&EPA
United States
Environmental Protection
Agency
Office of Science and
Technology
Washington, DC 20460
EPA-823-D-96-002
July 1996
The National Sediment
Quality Survey:
A Report to Congress on
the Extent and Severity of
Sediment Contamination in
Surface Waters of the
United States
Recycled/Recyclable
Printed with Soy/Canola Ink on paper that
contains at least 50% recycled fiber
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The National Sediment
Quality Survey:
A Report to Congress on the Extent and
Severity of Sediment Contamination in
Surface Waters of the United States
DRAFT
July 1996
United States Environmental Protection Agency
Office of Science and Technology
Standards and Applied Science Division
Washington, DC
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The National Sediment Quality Survey is a screening-level assessment of sediment quality
which 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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Draft National Sediment Quality Survey
Contents
Page
Tables v
Figures ix
Acknowledgments xiii
Executive Summary xv
"1 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
O Methodology 2-1
Background 2-2
Description of NSI Data 2-3
NSI Data Evaluation Approach 2-4
Sediment Chemistry Data 2-8
Tissue Residue Data 2-14
ToxicityData 2-14
Incorporation of Regional Comments on the Preliminary Evaluation of
Sediment Chemistry Data 2-14
Evaluation Using EPA Wildlife Criteria 2-15
O Findings 3-1
National Assessment 3-1
Watershed Analysis 3-8
Wildlife Assessment 3-11
Regional and State Assessment 3-11
EPA Region 1 3-17
EPA Region 2 3-24
EPA Region 3 3-31
EPA Region 4 3-38
EPA Region 5 3-46
EPA Region 6 3-54
EPA Region 7 3-61
EPA Region 8 3-68
EPA Region 9 3-74
EPA Region 10 3-81
Potentially Highly Contaminated Sites Not Identified by the NSI Evaluation 3-88
in
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4
5
6
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 4-7
EPA's Point and Nonpoint Source Sediment Contaminant Inventories 4-13
Conclusions and Discussions 5-1
Extent of Sediment Contamination 5-1
Sources of Sediment Contamination 5-3
Comparison of NSI Evaluation Results to Results of Previous Sediment
Contamination Studies 5-3
Comparison of NSI Evaluation Results to Fish Consumption Advisories 5-4
Sensitivity of Selected PCB Evaluation Parameters 5-5
Strengths of the NSI Data Evaluation 5-7
Limitations of the NSI Data Evaluation 5-9
Limitations of Data 5-9
Limitations of Approach 5-11
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: Expand the NSI's Coverage and Capabilities 6-2
Recommendation 4: Provide Better Access to Information in the NSI 6-3
Recommendation 5: Develop Better Monitoring and Assessment Tools 6-3
Recommendation 6: Address Basic Science Needs 6-4
Glossary Glossary-1
References References-1
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 Bioaccumulation Data F-l
G. Notes on the Methodology for Evaluating Sediment Toxicity Tests G-l
H. Additional Analyses forPCBs 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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Draft National Sediment Quality Survey
Tables
Page.
2-1 Number of Stations Evaluated in the NSI by State 2-4
2-2 NSI Data Evaluation Approach 2-9
3-1 National Assessment: Number of NSI Sampling Stations in Each Probability
of Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations are Located 3-3
3-2 Chemicals or Chemical Groups Most Often Responsible for Sampling Stations
Being Categorized as Tier 1 or Tier 2 3-6
3-3 Number of Stations Placed in Tier 1 or Tier 2 Based on Each Component of the
Evaluation Approach 3-11
3-4 River Reaches Located in Areas of Potential Widespread Sediment
Contamination That Have 10 or More Tier 1 Sampling Stations 3-14
3-5 Increased Number of Sampling Stations Identified as Tier 1 and Tier 2 if
Wildlife Criteria are Used in the National Assessment 3-15
3-6 Region 1: Number of NSI Sampling Stations in each Probability of Adverse
Effects Category and Number of River Reaches Where NSI Sampling
Stations are Located 3-18
3-7 Region 1: Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-22
3-8 Region 1: Water Bodies With Sampling Stations Categorized as Tier 1 That
are Located in Areas of Potential Widespread Sediment Contamination 3-22
3-9 Region 1: Chemicals Most Often Responsible for Stations Being Categorized
as Tier 1 or Tier 2 3-23
3-10 Region 2: Number of NSI Sampling Stations in Each Probability of
Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations are Located 3-25
3-11 Region 2: Watersheds Identified as Areas of Potential Widespread
Sediment Contamination 3-29
3-12 Region 2: Water Bodies With Sampling Stations Categorized as Tier 1 That
are Located in Areas of Potential Widespread Sediment Contamination 3-29
3-13 Region 2: Chemicals Most Often Responsible for Stations Being Categorized
as Tier 1 or Tier 2 3-30
3-14 Region 3: Number of NSI Sampling Stations in Each Probability of Adverse
Effects Category and Number of River Reaches Where NSI Sampling Stations
are Located 3-32
3-15 Region 3: Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-36
3-16 Region 3: Water Bodies With Sampling Stations Categorized as Tier 1 That
are Located in Areas of Potential Widespread Sediment Contamination 3-36
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TABLES (continued)
3-17 Region 3: Chemicals Most Often Responsible for Stations Being Categorized
as Tier 1 or Tier 2 3-37
3-18 Region 4: Number of NSI Sampling Stations in Each Probability of Adverse
Effects Category and Number of River Reaches Where NSI Sampling Stations
are Located 3-39
3-19 Region 4: Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-43
3-20 Region 4: Water Bodies With Sampling Stations Categorized as Tier 1 That
are Located in Areas of Potential Widespread Sediment Contamination 3-44
3-21 Region 4: Chemicals Most Often Responsible for Stations Being Categorized
as Tier 1 or Tier 2 3-45
3-22 Region 5: Number of NSI Sampling Stations in Each Probability of Adverse
Effects Category and Number of River Reaches Where NSI Sampling Stations
are Located 3-47
3-23 Region 5: Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-51
3-24 Region 5: Water Bodies With Sampling Stations Categorized as Tier 1 That
are Located in Areas of Potential Widespread Sediment Contamination 3-52
3-25 Region 5: Chemicals Most Often Responsible for Stations Being Categorized
as Tier 1 or Tier 2 3-53
3-26 Region 6: Number of NSI Sampling Stations in Each Probability of Adverse
Effects Category and Number of River Reaches Where NSI Sampling Stations
are Located 3-55
3-27 Region 6: Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-59
3-28 Region 6: Water Bodies With Sampling Stations Categorized as Tier 1 That
are Located in Areas of Potential Widespread Sediment Contamination 3-59
3-29 Region 6: Chemicals Most Often Responsible for Stations Being Categorized
as Tier 1 or Tier 2 3-60
3-30 Region 7: Number of NSI Sampling Stations in Each Probability of Adverse
Effects Category and Number of River Reaches Where NSI Sampling Stations
are Located 3-62
3-31 Region 7: Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-66
3-32 Region 7: Water Bodies With Sampling Stations Categorized as Tier 1 That
are Located in Areas of Potential Widespread Sediment Contamination 3-66
3-33 Region 7: Chemicals Most Often Responsible for Stations Being Categorized
as Tier 1 or Tier 2 3-67
3-34 Region 8: Number of NSI Sampling Stations in Each Probability of Adverse
Effects Category and Number of River Reaches Where NSI Sampling Stations
are Located 3-69
3-35 Region 8: Chemicals Most Often Responsible for Stations Being Categorized
as Tier 1 or Tier 2 3-73
VI
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Driil'l National Scdiiuuit Qiuilily Survey
TABLES (continued)
3-36 Region 9: Number of NSI Sampling Stations in Each Probability of Adverse
Effects Category and Number of River Reaches Where NSI Sampling Stations
are Located 3-75
3-37 Region 9: Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-79
3-38 Region 9: Water Bodies With Sampling Stations Categorized as Tier 1 That
are Located in Areas of Potential Widespread Sediment Contamination 3-79
3-39 Region 9: Chemicals Most Often Responsible for Stations Being Categorized
as Tier 1 or Tier 2 3-80
3-40 Region 10: Number of NSI Sampling Stations in Each Probability of Adverse
Effects Category and Number of River Reaches Where NSI Sampling Stations
are Located 3-82
3-41 Region 11: Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-86
3-42 Region 10: Water Bodies With Sampling Stations Categorized as Tier 1 That
are Located in Areas of Potential Widespread Sediment Contamination 3-86
3-43 Region 13: Chemicals Most Often Responsible for Stations Being Categorized
as Tier 1 or Tier 2 3-87
3-44 Potenitally Highly Contaminated Sites Not Identified in the NSI Evaluation 3-88
4-1 Correlations Among Sources and Chemical Class of Sediment Contaminants 4-4
4-2 Tier 1 and Tier 2 Stations Classifications by Chemical Class and Land Uses in
Areas of Potential Widespread Sediment Contamination 4-9
4-3 Comparison of Percent APC Agricultural Land Use to Percent of Tier 1 and Tier 2
Stations by Chemical Class 4-13
4-4 Comparison of Percent APC Urban Land Use 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
Tier 2 Based on the NSI Data Evaluation 5-5
5-2 National Sediment Inventory Database: Summary of QA/QC Information 5-10
vn
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Draft National Sediment Quality Survey
FIGURES
Figure Page
2-1 NSI Sediment Sampling Stations Evaluated 2-5
2-2 NSI Tissue Residue Sampling Stations Evaluated 2-6
2-3 NSI Toxicity Test Stations Evaluated 2-7
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 Metal 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 NSI Stations Categorized as Having a Higher Probability of Adverse
Effects (Tier 1) 3-4
3-3 National Assessment: Percent of River Reaches That Include Tier 1, Tier 2,
and Tier 3 Stations 3-5
3-4 National Assessment: Percent of NSI Measurements That Indicate
Potential Risk 3-7
3-5 Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects 3-9
3-6 Stations Categorized as Tier 1 or Tier 2 Due to Potential Human Health Effects 3-10
3-7 National Assessment: Areas of Potential Widespread Sediment Contamination 3-12
3-8 National Assessment: Watershed Classifications 3-13
3-9 Stations Categorized as Tier 1 or Tier 2 if of Wildlife Criteria are used to Complete
the Analysis 3-16
3-10 Region 1: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic
Life Effects and Potential Human Health Effects 3-19
3-11 Region 1: Percent of River Reaches The Include Tier 1, Tier 2, and
Tier 3 Stations 3-20
3-12 Region 1: Watershed Classifications 3-20
3-13 Region 1: Location of Sampling Stations Categorized as Tier 1 or Tier 2
and Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-21
3-14 Region 2: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic
Life Effects and Potential Human Health Effects 3-26
3-15 Region 2: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Stations 3-27
IX
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FIGURES (continued)
3-16 Region 2: Watershed Classifications 3-27
3-17 Region 2: Location of Sampling Stations Categorized as Tier 1 or Tier 2 and
Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-28
3-18 Region 3: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic
Life Effects and Potential Human Health Effects 3-33
3-19 Region 2: Percent of River Reaches That Include Tier 1, and Tier 3 Stations 3-34
3-20 Region 3: Watershed Classifications 3-34
3-21 Region 3: Location of Sampling Stations Categorized as Tier 1 or Tier 2
and Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-35
3-22 Region 4: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic
Life Effects and Potential Human Health Effects 3-40
3-23 Region 4: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Stations 3-41
3-24 Region 4: Watershed Classifications 3-41
3-25 Region 4: Location of Sampling Stations Categorized as Tier 1 or Tier 2
and Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-42
3-26 Region 5: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic
Life Effects and Potential Human Health Effects 3-48
3-27 Region 5: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Stations 3-49
3-28 Region 5: Watershed Classifications 3-49
3-29 Region 5: Location of Sampling Stations Categorized as Tier 1 or Tier 2
and Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-50
3-30 Region 6: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic
Life Effects and Potential Human Health Effects 3-56
3-31 Region 6: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Stations 3-57
3-32 Region 6: Watershed Classifications 3-57
3-33 Region 6: Location of Sampling Stations Categorized as Tier 1 or Tier 2
and Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-58
3-34 Region 7: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic
Life Effects and Potential Human Health Effects 3-63
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Draft National Sediment Quality Survey
FIGURES (continued)
3-35 Region 7: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Stations 3-64
3-36 Region 7: Watershed Classifications 3-64
3-37 Region 7: Location of Sampling Stations Categorized as Tier 1 or Tier 2
and Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-65
3-38 Region 8: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic
Life Effects and Potential Human Health Effects 3-70
3-39 Region 8: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Stations 3-71
3-40 Region 8: Watershed Classifications 3-71
3-41 Region 8: Location of Sampling Stations Categorized as Tier 1 or Tier 2 3-72
3-42 Region 9: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic
Life Effects and Potential Human Health Effects 3-76
3-43 Region 9: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Stations 3-77
3-44 Region 9: Watershed Classifications 3-77
3-45 Region 9: Location of Sampling Stations Categorized as Tier 1 or Tier 2
and Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-78
3-46 Region 10: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic
Life Effects and Potential Human Health Effects 3-83
3-47 Region 10: Percent of River Reaches That Include Tier 1, Tier 2, and
Tier 3 Stations 3-84
3-48 Region 10: Watershed Classifications 3-84
3-49 Region 10: Location of Sampling Stations Categorized as Tier 1 or Tier 2
and Watersheds Identified as Areas of Potential Widespread Sediment
Contamination 3-85
3-50 Location of Potentially Highly Contaminated Sites Not Identified in the
NSI Evaluation 3-89
4-1 Average Percent Contamination in 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 Tier 2 Stations vs. Total Urban Land Use in APCs 4-14
5-1 Comparison of (a) Location of Fish Consumption Advisories to
(b) Location of NSI Stations Categorized as Tier 1 or Tier 2 Due to
Potential Human Health Effects 5-6
5-2 NSI Stations That Would Be Categorized as Tier 1 or Tier 2 Based on
Potential Human Health Effects Without PCBs 5-8
XI
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Draft National Si-dimi-nt Quality Survey
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 their headquarters program offices
have participated in this project, provided technical guidance, and reviewed previous draft reports.
We greatly appreciate their efforts and helpful comments. 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 work-
shops in April 1993 (data compilation) 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. MacArthur 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 Ogden Environmental & Engineering in Kirkland, Washing-
ton; 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 evalua-
tion 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.
Xlll
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Draft National Sediment Quality Survey
Executive Summary
The National Sediment Quality Survey is the first
United States Environmental Protection Agency
(EPA) analysis of sediment chemistry and related
biological data from across the country to identify the
national extent and severity of sediment contamination.
This report to Congress was prepared in response to re-
quirements set forth in the Water Resources Develop-
ment Act (WRDA) of 1992, which directed EPA, in
consultation with the National Oceanic and Atmospheric
Administration (NOAA) and the U.S. Army Corps of
Engineers (COE), to conduct a comprehensive national
survey of data regarding the quality of aquatic sediments
in the United States. WRDA specifically directs EPA to
compile existing information on the quality, chemical and
physical composition, and geographic location of pol-
lutants in aquatic sediment, including the probable source
of such pollutants and identification of those sediments
which are contaminated. To comply with this mandate,
EPA's Office of Science and Technology (OST) devel-
oped the National Sediment Inventory (NSI), and com-
piled sediment chemistry and related biological data
stored in existing regional and national databases. EPA
also developed a screening-level assessment method to
analyze the NSI data.
This report presents the results of the evaluation of the
NSI data, including the screening level identification of sam-
pling stations across the country where contaminated sedi-
ments pose a potential threat to aquatic life and human
health, as well as watersheds across the country that might
have widespread sediment contamination problems. 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 assist 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, and allowing researchers to draw upon a large
data set of information to conduct new analyses that ulti-
mately will be relevant for local assessments and responses.
Description of the NSI
The NSI is the largest set of sediment chemistry and
related biological data ever compiled by EPA. It includes
approximately 2 million records for more than 21,000
monitoring stations across the country. To be efficient in
collecting usable data of similar types for inclusion in the
NSI, EPA sought data that were available in electronic
format, represented broad geographic coverage, and rep-
resented specific sampling locations identified by latitude
and longitude coordinates. The minimum data require-
ments for inclusion of computerized data in the NSI were
locational information, sampling date, latitude and lon-
gitude coordinates, and measured units. Additional data
fields such as sampling method and other quality assur-
ance/quality control information were retained in the NSI
if available, but were not required for a database 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)
• 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
• EPA Region 10/COE Seattle District's Sediment
Inventory
• EPA Region 9's Dredged Material Tracking
System (DMATS)
• EPA's Great Lakes Sediment Inventory
• EPA's Environmental Monitoring and Assess-
ment Program (EMAP)
• USGS (Massachusetts Bay) Data
In addition to sediment chemistry data, the NSI in-
cludes tissue residue, toxicity, benthic abundance,
histopathology, and fish abundance data. The sediment
chemistry, tissue residue, and toxicity data were evalu-
xv
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Kxecutivc Sumimirv
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 approach utilized chemical-specific screen-
ing values and an evaluation methodology developed
and reviewed by national experts to assess contami-
nated sediment. The evaluation methodology is meant
to be used for 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 sites which
pass through the screen would not be expected to ex-
hibit adverse effects; exceeding the screening levels
does not indicate the level or type of risk at a particu-
lar site, but can be used to target additional investiga-
tions. Thus, these tools and the evaluation results
improve the ability to identify and address contami-
nated sediment problems.
The following measurement parameters and tech-
niques were used alone or in combination to evaluate the
probability of adverse effects to aquatic life and human
health from exposure to sediment contaminants:
(1) Comparison of sediment chemistry measurements to
• Draft sediment quality criteria (SCQs)
• Other sediment quality screening values
- Sediment quality advisory levels (SQALs)
~ Effects range-median (ERM) and effects
range-low (ERL) values
- Probable effects levels (PELs) and
threshold effects levels (TELs)
- Apparent effects thresholds (AETs)
(2) Comparison of theoretical bioaccumulation poten-
tial (TBP) of measured sediment contaminants to
• EPA cancer and noncancer risk levels
• Food and Drug Administration (FDA) tolerance,
action, or guidance values
(3) Comparison of the molar concentration of acid vola-
tile sulfides ([AVS]) in sediment to the molar con-
centration of simultaneously extracted metals
([SEM]) in sediment
(4) Comparison of fish tissue contaminant levels to
• EPA cancer and noncancer risk levels
• FDA tolerance, action, or guidance values
(5) Acute effects based on sediment toxicity data
Based on the evaluation of these measurement param-
eters, EPA categorized NSI monitoring stations into "prob-
ability of adverse effects" categories, or tiers:
• Tier 1: higher probability of adverse effects
• Tier 2: intermediate probability of adverse
effects
• Tier 3: no indication of adverse effects (any sta-
tion not meeting the requirements for classifi-
cation as Tier 1 or Tier 2; includes stations for
which substantial data were available without
evidence of adverse effects, as well as stations
for which limited data were available to deter-
mine potential for adverse effects)
In addition to the analysis of potential adverse ef-
fects on aquatic life and human health, EPA also con-
ducted a separate analysis of potential wildlife effects from
exposure to contaminated sediment based on a compari-
son of sediment chemistry TBP values and fish tissue con-
centrations to EPA wildlife criteria.
EPA also developed a method to identify areas of
potential widespread sediment contamination (APCs). For
the purpose of the NSI data evaluation, APCs were de-
fined as watersheds in which 10 or more sampling sta-
tions were categorized as Tier 1 and in which at least 75
percent of all sampling stations were categorized as ei-
ther Tier 1 or Tier 2.
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 categorized as Tier 1 for potential aquatic life or
human health effects, 10,401 (49 percent) were catego-
rized as Tier 2, and 5,174 (25 percent) were categorized
as Tier 3. If the wildlife assessment had been included
with the aquatic life and human health assessment, 619
stations classified as Tier 3 for the aquatic life and human
health assessment would have been classified as Tier 2,
and 16 stations classified as Tier 2 would have been clas-
sified as Tier 1.
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Drill'! Nationnl Si'dinu'iit Quality Survey
Most of the NSI data were compiled from nonran-
dom monitoring programs. Such monitoring programs
focus their sampling efforts on areas where contamina-
tion is known or suspected to occur. As a result, assum-
ing all other factors are the same, the frequency of Tier 1
or Tier 2 classification based on the NSI data evaluation
is probably greater than that which would result from
purely random sampling.
The NSI sampling stations were located in 6,744 in-
dividual river reaches (or water body segments) across
the contiguous United States, or approximately 11 per-
cent of all river reaches in the country (based on EPA's
River Reach File 1). Thirty-five percent of all river reaches
evaluated had at least one station categorized as Tier 1,
and 42 percent had at least one station categorized as Tier
2 (but none as Tier 1). In 23 percent of the reaches evalu-
ated, all of the stations were categorized as Tier 3.
The NSI data evaluation identified 96 watersheds
throughout the United States as APCs (Figure 1). These
watersheds represent about 5 percent of all watersheds in
the country. In addition, 39 percent of all watersheds in
the country include at least one Tier 1 station, 15 percent
include at least one Tier 2 station but no Tier 1 stations,
and about 6 percent include only Tier 3 stations (Figure
2). Within the 96 APCs, 57 individual river reaches were
identified as having 10 or more Tier 1 sampling stations.
More stations were categorized as either Tier 1 or
Tier 2 based on potential aquatic life effects than on po-
tential human health effects. About 40 percent more sta-
tions were categorized as Tier 1 for potential aquatic life
effects (3,287 stations) than for potential human health
effects (2,327 stations). About 60 percent more stations
were categorized as Tier 2 due to potential aquatic life
effects (9,921 stations) than for potential human health
effects (6,196 stations).
Data related to more than 230 different chemicals
or chemical groups were included in the NSI evalua-
tion. Approximately 40 percent of these chemicals or
chemical groups (97) were present at levels that indi-
cated risks and resulted in stations being classified as
Tier 1 or Tier 2. PCBs, mercury, and DDT were the
chemicals most often responsible for stations being
categorized as Tier 1. The chemicals most often re-
sponsible for stations being categorized as either Tier
1 or Tier 2 were copper (7,172 stations), nickel (6,284),
lead (5,681), and PCBs (5,454).
Because PCBs were the contaminants most often re-
sponsible for Tier 1 classifications in the NSI evaluation,
and because EPA took a conservative approach in evalu-
ating the effects of PCB exposure, the Agency conducted
two separate analyses of PCB data to determine the im-
pact of the conservative approach on the overall classifi-
cation of NSI sampling stations. EPA first examined the
effect of excluding PCBs entirely from the NSI evalua-
tion. If PCBs were excluded, the number of Tier 1 sta-
tions 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 in-
crease reflects the movement of stations formerly classi-
fied as Tier 1 into Tier 2. In the second PCB evaluation,
EPA evaluated the effect on the overall results of using a
less conservative noncancer screening value (rather than
the cancer screening value) for predicting human health
risk associated with PCB sediment 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
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 state-of-the-art risk-based procedures to
address the probability of adverse effects to human health,
aquatic life, and wildlife. The results verify that sedi-
ment contamination is widespread and is an important
national concern. This was the first large-scale assess-
ment of sediment quality to go beyond "hot spot" identi-
fication. Through closer inspection of the thousands of
potentially contaminated locations, EPA distinguished 96
watersheds where contamination is likely to be most se-
vere and extensive.
The Agency and its state and federal partners can
address sediment contamination problems through wa-
tershed management approaches. Watershed management
programs focus on hydrologically defined drainage ba-
sins rather than areas defined by political boundaries.
These programs recognize that conditions of land areas
and activities within the watershed affect the water re-
source. Local management, stakeholder involvement, and
holistic assessments of water quality are characteristics
of the watershed approach. The National Estuary Pro-
gram 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 Programs have all recommended
actions to reduce sources of toxic contaminants to sedi-
ment. Numerous other examples of watershed manage-
ment programs are summarized in The Watershed
xvn
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Figure 1. Areas of Potential Widespread Sediment Contamination (APCs).
-------
Drut't National Sediment Quality Survey
At Least One
Tier 1 Station
39%
At Least One Tier 2 Station
and Zero Tier 1 Stations
15%
All Tier 3 Stations
6%
APCs
5%
No Data
35%
Figure 2. Watershed Classifications Based on Potential
Human Health Effects.
Approach 1993/94 Activity Report (USEPA, 1994g) and
A Phase I Inventory of Current EPA Efforts to Protect
Ecosystems (USEPA, 1995b).
The results of the NSI data evaluation confirm that
sediment contamination poses potential risks to aquatic
life, human health, and wildlife and is widespread in
many watersheds of the country. Based on the analy-
sis of the NSI data, potential sediment contamination
exists in all regions and states of the country. The
water bodies affected include streams, lakes, harbors,
near-shore areas, and oceans. Metals and persistent
organic chemicals are the contaminants most often
associated with sediment contamination.
The evaluation methodology was designed for the
purpose of a screening-level assessment of sediment qual-
ity; further evaluation would be required to confirm that
sediment contamination poses actual risks to aquatic life,
human health, or wildlife for any given site or water-
shed. Uncertainty is associated with site-specific mea-
sures, assessment techniques, exposure scenarios, and
default parameter selections. In addition, some poten-
tially contaminated sediment sites across the country have
been missed in the NSI data evaluation because the NSI
does not provide complete national coverage of sediment
quality data. Also, some data that were included in the
NSI were not evaluated because of questions concerning
data quality or a lack of locational information. On the
other hand, data in the inventory were collected between
1980 and 1993 and any single measurement of a chemi-
Aquatic Life or
cal at a site, taken any point in
time during that period, could
result in the categorization of the
site in Tier 1 or Tier 2. The
evaluation approach is conserva-
tive. Therefore, sites appearing
in Tier 1 or Tier 2 may hot cause
unacceptable impacts. Limita-
tions and uncertainties are dis-
cussed in detail in Chapter 5 and
elsewhere in the report.
Some of the most signifi-
cant sources of persistent and
toxic chemicals have been
eliminated or reduced as the
result of environmental con-
trols put into place during the
past 10 to 20 years. For ex-
ample, the commercial use of
PCBs and the pesticides DDT
and chlordane has been re-
stricted 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.
Because of the enormous cost of remediation, the pre-
ferred means for reducing health and environmental risks
from contaminated sediment is natural recovery through
continuing deposition of clean sediment. The feasibility
of natural recovery, as well as the long-term success of
expensive remediation projects, depends on the effective
control of pollutant sources. Although most active 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 organochlo-
rine pesticides are runoff and atmospheric deposition from
past applications on agricultural land. For other classes
of sediment contaminants, active sources continue to con-
tribute 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 polynuclear aromatic
hydrocarbons (PAHs). In addition, discharge limits for
municipal and industrial point sources are based on either
technology-based limits or state-adopted standards for pro-
tecti on of the water column, not necessarily for downstream
protection of sediment quality. Determining the local and
far-field effects of individual point and nonpoint sources
on sediment quality usually requires site-specific in-depth
study.
xix
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Executive Summary
The primary recommendation resulting from the NSI
data evaluation is that the states, in cooperation with EPA
and other federal agencies, should proceed with further
evaluations of the 96 watersheds identified as APCs. If
active watershed management programs are in place,
these evaluations should be coordinated within the con-
text of current or planned actions. Future efforts should
focus in particular on the 57 water body segments lo-
cated within the 96 APCs that had 10 or more stations
categorized as Tier 1. The purpose of these efforts should
be, as appropriate, to gather additional sediment chemis-
try and related biological data, and to conduct further
assessments of data to determine human health and eco-
logical risk, determine temporal trends, identify poten-
tial sources of sediment contamination and determine
whether potential sources are adequately controlled, and
determine whether natural recovery is a feasible option
for risk reduction.
Other recommendations resulting from the NSI
evaluation include the following:
• Federal, state, and local government agencies
should pool their resources and coordinate
their efforts to address their common sediment
contamination issues. These activities should
support efforts such as the selection of future
monitoring sites, the setting of priorities for
reissuance of National Pollutant Discharge
Elimination System (NPDES) permits and
permit synchronization, pollutant trading be-
tween nonpoint and point sources, and total
maximum daily load (TMDL) development.
• EPA should design future evaluations of NSI
data to determine the temporal trends of con-
tamination, and identify where and why condi-
tions are improving or worsening. The NSI
should expand to provide more complete na-
tional coverage of sediment quality data, both
in terms of the number of water bodies evalu-
ated and the suite of biological and chemical
information available to evaluate each site.
• EPA should continue its efforts to make the
NSI data and evaluation results more acces-
sible to other agencies' programs and to
States.
• EPA should continue to develop sediment qual-
ity criteria (SQCs), especially for metals. In the
absence of SQCs for additional nonionic organic
chemicals, EPA should also continue to develop
additional sediment quality advisory levels
(SQALs).
• Future monitoring programs should specify col-
lection of AVS and SEM measurements where
metals are a concern and site-specific total or-
ganic carbon (TOC) measurements where or-
ganic chemicals are a concern.
• Future sediment monitoring programs should
also collect tissue residue, biological effects, and
biological community measurements as well as
sediment chemistry measurements.
• EPA should continue to update the NSI evalua-
tion methodology as new assessment tools be-
come available and the state of the science
evolves.
• EPA and other federal agencies should fund or
otherwise support the development of tools to
better characterize the sources, fate, and effects
of sediment contaminants.
XX
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Chapter 1
Introduction
Draft Niitioiuil Si'dinu'iil Qiuility Survcv
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 aquatic sediments in the United States. The
statute specifically instructs EPA to compile all existing
information on the quantity, chemical and physical com-
position, and geographic location of pollutants in aquatic
sediment, including the probahle source of such pollut-
ants and identification of those sediments which are con-
taminated. The statute 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.
In addition, WRDA requires EPA to establish a compre-
hensive and continuing program to assess aquatic sedi-
ment 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
Sediment Inventory (NSI). The goals of the NSI are to
compile sediment quality information from available elec-
tronic databases, gather information from available elec-
tronic databases and published reports on sediment
contaminant sources, develop screening-level assessment
protocols to identify potentially contaminated sediment,
and produce biennial reports to Congress on the extent
and severity of sediment contamination nationwide. The
National Sediment Quality Survey is the first of these re-
ports to Congress. To ensure that future reports to Con-
gress accurately reflect contemporary conditions of the
Nation's sediment as science evolves, the NSI will de-
velop into a continually updated, centralized assemblage
of sediment quality measurements and state-of-the-art
assessment techniques. EPA anticipates that products
and services generated through the NSI will provide man-
agers at the federal, state, and local levels with relevant,
valuable information.
For this first report to Congress, OST has compiled
and analyzed historical data collected from 1980 to 1993
from across the country that are currently stored in large
electronic databases. This effort required a substantial
synthesis of multiple formats and the coordinated efforts
of many federal and state environmental information pro-
grams 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.
As data management systems and access capabilities con-
tinue to improve, EPA anticipates that a greater amount
of data will be readily available in electronic form in the
future.
This report presents the results of a screening-level
assessment of the NSI data. For this assessment, OST
examined sediment chemistry data, associated fish tissue
residue levels, and acute toxicity bioassay test results. The
purpose was to determine whether potential contamina-
tion problems either exist currently or existed over the
past 15 years at distinct monitoring locations. This re-
port identifies locations where available data indicate that
direct or indirect exposure to the sediment could cause
adverse effects to aquatic life and/or human health. How-
ever, because this analysis is based only on readily avail-
able electronic data, contamination problems likely 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
worsened since the sediment was sampled. Consequently,
this report does not definitively assess the current overall
condition of all sediments across the country, nor does it
identify uncontaminated sediment sites. This analysis
serves as a baseline for future assessments, which will
incorporate additional monitoring locations, as well as
supplementary data.
The National Sediment Quality Survey is presented
as a single report summarizing national, regional, and state
results from the evaluation of NSI data. Chapter 1 pro-
vides background information about sediment quality is-
sues. 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 the potential sources
of sediment contamination, including point and nonpoint
1-1
-------
sources. A discussion of the results is provided in Chap-
ter 5. Chapter 6 presents recommendations for improv-
ing the NSI for future evaluations of sediment quality.
Several appendices present detailed descriptions 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 Accu-
mulation 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 Recom-
mended by the National Sediment Inventory
Workshop, 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 sediment.
In fact, sediment has been described as the "ultimate
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 contaminants in
the aquatic environment.
Adverse effects on organisms in or near sediment
can occur even when contaminant levels in the overly-
ing water are low. Benthic (bottom-dwelling) organisms
can be exposed to contaminants in sediment through di-
rect contact, ingestion of sediment particles, or uptake
of dissolved contaminants present in the interstitial (pore)
water. 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
by affecting the food supply that sustainable populations
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
environment that provide habitat, feeding,
spawning, and rearing areas for many aquatic
organisms.
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
1-2
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Draft National Scdinifiil Quality Survey
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 Cali-
fornia, 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 river (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
established a cause-and-effect relationship between the
presence 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.
Contaminated sediments can also affect the food chain
base by eliminating food sources and, in some cases, al-
tering natural competition, which can impact the popula-
tion dynamics 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. Currently, over 1,500 water bod-
ies in the United States have fish consumption advisories
in place, affecting all but 4 states. Water supplies also
have been shut down because of contaminated sediments,
and in some places swimming is no longer allowed.
How extensive 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 in-
dustrial embayments (Dexter et al., 1981; Long, 1982;
Malins et al., 1980; Riley et al., 1981). These early as-
sessments demonstrated that Puget Sound sediments were
contaminated by many organic and inorganic chemicals
including PCBs, PAHs, and metals. Although contami-
nant concentrations in sediment tended to decrease rap-
idly with distance from the nearshore sources, researchers
also documented widespread low-level contamination in
thedeepwater sediments of the mainbasin 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 relation-
ships between hepatic lesions (liver tumors) and stomach
contents, 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
authors found that lesions were more likely to occur in
fish from sites with higher concentrations of chemical
contaminants in sediments. Certain lesions had a signifi-
cantly higher relative risk of occurrence at urban sites in
Puget Sound, San Francisco Bay, the vicinity of Los An-
geles, and San Diego Bay (Myers et al,, 1994). The re-
sults of this study provide strong evidence for the
involvement of sediment contaminants in causing hepatic
lesions in bottom fish and clearly indicate the usefulness
of these lesions as indicators of contaminant-induced ef-
fects in fish (Myers et al., 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 sedi-
ment contamination since the mid-1980s and has linked
elevated pollutant concentrations to the potential for ad-
verse biological effects in many urban areas, including
the Hudson-Raritan estuary, Boston Harbor, western
1-3
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Introduction
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 (IJC, 1987; Power and Chapman, 1992). The
USEPA (1993a) summarizes other recent assessment
studies. However, there is still no national-scale assess-
ment of the extent and severity of sediment contamina-
tion, particularly in freshwater areas. This report is the
result of EPA's first assessment to determine how wide-
spread the problem of sediment contamination is on a
national basis.
What are the potential sources of
sediment contamination?
Water bodies usually receive discharges of pol-
lutants 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, identifiable source of pollution such as a
pipe from a factory or a wastewater treatment plant.
Nonpoint source pollution is usually carried off the
land by stormwater runoff and includes pollutants from
agriculture, urban areas, mining, marinas and boating,
construction and other land modifications, and atmo-
spheric deposition. Many of the current suspected and
documented cases of sediment contamination are
caused by past industrial and agricultural uses of highly
persistent and toxic chemicals, such as PCBs and chlo-
rdane. While the use of such chemicals has since been
banned or tightly restricted, monitoring programs con-
tinue to study the extent and severity of their accumu-
lation in sediment, and subsequently in the tissues of
fish and shellfish. Other potential sediment contami-
nants, including heavy metals, PAHs, some pesticides,
and existing and new industrial chemicals, continue
to appear in point and nonpoint source releases. How-
ever, significant progress over the past 10 to 15 years,
achieved through industry pollution prevention initia-
tives, National Pollutant Discharge Elimination Sys-
tem (NPDES) permits, and national technology-based
effluent guideline limitations, has substantially re-
duced the discharge of toxic and persistent chemicals.
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 indus-
trial or municipal waste; in other cases, natural mixing
processes and dredging disperse the pollutants. In addi-
tion, rivers and streams can carry pollutants from up-
stream sources into larger downstream water bodies,
where they can contribute further to the problem of sedi-
ment contamination. Drifting atmospheric pollutants that
are eventually deposited in water bodies also contribute
to sediment contamination. For example, EPA estimates
that 76 to 89 percent of PCS 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 and 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
contents of storm sewers and sanitary sewers). Indus-
trial 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 contamina-
tion in some regions, as are runoff and seepage from land-
fills and Superfund sites, and urban and agricultural
runoff (Baudo and Muntau, 1990; Hoffman, 1985;
Livingston and Cox, 1985; Ryan and Cox, 1985). Agri-
cultural 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
environments (USEPA, 1993c). However, it is often dif-
ficult to determine the portion of these contaminants con-
tributed by nonpoint versus point source discharges
because the same contaminants can come from both
(Baudo and Muntau, 1990).
Kepone contamination in the James River in Vir-
ginia is an example of historical sediment contamina-
tion. 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
1-4
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Draft National Svdiim'iit Quality Survey
sediment and finfish and shellfish of the James River
downstream 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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Chapter 2
Methodology
Drill'! National Si'diincnl Quality Survey
EPA faced two primary challenges to
achieving the short-term goals of the
National Sediment Inventory (NSI) and fulfill-
ing 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 sediment quality measures suitable for all re-
gions of the country. The second challenge was to iden-
tify scientifically sound methods to determine whether a
particular sediment 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
to 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 are not always indicative of toxic effect
levels. Similar concentrations of a chemical can produce
widely different biological effects in different sediments.
This discrepancy occurs because toxicity is influenced
by the extent to which chemical contaminants bind to other
constituents in sediment. These other sediment constitu-
ents, such as organic ligands and inorganic oxides and
sulfides, are said to control the bioavaitobility of accu-
mulated contaminants. Toxicant binding, or sorption, to
sediment particles suspends the toxic mode of action in
biological systems. Because the binding capacity of sedi-
ment varies, the degree of toxicity exhibited also varies
for the same total quantity of toxicant.
The five general categories of sediment quality mea-
surements are sediment chemistry, sediment toxicity, com-
munity structure, tissue chemistry, and pathology (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, represented broad geographic cov-
erage, and represented specific sampling locations iden-
tified by latitude and longitude coordinates. EPA found
sediment chemistry and tissue chemistry to be the most
widely available 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 assumed
conditions of thermodynamic equilibrium (USEPA,
1993d). Other methods exist that rely on statistical cor-
relations of contaminant concentrations with incidence
of adverse biological effects (Barrick et al., 1988; FDEP,
1994; Long et al., 1995). In addition, fish tissue levels
can be predicted using sediment contaminant concentra-
tions, along with independent field measures of chemical
partitioning behavior and other known or assigned fish
tissue and sediment characteristics. EPA can evaluate risk
to consumers from predicted and field-measured tissue
chemistry data using established dose-response relation-
ships and standard consumption patterns. Evaluations
based on tissue chemistry circumvent the bioavailability
issue while also accounting for other mitigating factors
such as metabolism. The primary difficulty in using field-
measured tissue chemistry is relating chemical residue
levels to a specific sediment, especially for those fish spe-
cies 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 electronic
format EPA sought for the NSI. Sediment toxicity data
are typically in the form of percent survival, compared to
control mortality, for indicator organisms exposed to the
field-sampled sediment in laboratory bioassays. Although
these measures account for bioavailability and the antago-
nistic and synergistic effects of pollutant mixtures, they
do not address possible long-term reproductive or growth
effects, nor do they identify specific contaminants respon-
sible for observed acute toxicity. Indicator organisms also
might not represent the most sensitive species. Commu-
nity structure measures, such as fish abundance and
benthic diversity, and pathology measures are potentially
indicative of long-term adverse effects, yet there are a
2-1
-------
multitude of mitigating physical, hydroJogic, 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 weak-
nesses. 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 con-
taminated sediment in WRDA, EPA can identify loca-
tions where sediment chemistry measures exceed
"appropriate geochemical, lexicological, or sediment
quality criteria or measures." Again based on the statu-
tory definition, EPA can also use tissue chemistry and
sediment toxicity measures to identify aquatic sediments
that "otherwise pose a threat to human health or the en-
vironment" because there are either screening values
(e.g., EPA risk levels for fish tissue consumption) or con-
trol samples for comparison. It is unclear, however, how
EPA could evaluate community structure or pathology
measures to identify contaminated sediment, based on
the statutory definition, without first identifying appro-
priate 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 (or site), individually and
in combination using a variety of assessment methods.
Because of the limitations of the available sediment qual-
ity measures and assessment methods, EPA character-
izes this identification of contaminated sediment sites
as a screening-level analysis. Similar to a potential hu-
man illness screen, a screening-level analysis should pick
up all potential problems and note them for further study.
Thus, designation as a potentially contaminated site in
this analysis is not meant to be definitive, but is intended
to be inclusive of all potential problems. For this rea-
son, EPA elected to evaluate data collected from 1980
to 1993 and to evaluate each chemical or biological mea-
surement taken at a given site individually. A single
measurement of a chemical at a site, taken at any point
in time over the past 15 years, could be sufficient to
categorize the site as posing a potential threat 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 sampling
(e.g., a contaminated area might be sampled one year,
but not the next) or a function of natural events (e.g.,
floods can move contaminated sediment from one area
to another, or can bury contaminated sediment). There-
fore, EPA describes sites in terms of their "probability of
adverse effects to aquatic life or human health." Each
site falls into one of three categories (tiers): higher prob-
ability of adverse effects (Tier 1), intermediate probabil-
ity of adverse effects (Tier 2), or no indication of adverse
effects (Tier 3). Recognizing the imprecise nature of the
numerical assessment parameters, Tier 1 sites are distin-
guished from Tier 2 sites based on the magnitude of a
sediment chemistry measure or the degree of corrobora-
tion among the different types of sediment
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 de-
velopment of the NSI through pilot inventories in EPA
Regions 4 and 5 and the Gulf of Mexico Program.
Based on lessons learned from these three pilot inven-
tories, the Agency developed a document entitled
Framework for the Development of the National Sedi-
ment Inventory (USEPA, 1993a), which describes the
general format for compiling sediment-related data and
provides a brief summary of sediment quality evalua-
tion techniques. The format and overall approach were
then presented, modified slightly, and agreed upon at
an interagency workshop held in March 1993 in Wash-
ington, D.C. Following the workshop, EPA began
compiling and evaluating data for the NSI. Data from
several national and regional databases were included
as part of the effort.
In the spring of 1994, EPA conducted a preliminary
evaluation of NSI sediment chemistry data only. The
purpose of the assessment was to identify water bodies
throughout the United States where measured values of
sediment pollutants exceeded sediment chemistry levels
of concern. The results of that assessment were then dis-
tributed to the EPA Regional offices for their review. The
Regional offices were asked to review the preliminary
evaluation and to:
• Verify sites targeted as areas of concern.
• Identify sites that might be incorrectly targeted
as areas of concern.
• Identify potential areas of concern that were not
targeted, but should have been.
2-2
-------
Draft Nalioiuil Sfdinirnl Quality Survey
• 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 preliminary
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 fu-
ture reports to Congress.
In April 1994, EPA Headquarters held the Second
National Sediment Inventory Workshop (USEPA, 1994c).
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
participants provided the basis for the final approach
adopted to evaluate NSI data for this report to Congress.
Appendix 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)
- U.S. Army Corps of Engineers (COE)
- U.S. Geological Survey (USGS)
- EPA
- States
• 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
• EPA Region 10/COE Seattle District's Sediment
Inventory
• EPA Region 9's Dredged Material Tracking
System (DMATS)
• EPA's Great Lakes Sediment Inventory
• EPA's Environmental Monitoring and Assess-
ment Program (EMAP)
• USGS (Massachusetts Bay) Data
• EPA's National Point Source Inventory
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 elec-
tronically available data include locational information,
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 and 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 chemi-
cal composition of sediment-associated con-
taminants.
• Tissue residue: Measurement of chemical con-
taminants in the tissues of organisms.
• 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 as-
surance management associated with specific data sub-
mittals.
2-3
-------
Inherent in the diversity of
data sources are contrasting moni-
toring objectives and scope. Com-
ponent sources contain data
derived from different spatial sam-
pling plans, sampling methods, and
analytical methods. For example,
data from EPA's EMAP program
represent sampling stations that lie
on a standardized grid over a given
geographic area, whereas data in
EPA's STORET most likely repre-
sent state monitoring data sampled
from locations near known dis-
charges or thought to have elevated
contaminant levels. In contrast,
many of the data in NOAA's
COSED database represent sam-
pling stations purposely selected
because they are removed from
known discharges. From an assess-
ment point of view, STORET data
might be useful for developing a
list of contaminated sediment lo-
cations, but might overstate the
general extent of contaminated
sediment in the Nation by focus-
ing largely on areas most likely to
be problematic. On the other hand,
analysis of EMAP data might re-
sult in a more balanced assessment
in terms of the mix of contaminated
sites and uncontaminated sites.
Approximately 45 percent of monitoring records in the
NSI originate from the STORET database. Reliance on
these data is consistent with the stated objective of this
survey: to identify those sediments which are contami-
nated. However, one cannot accurately make inferences
regarding the overall condition of the Nation's sediment,
or characterize the "percent contamination," using all the
data in the NSI because uncontaminated areas are most
likely underrepresented.
NSI data do not evenly represent all geographic re-
gions in the United States, nor do the data represent a
consistent set of monitored chemicals. For example, sev-
eral of the databases are targeted toward marine environ-
ments 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 are
located in Washington, Florida, Illinois, California, Vir-
ginia, Ohio, Massachusetts, and Wisconsin. Each of these
states has more than 700 monitoring stations. Other states
of similar or larger size (e.g., Georgia, Pennsylvania) have
Table 2-1. Number of Stations Evaluated in the NSI by State
Region 1
Region 2
Region 3
Region 4
Region 5
Connecticut
Maine
Massachusetts
New Hampshire
Rhode Island
Vermont
New Jersey
New Yoil
Puerto Rico
Delaware
Districtof Columbia
Maryland
Pennsylvania
Virginia
West Virginia
Alabama
Florida
Georgia
Kentucky
Mississippi
North Carolina
South Carolina
Tennessee
Illinois
Indiana
Michigan
Minnesota
Ohio
Wisconsin
98
55
895
1
42
5
448
618
30
218
4
206
311
1,051
120
477
1,776
318
249
318
612
563
646
1,669
108
402
438
970
703
Region 6
Region 7
Region 8
Region 9
Region 10
Arkansas
Louisiana
New Mexico
Oklahoma
Texas
Iowa
Kansas
Missouri
Nebraska
Colorado
Montana
North Dakota
South Dakota
Utah
Wyoming
Arizona
California
Hawaii
Nevada
Alaska
Idaho
Oregon
Washington
1OT
460
101
286
662
128
103
327
253
202
38
161
43
47
44
124
1,443
36
96
267
95
291
2,225
far fewer sampling stations with data for evaluation. Fig-
ures 2-1, 2-2, and 2-3 depict the location of monitoring
stations with sediment chemistry, tissue residue, and tox-
icity data, respectively. Individual stations may vary con-
siderably in terms of the number of chemicals monitored.
Some stations have data that represent a large number of
organic and inorganic contaminants, whereas others have
measured values for only a few chemicals. Thus, the in-
ventory cannot be considered comprehensive even for
locations with sampling data, The reliance on readily
available electronic data has undoubtedly led to exclu-
sions of a vast amount of information available from
sources such as local and state governments and published
reports. Other limitations, including data quality issues,
are 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 contamina-
2-4
-------
•V
- t.
Alaska
Hawaii
,,,.-^-* A.
•> V- £:•; '••:'-'-:'\ij&'**'
• Station
Total ff: 17.884
Figure 2-1. NSI Sediment Sampling Stations Evaluated.
-------
• »'r" **•
.* • vs., %.•*-•»•-
.* * «*•***<*!** •• ^ "• r
•-'<• #%.:•' }. rr
-
h igure 2-2. NS1 Tissue Residue Sampling Stations Evaluated.
-------
Figure 2-3. NS1 Toxicity Test Stations Evaluated.
-------
Methodology
tion relies on measures of sediment chemistry, sediment
toxicity, and contaminant residue in tissue. Although the
NSI also contains benthic abundance, histopathology, and
fish abundance data, these types of data were not used in
the evaluation. Benthic and fish abundance cannot be
directly associated with sediment contamination based on
the statutory definition and currently available assessment
tools, and available fish liver histopathology data were
very limited.
The approach used to evaluate the NSI data focuses on
the protection of benthic organisms from exposure to con-
taminated sediments and the protection of humans from the
consumption offish thatbioaccumulate 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 evalu-
ation; however, they are presented separately. Table 2-2
presents the classification scheme used in the evaluation of
the NSI data. Each component, or evaluation parameter, of
the classification scheme is numbered on Table 2-2. Each
evaluation parameter is discussed under a section heading
that refers to these numbers. Figures 2-4 through 2-8 de-
pict the evaluation parameters and site classifications in flow-
chart format.
EPA analyzed the NSI data by evaluating each param-
eter in Table 2-2 on a station-by-station and sample-by-
sample basis. Each station was classified into a "probability
of adverse effects" category 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 divalent metals, whose concentrations were
summed), a single observation of elevated concentration
could place a site into Tier 1, the higher probability cat-
egory. In general, the methodology was constructed such
that a site classified as Tier 1 must be represented by a rela-
tively large set of data or by a highly elevated sediment
concentration of a chemical whose effects threshold level is
well characterized based on multiple assessment techniques.
Fewer data were required to classify a site as Tier 2. Any
station not meeting the requirements to be classified as Tier
1 or Tier 2 was classified as Tier 3. Stations in this category
include those for which substantial data were available with-
out evidence of adverse effects, as well as stations for which
limited data were available to determine potential for
adverse effects.
Individual evaluation parameters, applied to various
measurements independently, could lead to different site
classifications. If one evaluation parameter indicated a
higher probability of adverse effects, but other evaluation
parameters indicated an intermediate probability or did not
indicate adverse effects, a Tier 1 classification was assigned
2-8
to the station. For example, if a site was categorized as Tier
2 based on all sediment chemistry data, but was categorized
as Tier 1 based on toxicity data, the site was placed in Tier
1. This principle also applies to evaluating multiple con-
taminants within the same evaluation parameter. For ex-
ample, if the evaluation of sediment chemistry data placed
a site in Tier 1 for metals and in Tier 2 for PCBs, the site
was placed in Tier 1.
The numbered evaluation parameters used in the NSI
data evaluation are briefly described below. A detailed
description of the evaluation parameters is presented in
Appendix B.
Sediment Chemistry Data
Sediment chemistry screening values are reference
values above which sediment contaminant concentrations
could pose a significant threat to aquatic life. The sedi-
ment chemistry screening values used to evaluate the NSI
data for potential adverse effects of sediment contamina-
tion on aquatic life include both theoretically and em-
pirically based values. The theoretically based values rely
on the physical/chemical properties of sediment and
chemicals to predict the level of contamination that would
protect against an adverse effect to aquatic life. The em-
pirically based, or correlative, screening 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 draft 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 ef-
fects 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 limitations as-
sociated 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:
-------
I)r:itt National Sediment Quality Survey
Table 2-2. NSI Data Evaluation Approach (with numbered parameters)
Category of Site
Cl.idrkallon.
Tier 1:
Higher Probability of
Adverse Effects to
Aquatic Life or Human
Health
Tier 2:
Intermediate Probability
of Advene Effects to
Aquatic Life or Human
Health
Tier 3:
No indication of adverse
Data Dud to Determine C)*illflt*tt0ftl
Stdlment Chemlitry
Sediment chemistry value*
exceed draft icdimem quality
criteria for any one of the five
chemicali for which criteria have
been developed by EPA (must
have measured TOC) 1
OR
[SEM]-[AVS>5 for the sum of
molar concentration* of Cd, Cu,
Ni, Pb, and Zn' 2
OR
Sediment chemistry values
exceed two or more of the
relevant upper screening value*
(ERMs, AETs (high), PEU,
SQALs, SQCi) for any one
chemical (other than Cd, Cu, Ni,
Pb, and Zn) (can use default
TOC) 3
OR
Sediment chemistry TBP exceeds
FDA action level* or EPA risk
levels 4
[SEMHAVS] - 0 to 5 for the
sum of molar concentrations of
Cd, Cu, Ni, Pb, and Zn 3
OR
Sediment chemistry value*
exceed any one of the relevant
lower screening values (ERLs,
AETs (low), TELi, SQALs.
SQCi) for any one chemical (can
use default TOC) *
OR
Sediment chemistry TBP exceeds
FDA action levels or EPA risk
levels 7
OR
AND
OR
Tlilut Residue
Tissue levels of dioxin or PCBs
in resident species exceed EPA
risk levels S
Tissue levels in resident species
exceed FDA action levels or EPA
risk levels 9
Tissue levels in resident species
exceed FDA action levels or EPA
risk levels 10
OR
OR
Toilclty
Toxicity demonstrated by two or
more nonmicrobial acute toxicity
tests using two different species
(one of which must be a solid-
phase test) 11
Toxicity demonstrated by a
single-species nonmicrobial
toxicity test 12
Any station not categorized as Tier 1 or Tier 2. Available data (which may be very limited or quite extensive) do not
indicate a likelihood of adverse effects to aquatic life or human health.
"Metals: Cd = cadmium, Cu = capper, Ni = nickel, Pb = lead. Zn = zinc.
Don tf» ctamfciJ 1 y* f
hm» 1
<
M
V^TOC™-^ L-^SaLSSL11^ — •» -"TST
1- L~
UudriiultTOCoriX •" »"
todMvmlMTOC -»^
WkiTOCiMHWwl I_22 — ». concwmdonfer > ir"^
forth.ilul 1 com|»rli«i»*li*«ft /^ »
• SOC.»dSOAU / (^ThrlS
I'
UMimuuradTOC
yilMUdMnnkwIOC
nomntoddnmlol »>
CMXtmrrton for
cofnpwlion
WlthSQAU
i / ^r-
• /
DMdwmlal 1 /
r^ff^a^^c^l 1 1* > Exwtadithuc '
uTicrwnli««liMll 1 two up|>«r «cr»toin| wluM
^|i^^^^— —
I"
EneuifailaMornwra
latwierMnlninliMl
JL
'Unto ontorind by inotf»r p»rim«««r
Figure 2-4.
/-HJUmiV J-JJ*Vi * »«JU**LJU»»««'"»"'*«» mr-~-ff^*m «, v
Organic Chemicals and Metals Not Included in the AVS Analysis.
2-9
-------
Metlioilolotiv
Was AVS measured 1 y«» What was the resu
for the sample? 1 comparing [SEM] 0
s
Did chemical concentration 1 ^
exceed any screening values?! T*
no yes
i -
( Tier 2 J4
^•"-^4
0 > [SAV]-tW3Sl "1 f
1
Unless categorized by another parameter
It of I PEM]-[AVS]>5
»[AVS]?I
C^>
7"^
Figure 2-5. Aquatic Life Assessments: Sediment Chemistry Analysis for
Divalent Metals.
txt fxrlumwdl
tm
man nonmjcrobltl KUtt toxlcfty tut! 1
uilnf 2
-------
Drill! Niilioinil Sediment Ou;i
llthldwnloll I
nonpohr orpnkl I ^
Wm both Hdhwit chvnutrjr ud
Mi dWK fHldu* knb imuurad
ttttMtltol
J^
DM both ndlnwm dwnlitry TBP «ilu«
ml M> dnui raMM ImU oxMd
FDA talon tonli or EM rhk toMbl
Did dM wdlmw* chomltfry TIP or
IU tlf»u« raridin Iml cxcMd tht
FM ictlon Imh or £M Ullon Imbl
Unlnt cKiforlzwl by inodw ptmnMir
Figure 2-7. Human Health Assessments: Sediment Chemistry and Fish
Tissue Residue Analysis (excluding dioxins and PCBs).
Did levels of dioxin or PCBs
in fish tissue exceed EPA
risk levels?
no
yes
'Unless categorized by another parameter
Figure 2-8. Human Health Assessments: PCBs and Dioxin in Fish Tissue
Analysis.
2-11
-------
Methodology
• Acenaphthene (polynuclear aromatic
hydrocarbon, PAH)
• Diddrin (pesticide)
• Endrin (pesticide)
• Fluoranthene (PAH)
• Phenanthrene (PAH)
EPA developed these draft criteria using the equilib-
rium partitioning (EqP) approach (described in detail in Ap-
pendix 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 protective of aquatic life. It
combines the water-only effects concentration (the chronic
water quality criteria) and the organic carbon partitioning
coefficient of the chemical normalized to the organic car-
bon content of the sediment. The draft criterion is com-
pared to the measured dry-weight sediment concentration
of the chemical normalized to sediment organic carbon con-
tent. If the organic-carbon-normatized concentration of the
contaminant does not exceed the draft sediment quality cri-
terion, adverse effects should not occur to at least 95 per-
cent of benthic organisms. The draft SQCs are based on the
highest quality data available, which have been reviewed
extensively.
For the NSI dala evaluation, sediment chemistry
measurements with accompanying measured total organic
carbon (TOC) values can place a site in Tier 1 based ex-
clusively 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 sediment and, thus, the availability for uptake by
organisms (bioavailability). If draft SQCs based on mea-
sured TOC were not exceeded, or if none of the five non-
polar organic chemicals that have been assigned draft SQC
values were measured, the site was classified as Tier 3
unless otherwise categorized by another parameter. If a
sample for any of the five contaminants for which draft
SQCs have been developed did not have accompanying
TOC data, the measured concentration was compared 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.
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
2-12
to bioaccumulate is problematic because different sedi-
ments 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 (mor-
tality) to the metal concentration in the sediment in-
terstitial water (Adams et al., 1985; Di Toro et al.,
1990). Acid-volatile sulfide (AVS) is one of the ma-
jor chemical components that control the activities and
availability of metals in interstitial waters of anoxic
(lacking oxygen) sediments (Meyer et al.f 1994).
A large reservoir of sulfide exists as iron sulfide in
anoxic sediment. Sulfide will react with several divalent
transition metal cations (cadmium, copper, mercury,
nickel, lead, and zinc) to form highly insoluble compounds
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 of metals exceeds the
molar concentration of sulfide), typically at relatively high
dry-weight metal concentrations. This observation has
led to a laboratory measurement technique of calculating
the difference between simultaneously extracted metal
(SEM) concentration and acid volatile sulfide concentra-
tion from field samples to determine 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 bioaccumulation potential of mercury in
sediment. Specifically, under certain conditions mercury
binds to an organic methyl group and is readily taken up
by living organisms.
Sediment with measured [SEM] in excess of [AVS]
does not necessarily exhibit toxicity. This is because other
binding phases can tie up metals. However, research in-
dicates that sediment with [AVS] in excess of [SEM] will
not be toxic from metals, and the greater the [SEMRAVS]
difference, the greater the likelihood of toxicity from met-
als. Analysis of toxicity data for freshwater and saltwa-
ter sediment amphipods (crustaceans) from EPA's
Environmental Research Laboratory in Narragansett,
Rhode Island, revealed that 80 to 90 percent of the sedi-
ments were toxic at [SEM]-[AVS] = 5 (Hansen, 1995).
Thus, EPA selected [SEM]-[AVS] = 5 as the demarcation
line between Tier 1 and Tier 2. For the purpose of this
evaluation, where [SEM]-[AVS] was greater than 5, the
site was categorized as Tier 1. If [SEM]-[AVS] was be-
-------
I)r;il) National Scdimi'iil Oii;ili(y Survey
tween zero and 5, the site was categorized as Tier 2. If
[SEM]-[AVS] was less than zero, or if AVS or the five
AVS metals were not measured at the site, the site was
categorized as Tier 3 unless otherwise categorized by
another parameter.
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-
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.
Although the draft SQCs and SQALs were both de-
veloped using the EqP approach, the data used to derive
sediment SQALs came from limited sources and have
undergone limited peer review. 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
Quality Initiative "Tier II" water quality criteria meth-
odology. The data used to develop the latter values were
taken primarily from quality-screened studies in pub-
lished literature. The development of SQALs is discussed
in further detail in Appendix B of this report.
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 site us-
ing paired field and laboratory data. The developers of
the ERLs/ERMs define sediment concentrations below
the ERL as being in the "minimal-effects range," values
between the ERL and ERM in the "possible-effects
range," and values above the ERM in the "probable-ef-
fects range." In the FDEP (1994) approach, the lower of
the two guidelines for each chemical (the TEL) is as-
sumed to represent the concentration below which toxic
effects rarely occur. In the range of concentrations be-
tween the TEL and PEL, effects occasionally occur. Toxic
effects usually or frequently occur at concentrations above
the upper guideline (the PEL). In the Barrick et al. (1988)
approach, two thresholds were recognized, when pos-
sible, based on different indicator species. The AET-
low was the lowest concentration for which a particular
indicator showed an effect, and the AET-high was the
highest concentration at which effects were observed for
another indicator.
For the NSI data evaluation, the upper screening val-
ues were considered to be the ERM, PEL, draft SQC (when
using default TOC), SQAL, and AET-high for a given
chemical. The lower screening values were considered
to be the ERL, TEL, draft SQC (when using default TOC),
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 chemi-
cal served as both the high and low screening values.
For a site to be classified as Tier 1, a chemical mea-
surement 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 site
was classified as Tier 2. If sediment concentrations at a
site did not exceed any screening values or there were no
data for chemicals that have assigned screening values,
the site was categorized as Tier 3 unless otherwise cat-
egorized by another parameter.
Under this approach a site could be classified as Tier
1 from elevated concentrations of cadmium, copper, lead,
nickel, or zinc based only on a comparison of [SEM] to
[AVS] (that is, sites could not be classified as Tier 1 based
on an exceedance of two upper screening values for any
of the five metals). However, sites 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.
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 (con-
centration that does not change with time) of a contami-
nant in tissue's 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 or-
ganic chemicals. The TBP is estimated from the concen-
tration of contaminant in the sediment, the organic carbon
content of the sediment, the lipid content of the organ-
ism, and the relative affinity of the chemical for sediment
2-13
-------
Mi'thodoloyy
organic carbon and animal lipid content. This relative
affinity is measured in the field and is called a biota-sedi-
ment accumulation factor (BSAF, as discussed in detail
in Appendix C).
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 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 location. If tissue
residue levels for the same chemical for a resident spe-
cies at the same station did not exceed EPA risk levels or
FDA levels or there were no corresponding tissue data,
the station was classified as Tier 2. If neither TBP values
nor fish tissue residue levels exceeded EPA risk levels or
FDA levels, or if no chemicals with TBP values, EPA
risk levels, or FDA levels were measured, the site was
categorized as Tier 3 unless otherwise categorized 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 Ap-
pendix B.
Tissue Residue Data [8, 9,10]
Tissue residue data were used to assess potential ad-
verse effects to humans from the consumption of fish that
become contaminated through exposure to contaminated
sediment. Only those species considered benthic, non-
migratory (resident), and edible by human populations
were included in human health assessments. A list of
species included in the NSI and their characteristics is
presented in Appendix F.
Sampling stations at which human health screening
values for dioxin and PCBs were exceeded in fish tissues
were categorized as Tier 1. For these chemicals, corrobo-
rating sediment chemistry data was not required. If hu-
man health screening values for dioxin or PCBs in fish
tissue were not exceeded or if neither chemical was mea-
sured, the site was categorized as Tier 3 unless otherwise
categorized by another parameter.
For other chemicals, a tissue residue level exceed-
ing an FDA tolerance/action or guidance level or EPA
risk level, and a sediment chemistry TBP value exceed-
ing that level for the same chemical were required to clas-
sify a sampling station as Tier 1. If tissue residue levels
exceeded FDA levels or EPA risk levels but correspond-
ing TBP values were not exceeded at the same station
(or there were no sediment chemistry data from that sta-
tion), the sampling location was categorized 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 site was categorized as Tier 3 unless otherwise cat-
egorized by another parameter.
Toxicity Data [11, 12]
Toxicity data were used to classify sediment sites
based on their demonstrated adverse effects to aquatic life.
Acute nonmicrobial sediment toxicity tests with a mortal-
ity endpoint were evaluated. Toxicity test results that
lacked control data, or with control data that indicated
greater than 20 percent mortality (less than 80 percent sur-
vival), were excluded from further consideration. A re-
view of the American Society for Testing and Materials'
protocols and other protocols for sediment toxicity tests
suggests that mortality in controls can range from 10 to 30
percent, depending on the species, to be considered an
acceptable test result (API, 1994). Current amphipod test
requirements indicate that controls should have less than
10 percent mortality (API, 1994; USEPA, 1994b).
For the NSI data evaluation, EPA considers signifi-
cant toxicity as a 20 percent difference in survival from
control survival. For example, significant toxicity oc-
curs if control survival was 80 percent and experimental
survival was 60 percent or less.
For this evaluation parameter, corroboration of mul-
tiple tests was considered more indicative of a higher prob-
ability of adverse effects than the magnitude of the effect
in a single test. Toxicity demonstrated by two or more
single-species tests using two different test species (at least
one of which had to be a solid-phase test) placed a site in
Tier 1. A sampling station was categorized as Tier 2 if
toxicity was demonstrated by one single-species nonmi-
crobial toxicity test. If toxicity was not demonstrated by
a nonmicrobial toxicity test, or if toxicity test data were
not available, the site was categorized as Tier 3 unless
otherwise categorized by another parameter.
Incorporation of Regional Comments
on the Preliminary Evaluation of
Sediment Chemistry Data
Several reviewers from different EPA Regions and
states provided comments on the May 16,1994, prelimi-
nary evaluation of sediment chemistry data. These com-
2-14
-------
Draft .\;i(i(»n:il SfdifiH'iil Qiiiility .Siirvt-v
merits included more than 150 specific comments identi-
fying additional sites with contaminated sediment that had
not been identified in the preliminary evaluation. Since
the preliminary evaluation, the final NSI methodology
has been developed and implemented. The updated meth-
odology has been refined significantly to include tissue
residue and toxicity data as well as revised screening val-
ues. Data corresponding to any additional comments that
required further review were divided into two categories:
(1) data that reviewers stated represented false positives
and (2) data for additional water bodies that reviewers
stated represented areas of potentially higher levels of con-
tamination.
The false positives noted by the reviewers were iden-
tified in the NSI database by water body and chemical
listed in the comment. If stations on the specified water
bodies were categorized in the NSI evaluation as Tier 1
based only on the specific chemical(s) identified by the
reviewer, those stations were removed from Tier 1 and
placed in Tier 3. Stations that were identified in the NSI
evaluation as Tier 1 from other chemicals not identified
by the reviewer or because of toxicity data were not re-
moved from Tier 1.
Data for additional water bodies that reviewers iden-
tified as potential areas of significant contamination were
evaluated to determine whether stations along those wa-
ter bodies had already been categorized as Tier 1 based
on the NSI data evaluation Those stations which were
identified by the Regions as potential areas of signifi-
cant contamination but had not been categorized as Tier
1 as a result of the NSI evaluation are discussed sepa-
rately 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 as-
sessment of NSI data based on a comparison of sediment
chemistry TBP values and fish tissue values to EPA wild-
life criteria. This evaluation, however, was not included
with the results of evaluating the NSI data based on the
other parameters. The results of evaluating NSI data based
on wildlife criteria are presented in a separate section of
Chapter 3. Wildlife criteria based on fish tissue concentra-
tions were derived using methods similar to those employed
for deriving EPA wildlife criteria, as presented in the pro-
posed Great Lakes Water Quality Initiative Criteria Docu-
ments for the Protection of Wildlife (USEPA, 1995a). EPA
has proposed wildlife criteria for four contaminants: DDT,
mercury, 2,3,7,8-TCDD, and PCBs. The method used to
develop wildlife criteria for the NSI data evaluation is ex-
plained in detail in Appendix B.
2-15
-------
Chapter 3
Findings
Dnif'l Niiljon.il Sediment Qinility Survey
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
nationwide as part of the NSI data evaluation (Figure 3-
1). Of the sampling stations evaluated, 5,521 stations (26
percent) were categorized as having a higher probability
of adverse effects (Tier 1), 10,401 (49 percent) were cat-
egorized as having an intermediate probability of adverse
effects (Tier 2), and 5,174 (25 percent) were categorized
as having no indication of adverse effects (Tier 3). (See
Table 3-1.) The national distribution of Tier 1 stations is
illustrated in Figure 3-2. The distribution of Tier 1 sta-
tions depicted in Figure 3-2 must be viewed in the con-
text of the distribution of all sampling stations depicted
in Figure 3-1. Table 3-1 presents the number of stations
in each tier by EPA Region. The greater number of Tier 1
and Tier 2 stations in some Regions is to some degree a
function of a larger set of available data. Although there
are 17 times more Tier 1 stations in EPA Region 4 (south-
eastern states) than in EPA Region 8 (mountain states),
there are also 13 times more Tier 3 stations. The large
percentages of stations in Tier 1 and Tier 2 in all Regions
most likely reflect that sampling often takes place at loca-
tions where contamination problems are known or sus-
pected.
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 et
al., 1990). A river reach can be part of a coastal shore-
line, a lake, or a length of stream between two major
tributaries ranging from approximately 1 to 10 miles long.
NSI sampling stations were located in approximately 11
percent of all river reaches identified in the contiguous
United States (Table 3-1 and Figure 3-3). Thirty-five
percent of all river reaches evaluated in the United States
had at least one station categorized as Tier 1. Forty-two
percent of all reaches evaluated had at least one station
categorized as Tier 2 (but none as Tier 1). In 23 percent
of the reaches evaluated, all of the stations were catego-
rized as Tier 3. EPA has not yet catalogued river reaches
outside the contiguous United States (e.g., Alaska, Ha-
waii, Puerto Rico), and some sampling stations in the
ocean were not linked to a specific reach. Again, the
large percentages of river reaches with at least one Tier 1
or Tier 2 station probably reflect monitoring strategies
targeted toward contaminated areas. In most cases, the
smaller the percentage of reaches with available data, the
greater the percentage of river reaches with at least one
Tier 1 or Tier 2 station.
Not all sampling programs target only sites of known
or suspected contamination. The NSI includes the Na-
tional 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 (EMAP). These
are examples of sampling programs that do not target
sites of known or suspected contamination. Based on
these data alone, the percentage of stations placed in each
tier differs considerably from the percentage of stations
in each tier based on an evaluation of all the data in the
NSI. Smaller percentages of COSED and EMAP sta-
tions are categorized as Tier 1 (18 percent for COSED
and 14 percent for EMAP compared to 26 percent for all
NSI stations), greater percentages are categorized as Tier
2 (75 percent for COSED and 68 percent for EMAP com-
pared to 49 percent for all NSI stations), and smaller per-
centages are categorized as Tier 3 (7 percent for COSED
and 18 percent for EMAP compared to 25 percent for all
NSI stations). This may reflect the lower detection lim-
its of more sensitive analytical chemistry techniques, the
sensitivity of Tier 2 evaluation parameters, and the nearly
ubiquitous presence of intermediate levels of contami-
nation in areas sampled by these programs.
Data describing environmental concentrations of
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 some location at levels that indicated risks to
human health or the environment (Tier 1 or Tier 2). Table
3-2 presents the chemicals or chemical groups that re-
sulted in more than 1,000 stations being classified as Tier
3-1
-------
-4k
7
?v
>.
. •>-
w>
':
V •
v^
t
¥
5 *
- ••*
^•»" A.
Alaska Hawaii
-
•«•
• •*•* * jfcr . '• js^ur** ** «
;-M^-^S^Bil^
\^ V * *<• * ^ * .. ..*,A^** * *-!-/,_ tjf^** *-I****"*!« *^ !**^* * >**v- *
**. —* •** •*•*:! *X ^*-**^».*r * * ^ **r*"***JT^^ V * f .%'rfr^*ti_**i?
• Station
Total #: 21,096
Figure 3-1. Location of All NSI Sampling Stations.
-------
Table 3-1. National Assessment: Number of NSI Sampling Stations in Each Probability of Adverse Effects Category and Number of River Reaches
Where NSI Sampling Stations Are Located
State
Region 1
Region!
Region 3
Region 4
Regions
Regic«6
Region 7
Regions
Region 9
Region 10
Total for US.'
Station Evaluation
Tierl
#
298
355
318
1,157
1,418
382
330
68
468
727
5,521
V."
27
32
17
23
33
24
33
13
28
25
26
Tier 2
#
646
559
934
1,930
2,137
837
393
327
942
1,696
10,401
VI-
59
51
49
39
50
52
39
61
55
59
49
Tier 3
#
158
182
65S
1,872
735
397
288
140
289
455
5,174
W
14
17
34
38
17
24
28
26
17
16
25
River Reach Evaluation"
Timber of
Stations
Not
Iflpf^jilflq
byanRFl
Readf
361
173
92
343
108
124
t*A
hTA
794
497
2,492
Readies
Wat Least
1 Station
inlierl
59
116
209
566
594
266
246
61
119
147
2^71
teaches
Wet Least
1 Station
in Tier 2-
65
147
453
684
570
341
182
153
92
174
2,843
Readies
wrtll
Stations
in Her 3
7
29
226
520
268
192
88
91
43
72
1,530
Total #
Beaches
wtat Least
1 Station
Evaluated
131
292
8SS
1,770
1,432
799
516
305
254
393
6,744
Total
Readies
in Region
2,648
1,753
3,247
9,749
6,025
7,293
4,857
13,492
4,601
10,178
62,742
•/•ufall
Readies
in Region
wfat Least
1 Station
Evaluated
5
17
27
18
24
11
11
2
6
4
11
%of
Readies
Evaluated
n/at Least
1 Tierl
or Tier 2
Station
95
90
75
71
81
76
83
70
83
82
77
•River reaches based on EPA River Reach 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 inTier 1.
'Because some reaches occur in more than one Region, the total number of reaches in each cateogiy for the country might not equal the sum of reaches in the Regions.
-------
. •*• <
•
*••
•
Alaska
Hawaii
Puerto Rico
• Station
Total #: 5,521
Figure 3-2. NSI Stations Categorized as Having a Higher Probability of Adverse Effects (Tier 1).
-------
Reach File 1 River Reaches
in the Nation
(approx. 63,000)
Reach File 1 River Reaches
Evaluated
(6,744)
No Data Available
for Evaluation
89%
At Least One
Tier 1 Station
35%
River Reaches
Evaluated
11%
All Tier 3 Stations
23%
At Lent One Tier 2 Station
and Zero Tier 1 Stations
42%
Figure 3-3. National Assessment: Percent of River Reaches That Include Tier 1, Tier 2, and Tier 3 Stations.
-------
Table 3-2. Chemicals or Chemical Groups Most Often Responsible for
Sampling Stations Being Categorized as Tier 1 or Tier 2
Chemical or
Chemical Group
Copper
Nickel
Lead
PolycWori nated biphenyls
Arsenic
Cadmium
Mercury
Zinc
DDT (and metabolites)
Chromium
Diekirin
CNord&ne
Bsnzofajpyrene
Pyrene
Chtysene
Dibenza{a,ti)ajithr3cene
Benzo(a)anthracene
Bis(2-ethythexyl)phtha!aie
Naphloalene
Fltorart there
Fliforene
Silver
Total for all chemicals in
the NSI database
Number of Stations
Total # of
Stations
Evaluated
16,161
12,447
16,791
12,276
13,200
16,010
15,649
15.150
1 1.462
15,222
10,284
)£>,&97
5,435
5,798
5,300
4,896
5.120
3.559
5.246
5.SI4
5.175
8,022
21,096
Based on All Measurement
Parameters
Combined
Tien 1 & 2
7,172
6,284
5,681
5,454
5,392
4,808
4,333
3,468
3,422
3,070
2497
ZJ69
1,993
1,920
1,427
1.383
U66
1,190
use
1.114
1,107
1,096
15,922
Tierl
-
-
-
3,175
182
-
1,122
-
803
278
58
1)
287
431
166
337
214
347
254
210
201
302
5,521
Tier 2
7.172
6.284
5,681
2,279
5,210
4,608
3,211
3,468
2,6!9
2,792
2,539
2,158
1,706
1,489
1,261
1,046
1,152
843
932
904
906
794
10,401
Based on Aquatic
Life Parameters
Tlerl
-
-
-
963
182
-
1,122
-
7S>8
278
49
-
287
431
I6«
337
214
347
25-V
210
20|
302
3.287
Tierl
7,167
6,284
5,4 £5
1,219
4,658
4,773
3,127
3.451
2,203
2,786
1,006
1,303
1,051
1,489
1,261
1,018
1,106
S23
932
904
905
794
9,921
Based on Human
Health
Parameters
Tltrl
-
-
2,256
-
21
-
9
1]
-
-
-
-
-
-
-
-
2,327
Tier 2
5
-
328
3,198
605
41
103
17
1,402
7
2,456
1,697
1,990
10
30
1,092
847
406
5
11
5
-
6,1%
1 or Tier 2. Table 3-2 also separately identifies the num-
ber of 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 included:
• Comparison of sediment chemistry measure-
(SQCs).
• Comparison of sediment chemistry measure-
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 pa-
rameters included:
• Comparison of sediment
chemistry TBP to EPA risk lev-
els or FDA tolerance/action or
guideline levels.
• Comparison offish tissue lev-
els of PCBs anddioxin to EPA risk
levels. (A station can be classified
as Tier 1 without corroborating
sediment chemistry data.)
• Comparison of fish tissue lev-
els to EPA risk levels and FDA tol-
erance/action or guideline levels.
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. Of the 5,521
stations identified as Tier 1,3,175
stations were placed in Tier 1 be-
cause of elevated levels of PCBs,
1,122 were placed in Tier 1 be-
cause of elevated levels of mer-
cury, and 803 were placed in Tier
1 because of elevated levels of
DDT. Metals other than mercury,
silver, and arsenic are notably ab-
sent from Tier 1 in Table 3-2 since
the method for evaluating cad-
mium, copper, nicJceJ, Jead, and
zinc required the simultaneous
measurement of AVS, which was rarely available. The
[SEMHAVS] methodology for sediment assessment is
relatively new, and AVS measurements have not com-
monly been made during sediment analyses. It should
be noted that the total number of stations classified as
Tier 1 or Tier 2 for a given chemical as presented in
posed by that chemical. This is because, although there
may be few overall observations for some chemicals, the
frequency of detection in sediment and tissue and the
, --• —***»^M-»k7 »>*J1*I,L III J. IV-l 1 VI
Tier 2 risk may be high (see Appendix D, Table D-2 ).
Figure 3-4 illustrates the percent of sediment chemistry
and fish tissue residue measurements included in the NSI
that were above the detection limit for the chemicals
3-6
-------
Sediment and Fish Tissue
Measurements
(1,565,103)
Measurements Above
Detection Limit
(586.994)
Measurements Indicating
Potential Risk
(Tier 1 and Tier 2")
(142,004)
Not Detected
63%
No Way to Evaluate
35%
Other Organic*
3%
Tor most chemical groups, the percent of measurements in Her 1 and Tier 2 was similar to the percent of measurements in Tier 1 and Tier 2 for all classes of contaminants.
However, for PCBs, approximately 80 percent of the measurements were categorized as Tier 1 and for other metals, only 2 percent of the measurements were categorized as Tier 1.
Figure 3-4. National Assessment: Percent of NSI Measurements That Include Potential Risk.
-------
analyzed, the fraction of those measurements above de-
tection levels that indicate potential risk (Tier 1 or Tier 2),
and the distribution of measurements indicating risk by
chemical class.
More stations exhibit potential aquatic life effects than
potential human health effects. About 40 percent more
stations were categorized as Tier 1 for potential aquatic
life effects (3,287 stations) than for potential human health
effects (2,327 stations). Furthermore, about 60 percent
more stations were categorized as Tier 2 for potential
aquatic life effects (9,921 stations) than were categorized
as Tier 2 for potential human health effects (6,196 sta-
tions). The locations of stations categorized as Tier 1 or
Tier 2 for potential aquatic life effects are illustrated in
Figure 3-5, and the locations of those categorized as Tier
1 or Tier 2 for human health effects are illustrated in Fig-
ure 3-6.
EPA also analyzed the results to determine which
evaluation parameters were most sensitive; that is, which
parameters most often caused stations to be categorized
as either Tier 1 or Tier 2 (see Table 3-3). Most of the
stations categorized as Tier 1 (3,283 stations) or Tier 2
(9,882 stations) were placed in those categories because
measured sediment contaminant levels exceeded screen-
ing values. The comparison of fish tissue levels of PCBs
and dioxins to EPA risk levels triggered placement of the
second highest number of stations in Tier 1 (2,313 sta-
tions). The comparison of sediment chemistry TBP val-
ues to FDA action levels and EPA risk levels triggered
placement of the second highest number of stations in Tier
2 (5,671 stations). The AVS and toxicity parameters trig-
gered placement of the fewest stations in Tier 1 (8 sta-
tions each) and Tier 2 (146 stations for AVS and 183
stations for toxicity). These results reflect both data avail-
ability and evaluation parameter sensitivity.
For a number of reasons, known contaminated sedi-
ment sites in the United States might not have been cat-
egorized as Tier 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 sites were not evaluated for this
first report to Congress. However, additional databases
will be added to the NSI and more sampling stations 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
Lakes Sediment Quality Database). To reduce the
chances of overlooking sampling sites that have obvi-
ous sediment contamination problems, EPA sent a pre-
liminary evaluation of sediment chemistry data to each
EPA Region so knowledgeable staff would have an op-
portunity to list additional contaminated sediment sites
not identified in the NSI evaluation. The locations of
these sites are presented at the end of this chapter. De-
spite these efforts, some sediment sampling sites
known to have contamination problems still have not
been listed in this first report to Congress.
Watershed Analysis
Although sampling stations are an important unit of
assessment, the most significant sediment contamination
problems exist where multiple contaminated locations are
in close proximity or are distributed throughout a dis-
crete hydrologic unit. A single "hot spot" may not affect
a benthic community or accumulation of contaminants
in resident fish tissue to a great extent. However, wide-
spread contamination is more likely to adversely affect
benthic communities and lead to a greater extent of con-
taminant accumulation in resident fish.
The NSI data evaluation identified 96 watersheds
throughout the United States as areas of potential wide-
spread sediment contamination (APCs) (Figure 3-7).
APCs are watersheds that contain 10 or more Tier 1 sta-
tions and in which at least 75 percent of all stations have
been categorized as Tier 1 or Tier 2. These dual criteria
are based on empirical observation of the data. Expert
reviewers indicated that this was a reasonable approach
to obtaining a qualitative ranking. These watersheds con-
stitute 5 percent of all watersheds in the country (Figure
3-8), The watershed analysis also indicated that 39 per-
cent of all watersheds in the country contain at least one
Tier 1 station, 15 percent contain at least one Tier 2 sta-
tion but no Tier 1 stations, and 6 percent contain all Tier
3 stations. Thirty-five percent of all watersheds in the
country could not be evaluated because of a lack of data.
Contaminated areas may be concentrated in specific
river reaches in a watershed. Within the 96 APCs across
the country, 57 individual river reaches or water body
segments have 10 or more Tier 1 stations (Table 3-4).
These are localized areas within the watershed for which
an abundance of evidence indicates potentially severe
contamination. Because EPA's Reach File 1 was used to
index the location of NSI sampling stations, some sta-
3-8
-------
V
a- -'
Alaska
Hawaii
!?*
i
,u
.,-+&<-& A.
$*'•'••*' -r.£;W
&& ••«:'
-**y&* % »f^t4
•.-* i£*Z
*y$
i
. « " »*VJJ i-.-^ - - .«ff
1^ ', « -'
.X;.^,*^,^ . •••S'J'vK* "* '* * ' • * «*V*^* " "S"
' I.
Puerto Rico
• Station
Total #: 13^08
Figure 3-5. Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects.
-------
V
«" •'
A 5
-,• --
'
* »
.
Alaska
Hawaii
Puerto Rico
• Station
Total #: 8,523
Figure 3-6. Stations Categorized as Tier 1 or Tier 2 Due to Potential Human Health Effects.
-------
Drsif'l National Sediment Quality Survey
Table 3-3. Number of Stations Placed in Tier 1 and Tier 2 Based on Each Component of the Evaluation
Approach (see Table 2-2)
Measurement Parameter
Sediment chemistry values exceed draft sediment quality criteria
[SEM]-[AVS] comparison
Sediment chemistry values exceed screening values
Sediment chemistry TBP and fish tissue levels exceed risk levels or action levels
Sediment chemistry TBP exceeds risk levels or action levels
Fish tissue levels exceed risk levels or action levels
Tissue levels of PCBs or dfoxins exceed risk levels
Toxicity parameters
Number of
Stations in
Tiwi
97
8
3.283
126
NA
NA
2,3U
8
Number of
Stations In
Tier 2
NA
146
9,882
NA
5,671
2,789
NA
183
tions might not actually occur on the identified Reach
File 1 stream, but on a smaller stream that is hydrologi-
cally linked or is relatively close to the Reach File 1
stream.
Wildlife Assessment
As mentioned in Chapter 2, EPA conducted a sepa-
rate analysis of the NSI data to determine the number of
stations where chemical concentrations of DDT, mercury,
dioxin, and PCBs exceeded levels predicted to be protec-
tive of wildlife (i.e., EPA wildlife criteria). The wildlife
criteria used in this evaluation were derived using meth-
ods similar to those presented in the proposed Great Lakes
Water Quality Initiative Criteria Documents for the Pro-
tection of Wildlife (USEPA, 1995a). The only assumed
route of exposure of exposure for this evaluation was the
consumption of contaminated fish tissue by wildlife.
Data were available to evaluate a total of 7,796 NSI
stations using the wildlife criteria. Table 3-5 presents a
comparison of the stations categorized as Tier 1 or Tier 2
with and without the use of wildlife criteria. If wildlife
criteria had been used to complete the national assess-
ment, 619 stations classified as Tier 3 would have been
classified as Tier 2 and 16 stations classified as Tier 2
would have been classified.as Tier 1. Most of the change
in station classification is due to the increase in Tier 2
stations classified for DDT (from 2,619 to 4,276) and
mercury (from 3,211 to 5,199). Figure 3-9 presents all
16,541 stations classified as Tier 1 and Tier 2 when wild-
life criteria are used for the assessment.
Use of wildlife criteria resulted in the different clas-
sification for two reasons: (1) the wildlife criteria for DDT
and mercury are significantly lower (8 and 19 times lower,
respectively) than the EPA risk levels used in the corre-
sponding human health evaluations; (2) the lipid content
used in the wildlife TBP analysis {10.31 percent for whole
body) exceeded the lipid content used in the human health
TBP analysis (3.0 percent for fillet).
No additional stations were categorized as Tier 1 for
mercury or dioxins as a result of using wildlife criteria.
For a station to be categorized as Tier 1, both sediment
chemistry TBP and measured fish tissue concentrations
taken from that station had to exceed the wildlife criteria.
At very few stations in the NSI were both sediment chem-
istry and fish tissue levels for dioxin measured. In those
few cases where contaminants in both media were mea-
sured, there were no additional stations (stations not al-
ready classified as Tier 1) where both the sediment
chemistry TBP and fish tissue levels exceeded the wild-
life dioxin criteria. No additional stations were classified
as Tier 1 due to exceedance of the wildlife criteria for
mercury since sediment chemistry TBPs cannot be calcu-
lated for metals.
Regional and State Assessment
The remainder of this chapter presents more detailed
results from the evaluation of NSI data for stations lo-
cated in each of the EPA Regions and each state. The
3-11
-------
Figure 3-7. Areas of Potential Widespread Sediment Contamination (APCs).
-------
Draft National S«lim< m Oii.ili(\ SIIIMX
At Least One
Tier 1 Station
39%
At Least One Tier 2 Station
and Zero Tier 1 Stations
15%
All Tier 3 Stations
6%
APCs
5%
No Data
35%
Figure 3-8. National Assessment: Watershed Classification.
sections that follow present the number of sampling sta-
tions in each Region and state that have been categorized
as Tier 1, Tier 2, and Tier 3 and the chemicals that were
most often responsible for Tier 1 and Tier 2 classifica-
tions. Tables and figures similar to those presented in the
national assessment of station evaluation results and river
reach evaluation results are included. For each Region,
maps that identify the location of sampling stations cat-
egorized as Tier 1 and Tier 2 are provided. Areas of po-
tential widespread sediment contamination in each Region
have also been identified. The presentation format is iden-
tical for each Region.
3-13
-------
Table 3-4. River Reaches Located in Areas of Potential Widespread Sediment Contamination That Have
10 or More Tier 1 Sampling Stations
EPA Region
1
1
2
2
2
2
2
4
4
4
4
4
4
5
5
5
5
5
5
5
Cataloging Unit
Number
01090001
01090004
02030103
02030104
04120103
04120104
04130001
03060106
03080103
06010201
06010207
06020001
06030005
04030108
04030204
04040001
04040002
04040003
04090004
04100002
Cataloging Unit Name
Charles
Narragansett
Hackensack-Passaic
Sandy Hook-Staten Island
Buffalo-Eighteenmile
Niagara
Oak Ochaid-Twelvemile
Middle Savannah
Lower St. Johns
Watts Bar Lake
Lower Clinch
Middle
Tennessee-Chickamauga
Pickwick Lake
Menominee
Lower Fox
Little Calumet-Galien
Pike-Root
Milwaukee
Detroit
Raisin
RF1 Reach ID
01090001022
01090001015
01090001013
01090001024
01090004023
02030103023
02030104003
04120103007
04120103001
04120104007
04130001001
03060106047
03080103017
06010201026
06010201035
06010207022
06010207021
06010207003
06020001003
06030005046
04030108001
04030204001
04030204010
04030204004
04040001010
04040001006
04040002002
04040003001
04090004006
04090004014
04090004011
04090004004
04100002001
RF1 Reach Name
Boston Bay
Boston Bay
Atlantic Ocean
Boston Bay
Seekonk River
Rockaway River
Arthur Kill
Buffalo Creek
Lake Erie, U.S. Shore
Niagara River
Lake Ontario, U.S. Shore
Horse Creek
St. Johns River
Little River
Tennessee River
Poplar Creek
Poplar Creek, Brushy Fork
Clinch River
Lookout Creek
Wilson Lake
Menominee River
Fox River
Fox River
Fox River
Indiana Harbor
Calumet River
Lake Michigan
Milwaukee River
Detroit River
River Rouge
Detroit River
Detroit River
River Raisin
Number of Tier
1 Stations
72
42
37
16
16
26
10
26
17
12
14
10
10
15
10
19
17
16
29
22
10
13
12
10
15
12
15
48
27
12
II
10
16
Total Number of
Stations in
Reach
146
149
58
45
17
56
10
42
22
20
27
11
27
23
12
25
23
20
41
25
12
13
13
10
15
20
33
64
38
12
11
12
32
3-14
-------
Draff National Sediment Qinility Survey
Table 3-4. (Continued)
EPA Region
5
5
5
6
6
6
9
9
9
9
9
9
9
10
10
10
Cataloging Unit
Number
07010206
07120003
07120004
08040207
08080206
08090100
18030012
18050004
18070104
18070105
18070201
18070204
18070304
17110002
17H0013
171IOOI9
Cataloging Unit Name
Twin Cities
Chicago
Des Plainer
Lower Quachita
Lower Calcasieu
Lower Mississippi-New
Orleans
Tulare-Buena Vista Lakes
San Francisco Bay
Santa Monica Bay
Los Angeles
Seal Beach
Newport Bay
San Diego
Strait Of Georgia
Duwamish
Puget Sound
RF1 Reach ID
07010206001
07120003001
07120003006
07120004011
08040207005
08080206033
08080206034
08090100004
18030012014
18050004001
18070104003
1807010500!
18070201001
18070204002
18070304014
17110002019
17110013003
17110019086
17110019085
17110019068
17110019084
17110019087
17110019020
17 11 001 9022
RF1 Reach Name
Mississippi River
Chicago Sanitary Ship
Canal
Little Calumet River
Des Plains River
Bayou De Siard
Calcasieu River
Bayou D'lnde
Mississippi River
Kings River
San Francisco Bay
Pacific Ocean
Los Angeles River
Pacific Ocean
San Diego Creek
San Diego Bay
Bellingharn Bay
Elliot Bay
Pugel Sound
Puget Sound
Budd Inlet
Puget Sound
Puget Sound
Bainbridge Island
Sinclair Inlet
Number of Tier
1 Stations
10
35
13
11
11
13
11
13
10
11
20
12
18
11
30
13
41
119
105
41
32
32
31
25
Total Number or
Stations in Reach
15
36
42
20
11
40
30
23
12
27
37
31
47
22
46
26
100
232
264
112
57
164
88
44
Table 3-5. Increased Number of Sampling Stations Identified as Tier 1 and Tier 2 if Wildlife Criteria Are
Used in the National Assessment"
Chemical or Chemical
Group
DDT (and metabolites)
Dioxin
Mercury
PCBs
All Data
Number of Stations Excluding
Wildlife Assessment
Tierl
803
311
1,122
3,175
5,521
Tier 2
2,619
33
3,211
2,279
10,401
Number of Stations Including
Wildlife Assessment
Tierl
868
311
1,122
3,181
5,537
Tier 2
4,276
60
5,199
2,289
11,004
Tte wildlife assessment used a default lipid content of 10.3 1 percent to compute me segment cnemistry i BK
3-15
-------
' f
V ™»*
4 -*
•tl
V
'
i-
Alaska
Hawaii
-
#
: * „; ..,^-.--
. * *1**3 **•.'! •*
;:-vS^:-
*'*
*"* •'•*»
s- -*:/ 5-
•:A-'jL-~*£
> ~i
-. ; c-i -/-i:..x^aft--- ^^,- ^.m
^-:M-:i:¥sm-, Wi^mi
ii-.-t* - - - - A** >*_r*~*' ^Jr.VSaivt-.*
* * ~.fr+ .** *-**5 -*? <^T ** *" J*» * »*" *^V"->:"* 2
• !-£SS fef frfQI^Ii
•;'.-5.. >vi; • j. -t ^>t. .£*. *^*jy
: -•,-..'««••.>•"*«•« -,i*.«^
. - • .. .•:.v-...>* * »• * •• * "vr
.•« :.-;^.-%Vr^. *••;•* - • •• - .,.
- * '-
<.-
Puerto Rico
• Station
Total #: 16,541
Figure 3-9. Stations Categorized as Tier 1 or Tier 2 if Wildlife Criteria are Used to Complete the Analysis.
-------
Drutt Niitioiml Sediment Ojiulit> Survey
EPA Region 1
EPA evaluated 1,102 sampling stations in Region 1
as part of the NSI evaluation. This evaluation resulted
in 298 stations (27 percent) in Region 1 being catego-
rized as Tier 1, 646 (59 percent) as Tier 2, and 158 (14
percent) as Tier 3 (Table 3-6). Figure 3-10 identifies the
stations in Region 1 that were categorized as Tier 1 or
Tier 2 due to potential aquatic life effects and those cat-
egorized as Tier 1 or Tier 2 due to potential human health
effects.
The NSI sampling stations in Region 1 were located
in 131 separate river reaches, or 5 percent of all reaches
in the Region (Table 3-6). Of all river reaches evaluated
in Region 1, 45 percent included at least one Tier 1 sta-
tion, 50 percent included at least one Tier 2 station but no
Tier 1 stations, and 5 percent had only Tier 3 stations (Fig-
ure 3-11).
Out of a total of 61 watersheds located in Region 1,
3 (5 percent) were identified as APCs (Figure 3-12). In
addition, 39 percent of all watersheds in the Region had
at least one Tier 1 station hut were not categorized as
APCs, 11 percent had at least one Tier 2 station but no
Tier 1 stations, and less than 2 percent had only Tier 3
stations. Forty-three percent of the watersheds in Region
1 could not be evaluated because of a lack of data. The
locations of the APCs and the Tier 1 and Tier 2 stations in
Region 1 are illustrated in Figure 3-13.
Within the 3 watersheds in Region 1 identified as
APCs (Table 3-7), 14 water bodies have at least 1 sam-
pling station that has been categorized as Tier 1; 3 water
bodies have 10 or more sampling stations categorized as
Tier 1 (Table 3-8). The Massachusetts Bay area appears
to have the most significant potential widespread sedi-
ment contamination problem in Region 1. The waterbod-
ies listed on Table 3-8 are not inclusive of all locations
containing a Tier 1 or Tier 2 station because only water-
bodies within APC watersheds are listed. These sum-
mary results are also not inclusive of locations with
contaminated sediment not identified in this survey. The
data compiled for the NSI are primarily from large na-
tional electronic databases. Data from many sampling
and testing studies have not yet been incorporated into
the NSI. Thus, there may be additional locations with
sediment contamination that do not appear in this sum-
mary. On the other hand, data in the inventory were col-
lected between 1980 and 1993 and any single
measurement of a chemical at a site, taken any point in
time during that period, could result in the categorization
of the site in Tier 1 or Tier 2. The evaluation approach is
conservative. Therefore sites appearing in Tier 1 or Tier
2 may not cause unacceptable impacts. It is also impor-
tant to note that management programs may already exist
to address identified sediment contamination problems.
The chemicals most often responsible for stations
being categorized as Tier 1 or Tier 2 in Region 1 overall
and in each state in Region 1 are presented in Table 3-9.
3-17
-------
Table 3-6. Region 1: Number of NSI Sampling Stations in Each Probability of Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations Are Located
State
Connecticut
Maine
Massachusetts
New Hampshire
Rhode Island
Vermont
REGION Id
Station Evaluation
Tierl
#
20
13
242
4
16
3
298
%
20
24
27
57
38
60
27
Tler2
#
67
37
516
1
24
1
646
%
68
67
58
14
57
20
59
Tier 3
*
11
5
137
2
2
1
158
%
11
9
15
29
5
20
14
River Reach Evaluation*
Number of
Stations
Not
Identified
byanRFl
Reach6
8
28
316
-
9
-
361
Reaches
w/at Least
1 Station
In Tierl
16
9
25
2
6
3
59
Reaches
w/at Least
1 Station
In Tier 2C
24
7
27
-
7
-
65
Reaches
w/all
Stations
In Tier 3
4
2
-
2
-
-
7
Total #
Reaches
w/at Least
1 Station
Evaluated
44
18
52
4
13
3
131
Total
Reaches
instate
215
1,583
270
279
56
355
2,648
% of all
Reaches
Instate
w/at Least
1 Station
Evaluated
21
1
19
1
23
1
5
%of
Reaches
Evaluated
w/at Least
1 Tierl
or Tier 2
Station
91
89
100
-
100
-
95
•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.
-------
Figure 3-10. Region 1: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects (+) and Potential Human Health Effects (o).
-------
Reach File 1 River Reaches
in Region 1
(2,648)
Reach File 1 River Reaches
Evaluated
(131)
No Data AvatlaMa
for Evaluation
06%
All Tlar 3 Stations
5%
At Least One Tier 2 Station
and Zero T»r 1 Stations
50%
Figure 3-11. Region 1: Percent of River Reaches That Include Tier 1, Tier 2, and Tier 3 Stations.
At Least One
Tier 1 Station
39%
At Least One Tier 2 Station
and Zero Tier 1 Stations
11%
All Tier 3 Stations
2%
APCs
5%
No Data
43%
Figure 3-12. Region 1: Watershed Classifications.
3-20
-------
Figure 3-13. Region 1: Location of Sampling Stations Categorized as Tier 1 or Tier 2 and Watersheds Identified as Areas of Potential
Widespread Sediment Contamaination (APCs).
-------
Table 3-7. Region 1: Watersheds Identified as Areas of Potential Widespread Sediment Contamination
Cataloging
Unit Number
01090001
01090004
01090002
Name
Charles
Narragansett
Cape Cod
StateW
MA
MA, RI
MA,(RI)
Number of Stations
Tier]
195
28
15
Tier}
402
20
73
Tier 3
111
0
20
Percent of Stations
in Tier 1 or Tier 2
84
100
81
No data were available for states listed in parenthesis.
Table 3-8. Region 1: Water Bodies With Sampling Stations Categorized as Tier 1 That Are Located in
Areas of Potential Widespread Sediment Contamination
Water Body
Boston Bay
Atlantic Ocean
Seekonk River
Boston Hattaorand Mystic River Area
Buzzards Bay
Martha's Vineyard
Narragansett Bay
# of Tier 1
Stations
141
46
16
9
5
4
4
Water Body
Bats River
Potowomul River
Consnicut Island
Pawtuxet River
Acushnet River
Charles River
Taun ton River
# of Tier 1
Stations
3
3
2
2
1
1
I
3-22
-------
Table 3-9. Region 1: Chemicals Most Often Responsible for Stations Being Categorized as Tier 1 or Tier 2"
Region 1
Overall
Connecticut
Maine
Massachusetts
Chemical
Copper
Lead
Chromium
Nickel
Mercury
Arsenic
Zinc
Cadmium
Polychlorinated biphcnyls
Benzo(a)pvrene
DDT
Dibenn>(aji)anthracene
Benzo(a)anthracene
Pyre IK
Chrysene
Copper
Nickel
Lead
Cadmium
Zinc
Mercury
Chromium
Benzo(a)pyrene
Chrysene
Polychlorinated biphenyli
Arsenic
Polychlorinated biphenyls
Chromium
Nickel
Benzo(a)pyrene
Lead
DDT
Copper
Mercury
Dibenzo(a,h)aiUhracene
Lead
Copper
Mercury
ffTicrl
A Tier 2
Station
625
623
497
491
488
387
376
339
231
179
133
132
128
122
120
7T
55
49
45
40
39
32
28
24
23
3T
30
30
29
25
23
IfC
10
15
11
1 J
12
TTT
504
416
ITierl
Station
••
-•
59
--
176
14
-
-
74
5
17
13
8
7
2
-
-
-
-•
11
--
1
-
4
7
2
--
--
•-
1
162
1 Tier 2
Station
625
623
438
491
312
373
376
339
157
174
116
119
120
115
118
71
55
49
45
40
28
32
27
24
19
23
28
29
25
23
16
15
13
11
504
254
Massachusetts
(continued)
New
Hampshire
Rhode bland
Vermont
Chemical
Chromium
Nickel
Arsenic
Zinc
Cadmium
Polychlorinalcd biphenyls
Benzo(a)pyrene
DDT
Anthracene
Benzo(a)anthracene
Benzo(a)pyrene
Pnenanlhrcne
Acenaphthylene
Benio(b)fluonnlhene
Fluonmhcne
Chrysene
Acenaphthene
Lead
Copper
Nickel
Polychlorinated biphenyls
Benzo(i)pyrcne
Chromium
DDT
Arsenic
Beozo(a)anthracene
Dibenzo(a.h)anlhncene
Polychlorinated bipheayls
Dioxins
Aldrin
Arsenic
Cadmium
Copper
Dieldrin
Lead
Mercury
Nickel
#Tlerl
& Tier 2
Stations
411
377
317
314
278
149
98
4
3
3
3
3
3
3
3
2
2
35
32
28
25
25
23
23
22
21
20
3
1
1
1
1
1
1
1
'
1
ITIerl
Slattoni
53
-
14
-
-
54
2
3
2
2
2
2
-
-
-
1
-
••
-
-
5
-
3
3
—
"*
2
3
'
--
„
_.
"
_.
"
_.
#Ti«r2
Station
358
377
303
314
278
95
96
1
1
1
1
1
3
3
3
1
2
35
32
28
20
25
20
20
22
21
18
-
-
1
1
1
1
1
1
1
1
•Stations may be listed for more than one chemical.
3-23
-------
EPA Region 2
EPA evaluated 1,096 sampling stations in Region 2
as part of the NSI evaluation. This evaluation resulted
in 355 stations (32 percent) in Region 2 being catego-
rized as Tier 1, 559 (51 percent) as Tier 2, and 182 (17
percent) as Tier 3 (Table 3-10). Figure 3-14 identifies
the stations in Region 2 that were categorized as Tier 1
or Tier 2 due to potential aquatic life effects and those
categorized as Tier 1 or Tier 2 due to potential human
health effects.
The NSI sampling stations in Region 2 were located
in 292 separate river reaches, or 17 percent of all reaches
in the Region (Table 3-10). Of all river reaches evaluated
in Region 2, 40 percent included at least one Tier 1 sta-
tion, 50 percent included at least one Tier 2 station but no
Tier 1 stations, and 10 percent had only Tier 3 stations
(Figure 3-15).
Out of a total of 63 watersheds located in Region 2,
12 (19 percent) were identified as APCs (Figure 3-16).
In addition, 41 percent of all watersheds in the Region
had at least one Tier 1 station but were not categorized
as APCs, 30 percent had at least one Tier 2 station but no
Tier 1 stations, and none of the watersheds evaluated had
only Tier 3 stations. Less than 10 percent percent of the
watersheds in Region 2 could not be evaluated because
of a lack of data. The locations of the APCs and the Tier
1 and Tier 2 stations in Region 2 are illustrated in Figure
3-17.
Within the 12 watersheds in Region 2 identified as
APCs (Table 3-11), 52 water bodies have at least 1 sam-
pling station that has been categorized as Tier 1; 9 water
bodies have 10 or more sampling stations categorized as
Tier 1 (Table 3-12). Several areas in Region 2 appear to
have potential significant, widespread sediment contami-
nation problems. They include the Niagara River, Buf-
falo 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 waterbodies listed on Table 3-12 are not inclu-
sive of all locations containing a Tier 1 or Tier 2 station
because only waterbodies within APC watersheds are
listed. These summary results are also not inclusive of
locations with contaminated sediment not identified in this
survey. 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 may 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 a chemical at a site, taken any point in time
during that period, could result in the categorization of
the site in Tier 1 or Tier 2. The evaluation approach is
conservative. Therefore sites appearing in Tier 1 or Tier
2 may not cause unacceptable impacts. It is also impor-
tant to note that management programs may already exist
to address identified sediment contamination problems.
The chemicals most often responsible for stations
being categorized as Tier 1 or Tier 2 in Region 2 overall
and in each state in Region 2 are presented in Table 3-13.
3-24
-------
Table 3-10. Region 2: Number of NSI Sampling Stations in Each Probability of Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations Are Located
State
New Jersey
New York
Puerto Rico
REGION 2d
Station Evaluation
Tier!
#
142
208
5
355
%
32
34
17
32
Tier 2
#
228
310
21
559
%
51
50
70
51
Tier 3
#
78
100
4
182
%
17
16
13
17
River Reach Evaluation'
Number of
Stations
Not
Identified
byanRFl
Reach'
62
81
30
173
Reaches
w/at Least
1 Station
in Tier 1
59
58
-
U6
Reaches
w/at Least
1 Station
in Tier 2C
56
93
-
147
Reaches
w/afl
Stations
in Tier 3
14
15
-
29
Total#
Reaches
w/at Least
1 Station
Evaluated
129
166
-
292
Total
Reaches
in State
285
1,488
-
1,753
% ofaH
Reaches
instate
w/at Least
1 Station
Evaluated
45
11
-
17
% of
Reaches
Evaluated
w/at Least
ITierl
or Tier 2
Station
89
91
-
90
•River reaches based on EPA River Reach File 1 (RF1).
'Stations not identified by an RF1 teach 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.
u>
N>
-------
3
Figure 3-14. Region 2: Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effcects (+) and Potential Human Health Effects (o).
-------
Drsift National Sidiinciil Qiiiilily Survey
Reach File 1 River Reaches
in Region 2
(1,753)
Reach File 1 River Reaches
Evaluated
(292)
No Data Available
for Evaluation
83%
River Reaches
Evaluated
17%
All Tt«r 3 Station*
10%
At Least One Tier 2 Station
and Zero Tier 1 Station*
50%
Figure 3-15. Region 2: Percent of River Reaches That Include Tier 1, Tier 2, and Tier 3 Stations.
At Least One
Tier 1 Station
41%
APCs
19%
No Data
10%
At Least One Tier 2 Station
and Zero Tier 1 Stations
30%
Figure 3-16. Region 2: Watershed Classifications.
3-27
-------
:
x
Figure 3-17. Region 2: Location of Sampling Stations Categorized as Tier 1 or Tier 2 and Watersheds Identified as Areas of Potential
Widespread Sediment Contamination (APCs).
-------
Table 3-11. Region 2: Watersheds Identified as Areas of Potential Widespread Sediment Contamination
ClUloflnl
Unit Number
02030104
04120103
02030103
04130001
04120104
04120101
04150301
02040202
02030105
02030202
02040 105
02040301
Name
Sandy Hook-Staten bland
Buffalo-Eighteenmile
Hackensack-PaMaic
Oak Orchard-Twelvemile
Niagara
Chaulauqua-Conneaul
Upper St. Lawrence
Lower Delaware
Ran Ian
Southern Long Island
Middle Delaware-MuKonetcong
Mullica-Tomi
Slit*!)1
NY, NJ
NY
NY. NJ
NY
NY
NY, PA, OH
NY
PA. NJ
NJ
NY
PA, NJ
NJ
Number ofStllioni
Tier 1
60
59
43
39
24
21
21
18
13
11
11
10
Tier 2
21
33
58
46
16
86
5
2»
37
24
26
22
Tier 3
19
9
2
1
1
3
5
10
15
8
11
10
Percent of
Stilbni In Tier 1
or Tier 1
81
91
98
99
98
97
84
82
77
81
77
76
Table 3-12. Region 2: Water Bodies With Sampling Stations Categorized as Tier 1 That Are Located in
Areas of Potential Widespread Sediment Contamination
Water Body
Lake Ontario, U.S. Shore
Buffalo Creek
Rockaway River
Lake Erie. U.S. Shore
Arthur Kill
Suten bland
Smoke Creek
Hacketuack River
Manosquon River
Musconelcong River
Barnegal Bay
Eighleenmile Creek
Manalapan Bk.
Pomplon Creek
Rancocaj Creek, S. Br.
# of Tier t
Station!
31
30
26
24
22
21
21
10
10
1
8
6
6
6
5
4
3
3
2
2
2
2
2
2
2
2
Water Body
Shrewsbury River
Stony Bk.
Bats River
Beden Brook
Big Timber Creek
Cazenovia Creek
Cooper River
Cranbury Bk.
Great South Bay
Green Bk.
Mulliu River
Rancocai Creek, N. Br.
Rarilan Bay
Raritan River. N. Br.
Shinnecock Bay
South River
Toms River
Whippany River
# of Tier 1
Statloni
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3-29
-------
Table 3-13. Region 2: Chemicals Most Often Responsible for Stations Being Categorized as Tier 1 or Tier 2*
Region2
Overall
New Jersey
Chemical
Copper
Lead
Nickel
Polychlorinated biphenyls
Mercury
Cadmium
Zinc
DDT
Arsenic
Chromium
Qibrdane
Pyrene
Benzo(a)pyrene
Naphthalene
Huoranthene
DDT
Copper
Lead
Polychtorinaled biphenyls
Mercury
Anenic
Zinc
Chbrdane
#Tlcrl
&
Her 2
Slatloiw
546
467
443
442
388
360
358
351
282
247
229
214
180
155
151
195
192
191
181
158
151
143
139
#TUrl
Stations
-
„
151
144
-
-
114
6
26
-
64
36
30
41
48
-
-
43
70
6
-
-
» Tier 2
Stations
546
467
443
291
244
360
358
237
276
221
229
150
144
125
110
147
192
191
138
88
145
143
139
New Jersey
(continued)
New York
Puerto Rico
Chemical
Cadmium
Qiromkim
Copper
Nickel
Lead
For/chlorinated biphenyls
Cadmium
Mercury
Zinc
DDT
Pyrene
Chromium
Copper
Nickel
Arsenic
Lead
Mercury
Zinc
Silver
BU^ethytoexyOphlhalate
Diethylphthalate
Cadmiim
ffTterl
&
Tier 2
Stations
128
119
332
321
268
261
230
224
210
155
147
126
22
10
9
8
6
5
4
2
2
2
ftTierl
Stations
-
22
-
-
-
108
-
70
-
66
52
4
-
-
-
4
-
1
1
1
_
# Tier 2
Stations
128
97
332
321
268
153
230
154
210
89
95
122
22
10
9
8
2
5
3
1
,
2
3-30
-------
EPA Region 3
EPA evaluated 1,910 sampling stations in Region
3 as part of Ihe NSI evaluation This evaluation re-
sulted in 318 slalions {17 percenl) in Region 3 being
categorized as Tier 1, 934 (49 percent) as Tier 2, and
658 (34 percent) as Tier 3 (Table 3-14). Figure 3-18
identifies the stations in Region 3 that were catego-
rized as Tier 1 or Tier 2 due to potential aquatic life
effects and those categorized as Tier 1 or Tier 2 due to
potential human health effects.
The NSI sampling stations in Region 3 were lo-
cated in 888 separate river reaches, or 27 percent of
all reaches in the Region (Table 3-14). Of all river
reaches evaluated in Region 3, 24 percent included at
least one Tier 1 station, 51 percent included at least
one Tier 2 station but no Tier 1 stations, and 25 per-
cent had only Tier 3 stations (Figure 3-19).
Out of a total of 128 watersheds located in Region
3. 8 (6 percent) were identified as APCs (Figure 3-20).
In addition, 63 percent of all watersheds in the Region
had at least one Tier 1 station but were not categorized
as APCs, 22 percent had at least one Tier 2 station but no
Tier 1 stations, and 5 percent had only Tier 3 stations.
Four percent of the watersheds in Region 3 could not be
evaluated because of a lack of data. The locations of the
APCs and the Tier 1 and Tier 2 stations in Region 3 are
illustrated in Figure 3-21.
Within the 8 watersheds in Region 3 identified as
APCs (Table 3-15), 27 water bodies have at least 1
sampling station that has been categorized as Tier 1; 4
water bodies have 10 or more sampling stations cat-
egorized as Tier 1 (Table 3-16). The Delaware River;
Ihe Schuykill River in Pennsylvania (near Philadel-
phia); coastal areas of Lake Erie near Erie, Pennsyl-
vania; and the Ohio River near Pittsburgh appear to
have some of the most significant potential widespread
sediment contamination problems in Region 3. The
waterbodies listed on Table 3-16 are not inclusive of
all locations containing a Tier 1 or Tier 2 station be-
cause only waterbodies within APC watersheds are
listed. These summary results are also not inclusive
of locations with contaminated sediment not identi-
fied in this survey. The data compiled for the NSI are
primarily from large national electronic databases.
Data from many sampling and testing studies have not
yet been incorporated into the NSL Thus, there may
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 measurement of a chemical at a site,
taken any point in time during that period, could result in
the categorization of Ihe site in Tier 1 or Tier 2. The
evaluation approach is conservative. Therefore sites ap-
pearing in Tier 1 or Tier 2 may not cause unacceptable
impacts. It is also important to note that management
programs may already exist to address identified sedi-
ment contamination problems.
The chemicals most often responsible for stations
being categorized as Tier 1 or Tier 2 in Region 3 over-
all and in each state in Region 3 are presented in Table
3-17,
3-31
-------
Table 3-14. Region 3: Number of NSI Sampling Stations in Each Probability of Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations Are Located
State
Delaware
District of Columbia
Maryland
Pennsylvania
Virginia
West Virginia
REGION 3d
Station Evaluation
Tier 1
#
21
3
50
127
73
44
318
%
10
75
24
41
7
37
17
Her 2
#
35
1
68
106
691
33
934
%
16
25
33
34
66
27
49
Tier 3
#
162
-
88
78
287
43
658
%
74
-
43
25
27
36
34
River Reach Evaluation*
Stations
Not
Identified
by anRFl
Reach"
13
-
29
4
46
-
92
Reaches
w/at Least
1 Station
In Tier 1
10
3
31
78
61
30
209
Reaches
w/at Least
1 Station
In Tier 2C
7
-
36
27
362
23
453
Reaches
w/all
Stations
in Tier 3
22
-
30
34
112
31
226
Total #
Reaches
w/at Least
1 Station
Evaluated
39
3
97
139
535
84
888
Total
Reaches
in State
77
11
400
677
1279
993
3247
•A nf .»ii
Reaches
In State
w/at Least
1 Station
Evaluated
51
27
24
21
42
9
27
%of
Evaluated
w/at Least
1 Tierl
or Tier 2
Station
44
-
69
76
79
63
75
JRiver 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.
-------
Figure 3-18. Region 3: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects (+) and Potential Human Health Effects (o).
-------
Reach File 1 River Reaches
in Region 3
(3,247)
Reach File I River Reaches
Evaluated
(888)
No Data Available
for Evaluation
73%
At Least One Tier 2 Station
and Zero Tier 1 Station*
51%
All Tier 3 Station*
25%
Figure 3-19. Region 3: Percent of River Reaches That Include Tier 1, Tier 2 and Tier 3 Stations.
At Least One
Tier 1 Station
63%
APCs
6%
No Data
4%
All Tier 3 Stations
5%
At Least One Tier 2 Station
and Zero Tier 1 Stations
22%
Figure 3-20. Region 3: Watershed Classifications.
3-34
-------
-
J
Figure 3-21. Region 3: Location of Sampling Stations Categorized as Tier 1 of Tier 2 and Watersheds Identified as Areas of Potential
Widespread Sediment Contamination (APCs).
-------
Table 3-15. Region 3: Watersheds Identified as Areas of Potential Widespread Sediment Contamination
Cataloging
Unit Number
04120101
02040202
02060003
02040203
05030101
02040105
02070004
05030102
Name
Chautauqua-Conneaut
Lower Delaware
Gunpowder-Patapsco
Schuyllcill
Upper Ohio
Middle Delaware-Musconetcong
Conococheague-Opequon
Shenango
Sute(s)1
NY,PA,OH
PA.NJ
MD,(PA)
PA
WV.PA.OH
PA.NJ
WV.VAJUD,-
(PA)
OH.PA
Number of Stations
Tierl
21
18
17
12
12
11
11
11
Tier 2
86
29
7
23
29
26
12
1
TierS
3
10
5
9
12
11
6
3
No data were available for slates listed in parentheses.
Percent of Stations
In Tier 1 or Tier 2
97
82
83
80
77
77
79
80
Table 3-16. Region 3: Water Bodies With Sampling Stations Categorized as Tier 1 That Are Located in
Areas of Potential Widespread Sediment Contamination
Water Body
Delaware River
Lake Erie, U.S. Shore
Schuylkill River
Shenango River
Ohio River
Gunpowder Palls
Potomac River
Opequon Creek
Antietam Creek
Chartiers Creek
Conococheague Creek
Curtis Bay
Owynns Falls
Herring Run
# of Tier 1
Stations
13
10
10
10
- 7
4
4
3
2
2
2
2
2
2
Water Body
Patapsco River
Patapsco River, N. Br.
Raccoon Creek
Back River
Chesapeake Bay
Crum Creek
Darby Creek
Little Chartiers Creek
Little Gunpowder Falls
Neshannock Creek
Tulpehocken Creek
Walnut Creek
Wassahickon Creek
# of Tierl
Stations
2
2
2
1
1
1
1
1
1
1
1
1
1
3-36
-------
Table 3-17. Region 3: Chemicals Most Often Responsible for Stations Being Categorized as Tier 1 or Tier 2*
Region 3
Overall
Delaware
District of
Columbia
Maryland
Chemical
Nickel
Copper
Lead
Arsenic
Zinc
Polychlorinated biphenyls
Cadmium
Mercury
Chromium
Chlordane
DDT
Dieldrin
Benzo(a)pyrene
BHC
Dibenzo(a.h)anthracene
Polychlorinated biphenyls
DDT
Lead
Chromium
Arsenic
Nickel
BHC
Mercury
Benzo(a)pyrene
Copper
Polychlorinated biphenyls
Dioxins
Benzo(a)pyrene
Chlordane
Copper
Dieldrin
Nickel
Silver
Arsenic
Benzo(a)anthracene
Polychlorinaled biphenyls
Arsenic
Lead
# Tier 1
&
Tier 2
Stations
634
626
626
529
371
353
346
320
249
161
135
116
106
69
64
33~
27
24
19
18
15
13
12
12
8
2
2
2
2
2
2
1
1
1
70
68
#TI«rl
Stations
—
--
•-
1
-
243
-
42
12
9
-
6
2
4
3
2
--
--
-
3
-
2
••
-
-
--
-
1
•-
••
# Tier 2
Stations
634
626
626
528
371
110
346
278
237
161
126
116
100
67
60
24
24
' 17
18
15
13
9
12
8
—
2
2
2
2
2
--
1
1
70
68
Maryland
(continued)
Pennsylvania
Virginia
West Virginia
Chemical
Copper
Chromium
DDT
Chlordane
Zinc
Benzo(a)pyrene
Polychlorinated biphenyls
Lead
Chlordane
Nickel
Cadmium
Dieldrin
Copper
Zinc
DDT
Mercury
Copper
Nickel
Arsenic
Lead
Zinc
Mercury
Cadmium
Chromium
Polychlorinated biphenyls
Benzo(«)pyrene
Polychlorinated biphenyls
Lead
Chlordane
Dieldrin
Cadmium
Copper ,
Zinc
Heptachlor epoxide
Nickel
Aldrin
# Tier 1
&
Tier 2
Station
50
42
41
35
33
32
31
141
87
81
63
56
55
46
44
38
25
520
497
412
411
279
260
255
167
62
48
42
33
29
16
12
8
8
7
7
6
fTkrl
Stations
••
•-
4
--
-
-
-
112
-
--
-
~
~
-
-
6
3
•-
-•
-
-
-
34
•-
3
30
4
41
-
-
..
--
•-
-
•-
"•
-
# Tier 2
Stations
50
42
37
35
33
32
31
29
87
81
63
56
35
46
44
32
22
520
497
412
411
279
226
255
164
32
44
••
35
29
16
12
8
8
7
7
6
•Stations may be listed for more than one chemical.
3-37
-------
EPA Region 4
EPA evaluated 4,959 sampling stations in Region 4
as part of the NSI evaluation. This evaluation resulted in
1,157 stations (23 percent) in Region 4 being catego-
rized as Tier 1, 1,930 (39 percent) as Tier 2, and 1,872
(38 percent) as Tier 3 (Table 3-18). Figure 3-22 identi-
fies the stations in Region 4 that were categorized as Tier
1 or Tier 2 due to potential aquatic life effects and those
categorized as Tier 1 or Tier 2 due to potential human
health effects.
The NSI sampling stations in Region 4 were located
in 1,770 separate river reaches, or 18 percent of all reaches
in the Region (Table 3-18). Of all river reaches evalu-
ated in Region 4, 32 percent included at least one Tier 1
station, 39 percent included at least one Tier 2 station but
no Tier 1 stations, and 29 percent had only Tier 3 sta-
tions (Figure 3-23).
Out of a total of 308 watersheds located in Region
4,19 (6 percent) were identified as APCs (Figure 3-24).
In addition, 59 percent of all watersheds in the Region
had at least one Tier 1 station but were not categorized as
APCs, 17 percent had at least one Tier 2 station but no
Tier 1 stations, and 8 percent had only Tier 3 stations.
Ten percent of the watersheds in Region 1 could not be
evaluated because of a lack of data. The locations of the
APCs and the Tier 1 and Tier 2 stations in Region 4 are
illustrated in Figure 3-25.
Within the 19 watersheds in Region 4 identified as
APCs (Table 3-19), 65 water bodies have at least 1 sam-
pling station that has been categorized as Tier 1; 15 water
bodies have 10 or more sampling stations categorized as
Tier 1 (Table 3-20). Several areas in Region 4 appear to
have potential widespread sediment contamination prob-
lems. The most significant of these appear to be along
the Tennessee River, Lookout Creek in Tennessee and
Georgia, Wilson Lake and Mobile Bay in Alabama, the
St. Johns River in Florida, and other locations. It is im-
portant to emphasize here that Region 4 has significantly
more data in the NSI than do most other Regions, which
would to some degree account for the relatively large
number of Region 4 sampling stations categorized as Tier
1. The waterbodies listed on Table 3-20 are not inclusive
of all locations containing a Tier 1 or Tier 2 station be-
cause only waterbodies within APC watersheds are listed.
These summary results are also not inclusive of locations
with contaminated sediment not identified in this survey.
The data compiled for the NSI are primarily from large
national electronic databases. Data from many sampling
and testing studies have not yet been incorporated into
the NSI. Thus, there may be additional locations with
sediment contamination that do not appear in this sum-
mary. On the other hand, data in the inventory were col-
lected between 1980 and 1993 and any single
measurement of a chemical at a site, taken any point in
time during that period, could result in the categorization
of the site in Tier 1 or Tier 2. The evaluation approach is
conservative. Therefore sites appearing in Tier 1 or Tier
2 may not cause unacceptable impacts. It is also impor-
tant to note that management programs may already exist
to address identified sediment contamination problems.
The chemicals most often responsible for stations be-
ing categorized as Tier 1 or Tier 2 in Region 4 overall and
in each state in Region 4 are presented in Table 3-21.
3-38
-------
Table 3-18. Region 4: Number of NSI Sampling Stations in Each Probability of Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations Are Located
State
Alabama
Florida
Georgia
Kentucky
Mississippi
North Carolina
South Carolina
Tennessee
REGION 4J
Station Evaluation
Fieri
#
160
211
115
69
54
71
161
316
1,157
•/»
34
12
36
28
17
12
29
49
23
Tier!
#
178
672
100
131
142
294
254
159
1,930
%
37
38
32
52
45
48
45
25
39
Tier 3
#
139
893
103
49
122
247
148
171
1,872
%
29
50
32
20
38
40
26
26
38
River Reach Evaluation*
Stations
Not
Identified
by anRFl
Reach*
65
190
3
-
61
22
2
-
343
Reaches
w/at Least
1 Station
in Tier 1
68
70
75
49
21
50
105
132
566
Reaches
w/at Least
1 Station
in Tier 2C
57
115
57
60
47
156
138
63
684
Reaches
w/all
Stations
In Tier 3
57
126
54
26
35
107
28
97
520
Total #
Reaches
w/at Least
1 Station
Evaluated
182
311
186
135
103
313
271
292
1,770
Total
Reaches
instate
1,531
855
1,658
1,247
984
1,415
1,055
1,417
9,749
•/m nr ~n
Reaches
instate
w/at Least
1 Station
Evaluated
12
36
11
11
11
22
26
21
18
% of
Evaluated
w/at Least
1 Tierl
or Tier 2
Station
69
60
71
81
66
66
90
67
71
'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 total number of reaches in each category for the Region might not equal the sum of reaches in the states.
l*>
UJ
-------
.-
:
Figure 3-22. Region 4: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects (+) and Potential Human Health Effects (o).
-------
l)r :ill National Sediment Quality Survey
Reach File 1 River Reaches
in Region 4
(9,749)
Reach File 1 River Reaches
Evaluated
(1,770))
No Data Available
for Evaluation
82%
At Least One
Tier 1 Station
32%
At Least One Tier 2 Station
and Zero Tier 1 Stations
39%
All Tier 3 Stations
29%
Figure 3-23. Region 4: Percent of River Reaches That Include Tier 1, Tier 2, and Tier 3 Stations.
At Least One
Tier 1 Station
59%
APCs
6%
No Data
10%
All Tier 3 Stations
8%
At Least One Tier 2 Station
and Zero Tier 1 Stations
17%
Figure 3-24. Region 4: Watershed Classifications.
3-41
-------
-
Figure 3-25. Region 4: Location of Sampling Stations Categorized as Tier 1 or Tier 2 and Watersheds Identified as Areas of Potential
Widespread Contamination (APCs).
-------
Draft National Sediment Quality Survey
Table 3*19. Region 4: Watersheds Identified as Areas of Potential Widespread Sediment Contamination
Cataloging
Unit Number
06010201
06010207
06030005
06020001
03080103
03160205
06030001
03130002
03060106
03140102
06040001
06040005
08010100
06020002
06010104
03040201
08030209
03060101
03140107
Name
Watts Bar Lake
Lower Clinch
Pickwick Lake
Middle Tennessee- Chickamauga
Lower St. Johns
Mobile Bay
Guntersville Lake
Middle Chattahoochee-Lake
Harding
Middle Savannah
Choctawhatchee Bay
Lower Tennessee-Beech
Kentucky Lake
Lower Mississippi-Memphis
Hiwassee
Holston
Lower Pee Dee
Deer-Steele
Seneca
Perdido Bay
Stated
TN
TN
TN, AL,
(MS)
GA, TN,
(AL)
FL
AL
TN, AL,
(GA)
GA, (AL)
GA, SC
FL
TN, (MS)
KY.TN
AR, MS, KY,
MO. TN
GA, NC, TN
TN
NC, SC
MS, (LA)
NC, SC
FL, AL
Number of Stations
Tferl
63
61
49
47
32
31
25
21
20
19
15
15
14
13
12
11
11
10
10
Her 2
7
14
9
29
111
43
46
4
11
23
6
14
3
17
2
20
10
3
24
Tier 3
19
4
11
18
45
7
21
2
5
9
4
1
3
3
1
3
0
3
4
Percent of Stations
in Tier 1 or Tier 2
79
95
84
81
76
91
77
93
86
82
84
97
85
91
93
91
100
81
89
•No data were available for states listed in parentheses.
3-43
-------
Table 3-20. Region 4: Water Bodies With Sampling Stations Categorized as Tier 1 That Are Located in
Areas of Potential Widespread Sediment Contamination
Water Body
Tennessee River
St. Johns River
Lookout Creek
Mobile Bay
Wilson Lake
Poplar Creek
Clinch River
Choctawhatchee Bay
Ountersville Lake
Poplar Creek, Brushy Fork
Little River
Chattahoochee River
Watts Bar Lake
Mississippi River
Horse Creek
Black Bayou
Holston River
Kentucky Lake
Savannah River
Hiwassee River
Perdido Bay
Melton Hill Lake
Cherokee Lake
Fort Loudoun Lake
Gulf Of Mexico
Hartwell Reservoir
Lake Chickamauga
Pee Dee River
Pickwick Lake
Big Nance Creek
Black Creek
Catfish Creek
Crooked Creek
# of Tier 1
Stations
80
30
29
29
27
21
18
17
17
17
16
14
14
12
10
9
9
9
9
8
7
5
3
3
3
3
3
3
3
2
2
2
2
Water Body
Cypress Creek
Deer River
Long Cane Creek
Seneca River
Shoal Creek
Spring Creek
Twelvemile Creek
West Pont Lake
Beech Creek
Big Black Creek
Big Sandy Creek
Chatugue Lake
Conecross Creek
Coon Creek
Elevenmile Creek
Golden Creek
Hiwassee Lake
Jeffries Creek
Lake Harding
Lake Keowee
Lake Washington
Lafayette Creek
Little Horse Creek
Mountain Creek
Mud Creek
Nottely Lake
Oostanaula Creek
Pottsburg Creek
Rogers Creek
Sinking Creek
Steele Bayou
Sweetwater Creek
# of Tier 1
Stations
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3-44
-------
Drill'! Niitioniil Sedinu'iit Quality Sur\ c'\
Table 3-21. Region 4: Chemicals Most Often Responsible for Stations Being Categorized as Tier 1 or Her 2*
Rejion4
Overall
Alabama
Fiona.
fleo,,ia
153Siy
Chemical
Polychlorinated biphenyli
Lead
Copper
Mercury
Nickel
DDT
Cadmium
Anenic
Chromkim
Zinc
Chlordaiie
Benzo(a)pyrei»
Pyrene
DleUrln
Pluoranlhene
Anenic
Polychlorinated biphenyli
Cadmium
Nickel
Copper
Lead
DDT
Zinc
Chromium
Mercury
Polychlorinated hlphenyls
Lead
Copper
DDT
Cadmium
BenoMpyrane
Pymie
Annie
Chlordwe
klychlorlnavl blohenyls
Annie
Cadmium
Copper
Lead
Chlordne
Mercury
Nickel
DOT
Chromium
Nickel
Lead
Cadmium
•Tierl
&
Tier 2
Stations
1034
989
935
923
120
751
751
734
459
438
374
219
279
252
207
118
114
103
97
94
85
76
76
69
3«T
293
291
283
242
201
193
176
171
169
nr
62
60
60
46
45
43
38
36
33
ToT
76
69
*Tierl
Stations
669
-
-
235
-
157
37
26
-
7
28
62
9
34
4
98
,-
-
-
-
8
-
1
52
82
-
48
-
19
30
7
-
82
_
-
-
4
12
-
11
2
-
-
• Tier 2
Stations
365
989
935
611
820
594
751
697
433
431
367
261
217
243
173
114
16
103
97
94
85
68
76
68
211
291
283
194
208
174
146
164
169
62
60
60
46
41
31
38
25
31
foT
76
69
Kentucky
(continued)
Kisiaislppt
North
Carolina
South
Carolina
Tennessee
Chemical
Arsenic
Copper
Polychlorinaled biphenyli
Zinc
Chlordane
Dtcldrin
Mercury
DDT
Nickel
Arsenic
Polychlorinaled biphenyli
Cadmium
Chromium
Lead
Dieldrm
Copper
BenioCafeynne
Copper
Mercury
Lead
Nickel
Arsenic
Chromium
Cadmium
Polychlorlnnedblphenyli
Zinc
DDT
Lead
DDT
Mercury
Copper
PolycUodnated biphenyla
Nickel
Cadmium
Chromium
Arienic
Zinc
Pnlychlorlnuled biphenyla
Nickel
Lend
dercury
Copper
Arienic
Qntnium
Zinc
DDT
Dieldrm
• Tierl
Tier 2
Stations
65
SS
SO
43
41
40
35
99
66
63
44
33
32
28
24
22
13
ISO
133
128
99
75
72
62
60
45
27
198
188
144
141
132
131
129
63
62
58
230
164
137
134
130
118
87
83
57
52
• Tierl
Stations
3
-
48
-
3
3
3
31
-
1
IS
-
-
-
-
-
30
-
-
-
2
-
28
1
-
41
19
-
93
-
-
12
18
-
223
-
-
73
-
4
-
6
3
•Tier 2
Stations
62
SS
2
43
38
37
30
68
66
62
29
33
32
28
24
22
13
190
103
128
99
75
70
62
32
45
26
198
140
125
141
39
131
129
51
44
58
7
164
137
59
130
114
87
83
51
49
•Stations may be listed for more than one chemical
3-45
-------
EPA Region 5
EPA evaluated 4,290 sampling stations in Region 5
as part of the NSI evaluation. This evaluation resulted in
1,418 stations <33 percent) in Region 5 being categorized
as Tier 1, 2,137 (50 percent) as Tier 2, and 735 (17 per-
cent) as Tier 3 (Table 3-22). Figure 3-26 identifies the
stations in Region 5 that were categorized as Tier 1 or
Tier 2 due to potential aquatic life effects and those cat-
egorized as Tier 1 or Tier 2 due to potential human health
effects. It should be noted that the NSI includes sampling
data from the Great Lakes Sediment Inventory that, be-
cause of a lack of latitude and longitude data, were not
included in the NSI evaluation. Had those data been in-
cluded in the NSI evaluation, an additional 221 stations
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 (Table 3-22). Of all river reaches evaluated
in Region 5, 41 percent included at least one Tier 1 sta-
tion, 40 percent included at least one Tier 2 station but no
Tier 1 stations, and 19 percent had only Tier 3 stations
(Figure 3-27).
Out of a total of 278 watersheds located in Region 5,
36 (13 percent) were identified as APCs (Figure 3-28).
In addition, 59 percent of all watersheds in the Region
had at least one Tier 1 station but were not categorized as
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 could not be
evaluated because of a lack of data. The locations of the
APCs and the Tier 1 and Tier 2 stations in Region 5 are
illustrated in Figure 3-29.
Within the 36 watersheds in Region 5 identified
as APCs (Table 3-23), 102 water bodies have at least
1 sampling station that has been categorized as Tier 1;
18 water bodies have 10 or more sampling stations
categorized as Tier 1 (Table 3-24). The Detroit River,
Fox River, Milwaukee River, Mississippi River, Chi-
cago Ship Canal, and several coastal areas of Lake
Michigan and Lake Erie appear to have the most sig-
nificant potential widespread sediment contamination
problems in Region 5. It is important to emphasize
here that, like Region 4, Region 5 has significantly
more data in the NSI than do most other Regions,
which to some degree explains the relatively large
number of Region 5 sampling stations categorized as
Tier 1. The waterbodies listed on Table 3-24 are not
inclusive of all locations containing a Tier 1 or Tier 2
station because only waterbodies within APC water-
sheds are listed. These summary results are also not
inclusive of locations with contaminated sediment not
identified in this survey. The data compiled for the
NSI are primarily from large national electronic data-
bases. Data from many sampling and testing studies
have not yet been incorporated into the NSI. Thus,
there may be additional locations with sediment con-
tamination that do not appear in this summary. On
the other hand, data in the inventory were collected
between 1980 and 1993 and any single measurement
of a chemical at a site, taken any point in time during
that period, could result in the categorization of the
site in Tier 1 or Tier 2. The evaluation approach is
conservative. Therefore sites appearing in Tier 1 or
Tier 2 may not cause unacceptable impacts. It is also
important to note that management programs may al-
ready exist to address identified sediment contamina-
tion problems,
The chemicals most often responsible for stations
being categorized as Tier 1 or Tier 2 in Region 5 over-
all and in each state in Region 5 are presented in Table
3-25.
3-46
-------
Table 3-22.. Region 5: Number of NSI Sampling Stations in Each Probability if Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations Are Located
State
Illinois
Indiana
Michigan
Minnesota
Ohio
Wisconsin
REGION 5d
Station Evaluation
Tier 1
#
428
67
219
220
130
354
1,418
%
26
62
54
50
13
50
33
Tier 2
#
1,075
23
144
65
704
126
2,137
%
64
21
36
15
73
18
50
Tier 3
#
166
18
39
153
136
223
735
%
10
17
10
35
14
32
17
River Reach Evaluation'
Number of
Stations
Not
Identified
byanRFl
Reach"
g
3
20
-
71
6
108
Reaches
w/at Least
1 Station
in Tier 1
182
35
64
140
56
130
594
Reaches
w/at Least
1 Station
In Tier 2'
255
8
41
34
191
47
570
Reaches
w/all
Station!
in Her 3
30
1
11
90
57
82
268
Total #
Reaches
w/at Least
1 Station
Evaluated
467
44
116
264
304
259
1,432
Total
Reaches
Instate
920
559
1,145
1,355
1,054
1,174
6,025
% of all
Reaches
In State
w/at Least
1 Station
Evaluated
51
8
10
20
29
22
24
•/.of
Reaches
Evaluated
w/at Least
1 Tier 1
or Tier 2
Station
94
98
91
66
81
68
81
'River reaches based on EPA River Reach File 1 (RF1J.
'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.
-------
_
X
Figure 3-26. Region 5: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects (+) and Potential Human Health Effects (o).
-------
l)r;il'( Niilioiiiil Sfdiiiu'iil Quality Survey
Reach File 1 River Reaches
in Region 5
(6,025)
Reach File 1 River Reaches
Evaluated
(1,432)
No Data Available
for Evaluation
76%
At Least One
Tier 1 Station
41%
All Tier 3 Station*
19%
At Least One Tier 2 Station
and Zero Tier 1 Stations
40%
Figure 3-27. Region 5: Percent of River Reaches That Include Tier 1, Tier 2, and Tier 3 Stations.
At Least One
Tier 1 Station
59%
APCs
13%
No Data
18%
At Least One Tier 2 Station
and Zero Tier 1 Stations
7%
All Tier 3 Stations
3%
Figure 3-28. Region 5: Watershed Classifications.
3-49
-------
J
-
Figure 3-29. Region 5: Location of Sampling Stations Categorized as Tier 1 or Tier 2 and Watersheds Identified as Areas of Potential
Widespread Sediment Contamination (APCs).
-------
Table 3-23. Regio
n 5: Watersheds Identified as Areas of Potential Widespread Sediment Contamination
Cataloging
Uall Number
04090004
07120003
07120004
04040003
04030204
04040001
04040002
07140201
07010206
04110001
07140106
04120101
07070003
04100002
07140101
04030001
07040003
07080101
05120111
07120006
04090002
04100001
04100010
07040001
07140202
04030102
04030101
05030101
05120109
04060103
05030102
07130001
04100012
04110003
05040001
07090006
Name
Detroit
Chicago
Dei Plainee
Milwaukee
Lower Pox
Little Caluinet-Oallen
Pike-Root
Upper Ka.kHkil
Twin Cto'ea
Bluet-Rocky
Big Muddy
Cheulauqua-Conneaut
Cattle Rock
Railin
Cahokla-Joachim
St. Joeeph
Buffalo-White water
Copperaf-Duck
Middle WabaeVBuiaeron
Upper Pox
Lake St Clalr
Ottawa-Stony
Cedar-Portage
Ruih-Vermllllofi
Middle Kaakaakla
Door-Kewaunea
Menomlnea
Upper Ohio
Vermilion
Manletee
Shenango
Lower Illinola-Senachwlne Lake
Huron- Vermilion
Aehtabula-Chagrin
Tuaearawas
KUhwaukee
Staled)'
MI
IN. IL
WUL
WI
WI
IL. IN. (Ml)
WLIL
IL
WI, MN
OH
IL
NY, PA. OH
WI
MI. (OH)
MO, IL
IN. MI
WLMN
IL, IA
IN. IL
WLIL
MI
OH, MI
MI, OH
WI.MN
n.
WI
MI, WI
WV, PA. OH
IL, (IN)
Ml
OH, PA
IL
OH
OH
OH
IL. (WI)
N»ber of Station
Tier 1
85
64
61
60
49
45
34
31
26
24
23
21
20
18
18
17
17
17
15
IS
13
13
13
13
13
12
12
12
12
II
II
II
10
10
10
10
Tier 2
29
36
43
16
2
26
30
24
2
31
65
86
0
19
34
9
3
5
17
40
5
15
39
1
21
5
6
29
16
3
1
10
35
18
33
24
Tier 3
1
3
6
14
0
18
8
0
7
4
6
3
2
1
4
6
6
5
1
5
1
'
4
0
3
3
3
12
0
0
3
0
0
3
15
0
Ptrceal ot
or Tier I
99
97
95
84
100
80
89
100
80
93
94
97
91
97
93
81
77
II
97
92
95
97
93
100
92
85
86
77
100
100
80
100
100
90
11
100
•No data were available for stales lilted in parentheses.
3-51
-------
liable 3-24. Region 5: Water Bodies With Sampling Stations Categorized as Tier 1 That Are Located in
Areas of Potential Widespread Sediment Contamination
Water Body
Detroit River
Like Erie. U.S. Shore
Foi River
Miuiuippi River
Milwaukee River
Uke Michigan
Chict|0 Sanitary Ship Canal
Da Fluni River
Kaakaskia River
Calumet Rim
River Raisin
Indiana H.rbot
Wiiconain River
Wibuh River
Uke Si. Oair
Little Cilumet River
Rim Rouge
vfenominee River
Du Page River
Illinois River
Cahokia Canal
MiniMee Uke
ii| Muddy River, Caiey Fork
Black River
Crab Orchard Uke
Du Pa|e River. E. Br.
OuP>|e River. W.Br.
GrouaUle
Uke Mituwtonka
St. Joseph River
Tuacanwat River
Ashlabula River
CadirCnek
CTUMI.O Ship Caul
Root River
Chicago River, N. Br.
Nimiihillen Creek
—
Oh nathan Creek
— ,
Paw Paw River
....
Vermilion River. N. Pork
• of Tier 1
Station
64
60
58
56
55
45
41
27
21
19
16
15
15
14
13
13
13
12
9
9
t
«
7
7
7
7
7
7
7
7
7
6
J
S
5
;
4
4
4
4
3
— — — -•— ^»
3
3
3
3
}
"
3
•
3
1
3
3
Water Bod;
Becki Creek
Cattle Rock Flowage
Coldwitu River
Crib Orchard Creek
Crooked Creek
Hickory Creek
Kaibukia Creek, E. Fork
Kukukia River. Lake Pork
Uke Shelbyville
Little Creek
Porta ye River, E. Br.
Etamaey Creek
Saline River
Vermilion River
3arton Lake
Beaucoup Creek
3ig Bureau Creek
Bi| Muddy River, M. Pork
Buffalo Creek
Bums Ditch
Clark Lake
Coon River
3eep River
East River
Eliza Creek
Gar vi n Brook
Gilrnore Creek
Orouelile
Hog Creek
Kaskukia Creek, N. Pork
Kilbourn Ditch
Killbtick Creek
Uke Creek
Lemonweir River
J tile Crooked Creek
-ittle Roche A Cri Creek
(ill Creek
>ttawa Creek
Rend Lake
ugar Creek
wan Geek
>per Salt Fork Drainage Ditch
ermilion River. M. Pork
Bureau Creek
nil Town Drainage Ditch
Whitewater River
* of Tier 1
SUtlom
2
2
2
2
2
2
2
1
2
2
2
2
1
2
t
1
1
1
1
1
1
1
1
1
1
I
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3-52
-------
Draft National Sediment Quality Survey
Table 3-25. Region 5: Chemicak Most Often Responsible for Stations Being Categorized as Tier 1 or Tier 2'
Region 5
Overall
Illinois
Indiana
Michigan
Copper
Polychlorinated biphenyls
Lead
Dieldrin
Nickel
Cadmium
Arsenic
Zinc
Mercury
Chlordane
DDT
Chromium
Heptachlor epoxide
Pyrene
Fluoranthene
Dieldrin
Copper
Chlordane
Polychlorinated biphenyls
Uad
Cadmium
Arsenic
Nickel
Mercury
DDT
Polychlorinated biphenyls
Arsenic
Dieldrin
Chlordane
Heptachlor epoxide
Copper
Lead
BHC
DDT
Cadmium
Polychlorinated biphenyls
Copper
Uad
ffTierl
&
Tier 2
Stations
1,625
1,460
1,326
1.318
1,260
1,203
1,019
915
761
723
668
414
338
300
290
616
518
503
464
460
380
342
330
275
53
51
48
42
36
36
33
33
29
213
213
VTierl
Stations
"~
1,113
-
36
-
-
32
-
197
-
177
81
-
103
59
-
-
318
-
-
18
-
72
36
3
3
-
-
-
-
7
6
-
iTT
-
-
* Tier 2
Station
1,625
347
1,326
1.282
1,260
1.203
987
915
564
723
491
333
338
197
231
616
518
185
464
460
362
342
258
239
'
50
48
48
42
36
36
26
27
29
213
213
(continued)
Minnesota
Ohio
Wisconsin
Chemical
Nickel
DDT
Zinc
Mercury
Pyrene
Cadmium
Fluor an thene
Polychlorinated biphenyls
Dieldrin
Cadmium
DDT
Copper
Lead
Mercury
Dioxins
Chromium
Aldrin
Nickel
Copper
Lead
Arsenic
Cadmium
Zinc
Mercury
Chromium
Fluoranthene
Polychlorinated biphenyls
Polychlorinated biphenyls
Copper
Mercury
Lead
DDT
Cadmium
Dieldrin
Pyrene
Zinc
Nickel
# Tier 1
&
Tier 2
Stations
198
182
170
140
140
140
133
225
88
66
30
24
21
17
10
9
5
644
577
472
459
420
381
125
123
108
97
319
159
127
120
100
88
76
62
60
54
#Tierl
Stations
97
-
53
50
•-
20
216
-
-
-
-
-
-
10
-
-
-
-
-
2
-
-
16
19
17
65
304
-
42
-
15
-
-
21
-
-
# Tier 2
Stations
198
85
170
87
90
140
113
9
88
66
30
24
21
17
-
9
5
644
577
472
457
420
381
109
104
91
32
15
159
85
120
85
88
76
41
60
54
•Stations may be listed for more lhan one chemical.
3-53
-------
EPA Region 6
EPA evaluated 1,616 sampling stations in Region 6
as part of the NSI evaluation. This evaluation resulted
in 382 stations (24 percent) in Region 6 being catego-
rized as Tier 1, 837 (52 percent) as Tier 2, and 397 (24
percent) as Tier 3 (Table 3-26). Figure 3-30 identifies
the stations in Region 6 that were categorized as Tier 1
or Tier 2 due to potential aquatic life effects and those
categorized as Tier 1 or Tier 2 due to potential human
health effects.
The NSI sampling stations in Region 6 were located
in 799 separate river reaches, or II percent of all reaches
in the Region (Table 3-26). Of all river reaches evaluated
in Region 6, 33 percent included at least one Tier 1 sta-
tion, 43 percent included at least one Tier 2 station but no
Tier 1 stations, and 24 percent had only Tier 3 stations
(Figure 3-31).
Out of a total of 403 watersheds located in Region 6,
8 (2 percent) were identified as APCs (Figure 3-32). In
addition, 36 percent of all watersheds in the Region had
at least one Tier 1 station but were not categorized as
APCs, 21 percent had at least one Tier 2 station but no
Tier 1 stations, and 10 percent had only Tier 3 stations.
Thirty-one percent of the watersheds in Region 6 could
not be evaluated because of a lack of data. The locations
of the APCs and the Tier 1 and Tier 2 stations in Region
6 are illustrated in Figure 3-33.
Within the 8 watersheds in Region 6 identified as
APCs (Table 3-27), 17 water bodies have at least 1 sam-
pling station that has been categorized as Tier 1; 4 water
bodies have 10 or more sampling stations categorized as
Tier 1 (Table 3-28). The Calcasieu River and Mississippi
River in Louisiana appear to have some of the most sig-
nificant, potential widespread sediment contamination
problems in Region 6. The waterbodies listed on Table 3-
28 are not inclusive of all locations containing a Tier 1 or
Tier 2 station because only waterbodies within APC wa-
tersheds are listed. These summary results are also not
inclusive of locations with contaminated sediment not
identified in this survey. The data compiled for the NSI
are primarily from large national electronic databases.
Data from many sampling and testing studies have not
yet been incorporated into the NSI. Thus, there may 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 measurement of a chemical at a site,
taken any point in time during that period, could re-
sult in the categorization of the site in Tier 1 or Tier 2.
The evaluation approach is conservative. Therefore
sites appearing in Tier 1 or Tier 2 may not cause unac-
ceptable impacts. It is also important to note that man-
agement programs may already exist to address identified
sediment contamination problems.
The chemicals most often responsible for stations
being categorized as Tier 1 or Tier 2 in Region 6 overall
and in each state in Region 6 are presented in Table 3-29.
3-54
-------
Table 3-26. Region 6: Number of NSI Sampling Stations in Each Probability of Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations Are Located
State
Arkansas
Louisiana
New Mexico
Oklahoma
Texas
REGION 6J
Station Evaluation
Tier 1
#
18
111
4
122
127
382
%
17
24
4
43
19
24
Tier 2
#
39
270
40
95
393
837
%
36
59
40
33
59
52
Tier3
#
50
79
57
69
142
397
%
47
17
56
24
22
24
River Reach Evaluation*
Stations
Not
Identified
byanRFl
Reach"
. -
57
-
-
67
124
Reaches
w/at Least
1 Station
In Tier 1
17
45
4
97
104
266
Reaches
w/at Least
1 Station
in Tier 2C
31
68
28
59
160
341
Reaches
w/all
Stations
in Tier 3
40
29
28
41
56
192
Total #
Reaches
w/at Least
1 Station
Evaluated
88
142
60
197
320
799
Total
Reaches
instate
855
840
919
1,308
3,588
7,293
% of all
Reaches
instate
w/at Least
1 Station
Evaluated
10
17
7
15
9
11
% of
Evaluated
w/at Least
ITierl
or Tier 2
Station
55
80
53
79
83
76
•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.
en
Ui
-------
-
Figure 3-30. Region 6: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects (+) and Potential Human Health Effects (o).
-------
l)r;il( Vilinn.il Sediment Quality SMI \c\
Reach File 1 River Reaches
in Region 6
(7,293)
Reach File 1 River Reaches
Evaluated
(799)
No Data Available
for Evaluation
89%
At Least One
Tier 1 Station
33%
All Tier 3 Stations
24%
At Least One Tier 2 Station
and Zero Tier 1 Stations
43%
Figure 3-31. Region 6: Percent of River Reaches That Include Tier 1, Tier 2, and Tier 3 Stations.
At Least One
Tier 1 Station
36%
At Least One Tier 2 Station /
and Zero Tier 1 Stations
21%
All Tier 3 Stations
10%
APCs
2%
No Data
31%
Figure 3-32. Region 6: Watershed Classifications.
3-57
-------
-
-
Figure 3-33. Region 6: Location of Sampling Stations Categorized as Tier 1 or Tier 2 and Watersheds Identified as Areas of Potential
Widespread Sediment Contamination (APCs).
-------
Dnil't N;ilion;il Sediment Oiiiility Survey
Table 3-27. Region 6: Watersheds Identified as Areas of Potential Widespread Sediment Contamination
Cataloging
Unit Number
08080206
08090100
08010100
11070209
08040207
08030209
11070207
12040104
Name
Lower Calcasieu
Lower Mississippi-New Orleans
Lower Mississippi-Memphis
Lower Neosho
Lower Ouachita
Deer-Steele
Spring
Buffalo-San Jacinlo
State(s)1
LA
LA
AR, MS, KY,
MO, TN
OK, (AR)
LA
MS, (LA)
OK, MO, KS
TX
Number of Stations
Tier 1
26
16
14
13
12
11
10
10
Tier 2
52
34
3
3
0
10
25
23
Tier 3
22
1
3
4
0
0
6
3
Percent of Stations
in Tier 1 or Tier 2
78
98
85
80
100
100
85
92
•No data were available for states listed in parentheses.
Table 3-28. Region 6: Water Bodies With Sampling Stations Categorized as Tier 1 That Are Located in
Areas of Potential Widespread Sediment Contamination
Water Body
Calcasieu River
Mississippi River
Bayou D'Inde
Bayou De Siard
Buffalo Bayou
Fort Gibson Lake
Lake Hudson
Busch Island
Galveston Bay
# of Tier 1
Stations
15
15
11
11
5
4
3
2
2
Water Body
Neosho River
Pryor Creek
Greens Bayou
Lake Eucha
Mississippi River, Grand Pass
Mississippi River, Pass Loutre
Ouachita River
Spavinaw Lake
# of Tier 1
Stations
2
2
1
1
1
1
1
1
3-59
-------
Table 3-29. Region 6: Chemicals Most Often Responsible for Stations Being Categorized as Tier 1 or Tier 2*
Regon 6
Overall
Arkansas
Chemical
Nickel
Polychlorinated biphenyls
Arsenic
Copper
DDT
Cadmium
Lead
Chromium
Mercury
Chlodane
Silver
Zinc
Dieldrin
BHC
Dibenzo(a.h)anmracene
Arsenic
DDT
Mercury
Polychlorinated biphenyls
Lead
Dieldrin
Drains
Chlordane
Cadmium
Copper
Nickel
Arsenic
Chromiiirn
Polychlorinated biphenyls
Copper
DDT
SEM(est)b
Mercury
#Tierl
&
Tier 2
Stations
460
434
429
350
327
325
297
290
235
189
144
133
132
123
122
25
23
15
14
13
7
6
6
4
3
178
141
132
119
111
110
75
71
#Tierl
Stations
-
216
3
-
70
-
-
9
47
4
32
-
10
16
2
-
6
3
7
-
-
6
-
-
-
-
1
3
44
-
26
-
21
# Tier 2
Stations
460
218
426
350
257
325
297
281
188
185
112
133
122
107
120
25
17
12
7
13
7
-
6
4
3
178
140
129
75
111
84
75
50
Louisiana
(continued)
New Mexico
Oklahoma
Texas
'Stations may be listed for more than one chemical.
'Simultaneously extracted metals.
Chemical
Dibenzo(a,h)anthracene
Lead
Copper
Cadnium
Arsenic
Nickel
ImA
Zinc
Mercury
Chromium
Polychlorinated biphenyls
Chlordane
Polychlorinated biphenyls
Arsenic
Chlordane
Cadmium
DDT
Lead
Dieldrin
Copper
Mercury
Toxaphene
Nickel
Copper
Cadmium
Lead
Arsenic
Polychlorinated biphenyls
Chromium
DDT
Silver
Mercury
#Tierl
&
Tier 2
Stations
59
57
24
23
17
12
8
6
5
4
2
2
135
78
73
60
58
43
35
27
26
20
259
185
182
176
168
164
152
135
135
118
# Fieri
Stations
1
-
-
-
-
-
-
-
3
-
2
-
118
1
3
-
7
-
1
-
3
-
-
-
-
-
1
45
6
31
30
17
# Tier 2
Stations
58
57
24
23
17
12
8
6
2
4
-
2
17
77
70
60
51
43
34
27
23
20
259
185
182
176
167
119
146
104
105
101
3-60
-------
Draft National Sediment Quality Survey
EPA Region 7
EPA evaluated 1,011 sampling stations in Region
7 as part of the NSI evaluation. This evaluation re-
sulted in 330 stations (33 percent) in Region 7 being
categorized as Tier 1, 393 (39 percent) as Tier 2, and
288 (28 percent) as Tier 3 (Table 3-30). Figure 3-34
identifies the stations in Region 7 that were catego-
rized as Tier 1 or Tier 2 due to potential aquatic life
effects and those categorized as Tier 1 or Tier 2 due to
potential human health effects.
The NSI sampling stations in Region 7 were located
in 516 separate river reaches, or 11 percent of all reaches
in the Region (Table 3 -30). Of all river reaches evaluated
in Region 7, 48 percent included at least one Tier 1 sta-
tion, 35 percent included at least one Tier 2 station but no
Tier 1 stations, and 17 percent had only Tier 3 stations
(Figure 3-35).
Out of a total of 239 watersheds located in Region 7,
5 (2 percent) were identified as APCs (Figure 3-36). In
addition, 49 percent of all watersheds in the Region had
al least one Tier 1 station but were not categorized as
APCs, 16 percent had at least one Tier 2 station but no
Tier 1 stations, and 5 percent had only Tier 3 stations.
Twenty-eight percent of the watersheds in Region 7 could
not be evaluated because of a lack of data. The locations
of the APCs and the Tier 1 and Tier 2 stations in Region 7
are illustrated in Figure 3-37.
Within the 5 watersheds in Region 7 identified as
APCs (Table 3-31), 12 water bodies have at least 1 sam-
pling station that has been categorized as Tier 1; 1 water
body has 10 or more sampling stations categorized as Tier
1 (Table 3-32). The waterbodies listed on Table 3-32 are
not inclusive of all locations containing a Tier 1 or Tier 2
station because only waterbodies within APC watersheds
are listed These summary results are also not inclusive
of locations with contaminated sediment not identified in
this survey. The data compiled for the NSI are primarily
from large national electronic databases. Data from many
sampling and testing studies have not yet been incorpo-
rated into the NSI. Thus, there may be additional loca-
tions 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
measurement of a chemical at a site, taken any point
in time during that period, could result in the catego-
rization of the site in Tier 1 or Tier 2. The evaluation
approach is conservative. Therefore sites appearing
in Tier 1 or Tier 2 may not cause unacceptable im-
pacts. It is also important to note that management pro-
grams may already exist to address identified sediment
contamination problems.
The chemicals most often responsible for stations
being categorized as Tier 1 or Tier 2 in Region 7 over-
all and in each state in Region 7 are presented in Table
3-33.
3-61
-------
Table 3-30. Region 7: Number of NSI Sampling Stations in Each Probability of Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations Are Located
State
Iowa
Kansas
Missouri
Nebraska
REGION 7*
Station Evaluation
Tier 1
#
75
76
124
55
330
%
33
38
38
22
33
Tier 2
#
104
98
98
93
393
•/.
46
48
30
37
39
Tier 3
#
49
29
105
105
288
%
21
14
32
41
28
River Reach Evaluation1
Stations
Not
Identified
byanRFl
Reach"
-
-
-
-
-
Reaches
w/at Least
1 Station
in Tier 1
61
64
76
45
246
Reaches
w/at Least
1 Station
in Tier 2"
50
48
32
62
182
Reaches
w/all
Stations
in Tier 3
19
13
18
39
88
ToUlS
Reaches
w/at Least
1 Station
Evaluated
130
125
126
146
516
Total
Reaches
instate
1,198
1.184
1,364
1,265
4,857
Reaches
instate
w/at Least
1 Station
Evaluated
11
11
9
12
11
%of
Evaluated
w/at Least
ITlerl
or Tier 2
Station
85
90
86
73
83
•River reaches based on EPA River Reach File 1 (RF1).
'Stations not identified by an RFI 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.
-------
2 Figure 3-34. Region 7: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects (+) and Potential Human Health Effects (o).
-------
Reach File 1 River Reaches
in Region 7
(4,857)
Reach File 1 River Reaches
Evaluated
(516)
No Data Available
(or Evaluation
89%
At Least One Tier 2 Station
and Zero Tier 1 Stations
35%
All Tier 3 Stations
17%
Figure 3-35. Region 7: Percent of River Reaches That Include Tier 1, Tier 2, and Tier 3 Stations.
At Least One
Tier 1 Station
49%
At Least One Tier 2 Station \
and Zero Tier 1 Stations
16%
APCs
2%
No Data
28%
All Tier 3 Stations
5%
Figure 3-36. Region 7: Watershed Classifications.
3-64
-------
:
Figure 3-37. Region 7: Locations of Sampling Stations Categorized as Tier 1 or Tier 2 and Watersheds Identified as Areas of Potential
Widespread Sediment Contamination (APCs).
-------
Table 3-31. Region 7: Watersheds Identified as Areas of Potential Widespread Sediment Contamination
Cataloging
Unit Number
07140101
07080101
08010100
10270104
11070207
Name
Cahokia-Joachim
Copperas-Duck
Lower Mississippi-Memphis
Lower Kansas
Spring
State(s)
MO, IL
IL, IA
AR, MS, KY, MO, TO
MO, KS
OK, MO, KS
Number of Stations
Tierl
18
17
14
12
10
Tier 2
34
5
3
15
25
Tier 3
4
5
3
2
6
Percent of
Stations in Tier
1 or Tier 2
93
81
85
93
85
Table 3-32. Region 7: Water Bodies With Sampling Stations Categorized as Tier 1 That Are Located in
Areas of Potential Widespread Sediment Contamination
Water Body
Mississippi River
Kansas River
Spring River
Center Creek
Cedar Creek
Cow Creek
# of Tierl
Stations
17
7
5
3
2
1
Water Body
Duck Creek
Joachim Creek
Kill Creek
Stranger Creek
Turkey Creek
Wakarusa River
# of Tierl
Stations
1
1
1
1
1
1
3-66
-------
Drult National Sc'diiiu'iit Quality Survey
Table 3-33. Region 7: Chemicals Most Often Responsible for Stations Being Categorized as Tier 1 or Tier 2"
Region?
Overall
bwa
Kansas
Charted
Dieldrin
Chladane
Pblychtarinaled biphenyls
Arsenic
Heptachloreporide
Nickel
Cadmium
Lad
Copper
Ch ionium
Dtoxins
Zinc
Bis(2-ahylhotyl)phlhalale
DDT
Aldrin
Dieldrin
Chladane
PcJychkMinalMl biphenyl*
Heptachlorepoxide
Araenic
Copper
Cadmium
Nickel
EOT
Lead
Polychkxinated biphenyU
QJ crime
Dieldrin
#1Url
& 110-2
Station
336
329
305
171
138
121
115
84
74
50
44
43
37
33
31
126
91
71
54
34
17
14
14
12
10
68
67
62
filer 1
Station.
2
-
291
-
-
-
-
-
-
5
42
-
9
-
-
2
-
71
-
-
-
-
-
-
-
6S
-
-
*Tkr2
Station
334
329
14
171
138
121
115
84
74
45
2
43
28
33
31
124
91
-
54
34
17
14
14
12
10
-
67
62
Kansas
(continued
Missouri
Necndca
Chenfcd
Arsenic
Nickel
Cadnium
Lad
Chromium
Zinc
Copper
OUoidime
ftlychlcrinafed biphenyls
Dtidrin
HepBchlorepaxide
Araenic
Cadnium
led
Dioxins
Nckel
Copper
Dbldnn
Chlordane
Polydilorinated biphenyls
Aisenic
Cadnium
Nckel
Qimnum
Aldrin
Heptachlorcpoude
ffis(2-ethyUirayl)phtlialate
#Hefl
&Tio-2
Station
52
49
36
34
27
23
20
119
116
76
53
43
36
33
31
29
27
72
52
53
42
29
29
17
13
12
10
#Tkrl
Simian
-
-
-
-
1
-
-
-
102
-
-
-
-
-
29
-
-
-
-
50
-
-
-
2
-
-
4
#Her2
Stations
52
«
36
34
25
23
20
119
14
76
S3
43
36
33
2
29
27
72
52
-
42
29
29
15
13
12
6
•Sutions may be listed for more than one chemical
3-67
-------
EPA Region 8
EPA evaluated 535 sampling stations in Region 8 as
part of the NSI evaluation. This evaluation resulted in
68 stations (13 percent) in Region 8 being categorized as
Tier 1, 327 (61 percent) as Tier 2, and 140 (26 percent)
as Tier 3 (Table 3-34). Figure 3-38 identifies the stations
in Region 8 that were categorized as Tier 1 or Tier 2 due
to potential aquatic life effects and those categorized as
Tier 1 or Tier 2 due to potential human health effects.
The NSI sampling stations in Region 8 were located
in 305 separate river reaches, or 2 percent of all reaches
in the Region (Table 3-34). Of all river reaches evalu-
ated in Region 8, 20 percent included at least one Tier 1
station, 50 percent included at least one Tier 2 station but
no Tier 1 stations, and 30 percent had only Tier 3 sta-
tions (Figure 3-39).
Out of a total of 385 watersheds located in Region
8, none were identified as APCs. Fourteen percent of all
watersheds in the Region had at least one Tier 1 station,
12 percent had at least one Tier 2 station but no Tier 1
stations, and 9 percent had only Tier 3 stations (Figure
3-40). Sixty-five percent of the watersheds in Region 8
could not be evaluated because of a lack of data. The
locations of the Tier 1 and Tier 2 stations in Region 8 are
illustrated in Figure 3-41.
Lack of multiple sampling site data did not allow
identification of any watersheds in Region 8 as APCs.
Therefore, specific waterbodies with Tier 1 stations are
not listed in a separate table, as for other Regional sum-
maries. 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 may 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
measurement of a chemical at a site, taken any point
in time during that period, could result in the catego-
rization of the site in Tier 1 or Tier 2. The evaluation
approach is conservative. Therefore sites appearing
in Tier 1 or Tier 2 may not cause unacceptable im-
pacts. It is also important to note that management pro-
grams may already exist to address identified sediment
contamination problems.
The chemicals most often responsible for stations be-
ing categorized as Tier 1 or Tier 2 in Region 8 overall and in
each state in Region 8 are presented in Table 3-35.
3-68
-------
Table 3-34. Region 8: Number of NSI Sampling Stations in Each Probability of Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations Are Located
State
Colorado
Montana
North Dakota
South Dakota
Utah
Wyoming
REGION 8"
Tier 1
*
11
9
24
13
7
4
68
%
6
24
15
30
15
9
13
Station Evaluation
Tier 2
*
140
18
112
21
24
12
327
%
69
47
70
49
51
27
61
Tier 3
t
51
11
25
9
16
28
140
%
25
29
15
21
34
64
26
Mumbcr of
Stations
Not
Identified
by an RF1
Reach'
—
—
—
—
—
—
—
River Reach Evaluation1
Reaches
w/at Least
1 Station
In Tier 1
&
9
22
11
7
4
61
Reaches
w/at Least
1 Station
In Tier 2'
73
10
36
6
16
12
153
Reaches
w/all
Stations In
Tier 3
34
8
9
7
10
25
91
Total *
Reaches
w/at Least
1 Station
Evaloated
115
27
67
24
33
41
305
Total
Reaches
In State
2,178
5,490
992
1,611
1,034
2,421
13,492
% of all
Reaches
in State
w/at Least
1 Station
Evaluated
5
1
7
2
3
2
2
% of
Reaches
Evaluated
w/at Least
1 Tier 1 or
Tier 2
Station
70
70
87
71
70
39
70
•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 hi each category for the Region might not equal die sum of reaches in the states.
V
3
-------
Figure 3-38. Region 8: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects (+) and Potential Human Health Effects (o).
-------
Dull N;ili(iii.il St iliim n< <_)n;ili(\ Sin \( \
Reach File I River Reaches
in Region 8
(13,492)
Reach File 1 River Reaches
Evaluated
(305)
No Data Available
for Evaluation
08%
At Least One Tier 2 Station
and Zero Tier 1 Stations
50%
At Least One
Tier 1 Station
20%
All Tier 3 Stations
30%
Figure 3-39. Region 8: Percent of River Reaches That Include Tier 1, Tier 2, and Tier 3 Stations.
At Least One Tier 2 Station
and Zero Tier 1 Stations
12%
At Least One
Tier 1 Station
14%
Figure 3-40. Region 8: Watershed Classifications.
3-71
-------
-
Figure 3-41. Region 8: Locations of Sampling Stations Categorized as Tier 1 or Tier 2.
-------
l)r:ilt VitiniKil Sedimi'iil Quality Survey
Table 3-35. Region 8: Chemicals Most Often Responsible for Stations Being Categorized as Tier 1 or Tier 2"
Region 8
Overall
Colorado
Montana
North
Dakota
Chemical
Copper
Nickel
Cadmium
Arsenic
Lead
Zinc
Chromium
Poiychlorinated biphenyls
Mercury
Dieldrin
Aldrin
Toxaphene
Silver
BisO-ethylhexyOphthalate
Qtlordane
Cadmium
Copper
Arsenic
Nickel
Lead
Zinc
Mercury
Chromium
Poiychlorinated biphenyls
Dieldrin
Arsenic
Copper
Nickel
Poiychlorinated biphenyls
Chromium
Dieldrin
Aldrin
Toxaphene
Cadmium
)ioxins
Nickel
Copper
* Tier 1
&
Tier 2
Stations
195
192
169
155
74
56
53
40
35
20
12
12
11
10
9
— m
71
59
53
50
43
18
10
7
5
12
12
9
6
4
3
2
— Tib"
93
•••— — '
* Tier 1
Stations
—
-
-
22
-
-
1
29
12
-
-
-
1
4
-
_
-
-
-
-
6
4
-
9
-
2
• -— •—
* Tier 2
Stations
195
192
169
133
74
56
52
11
23
20
12
12
10
6
9
71
59
53
50
43
12
10
3
5
12
12
-
6
5
4
4
J
93
MM— — -
North Dakota
(continued)
South Dakota
Utah
Wyoming
.»— — "••
Chemical
Chromium
Arsenic
Cadmium
Poiychlorinated biphenyls
Mercury
Dieldrin
Aldrin
Bis(2-elhylhexyl)phthalate
Lead
Arsenic
Lead
Nickel
Cadmium
Copper
Zinc
Bis(2-ethylhcxyl)phthalate
Mercury
Chromium
Benzo(a)pyrene
Cadmium
Arsenic
Poiychlorinated biphenyls
Chlordane
Copper
Mercury
_aj
*eaa
Dieldrin
Silver
Zinc
Cadmium
Arsenic
Polychlorinated biphenyls
Copper
Bis(2-ethy!heiyl)phlhalaie
Mercury
ftckel
Silver
#Tierl
&
Tier 2
Stations
34
33
16
10
6
4
2
2
2
23
16
IS
9
9
6
3'
3
3
2
21
14
11
8
8
7
5
5
5
5
8
2
2
1
1
1
1
#Tierl
Stations
—
12
-
10
2
-
-
-
-
7
-
-
-
-
-
2
2
1
-
--
-
4
_
-
2
-
_
-
—
3
1
-
-
-
-
# Tier 2
Stations
34
21
16
--
4
4
2
2
2
16
16
15
9
9
6
1
1
2
2
21
14
7
8
8
5
Q
s
5
3
TT
5
i
2
1
1
1
1
•Stations may be listed for more than one chemical.
3-73
-------
EPA Region 9
EPA evaluated 1,699 sampling stations in Region 9
as part of the NSI evaluation. This evaluation resulted in
468 stations (28 percent) in Region 9 being categorized
as Tier 1, 942 (55 percent) as Tier 2, and 289 (17 per-
cent) as Tier 3 (Table 3-36). Figure 3-42 identifies the
stations in Region 9 that were categorized as Tier 1 or
Tier 2 due to potential aquatic life effects and those cat-
egorized as Tier 1 or Tier 2 due to potential human health
effects.
The NSI sampling stations in Region 1 were located
in 254 separate river reaches, or 6 percent of all reaches
in the Region (Table 3-36). Of all river reaches evalu-
ated in Region 9,47 percent included at least one Tier 1
station, 36 percent included at least one Tier 2 station but
no Tier 1 stations, and 17 percent had only Tier 3 sta-
tions (Figure 3-43).
Out of a total of 279 watersheds located in Region
9, 10 (less than 4 percent) were identified as APCs (Fig-
ure 3-44). In addition, 22 percent of all watersheds in
the Region had at least one Tier 1 station but were not
categorized as APCs, 10 percent had at least one Tier 2
station but no Tier 1 stations, and 5 percent had only Tier
3 stations. Fifty-nine percent of the watersheds in Re-
gion 9 could not be evaluated because of a lack of data.
The locations of the APCs and the Tier 1 and Tier 2 sta-
tions in Region 9 are illustrated in Figure 3-45.
Within the 10 watersheds in Region 9 identified as
APCs (Table 3-37), 19 water bodies have at least 1 sam-
pling station that has been categorized as Tier 1; 7 water
bodies have 10 or more sampling stations categorized as
Tier 1 (Table 3-38). San Diego Bay, San Francisco Bay,
and offshore areas around San Diego and Los Angeles
appear to have the most significant potential widespread
sediment contamination problems in Region 9. The water-
bodies listed on Table 3-38 are not inclusive of all loca-
tions containing a Tier 1 or Tier 2 station because only
waterbodies within APC watersheds are listed. These
summary results are also not inclusive of locations with
contaminated sediment not identified in this survey. The
data compiled for the NSI are primarily from large na-
tional electronic databases. Data from many sampling
and testing studies have not yet been incorporated into
the NSI. Thus, there may be additional locations with
sediment contamination that do not appear in this sum-
mary. On the other hand, data in the inventory were
collected between 1980 and 1993 and any single mea-
surement of a chemical at a site, taken any point in
time during that period, could result in the categoriza-
tion of the site in Tier 1 or Tier 2. The evaluation
approach is conservative. Therefore sites appearing
in Tier 1 or Tier 2 may not cause unacceptable im-
pacts. It is also important to note that management pro-
grams may already exist to address identified sediment
contamination problems.
The chemicals most often responsible for stations
being categorized as Tier 1 or Tier 2 in Region 9 overall
and in each state in Region 9 are presented in Table 3-39.
3-74
-------
Table 3-36. Region 9: Number of NSI Sampling Stations in Each Probability of Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations Are Located
State
Arizona
California
Hawaii
Nevada
REGION 9"
Tier 1
*
44
392
8
24
468
%
35
27
22
25
28
Station Evaluation
Tier 2
#
58
822
23
39
942
%
47
57
64
41
55
Tier 3
*
22
229
5
33
289
% •
18
16
14
34
17
Number of
Stations
Not
Identified
by an RF1
Reach'
—
758
36
—
794
Reaches
w/at Least
1 Station
In Tier 1
30
75
—
16
119
Reaches
w/at Least
1 Station
In Tier 2'
33
44
—
15
92
River Reach Evaluation'
Reaches
w/all
Stations In
Tier 3
11
26
—
6
43
Total #
Reaches
w/at Least
1 Station
Evaluated
74
145
—
37
254
Total
Reaches
In State
1,146
2,606
—
916
4,601
% of all
Reaches
In State
w/at Least
1 Station
Evaluated
7
6
—
4
6
% or
Reaches
Evaluated
w/at Least
1 Tier 1 or
Tier 2
Station
85
82
—
84
83
•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.
-4
-------
-J
-
Figure 3-42. Region 9: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects (+) and Potential Human Health Effects (o).
-------
Draft l\iition:il Sediment Qualify Survey
Reach File 1 River Reaches
Region 9
(4,601)
Reach File 1 River Reaches
Evaluated
(254)
At Least One Tier 1 Station
47%
River Reache*
Evaluated
6%
No Data Available
for Evaluation
94%
At Least One Tier 2 Station
and Zero Tier 1 Stations
36%
All Tier 3 Stations
17%
Figure 3-43. Region 9: Percent of River Reaches That Include Tier 1, Tier 2, and Tier 3 Stations.
At Least One Tier 2 Station
and Zero Tier 1 Stations
10%
All Tier 3 Stations
5%
At Least One
Tier 1 Station
22%
APCs
4%
No Data
59%
Figure 3-44. Region 9: Watershed Classifications.
3-77
-------
•J
X
Figure 3-45. Region 9: Location of Sampling Stations Categorized as Tier 1 or Tier 2 and Watersheds Identified as Areas of Potential
Widespread Sediment Contamination (APCs).
-------
I)mil Nalioniil Sediment Quality Survey
Table 3-37. Region 9: Watersheds Identified as Areas of Potential Widespread Sediment Contamination
Cataloging
Unit Number
18070104
18070201
18070304
18070204
18050004
18050003
18070105
18070107
18030012
18070301
Name
Santa Monica Bay
Seal Beach
San Diego
Newport Bay
San Francisco Bay
Coyote
Los Angeles
San Pedro Channel Islands
Tulare-Buena Vista Lakes
Aliso-San Onofre
State(s)
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
Number of Stations
Tierl
79
63
53
24
19
18
14
14
10
10
Tier 2
31
339
51
68
37
6
19
10
5
22
Tier 3
22
40
3
16
8
0
4
1
5
0
Percent of
Stations in Tier
lor Tier 2
83
91
97
85
88
100
89
96
75
100
Table 3-38. Region 9: Water Bodies With Sampling Stations Categorized as Tier 1
Areas of Potential Widespread Sediment Contamination
That Are Located in
Water Body
Pacific Ocean
San Diego Bay
San Francisco Bay
Los Angeles River
Santa Catalina Island
San Diego Creek
Kings River
Alamitos Creek
Calero Reservoir
Aliso Creek
# of Tierl
Stations
178
32
19
14
14
12
10
8
4
2
Water Body
Corte Madera Creek
Los Gatos Creek
Coyote Creek
Lexington Reservoir
Oso Creek
Peters Canyon Wash
San Diego River
San Juan Creek
Sweetwater River
# of Tierl
Stations
2
2
1
1
1
1
1
1
1
3-79
-------
Table 3-39. Region 9: Chemicals Most Often Responsible for Stations Being Categorized as Tier 1 or Tier 2*
Region 9
Overall
Chemical
Copper
DDT
Arsenic
Nickel
Cadmium
Pol/chlorinated biphenyls
Mercury
Lead
Bis(2-ethylhe«yl)phihalal
Chromium
Zinc
Silver
BHC
Ben2o(a)pyrei»
Dieldrin
Arsenic
Nickel
Lead
Zinc
Bis(2-cuiylheiyl)phthalat
Cadmium
DDT
Mercury
Silver
Copper
Polychlorinated biphenyli
# Tier 1
&
Tier 2
Stations
678
675
455
454
446
445
403
314
302
265
238
209
164
158
125
— w
55
50
37
28
26
24
23
22
15
573
418
#Tier 1
Stations
--
179
12
--
--
100
134
69
42
--
23
9
6
-
-•
8
-
--
15
--
9
12
7
168
-
.87
* Tier 2
Stations
678
496
443
454
446
345
269
314
233
223
238
186
155
152
125
72
47
50
37
28
11
24
14
10
8
472
573
331
California
(continued)
Hawaii
Nevada
Chemical
Cadmium
Nickel
Arsenic
Mercury
Bis(2-elhylhexyl)phthalal
Lead
Chromium
Nickel
Copper
Mercury
Arsenic
Lead
Zinc
DDT
Chromium
Polychlorinated biphenyls
Cadmium
Mercury
Arsenic
Copper
Nickel
Zinc
Lead
Polychlori rated biphenyls
Bis(2-ethylhexyl)phihalat
Cadmium
Chlordane
tTierl
&
Tier 2
Stations
406
373
357
336
264
253
239
20
19
16
16
14
13
10
10
S
8
29
27
14
11
11
10
9
8
8
8
* Tier 1
Stations
3
103
48
--
40
._
4
1
--
--
2
1
3
-
15
_
-
..
._
„
4
4
..
» Tier 2
Stations
406
373
354
233
216
253
199
20
19
12
15
14
13
8
9
5
8
14
27
14
11
11
10
5
4
8
8
3-80
-------
EPA Region 10
EPA evaluated 2,878 sampling stations in Region 10
as part of the NSI evaluation. This evaluation resulted in
727 stations (25 percent) in Region 10 being categorized
as Tier 1,1,696 (59 percent) as Tier 2, and 455 (16 per-
cent) as Tier 3 (Table 3-40). Figure 3-46 identifies the
stations in Region 10 that were categorized as Tier 1 or
Tier 2 due to potential aquatic life effects and those cat-
egorized as Tier 1 or Tier 2 due to potential human health
effects.
The NSI sampling stations in Region 10 were located
in 393 separate river reaches, or 4 percent of all reaches
in the Region (Table 3-40). Of all river reaches evaluated
in Region 10, 38 percent included at least one Tier 1 sta-
tion, 44 percent included at least one Tier 2 station but no
Tier 1 stations, and 18 percent had only Tier 3 stations
(Figure 3-47).
Out of a total of 219 watersheds located in Region
10,7 (3 percent) were identified as APCs (Figure 3-48).
In addition, 28 percent of all watersheds in the Region
had at least one Tier 1 station but were not categorized as
APCs, 14 percent had at least one Tier 2 station but no
Tier 1 stations, and 6 percent had only Tier 3 stations.
Forty-nine percent of the watersheds in Region 10 could
not be evaluated because of a lack of data. The locations
of the APCs and the Tier 1 and Tier 2 stations in Region
10 are illustrated in Figure 3-49.
Within the 7 watersheds in Region 10 identified as
APCs (Table 3-41), 34 water bodies have at least 1 sam-
pling station that has been categorized as Tier 1; 8 water
bodies have 10 or mote sampling stations categorized as
Tier 1 (Table 3-42). Puget Sound appears to have the
most significant potential widespread sediment contami-
nation problem in Region 10. The waterbodies listed on
Table 3-42 are not inclusive of all locations containing a
Tier 1 or Tier 2 station because only waterbodies within
APC watersheds are listed. These summary results are
also not inclusive of locations with contaminated sedi-
ment not identified in this survey. The data compiled for
the NSI are primarily from large national electronic data-
bases. Data from many sampling and testing studies have
not yet been incorporated into the NSI. Thus, there may
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 measurement of a chemical at a site,
taken any point in time during that period, could re-
sult in the categorization of the site in Tier 1 or Tier 2.
The evaluation approach is conservative. Therefore
sites appearing in Tier 1 or Tier 2 may not cause unac-
ceptable impacts. It is also important to note that man-
agement programs may already exist to address identified
sediment contamination problems.
The chemicals most often responsible for stations be-
ing categorized as Tier 1 or Tier 2 in Region 10 overall and
in each state in Region 10 are presented in Table 3-43.
3-81
-------
Table 3-40. Region 10: Number of NSI Sampling Stations in Each Probability of Adverse Effects Category and Number of River Reaches Where NSI
Sampling Stations Are Located
Slat*
Alaska
Idaho
Oregon
Washington
REGION 10"
Station Evaluation
Tier 1
*
21
43
81
582
727
*
8
45
28
26
25
Tier 2
*
191
36
158
1,311
1,696
%
71
38
54
59
59
Tier 3
*
55
16
52
332
455
%
21
17
18
15
16
River Reach Evaluation1
Number of
Stations
Not
Identified
by an RFI
Reach'
267
—
2
228
497
Reaches
w/at Least
1 Station
In Tier 1
—
30
45
75
147
Reaches
w/at Least
1 Station
In Tier 2'
—
16
43
115
174
Reaches
w/all
Stations In
Tier 3
—
7
25
40
72
Total*
Reaches
w/at Least
1 Station
Evaluated
—
53
113
230
393
Total
Reaches
In State
—
3.227
4,203
2,924
10,178
% of all
Reaches
In State
w/at Least
1 Station
Evaluated
—
2
3
8
4
% of
Reaches
Evaluated
w/at Least
1 Tier I or
Tier 2
Station
—
87
78
83
81
•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 t.
'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 3-46. Region 10: Stations Categorized as Tier 1 or Tier 2 Due to Potential Aquatic Life Effects (+) and Potential Human Health Effects (o).
-------
Reach File 1 River Reaches
in Region 10
(10,178)
Reach File 1 River Reaches
Evaluated
(393)
No Data Available
for Evaluation
96%
At Least One Tier 2 Station
and Zero Tier 1 Stations
44%
At Least One Tier 1 Station
38%
All Tier 3 Stations
18%
Figure 3-47. Region 10: Percent of River Reaches That Include Tier 1, Tier 2, and Tier 3 Stations.
At Least One Tier 2 Station
and Zero Tier 1 Stations
14%
All Tier 3 Stations
6%
At Least One
Tier 1 Station
28%
APCs
3%
No Data
49%
Figure 3-48. Region 10: Watershed Classifications.
3-84
-------
Figure 3-49. Region 10: Location of Sampling Stations Categorized as Tier 1 or Tier 2 and Watersheds Identified as Areas of Potential
Widespread Sediment Contamination (APCs).
-------
Table 3-41. Region 10: Watersheds Identified as Areas of Potential Widespread Sediment Contamination
Cataloging
Unit Number
17110019
17110013
17110002
17030003
17090012
17110014
17010303
Name
Puget Sound
Duwamish
Strait Of Georgia
Lower Yakima
Lower Willamette
Puyallup
Coeur D'Alene Lake
Stite(s)'
WA
WA
WA
WA
OR
WA
ID, (WA)
Number of Stations
Tier I
418
48
32
23
21
12
10
Tier 2
851
69
168
19
51
6
13
Tier 3
114
10
63
5
4
1
0
Percent of Stations
in Tier 1 or Tier 2
92
92
76
89
95
95
100
•No data were available for slate: listed in parentheses.
Table 3-42. Region 10: Water Bodies With Sampling Stations Categorized as Tier 1 That Are Located in
Areas of Potential Widespread Sediment Contamination
Water Body
Puget Sound
Budd Inlet
Elliot Bay
Bainbridge Island
Sinclair Inlet
Bellingham Bay
Yakima River
Willamette River
Carbon River
Columbia Slough
Green River
Coeur D'alene Lake
Dyes Inlet
Puyallup River
Coeur D'alene River
Johnson Creek
Chambers Creek
# of Tier 1
Stations
306
41
41
31
28
22
19
10
8
8
6
4
4
4
3
3
2
Water Body
Lake Whatcom
Sammish Bay
Sammish River
Whidbey Island
Spring Creek
Thompson Lake
Ahtanum Creek
Camano Island
Duwamish Waterway
Fidalgo Island
Padden Lake
Port Orchard
Port Susan
Spanaway Lake
Toppenish Creek
White Hall Creek
Wolf Lodge Creek
# of Tier 1
Stations
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
3-86
-------
Table 3-43. Region 10: Chemicals Most Often Responsible for Stations Being Categorized as Tier 1 or Tier 2*
Region 10
Overall
Alaska
Idaho
•Station! may be 1
Copper
Nickel
Arsenic
Uad
Benzo(a)pyrene
Pyttne
Mercury
Cadmium
Pol>dilori nated biphenyli
DibeimUMttlhnaM
Chryiene
Benzoce,cie
Naphthalene
Flucreoe
Chromium
•diromiuni
Aneiic
Copper
Nickel
Cadmium
Naphthalene
Polychlorinatedbiptienyls
Zinc
Phenanthrene
Fluorcnc
Anenic
PdychtoriMted bipnenyli
Letd
trier I
&
Tier!
Stations
L.409
1,231
SSI
803
770
760
754
710
709
704
669
5*9
547
54*
nr
5C
41
35
31
29
29
26
22
3T
32
32
#Iterl
Station*
..
55
-
103
160
133
289
245
B6
107
104
77
17
-
-
2
2
-
-
-
28
-
#Tier2
Station
1,409
1,176
get
700
610
627
7J4
421
464
618
562
483
4TO
8$
50
41
35
29
27
29
26
22
4
32
{continued}
Oregon
Wishiagton
Chemical
Copper
Zinc
DDT
Dieldrin
Tauphene
Silver
Copper
Nicttl
Aneiic
Polyctiloriiuted btphenyli
DDT
Zinc
Mercury
Cadmium
Chromium
Lad
Copper
Nickel
Aneiic
Lead
Benio(«)pyrcne
Pyrene
Mercury
Oiryxne
Dibenio(a,h)aatlincene
Benzo(a)utllmcene
f Tier 1
It
T«r2
Stations
29
28
28
25
21
14
11
125
107
86
84
73
59
S3
SI
46
44
1.315
1.156
1.017
7B8
754
735
683
612
681
646
*T«rl
Static™
..
•-
•-
-
8
-
-
1
46
19
-
7
3
-
-•
41
-
101
156
121
tt
240
104
*Tta2
Station!
29"
28
2!
25
21
14
3
125
107
85
38
!4
39
46
51
43
44
1.315
1,256
976
718
653
579
562
599
441
542
uled foi nwie than one chemicil.
3-87
-------
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 re-
ceiving streams that should have been but were not iden-
tified as locations of potential adverse effects, based on
the NSI data evaluation. The specific waterbodies that
reviewers of the preliminary evaluation identified as po-
tentially contaminated, but which are not presently in-
cluded in the NSI because data are inadequate to categorize
stations as Tier 1, are presented in Table 3-44 and Figure
3-50. 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-
34 or Figure 3-50.
Table 3-44. Potentially Highly Contaminated Sites Not Identified in the NSI Evaluation
Water Body
Onandaga Lake
Ley Creek
Kill van Kull
Newtown Creek
Scajaquada Creek
Skaneateles Creek
Hudson River
Southern reaches of the Maurice River
Elizabeth River
James River
Anacostia River
Lake O' the Pines
Linneville Bayou
Humboldt River Basin
Dry Lake
EPA Region
2
2
2
2
2
2
2
2
3
3
3
6
6
9
9
State
NY
NY
NY
NY
NY
NY
NY
NJ
VA
VA
DC
TX
TX
NV
AZ
Chemicals Potentially Present
pesticides, metals, PAHs, PCBs
mercury
metals, dioxin
PAHs
metals, PCBs
PCBs
PCBs
arsenic
PAHs
kepone
chlordane, PCBs
lead, zinc
lead, chromium
selenium
dioxin
3-88
-------
ewtown Creek
Kill van Kull
nthern Reaches of the
lurice River
ames River
lizabeth River
°° Figure 3-50. Location of Potentially Highly Contaminated Sites Not Identified in the NSI Evaluation.
-------
Chapter 4
Pollutant Sources
r 1"\°xic chemicals that accumulate in sediment and are
I associated with contamination problems enter the
JL 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 spe-
cific oudet, 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 de-
liver pollutants to surface water during and after rainfall
events. Some sources, such as landfills and mining sites,
are difficult to categorize as either a point or nonpoint source.
Although they represent discrete sources, pollution from
such land areas tends to result fromrainfall runoff and leach-
ing. Likewise, atmospheric deposition of pollutants, gen-
erally considered to be a nonpoint source of water pollution,
arises from the emission of chemicals from discrete station-
ary and mobile source points of origin. The CWA specifies
water vessels and other floating craft as point sources al-
though, 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 federal permit under the CWA. Per-
mitting authority, however, has been delegated to many
states. These permits often contain technology-based or
water quality-based pollutant discharge limits and moni-
toring requirements. More recently, replacement of ag-
ing combined sewer systems and other storm water
control measures has addressed the discharge of pollut-
ants from urban areas through municipal facilities. The
dredging of sediment 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 stationary
and mobile sources of air pollutants have also been es-
tablished 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 movement and
disposal of pollutants in landfills and other repositories
of hazardous waste are tracked and controlled. At sites
where past disposal practices, either purposeful or acci-
dental, have resulted in severe contamination,
remediation has been undertaken under the federal
Superfund laws. Where applicable, land development
projects are approved contingent on 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 bioac-
cumulative pollutants are difficult to monitor and regu-
late. It is beyond the scope of this baseline assessment
to determine the temporal trends of pollutant concentra-
tions in sediment on a national scale. Future reports to
Congress will address that issue. This baseline assess-
ment demonstrates that, at least from the perspective of
the past 15 years, sediment contamination remains a sig-
nificant and widespread problem.
Because of the high cost of remediation, the pre-
ferred solution to reducing health and environmental risks
from contaminated sediment problems is natural recov-
ery of contaminated sites through source reduction, con-
taminant degradation, and continuing deposition of clean
4-1
-------
Pollutant Sources
sediment. The feasibility of natural recovery, as well as
the long-term success of expensive remediation projects,
depends on the effective control of pollutant sources.
For some classes of sediment contaminants, such as
PCBs and organochlorine pesticides, use and manufac-
ture bans or severe restrictions have been in place for
many years. Past disposal and use of PCBs continue to
result in evaporation of this contaminant from some land-
fills and leaching from soils, but most active PCB sources
have been controlled. The predominant sources of or-
ganochlorine pesticides are runoff and atmospheric depo-
sition from past applications on agricultural land, and
occasional discharge from municipal treatment facili-
ties. For other classes of sediment contaminants, active
sources continue to contribute substantial environmen-
tal releases. For example, liberation of inorganic mer-
cury from fuel burning and other incineration operations
continues, as do urban runoff and atmospheric deposi-
tion 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 quality. Understand-
ing 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 by chemical class the extent of sedi-
ment contamination in the 96 watersheds iden-
tified as areas of potential widespread 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 sedi-
ment 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 report
can be grouped into six chemical classes: metals, PCBs,
pesticides, mercury, PAHs, and other organic chemicals.
Pesticides include the organochlorine pesticide com-
pounds assessed in this report, such as DDT and metabo-
lites, dieldrin, and chlordane. PAHs include both low-
and high-molecular-weight polynuclear aromatic hydro-
carbons, and other organics include all organics not oth-
erwise classified. Mercury is grouped separately from
other metals because of its unique behavior in the envi-
ronment (e.g., methylation and bioaccumulation poten-
tial) 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
chemical constituent of a particular chemical class places
a station into Tier 1 or Tier 2 and dividing by the total
number of stations in the watershed. Each constituent,
or any constituent representative of a chemical class,
might not have been measured at all stations in the wa-
tershed. In addition, 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 chemical class
measured by average extent of contamination (59 per-
cent), followed by PCBs and pesticides (both at 43 per-
cent), 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 com-
bined Tier 1 and Tier 2 also reflects the evaluation meth-
odology because a divalent transition metal concentration
cannot place a station into Tier 1 without an accompany-
ing acid-volatile sulfide concentration ([AVS]) measure-
ment, which is typically not available.
Figure 4-1 graphically displays the relative differ-
ences in certainty of assessing the probable effects of
4-2
-------
l)i-;il'i Nntioiiiil St-dimml On;ilil\ Survey
60%
50%-
40%-
30%-
20%-
10%
TIERS 1 & 2
Metals
PCBs
TIER1
Pesticides
Mercury
PAHs
Other
Organic*
Figure 4-1. Average Percent Contamination in APCs by Chemical Class
metals versus assessing the effects of PCBs. More con-
fidence can be placed in the assertion that PCBs exhibit
a "higher probability of adverse effects" than in making
this assertion for metals. The relatively high percent of
PCB contamination at the Tier 1 level reflects the rela-
tive certainty that elevated PCB levels in fish are associ-
ated with elevated levels in sediment. The relatively low
percent of metal contamination at the Tier 1 level prima-
rily reflects the lack of confirming data (i.e., AVS) re-
garding important binding phases and bioavailability, not
necessarily the lack of significance of metal contamina-
tion. In fact, the very high percent contamination indi-
cated at the combined Tier 1 and Tier 2 level demonstrates
the potential importance of this chemical class.
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 differ-
ences in extent of chemical class contamination on the
average in the 96 APCs is intended to provide some per-
spective 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 contamination. EPA focused this
review on studies appearing in peer-reviewed journals
and government reports pub-
lished after 1980. The majority
of studies related sediment con-
tamination to a source through
qualitative means, including as-
sociations of land use or specific
activity with the types of con-
taminants detected, and spatial
analyses. For example, orga-
nochlorine pesticide contamina-
tion is associated with
agricultural land use where past
application practices and hydro-
logic routes of rainfall runoff are
known. Some researchers made
the association with contamina-
tion source by more quantitative
means such as loadings measure-
ments, runoff or deposition esti-
mates, or mass balance models of
contaminant inputs. Most re-
search has focused on the chemi-
cals or chemical classes listed above. The studies
reviewed attributed sediment contamination from the six
classes of chemicals to four general nonpoint source cat-
egories and two general point source categories. 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 commercial
boating, and disposal of contaminated dredged material.
As mentioned at the beginning of this chapter, landfills
are not easily classified as a point or nonpoint source.
Evaporation and subsequent deposition of moderately
volatile contaminants from landfills represent an atmo-
spheric source, yet leachate is typically considered as nei-
ther "urban runoff nor a controlled point source.
Nonetheless, leachate from landfills is an important docu-
mented source of sediment contaminants. For example,
landfill leachate and past effluent discharges from elec-
tronics manufacturers have contaminated New Bedford
Harbor in Massachusetts with PCBs and heavy metals
(Garton et al., 1996). Boating and shipping activities
can be important sources of a variety of contaminants,
including PAHs and anti-fouling paint additives such as
tributyl tin and copper. As for dredged material disposal,
past dredging operations to maintain navigation chan-
nels could be responsible for contaminated sediment at
specifically designated dump sites. Dredging practices
are currently managed under federal, state, and local au-
thority to ensure that appropriate testing and safe dis-
4-3
-------
I'olllllillll SolllTfS
Table 4-1. Correlations Among Sources and Chemical Class of Sediment
Contaminants
Source/Chemical Class
Harvested Croplands
Inactive and Abandoned Mine Sites
Atmospheric Deposition
Urban Sources
Industrial Discharges
Municipal Discharges
Mercuiy
•
•
•
•
•
PCBs
•
*
•
PAHs
•
•
•
•
Metals
•
•
•
•
•
Pesticides
*
•
*
*
•
Other
Organics
•
•
•
•
• Ongoing source
* Source from past activities
posal occur. In addition to these sources, uncontrollable
and accidental point source releases, such as improper
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 particular
chemical or class of chemical contaminants at a given
location also varies. In addition, there is typically over-
lap 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 depos-
ited 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 in-
dustry-supplied release estimates, as well as specific spa-
als and other organics) continue,
the relative contribution compared
to nonpoint sources is an open
question and undoubtedly varies
substantially by watershed. A
brief summary of the literature re-
view 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 grow-
ers deliberately apply pesticides to
protect their yield from insects,
fungus, and weeds. In the past,
organochlorine compounds such
as L)L)T and chlordane were used without restriction to
rid harvested croplands of a broad range of unwanted
species. These compounds tend to be persistent in the
environment, 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 may be more easily degrad-
able and, in some cases, more narrowly targeted to
specific organisms. In addition, modem pesticides must
undergo federal 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 available
levels of concern in sediment, current-use pesticides were
not included in this evaluation of sediment quality. How-
ever, these compounds exhibit toxicity to non-target or-
ganisms. Furthermore, although these compounds have
shorter half-lives and greater water solubility than orga-
nochlorines in general, the chemical and physical prop-
erties of these compounds indicate significant
bioconcentration potential (Willis and McDowell, 1983).
Thus, further assessment of the presence of current-use
pesticides in fish and sediment is warranted.
The discharge of pollutants from agricultural lands
»• i i • . j- • _,• . . - to surface water is largely driven by precipitation. Con-
tad analysis studies, mdicate that municipal and industrial taminants also reach the aquatic ecosystem via irrigation
discharges of sediment contarmnants (particularly met- return flows through interflow "
4-4
-------
Most of the literature reviewed identifies agriculture 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 asso-
ciated with major agricultural areas of the United States
has been reported in numerous studies. For example,
the San Joaquin River, in the highly agricultural 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 organochlorines in bottom-
feeding Fish, a condition that is often a consequence of
sediment contamination. In the Yakima River in Wash-
ington, which drains a largely agricultural region, con-
centrations of DDT in fish for the years 1989-90 were
found to be similar to concentrations 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, Whitewood
Creek and the Belle Fourche River in South Dakota, Tar
Creek in Oklahoma, Iron Mountain in California, and
the Arkansas River and tributaries near Leadville, Colo-
rado. The persistence and mobility of heavy metals have
resulted in concentrations in sediments up to 65 miles
downstream of discharge similar to the elevated concen-
trations found in the mine tailings themselves (Henny et
al., 1994). Based on information 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 (Hemman,
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 put>-
lications describing the effects of mining on water quality;
however, few researchers have directly addressed the
effects of mining on sediments. A monitoring study per-
formed on Idaho's Lake Coeur d'Alene surface sediment
found that ores and wastes from a mining distort -were
the source of elevated sediment concentrations of sev-
eral heavy metals via transport down the Coeuird Alene
River (Horowitz et al., 1993). Moore etal. (1991) per-
formed an integrated sediment-water-biota monitoring
study on the effects of acid mine effluent on the Blackfoot
River in Montana. These researchers found elevated lev-
els of heavy metals in sediment from tributaries 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 sam-
pling sites from the mining district increased (Leigh,
1994). The author further suggests that similar occur-
rences of mercury contamination could exist throughout
the gold mining region of the Southern Piedmont be-
cause 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 compounds to aquatic environments in this
country (Keeler et al., 1993).
Atmospheric sources of mercury include coal com-
bustion, waste 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 bal-
ance studies from Wisconsin and Sweden indicating that
atmospheric deposition is responsible for most of the mer-
cury in lakes (USEPA, 1994a). The Swedish study also
points out that mercury deposited onto forest soils is
stored, for potentially long periods of time, before it en-
ters the lake through storm water runoff. This further
illustrates the relationship between atmospheric deposi-
tion and runoff.
Important sources of PCBs to the atmosphere include
transformer leakage, electric power generation, indus-
trial fuel combustion, landfills, sewage sludge combus-
tion, and waste oil combustion. 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 depo-
sition (Baker etal., 1993, cited in USEPA, 1994a). These
4-5
-------
Pollutant Sources
researchers have also estimated the percent contribution
of PCBs from atmospheric deposition for other Great
Lakes, keeping track of the fraction contributed from at-
mospheric 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 remaining 22 percent is
from other sources. Lakes Erie and Ontario receive only
about 13 percent and 7 percent, respectively, of their
annual PCB load from atmospheric 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 in-
dicative 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 etal. (1984) studied atmospheric
paniculate 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 ac-
count for the atmospheric contribution to upstream
waterborne 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 identified
atmospheric deposition as an important contributor, but
Jess 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
rivenne or upstream sources. In one study, estimates of
loadings to Narragansett Bay, Rhode Island, indicated
that atmospheric deposition contributes 2 percent of cop-
per and zinc and 33 percent of lead in sediment (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 cad-
4-6
mium input. Some portion of the riverine input origi-
nates from the air (USEPA, 1994a).
Atmospheric deposition is a significant source of di-
oxins and furans found in sediment. These highly per-
sistent compounds are grouped with "other organics" in
Figure 4-1. A recent review indicated that incineration
of industrial and municipal waste, residential and indus-
trial wood combustion, and electric power generation may
be the most important sources of dioxins and furans in
the environment (Johnson et al., 1992).
The category "urban sources" refers broadly to run-
off from roadways, residential and commercial areas, con-
struction sites, and marinas and shipyards. According 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 hydrocar-
bons (USEPA, 1992b). Much of the pollution in urban
runoff is associated with atmospheric deposition, par-
ticularly for mercury and PAHs. Other classes of chemi-
cals, such as metals and petroleum hydrocarbons, have
many land-based sources. Lead was formerly contrib-
uted by car exhaust, but most contributions now come
from exterior paints and industrial runoff. Cadmium is
also associated with paints. Zinc is associated with weath-
ering and abrasion of galvanized iron and steel. Car brake
linings and leaching and abrasion of copper pipes and
brass fittings contribute copper to runoff. Chromium is
contributed to runoff through car and machinery corro-
sion (Cohn-Lee and Cameron, 1991). Sources of petro-
leum 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 industrial
land use, followed by those associated with roads and
commercial land use, then those associated with residen-
tial land use. In contrast to atmospheric transport, which
can carry pollutants far from their original source, run-
off 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
-------
iuil Si'diitu'iil OualiU Survey
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 analytical
chemistry techniques, Brown et al. (1985) discovered that
crankcase oil was a primary contributor to sediment hy-
drocarbon contamination in Tampa, Florida. Gas chro-
matograms of used crankcase oil, storm water runoff, and
sediment samples all showed similar peaks, indicating
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 emis-
sions from commercial and residential fuel-burning fur-
naces and vehicular emissions. An inventory of sediment
contamination in Casco Bay, Maine, showed that the
highest PAH concentrations occurred at locations clos-
est 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 ratios than
those from remote areas. These ratios are indicative of
pollution caused by gas exhaust residues in urban run-
off. A study of ambienl air in the southern Lake Michi-
gan basin revealed that mercury concentrations, both
gaseous and paniculate, are significantly higher (approxi-
mately 5 times higher) in the Chicago urban/industrial
area than levels measured at the same time in surround-
ing 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 industrial and
municipal discharges. For example, sediment core
samples from southwestern Long Island, New York, re-
vealed levels of metals that increased to several times
the preindustrial concentrations, then decreased approxi-
mately 50 percent between the mid-1960s and late 1980s.
PCBs, chlordane, and other chlorinated orgamcs in sedi-
ment also decreased between the late 1960s and the late
1980s. Local improvements in wastewater treatment ana
national efforts to restrict the use of specific chemicals
are cited as explanations for the declines (Bopp et al.,
1993). As previously mentioned, pasteffluent discharges
from electronics manufacturers are linked to PCS con-
tamination in New Bedford Harbor, Massachusetts
(Carton et a]., 1996; Lake et al, 1992). Perhaps the best
example of pesticide contamination in sediment from pasi
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 sediment
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 determined that
the magnitude of current loading estimates for metals
and organics from major sources, such as industrial 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 Reau thorization Act of 1986
(SARA). Permit monitoring data indicate that munici-
pal sewage treatment plants and major industrial facili-
ties 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 organic s 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 sedi-
ment contaminants, primarily metals and other organics,
to municipal sewage treatment plants. The analysis of
these data addresses the hazard potential, 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
4-7
-------
Pollutant Soiuri's
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 sedi-
ment contamination by chemical class and patterns of
land use in the 96 areas of potential widespread sedi-
ment contamination (APCs). EPA identified individual
watersheds where land use appears to provide important
information concerning the types of contaminants
present, and summarized general trends that emerge by
looking at the percent of urban and agricultural land ar-
eas 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 obtain land use
data originally compiled by the U.S. Geological 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/in-
dustrial, 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 "to-
tal agricultural" land use category. This allowed com-
parison of attributes such as the percent 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 sampling
stations. First, the frequency and spatial extent of sam-
pling data in the NSI vary by watershed. A greater
amount of sampling in areas thought to be contaminated
will yield a larger number, and larger percentage, of Tier
1 and Tier 2 sites. Second, 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 prob-
lems of scale. Localized problems in specific reaches
might be caused by land use activity in the immediate
vicinity of the reach rather that the overall land use in
the watershed. Lastly, many individual pollutants and
chemical classes are associated with multiple types of
sources. Some classes of pollutants, like the highly per-
sistent PCBs, have been cycled in the environment for
many years and transported far from their original source.
4-8
These chemicals could not be expected to be associated
with any general land use category.
Table 4-2 lists each of the 96 APCs with the number
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 chemical
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 stations
were contaminated with pesticides, whereas no stations
exhibited mercury contamination and less than 10 per-
cent 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 contamina-
tion 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/for-
mulation or urban applications in the past. In the Coeur
D'Alene watershed in Idaho, there is very little agricul-
tural land use and almost no urban land use. In this wa-
tershed, where mining is a known source of
contamination, over 90 percent of the stations exhibited
metal contamination, whereas none indicated PAH or pes-
ticide contamination. In other watersheds with very low
percent urbanization, there was substantial contamina-
tion 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 examination of
percent agricultural and urban land use revealed some
general trends that are illustrated by these examples.
A high percentage of agricultural land use in a wa-
tershed tended to correspond with a markedly higher per-
cent contamination from pesticides and lower percent
contamination from metals, mercuiy, and PAHs. This
phenomenon is presented graphically in Figure 4-2 and
in tabular form on Table 4-3. For this analysis, EPA
grouped watersheds into quartiles based on percent total
-------
Table 4-2. Tier 1 and Tier 2 Station Classification by Chemical Class and Land Uses in Areas of Potential Widespread Sediment Contamination
(APCs)
El
fc
j { [ NuttbeTATStfttioMS Wtlh a Probability of Advene fcJTects
A OJtoginJ
tg.1 VMt NWK llSer iMerary
1 01090001 lairies 1 TER1 146
I | 1 TIER2 lit
1 01090004 JNamgan.**! 1 TIERI 1 8
1 1 JTIER2 1 TO
1 01090002 1 Cape Cod 1 TEW I 6
1 1 1 TIER2 1 27
1 P
ER1 | 20
ER2 1 45
2 1 0203010) llbckeDiict-PaiiBC 1 TIERI | 21
1 1 Insal 39
2 I 04130001 lOtkOrctanl-Tvebanife 1 HER] I 10
I 1 ITIEX2J »
2 02030104 1 S»dy Hoot-Suieo UU.0 lTlER.ll 5}
| 1 iTJERll It
2 | O412O104 |>C»0n I TIERI 1 5
1 1 TIER2 1 16
2! O41SO301 1 Upper SLlmm 1THU 1 1
1 1 T1ER2J *
1 | 02030105 jfartu. I TIEH.1 I 1
1 1 I TTER2 1 11
1 j 02040301 IMullia-Tomj 1 TIERI 1 2
1TER2 1 10
2 1 02040105 iMidAnfeware-Mucoaeteoae 1 TIERI 1
1 V 1 11001
2| 02030202 1 Southern Loq Istiod 1 TIERI I
-nER2| ]
3 J 02060003 iGuapawifcr-Pttipico 1 TTER1 1
II 1 TER2 |
3 02040203J
3 05030101
3 [ 02070004
3 02O402O2
3 O5O30HO
3 04120101
4 06010201
4 06010207
4 06030005
4 060200D1
4 0308)103
SchuylUll
Upper Otio
ConococlKague-OpeqiKll
Lower Delawaie
Shmwgo
Cbautaaqua-ConDeaut
WuuBaiLalte
Lower Clinch
Prt«™*lj«ke
Middle TenMtsee-Clfctaimigj
Lowa St. Iotas
TIER] I
TEK2
TIERI
T1EKZ
TORI
TTER2
TIERI
TIEIU
TIERI
T1ERZ
TIERI
TIER2
TIERI
TIER2
IlfcKI
TIER2
TEU
TIER2
TIERI
TIER2
TIERI
TIER2
S
0
0
0
2
1
7
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1
22
5
5
46
11
6
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14
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1
35
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68 1 35
4S« 1 54
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27 17
3 I 8
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79 31
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75 1 34
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61 I 15
40 1 19
30 1 »
0 | 17
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39 1 25
0 1
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ID I 13
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25 I 8
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0
101
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57
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76
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12
20
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5
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4
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195
402
28
20
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73
59
33
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58
39
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21
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11
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a
12
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29
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63
7
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9
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m
Totil
for
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708
48
108
1O1
103
86
100
41
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44
53
29
57
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110
89
79
«9
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1SS
ferceatof lofed AnalD fcadi Watershed
taMalU
23.43%
13.74%
5.90%
£.27%
3333*
2.23%
30.5M
935*
1.5]«
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8.54*
5.49*
2338%
13.47*
9.17*
13.08*
L88*
26.68*
3.93*
4.07*
9.71*
11.76%
1.93%
8.H*
6.99%
.aamntU/
InhBtrid
5.95*
3.58*
0.81*
354*
7.24*
44.43%
VOJJ*
3102%
0.85*
4.gT%
1.71*
1.53*
5.03*
5.10*
2.68*
152%
0.98*
13.51*
0.76%
1.13%
1.84%
1.74*
0.60*.
1-S8*
1.71*
Other
rtNM
4.56*
4.61*
1.77*
3.20%
5.65*
1.25*
7.70%
3.91*
1.29%
t»*
1.1!*
1.26*
5.06
4.32
2.78
2.18
0.89*
6.47
2.20*
2.05
1.2991
1.24
0.33
1.19*
1.57
"nptend
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7.41*
1.84%
4X85*
2.62*
10.48*
6.99*.
31.59*
3631*
25 .UK
6.04*
38.02*
4.29*
40.80*
41J7*
35.26%
5058*
21.76*
7441*
38.07*
27.72%
24.98*
40.733,
1950%
».03*
Otkcr
ISricrflnnl
0.04*
0.86*
4.12*
0.10*
O26*
329*
0.49*
0.24%
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0.49*
052%
0.16*
0.74*
0.11*
0.26*
0.34*
US*
1.90*
0-02*
0.21*
0.06%
0.04%
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1.72%
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5156%
22.90*
$0.94%
38.99*
8.42*
7.X3%
17.47*
28.47*
1555%
43.11*
33-98%
10.73*
26.70*
25.81*
43.13*
4324*
18.45*
12.85%
2158%
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56.28*
4451*
64.76%
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7.82%
9.96*
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7.97*
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2.88*
3.26*
4.11*
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051*
9.61*
5.36%
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5.20*
116*
4.07*
3.34*
25.04*
Other
1.47*
1.14*
1.37%
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1.33*
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122*
0.92%
0.21%
101*
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0.61*
1.88*
0.76*
246*
142%
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0.44*
0.21*
1.87%
1.63*
1.35%
1.44%
1.98%
Misting/
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6.23%
0.88*
21.98*
0.02*
3.64*
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13.03*
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3.82%
30.43*
7.86*
1622%
25.88*
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14.S2*
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0.27*
0.02%
1.40*
0.01*
0.16*
6.41%
O.OD*
0.36%
-------
£ Table 4-2. (Continued)
o
EP
Rig
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
Cataloging
Unit*
0603000
0313000
03O60106
0314010
06040005
06040001
06020002
08010100
06010104
03040201
03160205
08030209
03140107
03060101
04090004
07120003
07120004
04040003
040302O4
04040001
04040002
0714C&H
07010206
07140106
07070003
Name
GuntersrilleLake
Middle Chaoahoachee-Lake Harding
Middle Savannah
Choctawhatchee Bay
Kentucky Lake
Lower Tennessee-Beech
Hiwassee
Lower Mississippi-Memphis
Holsun
Lower Pee Dee
Mobile Bay
Deer-Slede
PerdidoBay
Seneca
etrat
Chicago
DCS Plains
Milwaukee
Lower Fox
(tie Calumet-Galien
Pike-Root
jper Kaslraslria
win Cities
g Muddy
Cattle Rock
Tier
'11KK1
TIER2
TIER1
T1ER2
TIER1
T1ER2
TTER1
TIER2
TIER1
TIER2
TORI
TTER2
TERl
1TER2
TTER1
TIER2
TIER1
TIER2
TIER1
TIER2
TIER1
TTER2
TIER1
TIER2
T1ER1
HER2
T1ER1
TIER2
T1ER1
TIER2
1TER1
TER2
1ER1
TIER2
TIER1
TIER2
TIER1
TIER2
TIER1
T1ER2
TIER1
TER2
TIER1
TIER2
TIER1
TIER2
TffiRl
TIER2
TIER1
TDHR2
Mercery
7
36
0
3
11
«
0
14
0
9
1
1
6
1
0
3
3
1
16
11
14
0
0
8
«
1
42
27
21
27
12
18
5
22
21
5
10
24
5
16
0
4
0
1
2
14
0
2
Other
Metals
1
60
1
8
11
10
7
32
0
25
0
11
0
18
1
3
1
6
0
16
13
38
0
7
0
15
1
8
21
90
23
52
4
53
6
38
3
27
14
48
4
40
0
8
0
2
2
61
0
1
itions V
PCBs
15
0
19
3
19
3
2
?
14
0
14
0
12
o
12
2
10
1
7
1
2
6
0
O
1
3
9
2
74
31
34
16
54
24
43
3
41
1
40
6
28
11
23
6
26
o
20
13
20
0
VUh • Probability of Advene Effects
Pesticides
3
11
4
14
3
13
9
11
0
2
0
13
0
6
0
IS
0
4
5
16
1
16
11
10
0
0
3
1
42
7
18
37
11
76
6
32
8
16
9
12
3
16
14
38
0
5
0
39
0
5
PAHs
0
0
0
2
2
2
2
15
0
0
0
o
0
0
0
0
0
, 0
0
1
4
21
0
0
1
9
0
0
53
19
0
0
0
0
20
6
5
14
7
0
1
3
0
0
0
0
0
0
0
0
Other
0
0
7
2
6
2
0
0
1
2
1
0
2
0
4
0
2
0
2
0
0
0
0
0
1
0
0
0
38
17
0
0
0
14
15
5
19
10
3
1
3
0
0
0
1
0
0
2
0
An
Chemkals-
25
46
21
4
20
11
19
23
15
14
15
6
13
17
14
3
12
2
11
20
31
43
11
10
10
24
10
3
85
29
64
36
61
43
60
16
49
2
45
26
34
30
31
24
26
2
23
65
20
0
Total
*of
Stations
92
27
36
51
30
25
33
20
15
34
81
21
38
16
115
103
110
90
51
89
72
55
35
94
22
Percent of Total Area In Each Watershed
Residential
0.97%
4.86*
3.75%
3.04%
1.25%
0.38%
2.65%
0.57%
4.73%
2.02%
4.22%
1.29%
8.04%
0.54%
42.87%
36.16%
21.71%
11.83%
8.94%
7.34%
12.02%
1.19%
21.99%
1.96%
1.05%
Commercial/
Industrial
0.33%
0.77%
1.78%
4.94%
0.33%
0.12%
0.511
0.88%
1.14%
0.55%
0.91%
0.57%
2.35%
0.02%
12.65%
19.12%
9.97%
5.78%
5.28%
6.16%
5.19%
0.39%
5.24%
0.91%
0.53%
Other
Urban
0.23%
0.95%
0.81%
1.10%
0.26%
0.20%
0.58%
0.35%
0.45%
0.47%
0.97%
0.77%
1.12%
0.02%
8.99%
8.10%
6.61%
4.20%
2.88%
2^9%
4.10%
0.69%
5.12%
0.66%
0.55%
Cropland
40.41%
15.41%
16.90%
3.03%
25.78%
28.06%
18.99%
49.87%
44.35%
32.03%
2.68%
74.35%
2.59%
0.12%
2435%
20.63%
48.40%
66.30%
76.15%
37.11%
33.68%
90.79%
48.03%
70.37%
40.77%
Other
Agricultural
0.05%
0.12%
0.18%
0.01%
0.00%
0.01%
0.11%
0.06%
0.01%
0.20%
0.43%
0.91%
0.16%
0.00%
0.18%
0.00%
0.31%
0.08%
0.04%
0.22%
0.04%
0.02%
0.03%
0.51%
0.05%
FonsUand
52.24%
75.59%
62.67%
61.80%
58.59%
65.47%
58.13%
21.07%
43.72%
54.90%
9.60%
18.66%
14.87%
13.24%
5.95%
4.45%
7.47%
6.64%
3.43%
12.87%
0.93%
5.83%
4.39%
2O.43%
37.43%
Baysi
Estuaries
0.00%
0.00%
0.00%
17.57%
0.00%
0.00%
0.00%
0.00%
0.00%
0.01%
18.20%
0.00%
8.08%
0.00%
0.78%
8.76%
0.00%
0.10%
0.11%
30.51%
43.58%
0.00%
0.00%
0.00%
0.00%
Other
Water
5.18%
0.98%
12.10%
3.14%
13.00%
3.01%
1.63%
25.08%
5.29%
9.43%
1.97%
3.34%
4.77%
0.58%
2,29%
1.14%
2.04%
4.68%
2.19%
2.12%
0.18%
1.05%
14.24%
3.60%
18.97%
Other
0.55%
1.27%
1.80%
1.25%
0.76%
1.82%
1.77%
2.09%
0.30%
0.38%
0.33%
0.03%
1.61%
0.36%
1.74%
1.63%
3.48%
0.41%
0.98%
1.08%
0.29%
0.04%
0.95%
1.56%
0.64%
Missing/
Unknown
0.05%
0.05%
0.00%
4.13%
0.03%
0.94%
15.63%
0.03%
0.00%
0.01%
60.70%
0.08%
56.39%
85.13%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
-------
Table 4-2. (Continued)
EPA Ici
Rrs
5 (
5 (
5
5
5
1
.UlaghJ
Unit* Sum 1
H100002I Raisin 1
j 1
M050001 1 St. Joseph
07040003 1 Buffalo- Whitewater
(Ml 10001 1 Black-Rocky
1
01 120006 I Upper Fox
1
S 1 05120111 iMMdkWiasb-Biuseron
5
S
5
5
5
5
5
5
5
5
5
5
5
5
5
6
6
6
«
01140202 1 Middle Kiskastia
OHM0001 \ Rush-VOTnllioii
05120109 1 Vennilkn
04030 108 IMauminee
04090002 1 Lake St. CUir
07140101 1 Cahokta-Joachim
04100010 1 Cedar-Pottage
04 10000 i|
1
0713OD01
04030102
04060103
05040001
07090006
04100012
04110003
08080206
08090100
11070209
08O40207
Ottawa-Stony
Lower Ulioois-Senaciwine Lake
Door-Kewaunee
Maniaee
Tincannrai
fiihwaukee
HuTOD-Vferaiboo
AAubola-Ch^grin
Lower Okaneu
Lower Moiusippi-New Orkam
LowaNeosho
UwecOuackita
Number ofStatJwB With • Probability of Advene Effect
Vr
TER1
TER2
TER1
TER2
ITER1
HER!
nERi
DEB2-
HERI
ITER2
T1ER1
TTER2
1TER1
•nEH2
TTER1
TIER2
TEU
TIER2
TIER
TIER2
TIER
TIER2
TIER
TTER2
TIER1
1TER2
TIER]
TTER2
HER]
TTER2
TTER1
TJER2
TlERl
TCR1
TlERl
TTER2
TOtl
TTER2
TTER1
TTER2
TTER1
TTER2
HERl
T1ER2
TTERl
TIER2
1TKR1
TH-R2
TlERl
T1ER2
(
feravr ft
I
2
O
O
0
1
2
M
0
12
7
9
1
4
0
2
2
5
8
t
10
4
g
24
5
3
6
0
0
2
7
0
O
0
1
0
21
5
5
12
18
3
11
0
0
0
5
Mher
lelafe
0
1
1
«
0
2
0
54
0
37
0
23
0
16
0
3
0
19
4
7
2
1}
1
25
0
46
,;
0
12
0
»
1
11
8
55
0
12
0
45
S
23
2
35
0
48
0
2
0
11
CBs
17
17
3
0
17
0
12
7
15
14
9
8
5
6
13
0
:
a
We
:;
i
12
12
1
1
30
1
i
•esfitite
7
13
7
i
0
«
7
4
0
27
0
30
B
22
0
3
0
26
0
2
&
g
2
41
3
4
3
10
0
15
0
6
2
12
2
4
0
34
0
o
s
2
6
1
40
0
13
tl
0
AHs
1
1
7
2
0
0
21
2
O
0
O
0
0
0
0
0
0
0
2
7
<
8
0
0
15
i
0
0
0
10
1
1
17
6
1
15
34
1
0
0
0
)1ber
0
6
3
6
0
0
9
1
0
0
0
0
0
(1
1
0
0
0
1
0
9
-1
4
0
4
'.
0
o
o
o
o
o
1
1
5
,
10
1
0
0
All
Tx-mkab-
18
19
17
9
17
3
24
31
15
40
15
17
13
22
13
1
12
16
12
6
n
5
18
34
13
39
13
15
11
10
12
5
11
3
10
SJ
10
24
10
35
10
\»
16
52
16
34
13
3
12
0
Total
tof
tatiotK
38
32
26
59
60
33
38
14
28
21
19
56
56
29
21
20
14
78
34
45
31
100
51
20
12
Fereenl of ToUl Are. In Each Watershed
esMennal
2.25%
3.081
0.74%
11.18%
10.36%
2-49%
1.21%
138%
3.92%
0.55%
11.44%
10.64%
1.85%
6.73%
2.04%
0.77%
0.45%
10.00*
235%
1.63*
18.31%
175*
3.0WS,
0.34%
3.38%
Conunerdal/
Industrial
1.00%
1.42%
0.29%
2.79%
2-44%
0.92%
0.40%
0.59%
1.00%
0.17%
3.81%
4-50%
1.28%
2.43%
1.04%
0.35%
0.20%
1.64%
1.05%
0.54%
3.14%
2.01*
126%
0.02%
0.33%
Other
Urban
0.74%
1.02%
0.40%
4.40%
2.38%
1.02%
0.60%
0.44%
0.73%
0.29%
X35%
4.32%
1.44%
2.93%
0.51%
0.46%
0.30%
1.71%
0.99%
0-91*
5.37*
0.44%
0.73%
0.05%
OJ1%
87.13%
79.21%
54.93%
66.45%
63.18*
79.64%
78.52%
80.68*
90.08%
10.13*
28.70%
4X42%
73.80%
75.57%
82J5&
38.47%
17.77%
53.74%
91.45*
85J8*
39.91*
30.87*
1.70*
4.48%
30.43%
Other
ericultanU
0.15%
1.25%
0.05%
0.20%
0.61%
0.09%
0.09%
0.06%
0.10%
0.01%
0.00%
0.11%
0.07%
0.30%
0.04%
0.87%
0.14%
0.04%
038%
0.17%
0.06%
021%
003%
0.01%
0.12%
5,46%
9.23%
37.00%
11.11*
10.84%
1331%
16.06%
943%
3.51%
67.58%
3.60%
33.25%
156%
6.t9%
8.96%
10.63%
73.75*
30.05%
2.99*
6.86%
27.41*
4J7*
134*
3.35*
52.72*
Bars*
Estuaries
0.01%
0.03%
0.00%
3.20%
0.00%
0.00%
0.00%
0.00%
0.00%
0.01*
33.06%
0.00%
17.41%
3.84%
O.00%
42.55%
0.00%
0.00*
0.00%
3.93%
4.86%
0.00%
1&26%
0.00*
0.00%
Otbrr
Water
2.90%
4.45%
6.50%
0.38%
7.42%
1.50%
3.01%
7.07%
0.15%
20.94%
4.87*
3.85%
2.10%
1.12%
4.04%
5.63%
6.82%
0.97%
0.30%
0.27%
0.63*
54.19*
39.49*
1.08*
8.96%
Other
0.35%
0.31%
0.08%
0.29%
2.77%
1.03*
0.10%
0.34%
0.50%
0.31%
0.17%
0.92%
0.49%
0.89%
0.82%
0.25%
0.57%
1.85%
038*
0.27*
0.30%
0.50%
0.53%
0.02%
3.36*
Mtafany
0.00%
0.00%
0.00%
0.00%
0.00%
0.03%
0.00%
0.00%
0.00%
0.01*
0.00*
0.00%
0.00%
0.00%
0.00%
0.00*
0.00%
0.00%
0.00*
0.04%
0.01%
4.67%
34.37*
9065*
0.00%
-------
Table 4-2. (Continued)
EPA
Reg.
6
7
7
7
9
9
9
9
9
9
9
»
9
9
10
10
10
10
10
10
10
Cmtalogins
Unltf
12040104
10270104
11070207
07080101
18070304
18O70104
18070201
18050003
18070204
18050004
18070105
18030012
18070107
18070301
17110019
17110013
17110002
17030003
17090012
17110014
17OI03O3
Name
Buffalo-San Jacitto
Lower Kansas
Spring
Copperas Duck
San Diego
Santa Monica Bay
Sea) Beach
Coyote
Newport Bay
San Francisco Bay
Los Angeles
Tulan-Buena Vista Labs
San Pedro Channel Islands
Aliso-San Oaof a
Puget Sound
DuwamUh
Strait ofGeonjia
Lower Yatma
Lower Willamette
Puyallup
CoenrD'AteneLaie
Number of Stations Witt • Probability of Advene Effects
Tier
TIER1
TTER2
TIER1
TER2
HHR1
TIER2
TTER1
TDER2
TERI
TIER2
TIER1
TIER2
ITER1
T1ER2
TIER1
TIE112
TIER1
TJH12
TERI
TIER2
TERI
TIER2
TTER1
TIER2
TERI
JJER2
TERI
TIER2
TERI
TIER2
TIER!
TIER2
TffiRl
TIER2
TTER1
TIER2
TERI
TIER2
TIER1
TTER2
TIER1
TIER2
Mercury
0
14
0
0
I
1
IS
X
15
33
5
38
14
8
10
13
10
33
4
16
0
1
7
3
5
7
98
449
0
27
1*
51
0
0
1
12
0
0
1
1
Other
Metals
1
26
1
14
0
....»
1
7
4
93
6
94
0
211
8
12
0
62
9
41
0
33
0
5
2
22
2
29
52
111*
3
107
1
ISO
0
4
0
51
3
e
t
13
FCfc
9
15
11
o
8
1
17
0
33
45
22
34
g
142
0
1
1
19
1
18
1
4
1
4
2
6
0
9
146
317
34
10
1
15
5
0
13
24
1
6
2
0
Pesticides
3
14
0
22
O
^
0
18
13
47
66
22
23
288
0
0
11
48
0
19
8
10
10
5
10
3
5
7
37
106
3
17
4
34
19
23
10
18
0
1
0
0
PAHs
11
0
0
O
1
7
39
4
18
2
30
0
1
0
B
•5
21
3
5
1
0
0
4
0
2
296
490
12
58
11
73
0
1
5
11
t
9
0
0
Otter
3
3
1
3
2
1
1
2
2
4
1
3
32
182
0
0
2
25
0
0
0
1
I
o
0
3
0
0
32
317
6
23
4
28
1
10
4
15
1
6
0
0
All
Chemicals-
10
23
12
15
10
25
17
5
53
51
79
31
63
339
18
6
24
66
19
37
14
19
10
5
14
10
10
22
418
851
48
69
31
168
23
19
21
51
12
6
10
13
TotaJ
for
Stations
36
29
41
27
107
132
442
24
108
64
37
20
25
32
1383
127
2«
47
76
19
23
Percent oTIbtal Are. in Each Watershed
Residential
23.31%
3.70%
1.84%
5.40%
1 1.02%
17.03%
41.18%
20.29%
19.51%
12.06%
3836*
1.76%
0.00%
3.18%
12.36%
12.99%
4.22*
1.13%
31.21%
5.85%
0.73%
CommercUK
Industrial
7.07%
1.82%
0.67%
2.53%
4.09%
7.90%
22.80%
9.69%
13.49%
7.21%
13.78%
1.53%
0.08%
1.26%
112%
2.97%
0.15%
0.52%
6.41%
0.55%
0.13%
Otter
Urtan
6.32%
1.83%
0.79*
1.58%
2.72%
2.86%
4.68%
9.13*
6.60%
3.48%
6.51%
0.70%
0.01%
1.22%
2.05*
4.23%
1.22*
0.26%
4.69*
0.79%
0.42*
Cropland
45.96%
82.75%
80.42%
68.60%
6.92%
1.18%
4.98%
607%
18.96%
4.43%
1.31%
55.36%
0.00%
4.37%
3.75%
6.82%
10.95*
25.97%
13.32%
3.78%
12.68*
Otter
AgrlcnltanJ
0.06*
0.91%
0.12*
0.18%
54.85%
20.81*
0.12%
23.27%
28. IS*
2736*
31.59%
38.72%
2J9%
60.80*
0.32*
0.55%
0.46*
55.06%
0.97%
4.44%
0.65*
ForesdiBd
13.38%
7.67%
14.27*
9.58%
9.62%
068*
0.00*
27.93%
0.25*
28.64*
6.65*
0.90%
0.00%
5.39%
4IJ5*
70.85*
28.13*
15.65%
39.03%
81.43%
75.10%
B«ys&
Estuaries
0.04*
0.00%
0.00%
0.00%
1.36%
0.41%
0.75%
1.58%
1.09%
14.20%
O.02*
0.00%
0.02%
0.03%
34.95*
0.00%
51.38*
0.00%
0.00%
0.00*
0.00%
Otter
Water
2.97%
0.92*
0.19*
9.04*
0.86%
0.20%
1.15*
1.38%
0.91%
1.98*
0.30*
0.74%
0.00%
0.26%
2.62*
0.96%
2.61*
t.23%
3.77%
0.68%
10.14*
Otter
0.80%
0.40*
1,7O%
0.54*
1.98%
0.96%
1.27*
0.66%
3.33%
0.65%
1.46%
OJ6%
0.18%
!.49%
0.48*
0.63%
0.20*
0.17*
0.61*
147%
0.14*
Missing/
Unknown
0.08%
0.00%
0.01*
2.55*
6.60%
47.95%
23.05*
0.01*
7.69*
0.00%
0.01*
0.03*
97.12%
22.01%
0.00%
0.00%
0.07*
0.01%
0.00%
0.01%
0.00*
•Because of the numerous chemicals mofutorcd at each station, the total in this cohin» is not equal to the sum of the ftumbers in the columns for the different chemical classes.
* Adapted from USGS land use and land cover cJassiGcatkn system for use with remote sensor data.
-------
70%T"
60%-
50%--
J0%
20*-'
10%--
0%
25%
50%
75%
100%
Percent Agricultural Land Use
Ave.Agrl. Use
Ava. Urban Use
Ave. Forest Use
10%
20%
36%
38%
18%
28%
63%
10%
17%
83%
5%
9%
Figure 4-2. Percent Tier 1 and Tier 2 Stations vs. Agricultural Land Use in
APCs.
Table 4-3. Comparison of Percent APC Agricultural Land Use to Percent
of Tier 1 and Tier 2 Stations by Chemical Class
Average Percent Agricultural Land Area in Group
Number of Watersheds in Group
Metals
PCBs
Pesticides
Mercury
PAHs
Others
Percent Total Agricultural Land Area
<25%
10%
32
66%
38%
37%
32%
30%
13%
25-50%
36%
34
60%
48%
39%
34%
17%
16%
50-75%
63%
13
58%
45%
40%
20%
12%
9%
>75%
83%
17
47%
42%
64%
18%
9%
12%
Overall
Average
39%
59%
43%
43%
29%
19%
14%
agricultural land use and calculated the average percent
contaminant by chemical class for each group. Some
general trends that would be expected were clearly evi-
dent. In watersheds with greater than 75 percent of the
land devoted to agriculture, pesticide contamination
jumped from under 40 percent of all stations to 64 per-
cent. In contrast, metals, mercury, and PAH contamina-
tion all steadily decreased, with all three classes exhibiting
a percent contamination in the over 75 percent agricul-
ture 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 per-
centage 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 ur-
ban area to best illustrate trends.
The percent PAH and metal con-
tamination were both 10 percent-
age points under the overall
average for the least urbanized
watershed group, then rose
sharply as the proportion of ur-
ban area crossed the 5 percent
threshold. The extent of metal
contamination rose to an average
of 71 percent, more than 10 per-
centage points above the overall
average of 59 percent, in water-
sheds 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 illustrate the effect
of atmospheric deposition from
local urban sources. Contamina-
tion from other organics also rose
steadily, but never varied more
than 6 percentage points from the
overall average. Pesticide con-
tamination 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 pre-
viously, this may reflect upstream delivery of contami-
nants, pesticide manufacture or formulation, or urban
applications in the past. As was the case with the agri-
culture analysis, the average percent PCB contamination
for the urban groups showed no trend and never varied
substantially from the overall average.
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.
4-13
-------
80%-r
70%--
80%--
iS 50%--
I = 40%--
30%- -
20%--
10%--
0%H 1 r
0% 5% 10%
25% 30% 35% 40%
Percent Total Urban Land Use
Ave. Urban Use 2% 7% 14%
Ave.Agri. Use 51% 38% 40%
Ave. Forest Use 29% 27% 29%
38%
26%
18%
Figure 4-3. Percent Tier 1 and Her 2 Stations vs. Urban Land Use in APCs.
Table 4-4. Comparison of Percent APC Urban Land Use to Percent of
Tier 1 and Tier 2 Stations by Chemical Class
Average Percent Urban Land Area in Group
Number of Watersheds in Group
Metals
PCBs
Pesticides
Mercury
PAHs
Others
Percent Total Urban Land Area
<5%
2%
32
49%
47%
50%
21%
9%
8%
5-10%
7%
18
61%
37%
39%
24%
25%
12%
10-20%
14%
19
59%
40%
32%
30%
23%
15%
>20%
38%
27
71%
45%
44%
40%
25%
20%
OveraU
Average
16%
59%
43%
43%
29%
19%
14%
The objective of the point source assessment com-
ponent of the NSI is to compile available data regarding
the purposeful discharge of sediment contaminants from
industrial facilities and municipal sewage treatment plants
and to determine the sediment hazard potential by chemi-
cal class, watershed, and industrial category. EPA has
produced a July 1996 draft National Sediment Contami-
nant Point Source Inventory: Analysis of Facility Release
Data based on 1994 permit monitoring records in EPA's
Permit Compliance System (PCS) and chemical release
estimates in the 1993 Toxic Release Inventory (TRI). The
report presents a sediment hazard analysis based on re-
lease amount, chemical toxicity, and inherent physical/
chemical properties of the contaminant. The report serves
as Volume II of the complete report to Congress on the
extent and severity of sediment
contamination in surface waters
of the United States. As previ-
ously stated, discharge limits for
point sources are not necessarily
protective of downstream sedi-
ment quality. The Agency be-
lieves an effective source control
strategy should focus on areas at
greatest risk on a watershed
scale. The report identifies 29
watersheds among the 96 areas
of potential widespread potential
sediment contamination (APCs)
where the potential for point
source contribution to sediment
contamination is the greatest.
The objective of the
nonpoint source assessment com-
ponent of the NSI is to prepare a
nationwide assessment of annual
nonpoint source contributions of
selected sediment contaminants
on a watershed basis. Given the
number and diversity of nonpoint
sources, the Agency is focusing
initial efforts on four major cat-
egories: harvested croplands,
urban areas, atmospheric dep-
osition, and inactive and aban-
doned mine sites (where informa-
tion is available). Although these
nonpoint sources do not consti-
tute the full range of sediment
contaminant sources, they are fre-
quently cited in the scientific lit-
erature as significant sources of
mercury, PCBs, PAHs, metals, pesticides, and other or-
ganic compounds.
The nonpoint source assessment is intended to be a
screening-level study that begins to correlate contami-
nated sediment sites with suspected sources of these con-
taminants. As part of this assessment, EPA is compiling
data from the Bureau of the Census, the U.S. Depart-
ment of Agriculture, the U.S. Department of the Interior's
U.S. Geological Survey and Bureau of Mines, and oth-
ers. EPA will compile information and data concerning
these nonpoint source activities to identify watersheds
for further investigation and assessment.
Given the breadth of nonpoint sources, EPA antici-
pates that the process of conducting future assessments
4-14
-------
will be iterative. Additional nonpoint sources will be
added to the inventory to more fully discriminate between
contaminant types and known sources and to character-
ize 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 sediment quality are ei-
ther limited or lacking.
4-15
-------
Dnifl National .Sfdiiiifiit Quality Survey
Chapter 5
Conclusions and Discussion
The National Sediment Inventory (NSI) is EPA's
largest compilation of sediment chemistry data
md related biological data. It includes approxi-
mately 2 million records for more than 21,000 monitor-
ing stalions across the country. EPA's evaluation of the
NSI data was the most geographically extensive investi-
gation of sediment contamination ever performed in the
United States. The evaluation was based on state-of-
the-art risk-based procedures to address the probability
of adverse effects to human health, aquatic life, and wild-
life. The results verify that sediment contamination is
widespread and is an important national concern. This
was the first large-scale assessment of sediment quality
to go beyond "hot spot" identification. Through closer
inspection of the thousands of potentially contaminated
locations, EPA distinguished 96 watersheds where con-
tamination is likely to be most severe and extensive.
The Agency and its state and federal partners can
address sediment contamination problems through wa-
tershed management approaches. Watershed manage-
ment programs focus on hydrologically defined
drainage basins rather than areas defined by political
boundaries. These programs recognize that conditions
of land areas and activities within the watershed af-
fect the water resource. Local management, stake-
holder involvement, and holistic assessments of water
quality are characteristics of the watershed approach.
The National Estuary Program is one example ot trie
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 Estu-
ary Programs have all recommended actions to reduce
sources of toxic contaminants to sediment. Numerous
other examples of watershed management programs are
summarized in The Watershed Approach 1993/94 Ac-
tivity Report (USEPA, I994g) andA P^e I Inventory
of Current EPA Efforts to Protect Ecosystems (USWA.
1995b).
This chapter presents some general conclusions
about the extent of sediment contamination in the
United States and sources of sediment conianunantt.
It also includes comparisons to other national studies
that address the extent of sediment contamination and
to a national survey of state-issued fish consumption
advisories. In addition* this chapter presents the re-
sults of an analysis of the sensitivity of parameters
used to evaluate potential human health effects from.
exposure to PCBs and mercury, which was performed
to show how the use of different screening values af-
fect the results. The chapter concludes with a discus-
sion of the strengths and limitations of the NSI data
and evaluation method.
Extent of Sediment Contamination
The results of the NSI data evaluation confirm that
sediment contamination is an important national con-
cern and poses potential risks to aquatic life, human
health, and wildlife. The data further confirm that sedi-
ment contamination is widespread in many watersheds
of the country. The analysis of the NSI data indicates
that potential sediment contamination exists in all re-
gions and states of the country. The water bodies af-
fected include streams, lakes, harbors, nearshore areas,
and oceans. A number of specific locations in the
United States had large numbers of stations that indi-
cate a higher probability of adverse effects (Tier 1 sta-
tions). Puget Sound, Boston Harbor, the Detroit River,
San Diego Bay, and portions of the Tennessee River
were among those locations. Several U.S. harbors
(e.g., Boston Harbor, Puget Sound, Los Angeles, Chi-
cago, Detroit) appear to have some of the most se-
verely 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 insuf-
ficient data were available in the NSI to make such a
determination. EPA's River Reach File 1 (RF1) delin-
eates 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 Stales contained NSI sampling
5-1
-------
Coiu'liisinns and Discussion
stations. More than 5,000 sampling stations in ap-
proximately 2,400 river reaches across the country (35
percent of all reaches evaluated) were classified as
Tier I. Another 10,000 sampling stations were classi-
fied as Tier 2. In total, over 5,000 river reaches in the
United States—approximately 75 percent of all river
reaches evaluated—include at least one Tier 1 or Tier
2 station. However, EPA cannot estimate the areal
extent or number of river miles of contaminated sedi-
ment because the NSI does not provide complete cov-
erage 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 evalua-
tion is probably greater than that which would result
from purely random sampling. Swartz et al. (1995) dem-
onstrated the effects of nonrandom sampling design
on the frequency of detecting contaminated sites.
They compared the percent of sediment sites that ex-
ceeded PAH screening effects levels (ERL, SQC, AET)
based on random site selection (Virginian Province
EMAP stations) to the percent of sites that exceeded
those levels based on site selection on the basis of
known PAH contamination (creosote-contaminated
Eagle Harbor, Washington). They found that the fre-
quency of exceeding a sediment guideline in sites
known to be contaminated was 5 to 10 times greater
than that for randomly selected sites.
The results of the NSI data evaluation must be
interpreted in the context of data availability. Many
states and EPA Regions appear to have a much greater
extent of sediment contamination than others. To
some degree, this appearance reflects the relative ex-
tent of readily available electronic data, not necessar-
ily the relative extent of sediment contamination. For
example, 182 of the 920 river reaches in Illinois con-
tain a Tier 1 station, whereas only 9 of the 5,490
reaches in Montana contain a Tier 1 station. How-
ever, the NSI includes sampling station data for over
50 percent of the river reaches in Illinois but less than
1 percent of the riverreaches in Montana. Therefore,
although the absolute number of Tier 1 and Tier 2
stations in each state is important, relative compari-
sons of the extent of sediment contamination between
states is not possible because the extent of sampling
and data availability vary widely.
The lack of important assessment parameters ham-
pered EPA's efforts to determine the full extent and
severity of sediment contamination. For example, a
Tier 1 classification based on divalent metal concen-
trations in sediment required an associated acid-vola-
tile sulfide (AVS) measurement. Also, a Tier 1
classification for potential human health effects re-
quired both sediment chemistry and fish tissue resi-
due data for all chemicals except PCBs and dioxins.
These data combinations frequently were not avail-
able. Table A-2 in Appendix A presents the total num-
ber of NSI stations where sediment chemistry data,
related biological data, and matched data (i.e., sedi-
ment chemistry and biological data taken at the same
station) were collected. AVS measurements were
available at only 1 percent of the evaluated stations.
Likewise, matched sediment chemistry 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 evalu-
ated stations.
The potential risk of adverse effects to aquatic life
and human health is greater in areas with a multitude
of contaminated locations. The assessment of indi-
vidual monitoring stations is useful for estimating the
number and distribution of contaminated spots and
overall magnitude of sediment contamination in the
United States. However, a single "hot spot" might
not pose a great threat to either the benthic commu-
nity 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 expo-
sure are much greater. EPA examined sampling sta-
tion classifications within watersheds to identify areas
of potential widespread sediment contamination
(APCs), where the exposure of benthic organisms and
resident fish to contaminated sediment would be fre-
quent. These are watersheds that include at least 10
Tier 1 sampling stations and in which at least 75 per-
cent of all sampling stations were categorized as either
Tier 1 or Tier 2. Approximately 5 percent of the water-
sheds in the United States (96 of 2,111) were classified
as APCs based on this assessment. In many of these
watersheds, the risk might be concentrated on certain
water bodies or river reaches. Within the 96 APCs, 57
river reaches include 10 or more Tier 1 stations.
Although the APCs were selected by means of a
screening exercise, EPA believes thai they represent the
highest priority for further risk analysis, temporal trend
assessment, contaminant source evaluation, and man-
agement action because of the preponderance of evi-
5-2
-------
Draft National Sediment Quality Snru-y
dence in these areas. The uncertainties and limita-
tions of this assessment leave some doubt about the
classification of any given sampling station. However,
the probability of making an erroneous classification
of all 10 Tier 1 stations and 75 percent of all stations in
a watershed is low. EPA chose the watershed as the
unit of spatial analysis 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 view-
ing the watershed holistically. Watershed management
is a vital component of community-based environmen-
tal protection.
Although only 11 percent of all river reaches in
the United States were evaluated as part of this study,
it is likely that the results identified most of the se-
verely contaminated areas in the country. This is be-
cause most of the NSI data were obtained from
monitoring programs targeted toward areas of known
or suspected contamination. For a number of reasons,
however, some potentially contaminated sediment sites
were missed. The most obvious reason is that the NSI
does not include all sediment quality data that have
ever been collected. For example, the NSI does not
include many EPA Superfund Program data and there-
fore sampling stations in the vicinity of hazardous
waste sites might not have been included in the NM
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
longitude) 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 dur-
ing the past 10 to 20 years. For example, the commer-
cial 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 municipjd
point source discharges and best management practices
for the control of nonpoint sources have greatly reduced
contaminant 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 af-
ter the implementation of source controls and improved
effluent 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, 1994d). 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 site. In the National Water Quality
Inventory report, the states frequently listed metals (e.g.,
5-3
-------
Conclusions im
-------
Drul'l Million;)! Sediment Quality Survey
Table 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 Tier 2 Based on the NSI Data
Evaluation
Chemical*
Mercury
PCBs
Chlordane
Dioxins
DDT and metabolites
Dieldrin
Selenium
Mirex
PAHs
Toxaphene
Hexachlorobenzene
Lead
Hexachlorobutadiene
Creosote*
Chromium
Copper
Zinc
# of Waterbodies with
Fish Advisories'
1,119
387
114
53
28
15
12
10
5
4
3
2
2
2
1
1
t
Number or River Reaches That Include
at Least One Tier 1 or Tier 2 Station
Based on the NSI Data Evaluation of
Human Health Fish Consumption
Advisories P
Tier 1
0
1,498
11
242
19
9
0
0
0
0
0
0
0
-
0
0
0
arameters'
Tier 2'
89
732
1,026
8
656
1,296
4
15
529
183
53
259
6
-
6
4
14
Total
89
2,230
1,037
250
675
1,305
4
15
529
183
53
259
6
-
6
4
14
"Other chemical groups responsible for fish consumption advisories (i.e., pesticides [24 water
bodies], "multiple" [4 water bodies], "not specified" [4 water bodies], and metals [6 water bodies])
could not be directly compared to NSI chemicals.
bNo reference values were available for creosote; therefore, it was not evaluated in the NSI data
evaluation.
•Does not include statewide advisories
Mercury: New York, New Jersey, Maine, Massachusetts, Michigan, coastal Florida
Chlordane: Missouri
PCBs: New York
Dioxin: coastal Maine
dA water body can be composed of numerous rivei reaches.
•River reaches that include at least one Tier 2 station but no Tier 1 stations.
grams per day. However, fish
advisories are often issued for
more exposed populations,
such as recreational or subsis-
tence fishers. The 0.2 ppm Ca-
nadian guideline limit for
mercury in fish that are part of
a subsistence diet yields 2,308
Tier 2 stations (56 percent of
all stations with detectable lev-
els) when applied to all edible
species in the NSI database.
Further details of the additional
mercury analyses are provided
in Appendix H.
The conclusion resulting
from these additional analyses
is that all three explanations
probably have an effect Most
fish advisories are issued to
protect infants from develop-
mental 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
advisories are not included in
the NSI, or are not evaluated
for sediment contamination be-
cause they are measurements in
pelagic or migratory fish.
Sensitivity to
Selected PCB
Evaluation
Parameters
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 stations (4 percent of all stations with
detectable levels), If data from all edible pelagic and
migratory species are included in the analysis, there
are 374 Tier 2 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 issu-
ing fishing advisories to protect against developmen-
tal effects among infants (USEPA, 1994e), yields 821
Tier 2 stations (20 percent of all stations with detect-
able levels) when applied to all edible species using
the consumption rate for an average consumer of 6.5
Because PCBs anddioxin 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 indicate a po-
tential sediment contamination problem and to place a site
in the Tier 1 category for those contaminants. Based on
the NSI data evaluation, PCBs were responsible for the
Tier 1 classification of more stations than any other chemi-
cal. Therefore, EPA conducted a sensitivity analysis of
some PCB evaluation parameters to determine the effect
on the number of stations classified as Tier 1 or Tier 2.
In the NSI evaluation, EPA selected a conservative ap-
proach for the analysis of PCBs. The approach is conserva-
tive because it does not require matching sediment chemistry
5-5
-------
Conclusions and Discussion
(a)
Figure 5-1. Comparison of (a) Location of Fish Consumption Advisories to (b) Location of NSI Stations
Categorized as Tier 1 or Tier 2 Due to Potential Human Health Effects.
5-6
-------
Drul't Niilioinil SrdiiiH'iil Qiiiility Survey
and tissue residue data for PCB, and it is based on a cancer
risk level of 105 (i.e., a 1 in 100,000 extra chance of cancer
over a lifetime of 70 years) applied to all PCBs congeners
or total PCB measurements. Some PCB congeners, mea-
sured individually or as a part of total PCB s, are considered
a greater threat for noncancer effects than for cancer. The
evaluation currently places 3,198 sediment stations and
2,256 tissue stations in Tier 1 based on human health pro-
tection using the 10'5 cancer risk level. Only 542 stations
included matching sediment and tissue data for PCBs. The
number of stations classified as Tier 1 would have decreased
significantly if this match had been required.
EPA performed additional evaluations to determine
the number of stations that exceed other screening val-
ues, less conservative than those selected for the PCB
evaluation used in this study. The complete results are
presented in Appendix H, which includes a comparison
of the number of sediment and fish tissue sampling sta-
tions with detectable levels of PCBs that exceed various
evaluation parameters for both aquatic life and human
health. The number of sediment stations that exceed the
respective levels is approximate because the additional
analyses were performed using default organic carbon
content.
Site evaluation based on PCB contamination 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
10'5 or greater, 85 percent or more of the stations with
detectable PCB levels are classified as Tier 1. About one-
half to two-thirds of the stations are classified as Tier 1
for exceedances of PCB levels protective of noncancer
health effects, cancer risk at a 1Q-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 factor,
and whether one is examining the set of fish tissue data
or sediment chemistry data. These three levels of protec-
tion vary within two orders of magnitude, a range that
covers most of the distribution of PCB measurements.
Although station classification for PCB contamina-
tion is quite sensitive to selection of evaluation param-
eters, 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 stations. If PCBs are dropped from the
analysis entirely, the total number of Tier 1 and Tier 2
stations remains about the same (less than a 5 percent
decrease), but the number of Tier 1 stations decreases by
approximately 40 percent. If PCBs are evaluated using a
noncancer human health threshold, the total number of
Tier 1 and Tier 2 stations decreases by less than 2 percent
and the number of Tier 1 stations decreases by approxi-
mately 12 percent. Figure 5-2 shows the location of Tier
1 and Tier 2 stations that exhibit potential human health
risks for all chemicals other than PCBs for comparison to
Figure 3-6 in the results section.
Strengths of the NSI Data
Evaluation
The National Sediment Quality Survey is the first
EPA analysis of sediment chemistry and related bio-
logical data from numerous databases to identify the
national extent and severity of sediment contamina-
tion. In future surveys, EPA will include data from
additional databases, thereby providing more complete
geographic coverage of the country. This first evalu-
ation of the NSI data, however, provides a baseline
screening assessment of sediment contamination prob-
lems throughout the country.
In the evaluation of NSI data, EPA has used the
most widely accepted sediment assessment techniques,
which were recommended by national experts in sedi-
ment quality assessment. These experts included per-
sons from federal and state governments as well as the
private sector. Because of the complex nature of the
reactions between different chemicals in different sedi-
ment types, in water, and in tissues, no single sedi-
ment assessment technique can be used to adequately
evaluate potential adverse effects due to exposure to
all contaminants. Uncertainties and limitations are as-
sociated with each of the evaluation techniques. To
compensate for those limitations, EPA has used mul-
tiple assessment techniques, alone and in combina-
tion, 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 highly reliable bench-
marks for protecting sediment quality and with mea-
sured TOC can indicate a higher probability for adverse
effects to aquatic life. In addition, EPA believes that
other sediment chemistry screening values (ERMs/
ERLs, PELs/TELs, AETs, and SQALs) are also reliable
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 sedi-
ment chemistry screening values (i.e., ERM, PEL, AET-
high, or SQAL) for classification of Tier 1 sites.
The screening values used to evaluate the NSI
data include both theoretical and correlative ap-
5-7
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!• igure 5-2. NSI Stations That Would Be Categorized as Tier I or Tier 2 Based on Potential Human Health Effects Without PCBs.
-------
proaches. The theoretical approaches (e.g., draft
SQCs, SQALs, and TBPs) are based on the best infor-
mation available concerning how chemicals react in
sediments and organisms and how organisms react to
those chemicals. The correlative approaches (i.e.,
ERMs/ERLs, PELs/TELs, and AETs) are based on
matched sediment and biological data gathered in the
field and in the laboratory, and they provide substan-
tial evidence of actual biological effects from sediments
contaminated with specific concentrations of the chemi-
cals of concern.
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 resi-
due and toxicity data. If high levels of PCBs or diox-
ins (which are highly hydrophobic organic chemicals
commonly found associated with sediments) were
measured in fish tissue at a given site, the site could
be categorized as Tier 1 with no corroborating sedi-
ment chemistry data. For other chemicals, high con-
centrations in tissues alone were not sufficient to
categorize a site as Tier 1; corroborating sediment
chemistry data were also required. For a site to be cat-
egorized as Tier 1 based on toxicity data alone, mul-
tiple 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 proposed SQCs for
only five nonionic organic chemicals, the Agency has
developed similar values, the SQALs, for an additional
35 chemicals as part of the NSI data evaluation. The
SQALs have allowed EPA to evaluate more chemi-
cals 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 sites based on this meth-
odology should be followed up with more intensive as-
sessment efforts, when appropriate (e.g., for water bodies
with multiple Tier 1 sites located in APCs). Two types of
limitations are associated with the evaluation of the NSI
data: limitations associated with the data themselves and
limitations associated with the evaluation of the data.
Limitations of Data
The NSI is a multimedia compilation of environmen-
tal monitoring data obtained from a variety of sources,
including state and federal program offices. Inherent in
the diversity of data sources are contrasting monitoring
objectives and scopes, which make comparison of data
from different data sets difficult. For example, several of
the databases contain only information from marine en-
vironments or other geographically focused areas. The
potential for inconsistencies in measured concentrations
of contaminants at different stations exists for samples
taken from different monitoring programs. For example,
sampling different age profiles in sediments, applying
different sampling and analysis methods, and sampling
for different objectives can affect the results of the NSI
evaluation. Although numerous data sets identified sam-
pling 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 Sediment
Quality Inventory, the Gulf of Mexico Program's Con-
taminated Sediment Inventory, and some data sets from
EPA's STORET). Furthermore, each monitoring program
used unique sampling and analysis protocols. For ex-
ample, PCBs, the chemical group most often responsible
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 source 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 qual-
ity of data was included in the NSI. None of this infor-
mation, 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.
5-9
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Table 5-2. National Sediment Inventory Database: Summary of QA/QC Information
Database
ODES
EMAP (VA and LA Provinces)
Seattle; U.S. Army Corps of
Engineers
Region 4
Gulf of Mexico
COSED
Great Lakes
DMATS
STORET
Massachusetts Bay (USGS)
Are There
QA/QC Reports
to Accompany
the Data?
Yes
Yes
Yes
Some
Some
Yes
Yes
Some
Unknown
Some
Were the Data
Peer-Reviewed?
Yes, 301 (h) data
Yes
Yes
No
No
Yes
Yes
Yes
Unknown
Yes
Are the Sampling
and Analytical
Methods Identified
in the Database?
Yes
Yes
Yes
Some
Some
Yes
Yes
Yes
No
Yes
Are the Detection
Limits for the
Analytes Included
in the Database?
Yes
Yes
Yes
Yes
Yes
Some
Yes
Yes
Yes
Yes
Comments
Data Qualifiers
Data Qualifiers
Data Qualifiers
Data Qualifiers
Data Qualifiers
Data Qualifiers
Data Qualifiers
Data reporting was also inconsistent among the dif-
ferent data sources. Inconsistencies that required resolu-
tion 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 sedi-
ment contaminant levels. Emissions of many prominent
contaminants declined during the 1980s, and significant
remediation efforts have taken place at many sites since
that time. In addition, dredging, burial, and scouring might
have removed contaminants from some sampling sites.
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.
Some data parameters are consistently absent through-
out the NSI database. (Refer to Appendix A, Tables A-l
and A-2, for information on the number of NSI stations at
which the various types of data were collected.) For ex-
ample, very few site-specific TOC or AVS data are avail-
able, and toxicity data or matched sediment chemistry and
biological data were available at relatively few stations.
For many of the fish tissue data included 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 problem;
however, no stations in the NSI could be placed in Tier 1
solely from measured concentrations of cadmium, cop-
per, nickel, lead, or zinc. This reflects in large part the
absence of AVS data, which are required to place sites
contaminated with those metals in Tier 1.
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 locations, if the sta-
tion names were not identical, the data could not be treated
as if they were collected from the same location. This very
5-10
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Dr.ill Vilional Si'dimi'iil Ou;i
likely resulted in an underestimate of the number of Tier 1
stations identified based on potential human health ef-
fects. The underestimate occurred because exceedances
of sediment TBP and tissue levels (EPA risk levels and
FDA levels) at the same station were required to catego-
rize 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 describ-
ing concentrations of nearly every chemical monitored in
all of the NSI data. Many monitoring programs use a
screening list of chemicals that are indicator pollutants
for contaminated sediments. Thus, many of the specific
chemicals assessed in the NSI data evaluation are not al-
ways 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 sedi-
ment contaminants was usually not presented with the data
included in the NSI and thus was not considered when the
significance of elevated contaminant concentrations in sedi-
ment was evaluated. Background conditions can be impor-
tant in an evaluation of potential adverse effects on aquatic
life because ecosystems can adapt to their ambient environ-
mental conditions. For example, high metals concentrations
in samples collected from a particular station might occur
from natural geological conditions at that location, as op-
posed to the effects of human activities.
Most data are associated with a specific location.
As a result, establishing the percent of waters with con-
taminated sediment is not possible because it is diffi-
cult to assess the extent to which a monitoring station
represents a larger segment of a water body. Further-
more, the NSI data are geographically biased. More
than 50 percent of all stations evaluated in the NSI are
located in 8 states (Washington, Florida, Illinois, Cali-
fornia, Virginia, Ohio, Massachusetts, and Wisconsin),
which have more than 700 monitoring stations each.
Finally, EPA did not verify reported latitude and longi-
tude coordinates for each site.
Limitations of Approach
Sediment Chemistry Screening Values
There are significant gaps in our knowledge concern-
ing sediment-pollutant chemistry (especially
bioavailability) and direct and indirect effects on aquatic
biota. The certainty with which sediment toxicity can be
predicted for each chemical using the various screening
values included in the NSI evaluation can vary signifi-
cantly based on the quality of the available data and the
appropriateness 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 high-
est quality toxicity and octanol/water partitioning data,
which have been reviewed extensively. The draft SQCs
have also undergone extensive field validation experi-
ments. However, data used to derive SQALs for addi-
tional chemicals have undergone only limited peer review.
The AET values used in this evaluation were based on
empirical data from Puget Sound. Direct application of
values from Puget Sound to a specific site or region in
another part of the country might be overprotective or
underprotective of the resources in that area. Extensive
collection of data and additional analyses would be re-
quired to develop AETs for other sites.
The bioavailability of metals in sediment is addressed
by the comparison of the molar concentration of sulfide
anions (i.e., acid-volatile sulfide [AVS]) to the molar con-
centration of metals (i.e., simultaneously extracted met-
als [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 bioavailable
or other non-measured phases might bind up the excess
metals. To apply the [SEM]-[AVS] difference to indicate
positive bioavailability and toxicity for this evaluation,
EPA used laboratory data that indicated the probability of
observed toxic effects at various [SEM]-[AVS] levels.
Based on these data, EPA defined the Tier 1 level as [SEM]-
[AVS ]>5. Thus, this use of [SEM]-[AVS] represents a hy-
brid 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 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
5-11
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Conclusions and Discussion
probability of observing adverse effects. Exceedance of
the identified level-of-concern concentrations is associ-
ated with a likelihood of adverse organism response, but
it does not demonstrate that a particular chemical is solely
responsible. In fact, a given site typically contains a mix-
ture of chemicals that contribute to observed adverse ef-
fects to some degree. Therefore, these correlative
approaches tend to result in screening values that are lower
than the theoretical draft SQCs and SQALs, which ad-
dress the effects of a single contaminant. The effects range
approaches to assessing sediment quality also do not ac-
count for such factors as organic matter content, AVS,
and particle size distribution, which can mitigate the
bioavailability and, therefore, 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
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 draft sediment quality criteria values are usu-
ally slightly higher for saltwater species. Because of limi-
tations of time and resources, sampled sites in the NSI
were not classified by salinity regime, and further site-
specific evaluations are required to more definitively as-
sess the toxicity at the sites. However, the application of
several different screening values should provide a rea-
sonable estimate of potential risks to aquatic life in fresh-
water, estuarine, and marine habitats.
Additional false positive and false negative classifi-
cations of potential risk to aquatic life from sediment con-
taminant concentrations could occur when a default value
for organic carbon content is applied. Draft SQCs and
SQALs are based on the partitioning of a chemical be-
tween organic carbon in the sediment and pore water at
equilibrium. 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 sedi-
ments (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. Sediments that
have an organic carbon content higher or lower than 1
percent TOC might be classified incorrectly.
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 de-
velopment of the draft sediment criteria. This analysis
indicated that varying the exposure medium (i.e., water
or sediment) resulted in an estimate of variability that
should be used for computing confidence limits for the
draft SQCs. The methodology used to derive the octanol/
water partitioning coefficient and the final chronic value
can also influence the degree of uncertainty associated
with the draft SQCs. Differences in the response of wa-
ter column and benthic organisms, and limitations in un-
derstanding the relationship of individual and population
effects to community-level effects, have also been noted
(Mancini andPlummer, 1994). Site-specific modifications
to screening values derived using the equilibrium parti-
tioning model have been recommended to better address
chemical bioavailability and species sensitivities (USEPA,
1993b). Sediment chemistry screening values developed
using the equilibrium partitioning approach also do not
address possible synergistic, antagonistic, or additive ef-
fects 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 use SQALs for this analysis in
most cases. The approach used to develop SQALs, and
to choose chemicals for which SQALs could not be de-
veloped, 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 repre-
sents a theoretical approach with field-measured compo-
nents. In addition to applying a site-specific or default
organic carbon content, the theoretical bioaccumulation
potential (TBP) calculation includes a field-measured
biota sediment accumulation factor (BSAF) to account
for the relative affinity of a chemical for fish tissue lipids
or sediment organic carbon. The BSAF will account for
5-12
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the effects of metabolism and biomagnification in the or-
ganism in which it is measured. The primary limitation of
this approach is the applicability of a field-measured BSAF,
or a percentile from a distribution of values, at a variety of
sites where the conditions may vary.
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 differ-
ent organisms and organic carbon in different sedi-
ments are similar and have distributional properties
similar to the field-measured values used to derive
BSAFs. Other simplifying assumptions are that chemi-
cals are similarly exchanged between the sediments
and tissues and that compounds behave alike, inde-
pendent of site conditions other than organic carbon
content. In reality, physical-chemical processes (e.g.,
diffusion through porous media and sediment mixing)
can vary and limit the rate at which chemicals can
exchange with bottom sediments. Uptake of contami-
nants by aquatic organisms is also a kinetic (rate-con-
trolled) process that can vary and be slowed, for
example, by awkward passage of a bulky molecule
across biological membranes. Also, a BSAF of 1 (ther-
modynamic equilibrium) was used to estimate TBPs
for many nonpolar organics. This BSAF might over-
estimate or underestimate the bioaccumulative poten-
tial for certain nonpolar organics because it is assumed
that there is no metabolic degradation or biotransfor-
mation of the chemical. Site-specific organic carbon
content was often not available, which leads to addi-
tional uncertainty concerning the comparability of
BSAFs among different locations. In addition, devel-
opment of the BSAFs used in the TBP evaluation re-
lied 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 re-
garding site classifications based on comparison of
estimated TBPs with FDA tolerance/action and guide-
line levels and EPA risk levels.
TBPs could not be calculated for polar organic com-
pounds or heavy metals. Therefore, sites could not be clas-
sified using FDA levels or EPA risk levels for those
chemicals using a TBP approach (although fish tissue
monitoring data are often available for many sites).
Uncertainties and numerous assumptions are associ-
ated with exposure parameters and toxicity data used to
derive EPA risk levels and FDA tolerance/action and
guideline levels. For example, the derivation of EPA risk
levels is based on the assumption that an individual con-
sumes on average 6.5 g/day of fish caught from the same
site over a 70-year period. Also, the TBP calculation for
human 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 ex-
posure 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 1 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.
5-13
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Draft Ntitioiiiil Sediment Quality Survey
Chapter 6
Recommendations
following discussion presents EPA's recom-
mendations for addressing current sediment con-
lamination problems throughout the country and
for improving the ability to conduct sediment assessments.
These recommendations relate to six activities or infor-
mation needs:
1. Further investigate conditions in the 96 targeted
watersheds.
2. Coordinate efforts to address sediment quality
through watershed management programs.
3. Expand the National Sediment Inventory's
(NSI's) coverage and capabilities.
4. Provide better access to information in the NSI.
5. Develop better monitoring and assessment tools.
6. Address basic science needs.
Recommendation 1: Further
Investigate Conditions in the 96
Targeted Watersheds
To characterize the extent and severity of sediment
contamination problems in the United States, EPA has
performed 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 extent and severity of sediment
contamination problems.
The primary recommendation resulting from the NSI
data analysis is that the states, in cooperation with EPA
and other federal agencies, should proceed with further
evaluations of the 96 watersheds identified as areas of
potential widespread sediment contamination (APCs). If
active watershed management programs are in place,
these evaluations should be coordinated within the con-
text of current or planned actions. Future monitoring and
assessment efforts should focus in particular on the 57
water body segments (or river reaches) located within
the 96 APCs that had 10 or more stations categorized as
Tier 1. The purpose of these efforts should be, as appro-
priate, to gather additional sediment chemistry data and
related biological data and conduct further assessments
of the data to determine human health and ecological
risk, determine temporal trends, identify potential sources
of sediment contamination and determine whether po-
tential sources are adequately controlled, and determine
whether natural recovery is a feasible option for risk re-
duction. Additional monitoring and analysis of data from
the 96 APCs will also be used to track and document the
effectiveness of management actions taken to address
sediment contamination problems over time. Trends in
sediment contamination in the 96 APCs over time will
be reported in future reports to Congress.
It should be noted that many state and federal gov-
ernment monitoring programs already do a good job of
gathering data at sites with known contamination prob-
lems (including some of the 96 APCs), and additional
monitoring at those locations will probably not be neces-
sary. However, for other locations not previously targeted
for focused monitoring, additional data might be required
to adequately assess potential sediment contamination
problems, especially in areas where significant human
health exposures occur. In addition, in some cases it might
be necessary to conduct baseline studies to determine
where to focus monitoring activities.
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 widespread 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
6-1
-------
other cases, the states should work with EPA to determine
the best course of action.
Recommendation 2: Coordinate
Efforts to Address Sediment
Quality Through Watershed
Management of Programs
The watershed approach is a community-based wa-
ter 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 solu-
tions that involve all stakeholders. It is within the water-
shed 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 future moni-
toring sites, setting of priorities for reissuance of NPDES
permits, permit synchronization, total maximum daily load
(TMDL) development, and pollutant trading between
nonpoint and point sources.
The NSI provides an important tool for targeting ef-
forts to further investigate the 96 APCs. It is also useful
for screening additional potential areas of concern where
there are known data gaps. In addition, the targeting tech-
nique used for identifying the APCs is directly applicable
to local-level analysis because it uses site-specific infor-
mation. As the NSI is expanded, it will provide further
information to help environmental managers better under-
stand which of the Nation's watersheds have sediment
contamination problems that pose the greatest risk to
aquatic life and human health, and track progress in ad-
dressing those problems.
There are many active watershed management efforts.
EPA recommends strengthening and expanding these ef-
forts, as appropriate, to better address sediment contami-
nation issues. The majority of the NSI data were obtained
by local watershed managers from monitoring programs
targeted toward areas of known or suspected contamina-
tion. NSI data and evaluation results can assist local wa-
tershed managers by providing additional data that they
may not have, enabling them to compare their sites to
others throughout the region or country, and allowing
researchers to draw upon a large data set of information to
conduct new analyses 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: Expand the
NSI's Coverage and Capabilities
The NSI is currently limited in terms of the number of
data sets it includes and the national coverage it provides.
Over 50 percent of the monitoring stations evaluated in the
NSI are located in eight states (Washington, Florida, Illinois,
California, Virginia, Ohio, Massachusetts, and Wisconsin).
In addition, only 11 percent of all river reaches in the United
States include one or more sampling stations that were as-
sessed as part of the NSI data evaluation.
EPA should continue compiling sediment chemistry
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.
Commentors on the preliminary evaluation of NSI data
identified several additional databases that should be in-
cluded in the NSI for future evaluations. Those databases
and others should be evaluated and added to the NSI in the
future as appropriate. 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.
Although some historical trend information is avail-
able, a comprehensive assessment of temporal trends is not
presented in the current report to Congress. Future evalua-
tions of the NSI data should be designed to determine where
and why sediment quality conditions are improving or wors-
ening. 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,
6-2
-------
atioinil Si'dimi'iil Quality Survey
and the National Oceanic and Atmospheric Administration's
NS&T database. In addition, in the search for additional
databases for use in future NSI data evaluations, EPA should
focus on obtaining sediment core data, which can provide
valuable information concerning historical trends in sedi-
ment 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.
Recommendation 4: Provide Better
Access to Information in the NSI
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 September 1996 the NSI data, organized by water-
shed and including maps and summary tables, should be
available on EPA's mainframe computer for on-screen
viewing 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 com-
prehensive CIS/modeling system, BASINS (Better As-
sessment 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 useful for identifying watersheds, water bodies, or sam-
pling stations where various sediment chemistry and/or
biological screening values have been exceeded. Identi-
fying 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 in-
tegrated 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 gener-
ating, 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
related 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
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. For example, many contami-
nants included in the NSI, such as kepone and tributyl
tin, could not be evaluated due to a lack of appropriate
screening values for comparison with measured values.
Although screening values were adopted or developed
for the NSI data evaluation wherever feasible, many data
for some potentially harmful contaminants were not evalu-
ated. To address this issue, EPA should continue to de-
velop sediment quality criteria (SQCs), especially for
metals. In the absence of SQCs for additional nonionic
organic chemicals, EPA should continue to develop addi-
tional sediment quality advisory levels (SQALs). The
development of additional SQCs and SQALs will improve
6-3
-------
Kctomim'iHlalions
assessment capabilities both for identifying potential prob-
lems and planning appropriate response actions.
The limitations associated with a lack of screening val-
ues were most apparent in the evaluation of sediment metals
data. As noted in the previous chapter, a large number of
stations had elevated concentrations of metals. However,
many of these stations could not be categorized as Tier 1
because of a lack of acid volatile sulfiide (AVS) and simulta-
neously extracted metals (SEM) data, which were required
to place stations in the Tier 1 category based on sediment
contamination from cadmium, copper, nickel, lead, or zinc.
AVS and SEM provide informatioo 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 poten-
tial impacts. Therefore, EPA recommends that future
monitoring programs also include TOC measurements
where organic chemicals are a concern.
EPA also recommends that future sediment moni-
toring programs collect tissue residue, biological effects
(i.e., toxicity, histopathology), and biological commu-
nity (e.g., benthic abundance and diversity) measure-
ments. These types of data are necessary to better assess
actual effects resulting from exposure to contaminated
sediment. 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. Matched sediment chemis-
try and tissue residue data should be collected where
human exposures are a concern.
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 will be developed. EPA should incorporate new
sediment assessment techniques into future NSI data
evaluations as they are developed, tested, and proven re-
liable. For example, 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 sediment assessment experts concerning biologi-
cal community conditions that can be directly related to
sediment quality problems. EPA should work to develop
these and other sediment assessment tools for
future assessments.
Recommendation 6: Address Basic
Science Needs
The National Sediment Quality Survey is the first at-
tempt to analyze sediment chemistry and biological data
from numerous databases from across the country in an
effort to identify the national extent and severity of sedi-
ment contamination. Because the data were not generated
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
science with respect to sediment chemistry data and related
biological data. These needs are in addition to those identi-
fied under Recommendations 3,4, and 5.
Identified below are the relevant issues and science
needs that should be addressed to better characterize the
sources, fate, and effects of sediment contaminants:
• 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.
6-4
-------
Glossary
Draft National Scdinicnt Quality Survey
Acid-volatile sulfide (AYS): 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 single dose of a chemical substance
through various routes of exposure. Refers to general-
ized toxic response with lethality usually being the ob-
served endpoint.
Apparent Effects Thresholds (AETs): Sediment
quality screening levels based on a biological effects cor-
relation approach. Designed to identify concentrations
of contaminants that are associated exclusively with sedi-
ments exhibiting statistically significant biological effects
relative to reference sediments. Two screening levels are
recognized when possible based on different indicators:
(1) AET-L, the lowest concentration for which a particu-
lar indicator showed an effect, and (2) AET-H, the high-
est concentration at which effects were observed for
another indicator. Based on empirical data from Puget
Sound. Developed by Barrick et al. (1988).
Benthic abundance: The quantity or relative de-
gree of plentifulness of organisms living in or on the bot-
tom 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 demon-
strate that a particular chemical is solely responsible.
Chronic toxicity: Response of an organism to re-
peated, long-term exposure to a chemical substance.
Combined sewer overflow: A discharge of a mix-
ture of storm water and untreated domestic wastewater
that occurs when the flow capacity of a sewer system is
exceeded during a rainstorm.
Contaminated sediment: Sediment that contains
chemical substances at concentrations that pose a known
or suspected threat to aquatic life, wildlife, or human
health.
Contaminated sediment site: A monitoring station
that has been categorized as having a higher probability
of causing adverse effects to aquatic life, wildlife, or hu-
man health based on an evaluation of monitoring data
contained in the NSI.
Demersal species: Swimming organisms that pre-
fer to spend the majority of their time on or near the bot-
tom of a water body.
Divalent metals: Metals that exist and are available
for reaction in a valence state of two (i.e., carrying an
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 stable system.
Effects range-median (ERM) and effects range-
low (ERL) values: Sediment quality screening levels
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-
ity for marine and estuarine sediments. Developed by
Long etal. (1995).
Elutriate phase toxicity test: Toxicity test in which
sediments are mixed with test water for a fixed period of
Glossary-1
-------
time, the sediments are then removed, and test organisms
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 ma-
terial 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 equi-
librium, 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: Ap-
proach used to relate the dry-weight sediment concen-
tration of a particular chemical that causes an adverse
biological effect to the equivalent free chemical concen-
tration in pore water and to that concentration sorbed to
sediment organic carbon or bound to sulfide. Based on
the theory that the partitioning of a nonionic organic
chemical between organic carbon and pore water and the
partitioning of a divalent metal between the solid and
solution phases are at equilibrium.
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., bacte-
ria) are used as the test organism. Microbial responses
in toxicity tests have been recommended as early warn-
ing indicators of ecosystem stress. However, questions
have been raised concerning the sensitivity of sediment
microbial 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
repository for sediment quality data and related biologi-
cal data. Results of the evaluation of data from the NSI
serve as the basis for the report to Congress on the extent
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 perma-
nently 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 re-
main 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.
Pore water: See Interstitial water.
Probable Effects Levels (PELs) and Threshold
Effects Levels (TELs): Biological effects correlation-
based sediment quality screening levels similar to ERMs/
ERLs. Used to develop similar effects-based guidelines
for the State of Florida. The lower of the two guidelines
Glossary-2
-------
Draft National Sediment Quality Survey
for each chemical (i.e., the TEL) is assumed to represent
the concentration below which toxic effects rarely occur.
In the range of concentrations between the two guide-
lines, effects occasionally occur. Toxic effects usually or
frequently occur at concentrations above the upper guide-
line value (i.e., the PEL).
Sediment quality advisory levels (SQALs): Equi-
librium partitioning-based sediment quality screening
values. Derived using the same approach used to develop
sediment quality criteria; however, SQALs are based on
data from limited sources and have undergone only lim-
ited review.
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 non-
ionic organic chemicals: acenaphthalene, dieldrin, en-
drin, fluoranthene, and phenanthrene.
Simultaneously extracted metals (SEM): Metal
concentrations that are extracted during the same analy-
sis in which the acid-volatile sulfide (AVS) content of the
sediment is determined.
Solid-phase toxicity test: 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): Measure of organic
carbon content of sediment expressed as a percent. Used
to normalize the dry-weight sediment concentration of a
chemical to the organic carbon content of the sediment.
U.S. Environmental Protection Agency (EPA) risk
levels: Levels of contaminant concentrations in an 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 Drug Administration (FDA) toler-
ance/action or guideline levels: FDA has prescribed
levels of contaminants that will render a food "adulter-
ated." The establishment of action levels (informal judg-
ments about the level of a food contaminant to which
consumers can be safely exposed) or tolerances (regula-
tions having the force of law) is the regulatory procedure
employed by FDA to control environmental contaminants
in food.
Glossary-3
-------
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References-6
-------
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
iata that could be used to evaluate the extent of sediment contamination throughout the United States. As a
result, emphasis was placed on gathering data sets with sediment chemistry data since those 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
Masschusetts Bay
Cape Arundel
City of Gloucester
Mile 106
South Carolina
Alabama
Mississippi
Georgia
North Carolina
Encina 301(h)
Morro Bay 301(h)
Hyperion 30l(h)
Goleta 301(h)
Tennessee
Kentucky
Florida
GLNPO/ARCS
Galveston Bay
San Diego Pre-30 l(h)
Orange County 301(h)
Oxnard301(h)
Los Angeles 301(h)
Thums Ocean Dumping
Puget Sound
Anchorage
Endicott403(c)
Kuparuk STP 403(c)
A-l
-------
Appendix A
San Francisco NEP
LA2 Ocean Dumping
LAS Ocean Dumping
COSED NOAA NS&T
Region 4 City of Tampa
Dept of Navy
EPA Region 4
FLDER
S FL Water Mgmt Dist.
USAGE
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
CityofBellingham
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 Townsend
Thurston County Dept of Public Health
U.S. Coast Guard
Great Lakes Heidelberg College, Tiffin, Ohio
Illinois EPA
Michigan Tech. Univ., Houghton, MI
Univ. of Wisconsin-Superior, WI
Michigan Dept. Natural Resources
Prudhoe Bay 403(c)
Port Valdez 403(c)
Ed Long
USAGE, Jacksonville
USAGE, Mobile
USAGE, Savannah
USAGE, Wilmington
USFWS
TVA
USAGE (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
PortofTacoma
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
Aqua Tech, Meimore, OHEG&G
Bionomics/Aqua Tech Environ. Cnstlt.
A-2
-------
Draft National Sediment Quality 'Survey
Applied Biology, Inc., Decatur, GA
Recra Research, Inc., Tonawanda, NY
USFWS, Columbia, MO - ARCS
Michigan State University
Virginian Province
Ohio EPA
Illinois Geological Survey
USEPA-GLNPO
USEPA-ERL-Duluth
EMAP Louisianian Province
DMATS USEPA Region 9
USGS A.D. Little, 1990
Massachusetts ACE_NED permit file #29-91-00473E
Bay 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
USGS ACE_NED permit file MA DPW Beverly-Salem Bridge
Massachusetts and By-Pass Project
Bay 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)
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
loadings data from the Permit Compliance System (PCS) and the Toxic Release Inventory (TRI). EPA recently re-
leased an updated version of the National Fish Advisories database, which was not available for this analysis. A
summary of the types of data available in the NSI is provided below.
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
Boehmetal., 1984
COM, 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
MA DEQE, 1986MA DPW, 1991
MA DEQE, 1982
MacDonald, 1991
NET Atlantic, 1990
Nolan etal., 1981
Penney etal., 1981
Phillips, 1985
Pruelletal., 1989
Ryan et al., 1982
Robinson et al., 1990
Shea etal., 1991
Shiaris et al., 1986
A-3
-------
Appendix A
Sediment chemistry. Sediment chemistry data include detailed analytical results, analyte sampled, remark codes,
sampling methods, analytical methods, sample weight, core depths, and grain size information. Percent organic carbon
and acid-volatile sulfide content of sediments are also included when available.
Tissue residue. Tissue residue data include detailed analytical results, analyte sampled, remark codes, sampling
methods, clean-up procedures, analytical methods, species, sex, anatomy sampled, life stage, and wet/dry reporting
basis.
Toxicity. Toxicity data include test conditions (DO, pH, flushing hardness, feeding, salinity, etc.), test species,
dilution, endpoints (e.g., mortality), and test duration. Solid-phase and elutriate data are provided when available.
Benthic abundance. Benthic abundance data include enumeration of species collected and numerous commu-
nity-level summaries/indices.
Histopathology. Histopathology data include the number of fish with body, branchial, and buccal pathologies;
number of species; and abundance.
Fish abundance. Fish abundance data include mean and standard deviation of fish length and abundance of
species.
For each data set included in the NSI, Table A-l identifies the number of stations at which the following param-
eters were measured:
• Sediment chemistry
• Tissue residue
• Benthic abundance
• Toxicity
• Histopathology
• Matched data
sediment chemistry and tissue residue
sediment chemistry and benthic abundance
sediment chemistry and toxicity
sediment chemistry and histopathology
sediment chemistry, tissue residue, and toxicity
sediment chemistry, benthic abundance, and toxicity
Table A-2 presents the total number of stations at which each of these parameters was measured and the number of
stations for which coordinates (i.e., latitude/longitude) were available. Only data from stations with coordinates could
be used to identify sites of potential concern.
The only quality assurance/quality control (QA/QC) information required for the data before they could be in-
cluded in the NSI was information on the source of the data and the location of the sampling station. If available,
information on the field and laboratory samples and methods used was included with the data.
How the Data Are Organized
The NSI data are contained in a series of tables that correspond to the different types of data described above. In
some cases multiple tables were created for one type of data. The primary table in the NSI is the station table. Each
record in the table corresponds to a unique sampling station. The records in the station table can be related to tables for
each type of data, such as sediment chemistry data, tissue residue data, etc. These tables can then be related to addi-
tional look-up tables that include ancillary information such as chemical or species names. Figure A-l illustrates the
relationship between the station, sediment chemistry, tissue residue, toxicity, and related look-up tables.
A-4
-------
Table A-l. Number of Stations at Which Various Types of Data Were Collected
Data Set
STORET
Region 4
ODES
COSED
Gulf of
Mexico
Great Lakes
DMATS
Mass. Bay
EMAP
LAProv.
VAProv.
Seattle
USCOE
Total
Number of Stations Where Measured
Sediment
Chemistry
12,907
1,024
1,317
1,104
210
761
213
979
260
202
2,116
mm
Tissue
Residue
6,057
1,722
26
202
199
8.206
Benttik
Abundance
2^92
476
259
212
365
3,904
Toricity
296
82
373
245
259
212
876
2343
Histopath-
ology
259
259
Sediment
Chemistry
and Tissue
Residue
1,533
37
26
169
198
1.963
Sediment
Chemistry
andBenthfc
Abundance
664
449
259
202
365
1,939
Sediment
Chemistry
andToxicity
70
6
369
188
259
202
707
1,801
Sediment
Chemistry
and
Histopath-
ology
259
259
Sediment
Chemistry,
Tissue
Residue,
andToxklty
2
26
163
198
389
Sediment
Chemistry,
Benthic
Abundance,
andToxicity
49
68
259
202
270
MS
-------
A|)|U'ii(li\ A
Table A-2. Number of Stations With Data Included in the NSI
Measurement Parameters
Sediment Chemistry
TOC
AVS
Tissue Residue
Toxicity
Elutriate Phase
Solid Phase
Benthic Abundance
Histopathology
Sediment Chemistry & Tissue
Sediment Chemistry & Toxicity
Sediment Chemistry & Abundance
Sediment Chemistry & Histopathology
Sediment Chemistry, Tissue, & Toxicity
Sediment Chemistry, Toxicity, & Abundance
Total Number of
Stations
21,093
6,170
425
8,206
2,343
630
1,865
3,904
259
1,963
1,801
1,939
259
389
848
Stations with Coordinates
Number
19,546
5,335
371
7,208
1,523
—
—
1,844
259
1,930
1,263
1,340
259
359
733
% of Total Number of
Stations
w/Coordinates'
76
21
1
28
6
—
—
7
1
8
5
5
1
1
3
Total number of stations with coordinates = 25,555.
A-6
-------
STATION
SOURCE/AGENCY/
STATION
SEDIMENT CHEMISTRY
SOURCE/AGENCY
STATION
SOURCE/FARM
TISSUE RESIDUE
SOURCE/AGENCY
STATION
SOURCE/FARM
SPECCODE
TOXICITY
SOURCE/AGENCY
STATION
SOURCE/SPECCODE/
PHASE
SEDIMENT CHEMISTRY
VARIABLE NAME
SOURCE/PARM
TISSUE RESIDUE
VARIABLE NAME
SOURCE/PARM
SPECIES NAME AND
LIFE HISTORY
SPECCODE
SPECIES NAME AND
TOXICITY PHASE USED
IN NSI EVALUATION
SOURCE/SPECCODE/
PHASE
Figure A-l. Organization of NSI Data.
A-7
-------
Appendix A
Table A-3 summarizes the tables that are available in version 1.1 of the NSI (the current version). Some of
these tables have not required updating since version 1.0 of the NSI (the version used to prepare the preliminary
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.
ALLSTAT.DBF
SOURCE
AGENCY
STATION
COUNTY
DEPTH
DEPT_MAX
DEPT_MIN
DREDGESI
DRWATERB
GEOCODE
INSTTT
LAT
LAT_2
LNG
LNG_2
LOCATION
LOC.CODE
NSIREACH
ORIGIN
ORG_NAME
REFER
SR.SCI
STATE
WATERBOD
EPA.REG
FIPS
FIPS_DIS
HUCLPIS
RF1_DIS
Station
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
STATION II DATE. DMATS NOTE: STATION = ID II " I! STATIONl II " II SERIES 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)
A-8
-------
DniH National Sediment Qu.ility Survey
Table A-3. Data Tables Available in the NSI
Table Name
Table Description
ALLSTAT.DBF
ALLSEDI.DBF
ALLTISS.DBF
ALLBIOT.DBF
ALLSEDM.DBF
ALLTISM.DBF
ALLELUT.DBF
LOADD.DBF
LOADS.DBF
LOADO.DBF
BIOTCODEDBF
ELUTPARM.DBF
SED_PARM.DBF
TIS_CODE.DBF
TIS_PARM.DBF
SEACOE.DBF
REMARK.WP
ALLSUPR.DBF
ALLBENA.DBF
ALLBENC.DBF
ALLHIST.DBF
ALLFISA.DBF
SPEC-CD.DBF
FISH-CD.DBF
Station
Sediment chemistry
Tissue residue
Biotoxicity
Sediment grain size and miscellaneous sediment chemistry
Miscellaneous tissue residue
Elutriate
PCS/TRI loadings
PCS/TR1 facilities (have loadings data)
Other PCS/TRI facilities (no associated loadings data)
Toxicity phase for biotoxicity table (ALLBIOT)
List of analytes for elutriate table (ALLELUT)
List of analytes for sediment tables (ALLSEDI, ALLSEDM)
List of species for tissue tables (ALLTISS, ALLTISM)
List of analytes for tissue tables (ALLTISS, ALLTISM)
EPA Region 10/COE Seattle District's Sediment Inventory Code file (important for
interpreting a large number of codes unique to this data source)
Text file on remark codes (important for remark codes other than "K" or "U")
Superfund facilities
Benthic species abundance
Benthic community
Histopathology
Fish abundance
Species codes for benthic data
Species codes for fish abundance data
A-9
-------
Appendix A
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
INSTRUME Instrument code to identify the final chemical analysis method(s) used for analyzing the
sample
MEAS_B AS Result is wet or dry weight basis (see also P)
NSIREVCD Preliminary evluation code (A=Reviewed in QA/QC of Preliminary Evaluation, U=Only
one (1) observation of this chemical in source, X=Deleted based on QA/QC of Preliminary
Evaluation (first run), Y=Duplicate Data, Z=Deleted based on QA/QC of Preliminary
Evaluation (second run))
P Result associated with PARM (ng/kg, ppb)
PARM Analyte measured (see also P and R)
R Remark code associated with PARM and P
SAMP_DTL Depth to bottom of sample interval (m)
S AMP_DTU Depth to top of sample interval (m)
SMP_EQP Sampling equipment code
SPHERE Sphere (i.e., environment) code from which the sample came
WET.WGT Total wet weight of sample (g)
ALLTISS.DBF
SOURCE
AGENCY
STATION
DATE
SAMPLE
SEQ
REPLICAT
ANATOMY
ANAT_CD
CAS
CLEANUP
COMPOSIT
DRY_WGT
EXT_MTHO
INSTRUME
NSIREVCD
LENGTH
LIFE_STA
MEAS_BAS
NUMBJND
P
PARM
P_STD
R
Tissue residue
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
STATION II DATE. DMATS NOTE: STATION = ID II " II STATION! II " II SERIES II' ' I
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
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
Preliminary evluation code (F=Field test, L=Lab test, W=Species cannot be resolved,
Y=Duplicate Data)
Length of specimen
Life stage code to identify the life stage of the sample
Result is wet or dry weight basis (see also P)
Number of organisms in sample
Result associated with PARM
Analyte measured (see also P and R)
Standard deviation of P associated with repeated measurements of PARM
Remark code associated with PARM and P
A-10
-------
SAMPTYPE
SEX
SMP_EQP
SPECCODE
SPECIMEN
TOT_REP
WEIGHT
WET_WGT
LIPIDS
SPEC BIO
Sample type
Sex code used to identify sex of sample
Sampling equipment code
Species code
Unique identifier for the individual organism being analyzed
Number of replicates
Weight of organism
Total weight of sample
% Extractable lipids
STORET taxonomic code
ALLBIOT.DBF
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
Biotoxicity
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
STATION II DATE. DMATS NOTE: STATION = ID II " II STATION! II " II SERIES II " I
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
A-ll
-------
Appendix A
P
P_CC
P2
PH
PHASE
PHOTO.PE
QASAMP1
QASAMP2
QASAMP3
RENEWAL
R
REBURIAL
RESPOJTY
SALINITY
SAMP_DTL
SAMP.DTU
SERIES
SIGNIF
SMP_EQP
SPECCODE
SPECIES
SPHERE
STD_TOX
TEMP
TESTDUR
TESTTYPE
TESTEXP
UNITS
UNITS2
WATERTYP
YOUNG
Result associated with ENDPOINT
Control-corrected analytical result associated with P
Result associated with ENDPOIN2
pH
Phase code to indicate the phase (i.e., medium) in which the bioassay organisms are housed
Photoperiod: Number of light hours vs. number of dark hours (e.g., 1608 = 16 hours light, 8
hours dark)
Control sample no. 1
Control sample no. 2
Control sample no. 3
Renewal (Y/N)
Remark code associated with ENDPOINT and P
ET50 (mean reburial time)
Type of bioassay response
Salinity of water in test chamber (ppt)
Depth to bottom of sample interval (m)
Depth to top of sample interval (m)
Bioassay series number
Significant difference from control
Sampling equipment code
Species code
Organism species
Sphere (i.e., environment) code from which the sample came
Standard Toxicant Result code to indicate whether the results of the standard toxicant
bioassay were acceptable
Water temperature (deg C)
Test duration (days)
Test used
Test exposure periods
Units associated with ENDPOINT and P
Units associated with ENDPOIN2 and P2
Water type
Number of young produced per adult female over 4 weeks
ALLSEDM.DBF Sediment grain size and miscellaneous sediment chemistry
SOURCE
AGENCY
STATION
DATE
SAMPLE
SUBSAMPL
REPLICAT
SEQ
CAS
CLEANUP
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
STATION II DATE. DMATS NOTE: STATION = ID II'' IISTATIONIII " II SERIES 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
A-12
-------
COARSE_M Method of analysis for analysis of coarse particles. Left blank if sample was not split into
fractions.
COMMENTS Comments
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 FARM
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)
S AMPJDTU 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 Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
AGENCY Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
STATION Monitoring station identification code. (ODES NOTE: STATION = STN_CD II " II
STATION II DATE. DMATS NOTE: STATION = ID II " IISTATIONIII " II SERIES II " II
SCAN.)
DATE Date of sample collection
SAMPLE Unique sample identifier code
SEQ 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
REPLICAT Unique replicate identifier code
ANAT_CD Organ/tissue sampled code
CAS CAS number for analyte
CLEANUP Sample cleanup code to indicate an additional step taken to further purify the sample
extracts or digestates
COMPOSIT A unique identifier to indicate a sample created by compositing tissues from several
individuals.
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
INSTRUME Instrument code to identify the final chemical analysis method(s) used for analyzing the
sample
LENGTH Length of specimen
LIPIDS Lipids (%)
A-13
-------
\|)|>rmli\ A
LIFE_STA
MEAS.BAS
NUMBJND
P
FARM
R
SEX
SMP.EQP
SPECCODE
SPEC_SCI
SPECIMEN
UNITS
WET_WGT
P ALP
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
Analyte measured (see also P and R)
Remark code associated with PARM and P
Sex code used to identify sex of sample
Sampling equipment code
Species code
Species scientific name
Unique identifier for the individual organism being analyzed
Units associated with PARM, P, and R
Total weight of sample
Nonnumeric result associated with PARM
ALLELUT.DBF
SOURCE
AGENCY
STATION
DATE
SAMPLE
SEQ
SUBSAMPL
REPLICAT
CAS
EXT_MTHO
INSTRUME
P
PARM
R
SAMP_DTL
SAMP_DTU
SAMPJEQP
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' Ml
STATION II DATE. DMATS NOTE: STATION = ID II " II STATIONIII " 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 (jig/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
E3KGYO 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
A-14
-------
Draft N;ili
-------
Appendix A
TIS.PARM.DBF List of analytes for tissue tables (ALLTISS, ALLTISM)
SOURCE
FARM
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
ALLSUPR.DBF Superfund facilities
STATE
ID
NAME
COUNTY
CNTY_FIP
C0305
C0326
LAT
LNG
NSIREACH
State postal code
Superfund identification
Facility name
County name
3-digit county FIPS code
C0305
C0326
Latitude (decimal degrees)
Longitude (decimal degrees)
Reach File 1 Reach
ALLBENA.DBF Benthic Species Abundance
SOURCE Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
AGENCY Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
STATION Monitoring station identification code. (ODES NOTE: STATION = STN_CD II " II
STATION II DATE. DMATS NOTE: STATION = ID II " IISTATIONIII " II SERIES II " I!
SCAN.)
DATE Date of sample collection
SAMPLE Unique sample identifier code
REPLICAT Unique replicate identifier code
BOTTOM Bottom type
AREA_BAS Area basis for reported data
COMM_BAS Basis for community abundance measurements
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
GENUS Organism genus
MESH_SZ Seive mesh size
N_REP Number of replicate samples
NUMBJND Total number of individuals
NUMB_SPE Total number of unique species
ORDER Organism order
P Result associated with PARM
PARM Analyte measured (see also P and R)
P_MEAN Mean P
P_STD Standard deviation of P
R Remark code associated with P and PARM
SAMP_DTL Depth to bottom of sample interval (m)
SAMP_DTU Depth to top of sample interval (m)
SPECIES Organism species
SPECCODE Species code
UNITS Units associated with PARM, P, and R
A-16
-------
ALLBENC.DBF Benthic Community
SOURCE Identification of data origin (e.g., REG4 is the Region 4 Pilot Study)
AGENCY Identification of group responsible for collecting data (e.g., NS&T is NOAA's National
Status and Trends Program)
STATION Monitoring station identification code. (ODES NOTE: STATION = STN_CD II " II
STATION II DATE. DMATS NOTE: STATION = ID II' ' IISTATIONIII " II SERIES II'
SCAN.)
DATE Date of sample collection
SAMPLE Unique sample identifier code
AMPHIPOD Number of amphipod
AMPHMABN Mean abundance of amphipods
AREA_B AS Area basis for reported data
ARTHROPO Number of arthropods in the sample
BIOMJTOT Total biomass (g)
BIOMMEAN Mean biomass per grab (g)
BI V_MABN Mean abundance of bivalves (g)
BSPINDEX Benthic species index
BSP_GRAB Number of grabs
BSP_MABN Mean abundance per grab
BSP_MDIV Mean Shannon-Wiener diversity index
BSPJvIEAN Mean number of species per grab
BSP_MEXP Expected mean number of species
BSPJTABN Total abundance
BSP TDIV pooled Shannon-Wiener diversity index
BSPJTOT Total number of species
CAPIMABN Mean abundance of capitellids
COMM_BAS Basis for community abundance measurements
CRUSTACE Number of crustaceans in the sample
DECAMABN Mean abundance of decapods
DOMINANC Numeric dominance in the sample
ECHINODE Number of echinoderms in the sample
EVENESS Eveness
ITI ITI
MED_DIAM 50% quartile diameter (phi)
MISC_TAX Number of miscellaneous taxa in sample
MOIST_M Sediment moisture content (%)
MOLLUSCS Number of molluscs in the sample
NEM ATODE Number of nematodes in the sample
OLIGOCHA Number of oligochaetes in the sample
PABN_AMP Percent abundance amphipods
PABN_BIV Percent abundance bivalves
PABN_GAS Percent abundance gastropods
PABNJTUB Percent abundance tubificids
PLYC.MWT Mean biomass per polychaete (g)
PLYCMABN Mean abundance of polychaetes
P_SENSIT Abundance of pollution sensitive organisms (%)
PJTOLER A Abundance of pollution tolerant organisms (%)
POLYCHAE Number of polychaetes in the sample
QUARDVTM Phi quartile deviation
Q1_PHI 25% quartile diameter (phi)
Q3_PHI 75% quartile diameter (phi)
RPDDEP M MeanRPDinmm
A-17
-------
Appendix A
SICL_B_M Mean silt/clay content (%)
SKEWNESS Phi quartile skewness
TUBIMABN Mean abundance of tubificids
ALLHIST.DBF
SOURCE
AGENCY
STATION
DATE
BODYPATH
BRNCPATH
BUCCPATH
FSP_ABN
FSPJTOT
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
STATION II DATE. DMATS NOTE: STATION = ID II " IISTATIONIII " II SERIES 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
STATION II DATE. DMATS NOTE: STATION = ID II " II STATIONI II ~' II SERIES 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.DBF
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
SPEC_SCI
SPEC COM
Species code
Species scientific name
Species common name
A-18
-------
Draft National Sediment Quality Survey
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 Appen-
dix D. For the purpose of discussion, the sediment evaluation parameters have been placed into three
groups: (1) those used to assess potential impacts to aquatic life, (2) those used to assess potential im-
pacts to human health, and (3) those used to assess potential impacts to wildlife. The uncertainties
associated with the use of these parameters in the NSI data evaluation are discussed in Chapter 5.
Aquatic Life Assessments
To evaluate the potential threat to aquatic life from chemical contaminants detected in sedi-
ments, measured concentrations of contaminants were compared to sediment chemistry screening
levels. The results of toxicity tests to indicate the actual toxicity of sediment samples to species of
aquatic organisms, 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 methodologies, 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, devel-
oped using an equilibrium partitioning approach (USEPA, 1992a, 1993a).
• Sediment quality advisory levels (SQALs) for selected nonionic organic chemicals, devel-
oped using an equilibrium partitioning approach (USEPA, 1992a, 1993a).
• The sum of simultaneously extracted divalent transition metals concentrations minus the
acid-volatile sulfide concentration ([SEMJ-[AVS]), also based on an equilibrium parti-
tioning 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).
B-l
-------
• Apparent effects thresholds (AETs) for selected nonionic 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.
Equilibrium Partitioning Approaches
The potential toxicity of sediment-associated nonionic organic chemicals and divalent metals is
indicated by the amount of the contaminant that is uncomplexed or freely available in the interstitial
(pore) water. The bioavailability and toxicity of nonionic organic chemicals and divalent metals in
sediments are mediated by several physical, chemical, and biological factors, including sediment
grain size, paniculate and dissolved organic carbon, and sulfide produced by sulfate-reducing bacte-
ria (DiToroetal., 1991,1992; Howard and Evans, 1993). For nonionic organic chemicals, sorption
to the organic carbon dissolved in the interstitial water and bound to sediment particles is the most
important factor affecting bioavailability. Sulfide, specifically the reactive solid-phase sulfide frac-
tion that can be extracted by cold hydrochloric acid (acid-volatile sulfide, or AVS), appears to con-
trol the bioavailability of most divalent metal ions because of the sulfide ions' high affinity for
divalent metals, resulting in the formation of insoluble metal sulfides in anaerobic sediments.
When the concentrations of nonionic organic chemicals and divalent metals were measured in
pore water extracted from spiked sediment used in toxicity tests, the biological effects observed in
those tests occurred at similar pore water concentrations, even when different types of sediments
were used, typically within a factor of 2 (Di Toro et al., 1991, 1992). Biological effects also oc-
curred at similar concentrations in tests with different sediment types containing different amounts
of organic carbon (OC) when (1) the dry weight sediment concentrations of nonionic organic chemi-
cals were normalized for organic carbon content (i.e., ug chemical/g^) and (2) when the difference
between molar concentrations of simultaneously extracted metals ([SEM]) in the sediment exceeded
the molar concentration of AVS ([AVS]) in the sediments by similar amounts (the mortality of
sensitive species increases in the range of 1.5 to 12.5 :mol of SEM per :mol of AVS), Most
importantly, Ihe effects concentrations in the sediment could be predicted from the effects concen-
trations determined in water-only exposures to these chemicals. Most measurements of sediment
chemical concentrations are made from whole sediment samples and converted to units of chemical
per dry-weight of sediment, because of the difficulties in extracting the pore water. However, when
dry-weight concentrations of nonionic organics and metals were used to plot concentration-re-
sponse curves of the toxicity of different sediments, biological effects occurred at different dry-
weight concentrations when measured in different sediments (Luoma, 1983; USEPA, 1993a). To
develop criteria or advisory levels for comparing the toxicity of different chemicals in different
sediments, it was necessary to examine the role of organic carbon and other complexing factors in
the bioavailability of chemicals in sediment.
In sediment, the partitioning of a nonionic organic chemical between organic carbon and pore
water and the partitioning of a divalent metal between the solid and solution phases are assumed to
be at equilibrium. The fugacity (activity) of the chemical in each of these phases is the same at
equilibrium. Fugacity describes mathematically the rates at which chemicals diffuse or are trans-
ported between phases (Mackay, 1991). Hence, an organism in the sediment is assumed to receive
an equivalent exposure from water only or from any equilibrated phase. The pathway of exposure
might include pore water (respiration), sediment carbon (ingestion), sediment organism (ingestion),
or a mixture of routes. The biological effect is produced by the chemical activity of the single phase
or the equilibrated system (Di Toro et al., 1991). The equilibrium partitioning approach uses this
partitioning theory to relate the dry-weight sediment concentration of a particular chemical that
causes an adverse biological effect to the equivalent free chemical concentration in pore water and to
the concentration sorbed to sediment organic carbon or bound to sulfide. The theoretical causal
resolution of chemical bioavailability in relation to chemical toxicity in different sediments differen-
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Draft National Sediment Quality Survey
tiates equilibrium partitioning approaches from purely empirical correlative assessment methods
(described later in this section).
The processes that govern the partitioning of chemical contaminants among sediments, pore
water, and biota are better understood for some kinds of chemicals than for others. Partitioning of
nonionic hydrophobic organic compounds between sediments and pore water is highly correlated
with the organic carbon content of sediments, but it does not account for all of the toxicity variation
observed between sediment and water-only experimental exposures. Other factors that can affect
biological responses are not considered in the model. The equilibrium partitioning approach has
been tested using only nonionic organic chemicals with octanol/water partition coefficients (log
K s) between 3.8 and 5.3. However, because the theory should be applicable to nonionic organic
chemicals with log Kows from 2.0 to 5.5 (Dave Hansen. EPA/ORD-Narragansett. pers. commun..
April 17. 1995), nonionic organic chemicals with log Kows in this range were evaluated for the
analysis of NSI data. For trace metals, concentrations of sulfides and organic carbon have been
identified as important factors that control the phase associations and, therefore, the bioavailability
of trace metals in anoxic sediments. However, models that can use these factors to predict the
bioavailability of trace metals in sediments are not fully developed (see below). Mechanisms that
control the partitioning of nonionic and nonpolar organic compounds with log Kovis 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 crite-
ria 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, permanently inundated, or intertidal sediments-
while accounting for sediment characteristics that affect the bioavailability of the chemical (Di Toro
et al 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 car-
bon (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 J multiplied by the sediment particle organic carbon par-
tition coefficient (K ). K 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 better than using the interstitial water concentration of the
chemical because organic carbon in the sediment can also bind the chemical and affect its
bioavailability and toxicity. The particle organic carbon partition coefficient (K.J is related to the
chemical's octanol/water partition coefficient (KJ by the following equation (Di Toro et al., 1991):
logKK = 0.00028 + 0.983(logK0J
The octanol/water partition coefficient for each chemical, which represents the likelihood of the
chemical to complex or sorb to organic carbon, must be measured with modern experimental tech-
niques to provide the most accurate estimate of this parameter. The concentration of the chemical on
sediment particles (C) is then equal to the dissolved concentration of chemical (Cd) multiplied by the
organic carbon content of the sediment (f J and the particle organic carbon partition coefficient
(K ) when f is greater than 0.2 percent (USEPA. 1993a), thus normalizing the dry-weight sedi-
ment'concenttation of the chemical to the organic carbon content of the sediment.
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Appendix It
The criterion threshold sediment concentration 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 organ-
isms can be exposed without producing toxic effects. This level is predicted to protect approxi-
mately 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,
1993c, 1993d, 1993e, 19930 using the aquatic life water quality criterion final chronic value (FCV,
in |U,g/L) and the partition coefficient between sediment and pore water (K , in L/g sediment) for the
chemical of interest (Di Toro et al., 1991; USEPA, 1993a). Thus, SQC ="K FCV. On a sediment
organic carbon basis, the sediment quality criterion, SQCoc, is:
) = FCV(ng / L) x Kx (L / kg) X (1 (T3 kgoc / goc )
where:
FCV = EPA aquatic life water quality criterion final chronic value and
K^. = organic carbon-water partitioning coefficient.
K^ 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)(foc)
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 sedi-
ment quality advisory levels (SQALs) using the following equation:
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
K^ = 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. However, data used to derive SQALs for additional chemicals came from
limited sources and have undergone only limited peer review. This section describes the sources'of
data used to calculate the values used in the SQAL equations: log Kows (used to derive K s) and
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Drsitt National Sediment Quality Survey
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, RCRA, 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 are meant to be used for
screening purposes only, the screening values are not regulatory criteria, site-specific cleanup stan-
dards, or remediation goals. The screening levels are set to be appropriately conservative, so sites
which pass through the screen would not be expected to exhibit adverse effects; 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 Environ-
mental Research Laboratory, Athens, Georgia; Environmental Research Laboratory, Duluth, Minne-
sota; and Environmental Research Laboratory, Narragansett, Rhode Island, provided guidance and
assisted in the development of the necessary values.
Method for Determination of Log Kows. Log Kow values were initially identified in summary
texts on physical-chemical properties, such as Howard (1990) and Mackay et al. (1992a,b) and ac-
companying volumes. Additional compendia of log Kow values were also evaluated, including De
Kock and Lord (1987), Couchette and Andren (1988), Klein et al. (1988), De Bruijn et al. (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 Sub-
stances Data Bank (HSDB) (National Library of Medicine); Internet databases such as CARL UN-
COVER; and 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/Bladen.
The determination of Kow values was based on experimental measurements taken primarily by
the slow-stir, generator-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). Recommended 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, draft report).
Selection of Chronic Toxicity Values. A hierarchy of sources for chronic toxicity values to
develop the SQALs 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 draft freshwater criteria documents.
5. Final chronic values developed from data in EPA's Aquatic Toxicity Information Retrieval
database (AQUIRE) and other sources.
B-5
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Appendix li
6a. Secondary chronic values developed from data in AQUIRE and other sources.
6b. Secondary chronic values from Suter and Mabrey (1994)
EPA draft 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 "Tier I") described in USEPA (1995c) available for the remaining chemi-
cals 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 draft 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 toxic-
ity 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) which 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
BHC, delta- 1,1,2,2-tetrachloroethane
chlorobenzene tetrachloroethene
dibenzofuran toluene
diethyl phthalate 1.1.1 -trichloroethane
di-n-butyl phthalate trichloroethene
ethylbenzene
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:
biphenyl fluorene
4-bromophenyl phenyl ether hexachlorethane
butyl benzyl phthalate malathion
diazinon methoxychlor
1,2-dichlorobenzene pentachlorobenzene
1,3-dichlorobenzene tetrachloromethane
1,4-dichlorobenzene tribromomethane
endosulfan mixed isomers 1,2,4-trichlorobenzene
alpha-endosulfan trichloromethane
beta-endosulfan m-xylene
Insufficient toxicity test data were found in AQUIRE for acenapthylene, endosulfan sulfate,
heptachlor epoxide, and trichlorofluoromethane. In addition, review of AQUIRE data records indi-
cated that no daphnid acute toxicity tests had been conducted for hexachlorobutadiene. These chemi-
cals 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;
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Draft National Sedinu-nt Quality Survey
Luoma, 1983). These differences have been reconciled by relating organism toxic response (mortal-
ity) 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, cad-
mium, lead, copper, and mercury. Acid-volatile sulfide (AVS) is one of the major chemical compo-
nents that control the activities and availability of metals in the pore waters of anoxic sediments
(Meyer etal., 1994).
AVS is operationally defined as the sulfide liberated from a sediment sample to which hydro-
chloric 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
etal., 1994).
Laboratory studies using spiked sediments and field-collected metal-contaminated sediments
demonstrated that when the molar ratio of SEM to AVS [SEM]/[AVS] was less than 1 (excess AVS
remained), no acute toxicity (mortality greater than 50 percent) was observed in any sediment for
any benthic test organism. When [SEM]/[AVS] was greater than 1 (excess metal remained), the
mortality of sensitive species (e.g., amphipods) increased in the range of 1.5 to 2.5 (imol of SEM per
limol AVS (Casas and Crecelius, 1994; Di Toro et al., 1992).
Experimental studies indicate that the lower limitof applicability for AVS is approximately 1 (imol
AVS/g sediment and possibly lower; other sorption phases, such as organic carbon, probably become
important for sediments with smaller AVS concentrations and for metals with large partition coeffi-
cients 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 etal., 1993;
Casas and Crecelius, 1994).
The AVS approach requires that all toxic SEMs present in amounts that would contribute sig-
nificantly to the [SEM] sum be measured to correctly predict only acute toxicity, so incomplete
analyses of metals would compromise the results (Di Toro et al., 1992); however, mercury presents
special problems and is not included in this evaluation. If the AVS content of sediment is low, as in
fully oxidized sediments, the metal-binding capacity of the sediment decreases and the method will
not work (Adams et al., 1992; Zhuang et al., 1994). Most benthic macroorganisms, including those
used in toxicity tests, survive in sediments that have a thin oxidized surface layer and then an anoxic
layer. The anoxic layer can have significant AVS concentrations that would reduce the metal activity
to which these organisms are exposed (Di Toro et al., 1992). 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. AVS can also 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, so that the toxicity of the
metals present in the sediment changes over time (Howard and Evans, 1993). However, the AVS
approach can be used to predict when a sediment contaminated with metals is not acutely toxic
(Ankley et al., 1993; Di Toro et al., 1992).
Selection of an [SEM]-[AVS] difference sufficiently high to place a sediment in the Tier 1
category 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
B-7
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Appendix 15
present. Using freshwater and saltwater sediment amphiod toxicity data, researchers at EPA's Envi-
ronmental Research Laboratory in Narragansett, Rhode Island, plotted [SEM]-[AVS] versus the per-
centage of sediments with a higher [SEM]-[AVSj 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 labora-
tory 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 re-
sponse, but it does not demonstrate that a particular chemical is solely responsible. Consequently,
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 conservative.
Effects Range-Medians and Effects Range-Lows
The effects range approach for deriving sediment quality guidelines involves matching dry-
weight sediment contaminant concentrations with associated biological effects data. Long and Mor-
gan (1990) originally developed informal guidelines using this approach for evaluation of NOAA's
National Status and Trends (NS&T) data. Data from equilibrium 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 sedi-
ments (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. Data entered into this biological effects database for sediments (BEDS) were
expressed on a dry-weight basis.
The authors identified the lower lOth-percentile concentration as the ERL and the 50th-percen-
tile concentration as the ERM. In terms of potential biological effects, sediment contaminant con-
centrations below the ERL are said to be 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."
The accuracy of these guidelines was evaluated based on the data in the database by 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 prob-
able-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 PAHs; 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 national sediment
quality criteria and freshwater effects-based criteria from Ontario. The authors attributed variability
in the database 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 inclu-
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Dr;iH National Sediment Quality Survey
sion 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.
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 (FDEP, 1994) to develop similar
weight-of-evidence, effects-based guidelines for Florida's coastal waters. Modifications to the Long
et al. (1995) approach increased the relevance of the resultant guidelines to Florida's coastal sedi-
ments 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, a threshold effects level (TEL) was calcu-
lated first as the square root of the product of the lower 15th-percentile concentration associated wi th
observations of biological effects (the ERL) and the 50th-percentile concentration of the no-ob-
served-effects data (the NER-M). A safety factor of 0.5 was applied to the TEL to define a no-
observable-effects level (NOEL). Next, a probable-effects level (PEL) was calculated as the square
root of the product of the 50th-percentile concentration of the effects data (the ERM) and the 85th-
percentile concentration of the no-observed-effects data (the NER-M). TELs and PELs were devel-
oped 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.
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. FDEP (1994) also 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 (PELs having a lower reliability than TELs) for evaluating sediment quality in
Florida's coastal waters, with less confidence in the values for mercury, nickel, total PCBs, chlor-
dane, lindane, and total DDT. The abilities of the TELs and PELs to correctly predict the toxicity of
sediment, based on an evaluation of independent sets of field data from Florida, the Gulf of Mexico,
California, and New York that were not included in the expanded database, were 86 percent and 85
percent, respectively. These limitations should be considered in the application of TELs and PELs.
Apparent Effects Thresholds
The AET approach is another empirical data evaluation approach similar to the effects range
approaches developed by Long et al. (1995) and FDEP (1994). Barrick et al. (1988) reported that
AETs can be developed for any measured chemical (organic or inorganic) that spans a wide concen-
tration range in the data set used to generate the AET. The AET concept was applied 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 bio-
logical indicators, application of the resulting sediment quality values enabled a wide range of bio-
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Appendix H
logical effects to be addressed in the management of contaminated sediments. Using sediment samples
from Puget Sound in Washington State, AET values were determined for 52 chemicals: 10 trace
metals, 15 individual polynuclear 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 sedi-
ment. 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 bio-
logical effects testing on subsamples of the same field sample.
2. Identified "impacted" and "nonimpacted" stations—Statistically tested the significance of
adverse biological 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. Checked for a preliminary AET—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 differ-
ent statistically relative to appropriate reference conditions. Two thresholds were recognized, when
possible, based on the different indicators. AET-L was the lowest concentration for which a particu-
lar indicator showed an effect, and AET-H was the highest concentration at which effects were
observed for another indicator. AET values based on Microtox bioassays were not used for the NSI
evaluation.
Sediment Toxicity Approaches
Methods for testing the acute and chronic toxicity of sediment samples to benthic freshwater
and marine organisms have been developed (see reviews in Burton et al., 1992, Lamberson et al.,
1992; API, 1994) 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
potentially contaminated field-collected sediments and mortality or sublethal effects on the organ-
isms were recorded. Results of whole sediment and elutriate toxicity tests were used in the evalua-
tion of the NSI.
Variations in sediment toxicity observed in 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 labora-
tory-related confounding factors (Lamberson et al., 1992). Recent studies indicate that aqueous
representations of whole sediment (e.g., elutriate) do not accurately predict the bioavailability of
some contaminants compared to whole-sediment exposures (Harkey et al., 1994). Acute sediment
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Drall National Sediment Quality Survey
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).
Sediment toxicity tests provide important information on the effects of multiple chemical exposures
to assist in the evaluation of sediment quality.
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.
Each of these evaluation parameters is described below.
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 nonpolar organic contami-
nants in the sediment. At present, the TBP calculation can be performed only for nonpolar organic
chemicals; however, methods for TBP calculations for metals and polar organic chemicals are under
development (USEPA and USACOE, 1994).
The environmental distribution of nonpolar organic chemicals is controlled largely by their
solubility in various media. Therefore, in sediments they tend to occur primarily in association with
organic matter (Karickhoff, 1981) and in organisms they are found primarily in the body fats or
lipids (Bierman, 1990; Geyer et al., 1982; Konemann and van Leeuwen, 1980; Mackay, 1982).
Bioaccumulation of nonpolar organic compounds from sediment can be estimated from the organic
carbon content of the sediment, the lipid content of the organism, and the relative affinities of the
chemical for sediment organic carbon and animal lipid content (USEPA and USACOE, 1994). It is
possible to relate the concentration of a chemical in one phase of a two-phase system to the concen-
tration in the second phase when the system is in equilibrium. The TBP calculation focuses on the
equilibrium distribution of a chemical between the sediment and the organism. By normalizing
nonpolar organic chemical concentration data for lipid in organisms, and for organic carbon in sedi-
ment, 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 (BS AF), as in the
following equation (USEPA and USACOE, 1994):
= BSAF(C,/fJf,
where TBP is expressed on a whole-body basis in the same units of concentration as Ct and
TBP = theoretical bioaccumulation potential (ppm);
C = concentration of nonpolar organic chemical in sediment (ppm);
BS AF = biota-sediment accumulation factor (ratio of the concentration of a chemi-
cal 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));
f = total organic carbon (TOC) content of sediment expressed as a decimal
fraction (i.e., 1 percent = 0.01); and
f = organism lipid content expressed as a decimal fraction (i.e. , 3 percent =
0.03) of fillet or whole-body dry weight.
B-ll
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Appendix 1$
BSAF values used in the TBP evaluation are discussed in Appendix C. If TOC measurements
were not available at a site, fK 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 meth-
odology uses a 3 percent value for human health assessments. The Great Lakes Water Quality
Initiative Technical Support Document for the Procedure to Determine Bioaccumulation Factors
(USEPA, 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, I992b),
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 Admin-
istration tolerance/action/guidance levels and EPA risk levels. These parameters are discussed be-
low.
FDA Tolerance/Action/Guidance Levels
The U.S. Food and Drug Administration (FDA) is responsible for the safety of the Nation's
foods, 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 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 environmental contaminants in food.
FDA established levels for several pesticides before they were banned. At the time (i.e., 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 developing risk-based stan-
dards. These standards have been derived by individually considering each chemical and the spe-
cies of fish it is likely to contaminate. FDA also considered (I) the amount of potentially contaminated
fish eaten and (2) the average concentrations of contaminants consumed. FDA has established ac-
tion levels in fish for 10 pesticides and methylmercury, tolerance levels for polychlorinated biphe-
nyls (PCBs), and guidance for 5 metals.
EPA Risk Levels
Potential impacts on humans are evaluated by estimating potential carcinogenic risks and non-
carcinogenic hazards associated with the consumption of chemically contaminated fish tissue In
this assessment it was assumed that the only source of contamination to fish is contaminated sedi-
ment. The procedures for estimating human health risks due to the consumption of chemically
contaminated fish tissue are based on Risk Assessment Guidance for Superfund (USEPA, 1989) and
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Driil'l National Scdiiiu-nt Quality Survey
Guidance for Assessing Chemical Contamination Data for Use in Fish Advisories, Volume II: Devel-
opment of Risk-Based Intake Limits (USEPA, 1994).
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 (1 in 100,000 extra chance of cancer over a
lifetime) cancer risk or a noncancer hazard in humans. A 10'5 risk level exceeds the lower bound
(i.e., 10*) 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 (GDI) of contaminants of concern:
CDI=(EPCXIRKEF)(ED)
(BW)(AT)
where:
GDI
EPC
IR
EF
ED
BW
AT
= chronic daily intake (mg/kg/day);
= exposure point concentration (contaminant concentration in fish);
= ingestion rate (6.5 g/day);
= exposure frequency (365 days/year);
exposure duration (70 years);
= body weight (70 kg); and
= averaging time (70 years x 365 days/year).
These are the same parameter values used by EPA to develop human health water quality crite-
ria. Carcinogenic risks are then quantified using the equation below:
Cancer risk, =CDIixSFi
where:
Cancer risk. = the potential carcinogenic risk associated with exposure to chemical i
(unitless);
CDI. = chronic daily intake for chemical / (mg/kg/day); and
SF. ' = slope factor for chemical i (mg/kg/day)-'.
The hazard quotient, which is used to quantify the potential for an adverse noncarcinogenic
effect to occur, is calculated using the following equation:
CDI,
RfD,
where:
GDI.
RfD'
= hazard quotient for chemical i (unitless);
= chronic daily intake for chemical i (mg/kg/day); and
= 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, an adverse noncarcino-
genic effect is highly unlikely to occur.
B-13
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Appendix It
Using these formulas, the fish tissue concentration (EPC) of a contaminant that equates to a
cancer risk of 10'5 or a hazard quotient that exceeds unity can be back-calculated.
Cancer risk:
Noncancer hazard:
EPC =
(IQ-'XBWXATXC,)
(IRXEFXEDXSF,)
Epc=(BW)(AT)(RfD,)(C,)
(IR)(EF)(ED)
where:
C,
= conversion factor (103g/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 are
due to bioaccumulation from 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 proposed 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 chemicals: DDT, mercury, 2,3,7,8-TCDD, and
PCBs. A Great Lakes Water Quality Wildlife Criterion (GLWC) is the concentration in the water of
a substance that, if not exceeded, protects avian and mammalian wildlife populations from adverse
effects resulting from the ingestion of surface waters and aquatic prey (USEPA, 1995a). Wildlife
values are calculated using the equation:
_ (NOAEL X SSF)
where:
WV
NOAEL
Wt
SSF
BAF
WA+(FAxBAF)
wildlife value (mg/L);
no-observed-adverse-effect level, as derived from mammalian or avian
studies (mg/kg-d);
average weight for the representative species identified for protection (kg);
average daily volume of water consumed by the representative species
identified for protection (L/d);
species sensitivity factor, an extrapolation factor to account for the differ-
ence in toxicity between species;
average daily amount of food consumed by the representative species
identified for protection (kg/d); and
bioaccumulation factor (L/kg), the ratio of the concentration of a chemical
in tissue, normalized to lipid, to the concentration in ambient water.
Chosen using guidelines for wildlife presented in appendix B to part 132,
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Draft National Si'dinii'iit Quality Survoy
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).
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 po-
tential wildlife exposure. Thus, the proposed EPA wildlife criteria cannot be compared directly to
the NSI fish tissue concentrations (either the derived TBPs or actual 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 (i.e., otter and mink) and avian species (i.e.,
kingfisher, herring gull, and eagle)—the same species used to derive the proposed EPA wildlife
criteria. The equation used to estimate wildlife values for fish tissue is presented below. (Exposure
assumptions used for each species are presented in USEPA, 1995a.)
[NOAEL X SSF] jj WtA
where:
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 pro-
posed EPA wildlife criteria for surface water. This exposure route, however, was not considered
when evaluating NSI data, even though sediment contamination might result in contamination of
surface water available for wildlife consumption. A sensitivity analysis was conducted to evaluate
the impact of excluding the surface water ingestion exposure route. Based on this analysis, ingestion
of surface water contributes less than 0.0001 percent of the total exposure (i.e., ingestion offish and
water). Therefore, excluding the water ingestion exposure route had no significant impact on the
evaluation of NSI data with regard to potential wildlife impacts.
Wildlife criteria derived for DDT, mercury, 2,3,7,8-TCDD, and PCBs based on fish tissue con-
centration are presented below.
B-15
-------
Appi-mlix I!
Chemical Fish Tissue
Criterion
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 actual fish tissue residue data contained in the NSI and to
TBPs calculated for DDT, 2,3,7,-TCDD, and PCBs. Mercury is not a nonpolar organic chemical,
and thus a TBP for mercury was not calculated. It should be noted that 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 Fac-
tors (USEPA, 1995b).
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Appendix B
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Draft Nnlioiuil Si-dinu-nt Quality 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 evalua-
tion. 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.
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 the
TBP calculation approach taken here addresses mainly the effects of chemical properties, BSAFs can differ depending
on the biota, dynamics of chemical loadings to the water body, food chain effects, and rate of sediment-water exchange.
Thus, actual BSAF values will depend on many site-specific variables including hydraulic, biological, chemical, and
ecological factors that affect bioavailability.
C-l
-------
Appendix ('
BSAF values were assigned only to nonpolar chemicals in the NSI. This section describes how the actual 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 de-
scribed 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.
Few, if any, steady-state sites were available, however, because environmental loadings of many of the chemicals
are variable or have declined since the 1960s. Barring this limitation, the BSAF data for Lake Ontario that were used
to estimate bioaccumulation factors (BAFs) for the Great Lakes Water Quality Initiative were recommended (Cook,
1995; USEPA, 1994a). The Lake Ontario BSAFs (Cook et al., 1994) are the primary source of values used in NSI TBP
assessments. Another source for BSAFs was the set of values reported for Lake Ontario salmonids (Oliver and Niirni,
1988). The Lake Ontario BSAFs are based on a large set of sediment and fish samples collected in 1987 (USEPA,
1990).
EPA Environmental Research Laboratory, Narragansett
EPA/ORD-Narragansett provided a second source of information for selecting BSAF values. Probability distribu-
tion curves for selecting BSAFs were presented by EPA/ORD-Narragansett for three chemical classes:
• PAHs
• PCBs
• Pesticides
EPA/ORD-Narragansett researchers developed cumulative probability curves for each chemical class from their data-
base of BSAFs (Hansen, 1995). The database from which general BSAF recommendations were summarized included
data from laboratory and field 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.
C-2
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Draft National Sediment Quality Survey
Overall the database contained more than 4,000 BS AF observations. Cumulative probability curves summarizing
the BSAF data in the database were provided by Hansen (1995) for PAHs, PCBs, and pesticides. BSAF values were
tabulated for several probability percentiles.
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 to achieve the 50th percentile protection level
(Table C-l). The BSAF values from EPA/ORD-Duluth were averages and were implemented in the analysis as such.
Since 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 recom-
mended 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)'unless the Oliver and Niimi values were all
that were available, as recommended by Cook (1995).
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
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 BSAF values from EPA/ORD-Duluth were used. BSAF values selected by this approach are expected to be
more conservative because food web transfer to pelagic fishes is a more important process for chemicals having high
log Kow values. Exposure through environmental media, as in direct contact with sediments by benthic organisms, is a
more important process for chemicals having low log Kow values.
Chemicals having no BSAF values available included halogenated compounds; compounds containing nitrogen,
sulfur, or phosphorus; and other compounds. These chemicals were assigned a default BSAF of 1.
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
Table C-l. EPA/ORD-Narragansett Data BSAF Distributions (kg sediment organic carbon/kg lipid)
Probability Percentile
50
70
80
90
95
100
Chemical Class
PAHs
0.29
0.55
0.94
1.71
2.84
4,19
PCBs
1.11
2.26
3.66
5.83
9.15
16.46
Pesticides
1.80
3.34
4.61
7.31
10.61
22.63
C-3
-------
Appendix C
Table C-2. Conventions for Assigning BSAFs to Nonpolar Organic Compounds in NSI
Category of Chemical
Dioxins
PCBs
Pesticides
PAHs
Halogenated and other
compounds
Source of BSAF
EPA/ORD-Dututha "pelagic," risk-based, chemical-specific BSAF
EPA/ORD-Duluth* "pelagic," risk-based BSAF for total PCBs
log Ko. < 5.5
EPA/ORD-Narragansettb "benthic" class-specific BSAF for
50th percentile protection level
log K w > 5.5
EPA/ORD-Duluth" "pelagic," risk-based, chemical-specific BSAF
if available; otherwise, use EPA/ORD-Narragansettb value
EPA/ORD-Narragansett1' "benthic," risk-based, class-specific BSAF for
50di percentile protection level
Default value of 1 unless chemical-specific value available from
EPA/ORD-Duluth"
BSAF Value Used in
Evaluation
0.059
1.85
1.80
See chemical-specific BSAF
given in Appendix D
0.29
1.0
•Cook, 1995.
"Hansen, 1995.
(USEPA, 1994b). Other screening values used for comparison to TBP values and tissue data are U S Food and Drug
Administration (FDA) tolerance/action/guidance levels and EPA wildlife criteria. The BSAF values used in the analy-
sis are presented in Appendix D along with the screening values discussed above.
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 hpid content can eliminate much site and species variation in the tendency of organisms to bioaccumuiate
nonpolar organic compounds (Esser, 1986). Lipid contents can vary naturally with species, site, season, age and
s.ze of fish, and trophic level In addition, reported Hpid contents can vary significantly depending on the
analytical method (Randall etal., 1991).
The purpose of this section is to evaluate the percent fish lipid content data from various sources and compare
these values to those se lected[for use in the NSI evaluation (i.e, 3.0 percent for fijlets 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 Food's (Dickey, 1990).
C-4
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Driil'l National Sedimi-nl On;ili(v Survey
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 since 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 advan-
tage of this database 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 Exler (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
-------
Appendix ('
Table C-3a. Lipid Contents of TYophic Level 3, Epibenthic Fishes
Specie! Name
Aplodinotus
grunniens
Aplodinotus
grunniens
Carpaides carpio
Carpoides cyprinus
Catostomut ardent
Catostomus
catostomus
Catostomus
catostomus
Catostomus
columbianus
Catostomus
commersoni
Catostomus
commersoni
Catostomus
commersoni
Catostomus
commersoni
Catnitomus
macrrtcheilus
Catostomus
occidentals
Coitus cognalut
Cyprinus carpio
Cyprinus carpio
Common Name
freshwater drum
freshwater drum
river carpsuckcr
quillback
Utah sucker
longnose sucker
(FW)
longnose sucker
bridgelip sucker
white sucker
white sucker
white sucker
white sucker
largescale sucker
Sacramento sucker
sculpin (FW)
carp
carp
Whole Fish Lipid
Content,
Percent (size)
mean = 5.8
(0.5 to 15.0, 3865
obs)
mean = 5.1
(0.3 to 13.0, 780 obs)
mean = 3.5
(1.1 to 8.2, 356 obs)
mean = 3.9
(2.5 to 7.2, 298 obs)
mean = 4.6
(0.7 to 10.4, 309 obs)
mean =6.1
(1.4 to 21 .8, 39 obs)
mean = 4.3
(0.2to 12.0,4102
obs)
mean = 6.7
(0.3 to 13.0, 752 obs)
mean = 9.8
(1.7 to 18.5, 3 obs)
8 (5.4 g)
9(15g)
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)
Fillet Lipid Content,
Percent (size)
mean = 1.9
(1.3 to 2.5, 3 obs)
mean = 4.93, standard
(error = 0.103, 905
obs)
mean = 4.4
(1.8 to 9.2, 184 obs)
mean = 3.2
(0.4 to 4.89, 78 obs)
mean = 1 .6
(0.1 to 6.7, 695 obs)
0.8 to 3.8 (not given)
mean = 7.05
(6.4 to 7.7, 32 obs)
5.41 ± 1.18
1.07 ±0.23
1.36 ±0.17
0.99 ± 0.22
2.25 ± 0.65
(not given)
mean = 1.7
(0.2 to 9.1 ,586 obs)
mean = 2.32
(standard error = 0.069,
157 obs)
mean = 1.6
(0.1 to 5.26, 482 obs)
Reference,
Comments
EPA (1992)
Exler (1987)
STORET
STORET
STORET
Owens etal. (1994)
STORET
STORET
Servos et al. (1994)
USEPA (1992)
STORET
Exter (1987)
STORET
USEPA (1992)
USEPA (1994)
Cook etal. (1991)
Kuehletal. (1987)
C-6
-------
Table C-3a. (Continued)
Species Name
Cyprinus carpio
Cyprinus carpio
Cyprinus carpio
Cyprinus carpio
Ctenophyaryngodon
idella
Erimyzon oblongui
Hypentelium
nigricans
Jclalurus furcatus
Iclalurus furcatus
Ictalurus melus
(Ameiurus melas)
Ictalurus natalii
(Ameiurus nalalis)
Iclalurus nebulosus
(Ameiurus nebulosus)
Ictalurus punctatus
Ictalurus punclatus
Iclalurus punctatus
Ictiobus bubalus
Common Name
carp
carp
carp
carp
grass carp
creek chubsucker
northern hogsucker
blue catfish
blue catfish
black bullhead
yellow bullhead
brown bullhead
channel catfish
channel catfish
channel catfish
smallmouth buffalo
Whole Fish Llpld
Content,
Percent (size)
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)
mean = 9.3
(0.5 to 25.1, 145 obs)
mean = 6.5
(0.3 to 17.0, 70002
obs)
mean = 3.9
(3.9 to 4.0. 3 obs)
mean = 4.4
(0.8 to 8.98, 637 obs)
mean = 7.3
(5.3 to 10.4, 5 obs)
mean = 2.9
(0.9 to 6.2, 91 lobs)
mean = 2.8
(0.5 to 7.5, 235 obs)
mean * 2.2
(1.3 to 4.1, 133 obs)
mean = 9.8
(3.4 to 23.0. 22 obs)
mean = 7.1
(0.3 to 15.0,7512
obs)
mean = 5.7
(2.2 to 11.9, 6 obs)
Fillet Llpld Content,
Percent (size)
mean = 9.0
(2.0 to 19.6, 6 obs)
mean - 4.3
(0.02 to 21.6. 16139
obs)
mean = 5.60
(standard error - 0.207,
163 obs)
mean = 5.2
(3 obs)
mean = 0.7
(0.5 to 0.99, 70 obs)
mean = 2.7
(2.0 to 3.0, 4 obs)
mean = 6.0
(1,5 to 12.0, 56 obs)
mean * 1.4
(0.15 to 5.1, 573 obs)
mean = 0.96
(0.1 to 3.2. 294 obs)
mean = 1.5
(0.4 to 3.3, 107 obs)
mean = 5.1
(1.1 to 11.5, 17 obs)
mean = 5.1
(0.2 to 17.3, 20655
obs)
mean = 4.26
(standard error = 0.417,
59 obs)
Reference,
Comments
Kuehl et al.(1987)
USEPA(1992)
STORET
Exler(1987)
USEPA(1992)
USEPA(1992)
STORET
USEPA (1992)
STORET
STORET
STORET
STORET
USEPA (1992)
STORET
Exler(1987)
USEPA (1992)
C-7
-------
Table C-3a. (Continued)
Speci« Name
Iciiobus bukalus
Ictiobus cyprinellus
kilobits cyprinellus
Iciiobus niger
Minytrema melanops
Minytrema tnetanopi
Moxostoma anisurum
Moxostoma
carinatum
Moxosloma
daquesntl
Moxoitoma
trtlttru rum
Moxoiloma
macrolepidotum
Moxostoma
macroltpidotum
Mugil cephalul
Mylachcilus caurinus
Prychocheilus
oregoni
Pfychocheilus
Scaphirhynchus
platorhynchus
Common Name
smallmouth buffalo
bigmouth buffalo
bigmouth buffalo
black buffalo
spotted sucker
spotted sucker
silver redhorse
river redhorse
black redhorse
golden redhorse
shorthead redhorse
shorthead redhorse
striped mullet
pearoouth
northern squiwfish
squawfish
shovelnose sturgeon
Whole Fish Lipld
Content,
Percent (size)
mean = 9.7
(2.8 to L7.J, 886 obs)
mean = 15.1
(5.7 to 22.6, 3 obs)
mean = 5.8
(0.4 to 16.2, 675 obs]
mean = 4.5
(0.9 to 7.4, 9 obs)
mean = 3,7
(0.7 to 5.9, 188 obs)
mean = 8.2
(6.2 to 8.5, 180 obs)
mean = 5.1
({.9 to 5.9, 193 obs)
mean = 5.0
(0.3 to 9.7, 1774 obs)
mean = 6.0
(0.8 to 16.1, 2018
obs)
mean = 19.8
{10.8 to 31.9,4 obs)
mean = 6.5
(0.4 to 10.9, 683 obs)
mean = 11.0 (9.36 to
12.91, 162 obs)
mean = 5,6 (0.8 to
12.0, 812 obs)
Fillet Lipid Content,
Percent (size)
mean = 4.8
(0.2 10 U.3. 595 obs)
mean = 4.1
(0.3 to 15, 1678 obs)
mean = 3.5
(1. 2 to 7. 1,42 obs)
mean = 1.5
(0.9 to 3.2, 197 obs)
mean = 2.1
(1.3 to 2.7, 7 obs)
mean =1.3
(0.5 to 2.4. 170 obs)
mean = 0.97
(0.7 10 1.8, 58 obs)
mean= 1.8
(0.6 to 2.8, 154 obs)
mean =3.0
0. 4 to 13.5, 342 obs)
mean = 3.79
(standard error = 0.357,
43 obs)
mean = 1.3
(0.7 to 3.0, It 7 obs)
mean = 2.2
(0.5 to 3.0, 7 otis)
mean = 7.4
(1.1 to 20.3, 392 obs)
Reference,
Comments
STORE!
USEPA (1992)
STORET
STORE!
USEPA (1992)
STORET
STORET
STORET
STORET
STORET
USEPA (1992)
STORET
Enter (1987)
STORET
STORET
USEPA (1992)
STORET
C-8
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Draft National Si'dinu-nt Quality Survey
Table C-3b. Lipid Contents of TVophic Level 3, Pelagic Fishes
Species Name
Acipcnser sp.
Acrocheilus alutaceus
Alosa
pseudoharengus
Alosa
pseudohartngus
Alosa sapidissuna
Alosa sapidissima
Anguilla rostrala
Aplodinolus
grunnifns
Archosargus
probatocephalus
Coregonus arledii
Coregonus
clupeafom
Coregonus hoyi
Dorosoma
cepedianum
Dorosoma pctenenst
Gad us
macrocephalus
tiiodon alosoides
Common Name
sturgeon (unknown)
chiselmouth
alewife
alewife
American shad
American shad
American eel
freshwater drum
sheepshead
cisco (lake herring)
lake whitefish
bloater
gizzard shad
threadfin shad
true or Pacific cod
goldeye
Whole Fish Lipid
Content,
Percent (tize)
mean = 5.0
(3.2 to 6.8, 47 obs)
7 (32 g)
mean = 8.9
(3.7to 15.2, 128 obs)
mean = 6.55
(5.9 to 7.6, 270 obs)
mean = 5.5
(1.0 to 19.7, 574 obs)
mean = 21.1
(16 to 25.5, 52 obs)
mean = 7.4
(1.3 to 18.0, 189 obs)
mean = 3.0
(0.5to 18.0, 9 obs)
mean = 3.2
(3.5 to 2.8, 74 obs)
Fillet Lipid Content,
Percent (size)
mean = 4.04
(7 obs)
mean = 0.55
(0.19 to 1.00,91 obs)
mean = 13.77
(standard error = 1.00,
11 obs)
mean = 11.66
(standard error = 0.885,
Hobs)
mean = 4.8
(0.3 to 21. 2, 459 obs)
mean = 2.41
(standard error = 0.040,
Sobs)
mean = 1.91
(standard error = 0.149,
69 obs)
mean = 5.86
(standard error = 0.451,
68 obs)
mean = 8.3
(3.2 to 17.0, 98 obs)
mean = 0.63
(standard error = 0.031,
18 obs)
Reference,
Comments
Exler(1987)
STORET
USEPA (1994)
STORET
STORET
Exler (1987)
Exler (1987)
STORET
Exler (1987)
Exler (1987)
Exler (1987)
STORET
STORET
STORET
Exler (1987)
STORET
C-9
-------
Table C-3b. (Continued)
Species Name
Platygobia (Hybopsis
in database) gracitis
Lepomis auritis
Lepomii cyanellus
Lepomis gibbosus
Lepomis gibbosus
Lepomis megalotis
Osmerus mordax
Osmerus mordax
Pimephales promelas
Lepomis macrochirus
Lepomis macrochirus
Lola lota
Lota lota
Lota lota
Oryzias latipes
Phoxinus
eryihrogasler
Common Name
flathead chub
redbreast sunfish
green sunfish
pumpkinseed
pumpkinseed
longear sunfish
rainbow smelt
rainbow smelt
fathead minnow
bluegill sunfish
bluegill sunfish
burbot
burbot
burbot
medaka
southern redbelly
dace
Whole Fish Lipid
Content,
Percent (size)
mean = 3.6
(1.3 to 8.1. 550 obs)
mean = 3.2
(2.2 to 7.8, 376 obs)
mean = 3.9
(2.2 to 7.7, 126 obs)
mean = 2.8
(1.0 to 7.2, 536 obs)
4 (16 g)
19 dg)
mean = 3.5
(2.4 to 4.6, 4 obs)
mean = 4.4
(0.1 to 8.7, 1034 obs)
8(0.175g)
mean = 5.6
(2.2 to 10.0. 762 obs)
Fillet Lipid Content,
Percent (size)
mean = 3.3
(0.68 to 8.14, 75 obs)
mean = 0.70
(standard error = 0.071,
Sobs)
mean = 2.42
(standard error = 0.107,
52 obs)
0.35 to 0.7
mean = 0.2
(0.1 to 0.3, 18 obs)
mean = 0.81
(standard error = 0.059,
13 obs)
Reference,
Comments
STORET
STORET
STORET
STORET
Exler (1987)
STORET
USEPA (1994)
Exler (1987)
Cook et al. (1991)
USEPA (1992)
STORET
Owens etal. (1994)
STORET
Exler (1987)
Schmieder et al.
(1992)
STORET
C-10
-------
Table C-3b. (Continued)
Species Name
Pomoxis annularis
Pomoxis annularis
Pomoxis
nigromaculalus
Pomoxis
nig romaculalus
Prosopium
Williamson!
Prosopium
williamsoni
Richardsonius
ballealus
Sebastes auriculatus
Sebasles marinus
Semotilus atromacula
Semotilus corporalis
Common Name
white crappie
white crappie
black crappie
black crappie
mountain whitefish
mountain whitefish
redside shiner
brown rockfish
redfish
creek chub
fallfish
Whole Fish Lipid
Content,
Percent (sire)
mean = 2.1
(0.4 to 5.8, 622 obs)
mean = 2.7
(0.7 to 8.4, 457 obs)
mean = 8.5
(0.5 to 13.8, 327 obs)
mean = 3.9
(1.0 to 5.0, 815 obs)
mean = 1.9
(0.25 to 3.9, 100 obs)
Fillet Llpid Content,
Percent (size)
mean = 1.0
(0.5 to 2.0, 7 obs)
mean = 0.4
(0.08 to 2.6, 936 obs)
mean = 1.1
(0.5 to 1.5, 3 obs)
mean = 1 .4
(0.13 to 5.3, 118 obs)
mean = 1.6,
(0.2 to 4. 1.532 obs)
3.4 to 11.8
(not given)
mean = 0.9
(0.85 to 0.96, 50 obs)
mean = 1.57
(81 obs)
mean = 1.63
(standard error = 0.092,
208 obs)
Reference,
Comments
USEPA (1992)
STORET
USEPA (1992)
STORET
STORET
Owens et al. (1994)
STORET
Exler (1987)
Exler (1987)
STORET
STORET
Table C-3c. Lipid Contents of Trophic Level 4, Epibenthic Fishes
Species Name
Pylodictis olivaris
Pylodictis olivaris
Common Name
flathead catfish
flathead catfish
Whole Fish Lipid
Content,
Percent (size)
mean = 3. 1
(0.5 to 8. 1,829 obs)
mean = 6.0
(1.6 to 8.7, 3 obs)
Fillet Lipid
Content, Percent
mean = 3.0
(0.2 to 21. 1,1315 obs)
mean = 1 .9
(0.6 to 3.1, 4 obs)
Reference,
Comments
STORET
USEPA (1992)
C-ll
-------
Appendix C
Table C-3d. Lipid Contents of TVophic Level 4, Pelagic Fishes
Species Name
Ambloplites rupestris
Ambloplites rupestris
Amia calva
Centropristis striata
Esox lucius
Esox lucius
Esox lucius
Esox niger
Leioslomus xanlhurus
Leiostomus xanlhurus
Lutjanus
campechanus
Micropogonias
undulatus
Micropterus
dolomieu
Micropterus
dolomieu
Micropterus
punctulatus
Micropterus
punctualtus
Common Name
rock bass
rock bass
bowfm
black sea bass
northern pike
northern pike
northern pike
chain pickerel
spot
spot
red snapper
Atlantic croaker
smaUmouth bass
smallmouth bass
spotted bass
spotted bass
Whole Fish Lipld
Content,
Percent (ilze)
mean = 2.3
(0.6 to 4.4, 759 obs)
mean = 1.9
(0.1 to 9.8, 8 10 obs)
mean = 5.2
(3.3 to 7.9, 300 obs)
mean = 3.4
(0.3 to 8.8, 1166 obs)
mean = 2.4
(0.6 to 4.9, 322 obs)
Fillet Lip id Content,
Percent (size)
mean = 1.0
(0.8 to 1.2, 3 obs)
mean = 0.7
(0.4 to 0.98, 129 obs)
mean = 0.5
(0.04 to 1.4, 230 obs)
mean = 2.00
(standard error = 0.221,
40 obs)
mean = 1.4
(0.6 to 2.6, 5 obs)
mean = 0.69
(standard error = 0.005,
224 obs)
mean = 1.3
(0.6 to 2.0, 5 obs)
mean = 4.90
(standard error = 2.93,
10 obs)
1.34 (55 obs)
3.17
(standard error = 0.529,
8 obs)
mean = 1.6
(0.8 to 4.4, 19 obs)
mean = 0.6
(0.01 to 2.3, 848 obs)
mean = 2.8
(0.9 to 4.5, 4 obs)
mean = 0.7
(0.1 to 1.8, 353 obs)
Reference,
Comments
USEPA (1992)
STORET
STORE!
Exler (1987)
USEPA (1992)
STORET
Exler (1987)
USEPA (1992)
STORET
Exler (1987)
Exler (1987)
Exler (1987)
USEPA (1992)
STORET
USEPA (1992)
STORET
C-12
-------
Draft i\;ilinn;)l SrdiiiU'iit Quality Survey
Table C-3d. (Continued)
Species Name
Micropterus
salmoides
Micropterus
salmoides
Morone americana
Morone chrysops
Morone chrysops
Morone saxalilis
Oncorhynchus
gorbuscha
Oncorhynchus
kisutch
Oncorhynchus
kisutch
Oncorhynchus mykiss
Oncorhynchus mykiss
Oncorhynchus nerka
Oncorhynchus
tshawytscha
Oncorhynchus
tshctwytscha
Perca fla vescens
Pomatomus saltalrix
Common Name
largemouth bass
largemouth bass
white perch
white bass
white bass
striped bass
pink salmon
echo salmon
coho salmon
rainbow trout
rainbow trout
sockeye salmon
chinook salmon
Chinook salmon
yellow perch
blucfish
Whole Fish Lipid
Content,
Percent (size)
mean = 4. 1
(0.3 to 10.6. 2924
obs)
mean = 4.5
(2.6 to 7. 1,249 obs)
mean = 4.6
(0.3 to 15.4, 615 obs)
ll(35g)
mean = 3.7
(2.4 to 5.1, 52 obs}
mean = 3.6
(1.2 to 9.1, 112 obs)
Fillet Lipid Content,
Percent (size)
mean = 1.6
(0.4 to 7.6, 54 obs)
mean =0.7
(0.04 to 9.2, 4548 obs)
mean = 2.7
(0.7 to 4.8, 11 obs)
mean = 3.9
(0.01 to 8.1, 847 obs)
mean = 2.33
(standard error = 0.381,
14 obs)
mean = 3.4S
(standard error = 0.141.
144 obs)
mean = 2.7
(0.4 to 10.7, 383 obs)
mean = 5,92
(standard error = 0.162,
217 obs)
mean = 5.0
(4.1 to 5.6, 3 obs)
mean = 8.56
(standard error = 0.392,
48 obs)
mean = 2.2
(0.04 10 17.7. 1957
obs)
mean = 10.44
(standard error = 1.692,
10 obs)
mean = 0.5
(O.I to 4.6, 280 obs)
mean = 4.27
(3 obs)
Reference,
Comments
USEPA (1992)
STORET
STORE!
USEPA (1992)
STORET
Exler (1987)
Exler (1987)
STORET
Exler (1987)
Branson et al. (1985)
VSEPA (1992)
Exler (1987)
STORET
Exler (1987)
STORET
Exler (1987)
C-13
-------
Table C-3d. (Continued)
Specici Name
Saimo clarki
(Onchorhynchus clarki)
Salmo gairdneri
(Onchorhynchus mykiss)
Salmo salar
Salmo trutta
Salmo trutta
Salvelinus namaycush,
Oncorhynchus mykiss,
Oncorhynchus spp.
Salvelinus malma
Salvelinus namaycush
Scomberomorus cavall
Scomberomorus macula
Stizostedion canadense
Stizostedion vitnum
Stizostedion vitnum
Stizostedion vitnum
Stizostedion vitreum
vitnum
Common Name
cutthroat trout
rainbow trout
Atlantic salmon
brown trout
brown trout
salmonids
Dolly Varden
lake trout
king mackerel
Spanish mackerel
sauger
walleye
walleye
walleye
walleye
Whole Fish Lip Id
Content,
Percent (size)
mean = 6.0
(1.5 to 8.9, 112 obs)
11 (2410 g)
mean = 7.1
(2.1 to 9.9, 3 obs)
mean = 15.9
(12.6to 18.3, 42 obs)
mean = 6.0
(0.8 to 16.3, 139 obs)
mean = 6.2
(0.3 to 15, 1089 obs)
Fillet Lipid Content,
Percent (size)
mean = 1.0
(0.2 to 1.7, 378 obs)
mean = 3.36
(standard error = 0.256,
24 obs)
mean = 6.34
(standard error = 1.72,
7 obs)
mean = 4.0
(1.6 to 8.1, 6 obs)
mean = 5.0
(0.14 to 14.8. 741 obs)
mean = 7.8
(2.5 to 20.0, 1883 obs)
mean = 2.00
(standard error = 0.188,
6 obs)
mean = 6.30
(standard error=3.810,
3 obs)
mean = 1.7
(0.3 to 10.0, 195 obs)
0.6 to 0.7
mean = 1.3
(0.3 to 6.0, 440 obs)
mean = 1.22
(standard error = 0.162,
Hobs)
mean = 1.6
(0.7 to 2.6, 13 obs)
Reference,
Comments
STORE!
Exler (1987)
Exler (1987)
USEPA (1992)
STORET
USEPA (1994)
USEPA (1992)
STORET
Exler (1987)
Exler (1987)
STORET
Owens et al. (1994)
STORET
Exler (1987)
USEPA (1992)
C-14
-------
Analysis ofLipids Data
Lipids data were analyzed for comparison with the screening value selected for the NSI evaluation by computing
averages. Eight averages of data for fishes of the following categories for data in STORE! (Table C-4a) and the
NSCRF (Table C-4b) were computed (and labeled A-H):
A. Trophic levels 3 and 4, whole body
B. Trophic levels 3 and 4, whole body, excluding migratory and saltwater fishes
C. Trophic level 4, pelagic, fillet
D. Trophic level 4, pelagic, fillet, excluding migratory and saltwater fishes
E. Resident, freshwater, demersal fishes, whole body
F. Resident, freshwater, pelagic fishes, whole body
G. Resident, freshwater, demersal fishes, fillet
H. Resident, freshwater, pelagic fishes, fillet.
Data for fillets and whole fish were evaluated separately. All analysis except "A" were of fishes in the NSI
exclusively. Summary statistics reported include the mean, standard error, range, and number of observations. The
matrices in Tables C-4a and C4-b indicate the categories of fishes 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 in the STORET database ranged from 0.753
to 4.49 percent; in the NSCRF, mean fillet values ranged from 1.6 to 4.9 percent. The mean whole-body percent lipid
content for various groups offish species in the STORET database ranged from 3.757 to 6.33 percent; in the NSCRF,
mean whole-body values ranged from 4.6 to 8.8 percent.
C-15
-------
n
Table C-4a. Lipid Analysis - STORET
Analysis
A
B
C
D
E
F
G
H
Matrix of fistes Included in Avenge
Trophic
Level
3
•
•
•
•
•
•
4
•
•
•
•
•
•
•
•
Position in Water
Column
Demersal
•
•
•
•
Pelagic
•
•
•
•
•
•
Mobility
Resident
•
•
•
•
•
•
•
•
Migratory
•
•
Habitat
freshwater
•
•
•
•
•
•
•
•
Saltwater
•
•
Tissue/
Orgm
whole
whole
ffllet
fillet
whole
whole
fillet
fillet
Lipid Content, %
Mean
5.97
5.97
2.5
0.753
6.33
3.757
4.49
1.06
Standard
Error
0.010
0.010
0.011
0.020
0.018
0.021
Number of
Observations
113,978
110,998
13,293
6793
91867
13025
42687
9378
Range
0.1-26.7
0.1-26.7
0.01-20
0.01-10
0.22-26.7
0.10-16.3
0.02-24
0.01-21.07
-------
Table C-4b. Lipid Analysis - NSCRF
Analysis
A
B
C
D
E
F
G
H
Trophic
Level
3
•
•
•
•
•
•
4
•
•
•
•
•
•
•
•
Matrix of Fishes Indnded la Average
Position In Water
Column
Demersal
•
•
•
•
Pelagic
•
•
•
•
•
•
Mobility
Resident
•
•
•
•
•
•
•
•
Migrator;
•
•
Habitat
Freshwater
•
•
•
•
•
•
•
•
Saltwater
•
•
Tissue/
Organ
whole
whole
fillet
fillet
whole
whole
fillet
fillet
Lipid Content, %
Mean
8.5
8.6
1.9
1.6
8.8
4.6
4.9
1.6
Standard
Error
0.328
0.116
0.338
1.02
0.697
0.106
Number of
Observations
249
246
122
103
233
7
34
117
Range
0.5-31.9
0.5-31.9
0.4-8.1
0.4-7.6
0.5-31.9
1.6-8.7
0.5-19.6
0.4-7.6
Data, for fillets and whole fish were evaluated separately. All analyses except "A" were of fishes in the NSI exclusively. Summary statistics reported include UK mean, standard error, range, and number of
observations. The matrices in TaHes C-4a and C-4b indicate rbe categories of Bshes averaged. The average of edible portions ftom USDA data was 4.1 percent lipid.
The mean fillet percent lipid content for various groups of fish species in the STORE! database ranged from 0.753 to 4.49 percent; in the NSCRF. mean fillet values ranged from 1.6 lo 4.9 percent. The mean
whole-body percent lipid content for various groups of Gin species in the STORET database ranged Sam 3 757 lo 6.33 percent: in the NSCRF, mean whole-body rallies ranged bom 4.6 to 8.S percenl.
-------
Appendix ('
References
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.
Dickey, L.E. 1990. Composition of foods, raw, processed, prepared—7990 supplement. Agriculture Handbook 8,
1990 Supplement. U.S. Department of Agriculture, Human Nutrition Information Service.
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, J. 1987. Composition of foods: Finfish and shellfish products. Agriculture Handbook No. 8-15. U.S. Depart-
ment of Agriculture, Human Nutrition and Information Service.
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.
Oliver, E.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.
Randall, R.C., H. Lee II, R.J. Ozretich, J.L. Lake, and R.J. Pruell. 1991. Evaluation of selected lipid methods for
normalizing pollutant bioaccumulation. Environ. Toxicol. Chem. 10:1431-1436.
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 in fish. 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 FY1994. 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-18
-------
l)r;itt Natinniil Sediment Quality Suni-v
Appendix D
Screening Values for
Chemicals Evaluated
Sediment Concentrations
Table D-1 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 (SQCJ
and sediment quality advisory levels (SQAL^.), which are in micrograms per gram (^g/g) organic carbon.
These values were multiplied by the organic carbon content (fM) 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 and are currently under technical review. These values were not developed for regulatory
purposes and should be used with caution (if at all) for purposes other than the evaluation 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 10'5 and a noncancer hazard quotient of 1 (USEPA, 1995a, 1995b). 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 poten-
tial 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 accommodates 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-specific data were
D-l
-------
Table D-l. Screening Values for Chemicals Evaluated
GUIDELINE VALUES INTENDED ONLY FOR SCREENING LEVEL HAZARD COMPARISON AMONG CHEMICALS
M«y Be Over- or C/Bderproteccfte of Sedfmeaf •( • Given LK((KH DepefffUng on SKe-Spedftc Condition
CAS N.mWr
83329
208968
67641
98862
107028
107131
15972608
116063
309002
62533
120127
999999933
7*40360
7440382
1912249
7440393
92875
71432
56553
999999955
50328
205992
191242
207089
65850
98077
Cb.mlc.l Nant
Ao^ipM^
Acenapbthylene
Acetone
Acclopbeaaae
Acrclein
Actylonitrile
AlacttortLasso
AldicanVTcmik
Aldrin
Aniline
Anthracene
Anthracene*. Pheoanthreoe
Antimony
Arsenic
Atiazine
Barium
Benzidine
Benzene
Bcozo(a>andiracene
Benzo(a)aathraceoc/ChryKne
Bcnzo(a)pyrene
Benzo
i
2
1.6
1
1
1
1
1
1
1
SQC_
(VtlfJ
130
180
EB-L
.016
.044
.0853
.0853
83
261
.261
.43
SedimeBt C»ac«Rt»tf«i
ER-M
(PP-)
.5
64
I.I
1.1
70
1.6
1.6
1.6
AEI-L
(PP-)
.5°
1.3"
.96"
.96°
ISO"
57>
1.6°
1.6'
1.6°
3.6°
.72°
3.6°
.65"
AET-H
(PP«)
21
13"
13'
691
200"
700°
5.1°
S.l"
3.6«
9.9"
2.6'
9.9'
.76'
SQAL_
(i*C)
130
180
5.7
TEL
(re->
0.00671
0.00587
0.0469
0.0469
7.24
00748
0.0748
0.0888
PEL
(PP-)
0.0889
0.128
0,245
0.245
41.6
0.693
0.693
0.763
Fbh Tinoe ConcenlrBtloa (ppm)
Cmcm.
. EPA
Rbk 10~*
0.2
1.3
0.0063
19
0.062
0.49
0.00047
3.7
0.15
0.15
0.015
0.15
1.5
0.0083
EPA NOB
C*ac«r
Haurd
QBdtfeBt
. 1
650
1100
1100
220
11
no
11
0.32
3200
3200
4.3
3.2
380
750
32
43000
FDA
Ct.id.nce;
Action/
TokrKBC*
Lent
03
68
BSAt
(ullku)
0.29'
1.0
1,0
1.80'
0.29'
0.29^
1.0
o.»'
0.29"
0.29'
0.29"
0.29'
-------
Table D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY FOR SCREENING LEVEL HAZARD COMPARISON AMONG CHEMICALS
M.j B« OT«r- >r [ladnprottcUn «f Snlixml « • Gi-tm L«olWi. D.pndiif o> Site-Specific Cotillon
CAS Nnbcr
100516
100447
7440417
319646
319S57
3198*8
58899
608731
92524
111444
108601
117817
542881
7440428
75274
74839
101553
1689845
85687
7440439
63252
1563«62
75150
133904
57749
5103719
Cke_kal Na»u
Benzyl alcohol
Benzyl chloride
Baymm
BHC, alpha
BllC.txa-
BHC.deta-
BHC, aanuna- (Undme)
BHC, Kchmcal grade
Bipbeoyl
BL^-cUoroettiyllcthcr
BisCZ-ctlotooofttTylicjker
BijO-<*yll!"yl)[tll>alae
BudUoomHliyQdlier
Boron
Bnnuomediaiie
Bnxnopbeiirl pbcoyl cteer. 4-
Bioiaioxyail
Bulyl boiiyl ptdulra
f aHnrinm
CarbaryVSevin
Carbofimn/furadan
Carbon disumde
Cttorambcii
Chloidaoe
dlordane. alpbXcU)-
Co«V
'
U
IJ
1A.6
U*
13
If
I
1
W
1
1
1.6
1.6
2
U
1J
SQC.
(tW«_)
ScdlBtnt CiHK»tralloQ
ER-L
(FT-)
1.2
ER-M
(PP-)
9.6
AET-L
-)
.87"-
1.9"
.9"
9.6°
SQAL_
(WO
13
037
0.37
110
'l30
1100
TEL
0.00032
0.00032
0.00032
0.00032
0.00032
0.112
0.676
0.00226
0.00226
PEL
<»->
0.00099
0.00099
0.00099
0.00099
000099
iS5
4.21
0.00479
0.00479
Flit Tim* CoBuMnltai
1.80"
1.80»
1.80*
B.2»»
1.0
1.0
1.0
4.771
4.77'
-------
Table D-l. (Continued)
GUIDELINE VALVES INTENDED ONLY FOR SCREENING LEVEL HAZARD COMPARISON AMONG CHEMICALS
May R« Ow- wr UaferpralMtlv* *f SctUMMl •! • Glvca L«c*tioa DefMMllag Ml Site-Specific Cawfltiau
CAS Mentor
5103742
5566347
999999247
999999248
108907
51O156
75003
75014
110758
74873
91587
95578
2921882
7440473
218019
7440508
108394
95487
106445
1319773
98828
21725462
57125
1861321
5JI90
72548
CUoxtane. bcuKtrans}-
Chlocdmc, JJUUm«(tr«ll)-
CUonboc-NaucUorC")-
atod™-N-»*1«<«-»>
Ctttorobciuenc
a*.*—
Chloroeditioe
CUoroedieae
ChloroetliylvinyL ether, 2-
Cblaronicthane
Chlotooiptuhilene, 2-
auoropbeaol, 2-
OitoipyrifM/DuntHia
Chromium
Chiyscoe
Coppe.
CresoL, m-
CrcsoUo-
Cresol, p-
Ciesols
Cumeoe
Cyanazine
cy«^
DCPA/Dacdul
ODD. cf'-
DDD, p. p'-
13
13
u
13
1,6
1
1
1
'
1
1
Z
1
'
1
1,3
13
SQC_
0-rtJ
ER.L
81
.384
34
.00158
.00158
ER-M
370
2.8
270
.027
.027
<«,«., C,
AET-L
(PP-)
26*
2.8-
390°
.63"
.63"
.67—
.63"
.016>
016*
mccilntiM
AET-H
2701
9.2"
1300-
.72'
.72'
3.6'
,72>
.043'
043'
SQAL_
(l**J
82
TEL
0.00226
0.00226
0.00226
0.00226
52J
0.108
18.7
0.00122
0.00122
PEL
0.00479
0.00479
0.00479
0.00479
160
0846
108
0.00781
0.00781
Ffth TiBM CHCMItnltM (pp«)
€••«•,
- EPA
Ebk 10'
0033
O.OK
0083
0.083
0.40
0.057
8.3
15
0.13
0.45
0.45
EPA No*
Ctmctr
Hourd
Qmkil
« 1
0.65
065
0.65
0.65
220
220
4300
270
860
54
32
54
400
540
540
54
54
430
22
220
110
FDA
Gifd»<*/
Act!—/
Tokruct
Level
03
03
03
03
11
5
5
BSAF
y
2.22-
4.77-
4.771
1.0
\.w
0.29*
1.80*
0.28'
0.28'
-------
Table D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY FOB SCREENING LEVEL HAZARD COMPARISON AMONG CHEMICALS
May Be O»er- or UnderproteetlTe »f Sediment at • Given Location Depending OB SlU-SpecUlc Conditions
CAS Number
3424826
72559
789026
50293
999999300
1163195
84742
117840
333415
53703
132649
96128
124481
1918009
95501
541731
106467
25321226
91941
7S7I8
75343
107062
75354
156605
156592
75092
Chemical Name
DDE, o,p'-
DDE, p, p'-
DDT, Of'-
DDT, p, p1-
DDT (Toul)
DccabcomodiplKayl oxide
Di-a-butyi phlhalalc
Di-a-oclyl phtlulale
DiaziaoarSpectncide
DA>enzoi>«ne, 1.2
Dibromocfalaromediaoe
Diomba
DicfalorobCDZcac. 1,2-
Dichlorobcozeae, 13-
DichlorobcnKW, 1,4-
DichlorolxMizeixa
Dichlcrobcnzkline, 3 J'-
Dicfalanxlifluoroaieduae
Dichlonxabanc 1,1-
Dicblocoeduiie U-
DichlotortlKoe, 1.1-
DictJorotbcnc, mu-lj-
DicUorocttykDc. c»-14-
DichloromHbaoe
Coo*
13
U
13
13
13
1
1,6
1
l.«
1
If
•
1
l.«
I,«
Ij6
1
1
1
1
1
1
1
1
SedlDMit CoKentritlon
SQC_
(nf«J
ER-L
(ppm)
.0022
.0022
.00158
.00158
.00158
.0634
ER-M
(ppm)
.027
.027
.027
.027
.0461
26
AET-L
(ppm)
.009*
.009"
.034'
.034*
.009*
1.4"
6.2*
.23-
.54*
0.05"
.11'
0.05-*
AET-H
(ppm)
.015'
.015"
.034'
.034"
.015-
IJf
6.2*
.97"
l.T
0.05"*
.12"
0.05-*
SQAl.,,
(M/«J
1100
.019
200
34
170
35
34
TEL
(ppm)
0.00207
0.00207
0.00119
0.00119
0.00389
0.00622
PEL
(Ppm)
0.374
0.374
0.00477
0.00477
0.0517
0.135
Fish Tissue Concentration (ppm)
> EPA
Risk lO'*
0.32
0.32
0.32
0.32
0.32
0015
0.077
1J
4.5
4.5
0.24
1.2
0.18
14
EPA NOD
Cancer
Haxard
Quotient
= 1
5.4
5.4
5.4
110
1100
220
9.7
43
220
320
970
960
960
2200
1100
97
220
110
650
FDA
GnKUxe/
Actloa/
Tolerance
Lent
5
5
5
5
5
BSAF
(unltless)
7.7-
7.71
1.6T
1.67'
l.T
1.0
1.0
1JO*
0.291
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
-------
Table D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY TO* SCREENING LEVEL HAZARD COMPARISON AMONG CHEMICALS
M.j Be Orer- «r Ofttifnnah* »F Sedluri 11 » Giro Lootta Dipoddig om SMr-Sfrctfic Cradlfex
CHS Namtxr
120832
94757
94*26
78875
542756
62737
115322
60571
846*2
119904
131113
105679
528290
99650
100254
51285
121142
606202
«88S7
122667
298044
959988
33213659
115297
72208
563122
CWmWJNio.
Dichkiopbcnol, 2,4-
Dkllw.iphmojyi.-KK jtid, 2,4-
Dichlon>plKix»ybiiliDo4c «ckl. 2.4-
Dichlofapropaiic. 1.2-
DicUon^ropene, 1.3-
Dichlarras
DicofoVKeHsuic
Dicldrin
Diediyl phlfailaH
mwn*thnryhrw,7JAiltr 3 T-
Dimeihyl pbdulaK
DimMhylpliciKil. 2.4-
DioilroUilKOc, 1>
DiaitrobeDzaK. 13-
Diiulrabciizeae, 1,4
Dbitroriieiial, 2,4-
DiniCratoluenc, 2.4-
Dnitracolunie. 2.6-
DncaeVDNBP
OipbcgylliydnirDC. 1,2-
DUulfotoo
EndotnU Jn, «lplu-
Eodosullao. bea-
Endnulf ra mixed jmnen
Entaia
EUuonBladcQ
C«i
5
'
1
'
1A6
1.6
1
1
1.6
1.6
I,«
1.6
1
SQC.,
u*C)
11
4.2
ER-L
(P»»)
EI-H
7.15&4
PEL
(PP-)
0.0043
FUk Tl»u» Cow»>tmk» (w-i
C«K«I.
-EPA
Ktsk l«*
1.6
0.62
0.37
0.24
.0067
7.7
0.13
EPA No.
C»ur
H*x>rd
QnUm
« 1
32
110
86
J.2
5.4
.54
86OJ
110000
220
4.3
1.1
4.3
21
22
11
11
0.43
65
65
65
5.2
5.4
FDA
CridaiceS
AOSamt
T»krn«
L*F«I
1
3
BSAF
(ullfeu)
1.0
1.80'
1.0
1.0
l.W
l.SO1
ISO'
1.80>
l.SO'
-------
Table D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY FOR SCREENING LEVEL HAZARD COMPARISON AMONG CHEMICALS
M.j B> OT«T- or Uadtrpntccttn of StdiaKat it • Choi Loc.tio. Dtpeadiag « Slu-SpcciCc CondJIku
CAS N._b«r
14 1786
100414
106934
206440
86737
944229
76448
1014573
118741
87683
77474
67721
51235042
123319
193395
78591
33820530
7439921
121755
108316
7439965
7439976
72435
78933
108101
22967926
ChcHkol Nanw
Etfayl acetate
Elhylbemene
Erhylcne ditromide
FUtofanthene
FlliOienc
Fooofos
Hepuchloc
HeplacUcr epoxkle
Hexachlorobenzene
Heuduorobuladicae
Hexaduarocyclopenudiene
Heuduoroethane
Hexazinooe
HydroqaiaODe
lDdeao(IJ3-cyn»e
bapbomoe
bapropalin
Lead
Miluhion
Maleic anhydride
Manganese
Mercury
Methoxychlor
Metbyl ethyl ketooe
Metfayl isobuyl ketone
Methyl Mercury
C«k
1
1.6
1
1
1.6
1
13
U
1
1
1
1.6
1
. 1
1
2
l.«
1,6
1
•
3
SOC_
(!*«_)
620
ER-L
<»")
.6
.019
46.7
.15
Scdimcait CoftcatratiM
ER-M
(PP-)
5.1
34
218
.71
AET-L
(»«)
.01»
2.5*
.54°
.022»
.011*
.69°
450*
J9°
AET-H
(PP-)
.037-
yf
3.6'
.23'
XT
Z6>
660"
2.1"
SQAL^
(«^_)
480
620
54
100
.067
1.9
TEL
0.113
0.0212
30^
0.13
PEL
1.494
0.144
112
0.696
Fl«h TisfB* C«nccatraII«B (ppm)
= EPA
Rlak l«'s
.0013
0.024
0.012
0.067
1.4
7.7
0.15
110
EPA NOB
H.urd
Quotient
>= 1
9700
1100
430
430
22
5.4
0.14
8.6
2.2
75
11
360
430
2200
160
220
1100
54
I.I
54
6500
860
1.1
FDA
Gotdi.ce;
ActioH/
Toteraaec
LCT>|
3
3
13
.
1
BSAF
(ualtleu)
1.0
0.2»>
0.29*
1.80*
IJO*
0.091
1.0
1.0
0.291
1.0
l.SO1
1.801
1.0
-------
Table D-l. (Continued)
GUIDELINE VALUES INTENDED ONLY FOR SCREENING LEVEL HAZARD COMPARISON AMONG CHEMICALS
May Be Over- Mr UadcrpraUctive »r SeilMMMI at a Glvea Lecatfea Depending •• Site-Spedflc C»*dfn»B*
CAS NeMber
91576
21087649
2385855
7439987
91203
91598
7440020
98953
100027
924163
621647
55185
86306
999999484
999999502
56382
12674112
11104282
11141165
53469219
12672296
11097691
11096825
608935
82688
8786S
Catnkal NapK
Methyhuphlhaleiie. 2-
M«ri brain
MitraSDechlonne
Molybdenum
Naphthalene
Naphdrylamine. 2-
Nickel
Nitrobenzene
Nitroptmiol, 4
Nitroiodi-o-butylamuie, N-
Nitro»Ddi-D-propylamine, N-
Ninosodinieltiylamme. N-
Nilro!od.r*cnyUniine. N-
PArls (high molecular weight)
PAHs Oow molecular weight)
Ptnthion ethyl
PCB(Aroclor-1016)
PCB(Aroclor-1221)
PCBcef
Acttoa/
Takrance
Level
0.1
70
2
2
2
2
2
2
2
BSAF
(aaltkai)
1.311
0.29*
1.85"
1.851
1.85'
1.85'
1.85'
1.85'
1.85'
0.04-
-------
Table D-l. (Continued)
CAS Nubcr
85O18
108952
298022
85449
13X363
1610180
7287196
23950585
1918167
129000
91225
7782492
7440224
122349
7440246
100425
13071799
886500
95943
1746016
79345
127184
56235
58902
961115
7440315
C»eBk*l N.B*
Pta.,*™.
Ttxaal
Phontt/F»moplioimimct
Pttbdic anhydride
Fuly .Jn.yli
„___
Promctym/Caparol
Proomudc
Propadilor
P^e
QuaoUnc
Selenium
Silver
Simazme
Stratum
Stymie
TertafoalCanttr
TulMUyn
TandJorobenzcne. 1,2,4.5-
Tor«*kxoe
1
1
1,4
1
I
5
1
1
1
1
1.6
If
If
1
SedlHMBt C«nc*atnli»
SQC,
&**J
180
ER-L
(PP-)
0.240
0.0227
.663
1
ER.M
1J
0.180
if
3.7
AET-L
(PP- )
1J'
.42*
1.0>
3.3'
6.11
.OST4
AET-H
(PP->
6.91
1.2"
3.1'
16"
6.1'
.14'
SQAL^
(Vt/tJ
18O
160
53
120
TEL
(PP>)
O.0867
O.0216
0.1S3
0.733
PEL
(PP>>
0.544
0.189
1.398
1.77
Fj»k Tknc C«c<»r>U« (ffm)
COKCM.
>EPA
RliklO*
0.014
0.009
0.90
6.9E-7
0.54
2.1
0.83
4.5
EPA Nu
Cancer
H>»rd
QMtfcm*
= I
6500
22
22000
0.22
160
43
810
140
320
54
54
54
6500
2200
0.27
11
3J
110
7J
320
320
6500
FDA
GmUmmem/
AeUxJ
Lnel
2
12
BSAF
(nHle»)
1,85'
0.29"
1.0
0.0591
1.0
1.0
1.0
-------
Table D-l. (Continued)
GUIDELINE VALVES INTENDED ONLY FOX SCJtECMNG LEVIL HAZASD C0MP4KISOH AMONG CHEMICALS
MIJ >t Orer-off GBta Bntoclh* «f StdtBKit •! ft Glrei Locittei DtyendiBg OB SUc-SpetMc Coarilll
Tridhloroptauil, 3,4 j6-
TiicUorophenoxyacetic acid, 2,4^-
TricfatoropbenoKypropiofiic icid, 2,4,5-
Triftui»linrrrcnM
Trimedl^befizeDe, 14,4-
Tnmtiololueae
Vanadium
Vinyl accute
Xyleocina-
Xyleoc, o-
Xyleoe-p-
Xyleaes
Ztoc
Dietio-toxic equivatuu
C*4«
1,6
1,6
1*6
1,«
1,6
1
1.6
1
1
•
1
1,5
1
1
1
'
ScAlHtal CovetntratlMi
SQC_
(m/C1
EK-L
llT't
150
ER-M
(»•)
410
AET-L
(B~>
.051'
.W
.04'
.04*
.04'
410>
AET-H
(«•«)
.064=
.1?
.12°
.12"
.12°
1600°
SQAL^
(M/lJ
89
10
65
920
17
210
25
^5
2.S
2.5
TEL
<»-)
124
TEL
Irr-l
271
FUh TtoM ConctntnllaR (pfw)
C*BCCB.
< EPA
Abk 10~f
.098
14
1.9
9S
18
98
14
3.6
6.9E-7
EPA NOB
C«Bccr
Biur4
QOOIteBI
« 1
2200
220
no
970
43
65
3200
110
1100
no
86
El
5.4
5.4
75
11000
22000
22000
22000
3200
FDA
Culduc*'
A.UoV
Telcritct
L«TC|
BSAF
(o.llk.1)
1.0
1.W
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1,D
1.0
1.0
1.0
0.025'
-------
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, 40CFR180.
6. Preliminary SQAL^ developed for this chemical is under technical review.
AET Criteria:
•Sediment concentration based on amphipods.
b Sediment concentration based on benthic organisms.
' Sediment concentration based on oysters.
BSAF Sources:
•Cook 1995.
'Hansen 1995.
-------
Appendix I)
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 repre-
sent 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 conserva-
tive screening values of the closely related chemicals were used in analyzing the chemicals without screening values.
This methodology was applied only for chemicals or chemical groups with more than 20 positive results. The follow-
ing chemicals and chemical groups were affected by this methodology: BHCs, chlordanes, cresols, DDT and metabo-
lites, 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 Tier 2 is often a reflection of the number of times that chemical is measured and detected in sediment samples.
Thus, chemicals that are measured and detected less frequently might 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 a station being categorized as Tier 1 or Tier 2.
D-12
-------
Dral'l Nalioniil Sediment Quality Survey
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)"-11
CAS Number
13329
208968
67641
107028
107131
15972608
309002
62533
120127
999999933
7440360
7440382
1912249
7440393
71432
9287S
56553
99999995S
50328
205992
191242
207089
65850
100516
7440417
92524
542881
111444
108601
117817
7440428
75274
74839
101553
85687
319846
319857
319868
Chemical Name
Acenaphthene
Acenaphthylene
Acetone
Acrolein
Acrylonitrile
Alachlor/Lasso
Aldrin
Aniline
Anthracene
Anthracene&Phenanthrene
Antimony
Arsenic
Atrarine
Barium
Benzene
Benzidine
Benzo(a)aiithraceiie
Benzo(a)anthncene/Chrysene
Benzo(a)pyrene
BenzcHbXluofamhene
Benzo(ghi)perylene
Benzo
-------
Appendix I)
Table D-2. (Continued)
CAS Number
58899
608731
7440439
75150
57749
999999247
999999248
5103719
5103742
5566347
108907
510156
75003
75014
110758
74873
91587
95578
2921882
7440473
218019
7440508
108394
95487
106445
1319773
21725462
57125
84742
117840
333415
53703
132649
124481
95501
541731
106467
25321226
91941
Chemical Name
BHC, gammaVLindane
BHC, technical grade
Cadmium
Carbon disulfide
Chlordane
Chlordane-Nonachlor(cis)-
Chlordane-Nonachlor(trans)-
Chlordane, alpha(cis)-
Chlordane, beta(trans)-
Chlordane, gamma(trans)-
Chloro benzene
Chlorobenzilate
Chloroethane
Chloroethene
Chloroethylvinyl ether, 2-
Chloromethane
Chloronaphthalene, 2-
Chlorophenol, 2-
Chlorpyrifos/Dursban
Chromium
Chrysene
Copper
Cresol, m-
Cresol, o
Cresol, p-
Cresols
Cyanazine
Cyanide
Di-n-butyl phthalate
Di-n-octyl phthalate
Diazinon/Spectracide
Dibenzo(a,h)anthracene
Dibenzofuran
Dibromochloromethane
Dichlorobenzene, 1,2-
Oichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Dichloro benzenes
Dichlorobenzidine, 3,3'-
Number of
Times
Measured
in Sediment
14442
169
27919
-
12432
1476
1992
4416
2833
967
2111
-
-
-
-
-
-
-
305
27504
6975
27956
988
1993
985
18
-
-
4651
4179
3712
7564
2564
2033
4402
4315
4352
27
-
Number of
Positive
Sediment
Results
999
166
15176
-
2170
9
31
1516
443
334
58
-
-
-
-
-
-
-
5
25216
3580
25452
780
745
84
1
-
-
986
435
249
2431
416
18
107
132
268
12
-
Number
of Times
Measured
in Tissue'
8750
115
6743
24
7316
4468
4569
6092
5841
85
819
22
557
706
534
744
655
629
793
5508
893
6284
-
51
49
-
326
14
637
650
172
824
126
562
892
797
887
-
_639
Number
of Positive
Tissue
Results'
1391
31
3321
21
4568
2101
2764
3659
3045
19
18
-
1
2
-
12
1
1
143
3283
149
5533
-
-
3
-
-
3
55
6
-
16
-
1
2
2
3
-
1
Tier 1
Level
Results
101
3
-
-
116
-
-
3
3
-
-
-
-
-
-
-
-
-
-
426
185
-
-
-
-
-
-
-
9
-
-
419
25
-
38
-
53
6
-
Tier 2
Level
Results
527
66
7206
-
4228
268
556
1157
847
207
4
-
-
2
-
-
-
-
-
4126
1618
11213
41
22
31
1
-
-
112
23
188
1732
51
-
23
22
41
3
-
D-14
-------
Draft National Sediment Quality Survi'\
Table D-2. (Continued)
CAS Number
75718
75343
107062
156605
75354
75092
120832
94757
78875
542756
115322
60571
84662
131113
105679
51285
121142
606202
122667
298044
1861321
53190
72548
3424826
72559
999999300
789026
50293
115297
959988
33213659
72208
563122
100414
206440
86737
944229
76448
1024573
Chemical Name
Dichlorodifluoromethane
Dichloroethane 1.1-
Dichloroethane 1,2-
Dichloroethene, trans-1,2-
Dichloroethene, 1,1-
Dichloromethane
Dichlorophenol, 2,4-
Dichlorophenoxyacetic acid, 2,4-
Dichloropropanc, 1,2-
Dichloropropene, 1,3-
Dicofol/Kelthane
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Dimethylphenol, 2,4-
Dinitmphenol, 2,4-
Dinitrotoluene, 2,4-
Dinitrotoluene, 2,6-
Diphenylhydrazine, 1,2-
Disulfoton
DCPA/Dacthal
ODD, o,p'-
DDD, p, p'-
DDE, o,p'-
DDE, p, p'-
DDT (Total)
DDT, o,p'-
DDT, p, p'-
Endosulfan mixed isomers
Endosulfan, alpha-
Endosulfan, beta-
End rin
Ethion/Bladen
Ethylbenzene
Fluoranthene
Fluorene
Fonofos
Hcptachlor
Heptachlor epoxide
Number of
Times
Measured
in Sediment
-
1918
1981
1393
-
2177
-
-
2015
-
-
14702
4188
4118
4541
-
-
-
-
-
129
6349
15311
5434
15961
3710
6056
16028
2606
5581
5886
12694
2953
2543
7562
6652
-
11952
12829
Number of
Positive
Sediment
Results
-
19
20
33
-
576
-
-
15
-
-
3113
367
135
80
-
-
-
-
-
76
977
4411
632
5980
736
567
3268
80
84
260
289
38
118
4563
2280
-
673
986
Number
of Times
Measured
In Tissue*
174
561
972
793
973
532
642
39
563
107
400
10243
654
653
640
631
636
636
509
23
827
3397
6252
3427
7656
5750
3479
5843
49
2832
2157
8192
170
807
953
797
288
7369
7480
Number
of Positive
Tissue
Results'
-
-
8
2
2
112
1
-
2
-
26
5583
2
-
1
-
1
1
•
-
586
428
2481
401
5715
4183
368
1677
12
53
10
893
-
50
216
14
-
1006
2896
Tier 1
Level
Results
-
-
-
-
-
-
-
-
-
-
-
89
34
-
-
-
-
-
-
•
-
73
572
118
823
122
25
371
-
-
-
-
-
1
234
231
-
-
-
Tier 2
Level
Results
-
-
-
-
-
11
-
-
-
-
-
6709
48
38
54
-
-
-
-
-
3
502
2574
222
3501
860
268
1839
20
45
42
8
-
42
1074
1141
-
210
1431
D-15
-------
Appendix I)
Table D-2. (Continued)
CAS Number
118741
87683
67721
193395
78591
33820530
7439921
121755
108316
7439965
7439976
72435
78933
108101
22967926
91576
21087649
2385855
7439987
91203
7440020
98953
100027
621647
86306
999999484
999999502
56382
608935
82688
87865
85018
108952
1336363
1610180
1918167
129000
12674112
11104282
Chemical Name
Hexachlorobenzene
Hexachlorobutadiene
Hexac hloroe thane
Indeno( 1 ,2,3-cd)pyrene
Isophorone
Isopropalin
Lead
Malathion
Maleic anhydride
Manganese
Mercury
Methoxychlor
Methyl ethyl ketone
Methyl isobutyl ketone
Methyl Mercury
Methylnaphthalene, 2-
Metribuzin
Mirex/Dechlorane
Molybdenum
Naphthalene
Nickel
Nitrobenzene
Nitrophenol, 4
Nitrosodi-n-propylamine, N-
Nitrosodiphenylanime, N-
PAHs (high molecular weight)
PAHs (low molecular weight)
Parathion ethyl
Pentachlorobenzene
Pentachloronitrobenzene/Quintozene
Pcntachlorophenol
Phenanthrene
Phenol
Polychlorinated biphenyls
Prometon/Pramitol
Propachlor
Pyrene
PCB(Aroclor-1016)
PCB(Aroclor-1221)
Number of
Times
Measured
In Sediment
10044
4198
3801
5874
3400
-
29979
4041
-
-
26142
9183
519
-
-
2629
-
5794
-
6823
21519
-
-
-
3730
1566
1604
-
114
-
5622
7067
4595
11296
-
-
7558
5098
5627
Number of
Positive
Sediment
Results
1445
128
4
1913
40
-
24971
38
-
-
16632
154
7
-
-
973
-
544
-
2820
18550
-
•
-
66
885
895
-
54
-
195
4078
864
4183
-
-
4555
46
7
Number
of Times
Measured
in Tissue'
6970
1161
636
756
635
392
6654
500
2
1000
9752
5912
20
26
9
-
289
4800
707
803
3120
635
606
645
661
-
-
499
404
390
1756
-
647
10642
289
1
952
3161
3568
Number
of Positive
Tissue
Results'
1519
14
-
20
4
15
3008
1
-
971
8424
63
11
-
8
-
-
915
169
22
974
-
1
1
3
-
-
4
30
2
149
-
12
7379
-
-
187
12
2
Tier 1
Level
Results
-
-
-
-
-
-
-
-
-
-
1951
•
-
-
-
71
-
-
-
291
-
•
-
•
-
93
112
-
-
-
-
335
-
8151
-
-
482
19
4
Tier 2
Level
Results
224
81
1
559
8
-
8883
26
-
5
5049
33
-
-
-
522
-
40
-
1247
9260
-
-
1
45
383
382
-
4
-
26
694
155
2620
-
-
1896
39
5
D-16
-------
Druft National Sediment Quality Survey
Table D-2. (Continued)
CAS Number
11141165
53469219
12672296
11097691
11096825
7782492
7440224
122349
7440246
100425
888888882
95943
1746016
79345
127184
56235
58902
7440315
108883
8001352
75252
120821
71556
79005
79016
75694
67663
95954
88062
93765
93721
1582098
7440622
108054
108383
95476
106423
1330207
7440666
Chemical Name
PCB(Aroclor-1232)
PCB(Aroclor-1242)
PCB(Aroclor-1248)
PCB(Aroclor-1254)
PCB(Aroclor-1260)
Selenium
Silver
Simazine
Strontium
Styrene
SEMjsst ([SEMHAVS])
Tetrachlorobenzene, 1,2,4,5-
Tetrachlorodibenzo-p-dioxin. 2,3,7,8-
Tetrachloroethane, 1,1,2,2-
Tetrachloroelhene
Tetrachlorom ethane
Tetrachlorophenol, 2,3,4,6-
Tin
Toluene
Toxaphene
Tribromomethane/Bromoform
Trichlorobenzene, 1,2,4-
Trichloroethane, 1.1,1-
Trichloroethane, 1,1,2-
Trichloroethene
Trichlorofluoromethane
Trichloromethane/Chloroform
Trichlorophenol, 2,4,5-
Trichlorophcnol, 2,4,6-
Trichlorophenoxyacetic acid, 2,4,5-
Trichlorophenoxypropionic acid, 2,4,5
Trifluralin/Treflan
Vanadium
Vinyl acetate
Xylene, m-
Xylene, o-
Xylene, p-
Xylenes
Zinc
Number of
Times
Measured
in Sediment
5417
6375
6314
7178
6885
-
11082
-
-
-
335
97
631
1683
2429
2010
-
-
2338
10912
2078
4256
2083
2035
2494
1096
2277
-
-
-
-
-
-
-
55
61
14
922
27065
Number of
Positive
Sediment
Results
13
435
559
1305
890
-
6256
-
-
-
335
1
38
49
109
15
-
-
325
75
44
87
63
14
75
9
76
-
-
-
-
-
-
-
31
1
2
48
26473
Number
of Times
Measured
In Tissue'
3195
4446
4464
5871
6035
2559
1739
289
45
191
-
398
908
978
973
979
71
382
814
'6566
818
1082
815
879
975
288
972
73
658
3
36
925
768
21
-
-
-
22
4580
Number
of Positive
Tissue
Results'
1
220
688
3343
3611
2079
515
-
45
-
-
12
391
33
49
4
-
264
116
643
7
46
23
7
19
15
37
-
-
-
-
193
465
-
-
-
-
13
4553
Tier 1
Level
Results
4
355
916
3664
3866
-
350
•
-
-
8
-
353
-
2
-
-
-
-
-
-
6
-
-
-
-
-
-
-
-
-
-
-
-
4
-
-
5
-
Tier 2
Level
Results
10
270
280
765
531
4
1083
-
-
-
161
-
23
2
17
-
-
-
28
684
-
49
10
-
1
-
•
-
-
-
-
-
-
-
6
1
2
11
5176
D-17
-------
Appendix I)
Table D-2. (Continued)
CAS Number
888888881
Chemical Name
Dioxin toxic equivalents
Number of
Times
Measured
in Sediment
56
Number of
Positive
Sediment
Results
56
Number
of Times
Measured
in Tissue'
590
Number
of Positive
Tissue
Results'
590
Tier 1
Level
Results
459
Tier 2
Level
Results
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
-------
Draft N;i(ion:il Sediiiii'iil (JiiiiEitv Survey
References
Barrick, R., S. Becker, L. Brown, H. Beller, and R. Pastorok. 1988. Sediment quality values refinement: 1988
update and evaluation ofPuget Sound AET. Vol. 1. Prepared for the Puget Sound Estuary Program, Office of
Puget Sound.
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.
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 dibeniofurans (CDDs and CDFs) and 1989 update. EPA/625/3-89/016. Risk Assessment
Forum, Washington, DC.
-. U.S. Environmental Protection Agency. 1993. Proposed sediment quality criteria for the protection of
benthic organisms. EPA 822/R93 Series Documents. USEPA. Office of Science and Technology, Health and
Ecological Criteria Division, Washington, DC.
. USEPA. 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.
. USEPA. 1995b. Health effects assessment summary tables FY1995. EPA/540/R-95/036. USEPA Office
of Solid Waste and Emergency Response, Washington, DC.
. USEPA. 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.
. USFDA. 1993b. Guidance document for cadmium in shellfish. U.S. Food and Drug Administration,
Center for Food Safety and Applied Nutrition, Washington, DC.
. USFDA. 1993c. Guidance document for chromium in shellfish. U.S. Food and Drug Administration,
Center for Food Safety and Applied Nutrition, Washington, DC.
. USFDA. 1993d. Guidance document for lead in shellfish. U. S. Food and Drug Administration, Center
for Food Safety and Applied Nutrition, Washington, DC.
. USFDA. 1993e. Guidance document for mercury in shellfish. U.S. Food and Drug Administration,
Center for Food Safety and Applied Nutrition, Washington, DC.
D-19
-------
Draft National Sediment Quality Survey
Appendix E
Cancer Slope Factors and
Noncancer Reference Doses
Used to Develop EPA Risk
Levels
Table E-1 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 FY1995. 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.
E-l
-------
Appendix K
Table E-l. Cancer Slope Factors and Noncancer Reference Doses Used to Develop EPA Risk Levels
CAS Number
83329
67641
98862
107028
107131
15972608
116063
309002
62533
120127
999999933
7440360
7440382
1912249
7440393
92875
71432
56553
999999955
50328
205992
207089
65850
98077
100516
100447
7440417
319846
319857
319868
58899
608731
Chemical Name
Acenaphthene
Acetone
Acetophenone
Acrolein
Acrylonitrile
Alachlor/Lasso
Aldicarb/Temik
Aldrin
Aniline
Anthracene
Anthracene & Phenanthrene
Antimony
Arsenic
Atrazine
Barium
Benzidine
Benzene
Benzo(a)anthracene
Benzo(a)anthracene/Chryaene
Benzo(a)pyiene
Benzo(b)fluoranthene
Benzo(k)fluoranthene
Benzole acid
Benzotrichloride
Benzyl alcohol
Benzyl chloride
Beryllium
BHC. alpha-
BHC, beta-
BHC. delta-
BHC, gamma- (Lindane)
BHC, technical grade
Cancer Slope
Factor(mg/kg/d)''
(Followed by source;
see footnotes)
5.40E-1'
8.00E-2"
1.70E+1'
5.70E-31
1.75E+01
2.22E-1"
2.30E+21
2.90E-2'
7.30E-1'
7.30E-1
7.30E+01
7.30E-1'
7.30E-2'
1.30E+11
.70E-11
4.30E+01
6.30E+01
1.80E+01
1.80E+0 .
.30E+0"
1.80E+01
Noncancer ReferenceDose
(mg/kg/d) (Followed by
source; see footnotes)
6.00E-2'
l.OOE-1'
l.OOE-1'
2.00E-211
l.OOE-3"1
l.OOE-2'
l.OOE-3'
3.00E-5'
3.00E-1'
3.00E-1
4.00E-4'
3.00E-4'
3.50E-21
7.00E-21
3.00E-31
4.00E+01
3.00E-1"
5.00E-3'
3.00E-41
Surrogate Chemical
Used (If necessary)
anthracene
benzo(a)anthracene
beta-BHC
E-2
-------
Draft National Sediment Quality Survey
Table E-L (Continued)
CAS Number
608731
92524
111444
108601
117817
542881
7440428
75274
74839
101553
1689845
85687
7440439
63252
1563662
75150
133904
57749
5103719
5103742
5566347
999999247
999999248
108907
510156
75003
75014
110758
74873
91587
95578
2921882
Ch«mlnl Name
BHC, technical grade
Biphcnyl
Bis(2-chloroethyl)ether
Bis(2-chloroisopropyl)etner
Bis(2-ethylhexyl)phtha!ate
Bis(chlorometliyl)echer
Boron
Bromodichloromethane
Bromomettiane
Bromophenyl phenyl ether, 4-
Bromoxyml
Butyl benzyl phthalate
Cadmium
Carbaryl/Sevin
Carbofuran/furadan
Carbon disulfide
Chloramben
Chlordane
Cblordane, alpha(cis)-
Chlordane, beta(trans)-
Chlordane, gamma(lrans)-
Ch!ordane-nonachlor(cis)-
Chtordane-nonachlor(trans)-
Chloro benzene
Chlorobenzilate
Chloroethane
Chloroethene
Chloroethylvinyl ether, 2-
Chloromethane
Chloronaphthalene, 2-
Chlorophenol, 2-
Chlorpyrit'os/Dursban
Cancer Slope
Factor! rag/kg/d)'
(Followed by lource;
see footnotes)
1.80E+01
1.10E+01
7.00E-2"
1.40E-21
2.20E+21
6.20E-21
UOE-t-O
1.30E+0
1.30E+0
1.30E+0
1.30E+0
L30E+0
2.70E-1*
1.90EtO"
1.30E-2"
Noncancer RefercnceDose
(mg/kg/d) (Followed by
sou ret; see footnotes)
5.00E-21
4.00E-2'
2.00E-2'
9.00E-2'
2.00E-2'
1.40E-3'
5.80E-2'
2.00E-2'
2.00E-1'
5.00E-4'
l.OOE-11
5.00E-3'
l.OOE-1'
l.SOE-2'
6.00E-51
6.00E-5
6.00E-5
6.00E-5
600E-5
6.00E-5
2.00E-2'
2.00E-2'
4.QOE-1'
2.50E-2'
8.00E-2'
5.00E-3'
3.00E-3'
Surrogate Chemical
Used (If neceuary)
chlordane
chlordane
chlordane
chtordane
chlordane
E-3
-------
Appendix K
Table E-l. (Continued)
CAS Number
7440473
218019
7440508
108394
95487
106445
1319773
98828
21725462
57125
1861321
53190
72548
3424826
72559
789026
50293
999999300
1163195
84742
117840
3334515
53703
132649
96128
124481
1918009
95501
541731
106467
25321226
91941
Chemical Name
Chromium
Chrysene
Copper
Cresol, m-
Cresol, o-
Cresol, p-
Cresols
Cumenc
Cyanazine
Cyanide
DCPA/Dacthal
ODD, o,p'-
DDD. p, p'-
DDE, o,p'-
DDE. p. p'-
DDT, o,p'-
DDT, p, p'-
DDT (Total)
Decabromodiphenyl oxide
Di-n-butyl phthalate
Di-n-octyl phthalate
Diazinon/Spectracide
Dibenzo(a,h)amhracene
Dibenzofuran
Dibromo-3-chloropropane, 1,2-
Dibromochloiomethanc
Dicamba
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,3-
Dichlorobenzene, 1,4-
Oichlorobenzenes
Dichlorobenzidine, 3,3'-
Cancer Slope
Factor(mg/kg/d) '
(Followed by source;
see footnotes)
7.30E-3'
8.40E-1"
2.40E-1
2.40E-1'
3.40E-1
3.40E-I'
3.40E-1
3.40E-11
3.40E-1
•
7.30E+0*
1.40E+0"
8.40E-21
2.40E-2'
2.40E-2
4.50E-11
Noncancer ReferenceDose
(mg/kg/d) (Followed by
source; see footnotes)
5.00E-31
3.71 E-2"
5.00E-21
5.00E-21
5.00E-3"
5.00E-3
4.00E-2'
2.00E-03"
2.00E-2'
l.OOE-2'
5.00E-4
5.00E-4J
5.00E-4
l.OOE-21
l.OOE-r
2.00E-2"
9.00E-4"
4.00E-3'
2.00E-2'
3.00E-2'
9.00E-2'
8.90E-2'
8.90E-2
Surrogate Chemical
Used (IT necessary)
p-Cresol
p.p'-DDD
p,p'-DDE
p.p'-DDT
p,p'-DDT
1,3- and 1,4-dichlorobcnzcne
E-4
-------
Draft National Sediment Omililv Survey
Table E-l. (Continued)
CAS Number
75718
75343
107062
75354
156605
156592
75092
120832
94757
94826
78875
542756
62737
115322
60571
84662
119904
131113
105679
528290
99650
100254
51285
121142
606202
88857
122667
298044
959988
33213659
115297
72208
Chemical Name
Dichlorodifluoromethane
Dichloroethanc 1,1-
Dichloroethane 1,2-
Dichloroeihene, 1,1-
Dichloroethene, trans- 1,2-
Dichloroethylene, cis-1,2-
Dichloromethanc
Dichlorophenol, 2,4-
Dkrhlorophenoiyacetic acid, 2,4-
Dichloroptienoxybutanoic acid, 2,4-
Dichlorapropanc, 1.2-
Dichloropropene, 1,3-
Dichlorvos
Dicofol/Kelthane
Dicldrin
Diechyl phlhaltte
DimethoxybenzidineJ,3'-
Dimethyl phthalatc
Dimethylphenol. 2,4-
Dinitrobenzene. 1.2-
Dinitrobenzene, 1,3-
Dinitrobenzene, 1,4
Dinitrophenol, 2,4-
Dinitrotoluene, 2,4-
Dinitrotoluene, 2,6-
Dinoseb/DNBP
Diphenylhydrazine. 1.2-
Disulfoton
Endosulfan, alpha-
Endo&ulfan, beta-
Endosulfan mixed isomers
Endrin
Cancer Slope
Factorl mg/kg/df'
(Followed by sourer,
see footnotes)
9.10E-2'
6.00E-11
7.50E-3'
6.80E-2k
1.75E-1"
2.90E-1'
4.40E-1-
1.60E+11
1.40E-2"
8.00E-1'
Noncancer ReferenceDose
(mt/kg/d) (Followed by
source; ice footnotes)
2.00E-1'
l.OOE-1*
9.00E-3'
2.00E-2'
l.OOE-2"
6.00E-21
3.00E-31
l.OOE-2'
8.00E-31
3.00E-4'
S.OOE-"
5.00E-51
8.00E-I'
l.OOE+1"
2.00E-2'
4.00E-4"
l.OOE-41
4.00E-4"
2.00E-3'
2.00E-3'
l.OOE-3"
l.OOE-31
4.00E-51
6.00E-3
6.00E-3
6.00E-3'
3.00E^'
Surrogate Chemical
Used (IT necessary)
endosulfan
endoiulfan
E-5
-------
Appendix K
Table E-l. (Continued)
CAS Number
563122
141786
100414
106934
206440
86737
944229
76448
1024573
118741
87683
74474
67721
51235042
123319
193395
78591
33820530
121755
108316
7439965
7439976
72435
78933
108101
22967926
21087649
2385855
7439987
91203
91598
7440020
Chemical Name
Ethion/Bladen
Ethyl acetate
Ethylbenzene
Ethylene dibromide
Fluoranthene
Fluorene
Fonofos
Heptachlor
Heptachlor epoxide
Hexach lorobenzene
Hexach lorobutadiene
Hexachlorocyclopentadiene
Hexach loroethane
Hexazinone
Hydroquinone
Indeno( 1 ,2,3-cd)pyrene
Isophorone
Isopropalin
Malatbion
Maleic anhydride
Manganese
Mercury
Methoxychlor
Methyl ethyl ketone
Methyl isobutyl ketone
Methyl Mercury
Metribuzin
Mirex/Dechlorane
Molybdenum
Naphthalene
Naphthylamine, 2-
Nickel
Cancer Slope
Factor(mg/kg/d)-'
(Fallowed by laurce;
see footnotes)
8.50E+1'
4.50E+01
9.10E+&
1.60E+01
7.80E-2'
1.40E-2'
7.30E-1'
9.50E-41
1.80E+0"
1.30E+2«
Noncancer ReferenceDose
(mg/kg/d) (Followed by
source; see footnotes)
5.00E-4'
9.00E-1'
l.OOE-11
4.00E-2'
4.00E-2'
2.00E-31
5.00E^»'
1.30E-51
8.00E-4'
2.00E-4"
7.00E-3'
l.OOE-31
3.30E-2'
4.00E-2"
2.00E-1'
1.50E-2'
2.00E-2'
l.OOE-1'
5.00E-31
l.OOE-4
5.00E-3'
6.00E-1'
8.00E-2"
l.OOE-4'
2.50E-2'
2.00E-4'
5.00E-3'
4.00E-2*
2.00E-2'
Surrogate Chemical
Used (If necessary)
methyl mercury
E-6
-------
Driif't Nalidiuil Si'dinu'iil Qimlily Sur\ey
Table E-l. (Continued)
CAS Number
98953
100027
974163
621647
55185
86306
S6382
12674112
11104282
11141165
53469219
12672296
11097691
1 1096825
608935
82688
87865
1089S2
298022
85449
1336363
1610180
7287196
23950585
1918167
129000
91225
7782492
7440224
122349
122349
100425
Chemical N.me
Nitrobenzene
Nttrophenol, 4
Nitrosodi-n-butylamine, N-
Nitrosodi-n-propylamine, N-
Nitrosodiethylamine, N-
Nitrosodiphenylamine, N-
Parathion ethyl
PCB(Aroclor-1016)
PCB(Aroclor-1221)
PCB(Arocfor-1232)
PCB(Aroclor-1242)
PCB(Aroclor-1248)
PCB(Aroclor-1254)
PCB(Aroclor-1260)
Pentach lore benzene
Pentachtoronittobenttne/Quintoze
Pentachlorophenol
Phenol
Phorate/Famophos/Thimet
Phlhalic anhydride
Polychlorinated biphenyls
Prometon/Pramitol
Prometym/Caparol
Pronamide
Ptopachlor
Pyrene
Quinoline
Selenium
Silver
Simazine
Strontium
Sly rent
Cancer Slope
F»ctor(mjj/kg/d) '
(Followed by source;
see footnotes)
5.40E+tf
7.00E+0'
1.50E+21
4.90E-3'
7.70E+0
7.70E+0
7.70E+0
7.70E+0
7.70E+0
7.70E+0
7.70E+0
1.20B-1'
7.70E+01
1.20E+1"
I.20E-1"
Noncanccr ReferenceDose
(mg/kg/d) (Followed by
source; see footnotes)
S.OOE-41
6.20E-2'
6.00E-3'
7.00E-5'
2.00E-5'
2.00E-5
2.00E-5
2.00E-5
2.00E-5
2.00E-5
8.00E-41
2.60E-1*
3.00E-21
6.00E-1'
2.00E-4"
2.00E+01
2.00E-5
1.50E-21
4.00E-31
7.50E-2'
1.30E-21
3.00E-21
S.OOE-31
5.00E-31
5.00E-31
6.00E-11
2.00E-1'
Surrogate Chemical
Uied (If necessary)
PCBs
PCBs
PCBs/Aroclor 1254
PCBs/Aroclor 1254
PCBs/Aroclor 1254
PCBs/Aroclor 1254
PCBs/Aroclor 1254
3.00E-31
Aroclor 1254 (RfD)
E-7
-------
Appendix K
Table E-l. (Continued)
CAS Number
13071799
886500
95943
1746016
79345
127184
56235
58902
961115
7440315
108883
8001352
75252
120821
71556
79005
79016
75694
67663
95954
88062
93765
93721
1582098
95636
118967
7440622
108054
108383
95476
1330207
7440666
Chemical Name
Terbufos/Counter
Terbutryn
Tetrachlorobenzene, 1,2,4,5-
Telrachlorodibenzo-p-dioxin, 2,3,7,8-
Tetrachloroethane, 1,1,2,2-
Tetrachloroethene
Telrachloromethanc
Tetrachlorophenol, 2,3,4,6-
Tetrachlorvinphos/Gardona/Stirof
Tin
Toluene
Toxaphene
Tribromomethane (Bromoform)
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethane, 1,1,2-
Trichloroethene
Trichlorofluoromethane
Trichloromethane (Chloroform)
Trichlorophenol, 2,4,5-
Trichlorophenol, 2.4,6-
Trichlorophenoxyacctic acid, 2,4,5-
Trichlorophenoxypropionic acid, 2,4,5-
Trifluralin/Trenan
Trimethylbenzene, 1,2,4-
Trinitrotoluene
Vanadium
Vinyl acetate
Xylene, m-
Xylene, o-
Xylenes
Zinc
Cancer Slope
Factor(mg/kg/d) '
(Followed by source;
see footnotes)
1.56E+5"
2.00E-I1
5.20E-2'
1.30E-1'
2.40E-2"
l.lOE-t-01
7.90E-"
5.70E-21
1.10E-2-
6.10E-3'
1.10E-2' '
7.70E-3'
3.00E-2'
Noncancer ReferenceDose
(mg/kg/d) (Followed by
source; see footnotes)
2.50E-5"
l.OOE-31
3.00E-41
l.OOE-2'
7.00E-4'
3.00E-21
3.00E-2'
6.00E-1'
2.00E-1'
2.00E-21
l.OOE-2'
9.00E-2'
4.00E-3'
6.00E-3'
3.00E-11
l.OOE-21
l.OOE-11
l.OOE-21
8.00E-3'
7.50E-31
5.00E-4'
5.00E-4'
7.00E-3'
l.OOE+0"
2.00E+Oh
2.00E+0*
2.00E+01
3.00E-1'
Surrogate Chemical
Used (If necessary)
E-8
-------
l)r:il( National Sediment Quality Survey
Codes:
'Integrated Risk Information System (IRIS).
hHealth Effects Assessment Summary Tables (HEAST).
'Environmental Criteria and Assessment Office (ECAO, as cited in Risk-Based Concentration Table).
"Withdrawn from HEAST, but use continued for screening assessments (USEPA, Risk-Based Concentration Table).
E-9
-------
Draft National Sediment Quality Survey
Appendix F
Species Characteristics
Related to NSI
Bioaccumulation Data
Table F-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 being 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 sub-
sistence 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 F
Table F-l. Species Characteristics Related to Tissue Residue Data
Species Code
615301010400
61 1829010000
872901010000
872901010600
872901010500
872901010.100
877601200100
875503060100
874701010200
874701010600
874701010300
874701010500
874701010100
883516020200
883516020100
877702060100
877702060200
877702060300
877702060400
877702060500
877702060600
877702060700
873401010100
884202010200
874101010100
883544260100
883516090100
883543030100
551539010100
877718020200
883102040500
618102000000
Scientific Name
Acanthomysis macropsis
Acartia spp.
Acipenser spp.
Acipenser fulvescens
Acipenser oxyrtiynchus
Acipenser transmomanus
Acrocheilus alutaceus
Allosmerus elongatus
Alosa aestivalis
Alosa chrysochloris
Alosa mediocris
Alosa pseudoharengus
Alosa sapidissima
Ambtoplites cavifrons
Ambloplites rupestris
Ameiuras bninneus
Ameiurus cams
Ameiurus melas
Ameiums natalis
Ameiurus nebulosus
Ameiurus platycephalus
Ameiurus sen-acanthus
Amia calva
Anarhichas denticulatus
Anguilla rostrata
Aplodinolus grunniens
Archoplites inlemiptus
Archosargus probaiocephalus
Arctica islandica
Arius felis
Artedius notospilotus
Asucidae
Common Name
Mysid shrimp
Copepod (unknown species)
Sturgeon (unknown Species)
Lake sturgeon
Atlantic sturgeon
White sturgeon
Chiselmouth
Whitebait smelt
Blueback herring
Skipjack herring
Hickory shad
Alewife
American shad
Roanoke bass
Rock bass
Snail bullhead
While catfish
Black bullhead
Yellow bullhead
Brown bullhead
Flat bullhead
Spotted bullhead
Bowfin
Northern wolffish
American eel
Freshwater drum
Sacramento perch
Shecpshead
Ocean quahog
Hardhead catfish
Bonehead sculpin
Crayfish (family)
Resident/Migratory*
E
M
M
R
M
M
R
M
M
M
M
M
M
R
R
R
R
R
R
R
R
R
R
R
M
M
R
M
R
M
R
R
Demersal/Pelagic'
E
P
D
D
D
D
P
P
P
P
P
P
P
P
P
D
D
D
D
D
D
D
E
D
P
E
P
P
D
D
D
D
Potentially Eatable
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
F-2
-------
Table F-l. (Continued)
Dnit'l National Sediment Quality Survey
Species Code
551519010000
S5I5I90I1300
883561010100
81Q60I05UOO
877718010100
8X3544030100
550000000000
550701160100
874701040000
874701040100
618901030100
618105010600
877601140100
618803010400
883528030300
877601030100
87080 2050100
870802Q5Q300
877604020000
877604020200
877604020100
877604020300
877604010000
877504010500
877604010100
877604010400
877604010200
877604011200
877604010300
87760401 1500
877604011600
877604012000
Scientific Name
Aslant spp.
Astane undata
Astroaotus ocellatus
Astropecten verrilli
Bagre marinus
Bairdietta cbrysoura
Bivalvia
Brachiuduntes rtcurvus
Brevofirlia spp.
Brtvrtonia tyrannus
Callinectes sapidus
Cambarus bartoni
Camposloma anomalum
Cancer magister
Caranx hippos
Carassius auratux
Carctlarhinu.i obscurus
CuKhttrhinus plttmbtus
Carpiodes spp.
Carpiodts carpio
Carpiodts cyprinua
Carpiodes velifer
Catostumux spp.
Catftsiimus urdens
Cattisittmux catusintnu*
Cutfjslvmtis calumbiUDiis
CaloMvmux cofnmtrsttni
Cutostiimus ItHipinnis
Cattnttttmu* mucrticheilus
Catoaomus occidentaHs
Catoslomus platyrhynchus
Catostamus snyderi
Common Name
Astarte clam (Unknown species)
Waved astarte
Oscar
Margined seastar
Gafflopsail catfish
Silver perch
Class of molluscs
Hooked mussel
Menhaden (unknown species)
Atlantic menhaden
Blue crab
Crayfish
Central stoneroller
Dungeness crab
Crevalle jack
Goldfish
Dusky shark
Brown shark (sandbar)
Carpsucker (unknown species)
River carpsucker
Qufflback
Highfin carpsucker
Sucker (unknown sp)
Utah sucker
Longnose sucker
Bridgelip sucker
White sucker
Flannelmouth sucker
Largescale sucker
Sacramento sucker
Mountain sucker
Klamath largescale sucker
Resident/ Migratory*
R
R
R
R
M
M
R
R
M
M
M
R
R
M
M
R
M
M
R
R
R
R
R
R
R
R
R
R
R
Demersal/Pel aglc'
D
D
P
D
E
P
D
D
P
P
D
D
E
D
P
E
E
E
D
D
D
D
D
D
D
D
D
D
D
D
D
D
Potcitdally Eatable
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
F-3
-------
Appendix F
Table F-l. (Continued)
Species Code
877604012100
883516000000
883516030100
883501010500
883502030100
900201010100
648933000000
648960063300
883561090100
885703010100
885703011100
877712010200
877601070100
551545020100
875501010800
875501010600
875501010900
883102000000
883102080000
883102080100
883102080700
883102080900
88.1102080200
551002010000
551002010100
551002010200
877601230100
877604060100
883544010200
883544010300
883544010400
877601761400
Scientific Name
Cutostomus tahoensis
Centrarchidue
Cenlrarchus macropttrus
Centropomus undecimalis
Ctntropristis slriata
Chelydru serptnlina
Chiroiwmidae
Chironomus riparius
Cichla ocellafis
Cilharichthys sordiduit
Citharichthys xanthostigma
Cichla Clarias fuscus
Clinostomus funduloides
Corbicula manilensis
Coregonus artedii
Coregonus clupeaformis
Coregonus hoyi
Cottidut
Conns spp.
Cottus aleuticus
Coitus bairdi
Cottus carolinae
Cottus criftnalus
Crassostrea spp.
Crassustrea gigas
Crasstistreu vir%inica
Ctenopharyngfidun idella
Cycleptus elongatus
Cynoscion nebultmus
Cynoscion nothus
Cynoscion regalis
CyprineUa tutrensis
Common Name
Tahoe sucker
Sunfish family
Flier
Common snook
Black sea bass
Snapping turtle
Midge family
Midge
Peacock cichlid
Pacific sanddab
Longfin sanddab
Whitespotted clarias
Rosyside dace
Asiatic clam
Cisco (lake herring)
Lake whitefish
Bloater
Sculpin family
Sculpin (unknown species)
Coastrange sculpin
Mottled sculpin
Banded sculpin
Slimy sculpin
Oysters (unknown species)
Pacific oyster
Eastern oyster
Grass carp
Blue sucker
Spotted sea trout
Silver sea trout
Weakfish
Red shiner
Resident/Migratory'
R
R
R
M
M
R
R
R
R
E
E
M
R
R
M
M
M
R
R
R
R
R
R
R
R
R
R
M
R
M
M
R
Demersal/Pelagic*
D
P
P
P
P
E
D
D
P
D
D
D
P
D
P
P
P
D
D
D
D
D
D
D
D
D
E
D
P
P
P
P
Potentially Eatable
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
F-4
-------
Table F-l, (Continued)
Spedci Code
877601761900
877601000000
S776010IOIOO
Sm050l0500
874701050100
874701050200
S5I2020 30100
885704040300
883544120500
877604030000
877604030200
877604030100
875801000000
875801010201
875801010202
875801010100
875801010400
875801010300
883520016700
883320010900
883320017600
883520018700
883520018800
8*0404021000
8S040402IIOO
879103040100
870802020100
88 0408010100
883544020100
877601260000
877601261500
883551020100
Scientific Name
Cyprinella spifoptera
Cyprinidue
Cyprinus carpio
Daxyatfs sabina
Dorosomu ceptdianum
Dorasoma petenenst
Elliptic complunata
Eopsttla exilix
Equetuf puaclatus
Erimyzan spp.
ErimyioR oblongus
Erimyzon media
Esocidae
Esox americanus americamu
Esox americanus vtrmtculatus
Esox lucius
ESOJC masquinongy
Esox niger
Ethioaona radiosam
Etheoitama sptciabilt
Elheosioma stigmaeum
Etheostoma whipplti
Etheoftama vtnalt
Fundulux zebrinus
Fundulus ulivaceus
Gadus macrticephalur
Caleejcerdit cu vier
Gambusia uffinis
Gtnyanemus tintatus
Gila spp.
Gila robuslu
Ginlla nigricans
Comnon Name
Spolfin shiner
Carp/goldfish (hybrid)
Common carp
Atlantic slingray
Gizzard ihad
Threadfin shad
Freshwater clam
Slender sole
Spotted dnim
Chubiucker (unknown ipeciei)
Creek chubiucker
Lake chubsucker
Pike
Redfln pickerel
Cress pickerel
Northern pike
Muikellunge
Chain pickerel
Omngebelly darter
Omngethroat darter
Speckled darter
Redfin darter
Banded darter
Plaini killifish
Blaclupoued lopminnow
Pacific cod
Tiger shark
Western mosquitofiih
White croaker
Chub (unknown ipeciei)
Koundtail chub
Opalcyt
Ruldent/ Migratory1
R
R
R
M
M
M
?
E
R
R
R
R
R
R
R
R
R
R
1 ^
M
R
M
R
R
M
Dementi/Pelagic*
P
E
D
D
P
P
D
D
D
E
E
P
P
P
P
D
D
P
E
P
E
E
E
P
Potentially Eatable
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
F-5
-------
Appendix F
Table F-l. (Continued)
Species Code
885704350100
551202060100
874701000000
622003030000
622003030700
875101010100
875101010200
885703110200
885704060100
885704060300
616923040100
877601050300
871602010100
877604050100
875503010100
885704220100
877702000000
877702010000
877702010200
877702010500
877604070100
877604070200
877604070300
883543020100
870600000000
877601300100
883544040100
884701030100
873201010000
873201010200
873201010100
873201010300
Scientific Name
Glyptocephalus zachi
Conidea angulata
Glupeidae
Hexagtnia spp.
Hexagenia timbata
Hiodon alosoides
Hiodon ttrgisus
Hippugtossina ilomuta
Hippoglossoides elas
Hippoglossoides platessoides
Hyalella azltca
Hybognathus placitus
Hydroltigus colliei
Hypentelium nigricans
Hypomesus prttiosus
Hypsapsftta guttulata
Ictaiuridue
Iclalurus spp.
Iclalurus furcalus
Iclalurus punctalus
Ictiabus bubal as
Ictiobus cyprintllut
Icllobus niger
Lafffjdttn rhotnboide s
Lumniftirmti
Lavinia exilicauda
Leiosttimus xanthurus
Lepidtigtibius Itpidus
Lepisosteus spp.
Lepuasteux ocultaux
Lepisosteus osseus
Ltfuoaeiu plalostomtts
Common Name
Rex sole
Freshwater mussel
Herring family
Burrowing mayfly (unknown species)
Mayfly
Goldeye
Mooneye
Bigmouth sole
Ralhead sole
American plaice
Freshwater amphipod
Plains minnow
Spotted rat fish
Northern hog sucker
Surf smelt
Diamond turfoot
Bullhead catfish family
Catfish (unknown species)
Blue catfish
Channel catfish
Smallmoulh buffalo
Bigmoulh buffalo
Black buffalo
Pinfish
Shark
Hitch
Spot
Bay goby
Gar (unknown species)
Spotted gar
Longnose gar
Shortness gar
Resident/ Migratory*
E
R
M
R
R
M
M
M
M
M
R
R
M
R
M
?
R
R
R
R
R
R
R
E
M
R
M
R
E
E
E
E
Demersal/Pelagic*
D
D
P
D
D
P
P
D
D
D
E
P
D
D
P
D
D
D
D
D
E
E
E
P
P
P
P
P
P
P
P
P
Potentially Eatable
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
F-6
-------
Table F-l. (Continued)
Draft National Scdinu'iit Quality Survey
Species Code
873201010400
883516050000
883516050100
883516050200
883516050500
883516050300
883516050600
883516050400
883516050700
883516050800
883516050900
883516051000
879103080100
618701150200
500501010300
883536010700
877601780400
877601780600
885704110100
814802010600
551531013600
551531011400
877601800200
551202430300
551547110100
883544070100
883516060000
883516060500
883516060100
883516060600
883516060300
883516060200
Scientific Name
Ltpisosteus spatula
Ltpomis .ipp.
Lepomis auritus
Lepomis cyanellus
Lepomis gibbosus
Lepomis guloma
Lepomis humilis
Lepomis macrochirus
Lepomis marginalus
Lepomis megalotis
Lepomis microtopfius
Lepomis punctatus
Lota lota
Loxorhynchus grandis
Lumbriculux variegatus
Luljanus campechanus
Luxilus chrysocephalus
Luxilus cornutus
Lyopsetta txilix
Lytechinus anamesus
Macoma irus
Macttmu nasulu
Macrhybupsis gelidu
Megulttnuias fiigantea
Mercenaria mercenaria
Micntpttifitniax unduluts
Micrdptfru.1 xpp.
Micntpterus cooxaf
Micrvpierux dulumieu
Wicroptcrux notiuit
Micropterus punctutatus
Wcroplerus salmoides
Common Name
Alligator gar
Sunfish (unknown species)
Redbreast sunfish
Green sunfish
Pumpkinsecd
Warmouth
Orangespotted sunfish
Bluegill
Dollar sunfish
Longear sur.fish
Rcdear sunfish
Spotted sunfish
Burbot
Decorator crab
Aqauatic worm
Red snapper
Striped shiner
Common shiner
Slender sole
Little gray sea urchin
Clam (macoma)
Bent-nosed macoma
Sturgeon chub
Washboard mussel
Quahog
Atlantic croaker
Bass (unknown species)
Redeye bass
Smallmouth bass
Swannee bass
Spotted bass
Largemouth Bass
Resident/Migratory'
E
R
R
R
R
R
R
R
, R
R
R
R
M
R
M
R
R
R
R
R
R
R
R
R
R
R
R
Demersal/Pelagic*
P
P
P
P
P
P
P
P
P
P
P
P
E
D
D
D
P
D
D
D
E
D
P
P
P
P
P
P
Potentially Eatable
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
F-7
-------
Appendix F
Table F-l. (Continued)
Species Code
877604080100
883502010000
883502010100
883502010400
883502010300
883502010500
883502010200
877604040000
877604040400
877604040700
877604040200
877604040900
877604041000
877604040100
877604041400
877604040300
877604041700
883601010100
883601010200
870802040100
55I701020IOO
877601170100
877601350100
550701010000
550701010200
550701010100
5001240.10500
500168040100
500125011900
500168040100
500125011900
500125011500
Scientific Name
Minytrema melanops
Morone spp.
Morone amcricana
Morone chrysops
Murone chrysops x saxalilix
Morone mississippiensis
Morone saxatilix
Moxoftoma spp.
Moxostoma anisurum
Moxostoma carinatum
Moxostoma congestion
Moxostoma duquesnei
Moxostoma trythrurum
Moxostoma macrolepidotum
Moxostoma pappillosum
Moxostoma poecilurum
Moxostoma rupiscartes
Mugil cephalus
Mugil cunma
Mustelus canis
Mya arenaria
Mylocheilus caurinus
Mylopharodon conocephalus
Mytilus spp.
Mytilux californianun
Mytilus edulis
Neanthts arenaceodcntata
Neoamphitrite robusta
Nephtys caecoides
Ntoampkitrite robusta
Niphtys caecoides
Vephtys incisa
Common Name
Spoiled sucker
Temperate bass (unknown species)
White perch
White bass
Hybrid striped bass (white/striped)
Yellow bass
Striped bass
Redhorse (unknown species)
Silver redhone
River redhorse
Gray redhorse
Black redhorse
Golden redhorse
Shorthead redhorse
V-lip redhone
Bhcktail redhorse
Striped jumprock
Striped mullet
White mullet
Smooth dogfish
Soft clam
Peamouth
Hardhead
Mussel (unknown species)
California mussel
Blue mussel
Sand worm
Terrebellid worm
Sand worm
Terrebellid worm
Sand worm
Red-lined worm
Resident/ Migratory*
E
E
M
M
E
M
M
R
R
R
R
R
R
R
R
R
R
M
M
M
R
R
R
R
R
R
R
R
R
R
R
R
Demersal/Pelagic"
D
P
P
P
P
P
P
D
D
D
D
D
D
D
D
D
D
E
E
E
D
E
E
D
D
D
D
D
D
D
D
D
PoUnttally Eatable
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
F-8
-------
Table F-l. (Continued)
Draft N;itioii:.il Sediment Quality Survey
Specks Code
877601100.100
877601100200
877601100100
S7760 1060 100
87760 150 1000
877601114100
877601 II 1400
877601110600
877601I181QO
877601112300
877702020200
877702021800
S77702022000
870703010100
500300000000
873501020800
875501020100
875501021100
875501020300
'75501020500
875501020600
878301020000
6I8I05030000
877601360100
883540020100
875503000000
875503030200
61 8 102020 100
6|79I80I0100
883502160400
885703030900
885703030100
Scientific Name
Nocumis asper
Nvcjtmis leploctphalus
Nocvmis micropogon
Nvftmixtmus crysoltuca.t
Nomipis amblnps
Ntttrupis bo
-------
Appendix F
Table F-l. (Continued)
Species Code
885703030400
817502010100
500166030400
617701010000
617701010100
617701010300
883520020100
883520030900
883560050100
877601370300
877601160200
811703050100
550905090100
885704140100
87760 1 840 100
885704151000
885704000000
885704160200
885704160400
880408110200
883544080100
872902010100
883525010100
883516070000
883516070100
883516070200
882602010100
875501060100
875501060200
551547070100
885704150400
Scientific Name
Paralichthys lethostigma
Purastichopus californicus
Pectinaria californiensis
Penaeus spp.
Penatus uzttcus
Penaeus xetiferus
Perca flavescens
Percina cupelandi
Phanerodan furcatus
Phoxinus erythrogaster
Pimephales promelas
Pilaster brcvispinus
Placopecten magellanicus
Platichthys.steUalus
Platygobio gracilis
Pleurontctes bilintatus
I
Pleuronectidae
Pleuronichlhys decurrtns
Pleuronichthys vertically
Poedlia vittata
Pogonias cromis
Polyodon spathula
Pamatomus sultatrix
Pomoxix spp.
Pomuxis annutaris
Piimnxis niffntmuculatux
Priunotus caralinus
Pronopium cylindruceum
Prosopium williamsoni
Protothaca stuminea
Pleuronectes americanus
Common Name
Southern flounder
California sea cucumber
Sandworm
Shrimp
Brown shrimp
White shrimp
Yellow perch
Channel darter
White seaperch
Southern rcdbelly dace
Fathead minnow
Starfish
Atlantic deep-sea scallop
Starry flounder
Flathead chub
Rock sole
En li h 1
g
Righleye flounder family
Curlfin sole
Homyhead lurbot
Cuban limia
Black drum
Paddleftsh
Bluefish
Crappie (unknown species)
White crappie
Black crappie
Northern searobin
Round whitefish
Mountain whitefish
Clam (Pacific littleneck)
Winter flounder
Resident/ Migratory*
M
R
R
R
R
R
R
R
R
R
R
R
R
M
R
E
M
M
M
M
E
M
M
M
R
R
R
R
M
M
R
M
Demersal/Pelagic'
D
D
D
D
E
E
P
D
P
P
P
D
D
D
E
D
p
D
D
D
P
P
P
P
P
P
P
D
P
P
D
D
Potentially Eatable
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
F-10
-------
Table F-l. (Continued)
Draft National Sediment Quality Survey
Speclei Cod*
885704150400
877601180000
877601)80100
877702030100
871304010300
890302010600
S5I52S04QIOO
877601090000
877601190100
875501030000
875501030500
875501030600
87S501000000
875501040000
875501040400
875501040100
875501040300
551547020100
872901020200
883544000000
883544090100
885003030100
885003050100
88500 3050200
885703040100
882601061600
883102310100
882601010300
882601012000
882601012100
8*2*01013900
882601012700
Scientific Name
Pleuronectts wntricanus
Ptychuchfllus spp.
Piychocheilm ongonemii
Pylodictis fflivaris
Raja binaculala
Rana caiesbtiana
Ran%ia cuntata
Rhinichlhys spp.
Sichardtonlus talteatut
Salmo spp.
Salmo solar
Salmo Hulla
Salmonidae
Salveltnut hybrid
Salve linusfbntinalis
Salvelinus malma
Salvtlinus namaycush
Saiulomiu glganUus
Scaphirhynchu* ptatorynchus
Sciatnldae
Sciotnaps ocelfatus
Scombtrjaponicus
Scombtromorus caiulla
Scambtromtirus macultaux
Scvphthalmus atfuvsus
Scvrpatna futtata
ScafpainkMltyi narmoriuus
Stbamts uaciculaln.t
S«baaes malifter
Sebastes mttanops
Sebaais xoneglcia
Stbttnt! pauelipinii
Common Nine
Winter flounder
Squawfish
Notlhern squawfish
Flallieod catfish
Winter skate
Bullfrog
Brackish water clam
Dace (unknown spccici)
Redtide shiner
Trout (unknown ipedn)
Atlantic ulmon
Brown IfXHtt
Trout (family)
Splake (hybrid)
Brook trout
Dolly varden
Lake trout
Oam (smooth wmhington)
ShovelnoK sturgeon
Drum family
Red drum
Chub mackerel
Kjng mackeral
Spanish nmckerel
Window pane
Cah'ibrnia «corpionfish
Cabezon
Brown rockfish
Quillback nockfiih
Black rockfiib
Golden icdfldi
Bocaccio
RnldtnUMtrttoiy*
M
R
R
R
M
?
R
R
E
E
E
E
E
E
R
M
M
R
M
M
M
M
M
Dcnumt/Feliglc'
D
E
E
E
D
P
D
P
P
P
P
P
P
P
D
P
D
D
P
P
P
P
Potutltliy Eatable
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
F-ll
-------
Appendix F
Table F-l. (Continued)
Species Code
882601012700
882601013000
877601080200
877601080100
877601080300
617704010900
551529020100
871001020100
883520040200
883520040100
880302020100
885703130300
885802011600
876202010100
885003040400
875501070100
883561400100
883561040500
551525020100
870802090200
884701300100
885801010100
880302030200
875802010200
050601010000
Scientific Name
Sebasus paucispinis
Sebastes proriger
Semotilus atromaculatus
Semoiilus corporalis
Semotitus lumbee
Sicyonia ingtntis
Solen sicarius
Squatus acanthitu
Stizastedion canadtnst
Sliioaedion vilreum
Srrongylura marina
Syacium papillosum
Symphurus atricauda
Synodus foetcns
Thunruis alanlicus
Thymaltus arcticus
Tilapia mossambica
Tilapia zitlii
Tresus capax
Triakis semifasciata
Tridentiger Irigonocephalus
Trinectes maculatus
Tytoitirus crocodilui
Umbra limi
Vauchtria
Common Name
Bocaccio
Redstripe rockfish
Creek chub
Fallfish
Sandhills crab
Rock shrimp
Razor clam
Spiny dogfish
Sauger
Walleye
Atlantic needlefish
Dusky flounder
California tonguefish
Inshore Uzardfish
Blackfm tuna
Arctic grayling
Mozambique tilapia
Redbelly lilapia
Horse clam
Leopard shark
Chameleon goby
Hogchoker
Houndfish
Central mudminnow
Macroalgae
Redden t/Mlgratory*
M
M
R
R
R
R
R
M
R
R
M
M
M
R
M
E
R
R
R
M
R
M
M
R
?
Demersal/Pelagic'
P
P
E
E
B
D
D
E
P
P
P
D
D
D
P
P
E
E
D
E
D
D
E
E
E
Potentially Eatable
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
"Fish species is considered: R = resident, M = migratory, E
bFish speciees is considered: D = demersal, P = pelagic, E =
= either resident or migratory. 7 = unknown.
either. 7 = unknown.
F-12
-------
Appendix G
Notes on the Methodology for
Evaluating Sediment Toxicity
Tests
esults 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 databases. Because
_/test results were reported differently in each database, appropriate interpretation of the test results was some-
times 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 reference 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 site from which the sediment sample was collected had been identi-
fied by latitude and longitude coordinates. Samples that were not properly identified were not analyzed. Tests from the
Great Lakes Sediment Inventory (GLSI) database were eliminated because sample locations were not appropriately identi-
fied. Sediment samples in the EPA Region 10/U.S. Army Corps of Engineers Seattle District's Sediment Inventory (SEACOE)
that were located in British Columbia were also excluded from the analysis.
Type of Test
Data from seven databases (Table G-l) were reviewed to determine whether they had reported the results of acute
sediment (solid-phase) and elutriate nonmicrobial toxicity tests in which the endpoint was mortality. Records pertain-
ing 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 eliminated from further analysis.
Only the DM ATS and COM databases clearly reported the phase (solid, elutriate, paniculate) 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 species, 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
U.S. Army Corps of
Engineers, Dredged
Material Tracking
System (DMATS)
EPA's Environmental
Monitoring and
Assessment Program,
Louisiaiiian Province
(EMAP-LA)
EPA's Environmental
Monitoring and
Assessment Program,
Virginian Province
(EMAP-VA)
Gulf of Mexico
Program's
Contaminated Sediment
Inventory (COM)
EPA's Great Lakes
Sediment Inventory
(GLSI)
EPA's Ocean Data
Evaluation System
(ODES)
EPA's Region 1Q/U.S.
Army Corps of Engineers
Seattle District's
Sediment
Inventory (SEACOE)
Sample
Locations
Identified
by
Lat/Long
Yes, all 74
Yes, all 259
Yes, all 179
Yes, all 42
No
Only 18 out
of 68
Only 18 out
of 68
Type
of Test
Solid and Elutriate
(identified in database)
Solid Phase
(not identified in
database, provided)
Solid and Elutriate
(not identified in
database, provided)
Solid Phase
(identified in database)
Not identified in database
Solid Phase
(not identified in
database)
Solid Phase
(not identified in
database)
Laboratory
Control Tests
Replicate control
test results provided
Not provided in D3
database, provided on
request
Not provided in D3
database, provided on
request
ERL-N: Yes
USAGE: No
GCRL". No,
provided on request
Not provided in database
Yes
Yes, some had to be
provided on request
Reference Sediment
Tests
Replicate reference
sediments tested with
each batch of
sediment samples
No
No
ERL-N: Yes
USACE: Yes
GCRL: No
No?
No
Yes
Comments
Used means of reference
sediment replicates in the
evaluation (contact: Alan Ota, EPA
Region 9)
Sediment sample test results were
calculated from the additional data
provided (contact: Kevin Summers,
EPA/ERLGB)
Sediment sample test results were
calculated from the additional data
provided (contact: Daryl Keith, EPA/
ERLN)
Used LI control data for tests done by
ERL-N (contacts: Phil Crocker, EPA;
John Scon, SAIC) and control data
obtained for GCRL (contact: Julia Lyle,
GCRL); for USACE tests used mean of
the reference test results as control
Sample location IDs and control test reults
were not provided; therefore, these data
were not evaluated for the NSI (contact:
Bob Hoke, SAIC)
Used controls (contact:
Tad Deschler, Tetra Tech)
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
Species Code
80509070600
615301010900
615301010400
615301010700
611829010000
616902010800
616800000000
610401010100
616302070900
650508331700
650508330100
885703010200
616915021500
617922010000
551002010100
551002010200
880404010100
610902010900
610902010100
815501010100
Species
Name
[canthomysis costata
Acanthomysis macropsis
Acanthomysis sculpta
Acartia spp.
Ampelisca abdita
Amphipods
Anemia salina
Asellus intermedius
CMronomus riparius
Chironomus tentans
Citharichthys stigmaeus
Corophium spinicorne
Crangon spp.
Crassostrea gigas
Crassostrea virginica
Cyprinodon variegatus
Daphnia magna
Daphnia pulex
Dendraster excentricus
Type of Toxicity Test
Liquid
(L)
Y
Y
Y
Y
Y
Y
Y
Elutriate
(E)
Y
Y
Y
Y
Y
Y
Paniculate
(P)
Y
Solid
(S)
Y
Y
Y,E
Y
E
E
E
Y
Y,E
Y
Y
Y
C
L most Common)
(LorE)
E
E
E
E
D
(L,E, or P)
E
A
(L,E,P,orS)
Unknown
E
-------
Table G-2. (Continued)
Species
Name
880404020700
881801010100
616915090200
622003030700
615301010700
616923040100
500501010300
814802010200
551531011600
551531011400
551531010000
615303140600
651530100000
615301210200
550701010100
500124030500
500124030000
500125011900
500124030200
551706040100
Species
Name
Fundulus grandis
Gasterosteus aculeatus
Grandidierella japonica
Hexagenia limbata
Holmesimysis sculpta
Hyallella azteca
Lumbriculus variegatus
Lytechinus pictus
Macoma balthica
Macoma nasuta
Macoma spp.
Metamysidopsis elongata
Mysid shrimp
Mysidopsis bahia
Mytilus edulis
Neanthes arenaceodentata
Neanthes spp.
Nephtys caecoides
Nereis virens
Panopea generosa
Type of Toxicity Test
Liquid
(L)
Y
Y
Y
Y
Y
Y
Elutriate
(E)
Y
Y
Y
Y
Y
Particulate
(P)
Y
Y
Y
Solid
-------
Table G-2. (Continued)
Species
Code
617701010200
MICROTOX
877601160200
551547070100
616942150400
080309070600
814903020400
611910030100
Species
Same
"aratanytarsus parthogenetic
Penaeus duorarum
Photobacterium phosphoreum
Pimephales promeles
Protothaca staminea
Rhepojcynius abronius
Selenasmun capricornutum
Strangylocentrotus purpuratus
Tigriopus californicus
Type of Toxicity Test
Liquid
00
Y
Y
Elutriate
(E)
Y
Particulate
(P)
Solid
(S)
E
Y
Y
Y,E
C
[L most Common)
(LorE)
E
E
E
D
CUE, or P)
A
(L,E,P,or S)
Unknown
E
E
-------
A|>|U'luli\ (i
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 organ-
isms 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 which 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 (ERL) in Narragansett. A low-salinity control test performed at the same time was not used in the
evaluation. The single ERL reference sediment sample was treated as a sediment toxicity test result. No
control tests were available from the USAGE 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 replicate results 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
-------
Draft National SccliiiU'iil (Jiiiiliiy Sur\i'v
• 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 and 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 apparently pristine (does not contain toxic
chemical contaminants) 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 USAGE 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.
Since 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 USAGE 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 charac-
teristics, not by toxic chemicals. Thus, the classification of some sites using the sediment toxicity tests might be
G-7
-------
.Appendix C
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 sediment sites
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,
contributed to the possibility of over- and underestimation of potential adverse effects associated with sediment
contaminated with 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 param-
eters on the number of sites where these chemicals were identified with the potential for adverse effects.
'he tendency for PCBs to bind to sediment and because of the relative toxicity of these chemicals to
humans EPA selected a very conservative approach for the analysis of PCBs in the current NSI evaluation. The
approach was conservative because (1) it did not require matching sediment chemistry data and tissue residue data
and (2) it used the cancer risk level of 10'' for all congener, aroclor, or total POT 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 stations where matching sediment
chemistry data and tissue residue data were available for analysis. In the following evaluatio i, the amount of PCB
sediment and fish t.ssue data exceeding screening values other than those used m the NSI analysis» compared to the
number of stations classified as Tier 1 or Tier 2
Figure H-1 is a cumulative density function graph depicting the maximum PCB concentration at each sedi-
ment sampling station where PCBs were detected. The various screening values that could be used to indicate
000
1.00E-04
1.00E-03 1.00E-02
100E-01 1.00E+00 .OOE+01 1.00E-KH 1.00E+03 1.00E+04 1.001
PCB In S«dl!n«nt Concentration (ppb)
Note: LeltM W«nWI«d on «» can* oonwpw"* to
PCB DM KWMlnj «luw I" T.M. H
, nf PCB Sediment Concentration Data (All Aroclors
Figure H-l. Cumulative Frequency Distribution
and Total PCB).
H-l
-------
Appi'iidiv II
adverse effects levels of PCBs in sediment are plotted as A through S in the figure and described in Table H-l.
The top two sections of Table H-l present the screening values of PCBs 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 detectable 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
Table H-l. Sediment Sampling Stations with Detectable Levels of PCBs That Exceed Various Screening
Values"-"
Type of Screening Value
Protection of Consumer*
Cancer Risk Level
10*
10"
10J
Noncancer Hazard Quotient of 1
FDA Tolerance Level
Wildlife Criteria
Associated Level (ppb)
0.25
2.5
25
40
360
29
Level Plotted in Figure
H-l Corresponds to
Letter
B
D
J
L
P
K
Number of Stations
with Detected PCBs
Exceeding Level
3,772
3,290
2,076
1,761
652
1,977
Percentage of Stations
with Detected PCBs
Exceeding Level
98.2
85.6
54.0
45.8
17.0
51.5
Protection of Consumers Using BSAF with Safety Factor'
Cancer Risk Level
10*
10»
10^
Noncancer Hazard Quotient of 1
FDA Tolerance Level
Wildlife Criteria
Protection of Aquatic Life
ER-L
ER-M
AET-L
AET-H
TEL'
PEL'
Other Protection Levels
TSCA« Level
0.063
0.63
6.3
9.9
90
7.2
22.7
180
1,000
3,100
21.6
189
50,000
A
C
E
0
M
F
I
N
Q
R
H
0
S
3,828
3.648
2,921
2,699
1.330
2,849
2,150
976
353
165
2,182
962
21
99.6
95.0
76.0
70.2
34.6
74.2
56.0
25.4
9.2
4.3
56.8
25.0
0.55
•MaKimum total or aroclor-specific value at a given station WM used.
'PCBs were detected at J.842 (41%) of the 9,401 itatioiu where collected samples were analyzed for them.
•For this presentation, measured levels wen compared to risk levels uiing a default organic carbon content (1%) and default organism lipid content (3%). Use of lite-specific organic carbon
would yield slightly different results.
'Levels used in the current National Sediment Quality Survey evaluation for human health.
•Levels used in the cumnt National Sediment Quality Survey evaluation for aquatic life (Tier 2).
levels used in the current National Sediment Quality Survey evaluation for aquatic life (Tier I).
Toxic Substances Control Act. 40 CFR Part 761, Subpart B, i 761.20.
H-2
-------
Drnl'l Viliomil Sediment Ou;ilil\ Survey
Tier 1 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).
Figure H-2 and Table H-2 present the comparison of different screening values and the corresponding number of
fish tissue stations with detected levels of PCBs exceeding the screening values.
1 nn
0.90
ffi
£ 0.80
2° J 0.70
•^
o o 0.60
"1
g c F(x) 0.50
I J °40
•E i 0.30
o «
asp
o •£• 0.20
° 0.10
*
'
-
_
—
—
^
_..
—
— •
«_
m-
s
:;;
/
^
Kramlng V.IUM In T*to H-2.
Figure H-2. Cumulative Frequency Distribution of PCB Fish Tissue Concentration Data (All Aroclors and
Total PCB).
Table H-2. Fish Tissue Sampling Stations with Detectable Levels of PCBs in Demersal, Resident, Edible
Fish That Exceed Various Screening Values*"
Type of Screening Value
Protection of Consumers
Cancer Risk Level
10-6
10-5'
10-4
Noncancer Hazard Quotient of 1
FDA Tolerance Level
Wildlife Criteria
Associated Level (ppb)
1.4
14
140
220
2,000
160
Level Plotted in Figure
H-2 Corresponds to
Letter
A
B
C
E
F
D
Number of Stations
with Detected PCBs
Exceeding Level
2,354
2,256
1,686
1,473
489
1,620
Percentage of Stations
with Detected PCBs
Exceeding Level
99.3
95.2
71.1
62.2
20.6
68.4
'Maximum total or aroclor-specific value at a given station was used.
'PCBs were detected at 2,370 (73%) of the 3,234 stations where collected samples were analyzed for them.
•Levels used in the current National Sediment Quality Survey evaluation for human health.
H-3
-------
Appendix II
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.
In 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 and Table H-3 present six screening values that could be applied for the protection of consumers
t 0.90
| « 0.80
1 I 0.70
li
S3 0.60
|i
c S
w $ r\ in
Jn m
je o.oo J — L- L-1
1.00E-02
Nob: L.tlwl id8M*«c
— - -
^
.
>
I
J
/
I
f
f
{
'
/
,
'
/
/
f
" ir"
r
1.00E-01 1.00E+00 1.00E+01 1.00E+02 1. 006+03 1.00E+04 1.00E+05
Concentration (ppb)
on ttw curve compond to rrwrcuy «B«ct» icnwilng vilum In Tabto 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"-11
Type of Screening Value
Protection of Consumers
Canadian Guideline"
Noncancer Hazard Quotient of 1 (1995)1
Noncancer Hazard Quotient of I (pre-
1995)"
Noncancer Hazard Quotient of 1 (pre-
1995 for infants)'
FDA Action Level1
Wildlife Criteria'
Associated Level (ppb)
200
1,100
3,231
646
1,000
57.3
Level Plotted in Figure
H-3 Corresponds to
Letter
B
E
F
C
D
A
Number of Stations
with Detected
Mercury Exceeding
Level
908
91
15
204
103
2,130
Percentage of Stations
with Detected Mercury
Exceeding Level
35.1
3.5
0.6
7.9
4.0
83.0
'Mercury wis delected al 2.589 (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)
McihyliiicfLury reference dose thai was available in IRIS in 1995 (UK)4 mg/kg-day).
•Corresponds lo mercury reference dose available in IRIS prior to 1995 (3x10* mg/kg-day).
•Corresponds to mercury reference dose available in IRIS prior to 1995 divided by a factor of 5 to protect against developmental effects among infants <6x 10 * mg/kg-day). 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 the wildlife analysis shown in Table 3-5 are slightly different because the data set used for that analysis included demersal, resident specirx {could be considered edible or not).
H-4
-------
Draft National Sediment Quality Survey
ingesting mercury-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 NS1 analysis,
result in only about 4 percent of stations with detectable levels classified as posing potential risk to human health.
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 stations would be classified as Tier 2 because of mercury contamination (Point
D). However, the proportion of stations with detectable levels of mercury that exceed some other human health
levels ranges from 20 percent to over 55 percent of stations.
1.00
3 0.90
£ J* 0.80
i!
3 J 0.70
is
a 1 ° 60
** E
F(x) 0.50
5 o
S0.40
0
£S 0.30
1 P 0.20
I £ 010
o 0.00
* 1.00
Not. Li
•-r •
. . —
._ — -
-
-
;
—
t
/
/
,
t '
,
t
f
/
.!_
]'iX"
• j._
II
•
E-02 1 .OOE-01 1 .OOE+00 1 .OOE+0 1 1 .OOE+02 1 .OOE+03 1 .OOE+04 1 .OOE+05
Concentration (ppb)
mer. kfcnlrfltd on (to curw oxrwpood to marcury «M» .cr^nlr* ViluM in Tibia H4.
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*"
Type of Screening Value
Protection of Consumers
Canadian Guideline"
Noncancer Hazard Quotient of I (1 995)'
Noncancer Hazard Quotient of 1 (pre-
1995)"
Noncancer Hazard Quotient of 1 (pre-
1995 for infants)'
FDA Action Level'
Wildlife Criteria'
Associated Level (ppb)
200
1,100
3,231
646
1,000
57.3
Level Plotted in Figure
H-4 Corresponds to
Letter
B
E
F
C
D
A
Number of Stations
with Detected
Mercury Exceeding
Level
2,308
353
37
821
374
3,623
Percentage of Stations
with Detected Mercury
Exceeding Level
55.8
7.8
0.9
19.9
9.0
87.6
•Mercury was detected at 4.135 (93%) of the 4,426 stations where collected samples were analyzed lor mercur
"Canadian guideline limit for mercury in fish that are part of a subsistence diet (Health and Wclff
'Methylmercury reference dose that was available in IRIS in 1995 (IxlO-1 rng/kg-day).
'Corresponds lo mercury reference dose available in IRIS prior (o 1995 (3x10" mg/kg-day).
, ions Hivided nv a factor of S lo protect aga nsl developmental effects among infants (6x10' mjj/kg-day). This value was
•Corresponds to mercury reference dose available in IRIS prior to 19!
formerly used by the EPA Office of Water.
'Level used in the current National Sediment Quality Survey evaluation for human health.
T ui r.k-,iu /liffiwiH because the dala sel used for that analysis included demersal, resident species (could be considered edible or not).
'The results ol the wildlife analysis shown in Table 3-5 are slightly dim
H-S
-------
Appendix I
NSI Data Evaluation
Approach Recommended at
the National Sediment
Inventory Workshop,
April 26-27, 1994
The original proposed approach for the integration and evaluation of NSI sediment chemistry and biological
iata 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 sites could be placed into one of the fol-
lowing 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 sites 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 site 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
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Appendix I
Table 1-1. Original Approach Recommended by NSI Workshop (April 1994)
Category of Site
Classification!
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
Data Used to Determine Classifications
Sediment Chemistry
(site is identified by any one of
the following characteristics)
Sediment chemistry values
exceed draft sediment quality
criteria for any one of the five
chemicals for which criteria have
been developed by EPA (must
have measured TOC)
OR
Sediment chemistry values
exceed all relevant AETs (high),
ERMs, PELs, and SQALs for any
one chemical(can use default
TOC)
OR
Sediment chemistry values >50
ppm for PCBs
OR
Sediment chemistry TBP exceeds
FDA action levels, EPA risk
levels, or wildlife criteria
OR
Elevated sediment chemistry
concentrations of PAHs
Sediment chemistry values
exceed at least two of the
sediment upper screening values
(i.e., ERM,, SQAL, PEL, high
AETXcan use default TOC)
OR
Sediment chemistry TBP exceeds
FDA action levels or wildlife
criteria
Sediment chemistry values
exceed one of the lower
screening values (ERL, SQAL,
TEL, lower AET)(can use default
TOC and AVS)
No exceedance of lower
screening values
AND
No sediment chemistry TBP
exceedances of FDA action levels
or wildlife criteria
OR
AND
AND
OR
OR
AND
Tissue Residue/
Histopathology
Human health thresholds for
dioxins or PCBs are exceeded in
resident species (not a consensus
agreement-participants evenly
divided on this issue)
Tissue levels in resident species
exceed FDA- action levels or EPA
risk levels or wildlife criteria
Presence of fish tumors
Tissue levels in resident species
exceed FDA action levels or
wildlife criteria
Tissue levels in resident species
are lower than FDA action levels
or wildlife criteria
OR
OR
AND
Toiicity
Toxicity demonstrated by two or
more acute toxicity tests (one of
which must be a solid-phase
nonmicrobial test)
Toxicity demonstrated by a
single-species toxicity test
(solid-phase, nonmicrobial)
Toxicity demonstrated by a
single-species toxicity test
(elutriate-phase, nonmicrobial)
No toxicity demonstrated in tests
using at least two species and at
least one solid-phase test using
amphipods
Not enough data to place a site in any of the other categories
1-2
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Draft National Sfdiim'iit Quality Survey
For a site to be classified as one with a high probability of adverse effects based on sediment chemistry data alone,
at least one of three criteria must be met: (1) sediment chemistry values exceed the draft 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 sediment
chemistry values to the draft 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 draft SQCs, the highest that a site could be classified would be medium-high potential for adverse effects.
For a site 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 site be classified as having a high probability of adverse
effects if fish tumors are present in resident species and elevated sediment chemistry concentrations for polynuclear
aromatic hydrocarbons (PAHs) are present.
The workshop participants were evenly divided on whether a site 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 sites. Methods are currently not adequate to
establish a direct causal relationship between benthic community changes and sediment contamination at specific sites
without additional data.
For a site 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, nonmicrobial test.
Medium-High Probability of Adverse Effects to Aquatic Life or Human
Health
Workshop participants suggested that a site 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 site to be classified as having a medium-high probability of adverse effects based on sediment chemistry data
alone the site 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 comparison of sediment
chemistry values to SQALs, default TOC values can be used.
A site 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 site 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 site 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 participant suggested that
default TOC and AVS values could be used. To classify a site as having a medium-low probability of adverse effects,
toxicity would be demonstrated by a single-species, nonmicrobial toxicity test using the elutriate phase as the test
1-3
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Appendix I
medium. Workshop participants did not propose any human-health-related criteria for placing a site 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 site 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.
Unknown Probability of Adverse Effects
Sites of unknown probability for causing adverse effects are those sites for which there are not enough data to
place them in any of the other categories. Sediments at the sites 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
sites into the five categories remained unresolved. For example, "elevated sediment chemistry concentrations of
PAHs" together with the presence of fish tumors is one criterion for placing a site 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 site in the high probability category, and exceedance of any two of
these values is sufficient to place a site in the medium-high probability category. But what if there are only two
relevant screening values for comparison for a given contaminant? Does a site 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 site 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
sites from only slightly contaminated sites. A separate analysis of wildlife criteria was, however, conducted.
1-4
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Draft National Si'dinu'iil Quality Survey
Workshop Participants
Sid Abel
EPA/OPPT (7406)
401 M Street, SW
Washington, DC 20460
(202) 260-3920; Fax (202) 260-0981
Jim Andreasen
EPA ORD-EMAP (8205)
401 M. Street, SW
Washington, DC 20460
(202) 260-5259; Fax (202) 260-4346
Gary Ankley
EPA ERL-Duluth
6201 Congdon Blvd.
Duluth, MN 55804
(218) 720-5603
Tom Armitage
EPA/OST (4305)
401 M Street, SW
Washington, DC 20460
(202) 260-5388
Bev Baker
EPA/OST (4305)
401 M Street, SW
Washington, DC 20460
(202) 260-7037
Rich Batiuk
EPA Chesapeake Bay Program Office
410 Severn Ave.
Annapolis, MD 21403
(410) 267-5731; Fax (410) 267-5777
Paul Baumann
National Biological Survey
Ohio State University
2021CoffeyRd.
Columbus, OH 43210
(614) 469-5701
Candy Brassard
EPA/OPP
7507C
410 M Street, SW
Washington, DC 20460
(703) 305-5398
Barry Burgan
EPA/OWOW (4503F)
401 M Street, SW
Washington, DC 20460
(202) 260-7060
Allen Burton
Biological Science Department F3301
Wright State University
Dayton, OH 45435
(513) 873-2201
Scott Can-
National Biological Survey
NFCR Field Research Station
TAMU-CC, Campus Box 315
6300 Ocean Dr.
Corpus Christi, TX 78412
(512) 888-3366; Fax (512) 888-3443
Charlie Chandler
USFWS/DEC
4401 N. Fairfax Dr., Suite 330
Arlington, VA 22203
(703) 358-2148; Fax (703) 358-1800
Peter Chapman
EVS Consultants
195 Pemberton Ave.
N. Vancouver, B.C.
Canada V7P2R4
(604) 986-4331
Tom Chase
EPA/OWOW (4504F)
401M Street, SW
Washington, DC 20460
(202) 260-1909; Fax (202) 260-9960
email: chase.tom@epamail.epa.gov
Greg Currey
EPA/OWEC (4203)
401 M Street, SW
Washington, DC 20460
(202) 260-1718
Kostas Daskalakis
NOAA/ORCA 21
1305 East Hwy.
Silver Spring, MD 20910
(301) 713-3028
1-5
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Appendix 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-Narragansett
27 Tarzwell Dr.
Narragansett, RI 02882
(401) 782-3027; Fax (401) 782-3030
Jon Harcum
Tetra Tech, Inc.
10306 Eaton PI., 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 Klewin
EPA Region 5 (WS-16J)
77 W. Jackson Blvd.
Chicago, IL 60604
(312) 886-4679; Fax (312) 886-7804
Fred Kopfler
Gulf of Mexico Program, Bldg. 1103
Stennis 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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Ed Long
NOAA (N/OMA 34)
7600 Sand Point Way, NE
Seattle, WA 98115
(206) 526-6338
Don MacDonald
MacDonald Environmental Sciences Ltd.
2376 Yellow Point Rd.
Ladysmith, BC
Canada VORZEO
(604) 722-3631
J Jun Malek
EPA Region 10
1200 Sixth Ave., WD-128
Seattle, WA 98101
(206) 553-1286; Fax (206) 553-1775
Audrey Massa
EPA Region 2
Marine and Wetlands Protection Branch
26 Federal Plaza
New York, NY 10278
(212) 264-8118; Fax (212) 264-4690
Deirdre Murphy
MD Dept. of Environment
2500 Broening Hwy.
Baltimore, MD 21224
(410) 631-3906; Fax (410) 633-0456
Arthur Newell
New York DEC
Division of Marine Resources
Bldg. 40, SUNY
Stony Brook, NY 11790-2356
(516) 444-0430; Fax (516) 444-0434
Tom O'Connor
NOAA Status and Trends Program
Bldg. SSMCY
1305 East West Highway
Silver Spring, MD 20901
(301) 713-3028
Robert Paulson WR/2
Wisconsin DNR
P.O. Box 7921
Madison, WI 53707-7921
(608) 266-7790; Fax (608) 267-2800
Mary Reiley
EPA/OST (4304)
401 M Street, SW
Washington, DC 20460
(202) 260-9456; Fax (202) 260-1036
John Scott
SAIC
J65 Dean Knauss Dr.
Narragansett, RI 02882
(401) 782-1900; Fax (401) 782-2330
Thomas Seal
Florida DEP
Mail Station 46
3900 Commonwealth Blvd.
Tallahassee, FL 32399-3000
(904) 488-0784
Mohsin Siddique
Water Quality Control Branch
2100MLKJr. Ave.,SE
Ste. 203
Washington, DC 20020
(202)404-1129
Gail Sloane
Florida DEP
Mail Station 46
3900 Commonwealth Blvd.
Tallahassee, FL 32399-3000
(904) 488-0784
Sherri Smith
Environment Canada
351 Street, Joseph Blvd., 8th Floor
Hull, Quebec, Canada
KIAOH3
(819) 953-3082; Fax (819) 953-0461
Betsy Southed and
EPA/OST (4305)
401 M Street, SW
Washington, DC 20460
(202) 260-3966
Mark Sprenger
EPAERT(MSIOI)
2890 Woodbridge Ave.
Edison, NJ 08837
(908) 906-6826
1-7
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Appendix I
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
Burnell 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
Xiaochun Zhang, WR/2
Wisconsin DNR
P.O. Box 7921
Madison, WI 53707
(608) 264-8888; Fax (608) 267-2800
1-8
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