus were not evaluated for this report
to Congress. As data management systems and access
capabilities continue to improve, EPA anticipates that a
greater amount of data will be readily available in elec-
tronic form.
This report presents the results of the screening-level
assessment of the NSI data. For this assessment, OST
examined sediment chemistry data, associated fish tissue
residue levels, and sediment toxicity test results. The
purpose was to determine whether potential contamina-
1-1
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Introduction
tion problems either exist currently or existed over the
past 15 years at distinct monitoring locations. This report
identifies locations where available data indicate that di-
rect or indirect exposure to the sediment could be associ-
ated with adverse effects to aquatic life or human health.
However, because this analysis is based on readily avail-
able electronic data, contamination problems exist at some
locations where data are lacking. Furthermore, because
the data analyzed were collected over a relatively long
period of time, conditions might have improved or wors-
ened since the sediment was sampled. Consequently, this
report does not definitively assess the current overall con-
dition of all sediments across the country, but serves as a
baseline for future assessments, which will include addi-
tional sampling stations, incorporate contemporary data,
and examine trends.
In addition to this and future reports to Congress,
EPA anticipates that products generated through the NSI
will provide managers at the federal, state, and local levels
with information. Many of the NSI data were obtained by
local watershed managers from monitoring programs tar-
geted toward areas of known or suspected contamina-
tion. NSI data and evaluation results can assist local
watershed managers by providing additional data that they
might not have, demonstrating the application of a weight-
of-evidence approach for identifying and screening con-
taminated sediment locations, and allowing researchers
to draw upon a large data set of information to conduct
new analyses that ultimately will be relevant for local as-
sessments.
The National Sediment Quality Survey summarizes
national, regional, and state results from the evaluation of
NSI data. Chapter 1 provides background information
about sediment quality issues. Chapter 2 is an overview
of the assessment methods used to evaluate the NSI data.
Chapter 3 contains the evaluation results on a national,
regional, and state basis. Chapter 4 presents information
on probable sources of sediment contamination, includ-
ing point and nonpoint sources. A discussion of the
results is provided in Chapter 5. Chapter 6 presents rec-
ommendations for evaluating and managing contaminated
sediments. Several appendices present detailed descrip-
tions of both the NSI data and the approach used to
evaluate the data;
A: Detailed Description of NSI Data
B: Description of Evaluation Parameters Used in
the NSI Data Evaluation
C Method for Selecting Biota-Sediment Accumu-
lation Factors and Percent Lipids in Fish
Tissue Used for Deriving Theoretical
Bioaccumulation Potentials
D: Screening Values for Chemicals Evaluated
E: Cancer Slope Factors and Noncancer Refer-
ence Doses Used to Develop EPA Risk Levels
F: Species Characteristics Related to NSI
Bioaccumulation Data
G: Notes on the Methodology for Evaluating
Sediment Toxicity Tests
H: Additional Analyses for PCBs and Mercury
i NSI Data Evaluation Approach Recommended
by the National Sediment Inventory Work-
shop, April 26-27,1994
Why Is Contaminated Sediment An
Important National Issue?
Sediment provides habitat for many aquatic organ-
isms and functions as an important component of aquatic
ecosystems. Sediment also serves as a major repository
for persistent and toxic chemical pollutants released into
the environment. In the aquatic environment, chemical
waste products of anthropogenic (human) origin that do
not easily degrade can eventually accumulate in sedi-
ment. In feet, sediment has been described as the "ulti-
mate sink," or storage place, for pollutants (Salomons et
al., 1987). If that were entirely true, however, we would
not need to be concerned about potential adverse effects
from these "stored" pollutants. Unfortunately, sediment
can function as both a sink and a source for contami-
nants in the aquatic environment.
Adverse effects on organisms in or near sediment
can occur even when contaminant levels in the overlying
water are low. Benthic (bottom-dwelling) organisms can
be exposed to contaminants in sediment through direct
contact, ingestion of sediment particles, or uptake of dis-
solved contaminants present in the interstitial (pore) wa-
ter. In addition, natural and human disturbances can
release contaminants to the overlying water, where pe-
lagic (open-water) organisms can be exposed. Evidence
from laboratory tests shows that contaminated sediment
can cause both immediate lethality (acute toxicity) and
long-term deleterious effects (chronic toxicity) to benthic
organisms. Field studies have revealed other effects, such
as tumors and other lesions, on bottom-feeding fish.
These effects can reduce or eliminate species of recre-
ational, commercial, or ecological importance (such as
crabs, shrimp, and fish) in water bodies either directly or
1-2
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National Sediment Quality Survey
by affecting the food supply that sustainable popula-
tions require. Furthermore, sediment contaminants might
not kill the host organism, but might accumulate in edible
tissue to levels that cause health risks to wildlife and
human consumers.
In summary, environmental managers and others are
concerned about sediment contamination and the assess-
ment of sediment quality for the following reasons
(adapted from Power and Chapman in "Assessing Sedi-
ment Quality," 1992):
• Various toxic contaminants found only in barely
detectable amounts in the water column can
accumulate in sediments to much higher levels.
• Sediments serve as both a reservoir for contami-
nants and a source of contaminants to the water
column and organisms.
• Sediments integrate contaminant concentrations
over time, whereas water column contaminant
concentrations are much more variable and dy-
namic.
• Sediment contaminants (in addition to water
column contaminants) affect bottom-dwelling
organisms and other sediment-associated organ-
isms, as well as both the organisms that feed on
them and humans.
• Sediments are an integral part of the aquatic en-
vironment that provide habitat, feeding, spawn-
ing, and rearing areas for many aquatic organ-
isms.
Contaminated sediments can affect aquatic life in a
number of ways. Areas with high sediment contaminant
levels can be devoid of sensitive species and, in some
cases, all species. For example, benthic amphipods were
absent from contaminated waterways in Commencement
Bay, Washington (Swartz et al., 1982). In Rhode Island,
the number of species of benthic molluscs was reduced
near an outfall where raw electroplating wastes and other
wastes containing high levels of toxic metals were dis-
charged into Narragansett Bay (Eisler, 1995). In Califor-
nia, pollution-tolerant oligochaete worms dominate the
sediment in the lower portion of Coyote Creek, which
receives urban runoff from San Jose (Pitt, 1995).
Sediment contamination can also adversely affect the
health of organisms and provide a source of contaminants
to the aquatic food chain (Lyman et al., 1987). For ex-
ample, fin rot and a variety of tumors have been found in
fish living above sediments contaminated by polycyclic
aromatic hydrocarbons (PAHs) located near a creosote
plant on the Elizabeth River in Virginia. These impacts
have been correlated with the extent of sediment contami-
nation in the liver (Van Veld et al., 1990). Liver tumors and
skin lesions have occurred in brown bullheads from the
Black River in Ohio, which is contaminated by PAHs from
a coke plant. The authors of the Black River study estab-
lished a cause-and-effect relationship between the pres-
ence of PAHs in sediment and the occurrence of liver
cancer in native fish populations (Baumann et al., 1987).
Examples of risks to fish-eating birds and mammals posed
by contaminated food chains include reproductive prob-
lems in Forster's terns on Lake Michigan near Green Bay
(Kubiak et al., 1989) and on mink farms where mink were
fed Great Lakes fish (Auerlich et al., 1973). In both cases,
high levels of polchlorinated biphenyls (PCBs) in fish were
identified as the cause of the reproductive failures. Con-
taminated sediments can also affect the food chain base
by eliminating food sources and, in some cases, altering
natural competition, which can impact the population dy-
namics of higher trophic levels (Burton et al., 1989; Landis
and Yu, 1995).
The accumulation of contaminants in fish tissue
(called bioaccumulation) and contamination of the food
chain are also important human health and wildlife con-
cerns because people and wildlife eat finfish and shell-
fish. In fact, the consumption of fish represents the most
significant route of aquatic exposure of humans to many
metals and organic compounds (USEPA, 1992a). Most
sediment-related human exposure to contaminants is
through indirect routes that involve the transfer of pollut-
ants out of the sediments and into the water column or
aquatic organisms. Many surface waters have fish con-
sumption advisories or fishing bans in place because of
the high concentrations of PCBs, mercury, dioxin, kepone,
and other contaminants. In 1995, over 1,500 water bodies
in the United States had fish consumption advisories in
place, affecting all but four states. Water supplies also
have been shut down because of contaminated sediments,
and in some places swimming is no longer allowed.
How Significant Is The Problem?
Puget Sound was one of the first areas in the country
to be studied extensively for sediment contamination.
Early studies from the 1980s demonstrated fairly exten-
sive sediment contamination, especially near major indus-
trial embayments (Dexter et al., 1981; Long, 1982; Malins
et al., 1980; Riley et al., 1981). These early assessments
demonstrated that Puget Sound sediments were contami-
nated by many organic and inorganic chemicals, includ-
ing PCBs, PAHs, and metals. Although contaminant
1-3
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Introduction
1
concentrations in sediment 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 et al., 1987).
Several recent studies conducted in other parts of
the country further illustrate the significance of sediment
contamination and its potential widespread impact. For
example, Myers et al. (1994) investigated the relationships
between hepatic lesions (liver tumors) and stomach con-
tents, liver tissue, and bile in three species of bottom-
dwelling fish captured from 27 urban and nonurban sites
on the Pacific Coast from Alaska to southern California,
as well as the relationship of such lesions to associated
chemical concentrations in sediments. In general, the au-
thors found that lesions were more likely to occur in fish
from sites with higher concentrations of chemical con-
taminants in sediments. Certain lesions had a significantly
higher relative risk of occurrence at urban sites in Puget
Sound, San Francisco Bay, the vicinity of Los Angeles,
and San Diego Bay (Myers et al., 1994). The results of this
study provide strong evidence for the involvement of sedi-
ment contaminants in causing hepatic lesions in bottom
fish and clearly indicate the usefulness of these lesions as
indicators of contaminant-induced effects in fish (Myers
etal., 1994).
Several recent assessments of existing data on the
Nation's marine (saltwater) and freshwater sediments (e.g.,
NRC, 1989) indicate potentially widespread and serious
contamination problems. The NOAA National Status and
Trends Program has monitored coastal sediment contami-
nation since the mid-1980s and has linked elevated pol-
lutant concentrations to the potential for adverse
biological effects in many urban areas, including the
Hudson-Ran tan estuary, Boston Harbor, western Long
Island, and the Oakland estuary of San Francisco Bay
(Long and Morgan, 1990; Power and Chapman, 1992).
The U.S. and Canadian governments have also identified
widespread contaminated sediments in the Great Lakes
(DC, 1987; Fox andTuchman, 1996; Power and Chapman,
1992). The USEPA (1993a) summarizes other recent as-
sessment studies. However, there is still no national-
scale assessment of the incidence and severity of
sediment contamination, particularly in freshwater areas.
This report is the result of EPA's first assessment to de-
termine how significant the problem of sediment contami-
nation is on a national basis.
What Are The Potential Sources Of
Sediment Contamination?
Water bodies usually receive discharges of pollut-
ants as a result of the various human activities, past and
present, that take place nearby. The cumulative effect of
historical, nonpoint, and point sources can contribute to
sediment contamination. A point source is a single, iden-
tifiable source of pollution such as a pipe from a factory
or a wastewater treatment plant. Nonpoint source pollu-
tion is usually carried off the land by storm water runoff
and includes pollutants from agriculture, urban areas,
mining, marinas and boating, construction and other land
modifications, and atmospheric deposition. Many of the
current suspected and documented cases of sediment
contamination are caused by past industrial and agricul-
tural uses of highly persistent and toxic chemicals, such
as PCBs and chlordane. While the use of such chemicals
has since been banned or tightly restricted, monitoring
programs continue to study the extent and severity of
their accumulation in sediment, and subsequently in the
tissues of fish and shellfish. Other potential sediment
contaminants, including heavy metals, PAHs, some pes-
ticides, and existing and new industrial chemicals, con-
tinue to appear in point and nonpoint source releases.
However, significant progress over the past 10 to 15 years,
achieved through industry pollution prevention initia-
tives, National Pollutant Discharge Elimination System
(NPDES) permits, and national technology-based efflu-
ent guideline limitations, has substantially reduced the
discharge of toxic and persistent chemicals. Surficial sedi-
ments are often less contaminated than deeper sediments
indicating improved sediment conditions with reduced
discharges over the past 10 to 15 years.
The characteristics of local sediment contamination
are usually related to the types of land use activities that
take place or have taken place within the area that drains
into the water body (the watershed). For example, har-
bors, streams, and estuaries bordered by industrialized
or urbanized areas tend to have elevated levels of the
metals and organic compounds typically associated with
human activities in these land use areas. Sometimes the
contamination is localized beneath an outfall of industrial
or municipal waste; in other cases, natural mixing pro-
cesses and dredging disperse the pollutants. In addi-
tion, rivers and streams can carry pollutants from upstream
sources into larger downstream water bodies, where they
can contribute further to the problem of sediment con-
tamination. Drifting atmospheric pollutants that are even-
tually deposited in water bodies also contribute to
sediment contamination. For example, EPA estimates that
1-4
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76 to 89 percent of PCB loadings to Lake Superior have
come from air pollution (USEPA, 1994a),
Point source releases, including accidental or delib-
erate discharges, have resulted in elevated localized sedi-
ment contamination. Purposeful and accidental
contaminant additions include effluent discharges, spills,
dumping, and the addition of herbicides to lakes and res-
ervoirs, Both industrial mid municipal point sources have
contributed a wide variety of contaminants to sediments.
Municipal point sources include sewage treatment plants
and overflows from combined sewers (which mix the con-
tents of storm sewers and sanitary sewers). Industrial
point sources include manufacturing plants and power-
generating operations.
The pervasiveness of organic and metal compounds
in sediments near urban and agricultural areas and the
association of large inputs of these contaminants with
runoff events tend to support the importance of contami-
nant contributions from nonpoint sources like atmo-
spheric deposition and land drainage. For example, mining
is a significant source of sediment contamination in some
regions, as are runoff and seepage from landfills and
Superfund sites, and urban and agricultural runoff (Baudo
and Muntau, 1990; Canfieldetal., 1994; Hoffman, 1985;
Livingston and Cox, 1985; Ryan and Cox, 1985). Agricul-
tural runoff can contribute selenium, arsenic, and mer-
cury and a wide variety of pesticides. Urban runoff is a
frequently mentioned source of heavy metals and PAHs.
Atmospheric deposition can be one of the major sources
of lead, arsenic, cadmium, mercury, PAHs, DDT and other
organochlorine pesticides, and PCBs in many aquatic en-
vironments (USEPA, 1993c). However, it is often difficult
to determine the portion of these contaminants contrib-
uted by nonpoint versus point source discharges be-
cause the same contaminants can come from both (Baudo
and Muntau, 1990).
Kepone contamination in the James River in Virginia
is an example of historical sediment contamination.
Kepone is a very stable organic compound formerly used
in pesticides. Although active discharges of kepone at
the production site in Hopewell, Virginia, terminated in
1980, high levels of kepone can still be found in the sedi-
ment and finish and shellfish of the lames River down-
stream from the original discharge site (Huggett and
O'Conner, 1988; Nichols, 1990). In fact, a fish advisory
exists on portions of the James River because of high
levels of kepone in tissues of fish taken from the river.
Historical sediment contamination problems such as those
on the James River are often further complicated by on-
going discharge sources. Such historical sediment con-
tamination problems can also slow the natural recovery
of aquatic systems because of the stable nature of the
chemicals responsible for the contamination. Historical
sediment contamination can also cause new problems.
For example, during heavy storms contaminated sedi-
ments can be uncovered, resuspended, and carried down-
stream, where they cause problems in areas that were
previously uncontaminated.
1-5
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Introduction
1-6
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Chapter 2
Methodology
EPA faced two primary challenges to achieving
the short-term goals of the National Sediment
Inventory (NSI) and fulfilling the mandate of
the Water Resources Development Act (WRDA) of 1992,
as described in the introduction to this report. The first
challenge was to compile a database of consistent sedi-
ment quality measures suitable for all regions of the coun-
try. The second challenge was to identify scientifically
sound methods to determine whether a particular sedi-
ment is "contaminated," according to 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 concentra-
tions of contaminants in sediment. In most cases, how-
ever, less obvious effects on biological communities and
ecosystems are much more difficult to identify and are
frequently associated with varying concentrations of sedi-
ment contaminants. In other words, bulk sediment chem-
istry measures arc not always indicative of toxic effect
levels. Similar concentrations of a chemical can pro-
duce widely different biological effects in different sedi-
ments. This discrepancy occurs because toxicity is
influenced by the extent to which chemical contaminants
bind to other constituents in sediment. These other sedi-
ment constituents, such as organic ligands and inorganic
oxides and sulfides, are said to control the bioavailability
of accumulated contaminants. Toxicant binding, or sorp-
tion, to sediment particles suspends the toxic mode of
action in biological systems. Because the binding ca-
pacity of sediment varies, the degree of toxicity exhib-
ited also varies for the same total quantity of toxicant.
The five general categories of sediment quality
measurements are sediment chemistry, sediment tox-
icity, community structure, tissue chemistry, and pa-
thology (Power and Chapman, 1992). Each of these
categories 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, repre-
sented broad geographic coverage, and represented
specific sampling locations identified by latitude and
longitude coordinates. EPA found sediment chemis-
try and tissue chemistry to be the most widely avail-
able sediment quality measures.
As described above, sediment chemistry measures
might not accurately reflect risk to the environment.
However, EPA has recently developed assessment meth-
ods that combine contaminant concentration with mea-
sures of the primary binding phase to address
bioavailability for certain chemical classes, under as-
sumed conditions of thermodynamic equilibrium
(USEPA, 1993d). Other methods, which rely on statisti-
cal correlations of contaminant concentrations with in-
cidence of adverse biological effects, also exist (Barrick
et al„ 1988; FDEP, 1994; Long et al., 1995). In addi-
tion, fish tissue levels can be predicted using sediment
contaminant concentrations, along with independent field
measures of chemical partitioning behavior and other
known or assigned fish tissue and sediment characteris-
tics. EPA can evaluate risk to consumers from predicted
and field-measured tissue chemistry data using estab-
lished dose-response relationships and standard consump-
tion patterns. Evaluations based on tissue chemistry
circumvent the bioavailability issue while also account-
ing for other mitigating factors such as metabolism. The
primary difficulty in using field-measured tissue chem-
istry is relating chemical residue levels to a specific sedi-
ment, especially for those fish species which typically
forage across great distances.
Sediment toxicity, community structure, and pathol-
ogy measures are less widely available than sediment
chemistry and fish tissue data in the broad-scale elec-
tronic format EPA sought for the NSI. Sediment toxic-
ity data are typically in the form of percent survival,
compared to control mortality, for indicator organisms
exposed to the field-sampled sediment in laboratory bio-
assays (USEPA, 1994b, c). Although these measures
account for bioavailability and the antagonistic and syn-
ergistic effects of pollutant mixtures, they do not ad-
dress possible long-term reproductive or growth effects,
nor do they identify specific contaminants responsible
for observed lethal toxicity. Indicator organisms also
might not represent the most sensitive species. Com-
munity structure measures, such as fish abundance and
benthic diveisity, and pathology measures are potentially
2-1
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Mvlliodol»j>> - I
indicative of long-term adverse effects, yet there are a
multitude of mitigating physical, hydrologic, and bio-
logical factors that might not relate in any way to chemi-
cal contamination.
The ideal assessment methodology would be based
on matched data sets of all five types of sediment qual-
ity measures to take advantage of the strengths of each
measurement type and to minimize their collective
weaknesses. Unfortunately, such a database does not
exist on a national scale, nor is it typically available on
a smaller scale. Based on the statutory definition of
contaminated sediment in the WRDA, EPA can iden-
tify locations where sediment chemistry measures ex-
ceed "appropriate geochemical, toxicological, or
sediment quality criteria or measures." Again based
on the statutory definition, EPA can also use tissue chem-
istry and sediment toxicity measures to identify aquatic
sediments that "otherwise pose a threat to human health
or the environment" because there are either screening
values (e.g., EPA risk levels for fish tissue consump-
tion) or control samples for comparison. However, EPA
believes it cannot accurately evaluate community struc-
ture or pathology measures to identify contaminated
sediment, based on the statutory definition, without first
identifying appropriate reference conditions to which
measured conditions could be compared.
For this analysis, EPA evaluated sediment chemis-
try, tissue chemistry, and sediment toxicity data, taken
at the same sampling station, individually and in com-
bination using a variety of assessment methods. Be-
cause 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. Similar to a potential hu-
man illness screen, a screening-level analysis should
pick up potential problems and note them for further
study. A screening-level analysis will typically identify
many potential problems that prove not to be signifi-
cant upon further analysis. Thus, classification of sam-
pling stations in this analysis is not meant to be
definitive, but is intended to be inclusive of potential
problems arising from presistent metal and organic
chemical contaminants. For this reason, EPA elected
to evaluate data collected from 1980 to 1993 and to
evaluate each chemical or biological measurement taken
at a given sampling station individually. A single mea-
surement of a chemical at a sampling station, taken at
any point in time over the past 15 years, may have been
sufficient to classify the sampling station as having an
increased probability of association with adverse effects
to aquatic life or human health.
EPA recognizes that sediment is dynamic and that
great temporal and spatial variability in sediment qual-
ity exists. This variability can be a function of sam-
pling (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, or can bury contaminated sediment).
Movement of sediment is highly temporal, and depen-
dent upon the physical and biological processes at work
in the watershed. Some deposits will redistribute while
others will 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 are
probable (Tier 1); associated adverse effects are possible,
but expected infrequently (Tier 2); or no indication of
associated adverse effects (Tier 3). A Tier 3 sampling
station classification does not neccesarily imply a zero
or minimal probability of adverse effects, only that avail-
able data (which may be substantial or limited) do not
indicate an increased probability of adverse effects. Rec-
ognizing the imprecise nature of the numerical assess-
ment 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 sedi-
ment quality measures.
The remainder of this chapter presents a short his-
tory of how EPA developed the NSI, a brief description
of the NSI data, and an explanation of the NSI data evalu-
ation approach.
Background
EPA initiated work several years ago on the devel-
opment of the NSI through pilot inventories in EPA Re-
gions 4 and 5 and the Gulf of Mexico Program. Based
on lessons learned from these three pilot inventories,
the Agency developed a document entitled Framework
for the Development of the National Sediment Inven-
tory (USEPA, 1993a), which describes the general for-
mat for compiling sediment-related data and provides a
brief summary of sediment quality evaluation techniques.
The format and overall approach were then presented,
modified slightly, and agreed upon at an interagency
workshop held in March 1993 in Washington, DC. Fol-
lowing the workshop, EPA began compiling and evalu-
ating data for the NSI. Data from several national and
regional databases were included as part of the effort.
2-2
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In the spring of 1994, EPA conducted a prelimi-
nary evaluation of NSI sediment chemistry data only.
The purpose of the assessment was to identify sampling
stations throughout the United States where measured
values of sediment pollutants exceeded sediment chem-
istry levels of concern. The results of that assessment
were then distributed to the EPA Regional offices for
their review. The Regional offices were asked to review
the preliminary evaluation and to:
• Verify sampling stations targeted as areas of
concern.
• Identify sampling stations that might be incor-
rectly targeted as areas of concern.
• Identify potential areas of concern that were not
targeted, but should have been.
• Inform EPA Headquarters of additional sedi-
ment quality data that should be included in
the NSI to make the inventory more accurate
and complete.
The EPA Regional offices completed their review of
the preliminary evaluation during the winter of 1994-
95. Regional comments on the results of the prelimi-
nary evaluation were incorporated into the NSI database.
EPA will add new data sets identified by the Regions to
the NSI and include them in the national assessment for
future reports to Congress.
In April 1994, EPA Headquarters held the Second
National Sediment Inventory Workshop (USEPA, 1994d).
The purpose of this workshop was to bring together ex-
perts in the field of sediment quality assessment to rec-
ommend an approach for integrating and evaluating the
sediment chemistry and biological data contained in the
NSI. The final approach recommended by workshop par-
ticipants provided the basis for the final approach adopted
to evaluate NSI data for this report to Congress. Appen-
dix I of this report provides a brief description of the
workshop approach and a list of attendees.
Description of NSI Data
The NSI includes data from the following data stor-
age systems and monitoring programs:
• Selected data sets from EPA's Storage and Re-
trieval System (STORET) (69 percent of sam-
pling stations)
- U.S. Army Corps of Engineers (USAGE)
- U.S. Geological Survey (USGS)
- EPA
- States
• NOAA's Coastal Sediment Inventory (COSED)
(5 percent of sampling stations)
• EPA's Ocean Data Evaluation System (ODES)
(6 percent of sampling stations)
• EPA Region 4's Sediment Quality Inventory (5
percent of sampling stations)
• Gulf of Mexico Program's Contaminated Sedi-
ment Inventory (1 percent of sampling stations)
• EPA Region 10/COE Seattle District's Sedi-
ment Inventory (8 percent of sampling stations)
• EPA Region 9's Dredged Material Tracking
System (DMATS) (1 percent of sampling sta-
tions)
• EPA's Great Lakes Sediment Inventory (less
than 1 percent of sampling stations)
• EPA's Environmental Monitoring and Assess-
ment Program (EMAP) (2 percent of sampling
stations)
• USGS (Massachusetts Bay) Data (3 percent of
sampling stations)
Although EPA elected to evaluate data collected since
1980 (i,e., 1980-93), data from before 1980 are still main-
tained in the NSI. 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 NSI. Additional data
fields providing details such as sampling method or other
quality assurance/quality control information were re-
tained in the NSI if available. Additional information
about available data fields ami NSI component databases
is presented in Appendix A of this report.
The types of data contained in the NSI include the
following:
• Sediment chemistry: Measurement of the
chemical composition of sediment-associated
contaminants.
• Tissue residue: Measurement of chemical con-
taminants in the tissues of organisms.
2-3
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• Benthic abundance: Measurement of the num-
ber and types of organisms living in or on sedi-
ments.
• Toxicity: Measurement of the lethal or suble-
thal effects of contaminants in environmental
media on various test organisms.
• Histopathology: Observation of abnormalities
or diseases in tissue (e.g., tumors).
• Fish abundance: Measurement of the number
and types of fish found in a water body.
The NSI 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 proce-
dures. However, EPA's STORET database is intended to
be a broad-based repository of data. Consequently, the
quality of the data in STORET, both in terms of database
entry and analytical instrument error, is unknown and
probably varies a great deal depending on the quality
assurance management associated with specific data sub-
mittals.
Inherent in the diversity of data sources are contrast-
ing 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 repre-
sent 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 the National Status and Trends Program data in
NOAA's COSED database represent sampling stations
purposely selected because they are removed from known
discharges. However, many other sampling stations in
the COSED database were located within highly urban-
ized bays and estuaries where chemical contamination
was expected. These sampling stations include data from
regional bioeffects assesments in which NOAA exam-
ined sediment quality in several highly urbanized areas.
These surveys were region-wide assessments, not point
source or end-of-pipe studies.
From an assessment point of view, STORET data
might be useful for developing a list of contaminated
sediment locations, but might overstate the general ex-
tent 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 two-thirds of sampling stations in the NSI
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. How-
ever, one cannot accurately make inferences regarding
the overall condition of the Nation's sediment, or char-
acterize the "percent contamination," using the data in
the NSI because uncontaminated areas are most likely
substantially underrepresented.
NSI data do not evenly represent all geographic re-
gions in the United State, nor do the data represent a
consistent set of monitored chemicals. For example, sev-
eral of the databases are targeted toward marine envi-
ronments or other geographically focused areas. Table
2-1 presents the number of stations evaluated per state.
More than 50 percent of all stations evaluated in the NSI
ate located in Washington, Florida, Illinois, California,
Virginia, Ohio, Massachusetts, and Wisconsin. Each of
these states has more than 700 monitoring stations. Other
states of similar or larger size (e.g., Georgia, Pennsylva-
nia) have far fewer sampling stations with data for evalu-
ation. Figures 2-1, 2-2, and 2-3 depict the location of
monitoring stations with sediment chemistry, tissue resi-
due, and toxicity data, respectively. Individual stations
may vary considerably in terms of the number of chemi-
cals 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 cannot be considered
comprehensive even for locations with sampling data.
The reliance on readily available electronic data has un-
doubtedly led to exclusions of a vast amount of informa-
tion available from sources such as local and state
governments and published reports. Other limitations,
including data quality issues, are discussed in Chapter 5
of this report.
NSI Data Evaluation Approach
The methodology developed for classifying sampling
stations according to the probability of adverse effects on
aquatic life and human health from sediment contami-
nation relies on measures of sediment chemistry, sedi-
ment toxicity, and contaminant residue in tissue.
Although the NSI also contains benthic abundance, his-
topathology, and fish abundance data, these types of data
were not used in the evaluation. Benthic and fish abun-
dance Cannot be directly associated with sediment con-
tamination based on the statutory definition and currently
available assessment tools, and available fish liver histo-
pathology data were very limited.
2-4
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Table 2-1. Number of Stations Evaluated in the NSI by State
Region 1
Connecticut
98
Region 6
Arkansas
107
Maine
55
Louisiana
460
Massachusetts
895
New Mexico
101
New Hampshire
7
Oklahoma
286
Rhode Island
42
Texas
662
Vermont
5
Region 2
New Jersey
448
Region 7
Iowa
228
New York
618
Kansas
203
Puerto Rico
30
Missouri
Nebraska
327
253
Region 3
Delaware
218
Region 8
Colorado
202
District of Columbia
4
Montana
38
Maryland
206
North Dakota
161
Pennsylvania
311
South Dakota
43
Virginia
1,051
Utah
47
West Virginia
120
Wyoming
44
Region 4
Alabama
477
Region 9
Arizona
124
Florida
1,776
California
1,443
Georgia
318
Hawaii
36
Kentucky
249
Nevada
96
Mississippi
318
,
North Carolina
612
South Carolina
563
Tennessee
646
Region 5
Illinois
1,669
Region 10
Alaska
267
Indiana
108
-
Idaho
95
Michigan
402
Oregon
291
Minnesota
438
Washington
2,225
Ohio
970
Wisconsin
703
The approach used to evaluate the NSI data focuses
on the protection of benthic organisms from exposure to
contaminated sediments and the protection of humans from
the consumption of fish that bioaccumulate contaminants
from sediment. In addition, potential effects on wildlife
from fish consumption were also evaluated. The wildlife
results were not included in the overall results of the NSI
data evaluation; however, they are presented separately.
Table 2-2 presents the classification scheme used in the
evaluation of the NSI data. Each component, or evalua-
tion parameter, of the classification scheme is numbered
on Table 2-2. Each evaluation parameter is discussed un-
der a section heading cross-referenced to these numbers.
Figures 2-4 through 2-8 depict the evaluation parameters
and sampling station classifications in flowchart format.
EPA analyzed the NSI data by evaluating each param-
eter in Table 2-2 on a measurement-by-measurement and
sampling station-by-sampling station basis. Each sampling
station was associated with a "probability of adverse ef-
fects" by combining parameters as shown
in Table 2-2 and Figures 2-4 through 2-8.
Because each individual measurement was
considered independently (except for diva-
lent metals, whose concentrations were
summed), a single observation of elevated
concentration could place a sampling sta-
tion into Tier I, (associated adverse effects
are probable). In general, the methodol-
ogy was constructed such that a sampling
station classified as Her 1 must be repre-
sented by a relatively large set of data or by
a highly elevated sediment concentration
of a chemical whose effects screening level
is well characterized based on multiple as-
sessment techniques. Fewer data were re-
quired to classify a sampling station as Tier
2. Any sampling station not meeting the
requirements to be classified as Tier 1 or
Tier 2 was classified as Tier 3. Sampling
stations in this category include those for
which substantial data were available with-
out evidence of adverse effects, as well as
sampling stations for which limited data
were available to determine the potential
for adverse effects.
Individual evaluation parameters, ap-
plied to various measurements indepen-
dently, could lead to different site
classifications. If one evaluation param-
eter indicated Tier 1, but other evaluation
parameters indicated Tier 2 or Her 3, a Her
1 classification was assigned to the sam-
pling station. For example, if a sampling station was cat-
egorized as Tier 2 based on all sediment chemistry data,
but was categorized as Her 1 based on toxicity data, the
station was placed in Tier 1. This principle also applies to
evaluating multiple contaminants within the same evalua-
tion parameter. For example, if the evaluation of sediment
chemistry data placed a sampling station in Her 1 for met-
als and in Tier 2 for PCBs, the station was placed in Her 1.
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 con-
tamination, environmental managers must balance Type I
errors (false positives: sediment classified as posing a threat
that does not) with Type II errors (false negatives: sedi-
ment that poses a threat but was not classified as such). In
2-5
-------
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-------
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Table 2-2. NSI Data Evaluation Approach (with numbered parameters)
Category of Sampling
Station
OmsHI cation*
Data U*ed to Determine OaaiifkatiQm
Sediment Otenjistiy
Tbtue Residue
Toxicity
Her 1:
Associated Adverse
Effects to Aquatic Life or
Human Heakh are
Probable
Sediment chemistry value*
exceed draft sediment quality
criteria far any one of the 6ve
cftetricais for which criteria have
beea developed by EPA (must
have measured TOO
1
OR
[SEM]*[AVS>5 for the sum of
molar ooacestratbns of Cd. Cu,
Ni Pb, and Zo*
2
OR
Sediment chemistry vsbes
exceed two or more of the
relevant qpper screening valuer
CERMj, AHTi (h£h), PELs,
SQALs, SQCs) for any one
chemical (other than Cd, Cu, Hi,
Pb, and Za) (can use de&uk
TOO
3
OR
Ussue lewis of dioxin or PCBs
m resklent species exceed EPA
risk levels
8
OR
Tbxfciy demonstrated by two or
more nosnicrobial scute toxicity
tests using two dM&reot specks
(one of which most be a sofld-
phase test)
11
OR
Sediment chemistry TDP
exceeds PDA levels or BPA rtik
leveb 4
AND
Tissue levels in reatient spec lot
exceed FDA toveb or EPA Hik
levels
9
Iter Z\
Associated Advene
Eflfecu to Aquatic LSe or
Hurron Heakh are
Poaifaie, but Expected
IreShsqtieraly
!SEM|.(AVS 1« 0 so 5 for the
cum of molar concenraUora of
Cd, Cu. Ni, Pb, and Zn
5
OR
Sedlrart chemistry values
exceed anyone of the relevant
lower screening vakxu OBRL),
AffI* (tow), H3L», SQALf,
SQCs) for any one chemical
(can use default TOC)
6
OR
Sediment chemistry IBP
exceed! FDA bveb or EPA
risk kveb ?
OR
Ibsuo levels in resident ipcctsi
exceed PDA levels or EPA risk
bvets
10
OR
Tbxfcly demonstrated by a
singfe««pocfes nonzntcrobiat
toxic ky test
12
Her 3:
No Indication of
Aucockled Adverse
Efiects
Any aanpfeag station not categorised as Her 1 or Tier 2. Available data (wftch imy be very limited or quite extensive) do
not indicate a bkefchood of adverse efiects to aquatic Bfe or hurmn heakh.
"Metals: Cd = cadmium. Oi = copper, Ni = nickel, Pb = lead, Za = zinc.
Does the dumieal
haveadnA SQCi
1
Wu TOC measured
tor the SBjiplinf imJon?
Wax TOC memir*d
for the sampling station!
Use measured TOC
value to determine TOC
rvocmaJ&ed chemtad
concentration for
comparijon with SQALs
Use measured TOC
value to deoermbe TOC
rtorma&ied chemical
concentration for
companion wfch draft SQC»
Utede6diTOCc(IX
to determirxs TOC
ramaiktd ch«nkal
concentration for
COmparocn with draft
SQCs and SQALt
Exceeded one or more
tower smwirig values
Unless catpjoraed bjr another parameter i
Figure 2-4. Aquatic Life Assessments: Sediment Chemistry Analysis for
Organic Chemicals and Metals Not Included in the AVS Analysis,
-------
Mi'thodology
I
Figure 2-5. Aquatic Life Assessments: Sediment Chemistry Analysis for
Divalent Metals.
Wu a toxicfty 1 f3
test performed? I
Wis toxicity demonstrated u*lng 2 or
mora nonmferobbl toxicity tens
u$fng 2 different spades (one of **hlch
wm *iotld-phue teit)?
-v
-------
National Sediment Quality Survey
Unless categorized by another parameter
Figure 2-7. Human Health Assessments: Sediment Chemistry and Fish
Tissue Residue Analysis (excluding dioxins and PCBs).
Unless categorized by another parameter
Figure 2-8. Human Health Assessments: PCBs and Dioxin in Fish Tissue
Analysis.
2-11
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Mi'tlio(lnli><>\
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 high rate of
"correct" classification (e.g., sediment definitely posing or
definitely not posing a threat) and a balance between Type
I and Type II errors. On the other hand, to retain a suffi-
cient 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.
The numbered evaluation parameters used in the NSI
data evaluation are briefly described below. A detailed de-
scription of the evaluation parameters is presented in Ap-
pendix B.
Sediment Chemistry Data
The sediment chemistry screening values used in this
report are not regulatory criteria, site-specific cleanup stan-
dards, or remediation goals. Sediment chemistry screen-
ing 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
both theoretically and empirically based values. The theo-
retically based values rely on the physical/chemical prop-
erties of sediment and chemicals to predict the level of
contamination that would not cause an adverse effect on
aquatic life. The empirically based, or correlative, screen-
ing values rely on paired field and laboratory data to relate
incidence of observed biological effects to the dry-weight
sediment concentration of a specific chemical.
The theoretically based screening values used as pa-
rameters in the evaluation of NSI data include the sedi-
ment quality criteria, sediment quality advisory levels, and
comparison of simultaneously extracted metals to acid-vola-
tile sulfide concentrations. Empirically based, correlative
screening values used in the NSI evaluation include the
effects range-median/effects range-low values, probable ef-
fects levels/threshold effects levels, and apparent effects
thresholds. The use of each of these screening values in
the evaluation of the NSI data is described below. Another
theoretically based evaluation parameter, the theoretical
bioaccumulation potential (which was used for human
health assessments), is also described below. The limita-
tions associated with the use of these screening values are
discussed in Chapter 5.
Sediment Chemistry Values Exceed EPA Draft
Sediment Quality Criteria [1]
This evaluation parameter was used to assess the po-
tential effects of sediment contamination on benthic spe-
cies. EPA has developed draft sediment quality criteria
(SQCs) for the following five nonionic organic chemicals:
• Acenaphthene (polynuclear aromatic
hydrocarbon, or PAH)
• Dieldrin (pesticide)
• Endrin (pesticide)
• Fluoranthene (PAH)
• Phenanthrene. (PAH)
EPA developed these draft criteria using the equi -
librium partitioning (EqP) approach (described in de-
tail in Appendix B) for linking bioavailability to toxicity.
The EqP approach involves predicting the dry-weight
concentration of a contaminant in sediment that is in
equilibrium with a pore water concentration that is pro -
tective of aquatic life. It combines the water-only ef-
fects concentration (the chronic water quality criteria)
and the organic carbon partitioning coefficient of the
chemical normalized to the organic carbon content of
the sediment. The draft criterion is compared to the
measured dry-weight sediment concentration of the
chemical normalized to sediment organic carbon con-
tent. If the organic-carbon-normalized concentration
of the contaminant does not exceed the draft sediment
quality criterion, adverse effects should not occur to at
least 95 percent of benthic organisms. The draft SQCs
are based on the highest quality data available, which
have been reviewed extensively.
For the NSI data evaluation, sediment chemistry mea-
surements with accompanying measured total organic car-
bon (TOC) values can place a site in Tier 1 based exclusively
on a comparison with a draft SQC. The amount of TOC in
sediment is one of the factors that determines the extent to
which a nonionic organic chemical is bound to the sedi-
ment and, thus, the availability for uptake by organisms
(bioavailability). If draft SQCs based on measured TOC
were not exceeded, or if none of the five nonpolar
organic chemicals that have been assigned draft SQC
values were measured, the sampling station was classi-
fied as Tier 3 unless otherwise categorized by another
parameter. Appendix B discusses the assumptions
2-12
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I
National Sediment Quality Survey
and limitations associated with the use of draft SQCs.
If a sample for any of the five contaminants for which
draft SQCs have been developed did not have accompa-
nying TOC data, the measured concentration was com-
pared to the draft SQC based on a default TOC value of
1 percent. In these instances, the draft SQC was treated
like other sediment quality screening values described
later in this section.
The assumption that the percent TOC for samples
without measured TOC is equal to 1 percent is based on
a review of values published in the literature. TOC can
range from 0.1 percent in sandy sediments to 1 to 4 per-
cent 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
the NSI evaluation. Consistent with the screening level
application, this value should not lead to an underesti-
mate of the bioavailability of associated contaminants
in most cases.
Comparison ofAVS to SEM Molar Concentrations
[2, 5]
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 recently been reconciled
by relating organism toxic response (mortality) to the
metal concentration in the sediment interstitial water
(Adams et al., 1985; Di Toro et al., 1990). Acid-vola-
tile sulfide (AVS) is one of the major chemical compo-
nents that control the activities and availability of metals
in interstitial waters of anoxic (lacking oxygen) sedi-
ments (Meyer et al., 1994).
A large reservoir of sulfide exists as iron sulfide in
anoxic sediment. Sulfide will react with several diva-
lent transition metal cations (cadmium, copper, mercury,
nickel, lead, and zinc) to form highly insoluble com-
pounds that are not bioavailable (Allen et al., 1993). It
follows in theory, and with verification (Di TorO et al.,
1990), that divalent transition metals will not begin to
cause toxicity in anoxic sediment until the reservoir of
sulfide is used up (i.e., the molar concentration pf met-
als exceeds the molar concentration of sulfide), typically
at relatively high dry-weight metal concentrations. This
observation has led to a laboratory measurement tech-
nique of calculating the difference between simulta-
neously extracted metal (SEM) concentration and acid
volatile sulfide concentration from field samples to de-
termine potential toxicity.
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 five metals: cadmium, copper, nickel, lead,
and zinc. Mercury was excluded from AVS comparison
because other important factors play a major role in de-
termining the bioaecumulation potential of mercury in
sediment. Specifically, under certain conditions mer-
cury 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, re-
search 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 fresh-
water and saltwater sediment amphipods (crustaceans)
from EPA's Environmental Research Laboratory in
Narragansett, Rhode Island, revealed that 80 to 90 per-
cent of the sediments were toxic at [SEM]-[AVS] > 5
(Hansen, 1995; see also Hansen et al., 1996). Thus,
EPA selected [SEM]-[AVS] = 5 as the demarcation line
between Tier 1 and Tier 2. For the purpose of this evalu-
ation, where [SEM]-[AVS] was greater than 5, the sam-
pling station was classified as Tier 1. If [SEM]-[AVS]
was between zero and 5, the sampling station was clas-
sified as Tier 2. If [SEM]-[AVS] was less than zero, or
if AVS or the five AVS metals were not measured at the
sampling station, the sampling station was classified as
Tier 3 unless otherwise classified by another parameter.
Appendix B discusses the assumptions and limitations
associated with the [SEM]-[AVS] approach.
Sediment Chemistry Values Exceed Screening
Values [3, 6]
Several sets of sediment contaminant screening val-
ues, developed using different methodologies, are avail-
able to assess potential adverse effects on benthic species.
The screening values selected for comparison with mea-
sured sediment levels are the draft SQCs using a default
TOC of 1 percent (for those samples which do not have
accompanying TOC data), sediment quality advisory lev-
els (SQALs) for freshwater aquatic life (developed using
the equilibrium partitioning approach discussed previ-
2-13
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Methodology
ously for the development of draft SQCs), the effects
range-median (ERM) and effects range-low (ERL) val-
ues developed by Long et al. (1995), the probable effects
levels (PELs) and threshold effects levels (TELs) devel-
oped for the Florida Department of Environmental Pro-
tection (FDEP, 1994), and the apparent effects thresholds
(AETs) developed by Barrick et al, (1988), The assump-
tions and approaches used to develop these screening
values are discussed in detail in Appendix B.
The draft SQCs and SQALs were both developed us-
ing the same EqP approach. However, the data used to
derive SQALs were not compiled from an exhaustive lit-
erature search, nor were the toxicity data requirements as
extensive as specified for draft SQCs. Toxicity values used
for SQAL development include final chronic values from
EPA ambient freshwater quality criteria and secondary
chronic values derived using EPA's Great Lakes Water Qual-
ity Initiative "Her II" water quality criteria methodology.
The data used to develop the latter values were taken pri-
marily from quality-screened studies in published litera-
ture. The development of SQALs is discussed in further
detail in Appendix B of this report EPA has also prepared
a document describing the derivation of the SQALs
(USEPA, 1996). The chemicals for which SQALs have
been developed are identified in Appendix D of this vol-
ume.
The ERLs/ERMs, PELs/TELs, and AETs relate the
incidence of adverse biological effects to the sediment
concentration of a specific chemical at a specific sam-
pling station using paired field and laboratory data. The
developers of the ERLs/ERMs define sediment concen-
trations below the ERL as being in the "minimal-effects
range," values between the ERL and ERM in the "pos-
sible-effects range," and values above the ERM in the
"probable-effects range." In the FDEP (1994) approach,
the lower of the two guidelines for each chemical (the
TEL) is assumed to represent the concentration below
which toxic effects rarely occur. In the range of concen-
trations between the TEL and PEL, effects occasionally
occur. Toxic effects usually or frequently occur at con-
centrations above the upper guideline (the PEL).
In independent analyses of the predictive abilities
of the ERL/ERMs and TEL/PELs, the precentages of
samples indicating high toxicity in laboratory bioassays
of amphipod survival were relatively low (10-12 per-
cent) when all chemical concentrations were in the mini-
mal effects range, intermediate (17-19 percent) in the
possible effects range, and higher (38-42 percent) in the
probable effects range. Furthermore, the percentages of
samples indicating high toxicity in any one of a battery
of 2-4 tests performed, including more sensitive bioas-
says with sublethal endpoints, were 5-28 percent, 59-64
percent, and 78-80 percent among samples within the
minimal, possible, and probable effects ranges (Long et
al,, in press).
The AET approach is not based on the probability
of incidence of adverse biological effects. The AET is
the highest concentration at which statistically signifi-
cant differences in observed adverse biological effects
from reference conditions do not occur, provided that
the concentration also is associated with observance of
a statisically significant difference in adverse biological
effects. Essentially, this identifies the concentration
above which an adverse biological effect always occurs
for a particular data set. Barrick et al. (1988) list spe-
cific AET values for several different species or biologi-
cal indicators. For the purposes of this assessment, EPA
defined the AET-low as the lowest AET among appli-
cable biological indicators, and the AET-high as the
highest AET among applicable biological indicators. By
the nature of how the AET is derived, less stringent val-
ues might evolve as more data sets become available.
For the NSI data evaluation, the upper screening val-
ues were considered to be the ERM, PEL, draft SQC
(when using default TOC value of 1 percent), SQAL,
and AET-high for a given chemical. The lower screen-
ing values were considered to be the ERL, TEL, draft
SQC (when using default TOC of 1 percent), SQAL, and
AET-low for a given chemical. Because they are not
based on ranges of effects, the single freshwater aquatic
life draft SQC and SQAL values for a given chemical
served as both the high and low screening values.
For a sampling station to be classified as Tier 1, a
chemical measurement must have exceeded at least two
of the upper screening values. If a sediment chemistry
measurement exceeded any one of the lower screening
values, the sampling station was classified as Tier 2. If
sediment concentrations at a sampling station did not
exceed any screening values or there were no data for
chemicals that have assigned screening values, the sam-
pling station was categorized as Her 3 unless otherwise
-categorized by another parameter.
Under this approach, a sampling station could be
classified as Tier 1 from elevated concentrations of cad-
mium, copper, lead, nickel, or zinc based only on a com-
parison of [SEM] to [AVS]; that is, sampling stations
could not be classified as Tier 1 based on an exceedance
of two upper screening values for any of the five metals.
However, sampling stations were classified as Tier 2 for
these five metals based on an exceedance of one of the
lower screening values if AVS data were not available.
2-14
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f , j National Sediment Quality Survey
r • ; i ; i t
Sediment Chemistry TBPs Exceed Screening
Criteria [4, 7]
This evaluation parameter addresses the risk to hu-
man consumers of organisms exposed to sediment con-
taminants. The theoretical bioaccumulation potential
(TBP) is an estimate of the equilibrium concentration
(concentration that does not change with time) of a con-
taminant 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 con-
tent 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 C). In prac-
tice, field measured BSAFs can vary by an order of mag-
nitude or greater for individual compounds depending
on location and time of measurement. For this evalua-
tion, EPA selected BSAFs that represents 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 sedi-
ment chemistry TBP value exceeded a screening value
derived using standard EPA risk assessment methodol-
ogy or the Food and Drug Administration (FDA) toler-
ance/action or guidance level, and if a corresponding
tissue residue level for the same chemical for a resident
species at the same sampling station also exceeded one
of those screening values, the station was classified as
Tier 1. Individual chemical risk levels were considered
separately ; that is, risks from multiple contaminants were
not added. 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 resident species at the same sampling station did
not exceed EPA risk levels or FDA levels or there were
no corresponding tissue data, the sampling station was
classified as Tier 2. If neither TBP values nor fish tis-
sue residue levels exceeded EPA risk levels or FDA lev-
els, 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 an-
other parameter. 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 [8, 9, 10]
Tissue residue data were used to assess potential
adverse effects on humans from the consumption of fish
that become contaminated through exposure to contami-
nated sediment. Only those species considered benthic,
non-migratory (resident), and edible by human popula-
tions were included in human health assessments. A
list of species included in the NSI and their characteris-
tics is presented in Appendix F.
Sampling stations at which human health screen-
ing values for dioxin and PCBs were exceeded in fish
tissues were classified as Tier 1. For these chemicals,
corroborating sediment chemistry data were not required.
If human health screening values for dioxin or PCBs in
fish tissue were not exceeded or if neither chemical was
measured, the sampling station was classified as Tier 3
unless otherwise classified by another parameter.
For other chemicals, both a tissue residue level ex-
ceeding an FDA tolerance/action or guidance level or
EPA risk level and a sediment chemistry TBP value ex-
ceeding that 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 corre-
sponding TBP values were not exceeded at the same sta-
tion (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 oth-
erwise classified by another parameter.
Toxicity Data [11,12]
Toxicity data were used to classify sediment sam-
pling stations based on their demonstrated lethality to
aquatic life in laboratory bioassays. Nonmicrobial sedi-
ment toxicity tests with a mortality endpoint were evalu-
ated. Toxicity test results that lacked control data, or
had control data that indicated greater than 20 percent
mortality (less than 80 percent survival), were excluded
from further consideration. The EPA has standardized
testing protocols for marine and freshwater toxicity tests.
A review of several protocols for sediment toxicity tests
suggests that mortality in controls may range from 10 to
30 percent, depending on the species, to be considered
an acceptable test result (API, 1994). Current amphi-
pod test requirements indicate that controls should have
less than 10 percent mortality (API, 1994; USEPA,
1994b).
2-15
-------
For the NSI data evaluation, EPA considered sig-
nificant toxicity as a 20 percent difference in survival
from control survival. For example, significant toxicity
occurred if control survival was 80 percent and experi-
mental survival was 60 percent or less.
Fortius evaluation parameter, corroboration of mul-
tiple tests was considered more indicative of probable
associated adverse effects than the magnitude of the ef-
fect in a single test Lethality demonstrated by two or
more single-species tests using two different test spe-
cies (at least one of which had to be a solid-phase test)
placed a sampling station in Tier 1. A sampling station
was classified as Tier 2 if toxicity was demonstrated by
one single-species nonmicrobial toxicity test. If lethal-
ity was not demonstrated by a nonmicrobial toxicity test,
or if toxicity test data were not available, the sampling
station was classified as Tier 3 unless otherwise classi-
fied by another parameter.
Incorporation ofRegional Comments on the
Preliminary Evaluation ofSediment
Chemistry Data
Several reviewers from different EPA Regions and
states provided comments on the May 16, 1994,
preliminary evaluation of sediment chemistry data. The
comments included more than 150 specific comments
identifying additional locations with contaminated sedi-
ment that had not been identified in the preliminary
evaluation. Since the preliminary evaluation, the final
NSI methodology has been developed and implemented.
The updated methodology has been refined significantly
to include tissue residue and toxicity data as well as
revised screening values. Data corresponding to any
additional comments that required further review were
divided into two categories: (1) data that incorrectly
identified contaminated sediment and (2) additional wa-
ter bodies that contain areas of sediment contamination.
The first category primarily addressed sampling stations
identified in the preliminary assessment as exceeding
sediment chemistry screening values for specific con-
taminants that reviewers stated were located in water
bodies that are not contaminated from the chemical(s)
in question.
EPA examined all NSI sampling stations that had
been identified in the preliminary evaluation as exceed-
ing a sediment quality screening value, but were located
in water bodies that reviewers of the preliminary evalu-
ation identified as not being contaminated by that spe-
cific contaminant or contaminants. If the sampling
station in question was classified in this final evalua-
tion as Tier 1 based only on the specific contaminant(s)
identified by the reviewer as not being a problem, the
sampling station was removed from the Tier 1 category
and placed in the Tier 3 category. Only a few sampling
stations were moved from the Tier 1 category to the Her
3 category as a result of this procedure. Stations identi-
fied in the NSI evaluation as Tier 1 based on other chemi-
cals not identified by the reviewer or because of toxicity
data were not removed from Her 1.
Additional water bodies that reviewers identified as
potential areas of significant contamination were evalu-
ated to determine whether sampling stations along those
water bodies were classified as Tier 1 based on the final
NSI data evaluation. Locations or water bodies identi-
fied by reviewers as potential areas of significant con-
tamination are discussed separately in the results
(Chapter 3).
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 sedi-
ment chemistry TBP values and fish tissue values to EPA
wildlife criteria developed for the Great Lakes. This
evaluation, however, was not included with the results
of evaluating the NSI data based on the other param-
eters. The results of evaluating NSI data based on wild-
life criteria are presented in a separate section of Chapter
3. Wildlife criteria based solely on fish tissue concen-
trations were derived for EPA wildlife criteria for water
that are presented in the Great Lakes Water Quality Ini-
tiative Criteria Documents for the Protection of Wild-
life (USEPA, 1995a). EPA has developed wildlife criteria
for four contaminants: DDT, mercury, 2,3,7,8-TCDD,
and PCBs. The method to adjust these wildlife criteria
for the NSI data evaluation is explained in detail in
Appendix B.
2-16
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Chapter 3
Findings
This chapter presents the results of the
evaluation of NSI data based on the
methodology described in Chapter 2. This dis-
cussion includes a summary of the results of national, re-
gional, and state assessments.
National Assessment
EPA evaluated a total of 21,096 sampling stations na-
tionwide as part of the NSI data evaluation (Figure 3-1).
Of the sampling stations evaluated, 5,521 stations (26 per-
cent) were classified as Tier 1, 10,401 (49 percent) were
classified as Tier 2, and 5,174 (25 percent) were classified
as Tier 3 (Table 3-1). This distribution suggests that state
monitoring programs (accounting for the majority of NSI
data) have been efficient and successful in focusing their
sampling efforts on areas where contamination is known
or suspected to occur. The frequency of Tier 1 classifica-
tion based on the evaluation of all NSI data is greater than
from data sets derived from purely random sampling.
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 Her
2 sampling stations in some Regions is to some degree a
function of a larger set of available data. Although there
are 17 times more Her 1 stations in EPA Region 4 (south-
eastern states) than in EPA Region 8 (mountain state), there
are also 13 times more Tier 3 stations.
The NSI sampling stations were located in 6,744 in-
dividual river reaches throughout the contiguous United
States (based on EPA's River Reach File 1; Bondelid and
Hanson, 1990). A river reach can be part of a coastal
shoreline, a lake, or a length of stream between two ma-
jor tributaries ranging from approximately 1 to 10 miles
long. NSI sampling stations were located in approxi-
mately 11 percent of all river reaches identified in the
contiguous United States (Table 3-1 and Figure 3-3).
Four percent of all river reaches in the United States con-
tained at least one sampling station classified as Tier 1.
Five percent of all reaches contained at least one sam-
pling station classified as Tier 2 (but none as filer 1). In
2 percent of reaches in the contiguous United States, all
of the sampling stations were classified as Her 3. EPA
has not yet catalogued river reaches outside the contigu-
ous 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 may be more pronounced in
some Regions compared to others, aid may bfe related to
the relative extent of sampling. The results presented on
Table 3-1 appear to indicate that the smaller the percent-
age of reaches with available data, the greater the likeli-
hood those reaches will contain a Her 1 or Her;2 sampling
station.
Not all sampling programs target only sites of known
or suspected contamination. The NSI includes data from
the National Oceanic and Atmospheric Administration's
(NOAA's) National Status and Trends Program, which
is part of the COSED database, and EPA's Environmen-
tal Monitoring and Assessment Program (EM AP). These
are examples of sampling programs in whict^ most sam-
pling stations are not targeted at locations of known or
suspected contamination. Based on these data alone, the
percentage of sampling stations placed in each tier dif-
fers considerably from the percentage of sampling sta-
tions in each tier based on an evaluation of all the data in
the NSI. Smaller percentages of COSED and EMAP
sampling stations are categorized as Tier 1 (18 percent
for COSED and 14 percent for EMAP compared to 26
percent for all NSI sampling stations), greater percent-
ages are categorized as Tier 2 (75 percent for COSED
and 68 percent for EMAP compared to 49 percent for all
NSI stations), and smaller percentages are categorized
as Her 3 (7 percent for COSED and 18 percent for EMAP
compared to 25 percent for all NSI sampling stations).
This may reflect the lower detection limits of more sen-
sitive analytical chemistry techniques, the sensitivity of
Tier 2 evaluation parameters, and the nearly ubiquitous
presence of lower to intermediate levels of contamina-
tion in areas sampled by these programs.
The NSI contains over 1.5 million individual records
of contaminant measurements in sediment and fish
3-1
-------
-------
Table 3-1. National Assessment: Evaluation Results for Sampling Stations and River Reaches by EPA Region
Region (State)
Station Evaluation
River Reach Evatuatioif
Herl
Tier 2
Her 3
Number of
Stations
Not
Identified
fayanRFl
Reaclf
Reaches
It
Least 1
Station fai
Her 1
Reaches
w/at
Least 1
Station in
Her24
Reaches
wfaU
Stations
in Tier 3
Total#
Reaches
Wat
Least 1
Station
Evaluated
Total
Reaches
in Region
% of all
Reaches
in Region
w&t
Least 1
Station
Evaluated
%ot
Reaches
Wat
Least 1
Her 1 or
Her 2
Station
#
%"
#
%b
#
%-
Region 1
(CT, ME, MA, NH, RI, VT)
298
27
646
59
158
14
361
59
65
7
131
2,648
5
5
Region 2
(NY, NJ, PR)
355
32
559
51
182
17
173
116
147
29
292
1,753
17
15
Region 3
(DE, DC, MD, PA, VA, WV)
318
17
934
49
658
34
92
209
453
226
888
3,247
27
20
Region 4
(AL, FL, GA, KY, MS. NC, SC,
TO)
1,157
23
1,930
39
1,872
38
343
566
684
520
1,770
9,749
18
13
Region 5
OL, IN, MI, MN, OH, WI)
1,418
33
2,137
50
735
17
108
594
570
268
1,432
6,025
24
19
Region 6
(AR, LA, NM, OK, TX)
382
24
837
52
397
24
124
266
341
192
799
7,293
11
8
Region 7
(IA, KS, MO, NE)
330
33
393
39
288
28
N/A
246
182
88
516
4,857
11
9
Region 8
(CO, MT, ND, SD, UT, WY)
68
13
327
61
140
26
N/A
61
153
91
305
13,492
2
2
Region 9
(AZ, CA, HI, NV)
468
28
942
55
289
17
794
119
92
43
254
4,601
6
5
Regon10
(AK, ID, OR, WA)
727
25
1,696
59
455
16
497
147
174
72
393
10,178
4
3
¦ftilal for U.S.1
5,521
26
10,401
49
5,174
25
2,492
2,371
2,843
1,530
6,744
62,742
11
8
¦River reaches based on EPA River Resell File 1 (RF1).
'Percent of all stations evaluated in the NSI in the Region.
'Stations not identified by an RF1 reach were located in coastal or open water areas.
*No stations in these reaches were included in Her 1.
•Because some reaches occur in more than one Region, the total number of teaches in each caleogry for the country might not equal the sum of reaches in the Regions.
-------
Figure 3-2. Sampling Stations Classified as Tier 1 (Associated Adverse Effects are Probable),
-------
11' . 1
National Sediment Quality Survey
At Least One
Tier 1 Station
4%
At Least One
Tier 2 Station and
Zero Tier 1 Stations
5%
All Tier 3 Stations
2%
Figure 3-3. National Assessment: Percent of River Reaches That Include
Her 1, Tier 2, and Tier 3 Sampling Stations.
tissue (Figure 3-4). Slightly more than one-third of these
measurements represent concentrations recorded as above
a detection limit. Using available assessment parameters,
EPA could evaluate nearly two-thirds (approximately
380,000) of these measurements for the probability of
association with adverse effects. Approximately one-
quarter of the measurements above detection (nearly 40
percent of measurements that could be evaluated) reflect
either a Tier 1 or Her 2 level of contamination. Figure 3-4
also shows the distribution of measurements at the Tier I
and Tier 2 level of contamination by chemical class.
Chemicals that have been measured over the past 15 years,
can be evaluated using the NSI evaluation approach, and
accumulate to levels associated with an increased prob-
ability of adverse effects are predominantly persistent, hy-
drophobic organic compounds and metals.
Data related to more than 230 different chemicals or
chemical groups were included in the NSI evaluation.
Approximately 40 percent of these chemicals or chemi-
cal groups (97) were present at levels that resulted in
classification of sampling stations as Tier 1 or Tier 2.
Table 3-2 presents the chemicals or chemical groups that
resulted in classification of more than 1,000 Tier 1 or
Tier 2 sampling stations. Sampling stations are reported
more than once in Table 3-2 because it is common for a
station to have elevated concentration levels for multiple
chemicals.
The contaminants most frequently at levels in fish
or sediment where associated adverse effects are prob-
able include PCBs (58 percent
of the 5,521 Tier 1 sampling
stations) and mercury (20 per-
cent of Tier 1 sampling sta-
tions). Pesticides, most notably
DDT and metabolites at 15 per-
cent of Tier 1 sampling stations,
and polynuclear aromatic hy-
drocarbons (PAHs), such as
pyreen at 8 percent of Her 1
sampling stations, also were
frequently at levels where as-
sociated adverse effects are
probable.
Dry weight measures of
divalent metals other than mer-
cury (e.g., copper, cadmium,
lead, nickel, and zinc) were not
used to place a sampling sta-
tion in Tier 1 without an asso-
ciated measurement of acid
volatile sulfide, a primary me-
diator of bioavailabilty not of-
ten available in the data base. The [SEM]-[AVS]
methodology for sediment assessment is relatively new, and
AVS measurements have not commonly been made during
sediment analyses. As a result, metals otter than mercury
(which also include arsenic, chromium, and silver) are solely
responsible for only 6 percent of Her 1 sampling stations
and overlap with mercury or organic compounds at an ad-
ditional 6 percent of Tier 1 sampling stations. In contrast,
metals other than mercury are solely responsible for about
28 pa-cent of the 15,992 Her 1 and Tier 2 sampling sta-
tions, and overlap with mercury or organic compounds at
an additional 28 percent of Tier 1 and Tier 2 sampling sta-
tions. The remaining 44 percent of Tier 1 and Her 2 sam-
pling stations are classified solely for mercury or organic
compounds.
Two important issues in interpreting the results of
sampling station classification are naturally occurring
"background" levels of chemicals and the effect of chemi-
cal mixtures. Site-specific naturally occulting (or back-
ground) levels of chemicals may be an important risk
management consideration in examining sampling sta-
tion classification. This is most often an issue for natu-
rally occurring chemicals such as metals and PAHs. In
addition, although the sediment chemistry screening lev-
els for individual chemicals are used as indicators of po-
tential adverse biological effects, other co-occurring
chemicals (which may or may not be measured) can cause
or contribute to any observed adverse effect at specific
locations.
3-5
-------
Sediment and Fish Tissue
Measurements
(1,565,103)
Measurements Above
Detection Limit
(586,994)
Measurements Indicating
Potential Risk
(Tier land Tier 2*)
(142,004)
PCBs
15%
Tier 2
«% PmHcMm'
/ 22%
NuVMytoEyahwfs
35%
'For Tier 1 alone: 27,358 measurements Indicate potential risk, distributed among PCBs (62 percent) PAH (13 percent), pesticides (S percent)
mercury (7 percent), other organlcs (5 percent), and other metals (4 percent)
Figure 3-4. National Assessment: Percent of NSI Measurements That Indicate Potential Risk.
-------
Table 3-2. Chemicals or Chemical Groups Most Often Associated With Tier 1 and Her 2 Sampling Station
Classifications
Chemical or
Chemical Group
Number of Stations
Total# of
Stations
Evaluated
Based on All Measurement Parameters
Based on
Aquatic Life
Parameters
Based oil
Human Health
Parameters
Combined
Tiers 1 & 2
Percent of
All Tier 1
and Tier 2
Stations
Tier!
Percent of
All Tier 1
Stations
Tier 2
Tier!
Tier 2
Tier 1
Tier 2
Copper
16,161
7,172
45
-
-
7,172
-
7,167
-
5
Nickel
12,447
6,284
39
-
-
6,284
-
6,284
-
-
Lead
16,791
5,681
36
-
-
5,681
-
5,415
-
328
Folychlorinated biphenyls
12,276
5,454
34
3,175
58
2,279
963
1,219
2,256
3,198
Arsenic
13,200
5,392
34
182
3
5,210
182
4,658
-
605
Cadmium
16,010
4t808
30
-
-
4,808
-
4,773
-
41
Mercury
15,649
4,333
27
1,122
20
3,211
1,122
3,127
-
103
Zinc
15,160
3,468
22
-
-
3,468
-
3,451
-
17
DDT (and metabolites)
11,462
3,422
21
803
15
2,619
798
2,203
21
1,402
Chromium
15,222
3,070
19
278
5
2,792
278
2,786
-
7
Dieldrin
10,284
2,597
16
58
1
2,539
49
1,006
9
2,456
Chlordane
10,697
2,169
14
11
<1
2,158
-
1,303
11
1,697
Benzo(a)pyrene
5,435
1,993
13
287
5
1,706
287
1,051
-
1,990
Pyrene
5,798
1,920
12
431
8
1,489
431
1,489
-
10
Chrysene
5,300
1,427
9
166
3
1,261
166
1,261
-
30
Dibetizo(a,h)anthracene
4,896
1,383
9
337
6
1,046
337
1,018
-
1,092
Benzo(a)anthracene
5,120
1,366
9
214
4
1,152
214
1,106
-
847
Bis(2-elhylhexyl)phthalate
3,559
1,190
7
347
6
843
347
823
-
406
Naphthalene
5,246
1,186
7
254
5
932
254
932
-
5
Fluoranthene
5,814
1,114
7
210
4
904
210
904
-
11
Fluorene
5,175
1,107
7
201
4
906
201
906
-
5
Silver
8,022
1,096
7
302
5
794
302
794
-
-
Total for all chemicals in
the NSI database
21,096
15,922
-
5,521
-
10,401
3,287
9,921
2,327
6,196
The total number of sampling stations classified as
Tier 1 or Tier 2 for a given chemical as presented in
Table 3-2 may not be representative of the potential risk
posed by that chemical. Although there may be few over-
all observations for some chemicals, the frequency of
detection in sediment and tissue and the frequency with
which those chemicals result in Tier 1 or Tier 2 risk may
be high. (See Appendix D, Table D-2.)
The results of the analysis for three chemicals (arsenic,
silver, and phthalate esthers) might be misleading. Arsenic is
typically analyzed in biota as "total arsenic", which includes
all forms of arsenic. The EPA risk level for comparison with
measured values was derived for the highly toxic effects of
inorganic arsenic. However, arsenic in the edible portions of
fish and shellfish is predominantly found in a nontoxic or-
ganic form (USEPA, 1995c). For this analysis, a precautionary
3-7
-------
approach was taken to account for the human health risk from
the small amount of inorganic arsenic included in total ar-
senic measures and for measures that, in fact, represent only
inorganic arsenic. Silver, like copper, cadmium, lead, nickel,
and zinc, binds to sulfide in sediment However, silver can-
not be evaluated like these other metals in the [SEMHAVS]
assessment for a number of reasons, including that one mol-
ecule of sulfide binds two molecules of silver rather than just
one as is the case for the other metals. Recent research sug-
gests that if any AVS is measured, silver will not be bioavail-
able or toxic to exposed aquatic organisms (Berry et al., 1996).
In the NSI data evaluation, silver is not evaluated on the basis
of AVS measurement, and exceedance of two upper thresh-
olds for aquatic life protection can classify a sampling station
as Tier 1. In the case of phthalate esthers, high concentra-
tions in samples might be an indication of contamination dur-
ing sample handling and not necessarily an indication of
sediment contamination at the sampling station.
Table 3-2 also separately identifies the number of
sampling stations categorized as Tier 1 or Tier 2 for aquatic
life effects and for human health effects. Evaluation pa-
rameters indicative of aquatic life effects include:
• Comparison of sediment chemistry measure-
ments to EPA draft sediment quality criteria
(SQCs).
• Comparison of sediment chemistry measure-
ments to other screening values (SQCs when
percent organic carbon is not reported, SQALs,
ERL/ERMs, PEL/TELs, and AETs).
• Comparison of [SEM] to [AVS].
• Results of toxicity tests.
Human health evaluation parameters included:
• Comparison of sediment chemistry TOP to EPA
risk levels or FDA tolerance/action or guide-
line levels.
• Comparison of fish tissue levels of PCBs and di-
oxin to EPA ride levels. (A sampling station can
be classified as Her 1 without corroborating sedi-
ment chemistry data.)
• Comparison of fish tissue levels to EPA risk lev-
els and FDA tolerance/action or guideline levels.
The evaluation results indicate that sediment contami-
nation associated with probable or possible but infrequent
adverse effects exists for both aquatic life and human
health. More sampling stations were classified as either
Her 1 or Tier 2 for aquatic life concerns than for human
health concerns. About 41 percent more sampling sta-
tions were classified as Tier 1 for aquatic life (3,287 sta-
tions) than for human health (2,327 stations). About 60
percent more sampling stations were classified as Tier 2
for aquatic life (9,921 stations) than were classified as
Tier 2 for human health (6,196 stations). The locations
of sampling stations classified as Tier 1 or Tier 2 for
aquatic life concerns are illustrated in Figure 3-5, and the
locations of those classified as Tier 1 or Tier 2 for human
health concerns are illustrated in Figure 3-6.
EPA analyzed the results to determine which evalua-
tion parameters most often caused sampling stations to
be classified as either Tier 1 or Tier 2 (see Table 3-3).
Most of the sampling stations classified as Tier 1 (3,283
stations) or Tier 2 (9,882 stations) were placed in those
categories because measured sediment contaminant lev-
els exceeded screening values. The comparison of fish
tissue levels of PCBs and dioxins to EPA risk levels trig-
gered placement of the second highest number of sam-
pling stations in Tier 1 (2,313 stations). The comparison
of sediment chemistry TBP values to FDA levels and EPA
risk levels triggered placement of the second highest num-
ber of sampling stations in Tier 2 (5,671 stations). The
AVS and toxicity parameters triggered placement of the
fewest sampling stations in Her 1 (8 stations each) and
Tier 2 (146 stations for AVS and 183 stations for toxic-
ity). These results reflect both data availability and evalu-
ation parameter sensitivity.
The lack of data required to apply some important
assessment parameters hampered EPA's efforts to deter-
mine the incidence and severity of sediment contamina-
tion. For example, a Tier 1 classification based on divalent
metal concentrations in sediment required an associated
acid-volatile sulfide (AVS) measurement. Also, a Tier I
classification for potential human health effects required
both sediment chemistry and fish tissue residue data for
all chemicals except PCBs and dioxins. These data com-
binations frequently were not available. Table A-2 in Ap-
pendix A presents the total number of NSI stations where
sediment chemistry data, related biological data, and
matched data (i,e., sediment chemistry and biological data
taken at the same sampling station) were collected. AVS
measurements were available at only 1 percent of the
evaluated stations. Likewise, matched sediment chemis-
try and fish tissue data were available at only 8 percent of
the evaluated stations. Toxicity data were also limited:
bioassay results were available at only 6 percent of the
evaluated stations.
To help judge the effectiveness of the NSI data evalu-
ation approach, EPA examined the agreement between
3-8
-------
-------
-------
Njilioiijil .SVcliinciH Qualify Sm^'cv
Table 3-3. Number of Sampling Stations Classified as Tier 1 and Tier 2 Based on Each Component of
the Evaluation Approach (see Table 2-2)
Measurement Parameter
Number of
Sampling
Stations In
Tier 1
Number of
Sampling
Stations in
Tier 2
Sediment chemistry values exceed draft sediment quality criteria
97
NA
ESEMHAVS] comparison
8
146
Sediment chemistry values exceed threshold values
3,283
9,882
Sediment chemistry TBP and fish tissue levels exceed risk levels or action levels
126
NA
Sediment chemistry TBP exceeds risk levels or action levels
NA
5,671
Fish tissue levels exceed risk levels or action levels
NA
2,789
Tissue levels of PCBb or dioxins exceed risk levels
2,313
NA
Toxicity test results
8
183
matched sediment chemistry and toxicity test results for
the 805 NSI sampling stations where both data types were
available and could be evaluated. The toxicity test data
indicate whether significant lethality to indicator organ-
isms occurs as a result of exposure to sediment. Tier 1
classifications for aquatic life effects from sediment chem-
istry data correctly matched toxicity test results for about
three-quarters of the sampling stations, with the remain-
der balanced between false positives (12 percent) and false
negatives (14 percent). In contrast, when Her 2 classifi-
cations from sediment chemistry data are added in, false
negatives drop to less than 1 percent at the expense of
false positives (which increase to 68 percent) and cor-
rectly matched sampling stations (which drop to 30 per-
cent). This result highlights the fact that classification in
Tier 2 is very conservative, and it does not indicate a high
probability of adverse effects to aquatic Mfe. If bioassay
test results for sublethal (chronic) endpoints such as re-
productive effects were included in the NSI evaluation,
the rate of false positives would likely decrease and cor-
rectly matched sampling stations would likely increase
for both tiers.
EPA also conducted a separate analysis of the corre-
lation of toxicity data and exceedances of SQCs and
SQALs (exclusive of other threshold values). From the
results of this study, there are 2,037 observations of a
SQC or SQAL exceedance at 916 sampling stations.
These 916 sampling stations are located in 405 distinct
RF1 reaches, which are in turn located in 218 distinct
watersheds. Matching toxicity test data are available at
39 of these 916 sampling stations. Toxicity test results
indicate that one or more SQC or SQAL exceedances are
associated with significant lethality (acute effects) to in-
dicator organisms slightly more than half of the time (22
of 39 sampling stations). SQCs and SQALs are levels set
to be protective of acute and chronic effects, such as ef-
fects on reproduction or growth, for 95 percent of benthic
species. The NSI currently does not contain matching
chronic toxicity test data to compare with sediment chem-
istry measures.
For a number of reasons, known contaminated sedi-
ment locations in the United States might not have been
classified as Her 1 or Tier 2 based on the evaluation of
NSI data. The NSI does not presently include data de-
scribing every sampled location in the Nation. There-
fore, numerous sampling stations were not evaluated for
this first report to Congress. However, additional data-
bases will be added to the NSI and more sampling sta-
tions will be evaluated for future reports to Congress.
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
(e.g., the Virginia State Water Control Board organic
chemical data reported in STORET). Databases with
obvious quality problems were not included in the NSI
data evaluation. Also, if a database included in the
NSI did not have associated locational information
(latitude/longitude), data in that database were not in-
cluded in the NSI data evaluation (e.g., EPA's Great
3-11
-------
l'imlin
Lakes Sediment Quality Database). To reduce the
chances of overlooking sampling locations that have
obvious sediment contamination problems, EPA sent
a preliminary evaluation of sediment chemistry data
to each EPA Region so knowledgeable staff would
have an opportunity to list additional contaminated
sediment locations not identified in the NSI evalua-
tion. These locations are presented at the end of this
chapter. Despite such efforts, some sediment sampling
locations known to have contamination problems still
have not been listed in this first report to Congress.
Watershed Analysis
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 mag-
nitude 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 ex-
posure are much greater. EPA examined sampling sta-
tion 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 may be more frequent. In
this report, EPA defines watersheds by 8-digit United
States Geological Survey (USGS) hydrologic unit codes
(the cataloging unit), which are roughly the size of a
county.
Watersheds containing 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 ei-
ther Tier 1 or Her 2. These dual criteria are based on
empirical observation of the data. NSI Sampling sta-
tions are located within 1,367 watersheds, or approxi-
mately 65 percent of the total number of watersheds in
the continental United States. To identify APCs, EPA first
examined the frequency distribution of the number of
Tier 1 sampling stations within these watersheds. The
upper 10 percent of watersheds with sampling stations
had 10 or more sampling stations classified as Tier 1.
Because approximately three-quarters of all sampling
stations in the nation are classified as Tier 1 or Tier 2,
EPA determined that APCs should also reflect at least
this distribution. This second requirement slightly re-
duced the number watersheds containing APCs.
The definition of "area of probable concern" v/as
developed for this report to identify watersheds for which
further study of the effects and sources of sediment con-
tamination, and possible risk reduction needs, would be
warranted. Where data have been generated through in-
tensive sampling in areas of known or suspected con-
tamination within a watershed, the APC definition should
identify watersheds which contain even relatively small
areas that are considerably contaminated. However, this
designation does not imply 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 sta-
tions 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 wa-
tershed of the size typically delineated by a USGS cata-
loging 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. 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 may both
include some watersheds with limited areas of contami-
nation and omit some watersheds with significant con-
tamination. However, given available data, EPA believes
it represents a reasonable screening analysis to identify
watersheds where further study is warranted.
The application of this procedure identified 96 wa-
tersheds that contain APCs. The location of these water-
sheds is depicted on Figure 3-7. The name and cataloging
unit number on Table 3-4 correspond to the labels on
Figure 3-7. These watersheds represent about 5 percent
of all watersheds in the continental United States (96 of
2,111). The watershed analysis also indicated that 39
percent of all watersheds in the country contain at least
one Tier 1 sampling station, 15 percent contain at least
one Tier 2 sampling station but no Tier 1 stations, and 6
percent contain all Tier 3 sampling stations (Figure 3-8).
Thirty-five percent of all watersheds in the country did
not include a sampling station.
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 one-quarter of the
watersheds in the country (488 of 2,111) met this require-
ment, and thus were eligible to contain an APC: approxi-
mately 20 percent (96 of 488) of these contain APCs.
Although a minimum amount of sampling was required
3-12
-------
-------
I'inclines
Ibble 3-4. USGS Cataloging Unit Numbers and Names for Watersheds Containing APCs
Map#
Cataloging Unit Number
Cataloging Unit Name
1
1090001
Charles
2
1090002
Cape Cod
3
1090004
Nairagansett
4
2030103
Hackensack-Passaic
5
2030104
Sandy Hook-Staten Island
6
2030105
Raritan
7
2030202
Southern Long Island
8
2040105
Middle Delaware-Musconetcong
9
2040202
Lower Delaware
10
2040203
Schuylkill
11
2040301
Muilica-Totns
12
2060003
Gunpowder-Patapsco
13
2070004
Conococheague-Opequon
14
3040201
Lower Pee Dee
15
3060101
Seneca
16
3060106
Middle Savannah
17
3080103
Lower St. Johns
18
3130002
Middle Chattahoochee-Lake Harding
19
3140102
Choctawhatehee Bay
20
3140107
Peidido Bay
21
3160205
Mobile Bay
22
4030102
Door-Kewaunee
23
4030108
Menominee
24
4030204
Lower Fox
25
4040001
Little Calumet-Galien
26
4040002
Pike-Root
27
4040003
Milwaukee
28
4050001
St. Joseph
29
4060103
Manistee
30
4090002
Lake St Clair
31
4090004
Detroit
32
4100001
Ottawa-Stony
33
4100002
Raisin
34
4100010
Cedar-Portage
35
4100012
Huron-Vermillion
36
4110001
Black-Rocky
37
4110003
Ashtabula-Chagrin
3-14
-------
National Sedinii'iil Qu^il'ilx Stir\c\
Table 3-4. (continued)
Map#
Cataloging Unit Number
Cataloging Unit Name
38
4120101
Chautauqua-Conneaut
39
4120103
Buffalo-Eighteenmile
40
4120104
Niagara
41
4130001
Oak Orchard-Twelvemile
42
4150301
Upper St. Lawrence
43
5030101
Upper Ohio
44
5030102
Shenango
45
5040001
Tuscarawas
46
5120109
Vermilion
47
5120111
Middle Wabash-Busseron
48
6010104
Holston
49
6010201
Watts Bar Lake
50
6010207
Lower Clinch
51
6020001
Middle Tennessee-Chickamauga
52
6020002
Hiwassee
53
6030001
Guntersville Lake
54
6030005
Pickwick Lake
55
6040001
Lower Tennessee-Beech
56
6040005
Kentucky Lake
57
7010206
Twin Cities
58
7040001
Rush-Vermillion
59
7040003
Buffalo-Whitewater
60
7070003
Castle Rock
61
7080101
Copperas-Duck
62
7090006
Kishwaukee
63
7120003
Chicago
64
7120004
Des Plaines
65
7120006
Upper Fox
66
7130001
Lower Illinois-Senachwine Lake
67
71401001
Cahokia-Joachim
68
7140106
Big Muddy
69
7140201
Upper Kaskaskia
70
7140202
Middle Kaskaskia
71
8010100
Lower Mississippi-Memphis
72
8030209
Deer-Steele
73
8040207
Lower Ouachita
3-15
-------
Finding
Table 3-4. (continued)
Map#
Cataloging Unit Number
Cataloging Unit Name
74
8080206
Lower Calcasieu
75
8090100
Lower Mississippi-New Orleans
76
10270104
Lower Kansas
77
11070207
Spring
78
11070209
Lower Neosho
79
12040104
Buffalo-San Jacinto
80
17010303
Coeur D'Alene Lake
81
17030003
Lower Yakima
82
17090012
Lower Willamette
83
17110002
Strait of Georgia
84
17110013
Duwamish
85
17110014
Puyallup
86
17110019
PugetSound
87
18030012
Tulare-Buena Vista Lakes
88
18050003
Coyote
89
18050004
San Francisco Bay
90
18070104
Santa Monica Bay
91
18070105
Los Angeles
92
18070107
San Pedro Channel Islands
93
18070201
Seal Beach
94
18070204
Newport Bay
95
18070301
Aiiso-San Oiiofre
96
18070304
San Diego
Figure 3-8. National Assessment: Watershed Classifications.
3-16
-------
for consideration as an APC, sampling effort alone did
not determine APC identification. In fact, other than
defining a ceiling, the total number of sampling stations
in a watershed is not indicative of the number of Tier 1
sampling stations. A simple statistical regression analy-
sis of total number of sampling stations versus number
of Tier 1 sampling stations for the nearly 500 watersheds
eligible to contain an APC (including at least 10 and up
to 200 sampling stations) resulted in a correlation coef-
ficient (R-square) of 0.44, a value which indicates a large
amount of variation.
APC designation could result from extensive sam-
pling throughout a watershed, or from intensive sampling
at a single or few contaminated locations. In compari-
son to the overall results presented in Figure 1, sampling
stations are located on an average of 46 percent of reaches
within watersheds containing APCs. On the average, 30
percent of reaches in watersheds containing APCs have
at least one Tier 1 sampling station, and 13 percent have
no Tier 1 sampling station but at least one Her 2 sam-
pling 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, 57 individual river reaches or water
body segments have 10 or more Tier 1 sampling stations
(Table 3-5). These are localized areas within the water-
shed for which an abundance of evidence indicates po-
tentially severe contamination. Because EPA's Reach File
1 was used to index the location of NSI sampling sta-
tions, some sampling stations might not actually occur
on the identified Reach File 1 stream, but on a smaller
stream that is hydrologically linked or is relatively close
to the Reach File 1 stream.
Volume 2 of this report contains more detailed in-
formation for each watershed containing an APC. This
information includes maps showing watershed bound-
aries, major waterways (RF1), and the location and clas-
sification of sampling stations. In addition, Volume 2
provides tables summarizing the sediment chemistry, fish
tissue, and toxicity test data collected within those wa-
tershed that were used for this evaluation.
Wildlife Assessment
As described in Chapter 2, EPA conducted a sepa-
rate 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 pro-
tective of wildlife (i.e., EPA wildlife criteria). The wild-
life criteria used in this evaluation were derived from
those presented in the Great Lakes Water Quality Initia-
tive Criteria Documents for the Protection of Wildlife
(USEPA, 1995a) subtracting out exposure from direct
water consumption. The only assumed route of expo-
sure for this evaluation was the consumption of contami-
nated fish tissue by wildlife.
Data were available to evaluate a total of 13,691 NSI
sampling stations using the wildlife criteria. Based on
wildlife criteria alone, 162 sampling stations would be
classified as Tier 1 (matched sediment chemistry and fish
tissue data), and 7,634 sampling stations would be clas-
sified as Tier 2 (sediment chemistry TBP or fish tissue
data). Figure 3-9 shows the location of Her 1 and Tier 2
sampling stations based on exceedance of wildlife crite-
ria. Table 3-6 presents a comparison of the sampling
stations classified as Tier 1 or Tier 2 with and without
the use of wildlife criteria. If wildlife criteria had been
used to complete the national assessment, 619 sampling
stations classified as Tier 3 would have been classified
as Tier 2 and 16 sampling stations classified as Tier 2
would have been classified as Tier 1. Most of the change
is from an increase in Tier 2 sampling stations classified
for DDT (from 2,619 to 4,276) and mercury (from 3,211
to 5,199).
Additional sampling stations would be classified as
Tier 1 or Tier 2 using wildlife criteria for two reasons:
(1) the wildlife criteria for DDT and mercury are signifi-
candy lower (8 and 19 times lower, respectively) than
the EPA risk levels used in the corresponding human
health evaluations; (2) the lipid content used in the wild-
life TBP analysis (10.31 percent for whole body) ex-
ceeded 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 mercury or dioxins wildlife criteria.
For a sampling station to be classified as Tier 1, both sedi-
ment chemistry TBP and measured fish tissue concentra-
tions 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
dioxin 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 dioxin criteria. No additional
sampling stations were classified as Her 1 for exceedance
of the wildlife criteria for mercury because sediment chem-
istry TBPs cannot be calculated for metals.
Regional and State Assessment
The remainder of this chapter presents more de-
tailed results from the evaluation of NSI data for sam-
3-17
-------
Table 3-5. River Reaches With 10 or More Tier 1 Sampling Stations Located in Watersheds Containing
APCs
EPA Region
Cataloging
Unit
Number
Cataloging Unit Name
RF1 Reach ID
RF1 Reach Name
Number of
Tierl
Stations
Total Number
of Stations in
Reach
1
01090001
Charts
01090001022
Boston Bay
72
146
01090001015
Boston Bay
42
149
01090001013
Atlantic Ocean
37
58
01090001024
Boston Bay
16
45
1
01090004
Narragansett
01090004023
Seekonk River
16
17
2
02030103
Hackensack-Passaic
02030103023
Rockaway River
26
56
2
02030104
Sandy Hook-Staten Island
02030104003
Arthur Ki
10
10
2
04120103
Biiffkb-Eghteermnile
04120103007
Buflab Creek
26
42
04120103001
Lake Brie, U.S. Store
17
22
2
04120104
Niagara
04120104007
Niagara River
12
20
2
04130001
Oak Orchard-lWelverrile
04130001001
Lake Ontario, U.S. Shore
14
27
4
03060106
Middle Savannah
03060106047
Horse Creek
10
11
4
03080103
Lower St Johns
03080103017
St Johns River
10
27
4
06010201
Walls Bar Lake
06010201026
Little River
15
23
06010201035
Tennessee River
10
12
4
06010207
Lower Clinch
06010207022
Poplar Creek
19
25
06010207021
Poplar Creek, Brushy
Fork
17
23
06010207003
Clinch River
16
20
4
06020001
Middle Temessee-Chrekamauga
06020001003
Lookout Creek
29
41
4
06030005
Pickwick Lake
06030005046
Wilson Lake
22
25.
5
04030108
Menominee
04030108001
Menominee River
10
12
5
04030204
Lower Fox
04030204001
Fox River
13
13
04030204010
Fox River
12
13
04030204004
Fox River
10
10
5
04040001
Little CahinEt-Galfcn
04040001010
Indiana Harbor
15
15
04040001006
Calumet River
12
20
5
04040002
Pike-Root
04040002002
Lake Michigan
15
33
5
04040003
Milwaukee
04040003001
Milwaukee River
48
64
5
04090004
Detroit
04090004006
Detroit River
27
38
04090004014
River Rouge
12
12
04090004011
Detroit River
11
11
04090004004
Detroit River
10
12
5
04100002
Raisin
04100002001
River Raisin
16
32
3-18
-------
Sediment (Juulity Survey
Thble 3-5. (Continued)
EPA Region
Cataloging
Unit
Number
Cataloging Unit Name
RF1 Reach ID
RF1 Reach Name
Number of
Tier 1
Stations
Total Number
of Stations in
Reach
5
07010206
Twin Cities
7010206001
Mississippi River
10
15
5
07120003
Chicago
7120003001
Chicago Sanitary Ship
Canal
35
36
7120003006
Little Calumet River
13
42
5
07120004
Des Plaines
7120004011
Des Plains River
11
20
6
08040207
Lower Ouachita
8040207005
Bayou De Siard
11
11
6
08080206
Lower Cakaseu
8080206033
Calcaseu River
13
40
8080206034
Bayou D'Inde
11
30
6
08090100
Lower Mississippi-New Orleans
8090100004
Mississippi River
13
23
9
18030012
Tblare-Buena Vista Lakes
18030012014
Kings River
10
12
9
18050004
San Francisco Bay
18050004001
San Francisco Bay
11
27
9
18070104
Santa Monica Bay
18070104003
Pacific Ocean
20
37
9
18070105
Los Angeles
18070105001
Los Angefcs River
12
31
9
18070201
Seal Beach
18070201001
Pacific Ocean
18
47
9
18070204
Newport Bay
18070204002
San Dego Creek
11
22
9
18070304
San Diego
18070304014
San Diego Bay
30
46
10
17110002
Strait of Georgia
17110002019
BeHingham Bay
13
26
10
17110013
Duwanrish
17110013003
EDiott Bay
41
100
10
17110019
Puget Sound
17110019086
Puget Sound
119
232
17110019085
Puget Sound
105
264
17110019068
Budd Inlet
41
112
17110019084
Puget Sound
32
57
17110019087
Puget Sound
32
164
17110019020
Bainbridge Island
31
88
17110019022
Sinclair Inlet
25
44
3-19
-------
LO
ro
o
Figure 3-9. Sampling Stations Classified as Tier 1 or Tier 2 Based on Wildlife criteria.
-------
Table 3-6. Increased Number of Sampling Stations Classified as Her 1 and Tier 2 by Including Wildlife
Criteria in the National Assessment"
Chemical or Chemical
Group
Number of Stations Excluding
Wildlife Assessment
Number of Stations Including
Wildlife Assessment
Her 1
Tier 2
Tier 1
Tier 2
DDT (and metabolites)
803
2,619
868
4,276
Dioxin
311
33
311
60
Mercury
1,122
3,211
1,122
5,199
PCBs
3,175
2,279
3,181
2,289
All Data
5,521
10,401
5,537
11,004
'The wildlife assessment used a default lipid content of 10.31 percent to compute the sediment chemistry TBP,
pling stations located in each of the EPA Regions and
each state. The sections that follow present the num-
ber of Tier 1» Tier 2, and Tier 3 sampling stations in
each Region and state and lists of the chemicals most
often responsible for Tier 1 and Tier 2 classifications.
Tables and figures similar to those presented in the
national assessment of sampling station evaluation re-
sults and river reach evaluation results are included.
Regional maps display the location of Tier 1 and Tier
2 sampling stations and APCs. The presentation for-
mat is identical for each Region.
These summary results are not inclusive of locations
with contaminated sediment not identified in this sur-
vey. The data compiled for the NSI are primarily from
large national electronic databases. Data from many sam-
pling and testing studies have not yet been incorporated
into the NSI. Thus, there might be additional locations
with sediment contamination that do not appear in this
summary. On the other hand, data in the inventory were
collected between 1980 and 1993 and any single mea-
surement of chemical at a sampling station, taken any
point in time during that period, could result in the clas-
sification of the sampling station in Tier 1 or Tier 2.
Because the evaluation is a screening level analysis, sam-
pling stations appearing in Tier 1 or Her 2 might not
cause unacceptable impacts. In addition, management
programs to address identified sediment contamination
might already exist.
It is important to emphasize here that some Re-
gions, such as Region 4 and Region 5, have signifi-
cantly more data in the NSI than do most other
Regions. This would, to some degree, account for the
relatively large number of sampling stations classified
as Tier 1 in these Regions.
3-21
-------
Findings
II
EPA Region. 1
Connecticut, Maine, Massachusetts, New Hampshire,
Rhode Island, Vermont
EPA evaluated 1,102 sampling stations in Region 1
as part of the NSI evaluation. Sediment contamination
where associated adverse effects to aquatic life are prob-
able (Tier 1) was found at 254 of these sampling sta-
tions, and possible but infrequent (Tier 2) at 613 of these
sampling stations. For human health, data for 44 sam-
pling stations indicated probable association with adverse
effects (Her 1), and 246 sampling stations indicated pos-
sible but infrequent adverse effects (Tier 2). Overall,
this evaluation resulted in the classification of 298 sam-
pling stations (27 percent) as Tier 1,646 (59 percent) as
Tier 2, and 158 (14 percent) as Tier 3. The NSI sam-
pling stations in Region 1 were located in 131 separate
river reaches, or 5 percent of all reaches in the Region.
Two percent of all river reaches in Region 1 included at
least one Tier 1 station, 3 percent included at least one
Tier 2 station but no Tier 1 stations, and less than one
percent had only Her 3 stations (Figure 3-10), Table 3-
7 (on the following page) presents a summary of sam-
pling station classification and evaluation of river reaches
for each state and for the Region as a whole.
Figure 3-10. Region 1: Percent of River Reaches
That Include Tier 1, Tier 2, and Iter
3 Sampling Stations.
This evaluation identified 3 watersheds containing
areas of probable concern for sediment contamination
(APCs) out of the 61 watersheds (5 percent) in Region 1
(Figure 3-11). In addition, 39 percent of all watersheds
in the Region had at least one Tier 1 sampling station but
were not identified as containing APCs, 11 percent had
at least one Tier 2 station but no Tier 1 stations, and 2
percent had only Tier 3 stations. Forty-three percent of
the watersheds in Region 1 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 3-12.
Within the three watersheds in Region 1 identified
as containing APCs (Table 3-8), 14 water bodies have at
least 1 Tier 1 sampling station; 3 water bodies have 10 or
more Tier 1 sampling stations (Table 3-9). The Massa-
chusetts Bay area appears to have the most significant
sediment contamination in Region 1. The water bodies
listed on Table 3-9 are not inclusive of all locations con-
taining a Tier 1 sampling station because only water bod-
ies within watersheds containing APCs are listed.
The chemicals most often associated with Her 1 and ,
Tier 2 sampling station classifications in Region 1 over-
all and in each state in Region 1 are presented in
Table 3-10.
At Least One
Tier 1 Station
39%
.y. / '"X
At Least One
Tier 2 Station and P
Zero Tier 1 Stations^"
11% £
11 APCs
B 5%
All Tier 3 Stations
2%
'No Data
43%
Total number of watersheds = 61
Figure 3-11. Region 1: Watershed Classifications.
At Least One
Tier 1 Station
9%
At Least One
Tier 2 Station and
Zero Tier 1 Stations
All Tier 3
Stations
<1%
3%
No Data
95%
Total number of river reaches = 2,648
3-22
-------
Table 3-7. Region 1: Evaluation Results for Sampling Stations and River Reaches by State
Station Evaluation
River Reach Evaluation^
Herl
Tier 2
Tier 3
Number of
Stations
Not
Identified
by an RF1
Reachb
Total#
Reaches
w/at
Least 1
Station
Evaluated
% of An
Reaches
inState
w/at
Least 1
Station
Evaluated
% of
Reaches
Wat
Least 1
Tierl or
Tier 2
Station
State
No.
%
No.
%
No.
%
Reaches
w/at
Least 1
Station in
Her 1
Reaches
w/at
Least 1
Station in
Tier2c
Reaches
w/All
Stations
in Tier 3
Total
Reaches
inState
Connecticut
20
20
67
68
11
11
8
16
24
4
44
215
21
19
Maine
13
24
37
67
5
9
28
9
7
2
18
1,583
1
1
Massachusetts
242
27
516
58
137
15
316
25
27
-
52
270
19
19
New Hampshire
4
57
1
14
2
29
-
2
-
2
4
279
1
1
Rhode Island
16
38
24
57
2
5
9
6
7
-
13
56
23
23
Vermont
3
60
1
20
1
20
-
3
-
-
3
355
1
1
REGION ld
298
27
646
59
158
14
361
59
65
7
131
2,648
5
5
•River reaches based on EPA River Reach File 1 (RF1).
"Stations not identified by an RF1 reach were located in coastal or open water areas.
cNo 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 Region might not equal the sum of reaches in the states.
-------
-------
Table 3-8. Region 1: Watersheds Containing Areas of Probable Concern for Sediment Contaminat
Cataloging
Unit Number
Number of Sampling
Stations
Percent of
Sampling
Stations in Tier t
or Tier 2
Name
State(s)"
Her 1
Her 2
Tier 3
01090001
Charfes
MA
195
402
111
84
01090004
Narragansett
MA, RI
28
20
0
100
01090002
Cape Cod
MA, (RI)
15
73
20
81
on
* No dati were available for states listed in parenthesis
Table 3-9. Region 1: Water Bodies With Sampling Stations Classified as Tier 1 Located in Watersheds
Containing APCs
# of Tier 1
# of Tier 1
Wiiter Body
Stations
Water Body
Stations
Boston Bay
141
Bass River
3
Atlantic Ocean
46
Potowomut River
3
Seckonk Elver
16
Conaricut Island
2
Boston Harbor and Mystic River Area
9
Pawtuxet River
2
Buzzards Bay
5
Acushnet River
1
Martha's Vineyard*
4
Charles River
1
Narragansett Bay
4
Taunton River
1
•Subsequent data review indicates these sampling stations may, in fact, be located in Buzzards Bay.
3
-------
I'ill
-------
EPA Region 2
New Jersey, New York, Puerto Rico
EPA evaluated 1,096 sampling stations in Region 2
as part of the NSI evaluation. Sediment contamination
where associated adverse effects to aquatic life are prob-
able (Tier 1) was found at 319 of these sampling sta-
tions, and possible but infrequent (Tier 2) at 523 of these
sampling stations. For human health, data for 37 sam-
pling stations indicated probable association with adverse
effects (Tier 1), and 533 sampling stations indicated pos-
sible but infrequent adverse effects (Tier 2). Overall,
this evaluation resulted in the classification of 355 sam-
pling stations (32 percent) as Tier 1, 559 (51 percent) as
Tier 2, and 182 (17 percent) as Her 3. The NSI sam-
pling stations in Region 2 were located in 292 separate
river reaches, or 17 percent of all reaches in the Region.
Seven percent of all river reaches in Region 2 included
at least one Tier 1 station, 8 percent included at least one
Tier 2 station but no Tier 1 stations, and 2 percent had
only Tier 3 stations (Figure 3-13). Table 3-11 (on the
following page) 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 12 watersheds containing
areas of probable concern for sediment contamination
(APCs) out of the 63 watersheds (19 percent) in Region
2 (Figure 3-14). In addition, 41 percent of all water-
2%
Total number of river reaches = 1,753
Figure 3-13. Region 2: Percent of River Reaches
That Include Iter 1, Tier 2, and Tier 3
Sampling Stations.
sheds in the Region had at least one Tier 1 sampling sta-
tion but were not identified as containing APCs, 30 per-
cent had at least one Tier 2 station but no Her 1 stations,
and none of the watersheds evaluated had only Her 3
stations. Ten percent percent of the watersheds in Re-
gion 2 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 3-15.
Within the 12 watersheds in Region 2 identified as
containing APCs (Table 3-12), 52 water bodies have at
least 1 Tier 1 sampling station; 9 water bodies have 10
or more Tier 1 sampling stations (Table 3-13). Several
areas in Region 2 appear to have significant sediment
contamination. They include the Niagara River, Buffalo
Creek, and Lake Erie near Buffalo, New York; Lake
Ontario between Rochester, New York, and the Niagara
River; the St. Lawrence River in the northern part of New
York; Arthur Kill in New York and New Jersey; the
Hackensack/Passaic watershed in New York and New
Jersey; the Atlantic Ocean beyond Staten Island; and oth-
ers. The water bodies listed on Table 3-13 are not inclu-
sive of all locations containing a Tier 1 sampling station
because only water bodies within watersheds containing
APCs are listed.
The chemicals most often associated with Tier 1 and
Tier 2 sampling station classifications in Region 2 over-
all and in each state in Region 2 are presented in
Table 3-14.
At Least One Tier 2 Station
and Zero Tier 1 Stations
30%
Total number of watersheds = 63
Figure 3-14, Region 2: Watershed Classifications.
3-27
-------
Table 3-11, Region 2: Evaluation Results for Sampling Stations and River Reaches by State
State
Station Evaluation
River Reach Evaluation*
Ueri
Tier 2
Her 3
Number of
Stations
Not
Identified
by an RFI
Reachk
Reaches
Wat
Least 1
Station in
Tier!
Reaches
Wat
Least 1
Station in
Her 2C
Reaches
Wall
Stations
in Tier 3
Total#
Reaches
Wat
Least 1
Station
Evaluated
Total
Reaches
in State
% of all
Reaches
inState
Wat
Least 1
Station
Evaluated
%of
Reaches
Wat
Least 1
Tier! or
Tier 2
Station
No.
%
No.
%
No.
%
New Jersey
142
32
228
51
78
17
62
59
56
14
129
285
45
40
New York
208
34
310
50
100
16
81
58
93
15
166
1,488
11
10
Puerto Rico
5
17
21
70
4
13
30
-
-
-
-
-
-
-
REGION 2d
355
32
559
51
182
17
173
116
147
29
292
1,753
17
15
•Rivet teaches based on EPA River Reach File 1 (RFI).
'Stations not identified by an RFI reach were located in coastal or open water areas,
•No stations ill these reaches were included in Her 1.
¦¦Because some reaches occur in more than one state, the total number of reaches in each category for the Region might tot equal the sum of reaches in the states.
-------
w Figure 3-15, Region 2: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing Areas of Probable Concern for
w Sediment Contamination (APCs).
?
-------
I'indinjis
Table 3-12. Region 2: Watersheds Containing Areas of Probable Concern for Sediment Contamination
Cataloging
Unit Number
Name
Statc(s)*
Number of Sampling
Stations
Percent of
Sampling
Stations in Her 1
or Tier 2
Tier 1
Her 2
"Her 3
02030104
Sandy Hook-Staten Island
NY, NJ
60
21
19
81
04120103
Bufftio-Eghteenmile
NY
59
33
9
91
02030103
Hackers ack-Passaic
NY, NJ
43
58
2
98
04130001
Oak Orchard-TVvelvsnile
NY
39
46
1
99
04120104
Niagara
NY
24
16
I
98
04120101
Chautauqua-Conneaur
NY, PA, OH
21
.. 86
3
97
04150301
Upper St Lawrence
NY
21
5
5
84
02040202
Lower Delaware
PA, NJ
18
29
10
82
02030105
Raritan
NJ
13
37
15
77
02030202
Southern Long Island
NY
11
24
8
81
02040105
Middfe Delaware-Musconetcong
PA, NJ
11
26
11
?7
02040301
Muffica-Toms
NJ
10
22
10
76
TMble 3-13. Region 2: Water Bodies With Sampling Stations Classified as Tier 1 Located in Watersheds
Containing APCs
# of Her 1
# of Tier 1
Water Body
Stations
Water Body
Stations
Lake Ontario, U.S. Shorn
31
Shrewsbury River
2
Buflhb Creek
30
Stony Bk.
2
Rockaway Riwr
26
Bass River
1
Lake Ere, U.S. Shore
24
Be den Brook
1
Atlantis Ocean
22
Big Timber Creek
1
Niagara River
21
Caasnovia Creek
1
St. Lawrence River
21
Cooper River
1
Arthur Kffl
10
Cranbury Bk.
I
Staten Island
10
Great South Bay
1
Sandy Hook Bay
8
Green Bk.
1
Delaware River
8
Hammonton C reek
1
Newark Bay
6
Matchaponix Bk.
1
Smoke Creek
6
Mistone River
1
Passak River
6
MuDica River
1
Hackensack River
5
Rahway River
1
Manasquan River
4
Rareiocas Creek, N. Br.
1
Musconetcong River
3
Raritan Bay
1
Tbnawanda Creek
3
Raritan River, N. Br.
1
Bamegat Bay
2
Raritan River, S. Br.
1
EighteereiiSe Creek
2
SB Rockaway Creek
1
Lower Bay
2
Sliinnecock Bay
1
Manalapan Bk.
2
South River
I
Moriches Bay
2
Toms River
1
Porrpton Creek
2
Wanaque Reservoir
1
Rancocas Creek, S. Br.
2
Whippany River
1
Saddb River
2
Yellow Brook
1
3-30
-------
jN;i(ion;iI Scdlfiiciif OuaJilvSur\< \
I ¦ _ I • i : ¦ ; V
Table 3-14. Region 2; Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling Station
Classifications*
Chemical
# Tier 1
& Tier 2
Stations
# Tier 1
Station
# Tier 2
Station
Chemical
# Tier 1
& Tier 2
Stations
# Tier 1
Station
# Tier 2
Station
Region 2
Overall
Copper
Lead
546
467
546
467
New Jersey
(continued)
Cadmium
Chromium
128
119
22
128
97
Nickel
443
-
443
New York
Copper
332
-
332
Polychiorinated biphenyls
442
151
291
Nickel
321
-
321
Mercury
388
144
244
Lead
268
-
268
Cadmium
360
--
360
Polychiorinated biphenyls
261
108
153
Zinc
358
--
358
Cadmium
230
..
230
DDT
351
114
237
Mercury
224
70
154
Arsenic
282
6
276
Zinc
210
_
210
Chromium
247
26
221
DDT
155
66
89
Chlordane
229
--
229
Pyrene
147
52
95
Pyrene
214
64
150
Chromium
126
4
122
Benzo(a)pyrene
180
36
144
Puerto Rico
Copper
22
-
22
Naphthalene
155
30
125
Nickel
10
-
10
Fluoranthene
151
41
110
Arsenic
9
-
9
New Jersey
DDT
195
48
147
Lead
8
-
8
Copper
192
-
192
Mercury
6
4
2
Lead
191
-
191
Zinc
5
--
5
Polychiorinated biphenyls
181
43
138
Silver
4
1
3
Mercury
158
70
88
Bis(2-ethylhexyl)phthalat
2
1
1
Arsenic
151
6
145
Diethyl phthalate
2
1
!
Zinc
143
-
143
Cadmium
2
_
2
Chlordane
139
139
'Stations may be listed for more ihao one chemical.
3-31
-------
E
imlrnjjs
EPA Region 3
Delaware, District of Columbia, Maryland, Pennsylva-
nia, Virginia, West Virginia
EPA evaluated 1,910 sampling stations in Region 3
as part of the NSI evaluation. Sediment contamination
where associated adverse effects to aquatic life are prob-
able CTier 1) was found at 86 of these sampling stations,
and possible but infrequent (Tier 2) at 915 of these sam-
pling stations. For human health, data for 239 sampling
stations indicated probable association with adverse ef-
fects (Her 1), and 222 sampling stations indicated pos-
sible but infrequent adverse effects (Tier 2). Overall,
this evaluation resulted in the classification of 318 sam-
pling stations (17 percent) as Tier 1,934 (49 percent) as
Her 2, and 658 (34 percent) as Tier 3. He NSI sam-
pling stations in Region 3 were located in 888 separate
river reaches, or 27 percent of all reaches in the Region.
Six percent of all river reaches in Region 3 included at
least one Her 1 station, 14 percent included at least one
Tier 2 station but no Tier 1 stations, and 7 percent had
only Tier 3 stations (Figure 3-16). Table 3-15 (on the
following page) 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 8 watersheds containing
areas of probable concern for sediment contamination
(APCs) out of the 128 watersheds (6 percent) in Region
3 (Figure 3-17). In addition, 63 percent of all water-
sheds in the Region had at least one Tier 1 sampling sta-
tion but were not identified as containing APCs, 22
percent had at least one Tier 2 station but no Tier 1 sta-
tions, and 5 percent had only Tier 3 stations. Four per-
cent of the watersheds in Region 3 did not include a
sampling station. The locations of the watersheds con-
taining APCs and the Tier 1 and Tier 2 sampling stations
in Region 3 are illustrated in Figure 3-18.
Within the 8 watersheds in Region 3 identified as
containing APCs (Table 3-16), 27 water bodies have at
least 1 Tier 1 sampling station; 4 water bodies have 10 or
more Tier 1 sampling stations (Table 3-17). The Dela-
ware River; the Schuykill River in Pennsylvania (near
Philadelphia); coastal areas of Lake Erie near Erie, Penn-
sylvania; and the Ohio River near Pittsburgh appear to
have some of the most significant sediment contamina-
tion in Region 3. The water bodies listed on Table 3-17
are not inclusive of all locations containing a Tier 1 sta-
tion because only water bodies within watersheds con-
taining APCs are listed.
The chemicals most often associated with Her 1 and
Tier 2 sampling station classifications in Region 3 over-
all and in each state in Region 3 are presented in
Table 3-18.
No Data/
73% /
% ;. J At Least One
ffcjgtJaTier 1 Station
HSpjf 6%
All Tier 3
Stations
7%
"yAt Least One
Tier 2 Station
and Zero Tier 1
Stations
14%
Total number of river reaches = 3,247
At Least One Her 2 Station
and Zero Tier 1 Stations
22%
Total number of watersheds = 128
Figure 3-16. Region 3: Percent of River Reaches Figure 3-17. Region 3: Watershed Classifications.
That Include Tier 1, Tier 2 and Tier 3
Sampling Stations.
3-32
-------
Table 3-15, Region 3: Evaluation Results for Sampling Stations and River Reaches by State
State
Station Evaluation
River Reach Evaluation*
Her 1
Her 2
Her 3
Number of
Stations
Not
Identified
by an RF1
Reachh
Reaches
Wat
Least 1
Station in
Herl
Reaches
Wat
Least 1
Station in
Her2«
Reaches
WaD
Stations
in Her 3
Total P
Reaches
Wat
Least 1
Station
Evaluated
Total
Reaches
inState
% of all
Reaches
inState
Wat
Least 1
Station
Evaluated
% of
Reaches
Wat
Least 1
Tier 1 or
Tier 2
Station
No.
%
No.
%
No.
%
Delaware
21
10
35
16
162
74
13
10
7
22
39
77
51
22
District of Columbia
3
75
1
25
¦
-
-
3
-
-
3
11
27
27
Maryland
50
24
68
33
88
43
29
31
36
30
97
400
24
17
Pennsylvania
127
41
106
34
78
25
4
78
27
34
139
677
21
16
Virginia .
73
7
691
66
287
27
46
61
362
112
535
1279
42
33
West Virginia
44
37
33
27
43
36
-
30
23
31
84
993
9
5
REGION 3d
318
17
934
49
658
34
92
209
453
226
888
3247
27
20
'River reaches based on EPA River Reach File 1 (RI'l).
'Stations not identified by an RF1 reach were located in coastal or open water areas.
'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 Region might not equal the sum of reaches in the states.
-------
I?
Figure 3-18, Region 3: Location of Sampling Stations Classified as Her 1 of Tier 2 and Watersheds Containing Areas of Probable Concern for
Sediment Contamination (APCs).
-------
Tfabie 3-16, Region 3; Watersheds Containing Areas of Probable Concern for Sediment Contamination
Cataloging
Unit Number
Name
State (s)*
Number of Sampling
Stations
Percent of
Sampling
Stations lit Tlerl
or Tier 2
Tlerl
Tlerl
Tier 3
04120101
Chaiteuqua-Conneaut
NY.PA.OH
21
86
3
97
02040202
Lower Delaware
PA.NJ
18
29
10
82
02060003
Gunpowder-Patapseo
MD,(PA)
17
7
5
83
02040203
Sctaytkfl
PA
12
23
9
80
05030101
Upper Oho
WyPA,OH
12
29
12
77
02040105
Middle Delaware-Musconetcong ¦
PAJSfJ
11
26
11
77
02070(X)4
Conoco che ague-Opequon
WV,VA,MD,(P-
A)
11
12
6
79
05030102
Shenango
OH,PA
11
1
3
80
•No dati were available for states listed in parentheses.
Table 3-17, Region 3: Water Bodies With Sampling Stations Classified as Her 1 Located in Watersheds
Containing APCs
# of Tier 1
# of Tier 1
W»ferBo
-------
Finding
Table 3-18, Regjon 3: Chemicals Most Often Associated With Tier 1 or Her 2 Sampling Station
Classifications*
# Tier 1
# Tier I
& Tier 2
# Tier 1
# Tier 2
& Tier 2
# Tier 1
# Tier 2
Chemical
Stations
Station
Station
Chemical
Stations
Station
Station
Region 3
Nickel
634
„
634
Maryland
Nickel
50
-
50
Overall
Copper
626
-
626
(continued)
Copper
42
--
42
Lead
626
-
626
Chromium
41
4
37
Arsenic
529
1
528
DDT
35
-
35
Zinc
371
-
371
Chlordane
33
-
33
Polychtorinated biphenyls
353
243
no
Zinc
32
--
32
Cadmium
346
-
346
Benzo(a)pyrcne
31
_
31
Mercury
320
42
278
Pennsylvania
Polychlorinated biphenyls
141
112
29
Chromium
249
12
237
Lead
87
--
87
Chlordane
161
_
161
Chlordane
81
-
81
DDT
135
9
126
Nickel
63
-
63
Dieldrin
116
-
116
Cadmium
56
_
56
Benzo(a)pyrene
106
6
100
Dieldrin
55
_
55
BHC
69
2
67
Copper
46
_
46
Dibenzo(aJi)anthraccne
64
4
60
Zinc
44
--
44
Delaware
Polychiorinated biphenyls
33
14
19
DDT
38
6
32
DDT
27
3
24
Mercury
25
3
22
Lead
24
_
24
Virginia
Copper
520
-
520
Chromium
19
2
17
Nickel
497
--
497
Arsenic
18
_
18
Arsenic
412
_
412
Nickel
15
-
15
Lead
411
-
411
BHC
13
_
13
Zinc
279
-
279
Mercury
12
3
9
Mercury
260
34
226
Benzotajpyiene
12
_
12
Cadmium
255
-
255
Copper
8
-
S
Chromium
167
3
164
District of
PolychJorinated biphenyls
4
2
2
Polychlorinated biphenyls
62
30
32
Columbia
Dioxlns
2
2
-
Benzo(a)pyrene
48
4
44
Benzo(a)pyrene
2
-
2
West Virginia
Polychlorinated biphenyls
42
41
-
Chlordane
2
-
2
Lead
35
¦: --
35
Copper
2
-
2
Chlordane
29
-
29
Dieldrin
2
_
2
Dieldrin
16
.
16
Nickel
2
..
2
Cadmium
12
-
12
Sliver
1
1
-
Copper
8
-
8
Arsenic
i
-
I
Zinc
8
..
8
Benzo(a)anthracene
1
1
HeptachJor epoxide
7
_
7
Maryland
Polychlorinated biphenyls
71
44
27
Nickel
7
-
7
Arsenic
70
_
70
Aldrin
6
-
6
Lead
68
-
68
'Stations may fee lifted for more than one chemical.
3-36
-------
EPA Region 4
Alabama, Florida, Georgia, Kentucky, Mississippi, North
Carolina, South Carolina, Tennessee
EPA evaluated 4,959 sampling stations in Region 4
as part of the NSI evaluation. Sediment contamination
where associated adverse effects to aquatic life are prob-
able (Tier 1) was found at 637 of these sampling sta-
tions, and possible but infrequent (Tier 2) at 1,888 of
these sampling stations. For human health, data for 561
sampling stations indicated probable association with ad-
verse effects (Tier 1), and 1,006 sampling stations indi-
cated possible but infrequent adverse effects (Tier 2).
Overall, this evaluation resulted in the classification of
1,157 sampling stations (23 percent) as Tier 1, 1,930 (39
percent) as Tier 2, and 1,872 (38 percent) as Tier 3. The
NSI sampling stations in Region 4 were located in 1,770
separate river reaches, or 18 percent of all reaches in the
Region. Six percent of all river reaches in Region 4 In-
cluded at least one Tier 1 station, 7 percent included at
least one Tier 2 station but no Tier 1 stations, and 5 per-
cent had only Tier 3 stations (Figure 3-19). Table 3-19
(on the following page) 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
areas of probable concern for sediment contamination
(APCs) out of the 308 watersheds (6 percent) in Region
4 (Figure 3-20). In addition, 59 percent of all water-
sheds in the Region had at least one Tier 1 sampling sta-
tion but were not identified as containing APCs, 17
percent had at least one Tier 2 station but no Tier 1 sta-
tions, and 8 percent had only Tier 3 stations. Ten per-
cent of the watersheds in Region 4 did not include a
sampling station. The locations of the watersheds con-
taining APCs and the Tier 1 and Tier 2 sampling stations
in Region 4 are illustrated in Figure 3-21.
Within the 19 watersheds in Region 4-identified as
containing APCs (Table 3-20), 65 water bodies have at
least 1 Tier 1 sampling station; 15 water bodies have 10 or
more Tier 1 sampling stations (Table 3-21). Several areas
in Region 4 appear to have potential sediment contamina-
tion. They include the Tennessee River and Lookout Creek
in Tennessee and Georgia, Wilson Lake and Mobile Bay
in Alabama, the St Johns River in Florida, and other loca-
tions. The water bodies listed on Table 3-21 are not inclu-
sive of all locations containing a Her 1 sampling station
because only water bodies within watersheds containing
APCs are listed.
The chemicals most often associated with Tier 1 and
Tier 2 sampling station classifications in Region 4 overall
and in each state in Region 4 are presented in Table 3-22.
No Data
82%
At Least One
Tier 1 Station
6%
At Least One
Tier 2 Station
and Zero Tier 1
Stations
All Her 3 7%
Stations
5%
Total number of river reaches - 9,749
Figure 3-19. Region 4; Percent of River Reaches
That Include Tier 1, Tier 2, and Tier 3
Sampling Stations.
At Least One
Tier 1 Station.
5i%
No Data
10%
Jl Tier 3 Stations
8%
At Least One Tier 2 Station
and Zero Tier 1 Stations
17%
Total number of watersheds = 308
Figure 3-20. Region 4i Watershed Classifications.
3-37
-------
Table 3-19. Region 4: Evaluation Results for Sampling Stations and River Reaches by State
State
State Evaluation
River Reach Evaluation*
Tier I
Ber2
Her 3
NimVberof
Stations
Not
Identified
by anRFl
Reach*
Reaches
Wat
Least 1
Station in
Her I
Reaches
Wat
Least I
Station in
Iter 2*
Reaches
Wall
Stations
to Her 3
Total#
Reaches
Wat
Least 1
Station
Evaluated
Total
Readies
instate
% of all
Reaches
inState
Wat
Least 1
Station
Evaluated
%of
Reaches
Wat
Least 1
Her 1 or
Her 2
Statioa
No.
%
No,
%
No.
%
Alabama
160
34
178
37
139
29
65
68
57
57
182
1,531
12
8
Florida
211
12
672
38
893
50
190
70
115
126
311
855
36
22
Georpa
115
36
100
32
103
32
3
75
57
54
186
1,658
11
8
Kentucky
69
28
131
52
49
20
-
49
60
26
135
1,247
11
9
Mississippi
54
17
142
45
122
38
61
21
47
35
103
984
11
7
Noah Carolina
71
12
294
48
247
40
22
50
156
107
313
1,415
22
15
South CatoBna
161
29
254
45
148
26
2
105
138
28
271
1,055
26
23
Tennessee
316
49
159
25
171
26
-
132
63
97
292
1,417
21
14
REGION ¥
1,157
23
1,930
39
1,872
38
343
566
684
520
1 7"»ti
. 18
13
•River reaches based on EPA River Reach FUe 1 (RF!>.
'Stations not identified by an RF1 reset were located to coastal or open water areas.
•No stations in these reaches were included in Tier 1.
^Because some reaches occur to more than one state, the total number of reaches in each category for the Region might not equal the sum of reaches in the states.
-------
Figure 3-21. Region 4; Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing Areas of Probable Concern for
Sediment Contamination (APCs).
-------
Findings.
Tbble 3-20. Region 4; Watersheds Containing Areas of Probable Concern for Sediment Contamination
Cataloging
Unit Number
Name
State(s)"
Number of Sampling
Stations
Percent of
Sampling
Stations in Tier 1
or Her 2
Tier I
Tier 2
Tier 3
06010201
Watts Bar Lake
TN
63
7
19
79
06010207
Lower Cftich
TN
61
14
4
95
06030005
Pickwick Lake
TN, AL, (MS)
49
9
11
84
06020001
Miridfc Tennessee- ChickanHuga
GA, TN, (AL)
47
29
18
81
03080103
Lower St Johns
FL
32
111
45
76
03160205
MobJb Bay
AL
31
43
7
91
06030001
GunlersvBte Lake
TN, AL, (CA)
25
46
21
77
03130002
Miridfe Chattahoochee-Lake
Harding
GA, (AL)
21
4
2
93
03060106
Middle Savannah
GA, SC
20
11
5
86
03140102
Cboctawhatchee Bay
CT
19
23
9
82
06040001
Lower Temessee-Beech
TN, (MS)
15
6
4'
84
06040005
Kentucky Lake
KY, TN'
15
14 .
1
97
08010100
Lower Mississippi-Mentha
AR, MS, KY,
MO, TN
14
3
3
85
06020002
Hlwassee
GA. NC. TN
13
17
3
91
06010104
Hofeton
TN
12
2
1
93
03040201
Lower Pee Dee
NC, SC
11
20
3
91
08030209
Deer-Steels
MS, (LA)
11
10
0
100
03060101
Seneca
NC, SC
10
3
3
81
03140107
Peidklo Bay
FL, AL
10
24
4
89
*Ko dsta were iviitiWe for stttet listed la pareothescs.
3-40
-------
Table 3-21. Region 4: Water Bodies With Sampling Stations Classified as Tier 1 Located in Watersheds
Containing APCs
# of Tier 1
# of Tier 1
Water Body
Stations
Water Body
Stations
Tennessee River
80
Cypress Creek
2
St. Johns River
30
Deer River
2
Lookout Creek
29
Long Cane Creek
2
Mobile Bay
29
Seneca River
2
Wilson Lake
27
Shoal Creek
2
Poplar Creek
21
Spring Creek
2
Clinch River
18
Twelvemile Creek
2
Choctawhatchee Bay
17
West Pont Lake
2
Guntersvilte Lake
17
Beech Creek
1
Poplar Creek, Bmsfcy Fork
17
Big Black Creek
1
Little River
1,6
Big Sandy Creek
1
Chattahoochee River
14
Chatugue Lake
1
Walts Bar Lake
14
Conecross Creek
I
Mississippi River
12
Coon Creek
1
Horse Creek
10
Hlevenmile Creek
1
Black Bayou
9
Golden Creek
1
Holston River
9
Hiwassee Lake
1
Kentucky Lake
9
Jeffries Creek
1
Savannah River
9
Lake Harding
1
Hiwassee River
8
Lake Keowee
1
Perdido Bay
7
Lake Washington
1
Melton Hill Lake
5
Lafayette Creek
1
Cherokee Lake
3
Little Horse Creek
1
Fort Loudoun Lake
3
Mountain Creek
1
Gulf Of Mexico
•3
Mud Creek
1
Reservoir
3
Notlely Lake
1
Lake Chickamauga
3
Oostanaula Creek
1
Pee Dee River
3
Pottsburg Creek
i
Pickwick Lake
3
Rogers Creek
1
Big Nance Creek
2
Sinking Creek
1
Black Creek
2
Steele Bayou
1
Catfish Creek
2
Sweetwater Creek
1
Crooked Creek
2
3-41
-------
Findings
Table 3-22. Region 4: Chemicals Most Often Associated With Her 1 or Tier 2 Sampling Station
Classifications*
# Tier 1
# Tier 1
& Tier 2
# Tier 1
# Tier 21
& Tier 2
#Tierl
# Tier 2
Chemical
Stations
Station
Station 1
Chemical
Stations
Station
Station
Region 4
Polychlorinated biphenyls
1034
669
365
Kentucky
Arsenic
65
3
62
Overall
Lead
989
_
989
(continued)
Copper
55
-
55
Copper
935
„
935
Polychlorinated biphenyls
50
48
2
Mercury
923
235
688
Zinc
43
-
43
Nickel
820
--
820
Chlordane
41
3
38
DDT
751
157
594
Dieldrin
40
' 3
37
Cadmium
751
..
751
Mercury
35
5
30
Arsenic
734
37
697
Mississippi
DDT
99
31
68
Chromium
459
26
433
Nickel
66
-
66
Zinc
438
_
438
Arsenic
63
1
62
Chlordane
374
7
367
Polychlorinated biphenyls
44
15
29
Benzo(a;pyrene
289
28
261
Cadmium
33
_
33
Pyrene
279
62
217
Chromium
32
_
32
Dieldrin
252
9
243
Lead
28
-
28
Fluoranther.e
207
34
173
Dieldrin
24
-
24
Alabama
Mercury
125
42
83
Copper
22
_
22
Arsenic
118
4
114
Senzo{a)pyren6
13
-
13
Polychlorinated biphenyls
114
98
16
North
Copper
150
150
Cadmium
103
-
103
Carolina
Mercury
133
30
103
Nickel
97
97
Lead
128
-
128
Copper
94
_
94
Nickel
99
-
99
Lead
85
_
85
Arsenic
75
_
75
DDT
76
8
68
Chromium
72
2
70
Zinc
76
_
76
Cadmium
62
62
Chromium
69
I
68
Polychlorinated biphenyls
60
28
32
Florida
Mercury
302
52
250
Zinc
45
-
45
Polychlorinated biphenyls
293
82
211
DDT
27
1
26
Lead
291
_
291
South
Lead
198
_
198
Copper
283
_
283
Carolina
DDT
188
48
140
DDT
242
48
194
Mercury
144
19
125
Cadmium
208
208
Copper
141
_
141
Benzo(a)pyrene
193
19
174
Polychlorinated biphenyls
132
93
39
Pyrene
176
30
146
Nickel
131
_
131
Arsenic
171
7
164
Cadmium
129
_
129
Chlordane
169
_
169
Chromium
63
12
51
Georgia
Polychlorinated biphenyls
111
82
29
Arsenic
62
18
44
Aisenic
62
62
Zinc
58
-
58
Cadmium
60
_
60
Tennessee
Polychlorinated biphenyls
230
223
7
Copper
60
_
60
Nickel
164
_
164
Lead
46
_
46
Lead
137
137
Chlordane
45
4
41
Mercury
134
75
59
Mercury
43
12
31
Copper
130
-
130
Nickel
38
„
38
Arsenic
118
4
114
DDT
36
11
25
Cadmium
87
-
87
Chromium
33
2
31
Zinc
83
83
Kentucky
Nickel
105
_
105
DDT
57
6
51
Lead
76
..
76
Dieldrin
52
3
49
Cadmium
69
-
69
'SuUoei may fee listed for matt than cm chemical.
3-42
-------
EPA Region. 6
Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin
EPA evaluated 4,290 sampling stations in Region
5 as part of the NSI evaluation. Sediment contamina-
tion where associated adverse effects to aquatic life are
probable (Tier 1) was found at 642 of these sampling
stations, and possible but infrequent (Tier 2) at 2,011 of
these sampling stations. For human health, data for 777
sampling stations indicated probable association with ad-
verse effects (Tier 1), and 1,469 sampling stations indi-
cated possible but infrequent adverse effects (Tier 2).
Overall, this evaluation resulted in the classification
of 1,418 sampling stations (33 percent) as Tier 1,2,137
(50 percent) as Tier 2, and 735 (17 percent) as Tier 3.
(It should be noted that the NSI includes sampling data
from the Great Lakes Sediment Inventory that, because
of a lack of latitude and longitude data, were not in-
cluded in the NSI evaluation. Had those data been
included in the NSI evaluation, an additional 221 sta-
tions would have been categorized as Tier 1, 392 as
Tier 2, and 84 as Tier 3.) The NSI sampling stations
in Region 5 were located in 1,432 separate river
reaches, or 24 percent of all reaches in the Region.
Ten percent of all river reaches in Region 5 included
at least one Tier 1 station, 10 percent included at least
one Tier 2 station but no Tier 1 stations, and 4 percent
had only Tier 3 stations (Figure 3-22). Table 3-23 (on
the following page) presents a summary of sampling sta-
tion classification and evaluation of river reaches for each
state and for the Region as a whole.
This evaluation identified 36 watersheds containing
arras of probable concern for sediment contamination
(APCs) out of the 278 watersheds (13 percent) in Re-
gion 5 (Figure 3-23). In addition, 59 percent of all wa-
tersheds in the Region had at least one Tier 1 sampling
station but were not categorized as containing APCs,
7 percent had at least one Tier 2 station but no Tier 1
stations, and 3 percent had only Tier 3 stations. Eigh-
teen percent of the watersheds in Region 5 did not in-
clude 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 3-24.
Within the 36 watersheds in Region 5 identified
as containing APCs (Table 3-24), 102 water bodies
have at least 1 Tier 1 sampling station; 18 water bod-
ies have 10 or more Tier 1 sampling stations (Table 3-
25). The Detroit River, Fox River, Milwaukee River,
Mississippi River, Chicago Ship Canal, and several
coastal areas of Lake Michigan and Lake Erie appear
to have the most significant sediment contamination
in Region 5. The water bodies listed on Table 3-25
are not inclusive of all locations containing a Tier 1
sampling station because only water bodies within
watersheds containing APCs are listed.
The chemicals most often associated with Tier 1
and Tier 2 sampling station classifications in Region 5
overall and in each state in Region 5 are presented in
Table 3-26.
No Data /
76% /
c-'.'j At Least One
WS Tier 1 Station
V-7 10%
All Tier 3
Stations
4%
' At Least One
Tier 2 Station
and Zero Tier 1
Stations
10%
Total number of river reaches = 6,025
7%
Total number of watersheds = 278
Figure 3-22. Region 5: Percent of River Reaches Figure 3-23. Region S: Watershed Classlflcations.
That Include Tier 1, Tier 2, and Tier 3
Sampling Stations.
3-43
-------
Table 3-23, Region 5: Evaluation Results for Sampling Stations and River Reaches by State
Slate
Station Evaluation
River Reach Evaluation*
•Berl
Tier 2
Her 3
Number of
Stations
Not
Identified
by anRFl
( Reach1'
Reaches
wfat
Least 1
Station in
Tier 1
Reaches
wfot
Least 1
Station in
Her 2"
Reaches
Wall
Stations
in Her 3
Total #
Readies
Wat
Least 1
Station
Evaluated
Tbtal
Reaches
inStale
%oraH
Reaches
in State
Wat
Least 1
Station
Evaluated
%Qf
Readies
Wat
Least 1
Tier lor
Tier 2
Station
No,
%
No.
%
No.
%
Hinois
428
26
1,075
64
166
W
8
182
255
30
467
920
51
48
Indiana
67
62
23
21
18
17
3
35
8
1
44
559
8
8
Michigan
219
54
144
36
39
10
20
64
41
11
116
1,145
10
9
Minnesota
220
50
65
15
153
35
-
140
34
90
264
1,355
20
13
Oto
130
13
704
73
136
14
71
56
191
57
304
1,054
29
23
Wisconsin
354
50
126
18
223
32
6
130
47
82
259
1,174
22
15
REGION 5'
1,418
33
2,137
50
735
17
108
594
570
268
1,432
6,025
24
19
*River roaches based on EPA River Reach Hie 1 (RF1).
^Stations not identified by an RF1 reach were located in coasial or open water areas.
cNo stations in these reaches were included in Her I,
^Because some reaches occur in more than oik slate, the total number of reaches in each category for the Region might not equal the sum of reaches in the slates.
-------
-------
Thble 3-24. Region 5: Watersheds Containing Areas of Probable Concern for Sediment Contamination
Cataloging
Unit Number
Number of Sanding
Stations
Percent of
Sampling
Name
State (s)*
TSep 1
Tier 2
Tier 3
or Her 2
04090004
Detroit
MI
85
29
1
99
07120003
Chicago
IN, EL.
64
36
3
97
07120004
DesPIaines
WI.IL
61
43
6
95
04040003
Milwaukee
WI
60
16
14
84
04030204
Lower Fox
WI
49
2
0
100
04040001
Little Calumst-Gafien
IL, IN, (MI) •
45
26
18
80
04040002
Pike-Root
WI, EL
34
30
8
89
07140201,
Upper Kaskaskia
IL
31
24
0
100
07010206
TWin Cities
WI, MN
26
2
7
80
04110001
Black-Rocky
OH
24
31
4
93
07140106
Big Muddy
IL
23
65
6
94
04120101
Chautauqua-Cormeaut
NY, PA, OH
21
86
3
97
07070003
Castk Rock
WI
20
0
2
91
04100002
Kaisil
MI, (OH)
18
19
1
97
07140101
Cabokia-Joachim
MO, IL
18
34
4
93
04050001
St. Joseph
IN, MI
17
9
6
81
07040003
Buffab-Whitewater
WI, MN
17
3
6
77
07080101
Copperas-Duck
IL, IA
17
5
5
81
05120111
Middle Wabash-Busseron
IN, IL
15
17
1
97
07120006
Upper Fox
WI, IL
15
40
5
92
04090002
Lake St Clair
MI
13
5
1
95
04100001
Ottawa-Stony
OH, MI
13
15
1
97
04100010
Cedar-Portage
MI, OH
13
39
4
93
07040001
Rush-Venriffion
WI, MN
13
1
0
100
07140202
Middle Kaskaskia •
IL
13
22
3
92
04030102
Door-Kewaunee
WI
12
5
3
85
04030108
Menominee
MI, WI
12
6
3
86
05030101
Upper Ohio
WV, PA, OH
12
29
12
77
05120109
\fermtBon
IL, (IN)
12
16
0
100
04060103
Manistee
MI
11
3
0
100
05030102
Shenango
OH, PA
11
1
3
80
07130001
Lower linois-Senachwine Lake
IL
11
10
0
100
04100012
Huron-\fentilkm
OH
10
35
0
100
04110003
Ashtabula-Chagrin
OH
10
18
3
90
05040001
Tbscarawas
OH
10
53
15
81
07090006
Kishwaukee
IL, (WI)
10
24
0
100
•No diu were available for states listed in parentheses.
3-46
-------
Table 3-25. Region 5: Water Bodies With Sampling Stations Classified as Tier 1 Located in Watersheds
Containing APCs
Water Body
# of Tier 1
Stations
Water Body
# of Tier 1
Stations
Detroit River
64
Becks Creek
2
Lake Erie, U.S. Shore
60
Castle Rock Flowage
2
Fox River
58
Coldwater River
2
Mississippi River
56
Crab Orchard Creek
2
Milwaukee River
55
Crooked Creek
2
Lake Michigan
45
Hickory Creek
2
Chicago Sanitary Ship Canal
41
Kaskaskia Creek, E. Fork
2
Des Plains River
27
Kaskaskia River, Lake Fork
2
Kaskaskia River
21
Lake Shelbyville
2
Calumet River
19
Little Creek
2
River Raisin
16
Portage River, E. Br.
2
Indiana Harbor
15
Ramsey Creek
2
Wisconsin River
15
Saline River
2
Wabash River
14
Vermilion River
2
Lake St. Clair
13
Barton Lake
1
Little Calumet River
13
Beaucoup Creek
1
River Rouge
13
Big Bureau Creek
1
Menominee River
12
Big Muddy River, M. Fork
1
Du Page River
9
Buffalo Creek
1
Illinois River
9
Bums Ditch
1
Cahokia Canal
8
Clark Lake
1
Manistee Lake
8
Coon River
1
Big Muddy River, Casey Fork
7
Deep River
1
Black River
7
East River
1
Crab Orchard Lake
7
Eliza Creek
1
Du Page River, E. Br.
7
Garvin Brook
1
Du Page River, W. Br.
7
Gilmore Creek
1
Grosse Isle
7
Grosse Isle
1
Lake Minnetonka
7
Hog Creek
1
St. Joseph River
7
Kaskaskia Creek, N. Fork
1
Tuscarawas River
7
Kilbourn Ditch
1
Lake Calumet
6
Killbuck Creek
1
Ashtabula River
5
Lake Creek
1
Cedar Creek
5
Lemon weir River
I
Fox Lake
5
Little Crooked Creek
1
Kishwaukee River, S. Br.
5
Little Roche A Cri Creek
1 ,
Lake Michigan,•Green Bay
5
Mill Creek
1
Chicago Ship Canal
4
Ottawa Creek
1
Root River
4
Petenwell Flowage
1
Salt Creek
4
Pigeon River
1
Vermilion River, Salt Fork
4
Piscasaw River
1
Big Muddy River
3
Rend Lake
1
Chicago River, N. Br.
3
Rocky River
1
Huron River
3
Sturgeon Bay
1
Kishwaukee River
3
Sugar Creek
1
3-
-------
I
'hidings
Table 3-25. (continued)
# of Tier 1
# of Tier 1
Water Body
Stations
Water Body
Stations
Manistee River
3
Swan Creek
I
Nimishillen Creek
3
Upper Salt Fork Drainage Ditch
I
Ohnathan Creek
3
Vermilion River, M. Fork
1
Paw Paw River
3
W Bureau Creek
1
Vermilion River, N. Fork
3
Wall Town Drainage Ditch
I
W Okaw River
3
Whitewater River
1
Table 3-26. Region 5: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling Station Classifications*
Chemical
# Tier J
& Tier 2
Stations
# Tier 1
Station
# Tier 2
Station
Chemical
# Tier 1
& Tier 2
Stations
# Tier I
Station
# Tier 2
Station
Region 5
Overall
Copper
Polychlorinated biphenyls
1,625
1,460
1,113
1,625
347
Michigan
(continued)
Nickel
DDT
198
182
97
198
85
Lead
1,326
~
1,326
Zinc
170
-
170
Dieldrin
1,318
36
1,282
Mercury
140
53
87
Nickel
1,260
-
1,260
Pyrene
140
50
90
Cadmium
1,203
-
1,203
Cadmium
140
-
140
Arsenic
1,019
32
987
Fluoranthene
133
20
113
Zinc
915
-
915
Minnesota
Polychlorinated biphenyls
225
216
¦ 9
Mercury
761
197
564
Dieldrin
88
-
88
Chlordane
723
-
723
Cadmium
66
_
66
DDT
668
Ill
491
DDT
30
-
30
Chromium
414
81
333
Copper
24
-
24
Heptachlor epoxide
338
-
338
Lead
21
-
21
Pyrene
300
103
197
Mercury
17
-
17
Fluoranthene
290
59
231
Dioxins
10
10
-
Illinois
Dieldrin
1019
33
986
Chromium
9
-
9
Copper
616
_
616
Aldrin
5
_
5
Chlordane
518
_
518
Ohio
Nickel
644
-
644
Polychlorinated biphenyls
503
318
185
Copper
577
-
577
Lead
464
-
464
Lead
472
_
472
Cadmium
460
-
460
Arsenic
459
2
457
Arsenic
380
18
362
Cadmium
420
-
420
Nickel
342
_
342
Zinc
381
_
381
Mercury
330
72
258
Mercury
125
16
109
DDT
275
36
239
Chromium
123
19
104
Indiana
Polychlorinated biphenyls
66
59
7
Fluoranthene
108
17
91
Arsenic
53
3
50
Polychlorinated biphenyls
97
65
32
Dieldrin
51
3
48
Wisconsin
Polychlorinated biphenyls
319
304
15
Chlordane
48
-
48
Copper
159
-
159
Heptachlor epoxide
42
-
42
Mercury
127
42
85
Copper
36
--
36
Lead
120
-
120
Lead
36
-
36
DDT
100
15
85
BHC
33
7
26
Cadmium
88
_
88
DDT
33
6
27
Dieldrin
76
-
76
Cadmium
29
-
29
Pyrene
62
21
41
Michigan
Polychlorinated biphenyls
250
151
99
Zinc
60
-
60
Copper
213
-
213
Nickei
54
-
54
Lead
213
-
213
'Suilosu m»y be listed for mote Ihaa one chemical.
3-48
-------
EPA Region 6
Arkansas, Louisiana, New Mexico, Oklahoma, Texas
EPA evaluated 1,616 sampling stations in Region 6
as part of the NSI evaluation. Sediment contamination
where associated adverse effects to aquatic life are prob-
able (Tier 1) was found at 222 of these sampling sta-
tions, and possible but infrequent (Tier 2) at 852 of these
sampling stations. For human health, data for 189 sam-
pling stations indicated probable association with adverse
effects (Tier 1), and 421 sampling stations indicated pos-
sible but infrequent adverse effects (Tier 2). Overall,
this evaluation resulted in the classification of 382 sam-
pling stations (24 percent) as Tier 1, 837 (52 percent) as
Tier 2, and 397 (24 percent) as Tier 3. The NSI sam-
pling stations in Region 6 were located in 799 separate
river reaches, or 11 percent of all reaches in the Region.
Three percent of all river reaches in Region 6 included
at least one Tier 1 station, 5 percent included at least one
Tier 2 station but no Tier 1 stations, and 3 percent had
only Tier 3 stations (Figure 3-25). Table 3-27 (on the
following page) 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 8 watersheds containing
areas of probable concern for sediment contamination
(APCs) out of the 403 watersheds (2 percent) in Region
6 (Figure 3-26). In addition, 36 percent of all water-
sheds in the Region had at least one Tier 1 sampling sta-
tion but were not identified as containing APCs, 21
percent had at least one Tier 2 station but no Tier 1 sta-
tions, and 10 percent had only Tier 3 stations. Thirty-
one percent of the watersheds in Region 6 did not include
a sampling station. The locations of the watersheds con-
taining APCs and the Tier 1 and Her 2 sampling stations
in Region 6 are illustrated in Figure 3-27.
Within the 8 watersheds in Region 6 identified as
containing APCs (Table 3-28), 17 water bodies have at
least 1 Tier 1 sampling station; 4 water bodies have 10 or
more Tier 1 sampling stations (Table 3-29). The
Calcasieu River and Mississippi River in Louisiana ap-
pear to have some of the most significant sediment con-
tamination in Region 6. The water bodies listed on Table
3-29 are not inclusive of all locations containing a Tier 1
sampling station because only water bodies within wa-
tersheds containing APCs are listed.
The chemicals most often associated with Her 1 or Tier
2 sampling station classifications in Region 6 overall and
in each state in Region 6 are presented in Table 3-30.
Total number of river reaches = 7.293
At Least One
Fe 1 Station
36%
At Least One -Cvv V
Tier 2 Station and iVVv
Zero Tier 1 StationsfOOvV
J APCs
21% fXVvvy^?—
2%
\ /
v J
Mo Data
All Tier 3 Stations^/
10%
31%
Total number of watersheds = 403
Figure 3-25. Region 6: Percent of River Reaches Figure 3-26. Region 6: Watershed Classifications.
That Include Tier 1, Tier 2, and
Tier 3 Sampling Stations.
3-49
-------
Table 3-27. Region 6: Evaluation Results for Sampling Stations and River Reaches by State
State
Station Evaluation
River Reach Evaluation"
Tierl
Her 2
Her 3
Number of
Stations
Not
Identified
by ail RFI
Reach"1
Reaches
Wat
Least 1
Station in
Tierl
Reaches
Wat
Least 1
Station in
Tier 2C
Reaches
Wall
Stations
in Her 3
Total#
Reaches
Wat
Least 1
Station
Evaluated
Total
Reaches
in State
% of all
Reaches
inState
Wat
Least 1
Station
Evaluated
%of
Reaches
Wat
Least 1
Tier lor
Her 2
Station
No.
%
No.
%
No.
%
Arkansas
18
17
39
36
50
47
-
17
31
40
88
855
10
6
Louisiana
111
24
270
59
79
17
57
45
68
29
142
840
17
13
New Mexico
4
4
40
40
57
56
-
4
28
28
60
919
7
3
Oklahoma
122
43
95
33
69
24
-
97
59
41
197
1,308
15
12
Texas
127
19
393
59
142
22
67
104
160
56
320
3,588
9
7
REGION
382
24
837
52
397
24
124
266
341
192
799
7,293
11
8
•River reaches based on EPA River Reach File 1 (RF1).
'Stations not identified by an RF1 reach were located in coastal or open water areas.
'No stations in these reaches were included in Her 1.
'Because some reaches occur in more than one state, the toutl number of reaches in each category for the Region might not equal the sum of reaches in the states.
-------
Figure 3-27. Region 6: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing Areas of Probable Concern for
Sediment Contamination (APCs).
-------
I
[•'inclines
Table 3-28. Region 6: Watersheds Containing Areas of Probable Concern for Sediment Contamination
Cataloging
Unit Number
Name
State(s)"
Number of Sampling
Stations
Percent of
Sampling Stations
in Tier 1 or Tier 2
Tier 1
Tier 2
Tier 3
08080206
Lower Calcasieu
LA
26
52
22
78
08090100
Lower Mississippi-New Orleans
LA
16
34
1
98
08010100
Lower Mississippi-Memphis
AR, MS, KY,
MO, TO
14
3
3
85
11070209
Lower Neosho
OK, (AR)
13
3
4
80
08040207
Lower Ouachita
LA
12
0
0
100
08030209
Deer-Steele
MS, (LA)
11
10
0
100
11070207
Spring
OK, MO, KS
10
25
6
85
12040104
BuSUo-San Jaciuo
TX
10
23
3
92
*Mo dais were available for states listed In parentheses.
Table 3-29. Region 6: Water Bodies With Sampling Stations Classified as Her 1 Located in Watersheds
Containing APCs
Water Body
# of Tier 1
Stations
Water Body
# of Tier 1
Stations
Calcasieu River
15
Neosho River
2
Mississippi River
15
Pryor Creek
2
Bayou D'lnde
11
Greens Bayou
Bayou De Siard
11
LakeEucha
1
Bufiab Bayou
5
Mississippi River, Grand Pass
1
Fort Gfcson Lake
4
Mississippi River, Pass Loutre
1
Lake Hudson
3 .
Ouachita River
1
Bosch Island
2
Spavinaw Lake
1
Galveston Bay
2
3-52
-------
National Si'diim iM Qimlilv Siir\ v\
Table 3-30. Region 6: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling Station
Classifications*
Chemical
# Tier 1
& Tier 2
Stations
# Tier 1
Station
# Tier 2
Station
Chemical
# Tier 1
& Tier 2
Stations
# Tier 1
Station
# Tier 2
Station
Region 6
Overall
Nickel
Polychlorinated biphenyls
460
434
216
460
218
Louisiana
(continued)
Dibenzo(a,h)anthracene
Lead
59
57
i
58
57
Arsenic
429
3
426
New Mexico
Copper
24
-
24
Copper
350
-
350
Cadmium
23
-
23
DDT
327
70
257
Arsenic
17
-
17
Cadmium
325
-
325
Nickel
12
-
12
Lead
297
--
297
Lead
8
-
8
Chromium
290
9
281
Zinc
6
-
6
Mercury
235
47
188
Mercury
5
3
2
Chlordane
189
4
185
Chromium
4
-
4
Silver
144
32
112
Polychlorinated biphenyls
2
2
-
Zinc
133
--
133
Chlordane
2
-
2
Dieldrin
132
10
122
Oklahoma
Polychlorinated biphenyls
135
118
17
BHC
123
16
107
Arsenic
78
1
77
Dibenzo(a,h)anthracene
122
2
120
Chlordane
73
3
70
Arkansas
Arsenic
25
-
25
Cadmium
60
-
60
DDT
23
6
17
DDT
58
7
51
Mercury
15
, 3
12
Lead
43
-
43
PolycMorinated biphenyls
14
7
7
Dieldrin
35
1
34
Lead
13
, --
13
Copper
27
-
27
Dieldrin
7
-
7
Mercury
26
3
23
Dioxins
6
6
--
Toxaphene
20
-
20
Chlordane
6
..
6
Texas
Nickel
259
-
259
Cadmium
4
--
4
Copper
185
-
185
Copper
3
--
3
Cadmium
182
-
182
Louisiana
Nickel
178
-
178
Lead
176
-
176
Arsenic
141
1
140
Arsenic
168
1
167
Chromium
132
3
129
Polychlorinated biphenyls
164
45
119
PolycMorinated biphenyls
119
44
75
Chromium
152
6
146
Copper
111
' -
111
DDT
135
31
104
DDT
110
26
84
Silver
135
30
105
SEM (est)'
75
-
75
Mercury
118
17
101
Mercury
71
21
50
'Stations may be listed for more than one chemical,
^Simultaneously extracted roeuds.
3-53
-------
rindiii^s
EPA Region 7
Iowa, Kansas, Missouri, Nebraska
EPA evaluated 1,011 sampling stations in Region 7 as
part of the NSI evaluation. Sediment contamination where
associated adverse effects to aquatic life are probable (Tier
1) was found at 32 of these sampling stations, and possible
but infrequent (Tier 2) at 242 of these sampling stations.
For human health, data for 299 sampling stations indicated
probable association with adverse effects (Tier 1), and 230
sampling stations indicated possible but infrequent adverse
effects (Tier 2). Overall, this evaluation resulted in the clas-
sification of 330 sampling stations (33 percent) as Tier 1,
393 (39 percent) as Her 2, and 288 (28 percent) as Tier 3.
The NSI sampling stations in Region 7 were located in 516
separate river reaches, or. 11 percent of all reaches in the
Region. Five percent of all river reaches in Region 7 in-
cluded at least one Tier 1 station, 4 percent included at least
one Her 2 station but no Tier 1 stations, and 2 percent had
only Tier 3 stations (Figure 3-28). Table 3-31 (on the fol-
lowing page) presents a summary of sampling station clas-
sification and evaluation of river reaches for each state and
for the Region as a whole.
This evaluation identified 5 watersheds containing
areas of probable concern for sediment contamination
Figure 3-28. Region 7: Percent of River Reaches
That Include Tier 1, Tier 2, and Tier 3
Sampling Stations.
(APCs) out of the 239 watersheds (2 percent) in Region
7 (Figure 3-29). In addition, 49 percent of all water-
sheds in the Region had at least one Tier 1 sampling sta-
tion but were not identified as containing APCs, 16
percent had at least one Tier 2 station but no Tier 1 sta-
tions, and 5 percent had only Tier 3 stations. Twenty-
eight percent of the watersheds in Region 7 did not
include a sampling station. The locations of the water-
sheds containing APCs and the Tier 1 and Tier 2 sam-
pling stations in Region 7 are illustrated in Figure 3-30.
Within the 5 watersheds in Region 7 identified as
containing APCs (Table 3-32), 12 water bodies have at
least 1 Tier 1 sampling station; 1 water body has 10 or
more Tier 1 sampling stations (Table 3-33). The water
bodies listed on Table 3-33 are not inclusive of all loca-
tions containing a Tier 1 sampling station because only
water bodies within watersheds containing APCs are
listed.
The chemicals most often associated with Tier 1 or
Tier 2 sampling station classifications in Region 7 over-
all and in each state in Region 7 are presented in
Table 3-34.
At Least One Tier 1 Station
49%
A
At Least One VvOvW /
Tier 2 Station and \^S-Syj /
Zero Tier 1 Stations yvV—j /
16 ... -i- o 01/^^3 .-^NoData
All Tier 3 Stations^1 — 28%
5%
Total number of watersheds = 239
Figure 3-29. Region 7: Watershed Classifications.
No Data
89%/^
\ At Least 1
(Tier One Station
8 5%
\
y"\7 At Least One Tier 2
^ Station and Zero
—/ Tier 1 Stations
All Tier 3 4%
Stations
2%
Total number of river reaches
= 4,857
3-54
-------
Table 3-31. Region 7: Evaluation Results for Sampling Stations and River Reaches by State
State
Station Evaluation
River Reach Evaluation"
Tier!
Tier 2
Her 3
Number of
Stations
Not
Identified
byanRFl
Reach1*
Reaches
Wat
Least 1
Station in
Her I
Reaches
Wat
Least 1
Station in
Her2c
Reaches
Wall
Stations
in Tier 3
Total#
Reaches
Wat
Least 1
Station
Evaluated
Ttrtal
Reaches
instate
% of an
Reaches
instate
Wat
Least 1
Station
Evaluated
% of
Reaches
Wat
Least 1
Her 1 or
Her 2
Station
No.
%
No.
%
No.
%
Iowa
75
33
104
46
49
21
-
61
50
19
130
1,198
11
9
Kansas
76
38
98
48
29
14
-
64
48
13
125
1,184
11
9
Missouri
124
38
98
30
105
32
-
76
32
18
126
1,364
9
8
Nebraska
55
22
93
37
105
41
-
45
62
39
146
1,265
12
8
REGION I4
330
33
393
39
288
28
-
246
182
88
516
4,857
11
9
'River reaches based on EPA River Reach File 1 (RF1).
'Stations not identified by an RFl reach were located in coastal or open water areas.
"No stations in these reaches were included in Tier I.
'Because some reaches occur in more than one state, the total number of reaches in each categoiy for the Region might not equal the sum of reaches in the states.
-------
Ul
OS
Figure 3-30. Region 7: Locations ©f Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing Areas of Probable Concern for
Sediment Contamination (APCs).
-------
Table 3-32. Region 7: Watersheds Containing Areas of Probable Concern for Sediment Contamination
Cataloging
Unit Number
Name
State(s)
Number of Sampling
Stations
Percent of
Sampling
Stations in Tier 1
or Tier 2
Tier 1
Tier 2
Her 3
07140101
Cahokia-Joachim
MO, IL
18
34
4
93
07080101
Copperas-Duck
II, IA
17
5
5
81
08010100
Lower Mississippi-Memphis
AR, MS, SCY,
MO, TN
14
3
3
85
10270104
Lower Kansas
MO, KS
12
15
2
93
11070207
Spring
OK, MO, KS
10
25
6
85
Table 3-33. Region 7: Water Bodies With Sampling Stations Classified as Tier 1 Located in Watersheds
Containing APCs
# of Tier 1
# of T!er 1
Water Body
Stations
Water Body
Stations
Mississi>pi River
17
Duck Creek
1
Kansas River
7
Joachim Creek
1
Spring River
5
Kin Creek
1
Center Creek
3
Stranger Creek
1
Cedar Creek
2
Hirkey Creek
1
Cow Creek
1
Wakarusa River
1
3-
-------
I incliiiiis
Table 3-34. Region 7: Chemicals Most Often Associated With Tier 1 or Tier 2 Sampling Station
Classifications*
# Tier 1
# Tier 1
& Tier 2
# Tier 1
# Tier 2
& Tier 2
# Tier 1
# Tier 2
Chemical
Stations
Station
Station
Chemical
Stations
Station
Station
Region?
Dieldrin
336
2
334
Kansas
Arsenic
52
—
52
Overall
Chlordane
329
_
329
(continued)
Nickel
49
-
49
Polychlorinaied biphenyls
305
291
14
Cadmium
36
-
36
Arsenic
171
_
171
Lead
34
_
34
Heptachlor epoxide
138
-
138
Chromium
27
1
26
Nickel
121
-
121
Zinc
23
_
23
Cadmium
115
-
115
Copper
20
-
20
84
-
84
Missouri
Chlordane
119
-
119
Copper
74
-
74
Polychlorinaied biphenyls
116
102
14
Chromium
50
5
45
Dieldrin
76
-
76
Dioxins
44
42
2
Heptachlor epoxide
53
-
53
Zinc
43
-
43
Arsenic
43
-
43
Bis(2-ethylhexyl)phthalal
37
9
28
Cadmium
36
-
36
DDT
33
_
33
Lead
33
-
33
AJdrin
31
-
31
Dioxins
31
29
2
Iowa
Dieldrin
126
2
124
Nickel
29
~
29
Chlordane
91
-
91
Copper
27
_
27
Polychlortoated biphenyls
71
71
-
Nebraska
Dieldrin
72
-
72
Heptachlor epoxide
54
-
54
Chlordane
52
-
52
Arsenic
34
_
34
Polychlorinaied biphenyls
50
50
-
Copper
17
-
17
Arsenic
42
-
42
Cadmium
14
-
14
Cadmium
29
-
29
Nickel
14
-
14
Nickel
29
-
29
EOT
12
-
12
Chromium
17
2
15
Lead
10
-
10
Aldrin
13
-
13
Kansas
Polychlorinaied biphenyls
68
68
-
Heptachlor epoxide
12
-
12
Chlordane
67
-
67
Bis(2-ethylhexyi)phthalat
10
4
6
Dieldrin
62
-
62
*5Ut!oet be lilted for more thus one chemical.
3-58
-------
EPA Region 8
Colorado, Montana, North Dakota, South Dakota, Utah,
Wyoming
EPA evaluated 535 sampling stations in Region 8 as
part of the NSI evaluation. Sediment contamination
where associated adverse effects to aquatic life are prob-
able (Tier 1) was found at 39 of these sampling stations,
and possible but infrequent (Tier 2) at 325 of these sam-
pling stations. For human health, data for 29 sampling
stations indicated probable association with adverse ef-
fects (Tier 1), and 19 sampling stations indicated pos-
sible but infrequent adverse effects (Tier 2). Overall,
this evaluation resulted in the classification of 68 sam-
pling stations (13 percent) as Tier 1,327 (61 percent) as
Tier 2, and 140 (26 percent) as Tier 3. The NSI sam-
pling stations in Region 8 were located in 305 separate
river reaches, or 2 percent of all reaches in the Region.
Less than 1 percent of all river reaches evaluated in Re-
gion 8 included at least one Tier 1 station, 1 percent in-
cluded at least one "Her 2 station but no Tier 1 stations,
and less than 1 percent had only Tier 3 stations (Figure
3-31). Table 3-35 (on the following page) presents a
summary of sampling station classification and evalua-
Figure 3-31. Region 8: Percent of River Reaches
That Include Her 1, Tier 2, and Tier 3
Sampling Stations.
tion of river reaches for each state and for the Region as
a whole.
None of the 385 watersheds in Region 8 were iden-
tified as watersheds containing areas of probable con-
cern for sediment contamination. Fourteen percent of
all watersheds in the Region had at least one Tier 1 sam-
pling station, 12 percent had at least one Tier 2 station
but no Tier 1 stations, and 9 percent had only Her 3 sta-
tions (Figure 3-32). Sixty-five percent of the watersheds
in Region 8 did not include a sampling station. The lo-
cations of the Tier 1 and Tier 2 sampling stations in Re-
gion 8 are illustrated in Figure 3-33.
Lack of multiple sampling site data did not allow
identification of any watersheds in Region 8 as contain-
ing APCs. Therefore, specific water bodies with Tier 1
sampling stations are not listed in a separate table, as for
other Regional summaries.
The chemicals most often associated with Tier 1 or
Tier 2 sampling station classifications in Region 8 over-
all and in each state in Region 8 are presented in
Table 3-36.
Total number of watersheds = 385
Figure 3-32. Region 8: Watershed Classifications.
No Data
98% \
\ At Least One
\T»r 1 Station
\ <1%
\ /
j /At Least One Her 2
— Station and Zero
j\ Tierl Stations
/ \ 1%
/All Tier 3
/ Stations
<1%
Total number of river reaches =
13,492
3-59
-------
Table 3-35. Region 8: Evaluation Results of NSI Sampling Stations and River Reaches by State
State
Station Evaluation
River Reach Evaluation^
Tier 1
Her 2
Tier 3
Number of
Stations
Not
Identified
by anRFl
Reactf*
Reaches
Wat
Least 1
Station in
TIerl
Reaches
Wat
Least 1
Station in
Tier?
Reaches
Wall
Stations
in Tier 3
Tbtal#
Reaches
Wat
Least 1
Station
Evaluated
Total
Reaches
instate
% of all
Reaches
instate
Wat Least
1 Station
Evaluated
%of
Reaches
Wat
Least 1
Her lor
Her 2
Station
No.
%
No.
%
No.
%
Colorado
11
6
140
69
51
25
-
8
73
34
115
2,178
5
4
Montana
9
24
18
47
11
29
-
9
10
8
27
5,490
1
<1
North Dakota
24
15
112
70
25
15
-
22
36
9
67
992
7
6
South Dakota
13
30
21
49
9
21
-
11
6
7
24
1,611
2
1
Utah
7
15
24
51
16
34
-
7
16
10
33
1,034
3
2
Wyoming
4
9
12
27
28
64
-
4
12
25
41
2,421
2
1
REGION 8d
68
13
327
61
140
26
-
61
153
91
305
13,492
2
2
'River reaches based on EPA River Reach File 1 (RF1).
^Stations not identified by an RF1 reach were located in coastal or open water areas.
®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 Region might not equal the sum of reaches in the states.
-------
-------
I'in
-------
Naliomil Sediment Qiiiilily Survey
EPA Region 9
Arizona, California, Hawaii, Nevada
EPA evaluated 1,699 sampling stations in Region 9
as part of the NSI evaluation. Sediment contamination
where associated adverse effects to aquatic life are prob-
able (Tier 1) was found at 433 of these sampling sta-
tions, and possible but infrequent (Tier 2) at 894 of these
sampling stations. For human health, data for 40 sam-
pling stations indicated probable association with adverse
effects (Tier 1), and 765 sampling stations indicated pos-
sible but infrequent adverse effects (Tier 2), Overall,
this evaluation resulted in the classification of 468 sam-
pling stations (28 percent) as Tier 1,942 (55 percent) as
Her 2, and 289 (17 percent) as Tier 3. The NSI sam-
pling stations in Region 9 were located in 254 separate
river reaches, or 6 percent of all reaches in the Region.
Three percent of all river reaches in Region 9 included
at least one Tier 1 station, 2 percent included at least one
Her 2 station but no Tier 1 stations, and 1 percent had
only Tier 3 stations (Figure 3-34). Table 3-37 (on the
following page) 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
areas of probable concern for sediment contamination
Figure 3-34. Region 9: Percent of River Reaches
That Include Tier 1» Tier 2, and Tier 3
Sampling Stations.
(APCs) out of the 279 watersheds (4 percent) in Region
9 (Figure 3-35). In addition, 22 percent of all water-
sheds in the Region had at least one Tier 1 sampling sta-
tion but were not classified as containing APCs, 10
percent had at least one Tier 2 station but no Tier 1 sta-
tions, and 5 percent had only Tier 3 stations. Fifty-nine
percent of the watersheds in Region 9 did not include a
sampling station. The locations of the watersheds con-
taining APCs and the Tier 1 and Her 2 sampling stations
in Region 9 are illustrated in Figure 3-36.
Within the 10 watersheds in Region 9 identified as
containing APCs (Table 3-38), 19 water bodies have at
least 1 Tier 1 sampling station; 7 water bodies have 10 or
more Tier 1 sampling stations (Table 3-39). San Diego
Bay, San Francisco Bay, and offshore areas around San
Diego and Los Angeles appear to have the most signifi-
cant sediment contamination in Region 9. The water bod-
ies listed on Table 3-39 are not inclusive of all locations
containing a Tier 1 sampling station because only water
bodies within watersheds containing APCs are listed.
The chemicals most often associated with Tier 1 or
Tier 2 sampling station classifications in Region 9 over-
all and in each state in Region 9 are presented in
Table 3-40.
At Least One Tier 2 Station
and Zero Tier 1 Stations
All Tier 3 Stationsx^^vfP^ ?
At Least One
feaJier 1 Station
^X22%
_^^tdAPCs
4%
No Data"-^,^
59%
Total number of watersheds = 279
Figure 3-35. Region 9: Watershed Classifications.
No Data
84%
At Least 1 Tier One
Station
3%
At Least One Tier 2
- Station and Zero
Tier 1 Stations
All Tier 3 2%
Stations
1%
Total number of river reaches = 4,601
3-63
-------
Table 3-37, Region 9: Evaluation Results for NSI Sampling Stations and River Reaches by State
Station Evaluation
River Reach Evaluation'
Herl
Her 2
Her 3
Number of
Stations
Not
Identified
by an RFI
Reach*
Total#
Reaches
Wat
Least 1
Station
Evaluated
% of all
Reaches
Instate
Wat
Least 1
Station
Evaluated
%or
Reaches
Wat
Least 1
Herl or
Her 2
Station
State
No.
%
No.
%
No.
%
Reaches
Wat
Least 1
Station In
Herl
Readies
Wat
Least 1
Station In
Her 2s
Reaches
Wall
Stations
In Tier 3
Total
Readies
IttState
Arizona
44
35
58
47
22
18
-
30
33
11
74
1,146
7
5
Califomia
392
27
822
57
229
16
758
75
44
26
145
2,606
6
5
Hawaii
8
22
23
64
5
14
36
-
-
-
-
-
-
-
Nevada
24
25
39
41
33
34
-
16
15
6
37
916
4
3
REGION 9"
468
28
942
55
289
17
794
119
92
43
254
4,601
6
5
•River reaches based on EPA River Reach File 1 (RFI),
'Stations not identified by an RFI reach were located in coastal or open water areas,
'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 Region might not equal the sum of reaches in the states.
-------
w Figure 3-36. Region 9: Location of Sampling Stations Classified as Tier 1 or Tier 2 and Watersheds Containing Areas of Probable Concern
gj for Sediment Contamination (APCs).
-------
Dable 3-38. Region 9; Watersheds Containing Areas of Probable Concern for Sediment Contamination
Cataloging
Unit Number
Name
State(s)
Number of Sampling
Stations
Percent of
Sampling
Stations In Tier 1
or Her 2
Tier 1
Tier 2
Her 3
18070104
Sana Monfca Bay
CA
79
31
22
83
18070201
Seal Beach
CA
63
339
40
91
18070304
San Diego
CA
53
51
3
97
18070204
Newport Bay
CA
24
68
16
85
18050004
SanFransfeco Bay
CA
19
37
8
88
18050003
Coyote
CA
18
6
0
100
18070105
Los Angebs
CA
14
19
4
89
18070107
Sail Pedro Channel Islands
CA
14
10
1
96
18030012
TUIare-Buena Vista Lakes
CA
10
5
5
75
18070301
AlisoSan Onofre
CA
10
22
0
100
liable 3-39, Region 9: Water Bodies With Sampling Stations Classified as Tier 1 Located in Watersheds
Containing APCs
# of Tier 1
# of Tier 1
Water Body
Stations
Water Body
Stations
Pacifis Ocean
178
Corte Madera Creek
2
San Diego Bay
32
Los Gates Creek
2
San Francisco Bay
19
Coyote Creek
1
Los Angeles River
14
Lcxitgton Reservor
1
Santa Catalna Island
14
Oso Creek
1
San Diego Creek
12
Peters Canyon Wash
1
Kiigs River
10
San Diego River
1
AJanAos Creek
8
San Juan Creek
1
Cakto Reservoir
4
Sweetwater River
1
Also Creek
2
3-66
-------
N;i(iun;il Si'diimnl Oulililv Sur\o\
Table 3-40. Region 9: Chemicals Most Often Associated with Her 1 or Her 2 Sampling Station
Classifications*
Chemical
# Tier I
& Tier 2
Stations
# Tier 1
Station
# Tier 2
Station
Chemical
# Tier 1
& Tier 2
Stations
# Tier 1
Station
# Tier 2
Station
Region 9
Overall
Copper
DDT
678
675
179
678
496
California
(continued)
Cadmium
Nickel
406
373
—
406
373
Arsenic
455
12
443
Arsenic
357
3
354
Nickel
454
-
454
Mercury
336
103
233
Cadmium
446
-
446
Bis(2-ethylhexyl)phthalat
264
48
216
Polychlorinated biphenyls
445
100
345
Lead
253
-
253
Mercury
403
134
269
Chromium
239
40
199
Lead
314
-
314
Hawaii
Nickel
20
-
20
Bis(2-ethylhexyl)phthalat
302
69
233
Copper
19
-
19
Chromium
265
42
223
Mercury
16
4
12
Zinc
238
-
238
Arsenic
16
1
15
Silver
209
23
186
Lead
14
_
14
BHC
164
9
155
Zinc
13
-
13
Benzo(a)pyrene
158
6
152
DDT
10
2
8
Dieldrin
125
--
125
Chromium
10
1
9
Arizona
Copper
72
¦-
72
Polychlorinated biphenyls
8
3
5
Arsenic
55
8
47
Cadmium
8
-
8
Nickel
50
50
Nevada
Mercury
29
15
14
Lead
37
--
37
Arsenic
27
-
27
Zinc
28
28
Copper
14
-
14
Bis(2-ethylhexyl)phthalat
26
15
11
Nickel
11
-
11
Cadmium
24
-
24
Zinc
11
_
11
DDT
23
9
14
Lead
10
-
10
Mercury
22
12
10
Polychlorinated biphenyls
9
4
5
Silver
15
7
8
B i s(2-ethy lhexy l)phthalat
8
4
4
California
DDT
640
168
472
Cadmium
8
-
8
Copper
573
-
573
Chlordane
8
-
8
Polychlorinated biphenyls
418
87
331
'Stations may be listed for more than one chemical.
3-67
-------
Findings
EPA Region 10
Alaska, Idaho, Oregon, Washington
EPA evaluated 2,878 sampling stations in Region
10 as part of the NSI evaluation. Sediment contamina-
tion where associated adverse effects to aquatic life are
probable (Tier 1) was found at 623 of these sampling
stations, and possible but infrequent (Her 2) at 1,658 of
these sampling stations. For human health, data for 112
sampling stations indicated probable association with ad-
verse effects (Her 1), and 1,285 sampling stations indi-
cated possible but infrequent adverse effects (Tier 2).
Overall, this evaluation resulted in the classification of
727 sampling stations (25 percent) in Region 10 as Tier
1, 1,696 (59 percent) as Tier 2, and 455 (16 percent) as
Tier 3. The NSI sampling stations in Region 10 were
located in 393 separate river reaches, or 4 percent of all
reaches in the Region. One percent of all river reaches
in Region 10 included at least one Tier 1 station, 2 per-
cent included at least one Her 2 station but no Tier 1
stations, and 1 percent had only Tier 3 stations (Figure
3-37). Table 3-41 (on the following page) presents a
summary of sampling station classification and evalua-
tion of river reaches for each state and for the Region as
a whole.
No Data
86%/
\ At Least One
\ Her 1 Station
\/ 2%
At Least One Her 2
s3r~ Station and Zero
A Tier 1 Stations
TUfTler 3 1%
Stations
1%
Total number of river reaches = 10,178
Figure 3-37. Region 10: Percent of River Reaches
That Include Tier 1, Tier 2, and Tier 3
Sampling Stations.
This evaluation identified 7 watersheds containing
areas of probable concern for sediment contamination
(APCs) out of the 219 watersheds (3 percent) in Region
10 (Figure 3-38). In addition, 28 percent of all water-
sheds in the Region had at least one Tier 1 sampling sta-
tion but were not categorized as containing APCs, 14
percent had at least one Tier 2 station but no Tier 1 sta-
tions, and 6 percent had only Tier 3 stations. Forty-nine
percent of the watersheds in Region 10 did not include a
sampling station. The locations of the watersheds con-
taining APCs and the Tier 1 and Tier 2 sampling stations
in Region 10 are illustrated in Figure 3-39.
Within the 7 watersheds in Region 10 identified as
containing APCs (Table 3-42), 34 water bodies have at
least 1 Tier 1 sampling station; 8 water bodies have 10 or
more Tier 1 sampling stations (Table 3-43). Puget Sound
appears to have the most significant sediment contami-
nation in Region 10. The water bodies listed on Table 3-
43 are not inclusive of all locations containing a Tier 1
sampling station because only water bodies within wa-
tersheds containing APCs are listed.
The chemicals most often associated with Tier 1 or
Tier 2 sampling station classifications in Region 10 over-
all and in each state in Region 10 are presented in
Table 3-44.
At Least One
Tier 2 Station and ^
Zero Tier 1 Stations, ¦/2v'
14% AJv\
At Least One .
Tier 1 Station
"^28%
\
All Tmr 3 Stations
fi% | —
3%
No Data
49%
Total number of watersheds = 219
Figure 3-38. Region 10: Watershed Classifications.
3-68
-------
Table 3-41, Region 10: Evaluation Results for NSI Sampling Stations and River Reaches by State
State
Station Evaluation
River Reach Evaluation*
Her 1
Tier 2
Tier 3
Number of
Stations
Not
Identified
by an RF1
Reachb
Reaches
Wat
Least 1
Station in
Tier 1
Reaches
Wat
Least 1
Station in
Her 2*
Reaches
Wall
Stations
teller 3
Total#
Reaches
Wat Least
1 Station
Evaluated
Total
Reaches
instate
% of all
Reaches
in State
Wat Least
1 Station
Evaluated
% of
Reaches
Wat
Least 1
Tkri or
Tier 2
Station
No.
%
No.
%
No.
%
Alaska
21
8
191
71
55
21
267
-
-
-
-
-
-
-
Idaho
43
•45
36
38
16
17
-
30
16
7
53
3,227
2
1
Oregon
81
28
158
54
52
18
2
45
43
25
113
4,203
3
2
Washington
582
26
1,311
59
332
15
228
75
115
40
230
2,924
8
6
REGION 10"
727
25
1,696
59
455
16
497
147
174
72
393
10,178
4
3
¦River reaches based on EPA River Reach File 1 (RF1).
•Stations not identified by 211RF1 reach were located in coastal or open water areas,
cNo stations in these reaches were included to Tier 1.
'Because some reaches occur in more than one state, the total number of reaches in each category for the Region might not equal the sum of reaches in the states.
-------
Figure 3-39, Region 10; Location of Sampling Stations Classified as Tier 1 or Her 2 and Watersheds Containing Arras of Probable Concern for
Sediment Contamination (APCs),
-------
Table 3-42. Region 10: Watersheds Containing Areas of Probable Concern for Sediment Contamination
Cataloging
Unit Number
Name
State(s)*
Number of Sampling
Stations
Percent of
Sampling
Stations in Tier 1
or Iter 2
fieri
Tier 2
Tier 3
17110019
Puget Sound
WA
418
851
114
92
17110013
Duwarrish
WA
48
69
10
92
17110002
Strait Of Georgia
WA
32
168
63
76
17030003
Lower Yakima
WA
23
19
5
89
17090012
Lower Willamette
OR
21
51
4
95
17110014
PuyallH>
WA
12
6
1
95
17010303
Coeur D'Afcne Lake
ID, (WA)
10
13
0
100
*No data were available for states listed in parentheses.
Table 3-43. Region 10: Water Bodies With Sampling Stations Classified as Tier 1 Located in Areas of
Probable Concern for Sediment Contamination
Water Body
# of Tier 1
Stations
Water Body
# of Tier 1
Stations
Puget Sound
306
Lake Whatcom
2
Budd Inlet
41
Samnish Bay
2
Elliot Bay
41
Samnish River
2
Bainbridg: Island
31
Whidbey Island
2
Sinclair Inlet
28
Spring Creek
2
Bellingham Bay
,22
Thonpson Lake
2
Yakima River
19
Ahtanum Creek
1
Willamette River
10
C amino Island
I
Carbon River
8
Duwanish Waterway
Columbia Slough
S
Fidalgo Island
1
Green River
6
Padden Lake
1
Coeur D'aiene Lake
4
Port Orchard
1
Dyes Inlet
4
Pott Susan
1
Puyallup River
4
Spanaway Lake
1
Coeur D'aiene River
3
Toppenish Creek
1
Johnson Creek
3
White Hall Creek
1
Chambers Creek
2
Wolf Lodge Creek
1
3-71
-------
Finding's
Ihble 3-44. Region 10: Chemicals Most Often Associated with Tier 1 or Tier 2 Sampling Station
Classifications*
Chemical
#Tier 1
& Tier 2
Stations
# "fieri
Station
# Tier 2
Station
Chemical
# Tier 1
& Tier 2
Stations
# Tier 1
Station
# Tier 2
Station
Region 10
Overall
Copper
Nickel
1,518
1,409
:
1,518
1,409
Idaho
{continued)
Cadmium
Copper
29
28
1IIU
29
28
Arsenic
1,231
55
1,176
Zinc
28
--
28
Lead
881
_
881
DDT
25
-
25
Benzo(a)pyrene
803
103
700
Dieldrin
21
-
21
Pyrcne
770
160
610
Toxaphene
14
--
14
Mercury
760
133
627
Silver
11
8
3
Cadmium
754
-
754
Oregon
Copper
125
_
125
Polychlorinated biphenyls
710
289
421
Nickel
107
-
107
Dibcnzo(a,h)anthracene
709
245
464
Arsenic
86
1
85
Chiysene
704
86
618
Polychlorinated biphenyls
84
46
38
Benzo(a)anthracene
669
107
562
DDT
73
19
54
Naphthalene
589
104
485
Zinc
59
--
59
Fluorcne
547
77
470
Mercury
53
7
46
Chromium
546
17
529
Cadmium
51
--
51
Alaska
Chromium
135
12
123
Chromium
46
3
43
Arsenic
89
-
89
Lead
44
-
44
Copper
50
-
50
Washington
Copper
1,315
--
1,315
Nickel
41
-
41
Nickel
1,256
--
1,256
Cadmium
35
_
35
Arsenic
1,017
41
^ 976
Naphthalene
31
2
29
Lead
788
-
788
Polychlorinated biphenyls
29
2
27
Benzo(a)pyrene
754
101
653
Zinc
29
-
29
Pyrene
735
156
579
Phenanthrene
26
-
26
Mercury
683
121
562
Fluorcne
22
-
22
Chrysene
682
83
599
Idaho
Arsenic
39
13
26
Dibenzo(a,h)anthracene
681
240
441
Polychlorinated biphenyls
32
28
4
Benzo(a)anthracene
646
104
542
Lead
32
-
32
*Sutiofti rtuy be listed for more than one chemical.
3-72
-------
Potentially Highly Contaminated
Sites Not Identified by the NSI
Evaluation
Several Regions and states provided comments on
the May 16, 1994, preliminary evaluation of sediment
chemistry data contained in the NSI. They identified
receiving streams that should have been but were not
identified as locations of potential adverse effects, based
on the NSI data evaluation. The specific water bodies
that reviewers of the preliminary evaluation identified
as potentially contaminated, but which are not presently
included in the NSI because data are inadequate to cat-
egorize sampling stations as Her 1, are presented in Table
3-45 and Figure 3-40. If a water body had previously
been identified as having at least one Tier 1 sampling
station using the NSI evaluation methodology, it was not
included in Table 3-45 or Figure 3-40,
Water Body
EPA Region
State
Chemicals Potentially Present
Onandaga Lake
2
NY
pesticides, metals, PAHs, PCBs
Ley Creek
2
NY
mercury
Kill van Kull
2
NY
metals, dioxin
Newtown Creek
2
NY
PAHs
Sc^jaquada Creek
2
NY
metals, PCBs
Skaneateles Creek
2
NY
PCBs
Hudson River
2
NY
PCBs
Southern reaches of the Maurice River
2
NJ
arsenic
Elizabeth River
3
VA
PAHs
James River
3
¥A
kepone
Anacostia River
' 3
DC
chlordane, PCBs
Lake 0' the Pines
6
TX
lead, zinc
Linneville Bayou
6
TX
lead, chromium
Humboldt River Basin
9
NV
selenium
Dry Lake
9
AZ
dioxin
Table 3-45. Potentially Highly Contaminated Sites Not Identified in the NSI Evaluation
3-73
-------
Figure 3-40, Location of Potentially Highly Contaminated Water Bodies Not Identified in the NSI Evaluation.
-------
Chapter 4
Pollutant Sources
Toxic chemicals that accumulate in sediment and
are associated with contamination problems
enter the environment from a variety of sources.
These sources can be broadly differentiated as point sources
and nonpoint sources. The term "point source" is defined
in 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
diffuse sources, such as urban areas or agricultural fields,
that tend to deliver pollutants to surface water during and
after rainfall events. Some sources, such as landfills and
mining sites, ate difficult to categorize as either a point or
nonpoint source. Although these land areas represent dis-
crete sources, pollution from such areas tends to result from
rainfall runoff and leaching. Likewise, atmospheric depo-
sition of pollutants, generally considered to be a nonpoint
source of water pollution, arises from the emission of chemi-
cals from discrete stationary and mobile source points of
origin. The CWA specifies water vessels and other float-
ing 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 water-
ways from municipal sewage treatment and industrial
facilities requires a permit under the CWA. Many states
have been authorized to issue permits in lieu of EPA.
These permits contain technology-based and water qual-
ity-based pollutant discharge limits and monitoring re-
quirements. More recently, replacement of aging
combined sewer systems and other storm water control
measures has addressed the discharge of pollutants from
urban areas through municipal facilities. The disposal
of sediment dredged to maintain navigation channels is
managed under both the CWA and the Marine Protec-
tion, 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 station-
ary 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 water bodies and accumulate in sediment. The
Toxic Substances Control Act (TSCA) and Federal In-
secticide, Fungicide, and Rodenticide Act (FIFRA) have
greatly reduced the toxic pollutant input to the environ-
ment through bans and use restrictions on many pesti-
cides and industrial-use chemicals.
Federal, state, and local laws have also addressed
land-based pollutant sources. Under the Resource Con-
servation and Recovery Act (RCRA), the transport, stor-
age, and disposal of pollutants in landfills and other
repositories of hazardous waste are tracked and con-
trolled. At sites where past disposal practices, either
purposeful or accidental, have resulted in severe con-
tamination, remediation has been undertaken under the
federal Superfund laws. Where applicable, land devel-
opment projects may be subject to an assessment of the
environmental impact conducted under National Envi-
ronmental Policy Act (NEPA) authority. Under the au-
thority of the Coastal Zone Management Act (CZMA),
EPA has developed nonregulatory management measures
to reduce pollutant delivery via nonpoint sources, such
as runoff from urban and agricultural areas.
The combined impact of these actions has yielded
improvements in water quality. In at least some docu-
mented cases, pollutant levels in sediment are also de-
creasing. (For example, see the discussion of the Palos
Verdes case study presented in Chapter 5.) However,
improvement in sediment quality might lag behind im-
provement in overlying water because of the persistent
nature of many pollutants, as well as the storage and
sink functions of sediment, and because the most toxic
bioaccumulative pollutants are difficult to monitor and
regulate. It is beyond the scope of this baseline assess-
ment to determine the temporal trends of pollutant con-
centrations in sediment on a national scale. Future
reports to Congress will address that issue.
Natural recovery of contaminated sediment can oc-
cur through source reduction, contaminant degradation,
and continuing deposition of clean sediment. The fea-
sibility of natural recovery, as well as the long-term suc-
cess of remediation projects, depends on the effective
control of pollutant sources. For some classes of sedi-
ment contaminants, such as PCBs and organochlorine
4-1
-------
I'ottnUmt .Sources
pesticides, use and manufacture bans or severe restric-
tions have been in place for many years. Past disposal
and use of PCBs continue to result in evaporation of
these contaminants from some landfills and leaching
from soils, but most active PCB sources have been con-
trolled. The predominant sources of organochlorine pes-
ticides are runoff and atmospheric deposition from past
applications on agricultural land, and occasional dis-
charge from municipal treatment facilities. For other
classes of sediment contaminants, active sources con-
tinue to contribute substantial environmental releases.
For example, liberation of inorganic mercury from fuel
burning and other incineration operations continues,
as do urban runoff and atmospheric deposition of met-
als and PAHs. In addition, discharge limits for munici-
pal and industrial point sources are based on
technology-based limits and state-adopted standards for
protection of the water column, not necessarily for down-
stream protection of sediment quality. Determining the
local and far-field effects of individual point and
nonpoint sources on sediment quality usually requires
site-specific study.
The purposes of this chapter are to:
• Present the extent of sediment contamination
by chemical class in the 96 watersheds identi-
fied as areas of probable concern for sediment
contamination (APCs).
• Identify the major source categories of these
chemical classes and summarize key studies
that link these source categories to sediment
contamination.
• • Analyze land use patterns and the extent of
sediment contamination by chemical class in
the 96 APCs.
• Briefly describe current EPA efforts to further
characterize point and nonpoint sources of sedi-
ment contaminants.
Extent of Sediment Contamination
by Chemical Class
The individual chemicals evaluated for this re-
port can be grouped into six chemical classes: met-
als, PCBs, pesticides, mercury, PAHs, and other
organic chemicals. Pesticides include the organochlo-
rine pesticide compounds assessed in this report, such
as DDT and metabolites, dicldrin, and chlordane.
PAHs include both low- and high-molecular-weight
polynuclear aromatic hydrocarbons, and other organ-
ics include all organics not otherwise classified. Mer-
cury is grouped separately from other metals because
of its unique behavior in the environment (e.g., me-
thylation and bioaccumulation potential) and because
of recent attention focused on its impact as a primary
sediment and fish contaminant of concern.
Figure 4-1 presents, by chemical class, the average
percent of stations that are contaminated in the 96 APCs.
For this analysis, the percent contamination is derived by
taking the number of stations where an individual chemi-
cal constituent of a particular chemical class places a sta-
tion into Tier 1 or Tier 2 and dividing by the total number
of stations in the watershed. Each constituent, or any con-
stituent representative of a chemical class, might not have
been measured at all stations in the watershed. In addi-
tion, the total number of stations in each watershed varies
extensively, as does the spatial extent of sampling within
the watershed. The resulting percent contamination by
chemical class varies a great deal—from 0 percent to 100
percent for each class—among the watersheds. Figure 4-1
presents the average value at both Tier 1 and combined
Tier 1 and Tier 2 contamination levels.
Figure 4-1 indicates that at the Tier 1 level of con-
tamination, PCBs are the dominant chemical class with
an average extent of contamination of 29 percent. Among
Tier 1 stations, all other classes of contaminants account
for contamination at a lower percent of the stations on
the average (6 to 10 percent). The relative importance
of PCBs reflects, in part, the fact that a station can be
designated Tier 1 for human health effects based on el-
evated fish tissue concentrations alone for this chemical
class, whereas elevated levels in fish tissue and corre-
sponding elevated levels in sediment are required for
all other classes. At the combined Tier 1 and Tier 2
level of contamination, metals are the dominant chemi-
cal class measured by average extent of contamination
(59 percent), followed by PCBs and pesticides (both at
43 percent), mercury (29 percent), and PAHs and other
organics (19 and 14 percent, respectively). The very
large increase in the relative importance of metals from
Tier 1 to combined Tier 1 and Tier 2 also reflects the
evaluation methodology because a divalent transition
metal concentration cannot place a station into Tier 1
without an accompanying acid-volatile sulfide concen-
tration ([AVS]) measurement, which is typically not
available.
Figure 4-1 graphically displays the relative differ-
ences in certainty of assessing the probable effects of
metals versus assessing the effects of PCBs. More con-
fidence can be placed in the assertion that PCBs exhibit
"probable association with adverse effects" than in mak-
4-2
-------
National Sediment Quality Survey
60%-
50%-
40%
30%-
20%-
10%-
TIERS 1 & 2
Metals
Pesticides
TIER 1
Mercury
PAHs
Other
Organics
Figure 4-1. Average Percent Contamination in Watersheds Containing
APCs by Chemical Class.
ing this assertion for metals. The relatively high per-
cent of PCB contamination at the Tier 1 level reflects
the relative certainty that elevated PCB levels in fish
are associated with elevated levels in sediment. The
relatively low percent of metal contamination at the Tier
1 level primarily reflects the lack of confirming data
(i.e., AVS) regarding important binding phases and
bioavailability, not necessarily the lack of significance
of metal contamination. In fact, the very high percent
contamination indicated at the combined Tier 1 and Her
2 level demonstrates the potential importance of this
chemical class. It should also be noted, however, that
correlative screening values such as ERMs do not indi-
cate causality, rather they are concentrations associated
with effects.
This analysis does not imply that certain chemical
classes are always dominant, nor that other chemical
classes can be dismissed altogether. In fact, contamina-
tion from constituents in any class may be of paramount
importance in a given watershed or location. The dif-
ferences in extent of chemical class contamination on
the average in the 96 APCs is intended to provide some
perspective to the ensuing sections of this chapter.
Major Sediment Contaminant
Source Categories
To identify the important sources of sediment con-
taminants, EPA searched the scientific and technical lit-
erature for studies that link specific pollutant sources to
evidence of sediment contamina-
tion. EPA focused this review on
studies appearing in peer-re-
viewed journals and government
reports published after 1980.
The majority of studies related
sediment contamination to a
source through qualitative
means, including associations of
land use or specific activity with
the types of contaminants de-
tected, and spatial analyses. For
example, organochlorine pesti-
cide contamination is associated
with agricultural land use where
past application practices and hy-
drologic routes of rainfall runoff
are known. Some researchers
made the association with con-
tamination source by more quan-
titative means such as loadings
measurements, runoff or deposi-
tion estimates, or mass balance
models of contaminant inputs. Most research has fo-
cused on the chemicals or chemical classes listed above.
The studies reviewed attributed sediment contamination
from the six classes of chemicals to four general nonpoint
source categories and two general point source catego-
ries. Table 4-1 summarizes the correlations of source
category to chemical class documented in literature.
Table 4-1 does not specifically list some important
sources that are difficult to categorize as a point or
nonpoint source. These sources include leachate from
landfills, direct inputs from recreational and commer-
cial boating, and disposal of contaminated dredged ma-
terial. As mentioned at the beginning of this chapter,
landfills are not easily classified as a point or nonpoint
source. Evaporation and subsequent deposition of mod-
erately volat le contaminants from landfills represent an
atmospheric source, yet leachate is typically considered
as neither "urban runoff' nor a controlled point source.
Nonetheless, leachate from landfills is an important
documented source of sediment contaminants. For ex-
ample, landfill leachate and past effluent discharges from
electronics manufacturers have contaminated New
Bedford Harbor in Massachusetts with PCBs and heavy
metals (Garton et al., 1996). Boaring and shipping ac-
tivities can be important sources of a variety of contami-
nants, including PAHs and antifouling paint additives
such as tributyl tin and copper. As for dredged material
disposal, past dredging operations to maintain naviga-
tion channels could be responsible for contaminated sedi-
ment at specifically designated dump sites. Dredging
4-3
-------
Pollutant Sources
Table 4-1. Correlations of Sources to Chemical Classes of Sediment
Contaminants
Source/Chemical Class
Mercury
PCBs
PAHs
Metals
Pesticides
Other
Qrganics
Harvested Croplands
~
Inactive and Abandoned Mine Sites
•
•
Atmospheric Deposition
•
•
•
•
•
•
Urban Sources
•
•
•
~
•
Industrial Discharges
•
~
•
•
~
•
Municipal Discharges
•
•
•
•
•
•
A" Sour-x from put activities
• Onjolnj source
practices are currently managed under federal, state, and
local authority to ensure that appropriate testing and safe
disposal occur. In addition to these sources, uncontrol-
lable and accidental point source releases, such as im-
proper disposal practices and spills, have occurred and
continue to occur.
A notable feature of Table 4-1 is the extent to which
multiple sources can be associated with each chemical
class. This is the primary factor in making source as-
sessment and effective source control such difficult tasks.
The table does not provide any indication of which
sources are the most significant. The significance of
any given source depends on the areal extent of the source
and intensity of the activity in the watershed. Because a
variety of sources are present (or were present in the
past) in most watersheds, and the extent and intensity
of each source vary, the most important source of a par-
ticular chemical or class of chemical contaminants at a
given location also varies. In addition, there is typically
overlap among source categories. The most obvious
overlap is between atmospheric deposition and urban
sources. For example, fuel combustion in urban areas
releases PAHs to the atmosphere, which are subsequently
deposited in various parts of the watershed or transported
to other areas.
Despite these cautions, the results of EPA's litera-
ture review allow some broad assertions regarding source
associations. For harvested croplands, organochlorine
pesticides are the major contaminants of concern. Inac-
tive and abandoned mine sites contribute mercury and
other heavy metals to sediment. Atmospheric deposi-
tion is a primary contributor of mercury, PCBs, and
PAHs. Urban sources are most closely associated with
metals and PAHs. Although permit monitoring records
and industry-supplied release es-
timates, as well as specific spa-
tial analysis studies, indicate that
municipal and industrial dis-
charges of sediment contaminants
(particularly metals and other or-
gan ics) continue, the relative con-
tribution compared to nonpoint
sources is an open question and
undoubtedly varies substantially
by watershed. A brief summary
of the literature review for major
source categories follows.
At many sites, elevated lev-
els of pesticides in the Nation's
sediment can be attributed to past
agricultural practices. Crop growers deliberately apply
pesticides tc protect their yield from insects, fungus, and
weeds. In the past, organochlorine compounds such as
DDT and chlordane were used without restriction to rid
harvested croplands of a broad range of unwanted spe-
cies. These compounds tend to be persistent in the en-
vironment, adsorptive to soil and sediment particles,
highly bioaccumulative in living tissue, and lethal to
many non-target organisms. As these effects became
apparent and regulatory authorities began restricting or
banning the use of persistent pesticides in the United
States, chemical manufacturers developed newer orga-
nophosphate pesticides that might be more easily de-
gradable and, in some cases, more narrowly targeted to
specific organisms. In addition, modern pesticides must
undergo fed; ral registration procedures designed to pro-
tect human health and the environment before they can
be approved for intended new uses.
Although the current-use pesticides are applied
throughout the country in large amounts, they are not
frequently analyzed in routine sediment monitoring, nor
are they frequently detected in sediment when included
in monitoring studies (Pereira et al„ 1994). Because of
the lack of monitoring data, and the absence of avail-
able levels of concern in sediment, current-use pesti-
cides were not included in this evaluation of sediment
quality. However, these compounds exhibit toxicity to
non-taiget organisms. Furthermore, although these com-
pounds have shorter half-lives and greater water solu-
bility than organochlorines in general, the chemical and
physical properties of some of these compounds indi-
cate significant bioconcentration potential (Willis and
McDowell, 1983). Thus, further assessment of the pres-
ence of current-use pesticides in fish and sediment is
warranted.
4-4
-------
National Sediment Quality Survey
The discharge of pollutants from agricultural lands
to surface water is largely driven by precipitation. Con-
taminants also reach the aquatic ecosystem via irriga-
tion return flows through interflow or ground water
seepage. Most of the literature reviewed identifies agri-
culture as the source of pesticides in sediment because
of upstream land use, chemical use, and the nature of
the chemicals detected in sediments. Contamination of
sediment associated with major agricultural areas of the
United States has been reported in numerous studies.
For example, the San Joaquin River, in the highly agri-
cultural central valley of California, has bed-sediment
concentrations of the pesticides DDT and dieldrin among
the highest of all major rivers in the United States
(Gilliom and Clifton, 1990). Researchers have also
found continued elevated levels of highly persistent or-
ganochlorines in bottom-feeding fish, a condition that
is often a consequence of sediment contamination. In
the Yakima River in Washington, which drains a largely
agricultural region, concentrations of DDT in fish for
the years 1989-90 were found to be similar to concen-
trations for the years 1970-76 (USGS, 1993).
Contaminant contributions from past mining activi-
ties are so significant that several former mining sites
in the United States have been included on the EPA
Superfund Program's National Priorities List of sites for
remediation, including the Clark Fork River Basin in
Montana, the Bunker Hill Complex in Idaho, White-
wood Creek and the Belle Fourche River in South Da-
kota, Tar Creek in Oklahoma, Iron Mountain in
California, and the Arkansas River and tributaries near
Leadville, Colorado. The persistence and mobility of
heavy metals have resulted in concentrations in sedi-
ments up to 65 miles downstream of discharge similar
to the elevated concentrations found in the mine tail-
ings themselves (Henny et al., 1994). Based on infor-
mation provided by the states, the Bureau of Mines
estimated that abandoned coal and metal mines and their
associated wastes adversely affect more than 12,000 miles
of rivers and streams and more than 180,000 acres of
lakes and reservoirs (Kleinman, 1989).
The primary sediment contaminants of concern as-
sociated with mining are heavy metals such as lead, mer-
cury, zinc, cadmium, copper, manganese, and silver.
These metals are primarily associated with historical
mining of silver, gold, lead, and zinc. A literature re-
view of studies related to mining pollution provided pub-
lications describing the effects of mining on water
quality; however, few researchers have directly addressed
the effects of mining on sediments. A monitoring study
performed on Idaho's Lake Coeur d'Alene surface sedi-
ment found that ores and wastes from a mining district
were the source of elevated sediment concentrations of
several heavy metals via transport down the Coeur
d'Alene River (Horowitz et al., 1993). Moore et al.
(1991) performed an integrated sediment-water-biota
monitoring study on the effects of acid mine effluent on
the Blackfoot River in Montana. These researchers found
elevated levels of heavy metals in sediment from tribu-
taries with known historical mine effluent input that were
higher than levels in nonaffected tributaries. In another
study from the gold mining region of northern Georgia,
elevated mercury concentrations decreased as distance
of the sampling sites from the mining district increased
(Leigh, 1994). The author further suggests that similar
occurrences of mercury contamination could exist
throughout the gold mining region of the Southern Pied-
mont because of the historical amalgamation processes
used by gold miners.
Atmospheric deposition is often identified as a ma-
jor source of mercury, PCBs, and PAHs to aquatic sys-
tems. Studies have also implicated atmospheric sources
as an important contributor of metals. Sources that emit
large amounts of many toxic chemicals to the atmosphere
include industrial point sources, fuel combustion in mo-
tor vehicles, volatilization of compounds from landfills
and open water, combustion of wood and other fuels to
produce heat, and waste incineration. In addition, long-
range atmospheric transport of organochlorine pesticides
from countries where their use is still permitted contrib-
utes these ci impounds to aquatic environments in this
country (Keeler et al., 1993).
Atmospheric sources of mercury include coal com-
bustion, wriste incineration, and paint application.
Sorensen et al. (1990) compared mercury levels in sedi-
ment cores from lakes in northern Minnesota with pre-
cipitation loadings from monitoring and concluded that,
on the average, direct wet atmospheric deposition ac-
counts for 60 percent of the mercury in lake sediment.
A 1994 EPA report to Congress entitled Deposition of
Air Pollutants to the Great Waters also describes mass
balance studies from Wisconsin and Sweden indicating
that atmospheric deposition is responsible for most of
the mercury in lakes (USEPA, 1994a). The Swedish
study also points out that mercury deposited onto forest
soils is stored, for potentially long periods of time, be-
fore it enters the lake through storm water runoff. This
further illustrates the relationship between atmospheric
deposition and runoff.
Sources of PCBs to the atmosphere include munici-
pal and haziirdous waste landfills, refuse and sewage
sludge incinerators, and occasional leakage from elec-
trical transfcrmers and capacitors (Keeler et al., 1993).
4-5
-------
Pollutant Sources
Researchers have developed a mass balance for PCBs in
Lake Superior that indicates that approximately 77 to
89 percent of the annual PCB input to the lake is from
atmospheric deposition (Baker et al., 1993, cited in
USEPA, 1994a). These researchers have also estimated
the percent contribution of PCBs from atmospheric depo-
sition for other Great Lakes, keeping track of the frac-
tion contributed from atmospheric deposition to upstream
lakes. For example, about 63 percent of PCB input to
Lake Huron is from direct atmospheric deposition, an
additional 15 percent is from atmospheric deposition to
the upstream Lakes Superior and Michigan, and the re-
maining 22 percent is from other sources. Lakes Erie
and Ontario receive only about 13 percent and 7 per-
cent, respectively, of their annual PCB load from atmo-
spheric sources.
Sources of atmospheric PAHs include stationary fuel
combustion, industrial production facilities, transporta-
tion, solid waste incineration, and forest and prairie fires.
Routine installation of catalytic converters in motor ve-
hicles, as well as other combustion emission controls,
have decreased PAH releases to the atmosphere. Atmo-
spheric transport of PAHs generated during fuel com-
bustion has often been inferred to account for the
appearance of PAHs in soils and sediments in regions
distant from known combustion sources, but quantifica-
tion of this process is scarce in the literature (Prahl et
al., 1984). Researchers typically state that the types of
PAHs detected in sediments at a particular study site are
indicative of combustion sources, thereby implying that
atmospheric deposition is probably the primary source
to the aquatic environment (Helfrich and Armstrong,
1986; Rice et al., 1993). In a rare attempt to quantify
this contribution, Prahl et al. (1984) studied atmospheric
particulate matter and surface sediment in Washington
State coastal sediments and estimated that atmospheric
transport accounted for about 10 percent of the PAHs in
sediment. However, unlike the examination of PCBs in
the Great Lakes described above, the authors did not
account for the atmospheric contribution to upstream
watcrborne inputs.
Metals are released to the atmosphere from sources
such as primary and secondary metal production and, in
the past, use of leaded gasoline. Mass balance studies
of metal inputs to the aquatic environment have identi-
fied atmospheric deposition as an important contribu-
tor, but less significant than riverine and upstream
sources. As was the case with the PAH mass balance in
Washington, these studies do not identify the atmospheric
portion of riverine or upstream sources. In one study,
estimates of loadings to Narragansett Bay, Rhode Island,
indicated that atmospheric deposition contributes 2 per-
cent of copper and zinc and 33 percent of lead in sedi-
ment (Bricker, 1993). Based on a mass balance study
on Delaware Bay, direct atmospheric deposition accounts
for 7 percent of the cadmium loading to the bay; rivers
(72 percent) and salt marshes (21 percent) account for
the remaining cadmium input. Some portion of the riv-
erine input originates from the air (USEPA, 1994a).
Atmospheric deposition is a significant source of
dioxins and furans found in sediment. These highly
persistent compounds are grouped with "other organ-
ics" in Figure 4-1. Municipal and industrial waste in-
cineration and residential and industrial wood
combustion were both listed as important sources of di-
oxins and furans to the environment in two recent re-
views (Voldner and Smith, 1989 and Johnson et al., 1992,
cited in Keeler et al., 1993).
The category "urban sources" refers broadly to run-
off from roadways, residential and commercial areas,
construction sites, and marinas and shipyards. Accord-
ing to EPA's National Urban Runoff Program (NURP)
studies, the principal toxic pollutants found in urban
runoff are metals, oil and grease, PAHs, and petroleum
hydrocarbons (USEPA, 1992b). Much of the pollution
in urban runoff is associated with atmospheric deposi-
tion, particularly for mercury and PAHs. Other classes
of chemicals, such as metals and petroleum hydrocar-
bons, have many land-based sources. Lead was formerly
contributed by car exhaust, but most contributions now
come from exterior paints and industrial runoff. Cad-
mium is also associated with paints. Zinc is associated
with weathering and abrasion of galvanized iron and
steel. Car brake linings and leaching and abrasion of
copper pipes and brass fittings contribute copper to run-
off. Chromium is contributed to runoff through car and
machinery corrosion (Cohn-Lee and Cameron, 1991).
Sources of petroleum hydrocarbons include disposal of
automobile and industrial lubricants, spillage from oil
storage facilities, and leakage from motor vehicles
(Brown et al., 1985). In addition to agricultural uses,
organochlorine pesticides were also used extensively in
urban and residential areas for a variety of pest control
purposes.
The association of urban sources and metal enrich-
ment of sediment is well documented in the literature.
For example, a study of storm water detention ponds in
Florida, Virginia, Maryland, and Minnesota found that
metal concentrations in surface sediments were typically
5 to 30 times higher than those in the parent soils
(Schueler, 1994). This study also reported the highest
metal concentrations in ponds associated with indus-
trial land use, followed by those associated with roads
4-6
-------
National Sediment Quality Survey
and commercial land use, then those associated with resi-
dential land use. In contrast to atmospheric transport,
which can carry pollutants far from their original source,
runoff of metals tends to affect areas in close proximity
to the source. For example, Yousef et al. (1985) sampled
water and sediments in detention ponds in Florida and
found that metals from highway runoff are retained by
bottom sediments close to the point of entry to the water-
way.
Hydrocarbons, PAHs, and mercury are also fre-
quently associated with urban sources. Using analyti-
cal chemistry techniques, Brown et al. (1985) discovered
that crankcase oil was a primary contributor to sediment
hydrocarbon contamination in Tampa, Florida. Gas
chromatograms of used crankcase oil, storm water run-
off, and sediment samples all showed similar peaks, in-
dicating that the type of petroleum found in sediment
very closely resembled that found in storm water runoff.
Sources of PAHs that are concentrated in urban areas
include emissions from commercial and residential fuel-
burning furnaces and vehicular emissions. An inven-
tory of sediment contamination in Casco Bay, Maine,
showed that the highest PAH concentrations occurred at
locations closest to the city of Portland (Kennicutt et al.,
1994). Mastran et al. (1994) found that sediments from
urban areas tend to have lower fluoranthene/pyrene ra-
tios than those from remote areas. These ratios are in-
dicative of pollution caused by gas exhaust residues in
urban runoff. A study of ambient air in the southern
Lake Michigan basin revealed that concentrations of
mercury, both gaseous and particulate, are significantly
higher (approximately 5 times higher) in the Chicago
urban/industrial area than levels measured at the same
time in surrounding areas (Keeler, 1994, as reported in
USEPA, 1994a).
In addition to the nonpoint source categories dis-
cussed above, municipal and industrial point sources
have been associated with sediment contaminated by
each of the chemical classes examined in this report.
Much of this contamination has been caused by past in-
dustrial and municipal discharges. For example, sedi-
ment core samples from southwestern Long Island, New
York, revealed levels of metals that increased to several
times the preindustrial concentrations, then decreased
approximately 50 percent between the mid-1960s and
late 1980s. PCBs, chlordane, and other chlorinated or-
ganics in sediment also decreased between the late 1960s
and the late 1980s. Local improvements in wastewater
treatment and national efforts to restrict the use of spe-
cific chemicals are cited as explanations for the declines
(Bopp et al., 1993). As previously mentioned, past ef-
fluent discharges from electronics manufacturers are
linked to PCB contamination in New Bedford Harbor,
Massachuselts (Garton et al., 1996; Lake et al., 1992).
Perhaps the best example of pesticide contamination in
sediment from past industrial activity is kepone in the
James River, Virginia. Kepone escaped undetected from
a manufacturing site for over 9 years and contaminated
miles of the James (Nichols, 1990).
A well-documented case of the effects of point
sources on sediment quality is the Newark Bay estuary
in New Jersey, which encompasses the Passaic River,
Hackensack River, Kill van Kull, and Arthur Kill.
Wenning et al. (1994) examined sediment core samples
from the lower Passaic River in New Jersey and con-
cluded that the sediment is heavily contaminated with
PCBs, PAHs, and metals from recent and historical mu-
nicipal and industrial discharges from local and upstream
sources. The authors identify industrial effluent, either
directly discharged or released through combined sewer
overflows, as the most likely primary source. Research-
ers have also measured high levels of dioxin in sedi-
ment in the estuary adjacent to an industrial site in
Newark where chlorinated phenols had been produced
(Bopp et al., 1991). In a recent study, researchers deter-
mined that the magnitude of current loading estimates
for metals and organics from major sources, such as in-
dustrial and municipal discharges and combined sewer
overflows, likely exceeds the capacity of the Newark Bay
estuary to absorb and dilute the various waste streams
(Crawford et al., 1995).
EPA has conducted an inventory and analysis of
point source releases of sediment contaminants in the
United States. This inventory includes examination of
data from effluent monitoring required by discharge per-
mits and chemical release estimates provided by indus-
try under the community right-to-know provision of the
Superfund Amendments and Reauthorization Act of
1986 (SARA). Permit monitoring data indicate that mu-
nicipal sewage treatment plants and major industrial fa-
cilities discharge all chemical classes of sediment
contaminants. Metals are monitored at the greatest num-
ber of facilities and released in the largest amounts.
Mercury, PAHs, and other organics are also released from
many facilities. PCBs and pesticides are less frequently
monitored, and a relatively small number of records in-
dicate positive detections. Industry-supplied release es-
timates provided under SARA indicate that
manufacturing facilities transfer the majority of their
sediment contaminants, primarily metals and other or-
ganics, to municipal sewage treatment plants. The analy-
sis of these data addresses the potential to adversley affect
4-7
-------
Pollutant Sources
sediment quality, but does not indicate whether these
discharges actively contribute to documented cases of
sediment contamination.
Land Use Patterns and Sediment
Contamination
The characteristics of local sediment contamination
are usually related to the types of land use activities that
take place or have taken place within the area that drains
into the water body (the watershed). The previous sec-
tion of this chapter provided numerous examples of these
relationships from published studies. For this report,
EPA examined the relationship between the extent of
sediment contamination by chemical class and patterns
of land use in the 96 APCs. 'EPA identified individual
watersheds where land use appears to provide impor-
tant information concerning the types of contaminants
present, and summarized general trends that emerge by
looking at the percent of urban and agricultural land
areas in watersheds.
This analysis was based on a comparison of the ex-
tent of contamination by chemical class (described ear-
lier in this chapter) within each watershed to the percent
of land area developed for certain uses within the water-
shed. EPA used the Agency's modeling tool, Better As-
sessment Science Integrating Point and Nonpoint
Sources (BASINS), for spatial analysis to quickly ob-
tain land use data originally compiled by the U.S. Geo-
logical Survey (USGS) on a watershed basis. Although
these land use data might be as much as 20 years old,
the data compiled for the NSI have also been collected
over the past 15 years. The original land use data are
divided into 10 categories. EPA combined residential,
commercial/industrial, and other urban land uses in the
"total urban" land use category for this analysis. EPA
also combined cropland and other agricultural land/
rangeland in a "total agricultural" land use category.
This allowed comparison of attributes such as the per-
cent of stations with pesticide contamination and the
percent total agricultural land use.
Several difficulties are associated with this approach
to comparing land use to the evaluation of NSI sam-
pling stations. First, the frequency and spatial extent of
sampling data in the NSI vary by watershed. Second,
the acreage of a land use activity is not indicative of the
intensity of that use. For example, a small amount of
land in a watershed might be devoted to an industrial
activity that contributes a large amount of pollution.
Most watersheds contain at least a small fraction of each
land use activity. There are also problems of scale.
Localized problems in specific reaches might be caused
by land use activity in the immediate vicinity of the reach
rather than the overall land use in the watershed. Lastly,
many individual pollutants and chemical classes are as-
sociated with multiple types of sources. Some classes of
pollutants, like the highly persistent PCBs, have been
cycled in the environment for many years and trans-
ported far from their original source. These chemicals
would not be expected to be associated with any general
land use category.
Table 4-2 lists each of the 96 APCs with the num-
ber of Tier 1 and Tier 2 stations by chemical class and
the percent land use information. In general, EPA found
that a diversified set of land uses yields a diversified set
of pollutants. However, in some cases a preponderance
of one land use type is associated with expected chemi-
cal classes of sediment contaminants. For example, the
Lower Yakima watershed in Washington, an intensive
fruit and vegetable growing region, is approximately
81 percent agricultural and only 2 percent urban. In
this watershed, nearly 90 percent of the sampling sta-
tions were contaminated with pesticides, whereas no sta-
tions exhibited mercury contamination and less than 10
percent exhibited contamination from metals or PAHs.
These percentages were substantially different from the
average values presented in Figure 4-1. Similar find-
ings were evident in other highly agricultural watersheds,
such as the Tulare-Buena Vista Lakes in California.
In some cases, the absence of a particular land use
in a watershed can provide clues about the source of in-
place contaminants. Some watersheds, such as the Lower
Mississippi-New Orleans in Louisiana and the
Hackensack-Passaic in New Jersey, have very low agri-
cultural land usage, yet a high percentage of contami-
nation from pesticides. High levels of contaminants in
recent sediment deposition may indicate upstream de-
livery of contaminants, whereas high levels in buried
sediment may be indicative of pesticide manufacture/
formulation or urban applications in the past. In the
Coeur D'Alene watershed in Idaho, there is very little
agricultural land use and almost no urban land use. In
this watershed, where mining is a known source of con-
tamination, over 90 percent of the stations exhibited
metal contamination, whereas none indicated PAH or
pesticide contamination. In other watersheds with very
low percent urbanization, there was substantial contami-
nation from all chemical classes except PAHs. This phe-
nomenon was evident in several nonurbanized
watersheds in the Southeast and upper Midwest, such
as Pickwick Lake and Guntersville Lake. Further ex-
4-8
-------
Table 4-2. Tier 1 and Tier 2 Station Classification by Chemical Class and Land Uses in Watersheds Containing Areas of Probable Concern for
Sediment Contamination (APCs)
Number of Stations With a Probability of Adverse Effects
Percent of Total Area is Each Watershed
EPA
Rat.
Cataloging
Ural#
Name
Her
Mercury
Other
Metals
PCBs
Pesticides
PAHs
Otter
All
Chemicals'
Total
#of
Stations
Residential
Commercial/
Industrial
Other
Urban
Cropland
Other
Agricultural
fisrestland
Bays &
Estuaries
Other
Water
Other
Missing/
Unknown
1
01090001
Charles
1
2
146
216
63
486
35
54
8
50
11
50
1
0
195
402
708
25.43%
5.95%
4.56%
3.06%
0.04%
39.57%
7,82%
5.86%
1.47%
6.23%
i
01090004
Narragaesetf
1
2
8
20
18
27
4
17
3
18
2
22
0
0
28
20
48
13.74%
3,58%
4.61%
7.41%
0.86%
51.56%
9.96%
6.27%
1.14%
0.88%
1
01090002
Cape Cod
1
2
6
27
3
60
8
33
1
33
5
34
0
0
15
73
108
5.90%
0.81%
1.77%
1.84%
4,12%
22.90%
35.05%
4.26%
13?%
21.98%
2
04120103
Buffalo-Bgbteerowie
1
2
20
45
7
79
29
31
29
31
43
17
29
15
59
33
101
8,27%
3.54%
3.20%
42.85%
0.10%
30.94%
10.31%
0.35%
©.43%
0.02%
2
02030103
Hackensack-Pa$$aic
1
2
21
39
12
75
13
34
23
42
10
13
4
19
43
58
103
33.33%
7,24%
5.65%
2.62%
0.26%
38.99%
0.00%
6.94%
1.33%
3.64%
2
04130001
Oak Orchard-TVelvemilc
1
2
10
30
20
61
4
15
8
20
4
12
2
13
39
46
¦ §6
2.25%
44.43%
1.25%
10.48%
3.29%
8.42%
26.77%
178%
0.29%
QM%
2
02030104
Sandy Hook-Siaieo Island
1
2
53
11
40
30
19
9
17
19
12
29
20
5
60
21
100
30,58%
10.23%
7,70%
6.99%
0.49%
7.83%
13.66%
7.27%
122%
13.03%
2
04120104
Niagara
1
2
5
16
0
29
11
9
13
U
19
9
16
M
24
16
41
9.35%
32.02%
3.91%
31.59%
0.24%
17.47%
0.02%
3.61%
0.92%
0,87%
2
04150301
Upper St. Lawrence
1
2
5
8
0
17
21
5
3
a
3
6
9
5
21
5
31
1.51%
0,85%
1.29%
36.31%
0.75%
28.47%
0.06%
26.73%
0,21%
3.82%
2
02030105
Ran tan
1
2
1
11
1
39
4
25
5
27
I
4
1
3
13
37
65
15,15%
4,87%
2,99%
25,86%
0,49%
26.55%
0.00%
165%
1.01%
20.43%
2
02040301
Mullica-Toms
1
2
2
10
0
24
2
10
2
11
I
15
5
4
10
22
42
8,54%
1.71%
1.18%
6.04%
0.52%
43.11%
7.97%
20.75%
132%
7.86%
2
02040105
Middle Delaware-Musconetcong
1
2
1
3
1
19
8
13
1
20
1
2
0
0
U
26
48
5,49%
1.53%
1.26%
38.02%
0.16%
33.98%
0.00%
168%
0.67%
16.22%
2
02030202
Southern Long Island
1
2
7
12
4
25
I
8
4
8
1
14
2
2
U
24
43
23.38%
5.03%
5,06%
4.29%
0.74%
10.73%
19,75%
3-26%
1.88%
25.88%
3
02060003
Gunpowder-Fatapsco
1
2
2
6
3
19
15
4
0
21
1
1
0
4
17
7
29
13-47%
5.10%
4,32%
40,80%
0,11%
26.70%
4.62%
4.11%
0.76%
0.01%
3
02040203
Schuylkill
1
2
0
5
1
16
U
6
0
14
0
0
2
0
12
23
44
9.17%
168%
2.78%
41.37%
0.26%
25.81%
0.00%
0.65%
146%
14.82%
3
05030101
Upper Ohio
1
2
0
0
0
29
12
0
0
9
0
0
0
1
12
29
53
13.08%
2.52%
118%
3526%
0.34%
43.13%
0.00%
1.07%
142%
0.00%
3
02070004
Cotiocochtague-Opcquoa
1
2
0
2
0
17
11
0
13
0
0
1
0
11
12
29
1.88%
0.98%
0,89%
50.58%
1.55%
43.24%
0.00%
0.51%
0.34%
0.02%
3
02040202
Lower Delaware
1
2
1
7
I
23
12
20
5
33
1
2
5
0
IS
29
57
26.68%
13.51%
6.47%
21.76%
1.90%
18.45%
0.18%
9.61%
1.17%
0.27%
3
05030102
Shenango
1
2
0
0
0
2
U
0
0
8
0
0
0
0
11
1
15
3.93%
0.76%
2.20%
74.41%
0.02%
12.85%
0.00%
5.36%
0.44%
0.02%
3
04120101
Chaitaaqua-Conaeaut
1
2
1
22
0
101
18
15
0
20
3
29
4
13
21
86
110
4.07%
1.13%
2.05%
38,07%
0.21%
21.58%
31.10%
0.18%
011%
1.40%
4
06010201
Waas Bar Lake
1
2
5
5
0
10
58
2
0
14
0
0
1
1
63
7
89
9.71%
1.84%
1.29%
27.72%
0.06%
52.32%
0.00%
5.20%
1.87%
0.01%
4
06010207
Lower Clinch
1
2
46
11
19
33
24
0
0
7
4
14
3
20
61
14
79
11.76%
1.74%
1.24%
24.98%
0,04%
56.28%
0.00%
116%
1.63%
0.16%
4
0IO3OOO5
Pickwick Lake
1
2
8
11
1
24
45
2
1
23
0
0
0
2
49
9
69
1.93%
0.60%
0.33%
40.73%
0.07%
44.51%
0,00%
4.07%
1.35%
6.41%
4
06020001
Middle Tertt)csscc-Chkkamauga
2
14
15
57
16
1
1
12
26
0
7
9
47
29
94
8.14%
1.58%
1.19%
19.50%
0.04%
64.76%
0.00%
3.54%
1.44%
0.00%
4
03080103
Lower St Johns
1
2
7
35
0
76
5
18
3
48
22
57
2
1
32
111
188
6.99%
1.71%
1.57%
9,03%
1.72%
51.60%
0.00%
25.04%
1.98%
0.36%
-------
Table 4-2. (Continued)
Number of Swk*tt Willi * Probability of Advene
Effects
Percent of TouU Area !a Bad) Watershed
Total
EPA
Catalog^;
Other
All
§ of
CcHBmerciaJ/
Other
Other
BaysJt
OAer
Minfogf
R*e,
Unit#
Name
Tier
Mercury
Meul?
PC?s
tadddes
Other
CbetakaU*
Stations
Residential
fcfktfUtal
Vtbm
Crooland
Arricvtturai
Rjrestfind
Esluwfes
Water
Other
Uninowo
4
06030001
OuctertvUltf Lai*
1
7
1
15
3
0
e
25
92
0.97 £
0J3%
0.23%
40.41%
0.05%
5234%
0,00%
5.18%
0.55%
0.05%
2
36
60
0
11
0
0
46
4
03130002
Middle Chauatheocfaee-Lake Harding
I
0
1
19
4
0
7
21
27
4.86%
0,77%
©.95%
1141%
0.12%
75.59%
0.00%
0.98%
1.27%
0.05%
2
3
8
3
14
2
2
4
4
03060106
Middle Savaoaah
I
II
1!
19
3
2
6
20
36
3.75$
1.7856
0,81%
16.90%
0.18%
62.67%
0.00%
1210%
1.80%
aoo%
2
6
10
3
13
2
2
11
4
03140102
Qoctawhatchee Bay
1
0
7
2
9
2
0
19
51
3.04%
4.94%
1.10%
3.03%
0.01%
61.80%
17.57%
3.14%
1.25%
4.13%
2
H
32
9
11
15
0
23
4
06040005
Kentucky Lake
1
0
0
14
0
0
1
15
30
125%
0.33%
0.26%
25.78%
0,00%
58,59%
0.00%
13.00%
0.76%
0.03%
2
9
25
Q
2
0
2
14
4
06040001
Lower Tennessee-Boedj
1
1
0
14
0
0
I
15
25
0.38%
0.12%
020%
28.06%
0.01%
65.47%
0:00%
3.01%
1.82%
0,94%
2
I
11
0
13
0
0
6
4
06020002
Hiwwsce
1
1
0
12
0
0
2
13
33
265*
0.51%
0.58%
18,99%
0.11%
58.13%
0.00%
1.63%
1.77%
15.63%
2
6
ES
0
6
0
0
17
4
08010100
Lower .Miisisrippl-Mesaphii
1
I
r
12
0
0
4
14
20
0.57%
0.81%
0.35%
49.87%
0.06%
21.07%
0,00%
25.08%
209%
0.03%
2
0
3
2
15
0
0
3
4
06010104
Holflon
1
3
1
10
0
0
2
12
15
4.73%
1.14%
0,45%
4435%
0.01%
43,72%
0.00%
529%
0.30%
0.00%
2
3
6
1
4
0
0
2
4
03040201
Lower Pee Dee
1
1
0
7
5
0
2
11
34
202%
0.55%
0.47%
3203%
0.20%
54.90%
0.01%
9,43%
0.38%
0.01%
2
16
16
1
16
I
0
20
4
03160205
Mobile Bay
1
11
13
2
1
4
0
31
81
4.22%
0,91%
0.97%
2.68%
0.43%
9,60%
18.20%
1.97%
(X33%
60.70%
2
14
38
6
16
21
0
43
4
08030209
Dier-Stee!*
1
0
0
0
11
0
0
11
21
1.23%
0.57%
0.77%
74.35%
©.91%
18.66%
0.00%
3.34%
0.03%
0.08%
2
0
7
0
10
0
0
10
4
Q3I4O107
PodtdoBay
1
s
0
1
0
1
1
10
38
8,04%
2.35%
1.12%
159%
0.16%
14.87%
8.08%
4.77%
1.61%
56.39%
2
8
IS
3
0
9
0
24
4
03060101
Seoeca
1
1
1
9
3
0
0
10
16
0,54%
0.02%
0.02%
0.12%
0.00%
13.24%
0.00%
0.58%
0.36%
85.13%
2
1
8
2
1
0
0
3
5
04090004
Detroit
I
42
21
74
42
53
38
85
ns
42.87%
1265%
8.99%
24.55%
0.18%
5.95%
0.78%
2.29%
1.74%
0.00%
2
27
90
31
1
19
17
29
5
07120003
Chicago
1
21
23
34
IS
0
0
64
103
36,16%
19.12%
8.10%
20,63%
0.00%
4.45%
8.76%
1.14%
1.63%
0.00%
2
27
52
Ifi
37
0
O
36
5
07120004
Des Plaines
1
12
4
54
a
0
1
61
110
21.71%
9.97%
6.61%
43.40%
0.31%
7.47%
0.00%
2.04%
3.48%
0.00%
2
18
53
24
76
0
0
43
5
&WM0003
Milwaukee
i
5
6
43
6
20
14
60
90
11.83%
5.78%
4.20%
65.30%
0.08%
6.64%
0.10%
4.68%
0,41%
0.00%
2
22
38
3
32
6
15
16
5
04030204
Lower Fox
1
21
3
41
8
5
5
49
51
8.94%
5.28%
2.88%
76.15%
0.04%
3.43%
0.11%
2,19%
0.98%
0-00%
2
5
27
1
16
14
19
2
5
04040001
Little Cdumet-GaUeo
1
10
14
40
9
7
10
45
89
7.34%
6.16%
2.59%
37.11%
0.22%
- 1287%
30.51%
2.12%
1.08%
©,00%
2
24
48
6
12
0
3
26
5
04040002
Pike-Root
5
4
2S
3
I
1
34
72
12.02%
5.19%
4.10%
33.68%
0.04%
0,93%
43.58%
0.18%
0.29%
0.00%
2
16
40
n
16
3
3
30
5
07140201
Upper Kaskaskia
0
0
23
14
0
0
31
55
1,19%
0.39%
0.69%
90.79%
0.02%
5.83%
0.00%
1.05%
0.04%
0.00%
2
4
8
6
38
0
0
24
5
07010206
Twin Cities
0
0
26
0
0
0
26
35
21.99%
5,24%
5.12%
48.03%
0.03%
4.39%
0.00%
14.24%
0.95%
0.00%
2
1
2
0
5
0
1
2
5
07140106
Big Muddy
2
2
20
0
0
0
23
94
1.96%
0.91%
0.66%
70.37%
0.51%
20.43%
0.00%
3.60%
1.56%
0.00%
2
14
61
13
39
0
0
65
5
07070003
Castle Rock
0
0
20
0
0
2
20
22
1.05%
0.53%
0.55%
40.77%
0.05%
37.43%
0.00%
18.97%
0,64%
0.00%
2
2
1
0
5
0
0
0
-------
Table 4-2. (Continued)
Number of Stations With a Probability of Adverse Effects
Percent of Total Area in Each Watershed
Total
EPA
Ctiaiogmg
Other
All
#of
Commercial/
Other
Other
Bays &
Other
Missing/
Reft*
Unit#
Name
Tier
Mercury
Metals
PCBi
Pesticides
PAHs
Other
Chemicals*
Stations
Residential
Industrial
Urban
Ciooland
Agricultural
Forestland
Ea futile*
Water
Other
Unknown
5
04100002
Raisin
1
1
0
17
7
1
©
18
39
2.25%
1.00%
0.74%
87.13%
0.15%
5,46%
0.01%
2.90%
0.35%
0.00%
2
2
7
17
13
2
6
19
5
04050001
St. Joseph
1
0
1
3
7
7
3
17
32
3.08%
1,42%
1,02%
79.21%
1.25%
9.23%
0.03%
4,45%
0.31%
0.00%
2
0
18
0
5
2
6
9
5
07040003
Buffalo-WhlEtwa^r
1
0
0
17
0
0
©
1?
26
0.74%
0,29%
0,40%
54.93%
0.05%
37,00%
0.0©%
6.50%
0.08%
0.00%
2
i
2
o
6
0
0
3
5
04U0001
Black-Rocky
1
2
0
12
7
21
9
24
53
11.18%
2.79%
4,40%
66.45%
0,20%
U.U%
3.20%
0.38%
0.29%
0.(30%
2
n
U
7
4
7
i
•*?
5
0?I20006
Upper Pox
1
0
©
15
0
0
0
15
60
10,36%
2.44%
2.38%
63.18%
0.61%
10.84%
0.00%
7.42%
2.77%
0.00%
2
12
37
H
27
9
9
40
5
0512011!
Middle Wabaih-Bussecon
1
7
0
9
0
0
0
15
33
2,49%
0.92%
1.02%
79.64%
0,09%
13.31%
0.00%
1.50%
1.03%
0.00%
2
9
23
ff
30
0
ft
17
5
07140202
Middle KaikaskUs
1
1
©
5
8
0
0
13
38
1,21%
0.40%
0.60%
78.52%
0.09%
16.06%
0.00%
3,01%
0.10%
0.00%
2
4
16
ft
fl
ft
77
5
07040001
Rush-Vermillion
1
0
0
13
0
0
13
14
1.38%
0.59%
0.44%
80.68%
0,06%
9.43%
0.00%
7.07%
0.34%
0.00%
2
2
3
9
3
9
p
1
5
05120109
Vermilion
1
8
0
4
0
0
0
12
28
- 3.92%
1.00%
0,73%
90.08%
0.10%
3.51%
. 0.00%
0,15%
0,50%
0,00%
2
2
19
1
26
0
0
16
5
04030108
Menominee
1
5
4
5
0
2
1
12
21
0.55%
0.17%
0.29%
10.13%
0.01%
<>738%
0.01%
20,94%
0.31%
0.01%
2
8
7
1
2
1
0
6
5
04090002
Lake St, Clair
1
1
2
10
S
5
9
13
19
18.44%
3.81%
2,35%
28.70%
0.06%
3,60%
38,06%
4,87%
an%
0.00%
2
10
13
6
S
8
5
5
5
07140101
Cahokia-Joachira
1
4
1
11
2
0
5
1$
56
10.64%
4.50%
4.32%
42,42%
-------
>3 Tiable 4-2. (Continued)
Nwa&er oC Swieos With x Pnbb&ty of A4vmc Meets
Pereeaj of Toul Area, ia Each Waiertbed
EPA
Ref»
Caltkfieg
Unilf
Nunc
Tier
Mercury
Other
Metals
PCBs
Pesticides
PAH*
Other
Aft
Chejaicals*
TotH
iof
Stations
Residential
CoMBKSCtll/
liKfau&ial
OUter
Utfaaa
Oopiitid
Other
Agricultural
Beralliad
B*y*&
Estuaries
Other
Water
Other
Mhttaj/
Uakccwi
6
12040104
BuMnSib Jacioto
1
2
0
14
i
26
9
15
3
14
1
11
3
3
10
23
36
2331%
632%
45.96%
0.06%
1338%
0.04%
197%
0.80%
0.08%
7
10210 lM
Lower Kansas
1
2
©
1
I
14
11
0
0
22
€
1
1
3
12
15
29
3.70%
1.82%
1.83%
82.75%
0.91%
7.67%
0.00%
0.92%
0.40%
0.00%
7
I10IO2S7
Spring
1
2
0
1
0
29
8
1
0
7
1
O
2
1
10
25
41
1,84%
0.67*
0.79%
80.42%
0.12%
14.27%
0.00%
0.19%
1.70%
0.01%
?
070S0101
Copperas-Duck
1
2
1
1
7
1?
0
0
18
0
1
1
2
17
5
2?
5.40%
2^3%
1.58%
68.60%
0.18%
9.58%
0.00%
9.04%
0.54%
2.55%
9
18070304
Sen Diego
1
2
IS
26
4
93
33
45
13
47
7
39
2
4
53
51
107
11.02%
4.09%
2.72%
6.92%
54.85%
9.62%
1.36%
0.86%
1.98%
6.60%
9
18070104
Santa Moaica Bay
2
15
33
6
94
22
34
66
21
4
18
1
3
79
31
132
17.03%
7,90%
2.86%
1.18%
20.81%
0.68%
0.41%
0.20%
0.96%
47.95%
9
18070201
Seal Beach
1
2
5
38
0
21!
8
142
23
288
2
30
32
182
63
339
442
41.18%
22.80%
4.68%
4.98%
0.12%
0.00%
0.75%
1.15%
157%
23.05%
9
18050003
Coyote
1
2
14
8
8
12
0
1
0
0
0
1
0
0
18
6
24
20,29%
9.69%
9.13%
6,07%
23.27%
27.93%
1.58%
138%
0.66%
0.01%
9
18070204
Newport Bay
1
2
10
13
0
62
1
19
11
48
0
8
2
25
24
68
108
19.51%
13.49%
6.60%
18.96%
28.16%
0.25%
1.09%
0.91%
333%
7.69%
9
18050004
San Francisco Bay
1
2
10
33
9
41
1
18
0
19
5
21
0
0
19
37
64
12.06%
7.21%
3.48%
4.43%
27.36%
28.64%
1430%
1.98%
0.65%
0.00%
9
18070105
Los Angeles
1
2
4
16
0
33
2
4
8
10
3
5
0
1
14
19
37
3836%
13.78%
6.51%
1.31%
31.59%
6.65%
0.02%
0.30%
1.46%
0.01%
9
18030012
Tulare-Buena Vista Lakes
1
2
0
1
0
5
1
4
10
S
1
0
I
0
10
5
20
1.76%
U3%
0.70%
55,36%
38.72%
0.90%
0.00%
0.74%
0.26%
0.03%
9
18070107
San Pedro Channel Islands
1
2
7
3
2
22
2
6
10
3
0
4
0
3
14
10
25
0.00%
0.08%
0.01%
0.00%
2.59%
0.00%
0.02%
0.00%
0.18%
97.12%
9
18070301
Aliso-Sac Ouofre
1
2
5
7
2
29
0
9
5
7
0
2
0
0
10
22
32
3.18%
1.26%
1.22%
4.37%
60.80%
5.39%
0.03%
0.26%
1.49%
22.01%
10
17110019
Puget Sound
1
2
98
449
52
1116
146
317
37
106
296
490
32
317
418
8SI
1383
12.36%
2.12%
Z05%
3.75%
0.32%
4135%
34.95%
2.62%
0.48%
0.00%
10
17110013
Duwamish
1
2
0
27
3
107
34
10
3
!7
12
SB
6
23
48
69
127
1199%
2.97%
4.23%
6.82%
035%
70J5%
0.00%
0.96%
0.63%
0.00%
10
17110002
Strait of Georgia
1
2
16
51
1
180
1
15
4
34
12
73
4
28
32
168
263
4.22%
0.75%
1.22%
10.95%
0.46%
28.13%
5138%
2.61%
0.20%
0,07%
10
17030003
Lower Yakima
1
2
0
0
0
4
5
0
19
23
0
1
1
10
23
19
47
1.13%
0.52%
0.26%
25.97%
55.06%
15.65%
0.00%
1.23%
0.17%
0.01%
10
17090012
Lower Willamette
1
2
1
12
0
51
13
24
10
18
5
11
4
IS
21
51
76
31.21%
6.41%
4.69%
13,32%
0.97%
19.01%
0.00%
3.77%
0.61%
0.00%
10
17110014
Puyallap
1
2
0
0
3
8
1
6
0
1
8
9
1
6
12
6
19
5.85%
0.55%
0.79%
3,78%
4.44%
81.43%
0.00%
0.68%
2-47%
0.01%
10
17010303
Cocur D'AJeoe Lake
1
2
1
t
8
13
2
0
0
0
0
0
0
0
10
13
23
0.73%
0.13%
0.42%
1268%
0.65%
75.10%
0.00%
10.14%
0.14%
0.00%
'Because of the numerous chemicals moaiiored at each station, the tola] |q this column is not equal to die sum of the numbers in tbe columns for the different chemical classes.
"Adapted from USGS land use and land cover classiHeatioo system for use with remote sensor data.
-------
Natimuil Sediment Quality Survey
Ave. Agri. Use
Ave. Urban Use
Ave. Forest Use
10%
20%
36%
amination of percent agricultural
and urban land use revealed some
general trends that are illustrated
by these examples.
A high percentage of agricul-
tural land use in a watershed
tended to correspond with a mark-
edly higher percent contamina-
tion from pesticides and lower
percent contamination from met-
als, mercury, and PAHs. This
phenomenon is presented graphi-
cally in Figure 4-2 and in tabular
form on Table 4-3. For this analy-
sis, EPA grouped watersheds into
quartiles based on percent total
agricultural land use and calcu-
lated the average percent of sam-
pling stations with contamination
by chemical class. Some general
trends that would be expected
were clearly evident. In water-
sheds with greater than 75 per-
cent of the land devoted to
agriculture, pesticide contamina-
tion jumped from under 40 per-
cent of all stations to 64 percent.
In contrast, metal, mercury, and
PAH contamination all steadily
decreased, with all three classes
exhibiting a percent contamina-
tion in the over 75 percent agri-
culture group at least 10
percentage points under the over-
all average for each class. PCBs
and other organics did not exhibit any trend and never
varied more than 5 percentage points from the overall
average.
In contrast, increasingly higher percentages of ur-
ban land use in watersheds correlated with steadily in-
creasing contamination from most chemical classes.
Figure 4-3 and Table 4-4 present the results of a trend
analysis for total urban land use. For this analysis; EPA
placed watersheds into groups of under 5 percent urban
area, 5 to 10 percent urban area, 10 to 20 percent urban
area, and greater than 20 percent urban area to best il-
lustrate trends. The percent PAH and metal contamina-
tion were both 10 percentage points under the overall
average for the least urbanized watershed group, then
rose sharply as the proportion of urban area crossed the
5 percent threshold. The extent of metal contamination
rose to an average of 71 percent, more than 10 percent-
age points above the overall average of 59 percent, in
Metal*
>
PCBs
Pesticides
X
Mercury
X
PAHs
Others
100*
Percent Agricultural Land Use
36%
19%
28%
63%
10%
17%
83%
5%
8%
Figure 4-2. Percent Tier 1 and Tier 2 Stations vs. Agricultural Land Use in
APCs.
Table 4-3. Comparison of Percent Agricultural Land Use in Watersheds
Containing APCs to Percent of Tier 1 and Tier 2 Stations by
Chemical Class
Percent Total Agricultural Land Area
<25% .
25-50%
50-75%
>75%
Overall
Average
Average Percent Agricultural Land Area in Group
10%
36%
63%
83%
39%
Number of Watersheds in Group
32
34
13
17
Metals
66%
60%
58%
47%
59%
PCBs
38%
48%
45%
42%
43%
Pesticides
37%
39%
40%
64%
43%
Mercury
32%
34%
20%
18%
29%
PAHs
30%
17%
12%
9%
19%
Others
13%
16%
9%
12%
14%
watersheds with more than 20 percent total urban land
use. Mercury contamination rose steadily and reached
a peak of 40 percent in the most heavily urbanized wa-
tersheds. The mercury and PAH trends perhaps illus-
trate the effect of atmospheric deposition from local
urban sources. Contamination from other organics also
rose steadily, but never varied more than 6 percentage
points from the overall average. Pesticide contamina-
tion initially decreased as percent urbanization increased,
but it rose more than 10 percentage points from the 10
to 20 percent urban group to the over 20 percent urban
group. As mentioned previously, this may reflect up-
stream delivery of contaminants, pesticide manufacture
or formulation, or urban applications in the past. As
was the case with the agriculture analysis, the average
percent PCB contamination for the urban groups showed
no trend and never varied substantially from the overall
average.
4-13
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Pollutant Sources
Metals
¦
PCBs
—A—
Pesticides
X
Mercury
X
PAHs
Others
15% 20%
Percent Total Urban Land Use
Ave. Urban Use
Ave. Agrl. Use
Ave. Forest Use
2% 7%
51% 38%
29% 27%
14%
40%
29%
35% 40%
38%
26%
18%
Figure 4-3. Percent Tier 1 and Tier 2 Stations vs. Urban Land Use in APCs.
Table 4-4. Comparison of Percent Urban Land Use in Watersheds
Containing APCs to Percent of Tier 1 and Tier 2 Stations by
Chemical Class
Percent Total Urban Land Area
<5%
5-10%
10-20%
>20%
Overall
Average
Average Percent Urban Land Area in Group
2%
7%
14%
38%
16%
Number of Watersheds in Group
32
18
19
27
Metals
49%
61%
59%
71%
59%
PCBs
47%
37%
40%
45%
43%
Pesticides
50%
39%'
32%
44%
43%
Mercury
21%
24%
30%
40%
29%
PAHs
9%
25%
23%
25%
19%
Others
8%
12%.
15%
20%
14%
based on 1994 permit monitor-
ing records in EPA's Permit
Compliance System (PCS) and
chemical release estimates in
the 1993 Toxic Release Inven-
tory (TRI). The report presents
a screening analysis to identify
probable point source contribu-
tors of sediment pollutants
based on release amount,
chemical toxicity, and inherent
physical/chemical properties of
the contaminant. The report
serves as Volume 3 of the com-
plete report to Congress on the
incidence and severity of sedi-
ment contamination in surface
waters of the United States. As
previously stated, discharge
limits for point sources are not
necessarily protective of down-
stream sediment quality. The
Agency believes an effective
source control strategy should
focus on areas at greatest risk
on a watershed scale. The re-
port identifies 29 watersheds
among the 96 APCs where the
potential for point source con-
tribution to sediment contami-
nation is the greatest.
EPA's Point and Nonpoint Source
Sediment Contaminant Inventories
As part of the National Sediment Inventory (NSI)
and mandate under the Water Resources Development
Act (WRDA) of 1992, EPA is conducting inventories of
point and nonpoint sources of sediment contaminants.
The objective of the point source assessment com-
ponent of the NSI is to compile available data regard-
ing the purposeful discharge of sediment contaminants
from industrial facilities and municipal sewage treat-
ment plants and to determine the potential to adversely
affect sediment quality by chemical class, watershed,
and industrial category. EPA has produced the Na-
tional Sediment Contaminant Point Source Inventory
The objective of the non-
point source assessment com-
ponent of the NSI is to prepare
a nationwide assessment of an-
nual nonpoint source contribu-
tions of selected sediment
contaminants on a watershed basis. Given the num-
ber and diversity of nonpoint sources, the Agency is
focusing its initial efforts on four major categories:
harvested croplands, urban areas, atmospheric dep-
osition, and inactive and abandoned mine sites (where
information is available). Although these nonpoint
sources do not constitute the full range of sediment
contaminant sources, they are frequently cited in the
scientific literature as significant sources of mercury,
PCBs, PAHs, metals, pesticides, and other organic
compounds.
The nonpoint source assessment is intended to be a
screening-level study that begins to correlate contami-
nated sediment locations with suspected sources of these
contaminants. As part of this assessment, EPA is com-
piling data from the Bureau of the Census, the U.S.
4-14
-------
Department of Agriculture, the U.S. Department of the
Interior's U.S. Geological Survey and Bureau of Mines,
and others. EPA will compile information and data con-
cerning these nonpoint source activities to identify wa-
tersheds for further investigation and assessment.
Given the breadth of nonpoint sources, EPA antici-
pates that the process of conducting future assessments
will be iterative. Additional nonpoint sources will be
added to the inventory to discriminate more fully be-
tween contaminant types and known sources and to char-
acterize their proximity to known or suspected
contaminated sediment sites. This iterative process will
allow EPA to identify regions of the country where
nonpoint sources are known to exist, but data on sedi-
ment quality are either limited or lacking.
4-15
-------
4-16
-------
Chapter 5
Conclusions and Discussion
T[he National Sediment Inventory (NSI) is EPA's
largest compilation of sediment chemistry data
and related biological data. It includes approxi-
mately 2 million records for more than 21,000 monitoring
stations across the country. EPA's evaluation of the NSI
data was the most geographically extensive investiga-
tion of sediment contamination ever performed in the
United States. The evaluation was based on procedures
to address the probability of adverse effects to aquatic
life and human health.
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 loca-
tion, nor a determination of the areal extent of contamina-
tion on a national scale. However, the evaluation results
strongly suggest that sediment contamination may be
significant enough to pose potential risks to aquatic life
and human health in some locations. The evaluation meth-
odology was designed for the purpose of a screening-
level assessment of sediment quality; further evaluation
would be required to confirm that sediment contamina-
tion poses actual risks to aquatic life or human health for
any given site or watershed.
Based on the number and percentage of sampling
stations containing contaminated sediment within water-
shed boundaries, EPA identified a number of watersheds
containing areas of probable concern for sediment con-
tamination (APCs) where additional studies may be
needed to draw conclusions regarding adverse effects
and the need for actions to reduce risks. Although the
APCs were selected by means of a screening exercise,
EPA believes that they represent the highest priority for
further ecotoxicological assessments, risk analysis, tem-
poral and spatial trend assessment, contaminant source
evaluation, and management action because of the pre-
ponderance of evidence in these areas. Although the
procedure for classifying APCs using multiple sampling
stations was intended to minimize the probability of mak-
ing an erroneous classification, further evaluation of con-
ditions in watersheds containing APCs is necessary
because the same mitigating factors that might reduce
the probability of associated adverse effects at one sam-
pling station may also affect neighboring sampling sta-
tions.
EPA chose the watershed as the unit of spatial analy-
sis because many state and federal water and sediment
quality management programs, as well as data acquisi-
tion efforts, are centered around 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, the Agency recognizes that contamina-
tion in some reaches in a watershed does not necessarily
indicate that the entire watershed is affected.
Watershed management is a vital component of com-
munity-based environmental protection. The Agency and
its state and federal partners can address sediment con-
tamination problems through watershed management ap-
proaches. Watershed management programs focus on
hydrologically defined drainage basins rather than areas
defined by political boundaries. These programs recog-
nize that conditions of land areas and activities within
the watershed affect the water resource. Local manage-
ment, stakeholder involvement, and holistic assessments
of water quality are characteristics of the watershed ap-
proach. The National Estuary Program is one example of
the watershed approach that has led to specific actions
to address contaminated sediment problems. Specifically,
the Narragansett (RI) Bay, Long Island Sound, New York/
New Jersey Harbor, and San Francisco Bay Estuary Pro-
grams have all recommended actions to reduce sources
of toxic contaminants to sediment. Numerous other ex-
amples of watershed management programs are summa-
rized in The Watershed Approach: 1993/94 Activity
Report (USEPA, 1994g) and A Phase I Inventory of Cur-
rent EPA Efforts to Protect Ecosystems (USEPA, 1995b).
This chapter presents some general conclusions
about the extent of sediment contamination in the United
States and sources of sediment contaminants. It. also
includes comparisons to other national studies that ad-
dress the extent of sediment contamination and to a na-
tional survey of state-issued fish consumption advisories.
In addition, this chapter presents the results of an analy-
5-1
-------
sis of the sensitivity of parameters used to evaluate po-
tential human health effects from exposure to PCBs and
mercury, which was performed to show how the use of
different screening values affect the results. The chap-
ter concludes with a discussion of the strengths and
limitations of the NSI data and evaluation method.
It'is important to understand both the strengths and
limitations of this analysis to appropriately interpret and
use the information contained in this report. The limita-
tions do not prevent intended uses, and future reports
to Congress on sediment quality will contain less uncer-
tainty. To ensure that future reports to Congress accu-
rately reflect current knowledge concerning the
conditions of the Nation's sediment as our knowledge
and application of science evolves, the NSI will develop
into a perodically updated, centralized assemblage of sedi-
ment quality measurements and assessment techniques.
Extent of Sediment Contamination
Based on the evaluation, sediment contamination
exists at levels where associated adverse effects are prob-
able CTier 1) in some locations within each region and
state of the country. The water bodies 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, Boston Harbor, the
Detroit River, San Diego Bay, and portions of the Ten-
nessee River were among those locations. Several U.S.
harbors (e.g., Boston Harbor, Puget Sound, Los Ange-
les, Chicago, Detroit) appear to have some of the most
severely contaminated sediments in the country. This
finding is not surprising since major U.S. 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 other water bodies in hundreds of
watersheds throughout the country contain sampling
stations classified as Tier 1. Many of these sampling
stations may represent isolated "hot spots" rather than
widespread sediment contamination, although insuffi-
cient data were available in the NSI to make such a deter-
mination. 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 11 percent of all river reaches in the United
States contained NSI sampling stations. More than 5,000
sampling stations in approximately 2,400 river reaches
across the country (4 percent of all reaches) were classi-
fied as Tier 1. Another 10,000 sampling stations were
classified as Tier 2. In total, over 5,000 river reaches in
the United States—approximately 8 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
because the NSI does not provide complete coverage for
the entire nation, sampling locations are largely based on
a nonrandom sampling design, and sediment quality can
vary greatly within very short distances.
Most of the NSI data were compiled from nonran-
dom monitoring programs. Such monitoring programs
focus sampling efforts on areas where contamination is
known or suspected to occur. As a result, assuming all
other factors are the same, the frequency of Tier 1 or Tier
2 classification based on the NSI data evaluation is prob-
ably greater than that which would result from purely
random sampling. Swartz et al. (1995) demonstrated the
effects of nonrandom sampling design on the frequency
of detecting contaminated sampling stations. They com-
pared the percent of sediment sampling stations that ex-
ceeded PAH screening effects levels (ERL, SQC, AET)
based on random sampling station selection (Virginian
Province EMAP stations) to the percent of sampling sta-
tions that exceeded those levels based on sampling sta-
tion selection on the basis of known PAH contamination
(such as creosote-contaminated Eagle Harbor, Washing-
ton). They found that the frequency of exceeding a sedi-
ment chemistry screening value in sampling stations
known to be contaminated was 5 to 10 times greater than
that for randomly selected sampling stations.
The percentage of all NSI sampling stations where
associated adverse effects are "probable" or "possible
but expected infrequently" (i.e., 26 percent in Tier 1 and
49 percent in Tier 2) does not represent the overall condi-
tion of sediment across the country: the overall extent of
contaminated sediment is much less, as is the percentage
of sampling stations where contamination is expected to
actually exert adverse effects. For example, a reasonable
estimate of the national extent of contamination leading
to adverse effects to aquatic life is between 6 and 12
percent of sediment underlying surface waters. This is
primarily because the majority of sampling stations in the
NSI are located in known or suspected areas of sediment
contamination (i.e., sampling stations were not randomly
selected). However, some individual data sets that are
included in the NSI, as well as the results of independent
investigations conducted by other researchers, can be
applied to represent the areal extent of sediment contami-
nation in their respective study areas. EPA's EMAP data
collection effort featured a probabilistic, or random, sam-
pling design. In the Virginian and Louisianian EMAP
5-2
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National Sediment Quality Survey
Provinces, located on the Mid-Atlantic and Gulf coasts
respectively, 104 of678 (15.3 percent) of sediment samples
were toxic to amphipods. With a 5 percent false positive
rate (statistical alpha=0.05), EMAP toxicity data suggest
that about 10 percent of marine and estuarine sites are
sufficiently contaminated to cause lethality to benthic
organisms (Richard Swartz, personal communication,
December 27,1996). In another recent study, Long et al.
(1996) examined amphipod survival in test sediment col-
lected from 1,176 locations in 22 estuarine areas through-
out the nation. These authors concluded that the areal
extent of toxic sediment comprised approximately 11 per-
cent of the combined study area.
To apply the NSI evaluation to estimate the areal
extent of toxic sediment in the United States, three fac-
tors must be accounted for: (1) most of the NSI data were
generated from sampling targeted toward areas of known
or suspected contamination, (2) sediment chemistry
screening values only identify sediment associated with
a probability of toxicity, and (3) toxicity is demonstrated
at some sampling stations where sediment chemistry
screening values are not exceeded. The latter condition
could be a result of false positives (i.e., laboratory toxic-
ity that would not be present in the field), toxic chemicals
present in the field but not measured or evaluated, or
toxicity that correlative screening values do not predict
(e.g., by definition 10 percent of toxic samples in the "ef-
fects distribution" lie blow the ERL).
Using information from available data and published
studies, the effects of each of the above factors can be
quantified. Swartz et al. (1995) suggest that exceeding a
sediment chemistry screening value at sites of known or
suspected contamination is 5 to 10 times more likely than
at sites where sediment is randomly sampled. However,
comparison of Tier 1 classification for Virginian and Loui-
sianan EMAP data to the entire NSI data base suggests
that the mix of sampling strategies in the NSI data base as
a whole results in screening value exceedance at 2 to 4
times as many sampling stations than purely random sam-
pling. Long et al., (in press), as well as a comparison of
matched sediment chemistry and toxicity data within the
NSI, suggest that approximately 40 percent of Tier 1 sam-
pling stations, and 20 percent of Tier 2 sampling stations,
would exhibit significant lethality to bottom dwelling
aquatic organism. Both data sets also suggest that sig-
nificant lethality occurs at approximately 10 percent of
Tier 3 stations, where no screening value is exceeded.
Alternatively, one could assume that significant labora-
tory toxicity at randomly sampled locations classified as
Tier 3 only represents "false positives", and therefore
that no toxicity occurs at Tier 3 sampling stations classi-
fied from random sampling.
In the NSI evaluation, 3,283 and 9,688 of the 17,884
sampling stations with sediment chemistry data available
were classified as Her 1 and Tier 2, respectively, for risk
to bottom dwelling aquatic organisms. Using a 40 per-
cent probability of lethality at Tier 1 and a 20 percent
probability of lethality at Tier 2, and further assuming 10
times less frequent Tier 1 and Tier 2 classification (upper
end of range from Swartz et al., 1995) in a random sample
and no lethality at Tier 3 sampling stations, the estimated
extent of sediment contamination in the United States
associated with lethality to bottom dwelling aquatic or-
ganisms is 2 percent. At the other extreme, assuming 2
times less frequent Tier 1 and Tier 2 classification (lower
end of range from EMAP/NSI comparisons) in a random
sample and a 10 percent probability of lethality at all re-
sulting Tier 3 sampling stations (11,399; including the
additional sampling stations previously classified as Tier
1 and Tier 2 before adjusting for random sampling), the
estimated extent of sediment contamination associated
with lethality to bottom dwelling aquatic organisms is 15
percent. Avoiding either extreme, assuming 2 to 5 times
less frequent Tier 1 and Tier 2 classification in a random
sample and a 10 percent probability of lethality for only
the original Tier 3 sampling stations (4,913; prior to ad-
justing for random sampling), the range narrows to 6 to
12 percent—about 1,000 to 2,000 toxic sampling stations
out of approximately 18,000. This range encompasses
the areal extent point estimates from EMAP toxicity data
and Long et al. (1996). EPA believes these are reasonable
estimates of the extent of sediment contamination across
the United .States.
The results of the NSI data evaluation must be inter-
preted in the context of data availability. Many states
and EPA Regions appear to have a much greater inci-
dence 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,
182 of the 920 river reaches in Illinois contain a Tier 1
sampling station, whereas only 9 of the 5,490 reaches in
Montana contain a Tier 1 sampling station. However, the
NSI includes sampling station data for over 50 percent of
the river reaches in Illinois but less than 1 percent of the
river reaches in Montana. Therefore, although the abso-
lute number of Tier 1 and Tier 2 stations in each state is
important, relative comparisons of the incidence of sedi-
ment contamination between states is not possible be-
cause the extent of sampling and data availability vary
widely.
For a number of reasons, some potentially contami-
nated sediment sites were missed in this evaluation. The
most obvious reason is that the NSI does not include all
5-3
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Conclusions and Discussion
sediment quality data that have ever been collected. For
example, the NSI does not include many EPA Superfund
Program data and therefore sampling stations in the vi-
cinity of hazardous waste sites might not have been in-
cluded in the NSI evaluation. Additional data sets will be
added to the NSI for future evaluations to provide better
national coverage. In addition, some data in the NSI were
not evaluated because of questions concerning data qual-
ity or because no locational information (latitude and lon-
gitude) was available.
Sources of Sediment
Contamination
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 10 to 20 years. For example, the commercial use
of PCBs and the pesticides DDT and chlordane has been
restricted or banned in the United States. In addition,
effluent controls on industrial and municipal point source
discharges and best management practices for the con-
trol of nonpoint sources have greatly reduced contami-
nant loadings to many of our rivers and streams.
The results of better controls over releases of sedi-
ment contaminants are evident from studies such as that
conducted by Swartz et al. (1991) on the Palos Verdes
Shelf. These researchers examined sediment cores col-
lected at two sites on the Palos Verdes Shelf near the Los
Angeles County Sanitation District's municipal waste-
water outfalls, and at two reference sites in Santa Monica.
They found that the vertical distribution of sediment tox-
icity near the outfalls was significantly correlated with
profiles of total organic carbon and sediment chemical
contamination. Dating of core horizons showed that sedi-
ment toxicity also was significantly correlated with his-
torical records of the mass emission rate of suspended
solids from the outfalls. The vertical profiles showed
that the toxicity of surficial sediments increased after the
initiation of the discharge in the 1950s, remained rela-
tively high until the early 1970s, and then decreased after
the implementation of source controls and improved ef-
fluent treatment (Swartz et al., 1991).
Based on the NSI data evaluation, metals and persis-
tent organic chemicals are the contaminants most often
associated with sediment contamination. Despite recent
progress in controlling sediment contaminant releases to
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.
Some correlations between land use and sediment
contamination caused by specific classes of chemicals
were identified in Chapter 4. Agricultural land use was
correlated with the extent of sediment contaminated with
organochlorine pesticides in APC watersheds, especially
those with more than 75 percent of land area devoted to
crop production or rangeland. In contrast, the extent of
sediment contaminated with PAHs, mercury, and other
metals in APC watersheds correlated with the extent of
urban land use. Land use did not appear to be associated
with the extent of PCB contamination.
Comparison of NSI Evaluation
Results to Results of Previous
Sediment Contamination Studies
The results of this study are consistent with the find-
ings of other national assessments of sediment contami-
nation. For example, in EPA's 1992 National Water
Quality Inventory report, 27 states identified 770 known
contaminated sediment sites (USEPA, 1994e). The iden-
tified "sites" probably best correlate to river reaches from
this analysis in terms of areal extent. The NSI evaluation
identified approximately 2,400 river reaches in 50 states
that contain a Tier 1 sampling station. In the National
Water Quality Inventory report, the states frequently listed
metals (e.g., mercury, cadmium, and zinc), PCBs, DDT (and
its by-products), chlordane, and priority organic chemi-
cals as the cause of sediment contamination. They iden-
tified industrial and municipal discharges (past and
present), landfills, resource extraction, abandoned haz-
ardous waste disposal sites, and combined sewer over-
flows as the most important sources of sediment
contamination.
In a 1987 overview of sediment contamination (which
was based on a limited amount of national data), EPA
estimated that hundreds of sites located in all regions of
the United States have in-place sediment contaminants
at concentrations of concern (USEPA, 1987). The study
identified harbor areas, both freshwater and marine, as
some of the most severely impacted areas in the country.
The study identified municipal and industrial point source
discharges, urban and agricultural runoff, combined sewer
overflows, spills, mine drainage, and atmospheric depo-
sition as frequently cited sources of sediment contami-
nation.
In 1994, the National Oceanic and Atmospheric Ad-
ministration (NOAA) released its Inventory of Chemical
Concentrations in Coastal and Estuarine Sediments
(NOAA, 1994). This study categorized 2,800 coastal sites
as either "high" or "hot" based on the contaminant con-
centrations found at the sampling locations. NOAA did
5-4
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National Sediment Quality Survey
not use risk-based screening values for its analysis. Us-
ing the National Status and Trends Mussel Watch data
set, "high" values were defined as the mean concentra-
tion for a specific chemical plus one standard deviation.
High values corresponded 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 intracoastal 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.
Comparison of NSI Evaluation
Results to Fish
Consumption
Advisories
Table 5-1.
EPA recently published a Na-
tional Listing of Fish Consumption
Advisories issued by state gov-
ernments. As of 1994, 1,532 fish
consumption advisories were in
place in 46 states. (Each advisory
might apply to several water body
segments, or reaches, as defined
in this study.) Mercury was the
contaminant most often associ-
ated with fish consumption advi-
sories; 1,119 water bodies had
advisories that included mercury.
States also issued a large number
of advisories because of high lev-
els of chlordane, PCBs, and diox-
ins in fish tissue.
A direct comparison of the
fish advisory contaminants and
NSI contaminants is not possible
because states often issue advi-
sories for groups of chemicals.
Nevertheless, five of the top six
contaminants associated with fish
advisories (PCBs, DDT, dieldrin,
chlordane, and dioxins) are also
among the contaminants most of-
ten responsible for the Tier 1 clas-
sification of water bodies based on
potential human health effects
(Table 5-1). As illustrated in Fig-
ure 5-1, many sampling stations
categorized as Tier 1 or Tier 2 for
human health effects are located in water bodies for which
fish consumption advisories have been issued for the
chemical(s) responsible for the Tier 1 or Tier 2 categoriza-
tion. Tier 1 and Tier 2 stations are located predominantly
where data have been collected and compiled for the NSI,
whereas fish consumption advisories are located in states
with active fish advisory programs. Unlike the NSI data
evaluation, which is applied consistently to available data,
risk assessment methods used by states may vary.
Although there is good agreement for other chemi-
cals, mercury is notably absent from the Tier 1 category
in Table 5-1. Using the NSI evaluation methodology, mer-
cury cannot place a sampling stations in Tier 1 for poten-
tial human health effects. For chemicals other than PCBs
and dioxins, sediment chemistry and fish tissue data must
both indicate human health risk for Tier 1 assignment.
Comparison of Contaminants Most Often Associated With Fish
Consumption Advisories and Those Which Most Often Cause
Stations to Be Placed in Tier 1 or Tier 2 Based on the NSI Data
Evaluation
Number of River Reaches That Include
at Least One Tier 1 or Tier 2 Station
Based oil the NSI Data Evaluation of
Human Health Fish Consumption
Advisories Parameters"
# of Water Bodies with
Chemical*
Fish Advisories"
Tier 1
Tier 2'
Total
Mercury
1,119
0
89
89
PCBs
387
1,498
732
2,230
Chlordane
114
11
1,026
1,037
Dioxins
53
242
8
250
DDT and metabolites
28
19
656
675
Dieldrin
15
9
1,296
1,305
Selenium
12
0
4
4
Mirex
10
0
15
15
FAHs
5
0
529
529
Toxaphene
4
0
183
183
Hexachlorobenzene
3
0
53
53
Lead
2
0
259
259
Hexachlorobutadiene
2
0
6
6
Creosote*
2
-
-
.
Chromium
1
0
6
6
Copper
1
0
4
4
Zinc
1
0
14
14
'Other chemical groups responsible for fish consumption advisories (i.e., pesticides (24 water bodies], "multiple" [4
water bodies], "not specified" f4 water bodies), and meials [6 water bodies]) could not be directly compared to NSI
chemicals.
No reference values were available for creosote; therefore, it was noi evaluated in the NSI data evaluation,
cDocs not include statewide advisories
Mercury: New York, New Jersey, Maine, Massachusetts, Michigan, coastal Florida
Chlordane: Missouri
PCBs: New York
Dloxin: coastal Maine
dA water body can be composed of numerous river reaches.
*River reaches that include at least one Her 2 sampling station but no Tier i sampling stations.
5-5
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Figure 5-1. Tier 1 and Tier 2 Sampling Stations for Potential Risk to Human Health Located Within Water Bodies with Fish Consumption Advisories
in Place for the Same Chemical Responsible for the Tier 1 or Tier 2 Classification.
-------
National Sediment Quality Survey
Unfortunately, the bioaccumulation potential of mercury
based on concentrations in sediment cannot be assessed
because the biota sediment accumulation factors (BSAFs)
used for this study apply only to nonionic organic com-
pounds. In addition, available fish tissue data for mer-
cury did not place a large number of sampling stations in
Tier 2 for potential human health effects, compared to the
number of fish consumption advisories issued.
There are three possible explanations for the rela-
tively small number of sampling stations categorized as
Tier 2 for mercury in comparison to the number of fish
consumption advisories in place for mercury. The first
explanation is that the NSI evaluation was limited to data
from resident demersal species, whereas data used in sup-
port of issuing state fish advisories probably included
pelagic and migratory species. The second possible ex-
planation is that the evaluation parameters used in the
analysis were not as stringent as the ones used to sup-
port fish consumption advisory issuance. The third ex-
planation is that the NSI does not include all of the data
used by the states to issue fish advisories.
To examine these possible explanations, EPA per-
formed additional analyses of mercury fish tissue data
included in the NSI. The current evaluation, using a fish
tissue screening value of 1 part per million (ppm), yields
103 Tier 2 sampling stations (4 percent of all stations with
detectable levels). If data from all edible pelagic and mi-
gratory species are included in the analysis, there are 374
Tier 2 sampling stations (9 percent of all stations with
detectable levels). A fish tissue threshold of 0.6 ppm,
derived using the more stringent reference dose (0.00006
mg/kg-day) recommended to states for issuing fishing
advisories to protect against developmental effects
among infants (USEPA, 1994f), yields 821 Tier 2 sampling
stations (20 percent of all stations with detectable levels)
when applied to all edible species using the consumption
rate for an average consumer of 6.5 grams per day. How-
ever, fish consumption advisories are often issued for
more highly exposed populations, such as recreational or
subsistence fishers. The 0.2 ppm Canadian guideline limit
for mercury in fish that are part of a subsistence diet
yields 2,308 Tier 2 sampling stations (56 percent of all
stations with detectable levels) when applied to all edible
species in the NSI database. Further details of the addi-
tional mercury analyses are provided in Appendix H.
The conclusion resulting from these additional analy-
ses is that all three explanations for the discrepancy in
numbers of fish advisories and Tier 1 and Tier 2 sampling
stations for mercury probably have an effect. Most fish
consumption advisories are issued to protect infants from
developmental effects for populations where exposure is
greater than 6.5 grams of fish per day. It is also likely that
many of the data used to develop state fish consumption
advisories are not included in the NSI, or are not evalu-
ated for sediment contamination because they are mea-
surements in pelagic or migratory fish.
Sensitivity of Selected PCB
Evaluation Parameters
Because PCBs and dioxin are extremely hydrophobic
chemicals commonly associated with sediment, and be-
cause of their toxicity to humans, EPA believes that el-
evated levels of PCBs and dioxins in fish tissue of
resident, demersal species are sufficient evidence to indi-
cate a higher probability of adverse human health effects
and to place a sampling station in Tier 1. Based on the
NSI data evaluation, PCBs were responsible for the Tier 1
classification of more sampling stations than any other
chemical. Therefore, EPA conducted a sensitivity analy-
sis of some PCB evaluation parameters to determine the
effect on the number of sampling stations classified as
Tier 1 or Tier 2.
In the NSI evaluation, EPA selected a precautionary
approach for the analysis of PCBs. The approach is pre-
cautionary because it does not require matching sedi-
ment chemistry and tissue residue data for PCB, and it is
based on the risk of cancer for all PCBs congeners or
total PCB measurements. However, some PCB congeners
are considered a greater threat for noncancer effects than
for cancer. The evaluation currently places 2,256 tissue
sampling stations in Tier 1 based on human health cancer
risk. Only 542 of these sampling stations included match-
ing sediment and tissue data for PCBs. Therefore, the
number of sampling stations classified as Tier 1 would
have decreased significantly if this match had been re-
quired.
EPA performed additional evaluations to determine
the number of sampling stations that exceed other screen-
ing values which are less precautionary than those se-
lected for the PCB evaluation in this study. The complete
results are presented in Appendix H, which includes a
comparison of the number of sediment and fish tissue
sampling stations with detectable levels of PCBs that ex-
ceed various evaluation parameters for both aquatic life
and human health.
Sampling station evaluation based on PCB contami-
nation is quite sensitive to the selection of evaluation
parameters. For protection of fish consumers, there are
essentially three distinct levels of protection. Using an
EPA cancer risk of 10"s (i.e., a 1 in 100,000 extra chance of
cancer over a lifetime of 70 years) or greater, 85 percent or
5-7
-------
<'(inclusions stud Discussion
more of the sampling stations with detectable PCB levels
are classified as Tier 1. About one-half to two-thirds of
the sampling stations are classified as Tier 1 for
exceedances of PCB levels protective of noncancer
health effects, cancer risk at a 10"4 risk level, or levels
exceeding the wildlife criterion. Less than one-third of
the stations are classified as Tier 1 using the FDA level
of protection. As documented in Appendix H, these per-
centages vary depending on use of a BSAF safety fac-
tor, and whether one is examining the set of fish tissue
data or sediment chemistry data. These three levels of
protection vary within two orders of magnitude, a range
that covers most of the distribution of PCB measure-
ments.
Although sampling station classification for PCB
contamination is quite sensitive to selection of evalua-
tion parameters, overall station classification using the
complete NSI evaluation for all chemicals is more robust
Using the selected PCB evaluation parameters, there are
15,922 total Tier 1 and Tier 2 sampling stations. If PCBs
are dropped from the analysis entirely, the total number
of Tier 1 and Tier 2 sampling stations remains about the
same (less than a 5 percent decrease), but the number of
Tier 1 sampling stations decreases by approximately 40
percent. If PCBs are evaluated using a noncancer hu-
man health threshold, the total number of Tier 1 and Tier
2 sampling stations decreases by less than 2 percent and
the number of Tier 1 sampling stations decreases by ap-
proximately 12 percent Figure 5-2 shows the location of
Tier 1 and Tier 2 sampling stations that exhibit potential
human health risks for all chemicals other than PCBs for
comparison to Figure 3-6 in the results section. Approxi-
mately 78 percent (6,670 of 8,523) of the total number of
Tier 1 and Tier 2 sampling stations indicating human health
riskremainafterexcludingPCBs from the evaluation.
Strengths of the NSI Data
Evaluation
For this report to Congress, EPA has compiled the
most extensive data base of sediment quality informa-
tion currently available in electronic format. To evaluate
these data, EPA has applied sediment assessment tech-
niques in a weight-of-evidence approach recommended
by national experts. The process to produce this report
to Congress has engaged a broad array of government,
industry, academic, and professional experts and stake-
holders in development and review stages. The evalua-
tion approach utilizes sediment chemistry, tissue residue,
and toxicity test results. The assessment tools employed
in this analysis have been applied in North America with
results 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 to 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 ef-
fects from exposure to all contaminants. Uncertainties
and limitations are associated with all sediment quality
evaluation techniques. To compensate for those limita-
tions, EPA has used multiple assessment techniques, alone
and in combination, to evaluate the NSI data. For example,
EPA developed draft SQCs based on the best scientific
data available and extensive peer review. Therefore, EPA
believes that the draft SQCs are reliable benchmarks for
protecting sediment quality, and with measured TOC can
indicate a higher probability for adverse effects to aquatic
life. In addition, EPA believes that other sediment chemis-
try screening values (ERMs/ERLs, PELs/TELs, AETs, and
SQALs) are also useful indicators of probability for aquatic
life impacts. The Agency applied a weight-of-evidence
approach for evaluating contaminant levels using these
screening values, requiring the exceedance of multiple
upper sediment chemistry screening values (i.e., ERM,
PEL, AET-high, or SQAL) for classification ofTier 1 sam-
pling stations.
The screening values used to evaluate the NSI data
include both theoretical and correlative approaches. The
theoretical approaches (e.g., draft SQCs, SQALs, and
TBPs) are based on the best information available con-
cerning how chemicals react in sediments and organisms
and how organisms react to those chemicals. The correla-
tive approaches (i.e., ERMs/ERLs, PELs/TELs, and AETs)
are based on matched sediment and biological data gath-
ered in the field and in the laboratory, and they provide
substantial evidence of actual biological effects from sedi-
ments contaminated with specific concentrations of the
chemicals.
The NSI evaluation approach includes assessments
of potential impacts to 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 end-
point is used to assess environmental impacts.
Because sediment chemistry data are not the only
indicators of potential environmental degradation due to
sediment contamination, the NSI data evaluation approach
also includes evaluations of fish tissue residue and toxic-
ity data. If high levels ofPCBs or dioxins (which are highly
5-8
-------
-------
hydrophobic organic chemicals commonly found associ-
ated 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 stations to be categorized as
Tier 1 based on toxicity data alone, multiple toxicity tests
with positive results using two different test species were
required. One of the tests had to be a solid-phase test.
Although EPA has developed draft SQCs for only
five nonionic organic chemicals, the Agency has devel-
oped similar values, the SQALs, for an additional 35 chemi-
cals as part of the NSI data evaluation. The SQALs have
allowed EPA to evaluate more chemicals using multiple
assessment techniques, thereby adding more weight of
evidence to the results of this evaluation.
limitations of the NSI Data
Evaluation
This methodology was designed for the purpose of
a screening-level assessment of sediment quality, A con-
siderable 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 water bodies with multiple Tier 1 sampling stations
located in APCs). Two types of limitations are associ-
ated with the evaluation of the NSI data: limitations asso-
ciated with the data themselves and limitations associated
with the evaluation of the data.
Limitations of Data
The NSI is a multimedia compilation of environmen-
tal monitoring data obtained from a variety of sources,
including state and federal government offices. Inherent
in the diversity of data sources are contrasting monitor-
ing objectives and scopes, which make comparison of
data from different data sets difficult. For example, sev-
eral of the databases contain only information from ma-
rine environments or other geographically focused areas.
The potential for inconsistencies in measured concentra-
tions 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 evaluation. Although numerous data sets identi-
fied sampling and laboratory methods, most data did not
have this information. In addition, some data sets included
in the NSI were not peer-reviewed (i.e.. Region 4's Sedi-
ment Quality Inventory, the Gulf of Mexico Program's
Contaminated Sediment Inventory, and some data sets
from EPA's STORET). Furthermore, each monitoring pro-
gram used unique sampling and analysis protocols. For
example, PCBs, the chemical group most often respon-
sible for placing sites in Tier 1, were measured by nearly
all of the 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
was information on the sour® of the data and the loca-
tion 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. None of this
information, however, was required before a data set could
be included in the NSI. Evaluation of such information
can provide an indication of the quality of the data used
to target a specific site. Table 5-2 presents a summary of
the known QA/QC information associated with each of
the data sets included in the NSI.
Data reporting was also inconsistent among the dif-
ferent data sources. Inconsistencies that required reso-
lution 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 pres-
ence of control data. Control data were not often initially
reported with the data, and significant follow-up work
was required to acquire such data. In addition, 4 of the 11
sources of toxicity test data used in the NSI evaluation
did not report the use of laboratory replicates.
Some of the data included in the NSI were compiled
as early as 1980 (the data cover the period of 1980-93) and
might not reflect current conditions. The analysis did
not include a temporal assessment of trends in sediment
contaminant levels. Emissions of many prominent
contaminants declined during the 1980s, and significant
remediation efforts have taken place at many locations
since that time. In addition, dredging, burial, and scour-
ing might have removed contaminants from some sam-
pling stations. The lack of a trend analysis in sediment
contamination over time is an important limitation of this
study and will be investigated in future NSI evaluations.
5-10
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Table 5-2. National Sediment Inventory Database: Summary of QA/QC Information
Database
Are There
QA/QC Reports
lo Accompany
the Data?
Were the Data
Peer-Reviewed?
Are the Sampling
and Analytical
Methods Identified
In the Database?
Are the Detection
Limits for the
Analytes Included
in the Database?
Comments
ODES
Yes
Yes, 301(h) data
Yes
Yes
Data Qualifiers
EMAP (VA and LA Provinces)
Yes
Yes
Yes
Yes
Data Qualifiers
Seattle; U.S. Army Corps of
Engineers
Yes
Yes
Yes
Yes
Data Qualifiers
Region 4
Some
No
Some
Yes
Data Qualifiers
Gulf of Mexico
Some
No
Some
Yes
Data Qualifiers
COSED
Yes
Yes
Yes
Some
Great Lakes
Yes
Yes
Yes
Yes
DMATS
Some
Yes
Yes
Yes
Data Qualifiers
STORET
Unknown
Unknown
No
Yes
Data Qualifiers
Massachusetts Bay (USGS)
Some
Yes
Yes
Yes
Some data parameters are consistently absent
throughout the NSI database. (Refer to Appendix A, Tables
A-1 and A-2, for information on the number of NSI sta-
tions at which the various types of data were collected.)
For example, very few site-specific TOC or AVS data are
available, and toxicity data or matched sediment chemis-
try and biological data were available at relatively few
sampling stations. For many of the fish tissue data in-
cluded in the NSI, the species was not identified.
The lack of AVS data in the NSI was a significant
limitation for the evaluation of metals data. The NSI in-
cludes a relatively large amount of metals data, and the
data indicate that metals concentrations in sediment are
elevated in many areas. At some stations the elevated
metals concentrations might indicate a potential prob-
lem; however, no sampling stations in the NSI could be
placed in Tier 1 solely from measured concentrations of
cadmium, copper, nickel, lead, or zinc. This reflects in
large part the absence of AVS data, which are required to
place sampling stations contaminated with those metals
in Tier I.
The unavailability of matching sediment chemistry
and tissue residue data also limited the NSI data evalua-
tion. 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 sedi-
ment 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 loca-
tion. This very likely resulted in an underestimate of the
number of Tier 1 stations identified based on potential
human health effects. The underestimate occurred be-
cause 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 moni-
toring programs in the suite of chemicals analyzed also
represents an area of uncertainty in the NSI data evalua-
tion. Certain databases contain primarily information de-
scribing concentrations of metals or pesticides, whereas
others (e.g., STORET and ODES) contain data describing
concentrations of nearly every chemical monitored in all
of the NSI data. Many monitoring programs use a screen-
ing list of chemicals that are indicator pollutants for
contaminated sediments. Thus, many of the specific
chemicals assessed in the NSI data evaluation are not
always measured in samples. In addition, 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 and thus was not considered
when the significance of elevated contaminant concen-
5-11
-------
Conclusions sind Discussion
trations in sediment was evaluated. Background condi-
tions can be important in an evaluation of potential ad-
verse effects on aquatic life because ecosystems can
adapt to their ambient environmental conditions. For ex-
ample, high metals concentrations in samples collected
from a particular station might occur from natural geo-
logical conditions at that location, as opposed to the ef-
fects of human activities.
Most data are associated with a specific location. As
a result, establishing the extent of contaminated sedi-
ment within a water body is not possible because it is
difficult to assess the extent to which a monitoring sta-
tion represents a larger segment of a water body. Fur-
thermore, the NSI data are geographically biased. More
than 50 percent of all sampling stations evaluated in the
NSI are located in 8 states (Washington, Florida, Illinois,
California, Virginia, Ohio, Massachusetts, and Wiscon-
sin), which have more than 700 monitoring stations each.
Finally, EPA did not verify reported latitude and longi-
tude coordinates for each sampling station.
Limitations of Approach
Sediment Chemistry Screening Values
There are significant gaps in our knowledge con-
cerning sediment-pollutant chemistry (especially bioavail-
ability) and direct and indirect effects on aquatic biota.
The certainty with which sediment toxicity can be pre-
dicted for each chemical using the various screening val-
ues included in the NSI evaluation can vary significantly
based on the quality of the available data and the appro-
priateness of exposure assumptions. For example, draft
SQCs and SQALs are not equivalent, even though they
were developed using the same methodology. EPA has
proposed SQCs for five chemicals based on the highest
quality toxicity and octanol/water partitioning data, which
have been reviewed extensively. The draft SQCs have
also undergone extensive field validation experiments.
However, SQALs for additional chemicals are in many
cases based on a less extensive toxicity data set and have
not been field validated. The AET values used in this
evaluation were based on empirical data from Puget Sound.
Direct application of values from Puget Sound to a spe-
cific location or region in another part of the country
might be overprotective or underprotective of the re-
sources in that area. Extensive collection of data and ad-
ditional analyses would be required to develop AETs for
other locations.
The bioavailability of metals in sediment is addressed
by the comparison of the molar concentration of sulfide
anions (i.e., acid-volatile sulfide [AYS]) to the molar con-
centration of metals (i.e., simultaneously extracted metals
[SEM]). The [SEM]-[AVS] difference is most applicable
as an indicator of when metals are not bioavailable. If
[AVS] exceeds [SEM], there is sufficient binding capacity
in the sediment to preclude metal bioavailability. How-
ever, if [SEM] exceeds [AVS], metals might be bioavail-
able or other nonmeasured phases might bind up the
excess metals. To apply the [SEM]-[AVS] difference to
indicate positive bioavailability and toxicity for this evalu-
ation, EPA used laboratory data that indicated the prob-
ability of observed toxic effects at various [SEM]-[AVS]
levels. Based on these data, EPA defined the Tier 1 level
as [SEM]-[AVS]>5, Thus, this use of [SEM]-[AVS] repre-
sents a hybrid of a theoretical approach and a correlative
approach.
Only those chemicals for which sediment chemistry
screening values (i.e., draft SQCs, SQALs, ERLs/ERMs,
PELs/TELs, and AETs) are available were evaluated in
the analysis of NSI data. Therefore, the methodology
could not identify contamination associated with chemi-
cal classes such as ionic organic compounds (e.g„ alkyl
phenols) and organometallic complexes (e.g., tributyl tin).
Biological effects correlation approaches such as
ERMs or PELs are based on the evaluation of paired field
and laboratory data to relate incidence of adverse bio-
logical effects to the dry-weight sediment concentration
of a specific chemical at a particular sampling station.
Researchers use these data sets to identify level-of-con-
cem chemical concentrations based on the probability of
observing adverse effects. Exceedance of the identified
level-of-concern concentration is associated with a likeli-
hood of adverse organism response, but it does not dem-
onstrate 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. Therefore, these correlative approaches
tend to result in screening values that are lower than the
theoretical draft SQCs and SQALs, which address the
effects of a single contaminant. However, these correla-
tive approaches are better at predicting toxicity in com-
plex mixtures of contaminants in sediment. The effects
range approaches to assessing sediment quality also do
not account for such factors as organic matter content
and AVS, which can mitigate the bioavailability and, there-
fore, the toxicity of contaminants in sediment.
Another concern is the application of screening val-
ues based on freshwater data (draft SQCs and SQALs)
and those based on saltwater data alone (ERLs/ERMs,
PELs/TELs, and AETs) to evaluate sediment contaminant
concentrations in the NSI from both freshwater and salt-
water habitats. Freshwater organisms exhibit tolerance to
5-12
-------
N:i(iiin;il Si'dimcnl 0";>'¦'> Survey
toxic chemicals similar to that of saltwater species when
tested in their respective water; however, estuarine or-
ganisms might be less tolerant if osmotically stressed
(Rand and Petrocelli, 1985). Thus, the relative toxicity of a
chemical in water (i.e., its chronic threshold water con-
centration) is usually within an order of magnitude for
saltwater and freshwater species, although final chronic
values and proposed sediment quality criteria values are
usually slightly higher for saltwater species. Ingersoll et
al., (1996) reported similar reliability and predictive ability
between marine and freshwater guidelines. In addition
Long et al., (1995) compared the ERLs and ERMs with
comparable values derived for freshwater by the Ontario
Ministry of the Environment and the agreement was ex-
tremely good. Because of limitations of time and re-
sources, sampling stations in the NSI were not classified
by salinity regime, and further site-specific evaluations
are required to more definitively assess the toxicity at the
stations. However, the application of several different
screening values should provide a reasonable estimate
of probability of risk to aquatic life in freshwater, estua-
rine, and marine habitats.
Additional false positive and false negative classifi-
cations of risk to aquatic life from sediment contaminant
concentrations could occur when a default value for or-
ganic carbon content is applied. Draft SQCs and SQALs
are based on the partitioning of a chemical between or-
ganic carbon in the sediment and pore water at equilib-
rium. Because the organic carbon content of most
sediment samples in the NSI is unknown, these sediment
samples were assumed to contain 1 percent organic car-
bon. Total organic carbon (TOC) can range from 0.1 per-
cent 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 per-
cent 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 per-
cent. In contrast, the concentration ranges of contami-
nants normalized to dry weight typically varied by several
orders of magnitude. Therefore, normalizing dry-weight
concentrations to a relatively narrow range of TOC con-
centrations had little influence on relative concentrations
of contaminants among samples. Similar findings were
reported by Barrick et al., (1988) for AETs and Long et al.
(1995) for ERMs calculated using sediment concentra-
tions normalized to TOC concentrations.
Uncertainty associated with the equilibrium partition-
ing theory for developing draft SQCs and SQALs includes
the degree to which the equilibrium partitioning model
explains the available sediment toxicity data (USEPA,
1993d). An analysis of variance using freshwater and salt-
water organisms in water-only and sediment toxicity tests
(using different sediments) was conducted to support
development of the proposed sediment criteria. This
analysis indicated that varying the exposure medium (Le.,
water or sediment) resulted in an estimate of variability
that should be used for computing confidence limits for
the draft SQCs. The methodology used to derive the
octanol/water partitioning coefficient and the final chronic
value can also influence the degree of uncertainty asso-
ciated with the draft SQCs. Differences in the response
of water column and benthic organisms, and limitations
in understanding the relationship of individual and popu-
lation effects to community-level effects, have also been
noted (Mancini and Plummer, 1994). Site-specific modifi-
cations to screening values derived using the equilib-
rium partitioning model have been recommended to better
address chemical bioavailability and species sensitivi-
ties (USEPA, 1993b). Sediment chemistry screening val-
ues developed using the equilibrium partitioning
approach also do not address possible synergistic, an-
tagonistic, or additive effects of contaminants.
Based on the theoretical calculations used to com-
pute SQAL values, it is possible that SQALs might be
orders of magnitude larger or smaller than other screen-
ing values used for the analysis (ERLs/ERMs, PELs/TELs,
and AETs). This might be a result of the limited aquatic
toxicity data used to develop SQAL values for some of
the contaminants for which water quality criteria are un-
available. EPA did not develop SQALs for this analysis
in those cases where toxicity data were considered inad-
equate. The approach used to develop SQALs, and to
choose chemicals for which SQALs could not be devel-
oped, is presented in Appendix B.
Fish Tissue Screening Values
The approach used to assess sediment chemistry
data for the potential 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 in-
cludes a field-measured biota sediment accumulation fac-
tor (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 applicabil-
ity of a field-measured BSAF, or a percentile from a distri-
bution of values, at a variety of sites where the conditions
may vary.
5-13
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Conclusions and Discussion
TBPs were assumed to be equivalent to levels de-
tectable 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 car-
bon in different sediments are similar and have distribu-
tional 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, indepen-
dent of site conditions other than organic carbon con-
tent, 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) pro-
cess that can vary and be slowed, for example, by awk-
ward passage of a bulky molecule across biological
membranes. Also, a BSAF of 1 (thermodynamic equilib-
rium) was used to estimate TBPs for many nonpolar or-
ganics. This BSAF might overestimate or underestimate
the bioaccumulative potential for certain nonpolar organic
chemicals because it is assumed that there is no meta-
bolic degradation or biotransformation of such chemi-
cals. Site-specific organic carbon content was often not
available, which leads to additional uncertainty concern-
ing the comparability of BSAFs among different loca-
tions. In addition, development of the BSAFs used in the
TBP evaluation relied on a large amount of data that have
not been published or peer-reviewed. Because of these
factors, actual residue levels in fish resulting from direct
and/or indirect exposure to contaminated sediment might
be higher or lower. There is therefore uncertainty regard-
ing sampling stations classifications based on compari-
son of estimated TBPs with FDA tolerance/action and
guideline levels and EPA risk levels.
TBPs could not be calculated for polar organic com-
pounds or heavy metals. Therefore, sampling stations
could not be classified using FDA levels or EPA risk lev-
els for those chemicals using a TBP approach (although
fish tissue monitoring data are often available for many
stations).
Uncertainties and numerous assumptions are asso-
ciated with exposure parameters and toxicity data used to
derive EPA risk levels and FDA tolerance/action and guide-
line levels. For example, the derivation of EPA risk levels
is based on the assumption that an individual consumes
on average 6.5 g/day of fish caught from the same site
over a 70-year period. Also, the TBP calculation for hu-
man health assessments assumes fish tissue contains 3
percent lipid. This value is intended to be indicative of
the fillet rather than the whole body. Generally, the expo-
sure assumptions and safety factors incorporated into
toxicity assessments might overestimate risks to the gen-
eral population associated with sediment contamination,
but might underestimate risks to populations of subsis-
tence or recreational fishers.
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 deter-
mine which of the stations in Tier I should be considered
the "most" contaminated. Such a numerical ranking sys-
tem was intentionally not used for the NSI data evalua-
tion because EPA does not believe that such ranking is
appropriate for a screening-level analysis such as this,
given the level of uncertainty.
5-14
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National Sediment Quality Survey
Chapter 6
Recommendations
The following discussion presents EPA's recom-
mendations for addressing sediment con-
tamination throughout the country and for im-
proving the ability to conduct sediment quality assess-
ments. These recommendations relate to five activities
or information needs:
1. Further investigate conditions in the 96 targeted
watersheds.
2. Coordinate efforts to address sediment quality
through watershed management programs.
3. Incorporate a weight-of-evidence approach and
measures of chemical bioavailability into sedi-
ment monitoring programs.
4. Evaluate the National Sediment Inventory's
(NSI's) coverage and capabilities and provide
better access to information in the NSI.
5. Develop better monitoring and assessment
tools.
Recommendation 1: Further
Investigate Conditions in the 96
Targeted Watersheds
To characterize the incidence and severity of sedi-
ment contamination in the United States, EPA has per-
formed a screening-level analysis of the information in
the NSI, the results of which are presented in Chapter 3.
As mentioned previously, the results of the NSI data
evaluation alone should not be used as justification for
taking corrective actions at potentially contaminated sites.
The initial evaluation of NSI data was performed as a
means of screening and targeting. Additional, site-spe-
cific data and information should be gathered to verify
the NSI evaluation results and to support a comprehen-
sive assessment of the incidence and severity of sedi-
ment contamination problems.
The primary recommendation resulting from the NSI
data analysis is to encourage further investigation and
assessment of contaminated sediment. States, in coop-
eration with EPA and other federal agencies, should pro-
ceed with further evaluations of the 96 watersheds
containing areas of probable concern for sediment con-
tamination (APCs). In many cases, it is likely that much
additional investigation and assessment has already oc-
curred, especially in well known areas at risk for contami-
nation, and some areas have been remediated. If active
watershed management programs are in place, these
evaluations should be coordinated within the context of
current or planned actions. Future monitoring and as-
sessment efforts should focus on areas such as the 57
water body segments (or river reaches) located within
the 96 watersheds containing APCs that had 10 or more
stations categorized as Tier 1. The purpose of these ef-
forts should be, as appropriate, to gather additional sedi-
ment chemistry data and related biological data and
conduct further assessments of the data to determine
human health and ecological risk, determine temporal and
spatial trends, identify potential sources of sediment con-
tamination and determine whether potential sources are
adequately controlled, and determine whether natural re-
covery is a feasible option for risk reduction. Additional
monitoring and analysis of data from the 96 watersheds
containing APCs will also be used to track and document
the effectiveness of management actions taken to ad-
dress sediment contamination problems over time. Trends
in sediment contamination in the 96 APCs over time will
be reported in future reports to Congress.
Available options for reducing health and environ-
mental risks from contaminated sediment include physi-
cal removal and land disposal; subaqueous capping; in
situ or ex situ biological, physical/chemical, or thermal
treatment to destroy or remove contaminants; and natu-
ral recovery through continuing deposition of clean sedi-
ment. Assuming further investigation reveals the need
for management attention to reduce risks, the preferred
means depends on factors such as the degree and extent
of contamination, the value of the resource, the cost of
available options, likely human and ecological exposure,
and the acceptable time period for recovery. If risk man-
agers anticipate a lengthy period of time prior to recovery
of the system, state and local authorities can consider
6-1
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Recommendations
consider options such as placing a fish consumption
advisory on water bodies or portions of water bodies
where a significant human health risk exists.
Many state and federal government monitoring pro-
grams already do a good job of gathering data at loca-
tions with known contamination problems (including
some of the 96 APCs), and additional monitoring at those
locations will probably not be necessary. However, for
other locations not previously targeted for focused moni-
toring, additional data might be required to adequately
assess potential sediment contamination problems, es-
pecially in areas where significant human health expo-
sures occur. In addition, in some cases it might be
necessary to conduct baseline studies to determine where
to focus monitoring activities.
Further investigation might reveal that risks are mini-
mal or that natural recovery has diminished risk or will
diminish risk in an acceptable time period, or it might
verify that current contamination is significant and un-
likely to sufficiently improve under existing conditions.
Following verification of sediment contamination prob-
lems based on these additional assessments, appropriate
actions (e.g., remediation, permit review, TMDL assess-
ment, best management practices for nonpoint sources,
or "no action") should be taken to address the problem.
In many cases, the mechanisms for corrective actions are
already in place (e.g., permit review, TMDL assessments)
and responsible parties have already been identified. In
other cases, the states should work with EPA to deter-
mine the best course of action.
Recommendation 2: Coordinate
Efforts to Address Sediment
Quality Through Watershed
Management Programs
The watershed approach is a community-based water
resource management framework that requires a high
level of interprogram coordination to consider all factors
contributing to water and sediment quality problems and
to develop integrated, science-based, cost-effective so-
lutions that involve all stakeholders. It is within the
watershed framework, therefore, that EPA recommends
that federal, state, and local government agencies pool
their resources and coordinate their efforts to address
their common sediment contamination issues. These
activities should support efforts such as selection of fu-
ture monitoring sites, setting of priorities for reissuance
of NPDES permits, permit synchronization, total maxi-
mum daily load (TMDL) development, and pollutant
trading between nonpoint and point sources.
The NSI provides an important tool for targeting
efforts to further investigate the 96 watersheds contain-
ing APCs. It is also useful for screening additional po-
tential areas of concern where there are known data gaps.
In addition, the targeting technique used for identifying
the APCs is directly applicable to local-level analysis
because it uses site-specific information. As the NSI is
expanded, it will provide further information to help
environmental managers better understand which of the
Nation's watersheds have sediment contamination prob-
lems that pose the greatest risk to aquatic life and human
health, and track progress in addressing those problems.
There are many active watershed management ef-
forts. EPA recommends strengthening and expanding
these efforts, as appropriate, to better address sediment
contamination issues. The majority of the NSI data were
obtained by local watershed managers from monitoring
programs targeted toward areas of known or suspected
contamination. NSI data and evaluation results can as-
sist local watershed managers by providing additional
data that they may not have, enabling them to compare
their sites to others throughout the region or country,
demonstrating the application of a weight-of-evidence
approach for identifying and screening contaminated
sediment locations, and allowing researchers to draw
upon a large data set of information to conduct new analy-
ses that ultimately will be relevant for local assessments
and responses.
An important component of watershed management
is to educate and engage all stakeholders in government,
industry, and the community. The NSI can help explain
the need to establish pollution prevention initiatives for
point sources and nonpoint sources that might go be-
yond current practices. For example, chemical use prac-
tices in industry and by landowners, homeowners, and
local governments might need to be changed to prevent,
reduce, or eliminate potential sources of sediment con-
taminants.
Recommendation 3: Incorporate a
Weight-of-Evidence Approach and
Measures of Chemical
Bioavailability into Sediment
Monitoring Programs
As stated in Chapter 2 of this volume, the ideal as-
sessment 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 amount of
6-2
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National Sediment QuaiiH Survey
contaminant present, but cannot definitively indicate
an effect. On the other hand, toxicity tests or benthic
community surveys can indicate an effect, but cannot
definitively implicate a chemical cause. However,
matched sediment chemistry data and toxicity tests, es-
pecially linked through innovative toxicity identifica-
tion evaluation (TIE) approaches, can provide a
preponderance of evidence implicating a chemical cause
of a biological effect. This advocacy of a weight-of-
evidence approach is supported by the consensus of par-
ticipants in an expert workshop on sediment ecological
risk assessment sponsored by the Society of Environ-
mental Toxicology and Chemistry held in Pacific Grove,
California, in April 1995. These scientists concluded
that no single approach provides the best answer for risk
assessment, but each endpoint has strengths and weak-
nesses and the best approach is to use multiple endpoints
(Ingersoll et al., 1997). Toward this end, monitoring
programs should be planned and executed to support
weight-of-evidence assessments.
EPA recommends that future sediment monitoring
programs collect tissue residue, biological effects (i.e.,
toxicity, histopathology), and biological community
(e.g., benthic abundance and diversity) measurements.
These types of data are necessary to better assess actual
effects resulting from exposure to contaminated sedi-
ment. Matched sediment chemistry and tissue residue
data should be collected where human exposures are a
concern. In areas where aquatic life effects are a con-
cern, monitoring programs should collect matched sedi-
ment chemistry and biological effects data and biological
community measurements. There is a need to evaluate
matched sediment chemistry and toxicity data to deter-
mine the predictive ability of screening values to cor-
rectly classify toxicity and minimize both Type I (false
positive) and Type II (false negative) errors.
Collection of measures of chemical bioavailability
is critical to the success of weight-of-evidence assess-
ments. As noted in the previous chapter, a large number
of stations had elevated concentrations of metals. How-
ever, many of these stations could not be categorized as
Tier 1 because of a lack of acid volatile sulfiide (AVS)
and simultaneously extracted metals (SEM) data, which
were required to place stations in the Tier 1 category
based on sediment contamination from cadmium, cop-
per, nickel, lead, or zinc. AVS and SEM provide informa-
tion necessary to assess the bioavailability of metals in
sediment, and future sediment monitoring programs
should specify collection of AVS and SEM measurements
where metals are a concern.
Total organic carbon (TOC) data were also lacking
for many monitoring stations with data in the NSI. TOC,
like AVS and SEM, provides information related to the
bioavailability of contaminants—in this case, nonionic
organic chemicals. Because of the lack of site-specific
TOC data, a default TOC value was used in the NSI evalu-
ation in the comparison of measured sediment chemistry
values to screening values. This approach resulted in
the possible overestimation or underestimation of po-
tential impacts. Therefore, EPA recommends that future
monitoring programs also include TOC measurements
where organic chemicals are a concern.
Recommendation 4: Evaluate the
NSI's Coverage and Capabilities
and Provide Better Access to
Information in the NSI
The NSI is currently limited in terms of the number
of data sets it includes and the national coverage it pro-
vides. Over 50 percent of the monitoring stations evalu-
ated in the NSI are located in eight states (Washington,
Florida, Illinois, California, Virginia, Ohio, Massachu-
setts, and Wisconsin). In addition, only 11 percent of all
river reaches in the United States include one or more
sampling stations that were assessed as part of the NSI
data evaluation.
EPA should continue compiling sediment chemis-
try data and related biological data in the NSI to:
• Obtain a greater breadth of coverage across the
United States.
• Increase the number of water bodies evaluated.
• Include additional data for more chemicals of
concern.
• Provide more recent data for evaluation for fu-
ture reports to Congress.
During the course of developing and compiling the
NSI, commentators and reviewers identified several ad-
ditional databases that should be included in the NSI for
future evaluations. Those databases and others should
be evaluated and added to the NSI in the future as appro-
priate. EPA plans to obtain the most recent data from
databases currently in the NSI (e.g., STORET and ODES)
and add new data from recent monitoring efforts targeted
at specific water bodies, states, or other areas that are
currently underrepresented in the NSI.
6-3
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Recommendations
Although some historical trend information is avail-
able, a comprehensive assessment of temporal trends is
not presented in the current report to Congress. EPA
should consider whether to design future evaluations of
the NSI data to determine where and why sediment qual-
ity conditions are improving or worsening. EPA plans to
develop an approach for assessing temporal trends that
might include, for example, a statistical analysis of recent
and older data from national databases that are updated
on a regular basis, such as STORET, ODES, and the Na-
tional Oceanic and Atmospheric Administration's NS&T
database. In addition, in the search for additional data-
bases for use in future NSI data evaluations, EPA should
focus on obtaining sediment core data, which can pro-
vide valuable information concerning historical trends in
sediment contamination. An assessment of temporal
trends in sediment contamination will provide valuable
information concerning the effectiveness of measures
taken to control the release of sediment contaminants.
The NSI can be a powerful tool for water resource
managers at the national, regional, state, watershed, and
water body levels. It provides in a single place a wealth
of information that could be very useful, especially with
improved access and availability. Multiple agencies
should have access to the same data for decision makers
in regional management, state-level management, and
watershed-level management.
Plans are under development to make this happen.
By the summer of 1997 the NSI data, organized by water-
shed and including maps and summary tables, should be
available on EPA's mainframe computer for on-screen view-
ing and download. In addition, near future plans are to
make this information available on EPA's World Wide Web
site. EPA has also included the NSI data in its compre-
hensive GIS/modeling system, BASINS (Better Assess-
ment Science Integrating Point and Nonpoint Sources).
Future activities should include the addition of the NSI
evaluation tools to BASINS to allow users to query the
NSI evaluation results. For managers, this could be use-
ful for identifying watersheds, water bodies, or sampling
stations where various sediment chemistry and/or bio-
logical screening values have been exceeded. Identify-
ing potential point and nonpoint sources of sediment
contaminants is also critical.
Increased access to data and information in the NSI
has many implications. At the national level, the data
and information can:
* Demonstrate the need and provide impetus for
increased pollution prevention efforts.
• Demonstrate the need for safer or biodegrad-
able chemicals.
• Determine relative risk compared to other prob-
lems.
At the state and watershed level, better access to
NSI information can help in:
• Educating and involving the public.
• Setting goals and prioritizing activities and ex-
penditures.
• Evaluating the adequacy and effectiveness of
control actions, clean-up activities, and other
management actions.
Related to source identification are plans under way
at the Agency for one-stop reporting of and access to
integrated information about the environmental perfor-
mance and emissions of major industrial facilities and other
pollution sources. States and EPA will give every major
industrial facility and other type of facility generating,
storing, and disposing of hazardous and toxic wastes a
unique identifying number. This number will be used by
states and EPA to link all environmental information re-
lated to the facility. NSI development will be linked to
these Agency-level efforts.
Interagency and intergovernmental cooperation is
essential for enhancing NSI information, coverage, and
comprehensiveness. Reporting of water quality informa-
tion and environmental indicator development at the Of-
fice of Water are important ongoing efforts related to the
collection of information from state agencies (through
305(b) reporting), other federal agencies, and the private
sector. Efforts for future data collection for the NSI should
be integrated into these related initiatives.
Recommendation 5: Develop Better
Monitoring and Assessment Tools
The National Sediment Quality Survey is the first
attempt to analyze sediment chemistry and biological data
from numerous databases from across the country in an
effort to identify the national incidence and severity of
sediment contamination. Because the data were not gen-
erated by a single monitoring program designed at the
outset to provide this national picture, numerous hurdles
had to be overcome to analyze the data with as little bias
and as much scientific validity as possible. This exercise
itself provided an opportunity to assess the needs to
develop better basic and applied science with respect to
sediment chemistry data and related biological data.
6-4
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National Sediment Quality Survey
To ensure effective quality control and quality as-
surance management, monitoring programs should adopt
standard sample collection, storage, analyses, and docu-
mentation procedures. Lack of available quality control
information and the recognized limitations of some past
sampling and analyses methods necessarily restricts the
interpretation of much of the historical data base. How-
ever, these limitations should be eliminated in the future
through current practices such as "clean" laboratory tech-
niques, lowered analytical detection limits, and better
record keeping. Modernization of federal and other data
repositories to accommodate the storage of much addi-
tional valuable and relevant information should help fa-
cilitate the process.
During the evaluation of information in the NSI, ana-
lysts continually came up against the limitations of avail-
able tools and techniques to assess the sediment
contaminant information. Although screening values were
adopted or developed for the NSI data evaluation wher-
ever feasible, many data for some potentially harmful con-
taminants were not evaluated. For example, many
contaminants included in the NSI, such as kcponc and
tributyl tin, could not be evaluated due to a lack of appro-
priate screening values for comparison with measured
values.
The sediment quality evaluation tools used for the
current NSI data evaluation should be used as the basis
for further methods development. As sediment quality
data become more available and the state of the science
for sediment assessment evolves, assessment methods
will also evolve. For example, new and better screening
values and laboratory tests for biological effects will be
developed. EPA should incorporate new sediment as-
sessment techniques into future NSI data evaluations as
they are developed, tested, and proven reliable. For ex-
ample, although biological community data were included
in the NSI, the data were not evaluated for this report to
Congress because there is little agreement among sedi-
ment assessment experts concerning biological commu-
nity conditions that can be directly related to sediment
quality problems, EPA should work to develop these and
other sediment assessment tools for future assessments,
EPA needs to evaluate the ecological relevance of the
assessment tools used to evaluate contaminated sedi-
ment.
Other relevant issues and science needs that should
be addressed to better characterize the sources, fate, and
effects of sediment contaminants include:
• Methods to better predict the fate and transport
of sediment contaminants.
• Methods to predict or track atmospheric sources
and cross-media transfers of sediment contami-
nants such as mercury, pesticides, PCBs, and
PAHs.
• Bioavailability of compounds other than non-
ionic organics.
• Estimates of land use impacts on sediment con-
ditions {predictive capabilities).
• Methods for fingerprinting chemicals for source
identification.
In the context of the budget process, EPA and other
federal agencies should evaluate whether to request fund-
ing to support the development of tools to better charac-
terize the sources, fate, and effects of sediment contami-
nants.
6-5
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National Sviliment ()ui\lil\ Survey
Glossary
Acid-volatile sulfide (AVS): Reactive solid-phase
sulfide fraction that can be extracted by cold hydrochlo-
ric acid. Appears to control the bioavailability of most
divalent metal ions because of the sulfide ions' high af-
finity 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 general-
ized toxic response with lethality usually being the ob-
served endpoint.
Apparent Effects Thresholds (AElfc): Sediment
chemistry screening values based on a biological effects
correlation approach. The AET is the highest concen-
tration at which statistically significant differences in
oberseved adverse biological effects from reference con-
ditions do not occur, provided that the concentration also
is associated with observance of a statistically signifi-
cant difference in adverse biological effects. Based on
empirical data from Puget Sound, EPA defined the AET-
low as the lowest AET among applicable biological indi-
cators, and the AET-high as the highest AET among
applicable biological indicators.
Benthic abundance: The quantity or relative degree
of plentifulness of organisms living in or on the bottom
of streams, rivers, or oceans.
Benthic organisms: Species living in or on the bot-
tom of streams, rivers, or oceans.
Bioavailability: The fraction of chemical present that
is available for uptake by aquatic organisms.
Biological community: An assemblage of organ-
isms that are associated in a common environment and
interact with each other in a self-sustaining and self-regu-
lating relationship.
Biological effects correlation approach: A method
for relating the incidence of adverse biological effects to
the dry-weight sediment concentration of a specific chemi-
cal 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 likeli-
hood of adverse organism response, but does not dem-
onstrate that a particular chemical is solely responsible.
Cataloging unit: Sometimes referred to as a hydro-
logic unit, corresponds to a watershed that was delin-
eated by the U.S. Geological Survey. A watershed is an
area that drains ultimately to a particular watercourse of
body of water. There are approximately 2,100 cataloging
units in the contiguous United States, which are, on av-
erage, somewhat larger than counties. Each cataloging
unit is uniquely identified with an 8-digit hydrologic unit
code (HUG).
Chronic toxicity: Response of an organism to re-
peated, long-term exposure to a chemical substance. Typi-
cal observed endpoints include growth and reproduction.
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 ex-
ceeded 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 water body.
Divalent metals: Metals that are available for reac-
tion 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) envi-
ronment, which interact to produce a system which can
be defined by its functionality and structure.
Effects range-median (ERM) and effects range-low
(ERL) values: Sediment chemistry screening values
based on a biological effects correlation approach. Rep-
resent chemical concentration ranges that are rarely (i.e.,
below the ERL), sometimes (i.e., between ERL and ERM),
and usually (i.e., above the ERM) associated with toxic-
Glossary-1
-------
Cilossarj
ity for marine and estuarine sediments. Ranges are de-
fined by the tenth percentile and fiftieth percentile of the
distribution of contaminant concentrations associated
with adverse biological effects.
Elutriate phase toxicity test: Toxicity test in which
sediments are mixed with test water for a fixed period of
time, the test water is then siphoned off, and test organ-
isms are introduced to the test water (the elutriate) in the
absence of sediments. Useful for representing the expo-
sure to chemicals that can occur after sediments have
been resuspended into the water column or after they
have passed through the water column as part of dredged
material disposal operations.
Equilibrium concentration: The concentration at
which a system is in balance due to equal action by op-
posing forces within the system. When the partitioning
of a nonionic organic chemical between organic carbon
and pore water and partitioning of a divalent metal be-
tween 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 include pore water (respiration), sedi-
ment 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 be-
tween organic carbon and pore water and the partition-
ing of a divalent metal between the solid and solution
phases are at equilibrium.
Histopathology: The study of diseases associated
with tissue changes or effects.
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.
Interstitial water: Water in an opening or space, as
between rock, soil, or sediment (i.e., pore water).
Microbial toxicity test: Type of toxicity test in which
members of the microbial community (i.e., bacteria) are
used as the test organism. Microbial responses in toxic-
ity tests have been recommended as early warning indi-
cators of ecosystem stress. However, questions have
been raised concerning the sensitivity of sediment mi-
crobial toxicity testing.
Molar concentration: The ratio of the number of
moles (chemical unit referring to the amount of an ele-
ment 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.
National Sediment Inventory (NSI): A national com-
pilation 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 coun-
try (i.e., the National Sediment Quality Survey). Eventu-
ally, all compiled NSI data will be incorporated into the
new, modernized STORET, where they will be permanently
stored.
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 com-
pounds 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 in-
clude atmospheric deposition, agriculture, silviculture,
urban runoff, mining, construction, dams and channels,
inappropriate land disposal of waste, and saltwater intru-
sion,
Nonpolar organic chemicals: Compounds that do
not exhibit a strong dipole moment (there is little differ-
ence between the electrostatic forces holding the chemi-
cal 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.
Point source pollution: Pollution contributed by any
discernible, confined, and discrete conveyance includ-
ing, 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 dis-
charged.
GIossary-2
-------
Satioiml Svdinu nt Qutilily Snrvi-%
Pore water: See Interstitial water.
Probable effects levels (PELs) and threshold effects
levels (TELs): Biological effects correlation-based sedi-
ment chemistry screening values similar to ERMs/ERLs.
A generalized approach used to develop effects-based
guidelines for the state of Florida and others. The lower
of the two guidelines for each chemical (i.e., the TEL) is
assumed to represent the concentration below which toxic
effects rarely occur. In the range of concentrations be-
tween the two guidelines, effects occasionally occur.
Toxic effects usually or frequently occur at concentra-
tions above the upper guideline value (i.e., the PEL).
Ranges are defined by specific percentiles of both the
distribution of contaminant concentrations associated
with adverse biological efects and the "no effects" distri-
bution.
River Reach: A stream segment between the con-
secutive 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. The more detailed
version of the Reach File (RF3) was not used for the Na-
tional Sediment Inventory.
Sampling Station: A specific location associated
with latitude/longitude coordinates where data have been
collected. Defined by the data source, sponsoring agency,
and station identification code. Multiple sampling sta-
tions can have the same latitude/longitude coordinates if
labeled with a different station identification code for sam-
pling performed on different dates or by different spon-
soring agencies.
Sediment quality advisory levels (SQALs): Equilib-
rium partitioning-based sediment chemistry screening val-
ues. Derived using the same approach used to develop
sediment quality criteria; however, SQALs may be based
on a limited set of aquatic toxicity data.
Sediment quality criteria (SQCs): Published draft
sediment quality criteria for the protection of aquatic life.
Based on the equilibrium partitioning-based approach
using the highest quality toxicity and octanol/water par-
titioning data, which have been reviewed extensively.
Draft SQCs have been developed by EPA for five noil-
ionic organic chemicals: acenaphthalene, dieldrin, en-
drin, fluoranthene, and phenanthrene.
Simultaneously extracted metals (SEM): Metal con-
centrations that are extracted during the same analysis in
which the acid-volatile sulfide (AVS) content of the sedi-
ment is determined.
Solid-phase toxicity test: A toxicity test in which
test organisms are exposed directly to sediments. Sedi-
ments 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 expo-
sure. Solid-phase toxicity tests integrate multiple expo-
sure 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 contami-
nant in tissues if the sediment in question were the only
source of contamination to the organism. TBP is esti-
mated from the organic carbon content of the sediment,
the lipid content of the organism, and the relative affini-
ties of the chemical for sediment organic carbon and ani-
mal lipid content.
Total organic carbon (TOC): A measure of the or-
ganic carbon content of sediment expressed as a percent
Used to normalize the dry-weight sediment concentra-
tion 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 expo-
sure medium that pose a potential carcinogenic risk (e.g.,
10"5, or a 1 in 100,000 extra chance of cancer over a life-
time) 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 con-
taminated fish tissue.
U.S. Food and Drag Administration (FDA) tolerance/
action or guideline levels: FDA has prescribed levels of
contaminants that will render a food "adulterated." The
establishment of action levels (the level of a food con-
taminant to which consumers can be safely exposed) or
tolerances (regulations having the force of law) is the
regulatory procedure employed by FDA to control envi-
ronmental contaminants in the commercial food supply.
Glossary-3
-------
Glossary-4
-------
Acronyms
AET: apparent effects threshold
APC: area of probable concern for sediment con-
tamination
AVS: acid volatile sulfide
BASINS: Better Assessment Science Integrating
Point and Nonpoint Sources (EPA model-
ing tool)
BSAF: biota-sediment accumulation factor
CAA: Clean Air Act
CAS: Chemical Abstract Service
COSED: Coastal Sediment Inventory
CWA: Clean Water Act
CZMA: Coastal Zone Management Act
DMATS: Dredged Material Tracking System
EMAP: Environmental Monitoring and Assessment
Program !
EPA: U. S. Environmental Protection Agency
ERL: effects range-low value
ERM: effects range-median value
FDA: Food and Drug Administration
FIFRA: Federal Insecticide, Fungicide, and Roden-
ticide Act
MPRSA: Marine Protection, Research, and Sanctu-
aries Act
NEPA: National Environmental Policy Act
NOAA: National Oceanic and Atmospheric Admin-
istration
NPDES: National Pollutant Discharge Elimination
System
NSI: National Sediment Inventory
NURP: National Urban Runoff Program
ODES: Ocean Data Evaluation System
OST: Office of Science and Technology, U. S. En-
vironmental Protection Agency
PAH: polynuclear aromatic hydrocarbon
PCB: polychlorinated biphenyls
PCS: Permit Compliance System
PEL: probable effects level
QA/QC: quality assurance/quality control
RCRA: Resource Conservation and Recovery Act
RF1: River Reach File 1
SEM: simultaneously extracted metals
SQAL: sediment quality advisory level
SQC: sediment quality criteria
STORET: Storage and Retrieval System
TBP: theoretical bioaccumulation potential
TEL: threshold effects level
TIE: toxicity identification evaluation
TMDL: total maximum daily load
TOC: total organic carbon
TRI: Toxic Release Inventory
TSCA: Toxic Substance Control Act
USACE: U. S. Army Corps of Engineers
USGS: U. S. Geological Survey
WRDA: Water Resources Development Act of 1992
Acronyms-1
-------
Acn tin ins
-------
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concentrations in water, sediments, plankton, and fish
of eighty northern Minnesota lakes. Environ. Set
Technol. 24(11)1716-1727.
Swartz, R.C., W.A. Deben, K.A. Sereo, and J.O.
Lamberson. 1982. Sediment toxicity and distribution
of amphipods in Commencement Bay, Washington,
USA. Mar. Poll Bull. 13:359-364.
Swartz, R.C., D.W. Schults, l.O. Lamberson, R.J.
Ozretich, , and J.K. Stull. 1991. Vertical profiles
of toxicity, organic carbon, and chemical con-
taminants in sediment cores from the Palos
Verdes Shelf and Santa Monica Bay, California.
Mar. Environ. Res. 31:215-225.
Swartz, R.C., D.W. Schults, R.J. Ozretich, J.O.
Lamberson, EA. Cole, T.H. DeWitt, M J. Redmond,
and S.P. Fenaro. 1995. PAH: A model to predict the
toxicity of polynuclear aromatic hydrocarbon mixtures
in field-collected sediments. Environ. Toxicol. Chem.
14(11): 1977-1987.
USEPA. 1987. An overview of sediment quality in
the United States. EPA-905/9-88-002. U.S.
Environmental Protection Agency, Office of ,
Water, Washington, DC.
. 1992a. Proceedings of EM's contaminated
sediment management strategy forums. Chicago, EL,
April 21-22; Washington, DC, May 27-28 and June
16,1992. EPA 823-R-92-007.
. 1992b. Environmental impacts of stormwater
discharges: A national profile, U.S. Environmental
Protection Agency, Office of Water, Washington, DC.
. 1993a. Framework for the development of the
National Sediment Inventory. U.S. Environmental
Protection Agency, Office of Science and Technology,
Washington, DC.
. 1993b. Guidelines for deriving site-
specific sediment quality criteria for the protec-
tion of benthic organisms. EPA-822-R-93-017.
U.S. Environmental Protection Agency, Office of
Science and Technology, Health and Ecological
Criteria Division, Washington, DC.
. 1993c. Identification of sources contributing to
the contamination of the great waters by toxic
compounds. U.S. Environmental Protection Agency,
Office of Manning and Standards, Durham, NC.
. 1993d. Technical basis for establishing
sediment quality criteria for nonionic organic
contaminants for the protection of benthic organisms
by using equilibrium partitioning. Draft. EPA 822-R-
93-011. U.S. Environmental Protection Agency, Office
of Science and Technology, Health and Ecological
Criteria Division, Washington, DC.
. 1994a. Deposition of air pollutants to the great
waters. U.S. Environmental Protection Agency,
Office of Air Quality, Research Triangle Park, NC
. 1994b. Methods for assessing the toxicity of
sediment-associated contaminants with estuarine and
marine amphipods. EPA 600-R-94-025. US. Envi-
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Development, Washington, DC.
. 1994c. Methods for measuring the toxicity and
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——. 19944. Proceedings of the National Sediment
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Office of Science and Technology, Washington, DC.
References-5
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I
Kcl'cmu't's
. 1994e, National water quality inventory:
1992 report to Congress. EPA-841-R-94-001. U.S.
Environmental Protection Agency, Office of Water,
Washington, DC.
, 1994f. Guidance for assessing chemical
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. 1994g. The Watershed Protection Approach:
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. 1995a. Great Lakes Water Quality Initiative
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. 1995b. A Phase I Inventory of Current EPA
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. 1995c. Guidance for Assessing Chemical
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Volume 1: Fish Sampling and Analysis (Second
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DC.
. 1996. Derivation of EPA's Sediment Quality
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Workshop on the Estimation of Atmospheric
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Ontario, Joint Water Quality Board/Science
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Board of the International Joint Commission
Windsor, Ontario, ISBN 1-895085-20-9, 94 pp.
Wenning, R.J., N.L. Bonne vie, and S,L. Huntley.
1994. Accumulation of metals, polychlorinated
biphenyls, and polycyclic aromatic hydrocarbons in
sediments from the lower Passaic River, New
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81.
Willis, G.H., and L.L. McDowell. 1983. Environmen-
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Florida, Department of Civil Engineering and
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Department of Transportation.
References-6
-------
\sllt«>il:ll Si cliiiH iiI <_)u.ilil v Stiru-,\
Appendix A
Detailed Description of
NSI Data
Sources of the NSI Data
The scope of the data compilation component of the NSI was to collect, review, and compile readily available
data that could be used to evaluate the incidence of sediment contamination throughout the United States.
As a result, emphasis was placed on gathering data sets with sediment chemistry data since thosfe were the
most prevalent data available on a national basis. The minimum data elements for inclusion in the NSI were date of
sample collection, latitude/longitude, reliable units (e.g., mg/kg), and source of data. The electronic data sources
used for the NSI are listed below. ;
• EPA's Storage and Retrieval System (STORET)
• EPA's Ocean Data Evaluation System (ODES)
• NOAA's Coastal Sediment Inventory (COSED)
• EPA Region 4's Sediment Quality Inventory
• EPA Gulf of Mexico Program's Contaminated Sediment Inventory
• EPA Region 10/COE Seattle District Sediment Inventory
• EPA's Great Lakes Data Base
• EPA's Environmental Monitoring and Assessment Program (EMAP)
• EPA Region 9 Dredged Material Tracking System (DMATS)
• USGS Massachusetts Bay Data (metals only)
• National Source Inventory (PCS and TRI)
In several cases, the readily available data sources for the NSI were compilations of existing data. For example, the
EPA Gulf of Mexico Program's Contaminated Sediment Inventory included data from ODES, STORET, and EMAP.
Since those data sources had been reviewed independently, they were deleted from the Gulf of Mexico Inventory
before that data set was added to the NSI. A similar screening of data was conducted for the other data sets included in
the NSI. Below is a summary of the remaining contributors to the individual data sets:
STORET Numerous federal and state agencies
ODES Boston Harbor Tennessee
Masschusetts Bay
Cape Arundel
City of Gloucester
Mile 106
South Carolina
Alabama
Mississippi
Georgia
North Carolina
Encina 301(h)
Morro Bay 301(b)
Hyperion 301(h)
Kentucky
Florida
GLNPO/ARCS
Galveston Bay
San Diego Pre-301(h)
Orange County 301(h)
Oxnard 301(h)
Los Angeles 301(h)
Thums Ocean Dumping
Puget Sound
Anchorage
Endicott 403(c)
A-l
-------
Goleta 301(h)
San Francisco NEP
LA2 Ocean Dumping
LA5 Ocean Dumping
COSED NOAA NS&T
Region 4 City of Tampa
Dept of Navy
EPA Region 4
Florida DER
South Florida Water Mgmt DisL
USACE
Gulf of Mexico ADEM (Mobile)
Army Corps Eng.
EPA-Houston
ERL-N
GCRL, Mississippi
Seattle COE Department of Social and Health Services
Department of Ecology
U.S. Fish and Wildlife Service
Puget Sound Water Quality Authority
Tetra Tech, Inc.
Department of Fisheries
Department of Natural Resources
Department of Wildlife
EPA Region 10
Batelle Northwest Sequim Laboratory
Environmental Systems Corporation
Department of Health
College of Ocean and Fisheries Science
PTI Environmental Services
National Oceanic and Atmospheric Admin. Fish
and Wildlife Health Consultants
City of Bellingham
U.S. Army Corps of Engineers, Seattle
Columbia Northwest, Inc.
Hulbert Mill
King County
Municipality of Metropolitan Seattle
Wildlife Health Consultants
U.S. Navy '
City of Olympia, LOTT treatment plant
Port of Bellingham
Port of Everett
Port of Olympia
Port of Port Town send
Thurston County Dept of Public Health
U.S. Coast Guard
Great Lakes Heidelberg College, Tiffin, Ohio
Illinois EPA
Michigan Tech. Univ., Houghton, MI
Kuparuk STP 403(c)
Prudhoe Bay 403(c)
Port Valdez 403(c)
Ed Long
USACE, Jacksonville
USACE, Mobile
USACE, Savannah
USACE, Wilmington
USFWS
TVA
USACE (Mobile)
USEPA Region 6
USGS
Department of Parks and Recreation
Environmental Information Consultants
South. CA Coastal Water Research
Proj., Army Corps of Engineers, San
Francisco
Environmental Science Associates, Inc.
E.V.S. Consultants, Sausalito, CA
Marine Bioassay Labs, Watsonville,
CA
MEC Analytical Systems, Watsonville,
CA
San Francisco Port Commission
ToxScan, Inc., Watsonville, CA
Tetra Tech, Inc., Lafayette, CA
Port of Grays Harbor
Port of Tacoma
Tristar Marine ^
Morton Marine
Port of Seattle
South Park Marina
U.S. Oil and Refining Company
Weyerhauser
Day Island Yacht Club
Shell Oil
Capital Regional District, Victoria, BC
Environment Canada Greater
Vancouver Regional District
E.V.S. Consultants, Seattle, WA
E.V.S. Consultants, Vancouver, BC
British Petroleum Oil Company
American Petroleum Institute
US Army COE, Buffalo District
Beak Consultants, Inc
Ontario Ministry of the Environment
A-2
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\;ifion;iJ Sediment Quality Survey
EMAP
DMATS
USGS
Massachusetts
Bay
USGS
Massachusetts
Bay
Univ. of Wisconsin-Superior, WI
Michigan Dept. Natural Resources
Ohio EPA
Illinois Geological Survey
USEPA-GLNPO
USEPA-ERL-Duluth
Louisianian Province
USEPA Region 9
A.D. Little, 1990
ACE_NED permit file #29-91-00473E
ACE_NED permit file 199102068
ACE_NED permit file 09-89-2777
ACE_NED permit file 09-89-530
ACE.NED permit file 1989-2911
ACE_NED permit file 199101096
ACE_NED permit file 20-87-2002
ACE_NED permit file 20-89-2206
ACE_NED permit file 22-87-927
ACE_NED permit file 23-198902070
ACE_NED permit file 24-87-912
ACE_NED permit file 24-89-1180
ACE_NED permit file 25-81-374
ACE_NED permit file 25-86-1007
ACE_NED permit file 25-86-290E
ACE_NED permit file 25-86-641
ACE_NED permit file Boston Harbor
ACE_NED permit file Bridge marine- Salisbury, MA
ACE_NED permit file CENED-OR (1145-2-303b)
ACE_NED permit file HULL-72-CHA30
ACE_NED permit file Long Wharf Boston
ACE_NED permit file MA DPW Beverly-Salem Bridge
and By-Pass Project
ACE_NED permit file
MA-HULL-81 -180
ACE_NED permit file MA-HULL-84-210
ACE_NED permit file MWRA- Stoney Brook Conduit
ACE_NED permit file Massport Bird Island Flats -
Harborwalk phase III
ACE_NED permit file Navigation Improvement Study
Dredge Material Disposal Plan Supplement to Feasibility
Rep
USACOE.1981
Wong, 1983
USEPA MBDS, 1989
USACOE, 1990b (DAMOS)
Aqua Tech, Melmore, OHEG&G
Bionomics/Aqua Tech Environ. Cnstlt.
Applied Biology, Inc., Decatur, GA
Recra Research, Inc., Tonawanda, NY
USFWS, Columbia, MO - ARCS
Michigan State University
Virginian Province
ACE_NED permit file Navigation
Improvement Study Feasibility
Report and Environmental
Assessment; Mystic RI
ACE_NED permit file Navigation
Improvement Study Dredge
Material Disposal Plan Supplement
to Feasibility Rep
Boehm, 1983
Bajek, 1983
Battelle, 1984; 1987 a, b
Boehm & Farrington, 1984
Boehm etal., 1984
CDM, 1980
Cudmore, 1988
Enseco, 1987a
Enseco, 1987b
GCA Corp., 1982
Gardner et al., 1986
Gardner et al., 1988
Hubbard, 1987
Jason M. Cortell & Assoc., 1982
Jason Cortell, 1990
MA DEQE, 1985
MADEQE, 1986 MA DPW, 1991
MA DEQE, 1982
MacDonald, 1991
NET Atlantic, 1990
Nolan et al., 1981
Penney etal., 1981
Phillips, 1985
Pruell et al., 1989
Ryan et al., 1982
Robinson et al., 1990
Shea et al., 1991
Shiaris et al., 1986
Types of Data Included in the NSI
In addition to sediment chemistry data, tissue residue, benthic abundance, toxicity (solid-phase and elutriate),
histopathology, and fish abundance data have been gathered and included in the NSI, although only the sediment
chemistry, tissue residue, and toxicity data have been evaluated for this report to Congress. The NSI also includes
A-3
-------
\|>|H'ti
-------
Table A-l. Number of Sampling Stations at Which Various Types of Data Were Collected
Data Set
Number of Stations When Measured
Sediment
Chemistry
Tissue
Residue
Benthlc
Abundance
Toxicity
Histopath-
ology
Sediment
Chemistry
and Tissue
Residue
Sediment
Chemistry
and Benthlc
Abundance
Sediment
Chemistry
and Toxicity
Sediment
Chemistry
and
Hlstopath-
ology
Sediment
Chemistry,
Tissue
Residue,
and Toxicity
Sediment
Chemistry,
Beothk
Abundance,
and Toxicity
STORET
12,907
6,057
1,533
Region 4
1,024
ODES
1317
1,722
2,592
296
37
664
70
2
49
COSED
1,104
Gulf of
Mexico
210
82
6
Great Lakes
761
26
476
373
26
449
369
26
68
DMATS
213
202
245
169
188
163
Mass. Bay
979
EMAP
LAProv.
VAProv.
260
200
199
259
212
259
212
259
198
259
202
259
202
259
198
259
202
Seattle
USCOE
2,116
365
876
365
707
270
Total
21,093
8,206
3,904
2,343
259
1,963
1,939
1,801
259
389
848
-------
Ibble A-2. Number of Sampling Stations With Data Included in the NSI
Measurement Parameters
Total Number of
Stations
Stations with Coordinates
Number
% of Total Number
of Stations
^Coordinates*
Sediment Chemistry
21,093
19,546
76
TOC
6,170
5,335
21
AVS
425
371
1
Tissue Residue
8,206
7,208
28
Toxicity
2,343
1,523
6
Elutriate Phase
630
—
, _
SoBd Phase
1,865
—
—
Benthic Abundance
3,904
1,844
7
Histopathotogy
259
259
1
Sediment Chemistry & Tissue
1,963
1,930
8
Sediment Chemistry & Toxicity
1,801
1,263
5
Sediment Chemistry & Abundance
1,939
1,340
5
Sediment Chemistry & Histopathotogy
259
259
1
Sediment Chemistry, Tissue, & Toxicity
389
359
1
Sediment Chemistry, Toxicity, & Abundance
848
733
3
Tola] number of stations with coordinates = 25,555.
evaluation of sediment chemistry data described in Chapter 2). Key changes to the data set from version 1.0
include the following:
• Inclusion of Regional/state review codes. (See data element NSIREVCD in tables ALLSEDI and ALLTISS.)
• Resolution of species codes for tissue residue data.
• Inclusion of biotoxicity control data for EMAP programs.
• Revised loadings data from Permit Compliance System (PCS) and Toxic Release Inventory (TRI). Facili-
ties with no loadings data are included as a separate table.
• Inclusion of species information and toxicity phase for purposes of the NSI evaluation methodology.
The remainder of this section contains a listing of the field names and descriptions associated with each
table in the NSI.
A-6
-------
NalitinnI Sediment (,)u;ilitv Survey
Figure A-l. Organization of NSI Data.
A-7
-------
Thble A-3. Data TMbles Available In the NSI
Table Name
Table Description
ALLSTAT.DBF
Station
ALLSBDI.DBF
Sediment chemistry
ALLTISS.DBF
Tissue residue
ALLBIOT.DBF
Biotoxicity
ALLSEDM.DBF
Sediment grain size and miscellaneous sediment chemistry
ALLTISM.DBF
Miscellaneous tissue residue
ALLBLUT.DBF
Elutriate
LOADD.DBF
PCS/TRI loadings
LOADS.DBF
PCS/TRI facilities (have loadings data)
LOADO.DBF
Other PCS/TRI facilities (no associated loadings data)
BIOTCODE.DBF
Toxicity phase for biotoxicity table (ALLBIOT)
ELUTPARM.DBF
list of analytes for elutriate table (ALLELUT)
SEDJ?ARM.DBF
List of analytes for sediment tables (ALLSEDI, ALLSEDM)
TIS_CODE.DBF
List of species for tissue tables (ALLTISS, ALLTISM)
TIS.PARM.DBF
List of analytes for tissue tables (ALLTISS, ALLTISM)
SEACOB.DBF
EPA Region 10/COE Seattle District's Sediment Inventory Code file (important for
interpreting a large number of codes unique to this data source)
REMARK. WP
Text file on remark codes (important for remark codes other than "K" or "U")
ALLSUPR.DBF
Superfund facilities
ALLBENA.DBF
Benthic species abundance
ALLBENC.DBF
Benthic community
ALLHIST.DBF
Histopathology
ALLFISA.DBF
Fish abundance
SPEC-CD.DBF
Species codes for benthic data
FISH-CD.DBF
Species codes for fish abundance data
A-8
-------
Njiiiomil Nt ilinn iH Qu;ilil> Siirtcy
ALLSTAT.DBF
Station
SOURCE
AGENCY
STATION
COUNTY
DEPTH
DEPT.MAX
DEPT_MIN
DREDGESI
DRWATERB
GEOCODE
INSTIT
LAT
LAT_2
LNG
LNG_2
LOCATION
LOC_CODE
NSIREACH
ORIGIN
ORG.NAME
REFER
SR.SCI
STATE
WA1ERBOD
EPA_REG
FIPS
FIPS_DIS
HUCJDIS
RFl.DIS
Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
Monitoring station identification code. (ODES NOTE: STATION = STNJCDII'' II STA-
TION II DATE. DMATS NOTE: STATION = ZD II " IISTATIONIII " II SERIES II' * II
SCAN.)
County
Water depth (m)
Maximum water depth (m)
Minimum water depth (m)
Dredged site
Dredged water body
Geologic code
Institution
Latitude (decimal degrees)
Latitude #2 forming a rectangle (decimal degrees)
Longitude (decimal degrees)
Longitude #2 forming a rectangle (decimal degrees)
Location
Location code
Reach File 1 reach
Origin
Organization name
Reference, literature citation
Senior scientist
State
Waterbody
EPA Region
FIPS code
Distance to nearest FIPS (mile)
Distance to nearest catologic unit (mile)
Distance to RF1 reach (mile)
ALLSEDI.DBF
SOURCE
AGENCY
STATION
DATE
SAMPLE
SUBSAMPL
REPLICAT
SEQ
CAS
CLEANUP
COMMENTS
DRY.WGT
Sediment chemistry
Identification of data origin (e.g,, REG4 is the Region 4 Pilot Study)
Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
Monitoring station identification code. (ODES NOTE: STATION = STN_CD II " II STA-
TION II DATE. DMATS NOTE: STATION = ID II " II STATIONI li'' II SERIES II " II
SCAN.)
Date of sample collection
Unique sample identifier code
Unique subsample identifier code
Unique replicate identifier code
Computer-generated sequence number when multiple samples were taken; SOURCE,
AGENCY, STATION, and DATE were identical; and no SAMPLE, SUBSAMPL, or
REPLICAT codes were provided
CAS number for analyte
Sample cleanup code to indicate an additional step taken to further purify the sample
extracts or digestates
Comments
Percent of total sample remaining after drying
A-9
-------
\|>|K'ii
-------
Niitioiv.il Sediment Quality Survey
SAMPTYPE
Sample type
SEX
Sex code used to identify sex of sample
SMP_EQP
Sampling equipment code
SPECCODE
Species code
SPECIMEN
Unique identifier for the individual organism being analyzed
TOT_REP
Number of replicates
WEIGHT
Weight of organism
WET_WGT
Total weight of sample
LIPIDS
% Extractable lipids
SPEC_BIO
STORET taxonomic code
ALLBIOT.DBF
Biotoxicity
SOURCE
AGENCY
STATION
DATE
SAMPLE
REPLICAT
SEQ
AMMONIA
ABNORMAL
BIOASS_DA
BIOASSAY
BIOMASS
COMMENTS
COM_NAME
DIL_UNIT
DILUTION
DOX
ENDPOIN2
ENDPOINT
E_QUALIF
EMERGENC
EXT_MTHO
FEEDING
FLUSH
GENUS
HARDNESS
HOLD_TIM
LFSTG_EN
LFSTG_ST
MEASURED
NAME
NUM_ORGA
P
Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
Monitoring station identification code. (ODES NOTE: STATION = STNjCD II ' ' II STA-
TION II DATE. DMATS NOTE: STATION = ID II " IISTATIONIII " II SERIES II " II
SCAN.)
Date of sample collection
Unique sample identifier code
Unique replicate identifier code
Computer-generated sequence number when multiple samples were taken; SOURCE,
AGENCY, STATION, and DATE were identical; and no SAMPLE, SUBSAMPL, or
REPLICAT codes were provided
Ammonia concentration (mg/L)
Abnormality
Bioassay date
Type of bioassay reported
Biomass
Comments
Common name
Concentration/Dilution units
Concentration/Dilution
Dissolved oxygen (mL/L)
Endpoint #2 of bioassay test
Endpoint of bioassay test
EMERGENC qualifier
Emergence after 10 days
Extraction method code to indicate the method used to extract or digest the sample matrix
and remove or isolate the chemical of concern
Feeding of species tested
Flushing rate in percent of chamber volume exchanged/24 hours
Organism genus
Hardness
Holding time of sample prior to analysis (weeks)
Life stage end—for bioassays that span more than one life stage, record predominant life
stage at the end of the bioassay
Life stage start—for bioassays that span more than one life stage, record predominant life
stage at the start of the bioassay
Measured (Y/N)
Genus and species name (linked to PHASE)
Number of organisms
Result associated with ENDPOINT
A-ll
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\|>|H>ii
-------
DRY.WGT
Percent of total sample remaining after drying
EXT_MTHO
Extraction method code to indicate the method used to extract or digest the sample matrix
and remove or isolate the chemical of concern
FINE_MTH
Method of analysis for analysis of fine particles. Left blank if sample was not split into
fractions.
INSTRUME
Instrument code to identify the final chemical analysis method(s) used for analyzing the
sample
meas_bas
Result is wet or dry weight basis (see also P)
P
Result associated with PARM
PARM
Analyte measured (see also P and R)
PHI_B
Phi boundaries in phi units, between the coarse and fine fractions
PHI_MAX
Phi boundary maximum at the fine end of the analyzed range
PHI_MIN
Phi boundary minimum at the coarse end of the analyzed range
R
Remark code associated with PARM and P
SAMP_DTL
Depth to bottom of sample interval (m)
SAMP_DTU
Depth to top of sample interval (m)
SMP.EQP
Sampling equipment code
SPHERE
Sphere (i.e., environment) code from which the sample came
TOT.WGT
Total weight of sample (g)
UNITS
Units associated with PARM, P, and R
WET_WGT
Total wet weight of sample (g)
P_ALP
Nonnumeric result associated with PARM
ALLTISM.DBF
Miscellaneous tissue residue
SOURCE
AGENCY
STATION
DATE
SAMPLE
SEQ
REPLICAT
ANAT_CD
CAS
CLEANUP
COMPOSIT
DRY_WGT
EXT_MTHO
INSTRUME
LENGTH
LIPIDS
LIFE_STA
MEAS_BAS
NUMB_IND
P
Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
Monitoring station identification code. (ODES NOTE: STATION = STN_CD II " II STA-
TION II DATE. DMATS NOTE: STATION = ID II " IISTATIONIII " II SERIES II " II
SCAN.)
Date of sample collection
Unique sample identifier code
Computer-generated sequence number when multiple samples were taken; SOURCE,
AGENCY, STATION, and DATE were identical; and no SAMPLE, SUBSAMPL, or
REPLICAT codes were provided
Unique replicate identifier code
Organ/tissue sampled code
CAS number for analyte
Sample cleanup code to indicate an additional step taken to further purify the sample
extracts or digestates
A unique identifier to indicate a sample created by compositing tissues from several
individuals.
Percent of total sample remaining after drying
Extraction method code to indicate the method used to extract or digest the sample matrix
and remove or isolate the chemical of concern
Instrument code to identify the final chemical analysis method(s) used for analyzing the
sample
Length of specimen
Lipids (%)
Life stage code to identify the life stage of sample
Result is wet or dry weight basis (see also P)
Number of organisms in sample
Result associated with PARM
A-13
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Appendix A
PARM
Analyte measured (see also P and R)
R
Remark code associated with PARM and P
SEX
Sex code used to identify sex of sample
SMPJEQP
Sampling equipment code
SPECCODE
Species code
SPEC_SCI
Species scientific name
SPECIMEN
Unique identifier for the individual organism being analyzed
UNITS
Units associated with PARM, P, and R
WET_WGT
Total weight of sample
P_ALP
Nonnumeric result associated with PARM
ALLELUTJ3BF
SOURCE
AGENCY
STATION
DATE
SAMPLE
SEQ
SUBSAMPL
REPLICAT
CAS
EXT.MTHO
INSTRUME
P
PARM
R
SAMP_DTL
SAMP_DTU
SAMP_EQP
Elutriate
Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
Monitoring station identification code. (ODES NOTE: STATION = STNjCD II ' ' II STA-
TION II DATE. DMATS NOTE: STATION = ID II " IISTATIONIII " II SERIES II " II
SCAN.)
Date of sample collection
Unique sample identifier code
Computer-generated sequence number when multiple samples were taken; SOURCE,
AGENCY, STATION, and DATE were identical; and no SAMPLE, SUBSAMPL, or
REPLICAT codes were provided.
Unique subsample identifier code
Unique replicate identifier code
CAS number for analyte
Extraction method code to indicate the method used to extract or digest the sample matrix
and remove or isolate the chemical of concern
Instrument code to identify the final chemical analysis method(s) used for analyzing the
sample
Result associated with PARM (|ig/L)
Analyte measured (see also P and R)
Remark code associated with PARM and P
Depth to bottom of sample interval (m)
Depth to top of sample interval (m)
Sampling equipment code
LOADD.DBF
PCS/TRI loadings
ID
Facility identification number
CAS
CAS number for analyte
CHEMICAL
Analyte name
SIC
SIC code for facility
E3KGY0
PCS loadings using below detection limit (dl) equal to 0.0 assumption
E3KGYE
PCS loadings using below detection limit equal to 0.5-dl assumption
E3KGY1
PCS loadings using below detection limit equal to dl assumption
E3FLOO
PCS flow using below detection limit equal to 0.0 assumption
E3FLOE
PCS flow using below detection limit equal to 0.5-dl assumption
E3FL01
PCS flow using below detection limit equal to dl assumption
E6KGYE
TRIPOTW transfers
E6KGY75
75 percent of TRI POTW transfers
A-14
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LOADS.DBF
PCS/TRI facilities (have loadings data)
ID Facility identification number
CODE "PCS" or "TRI"
SPC State postal code
LAT Latitude (decimal degrees)
LNG Longitude (decimal degrees)
NSIREACH Reach File 1 Reach
LOADO.DBF Other PCS/TRI facilities (no associated loadings data)
ID Facility identification number
SPC State postal code
LAT Latitude (decimal degrees)
LNG Longitude (decimal degrees)
NSIREACH Reach File 1 Reach
BIOTCODE.DBF Toxicity phase for biotoxicity table (ALLBIOT)
NAME Genus and species name
PHASE Toxicity phase listed in source of data (when available)
SOURCE Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
NSIPHASE Toxicity phase used by NSI
ELUTPARM.DBF List of analytes for elutriate table (ALLELUT)
SOURCE
PARM
CAS
LNAME
Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
Analyte measured (see also P and R)
CAS number for analyte
Analyte long name
SED_PARM.DBF List of analytes for sediment tables (ALLSEDI, ALLSEDM)
SOURCE
PARM
CAS
LNAME
Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
Analyte measured (see also P and R)
CAS number for analyte
Analyte long name
TIS_CODE.DBF List of species for tissue tables (ALLTISS, ALLTISM)
SPECCODE
SPEC_SCI
SPEC_COM
RES_MIG
BOT.PEL
EDIBLE
Species code
Species scientific name
Species common name
Species resident, migratory, or either
Species benthic, pelagic, or either
Species considered edible by humans
TIS_PARM.DBF List of analytes for tissue tables (ALLTISS, ALLTISM)
SOURCE Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
PARM Analyte measured (see also P and R)
A-15
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CAS
LNAME
CAS number for analyte
Analyte long name
ALLSUPR.DBF
STATE
ID
NAME
COUNTY
CNTYJPIP
C0305
C0326
LAT
LNG
NSIREACH
Superfund facilities
State postal code
Superfund identification
Facility name
County name
3-digit county FIPS code
C0305
C0326
Latitude (decimal degrees)
Longitude (decimal degrees)
Reach FUe 1 Reach
ALLBENA.DBF Benthic species abundance
SOURCE
AGENCY
STATION
DATE
SAMPLE
REPLICAT
BOTTOM
AREAJ3AS
COMMJBAS
EXT_MTHO
GENUS
MESH_SZ
NJREP
NUMBJND
NUMB„SPE
ORDER
P
PARM
P_MEAN
P_STD
R
SAMP_DTL
SAMP_DTU
SPECIES
SPECCODE
UNITS
Identification of data origin (e.g.» REG4 is the Region 4 Pilot Study)
Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
Monitoring station identification code. (ODES NOTE: STATION = STNjCD II' ' II STA-
TION II DATE. DMATS NOTE: STATION = ID II " IISTATIONIII»* II SERIES II " II
SCAN.)
Date of sample collection
Unique sample identifier code
Unique replicate identifier code
Bottom type
Area basis for reported data
Basis for community abundance measurements
Extraction method code to indicate the method used to extract or digest the sample matrix
and remove or isolate the chemical of concern
Organism genus
Seive mesh size
Number of replicate samples
Total number of individuals
Total number of unique species
Organism order
Result associated with PARM
Analyte measured (see also P and R)
MeanP
Standard deviation of P
Remark code associated with P and PARM
Depth to bottom of sample interval (m)
Depth to top of sample interval (m)
Organism species
Species code
Units associated with PARM, P, and R
ALLBENC.DBF Benthic community
SOURCE Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
A-16
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AGENCY
STATION
DATE
SAMPLE
AMPHIPOD
AMPHMABN
AREA_BAS
ARTHROPO
BIOM_TOT
BIOMMEAN
BIV_MABN
BSPINDEX
BSP_GRAB
BSP_MABN
BSP_MDIV
BSP.MEAN
BSP_MEXP
BSP_TABN
BSP_TDIV
BSP.TOT
CAPIMABN
COMM.BAS
CRUSTACE
DECAMABN
DOMINANC
ECHINODE
EVENESS
ITI
MEDJDIAM
MISC.TAX
MOIST.M
MOLLUSCS
NEMATODE
OLIGOCHA
PABN_AMP
PABN_BIV
PABN_GAS
PABN.TUB
PLYC_MWT
PLYCMABN
P_SENSIT
P_TOLERA
POLYCHAE
QUARDVTM
Ql.PHI
Q3_PHI
RPDDEP.M
SICL_B_M
SKEWNESS
TUBIMABN
I
i N:iXiofi;il ScrJiiueuf Qaitliir Siu voy
Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
Monitoring station identification code. (ODES NOTE: STATION = STN_CD II " II STA-
TION II DATE. DMATS NOTE: STATION = JD II " IISTATIONIII " II SERIES II' * II
SCAN.)
Date of sample collection
Unique sample identifier code
Number of amphipod
Mean abundance of amphipods
Area basis for reported data
Number of arthropods in the sample
Total biomass (g)
Mean biomass per grab (g)
Mean abundance of bivalves (g)
Benthic species index
Number of pabs
Mean abundance per grab
Mean Shannon-Wiener diversity index
Mean number of species per grab
Expected mean number of species
Total abundance
Pooled Shannon-Wiener diversity index
Total number of species
Mean abundance of eapitellids
Basis for community abundance measurements
Number of crustaceans in the sample
Mean abundance of decapods
Numeric dominance in the sample
Number of echinoderms in the sample
Eveness
ITI
50% quartile diameter (phi)
Number of miscellaneous taxa in sample
Sediment moisture content (%)
Number of molluscs in the sample
Number of nematodes in the sample
Number of oligochaetes in the sample
Percent abundance amphipods
Percent abundance bivalves
Percent abundance gastropods
Percent abundance tubificids
Mean biomass per polychaete (g)
Mean abundance of polychaetes
Abundance of pollution sensitive organisms (%)
Abundance of pollution tolerant organisms (%)
Number of polychaetes in the sample
Phi quartile deviation
25% quartile diameter (phi)
75% quartile diameter (phi)
Mean RPD in mm
Mean silt/clay content (%)
Phi quartile skewness
Mean abundance of tubificids
A-17
-------
ALLfflSTJDBF
SOURCE
AGENCY
STATION
DATE
BODYPATH
BRNCPATH
BUCCPATH
FSP_ABN
FSP_TOT
MNMDTRSH
Histopathology
Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
Monitoring station identification code. (ODES NOTE: STATION = STN_CD II " II STA-
TION II DATE. DMATS NOTE: STATION = ID II' * IISTATIONIII " II SERIES II4 ' II
SCAN.)
Date of sample collection
Number of fish with body pathologies
Number of fish with branchial pathologies
Number of fish with buccal pathologies
Abundance (number/trawl)
Number of species
Manmade trash (Y/N)
ALLFISA.DBF
SOURCE
AGENCY
STATION
DATE
LEN_MEAN
LEN_STD
P
PARM
SPECCODE
UNITS
Fish abundance
Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
Monitoring station identification code. (ODES NOTE: STATION = STNjCD II " II STA-
TION II DATE. DMATS NOTE: STATION = ID II " II STATIONI II " II SERIES II ' ' II
SCAN.)
Date of sample collection
Mean length (in)
Standard deviation length (in)
Result associated with PARM
Analyte measured (see also P)
Species code
Units associated with PARM and P
SPEC-CD JDBF
SPECCODE
SPEC_SCI
SPEC COM
Species codes for benthic data
Species code
Species scientific name
Species common name
FISH-CD.DBF Species codes for fish abundance data
SPECCODE Species code
SPECJSCI Species scientific name
SPEC_.COM Species common name
A-18
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National Sediment Quality Sur* cv
Appendix B
Description of Evaluation
Parameters Used in the NSI
Data Evaluation
Chapter 2 of this document presented 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 D. For the
purpose of discussion, the sediment evaluation parameters have been placed into three groups: (1) those used to assess
potential impacts on aquatic life, (2) those used to assess potential impacts on human health, and (3) those used to assess
potential impacts on wildlife. The uncertainties associated with the use of these parameters in the NSI data evaluation arc
discussed in Chapter 5.
Aquatic Life Assessments
To evaluate the potential threat to aquatic life from chemical contaminants detected in sediments, measured concen-
trations 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, when available, were also evaluated for the NSI.
Sediment chemistry screening levels are reference values above which sediment contaminant concentrations could
pose a significant threat to aquatic life. Several different approaches, based on causal or empirical correlative method-
ologies, have been developed for deriving screening levels of sediment contaminants. Each of these approaches
attempts to predict contaminant concentration levels that could result in adverse effects to benthic species, which are
extrapolated to represent the entire aquatic community for this evaluation. For the purpose of this analysis, the
screening levels selected include the following:
• EPA's draft sediment quality criteria (SQCs) for five nonionic organic chemicals, developed using an equilib-
rium partitioning approach (USEPA, 1992a, 1993a).
• Sediment quality advisory levels (SQALs) for selected nonionic organic chemicals, developed using an
equilibrium partitioning approach (USEPA, 1992a, 1993a).
• The sum of simultaneously extracted divalent transition metals concentrations minus the acid-volatile sulfide
concentration ([SEM] - [AVS]), also based on an equilibrium partitioning approach.
• Effects range-median (ERM) and effects range-low (ERL) values for selected nonionic organics and metals
developed by Long et al. (1995).
• Probable effects levels (PELs) and threshold effects levels (TELs) for selected nonionic organics and metals
developed for the Florida Department of Environmental Protection (FDEP, 1994).
• Apparent effects thresholds (AETs) for selected organics and metals developed by Barrick et al. (1988).
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
-------
\|>|H'it
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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 have not been evaluated using equilibrium
partitioning approaches.
Draft Sediment Quality Criteria
The equilibrium partitioning model was selected for the development of sediment quality criteria because it can be
applied to predict sediment contaminant concentrations below which biological effects are not expected to occur based
on the toxicity of individual nonionic organic chemicals—and hence can protect benthic aquatic life in bedded, perma-
nently inundated, or intertidal sediments—while accounting for sediment characteristics that affect the bioavailability of
the chemical (Di Toro etal., 1991; USEPA, 1993a). 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 (f^) is greater than
0.2 percent.
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
(f^.) multiplied by the sediment particle organic carbon partition coefficient (K^). 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 concentra-
tion 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 (KJ is related to the chemical's octanol/water partition
coefficient (Kow) by the following equation (Di Toro et al., 1991):
logK^ = 0.00028 + 0.983(logKow)
Hie 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 concentra-
tion of chemical (Cd) multiplied by the organic carbon content of the sediment (f^) and the particle organic carbon
partition coefficient (K^.), when f^ is greater than 0.2 percent (USEPA, 1993a), thus normalizing the dry-weight sediment
concentration of the chemical to the organic carbon content of the sediment.
The criterion for the dissolved concentration of chemical (Ct) is derived from the final chronic value (FCV) of EPA's
water quality criteria (USEPA, 1985). Freshwater and saltwater FCVs 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 the highest levels of a chemical to which organisms can be exposed without producing toxic effects. This level
is predicted to protect approximately 95 percent of aquatic life under certain conditions. 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). Thus, if the concentration of a chemical in
sediment, measured with respect to the sediment organic carbon content, does not exceed the sediment quality criterion,
then no adverse biological effects from that chemical would be expected (USEPA, 1992a, 1993a).
EPA has developed and published draft freshwater sediment quality criteria (SQCs) for the protection of aquatic life
for five contaminants: acenaphthene, dieldrin, endrin, fluoranthene, and phenanthrene. These draft SQCs are based on
the equilibrium partitioning approach (USEPA 1993b, c, d, e, f) using the aquatic life water quality criterion final chronic
value (FCV, in jig/L) and the partition coefficient between sediment and pore water (Kp, in L/g sediment) for the chemical
B-3
-------
Appendiv B
chemical of interest (Di Toro et al., 1991; USEPA, 1993a). Thus, SQC = Kp FCV. On a sediment organic carbon basis,
the sediment quality criterion, SQC^., is:
SQC,, (m I gx) = FCV(jig / L) X (L / kg) % dO"3 kgoc / goc)
where:
FCV = EPA aquatic life water quality criterion final chronic value and
Koc = organic carbon-water partitioning coefficient.
is presumed to be independent of sediment type for nonionic organic chemicals, so that the SQC^ is also
independent of sediment type. Using a site-specific organic carbon fraction, f^ (g^/g sediment), the SQC^, can be
expressed as a sediment-specific value, the SQC:
SQC = (SQCoc)(fK)
Sediment Quality Advisory Levels
EPA intends to develop sediment quality criteria for additional chemicals in the future. In the interim, EPA's
Office of Science and Technology developed equilibrium partitioning-based sediment quality advisory levels (SQALs)
using the following equation:
SQAL^g / g„) = [FCV, SCV^g/ L)1 % K„(L / kg) % (10"3kgoc /gx)
where:
SQAL^ = calculated sediment quality advisory level;
FCV, SCV = EPA aquatic life chronic criterion (final chronic value, FCV), or other chronic threshold
water concentration (secondary chronic value, SCV); and
= organic carbon-water partitioning coefficient.
As noted in Chapter 2, EPA has proposed sediment quality criteria (SQCs) for five chemicals based on the highest
quality toxicity and octanol/water partitioning (Kow) data, which have been reviewed extensively. This section de-
scribes the sources of data used to calculate the values used in the SQAL equations: log Kows (used to derive K^s) and
chronic threshold water concentrations. A detailed description of the methods and data used to develop SQALs for
specific chemicals using the equilibrium partitioning approach will be published by EPA as a separate document.
SQALs for use in the NSI data evaluation were developed in conjunction with other programs at EPA (established
under the Resource Conservation and Recovery Act, RCR A, and the Superfund Amendments and Authorization Act,
SARA) to provide the same values for conducting screening-level evaluations of sediment toxicity for these programs.
The SQALs (as well as the other sediment chemistry threshold levels) are meant to be used/or screening purposes only.
The screening values are not regulatory criteria, site-specific cleanup standards, or remediation goals. The screening
levels are set to be appropriately conservative, so samples that do not exceed the screen would not be expected to
exhibit adverse effects from the action of the specific chemical evaluated; exceeding the screening levels does not
indicate the level or type of risk at a particular site, but can be used to target additional investigations. EPA's Office of
Research and Development (ORD), including staff from Environmental Research Laboratory, Athens, Georgia; Envi-
ronmental Research Laboratory, Duluth, Minnesota; and Environmental Research Laboratory, Narragansett, Rhode
Island, provided guidance and assisted in the development of the necessary values. The SQALs used for the NSI data
evaluation are presented with other screening values in Table D-l of Appendix D.
Method for Determination of Log Kob$. Log Kow values were initially identified in summary texts on physical-
chemical properties, such as Howard (1990) and Mackay et al. (1992a, b) and accompanying volumes. Additional
compendia of log Kow values were also evaluated, including De Kock and Lord (1987), Doucette and Andren (1988),
Klein et al. (1988), De Bruijn etal. (1989), Isnard and Lambert (1989), Leo (1993), Noble (1993), and Stephan (1993).
To supplement these sources, on-line database searches were conducted in ChemFate, TOXLINE, and Hazardous
Substances Data Bank (HSDB) (National Library of Medicine); Internet databases such as CARL UNCOVER; and
B-4
-------
EPA databases such as ASTER, OLS, and the ORD BBS, Original references were identified for the values, and
additional values were identified. In cases where log Kow values varied over several orders of magnitude or measured
values could not be identified, detailed on-line searches were conducted using TOXLIT, Chemical Abstracts, and
DIALOG. Values identified from all of these sources and the method used to obtain each log Kow value were compiled
for each chemical. A few chemicals lacked experimentally measured log Kows, and no log Kow data were available from
any source for butachlor, DCPA/Dacthal, and Ethion/B laden.
The determination of Kow values was based on experimental measurements taken primarily by the slow-stir, gen-
erator-column, and shake-flask methodologies. The SPARC Properties Calculator model was also used to generate
Kow values, when appropriate, for comparison with the measured values. Values that appeared to be considerably
different from the rest were considered to be outliers and were not used in the calculation.
For each chemical, the available value based on one of these methods was given preference. If more than one such
value was available, the log Kow value was calculated as the arithmetic mean of those values (USEPA, 1994), Recom-
mended log Kows were finalized by ORD-Athens based on recommended criteria, and the justification for selection of
each value was included in the report (Karickhoff and Long, April 10,1995, report).
Selection of Chronic Toxicity Values. A hierarchy of sources for chronic toxicity values to develop the SQ ALs was
prepared. The following sources were identified and ranked from most to least confidence in the chronic values to be used:
1. Sediment quality criteria (SQCs).
2. Final chronic values from the Great Lakes Initiative (USEPA, 1995c),.
3. Final chronic values from the National Ambient Water Quality Criteria documents.
4. Final chronic values from freshwater criteria documents.
5. Final chronic values developed from data in EPA's Aquatic Toxicity Information Retrieval database (AQUIRE)
and other sources.
6a. Secondary chronic values developed from data in AQUIRE and other sources.
6b. Secondary chronic values from Suter and Mabrey (1994)
EPA SQCs were available for five chemicals: acenapthene, dieldrin, endrin, fluoranthene, and phenanthrene. There
were no final chronic values (FCVs) obtained by the aquatic life criteria methodology (referred to as "Her I") de-
scribed in USEPA (1995c) available for the remaining chemicals in the NSI. Two SQALs were based on the FCVs
from National Ambient Water Quality Criteria documents, for gamma-BHC/Lindane and toxaphene. No FCVs were
available from criteria documents.
Thirteen SQALs were based on work conducted by Oak Ridge National Laboratories (Suter and Mabrey, 1994)
using the USEPA (1995c) methodology for obtaining secondary chronic values ("Tier II"). This methodology was
developed to obtain whole-effluent toxicity screening values based on all available data, but the SCVs could also be
calculated with fewer toxicity data than are required for the criteria methodology. 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 NSI. SCVs from Suter and Mabrey
(1994) were used to develop SQALs for the following chemicals:
benzene napthalene
chlorobenzene 1,1,2,2-tetrachloroethane
delta-BHC tetrachloroethene
dibenzofuran , toluene
diethyl phthalate 1,1,1-trichloroethane
di-n-butyl phthalate
ethylbenzene
trichloroethene
B-S.
-------
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 SCVs:
Insufficient toxicity test data were found in AQUIRE for acenapthylene, endosulfan sulfate, heptachlor epoxide,
and trichlorofluoromethane. In addition, review of AQUIRE data records indicated that no daphnid acute toxicity
tests had been conducted for hexachlorobutadiene. These chemicals were dropped from further development of
SQALs.
Acid-Volatile Sulfide Concentration
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). Metals form insoluble complexes with the reactive pool of solid-phase
sulfides in sediments (iron and manganese sulfides), restricting their bioavailability. The metals that can bind to these
sulfides have sulfide solubility parameters smaller than those of iron sulfide and include nickel, zinc, cadmium, lead,
copper, and mercury. Acid-volatile sulfide (AVS) is one of the major chemical components that control the activities
and availability of metals in the pore waters of anoxic sediments (Meyer et al., 1994).
AVS is operationally defined as the sulfide liberated from a sediment sample to which hydrochloric acid has been
added at room temperature under anoxic conditions (Meyer et al., 1994). The metals concentrations that are extracted
during the same analysis are termed the simultaneously extracted metals (SEM). SEM is operationally defined as
those metals which form less soluble sulfides than do iron or manganese (i.e., the solubility products of these sulfides
are lower than that of iron or manganese sulfide) and that are at least partially soluble under the same test conditions
in which the AVS content of the sediment is determined (Allen et al., 1993; Di Toro et al., 1992; Meyer et al., 1994).
Laboratory studies using spiked sediments and field-collected metal-contaminated sediments demonstrated that
when the molar ratio of SEM to AVS [SEM]/[AVS] was less than 1 (excess AVS remained), no acute toxici ty (mortal-
ity 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 |imol of SEM per |imol AVS (Casas and Crecelius, 1994; Di Toro et al., 1992).
Experimental studies indicate that the lower limit of applicability for AVS is approximately 1 pmol AVS/g sedi-
ment 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), However, the AVS approach can be used to predict when a sediment contami-
nated with metals is not acutely toxic (Ankley et al., 1993; Di Toro et al., 1992).
There are several important factors to consider in interpreting the [SEMHAVS] difference. First, all toxic SEMs
present in amounts that contribute significantly to the [SEM] sum should be measured. However, because mercury
presents special problems, it is not included in the current SEM analysis. Second, if the AVS content of sediment is
biphenyl
4-bromophenyl phenyl ether
butyl benzyl phthalate
diazinon
1.2-dichlorobenzene
1.3-dichlorobenzene
1.4-dichlorobenzene
endosulfan mixed isomers
alpha-endosulfan
beta-endosulfan
fluorene
hexachlorethane
malathion
methoxychlor
pentachlorobenzene
tetrachl oro methane
tribromomethane
1,2,4-trichlorobenzene
trichloromethane
m-xylene
B-6
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(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 an appropriate carbon source
occur under low oxygen conditions for the sulfate-reducing bacteria. Lastly, AVS can vary when sediments are oxgenated
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.
Biological Effects Correlation Approaches
Biological effects correlation approaches are based on the evaluation of paired field and laboratory data to relate
incidence of adverse biological effects to the dry-weight sediment concentration of a specific chemical at a particular
site. Researchers use these data sets to identify level-of-concern chemical concentrations based on the probability of
observing adverse effects. Exceedance of the identified level-of-concern concentrations is associated with a likelihood
of adverse organism response, but it does not demonstrate that a particular chemical is solely responsible. Conse-
quently, correlative approaches do not indicate direct cause-and-effect relationships. In fact, a given site typically
contains a mixture of chemicals that contribute to observed adverse effects to some degree. These and other potentially
mitigating factors tend to make screening values based on correlative approaches lower than screening values based on
effects caused by a single chemical. However, correlative procedures differ from one another by design and, subse-
quently, in how they relate to sediment toxicity. For example, ERMs are levels usually associated with adverse effecs,
whereas AETs are levels intended to always be associated with adverse effects. Thus, when in error, ERMs minimize
false negatives relative to AETs and AETs minimize false positives relative to ERMs (Ingersoll et al., 1996).
Effects Range-Medians and Effects Range-Lows
The effects range approach for deriving sediment quality guidelines involves matching dry-weight sediment con-
taminant concentrations with associated biological effects data. Long and Morgan (1990) originally developed informal
guidelines using this approach for evaluation of NOAA's National Status and Trends (NS&T) data. Data from equilib-
rium partitioning modeling, laboratory, and field studies conducted throughout North America were used to determine
the concentration ranges that are rarely, sometimes, and usually associated with toxicity for marine and estuarine
sediments (Long et al., 1995). Effects range-low (ERL) and effects range-median (ERM) values were derived by Long et
al. (1995) for 28 chemicals or classes of chemicals: .9 trace metals, total PCBs, 13 individual polynuclear aromatic
hydrocarbons (PAHs), 3 classes of PAHs (total low molecular weight, total high molecular weight, and total PAH), and
2 pesticides (p,p'-DDE and total DDT), For each chemical, sediment concentration data with incidence of observed
adverse biological effects were identified and ordered. The authors identified the lower lOth-percentile concentration as
the ERL and the 50th-percentile concentration as the ERM. In terms of potential biological effects, sediment contami-
nant concentrations below the ERL are defined as in the "minimal-effects range," values between the ERL and ERM are
in the "possible-effects range," and values above the ERM are in the "probable-effects range." Data entered into this
biological effects database for sediments (BEDS) were expressed on a dry-weight basis.
The accuracy of these guidelines was evaluated using the data in the database not associated with adverse effects
and noting whether the incidence of effects was less than 25 percent in the minimal-effects range, increased consistently
with increasing chemical concentrations, and was greater than 75 percent in the probable-effects range. Long et al.
B-7
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Appendix 15
tently with increasing chemical concentrations, and was greater than 75 percent in the probable-effects range. Long et
al. (1995) reported that these sediment quality guidelines were most accurate for copper, lead, silver, and all classes of
PAHs and most of the individual FAHs; however, accuracy was low for nickel, chromium, mercury, total PCBs, and
DDE and DDT. The guidelines generally agreed within factors of 2 to 3 with other guidelines, including the freshwater
effects-based criteria from Ontario. The authors attributed variability in the concentrations associated with effects to
differences in sensitivities of different taxa and physical factors that affect bioavailability, but they argued that because
of the synergistic effects of multiple toxicants, the inclusion of data from many field studies in which mixtures of
chemicals were present in sediments could make the guidelines more protective than guidelines based on a single
chemical. The authors also emphasized that ERLs and ERMs were intended to be used as informal screening tools
only.
Although the ERL and ERM guidelines were not based upon deterministic or cause-effects studies, their accuracy
in correctly predicting nontoxicity and toxicity has been determined empirically among field-collected samples (Long
et al., in press). Analyses were performed with matching laboratory bioassay data and chemical data from 989 samples
collected in regions of the Atlantic, Pacific, and Gulf coasts. Data were gathered from results of amphipod survival
tests (Ampelisca abdita and Rhepoxynius abronius) for all 989 samples. Data from a battery of sensitive bioassays
(fertilization success of urchin gametes, embryological development of mollusc embryos, and microbial biolumines-
cence) were gathered for 358 of these samples. The percentages of samples indicating non-toxicity (not significantly
different from controls, p > 0.05), significant toxicity (p < 0.05), and high toxicity (p < 0.05 and mean response >20
percent difference from controls) were determined for the results of the amphipod tests alone and for the results of any
one of the tests performed.
Results of the analyses (summarized in Table B-l) suggest that highly toxic responses occurred in 12 percent of
the samples in the amphipod tests and 28 percent of the samples in any one of the tests performed when all chemical
concentrations were less than their respective ERL values. These samples were analogous to those classified as Her 3
in this report (i.e., all chemical concentrations less than the screening values). When one or more chemicals exceeded
ERL concentrations, but all concentrations were lower than the ERM concentrations (analogous to Tier 2), the percent-
ages of samples indicating high toxicity were 19 percent in the amphipod tests and 64 percent in any one of the tests
performed. The incidence of high toxicity in the amphipod tests increased from 10 percent when only one ERL value
was exceeded to 58 percent when 20-24 ERLs were exceeded. The incidence of toxicity in any one of the tests
increased from 29 percent when only one ERL was exceeded to 91 percent when 20-24 ERLs were exceeded. In
samples analogous to those classified as Tier 1 (one or more ERMs exceeded), the incidence of high toxicity was 42
percent in amphipod tests and 80 percent in any one of the battery of tests performed. If both the significant and highly
toxic results were combined in the Tier 1 samples, the percentage of samples indicating toxicity increases to 55 percent
in amphipod tests and 87 percent in any one of the tests. As with the ERLs, the incidence of toxicity increased with
increasing number of chemicals that exceeded the ERMs.
Probable Effects Levels and Threshold Effects Levels
A method slightly different from that used by Long et al. (1995) to develop ERMs and ERLs was used by the
Florida Department of Environmental Protection (FDEF, 1994) to develop similar correlative, effects-based guidelines
Table B-l. Incidence of Toxicity in Amphipod Survival Tests Alone and Any One of 2-4 Tests Performed in
Samples Analogous to Those Classified as Tier 1,2, or 3 (from Long et al., in press)
Amphipod Tests Alone
Any Test Performed
Chemical
Concentrations
Analogous
Tier
% Not
Toxic
% Signif.
Toxic
% Highly
Toxic
% Not
Toxic
% Signif.
Toxic
% Highly
Toxic
ail < ERLs
Tier 3
64
23
12
67
5
28
> I or more ERLs
Tier 2
59
22
19
20
15
64
> 1 or more ERMs
Tierl
45
13
42
13
7
80
B-8
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Nsiliomil Sediment Qiisililv Survey
for Florida's coastal waters. Modifications to the Long et al. (1995) approach increased the relevance of the resultant
guidelines to Florida's coastal sediments by making information in the database more consistent and by expanding the
information used to derive sediment quality assessment guidelines with additional data from other locations in the
United States and Canada, particularly Florida and the southeastern and Gulf of Mexico regions (FDEP, 1994). Three
effects ranges were developed with a method that used both the chemical concentrations associated with biological
effects (the "effects" data) and those associated with no observed effects (the "no-effects" data). In this method, the
threshold effects level (TEL) is the geometric mean of the lower 15th-percentile concentration of the effects data (the
ERL) and the 50th-pereentile concentration of the no-effects data. The probable-effects level (PEL) is the geometric
mean of the 50th-percentile concentration of the effects data (the ERM) and the 85th-percentile concentration of the
no-effects data. Essentially, the PEL and TEL reflect the ERM and ERL values adjusted upward or downward depend-
ing on the degree of overlap between the distributions of "effects" and "no effects" data. TELs and PELs have been
developed for 33 chemicals: 9 trace metals, total PCBs, 13 individual polynuclear aromatic hydrocarbons (PAHs), 3
classes of PAHs (total low molecular weight, total high molecular weight, and total PAH), 6 pesticides (chlordane,
dieldrin, p.p' -DDD, p.p* -DDE, p,p' -DDT), and total DDT (FDEP, 1994).
As was the case with the Long et al. (1995) approach, in the FDEP (1994) approach the lower of the two guidelines
for each chemical (i.e., the TEL) was assumed to represent the concentration below which toxic effects rarely occurred.
In the range of concentrations between the TEL and PEL, effects occasionally occurred. Toxic effects usually or
frequently occurred at concentrations above the upper guideline value (i.e., the PEL). TEL and PEL values were
developed on a sediment dry-weight basis.
Although the extensive database and evaluation of effects data make this approach applicable to many areas of the
country, the available data still have limitations. For example, FDEP (1994) noted that there is a potential for
underprotection or overprotection of aquatic resources if the bioavailability of sediment-associated contaminants and
other factors affecting toxicity are not included. Most of the TELs and PELs were within a factor of 2 to 3 of other
sediment quality guideline values. Most were deemed reliable for evaluating sediment quality in Florida's coastal
waters, with less confidence in the values for mercury, nickel, total PCBs, chlordane, lindane, and total DDT. An
evaluation of independent sets of field data from Florida, the Gulf of Mexico, California, and New York showed that
TELs and PELs correctly predict the toxicity of sediment in 86 percent and 85 percent of the samples, respectively.
As with ERLs and ERMs, the accuracy of fEL and PEL guidelines to correctly predict nontoxicity and toxicity
has been determined empirically among field-collected samples (Long et al., in press). Analyses were performed with
matching laboratory bioassay data and chemical data from 989 samples collected in regions of the Atlantic, Pacific,
and Gulf coasts. Data were gathered from results of amphipod survival tests (Ampelisca abdita and Rhepoxynius
abronius) for all 989 samples. Data from a battery of sensitive bioassays (fertilization success of urchin gametes,
embryological development of mollusc embryos, and microbial bioluminescence) were gathered for 358 of these
samples. The percentages of samples indicating nontoxicity (not significantly different from controls, p > 0.05),
significant toxicity (p < 0.05), and high toxicity (p < 0.05 and mean response >20 percent difference from controls)
were determined for the results of the amphipod tests alone and for the results of any one of the tests performed.
Results of the analyses (summarized in Table B-2) suggest that highly toxic responses occurred in 10 percent of
the samples in the amphipod tests and 5 percent of the samples in any one of the tests performed when all chemical
concentrations were less than their respective TELvalues, These samples were analogous to those classified as Tier 3
in this report (i.e., all chemical concentrations less than the screening values). When one or more chemicals exceeded
TEL concentrations, but all concentrations were lower than the PEL concentrations (analogous to Tier 2), the percent-
ages of samples indicating high toxicity were 17 percent in the ampipod tests alone and 59 percent in any one of the
tests performed. The incidence of high toxicity in the amphipod tests increased from 13 percent when only one TEL
value was exceeded to 52 percent when 20-27 TELs were exceeded. The incidence of toxicity in any one of the tests
increased from 31 percent when 1-5 TELs were exceeded to 63 percent when 20-27 TELs were exceeded. In samples
analogous to those classified as Tier 1 (one or more PELs exceeded), the incidence of high toxicity was 38 percent in
amphipod tests and 78 percent in any one of the battery of tests performed. If both the significant and highly toxic
results were combined in the Her 1 samples, the percentage of samples indicating toxicity increases to 51 percent in
amphipod tests and 86 percent in any one of the tests. As with the TELs, the incidence of toxicity increased with
increasing number of chemicals that exceeded the PELs.
B-9
-------
Thble B-2. Incidence of Toxicity in Amphipod Survival Tests Alone and Any One of 2-4 Tests Performed in
Samples Analogous to Those Classified as Tier 1,2, or 3 (from Long et al., in press)
Chemical
Concentrations
Analogous
Her
Amphipod Tests Alone
Any Test Performed
% Not
Toxic
% Signif.
Toxic
% Highly
Toxic
% Not
Toxic
% Signlf.
Toxic
% Highly
Toadc
all 1 or more TELs
Tier 2
62
21
17
22
19
59
> 1 or more PELs
Tierl
49
13
38
14
8
73
Apparent Effects Thresholds
The AET approach is another empirical data evaluation approach to defining concentrations in sediment associ-
ated with adverse effects, Barrick et al. (1988) reported that AETs can be developed for any measured chemical
(organic or inorganic) with a wide concentration range in the field. The AET concept applies to matched field data for
sediment chemistry and any observable biological effects (e.g., bioassay responses, infaunal abundances at various
taxonomic levels, bioaccumulation). By using these different biological indicators, application of the resulting sedi-
ment quality values enables a wide range of biological effects to be addressed in the management of contaminated
sediments. Using sediment samples from Puget Sound in Washington State, AET values have been developed for 52
chemicals: 10 trace metals, 15 individual polynuelear aromatic hydrocarbons (PAHs), 3 pesticides (p,p'-DDD, p,p'-
DDE, p,p'-DDT), 6 halogenated organics, and 18 other compounds.
The focus of the AET approach is to identify concentrations of contaminants that are associated exclusively with
sediments exhibiting statistically significant biological effects relative to reference sediments. AET values were based
on measured chemical concentrations per dry weight of sediment, AETs for each chemical and biological indicator
were developed using the following steps (Barrick et al., 1988).
1. Collected "matched" chemical and biological effects data—Conducted chemical and biological effects test-
ing on subsamples of the same field sample.
2. Identified "impacted" and "nonimpacted" stations—Statistically tested the significance of adverse biologi-
cal effects relative to suitable reference conditions for each sediment sample and biological indicator.
3. Identified the AET using only "nonimpacted" stations—For each chemical, the AET was identified for a
given biological indicator as the highest detected concentration among sediment samples that did not exhibit
statistically significant effects.
4. Verified that statistically significant biological effects were observed at a chemical concentration higher than
the AET; otherwise, the AET was only a preliminary minimum estimate.
5. Repeated steps 1-4 for each biological indicator.
For a given data set, the AET value for a chemical is the sediment concentration above which a particular adverse
biological effect for individual biological indicators (amphipod bioassay, oyster larvae bioassay, Microtox bioassay,
and benthic infaunal abundance) is always significantly different statistically relative to appropriate reference condi-
tions. Two thresholds were recognized in the evaluations conducted in this report, when possible, based on the differ-
ent indicators. EPA defined the AET-low as the lowest AET among applicable biological indicators, and the AET-high
as the highest AET among applicable biological indicators. The use of the high/low AET values is not a recommenda-
tion of the authors of the approach; rather it was developed for the NSI evaluation. The two thresholds were used in
this evaluation to give a range of effects values (as with the ERL/ERMs and TEL/PELS). AET values based on
Microtox bioassays were not used for the NSI evaluation.
B-10
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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 acute and chronic 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, 1994b, c) and used primarily for dredged material evaluation (USEPA and USACOE, 1994), The NSI data
contain acute sediment toxicity results from tests in which organisms were exposed to field-collected sediments and
mortality was recorded. Results of whole sediment and elutriate toxicity tests were used in the evaluation of the NSI.
Variations in observed toxicity from tests of the same sediment sample may be attributed to the relative sensitivities
of the species used in the tests; disruption of geochemistry and kinetic activity of bedded sediment contaminants during
sampling, handling, and bioturbation; and laboratory-related confounding factors (Lamberson et al„ 1992). Recent
studies indicate that aqueous representations of whole sediment (e.g., elutriate) do not accurately predict the bioavail-
ability of some contaminants compared to whole-sediment exposures (Harkey et al., 1994). Acute sediment toxicity tests
have been widely accepted by the scientific and regulatory communities and the results can be readily interpreted,
although more work is needed on chronic testing (Thomas et al,, 1992), Appendix G presents the methodology for
evaluating sediment toxicity tests as applied in the NSI data evaluation.
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 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 USACOE, 1994),
The TBP calculation is used as a screening mechanism to represent the magnitude of bioaccumulation likely to be
associated with nonpoiar organic contaminants in the sediment. At present, the TBP calculation can be performed only
for nonpoiar organic chemicals; however, methods for TBP calculations for metals and polar organic chemicals are under
development (USEPA and USACOE, 1994).
The environmental distribution of nonpoiar organic chemicals is controlled largely by their solubility in various
media. Therefore, in sediments they tend to occur primarily in association with organic matter (Karickhofif, 1981) and in
organisms they are found primarily in the body fate or lipids (Bierman, 1990; Geyer et al., 1982; Konemann and van
Leeuwen, 1980; Mackay, 1982), Bioaccumulation of nonpoiar 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 USACOE, 1994). It is possible to relate the concentra-
tion 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 nonpoiar 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 USACOE,
1994).
The TBP can be calculated relative to the biota-sediment accumulation factor (BSAF), as in the following equation
(USEPA and USACOE, 1994):
TBP = BSAF(C,/f,Jf,
where TBP is expressed on a whole-body basis in the same units of concentration as C1 and
TBP = theoretical bioaccumulation potential (ppm);
C = concentration of nonpoiar organic chemical In sediment (ppm);
B-ll
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Appendix B
C, = 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));
= 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.
BSAF values used in the TBP evaluation are discussed in Appendix C. If TOC measurements were not available
at a site, f^ 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 assess-
ments. The Great Lakes Water Quality Initiative Technical Support Document for the Procedure to Determine
Bioaccumulation Factors (USEPA, 1995b) uses a 3.10 percent lipid value for trophic level 4 fish and 1.82 percent for
trophic level 3 fish in its human health assessments.
As part of the NSI 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 evaluation (Appendix C). The mean fillet percent lipid content
for various groups of fish species in the STORET database ranged from 0.753 to 4.49 percent; in the NSCRF, mean
fillet values ranged from 1.6 to 4.9 percent. The mean whole-body percent lipid content for various groups of fish
species in the STORET database ranged from 3.757 to 6.33 percent; in the NSCRF, mean whole-body values ranged
from 4.6 to 8.8 percent
In the NSI data evaluation approach, TBP values were compared to U.S. Food and Drug Administration tolerance/
action/guidance levels and EPA risk levels. These parameters an® discussed below.
FDA Tolerance/Action/Guidance Levels
Hie U.S. Food and Drug Administration (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
employed by FDA 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. These standards have been
derived by individually considering each chemical and the species of fish it is likely to contaminate. FDA also
considered (1) the amount of potentially contaminated fish eaten and (2) the average concentrations of contaminants
consumed. FDA has established action levels in fish for 10 pesticides and methylmercury, tolerance levels for poly-
chlorinated biphenyls (PCBs), and guidance for 5 metals.
EPA Risk Levels
Potential impacts on humans are evaluated by estimating potential carcinogenic risks and noncarcinogenic haz-
ards 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
B-12
-------
XiiliajJiiJ Si'fJiim-nt Quiilit t .S>/ri< »
(USEPA, 1989) and Guidance for Assessing Chemical Contamination Data for Use in Fish Advisories, Volume II;
Development of Risk-Based Intake Limits (USEPA, 1994a).
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 s risk level exceeds the lower bound (i,e„ 10'6) but is lower than the upper bound (i.e., 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 (CD!) of
contaminants of concern:
CDI= (EPC)(IR)(EF)(ED)
(BW)(AT)
where:
CDI = chronic daily intake (mg/kg/day);
EPC = exposure point concentration (contaminant concentration in fish);
IR = ingestion rate (6.5 g/day);
EF = exposure frequency (365 days/year);
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 used by EPA to develop human health water quality criteria. Carcinogenic
risks are then quantified using the equation below:
Cancer ris^ =001,2 SF,
where:
Cancer risk, = the potential carcinogenic risk associated with exposure to chemical i (unitless);
CDI. = chronic daily intake for chemical i (mg/kg/day); and
SF. = slope factor for chemical i (mg/kg/day)"1.
The hazard quotient, which is used to quantify the potential for an adverse noncarcinogenic effect to occur, is
calculated using the following equation:
CDI
110 _
' RfD,
where:
HQ. = hazard quotient for chemical i (unitless);
CDI. = chronic daily intake for chemical i (mg/kg/day); and
RfD, = reference dose for chemical i (mg/kg/day).
If the hazard quotient exceeds unity (i.e., 1), an adverse health effect might occur. The higher the hazard quotient,
the more likely that an adverse noncarcinogenic effect will occur as a result of exposure to the chemical. If the
estimated hazard quotient is less than unity, noncarcinogenic effects are unlikely to occur.
Using these formulas, the fish tissue concentration (EPC) of a contaminant that equates to a cancer risk of 10 s or
a hazard quotient that exceeds unity can be back-calculated.
Cancer risk:
B-13
-------
V|»|H'ii(ii\ 1?
EPC =
(1Q-5)(BW)(AT)(C,)
(rR)(EF)(ED)(SFi)
Noncancer hazard:
EPC =
(BW)(AT)(RfDj )(C,)
(IR)(EF)(ED)
whore:
C, = conversion factor (10' g/kg).
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
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
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.
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 (USEPA, 1995a). EPA has developed Great Lakes Water Quality Wildlife Criteria for four chemi-
cals: DDT, mercury, 2,3,7,8-TCDD, and PCBs. A Great Lakes Water Quality Wildlife Criterion (GLWC) is the concentra-
tion 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, 1995a). Wildlife values are calculated
using the equation:
(NOAEL ,gSSF) jWtA
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);
WtA = average weight for the representative species identified for protection (kg);
WA = average daily volume of water consumed by the representative species identified for protec-
tion (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 protec-
tion (kg/d); and
BAF ss bioaccumulation factor (L/kg), the ratio of the concentration of a chemical in tissue, normal-
ized 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, 1995a).
B-14
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National Sediment Quality Suim'v
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). Therefore, it was necessary 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 (ie., otter and mink) and avian species (i.e., king-
fisher, 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 pre-
sented in USEPA, 1995a.)
where:
WV rNOAEI^SSFj^Wt,
Fa
WV
vnih
NOAEL
SSF
Wt.
wildlife value for fish tissue (mg/kg);
no-observed-adverse-effect level (mg/kg-day);
species sensitivity factor
average weight of animal in kilograms (kg); and
average daily amount of food consumed (kg/day).
Secondly, the geometric mean of the wldlife 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 sensitiv-
ity analysis was conducted to evaluate the impact of excluding the surface water ingestion exposure route. Based on
this analysis, ingestion of surface water contributes less than 0.0001 percent of the total exposure (i.e., ingestion of fish
and water). Therefore, excluding the water ingestion exposure route had no significant impact on the evaluation of
NSI data with regard to potential wildlife impacts.
Wldlife criteria derived for DDT, mercury, 2,3,7,8-TCDD, and PCBs based on fish tissue concentration are presented below.
Fish Tissue
Chemical Criterion fmp/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 calcu-
lated for DDT, 2,3,7,-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, 1995b).
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A|)|R'i)(li\ B
References
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Long, E.R., L J. Field, and D.D. MacDonald. In press. Predicting toxicity in marine sediments with numerical
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sediment quality guidelines. Submitted to Environ, Toxicol, Chem.
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Thirteenth Pellston Workshop, ed. J.L. Hamelink, P.F. Landrum, H.L. Bergman, and W.H. Benson, pp.155-170.
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tion methods compendium, pp. 3-1-3-10. EPA 823-R-92-006. U.S. Environmental Protection Agency, Office of
Water, Washington, DC.
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Protection Agency, Office of Science and Technology, Washington, DC.
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ton, DC.
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National Sediment Survey
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822/R93-013. U.S. Environmental Protection Agency, Office of Science and Technology, Health and Ecological
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R93-015. U.S. Environmental Protection Agency, Office of Science and Technology, Health and Ecological
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016. U.S. Environmental Protection Agency, Office of Science and Technology, Health and Ecological Criteria
Division, Washington, DC.
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Development, Duluth, MN.
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Army Corps of Engineers, Washington, DC.
Zhuang, Y., H.E. Allen, and G. Fu. 1994. Effect of aeration of sediment on cadmium binding. Environ. Toxicol.
Chem. 13(5):717-724.
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National .^i-dSmciii Qiralil.v Survey
Appendix C
Method for Selecting Biota-
Sediment Accumulation
Factors and Percent Lipids in
Fish Tissue Used for Deriving
Theoretical Bioaccumulation
Potentials
Theoretical bioaccumulation potentials (TBPs) are empirically derived potential concentrations that might
occur in the tissues of fish exposed to contaminated sediments. TBPs are computed for nonpolar organic
chemicals as a function of sediment concentrations, fish tissue lipid contents, and sediment organic carbon
contents. Four separate pieces of information are required to compute the TBP for nonpolar organic chemicals:
1. Concentration of nonpolar organic compound in sediment.
2. Organic carbon content of the sediment.
3. Biota-sediment accumulation factor (BSAF).
4. Lipid content in fish tissue.
The details of the TBP calculations and related assumptions are found in Appendix B of this report to Congress.
This appendix describes the approach used to develop the BSAFs used in the NSI TBP evaluation and to evaluate fish
tissue lipid content data from selected information sources for comparison to the values used in the NSI TBP evalu-
ation. The BSAF values used for each chemical evaluated are presented in Appendix D.
Chemicals considered for fish tissue residue evaluation as part of the NSI data evaluation have at least one
screening value available, and the sum of positive sediment results and positive tissue results is greater than 20
observations. BSAF values were assigned to all nonpolar organic chemicals in the NSI having available screening
values. These screening values are risk-based concentrations (RBCs) developed either from carcinogenic potency
slopes or from oral reference doses. Carcinogenic potency slopes and reference doses were obtained from IRIS
(USEPA, 1995) and HEAST (USEPA, 1994b). Other screening values used for comparison to TBP values and tissue
data are U.S. Food and Drug Administration (FDA) tolernnce/action/guidance levels and EPA wildlife criteria. The
BSAF values used in the analysis are presented in Appendix D along with the screening values discussed above.
Method for Selecting BSAFs
Biota-sediment accumulation factors (BSAFs) are transfer coefficients that relate concentrations in biota to con-
centrations 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 partitioning theory, field measured BSAFs empirically account for factors such as metabolism and
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food chain biomagnificaiton. BSAFs can vary depending on the biota, dynamics of chemical loadings to the water
body, 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 BASF, measured at one location at a point in time, when applied to another location at another point
in time depends on two factors: (1) 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, and (2) the degree of
similarity between environmental conditions at the place of measurement and place of application.
BSAF values were assigned only to nonpolar chemicals in the NSI. This section describes how the BSAF values
used for the TBP assessment were selected from recommended values for specific chemicals.
Sources of Recommended BSAFs
BSAFs used in the NSI TBP evaluation were obtained from the EPA Office of Research and Development (EPA/
ORD) Environmental Research Laboratories at Duluth, Minnesota (Cook, 1995) and Narragansett, Rhode Island
(Hansen, 1995). In some cases (i.e., EPA/ORD-Duluth), BSAFs were provided for specific chemicals; in other cases
(i.e., EPA/ORD-Narragansett), BSAFs were provided by chemical class. Recommended BSAFs from each laboratory
are described below.
EPA Environmental Research Laboratory, Duluth
BSAF recommendations obtained from EPA/ORD-Duluth included mainly chemical-specific values for:
• PCB congeners
• Pesticides
• Dioxins/Furans
• Chlorinated benzenes
The recommended values from EPA/ORD-Duluth were based on BSAF data compiled from various sites and studies.
Data were selected based on the following criteria (Cook, 1995):
• The primary source of chemical exposure to food webs was through release of chemicals in sediments.
• The BSAF was derived for pelagic organisms (i.e., fish).
• Chemicals in sediments and biota were at roughly steady state with respect to environmental loadings of the
chemical.
Pelagic BSAF data which predict relative bioaccumulation potentials of different chemicals are available for
ecosystems in which sediments are a primary source of the chemicals to pelagic food webs through release of chemi-
cals to the water. Little or no BSAF data exist for sites in which water and sediments are at steady-state with respect
to external chemical loadings. The best BSAF data for fish are those measured for Lake Ontario and used to estimate
BAFs in the Technical Support Document (TSD) for the Great Lakes Water Quality Initiative (GLWQI) (Cook, 1995;
Cook et al., 1994; USEPA, 1994a). The lake Ontario BSAFs are based on a large set of sediment and fish samples
collected in 1987 (USEPA, 1990). The BSAFs for PCDDs, PCDFs and co-planar PCB congeners are available from
ORD-Duluth data. Additional BSAFs for PCBs and pesticides are available from the data of Oliver and Niimi
(1988). These contemporary BSAFs are estimated to be approximately 20 to 25 percent of BSAFs when Lake
Ontario surface sediments and water are at steady-state with chemical loading to the ecosystem; a condition which
probably existed in the 1960s. EPA has measured BSAFs in the Fox River and Green Bay in Wisconsin and find
similar values despite much different species and exposure conditions (Cook, 1995).
EPA Environmental Research Laboratory, Narragansett
EPA/ORD-Narragansett provided a second source of information for selecting BSAF values. Probability distri-
bution curves for selecting BSAFs were presented by EPA/ORD-Narragansett for three chemical classes:
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N;t(ion;il Scdiiiiciif (_>«';iIil_\ S*11 \cv
• PAHs
• PCBs
• Pesticides
EPA/ORD-Narragansett researchers developed cumulative probability curves for each chemical class from their da-
tabase of BSAFs (Hansen, 1995). The database from which general BSAF recommendations were summarized in-
cluded data from laboratory and Held studies conducted with both freshwater and marine sediments. Data must be
from species that directly contact sediments or feed on organisms that live in sediments, i.e., benthic invertebrates and
benthically coupled fishes.
Overall the database contained more than 4,000 BSAF observations. Cumulative probability curves summariz-
ing the BSAF data in the database were provided by Hansen (1995) for PAHs, PCBs, and pesticides. BSAF values
were tabulated for several probability percentiles. These findings have been published in Tracey and Hansen, 1996.
Approach for Selecting BSAFs from Recommended Values
The general approach for selecting a BSAF for a chemical follows:
• Use a chemical-specific value for the BSAF, if available.
• If no chemical-specific value is available, use a BSAF derived for a chemical category.
• For chemicals having no specific information on the BSAF, use a default value of 1.
The EPA/ORD-Narragansett values for the BSAF were selected as the 50th percentile of the distribution of
BSAFs by chemical class (Table C-l). The BSAF values from EPA/ORD-Duluth were averages of individual data
points for specific chemicals. The preference for central tendency measures reflects risk management that imples an
approximate 50 percent chance of bioaccumulation to a predicted level. Other components of the EPA risk levels for
fish tissue chemical residues and FDA action/tolerance/guidance, such as toxic potency (cancer potency factor and
oral reference doses) and exposure frequency, reflect more precautionary and protective risk management.
Because there was some overlap between the categories of chemicals for which BSAF values were recommended,
the following approach was used to assign BSAFs to specific chemicals in the NSI (Table C-2). For dioxins and
furans, chemical-specific values recommended by EPA/ORD-Duluth were applied; for PCBs, the value for total
PCBs recommended by EPA/ORD-Duluth was used. When using BSAFs from USEPA (1994a), values from the
study by Cook et al. (1994) were preferred over values reported by Oliver and Niimi (1988).
Pesticides received recommendations from both laboratories. The BSAFs developed by EPA/ORD-Narragansett
were for benthic organisms and demersal (bottom-dwelling) fishes. The BSAFs developed by EPA/ORD-Duluth, on
Table C-l. EPA/ORD-Narragansett Data BSAF Distributions (kg sediment organic carbon/kg lipid)
Probability Percentile
Chemical Class
PAHs
PCBs
Pesticides
50
0.29
1.11
1.80
70
0.55
2.26
3.34
80
0.94
3.66
4.61
90
1.71
5.83
7.31
95
2.84
9.15
10.61
100
4.19 ,
16.46
22.63
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\|>|H-ii(li\ C
Table C-2. Conventions for Assigning BSAFs to Nonpolar Organic Compounds in NSI
Category of Chemical
Source of BSAF
BSAF Value Used in
Evaluation
Dioxins
EPA/ORD-Duhith' "pelage" chemical-specific BSAF
0.059
PCBs
EPA/ORD-Duluth' "pelagfc" BSAF for total PCBs
1.85
Pesticides
bgKw<5.5
EPA/ORD-Narragansett6 "benthic" class-specific BSAF for
50th percentile protection level
1.80
logKow^5.5
EPA/ORD-Duluth' "pelagfc" chemical-specific BSAF
if available; otherwise, use EPA/ORD-Narragansettb value
See chemical-specifc BSAF
given in Appendix D
PAHs
EPA/ORD-Narragansettb "benthic" class-specific BSAF for 50th
percentile protection level
0.29
Halogenated and other
compounds
Default value of 1 unless chemical-specific value available from
EPA/ORD-Duluth1
1.0
•Cook, 1995.
'Hansen, 1995.
the other hand, were for benthically coupled pelagic (open-water) fishes. BSAFs from EPA/ORD-Narragansett were
used for pesticides having log Kow values less than 5.5. For pesticides having log Kow values greater than or equal to
5.5, the BS AF values from EPA/ORD-Duluth were used. BSAF values selected by this approach are more appropri-
ate because food web transfer to pelagic fishes is considered to be a more important process for chemicals having
high log Kw values. Exposure through environmental media, as in direct contact with sediments by benthic organ-
isms, is a more important process for chemicals having low log Kow values. Chemicals having no recommended
BSAF values available were assigned a default BSAF of 1.
Evaluation of Tissue Lipid Content
Fish tissue lipid content enters the risk screening assessment as the normalizing factor in the numerator of the
TBP equation. Normalizing by organic carbon content removes much of the site-to-site variation in the sorption of
nonpolar organic chemicals by sediments (Karickhoff et al., 1979). In a similar manner, normalizing by lipid content
can eliminate much site and species variation in the tendency of organisms to bioaccumulate nonpolar orgjinic com-
pounds (Esser, 1986). Lipid contents can vary naturally with species, site, season, age and size of fish, and trophic
level. In addition, reported lipid contents can vary significantly depending on the analytical method (Randall et al.,
1991).
The purpose of this section is to evaluate the percent fish lipid content data from various sources and compare
these values to those selected for use in the NSI evaluation (i.e., 3.0 percent for fillets for human health TBP evalua-
tions and 10.31 for whole body wildlife TBP evaluations).
The remainder of this section describes the lipid data sources evaluated and analysis of the lipid content data.
Sources of Lipid Data
Lipid data used for comparison with the percent lipid values selected for the NSI evaluation were obtained from
three major sources:
• EPA's water monitoring database, STORET.
• National Study of Chemical Residues in Fish, or NSCRF (USEPA, 1992).
• U.S. Department of Agriculture's (USDA's) Composition of Foods (Dickey, 1990).
C-4
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Additional sources included examples of whole fish and fillet lipid contents taken from the recent literature.
Each of the three major sources is described in the following paragraphs.
STORET
The STORET database was the single largest source of reported data on fish tissue lipid contents. Data stored
under various parameter codes for lipid content in STORET were converted into units of percentage. Some screening
of the data was performed as follows:
• Records were retrieved from January 1990 to March 1995.
• Reported lipid contents greater than 35 percent were eliminated because they were significantly greater than
the 90th percentile.
• Only records having an anatomy code of "whole organism" or "fillet" were included. Records with a code of
"fillet/skin" or "edible portion" were excluded.
• Data that appeared to be reversed (i.e., fillet percent lipid was greater than whole organism lipid) were also
not considered.
• Also not considered were records in which the minimum and maximum were equal, or very nearly equal,
when the number of observations was large.
There is less consistency in the data obtained from STORET relative to the NSCRF data because the analyses in
STORET were conducted by numerous laboratories around the Nation. Data reported under different parameter
codes (i.e., different methods for lipids) were grouped for the analysis. Moreover, the quality of the data in STORET
is unknown. STORET data are compiled by species in Table C-3. The fishes are divided by trophic level and habitat
into four subtables (Tables C-3a through C-3d) for the combinations of trophic levels 3 and 4 and epibenthic (bottom-
dwelling) and pelagic (water column-dwelling) habitat.
National Study of Chemical Residues in Fish
The second largest database on fish tissue lipid content was available from the NSCRF (USEPA, 1992) (Table C-3).
This set of lipid analysis data was taken in conjunction with analyses for dioxins/furans. An advantage of this data-
base is that all of the lipid measurements were performed by the same laboratory using the same method. The data
were screened to exclude data for fish species for which two or fewer observations were made.
USDA Report on Composition of Foods
A summary of a relatively small database on the composition of fish and shellfish foods and food products was
available from USDA (Dickey, 1990). The section on fish and shellfish in the report coordinated by Dickey (1990)
came from an earlier USDA report by Exlcr (1987). Data presented by Exler (1987) for various fish species were
summarized from the USDA's Nutrient Data Bank (NDB). Records in the NDB are based primarily on published
scientific reports and technical journal articles. To a lesser extent, the NDB contains unpublished data from indus-
trial, government, and academic institutions under contract with the Human Nutrition Information Service. Lipids
data are given in percentage of edible portion, where "edible portion" is the part of food customarily considered
edible in the United States. Records were available for 32 fishes.
C-5
-------
Vppomlk ("
Table C-3a. Lipid Contents of Trophic Level 3, Epibenthic Fishes
Spec&a Name
Common Name
mole Fish Lipid
Content,
Percent (size)
Fillet Lipid Content,
Percent (stee)
Refereocf,
Comments
Aplodtnotus
grunntcns
freshwater drum
mean as 1.9
(1,3 to 2.5, 3 obs)
EPA (1992)
Apfodinotus
grunrtiens
freshwater drum
mean m 4.93, standard
(error = 0.103, 905
obs)
Exler (1987)
Carpoides carpio
river carpsucker
mean * 5.8
(0.5 to 15.0, 3865
obs)
mean * 4.4
(1.8 to 9.2, 184 obs)
STORET
Carpoides cyprmus
quflfcack
mean as 5.1
(0,3 to L3,0, 780
obs)
mean = 3.2
(0.4 to 4,89, 78 obs)
STORET
Catostomus aniens
Utah sucker
mean = 3.5
(1.1 to 8.2, 356
obs)
mean » 1.6
(0.1 to 6.7, 695 obs)
STORET
Colostomas
catostomus
kmgnose sucker
(FW)
0.8 to 3.8 (not given)
Owens et aL (1994)
Catostomus
catostomus
Iongnose sucker
mean = 3.9
(2.5 to 7.2, 298
obs)
mean = 7.05
(6.4 to 7.7, 32 obs)
STORET
Catostomus
columbkmus
bridgclip sucker
mean = 4.6
(0.7 to 10.4, 309
obs)
STORET
Catostomus
commersoni
white sucker
5.41 ± i.18
1.07 ± 0,23
1.36 ±0.17
0.99 ± 0.22
2.25 ± 0.65
(not given)
Servos et aL (1994)
Catostomus
commersoni
white sucker
mean = 6.1
(1.4 to 21.8, 39
obs)
USEPA (1992)
Catostomus
commersoni
white sucker
mean = 4.3
(0.2 to 12.0, 4102
obs)
mean = 1.7
(0.2 to 9.1, 586 obs)
STORET
Catostomus
commersoni
white sucker
mean = 2.32
(standard error =
0.069, 157 obs)
ExJcr (1987)
Catostomus
macrochetius
largescafc sucker
mean = 6.7
(0.3 to 13.0, 752
obs)
mean = 1.6
(0.1 to 5.26, 482
obs)
STORET
Catostomus
occidentals
Sacramento sucker
mean = 9.8
(1.7 to 18.5, 3 obs)
USEPA (1992)
Cottus cognatus
scutpin (FW)
8 (5.4 g)
USEPA (1994a)
Cyprinus carpio
carp
9 (15 g)
Cook et aL (1991)
Cyprtnus carpio
carp
18.7 (69.5 g)
15.7 (56.0 g)
13.0 (37.5 g)
16.6 (36.5 g)
17.5 (29.0 g)
Kuehlet aL (1987)
€-6
-------
Nutioiuil .Sediment Quality JS«r\cy
Table C-3a. (Continued)
Species Name
Common Name
Whole Fish Lipid
Contenfl,
Percent (siae)
Fillet Lipid Content,
Percent (size)
Reference,
Comments
Cyprinus carpio
carp
18.7 (69.5 g)
15.7 (56.0 g)
: 13.0 (37.5 g)
16.6 (36.5 g)
17.5 (29.0 g)
Kuehl et aL (1987)
Cyprinus carpio
carp
mean = 9.3
(0.5 to 25.1, 145
obs)
mean = 9.0
(2.0 to 19.6, 6 obs)
USEPA (1992)
Cyprinus carpio
carp
¦ mean = 6.5
(0.3 to 17.0,70002
obs)
msan = 4.3
(0.02 to 21.6, 16139
obs)
STORET
Cyprinus carpio
carp
mean = 5.60
(standard error =
0.207, 163 obs)
Exler (1987)
Ctenophyaryngodo-
n idclla
grass carp
msan = 5.2
(3 obs)
USEPA (1992)
Erimyzon oblongus
creek chubsucker
mean = 3.9
(3.9 to 4.0, 3 obs) .
USEPA (1992)
Hypentelium
nigricans
northern hogsucker
mean = 4.4
(0.8 to 8.98, 637
obs)
mean = 0.7
(0.S to 0.99, 70 obs)
STORET
Ictalurus furcatus
blue catfish
mean = 7.3
(5.3 to 10.4, 5 obs)
mean = 2.7
(2.0 to 3.0, 4 obs)
USEPA (1992)
Ictalurus furcatus
blue catfish
mean = 6.0
(1.5 to 12.0, 56 obs)
CTAOCT
O lUKbi
fctalurus melus
(Ameiurus melus)
black bullhead
mean = 2.9
(0.9 to 6.2, 911 obs)
mean = 1.4
(0.15 to 5.1, 573 obs)
STORET
Ictalurus natalis
(Ameiurus natalis)
yefow bullhead
mean = 2.8
(0.5 to 7.5, 235 obs)
mean = 0.96
(0.1 to 3.2, 294 obs)
STORET
Ictalurus nebulosus
(Ameiurus
nebulosus)
brown bullhead
mean = 2.2
(1.3 to 4.1, 133 obs)
mean= 1.5
(0.4 to 3.3, 107 obs)
STORET
Ictalurus punctatus .
channel catfish
mean = 9.8
(3.4 to 23.0, 22 obs)
mean = 5.1
(1.1 to 11.5, 17 obs)
USEPA (1992)
Ictalurus punctatus
channel catfish
mean = 7.1
(0.3 to 15.0,7512
obs)
msan = 5.1
(0.2 to 17.3, 20655
obs)
STORET
Ictalurus punctatus
channel catfish
mean = 4.26
(standard error =
0.417, 59 obs)
Exler (1987)
Ictiobus bubalus
smaltouth bufiafo
mean = 5.7
(2.2 to 11.9, 6 obs)
USEPA (1992)
C-7
-------
Table C-3a. (Continued)
Spedes Name
Common Name
Whole Fish Lipid
Content,
Percent (she)
Fillet lipid Content,
Percent (size)
Reference;
Comments
fcliobus bubalus
smalhmoush buffaJo
mean = 9.7
(2-8 to 17.3, 886
obs)
mean = 4.8
(0.2 to 14.5, 595 obs)
STORET
Ictiobus cyprinettus
bigmouth buffalo
mean = 15.1
(5.7 to 22.6, 3 obs)
USEPA (1992)
la lob us cyprincllus
bigmouh buffalo
mean = 5.8
(0,4 to 16.2, 675
obs)
mean = 4.1
(0.3 to 15, 1678 obs)
STORET
Ictiobus niger
black buflaJo
trean = 3.5
(1.2 to 7.1, 42 obs)
STORET
Minytmma
melmops
spotted sucker
mean = 4.5
(0.9 to 7.4, 9 obs)
USEPA (1992)
Mwytrcma
mclanops
spotted sucker
mean = 3.7
(0.7 to 5.9, 188
obs)
mean = 1.5
(0.9 to 3.2, 197 obs)
STORET
Moxostoma
artisurum
silver redhorse
mean = 8.2
(6.2 to 8.5, 180
obs)
mean = 2.1
(1.3 to 2.7, 7 obs)
STORET
Moxostoma
carina! um
river redhorse
mean = 5.1
(1.9 to 5.9, 193
obs)
mean = 1.3
(0.5 to 2.4, 170 obs)
STORET
Moxostoma
duquesnei
black redhorse
mean = 5.0
(0.3 to 9.7, 1774
obs)
mean = 0.97
(0.7 to 1.8, 58 obs)
STORET
Moxostoma
erythrurum
golden redhorse
mean = 6.0
(0.8 to 16.1, 2018
obs)
mean =1.8
(0.6 to 2.8, 154 obs)
STORET
Moxostoma
macrolepidotum
shotthead redhorse
mean = 19.8
(10.8 to 31.9, 4
obs)
USEPA (1992)
Moxostoma
macrolepidotum
shorthead redhorse
mean = 6.5
(0.4 to 10.9, 683
obs)
mcan = 3.0
(1.4 to 13.5, 342 obs)
STORET
Mugil cephalus
striped muBet
mean = 3.79
(standard error =
0.357, 43 obs)
ExJer (1987)
Mylochcilus
caurmus
peanciah
mean = 11.0 (9.36
to 12.91, 162 obs)
STORET
Ptychochetlus
ottgoni
northern squawfish
mean = 5.6 (0.8
to 12.0, 812 obs)
mean = 1.3
(0.7 to 3.0, 117 obs)
STORET
Ptychochetlus
squawfeh
mean = 2.2
(0.5 to 3.0, 7 obs)
USEPA (1992)
Scaphirhynchus
platorhynchus
shovelnose sturgeon
mean = 7.4
(1.1 to 20.3, 392 obs)
STORET
C-8
-------
National Sejtliim>nt Quality Suvma
Table C-3b. Lipid Contents of Ttophie Level 3, Pelagic Fishes
Species Name
Common Name
Whole Fish lipid
Content,
Percent (siae)
Fillet Lipid Content,
Percent (sia*>)
Reference,
Comments
Acipenser sp.
sturgeon (unknown)
mean = 4.04
(7 obs)
Exler (1987)
Acmcheilus
atutaceus
chiselmoulh
mean = 5,0
(3.2 to 6.8, 47 obs)
mean = 0,55
(0,19 to 1.00, 91 obs)
STORET
Atosa
pseudoharengus
ale wife
7 (32 g)
USEPA (1994a)
Alosa
pseudoharengus
ale wife
mean = 8.9
(3.7 to 15.2, 128
obs)
STORET
Alosa sapidissima
Amerfcan shad
mean = 6.55
(5.9 to 7.6, 270
obs)
STORET
Alosa sapidissima
American shad
mean = 13.77
(standard error = 1.00,
11 obs)
Exler (1987)
Anguilla mstrata
American eel
mean = 11.66
(standard error =
0.885, 14 obs)
Exler (1987)
Aphdinotus
grunniens
freshwater dnim
mean = 5,5
(1.0 to 19.7, 574
obs)
mean = 4.8
(0.3 to 21.2, 459 obs)
STORET
Archosargus
pmbalocephatus
sheepshcad
mean = 2.41
(standard error =
0.040, 5 obs)
Exler (1987)
Coregonus artedii
cisoo (lake herring)
mean = 1.91
(standard error =
0.149, 69 obs)
Exler (1987)
Coregonus
clupeaform
lake whitefish
mean = 5,86
(standard error =
0.451, 68 obs)
Exler (1987)
Coregonus hoyi
bloater
mean = 21.1
(16 to 25.5, 52 obs)
msan = 8.3
(3.2 to 17.0, 98 obs)
STORET
Domsoma
cepedianum
gizzard shad
mean = 7.4
(1.3 to 18.0, 189
obs)
STORET
Domsoma
petenense
threadfin shad
mean = 3.0
(0.5 to 18.0, 9 obs)
STORET
Gadus
macmcephalus
true or Pacific cod
mean = 0.63
(standard error =
0.031, 18 obs)
Exler (1987)
Hiodon absoides
goldcye
msan = 3.2
(3.5 to 2.8, 74 obs)
STORET
C-9
-------
VppcndK ('
Table C-3b. (Continued)
Spedes Name
Common Name
Whole Hsh Lipid
Content,
Percent (size)
Fillet Lipid Content,
Percent (she)
Reference,
Comments
Plalygobia
(Hybopsis h
database) gracilis
fbthead chub
mean = 3,3
(0.68 to 8.14, 75 obs)
STORET
Lepomis auritis
redbreast sunfish
mean = 3.6
<1.3 to 8.1, 550
obs)
STORET
Lepomis cyanettus
green stnfish
mean = 3.2
(2.2 to 7.8, 376
obs)
STORET
Lepomis gibbosus
punpkinsccd
mean = 3.9
(2.2 to 7.7, 126
obs)
STORET
Lepomis gibbosus
purrpkircced
mean = 0.70
(standard error =
0.071, 8 obs)
Exler (1987)
Lepomis megalotis
longear sunfish
mean = 2.8
(1.0 to 7.2, 536
obs)
STORET
Osmerus mordax
rainbow smelt
4 (16 g)
USEPA (1994)
Osmerus mordax
rainbow smelt
mean = 2.42
(standard error =
0.107, 52 obs)
Exler (1987)
Pimephales
promelas
fathead minnow
19 (1 8)
Cook et al (1991)
Lepomis
macmchirus
bliegffl sunfish
mean = 3.5
(2.4 to 4.6, 4 obs)
USEPA (1992)
Lepomis
macmchirus
bliegill smfish
mean = 4.4
(0.1 to 8.7, 1034
obs)
STORET
Lota lota
bubot
0.35 to 0.7
Owens et al. (1994)
Lota lota
burbot
mean = 0.2
(0.1 to 0.3, 18 obs)
STORET
Lota lota
burbot
mean = 0.81
(standard error =
0.059, 13 obs)
Exler (1987)
Oryzias latlpes
medaka
8 (0.175 g)
Schirieder et al
(1992)
Phoxinus
etythrogaster
southern redbeDy
dace
mean = 5.6
(2.2 to 10.0, 762
obs)
STORET
C-IO
-------
N:ili»ii:il Svriimonl Qu:iJit\ Surrey
Table C-3b. (Continued)
Species Name
Common Name
WlMjIe Fish Lipid
Content,
Percent (sire)
Fillet Lipid Content,
Percent (size)
Reference,
Comments
Pomoxis annularis
white crappie
mean = 1.0
(0.5 to 2.0, 7 obs)
USEPA (1992)
Pomoxis annularis
white crappb
mean = 2.1
(0.4 to 5.8, 622
obs)
mean = 0,4
(0.08 to 2.6, 936 obs)
STORET
Pomoxis
nigmmacuhuus
black crappie
mean = 1.1
(0.5 to 1.5, 3 obs)
USEPA (1992)
Pomoxis
nigmmaculatus
black crappie
mean = 2.7
(0.7 to 8.4, 457
obs)
mean = 1.4
(0.13 to 5.3, 118 obs)
STORET
Pmsopium
williamsoni
mountain whitefch
mean = 8.5
(0.5 to 13.8, 327
obs)
mean = 1.6,
(0.2 to 4.1, 532 obs)
STORET
Pmsopium
williamsoni
mountain whitefish
3.4 to 11.8
(not given)
Owens et al (1994)
Hichanhonius
baUeatus
rcdsidc shiner
mean = 0.9
(0.85 to 0.96, 50 obs)
STORET
Sebastes
auriculatus
brown rockfish
mean = 1.57
(81 obs)
Exler (1987)
Sebastes marinus
redfish
mean = 1.63
(standard error =
0.092, 208 obs)
Exler (1987)
Semotiius
atmmacula
creek chub
mean = 3.9
(1.0 to 5.0, 815
obs)
STORET
Semotiius
corporalis
feBfish
mean = 1.9
(0.25 to 3.9, 100
; obs)
STORET
Table C-3c. Lipid Contents of Trophic Level 4, Epibenthic Fishes
Species Name
Common Name
Whole Fish Lipid
Content,
Percent (size)
Fillet Lipid
Content, Percent
Reference,
Continents
PyloMctis olivaris
flathead catfish
mean = 3.1 (0.5 to
8.1,829 obs)
mean = 3.0 (0.2 to
21,1,1315 obs)
STORET
Pylodictis olivaris
flathead catfish
mean = 6.0
(1.6 to 8.7,3 obs)
mean= 1.9
(0.6 to 3.1,4 obs)
USEPA (1992)
C-ll
-------
Table C-3d. Lipid Contents of Trophic Level 4, Pelagic Fishes
Species Name
Common Name
Whole Fish Lipid
Content,
Percent (size)
Fillet Lipid Content,
Percent (size)
Reference,
Comments
Ambtoplites
rupestris
rock bass
mean = 1.0
(0.8 to 1.2, 3 obs)
USEPA (1992)
Ambtoplites
rupestris
rock bass
mean = 2.3
(0.6 to 4.4, 759
obs)
mean = 0.7
(0.4 to 0.98, 129 obs)
STORET
Amia caiva
bowfin
mean = 0.5
(0.04 to 1.4, 230 obs)
STORET
Ceniropristis
striata
black sea bass
mean — 2.00
(standard error =
0.221, 40 obs)
Exler (1987)
Esox lucius
northern pike
mean = 1.4
(0.6 to 2.6, 5 obs)
USEPA (1992)
Esox lucius
northern pike
mean = 1.9
(0.1 to 9.8, 810
obs)
STORET
Esox lucius
northern pike
mean = 0.69
(standard error =
0.005, 224 obs)
Exler (1987)
Esox niger
chain pickerel
mean= 1.3
(0.6 to 2.0, 5 obs)
USEPA (1992)
Leiostamus
xanthurus
spot
mean = 5.2
(3.3 to 7.9, 300
obs)
STORET
Lciastomus
xanthurus
spot
mean = 4.90
(standard error = 2.93,
10 obs)
Exler (1987)
Littjanus
campechanus
red snapper
1.34(55 obs)
Exler (1987)
Micropogonias
undulatus
Altaic croaker
3.17
(standard error =
0.529, 8 obs)
Exler (1987)
Micropterus
dolomteu
smaflmouth bass
mean = 1.6
(0.8 to 4.4, 19 obs)
USEPA (1992)
Micropterus
dolomicu
smaUmouth bass
mean = 3.4
(0.3 to 8.8, 1166
obs)
mean = 0.6
(0.01 to 2.3, 848 obs)
STORET
Micropterus
punctulatus
spotted bass
msan = 2.8
(0.9 to 4.5, 4 obs)
USEPA (1992)
Micropterus
punctualtus
spotted bass
mean = 2.4
(0.6 to 4.9, 322
obs)
mean = 0.7
(0.1 to 1.8, 353 obs)
STORET
C-12
-------
National Sediment Quality Svmev
Table C-3d. (Continued)
Species Name
Common Name
Whole Fish lipid
Content,
Percent (size)
Fillet lipid Content,
Percent (size)
Reference,
Comments
Micmpterus
sal mo ides
largemouih bass
mean = 1.6
(0.4 to 7.6, 54 obs)
USEPA (1992)
Micmpterus
salmoides
large mouth bass
mean = 4.1
(0.3 to 10.6, 2924
obs)
msan = 0,7
(0.04 to 9.2, 4548
obs)
STORET
Momne amcricana
white perch
mean = 4.5
,(2.6 to 7.1, 249
obs)
STORHT
Momne chrysops
white bass
mean = 2.7
(0.7 to 4.8, 11 obs)
USEPA (1992)
Momne chrysops
white bass
mean = 4.6
(0.3 to 15.4, 615
obs)
mean — 3.9
(0.01 to 8.1, 847 obs)
STORET
Momne saxatitis
striped bass
mean = 2.33
(standard error =
0.381, 14 obs)
Exler (1987)
Oncorhynchus
gorbuscha
pink salmon
mean = 3.45
(standard error =
0.141, 144 obs)
Eifer (1987)
Oncorhynchus
kisutch
coho salmon
mean = 2.7
(0.4 to 10.7, 383 obs)
STORET
Oncorhynchus
kisutch
coho salmon
rrean = 5.92
(standard error =
0.162, 217 obs)
Exler (1987)
Oncorhynchus
mykiis
rainbow trout
11 (35 g)
Branson et aL
(1985)
Oncorhynchus
my kiss
rakibow trout
mean = 5.0
(4.1 to 5.6, 3 obs)
USEPA (1992)
Oncorhynchus
nsrka
sockeye salmon
mean = 8.56
(standard error =
0.392, 48 obs)
Exler (1987)
Oncorhynchus
tshawytscha
Chinook salmon
mean = 3.7
(2.4 to 5.1, 52 obs)
mean = 2.2
(0.04 to 17.7, 1957
obs)
STORET
Oncorhynchus
tshawytscha
Chinook salmon
mean = 10.44
(standard error =
1.692, 10 obs)
Exler (1987)
Petva ftavescens
yelfow perch
irean = 3.6
(1.2 to 9.1, 112
obs)
mean = 0.5
(0.1 to 4.6, 280 obs)
STORET
Pomatomus
saltatrix
blue fish
mean = 4.27
(3 obs)
Exler (1987)
C-13
-------
Table C-3d. (Continued)
Specks Name
Common Name
Whole Fish Lipid
Content,
Percent (size)
Fillet lipid Content,
Percent (size)
Reference,
Comments
Salmo clarki
(Onchorhynchus
clarki)
cutthroat trout
mean = 1.0
(0.2 to 1.7, 378 obs)
STORET
Salmo gairdneri
(Onchorhynchtu
mykiss)
rainbow trout
mean = 3.36
(standard error =
0.256, 24 obs)
Exfcr (1987)
Salmo salar
Atlantic salmon
mean = 6.34
(standard eiror = 1.72,
7 obs)
Exler (1987)
Salmo trutta
brown trout
mean = 4.0
(1.6 to 8.1, 6 obs)
USEPA (1992)
Salmo trutta
brown trout
mean = 6.0
(1.5 to 8.9, 112
obs)
mean = 5.0
(0.14 to 14.8, 741
obs)
STORET
Salvelinus
namaycush,
Oncorhynchus mykiss,
Oncorhynchus spp.
salmo nkls
11 (2410 g)
USEPA (1994a)
Satvelinus matma
DoBy Varden
mean = 7.1
(2.1 to 9.9, 3 obs)
USEPA (1992)
Salvelinus namaycush
lake trout
mean = 15.9
(12.6 to 18.3, 42
obs)
mean = 7.8
(2.5 to 20.0, 1883
obs)
STORET
Scom.bcromorus
cavall
king mackerel
mean = 2.00
(standard error =
0.188, 6 obs)
Exler (1987)
Scombemmorus
macula
Spanish mackerel
mean = 6.30
(standard error=3.810,
3 obs)
Exfcr (1987)
Stizostedion
canadense
sauger
mean ss 6.0
(0.8 to 16.3, 139
obs)
mean s 1.7
(0.3 to 10.0, 195 obs)
STORET
Stizostedion vitreum
walleye
0.6 to 0.7
Owe® et al (1994)
Stizostedion vismum
walleye
mean = 6.2
(0.3 to 15, 1089
obs)
mean =1.3
(0.3 to 6.0, 440 obs)
STORET
Stizostedion vitreum
walleye
mean = 1.22
(standard error =
0.162, 14 obs)
Exler (1987)
Stizostedion vixreum
walleye
msan = 1.6
(0.7 to 2.6, 13 obs)
USEPA (1992)
C-14
-------
"S;i(i
-------
Table C-4a. Lipid Analysis - STORET
Analysis
Matrix of Fishes Included in Avenge
Tissue/
Organ
Lipid Content, %
"Ifcophlc
Level
Position in Water
Column
Mobility
Habitat
Mca-
n
Standard
Error
Number of
Observatio-
ns
Range
3
4
Demersal
Pelagic
Resident
Migratory
Freshwat-
er
Saltwater
A
•
•
•
•
•
•
•
•
whole
5.97
113,978
0.1-26.7
B
•
•
•
•
•
•
whole
5.97
0.010
110,998
0.1-26.7
C
•
•
•
•
•
•
fillet
2.5
13,293
0.01-20
D
•
•
•
•
fillet
0.753
0.010
6793
0.01-10
E
•
•
•
•
•
whole
6.33
0.011
91867
0.22-26.7
F
•
•
•
•
•
whole
3.757
0.020
13025
0.10-16.3
G
•
•
•
•
m
Sllet
4.49
0.018
42687
0.02-24
H
•
•
•
•
•
fillet
1.06
0.021
9378
0.01-21.-
07
-------
Table C-4b. Lipid Analysis - NSCRF
Analysis
Matrix of Fishes Included in Average
Tissue/
Organ
lipid Content, %
TVophic
Level
Position in Water
Coliam
Mobility
Habitat
Mean
Standard
Em»r
Number of
Observations
Range
3
4
Demeisal
Pelagic
Resident
Migratory
fteshmter
Saltwater
A
•
•
•
•
•
•
•
•
whole
8,5
249
0.5-31.9
B
•
m
•
•
•
•
whole
8.6
0.328
246
0.5-31.9
C
m
•
•
•
•
•
fillet
1.9
122
0,4-8.1
D
•
•
•
•
fillet
1.6
0.116
103
0.4-7.6
E
•
•
•
•
•
whole
8.8
0.338
233
0,5-31.9
F
•
•
•
•
m
whole
4.6
1.02
7
1.6-8.7
0
•
•
•
•
fflet
4.9
0,697
34
0.5-19.6
H
•
•
•
•
•
ffllet
1.6
0,106
117
0.4-7.6
Data for fillets and whole fish were evaluated separately. All analyses except MA" were of fishes in the NSI exclusively. Summary statistics reported include the mean, stao<£ard error, range, and number of
observations. The matrices in Tables C-4a and C-4b indicate the categories of fisbes averaged. The average of edible portions from USDA data was 4.1 percent lipid.
The mean fillet percent lipid content for various groups of fish species is the S TO RET database ranged from 0.753 to 4.49 percent; in the NSCRF, mean fillet values ranged from 1.6 to 4.9 percent. The mean
whole-body percent lipid content for various groups of fish species in the S TO RET database ranged from 3.757 to 6.33 percent; in the NSCRF, mean whole-body values ranged from 4.6 to 8.8 percent.
-------
References
Branson, D.R., I.T. Takahashi, W.M. Parker, and G.E. Blau. 1985. Bioeoncentration of 2, 3,7,8-
tetrachlorodibenzo-p-dioxin in rainbow trout. Environ. Toxicol. Chem. 4:799-788.
Cook, P.M. 1995. Pelagic BSAFs for NSI methodology. Memorandum from P.M. Cook, ERL, Duluth, to C. Fox,
USEPA Office of Water, March 29,1995.
Cook, P.M., G.T. Ankley, R.J. Erickson, B.C. Butterworth, S.W. Kohlbry, P. Marquis, and H. Corcoran. 1994. The
biota-sediment accumulation factor (BSAF): Evaluation and application to assessment of organic chemical
bioaccumulation in the Great Lakes. In preparation, (cited in: USEPA, 1994a)
Dickey, L.E. 1990. Composition of foods, raw, processed, prepared—1990 supplement. Agriculture Handbook 8,
1990 Supplement U.S. Department of Agriculture, Human Nutrition Information Service, Washington, DC.
Esser, H.O. 1986. A review of the correlation between physicochemical properties and bioaccumulation. Pestic. Sci.
17:265-276. (as cited by Randall et al., 1991).
Exler, 1.1987. Composition of foods: Finfish and shellfish products. Agriculture Handbook No. 8-15. U.S. Depart-
ment of Agriculture, Human Nutrition and Information Service, Washington, DC.
Flint, R.W. 1986. Hypothesized carbon flow through the deep water Lake Ontario food web. J. Great Lakes Res.
12:344-354.
Hansen. 1995. Assessment tools that can be used for the National Sediment Inventory. Memorandum from D.J.
Hansen, ERL, Narragansett, to C. Fox, USEPA Office of Water, February 28,1995.
KarickhofF, S.W., D.S. Brown, and T.A. Scott. 1979. Sorption of hydrophobic pollutants on natural sediments.
Water Res. 13:241-248.
Kuehl, D.W., P.M. Cook, A.R. Batterman, D.B. Lothenbach, and B.C. Butterworth. 1987. Bioavailability of
polychlorinated dibenzo-p-dioxins and dibenzofurans from contaminated Wisconsin River sediment to carp.
Chemosphere 18:1997-2014.
Oliver, B.G., and A.J. Niimi. 1988. Trophodynamic analysis of polychlorinated biphenyl congeners and other
chlorinated hydrocarbons in the Lake Ontario ecosystem. Environ. Sci. Technol. 22:388-397.
Owens, J.W., S.M. Swanson, and D.A. Birkholz. 1994. Bioaccumulation of 2, 3,7, 8-tetrachlorodibenzo-p-dioxin,
2,3,7,8-tetrachlorodibenzofuran and extraetable organic chlorine at a bleached kraft mill site in a northern
Canadian river system. Environ. Toxicol. Chem. 13:343-354
Randall, R.C., H. Lee II, R J. Ozretich, J.L. Lake, and RJ. Praell. 1991. Evaluation of selected lipid methods for
normalizing pollutant bioaccumulation. Environ. Toxicol. Chem. 10:1431-1436.
Tracey, G.A. and D.J. Hansen. 1996. Use of biota-sediment accumulation factors to assess similarity of nooionic
organic chemical exposure to benthically-coupled organisms of differing trophic mode. Arch, Environ. Contam.
Toxical. 30:467-475.
Schmeider, P., D. Lothenbach, R. Johnson, R. Erickson, and J. Tietge. 1992. Uptake and elimination kinetics of
'H-TCDD in medaka. Toxicologist 12:138.
Servos, M.R., S.Y. Huestis, D.M. Whittle, G. J. Van Der Kraak, and K.R. Munkittrick. 1994. Survey of receiving-
water environmental impacts associated with discharges from pulp mills. 3. Polychorinated dioxins and furans
in muscle and liver of white sucker (Catostomus commersoni). Environ. Toxicol. Chem. 13:1103-1115.
C-18
-------
Niitinir.il St'tliinciH Quality Survey
USEPA. 1990. Lake Ontario TCDD bioaccumulation study—Final report. U.S. Environmental Protection Agency,
Region 2, New York, NY.
USEPA. 1992. National study of chemical residues infish. 2 vols. EPA 823-R-92-008a,b. U.S. Environmental
Protection Agency, Office of Science and Technology, Washington, DC.
USEPA. 1994a. Great Lakes Water Quality Initiative technical support document for the procedure to determine
bioaccumulation factors—July 1994. EPA-822-R-94-002. U.S. Environmental Protection Agency, Office of
Water, Office of Science and Technology, Washington, DC.
USEPA, 1994b. Health effects assessment summary tables FY 1994. Supplement number 2. EPA/540/R-94/114,
NTIS PB94-921102. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response,
Washington, DC. November.
USEPA, 1995. Integrated Risk Information System (IRIS). Online. U.S. Environmental Protection Agency, Office
of Health and Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH.
C-19
-------
Appendix ('
-------
Appendix D
Screening Values for
Chemicals Evaluated
Sediment Concentrations
Table D-l presents the screening values used in the evaluation of NSI sediment chemistry data. Values listed
in this table are in parts per million (ppm) except for the values for EPA draft sediment quality criteria
(SQC^) and sediment quality advisory levels (SQALoc), which are in micrograms per gram (fig/g) organic
carbon. These values were multiplied by the organic carbon content (f^) of the sediment sample, when known, or
the default value if unknown (f^. = 0.01). SQALs used in this analysis were calculated specifically for use in the
screening analysis of NSI data. Effects range-low (ERL) and effects range-median (ERM) values were taken from
Long et al. (1995). Apparent effects threshold-low (AET-L) and apparent effects threshold-high (AET-H) values
listed are values that have been normalized to dry weight. AET-Ls and AET-Hs were taken from Barrick et al.
(1988). Threshold effects levels (TELs) and probable effects levels (PELs) were taken from FDEP (1994).
Fish Tissue Concentrations
Fish tissue concentrations are presented in the right columns of Table D-l. EPA risk levels were calculated for
both a human health cancer risk of 10 s and a noncancer hazard quotient of 1 (USEPA, 1995a, b). Other available
EPA sources were consulted as necessary for risk-based concentrations to be used in a screening analysis, including
the Environmental Criteria and Assessment Office (as cited in USEPA, 1995c). FDA guidance/action/tolerance
levels were obtained from the FDA Office of Seafood (DHHS, 1994; 40 CFR 180.213a and 180.142; USFDA, 1993a,
b, c, d, e).
Biota-Sediment Accumulation Factors
The final column in Table D-l presents the biota-sediment accumulation factors (BSAFs) used in the analysis.
The BSAFs were adopted for use in the theoretical bioaccumulation potential (TBP) calculations that represent
potential concentrations that might occur in tissues of fish exposed to contaminated sediments. The methodology
used in deriving BSAFs and other parameters used in the TBP calculations are described in Appendix C of this
document.
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 exist as specific chemicals. To perform a screening analysis that accommo-
dates the way the data exist in the NSI and provides a reasonably conservative risk-based approach, chemical data
were combined in particular cases.
Two of the chemical groups affected by this approach are polychlorinated biphenyls (PCBs) and dioxin com-
pounds. The data for PCBs in the NSI occur in three ways: (1) total PCBs, (2) PCB congeners, and (3) PCB aroclors.
The data for the PCB congeners were summarized (excluding as appropriate the lower chlorinated homologs that
may be present as laboratory artifacts) to provide a total PCB value where one was not provided by the original
database. This summarization enabled comparisons to the screening values available for total PCBs. Aroclor-spe-
D-l
-------
Table D-l. Screening Values for Chemicals Evaluated
GUTDEUNE VAUJESINTENDED ONLY FOR SCREENC-LEVEL HAZARD COMPARISON AMONG CHEMICALS
Ma^BcQretverUadeffcvtectire efS*daxBt*taGh*aLo«ftJfe«Ofcratrik
1
0.2
a
1.0
1597260$
AkchbtfLawo
1
1.3
110
116063
AHkarts/Tcnisk
11
309002
Aidrin
1,3
0.0063
0,32
0.3
1.80*
62533
AnSnc
19
120127
Anthracene
!
.0853
1.1
.96°
13*
0.0469
0.245
3200
0.29s
999999933
Anthracene & Phemtnlhttae
1
180
.0853
1.1
.96*
6.9*
180
0.0469
0.245
3200
0.29*
7440360
Antta»ny
150*
200*
4.3
7440382
Ancrae
2
8.2
70
57b
700°
7.24
41.6
0.062
3.2
68
1912249
Atraane
0.49
380
7440393
Dirkori
750
92875
Bcraadjoc
0,00047
32
71432
Benzene
1,6
5.7
3.7
1.0
56553
Bcrau{B)anthraa:nc
1
.261
1,6
1,6s
5,1**
0.0748
0.693
0.15
0.29*
999999955
Bcmo(a)anthniccne.'Cbfyse-
ne
1
.261
1.6
l£*
5.1**
0,0748
0.693
0.15
0.29"*
50328
Benaj(i»)pyreae
1
.43
1.6
1.6°
3.6"
0.0888
0.763
0.015
0.29s
205992
Berne (b)&ioRinthf dc
1
3.6®
9.9*
0.15
0.29*
191242
Beamfj^Tperyfct*
1
.72*
2.6*
207089
QetQofk)lbofiusbrnB
1
3.6°
9.9*
!.5
0.291"
65850
Qcszoicacid
.65°*
.76*
43000
98077
IkmotricWoride
1
0.0083
-------
Table D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY FOR SCREENING-LEVEL HAZARD COMPARISON AMONG CHEMICALS
Ma; Be Orer» or Uiiieiprfcdhe of Seincii at * Given Location Depodkie on Site-Specific CtaStlon
CAS Number
Name
Code
Sediment Cotxcttratfon
Fbh Tissue CooceoCnitk-o (fpo)
BSAF
(unities*)
SQC-
ER-L
ER-M
{ppuj
AET-L
(P»)
AET-n
(f!*^
SQM^
WzJ
TEL
)
PEL
<&**$
Cbd»&
a EPA
Risk I4H
EPA
Neacaac*-
r
Haxsitl
Qnotteet
= 1
FDA
Guidance/
Action/
Tokmocc
Level
100516
Benzyl alcohol
.073"
Ml*
3200
100447
Benzyl cfck>ridt
1
0.63
744041?
BeryEaim
.025
54
319846
BHC, *3pha-
u
0,00032
0.00099
0.017
0.3
1.80"
319857
BHC, bcta-
u
0.00032
0.00099
0.060
03
1,80s
319868
BHC, dtKa»
U,6
13
0.00032
0.00099
0,060
03
IJQ*
58899
BHC, gamra- (Lindane)
13. 6
0.37
0,00032
0.00099
0.083
3.2
03
1.80*
60873I
BHC, technical grade
1,3
0.37
0.00032
0.00099
0,060
3.2
03
1.81?
92524
Blphenyl
1,6
110
540
0.29*
111444
Bs(2-chiorocthyl)elhcr
1
0.098
108601
Bis(2-c^orois»prDpy3)cther
1
1.5
430
11781?
Bts(2-ethylh5xyf)phthala'i
1.6
1.3*
1.9°
0.182
2.65
7.7
220
1,0
542881
Bb(chioromcthy!}ether
0.00049
7440428
Bvrun
970
75274
Bro mcxlkhloro methane
I
1.7
220
74839
Broisoiaethaoe
1
15
101553
Broraophenvi phenyl ethen 4-
1,6
130
620
1,0
1689845
Brortojcynil
220
85687
Biayl berasl ph?hatate
1.6
.9*
.9°
1100
2200
1.0
7440439
Cadmium
2
1.2
9,6
5.1*
9.6°
0.676
4,21
5.4
3
63252
CaxttaryVSevin
1100
1563662
Ca^ftrarvlanuUn
54
75150
Caibon disulfide
1100
133904
CMorarcben
160
57749
Chkutiane
13
0.00226
0.00479
0.083
0.65
03
4.77*
5103719
C Mordant, a^pha(ds>-
13
0.00226
0,00479
0.083
0.65
03
4.77*
-------
Table D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY FOR 6CRTO%TNC-LEVEL HAZARD COMPARISON AMONG CHEMICALS
May Be Onr» «eVtArftvUtAre 01SttiatdL •! • Given Lae»lfea Dcpeedac mSUt-Sf**16c GMdtfens
CAS Number
Chtnbl Nmbb
Oxfc
Sedkseot C»se«aCmtioa
flibTluue CoBccatiafibB (ffcci
BSAF
(uafOtfts)
5QC„
(WfeJ
ER-L
ER-M
(pp™)
AET-L
AET-H
(Jr*»)
SQAL„
TEL
(PP»»
PEL
(n*!0
Cboetft
» EPA
Risk 104
EPA
Neaesaec*
r
QuBtkui
-1
FDA
CtfdtBee/
Artfarf
TOeiwec*
Level
5103742
ChJortUns, bet*(tma>-
U
0.00226
0.00479
0.083
0.65
03
2*
5566347
Chterdww, gsjrmi(6ra«it)-
w
0.00226
0.00479
0.083
0.65
03
2.22*
999999247
CHonJ«j»-NonBchiof(crij>-
13
0.00226
0,00479
0.083
0.65
03
4.77*
999999248
ChfcrdJU*-Nor«cHor•
U
0.00226
0.00479
0.083
0.65
0.3
4.77*
108907
Chbrobcrecac
1,6
82
220
1.0
510156
CKbfobersrskte
0.40
220
750Q3
Chksme thane
!
4300
75014
CHbmeihefle
1
0.057
110758
Chbrocthyfviriyi ctfwr, 2-
1
270
74873
Chfarorottbane
I
8.3
91587
ChkHoruphthtknc, 2-
I
860
93578
Chkjrophenol, 2-
54
2921882
Chbjpyrifos'DwsbKi
1
32
1.80*
7440473
Chromium
2
81
370
260?
270-
52.3
160
54
n
218019
Chryseae
1
-384
2.8
Z&
9,2**
0-108
0.846
15
0.29s
7440508
Copper
34
270
390"
1300*
18.7
108
400
IGS394
Cresol n>-
.63*®
,72*
540
95487
Creaol 0-
,63^
.72"
540
106445
Crcsot, p-
.67
-------
Table D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY FOR SCREENING-LEVEL HAZARD COMPARISON AMONG CHEMICALS
May Be Orer* or Underptotective of Sediment at a Gtren Location Depending on Site-Specific Conditions
CAS Nunter
QwttJcal Name
Code
Sedbnent Concentration
Fish tissue Concentration (ppn)
BSAF
(uaftless)
SQC,
(MSfeJ
ER-L
(Pf*n)
ER-M
(ppra)
AET-L
(Pl*n)
AET-n
(ppm)
SQAL^
TEL
(ppra)
PEL
(Piro)
Concea
= EPA
Rbk It*
EPA
None* nee-
r
Hazard
Quotient
= 1
FDA
Guidance/
Action'
Ibfenacc
Level
3424826
DDE, o,p'-
13
.0022
.027
.009s
.015'
0.00207
0.374
0.32
5
7.7*
72559
DDE, p, p'-
13
,0022
.027
.009s
.015*
0.00207
0.374
0.32
5
7.7*
789026
DDT, o,p'-
1,3
.00158
.027
.034b
.034*
0.00119
0.00477
0.32
5.4
5
1.67*
50293
DDT, p, p'-
13
.00158
.027
.034*
.034*
0.00119
0.00477
0.32
5.4
5
1.67*
999999300
DDT (Total)
13
.00158
.0461
.009*
.015*
0.00389
0.0517
0.32
5.4
5
7.7*
1163195
Deeabroraodipbcnyi oxide
i
110
. . $4742
Di-rv butyl phttabte
1.6
1.4M
1.4"
..1100
... .
1100
1.0
117840
Di-n-octyl phihalaie
1
6.2b
6.2*
220
1.0
333415
DiazinorVSpectracide
1,6
.019
9.7
1.80*
53703
Dibenzo(aJi)anthracene
1
.0634
.26
,23«
.97*
0.00622
0.135
0.015
0.29*
132649
Dibcnzofuran
1.6
.54®
1.7'
200
43
1.0
96128
Dtbromo-3-chbropropane,
1,2-
I
0.077
124481
DiroroocWoromcihax*
1
1.3
220
1.0
1918009
Dicarrfca
320
95501
Dichfcrobcnzcne, 1,2-
1.6
0.058*
0.05*6
34
970
1.0
541731
Dchbrobenzene, 13-
1.6
170
960
1.0
106467
Dk^lorobenzrne, 1,4-
1.6
.11*
.12**
35
4.5
1.0
25321226
Dichlorobenzenes
1
0.05**
0.05**
34
4.5
960
1.0
91941
Dichbrobenadine, 33'-
0.24
75718
Dchbrodifluoronethane
1
2200
75343
Dichbroethane 1,1-
1
1100
1.0
107062
Dfchb roe thane 1,2-
1
1.2
1.0
75354
Dfchloroetheoe, 1,1-
1
0,18
97
156605
Dichbroethene, trans-1,2-
1
220
1.0.
156592
DicWoroethyfene, cs-1,2-
1
110
75092
Dichbro methane
1
14
650
1.0
-------
Ifcble D-l. (Continued)
CUH>EUNE VALUES EXTENDED ONLYFO* SCREENIN'G-LEVTL UAZ^D COMPARISON AMONG CHEMICALS
May Ik Orcr- «r UWetfreteefrr* ef SeAerel af a Chts UoiiM Dtfiirfbi mi SHc*S?edBc CMMnt
CASNwHber
Chrakal Naax>
OxSo
SHhneat Gmxacm&m
FWiUmbd CwMifUwi
B5AF
)
SQCm
W
ER-C.
m-M
(Km*
AET-L '
(ftwd
AET-Q
(ppw)
SQAL„
o>»y
TEL
(fftnj
PEL
1
FDA
GiMikc/
Actioa/
Ifeicruec
Lerci
120832
DfeMorof»I)«iQi 2,4-
32
94737
DichbropiEaDX>*cetk: add, 2,4-
5
110
1
94826
DidNompheooxybocanob *dd,
2,4-
86
78875
Dk^hropfcpane, 1,2-
1
1.6
1,0
542756
DjchfaropropeEe, 1,3-
1
0,62
3.2
62737
EScUorvoj
1
0.37
5.4
115322
DicofbVKckbane
0.24
60571
Diddm
I J,6
U
u
7.15E-4
0.0043
.0067
.54
.3
1.80*
84662
Dkohyi pbthaiair
1,6
0.2"
0.2s
63
8600
1.0
I19904
Dmthcxybenadine.3,3'-
7.7
131113
Dimethyl phtahie
1
0.16*
0.16°
110000
1.0
105679
Dimtihyiphcnol, 2,4-
.029*
.21"
220
528290
DiirrobeiHtne, 1,2-
4.3
99650
Dntrobeszene, 13-
1.1
100254
Dinfero benzene, 1,4
4.3
51285
Dhfcrophenol, 2,4-
22
121142
DHtictolaene, 2,4-
22
606202
DiraEroEol*nc, 2,6-
11
88857
DinoscWDNBP
1!
12266?
D^henyih)dJrt«iDe. 1,2-
.
0.13
298044
Dsu&hob
I
0.43
959988
Endosul&n, *lphi-
1.6
.29
65
1 M8>
33213659
Endosylfea bets-
1,6
1.4
65
1.80s
115297
Ecdosul&a maed isomer*
1.6
34
65
1.80*
72208
Endria
1,6
4.2
4J
3.2
1.80*
563122
Btaen/BSadeB
I
5.4
1.80*
-------
Table D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY FOR SCREENING-LEVEL HAZARD COMPARISON AMONG CHEMICALS
May Be Over- or Uncle (protective of Sediment at • Given Location DepemSng on S He-Specific Condfttons
CAS Nunier
Qxidcal Name
Code
Sedhmtf Concentration
FbhTbsue Concentration (ppra)
BSAF
(uohlets)
SQC.
(M8/8,e>
ER-L
(ppa)
ER-M
(ppa)
AET-L
(pf*n)
AET-H
(Pl*n)
SQAL^
TEL
(ppn)
PEL
(PI*")
ConcciL
= EPA
Risk 1«H
EPA
Nomine-
r
Hianl
Quotient
= 1
EDA
Cttfdaoee/
Action/
Tolerance
Level
141786
Ethyl acetate
1
9700
100414
Ethyfcenzene
1.6
.0Ib
.037°
480
1100
1.0
106934
Flhytene dihromide
I
.0013
206440
Fiioraniheoe
I
620
.6
5.1
2.5®
30*
620
0.113
1.494
430
0.29*
86737
Fluorene
1.6
.019
.54
.54®
3.6*
54
0.0212
0.144
430
0.29*
944229
Fonofos
1
22
76448
Heptachbr
1.3
0.024
5.4
.3
1.80*
1024573
Heptachbr epoxide
U
0.012
0.14
.3
1.80b
118741
Hexachloro benzene
1
.022*
.23°
0.067
8.6
0.09*
87683
Hexachbrobutadicne
1
.011*
.27®
1.4
2.2
1.0
77474
HexachbrocycbpenUdiene
1
75
67721
Hexachbroethane
1.6
100
7.7
11
1.0
51235042
Hexazinone
1
360
123319
Hydroqienone
430
193395
lndeno(l,2P3-ed)pyrere
1
.69°
X6*
0.15
0.29"
78591
Isophorone
1
no
2200
1.0
33820530
Isopropain
160
7439921
Lead
2
46.7
218
4506
660-
30.2
112
1.3
121755
Malathton
1.6
.067
220
1.80*
108316
Mafeic anhydride
1100
7439965
Manganese
54
7439976
Mercwy
.15
.71
.59*
2.1**
0.13
0.696
1.1
1
72435
Methoxychbr
1.6
1.9
54
1.80*
78933
Methyl ethyl ketone
1
6500
1.0
108101
Methyl isobutyl ketone
1
860
22967926
Methyl nrrcwy
3
1.1
1
-------
Table D-l. (Continued)
GUIDELINE VALDE3 ^tended only for screening-level hazard comparison among chemicals
M»| B« Or*** or IMttfntcdn ef Stdfanrnt at ¦ Ctrci Lw»M&9m
CASNisrbcr
OxmiadNum
Code
ScdkscotCoactfltnUMt
lUtTiutx GMntsbMiMQfiid
BSAF
(unities*)
SQC„
er.l
{«¦**
ER-M
AEF»L
(PP*J
AET-II
(WW#
SQAL„
W&J
TEL
(R**)
PEL
Ceoccsv
m EPA
Rbklfr*
EPA
Nwapincc»
r
ELuMfd
Quotient
-1
FDA
Grfduee/
Tbfc rente
Level
91576
Mc&^phthoboe, 2-
1
.07
.67
.67*
1,9*
0.0202
0501
21087649
Metribum
270
2385855
Mircx/Dechlomne
U
0.060
2.2
0.1
UI*
7439987
Molybdenum
54
mm
NtpkJalcne
1,6
,16
2,1
XI-
%T>
4?
0.0346
0391
430
0,29*
91598
NspbtbyfcmA*, 2-
0.00083
7440020
Nickc!
2
20.9
51.6
15.9
42.8
220
70
9S953
Nitrobenzene
5.4
100027
Njerophesoi, 4
670
924163
Nnosod^r^bix^Kisirc, N-
0.020
621647
Nirosodi-ii-piopyluDine, N-
aois
55185
NbtHOdinsthyiHnins, N-
0.0021
86306
Nirosodipbtnytaniie, N-
.028*
,13°
22
999999484
PAK$ (faS$) mofecuh* weight)
1.7
9.6
n*»
69*Aa
0.655
6676
999999502
PAHs (faw a»fecul« weight)
,552
3,16
24^
0312
L442
56382
P*r&£h*3n ethyl
65
12674111
PCB (Arocbr-1016)
1.4
.0227
.180
t&
3.1*
0,0216
aiss
aoi4
0.75
2
L85*
11104282
PCB (Arocbr-1221)
1.4
.0227
.180
1.0°
3.1*
0,0216
0.189
0.014
0.22
2
1.85*
11141165
PCB (Aroctor-1232)
M
.0227
.180
U?
3.1*
0.0216
0.189
aoi4
0.22
2
1.85*
53469219
PCB (Arocbr-1242)
1.4
.0227
.180
1.04
3.t*
0.0216
0.189
0,014
0.22
2
1.85*
12672296
PCB (Aroclor-1248)
1,4
.0227
.180
I.06
3.1*
0.0216
0.189
0.014
0.22
2
1.85*
11097691
PCB (Aroclor-1254)
1.4
.022?
.180
L0>
3.1*
0.0216
0.189
0.014
0.22
2
1.85*
11096825
PCB (Arocbr-1260)
1.4
.0227
.180
1.0*
3.1*
0.0216
0.189
0.014
0.22
2
1.85*
608935
PenticWorobenzetrc
1.6
69
8.6
0.04*
82688
PentacWoroniTOb
-------
Table D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY FOR SCREENING-LEVEL HAZARD COMPARISON AMONG CHEMICALS
May Be Over- or Uode (protective of Sc dental al • Ghtn Location Dependng en SL3«SpedSe CondMooi
CASNimter
Chemical Nan
Code
Scdbaent Cooccotxvtfcm
Rib JHtat Concentration (ppm)
BSAF
(tablets)
SQC,
(vstej
ER-L
(ffOl)
Bt-M
(ppm)
AET-L
(ffOl)
AUT-H
(ppm)
sqalk
TEL
(pH
PEL
(FPU)
Coacea
¦ EVA
Risk 104
EPA
r
Hazard
Quotient
« 1
FDA
Guidance/
Actlonf
Inference
Level
85018
Pheoutbrene
1
180
0.240
1.5
1.5"
6.9*
180
0.0867
0.544
108952
Phenol
.42b
1.2**
6500
298022
Phonte/Fannphoa^nimet
1
2.2
8S449
PMtaic anhydride
22000
1336363
Polychbrsaied bpbesyfe
1,4
0.0227
0.180
1.0»
3.1'
0.0216
0.189
0.014
0.22
2
1.85*
1610180
Proraeton'Pnunitol
160
7287196
Prometyn/Capvol
43
23950585
Proaanade
810
1918167
Propachlor
140
129000
Pyrene
1
.665
2.6
3J®
16**
0.153
1.398
320
0.29*
91225
Quinofine
1
0.009
7782492
Sekimim
54
7440224
Stiver
1
3.7
6.1*
6.1*
0.733
1.77
54
122349
Stnnzne
5
0.90
54
12
7440246
Strontium
6500
100425
Styrene
1
2200
13071799
Terbufbs/Cooter
1
0.27
886500
Terbutryn
11
95943
Tetrnchlorobeincne, 1,2,4,5-
1
3.2
1.0
1746016
Tetrechloro<12>en20-p-dbxvi,23>7-
.8-
1
6.9E-7
0.059*
79345
Tctrachloroethane, 1,1,2,2-
1.6
160
0.54
1.0
127184
TctracUoroetheoo
1.6
.057*
.14®
53
2.1
110
1.0
56235
TcCrachloromcthano
1.6
120
0.83
7.5
1.0
58902
TctrachlorophfcooL 2,3,4,6-
320
961115
Tctrnchbrvinphos/Gardoni/Stiof
1
4.5
320
7440315
Tm
6500
-------
Tabic D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY FOR SCREENING-LEVEI, HAZARD COMPAX1SON AMONG CHEMICMS
May B« Ore* «r IMrtpetfetln at fetfmrnf at i Gtvca Leettfe* Dcptadtag m Ske4Ffe46e OmUom
CAS Ni*riber
Genticst! N*me
Code
Scdbxret Cwmft'itlwi
fkbTluK
BSAF
(atfes*)
SQC„
ER-L
(?|Nt$
ER-M
Afrr-L
(pproj
AET-n
(w»>
SQAI*
TEL
(&**$
PEL
Cp?e$
Co see B.
-EPA
Rkk It*
ETA
Neoatxsce-
r
BtBHtt
Qnolfcnt
-1
SUA
CMdftnec/
Actio ts?
Ifeteranee
Level
108883
Toluene
1,6
89
2200
1.0
8001352
ToxAphcno
1.6
10
.098
I JO
75252
TribromottrthAne (Brcrrofenn)
1,6
65
14
220
1.0
120821
Tfiiiofobancne, 1,2,4-
Ifi
.051*
.064*
920
no
1.0
71556
TrichbrDcthine, 1,1.1-
1,6
17
970
1.0
79905
Trichbrocthaoe, 1,1,2-
1
1.9
43
1.0
79016
Trfchbrocthmc
1.6
210
9.8
65
L0
75694
TnchbroSjoroar thane
1
3200
1.0
67663
Triddoronjsiiuuac (Chiorolbmj
1
18
no
1.0
95954
Trichlorophenal, 2,4,5-
1100
88062
Itichlorophenol, 2,4,6*
9.8
93765
TricMorophenoxyacedc t&d, 2,4,5-
no
93721
TrkH»roplc4K>x>pivpbnb acid.
2,4,5-
86
1582098
Tnftaafic^Tccfiaa
14
31
95636
Trinxthyifoeizeae, 1,2,4-
1
5.4
118967
TnatrotDiHeHe
3.6
5.4
7440622
V^mdaim
75
108054
Vinyl tcctaic
i
11000
108383
Xylene, to
1,6
.04*
.12*
2.5
22000
1.0
95476
Xyfeoe, o-
1
.04*
.12"
2.5
2200©
1.0
106423
Xyfenc, p-
1
.04*
.12*
2.5
1.0
1330207
Xyfcacs
1
.04*
.12*
Z5
22000
1.0
7440666
Zbc
150
410
410^
1600s
124
271
3200
S88888881
Dbsa-taxie «|iav*kn3
1
«.9E-7
0025*
-------
Table D-l. (Continued)
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. Preliminaiy SQALM developed for this chemical is under technical review.
AET Criteria:
' Sediment concentration based on am phi pods.
6 Sediment concentration based on benthic organisms.
•Sediment concentration based on oysters.
BSAF Sources:
'Cook, 1995.
'Hansen, 1995,
-------
Appendix I)
cific data were analyzed separately. In addition, the dioxin congeners were evaluated using the toxicity equivalence
factor (TEF) approach (USEPA, 1989). This approach involves summarizing specific dioxin congeners based on
their toxicity as compared to 2,3,7,8-tetrachlorodibenzo-p-dioxin, for which screening values are available. PCBs
and dioxin represent the only cases where chemical data were actually combined for the NSI evaluation.
Because EPA typically performs risk-based screening by analyzing closely related chemicals with the same risk-
based concentrations, this methodology was applied to the NSI 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 screen-
ing values. This methodology was applied only for chemicals or chemical groups with more than 20 positive results.
The following chemicals and chemical groups were affected by this methodology; BHCs, chlordanes, cresols, DDT
and metabolites, dichlorobenzenes, endosulfans, methylmercury, anthracene and phenanthrene, benzo(a)anthracene/
chrysene, xylenes, and PCBs (in applying screening values to aroclors with no available screening values).
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 Her 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 not often be identified as posing a
potential risk to aquatic life or human health, even though the chemical is highly toxic. Table D-2 lists the number
of times each chemical included in the NSI evaluation was measured and detected (i.e., a positive result) in sediment
and fish tissue and the number of times each chemical was responsible for Tier 1 or Her 2 sampling stations being
classified.
D-12
-------
Table D-2, Frequency of Detection of Chemicals in Sediment and Fish Tissue and Number of Detections
Resulting in Risk (Tier 1 or Tier 2)*-k
CAS Number
Chemical Name
N umber of
Times
Measured
ill SedjmeM
Number or
FosKfte
Sediment
Results
Ntmber
of Times
Measur-
ed
In
Tissue'
Number
of
Positive
Tissue
Results'
Her I
Level
Results
Tier 2
Level
Results
83329
Acenapbshene
6126
1567
777
41
144
359
208968
Aceraphthylene
5774
1286
-
-
74
958
67641
Acetone
547
48
22
16
-
107028
Acrofesi
-
-
464
-
-
-
107131
ActybnMc
1034
9
464
-
-
7
15972608
Ahchbr/Lasso
-
-
976
1
-
-
309002
AMrio
14311
658
8029
612
2
712
62533
Anilte
-
-
10
-
-
-
120127
Anthracene
5211
1798
748
63
168
728
999999933
Anthracene & Phenantbrene
260
199
4
-
82
95
7440360
Antimony
S923
2980
1275
99
-
56
7440382
Arsenic
22281.
18791
5528
2113
189
8613
1912249
Atrazme
-
-
880
-
-
-
7440393
Bariim 1
-
-
986
837
-
-
71432
Benzene
2248
136
976
90
-
16
92875
BenzHi*
-
-
537
-
-
-
56553
BeniD(a)H)tt>raeene
6718
3236
820
153
241
1540
999999955
Benzo(a)antaaeene/Ctarysetie
272
243
-
-
146
76
50328
Beim>{a)pyrcne
7011
3263
831
58
317
2292
205992
Benjo(b)fliKjranihene
4179
1249
717
26
-
441
191242
BenzaCgbOpeiyfene
6034
2016
-
-
-
259
207089
fknroOOfluoraritbeiie.
4192
1093
651
21
-
113
65850
Beraoic ackl
1724
247
121
5
-
41
100516
Benzyl alcohol
1910
90
120
-
-
13
7440417
Berlin
-
-
1301
81
-
39
92524
B phenyl
1215
873
564
138
-
2
542881
B B(ehtareaeth5tl)elher
-
-
76
-
-
-
111444
Bis(2-cbbroethyl)etber
-
-
636
3
-
3
108601
BS(2-chbroEopropyI)ether
-
-
34
1
-
-
117817
Bis(2-ethyliexyl)phthakte
4606
1998
647
91
401
1109
7440428
Boron
-
-
44
21
-
-
75274
Bromodichbromethare
-
-
560
4
-
-
74839
Bro mo methane
-
-
491
3
-
-
101553
Bron»phenyl phenyl ether, 4-
2698
20
656
1
-
7
85687
Butyl benzyl phthabte
4069
333
_ 634
4
1
51
319846
BHC, alpha-
9109
219
8148
1670
11
461
319857
BHC, bcta-
6761
241
3060
209
-
257
319868
BHC, deta-
4891
99
2156
65
1
94
D-13
-------
\p|H*ndi\ I)
Table D-2. (Continued)
CAS Number
Chemical Name
Number of
Times
Measured
in Sediment
Number of
Positive
Sediment
Results
Number
of Times
Measured
io Tissue'
Number
of
Positive
Tissue
Results'
Tier 1
Level
Results
Tier 2
Level
Results
58899
BHC, pimm-/L«xla(ie
14442
999
8750
1391
101
527
608731
BHC, teetotal grade
169
166
115
31
3
66
7440439
Cadmfam
27919
15176
6743
3321
-
7206
75150
Carbon dMUe
-
-
24
21
-
-
57749
Chbrdane
12432
2170
7316
4568
116
4228
999999247
Chbniair-Nonachfer(cE>
1476
9
4468
2101
-
268
999999248
Chtaiane-Nonachbr(!ians)-
1992
31
4569
2764
-
556
5103719
CUoidare, a(pha(cis)-
44-16
1516
6092
3659
3
1157
5103742
Chtordanc, bcta(trans).
2833
443
5841
3045
3
847
5566347
Chlordane, gmtm(trans)-
967
334
85
19
-
207
108907
Chbroberaens
2111
58
819
18
•
4
510156
Chbrobenabte
-
-
22
-
-
-
75003
Cbfaroctkirc
-
-
557
1
-
-
75014
Chbroelbene
-
-
706
2
-
2
110758
ChtonjclbyMnyl ether, 2-
-
-
534
-
-
-
74873
Cbbromettiane
-
-
744
12
-
-
91587
Chbronapbttakne, 2-
-
-
655
I
-
-
95578
Chbmphciiol 2-
-
-
629
1
-
-
2921882
Chlorpynfcsfflursban
305
5
793
143
-
-
7440173
Chcomlm
27504
25216
5508
3283
426
4126
218019
Ckysene
6975
3580
893
149
185
1618
7440508
Copper
27956
25452
6284
5533
-
11213
108394
Ctesol, m-
988
780
-
-
-
41
95487
Crcsol o
1993
745
51
-
-
22
106445
Cresol p-
985
84
49
3
-
31
1319773
Cresois
18
1
-
-
-
1
21725462
Cyamate
-
-
326
-
-
-
57125
Cyanic
-
-
14
3
-
-
84742
Di-n-butyi phtbaMe
4651
986
637
55
9
112
117840
Di-ri-octyl phlMhle
4179
435
650
6
-
23
333415
DkzinoiVSpcctracHe
3712
249
172
-
-
188
53703
Dibemo(aii)aiatacene
7564
2431
824
16
419
1732
132649
Dibcnzofuran
2564
416
126
-
25
51
124481
Dirornochlorometharc
2033
18
562
I
-
-
95501
Dchbrobccnene, 1,2-
4402
107
892
2
38
23
541731
Dfchbrobenasns, 13-
4315
132
797
2
-
22
106467
Dchbrobcraiix, 1,4-
4352
268
887
3
53
41
25321226
Dichferabenzenes
27
12
-
-
6
3
91941
Dfcllbroberradire, 3,3'-
-
-
639
1
-
-
D-14
-------
Nnlioiuil Scd'um-nt OustVit.v Sur\i>.\
liable D-2. (Continued)
CAS Number
Chemical Name
Number of
Urns
Mewived
in Sediment
Number of
Positive
Sediment
Results
Number
of Times
Measured
In Tissue"
Number
of
Positive
Tissue
Results'
Tier 1
Level
Results
Tier 2
Level
Remits
75718
Dichbrodifboroirelhar*
-
-
174
-
-
-
75343
Dlchloroethane 1,1.
1918
19
561
-
-
-
107062
Dchtoroelhane 1,2-
1981
20
972
8
-
-
156605
Dchbroethene, trans-1,2-
1393
33
793
2
-
-
75354
Dfchbroelhene, 1,1-
-
-
973
2
-
-
75092
DiehlDromethaifc
2177
576
532
112
-
11
120832
Dishbrophenol 2,4-
-
-
642
1
-
-
94757
DichloropheiKHtyacetk: acid, 2,4-
-
-
39
-
-
-
78875
Dfchbropropaic, 1,2-
2015
15
563
2
-
-
542756
Dfchbtopropene, 1,3-
-
-
107
-
-
-
115322
DicofoVKellhane
-
-
400
26
-
-
60571
DicUrin
14702
3113
10243
5583
89
6709
84662
Diethyl phlhalate
4188
367
654
2
34
48
131113
Dmiliyl phihalatc
4118
135
653
-
-
38
105679
Dime fbyip tic rwl 2,4-
4541
80
640
1
-
54
51285
Binkrophenol, 2,4-
-
-
631
-
-
-
121142
Dinitrotoluene, 2,4-
-
-
636
1
-
-
606202
DUtrolotieiK, 2,6-
-
-
636
1
-
-
122667
Dip he ny[hydrazine, 1,2-
-
-
509
-
-
-
298044
Disulfbton
-
-
23
-
-
-
1861321
DCPA/Daclhal
129
76
827
586
-
3
53190
DDD, o,p'-
6349
977
3397
428
73
502
72548
DDD, p, p'-
15311
4411
6252
2481
572
2574
3424826
DDE, o,p'-
5434
632
3427
401
118
222
72559
DDE, p, p'-
15961
5980
7656
5715
823
3501
999999300
DDTflbla!)
3710
736
5750
4183
122
860
789026
DDT, o,p'-
6056
567
3479
368
25
268
50293
DDT, p, p'-
16028
3288
5843
1677
371
1839
115297
EndosuKm ttixed booths
2606
80
49
12
-
20
959988
Endosuifan, afcha-
5581'
84
2832
53
-
45
33213659
Endosufin, beta-
5886
260
2157
10
-
42
72208
Endrm
12694
289 '
8192
893
-
8
563122
Ethion/Bladen
2953
38
170
-
-
-
100414
Ettiytoenzenc
2543
118
807
50 .
1
42
206440
Fkxtramhene
7562
4563
953
216
234
1074
86737
Fluorene
6652
2280
797
14
231
1141
944229
Forofos
-
-
288
-
-
-
76448
Hcptachlor .
11952
673
7369
1006
-
210
1024573
Hcptachbr epoxide
12829
986
7480
2896
-
1431
D-15
-------
Table D-2. (Continued)
CAS Number
CbemlcalName
Ntmberof
Times
Measured
In Sediment
Number of
Positive
Sediment
Results
Number
or Times
Measured
in Tissue'
Number
of
Positive
Tissue
Results
Tier 1
Level
Results
Her 2
Level
Results
118741
Henachbrobenzsne
10044
1445
6970
1519
-
224
87683
Hexachbrobutadtoie
4198
128
1161
14
-
81
67721
Heme hb methane
3801
4
636
-
-
1
193395
IndenotlAS-cdJpyrene
5874
1913
7S6
20
-
559
78591
feopbarone
3400
40
635
4
-
8
33820530
ItopiopaBn
-
-
392
15
-
-
7439921
Lead
29979
24971
6654
3008
-
8883
121755
Mahlhton
4041
38
500
1
-
26
108316
Mack: anhydride
-
-
2
-
-
-
7439965
Mangansse
-
-
1000
971
-
5
7439976
Mcrcuiy
26142
16632
9752
8424
1951
5049
72435
MethojQchfor
9183
154
5912
63
-
33
78933
Methyl ethyl ketone
519
7
20
11
-
-
108101
Methyl isGbutyi ketone
-
-
26
-
¦
-
22967926
Methyl meicury
-
-
9
8
-
-
91576
Metl^taphthakne, 2-
2629
973 .
-
¦
71
522
21087649
Metribuzh
-
-
289
-
-
-
2385855
Mrex/Dechbrane
5794
544
4800
915
-
40
7439987
Molybdenum
-
-
707
169
-
-
91203
Naphthalene
6823
2820
803
22
291
1247
7440020
Nickel
21519
18550
3120
974
-
9260
98953
Nitoberasne
-
-
635
-
-
-
100027
Niropbenol, 4
-
-
606
1
-
-
621647
NtosodJ-n-piopytaifce, N-
-
-
645
I
-
1
86306
NkrosodipbenyiiiTwie, N-
3730
66
661
3
-
45
999999484
PAHs (high niobctihr weight)
1566
885
-
-
93
383
999999502
PAHs (bw molecular weight)
1604
895
-
-
112
382
56382
Parathbn ethyl
-
-
499
4
-
-
608935
PentachbrobetEBne '
114
54
404
30
-
4
82688
PenQchbrantobenane/Quiruozene
-
-
390
2
-
-
87865
Pentachbropbenol
5622
195
1756
149
-
26
85018
PiwBMhitne
7067
4078
.
-
335
694
108952
Ftercl
4595
864
647
12
-
155
1336363
Polychbriravtx) b|>henyls
11296
4183
10642
7379
8151
2620
1610180
Promton/Paniol
-
-
289
-
-
-
1918167
Propachtor
-
-
1
-
-
-
129000
Pjitne
7558
4555
952
187
482
1896
12674112
PCB (Aiocbr-1016)
5098
46
3161
12
19
39
11104282
PCB (Arocbr-1221)
5627
7
3568
2
4
5
D-I6
-------
National Sediment Quality Survey
Table D-2. (Continued)
CAS Numfwr
Ok mica I Name
Number of
limes
Measured
to Sediment
Number or
HjilUve
Sediment
Result?
Number of
Times
Measured
fa Tissue1
Number
sf
Positive
Tissue
Results'
Tterl
Lewi
Results
Tier 2
level
Results
11141165
PCB (Arocbr-1232)
5417
13
3195
1
4
10
53469219
PCB (Arocbr-1242)
6375
435
4446
220
355
270
12672296
PCB (Arocbr-1248)
6314
559
4464
688
916
230
1109769!
PCB (Arocbr-l2J4)
7178
1305
5871
3343
3664
765
11096825
PCB (Atocbr-1260)
6885
890
6035
3611
3866
531
7782492
Selenium
-
-
2559
2079
-
4
7440224
SDwf
11082
6256
1139
515
350
1083
122349
Sirazsne
-
-
289
-
-
-
7440246
Sttonttan
-
•
45
45
-
-
100425
Stjirene
-
-
191
-
-
-
8S8888882
SFA1 est flSEMMAVS])
335
335
-
-
8
161
95943
TcttachbnDbenzenc, 1,2,4,5-
97
1
398
12
.
-
1746016
TeErachbrod iber*zn- p- d b xii. 2,3,7,8-
631
38
908
391
353
23
79345
litrachbroettaMs, 1,1,2,2-
1683
49
978
33
-
2
127184
•fttrachbrociheiK
2429
109
973
49
2
17
56235
Tettachbromeitans
2010
15
979
4
-
-
58902
TfctraeMjtophenol, 2,3,4,6-
-
-
71
-
-
-
7440315
Tta
-
-
382
264
-
-
108883
Tohicrc
2338
325
814
116
-
28
8001352
Hsxaphene ;
10912
75
6566
643
-
684
75252
TltwromoixietteJW/Broniofcrai
2078
44
818
7
-
-
120821
HiMotobiaaene, 1,2,4-
4256
87
10S2
46
6
49
71556
Hfchbroetfai*:, 1,1,1-
2083
63
815
23
-
10
79005
TWchtoroeUane, 1,1,2-
2035
14
879
7
-
-
79016
TftHbroefene
2494
75
975
19
,
1
75694
Uxhbroftoromelhane
1096
9
288
15
-
-
67663
HfcUoromeltaneClkMofcim
2277
76
972
37
-
-
95954
Ubhbropheno!. 2,4,5-
-
-
73
-
-
-
88052
H-fchbropbenoI, 2,4,6-
-
-
658
-
-
-
93765
"Dxhbfophenoxyacetfc acid, 2.4,5-
-
-
3
-
-
-
93721
IttUDrophtnoxjpropijrt: acid, 2,4,5
-
-
36
-
.
-
1582098
THtaaiimelhti
-
-
925
193
-
-
7440622
\Sinadium
-
-
768
465
-
-
108054
Vinyl acetate
-
-
21
-
-
-
108383
Xylene, m-
55
31
-
-
4
6
95476
Xyfcre, o-
61
1
-
.
1
106423
Xylene. p-
14
2
-
-
-
2
1330207
Xylercs
922
48
22
13
5
11
7440666
Zinc
27065
26473
4580
4553
-
5176
D-17
-------
\|>|H'IUli\ I)
¦Bible D-2. (Continued)
Number
Number of
Number of
Number
or
Times
Positive
or lines
Positive
Tier 1
Tier 2
Measured
Sediment
Measured
Tissue
Level
Level
CAS Number
Chemkal Name
In Sediment
Results
in Tissue*
Results'
Results
Results
833888881
Dbxxi toxic equhakns
56
56
590
590
459
45
¦Results presented at observation level. Multiple observations may have occurred at a given station.
^Observations recorded here correspond only to stations with available latitude/longitude coordinates.
'Fish tissue results are presented for demersal, resident, and edible species only.
D-18
-------
National Sediment Quality Survey
References
Barrick, R., S. Becker, L. Brown, H, Beller, and R. Pastorok. 1988. Sediment quality values refinement: 1988
update and evaluation of Puget Sound AET. Vol. 1. Prepared for the Puget Sound Estuary Program, Office of
Puget Sound. •
Cook, P.M. 1995. Pelagic BSAFs for NSI methodology. Memorandum from P.M. Cook, ERL Duluth, to C. Fox,
EPA, Office of Water, March 29, 1995.
DHHS. 1994. Action levels for poisonous or deleterious substances in human food and animal feed. U.S. Food
and Drug Administration, Department of Health and Human Services, Public Health Service, Washington, DC.
FDEP. 1994. Approach to the assessment of sediment quality in Florida coastal waters, Vol. 1. Development and
evaluation of sediment quality assessment guidelines. Prepared for Florida Department of Environmental
Protection, Office of Water Policy, Tallahasee, FL, by MacDonald Environmental Sciences Ltd., Ladysmith,
British Columbia.
Hansen. 1995. Assessment tools that can be used for the National Sediment Inventory. Memorandum from D, J.
Hansen, ERL, Narragansett, to C. Fox, Office of Water, February 28, 1995.
Long, E.R., D.D. MacDonald, S.L. Smith, and F.D. Calder, 1995. Incidence of adverse biological effects within
ranges of chemical concentrations in marine and estuarine sediments. Environ. Manage. 19(l);81-97.
USEPA. 1989. Interim procedures for estimating risks associated with exposures to mixtures of chlorinated
dibenzo-p-dioxins and dibemofurans (CDDs and CDFs) and 1989 update. EPA/625/3-89/016 U.S. Environ-
mental Protection Agency, Risk Assessment Forum, Washington, DC.
. 1995a. Integrated Risk Information System (IRIS). Online. U.S. Environmental Protection Agency,
Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati,
OH.
. 1995b. Health effects assessment summary tables FY 1995. EPA/540/R-95/036. U.S. Environmental
Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC.
. 1995c. Risk-based concentration table, January-June 1995. USEPA Region 3.
USFDA. 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.
D-19
-------
Vppi'iulK I)
-------
Appendix E
Cancer Slope Factors and
Noncancer Reference Doses Used
to Develop EPA Risk Levels
T able E-l presents the cancer slope factors and noncancer reference doses that were used to calculate the EPA
risk levels and hazard quotients used in the analysis. The calculations for the EPA risk levels and hazard
quotients used in the analysis appear in Appendix B. The slope factors and reference doses were obtained
from the following sources:
• Health Effects Assessment Summary Tables FY 1995. EPA/540/R-95/036. U.S. Environmental Protection
Agency, Office of Solid Waste and Emergency Response, Washington, DC.
• Integrated Risk Information System (IRIS). Online. U.S. Environmental Protection Agency, Office of Health
and Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH.
• Risk-Based Concentration Table, January-June 1995. U.S. Environmental Protection Agency, Region 3,
Philadelphia, PA.
E-l
-------
Apprntlis K
Tfrble E-l. Cancer Slope Factors and Noncancer Reference Doses Used to Develop EPA Risk Levels
CAS Number
Chemical Names
Cancer Slope factor
((nig/kg/d)*1)
(Followed by source;
see footnotes)
Noncancer Reference Dose
(mg/kg/d)
(Followed by sources
see footnotes)
Surrogate 'Chemical
Used (if neccessary)
83329
Acen&phthene
6.00E-21
67641
Acetone
l.OOE-l'
98362
Acetophenone
1.00E-11
107028
Acrolein
2,00E-2S
107131
Acjylonitrile
5.40E-11
1.00E-3"
15972608
Alachlor/Lasso
8.00E-2"
1.00E-21
116063
Aldlcarb/Temik
1.00E-3'
309002
Aldrin
1.70E+11
3.00E-5'
62533
Aniline
5.70E-31
120127
Antthacene
3.00E-11
999999933
Anthracene & Phenanihrene
3.00E-1
anthracene
7440360
Antimony
4.00E-4'
7440382
Arsenic
1.75E+01
3.00E-41
1912249
Airazine
2.22E-1*
3.50E-2'
7440393
Barium
7.00E-2'
92875
Benzidine
2.30E+21
3.00E-3'
71432
Benzene
2.90E-21
56553
Benzo(a)anthracene
7.30E-1®
999999955
Benzo(a)anthracene/Chiysene
7.30E-1
benzo(a)artthracene
50328
Benzo{a)p3irene
7.30E+01
205992
Benzo(b}fluoranthene
7.30E-1'
2070S9
Benzo(k)fluoranthene
7.30E-25
65850
Benzoic acid
4.00E+01
98077
Benzatrichloride
1.30E+11
100516
Benzyl alcohol
3.00E-lb
100447
Benzyl chloride
1.70E-11
7440417
Beryllium
4.30E+tf
5.00E-3'
319846
BHC, alpha-
6.30E+01
319857
BHC, bctt-
1.80E+Q1
319868
BHC, delta-
1.80E+0
beta-BHC
58899
BHC, gamma- (Lindane)
1.30E+0®
3.00E-4'
608731
BHC, technical grade
1.80E+01
E-2
-------
Niilioiiii) Sediimiil Kurvcv
j * *
Table E-l. (Continued)
CAS Number
Chemical Name
Cancer Slope Factor
((mg/kg/d)'1)
(Followed by source;
see footnotes)
Noncancer Reference
Dose (mg/kg/d)
(Followed by source;
see footnotes)
Surrogate Chemical
Used (If neeessaiy)
608731
BHC, technfcal gptde
1.80E+01
92524
Biphenyl '
5.00E-2'
111444
Bis(2-chforoethyl)ether
1.1OE+0
108601
Bis(2-chbroisopropyl)ether
7.00E-2"
4.00E-2'
117817
Bis(2-ethylhex5%>hthalate
1.40E-2'
2.00E-21
542881
Bis(chtaromethyI)ether
2.20E+2'
7440428
Boron
9.00E-21
75274
BrornodicMoromethaiiB
6.20E-21
2.00E-2'
74839
Bro it» methane !
1.40E-3'
101553
Bromuphsnyl phenyl ether, 4-
5.80E-2'
1689845
Bromoxyml
2.00E-21
8S687
Butyl benzyl phthalate
2.00E-11
7440439
Cadmium
5.00E-41
63252
CarbaiyVSevin
1.00E-1'
1563662
Carbofaan/furadan
5.00E-3'
75150
Carbon disulfide
1.00E-1'
133904
Chloramben
1.50E-2'
57749
Chlordane
1.30E+O
6.00E-51
5103719
CMordane, alpha(cis)-
1.30E+0
6.00E-5
chlordane
5103742
CHordane, beta(trans)-
1.30E+0
6.00E-5
chbrdane
5566347
Chlordane, gamma(trara>
1.30E+0
6.00E-5
chtordane
999999247
Chlofda«-r»nachlor(cis)-
1.30E+0
6.00E-5
chbrdane
999999248
Chtordane-nonachbr(trans)-
1.30E+0
6.00E-5
chlordane
108907
Chlorobenzene
2.00E-2'
510156
Chtorobenrilate
2.70E-1'
2.00E-2'
75003
Chtoroethane
4.00E-1®
75014
CWoroethene
1.90E+0"
110758
Chloroethytvinyl ether, 2-
2.50E-2'
74873
Chloromethane
1,30E-2h
91587
Chloconaphthalcre, 2-
8.00E-21
95578
Chlorophenol, 2-
5.00E-31
2921882
Chkapyrifbs/Dursban
3.00E-3'
E-3
-------
Vj)|K'luli\ 1%
liable E-l. (Continued)
CAS Number
Chemical Name
Cancer Slope Factor
((mg/kg/d)-')
(Followed by source;
see footnotes)
Noncancer Reference
Dose (mg/kg/d)
(Followed by source;
see footnotes)
Surrogate Chemical
Used (if nectssaty)
7440473
Chrotrium
5.00E-3'
218019
Chrysene
7.30E-3*
7440508
Copper
3.71E-2"
108334
C re sol n>
5.00E-21
95487
Cresol, o-
5.00E-21
106445
Cresot p-
5.00E-3"
1319773
Cresob
5.00E-3
p-Cresol
98828
Current
4.00E-2'
21725462
Cyanazix
8.40E-1"
2.00E-03"
57125
Cyanide
2.0OE-21
1861321
DCPA/Daclhal
1.00E-2'
53190
DDD, o,p'-
2.40E-1
p,p'-DDD
72548
DDD, p,p -
2.40E-1'
3424826
DDE, o,p-
3.40E-1
p,p'-DDE
72559
DDE, p,p'-
3.40E-11
789026
DDT, o,p'-
3.40B-1
5.00E-4
p,p'-DDT
50293
DDT, p,p-
3.40E-1'
5.00E-4i
999999300
DDT (Total)
3.40E-1
5.00E-4
p.p'-DDT
1163195
DecabroiTodpheny] oxide
1.00E-21
84742
Di-ivbutyl phthalate
1.00E-11
117840
Di-i>-oct>1 pllMalc
2.00E-2"
3334515
Diazinon/S pectiacide
9.00E-4"
53703
DjbeiHD(a4i)arthracene
7.30E+0*
132649
Dibemofuran
4.00E-3®
96128
Dibromo-3-chloropropane, 1,2-
I.40E+0"
124481
D&raniocHloronE thane
8.40E-2
2.00E-21
1918009
Dfcairf»a
3.00E-2'
95501
Dichtorobenzfine, 1,2-
9.00E-2'
541731
DichtorobereEne, 1,3-
8.90E-2'
106467
Dichlorobenzene, 1,4-
2.40E-2»
25321226
DkWorobenzEncs
2.40E-2
8.90E-2
1,3-and 1,4-
dicUorobeiizene
91941
Dichbrobenzidu;, 3,3-
4.50E-1'
E-4
-------
National Sdlimcut Quality Survey
Table E-l, (Continued)
CAS Number
Chemical Name
Cancer Slope Factor
(Cme/kg/d)'}
(Followed by source;
see footnotes)
Noncancer Reference
Dose (mg/kgtf)
(Followd by source;
see footnotes)
Surrogate Chemical
Used (if necessary)
75718
DchlorodltoroiTeihaiie
2.006-1'
75343
Dicbkxoethime 1,1-
1.00E-1'
S07062
Dfchkwoethane 1,2-
9.10E-2'
75354
Dfchkxoethene, 1,1-
6.00E-1'
9.00E-3'
156605
DichtoroetheiB, trans-1,2-
2.00 E-2'
156592
Dfchkjiocthylene, cis-1,2-
1.0GE-2*
75092
rHchlwomethane
7.50E-3'
6.00 E-2'
120832
Dchloropihenol, 2,4-
3.00E-3(
94757
McltaophetioxyaceUc acid, 2,4-
1.00E-2'
94826
DfcWorophenoxybutaiuic acid, 2,4-
8.00E-31
78875
Dfchloropropanc, 1,2-
6.80E-2"
542756
Dichkwopropene, 1,3-
1.75E-1'
3.00E-4'
62737
Dichtorvos
2.90E-1'
5.00E-*
115322
DicofeWCeMiaiie
4.40E-1*
60571
Dfeldrm
1.60E+1"
5.00E-5"
84662
Dclhyl phihalate
8.00E-1'
119904
Djmethoxyberajdinc,3,3'-
1.4GE-21"
131113
Dkrethyl phihalate
1.00E+1"
105679
Dimelhylphenol, 2,4-
2.00B-2*
528290
DiiiUobcnzB», 1,2-
4.a>E-4"
99650
Dinteobcnzcne, 1,3-
t.OOE-4'
100254
DWttobetHene, 1,4-
4.00E-4'
51285
Dintaophenol, 2,4-
2.00E-3'
121142
DMtrotoIuene, 2,4-
2.00E-3'
606202
DWtrotoluenc, 2,6-
1.00E-3"
88857
Dkioseb/DNBP
1.00E-3'
122667
Dqphenylhydruiie, 1,2-
8.0CE-1'
298044
Disufibton
4.00E-5'
959988
EndosuKan, alpha-
6.00E-3
erxiosulfan
33213659
EndosuKan, beta-
6.00E-3
endosufan
115297
Endosulfim mixed isomers
6.00E-3'
72208
Endrin
3.00E-41
E-5
-------
\|>|)on
-------
NitfiorinJ SV
-------
Appendix K
Table E-l. (Continued)
CAS Number
Chemical Name
Cancer Slope Factor
((mg/kg/d)-1)
(Followed by source;
see footnotes)
Noncancer Reference
Dose (mg/kg/d)
(Followed by sounce;
see footnotes)
Surrogate Chemical
Used (if necessaiy)
13071799
Terbufos/Counter
2.50E-5"
886500
Terbutryn
1.00B-31
95943
Tetrachlorobenzene, 1,2,4,5-
3.00E-4'
1746016
Tetrachtorodfoenzo-p-dhxin, 2,3,7,8-
1.56E+5h
79345
Tetrachloroe thane, 1,1,2,2-
2.00E-11
127184
Tetrachloroethene
5.20E-2®
1.00B-21
56235
Tetrachlororre thane
1.30E-11
7.00E-4'
58902
Tetrachlorophenol, 2,3,4,6-
3.00E-2'
961115
Tetrachtorvinphos/Gardona/S tirof
2.40E-2"
3.00B-21
7440315
Hn
6.00B-1"
108883
Toluene
2.00E-11
8001352
Toxaphene
1.10E+01
75252
TVfcromorre thane (Bromofbrm)
7.90E-"
2.00E-21
120821
IVichlorobenzeiK, 1,2,4-
1.00E-21
71556
THchkjroethane, 1,1,1-
9.00E-2"
79005
Ttichloroethane, 1,1,2-
5.70E-2'
4.00E-31
79016
Urichloroe there:
1.10E-2"
6.00E-3'
75694
TticHorofluoroirethane
3.00E-11
67663
TYichloro methane (Chloroform)
6.10E-3'
1.00E-2i
95954
HichJorophenol, 2,4,5-
1.00E-1'
88062
Ttichlorophenol, 2,4,6-
1.10E-21
93765
Ttichlorophenoxyacetic ackl, 2,4,5-
1.00E-21
93721
TYichtoropteroxypropionic acid,
2,4,5-
8.00E-31
1582098
HifluraSn/Iteflan
7.70E-3'
7.50E-3'
95636
THnrthylbenzEne, 1,2,4-
5.00E-4®
118967
THnkrotokjene
3.00E-21
5.00E-4'
7440622
Vanadium
7.00E-3"
108054
Vinyl acetate
1.00E+0"
108383
Xylene, m-
2.00E+0h
95476
Xylene, o-
2.00E+0"
1330207
Xylenes
2.00E+01
7440666
Zinc
3.00E-11
E-8
-------
Nillioiliil Si'fliiiiriif Shims
Codes:
'Integrated Risk Information System (IRIS).
'Health Effects Assessment Summary Tables (HEAST).
'Environmental Criteria and Assessment Office (ECAO, as cited in Risk-Based Concentration Table).
"Other EPA documents, as cited in Risk-Based Concentration Table.
"Withdrawn from HEAST, but use continued for screening assessments (USEPA, Risk-Based Concentration Table).
E-9
-------
Appendix
E-10
-------
Appendix F
Species Characteristics
Related to NSI
Bioaccumulation Data
TableF-l presents the species used in tissue residue analyses whose results are included in the NSI, For each
species listed, Table F-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.
F-l
-------
Appendix I'
Thble F-l. Species Characteristics Related to Tissue Residue Data
Specks Ode
Scientific Name
Common Name
Resident/Migratory"
Demersal/Pelagic'
Potenialiy Eatable
615301010-100
Acmlfiomysis mscmpsb
Mysid shrimp
E
E
611829010000
Acartia spp.
Copepod (unknown species)
M
P
872901010000
Acipenserspp.
Sturg»n (unknown Specfes)
M
D
Y
872901010600
Acipeaser fulvescens
Lake sturgeon
R
D
Y
872901010500
Aclpenser oxyrhynchus
Atlantic sturgeon
M
D
Y
872901010300
Acipenser tranmonlanus
White sturgeon
M
D
Y
877601200100
Acrocheihts alutaceus
Chisetaotlh
R
P
87S503060100
Albsmerus ?Lor,go!us
Whitebait smelt
M
P
Y
874701010200
Absa aestivalis
Bijeback herring
M
P
Y
874701010600
Absa chrysochhris
Skipjack herring
M
P
Y
874701010300
Absa mediocris
Hickosy shad
M
P
Y
874701010500
Absa pseudohairngus
Afcwift
M
P
Y
874701010100
Absa sapidissima
American shad
M
P
Y
383516020200
Ambbplites cavifmtts
Roanoke bass
R
P
Y
883516020100
Ambbplites rupestris
Rock bass
R
P
Y
877702060100
Ameiurus brimneus
Snal bullhead
R
D
Y
877702060200
Ameiurus coins
White cattish
R
D
Y
877702060300
Amriunis mehs
Black bullhead
R
D
Y
877702060400
Ameiurus nataUs
YeBow bullhead
R
D
Y
877702060500
Ameiurus nebubsus
Brawi billhead
R
D
Y
877702060600
Ameiurus platycephaius
Flat bullhead
R
D
Y
877702060700
Ameiurus serracanthus
Spo&d bullhead
R
D
Y
873401010100
Amia caiva
Bow&t
R
E
Y
884202010200
Anarhichas denticulatus
Northern woKfth
R
D
Y
874101010100
Anguilb rostmia
Ametfcaneel
M
P
Y
883544260100
Apbdinotus gmmtkns
Freshwater dram
M
E
Y
883516090100
ArchopUles imermptta
Sacramento perch
R
P
Y
883543030100
Archosargus pmbatocephaha
Sheepshead
M
P
Y
551539010100
Arctica isbndica
Ocean quabog
R
D
Y
877718020200
Aritisfelis
Hardhead catfish
M
D
Y
883102040500
Artedius notospilotus
Bonehead sculpm
R
D
618102000000
Astacidae
Crayfish (family)
R
D
Y
F-2
-------
Table F-l. (Continued)
Species Code
Scientific Name
Common Name
Resident/Migratory"
Demersal/Pelagic*
Potentially Eatable
551519010000
Astarte spp.
Astarte clam (Unknown species)
R
D
551519011300
Astarte undata
Waved astarte
R
D
883561010100
Astro not us oceUatus
Oscar
R
P
Y
810601051100
Aslropecten verrilli
Margined seas tar
R
D
877718010100
Bagre marinus
GafitopsaQ catfish
M
E
Y
883544030100
BairdieUa chrysoura
Silver perch
M
P
Y
550000000000
Bivatvia
Class of molluscs
R
D
Y
550701160100
Brachiodontes recurvus
Hooked mussel
R
D
Y
874701040000
Brevoortia spp.
Menhaden (unknown species)
M
P
Y
874701040100
Brevoortia tyrannus
Atlantic menhaden
M
P
Y
618901030100
Callinectes sapid us
Btoe crab
M
D
Y
618105010600
Cambarus bartoni
Crayfish
R
D
Y
877601140100
Campostoma anomalum
Central stone roller
R
E
618803010400
Cancer magister
Dungcness crab
M
D
Y
883528030300
Caranx hippos
CrevaQc jack
M
P
Y
877601030100
Carassius aural us
Goldfish
R
E
870802050100
Carcharhinus obscurus
Dusky shark
M
E
Y
870802050300
Carcharhinus plumbeus
Brown shark (sandbar)
M
E
Y
877604020000
Carpiodes spp.
Carpsucker (unknown species)
R
D
Y
877604020200
Carpiodes carpio
River carpsucker
R
D
Y
877604020100
Carpiodes cyprinus
QuiQback
R
D
Y
877604020300
Carpiodes velifer
High fin carpsucker
R
D
Y
877604010000
Catostomus spp.
Sucker (unknown sp)
R
D
Y
877604010500
Colostomas aniens
Utah sucker
R
D
Y
877604010100
Catostomus catostomus
Longnose sucker
R
D
Y
877604010400
Catostomus columbianus
BridgeHp sucker
R
D
Y
877604010200
Catostomus commersoni
Whfie sucker
R
D
Y
877604011200
Catostomus latipinnis
Flannebrouth sucker
R
D
Y
877604010300
Catostomus macrocheilus
Largescale sucker
R
D
Y
877604011500
Catostomus occidentalis
Sacramento sucker
R
D
Y
877604011600
Catostomus platyrhynchus
Mountain sucker
R
D
Y
877604012000
Catostomus snyderi
Klamath largescale sucker
R
D
Y
F-3
-------
Appendix. F
Ibble F-l. (Continued)
Speclej Code
Scientific Name
Common Name
Resident/Migratory*
Demcrsal/ft lagle1"
Potentially Eatable
8776W012100
Colostomas tahoensis
"fthoesiKker
R
D
Y
883516000000
Cenlrarchidae
Sut&h
R
P
Y
883516030100
Centramhus macmplerus
FHer
R
P
Y
883501010500
Ctnneptsmus undecimalis
Common snook
M
P
Y
883502030100
CeMmpristis striata
Black sea bass
M
P
Y
900201010100
Chefydra serpentina
Snapping turtle
R
E
Y
648933000000
Chinnomidae
MHgs 6m%
R
D
648960063300
Chimnomus riparius
Midge
R
D
883561090100
Cichla oceitaris
Peacock cicMd
R
P
Y
885703010100
Cilkarichshys sordidus
Pacific sanddab
E
D
88570301U00
Citharichtkp xamhostigma
Longlki sanddab
E
D
877712010200
Cichla Clarias fuscus
Whiespotted clarias
M
D
Y
877601070100
CBmstomus funduloides
Rosyside dace
R
P
551545020100
Corbkuta manilensis
Asiatic clam
R
D
Y
875501010800
Corrganus artedii
Cisco (lake herring)
M
P
Y
875501010600
Cottgonus ciupeafcrmis
Lake wijiefish
M
P
Y
875501010900
Cottgonus hoy!
IS baler
M
P
Y
883102000000
Cottidae
Scu^in family
R
D
Y
883102080000
Coitus spp.
Sculp in (unknown species)
R
D
883102080100
Callus aleulicus
Coastraitge scii^r
R
D
883102080700
Cottus bairdi
Moiled sculpm
R
D
883102080900
Cottus camiinae
Banded scuipii
R
D
883102080200
Coitus cognatus
Slimy sculpii
R
D
551002010000
Crassastrta spp.
Oysters (unknown species)
R
D
Y
551002010100
Crassostira gigas
Pacific oyster
R
D
Y
551002010200
Crmsostrea virginica
Eastern oyster
R
D
Y
877601230100
Ctenopharyngodon idtUa
Glass carp
R
E
Y
877604060100
Cycleptm ebngatus
Bbe sucker
M
D
Y
883544010200
Cynoscion nebubsus
Spotted sea trout
R
P
Y
883544010300
Cynoscion nothus
Silver sea trout
M
P
Y
883544010400
Cyrtoscion tvgalis
Weakfish
M
P
Y
877601761400
Cyprinetla lutrensis
Red shiner
R
P
F-4
-------
Table F-l. (Continued)
Species Code
Scientific Name
' Connnn Name
Resident/Migratory"
Demersal/Pelagic"
Potentially Eatable
877601761900
CyprineUa spiloptera
Spotfin shiner
R
P
877601000000
Cyprbshlae
Carp/goldfish (hybrid)
R
E
Y
877601010100
Cyprinus carpb
Common carp
R
D
Y
871305010500
Dasyatis sabina
Atlantic stingray
M
D
Y
874701050100
Domsoma cepedianum
Gizzard shad
M
P
874701050200
Dorosoma pesemnse
Throadfin shad
M
P
551202030100
Etiiplio compkmata
Freshwater clam
j
D
Y
885704040300
Eopsetta ex ills
Slender sole
E
D
Y
883544120500
Equetus punctata:
Spotted dram
R
D
877604030000
Erimyzon spp.
Chubsucker (unknown speces)
R
E
877604030200
Erimywn obbngus
Creek chubsucker
R
E
877604030100
Erimyzon sucetla
Lake chubsucker
R
E
875801000000
Esocidae
Pike
R
P
Y
875801010201
Esox americanus americanus
Red fin pickerel
R
P
Y
875801010202
Esox americanus vermiculalus
Crass pickerel
R
P
Y
875801010100
Esox lucius
Northern pike
R
P
Y
875801010400
Esox masquinongy
Musketage
R
P
Y
875801010300
Esox niger
Cham pickerel
R
P
Y
883520016700
Elheosioma radiosum
Orangebely darter
R
D
883520010900
Etheostoma spectabile
Orangethroat darter
R
D
883520017600
Etheostoma stigmaeum
Speckled darter
R
D
883520018700
Etheostoma whipplei
Redfil darter
R
D
883520018800
Etheostoma zpnale
Banded darter
R
D
880404021000
Fundulus zebrinus
Plate kSBfish
R
P
880404021100
Fundulus olivaceus
Blackspotted topminnow
R
P
879103040100
Gad us macmcephalus
Paci& cod
M
E
Y
870802020100
Gainocenio cuyier
Tiger shark
M
E
Y
880408010100
Gambusia qffinis
Western mosqutofish
R
P
883544020100
Genyonemus tineas us
While croaker
M
E
Y
877601260000
Gila spp.
Chub (unknown species)
R
E
877601261500
Gila mbusta
Round tail chub
R
E
883551020100
Girelfa nigricans
Opaleye
M
P
F-5
-------
Append!* !•"
Table F-l. (Continued)
Species Code
Scientific Name
Common Name
Resident/Migratory"
Demersal/Pelagic"
Potentially Eatable
8SS704350100
Gfyplacephalus zachi
Rex sole
E
D
S51202060100
Gonidea angulata
Freshwater mussel
R
D
Y
874701(KXHX)0
Glupetdae
Herring family
M
P
Y
622003030000
Htxagenm spp.
Bunowiig mayfty (unknown species)
R
D
622003030700
Hexagtnia limbata
Mayfly
R
D
875101010100
Hiodon absoides
GoHcyc
M
P
Y
t7S1010102X)
Hiodon lergiius
Mooreye
M
P
Y
885703110200
Hippoghssina stcmata
Bigmouth sole
M
D
Y
885704060100
Hippogbssoides das
Flathead sole
M
D
Y
885704060300
Hippogbssoides platessoides
American plaice
M
D
Y
6169230-10100
Hyalella aztcca
Freshwater amphipod
R
E
877601050300
Hybognaihus placUus
Plains minnow
R
P
871602010100
Hydmlagus colliei
Spotted rat fish
M
D
877604050100
Hyptnteiium nigricans
Northern hog sucker
R
D
Y
875503010100
Hypomesus pitliosus
Surf smelt
M
P
Y
885704220100
Hypsopseua guUubia
Diamond turbot
7
D
Y
877702000000
Iclalaridae
Bullhead catfish family
R
D
Y
877702010000
Iclalurus spp.
Catfish (unknown specks)
R
D
Y
877702010200
Ictahtrus furcmus
Bkie catfish
R
D
Y
877702010500
let alums ptwclalus
Channel catfish
R
D
Y
877604070100
IcSbbus buhalus
Stralkiouth buflato
R
E
Y
877604070200
lesiobus cyprinelius
Bgrouth buffab
R
E
Y
877604070300
Ictbbus niger
Black buflab
R
E
Y
8S3543020100
Lagodon rkomboides
Pinfish
E
P
870600000000
Lamntfermes
Shark
M
P
Y
877601300100
Lavinia exiiimuda
Hitch
R
P
8S3S44040100
Lefoslomus xanthums
Spot
M
P
Y
884701030100
Lepidogobius lepidus
Bay gaby
R
P
873201010000
Lepisosteus spp.
Gar (unknown species)
E
P
Y
873201010200
Lepisosteus oculatus
Spotted gar
E
P
Y
873201010100
Lepisosteus osseas
Longnosc gar
E
P
Y
873201010300
Lepisosteus platostomus
Shortnose gar
i
P
Y
F-6
-------
Table F-l. (Continued)
Species Code
Scientific Name
, Cotrwncm Name
Residert/Migratoiy*
Demersal/Pelagic1
Potentially Eatable
873201010400
Lepisosteus spatula
Alligator gar
E
P
Y
883516050000
Lepomis spp.
Sunfeh (unknown species)
R
P
Y
883516050100
Lepomis auritus
Redbreast sunfish
R
P
Y
883516050200
Lepomis cyanettm
Green sunfish
R
P
Y
883516050500
Lepomis gibbosus
Pumpkins eed
R
P
Y
883516050300
Lepomis gulosus
Warmouth
R
P
Y
883516050600
Lepomis hum:Us
Orangespotted sunfish
R
P
Y
8S3516050400
Lepomis macmchirus
Blue gill
R
P
Y
883516050700
Lepomis marginalus
Dollar sunfeh
R
P
Y
883516050800
Lepomis megabits
Longear sunfeh
R
P
Y
883516050900
Lepomis micmlophus
Redearsunfish
R
P
Y
883516051000
Lepomis punctaius
Spotted sunfish
R
P
Y
879103080100
Lota lota
Burbot
M
E
Y
618701150200
Loxorhynchus gnmdis
Decorator crab
R
D
500501010300
Lumbriculus variegatus
Aqauatic worm
R
D
88353601Q700
Lutjanus campechanus
Red snapper
M
D
Y
877601780400
Lwcilus chrysocephalus
Striped shiner
R
P
877601780600
hixitus comutus
Common shiner
R
P
885704110100
Lyopsella exitis
Slender sob
M
D
Y
814802010600
Lytechinus anamesus
Litle gray sea urehii
R
D
551531013600
Macoma ims
Clam (macoma)
R
D
Y
551531011400
Macoma nasuta
Bent-nosed macoma
R
D
877601800200
Macrhybopsis gelida
Surgeon chub
R
E
551202430300
Megatonaias gigantea
Washboard mussel
R
D
Y
551547110100
Mercenaria meivenaria
Quahog
R
D
Y
883544070100
Micmpogonias mduktts
Atlantic croaker
M
P
Y
883516060000
Micmpterus spp.
Bass (unknown species)
R
P
Y
883516060500
Micmpterus coosae
Redeye bass
R
P
Y
883516060100
Micmpterus dolomieu
SmaCmoiith bass
R
P
Y
883516060600
Micmpterus notius
Swameebass
R
P
Y
883516060300
Micmpterus punctulaus
Spotted bass
R
P
Y
883516060200
Micmpterus salmoides
Laigemoulh Bass
R
P
Y
F-7
-------
\|)|RMl(li\ I''
TkbleF-1. (Continued)
Speclei Code
Scientific Name
Conrnun Name
Resident/Migratory*
Dc me rsaVPc Ingle'
Potentially Eatable
8776O408O100
Minytnma mekmops
Spotted sucker
E
D
Y
883502010000
Momne spp.
Tfemperate bass (unknown species)
E
P
Y
883502010100
Momm amcricana
While perch
M
P
Y
883502010400
Momne chrysops
White bass
M
P
Y
883502010300
Morom chrysops x saxatilis
Hybrid striped bass (whie/strfied)
E
P
Y
8S3502010500
Momne mlssissippiensis
YeSow bass
M
P
Y
S83502010200
Monme saxatilis
Striped bass
M
P
Y
877604040000
Moxostoma spp.
Redborse (unknown species)
R
D
Y
877604040400
Moxostoma anisumm
Stiver red horse
R
D
Y
877604040700
Moxostoma carinaium
River redborse
R
D
Y
877604040200
Moxostoma conges!um
Gtayredhorse
R
D
Y
877604040900
Moxostoma duquesnei
Black redborse
R
D
Y
877604041000
Maxostoma etythrumm
Golden redborse
R
D
Y
877604040100
Moxostoma macmlepidotum
Shorthead rcdboree
R
D
Y
877604041400
Moxostoma papptUosum
V-^j redhoree
R
D
Y
877604040300
Moxostoma poectturum
Blacktail redborse "
R
D
Y
877604041700
Moxostoma rupiscarles
Striped jumprock
R
D
Y
883601010100
Mugil cephahts
Striped millet
M
E
Y
883601010200
Mugil currma
White mulfct
M
E
Y -
870802040100
Musteius amis
Smooth dogfish
M
E
Y
551701020100
Mya armaria
Soft clam
R
D
Y
87760U70100
Mylocheilus caurinus
Peanwuih
R
E
877601350100
Mybpharodort conacephabts
Hard lead
R
E
350701010000
Mytilus spp.
Mussel (unknown species)
R
D
Y
550701010200
Mytilus califamimus
CaBbmia mussel
R
D
Y
550701010100
Mytilus edutis
Bkie mussel
R
D
Y
500124030500
Necmlhes arenaceodensala
Sand worm
R
D
500168040100
Neoampkllrile robusta
TfecrebeBd worm
R
D
500125011900
Nepktys caecoides
Sand worm
R
D
500168040100
Neoamphitrite robusta
TferrebeJki worm
R
D
500125011900
Nephtys caecoides
Sand worm
R
D
500125011500
Nepktys mcisa
Red-lined worm
R
D
F-8
-------
I
Table F-l. (Continued)
Species Code
Scientific Name
Common Name
Reside nl/Migratoiy'
Demersal/Pelagic1
Potentially Eatable
877601100300
Nocomis asper
Redspot chub
R
E
877601100200
Nocomis leptocephalus
Bbebead chub
R
E
877601100100
Nocomis micropogon
River chub
R
E
877601060100
Notemigonus crysoleucas
Golden shiner
M
P
877601501000
Notrvpis amblops
Bigcyc chub
R
E
877601114100
Notrvpis boops
Bigeye shher
R
P
877601111400
Notrvpis buchanani
Ghost shiner
R
P
877601110600
Notrvpis hudsonius
Spottail shiner
R
P
877601118100
Notrvpis nubilus
Ozark minnow
R
E
877601112300
Notrvpis stramineus
Sand shiner
R
P
877702020200
Noturus insignis
Margined madtrom
R
D
877702021800
Noturus miurus
Brindled madtom
R
D
877702022000
Noturus phaeus
Brown madtom
R
D
870703010100
Odoruaspis taurus
Sand tiger
M
E
Y
500300000000
OUgochaeles
Aquatk worms
R
D
875501020800
Oncorhynchus clarki
Cutthroat trout
E
P
Y
875501020100
Oncorhynchus gorbuscha
Pink salmon
M
P
Y
875501021100
Oncorhynchus mykiss
Rainbow trout
E
P
Y
875501020300
Oncorhynchus kisutch
Coho salmon
M
P
Y
875501020500
Oncorhynchus nerka
Sockeye salmon
M
E
Y
875501020600
Oncorhynchus tshawytscha
Chinook salmon
M
E
Y
878301020000
Opsanus spp.
Toadfish (unknown species)
R
. D
618105030000
Orconectes spp.
Crayfish
R
D
Y
877601360100
Orthodon micrvlepidotus
Sacramento blackfish
R
P
883540020100
Orthopristis chrysoptera
Pigfish ;
R
P
Y
875503000000
Osmeridae
Smelt (species unknown)
M
P
Y
875503030200
Osmerus mordax
Rainbow smelt
M
P
Y
618102020100
Pacifastacus leniusculus
Crayfish
R
D
Y
617918010100
Pandalus borealis
Maine shrimp
R
D
883502160400
Paralabrax nebulifer
Barred sand bass
E
D
Y
885703030900
ParaOchthys californicus
California halibut
M
D
Y
885703030100
Paraiichthys dentatus
Summer flounder (fluke)
M
D
Y
F-9
-------
A|)|)t'll(li\ I'
¦
"IbbIeF-1. (Continued)
Specks Code
Scientific Name
Convnon Name
Resident/Migratory"
Demersal/Pelagic*
Potentially Eatable
885703030400
Paralichihys lethostigma
Sotfhem flounder
M
D
Y
817502010100
Parasiichopus califomicus
California sea cucumber
R
D
500166030400
Pectinaria califomiensis
Sand worm
R
D
617701010000
Penaeus spp.
Shrimp
R
D
Y
617701010100
Pcnaeus aztecus
Brown shrimp
R
E
Y
617701010300
Penaeus setiferus
Whie shrimp
R
E
Y
883520020100
Perca flavescens
Yellow perch
R
P
Y
883520030900
Percina copelandi
Channel darter
R
D
883560050100
Phanerodon furcaius
White seaperch
R
P
Y
877601370300
Phoxlnus erythrogaster
Soiahern redbeDy dace
R
P
877601160200
Pimephales promelas
Fathead minnow
R
P
811703050100
Pisaster brevispirtus
Starfish
R
D
550905090100
Placopectert mageltanlcus
Atlantic deep-sea scaHup
R
D
885704140100
Ptailchthys steUaius
Starry flounder
M
D *
Y
877601840100
Platygobio gracilis
Flathead chub
R
E
885704151000
Pleuronectes bilineaius
Rock sole
E
D
Y
885704130100
Pleuronectes vetulus
English sob
M
D
Y
885704000000
Pleuronectidae
Rigjiteye flounder family
M
D
Y
885704160200
Pleuronichihys decurrens
CurBn sole
M
D
V
885704160400
Pleuronichihys verticalis
Hornybead turbot
M
D
Y
880408110200
Poecilia viltata
Cuban limia
E
P
883544080100
Pogonlas cromis
Black dmm
M
P
Y
872902010100
Pofyodon spaihula
Paddb&b
M
P
Y
883525010100
Pomatomus sallairix
B be fish
M
P
Y
883516070000
Pomoxis spp.
Crappie (unknown species)
R
P
Y
883516070100
Pomoxis annularis
White crappie
R
P
Y
883516070200
Pomoxis nigromaculatus
Black crappie
R
P
Y
882602010100
Pricnotus carvlinus
Northern searobin
R
D
Y
875501060100
Prosoplum cylindraceum
Round whie fish
M
P
Y
875501060200
Prosopium williamsoni
Mountain whiefish
M
P
Y
551547070100
P/otothaca staminea
Clam (Pacific littleneck)
R
D
Y
885704150400
Pleuronectes americanus
Winter flounder
M
D
Y
F-10
-------
Table F-l. (Continued)
Specks Code
Scientific Name
Common Name
Reside nt/Mijra tor)"
Demersal/Pelagic'
Potentially Eatable
885704150400
Pkumuectes americanus
Winter flounder
M
D
Y
877601180000
Ptychocheiius spp.
Sqwwfisb
. R
E
Y
877601180100
Ptychocheiius omgommis
Northern squawfish
R
E
Y
877702030100
Pylodictis olivaris
Flathead catfish
R
E
Y
871304010300
Raja binocuhta
Winter skate
I
M
D
Y
890302010600
Ram catesbeiana
Bullfrog
?
P
Y
551525040100
Rangia cuneota
Brackish water clam
R
D
Y
877601090000
Rhmkhl'nys spp.
Dace (unknown species)
R
D
877601190100
Richatdsomm ball eat us
Redside shiner
R
P
875501030000
Salmo spp.
Th>ut (unknown species)
E
P
Y
875501030500
Salmo salar
Atlantic sahen
M
P
Y
875501030600
Salrrw trutta
Brown trout
E
P
Y
875501000000
Salmantdae
Hmr (family)
E
P
Y
875501040000
SaJvefcus hybrid
Splake (hybrid)
E
P
Y
875501040400
SalvcBnus fontmalis
Brook trout
E
P
Y
875501040100
Satvelinus malma
Dolly vaidea
E
P
Y
S75501040300
Salvelinus namaycush
Lake trout
E
P
Y
551547020100
Saxidomus gigtmlem
Clam (smooth Washington)
R
D
Y
872901020200
Scaphirhynchus plmorynchus
Staovehose sturgeon
M
D
Y
883544000000
Scimnidoe
Drum 6n%
M
E
Y
883544090100
Sciaenaps oceBatus
Red drum
M
E
Y
885003030100
Scomber japonlcus
Chub mackerel
M
P
Y
885003050100
Scomberomoms cavalla
Khg mackeral
M
P
Y
885003050200
Scombcmmoms maculatus
Spanish mackerel
M
P
Y
885703040100
Scophlhalmus aquosus
Windowpane
M
D
Y
882601061600
Scorpaena guttata
CaKbmia scorpionfish
R
D
Y
883102310100
Scorpaenichtkys marmomtus
Cabezon
R
D
882601010300
Sebastes auricukttm
Brown rock&h
M
P
Y
882601012000
Sebastes maliger
Qulback rockfish
M
P
Y
882601012100
Sebastes melanops
Black rockfish
M
P
Y
882601013900
Sebastes nonegicus
Goto redfisb
M
P
Y
882601012700
Sebastes paacispinis
Bocaccio
M
P
Y
F-ll
-------
Appendix F
Tbble F-l. (Continued)
Specks Code
ScientilSc Name
Common Name
Resident/Migratory'
Demersal/Pelagic'
Potentially Eatable
882501012700
Scbastcs paucispinis
Bocaccb
M
P
y
882601013000
Sebastes pmrigcr
Redstripe rockfish
M
P
Y
877601080200
Seawlilus almmaculatus
Creek chub
R
E
877601080100
SemotUus corporate
Falffish
R
E
877601080300
Semelltus Umbee
Sandhills crab
R
E
617704010900
Sicycnta mgentis
Rock shitap
R
D
5S1529020100
Solrn sicarius
Razor clam
R
D
871001020100
Squalus aeanthias
Spiny dogfish
M
E
Y
883520040200
Stizpstedion canadcnse
Sauger
R
P
Y
883520040100
Slisostedion vitmum
Walleye
R
P
Y
880302020100
Strongylura marina
Atlantic needlefish
M
P
88S703130300
Syaciam papillosum
Dusky fbunder
M
D
Y
885802011600
Symphurus atricauda
CaEfomia tonpiefish
M
D
876202010100
Sywdus fattens
Inshore izartfisb
R
D
885003040400
Thumus atlanticus
Blackfti tuna
M
P
Y
87S50I070100
Thymallus aKticus
Arctic gray&g
E
P
Y
883561400100
Tilapia mossambica
Mozambique tilapia
R
E
Y
883561040500
1llapia zilUi
RedbeDy tilapia
R
E
Y
551525020100
Tmsus capax
Honse clam
R
D
Y
870802090200
Trbikis semfasciala
Leopard shark
M
E
Y
884701300100
Tridtnligcr trigonocephaly
Chamefcon goby
R
D
88SS01010100
Trinectes maculalus
Hagehoker
M
D
880302030200
fylosurus crocodibts
HoureJfisb
M
E
Y
875802010200
Umbra limi
Central roodmhnow
R
E
050601010000
Vaucheria
Mactoal^e
?
E
*Filh spedei is cooiidcrtd: R =» red dent, M » migratory, E = either resident or migratory, ? = unknown.
^FWi ipcdets is coQitderedi D =* demersal, P =- pelagic, E = either, ? = unknown.
F-12
-------
!
I
NiilioiuiJ Qti:iJify Survey
Appendix G
Notes on the Methodology for
Evaluating Sediment Toxicity
Tests
Results of sediment toxicity tests conducted around the United States were submitted with several databases
for evaluation in the NSI. Additional processing of records was required for most of the data. Because
test results were reported differently in each database, appropriate interpretation of the test results was
sometimes confusing. This section explains how the toxicity test data were handled for the NSI evaluation with
respect to issues related to sampling date, type of test, sample location identification, and results of control or refer-
ence tests conducted during the toxicity tests.
Sampling Date
Only those tests in the databases for which the sediment samples were obtained between January 1, 1980, and
December 31, 1993, were evaluated. Tests before and after that period were eliminated.
Sample Location
Records were examined to determine whether the sampling station from which the sediment sample was col-
lected had been identified by latitude and longitude coordinates. Samples that were not referenced to a specific
location were not considered in this study. Tests from the Great Lakes Sediment Inventory (GLSI) database were not
considered because sample locations were not appropriately identified. Sediment samples in the EPA Region 10/lJ.S.
Army Corps of Engineers Seattle District's Sediment Inventory (SEACOE) from sampling stations located in British
Columbia were also not considered in the analysis.
Type of Test
Data from seven databases (Table G-l) were reviewed to determine whether they had reported the results of
sediment (solid-phase) and elutriate nonmicrobial toxicity tests in which the endpoint was mortality. Records per-
taining to chronic toxicity tests, microbial toxicity tests, tests that were not conducted with sediment or elutriate, and
tests in which the endpoint was not percent mortality (or percent survival, which could be converted to percent
mortality) were excluded from further consideration.
Only the DMATS and GOM databases clearly reported the phase (solid, elutriate, particulate) of sediment sample
used in the bioassays conducted; ODES provided this information for some of the tests. If the phase was not indi-
cated, this information was obtained or best professional judgment was used to identify the phase used in the tests.
For some tests, comparison of species with those used in standard EPA test protocols or with species used in other
sediment toxicity tests in the databases permitted assignment of phase with certainty. Other species might be used in
sediment-, elutriate-, and particulate-phase tests, and the phase was assigned with uncertainty. Table G-2 presents a
list of species used in toxicity tests whose results are included in the NSI. Table G-2 also presents the type of toxicity
test for which each species is generally used (i.e., liquid-phase, elutriate-phase, suspended particulate-phase, sedi-
ment/solid-phase). The data presented in Table G-2 are the basis for determining whether the toxicity test of concern
was conducted using the solid or elutriate phase. A "Y" entered in Table G-2 indicates that the phase was given with
the test results; an "E" indicates that the phase was estimated using best professional judgment based on the species
used in the toxicity test.
. . ' ' G-l
-------
Table G-l. Toxicity Test Database Characteristics
Database
Sample
Locations
Identified
by
Lat/Long
¦type
of Ttest
Laboratory
Control Tests
Reference Sediment
Tests
Comments
U.S. Army Corps of
Engineers, Dredged
Material Tracking
System (DMATS)
Yes, all 74
Solid and Elutriate
(identified in database)
Replicate control
test results provided
Replicate reference
sediments tested with
each batch of
sediment samples
Used means of reference
sediment replicates in the
evaluation (contact: Alan Ota, EPA
Region 9)
HPA's Environmental
Monitoring and
Assessment Program,
Louisianian Province
(EMAP-LA)
Yes, all 259
Solid Phase
(not identified in
database, provided)
Not provided In D3
database, provided on
request
No
Sediment sample test results were
calculated from the additional data
provided (contact: Kevin Summers,
EPA/ERLGB)
EPA's Environmental
Monitoring and
Assessment Program,
Virginian Province
(EMAP-VA)
Yes, all 179
Solid and Elutriate
(not identified in
database, provided)
Not provided in D3
database, provided on
request
No
Sediment sample test results were
calculated from the additional data
provided (contact: Daryl Keith, EPA/
ERLN)
Guif of Mexico
Program's
Contaminated Sediment
Inventory (GOM)
Yes, all 42
Solid Phase
(identified in database)
ERL-N: Yes
USACE: No
GCRL: No,
provided on request
ERL-N: Yes
USACE: Yes
GCRL: No
Long Island Sound reference sediment
was used to generate control data for tests
done by ERL-N (contacts: Phil Crocker,
EPA; John Scott, SAIC) and control data
obtained for GCRL (contact: Julia Lyle,
GCRL); for USACE tests used mean of
the reference test results as control
EPA's Great Lakes
Sediment Inventory
(GLSI)
No
Not identified in database
Not provided in database
No?
Sample location IDs and control test reults
were not provided; therefore, these data
were not evaluated for the NSI (contact:
Bob Hoke, SAIC)
EPA's Ocean Data
Evaluation System
(ODES)
Only 18 out
of 68
Solid Phase
(not identified in
database)
Yes
No
Used controls (contact:
Tad Deschler, Tetra Tech)
EPA's Region 10/U.S.
Army Corps of Engineers
Scsttlc District's
Sediment
Inventory (SEACOE)
Only 18 out
of 68
Solid Phase
(not identified in
database)
Yes, some had to be
provided on request
Yes
Used controls (contact: Roberts Feins,
Environmental Information Consultants;
John Armstrong, EPA Region 10; and
Gary Braun, Tetra Tech, for Puget Sound
Estuary Program Reports, 1988)
-------
Table G-2. Test Species Used in Sediment Bioassay Test Results Included in the NSI
0
1
W
Type of Toxicity Test
Species
Liquid
Elutriate
Particulate
Solid
C
(L most Common)
D
A
Species Code
Name
(L)
(E)
(P)
(S)
(L or E)
(L,E, or P)
(L,EJP,or S)
Unknown
80509070600
•
E
615301010900
Acanthomysis costata
Y
Y
615301010400
Acanthomysis macropsis
Y
Y
Y
615301010700
Acanthomysis sculpta
Y
E
611829010000
Acartia spp. spp.
Y
Y
616902010800
Ampelisca abdita
Y,E
616800000000
Amphipods
Y
610401010100
Anemia salina
Y
Y
616302070900
Asellus intermedius
E
650508331700
Chironomus riparius
E
650508330100
Chironomus tentans
E
885703010200
Citharichthys stigmaeus
Y
Y
616915021500
Corophium spinicome
Y,E
617922010000
Crangon spp. spp.
Y
Y
Y
551002010100
Crassostrea gigas
Y
Y
E
551002010200
Crassostrea virginica
Y
880404010100
Cyprinodon variegatus
Y
Y
610902010900
Daphnia magna
E
610902010100
Daphnia pulex
E
815501010100
Dendraster excentricus
E
-------
"Ikble G-2. (Continued)
Type of Toxicity Test
Species
Species
Liquid
Elutriate
Particulate
Solid
C
(L most Common)
D
A
Name
Name
(L)
m
(P)
(S)
(LorE)
(L,E, or P)
(L,E,P,®rS)
Unknown
880404020700
Fundulus grandis
¥
Y
881801010100
Gasterosteus aculeatus
E
616915090200
Grandidierella japonica
Y
622003030700
Hexagenia limbata
E
615301010700
Holmesimysis sculpta
Y
Y
Y
E
616923040100
Hyallella azteca
E
500501010300
Lumbriculus variegatus
B
814802010200
Lytechitms pktus
Y
Y
551531011600
Macoma balthica
E
551531011400
Macoma nasuta
Y
YJS
551531010000
Macoma spp.
E
615303140600
Metamysidopsis elongata
Y
Y
Y
651530100000
Mysid shrimp
Y
Y
Y
615301210200
Mysidopsis bahia
Y
Y
550701010100
Mytilus edulis
Y
Y
E
500124030500
Neanthes arenaceodentata
Y,E
500124030000
Neanthes spp.
E
500125011900
Nephtys caecoides
Y»E
500124030200
Nereis virens
Y
551706040100
Panopea generosa
E
-------
Table G-2. (Continued)
Type of Toxicity Test
Species .
Species
Liquid
Elutriate
Particulate
Solid
C
(L most Common)
D
A
Code
Name
(L)
-------
Vl»JK«ll(li\ (J
Only DMATS contained elutriate test results in addition to sediment test results; all other tests evaluated were
sediment (solid- phase) test results.
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 run under the same conditions in which the same 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. The GLSI database did
not contain any control test data; because of this, as well as the lack of station-identifying coordinates, the GLSI
database was eliminated from evaluation for the NSI. For the other databases, control test results were matched to the
sediment test results and were treated as follows:
• Multiple control and reference sample test results were reported for each sediment tested in the DMATS
database. These were determined to be replicate test results. Because the sediment samples tested in DMATS
were usually fine-grained and the laboratory performance controls were sand, the reference sediment samples
were used as "controls" to evaluate toxicity of sediment samples. The percent mortality for the reference
replicates were averaged for each reference site to obtain the mean percent mortality for the reference sedi-
ment for comparison with the sediment sample test result.
• The D3 version of both the EMAP-LA and EMAP-VA databases contained control-corrected results for the
sediment samples tested. The control-corrected results were obtained using the following equation:
percent survival of organisms in sediment sample test = control-corrected percent survival
percent survival of organisms in control test percent survival
• EMAP-LA provided a revised database on request that contained the percent survival of the controls. The
sediment sample test results were calculated according to this equation:
percent survival of organisms in sediment sample test =
control-corrected percent survival X percent survival of organisms in control test
100
• EMAP-VA provided a revised database on request that contained the mean percent mortality of controls and
the mean percent mortality of the sediment sample tests for each station, as well as the control-corrected
percent survival.
• The GOM database reported control test results for tests conducted by EPA's Environmental Research Labo-
ratory in Narragansett. A low-salinity control test performed at the same time was not used in the evaluation.
The single reference sediment sample was treated as a sediment toxicity test result. No control tests were
available from the USACE data set within this database; the mean of reference sediment toxicity test results
was used as the "control" for these test data. No control test results were found in the GOM database for the
GCRL data set. Total percent mortality of pooled control test replicates were provided by Julia Lytle of
GCRL and entered into the database for the NSI analysis.
• The ODES database reported single-value control results for the ARSR and OSE data sets. (Whether these
were means of replicate tests is unknown.) One sediment test result in ARSR was matched to two different
control test results; however, the one control test result that was not matched elsewhere in the data set was
eliminated for the analysis.
G-6
-------
National Sediment Quality Survey
* The SEACOE database contained single-value control test results for the ALCTRAZ data set and several
series of control test results for other data sets (e.g., EVCHEM and EBCHEM). Information on the correct
control series was obtained, and the proper control test results were evaluated in the computer program.
Means were calculated for replicates in the series Mid used to evaluate the sediment sample test results.
Results of control tests reported as "percent survival" were converted to "percent mortality" by the following
calculations:
percent mortality = 100 - percent survival
percent mortality = number of surviving organisms/total number of organisms in test
Sometimes entries in databases reversed "mortality" and "survival" (e.g., PSE data set in the ODES database).
Any questions concerning the designation were checked and corrected if necessary. If replicate sediment toxicity test
results were provided for a sampling site in the database, a mean was calculated and compared to the mean control
mortality. (Some databases provided only the means, e.g., EMAP-LA, EMAP-VA.) For the purpose of the NSI
evaluation, if the control had greater than 20 percent mortality (less than 80 percent survival), that test was excluded
from further consideration.
Reference Sediment Stations
Some data sets included data for reference sediments that were run simultaneously with the control and sediment
samples. Reference sediment is sediment collected from a field site that is appreciably free of toxic chemical con-
taminants and has grain size, total organic carbon, sulfide and ammonia levels, and other characteristics similar to the
sediment samples to be tested for toxicity. Because reference sediments should match the characteristics of the
sediment samples more closely than the sand or sediment used for the laboratory (performance) control, they should
provide information on the appropriateness of using a particular test organism since the suitability and survival of
different species can be affected by these other physical and chemical characteristics of the sediment.
* As noted previously, DMATS provided several reference sediment samples for each toxicity test, along with
control test results. The number of such reference sediment samples varied for different test dates, and these
sediment samples were determined to represent replicates. The average percent mortality was determined
from each set of replicates and this was used as a "control" to evaluate the toxicity of sediment samples in
this database. If percent mortality of the mean reference test result exceeded 20 percent, the sediment
toxicity tests that were run with that reference sediment were not used in the evaluation.
* Reference sediment test results were not identified in the EMAP-LA, EMAP-VA, or ODES databases.
* In the GOM database, a reference sediment test was run in tests conducted by EPA's Environmental Re-
search Laboratory in Narragansett. This single reference sediment sample was treated as a sediment toxicity
test result. Reference sediment tests in the USACE data set were averaged and used as the control for
analysis since other control test data were not provided in the data set.
* Reference sediment toxicity test results in the SEACOE database were treated as a sample site.
Because reference toxicity test results were not available for all of the sediment toxicity tests, reference sediment
sample test results were not used as "controls" in the evaluation of sediment toxicity test data in the NSI, with the
exception of the DMATS data and the USACE data in the GOM database. The remaining reference sediment test
results were compared with the control results to determine whether significant toxicity was indicated at that field
site; i.e., they were treated like a sediment toxicity test result (see below).
It should be noted, however, that careful examination of such reference test results could improve the interpreta-
tion of sediment toxicity tests; i.e., they might indicate that test organisms were adversely affected by sediment
characteristics, not by toxic chemicals. Thus, the classification of some sites using the sediment toxicity tests might
G-7
-------
be inappropriate because the control test result did not adequately explain the result, based on the test organism's
health or sensitivity to test conditions.
Test Results
For the NSI evaluation protocol for sediment toxicity test data, significant toxicity was indicated if there was a
difference of 20 percent survival from control survival (e.g., if control survival was 100 percent and 80 percent or less
of the test organisms survived, or if control survival was 80 percent and 60 percent or less of the test organisms
survived, significant toxicity was indicated). Although a number of different test species and protocols were used in
the tests evaluated, this threshold provides a preliminary indication of sediment toxicity for classifying sampling
stations for the NSI.
G-8
-------
Appendix H
Additional Analyses for PCBs
and Mercury
To perform the screening analysis for the National Sediment Quality Survey using NSI data, EPA selected
reasonably conservative screening values, including theoretically and empirically derived risk-based screen-
ing levels. The limited number of sediment criteria available for use in this type of evaluation, however, contribut-
ed to the possibility of over- and underestimation of potential adverse effects associated with sediment contaminated for
some chemicals. Two chemicals where this issue is particularly relevant are PCBs and mercury. EPA conducted further
analyses on PCBs and mercury to determine the effect of using different assessment parameters on the number of sampling
stations where these chemicals were identified as associated with a probability of adverse effects.
Because of the tendency for PCBs to bind to sediment and because of the relative toxicity of these chemicals to
humans, EPA selected a precautionary approach for the analysis of PCBs in the NSI evaluation. The approach was
precautionary because (1) it did not require matching sediment chemistry data and tissue residue data for Tier 1
classification and (2) it used the cancer risk level of 10 s for all congener, aroclor, or total PCB measurements to
evaluate human health effects related to PCB contamination. EPA applied the cancer slope factor for aroclor 1260,
the most potent commercial mixture, to all measures. It should be noted that there were only 542 sampling stations
where matching sediment chemistry data and tissue residue data were available for analysis. In the foEowing evalu-
ation, the amount of PCB sediment and fish tissue data exceeding screening values other than those used in the NSI
analysis is compared to the number of sampling stations classified as Tier 1 or Her 2.
Figure H-l is a cumulative density function graph depicting the maximum PCB concentration at each sediment sam-
pling station where PCBs were detected. The various screening values that could be used to indicate adverse effects levels
1
*!
•s
*
1.00
0.90
0.80
0.70
0.80
F(x> 0.50
0.40
0.30
0.20
0.10
0.00
1,006-04 1.0QE-Q3 1.00E-O2 1.00E-01 1.00E-HJO 1.00E+01 1.00E+02 1.00E+03 1.00E+04 1.00E+05 1.00E+06
PCB In Sedlmont Concontrallon (ppb)
I:
Note: Lofiara kknllfiod on saury# eeftetpcnd to PCB effect* icrwMitfjg yalwe* hi Table H-1,
Figure H-l. Cumulative Frequency Distribution of PCB Sediment Concentration Data (All Aroclors
and Total PCB).
H-l
-------
ApjH'ndh II
ofPCBs in sediment are plotted as A through S in the figure and described in Table H-l. The top two sections of Table H-
1 present the screening values ofPCBs in sediment that are protective of human or wildlife consumers. The levels shown
were derived using the theoretical bioaccumulative potential (TBP) analysis with the default lipid content (3 percent),
default organic carbon content (1 percent), and BSAFs with and without the safety factor of 4. (See Appendices B and C for
further explanation.) Depending on the screening value, the number of sediment chemistry sampling stations with detect-
able PCBs exhibiting potential human health or aquatic life effects varies from under 1 percent to over 99 percent The
screening values selected for the NSI evaluation classify approximately 85 percent of sediment chemistry sampling stations
in Her 2 for human health effects (Point D). For aquatic life effects, the selected screening values classify 25 percent of
sampling stations as Tier 1 (Point O) and 57 percent of sampling stations as Tier 2 (Point H).
Ibble H-l, Sediment Sampling Stations with Detectable Levels of PCBs That Exceed Various Screening
Values*,b
TVpc of Screening Value
Associated Level
(PPb)
Level Plotted in
Figure H-l
Corresponds to Letter
Number of Stations
with Detected PCBs
Exceeding Level
Percentage of Stations
with Detected PCBs
Exceeding Level
Protection of Consumers
Cancer Risk Level
lO6
0125
B
3,772
98.2
I0M
2.5
D
3,290
85.6
1»4
25
J
2,076
54.0
Nonoancer Hazard Quotient of 1
4°
L
1,761
45.8
FDA "Iblcraiicc Level
360
P
652
17.0
Wliifc Criteria
29
K
1,977
51.5
Protection of Consumers Using BSAF with Safety Factor'
Cancer Risk Level
10s
0,063
A
3,828
99.6
10'
0.63
C
3,648
95.0
10*
6.3
E
2,921
76.0
Noncancer Hazard Quotient of 1
9.9
G
2,699
70.2
FDA Tblcrance Level
90
M
1,330
34.6
Widife Criteria
7.2
F
2,849
74.2
Protection of Aquatic Life
ER-L
22.7
I
2,150
56.0
ER-M
180
N
976
25.4
AET-L
1,000
Q
353
9.2
AET-H
3,100
R
165
4.3
TEL-
21.6
H
2,182
56.8
PEL'
189
O
962
25.0
Other Protection Levels
TSCA« Level
50,000
s
21
0.55
tots) or uoclor-jpecific value si a gives station was used.
IPCB» wc*e detected at 3,842 (41%) of the 9,401 stations where collected samples were analyzed foe them.
•For LhU presentation, me Mured level* were compared to risk level* using a default organic carbon content (! %) and default organism lipid content (3%), Use of site-specific organic carbon
would yield slighUy different results.
'Levels used ia the current National Sediment Quality Survey evaluation for human health.
•Levels ujed ia the current National Sediment Quality Survey evaluation for aquatic life (Tier 2).
tovtU uied ia the current National Sediment Quality Survey evaluation for aquatic life (Tier I).
•Tbxte Subaance* Cootrol Act 40 CFR Part 761, Subpart B, f 761.20.
H-2
-------
Figure H-2 and Table H-2 present the comparison of different screening values and the corresponding number of
fish tissue sampling stations with detected levels of PCBs exceeding the screening values. The 10"5 cancer risk level
(Point B) was one of the most conservative thresholds: concentrations exceeded this level at approximately 95
percent of tissue residue sampling stations where PCBs were detected. These sampling stations were clssified as Tier
1 for potential human health risk.
m
o
CL .
Ii
¦B
ss
1.00
0.90
0.80
0.70
0.60
F{x) 0.50
0.40
0.30
0.20
0.10
0.00
I
1.00E-03 1.00E-02 1.00E-01 1.00E+00 1.0QE+01 1.00E+02 1.00E+03 1.00E+04 1.00E+05 1.00E-K36
PCB In Fish Tlmua Concentration (ppb)
Net«: Lagan Idsrrtfted on Mw
-------
Appendix II
111 contrast to the PCB evaluation, the evaluation of mercury detected in fish tissue residue in the NSI analysis was
substantially less conservative than that which would result from use of different screening values. To determine the
possible outcomes of different data evaluations, EPA performed additional analyses of mercury fish tissue data included in
the NSI. Figure H-3 aid Table H-3 present six screening values that could be applied for the protection of consumers
ingesting mercuiy-contaminated fish. As shown in these displays, both EPA's current noncancer reference dose recom-
mended for general use (Point E) and the FDA action level (Point D), the screening value used in the current NSI analysis,
result in only about 4 percent of sampling stations with detectable levels classified as posing potential risk to human health.
1.00E-Q2 1.00E-01 1.00E+00 1.00E+01 1.00E+02 1.0OE+O3 1.00E+04 1.00E+05
Concentration (ppb)
NetK Uttart WarSffcd on tfw eurvs eorrMpond to affects ecrwitofl vafcrn in Ta«* H-3.
Figure H-3. Cumulative Frequency Distribution of Mercury Fish Tissue Data for Demersal, Resident, and
Edible Species.
Table H-3. Fish Tissue Sampling Stations with Detectable Levels of Mercury in Demersal, Resident,
Edible Fish Species That Exceed Various Screening Values*-"
Type of Screening Value
Associated Level
(ppb)
Level Plotted in
Figure H-3
Corresponds to Letter
Number of Stations
with Defected
Mercury Exceeding
Level
Percentage of Stations
with Detected Mercury
Exceeding Level
Protection of Consumers
Canadian Guideline*
200
B
908
35.1
Noncanxr Hazard Quotient of 1 (1995)°
1,100
E
91
3.5
Noncancer Hazard Quo tent of 1 (pre-
199 sy
3,231
F
15
0.6
Noncancer Hazard Quotient of 1 (pre-
1995 for Wants)"
646
C
204
7.9
FDA Action Level'
1,000
D
103
4.0
Wadlifc Criteria*
57.3
A
2,150
83.0
•Mercury wat detected u 2489 (90%) of the 2,861 stations where collected samples were analyzed for mercury.
^Canadian guideline limit for mercury in fish that are part of a subsistence diet (Health and Welfare Canada, 1979),
~Methyl mercury reference date ihit was available in IRIS in 1995 (1x10** mg/kg-day).
^Comjpoflds to mercury reference dose available in IRIS prior to 1995 (3x!0"* mg/kg-day).
•Corrtspoods ta mercury reference date available In IRIS prior to 1995 divided by a factor of 5 to protect against developmental effects among infants (6x10-® mg/kg-day). This value was
formerly u*d by the EPA Office of Water.
level uied in Ihe current National Sediment Quality Survey evaluation for human health.
The result* of the wildlife analysis shown In Table 3-5 are slightly different because the data set used for that analysis included demersal, resident species (could be considered edible or not).
H-4
-------
The NSI evaluation restricted the data analyzed to demersal, resident, and edible species. Figure H-4 and
Table H-4 present the same six mercury screening values with the data for all fish species considered edible by
humans with detectable levels of mercury in the NSI. If all edible fish species were analyzed using selected
screening values, 9 percent of sampling stations would be classified as Tier 2 because of mercury contamination
(Point D). However, the proportion of sampling stations with detectable levels of mercury that exceed some
other human health levels ranges from 20 percent to over 55 percent of sampling stations.
I
II
61
*
I
I I
1
*
1.00
OJO
0.80
0.79
0.60
F(x) 0.S0
0.40
0.30
0.20
0.10
0.00
1.0QE-02
t
1.00E-01 1.00E+00 1.00E+01 1.00H+02
Concentration (nib)
1.00E-HS3
1.00E+04 1.00E+OS
No»: Mrtm* ktentPMl oft ifei cemnpend 6a mercury •cfwjrtfta vrtJt* In Tafeia W
Figure H-4. Cumulative Frequency Distribution of Mercury Fish Tissue Data for All Edible Species.
Table H-4. Fish Tissue Sampling Stations with Detectable Levels of Mercury in Edible Fish Species That
Exceed Various Screening Values**1'
Type of Screening Value
Associated Level •
(ppb)
Level Hotted in
Figure H-4
Corresponds to Letter
Number of Stations
with Detected
Mercury Exceeding
Level
Percentage of Stations
•with Detected Mercury
Exceeding Level
Protection of Consumers
Canadian Guidefine1'
• 200
B
2,308
55.8
Noncancer Hazard Quotient of 1 (1995)®
1,100
E
353
7.8
Noncancer Hazard Quotient of I (pre-
1995)'
• 3,231
F
3?
0.9
Noncancer Hazard Quotient of I (jpre-
1995 for infants)"
646
C
821
19.9
FDA Action Level'
1,000
D
374
9.0
Wildlife Criteria'
: 57,3
A
3.623
87.6
~Mercury was delected at 4,135 (93%) of the 4,426 stations when? collected samples were analyzed for mercury.
"Canadian guideline limit for mercury in fish that are part of a subsistence diet (Health end Welfare Canada, 1979).
•Methyl mercury reference dose that was available ia IRIS in 1995 (lxl0J mg/kg-day).
Corresponds to mercury reference dose available in IRIS prior to 1995 (SxlO4 mgfltg-day).
¦Corresponds to mercury reference dose available in IRIS prior to 1995 divided by a factor ef 5 to protect against developmental effects among infants (SxlO-1 mg/Vg-doy). This value was
formerly used by the EPA Office of Water.
'Level used in the current National Sediment Quality Survey evaluation for human health.
•The results of ihe wildlife analysis shown in Table 3-5 are slightly different because the data set used for that analysis included demersal, resident species (could be considered edible or not).
H-5
-------
\ |>|)tluli\
H-6
-------
Appendix I
NSI Data Evaluation
Approach Recommended at
the National Sediment
Inventory Workshop,
April 26-27, 1994
T[he original proposed approach for the integration and evaluation of NSI sediment chemistry and biological
data was developed at the Second National Sediment Inventory Workshop held on April 26 and 27, 1994, in
Washington, D.C. The proposed workshop approach was modified, however, to address inconsistencies
found in trying to implement the approach and to address the concerns of the many experts in the field of sediment
quality assessment who commented on the workshop approach. This appendix presents the NSI data evaluation
approach developed by the April 1994 workshop participants. The actual approach that EPA used in the NSI data
evaluation is presented in Chapter 2. A list of workshop participants is provided at the end of this appendix.
Using the approach recommended by workshop participants, sediment sampling stations could be placet! into one
of the following five categories based on an evaluation of data compiled for the NSI:
* High probability of adverse effects to aquatic life or human health
* Medium-high probability of adverse effects to aquatic life or human health
* Medium-low probability of adverse effects to aquatic life
* Low probability of adverse effects to aquatic life or human health
* Unknown probability of adverse effects to aquatic life or human health.
Using the workshop approach, contaminated sediment sampling stations could be placed into one of the five
categories based on an evaluation of the following types and combinations of data:
* Sediment chemistry data alone
* Toxicity data alone
* Tissue residue data alone
* Sediment chemistry and tissue residue data
* Sediment chemistry and histopath-ological data
* Sediment chemistry, sediment toxicity, and tissue residue data.
The overall approach developed by workshop participants is summarized in Table 1-1 and is described below.
High Probability of Adverse Effects to Aquatic Life or Human Health
Based on the evaluation approach proposed by the April 1994 workshop participants, a sampling station could be
classified as having a high probability of adverse effects to aquatic organisms or human health based on sediment
chemistry data alone, toxicity data alone, tissue residue data alone, or a combination of sediment chemistry and tissue
residue or histopathological data.
1-1
-------
Appviwlix
"Bible 1-1. Original Approach Recommended by NSI Workshop (April 1994)
Category of
Sanding
Station
Classifications
Data Used to Determine Classifications
Sediment Chemlstiy
(santing station is identified
by
any one of
the following characteristics)
Tissue Residue/
His to pathology
Tbxicity
High Probabffity
of Adverse
Effects lo
Aquatic Life or
Hunan Health
Sediment chemistry values exceed
sediment draft quaky criteria for
any one of the five chemicals for
which criteria have been
dewbped by EPA (based on
measured TOC)
OR
Human health thresholds
for dtoxin or PCBs are
exceeded in resident
species (not a consensus
agreement—participants
evenly divided on this
issue)
OR
Toxicity den»nsirated by
t wo or ma re acute
toxicity tests (one of
which must be a solid-
phase nonnicrobial test)
OR
Sediment chenistiy values exceed
a! relevant AEft (high), ERMs,
PELs, and SQALs for any ore
chemical (can use default TOG)
OR
Sediment chemistry values >50
ppmfbr PCBs
OR
Sediment chemistry TOP exceeds
FDA action levels, EPA risk
levels, or wfldtfe criteria
AND
Tissue bvels in resident
species exceed FDA
action levels or EPA risk
levels, or wiMlfe criteria
OR
Elevated sediment chenistiy
concentrations of PAHs
AND
Presence offish tumors
—
Medium-High
Pwbablftyof
Adverse Effects
Jo Aquatic Life
or Hunan
Healh
Sediment chemistry values exceed
at least two of the sediment tipper
screening vahss (Le., ERM,
SQAL, PEL, high AED (can use
default TOG)
OR
Tissue levels in resident
species exceed FDA
action bvels or wildlife
criteria
OR
Toxicity detnansirated by
a single-species toxicity
test (sold-phase,
nonnicrabiaf)
OR
Sediment chemistry IBP exceeds
FDA action levels or wfldfife
criteria
Medium-Low
Probability of
AdwtseEfifects
to Aquatic Life
Sediment chemistry values exceed
one of the lower scteerfng valies
(ERL, SQAL, TEL, tower AET)
(can use defeul TOC and AVS)
OR
Toxicity demonstrated by
a singb species toxicity
test (elutriate-phase,
nonnicrobial)
Low Probability
of Adverse
EfTccts to
Aquatic Life or
Human Health
No exceedance of tower
screening values
AND
No sediment chemistry IBP
exceedances of FDA action bvels
or wiMlfe criteria
AND
Tissue bvels in resident
species are tower than
FDA action bvels or
wili life criteria
AND
No toxicity demonstrated
in tests using at bast two
species and at least one
soSd-phase test using
amphipods
Unknown
Not enough data to place a site in any of the other categories.
1-2
I
-------
i\si(i«mil Scdinioiil Qiuilily Survey
For a sampling station to be classified as one with a high probability of adverse effects based on sediment chem-
istry data alone, at least one of three criteria must be met: (1) sediment chemistry values exceed the sediment quality
criteria (SQCs) developed by EPA for acenaphthene, dieldrin, endrin, fluoranthene, or phenanthrene; (2) sediment
chemistry values exceed all appropriate screening values for a given chemical (i.e., high apparent effects thresholds
(AETs), effects range-medians (ERMs), probable effects levels (PELs), and sediment quality advisory levels (SQALs));
and/or (3) sediment chemistry values exceed 50 ppm for polychlorinated biphenols (PCBs). When comparing sedi-
ment chemistry values to the SQCs, measured total organic carbon (TOC) must be used. Workshop participants sug-
gested using default TOC values in the comparison of sediment chemistry values to SQALs if actual measured TOC
values are not available. However, if default TOC values are used in a comparison of sediment chemistry measure-
ments to.SQCs, the highest that a sampling station could be classified would be medium-high potential for adverse
effects.
For a sampling station to be classified as having a high probability of adverse effects based on a combination of
sediment chemistry and tissue residue data, sediment chemistry theoretical bioaccumulation potential (TBP) and tissue
levels in resident, nonmigratory species must exceed FDA tolerance/action/guidance levels, EPA risk levels, or EPA
wildlife criteria. Workshop participants also recommended that a sampling station be classified as having a high
probability of adverse effects if fish tumors are present in resident species and elevated sediment chemistry concentra-
tions for polynuclear aromatic hydrocarbons (PAHs) are present.
The workshop participants were evenly divided on whether a sampling station could be classified as having a high
probability of adverse effects based solely on the exceedance of human health screening values for dioxins or PCBs in
resident fish species. Participants did agree that benthic community data in combination with sediment chemistry data
could be used in the future, but not for the current evaluation, to classify sediment sampling station. Methods are
currently not adequate to establish a direct causal relationship between benthic community changes and sediment
contamination at specific sampling stations without additional data.
For a sampling station to be classified as having a high probability of adverse effects based on toxicity data alone,
toxicity must be demonstrated by two or more acute toxicity tests, at least one of which must be a solid-phase, nonmi-
crobial test. :
Medium-High Probability of Adverse Effects to Aquatic Life or Human Health
Workshop participants suggested that a sampling station could be classified as having a medium-high probability
of adverse effects on aquatic life or human health based on sediment chemistry data alone, toxicity data alone, or tissue
residue data alone.
For a sampling station to be classified as having a medium-high probability of adverse effects based on sediment
chemistry data alone, the station must meet at least one of two criteria: (1) sediment chemistry values exceed at least
two of the sediment chemistry upper screening values (i.e., appropriate ERMs, SQALs, PELs, or AET-highs) or (2)
sediment chemistry TBP values exceed FDA tolerance/action/guidance levels or EPA wildlife criteria. In the compari-
son of sediment chemistry values to SQALs, default TOC values can be used.
A sampling station could also be classified as having a medium-high probability of adverse effects if toxicity is
demonstrated by a single-species, nonmicrobial toxicity test using the solid phase as the testing medium or if actual fish
tissue residue levels exceed FDA tolerance/action/guidance levels or EPA wildlife criteria.
Medium-Low Probability of Adverse Effects to Aquatic Life
Workshop participants suggested that a sampling station could be classified as having a medium-low probability
of adverse effects to aquatic life based on either sediment chemistry data alone or toxicity data alone. A sampling
station could be classified as having a medium-low probability of adverse effects if sediment chemistry values exceed
at least one of the lower sediment chemistry screening values (i.e., ERL, TEL, SQAL, or AET-low). Workshop
participants suggested that default TOC and AVS values could be used. To classify a sampling station as having a
medium-low probability of adverse effects, toxicity would be demonstrated by a single-species, nonmicrobial toxicity
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test using the elutriate phase as the test medium. Workshop participants did not propose any human-health-related
criteria for placing a sampling station in the medium-low probability of adverse effects category.
Low Probability of Adverse Effects to Aquatic Life and Human Health
Using the workshop approach, for a sampling station to be classified as having a low probability of adverse effects
on aquatic life and human health, all of the following criteria must be met: (1) there are no exceedances of the lower
sediment chemistry screening values (i.e., ERL, TEL, SQAL, or AET-low); (2) there is no toxicity demonstrated in
tests using at least two species and at least one solid-phase test using amphipods; (3) there are no TBP exceedances of
FDA tolerance/action/guidance levels and EPA wildlife criteria; and (4) tissue levels of resident species are below
FDA levels and EPA wildlife criteria.
Unlmown Probability of Adverse Effects
Sampling station of unknown probability for causing adverse effects are those stations for which there are not
enough data to place them in any of the other categories. Sediments at the sampling stations might or might not cause
adverse impacts to aquatic life or human health.
Modifications to Workshop Approach
The approach for evaluating NSI data recommended by the April 1994 workshop participants provides the frame-
work for the final evaluation approach actually used to evaluate the NSI data. Workshop participants had less than 4
hours to reach consensus on their recommendations for the approach following a day and a half of debate covering
many challenging issues. As a result, some of the specific issues concerning how data were to be evaluated to place
sampling stations into the five categories remained unresolved. For example, "elevated sediment chemistry concentra-
tions of PAHs" together with the presence of fish tumors is one criterion for placing a sampling station in the high
probability of adverse effects category. However, how "elevated" do sediment chemistry concentrations of PAHs have
to be to meet this criterion? As another example, sediment chemistry values that exceed all relevant AETs, ERMs,
PELs, and SQAL values for any one chemical are sufficient to place a sampling station in the high probability category,
and exceedance of any two of these values is sufficient to place a sampling station in the medium-high probability
category. But what if there are only two relevant screening values for comparison for a given contaminant? Does a
sampling station at which both values are exceeded for a given chemical belong in the high or medium-high probability
category?
A significant modification in the final approach used to evaluate the NSI data was the reduction in the number of
categories from five to three, eventually combining the medium-high and medium-low categories and the low and
unknown categories proposed in the workshop approach. In addition, the following evaluation parameters were dropped
from the final approach:
• Sediment chemistry values > 50 ppm for PCBs
- Expert reviewers of the methodology believed that this parameter was not necessary; i.e., a sampling
station that was targeted as a higher probability for adverse effects by this parameter would already have
been targeted at a much lower concentration using other parameters.
• Elevated sediment chemistry concentrations of PAHs and presence of fish tumors
- Available fish liver histopathology data in the NSI are very limited; therefore, this evaluation parameter
was not considered further.
In the final approach adopted for the evaluation of the NSI data, the EPA wildlife criteria were not included in the
TBP and fish tissue residue parameters. Reviewers of the methodology felt that the wildlife criteria values were overly
conservative for this screening assessment and thus could not be used to distinguish potentially highly contaminated
sampling stations from only slightly contaminated station. A separate analysis of wildlife criteria was, however,
conducted.
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National Ni
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Appendix I
i
i
Dom DiToro
Manhattan College
Environmental Engineering
Bronx, NY 10471
(718) 920-0276; Fax (718) 543-7914
Bob Engler
COE-WES
3909 Halls Ferry Road
Vicksburg, MS 39180-6199
(601) 634-3624
Jay Fields
NOAA/HAZMAT
7600 Sand Point Way, NE
Seattle, WA 98115
(206) 526-6404
Catherine Fox
EPA/OST (4305)
401 M Street, SW
Washington, DC 20460
(202) 260-1327; Fax (202) 260-9830
Tom Fredette
COE New England District
424 Trapels Rd.
Waltham, MA 02254
(617) 647-8291; Fax (617) 647-8303
Marilyn Gower
EPA Region 3
2530 Riva Rd., Suite 300
Annapolis, MD 21401
(410) 224-0942
Dave Hansen
EPA ERL-Narragansctt
27 Tarzwell Dr.
Narragansett, RI 02882
(401) 782-3027; Fax (401) 782-3030
Jon Harcum
Tetra Tech, Inc.
10306 Eaton PL, Ste.340
Fairfax, VA 22030
(703) 385-6000; Fax (703) 385-6007
Rick Hoffmann
EPA/OST (4305)
401 M Street, SW
Washington, DC 20460
(202) 260-0642; Fax (202) 260-9830
Bob Hoke
SAIC
411 Hackensack Ave.
Hackensack, NJ 07601
(201) 489-5200; Fax (201) 489-1592
Chris Ingersoll
NBS
Midwest Science Center
4200 New Haven Rd.
Columbia, MO 65201
(314) 875-5399
Doug Johnson
EPA Region 4
345 Courtland Street, NE
Atlanta, GA 30365
(404) 347-1740; Fax (404) 347-1797
Ken Klewio
EPA Region 5 (WS-16J)
77 W. Jackson Blvd.
Chicago, 1L 60604
(312) 886-4679; Fax (312) 886-7804
Fred Kopfler
Gulf of Mexico Program, Bldg. 1103
S tennis Space Center, MS 39529
(601) 688-3726; Fax (601) 688-2709
Paul Koska
EPA Region 6
1445 Ross Ave.
Dallas, TX 75115
(214) 655-8357
Mike Kravitz
EPA/OST
401 M Street, SW
Washington, DC 20460
(202) 260-8085
Peter Landrum
Great Lakes ERL
2205 Commonwealth Blvd.
Ann Arbor, MI 48105
(313) 741-2276
Matthew Liebman
EPA Region 1
JFK Federal Bldg., WQE
Boston, MA 02203
(617) 565-4866; Fax (617) 565-4940
email: bays@epamail.epa.gov
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Niilioiiiil Sc
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Jerry Stober
ESD-Athens
College Station Rd.
Athens, GA 30113
(706) 546-2207; Fax (706) 546-2459
Rick Swartz
EPA ERL-Newport
Hatfield Marine Science Center
Marine Science Drive
Newport, OR 97365
(503) 867-4031
Nelson Thomas
EPA ERL-Duluth
6201 Congdon Blvd.
Duluth, MN 55804
(218) 720-5702
Rachel Friedman-Thomas
Washington Dept. of Ecology
Mail Slot 47703
Olympia, WA 98504-7703
(206) 407-6909; Fax (206) 407-6904
Bumell Vincent
EPA/ORD
401 M Street, SW
Washington, DC 20460
(202) 260-7891; Fax (202) 260-6932
Mark Wildhaber
NBS
Midwest Science Center
4200 New Haven Rd.
__ Columbia, MO 65201
(314) 876-1847
Craig Wilson
California SWRCB
901 P Street
Sacramento, CA 95814
(916)657-1108
Drew Zacherle
Tetra Tech, Inc.
10306 Eaton PL, Ste. 340
Fairfax, VA 22030
(703) 385-6000; Fax (703) 385-6007
Chris Zarba
EPA/OST (4304)
401 M Street, SW
Washington, DC 20460
(202) 260-1326
Xiaochuo Zhang, WR/2
Wisconsin DNR
P.O. Box 7921
Madison, WI 53707
(608) 264-8888; Fax (608) 267-2800
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rtu.s. OOVERHffiOT PRINTING OFFICE: 1996-619-098/9(1685
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