DACW67-85-0029
Work Order 0001C
TC3090-02; Task 6
Final Report
DEVELOPMENT OF SEDIMENT QUALITY VALUES
FOR PUGET SOUND
Volume 1
by
Tetra Tech, Inc.
Prepared for
Resource Planning Associates
for
Puget Sound Dredged Disposal Analysis
and Puget Sound Estuary Program
September, 1986
Tetra Tech, Inc.
11820 Northup Way, Suite 100
Bellevue, Washington 98005
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PREFACE
(Prepared by the sponsoring agencies)
The attached report, Development of Sediment Quality Values for Puget
Sound, details the results of the early stages of a joint effort by the Puget
Sound Estuary Program (PSEP) and the Puget Sound Dredged Disposal Analysis
(PSDDA). The work was performed by Tetra Tech, Inc., with funding and support
from the U.S. Environmental Protection Agency, the U.S. Army Corps of Engineers,
and the Washington Departments of Ecology and Natural Resources.
The sediment quality values study was commissioned in response to
a growing need on the part of federal, state, and local agencies to make
decisions concerning the regulation and management of contaminated marine
sediments. Recent studies have indicated that sediment contamination may
be linked to adverse impacts in marine biota, and that consumption of marine
organisms exposed to contaminated sediments may pose risks to human health.
Ideally, the development of sediment quality values would be guided
by definitive cause and effect information relating the individual and
collective effects of specific contaminants to biological effects in a
variety of aquatic species. To date, very little information of this type
is available and it is unlikely that it will be available in the near future.
The necessary laboratory studies will take many years to complete. In
the interim, in the interest of protecting human health and environmental
quality, regulatory agencies must proceed with sediment management decisions
based on the best information available - information that may be theoretically
and/or empirically derived. It is on this premise that the PSDDA and PSEP
sediment quality values study has been based.
The goals of the sediment quality values study were to evaluate options
for sediment management and to identify numerical values for concentrations
of chemicals in sediments that appear to be associated with adverse biological
effects in Puget Sound. We feel that these goals have been realized.
It is important to note, however, that the results and recommendations
presented in the attached report are considered by the sponsoring agencies
to be interim. As additional laboratory information concerning chemical/
biological cause and effect relationships is developed, and as supplemental
field data become available, the numerical concentrations adopted as the
recoronended sediment quality values, as well as the approaches to establishing
and using sediment quality values, are expected to change. In addition,
it should also be noted that the sediment quality values presented in the
report have been developed using Puget Sound data only. Although the approaches
considered in this study may prove useful in developing sediment quality
values from other data sets for other locales, application of the Puget
Sound values to to other areas would not be advised without specific study.
The first stage of the sediment quality values study is now complete;
however, the agencies are by no means finished with their work. The attached
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report contains an evaluation of approaches and ranges of chemical values
that can be applied in sediment management, but does not identify the specific
values that the agencies should adopt for regulation or how these numerical
values should be modified for application in specific regulatory programs.
Both PSDDA and PSEP technical advisory groups are in the process of evaluating
the options presented in the report and defining specific values for different
regulatory uses in Puget Sound. In the coming year, it is anticipated
that the agencies involved in PSDDA will adopt sediment quality values
for use in regulating the disposal of dredged material. PSEP anticipates
using sediment quality values as a tool 1n classifying and prioritizing
areas for source control and remedial action, and in establishing discharge
limits that can more effectively protect the quality of marine sediment.
The preliminary results of the PSDDA/PSEP sediment quality values
study were initially presented in a series of four separate draft reports.
These reports were distributed to a variety of technical and management
experts at a number of agencies and consulting firms for their review.
The draft reports generated significant interest and controversy, and many
comments were received. The sponsoring agencies regard all comments as
important, useful, and constructive, and have seriously considered all
concerns in preparation of the final report. In addition, the agencies,
with the assistance of Tetra Tech, have prepared a "response to comments"
section, which is included in this final report as Appendix H. The final
report represents the revision and synthesis of four draft reports into
a single document.
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CONTENTS
Page
PREFACE ii
LIST OF FIGURES vi1
LIST OF TABLES v1ii
ACKNOWLEDGMENTS i x
EXECUTIVE SUMMARY x
STUDY PURPOSE !
BACKGROUND 1
OBJECTIVES 1
REPORT ORGANIZATION 2
I. EVALUATION OF APPROACHES FOR THE DEVELOPMENT OF
SEDIMENT QUALITY VALUES FOR PUGET SOUND 5
1.0 INTRODUCTION 5
2.0 APPROACHES TO DETERMINING SEDIMENT QUALITY VALUES 5
2.1 Reference Approach 5
2.2 Water Quality Criteria Approach 11
2.3 Equilibrium Partitioning Approach (Sediment-Water) 13
2.4 Equilibrium Partitioning Approach (Sediment-Biota) 18
2.5 Field Bioassay Approach 22
2.6 Screening Level Concentration Approach 22
2.7 Apparent Effects Threshold Approach 26
2.8 Spiked Bioassay Approach 32
3.0 FINAL SELECTION OF APPROACHES FOR TESTING
WITH PUGET SOUND DATA 33
3.1 Rationale for Selection 33
3.2 Summary of Approaches Selected for Testing
with Puget Sound Data 36
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II. APPLICATION OF RECOMMENDED SEDIMENT QUALITY VALUE APPROACHES
TO PUGET SOUND DATA
37
4.0 INTRODUCTION 37
5.0 METHODS 38
5.1 Compilation of Matched Chemical/Biological Data
from Puget Sound 38
5.2 Application of the Equilibrium Partitioning Approach 44
5.3 Application of the Apparent Effects Threshold Approach 48
5.4 Application of the Screening Level Concentration
Approach 53
6.0 RESULTS 57
6.1 Sediment Quality Values Generated by the Equilibrium
Partitioning and Apparent Effects Threshold Approaches 57
6.2 Sediment Quality Values Generated by the Screening
Level Concentration Approach 67
6.3 Comparison of Sediment Quality Values to Sediment
Concentrations in Puget Sound 73
7.0 UNCERTAINTY ANALYSIS: TEST OF GENERATED SEDIMENT
QUALITY VALUES 77
7.1 Accuracy of Sediment Quality Values (Sensitivity
and Efficiency) 78
7.2 Precision of Sediment Quality Values 92
8.0 RECOMMENDED USES OF SEDIMENT QUALITY VALUES 109
8.1 Screening Technique to Identify Need for Further
Testing 109
8.2 Determination and Ranking of Problem Areas 110
8.3 Identification of Potential Problem Chemicals 111
8.4 Identification of Appropriate Sediments for Open-Water
Disposal 111
8.5 Prioritization of Laboratory Cause-Effect Studies 112
8.6 Appropriate Normalizations of Sediment Quality Values 113
8.7 Use of Conventional Variables in Sediment Management 114
8.8 Summary of Recommendations for Uses of Sediment
Quality Values 115
8.9 Recommendations for Future Studies 117
REFERENCES 118
APPENDIX A - SEDIMENT DATA COMPILED IN SEDIMENT QUALITY VALUES
DATABASE A-l
APPENDIX B - STATION LISTING OF CHEMICALS EXCEEDING SEDIMENT
QUALITY VALUES B-l
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APPENDIX C - SUMMARY OF REVIEW OF DATA FOR INCLUSION IN PUGET
SOUND DATABASE C-l
APPENDIX D - SUMMARY OF PATTERN RECOGNITION STUDIES D-l
APPENDIX E - RECOMMENDED CONTAMINANTS OF CONCERN
FOR MANAGEMENT OF DREDGED MATERIAL E-l
APPENDIX F - RECOMMENDED ANALYTICAL DETECTION LIMITS F-l
APPENDIX G - RECOMMENDATIONS FOR ANCILLARY SEDIMENT VARIABLES G-l
APPENDIX H - RESPONSE TO COMMENTS ON DRAFT REPORTS H-l
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Num
1
2
3
4
5
6
7
8
9
10
11
12
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Page
3
9
10
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19
24
27
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43
68
69
70
80
102
FIGURES
Flow chart of tasks for the development of sediment
quality values
Modified reference approach; Fourmile Rock
Modified reference approach: Port Gardner
Sediment-water equilibrium partitioning approach
Sediment-biota equilibrium partitioning approach
Calculation of screening level concentration (SLC) for
SLC approach
AET approach: Lead
AET approach: 4-Methyl phenol
Location of chemical and biological samples Included 1n
Puget Sound database
SSLC values for HPAH
SSLC values for naphthalene
SSLC values for mercury
Sensitivity and efficiency as measures of accuracy
Example confidence limits for benthic AET determined for lead
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TABLES
Number Page
1 Approaches reviewed for establishing sediment quality values 6
2 Summary of data sets used in this project 41
3 Geographic distribution of compiled Puget Sound data 42
4 Data used for application of EP approach 45
5 Reference area taxa used for the limited application of the
SLC approach 55
6 EP and AET sediment quality values (dry weight) 58
7 EP and AET sediment quality values (organic carbon normalized) 61
8 AET sediment quality values (fine-grained sediment normalized) 64
9 SLC values for target compounds 72
10 Detection frequency and percentile values for chemical
concentrations used in Puget Sound database 74
11 Biologically impacted stations based on multiple biological
indicators 82
12 Biologically impacted stations based on single biological
indicators 83
13 Evaluation of accuracy of EP and AET approaches 84
14 Biologically impacted stations not indicated by sediment
quality value approaches 88
15 Accuracy of AET as related to data sets used for generation
and evaluation 91
16 Efficiency of EP and AET values for PCBs and p,p'-DDT 93
17 Percent efficiency of SLC sediment quality values 94
18 Minimum estimate of precision of EP sediment quality values 100
19 Evaluation of lower and upper limits of AET with biological
data 104
20 Lower and upper limits of AET sediment quality values
(dry weight) 107
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ACKNOWLEDGMENTS
This document was prepared by Tetra Tech, Inc., under the direction
of Mr. Robert C. Barrick, for Resource Planning Associates (RPA) and the
U.S. Army Corps of Engineers (COE), Seattle District, in partial fulfillment
of Contract No. DACW67-85-0029. Dr. Gary Minton of RPA was the Program
Manager. Dr. Steven Dice of U.S. COE was the Project Officer; Mr. Keith
Phillips of U.S. COE and the Puget Sound Dredged Disposal Analysis was
Technical Coordinator. Ms. Catherine Krueger of U.S. EPA provided coordination
with the Puget Sound Estuary Program.
Primary authors of this report were Mr. Harry R. Beller, Mr. Robert
C. Barrick, and Dr. D. Scott Becker. Technical assistance was provided
by Dr. Gordon R. Bilyard, Dr. Thomas C. Ginn, Mr. Michael W. Madland, Ms. Nancy
A. Musgrove, Ms. Julia F. Wilcox, and Dr. Les G. Williams. Pattern recognition
analyses and technical assistance were provided by Dr. Gerald A. Erickson
of G.A. Erickson & Associates.
Ms. Marcy Brooks-McAul i f fe, Dr. James Erkmann, Ms. Carol Newlin, and
Ms. Theresa M. Wood assisted in technical editing and report production.
Graphics and word processing support was provided by Mr. A. Brian Carr,
Ms. Betty Dowd, Ms. Lisa M. Fosse, Ms. Gretchen A. Gramann, Ms. Gail Singer,
and Ms. Nancy J. Southorn.
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EXECUTIVE SUMMARY
STUDY PURPOSE
This report presents the results of a joint effort by the Puget Sound
Estuary Program (PSEP) and the Puget Sound Dredged Disposal Analysis (PSDDA)
to develop sediment quality values for Puget Sound. These values represent
concentrations of chemicals in sediments that are expected to be associated
with adverse biological effects based either on field evidence or theoretical
predictions. Ideally, the development of sediment quality values should
be guided by definitive cause and effect information that relates the individual
and collective effects of contaminants to a range of biological effects.
Very little cause-effect information is available. The sediment quality
values discussed in this report are interim estimates that have been developed
as only one of several tools to assist managers in making sediment management
decisions. The study objectives were to:
1. Compile chemical and biological data from Puget Sound appropriate
for use in the development of sediment quality values
2. Evaluate techniques that can be used to develop chemical
specific values
3. Evaluate the reasonableness of the values generated using
different techniques (i.e., their ability to correctly identify
sites with known biological impacts)
4. Evaluate the appropriateness of using the values in different
regulatory applications
5. Identify future studies that will be needed to refine or
verify the sediment quality values that are generated.
The scope of the fourth objective was refined during the project. Participants
at a joint PSDDA/PSEP technical workshop concurred that a range of sediment
quality values from several approaches should be provided to enable program
managers to determine appropriate values for individual programs. Appropriate
uses of the sediment quality values in different regulatory applications
are discussed. Results that are pertinent to each objective are summarized
in this report.
Material presented in appendices to this report addresses additional
objectives related to statistical analyses of a subset of the chemical and
biological data using pattern recognition techniques (Appendix D), recomended
contaminants of concern (Appendix E), analytical detection limits (Appendix F),
and appropriate uses of conventional sediment variables (Appendix G) for the
management of dredged material. Responses to comments received on draft
reports used to prepare this final report are summarized in Appendix H.
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SUMMARY OF RESULTS AND CONCLUSIONS
Eight possible approaches to establishing sediment quality values
were evaluated based on (in order of decreasing importance):
t The plausibility and scientific defensibility of their theoreti-
cal bases and critical assumptions
• The quantity of data required, and the current availability
of data (i.e., for generation of sediment quality values
during the present project)
• The range of chemicals for which the approach is appropriate
• The range of biological effects information that can be
incorporated into the approach.
Of the eight approaches reviewed, three were selected as the most appropriate
for evaluation in this project with available Puget Sound data. These
included the sediment-water equilibrium partitioning (EP), apparent effects
threshold (AET), and screening level concentration (SLC) approaches.
For a given nonpolar, nonionic organic compound, the sediment-water
equilibrium approach establishes a sediment quality value as the sediment
concentration [normalized to total organic carbon (TOC) content] corresponding
to an interstitial water concentration equivalent to the U.S. EPA water
quality criterion for the contaminant. The relationship between sediment
concentrations and interstitial water concentrations is calculated with
an estimated sediment organic matter-interstitial water partition coefficient.
Field data are not required to generate sediment quality values using this
theoretical approach, but are used to validate the approach.
The AET value is the sediment concentration of a contaminant above
which statistically significant biological effects (e.g., amphipod mortality,
oyster larvae abnormality, depression in the abundance of benthic infauna)
would always be expected. The approach was developed for use with any
organic or inorganic contaminant, and does not require a priori assumptions
concerning the specific mechanism for interactions between contaminants
and organisms. AET are empirically derived from matched field data for
sediment chemistry and a range of biological effects indicators.
The SLC approach estimates the sediment concentration of a contaminant
above which less than 95 percent of the total enumerated species of benthic
infauna are present. This approach was originally developed and recommended
for use with nonpolar organic compounds normalized to organic carbon content
in sediments (Battelle 1986). Possible use of the SLC approach with dry-weight
normalized data (and contaminants other than nonpolar organic compounds)
was also examined in the current study. This modification was evaluated
for the SLC approach because (as with the AET approach, but unlike the
equilibrium partitioning approach) a priori assumptions concerning the
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specific mechanism for interactions between contaminants and organisms
are not necessary. SLC are empirically derived from matched field data for
sediment chemistry and the abundance of individual species of benthic infauna.
Project constraints permitted the testing of this approach for only three
contaminants (naphthalene, high molecular weight polycyclic aromatic hydro-
carbons, and mercury), although the approach is not considered to be limited
to these contaminants.
The application and evaluation of the selected sediment quality value
approaches required that a large database of matched chemical and biological
data be compiled. Of 11 Puget Sound data sets reviewed, paired data from
7 studies were included in the final database. These data included recent
studies in Commencement Bay (Tetra Tech 1985), eight urban and nonurban
embayments of Puget Sound (Battelle 1985a), Everett Harbor (U.S. Department
of the Navy 1985), and Duwamish River (Chan et al. 1985a,b). Municipality
of Metropolitan Seattle (Metro) data for the Alki Extension project (Osborn
et al . 1985; Trial and Michaud 1985) and the Toxicant Pretreatment Planning
Study (TPPS; Phase III, Comiskey et al. 1984; Romberg et al. 1984) were
also included in the database.
Using the three selected approaches, sediment quality values were
calculated for 73 individual or classes of U.S. EPA priority pollutants
and other contaminants, and 3 conventional variables (e.g., TOC) and compared,
when possible. In general, the magnitude of the sediment quality values
for a given contaminant ranked SLC < AET < equilibrium partitioning.
The AET (normalized to sediment dry-weight, organic carbon content,
and percent fine-grained material) and equilibrium partitioning approaches
were tested with respect to the frequencies at which they correctly identified
impacted stations and misidentified nonimpacted stations. Stations were
designated as impacted or nonimpacted by independent statistical compari-
sons of biological data to reference conditions. These impacted/nonimpacted
designations were based on four biological indicators: amphipod bioassays,
oyster larvae bioassays, Microtox bioassays, and benthic infaunal analyses.
A subset of impacted stations was designated as severely impacted based
on somewhat arbitrary criteria: >50 percent amphipod mortality or oyster
larvae abnormality, or statistically significant depressions in the abundance
of more than one major taxonomic group of benthic infauna (including Mollusca,
Polychaeta, Crustacea). This subset of severely impacted stations was
only used as part of the validation check on sediment quality values, and
not to generate sediment quality values.
The 40 sediment quality values from the equilibrium partitioning approach
correctly identified between 13 and 43 percent of the impacted stations,
and between 0 and 46 percent of severely impacted stations, depending on
the biological indicator used for validation. This approach misidentified
between 0 and 67 percent of the nonimpacted stations, depending upon the
biological indicator used for validation.
The 64 sediment quality values from the AET approach (using dry-weight
normalization) correctly identified between 54 and 94 percent of the impacted
stations, and between 92 and 100 percent of severely impacted stations,
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depending on the biologial indicator used for validation. Corresponding
AET values generated from chemical data normalized to TOC or percent fine-
grained material in sediments correctly identified between 37 and 88 percent
of the impacted stations, and between 62 and 100 percent of the severely
impacted stations, depending on the type of normalization, and the biological
indicator used for validation. The AET approach misidentified between
0 and 69 percent of the nonimpacted stations, depending upon the type of
normalization used for chemical concentrations and biological indicator
used for validation.
A detailed evaluation of the accuracy of SLC values was beyond the
project scope, because SLC were generated for only three chemicals. It was
assumed that such a small number of chemicals could not be expected to correctly
identify all impacted stations. Hence, a preliminary evaluation was made
only of the number of nonimpacted stations misidentified using each of
these three SLC values. The approach misidentified between 15 and 70 percent
of the nonimpacted stations, depending on the chemical, type of normalization
of chemical concentrations, and biological indicator used for validation.
Of the sediment quality values developed from field data (i.e., AET
and SLC), the most accurate values were those generated from chemical data
normalized to sediment dry weight. Potential reasons that sediment quality
values normalized to organic carbon could be less predictive of biological
effects than dry-weight normalized values are discussed in the report.
Organic carbon normalization may be less predictive if sediment/interstii-
tial water systems are not at equilibrium in the environment, if all organic
matter in sediments does not have uniform affinity for nonpolar, nonionic
pollutants, or if interstitial water is not the predominant route of contami-
nant uptake by organisms.
Precision of sediment quality values developed for each approach was
estimated for selected components of uncertainty that could be quantified.
The uncertainty of the equilibrium partitioning approach was estimated
to range from less than one to six orders of magnitude of the calculated
values, primarily because of uncertainty in the estimation of theoretical
constants and the applicability of water quality criteria used in the approach.
For a given set of field data, the uncertainty of the AET approach was
estimated to range from much less than one to two orders of magnitude of
the values generated, primarily because of potential misclassification
of nonimpacted stations that are used to define the AET. An evaluation
of the precision of SLC was beyond the project scope. A statistical evaluation
of the precision of SLC has recently been completed by Battelle (1986);
95-percent confidence intervals are calculated for SLC values developed
for nine contaminants in marine sediments.
The sediment quality values, in some cases with application of a "safety
factor," are recommended for:
• Trigger levels for screening decisions on the need for further testing
• Prioritization of potential problem areas
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• Identification of problem chemicals in an impacted sediment
• Identification of acceptable sediments for open-water disposal
• Prioritization of laboratory studies for determining cause-effect
relationships.
Additional studies for refining or verifying the sediment quality values
generated in this project are also recommended. Elements of these recommended
studies would enlarge the database to improve the reliability of field-based
sediment quality values, develop new kinds of sediment quality values (i.e.,
for different biological indicators), and improve the understanding of
the interactions between sediment-bound contaminants and biological effects.
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STUDY PURPOSE
BACKGROUND
In recent years, high concentrations of U.S. EPA priority pollutants
and other toxic chemicals have been identified in the sediments of a number
of bays in Puget Sound. Relatively little is known about the ways in which
exposure to contaminated sediments affects marine life; however, recent field
surveys have found increasing frequencies of biological abnormalities in fish
harvested in areas of high sediment contamination. In addition, exposure to
contaminated sediments has been linked to adverse environmental impacts such
as bioaccumulation in fish and invertebrates, and modified benthic community
structure. The risks to human health from consumption of marine organisms
exposed to contaminated sediments is also an issue of concern.
Currently, a set of sediment quality values that can be used in judging
the significance of chemical contamination and as a basis for regulatory
action is needed because it is apparent that national water quality criteria
alone are insufficient to ensure protection of estuarine and marine ecosystems
consistent with provisions of the Clean Water Act. Sediment quality values
represent chemical-specific concentrations in sediments that are expected to
be associated with adverse biological effects based either on field or
laboratory evidence, or theoretical predictions. Contaminated sediments
can serve as an important reservoir for continual recontamination of the
overlying water column. Organisms may be harmed as a result of consumption
of prey organisms that are intimately associated with sediments. Thus,
specific limits for both aqueous and sediment phase contaminant levels
are required. When fully developed, sediment quality values that define
environmentally acceptable levels of contaminants in sediments will be
one of several important tools in environmental decision-making.
OBJECTIVES
The Puget Sound Dredged Disposal Analysis (PSDDA) and Puget Sound
Estuary Program (PSEP) jointly sponsored the current work as part of an
overall goal to develop a coherent interim management program for contami-
nated sediments. The specific study objectives were to:
1. Compile chemical and biological data from Puget Sound appropriate
for use in the development of sediment quality values
2. Evaluate approaches that can be used to develop chemical-specific
sediment quality values and select one or more approaches
for evaluation with Puget Sound data
3. Evaluate the reasonableness of the values generated using
different techniques (i.e., their ability to correctly identify
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sites with known biological impacts) by conducting an uncertainty
analyses
4. Evaluate the appropriateness of using the values in different
regulatory applications based on the results of the uncertainty
analyses, and based on the advantages and limitations of
the approaches used for their development
5. Identify future studies that will be needed to refine or
verify the sediment quality values that are generated.
The fourth objective was clarified in discussions at a joint meeting of
the U.S. EPA Region X Sediment Criteria Work Group and the PSDDA Evaluation
Procedures Work Group held in January, 1986. Participants concurred that
program managers should be provided with a variety of sediment quality
values for several approaches to enable the managers to determine appropriate
values for individual programs. Hence, a single sediment quality value
for each contaminant is not recommended in this report, although approp-
riate uses of sediment quality values in different regulatory applications
are discussed.
A separate task was conducted as part of the overall work conducted
for PSDDA/PSEP to develop a comparative risk assessment document for environ-
mental and health concerns associated with the management of dredged material.
The results of this concurrent task are reported elsewhere (Tetra Tech
1986a). A flow chart of all tasks is shown in Figure 1.
REPORT ORGANIZATION
The preliminary results of the PSDDA/PSEP sediment quality values
study were presented in a series of four separate draft reports. Comments
received from peer review of these reports were used to refine the draft
reports into this final report. A summary of comments addressed by PSDDA/PSEP
on the draft reports and their responses is given in Appendix H.
Section I of the report addresses the evaluation of different approaches
that were considered for development of sediment quality values. Approaches
that can use existing data to generate chemical-specific sediment quality values
are selected in this section for application in Puget Sound. Section II
of the report describes the application of the selected approaches to data
compiled from Puget Sound investigations. An uncertainty analysis is also
summarized in this section that: (1) evaluates the ability of the sediment
quality values generated by different approaches to identify stations known
to be impacted according to one or more biological indicators, and (2)
estimates confidence intervals for the sediment quality values. Recommendations
for uses of sediment quality values and future studies are also summarized.
Several appendices have been incorporated into the final report to
summarize Puget Sound data recommended for use in this study (Appendices A-C),
and additional tasks undertaken in support of the overall sediment quality
values study. These additional tasks included:
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DEVELOPMENT OF SEDIMENT QUALITY VALUES
FOR PUGET SOUND
TASK 1
TASK 2
TASK 3
TASK 4
TASK 5
TASK 6
f
identify Appropriate Data Sets
Build Database
I
Evaluate Sediment
Quality Approaches a
I
Apply Approaches
To Database a
i
Evaluate Uncertainty
Of Sediment
Quality Values a
~r~
Final Report
1
Conduct Multivariate
Analyses (ARTHUR)
(data subset only)a
(incorporate
trends)
Conduct Risk Workshop
I
Recommend Exposure
Assessment Approach b
(with example application)
a Indicates draft reports integrated into the final report
b Risk exposure assessment prepared as a separate
final report (Tetra Tech 1986 a)
Figure 1. Flow chart of tasks for the development of sediment
qua!ity values.
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• A statistical evaluation of a data subset using pattern
recognition techniques (Appendix D; the objective was to
investigate underlying trends among multiple chemical and
biological variables that might be considered in the development
of sediment quality values)
• A recommendation of contaminants of concern for the management
of dredged material (Appendix E) and appropriate detection
limits (Appendix F)
§ An evaluation of the potential use of conventional chemical
variables (e.g., sulfides, volatile solids) in the management
of contaminated sediments (Appendix G).
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I. EVALUATION OF APPROACHES FOR THE DEVELOPMENT OF SEDIMENT
QUALITY VALUES FOR PUGET SOUND
1.0 INTRODUCTION
In Section I, the conceptual basis for eight approaches to determining
sediment quality values is presented (Table 1) and the limitations and
advantages of each approach are addressed. Based on a set of selection
criteria discussed in Section 3.1, recommendations were made for the application
of some of these approaches for the development of chemical-specific sediment
quality values for use in Puget Sound.
2.0 APPROACHES TO DETERMINING SEDIMENT QUALITY VALUES
Information from other reviews of approaches to determining sediment
quality values was incorporated into this section as appropriate (Battel le
1985b; JRB Associates 1984a,b).
2.1 Reference Approach
2.1.1 General Concept—
In the reference approach, sediment quality values are based on chemical
contaminant concentrations in a reference area that is pristine or is considered
to have acceptably low levels of contamination and no apparent biological
disturbance. This is often referred to as the background approach.
2.1.2 Advantages—
The primary advantage of the reference approach is that it has minimal
data requirements. The establishment of reference contaminant concentrations
does not require the collection of extensive field data, particularly in
areas where historical data are available. In its elementary form, the
reference approach does not require analysis of site-specific biological
indicators (e.g., sediment toxicity, benthic infaunal abundances) for samples
to be compared with reference levels. However, evidence of acceptable
biological conditions in the reference area will, in most cases, require
some site-specific biological assessments.
The reference approach is the only method for establishing sediment
quality values that does not require quantitative toxicological data for
priority pollutants and other contaminants of concern in sediments.
2.1.3 Limitations—
The primary limitation of the reference approach is that it is difficult
to defend on legal and scientific grounds. The choice of a suitable reference
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TABLE 1. APPROACHES REVIEWED FOR
ESTABLISHING SEDIMENT QUALITY VALUES
Approach
Concept
Reference Approach
Water Quality Criteria
Approach
Equilibrium Partitioning
(Sediment-Water) Approach
Equilibrium Partitioning
(Sediment-Biota) Approach
Field Bioassay Approach
Screening Level
Concentration (SLC)
Approach
Sediment quality values are based on chemical
concentrations in a pristine area or an area
with acceptably low levels of contamination.
Contaminant concentrations in in interstitial
water are measured directly and compared
with U.S. EPA water quality criteria.
A theoretical model is used to describe the
equilibrium partitioning of a contaminant
between sedimentary organic matter and inter-
stitial water. A sediment quality value
for a given contaminant is the organic carbon
normalized concentration that would correspond
to an interstitial water concentration equivalent
to the U.S. EPA water quality criterion for
the contaminant.
Acceptable contaminant body burdens for benthic
organisms are based on existing regulatory
limits. Sedimentary contaminant concentrations
that would correspond to these body burdens
under thermodynamic equilibrium are established
as sediment quality values.
Relationships between chemical concentrations
and biological responses are established
by exposing test organisms to field-collected
sediments with measured contaminant concen-
trations.
The SLC approach estimates the sediment concen-
tration of a contaminant above which less
than 95 percent of the total enumerated species
of benthic infauna are present. SLC values
are empirically derived from paired field
data for sediment chemistry and species-specific
benthic infaunal abundances.
6
-------
TABLE 1. (Continued)
Apparent Effects
Threshold (AET) Approach
An AET is the sediment concentration of a
contaminant above which statistically significant
biological effects (e.g., amphipod mortality
in bioassays, depressions in the abundance
of benthic infauana) would always be expected.
AET are empirically derived from paired field
data for sediment chemistry and a range of
biological effects indicators.
Spiked Bioassay Approach
Dose-response relationships are established
by exposing test organisms to sediments that
have been spiked with known amounts of chemicals
or mixtures of chemicals. Sediment quality
values are determined for sediment bioassays
in the manner that aqueous bioassays were
used to establish U.S. EPA water quality
criteria.
7
-------
area is largely subjective and, depending on the selection criteria, can
either overprotect or underprotect against actual environmental impacts:
• The selection of a pristine reference site could enforce
unnecessarily restrictive sediment quality values that may
not be realistically attainable and that would not account
for the assimilative capacity of sediments. Sediment quality
values set at some specified factor above reference concentration
would be an arbitrary solution to this problem, and would
ultimately be difficult to defend on technical grounds.
Sediment quality values set as detection limits (when chemicals
are not detected in reference sediments) or as multiples
of detection limits would also be difficult to defend.
t The choice of a relatively contaminated site as a reference
area could be questioned on legal and technical grounds
as being too lenient. Such a situation could arise if sediment
quality values were established to evaluate dredged material
for disposal at an established dredge disposal site (see
Section 2.1.4).
2.1.4 Modified Reference (Background) Approach—
The U.S. EPA Region X and the Washington Department of Ecology have
established" sediment quality values for two Puget Sound dredge disposal
sites (Fourmile Rock and Port Gardner) based on a modified reference approach
(U.S. EPA/Washington Department of Ecology 1984, 1985). Similar interim
criteria have been established for other areas in Puget Sound. A noteworthy
attribute of this approach is its inclusion of biological effects (bioassay)
data.
Flow diagrams of the approaches for each site are presented in Figures 2
and 3. In both cases, dredged materials are evaluated for their suitability
for open-water disposal based on comparisons of chemical (conventional
and priority pollutant) and biological (bioassay) results with those of
the reference areas (i.e., the disposal sites). This approach, which forbids
the open-water disposal of dredged materials that are more chemically contami-
nated or more biologically detrimental than sediments of the disposal site,
is designed to prevent degradation of the disposal site. A modified reference
approach was considered appropriate in light of: (1) the immediate necessity
for establishing disposal criteria, (2) the lack of an extensive assessment
of the biological health of the disposal site, and (3) the lack of toxicological
data for the contaminants of concern in sediments.
The use of site-specific bioassay data significantly improves the
reference approach because it provides more defensible criteria upon which
to base sediment quality values.
A limitation of the modified reference approaches used by U.S. EPA/Washing-
ton Department of Ecology is the lack of sufficient data for the reference
areas (the Fourmile Rock disposal site, the Port Gardner disposal site,
and central Puget Sound) (Thornton 1985). This lack of data limits the
8
-------
MODERATE
AND HIGH
CONCERN
AREAS
LOW
CONCERN
AREA
CHEMICAL TESTING
-CONVENTIONAL
-HEAVY METALS
VES
VES
YES
YES
YES
YES
IN-WATER
DISPOSAL
NOT APPROVED
IN-WATER
DISPOSAL
NOT APPROVED
IN-WATER
DISPOSAL
NOT APPROVED
IN-WATER
DISPOSAL
NOT APPROVED
IN-WATER
DISPOSAL
NOT APPROVED
WHERE IS
LOCATION
OF DREDGING?
AHPHIPOD BIOASSAY
-SEDIMENT TEST
OYSTER LARVAE BIOASSAY
-SEDIMENT TEST
ARE OIL AND GREASE
CONCENTRATIONS GREATER
THAN 0.IS?
CHEMICAL TESTING
-BASE/NEUTRAL PRIORITY
POLLUTANTS
CHEMICAL testing
-CONVENTIONAL
-HEAVY METALS
-PRIORITY POLLUTANTS
ARE HEAVY METALS
CONCENTRATIONS GREATER
THAN AMBIENT LEVELS AT
THE 4-MILE ROCK SITE?
ARE BASE/NEUTRAL
PRIORITY POLLUTANT
CONCENTRATIONS GREATER
THAN AMBIENT LEVELS AT
THE 4-MILE ROCK SITE?
IS THE MEAN SURVIVAL
RATE GREATER THAN OR
EQUAL TO THE MEAN
SURVIVAL RATE AT THE
4-MILE ROCK SITE?
ARE HEAVY METALS AND
PRIORITY POLLUTANT
CONCENTRATIONS GREATER
THAN AMBIENT LEVELS AT
THE 4-MILE ROCK SITE?
IS THE MEAN MORTALITY/
ABNORMALITY RATE LESS
THAN OR EQUAL TO THE
MORTALITY/ABNORMALITY
RATE AT THE 4-MILE
ROCK SITE?
IN-WATER DISPOSAL APPROVED
REFERENCE: U.S. EPA/WDOE (1984).
Figure 2. Modified reference approach: Fourmile Rock.
9
-------
MODERATE
AND HIGH
CONCERN
AREAS
LOW
CONCERN
AREA
VES
YES
ARE BASE/NEUTRAL
PRIORITY POLLUTANT
CONCENTRATIONS GREATER
THAN ONE STANDARD
DEVIATION ABOVE CENTRAL
PUGET SOUND LEVELS?
YES
YES
IN-WATER DISPOSAL APPROVED
IN-WATER
DISPOSAL
NOT APPROVED
IN-WATER
DISPOSAL
NOT APPROVED
IN-WATER
DISPOSAL
NOT APPROVED
IN-WATER
DISPOSAL
HOT APPROVED
WHERE IS
LOCATION
OF DREDGING?
AMPHIPOD BIOASSAY
-SEDIMENT TEST
IS THE MEAN SURVIVAL
RATE GREATER THAN OR
EQUAL TO 16 OF 20?
CHFMICAL TESTING
-BASE/NEUTRAL PRIORITY
POLLUTANTS
ARE OIL AND GREASE
CONCENTRATIONS GREATER
THAN 0.1*?
CHEMICAL TESTING
-CONVENTIONAL
-HEAVY METALS
CHEMICAL TESTING
-CONVENTIONAL
-HEAVY METALS
-PRIORITY POLLUTANTS
ARE HEAVY METALS
CONCENTRATIONS GREATER
THAN AVERAGE AMBIENT
LEVELS IN CENTRAL
PUGET SOUND?
ARE HEAVY METALS AND
PRIORITY POLLUTANT
CONCENTRATIONS GREATER
THAN ONE STANDARD
DEVIATION ABOVE AVERAGE
AMBIENT LEVELS IN
CENTRAL PUGET SOUND?
REFERENCE: U.S. EPA/WDOE (1985).
Figure 3. Modified reference approach: Port Gardner.
10
-------
statistical confidence that available chemical data are truly representative
of the reference areas.
2.2 Water Qual ity Crit e r ia Approach
2.2.1 General Concept—
Contaminant concentrations in interstitial water are measured directly
and compared with U.S. EPA water quality criteria.
2.2.2 Background—
U.S. EPA Region VI developed this method to take advantage of the
existing toxicological database used to establish national water quality
criteria. Relevant criteria consist of 24-h average concentrations and
maximum permissible concentrations for the protection of saltwater aquatic
organi sms.
The only data requirement for this approach is interstitial water
contaminant concentrations. Site-specific biological data are not necessary.
2.2.3 Advantages—
The principle advantage of the water quality criteria approach is
that it relies on existing toxicological data and only requires site-specific
collection of chemical data.
2.2.4 Limitations—
A critical limitation for any approach based on U.S. EPA water quality
criteria is that criteria are available for only some of the priority
pollutants: 10 inorganic pollutants (arsenic, silver, cadmium, chromium,
copper, mercury, nickel, lead, selenium, and zinc) and 9 organic pollutants
[PCBs and selected chlorinated hydrocarbon pesticides (chlordane, dieldrin,
DDT, endosulfan, endrin, heptachlor, lindane, and toxaphene)]. Polycyclic
aromatic hydrocarbons, a significant class of priority pollutants in Puget
Sound, are a noteworthy omission from this list.
Practical difficulties exist with the collection and analysis of inter-
stitial water samples. Standardized and validated procedures for interstitial
water analysis have not been established. Distinctions between dissolved,
colloidal, and suspended phases of a contaminant are operational and therefore
are seldom comparable among different laboratory procedures.
Important assumptions of this approach cannot be validated with existing
data. These assumptions relate to: (a) the applicability of sediment-
free laboratory bioassays to benthic biota under field conditions, and
(b) the route of uptake of contaminants in sediments.
7.7.4.1 AddI icabi 1 it.v of Water Quality Criteria Bioassays to Benthic
Biota Under Field Conditions—Sediment-free bioassays conducted largely
with nektonic organisms may not be applicable to benthic biota (e.g., deposit-
11
-------
feeding organisms). The application of toxicological data from a given
species to organisms from different phyla with different feeding habits
cannot be justified with current biological response data. Although bioassays
of marine benthic invertebrates are included in the database for U.S. EPA
water quality criteria (Stephan et al. 1983), test organisms were typically
nektonic.
Under field conditions, the presence of dissolved or colloidal organic
matter may be an important factor in uptake and toxicity. However, dissolved
organic matter was not a controlled variable in toxicity tests used to
establish U.S. EPA water quality criteria. The effect of dissolved or
colloidal organic matter on uptake and toxicity of contaminants has been
documented (Jenne and Luoma 1975 and references therein; George and Coombs
1977; Khalid 1980 and references therein; Landrum et al. 1985).
The water quality criteria approach is based on toxicological data
for chemicals tested individually. Thus, the approach is not designed
to address interactive effects of contaminants. To the extent that interactive
effects occur in the environment, uncertainty of these sediment quality
values would increase.
2.2.4.2 The Route of Uptake of Contaminants in Sediments—For this
approach, interstitial water is assumed to be the primary source of contaminants
to benthic biota, or alternatively, benthic systems are assumed to be at
equilibrium such that sediment ingestion will not enhance the amount of
contaminants available from interstitial water (JRB Associates 1984a).
Comprehensive data regarding compound-specific uptake routes for benthic
organisms (e.g., partitioning from interstitial water vs. assimilation
by ingestion of sediment) are not available. Biological uptake routes
vary with physiochemical properties of contaminants, feeding habits and
digestive physiology of benthic organisms, and the nature of contaminant-
sediment associations (which in turn can vary according to site-specific
environmental conditions, such as organic matter influx, pH, and redox
conditions).
Existing studies are indicative of the complex factors involved in
the relative biological availability of contaminants in dissolved and parti-
culate form. For example, Roesijadi et al. (1978) found that polychaetes
took up polycyclic aromatic hydrocarbons (PAH) more readily from an aqueous
than from a sediment phase, whereas Fowler et al. (1978) found that contaminated
sediments were a more significant source of polychlorinated biphenyls (PCBs)
for polychaetes than water. In a series of laboratory bioassays in which
midges were exposed to Kepone-contaminated water, sediment, or food, Adams
et al. (1984) concluded that interstitial water and/or water at the sedi-
ment/water interface were the sources for pollutant uptake. Jenne and
Luoma (1975) and Luoma and Jenne (1975) noted that the bioavailability
of a metal is related to the magnitude of its sediment-water distribution
coefficient, which in turn is related to the type of phase (e.g., iron
oxide or humic matter) with which it is associated. A given metal can
have various phase associations depending in part on the chemical environment
(e.g., redox conditions) at the sediment/ water interface. In general,
12
-------
it has been observed that the uptake of pollutants by benthic organisms
is more rapid from the aqueous phase. However, sediments can constitute
a much larger pool of contaminants than water (on an equal volume basis)
because of their higher sorption capacity for many pollutants (Jenne and
Luoma 1975; Luoma and Jenne 1975; Neff 1984).
2.3 Equilibrium Partitioning Approach (Sediment-Water)
2.3.1 General Concept—
A simple model is used to describe the equilibrium partitioning of
a contaminant between sedimentary organic matter and interstitial water.
A sediment quality value for a given contaminant is the sediment concentration
(normalized to organic carbon content) that would correspond to an interstitial
water concentration equivalent to the U.S. EPA water quality criterion
for the contaminant.
2.3.2 Background—
The equilibrium partitioning approach (Figure 4) uses the same toxi-
cological database as the water quality criteria approach, but avoids the
difficulties associated with the direct measurement of contaminant concen-
trations in interstitial water. The estimation of interstitial water c°n~
centrations from sediment concentrations involves the assumption that the
distribution of a contaminant between sediment and interstitial water phases
is governed by rapid and continuous exchange between these two phases.
This assumption of thermodynamic equilibrium at the sediment-water interface
implies that the sediment phase/aqueous phase concentration ratio is a
constant for a given sediment. The distribution can be represented as:
where:
Kp = is the thermodynamic sediment-water partition coefficient for
"contaminant x"
C* = sediment concentration of "contaminant x" (dry wt)
Cx = interstitial water concentration of "contaminant x".
iw
Laboratory sorption experiments have demonstrated that Kq values of
nonpolar nonionic organic contaminants are significantly correlated with
sedimentary organic carbon content (e.g., Karickhoff et al. 1979; Sctoarzenbach
and Westall 1981; Means et al. 1980). In support of these findings, field
studies (e.g., Choi and Chen 1976; Abdullah et al. 1982) have reported
significant correlations of organic carbon content with nonionic organic
13
-------
Determine sediment concentration of
contaminant X normalized to organic
carbon content, [Cx, ].
L s/ocJ
I
Find appropriate K value for con-
uw
taminant X.
1
Find in literature an appropriate
equation to relate K to K of the
ow oc
form: log K = a log K + b.
3 oc 3 ow
1
Find or estimate a water quality
criterion for contaminant X, [Cx, 1.
L w/crJ
1
Calculate an organic carbon-normalized
sediment quality value, [C*^cr], with
tCs/cr^ = Koc x ^w/cr-'-
i
Compare [C*/0(;] to [cJ/crJ.
Figure 4. Sediment-water equilibrium partitioning approach.
See text for definition of terms.
14
-------
pollutant concentrations in sediment, although this correlation has not
been observed in all field studies (Glooschenko et al. 1976). Because
organic matter is apparently the sedimentary fraction that mediates sediment-
water distributions, sedimentary concentrations of hydrophobic pollutants
are normalized to organic carbon content for the equilibrium partitioning
approach. Thus, a thermodynamic partition coefficient for organic carbon-
normalized contaminant concentrations is appropriate for nonpolar, nonionic
compounds:
where:
Kx = organic carbon-normalized partition coefficient for contaminant x
oc
f = fraction (on a wt/wt basis, in decimal form) of organic carbon
oc in the sediment (dry wt).
If a k£c value and a water quality criterion (Cjj/Cr) for contaminant x
are known, an organic carbon normalized sediment quality value (C|/cr)
can be determined as:
rx = K* x Cx
s/cr oc w/cr.
Because Koc values are not available in published literature for all
contaminants of concern, more widely available octanol-water partition
coefficients (Kow values) are used to estimate Koc values. Several laboratory
studies have demonstrated that Kow and K0c values for a given nonpoar,
nonionic organic pollutant are highly correlated (Chiou et al. 1983; Karickhoff
et al. 1979; Means et al. 1980). This relationship is expressed in the
form
1og Kqq = a 1og Kow + ^»
where a and b are empirically derived constants. This correlation implies
that the partitioning of a nonpolar, nonionic organic compound between
water and an irrmiscible organic solvent (octanol) is mechanistically analogous
to its distribution between water and sedimentary organic matter (Lambert
1967, 1968; Chiou et al. 1983).
? i 2 1 Ionizable Organic Compounds—The predictive relationship
invofvTngiuT-and Koc described for non-polar, nonionic compounds cannot
iustifiably be applied to polar, ionizable organic compounds. Ionizable
organic compounds such as phenols can interact with sedimentary organic
matter and mineral surfaces via mechanisms that are not possible for nonionic
15
-------
organic compounds (e.g., H-bonding, ion exchange, ligand exchange) (Isaacson
and Frink 1984; Schellenberg et al. 1984). Although organic carbon can
play an important part in ionic organic pollutant-sediment associations,
the pH and ionic strength of the aqueous phase are also of great importance
and can influence chemical speciation and complicate sorption behavior
(Schellenberg et al. 1984; Westall et al. 1985). Thus, organic carbon
normalization and use of Kow values to predict interstitial water concentration
of ionizable organic compounds is not a scientifically valid approach.
2.3.2.2 Metals and Metalloids—Predictive distribution coefficients
are at least as difficult to determine for trace metals as for ionizable
organic compounds. Whereas sediment-water distributions of metals can
be influenced by organic carbon content, they can also be strongly influenced
by iron and manganese oxide and hydrous oxide surfaces (these phases can
scavenge metals under oxidizing conditions), sulfides (mercury, zinc, cadmium,
copper, and lead form insoluble sulfides under reducing conditions), carbonates,
and phosphate ion concentrations (particularly relevant for lead) (Brannon
et al. 1976; Crecelius 1975; Davies-Colley et al. 1984; Jenne 1977; Jenne
and Luoma 1975; Khalid 1980; Lion et al. 1982; Luoma and Jenne 1975).
Associations with oxides, sulfides, and other phases can affect the K[>
of a given metal as well as the relative rates of metal adsorption and
desorption. For example, an iron oxide or organic matter coating on a
sediment particle can impede the desorption rate of a metal , increasing
the time required to reach equilibrium (Jenne 1977). On a site-specific
basis, redox conditions and pH can influence the relative importance of
sedimentary phases (e.g., oxides vs. sulfides) as well as the chemical
speciation and/or oxidation state of the metal pollutant. Physiocochemical
properties and environmental behavior of a given metal can vary dramatically
depending on its species. In summary, sediment-metal associations are
too variable on a site-specific basis and too poorly understood to allow
for accurate quantitative predictions of sediment-water distribution in
the environment.
The remainder of the equilibrium partitioning section is relevant
to nonpolar, nonionic organic pollutants, as the necessary predictive relation-
ships cannot be reliably determined for ionizable organic pollutants and
trace metals.
2.3.3 Advantages—
The equilibrium partitioning approach takes advantage of an existing
toxicological database (U.S. EPA water quality criteria) and does not require
the collection of biological data to generate sediment quality values.
It requires the collection of only one ancillary chemical variable, organic
carbon content. Estimation of sediment-water partitioning based on Kow
have a firm theoretical and empirical basis.
Z.3.4 Limitations—
The limitations of the water quality criteria approach discussed previously
also apply to the equilibrium partitioning approach: the limited number
of nonpolar organic compounds for which established U.S. EPA water quality
16
-------
criteria are available; uncertainty in the applicability of sediment-free
laboratory bioassays to benthic biota under field conditions; uncertainty
in the route of biological uptake of sedimentary contaminants; the uncertainty
introduced by interactive effects of contaminants in the environment.
Additional sources of uncertainty are discussed below.
2.3.4.1 The Equi 1 i brium Assumption—The assumption that steady-state
equilibrium exists in all aquatic environments is uncertain (Prahl and
Carpenter 1983) and can be violated by kinetic factors. Hydrophobic organic
contaminants incorporated within a sedimentary organic matter matrix (e.g.,
fecal pellets or humic substances) may take weeks, months, or longer to
attain equilibrium concentrations with an aqueous phase (Freeman and Cheung
1981; DiToro and Horzempa 1982; Karickhoff and Morris 1985a,b). The fact
that significant percentages of sediment-associated hydrophobic compounds
may be entrapped within refractory organic phases brings the equilibrium
assumption into question.
Three factors increase the uncertainty of predictions of interstitial
water contaminant concentrations based on Kow relationships: 1) there
is considerable variation among Kow values available in the literature
for a given compound, 2) colloidal or dissolved organic matter in interstitial
water may cause deviations from Kq values predicted from experiments with
a "pure" aqueous phase, and 3) laboratory-determined Kq values, from which
Koc~Kow relationships derive, are dependent on sediment/water (volume/volume)
ratios and may not be quantitatively equivalent to in situ Kg values.
2.3.4.2 Variations in Knyj Values—Variations in reported Kow values
for a given compound are common in scientific literature (Kenaga and Goring
1980; Rapaport and Eisenreich 1984). In some cases, reported Kow values
for a compound can differ by over an order of magnitude. These discrepancies
result in part from the use of different techniques for Kow determination.
Techniques for Kow determination include the use of shake flasks, generator
columns, reverse-phase high-pressure liquid chromatography (RPHPLC; e.g.,
Rapaport and Eisenreich 1984; Veith et al. 1979b), and approximations based
on fragment constants (Hansch and Leo 1979).
2.3.4.3 Effects of Dissolved Organic Matter on Partition Coefficient—
The presence of dissolved organic matter in interstitial water could enhance
the solubility of hydrophobic organic compounds and contribute to the un-
certainty of estimated partition coefficients. Associations of hydrophobic
compounds (e.g., DDT, PCBs) with dissolved organic matter have been documented
with various analytical procedures (Carter and Suffet 1982; Hassett and
Anderson 1979; Landrum et al. 1984). In addition to reducing actual sediment-
water partition coefficients, dissolved organic matter could also have
unpredictable effects on contaminant toxicity (see water quality criteria
approach limitations).
2.3.4.4 Effects of Sediment/Water (vol-.vol) Ratios on Partition Coeffi-
cients (The "Solids Effect")—Laboratory studies have demonstrated that
KQ and K0c values generated in sorption experiments are not constants,
but are strongly dependent on the relative volumes of sediment and water
in the test systems (O'Connor and Connolly 1980; Voice et al. 1983; Voice
17
-------
and Weber 1985). Although this finding has been explained as a laboratory
artifact (Gschwend and Wu 1985), it has been shown that partition coefficients
(Koc values) determined by measuring aqueous and sedimentary PCB concentrations
in water column-suspended solid versus interstitial water-sediment samples
differed by over an order of magnitude (Voice and Weber 1985). This difference
in observed Koc values was consistent with the Koc-solids concentration
relationship observed in many laboratory sorption studies. Thus, partition
coefficients, which are ultimately based on sorption experiments with relatively
low sediment-water ratios, may not compare well to in situ sediment-water
partitioning, which involves very high sediment-water ratios.
Other factors that may affect the applicability of laboratory sorption
studies to field samples (e.g., salinity, temperature, sediment particle
size) have been discussed elsewhere (JRB Associates 1984b) and are considered
to be less significant sources of uncertainty.
2.4 Equilibrium Partitioning Approach (Sediment-Biota)
2.4.1 General Concept—
Acceptable contaminant body burdens for benthic organisms are based
on existing regulatory limits [e.g., U.S. FDA action limits or U.S. EPA
Reference Dose values (RfD)] or, in lieu of established limits for certain
compounds, on U.S. EPA water quality criteria. Sedimentary contaminant
concentrations that would correspond to these body burdens under thermodynamic
equilibrium are established as sediment quality values (Figure 5).
2.4.2 Background--
This approach has been investigated by U.S. EPA/Environmental Research
Laboratory-Narragansett, the U.S. Army Corps of Engineers (COE), and Battelle
(1985a). McFarland (1984) suggested a sediment-biota approach as a method
of screening sediments for bioaccumulation potential of nonpolar organic
compounds. Several underlying assumptions of the approach limit its appli-
cability to nonpolar, nonionic organic contaminants. There are a number
of assumptions inherent in this approach:
1. Thermodynamic equilibrium exists among sediment, organisms,
and interstitial water
2. Hydrophobic pollutants associate predominantly with lipids
in all aquatic organisms, and the affinity of lipids for
these pollutants is equivalent for all organisms; similarly,
hydrophobic pollutants associate predominantly with organic
carbon in all sediments and the affinity of organic carbon
for these pollutants is equivalent in all sediments
3. The equilibrium distribution of hydrophobic organic pollutants
between lipids and sedimentary organic carbon (i.e., the biocon-
centration factor) is constant regardless of the type of organism
or sediment and regardless of the specific compound; this assump-
tion is not necessary if organism-specific bioconcentration
18
-------
Determine sediment concentration of
contaminant X normalized to organic
carbon content, [Cx, 1.
L s/ocJ
t
Find appropriate regulatory
(e.g., U.S. FDA) body burden
limit, [CB/ ]. Normalize to
lipid content.
JL
1
If FDA limit is not
available, use water
quality criterion,
^w/cr^ t0 estimate
body burden [c!h.
o
I
Find appropriate Kqw value
for contaminant X.
i
r
Find appropriate relation-
ship of the form:
log BCF = a log Kqw + b
where BCF is a bio-
concentration factor
(lipid normalized).
log [C*] = log BCF + log [C*/cr].
Use log [C*/cr] « log [cjj/cr] + o.28
to set sediment quality value, [C*^cr],
I
Compare [Cj/0(;] to [Cj/cp].
Figure 5. Sediment-biota equilibrium partitioning approach.
See text for definition of terms.
19
-------
factors are established for specific compounds in laboratory
studies.
The equilibrium expression used to establish sediment quality values
is based on:
BCFB-S ¦ CX/oc
where:
BCFg_r contaminant x.
If BCFg-s is not given a constant value, an empirically derived value for
sedimentary organic matter/1ipid partitioning would have to be established
for all relevant compounds and benthic species. This would require a consid-
erable amount of bioaccumulation research.
20
-------
Because U.S. FDA limits have been established for few contaminants,
a method has been devised to calculate tissue body burden limits from water
quality criteria. The operative assumption for this method is that equilibrium
body burdens of organisms in interstitial water with acceptable contamination
levels will be acceptable. The calculation is based on the expression:
BCFB-W =
log[Cg] = log[BCFB_w] + log [C*/Cp]
where:
[Cq] = 1ipid-normalized body burden of contaminant x
D
[BCFg_w] = bi©concentration factor (partition coefficient) of contaminant x
between biota and water
[Cx, 1 = water quality criterion for contaminant x
w/cr
BCFfj-w is often estimated by Kow, as strong linear log-log correlations
between BCFg_^ and Kow have been reported for some compounds in fish (Neely
et al. 1974; Veith et al. 1979b, 1980; Gossett et al. 1983).
2.4.3 Advantages-
Sediment quality values can be established with only FDA guidelines
(or water quality criteria and Kow values, if necessary).
2.4.4 Limitations—
The assumptions involved in this approach require extensive validation
and study. The equilibrium assumption is difficult to validate in field
and laboratory studies. Poor correlations between partition coefficients
and bioconcentration factors have been observed with compounds that are
rapidly metabolized by fishes (e.g., PAH) (Oliver and Niimi 1985; Connor
1984). Bioconcentration of "bulky" (i.e., sterically hindered) and high
molecular weight compounds also deviates from behavior predicted from partition
coefficients, presumably because the partitioning behavior of these compounds
differs from that of smaller molecular volume, lower weight compounds (Oliver
and Niimi 1985; Mackay 1982; Kenaga and Goring 1980).
The assumption that lipids control water-organism partitioning has
been supported by laboratory studies of fish bioaccumulation from contaminated
water (Chiou 1985; Schnoor 1982), but was not confirmed in a laboratory
study of polychaetes in PCB-spiked sediments (McLeese et al. 1980). The
21
-------
assertion that all lipids have an equal affinity for hydrophobic pollutants
and that no other factor (e.g., surface area/volume ratios) significantly
influences partitioning need to be supported by further studies.
The applicability of FDA limits to environmental quality is not well
founded, as the limits were designed for the protection of human health
and also take into account additional socioeconomic factors. The use of
water quality criteria to determine acceptable tissue levels of benthic
organisms also requires validation. Compounds that do not have established
FDA body burden limits or water quality criteria cannot be treated with
the sediment-biota approach. U.S. FDA action levels (or tolerance limits)
are available for the following nonpolar organic pollutants: aldrin, chlordane,
DDT/DDE/DDD, dieldrin, endrin, heptachlor/heptachlor epoxide, hexachlorocyclo-
hexane, Kepone, Mi rex, PCBs, and toxaphene.
2.5 Field Bioassav Approach
2.5.1 General Concept-
Relationships between chemical concentrations and biological responses
are established by exposing test organisms (e.g., Rhepoxynius abroni us,
a marine amphipod) to field-collected sediments with measured contaminant
concentrations. Mortality or sublethal effects are compared quantitatively
to effects observed in reference sediments.
2.5.2 Background—
This empirical assessment of sediment toxicity has been implemented
by the U.S. EPA and U.S. Army Corps of Engineers to test the suitability
of dredged materials for ocean disposal (U.S. EPA/COE 1977). Bioassay
techniques have been developed that provide a high degree of statistical
confidence (P<0.05) for determining whether or not mortality of test organisms
differs from that for a relatively uncontaminated control sediment (Swartz
et al. 1985). Sediment quality values could be established at contaminant
concentrations that correlate with statistically significant difference
in mortality between a test sediment and a control sediment.
In its simplest form, the field bioassay approach cannot set contaminant-
specific sediment quality values. The approach treats sediment toxicity
as a "black box," in that the total effect of all toxic agents (even those
that have not been chemically analyzed and quantified) is being measured.
Thus, the approach is useful for identifying problem sediments, but requires
integration with another approach to yield chemical-specific sediment quality
values (e.g., see Section 2.7).
2.6 Screening Level Concentration Approach
2.6.1 General Concept—
The presence of a given benthic species is related to sedimentary
contaminant concentrations to determine the minimum concentration (for
a given compound) that was not exceeded in 90 percent of the samples that
22
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contained the species. This process is carried out for numerous species
and a "screening level concentration" (SLC) is estimated as the contaminant
concentration above which less than 95 percent of the total enumerated
species of benthic infauna are present.
2.6.2 Background—
The SLC approach (originally termed toxicity endpoint approach) was
suggested as an interim method to establish national sediment criteria
for nonpolar organic contaminants (Battel 1 e 1985a). Still in the developmental
stage, this approach is currently being evaluated as a screening test for
nonpolar organic compounds to distinguish between concentrations that pose
a threat to biota and those that do not (Battelle 1986).
The approach requires that all stations at which a particular benthic
invertebrate species is present be arranged sequentially with respect to
increasing sediment concentration of the target contaminant. The concentration
at the station representing the 90th percentile of the total number of
stations at which the species was present is termed the "Species Screening
Level Concentration" (SSLC). SSLC values are calculated for a number of
species, and arranged sequentially with respect to increasing contaminant
concentration. The concentration above which 95 percent of the SSLC are
found is termed the "Screening Level Concentration" (SLC). This is consistent
with the water quality criteria goal of protecting 95 percent of aquatic
biota from adverse effects.
At present, the following minimum data requirements are recommended
to calculate the SLC for a compound (Battelle 1986):
• 20 stations for each SSLC calculation
• 10 taxa (not necessarily species level) for each SLC calculation
• Stations that span a gradient of contamination
• Taxa that are taxonomically homogeneous (e.g., all to species
level, all to genus level).
Increasing the number of species and stations beyond the minimum requirements
is thought to increase the power of the analysis.
The SLC approach can be displayed graphically, as shown in Figure 6.
For each species of interest, concentrations of a given contaminant (normalized
to organic carbon content) are plotted in order of increasing concentration
for all sample sites where the species was present (Figure 6a). As shown,
the SSLC is the minimum contaminant concentration that was not exceeded
in sediments from 90 percent of the stations containing the species. SSLC
values for selected species are then plotted in a cumulative frequency
graph (Figure 6b). The SLC is then determined as the SSLC concentration
above which 95 percent of the SSLC values fall.
23
-------
90th Percentile Concentration
c
U
o
c
o
*
t-
¦*-»
c
a>
w
c
o
1000 -
SSLC for "A"
100 -
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Sites Where Species "A" is Present
a. Calculation of Species Screening Level Concentration (SSLC)
c
©
D U
U —
~J c
C
-------
2.6.3 Advantages—
The SLC approach uses site-specific field data and is based on an
objective method designed to be consistent with the goals of U.S EPA water
quality criteria. The approach is not theoretically limited to any one
kind of chemical contaminant, although organic carbon normalization (favored
by the developers of the approach) limits its use to nonpolar organic com-
pounds. With appropriate normalization, the approach could be applied
to metals, polar organic compounds, and nonpolar organic compounds.
2.6.4 Limitations—
The SLC approach requires a considerable amount of field data that
span a wide range of contaminant concentrations. It requires that infaunal
taxonomic identification be made at the species level, which is a limitation
in terms of the time and effort required.
Two implicit assumptions are included in this approach:
• A critical a posteriori interpretation made after generation
of SLC values is that the presence/absence of various benthic
species is influenced by the toxic effects of contaminants.
However, environmental variables (e.g., substrate depth,
sediment texture) can be the primary determinants of species
presence at a given site. Unless an a priori attempt is
made to account for "natural" environmental factors in assessing
species presence/absence before calculating SSLC, any a
posteriori toxicological interpretation of the final SLC
values is complicated.
• It is assumed that the effects of a single contaminant can
be discerned from those of all contaminants combined (assuming
that presence/absence is truly determined by contaminants).
This approach does not attempt to distinguish between an undisturbed,
relatively abundant population and a population that has undergone a severe
decline in abundance because of a toxic response (or other factor). Presence/
absence is an extreme criterion with which to assess adverse effects.
In light of this, it might be more reasonable to consider the number of
individuals of each species present rather than their simple presence or
absence. Benthic infaunal abundances are generally a more sensitive indicator
of disturbance than presence/absence.
Selection criteria for species are not stipulated in this approach.
However, the species selected to determine SSLC values can have a great
effect on the SLC (i.e., especially when interpreted as a sediment quality
value). If over 5 percent of the selected species are sensitive to contami-
nants, a low and presumably protective SLC value will result (assuming
that natural factors have been accounted for). However, if sensitive species
constitute a much smaller portion of the total species (i.e., <<5 percent)
a high SLC could result. Contaminant concentrations sufficient to ensure
25
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the survival of a hardy species may not be acceptable as sediment quality
values designed to protect 95 percent of all aquatic life.
The uncertainty of the SLC approach is potentially increased by the
existence of interactive effects; the increase in uncertainty will be less
pronounced when large data sets collected from diverse areas are used to
generate sediment quality values with this approach. Additivity and synergism
can produce a comparatively low SSLC for a given chemical by causing species
absence at concentrations that would not eliminate a species in the absence
of these interactive effects. This would reduce the pool of "nonimpacted"
stations used to generate an SSLC. If a large database is used such that
chemicals occur over a wide range of concentrations at stations where additivity
and synergism are not operative, then the SSLC will be properly set.
Antagonism could potentially increase sediment quality values set
by the SLC approach by allowing a species to survive in a sample at a concentra-
tion (of a given chemical) that would normally eliminate the species.
With a large database and the 90-percent safety factor in SSLC values,
such cases would probably have little effect. A final limitation concerning
the influence of unmeasured contaminants (common to all of the chemical-specific
approaches, and especially empirical approaches that use field data to
generate sediment quality values) is discussed in the Limitations section
of the AET approach (Section 2.7.4).
2.7 Apparent Effects Threshold (AET) Approach
2.7.1 General Concept—
Chemical data are classified according to the absence or presence
of associated biological effects to determine concentrations of contaminants
above which statistically significant biological effects (e.g., depres-
sions in benthic infaunal abundances) would always be expected to occur.
2.7.2 Background—
AET were used to determine the contaminants of concern and the regions
of highest concern in a Superfund study of Commencement Bay (Tetra Tech
1985). The empirical relationships used to establish AET do not prove
a cause-effect relationship between contaminants and effects. The focus
of this approach is to identify concentrations of contaminants that are
associated exclusively with sediments having statistically significant
biological effects (relative to reference sediments).
A pictorial representation of the approach for two chemicals is presented
in Figures 7 and 8. Three subpopulations of all sediments analyzed for chem-
istry and biological effects are represented by bars in the figure, and include:
1. Sediments that did not exhibit significant infaunal depressions
2. Sediments that did not exhibit significant toxicity
3. Sediments that exhibited either toxicity or infaunal depression.
26
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LEAD
680X
72X
33X
ELEVATION
ABOVE
REFERENCE
1000X
100X
10X
1X = 92 mg/kg DW
NO BENTHIC DEPRESSIONS
(32 SITES)
NO SEDIMENT TOXICITY
(28 SITES)
(2B SITES)
sp-m
BENTHIC DEPRESSIONS AND/OR SEDIMENT TOXICITY OBSERVED
I BS-19
I SMI
2
••• • •
SP-14
I
a-*
«tt
SM2 I
RS-24
RS-tt
11 ppm
aOOppm 660 ppm
6300 ppm
f'
k r —r—r t
"i—riii — r 4
-—i—
1 i ' | 1
r i i ia
u-r-r-,
10
100
1000
10000
- POTENTIAL
APPARENT-
- APPARENT
MAXIMUM-
EFFECT
BENTHIC
TOXICITY
OBSERVED
THRESHOLD
EFFECT
THRESHOLD
LEVEL AT A
THRESHOLD
BIOLOGICAL
STATION
Figure 7. AET approach: Lead (dots in lowest bar designate stations from
Tetra Tech, 198b).
-------
4-METHYL PHENOL
ELEVATtON I 1X - 13 ^grtig DW
ABOVE J |
I I L.
REFERENCE
I
_l l I l I l
10X
I
_l ¦ ' T 1 I i—l
KMX
I
1000X
_1 1 1 I 1 I 1 I
NO BENTHIC DEPRESSIONS
(32 SITES)
NO SEDIMENT TOXICITY
(28 SITES)
(29 SITES)
BENTHIC DEPRESSIONS AND/OR SEDIMENT TOXICITY OBSERVED
CONCENTRATION
(tjgkg DW) JuJo
POTENTIAL
EFFECT
THRESHOLD
— APPARENT
BENTHIC
EFFECTS &
TOXICITY
THRESHOLD
-/J-
SP-H
RS-24
670 ppb
I II 1 I
10.000
96.000
MAXIMUM
OBSERVED
LEVEL AT A
BIOLOGICAL
STATION
Figure 8. AET approach: 4-Methyl phenol (dots in lowest bar designate stations
from Tetra Tech, 1985).
-------
The horizontal axis in each figure represents sedimentary concentra-
tions of the contaminant of concern (i.e., lead or 4-methyl phenol) on
a log scale. The AET for lead was based on lead concentration ranges corres-
ponding to sediments that did not exhibit significant biological effects.
The AET for 4-methyl phenol were determined analogously.
The potential effect threshold (Figure 7 and 8) is the contaminant
concentration below which no statistically significant biological effects
were observed in any sample. Note that this threshold for 4-methyl phenol
is equal to the detection limit for the compound. The threshold is designated
as "potential" because toxicity or benthic effects were found at some,
but not all, of the stations with higher lead or 4-methyl phenol concentra-
tions. The toxicity or benthic effects observed at these stations could
have resulted from other contaminants or physical conditions (e.g., grain
size). Because of these factors, the potential threshold is not used to
set sediment quality values.
Apparent benthic effect thresholds and apparent toxicity thresholds
correspond to concentrations above which al_[ samples were observed to have
infaunal depressions or toxicity, respectively. The purpose of treating
data in this manner is to reduce the weight given to samples in which factors
other than the contaminant examined (e.g., other contaminants, environmental
variables) may be responsible for sediment toxicity. An application of
the AET approach to 56 Commencement Bay samples (Tetra Tech 1985) (Figures 7
and 8) illustrates how potential effects of compounds may be distinguished
from one another. For example, sediment from Station SP-14 exhibited severe
toxicity and depressed infaunal abundances, potentially related to a greatly
elevated level of 4-methyl phenol (7,400 times reference levels; Figure 8).
The same sediment from Station SP-14 contained a low concentration of lead
that was not critical in establishing the AET for lead (Figure 7). Despite
the toxic effects displayed by the sample, sediments with higher lead concentra-
tions exhibited no statistically significant biological effects. These
results were interpreted to suggest that the effects at Station SP-14 were
more likely associated with 4-methyl phenol (or a covarying substance)
than with lead. A converse argument can be made for lead and 4-methyl
phenol in sediments from Station RS-18. Hence, the AET approach helps
to identify different contaminants that are most likely associated with
observed effects at each biologically impacted site. Based on the results
for these two contaminants, effects at four of the 29 sites may be associated
with elevated concentrations of 4-methyl phenol and effects at seven other
sites may be associated with elevated lead concentrations.
AET are not limited to site-specific biological indicators [such as
infaunal abundances and sediment toxicity (e.g., amphipod, oyster larvae,
or bacterial luminescence bioassays)]. They can also be established from
biological effects data that are not site-specific, such as fish histopathology
or fish bioaccumulation data. Biological indicators that are not site-
specific (e.g., fish histopathology data) will introduce additional uncertainty
to AET because they will require averaging of chemical data over large
areas. Hence, associations between chemical and biological data would
29
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be weaker than for site-specific chemical and biological data (e.g., amphipod
bioassays), which can be based on aliquots of the same sediment homogenate.
The AET approach does not intrinsically require normalization (although
use of dry-weight normalized data to correct for sediment moisture content
is assumed to be desirable). There are a number of reasons that normalization
of data to organic carbon content, metal hydroxide content, or grain size
may contribute to a better understanding of sediment quality values [e.g.,
sediment toxicity may be reduced by the "binding" of contaminants to sediment
organic carbon, Adams et al. (1984)]. The comparison of sites with similar
grain size distributions could minimize potential biological effects of
natural environmental factors. At present, there are insufficient data
to recommend the use of one normalization method over another.
2.7.3 Advantages—
Like the field bioassay approach, the AET approach relies on empirical
biological data to establish sediment quality values. However, the AET
approach is an attempt to alleviate a major limitation of the field bioassay
approach, namely, that contaminant-specific biological effects cannot be
discerned from overall contaminant effects in bioassays of field samples.
There are no constraints on the type of contaminant (e.g., metals vs. organic
compounds) for which AET can be established. A variety of biological effects
indicators (e.g., fish or benthic invertebrate histopathology, bioaccumulation,
sediment toxicity) can be used to determine AET for a given chemical.
A chemical's AET determined for a diverse set of biological indicators
can be compared to yield a more comprehensive perspective on appropriate
sediment quality values. By definition, observed biological effects always
occur above the AET (for the given data set), hence the approach provides
a sediment quality value that is based on non-contradictory evidence of
environmental effects.
2.7.4 Limitations—
The AET approach requires the collection of extensive field data for
chemical variables and at least one biological indicator. If toxic and
benthic effects thresholds are to be established, synoptic bioassay and
infaunal abundance data are required in addition to chemical data.
The AET approach allows for the possibility that sediment quality
values could be set at a level higher than required for complete environmental
protection. This is because biological effects can be observed at levels
well below the AET (that is, the potential effect threshold can be much
lower than the AET).
AET uncertainty is increased by the possibility of interactive effects;
the increase in uncertainty will be less pronounced when large data sets
collected from diverse areas are used to generate AET. Additivity and
synergism can produce a comparatively low AET for a given chemical by causing
impacts at concentrations that would not cause impacts in the absence of
these interactive effects. This would effectively reduce the pool of non-
impacted stations used to generate AET. This effect is reduced if a large
30
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database is used such that chemicals occur over a wide range of concentrations
at stations where additivity and synergism are not operative. Antagonism
will produce comparatively high AET if the AET is established at a station
where antagonism occurs. A large database could not rectify this elevation
of AET because the station at which antagonism occurred would tend to be
the nonimpacted station with the highest concentration.
Major sources of uncertainty in determining AET include:
1. The statistical error (P<0.05) associated with the significance
of bioassay and benthic infauna results (i.e., a classification
error)
2. The analytical error associated with the quantification
of chemical results
3. The uncertainty associated with the difference between the
maximum concentration not associated with an effect and the
next highest concentration that is associated with an effect
(e.g., the concentration range between the AET and the 4-methyl
phenol concentration at Station SP-16 in Figure 8)
4. The uncertainty that adequate sampling has been performed
to ensure that a wide range of chemical concentrations and
effects has been evaluated.
As with any field-based approach, or even a verification study for a theoretical
approach, collection of extensive field data enhances the reliability of
the results.
Another source of uncertainty common to all of the chemical-specific
approaches (especially approaches that generate sediment quality values
based on field data), is the possibility of effects being caused by unmeasured,
covarying chemicals. Such chemicals would not be expected to substantially
decrease the ability of AET to predict biologically impacted stations (excluding
interactive effects already discussed). If an unmeasured chemical (or
group of chemicals) varies consistently in the environment with a measured
chemical, then the AET established for the measured contaminant will (indirect-
ly) apply to or result in management of the unmeasured contaminant. In
such cases, a measured contaminant would be used as an "indicator" for
an unmeasured contaminant (or group of unmeasured contaminants). Because
all potential contaminants cannot be measured routinely, management strategies
must rely to some extent on "indicator" chemicals.
If an unmeasured chemical (or group of chemicals) does not always
covary with a measured chemical (e.g., if a certain industrial process
releases an unusual mixture of contaminants), the effect should be discerned
if a sufficiently large data set is used to establish AET. Use of a large
data set comprising samples from a variety of areas with wide-ranging chemical
concentrations would decrease the likelihood that an unreal i sti cal 1 y low
AET would be set. Because AET are set by the highest concentration of
a given chemical in samples without observed biological impacts, AET will
31
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not be affected by less contaminated samples in which unmeasured contaminants
cause biological impacts.
If an unmeasured toxic chemical does not covary with any of the measured
chemicals, it is likely that neither the AET nor any of the other chemical-
specific approaches reviewed could predict impacts at stations where the
chemical is inducing toxic effects. However, the predictive success can
be tested in a validation of each chemical-specific approach using field
data. Approaches that rely upon biological testing alone (e.g., the field
bioassay approach; Section 2.5) could indicate the impact, but would not
indicate the chemical of concern.
2.8 Spiked Bioassay Approach
2.8.1 General Concept—
Dose-response relationships are determined by exposing test organisms
(e.g., Rhepoxynius abronius) to sediments that have been spiked with known
amounts of chemicals (or mixtures of chemicals). Sediment quality values
can be determined for sediment bioassays in the manner that aqueous bioassays
were used to establish U.S. EPA water quality criteria.
2.8.2 Background-
Research on spiked sediment bioassays is being conducted for certain
chemicals by the U.S. EPA/Office of Research and Development and by Battelle.
The research includes investigations of the effect of sedimentary organic
matter on chemical toxicity.
2.8.3 Advantages—
In contrast to the field-based approaches considered in this project (e.g.,
AET, SLC), the spiked bioassay approach can establish cause-and-effect relation-
ships between chemicals and toxic biological responses. Chemicals can be
tested individually or in combination. The spiked bioassay is the only
systematic and reliable method available for identifying and quantifying
interactive effects (e.g., additivity, synergism, and antagonism).
2.8.4 Limitations—
The major limitation of the spiked bioassay approach is the amount
of research effort required to test the range of contaminants potentially
occurring in Puget Sound, both individually and in combinations. To establish
sediment quality values based on spiked bioassays, a wide range of test
organisms would require testing. To enhance the reliability of the sediment
quality values, a range of sediment types (e.g., with varying organic carbon
content and particle size distributions) would also be required.
A source of uncertainty for the spiked bioassay approach concerns
the degree to which spiked chemical-sediment associations resemble chemical-
sediment association that exist in the environment (e.g., in sediments
32
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that have been extensively reworked by benthic organisms and microbes).
This issue is difficult to research and is not currently well understood.
3.0 FINAL SELECTION OF APPROACHES FOR TESTING WITH PUGET SOUND DATA
Of the eight approaches discussed in the previous sections, three
[the water quality criteria (Section 2.2), field bioassay (Section 2.5),
and spiked bioassay (Section 2.8) approaches] are not evaluated further
for application or testing in this project for the following reasons:
• U.S. EPA water quality criteria are integrated into the
sediment-water and, for some compounds, the sediment-biota
equilibrium partitioning approaches.
• Because the focus of this project is on approaches that
can generate chemical-specific sediment quality values,
the field bioassay approach will be considered a part of
the AET approach in this discussion. The field bioassay
approach in its simplest form is not designed to generate
chemical-specific sediment quality values.
• The spiked bioassay approach has not yet generated sufficient
data to establish sediment quality values and cannot be
evaluated in the present project. However, its potential
role in future work is discussed in Section 8.5.
The remaining five approaches (reference, sediment-water and sediment-
biota equilibrium partitioning, SLC, and AET) were evaluated with regard
to their most appropriate uses for establishing sediment quality values.
3.1 Rationale for Selection
Approaches were evaluated with regard to the following criteria (in
approximate order of decreasing importance):
• The plausibility and scientific defensibility of the theo-
retical basis and critical assumptions associated with each
approach
• The quantity of data required for each approach, and the
current availability of data (i.e., for generation of sediment
quality values during the present project)
• The range of chemicals for which each approach is appropriate
(i.e., metals; nonpolar, nonionic organic compounds; and
polar, ionizable organic compounds)
§ The range of biological effects information that can be
incorporated into each approach.
33
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3.1.1 Reference Approach—
The reference approach (Section 2.1) provides a toxicologically sound
way to establish sediment quality values if: (1) the reference site is
determined to have no detrimental effects and (2) the goal of the program
is to ensure the absence of toxic effects by considering any contamination
above reference levels as unacceptable.
The most practical use of the reference approach is as a screening
tool; sediments with contamination above that of an acceptable reference
site can easily be identified. After screening, specific contaminant levels
requiring remedial action would be established by another approach. This
screening approach has minimal data requirements (i.e., only chemistry
data are required). The approach is appropriate for a wide range of chemicals.
It is not expected that application of this screening approach to Puget
Sound biological/chemical data would yield any additional information on
its most appropriate use. Therefore, this approach is not recommended
for testing with Puget Sound data in this project.
3.1.2 Sediment-Water Equilibrium Partitioning Approach—
The sediment-water equilibrium partitioning approach (Section 2.3)
involves plausible assumptions for estimating interstitial water concentrations
of hydrophobic pollutants based on sedimentary concentrations. The toxicologi-
cal assumptions require validation by comparison of calculated sediment
quality values to observed site-specific biological effects. Such tests
were conducted with existing Puget Sound data in this project (Section
7.1.1). An appealing aspect of this approach is that it has few data require-
ments (sedimentary contaminant concentration and organic carbon content).
Biological data are not required (except for validation of the approach).
To use this approach for more than the nine nonpolar, nonionic organic
compounds or mixtures for which water quality criteria exist, a method
was used to estimate appropriate criteria when established criteria were
unavailable (Section 5.2.3.3).
3.1.3 Sediment-Biota Equilibrium Partitioning Approach—
The sediment-biota approach (Section 2.4) is not recommended for use
in this project because sufficient data do not exist to validate the approach
and to generate definitive sediment quality values.
The minimal data requirements of the sediment-biota equilibrium Par*j~
tioning approach (only sedimentary contaminant concentrations are required)
derive from the series of assumptions used to estimate tissue concentrations
and their toxicological significance. As discussed previously, data are
not available to confirm certain critical assumptions of the approach (e«9'»
thermodynamic equilibrium exists between organisms, sediment, and interstitial
water; sediment organic mattei—lipid distribution coefficients are constant
for all hydrophobic compounds, all benthic organisms, and all sediments;
and U.S. FDA body burden limits and the U.S. EPA water quality criteria
used to estimate body burdens are sufficient to determine potential biological
34
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effects for aquatic organisms). The toxicological assumptions of the sediment-
biota approach can only be tested with bioaccumulation data that can be
related to toxicological data. Comprehensive data of this type are not
available and could be difficult to interpret (e.g., it is unclear whether
metabolites and conjugated compounds should be measured along with parent
compounds in tissue). Metabolites can be the most toxicological ly active
forms of pollutants. In contrast to the sediment-biota approach, the sediment-
water approach has more laboratory and field evidence that supports some
of its critical assumptions. The unproven toxicological assumptions of
the sediment-water approach are testable when bioassay or benthic infaunal
data are available.
3.1.4 Screening Level Concentration Approach—
The SLC approach (Section 2.6) is still under development but can
be applied with available data. Four aspects of the approach limit its
defensibility:
1. The interpretation that species presence/absence (without
accounting for natural factors) is indicative of toxic effects
of contaminants is uncertain
2. Presence/absence is an insensitive measure of the health
of a population
3. Selection criteria for species are not established, thus,
sensitive species may not be represented
4. There is no mechanism to help discern the effects of individual
contaminants from the combined effects of all contaminants
present.
The approach could be modified to address the first three of these limita-
tions. The recommended modifications would constitute a major change in
the overall approach:
• Data could be grouped according to important natural variables
(e.g., grain size, depth) to reduce the effects of these
variables on biological observations
• Instead of presence/absence, a quantitative measure of infaunal
abundance relative to that at an appropriate reference site
is suggested as a new criterion (e.g., sample sites that
displayed <80 percent depression in the abundance of a species
could be used to determine the SSLC)
• A minimum number of sensitive species (e.g., a minimum of
5 percent of the total species considered) is recommended
for determination of SSLC values.
The possible effects of the suggested modifications on the determination
of sediment quality values have not been previously tested. The fourth
35
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limitation discussed above is not readily overcome. A limited test of
the modified approach was conducted in this project (Section 7.1.4).
3.1.5 Apparent Effects Threshold Approach—
The AET approach is based on empirical evidence of biological effects,
and 1s primarily limited by the quantity of data required to interpret
apparent concentration-effects relationships properly. The approach can
be used for any chemical contaminant and for any observable biological
effects (e.g., bioassays, infaunal abundances, fish histopathology, bioaccumu-
lation). The potential for the AET approach to disregard impacted stations
and other aspects of AET uncertainty was tested in this project with matched
biological/chemical data from Puget Sound (Section 7.1.1).
3.2 Summary of Approaches Selected for Testing with Puqet Sound Data
The sediment-water equilibrium partitioning and AET approaches were
selected for testing with Puget Sound data. Test results are presented
in Section II. The equilibrium partitioning sediment-water approach was
used to generate sediment quality values only for nonpolar, nonionic pol-
lutants. Sediment quality values from this approach were normalized to
organic carbon content as specified in equilibrium partitioning theory.
The compiled Puget Sound database was used to test the success of these
values in predicting biological effects. AET sediment quality values were
generated and tested with the same compiled Puget Sound data used to test
the equilibrium partitioning approach. AET were generated for four site-
specific biological indicators for which data are available (amphipod bioassay,
oyster larvae bioassay, benthic infaunal abundances, and Microtox bioassay).
Chemical data were normalized to dry weight sediment, organic carbon content,
and to percent of fine-grained material (<63 um).
A limited test was performed for three sediment quality values derived
with the SLC approach, after the approach was modified according to the
factors discussed 1n Section 3.1.4. The test was limited because the approach
is time consuming to apply and computerized species-level infauna data
were available for only a few studies (e.g., Commencement Bay). Nonpolar,
nonionic compound concentrations were normalized to organic carbon content.
Metals were normalized to dry weight sediment. A summary of the results
is given in Section 6.2.
36
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II. APPLICATION OF RECOMMENDED SEDIMENT QUALITY VALUE
APPROACHES TO PUGET SOUND DATA
4.0 INTRODUCTION
In this section, sediment quality values are developed for the three
approaches selected in Section 3.2 (i.e., sediment-water equilibrium parti-
tioning, AET, and SLC approaches). Ideally, the development of sediment
quality values should be guided by definitive cause and effect information
that relates the individual and collective effects of contaminants to a
range of biological effects. However, very little cause-effect information
is available. The sediment quality values discussed in this section are
interim estimates that have been developed from or validated with chemical
and biological data from Puget Sound field investigations.
A large Puget Sound database that comprises matched biological and
chemical data from 190 stations was assembled from seven different studies
to generate and test sediment quality values for the three selected approaches
(see Appendices A and C). The site-specific biological indicators used
to assess the sediment quality values include sediment bioassays [amphipod
mortality, oyster larvae abnormality, and bacterial luminescence (Microtox)],
and depressions in the abundance of major benthic taxonomic groups (e.g.,
Mollusca, Crustacea, and Polychaeta) or total benthos. Abundances of individual
benthic infaunal species were used only with the SLC approach, although
these data could be used in the AET approach as well.
The methods used to apply each approach are described in detail in
Section 5.0. Chemical-specific sediment quality values derived for each
approach are tabulated in Section 6.0. A comparison of the magnitude of
these values with the distribution of chemical concentrations in the compiled
Puget Sound database (e.g., 50th, 75th, and 90th percentile concentrations)
is presented in Section 6.3. This comparison places the sediment quality
values in perspective with the level of contamination observed in Puget
Sound sediments. Existing Puget Sound interim sediment quality criteria
for the Fourmile Rock disposal site are also summarized in Section 6.3
for comparison with the sediment quality values generated in this project.
In Section 7.0, the contaminant-specific sediment quality values generated
by the sediment-water equilibrium partitioning (EP) and AET approaches
are evaluated with two measures of uncertainty:
• "Accuracy," defined as the ability to predict biologically
impacted sediments based on contaminant concentrations in
the sediments
• "Precision," defined as the expected variability of sediment
quality values given the particular constraints in the design
and use of each approach.
37
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The most appropriate use of the proposed sediment quality values for
managing contaminated sediments will be influenced by the results of this
uncertainty analysis. The analysis focuses on quantifiable uncertainty,
although each of the approaches has unquantifiable components of uncertainty.
The SLC approach was evaluated separately from the EP and AET approaches
because its application was restricted to a small subset of the database
and sediment quality values for only a limited number of chemicals were
generated.
The overall accuracies of the sediment quality values generated by
the EP and AET approaches were evaluated with the same techniques and the
same data sets. To more fully test the predictive success of AET, additional
accuracy analyses were carried out for AET generated and evaluated with
independent data sets (Section 7.1.2). The success of selected sediment
quality values generated by the EP and AET approaches in individually predicting
biological effects was also examined (e.g., a comparison of the AET value
for PCBs and the EP value for PCBs; Section 7.1.3).
Precision analyses of the EP and AET approaches resulted in estimated
confidence limits for contaminant-specific sediment quality values. A
precision analysis of the SLC approach was beyond the scope of this project;
however, confidence intervals for nine SLC values generated from a national
database have been determined by Battelle (1986a).
In Section 8.0, the applicability of the sediment quality values generated
in this project to a range of sediment management issues is discussed.
Recommendations for their use in decision-making and for further studies
to refine the values are also summarized (Section 8.0).
5.0 METHODS
5.1 Compilation of Matched Chemical/Biological Data From Puget Sound
A large database consisting of matched chemical/biological data (i.e.,
chemical and biological data representing the same station) was compiled
for the application and testing of the selected sediment quality value
approaches. The database was compiled for two reasons:
• To generate sediment quality values for field-based approaches
(i.e., the AET and SLC approaches)
t To test the success of sediment quality values (from each
of the approaches) at predicting biological impacts in Puget
Sound sediments from chemical concentrations in the sediments.
Potential biases related to generating and testing AET with the same database
are addressed in detail in Section 7.1.2, in which the predictive success
is evaluated for AET generated and tested with independent data sets.
38
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Available Puget Sound data sets containing field measurements of sediment
chemistry and at least one site-specific indicator of biological effects
were identified and reviewed for use in this project. Data sets from the
following Puget Sound studies were identified (for the sake of brevity,
incomplete names for studies are used below; the full study names are included
in the references):
• Alki Extension (Osborn et al. 1985; Trial and Michaud 1985)
• Commencement Bay (Tetra Tech 1985)
• Duwamish Head (Stober and Chew 1984a)
• Duwamish River I (Chan et al. 1985b)
• Duwamish River II (Chan et al. 1985a)
• Eagle Harbor (not completed in time for this project)
• Eight Bay (Detailed Survey) (Battelle 1985a)
• Everett Harbor (U.S. Department of the Navy 1985)
• OMPA 2, 19 (Maiins et al. 1980, 1982)
• Seahurst (Dinnell et al. 1984; Landolt et al. 1984; Nevissi
et al. 1984; Stober and Chew 1983, 1984b; Word et al. 1984)
t TPPS (Phase III) (Comiskey et al. 1984, Romberg et al. 1984)
5.1.1 Review of Available Data—
A general review of the available data sets was carried out in three
basic steps:
• All available data sets were reviewed for synoptic collection
of data, and only synoptically collected chemical and biological
data were considered further. [Note: a synoptic data set
was defined in this project as one for which toxicity data
were collected on the same sediment homogenate used for
sediment chemistry, and replicate benthic infaunal samples
were collected at the identical station location and time,
or at nearly the same time, as sediment chemistry samples.)
• Each data set was reviewed for documentation of quality
assurance (OA) methods and summaries of QA review (such
documentation was typically provided in the reports in which
the data were presented)
• Data were subjected to a more detailed review that focused
on issues related to data comparability.
39
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•i Synoptical ly collected data were used to reduce the possibility that
patchy (spatially variable) sediment contamination could result in biological
and chemical data based on dissimilar sediment samples representing the
same station." Because the toxic responses of "stationary" organisms
(e.g., bioassay organisms confined to a test sediment, or infaunal organisms
largely confined to a small area) were assumed to be affected by direct
association with contaminants in the surrounding environment, it was considered
essential that chemical and biological data be collected from nearly identical
subsamples from a given station. Overall, synoptic data were considered
to provide the most reliable basis for deriving or validating sediment
quality values with site-specific biological field data.
A detailed QA review of all data that were considered for inclusion
in the database was beyond the scope of this project. However, the chemical
and biological methods were reviewed for every data set considered in an
attempt to ensure comparability of chemical, bioassay, and benthic infaunal
data from all studies.
In this QA review, analytical techniques, detection limits, and the
chemical scope of pollutants analyzed (e.g., polar and nonpolar semivolatile
organic compounds, metals, volatile organic compounds) were all considered.
The scope of chemicals analyzed in a data set is especially important for
sediment quality value approaches that are based on field data (e.g., the
SLC and AET approaches). The availability of a wide diversity of chemical
data increases the probability that toxic agents (or chemicals that covary
with toxic agents) can be identified in sediments with observed biological
impacts. No entire data sets were excluded from the database as a result
of the review of chemical data.
The QA review of benthic infaunal data focused on sampling methods,
and in particular, on subsampling techniques (e.g., cores taken from grab
samples), and on the level of replication. The QA review of toxicological
data focused on sediment storage (fresh vs. frozen) and on the general
acceptance of bioassay methods used.
More detailed treatment of the review and of rationales for excluding
data are included in Appendix C.
5.1.2 Summary of Data Used in this Project—
The data sets that were included in the compiled Puget Sound database
are summarized in Table 2. The geographical distribution of these data
in Puget Sound is presented in Table 3 and Figure 9. More detailed maps
with sample locations are included in Appendix A. The compiled chemical
data and variables used for normalization [i.e., total organic carbon content,
percent of fine-grained material (percent silt and clay)] are tabulated
in Appendix A.
40
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TABLE 2. SUMMARY OF DATA SETS USED IN THIS PROJECT
Study
Number
of Stations
Chemi stry
Benthic
Infauna
Amphi pod
Bioassay
Oyster
Larvae
Microtox
Alki Extension
11
11
—
—
—
Commencement Bay
56
54
56
56
50
Duwamish River I
9
—
9
—
—
Duwamish River II
31
—
31
—
—
Eight Bay
48
—
48
—
—
Everett Harbor
6
—
6
—
—
TPPS (Phase III A&B)
29
29
—
—
—
Total
190
94
150
56
50
41
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TABLE 3. GEOGRAPHIC DISTRIBUTION OF COMPILED PUGET SOUND DATA
Available Data
Study Area
Chemi stry
Benthic Infauna
Bioassay
Reference3
Alki Point
X
X
4,7
Bellingham Bay
X
X
1
Cam Inlet
X
X
X
6
Case Inlet
X
X
1
Commencement Bay
X
X
6
Dabob Bay
X
X
1
Duwamish River
X
X
2
Elliott Bay
X
X
1
X
X
3,5
Everett Harbor/
X
X
1
Port Gardner
X
X
8
Samish Bay
X
X
1
Sequim Bay
X
X
1
Sinclair Inlet
X
X
1
East Passage and
X
X
3,5
Central Basin
a References:
1. (Battelle 1985b).
2. (Chan et al. 1985a,b).
3. (Comiskey et al. 1984).
4. (Osborn et al. 1985).
5. (Romberg et al. 1984).
6. (Tetra Tech 1985).
7. (Trial and Michaud 1985).
8. (U.S. Dept. of the Navy 1985).
42
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FOKT
SUSAN\
EVERETT
fvfflfrr .
east • • : .
PASSAGf ¦
rComm«M«Mn(
V/TACOMA
MILES
Figure 9. Location of chemical and biological samples included
in Puget Sound database.
43
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5.2 Application of the Equilibrium Partitioning Approach
5.2.1 Synopsis of Approach—
A simple model is used to describe the equilibrium partitioning of
a contaminant between sedimentary organic matter and interstitial water
(see Section 2.3). A sediment quality value for a given contaminant is
the sediment concentration (normalized to organic carbon content) that
would correspond to an interstitial water concentration equivalent to the
U.S. EPA water quality criterion for the contaminant.
5.2.2 Chemicals Used for Application of the EP Approach—
The theoretically based requirements of the EP approach (e.g., the
need for a quantitative, environmentally invariant partition coefficient)
restrict its application to nonpolar, nonionic (neutral) organic compounds
(Section 2.3.2).
Forty nonpolar, nonionic priority pollutants were used in the application
of the EP approach for this project (Table 4). Neutral organic priority
pollutants that were reported as not detected in the Puget Sound data compiled
for this project were not used in the EP application (e.g., hexachlorocyclo-
pentadiene, halogenated ethers, a number of volatile organic compounds,
and several chlorinated pesticides).
5.2.3 Literature Data Used for the EP Application-
Sediment quality values for each contaminant were generated using
three types of information obtained from the literature (Table 4):
1. Octanol-water partition coefficients (K0w)
2. Equations to approximate Koc (a sediment organic carbon-
water partition coefficient) from Kow
3. U.S. EPA water quality criteria for saltwater organisms
(or estimates of criteria) for each contaminant.
The literature values and sources used for this project are indicated in
Table 4.
5.2.3.1 Knw Values—Recently determined Kow values were used when
available. Direct determinations of K0w values were preferred. For some
compounds, estimations based on reverse-phase high pressure chroma-
tography were the most reliable values available. Calculated Kow values
[e.g., those estimated with fragment constants (Hansch and Leo 1979)] were
used only if empirically determined values were not available.
5.2.3.2 Knyi-Knr Equations—Three Kow-Koc equations were used for
this project. These are regression equations of the form log Kqc " a l09 lW"b>
where a and b are empirically derived constants. An attempt was made to
44
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TABLE 4. DATA USED FOR APPLICATION OF EP APPROACH
Chemical
log K
ow
WQC (ppb)
Kni -K equation*
ow oc
Low molecular weight PAH
naphthalene
acenaphthylene
acenaphthene
f1uorene
phenanthrene
anthracene
High molecular weight PAH
fluoranthene
pyrene
benzo(a)anthracene
chrysene
benzof1uoranthenes
benzo(a)pyrene
i ndeno(l,2,3-c,d)pyrene
dibenzo(a,h)anthracene
benzo(g,h,i jperylene
Total PCBs
3.36*
4.07;
3.92,
4.23®
4.57
4.54
5.22*
5.18
5.91,
5.79
6.57
6.42 J
7.66
6.50;
7.05
b,f
6.46
1175r s
150„
355
150r'S
isof-5
150
r,s
r,s
r »s
r,s
150r,S
150r'S
r,s
r,s
150
150
150
150
150
150
0.03
S,0
Total chlorinated benzenes
1.3-di chlorobenzene
1.4-dichlorobenzene
1,2-di chlorobenzene
1,2,4-trichlorobenzene
hexachlorobenzene (HCB)
3.48
3.38
3.38?
3.98?
5.47
65*'P
65 ,P
65S'P
65S'P
65S'P
v
V
V
V
V
Total phthalates
dimethyl phthalate
diethyl phthalate
di-n-butyl phthalate
butyl benzyl phthalate
bis(2-ethylhexyl)phthalate
di-n-octyl phthalate
Pesticides
p,p'-DDE
p.p'-OOO
p,p'-DDT
aldrin
chlordane
dieldrin
heptachlor
gamma-HCH (lindane)
1.61)
1.40
4.13!
4.05:
4.20;
8.06
5.69|
6.02,1
6.36
7.4 h
6.00
6.2 h
5.44 J
3.72
1472
1472
1472
1472
r,s
r,s
r,s
r,s
i472[>*
1472 ,S
1.8P
o.o^r
1.3
0.004
0.0019;
0.0036
0.16^
u
u
u
u
u
u
u
u
u
u
u
u
u
u
45
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TABLE 4. (Continued)
Chemical
,o» C
WQC2 (ppb)
3
K ,-K equation
ow oc
Miscellaneous extractables
hexachloroethane
3-93h
470*1
u
hexachlorobutadiene
4.28"
16
u
Volatile organics
trichloroethene
2.42^
1000I*
u
tetrachloroethene
2.53r
225j
u
ethyl benzene
3.15
215
V
1 Sources and notes for log Kow values:
a Karickhoff (1981)
b Callahan et al. (1979)
c Veith et al. (1980)
d Rapaport and Eisenreich (1984)
® Miller et al. (1985)
' Average of the benzofluoranthene isomers (b and k)
9 Means et al. (1980)
^ Veith et al. (1979b)
\ Veith et al. (1979a)
J Average of log Kows for Aroclors 1242, 1254,and 1260
k Miller et al. (1984)
1 McDuffie (1981)
m Chiou et al. (1981)
n Briggs (1981)
2 Water quality criteria (U.S. EPA 1980) are derived from the following tests all values
apply to bioassays conducted with saltwater organisms):
0 Chronic (24 hr. average) criterion
P Estimated chronic criterion (0.5 times the lowest concentration at which
chronic effects were observed)
9 Acute (maximum permissible) criterion
r Estimated acute criterion (0.5 times the lowest concentration at which
acute effects were observed)
s Class value; used for PCBs and individual compounds for which specific
criteria are not available.
3 Regression equations used and their sources are:
t log K = 0.989 log K - 0.346 (Karickhoff 1981)
u log K - 0.843 log Kow + 0.158 (JRB Associates 1984b)
v log Koc - 0.72 log Kow + 0.49 (Schwarzenbach and Westall 1981).
oc ow
46
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use equations that were specific to the chemical classes to which they
were applied. For example, the equation used for substituted benzene priority
pollutants (Table 4), was developed from laboratory data for 12 substituted
benzenes (e.g., representatives of mono- through tetramethylbenzenes and
mono- through tetrachlorobenzenes). Similarly, the equation used for PAH
was developed from laboratory data for a series of PAH (Table 4). Sufficient
phthalate data were not available to develop an equation specifically for
phthalates. Thus, an equation developed for priority pollutants with widely
ranging Kow values was considered the most appropriate for phthalates (Table 4;
JRB Associates 1984b).
5.2.3.3 Water Quality Criteria—U.S. EPA water quality criteria are
available for nine organic priority pollutants (PCBs, DDT, chlordane, dieldrin,
endosulfan, endrin, heptachlor, lindane, and toxaphene). Of these nine
pollutants, chronic saltwater criteria are available for PCBs, DDT, chlordane,
dieldrin, and heptachlor. It is desirable to apply the EP approach to
as wide a range of nonionic organic chemicals as possible to account for
the occurrence of pollutants in the environment from a variety of sources.
When published water quality criteria were unavailable for the relevant
priority pollutants, water quality criteria were estimated according to
a procedure used by developers of the EP approach (JRB Associates 1984b).
The procedure involves estimating a water quality criterion as one-half
the lowest concentration observed to cause biological effects on saltwater
organisms [biological effects data were taken from U.S. EPA (1980)]. This
procedure is consistent with the most recent U.S. EPA guidelines for developing
water quality criteria (Continuous Maximum Concentrations) from Final Acute
Values (Stephan et al. 1985).
Actual water quality criteria, when eventually established by U.S. EPA
for compounds that currently have no established criteria, are likely to
be different (probably lower) than the values estimated in the present
study. Because EP sediment quality values are expected to be most reliable
when based on established water quality criteria, compound-specific uncertainty
analyses were conducted for compounds with established chronic water quality
criteria (Sections 7.1.3 and 7.2.1).
Actual and estimated water quality criteria used in this project are
listed in Table 4. Chronic (24-h average) criteria were used when available
because they were considered to be more environmentally protective than
acute (maximum permissible) criteria. Chronic effects are potentially
important in sediments, which are long-term reservoirs for many nonpolar
organic pollutants. Developers of the equilibrium partitioning approach
have recommended the use of chronic criteria when available (JRB Associates
1984b). When chronic water quality criteria were not available, the following
values were used as water quality criteria based on available data in U.S. EPA
(1980) (in order of preference):
• Estimates of chronic criteria (based on chronic toxicity
data)
• Acute criteria
47
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t Estimates of acute criteria (based on acute toxicity data)
• Estimates of chronic criteria for a chemical group (e.g.,
chlorinated benzenes)
• Estimates of acute criteria for a chemical group.
Class criteria were applied to individual compounds within a chemical group
[consistent with the recommendations of U.S. EPA (1980)] except for the
PCB class criterion, which was intended to be used for the entire PCB class,
not for individual PCB congeners (U.S. EPA 1980).
Octanol-water partition coefficients (
-------
5.3.3 Data Used for the AET Application—
5.3.3.1 Chemical Data—AET were developed for chemical concentrations
(Appendix A) normalized to three variables: sediment dry weight, sediment
organic carbon content (expressed as percent of dry weight sediment), and
fine-grained particle content (expressed as the percent of silt and clay,
or <63 urn particulate material, in dry weight of sediment). A discussion
of these normalization .parameters is included in Appendix G (Ancillary
Sediment Variables).
5.3.3.2 Biological Data—AET were developed for four biological effects
indicators: amphipod bioassays (with raw data presented as percent mortality),
oyster larvae bioassays (with raw data expressed as percent abnormality),
benthic infaunal abundances, and bacterial luminescence bioassays (Microtox
bioassay; with raw data expressed as percent decrease in luminescence).
The selection of indicators was limited by available biological data.
5.3.4 Treatment of Biological Data for AET Application—
The AET approach relies on a binary assessment of biological effects
data for a given indicator: a station is classified as either having or
not having statistically significant effects. For the sake of brevity,
stations with significant effects will be termed "impacted" and those with
no statistically significant effects will be termed "nonimpacted." A discussion
of the impacted/nonimpacted designation of biological effects for each
indicator is presented in this section.
The primary test used to evaluate bioassay and benthic impacts was
the t-test, a test that is mathematically equivalent to the single-classifi-
cation ANOVA based on two groups (Sokal and Rohlf 1981). Although one
assumption of ANOVA is that the data are distributed normally, the consequences
of non-normality are not too serious; only very skewed distributions have
a marked effect on test results (Snedecor and Cochran 1967; Zar 1974; Sokal
and Rohlf 1981).
In the amphipod bioassay, for example, there was no reason to expect
distributions to be markedly skewed. The five replicate values for each
test were generated from subsamples of a homogeneous composite under carefully
controlled conditions. These test conditions suggest that the values of
all five replicates should be very similar and that the random error encountered
among replicates should be relatively small. Transformation of the bioassay
results therefore were not considered necessary.
For benthic results, there was considerable reason to believe that
the abundance data were strongly skewed, as that pattern is typical of
benthic infaunal assemblages (Gray 1981). Accordingly, data for abundance
of infauna were log^-transformed before statistical analyses were conducted.
5.3.4.1 Amphipod Bioassay—Significant mortalities of the amphipod
Rhepox.ynius abronius were determined by statistically comparing results
of tests on sediments from potentially impacted sites with those from a
49
-------
reference area. All comparisons were made within respective studies (i.e.,
data were not compared among studies). The reference areas used for the
different studies included Carr Inlet and Sequim Bay.
Stations with amphipod bioassay data were evaluated for statistically
significant mortality as follows:
• All replicates from all stations in the reference area used
for each study were pooled, and a mean mortality and standard
deviation were calculated
• Results from each potentially impacted site were then compared
statistically with the reference conditions using pairwise
analysis
• An ^max'test (Sokal and Rohlf 1969) was used to test for
homogeneity of variances between each pair of mean values
• If variances were homogeneous, a t-test was used to compare
the two means
• If variances were not homogeneous, an approximate t-test
(Sokal and Rohlf 1969) was used to compare means
• Error rates for significance were adjusted for multiple
comparisons using Bonferroni's technique (Miller 1981).
That is, an experimentwise error rate of 0.05 for each study
was achieved by testing each pairwise comparison at an error
rate equal to 0.05 divided by the total number of comparisons
made for each study.
5.3.4.2 Oyster Larvae Bioassay—Significant abnormalities in the
larvae of the Pacific oyster, Crassostrea gigas. were determined by statis-
tically comparing results of tests on sediments from potentially impacted
sites with those from a reference area. Oyster larvae data were available
from Tetra Tech (1985). The reference area for that study was Carr Inlet.
Stations with oyster bioassay data were evaluated for statistically
significant abnormality as follows:
t All replicates from all stations in the reference area were
pooled, and a mean abnormality and standard deviation were
calculated
• Results from each potentially impacted site were then compared
statistically with the reference conditions using a t-test.
t Error rates for significance were adjusted for multiple
comparisons using Bonferroni's technique (Miller 1981).
That is, an experimentwise error rate of 0.05 for each study
was achieved by testing each pairwise comparison at an error
50
-------
rate equal to 0.05 divided by the total number of comparisons
made for each study.
5.3.4.3 Benthic Infaunal Analysis—Significant depressions of the
total abundance of benthic infauna, and of abundances of polychaetes, molluscs,
and crustaceans were determined separately by comparing values from potentially
impacted sites with those from reference areas. Comparisons were made
within respective studies unless reference data were not available for
a particular study. In those cases, comparisons were made among studies.
Although the AET approach can be used with species-level data for
benthic infauna, higher level taxa (i.e., Polychaeta, Mollusca, Crustacea)
were used to set AET values in this project for two major reasons. First,
because the AET approach is based on pair-wise statistical comparisons
with reference conditions, the benthic taxa must either be abundant enough
or have a low enough variance to allow major depressions to be discriminated
statistically. If these criteria are not met, it may be very difficult
to distinguish a depression and, in some cases, complete absence of a taxon
may not be indicated as a significant impact. Therefore, use of taxa that
are either rare or highly variable may result in a relatively insensitive
indicator of environmental impact.
The second major reason for using higher taxa was that comparisons
with bioassay results (i.e., amphipod mortality and oyster larvae abnormality)
as part of the Commencement Bay Nearshore/Tideflats Remedial Investigation
(Tetra Tech 1985) showed that impacted or non-impacted designations made
by benthic and bioassay indicators agreed at 67-79 percent of the 48 stations
evaluated. This level of agreeement is significant (P<0.05, binomial test),
and suggests that benthic comparisons based on higher taxa were as sensitive
as the bioassays and were responding to similar stimuli. This independent
corroboration of the use of higher level taxa contributed to its acceptance
for setting benthic infauna AET.
Reference data for each potentially impacted site were selected based
on samples collected during the same season, at a similar depth, and in
sediments with similar particle size characteristics (i.e., percent silt
plus clay) as those of the potentially impacted site. In this manner,
all comparisons were stratified by three of the major natural variables
known to influence the abundance and distribution of benthic macroi nverte-
brates. The reference areas used for different studies included Carr Inlet,
Blair Waterway (Commencement Bay), and central Puget Sound (Pt. Williams
to Pt. Robinson). Although not a pristine reference area, Blair Waterway
stations sampled in the Commencement Bay Nearshore/Tideflats Remedial Investi-
gation (Tetra Tech 1985) exhibited relatively low chemical contamination,
no toxicity (except one station), and sediment characteristics similar
to those found throughout the other waterways. Thus, Blair Waterway was
used as a best estimate of unimpacted waterway conditions in fine-grained
sediments of Commencement Bay.
Stations with infaunal data were evaluated for statistically significant
benthic depressions as follows:
51
-------
• All abundances were logio~transformed
0 All replicates from each set of reference conditions were
pooled, and a mean and standard deviation were calculated
for each of the four benthic groups (i.e., total benthos,
Polychaeta, Mollusca, and Crustacea)
• The 1ogio~transformed abundance of each benthic group at
each potentially impacted site was then compared statistically
with the appropriate reference conditions using pairwise
analysis
• An Fmax"tes1: (Sokal and Rohlf 1969) was used to test for
homogeneity of variances between each pair of values
t If variances were homogeneous, a t-test was used to compare
the two means
• If variances were not homogeneous, an approximate t-test
(Sokal and Rohlf 1969) was used to compare means
• Error rates for significance were adjusted for multiple
comparisons using a Bonferroni's technique (Miller 1981).
That is, an experimentwise error rate of 0.05 for each benthic
group in each study was achieved by testing each pairwise
comparison at an error rate equal to 0.05 divided by the
total number of comparisons made for each study.
Appropriate reference data (i.e., with respect to season, depth, and sediment
texture) were available for most potentially impacted sites. However appro-
priate reference data could not be found for 17 Phase IIIA and 6 Phase
111B Metro TPPS stations. Thus, biological effects could not be assessed
for these stations.
5.3.4.4 Microtox Bloassay—-Significant Microtox toxicity for samples
from Carmencenent Bay (Tetra Tech 1985) was assessed by statistically comparing
the predicted decrease in luminescence in the presence of a 15-g sediment
sample to that observed for sediments from a reference area (Carr Inlet).
The following procedure was used (Williams et al. 1986):
t For each sample, decrease in luminescence for a 15-g sample
was predicted with a least-squares regression of the percent
decrease in luminescence vs. the logarithm of the standardized
sample dilution, where five serial dilutions of supernatants
from samples of 13.0 to 26.4 g were used as values for the
independent variable
• Statistical significance of the predicted luminescent response
as compared to that of control sediment was determined using
a t-test with a comparisonwise error rate of 0.001, which
yields an experimentwise error rate of 0.05 (Zar 1974).
52
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5.4 Application of the Screening Level Concentration Approach
5.4.1 Synopsis of Approach—
In the SLC approach, the presence of a given benthic species is correlated
to sedimentary contaminant concentrations to determine the minimum concentration
(for a given compound) that was not exceeded in 90 percent of the samples
containing the species (Section 2.6). This process is carried out for
numerous species and a "screening level concentration" (SLC) is estimated
as the contaminant concentration above which no more than 95 percent of the
total enumerated species of benthic infauna are present (see Section 2.6.2).
5.4.2 Modifications of the Original Approach—
Before the SLC approach was applied on a limited scale to data from
Commencement Bay, several modifications to the original approach were made
to respond to the constraints identified in Section 3.1.4. Because the
characteristics of benthic invertebrate assemblages are influenced by depth
(e.g., Sanders et al. 1965; Jumars and Fauchald 1977) and sediment character
(e.g., Gray 1974; Rhoads 1974), effects of these natural variables may
confound or obscure effects from chemical contamination. The stations
used in the limited application were therefore stratified by depth and
percent silt plus clay (i.e., an index of sediment character).
The species list used to generate each SLC is developed in part using
data from contaminated stations. Hence, it is possible that >95 percent
of the SSLC values (explained in Section 2.6.2) will be calculated for
pollution-tolerant species. In such a case, the resulting SLC would be
established by a pollution-tolerant species and thereby may not be sufficiently
protective if applied beyond the immediate data set. To avoid the potential
influence of pollution-tolerant species, the species list used in this
limited application was developed from data collected in a Puget Sound
reference area (with similar physical conditions as at the contaminated
stations). This list therefore represents the species expected to be present
at the contaminated stations in the absence of contamination.
The use of presence as a measure of the health of a population may be a
relatively insensitive measure. For example, it is uncertain whether a single
individual at a station implies that a species has not been unreasonably
affected by chemical contamination. Presence may also be overly sensitive to
methodological errors. For instance, if a single individual was introduced to
a station (e.g., through inadequate cleaning of the sieves between stations) or
was identified incorrectly (e.g., because it was damaged), the designation of
the entire station could change from impacted to nonimpacted with respect to
that species. To evaluate the use of presence as the index of species health,
an 80 percent depression in abundance from that observed at the reference
stations was used as an alternative index of impact (i.e., 80 percent depression
corresponds to absence). The 80 percent level was derived from Tetra Tech
(1985), in which most (i.e., 33 of 37) statistically significant (P<0.05)
depressions in the abundance of major taxonomic groups (i.e., total benthos,
Polychaeta, Mollusca, Crustacea) from Commencement Bay stations exceeded
53
-------
an 80 percent depression from reference abundances (see also discussion
in Appendix H).
5.4.3 Data Used for the Limited Application—
The data used for the limited application of the toxicity endpoint
approach were taken from the Commencement Bay Nearshore/Tideflats Superfund
Remedial Investigation (Tetra Tech 1985). Twenty-nine stations (4 reference,
25 contaminated) were selected within the depth range of 5-18 m and the
range of percent silt plus clay of 55-89 percent. The station group included
representatives from all seven industrial waterways and spanned relatively
wide ranges of degree and kind of chemical contamination.
Twenty taxa (15 species, 1 species group, 3 genera, 1 family) were
sufficiently abundant (i.e., > 5.0 individuals per station) to calculate
an 80 percent depression that would require a minimum of one individual
per station. The taxa spanned a wide taxonomic range, including 4 phyla
and at least 5 classes (Table 5). Several taxa identified to the family
and genus level were included with the species-specific data because the
taxonomic laboratory could not routinely identify those taxa to lower levels.
Inclusion of these taxa presumably increased the power of the limited applica-
tion by providing four additional SSLC values. In addition, one of the
higher level taxa (i.e., Amphiuridae) is thought to be pollution-sensitive
(Word et al. 1977; Thompson 1982).
The reference taxa were quite representative of the species captured
at all 52 stations in Commencement Bay (Tetra Tech 1985), as 7 were included
within the 10 most abundant taxa of the 52-station data set and all but
Syllis heterochaeta were included in the 63 most abundant taxa of the 52-station
data set. Pollution-sensitive species accounted for at least 25 percent
of the reference taxa. Four of these species (i.e., Praxi 11 e 11 a gracil is,
Axinopsida serricata, Nucula tenuis, Euphilomedes producta) were identified
as potential pollution-sensitive taxa in pattern recognition analyses of
Commencement Bay data (Appendix D), and a fifth taxon (i.e., Amphiuridae)
has been identified as apparently pollution-sensitive by Word et al. (1977)
and Thompson (1982).
Using the 20 reference species and the 25 contaminated sites described
earlier, SLC were calculated for high molecular weight polycyclic aromatic
hydroca'rbons (HPAH), naphthalene, and mercury. SLC were calculated for
HPAH and naphthalene on both a dry-weight and an organic carbon normalized
basis. The SLC approach was orginally developed and recommended for use
with nonpolar organic compounds normalized to organic carbon content in
sediments (Battelle 1986). The reason for also testing dry-weight normalization
was that the SLC approach (as with AET, but unlike the equilibnum
approach) requires no a priori assumptions concerning the specific mechanism
for interactions between contaminants and organisms.
54
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TABLE 5. REFERENCE AREA TAXA USED FOR THE LIMITED APPLICATION
OF THE SLC APPROACH
Number
of
Taxon
Reference
Stations0
Taxon
Code3
Abundance'*
80 Percent
Presence
Annelida
Polychaeta
Chaetozone spp.
1
13.8
20
23
Eteone longa
2
9.8
6
18
Euchone spp.
3
10.5
10
16
Glycera capitata
4
15.5
17
21
Lumbrineris sp. qr. 1
5
149.5
19
23
Nephtys ferruginea
6
8.3
14
19
Notomastus tenuis
7
10.8
6
16
Praxi1 lei la graci1i s^
8
32.5
3
10
Syllis heterochaeta
9
5.8
1
2
Tharyx multifilis
10
710.8
21
24
Mollusca
Gastropoda
Mitrella gouldi
11
10.3
8
14
Pelecypoda
Axinopsida serricata^
12
1,266.8
13
24
Compsomyax subdiaphana
13
6.0
6
7
Macoma carlottensis
14
99.5
17
24
Nucula tenuis*1
15
18.3
7
14
Psephidia lordi
16
17.5
9
19
Arthropoda
Crustacea
Euphilomedes carcharodonta
17
16.8
17
22
Euphilomedes producta"
18
98.5
13
21
Pinnixa spp.
19
5.0
14
18
Echinodermata
Stelleroidea
Amphiuridaee
20
6.0
11
18
a Code number used to identify each taxon in Figures 10-12.
b Mean abundance of each taxon at the four reference stations. The abundance
of each taxon at each reference station was based on four replicate 0.06-m2
grab samples.
c Number of stations at which the abundance of each taxon was <80 percent
depressed relative to the reference abundance or at which each species
was present.
55
-------
TABLE 5. (Continued)
d Identified as a potential pollution-sensitive species in Commencement
Bay in Appendix D.
e Identified as a pollution-sensitive taxon by Word et al. (1977) and Thompson
(1982).
56
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6.0 RESULTS
6.1 Sediment Quality Values Generated by the Equilibrium Partitioning
and Apparent Effects Threshold Approaches
Contaminant-specific sediment quality values are presented in Tables 6
through 8. For comparative purposes, the values are grouped according to
their normalization. EP sediment quality values can be compared roughly
to dry-weight AET if an organic carbon content is assumed (Table 6). As
noted in Table 6, a 1 percent organic carbon content is assumed; however,
to adjust the sediment quality values for a different organic carbon content,
multiply by the percent organic carbon. (The mean and median organic carbon
content of the compiled Puget Sound database are 2.0 and 1.3, respectively.)
EP and AET sediment quality values normalized to organic carbon content
are presented together in Table 7. Fines-normalized AET are presented
in Table 8. The "greater than (>)" AET values in Tables 6-8 indicate that
a definite AET could not be established because there were no impacted
stations with chemical concentrations above the highest concentration among
nonimpacted stations. These "greater than" values were not used in testing
the AET approach (Section 7.0), although they indicate a minimum potential
value for the AET of the particular chemical.
Sediment quality values generated by the EP approach generally are
higher, in some cases by orders of magnitude, than corresponding AET values
generated for various biological effects indicators (Tables 6 and 7). However,
EP values for several chemicals are comparable to or considerably less
than the corresponding AET (e.g., p,p'-DDT and PCBs). Because EP sediment
quality values are established largely by Kow values and water quality
criteria, relatively low water quality criteria (e.g., established chronic
water quality criteria for certain chlorinated pesticides and PCBs; Table 4)
or low Kow values (e.g., for dimethyl phthalate; Table 4) will set relatively
low sediment quality values for these chemicals.
6.1.1 Comparison with Historical Values—
6.1.1.1 Equilibrium Partitioning—The EP sediment quality values
presented in Table 7 are comparable to corresponding values generated in
a recent report that developed the sediment-water equilibrium partitioning
approach for Puget Sound (JRB Associates 1984b). Differences between the
two sets of values are attributable to the use of different Kow values
(Kow values reported in more recent literature were used when possible
in the present report) or different K0w"Koc equations (chemical class-specific
Kow-Koc equations were used when possible in the present report).
The EP values in Table 7 differ from EP values generated by Quinlan
et al. (1985). In their report, the equilibrium partitioning approach
included use of observed sediment-water partition values (K^), Kow-Koc
equations, Koc-aqueous solubility equations, U.S. EPA water quality criteria,
and aqueous bioassay toxicity data from unspecified sources. Values were
presented on a dry weight basis assuming a 2 percent organic carbon content.
Of the 10 compounds for which EP values were established in both Quinlan et
al. (1985) and the present report, 4 compounds or compound groups (PCBs,
57
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TABLE 6. EP AND AET SEDIMENT QUALITY VALUES (DRY WEIGHT)3,5
(ug/kg dry weight for organics; mg/kg dry weight for metals)
Amphipod
Oyster
Benthic
Microtox
Chemical
EP *
AET
AET
AET
AET
Low molecular weight PAH
5200
5200
6100
5200
naphthalene
11000
2100
2100
2100
2100
acenaphthylene
7200
560
>560
640
>560
acenaphthene
12000
630
500
500
500
fluorene
10000
540
540
640
540
phenanthrene
22000
2100
1500
3200
1500
anthracene
21000
960
960
1300
960
High molecular weight PAH
18000
17000
>51000
12000
fluoranthene
5200
3900
2500
6300
1700
pyrene
89800
4300
3300
>7300
2600
benzo(a)anthracene
470000
1600
1600
4500
1300
chrysene
360000
2800
2800
6700
1400
benzof1uoranthenes
2100000
3700
3600
8000
3200
benzo(a)pyrene
1500000
2400
1600
6800
1600
indeno(l,2,3-c,d)pyrene
26000000
690
690
>5200
600
dibenzo(a,h)anthracene
1800000
260
230
1200
230
benzo(g,h,i)perylene
6300000
740
720
5400
670
Total PCBs
120
2500
1100
1100
130
Total chlorinated benzenes
680
400
400
170
1,3-dichlorobenzene
640
>170
>170
>170
>170
1,4-di chlorobenzene
550
260
120
120
110
1,2-di chlorobenzene
550
>350
50
50
35
1,2,4-trichlorobenzene
1500
51
64
64
31
hexachlorobenzene (HCB)
17000
130
230
230
70
Total phthalates
>5200
3400
>70000
3300
dimethyl phthalate
480
160
160
160
71
diethyl phthalate
320
>73
>73
97
>48
di-n-butyl phthalate
64000
>5100
1400
>5100
1400
butyl benzyl phthalate
55000
>470
>470
470
63
bi s(2-ethylhexyl)phthalate
74000
>3100
1900
1900
1900
di-n-octyl phthalate
130000000
>590
>420
>68000
Pesticides
p,p'-DDE
6300
15
9
p.p'-DDD
3100
43
2
—
p,p'-DDT
3.3
3.9
>6
11
aldrin
32000
—
chlordane
6.6
—
dieldrin
4.6
—
heptachlor
2.0
—
gamma-HCH (1i ndane)
3.2
58
-------
TABLE 6. (Continued)
Chemical
Epc»d
Amphi pod
AET
Oyster
AET
Benthic
AET
Mi cr
AET
560
420
1200
1200
63
63
>72
>72
1200
670
670
670
>50
29
29
29
>140
>140
>140
>140
14000
930
290
270
270
120
310
370
370
370
670
670
670
670
260
260
270
270
240
240
250
250
540
540
540
540
73
73
73
57
>690
650
650
650
220
130
75
40
1600
__
——
—
440
>210
140
140
140
1200
>50
37
37
33
—
>160
120
120
100
5.3
26
3.2
26
—
93
700
85
700
—
>5.5
0.45
>0.50
0.36
—
6.7
9.6
5.8
9.6
—
>130
>37
59
27
—
800
390
310
390
—
27000
37000
37000
37000
—
700
660
300
530
—
230
480
>1000
480
—
2.1
0.59
0.88
0.41
—
>120
39
49
28
—
>1.0
—
>63
—
—
>3.7
>0.56
5.2
>0.56
—
0.4
0.24
0.24
0.24
—
870
1600
260
1600
__
15%
15%
15%
15%
—
27%
22%
22%
22%
—
>98%
>89%
>97%
88%
Phenols
phenol
2-methylphenol
4-methylphenol
2,4-dimethylphenol
pentachlorophenol
Miscellaneous extractables
hexachloroethane
hexachlorobutadiene
1-methylphenanthrene
2-methylnaphthalene
bi phenyl
dibenzothiophene
dibenzofuran
benzyl alcohol
benzoic acid
N-ni trosodi phenyl ami ne
Volatile organics
trichloroethene
tetrachloroethene
ethyl benzene
total xylenes
Metals
antimony
arsenic
beryl 1ium
cadmium
chromium
copper
i ron
lead
manganese
mercury
nickel
selenium
si 1ver
thai 1ium
zinc
Conventional variables
total organic carbon
total volatile solids
percent fine-grained
59
-------
TABLE 6. (Continued)
a Dashes in EP column indicate chemicals not appropriate for approach (because of
chemical characteristics) or chemicals for which water quality criteria are not available
or easily estimated.
b ">" in AET columns indicate that a definite AET could not be established because
there were no impacted stations with chemical concentrations above the highest concen-
tration among nonimpacted stations.
c Equilibrium Partitioning approach.
^ Assumes a constant organic carbon content of 1 percent for comparison purposes.
To adjust the value for a different organic carbon content, multiply by the percent
organic carbon.
60
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TABLE 7. EP AND AET SEDIMENT QUALITY VALUES (ORGANIC CARBON NORMALIZED)3'5
(mg/kg organic carbon for organics and metals)
Amphipod
Oyster
Benthic
Microtox
Chemical
EP
AET
AET
AET
AET
Low molecular weight PAH
396
370
>6100
>530
naphthalene
1100
>200
99
>330
>170
acenaphthylene
720
27
>27
640
>27
acenaphthene
1200
>72
16
>100
>57
fluorene
1000
58
23
>640
>71
phenanthrene
2200
180
120
>3200
>160
anthracene
2100
>79
>79
1300
>79
High molecular weight PAH
960
960
>51000
1500
fluoranthene
520
160
160
>6300
>190
pyrene
8980
>210
>210
>7300
>210
benzo(a)anthracene
47000
no
no
>4500
>160
chrysene
36000
110
110
>6700
>200
benzofluoranthenes
210000
230
230
>8000
>430
benzo(a)pyrene
150000
99
99
>6800
>140
indeno(1,2,3-c,d)pyrene
2600000
33
33
>5200
>87
dibenzo(a,h)anthracene
180000
120
120
>1200
33
benzo(g,h,i jperylene
630000
>31
31
>5400
>67
Total PCBs
12
130
>46
270
12
Total chlorinated benzenes
21
21
21
18
1,3-dichlorobenzene
64
>15
>15
>15
>15
1,4-dichlorobenzene
55
6.5
3.1
>16
>16
1,2-dichlorobenzene
55
>3.2
2.3
2.3
2.3
1,2,4-trichlorobenzene
150
>2.8
2.7
2.7
0.81
hexachlorobenzene (HCB)
1700
5.4
9.6
9.6
2.3
Total phthalates
>310
>310
>12000
220
dimethyl phthalate
48
>22
>22
>22
>19
diethyl phthalate
32
>5.3
>5.3
>78
>5.3
di-n-butyl phthalate
6400
260
260
>3400
220
butyl benzyl phthalate
5500
>9.2
>9.2
64
4.9
bis(2-ethylhexyl)phthalate
7400
78
59.6
59.6
47
di-n-octyl phthalate
13000000
>57
>57
>12000
—
Pesticides
p,p*-DDE
630
0.9
—
>5.0
p,p'-DDD
310
2.2
—
2.0
p,p'-DDT
0.33
>1.2
>0.39
>3.7
aldrin
3200
__
chlordane
0.66
dieldrin
0.46
heptachlor
0.20
_
gamma-HCH (lindane)
0.32
——
—
61
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TABLE 7. (Continued)
r
Amphipod
Oyster
Benthic
Microtox
Chemical
EP
AET
AET
AET
AET
Organic acids
phenol
—
>39
>39
>39
33
2-methylphenol
—
3.1
3.1
>10
>10
4-methylphenol
—
37
37
81
81
2,4-dimethylphenol
—
>1.3
>1.3
>1.3
0.63
pentachlorophenol
—
>11
>11
>11
>11
Miscellaneous extractables
hexachloroethane
1400
—
—
—
—
hexachlorobutadiene
93
16
11
11
3.9
1-methylphenanthrene
—
22
22
>29
>29
2-methylnaphthalene
—
38
38
>64
>64
biphenyl
—
9.4
7.0
12
12
dibenzothiophene
—
>19
8.2
14
14
di benzofuran
—
15
15
>58
>58
benzyl alcohol
—
5.0
5.0
5.0
5.0
benzoic acid
—
>170
>170
>170
>170
N-ni trosodi phenyl ami ne
—
>11
>11
11
>11
Volatile organics
trichloroethene
160
—
—
—
—
tetrachloroethene
44
>22
>22
>22
>22
ethyl benzene
120
>3.8
>3.8
>3.8
>3.8
total xylenes
—
>12
>12
>12
>12
Metals
antimony
—
1900
3300
550
3300
arsenic
—
32000
88000
6300
88000
beryl 1ium
—
>470
>64
>350
42
cadmium
—
1100
1200
580
1200
chromium
—
>5900
>5400
>35000
5100
copper
—
49000
49000
17000
48000
iron
—
>5700000
>5700000
22000000
4600000
lead
—
84000
66000
22000
66000
manganese
—
>83000
>83000
>1100000
61000
mercury
—
210
210
>780
77
nickel
—
>6800
>6800
>32000
6300
selenium
—
44
—
>8400
silver
—
>130
>100
490
100
thai 1ium
—
>105
>105
>105
>105
zi nc
—
72000
>200000
53000
>200000
a Dashes in EP column indicate chemicals not appropriate for approach (because of chemical
characteristics) or chemicals for which water quality criteria are not available or {
easily estimated.
62
-------
TABLE 7. (Continued)
b ">" in AET columns indicate that a definite AET could not be established because
there were no impacted stations with chemical concentrations above the highest concen-
tration among nonimpacted stations.
c Equilibrium Partitioning approach.
63
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TABLE 8. AET SEDIMENT QUALITY VALUES (FINE-GRAINED SEDIMENT NORMALIZED)*'6
(ug/kg fines for organics; mg/kg fines for metals)
Chemical
Amphipod
Oyster
Benthic
Microtox
AET
AET
AET
AET
16000
16000
>92000
29000
4300
4300
>9500
>9500
1200
>1200
>9600
>1200
2300
890
3100
3100
2300
1000
>9600
3900
7100
5400
>47000
8800
3400
3400
>19000
3400
42000
42000
>770000
82000
7100
7100
>94000
10000
9300
9300
>110000
9500
4600
4600
>67000
8800
5800
5800
>100000
11000
9990
9990
>120000
>24000
4300
4300
>100000
7800
1600
1600
>78000
>4800
580
580
>17000
1800
1500
1500
>80000
>3700
4300
1400
4800
230
930
510
>12000
1000
>270
>270
>270
>270
350
160
>5100
>880
>480
62
130
130
90
90
90
51
230
290
290
110
21000
21000
77000
18000
>420
>420
>420
>3900
>130
>130
>1100
>130
18000
18000
>59000
18000
>580
>580
580
110
4000
3400
3400
3400
1100
1100
75000
26
— —
51
74
30
5.6
>8.2
>200
Low molecular weight PAH
naphthalene
acenaphthylene
acenaphthene
fluorene
phenanthrene
anthracene
High molecular weight PAH
fluoranthene
pyrene
benzo(a)anthracene
chrysene
benzof1uoranthenes
benzo(a)pyrene
indeno(l,2,3-c,d)pyrene
dibenzo(a,h)anthracene
benzo(g,h,i)perylene
Total PCBs
Total chlorinated benzenes
1.3-di chlorobenzene
1.4-di chlorobenzene
1,2-dichl orobenzene
1,2,4-trichlorobenzene
hexachlorobenzene (HCB)
Total phthalates
dimethyl phthalate
diethyl phthalate
di-n-butyl phthalate
butyl benzyl phthalate
bi s(2-ethylhexyl) phthalate
di-n-octyl phthalate
Pesticides
p,p'-DDE
p,p'-DDD
p,p'-DDT
aldrin
chlordane
dieldrin
heptachlor
gamma-HCH (lindane)
64
-------
TABLE 8. (Continued)
Amphipod
Oyster
Benthic
Microtox
Chemical
AET
AET
AET
AET
Organic acids
phenol
>3800
>3800
>14000
>3800
2-methylphenol
140
140
>570
>570
4-methylphenol
1600
1600
4500
4500
2,4-dimethylphenol
>68
32
36
36
pentachlorophenol
>250
>250
>250
>250
Miscellaneous extractables
hexachloroethane
—
—
—
—
hexachlorobutadiene
370
350
350
200
1-methylphenanthrene
960
960
1600
1600
2-methylnaphthalene
1600
1600
3500
3500
biphenyl
460
460
640
640
dibenzothiophene
430
430
780
780
di benzofuran
960
960
3200
3200
benzyl alcohol
120
120
170
170
benzoic acid
>3400
>3400
>3400
>3400
N-ni trosodi phenyl ami ne
500
500
500
500
Volatile organics
trichloroethene
—
—
—
—
tetrachloroethene
>1000
>1000
>1000
>1000
ethyl benzene
>180
>180
>180
180
total xylenes
>530
>530
>530
>530
Metals
antimony
410
410
25
410
arsenic
6600
6600
1600
6600
beryl 1ium
>19
>7.8
>6.7
>7.8
cadmium
170
170
25
170
chromium
>590
>590
>4300
>590
copper
6800
6800
970
6800
iron
>800000
>80000
500000
>800000
1 ead
7700
7700
970
7700
manganese
>7700
>7700
>18000
>7700
mercury
11
11
8
11
nickel
>780
>780
>3300
>780
selenium
1.5
—
66
—
si 1ver
1 >22
>22
>65
>22
thai 1ium
2.6
2.6
2.6
2.6
zinc
16000
16000
4100
>16000
a Dashes in EP column indicate chemicals not appropriate for approach (because of
chemical characteristics or chemicals for which water quality criteria are not available
or easily estimated.
65
-------
TABLE 8. (Continued)
b ">" in AET columns indicate that a definite AET could not be established because
there were no impacted stations with chemical concentrations above the highest concen-
tration among nonimpacted stations.
66
-------
DDT, butyl benzyl phthalate, tetrachloroethene) have similar values. Calculated
EP values for other compounds differ by one to three orders of magnitude
[the Quinlan et al. (1985) values are consistently lower]. These differences
are attributable largely to the differences in toxicity data used in the
reports. Toxicity values were often orders of magnitude lower in the Quinlan
et al. (1985) report than in the U.S. EPA (1980) report used for the present
project.
6.1.1.2 AET—Fifty-six samples from the compiled Puget Sound data
set were used previously to generate toxicity AET (amphipod and oyster
larvae bioassays) and benthic AET as part of the Commencement Bay Nearshore/
Tideflats Superfund Remedial Investigation (Tetra Tech 1985). In some
cases, AET generated from the Puget Sound data set (Tables 6, 7, and 8)
are higher than those generated from the reduced data set. (AET can only
increase when the chemical/biological data set is expanded, because AET
are established by the highest concentration associated with a station
without biological impacts.)
Major increases in AET generated in this project compared with those
determined in the Commencement Bay study were noted for three chemical groups:
PCBs (amphipod and/or benthic AET increased, depending on normalization),
HPAH (benthic AET increased), and total phthalates (benthic AET increased;
the changes were driven by dioctyl phthalate). For example, the amphipod
AET for PCBs increased from 420 ug/kg (dry weight) to 2,500 ug/kg. The benthic
AET for HPAH increased from 17,000 ug/kg (dry weight) to >51,000 ug/kg.
The benthic AET for total phthalates increased from 5,200 ug/kg (dry weight)
to >70,000 ug/kg. The benthic AET for silver increased from >0.56 to 5.2
mg/kg (dry weight).
It is noteworthy that the benthic AET for LPAH, HPAH, and most of
the 16 priority pollutant PAH were set by a single station (7WP-11 from
the Metro TPPS study, Appendix Table A5). Concentrations of PAH in sediment
from this West Point station were far above those of the other nonimpacted
benthic stations in the Puget Sound data set. The effect of having one
"anomalous" station establish AET values are discussed further in the uncer-
tainty analysis for AET (Section 7.2.2.2; Classification Errors).
6.2 Sediment Quality Values Generated by the Screening Level Concentration
Approach
SSLC values are graphed in Figures 10-12 for each of three chemicals
or chemical groups for which the SLC approach was applied. To generate
these values, the minimum number of stations at which a species must either
be <80 percent depressed or present (depending upon the index used) was
arbitrarily set at six. As shown in Table 5, Syl1is heterochaeta was thereby
eliminated from both analyses and Praxil1 ell a gracilis was eliminated from
the analysis based on the 80 percent depression index. In most cases,
the number of impacted stations based on the presence index exceeded the
number based on the 80 percent depression index. When <10 stations were
available for a species, a 90th percentile concentration could not be used
as the SSLC. In such cases, the second highest contaminant concentration
was used as the SSLC, even though it represented <90 percent of the stations.
67
-------
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20
40
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80
100
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20 40 60 80 100
PERCENT OF TOTAL TAXA
• 80% PERCENT DEPRESSION NDEX
o—o PRESENCE INDEX
• POTENTIAL POLLUTION-SENSITIVE TAXON
Figure 10. SSLC values for HPAH. Numbers near points are
taxon codes (Table 5). Arrows on ordinates
indicate SLC values.
68
-------
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Figure 11. SSLC values for naphthalene. Numbers near points
are taxon codes (Table 5). Arrows on ordinate
indicate SLC values.
69
-------
.50
p--O-O-O-O-O
.40 -
*/
>. .20 -h
.10 -
60
20
40
100
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80
PERCENT OF TOTAL TAXA
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O O PRESENCE INDEX
• POTENTIAL POLLUTION-SENSITIVE TAXON
Figure 12. SSLC values for mercury. Numbers near points are
taxon codes (Table 5). Arrows on ordinate indicate
SLC values.
70
-------
SLC values based on Figures 10-12 are presented in Table 9. Because
18 and 19 species were used for the 80 percent depression and presence
analyses, respectively, the species having the lowest SSLC for each compound
represented approximately 5 percent of the total number of species and
therefore set the SLC. Each of these species is identified in Table 9.
6.2.1 Patterns Observed Using the Limited Application—
The SSLC index based on species presence appeared to be less sensitive
than the index based on an 80 percent depression from reference abundances.
Most SSLC values based on presence exceeded those based on an 80 percent
depression for all compound/norma 1 ization combinations. In addition, SLC
values based on presence exceeded those based on an 80 percent depression
by 12-56 percent for all compound/normalization combinations (Table 9).
One major effect of using an 80 percent depression as the index was a substan-
tial reduction in the number of stations used for many SSLC analyses relative
to the number of stations available when presence was used. However, this
potential limitation did not appear to alter the relative sensitivities
of the two indices.
The SSLC plots were characterized by an initially increasing line
followed by a series of plateaus at higher chemical concentrations, and
rarely approached linearity. Most wide plateaus (i.e., comprising many
stations) were found at the highest observed SSLC values, suggesting that
those concentrations act as a common limitation, or "ceiling," for many
of the less sensitive taxa. Wide plateaus were not found for the lowest
observed SSLC values, suggesting that the most sensitive taxa exhibited
a graded response to increasing chemical concentrations.
The relative magnitude of the SSLC for several taxa sometimes varied
substantially depending upon which index (i.e., presence or 80 percent
depression) was used. The most common pattern in the SSLC plots was for
some of the lowest SSLC values calculated using the 80 percent depression
index to increase when recalculated using the presence Index. The most
sensitive taxa in this respect were Eteone 1onga (Code=2) and Amphiuridae
(Code=20). For most compound/normalization combinations, SSLC values for
these two taxa rose from among the lowest observed when based on the 80
percent depression index, to among the highest observed when based on the
presence index. SSLC values for Notomastus tenuis (Code=7) also showed
this pattern, but to a lesser degree. In contrast, the lowest SSLC values
based on the presence index rarely increased substantially when recalculated
using the 80 percent depression index. These patterns suggest that SSLC
values based on species presence may be less stable when evaluating the
most sensitive taxa than are SSLC values based on an 80 percent depression
of a species.
Taxa thought to be pollution sensitive did not consistently exhibit
the lowest SSLC values. SSLC values of the five species identified as
potentially pollution sensitive in Table 5 ranged from among the lowest
to among the highest observed values for most SSLC plots. Sensitive taxa
sometimes having the lowest SSLC, and thereby setting the SLC, included
71
-------
TABLE 9. SLC VALUES FOR TARGET COMPOUNDS
Compound
SLC®
mg/kg Organic Carbon
80 Percent Presence
,b
mg/kg Dry Weight
80 Percent Presence
HPAH
200
230
4.9
5.8
(7)
(8)
(13)
(13)
Naphthalene
33
37
0.55
0.86
(2)
(8)
(15)
(13)
Mercury
0.20
0.29
(13)
(13)
a The code for the taxon that set each SLC is given in parentheses
below each concentration (see Table 5 for a description of
each code).
b For each method of normalization, organic carbon and dry
weight, SLC are given for analyses using indices based on 1)
an 80 percent depression from reference abundance and 2) taxon
presence.
72
-------
Praxillella gracilis (three cases) and Nucula tenuis (one case). Both
of these species were identified as potentially sensitive to toxic chemicals
in a pattern recognition analysis of Commencement Bay chemical and biological
data (Appendix D). The species that set the greatest number of SLC values
(five) was Compsomyax subdiaphana. This latter species was not identified
in the pattern recognition study (Appendix D). Additional comparisons
are presented by Battelle (1986a).
6.2.2 Comparison with Other Sediment Quality Value Approaches—
For the three compounds for which SLC values were established, sediment
quality values based on the SLC approach were lower than the corresponding
AET, which were lower than the corresponding EP values. The consistently
low sediment quality values generated for the three chemicals by the SLC
approach probably resulted largely from their reliance upon sensitive taxa
(generally species level). By contrast, the benthic indicator used in
this application of the AET approach is based on higher level taxa (i.e.,
phylum and class level), without regard to pollution sensitivity. (The
AET approach could be applied to species-level benthic data). The differences
between sediment quality values generated by the SLC and AET approaches
may relate to the data used to set these values: the 29 stations used
to establish SLC values had narrower ranges of concentration for the chemicals
examined than did the larger set of Puget Sound stations used to establish
AET. Determination of SLC values based on a larger data set is recommended.
In addition, development of AET for apparently sensitive taxa is recommended.
Such AET may enable more sensitive indication of chemical impacts on benthic
infaunal communities than AET based on major taxa.
6.3 Comparison of Sediment Quality Values to Sediment Concentrations—
Puget Sound
The detection frequency and concentration percentiles (e.g., 50th,
75th, and 90th percentiles) of individual chemicals in the Puget Sound
database assembled for this study are shown in Table 10. This range of
chanical concentrations in environmental samples (mostly from urban embayments)
provides a useful perspective for the sediment quality values generated
in this study (e.g., Tables 6 and 9). The following comparison does not
necessarily indicate the percentage of all Puget Sound sediment ^at niay
exhibit biological effects because biological testing is necessary to confirm
such predicted effects, and the existing database is skewed toward more
highly contaminated sediments from urban embayments of Puget Sound.
The SLC values generated in this study fell between the 50th and 90th
oercentile concentrations of the corresponding chemicals in sediment samples
Contained in the database. It should be noted that these are preliminary
SLC values developed from a relatively small data set. In general, AET
values were at or above the 90th percentile concentration for most chemicals.
All but 7 of the 40 EP sediment quality values (dry weight, 1 percent organic
carbon content assumed) exceeded the concentration of the corresponding
chemical in 100 percent of the sediment samples in the Puget Sound database.
73
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TABLE 10. DETECTION FREQUENCY AND PERCENTILE VALUES FOR CHEMICAL
CONCENTRATIONS USED IN PUGET SOUND DATABASE
(ug/kg dry weight for organics; mg/kg dry weight for metals)
Chemical
Detection
Frequency®
Concentration
Percentiles^
50% 75% 90%
Max imum
Detected
Concentration*-
Fourmile
Rock Interim
Sed iment
Criteria"
Low molecular weight PAH
161/212
610
1450
3200
55000
855
naphthalene
150/212
100
340
920
5900
«...
acenaphthylene
126/212
40
100
280
3300
acenaphthene
96/181
40
100
200
4000
- - -
fluorene
144/212
60
140
320
4800
...
phenanthrene
185/212
220
540
1200
34000
anthracene
163/212
83
200
450
10000
...
High molecular weight PAH
165/181
2800
7000
14000
260000
14000
fluoranthene
191/212
420
880
1900
71000
pyrene
190/212
520
1000
2000
63000
•» - -
benzo(a)anthracene
171/212
200
530
920
15000
...
chrysene
175/212
260
720
1600
35000
...
benzofluoranthenes
143/181
410
1600
3300
29000
«...
benzo(a)pyrene
160/212
200
640
1400
23000
indeno(l,2,3-c,d)pyrene
94/181
140
390
600
9100
dibenzo(a,h)anthracene
76/212
34
170
490
4000
benzo(g,h,i)perylene
102/181
140
400
800
11000
Total PCBs
168/200
91
140
290
5400
760
Total chlorinated benzenes
52/166
49
600
1300
17000
•» • »
1,3-dichlorobenzene
20/175
U 5
U 40
U 100
170
1,4-dichlorobenzene
49/175
U 20
U 40
U 100
7700
...
1,2-dichlorobenzene
20/175
U 5
U 40
U 100
9000
1,2,4-tr i ch1oroben zene
8/166
U 10
U 40
U 170
260
...
hexachlorobenzene (HC8)
27/206
U 20
U 20
500
730
---
Total phthalates
130/166
660
1400
3600
70000
• « -
dimethyl phthalate
46/175
U 40
U 50
100
350
diethyl phthalate
62/175
U 10
U 40
100
320
di-n-butyl phthalate
99/175
U 80
210
850
5100
butyl benzyl phthalate
45/166
U 25
U 80
180
1800
bis(2-ethylhexyl)phthalate
46/175
U 25
210
750
3100
-- -
di-n-octyl phthalate
67/166
U 40
260
1500
69000
...
Pesticides
P.P'-DOE
72/175
U 1
3
8.9
47
P.P'-DDD
47/150
U 1
U 1
12
175
---
P.P'-DDT
28/206
U 1
U 25
U 50
77
9e
aldrin
• • m
• • m
• • •
chlordane
• • m
m _ s
* ••
« ««•
mmm
...
dieldrin
— - ~
~ _
w tm wm
• w —
~ «
...
heptachlor
_ • •
• ••
• m a»
w W —
gamma-HCH (lindane)
—
---
74
-------
TABLE 10. (Continued)
Fourmile
Concentration
Maximum
Rock Interim
Detection
Percentiles
Detected
Sediment
Frequency®
50% 75% 90%
Concentration0
Criteria
Organic acids
phenol
63/165
U
40 U
100
280
1700
2-methylphenol
26/52
U
10
22
45
72
—
4-methylphenol
47/52
150
290
650
96000
—
2,4-dimethylphenol
9/165
u
10 U
50 U
200
50
—
pentachlorophenol
9/165
u
200 U
200 U
1200
140
—
Miscellaneous extractables
hexachloroethane
2/166
u
50 U
50 U
200
2800
—
hexachlorobutadiene
31/175
u
25 U
130
320
730
—
1-methylphenanthrene
72/96
29
78
180
1300
—
1-methylnapthalene
86/96
70
190
390
1200
—
biphenyl
69/96
11
56
100
1100
—
dibenzothiophene
37/56
33
110
190
1100
—
dibenzofuran
47/52
120
200
350
2000
—
benzyl alcohol
30/52
14
28
53
140
—
benzoic acid
12/52
u
25
57
370
690
—
N-nitrosodiphenyl amine
29/166
u
5 U
500 U
2000
610
—
Volatile organics
trichloroethene
0/69
u
10 u
20 U
20
U 20
—
tetrachloroethene
13/80
u
10 u
20 U
20
210
—
ethyl benzene
11/80
u
10 u
20 U
20
50
—
total xylenes
7/15
u
20
100
140
160
—
Metals (mg/kg dry weight)
antimony
115/167
0.34
0.94
1.56
420
—
arsenic
211/211
10
17
38
9700
19
beryl 1ium
156/156
0.29
0.39
3.53
5.5
—
cadmium
193/212
0.48
1.78
3.07
180
0.9
chromium
167/167
35
53
66
129
—
copper
212/212
49
83
167
11400
115
iron
108/108
19000 27000 32000
53000
—
lead
212/212
39
92
190
6250
158
manganese
108/108
200
400
520
1000
—
mercury
204/212
0.20
0.46
0.98
52
1.4
nickel
167/167
27
41
50
118
—
selenium
62/156
U 0.40
1.0
1.0
63
—
silver
153/167
0.30
0.74
2.8
5.4
—
thai 1ium
37/156
U 0.1
U 0.1
0.2
3.2
—
zinc
212/212
91
140
292
3320
450
Conventional variables
total volatile solids
160/160
6.15%
9.30%
12.9 %
44.7%
10%
total organic carbon*
212/212
1.31%
2.30%
4.50%
16.0%
10%
fine-grained material
212/212
61.0 %
81.0 %
90.0 %
98.0%
—
75
-------
TABLE 10. (Continued)
Detection frequency is the number of samples in which the chemical was detected
compared with the total number of samples for which data were reported (only samples
with associated biological effects data are included in the total number).
^ The indicated percentage of samples in the data set had concentrations that fell
below the concentrations listed. The percentiles were determined using all data reported
(i.e., detection limits were Included).
c Maximum detected concentration for the number of samples shown in the detection
frequency column.
d In-water disposal at the Fourmile Rock site is not allowed if any pollutant exceeds
listed concentration.
e Summation of p,p'-DDD, p,p'-DDE, and p,p'-DDT.
f Organic carbon content for the Alki Extension Study samples was estimated from total
volatile solids data using a regression equation (Appendix A - Table 5).
76
-------
The same trend applied to EP values normalized to organic carbon (Table 7)
when compared to organic carbon normalized Puget Sound data (e.g., Appendix
Table B1). All but 8 of the 40 EP values normalized to organic carbon
exceeded the concentration of the corresponding chemical (normalized to
organic carbon) in 100 percent of the sediment samples in the database.
Fourmile Rock interim sediment criteria for the disposal of dredged
material have been used as guidelines for sediment management by some agencies.
"Permissible" sediment concentrations of the 10 U.S. EPA priority pollutants
or pollutant groups used as part of these guidelines are also summarized
in Table 10 for comparison with the sediment quality values derived in
this report. With some exceptions, the Fourmile Rock dry-weight criteria
listed in Table 10 are 2-36 times lower than the dry-weight normalized
EP, AET, and SLC sediment quality values listed in Tables 6 and 9. Notable
exceptions include mercury (the oyster larvae, Microtox, and benthic AET,
and the mercury SLC concentration are exceeded by the Fourmile Rock criteria),
PCBs (the EP value and Microtox AET for PCBs are exceeded), high molecular
weight PAH (the SLC concentration and Microtox AET for HPAH are exceeded),
and zinc (only the benthic AET is exceeded).
7.0 UNCERTAINTY ANALYSIS: TEST OF GENERATED SEDIMENT QUALITY VALUES
The goal of testing sediment quality value approaches in this project
is to assess their potential use for various aspects of sediment management.
The uncertainty analysis comprised two components: accuracy (i.e., the
ability of an approach to predict biological effects), and precision (i.e.,
the expected variability of sediment quality values given the particular
constraints in the design and use of an approach).
Two aspects of accuracy were considered in this analysis (and are defined
in detail in Section 7.1). The first aspect, sensitivity, represents the
ability of sediment quality values to correctly identify all stations in a
data set that actually have biological impacts. The second aspect of accuracy,
efficiency, is independent of sensitivity and represents the ability of sediment
quality values to identify only stations that actually have biological
impacts. The results of these accuracy tests are presented in four sections:
1. The overall sensitivity and efficiency of the EP and AET
approaches when the accuracy test included all possible
appropriate chemicals for each approach (Section 7.1.1)
2. A further test of the sensitivity and efficiency of AET
values that were generated with one database and tested
with an independent database (Section 7.1.2)
3. A comparison of the efficiency (but not sensitivity) of
individual EP and AET sediment quality values for selected
chemicals that were common to both approaches (Section 7.1.3;
the sensitivity of a sediment quality value for only one chemical
was not considered a good measure of the predictive success
of an approach, because no single chemical is expected to
account for all biologically impacted stations in the database)
77
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4. The efficiency (but not sensitivity) of the SLC sediment
quality values (Section 7.1.4).
The SLC accuracy test was conducted independently of the EP and AET evaluations
because SLC values were generated for only 3 chemicals (selected from over
60 chemicals that were considered appropriate), and were based on a relatively
small data subset. A preliminary, less thorough uncertainty evaluation
was thus considered appropriate for the SLC approach. Generation of SLC
sediment quality values for a large number of chemicals and for a large
database would have required a level of effort beyond the scope of the
present project.
The accuracy evaluation was considered the best way to evaluate the
overall ability of each approach to predict biological impacts. The accuracy
analysis could not quantify various elements of uncertainty in each approach,
but instead provided an estimate of how the combined uncertainties of an
approach would affect its ultimate predictive success. This was considered
particularly useful because numerous factors that affected the uncertainty
of the AET and EP approaches were not quantifiable, and some may partially
offset the effect of others.
The precision evaluation consists of two sections:
1. Estimated Minimum Confidence Limits for EP Sediment Quality
Values (Section 7.2.1)
2. Estimated Confidence Limits for AET Sediment Quality Values
(Section 7.2.2).
The EP approach, which is theoretically based, requires a number of estimations
and assumptions to derive a sediment quality value (e.g., estimation of
Kor values from Kow values, assumption of thermodynamic equilibrium; see
Section 2.3) . Precision could only be estimated for the EP values for
chemicals with established chronic water quality criteria. The uncertainty
associated with estimated water quality criteria was not possible to quantify.
Quantifiable and unquantifiable elements of uncertainty are found
in all of the sediment quality value approaches (e.g., see discussion of
AET approach in Section 2.7, and discussion of SLC approach in Section
2.6). For AET values, confidence limits reflect a consideration of the
"weight of evidence" supporting the empirical approach. A method for determin-
ing precision of SLC values is discussed by Battelle (1986a).
7.1 Accuracy of Sediment Quality Values (Sensitivity and Efficiency)
Any use of a sediment quality value approach (e.g., to screen areas
for further testing and evaluation, to identify problem sediments in a
study area) requires that the approach be an accurate predictor of detrimental
biological effects. "Accuracy," in this sense, has two aspects:
78
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• Sensitivity—the approach should be capable of identifying
a high percentage of sites that exhibit adverse biological
effects potentially associated with chemical contamination
• Efficiency—the approach should not identify a large number
of stations that do not exhibit any adverse biological effects
potentially related to chemical pollution.
The concepts of sensitivity and efficiency are presented in Figure 13.
Sensitivity and efficiency are distinct and can be mutually exclusive.
For example, a sediment quality value approach that sets values for a wide
range of chemicals near their analytical detection limits will probably
be sensitive but inefficient. That is, it will indicate all sediments
with severe biological effects but will also identify many biologically
unaffected sediments with only slightly elevated chemical concentrations.
Thus, the approach would have limited effectiveness at identifying sites
of potential concern from a large sample pool.
Conversely, a sediment quality value approach that sets values at
the high range of environmental concentrations may be efficient but insen-
sitive. That is, a high percentage of the predicted problem stations identified
by the approach may indeed be biologically impacted, but the approach may
fail to indicate biologically impacted stations with moderate to high chemical
concentrations. Such an approach could be useful for indicating grossly
contaminated sediments but would not be adequate for most sediment management
purposes.
With the matched chemical/bio 1 ogical data compiled for this project,
it was possible to quantitatively evaluate the sensitivity and efficiency
of the different sediment quality approaches.
7.1.1 Sensitivity and Efficiency of the EP and AET Approaches
The primary objective of this test was to determine the sensitivity
and efficiency of each approach for each biological indicator. The
and AET approaches were tested using the same procedure and the same sets
of chemical/biological data. The test was carried out in three steps,
as described below:
1 The chemical database (Appendix A) was subdivided into groups
of stations that were tested for the sane biological effects
indicators.
For example all chemistry stations with associated amphipod bioassay data
were grouped together. Thus, four station groupings were established according
to available station-specific biological data (amphipod bioassay, oyster
larvae bioassay, benthic infaunal, Microtox bioassay). Only Commencement
Bay stations were included in all four groups. The oyster larvae and Microtox
groups consisted solely of Commencement Bay stations. The numbers of stations
included in each group were: amphipod (150), benthic (94), oyster larvae (56),
79
-------
BIOLOGICALLY
MPACTED
O NO
[a> no
r ® YES
YES
PREDICTED TO
BEIMPACTED
NO
YES
NO
YES
FOR A GIVEN DATA SET WfTH PARED CHEMICAL/BIOLOGICAL DATA:
A ALL STATIONS PREDICTED BY APPROACH TO BE IMPACTED (Le„ PREDICTED PROBLEM STATIONS).
B ALL STATIONS KNOWN TO BE IMPACTED BASED ON A GIVEN BIOLOGICAL INDICATOR
C ALL STATIONS SUCCESSFULLY PREDICTED TO BE MPACTED.
SENSITIVITY = C/B x 100 = 5/7 x 100 = 71%
EFFICIENCY = C/A x 100 = 5/9 x 100 = 56%
Figure 13. Sensitivity and efficiency as measures of accuracy.
-------
and Microtox (50) (Table 2). Specific stations that compose each group
can be determined from Appendix A.
2. The stations of each group were classified as "impacted"
or "nonimpacted" based on the appropriate statistical criteria
discussed in a previous section (see "Treatment of Biological
Data for AET Application" in Section 5.3.4). Biologically
impacted stations were evaluated further for classification
as "severely impacted" (i.e., having an especially high
magnitude of biological effects).
Severe amphipod and oyster impacts were designated as values above 50 percent
(absolute) mortality and abnormality, respectively. These severe toxicity
values are somewhat arbitrary, but reflect guidelines presented in Tetra
Tech (1985) and correspond with "severe toxicity" guidelines proposed by
the U.S. COE for the management of dredged material (U.S. COE 1985). The
severe impact guideline used for benthic infaunal data was set arbitrarily
as the occurrence of benthic depressions in more than one major taxonomic
group (i.e., two or more depressions among Mollusca, Crustacea, and Poly-
chaeta). This guideline was chosen based on the consideration that significant
depressions in more than one taxonomic group were indicative of impacts
across a fairly broad range of organisms and were unlikely to be driven
solely by highly sensitive species. A severe impact criterion was not
developed for Microtox data.
The results of classifying impacted and severely impacted stations
are presented in Tables 11 and 12. The stations with multiple indicator
data [i.e., Commencement Bay stations (Table 11)] are presented separately
from stations with only single indicators (Table 12). The respective number
of impacted and severely impacted stations in each group were: amphipod
(43 and 13), benthic (28 and 13), oyster larvae (16 and 4), and Microtox
(29; severe impacts not designated).
3. For each approach, sediment quality values for all applicable
chemicals (40 for the EP approach and 64 for the AET approach)
were compared with the corresponding chemical data for each
station. When one or more chemicals exceeded the appropriate
sediment quality values at a given station, then biological
impacts were considered to be predicted at that station.
Station-by-station listings of all chemicals that exceeded their sediment
Quality values for the EP (organic carbon-normalized), AET (dry weight),
AET (organic carbon-normalized), and AET (fines-normalized) approaches
are presented in Appendix Tables B1 through B4. Note that besides Commencement
Bay stations, only some kinds of biological indicator data were available
at each station.
Results of the accuracy tests (i.e., quantitative measures of sensitivity
and efficiency; see Figure 13) of each approach are summarized in Table 13
for the different sets of biological indicator stations in the database.
This table can be constructed from the information in Appendix Tables B1-B4
and the following three formulas:
81
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TABLE 11. BIOLOGICALLY IMPACTED STATIONS BASED ON
MULTIPLE BIOLOGICAL INDICATORS3
Station
Amphipod
Oyster
Benthic
Micro
1 BL-13
Y
1 BL-25
X
A
1 BL-31
x
1 CI-11
*X
*X
X
x
1 CI-13
X
*X
x
1 C1—16
X
*x
x
1 CI-17
y
1 C1—20
X
X
A
1 HY-12
X
y
1 HY-14
X
A
y
1 HY-17
X
*x
A
x
1 HY-22
X
X
*x
f\
y
1 HY-23
X
X
*x
A
x
1 HY-24
A
y
1 HY-32
*x
A
1 HY-37
x
y
1 HY-42
X
A
y
1 HY-43
A
y
1 HY-47
X
X
A
x
1 HY-50
A
y
1 MI—11
X
A
1 MI—13
y
1 MI-15
X
A
Y
1 RS-13
X
X
A
1 RS-18
*X
*x
*x
x
1 RS-19
*X
X
X
x
1 RS-20
X
x
1 RS-24
X
1 SI-11
X
X
1 S1—12
X
X
X
1 SI-15
X
1 SP-U
X
1 SP-12
X
X
1 SP-14
*X
*x
*x
X
1 SP-15
*X
*x
*x
X
1 SP-16
X
x
x
X
2 B 15
X
a X indicates statistically significant biological impacts,
b Severe impacts not estimated for Microtox data.
* Indicates severe biological impacts.
82
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TABLE 12. BIOLOGICALLY IMPACTED STATIONS BASED
ON SINGLE BIOLOGICAL INDICATORS
Amphi pod
Bioassay
Station
Benthic
Infauna
Station
3
BH-05
5
AP-04
3
BH-23*
6
EB-33*
3
CS-15
6
EB-35
3
CS-17
6
EB-36
3
DB-15
6
WP-16
3
EV-Ol*
7
EB-33*
3
EV-02
7
EB-35
3
EV-03
7
EB-36*
3
EV-04*
7
EB-38*
3
EV-05*
7
WP-03
3
EV-06
3
SC-08
3
SC-14
3
SC-20
3
SM-01
3
SM-03
3
SM-20
4
DR-07
4
DR-08*
9
DR-10*
9
DR-11
9
DR-16
9
DR-25
9
DR-26*
9
DR-27*
* Severely impacted stations.
83
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TABLE 13. EVALUATION OF ACCURACY OF EP AND AET APPRAOCHES
Sensitivity (%)a Sensitivity (%)a
Approach (Impacted Stations) (Severely Impacted Stations) Efficiency (%)a
Equilibrium Partitioning
Amphipod
30
(13/43)b
31
(4/13)
34
(13/38)
Oyster
13
(2/16)
0
(0/4)
33
(2/6)
Benthic
43
(12/28)
46
(6/13)
39
(12/31)
Microtox
14
(4/29)
c
100
(4/4)
AET - DW<1
Amphipod
54
(23/43)
92
(12/13)
100e (23/23)
Oyster
94
(15/16)
100
(4/4)
100
(15/15)
Benthic
82
(23/28)
92
(12/13)
100
(23/23)
Microtox
90
(26/29)
100
(25/25)
AET - 0C<1
Amphi pod
40
(17/43)
62
(8/13)
100
(17/17)
Oyster
63
(10/16)
100
(4/4)
100
(10/10)
Benthic
68
(19/28)
69
(9/13)
100
(19/19)
Microtox
59
(17/29)
100
(17/17)
AET - Fines'*
Amphipod
37
(16/43)
69
(9/13)
100
(16/16)
Oyster
88
(14/16)
100
(4/4)
100
(14/14)
Benthic
57
(16/28)
62
(8/13)
100
(16/16)
Microtox
55
(16/29)
•
——
100
(16/16)
AET (DW, OC or
Fines)f
Amphipod
58
(25/43)
92
(12/13)
100
(25/25)
Oyster
100
(16/16)
100
(4/4)
100
(16/16)
Benthic
82
(23/28)
92
(12/13)
100
(23/23)
Microtox
93
(27/29)
100
(27/27)
a Terms defined in text,
b Fraction from which percent was calculated.
c Severe impacts not determined for Microtox data.
d DW » normalized to dry weight; OC » normalized to organic carbon; fines * normalize<
to percent fine-grained material.
e By definition; the approach is designed such that all sediments with concentration'
exceeding their AET are biologically impacted.
f Stations identified by any of the three normalized AET.
84
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1. Sensitivity
(impacted stations)
Sensitivity
(severely impacted =
stations)
3. Efficiency
Depending on the biological indicator being tested (Table 13), and
using these formulas, the EP approach correctly predicted from 13 to 43
percent of the stations with statistically significant biological effects.
The EP approach also correctly predicted from 0 to 46 percent of the severely
impacted stations. From 33 to 100 percent of all stations predicted by
the EP approach to be impacted had observed biological effects. Although
the EP approach may be sensitive for untested indicators of biological
effects at the apparently unimpacted stations, it did not correctly predict
a majority of the impacted or severely impacted stations identified by
any of the available biological indicators. Only EP values normalized
to organic carbon were tested for accuracy.
The accuracy of the AET approach (dry weight normalization) in correctly
predicting stations with observed biological effects ranged from 54 to 94
percent, depending on the biological indicator being tested (Table 13). The
approach also correctly predicted from 92 to 100 percent of the severely
impacted stations when chemical concentrations were normalized to dry weight.
Alternative methods of normalizing AET values resulted in lower sensitivity
(Table 13). By definition, 100 percent of the stations predicted by the
AET approach to be impacted had observed biological effects. This result
was a direct consequence of generating and testing AET with the same database
(because the AET is set at the level above which biological effects always
occur in the data set used to generate AET). A more realistic assessment
of AET efficiency is given in Section 7.1.2, based on AET generated and
tested with independent databases.
In contrast to this predetermined result for efficiency, there is
nothing in the design of the AET approach that, a priori, ensures high
sensitivity (i.e., identification of a high percentage of impacted stations)
when generated and subsequently tested with the same database (as in Table 13).
Application of the AET approach to develop sediment quality values for
a chemical does not ensure that an AET will even be set for a chemical.
For example, chemicals that do not produce biological effects or covary
with another chemical that produces biological effects may occur in completely
overlapping concentration ranges in nonimpacted and biologically impacted
sediments, or even in higher concentration in the group of nonimpacted
stations. If only such chemicals were used to develop AET, the approach
would likely have poor sensitivity regardless of the database used for
testing. The sensitivity test was only conducted with chemicals with estab-
correctly predicted stations with impacts 1f)n
all stations with impacts x
correctly predicted stations with
severe impacts
all stations with severe impacts
x 100
correctly predicted stations with impacts x inn
all stations predicted to be impacted
85
-------
lished AET (i.e., the concentration of the chemical was higher in sediment
from >1 station withstatistica1ly significant biological effects than
in sediment from any of the nonimpacted stations).
In addition, the number of stations that are predicted to be impacted
using any one AET value was typically only a small percentage of all impacted
stations. This result was interpreted to mean that biological effects
in Puget Sound probably result from a number of chemicals derived from
several different sources. However, the setting of AET values for several
chemicals could result in the same small subset of impacted stations being
predicted to be impacted by each chemical. Even if the same database was
used to generate and test these AET, the result would be poor sensitivity.
As a further check on the sensitivity of the AET approach, AET generated
and tested with independent databases was also performed (see Section 7.1.2).
Several trends in the accuracy of AET are apparent from Table 13:
• The sensitivity of AET for identifying severely impacted
stations is comparable to or greater than their sensitivity
for identifying impacted stations overall. This trend applies
to AET normalized to dry weight, organic carbon, and percent
fine-grained sediments and to all four biological indicators
assessed.
• The ability of the AET approach to indicate stations with
significant amphipod toxicity is less than its ability to
indicate stations with significant oyster larvae abnormalities,
Microtox bioassay effects, or benthic infaunal depressions.
This trend is consistent whether AET are normalized to dry
weight, organic carbon, or percent fine-grained sediment
• Dry-weight AET are consistently more sensitive than those
normalized to organic carbon or percent fine-grained sediment
(this applies to impacted stations and severely impacted
stations). In fact, the latter two AET identified very
few impacted stations that were not already identified by
the dry-weight AET alone [compare "AET-DW" and "AET (DW,
OC, or fines)" in Table 13].
The relative sensitivities of dry-weight and organic carbon normalized AET
were further tested. The overall sensitivity of dry-weight AET (Table 13)
was compared to the sensitivity of AET when nonpolar organic compounds
were normalized to organic carbon content and metals and polar organic
compounds were normalized to dry weight sediment. Based on Cerent
of organic carbon normalization, the sensitivity of AET would be expectea
to improve if nonpolar organic compounds were normalized to organic caroon
content. However, the sensitivity of AET when nonpolar organic compounds
were normalized to organic carbon was lower than when the same compounds
were normalized to sediment dry weight, regardless of the biological indicator.
Sensitivity decreased from 54 to 44 percent for amphipod bioassay stations,
from 94 to 88 percent for oyster larvae bioassay stations, from 82 to 75
percent for benthic infaunal stations, and from 90 to 76 percent for Microtox
86
-------
bioassay stations. This analysis can be carried out with the information
in Appendix Tables B-2 and B-3 and using the formulas presented in this
section.
Although these results do not provide a mechanistic explanation for
the predictive success of dry weight normalization relative to organic
carbon normalization of AET, they suggest that the mass loading of contaminants
in sediments may be the predominant factor influencing toxicity to benthic
organisms (although organic carbon interactions may be a secondary factor).
For additional discussion of this issue, see Section 8.6 and Appendix H.
The EP and AET approaches were tested further by examining the list
of stations that were biologically impacted (according to available indicators)
but were not correctly identified as impacted by one or both of the approaches.
These stations are listed by biological effects indicator in Table 14.
The stations listed for the AET approach were not correctly predicted as
by any of the three sets of AET values (normalized to dry weight, organic
carbon, and percent fine-grained sediment). If on 1y dry-weight AET had
been used in the evaluation, this list would be essentially the same.
The largest percentage of biologically impacted stations that were
not correctly predicted by either approach is in the amphipod bioassay
data group. Fifteen of the 43 impacted amphipod bioassay stations were
not predicted by either approach. Because natural variables (e.g., fine
sediment texture) can influence amphipod mortality even in relatively uncontami-
nated sediments (Ott 1985; Tetra Tech 1985), it is possible that the toxicity
observed at these stations was not related to chemical contamination.
Of the 18 impacted stations not predicted by the AET approach, 28 percent
had over 90 percent fine-grained material, 22 percent had between 85 and
90 percent fine-grained material, and 27 percent had between 75 and 85
percent fine-grained material. Overall, 77 percent of the stations displaying
significant amphipod toxicity had fine-grained sediment content of over
75 percent.
These results support, but do not prove, the contention that fine-
grained material may have been responsible for impacts observed at many
of the amphipod data stations not identified by the AET approach. Alterna
tively, the toxicity may be attributable to some unidentified chemical
with a distribution that does not co-vary with the chemicals studied.
Other factors (e.g., viruses) could contribute to unexplained amphipod
toxicity. However, five of these 18 stations are from areas that are not
highly industrialized or populated (e.g., Dabob Bay, Case Inlet, Samish
Bay) and have relatively low concentrations of U.S. EPA priority pollutants.
These characteristics were not shared by most of the remaining stations
that exhibited significant amphipod toxicity, but were not identified by
the EP approach. For example, three of the stations not predicted by the
EP approach (1C1-11, 1RS-18, 1SP-14) were each severely impacted according
to three biological indicators and exhibited elevated concentrations of
numerous chemicals, including PAH (see Tables 11 and 14, and Appendix A).
87
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TABLE 14. BIOLOGICALLY IMPACTED STATIONS NOT
INDICATED BY SEDIMENT QUALITY VALUE APPROACHES
Amphipod Oyster Benthic Microtox
EP AET EP AET EP AET EP AET
1
CI-11*
1
CI-11* none
1
CI-11*
1
BL-13
1
BL-25
1
BL-25
1
CI-13
1
CI-13*
1
BL-31
1
C1-20
1
CI-16
1
CI-16*
1
CI-11
1
HY-14
1
MI—11
1
MI-11
1
CI-20
1
HY-17*
1
CI-13
1
MI—15
1
MI—15
1
HY-12
1
HY-32*
1
HY-32*
1
CI-16
1
HY-37
1
RS-13
1
HY-17
1
RS-18*
1
CI — 17
1
RS-18*
1
HY-47
1
RS-19
1
HY-12
1
RS-19*
1
HY-50
1
RS-20
1
HY-14
1
RS-13
1
SI-11
1
HY-17
1
RS-24
1
RS-18*
1
SI-12
1
HY-24
1
SI—12
1
SI-12
1
RS-19
1
SP-14*
1
HY-43
1
SI—15
1
SP-12
1
SP-15*
1
HY-47
1
SP-14*
1
SP-14*
1
SP-16
1
HY-50
1
SP-15*
1
SP-15*
5
AP-04
5
AP-04
1
MI—13
1
SP-16
1
SP-16
7
WP-03
1
MI-15
1 MI-15
3
BH-05
3
BH-05
1
RS-18
3
BH-23*
3
BH-23*
1
RS-19
3
CS-15
3
CS-15
1
RS-20
3
CS-17
3
CS-17
1
SI-11
3
DB-15
3
DB-15
1
SI-12
3
EV-01*
1
SP-11
1 SP-11
3
EV-02
3
EV-02
1
SP-12
3
EV-03
3
EV-03
1
SP-14
3
EV-04*
1
SP-15
3
EV-05*
1
SP-16
3
EV-06
3
EV-06
3
SC-08
3
SC-14
3
SC-20
3
SM-01
3
SM-03
3
SM-03
3
SM-20
3
SM-20
9
DR-11
9
DR-16
9
DR-16
* Severely impacted station as defined in text; all stations listed exhibited sta-
tistically significant biological effects.
88
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7.1.2 Sensitivity and Efficiency Analysis of AET Generated and Tested
with Independent Data Sets—
In the preceding section, AET were generated and tested with the same
data. AET were generated with the entire Puget Sound database because
large data sets (with wide-ranging chemical concentrations) are expected
to enhance the reliability of field-based sediment quality values (e.g.,
AET, SLC values). The entire Puget Sound database was also used for evaluation
of AET (and EP) values because the reliability of the accuracy analysis
was also expected to be highest with a large database.
For this section, AET (dry weight) were generated with Commencement
Bay data alone and were tested with chemical data from the 134 remaining
stations in the Puget Sound database, for three reasons: (1) to account
for potential bias resulting from using the same data for establishing
and then testing AET, (2) to examine the accuracy of AET developed from
one area and applied to other areas, and (3) to enable AET to be determined
from chemical data that are matched with biological effects data for any
of the four biological indicators used in this project (the Commencement
Bay data set is unique in this respect).
The evaluation procedure was analogous to that described in the preceeding
section:
t The chemical database (from the 134 non-Commencement Bay
samples) was subdivided into groups of stations tested for
the same biological indicators (either amphipod bioassay
or benthic infaunal analysis; Microtox and oyster larvae
bioassays were not performed for these samples)
• The stations of each group were classified as "impacted"
(and "severely impacted") or "nonimpacted" (see Table 12
for listing of impacted and severely impacted stations)
• Commencement Bay AET for all appropriate chemicals were
compared to the corresponding data for the non-Commencement
Bay stations; stations predicted to have biological impacts
were identified as those stations with one or more chemicals
exceeding AET
• Measurements of accuracy (sensitivity and efficiency as
percent) were calculated for each subgroup of stations as
in Section 7.1.1, i.e.:
Sensitivity _ correctly predicted stations with impacts 1fin
(impacted stations) ~ all stations with impacts x
Sensitivity correctly predicted stations with
(severely impacted = severe impacts
stations) all stations with severe impacts x 00
89
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Efficiency
= correctly predicted stations with impacts
all stations predicted to be impacted
Station-by-station listings of chemicals exceeding Commencement Bay
AET are included in Appendix Table B6, along with the Commencement Bay
AET themselves (Appendix Table B6-A). The results of this analysis and of the
corresponding analysis performed for Table 13 are tabulated in Table 15.
These results suggest that the original accuracy analysis was not
biased. In fact, the sensitivity of AET generated and tested with these
different databases slightly exceeded the sensitivity observed for AET
generated and tested with the one Puget Sound database. However, the efficiency
of Commencement Bay AET for both biological indicators (31 and 37 percent)
is a more reliable estimate of true performance than the 100 percent efficiency
reported in Table 13. Efficiency might improve if two larger independent
databases are used in such accuracy analyses, because more reliable AET
would be expected from a larger database.
7.1.3 Efficiency of EP and AET Sediment Quality Values for Selected Chemicals—
To apply the EP approach to the maximum number of nonpolar organic
compounds in the initial test of accuracy (Section 7.1.1), it was necessary
to estimate water quality criteria for the compounds that do not have estab-
lished criteria. In the present section, the accuracy of individual EP
sediment quality values was tested only for chemicals with established
U.S. EPA chronic water quality criteria. These sediment quality values
were tested individually because they were considered the most reliable
EP values available. For comparison, AET accuracy was also tested for
the same chemicals.
PCB and p,p'-DDT were selected for this evaluation for two reasons:
(1) they are among the few nonpolar organic compounds with established
chronic water quality criteria, and (2) they accounted for virtually all
of the predicted problem stations indicated by the EP approach. Of the
63 Puget Sound stations at which EP sediment quality values were exceeded,
PCBs were exceeded at 58 stations, p,p'-DDT at 10 stations, fluoranthene
at 5 stations, diethyl phthalate at 3 stations, lindane, heptachlor, and
phenanthrene each at 2 stations, and dieldrin at one station (Appendix
Table Bl). The EP values for the other nonpolar organic chemicals were
never exceeded at any stations in the Puget Sound database.
The only appropriate measure of accuracy for individual chemicals is
efficiency. Sensitivity is considered an inappropriate measure of accuracy
for individual sediment quality values because a single chemical is not
expected to account for biological impacts in all sediment samples. The
measure of efficiency was analogous to that defined previously (Sections
7.1.1 and 7.1.2), but was only based on a single chemical (i.e., either
PCBs or p.p'-DOT).
The accuracy analysis was carried out as in previous tests, except
AET generated with Conrnencement Bay data were tested with the 134 non-Commence-
90
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TABLE 15. ACCURACY OF AET AS RELATED TO DATA SETS USED
FOR GENERATION AND EVALUATION
AET Tested
Sensitivity
("Impacted")
Sensitivity
("Severely Impacted")
Efficiency
Amphipod (DW)a
Benthic (DW)a
54 (23/43)
82 (23/28)
92 (12/13)
92 (12/13)
100b (23/23)
100b (23/23)
Amphipod (DW)C
Benthic (DW)C
72 (18/25)
90 ( 9/10)
100 ( 8/8)
100 ( 4/4)
37 (18/49)
31 ( 9/29)
a From Table 12, AET generated and evaluated with the same data set.
b As noted in Table 12, efficiency is 100 percent by definition.
** AET generated with data from one data set (Commencement Bay, 56 samples)
and evaluated with data from several independent studies (Puget Sound data
in the complied database, excluding Commencement Bay; 134 samples).
91
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ment Bay stations in the database. If the entire Puget Sound database
were used to generate and evaluate individual AET, the efficiency would
be 100 percent by definition for all chemicals.
The results of this accuracy evaluation are presented in Table 16.
The number of gaps in the table preclude detailed interpretation. It is
noteworthy that the EP efficiency percentages for PCBs are similar to those
listed for the overall EP approach in Table 13 (Section 7.1.1). This is
not surprising, as PCBs accounted for over 90 percent of the predicted
impacted stations for the EP approach. For either chemical, efficiencies
of the EP and AET sediment quality values are roughly comparable when evaluated
with the amphipod bioassay station group. For the benthic infaunal station
group, efficiency of the AET for PCBs was greater than the EP value for
PCBs (100 percent vs. 43 percent, respectively).
It should again be noted that sensitivity and efficiency can be mutually
exclusive; this evaluation for individual chemicals does not yield information
on the overall sensitivity of either approach.
7.1.4 Efficiency of SIC Sediment Quality Values: Preliminary Evaluation—
The evaluation of the SLC approach could not be conducted as thoroughly
sr that of the EP and AET approaches because SLC values were not developed
tjr all appropriate chemicals and did not incorporate data from a large
number of stations. It was not considered appropriate to test the overall
sensitivity and efficiency of the approach because sediment quality values
for only three contaminants were developed. These three contaminants (naph-
thalene, HPAH, and mercury) could not be expected to account for biological
impacts potentially associated with other contaminants. Hence, the same
measure of efficiency was used based only on a single chemical (i.e., either
HPAH, naphthalene, or mercury) as in the previous section:
Efficiency = correctly predicted stations with impacts x 100
(percent based all stations predicted to be impacted
on chemical x)
The efficiencies of SLC values for HPAH, naphthalene, and mercury
(based on stations grouped by biological indicator) are presented in Table 17).
For the range of compound/normalization combinations, SLC values were most
accurate relative to the Microtox bioassay (i.e., 50-70 percent agreement)
and least accurate relative to the oyster abnormality bioassay (i.e., 15-
30 percent agreement). Efficiencies with respect to benthic depressions
(i.e., 36-48 percent agreement) and the amphipod mortality bioassay (i.e.,
32-52 percent agreement) were intermediate in magnitude. For both HPAH
and naphthalene, efficiencies of all four biological indicators were higher
for dry-weight normalizations than for organic-carbon normalizations.
7.2 Precision of Sediment Quality Values
The effective management of contaminated sediments requires knowledge
of the confidence limits for the estimates of sediment quality values.
These confidence limits reflect a degree of uncertainty in the derivation
92
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TABLE 16. EFFICIENCY OF EP AND AET VALUES FOR PCBs AND p,p'-DDT
Efficiency®
PCB p.p'-DDT
Equilibrium Partitioning
Amphipod
Oyster
Benthic
Microtox
29
50
43
100
(10/34)
(2/4)
(12/28)
(4/4)
100 (2/2)
0 (0/2)
25 (2/8)
(Not exceeded)
AET (dry weight)
Amphipod
Oyster
Benthic
Microtox
38
100
(9/24)
NAb
(3/3)
NAb
100 (1/1)
NAD
c
NAb
AET (organic carbon)
Amphipod
Oyster
Benthic
Microtox
26
(7/27)
NAb
—C
NAb
100 (2/2)
NAb
—c
NAb
a Efficiency =
Stations Correctly Predicted (based on Chemical X) to be Impacted inn
All Stations Predicted to be Impacted (based on Chemical X)
b Not applicable: oyster larvae and Microtox bioassays were only performed
on Commencement Bay samples.
c Definite AET could not be established because there were no impacted
stations with chemical concentrations above the highest concentration among
nonimpacted stations.
93
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TABLE 17. PERCENT EFFICIENCY OF SLC SEDIMENT QUALITY VALUES
Biological Indicators3'1*
Sediment Bioassay
Compound
Normalization
Benthic
Amphipod
Oyster
Microtox
HPAH
OC
36 (19/53)
35 (12/34)
18 (6/34)
61 (14/23)
HPAH
DW
42 (16/38)
38 (12/32)
25 (8/32)
65 (15/23)
Naphthalene
OC
38 (6/16)
45 (9/20)
15 (3/20)
50 (6/12)
Naphthalene
DW
48 (10/21)
52 (17/33)
30 (10/33)
70 (14/20)
Mercury
DW
48 (21/44)
32 (23/72)
17 (12/72)
64 (16/25)
* Indicators included depressed abundances of at least one major benthic
group (i.e., total benthos, Polychaeta, Mollusca, Crustacea), amphipod
mortality, oyster abnormality, and change in bacterial luminescence (i.e.,
Hicrotox bioassay).
b Efficiency (percent) -
Stations Correctly Predicted (Based on Chemical X) with Impacts ,nn
All Stations Predicted to be Impacted (Based on Chemical X) * '
94
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of the values. All of the approaches used to derive sediment quality values
have sources of error that affect the reproducibility of the values. Some
components of variability can be reasonably estimated (e.g., analytical
error of chemical and biological results, or the standard error in a regression
curve). Other components of variability are poorly defined (e.g., the
true representativeness of empirical data used in the AET and SLC approaches,
or the uncertainty in interstitial water concentrations predicted by the
EP approach). Because of the unquantifiable nature of these latter sources
of error, confidence limits presented in this section are only rough estimates
of the true precision of sediment quality values. A statistical confidence
that incorporates all aspects of uncertainty cannot be assigned.
7.2.1 Estimated Minimum Confidence Limits for EP Values—
As discussed in the Section 5.2, sediment quality values for the equi-
librium partitioning approach are generated with the equation:
EP value = Koc x water quality criterion
For many chemicals, Koc values and water quality criteria are unavailable
and must be estimated. Both of the variables have various degrees of quanti-
fiable uncertainty and unquantifiable uncertainty. In this section, estimates
are made of the minimum uncertainty associated with EP values for nonpolar,
nonionic chemicals for which chronic (24-h) criteria have been established
by the U.S. EPA (i.e., PCBs, p,p'-DDT, chlordane, dieldrin, and heptachlor).
EP values based on established chronic water quality criteria are considered
to be the most reliable sediment quality values for the approach.
7.2.1.1 Knr Values—Three major factors contribute to the uncertainty
of interstitial water contaminant concentrations predicted from Koc values:
t The uncertainty in measured and estimated Kow and Koc values
• The possibility that laboratory determinations of Koc values
do not accurately simulate conditions in nature [e.g., because
sediment:water (volume:volume) ratios and dissolved organic
matter concentrations can be markedly different under laboratory
and natural conditions; discussed in Section 2.3.4]
• Possible violation of the assumption that sediment-interstitial
water partitioning is at equilibrium under conditions in
nature (discussed in Section 2.3.4).
In this report, only the first factor will be discussed because quantita-
tive estimates of the uncertainty associated with the latter two factors
are difficult to make with available data. Because Koc values are not
widely available in published literature for many nonpolar chemicals, more
widely available KoW values are used to estimate Koc according to the linear
relationship:
log Kqc = a +b
95
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where a and b are empirically derived constants. Two major sources of
uncertainty characterize this relationship: uncertainty in measured or
estimated Kow and uncertainty in the Kow"^oc regression relationship.
Variation in reported Kow values in the scientific literature are common
and result in part from the variety of techniques by which they are measured
or estimated (e.g., generator columns, shake flasks, approximations based
on reverse-phase high-pressure liquid chromatography, estimations based
on fragment constants). In some cases, reported Kow values for a compound
can vary by over one order of magnitude (e.g., Kenga and Goring 1980; Rapaport
and Eisenreich 1984).
For nonpolar compounds, Koc values predicted by a Kow~^oc regression
equation are generally within 0.5 log units of measured Kqc values (Karickhoff
1981). Accordingly, the regression equation used to estimate Koc for PCBs,
p,p'-DDT, chlordane, dieldrin, and heptachlor in this study was found to
have a standard error of approximately 0.5 log units (JRB Associates 1984b).
However, for some substances, deviations from measured Koc values may be
greater than 2 log units (Lyman et al. 1982). The variation associated
with each measured Koc or Kow value is not included in the regression analysis.
Consequently, regression statistics will provide a minimum estimate of
the amount of variation (expressed as a standard deviation or 95 percent
confidence interval) in predicted Koc values.
7.2.1.2 Water Quality Criteria—Ma.ior uncertainties in establishing
LiiCs precision of chronic water quality criteria are:
• Adequacy of the existing aquatic toxicity database for estab-
lishing a criterion according to U.S. EPA (1980) guidelines
and for determining a statistical estimate of uncertainty
• Comparability of criterion values for the various contaminants,
which were determined using a stratified evaluation of two
kinds of toxicological data (i.e., systemic toxicity and
bioaccumulation potential)
• Validity of the assumption that the sediment-free bioassay
or bioaccumulation data for primarily nektonic organisms
are applicable to benthic biota (e.g., deposit-feeding infauna)
under field conditions (discussed in Section 2.2.4)
• Validity of the assumption that interstitial water is the
sole source of contaminants to benthic biota, or alternatively,
that benthic systems follow sediment-biota equilibrium partition-
ing and are unaffected by the route of contaminant exposure
(e.g., dermal absorption vs. ingestion of sediments) (discussed
in Section 2.2.4)
Only the first factor above will be quantified in this report because
the other three factors are not quantifiable with available data. However,
uncertainties associated with assumptions of the approach will influence
the degree of accuracy of the approach when tested with field data.
96
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According to U.S. EPA (1980) guidelines, chronic water quality criteria
for each priority pollutant are determined using a stratified evaluation
of systemic toxicity and bioaccumulation in tissues. Factors considered
in evaluation of systemic toxicity include chronic effects in animals and
plant toxicity. Where there are insufficient chronic data in biotic groups
(e.g., major phyletic groups that have not been tested), chronic toxicity
for the group may be estimated by dividing the acute toxicity criterion
(i.e., the maximum allowable concentration) by a geometric mean acute tox-
icity/chronic toxicity ratio. Acute/chronic ratios may range from 2 to
100 (Welch 1980). Contaminant concentrations in water that may result
in bioaccumulation considered harmful to humans through the ingestion of
seafood or to sensitive wildlife are determined by:
CWqc = A/(L x BCFn)
where:
C = concentration in water
wqc
A = U.S. Food and Drug Administration (FOA) action level (e.g., 5
mg/kg PCBs) or a maximum permissible tissue concentration in
a sensitive species (e.g., 0.15 mg/kg DDT in brown pelicans)
L = average percent lipids in seafood (i.e., 16 percent expressed
as a decimal) or in the prey of a sensitive species
BCFn = lipid-normalized geometric mean bioconcentration factor for aquatic
organi sms.
The final chronic water quality criterion is the minimum of the various
chronic animal toxicity, plant toxicity, and bioaccumulation values. Geometric
mean BCFn values in combination with FDA action levels or maximum permissible
tissue concentrations were used to establish chronic water quality criteria
for the five nonpolar, nonionic substances discussed above. Thus, data
available in the U.S. EPA (1980) water quality criteria may be used to
calculate the standard deviation of the mean log BCFn as a measure of the
minimum uncertainty for each substance:
• PCBs (mean log BCFn=4.02, SD=0.34, N=5)
• DDT (mean log BCFn=4.25, SD=0.59, N=32)
• Chlordane (mean log BCFn=3.67, SD=0.03, N=2)
• Dieldrin (mean log BCFn=3.19, SD=0.18, N=2)
• Heptachlor (mean log BCFn=3.72, SD=not determined, N=l).
A major limitation of this approach is that uncertainty in determination
of U.S. FDA action levels, maximum permissible tissue concentrations, and
average percent lipid content in seafood or the prey of sensitive wildlife
is not taken into consideration. U.S. FDA action levels are determined
97
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by a combination of human-health risk assessment, economic impact analysis,
and regulatory policy- and decision-making procedures. Consequently, un-
certainty estimates for U.S. FDA action levels are not feasible since they
are determined in part by unquantified professional judgement.
As indicated above, where data were insufficient to determine chronic
water quality criteria, estimates of water quality criteria were determined
from the minimum chronic toxicity value available (cf. Table 4). In general,
the data are too limited to quantify uncertainty for chronic water quality
criteria estimated from minimum chronic toxicity values. With the exception
of particularly sensitive commercially important species, maximum allowable
water quality criteria are defined as the EC50 concentration for the 5th
percentile of species ranked in decreasing order of sensitivity. Thus,
where acute toxicity data are sufficient to determine a maximum allowable
water quality criterion, it is possible to evaluate uncertainty using linear
estimation techniques (e.g., regression of probit-transformed cumulative
percent frequency of species tested on log transformed EC50 values). This
method would provide a minimum estimate of variance associated with the
acute EC50 criterion concentration. This method also requires extensive
data evaluation and is beyond the scope of the present project.
A major limitation in determination of sediment quality values is
the uncertain comparability of the two kinds of toxicological data (i.e.,
systemic toxicity and bioaccumulation potential) used to derive chronic
water quality criteria. In general, sediment quality values determined
from acute toxicity data approximate the sediment concentration needed
to be protective of aquatic biota. Sediment quality values determined
from chronic toxicity data approximate the sediment concentration needed
to be protective of human health or sensitive wildlife. Given the guidelines
(U.S. EPA 1980) used to establish water quality criteria, sediment quality
values determined from BCF may be more conservative than those estimated
from acute toxicity values bfapproximately one order of magnitude.
7.2.1.3 Estimated Uncertainty in EP Sediment Quality Values—As a
first step in approximating the miniumum amount of uncertainty, EP values
were estimated for the five chemicals for which there are chronic water
quality criteria using the relationship:
log EP value = log Koc + log Cxw/cr [Eq. 1]
where:
EP value - sediment quality concentration normalized to organic carbon
content for chemical x (Cxs/cr)
Kxoc - sediment organic carbon-water partition coefficient for
chemical x
Cxw/cr " water quality criteria concentration for chemical x.
98
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An estimate of the variance of each predicted log Koc was determined from
the standard error (0.48 log units) reported for the appropriate Kow-Koc
regression equation (JRB Associates 1984b). Estimates of the variance
of each log Cxw/cr value was determined from the standard deviation of
the mean of the log BCFn values reported by the U.S. EPA (1980). Each
variable (i.e., EP value, Koc, Cxw/cr) was assumed to be log-normally
distributed (Gumbel 1958). The total variance in the EP value was estimated as:
Vs - Vk + Vw [Eq. 2]
where:
Vs = variance of log EP value
o
Vk = variance of log KQC = (std. dev. of predicted log Koc value)
V = variance of the log BCF„ value described above,
w n
The arithmetic mean and standard deviation given the uncertainties in the
derivation of each EP value were then determined by propagation of errors
using the equations:
[exp (aU ) x exp (aS )2]
mean EP value = ~5—
and
standard deviation of EP = exp (aUs) x [exp 2(aS$)2 - exp (aS$)2]0'5
where:
a « In 10 - 2.303
U$ = mean log EP value from Eq. 1
Ss - (Vs)0-5.
Values used to estimate the uncertainty of EP sediment quality values
for each of the five substances are shown in Table 18. The combined uncer-
tainty for these substances may be expressed as the coefficient of variation
(i.e., the standard deviation divided by the mean, expressed as a percent).
Coefficients of variation ranged from 1.5 for heptachlor to 4.5 for p,p'-
ODT. These are extreme minimum estimates of uncertainty based on considera-
tion of only two factors that may affect determination of uncertainty in
the EP sediment quality values. For the substances listed in Table 18,
there are a number of factors that could contribute to uncertainty estimates
(see Section 2.3.4). Although uncertainty for many of these factors remains
unquanti fied, it is likely that the true error associated with predicted
EP values is in the range of one to six orders of magnitude of the generated
value.
99
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TABLE 18. MINIMUM ESTIMATE OF PRECISION OF EP SEDIMENT QUALITY VALUES
Chemical
SE„ a
oc
SD k
WQC
CVc
95 Percent d
Confidence Interval
Lower Upper
PCB
0.48
0.34
2.3
0
66,000
p.p'-DDT
0.48
0.59
4.5
0
2,300
chlordane
0.48
0.03
1.6
0
2,700
dieldrin
0.48
0.18
1.7
0
490
heptachlor
0.48
1.5
0
800
a Standard error of regression equation log K =0.843 log K +0.158 (n=19;r=0.96)
(JRB Associates 1984b). c ow
k Standard deviation of U.S. EPA chronic water quality criterion.
c Coefficient of variation of EP sediment quality value [i.e., (SD/mean)*100].
^ 95 percent confidence intervals were established as the EP sediment quality value
(Table 4) +1.96(CV*EP sediment quality value); assuming a normal distribution of
mean predicted EP sediment quality values for a given chemical. Units are ug/kg
organic carbon.
100
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7.2.2 Estimated Confidence Intervals for AET Values—
For a given data set, major sources of uncertainty in determining
AET include:
• The uncertainty defined by the concentration range between
the AET (determined by a nonimpacted station) and the next
highest concentration that is associated with a statistically
significant effect
• A classification error associated with the statistical signifi-
cance of biological indicator results (i.e., whether a station
is properly classified as impacted or nonimpacted)
• The weight of evidence or number of observations supporting
a given AET value
• The analytical error associated with quantification of chemical
results (this error is also implicit in the derivation of
EP values, but was not quantified).
7•2•2•1 Uncertainty Above the AET Concentration—The most obvious
uncertainty in the AET value is the concentration range between the AET
and the next highest concentration in the data set (e.g., Figure 14).
By definition, this next highest concentration is always associated with
a statistically significant effect. It is assumed that the AET could be
anywhere within the range because there are no data on the potential effects
associated with sediment concentrations within this range. For the available
data set, adjusting AET within this concentration range (up to but not
including the next highest concentration) will have no effect on the apparent
accuracy" of the AET approach. The upper limit of AET should include
this range of concentrations.
7.2.2.2 Classification Errors—Impacted stations (for each biological
indicator) were defined as those exhibiting biological effects that were
statistically significant relative to reference conditions. The potential
error in misclassifying impacted and nonimpacted stations is defined by
two statistical errors: 1) the probability that a station determined to
be impacted was actually nonimpacted (a Type I error) and 2) the probabil-
ity that a station determined to be nonimpacted was actually impacted (a
Type II error). If a Type I error was made for a station at which sediment
concentrations of a contaminant exceeded the AET, the correct AET will
be higher. If a Type II error was made for the station that set the AET,
the correct AET will be lower.
The Type I error (set experimentwise) for each study and each biological
indicator is P<0.05. The probability (P) of falsely classifying any one
of the impacted stations with contaminant concentrations above AET ranged
from 0.1 to 0.8 percent, depending on the exact number of stations tested
in each of the studies. The probability of falsely classifying more than
one of the impacted stations is even lower (i.e., equal to Pn, where n
101
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LEAD
r.
AET UNCERTAINTY
NO BENTHIC DEPRESSIONS' '
»• t— • ¦¦¦ii —— t • i
BENTHIC DEPRESSIONS AND/OR SEDIMENT TOXICITY OBSERVED
I
11 ppm
300 ppm
6300 ppm
CONCENTRATION II ' ' 1 ' ' ' ' '| 1 4
^ 1 1 1 1 1 I J " T ¦ 1 1 1
i ¦ ' I
(mgfcg DW) | 10 100
1000
10000
APPARENT-
MAXIMUM -
BENTHIC
OBSERVED
EFFECT
LEVEL AT A
THRESHOLD
BIOLOGICAL
STATION
Figure 14. I xan.pl o confidence limits for benthic AET determined for lead. [Sources
of una rUiinity include potential misclassification of 3 impacted stations
and the concentration range between the lead benthic AET and the next high-
est concentration at an impacted station (i.e., station CI-13)].
-------
is the total number of impacted stations misclassified). Because of this
low probability, and the uncertainty already incorporated in the previous
discussion (see "Uncertainty Above the AET Concentration", Section 7.2.2.1),
any potential error associated with misclassifying impacted stations is
assumed to be negligible in setting the upper limit of AET. This assumption
is environmentally protective.
The lower limit of AET is affected by the probability of making Type II
errors in the classification of stations. This probability varies for
each sample and its calculation is beyond the scope of the present effort.
For a given number of samples, the probability of making a Type II error
increases as the probability of making a Type I error decreases. For the
purpose of analysis, a potential Type II error of 40 percent was assumed
(i.e., a statistical power of 60 percent).
The AET will only be affected by a Type II error if the nonimpacted
station setting the AET has been misclassified (i.e., the AET would then
be equal to the next lower concentration found at a nonimpacted station).
Two types of events would cause the AET to be substantially lowered because
of mi scl as si f i cati on errors: 1) several stations spanning a large range
of concentrations were misclassified or 2) only one or two stations were
misclassified, but a large concentration difference existed between the
station setting the original AET and the station setting the revised AET
(Figure 14). The latter event is of concern because of the sensitivity
of the AET to an apparently nonimpacted station with anomalously high concentra-
tions of a contaminant. To ensure protective sediment quality values,
the lower confidence limit of AET should incorporate this uncertainty.
Assuming a 40 percent probability of mi scl assi fyi ng each of the nonimpacted
stations, the total probability of misclassifying three stations at and
just below the AET is approximately 5 percent (i.e., P=0.4 , where n is
the number of stations misclassified). Hence, for a given data set there
is a 95 percent probability that the AET is greater than the concentration
round at the nonimpacted station with the fourth highest concentration
of each contaminant. If the true probability of making a Type II error
is only 20 percent, then misclassification of two stations at and just
below the AET would yield a similar 95 percent confidence.
Uncertainty analyses for AET were conducted for concentrations normalized
to dry weight of sediment, which was consistently the most accurate set
of AET in indicating problem sediments (see Section 7.1.1). The lower
limits of AET were initially calculated assuming mi scl assi fi cation of the
three nonimpacted stations with the highest concentrations of each contaminant.
For all AET values, these lower limits averaged approximately one-half
the original AET value, and ranged from 1.2 to 26 times lower than the
original AET.
A test of the "accuracy" of these lower limits in correctly predicting
impacted sediments was conducted (i.e., see Lower Limit II in Table 19)
and compared with the evaluation summarized in Table 12 for the original
AET (dry weight). All of the stations with significant oyster larvae and
Microtox bioassay results were correctly identified using the lower limits
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TABLE 19. EVALUATION OF LOWER AND UPPER LIMITS OF AET
WITH BIOLOGICAL DATA
Sensitivity
Sensitivity
Approach
(Impacted Stations)
(Severely Impacted Stations)
Efficiency
AET - DW
Upper Limita
Amphi pod
54
(23/43)b
92
(12/13)
100c
(23/23)
Oyster
94
(15/16)
100
(4/4)
100
(15/15)
Benthic
82
(23/28)
92
(12/13)
100
(23/23)
Microtox
90
(26/29)
—d
100
(25/25)
AET - DW
Lower Limit Ie
Amphi pod
70
(30/43)
100
(13/13)
46
(30/65)
Oyster
100
(16/16)
100
(4/4)
33
(16/48)
Benthic
96
(27/28)
100
(13/13)
43
(28/65)
Microtox
93
(27/29)
———
59
(27/46)
AET - DW
Lower Limit II*
Amphi pod
79
(34/43)
100
(13/13)
40
(34/84)
Oyster
100
(16/16)
100
(4/4)
33
(16/49)
Benthic
96
(27/28)
100
(13/13)
38
(27/71)
Microtox
100
(29/29)
40
(29/48)
a DW - dry weight normalized; accuracy data for the upper limit of the AET are identical
to those for the AET - DW in Table 13 because no data points lie between the AET and the
upper limit in the defined data set (see text).
b Fraction from which percent was calculated.
c By definition; the approach is designed such that all sediments with concentrations
exceeding their AET are biologically impacted. This constraint does not apply to the
lower 1imit of AET.
d Severe impacts not determined for Hicrotox data.
e Lower limit assuming a 20 percent misclassification error for two nonimpacted stations
at and just below the AET. Results in a 95 percent confidence that the AET is greater
than the lower limit set by the nonimpacted station with the next highest concentration.
Note that for the different indicators, from 33 to 91 percent of all apparently nonimpacted
stations in the data set were indicated by the Lower Limit I of AET.
f Lower limit assuming a 40 percent misclassification error for three nonimpacted stations
at and just below the AET. Results in a 95 percent confidence that the AET is greater
than the lower limit set by the nonimpacted station with the next highest concentration.
Note that for the different indicators, from 47 to 91 percent of all apparently nonimpacted
stations in the data set were indicated by the Lower Limit II of AET.
104
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for AET, and all but one station with significant benthic effects were
correctly identified. Although a higher percentage of stations with signifi-
cant amphipod bioassay responses was identified using lower limits of AET
than with the original AET, 9 of 43 impacted stations were missed (6 of
the 9 stations had a percent fine-grained material content exceeding 75
percent). More importantly, from 47 to 91 percent of the apparently nonimpacted
stations were predicted to exhibit biological impacts using these lower
limits of AET for different biological indicators (Table 19). Thus, the
efficiency (i.e., discriminating power) of the AET approach is substan-
tially reduced by extending the lower limit of the AET to account for a
potential 40 percent misclassification error of nonimpacted stations at
a 95 percent confidence level.
This analysis was repeated assuming a potential 20 percent miscl assi fica-
tion error of nonimpacted stations (i.e., mi scl assi fication of 2 stations
to yield a 95 percent confidence). A test of the "accuracy" of these lower
limits is also presented in Table 19 (Lower Limit I). Two stations exhibiting
significant Microtox bioassay responses and one station exhibiting significant
benthic effects were not indicated using these limits. Thirty percent
of the stations with significant amphipod bioassay responses were not pre-
dicted. Sediments at most of the latter stations had a high percentage
of fine grained material. All severely impacted stations (as defined for
each biological indicator in the "Accuracy" section) were correctly predicted
using these lower limits.
A majority of the apparently nonimpacted stations were also predicted
to be impacted stations using these lower limits of AET (except using the
ower limits for the amphipod bioassay AET). These results suggest that
apparently nonimpacted and impacted stations are well distinguished only
at concentrations close to the originally derived AET. Hence, the small
improvement in sensitivity for identifying impacted stations using lower
limits of AET must be balanced against a large decrease in efficiency of
identifying only impacted stations.
These trends suggest that the lower limit of AET should be set with
the assumption that only one station was mi scl assi f i ed (i.e., the station
originally setting the AET). Such a lower limit will account for the possi-
. }y of anomalous chemical results setting the original AET, and will
misidentify a minimum number of apparently nonimpacted stations. The assumption
of a single misclassification implies a Type II error of only 5 percent
to yield a 95 percent confidence in the lower limit (rather than the 40
or 20 percent assumed in the analyses presented in Table 19). Because
of the low Type I error established for the biological indicator tests
(i.e., <1 percent for individual statistical comparisons), such a low Type II
error may be unrealistic. [Note that if a lower confidence of 80 percent
is acceptable, a single misclassification then implies a Type II error
of 20 percent].
The lower limit of AET are best suited as guidelines for use of biological
screening tests for contaminated sediments. These lower limits should be set
protectively by assuming misclassification of at least two nonimpacted stations
unless an administrative decision is made to assume a single misclassification.
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Lower limits (defined as Lower Limit I) and upper limits of dry-weight
normalized AET are presented in Table 20.
7.2.2.3 Weight of Evidence—The number of observations supporting
an AET will affect its uncertainty; AET based on a large database are more
reliable than those based on a small database. Although the effect of
weight of evidence on AET uncertainty is difficult to quantify, it is indirectly
incorporated in the approach for estimating confidence limits for AET (discussed
in the two preceding sections). The number of stations used to establish
an AET would be expected to have a marked effect on confidence limits since
small data sets would tend to have less continuous distributions of chemical
concentrations than large data sets. That is, small data sets would tend
to have larger concentration gaps between stations (and correspondingly
wider confidence limits) than larger data sets.
7.2.2.4 Chemical Analysis Error—The analytical precision of chemical
analyses in the concentration range of most AET in Table 6 (i.e., several
hundred parts per billion to several parts per million) is expected to
average approximately ±30 percent for organic compounds and ±5 percent
for metals and metalloids (Horwitz et al. 1980; U.S. EPA 1984; Tetra Tech
1985). This uncertainty is typically less than, and independent of, the
variability represented by the uncertainty discussed in the previous sections.
Hence, the lower and upper limits of AET set by the other sources of error
are expected to exceed the analytical error in most chemical results.
Chemical data that are compared with AET (and other sediment quality
values) derived in this report should take analytical error into account.
In particular, analytical detection limits are recommended to be sufficiently
low to ensure confident quantification of contaminants present at concentra-
tions (dry weight) at least half the corresponding AET (see Appendix F).
Detection limits specified by the PSEP protocol for analysis of organic
compounds in sediments by gas chromatography/mass spectroscopy range from
1-50 ug/kg (dry weight) (Tetra Tech 1986b). Recommended detection limits
for gas chromatography/electron capture analyses are 0.1-5 ug/kg for pesticides
and 5-20 ug/kg for PCBs. Comparision of these values with AET in Table 6
reveals that detection limits of 1/2 AET values (e.g., for the lowest AET,
usually Microtox) are reasonable. The most serious problems would probably
be presented by p.p'-ODT, 2-methylphenol, 2,4-dimethyl phenol, N-nitrosodiphenyl
amine, and benzyl alcohol. Detection limits of 1/2 lowest AET for chlorinated
benzenes (1,2-dichloro- and 1,2,4-trichlorobenzene) could also be difficult
to attain, but are technically feasible with existing protocols.
The AET for DDT isomers indicate that mass spectral analysis wi>1
not be sufficiently sensitive for the screening of these compounds; detection
by electron capture (or equivalent) is recommended with dual column verification
and confirmation by mass spectral analyses whenever possible to minimize
problems with "false positives".
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TABLE 20. LOWER AND UPPER LIMITS OF AET SEDIMENT QUALITY VALUES (DRY WEIGHT)3
(ug/kg dry weight for orgam'cs; mg/kg dry weight for metals)
Chemical
Amphipod
AET
Oyster
AET
Benthic
AET
Microtox
AET
Low molecular weight PAH
naphthalene
acenaphthylene
acenaphthene
fluorene
phenanthrene
anthracene
High molecular weight PAH
fluoranthene
pyrene
benzo(a)anthracene
chrysene
benzof1uoranthenes
benzo(a)pyrene
i ndeno(1,2,3-c,d)pyrene
dibenzo(a,h)anthracene
benzo(g,h,i jperylene
Total PCBs
Total chlorinated benzenes
1.3-dichlorobenzene
1.4-dichlorobenzene
1,2-dichlorobenzene
1,2,4-trichlorobenzene
hexachlorobenzene (HCB)
Total phthalates
dimethyl phthalate
diethyl phthalate
di-n-butyl phthalate
butyl benzyl phthalate
bis(2-ethylhexyl)phthalate
di-n-octyl phthalate
Pesticides
p,p*-DDE
p,p'-DDD
p,p'-0DT
aldrin
chlordane
dieldrin
heptachlor
gamma-HCH (lindane)
4200- 6000
1600- 4300
280- 760
500- 2400
480- 970
1500- 2200
560- 1300
13000-
2300-
3300-
1300-
2300-
3200-
1400-
580-
U 200-
670-
19000
4000
5500
1800
4600
4100
3900
760
310
770
1800- 3800
310- 1200
61- >170
120- 280
94- >350
34- 250
96- 220
3400->5200
110- 340
47- > 73
1500->5100
125- >470
1300->3100
170—> 590
11- 40
29- 70
1.4- 5.7
3900- 6000
1200- 4300
160- >560
230- 2400
280- 3000
1100- 1700
490- 1300
12000-
1900-
2600-
860-
2300-
2800-
1600-
570-
160-
610-
17500
3500
4200
2200
4600
3650
1900
760
250
730
250- 1400
240- 660
40- >170
64- 170
27- 72
34- 250
96- 720
1600- 3500
110- 340
47- > 73
1000- 1400
83- >470
820- 2900
47-> 420
1.3- >6
4500-11000
1200- 4300
330- 3900
290- 2400
490- 2600
1200- 4200
560- 1500
17000-
2400-
2800-
1700-
2300-
3900-
2200-
1000-
260-
1800-
>51000
8000
>7300
9400
10300
8400
9700
>5200
1400
7900
480- 1400
240- 660
56- >170
71- 170
27- 72
27- 250
70- 720
6200->70000
110- 340
73- 310
2000->5100
290- 800
840- 2900
3600->68000
5- 9.9
1- 11
5.8- 14
3700- 6000
1200- 4300
190- >560
190- 2400
280- 3000
1100- 1700
470- 1300
8800-
1500-
1600-
790-
1100-
2800-
980-
450-
160-
380-
13000
1800
3200
1500
1500
3500
1900
620
250
710
120- 135
150- 190
40- >170
63- 115
25- 36
U 5- 33
U 10- 95
970- 3400
U 50- 100
13- > 48
840- 1450
U 25- 82
430- 2900
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TABLE 20. (Continued)
Amphipod
AET
Oyster
AET
Benthic
AET
Microtox
AET
Phenols
phenol
2-methylphenol
4-methylphenol
2,4-dimethyl phenol
pentachlorophenol
Miscellaneous extractables
hexachloroethane
hexachlorobutadi ene
1-methylphenanthrene
2-methylnaphthalene
biphenyl
dibenzothiophene
dibenzofuran
benzyl alcohol
benzoic acid
N-ni trosodi phenyl ami ne
Volatile organics
trichloroethene
tetrachloroethene
ethyl benzene
total xylenes
Metals
antimony
arsenic
beryl 1ium
cadmium
chromium
copper
i ron
lead
manganese
mercury
nickel
selenium
silver
thai 1ium
zinc
Conventional variables
total organic carbon
total volatile solids
percent fine-grained
420- 1000
38- 70
600- 2500
>50
HJ100
180- 720
260- 360
460- 800
140- 265
180- 245
250- 1900
57- 120
430- >690
24- 600
140- >210
33- > 50
100- >160
390- 490
27- 70
420- 880
U 10- 49
>U100
140- 280
270- 520
460- 800
110- 265
170- 245
270- 1900
42- 120
390- 680
20- 210
78- 160
18- 49
53- 150
420- 1600
43- >72
560- 880
U 10- 49
>U100
140- 280
270- 520
460- 800
150- 300
180- 310
270- 1900
57- 120
390- 680
61- 120
78- 160
18- 49
53- 150
260- 1600
43- >72
420- 880
U 10- 49
>U100
U 25- 125
220- 520
440- 800
110- 300
110- 310
270- 1900
35- 60
330- 680
U 5- 130
U 10- >140
U 10- 36
U 20- 105
2.0- 25
3.1- 35
3.1- 3.25
3.1- 35
86- 690
90- 1400
49- 85.5
39- 1400
5.3- >5.5
0.32->0.45
>0.49
0.28- 0.36
5.7- 9.5
4.4- 15
3.4- 6.6
3.4- 15
82- >130
29- >37
52- 59.5
24- 28
310- 2100
290- 2100
170- 2100
160- 2100
24000-36000
20000-52000
34000-52000
18000-52000
610- 720
500- 720
210- 420
210- 650
200- 470
200- 550
630->1000
150- 550
1.7- 2.2
0.49- 1.0
0.52- 0.97
- 0.46
110- >120
23- 39.5
40- 49.5
21- 29
U 1-U 1.3
—
U 1- > 63
—
1.6-> 3.7
>0.50->0.56
3.7- 5.3
0.44->0.56
0.2- 0.45
0.2- 0.45
0.14- 0.46
0.11- 0.45
490- 900
340- 3200
210- 265
200- 3200
11- 15.5
18- 34
92->98
6-15.5
11-44
>86
6-15.5
12-44
>96
4-15.5
9-44
81-88.5
a Lower limit of AET is as defined for Lower Limit I in Table 19; Upper limit of
AET is just less than the next highest concentration above the AET (found at an
impacted station).
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8.0 RECOMMENDED USES OF SEDIMENT QUALITY VALUES
Sediment quality values derived in this project are appropriate tools
for certain aspects of sediment management in Puget Sound, but will be
only one component of an overall management approach. The following uses
may be appropriate for sediment quality values:
• Application as trigger levels for screening decisions on
the need for further chemical and/or biological testing
and evaluation of sediments (i.e., dredged material testing
or preliminary site investigations)
• Determination of the extent and relative priority of potential
problem areas to be managed (e.g., for Superfund remedial
investigations, Toxic Action Plans, or NPDES enforcements)
• Identification of potential problem chemicals in problem sediments
• Identification of "acceptable sediments" for dredged materials
that may be placed in unconfined, open-water sites (i.e.,
to preclude the degradation of existing or planned sites)
t Prioritization of laboratory studies for determining cause-
effect relationships.
These potential uses are discussed in Sections 8.1 through 8.5. Reconmendations
related to chemical normalization and use of conventional sediment variables
are ?Pven in Sections 8.6 and 8.7. A brief summary of all reconmendations for
use of the sediment quality values generated in this project is given in
Section 8.8. Recommendations for future studies are surrmarized in Section 8.9.
8J: Screening Technique to Identify Need for Further Testing
Comparisons of the accuracies of the EP and AET approaches (Table 13)
indicate that dry-weight AET are the most successful predictors of sediments
with biological impacts, based on available data. However, the method
of establishing AET allows for the possiblity that impacted stations can
be overlooked. For screening level applications, it is preferable to err
on the side of underestimating threshold levels to ensure that all potential
stations of concern are identified. The lower limits of AET discussed
previously (Section 7.2.2.2), were recommended as possible screening guidelines
for the need of conducting biological tests on contaminated sediments.
The lowest AET for a chemical (i.e., the most sensitive according to a
range of biological indicators) is also recommended for use in screening
for potentially impacted sediments.
To investigate the performance of this proposed approach, the lowest
dry-weight AET (regardless of biological indicator) for each chemical or
chemical group were applied to the entire Puget Sound data set. The purpose
was to simulate a potential application by sediment quality managers (i.e.,
use of the most environmentally protective AET for screening). This approach
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cannot be used to assess accuracy because stations with different biological
effects data have all been grouped together.
Station-by-station listings of chemicals or chemical groups that exceeded
the lowest corresponding dry-weight AET are presented in Appendix Table B-5.
In total, 124 of the 190 stations in the Puget Sound data set had chemical
or chemical group concentrations that exceeded the lowest corresponding
AET. This compares to a total of 50 stations predicted by the AET approach
to be biologically impacted when stations were partitioned according to
available biological effects data. Sixty-three stations were predicted
by the EP approach to be impacted.
Besides correctly predicting all but 10 of the 72 biologically impacted
stations, the lowest AET predicted impacts at 55 stations for which biological
effects were not observed. For example, many Bellingham Bay stations were
predicted by the approach to be of concern because of mercury. This is
noteworthy because potential mercury sources existed historically in this
area (i.e., chloralkali plants). Some stations in Bellingham Bay, Dabob
Bay, Sequim Bay, and West Point were also predicted to be of concern for
elements that have significant natural sources (e.g., crustal elements
such as nickel). These results suggest that elements not previously considered
as being of concern in Puget Sound (e.g., chromium) warrant further evaluation.
It is recommended that additional matched biological effects and chemical
data be collected for sediments having high, but apparently naturally-derived
concentrations of crustal elements to better define AET for these elements.
The SLC approach, developed specifically for screening applications,
is recommended for further study. The approach, as modified in the limited
application, generated sediment quality values consistently below AET and
could prove to be environmentally protective as a screening tool.
8.2 Determination and Ranking of Problem Areas
AET were particularly effective at predicting severely impacted sediments,
and are designed to identify impacted sediments in general. Determination
of the extent and ranking of problem areas is an important use of sediment
quality values such as the AET because of their ability to guide efficient
allocation of resources. AET are recommended as tools for defining problem
sediments. Because AET appear less able to predict amphipod bioassay impacts
than impacts for other available biological tests, it is recommended that
amphipod bioassays be used in conjunction with dry-weight AET to determine
and rank problem areas if resources do not permit a broad suite of biological
tests to be conducted.
SLC were intended more for protecting healthy benthic infaunal comnunities
than for effectively distinguishing existing problem areas. As such, SLC
may be more sensitive than required in defining problem areas for high
priority remedial action. However, SLC are recommended as possible goals
for sediment cleanup actions after problem areas have been defined, as
are the lower confidence limits of the lowest AET (i.e., most sensitive)
determined for a range of biological indicators.
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8.3 Identification of Potential Problem Chemicals
Ideally, an approach used to identify potential problem chemicals should
be capable of encompassing a wide range of chemicals. The approach should be
accurate (i.e., efficient for individual chemicals). The restricted applic-
ability of the EP approach to nonpolar, nonionic organic compounds limits
its effectiveness at identifying potential problem chemicals.
The AET approach, which can be applied for any measured chemical,
attempts to ensure with high probability that sediments with chemicals
exceeding their AET will have associated biological effects. The approach
is recommended for identification of problem chemicals because it more
accurately identified problem sediments with available biological indicators
than other approaches (see Table 13 in Section 7.1.1).
The SLC approach is also recommended as a possible tool for identifying
potential problem chemicals. Like the AET approach, it is recommended
for application to any measured chemical. It also had a high level of
efficiency according to one biological indicator for the chemicals for
which it was tested (i.e., sediments with HPAH, naphthalene, or mercury
concentrations above SLC often had associated Microtox bioassay impacts).
Identification of Appropriate Sediments for Open-Water Disposal
The EP approach may be sensitive to potential effects i.n sediments with
low concentrations of selected contaminants, but had poor accuracy in correctly
identifying severely impacted sediments. This may result in part because
development of sediment quality values for metals and polar organic compounds
was not considered appropriate using the EP approach. Hence, EP values
for these substances were not available, although exposure to metals and
polar organic compounds could potentially result in severe biological impacts.
Ideally, an approach used to identify "acceptable sediments" would perfectly
predict all impacted sediments, and would easily identify marginally impacted
sediments as well (i.e., the approach should be protective without exception).
Hence, EP values are recommended for use in identifying appropriate sediments
for open-water disposal only when used in conjunction with sediment quality
values developed by an alternative approach (e.g., AET or SLC) for metals,
polar organic compounds, and ionic nonpolar organic compounds, or with
a set of confirming biological tests.
The "potential" effect threshold (discussed in Section 2.7.2 of the
AET approach) represents the concentration below which there are no statisti-
cally significant biological effects in any sample. This threshold is
far more protective than the AET. It is likely too protective in identifying
"acceptable" sediments because Puget Sound reference conditions exceed
the threshold set for most chemicals, and is not recommended.
AET values, unless used with a safety factor or confirming biological
tests, are not well suited for the identification of acceptable sediments
for disposal at unconfined open-water sites. The AET approach is designed
to predict sediment concentrations above which biological effects are always
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expected. Hence, AET are recommended for identifying "acceptable" sediments
for disposal if a safety factor is applied that would adjust the sediment
quality value to less than the lower confidence limits of the lowest AET
determined for each chemical (i.e., the most sensitive AET for a range
of biological indicators). A large number of sediments may be misidentified
as potentially impacted according to this guideline, but sediments that
pass the guideline would likely be without hazard to the environment. This
safety factor could be determined arbitrarily or with respect to defined
reference conditions. Otherwise, a set of biological tests should be used
in conjunction with the original AET values to identify "acceptable" sediments.
SLC values are better suited to identifying sediment concentrations
that are protective of sensitive taxa, which presumably would not be adversely
affected by exposure to "acceptable sediments." SLC are recommended for
this purpose once SLC values have been developed for a wider range of chemi-
cals. By setting a lower concentration cutoff in the SLC approach (e.g.,
the concentration that is protective of 99 percent of the taxa rather than
95 percent), more protective sediment quality values could also be developed
for use in identifying "acceptable sediments."
Alternatively, "acceptable sediments" can be defined as those that
already exist in the environment (away from urban centers). This definition
suggests use of reference area concentrations to define "acceptable sediments",
or continuance of a "non-degradation" policy at disposal sites by not permitting
sediments with higher than average concentrations found at the site (i.e.,
the goal of the Fourmile Rock Interim Criteria). For long-term management,
chemical-specific sediment quality values based on biological effects data
are recommended over acceptance of these more arbitrary guidelines.
8.5 Prioritization of Laboratory Cause-Effect Studies
Definitive cause-effect relationships can only be determined under
controlled laboratory conditions, where chemicals can be added to sediments
at known concentrations and biological responses of test organisms can
be compared to those of control organisms. Such studies would be very
useful for establishing sediment quality values. However, considerable
time and effort will be required to account for responses of a wide variety
of marine organisms to all potential problem chemicals and combinations
of chemicals over wide concentration ranges.
It is recommended that results of applications of sediment quality
values be used to guide laboratory cause-effect studies. Chemicals that
are frequently identified as problem chemicals should receive high priority
in laboratory studies. Assuming that the sediment quality values used are
accurate, this prioritization will focus efforts on chemicals that are poten-
tially of greatest concern and will provide information about concentration
ranges for their threshold effect levels. Results of laboratory studies will,
in turn, provide the most reliable means of testing the accuracy of sediment
quality values.
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8.6 Appropriate Normalizations of Sediment Quality Values
AET developed from chemical data normalized to sediment dry weight
were consistently more accurate in correctly identifying biologically impacted
stations than those developed from data normalized to organic carbon content
or percent fine-grained sediment. This trend was observed for nonpolar
organic compounds as well as other contaminants (Section 7.1.1). Dry-weight
normalization also was the most predictive of biological effects in statistical
pattern recognition analyses (Appendix D). Hence, dry-weight normalization
is recommended as an appropriate normalization technique for sediment quality
values developed from field data (e.g., AET and SLC).
Reasons that dry-weight normalization may have outperformed organic
carbon normalization are summarized in this section and discussed in more
detail in Appendix H (Response to Comments). It is unlikely that the database
used for testing of AET influenced the success of organic carbon vs. dry-weight
AET. The amphipod bioassay and benthic infaunal stations were compiled
from numerous studies and study areas. The oyster larvae and Microtox
bioassay samples were taken from the Commencement Bay study only. However,
evidence of the better predictive success of dry-weight AET relative to
organic carbon AET does not consist solely of oyster larvae and Microtox
bioassay AET (although they are consistent with the trends observed for
amphipod bioassay and benthic AET).
Dry-weight normalization assumes that mass loading of a contaminant
in sediment is a predominant factor influencing toxicity to exposed organisms
(although organic carbon interactions may be a secondary factor). Organic
carbon normalization theory assumes that interstitial water is the primary
source of nonpolar organic contaminants to biota (e.g., Battelle 1986),
and that, under equilibrium conditions, the distribution of nonpolar contami-
nants between sedimentary organic matter and water (Koc) is constant (and
predictable). Organic carbon tends to act as a sink for nonpolar contami-
nants (i.e., organic carbon content and sediment toxicity should be inversely
related). Hence, as sediment organic carbon content increases, toxicity
"threshold" values expressed per gram of bulk sediment should decrease.
If contaminant concentrations are normalized to organic carbon content,
then threshold values should be constant for that contaminant in all sediments.
The empirical AET and SLC approaches do not favor one of these mechanistic
explanations over the other, but can operate whether one, a combination
of the two, or alternative assumptions are appropriate. Further studies
are recommended to elucidate the underlying mechanism. For contaminated
sediments in the environment, organic carbon normalization could be less
predictive than dry-weight normalization if one or more of the following
three alternative assumptions are true:
• Sediment/interstitial water systems are not at equilibrium
• All sediment organic matter does not have uniform affinity
for hydrophobic pollutants
• Interstitial water is not the predominant route of contaminant uptake.
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If sediment-water equilibrium is not attained in the environment, organic
carbon content may not correlate well with the bioavailable, toxic portions
of nonpolar organic compound loadings in sediment. If different kinds
of organic matter differ in their affinity for a given chemical, then normaliza-
tion to bulk organic carbon content may introduce errors. Finally, release
or binding of contaminants through direct alteration of sediment matrices
by benthic organisms (e.g., Shaw and Wiggs 1980) could reduce the importance
of sediment-water equilibrium (and hence, organic carbon partitioning)
as a predominant mechanism controlling contaminant uptake.
8.7 Use of Conventional Variables in Sediment Management
As summarized in Appendix 6, conventional variables were evaluated
as tools for sediment management in two ways: (1) conventional variables
were used to normalize chemical concentrations when generating AET and
SLC values, and (2) conventional variables were themselves used as indicators
of sediment quality (e.g., a total organic carbon AET was developed, potentially
as an indicator of organic enrichment). For both the AET and SLC approaches,
use of chemicals normalized to dry weight was consistently more accurate
(by measures defined in Section 7.1) than use of chemicals normalized to
organic carbon content or percent fine-grained sediment (Tables 13 and 17
in Sections 7.1.1 and 7.1.4, respectively).
Sediment quality values were established using the AET approach for
three conventional variables: total organic carbon, total volatile solids,
and percent fine-grained material. Because the AET for organic carbon
and total volatile solids (Table 6) are at levels that will seldom be exceeded
in Puget Sound, these variables may be of limited use for screening samples.
For the three stations exceeding these values (1M1-13, 1SP-14, and 3EV-04;
Appendix Table B-2), other problem chemicals were also indicated. Thus,
the conventional variables did not identify predicted biologically impacted
stations that were not already identified by chemical variables. AET for
fine-grained particulate material were of limited use because some nonimpacted
stations had very high fine-grained sediment content (up to 98 percent).
All conventional variables for which some data were available (e.g.,
oil and grease, and sulfides) were not evaluated. A general relationship
was observed in Commencement Bay sediments between high levels of organic
carbon and sulfides. Oil and grease concentrations in Commencement Bay
sediments did not correlate well with the distribution of other chemical
concentrations. However, high oil and grease concentrations were observed
in a waterway that received considerable storm drain runoff (City Waterway;
Tetra Tech 1985). This variable may serve as an independent indicator
of contamination for specific urban sources. Data for other variables
(e.g., chemical oxygen demand) were unavailable.
It is recommended that sediment conventional variables not be used
to identify "acceptable sediments" because substantial chemical contamination
can be found in sediments that appear "acceptable" according to conventional
measurements. These variables can be used to indicate gross contamination
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of sediments, and as a cost effective screening tool in a preliminary problem
identi fication.
Overall, conventional variables appeared to be of limited use in the
application of sediment quality values. This does not indicate that variables
such as organic carbon content are not useful for interpreting geochemical
distributions of chemicals (particularly nonpolar organic compounds), but
may indicate that conventional variables are not the predominant factors
controlling bioavailability and sediment toxicity.
8.8 Summary of Recommendations for Uses of Sediment Quality Values
The recommendations summarized in this section are specific to the
potential application of the sediment quality values generated in this
project to management of sediments in Puget Sound:
1. For long-term management of contaminated sediments, chemical-specific
sediment quality values based on biological effects data are recommended
over existing guidelines related to reference area chemical concentrations.
2. Program managers should review a variety of sediment quality
values for several approaches when making decisions on appropriate values
or their modification for specific applications (e.g., see summary of data
in Tables 6-10 for three approaches and existing Puget Sound guidelines)
3. After development of SLC for a wider range of chemicals than
determined in this project, SLC are recommended especially for use in screening
decisions on the need for further chemical or biological testing of sediments
in regulatory programs (Section 8.1).
4. The lower confidence limits established for the lowest AET (i.e.,
most sensitive) for a chemical based on a range of biological tests are
also recommended for use in making screening decisions concerning the need
for biological testing of contaminated sediments (Section 8.1).
5. AET normalized to sediment dry weight are recommended as the
most accurate sediment quality values for correctly identifying impacted
sediments according to available biological indicators (Table 13, and Sections
7.1.1 and 8.2).
6. AET and SLC are both recommended as possible tools for the identifi-
cation of problem chemicals in biologically impacted sediments. Both of
the approaches can be applied to any measured chemical and their apparent
high efficiency of identifying problem sediments based on individual chemical
values (Section 8.3).
7. Although not generally recommended over other sediment quality
values because of low accuracy in identifying severely impacted sediments,
selected EP values for chemicals with established chronic water quality
criteria (e.g., PCBs and p,p'-DDT) appear to be protective guidelines that
may have use in identifying "acceptable" sediments for open-water disposal.
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These values must be used in conjunction with additional sediment quality
values from other approaches (e.g., AET or SLC) for metals, polar organic
compounds, and ionic nonpolar organic compounds, or with a set of confirming
biological tests (Sections 7.1.3 and 8.4).
8. AET are recommended for use in identifying "acceptable" sediments
for open-water disposal only when a safety factor is applied that would
adjust the sediment quality value to less than the lower confidence limits
of the most sensitive AET for a range of biological indicators. Otherwise,
a set of confirming biological tests should be used in conjunction with
the original AET values to determine "acceptable" sediments (Sections 7.2.2.2
and 8.4)
9. SLC are recommended as especially appropriate sediment quality
values for use in identifying "acceptable" sediments, but SLC values for
a wider range of chemicals than determined in this project will be required
(Section 8.4).
10. SLC are recommended as possible remedial action cleanup goals
for contaminated sediments. The lower confidence intervals of the most
sensitive AET for a range of biological indicators are also recommended
for this purpose (Section 8.4).
11. Sediment quality values based on field data (e.g., AET SLC)
should be used to help determine appropriate chemicals for focusing laboratory
cause-effect studies (Section 8.5).
12. Dry-weight normalization is recommended as an appropriate method
for generating sediment quality values from field data. Normalization
to organic carbon content or percent fine-grained material is also appropriate
but results in generally lower predictive success in correctly identifying
biologically impacted sediments.
13. Chemical detection limits should be low enough to ensure confident
quantification of contaminant concentrations (dry weight) at less than
one half the corresponding sediment quality value for the chemical, and
preferably one tenth the value (Section 7.2.2.4 and Appendix F).
14. Sediment quality values for conventional variables (e.g., sulfides,
organic enrichment) were ineffective in predicting biologically impacted
sediments. These values should not be used to identify "acceptable" sediments
for open-water disposal without confirming evidence, because high concentrations
of toxic contaminants can be present in samples that have apparently "normal"
values for bulk sediment variables.
116
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8.9 Recommendations for Future Studies
The following recommendations are included as specific suggestions
for refining or verifying the sediment quality values generated in this
project:
1. Further study of the SLC approach is recommended using a larger
biological database and a wider range of chemicals than tested in this
project (Sections 6.2.2 and 8.1). The approach should be applied in both
its original and modified forms to determine the sensitivity of the underlying
assumptions (Section 2.6).
2. AET values should be developed for species-level taxa that are
apparently sensitive to toxic contamination in addition to the existing
AET for major taxonomic groups (see Section 6.2.2).
3. Additional matched bioeffects and chemical data should be collected
for sediments that contain high, but apparently naturally-derived, concentra-
tions of crustal elements (e.g., nickel, chromium) to better define AET
for these metals (Section 8.1).
4. The AET and SLC sediment quality values derived in this report
should be used to test an independent data set containing a range of chemical
and biological indicator data (e.g., results from the Elliott Bay Toxics
Action Plan). Such a comparison will test the predictive power of these
empirical sediment quality values in identifying problem sediments. In
this study, multiple biological indicator data were only available for
Commencement Bay samples.
5. Sediment quality values should be developed for other biological
indicators (e.g., fish bioaccumulation or histopathology AET) than available
for this project because of the potential for chronic effects to occur
at lower concentrations than might produce a response in acute bioassays,
or in organisms with different sensitivities than benthic infauna.
6. Mechanistic studies should be undertaken to determine why dry-
weight normalization appears to yield sediment quality values that more
accurately predict biological impacts than sediment quality values normalized
to organic carbon (see potential hypotheses in Section 8.6).
117
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Coastal Water Research Project Biennial Report 1981-1982. W. Bascom (ed).
SCCWRP, Long Beach, CA.
Thornton, J. 26 July 1985. Technical memorandum. Washington Department of
Ecology, Olympia, WA.
Trial, W., and J. Michaud. 1985. Alki wastewater treatment plant outfall
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U.S. Army Corps of Engineers. 1985. Decision-making framework for management
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U.S. Department of the Navy. 1985. Final environmental impact statement.
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127
-------
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-------
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129
-------
APPENDIX A
SEDIMENT DATA COMPILED
IN SEDIMENT QUALITY VALUES DATABASE
-------
APPENDIX A. SEDIMENT DATA
CONTENTS
Number Page
Table A-l Commencement Bay - Main Sediment Survey A-3
Table A-2 Commencement Bay - Blair Waterway Dredging Study A-77
Table A-3 Eight Bay A-103
Table A-4 Duwamish River I A-l19
Table A-5 Alki Extension A-122
Table A-6 TPPS Phases 3A and 3B A-130
Table A-7 Everett Harbor A-138
Table A-8 Duwamish River II A-140
Figure A-l Locations of Commencement Bay stations sampled for
benthic macroinvertebrates and sediment bioassays
during March and July A-100
Figure A-2 Locations of reference stations sampled in Carr Inlet A-102
Figure A-3 Sequim Bay sampling stations A-l10
Figure A-4 Sinclair Inlet sampling stations A-l11
Figure A-5 Case Inlet sampling stations A-l12
Figure A-6 Dabob Bay sampling stations A-l13
Figure A-7 Elliott Bay - Fourmile Rock sampling stations A-114
Figure A-8 Samish Bay sampling stations A-l15
Figure A-9 Everett Harbor - Port Gardner sampling stations A-l16
Figure A-10 Bellingham Bay sampling stations (Inner Harbor) A-l17
Figure A-l1 Bellingham Bay sampling stations (Outer Harbor) A-l18
Figure A-12 Sediment sampling station locations for dredged
material characterization A-l21
A-11
-------
Figure A-13 Plot of total organic carbon with total volatile
solids A-124
Figure A-14 Regression of total organic carbon on total volatile
solids for total volatile solids <10 percent A-125
Figure A-15 Sediment collection stations offshore of Point
Williams, sampled May 26, 1984 A-126
Figure A-16 Sediment collection stations offshore of Alki Point,
sampled May 25-26, 1984 A-127
Figure A-17 Point Williams benthos reference sampling station
locations A-128
Figure A-18 Alki Point benthos sampling station locations A-129
Figure A-19 Map showing the 26 stations in the central basin of
Puget Sound and Elliott Bay sampled during Phase III
of the TPPS program A-137
Figure A-20 Sediment sampling locations in the East Waterway A-139
A-111
-------
Each study used for this report has been assigned a group number (i.e.,
1-9). This number is in the first column of Tables A-3 through A-8. The
study in Table A-l is group 1 and the study in Table A-2 is group 2. Station
numbers have been assigned for this study in addition to the original
investigators' station numbers. These station numbers are in column 3 of
Tables A-l and A-2 and in column 2 of Tables A-3 through A-8. The station
number prefix corresponds to the geographical location of the station as fol lows:
AP A1 ki Point
B Blair waterway, Commencement Bay
BH Bellingham Bay
BL Blair waterway, Commencement Bay
CI City waterway, Commencement Bay
CR Carr Inlet
CS Case Inlet
DB Dabob Bay
DR Duwamish River (includes East and West waterways)
EB Elliott Bay
EV Everett Harbor
HY Hylebos waterway, Commencement Bay
MD Middle waterway, Commencement Bay
MI Milwaukee waterway, Commencement Bay
PW Point Wi11iams
RS Ruston - Point Defiance Shoreline
SC Sinclair Inlet
SI Sitcum waterway, Commencement Bay
SP St. Paul waterway, Commencement Bay
SM Samish Inlet
SQ Sequim Bay
WP West Point
All organic compounds are expressed as ug/kg (ppb) dry weight and
metals are expressed as mg/kg (ppm) dry weight.
Toxicity, benthic, and microtox codes are indicated for all stations.
The toxicity code is defined as:
0s No data available
1 a No significant* oyster larvae abnormality or amphipod mortality
2 * Significant® oyster larvae abnormality
3 = Significant* amphipod mortality
4 = Both significant® oyster larvae abnormality and amphipod
mortality.
A-l
-------
The benthlc code is defined as:
0 * No data available
1 3 No significant® depressions in benthic Infaunal abundances
2 * Significant® depressions in benthic Infaunal abundances of one
major taxonomlc group
3 » Significant* depressions in benthic Infaunal abundances of
more than one major taxonomlc group.
The microtox code 1s defined as:
0 * No data available
1 ¦ No significant® decrease 1n bacterial luminescence
2 ¦ Significant* decrease 1n bacterial luminescence,
* Significance implies statistically significant difference (P>0.05) from
reference conditions.
-------
Table A-l. COMENCEMENT BAY - MAIN SEDIMENT QUALITY SURVEY3
STATION#
TOX BENTHIC MICRO
CODE CODE CODE
1 BL-11
i
1 BL-13
l
1
->
1 8L-21
I
1 BL-25
3
1
1
1 BL-2B
1
i_
1
1 BL-31
1
1
2
1 CI-11
4
2
2
1 CI-13
2
3
2
: CI-16
2
3
2
1 CI-17
1
1
2
1 CI-20
4
1
1
1 CI-22
1
1
1
1 CR-11
1
1
1
1 CR-12
1
1
1
1 CR-13
1
1
1
1 CR-14
1
1
1
1 HY-12
2
1
2
1 HY-14
1
2
2
1 HY-17
2
3
2
1 HY-22
4
3
2
1 HY-23
4
3
2
1 HY-24
1
1
2
1 HY-28
1
1
1
1 HY-32
1
3
1
1 HY-37
1
2
2
1 HY-42
3
1
2
1 HY-43
1
1
2
1 HY-44
1
1
1
: HY-47
2
2
2
1 HY-50
1
1
2
i ro-12
1
1
1
1 MI-ll
3
1
1
1 MI-13
1
1
2
1 MI-15
3
1
2
1 RS-12
1
1
1
1 RS-13
4
1
1
1 RS-14
1
1
1
1 RS-18
4
3
2
1 RS-19
4
2
2
1 RS-20
1
2
2
1 RS-22
1
Q
1
1 RS-24
3
a
1
1 SI-11
1
2
2
1 SI-12
3
2
2
1 SI-15
3
1
1
1 SP-11
1
1
2
1 SP-1Z
2
1
2
1 SP-14
4
3
2
1 SP-15
4
3
2
: sp-14,
U
2
7
The SO stations listed on this page have biological effects data arei are used
for this report. Additional stations and associated chemical data are include
on subsequent pages of this Table A-l. Where replicate data have been provided,
the mean value 1m used for calculations.
A-3
-------
MAIN SEOIHENT QUALITY SURVEY ORGANIC CHEMICALS - Values In ppb dry weight
PHENOLS
2,4-di-
methyl-
Drainsye
Survey
Station
Sample
Hep
phenol
pher
7110019-BL-000-
KSQS
81-11
SOSC -
BIO
U10
7110019-BL-000-
hsqs
BL-12
S01C -
B25
U10
7110019-BL-000-
HSQS
Bl-13
SOSC -
BIO
U10
7110019-BI-000-
HSQS
BL-14
S01C -
82S
U10
7110019-81-000-
HSQS
81-lb
S01C -
IS
U10
7110019-BL-000-
HSQS
8L-16
S01C -
220
17
7U0019-BL-000-
HSQS
Bt-17
SQ1C -
01
84
U10
7110019-81-000-
hsqs
8L-17
S01C -
02
200
U1U
7110019-81-000-
HSQS
8L-18
S01C -
220
U10
7110019-BI-000-
HSQS
BL-19
S01C -
130
U10
7110019-BL-000-
HSQS
81-20
S01C -
230
U10
7110019-Bl-UUU-
MSQS
81-21
S05C -
240
U10
7110019-BL-000-
HSQS
BL-22
S01C -
140
U10
7110019-81-000-
HSQS
81-23
S01C -
5S0
U10
7110019-BL-000-
HSQS
BL-24
S01C -
82
U10
7110019-BL-000-
HSQS
81-25
SObC -
63
U10
7110019-8L-000-
HSQS
BL-26
S01C -
420
U10
7110019-8L-000-
HSQS
8L-27
S01C -
110
oio
7110019-81-000-
HSQS
BL-2B
SOSC -
62
U10
7110019-BL-000-
HSQS
BL-29
SU1C -
190
U10
7110019-81-000-
HSQS
BL-30
S01C -
240
U10
7110019-W.-000-
HSQS
BL-31
SU5C -
420
U10
7110019-BI-000-
HSQS
BL-32
S01C -
160
010
7H0019-HY-000-
HSQS
CB-11
S01C -
200
23
7110019-C8-000-
HSQS
CB-12
MIC -
y2
U10
7110019-CB-000-
HSQS
CB-13
S01C -
70
U10
7110019-CB-000-
HSQS
CB-14
S01C -
92
19
7110019-CI-000-
HSQS
Cl-U
S02C -
Z1100
U50
7110019-CI-000-
HSQS
CI-12
S01C -
Z270
U10
7110019-CI-000-
HSQS
Cl-13
S05C -
Z70
U10
7110019-C1-000-
HSQS
Cl-14
SOIC -
Z130
U10
7110019-CI-000-
HSQS
Ci-lS
S01C -
Z240
77
7110019-CM-UOU-
HSQS
C1-16
S05C -
U10
50
7110019-C1-000-
HSQS
CI-17
SOSC -
01
ziyo
U10
711UO19-CI-0OO-
HSQS
CI-17
SOSC -
02
Z360
010
7110019-C1-000-
HSQS
CI —18
SOIC -
Z160
38
7110019-CI-000-
HSQS
CI-19
SOIC -
Z200
010
7110019-C1-000-
HSQS
C1-20
SOSC -
Z1200
29
7110019-C1-000-
HSQS
CI-21
SOIC -
Z20
U20
7110019-C1-000-
HSQS
C1-22
SOSC -
Z30
U10
711U019-CK-000-
MSQS
CR-U
soic -
U10
010
7U0019-CR-000-
MSQS
CR-12
SOSC -
U10
U10
7110019-CH-000-
HSQS
CR-13
SOIC -
44
U10
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry welqht
PHENOLS
2,4-di-
Drainaye
roethyl-
Survey
Station Sample
Rep
phenol
phenol
7110019-CK-0Q0-
MSQS
CR-14
S05C
62
UIO
7110019-HY-000-
msqs
HY-11
SOIC
.
290
UIO
7110019-HY-000-
MSQS
HY-12
SOIC
.
500
UIO
7110U19-HY-0UU-
MSQS
HY-13
SOIC
-
130
UIO
7110019-HY-OOU-
MSQS
HY-14
S05C
-
280
UIO
7110U19-HY-000-
MSQS
HY-15
SOIC
.
43
UIO
7110019-HY-000-
MSQS
HY-16
SOIC
-
2100
U20
7110019-HY-000-
MSQS
HY-17
SOIC
-
250
UIO
7110019-HY-000-
MSQS
HY-18
soic
.
120
U20
711U019-HV-U0U-
MSQS
HY-19
SOIC
-
2190
U20 .
7110019-HY-000-
MSQS
HY-20
SOIC
.
01
Z650
UIO
7110019-HY-000-
MSQS
HY-20
SOIC
-
02
ZbO
U1U
7110019-HY-UUU-
MSQS
HY-21
SOIC
.
UiO
UIO
7110019-HY-UUO-
MSQS
HY-22
SOSC
-
Zb30
UIO
7110019-HY-UU0-
MSQS
HY-23
SOIC
-
B25
UIO
7110019-HY-000-
MSQS
HY-24
soic
_
B2b
UIO
7110019-HY-000-
MSQS
HY-25
SOIC
.
Z74
UIO
7110019-HY-000-
MSQS
HY-26
SU1C
-
Z20
U20
711UU19-HY-000-
MSQS
HY-27
SOIC
.
BIO
UIO
7110019-HY-000-
MSQS
HY-28
SOIC
-
Z110
UIO
7110019-HY-000-
MSQS
HY-29
SOIC
_
BIO
UIO
7110019-HY-000-
MSQS
HY-30
SOIC
-
140
UIO
7110019-HY-000-
MSQS
HY-31
SOIC
-
01
68
UIO
7110019-HY-000-
MSQS
HY-31
SOIC
_
02
61
UIO
7110019-HY-000-
MSQS
HY-32
SOIC
_
110
UIO
711U019-HY-000-
MSQS
HY-33
SOIC
-
160
UIO
7110019-HY-000-
MSQS
HY-34
SU1C
.
90
14
7110019-HY-0U0-
MSQS
HY-3S
SOIC
.
110
22
7110019-HY-000-
MSQS
HY-36
SOIC
_
490
UIO
7110019-HY-000-
MSQS
HY-37
SOIC
_
420
UIO
7110019-HY-0U0-
MSQS
HY-j8
SOIC
_
200
UIO
7110019-HY-000-
MSQS
HY-39
SOIC
.
BIO
UIO
7110019-HY-UUO-
MSQS
HY-40
SOIC
_
Z110
UIO
7110019-HY-000-
MSQS
HY-41
SOIC
_
Z4U
U20
7110019-HY-000-
MSQS
HY-42
SOIC
_
300
UIO
7110019-HY-000-
MSQS
HY-43
SOIC
.
3bU
UIO
711UQ19-HY-UOO-
MSQS
HY-44
soic
.
UIO
UIO
7110U19-HY-000-
MSQS
HY-45
SOiC
_
BIO
010
7110019-HY-000-
MSQS
HY-46
SOIC
_
BIO
U1U
7U0019-HY-000-
MSQS
HY-47
SOiC
_
Z120
U20
7110019-HY-000-
MSQS
HY-48
SOIC
.
Z57
UIO
7110019-HY-UUO-
MSQS
HY-49
SOIC
_
110
UIO
7110U19-HY-U0U-
MSQS
HY-SO
SOSC
-
330
UIO
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
PHENOLS
Urainaye
Survey
Station
Samp 1e
Kep
7110019-HY-000-
Msgs
HY-sl
SU1C
711UU19-MD-000-
msqs
MO-11
S01C
-
7110019-MD-000-
MSIIS
MD-12
S05C
•
7110019-MO-OOO-
Msgs
HO-13
S01C
-
7110019-MI-000-
MSQS
HI —11
S01C
-
U2
7110019-NI-000-
Msgs
MI-12
S01C
-
7110019-MI-U00-
MSQS
MI—13
S01C
-
7110019-MI-000-
MSQS
MI-14
S01C
-
7110019-MI-U0O-
MSQS
MI-IS
S01C
-
711UU19-RS-OUO-
Msgs
RS-11
S01C
-
7110019-KS-000-
MSQS
RS-12
S01C
-
7110U19-RS-OUO-
msqs
RS-13
S01C
-
7110019-RS-000-
MSQS
RS-14
S05C
-
01
7110O19-RS-OUO-
Msgs
RS-14
S05C
-
02
7110019-RS-000-
MSQS
RS-15
S02C
-
7110019-RS-000-
Msgs
RS-16
S01C
-
7110019-RS-000-
MSQS
RS-17
S01C
-
711UU19-RS-000-
Msgs
RS-18
S01C
-
7110019-RS-000-
MSQS
RS-19
S01C
-
7110019-RS-000-
MSQS
RS-20
S01C
-
7110019-RS-000-
MSQS
RS-21
S01C
-
7110019-RS-000-
MSQS
RS-22
S01C
•
7110019-RS-000-
MSQS
RS-24
S05C
-
7110019-SI-000-
MSQS
SI-11
SOSC
-
7U0019-SI-000-
MSQS
SI-12
S01C
-
7110019-SI-000-
MSQS
S1-13
S01C
-
7110019-SI-000-
MSQS
S1—14
S01C
-
711U019-SI-000-
MSQS
S1—15
SOSC
7110019-SP-0U0-
Msgs
SP-11
SOSC
-
7110al9-SP-OO0-
msqs
SP-12
SOSC
-
7110019-SP-000-
Msgs
SP-13
S01C
-
7110019-SP-OOO-
MSQS
SP-14
S01C
-
7110019-SP-000-
msqs
SP-15
S05C
-
7110019-SP-000-
MSQS
SP-16
SOSC
-
7110019-DP-000-
Msgs
WBS
CTL
-
01
711U019-UP-000-
Msgs
UBS
CTL
-
02
Muaber of Observations: 123
Values in ppb dry weight
2 ,4-di -
methyl -
phenol phenol
2150
010
2850
U10
2360
U10
Z20U
U10
Z60
U10
BIO
37
Z150
29
BIO
29
Z20
U10
260
U10
51
U10
230
U10
270
U10
250
U10
130
U10
420
210
110
23
310
U10
99
U10
56
U10
220
U10
49
U10
43
U10
Z160
U10
Z190
U10
Z220
19
Z16
26
Z120
U10
Z130
U10
Z25
U10
Z160
71
Z1700
U40
Z110
U10
Z240
U10
U10
U10
U10
U10
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weiyht
PHENOLS
2-
4-
methyl-
methyl-
Drainage
Survey
Station
Sample
Rep
phenol
phenol
1
110019-BL-000-
Msgs
BL
11
S05C
.
Ll(l
48
1
11OOI9-BL-0UU-
MSQS
BL
12
S01C
-
14
84
1
110019-BL-000-
MSQS
BL
13
S05C
-
L10
120
1
110019-BL-000-
Msgs
BL
14
SU1C
-
U10
62
1
11U019-BL-000-
Msgs
BL
15
S01C
-
12
63
1
110019-BL-OUO-
Msgs
BL
16
S01C
-
52
320
1
110019-BL-000-
MSQS
BL
17
SU1C
-
01
12
80
1
110019-BL-0U0-
MSQS
BL
17
S01C
-
02
62
410
1
110U19-BL-0U0-
MSQS
BL
18
S01C
-
29
410
1
110019-BL-0U0-
MSQS
BL
19
S01C
-
15
110
I
110019-BL-UU0-
MSQS
BL
20
S01C
-
U10
110
1
110U19-BL-000-
MSQS
BL
21
SUbC
-
U10
420
1
110019-BL-000-
MSQS
BL
22
S01C
-
11
160
1
110U19-BL-0U0-
MSQS
BL
23
S01C
-
12
92
1
11U019-BL-0OU-
MSQS
BL
24
S01C
-
U10
18U
I
110U19-BL-UU0-
MSQS
BL
2b
S05C
-
U10
170
I
11UU19-BL-OOQ-
MSQS
BL
26
sine
-
14
120
1
110019-BL-000-
MSQS
BL
27
SU1C
-
26
240
1
110019-BL-000-
MSQS
BL
28
S05C
-
16
190
1
110019-BL-000-
MSQS
BL
29
S01C
-
U10
660
1
110U19-BL-000-
MSQS
BL
30
S01C
-
15
240
1
110019-BL-000-
MSQS
BL
31
S05C
-
Lin
230
1
11U019-BL-000-
hsqs
BL
32
S01C
.
22
340
1
110019-HT-000-
Msgs
CB
11
S01C
-
11
130
1
11U019-CB-0U0-
MSQS
CB
12
S01C
-
U10
47
1
110019-CB-000-
HSQS
CB
13
S01C
-
L10
210
1
110019-CB-000-
MSQS
CB
14
S01C
-
U10
61
1
110019-CI-000-
MSQS
CI
11
S02C
.
U50
460
1
11U019-CI-0UU-
MSQS
CI
12
S01C
-
74
550
1
110019-CI-000-
MSQS
CI
13
SObC
-
36
270
1
110U19-CI-0U0-
MSQS
CI
14
S01C
-
49
310
1
11U019-CI-UU0-
MSQS
CI
15
S01C
-
67
940
1
110U19-CW-000-
MSQS
CI
16
SOt)C
-
46
1200
1
110019-CI-000-
MSQS
CI
17
SOSC
.
01
L10
700
1
11U019-CI-000-
MSQS
CI
17
SU5C
-
02
3U
bUO
I
110019-CI-000-
MSQS
CI
18
S01C
-
4U
670
1
U0019-CI-000-
MSQS
CI
19
S01C
-
37
240
1
11U0I9-LI-UU0-
MSQS
CI
20
SObC
-
43
230
1
110U19-CI-000-
MSQS
CI
21
S01C
-
35
330
1
110019-CI-0U0-
MSQS
CI
22
SUbC
-
3B
150
1
110019-CK-0U0-
MSQS
CR
11
S01C
-
U10
U10
1
I10019-CH-0U0-
MSQS
CK
12
SU5C
-
U1U
U10
1
110019-CK-000-
MSQS
CR-
13
S01C
-
010
26
1
110019-CR-000-
MSQS
CR-
14
S05C
-
U10
32
-------
MAIN SEDIMENT QUALITY SUKVEV ORGANIC CHEMICALS - Values in ppb dry weight
PHENOLS
2- 4-
methyl- methyl-
Drainage
Survey
Station
Sample
Kep
phenol
phenol
1
0019-HY-000-
Msgs
HY-11
S01C
U10
74
1
0019-HY-OOO-
NSOS
HY-12
S01C
-
U10
320
1
0019-HY-000-
MSQS
HY-13
S01C
-
U10
67
1
0019-MY-000-
nsqs
HY-14
S05C
-
U10
26
1
0019-HY-000-
Hsys
HY-15
S01C
-
U10
38
1
0019-HY-000-
Msgs
HY-16
S01C
U20
no
1
001V-HY-000-
NSOS
HY-17
S01C
U10
39
1
0019-HY-000-
NSOS
NY-18
S01C
U20
67
1
0019-HY-000-
NSQS
HY-19
S01C
U20
71
1
0019-HY-000-
NSQS
HY-20
S01C
01
9S
U10
1
0019-HY-000-
NSQS
HY-20
S01C
02
U10
U10
1
II019-HY-000-
NSQS
MY-21
S01C
U10
U10
1
0019-HY-000-
NSQS
HT-22
S05C
15
88
1
0019-HY-000-
NSQS
HY-23
S01C
U10
21
1
0019-HY-000-
NSQS
HY-24
S01C
U10
110
1
0019-HY-000-
NSQS
HY-25
S01C
U10
70
1
0019-HY-000-
NSQS
HY-26
S01C
U20
160
I
0019-MY-000-
NSQS
HY-27
S01C
U10
110
1
UU19-HY-000-
NSQS
HY-28
S01C
U10
180
1
0019-HY-000-
NSQS
HY-29
S01C
-
U10
U10
? 1
0019-HY-000-
NSQS
HY-30
S01C
-
U10
61
CO 1
0019-HY-000-
NSQS
HY-31
S01C
-
01
U10
U10
1
0019-HY-000-
NSQS
HY-31
S01C
-
02
U10
U10
1
0019-HY-000-
NSQS
HY-32
S01C
-
2b
190
1
0019-HY-000-
NSQS
HY-33
S01C
-
U10
75
1
0019-HY-000-
NSQS
HY-34
S01C
-
16
520
1
0019-HY-000-
NSQS
HY-36
S01C
-
34
190
1
0019-HY-000-
NSQS
HY-36
S01C
-
U10
63
1
0019-HY-000-
NSQS
HY-37
S01C
-
11
50
1
0019-HY-000-
NSQS
HY-38
S01C
-
U10
71
1
0019-HY-000-
NSQS
HY-39
S01C
-
21
120
1
0019-HY-000-
NSQS
HY-40
S01C
-
31
190
1
0019-HY-000-
NSQS
HY-41
S01C
-
U20
L20
1
0019-HY-000-
NSQS
HY-42
S01C
-
L10
45
1
0019-HY-000-
NSQS
HY-43
S01C
-
U10
45
1
0019-HY-000-
NSQS
HY-44
S01C
-
U10
U10
1
0019-HY-000-
NSQS
HY-45
S01C
-
U1U
55
1
0019-HY-000-
NSQS
HY-46
S01C
-
U10
90
1
0019-HY-000-
NSQS
HY-47
S05C
-
14
49
1
0019-HY-000-
NSQS
HY-48
S01C
-
U10
100
1
0019-HY-000-
NSQS
HY-49
S01C
-
14
120
1
0019-HY-000-
NSQS
HY-50
S05C
-
27
290
1
0019-HY-000-
NSQS
HY—S1
S01C
-
15
250
1
0019-MD-000-
NSQS
MO-11
S01C
-
68
620
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
PHENULS
2-
4-
methyl-
methyl-
Uralnage
Survey
Station Sample
Rep
phenol
phenol
17110O19-MU-OU0-
MSQS
MU-12 SOSC -
63
670
17110U19-MD-000-
MSQS
MO-13 S01C -
36
450
17110019-HI-000-
NSQS
MI — 11 S01C -
01
18
150
17110019-MI-000-
MSQS
MI—11 S01C -
02
15
140
17110019-N1-000-
MSQS
MI—12 S01C -
30
260
17110019-MI-000-
MSQS
MI-13 S01C -
19
140
17110019-MI-000-
MSQS
MI—14 S01C -
23
250
17110019-MI-000-
MSQS
MI-15 S01C -
22
220
17110019-RS-000-
MSQS
RS-11 S01C -
19
380
17110019-RS-000-
MSQS
R S—12 S01C -
13
13U
17110019-RS-000-
MSQS
RS-13 S01C -
72
560
17110019-RS-000-
MSQS
RS-14 S05C -
01
U10
500
17110019-RS-000-
MSQS
RS-14 S05C -
02
U10
270
17110019-RS-000-
MSQS
RS-15 S01C -
U10
U10
17110019-RS-000-
MSQS
RS-16 S01C -
46
380
17110019-RS-000-
MSQS
RS-17 S01C -
13
51
17110019-RS-000-
MSQS
RS-18 S01C -
71
190
17110019-RS-000-
MSQS
RS-19 S01C -
U1U
L10
17110019-RS-000-
MSQS
RS-20 S01C -
U10
15
17110019-RS-000-
NSQS
RS-21 S01C -
18
7y
17110019-KS-000-
MSQS
RS-22 S01C -
U10
U10
lO 17110019-RS-000-
MSQS
RS-24 SOSC -
U10
U1U
17110019-SI-000-
MSQS
SI-11 SObC -
14
110
17110019-S1-000-
MSQS
SI-12 S01C -
17
180
17110019-S1-000-
MSQS
SI-13 S01C -
19
230
17110019-SI-000-
MSQS
SI-14 S01C -
18
96
17110019-SI-000-
MSQS
SI-15 S05C -
L10
73
17110019-SP-000-
MSQS
SP-11 S05C -
U10
250
17110019-SP-000-
MSQS
SP-12 SOSC -
35
390
17110019-SP-000-
MSQS
SP-13 S05C -
100
1900
17110019-SP-000-
MSQS
SP-14 S01C -
U40
96000
17110019-SP-000-
MSQS
SP-15 S05C -
U10
2600
17110019-SP-000-
MSQS
SP-16 S05C -
U10
890
17110019-0P-000-
MSQS
MBS CTL -
01
U10
U10
17110019-DP-000-
MSQS
WBS CTL -
02
U10
U10
Number of Observations: 123
-------
MAIN SEDIMENT DUAL ITT SURVEY ORGANIC CHEMICALS - Values
SU8ST1TUTEO PHENOLS
in ppb dry
weight
2-
chloro-
2,4-di-
chloro-
Drainage
Survey
Station
Samp
e
Rep
phenol
phenol
0019-BL-000-
HSQS
BL-11
S05C
U5
UIO
0019-8L-000-
MSQS
8L-12
S01C
_
US
UIO
OU19-BI-OOU-
nsgs
BL-13
SObC
-
U5
UIO
0019-81-000-
MSQS
BL-14
SU1C
.
US
UIO
0Q19-BL-U00-
Msgs
BL-15
S01C
-
us
UlU
OOlS-dl-OUO-
MSQS
BL-16
S01C
-
us
UIO
0019-81-000-
Msgs
Bl-17
S01C
.
U1
U5
UIO
0019-BL-000-
Msgs
Bl-17
S01C
-
02
US
UIO
0019-BL-000-
Msgs
BL-18
S01C
-
us
UIO
UU19-8L-000-
Msgs
BL-19
S01C
-
us
UIO
0019-8L-000-
Msgs
BL-20
S01C
-
U5
UIO
OU19-BI-000-
Msgs
BL-21
SOSC
.
U5
UIO
0019-81-000-
Msgs
BL-22
S01C
-
U5
UIO
0019-BL-000-
Msgs
BL-23
S01C
.
US
UIO
0019-81-000-
NSQS
BL-24
S01C
-
US
UlU
uoiy-Bi-ouu-
Msgs
BL-25
S05C
.
U5
UIO
0019-BL-000-
Msgs
8L-26
S01C
-
U5
UIO
0019-6L-000-
Msgs
BL-27
S01C
-
U5
UIO
0019-81-000-
Msgs
BL-28
SOSC
-
U5
UIO
UU19-BI-000-
Msgs
BL-29
S01C
.
U5
UIO
0019-BL-000-
Msgs
BL-30
S01C
-
US
UIO
0019-BL-000-
Msgs
BL-31
SOSC
.
U5
UIO
0019-BI-000-
Msgs
BL-32
S01C
.
US
UIO
0U19-HY-OO0-
Msgs
CB-11
S01C
-
US
UIO
0019-CB-000-
Msgs
C8-12
S01C
-
U5
UIO
OOIS-CB-OOO-
Msgs
CB-13
S01C
-
U5
UIO
0019-CB-000-
Msgs
CB-14
S01C
_
U5
UIO
0019-CI-000-
Msgs
CI-11
S02C
-
U25
UbO
0019-CI-000-
MSQS
C1 -12
S01C
-
US
UIO
0019-CI-000-
Msgs
Cl-13
S05C
-
U5
UIO
0019-C1-000-
MSQS
C1-14
S01C
-
U5
UIO
0019-CI-000-
MSQS
CI-15
S01C
_
US
UIO
0019-CW-000-
Msgs
C1 -16
S05C
-
Ub
UIO
0019-CI-000-
Msgs
C1-17
SOSC
-
01
US
UIO
0019-C1-000-
Msgs
C1-17
SOSC
-
02
US
UIO
0019-CI-000-
MSQS
C1-18
S01C
-
Ub
UlU
0019-C1-000-
Msgs
CI -19
S01C
-
U5
UIO
0019-C1-000-
Msgs
C1-20
SOSC
.
U5
UlU
0019-C1-000-
Msgs
Cl-21
S01C
-
UIO
U20
0019-C1-000-
msqs
CI-22
SU5C
-
US
UIO
0019-CR-000-
Msgs
CR-11
S01C
.
U5
UIO
0019-CR-000-
MSQS
CR-12
SOSC
-
US
UIO
2,4,6-
4-chloro- tr1-
3-methyl chloro-
phenol phenol
U10
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
uso
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
U20
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
USO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
U20
UIO
UIO
UIO
2,4- 4,6-
penta- di- di-
chloro- 2-nitro- nitro- nitro-o- 4-nitro-
phenol phenol phenol cresol phenol
14U UIO ulOO U100
U2b UIO ulOO UlOO
U25 UIO UlOO UlOO
S8 U1U U1UO U1UO
44 UIO UlOO UlOO
U25 UIO UlOO UlOO
81 UIO UIOU UlOO
UlOO UIO UlOO UlUU
78U UIO UlOO UlOO
USO UIO UlOO UlOO
USO UIO UlOO UlOO
U25 UIO UlOO UlOO
U50 UIO UlOO UlOO
U25 UIO UlOO UlUO
140 UIO UlOO UlOO
U25 UIO UlOO UlOO
U25 UIO UlOO UlOO
UlOO UIO UlOO UlOO
USO UIO UlOO UlOO
92 UIO UlOO UlOO
860 UIO UlOO UlOO
U25 UIO UlUU UlOO
U2S UIO UlOO UlOO
UbO UIO UIOU UIOU
UlOO UIO UlOO UlOO
U50 UIO UlOO UlOO
U50 UIO UlUO UlUO
U130 USO U500 USOO
UlOO UIO UlUU UlOO
64 UIO UlOO UlOO
77 UIO UIOU UlOO
67 UlU UIOU UlUU
57 UIO UlOO UlOO
45 UlU ulOO UlUO
U25 UIO UlOO UlOO
57 UIO UlUO UlUU
56 UIO ulOO UlOO
48 UIO UlOO UlOO
UBO U20 U2UU U200
U50 UIO UIOU UlUO
U50 UIO UlUO UlUO
UbO UIO UlOO UlUO
-------
MAIN SEDIMENT QUALITY SUKVEY ORGANIC CHEMICALS - Values in ppb dry weluht
SUBSTITUTED PHENOLS
2-
2,4-di-
4-chloro
Drainage
chloro-
chloro-
3-methyl
Survey
Station
Sample
Rep
phenol
pnenol
phenol
I
110019-CR-000-
MSQS
CR-13
SOIC
_
U5
U10
U10
1
110019-CR-000-
MSQS
CR-14
S05C
-
U5
U10
U10
1
110019-HY-OUO-
MSQS
HY-11
SOIC
-
US
U10
U10
1
11UOI9-HY-OUO-
MSQS
HY-12
SOIC
-
US
U10
U10
1
110019-HY-000-
Msgs
HY-13
SOIC
-
U5
U10
U10
1
110019-HY-000-
msqs
HY-14
S05C
-
U5
U10
U10
1
110019-HY-000-
MSQS
HY-15
SOIC
-
US
U10
U10
1
110019-HY-000-
msqs
HY-16
SOIC
-
U10
U20
U10
1
110U19-HY-00U-
MSQS
HY-17
SOIC
-
U5
U10
U10
1
110019-HY-000-
ksqs
HY-18
SOIC
.
U10
U20
U10
1
110019-HY-000-
MSQS
HY-19
SOIC
-
U10
U20
U10
1
110019-HY-000-
MSQS
HY-20
SOIC
-
01
US
U10
U10
1
110019-HY-000-
MSQS
HY-20
SOIC
-
U2
U5
U10
U10
1
110019-HY-000-
MSQS
HY-21
SOIC
-
U5
U10
U10
1
110019-HY-000-
MSQS
HY-22
S05C
-
US
U10
U10
1
110019-HT-000-
MSQS
HY-23
SOIC
-
U5
U10
U10
1
110U19-HY-000-
MSQS
HY-24
SOIC
-
U5
U10
U10
1
110019-HY-000-
MSQS
HY-25
SOIC
-
U5
U10
U10
1
1I0019-HY-000-
MSQS
HY-26
SOIC
-
U10
U20
U10
1
110019-HY-000-
MSQS
HY-27
SOIC
_
U5
U10
U10
1
110019-HY-000-
MSQS
HY-28
SOIC
-
US
U10
U10
1
110019-HY-000-
MSQS
HY-29
SOIC
-
Ub
U10
U10
1
110019-HY-000-
MSQS
HY-30
SOIC
-
US
U10
U10
1
1IU019-HY-000-
MSQS
HY-31
SOIC
-
U1
US
U10
uiu
1
110019-HY-000-
MSQS
HY-31
SOIC
-
02
US
U10
U10
1
110U19-HY-000-
MSQS
HY-32
SOIC
-
U5
U10
uio
1
11U019-HY-000-
MSQS
HY-33
SOIC
-
U5
U10
U10
1
11UU19-HY-000-
MSQS
HY-34
SOIC
-
U5
U10
uio
1
110019-HY-000-
MSQS
HY-35
SOIC
_
U5
U10
uio
1
110019-HY-000-
MSQS
HY-36
SOIC
-
US
U10
UIO
1
110019-HY-000-
MSQS
HY-37
SOIC
-
US
U10
UIO
1
110019-HY-000-
MSQS
HY-38
SOIC
.
US
U10
UIO
I
110019-HY-000-
MSQS
HY-39
SOIC
-
U5
U10
UIO
1
110019-HY-000-
MSQS
HY-40
SOIC
_
U5
U10
UIO
1
110019-HY-000-
MSQS
HY-41
SOIC
_
U10
U20
U20
1
110019-HY-000-
MSQS
HY-42
SOIC
_
US
U10
UIO
1
11U019-HY-U0O-
MSQS
HY-43
SOIC
.
U5
U10
UIO
1
110019-HY-000-
MSQS
HY-44
SOIC
-
US
U10
UIO
1
110019-HY-00U-
MSQS
HY-4S
SOIC
-
US
U10
UIO
1
11U019-HY-000-
MSQS
HY-46
SOIC
-
U5
U10
UIO
1
110019-HY-000-
MSQS
HY-47
S05C
-
U5
U10
UIO
1
110019-HY-000-
MSQS
HY-48
SOIC
-
U5
U10
UIO
2.4.6-
2,4-
4,6-
tri-
penta-
di-
di -
chloro-
Chloro-
2-nitro- nitro-
ni tro-o-
4-n1tro-
phenol
phenol
phenol pfienol
cresol
phenol
UIO
USO
UIO
U100
U100
UIO
U50
UIO
U1U0
U1U0
UIO
U50
UIO
U100
U100
UIO
U2S
UIO
U100
U100
UIO
USO
UIO
U100
U100
UIO
U100
UIO
U100
U100
UIO
U50
UIO
U100
U100
U20
USO
U20
U100
U200
UIO
U50
UIO
U100
U100
U20
U50
U20
U100
U2U0
U20
U50
U20
U100
U200
UIO
USO
UIO
U100
UlUO
UIO
U2S
UIO
U100
U100
UIO
USO
UIO
U100
UlUO
UIO
U100
UIO
U100
U100
UIO
USO
UIO
U100
U100
UIO
U50
UIO
U100
U100
UIO
U50
UIO
U100
U10U
U20
U50
U20
U100
U200
UIO
U25
UIO
U100
UlUO
UIO
U2S
UIO
U100
U100
UIO
U50
UIO
U100
U10U
UIO
USO
UIO
U100
uioo
160
U2S
UIO
U1UU
U100
140
U50
UIO
U100
UIOO
UIO
71
UIO
U1U0
UIOO
UIO
U25
UIO
U100
UIOO
UIO
120
UIO
U100
UIOO
UIO
110
UIO
U100
UIOO
UIO
USO
UIO
U100
UIOO
UIO
U25
UIO
U100
UIOO
UIO
USO
UIO
U100
UIOO
UIO
U50
UIO
U100
UIOO
UIO
USO
UIO
U100
UIOO
U20
U100
U20
U200
U200
UIO
U100
UIU
U100
UIOO
UIO
U100
UIO
U100
UIOO
UIO
U100
UIO
U100
UIOO
UIO
USO
UIO
U100
UIOO
UIO
U25
UIO
U100
UIOO
UIO
U100
UIO
U100
UIOO
UIO
USO
UIO
U100
UlUO
-------
MAIN SEDIMENT QUALITr SURVEY ORGANIC CHEMICALS
SUBSTITUTED PHENOLS
Drainage
Survey
Station
Sample
Rep
17110U19-HY-000-
HSQS
HY-49
S01C
_
171I0O19-HY-OUO-
MSQS
HY-50
SObC
.
17110019-HY-000-
HSQS
HY-51
S01C
_
17110019-MU-000-
msqs
MO-11
S01C
-
171I0019-K)-U00-
Msgs
MO-12
S05C
-
1711UU19-MD-000-
MSQS
MD-13
S01C
-
17110019-MI-OOO-
MSQS
Ml — 11
S01C
-
01
17110019-MI-000-
Msgs
MI-11
S01C
-
02
17U0019-KI-O0U-
Msgs
Ml-12
S01C
-
17110019-MI-000-
Msgs
MI— 13
S01C
.
1711U019-MI-000-
MSQS
MI — 14
S01C
-
17110019-MI-000-
MSQS
MI-15
S01C
-
1711UU19-RS-000-
MSQS
RS-11
S01C
.
17U0019-RS-000-
msqs
KS-12
S01C
-
17110019-RS-000-
MSQS
RS-13
S01C
-
17110019-RS-000-
Msgs
RS-14
S05C
-
01
17110019-RS-000-
MSQS
KS-14
S05C
-
02
1711U019-RS-U00-
MSQS
RS-15
S02C
.
17110019-RS-000-
msqs
RS-16
S01C
_
17110O19-RS-OUO-
MSQS
RS-17
S01C
-
17110019-RS-000-
msqs
RS-18
S01C
_
17110019-RS-000-
Msgs
RS-19
S01C
.
17110019-RS-000-
MSQS
RS-20
S01C
.
17110019-RS-000-
Msgs
RS-21
S01C
.
17110019-RS-000-
Msgs
RS-22
S01C
.
17110019-RS-000-
MSQS
RS-24
S05C
-
17110019-SI-000-
MSQS
SI-U
S05C
-
17110019-SI-000-
MSQS
SI-12
S01C
_
17110019-SI-U00-
MSQS
SI -13
S01C
.
17110019-SI-000-
MSQS
SI -14
S01C
.
17110019-SI-U00-
MSQS
SI-15
S05C
.
17110019-SP-000-
MSQS
SP-11
SObC
.
17110019-SP-000-
msqs
SP-12
sobc
17110019-SP-OOU-
Msgs
SP-13
S01C
-
17110019-SP-000-
Msgs
SP-14
S01C
_
17110019-SP-000-
MSQS
SP-lb
SUbC
_
17110019-SP-000-
Msgs
SP-16
SUbC
-
1711U019-0P-QU0-
Msgs
UBS
CTL
-
01
17110019-OP-UOO-
MSQS
WBS
CTL
-
02
Values in ppb dry weiyht
2.4,6-
2,4-
4 ,6-
2-
2,4-di-
4-chloro-
trl -
penta-
di -
di-
chloro-
chloro-
3-methyl
chloro-
chloro-
2-nitro-
nitro-
nitro-o-
4-nitro-
phenol
phenol
phenol
phenol
phenol
phenol
phenol
cresol
phenol
U5
U10
U10
U10
150
U10
U100
U100
U5
U10
U10
U10
U50
U10
U1UU
UlUO
US
U10
U10
U10
U25
U10
U100
U100
U5
010
010
U10
620
U10
U1U0
U100
U5
010
U10
2b
77
U10
U100
U100
U5
U10
U10
010
49
U10
U100
U100
Ut>
U10
010
010
UbO
U10
U100
U100
US
U10
U10
U10
UbU
U10
U100
U1UU
ut)
U10
U10
U10
U2b
U10
U100
UlUO
Ub
010
U10
U10
U2b
U10
U100
UlOU
U5
010
U10
U10
UbU
U10
U100
U100
Ub
U10
U10
U10
U2b
U1U
U100
UlUO
U5
U10
U10
U10
UbO
U10
U100
U100
U5
U10
U10
U10
UbO
U10
U100
U100
U5
U10
010
U10
U25
U10
U100
U100
U5
U10
U10
U10
U2b
U10
U100
UlOU
Ub
U10
U10
U10
U2b
U10
U100
U100
U5
U10
U10
U10
U5U
U10
U1U0
UlUO
U5
U10
U10
U10
U25
U10
U10U
U100
U5
U10
U10
U10
34
U10
U100
U100
Ub
U10
U10
U10
U2b
U10
U100
U100
U5
U10
U10
U10
U25
U10
U100
U100
U5
U10
U10
010
U100
U10
U100
U100
Ub
U10
010
U10
UbO
U10
UlUO
U100
U5
U10
U10
U1U
UbO
U1U
U100
U100
U5
U10
U10
U10
U25
U10
U10U
UlOU
Ub
U10
U10
U10
U75
U10
UlUO
U100
Ub
U10
010
U10
U1U0
U1U
U1UU
U100
Ub
U10
U10
U10
L25
U10
U100
UlUO
U5
U10
010
U10
UbO
U1U
uiuo
U100
Ub
U10
U10
U10
U100
U10
U100
U100
U5
U10
U10
U10
UbO
U10
UlUO
U100
Ub
U10
U10
U10
U50
U10
U100
U100
Ub
U10
U10
U10
U1U0
U10
U1UU
UlUO
U20
U40
U40
U40
U100
U4U
U400
U4UU
Ub
U10
U10
U10
UbO
uio
UlUO
UlOU
Ub
U10
U10
U10
U25
U10
U100
U100
Ub
U10
U10
ulu
U2S
UIO
U100
UlUO
Ub
U10
U10
U10
U50
uio
U10U
U100
Number of Observations:
123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS -
SUBSTITUTED PHENOLS
Values in ppb dry weight
Drainage
17110019-BL-000-
1711U019-BL-000-
17110019-BL-000-
17110U19-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
1711CKJ19—BL—OOO-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17U0019-BL-000-
17110019-BL-000-
_ 17110019-BL-000-
, 17110019-BL-000-
»-* 17110019-BL-000-
W 17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-HY-000-
17110019-CB-000-
17110019-CB-000-
17110019-CB-000-
1711U019-CI-000-
17110019-CI-000-
1711U019-C l-OOU-
miOO^-CI-OOO-
miinm-CI-OOO-
1711U019-CU-000-
17110019-CI-000-
17110019-C1-000-
17110019—CI —000—
17110019—C1—000—
1711UU19-CI-00U-
17110019-CI-000-
17110019-CI-000-
17110019-CR-000-
1711UU19-CK-000-
2,4 ,b-
tri -
chloro
Survey Station Sample Rep phenol
MSQS
BL-11
SOSC
-
U10
MSQS
BL-12
S01C
-
U10
MSQS
BL-13
SOSC
-
U10
MSQS
BL-14
SUIC
-
U10
MSQS
BL -15
SOIC
-
U10
MSQS
BL-16
SOIC
-
U1U
MSQS
BL-17
soic
-
01
U10
MSQS
BL-17
SUIC
.
02
U1U
MSQS
BL-IB
SOIC
-
U10
MSQS
BL-19
SOIC
-
U10
MSQS
BL-2U
SOIC
-
U10
MSQS
BL-Z1
SOSC
-
U10
MSQS
BL-22
SOIC
-
U10
MSQS
BL-23
soic
-
U10
MSQS
BL-24
soic
-
U10
MSQS
Bl-25
sosc
-
U10
MSQS
BL-26
soic
-
U10
MSQS
BL-27
soic
-
U10
MSQS
BL-28
S05C
-
010
MSQS
BL-29
SOIC
-
010
MSQS
BL-30
SOIC
-
010
MSQS
BL-31
SOSC
-
U10
MSQS
BL-32
SOIC
.
U1U
MSQS
CB-11
SOIC
-
010
MSQS
CB-12
SOIC
-
010
MSQS
CB-13
SOIC
-
01U
MSQS
CB-14
SOIC
-
010
MSQS
CI-11
S02C
-
UbO
MSQS
CI-12
SOIC
-
010
MSQS
C1-13
SOSC
-
U10
MSQS
CI-14
SOIC
•
um
MSQS
CI-IS
SOIC
.
010
MSQS
CI -16
SOSC
-
U10
MSQS
CI-17
SOSC
.
01
U10
MSQS
Cl-17
SOSC
-
02
010
MSQS
CI-18
SOIC
-
010
MSQS
Cl-19
SOIC
-
010
MSQS
CI-20
SU5C
-
010
MSQS
CI-21
SOIC
-
U20
MSQS
C1 -22
SObC
-
010
MSQS
CR-11
SOIC
-
Old
MSQS
CR-12
S05C
-
U10
-------
MAIN SEDIMENT QUALITY SURVET OKGANIC CHEMICALS - Values In ppb dry wetyht
SUBSTITUTED PHENOLS
2,4,5-
tri -
Chloro-
Drainage
Survey
Station
Sample
Hep
phenol
17110019-CR-000-
MSQS
CR-13
S01C
.
UIO
17110U19-CR-00U-
MSQS
CR-14
S05C
-
UlU
17110019-HY-000-
MSOS
HY-U
S01C
-
UlU
17110019-HY-000-
Msgs
HY-12
SOIC
-
UlU
17110019-HY-000-
MSQS
NY-13
S01C
-
U10
17110019-HY-000-
MSOS
HY-14
S05C
-
UlU
171I0019-MY-000-
MSQS
HY-15
S01C
-
UlU
17110U19-HY-000-
Msgs
HY-16
S01C
-
UlU
17U0019-HY-000-
Msgs
HY-17
S01C
-
U10
17110019-HY-OOU-
MSQS
HY-18
S01C
-
U2U
17110019-HY-000-
Msgs
MY—19
SU1C
-
U2U
17110019-HY-000-
MSQS
HY-20
S01C
-
01
U10
17110019-HY-000-
MSQS
HY-20
SOIC
-
02
U10
17110U19-HY-U00-
MSQS
HY-21
S01C
-
UlU
17110019-HY-000-
MSQS
HY-22
sosc
-
UlU
17110019-HY-000-
MSQS
HY-23
soic
-
UlU
17110019-HY-000-
MSQS
HY-24
S01C
-
U10
I7110019-HY-0U0-
MSQS
MY—25
S01C
-
UlU
17110019—HY-OOO-
MSQS
HY-26
S01C
-
U2U
17110019-HY-0O0-
MSQS
MY-27
SOIC
-
U10
17110Q19-HY-000-
MSQS
HY-28
SOIC
-
UlU
17110019-HY-Q0U-
MSQS
HY-29
SUIC
-
UlU
17110U19-HY-000-
MSQS
HY-30
SOIC
-
Uio
17110019-HY-0U0-
MSQS
HY-31
SOIC
-
01
UlU
17110019-HY-000-
MSQS
HY-31
SOIC
-
02
UlU
17U0U19-HY-000-
MSQS
HY — 32
SOIC
-
U10
17110019-HY-000-
MSQS
HY-33
SOIC
-
UlU
17110019-HY-000-
MSQS
HY-34
SOIC
-
UlU
17110019-HY-000-
MSQS
HY-35
SOIC
-
uio
17110U19-HY-000-
MSQS
HY-3b
SOIC
-
UlU
17U0019-HY-000-
MSQS
HY-37
SOIC
-
UlU
17110019-HY-000-
MSQS
HY-38
SOIC
-
uio
1711U019-HY-000-
MSQS
HY-39
SOIC
-
uio
17110019-HY-000-
MSQS
HY-4U
SOIC
-
uio
17110019-HY-000-
MSQS
HY-41
SOIC
-
U20
17110U19-HY-000-
MSQS
HY-42
SOIC
-
UIO
17110019-HY-000-
MSQS
HY-43
SOIC
-
UIO
17UOU19-HY-UOO-
MSQS
HY-44
SUIC
-
UlU
1711U019-HY-000-
MSQS
HY-4b
SOIC
-
UlU
17110U19-HY-UUO-
MSUS
HY -46
SUIC
-
UlU
1711UU19-HY-UUO-
MSQS
HY-47
SUbC
-
UlU
17110019-HY-U00-
MSUS
HY-48
SOIC
-
UlU
-------
MAIN SEDIMENT QUALITY SJ«VEY JkjANIC CHEMICALS -
SUBSTITUTED PHENOLS
Values in ppb dry weiyht
tri-
chloro
Drainage
Sorve>
Stati on
Sample
Rep
phe
110019-HY-000-
MSQS
HY-49
S01C
_
UIO
110019-HY-U0Q-
MS(JS
HY-50
SOSC
-
U1U
110019-HY-000-
Msgs
HY-S1
sine
-
U10
110019-MO-000-
MSQS
MU-11
S01C
.
1M
110019-HJ-000-
MSQS
MO-12
S05C
-
29
110019-MO-000-
MSQS
MD-13
SU1C
-
U10
11U019-M1-000-
MSQS
Ml—11
S01C
-
U1
U10
110019-MI-000-
MSQS
MI-11
S01C
.
U2
U10
U0019-MI-000-
MSQS
MI-12
S01C
-
U10
110019-MI-00U-
MSQS
MI-13
S01C
.
UIO
11O019-MI-0U0-
MSQS
MI-14
S01C
-
U10
110019-MI-000-
MSQS
MI-15
sine
-
1)10
110019-RS-000-
MSQS
RS-11
soic
-
U10
110019-RS-000-
MSQS
RS-12
S01C
-
U1U
110019-RS-000-
MSQS
RS-13
SOIC
-
U10
110U19-RS-000-
MSQS
KS-14
SU5C
-
01
U10
110019-RS-000-
MSQS
KS-14
S05C
_
02
UIO
110019-RS-000-
MSQS
RS-15
SOIC
-
U1U
110019-KS-000-
MSQS
KS-16
SOIC
-
UIO
110019-RS-000-
MSQS
RS-17
SOIC
-
UIO
11O019-RS-UOU-
MSQS
RS-18
SU1C
-
UIO
110019-KS-000-
MSQS
RS-19
SOIC
.
U1U
110019-RS-000-
MSQS
RS-20
SOIC
-
UIO
110019-RS-000-
MSQS
RS-21
SOIC
.
U1U
110019-RS-000-
MSQS
RS-22
SOIC
.
UIO
110019-RS-000-
MSQS
RS-24
S05C
.
UIO
11U019-SI-000-
MSQS
SI-11
S05C
-
UIO
11OU19-SI-0UU-
MSQS
Sl-12
SOIC
.
UIO
110019-SI-U0U-
MSQS
SI-13
SOIC
.
UIO
110019-SI-000-
MSQS
SI-14
SOIC
.
UIO
110019-SI-000-
MSQS
SI-15
SOSC
-
UIO
110019-SP-000-
MSQS
SP-11
S05C
_
UIO
11U019-SP-000-
MSQS
SP-12
SOSC
-
UIO
110019-SP-000-
MSQS
SP-13
S06C
.
UIO
110019-SP-000-
MSQS
SP-14
SOIC
.
U40
110019-SP-OOO-
MSQS
SP-15
S05C
-
UIO
110019-SP-000-
MSQS
SP-16
S05C
.
UIO
11UO19-DP-0UO-
MSQS
UBS
CTL
.
01
UIO
110019-DP-000-
MSQS
WBS
CTL
.
02
UIO
Number of Observations:
123
-------
MAIN SEDIMENT QUALITY SUKVEr 0HGAN1C CHEMICALS - Values i
LOW MOLECULAR HEIGHT AROMATIC HYDROCARBONS
2-
methy1
naphth
Drainage
Survey
Station
Sample
Rep
alene
7U0019-BI-000-
MSQS
BL-U
SOSC
_
65
7UU019-BL-000-
Msgs
BL-12
S01C
-
130
711U019-BL-0UU-
MSQS
BL-13
S05C
-
110
7110019-81-000-
msqs
BL-H
SOIC
-
140
7110019-BL-000-
Msgs
BL-15
S01C
-
71
7110019-BL-000-
Msgs
Bl-16
SOIC
-
380
7U0019-BL-000-
Msgs
Bl-17
SOIC
-
01
120
7110019-BL-000-
Msgs
BL-17
S01C
-
02
270
71IU019-8L-000-
Msgs
Bl-18
SOIC
-
280
711UU19-BL-OUO-
Msgs
BL-19
SOIC
-
100
7110019-BL-OOU-
Msgs
BL-20
SOIC
-
120
7110U19-BL-000-
Msgs
Bl-21
SOSC
-
28U
7U0019-BL-000-
msqs
BL-22
SOIC
-
150
7110019-BL-000-
Msgs
BL-23
SOIC
-
170
711U019-BL-000-
Msgs
8L-24
Suic
-
240
7110019-BL-000-
Msgs
BL-25
SObC
-
180
7I10019-BL-0U0-
Msgs
BL-26
SOIC
-
240
7110019-Bl-000-
msqs
BL-27
SOIC
-
IbO
7110U19-BL-000-
Msgs
BL-28
SObC
-
140
7110019-BL-OOO-
Msgs
BL-29
SOIC
-
320
7110019-BL-000-
MSQS
BL-30
SOIC
-
130
7110019-BL-000-
Msgs
BL-31
SObC
-
78
7110019-BL-000-
Msgs
BL-32
SOIC
-
270
7110019-HY-000-
Msgs
CB-U
SOIC
-
130
7110019-CB-000-
MSQS
CB-12
SOIC
-
93
7110019-CB-000-
Msgs
CB-13
SOIC
-
160
7110019-CB-000-
MSQS
CB-14
SOIC
-
120
7110U19-CI-000-
MSQS
CI-U
S02C
-
590
7110019-CI-000-
Msgs
CI-12
SOIC
.
740
7110019-C1-000-
Msgs
CI-13
S05C
-
330
7110019-CI-000-
MSQS
CI-14
SOIC
-
290
7110U19-C1-000-
MSQS
Cl-lb
SOIC
-
700
7110019-CW-000-
msqs
CI-16
SObC
-
460
71100I9-CI-OOO-
msqs
CI-I7
SOSC
-
01
600
7110019-C1-000-
MSQS
CI-17
SOSC
-
02
530
7110019-C1-000-
MSQS
CI-18
SOIC
.
480
7110019-CI-000-
MSQS
CI-19
SOIC
-
320
711001V-C1-U00-
MSQS
C1-2U
SObC
-
360
7110019-CI-000-
MSQS
CI-21
SOIC
-
890
7110Q19-CI-OOU-
MSQS
C1 - 22
S05C
.
460
7110019-CK-000-
MSQS
UR-ll
SUIC
-
05
7H0019-CK-000-
MSQS
CK-12
SObC
-
U5
7U0019-CR-000-
MSQS
Crt-13
SOIC
-
05
ppb dry weight
naphtha- acenaph- acenaph-
lene
thylene
thene
300
22
U5
310
57
39
280
57
39
230
98
270
240
33
2b
590
43
50
400
51
56
390
bl
44
330
33
38
260
36
2b
360
32
38
360
37
38
750
46
88
850
49
120
530
44
65
480
65
54
1100
92
140
110
11
38
380
38
b4
870
68
190
410
33
58
150
34
31
750
44
92
330
43
30
68
05
7.1
85
21
9.8
83
25
10
1100
180
460
5500
250
220
1100
170
110
1700
110
74
2100
250
360
1300
190
130
1200
230
170
1900
330
180
950
180
100
830
140
9U
980
190
130
2400
b50
380
120U
330
190
7 .4
05
Ub
13
Ub
U5
U5
Ob
05
anthra-
cene/
phenan- anthra- phenan-
threne cene threne
98 44
240 120
22U 110
1200 780
140 69
340 110
220 91
250 98
230 91
160 68
200 86
230 82
30C 160
420 220
310 140
27U IbO
540 220
180 62
290 170
770 230
210 77
670 220
470 220
260 91
85 14
82 20
88 15
1800 460
190C 660
830 310
540 190
2bO„ 830
79Q 330
1100 b30
1200 450
760 400
510 230
570 330
2700 1600
1500 960
14 6.7
12 8.6
16 .5
f luorene
19
52
48
220
35
73
62
62
62
34
45
47
84
120
76
69
120
43
68
190
48
110
110
53
14
16
16
02b
290
160
110
490
200
280
300
190
110
160
600
280
U5
05
U6
-------
MAIN SEDIMENT QUALITr SURVEr ORGANIC CHEMICALS - Values in pnh dry weight
LOW MOLECULAR WEIGHT AROMATIC HYDROCARBONS
metny1
napntn- naphtha- acenaph-
Drainage
Survey
Station
Sample
Rep
alene
tene
thyli
1711QG19-CR-000-
Msys
CR-14
S05C
_
US
7.5
05
17110019-HY-000-
Msgs
HY-11
S01C
-
50
79
45
17U0019-HY-000-
MSQS
HY-12
S01C
-
58
160
42
1711U019-HY-00U-
Msgs
HY-13
S01C
.
HZ
190
49
17110019-HY-000-
msqs
HY-14
SOSC
-
55
130
36
17110019-HY-0U0-
Msgs
HY-15
S01C
-
68
160
53
1711UU19-HY-000-
MSQS
HY-16
S01C
-
100
190
110
17110019-HY-UU0-
Msgs
HY-17
S01C
-
69
240
61
17110019-HY-OUO-
Msgs
HY-18
sole
-
120
300
67
17110019-HY-UOU-
MSQS
HY-19
S01C
-
130
250
87
17110019-HY-000-
MSQS
HY-20
sole
-
01
220
610
87
17110019-HY-UUO-
MSQS
HY-20
soic
-
0
200
580
130
17110019-HY-000-
MSQS
HY-21
S01C
-
270
610
81
17110019-HY-OUO-
MSQS
HY-22
S05C
-
390
1600
100
17U0019-HY-000-
MSQS
HY-23
SOIC
-
180
380
79
17110019-HY-000-
MSQS
HY-24
SOIC
-
130
360
50
17110019-HY-000-
MSQS
HY-25
SOIC
-
150
2600
42
17110U19-HY-000-
MSQS
HY-26
SOIC
-
380
480
57
17110019-HY-000-
MSQS
HY-27
SOIC
-
91
280
56
17110019-HY-OOO-
MSQS
HY-28
SOIC
-
120
480
61
miOOlS-HY-OOO-
MSQS
HY-29
SOIC
-
91
300
48
17110019-HY-000-
MSQS
HY-30
SOIC
.
74
240
34
17110019-HY-000-
MSQS
HY-31
SOIC
-
01
80
340
68
17110019-HY-000-
MSQS
HY-31
SOIC
-
02
77
300
55
1711U019-HY-000-
MSQS
HY-32
SOIC
-
150
510
77
17U0019-HY-000-
MSQS
HY-33
SOIC
-
210
1100
93
17110019-HY-00U-
MSQS
HY-34
SOIC
-
150
740
130
1 711U019-HY-000-
MSQS
HY-35
SOIC
.
280
740
94
17110U19-HY-00U-
MSQS
HY-36
SOIC
-
380
840
120
17110019-HY-000-
MSQS
HY-37
SOIC
-
170
920
93
1711U019-HY-000-
MSQS
HY-38
SOIC
-
260
740
110
17110019-HY-000-
MSQS
HY-39
SOIC
-
390
1200
100
17110U19-HY-OOU-
MSQS
HY-40
SOIC
-
330
1100
88
17U0019-HY-000-
MSQS
HY-41
SOIC
-
250
750
62
17110019-HY-000-
MSQS
HY-42
SOIC
-
160
530
87
17110019-HY-000-
MSQS
HY-43
SOIC
-
130
490
90
17110019-HY-0OU-
MSQS
HY-44
SOIC
-
42
23
7.8
17110019-HY-000-
MSQS
HY-45
SOIC
-
290
850
95
17110019-HY-OOU-
MSQS
HY-46
SU1C
-
340
980
94
17U0019-HY-000-
MSQS
HY-47
S05C
-
160
670
80
171100ly-HY-000-
MSQS
HY-48
SOIC
-
270
760
76
17110019-HY-000-
MSQS
HY-49
SOIC
-
100
200
38
17110019-HY-000-
MSQS
HY-bO
S05C
-
190
400
27
anthra
cene/
acenaph- phenan- anthra- phenan
thene
f 1 uorene
threne
cene
U5
US
16
22
7y
82
44U
160
39
55
420
190
49
85
510
460
51
120
700
390
34
53
490
260
200
280
1600
1300
86
120
720
440
110
110
970
700
120
150
680
680
61
89
580
Z290
71
87
C
C
97
110
750
Z360
450
480
1200
Z580
88
160
2300
Z930
61
94
Z620
Z230
40
65
2360
Z170
43
63
2450
Z330
44
58
400
160
79
100
570
240
39
57
310
190
34
45
240
110
36
48
290
120
32
59
300
270
85
85
420
220
85
98
550
260
130
140
570
280
92
100
460
260
190
200
750
430
76
100
460
220
110
150
620
310
67
75
Z320
2170
60
92
2390
Z180
58
85
Z430
Z150
83
160
720
380
90
110
530
220
05
8.6
42
26
73
95
Z230
Z130
100
220
Z470
Z200
51
86
Z420
Z150
110
88
Z310
Z130
22
26
120
56
54
31
190
83
-------
MAIN SEOIMENT QUAL1TY SURVEY UKGANIC CHE*i:^> -
LUW MOLECULAR WEIGHT AROMATIC HYDROCARBONS
Oralnaye
Survey
Station Sampl
e
U019-HY-000-
MSQS
HY-bl
S01C
0019-MU-000-
MSQS
KM1
S01C
0019-MD-0O0-
MSQS
MO-12
SOSC
U019-M)-000-
MSQS
MD-13
S01C
0019-HI-0(H)-
MSQS
MI-11
S01C
0019-HI-000-
MSQS
HI — 11
S01C
0019-MI-000-
HSQS
Ml-12
S01C
0019-H1-000-
msqs
Ml-13
S01C
0019-MI-000-
HSQS
MI -14
S01C
0019-M1-000-
MSQS
MI-IS
S01C
0019-RS-000-
MSQS
RS-11
S01C
0019-KS-000-
MSQS
RS-12
suic
0019-RS-000-
MSQS
RS-13
S01C
0019-RS-000-
MSQS
RS-14
S05C
0019-RS-000-
MSQS
RS-14
SOSC
-
0019-RS-000-
MSQS
RS-15
S02C
-
0019-RS-000-
MSQS
RS-16
SUIC
-
0019-KS-000-
MSQS
KS-17
SUIC
-
0019-RS-000-
MSQS
RS-18
SUIC
-
0019-RS-000-
MSQS
RS-19
SUIC
-
OU19-RS-UOU-
MSQS
RS-20
S01C
-
U019-RS-000-
MSQS
RS-21
SUIC
-
0019-RS-000-
MSQS
RS-22
SUIC
-
0019-RS-000-
MSQS
RS-24
S05C
-
U019-SI-000-
MSQS
SI-11
SOSC
-
0019-SI-000-
MSQS
SI-12
SUIC
-
OOly-SI-OOO-
MSQS
SI -13
S01C
-
0019-SI-000-
MSQS
S1 -14
SUIC
-
0019-SI-000-
MSQS
SI-IS
S05C
-
0019-SP-000-
HSQS
SP-11
S05C
-
0019-SP-000-
MSQS
SP-12
S05C
-
0019-SP-Q00-
MSQS
SP-13
S01C
-
0019-SP-000-
MSQS
SP-14
S01C
-
U019-SP-000-
MSQS
SP-15
S06C
-
0019-SP-000-
MSQS
SP-16
SU5C
-
0019-DP-000-
MSQS
WUS
CTL
-
0019-DP-000-
MSQS
WBS
CTL
-
.'jluos in pph -jry weiyht
2-
nethy1
naphth-
naphtha-
acenaprv
alene
1 ene
thylene
170
250
40
910
2900
530
67U
210U
560
320
1200
600
36U
Z910
150
320
Z890
170
450
1300
170
270
Z680
110
310
7 70
110
220
Z550
56
320
620
120
190
330
76
440
1200
140
350
55
120
180
540
97
12
36
6.4
830
1900
210
250
450
94
1200
lyoo
290
61
150
22
21
83
26
1100
1200
120
U5
U5
U5
33
63
16
270
Z910
120
180
Z74U
65
230
530
49
720
1400
70
380
Z860
56
130
Z560
150
200
Z720
110
390
2700
290
810
4400
410
70
Z 270
75
110
Z290
45
U5
U5
U5
U5
190
U5
Number of Observations: 123
acenapn-
phenan-
anthra
thene
fluorene
threne
cene
21
36
170
62
350
410
2100
440
SOU
54U
1100
380
190
230
830
380
160
iyo
Z870
440
150
18
Z890
500
150
180
740
390
120
ISO
Z570
310
110
170
670
350
60
91
Z340
110
no
180
820
500
160
240
940
410
390
490
1100
330
180
220
1000
740
140
160
740
380
11
16
89
43
yuo
790
1900
710
140
220
640
330
2500
3100
11000
1400
U5
140
570
400
14
22
21U
56
790
1100
1800
1200
U5
US
11
8.7
J 5
14
67
43
240
250
Z480
220
120
120
Z480
210
68
88
350
140
640
610
1800
460
87
130
Z460
160
73
95
Z330
120
120
160
Z540
140
290
370
950
450
270
240
Z660
Z8b
36
42
Z160
Z36
27
32
Z150
Z39
US
05
U5
05
U5
U5
Z6
Z4
-------
MAIN SEDIMENT DUALITY SOHVEY ORGANIC CHEMICALS -
HIGH MOLECULAR WEIUHT PAH
Values in
ppb dry
wei ght
benzo(a)
f luor-
anthra
Drainage
Survey
Station
Sample Rep
anthene
pyrene
cene
110019-BL-OU0-
MSgS
BL-11
S05C
200
190
100
110019-BL-U00-
MSQS
Bl-12
S01C
-
510
530
270
110019-BL-000-
MSQS
BL-13
S05C
-
460
410
240
llOOiy-BL-OOU-
Msgs
Bt-14
S01C
-
3600
2900
2200
110U19-BL-000-
Msgs
BL-15
S01C
-
290
27U
140
110U19-BL-0U0-
Msgs
BL-16
S01C
-
530
470
220
llUOly-BL-OOO-
Msgs
BL-17
S01C
01
440
420
180
110U19-BL-000-
Msgs
BL-17
S01C
02
420
380
150
110019-BL-000-
Msgs
BL-18
S01C
-
390
330
210
110019-BL-000-
Msgs
BL-19
S01C
-
320
320
140
110019-BL-000-
Msgs
BL-20
S01C
-
430
360
170
110019-BL-000-
Msgs
BL-21
S05C
-
480
420
200
110U19-BL-U0Q-
Msgs
BL-22
S01C
-
740
600
370
110019-BL-000-
Msgs
BL-23
SU1C
-
1200
1000
600
110019-BL-000-
Msgs
Bl-24
S01C
-
650
530
290
11UO19-BI-OU0-
Msgs
BL-25
SObC
-
600
560
230
110019-BL-000-
Msgs
BL-26
S01C
-
800
760
320
110019-BL-000-
Msgs
BL-27
S01C
.
280
220
95
110019-BL-000-
Msgs
BL-28
SU5C
-
1200
620
350
110019-BI-000-
Msgs
BL-29
S01C
-
1800
1200
770
11U019-BL-0U0-
Msgs
BL-30
S01C
-
330
290
130
110019-BL-000-
Msgs
BL-31
SObC
-
890
620
260
110U19-BL-OOU-
Msgs
BL-32
S01C
.
980
730
510
110019-HY-000-
MSQS
CB-11
S01C
-
390
350
170
110019-CB-0U0-
Msgs
CB-12
S01C
.
51
51
25
110U19-CB-000-
Msgs
CB-13
S01C
-
48
52
21
110019-CB-000-
Msgs
CB-14
S01C
-
47
53
27
11U019-CI-000-
Msgs
CI-U
S02C
-
2400
2200
980
110019-CI-000—
Msgs
CI-12
S01C
-
2400
3700
1100
110019-CI-000-
Msgs
CI-13
SObC
.
1200
2000
560
110019-C1-000-
Msgs
CI-14
S01C
-
800
1200
270
110U19-C1-000-
Msgs
CMS
S01C
-
2800
3600
870
11U019-CW-000-
Msgs
Cl-16
SU5C
.
860
1300
330
110019-CI-000-
Msgs
CI-17
S05C
01
1300
2100
780
110019—C1-000-
Msgs
CI -17
S05C
U2
2500
1000
630
110019-C1-000-
Msgs
CI-18
SU1C
-
860
1500
4 30
110019-CI-000-
Msgs
CI-19
SU1C
-
710
1100
370
110019-C1-000-
Msgs
CI-20
S05C
.
7 90
900
500
110019-CI-UUO-
Msgs
CI-21
S01C
-
2700
4700
19U0
UU019-CI-0U0-
Msgs
CI-22
SU5C
-
lbOU
2600
1300
110019-CR-000-
Msgs
CR-11
S01C
.
16
16
5.5
nooiy-cR-ouo-
Msgs
CK-12
SObC
-
14
14
5.8
110U19-CR-000-
Msgs
CR-13
S01C
-
11
11
U5
i ndeno
benzo(b)
benzo(k)
(1,2.
fluor-
fluor-
benzo(a)
3-cd)
chrysene
anthene
anthene
pyrene
pyrene
200
C
c
52
59
530
C
c
29U
130
500
C
c
200
110
27UU
C
c
1200
310
310
C
C
140
77
390
C
C
420
110
290
C
C
240
89
330
C
C
240
100
400
c
C
270
130
250
C
C
230
85
320
c
C
250
86
330
c
C
300
100
520
c
C
440
160
740
C
C
740
280
550
c
C
350
170
420
C
C
350
14U
600
c
C
420
160
120
c
C
100
36
510
c
C
450
170
850
c
C
770
270
180
c
C
150
68
490
c
C
280
110
560
C
C
510
250
330
c
C
200
94
51
c
C
34
15
43
c
C
51
14
51
c
C
32
lb
1600
c
c
1300
630
1500
c
c
1100
680
810
c
c
860
420
400
c
c
460
200
12U0
c
c
1300
541
610
c
c
640
160
880
c
c
1200
480
730
c
c
1400
250
6yo
c
c
760
260
490
c
c
590
290
640
c
c
670
240
1500
c
c
2400
670
1300
c
c
1200
410
9.2
c
c
U5
U5
11
c
c
7 .1
Ob
U5
c
c
05
U5
-------
MAIN SEDIMENT QUALITY SUKVEY ORGANIC CHEMICALS - Values in ppb dry weight
HIGH MOLECULAH WEIGHT PAH
fluor-
UraiMije Survey Station Sample Rep antbene
17110019-CK-000-
MSQS
CR-14
S05C
_
16
17110019-HY-00U-
MSQS
HY-11
SOIC
-
1300
17H0019-HY-000-
MSQS
HY-12
S01C
-
680
17110U19-HY-01W-
MSQS
HY-13
S01C
-
1900
17110019-HT-000-
msqs
HY-14
S05C
.
2600
17110019-HY-000-
MSQS
HY-15
S01C
-
100G
17110019-Hr-000-
MSQS
HY-16
S01C
-
6400
17110019-HY-000-
msqs
HY-17
S01C
-
3900
1/U0019-HY-000-
HSQS
HY-18
SU1C
.
3300
17110019-MY-000-
MSQS
HY-19
S01C
-
5800
17110019-HY-000-
MSQS
HY-20
S01C
.
01
1800
17110019-HY-OOU-
MSQS
HY-2U
S01C
-
02
2000
1711U019-HY-000-
MSQS
HY-21
S01C
-
2000
17110019-HY-000-
MSQS
HY-22
S05C
-
3600
17110019-HY-000-
MSQS
HY-23
S01C
.
2500
17110019-HY-000-
MSQS
HY-24
S01C
-
1600
17110019-HY-000-
MSQS
HY-25
soic
-
1400
17UU019-HY-000-
MSQS
HY-26
S01C
-
2600
17110019-HY-000-
MSQS
HY-27
SOIC
.
720
17110U19-HY-000-
MSQS
HY-28
SOIC
-
1300
17110019-HY-000-
MSQS
HY-29
SOIC
_
600
1711001S-HY-OOO-
MSQS
HY-30
SOIC
-
790
miOOig-HY-UOO-
MSQS
HY-31
SOIC
-
U1
450
1711U019-HT-000-
MSQS
HY-31
SOIC
.
02
520
17U0019-HY-000-
MSQS
HY-32
SOIC
-
800
17110Q19-HY-OOU-
MSQS
HY-33
SOIC
-
830
17110019-HY-000-
MSQS
HY-34
SOIC
.
1400
171HX)19-HY-000-
MSQS
HY-35
SOIC
-
950
17U0019-HT-000-
MSQS
HY-36
SU1C
-
14U0
1711U019-HY-UUU-
MSQS
HY-37
SOIC
-
7 4 (J
17110019-HY-000-
MSQS
HY-38
SOIC
.
1200
17110U19-HY-000-
MSQS
HY-39
SOIC
-
Z490
17U0019-HY-000-
MSQS
HY-40
SOIC
.
Z780
1711U019-HY-0UU-
MSQS
HY-41
SOIC
-
Z700
17110019-HY-000-
MSQS
HY-42
SOIC
-
170
17110U19-HY-000-
MSQS
HY-43
SOIC
-
840
17110019-HY-000-
MSQS
HY-44
SOIC
-
72
1711U019-HY-000-
MSQS
HY-45
SOIC
-
Z650
17U0019-HY-000-
MSQS
HY-46
SOIC
-
1100
17110Q19-HY-U00-
MSQS
HY-47
S05C
.
Z690
17110019-HY-000-
MSQS
HY-48
SOIC
-
Z6bO
17110019-HY-000-
MSQS
HY-4y
SOIC
-
340
17110019-HY-OOU-
MSQS
HY-50
S05C
-
2bO
1 ndeno-
benzo(a)
benzo(b)
benzo(k)
(1
anthra-
f luor-
f1uor-
benzofa)
3-cd)
cene
chrysene
anthene
anthene
pyrene
pyrene
7.0
10
C
C
8.5
U5
570
1200
C
c
4 70
260
1300
1800
C
c
1200
580
1100
3300
C
c
1700
74ll
1600
2800
c
c
1300
600
1100
2600
c
c
53U0
1200
2200
6100
c
c
1800
1700
1200
2700
c
c
2400
69
2300
3900
c
c
4200
940
3500
5500
c
c
2100
1500
1300
2300
c
c
2600
920
C
C
c
c
2900
760
1300
2300
c
c
1400
720
2300
2700
c
c
6100
2700
1000
2300
c
c
2000
150
670
2300
c
c
1300
690
880
1500
c
c
530
350
1300
2600
c
c
1700
630
370
980
c
c
330
230
770
1100
c
c
67U
240
360
950
c
c
440
270
370
900
c
c
390
160
320
700
c
c
270
120
300
540
c
c
210
110
400
940
c
c
510
200
560
1100
c
c
2800
390
700
1100
c
c
51C
350
480
1200
c
c
520
420
760
1600
c
c
100C
410
500
940
r
>-
c
580
260
400
950
c
r
550
290
260
Z570
c
c
390
210
380
Z760
c
c
5?0
210
350
Z650
c
u
510
210
830
1800
r
c
890
300
560
650
c
c
580
380
26
62
c
r
42
20
330
Z410
c
c
310
130
520
Z770
c
c
480
220
310
Z560
c
c
7 j
17o
310
Z4B0
c
r
U
- ' -i
190
90
160
c
c
7S
be
110
170
c
r
• i ¦->
71
pyrene
18
1200
1300
2200
3300
3400
4800
4300
3300
5800
1700
2000
2100
2600
1900
1500
1400
3500
790
1200
800
480
460
500
1000
1300
1300
900
1400
880
1100
Z71U
Z720
2610
140
710
6b
Z590
Z910
Z640
Z640
200
290
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weioht
HIGH MOLECULAR WEIGHT PAH ' weignt
Urai naye
Survey
Station
Sample
17110019-HY-O00-
MSQS
HY-51
S01C
.
17110019-MD-000-
MSQS
MO-11
S01C
I71100ly-M0-O00-
Msgs
M0-12
S05C
_
1711OU19-MD-U00-
Msgs
MO-13
S01C
•
17110019-MI-000-
MSQS
HI -11
S01C
1711UU19-MI-000-
Msgs
HI—11
S01C
17110019-M1-000-
Msgs
MI — 12
S01C
1711U019-MI-0U0-
Msgs
MI-13
S01C
_
17110019—MI-000-
Msgs
HI —14
S01C
17110019-MI-000-
Msgs
MI —15
S01C
•
17110019-KS-000-
Msgs
RS-11
S01C
17110019-RS-U0U-
Msgs
RS-12
S01C
17110019-KS-000-
Msgs
RS-13
S01C
17110019-KS-000-
Msgs
RS-14
S05C
17110019-RS-U00-
Msgs
RS-14
S05C
_
17110019-K S-UOU-
Msgs
RS-15
S02C
1711001y-RS-000-
Msgs
RS-16
S01C
_
17110019-RS-000-
MSQS
RS-17
S01C
17110019-RS-000-
MSQS
RS-18
S01C
17110019-RS-00U-
MSQS
RS-19
S01C
_
17110019-RS-000-
MSQS
RS-20
S01C
_
17110U19-RS-000-
MSQS
RS-21
S01C
.
17110019-RS-U00-
Msgs
KS-22
S01C
1 7110019-RS-000-
Msgs
RS-24
S05C
17110019-SI-000-
MSQS
SI-11
S05C
1711U019-SI-000-
MSQS
SI-12
S01C
_
1 7110019-SI-000-
MSQS
SI-13
S01C
_
17110U19-SI-000-
Msgs
SI —14
S01C
1 7110019-SI-000-
MSQS
SI-15
SU5C
17110019-SP-000-
MSQS
SP-11
SU5C
1 7110019-SP-000-
MSQS
SP-12
SU5C
17110U19-SP-000-
MSQS
SP-13
S01C
17110019-SP-000-
MSQS
SP-14
S01C
17110019-SP-0UU-
MSQS
SP-15
S05C
17110019-SP—000-
MSQS
SP-16
SUbC
1 7110019-DP-000-
MSQS
WBS
CTL
17110U19-DP-U00-
MSQS
MBS
CTL
•
U1
02
01
02
f 1 uor-
anthene
210
2800
1500
1300
1500
1500
1300
980
1200
550
1000
1000
1300
1800
1600
79
1300
640
8100
850
180
3600
11
160
780
850
840
1100
530
520
550
1700
Z3O0
150
140
01 Ub
02 U5
pyrene
190
2900
1600
1600
1600
1500
1200
Z970
1000
Z480
1300
1300
1200
1700
1600
100
1900
620
5600
680
210
2100
12
110
Z680
Z920
67n
930
Z600
Z490
Z680
14U0
Z290
Z330
Z11U
U5
U5
benzo(a)
anthra-
cene
85
1200
710
530
620
560
450
330
420
100
650
480
1100
710
620
41
500
590
3200
350
76
1800
Lb
97
860
400
250
710
300
110
140
550
93
32
41
U5
Ub
Number of Observations: 123
i ndeno-
benzo(b)
benzo(k)
(1,2,
f luor-
f1uor-
benzoja)
3-cd)
chrysene
anthene
anthene
Pyrene
pyrene
160
C
C
110
30
1500
C
C
1600
710
920
C
C
1600
100
530
C
C
77U
JbO
820
C
C
yeo
480
880
C
C
920
420
610
c
C
63C
320
46U
c
C
480
23U
710
c
C
560
200
350
c
C
180
95
870
c
C
1000
350
650
c
c
1000
460
1400
c
C
980
600
1400
c
C
880
320
880
c
C
790
320
54
c
C
43
19
750
c
C
540
250
1000
c
C
880
17
4700
c
C
4000
770
400
c
C
2 70
90
92
c
C
110
58
2300
c
c
14u0
480
7.0
c
c
5 .6
Ub
9b
c
c
iou
40
1200
c
c
1400
570
630
c
c
730
180
400
c
c
600
210
710
c
c
h 70
270
260
c
c
410
190
170
c
c
84
U5
220
c
c
160
61
660
c
c
370
170
Z59
c
c
100
67
Z56
c
c
21
U5
Z69
c
c
45
23
U5
U5
U5
Ub
Ob
U5
U5
U5
Ub
U5
-------
MAIN SEDIMENT OUALITr SURVEY ORGANIC CHEMICALS - Values in ppt> dry weight
HIGH MOLECULAR HEIGHT PAH
tota 1
dibenzo-
benzo-
benzo
(a,h)an-
(ghi)
f luor
Drainage
Survey
Station
Samp 1e
Rep
thracene
perylene
anthei
17110019-BL-000-
MSOS
BL-11
S05C
22
78
260
1711U019-BL-00U-
Msgs
BL-12
S01C
-
34
150
1200
17110O19-BL-OOU-
MSgS
BL-13
S05C
-
30
120
540
17110U19-BL-OUO-
Msgs
BL-14
S01C
-
120
240
1100
17110U19-8L-000-
Msgs
BL-15
S01C
-
23
81
330
17111W19-BL-000-
msqs
BL-16
S01C
-
41
100
410
17110019-BL-000-
Msgs
BL-1 7
S01C
-
01
36
100
400
17110019-BL-000-
Msgs
BL-17
S01C
-
02
24
110
420
17110019-BL-000-
msqs
BL-18
S01C
-
53
130
480
17110019-BL-000-
Msgs
BL-19
S01C
-
16
77
510
17110U19-BL-000-
Msgs
BL-2U
S01C
.
21
89
360
1711UU19-BL-000-
Msgs
BL—21
S05C
-
23
90
420
17U0019-BI-000-
Msgs
BL-22
S01C
.
33
150
630
17U0019-BL-000-
Msgs
BL-23
S01C
-
62
240
1000
17110019-BL-OOO-
Msgs
BL-24
S01C
-
65
150
58C
1711U019-BL-000-
Msgs
BL-25
S05C
-
42
130
52C
17110019-BL-000-
Msgs
BL-26
S01C
-
50
170
:60
17110019-BL-000-
Msgs
BL-2 7
S01C
-
15
38
140
17110019-BL-000-
Msgs
BL-28
S05C
-
36
130
1300
17110019-BL-000-
Msgs
BL-29
S01C
-
110
210
560
17U0019-BL-000-
MSQS
BL-30
S01C
-
26
50
110
17110019-BL-0OU-
MSQS
BL- 31
SuSC
-
43
89
150
17110019-BL-000-
msqs
BL-32
S01C
-
90
190
800
17110U19-HY-00U-
Msgs
CB-11
S01C
-
2i
91
330
17110U19-CB-000-
Msgs
CB-12
S01C
-
US
14
51
17110019-CB-000-
MSQS
CB-13
S01C
-
o .4
12
33
17110019-CB-000-
MSOS
CB-14
S01C
-
U5
15
31
17110019-CI-000-
Msgs
CI-11
S02C
-
U25
780
3200
17110019-CI-000-
Msgs
CI-12
S01C
-
260
740
2500
17110019-CI-0QU-
MSQS
Cl-13
SObC
.
160
390
2100
17110019—CI —(M)0-
Msgs
C 1-1«
S01C
-
69
200
1100
17110019-CI-000-
MSQS
CI-1S
501C
-
160
420
2500
17110019-CW-000-
msqs
Cl-lf
S05C
-
50
230
640
17UOC19-CI-UOO-
Msgs
CI- 17
S05C
-
01
180
480
2600
17lluyi9-CI-000-
MSQS
CI-17
S05C
-
02
95
400
750
t71100i9-CI-000-
Msgs
CI-IB
S01C
-
110
290
1500
17U0019-CI-000-
MSQS
CI-19
S01C
-
:io
290
1500
17110019-CI-000-
MSjS
CI - 20
S05C
-
67
260
690
i7110019-C1-000-
Msgs
C1-21
SU1C
-
270
640
4400
I 7il0019-CI -UOO-
Msgs
Cl-22
sos;
-
151
2600
iai0019-CR-0l)t'-
msqs
CR-11
soic
-
U5
U5
18
i7:iooi9-CR-;oo-
Msgs
CR-12
SU5C
-
,5
U5
17
benzofa)
anthracene/
chrysena
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry weiqht
HIGH MOLECULAR WEIGHT PAH 9
Drai naje
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17U0019
17110019
17110019
17110019
, 17110019
17110019
J 17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019-
17110019
17110019-
1 711U019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019
17110019-
17110019-
17110019-
CR-OOO-
-CR-OOO-
HY-OOO-
HY-OOO-
-HY-OOO-
-HY-OOO-
-HY-OOO-
-HY-OOO-
HY-OOO-
-HY-OUO-
-HY-OOO-
-HY-OOO-
HT-OOO-
-HY-OOO-
-HY-OOO-
-HY-OOO-
-HY-OOO-
HY-OOO-
-HY-OOO-
-HY-OOO-
¦HY-OOO-
-HY-OOO-
-HY-OOO-
HY-OOO-
-HY-OOO-
•HY-OUO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOU-
HY-OOO-
HY-OOO-
HY-OOO-
HS(JS
Msgs
MSUS
Msgs
msqs
Msqs
MSQS
Msgs
Msgs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSUS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
total
dibenzo-
benzo-
benzo-
(a ,h)an-
(ghi)
f luor-
Station
Sample
Rep
thracene
perylene
anthei
CR-13
S01C
_
U5
U5
U5
Ck-14
S05C
-
US
U5
15
HY-11
S01C
-
82
100
1200
HY-12
S01C
-
2t>0
740
2100
HY-13
S01C
-
280
670
3800
HY-14
S05C
-
230
720
3600
HY-15
S01C
-
470
1100
5500
HY-16
S01C
-
S80
1900
8800
HY-17
S01C
-
bB
75
3700
HY-18
SU1C
-
280
1000
4800
HY-19
S01C
-
480
1100
5500
HY-20
S01C
-
01
340
740
5800
HY-20
S01C
-
02
240
610
5800
HY-21
S01C
-
270
610
6100
HY-22
S05C
-
1500
US
8500
HY-23
S01C
-
440
1100
2400
HY-24
S01C
-
170
610
2400
HY-25
S01C
-
110
440
1500
HY-26
SU1C
-
160
690
6300
HY-27
S01C
-
79
280
1200
HY-28
S01C
-
100
340
1800
HY-29
S01C
-
84
210
73U
HY-30
S01C
-
45
200
570
HY-31
S01C
-
01
41
170
540
HY-31
S01C
-
02
29
140
550
HY-32
S01C
-
75
290
3200
HY-33
SU1C
-
140
650
2200
HY-34
SU1C
-
100
290
1100
HY-35
S01C
-
140
310
1100
HY-36
sine
-
150
350
1600
HY-37
SU1C
-
110
340
900
HY-38
SU1C
-
110
290
1000
HY-39
S01C
-
82
210
1400
HY-40
S01C
-
65
210
920
HY-41
S01C
-
53
190
810
HY-42
S01C
-
100
320
1200
HY-43
S01C
-
120
420
670
HY-44
S01C
-
U5
19
150
HY-45
S01C
-
35
140
1100
HY-46
S01C
-
86
200
1500
HY-47
S05C
-
45
160
670
HY-48
S01C
-
58
160
1200
benzo(a)
anthracene/
chrysene
3300
-------
"AIH SCO1HENT DUAL ITT SURVEY ORGANIC CHEMICALS -
HIGH MULECUtAR HEIGHT PAH
Values in ppb dry weight
Oralnaye
0019-HY-0C0-
0019-HT-000-
00I9-HT-000-
0019-NMM0-
U019-M9-000-
0019-MO-000-
0019-MI-000-
0019-Ml-000-
0019-MI-U00-
U019-MI-000-
0019-Ml-000-
0019-MI-000-
0019-RS-000-
0019-RS-OOU-
0019-RS-000-
0019-RS-000-
0019-RS-000-
0019-RS-OOU-
0019-RS-00U-
0019-KS-000-
0019-RS-000-
0019-RS-000-
G019-RS-000-
0019-RS-000-
0019-RS-000-
0019-RS-000-
0019-SI-000-
0019-SI-000-
0019-SI-000-
0019-SI-000-
0019-SI-000-
0019-SP-000-
0019-SP-000-
0019-SP-000-
0019-SP-000-
0019-SP-000-
0019-SP-000-
0019-OP-000-
0019-DP-000-
Survey Station Sample Rep
MSQS
MSQS
msqs
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
total
dibenzo- benzo- benzo-
(a.h)an- (ghi) fluor-
thracene perylene anthenes
HT-49
S01C
-
18
54
200
MT-50
S05C
-
19
63
420
«r-si
S01C
-
U5
49
190
MO-11
S01C
-
140
740
1800
MD-12
SOSC
-
110
670
1400
MO-13
SOIC
-
110
340
940
MI-11
S01C
-
01
190
360
2400
MI—11
S01C
-
U2
130
320
2400
MI -12
S01C
-
61
240
940
Ml-13
S01C
-
48
160
1300
MI-14
SOIC
-
48
180
830
MI-15
S01C
-
29
100
910
RS-1I
S01C
-
85
320
940
RS-12
S01C
-
170
370
1700
RS-13
S01C
-
230
460
3000
RS-14
S05C
-
01
140
250
1200
RS-14
SOSC
-
02
79
260
940
RS-15
S02C
-
US
17
64
RS-16
soic
-
42
230
730
RS-17
suic
-
21
23
1000
RS-18
suic
-
320
U5
4200
KS-19
soic
-
21
76
400
RS-20
soic
-
9.6
57
190
RS-21
soic
-
200
480
1900
RS-22
soic
-
US
U5
21
RS-24
S05C
-
14
37
250
Sl-11
SOSC
-
ISO
570
1500
SI-12
suic
-
SB
280
810
SI-13
soic
-
42
170
610
Sl-14
soic
-
91
220
830
SI-15
SOSC
-
53
180
490
SP-U
S05C
-
U5
US
360
SP-12
SOSC
-
U5
55
610
SP-13
SOIC
-
37
160
580
SP-14
SOIC
-
US
67
140
SP-lb
S05C
-
U5
18
130
SP-16
SOSC
-
U5
25
170
HBS
CTl
-
01
US
U5
UBS
CTL
-
02
US
U5
Number of Observations: 1 if3
benzo(a)
anthracene/
chrysene
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS -
CHLOKIMATED AROMATIC HYURuCAkBONS
Values in ppb dry weight
1,3-di-
1,4-di-
1,2-di -
Drainage
Chloro-
chloro-
chloro-
Survey
Station
Sample Rep
beruene
benzene
benzere
17110019-8L-0U0-
MSQS
BL-11
SQ5C
_
US
U5
U5
17110Uly-BL-UUU-
msus
BL-12
S01C
-
US
U5
U5
17110019-BL-000-
MSQS
BL-13
S05C
-
U5
16
US
17110019-BI-0U0-
MSQS
BL-14
S01C
-
98
U5
U5
1711OO19-BL-U0O-
MSUS
BL-1S
S01C
-
U10
U10
U10
17110019-BL-000-
MSQS
Bl-16
S01C
-
USO
U50
U50
17110019-BL-UU0-
MSQS
BL-17
S01C
U1
U5
27
18
17110019-BL-000-
MSQS
BL-17
S01C
02
USO
U50
USO
1711O019-BL-000-
MSQS
HL-ia
S01C
-
USO
U50
U50
17110019-BL-000-
MSQS
BL-19
S01C
_
210
34
18
1711U019-BL-U00-
MSQS
BL-20
S01C
_
200
48
36
17110019-BL-000-
MSQS
BL-21
sosc
-
E170
U25
U25
17110019-BL-000-
MSQS
BL-22
S01C
-
72
100
19
1711U019-BL-000-
MSQS
BL-23
S01C
.
26
94
25
1711O019-BL-U00-
MSQS
BL-24
S01C
-
120
67
2S
17110019-BL-000-
MSQS
BL-25
S05C
•
S6
46
27
17110019-BL-000-
MSQS
BL-26
SOIC
_
IB
76
46
17110019-BL-000-
MSQS
BL-27
S01C
_
U20
U20
U20
17110019-BL-000-
MSQS
BL-28
sosc
.
US
U5
U5
17110019-BL-000-
MSQS
BL-29
soic
_
98
34
U5
17110019-BL-UUU-
MSQS
BL-30
SOIC
.
88
24
us
17110019-BL-000-
MSQS
BL-31
SOSC
_
14
7.8
U5
17110019-BL-000-
MSQS
BL-32
SOIC
_
110
S9
US
17110019-HY-000-
MSQS
CB-11
SOIC
-
U5
32
US
17110019-CB-000-
MSQS
CB-12
SOIC
_
US
U5
U5
17110019-CB-000-
MSQS
CB-13
SOIC
_
U5
US
U5
1 7110019-CB-000-
MSQS
ca-14
SOIC
-
U5
U5
U5
17110019-CI-000-
MSQS
CI-11
S02C
.
U2S
290
37
17110019-CI-000-
MSQS
CI-12
SOIC
-
U20
U20
U20
17110019-CI-OOO-
MSQS
CI-13
sosc
-
US
72
18
17110019-CI-OOO-
MSQS
CI -14
SOIC
-
U5
100
24
17110019-CI-OOO-
MSQS
Cl-lb
soic
-
U5
190
16
17110019-CW-000-
MSQS
CI-16
sosc
.
S7
260
350
17110U19-CI-UOO-
MSQS
CI-17
S05C
01
23
88
20
17110019-CI-000-
MSQS
CI -17
S05C
02
U5
150
25
17110019-C1-000-
MSQS
CI-IB
SOIC
-
18
76
17
17110019-CI-U0U-
MSQS
CI-19
SOIC
-
U5
51
14
17110019-C1-000-
MSQS
CI-20
SOSC
-
US
64
16
17110019-C1-000-
MSQS
C1-21
SOIC
-
U10
U10
U10
17110019-C1-000-
MSQS
C1-22
SOSC
-
Ub
27
US
1 7110U19-CR-000-
MSQS
CK-11
SOIC
-
U5
US
U5
1711OO19-CR-0U0-
MSQS
CR-12
S05C
-
US
us
US
tri -
chloro-
hexa-
Chloro-
naph-
chloro-
benzene
thalene
benzene
US
US
U10
'J 5
U5
U10
U5
U5
U10
US
US
U1U
U5
US
U10
U5
U5
U10
U5
U5
U10
US
U5
U10
U5
U5
U10
US
U5
U10
U5
U5
U10
U5
U5
uio
US
U5
U10
US
US
UlU
U5
US
UIO
US
us
UlU
U5
U5
UIO
US
US
UIO
U5
U5
UIO
U5
US
UIO
US
us
UIO
J 5
US
UIO
US
U5
UIO
U5
US
40
U5
U5
uio
U5
US
UIO
U5
US
UIO
U2S
U25
USO
US
US
uio
US
U5
UIO
U5
U5
UIO
U5
U5
UIO
US
U5
UIO
U5
U5
UIO
U5
U5
UIO
us
US
UlU
U5
US
UIO
us
US
UIO
U10
U10
U20
U5
US
UIO
US
us
UIO
US
Ub
UIO
-------
MAIN SEUIMENT QUALITY SUKVEY OkGANIC CHEMICALS -
CHI URINATED AKUMATIC MYOKOCAKBONS
Values in ppb dry weight
1,3-di-
1,4-di-
1,2-di -
Dralnaye
chloro-
chloro-
chloro-
Survey
Station
Sample
Rep
benzene
benzene
benzene
17110019-CR-000-
MSQS
CR-13
SQ1C
U5
US
U5
1711UU19-CR-UOO-
Msgs
CR-14
S05C
-
US
US
U5
1711U019-HY-00U-
MSQS
HY-11
S01C
-
Kb
Ub
US
17110Q19-HY-UUO-
MSQS
HY-12
S01C
-
US
19
L5
17110O19-HY-UOO-
Msgs
HY-13
SU1C
-
28
05
US
17H0019-HY-000-
MSQS
HY-14
SObC
-
US
21
US
17110019-HY-000-
MSQS
HY-15
S01C
-
28
U5
U5
17110019-HY-000-
MSQS
HY-16
S01C
.
U5
U5
Ub
17110019-HY-000-
MSQS
HY-17
S01C
-
Ub
22
U5
17U0019-HY-000-
MSQS
HY-16
S01C
-
U5
18
U5
17110U19-HY-000-
MSQS
HY-19
S01C
-
US
US
U5
17110019-HY-OO0-
MSQS
HY-20
SU1C
.
01
U5
87
U5
17110U19-HY-UO0-
MSQS
HY-20
S01C
-
02
Ub
68
U5
17110U19-HY-OUO-
MSQS
HY-21
S01C
-
Ub
94
U5
17110019-HY-000-
MSQS
HY-22
SU5C
-
85
180
73
17110019-HY-000-
MSQS
HY-23
S01C
-
U5
53
U5
17110019-HY-000-
MSQS
HY-24
S01C
-
19
39
Ub
1711U019-HY-000-
MSQS
HY-25
SG1C
-
22
67
19
17110019-HY-000-
MSQS
HY-26
S01C
-
U20
U20
U20
17110019-HY-000-
MSQS
HY-27
S01C
-
33
35
1)5
17110019-HY-OQQ-
MSQS
HY-28
S01C
.
Ub
42
U5
17110019-HY-OUO-
MSQS
HY-29
S01C
-
36
US
U5
17110019-MY-000-
MSQS
HY-30
S01C
_
150
U5
U5
17110019-HY-QUO-
MSQS
HY-31
S01C
-
01
110
2b
U5
17110019-HY-000-
MSQS
HY-31
S01C
-
02
210
30
U5
1711Q019-HY-000-
MSQS
HY-32
S01C
.
U5
40
US
1711U019-HY-000-
MSQS
HY-33
S01C
-
22
98
U5
17110019-HY-UO0-
MSQS
HY-34
SU1C
.
Ub
26
US
1711UU19-HY-000-
MSQS
HY-35
S01C
-
46
64
U5
17110019-HY-000-
MSQS
HY-36
S01C
-
8U
180
US
17U0019-HY-000-
MSQS
HY-37
S01C
-
8.2
56
8.8
17110019-HY-000-
MSQS
HY-38
S01C
-
110
Ub
U5
17110019-HY-000-
MSQS
HY-39
S01C
.
65
150
Ub
17UU019-HY-0U0-
MSQS
HY-40
S01C
.
44
lbO
U5
17110U19-HY-000-
MSQS
HY-41
S01C
-
Ub
81
14
1711U019-HY-000-
MSQS
HY-42
S01C
-
23
64
14
17110Q19-HY-U0U-
MSQS
HY-43
S01C
.
13
71
9.2
17110019—HY—000-
MSQS
HY-44
S01C
-
Ub
U5
U5
17110019-HY-000-
MSQS
HY-45
S01C
-
40
80
U5
17110019-HY-UUO-
MSQS
HY-46
SU1C
-
100
200
46
17110019-HY-000-
MSQS
HT-47
SObC
-
19
120
22
17110U19-HY-000-
MSQS
HY-4(J
Sj 1 Lr
-
'Jb
200
Ub
tri-
chloro-
hexa-
chloro-
naph-
chloro-
benzene
thalene
benzene
U5
Ub
U10
U5
U5
U10
U5
Ub
U10
U5
Ub
22
U5
U5
31
U5
Ub
U10
U5
U5
U10
U5
U5
uio
Ub
U5
U10
Ub
U5
UIO
Ub
Ub
UIO
Ub
Ub
61
U5
Ub
63
U5
U5
78
260
U5
730
U5
Ub
UIO
27
U5
67
26
U5
35
U5
Ub
33
U5
us
UIO
U5
us
70
Ub
us
UIO
U5
us
UIO
U5
us
25
Ub
Ub
23
31
Ub
b4
Ub
Ub
b6
14
US
24
38
Ub
84
b4
Ub
22U
34
Ub
96
38
Ub
140
120
U5
77
90
U5
130
100
Ub
230
64
Ub
23U
bl
U5
130
U5
U5
UIO
110
Ub
120
260
Ub
320
51
05
100
39
•ob
6y
-------
MAIN SEDIMENT DUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weiaht
CHLORINATED AROMATIC HYDROCARBONS
Drai naye
171iouiy
17110019
l7l10019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110U19
17110019
17110019
17110019
17110019
3» 17110019
Ko 17110U19
-J 17110019
17110019
17110019
17110019
17110019
17110019
17110019
1 7110019'
17110019
17110019-
17110019'
17110019-
17110019'
17110019-
17110019
17110019
17110019
17110019
17110019-
-HY-OOO-
-HY-OOO-
-HY-OOO-
MO-OOO-
-MD-OOO-
-MO-UOO-
MI-OOO-
-MI-OOO-
-MI-OUO-
-M1-000-
-MI-OOO-
MI-OOO-
-RS-OOO-
RS-OOO-
-RS-OOO-
RS-OUO-
-RS-OOO-
KS-OOO-
RS-OOO-
RS-OOO-
RS-OUO-
RS-OOO-
RS-OOO-
RS-OOO-
•KS-000-
RS-OUO-
SI-OOO-
SI-OOU-
Sl-OOO-
SI-OOO-
SI-OOO-
SP-OOO-
SP-OOO-
SP-OOO-
SP-OOO-
SP-OOO-
SP-OOO-
OP-OOO-
UP-OOO-
MSQS
MSQS
Msgs
MSQS
MSQS
MSQS
MSUS
msqS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-49
HY-50
HY-51
MO-11
MO-12
MO-13
MI - H
MI — 11
MI-12
MI -13
Ml-14
MI-15
RS-11
RS-12
RS-13
R S—14
RS-14
RS-lb
RS-16
RS-17
RS-18
RS-19
RS-2U
RS-21
KS-22
RS-24
SI-11
S1-12
SI—13
SI —14
SI-15
SP-11
SP-12
SP-13
SP-14
SP-15
SP-16
WbS
UBS
l ,3-ai-
1,4-di-
1 .2-'
chloro-
chloro-
chl o
Sample
Kep
benzene
benzene
benzi
S01C -
16
10
U5
S05C -
U10
U10
U10
S01C -
28
Ub
Ub
S01C -
Ub
180
97
S05C -
U5
63
3b
S01C -
Ub
4U
14
S01C -
U1
Ub
22
Ub
S01C -
U2
Ub
19
Ub
suic -
43
33
U5
S01C -
12
17
Ub
S01C -
Ub
18
Ub
S01C -
36
17
Ub
S01C -
Ub
60
US
S01C -
40
2b
Ub
S01C -
US
110
16
SObC -
01
Ub
26
US
S05C -
02
Ub
29
Ub
S02C -
Ub
Ub
Ub
S01C -
Ub
40
Ub
S01C -
Ub
38
9.4
S01C -
Ub
2S0
18
S01C -
Ub
10
Ub
S01C -
12
Ub
Ub
S01C -
US
73
Ub
S01C -
U5
U5
US
SObC -
Ub
Ub
Ub
S05C -
US
24
16
S01C -
US
24
US
S01C -
US
23
Ub
S01C -
19
30
US
SObC -
US
17
US
SObC -
8.6
11
US
SOSC -
12
13
Ub
SUIC -
Ub
US
Ub
S01C -
Ub
Ub
Ub
S05C -
Ub
10
Ub
SObC -
lb
12
U5
CTL -
01
Ub
Ub
Ub
CTL -
02
Ub
Ub
Ub
Number of Observations:
123
tri-
ch1oro-
tiexa-
chloro-
naph-
cbloro-
beozene
thalene
benzene
US
Ub
U10
Ub
Ub
UlU
US
Ub
1/
16
Ub
U10
7.3
Ub
U10
Ub
U5
U1Q
Ub
Ub
UliJ
Ub
Ub
U10
Ub
Ub
UK)
Ub
Ub
ulu
Ub
Ub
Ull)
Ub
Ub
UlU
Ub
Ub
U10
Ub
US
U10
Ub
Ub
U10
Ub
Ub
U10
Ub
Ub
UlU
Ub
US
UlU
Ub
Ub
U10
Ub
US
U10
Ub
Ub
U10
Ub
U5
UlU
Ub
US
U10
Ub
Ub
U10
Ub
Ub
uic
US
Ub
UlU
Ub
Ub
U10
Ub
Ub
UlU
Ub
US
U10
Ub
Ub
UlU
US
U5
U10
Ub
US
U10
Ub
Ub
U10
Ub
Ub
UlU
Ub
Ub
U10
Ub
Ub
UlU
Ub
Ub
U10
Ub
Ub
UlU
Ub
Ub
U10
-------
MAIN SEDIMENT QOAUTr SUKVElf ORGANIC CHEMICALS -
CHLORINATED All CHATIC HYDROCARBONS
Values in ppb dry weight
hexa-
nexa-
chloro-
nexa-
chloro-
Cyclo-
chloro-
buta-
penta-
Drainage
Survey
Station
Sample
Rep
ethane
diene
diene
17110O19-8L-UUO-
MSQS
BL-11
S05C
USO
025
17110019-BL-000-
Msgs
BL-12
S01C
-
U50
U2S
17110019-8L-000-
msqs
BL-13
SOSC
.
ut>0
U2S
17110019-Bl-UW-
msqs
BL-14
S01C
.
USO
02S
17110019-BL-OOU-
MSQS
BL-lb
S01C
USO
U25
17110U19-BL-0U0-
MSQS
BL-16
S01C
-
USO
U2S
17110019-BL-000-
MSQS
BL.-17
S01C
-
01
USO
U2S
17110019-BL-000-
msqs
BL-17
S01C
-
02
USO
USO
171100I9-W.-000-
MSQS
BL-18
S01C
.
USO
USO
17110019-81-000-
MSQS
BL-19
S01C
-
USO
U25
17110U19-BL-000-
MSQS
Bl-20
S01C
.
USO
U25
17110U19-BI-QUO-
MSQS
Bl-21
SOSC
.
USO
U2S
1711U019-BL-000-
MSQS
Bl-22
SU1C
.
U100
U25
17U0019-BL-000-
MSQS
BL-23
S01C
-
U50
U25
17UUU19-W.-UUO-
MSQS
BL-24
S01C
-
USO
U2S
17U0019-BI-000-
MSQS
BL-25
S05C
-
USO
025
_ 17110U19-BL-000-
MSQS
BL-26
S01C
.
USO
U2S
i 17110019-61-000-
MSQS
BL-27
S01C
.
USO
U50
Jg 17110019-BL-000-
HSQS
BL-28
SOSC
_
USO
025
17U0019-BL-000-
MSQS
Bl-29
S01C
-
USO
U25
17110019-BL-000-
MSQS
BL-30
S01C
.
USO
U25
17110019-BI-000-
MSQS
BL-31
S05C
.
USO
025
17110019-61-000-
MSQS
BL-32
S01C
.
ut>u
U25
1711UU19-HY-000-
MSQS
CB-U
S01C
-
U50
64
171I0019-CB-000-
MSQS
CB-12
S01C
-
U5U
025
17110019-CB-OOO-
MSQS
CB-13
S01C
-
uso
025
17110019-CB-000-
MSQS
CB-14
S01C
.
U50
U25
17110019-CI-000-
MSQS
Cl-ll
S02C
-
U250
U130
17110Q19-CI-000-
MSQS
Cl-12
S01C
-
U100
U25
17110019-CI-0O0-
MSQS
Cl-13
SOSC
-
USO
025
17110019-C1-000-
MSQS
CI-14
SulC
-
usu
U25
17110U19-CI-000-
Msqs
CI-15
S01C
-
U50
025
17110019-CW-000-
MSQS
CI-16
SUbC
-
U50
U25
17110019-CI—000—
MSQS
Cl-17
SOSC
-
01
U50
U25
17U0019-CI-OUO-
MSQS
CI-17
S05C
.
02
USO
025
17110019-CI-000-
MSQS
CI-IB
S01C
-
USO
U2S
17110019-CI-OUO-
MSQS
CI-19
S01C
-
U50
U25
17110019-CI-OOU-
MSQS
C1-20
S05C
.
U50
U25
17110019-C1-000-
HSQS
C1 -21
S01C
-
U100
USO
17110019-CI-000-
MSQS
C1-22
sosc
-
U50
U25
17110019-CR-000-
MSQS
CR-11
S01C
-
USO
U25
-------
main sediment quality survey organic CHEMICALS
CHLOKlNATEl) ALIPHATIC hyukocakbuns
«a lues in ppb dry weight
Uralnaye
17110019-CR-0U0-
17110U19-CR-000-
17110019-CR-UU0-
17110019-HY-000-
17110019-HV-000-
17110019-HY-000-
17110019-HY-000-
1711U019-HY-0UU-
17110019-HY-000-
17110U19-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-OOO-
17110019-HY-U00-
17110019-HY-000-
17110019-HY-000-
¦f* 17110019-HY-000-
ro 17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
l 7110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-00U-
17110019-HY-000-
17110019-HY-000-
17110U19-HY-00U-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
1711UU19-HY-UOO-
17110019-HY-000-
17110U19-HY-UU0-
17110019-HY-000-
171100iy-HY-0UU-
17110019-HY-000-
17110019-HY-0U0-
17110019-HY-000-
Survey Station Sample Rep
MSQS
CR-12
susc
_
MSQS
CR-13
soic
_
Hsgs
CR-14
S05C
_
MSQS
HY-11
SOIC
_
MSQS
HY-12
SOIC
_
MSQS
HY-13
SOIC
_
MSQS
HY-14
S05C
MSQS
HY-15
SOIC
_
MSQS
HY-16
SOIC
_
MSQS
HY-17
SOIC
_
MSQS
HY-18
SOIC
_
MSQS
HY-19
SOIC
-
MSQS
HY-20
SOIC
.
MSQS
HY-20
SOIC
-
MSQS
HY-21
SOIC
.
MSQS
HY-22
S05C
_
MSQS
HY-23
SOIC
_
MSQS
HY-24
SU1C
MSQS
HY-25
SOIC
MSQS
HY-26
SOIC
MSQS
HY-27
SOIC
_
MSQS
HY-28
SOIC
_
MSQS
HY-29
SOIC
MSQS
HY-30
SOIC
.
MSQS
HY-31
SOIC
•
MSQS
HY-31
SOIC
_
MSQS
HY-32
SOIC
_
MSQS
HY-33
SOIC
_
MSQS
HY-34
SOIC
_
MSQS
HY-35
SOIC
MSQS
HY-36
SOIC
_
MSQS
HY-37
SOIC
_
MSQS
HY-38
SOIC
_
MSQS
HY-39
SOIC
_
MSQS
HY-40
SQ1C
.
MSQS
HY-41
smc
_
MSQS
HY-42
SOIC
-
MSQS
HY-43
SOIC
_
MSQS
HY-44
SOIC
_
MSQS
HY-45
SOIC
_
MSQS
HY-46
SOIC
-
hexa-
nexa-
chloro-
hexa-
chloro-
cyclo-
cnloro-
buta-
penta-
ethane
di ene
diene
U50
U25
U50
U25
USO
U25
USO
U25
USO
U25
USO
U25
U50
E1S
U50
U25
U50
U25
U50
31
U50
U25
USO
U25
U50
140
U50
160
U50
170
28U0
730
U50
170
140
140
USO
130
USO
U25
U50
U25
U50
120
U50
U2S
U50
U25
U50
50
USO
48
U50
98
USO
130
U50
34
USO
190
U50
360
USO
130
U50
210
U50
300
U50
3S0
U50
680
U50
270
UbU
180
J5U
U25
uso
440
J3 0
940
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS -
CHLORINATED ALIPHATIC HYDROCARBONS
Values in ppb dry weight
hexa-
hexa-
chloro-
hexa-
chloro-
cyclo-
Drainage
chloro-
buta-
penta-
Survey
Station
Sample
Rep
ethane
diene
dfene
7110019-HV-000-
MSQS
HY-47
SOSC
uso
290
711U019-HY-U00-
MSQS
HY-48
S01C
-
USO
220
7110019-HY-000-
MSQS
HY-49
S01C
-
USO
U25
7110U19-HY-000-
MSQS
HY-SO
S05C
.
USO
U2S
7U0U19-HT-00O-
MSQS
HY-S1
S01C
-
uso
32
7 HOC 19-MO-000-
MSQS
MD-11
sine
-
uso
U2S
7110019-MD-000-
MSQS
MO-12
SU5C
-
USO
U25
7U0019-MU-000-
MSQS
MO-13
SU1C
-
USO
U2S
7U0019-M1-000-
MSQS
MI-11
S01C
-
U1
U50
U2S
7110U19-MI-00U-
MSQS
Ml-il
S01C
-
U2
U50
U25
7U0019-MI-000-
MSQS
Ml-12
SU1C
-
U5U
U25
7110019-MI-000-
MSQS
Ml-13
S01C
-
U50
U2S
7U0019-M1-0UU-
MSQS
MI-14
S01C
•
USO
U2S
7110019-HI-000-
MSQS
MI-IS
S01C
-
USO
U2S
711UQ19-R5-000-
MSQS
RS-11
S01C
-
U50
U2S
7110019-RS-000-
MSQS
HS-12
S01C
.
USO
U2S
7U0019-RS-000-
MSQS
RS-13
S01C
-
USO
U25
7110019-RS-000-
MSQS
RS-14
SOSC
-
01
U50
U2S
7U0019-RS-000-
MSQS
RS-14
SOSC
-
02
U50
U2S
71100I9-RS-000-
MSQS
RS-1S
S02C
-
USO
U2S
7UQ019-RS-000-
MSQS
RS-16
S01C
-
USO
U25
7H0019-RS-000-
MSQS
RS-17
S01C
-
USO
U2S
7110019-RS-000-
MSQS
RS-18
suic
-
USO
U25
7110019-RS-000-
MSQS
RS-19
S01C
-
USO
U2S
7110019-RS-000-
MSQS
RS-20
S01C
-
U50
U25
7U0019-RS-000-
MSQS
RS-21
S01C
.
USO
U2S
7110019-HS-000-
MSQS
RS-22
S01C
-
U50
02 S
7U0019-RS-00U-
MSQS
RS-24
SOSC
-
USU
U2S
7110019-SI-000-
MSQS
SI-11
SOSC
-
USO
U2S
7110019-SI-000-
MSQS
SI-12
S01C
-
U50
U25
7110U19-SI-000-
MSQS
SI — 13
S01C
-
U50
U25
711Q019-SI-00U-
MSQS
SI-14
S01C
-
USO
U25
7110019-SI-000-
MSQS
Si-lb
SOSC
-
USO
U25
7110019-SP-OUO-
MSQS
SP-11
SOSC
-
USO
02 S
7110019-SP-U00-
MSQS
SP-12
S05C
-
USO
U25
7U0019-SP-000-
MSQS
SP-13
S01C
-
USO
U25
7110019-SP-000-
MSQS
SP-14
S01C
-
USO
U2S
-------
MAIN SEUIMENT QUALITY SURVEY ORGANIC CHEMICALS -
CHLORINATEU ALIPHATIC HYDROCARBONS
Values in ppb dry weiyht
Drainaye
Survey
Station
Sample
17U0019-SP-000-
MSUS
SP-15
S05C -
1711UU19-SP-UOO-
Msgs
SP-16
S05C -
17110019-UP-000-
MSOS
WBS
CTL -
i7iiooiy-op-uuo-
Msgs
WBS
CTL -
Number of Observations:
123
3»
u>
hexa-
hexa-
chloro-
hexa-
chloro-
cyclo-
Rep
ch1oro-
buta-
penta-
ethane
di ene
diene
U50
U25
UbO
U2b
01
USO
U2S
02
USO
U2b
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weiqht
PHTHALATES
di -n-
dimethyl
diethyl
butyl
Drainage
phtha-
phtha-
phtha
Survey
Station
Samp
e
Rep
late
1 ate
late
711U019-BL-0Q0-
MSQS
BL-11
S05C
-
U50
U10
Z140
7110019-BL-000-
MSQS
BL-12
S01C
-
L50
43
Z1200
711UU19-BI-000-
msqs
BL-13
S05C
-
L50
U10
2210
711UU19-BL-000-
MSQS
Bt-14
S01C
-
050
53
Z1100
711UOiy-Bl-OOU-
MSQS
BL-lb
S01C
-
050
U10
Z420
7110019-BL-000-
MSQS
BL-16
S01C
-
U50
U10
Z230
7110019-BL-000-
MSQS
BL-17
S01C
.
01
L50
U10
B25
7110019-BL-000-
MSQS
BL-17
S01C
-
02
UbO
U10
Z370
7110019-BL-000-
MSQS
BL-18
S01C
-
U50
U10
Z1400
7110019-BL-000-
MSQS
BL-19
S01C
-
U50
U10
Z1200
7110U19-BL-000-
MSQS
BL-20
S01C
-
U50
U10
ZS
7110019-BL-000-
MSQS
BL-21
S05C
-
U50
U10
Z530
7110019-BL-000-
MSQS
BL-22
S01C
-
050
U10
Z760
7110019-BL-000-
MSQS
Bl-23
S01C
-
U50
U10
Z1600
7110019-BI-000-
MSQS
BL-24
S01C
-
UbO
U10
Z1000
7110019-BL-000-
MSQS
BL-25
S05C
-
L50
U10
B25
7110019-BL-000-
MSQS
BL-26
S01C
.
U50
U10
Z180
7110019-BL-000-
MSQS
BL-27
SU1C
-
U50
U10
Z340
7U0019-BL-000-
MSQS
Bl-28
SU5C
-
L50
U10
B2b
7110U19-BI-000-
MSQS
BL-29
S01C
.
y7
010
B25
7110O19-BL-OOU-
MSQS
BL-30
S01C
-
UbO
U10
B25
711U019-BL-UOU-
MSQS
BL-31
SObC
-
U50
U10
Z420
711U019-BL-00U-
MSQS
BL-32
S01C
-
U50
U10
Z85
7110019-HY-000-
MSQS
C8-11
S01C
-
U50
U10
Z9800
7110019-CB-000-
MSQS
CB-12
S01C
-
U100
L20
Z3700
7110019-CB-000-
MSQS
CB-13
S01C
-
U50
U10
Z150
7110019-CB-U00-
MSQS
CB-14
SU1C
-
UbO
U10
Z280
711001V-CI-000-
MSQS
CI-11
S02C
-
U250
UbO
U50
7110019-CI-000-
MSQS
Cl-12
S01C
-
84
U10
Z510
7U0019-CI-UO0-
MSQS
CI -13
SOSC
-
58
U10
Z50
711U019-CI-000-
MSQS
CI—14
S01C
-
66
23
Z15
7110019-CI-UO0-
MSQS
CI-15
S01C
-
L50
31
Z70
7110019-CW-000-
MSQS
CI -16
S05C
-
U50
U10
21600
711U019-C1-U00-
MSQS
CI-17
S05C
-
01
78
38
Z270
7110019-CI-000-
MSQS
Cl-17
SOSC
-
02
U50
U10
Z1000
71L0019-CI-OUO-
MSQS
CI-18
S01C
.
150
26
B25
7110019-CI-OUU-
MSQS
CI-19
S01C
-
L50
U10
B25
7110019-CI-UU0-
MSQS
C1-20
SU5C
.
U5U
U10
Z130
7110019-CI-000-
MSQS
CI-21
S01C
-
L10U
44
Z140
711UU19-CI-000-
MSQS
CI-22
S05C
-
U50
U10
Z20
7110019-CR-000-
MSQS
CR-11
S01C
-
U50
U10
Z760
7U0019-CR-000-
MSQS
CR-12
S05C
-
UbO
13
82b
butyl
benzol
phtha-
1 ate
63
93
83
64
025
U25
245
U25
U25
U25
U25
U25
U25
U25
U25
Z25
U25
U2b
U25
L25
L25
U2b
U25
U25
U60
U25
025
U130
660
210
130
150
U25
L25
U25
33
56
U25
LbO
L2b
025
U25
Dis(2-
ethy1-
hexy1)-
phtha-
1 ate
460
1000
760
780
120
B25
B25
U25
U25
025
U25
U25
B25
U25
U25
B25
B2b
025
325
B2b
B2b
B25
B25
B25
B2b
B25
B25
U130
6600
31U0
lbOO
1800
860
550
700
790
930
LI25
710
4 JO
825
325
di -n-
octy 1
phtha-
late
025
U25
U2b
27
U25
U25
U2b
U25
025
U25
U25
U25
U2b
U25
U2b
U25
U2b
U25
U2b
L25
U25
U25
U2b
U25
2bO
L25
U25
U130
290
130
49
U25
U25
U25
U25
U25
U25
U25
050
U25
U25
U2b
-------
PhIha|SATF^NT yUALITY SURVEr 0KGANIC CHEMICALS - val'^s in PPb dry weight
Drai nage Survey
17110019-CR-000- MSQS
17110019-CK-OOU- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-UOO- MSgS
17110019-HY-000- MSQS
17110019-HY-OOU- MSgS
17110019-HY-000- MSgS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-0U0- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-0U0- MSQS
17110U19-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
1711U019-HY-000- MSQS
U> 17110019-HY-000- MSQS
00 17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-00U- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-OOU- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
1 7110019-HY-000- MSQS
17110019-HY-000- MSQS
1711OO19-HY-0UO- MSQS
1711001y-HY-UUU- MSgS
17110019-HY-000- MSQS
Station
Sample
Rep
CR-13
S01C -
CR-14
SOSC -
HY-11
S01C -
HY-12
S01C -
HY-13
S01C -
HY-14
S05C -
HY-15
SU1C -
HY-16
S01C -
HY-17
S01C -
HY-18
sine -
HY-19
sine -
HY-20
soic -
01
HY-20
S01C -
U2
HY-21
SOIC -
HY-22
S05C -
HY-23
SOIC -
HY-24
SOIC -
HY-25
SOIC -
HY-26
SOIC -
HY-27
SOIC -
HY-28
SOIC -
HY-29
SOIC -
HY-30
SOIC -
HY-31
SOIC -
01
HY-31
SOIC -
02
HY-32
SOIC -
HY-33
SOIC -
HY-34
SOIC -
HY-35
SOIC -
HY-36
SOIC -
HY-37
SOIC -
HY-38
SU1C -
HY-39
SOIC -
HY-40
SOIC -
HY-41
SOIC -
HY-42
SOIC -
HY-43
sine -
HY-44
SOIC -
HY-45
SOIC -
HY-46
SOIC -
HY-47
SOSC -
HY-48
sine -
di -n-
dimethyl
di ethyl
butyl
phtha-
phtha-
phtha
1 ate
1 ate
1 ate
U50
10
825
U50
18
230
150
010
BIO
UbO
U10
Z5100
220
U10
810
U50
010
2270
74
U10
BIO
180
U10
BIO
U50
U10
825
550
U10
Z260
200
U10
Z260
890
37
Z12
680
74
Z130
1100
42
Z100
U50
U10
Z560
350
U10
Z92
120
47
Z100
U50
U10
Z930
50
65
350
L50
U10
BIO
U50
U10
010
73
U10
BIO
71
U10
BIO
L50
27
B25
U100
020
Z260
50
48
B25
UbO
U10
Z450
L50
26
BIO
52
22
810
1000
U10
BIO
U50
U10
B25
64
33
BIO
L50
010
Z15
270
50
Z180
510
30
Z140
U50
010
B25
UbO
010
B25
L50
U10
825
L50
010
U10
L50
44
Z50
U50
010
Z1500
B5
120
Z530
Dis(2-
butyl
ethy t -
di -n-
benzyl
hexy1) -
OCty 1
phtha-
phtha-
phtha-
late
1 ate
1 ate
025
825
U25
025
825
U25
025
B25
39
025
B25
U25
67
Z360
44
025
B25
U25
025
B25
U2b
U25
B25
U25
U25
B25
025
U25
U25
U25
100
1500
U25
U25
920
50
025
U25
U25
025
86U
U25
U25
3000
U25
no
710
U25
470
810
47
U25
530
U25
33
U25
U25
300
825
U2S
U25
B2S
U25
580
B25
U25
025
025
U50
U25
B25
L25
050
Z200
050
58
825
U25
025
825
025
350
B25
U25
890
Z10
L25
025
B25
U25
025
B25
U25
U25
B25
U25
130
320
U25
100
480
025
62u
1500
02b
U25
Z90
U25
U25
Z40
U25
U25
825
U25
29
U25
U2b
U25
440
U25
U25
U25
U25
250
370
U25
-------
Siafflf" "UALITr SURVEY CHEMICALS - values In ppb dry weight
Drainage
Survey
Station Sample
0019-HY-000-
HSQS
HY-49
S01C -
0019-HY-000-
HSQS
HY-50
SOSC -
0019-HY-000-
hsqs
HY-51
S01C -
0019-MD-000-
HSQS
HO-11
S01C -
0019-HD-000-
MSQS
HO-12
SOSC -
0O19-W-UO0-
hsqs
MD-13
S01C -
UU19-HI-000-
hsqs
HI — 11
S01C -
Q019-HI-000-
HSQS
HI-11
S01C -
U019-HI-000-
HSQS
HI—12
S01C -
0019-MI-000-
MSQS
HI —13
S01C •
0019-MI-000-
HSQS
HI—14
S01C -
0019-HI-000-
HSQS
MI-IS
S01C -
0019-RS-000-
HSQS
RS-11
S01C -
0019-RS-000-
MSQS
RS-12
S01C -
0019-RS-000-
MSQS
RS-13
S01C -
0019-RS-000-
MSQS
RS-14
S05C -
0019-RS-000-
MSQS
RS-14
S05C -
0019-RS-000-
MSQS
KS-15
S02C -
0019-RS-000-
MSQS
RS-16
S01C -
Q019-RS-QQ0-
MSQS
RS-17
S01C -
0019-RS-000-
MSQS
RS-18
S01C -
0019-RS-000-
HSQS
RS-19
S01C -
0019-RS-000-
MSQS
RS-20
S01C -
0019-RS-000-
MSQS
RS-21
S01C -
QQ19-RS-U00-
HSQS
RS-22
S01C •
0019-RS-000-
MSQS
RS-24
SOSC -
0019-SI-000-
HSQS
SI-U
S05C -
0019-SI-000-
HSQS
SI -12
S01C -
Q019-S1-0Q0-
HSQS
SI-13
S01C -
0019-S1-000-
MSQS
SI-14
S01C -
OU19-SI-000-
MSQS
Sl-15
SOSC -
0019-SP-000-
HSQS
SP-11
SOSC -
0019-SP-000-
MSQS
SP-12
S05C -
0019-SP-000-
HSQS
SP-13
SU1C -
0019-SP-000-
MSQS
SP-14
S01C -
0019-SP-000-
MSQS
SP-1S
S05C -
0019-SP-000-
HSQS
SP-16
S05C -
0019-DP-000-
HSQS
UBS
CTL -
0019-DP-000-
MSQS
WBS
CTL -
Number of Observations: 123
d1 -n-
dimethyl
diethyl
butyl
phtha-
phtha-
phtha
late
late
late
66
U10
2460
USO
U10
Z1000
68
U10
BIO
USO
U10
Z170
USO
U10
21400
U50
U10
2350
USO
U10
B25
62
U10
825
59
U10
BIO
110
U10
B25
110
U10
BIO
U50
U10
B2S
USO
U10
Z850
71
010
2230
U50
U10
ZS60
L50
U10
Z470
USO
U10
Z1200
USO
U10
Z1300
U50
U10
Z6700
LSO
U10
Z30
USO
U10
B2S
USO
U10
21600
USO
U10
Z740
USO
U10
21400
LSO
U10
Z120
USO
U10
Z940
U50
U10
B2S
USO
U10
B25
U50
U10
BIO
88
U10
UIO
USO
U10
B25
LSO
U10
B25
LSO
U10
B25
U50
uio
BIO
U50
U10
UIO
U50
UIO
B25
L50
UIO
B25
210
U1U
Z160
240
UIO
BIO
bis(2-
butyl
ethyl-
d1 -n-
benzyl
hexyl )-
octyl
phtha-
phtha-
phtha-
late
late
late
U25
B25
U25
U25
B25
U25
U25
B25
U25
U25
1200
U25
U25
1900
U25
U25
300
U25
U2S
B25
U25
L25
B25
U25
U25
B2S
U25
U25
B25
U25
U25
B25
U25
U25
B25
U25
U25
B2S
U25
L25
B25
L25
U25
B2S
025
U25
B25
U25
U25
B25
U25
U25
B2S
U25
U25
B2S
230
U25
B2S
L25
U25
U25
U25
U25
B25
025
U2S
B25
U25
U25
B25
U25
U25
825
U25
U25
B25
U25
U25
U25
U25
U25
B25
U25
U2S
B25
U25
U25
825
U25
U25
B25
U25
U25
825
U25
U2S
B25
U25
U25
B25
U25
U2S
1325
U25
L25
825
25
U25
U25
U25
U25
825
U25
U25
1325
U25
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS -
MISCELLANEOUS OXYbENATEU COMPOUNDS
Values in ppb dry weight
Drainage
Survey
Station
17U0019-BL-000-
MSUS
8L-11
1711U019-BL-000-
MSQS
BL-12
17110019-BL-000-
Msgs
BL-13
17110U19-BL-U00-
MSlJS
BL-14
17110019-BL-000-
MSQS
BL-1S
17110019-BL-000-
msqs
BL-16
17110019-BL-000-
MSQS
BL— 17
17110019-BL-000-
Msgs
BL—17
17110019-BL-000-
MSQS
BL-18
17110019-BL-000-
MSQS
BL-19
17110019-BL-000-
MSQS
BL-20
17110019-BL-000-
MSQS
BL-21
17110019-BL-OOO-
MSQS
BL-22
17110019-BL-000-
MSQS
BL-23
1711OO19-BL-U0O-
MSQS
BL-24
17110019-BL-000-
MSQS
BL-25
1711U019-BL-000-
MSQS
BL-26
17110019-BL-00U-
MSQS
BL-27
17110019-BL-000-
MSQS
BL-28
17110019-BL-000-
MSQS
BL-29
' 17110019-BL-000-
MSQS
BL-30
tn 17110019-BL-000-
MSQS
BL-31
17110019-BL-000-
MSQS
BL-32
17110019-HY-000-
MSQS
CB-11
1711U019-CB-000-
MSQS
CB-12
17110019-CB-000-
MSQS
CB-13
17110019-C B-000-
MSQS
CB-14
17110019-CI-000-
MSQS
CI-11
17110019-CI-00U-
MSQS
CI-12
17110019-C1-000-
MSQS
C1-13
17110019-C1-000-
MSQS
C1—14
17110019-CI-000-
MSQS
CI-15
17110019-CW-000-
MSQS
C1-16
17110019-CI-000-
MSQS
CI—17
17110019-C I-000-
MSQS
CI-17
17110019-CI-000-
MSQS
CI -18
i7iiooiy-ci-ooo-
MSQS
CI-19
17110019-CI-000-
MSQS
CI-20
17110019-CI-000-
MSQS
CI-21
17110019-CI-000-
MSQS
CI-22
17110019-CR-000-
MSQS
CR-11
17110019-CR-000-
MSQS
CR-12
17110019-CR-000-
MSQS
CR-13
17110U19-CR-000-
MSQS
CR-14
benzy1
benzoi c
dibenzo-
Sample
Rep
alcohol
acid
furan
SU5C -
22
U25
33
SOIC -
34
260
75
S05C -
16
390
76
SOIC -
29
U2b
210
SOIC -
L10
U25
52
SU1C -
36
8000
100
SOIC -
01
16
U25
71
SOIC -
02
58
450
89
SOIC -
27
360
76
SOIC -
L10
250
58
SOIC -
14
180
77
S05C -
57
250
72
SOIC -
U10
89
120
SOIC -
U10
U25
170
SOIC -
17
250
100
S05C -
42
330
96
SOIC -
16
1500
180
SOIC -
23
390
46
S05C -
20
650
87
SOIC -
23
230
240
SOIC -
38
230
70
S05C -
11
68
110
SOIC -
27
200
130
SOIC -
30
40
60
SOIC -
42
L25
15
SOIC -
61
U25
18
SOIC -
80
95
24
S02C -
140
U130
370
SOIC -
110
790
260
S05C -
25
690
180
SOIC -
54
U25
130
SOIC -
51
310
450
S05C -
U10
U25
210
S05C -
01
33
330
190
S05C -
02
12
U25
250
SOIC -
29
330
120
SOIC -
44
460
130
S05C -
18
U25
160
SOIC -
31
350
310
S05C -
29
U25
170
SOIC -
U10
U25
U5
S05C -
U10
430
U5
SOIC -
U10
210
U5
S05C -
U10
200
U5
-------
MAIN SEDIMENT gUALITY SUKVEY ORGANIC CHEMICALS - Values in ppb dry weight
MISCELLANEOUS OXYGENATED COMPOUNDS
jenzyl benzoic dibenzo-
Drainage
Survey
Station
Sample
Rep
alcohol
acid
furan
7110019-HY-000-
MSUS
HY-11
SOIC
45
U25
66
7110019-HY-000-
MSQS
HY-12
SOIC
-
14
U25
66
71100I9-HY-000-
Msgs
HY-13
SOIC
-
25
54
82
7110019-HY-000-
msqs
HY-14
S05C
-
U10
J25
79
71I0019-HY-000-
msqs
HY-15
SOIC
-
13
150
60
7U0019-HY-000-
msqs
HY-16
SOIC
-
U10
U25
170
7110019-HY-000-
Msgs
MY-17
SOIC
-
010
U25
120
M10019-MY-000-
MSQS
HY-18
SOIC
-
24
U25
97
7110019-HY-000-
Msgs
HY-19
SOIC
-
U10
U25
130
7110019-HY-000-
Msgs
HY-20
SOIC
-
01
95
U25
110
7110019-HY-000-
MSQS
HY-20
SOIC
.
02
66
U25
68
7110019-HY-000-
MSQS
HY-21
SOIC
-
500
250
130
7110019-HY-000-
MSQS
HY-22
S05C
-
U10
U25
480
711U019-HY-0UQ-
MSQS
HY-23
SOIC
-
41
470
120
7110019-HY-000-
MSQS
HY-24
SOIC
-
33
U25
100
7U0019-HY-000-
MSQS
HY-25
SOIC
-
U10
U2b
77
7110019-HY-000-
MSQS
HY-26
SOIC
-
U10
670
72
7110019-HY-000-
MSQS
HY-27
SOIC
-
63
230
74
7110019-HY-000-
MSQS
HY-28
SOIC
-
U10
L25
120
7110019-HY-000-
MSQS
HY-29
SOIC
-
48
170
66
7110019-HY-000-
MSQS
HY-30
suic
-
21
U25
50
7110019-HY-000-
MSQS
HY-31
SOIC
.
01
U10
U25
55
7110019-HY-000-
MSQS
HY-31
SOIC
.
02
U10
U25
57
7110019-HY-000-
MSQS
HY-32
SOIC
-
29
U25
94
7110019-HY-000-
MSQS
HY-33
SOIC
-
56
U25
130
7110019-HY-000-
MSQS
HY-34
SOIC
-
18
220
120
7110019-HY-000-
MSQS
HY-35
SOIC
.
38
U25
150
7110019-HY-000-
MSQS
HY-36
SOIC
-
35
550
300
7110019-HY-000-
MSQS
HY-37
SOIC
-
14
U25
120
7110U19-HY-000-
MSQS
HY-38
SOIC
.
50
290
160
7110019-HY-000-
MSQS
HY-39
SOIC
-
U10
250
110
7110019-HY-000-
MSQS
HY-40
SOIC
-
iOO
1)25
140
7110019-HY-000-
MSQS
MY-41
SOIC
-
340
U50
110
7110019-HY-0O0-
MSQS
HY-42
SOIC
-
LI 0
U25
170
7110019-HY-00U-
msqs
HY-43
SOIC
-
U10
U25
160
7110019-HY-000-
MSQS
HY-44
SOIC
-
U10
'J 2 b
8.0
7110019-MY-000-
MSQS
HY-45
SOIC
.
33
U2b
110
711Q019-MY-000-
msqs
HY-46
SOIC
-
U!0
'J 2 5
ISO
7110Q19-HY-UGO-
MSQS
HY-47
sosc
-
U10
U25
no
7110019-HY-000-
MSQS
HY-48
SOIC
-
61
U25
130
7110019-HY—O'Jli—
msqs
HY-4y
SOIC
.
74
140
50
71iOlli9-H?-000-
msqs
HY-bU
SCbC
-
7 3
U<:b
S4
711001S-HY-000-
Msgs
HY-b 1
SOIC
-
79
ISO
49
Miaul9-Mi)-00'J-
Msgs
MO-11
SJ1C
-
47
U /?b
440
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
MISCELLANEOUS OXYGENATED COMPOUNDS
Drainage
Survey
Station
Sampl e
Rep
0019-MD-000-
MSOS
HJ-12
SUSC
_
0019-MU-000-
MSllS
MD-13
SOIC
_
0019-MI-000-
Msgs
MI-11
SOIC
-
01
0019-MI-000-
MSOS
MI -11
SOIC
-
02
0019-MI-000-
MSQS
MI-12
SU1C
-
0019-HI-000-
MSQS
MI-13
SOIC
-
0019-MI-000-
Msgs
MI-14
SOIC
_
0019-MI-000-
Msgs
MI-15
SOIC
-
0019-RS-000-
Msgs
RS-11
SOIC
-
0019-RS-000-
MSQS
RS-12
SOIC
.
0019-KS-000-
Msgs
RS-13
SOIC
.
0019-RS-0O0-
MSQS
RS-14
S05C
_
01
0019-RS-000-
MSQS
RS-14
S05C
_
02
D019-RS-000-
MSQS
RS-15
SOIC
_
0019-RS-000-
MSQS
RS-16
SOIC
_
0019-RS-000-
Msgs
RS-17
SOIC
.
0019-RS-000-
MSQS
RS-18
SOIC
.
0019-RS-000-
MSQS
RS-19
SOIC
_
0019-RS-000-
MSQS
RS-20
SOIC
_
0019-RS-000-
MSQS
RS-21
SOIC
_
0019-RS-000-
MSQS
RS-22
SOIC
_
0019-RS-000-
MSQS
RS-24
S05C
_
0019-SI-000-
MSQS
Sl-11
S05C
_
0019-SI-000-
MSQS
S1-12
SOIC
_
0019-SI-000-
MSQS
SI-13
SOIC
_
0019-SI-000-
MSQS
SI-14
SOIC
_
0019-SI-000-
MSQS
SI-IS
S05C
0019-SP-000-
MSQS
~ SP-11
S05C
_
0019-SP-000-
MSQS
SP-12
S05C
_
0019-SP-000-
MSQS
SP-13
S05C
.
0019-SP-000-
MSQS
SP-14
SOIC
_
0019-SP-000-
MSQS
SP-lb
S05C
_
U019-SP-000-
MSQS
SP-16
SOSC
_
0019-DP-000-
MSQS
WHS
CTL
.
01
0019-DP-000-
MSQS
WBS
CTL
-
02
Number of Observations: 123
Values in ppb dry weight
benzyl
benzoic
dibenzo-
alcohol
acid
furan
29
U25
540
23
U25
190
17
U25
280
32
U2S
260
43
310
320
23
U25
2bO
48
230
250
31
U25
130
15
110
150
3b
U2b
150
21
U25
400
U)0
U25
210
U10
U25
150
U10
L25
12
U10
250
920
10
200
190
24
U25
2000
U10
U25
110
U10
U25
14
U1U
260
820
U10
U25
U5
U10
U2S
17
25
U25
310
15
U25
190
26
170
110
40
140
610
13
U25
130
U10
U25
170
61
U25
200
180
630
420
U10
U100
280
U10
U25
40
130
U25
38
U10
U25
U5
U10
U25
U5
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
OftGANONITROGEN COMPOUNDS
N-
nitroso-
2,6-d1-
2,4-di-
Uralnaye
Survey
ni tro-
dipropyl
- nitro-
nitro-
Station
Sample
Rep
benzene
amine
toluene.
toluene
17110019-BL-000-
msqs
BL-ll
SOSC
•
US
U10
U10
U5
17110019-BI-000-
MSQS
Bl-12
S01C
-
US
U10
U10
US
17110019-BL-000-
MSQS
BL-13
SOSC
-
U5
U10
U10
US
17110019-BL-000-
MSQS
BL-14
S01C
•
US
U10
U10
US
17110019-BL-000-
MSQS
BL-15
SU1C
-
US
U10
U10
US
17110019-BL-000-
MSQS
BL-16
S01C
.
US
U10
U10
US
17110019-BL-000-
MSQS
BL-17
S01C
-
01
US
U10
U10
US
1711U019-BL-000-
MSQS
BL—17
S01C
.
02
us
U10
U10
U5
17110019-BL-000-
MSQS
BL-18
S01C
.
us
U10
U10
US
17U0019-BL-000-
MSQS
BL-19
S01C
•
us
U10
U10
US
17U0019-BL-000-
MSQS
BL-20
S01C
-
U5
U10
U10
US
1711U019-BL-000-
MSQS
BL-21
SOSC
-
US
U10
U10
US
17110019-BL-000-
MSQS
8L-22
S01C
.
U5
U10
U10
US
17110019-BL-OOO-
MSQS
BL-23
S01C
-
US
U10
U10
us
UllOOH-BL-OOO-
MSQS
BL-24
S01C
-
US
U10
U10
us
17110019-BL-000-
MSQS
BL-25
SOSC
-
US
U10
U10
us
17110019-81-000-
MSQS
BL-26
S01C
-
us
U10
U10
us
17110019-BL-000-
MSQS
BL-27
S01C
-
U5
U10
U10
us
17110019-BL-000-
MSQS
BL-2B
SOSC
-
us
U10
U10
U5
17110019-BL-000-
MSQS
BL-29
S01C
.
U5
U10
U10
us
17110019-BL-000-
MSQS
BL-30
S01C
-
us
U10
U10
us
17110019-BL-000-
MSQS
BL-31
SOSC
-
us
U1U
U10
U5
17110019-BL-000-
MSQS
BL-32
S01C
-
US
U10
U10
us
17UUU19-HT-OUO-
MSQS
CB-11
S01C
-
U5
U10
U10
us
17110019-CB-000-
MSQS
CB-12
SOIC
.
U5
U10
U10
U5
17110019-CB-000-
MSQS
CB-13
S01C
_
US
U10
U10
us
17110019-CB-000-
MSQS
C8-14
SOIC
-
Ub
U10
U10
US
17110019-CI-000-
MSQS
CI-11
S02C
.
U2S
U50
uso
U2S
17110019-CI-000-
MSQS
CI-12
SOIC
.
US
U10
U10
US
17110019-CI-000-
MSQS
CI—13
SOSC
-
U5
U10
U10
US
17110019-C1-000-
MSQS
CI —14
SOIC
-
US
U10
U10
U5
1711U019-CI-000-
MSQS
Cl-lS
SOIC
-
US
U10
U10
Ub
17110019-CM-000-
MSQS
Cl-16
S05C
.
us
U10
U10
US
17110019-CI-000-
MSQS
CI —17
SOSC
-
01
U5
U10
U10
US
17110019-CI-000-
MSQS
C1—17
S05C
-
02
US
U10
010
us
17110U19-CI-000-
MSQS
CI-18
SOIC
.
US
U10
U10
U5
17110U19-CI-UOO-
MSQS
CI-19
SOIC
-
U5
U10
U10
U5
17110019-CI-000-
MSQS
CI-20
SOSC
.
US
U10
U10
us
17110019-C1-000-
MSQS
C1—21
SOIC
-
U10
U20
U20
U10
17110019-C1-000-
MSQS
CI-22
SOSC
-
US
U10
U10
U5
17110019-CR-000-
MSQS
CH-11
SOIC
-
U5
U10
U10
US
1711U019-CR-000-
MSQS
CR-12
SOSC
-
US
U10
U10
U5
N- 1,2-di- 3,3'-di - N-
nitroso- phenyl- chloro- nitroso-
diphenyl- hydra- benzi- benzi- dimethyl
amine zine dine dine amine
US U5 U100
U5 1200 U100
U5 U5 U100
US US U10U
U5 U5 U100
59 US U10U
U5 US U100
U5 US U100
US U5 U100
US U5 U100
US U5 U100
U5 US U100
U5 US U100
US US U100
US US U100
U5 U5 U10U
90 280 U100
US US U100
U5 U5 U100
US U5 U100
US U5 U100
U5 US U10U
U5 U5 U10U
U5 U5 U1U0
U5 US U100
US U5 UlUU
U5 U5 U100
U25 U2S USOO
U5 US U100
U5 U5 UlUO
U5 U5 U100
US U5 U100
220 US U100
U5 US U100
US U5 U100
120 U5 UlUO
U5 US U100
U5 U5 U100
U10 U10 U200
US U5 U100
U5 US U100
U5 US UlUU
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS -
OKGANONITROGEN compounds
Values in ppb dry weight
Drainage Survey
17110019-CR-000- MSQS
17110019-CR-OOO- MSQS
17110019-HY-00U- MSQS
17110019-HY-00U- NSqS
17110019-HY-000- MSQS
1711UO19-HY-OOO- MSQS
17110019-HY-000- MSQS
1711OO19-HY-UO0- MSQS
1711U019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
1711OO19-HY-OO0- MSQS
1711UO19-HY-UO0- MSQS
171I0019-HY-000- MSQS
17110019-HY-000- MSQS
17U0019-HY-000- MSQS
17110019-HY-000- MSQS
1711U019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-OOO- MSQS
17110019-HY-000- MSQS
17110019-HY-OOO- MSQS
17110019-HY-000- MSQS
17110019-HY-U00- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
1711Q019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110U19-HY-000- MSQS
17110019-HY-000- MSQS
17110019-HY-000- MSQS
17110U19-HY-000- MSQS
17110019-HY-000- MSQS
nitro-
Station Sample Kep benzene
CR-13 S01C - U5
CR-14 S05C - U5
HY-11 S01C - U5
HY-12 S01C - U5
HY-13 S01C - U5
HY-14 SObC - U5
HY-15 S01C - US
HY-16 SU1C - US
HY-17 S01C - US
HY-18 S01C - US
HY-19 S01C - U5
HY-20 S01C - 01 U5
HY-20 S01C - 02 US
HY-21 S01C - US
HY-22 S05C - U5
HY-23 S01C - US
HY-24 S01C - U5
HY-2S S01C - US
HY-26 S01C - U5
HY-27 S01C - US
HY-28 S01C - U5
HY-29 S01C - US
HY-30 S01C - U5
HY-31 S01C - 01 U5
HY-31 S01C - 02 U5
HY-32 S01C - US
HY-33 S01C - US
HY-34 S01C - U5
HY-3S S01C - US
HY-36 S01C - U5
HY-37 S01C - U5
HY-38 S01C - U5
HY-39 S01C - US
HY-40 S01C - US
HY-41 S01C - US
HY-42 S01C - U5
HY-43 SU1C - U5
HY-44 SU1C - U5
HY-45 S01C - U5
HY-46 S01C - US
HY-47 S05C - U5
HY-48 S01C - US
N-
nitroso-
2 ,6-di -
2,4-di-
dipropyl
- nitro-
nitro-
amine
toluene
toluene
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
us
U10
U10
us
U10
U10
us
U10
U10
U5
U10
U10
US
U10
U10
U5
U10
U10
US
U10
U10
U5
U10
U10
US
U10
U10
US
U10
U10
U5
U10
U10
U5
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
U5
U10
U10
U5
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
U5
U10
U10
US
U10
U10
U5
U10
U10
US
U10
U10
US
U10
U10
US
U10
U10
U5
U10
U10
US
U10
U10
U5
U10
U10
U5
U1U
U10
US
U10
U10
U5
N- 1,2-di- 3,3'-di- N-
nitroso- phenyl- chloro- nitroso-
diphenyl- hydra- benzi- benzi- dimethyl
amine zine dine dine amine
U5 U5 U100
US U5 U100
US US U100
U5 U5 U100
U5 US U100
U5 US U100
U5 U5 U100
U5 U5 U100
U5 U5 U100
U5 US U100
US US U100
U5 U5 U100
U5 U5 U100
U5 Ub U100
U5 US U100
US U5 U100
U5 U5 U100
U5 U5 U100
U5 U5 U100
U5 US UIOO
US U5 UIOO
U5 U5 UIOO
U5 US UIOO
77 US UIOO
50 U5 UIOO
Ub US UIOO
96 U5 UIOO
U5 US UIOO
U5 U5 UIOO
U5 US U10U
U5 U5 UIOO
U5 US UIOO
U5 US UIOO
U5 U5 UIOO
U5 US UIOO
U5 U5 U1U0
U5 U5 UIOO
28 34 UIOO
U5 U5 UIOO
U5 U5 UIOO
U5 U5 UIOO
U5 US U10U
-------
MAIM SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values In pob drv weloht
ORCAMOMITRUGEN COHPUUNDS ^
Drainage
Survey
Station
Sample
Rep
17110019-HY-OOO-
MSQS
HV-49
SU1C
1711O019-HY-OUO-
NSQS
HY-50
SOSC
_
17110019-HY-000-
nsqs
HY-51
S01C
.
17110019-MD-OUO-
hsqs
HD-11
S01C
_
17110019-HO-000-
NSQS
HD-12
SOSC
_
17110U19-H0-000-
HSQS
HO-13
S01C
_
17U0019-HI-000-
HSQS
NI-U
SOIC
-
01
17110U19-HI-000-
NSQS
HI—11
S01C
-
02
17110019-MI-000-
MSQS
MI-12
SOIC
-
17110019-MI-000-
NSQS
HI-13
SOIC
.
17110019-HI-000-
NSQS
HI -14
SOIC
.
1711U019-NI-000-
NSQS
HI-15
SOIC
.
17110019-RS-000-
NSQS
RS-11
SOIC
.
17110019-RS-000-
NSQS
RS-12
SOIC
.
17110019-RS-000-
NSQS
RS-13
SOIC
_
17U0019-HS-000-
MSQS
RS-14
SOSC
_
01
17110019-RS-000-
MSQS
RS-14
SOSC
.
02
J» 17110019-RS-000-
MSQS
RS-15
S02C
.
L17110019-RS-000-
MSQS
RS-16
SOIC
.
O17110019-RS-000-
MSQS
RS-17
SOIC
_
17110019-RS-000-
HSQS
RS-18
SOIC
_
17110019-RS-000-
NSQS
RS-19
SOIC
17110019-RS-000-
NSQS
RS-20
SOIC
_
1711O019-RS-OOU-
MSQS
RS-21
SOIC
-
17110U19-RS-000-
HSQS
RS-Z2
SOIC
_
1711UU19-RS-Q00-
HSQS
RS-24
SOSC
_
17110019-SI-000-
NSQS
SI-11
SOSC
_
17110019-S1-000-
MSQS
SI-12
SOIC
_
17U0019-S1-000-
NSQS
SI-13
SOIC
17110019-S1-000-
HSQS
SI-14
SOIC
_
17110019-SI-000-
HSQS
SI-IS
SOSC
_
17110019-SP-UOO-
HSQS
SP-11
SOSC
_
17110019-SP-000-
HSQS
SP-12
SOSC
_
17110019-SP-000-
HSQS
SP-13
SOIC
_
17110019-SP-000-
HSQS
SP-14
SOIC
_
1711U019-SP-000-
HSQS
SP-15
S05C
.
17110019-SP-000-
HSQS
SP-16
SOSC
_
17110019-QP-000-
HSQS
WHS
CTL
.
01
17110019-DP-000-
HSQS
WBS
CTl
-
02
N-
nitroso-
2,6-d1-
2,4-di-
nitro-
di propyl
- nltro-
nitro-
benzene
amine
toluene
toluene
U5
U10
U10
U5
U5
U10
U10
US
US
U10
40
U5
US
U10
U10
US
US
U10
U10
U5
US
U10
U10
U5
US
U10
U10
US
US
U10
U10
US
Ub
U10
U10
U5
US
U10
U10
US
us
U10
U10
US
us
U10
U10
us
us
U10
U10
us
us
010
U10
us
us
U10
U10
U5
us
U10
U10
us
us
U10
U10
U5
us
U10
U10
US
U5
U10
U10
U5
U5
U10
U10
US
us
U10
U10
Ub
us
U10
U10
U5
us
U10
U10
U5
Ub
U10
U10
US
U5
U10
U10
U5
US
U10
U10
U5
U5
U10
U10
U5
U5
U10
U10
Ub
us
U10
U10
U5
U5
U1U
U10
U5
US
U10
U10
US
U5
U10
U10
U5
U5
U10
U10
U5
US
U10
U10
US
U5
U10
U10
U5
Ub
U10
U10
US
US
U10
U10
US
U5
U10
U10
U5
U5
U10
U10
US
Nuaber of Observations:
123
N- 1,2-di- 3,3'-d i- N-
nltroso- phenyl- chloro- nitroso-
d1phenyl - hydra- benzl- benzl- dimethyl
amine zlne dine dine amine
U5 US U100
U5 U5 ulOO
U5 Ub U100
US Ub ulUO
U5 U5 UlOO
Ub U5 UlOO
U5 U5 UlOO
U5 U5 UlOO
U5 Ub UlOO
US Ub UlOO
U5 U5 UlOO
U5 US UlOO
U5 U5 UlOO
US U5 UlOO
U5 U5 UlOO
U5 U5 UlOO
U5 U5 UlOO
US U5 UlOO
U5 U5 UlOO
Ub U5 UlOO
610 U5 UlOO
U5 U5 ulOO
U5 Ub UlOO
US U5 U1UU
U5 U5 UlOO
40 US UlOO
U5 U5 UlOO
130 U5 UlUO
Ub U5 UlOO
U5 U5 U10U
U5 U5 UlOO
US U5 UlUO
U5 U5 UlOO
Ub U5 UlOO
Ub Ub UlOO
Ub US UlOO
US US UlOO
33 Ub UlOO
43 Ub UlOO
-------
PESTICIDES*!11 QUALITY SURVEY O^ANIC CHEMICALS - Values in ppb dry weight
L)ra1naye
Sur*ey
Station Sample
0019-BL-000-
MSUS
BL-11
S05C
0019-BL-000-
NSQS
Bl-12
SOIC
QU19-BI-000-
MSQS
BL-13
sosc
_
0019-BL-000-
HSQS
BL-14
SOIC
•
0019-BL-000-
MSQS
Bl-15
SOIC
0019-BL-000-
MSQS
BL-16
SU1C
_
0019-BL-000-
MSQS
BL-17
SOIC
.
0019-BL-000-
nsqs
BL-17
SOIC
_
0019-BL-000-
MSQS
BL-1B
SOIC
_
0019-BL-000-
MSQS
81-19
SOIC
_
0019-BL-000-
MSQS
BL-ZO
SOIC
0019-BL-000-
MSQS
Bl-21
SU5C
_
0019-BI-U00-
MSQS
BL-22
SOIC
0019-BL-000-
MSQS
81-23 SOIC
•
0019-BI-000-
MSQS
BL-24
SOIC
0019-BL-000-
MSQS
Bl-25
SOSC
_
0019-BL-000-
MSQS
BL-26
SOIC
_
0019-BL-000-
MSQS
BL-27
SOIC
0019-BL-000-
MSQS
Bl-28
SOSC
U019-BL-000-
MSQS
BL-29
SOIC
0019-BL-000-
MSQS
BL-30
SOIC
_
0019-8L-000-
MSQS
BL-31
S05C
0019-BL-000-
MSQS
BL-32
SOIC
0019-HY-000-
MSQS
CB-11
SOIC
0019-CB-000-
MSQS
CB-12
SOIC
_
0019-CB-000-
HSQS
CB-13 SOIC
OU19-CB-OCO-
MSQS
CB-14
SOIC
_
0019-CI-000-
MSQS
CI-11
S02C
0019-CI-000-
MSQS
CI—12 SMC
0019-CI-U00-
MSQS
CI-13
S05C
_
0019-CI-000-
MSQS
CI-14
SOIC
_
0019-CI-000-
MSQS
CI-15
SOIC
0019-CW-000-
MSQS
CI—16 S05C
•
019-CI-000-
MSQS
CI-17
SOSC
_
019-CI-000-
MSQS
CI-17
S05C
_
019-CI-000-
MSQS
CI—18
SOIC
_
019-CI-000-
MSQS
CI —19
SOIC
_
019-CI-OOO-
MSQS
CI-20
SOSC
019-CI-000-
MSQS
CI-21
SOIC
019-CI-000-
MSQS
CI — 22
SOSC
-
oiy-CR-ooo-
MSQS
CR-11
SOIC
_
019-CK-U00-
MSQS
CR-12
SU5C
U19-CK-UU0-
MSQS
CR-13
SOIC
-
019-CR-000-
MSQS
CR-14
SOSC
_
U19-HY-00U-
MSQS
HV-11
SOIC
-
Rep 4.4'-DDE 4,4'-DOO 4,4'-DDT aldrin
USO
USO
USO
USU
U40
U40
U40
U40
USO
USO
USO
USO
U2S
U2S
U25
U25
U2S
U2S
02S
U2S
U50
USO
USO
USU
01
USO
USO
USO
USO
02
USO
USO
USO
oso
U50
USO
USO
USO
U25
U25
U2S
U25
U2S
U25
U25
U25
USO
U50
U5U
USO
USO
USO
USO
USO
USO
USO
USO
USO
USO
USO
USO
U50
U50
USO
U50
USO
U50
U50
USO
U50
usu
USU
U50
U50
USO
USO
U50
U50
U50
USO
U50
USO
USO
USO
U50
USO
USO
USO
USO
USO
U25
U25
U25
U2S
U25
U25
U2S
U25
U25
U2S
U25
U25
U2S
U25
U25
U25
U25
U2S
U25
U2S
USO
U50
USO
U50
USO
USO
USO
U50
U2S
U2S
U25
U25
U25
U25
U25
U2S
U25
U25
U2S
U25
USO
USO
USO
USO
01
U25
U25
U25
U2S
02
USO
USO
USO
USO
U25
U25
U25
U25
U25
U2S
U2S
U2S
USO
USO
U50
USO
UbU
USO
U50
U50
U25
U25
U25
U25
U2S
U2S
U2S
U2S
U2S
U25
U25
U25
U2S
U2S
U25
U25
U2S
U2S
U2S
U25
U25
U25
U2S
U2S
dieldrin
a-HCH
b-HCH
d-HCH
y-HCH
USO
U50
USU
USO
USO
U40
U40
U40
U40
U40
USO
USO
USO
USU
USO
U25
U25
U25
U25
U25
U2S
U25
U2S
U2S
U2S
USO
USO
USO
USO
UbO
USU
U50
USO
USO
U5U
USO
USO
U50
USU
USO
USO
USO
U50
USO
USO
U25
U25
U25
U2S
U2S
U2S
U2S
U2S
U2S
U2b
USO
U50
USO
U5U
USO
U50
USO
USO
USU
USO
U50
USO
USO
USO
USO
U50
USO
USO
U5U
USU
U50
U50
USO
USO
USO
U50
U50
U50
U50
USO
U50
USO
USO
USO
USO
USO
U50
U50
U5U
USO
IJ50
U50
U50
U50
U50
U50
USO
USO
U50
USO
U50
U50
U50
USO
USO
U25
U25
U25
U2S
U25
IJ25
U25
U2S
U25
U2S
U2S
U25
U25
U25
U2S
U25
U25
U25
U2S
U25
U25
U25
U25
U2S
U25
USO
U50
U50
USO
USO
U50
U50
U50
USO
USO
U25
U2S
U25
U25
U25
U25
U2S
U25
U2S
U25
U25
U2S
U25
U25
U25
USO
U50
USO
USU
USO
U25
U25
U25
U25
U25
USO
USO
USO
USO
USU
U25
U25
U25
U25
U2S
U25
U25
U2S
U2S
U2S
USO
USO
U50
U50
U50
U50
U50
USO
USU
USU
U25
U25
U25
U25
U25
U2S
U25
U25
U2S
U2S
U25
U25
U2S
U25
U25
U25
U25
U25
U25
U2S
U25
U25
U2S
U25
U25
U25
U25
U25
U25
U2S
-------
MAIN SEDIMENT DUALITY SURVEY UHGANIC CHEMICALS - Values in ppb dry weight
PESTICIDES I
Orainaye
Survey
Station Sample
Rep
4,4'-UDE
4,4' -UUD
4 ,4' -DDT
aid
UOlS-MT-UOU-
nsqs
HY-12
SOIC -
U50
USO
USO
USO
0019-NT-UOO-
nsqs
HY-13
SOIC •
U25
U25
U25
U25
0019-MY-UUU-
NSQS
HY-14
S05C -
USO
USO
USO
USO
OOH-HT-UOO-
nsqs
MY-1S
SOIC -
U25
U25
U2S
U25
OUI9«MY»UOQ»
nsqs
HY-16
SOIC -
U2S
U25
U2S
U2S
OOK-HY-OOO-
nsqs
MY-17
SOIC *
USO
USO
USO
USO
OU19-MY-OUU-
NSQS
HY-18
SOIC -
U25
U25
U25
U2S
0019-HT-000-
NSQS
HY-19
soic -
U2S
U2S
U25
U2S
0019-HY-IW0-
NSQS
HY-20
SOIC -
01
U2S
U2S
U2S
U2S
0019-HY-UUO-
NSQS
HY-20
SOIC •
02
U25
U25
U25
U2S
0019-MY-000-
NSQS
MY-21
SOIC -
U2S
U2S
U2S
U2S
0019-HY-000-
NSQS
HY-22
SOSC •
USO
USO
USO
USO
(J01t«HY«UQO-
NSQS
MY-23
SOIC -
U2S
U25
U2S
U2S
0019-MY-000-
NSQS
HY-24
soic -
USO
USO
USO
USO
0O19-HY*O0U-
NSQS
HY-25
SOIC -
USO
USO
USO
USO
00l9«MT-000-
NSQS
HY-26
SOIC -
USO
USO
USO
USO
O019-HY-00O-
NSQS
HY-27,SOIC -
U25
U2S
U2S
U2S
Q019-MY-000-
NSQS
HY-28
SOIC -
U50
USO
USO
USO
OOI9«HY-O00-
NSQS
MY-29
SOIC *
U2S
U2S
U2S
U25
UU19-HY-OOU-
NSQS
HY-30
SOIC -
U25
U2S
U2S
U25
0019-HY-000-
NSQS
HY-31
soic -
01
U50
USO
USO
U50
0019-MY-000-
NSQS
HY-31
SOIC -
02
U50
USO
USO
USO
0019-HT-000-
NSQS
HY-32
soic -
U25
U2S
U2S
U25
0019*HY-000-
NSQS
HY-33
SOIC -
U50
USO
USO
USO
0019-MY-000-
NSQS
MY-34
SOIC -
U2S
U25
U2S
U25
0019-MY-UOU-
NSQS
HY-35
SOIC -
U25
U2S
U25
U2S
0U19-HY-000-
NSQS
HY-36
SOIC -
U25
U25
U2S
U25
0019-NT-000-
NSQS
HY-37
SOIC -
USO
U50
USO
USO
0019-MT-UOO-
NSQS
HY-38
SOIC -
U25
U2S
U2S
U2S
W19-HY-0U0-
NSQS
HY-39
SOIC -
U25
U2S
U2S
U25
UU19-HY-000-
NSQS
HY-4U
SOIC -
USO
USO
USO
USO
0019-HY-000-
NSQS
HY-41
SOIC •
USO
USO
USO
USO
0019-MY-00U-
NSQS
HY-42
SOIC -
USO
USO
U50
USO
O019-HY-OOO-
NSQS
HY-43
SOIC -
U50
USO
USO
USO
0019-MY-000-
NSQS
HY-44
SOIC -
USO
USO
USO
USO
U019-MY-000-
NSQS
HY-4S
SOIC -
U25
U25
U2S
U2S
0019-HY-OUU-
NSQS
HY-46
SOIC •
U2S
U25
U2S
U25
0019-HY-000-
NSQS
HY-47
S05C -
USO
USO
USO
USO
0019-HY-000-
NSQS
HY-48
SOIC •
U25
U2S
U2S
U2S
0019-HY-000-
NSQS
HY-49
SOIC -
U25
U25
U2S
U2S
0019-HY-0UU-
NSQS
HY-SO
S05C -
U25
U25
U25
U2S
0019-HY-000-
NSQS
HY-51
SOIC -
U25
U25
U25
U25
0019-MU-000-
NSQS
HO-11
SOIC -
USO
U50
USO
U50
0019-W-000-
NSQS
MD-12
SOSC -
U50
USO
USO
USO
0019-ND-000-
NSQS
MD-13
SOIC -
USO
U50
USO
U50
dieldrin
a-HCH
b-HCH
d-HCH
y-HCH
U50
USO
USO
USO
U50
U2S
U25
U25
U2S
U2S
USO
USO
USO
USO
USO
U2S
U25
U25
U25
U25
UZS
U2S
U25
U2S
U2S
USO
USO
U50
USO
USO
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U25
U2S
U2S
U2S
USO
USO
USO
USO
USO
U2S
U2S
U2S
U2S
U2S
USO
USO
USO
USO
USO
USO
USO
USO
USO
USO
U50
USO
USO
USO
USO
U25
U25
U2S
U2S
U2S
USO
USO
U50
U50
USO
U25
U25
U2S
U2S
U25
U2S
U2S
U25
U2S
U25
U50
USO
U50
USO
USU
U50
U50
USO
U50
U50
U2S
U2S
U25
UZS
U2S
USO
U50
USO
U50
USO
U2S
U2S
U2S
U2S
U2S
U25
U25
U25
U2S
U25
U2S
U2S
U25
U25
U25
USO
U50
USO
USO
U50
U25
U25
U2S
U2S
U25
U25
U25
U25
U2S
U2S
USO
USO
USO
USO
USO
USO
USO
U50
USO
USO
U50
U50
USO
USO
USU
USO
USO
USO
U50
USO
USO
USO
USO
U50
U50
U2S
U25
U2S
U2S
U2S
U25
U2S
U25
U2S
U2S
USO
U50
USO
USO
U50
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U2S
U25
U25
U2S
U25
U25
U2S
U2S
U25
U25
U2S
U25
U25
U50
USO
U50
U5U
uso
U50
USO
USO
USO
USO
USO
USO
U50
usu
USO
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
PESTICIDES I
Drainage
0019-MI-000-
0019-M1-000-
0019-HI-000-
0019-MI-000-
0019-HI-000-
U019-H1-000-
0019-RS-000-
0019-RS-000-
0019-HS-000-
0019-RS-000-
0019-RS-000-
0019-RS-000-
0019-RS-0U0-
0019-RS-000-
0019-RS-000-
0019-RS-000-
0019-RS-000-
0019-RS-000-
0U19-RS-000-
0019-RS-000-
0019-SI-000-
0019-SI-000-
0019-SI-000-
0019-SI-000-
0019-SI-000-
0019-SP-000-
0019-SP-000-
0019-SP-000-
0019-SP-000-
0019-SP-000-
0U19-SP-000-
0019-DP-000-
0019-QP-000-
Survey
Station
Sample
Rep
MSQS
HI—11
S01C
_
01
MSQS
MI-11
S01C
-
02
msqs
MI—12
S01C
-
MSQS
MI-13
S01C
-
MSQS
MI-14
S01C
-
MSQS
MI-IS
S01C
.
MSQS
RS-11
S01C
.
MSQS
RS-12
suic
.
MSQS
RS-13
S01C
-
MSQS
RS-14
SOSC
-
01
MSQS
RS-14
S05C
.
02
MSQS
RS-15
S02C
-
MSQS
RS-16
S01C
.
MSQS
RS-17
SUIC
.
MSQS
RS-18
S01C
.
MSQS
RS-19
SOIC
_
MSQS
RS-20
SOIC
-
MSQS
RS-21
S01C
.
MSQS
RS-22
SOIC
-
MSQS
RS-24
S05C
-
MSQS
SI-11
SOSC
.
MSQS
SI —12
SOIC
_
MSQS
S1-13
SOIC
.
MSQS
SI-14
SOIC
.
MSQS
SI-IS
SOSC
_
MSQS
SP-11
SOSC
_
MSQS
SP-12
SOSC
.
MSQS
SP-13
SOIC
_
MSQS
SP-14
SOIC
.
MSQS
SP-15
SU5C
_
MSQS
SP-16
S05C
-
MSQS
WBS
CTL
-
01
MSQS
WBS
CTL
-
02
4,4'-DDE 4,41 -DDD 4,4'-I
U2S
U25
U25
U25
U25
U25
U2S
U25
U25
U25
U25
U25
U25
U2S
U25
U25
U25
U25
U25
U25
U2S
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U2S
U25
U25
U2S
U25
025
U2S
U2S
U25
U25
USO
USO
USO
U50
USO
U50
U25
U25
U25
U25
U25
U25
U25
U2S
U2S
U2S
U25
U25
USO
U50
U50
U50
U50
USO
U25
U25
U2S
U25
U25
U25
USU
U50
USO
U25
U25
U25
U25
U25
U2S
U25
U25
U25
U50
U50
U50
U25
U25
U25
U25
U2S
U25
U25
U25
U25
U25
U25
U25
aldrin
dieldrin
a-HCH
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U2S
U25
U25
U2S
U25
U25
U25
J25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U2S
U2S
USO
USO
U5U
JSO
U50
U50
1)25
U25
U25
U25
U25
U25
U25
U25
U25
1125
U25
U25
U50
USO
U50
J50
USO
U50
U25
U2S
U2S
U25
U25
U25
1150
U50
U50
U25
U25
U25
U25
U25
U25
U25
U25
U25
U50
U50
U50
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
U25
b-HCH
d-HCH
g-HCH
U25
U25
U2S
U25
U25
U2S
U25
U25
U25
U25
U25
U25
U25
U2S
U2S
U25
U25
U25
U25
U25
U2S
U25
U2S
U25
U25
U25
U2S
U25
U25
U2S
U25
U2S
U2S
U25
U25
U25
U25
U25
U25
U25
U2S
U25
USO
U50
USU
U50
U50
USO
U25
U2S
U25
U25
U25
U2S
U25
U25
U25
U25
U25
U2S
USO
USO
U5U
U50
U50
USO
U2S
U25
U25
U25
U25
U25
U50
U50
USO
U25
U25
U25
U25
U25
U2S
U25
U25
U25
USO
U50
U50
U25
U25
U25
U25
U25
U25
U25
IJ25
U25
U25
U25
U2S
Number of Observations: 123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry weight
PCBS
Urainage
Survey
Station
Samp Ie
0019-BL-000-
MSQS
BL -11
S05C
0019-BI-000-
Msqs
BL-12
S01C
-
0019-BL-000-
MSQS
Bl-13
S05C
.
0019-BL-000-
MSQS
BL-14
S01C
_
0019-81-000-
MSQS
BL-15
S01C
-
0019-BL-000-
Msgs
BL-16
S01C
.
0019-BI-000-
Msgs
BL-17
S01C
-
0019-81-000-
MSQS
BL-17
S01C
_
0019-81-000-
MSQS
BL-18
S01C
_
0019-BL-0UU-
HSQS
BL-19
S01C
.
0019-BL-000-
MSQS
BL-20
S01C
_
0019-Bt-000-
MSQS
BL-21
SObC
.
0019-BL-000-
MSQS
BL-22
S01C
.
0019-BL-000-
MSQS
BL-23
S01C
_
OOlV-Bt-OOO-
HSQS
BL-24
S01C
-
0019-BL-000-
MSQS
BL-25
SOSC
_
0019-81-000-
HSQS
BL-26
S01C
-
0019-BL-000-
MSQS
BL-27
S01C
.
0019-BL-000-
HSQS
BL-28
SOSC
_
0019-BL-000-
MSQS
BL-29
S01C
0019-BI-000-
MSQS
BL-30
S01C
.
0019-BI-000-
MSQS
BL-31
SOSC
.
0019-61-000-
MSQS
BL-32
S01C
_
0019-HY-000-
MSQS
CB-11
S01C
_
0019-CB-000-
MSQS
CB-12
S01C
.
0019-CB-000-
MSQS
CB-13
S01C
-
0019-C8-000-
MSQS
CB-14
S01C
-
0019-C1-000-
MSQS
CI-11
S02C
-
0019-CI-000-
MSQS
CI-12
S01C
.
0019-CI-000-
MSQS
CI-13
S05C
.
0019-C1-000-
MSQS
C1-14
S01C
-
0019-CI-000-
MSQS
C1-15
S01C
0019-CW-000-
MSQS
C1-16
SOSC
_
0019-C1-000-
MSQS
CI-17
SOSC
_
0019-CI-000-
MSQS
CI-17
SOSC
-
0019-CI-000-
MSQS
CI-18
sine
_
0019-C1-000-
MSQS
CI-19
S01C
_
0019-C1-000-
HSQS
CI-20
SOSC
-
0019-CI-000-
HSQS
C1-21
S01C
.
0019-C1-000-
HSQS
CI-22
SOSC
.
0019-CR-000-
MSQS
CR-11
S01C
-
0019-CR-000-
MSQS
CR-12
SOSC
_
0019-CR-000-
MSQS
CR-13
S01C
-
0019-CR-000-
MSQS
CR-14
SOSC
.
0019-MY-000-
MSQS
HY-11
S01C
-
PC8-1016 PCB-1221 PCB-1232
01
02
01
02
Total
PCB-1242
PCB-1248
PCB-1254
PCB-1260 PCBs
U10
U10
C
C 17
U10
U10
C
C 68
U10
U10
C
C 22
U100
U100
C
C U110
U10
U10
C
C 15
U100
U100
C
C U110
1)10
U10
C
C 22
U100
U100
C
C 200
U10
U10
C
C 13
U10
UlO
C
C 9.4
U10
U10
c
C 13
U10
UlO
c
C 6.7
U10
UlO
c
C 11
U10
UlO
c
C 17
U10
UlO
c
C 18
U10
UlO
c
C 38
U100
U100
c
C U100
U7
U7
c
C 6.8
U80
U80
c
C 84
UBO
U80
c
C 140
U10
UlO
c
C 30
U10
UlO
c
C 3
U10
UlO
c
C 14
U100
U100
c
C U100
U10
UlO
c
C U8
U10
UlO
c
C U8
U10
UlO
c
C U8
U1200
U1200
c
C 15
U130
U130
c
C 79
U130
U130
c
C 140
U130
U130
c
C 57
U130
U13U
c
C 51
U15
U15
c
C 36
U15
U15
c
C 50
U130
U130
c
C 100
U130
U130
c
C 95
U130
U130
c
C 49
U10
UlO
c
C 19
U100
U100
c
C U90
UBO
U80
c
C 32
U7
U7
c
C U7
U7
U7
c
C U7
U7
U7
c
C U7
U7
U7
c
C U7
U130
U130
c
C U130
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
PCBS
Drainage
Survey
Station
Samp 1e
Rep
0019-HY-000-
MSQS
HY-12
S01C
0019-HY-000-
MSQS
HY-13
S01C
_
0019-HY-000-
MSQS
HY-14
S05C
_
0019-HY-000-
Msgs
HY-15
S01C
_
0019-HY-000-
MSQS
HY-I6
S01C
_
0019-HY-000-
MSQS
HY-17
S01C
.
0019-HY-000-
MSQS
HY-18
S01C
_
0019-HY-000-
MSQS
HY-19
S01C
0019-HY-000-
MSQS
HY-20
S01C
.
01
0019-HY-000-
MSQS
HY-20
S01C
_
02
0019-HY-000-
MSQS
HY-21
S01C
_
0019-HY-000-
MSQS
HY-22
S05C
.
0019-HY-000-
MSQS
HY-23
S01C
.
0019-HY-000-
MSQS
HY-24
S01C
.
0019-HY-000-
MSQS
HY-25
S01C
_
0019-HY-000-
MSQS
HY-26
S01C
.
0019-HY-000-
MSQS
HY-27
SOIC
_
0019-HY-000-
MSQS
HY-28
S01C
_
0019-HY-000-
MSQS
HY-29
SOIC
•
0019-HY-000-
MSQS
HY-30
SOIC
_
0019-HY-000-
MSQS
HY-31
SOIC
.
01
0019-HY-000-
MSQS
HY-31
SOIC
_
02
0019-HY-000-
MSQS
HY-32
SOIC
.
0019-HY-000-
MSQS
HY-33
SOIC
-
0019-HY-000-
MSQS
HY-34
SOIC
.
0019-HY-000-
MSQS
HY-35
SOIC
_
0019-HY-000-
MSQS
HY-36
SOIC
.
0019-HY-000-
MSQS
HY-37
SOIC
_
0019-HY-000-
MSQS
HY-38
SOIC
•
0019-HY-000-
MSQS
HY-39
SOIC
_
0019-HY-OOO-
MSQS
HY-40
SOIC
_
0019-HY-000-
MSQS
HY-41
SOIC
.
0019-HY-000-
MSQS
HY-42
SOIC
_
0019-HY-000-
MSQS
HY-43
SOIC
.
0019-HY-000-
MSQS
HY-44
SOIC
.
0019-HY-000-
MSQS
HY-45
SOIC
0019-HY-000-
MSQS
HY-46
SOIC
.
0019-HY-000-
MSQS
HY-47
S05C
_
0019-HY-000-
MSQS
HY-48
SOIC
.
0019-HY-000-
MSQS
HY-49
SOIC
_
0019-HY-000-
MSQS
HY-50
S05C
.
0019-HY-000-
MSQS
HY-51
SOIC
-
0019-MU-000-
MSQS
MO-11
SOIC
.
U019-MU-000-
MSQS
MD-12
S05C
-
0019-MO-000-
MSQS
MD-13
SOIC
-
Values in ppb dry weight
PCB-1U16 PC B-1221 PCB-1232
Total
PCB-1242
PCB-1248
PCB-12S4
PCB-1260
PCBS
U80
U80
C
c
110
U130
U130
C
c
77
USO
U50
c
c
85
U100
U10U
c
c
130
U150
U150
c
c
330
U130
U130
c
c
170
U1S0
U150
c
c
150
U150
U150
c
c
210
U130
U130
c
c
210
U130
U130
c
c
320
U130
U130
c
c
330
U1500
U1500
c
c
2001)
U150
U150
c
c
1500
U130
U130
c
c
250
U130
U130
c
c
190
U100
U100
c
c
170
U130
U130
c
c
860
U900
U900
c
c
120
U100
U100
c
c
220
U130
UI30
c
c
240
uino
U100
c
c
110
U100
USO
c
c
110
U100
U100
c
c
130
U100
U100
c
c
U90
U100
U100
c
c
200
U1000
U1000
c
c
140
U100
U100
c
c
110
U1000
U50
c
c
420
U130
U130
c
c
210
U900
U900
c
c
U1000
U1000
c
c
190
U100
U100
c
c
U95
U10000
U1000
c
c
1100
U1000
U1000
c
c
U100
U80
U80
c
c
1165
U900
U900
c
c
130
U1000
U1000
c
c
280
U1000
U1000
c
c
U80
U80
c
c
U85
U100
U100
c
c
20
U10
U10
c
c
33
U100
U100
c
c
U95
U15
U15
c
c
29
U10
U10
c
c
130
U10
U10
c
c
38
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values 1n ppb dry weight
PCBS
Drainage
Survey
Station
Sample
Rep
17110019-MI-000-
MSQS
HI — 11
S01C
01
17110019-MI-U0O-
MSQS
MI—11
S01C
-
02
17U0019-NI-000-
MSQS
MI —12
S01C
-
17U0019-MI-000-
msqs
MI—13
S01C
.
1711001V-MI-000-
MSQS
MI—14
S01C
.
17110019-NI-000-
Msqs
MI—15
S01C
.
17110019-RS-000-
MSQS
RS-11
S01C
-
17110019-RS-000-
MSQS
RS-12
S01C
-
17110019—RS-OOO-
MSQS
KS-13
S01C
-
17110019-RS-UOO-
msqs
RS-14
SOSC
-
01
17110019-RS-000-
MSQS
RS-14
S05C
-
02
17110019-RS-000-
MSQS
RS-15
S02C
-
17110019-RS-000-
MSQS
RS-16
S01C
-
17110U19-RS-0U0-
MSQS
RS-17
S01C
-
17110019-RS-000-
MSQS
RS-18
S01C
-
17110019-RS-000-
MSQS
RS-19
S01C
.
17110019-RS-000-
MSQS
RS-20
S01C
.
17110019-RS-000-
MSQS
RS-21
S01C
-
17110019-RS-000-
MSQS
RS-22
S01C
-
17110019-RS-000-
MSQS
RS-24
SOSC
.
17U0019-SI-000-
MSQS
SI-11
S05C
-
-W 17110019-SI-000—
MSQS
SI—12
S01C
.
• 17110019-SI-000-
MSQS
SI —13
S01C
.
£ 17110019-SI-0U0-
MSQS
SI—14
S01C
.
17110019-SI-000-
MSQS
SI—15
SOSC
-
17110019-SP-OOO-
MSQS
SP-11
SOSC
-
17110019-SP-000-
MSQS
SP—12
SOSC
-
17110019-SP-000-
MSQS
SP-13
S01C
-
17110019-SP-000-
MSQS
SP-14
S01C
-
17110019-SP-000-
MSQS
SP-15
SOSC
-
17110019-SP-000-
MSQS
SP-16
SOSC
-
17110019-0P-UOO-
MSQS
MBS
CTL
-
01
17110019-DP-000-
MSQS
UBS
CTL
-
02
PCB-1016 PCB-1221 PCB-1232
Nuitber of Observations: 123
Total
PCB-1242
PCB-1248
PCB-1254
PCB-1260
PCBs
U10
U10
C
c
14
U100
U100
C
c
56
U100
U100
C
c
87
U10
U10
C
c
13
U100
U100
C
c
63
U10
U10
c
c
16
U7
U7
C
c
U7
U80
U8U
c
c
16
U10
U10
c
c
14
UlbO
U1S0
C
c
18
U15
U15
c
c
15
U7
U7
C
c
U7
U200
U200
C
c
580
U100
U10G
c
c
510
U15
U1S
c
c
26
U7
U7
c
c
14
U7
U7
c
c
4
U150
U15U
c
c
580
U7
U7
c
c
U6
U7
U7
c
c
17
U10
U10
c
c
20
U10
U10
c
c
18
U10
U10
c
c
35
U100
U100
c
c
140
U10
U10
c
c
16
U100
U100
c
c
U90
U100
U100
c
c
uion
U130
U130
c
c
79
U180
U180
c
c
U180
U100
U100
c
c
U90
U80
U80
c
c
U90
U6
U6
c
c
U6
U7
U7
c
c
U7
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
VOLATILE HALOGENATEl) ALKENES
Drainage
Survey
Station
Sample
Rep
0019-BL-000-
HSQS
BL-11
S05C -
0019-BL-000-
HSQS
BL-12
S01C -
0019-BL-000-
HSQS
UL-13
SOSC -
CW19-BL-000-
NSQS
BL-14
S01C -
0019-BL-000-
HSQS
BL-15
S01C -
0019-BL-000-
HSQS
BL-16
S01C -
0019-BL-000-
HSQS
BL-17
S01C •
01
0019-BL-000-
HSQS
BL-17
S01C -
02
0019-BL-000-
HSQS
BL-18
SU1C -
OU19-BL-ODO-
HSQS
BL-19
S01C -
0019-BL-000-
HSQS
BL-20
S01C •
0019-BL-000-
HSQS
BL-21
SObC -
0019-BL-000-
HSQS
BL-22
SU1C •
0019-BL-OOO-
HSQS
BL-23
S01C •
0019-BL-000-
HSQS
BL-24
S01C •
0019-BL-000-
HSQS
BL-25
SOSC -
0019-BL-000-
HSQS
BL-26
S01C •
0019-BL-000-
HSQS
BL-27
S01C -
0019-BL-000-
HSQS
BL-28
SOSC -
0019-BL-000-
HSQS
BL-29
S01C -
0019-BL-000-
HSQS
BL-3U
S01C -
0019-BL-000-
HSQS
BL-31
S05C -
0019-BL-000-
HSQS
BL-32
S01C -
0019-HY-000-
HSQS
CB-11
S01C •
0019-CB-000-
HSQS
CB-12
suic -
0019-CB-000-
HSQS
C8-13
soic -
0019-CB-000-
HSQS
CB-14
soic -
0019-CI-000-
HSQS
CI-11
S02C -
00I9-CI-UOO-
HSQS
C1—12
soic -
0019-CI-000-
HSQS
CI —13
S05C -
0019-CI-000-
MSQS
CI —14
SOIC -
0019-CI-000-
NSQS
CI —15
SOIC -
0019-CH-000-
MSQS
CI —16
S05C -
0019-CI-000-
MSQS
CI-17
S05C -
01
0019-CI-000-
NSQS
CI-17
SOSC -
02
0019-CI-000-
MSQS
C1-18
SOIC -
0019-C1-000-
MSQS
CI —19
SOIC -
0019-CI-000-
MSQS
CI-20
SOSC -
0019-C1-000-
MSQS
CI-21
SOIC -
0019-CI-OUO-
MSQS
CI-22
SOSC -
0019-CK-000-
MSQS
CR-11
SOIC -
019-CR-000-
MSQS
CR-12
SU5C -
019-CR-000-
MSQS
CR-13
SOIC -
vi nyl
chloride
1,1-di-
chloro-
ethene
1,2- cis-1,3-
trans- di-
dichloro chloro-
ethylene propene
trans-
1,3-d i- tri-
chloro- chloro-
propene ethene
tetra-
chloro-
ethene
U10
UIO
U10
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS -
VOLATILE HALOGENATEU ALKENES
Values in ppb dry weight
1,1-di- trans-
vinyl chloro- dichloro
Drainage
Survey
Station
Sample
Rep
chloride
ethene
et
1
0019-CR-000-
NSQS
CR-14
SUSC
1
0O19-HY-OUO-
NSQS
HY-11
S01C
-
1
0019-HY-000-
MSQS
HY-12
S01C
•
i
0019-HY-000-
MSQS
HY-13
SOIC
.
l
0019-MY-000-
MSQS
HY-14
S05C
-
l
UU19-HY-000-
NSQS
HY-IS
S01C
-
l
0019-HY-000-
MSQS
HY-16
S01C
•
l
0019-MY-000-
MSQS
HY-17
S01C
-
UIO
UIO
UIO
l
0019-NY-000-
MSQS
HY-18
S01C
-
i
0U19-HY-000-
MSQS
MY-19
S01C
-
l
0019-HY-000-
MSQS
HY-20
S01C
-
01
l
0019-HY-000-
MSQS
HY-20
S01C
-
02
l
U019-WY-0U0-
NSQS
HY-21
S01C
-
UIO
UIO
UIO
l
0019-HY-000-
MSQS
HY-22
S05C
-
UIO
UIO
UIO
l
0019-HY-000-
MSQS
HY-23
S01C
-
UIO
UIO
UIO
l
001S-HY-000-
NSQS
HY-24
S01C
-
l
0019-HY-000-
NSQS
HY-25
S01C
-
UIO
UIO
UIO
l
0019-HY-000-
NSQS
HY-26
S01C
-
l
0019-HY-000-
NSQS
HY-27
S01C
-
» l
0019-HY-000-
NSQS
HY-28
S01C
-
UIO
UIO
UIO
£ 1
0019-HY-000-
MSQS
HY-29
S01C
.
S i
0019-HY-000-
MSQS
HY-30
S01C
-
1
0019-HY-000-
NSQS
HY-31
S01C
-
01
1
0019-HY-000-
NSQS
HY-31
soic
02
1
0019-HY-000-
NSQS
HY-32
S01C
.
1
0019-HY-000-
MSQS
HY-33
SOIC
-
1
0019-HY-000-
MSQS
HY-34
SU1C
•
1
0019-HY-000-
NSQS
HY-35
SOIC
.
1
0019-HY-000-
NSQS
NY-36
SOIC
-
1
0019-HY-000-
NSQS
HY-37
SOIC
-
1
0019-HY-000-
NSQS
MY-38
SOIC
•
1
0019-HY-000-
NSQS
HY-39
SOIC
-
I
0019-HY-000-
NSQS
HY-40
SOIC
.
1
0019-HY-000-
MSQS
HY-41
SOIC
-
UIO
UIO
UIO
I
0019-HY-000-
MSQS
HY-42
SOIC
-
UIO
UIO
UIO
1
0019-HY-000-
MSQS
HY-43
SOIC
-
UIO
UIO
UIO
1
0019-MY-000-
NSQS
HY-44
SOIC
-
UIO
UIO
UIO
1
0019-HY-000-
NSQS
HY-45
SOIC
-
UIO
UIO
UIO
1
0019-HY-000-
NSQS
HY-46
SOIC
.
1
0019-HY-000-
MSQS
HY-47
S05C
.
1
0019-HY-000-
MSQS
HY-40
SOIC
.
1
0019-HY-000-
MSQS
HY-49
SU1C
.
1
U019-HY-000-
MSQS
HY-50
SObC
-
cis-1,3- trans-
di- 1,3-di- tri- tetra-
chloro- chloro- chloro- chloro-
propene propene ethene ethene
UIO UIO UIO 210
U10 UIO UIO 160
UIO UIO UIO 67
UIO UIO UIO 170
UIO UIO UIO 140
UIO UIO UIO 140
UIO UIO UIO 47
UIO UIO UIO 78
UIO UIO UIO 140
UIO UIO UIO 57
UIO UIO UIO 68
-------
"AIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS
VOLATILE HALOUENATEO ALKENES
Drainage
Survey
Station
Sample
Rep
0019-HY-000-
MSQS
HY-51
SU1C
0019-MO-000-
NSQS
MO-11
S01C
-
0019-MD-000-
Msqs
MU-12
S05C
-
0019-MD-000-
MSQS
MO-13
S01C
0019-HI-000-
NSQS
MI —11
S01C
-
01
0019-HI-000-
MSQS
MI —11
S01C
02
0019-MI-OOO-
MSQS
MI —12
S01C
•
0019-MI-000-
MSQS
MI-13
SU1C
-
0019-M1-000-
MSQS
MI-14
S01C
_
0019-MI-000-
MSQS
MI-IS
S01C
0019-RS-000-
MSQS
RS-11
S01C
.
0019-RS-000-
MSQS
RS-12
S01C
0019-RS-000-
MSQS
RS-13
S01C
-
0019-HS-000-
MSQS
RS-14
S05C
•
01
0019-RS-000-
MSQS
RS-14
S05C
-
02
0019-HS-000-
MSQS
RS-15
S02C
.
0019-RS-000-
MSQS
RS-16
S01C
•
0019-RS-000-
MSQS
RS-17
S01C
•
0U19-RS-000-
NSQS
RS-18
S01C
•
0019-RS-000-
MSQS
RS-19
S01C
•
O019-RS-0UO-
MSQS
RS-20
S01C
.
0019-RS-000-
MSQS
RS-21
S01C
_
0019-RS-000-
NSQS
RS-22
S01C
•
0019-RS-000-
MSQS
RS-24
S05C
0019-SI-000-
MSQS
SI-11
S05C
.
0019-SI-000-
MSQS
SI-12
SU1C
•
0019-SI-000-
MSQS
SI-13
S01C
•
0019-SI-000-
MSQS
SI-14
S01C
•
0019-SI-000-
MSQS
SI-IS
SOSC
_
0019-SP-000-
NSQS
SP-11
S05C
.
0019-SP-000-
MSQS
SP-12
SOSC
_
0019-SP-000-
NSQS
SP-13
S01C
•
0019-SP-000-
NSQS
SP-14
S01C
0019-SP-000-
NSQS
SP-15
SOSC
.
0019-SP-000-
MSQS
SP-16
SOSC
•
0019-UP-000-
MSQS
WBS
CTL
•
01
0019-0P-000-
NSQS
WBS
CTL
-
02
Number of Observations: 123
Values in ppb dry weight
1,2- c1s-1,3- trans-
1,1-di- trans- di- 1,3-di- tri- tetra-
vinyl chloro- dichloro chloro- chloro- chloro- chloro-
chloride ethene ethylene propene propene ethene ethene
U10 U10 U10 U10 U10 U10 U10
UIO U10 U10 UIO Uio UIO UIO
UIO Uio UIO UIO UIO UIO UIO
UIO Uio UIO UIO UIO UIO UIO
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
VOLATILE AROMATIC HYDROCARBONS
Drainage
Survey
Station
Sample
Rep
0019-BL-000-
MSQS
BL-11
SOSC
0019-BL-000-
MSQS
BL-12
S01C
_
0019-BL-000-
MSQS
BL-13
SOSC
-
0019-BL-000-
MSQS
BL-14
S01C
-
0019-BL-000-
MSQS
81-15
S01C
•
0019-BL-000-
MSQS
BL-16
S01C
-
0019-BL-000-
MSQS
BL-17
S01C
.
01
0019-BL-000-
MSQS
BL-17
S01C
-
02
0019-BL-000-
MSQS
8L-18
S01C
-
0019-BL-000-
MSQS
BL-19
S01C
•
Q019-BL-00U-
MSQS
BL-20
S01C
•
0019-BL-000-
MSQS
BL-21
S05C
-
U019-BL-000-
MSQS
BL-22
S01C
•
OOlS-Bl-OUO-
MSQS
BL-23
S01C
-
0019-BL-000-
MSQS
BL-24
S01C
0019-BL-000-
MSQS
BL-25
SOSC
.
0019-8L-000-
MSQS
BL-26
S01C
•
0019-BL-000-
MSQS
BL-27
S01C
•
0019-BL-000-
MSQS
8L-28
SOSC
-
U019-BL-000-
MSQS
BL-29
S01C
-
0019-BI-000-
MSQS
BL-30
SU1C
-
0019-BL-000-
MSQS
BL-31
SOSC
.
0019-BL-000-
MSQS
BL-32
S01C
•
0019-HY-000-
MSQS
CB-U
S01C
.
0019-CB-000-
MSQS
CB-12
S01C
-
0019-CB-000-
MSQS
CB-13
S01C
-
0019-CB-000-
MSQS
CB-14
S01C
.
0019-CI-OOO-
MSQS
CI-11
S02C
-
0019-CI-000-
MSQS
C1-12
S01C
-
0019-CI-000-
MSQS
C1—13
SUbC
-
0019-CI-000-
MSQS
CI-14
S01C
.
0019-CI-000-
MSQS
CI-1S
S01C
•
0019-CU-000-
MSQS
CI—16
SOSC
.
0019-Cl-OOO-
MSQS
CI-17
S05C
.
01
0019-CI-U00-
MSQS
Cl-17
sosc
-
02
0019-CI-000-
MSQS
CI—18
soic
.
OOI9-CI-UOO-
MSQS
C1—19
suic
.
0019-CI-000-
MSQS
CI-20
SOSC
-
0019-CI-000-
MSQS
CI-21
SOIC
0019-CI-000-
MSQS
C1-22
SOSC
-
U019-CR-000-
MSQS
CR-11
S01C
-
0019-CR-000-
MSQS
CR-12
SOSC
-
0019-CR-000-
MSQS
CR-13
S01C
-
0019-CR-000-
MSQS
CR-14
SOSC
-
benzene toluene
ethyl -
benzene
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
UIO
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
VOLATILE AROMATIC HYOROCARbllNS
ethyl -
Drainage Survey Station Sample Rep benzene toluene benzene
17110019-HY-O00-
MSQS
HY-U
S01C
-
17110019-HY-000-
MSgS
HY-12
S01C
-
17110019-HY-000-
MSOS
HY-13
S01C
-
17110019-HY-000-
MSQS
HY-14
SObC
.
17110019-HY-000-
MSQS
HY-15
S01C
-
17110019-HY-000-
MSQS
HY-16
S01C
-
17110019-HY-000-
Msgs
HY-17
S01C
-
U10
U10
50
17110019-HY-000-
MSQS
HY-W
S01C
-
17110019-HY-000-
MSQS
HY-19
S01C
.
17110019-HY-000-
Msgs
HY-20
S01C
-
01
17110019-HY-000-
MSQS
HY-20
S01C
.
02
17110019-HY-OOO-
MSQS
HY-21
S01C
-
U10
UIO
UIO
miOO^-HY-OOO-
MSQS
HY-22
sosc
-
U10
UIO
17
17110019-HY-000-
MSQS
HY-23
S01C
-
U10
UIO
31
17110019-HY-000-
MSQS
HY-24
S01C
-
17110019-HY-000-
MSQS
HY-25
S01C
-
U10
UIO
30
17110019-HY-000-
MSQS
HY-26
S01C
-
17110019-HY-000-
MSQS
HY-27
S01C
-
17110019-HY-000-
MSQS
HY-2B
S01C
-
U10
UIO
33
j, 17110019-HY-OOO-
MSQS
HY-29
S01C
-
i 17110019-HY-000-
MSQS
HY-30
S01C
-
J217110019-HY-000-
MSQS
HY-31
S01C
-
01
17110019-HY-000-
MSQS
HY-31
S01C
-
02
1711U019-HY-000-
MSQS
HY-32
S01C
-
17110019-HY-U00-
MSQS
HY-33
S01C
-
17110019-HY-000-
MSQS
HY-34
S01C
-
17110019-HY-000-
MSQS
HY-35
S01C
-
1711OO19-HY-UO0-
MSQS
HY-36
S01C
-
17110019-HY-000-
MSQS
HY-37
S01C
-
17110019-HY-000-
MSQS
HY-38
S01C
.
17110019-HY-000-
MSQS
HY-39
S01C
-
17110019-HY-000-
MSQS
HY-40
S01C
-
17110019-HY-000-
MSQS
HY-41
S01C
-
U10
UIO
L10
17110019-HY-000-
MSQS
HY-42
S01C
-
UIO
UIO
18
17110019-HY-000-
MSQS
HY-43
S01C
-
U10
UIO
37
17110019-HY-000-
MSQS
HY-44
S01C
-
U10
UIO
10
17110019-HY-000-
MSQS
HY-45
S01C
-
UIO
UIO
13
17110019-HY-000-
MSQS
HY-46
S01C
-
17110019-HY-000-
MSQS
HY-47
SOSC
-
17110019-HY-000-
MSQS
HY-48
S01C
-
17110019-HY-OOO-
MSQS
HY-49
S01C
-
17110019-HY-000-
MSQS
HY-50
SOSC
-
17110019-HY-000-
msqs
HY-51
S01C
-
17110019-MD-U00-
Msgs
MO-11
SU1C
-
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weiqht
VOLATILE AROMATIC HYDROCARBONS
ethyl-
Drjlnije Survey Station Sample Rep benzene toluene benzene
17110019-MD-000-
MSQS
MO-12
sosc
-
17110U19-MD-000-
MSQS
MO-13
S01C
-
17110019-MI-000-
MSQS
MI-11
S01C
-
01
17110019-Ml-OOO-
MSQS
MI-11
S01C
.
02
17110019-NI-000-
MSQS
MI-12
S01C
_
17110019-NI-000-
MSQS
MI-13
S01C
.
17U0019-NI-000-
MSQS
MI-14
S01C
•
1711U019-NI-UO0-
MSQS
HI —15
S01C
.
17110019-RS-000-
MSQS
RS-11
S01C
•
17110019-RS-U00-
MSQS
RS-12
S01C
.
17110019-RS-000-
MSQS
RS-13
S01C
-
17110019-RS-000-
MSQS
RS-14
SObC
-
01
1711U019-RS-000-
MSQS
RS-14
SOSC
-
02
1711U019-KS-000-
MSQS
RS-1S
SU2C
.
17110U19-RS-000-
MSQS
RS-16
S01C
-
17110019-RS-000-
MSQS
RS-17
S01C
.
17110U19-RS-000-
MSQS
RS-18
sine
17110019-RS-000-
MSQS
RS-19
SU1C
-
17110019-RS-000-
MSQS
RS-20
S01C
•
.17110019-RS-000-
MSQS
RS-21
S01C
.
17110019-RS-000-
MSQS
RS-22
S01C
-
'17110019-RS-U00-
MSQS
RS-24
SUbC
-
17110019-SI-000-
MSQS
SI-U
SOSC
.
U10
U10
U10
17110019-SI-000-
MSQS
S1-12
S01C
.
17110019-SI-000-
MSQS
S1-13
S01C
.
17110019-SI-000-
MSQS
SI —14
SU1C
.
171100I9-SI-000-
MSQS
SI-15
SOSC
.
17110019-SP-000-
MSQS
SP-11
S05C
.
17110019-SP-000-
MSQS
SP-12
SOSC
.
U10
U10
U10
1711O019-SP-OOU-
MSQS
SP-13
S01C
-
17110U19-SP-000-
MSQS
SP-14
S01C
-
U10
U10
U10
17110019-SP-000-
MSQS
SP-15
SOSC
-
U10
U10
U10
17110019-SP-000-
MSQS
SP-16
SOSC
-
17110U19-DP-000-
MSQS
WBS
CTL
.
01
17110019-DP-000-
MSQS
WBS
CTL
-
02
Nunber of Observations:
123
-------
MAIN SEDIMENT QUALITY SURVEY ORGANIC CHEMICALS - Values in ppb dry weight
VOLATILE AROMATIC HYDROCARBONS
total
styrene xylenes o-xylene
Drainage
Survey
Station
Sample
Rep
17110019-BL-00U-
MSQS
BL-11
S05C
_
17110019-BL-000-
MSQS
BL-12
S01C
17110019-BL-000-
MSOS
8L-13
S05C
.
17110019-BL-O00-
MSgS
BL-14
S01C
_
17110019-BL-000-
MSQS
BL-15
SU1C
-
17110019-BL-000-
MSQS
BL-16
S01C
.
17110019-BL-OOO-
MSQS
BL-17
SU1C
.
01
^llOO^-BL-OUO-
Msgs
BL-17
S01C
•
02
17110019-BL-000-
MSQS
BL-18
S01C
.
17110019-BL-000-
MSQS
BL-19
S01C
.
17110019-BL-000-
MSQS
BL-20
S01C
.
17110019-BL-000-
msqs
BL-21
S05C
.
17110019-BL-000-
MSQS
BL-22
S01C
-
17110019-BL-000-
Msgs
BL-23
S01C
-
17110019-BL-000-
MSQS
BL-24
SU1C
-
17110019-BL-000-
Msgs
BL-2S
sosc
.
17110019-BL-000-
MSQS
BL-26
soic
-
17110019-BL-000-
MSOS
BL-27
S01C
•
17110019-BL-000-
msqs
BL-28
SOSC
•
17110019-BL-000-
MSQS
BL-29
SOIC
•
i 17110019-BL-OOO-
Msgs
BL-30
SOIC
•
W17110019-BL-000-
MSQS
BL-31
S05C
.
*** 17110019-BL-000-
MSQS
BL-32
SOIC
17110019-HY-000-
MSQS
CB-U
SOIC
•
17110019-CB-000-
msqs
CB-12
SOIC
•
17110019-CB-000-
MSQS
CB-13
SOIC
•
17110019-CB-000-
Msgs
CB-14
SOIC
•
17110019-CI-000-
msqs
CI-11
SU2C
.
17110019-CI-000-
MSQS
CI—12
SOIC
.
17110019-CI-000-
Msgs
CI-13
S05C
•
17110019-CI-000-
MSQS
CI—14
SOIC
•
17110019-CI-000-
MSQS
CI-15
SOIC
.
17110019-CW-000-
Msgs
CI-16
S05C
•
17110019-CI-000-
MSQS
CI-17
S05C
01
17110019-CI-000-
MSQS
CI — 17
S05C
-
02
17110019-CI-000-
MSQS
C I—18
SOIC
.
17110019-CI-000-
MSQS
CI —19
SOIC
-
17110019-CI-000-
MSQS
CI-20
SOSC
.
17110019-C1-000-
MSQS
CI-21
SOIC
.
17110019-CI-000-
MSQS
C1—22
SOSC
17110019-CR-000-
MSQS
CR-11
SOIC
-
17110019-CR-000-
MSQS
CR-12
S05C
-
17110019-CR-000-
MSQS
CR-13
SOIC
-
17110019-CR-000-
MSQS
CR-14
SU5C
-
U20 U20 C
U20 U20 C
U20 U2U
U20 U20 C
U20 U2U C
U20
-------
MAIN SEDIMENT QUALITY SURVEY OKUANIC CHEMICALS - Values in ppb dry weiqht
VOLATILE AROMATIC HYDROCARBONS
total
Oriliuye
Su rvey
Station
Sample
Rep
styrene
xylenes
0
0019-HY-000-
MSQS
MY-ll
SOIC
_
U019-HY-000-
MSQS
HY-12
S01C
-
0019-HY-000-
NSQS
HY-13
S01C
-
0019-HY-000-
MSQS
HY-I4
SOSC
•
0019-HY-U00-
NSQS
HY-15
SOIC
•
0019-HY-000-
MSQS
HY-16
SU1C
0019-HY-000-
NSQS
NY-17
SOIC
U20
160
c
0U19-HY-OOO-
MSQS
HY-18
SU1C
•
OOI9-MY-OUO-
NSQS
HY-19
SOIC
•
0U19-HY-000-
Nsqs
HY-ZO
SOIC
-
01
0019-HY-000-
NSQS
HY-20
SOIC
•
U2
0019-HY-000-
NSQS
HY-21
SOIC
.
ueo
U20
c
0019-HY-000-
NSQS
HY-22
SOSC
-
u?o
70
c
0019-HY-000-
NSQS
HY-23
SOIC
-
U20
110
c
0019-HY-000-
NSQS
HY-24
SOIC
-
0019-HY-000-
MSQS
HY-25
SOIC
U20
98
c
0019-HY-000-
NSQS
HY-26
SU1C
.
0019-MY-000-
NSQS
HY-27
SOIC
0019-HY-000-
NSQS
HY-28
SOIC
-
U20
100
c
0019-HY-0UO-
NSQS
HY-29
SOIC
UU19-HY-000-
MSQS
HY-30
SOIC
OOIS-MY-OOO"
MSQS
HY-31
SOIC
•
01
OOlV-HY-UOO-
NSQS
HY-31
SOIC
•
02
0019-HY-000-
NSQS
HY-32
SOIC
•
0019-HY-000-
NSQS
HY-33
SOIC
0019-HY-000-
NSQS
HY-34
SOIC
•
0019-HY-0Q0-
NSQS
HY-35
SOIC
.
UO19-HY-000-
MSQS
HY-36
SOIC
_
0019-HY-000-
NSQS
HY-37
SU1C
•
0019-HY-000-
MSQS
HY-38
SOIC
•
0019-HY-000-
MSQS
HY-39
SOIC
•
0019-HY-000-
MSQS
HY-40
SOIC
-
0019-HY-000-
MSQS
HY-41
SOIC
.
U20
L20
c
0019-HY-000-
NSQS
HY-42
SOIC
U20
53
c
U019-HY-0UU-
MSQS
HY-43
SOIC
-
U20
120
c
0019-HY-000-
Msgs
HY-44
SOIC
•
U20
30
c
0019-HY-000-
Nsgs
HY-45
SOIC
U20
49
c
0019-MY-UUO-
MSQS
HY-46
SOIC
-
0019-HY-000-
Msgs
HY-47
SOSC
0019-MY-000-
MSQS
HY-48
SOIC
•
0019-HY-000-
MSQS
HY-49
SOIC
•
0019-HY-OO0-
MSQS
HY-SO
S05C
•
0019-HY-000-
MSQS
HT-S1
SU1C
-
0019-MJ-0O0-
MSQS
MO-11
SOIC
-
o-x/lene
-------
MAIN SEDIMENT DUALITY SURVEY ORGANIC CHEMICALS - Values In ppb dry weight
VOLATILE AROMATIC HYDROCARBONS
Drainaye
total
Survey Station Sample Hep styrene xylenes o-xylene
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019-
17110019
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
17110019-
3» 17110019-
tln 17110019
17110019-
17110019-
17110019-
17110019
17110019
17110019
17110019
17110019
17110019
17110019-
17110019-
17110019-
17110019
-MD-OOO-
-MO-OOO-
-MI-OOO-
¦MI-OUO-
-MI-OOO-
-MI-OOO-
•MI-OOO-
•MI-OOO-
RS-OUO-
RS-OUO-
•RS-OOO-
RS-OOO-
RS-OOO-
RS-OOO-
RS-OOO-
RS-OUO-
RS-OOO-
RS-OOO-
RS-OOO-
RS-OOO-
RS-OOO-
RS-OOO-
SI-OOO-
S1-000-
SI-OOO-
S1-000-
SI-OOO-
SP-OOO-
SP-OOO-
SP-OOO-
SP-OOO-
SP-OOO-
SP-OOO-
DP-OOO-
DP-OOO-
MSQS
Msgs
NSljS
Msgs
Msgs
Msgs
Msgs
Msgs
Msqs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
MSQS
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
Msgs
MO-12 S05C
MU-13 S01C
MI-11 S01C
MI-11 S01C
MI-12 S01C
MI-13 S01C
MI-14 S01C
MI-IS S01C
RS-11 S01C
RS-12 S01C
RS-13 S01C
RS-14 SU5C
RS-14 S05C
RS-15 S01C
RS-16 S01C
RS-17 S01C
RS-18 S01C
RS-19 S01C
RS-20 S01C
RS-21 S01C
RS-22 S01C
RS-24 S05C
SI-11 SOSC
SI-12 S01C
SI—13 S01C
Sl-14 S01C
SI-15 SOSC
SP-11 SU5C
SP-12 S05C
SP-13 S05C
SP-14 S01C
SP-15 S05C
SP-16 S05C
UBS CTL
HBS CTL
01
02
01
02
U20
U20
C
U20
U20
C
U20
U20
C
U20
U20
C
01
02
Number of Observations: 123
-------
MAIN SEDIMENT TENTATIVELY IDENTIFIED ORGANIC CHEMICALS DATA
1-methyl -
2-(I- penta-
methyl- 2- chloro-
ethyl) methoxy cyclo-
Drainage
Survey
Station
Sample
Rep
benzene
phenol
pentane
1
0019-BL-000-
Msgs
BL-11
SObC
E81
E20
U
1
0019-81-000-
MSIJS
BL-12
S01C
-
1
0019-81-000-
Msgs
BL-13
SOSC
-
E87
E35
U
1
0019-BL-000-
NSQS
BL-14
S01C
-
E31
U
U
1
0019-BL-000-
MSQS
BL-15
S01C
-
E140
E28
U
I
0019-BL-000-
Nsgs
BL-16
S01C
-
£25
E29
U
1
0019-BL-000-
NSQS
BL-17
S01C
-
01
ES6
E18
U
1
U019-BL-000-
Msgs
BL-17
S01C
-
02
1
0019-BL-000-
Msgs
BL-18
S01C
-
E12
E12
U
1
0019-81-000-
MSQS
BL-19
S01C
-
E47
E17
u
1
0019-BL-000-
NSQS
BL-20
S01C
.
El 30
E23
u
1
0019-BL-000-
Nsgs
BL-21
S05C
-
El.7
E18
u
1
0019-BL-000-
NSQS
BL-22
S01C
-
E990
E29
u
1
0019-BL-000-
NSQS
BL-23
S01C
-
El 10
E39
u
1
0019-BL-000-
NSQS
81-24
S01C
-
E30
E43
u
1
0019-BL-000-
NSQS
BL-2S
SOSC
-
£42
E15
u
1
0019-8L-000-
NSQS
BL-26
S01C
-
E52
E47
El.8
1
0019-BL-000-
NSQS
BL-27
S01C
-
El.3
E8.9
u
»1
0019-BL-000-
NSQS
BL-28
SOSC
-
ES.5
E14
u
Ull
0019-BL-000-
NSQS
BL-29
S01C
-
E28
Ell
u
o»i
0019-BL-000-
NSQS
BL-30
S01C
-
E b 3
E39
u
1
0019-BL-000-
NSQS
BL-31
S05C
-
E230
E49
El .1
1
0019-BL-000-
NSQS
BL-32
S01C
-
El 20
El 60
u
1
0019-HT-000-
MSQS
CB-11
S01C
-
E1S0
E100
Ell
1
0019-CB-000-
NSQS
CB-12
S01C
-
E9S
E42
u
1
0019-CB-000-
NSQS
CB-13
S01C
-
£39
ES3
u
1
0019-CB-000-
MSQS
CB-14
S01C
.
E160
U
u
1
U019-CI-000-
NSQS
CI-11
S02C
.
E330
u
u
1
0019-CI-000-
MSQS
CI-12
S01C
-
U
u
u
1
0019-C1-000-
NSQS
Cl-13
S05C
-
E86
E120
u
1
0019-C1-000-
MSQS
CI -14
S01C
-
E46
E1S0
u
1
0019-CI-000-
MSQS
Cl-lb
S01C
-
E86
u
u
1
0019-CW-000-
NSQS
CI-16
SOSC
-
El 70
u
u
1
0019-CI-000-
MSQS
C1-17
SUbC
-
01
E180
E160
u
1
0019-CI-000-
MSQS
CI-17
S05C
-
02
1
0019-C1-000-
MSQS
C1-18
S01C
-
E51
ESI
u
1
0019-CI-000-
MSQS
C1-19
S01C
-
U
U
u
1
0019-CI-000-
MSQS
CI-20
SOSC
-
Eb60
ES80
u
1
0019-C1-000-
MSQS
CI-21
S01C
-
ESS
E230
u
1
0019-CI-000-
MSQS
CI-22
SOSC
-
E8S
E43
u
1
0019-CR-000-
MSQS
CR-11
S01C
-
E12
U
u
1
0019-CR-000-
MSQS
CH-12
S05C
-
U
U
u
1
0019-CR-000-
Msgs
CR-13
S01C
-
U
El .3
u
1
0019-CR-000-
msos
CR-14
SUbC
-
u
u
u
1- 2,6-tli
methyl methyl
naphth- 1,1' naphth
alene biphenyl alene
E9.0
E22
E33
Ell
U
E16
E12
E1S
E18
E12
E32
E5S
E34
E13
E34
E8.9
Ell
E30
Ell
Ell
E44
E21
E15
El 70
E19
E110
£200
E76
E46
El 10
E140
E150
E56
u
E270
E73
E8S
U
U
U
U
-------
MAIN SEDIMENT SURVEY TENTATIVELY IDENTIFIED ORGANIC CHEMICALS DATA
1-methyl
-
2-( 1-
penta-
methyl-
2-
chloro-
Drainage
ethyl)
methoxy
cyclo-
Survey
Station
Sampl e
Hep
benzene
phenol
pentane
17110019-HY-000-
MSOS
HY-11
S01C
_
17110019-HY-000-
MSQS
HY-12
S01C
.
E220
U
Ell
17110019-HY-0U0-
Msgs
HY-13
SU1C
-
17110019-HY-000-
MSQS
HY-14
S05C
-
E970
U
E4.9
17110019-HY-OUU-
Msgs
HY-15
S01C
-
Eb 10
U
E22
17110019-HY-000-
MSQS
HY-16
S01C
-
E2000
U
U
17110019-HY-000-
MSQS
HY-17
S01C
.
£2800
U
E8.4
17110019-HY-000-
MSQS
HY-18
S01C
.
17110019-HY-000-
MSQS
HY-19
S01C
.
£710
U
U
17110019-HY-000-
MSQS
HY-20
S01C
.
01
E230
U
E18
17110019-HY-000-
MSQS
HY-20
S01C
-
02
17110019-HY-000-
MSQS
HY-21
S01C
-
E190
U
Ell
17U0019-HY-000-
MSQS
HY-22
S05C
-
£1000
U
E17
17110019-HY-000-
MSQS
HY-23
S01C
.
E87
U
E6.0
17110019-HY-000-
Msqs
HY-24
S01C
-
E180
U
E13
17110019-HY-000-
MSQS
HY-25
S01C
-
£53
U
£6.7
17110019-HY-000-
MSQS
HY-26
S01C
-
E9.5
U
El .9
17110019-HY-000-
MSQS
HY-27
S01C
-
E300
u
E22
17110019-HY-000-
MSQS
HY-28
S01C
-
El 100
u
E15
17110019-HY-000-
MSQS
HY-29
S01C
-
E550
u
E21
17110019-HY-000-
MSUS
HY-30
S01C
.
E44
u
U
17110019-HY-000-
MSQS
HY-31
S01C
.
01
E400
u
E5.8
17110019-HY-000-
MSQS
HY-31
S01C
.
02
1711U019-HY-0U0-
MSQS
HY-32
S01C
.
E13
u
E25
17110019-HY-000-
MSQS
HY-33
S01C
-
E340
u
E30
17110019-HY-000-
MSQS
HY-34
S01C
.
E210
u
E23
17110019-HY-000-
MSQS
HY-35
S01C
-
£150
u
E29
17110019-HY-000-
MSQS
HY-36
S01C
-
E310
u
£200
17110019-HY-000-
MSQS
HY-37
S01C
.
E310
u
E54
17110019-HY-000-
MSQS
HY-38
S01C
-
E140
u
£34
17110U19-HY-000-
MSQS
HY-39
S01C
-
E210
u
E52
17110019-HY-0U0-
MSQS
HY-40
S01C
-
E120
u
£31
17110019-HY-000-
MSQS
HY-41
suic
-
E210
u
E47
17110019-HY-000-
MSQS
HY-42
S01C
-
£82
u
E68
17110019-HY-000-
MSQS
HY-43
S01C
.
E180
u
£72
17110019-HY-000-
MSQS
HY-44
S01C
-
£7.7
u
U
17110019-HY-000-
MSQS
HY-45
S01C
.
El 50
u
£44
1711U019-HY-000-
MSQS
HY-46
S01C
-
El 90
u
E270
17110019-HY-000-
MSQS
HY-47
sosc
-
El 20
E48
E58
17110019-HY-000-
MSQS
HY-48
S01C
-
U
E41
E17
1711U019-HY-000-
MSQS
HY-49
S01C
-
17110019-HY-000-
MSQS
HY-50
S05C
-
E33
£69
U
17110019-HY-000-
MSQS
HY-51
S01C
-
17110019-MD-000-
MSQS
HD-11
S01C
-
E15
El 30
U
1- 2,6-di
methyl methyl
naphth- 1,1' naphth
alene biphenyl alene
E24
E23
ElOO
U
E36
E20
E50
E51
El 10
U
u
E17
E9.1
E45
E26
E48
E8.4
E15
El 10
E54
E46
E48
E310
E74
E63
E41
U
E31
E64
E63
El.3
£51
E41
E27
£30
E19
El 50
-------
MAIN SEDIMENT SUKVET TENTATIVELY IDENTtFI ED ORGANIC CHEMICALS DATA
1-methyl
-
Z-(l-
penta-
methyl-
2-
chloro-
Ortfnaye
Survey
Station
ethyl)
methoxy
O
-c
0
1
Sample
Rep
benzene
phenol
pentane
0019-IC-000-
MSQS
HD-12
S05C
_
Eld
£930
U
0019-MD-000-
HSQS
MO-13
S01C
-
£190
E170
U
ooi9-m-ooo-
HSQS
MI—11
S01C
.
01
E210
£110
U
0019-HI-000-
HSQS
MI-11
S01C
_
02
U019-HI-000-
HSQS
Nl-12
SOIC
.
0019-MI-000-
HSQS
MI-13
S01C
-
E310
£190
u
0019-KI-000-
HSOS
MI-14
S01C
_
0019-HI-000-
HSQS
HI-IS
SOIC
-
E260
£160
u
0019-KS-000-
HSQS
RS-ll
S01C
-
EZbO
E83
u
0019-RS-000-
HSQS
RS-12
S01C
_
E73
E32
u
0019-KS-U00-
HSQS
RS-13
S01C
-
E1S0
ESS
u
0019-RS-000-
HSQS
RS-14
S05C
-
01
E170
u
u
0019-KS-000-
HSQS
RS-14
SOhC
.
02
0019-RS-000-
HSQS
RS-15
S02C
-
E22
u
u
0019-RS-0OU-
HSQS
RS-16
S01C
-
£2700
u
u
0019-ftS-000-
HSQS
RS-17
S01C
-
E140
u
u
0019-RS-000-
HSQS
RS-18
S01C
-
E570
u
u
0019-RS-000-
HSQS
RS-19
SU1C
-
Eb 7
Eb .0
u
0019-RS-000-
HSQS
RS-20
S01C
-
0
U
u
0019-RS-000-
HSQS
RS-21
SOIC
-
E95
£29
u
0U19-RS-000-
HSQS
RS-22
S01C
-
U
U
u
0019-RS-000-
HSQS
RS-24
S05C
.
E4.S
£1.5
E5.7
QOH-Sl-OOO-
HSQS
SI-11
S05C
.
E2300
E300
U
OQ19-SI-UOO-
HSQS
SI-12
SOIC
-
£190
E310
U
0019-51-000-
HSQS
SI—13
SOIC
-
0019-SI-000-
HSQS
SI-14
SOIC
-
E160
E160
U
0019-SI-000-
HSQS
SI-15
sosc
-
E210
£150
0
0019-SP-000-
HSQS
SP-11
S05C
-
E560
E370
U
0019-SP-000-
HSQS
SP-12
sosc
-
E600
£360
u
0019-SP-000-
HSQS
SP-13
SOIC
-
E530
£560
u
0019-SP-000-
HSQS
SP-14
SOIC
.
£6600
E3900
u
0019-SP-000-
HSQS
SP-15
SOSC
-
El 400
£1500
li
0019-SP-000-
HSQS
SP-16
SOSC
.
E300
E340
u
0019-DP-000-
HSQS
W8S
CTL
-
01
0019-OP-000-
HSQS
W8S
CTL
-
02
Nuaber of Observations: 123
1- 2,6-di
methyl methyl
naphtfi- 1,1' naphth
alene blphenyl alene
E260
E75
E100
£100
E40
E61
£34
E80
E84
E3.9
U
£57
El 100 E190U
£23
£5.2
£120
U
E3.0
E100
£56
E210
E72
£84
EB5
£64
E310
ll
E14
-------
MAIN SEDIMENT SURVEY TENTATIVELY IDENTIFIED ORGANIC CHEMICALS UATA
2,3,6-
tri-
2-
1-
9-
ntethyl
dlbenzo-
methyl
methyl
hexa-
1so-
naphth-
thlo
phenan-
phenan-
decenoi c
pimar<
Oralnaye
Survey
Station
Sample
alene
phene
threne
threne
acid
dlene
17110019-BL-000-
HSQS
81-11
SU5C -
U
£54
E25
£270
£86
1711QU19-BL-OOU-
HSQS
BL-12
S01C -
1711U019-BL-000-
HSQS
81-13
50 bC -
E26
E83
E59
£410
E1500
17110019-BL-000-
HSQS
BL-14
S01C -
E72
E98
£92
E720
U
171L0019-BL-000-
HSQS
8L-15
S01C -
Ell
E51
E38
U
El 10
17110019-BL-000-
HSQS
BL-16
S01C -
E31
E42
£44
£440
E230
17110019-BL-000-
HSQS
Bl-17
S01C -
01
E20
E18
E38
£180
E97
17110019-BL-000-
Hsqs
BL-17
S01C -
02
1711O019-BL-000-
HSQS
BL-18
S01C -
E32
E96
E67
E140
£92
171I0019-BL-000-
HSQS
BL-19
S01C -
£15
£63
£49
£110
E1Z0
17110019-BL-000-
HSQS
BL-20
SU1C -
E25
E50
£48
£210
£270
17110019-BL-0W-
HSQS
BL-21
SObC -
£23
£85
E4a
E160
£170
17110019-BL-000-
HSQS
81-22
S01C -
E3Z
E92
E37
E91
E230
17110019-BL-000-
HSQS
BL-23
SU1C -
E46
E62
ESS
U
E170
17110019-BL-000-
HSQS
8L-24
S01C -
£43
E130
£110
E85
E240
17110019-61-000-
HSQS
81-25
SUSC -
E20
E31
E42
£260
E120
17UQ019-BI-000-
HSQS
81-26
SU1C -
E67
E61
£34
£340
£180
1711U019-8L-000-
HSQS
BL-27
S01C -
E19
£38
E24
E30
E84
y 17110019-BL-000-
HSQS
BL-28
S05C -
E26
£38
£42
£100
£110
17110019-BL-000-
HSQS
Bl-29
S01C -
£46
E71
E82
£240
E91
*©17110019-BL-000-
HSQS
BL-30
S01C -
E20
£26
E29
£170
£140
1711U019-BL-000-
HSQS
BL-31
SU5C -
Ell
£46
E90
E200
E23U
17110019-BL-000-
HSQS
BL-32
S01C -
E41
E310
£140
E160
E530
17110019-HY-000-
HSQS
CB-11
S01C -
Ely
E26
£42
U
E280
17110019-CB-000-
HSQS
CB-12
SU1C -
Ell
E38
E39
U
El 30
17110019-CB-000-
HSQS
CB-13
S01C -
0
£40
ESQ
E900
E160
17110019-CB-000-
HSQS
CB-14
501C -
u
E53
£71
E220
E300
17110O19-CI-O0U-
HSQS
CI-U
S02C -
£190
U
U
El 200
£240
1711OO19-CI-O0O-
HSQS
CI—12
sovc •
E190
E290
£500
E4380
E600
17110019-CI-000-
HSQS
CI-13
S05C -
E110
E73
£110
£600
£200
17110019-C1-000-
HSQS
CI —14
S01C -
E31
£54
U
E460
E190
17110019-CI-000-
HSQS
CI-15
S01C -
£130
E250
£160
£300
£150
17UQ019-CW-U0O-
HSQS
CI-16
S05C -
E130
U
U
U
£490
17110019-C1-000-
HSQS
CI-17
S05C -
01
E180
£210
£310
U
E530
17U0019-CI-000-
HSQS
CI—17
S05C -
02
17110019-CI-000-
HSQS
CI —18
S01C •
£49
E110
E180
U
E98
17110019-CI-000-
HSQS
CI —19
S01C -
E89
E180
E180
E 720
E43U
17110019-C1-000-
HSQS
CI-20
S05C -
E250
E490
U
U
El 400
17110019-01-000-
HSQS
CI —21
S01C -
E1U0
E110
E3au
E6y0
E190
17110019-CI-000-
HSQS
C1—22
SU5C -
E99
E260
E270
E970
E370
17110019-CR-000-
HSQS
CR-11
SU1C -
u
u
U
£62
U
17110019-CR-000-
HSQS
CH-12
SU5C -
u
u
u
Ef.3
U
171I0019-CR-000-
MSC/S
CR-13
S01C -
u
u
u
E530
U
1711U019-CR-000-
HSQS
CR-14
SU5C -
u
u
u
E2B0
U
-------
main sediment survey tentatively identified
ORGANIC CHEMICALS DATA
Drainage
0019-HY-000-
0019-HY-0U0-
0019-HY-000-
UO19-KY-OO0-
U019-HT-000-
0019-HY-U00-
0019-HY-000-
0019-HY-000-
0U19-HY-000-
0019-HY-000-
OQ19-HY-UOO-
0019-HY-000-
0019-MY-000-
0019-HY-000-
J019-HY-000-
0019-HY-000-
0019-HY-000-
0019-HY-000-
0019-HY-000-
0019-HY-000-
0019-HY-000-
0019-MY-000-
0019-MY-000-
0019-KY-000-
0Q19-HY-Q00-
0019-HY-000-
QG19-HY-000-
0019-HY-000-
0019-HY-000-
0019-HY-000-
0019-HY-0U0-
0019-HY-000-
0019-HY-0O0-
0019-HY-000-
0019-HY-000-
0019-HY-000-
UU19-HY-000-
0019-HY-000-
(XJ19-HY-000-
0019-HY-000-
U019-HY-000-
0019-HY-000-
0019-HY-OUO-
U019-MU-000-
Survey Station Sample
2.3.6-
trl-
methy1
naphth-
alene
dibenzo-
thlo
phene
2-
methyl
phenan-
threne
HSQS
MSQS
HSQS
MSQS
MSQS
MSQS
MSQS
msqs
HSQS
msqs
HSQS
HSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HV-11 SOIC -
HY-1Z S01C -
HY-13 S01C -
HY-14 SOSC -
HY-1S S01C -
HY-16 SOIC -
HY-17 SOIC -
HY-18 SUlC -
HY-19 SOIC -
HY-20 SOIC -
HY-20 SOIC -
HY-21 SOIC -
HY-22 S05C -
HY-23 SOIC -
HY-24 SOIC -
HY-25 SOIC -
HY-26 SOIC -
HY-27 SOIC -
HY-28 SOIC -
HY-29 SOIC -
HY-30 SOIC -
HY-31 SOIC -
HY-31 SOIC -
HY-32 SOIC -
HY-33 SOIC -
HY-34 SOIC -
HY-3S SOIC -
HY-36 SOIC -
HY-37 SOIC -
HY-38 SOIC -
HY-39 SOIC -
HY-40 SOIC -
HY-41 SOIC -
HY-42 SOIC -
HY-43 SOIC -
HY-44 SOIC -
HY-45 SOIC -
HY-46 SOIC -
HY-47 S05C -
HY-48 SOIC -
HY-49 SOIC -
HY-50 S05C -
HY-51 SOIC -
MD-11 SOIC -
01
02
01
02
El 10
E63
U
E120
E190
E270
U
E310
E200
E360
U
E50
0
E62
u
E46
E320
E740
E84
0
E100
E6b
E44
E51
E33
E56
U
E45
U
E44
0
U
£21
E23
E43
El 10
E39
E67
E74
E35
U
E100
U
E70
u
E380
E110
E64
£74
E84
E100
U
E39
£56
E35
E38
El 70
E40
E110
E140
U
E6.4
u
E100
E31
E41
E46
E75
E19
E26
E32
E260
E180
1- 9-
methyl hexa- 1so-
phenan- decenoic piraara-
threne acid diene
El 70
El 700
£320
U
u
E290
E390
E1600
£820
U
E5700
E47O0
E260
El 300
E31U
E27
El 300
E17U
E72
E3500
E85
E62
E3800
El 30
E530
E850
E480
U
E1500
E62
E61
E760
E2t>0
E8Z
E800
£170
E39
U
E190
E26
E820
U
U
E1100
U
U
El 100
U
E19
U
£59
U
U
E300
E78
U
E220
U
E640
E200
E93
E2300
E ISO
ElbU
E1300
E300
E330
E7300
El 200
U
£760
£470
E94
E2300
E270
EUO
E880
E21U
E93
E680
E430
E36
E14U0
E420
E74
E1600
E620
U
ElbOO
E630
E5.1
E590
E28
El 30
U
E200
in
E290
E140
El 70
E71U
E540
£470
£190
E69
E760
£150
E210
E750
E230
-------
MAIN SEDIMENT QUALITY SURVEY TENTATIVELY IDENTIFIED ORGANIC CHEMICALS DATA
2,3,5-
tri- 2-
methyl dlbenzo- methyl
rtaphth- th1o phenan-
Drai naye
Survejr
Station Sample
alene
phene
threne
1711OO19-M0-OOO-
HSQS
MO-12
S05C -
E240
E470
17110019-MO-OOO-
MSOS
MO-13
SOIC -
E6S
E120
17110019-MI-000-
HSQS
MI-11
S01C -
01
El 10
El 10
17110019-MI-000-
msqs
MI-11
S01C -
02
17110019-m-ooo-
hsqs
MI-12
SUIC -
17110019-W-000-
HSQS
MI-J3
S01C -
E68
El 10
17110019-M1-000-
HSQS
MI-14
S01C -
17110019-HI-000-
HSQS
Ml-15
SOIC -
U
E63
17U0019-RS-000-
MSQS
RS-11
S01C -
£70
E180
17110019-RS-000-
hsqs
RS-12
S01C -
E53
E83
17110019-RS-000-
HSQS
RS-13
S01C -
E98
£180
17110019-RS-000-
HSQS
HS-14
S05C -
01
U
E86
17110019-RS-000-
HSQS
RS-14
S05C -
02
17110019-RS-000-
HSUS
RS-15
S02C -
ES.2
£9.2
17110019-RS-000-
MSQS
RS-16
SOIC -
E130
El 30
17110019-RS-000-
HSQS
RS-17
S01C -
E97
E180
17110019-RS-000-
HSQS
RS-18
SOIC -
Ell (X)
E2400
__ 17110019-RS-000-
NSQS
R5-19
SOIC -
E97
El 00
i 17110019-RS-000-
HSQS
KS-20
SOIC -
Elb
E38
CT 17110019-KS-000-
HSQS
RS-21
SOIC -
E190
£360
17110019-RS-000-
MSQS
RS-22
SOIC -
U
U
17110019-RS-000-
HSQS
RS-24
S05C -
U
U
17110019-S1-000-
HSQS
Sl-11
S05C -
E170
E2B0
17110019-SI-000-
hsqs
SI-12
SOIC -
E76
El 30
17110019-SI-000-
HSQS
SI-13
SOIC -
17110019-S1-000-
MSQS
SI-14
soic -
E260
E660
17110019-SI-000-
HSQS
SI-15
S05C -
U
E230
17110U19-SP-000-
MSQS
SP-11
S05C -
u
E110
17110019-SP-OOO-
MSQS
SP-12
SObC -
u
El 40
17110019-SP-000-
MSQS
SP-13
SUIC -
u
E64
17110019-SP-OOO-
MSQS
SP-14
SOIC -
u
U
17110019-SP-000-
HSQS
SP-15
SCI5C -
u
E52
17110019-SP-OOO-
HSQS
SP-16
SU5C -
u
£40
17110019-DP-000-
MSQS
UBS
CTL -
01
17110019-UP-OOO-
MSQS
W8S
CTL -
02
NumDer of Observations: 123
1-
9-
methyl
hexa-
Iso-
phenan-
decenoic
p1 ma ra -
threne
acw
alene
E220
E1000
E930
El 50
u
£240
E210
El 600
E430
E150
E560
E560
E160
E310
EblO
E320
E230
E190
E89
El 300
E1S0
E200
U
£150
E160
£1300
0
ES.2
U
E14
U
E160
E310
E120
E340
E170
El 300
U
U
E140
E670
U
ES2
E66
E14
E31Q
U
E81
U
E770
U
£18
E280O
£36
E130
El 100
E870
U
U
E960
E430
E650
E350
E370
E540
£280
U
E2100
E750
E36
E2200
E710
E64
El 300
E410
U
E990
E59U0
E96
E2U00
E550
E61
El 600
£310
-------
>
o>
N
MAIN SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage Survey Station Sample R
,0019-81-000- MSQS BL-U S05C -
0019-BL-000- MSQS BL-12 S01C -
0019-BL-000- MSQS BL-13 S05C -
0019-BL-000- MSQS BL-14 SOIC •
0019-BL-000- MSQS 8L-15 S01C -
0019-BL-000- MSQS BL-16 S01C -
0019-BL-000- MSQS BL-17 S01C -
0019-BL-000- MSQS Bl-18 SOIC -
0019-BI-000- MSQS BL-19 S01C -
0019-BL-000- MSQS BL-20 S01C -
0019-BI-000- MSQS BL-21 S05C -
0019-BL-000- MSQS 8L-2Z S01C -
0019-BL-000- MSQS BL-23 SOIC -
0019-BL-000- MSQS BL-24 S01C -
0019-BI-000- MSQS BL-25 S05C -
0019-BL-000- MSQS BL-26 S01C -
0019-BI-000- MSQS BL-27 S01C -
0019-BI-000- MSQS BL-28 SU5C -
0019-81-000- MSQS BL-29 S01C -
J19-BL-000- MSQS BL-30 S01C -
0019-BL-000- NSQS BL-31 S05C -
0019-BL-000- MSQS BL-32 S01C -
0019-HY-000- MSQS CB-11 S01C -
0019-CB-000- MSQS CB-12 SOIC -
0019-CB-000- MSQS CB-13 SOIC -
0019-CB-000- MSQS CB-14 SOIC -
0019-CI-OOU- MSQS CI-11 S02C -
0019-CI-000- MSQS CI-12 SOIC -
0019-CI-000- MSQS CI-13 S05C -
0019-CI-000- MSQS Cl-14 SOIC -
0019-C1-000- MSQS CI-15 SOIC -
0019-CU-000- MSQS Cl-16 SOSC -
0019-CI-000- MSQS CI-17 SOSC -
0019-C1-000- MSQS CI-1U SOIC -
0019-CI-000- MSQS C1-19 SOIC -
OU19-CI-000- MSQS CI-20 S05C -
0019-C1-000- MSQS CI-21 SOIC -
0019-C1-000- MSQS Cl-22 S05C -
0019-CR-000- MSQS CR-11 SOIC -
0019-CR-000- MSQS CR-12 SOSC -
0019-CR-000- MSQS CR-13 SOIC -
0019-CR-000- MSQS CR-14 S05C -
0019-HY-000- MSQS HY-11 SOIC -
0019-HY-000- MSQS HY-12 SOIC -
0019-HY-000- MSQS HY-13 SOIC -
0019-HY-000- MSQS HY-14 SOSC -
0019-HY-000- MSQS HY-15 SOIC -
0019-HY-000- MSQS HY-16 SOIC -
Antimony
Arsenic
Barium
0.36
26
15
0.S2
31
20
0.70
3b
21
0.64
25
21
0.50
36
18
0.78
34
21
0.70
33
21
o.su
36
15
0.68
21
18
O.SO
28
18
0.52
18
17
0.66
21
17
0.58
19
IB
0.58
16
19
0.60
15
19
0.48
22
18
UO.l
5.4
8.2
0.18
7.0
12
0.44
13
17
0.34
8.0
17
0.34
7.6
18
0.38
12
22
0.48
26
32
0.28
8.4
22
0.30
9.6
23
0.70
14
24
0.86
21
42
1.1
25
44
1.2
30
44
1.2
33
47
1.4
33
57
1.0
20.
33
0.96
28.
48
0.94
30.
46
U.78
28.
50
0.88
29.
60
0.28
11.
23
0.22
8.0
16
0.13
2.4
5.6
UO.l
3.8
6.8
UO.l
3.8
7.3
0.14
3.8
7.8
1.7
100
24
1.1
40
28
1.2
60
28
1.0
32
22
1.1
40
22
12
79
25
Beryllium Cadmium Chromium
0.23
3.0
11
0.27
3.3
13
0.28
3.4
13
0.25
3.1
12
0.25
3.1
11
0.28
3.3
13
0.27
3,5
13
0.20
2.8
10
0.25
3.1
12
0.23
2.9
11
0.22
2.8
11
0.23
3.0
11
0.25
3.1
12
0.24
2.9
13
0.26
3.2
12
0.23
3.0
11
0.12
1.6
5.8
0.16
2.0
7.7
0.22
3.0
11
0.19
1.9
7.6
E0.18
2.1
7.3
0.21
2.5
8.3
0.26
2.8
10
0.22
2.3
7.1
0.24
2.4
7.6
0.25
2.4
9.1
0.22
4.7
36
0.27
6.2
37
0.29
6.7
35
0.29
6.5
34
0.29
6.9
35
0.21
5.7
27
0.26
5.8
29
0.29
6.5
31
0.25
5.0
26
0.27
5.1
27
0.16
2.7
12
0.12
1.5
8.4
0.082
1.1
9.9
E0.11
1.4
11
EO .07 3
1.1
9.6
E0.082
1.5
11
0.32
3.4
22
0.39
2.9
29
0.36
3.0
27
0.30
2.5
22
0.33
2.3
23
0.41
3.6
28
-------
MAIN SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values In ppm dry weiyht
Survey Station Sample R<
Drainage
i
U
0019
0019
0019
0019
0019
019
019
019
019
0019
0019
0019-
019
019-
0019-
0019-
0019-
0019-
0019-
0019
0019-
0019-
0019
0019-
0019-
0019-
0019-
0019-
0019-
0019-
0019-
0019-
0019
0019'
0019
0019
0019
0019
0019
0019-
0019
0019
0019
0019-
0019
0019
0019
0019
i-Hr-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
¦HY-OOO-
HY-OOO-
¦HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-UOO-
HY-OOO-
HY-OOO-
HY-OOO-
•HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-OOO-
HY-UOO-
HY-OOO-
HY-OOO-
•HY-OOO-
-HY-OOO-
-HY-OOO-
-HY-OOO-
-MD-OOO-
-MO-OOO-
-MO-OOO-
-MI-OOO-
Ml-OOO-
H1-000-
MI-OOO-
Ml-OOO-
RS-OOO-
RS-OOO-
•RS-OOO-
RS-OOO-
RS-OOO-
HSQS
msqs
hsos
MSt)S
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
NSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
MSQS
HY-17 S01C -
HY-18 S01C -
HY-19 S01C -
HY-20 S01C
HY-21 S01C
HY-22 S05C
HY-23 S01C
HY-24 S01C
HY-25 S01C
HY-26 S01C
HY-27 S01C
HY-28 S01C
HY-29 S01C
HY-30 S01C
HY-31 S01C
HY-32 S01C
HY-33 S01C
HY-34 S01C
HY-35 S01C
HY-36 S01C -
HY-37 S01C -
HY-38 S01C -
HY-39 S01C -
HY-« S01C -
HY-41 S01C -
HY-42 S01C -
HY-43 S01C -
HY-44 S01C -
HY-4S S01C -
HY-46 S01C -
HY-47 SUSC -
HY-48 S01C -
HY-49 S01C -
HY-50 S05C -
HY-51 S01C -
MD-11 S01C -
MO-12 S05C -
MD-13 S01C •
HI—11 S01C -
MI—12 S01C -
Ml-13 S01C ¦
Ml-14 S01C -
Ml-16 S01C ¦
RS-11 S01C •
KS-12 S01C •
RS-13 S01C ¦
RS-14 S05C
RS-15 S02C
Antimony
Arsenic
Barium
1.5
86
36
1.5
80
50
3.4
70
29
1.0
52
35
0.82
67
30
1.0
90
32
0.88
66
35
0.96
85
36
0.96
53
27
0.70
44
23
1.1
28
23
1.4
39
27
0.88
26
27
0.78
28
27
0.60
18
18
1.1
27
27
0.50
20
19
0.70
25
13
0.68
22
30
0.74
30
37
1.0
20
28
0.86
18
24
0.38
20
22
0.68
25
47
0.44
22
33
0.72
15
33
0.62
14
39
UO.l
5.8
5.1
0.34
20
23
0.32
32
27
0.56
25
32
0.40
20
35
2.4
16
25
1.3
12
27
0.30
12
24
0.90
15
36
1.2
39
36
1.9
67
26
0.38
10
40
0.54
12
50
0.48
12
41
0.40
10
32
0.48
9.5
28
0.54
16
40
1.2
16
31
1.4
20
28
3.1
32
22
0.19
16
11
Ber>l1ium
Cadmi um
Chromium
0.40
0.40
0.35
0.41
0.37
0.37
0.39
0.45
0.40
0.31
0.34
0.36
0.31
0.50
0.23
0.30
0.23
0.23
0.29
0.31
0.32
0.24
0.26
0.32
0.26
0.29
0.28
0.10
0.27
0.31
0.29
0.24
0.24
0.25
0.24
E0.16
E0.16
E0.16
0.27
0.28
0.27
0.27
0.25
E0.15
0.22
£0.15
0.22
EO.099
3.6
29
4.0
35
3.5
27
3.5
32
3.0
30
3.6
31
2.8
26
3.4
37
2.3
30
2.0
21
2.2
25
2.5
26
2.2
22
2.1
24
1.3
16
2.1
24
1.7
23
1.8
18
1.8
22
2.1
24
2.0
23
1.5
17
1.5
16
1.9
22
1.7
18
1.8
20
1.6
20
0.62
5.4
1.8
14
2.3
19
1.8
21
1.4
14
2.6
9.9
2.8
9.0
1.3
12
3.4
18
3.4
15
3.5
14
1.4
13
2.0
14
1.9
13
1.6
lb
1.5
12
2.2
12
2.2
16
2.5
18
3.1
16
1.4
15
-------
MAIN SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Orainaye
Survey
Station
Samp
17110019-RS-000-
NSQS
RS-16
S01C
17110019-RS-000-
Msgs
RS-17
S01C
17110019-RS-000-
nsqs
RS-18
S01C
17110019-RS-000-
NSQS
RS-19
S01C
17110019-RS-000-
NSQS
RS-20
S01C
17110O19-RS-OOU-
NSQS
RS-21
S01C
17110019-RS-000-
NSQS
RS-22
S01C
17U0019-RS-000-
NSQS
RS-24
SOSC
17110019-S1-000-
NSQS
SI-11
S05C
17110019-SI-000-
NSQS
S1-12
S01C
17U0019-SI-000-
NSQS
SI-13
S01C
17110019-SI-000-
NSQS
S1—14
SU1C
17110019-SI-000-
NSQS
SI-IS
S05C
17110019-SP-OOO-
NSQS
SP-11
S05C
17110019-SP-000-
NSQS
SP-12
S05C
17110019-SP-OOO-
NSQS
SP-13
S01C
17110019-SP-000-
NSQS
SP-14
S01C
17110019-SP-000-
NSQS
SP-15
S05C
17110019-SP-000-
NSQS
SP-16
S05C
17110019-DP-000-
NSQS
WBS
CTL
17110019-DP-000-
NSQS
UBS
CTL
e Rep
Antimony
Arsenic
Barium
-
7.8
136
23
-
190
12200
56
-
420
9700
71
-
36
1550
102
-
1.8
90
15
-
205
9000
153
-
5.3
85
14
-
26
700
103
-
1.3
93
19
-
0.82
33
18
-
0.70
28
18
-
1.1
23
26
-
0.46
10
21
-
0.44
6.0
11
-
0.54
7.0
16
-
0.72
12
19
-
0.66
7.0
50
-
0.13
7.4
12
-
0.26
5.5
17
02
U1
2.8
10
01
U1
2.1
9.9
i
at
^Nuaber of Observations: 117
Beryl 1 iuin
EG.17
0.48
0.33
0.20
EO .18
0.55
0.10
0.28
0.22
0.21
0.21
0.18
0.25
0.13
0.17
0.20
0.18
0.12
0.18
0.12
0.11
Cadmi um
Chrorni uin
3.4
14
76
18
184
23
16
12
3.0
15
105
46
2.2
7.6
9.6
15
4.4
13
3.2
11
2.3
11
1.6
13
1.6
11
1.3
10
1.7
14
2.4
18
2.7
21
0.82
6.7
0.86
8.1
0.24
18
0.22
17
-------
MAIN SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage
Survey
Station
Sample
Rep Copper
Iron
Lead
0019-BL-000-
MSQS
BL-11
S05C
_
41
17300
46
0U19-BL-000-
MSQS
BL-12
SOIC
-
74
18000
66
0019-BL-000-
MSQS
BL-13
SUSC
-
63
19500
64
0019-BL-000-
Msgs
BL-14
SOIC
-
59
16500
53
0019-BL-000-
MSQS
BL-1S
SOIC
-
57
17000
54
0019-BL-000-
MSQS
BL-16
SOIC
-
71
18100
73
0019-BL-000-
MSQS
81-17
SOIC
-
64
18600
61
0019-BL-000-
MSQS
BL-18
SOIC
-
49
14500
41
0019-BL-000-
MSQS
BL-19
SOIC
-
54
16600
53
0019-BL-000-
MSQS
BL-20
SOIC
-
52
16400
53
0019-BL-000-
MSQS
BL-21
SOSC
-
54
15200
49
0019-BL-000-
MSQS
BL-22
SOIC
-
54
16200
52
0019-BL-000-
MSQS
Bl-23
SOIC
-
53
16900
55
0O19-BL-0U0-
MSQS
BL-24
SOIC
-
101
15300
58
0019-BL-000-
MSQS
BL-25
SOSC
-
57
17200
56
0019-BL-000-
MSQS
BL-26
SOIC
-
53
16900
52
0019-BL-000-
MSQS
BL-27
SOIC
-
20
9150
19
0U19-BL-000-
MSQS
BL-28
S05C
_
28
11100
27
0019-BL-000-
MSQS
BL-29
SOIC
-
48
17500
47
0019-BL-000-
MSQS
BL-30
SOIC
-
32
11700
35
0019-BL-000-
MSQS
BL-31
SUSC
-
29
12200
32
OU19-BL-0OO-
MSQS
BL-32
SU1C
-
41
14500
39
0019-HY-000-
MSQS
CB-11
SOIC
-
101
14800
54
0019-CB-000-
MSQS
CB-12
SOIC
-
25
12800
26
0019-CB-000-
MSQS
CB-13
SOIC
.
27
13700
27
0019-CB-000-
MSQS
CB-14
SOIC
-
36
14400
30
0019-CI-000-
MSQS
CI-11
S02C
-
155
16100
725
0019-CI-000-
MSQS
C1-12
SOIC
.
203
16800
595
0019-CI-000-
MSQS
CI-13
S05C
-
185
19200
450
0019-CI-000-
MSQS
CI—14
SOIC
.
176
19600
388
0019-CI-000-
MSQS
CI-1S
SOIC
-
188
22700
453
0019-CW-000-
MSQS
C1-16
S05C
-
158
15800
240
0019-CI-000-
MSQS
CI-17
SOSC
_
168
17900
300
0019-CI-000-
MSQS
C1—18
SOIC
.
166
17400
291
0019-CI-000-
MSQS
CI —19
SOIC
-
156
16200
204
0019-CI-000-
MSQS
CI-20
S05C
-
158
17600
211
0019-CI-000-
MSQS
CI-21
SOIC
-
71
11100
88
0019-CI-000-
MSQS
CI-22
SObC
-
40
8360
49
0019-CR-000-
MSQS
CR-11
SOIC
-
4.9
6230
11
0U19-CR-000-
MSQS
CR-12
S05C
-
7.3
8150
13
0019-CR-000-
MSQS
CR-13
SOIC
-
5.2
6530
10
0019-CR-000-
MSQS
CR-14
SOSC
-
7.8
6980
12
0019-HY-000-
MSQS
HY-11
SOIC
-
125
25000
82
0019-HY-000-
MSQS
HY-12
SOIC
-
143
24300
93
0019-HY-000-
MSQS
HY-13
SOIC
-
150
23100
96
0U19-HY-000-
MSQS
HY-14
S05C
-
114
19900
71
0019-HY-000-
MSQS
HY-15
SOIC
-
114
19300
76
0019-HY-000-
MSQS
HY-16
SOIC
-
220
22800
129
Manganese
Nickel
Selenium
79
12
U1.0
91
13
U1.0
99
13
U1.0
81
12
U1.0
94
12
U1.0
101
13
U1.0
103
13
U1.0
76
10
U1.0
97
U1.0
99
12
U1.0
92
11
U1.0
93
12
U1.0
104
12
U1.0
100
13
U1.0
106
13
U1.0
105
12
U1.0
75
7.8
U1.0
96
9.2
U1.0
128
13
U1.0
101
9.7
U1.0
114
9.9
U1.0
137
11
U1.0
163
13
in .o
121
11
U1.0
139
12
U1.0
151
13
U1.0
116
40
U1.0
128
33
U1.0
136
28
U1.0
145
27
U1.0
178
25
U1.0
107
19
U1.0
154
21
U1.0
139
22
U1.0
140
20
U1.0
150
21
U1.0
85
11
U1.0
74
9.0
Ul.O
83
11
U1.0
132
14
Ul.O
80
12
Ul.O
76
13
Ul.O
204
22
U1.0
145
27
Ul.O
144
26
U1.0
131
22
Ul.O
124
24
Ul.O
134
32
U1.0
-------
MAIN SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Oralnage
Survey
Station Sample Rep
Copper
Iron
Lead
0019-HY-000-
MSQS
HY-17 SOIC -
204
26700
114
0019-HY-000-
Msgs
HY-18 SOIC -
264
29900
131
0019-HY-000-
MSQS
HY-19 SOIC -
186
22700
117
0019-HY-000-
MSQS
HY-20 SOIC -
177
25700
125
0019-HY-000-
MSQS
HY-21 SOIC -
153
21500
109
0019-HY-000-
MSQS
HY-22 S05C -
239
25500
181
00I9-HY-000-
MSQS
HY-23 SOIC -
147
22000
110
0019-HY-000-
MSQS
HY-24 SOIC -
192
26400
139
0019-HY-000-
MSQS
HY-25 SOIC -
122
20900
78
0019-HY-000-
MSQS
HY-26 SOIC -
91
17800
64
0019-HY-000-
MSQS
HY-27 SOIC -
118
18000
93
0019-HY-000-
MSQS
HY-28 SOIC -
125
20100
92
0019-HY-U00-
MSQS
HY-29 SOIC -
107
18000
79
0019-HY-000-
MSQS
HY-30 SOIC -
9a
22400
56
0019-HY-000-
MSQS
HY-31 SOIC -
60
13700
46
0019-HY-000-
MSQS
HY-32 SOIC -
110
17000
91
0019-HY-000-
MSQS
HY-33 SOIC -
79
14800
56
0019-HY-000-
MSQS
HY-34 SOIC -
62
13700
53
0OI9-HY-O0O-
MSQS
HY-35 SOIC -
99
1S60O
83
0019-HY-000-
MSQS
HY-36 SOIC -
106
17100
106
0019-HY-000-
MSQS
HY-37 SOIC -
98
17000
76
0019-HY-000-
MSQS
HY-38 SOIC -
76
14400
69
0019-HY-000-
MSQS
HY-39 SOIC -
73
13700
63
0019-HY-000-
MSQS
HY-40 SOIC -
262
16500
102
0019-HY-000-
MSQS
HY-41 SOIC -
78
15000
73
0019-HY-000-
MSQS
HY-42 SOIC -
96
15600
172
0019-HY-000-
MSQS
HY-43 SOIC -
115
15000
81
0019-HY-000-
MSQS
HY-44 SOIC -
14
6790
8.3
0019-HY-000-
MSQS
HY-45 SOIC -
65
20100
56
0019-HY-000-
MSQS
HY-46 SOIC -
112
23100
134
0019-HY-000-
MSQS
HY-47 S05C -
83
14800
69
0019-HY-00U-
MSQS
HY-48 SOIC -
71
13600
48
0019-HY-000-
MSQS
HY-49 SO\C -
42
14000
42
0019-HY-U00-
MSQS
HY-50 S05C -
40
14900
42
0019-HY-000-
MSQS
HY-51 SOIC -
46
14800
23
0019-M0-000-
MSQS
MO-11 SOIC -
176
14800
188
0019-MO-000-
MSQS
Ml)-12 S05C -
311
12800
303
0019-MD-000-
MSQS
MO-13 SOIC -
554
13500
190
0019-MI-000-
MSQS
MI-11 SOIC -
58
13900
49
0019-M1-000-
MSQS
MI-12 SOIC -
77
15700
75
0019-MI-000-
MSQS
Ml — 13 SOIC -
71
14700
78
0019-MI-000-
MSQS
MI-14 SOIC -
60
15100
62
0019-H1-000-
MSQS
Ml-15 SOIC -
46
14200
48
0019-RS-000-
MSQS
RS-11 SOIC -
69
10800
80
0019-RS-000-
MSQS
RS-12 SOIC -
41
12900
58
0019-RS-000-
MSQS
RS-13 SOIC -
67
10900
99
0019-RS-000-
MSQS
RS-14 S05C -
155
14600
104
0019-RS-000-
MSQS
RS-15 S02C -
55
8600
38
Manganese
Ni ckel
Selenium
197
30
01.0
250
39
U1.0
135
29
U1.0
204
36
01.0
171
38
U1.0
205
52
01.0
203
56
U1.0
203
39
01.0
165
30
01.0
132
24
Ul .0
131
25
01.0
148
23
01.0
136
22
U1.0
210
25
01.0
98
15
U1.0
118
21
01.0
105
22
01.0
88
14
01.0
127
20
01.0
143
20
01.0
134
23
01.0
120
15
U1.0
110
18
01.0
141
21
01.0
160
18
01.0
151
21
01.0
128
20
U1.0
55
6.9
U1.0
140
20
01.0
171
39
01.0
133
19
U1.0
149
14
Ul.O
122
12
Ul.O
132
12
01.0
161
13
Ul.O
95
13
Ul.O
86
11
Ul.O
106
12
Ul.O
103
12
Ul.O
120
13
01.0
118
12
Ul.O
136
14
01.0
145
12
Ul.O
110
12
Ul.O
150
20
Ul.O
106
19
Ul.O
109
20
Ul.O
119
17
Ul.O
-------
MAIN SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019'
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
17110019
-RS-OOO-
•RS-OOO-
-RS-0O0-
¦RS-OOO-
RS-OOO-
¦RS-OOO-
¦RS-OOO-
RS-OOO-
SI-OOO-
SI-OOO-
SI-OOO-
-S1-000-
SI-OOO-
SP-OOO-
-SP-OOO-
¦SP-OOO-
-SP-OOO-
-SP-OOO-
-SP-OOO-
¦OP-OOO-
-OP-OOO-
NSQS
hsqs
HSQS
hsqs
hsqs
hsqs
HSQS
HSQS
HSQS
HSQS
HSQS
HSQS
HSQS
HSQS
HSQS
HSQS
HSQS
HSQS
HSQS
HSQS
HSQS
Station Sample
Rep
Copper
Iron
Lead
RS-16 S01C -
458
12100
155
RS-17 S01C -
8320
50000
2680
RS-18 S01C -
11400
52900
6250
RS-19 SU1C -
2240
24000
1020
RS-20 S01C •
137
15900
78
RS-21 S01C -
14300
115000
4970
RS-22 S01C •
87
10300
98
RS-24 S05C -
38b
37100
531
SI-11 S05C -
292
14200
661
SI-12 S01C -
191
12800
496
SI—13 S01C -
168
12300
310
SI—14 S01C -
148
11700
212
SI-IS SOSC -
74
12800
128
SP-11 SOSC -
65
8310
24
SP-12 SOSC -
56
10600
29
SP-13 S01C -
82
11900
52
SP-14 S01C -
275
9270
60
SP-1S SOSC •
32
6650
11
SP-16 S05C -
29
10000
11
MBS CTL -
02
7.2
9900
2.5
WBS CTL -
01
5.2
9420
3.2
I
Ol
^4
Number of Observations: 117
Manganese
Nickel
Selenijm
186
16
U1.0
2U2
64
26
748
93
24
137
23
1.4
232
19
U1.0
746
350
25
99
10
U1.0
484
28
U1.0
114
12
Ul.O
118
11
U1.0
102
10
Ul.O
112
12
Ul.O
105
11
Ul.O
70
8.3
Ul.O
88
10
Ul.O
107
12
Ul.O
556
40
Ul.O
83
7.7
U1.0
85
8.9
Ul.O
132
29
U1
128
28
U1
IV-10
-------
(WIN SEDIMENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Oralnaye
Survey
Station
Sample
Rep Silver
0019-BL-000-
NSQS
BL-11
S05C
U.17
0019-8L-000-
MSQS
BL-12
S01C
_
0.30
OOW-UL-OOO-
NSQS
BL-13
S05C
_
0.14
0019-BL-000-
NSQS
BL-14
S01C
_
0.22
0019-BL-000-
MSQS
BL-15
S01C
_
0.26
0019-BL-000-
MSQS
BL-16
S01C
.
ti.18
0019-8L-000-
NSQS
8L-17
SU1C
_
0.19
0019-8L-000-
NSQS
BL-18
S01C
.
0.18
0019-BL-000-
NSQS
8L-19
S01C
_
0.24
0019-BL-000-
NSQS
BL-20
S01C
.
0.22
0019-BL-000-
NSQS
BL-21
S05C
_
0.17
0019-BL-UOO-
NSQS
BL-22
S01C
_
0.40
0019-61-000-
NSQS
BL-23
S01C
.
0.22
0019-BL-000-
NSQS
BL-24
S01C
_
0.34
0019-BL-000-
NSQS
BL-25
S05C
.
0.20
0019-8L-000-
NSQS
BL-26
S01C
.
0.20
0019-BL-000-
MSQS
BL-27
S01C
-
UO.l
0019-BL-000-
NSQS
BL-28
S05C
.
0.13
0019-BL-000-
NSQS
BL-29
S01C
_
0.18
0019-BL-000-
NSQS
BL-30
S01C
-
0.20
OOH-BL-OOO-
MSQS
BL-31
S05C
.
0.15
0019-BL-000-
MSQS
BL-32
S01C
.
0.22
0019-HY-000-
NSQS
CB-11
S01C
_
0.34
0019-CB-000-
NSQS
CB-12
S01C
_
UO.l
0019-CB-000-
NSQS
CB-13
S01C
_
0.10
U019-C6-000-
NSQS
CB-14
S01C
_
0.11
0019-CI-000-
NSQS
CI-11
S02C
.
UO.l
0019-CI-000-
MSQS
CI-12
S01C
.
O.U
0019-CI-000-
NSQS
CI-13
SOSC
_
0.11
0019-CI-000-
NSQS
CI-14
S01C
_
0.11
0019-CI-000-
NSQS
CI-1S
S01C
UO.l
0019-CU-000-
NSQS
CI-16
S05C
_
UO.l
0019-CI-000-
NSQS
CI-17
S05C
.
UO.l
0019-CI-000-
NSQS
CI-18
S01C
_
0.11
0019-CI-000-
NSQS
CI-19
S01C
.
0.12
0019-CI-000-
NSQS
CI-20
SOSC
_
0.13
0019-CI-000-
NSQS
Cl-21
S01C
.
0.44
0019-CI-000-
MSQS
CI-22
S05C
.
0.40
0019-CR-000-
NSQS
CR-11
S01C
_
UO.l
0019-CR-000-
NSQS
CR-12
S05C
•
UO.l
0019-CR-000-
NSQS
CH-13
501C
_
UO.l
0019-CR-000-
NSQS
CR-14
SOSC
_
UO.l
0019-HY-UU0-
NSQS
HY-11
S01C
.
0.24
OOH-HY-OOO-
NSQS
HY-12
S01C
_
0.26
0019-HY-000-
NSQS
MY-13
S01C
„
0.26
0019-HY-000-
NSQS
HY-14
S05C
_
0.24
0019-HY-000-
MSQS
HY-15
SU1C
-
0.22
0019-HY-000-
MSQS
HY-16
S01C
-
0.26
U0.1 63
UO.l 91
UO.l 85
UO.l 68
UO.l 70
UO.l 88
UO.l 82
UO.l 67
UO.l 68
UO.l 65
UO.l 65
UO.l 69
UO.l 68
0.10 90
0.11 70
UO.l 66
UO.l 29
UO.l 37
UO.l 62
UO.l 39
UO.l 35
UO.l 44
UO.l 72
UO.l 26
UO.l 28
UO.l 33
UO.l 325
UO.l 282
UO.l 247
UO.l 234
UO.l 270
UO.l 254
UO.l 227
UO.l 236
UO.l 164
UO.l 165
UO.l 72
UO.l 44
0.11 15
UO.l 19
0.20 15
0.24 17
UO.l 176
UO.l 198
UO.l 207
UU.l 180
UO.l 1H6
0.16 317
Cyanide
Cobalt Mercury
0.069
0.078
0.20
0.22
0.20
0.15
0.16
0.18
0.21
0.20
0.13
0.10
0.15
0.099
0.15
0.12
0.051
0.070
0.082
0.094
0.085
0.11
0.14
0.053
0.063
0.066
0.53
0.45
1.1
0.10
0.28
0.11
0.11
0.96
0.20
0.24
0.32
0.22
0.055
0.098
0.049
0.034
0.078
0.46
0.39
0.33
0.048
0.17
-------
MAIN SEDIMENT gUALITr SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage Survey Station Sample Rep
i
CT»
IO
17110019-Hr-ooo- Hsgs hy-17 S01C
17110019-HY-000- MSQS HY-18 S01C
17110019-HY-000- MSQS HY-19 S01C
17110019-HY-00U- MSQS HY-20 S01C
1711U019-HY-000- MSQS HY-21 S01C
17U0019-HY-000- MSQS HY-22 S05C
17110019-HY-U00- MSQS HY-23 S01C
17110019-HY-0U0- MSQS HY-24 S01C
17110019-HY-000- MSQS HY-25 S01C
17110019-HY-000- MSQS HY-26 S01C
17110019-HY-000- MSQS HY-27 SU1C
17110019-HY-000- MSQS HY-28 S01C
17110019-HY-000- MSQS HY-29 S01C
17110019-HY-000- MSQS HY-30 S01C
17110019-HY-000- MSQS HY-31 S01C
17110019-HY-000- MSQS HY-32 S01C
I7110019-HY-000- MSQS HY-33 S01C
17110019-HY-000- MSQS HY-34 S01C
171100I9-HY-000- MSQS HY-35 S01C
17110019-HY-000- MSQS HY-36 S01C
17110019-HY-000- MSQS HY-37 SU1C
17110019-HY-000- MSQS HY-38 S01C
17110019-HY-000- MSQS HY-39 S01C
17110019-HY-000- MSQS HY-40 S01C
17110019-HT-000- MSQS HY-41 S01C
17110019-HY-000- MSQS HY-42 S01C
17110019-HY-000- MSQS HY-43 S01C
17110019-HY-000- MSQS HY-44 S01C
17110019-HY-000- MSQS HY-45 S01C
17110019-HY-000- MSQS HY-46 S01C •
17110019-HY-000- MSQS HY-47 S05C
17110019-HY-000- MSQS HY-48 S01C •
17110019-HY-000- MSQS HY-49 S01C
17110019-HY-000- MSQS HY-50 S05C ¦
17110019-HY-000- MSQS HY-51 S01C •
17110019-MD-000- MSQS MO-11 S01C ¦
17110019-MD-000- MSQS MO-12 S05C -
17U0019-MO-000- MSQS MD-13 S01C ¦
17110019-MI-000- MSQS MI-11 S01C •
17110019-MI-000- MSQS MI-12 S01C ¦
17110019-M1-000- MSQS Nl-13 S01C ¦
17110019-MI-000- MSQS MI-14 S01C -
17110019-M1-000- MSQS MI-IS S01C ¦
17110019-RS-000- MSQS RS-11 S01C ¦
17110019-RS-OOO- MSQS RS-12 S01C •
17110019-RS-000- MSQS RS-13 S01C ¦
17110019-RS-000- MSQS RS-14 S05C -
17110019-RS-000- MSQS RS-15 S02C -
Si lver
Thai 1ium
Zinc
0.46
U0.1
268
0.24
U0.1
294
0.40
UO. 1
273
0.48
00.1
2S5
0.42
UO.l
202
0.40
00.1
242
0.40
UO.l
190
0.42
UO.l
258
0.36
UO.l
149
0.28
UO.l
123
0.24
UO.l
144
0.26
UO.l
146
0.17
UO.l
130
0.16
UO.l
120
0.17
UO.l
77
0.32
UO.l
143
0.18
UO.l
84
0.16
UO.l
105
0.24
UO.l
112
0.36
UO.l
124
0.24
UO.l
109
0.24
UO.l
88
0.32
UO.l
75
0.42
UO.l
116
0.36
UO.l
91
0.30
UO.l
115
0.26
UO.l
107
0.20
UO.l
21
0.26
UO.l
69
0.38
UO.l
89
0.42
UO.l
95
0.34
UO.l
74
0.26
UO.l
43
0.26
UO.l
41
0.24
UO.l
46
0.22
UO.l
178
0.56
UO.l
208
0.26
UO.l
158
0.40
UO.l
105
0.4b
UO.l
135
0.50
UO.l
120
0.36
UO.l
88
0.28
IX).1
63
0.26
UO.l
91
0.20
UO.l
60
0.28
UO.l
80
0.44
UO.l
91
0.24
UO.l
32
Cyanide Cobalt Mercury
0.30
0.39
0.32
0.28
0.056
0.50
0.40
0.49
0.27
0.25
0.32
0.28
0.28
0.22
0.19
0.38
0.23
0.21
0.30
0.38
0.22
0.22
0.22
3.2
0.34
0.31
0.18
0.20
0.32
0.44
0.21
0.13
0.11
0.11
0.13
0.18
0.32
3.4
0.18
0.17
0.16
0.16
0.12
0.32
0.30
0.39
0.30
0.12
-------
MAIN SEUINENT QUALITY SURVEY INORGANIC CHEMICALS - Values in ppm dry weight
Drainage
Survey
Station
Sample
Rep
Si 1 ver
Thai 1ium
Zinc
7110019-RS-000-
MSQS
RS-16
S01C
_
0.26
0.11
103
7110019-RS-000-
msqs
RS-17
S01C
_
0.36
UO.l
2040
7110019-RS-000-
NSQS
US-18
S01C
_
0.30
3.2
3320
7U0019-RS-000-
MSQS
RS-19
S01C
_
0.22
0.46
906
7110019-RS-000-
MSQS
RS-20
S01C
-
0.17
UO.l
140
711U019-RS-000-
NSQS
RS-21
S01C
.
0.20
0.80
4210
7U0019-RS-U0O-
MSQS
RS-22
SU1C
-
U.28
UO.l
201
7110019-RS-000-
msqs
RS-24
S05C
-
0.44
UO.l
1620
711OO19-SI-OO0-
MSQS
Sl-ll
S05C
-
0.50
0.16
491
7U0019-SI-000-
MSQS
SI—12
S01C
-
0.46
0.14
337
7110019-SI-000-
MSQS
SI—13
SOIC
-
0.60
UO.l
254
7U0019-SI-000-
MSQS
SI-14
S01C
-
0.54
UO.l
205
7110019-S1-000-
MSQS
SI-IS
SU5C
-
0.38
UO.l
109
71100I9-SP-000-
MSQS
SP-ll
S05C
-
0.22
0.11
60
71100I9-SP-000-
NSQS
SP-12
S05C
-
0.30
0.14
62
7110019-SP-OOO-
MSQS
SP-13
SOIC
-
0.40
0.16
106
7110019-SP-000-
NSQS
SP-14
SOIC
-
0.26
0.12
125
7110019-SP-OOO-
NSQS
SP-15
SOSC
-
0.13
UO.l
29
7110019-SP-000-
MSQS
SP-16
SOSC
-
0.17
UO.l
30
7U<»l9-DP-0l)0-
MSQS
was
CTL
-
02
U0.2
U0.5
21
7110019-OP-000-
MSQS
UBS
CTL
-
01
U0.2
UU.5
20
Nuaber of Observations: 117
Cobalt
Mercury
S.l
5.1
0.75
29
52
3.2
0.59
17
0.14
0.41
0.29
0.20
0.19
0.16
0.16
0.17
0.35
0.36
0.14
0.094
0.10
U0.1
U0.1
-------
MAIN SEDIMENT QUALITY SURVEY SEDIMENT GRAIN SIZE
Rep
Drainage
Survey
Station
Sample
0019-BL-000-
MSQS
BL-11
S05C
0019-BL-0U0-
msqs
Bl-12
S01C
.
0019-BL-000-
MSQS
Bl-13
S05C
.
0019-BL-000-
MSQS
BL-14
SOIL
-
0019-BL-000-
MSQS
BL-15
S01C
-
0019-BL-000-
MSQS
Bl-16
S01C
-
0019-BL-000-
MSQS
Bl-16
S01C
-
0019-BL-Q00-
Msqs
BL-17
S01C
-
0019-BL-000-
MSQS
BL-18
S01C
-
0019-BL-000-
MSQS
BL-19
S01C
•
0019-BL-000-
MSQS
ttL-20
S01C
•
U019-BL-U00-
MSQS
BL-21
S05C
•
U019-BL-00U-
MSQS
BL-22
SU1C
•
0U19-BL-00U-
MSQS
BL-23
S01C
0U19-BL-UUU-
MSQS
BL-24
S01C
-
0019-BL-U00-
MSQS
BL-25
S05C
-
0019-BL-000-
MSQS
BL-26
S01C
.
U019-BL-U00-
MSQS
BL-27
SU1C
-
0019-BL-U00-
MSQS
BL-28
S06C
•
0019-BL-000-
MSQS
BL-29
S01C
-
0019-BL-000-
MSQS
BL-29
SU1C
_
0019-8L-000-
MSQS
BL-3U
S01C
•
U019-BL-0U0-
MSQS
BL-31
S05C
-
0019-BL-000-
MSQS
BU-32
S01C
-
0019-HY-000-
MSQS
CB-11
S01C
-
0019-CB-000-
MSQS
CB-12
S01C
-
0019-CB-000-
MSQS
CB-13
S01C
•
0019-CB-000-
MSQS
CB-14
S01C
-
0019-C1-000-
MSQS
CI-11
S02C
UU19-CI-000-
MSQS
C1-12
S01C
•
0019-CI-000-
MSQS
CI—13
S05C
-
0019-CI-000-
MSQS
C1—14
S01C
•
0019-CI-000-
MSQS
CI—15
S01C
•
0U19-CW-000-
MSQS
CI-16
S05C
.
0019-CI-000-
MSQS
CI-17
S05C
-
0019-CI-000-
MSQS
Ci-18
S01C
•
0019-CI-000-
MSQS
C1-19
S01C
-
0019-CI-000-
MSQS
CI-2U
S05C
-
0019-CI-000-
MSQS
CI-21
S01C
-
0019-CI-000-
MSQS
CI-22
SU5C
.
0019-CR-000-
MSQS
CR-11
S01C
-
0019-CR-000-
MSQS
CR-12
S05C
-
0019-CK-000-
MSQS
CR-13
S01C
.
0019-CR-000-
MSQS
CR-14
S05C
-
O019-CR-UOO-
MSQS
CR-14
S05C
-
0U19-HY-000-
MSQS
HY-11
S01C
-
0U19-HY-0U0-
MSQS
HY-12
S01C
-
02
01
02
01
01
02
1
Rocks
0.210
0.806
0.478
2.144
0.809
1.370
0.127
0.189
0.020
0.000
0.000
0.140
0.810
O.OUO
0.115
0.350
0.174
0.625
0.575
0.000
0.166
0.420
0.258
0.000
0.363
0.055
0.334
0.164
2.377
0.168
0.063
0.077
4.933
1.626
0.221
0.071
0.178
0.500
0.218
0.191
0.361
0.110
0.095
0.086
0.000
0.173
0.166
t
Sand
44.587
13.908
15.517
23.600
17.430
10.311
8.317
11.524
48.129
13.579
24.321
35.761
35.995
32.400
10.957
11.830
33.481
75.463
62.920
24.166
24.570
42.318
39.845
21.017
15.375
30.482
18.955
16.393
58.270
29.629
21.631
12.519
32.350
24.723
27.466
29.192
16.968
19.782
57.578
71.784
95.299
87.076
92.243
76.022
78.696
30.255
21.307
1
Silt
39.347
60.738
61.660
54.416
61.256
58.713
65.714
63.287
36.431
63.669
54.604
45.848
43.403
39.864
66.032
63.811
49.638
18.904
26.548
62.603
62.119
46.146
50.258
64.089
61.138
56.743
66.426
67.213
29.387
55.196
60.071
66.046
47.581
54.482
54.637
54.278
65.669
63.443
32.918
22.396
4.340
12.814
7.662
18.495
21.304
46.828
45.984
%
Clay
15.855
24.548
22.345
19.840
20.505
29.606
25.841
25.000
15.420
22.752
21.074
18.251
19.792
27.735
22.895
24.009
16.708
5.008
9.957
13.231
13.145
11.117
9.639
14.894
23.123
12.719
14.286
16.230
9.965
15.008
18.234
21.359
15.136
19.168
17.676
16.459
17.185
16.275
9.285
5.629
0.000
0.000
0.000
5.396
0.000
22.745
32.543
-------
MAIN SEDIMENT QUALITY SUKVEr SEDIMENT GRAIN SIZE
Ordinate
Survey
Station
Sample
0019-HY-000-
MSQS
HY-13
S01C
0019-HY-000-
MSQS
HY-14
S05C
•
0019-HT-000-
nsqs
HY-15
S01C
•
0019-HT-000-
MSQS
HY-16
SU1C
.
0019-HY-000-
NSQS
HY-17
soic
-
0019-HY-000-
NSQS
HY-18
S01C
•
0019-NY-000-
MSQS
HY-18
SOIC
-
0019-HY-000-
NSQS
HT-19
SOIC
•
0019-HY-000-
NSQS
MY-20
SOIC
-
0019-MY-000-
NSQS
HY-21
SOIC
•
0019-HY-000-
NSQS
HY-2Z
S05C
-
0019-HY-000-
NSQS
HY-23
SOIC
-
0019-HY-000-
NSQS
HY-24
SOIC
-
0019-HY-000-
NSQS
HY-25
SOIC
-
0019-HY-000-
NSQS
HY-26
SOIC
-
0019-NY-000-
NSQS
HY-27
SOIC
-
0019-HY-000-
NSQS
HY-28
SOIC
-
0019-HY-000-
NSQS
HY-29
SOIC
-
0019-HY-000-
NSQS
HY-29
SOIC
-
0019-HY-000-
NSQS
HY-30
SOIC
•
0019-HY-000-
NSQS
HY-31
SOIC
-
0019-NY-000-
NSQS
MY-32
SOIC
-
0019-HY-000-
NSQS
HY-33
SOIC
-
0019-MY-000-
NSQS
HY-34
SOIC
-
0019-HY-000-
NSQS
HY-35
SOIC
-
0019-HT-000-
NSQS
HY-36
SOIC
-
0019-HY-000-
NSQS
HY-37
SOIC
-
0019-HY-000-
NSQS
HY-38
SOIC
-
0019-HY-000-
NSQS
HY-39
SOIC
-
0019-HY-000-
NSQS
HY-39
SOIC
-
U019-MT-0U0-
NSQS
HT-40
SOIC
0019-HY-000-
NSQS
MY-41
SOIC
-
0019-HY-000-
NSQS
HY-42
SOIC
.
0019-HY-000-
NSQS
HY-43
SOIC
•
0019-HY-000-
NSQS
HY-44
SOIC
*
0019-HY-000-
NSQS
HY-45
SOIC
-
0019-HY-000-
NSQS
HY-46
SOIC
-
0019-HY-000-
NSQS
HY-47
S05C
.
0019-HY-000-
NSQS
HY-48
SOIC
-
0019-HY-000-
NSQS
HY-49
SOIC
-
0019-HY-000-
NSQS
HY-50
S05C
0019-HY-0U0-
NSQS
HY-51
SOIC
-
0019-HY-000-
NSQS
HY-51
SOIC
-
O019-N)-U00-
NSQS
MO-11
SOIC
-
0019-MO-000-
NSQS
MO-11
SOIC
-
0019-MO-000-
NSQS
MO-12
S05C
-
0019-HD-000-
MSQS
MO-13
SOIC
-
0019-N1-000-
MSQS
MI -11
SOIC
-
Kep
1 t
kocks Sand
U1
02
01
02
01
02
01
02
01
02
0.625
1.857
0.384
17.134
5.925
2.52b
0.411
0.770
0.114
1.118
2.106
0.390
0.281
0.765
6.838
9.374
6.976
1.895
4.460
0.000
2.329
5.284
S2.933
4.892
0.914
1.887
0.811
1.737
11.709
8.080
0.499
0.820
0.604
6.121
2.448
1.018
6.278
0.388
1.235
3.512
0.302
0.164
0.093
o.07a
3.774
0.653
35.355
0.048
23.290
50.208
31.464
38.596
27.142
26.573
25.585
36.062
12.986
18.392
22.367
13.160
18.203
8.563
36.391
27.760
31.924
35.213
34.812
18.323
47.634
33.568
26.409
46.379
28.926
31.855
21.703
45.264
44.429
45.517
19.040
38.020
21.330
36.725
91.935
30.365
31.627
21.324
39.689
13.929
13.975
20.526
20.980
25.039
38.572
43.205
40.046
13.951
1
Silt
43.962
29.619
40.532
27.828
40.833
41.934
44.353
41.647
55.027
54.634
50.364
67.965
50.772
57.722
38.192
44.214
45.226
42.976
40.258
47.035
34.548
40.623
13.940
36.213
46.545
44.182
51.081
39.495
30.287
32.295
55.157
42.857
55.699
38.156
5.617
48.912
55.043
55.663
41.492
62.798
66.546
61.179
60.327
57.903
43.912
46.268
17.543
62.500
1
Clay
32.122
18.316
27.619
16.442
26.101
28.969
29.651
21.521
31.874
25.856
25.163
16.485
30.744
32.951
18.579
18.652
15.873
19.917
20.469
34.642
15.490
20.525
6.718
12.516
23.615
22.075
26.405
13.504
13.575
14.108
25.304
18.303
22.366
18.998
0.000
19.706
7.051
22.625
17.585
19.762
19.177
18.131
18.600
16.980
13.743
9.874
7.056
23.SOI
-------
MAIN SEDIMENT QUALITY SURVEY SEDIMENT GRAIN SIZE
Drainage
Su rvey
Station
Sample
Rep
1
Rocks
1
Sand
%
Silt
i
Clay
OOly-Ml-OOO-
MSQS
HI —12
S01C -
0.144
8.833
63.658
27.365
ouiy-Mi-uoo-
MSQS
MI — 13
S01C -
0.103
10.432
64.234
25.231
0019-MI-000-
MSQS
MI —14
S01C -
0.121
14.095
68.297
17.486
UU19-MI-OO0-
MSQS
MI-IS
S01C -
0.000
14.892
69.155
15.953
UO19-KS-0UO-
MSQS
RS-11
S01C -
3.878
68.839
18.251
9.031
ooiy-RS-uoo-
MSQS
RS-12
S01C -
1.723
68.998
21.220
8.060
0019-RS-000-
MSQS
RS-13
S01C -
0.389
87.043
12.569
0.000
0019-RS-000-
MSQS
RS-14
S05C -
29.864
46.359
12.456
11.321
0019-RS-000-
MSQS
RS-15
S05C -
0.080
96.259
3.661
0.000
0019-RS-000-
MSQS
RS-16
S01C -
12.157
64.969
14.292
8.582
0019-RS-000-
MSQS
RS-17
S01C -
y.912
67.661
14.666
7.762
UU19-RS-000-
MSQS
RS-ltt
S01C -
7.744
58.912
25.209
8.134
0019-RS-000-
MSQS
RS-19
S01C -
7.339
89.469
3.192
0.000
0019-RS-000-
MSQS
KS-20
S01C -
5.193
88.993
5.814
0.000
0019-RS-000-
MSQS
RS-21
S01C -
1.844
48.593
36.375
13.188
U019-KS-000-
MSQS
RS-22
SG1C -
38.860
59.863
1.277
0.000
0019-RS-UU0-
MSQS
RS-24
susc -
8.961
74.976
11.828
4.235
0019-SI-000-
MSQS
SI-11
S05C -
0.068
20.059
62.754
17.119
0019-SI-U00-
MSQS
SI-12
SU1C -
0.172
23.747
59.367
16.714
0019-SI-OOO-
MSQS
SI-13
S01C -
0.081
18.261
65.779
15.880
0019-SI-000-
MSQS
SI—14
S01C •
1.531
47.859
40.848
9.762
0019-SI-000-
MSQS
SI-15
S05C -
1.155
18.328
61.876
18.641
0019-SP-000-
MSQS
SP-11
S05C -
6.015
65.887
19.746
8.352
0019-SP-000-
MSQS
SP-12
SObC -
3.140
47.518
40.175
9.166
0019-SP-000-
MSQS
SP-13
sosc -
1.118
29.538
57.223
12.121
0019-SP-000-
MSQS
SP-14
soic •
1.500
31.900
50.750
15.850
0019-SP-000-
MSQS
SP-15
sosc -
1.014
73.085
25.901
0.000
0019-SP-000-
MSQS
SP-I6
S05C -
0.000
45.149
46.759
8.092
0019-DP-000-
MSQS
UBS
CTL -
01
0.000
97.188
2.812
0.000
0019-DP-000-
MSQS
WBS
CTL -
02
0.000
97.971
2.029
0.000
0019-DP-000-
MSQS
W8S
CTL -
03
0.000
97.274
2.726
0.000
Number of Observations: 126
-------
MAIN SEDIMENT QUALITY SURVEY SEOIMENT CONVENTIONALS
Total
Total
Total
Uraina^e
Survey
Station Sample Rep
Solids
Vol at i 1 e
Oryani
1
Solids *
Carbon
71I0019-BL-000-
HSQS
Bl-11
S05C
_
55.5
3.2
1.29
711U019-IM.-000-
HSQS
Bt-12
S01C
-
48.6
4.y
2.21
71 IOU19-B^.-OUO-
HSyS
81-13
S03C
-
49.2
5.1
2.03
7110U19-BL-000-
HSQS
8L-14
S01C
-
48.8
5.2
2.12
7IUW19-BL-000-
HSQS
BL-1S
S01C
-
53.1
3.8
1.39
7110019-81-000-
hsqs
BL-16
S01C
.
47.9
5.0
1.96
711O019-81-000-
hsqs
BL-17
S01C
-
44.6
4.4
1.71
7110019-84.-000-
HSQS
8L-18
S01C
.
59.5
3.1
1.03
7110019-Bt-000-
nsqs
BL-19
S01C
-
53.6
3.7
1.40
7110O19-BI-UOO-
HSQS
BL-20
S01C
-
56.5
3.4
1.35
711UUl9-Bt-OOtl-
HSQS
Bl-21
sosc
.
59.9
3.2
1.14
niooi«-M.-ooo-
HSQS
BL-22
S01C
-
59.6
3.2
1.27
7110019-81-000-
HSQS
BL-23
S01C
-
56.1
3.8
1.49
711UM9-H.-QOO-
HSQS
BL-24
S01C
-
49.3
4.1
1.76
7110019-81-QOO-
NSQS
BL-25
sosc
-
54.2
3.4
1.47
7110019-Bl-000-
nsqs
BL-26
S01C
-
55.5
3.4
1.43
7110019-81-000-
hsqs
BL-27
S01C
.
78.7
1.4
0.48
7110019-81-000-
NSQS
BL-28
sosc
.
73.2
1.9
0.73
7110019-81-000-
NSQS
BL-29
soic
-
63.4
2.6
1.12
7110019-81-000-
NSQS
Bl-30
S01C
.
64. $
2.5
1.09
7110019-81-000-
NSQS
BL-31
SObC
65.8
2.7
1.09
7110019-81-000-
NSQS
BL-32
SOIC
-
60.8
3.4
1.42
7110019-HY-000-
NSQS
CB-11
SOIC
-
56.7
5.0
2.46
7110019-C8-000-
NSQS
CB-12
SOIC
.
66.4
3.4
1.28
7110019-CB-000-
NSQS
CB-13
SOIC
.
64.3
3.3
1.27
7110019-CI-000-
HSQS
CB-14
SOIC
.
63.6
3.4
1.24
711U019-C1-000-
NSQS
Cl-11
S02C
.
45.3
13.5
8.86
7110Q19-CI-000-
NSQS
Cl-12
SOIC
_
42.0
12.3
7.71
7110019-CI-000-
HSQS
C1-13
SOSC
.
38.2
11.4
6.50
7110019-C1-000-
NSQS
C1-14
SOIC
-
35.0
11.0
6.24
7UU019-CI-000-
HSQS
CI-15
SOIC
.
40.6
11.0
S.94
7110019-CM-000-
HSQS
CI-16
sosc
-
28.6
17.3
10.9
7U0019-C1-000-
NSQS
CI-17
SOSC
-
41.8
9.8
5.64
7110019-C1-000-
NSQS
CI-18
SOIC
.
42.8
10.3
5.94
7U0019-CI-000-
HSQS
Cl-19
SOIC
-
41.7
8.8
4.90
7110019-C1-000-
HSQS
CI-20
sosc
.
44.7
8.S
4.59
7110019-C1-UOO-
HSQS
C1-21
SOIC
.
57.7
0.3
2.82
7110019-CI-000-
HSQS
CI-22
SOSC
.
70.4
3.2
1.21
7110019-CR-000-
HSQS
CR-11
SOIC
_
75.7
0.9
0.35
7110019-CK-000-
HSQS
CR-12
SOSC
.
75.6
1.0
0.26
7110019-CR-000-
HSQS
CR-13
SOIC
.
76.8
0.77
0.19
7110019-CR-000-
HSQS
CR-14
SOSC
-
73.8
1.2
0.43
7110019-MY-000-
HSQS
HY-11
SOIC
-
42.9
16.0
6.81
7110019-MY-000-
HSQS
HY-12
SOIC
.
41.2
11.7
5.72
7110019-HY-000-
HSQS
HY-13
SOIC
-
40.8
12.3
5.49
7110019-MY-000-
HSQS
HY-14
SOSC
-
51.5
8.4
4.51
Carbon
ate i
Hydrogen
1
Ni trogen
1
0.16
0.19
0.088
0.10
U .1)63
0.10
0.10
0.062
0.082
0.077
0.064
0.069
0.089
0.10
0.083
0.087
0.046
0.047
0.072
0.067
0.066
0.082
0.71
0.076
1.15
0.073
0.35
0.34
0.29
0.34
0.28
0.49
0.27
0.22
0.22
0.20
0.13
0.16
0.24
0.035
0.024
0.052
0.25
0.21
0.21
0.17
-------
MAIN SEDIMENT QUALITY SURVEY SEDIMENT CONVENT IONALS
Drainage
1711U019-HY-000-
17110019-HY-000-
17110U19-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-OUO-
17110019-HY-000-
17110019-HY-000-
17110U19-HY-000-
17110019-HY-0U0-
17110019-HY-000-
17110019-HY-000-
171I0019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110O19-HY-0UO-
17110019-HY-000-
17iiooi9-hy-ooo-
I7110019-HY-000-
wi 17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110U19-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
17110019-HY-000-
1711(HJ19-HY-000-
1711O019-HY-000-
17110019-MD-000-
17110019-HD-000-
1711OU19-MO-0OU-
17110019-MI-000-
1711UU19-MI-000-
17110019-MI-000-
17110019-MI-000-
17110019-Ml-000-
Survey Station
MSQS
HY-15
Msgs
HY-16
MSQS
HY-17
Msgs
HY-1B
MSOS
HY-19
MSQS
HY-20
MSQS
HY-21
MSQS
HY-22
MSQS
HY-23
MSQS
HY-24
MSQS
HY-25
MSQS
HY-26
MSQS
HY-27
MSQS
HY-26
MSQS
HY-29
MSQS
HY-30
MSQS
HY-31
MSQS
HY-32
MSQS
HY-33
MSQS
HY-34
MSQS
HY-35
MSQS
HY-36
MSQS
HY-37
MSQS
HY-38
MSQS
HY-39
MSQS
HY-40
MSQS
HY-41
MSQS
HY-42
MSQS
HY-43
MSQS
HY-44
MSQS
HY-45
MSQS
HY-46
Msqs
HY-47
MSQS
HY-48
MSQS
HY-49
MSQS
HY-50
MSQS
HY-51
MSQS
MO-11
MSQS
MO-12
MSQS
MD-13
MSQS
MI—11
MSQS
MI-12
MSQS
MI —13
MSQS
Ml—14
MSQS
MI-15
Total
Sample Rep
Sol ids
1
S01C -
49 .u
S01C -
38.7
SU1C -
3y .1
S01C -
37.2
S01C -
33.8
S01C -
40.1
S01C -
37.1
S05C -
37.6
S01C -
36.9
S01C -
38.0
S01C -
45.1
S01C -
Si .8
S01C -
46.5
501C -
38.5
S01C -
47.6
S01C -
40.6
S01C -
59.9
S01C -
51.1
S01C -
59.3
S01C -
52.6
S01C -
52.6
S01C -
47.8
S01C -
51.5
S01C -
47.7
S01C -
61.4
S01C -
51.4
S01C -
57.8
S01C -
49.7
S01C -
55.2
S01C -
78.4
S01C -
54.5
S01C -
51.6
S05C -
51.8
S01C -
63.4
S01C -
55.5
S05C -
56.9
S01C -
59.9
S01C -
38.8
S05C -
49.2
S01C -
63.0
S01C -
55.9
S01C -
53.3
S01C -
57.4
suic -
55.7
S01C -
60.7
Total Total
Volatile Organic
Sol ids X Carbon %
8.0
3.78
25.9
12.21
12.2
5.22
15.1
6.39
20 .y
9.01
11.0
4.45
11.4
4.59
11.3
4.44
10.5
3.78
11.4
5.12
7.8
3.15
7.5
3.24
8.7
2.24
14.6
3.10
8.6
2.84
8.0
2.28
4.4
1.65
8.1
3.83
6.1
1.81
10.8
6.47
6.6
2.62
7.7
3.74
6.4
2.48
9.4
4.09
4.2
1.57
6.7
2.25
5.4
1.96
6.3
2.39
5.7
2.89
1.3
0.26
5.2
1.55
6.5
0.43
6.5
1.84
4.6
2.39
6.0
2.36
5.7
2.25
4.8
1.81
11.4
7.27
6.8
4.04
5.3
7.26
8.8
2.30
10.1
2.26
11.0
2.17
5.3
1.97
4.7
1.52
Carbon- Hydroyen Nitroyen
ate I * i
0.15
0.22
0.18
0.22
0.24
0.19
0.19
0.18
0.16
0.22
0.17
0.13
0.13
0.13
0.12
0.11
.092
0.12
0.075
0.16
0.12
0.15
0.12
0.14
0.079
0.11
0.10
0.12
0.11
0.021
0.085
0.11
0.89
0.12
0.11
0.11
0.091
0.44
0.16
0.04
0.11
0.13
0.12
0.11
0.076
-------
MAIN SEDIMENT QUALITY SURVEY SEDIMENT CONVENTIONALS
Total
Total
Total
Drainage
Sur»«jr
Station Sample
Kep
Solids
Volatile
Organic Carbon- Hydrogen
Nitrogen
1
Solids 1
Carbon X ate X X
t
i7iiuoi»-«s-ooo-
MSQS
KS-ll
SOIC
_
72.4
3.7
2.35
0.59
171I0019-RS-000-
NSQS
RS-12
SOIC
-
68.3
4.8
2.57
0.087
17110019-KS-000-
nsqs
RS-13
S01C
-
60.1
9.1
0.69
0.11
17II00I9-RS-0U0-
MSQS
RS-14
sosc
-
40.6
22.2
15.1
0.28
miuoHMS-ooo-
NSQS
RS-15
S02C
-
76.3
1.2
0.36
0.028
17U0019-RS-000-
KSQS
RS-16
SOIC
-
30 .8
28.6
20.5
0.29
17U0O19-RS-OUO-
NSQS
RS-17
SOIC
.
59.5
3.9
1.90
0.079
17UUUI9-RS-000-
NSQS
RS-1B
SOIC
-
42.0
19.6
8.83
0.20
17UU019-RS-000-
NSQS
RS-19
SOIC
-
79.4
1.0
0.58
0.032
17110019-*S-000-
NSQS
RS-20
SOIC
.
77.3
1.5
0.28
0.024
17U0019-RS-000-
NSQS
RS-21
SOIC
-
42.1
11.2
3.13
0.16
17110019-RS-000-
NSQS
RS-22
SOIC
.
8S.3
0.8
0.28
0.020
17111W19-RS-000-
NSQS
RS-24
sosc
-
66.9
2.7
0.80
0.070
17110019-SI-000-
NSQS
Sl-11
sosc
.
60.0
4.8
2.10
0.11
171I0019-SI-000-
NSQS
SI—12
SOIC
-
62.2
3.7
1.56
.095
17U0019-SI-000-
NSQS
SI-13
SOIC
.
62.4
4.1
1.79
1.60
17UUOI9-SI-OUO-
NSQS
SI-14
SOIC
-
68.1
3.7
1.61
.084
17UU019-SI-000-
NSQS
SI-IS
sosc
-
61.5
4.9
2.46
0.13
17110019-SP-OOO-
NSQS
SP-U
sosc
-
S7.2
7.9
3.50
0.12
17110019-SP-000-
NSQS
SP-12
sosc
-
55.0
8.6
4.67
.16
17110019-SP-OUO-
NSQS
SP-13
SOIC
.
46.8
13.2
5.67
.19
17110019-SP-000-
NSQS
SP-14
SOIC
¦
30.5
44.7
16.0
.79
1711M19-SP-OUU-
NSQS
SP-U
sosc
-
65.8
4.3
2.06
.084
17110019-SP-OOO-
NSQS
SP-16
sosc
-
64.4
3.6
1.47
1.19
17UU019-UP-000-
NSQS
MS
CTL
.
U1
80.6
0.56
0.07
»7hooi*-op-oqo-
NSQS
UBS
CTL
-
02
80.2
0.57
0.17
Q0.01
INtur of Observations: U7
a. Reference:
Tetra Tech, Inc. 1985. Commencement Bay nearshore tideflats remedial
investigation. Final Report Prepared for Washington Department of Ecoloav and
U.S. EPA by Tetra Tech, Inc. Bellevue, UA.
-------
Table A-2. COMffiNCOffiNT BAY - BLAIR WATERWAY DREDGING STUDY &
CTATTmi* T0X BENTH1C MICRO
STATION# C(M)E C0DB C0DE
2 B03 110
2 B04 1 1 D
2 B09 110
2 B1Q 1 1 a
2 B12 110
2 915 3 10
Th* 6 stations listed an this pegs have biological effects data and are used for
this report. Additional stations and associated chsmlcal data are Include an
subsequent pages of this Table A-l. Mwre replicate data have been provided,
thft nttn valus Is used for calculations..
A-77
-------
BLAIH DREDGING PROJECT SURFACE SEDIMENT
PHENOLS
Online
17110019-81.-000-
17110019-BL-000-
17110019-Bt-OOO-
lfllOQW-Bl-OOQ-
17110019-81-000-
1711UU11-BI-0UU-
17U001i-«i.-000-
1?110019-W.-000-
17110019-H.-UW-
17110019-11-000-
17U0019-BL-000-
17U0019-W.-000-
17110019-W.-000-
17110019-II-000-
171100H-BL-0UU-
17110019-BL-000-
17110011-B.-000-
miQOM-MMNO-
17110019-81-000-
171I0019-BL-OUU-
1IUQ019-W.-OOU-
171J0U19-BI-U00-
Survey
Station
Sanple
URSOOl
BQ2
SOIC -
IffiSOOl
BU2
SOIC -
US S0U1
803
SOSC -
UtfSQOl
1104
SOIC •
URSOOl
804
SOIC -
URSOOl
aot
SOIC -
UMOOl
BQ4
SOIC -
URSOOl
B04
SOIC -
URSOOl
807
SOIC -
WS001
807
SOIC -
URSOOl
809
SOSC -
WS001
809
SOSC -
WS001
BIO
S05C -
URSOOl
B1U
sosc -
URSOOl
BU
SOIC -
URSOOl
811
SOIC -
URSOOl
812
susc -
URSOOl
*14
SOIC -
UR5001
BIS
S05C -
URSOOl
B17
SOIC -
uasooi
sir
SOIC -
URSOOl
BIB
SOIC -
Muater »f OkMrv«tto«$: 2?
CHEMICALS
- Values in ppb dry weight
2,4-di-
«ethyl-
Wep phenol phenol
U10
U10
Z40
4.8
01
Z6S
4.2
02
Z29
9.8
03
Z43
U2
04
010
U10
OS
U10
U10
U10
U10
020
U20
01
ztoo
2.3
02
Z370
U1
02
01
Z4
U2
Z22
Ul
ZS6
3.0
Z53
7.2
01
U1
U2
02
Z34
2.3
Z30
U2
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values i
PHENOLS
2- 4-
methyl- methyl-
Drainage
Survey
Station
Sample
Rep
phenol
phenol
17110019-BI-000-
URS001
B02
S01C -
U10
UIO
17110019-BL-0O0-
URS001
803
SOSC -
17110019-BL-000-
URS001
604
S01C -
01
17110019-BL-000-
URS0U1
BU4
S01C -
02
17110019-BL-000-
URS001
B04
S01C -
03
17110019-BL-000-
URS001
B04
S01C -
04
U10
160
17110019-BL-000-
URS001
BO 4
S01C -
05
U10
240
17110019-8L-000-
URS001
B07
S01C -
UIO
110
17110019-BL-000-
URS001
609
SOSC -
U20
92
17110019-BL-000-
ORSOOl
BIO
S05C -
01
17110019-BL-000-
URSU01
BIO
SOSC -
02
17110019-BL-000-
URS001
611
S01C -
01
17110019-BL-000-
URS001
B12
S05C -
17110019-BL-000-
URSOOl
614
S01C -
17110019-BL-000-
URS001
BIS
SOSC -
17110019-8L-000-
URSOOl
817
S01C -
01
17110019-BL-000-
URSOOl
817
S01C -
02
17110019-BL-000-
URSOOl
618
S01C -
vo Number of Observations: 18
ppb dry weight
-------
BLAIR UftEOING PROJECT SURFACE SEOIHENT ORGANIC CHEMICALS - Values 1
SUBSTITUTED PHENOLS
2- 2,4-dl-
chloro- chloro-
Drainage
Survejr
Station
Sample
Rep
phenol
phenol
17110019-81-OUO-
UKS001
802
S01C
_
17110019-81-000-
IWSU01
802
S01C
-
U5
Ul U
1711UU19-BI-OUO-
URSOOl
8U3
S05C
-
U1
U2
17110019-BL-000-
ursooi
804
SU1C
.
01
U2
U4
17110019-BL-000-
UN SOUl
BU4
S01C
-
02
U2
U4
17110019-BL-000-
UHS001
804
S01C
-
03
U2
US
17U0019-BL-000-
URS001
B04
S01C
-
04
US
U10
17110019-81-000-
URS001
804
S01C
-
05
U5
UIO
17110W19-8L-000-
UKSOUl
8U7
S01C
-
17110019-81-000-
URS001
807
S01C
.
US
U10
17110019-W.-000-
URSOOl
B09
S05C
.
17110019-8L-000-
URSOOl
809
S05C
-
U10
U20
17110019-8L-000-
URS001
810
S05C
-
01
U1
U2
17110019-81-000-
URSU01
810
sosc
-
02
U1
Ul
17110019-81-000-
URS001
Bll
S01C
.
02
17110019-BL-QOQ-
URS001
Bll
S01C
-
01
U3
U6
17110019-8L-000-
URS001
812
S05C
-
U1
U2
17110019-BL-000-
UHSUOl
814
S01C
-
U2
U2
171W019-BL-000-
URSOOl
BIS
SOSC
-
U3
U4
17110019-81-000-
URS001
817
S01C
-
01
U1
US
1711U019-8L-0U0-
URS001
817
S01C
-
02
U1
Ul
17110019-8L-000-
URS001
B18
S01C
-
U2
U3
duMber of Observations: 22
ppb dry weight
2.4.6-
2.4-
4.6-
4-chloro-
tr1-
penta-
dl-
di-
3-methyl
chloro-
chloro-
2-nitro-
nitro-
nitro-o-
4-nitr
phenol
phenol
phenol
phenol
phenol
cresol
phenol
UIO
UIO
U25
UIO
UlOO
UlOO
6.1
Ul
S
U2
UlOO
US
1)70
Ul
Ul
U2
US
U150
U60
(J 1300
B1
Ul
U6
U3
U2
U2
U20
U2
U3
U3
U5
U150
UlOO
U1S00
UIO
UIO
31
UIO
UlOO
UlOO
UIO
UIO
50
U1U
UlOO
UlOO
UIO
UIO
U25
IJ10
UVOO
UlOO
U20
U20
USO
U20
UlOO
U200
U2
Ul
Ul
U2
U60
U30
U6
Ul
Ul
Ul
Ul
U60
U30
U20
U2
U2
U3
U6
U190
UlOO
U1900
Ul
Ul
02
U2
U90
U40
V9
Ul
Ul
Ul
U3
U90
U40
U23
Ul
Ul
Ul
US
UlOO
USO
U 22
U2
Ul
Ul
U2
U130
U70
U1300
Ul
Ul
Ul
Ul
U60
Ul
U13
Ul
Ul
U2
U4
two
U40
U80
-------
BUM OR EDGING PHOJECT SURFACE SEOIMENT ORGANIC CHEMICALS
LOW MOLECULAR WEIGHT AROMATIC HYDROCARBONS
methyl
naphth
Drainage
Survey
Station
Sample
Rep
alene
17110019-BL-000-
URS001
802
S01C
17110019-61-000-
URS001
B02
S01C
.
75
17110019-BL-000-
URSOOl
B03
S05C
-
SO
17110019-Bl-QOO-
URSOOl
B04
SU1C
01
67
17110019-BL-OOO-
UHS001
B04
S01C
-
02
no
1711U019-BL-000-
URS001
B04
S01C
.
03
100
17110019-BL-000-
URS0Q1
BO*
SU1C
-
04
240
17110019-BL-000-
URS001
604
S01C
-
05
180
17110019-BL-OOO-
URS001
B07
S01C
-
17110019-BL-000-
URSOOl
BU7
S01C
-
100
17ll0l)19-BL-000-
URSOOl
B09
sosc
-
100
17110Q19-BL-000-
URSOOl
B09
S05C
-
17110019-BL-000-
URSOOl
BIO
S05C
-
01
46
17110019-BI-000-
URS001
BIO
sosc
-
02
SI
17110019-BL-000-
URSOOl
Bll
S01C
_
02
171100t9-ei-000-
URS001
Bll
S01C
-
01
27
17U0019-BL-000-
URSOOl
812
sosc
-
24
17110019-BL-000-
URSOOl
B14
S01C
-
65
17110019-81-000-
URS001
815
sosc
-
44
17110019-BL-000-
URSOOl
B17
S01C
.
01
27
I7110U19-8L-000-
URSOOl
817
soic
.
02
34
17110019-81-000-
URSOOl
BIB
S01C
-
48
Nuwber of Observations: 22
Values in ppb dry weight
naphtha-
acenaph-
acenaph-
phenan-
anthi
lene
thylene
thene
fluorene
threne
cene
Z590
20
13
20
76
33
200
IS
44
63
330
130
680
13
140
150
540
260
45
20
220
190
690
310
710
35
310
320
1400
720
Z970
70
230
210
440
230
Z570
80
240
230
430
260
BS
40
52
84
290
120
B5
40
86
140
280
84
81
5.9
10
16
110
18
80
6.0
11
17
120
22
79
11
14
22
150
65
110
7.8
19
27
170
48
310
17
76
92
430
130
210
14
93
120
570
290
66
2.0
14
15
97
28
62
n
12
16
82
30
370
s.o
140
110
350
95
-------
KIAJK OKEUCINti PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS
K16H MXECULMt HEIGHT PAH
fluor-
Drainage
Survey
Station
Sample
Rep
anthene
1711OQI9-BI-0UU-
URS001
802
S01C
_
lTHUOH-Bi.-OOO-
URS001
B02
S01C
-
210
17110019-BL-000-
URS001
B03
S05C
-
480
17110019-81-000-
URS001
B04
S01C
-
01
1400
17110019-BL-000-
URS001
004
S01C
-
02
1900
17110019-81-000-
URS001
B04
S01C
.
03
2900
1711OO19-01-OOO-
URS001
804
S01C
-
04
2200
17110019-81-000-
URS001
804
S01C
-
05
2300
17110019-81-000-
URS001
807
S01C
-
17110019-81-000-
URS001
807
S01C
-
670
1/110019-Bl-000-
URS001
809
sosc
-
17110019-BL-000-
URS001
809
sosc
-
910
17110019-BL-000-
OR SOU 1
BIO
sosc
.
01
120
17110019-81-000-
URS001
810
sosc
-
02
120
17U0019-BL-000-
ORS001
811
S01C
.
02
17110019-BL-000-
URS001
Bll
soic
-
01
250
17110019-BL-000-
0HS0U1
812
sosc
-
320
17110019-81-000-
URS001
B14
SOIC
-
670
17110019-BL-0U0-
URS001
815
S05C
-
1100
17110019-8L-0U0-
UflSOOl
B17
SOIC
-
01
160
17110019-81-000-
ORS0U1
B17
SOIC
-
02
140
17110019-8L-000-
URS001
B18
SOIC
•
470
Nuatwr of Observations: 22
- Values in ppb dry weiyht
i ndeno-
benzo(a)
benzo(b)
benio(k)
(1.2.
anthra-
fluor-
fluor-
Denzo(a)
3-cd)
jyrene
cene
chrysene
anthene
anthene
pyrene
pyren
220
82
160
C
C
120
55
470
160
400
C
C
140
120
1100
680
680
630
580
540
310
1700
710
1300
C
C
570
330
2100
880
1600
C
c
750
510
1600
810
980
c
C
850
280
1800
750
1100
C
c
660
390
580
230
410
c
C
360
130
640
310
350
c
C
360
150
110
41
81
c
C
4b
1)1
no
42
92
c
C
49
29
240
no
300
c
C
120
86
260
110
260
C
c
130
58
550
220
430
c
c
210
130
730
340
720
380
300
250
15U
150
70
130
C
c
73
55
130
62
110
87
72
69
48
310
140
190
C
C
no
52
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT
HIGH MOLECULAR HEIGHT PAH
Dralnaye
17110019-BL-OOU-
17110019-BL-000-
17110019-BL-000-
17110019-BI-0U0-
17110019-BL-000-
17110019-BL-OUO-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110U19-BL-000-
1711Q019-BL-000-
17110019-BL-000-
17110019-BI-000-
171I0019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
1711001V-BL-OOO-
» 17110019-BL-000-
S 17110019-BL-000-
17110019-BL-000-
Survey
Station
Sample
UKS001
B02
S01C
.
UfiSOOl
BUZ
S01C
-
URSU01
BO 3
SOSC
.
URSOOl
B04
S01C
-
URS001
BU4
S01C
-
URSOOl
B04
S01C
-
URS001
B04
S01C
-
UKSOOl
B04
S01C
-
URSOOl
BO 7
S01C
-
URS001
BU7
S01C
-
URSOOl
B09
SOSC
-
UKSOOl
B09
SOSC
-
URSOOl
BIO
S05C
-
URS001
BIO
SOSC
-
URSOOl
Bll
S01C
-
URSOOl
BU
S01C
.
URSOOl
B12
S05C
.
UKSOOl
B14
S01C
-
URSOOl
B15
sosc
-
URSOOl
B17
S01C
-
URSOOl
B17
S01C
.
URSOOl
BIS
S01C
.
Number of Observations: 22
IANIC CHEMICALS - Values !n ppb dry weight
total
dibenzo- benzo- benzo-
(a,h)an- (ghi) f!uor-
Rep thracene perylene anthenes
16
57
240
45
110
550
01
130
310
02
120
320
1700
03
160
470
2400
04
93
250
1300
05
110
320
1400
32
130
500
56
140
490
01
U1
25
no
02
14
26
110
02
01
40
83
490
U1
U1
330
44
120
640
50
130
01
19
23
730
02
6.6
41
9.8
45
260
-------
BLAIR DREIWIRG PROJECT SURFACE SEDIMENT. ORGANIC CHEMICALS - Values in ppb dry weight
CHLORINATED AROMATIC HYDROCARBONS
1,2.4- 2-
1,3-di-
1,4-di-
1,2-di-
tri-
chloro-
hexa-
chloro-
chloro-
chloro-
chloro-
naph-
chloro-
Drainage
Survey
Station
Sample
Rep
benzene
benzene
benzene
benzene
thalene
benzene
17110019-BL-000-
ORSOOl
BO 2
S01C -
17110019-BL-000-
URSOOl
802
S01C -
U10
U10
U10
U5
Ub
U10
17110019-BL-000-
URSOOl
003
S05C -
25
21
50
U1
U1
U1
17110019-BL-000-
URS001
B04
S01C -
01
U1
21
U1
U1
U1
U1
17110019-BL-000-
URSOOl
B04
S01C -
02
U2
32
U1
U1
U1
U1
17110019-BL-000-
URSOOl
B04
S01C -
03
U1
25
U1
U1
U1
U30
17110019-BL-OOO-
ORSOOl
BU4
S01C -
04
130
110
U5
U5
U5
U10
17110019-BL-000-
URSOOl
B04
S01C -
Ob
170
63
U5
U5
U5
U10
17110019-BL-000-
URSOOl
B07
S01C -
17110019-BL-000-
URSOOl
BO 7
S01C -
U5
US
U5
U5
U5
U10
17U0019-BL-000-
UKSOUl
B09
S05C -
17110019-BL-000-
URSOOl
B09
S05C -
US
U5
U5
U5
U5
U10
1711UO19-BL-0UO-
URSOOl
BIO
S05C -
01
U1
U1
U1
U1
01
U1
17110019-W.-000-
URSOOl
BIO
SUSC -
02
U1
U1
U1
Hi
U1
U1
17110019-BL-000-
URSOOl
Bll
S01C -
02
17110019-BL-000-
URSOOl
Bll
S01C -
01
U2
U2
U2
U2
U1
U1
17110019-BL-000-
URSOOl
B12
S05C -
U1
U1
U1
U1
U1
U1
17110019-BL-000-
URSOOl
B14
S01C -
U1
U1
U1
U1
U1
U1
17110019-BL-000-
URSOOl
B15
SOSC -
U1
U1
U1
U2
U1
U1
17110019-BL-000-
URSOOl
B17
S01C •
01
U1
U1
U1
U1
U1
U1
17110019-BL-000-
URSOOl
817
S01C -
02
U1
U1
U1
U1
U1
U1
17110019-BL-OOO-
URSOOl
B18
S01C -
U1
U1
U1
U1
U1
U1
Nwber of Observations: 22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT
CHLORINATED ALIPHATIC HYDROCARBONS
Dralnaye
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17U0019-BL-000-
1711U019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
1711U019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17U0019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
• 17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
Survey
Station
Sample
URSOOl
BO 2
S01C
URSOOl
B02
S01C
URSOOl
BO 3
S05C
URSOOl
B04
S01C
URSOOl
B04
S01C
URSOU1
804
S01C
URSOOl
B04
S01C
URSUOl
BOA
S01C
URSOOl
B07
S01C
URSOOl
B07
S01C
URSOOl
B09
S05C
URSOOl
B09
S05C
URSOOl
BIO
S05C
URSOOl
BIO
S05C
URSOOl
Bll
S01C
URSOOl
811
S01C
URSOOl
B12
S05C
URSOOl
B14
S01C
URSOOl
B15
S05C
URSOOl
B17
S01C
URSUOl
B17
S01C
URSOOl
Bit)
S01C
Nunber of Observations: 22
CHEMICALS - Values in ppb dry weight
hexa-
chloro-
Rep ethane
hexa-
hexa- chloro-
chloro- cyclo-
buta- penta-
diene diene
U50
U25
U4
U2
01
US
U2
02
U80
U4
03
U6
U3
04
USO
U25
05
U50
U25
UbO
U25
U50
U25
01
U20
U2
02
U20
U1
02
01
U80
U6
U4
U2
U30
U3
U40
U4
01
U5
U2
02
U2S
U1
U30
U3
-------
01AIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values 1n ppb dry weight
PHTHALATES
di-n-
butyl
dimethyl
diethyl
butyl
benzyl
phtha-
phtha-
phtha
phtha-
UrilMge
Survey
Station
Sample
Rep
late
late
late
late
0019-81-000-
UKSU01
802
S01C -
U019-BL-000-
UKSOOl
802
S01C -
B50
U10
167
73
0019-8L-000-
URS001
803
sosc -
24
in
Z30
Z46
0019-8L-000-
URS001
804
SU1C -
01
29
UI
2170
Z19
0019-BL-000-
URSOOl
804
S01C -
02
48
in
2430
Z42
0019-8L-000-
URSOOl
804
SOIC -
03
23
01
Z260
Z420
0019-BL-000-
URS001
804
S01C -
04
B50
010
Z34
61
0019-BL-000-
URS001
B04
SOIC -
OS
Z47
U10
Z64
81
0019-BL-000-
UKSOOl
807
SOIC -
0019-BL-00U-
UKSOOl
807
SOIC -
BSO
U10
U10
26
0019-BL-000-
UKSOOl
809
sosc -
0019-81-000-
URSOOl
B09
S05C -
Z160
U10
Z1300
U25
0019-BL-000-
UKSOOl
810
sosc -
01
U1
Ul
81
Z22
0019-BL-000-
URSOOl
BIO
sosc -
02
01
1.9
Z40
Z60
0019-BL-000-
URSOOl
811
SOIC -
02
Z160
0019-BL-000-
URSOOl
BU
soic -
01
9.7
8.8
B1
0019-BL-000-
URSOOl
B12
sosc -
IS
4.7
Z260
Z50
0019-8L-000-
URSOOl
B14
soic -
40
4.8
Ul
Z17
0019-BL-000-
URSOOl
Bib
sosc -
41
2.9
B1
Z14
0019-BL-000-
URSOOl
B17
SOIC -
01
U1
Ul
Z60
B1
0019-BL-000-
URSOOl
B17
SOIC -
02
2.6
Ul
B1
Z3
0019-8L-000-
URSOOl
818
SOIC -
S.6
1.6
Z120
Z80
Nuaber of Obscrvittons: 22
bis(2-
ethyl-
di -n-
hexyl )-
octyl
phtha-
phtha
late
late
Z380
025
Z1300
Z17
Z580
Ul
Z370
Z22
Z1300
Ul
Z760
U25
1200
U25
Z230
025
Z180
420
Z110
Z7
Z80
Z5
Z600
Z10
Z330
Z6
Z260
Z10
Z240
260
Z10
Z6
81
Ul
Z160
B1
-------
BLAIR DREDGINU PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values in ppb dry weight
MISCELLANEOUS OXYGENATED COMPOUNDS
benzyl benzoic dibenzo-
Drainage
Survey
Station
Sample
Rep
alcohol
acid
furan
17110019-BL-000-
URSOOl
B02
S01C -
U10
U25
25
17110019-BL-000-
URSOOl
B03
SOSC -
1711U019-BL-000-
URSOOl
BOA
S01C -
01
I7110019-BL-000-
URSOOl
BOA
S01C -
02
17110019-BL-000-
URSOOl
BOA
S01C -
03
17110019-BL-OOO-
UKSOOl
BOA
S01C -
OA
U10
U25
iyo
1711O019-BL-000-
URSOOl
BOA
S01C -
05
U10
U2S
220
17110019-BL-OOO-
UKSOOl
B07
S01C -
U10
U25
BO
17110019-BL-000-
URSOOl
B09
SOSC -
U10
U2S
110
17110019-BL-000-
UKSOOl
BIO
SOSC -
01
17110019-BL-000-
URSOOl
BIO
S05C -
02
1711U019-BL-000-
URSOOl
BU
S01C -
01
17110019-BL-000-
URSOOl
B12
S05C -
17110019-BL-000-
URSOOl
B14
S01C -
17110019-BL-000-
URSOOl
BIS
SOSC -
17110019-BL-000-
URSOOl
B17
sole -
01
17110019-BL-000-
URSOOl
B17
S01C •
02
17110019-BL-000-
URSOOl
B18
S01C -
Number of Observations: 18
oo
-------
BLAIK DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values
ORGANONITROGEN CONPUUNOS
N-
nitroso-
nitro- dipropyl
Drainage
Survey
Station
Sample
Rep
bemene
amine
17110019-81-000-
URSOOl
B02
S01C
•
17110019-81-000-
URS001
602
S01C
-
US
UIO
17110019-81-000-
URSOOl
B03
SOSC
-
Ul
Ul
17110019-81-000-
ursuoi
BOA
SOtC
-
01
U1
Ul
17110019-61-000-
URS001
B04
S01C
.
02
Ul
U2
1711OO19-0L-OOO-
URSOOl
B04
S01C
.
03
U1
Ul
17110U19-BI-U0O-
URSOOl
B04
S01C
-
04
U5
UIO
17U0019-BL-000-
URSOOl
BOA
S01C
.
OS
U5
UIO
1711QOI9-BL-000-
URSOOl
807
S01C
-
17U0019-BL-000-
URS001
807
S01C
-
US
UIO
17U001«-BL-000-
URSOOl
B09
SOSC
.
17U0019-BL-000-
URSOOl
B09
SOSC
-
U5
UIO
1711U019-BL-000-
URS001
810
SOSC
-
01
Ul
Ul
17110019-BL-000-
URS001
BIO
SOSC
-
02
Ul
Ul
17110019-81-000-
URS001
811
S01C
-
02
1711U019-BUOOO-
URSOOl
811
SU1C
-
01
U2
U2
17110U19-BL-000-
URSOOl
812
SOSC
-
Ul
13
17110019-BL-000-
URSOOl
B14
S01C
-
Ul
Ul
17110019-BL-OOO-
URSOOl
BIS
SOSC
.
Ul
Ul
171HW19-BL-000-
URSOOl
B17
SU1C
-
01
Ul
Ul
17110019-BL-OOO-
URSOOl
B17
SOIC
.
02
Ul
4.5
1711U019-BL-00U-
URSOOl
BIB
S01C
-
Ul
Ul
Muafccr »f Observations: 22
n ppt> dry weiyht
N- 1,2-di- 3,3'-di- N-
2,6-di- Z,4-d1- nitroso- phenyl- chloro- nitroso-
nitro- nitro- diphenyl- hydra- Denzi- benzi- dimethyl
toluene toluene amine zine dine dine amine
UIO
us
US
U5
U1Q0
Ul
UIO
24
Ul
U2
UIO
Ul
Ul
Ul
U2
46
Ul
U20
Ul
37
Ul
UIO
U5
US
U5
U1Q0
UIO
U5
US
U5
U100
UIO
us
US
U5
U100
UIO
us
US
US
U100
Ul
Ul
6.7
Ul
Ul
Ul
Ul
6.1
Ul
Ul
9.9
Ul
Ul
Ul
13
Ul
Ul
Ul
15
Ul
Ul
Ul
20
Ul
Ul
Ul
Ul
Ul
Ul
Ul
3.9
Ul
Ul
Ul
5.0
Ul
-------
BLAIR OKEDGINU PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values
PESTICIDES I
Uralnaye
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
1711O019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-OOO-
17110019-BL-000-
1711U019-BL-000-
17110019-BL-000-
171I0019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-81-000-
^ 17110019-BL-000-
co
vO
Survey
Station
Sample
Rep
4.4'-DDE
4 ,41 -1
URS001
B02
S01C
URS001
BO 2
S01C
-
USD
U5U
URS001
BU3
S05C
-
U0.02
UO .05
URS001
B04
S01C
.
01
0.Z7
UO .04
URS0U1
804
S01C
-
02
0.55
3.1
URS001
B04
S01C
-
03
1.7
0.8
URS001
B04
S01C
-
04
UbO
U50
URS001
B04
S01C
-
05
UbU
U5U
URS001
B07
S01C
-
URSOOl
BO 7
S01C
.
U50
U5U
URS001
B09
S05C
-
URS001
B09
S05C
.
U50
U50
URSOOl
BIO
S05C
-
01
UO.U1
UO .04
URS001
BIO
S05C
-
02
U0.U1
UO .04
URS001
BU
S01C
.
02
URS001
BU
S01C
-
01
UO .02
UO .04
URS001
B12
S05C
-
U0.01
U0.04
URS001
B14
S01C
-
U0.02
UO .04
URS001
BIS
S05C
.
0.73
1.7
URS001
B17
S01C
.
01
U0.01
U0.03
URS001
B17
S01C
-
02
U0.01
U0.03
URSOOl
B18
S01C
-
0.12
1.3
Number of Observations: 22
n ppb dry weight
4,4'-DDT
aldrin
dieldrin
a-HCH
b-HCH
d-HCH
g-HCH
U50
U50
U50
U50
U50
U50
U50
1.3
UO .02
UO .02
UO .02
UO .05
UO .03
U0.U2
2.2
UO.Ol
UO.Ol
UO .02
UO .05
UO .03
UO .02
7.1
U0.01
UO.Ol
UO .02
U0.U5
UO .03
U0.U2
2.5
UO .01
UO.Ol
UO .02
UO .05
UO .03
UO .02
U50
U50
U50
U50
U50
U50
U50
UbU
U50
U50
U50
U5U
U50
U50
U50
U5U
U50
USO
U50
U50
U50
U50
U50
U50
U50
U50
U50
U50
U0.04
UO.Ol
UO.Ol
UO.Ol
UO .04
UO .03
UO .02
1.6
U0.01
UO.Ol
UO.Ol
UO .04
UO .03
UO .02
U0.05
UO .01
UO.Ol
UO .02
UO .05
UO .03
UO .02
1.4
UO.Ol
UO.Ol
UO.Ol
UO .05
UO .03
UO .02
3.0
UO.Ol
UO.Ol
UO .02
UO .05
UO .03
UO .02
5.8
0.44
UO.Ol
UO.Ol
UO .05
UO .03
UO.OZ
1.2
UO.Ol
UO.Ol
UO.Ol
UO .03
UO .02
UO.Ol
U0.03
UO.Ol
UO.Ol
UO.Ol
UO .03
UO .02
UO.Ol
15
UO.Ol
UO.Ol
UO.Ol
UO .03
UO .02
UO.Ol
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values
PCBS
Orainaye
17110019-BL-000-
1711U019-BL-000-
17110019-BL-OOO-
17U0U19-BL-Q00-
17110019-BL-OOO-
17110019-BL-000-
17110019-BL-OOO-
17110019-BL-OOO-
17110019-BL-4100-
171100I9-BL-000-
17110019-BL-OOO-
17110019-BL-000-
17UU019-6L-000-
171I0019-BL-000-
17110019-BL-900-
17110019-BL-000-
17110019-BL-000-
17U0019-BL-000-
17110019-BL-000-
17H0019-BL-000-
> 17110019-BL-000-
^ 17110019-BL-000-
Survey
Station Sample
Rep
PCB-1016
PCB
URSOOl
B02
S01C
URSOOl
B02
S01C
.
URSOOl
803
sosc
.
U22
U22
URSOOl
B04
S01C
-
01
U20
U20
tnsooi
804
S01C
-
02
U20
U20
URSOOl
804
S01C
.
03
U20
U20
URSOOl
804
S01C
-
04
URSOOl
B04
S01C
-
OS
URSOOl
B07
S01C
-
URSOOl
B07
S01C
.
URSOOl
B09
sosc
.
URSOOl
B09
sosc
-
URSOOl
BIO
sosc
-
01
U17
U17
URSOOl
BIO
sosc
-
02
017
U17
URSOOl
Bll
soic
.
02
URSOOl
Bll
S01C
-
01
U21
U21
IttSOOl
812
SOSC
-
U19
U19
URSOOl
814
SOIC
.
U20
U20
URSOOl
BIS
SOSC
-
uia
U18
URSOOl
B17
SOIC
-
01
U14
U14
URSOOl
B17
SOIC
.
02
U14
U14
URSOOl
B18
SOIC
-
U14
U14
Muabcr of Observations: 22
n ppb dry weight
Total
PCB-1232 PCB-1242 PCB-1248 PCB-1254 PCB-1260 PCBs
C
U22
U22
U22
U22
U22
47
U20
46
U20
020
U2U
U20
69
U20
U20
U20
U20
210
U20
U20
84
19
60
16
C
U17
017
U17
U17
U17
U17
U17
U17
U17
U17
14
U21
U21
U21
U21
U19
019
U19
U19
U19
U20
1120
26
1)20
U20
U18
U18
36
U18
U18
U14
U14
U14
U14
U14
U14
U14
8.2
U14
U14
U14
U14
U14
U14
U14
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT ORGANIC CHEMICALS - Values in ppb dry weight
VOLATILE HALOGENATED ALKENES
1.2-
cis-1,3-
trans-
1,1-di-
trans-
di-
1,3-di-
tri-
tetra-
vinyl
chloro-
dichloro
chloro-
chloro-
chloro-
chl oro
Drainage
Survey
Station
Sample
Rep
chloride
ethene
ethylene
propene
propene
ethene
ethene
17110019-BL-000-
URSOOl
B02
S01C -
U5
U5
BS
U5
U5
U5
U5
171IOO19-BL-000-
URS001
B02
S01C -
17110019-BL-000-
URS001
BO 3
SOSC -
U5
BS
BS
US
U5
US
U5
17110019-BL-000-
URS001
B04
S01C •
01
U5
BS
BS
US
U5
U5
U5
17110019-BL-000-
URS001
B04
S01C -
02
U5
US
BS
U5
US
US
U5
17110019-BL-000-
URS001
B04
S01C -
03
US
U5
BS
U5
U5
U5
U5
17110019-BL-000-
URS001
B04
S01C •
04
17110019-BL-000-
URSOOl
BU4
S01C -
05
17110019-BL-000-
URS001
B07
S01C -
US
U5
U5
US
US
U5
U5
17110019-BL-000-
URS001
B07
S01C -
17110019-BL-000-
URS001
B09
SOSC -
US
US
85
U5
U5
US
U5
17110019-BL-000-
URS001
B09
S05C •
17110019-BL-000-
URS0U1
BIO
SOSC -
01
US
US
BS
U5
U5
U5
U5
17110019-BL-000-
URS001
BIO
SOSC -
02
17110019-BL-000-
URS001
Bll
S01C -
02
U5
us
B5
U5
U5
US
U5
17110019-BL-000-
URSOOl
Bll
S01C -
01
U5
BS
Zl.l
U5
U5
U5
U5
17110019-BL-000-
URS001
B12
SOSC -
US
U5
85
U5
U5
U5
Ub
17110019-BL-000-
URSOOl
B14
S01C -
US
US
85
US
US
U5
U5
17110019-BL-000-
URSOOl
B15
SOSC -
U5
U5
B5
US
US
U5
U5
17110019-BL-000-
URSOOl
B17
S01C -
01
U5
ZO.SO
B5
US
U5
US
US
17110019-BL-000-
URSOOl
B17
S01C -
02
17110019-BL-000-
URSOOl
B18
S01C -
U5
U5
B5
U5
U5
U5
Ub
Number of Observations: 22
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT
VOLATILE AROMATIC HYDROCARBONS
Drainage
Survey
Station
Samp!
17110019-BL-000-
URS001
B02
S01C
17110019-BL-000-
URS001
802
S01C
17U0019-BL-000-
URS001
B03
S05C
17110019-BL-000-
URS001
B04
S01C
17110019-8L-000-
URS001
B04
S01C
17110019-BL-000-
URS001
B04
S01C
17U0019-BL-000-
URS001
B04
S01C
17110019-BL-000-
URSCKJ1
B04
S01C
17110019-BL-000-
URS001
B07
S01C
17110019-BL-000-
URS001
B07
S01C
17110019-BL-000-
URS001
B09
S05C
17110019-BL-000-
URS001
B09
S05C
17110019-BL-000-
URS001
BIO
sosc
17110019-BL-000-
URS001
BIO
sosc
17110019-BL-000-
URS001
Bll
soic
17110019-BL-000-
URSOUl
Bll
soic
17110019-BL-000-
URS001
B12
sosc
17110019-BL-000-
URSOUl
B14
soic
17110019-8L-000-
URS001
B15
S05C
17110019-BL-000-
URS001
B17
soic
»17110019-BL-OOO-
UKS001
B17
soic
17110019-Bl-000-
URS001
BIB
soic
N
Nutter of Observations: 22
IGANIC CHEMICALS - Values in ppb dry weight
ethyl -
Rep benzene toluene benzene
B5
U5
U5
85
Z0.19
0
01
Z1.6
ZO .36
U5
02
85
U5
U5
03
U5
us
Ub
04
05
20.35
U5
U5
B5
BS
U5
01
B5
U5
US
02
02
Z0.15
U5
U5
01
B5
U5
US
B5
US
Ub
B5
20.35
U5
US
US
U5
01
23.2
U5
U5
02
B5
B5
U5
-------
BLAIR OREOGING PROJECT SURFACE SEDIMENT TENTATIVELY IDENTIFIED ORGANIC CHEMICALS - Values in ppb dry weight
Drainage
Survey
Station
Sample
7110019-BL-OOO-
UKSOOl
802
S01C
7110019-BL-000-
URS001
BO 3
S05C
7110019-BL-0UU-
URSOOl
B04
S01C
7110019-BL-000-
URS001
B04
S01C
7110019-BL-000-
URSOOl
804
S01C
7110019-BL-000-
URSOOl
B04
S01C
7110019-BL-000-
URSOOl
804
S01C
7110019-BL-000-
URSOOl
B07
S01C
711O019-BL-000-
URSOOl
B09
S05C
7110019-BL-000-
URSUOl
BIO
S05C
7110019-BL-OOO-
UKSOOl
BIO
S05C
7110019-BL-000-
URSOOl
Bll
S01C
7110019-BL-000-
URSOOl
812
S05C
7110019-BL-000-
URSOOl
B14
S01C
7110019-BL-000-
URSOOl
B15
S05C
7110019-BL-000-
URSOOl
B17
S01C
7110019-BL-000-
URSOOl
B17
S01C
7110019-BL-000-
URSOOl
818
S01C
1-methyl -
2-( 1-
methyl- 2-
ethyl) methoxy
Rep benzene phenol
penta- 1-
chloro- methyl
cyclo- naphth-
pentane alene
2,6-di-
methyl
1,1' naphth-
biphenyl alene
68 64
01 23 77
02 130 130
03 140 94
04
05
01 53 69
02 58 66
01 30 33
6.9 16
88 78
80 76
01 32 8.8
02 35 45
17 42
Number of Observations: 18
-------
BLAIR DREDGING PROJECT SURFACE SEDIMENT TENTATIVELY IDENTIFIED ORGANIC CHEMICALS - Values
2.3,5-
tri-
2-
1-
methyl
dibenzo-
methyl
methyl
naphth-
thlo
phenan-
phenan
Oralnage
Survey
Station Sample
alene
phene
threne
threne
171I0019-BI-000-
URSOOl
B02
S01C -
J7110019-BL-000-
URSOOl
803
S05C -
23
32
82
1711Q019-BI-000-
URS001
804
S01C -
01
SO
60
47
17110019-BL-000-
URSOOl
804
S01C -
02
62
85
76
17110019-BI-000-
URS001
B04
S01C -
03
SO
120
90
17110019-Bt.-000-
URSOOl
B04
S01C -
04
i7110019-IM.-000-
URSOOl
804
S01C -
Ob
17110019-81-000-
URSOOl
BO 7
S01C -
17U0019-81-000-
URSOOl
809
S05C -
17110019-81-000-
URSOOl
810
S05C -
01
29
8.5
60
17110019-81-000-
URSOOl
BIO
SOSC -
02
42
U1
36
17110019-81-000-
URSOOl
BU
SOIC -
01
29
11
48
17110019-81-000-
URSOOl
812
SOSC -
18
15
23
17U0019-W.-000-
URSOOl
B14
SOIC -
66
37
16
17110019-81-000-
URSOOl
B15
SOSC -
56
49
69
17H0019-BI-000-
URSOOl
817
SOIC -
01
16
40
14
17110019-81-000-
URSOOl
817
SOIC -
02
31
9.7
34
17110019-81-000-
URSOOl
B18
SOIC -
24
38
42
>
%o
Hunter of Observations: IB
1n ppb dry weight
9-
hexa- iso-
decenoic pimara-
acid diene
-------
BLAIR UREDUIHG PROJECT SURFACE SEDIMENTS INORGANIC CHEMICALS - Values in ppm dry weignt
Drainage
Survey Station
Sample
Rep
Antimony
Arsenic
Barium
Beryl 1 i ura
Cadmium
Chromi urn
7110019-BL-000-
URS001
BO 2
S01C -
0.1
38
23.1
0.14
0.1B
18.9
7110019-8L-000-
UHSU01
B03
S05C -
0.5
4y
25.1
0.15
0.44
20.0
7110019-BL-000-
URS0U1
B04
S01C -
01
0.7
53
22.4
0.12
0.50
18.0
7110019-BL-000-
URS001
604
S01C -
02
0.7
52
23.0
0.13
0.60
19.6
7110019-BL-000-
URSOOl
B04
S01C -
03
0.7
53
23.5
0.15
0.51
18.6
7110019-BL-000-
URSOOl
BU7
S01C -
0.4
35
24.1
0.11
0.17
18.5
7110019-BL-000-
URSOOl
B09
SuSC -
0.3
15
14.5
0.08
U0.10
11.1
711U019-BI-000-
URS001
BIO
SOSC -
0.3
25
25.2
0.14
UC .10
13.0
7110019-BL-Q00-
URSOOl
811
S01C -
0.5
48
21.5
0.13
0.30
18.2
7110019-BL-000*
URSOOl
812
S05C -
0.5
44
22.6
0.14
0.27
17.5
711QQ19-BL-000-
URSOOl
814
S01C -
0.4
39
28.1
0.15
0.28
18.7
7110019-BL-000-
UKS001
BIS
S05C -
0.8
46
29.0
0.13
0.50
18.4
7110019-BL-000-
UHSOOl
B17
S01C -
0.3
16
16.1
1)0.05
U0.10
10.4
7110019-BI-000-
URSOOl
BIB
S01C -
0.3
9.5
10.9
U0.05
U0.10
11.2
Number of Observations: 14
T*
WD
tn
-------
Drainage
17110019-BL-000-
17110019-BL-000-
17110019-Bl-OOO-
miOOig-BL-OOO-
17U0019-BL-000-
17110019-BL-OOO-
miOOM-Bl-OOO-
17110U19-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-000-
17110019-BL-UO0-
UECT SURFACE SEDIMENTS
INORGANIC CHEMICALS - Values
in ppm
Survey
Station
Sample
Rep
Copper
I ron
Lead
URS001
B02
SU1C
.
52.9
20700
31.9
URS001
B03
SOSC
-
75.7
20400
46.0
UKSU01
B04
S01C
-
01
62.8
19800
49.4
UBS001
B04
S01C
-
02
62.5
19600
52.2
URS001
BOA
SU1C
-
03
66.0
20500
52.4
UKS001
BO 7
S01C
-
60.6
19800
36.7
URSU01
B09
SOSC
-
25.8
12000
15.5
UKS001
BIO
SOSC
-
31.7
15800
18.1
URS001
BU
S01C
-
5S.S
22000
38.0
URS001
B12
SOSC
-
53.2
19600
31.3
URS001
B14
S01C
-
60.0
21800
43.0
URS001
BIS
SOSC
-
11.0
2U200
59.0
URS001
B17
S01C
•
23.6
11600
12.6
tmsooi
B18
SU1C
-
15.2
9620
8.1
ttuMber of Observations: 14
>
i
so
Ok
dry weight
Manganese
Ni ckel
Selenium
114
13.6
DO .05
110
12.5
U0.05
111
12.3
UO .05
112
12.3
UO.Ub
115
12.7
DO .05
135
12.5
UO .05
87.4
9.0
U0.05
125
11.0
UO .05
108
12.4
U0.05
103
12.2
U0.05
142
12.7
UO .05
138
12.1
1)0.05
83.7
8.4
U0.05
75.7
8.5
UO .05
-------
BLAIK DREDGING PROJECT SURFACE SEDIMENTS INORGANIC CHEMICALS - Values In ppm dry weight
Drainage
Survey Station
Sample
17110019-BI-000-
URS001
802
S01C -
17110019-BL-000-
URS001
BO 3
SOSC -
17110019-BL-000-
URS001
B04
S01C -
17110019-BL-000-
URS001
B04
S01C -
17110019-BL-000-
URS001
B04
S01C -
17110019-BL-000-
URS001
B07
S01C -
17110019-Bl-OOO-
URS001
B09
SOSC -
17110019-BI-000-
URS001
BIO
SOSC -
17110019-BL-000-
URS001
811
S01C -
17U0019-BL-000-
URS001
B12
SOSC -
17110019-BL-000-
URS001
B14
S01C -
17110019-BL-000-
URS001
B15
SOSC -
17110019-BL-000-
URS001
B17
S01C -
17110019-BL-000-
URS001
B18
S01C -
Rep
Si 1ver
Thai 1ium
Z1nc
Cyanide
Cobalt Mercury
0.14
UO.l
79.7
0.15
0.32
UU.l
91.2
0.22
01
0.27
UO.l
89.3
0.23
02
0.28
UO.l
90.6
0.15
03
0.27
UO.l
90.8
0.14
0.26
UO.l
72.3
0.21
0.08
UO.l
37.6
0.08
0.16
UO.l
40.3
0.13
0.19
UO.l
87.8
0.19
0.18
UO.l
73.9
0.12
0.27
UO.l
91.0
0.22
0.22
UO.l
118.3
0.22
0.08
UO.l
33.3
0.28
O.OS
UO.l
26.6
UO .04
Number of Observations: 14
-------
tU-Alfi MEOGING PROJECT SURFACE SEDIMENT GRAIN SIZE
* * * I
Drainage
Survey
Station
Sample
Rep Rocks
Sand
SI !t
Clay
17110019-BL-000-
IttiSOOl
802
SOIC
_
0.606
18.025
61.001
20.367
i;nooi9-w.-ooo-
UftSOOl
803
SU5C
-
1.316
15.900
63.S52
19.232
17110019-#L-000-
UftSOOl
804
S01C
-
0.397
29.892
53.739
15.972
17110019-8L-000-
UA SOU I
807
S01C
.
0.212
11.992
70.239
17.SS7
17110019-81.-000-
UftSOOl
809
SObC
-
0.032
61.591
32.398
5.979
17HO019-BL-IWO-
0HS001
810
SUiC
-
0.008
27.553
61.803
10.636
17110019-8L-000-
UftSOOl
811
S01C
.
0.372
29.980
48.970
20.678
17110U19-8L-000-
UftSOOl
812
sosc
-
0.219
18.997
61.325
19.459
17110019-8L-000-
URS001
814
soic
-
0.356
8.964
70.387
20.293
171I0019-BL-000-
1MS001
BIS
S05C
.
0.014
28.714
54.846
16.427
i;i 10019-0.-000-
UftSOOl
817
SOIC
-
0.196
67.464
27.815
4.525
17U0019-8L-000-
U8SU01
818
SOIC
-
0.192
75.608
21.189
3.010
NMbcr of Observations: 12
>
t
-------
BLAIR UKEUUING PROJECT SURFACE SEOIMENT CONVENTIONALS
Drainage
Survey
Statl
17110019-BL-000-
URSOUl
B02
17110019-8L-000-
URSOOl
BO 3
17110019-BL-000-
UHSOOl
BU4
1711OU19-BL-U00-
URSOOl
BO 4
1711UU19-UL-OUO-
URSOOl
B04
17110019-61-000-
UHSOOl
BO 7
17110U19-BL-000-
UKSOOl
BU9
17110019-BI-000-
URSOOl
eio
17110019-UL-000-
URSOOl
Bll
1711U019-BL-000-
URSOOl
B12
1711U019-BI-000-
UKSOOl
B14
17110019-BL-000-
URSOOl
002
17110U19-BL-000-
UHSOOl
B03
17110019-BL-U00-
URSOOl
B04
1711IJ019-BL-000-
URSOOl
804
1711OO19-BL-U0U-
URSOUl
B04
1711U019-BL-000-
URSOOl
BO 7
17110019-BL-000-
URSOOl
B09
17110019-BL-000-
URSOOl
BIO
17U0019-BL-000-
URSOOl
Bll
17110019-BL-000-
URSOOl
B12
17110019-BL-000-
URSOOl
B14
171IOU19-BL-000-
URSOUl
Bib
17110019-BL-000-
URSOOl
B17
17110U19-BL-000-
URSOOl
BIB
Number of UDservations:
14
Total
Total
Sample
Rep
Sol ids
Yolatile
t
Sol ids I
S01C -
52.1
4.04
S05C -
46.0
5.66
S01C -
01
51.1
5.02
S01C -
02
SI .4
4.S4
S01C -
03
51.1
4.64
S01C -
SI .6
4.19
SOSC -
66.3
2.14
S05C -
5B.5
3.90
SOIC •
48.4
4.82
SOSC -
53.6
3.97
S01C -
49.5
4.53
S01C -
52.1
4.04
SOSC -
46.0
5.66
S01C -
01
51.1
S.U2
S01C •
02
51.4
4.54
S01C -
03
51.1
4.64
S01C -
51.6
4.19
SOSC -
66.3
2.14
S05C -
58.5
3.90
S01C -
48.4
4.82
SOSC •
53.6
3.97
SOIC -
49.5
4.S3
susc -
54.6
4.91
SOIC -
80.0
1.91
SU1C -
72.8
1.29
Total
Oryanic Carbon- Hydrogen Nitroyen
Carbon I ate lit
1.23
0.065
2.18
0.104
1.70
0.079
1.69
0.077
1.63
0.095
1.49
0.083
0.73
0.031
1.32
0.069
1.72
0.085
1.41
0.082
1.69
0.083
1.23
0.065
2.18
0.104
1.70
0.079
1.69
0.077
1.63
0.095
1.49
0.083
0.73
0.031
1.32
0.U69
1.72
0.085
1.41
0.082
1.69
0.083
1.4B
0.202
0.70
0.136
0.29
0.006
a. Reference:
Tetra Tech, Inc. 1985. Commencement Bay nearshore tideflats remedial
investigation. Final Report Prepared for Washington Department of Ecology and
U.S. EPA by Tetra Tech, Inc. Bellevue, WA.
-------
• STATIONS SAMPLED FOR BENTHOS
ANO BIOASSAYS DURING MARCH
¦ STATIONS SAMPLED FOR BENTHOS
ANO BIOASSAYS DURING JULY
O
o
COMMENCEMENT
BAY
MW
HY-28
HY-M
Hw2
HY-23
MM1
N
4000
fEIT
METERS
1000
Figure A-l. Locations of Commencement Bay stations sampled
for benthic macroinvertebrates and sediment
bioassays during March and July.
Reference: Tetra Tech 1985.
-------
c RS-22 (BKMSSM ONLY)
J»
I
STATIONS SAMPLED FOR BENTHOS
ANO BIOASSAVS DURING JANUARY
STATIONS SAMPLED FOR BENTHOS
AND BIOASSAYS DURING JULY
HS-11
RUSTON
COMMENCEMENT
BAY
RS-13
TACOMA
4000
J I FEET
I METERS
1000
RS-12
Figure A-l. (Continued).
-------
Mi
iV&si"-;-.
'
f.' v:';-••••!¦•
• SURFICIAL SEDIMENT CHEMISTRY — JANUARY
SURFICIAL SEDIMENT CHEMISTRY, BENTHIC
MAGROINVERTEBRATES. AND SEDIMENT
TOXICITY — MARCH
FISH HISTOPATHOLOGY AND BIOACCUMULATION
JUNE
V."v : ••••. :•
Figure A-2. Locations of reference stations sampled in Carr
Inlet.
Reference: Tetra Tech 1985.
A-102
-------
TABLE A 3
RIGHT BAY3
2.4.6
tri -
total
4.
4'-
4.4'
4 .
4 '
ch loro
STATION*
PCBsb
DDT
DDE
ODD
phenol
3 SQ 14
Uc
20
U
1
U 1
u
1
U 500
3 SQ-17
u
20
U
1
U 1
u
1
U 500
3 SQ-18
u
20
U
1
U 1
u
1
U 500
3 SQ-20
u
20
U
1
U 1
u
1
U 500
3 SC 06
1253
U
1
U 1
u
1
U 400
3 SC-07
588
U
1
U 1
u
1
U 400
3 SC 08
646
U
1
U 1
u
1
U 400
3 SC-14
1672
U
1
U 1
u
1
3 SC-17
231
U
1
U 1
u
1
U 500
3 SC-18
229
U
1
U 1
u
1
U 200
3 SC-19
u
60
u
1
U 1
u
1
U 500
3 SC-20
384
u
1
U 1
u
1
U 500
3 CS 01
u
20
u
1
U 1
u
1
U 500
3 CS 11
u
20
u
1
U 1
u
1
U 500
3 CS-15
u
20
u
1
U 1
u
1
U 500
3 CS-17
u
20
u
1
U 1
u
1
U 500
3 DB-01
u
20
u
1
U 1
u
1
U 200
3 DB-05
u
20
u
1
U 1
u
1
U 200
3 DB 07
u
20
u
1
U 1
u
1
U 200
3 DB-15
u
20
0
1
U 1
u
1
U 200
3 EB-09
330
u
1
U 1
u
1
U 400
3 EB-10
279
u
1
U 1
u
1
U 400
3 EB-12
78
u
1
U 1
u
1
U 400
3 EB-17
646
u
1
U 1
u
1
U 400
3 EB 20
640
u
1
U 1
u
1
U 200
3 EB-22
687
u
1
U 1
u
1
U 200
3 EB 23
148
u
1
U 1
u
1
U 200
3 EB-24
69
u
1
U 1
u
1
U 200
3 SM 01
u
20
u
1
U 1
u
1
U 200
3 SM-03
u
20
u
1
U 1
u
1
U 200
3 SM 07
u
20
u
1
0 1
u
1
U 200
3 SM-20
u
20
u
1
U 1
u
1
U 200
3 EV-01
445
u
1
U 1
u
1
U
3 EV-02
84
u
1
U 1
u
1
U
3 EV-03
516
u
1
U 1
u
1
U
3 EV 04
965
u
1
U 1
u
1
U
3 EV-05
394
u
1
U 1
u
1
U
3 EV-06
124
u
1
U 1
u
1
U
3 EV 07
155
u
1
U 1
u
1
U
3 EV-11
171
u
1
U 1
u
1
U
3 BH-03
74
u
1
U 1
u
1
U 200
3 BH 04
54
u
1
U 1
0
1
V 200
3 BH 05
27
u
1
U 1
u
1
U 200
3 BH 07
31
u
1
U 1
u
1
U 200
3 BH 11
54
u
1
U 1
u
1
U 100
3 BH 12
53
u
1
U 1
u
1
U 100
3 BH 23
u
20
u
1
U 1
u
1
U 100
3 BH 24
0
20
u
1
U 1
u
1
U 100
2.4. -di-
¦ethy1
phenol
penta-
chioro
phenol
phenol
acenaph-
thene
1.2.4
tri -
chloro-
benzene
hexa-
ch loro
benzene
u
250
U1500
u
100
U
100
U
200
U 800
u
250
U1500
u
100
U
100
U
200
U 800
u
250
U1500
u
100
U
100
U
200
U 800
u
250
1)1 500
u
100
U
100
U
200
U 800
u
200
U1600
La200
U
200
U
200
U 800
u
200
U1600
u
200
L
200
U
200
U 800
u
200
U1600
220
U
200
U
200
U 800
U
200
U
200
U 800
u
250
U1500
u
100
u
100
U
200
U1000
u
100
U
600
u
40
u
40
U
80
U 400
u
250
U1500
u
100
u
100
U
200
U1000
u
250
U1500
u
100
U1200
u
200
U1000
u
250
U1500
560
U
100
u
200
U 800
u
250
U1500
u
100
U
100
u
200
U 800
u
250
U1500
u
100
U
100
u
200
U 800
u
250
(J 1500
u
100
0
100
u
200
U 800
u
100
U
600
u
40
u
40
u
80
U 400
u
100
U
600
u
40
u
40
u
80
U 400
u
100
U
600
u
40
u
40
u
80
U 400
u
100
U
600
u
40
u
40
u
80
U 400
u
200
U1200
u
80
u
80
u
160
U 800
u
200
U1200
u
80
630
u
160
U 800
u
200
U1200
u
80
u
80
u
160
U 800
u
200
U1200
u
80
u
80
u
160
U 800
u
100
U
800
u
100
u
100
u
100
U 400
u
100
U
800
u
100
L
100
u
100
U 400
u
100
U.
800
u
100
U
100
u
100
U 400
u
100
U
800
u
120
L
100
u
100
U 400
u
100
U
600
u
40
U
40
u
80
U 400
u
100
U
600
u
40
U
40
u
80
U 400
u
100
U
600
u
40
U
40
u
80
U 400
u
100
U
600
u
40
U
40
u
80
U 400
u
U
250
370
u
U
u
U
190
110
u
U
u
U
u
280
u
U
u
U
1400
3300
u
U
u
u
u
240
u
U
u
u
u
120
u
U
u
u
u
480
u
u
u
u
u
U
u
u
u
100
u
600
u
40
U
40
u
80
U 400
u
100
u
600
u
40
150
u
80
U 400
u
100
u
600
u
40
U
40
u
80
U 400
u
100
u
600
u
40
110
u
80
U 400
u
50
u
300
u
20
L
20
u
40
U 200
u
50
u
300
u
20
u
20
u
40
U 200
u
50
u
300
u
20
u
20
u
40
U 200
u
50
u
300
u
20
u
20
u
40
U 200
-------
1.2 di
chloro-
STATION# benzene
3
SQ-14
U
100
3
SQ- 17
U
100
3
SQ-18
U
too
3
SQ 20
U
100
3
SC-06
U
200
3
SC-07
U
2O0
3
SC-08
U
200
3
SC-14
U
200
3
SC 17
0
200
3
SC 18
u
40
3
SC-19
0
200
3
SC 20
11
200
3
CS-01
u
100
3
CS-11
u
100
3
CS-15
u
100
3
CS 17
u
100
3
D8-01
u
40
3
D8-0S
V
40
3
Dfl-07
V
40
3
OB-15
u
40
3
EB-09
u
80
3
EB-10
u
80
3
EB-12
u
80
3
EB-17
u
80
3
EB-20
0
100
3
EB-22
11
100
3
EB-23
u
100
3
EB 24
u
100
3
SM-01
u
40
3
SM 03
u
40
3
SK07
u
40
3
SN-20
u
40
3
EV-01
u
3
EV-02
u
3
EV-03
u
3
EV-04
u
3
EV-05
u
3
EV 06
u
3
EV-07
u
3
EV-11
u
3
BH-03
u
40
3
BH 04
u
40
3
BH 05
u
40
3
BH 07
1!
40
3
BH 11
u
20
3
BH 12
u
20
3
BH 23
U
20
3
BH 24
u
20
1.2 di
1 .4 di 2,6 di phenyl
chloro- nitro- hydra-
benzene toluene zine
U
100
U
500
u
250
U
100
u
500
u
250
U
100
u
500
u
250
U
100
u
5O0
u
250
U
200
u
400
0
200
U
200
u
4O0
u
200
U
200
u
400
u
200
U
200
V
4O0
0
200
U
200
V
500
u
250
u
40
u
200
u
100
u
200
u
500
u
250
u
200
u
5O0
u
250
(J
100
u
500
u
250
V
100
u
500
0
250
u
100
u
500
u
250
u
100
u
500
u
250
u
40
u
2O0
u
100
u
40
u
200
u
100
u
40
u
200
u
100
u
40
u
200
u
100
u
80
u
400
u
200
u
80
u
4O0
l)
200
u
80
u
400
u
200
u
80
u
400
u
200
u
100
u
200
u
100
u
100
u
200
u
100
u
100
u
2O0
u
100
u
100
u
200
u
100
u
40
u
100
u
100
u
40
u
100
u
100
u
40
u
100
u
100
u
40
u
100
u
lOO
u
u
u
u
u
u
u
u
u
u
u
u
0
u
u
V
u
u
u
u
u
u
u
u
u
40
u
100
u
100
u
40
u
lOO
u
100
u
40
u
100
V
100
U
40
u
100
u
100
u
20
u
J 00
IJ
50
u
20
I)
100
u
50
i;
20
u
100
11
50
u
20
1!
100
V)
50
1.3 di
chloro-
benzene
U 100
U 100
U 100
U 100
U 200
U 200
U 200
U 200
U 200
U 40
U 200
U 200
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
U 80
U 80
U 80
U 80
U 100
U 100
U 100
U 100
U 40
U 40
U 40
a 40
0
u
u
u
u
u
V
u
U 40
U 40
U 40
U 40
U 20
U 20
H 20
i: 20
f luor-
anthene
hexa
chloro
but a
d iene
hexa
chloro
cyclo
penta -
d iene
i so-
phorone
naphtha-
lene
n n i t ro
sod i
phenyl
amine
t)is
(2 et hy
hexy1)
phtha
late
U 100
U
400
<11500
U
100
u
100
U2500
U 200
U 100
u
400
ui 500
u
100
u
100
U2500
U 200
U 100
u
400
U1500
u
100
u
100
U2500
U 200
U 100
u
400
111500
u
100
u
100
U2500
U 200
200
u
400
U1200
u
200
L
200
U2000
940
1300
0
400
U1200
u
200
L
200
U2000
420
490
u
400
1J1200
u
200
L
200
U2000
380
650
u
400
U1200
u
200
U
200
I'2000
740
520
u
400
U1500
u
100
U
100
02500
Ul 100
640
u
160
U
600
u
40
57
U1000
U 600
200
u
400
U1500
u
100
U
100
U2500
U 100
5200
u
400
U1500
u
100
1200
U2500
U 780
U 100
u
400
U1500
u
100
U
100
U2500
U 200
U 100
u
400
U1500
u
100
u
100
02500
U 200
U 100
u
400
U1500
u
100
u
100
U2500
U 200
U 100
u
400
U1500
u
100
u
100
02500
U 200
U 40
u
160
U
600
u
40
u
40
U1000
U 120
U 40
u
160
u
600
u
40
u
40
U1000
U 190
U 40
u
160
u
600
u
40
u
40
U1000
U 310
U 40
V
160
u
600
u
40
u
40
U1000
U 360
610
u
320
01200
u
80
u
80
02000
310
2300
u
320
01200
u
80
420
U2000
U 80
220
u
320
HI 200
u
80
u
80
02000
530
U 50
u
320
U1200
u
80
u
80
U2000
o
CO
820
u
200
0
600
u
100
120
UIOOO
U 100
370
u
200
u
600
u
100
100
01000
210
240
u
200
u
600
u
100
L
100
01000
U 100
190
u
200
u
600
u
100
L
100
01000
L 100
76
u
160
u
600
u
40
U
40
UIOOO
2800
97
u
160
u
600
u
40
U
40
UIOOO
340
68
u
160
u
600
u
40
U
40
UIOOO
180
U 40
u
160
u
600
u
40
u
40
UIOOO
740
4800
u
u
u
1300
u
U
770
u
u
u
750
L'
u
830
u
u
u
1800
V
u
4100
u
u
U
5900
u
I'
1800
u
u
u
590
0
830
790
u
u
u
790
u
190
1400
u
IJ
u
1400
u
290
200
u
u
u
u
u
870
710
u
160
i;
600
u
40
220
UIOOO
390
1400
i;
160
i;
600
u
40
370
UIOOO
290
480
i;
160
L:
600
40
260
UIOOO
260
1500
i:
160
u
600
u
40
290
UIOOO
310
550
V
80
V
300
20
yi
0 500
I' 70
200
r
BO
V
300
r
20
100
U 500
U 160
20(1
r
80
i:
ton
1
20
t: 500
U 200
U 21)
c
80
r
Kid
I'
2(1
140
r sou
1 76U
-------
benzyl
di
n-
di
ri-
di
-
di
butyl
butyl
octyl
ethyl
aethyi
phtha-
phtha-
phtha-
phtha-
phtha
STATION#
late
late
la
te
late
late
3
SQ- 14
U
100
U
100
U
100
U
100
U 100
3
SQ-17
U
100
U
100
U
100
I)
100
U 100
3
SQ- 18
U
100
U
100
U
100
U
100
U 100
3
SQ 20
U
100
U
100
U
100
U
100
U 100
3
SC-06
U
200
U
200
u
200
U
200
U 200
3
SC-07
U
200
U
200
u
200
U
200
U 200
3
SC-08
U
200
U
200
u
200
U
200
U 200
3
SC-14
U
200
U
200
u
200
U
200
U 200
3
SC-17
U
100
U
100
u
100
U
100
U 100
3
SC-18
U
40
U
40
u
40
U
40
U 40
3
SC-19
U
100
U
100
u
100
U
100
U 100
3
SC-20
U
100
U
100
u
100
U
100
U 100
3
CS-01
U
100
U
100
u
100
U
100
U 100
3
CS-11
U
100
U
100
u
100
u
100
U 100
3
CS-15
U
100
U
100
u
100
u
100
U 100
3
CS-17
U
100
U
100
u
100
u
100
U 100
3
DB-01
U
40
U
40
u
40
u
40
U 40
3
DB-05
U
40
u
75
u
40
u
40
U 40
3
DB-07
U
40
u
40
u
40
u
40
U 40
3
DB-15
U
40
u
40
u
40
u
40
U 40
3
EB-09
U
80
u
80
u
80
u
80
U 80
3
EB-10
u
80
u
80
u
80
u
80
U 80
3
EB-12
u
80
u
80
u
80
u
80
U 80
3
EB-17
u
80
u
80
u
80
u
80
U 80
3
EB-20
u
100
u
100
u
100
u
100
U 100
3
EB-22
u
100
u
100
u
100
u
100
U 100
3
EB-23
u
100
u
100
u
100
u
100
U 100
3
EB-24
u
100
u
100
u
100
u
100
U 100
3
SM-01
u
40
u
40
69
u
40
U 40
3
SM-03
u
40
u
40
160
u
40
U 40
3
SM-07
u
40
u
40
u
40
u
40
U 40
3
SM-20
u
40
u
40
100
u
40
U 40
3
EV-01
u
u
u
u
U
3
EV-02
u
u
u
u
U
3
EV-03
u
u
60
u
U
3
EV-04
440
u
u
u
U
3
EV-05
u
u
u
u
u
3
EV-06
u
u
u
u
u
3
EV-07
u
u
u
u
u
3
EV-11
u
u
u
u
u
3
BH-03
u
40
u
40
u
40
u
40
U 40
3
BH 04
u
40
u
40
u
40
u
40
U 40
3
BH-05
u
40
u
40
u
40
u
40
U 40
3
BH-07
u
40
u
40
590
u
40
U 40
3
BH-U
u
20
u
20
u
20
u
20
U 20
3
BH-12
u
20
u
20
u
20
u
20
U 20
3
BH -23
u
20
u
20
300
u
20
U 20
3
BH - 24
u
20
u
20
u
20
u
20
U 20
tota)
benzo(a)- benzo
anthra- benzo(a) fluor-
cene pyrene anthenes
U 100
U
250
U 200
U 100
U
250
U 200
U 100
U
250
U 200
U 100
U
250
U 200
L 200
350
570
1000
1100
1600
380
360
590
490
480
980
350
380
980
550
220
630
230
u
250
U 100
1900
1100
1900
U 100
u
250
U 200
U 100
u
250
U 200
U 100
u
250
U 200
U 100
u
250
U 200
U 40
u
100
U 40
U 40
u
100
U 40
U 40
u
100
U 40
U 40
u
100
U 40
350
u
200
490
1400
1400
2200
U 80
u
200
U 80
U 80
u
200
U 80
690
u
100
700
320
200
370
U 100
u
100
U 100
150
140
240
U 40
u
100
U 40
U 40
u
100
U 40
U 40
u
100
U 40
U 40
u
100
U 40
810
250
620
160
u
150
U
u
U
370
u
310
910
u
1000
U
u
U
540
u
460
U
u
U
250
u
100
U 40
430
u
100
U 40
U 40
u
100
U 40
700
u
100
770
150
u
50
190
80
u
50
U 20
U 20
u
50
U 20
U 20
u
50
U 20
benzo
acenaph- anthra- (ghi)
thylene cene perylene
U
100
U
100
U
500
U
100
u
100
U
500
U
100
u
100
u
500
U
100
u
100
u
500
U
200
u
200
u
800
U
200
580
L
800
U
200
u
200
u
800
U
200
u
200
u
800
u
100
u
100
u
500
u
40
200
u
200
u
100
u
100
u
500
u
100
u
100
looo
u
100
u
100
u
500
u
100
u
100
u
500
u
100
u
100
u
500
u
100
u
100
u
500
u
40
u
40
u
200
u
40
u
40
u
200
u
40
u
40
u
200
u
40
u
40
u
200
u
80
u
80
u
400
u
80
460
u
400
u
80
u
80
u
400
u
80
u
80
u
400
u
100
180
u
400
u
100
L
100
u
400
u
100
u
100
u
400
L
100
L
100
L
400
u
40
u
40
u
200
u
40
u
40
u
200
u
40
u
40
u
200
u
40
u
40
u
200
u
700
u
u
27
u
u
110
u
770
410
u
u
890
u
u
u
u
120
480
u
u
u
u
u
40
u
40
u
200
u
40
110
u
200
u
40
u
40
u
200
ii
40
280
u
200
11
20
130
u
100
u
20
38
u
100
u
20
u
20
u
100
u
20
u
20
u
100
chrysene
U 100
U 100
U 100
U 100
L 200
1000
340
510
370
450
190
2000
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
370
1500
U 80
U 80
560
220
U 100
140
U 40
U 40
U 40
U 40
650
160
210
340
750
U
460
U
250
540
U 40
820
170
93
U 20
U 20
-------
STATION# fluorene
3 SQ-14
0
100
3 SQ-17
U
100
3 SQ-18
U
100
3 SQ 20
U
100
3 SC-06
U
200
3 SC-07
L
200
3 SC-08
U
200
3 SC-14
U
200
3 SC-17
U
10O
3 SC-18
U
40
3 SC-19
U
100
3 SC-20
980
3 CS-01
U
100
3 CS-11
U
100
3 CS-15
u
100
3 CS-17
u
100
3 DB-01
u
40
3 DB-05
u
40
3 DB-07
u
40
3 DB-15
0
40
3 EB 09
u
80
1
3 EB-10
490
o
3 EB-12
u
80
o*
3 EB-17
u
80
3 EB-20
u
100
3 EB-22
L
100
3 EB-23
u
100
3 EB 24
L
100
3 SM-01
U
40
3 SN-03
U
40
3 SM-07
u
40
3 SN-20
U
40
3 EV-01
410
3 EV-02
140
3 EV 03
290
3 EV-04
2100
3 EV-05
250
3 EV-06
u
3 EV-07
400
3 EV 11
u
3 BH-03
u
40
3 BH 04
210
3 BH-05
u
40
3 BH-07
150
3 BH 11
32
3 BH 12
66
3 BH 23
u
20
3 BH-24
u
20
dibenzo indeno
(a,h)an- (1,2,3-cd)
thracene pyrene pyrene
U
500
U
500
U 100
U
500
U
500
U 100
U
500
U
500
U 100
U
500
U
500
U 100
U
800
U
800
580
U
800
L
800
1800
U
800
u
80O
570
U
800
u
800
1000
U
50O
u
500
620
U
200
u
200
680
U
500
u
500
330
u
500
840
8400
u
500
u
500
U 100
u
500
u
500
U 100
u
500
u
500
U 100
u
500
u
500
U 100
u
200
u
200
U 40
u
200
u
200
U 40
u
200
u
200
U 40
u
200
u
200
U 40
u
400
u
400
1100
u
400
u
400
3400
u
400
u
400
300
u
400
u
400
U 80
u
400
u
400
1400
u
400
u
400
550
u
400
u
400
670
L
400
L
400
300
u
200
u
200
88
u
200
u
200
120
u
200
u
200
U 40
u
200
u
200
U 40
u
u
2500
u
u
510
u
u
880
u
u
2100
u
u
1500
u
u
630
u
u
1600
u
u
200
u
200
u
200
620
u
200
u
200
1100
u
200
u
200
390
u
200
u
200
1300
u
100
u
100
380
u
100
u
100
230
u
100
1)
100
180
u
100
u
100
180
phenan
threne
U 100
U 100
U 100
U 100
ISO
1200
L 200
520
1200
510
110
8900
U 100
U 100
U 100
U 100
U 40
U 40
U 40
U 40
300
2100
210
U 80
480
190
U 100
200
170
U 40
U 40
U 40
1900
380
970
4700
U
630
1600
370
240
570
210
880
170
170
110
230
chloro
form
ethyl -
benzene
tetra-
chloro-
ethene
trl -
chloro
ethylene
antlaony arsenic beryllii
U
13
U
13
U
13
u
13
u
0.1
5.6
4.6
U
13
U
13
U
13
u
13
u
0.1
7.3
5.5
U
13
u
13
U
13
u
13
U
0.1
6.9
5.5
U
15
0
15
0
15
u
15
u
0.1
6.9
5.3
U
12
u
12
U
12
(J
12
0.2
9
0.31
u
11
0
11
0
11
0
11
2.0
67
0.29
u
14
u
14
U
14
u
14
U
0.1
14
0.34
u
16
u
16
U
16
u
16
0.2
14
0.31
u
14
u
14
u
14
u
14
1.4
39
4.8
u
13
u
13
u
13
u
13
0.1
15
4.1
u
10
u
10
u
10
u
10
2.0
25
3.6
u
7.7
u
7.7
u
7.7
u
7 7
0.3
14
4 . 5
u
14
u
14
u
14
u
14
U
0.1
8.2
3.7
u
14
u
14
u
14
u
14
U
0.1
6.9
3.5
u
17
u
17
u
17
u
17
U
0.1
6.5
4 . 7
u
18
u
18
u
18
u
18
u
0 1
8 1
4.2
u
7.2
u
7.2
0
7.2
u
7.2
u
0.1
1.9
3.9
u
7.9
0
7.9
u
7.9
u
7.9
u
0.1
2.1
4.0
u
10
u
10
u
10
u
10
u
0.1
3.4
4.7
u
16
u
16
u
16
0
16
u
0.1
5.6
5.5
u
11
u
11
u
11
u
11
0.1
12
0.31
u
12
u
12
u
12
u
12
0.1
11
0.31
u
14
u
14
u
14
u
14
u
0.1
7.2
0.39
u
10
u
10
u
10
u
10
0
0.1
14
0.31
u
10
u
10
u
10
u
10
1.3
31
0 33
u
11
u
11
u
11
u
11
u
0.1
9
0.32
u
8
u
8
u
8
u
8
u
0.1
6.6
0.24
u
6.9
u
6.9
u
6.9
u
6.9
u
0 1
5.7
0.20
u
4.1
u
4.1
u
4.1
u
4.1
u
0 1
4.5
0.36
u
13
u
13
u
13
u
13
u
0 1
8.0
0.34
u
4.3
u
4.3
u
4.3
u
4.3
u
0 1
4.6
0 37
u
4.5
u
4.5
u
4.5
u
4 5
u
0 1
5.5
0.38
u
15
u
15
u
15
u
15
u
0.1
12.
0.37
u
15
u
15
u
15
u
15
u
0.1
9.9
0.36
u
13
u
13
u
13
u
13
u
0.1
14
0.30
L
18
L
18
u
18
u
18
0.1
18
0.25
u
16
u
16
u
16
u
16
0.2
8 5
0.28
u
9.3
u
9.3
u
9.3
u
9.3
u
0 1
6 1
0.18
u
12
u
12
u
12
u
12
u
0.1
7 7
0.28
u
10
u
10
u
10
u
10
u
0. 1
6.8
0.25
u
15
u
15
u
15
u
15
u
0. 1
8 5
0.31
u
5.0
L
5.0
L
5.0
L
5 0
u
0. 1
7 9
0.37
u
5 2
W
5.2
u
5 2
u
5 2
11
0 1
11 .
0 41
u
5
u
5
L
5
u
5
u
0 1
8 9
0.41
u
10
u
10
u
10
u
10
0 2
10
0.53
u
10
u
10
u
10
u
10
0 2
6 9
0.38
u
10
u
10
i:
10
II
10
0 2
10
0 52
u
10
u
10
L
10
I'
10
r
o i
8 5
0 49
-------
STATION# cad*lu« chroaiua
3 SQ 14
0.9
60
3 SQ 17
0 9
67
3 SQ-18
1.1
66
3 SQ-20
0.9
65
3 SC-06
3.6
93
3 SC-07
1 .1
79
3 SC-08
0.9
71
3 SC-14
1.2
65
3 SC-17
1.8
129
3 SC-18
1.2
73
3 SC-19
2.3
80
3 SC-20
2.0
86
3 CS-01
0.7
38
3 CS-11
0.6
41
3 CS-15
1.1
57
3 CS-17
1.9
57
3 DB-01
0.1
49
3 DB-05
0.1
51
3 DB-07
0.2
60
3 DB-15
0.4
76
3 EL -09
1.0
41
3 EL-10
2.0
41
3 EL-12
0.4
43
3 EL-17
0.7
40
3 EL-20
0.7
49
3 EL-22
0.5
38
3 EL-23
0.2
25
3 EL-24
0.2
22
3 SM-01
0.14
32
3 SN-03
0.21
43
3 SN-07
0.17
35
3 SN-20
0.14
40
3 EV-01
1.9
50
3 EV 02
0.83
54
3 EV-03
1.5
48
3 EV-04
1. 1
67
3 EV-05
3.1
62
3 EV-06
1.1
34
3 EV-07
1.9
53
3 EV-11
0.9
48
3 BH-03
0.98
68
3 BH-04
1.2
81
3 BH-05
0.55
86
3 BH-07
0.88
82
3 BH-11
0.31
63
3 BH-12
0.50
57
3 BH-23
0.33
66
3 BH-24
0.36
69
lead iiercury nickel
6.8
0.041
37
9.0
0.068
41
8.5
0.060
39
9.0
0.055
38
132
1.38
44
233
1.28
39
151
1 .21
45
175
1.57
43
194
0.70
43
131
0.72
39
360
2.07
45
163
1.64
44
23
0.12
21
9.3
0.054
22
19
0.11
33
13
0.082
31
0.4
0.016
27
U 0.1
0.022
30
5.6
0.029
33
9.9
0.047
46
245
1.69
33
607
1.08
33
35
0.28
37
100
0.58
26
176
0.78
24
51
0.51
27
29
0.16
18
23
0.16
17
5
0.080
20.
5
0.073
27.
6
0.069
19.
6
0.063
21.
38
0.21
44
25
0.20
48
47
0.23
43
82
0.26
45
40
0.18
51
25
0.20
34
34
0.23
50
15
0.12
46
46
1.35
73.
37
1.69
89.
13
0.81
Ill
18
0.97
105
13
0.54
118
11
0.64
72
10
0.54
102
8
0.59
117
copper
43
48
48
44
205
807
231
299
240
170
293
198
56
30
59
52
28
33
50
74
112
165
106
101
152
89
39
20
33
40
33
40
85
81
82
111
101
49
79
70
400
72
69
72
79
61
62
67
eleniua
p
si 1 ver
thai 1 lua
zinc
0.8
0.107
0.2
76
1 .0
0.226
U 0. 1
88
0.7
0.223
II 0.1
85
0.4
0.2
U 0.1
83
0.4
3.7
0.2
330
0.3
2.3
U 0.1
873
0.2
2.29
0.1
311
0.3
2.32
U 0.1
272
0.6
1.29
0.1
328
0.7
1.56
0.1
227
0.1
1.36
0.2
343
0.7
2.67
U 0. 1
235
0.4
0.569
U 0.1
82
0.3
0.119
0.1
57
0.7
0.364
U 0.1
98
0.7
0.262
U 0.1
84
0.3
0.37
0 0.1
72
0.1
0.37
0.1
77
0.6
0.78
0.1
86
1.0
0.218
0.1
102
0.1
0.741
U 0.1
434
0.3
0.651
U 0. 1
687
0.3
0.675
U 0.1
120
0.1
0.65
110.1
192
0.1
0.6
0.1
460
0.2
0.671
U 0.1
116
0.1
0.176
U 0.1
69
0.1
0.254
U 0. 1
44
0.4
0.104
U 0.1
74
0.3
0.13
U 0.1
75
0.1
0.108
U 0.1
76
0.1
0.118
U 0.1
81
0.2
0.368
0.5
313
0.1
0.218
0.2
141
0.2
0.334
0.2
237
0.2
0. 155
0.3
1074
0.4
0.415
0.5
249
0.1
0.184
0.2
78
0.2
0.372
0.4
132
0.1
0.344
V 0.1
88
0.4
0.254
0.1
102
0.2
0.377
0.2
135
0.1
0.221
0.2
111
0.1
0.28
0.1
117
0.1
0.203
U 0.1
113
0.3
0.207
0.1
97
0.2
0.236
0.1
114
0.1
0.217
0.1
115
-------
* total
% total
depth
volatile
organic
STATION*
(¦)
solids
* silt
X clay
carbon
3 SQ-14
23.8
9. 19
35. 18
28.71
2.09
3 SQ-17
25.6
10.44
47.81
35.25
2.30
3 SQ-18
24.7
11.58
45.52
33.93
2.28
3 SQ 20
18.9
9.55
48.40
35.47
2.23
3 SC-06
6.7
9.78
60.30
26.46
2.93
3 SC-07
7.6
6.23
30.85
24.63
2.12
3 SC-08
13.4
11.08
52.04
37.83
2.87
3 SC-14
14.9
12.93
58.36
33.20
3.04
3 SC-17
14.6
9.63
45.21
33.02
2.40
3 SC-18
16. B
9.59
46.04
31.77
2.58
3 SC-19
15.B
7.82
29.93
19.07
2.33
3 SC-20
IB.3
10.92
39.82
28.74
3.28
3 CS-01
40. S
10.33
54.61
34.79
2.01
3 CS-U
21.3
6.18
21.01
17.96
1.36
3 CS-15
28.3
11.84
43.71
37.64
2.42
3 CS-17
24.1
11 .87
31.42
46.71
2.69
3 DB-01
112.8
4.14
12.43
7.82
0.83
3 DB-05
88.4
5.51
13.25
10.41
1.39
3 DB-07
97.5
7.66
24.91
24.45
2.14
3 DB-15
109.7
13.24
49.37
40.31
2.65
3 EL-09
173.1
7.02
31.96
23.72
1.77
3 EL-10
184. 1
6.68
41.80
27.10
2.35
3 EL-12
189.0
9.89
50.94
37.68
2. 14
3 EL-17
170.7
7.32
34.01
21.90
1.92
3 EL-20
173.7
6.76
34.00
18.34
1.39
3 EL-22
179.2
8.20
36.65
25.72
1.93
3 EL-23
121.9
8.01
13.82
10.67
1.11
3 EL-24
137.2
6.22
6.40
7.27
3.80
3 SH-01
13.4
5.71
61.58
19.59
1.32
3 SM-03
10.1
7.50
55.15
25.73
1.89
3 SH-07
23.5
6.12
57.53
27.38
1.32
3 S* 20
30.5
6.64
61.43
25.84
1.39
3 EV 01
16.8
20.14
42.86
20.02
8.98
3 EV 02
16.8
13.42
62.37
20.73
4. 17
3 EV 03
13.4
25.44
40.98
22.56
11.00
3 EV-04
13.7
35.06
34.24
19.58
15 42
3 EV-05
10.1
25.99
45.49
21.83
9.22
3 EV-06
11.9
10.52
28.22
11.96
4.51
3 EV-07
16.5
18.13
48.29
19.25
6.67
3 EV-11
97.5
7.36
50.69
18.39
2.29
3 BH-03
5.5
26.93
50.03
16.88
12.15
3 BH-04
11.9
13.72
58.66
24.93
4.83
3 BH-05
11.9
9.20
71 .35
25.17
2.34
3 BH-07
10. 1
10.67
63.57
28.12
3.15
3 BH-11
7.0
7.25
60.65
37.42
2.07
3 BH-12
6. 1
9.33
36. 14
28.08
3.69
3 BH-23
17.4
8.14
67.44
27.92
2.01
3 BH-24
11.0
6.50
64.97
32.48
2.09
BENTHIC
CODE
TOX MICRO
CODE CODE
0 1 0
0 1 0
0 1 0
0 1 0
0 3 0
0 1 0
0 10
0 3 0
0 10
O 10
0 1 0
0 3 0
0 1 0
0 1 0
0 3 0
0 3 0
0 1 0
0 10
0 1 0
0 1 0
0 1 0
O 1 0
0 1 0
0 1 0
0 1 0
0 1 0
0 1 0
0 1 0
0 1 0
0 3 0
0 1 0
0 1 0
0 3 0
0 1 0
0 1 0
0 3 0
0 3 0
0 1 0
0 1 0
0 1 0
0 1 0
0 1 0
0 10
0 1 0
0 1 0
0 1 0
0 3 0
0 1 0
-------
a. Reference:
Battelle Warine Research laboratory. 1985. Detailed chemical and biological analyses of selected sediments from Puget Sound. Draft Final
Report. U.S. EPA Region X, Seattle, MA. 300 pp.
b. Total FCB6 are the sua of detected Aroclors.
c. U = undetected at detection limit shew. Detection limits were not available for all the chemicals for the "EV" stations.
d. L = less than the value shc*n. For purposes of this report, these were considered undetected.
e. Silver data Mere supplied by Eric Crecelius, Battelle Pacific NW Laboratories, personrtal comnuaicatlcn by H. Beller, November 22,1985.
J»
t
O
v£>
-------
Nautical Milaa
Kiapot Pt.
SEQUIM BAY £
122° -00V
Schoolhouse Pt
Samplings
• 1084
0«983
Figure A-3. Sequim Bay Sampling Stations
Reference: BatteUe 1985.
A-UO
-------
erron
47
-------
'/"/'f/f/ti
HARTSTENE ISLAND i
Samplings
• 1984
0'983
Figure A-5.
Reference:
Case Inlet Sampling Stations
Battelle 1985.
A-112
-------
Samplings
01983
• 1904
Figure A-6. Dabob Bay Sampling Stations
Reference: Battel le 1985. A-113
-------
7
O
4
0
47°- 38' N
p 0 V2 1
f Nautical Miles
^Magnolia Bluff
Four Mile
o8
J1 ®
47°-36'N 14
,''o3\ ilil^
\ / # \ ®1 90
® ^ 1
16^?° ®
0 „19 . 23
o
15
O18 °21
013
Samplings
O I983
0 1984
ELLIOTT BAY
122°-26W^^^
Figure A-7. Elliott Bay - Fourmile Rock Sampling Stations
Reference: Battelle 1985.
A- 1X4
-------
Governor's Pt
Nautical Mil«s
Whiskey Rock
SAMISH BAY
Samish Is
Samplings
01983
• 1984
Figure A-8. Samish Bay Sampling Stations
Reference: Battelle 1985.
A-115
-------
\ Priest Pt.
\r ""f"
25
A
o18
Gedney Is.
016
°22 °23
EVERETT HARBOR
>11
9o
8° ,
Pt. Gardner
Mukilteo
Nautical Miles
01983
• 1964
Figure A-9. Everett Harbor - Port Gardner Sampling Stations
Reference: Battelle 1985.
A-116
-------
Nautical Milas
BELLINGHAM
48 °-44'N
BELLINGHAM BAY
Starr Rock'
24®
Bellingham
South
122°- 3CW
Figure A-10. Bellingham Bay Sampling Stations (Inner Harbor)
Reference: Battel le 1985.
A-117
-------
Nooksack R
O
13
Marietta
Nautical Miles
BELLINGHAM
021
o
22
/r
0
1
BELLINGHAM BAY
Samplings
• 1984
01963
South Bellingham
Figure A—11. Bellingham Bay
Reference: Battelle 1985.
Sampling Stations (Outer Harbor)
A -118
-------
TABLE A-4
DUWAMISH RIVER I d
bis (2
dl -n-
ethyl -
hexa-
STATION
*
diaethyI
diethyl
butyl
hexy 1
chloro-
2,6-di
phtha-
phtha-
phtha-
phtha-
buta-
nltro-
late
late
late
late
diene
toluene
4 DR-01
CI
8.5
4.5
13
280
Ub1.3
U 3.0
4 DR 02
C2
3.1
1.7
7.2
120
U 0.8
U 2.6
4 DR-03
C3
11
4.4
36
1000
U 1.3
U 5.3
4 DR-04
C4
6.4
1.9
13
260
U 1 .3
U 3.3
4 DR-05
C5
8.2
2.4
24
500
U 1.3
U 2.9
4 DR-06
C6
11
4.7
130
580
U 1 .3
U 4.7
4 DR-07
C7
12
3.6
31
740
U 1 .9
U 5.1
4 DR 08
C8
4.8
18
60
2800
U 10
U 5.3
4 SQ 09
C9
11
15
17
100
U 1 3
U 3.2
STATION #
anthra-
cene
1-aethyl-
phenan-
threne
f luor-
anthene
pyrene
benzol a I
anthra-
cene
chrysene
4
DR-01
CI
2.1
X*
1
4
DR - 02
C2
7.6
4
DR-03
C3
15
»—•
4
DR-04
C4
6.5
•£>
4
DR - 05
C5
13
4
DR-06
C6
7.4
4
DR-07
C7
26
4
DR-08
C8
210
4
SQ-09
C9
U 2.3
u
1.3
10
32
9.4
24
13
27
170
4.4
37
64
220
110
160
170
300
1100
53
31
52
230
120
160
160
270
1200
35
7.4
17
100
33
140
50
180
590
2.7
21
36
260
57
110
120
250
1400
16
STATION #
4.4'-DDE 4,4'-DDD 4,4'-DDT
Total
PCBs
naphtha-
lene
2-
¦ethyl
naphth-
alene
4
DR-01
CI
U
0.5
0.6
U
0.5
23
4
DR-02
C2
U
0.5
0.9
U
0.5
38
4
DR-03
C3
u
0.5
3.9
U
0.5
31
4
DR-04
C4
u
0.5
0.6
U
0.5
13
4
DR-05
C5
u
0.5
2.6
U
0.5
76
4
DR-06
C6
u
0.5
3.2
u
0.5
41.6
4
DR 07
C7
u
0.5
5.6
22
120
4
DR 08
C8
u
0.5
71
0.8
3900
4
SQ 09
C9
u
0.5
U 0.5
u
0.5
2.7
2.6
71
11
5.8
12
6.0
8.3
99
3.0
5.5
110
13
2.8
14
9 2
9.7
140
9 1
1,3-dl
1.4 dl-
1 ,2-di-
hexa
chloro-
chloro-
ch loro
isophor-
chloro-
benzene
benzene
benzene
one
benzene
U 40
U 40
U 40
U
2.0
U 0 5
U 40
U 40
U 94
U
2.0
U 0.5
U 40
U 40
U 40
U
2.7
U 0.5
U 40
U 40
U 40
U
1 .9
U 0.5
U 40
U 40
U 40
U
3.0
U 0.5
U 40
U 40
U 40
U
2.7
U 0 5
U 40
U 40
U 40
u
3 . 3
U 0.5
U 40
U 40
U 40
u
3 . 5
1 .7
U 40
U 40
U 40
u
3.3
U 0 5
total
indeno-
benzo-
(1.2.
dibenzo
benzo-
f luor
benzol a)
3-cd)
(a,h(an-
(ghi)
anthenes
pyrene
pyrene
thracene
perylene
7.4
5 . 6
U 3.0
U
3.0
4.5
18
7 . 2
4.4
U
2.9
12
150
61
44
U
7.0
93
29
26
21
U
3.0
42
76
49
29
u
3.3
92
59
44
19
u
5.9
80
200
95
59
u
4.3
67
720
400
170
42
180
9.1
8.3
6.6
u
3.3
8.3
1,1' acenaph- acenaph- fluorene phenan-
blphenyl thylene thene threne
U
1 .8
U 1 .9
U
1.9
U
1.7
23
2.7
U 1 .9
U
1.9
<0
CM
54
7.4
U 3.9
u
3.9
U
3.5
150
U
2.2
U 2.3
u
2.3
U
2 . 1
80
6.8
U 1 .7
9.4
U
2.1
120
U
14
U 3.6
3.6
4.8
97
U
4.3
1 . 7
23
14
190
29
2400
100
91
560
U
7.9
U 37
u
2.8
2.5
U 40
-------
STATION
*
* total
* total
X silt
\ clay
organic
volatlle
TOX
BENTHIC
MICR(
carbon
sol ids
ARSENIC
CADMIUM
COPPER
LEAD
ZINC
MERCURY
CODE
CODE
CODE
4 DR-01
CI
0
13.1
0.36
2.6
8.6
0.13
14
9.4
57
0 01
3
0
0
4 DR-02
C2
0
9.9
0.63
3. 1
12
0.15
16
8.9
57
0.01
1
0
0
4 OR 03
C3
2 6
34 .5
3.5
11
17
0.45
32
20
91
0.04
1
0
0
4 DR-04
C4
2.9
13.7
2 8
7.2
14
0 26
22
14
71
0.02
1
0
0
4 DR-05
C5
3.4
42.3
2.1
8.2
18
0.42
32
17
84
0.04
1
0
0
4 DR-06
C6
5.0
39.9
2.3
8.2
17
0.35
32
17
79
0.07
1
0
0
4 DR-07
C7
4 6
49.2
1 .8
8. 1
20
0.40
35
24
90
0 05
3
0
0
4 DR 06
C8
2.8
84 .8
2.2
8 7
34
3.1
120
160
270
0.42
3
0
0
4 SQ 09
C9
4.5
68.8
1.4
7.6
22
0.64
35
12
93
U0.03
1
0
0
a. Reference:
Chan, S.-L., M.H. Schiewe,
D.W. Brown. 1985. Analyses of sediment samples for U.S. Army Corps of Engineers Seattle Harbor navigation project
operations and maintenance sampling and testing of Duwamish River sediments. Draft report. 15 pp. plus appendices.
b. U = undetected at detection limit shown.
-------
oo
r
Figure a-12. Sediment Sampling Station Locations for Dredged Material
Characterization.
Reference: Chan et al. 1985.
A-121
-------
TAI1LK A 5. AI.K 1 EXTENSION®
1,2-dl
phenyJ
1,4 di
STATION#
hydra
chloro
naphtha
acenaph-
acenaph
anthra
phenan-
f luor
phenol
zlne
benzene
fluorene
lene
thene
thylene
cene
threne
anthene
pyrene
chryse
5
AP-01
LSKR04
74 . 1
U
5.8
27.3
3.9
3.9
U
3
U
2.7
9. 1
35.1
42.9
49 4
20 8
5
AP-02
LSKR05
Ub4 .3
U
5.8
2.7
U 3.4
U 1.6
U
3
U
2 7
U 2.9
1 .3
2.6
5.3
U 18
5
AP-03
LSKR06
5 3
U
5.8
U 3
U 3.4
U 1.6
U
3
U
2.7
U 2.9
1.3
2.7
2.6
1 .3
S
AP-04
LSJR02
74.2
U
5.8
82 3
25 8
II 1.6
11
.3
4.8
95.2
172.
206.
338
101 .
5
AP-05
LSLR02
1.7
0.2
13.8
U 3.4
6.3
U
3
U
2.7
10.0
10.0
12.6
20 1
43.9
5
AP-06
LSLP02
U 4 3
u
5.8
10.4
U 3.4
U 1 .6
U
3
u
2.7
9.1
7.8
11.7
20 8
22 1
5
AP-07
LSKN02
U 4 .3
u
5.8
6.6
U 3.4
14 .5
u
3
u
2.7
4.0
14.5
19.8
36 9
17.2
5
PW-01
LSUV01
U 4.3
u
5.8
U 3
U 3.4
U 1.6
u
3
u
2.7
U 2.9
2.6
21 .9
219
3 9
5
PW 02
LSUII01
31 . 7
u
5.8
U 3
2.6
1.3
1
.3
u
2.7
5.3
15 9
26 4
33 0
9.3
5
PW-03
LSUU02
U 4 3
u
5 8
4 1
U 3.4
6.8
u
3
u
2.7
2.7
5.5
9 6
15 0
8.2
5
P* 04
LSUU03
12 1
u
5.8
22.9
U 3.4
4.0
u
3
u
2.7
4.0
12 1
25 6
35.0
16 2
total
Indeno
dl-n-
dl -n
benzo(a)
benzo-
(1.2,
dlbenzo-
benzo-
dlaethyl
diethyl
buty 1
octyl
STATION#
anthra-
benzo(a)
fluor-
3-cd)
(a,h(an-
(ghl)
phtha-
phtha
phths-
phtha
total
4.4'
cene
pyrene
anthenes
pyrene
thracene
perylene
late
late
late
late
PCBs
DDE
5
AP-01
LSKR04
13.0
24 .7
48.1
U32.8
U34.4
6.5
U 3.7
5.2
46.8
U 7 . 5
16
lC0 . 65
5
AP-02
LSKR05
U IB
U 12 5
1112.4
U32.8
U34.4
U31.7
U 3.7
2.7
51 .8
14.6
19
L0.66
5
AP-03
LSKR06
U 18
U 12.5
2.6
U32.8
U34.4
U31 .7
U 3.7
U 3.6
69.7
U 7.5
29
L0.66
5
AP-04
LSJR02
74.2
100.0
U12.4
1.6
3.2
33.9
U 3.7
4.8
48.4
4 . 8
34
1 .61
5
AP-05
LSLR02
26.4
21 .3
57 .7
18.8
2.5
13.8
U 3.7
21.3
82.8
U 7.5
14
L0.63
5
AP-06
LSLP02
10.4
10.4
33.8
9.1
U34 .4
10.4
U 3.7
6.5
63.7
2 6
6.6
L0.65
5
AP-07
LSKN02
11.9
13.2
60.7
U32.8
U34.4
10.6
U 3.7
5.2
71.2
U 7 5
11
L0.66
5
PW-01
LSUV01
2.6
U 12.5
U12.4
U32.8
U34.4
U31 .7
U 3.7
U 3.6
29.6
U 7.5
13
L0.64
5
PW-02
LSUU01
5.3
13.2
33.1
U32.8
U34.4
4.0
U 3.7
6.6
44 .0
U 7.5
8.6
U0.07
5
PW-03
LSUU02
5.5
4.1
27 . 3
U32 8
U34.4
6.8
U 3 7
6.8
61 . 5
U 7.5
5.6
L0.68
5
PW 04
LSUU03
6.7
21 .6
51 . 3
U32.8
U34.4
9.4
1 . 4
4.0
31 . 0
U 7.5
25. 2
L0.67
STATION#
tetra- 1,2-di- 1,3-dl-
4.4'- 4,4'- chloro- ethyl- chloro- chloro
DDO DDT ethane benzene benzene benzene
hexa-
chloro-
buta-
dlene
butyl
benzyl
phtha-
late
2,4.6-
trl -
chloro-
phenol
penta-
chloro-
phenol
2,6-di-
nltro
toluene
1,2.4
trl-
chloro-
benzene
5
AP-01
LSKR04
U
0.08
u
0.10
U 5
U
5
U
3.5
U
4
UO .31
U
7.5
U
23
U
209
U
230
U
10
5
AP-02
LSKR05
U
0. 08
u
0. 10
0.04
U
5
U
3.5
U
4
U0.31
U
7.5
u
23
u
209
u
230
U
10
5
AP-03
LSKR06
U
0.08
u
0 . 10
0.01
0.
04
u
3.5
U
4
UO. 31
u
7.5
u
23
u
209
u
230
U
10
5
AP-04
LSJR02
U
0.08
u
0 .10
0.03
0
.05
u
3.5
U
4
UO . 31
V
7.5
u
23
u
209
u
230
u
10
5
AP-05
LSLR02
U
0.08
u
0.10
U 5
u
5
u
3.5
U
4
UO .31
V
7.5
u
23
u
209
u
230
u
10
5
AP-06
LSLP02
U
0.08
u
0. 10
U 5
u
5
u
3 5
U
4
U0.31
u
7 . 5
u
23
u
209
u
230
u
10
5
AP-07
LSKN02
U
0.08
u
0.10
0.03
u
5
u
3.5
u
4
UO. 31
u
7.5
u
23
u
209
u
230
u
10
5
PW 01
LSUV01
U
0.08
u
0.10
U 5
u
5
u
3 . 5
u
4
U0.31
u
7 . 5
u
23
u
209
u
230
u
10
5
PW-02
LSUU01
u
0.08
u
0.10
U 5
u
5
u
3.5
u
4
UO . 31
u
7 5
u
23
u
209
IJ
230
u
10
5
PN 03
LSUU02
u
0 08
u
0.10
0.06
0
03
u
3 5
u
4
UO 31
u
7 . 5
u
23
u
209
u
2 30
u
10
5
PW 04
LSUU03
u
0.08
u
0 . 10
0 . 01
u
5
u
3 5
u
4
UO 31
u
7 5
u
23
u
209
I'
230
u
10
-------
STATION#
hexa
chloro-
benzene
hexa
chlor -
ethane
hexa
ch lor
cyclo
pent a
d i ene
b 1 s ( 2
ethyl -
hexy 1 )
phtha
late
2.4 di
nethy1 -
phenol
N-ni-
troso
diphenyl
amine
s i 1 ver
cadaiua
chronius
copper
5
AP 01
LSKR04
u
20
U
12
U
110
u
10
u
5
U
4
8
4.9
0 . 35
LO . 23
23
3.8
5
AP 02
LSKR05
u
20
u
12
u
110
u
10
u
5
u
4
8
9.3
0 26
LO. 21
24
4 . 2
5
AP 03
LSKR06
u
20
u
12
u
110
u
10
u
5
y
4
8
4.7
0 . 28
LO 18
20
4 . 3
5
AP 04
LSJR02
u
20
u
12
u
110
u
10
u
5
u
4
8
11
0.48
LO . 32
31
11
5
AP-05
LSLR02
u
20
u
12
u
110
u
10
u
5
u
4
8
14
L0.21
LO . 21
28
9.9
5
AP 06
LSLP02
u
20
u
12
u
110
u
10
u
5
u
4
8
31
L0.23
LO. 23
31
11.6
5
AP-07
LSKN02
u
20
u
12
u
110
u
10
u
5
u
4
8
15
L0.20
LO. 20
44
18 4
5
PW 01
LSliVOl
u
20
1!
12
u
110
u
10
u
5
u
4
8
2.0
0 . 33
LO . 22
23
3.6
5
PN 02
LSUU01
u
20
U
12
u
110
u
10
u
5
u
4
8
19
LO. 17
LO. 17
26
11
5
PW 03
LSUU02
u
20
U
12
u
110
u
10
u
5
u
4
8
0.34
LO. 19
27
13
5
PW 04
LSUU03
u
20
u
12
u
110
u
10
u
5
u
4
8
20
0.29
LO. 16
31
16
* total
X total
STATION#
depth
volatile
organic
\
BENTHIC
TOX
MICRC
¦ercury
nickel
lead
antiaony
zinc
(¦)
solids
carbon
f lnese
CODE
CODE
CODE
5
AP-01
LSKR04
0.022
18
4.7
LO. 05
22
22
1
0.57
0.54
1
0
0
5
AP-02
LSKR05
LO.012
17
5.8
L0.05
21
22
1
0.57
0.60
1
0
0
5
AP 03
LSKR06
0.020
17
5.8
LO. 04
21
22
1
0.57
0.60
1
0
0
5
AP-04
LSJR02
0.05
23
16
LO. 08
37
49
2
0.74
4.1
1
0
0
5
AP-05
LSLR02
0.056
23
14
LO. 05
43
98
4
1 .07
5.4
1
0
0
5
AP 06
LSLP02
0.053
27
10
L0.05
44
112
2
0.74
3.8
1
0
0
5
AP-07
LSKN02
0.053
22
13
L0.04
49
22
4
1 .07
1.9
1
0
0
5
PW-01
LSUV01
LO.023
15
5
L0.05
21
22
1
0.57
0.89
1
0
0
5
PW-02
LSUU01
0.053
23
20
L0.04
38
47
2
0.74
2.1
1
0
0
5
PW 03
LSUU02
0.051
23
11
L0.04
40
96
2
0.74
3.0
1
0
0
5
PW-04
LSUU03
0.055
23
13
LO. 04
44
110
2
0.74
2.4
1
0
0
a. References:
Osborn, J.G., D.E. Weitkamp, and T.H. Schadt. 1985. A1ki wastewater treatment plant outfall inprovements predesign study. Technical Report No.
6.0, Marine Biology. Municipality of Metropolitan Seattle. 50 pp.
Trial, W., and J. Michaud. 1985. Alki wastewater treatment plant outfall improvements predesign study. Technical Report No. 8.3, Water Quality.
Municipality of Metropolitan Seattle. 89 pp.
b. U= Undetected at detection limit shown. Detection limits are from:
Romberg, G.P., S.P. Pavlou, and E.A. Crecelius. 1984. Presence, distribution, and fate of toxicants in Puget Sound and Lake Washington. METRO
Toxicant Program Report No. 6A. Toxicant Pretreatment Planning Study Technical Report CI. Municipality of Metropolitan Seattle, Seattle, WA.
231 pp.
c. L ~ Less than the value shown. For purposes of this report, these were considered undetected.
d. Total organic carbon values were estimated from a regression plot of total volatile solids with total organic carbon. See Figures A-13and A-14 .
The regression in figure A-13was used for the estimate and includes only data in the range of volatile solids found in the Alki Study (i.e., <10%).
e. Grain size data were unavailable for station AP-03. Because or its proximity to AP-02, an estimated value of 0.6X fine grained material was
assigned for use in calculations .
-------
o
m
cc
<
o
o
z
<
o
cc
o
<
H
O
H
Z
Ui
O
CC
UJ
0.
3.5
10.5
17.5
24.5
31.5
38.5
7 14 21 28 35
PERCENT TOTAL VOLATILE SOLIDS
42
119 cast* plotted. Regression statistics of TOC on V50L10S:
Correlation .90784 R Squared .02417 S.E. of Est 1.2804* Sig. .0000
Intercept^.E.) -.56a53( .19400) Slope(S.E.) .42116( .01798)
Figure A-13. Plot of total organic carbon with total
volatile solids.
A-124
-------
o
m
tr
<
o
o
z
<
a
a:
o
<
h-
o
Ul
o
ac
Ui
a.
1.75+
1.5+
..25+
.75+
.25+
PERCENT TOTAL VOLATILE SOLIDS
47 casts plotted. Rtgrtssion statistic* at TOC on VSOLIOS:
rarr.liti:n .75506 R Squared .57011 S.E. of Est .33882 Sig. .0000
Int»rc«ot(S.E.) ,40340< .10455) Sl0P«
-------
POINT WILLIAMS
LSUV01
LSUU01
FAUNTLERCH
CCVE
BRACE
POINT
DEPTH CONTOURS IN
METERS ATMUW
SCALE IN METERS
Rgure A-15.
Sediment collection stations offshore of
Point Williams, sampled May 26,1984.
Reference: Qsborn et al. 1985.
A-126
-------
(
/
/
/
/
/
/
/
183
LSKN02
\
\
\
X
/
ALKI POINT
ALKlWTP
\
LSKR04•
\
J
OEPTH CONTOURS IN
meters at mllw
0 100 200 300 400 500
4^
500 1.000 1.500
SCALE IN METERS
SCALE IN FEET
Rgure A-16.
Sediment collection stations offshore of
Aiki Point, sampled May 25-26,1984.
Reference: Osborn et al. 1985.
A-127
-------
V
\
f
t.
POINT WILLIAMS
r
LSUV01
LSUU03
r lsuuoi
LSUU02
FAUNTLEROY
COVE
BRACE
POINT
DEPTH CONTOURS IN
METERS AT MLLW
SCALE IN METERS
SCALE IN FEET
500
1.000
1.500
Figure A-17.
Point Williams benthos reference
sampling station locations.
Reference: Osborn et al. 1985.
A-128
-------
/
/
183
\
LSKN02
\
ALKI POINT
ALKl WTP
LSKR04 •
™KR05
CR06 \
LSLR02
J
/ I \
06PTH CONTOURS IN
METERS AT MLLW
0 100 200 300 400 500
4^
SCALE IN METERS
SCALE IN FEET
500 1000 1.500
Figure A-18.
Alki Point benthos sampling
station locations.
Reference: Osborn et al. 1985.
A-129
-------
TABLE A-6. TPPS PHASE 3 A * B4
STATION # phenol
6
EB-30
401230
1824
Lc
52
7
EB-30
401230
2081
I)b
4.3
6
EB-31
401630
1816
u
4.3
7
EB-31
401630
2078
u
4.3
6
EB-32
401830
1825
11
4.3
7
EB-32
401830
2077
u
4.3
e
EB-33
401406
1778
u
4.3
7
EB-33
401406
2080
u
4.3
6
EB-34
401512
1780
u
4.3
7
E8-34
401512
2071
u
4.3
6
EB-35
401603
1775
u
4.3
7
EB-35
401603
2079
u
4.3
0
EB-36
401606
1776
u
4.3
7
EB-36
401606
2072
L
236
6
EB-37
401612
1777
u
4.3
7
EB-37
401612
2073
u
4.3
6
EB-38
401706
1778
u
4.3
7
EB-38
401706
2074
u
4.3
8
EB-38
401810
1814
L
40
7
EB-39
401810
2075
u
4.3
6
MP-01
400330
1806
u
4.3
7
WP-01
400330
2088
u
4.3
«
WP-02
400430
1807
0
4.3
7
WP-02
400430
2089
u
4.3
6
WP-03
400510
1788
u
4.3
7
WP-03
400510
2090
u
4.3
6
W-04
400530
1809
u
4.3
7
WP-04
400530
2091
29
6
WP-05
400621
1810
u
4.3
7
HP-OS
400621
2092
u
4.3
«
KT-06
400712
1811
u
4 3
7
WP-06
400712
2084
u
4 3
6
BP-07
400730
1812
u
4.3
7
WP-07
400730
2093
u
4.3
6
HP-08
400810
1813
u
4.3
7
HP-oa
400810
2083
u
4.3
6
WP09
400830
1815
u
4.3
7
WP-09
400830
2082
u
4.3
6
WP-10
400210
1787
u
4 3
7
HP-10
400210
2076
u
4 3
6
W-ll
400310
1789
u
4 3
7
WP-11
400310
2087
u
4.3
6
KP-12
400275
1786
u
4 3
7
WP-12
400275
2069
u
4.3
6
WP-13
400375
1784
u
4 3
7
WP-13
400375
2070
u
4.3
6
WP 14
400575
1785
7
WP 14
400575
2085
u
4 3
6
WP 15
400775
1817
u
4 3
7
HP 15
400775
2094
u
4 3
6
WP 16
400875
1816
IJ
4 3
7
HP It.
100875
20B».
!:
4 J
1,2 di-
phenyl- n nltroso-
hydra- diphenyl acenaph
zlne aalne thene
U
5.8
U
4.8
L
37
U
5.8
u
4.8
25
U
5.B
u
4.8
L
68
U
5.8
L
7
L
34
U
5.8
U
4.8
U
3
u
5.8
L
30
L
61
u
5.8
L
132
L
460
u
5.8
U
4.8
U
3
u
5.8
U
4.8
L
694
u
5.8
L
46
81
u
5.8
U
4 .8
L
304
u
5.8
276
U
3
u
5.8
L
54
U
3
u
5.8
L
308
U
3
u
5.8
U
4.8
L
78
0
5.8
191
L
57
u
5.8
L
61
L
293
u
5.8
L
18
85
u
5.8
I)
4.8
804
u
5.8
U
4.8
L
43
V
5.8
u
4.8
177
u
5.8
u
4.8
L
3
V
5.8
u
4.8
142
u
5.8
u
4.8
L
29
u
5.8
u
4.8
U
3
V
5.8
u
4.8
U
3
u
5.8
u
4.6
u
3
u
5.8
u
4.8
L
10
u
5.8
486
U
3
u
5.8
u
4.8
u
3
u
5 8
L
542
L
64
u
5.8
U
4.8
4
u
5.8
u
4.8
U
3
u
5.8
u
4.8
12
u
5.8
u
4.8
L
104
u
5.8
L
3
L
37
u
5.8
u
4 8
L
488
u
5 8
u
4 8
U
3
u
5.8
L
14
L
35
u
5 8
U
4.8
L
12
u
5.8
L
196
1335
u
5.8
U
4 8
102
u
5.8
L
75
U
3
u
5 8
L
22
U
3
u
5 8
U
4 8
u
3
u
5 8
18
u
3
u
5 8
63
1.
106
u
5 8
u
4 8
L
68
u
5.8
u
4 B
14
u
5 8
i:
4 B
i:
3
i:
r. »
r
1 >i
t
2.4-dl-
¦ethyl-
phenol
U 5
U 5
L 24
U 5
L 30
U 5
U 5
U 5
U 5
U 5
u s
U 5
I) 5
U 5
U 5
0 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
U 5
t! 5
C ¦)
acenaph anthra-
thylene cene
L
34
79
U
2.7
86
L
63
149
25
64
U
2.7
174
L
56
82
L
431
L
49
U
2.7
L
65
U
2.7
53
L
46
217
V
2.7
3936
L
42
1636
4013
67
U
2.7
U
2.9
L
75
98
L
3
1274
L
276
L
67
42
871
U
2.7
1080
U
2.7
U
2 9
L
31
L
136
12
16
247
277
50
360
U
2.7
U
2.9
L
4
4
U
2.7
124
23
106
U
2.7
U
2.9
L
35
63
L
60
203
67
34
L
48
78
57
189
L
91
L
26
L
33
22
L
460
L
21
U
2.7
L
4
L
32
517
51
126
2278
9964
643
1273
U
2.7
L
29
L
14
U
2 9
U
2.7
U
2 9
U
2 7
26
L
95
32
U
2 7
74
20
1 15
U
2 7
1.
47
naphtha phenan
lene threne
241
241
93
303
444
313
172
184
275
367
152
220
L
316
316
L
108
203
U
1.6
220
133
681
L
215
4293
U
1.6
2574
U
1.6
157
128
U
2.9
L
52
261
236
3822
U
1.6
232
184
2970
578
1683
498
U
2.9
129
477
U
1.6
61
308
1032
139
1166
u
1 6
u
2.9
u
16
14
u
1 6
187
33
259
u
1 6
u
2.9
u
16
298
L
42
285
27
343
u
1.6
397
65
392
L
65
104
U
16
61
u
1.6
84
u
1.6
135
u
1.6
401
u
16
383
2669
33630
328
3150
u
1 6
56
u
1 6
u
2.9
u
1 6
u
2.9
u
1 6
132
u
1 6
124
L
45
298
126
4H9
IJ
1 b
50 i
11
MH
fluor-
ene
220
38
418
29
124
L 70
L 276
U 3.4
L 429
115
2683
118
U 3.4
L 97
490
86
L 173
218
704
U 3.4
150
4
324
79
U 3.4
U 3.4
L 106
14
U 3.4
L 44
L 38
15
L 48
31
L 65
45
L 293
10
L 21
63
4804
643
L 29
U 3.4
L 222
I 24
475
-117
32
I. 50
! J 1
-------
STATION #
dibenzo
(a,h)an -
thracene
benzo(a)
anthra-
cene
benzo(a)-
pyrene
tota 1
benzo
fluor-
anthenes
i
*
6
F.B 30
401230
1824
U 34 4
289
814
1759
•
7
KB 30
401230
2081
U 34 .4
556
758
3283
•
6
EB-31
401630
1818
392
339
1305
2794
•
7
EB 31
401630
2078
U 34.4
123
245
270
•
6
EB-32
401830
1825
U 34 .4
U
18
U
12.5
U
12.4
•
7
EB-32
401830
2077
U 34 .4
257
173
185
6
EB-33
401406
1779
4023
862
9770
17529
7
EB-33
401406
2080
U 34.4
u
18
u
12.5
U
12.4
•
6
EB 34
401S12
1780
U 34.4
L
122
9959
18041
•
7
EB-34
401512
2071
U 34.4
805
1022
1084
6
EB-35
401603
1775
U 34.4
9481
u
12.5
u
12.4
7
EB-35
401603
2079
1048
3125
5882
11213
6
EB-36
401606
1776
U 34.4
535
903
2475
7
EB-36
401606
2072
U 34.4
138
154
338
6
EB-37
401612
1777
850
621
2092
3791
7
EB-37
401612
2073
U 34 .4
5414
2930
4140
6
EB-36
401706
1778
471
573
976
3570
7
EB-38
401706
2074
U 34.4
851
990
2574
6
EB-39
401810
1814
U 34.4
905
1432
3266
7
EB-39
401810
2075
U 34.4
844
1450
3463
6
WP-01
400330
1806
L 10
341
490
1294
7
WP-01
400330
2088
U 34.4
81
120
110
6
WP-02
400430
1807
U 34.4
740
1002
2640
7
WP-02
400430
2089
172
1715
3602
4117
6
WP-03
400510
1788
U 34 .4
U
18
u
12.5
u
12.4
7
WP-03
400510
2090
U 34 .4
U
18
65
83
6
WP-04
400530
1809
U 34 .4
U
18
642
160
7
WP-04
400530
2091
U 34.4
273
177
273
6
WP-05
400621
1810
U 34.4
U
18
u
12.5
u
12.4
7
WP-05
400621
2092
U 34.4
194
323
310
6
WP-06
400712
1811
U 34.4
L
46
L
339
2534
7
WP-06
400712
2084
U 34.4
313
194
463
6
WP-07
400730
1812
780
265
1852
3452
7
WP 07
400730
2093
U 34 .4
1757
1622
1757
6
WP 08
400810
1813
U 34.4
298
L
95
1712
7
WP-08
400810
2083
U 34.4
95
109
136
6
WP-09
400830
1815
U 34.4
209
572
1032
7
WP-09
400830
2082
U 34.4
162
471
412
6
WP-10
400210
1787
L 26
1151
349
1294
7
WP-10
400210
2076
71
344
816
855
6
WP-U
400310
1789
L 203
14947
22598
129359
7
WP-11
400310
2087
1155
4462
6824
8005
6
WP-12
400275
1786
U 34.4
643
1877
3405
7
WP-12
400275
2069
U 34 .4
235
447
782
6
WP-13
400375
17B4
U 34.4
U
18
L
1263
L
2274
7
WP 13
400375
2070
U 34 .4
2B9
500
737
6
WP-14
400575
1785
7
WP-14
400575
2085
U 34 .4
174
L
74
L
113
6
WP -15
400775
1817
U 34 4
685
2173
3928
7
WP 15
400775
2094
U 34 .4
402
402
1322
6
WP-16
400875
1816
U 34.4
U
18
651
3077
7
WP 16
400875
2086
U 34 .4
110
121
161
indeno
benzo
1 .
2 di
fluor-
(1,2.3-cd)
(ghi )
c h 1 o r o
:hrysene
anthene
pyrene
pyrene
perylene
benzene
1050
446
34 1
551
650
I'
3 5
631
732
303
707
¦155
II
3 . 5
1201
522
522
653
107 1
II
3 . 5
179
294
64
319
L
54
U
3 . 5
I 18
528
U
32.8
688
I'
31.7
u
3 . 5
350
374
68
397
65
u
3.5
1121
1178
2874
1494
8046
u
3.5
) 18
1703
U
32.8
1189
U
31.7
u
3.5
445
1306
U
32 8
1878
u
31.7
u
3.5
1362
1022
341
1424
341
L
8978
10376
5367
U
32.8
6440
u
31.7
U
3.5
5147
5882
4412
6250
5147
L
18
1605
468
U
32.8
1137
u
31.7
U
3.5
277
369
308
718
174
U
3.5
2255
65
U
32.6
95
1699
U
3.5
7962
11147
L
105
11147
L
115
L
51
1552
606
U
32.8
774
808
U
3.5
1802
1624
257
U 7.6
192
U
3.5
804
3518
U
32.8
4774
U
31.7
U
3.5
1385
1450
2165
2078
2597
U
3.5
886
858
150
913
163
U
3.5
96
116
41
146
32
U
3.5
1171
1572
143
2712
200
u
3.5
2058
2744
1012
3774
1235
u
3.5
' 18
U 7.6
U
32.8
U 7.6
u
31 . 7
u
3.5
82
76
U
32 8
136
u
31.7
u
3.5
227
428
u
32.8
521
u
31.7
u
3.5
532
614
191
846
150
u
3.5
! 18
U 7.6
u
32.8
U 7.6
u
31.7
u
3.5
220
401
142
466
142
u
3.5
33
488
L
136
705
L
271
u
3.5
522
746
313
955
284
u
3.5
1455
622
u
32.8
661
1587
u
3.5
2297
1757
2432
2838
2568
u
3.5
106
311
389
441
765
u
3.5
115
123
L
7
176
10
u
3.5
725
167
U
32 8
321
L
46
u
3.5
250
412
U
32.8
588
U
31.7
u
3.5
4140
1203
362
1811
310
u
3.5
383
689
523
829
944
u
3.5
35409
71352
9093
62811
10658
u
3.5
6693
6299
5249
7349
5381
u
3.5
231
260
U
32.8
402
U
31.7
u
3.5
279
U 7.6
92
U 7.6
109
u
3.5
1 18
U 7.6
u
32 8
U 7.6
U
31.7
u
3.5
368
342
95
421
129
u
3.5
206
2375
L
185
290
L
201
u
3.5
2440
714
893
1042
1756
u
3.5
575
833
316
1379
270
u
3 . 5
J 18
1302
U
32 8
1509
u
31.7
u
3.5
144
203
L
25
243
L
25
u
3.5
-------
hexa-
benzyl
1,3-di-
1
4 dl
chloro-
butyl
chloro
chloro-
buta-
phtha-
STATION
*
benzene
benzene
dlene
late
* 6 EB 30
401230
1824
U 4
U
3
U 0.31
160
• 7 EB 30
401230
2081
U 4
U
3
13
U
7.5
• 6 EB 31
401630
1818
U 4
u
3
U 0.31
217
• 7 EB 31
401630
2078
U 4
u
3
7
54
• 6 EB-32
401830
1825
U 4
u
3
U 0.31
I
32
• 7 EB-32
401830
2077
1) 4
IJ
3
7
I
58
6 EB-33
401406
1779
U 4
u
3
U 0.31
1724
7 EB-33
401406
2080
U 4
u
3
11
u
7.5
• 6 EB-34
401512
1780
U 4
u
3
U 0.31
u
7.5
• 7 EB-34
401512
2071
U 4
L
7740
22
124
6 EB-3S
401603
1775
D 4
V
3
U 0.31
u
7.5
7 EB-35
401603
2079
U 4
u
3
U 0.31
1820
6 EB-36
401606
1776
U 4
u
3
U 0.31
334
7 EB-36
401606
2072
U 4
u
3
15
67
• 6 EB-37
401612
1777
U 4
u
3
U 0.31
u
7.5
• 7 EB-37
401612
2073
U 4
L
45
6
178
6 EB 38
401706
1776
U 4
u
3
U 0.31
u
7.5
7 EB-38
401706
2074
U 4
u
3
2
812
* 6 EB-39
401810
1814
D 4
u
3
U 0.31
L
578
• 7 EB-39
401810
2075
U 4
u
3
6
U
7.5
• « WP-01
400330
1606
U 4
V
3
U 0.31
U
7.5
7 W-01
400330
2088
U 4
0
3
1
11
• 6 WP-02
400430
1807
U 4
u
3
V 0.31
L
20
7 WP-02
400430
2089
U 4
0
3
2
L
34
* S WP-03
400510
1788
D 4
u
3
U 0.31
U
7.5
7 WP-03
400510
2090
U 4
u
3
U 0 31
12
• 6 WP 04
400530
1809
U 4
u
3
U 0.31
U
7.5
7 WP-04
400530
2091
U 4
u
3
11
U
7.5
* 6 WP-05
400621
1810
U 4
u
3
U 0.31
U
7.5
7 WP-05
400621
2092
U 4
u
3
4
U
7.5
• 6 WP-06
400712
1811
U 4
0
3
U 0.31
U
7.5
7 WP-06
400712
2084
U 4
u
3
3
U
7.5
* e wp-07
400730
1812
U 4
u
3
U 0.31
L
11
7 WP-07
400730
2093
U 4
u
3
7
U
7.5
• 6 WP-08
400810
1813
U 4
u
3
U 0.31
L
17
7 WP-08
400610
2083
U 4
u
3
5
26
• 6 WP-09
40083O
1815
V 4
u
3
V 0.31
L
11
7 WP-09
400830
2082
U 4
u
3
U 0.31
U
7.5
• 6 WP-10
400210
1787
U 4
u
3
U 0.31
U
7.5
7 WP-10
400210
2076
U 4
u
3
U 0.31
23
• 6 WP-11
400310
1789
U 4
u
3
U 0.31
u
7 5
7 WP-11
400310
2087
U 4
u
3
L 0.3
u
7.5
6 WP-12
400275
1786
U 4
u
3
U 0.31
u
7.5
7 WP-12
400275
2069
U 4
u
3
6
307
6 WP-13
400375
1784
U 4
u
3
U 0.31
u
7.5
7 WP-13
400375
2070
U 4
u
3
5
L
26
6 WP 14
400575
1785
7 WP -14
400575
2085
U 4
u
3
U 0.31
259
6 WP 15
400775
1817
U 4
u
3
U 0.31
u
7 5
7 WP 15
400775
2094
U 4
u
3
U 11
287
6 WP 16
400875
1816
U 4
u
3
U 0 31
u
7 5
7 WP 16
400875
2086
U 4
u
3
6
28
di n di-n
diethyl butyl- dlaethyl octyl
phtha- phtha phtha- phtha
late late late late 4.4' DDD 4.4' DDE
L
11
108
L
5
244
a
2
U
3.6
328
U
3.7
L
40
U
0 08
3
u
3.6
L
13
U
3.7
14386
3
3
51
515
L
10
539
U
0 08
4
L
25
L
32
U
3.7
482
U
0.08
1
42
888
L
30
678
U
0.08
5
U
3.6
L
106
U
3.7
2615
U
0.08
11
u
3.6
405
U
3.7
3243
30
30
L
90
531
U
3.7
L
2082
U
0.08
6
43
310
L
15
1269
U
0.08
22
U
3.6
2147
U
3.7
9481
175
47
L
83
2941
L
88
37868
U
0.08
U
0.07
L
23
211
U
3.7
27759
u
0.08
37
318
4103
u
3.7
303
u
0.08
10
L
29
281
u
3 7
948
u
0.08
9
45
860
u
3.7
13376
u
0.08
14
L
88
175
u
3.7
1717
u
0.08
7
20
832
18
4158
u
0.08
4
L
45
578
u
3.7
6784
u
0.08
8
U
3.6
L
193
u
3 7
U
7.5
u
0.08
6
U
3.6
L
25
u
3 7
U
7.5
u
0.08
5
9
112
L
20
173
u
0.08
L
0.3
u
3.6
200
L
17
431
u
0.08
1
L
17
103
L
31
L
5
u
0.08
L
3
U
3.6
L
14
u
3.7
L
352
u
0.08
0.1
11
423
u
3.7
423
u
0.08
L
0.4
U
3.6
L
28
u
3.7
414
u
0.08
0.1
97
164
u
3.7
205
L
0.3
1
u
3.6
187
u
3.7
461
u
0.08
U
0.07
L
23
1423
u
3.7
246
u
0.08
U
0.07
L
50
L
45
u
3 7
1165
u
0.08
1
22
194
u
3.7
254
L
0.5
1
L
11
198
u
3.7
489
u
0.08
2
L
8
1622
u
3.7
500
u
0 08
1
L
10
101
u
3.7
L
259
u
0.08
1
47
2035
u
3.7
258
L
1
3
L
8
73
L
4
181
10
3
12
162
u
3 7
235
L
0 4
2
U
3.6
L
43
L
23
2329
u
0.08
1
17
94
U
3 7
3571
u
0.08
L
0. 1
u
3.6
U
2 4
U
3.7
1423
12
3
L
9
262
U
3.7
U
7.5
2
1
U
3.6
U
2 4
u
3.7
6166
V
0.08
2
50
475
u
3.7
249
u
0 08
U
0.07
u
3.6
u
2 4
u
3.7
L
530
u
0.08
9
18
4474
u
3.7
2605
u
0 08
L
5
u
3.6
844
L
18
68602
L
1
L
2
L
27
247
U
3 7
625
U
0 08
1
57
402
L
9
1264
u
0 08
3
u
3 6
L
89
1'
3 7
1331
12
5
51
339
I.
11
367
L
0.3
1
-------
STATION «
4,4' DDT
tota I
PCBsd
2,4,6-
tr i -
chloro-
phenol
penta
chloro-
pheno1
6
EB-30
401230
1824
U 0 10
404
U
23
U
209
7
EB 30
401230
2081
U 0.10
230
I)
23
U
209
6
EB-31
401630
1818
U 0.10
454
u
23
u
209
7
EB 31
401630
2078
U 0.10
255
u
23
u
209
6
EB-32
401830
1825
U 0.10
117
u
23
u
209
7
EB-32
401830
2077
14
327
u
23
u
209
6
EB-33
401406
1779
U 0.10
1060
u
23
u
209
7
EB-33
401406
2080
L 1
2280
u
23
u
209
6
EB-34
401512
1780
U 0.10
955
u
23
u
209
r
EB-34
401512
2071
77
2070
u
23
u
209
6
EB-35
401603
1775
U 0.10
3940
u
23
u
209
7
EB-35
401603
2079
U 0.10
965
u
23
u
209
6
EB-36
401606
1776
U 0.10
3970
u
23
u
209
7
EB-36
401606
2072
15
487
I)
23
u
209
6
EB-37
401612
1777
U 0.10
915
u
23
u
209
7
EB-37
401612
2073
67
1130
u
23
u
209
6
EB-38
401706
1778
U 0.10
730
u
23
u
209
7
EB-38
401706
2074
28
315
u
23
u
209
6
EB-39
401810
1814
U 0.10
925
u
23
u
209
7
EB-39
401810
2075
48
468
u
23
u
209
6
WP 01
400330
1806
U 0.10
109
u
23
u
209
7
WP-01
400330
2088
1
15
u
23
u
209
6
WP-02
400430
1807
U 0.10
79
u
23
u
209
7
WP-02
400430
2089
U 0.10
149
u
23
u
209
6
WP-03
400510
1788
U 0.10
18
u
23
u
209
7
WP-03
400510
2090
U 0.10
25
u
23
u
209
6
WP-04
400530
1809
1
123
u
23
u
209
7
WP-04
400530
2091
2
68
u
23
u
209
6
WP-05
400621
1810
U 0.10
11
u
23
u
209
7
WP-05
400621
2092
U 0.10
U 0.5
u
23
u
209
6
WP-06
400712
1811
U 0.10
49
u
23
u
209
7
WP-06
400712
2084
U 0.10
70
u
23
u
209
6
WP-07
400730
1812
U 0.10
151
u
23
u
209
7
WP 07
400730
2093
10
130
u
23
u
209
6
WP-08
400810
1813
U 0.10
77
u
23
u
209
7
WP-08
400810
2083
U 0.10
161
u
23
u
209
6
WP-09
400830
1815
U 0.10
251
u
23
u
209
7
WP-09
400830
2082
L 1
130
u
23
u
209
6
WP-10
400210
1787
U 0.10
53
u
23
u
209
7
WP-10
400210
2076
U 0.10
41
u
23
u
209
6
WP-11
400310
1789
U 0.10
1080
u
23
u
209
7
WP-11
400310
2087
U 0.10
131
u
23
u
209
6
WP-12
400275
1786
U 0.10
102
u
23
u
209
7
WP-12
400275
2069
U 0.10
106
u
23
u
209
6
WP 13
400375
1784
U 0.10
480
u
23
u
209
7
WP 13
400375
2070
U 0.10
87
u
23
u
209
6
WP-14
400575
1785
7
WP- 14
400575
2085
U 0 10
145
u
23
u
209
6
WP 15
400775
1817
U 0.10
146
u
23
u
209
7
WP 15
400775
2094
11
221
u
23
u
209
6
WP 16
400875
1816
U 0.10
275
u
23
u
209
7
WP-16
400875
2086
U 0.10
76
u
23
u
209
2,6-di
nitro
toluene
II 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
U 230
1.2,4-
tri
ch loro-
benzene
U 10
U 10
U 10
U 10
U 10
U 10
(J 10
U 10
V 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
hexa
chloro-
benzene
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
V 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
U 20
I! 20
hexa-
chloro-
ethane
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
U 12
hexa
chloro-
cyclo
penta-
diene
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
U 110
l< 110
U 110
bis(2
ethyl -
hexy1)
phtha-
late
U 10
U 10
(J 10
L' 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
U 10
-------
STATION
#
ant iaony
arsenic
beryl 1lua
cadaluB
6 EB-30
401230
1824
0.50
13
0.36
0.25
7 EB-30
401230
2081
0.67
14
0.40
0.46
6 EB-31
401630
1818
0.80
12
0.45
0.23
7 EB-31
401630
2078
0.53
8.0
0.070
0.050
S EB-32
401830
1825
1.0
10
0.44
0.30
7 EB-32
401830
2077
0.67
11
0.24
0.14
6 EB-33
401406
1779
3.2
8.8
0.33
0.99
7 EB-33
401406
2080
1.5
15
0.35
2.1
« EB-34
401312
1780
2.3
12
0.30
0.32
7 EB-34
401512
2071
0.87
13
0.37
0.39
6 EB-3S
401603
1775
3.3
15
0.21
0.48
7 EB-39
401603
2079
2.4
16
0.24
2 2
S EB-36
4016O6
1776
1.3
12
0.41
0. 15
7 EB 36
401606
2072
0.46
9.1
0.16
0.34
6 EB-37
401612
1777
3.7
16
0.30
0.70
7 EB-37
401612
2073
1.0
14
0.11
0.36
6 EB-38
401706
1778
3.2
15
0.40
0.86
7 EB-38
401706
2074
0.60
12
0.20
0.63
6 EB-39
401810
1814
1.0
12
0.37
0.47
7 EB-39
401810
2075
0.94
11
0.18
0.33
6 HP-Ol
400330
1806
0.80
9.2
0.26
0.33
7 HP-Ol
400330
2088
L 0.30
6.1
0.32
0.10
8 WP-02
400430
1807
0.30
7.2
0.S1
0.43
7 WP-02
400430
2089
0.33
8.4
0.26
0.10
6 WP-03
400510
1788
1.3
4.0
0.12
0.060
7 W-03
400510
2090
L 0 .30
1.9
0.16
0.080
8 WP-04
400530
1809
0.80
3.2
0.19
0.060
7 WP-04
400530
2091
0.33
4.5
0.24
0.070
8 WP-OS
400621
1810
L 0.30
15
0.11
0.070
7 WP-05
400621
2092
0.60
4.2
0.16
L 0.010
6 WP-06
400712
1811
0 40
1.4
0.23
0.14
7 WP-06
400712
2084
0.40
6.8
0.24
0.080
8 WP-07
400730
1812
0.30
4.7
0.41
0.23
7 WP-07
400730
2093
0.33
5.3
0.34
0.030
6 WP-08
400810
1813
0.70
13
0.22
0. 10
7 WP-08
400810
2083
0.33
3.8
0.21
0.030
8 WP-09
400830
1815
L 0 .30
29
0.28
0. 18
7 WP-09
400830
2082
0.33
6.8
0.33
0.14
6 WP-10
400210
1787
0.60
5.2
0.15
0.070
7 WP -10
400210
2076
0.33
3.0
0.090
0 15
8 WP-11
400310
1 789
1.3
6.6
0.20
0.40
7 WP-11
400310
2087
0.33
3.8
0.21
0.10
6 WP-12
400275
1786
0.40
11
0.47
0 35
7 WP-12
400275
2069
0.33
10
0.49
0.20
8 WP-13
400375
1784
3 0
12
0.49
0.31
7 WP-13
400375
2070
0.40
9.5
0.34
0.40
6 WP 14
400575
1785
19
12
0. 50
0.33
7 WP-14
400575
2085
0.33
11
0.36
0.11
6 WP- 15
400775
1817
0 80
8.9
0.47
0.34
7 WP-15
400775
2094
0 33
9.9
0 47
0.11
6 WP 16
400875
1816
0 50
9.4
0 42
0 47
7 WP 16
400875
2086
L 0.30
12
0.40
0. 10
ooiua
copper
lead
¦ercury
nickel
selenli
54
62
55
0.43
32
L 0.20
47
50
81
0 29
38
0.30
37
55
68
0.63
27
L 0 .33
11
13
30
0.060
13
L 0 .20
47
50
62
0.53
31
0 20
47
46
72
0.34
43
0.34
60
130
89
0.98
44
0.48
61
87
210
1.0
44
L 0 .20
48
52
54
1.6
40
0.56
97
55
94
0.34
51
L 0 20
63
120
670
1.6
41
L 0.20
57
120
430
1 3
49
L 0 20
63
57
88
0 77
56
0.40
45
34
49
0 22
50
L 0.20
60
62
90
0.72
48
0 78
59
54
98
3.6
50
L 0.20
59
61
95
0.62
49
0 48
53
54
100
0.72
58
L 0 .20
54
82
82
0.65
39
L 0 .20
49
58
120
0.61
45
L 0.20
36
32
51
0.20
36
L 0.20
45
15
22
0.17
24
0.37
38
37
43
0.58
31
L 0 .20
46
25
27
0.22
34
0.70
8.9
13
13
0.14
21
L 0 .20
20
7.6
10
L 0.050
23
L 0.20
13
17
18
0.11
18
L 0 .20
26
14
28
0.080
24
0.30
24
12
14
L 0.050
23
L 0. 20
18
6.3
13
L 0.050
19
L 0.20
21
34
42
0. 13
18
L 0 .20
33
14
24
0.11
28
L 0 20
16
20
12
0 22
23
L 0 .20
23
13
23
0.090
26
L 0.20
24
33
17
0.15
8.3
L 0 .20
21
10
13
0.47
19
L 0.20
62
28
19
L 0.050
47
L 0 .20
42
21
21
0.11
40
L 0.20
17
29
19
0.15
17
L 0 20
19
7.2
13
L 0.050
25
L 0.20
37
27
31
0.37
31
L 0 .20
29
11
17
0.27
32
L 0 .20
40
36
35
0.36
27
0.78
50
35
39
0.11
39
0 80
43
39
33
0.34
30
0.78
45
34
40
0.11
42
0.59
48
41
42
0.39
33
63
53
40
44
0 88
40
0 90
39
46
35
0.33
30
0 . 56
52
40
43
0 20
36
1 .7
63
74
290
0 33
44
0. 56
51
3a
38
0 13
40
0 43
-------
STATION
•
siIver
thai 1\um
zinc
*
6
EB 30
401230
1824
3.9
L 0.10
100
•
7
EB-30
401230
2061
0.24
L 0.10
100
*
6
EB 31
401630
1618
3.6
L 0. 10
120
•
7
EB 31
401630
2078
0.050
L 0. 10
50
*
6
EB-32
401830
1825
2.3
L 0.10
110
•
7
EB-32
401830
2077
0.74
L 0. 10
100
6
EB-33
401406
1779
4.6
L 0.10
140
7
EB- 33
401406
2080
1-1
L 0.10
170
•
6
EB-34
401512
1780
3.4
L 0.10
100
•
27
EB-34
401512
2071
2.1
L 0. 10
110
6
EB-35
401603
1775
4.1
L 0.10
300
7
EB-35
401603
2079
0.94
L 0.10
260
6
EB-36
401606
1776
5.4
L 0.10
150
7
EB 36
401606
2072
1.3
L 0. 10
86
*
6
EB 37
401612
1777
3.9
L 0. 10
120
•
7
EB 37
401612
2073
15
L 0.10
110
6
EB 38
401706
1778
5.2
L 0.10
130
7
EB 38
401706
2074
ro
to
L 0. 10
110
•
6
EB-39
401810
1814
2.1
L 0.10
120
«
7
EB-39
401810
2075
2.4
L 0. 10
120
*
6
WP01
400330
1806
2.8
L 0.10
72
7
WP-01
400330
2088
0.24
L 0.10
52
*
6
WP-02
400430
1807
4.2
L 0.10
120
7
WP-02
400430
2089
0 45
L 0.10
68
*
6
WP-03
400510
1788
1.0
L 0.10
45
7
WP-03
400510
2090
0.030
L 0.10
29
*
6
WP 04
400530
1809
1.0
L 0.10
59
7
WP -04
400530
2091
0.21
L 0. 10
45
•
6
WP-05
400621
1810
0.050
0.10
57
7
WP-05
400621
2092
L 0.020
L 0.10
26
«
6
WP-06
400712
1811
1.3
L 0.10
78
7
WP-06
400712
2084
0.20
L 0.10
54
*
6
WP-07
400730
1812
2.9
L 0.10
81
7
WP-07
400730
2093
0.060
L 0.10
38
•
6
WP08
400810
1813
1.1
L 0.10
51
7
WP-08
400810
2083
0.030
L 0.10
32
*
6
WP-09
400830
1815
0.21
L 0.30
86
7
WP-09
400830
2082
0.28
L 0.10
71
*
6
WP-10
400210
1787
1.0
L 0.10
46
7
WP-10
400210
2076
0.030
L 0.10
38
•
6
WP11
400310
1789
1.6
L 0.10
83
7
WP-11
400310
2087
0.24
L 0.10
42
6
WP12
400275
1786
3.7
L 0.10
120
7
WP12
400275
2069
0.51
L 0.10
100
6
WP-13
400375
1784
3 7
L 0. 10
110
7
WP -1 3
400375
2070
0.56
L 0.10
100
6
HP 14
400575
1785
3.7
L 0.10
120
7
WP14
400575
2085
0.61
L 0.10
140
6
WP-15
400775
1817
3.3
L 0.10
120
7
WP -15
400775
2094
0.58
L 0.10
98
6
WP- 16
400875
1816
5 0
L 0.10
130
7
WP-16
400875
2086
0.58
L 0. 10
110
X total
TOX
BFNTHIC
MICRC
organic
CODE
CODE
CODE
ron
¦anganese
carbon
X silt *
clay
35000
420
1.0
81.5
0
0
0
0
30000
380
0.40
82.7
0
0
0
0
31000
380
1.7
95.9
0
0
0
0
14000
130
0.04
93.3
0
0
0
0
32000
340
1.2
90.6
0
0
0
0
30000
330
O 40
83. 1
0
0
0
0
31000
300
0.90
82.1
0
0
3
0
30000
310
0 60
80.6
0
0
3
0
25000
300
0 67
93.6
0
0
0
0
25000
280
0.50
89.8
0
0
0
0
19000
220
0 77
31 .9
0
0
2
0
19000
200
0.44
38.8
0
0
2
0
31000
400
0.60
86.6
0
0
2
0
19000
210
0.62
80.0
0
0
3
0
32000
330
1 .3
72.2
0
0
0
0
25000
300
0.50
80.3
0
0
0
0
37000
420
1.3
86.6
0
0
1
0
26000
310
1 2
62. 1
0
0
3
0
28000
580
0.92
78.7
0
0
0
0
24000
300
0.14
69.6
0
0
0
0
29000
390
0.53
18.9
0
0
0
0
23000
440
0 14
16.3
0
0
1
0
32000
520
0.80
29.4
0
0
0
0
18000
380
0.27
24.0
0
0
1
0
16000
200
0.42
10.5
0
0
0
0
10000
160
0.18
4.7
0
0
2
0
22000
580
1.2
16.4
0
0
0
0
15000
340
0.16
9.0
0
0
1
0
11000
500
0.31
7. 1
0
0
0
0
12000
420
0.15
2 4
0
0
1
0
21000
570
1.4
14. 1
0
0
0
0
17000
520
0 28
9.7
0
0
1
0
39000
420
0.18
10.5
0
0
0
0
16000
460
0.27
5.1
0
0
1
0
16000
380
0.50
8.8
0
0
0
0
13000
630
0.06
5.9
0
0
1
0
28000
650
0.48
41. 5
0
0
0
0
26000
1000
0.20
18.6
0
0
1
0
20000
340
0.38
12.6
0
0
0
0
12000
360
0.08
4 8
0
0
1
0
19000
280
0.60
23.4
0
0
0
0
13000
190
0.10
6.7
0
0
1
0
32000
580
0.95
91.2
0
0
1
0
32000
460
0.70
96.6
0
0
1
0
36000
520
0.90
91 .8
0
0
1
0
29000
380
0.26
88 5
0
0
1
0
34000
630
0.75
96.2
0
1
0
30000
380
0.59
91.1
0
0
1
0
28000
430
0.76
97.0
0
0
1
0
30000
450
0.45
92.9
0
0
1
0
27000
410
1.3
95.9
0
0
2
0
30000
500
0.75
93. 2
0
0
1
0
-------
* These stations were not used for developing sediment quality values as appropriate biological reference stations were not available.
a. Reference:
Romberg, G.P., S.P. Pavlou, and E.A. Crecelius. 1984. Presence, distribution, and fate of toxicants in Puget Sound and Lake Washingtor
Toxicant Program Report No. 6A. Toxicant Pretreatment Planning Study Technical Report CI. Municipality of Metropolitan Seattle, Seattle, WA
231 pp.
b. U » Undetected at detection limit shown. Detection limits are from Romberg et al. 1984.
c. L « Less than the value shown. For purposes of this report, these *ere considered undetected.
d. Total PCBs are the sum of detected Aroclors.
-------
Figure A-19. MAP SHOWING THE 26 STATIONS IN THE CENTRAL BASIN OF PUGET SOUND
AND ELLIOTT BAY SAMPLED DURING PHASE III OF THE TPPS PROGRAM.
Note: These are "corrected" station locations based on a miniranger
survey and some may vary from those illustrated in subsequent
figures. The corresponding station numbers listed in Table A-6
have 40 or 400 preceeding the station numbers shown on this map.
Reference: Romberg et al. 1984.
A-137
-------
TABI.K A 7 KVKKETT HARBOR3
dibenzo-
1 aethyl
naphtha
1.1'
acenaph-
acenaph-
thio-
phenan-
anthra-
phenan
f 1 uor
STATION
•
lene
bipheny1
thylene
thene
fluorene
phene
threne
cene
threne
anthene
pyrent
8 EV-20
EEW-1
1045
44
U
6
558
315
150
779
168
68
838
562
8 EV 21
EEW 2
1762
8
U
6
64
33
9
94
12
9
74
110
8 EV-22
EEW-3
94
b 11
18
35
41
44
192
80
77
272
299
8 EV-23
EEW- 4
17
UD 6
6
9
16
22
92
26
62
154
464
8 EV 24
EEM 5
76
16
25
25
42
U 6
229
61
U 6
246
1029
8 EV-25
EEH 6
345
18
43
40
61
39
222
90
96
348
447
total
indeno-
benzol a)
benzo-
(1
.2
dlbenzo-
benzo-
* total
* total
anthra-
tluor-
benzo(a)
3-
cd)
-------
m
STATINS
COMPOSITES
?o*rZA*o»t*
EEV-I
EEV-T
EEV-J
EEU-J
EEW-6
Figure A-20. Sediment sampling locations in the East Waterway.
Reference: U.S. Navy 1985.
A-139
-------
TABLE A H OUWANISH RIVER
Methyl
total
naphtha
naphth-
1 .
1'
STATION *
PCBs
lene
a lene
bi pheny
g
DR-10
CA1
5400
1400
390
11
9
DK11
CA2
530
200
49
8.9
9
DR-12
CA3
k 24
7.4
6.1
2 8
9
DR-13
CB1
M° 215
M
201
N
73.4
MU16.5
9
DR-14
CB2
150
860
200
78
9
DR-15
CB3
15
5.7
U
2.9
U
2.5
9
DR-16
CB4
19
UC
3.5
U
3.7
U
3.1
9
DR-17
CBS
N 14.9
MU
3.1
MU
3.2
MU
2 .7
9
DR-18
CC1
74
69
45
6.5
9
DR-19
CC2
330
520
120
29
9
DR-20
CC3
15
U
2.8
U
2.8
U
2.5
9
DR 21
CC4
22
32
17
u
2.4
9
DR-22
CCS
180
58
18
u
1.3
9
DR-23
CD1
1800
120
41
u
4.1
9
DR-24
CD2
16
9.5
u
3.9
u
3.3
9
DR-25
CE1
790
190
77
11
9
DR-26
CE2
170
160
69
9.0
9
DR-27
CE3
40
75
25
u
4.5
9
DR-28
CF1
2500
360
190
54
9
DR-29
CF2
2200
80
39
6.4
9
DR-30
CF3
650
180
130
23
9
DR-31
CF4
560
130
51
7.4
9
DR-32
CF5
88
8.9
6.0
u
2.7
9
DR-33
CGI
1200
46
27
u
1.6
9
DR-34
CG2
1300
46
32
5.0
9
DR-35
CG3
620
44
23
6.1
9
DR-36
CG4
150©
100
41
u
2.0
9
DR -37
CG5
8.7
U
1 7
u
1.7
u
1 .4
9
DR-38
CHI
1400
40
26
5.2
9
DR-39
CW/A1
N 124
M
75
N
15
MU
3.7
9
SQ-21
SEQUIN
37
7.0
16
u
2.8
1 methyl-
acenaph-
phenan-
anthra-
phenan-
f luor -
thene
f luorene
threne
cene
threne
anthene
71
98
970
170
70
1400
60
60
300
130
32
620
7.7
6.8
32
2.0
1 .9
9 2
N 32
M
26.4
N 128
M
61
M
7.4
M 125
480
480
1500
170
68
850
U 2.5
U
2.2
11
2.8
2.0
13
U 3. 1
U
2.7
2.6
U
2.2
2.3
U 2 0
MU2.7
MU
2.4
M 6.1
MU
2.5
MU
1 9
MU 2 .3
18
26
160
50
37
210
140
120
310
120
14
460
U 2.5
U
2.2
12
U
1.9
U
1.7
22
U 2.4
u
2.1
17
U
1 8
u
1.7
36
19
12
88
30
7.7
150
67
26
330
95
14
61
U 3.3
u
2.9
15
3.7
u
2.3
21
150
150
810
140
51
960
79
75
290
53
24
270
40
32
190
25
13
160
400
320
1000
280
82
1900
47
29
240
100
72
680
26
27
130
53
33
200
41
33
220
76
33
380
U 2.7
u
2.4
21
5.9
u
1.8
38
25
17
170
66
26
430
29
15
200
79
28
440
34
22
100
32
18
190
45
30
170
79
24
290
U 13
u
1 2
U 1.0
V
0.99
u
0.93
U 0 .89
34
17
160
90
29
380
M 35
N
36
M 355
N
84
M
42
M 540
U 2.8
3.9
48
2.0
u
19
63
-------
790
4.6
350
780
38
2.0
1.8
770
830
40
36
250
1200
60
870
280
100
1700
880
340
600
56
580
580
310
830
J. 87
470
830
45
benzo(a)
anthra-
cene
chrysene
benzo(a)
pyrene
dlbenzo
(a,h)an -
thracene
hexa
chloro-
benzene
4.4'-DDE
4,4'-DDD
4.4'-DDT
590
1000
390
46
0.66
41
38
U
1 .2
280
480
170
51
U
0.15
4.1
7.8
U
0.53
3.2
2.5
U 2.1
U
2.0
0.66
U
0.071
U
0. 14
u
0. 15
M 177
N
355
M 280
N
38
M
0.26
M
0.93
M
3.6
mjo.19
220
330
160
14
0.21
0.61
6.1
u
0.21
4.2
4.7
3.1
U
2.6
U
0.087
U
0.082
U
0.14
u
0.31
U 1.9
U
2.0
U 2.0
U
1.8
0.54
U
0.083
U
0.16
u
0. 17
MU2.8
NU
3.1
MU2.8
HU7.3
MU0.092
MU0.087
NU0.14
MU0.56
92
180
110
11
0.15
0.26
1.9
U
0.32
190
370
150
19
U
0.14
1.4
4.3
u
0.33
U 2.8
U
3.1
U 2.9
U
3.3
u
0.089
U
0.081
U
0. 15
u
0 12
2.0
3.0
U 1.9
U
2.2
u
0.084
u
0 075
U
0.14
u
0. 12
180
200
54
U
2.6
u
0. 16
0.94
2.4
u
0.21
500
810
470
97
1.2
11
29
0.44
5.7
8.1
5.8
U
3.4
u
0.10
u
0 095
0.22
u
0 14
250
510
150
23
0.27
4.0
14
u
0.41
87
200
54
17
u
0.12
1.1
2.3
u
0.30
25
20
3.7
U
3.8
0.24
2.3
3.4
u
0.21
920
860
320
50
0.77
15
43
0.96
450
700
240
22
0.65
15
35
0 69
120
230
69
11
u
0. 14
3.3
9.2
u
0.85
240
550
150
21
u
0.14
3.6
24
0.49
12
15
8.2
U
3.2
u
0.092
0.30
0.32
u
0.14
340
530
230
20
0.24
10
14
u
0.23
230
420
210
29
0.40
7.2
16
u
0.35
320
260
69
U
5.5
0.24
5.6
15
2.4
170
460
160
U
3.2
u
0.25
9.9
25
u
0.33
U 1.0
u
1 1
U 1.1
U
1.3
u
0.15
u
0.12
U
0.25
u
0.20
850
440
130
33
0.38
0.83
14
u
0.38
N 250
N
410
N 370
N
67
MU0.094
M
0.72
N
2.3
M
0.67
7.2
7.9
4.1
U
3.0
u
0.28
0.26
0.52
u
0.38
-------
total
total
volatile
organic
TOX
BENTHIC
MICRO
STATION •
arsenic
cadaiua
copper
lead
¦ercury
zinc
solids
carbon
silt
CODE
CODE
CODE
9 OR-10
CA1
2.9
2.4
83
130
0.83
202
4.67
1.42
50.27
3
0
0
9 Wi ll
CA2
7.2
0.97
45
82
0.29
78.2
3.94
0 99
88.00
3
0
0
9 OR-12
CAS
1.2
U
0.05
8.7
5.5
0.08
18.4
1.41
0.59
4.63
1
0
0
9 OR-13
CB1
4.3
0.18
28
86
0.42
58.4
1.49
0.55
12.94
1
0
0
9 DR-14
CB2
3.6
0. 13
39
700
0.37
63.9
3.63
0.83
21.15
1
0
0
9 OR-15
CBS
N
3.1
N
0.07
N
17
M
18
M 0.06
* 30.7
N
1.53
N 0.35
M
19.66
1
0
0
9 OR-16
CB4
3.5
I)
0.05
20
4.4
0. 14
34.2
171
0.35
37.88
0
0
9 OB IT
CBS
2.7
0.05
18
6.0
0.04
26
1.81
0.34
48.07
1
0
0
9 OR-18
cci
3.0
0.09
19
14
0.07
30.7
1.50
0.38
15.61
1
0
0
9 OR-19
CC2
4.6
0.30
38
50
0.23
66.1
2.12
2 10
15.25
1
0
0
9 DR-20
CCS
1.7
0.11
13
22
0.08
24.6
2.50
0.39
26.25
1
0
0
9 m-21
CC4
2.5
U
0.05
15
15
0.05
27.8
N
1.71
N 0 45
13.68
1
0
0
9 OR-22
CCS
3.1
U
0.05
15
6.0
0.02
21.3
2.26
0.61
31.84
1
0
0
9 OR-23
CD1
24
1.38
115
103
0.68
188
6.20
1 36
65.42
1
0
0
9 DR-24
CD2
1.7
V
0.05
18
19
0.05
22.5
1.86
N 0 .45
14.20
1
0
0
9 OR-25
CE1
29
1.73
76
131
0.33
S23
4.97
0.72
51.59
3
0
0
9 OR-26
CE2
M
47
N
2.64
N
63
H
128
N 0.15
N 1211
N
3.74
N 0 .57
38.58
3
0
0
9 OR-27
CE3
57.
10.4
06
128
2.3
2600
3.87
0.52
31.86
3
0
0
9 OR-28
CF1
19
1.68
74
94
0.23
203
5.04
2.00
58.23
1
0
0
9 DR 29
CF2
16
1.79
96
130
0.46
336
6.26
1.66
79.64
1
0
0
9 OR-30
CF3
3.3
0.36
37
62
0.27
74.8
3.90
1.06
22.01
1
0
0
X*
9 08-31
CF4
7.4
0.81
63
77
0.33
126
N
5.16
N 1.34
75.62
1
0
0
•
9 DR-32
CF5
2.9
0. 15
18
26
0.08
39
2.03
0.70
39.23
1
0
0
1 •
A
9 OR-33
CGI
13
0.58
96
95
0.23
167
6.28
1.25
68.2
1
0
0
IS)
9 DR-34
CG2
9.3
0.4
79
87
0.28
141
5.97
1 20
79.85
1
0
0
9 OR-33
CG3
M
10
N
0.31
N
48
N
56
N 0 .58
N 86 4
N
5.08
N 1.20
N
61.04
1
0
0
9 DR-36
CG4
7.9
0.35
59
71
1.1
116
5.64
1 .36
69.49
1
0
0
9 DR-37
CG5
7.1
0. 12
32
39
0. 11
56.7
3.64
N 1.04
55.6
1
0
0
9 DR-38
CHI
8.8
1.16
65
91
0 11
130
N
7.50
1 62
78.74
1
0
0
9 DR-39
CM/A1
13
2.05
55
50
0.85
110
2.18
1 11
17.77
1
0
0
9 SQ-21
SEQUIM
1
0
0
a. Reference:
Chan, S.-l.. H.H. Schiewe, D.M. Brown. 1985. Analyses of sediment samples for U.S. Army Corps of Engineers East, West and Ouwamish Waterway
navigation improvement project operations and maintenance sampling and testing of Ouwamish River sediments. Unpublished data.
b. M 1 mean of replicate measurements.
c. U * undetected at detection limit shown.
-------
APPENDIX B
STATION LISTINGS OF CHEMICALS EXCEEDING
SEDIMENT QUALITY VALUES
-------
APPENDIX B. STATION LISTINGS OF CHEMICALS
EXCEEDING SEDIMENT QUALITY VALUES
CONTENTS
Number Page
Table B-l Station listing of organic carbon normalized chemicals
exceeding equilibrium partitioning sediment quality
values B-2
Table B-2 Station listing of chemicals exceeding dry weight AET B-13
Table B-3 Station listing of chemicals exceeding organic carbon
normalized AET B-36
Table B-4 Station listing of chemicals exceeding fines normalized
AET B-48
Table B-5 Station listing of chemicals exceeding lowest dry
weight AET B-65
Table B-6 Station listing of chemicals exceeding Commencement
Bay dry weight normalized AET (non-Commencement Bay
stations) B-98
Table B-6A Commencement Bay AET sediment quality values B-l19
-------
STATION LISTINGS OF CHEMICALS EXCEEDING
SEDIMENT QUALITY VALUES
The dry weight values are those found in the original data reports (see
Appendix A). Organic carbon and fines normalized values are calculated and
not adjusted for the proper number of significant figures (i.e., typically 2).
Toxicity, benthic, and microtox codes are indicated for all stations.
The toxicity code is defined as:
0 = No data available
1 = No significanta oyster larvae abnormality or amphipod mortality
2 = Significant® oyster larvae abnormality
3 = Significant* amphipod mortality
4 = Both significant® oyster larvae abmnormality and amphipod
mortality.
The benthic code is defined as:
0 = No data available
1 = No significant* depressions in benthic infaunal abundances
2 = Significant® depressions in benthic infaunal abundances of one
major taxonomic group
3 - Significant® depressions in benthic infaunal abundances of
more than one major taxonomic group.
The microtox code is defined as:
0s No data available
1 = No significant# decrease in bacterial luminescence
2 = Significant® decrease in bacterial luminescence.
a Significance implies statistically significant difference (P>0.05) from
reference conditions.
B-I
-------
TABLE B-l. STATION LISTING OF ORGANIC CARBON NORMALIZED CHEMICALS
EXCEEDING EQUILIBRIUM PARTITIONING SEDIMENT QUALITY VALUES
Organics expressed as ppb organic carbon, metals ppm organic carbon
Group: 1 Station: HY-22
Toxicity code: 4 Benthic code: 3 Microtox code 2
total PCBs
Concentration OC
45045.0
Group: 1 Station: HY-23
Toxicity code: 4 Benthic code:
total PCBs
Microtox code 2
Concentration OC
39682.5
Group: 1 Station: HY-37
Toxicity code: 1 Benthic code:
total PCBs
Microtox code 2
Concentration OC
16935.5
Group: 1 Station: HY-42
Toxicity code: 3 Benthic code:
total PCBs
1 Microtox code 2
Concentration OC
46025.1
Group: 2 Station: B04
Toxicity code: 1 Benthic code: 1
4,4'-DDT
Microtox code 0
Concentration OC
234
Group: 2 Station: B15
Toxicity code: 3 Benthic code:
4,4'-DDT
1 Microtox code 0
Concentration OC
391.89
B-2
-------
Group: 3 Station: EB-09
Toxicity code: 1 Benthic code:
total PCBs
Microtox code 0
Concentration OC
18644.1
Group: 3 Station: EB-17
Toxicity code: 1 Benthic code:
total PCBs
Microtox code 0
Concentration OC
33645.8
Group: 3 Station: EB-20
Toxicity code: 1 Benthic code: 0
total PCBs
Microtox code 0
Concentration OC
46043.2
Group: 3 Station: EB-22
Toxicity code: 1 Benthic code:
total PCBs
Microtox code 0
Concentration OC
35595.9
Group: 3 Station: EB-23
Toxicity code: 1 Benthic code: 0
total PCBs
Microtox code 0
Concentration X
13333.3
Group: 3 Station: SC-06
Toxicity code: 1 Benthic code: 0 Microtox code 0
total PCBs
Concentration OC
42764.5
B-3
-------
Group: 3 Station: SC-07
Toxicity code: 1 Benthic code: 0
total PCBs
Microtox code 0
Concentration X
27735.8
Group: 3 Station: SC-08
Toxicity code: 3 Benthic code;
total PCBs
0 Microtox code 0
Concentration OC
22508.7
Group: 3 Station: SC-14
Toxicity code: 3 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
55000.0
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code: 0
4,4'-DDT
Microtox code 0
Concentration 0C
1222.22
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code:
total PCBs
0 Microtox code 0
Concentration 0C
177272.7
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code:
total PCBs
3 Microtox code 0
Concentration 0C
117777.8
B-4
-------
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code:
fluoranthene
total PCBs
Microtox code 0
Concentration OC
697013.0
511688.3
Group: 6 Station: EB-36
Toxicity code: 0 Benthic code:
total PCBs
Microtox code 0
Concentration OC
661666.7
Group: 6 Station: EB-38
Toxicity code: 0 Benthic code:
total PCBs
Microtox code 0
Concentration OC
56153.8
Group: 6 Station: WP-13
Toxicity code: 0 Benthic code: 1
total PCBs
Microtox code 0
Concentration X
53333.3
Group: 6 Station: WP-15
Toxicity code: 0 Benthic code: 1
total PCBs
Microtox code 0
Concentration OC
19210.5
Group: 6 Station: WP-16
Toxicity code: 0 Benthic code:
total PCBs
Microtox code 0
Concentration OC
21153.8
B-5
-------
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code: 3
Microtox code 0
total PCBs
Concentration OC
380000.0
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code:
fl uoranthene
total PCBs
Microtox code 0
Concentration 0C
1336818
219318.2
Group: 7 Station: EB-36
Toxicity code: 0 Benthic code:
diethyl phthalate
total PCBs
M'-DDT
Microtox code 0
Concentration 0C
51290.3
78548.4
2419.35
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code:
total PCBs
4,4'-DDT
Microtox code 0
Concentration 0C
26250.0
2333.33
Group: 7 Station: WP-01
Toxicity code: 0 Benthic code:
414'-DDT
1 Microtox code 0
Concentration 0C
714.29
B-6
-------
Group: 7 Station: WP-02
Toxicity code: 0 Benthic code:
fluoranthene
total PCBs
Microtox code 0
Concentration OC
1016296
55185.2
Group: 7 Station: WP-03
Toxicity code: 0 Benthic code:
total PCBs
Microtox code 0
Concentration OC
13888.9
Group: 7 Station: WP-04
Toxicity code: 0 Benthic code:
diethyl phthalate
total PCBs
4,4'-DDT
Microtox code 0
Concentration OC
60625.0
42500.0
1250.00
Group: 7 Station: WP-06
Toxicity code: 0 Benthic code:
total PCBs
1 Microtox code 0
Concentration OC
25000.0
Group: 7 Station: WP-07
Toxicity code: 0 Benthic code: 1
fluoranthene
total PCBs
4,4'-DDT
Microtox code 0
Concentration OC
650740
48148.1
3703.70
Group: 7 Station: WP-08
Toxicity code: 0 Benthic code: 1
diethyl phthalate
total PCBs
Microtox code 0
Concentration OC
78333.3
268333.3
B-7
-------
Group: 7 Station: WP-09
Toxicity code: 0 Benthic code: 1 Microtox code 0
Concentration X
total PCBs
4,4'-DDT
65000.0
500.00
Group: 7 Station: WP-10
Toxicity code: 0 Benthic code:
fluoranthene
total PCBs
Microtox code 0
Concentration 0C
861250.0
51250.0
Group: 7 Station: WP-11
Toxicity code: 0 Benthic code:
phenanthrene
fluoranthene
total PCBs
Microtox code 0
Concentration 0C
3150000
6299000
131000.0
Group: 7 Station: WP-12
Toxicity code: 0 Benthic code:
total PCBs
1 Microtox code 0
Concentration 0C
15142.9
Group: 7 Station: WP-13
Toxicity code: 0 Benthic code:
total PCBs
1 Microtox code 0
Concentration 0C
33461.5
Group: 7 Station: WP-14
Toxicity code: 0 Benthic code: 1
total PCBs
Microtox code 0
Concentration 0C
24576.3
B-8
-------
Group: 7 Station: WP-15
Toxicity code: 0 Benthic code:
total PCBs
M'-DDT
Microtox code 0
Concentration OC
49111.1
2444.44
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code:
total PCBs
Microtox code 0
Concentration OC
380281.7
Group: 9 Station: DR-11
Toxicity code: 3 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
53535.4
Group: 9 Station: DR-13
Toxicity code: 1 Benthic code: 0
total PCBs
Microtox code 0
Concentration OC
39090.9
Group: 9 Station: DR-14
Toxicity code: 1 Benthic code: 0
total PCBs
Microtox code 0
Concentration OC
18072.3
Group: 9 Station: DR-18
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
19473.7
B-9
-------
Group: 9 Station: DR-19
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
15714.3
Group: 9 Station: DR-22
Toxicity code: 1 Benthic code:
total PCBs
Microtox code 0
Concentration OC
29508.2
Group: 9 Station: DR-23
Toxicity code: 1 Benthic code;
total PCBs
Microtox code 0
Concentration OC
132352.9
Group: 9 Station: DR-25
Toxicity code: 3 Benthic code:
total PCBs
Microtox code 0
Concentration OC
109722.2
Group: 9 Station: DR-26
Toxicity code: 3 Benthic code:
total PCBs
Microtox code 0
Concentration OC
29824.6
Group: 9 Station: DR-27
Toxicity code: 3 Benthic code;
dieldrin
heptachlor
1indane
Microtox code 0
Concentration OC
577
904
692
B-10
-------
Group: 9 Station: DR-28
Toxicity code: 1 Benthic code:
total PCBs
Microtox code 0
Concentration OC
125000.0
Group: 9 Station: DR-29
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
132530.1
Group: 9 Station: DR-30
Toxicity code: 1 Benthic code: 0
total PCBs
Microtox code 0
Concentration 0C
61320.8
Group: 9 Station: DR-31
Toxicity code: 1 Benthic code: 0
total PCBs
Microtox code 0
Concentration 0C
41791.0
Group: 9 Station: DR-32
Toxicity code: 1 Benthic code: 0
total PCBs
Microtox code 0
Concentration 0C
12571.4
Group: 9 Station: DR-33
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration 0C
96000.0
B-ll
-------
Group: 9 Station: DR-34
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
108333.3
Group: 9 Station: DR-35
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
51666.7
Group: 9 Station: DR-36
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
110294.1
Group: 9 Station: DR-38
Toxicity code: 1 Benthic code:
total PCBs
0 Microtox code 0
Concentration OC
86419.8
B-12
-------
TABLE B-2. STATION LISTING OF CHEMICALS EXCEEDING
DRY WEIGHT NORMALIZED AET
Organics expressed as ppb dry weight, metals ppm dry weight
Group: 1 Station: BL-13
Toxicity code: 1 Benthic code:
Microtox
butyl benzyl phthalate
Microtox code 2
Concentration DW
83.0
Group: 1 Station: CI-11
Toxicity code: 4 Benthic code:
Amph i pod
phenol
benzo(ghi)perylene
1,4-dichlorobenzene
benzyl alcohol
lead
Oyster
phenol
phenanthrene
benzo(ghi)perylene
1,4-dichlorobenzene
benzyl alcohol
lead
nickel
Benthic
1,4-dichlorobenzene
benzyl alcohol
lead
zinc
Microtox code 2
Concentration DW
1100
780.0
290.0
140
725
Concentration DW
1100
1800
780.0
290.0
140
725
40.0
Concentration DW
290.0
140
725
325.0
B-13
-------
Microtox
phenanthrene
fluoranthene
chrysene
benzo(ghi)perylene
1,2-d1chlorobenzene
1,4-dichlorobenzene
benzyl alcohol
lead
mercury
High molecular wt. PAH
Chlorinated benzenes
Concentration DW
1800
2400
1600.0
780.0
37.0
290.0
140
725
.53
13090.0
327.0
Group: 1 Station: CI-13
Toxicity code: 2 Benthic code:
Oyster
bis(2-ethylhexy1)phthalate
benzoic acid
mercury
Total phthalates
Benthic
bis(2-ethylhexy1)phthalate
benzoic acid
cadmium
lead
mercury
Microtox
bis(2-ethy1hexyl)phtha1ate
butyl benzyl phthalate
total PCBs
benzoic acid
mercury
Total phthalates
Microtox code 2
Concentration DW
3100.0
690
1.10
3548.0
Concentration 0W
3100.0
690
6.70
450
1.10
Concentration DW
3100.0
210.0
140.0
690
1.10
3548.0
B-14
-------
Group: 1 Station: CI-16
Toxicity code: 2 Berithic code:
3
Microtox code 2
Oyster
Concentration DW
N-nitrosodiphenylamine
1,2-dichlorobenzene
1,4-dichlorobenzene
di-n-butyl phthalate
4-methylphenol
Chlorinated benzenes
Benthic
2,4-dimethylphenol
N-nitrosodiphenylamine
1,2-dichlorobenzene
1,4-dichlorobenzene
4-methylphenol
Chlorinated benzenes
Microtox
N-nitrosodiphenylamine
1,2-dichlorobenzene
1,4-dichlorobenzene
di-n-butyl phthalate
4-methylphenol
Chlorinated benzenes
220.0
350.0
260.0
1600.0
1200
667.0
Concentration DW
50.0
220.0
350.0
260.0
1200
667.0
Concentration DW
220.0
350.0
260.0
1600.0
1200
667.0
Group: 1 Station: CI-17
Toxicity code: 1 Benthic code:
Microtox
fluoranthene
1,4-dichlorobenzene
Microtox code 2
Concentration DW
1900
119.0
Group: 1 Station: C1-20
Toxicity code: 4 Benthic code: 1
Amphipod
phenol
l,l'-biphenyl
dibenzothiophene
Microtox code 1
Concentration DW
1200
270.0
250.0
B-15
-------
Oyster
Concentration DW
phenol
1,1'-bi phenyl
dibenzothiophene
1200
270.0
250.0
Group: 1 Station: HY-12
Toxicity code: 2 Benthlc code:
Oyster
phenol
benzo(ghi)perylene
dibenzo(a,h)anthracene
di-n-butyl phthalate
Total phthalates
Microtox
chrysene
benzo(ghi)perylene
di-n-butyl phthalate
mercury
Total phthalates
Microtox code 2
Concentration DW
500
740.0
260.0
5100.0
5100.0
Concentration DW
1800.0
740.0
5100.0
.46
5100.0
Group: 1 Station: HY-14
Toxicity code: 1 Benthic code:
Microtox
fluoranthene
chrysene
benzo(gh1)perylene
High molecular wt. PAH
Microtox code 2
Concentration DW
2500
2800.0
720.0
16650.0
Group: 1 Station: HY-17
Toxicity code: 2 Benthic code:
Oyster
benzo(a)pyrene
Total benzofluoranthenes
pyrene
tetrachloroethene
ethyl benzene
total xylenes
High molecular wt. PAH
Microtox code 2
Concentration DW
2400.0
3700
4300
210
50
160
18402.0
B-16
-------
Benthic
Concentration DW
tetrachloroethene
ethylbenzene
total xylenes
arsenic
zinc
210
50
160
86.0
268.0
Microtox
Concentration DW
fluoranthene
benzo(a)pyrene
chrysene
ethylbenzene
total PCBs
total xylenes
High molecular wt. PAH
3900
2400.0
2700.0
50
170.0
160
18402.0
Group: 1 Station: HY-22
Toxicity code: 4 Benthic code:
Amph i pod
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
dibenzothiophene
1-methylphenanthrene
High molecular wt. PAH
Chlorinated benzenes
Microtox code 2
Concentration DW
2300.0
6100.0
8500
1500
2700.0
260.0
730.0
730.0
320.0
530.0
30000.0
1328.0
B-17
-------
Concentration DW
phenol
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
dibenzo(a,h)anthracene
indeno(1,2,3-cd)pyrene
1,2,4-trichlorobenzene
1,2-dichlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
bi s(2-ethylhexy1)phthai ate
total PCBs
nickel
dibenzothiophene
1-methylphenanthrene
High molecular wt. PAH
Total phthalates
Chlorinated benzenes
Benthic
Total benzofluoranthenes
d1benzo(a,h)anthracene
1,2,4-trichlorobenzene
hexachlorobenzene
1,2-dichlorobenzene
l,4-d1chlorobenzene
hexachlorobutad iene
bis(2-ethylhexy1)phthalate
total PCBs
arsenic
dibenzothiophene
1-methylphenanthrene
Chlorinated benzenes
530
2300.0
6100.0
8500
1500
2700.0
260.0
73.0
180.0
730.0
3000.0
2000.0
52.0
320.0
530.0
30000.0
3560.0
1328.0
Concentration DW
8500
1500
260.0
730.0
73.0
180.0
730.0
3000.0
2000.0
90.0
320.0
530.0
1328.0
B-18
-------
Microtox
Concentration DW
fluoranthene
benzo(a)pyrene
chrysene
1,2,4-trichlorobenzene
hexachlorobenzene
1,2-dichlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
total PCBs
mercury
dibenzothiophene
1-methylphenanthrene
High molecular wt. PAH
Total phthalates
Chlorinated benzenes
3600
6100.0
2700.0
260.0
730.0
73.0
180.0
730.0
3000.0
2000.0
.50
320.0
530.0
30000.0
3560.0
1328.0
Group: 1 Station: HY-23
Toxicity code: 4 Benthic code:
Amphipod
phenanthrene
benzo(ghi)perylene
dibenzo(a,h)anthracene
dimethyl phthalate
Oyster
phenanthrene
benzo(a)pyrene
benzo(ghi)perylene
dibenzo(a.h)anthracene
dimethyl phthalate
tetrachloroethene
total PCBs
nickel
Benthic
dimethyl phthalate
tetrachloroethene
total PCBs
Microtox code 2
Concentration DW
2300
1100.0
440.0
350.0
Concentration DW
2300
2000.0
1100.0
440.0
350.0
170
1500.0
56.0
Concentration DW
350.0
170
1500.0
B-19
-------
Microtox
Concentration DW
phenanthrene
fluoranthene
benzo(a)pyrene
chrysene
benzo(ghi)perylene
hexachlorobutadiene
butyl benzyl phthalate
dimethyl phthalate
total PCBs
total xylenes
High molecular wt. PAH
2300
2500
2000.0
2300.0
1100.0
170.0
110.0
350.0
1500.0
110
13790.0
Group: 1 Station: HY-24
Toxicity code: 1 Benthic code:
Microtox
chrysene
hexachlorobutadiene
butyl benzyl phthalate
dimethyl phthalate
total PCBs
mercury
Microtox code 2
Concentration DW
2300.0
140.0
470.0
120.0
250.0
.49
Group: 1 Station: HY-37
Toxicity code: 1 Benthic code:
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexach1orobutad i ene
total PCBs
Chlorinated benzenes
Microtox code 2
Concentration DW
34.0
96.0
130.0
420.0
203.0
Group: 1 Station: HY-42
Toxicity code: 3 Benthic code:
Amphipod
1,2,4-trichlorobenzene
hexachlorobenzene
Microtox code 2
Concentration DW
64.0
230.0
B-20
-------
Microtox
chrysene
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
total PCBs
Chlorinated benzenes
Concentration DW
1800.0
64.0
230.0
270.0
1100.0
395.0
Group: 1 Station: HY-43
Toxicity code: 1 Benthic code:
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
ethylbenzene
total xylenes
Chlorinated benzenes
Microtox code 2
Concentration DW
51.0
130.0
180.0
37
120
274.2
Group: 1 Station: HY-47
Toxicity code: 2 Benthic code:
Oyster
1,4-dichlorobenzene
hexachlorobutadiene
di-n-butyl phthalate
Benthic
1,4-dichlorobenzene
hexachlorobutad iene
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
di-n-butyl phthalate
Chlorinated benzenes
Microtox code 2
Concentration DW
120.0
290.0
1500.0
Concentration DW
120.0
290.0
Concentration DW
51.0
100.0
120.0
290.0
1500.0
312.0
B-21
-------
Group: 1 Station: HY-50
Toxicity code: 1 Benthic code:
Microtox
benzyl alcohol
1 Microtox code 2
Concentration DW
73
Group: 1 Station; MI-13
Toxicity code: 1 Benthic code: 1
Microtox
dimethyl phthalate
FINES
Microtox code 2
Concentration OW
110.0
.89
Group: 1 Station: RS-13
Toxicity code: 4 Benthic code: 1
Amphipod
2-methylphenol
Oyster
m m m m m m
2-methylphenol
Microtox code 1
Concentration DW
72
Concentration DW
72
B-22
-------
Group: 1 Station: RS-18
Toxicity code: 4 Benthic code: 3
Amphipod
N-nitrosodiphenyl amine
acenaphthene
anthracene
phenanthrene
f1uorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
2-methylphenol
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
iron
lead
manganes
thai 1ium
zinc
mercury
l.l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Low molecular wt. PAH
High molecular wt. PAH
Microtox code 2
Concentration DW
610.0
2500.0
1400.0
11000
3100.0
8100
3200.0
4000.0
4200
4700.0
320.0
770.0
5600
71
2000
1200
420.0
9700.0
184.00
11400
52900
6250
748
3.20
3320.0
52.00
1100.0
1100.0
1300.0
20190.0
30890.0
B-23
-------
Oyster
N-nitrosodiphenyl amine
acenaphthene
anthracene
phenanthrene
fluorene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
1,4-dichlorobenzene
2-methylphenol
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
iron
lead
manganes
nickel
thallium
zinc
mercury
1,1'-bi phenyl
dibenzothiophene
1-methylphenanthrene
Low molecular wt. PAH
High molecular wt. PAH
Concentration DW
610.0
2500.0
1400.0
11000
3100.0
3200.0
4000.0
4200
4700.0
320.0
770.0
5600
250.0
71
2000
1200
420.0
9700.0
184.00
11400
52900
6250
748
93.0
3.20
3320.0
52.00
1100.0
1100.0
1300.0
20190.0
30890.0
B-24
-------
Benthic
N-nitrosodiphenyl amine
acenaphthene
anthracene
phenanthrene
fluorene
fluoranthene
1,4-dichlorobenzene
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
iron
lead
thai!ium
zinc
mercury
1,1'-biphenyl
dibenzothiophene
1-methylphenanthrene
Low molecular wt. PAH
Concentration DW
610.0
2500.0
1400.0
11000
3100.0
8100
250.0
2000
1200
420.0
9700.0
184.00
11400
52900
6250
3.20
3320.0
52.00
1100.0
1100.0
1300.0
20190.0
B-25
-------
Microtox
Concentration DW
N-nitrosod1phenyl amine
acenaphthene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)pyrene
chrysene
1,4-dichlorobenzene
dibenzofuran
2-methy1naphthaiene
antimony
arsenic
cadmium
copper
iron
lead
manganes
thai 1ium
zinc
mercury
1,1'-biphenyl
dibenzothiophene
1-methylphenanthrene
Low molecular wt. PAH
High molecular wt. PAH
Chlorinated benzenes
610.0
2500.0
1400.0
11000
3100.0
8100
4000.0
4700.0
250.0
2000
1200
420.0
9700.0
184.00
11400
52900
6250
748
3.20
3320.0
52.00
1100.0
1100.0
1300.0
20190.0
30890.0
268.0
Group: 1 Station: RS-19
Toxicity code: 4 Benthic code:
Amphipod
antimony
arsenic
cadmium
copper
lead
thallium
zinc
mercury
Microtox code 2
Concentration DW
36.00
1550.0
16.00
2240
1020
.46
906.0
3.20
B-26
-------
Oyster
Concentration DW
phthalate
di-n-butyl
antimony
arsenic
cadmium
copper
lead
thai 1ium
mercury
Benthic
arsenic
cadmium
copper
lead
thai 1ium
zinc
mercury
Microtox
di-n-butyl phthalate
antimony
arsenic
cadmium
copper
lead
thallium
mercury
1600.0
36.00
1550.0
16.00
2240
1020
.46
3.20
Concentration DW
1550.0
16.00
2240
1020
.46
906.0
3.20
Concentration DW
1600.0
36.00
1550.0
16.00
2240
1020
.46
3.20
Group: 1 Station: RS-20
Toxicity code: 1 Benthic code:
Benthic
arsenic
Microtox
mercury
Microtox code 2
Concentration DW
90.0
Concentration DW
.59
B-27
-------
Microtox code 1
Concentration DW
antimony
arsenic
cadmium
Iron
manganes
zinc
Group: 1 Station: SI-11
Toxicity code: 1 Benthic code: 2
Benthic
arsenic
lead
zinc
Microtox
lead
Group: 1 Station: SI-12
Toxicity code: 3 Benthic code: 2
Benthic
N-nitrosod1phenylamine
lead
zinc
Microtox
N-n1trosod1pheny1 amine
Group: 1 Station: SI-15
Toxicity code: 3 Benthic code: 1
Amph i pod
1-methylphenanthrene
26.00
700.0
9.60
37100
484
1620.0
Microtox code 2
Concentration DW
93.0
661
491.0
Concentration DW
661
Microtox code 2
Concentration DW
130.0
496
337.0
Concentration DW
130.0
Microtox code 1
Concentration DW
370.0
B-28
-------
Group: 1 Station: SP-12
Toxicity code: 2 Benthic code: 1 Microtox code 2
Microtox
benzyl alcohol
Concentration DW
61
Group: 1 Station: SP-14
Toxicity code: 4 Benthic code:
Amph ipod
phenol
naphthalene
4-methylphenol
2-methylnaphthalene
manganes
total volatile sol ids
total organic carbon
l,l'-biphenyl
Low molecular wt. PAH
Oyster
phenol
naphthalene
4-methylphenol
2-methylnaphthalene
manganes
nickel
total volatile solids
total organic carbon
1,1'-b i phenyl
Low molecular wt. PAH
Benthic
phenol
naphthalene
4-methylphenol
2-methylnaphthalene
total volatile solids
total organic carbon
1,1'-biphenyl
Microtox code 2
Concentration DW
1700
4400.0
96000
810
556
44.70
16.00
310.0
6065.0
Concentration DW
1700
4400.0
96000
810
556
40.0
44.70
16.00
310.0
6065.0
Concentration DW
1700
4400.0
96000
810
44.70
16.00
310.0
B-29
-------
Microtox
Concentration DW
phenol
naphthalene
4-methylphenol
2-methylnaphthalene
manganes
total volatile solids
total organic carbon
1,1'-biphenyl
Low molecular wt. PAH
1700
4400.0
96000
810
556
44.70
16.00
310.0
6065.0
Group: 1 Station: SP-15
Toxicity code: 4 Benthic code:
Amphipod
4-methylphenol
Oyster
4-methylphenol
Benthic
4-methylphenol
Microtox
4-methylphenol
Microtox code 2
Concentration DW
2600
Concentration DW
2600
Concentration DW
2600
Concentration DW
2600
Group: 1 Station: SP-16
Toxicity code: 4 Benthic code: 2
Amphipod
benzyl alcohol
Oyster
m m m m m ^
4-methylphenol
benzyl alcohol
Benthic
4-methylphenol
benzyl alcohol
Microtox code 2
Concentration DW
130
Concentration DW
890
130
Concentration DW
890
130
B-30
-------
Microtox
Concentration DW
4-methylphenol
benzyl alcohol
890
130
Group: 2 Station: B15
Toxicity code: 3 Benthic code:
Amphipod
4,4'-0DT
1 Microtox code 0
Concentration DW
5.8
Group: 3 Station: EV-01
Toxicity code: 3 Benthic code: 0
Amph i pod
fluoranthene
thai 1ium
Microtox code 0
Concentration DW
4800
.50
Group: 3 Station: EV-04
Toxicity code: 3 Benthic code:
Amph i pod
phenol
acenaphthene
naphthalene
acenaphthylene
phenanthrene
fluorene
fluoranthene
zinc
total volatile solids
total organic carbon
Low molecular wt. PAH
Microtox code 0
Concentration DW
1400
3300.0
5900.0
770.0
4700
2100.0
4100
1074.0
35.06
15.42
17180.0
B-31
-------
Group: 3 Station: SC-20
Toxicity code: 3 Benthic code:
Amphipod
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(ghi)perylene
pyrene
Low molecular wt. PAH
High molecular wt. PAH
Microtox code 0
Concentration DW
8900
980.0
5200
1900.0
1000.0
6400
11080.0
20140.0
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code:
Amphipod
4,4'-DDT
Microtox code 0
Concentration DW
22.0
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code: 0
Amph i pod
acenaphthylene
total PCBs
4,4'-DDD
Microtox code 0
Concentration DW
2400
3900.0
71.00
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
benzo(a)pyrene
Total benzofluoranthenes
benzo(gh1)perylene
dibenzo(a,h)anthracene
butyl benzyl phthalate
mercury
4,4'-DDE
Microtox code 0
Concentration DW
132.0
9770.0
17529
8046.0
4023
1724
.98
11.00
B-32
-------
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code: 2
Microtox code 0
Benthic
Concentration DW
anthracene
phenanthrene
fluorene
benzo(a)anthracene
chrysene
total PCBs
lead
zinc
mercury
4,4'-DDE
4,4'-DDD
Low molecular wt. PAH
3936.0
4293
2683.0
9481.0
10376
3940.0
670
300.0
1.60
47.00
175.00
11431.0
Group: 6 Station: EB-36
Toxicity code: 0 Benthic code:
Benthic
acenaphthylene
total PCBs
silver
4,4'-DDE
Microtox code 0
Concentration DW
4013
3970.0
5.40
37.00
Group: 6 Station: WP-16
Toxicity code: 0 Benthic code:
Benthic
4,4'-DDD
Microtox code 0
Concentration DW
12.00
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code:
Benthic
total PCBs
mercury
4,4'-DDE
4,4'-DDD
Microtox code 0
Concentration DW
2280.0
1.00
30.00
30.00
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code: 2 Microtox code 0
B-33
-------
Benthlc
Concentration DW
N-nitrosodiphenylamine
anthracene
Total benzofluoranthenes
butyl benzyl phthalate
lead
zinc
mercury
276.0
1636.0
11213
1820
430
260.0
1.30
Group: 7 Station: EB-36
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
diethyl phthalate
M'-DDT
4,4*-DDE
Microtox code 0
Concentration DW
308.0
318.0
15.0
10.00
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code:
Benthic
mm mm mm m
butyl benzyl phthalate
4,4'-DDT
Microtox code 0
Concentration DW
812.0
28.0
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code:
Amphlpod
total PCBs
4,4'-DDE
Microtox code 0
Concentration DW
5400.0
41.00
Group: 9 Station: DR-26
Toxicity code: 3 Benthic code: 0
Amphipod
zinc
Microtox code 0
Concentration DW
1211.0
B-34
-------
Group: 9 Station: DR-27
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
cadmium 10.40
zinc 2600.0
mercury 2.30
B-35
-------
TABLE B-3. STATION LISTING OF CHEMICALS EXCEEDING
ORGANIC CARBON NORMALIZED AET
Organics expressed as ppb organic carbon, metals ppm organic carbon
Group: 1 Station: BL-31
Toxicity code: 1 Benthic code: 1
Microtox
phenol
Group: 1 Station: CI-11
Toxicity code: 4 Benthic code: 2
Oyster
1,4-dichlorobenzene
Microtox code 2
Concentration OC
38532.1
Microtox code 2
Concentration OC
3273.1
Group: 1 Station: CI-13
Toxicity code: 2 Benthic code:
Microtox
bis(2-ethylhexyl)phthalate
Group: 1 Station: CI-16
Toxicity code: 2 Benthic code:
Oyster
1,2-dichlorobenzene
Benthic
1,2-dichlorobenzene
Microtox
1,2-dichlorobenzene
Microtox code 2
Concentration OC
47692.3
Microtox code 2
Concentration OC
3211.0
Concentration OC
3211.0
Concentration OC
3211.0
B-36
-------
Group: 1 Station: HY-22
Toxicity code: 4 Benthic code: 3
Microtox code 2
Amphipod
Concentration OC
benzo(a)pyrene
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
hexachlorobenzene
hexachlorobutadiene
Chlorinated benzenes
Oyster
benzo(a)pyrene
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
1,2,4-trichlorobenzene
hexachlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthaiate
Chlorinated benzenes
Benthic
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
Chlorinated benzenes
1,2,4-trichlorobenzene
hexachlorobenzene
Microtox
dibenzo(a,h)anthracene
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
bis(2-ethylhexyljphthalate
total PCBs
Chlorinated benzenes
137387.4
33783.8
60810.8
16441.4
16441.4
29909.9
Concentration OC
137387.4
33783.8
60810.8
5855.9
16441.4
4054.1
16441.4
67567.6
29909.9
Concentration 0C
16441.4
67567.6
29909.9
5855.9
16441.4
Concentration 0C
33783.8
5855.9
16441.4
16441.4
67567.6
45045.0
29909.9
Group: 1 Station: HY-23
Toxicity code: 4 Benthic code:
Microtox
hexachlorobutadiene
total PCBs
Microtox code 2
Concentration 0C
4497.4
39682.5
B-37
-------
Group: 1 Station: HY-24
Toxicity code: 1 Benthic code:
Microtox
butyl benzyl phthalate
Microtox code 2
Concentration OC
9179.7
Group: 1 Station: HY-37
Toxicity code: 1 Benthic code:
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
total PCBs
Microtox code 2
Concentration OC
1371.0
3871.0
5241.9
16935.5
Group: 1 Station: HY-42
Toxicity code: 3 Benthic code:
Amphipod
hexachlorobenzene
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
total PCBs
Microtox code 2
Concentration OC
9623.4
Concentration OC
2677.8
9623.4
11297.1
46025.1
Group: 1 Station: HY-43
Toxicity code: 1 Benthic code:
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
Microtox code 2
Concentration OC
1764.7
4498.3
6228.4
Group: 1 Station: HY-47
Toxicity code: 2 Benthic code:
Oyster
1,2,4-trichlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
Microtox code 2
Concentration OC
2771.7
6521.7
15760.9
B-38
-------
Benthic
Concentration OC
hexachlorobutadlene
1,2,4-trichlorobenzene
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
Amphipod
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
1,4-d1chlorobenzene
2-methylphenol
4-methylphenol
dibenzofuran
2-methylnaphthalene
Low molecular wt. PAH
High molecular wt. PAH
1,l'-biphenyl
1-methylphenanthrene
15760.9
2771.7
Concentration OC
2771.7
5434.8
15760.9
1 Microtox code 2
Concentration OC
1336.4
1 Microtox code 1
Concentration OC
71014.5
188405.8
159420.3
142029.0
434782.6
202898.6
33333.3
86956.5
15942.0
10434.8
81159.4
57971.0
63768.1
528985.5
1488406
11594.2
28985.5
Group: 1 Station: MI-13
Toxicity code: 1 Benthic code:
Microtox
2,4-dimethylphenol
Group: 1 Station: RS-13
Toxicity code: 4 Benthic code:
B-39
-------
Oyster
Concentration OC
acenaphthene
naphthalene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(gh i)pery1ene
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
1,2-dichlorobenzene
1,4-dichlorobenzene
2-methylphenol
4-methylphenol
dibenzofuran
2-methylnaphtha!ene
Low molecular wt. PAH
High molecular wt. PAH
1,1'-biphenyl
dibenzothiophene
1-methylphenanthrene
56521.7
173913.0
159420.3
71014.5
188405.8
159420.3
142029.0
434782.6
202898.6
66666.7
33333.3
86956.5
2318.8
15942.0
10434.8
81159.4
57971.0
63768.1
528985.5
1488406
11594.2
14202.9
28985.5
Group: 1 Station: RS-18
Toxicity code: 4 Benthic code:
Amphipod
dibenzofuran
antimony
arsenic
cadmium
copper
selenium
mercury
1,1'-biphenyl
Microtox code 2
Concentration 0C
22650.1
4756.5
109852.8
2083.8
129105.3
271.8
588.9
12457.5
B-40
-------
Concentration OC
acenaphthene
phenanthrene
fluorene
dibenzofuran
antimony
arsenic
cadmium
copper
lead
mercury
1,1'-biphenyl
dibenzothiophene
Benthic
antimony
arsenic
cadmium
copper
lead
1,1'-biphenyl
Mlcrotox
antimony
arsenic
cadmium
copper
lead
mercury
1,1'-biphenyl
28312.6
124575.3
35107.6
22650.1
4756.5
109852.8
2083.8
129105.3
70781.4
588.9
12457.5
12457.5
Concentration 0C
4756.5
109852.8
2083.8
129105.3
70781.4
12457.5
Concentration 0C
4756.5
109852.8
2083.8
129105.3
70781.4
588.9
12457.5
Group: 1 Station: RS-19
Toxicity code: 4 Benthic code:
Amphipod
di-n-butyl phthalate
dibenzofuran
antimony
arsenic
cadmium
copper
lead
selenium
zinc
mercury
1-methyIphenanthrene
Microtox code 2
Concentration 0C
275862.1
18965.5
6206.9
267241.4
2758.6
386206.9
175862.1
241.4
156206.9
551.7
24137.9
B-41
-------
Oyster
Concentration OC
fluorene
di-n-butyl phthalate
dibenzofuran
antimony
arsenic
cadmium
copper
lead
mercury
dibenzothiophene
1-methylphenanthrene
Benthic
24137.9
275862.1
18965.5
6206.9
267241.4
2758.6
386206.9
175862.1
551.7
16724.1
24137.9
Concentration OC
antimony
arsenic
cadmium
copper
lead
zinc
dibenzothiophene
Microtox
6206.9
267241.4
2758.6
386206.9
175862.1
156206.9
16724.1
Concentration 0C
di-n-butyl phthalate
antimony
arsenic
cadmium
copper
lead
mercury
Total phthalates
dibenzothiophene
275862.1
6206.9
267241.4
2758.6
386206.9
175862.1
551.7
275862.1
16724.1
Group: 1 Station: RS-20
Toxicity code: 1 Benthic code:
Benthic
antimony
arsenic
cadmium
copper
lead
Microtox code 2
Concentration 0C
642.9
32142.9
1071.4
48928.6
27857.1
B-42
-------
Microtox
•mmmmmm
di-n-butyl phthalate
copper
iron
manganese
nickel
mercury
Total phthalates
Group: 1 Station: RS-24
Toxicity code: 3 Benthic code: 0
Amphi pod
antimony
arsenic
cadmium
zinc
Group: 1 Station: SI-11
Toxicity code: 1 Benthic code: 2
Benthic
lead
Group: 1 Station: SI-12
Toxicity code: 3 Benthic code: 2
Benthic
lead
Group: 1 Station: SP-14
Toxicity code: 4 Benthic code: 3
Amphipod
4-methylphenol
Oyster
4-methylphenol
Concentration OC
264285.7
48928.6
5678571
82857.1
6785.7
210.7
264285.7
Microtox code 1
Concentration OC
3250.0
87500.0
1200.0
202500.0
Microtox code 2
Concentration 0C
31476.2
Microtox code 2
Concentration 0C
31794.9
Microtox code 2
Concentration 0C
600000.0
Concentration 0C
600000.0
B-43
-------
Benthic
Concentration
OC
4-methylphenol
600000.0
Microtox
Concentration
0C
4-methylphenol
600000.0
Group: 1 Station: SP-15
Toxicity code: 4 Benthic code: 3
Microtox code 2
Amphipod
Concentration
OC
4-methylphenol
126213.6
Oyster
Concentration
0C
4-methylphenol
126213.6
Benthic
Concentration
0C
4-methylphenol
126213.6
Microtox
Concentration
OC
4-methylphenol
126213.6
Group: 1 Station: SP-16
Toxicity code: 4 Benthic code: 2
Microtox code 2
Amphipod
Concentration
0C
4-methylphenol
benzyl alcohol
60544.2
8843.5
Oyster
Concentration
OC
4-methylphenol
benzyl alcohol
60544.2
8843.5
Benthic
Concentration
OC
benzyl alcohol
8843.5
Microtox
Concentration
0C
benzyl alcohol
8843.5
B-44
-------
Group: 3 Station: SC-20
Toxicity code: 3 Benthic code:
Amphipod
phenanthrene
Microtox code 0
Concentration OC
271341.5
Group: 3 Station: SM-01
Toxicity code: 3 Benthic code:
Amphipod
bis(2-ethylhexyl)phthalate
Microtox code 0
Concentration OC
212121.2
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code:
Amphipod
4,4'-DDT
Microtox code 0
Concentration OC
1222.22 *
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code:
Amphipod
acenaphthylene
bis(2-ethylhexyl)phthalate
total PCBs
4,4'-DDD
Microtox code 0
Concentration OC
109090.9
127272.7
177272.7
3227.27
Group: 6 Station: E8-33
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
butyl benzyl phthalate
silver
Microtox code 0
Concentration OC
14666.7
191555.6
511.1
8-45
-------
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code: 2
Microtox code 0
Benthic
Concentration OC
total PCBs
lead
si lver
4,4,-DD0
511688.3
87013.0
532.5
22727.27
Group: 6 Station: E8-36
Toxicity code: 0 Benthic code:
Benthic
acenaphthylene
total PCBs
silver
Microtox code 0
Concentration OC
668833.3
661666.7
900.0
Group: 6 Station: WP-16
Toxicity code: 0 Benthic code:
Benthic
lead
Microtox code 0
Concentration OC
22307.7
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code:
Benthic
total PCBs
lead
4,4*-DDD
Microtox code 0
Concentration 0C
380000.0
35000.0
5000.00
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
1,2-dichlorobenzene
butyl benzyl phthalate
copper
lead
zinc
Microtox code 0
Concentration 0C
62727.3
4090.9
413636.4
27272.7
97727.3
59090.9
B-46
-------
Group: 7 Station: EB-36
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenyl amine
Microtox code 0
Concentration OC
49677.4
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code:
Benthic
butyl benzyl phthalate
Microtox code 0
Concentration OC
67666.7
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code:
Amphipod
total PCBs
4,4'-D0D
4,4'-ODE
Microtox code 0
Concentration OC
380281.7
2676.06
2887.32
Group: 9 Station: DR-25
Toxicity code: 3 Benthic code:
Amphipod
zinc
Microtox code 0
Concentration OC
72638.9
Group: 9 Station: DR-26
Toxicity code: 3 Benthic code:
Amphipod
zinc
Microtox code 0
Concentration OC
212456.1
Group: 9 Station: DR-27
Toxicity code: 3 Benthic code: 0
Amphipod
cadmium
zinc
mercury
Microtox code 0
Concentration OC
2000.0
500000.0
442.3
B-47
-------
TABLE B-4. STATION LISTING OF CHEMICALS EXCEEDING
FINES NORMALIZED AET
Organics expressed as ppb fine grained material,
metals ppm fine grained material
Group: 1 Station: CI-11
Toxicity code: 4 Benthic code:
Amphipod
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
1,4-dichlorobenzene
benzyl alcohol
dibenzothiophene
Oyster
acenaphthene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
1,2-dichlorobenzene
1,4-dichlorobenzene
benzyl alcohol
Chlorinated benzenes
dibenzothiophene
Benthic
benzyl alcohol
lead
Microtox
benzyl alcohol
Microtox code 2
Concentration Fines
1981.7
1600.6
736.8
355.7
482.7
Concentration Fines
1168.7
1981.7
1600.6
94.0
736.8
355.7
830.8
482.7
Concentration Fines
355.7
1842.0
Concentration Fines
355.7
Group: 1 Station: CI-13
Toxicity code: 2 Benthic code:
Oyster
bis(2-ethylhexyl)phthalate
Benthic
bis(2-ethylhexyl)phthalate
B-48
Microtox code 2
Concentration Fines
3959.1
Concentration Fines
3959.1
-------
Microtox
bis(2-ethylhexyl)phthalate
butyl benzyl phthalate
Concentration Fines
3959.1
268.2
Group: 1 Station: CI-16
Toxicity code: 2 Benthic code:
Oyster
2,4-dimethylphenol
1,2-dichlorobenzene
1,4-dichlorobenzene
4-methylphenol
Chlorinated benzenes
Benthic
2,4-dimethylphenol
1,2-dichlorobenzene
Microtox
2,4-dimethylphenol
1,2-dichlorobenzene
Microtox code 2
Concentration Fines
67.9
475.2
353.0
1629.3
905.6
Concentration Fines
67.9
475.2
Concentration Fines
67.9
475.2
Group: 1 Station: CI-20
Toxicity code: 4 Benthic code:
Oyster
2,4-dimethylphenol
Microtox code 1
Concentration Fines
36.4
Group: 1 Station: HY-17
Toxicity code: 2 Benthic code:
Microtox
total PCBs
Microtox code 2
Concentration Fines
254.0
B-49
-------
Group: 1 Station: HY-22
Toxicity code: 4 Benthic code: 3
Amphipod
benzo(a)pyrene
Total benzofluoranthenes
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
Chlorinated benzenes
Oyster
benzo(a)pyrene
Total benzofluoranthenes
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
1,2,4-trichlorobenzene
hexachlorobenzene
1,2-dichlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
total PCBs
Chlorinated benzenes
Benthic
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
Microtox
benzo(a)pyrene
dibenzo(a.h)anthracene
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl)phthalate
total PCBs
Chlorinated benzenes
Microtox code 2
Concentration Fines
8077.3
11255.3
1986.2
3575.2
344.3
966.6
966.6
3972.5
1758.5
Concentration Fines
8077.3
11255.3
1986.2
3575.2
344.3
966.6
96.7
238.3
966.6
3972.5
2648.3
1758.5
Concentration Fines
344.3
966.6
966.6
3972.5
Concentration Fines
8077.3
1986.2
344.3
966.6
966.6
3972.5
2648.3
1758.5
B-50
-------
Group: 1 Station: HY-23
Toxicity code: 4 Benthic code:
Oyster
total PCBs
Microtox
hexach1orobutad iene
butyl benzyl phthalate
total PCBs
Microtox code 2
Concentration Fines
1735.1
Concentration Fines
196.6
127.2
1735.1
Group: 1 Station: HY-24
Toxicity code: 1 Benthic code: 1
Microtox
butyl benzyl phthalate
total PCBs
Microtox code 2
Concentration Fines
576.6
306.7
Group: 1 Station: HY-37
Toxicity code: 1 Benthic code:
Microtox
hexachlorobenzene
total PCBs
Microtox code 2
Concentration Fines
123.9
542.0
Group: 1 Station: HY-42
Toxicity code: 3 Benthic code:
Amphipod
hexachlorobenzene
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexach1orobutad i ene
total PCBs
Microtox code 2
Concentration Fines
294.6
Concentration Fines
82.0
294.6
345.8
1409.0
B—51
-------
Group: 1 Station: HY-43
Toxicity code: 1 Benthic code: 1 Microtox code 2
Microtox
Concentration Fines
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
89.2
227.4
314.9
Group: 1 Station: HY-47
Toxicity code: 2 Benthic code:
Oyster
hexachlorobutadiene
Benthic
hexachlorobutadiene
Microtox
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
Microtox code 2
Concentration Fines
370.4
Concentration Fines
370.4
Concentration Fines
65.1
127.7
370.4
B-52
-------
Group: 1 Station: RS-13
Toxicity code: 4 Benthic code: 1
Amphipod
acenaphthene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
naphthalene
1,4-dichlorobenzene
2-methylphenol
4-methylphenol
dibenzofuran
benzyl alcohol
2-methy1 naphtha 1ene
Low molecular wt. PAH
High molecular wt. PAH
Chlorinated benzenes
1,1'-biphenyl
dibenzothiophene
1-methylphenanthrene
Microtox code 1
Concentration Fines
3102.6
8751.0
3898.2
10342.1
8751.0
7796.3
23866.3
11137.6
3659.5
1829.8
4773.3
9546.5
9546.5
875.1
572.8
4455.1
3182.2
167.1
3500.4
29037.4
81702.5
1002.4
636.4
779.6
1591.1
B-53
-------
Concentration Fines
acenaphthene
3102.6
phenanthrene
8751.0
f luorene
3898.2
fluoranthene
10342.1
benzo(a)anthracene
8751.0
benzo(a)pyrene
7796.3
Total benzofluoranthenes
23866.3
chrysene
11137.6
benzo(ghi)perylene
3659.5
dibenzo(a,h)anthracene
1829.8
indeno(l,2,3-cd)pyrene
4773.3
pyrene
9546.5
naphthalene
9546.5
1,2-dichlorobenzene
127.3
1,4-dichlorobenzene
875.1
2-methylphenol
572.8
4-methylphenol
4455.1
dibenzofuran
3182.2
benzyl alcohol
167.1
2-methylnaphthalene
3500.4
Low molecular wt. PAH
29037.4
High molecular wt. PAH
81702.5
Chlorinated benzenes
1002.4
1,1'-biphenyl
636.4
dibenzothiophene
779.6
1-methylphenanthrene
1591.1
B-54
-------
Group: 1 Station: RS-18
Toxicity code: 4 Benthic code:
3
Microtox code 2
Amphipod
N-nitrosodiphenylamine
acenaphthene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
naphthalene
1,4-dichlorobenzene
2-methylphenol
dibenzofuran
2-methy1naphtha1ene
antimony
arsenic
cadmium
copper
lead
selenium
thallium
mercury
Low molecular wt. PAH
High molecular wt. PAH
1,l'-biphenyl
dibenzothiophene
1-methy1phenanthrene
Concentration Fines
1829.6
7498.5
4199.2
32993.4
9298.1
24295.1
9598.1
11997.6
12597.5
14097.2
959.8
2309.5
16796.6
5698.9
749.9
213.0
5998.8
3599.3
1259.75
29094.2
551.9
34193.2
18746.3
71.99
9.60
156.0
60557.9
92651.5
3299.3
3299.3
3899.2
B-55
-------
Oyster
N-nitrosodiphenylamine
acenaphthene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
naphthalene
1,4-dichlorobenzene
2-methylphenol
dibenzofuran
2-methy1naphtha1ene
antimony
arsenic
cadmiurn
copper
lead
selenium
thai 1ium
mercury
Low molecular wt. PAH
High molecular wt. PAH
Chlorinated benzenes
1,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Concentration Fines
1829.6
7498.5
4199.2
32993.4
9298.1
24295.1
9598.1
11997.6
12597.5
14097.2
959.8
2309.5
16796.6
5698.9
749.9
213.0
5998.8
3599.3
1259.75
29094.2
551.9
34193.2
18746.3
71.99
9.60
156.0
60557.9
92651.5
803.8
3299.3
3299.3
3899.2
B- 56
-------
Concentration Fines
N-nitrosodiphenylamine
acenaphthene
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
lead
selenium
thallium
zinc
mercury
1 ,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Microtox
mm mmm mm
N-nitrosodiphenylamine
acenaphthene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
chrysene
pyrene
dibenzofuran
2-methy1naph tha1ene
antimony
arsenic
cadmium
copper
lead
thallium
mercury
Low molecular wt. PAH
High molecular wt. PAH
1,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
1829.6
7498.5
5998.8
3599.3
1259.75
29094.2
551.9
34193.2
18746.3
71.99
9.60
9958.0
156.0
3299.3
3299.3
3899.2
Concentration Fines
1829.6
7498.5
4199.2
32993.4
9298.1
24295.1
9598.1
11997.6
14097.2
16796.6
5998.8
3599.3
1259.75
29094.2
551.9
34193.2
18746.3
9.60
156.0
60557.9
92651.5
3299,3
3299.3
3899.2
B-57
-------
Group: 1 Station: RS-19
Toxicity code: 4 Benthic code: 2
Amphipod
anthracene
phenanthren^
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
naphthalene
di-n-butyl phthalate
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
lead
selenium
thallium
zinc
mercury
Low molecular wt. PAH
High molecular wt. PAH
Total phthalates
1,1'-biphenyl
dibenzothiophene
1-methylphenanthrene
Microtox code 2
Concentration Fines
12539.2
17868.3
4388.7
26645.8
10971.8
8463.9
12539.2
12539.2
2382.4
658.3
2821.3
21316.6
4702.2
50156.7
3448.3
1912.2
1128.53
48589.3
501.6
70219.4
31974.9
43.89
14.42
28401.3
100.3
40188.1
98338.6
50156.7
721.0
3040.8
4388.7
B-58
-------
Concentration Fines
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
naphthalene
1,4-dichlorobenzene
di-n-butyl phthalate
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
lead
selenium
thallium
zinc
mercury
Low molecular wt. PAH
High molecular wt. PAH
Total phthalates
1 ,l'-biphenyl
dibenzothiophene
1-methylphenanthrene
Benthic
dibenzofuran
antimony
arsenic
cadmium
copper
iron
lead
thai 1ium
zinc
mercury
1,1'-biphenyl
dibenzothiophene
1-methylphenanthrene
12539.2
17868.3
4388.7
26645.8
10971.8
8463.9
12539.2
12539.2
2382.4
658.3
2821.3
21316.6
4702.2
313.5
50156.7
3448.3
1912.2
1128.53
48589.3
501.6
70219.4
31974.9
43.89
14.42
28401.3
100.3
40188.1
98338.6
50156.7
721.0
3040.8
4388.7
Concentration Fines
3448.3
1128.53
48589.3
501.6
70219.4
752351.1
31974.9
14.42
28401.3
100.3
721.0
3040.8
4388.7
B-59
-------
Microtox
Concentration Fines
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
chrysene
pyrene
di-n-butyl phthalate
total PCBs
dibenzofuran
antimony
arsenic
cadmium
copper
lead
thallium
mercury
Low molecular wt. PAH
High molecular wt. PAH
Total phthalates
1 ,l'-biphenyl
dibenzothiophene
1-methy1phenanthrene
Group: 1 Station: RS-20
Toxicity code: 1 Benthic code: 2
Benthic
antimony
cadmium
copper
lead
mercury
Group: 1 Station: SP-12
Toxicity code: 2 Benthic code: 1
Oyster
benzyl alcohol
12539.2
17868.3
4388.7
26645.8
10971.8
8463.9
12539.2
21316.6
50156.7
438.9
3448.3
1128.53
48589.3
501.6
70219.4
31974.9
14.42
100.3
40188.1
98338.6
50156.7
721.0
3040.8
4388.7
Microtox code 2
Concentration Fines
30.98
51.6
2358.0
1342.5
10.2
Microtox code 2
Concentration Fines
123.6
B-60
-------
Group: 1 Station: SP-14
Toxicity code: 4 Benthic code: 3
Amphipod
naphthalene
4-methylphenol
1,1'-biphenyl
Oyster
naphthalene
4-methylphenol
1,1'-biphenyl
Benthic
4-methylphenol
Microtox
4-methylphenol
Group: 1 Station: SP-15
Toxicity code: 4 Benthic code: 3
Anphipod
4-methylphenol
Oyster
4-methylphenol
Benthic
4-methylphenol
Microtox
4-methylphenol
Microtox code 2
Concentration Fines
6606.6
144144.1
465.5
Concentration Fines
6606.6
144144.1
465.5
Concentration Fines
144144.1
Concentration Fines
144144.1
Microtox code 2
Concentration Fines
10038.6
Concentration Fines
10038.6
Concentration Fines
10038.6
Concentration Fines
10038.6
B-61
-------
Group: 1 Station: SP-16
Toxicity code: 4 Benthic coder
Amphipod
benzyl alcohol
Oyster
4-methy1 phenol
benzyl alcohol
Benthic
benzyl alcohol
Microtox
benzyl alcohol
Microtox code 2
Concentration Fines
237.0
Concentration Fines
1622.6
237.0
Concentration Fines
237.0
Concentration Fines
237.0
Group: 2 Station: B15
Toxicity code: 3 Benthic code: 1
Amphipod
4,4'-DDT
Microtox code 0
Concentration Fines
8.17
Group: 3 Station: EV-01
Toxicity code: 3 Benthic code: 0
Amphipod
fluoranthene
Microtox code 0
Concentration Fines
7633.6
Group: 3 Station: EV-04
Toxicity code: 3 Benthic code: 0
Amphipod
acenaphthene
acenaphthylene
phenanthrene
fluorene
fluoranthene
naphthalene
Low molecular wt. PAH
Microtox code 0
Concentration Fines
6131.5
1430.7
8732.8
3901.9
7618.0
10962.5
31921.2
B-62
-------
Group: 3 Station: SC-20
Toxicity code: 3 Benthic code:
Amphipod
phenanthrene
fluoranthene
pyrene
Low molecular wt. PAH
Microtox code 0
Concentration Fines
12981.3
7584.6
9334.9
16161.0
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code: 0
Amphipod
4,4'-DDT
Microtox code 0
Concentration Fines
40.89
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code: 0
Amphipod
acenaphthylene
total PCBs
4,4'-DDD
Microtox code 0
Concentration Fines
2739.7
4452.1
81.05
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code: 3
Benthic
butyl benzyl phthalate
Microtox code 0
Concentration Fines
2099.9
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code: 2
Benthic
total PCBs
lead
4,4'-DDD
4,4'-DDE
Microtox code 0
Concentration Fines
12351.1
2100.3
548.6
147.3
B-63
-------
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code:
Benthic
4,41-DDD
Microtox code 0
Concentration Fines
37.22
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code:
Benthic
N-nitrosodiphenylamine
butyl benzyl phthalate
di-n-octyl phthalate
lead
Total phthalates
Microtox code 0
Concentration Fines
711.3
4690.7
97597.9
1108.2
110309.3
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code:
Benthic
butyl benzyl phthalate
Microtox code 0
Concentration Fines
1307.6
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code: 0
Amphipod
total PCBs
4,4'-DDD
4,4'-DDE
Microtox code 0
Concentration Fines
10742.0
75.59
81.56
B-64
-------
TABLE B-5. STATION LISTING OF CHEMICALS EXCEEDING
LOWEST DRY WEIGHT NORMALIZED AET
Organics expressed as ppb dry weight, metals ppm dry weight
Group: 1 Station: BL-13
Toxicity code: 1 Benthic code: 1 Microtox code 2
Lowest AET
butyl benzyl phthalate
Concentration DW
83.0
Group: 1 Station: CI-11
Toxicity code: 4 Benthic code:
Lowest AET
phenol
phenanthrene
fluoranthene
chrysene
benzo(ghi)perylene
1,2-dichlorobenzene
1,4-dichlorobenzene
benzyl alcohol
lead
nickel
zinc
mercury
High molecular wt. PAH
Chlorinated benzenes
Microtox code 2
Concentration DW
1100
1800
2400
1600.0
780.0
37.0
290.0
140
725
40.0
325.0
.53
13090.0
327.0
Group: 1 Station: C1—13
Toxicity code: 2 Benthic code: 3 Microtox code 2
Lowest AET Concentration DW
bis(2-ethylhexyl)phthalate 3100.0
butyl benzyl phthalate 210.0
total PCBs 140.0
benzoic acid 690
cadmium 6.70
lead 450
mercury
1.10
Total phthalates 3548.0
B-65
-------
Group: 1 Station: C1-16
Toxicity code: 2 Benthic code: 3 Microtox code 2
Lowest AET Concentration DW
2,4-d1methylphenol 50.0
N-nitrosodiphenylamine 220.0
1,2-dichlorobenzene 350.0
1,4-dichlorobenzene 260.0
dl-n-butyl phthalate 1600.0
4-methylphenol 1200
Chlorinated benzenes 667.0
Group: 1 Station: CI-17
Toxicity code: 1 Benthic code: 1 Microtox code 2
Lowest AET Concentration DW
fluoranthene 1900
1,4-dichlorobenzene 119.0
Group: 1 Station: CI-20
Toxicity code: 4 Benthic code: 1 Microtox code 1
Lowest AET Concentration DW
phenol 1200
l.l'-biphenyl 270.0
dibenzothiophene 250.0
Group: 1 Station: HY-12
Toxicity code: 2 Benthic code: 1 Microtox code 2
Lowest AET Concentration DW
phenol 500
chrysene 1800.0
benzo(gh1)perylene 740.0
dibenzo(a,h)anthracene 260.0
dl-n-butyl phthalate 5100.0
mercury .46
Total phthalates 5100.0
B-66
-------
Group: 1 Station: HY-14
Toxicity code: 1 Benthic code: 2 Microtox code 2
Lowest AET Concentration DW
fluoranthene 2500
chrysene 2800.0
benzo(ghi)perylene 720.0
High molecular wt. PAH 16650.0
Group: 1 Station: HY-17
Toxicity code: 2 Benthic code:
Lowest AET
fluoranthene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
pyrene
tetrachloroethene
ethyl benzene
total PCBs
total xylenes
arsenic
zinc
High molecular wt. PAH
Microtox code 2
Concentration DW
3900
2400.0
3700
2700.0
4300
210
50
170.0
160
86.0
268.0
18402.0
B-67
-------
Group: 1 Station: HY-22
Toxicity code: 4 Benthic code:
Lowest AET
phenol
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
1,2,4-trichlorobenzene
hexachlorobenzene
1,2-d ichlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
bis(2-ethylhexyl}phthalate
total PCBs
arsenic
nickel
mercury
dibenzothiophene
1-methylphenanthrene
High molecular wt. PAH
Total phthalates
Chlorinated benzenes
Microtox code 2
Concentration DU
530
3600
2300.0
6100.0
8500
2700.0
1500
2700.0
260.0
730.0
73.0
180.0
730.0
3000.0
2000.0
90.0
52.0
.50
320.0
530.0
30000.0
3560.0
1328.0
Group: 1 Station: HY-23
Toxicity code: 4 Benthic code: 3
Lowest AET
phenanthrene
fluoranthene
benzo(a)pyrene
chrysene
benzo(gh i)pery1ene
d ibenzo(a,h)anthracene
hexachlorobutadiene
butyl benzyl phthalate
dimethyl phthalate
tetrachloroethene
total PCBs
total xylenes
nickel
High molecular wt. PAH
Microtox code 2
Concentration DW
2300
2500
2000.0
2300.0
1100.0
440.0
170.0
110.0
350.0
170
1500.0
110
56.0
13790.0
B-68
-------
Group: 1 Station: HY-24
Toxicity code: 1 Benthic code:
Lowest AET
chrysene
hexachlorobutadiene
butyl benzyl phthalate
dimethyl phthalate
total PCBs
mercury
Microtox code 2
Concentration DW
2300.0
140.0
470.0
120.0
250.0
.49
Group: 1 Station: HY-37
Toxicity code: 1 Benthic code:
Lowest AET
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
total PCBs
Chlorinated benzenes
Microtox code 2
Concentration DW
34.0
96.0
130.0
420.0
203.0
Group: 1 Station: HY-42
Toxicity code: 3 Benthic code: 1
Lowest AET
chrysene
1,2,4-trichlorobenzene
hexachlorobenzene
hexach1orobutad i ene
total PCBs
Chlorinated benzenes
Microtox code 2
Concentration DW
1800.0
64.0
230.0
270.0
1100.0
395.0
Group: 1 Station: HY-43
Toxicity code: 1 Benthic code:
Lowest AET
1,2,4-trichlorobenzene
hexachlorobenzene
hexachlorobutadiene
ethyl benzene
total xylenes
Chlorinated benzenes
Microtox code 2
Concentration DW
51.0
130.0
180.0
37
120
274.2
B-69
-------
Group: 1 Station: HY-47
Toxicity code: 2 Benthic code: 2 Microtox code 2
Lowest AET
1,2,4-trichlorobenzene
hexachlorobenzene
1,4-dichlorobenzene
hexachlorobutadiene
di-n-butyl phthalate
Chlorinated benzenes
Concentration DW
51.0
100.0
120.0
290.0
1500.0
312.0
Group: 1 Station: HY-50
Toxicity code: 1 Benthic code:
Lowest AET
benzyl alcohol
Microtox code 2
Concentration DW
73
Group: 1 Station: MI-13
Toxicity code: 1 Benthic code:
Lowest AET
dimethyl phthalate
FINES
Microtox code 2
Concentration DW
110.0
.89
Group: 1 Station: RS-13
Toxicity code: 4 Benthic code:
Lowest AET
2-methylphenol
1 Microtox code 1
Concentration DW
72
B-70
-------
Group: 1 Station: RS-18
Toxicity code: 4 Benthic code: 3
Lowest AET
N-nitrosodiphenylamine
acenaphthene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
d ibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
1,4-dichlorobenzene
2-methylphenol
dibenzofuran
2-methylnaphthalene
antimony
arsenic
cadmium
copper
iron
lead
manganes
nickel
thallium
zinc
mercury
1,1'-biphenyl
dlbenzothiophene
1-methylphenanthrene
Low molecular wt. PAH
High molecular wt. PAH
Chlorinated benzenes
Microtox code 2
Concentration DW
610.0
2500.0
1400.0
11000
3100.0
8100
3200.0
4000.0
4200
4700.0
320.0
770.0
5600
250.0
71
2000
1200
420.0
9700.0
184.00
11400
52900
6250
748
93.0
3.20
3320.0
52.00
1100.0
1100.0
1300.0
20190.0
30890.0
268.0
B-71
-------
Group: 1 Station: RS-19
Toxicity code: 4 Benthic code: 2 Kicrotox code 2
Lowest AET Concentration DW
di-n-butyl phthalate 1600.0
antimony 36.00
arsenic 1550.0
cadmium 16.00
copper 2240
lead 1020
thallium .46
zinc 906.0
mercury 3.20
Group: 1 Station: RS-20
Toxicity code: 1 Benthic code: 2 Microtox code 2
Lowest AET Concentration 0W
arsenic 90.0
mercury .59
Group: 1 Station: RS-24
Toxicity code: 3 Benthic code: 0 Microtox code 1
Lowest AET Concentration DW
antimony 26.00
arsenic 700.0
cadmium 9.60
copper 385
iron 37100
lead 531
manganes 484
zinc 1620.0
Group: 1 Station: SI-11
Toxicity code: 1 Benthic code: 2 Microtox code 2
Lowest AET Concentration DW
arsenic 93.0
lead 661
zinc 491.0
B-72
-------
Group: 1 Station: SI-12
Toxicity code: 3 Benthic code: 2
Lowest AET
N-n i trosodi phenyl amine
lead
zinc
Group: 1 Station: SI—15
Toxicity code: 3 Benthic code: 1
Lowest AET
1-methylphenanthrene
Group: 1 Station: SP-12
Toxicity code: 2 Benthic code: 1
Lowest AET
benzyl alcohol
Group: 1 Station: SP-14
Toxicity code: 4 Benthic code: 3
Lowest AET
phenol
naphthalene
4-methylphenol
2-methylnaphthalene
manganes
nickel
total volatile solids
total organic carbon
l.l'-biphenyl
Low molecular wt. PAH
Group: 1 Station: SP-15
Toxicity code: 4 Benthic code: 3
Lowest AET
4-methylphenol
Microtox code 2
Concentration DW
130.0
496
337.0
Microtox code 1
Concentration DW
370.0
Microtox code 2
Concentration DW
61
Microtox code 2
Concentration DW
1700
4400.0
96000
810
556
40.0
44.70
16.00
310.0
6065.0
Microtox code 2
Concentration DW
2600
B-73
-------
Group: 1 Station: SP-16
Toxicity code: 4 Benthic code: 2
Lowest AET
4-methylphenol
benzyl alcohol
Mlcrotox code 2
Concentration DW
890
130
Group: 2 Station: B03
Toxicity code: 1 Benthic code:
Lowest AET
1,2-dichlorobenzene
1 Microtox code 0
Concentration DW
50.0
Group: 2 Station: B04
Toxicity code: 1 Benthic code:
Lowest AET
fluoranthene
butyl benzyl phthalate
mercury
Microtox code 0
Concentration DW
2140
125.0
.52
Group: 2 Station: B09
Toxicity code: 1 Benthic code:
Lowest AET
dimethyl phthalate
Microtox code 0
Concentration DW
160.0
Group: 2 Station: B15
Toxicity code: 3 Benthic code:
Lowest AET
4,4'-DOT
Microtox code 0
Concentration DW
5.8
B-74
-------
Group: 3 Station: BH-03
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
copper 400
nickel 73.5
mercury 1.35
total volatile solids 26.93
Group: 3 Station: BH-04
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
nickel 89.6
mercury 1.69
Group: 3 Station: BH-05
Toxicity code: 3 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
nickel 111.0
mercury .81
FINES .97
Group: 3 Station: BH-07
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
nickel 105.0
mercury .97
FINES .92
Group: 3 Station: BH-11
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
nickel 118.0
mercury .54
FINES .98
B-75
-------
Group: 3 Station: BH-12
Toxicity code: 1 Benthic code: & Microtox code 0
Lowest AET Concentration DW
nickel 72.0
mercury .64
Group: 3 Station: BH-23
Toxicity code: 3 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
nickel 102.0
mercury .54
FINES .95
Group: 3 Station: BH-24
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
nickel 117.0
mercury .59
FINES .97
Group: 3 Station: CS-01
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
phenol 560
silver .57
FINES .89
Group: 3 Station: DB-07
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
silver .78
B-76
-------
Group: 3 Station: DB-15
Toxicity code: 3 Benthic code: &
Lowest AET
nickel
FINES
Microtox code 0
Concentration DW
46.0
.90
Group: 3 Station: EB-09
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 330.0
silver .74
zinc 434.0
mercury 1.69
Group: 3 Station: EB-10
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
acenaphthene 630.0
phenanthrene 2100
fluoranthene 2300
chrysene 1500.0
pyrene 3400
total PCBs 279.0
lead 607
silver .65
zinc 687.0
mercury 1.08
High molecular wt. PAH 12200.0
Group: 3 Station: EB-12
Toxicity code: 1 Benthic code: 0
Lowest AET
silver
FINES
Microtox code 0
Concentration DW
.68
.89
B-77
-------
Group: 3 Station: EB-17
Toxicity code: 1 Benthlc code:
Lowest AET
total PCBs
silver
mercury
Mlcrotox code 0
Concentration DW
646.0
.65
.58
Group: 3 Station: EB-20
Toxicity code: 1 Benthlc code: 0
Lowest AET
total PCBs
silver
zinc
mercury
Microtox code 0
Concentration DW
640.0
.60
460.0
.78
Group: 3 Station: EB-22
Toxicity code: 1 Benthlc code:
Lowest AET
total PCBs
silver
mercury
Mlcrotox code 0
Concentration DW
687.0
.67
.51
Group: 3 Station: EB-23
Toxicity code: 1 Benthic code: 0
Lowest AET
total PCBs
Mlcrotox code 0
Concentration DW
148.0
Group: 3 Station: EV-01
Toxicity code: 3 Benthic code: 0
Lowest AET
phenanthrene
fluoranthene
total PCBs
nickel
thallium
zinc
Mlcrotox code 0
Concentration DW
1900
4800
445.0
44.0
.50
313.0
B-78
-------
Group: 3 Station: EV-02
Toxicity code: 3 Benthic code: 0
Lowest AET
nickel
Microtox code 0
Concentration DW
48.0
Group: 3 Station: EV-03
Toxicity code: 3 Benthic code:
Lowest AET
total PCBs
nickel
total volatile sol ids
Microtox code 0
Concentration DW
516.0
43.0
25.44
B-79
-------
Group: 3 Station: EV-04
Toxicity code: 3 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
phenol 1400
acenaphthene 3300.0
naphthalene 5900.0
acenaphthylene 770.0
phenanthrene 4700
fluorene 2100.0
fluoranthene 4100
butyl benzyl phthalate 440.0
total PCBs 965.0
nickel 45.0
thallium .30
zinc 1074.0
total volatile solids 35.06
total organic carbon 15.42
Low molecular wt. PAH 17180.0
Group: 3 Station: EV-05
Toxicity code: 3 Benthic code:
Lowest AET
fluoranthene
total PCBs
nickel
thai 1ium
total volatile solids
Microtox code 0
Concentration DW
1800
394.0
51.0
.50
25.99
Group: 3 Station: EV-07
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
phenanthrene 1600
total PCBs 155.0
nickel 50.0
thallium .40
B-80
-------
Group: 3 Station: EV-11
Toxicity code: 1 Benthic code: 0
Lowest AET
total PCBs
nickel
Group: 3 Station: SC-06
Toxicity code: 1 Benthic code: 0
Lowest AET
total PCBs
nickel
silver
zinc
mercury
Group: 3 Station: SC-07
Toxicity code: 1 Benthic code: 0
Lowest AET
total PCBs
copper
silver
zinc
mercury
Group: 3 Station: SC-08
Toxicity code: 3 Benthic code: 0
Lowest AET
total PCBs
nickel
silver
zinc
mercury
FINES
Microtox code 0
Concentration DW
171.0
46.0
Microtox code 0
Concentration DW
1253.0
44.0
3.70
330.0
1.38
Microtox code 0
Concentration DW
588.0
807
2.30
873.0
1.28
Microtox code 0
Concentration DW
646.0
45.0
2.29
311.0
1.21
.90
B-81
-------
Group: 3 Station: SC-14
Toxicity code: 3 Benthlc code:
Lowest AET
total PCBs
nickel
silver
zinc
mercury
FINES
Microtox code 0
Concentration DW
1672.0
43.0
2.32
272.0
1.57
.92
Group: 3 Station: SC-17
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
nickel
silver
zinc
mercury
Microtox code 0
Concentration DW
231.0
43.0
1.29
328.0
.70
Group: 3 Station: SC-18
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
silver
mercury
Microtox code 0
Concentration DW
229.0
1.56
.72
Group: 3 Station: SC-19
Toxicity code: 1 Benthic code: 0
Lowest AET
lead
nickel
silver
zinc
mercury
Microtox code 0
Concentration DW
360
45.0
1.36
343.0
2.07
B-82
-------
Group: 3 Station: SC-20
Toxicity code: 3 Benthic code:
Lowest AET
phenanthrene
fluorene
fluoranthene
benzo(a) anthracene
chrysene
benzo(ghi)perylene
pyrene
total PCBs
nickel
silver
mercury
Low molecular wt. PAH
High molecular wt. PAH
Microtox code 0
Concentration DW
8900
980.0
5200
1900.0
2000.0
1000.0
6400
384.0
44.0
2.67
1.64
11080.0
20140.0
Group: 3 Station: SM-01
Toxicity code: 3 Benthic code: 0
Lowest AET
bi s(2-ethylhexyl)phthalate
Microtox code 0
Concentration OW
2800.0
Group: 3 Station: SQ-17
Toxicity code: 1 Benthic code:
Lowest AET
nickel
Microtox code 0
Concentration DW
41.0
Group: 4 Station: DR-03
Toxicity code: 1 Benthic code: 0
Lowest AET
4,4'-DDD
Microtox code 0
Concentration DW
3.90
Lowest AET
4,4'-DDD
Concentration DW
2.60
B-83
-------
Group: 4 Station: DR-06
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET
4,4'-DDD
Concentration DW
3.20
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code:
Lowest AET
4,4'-DOT
4,4'-ODD
Microtox code 0
Concentration 0W
22.0
5.60
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code:
Lowest AET
acenaphthylene
b i s(2-ethylhexy1)phthalate
total PCBs
zinc
mercury
4,4'-D0D
Microtox code 0
Concentration DW
2400
2800.0
3900.0
270.0
.42
71.00
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code: 3
Lowest AET
N-n1trosod1phenyl amine
benzo(a)pyrene
Total benzofluoranthenes
benzo(gh1)perylene
d1benzo(a,h)anthracene
1ndeno(l,2,3-cd)pyrene
butyl benzyl phthalate
total PCBs
Iron
manganes
nickel
silver
mercury
4,4'-0DE
High molecular wt. PAH
Total phthalates
Microtox code 0
Concentration DW
132.0
9770.0
17529
8046.0
4023
2874.0
1724
1060.0
31000
300
44.0
4.60
.98
11.00
46897.0
4445.0
B-84
-------
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code:
Lowest AET
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
chrysene
pyrene
di-n-butyl phthalate
total PCBs
lead
nickel
silver
zinc
mercury
4,4'-DDE
4,4'-DDD
Low molecular wt. PAH
High molecular wt. PAH
Total phthalates
Microtox code 0
Concentration DW
3936.0
4293
2683.0
5367
9481.0
10376
6440
2147.0
3940.0
670
41.0
4.10
300.0
1.60
47.00
175.00
11431.0
31664.0
11628.0
Group: 6 Station: EB-36
Toxicity code: 0 Benthic code: 2
Lowest AET
N-nitrosodiphenylamine
acenaphthylene
chrysene
butyl benzyl phthalate
total PCBs
iron
manganes
nickel
silver
mercury
4,4'-DDE
Total phthalates
Microtox code 0
Concentration DW
54.0
4013
1605.0
334.0
3970.0
31000
400
56.0
5.40
.77
37.00
28327.0
B-85
-------
Group: 6 Station: EB-38
Toxicity code: 0 Benthic code: 1
Lowest AET
Mlcrotox code 0
Concentration DW
N-nitrosodiphenylamine 61.0
chrysene 1552.0
benzo(ghi)perylene 808.0
dibenzo(a,h)anthracene 471.0
total PCBs 730.0
iron 37000
manganes 420
nickel 49.0
silver 5.20
mercury .62
Group: 6 Station: WP-12
Toxicity code: 0 Benthic code;
Lowest AET
N-nitrosodiphenylamine
benzo(a)pyrene
iron
manganes
silver
Total phthalates
FINES
Microtox code 0
Concentration DW
75.0
1877.0
32000
580
3.70
6166.0
.91
Group: 6 Station: WP-13
Toxicity code: 0 Benthic code:
Lowest AET
total PCBs
iron
manganes
silver
FINES
Microtox code 0
Concentration DW
480.0
36000
520
3.70
.92
Group: 6 Station: WP-14
Toxicity code: 0 Benthic code:
Lowest AET
iron
manganes
silver
FINES
Microtox code 0
Concentration DW
34000
630
3.70
.96
B-86
-------
Group: 6 Station: WP-15
Toxicity code: 0 Benthic code: 1
Lowest AET
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
total PCBs
iron
manganes
silver
High molecular wt. PAH
FINES
Microtox code 0
Concentration DW
2173.0
3928
2440.0
1756.0
893.0
146.0
28000
430
3.30
13631.0
.97
Group: 6 Station: WP-16
Toxicity code: 0 Benthic code:
Lowest AET
total PCBs
iron
manganes
nickel
silver
4,4'-DDD
FINES
Microtox code 0
Concentration DW
275.0
27000
410
44.0
5.00
12.00
.96
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code: 3
Lowest AET
fluoranthene
total PCBs
iron
manganes
nickel
silver
mercury
4,4'-DDE
4,4'-DDD
Total phthalates
Microtox code 0
Concentration DW
1703
2280.0
30000
310
44.0
1.10
1.00
30.00
30.00
3648.0
B-87
-------
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code:
Lowest AET
N-nitrosodiphenyl amine
anthracene
phenanthrene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
dibenzo(a,h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
butyl benzyl phthalate
di-n-butyl phthalate
dimethyl phthalate
total PCBs
lead
nickel
silver
zinc
mercury
High molecular wt. PAH
Total phthalates
Microtox code 0
Concentration DW
276.0
1636.0
2574
5882
3125.0
5882.0
11213
5147.0
5147.0
1048
4412.0
6250
1820
2941.0
88.0
965.0
430
49.0
.94
260.0
1.30
48106.0
42800.0
Group: 7 Station: EB-36
Toxicity code: 0 Benthic code: 3
Lowest AET
N-nitrosodiphenyl amine
butyl benzyl phthalate
d1-n-butyl phthalate
diethyl phthalate
4,4'-DDT
total PCBs
nickel
silver
4,4'-DDE
Total phthalates
Microtox code 0
Concentration DW
308.0
67.0
4103.0
318.0
15.0
487.0
50.0
1.30
10.00
4791.0
B-88
-------
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code:
Lowest AET
phenanthrene
chrysene
butyl benzyl phthalate
4,4'-DDT
total PCBs
manganes
nickel
silver
mercury
Total phthalates
Microtox code 0
Concentration DW
2970
1802.0
812.0
28.0
315.0
310
58.0
2.20
.72
5840.0
Group: 7 Station: WP-01
Toxicity code: 0 Benthic code: 1
Lowest AET
manganes
Microtox code 0
Concentration DW
440
Group: 7 Station: WP-02
Toxicity code: 0 Benthic code:
Lowest AET
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
pyrene
total PCBs
manganes
High molecular wt. PAH
Lowest AET
manganes
Microtox code 0
Concentration DW
2744
1715.0
3602.0
4117
2058.0
1235.0
1012.0
3774
149.0
380
20429.0
Concentration DW
340
B-89
-------
Group: 7 Station: WP-05
Toxicity code: 0 Benthic code:
Lowest AET
di-n-butyl phthalate
manganes
Microtox code 0
Concentration DW
1423.0
420
Group: 7 Station: WP-06
Toxicity code: 0 Benthic code:
Lowest AET
manganes
Microtox code 0
Concentration DW
520
B-90
-------
Group: 7 Station: WP-07
Toxicity code: 0 Benthic code: 1 Microtox code 0
Lowest AET
Concentration DW
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
chrysene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
di-n-butyl phthalate
4,4'-DDT
manganes
High molecular wt. PAH
1757
1757.0
1622.0
2297.0
2568.0
2432.0
1622.0
10.0
460
17028.0
Group: 7 Station: WP-08
Toxicity code: 0 Benthic code:
Lowest AET
di-n-butyl phthalate
total PCBs
manganes
mercury
Microtox code 0
Concentration DW
2035.0
161.0
630
.47
Group: 7 Station: WP-09
Toxicity code: 0 Benthic code:
Lowest AET
manganes
nickel
Microtox code 0
Concentration DW
1000
40.0
Group: 7 Station: WP-10
Toxicity code: 0 Benthic code: 1
Lowest AET
benzo(gh1)perylene
manganes
Total phthalates
Microtox code 0
Concentration DW
944.0
360
3705.0
B-91
-------
Group: 7 Station: WP-11
Toxicity code: 0 Benthic code:
Lowest AET
acenaphthylene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
total PCBs
Low molecular wt. PAH
High molecular wt. PAH
Microtox code 0
Concentration DW
643.0
1273.0
3150
643.0
6299
4462.0
6824.0
8005
6693.0
5381.0
1155
5249.0
7349
131.0
6139.0
51417.0
Group: 7 Station: WP-12
Toxicity code: 0 Benthic code:
Lowest AET
butyl benzyl phthalate
iron
manganes
FINES
Microtox code 0
Concentration 0W
307.0
32000
460
.97
Group: 7 Station: WP-13
Toxicity code: 0 Benthic code:
Lowest AET
di-n-butyl phthalate
Iron
manganes
nickel
Total phthalates
FINES
Microtox code 0
Concentration DM
4474.0
29000
380
42.0
7123.0
.89
B-92
-------
Group: 7 Station: WP-14
Toxicity code: 0 Benthic code:
Lowest AET
N-nitrosodiphenyl amine
fluoranthene
butyl benzyl phthalate
total PCBs
iron
manganes
nickel
silver
mercury
Total phthalates
FINES
Microtox code 0
Concentration DW
63.0
2375
259.0
145.0
30000
380
40.0
.61
.88
69723.0
.91
Group: 7 Station: WP-15
Toxicity code: 0 Benthic code: 1
Lowest AET
butyl benzyl phthalate
4,4'-DDT
total PCBs
iron
manganes
silver
FINES
Microtox code 0
Concentration DM
287.0
11.0
221.0
30000
450
.58
.93
Group: 7 Station: WP-16
Toxicity code: 0 Benthic code:
Lowest AET
iron
manganes
nickel
silver
FINES
Microtox code 0
Concentration DW
30000
500
40.0
.58
.93
Group: 8 Station: EV-20
Toxicity code: 1 Benthic code:
Lowest AET
acenaphthene
Microtox code 0
Concentration DW
558.0
B-93
-------
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 5400.0
mercury .83
4,4'-DDE 41.00
4,4'-DDD 38.00
Group: 9 Station: DR-11
Toxicity code: 3 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 530.0
4,4'-DDD 7.80
Group: 9 Station: DR-13
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 215.0
mercury .42
4,4'-DDD 3.60
Group: 9 Station: DR-14
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 150.0
lead 700
4,4'-DDD 6.10
Group: 9 Station: DR-19
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 330.0
4,4'-DDD 4.30
B-94
-------
Group: 9 Station: DR-22
Toxicity code: 1 Benthic code:
Lowest AET
total PCBs
4,4'-DDD
Microtox code 0
Concentration DW
180.0
2.40
Group: 9 Station: DR-23
Toxicity code: 1 Benthic code: 0
Lowest AET
total PCBs
mercury
4,4'-DDE
4,4'-DDD
Microtox code 0
Concentration DW
1800.0
.68
11.00
29.00
Group: 9 Station: DR-25
Toxicity code: 3 Benthic code:
Lowest AET
total PCBs
zinc
4,4'-DDD
Microtox code 0
Concentration DW
790.0
523.0
14.00
Group: 9 Station: DR-26
Toxicity code: 3 Benthic code:
Lowest AET
total PCBs
zinc
4,4*-DDD
Microtox code 0
Concentration DW
170.0
1211.0
2.30
Group: 9 Station: DR-27
Toxicity code: 3 Benthic code:
Lowest AET
cadmium
zinc
mercury
4,4'-DDD
Microtox code 0
Concentration DW
10.40
2600.0
2.30
3.40
B-95
-------
Group: 9 Station: DR-28
Toxicity code: 1 Benthlc code: 0
Lowest AET
fluoranthene
total PCBs
4,4'-DDE
4,4'-DDD
Hlcrotox code 0
Concentration DW
1900
2500.0
15.00
43.00
Group: 9 Station: DR-29
Toxicity code: 1 Benthic code: 0
Lowest AET
total PCBs
zinc
mercury
4,4'-DDE
4,4'-DDD
Mlcrotox code 0
Concentration DW
2200.0
336.0
.46
15.00
35.00
Group: 9 Station: DR-30
Toxicity code: 1 Benthlc code: 0
Lowest AET
total PCBs
4,4'-DDD
Mlcrotox code 0
Concentration DW
650.0
9.20
Group: 9 Station: DR-31
Toxicity code: 1 Benthlc code: 0
Lowest AET
total PCBs
4,4'-DDD
Mlcrotox code 0
Concentration DW
560.0
24.00
Group: 9 Station: DR-33
Toxicity code: 1 Benthlc code:
Lowest AET
total PCBs
4,4*-DDE
4,4'-DDD
Mlcrotox code 0
Concentration DW
1200.0
10.00
14.00
B-96
-------
Group: 9 Station: DR-34
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 1300.0
4,41-DDD 16.00
Group: 9 Station: DR-35
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 620.0
mercury .58
4,4'-DDD 15.00
Group: 9 Station: DR-36
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 1500.0
mercury 1.10
4,4'-DDE 9.90
4,4'-DDD 25.00
Group: 9 Station: DR-38
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
total PCBs 1400.0
4,4'-DDD 14.00
Group: 9 Station: DR-39
Toxicity code: 1 Benthic code: 0 Microtox code 0
Lowest AET Concentration DW
mercury .85
4,4'-DDD 2.30
B-97
-------
TABLE B-6. STATION LISTING OF CHEMICALS EXCEEDING COMMENCEMENT BAY DRY
WEIGHT NORMALIZED AET (NON-COMMENCEMENT BAY STATIONS)
Organic compounds expressed as ppb dry weight, metals as ppm dry weight
Group: 3 Station: BH-03
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
copper 400
nickel 73.5
mercury 1.35
total volatile solids 26.93
Group: 3 Station: BH-04
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 89.6
mercury 1.69
Group: 3 Station: BH-05
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 111.0
Group: 3 Station: BH-07
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 105.0
B-98
-------
Group: 3 Station: BH-11
Toxicity code: 1 Benthic code: 0
Microtox code 0
Amphipod Concentration DW
nickel 118.0
Group: 3 Station: BH-12
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 72.0
Group: 3 Station: BH-23
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 102.0
Group: 3 Station: BH-24
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
nickel 117.0
Group: 3 Station: CS-01
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
phenol 560
B-99
-------
Group: 3 Station: DB-15
Toxicity code: 3 Benthic code: 0
Amphipod
nickel
Group: 3 Station: EB-09
Toxicity code: 1 Benthic code: 0
Amphipod
mercury
Group: 3 Station: EB-10
Toxicity code: 1 Benthic code: 0
Amphipod
acenaphthene
phenanthrene
zinc
Group: 3 Station: EB-17
Toxicity code: 1 Benthic code: 0
Amph i pod
total PCBs
Group: 3 Station: EB-20
Toxicity code: 1 Benthic code: 0
Amphipod
total PCBs
Microtox code 0
Concentration DW
46.0
Microtox code 0
Concentration DW
1.69
Microtox code 0
Concentration DW
630.0
2100
687.0
Microtox code 0
Concentration DW
646.0
Microtox code 0
Concentration DW
640.0
B-100
-------
Group: 3 Station: EB-22
Toxicity code: 1 Benthic code: 0
Microtox code 0
Amph1pod
total PCBs
Group: 3 Station: EV-01
Toxicity code: 3 Benthic code: 0
Concentration DW
687.0
Microtox code 0
Amphipod
phenanthrene
fluoranthene
total PCBs
nickel
thallium
Concentration DW
1900
4800
445.0
44.0
.50
Group: 3 Station: EV-02
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amph1pod
nickel
Concentration DW
48.0
Group: 3 Station: EV-03
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
nickel
total volatile solids
Concentration DW
516.0
43.0
25.44
B-101
-------
Group: 3 Station: EV-04
Toxicity code: 3 Benthic code:
Microtox code 0
Amphipod
phenol
acenaphthene
naphthalene
phenanthrene
fluorene
fluoranthene
total PCBs
nickel
thailium
zinc
total volatile solids
total organic carbon
Low molecular wt. PAH
Concentration DW
1400
3300.0
5900.0
4700
2100.0
4100
965.0
45.0
.30
1074.0
35.06
15.42
17180.0
Group: 3 Station: EV-05
Toxicity code: 3 Benthic code:
Microtox code 0
Amphipod
nickel
thallium
total volatile solids
Concentration DW
51.0
.50
25.99
Group: 3 Station: EV-07
Toxicity code: 1 Benthic code:
0 Microtox code 0
Amphipod
phenanthrene
nickel
thailium
Concentration DM
1600
50.0
.40
B-102
-------
Group: 3 Station: EV-11
Toxicity code: 1 Benthic code: 0 Mlcrotox code 0
Amphipod Concentration DW
nickel 46.0
Group: 3 Station: SC-06
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 1253.0
nickel 44.0
mercury 1.38
Group: 3 Station: SC-07
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 588.0
copper 807
zinc 873.0
mercury 1.28
Group: 3 Station: SC-08
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 646.0
nickel 45.0
mercury 1.21
Group: 3 Station: SC-14
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 1672.0
nickel 43.0
mercury 1.57
B-103
-------
Group: 3 Station: SC-17
Toxicity code: 1 Benthic code:
0
Microtox code 0
Amphipod
nickel
Group: 3 Station: SC-19
Toxicity code: 1 Benthic code: 0
Amphipod
nickel
mercury
Group: 3 Station: SC-20
Toxicity code: 3 Benthic code: 0
Amphipod
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(ghi)perylene
pyrene
nickel
mercury
Low molecular wt. PAH
High molecular wt. PAH
Group: 3 Station: SQ-17
Toxicity code: 1 Benthic code: 0
Amphipod
nickel
Concentration DW
43.0
Microtox code 0
Concentration DW
45.0
2.07
Microtox code 0
Concentration DW
8900
980.0
5200
1900.0
1000.0
6400
44.0
1.64
11080.0
20140.0
Microtox code 0
Concentration DW
41.0
B-104
-------
Group: 4 Station: DR-07
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amph i pod
4,4'-DDT
Concentration DW
22.0
Group: 4 Station: DR-08
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod
total PCBs
Concentration DW
3900.0
Group: 6 Station: EB-33
Toxicity code: 0 Benthic code:
3 Microtox code 0
Benthic
N-nitrosodiphenylamine
benzo(a)pyrene
Total benzofluoranthenes
benzo(ghi)perylene
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
butyl benzyl phthalate
di-n-octyl phthalate
antimony
iron
manganes
nickel
mercury
High molecular wt. PAH
Concentration DW
132.0
9770.0
17529
8046.0
4023
2874.0
1724
2615
3.20
31000
300
44.0 *
46897.0
B-105
-------
Group: 6 Station: EB-35
Toxicity code: 0 Benthic code:
Microtox code 0
Benthic
Concentration DM
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
chrysene
pyrene
di-n-octyl phthalate
total PCBs
antimony
lead
manganes
nickel
zinc
mercury
Low molecular wt. PAH
High molecular wt. PAH
Total phthalates
3936.0
4293
2683.0
5367
9481.0
10376
6440
9481
3940.0
3.30
670
220
41.0
300.0
1.60
11431.0
31664.0
11628.0
Group: 6 Station: EB-36
Toxicity code: 0 Benthic code: 2 Microtox code 0
Benthic Concentration DW
N-nitrosodiphenylamine 54.0
di-n-octyl phthalate 27759
total PCBs 3970.0
iron 31000
manganes 400
nickel 56.0 *
mercury .77
Total phthalates 28327.0
B-106
-------
Group: 6 Station: E8-38
Toxicity code: 0 Benthic code: 1 Mfcrotox code 0
Benthic
Concentration OW
N-nitrosodiphenylamine
Total benzofluoranthenes
benzo(ghi)perylene
dibenzo(a,h)anthracene
di-n-octyl phthalate
antimony
iron
manganes
nickel
mercury
61.0
3570
808.0
471.0
1717
3.20
37000
420
49.0
.62
Group: 6 Station: WP-12
Toxicity code: 0 Benthic code:
1 Hicrotox code 0
Benthic
Concentration 0W
N-nitrosodiphenylamine
benzo(a)pyrene
Total benzofluoranthenes
di-n-octyl phthalate
iron
manganes
Total phthalates
75.0
1877.0
3405
6166
32000
580
6166.0
Group: 6 Station: WP-13
Toxicity code: 0 Benthic code:
1 Hicrotox code 0
Benthic
Concentration OW
di-n-octyl phthalate
iron
manganes
530.0
36000
520
8-107
-------
Group: 6 Station: WP-14
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
Concentration DW
iron
manganes
34000
630
Group: 6 Station: WP-15
Toxicity code: 0 Benthic code:
1 Microtox code 0
Benthic
Concentration DW
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
di-n-octyl phthalate
iron
manganes
High molecular wt. PAH
2173.0
3928
2440.0
1756.0
893.0
625.0
28000
430
13631.0
Group: 6 Station: WP-16
Toxicity code: 0 Benthic code:
Benthic
di-n-octyl phthalate
iron
manganes
nickel
Microtox code 0
Concentration DW
1331
27000
410
44.0 *
B-108
-------
Group: 7 Station: EB-33
Toxicity code: 0 Benthic code: 3
Microtox code 0
Benthic
di-n-octyl phthalate
total PCBs
iron
manganes
nickel
mercury
Group: 7 Station: EB-35
Toxicity code: 0 Benthic code: 2
Benthic
N-nitrosodiphenylamine
anthracene
phenanthrene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
butyl benzyl phthalate
di-n-octyl phthalate
lead
nickel
zinc
mercury
High molecular wt. PAH
Total phthalates
Concentration DW
3243
2280.0
30000
310
44.0
1.00
Microtox code 0
Concentration DW
276.0
1636.0
2574
5882
3125.0
5882.0
11213
5147.0
5147.0
1048
4412.0
6250
1820
37868
430
49.0 *
260.0
1.30
48106.0
42800.0
B-109
-------
Group: 7 Station: EB-36
Toxicity code: 0 Benthic code: 3
Microtox code 0
Benthic
Concentration DW
N-nitrosodiphenyl amine
4,4'-DDT
manganes
nickel
308.0
15.0
210
50.0
Group: 7 Station: EB-38
Toxicity code: 0 Benthic code: 3 Microtox code 0
Benthic
phenanthrene
butyl benzyl phthalate
di-n-octyl phthalate
4,4'-DDT
manganes
nickel
mercury
Total phthalates
Concentration DW
2970
812.0
4158
28.0
310
58.0 *
.72
5840.0
Group: 7 Station: WP-01
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
manganes
Concentration DW
440
B-110
-------
Group: 7 Station: WP-02
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
pyrene
manganes
High molecular wt. PAH
Concentration DW
2744
1715.0
3602.0
4117
1235.0
1012.0
3774
380
20429.0
Group: 7 Station: WP-03
Toxicity code: 0 Benthic code:
2 Microtox code 0
Benthic
di-n-octyl phthalate
Concentration DW
423.0
Group: 7 Station: WP-04
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
manganes
Concentration DW
340
Group: 7 Station: WP-05
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
manganes
Concentration DW
420
B-lll
-------
Group: 7 Station: WP-06
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
manganes
Concentration DW
520
Group: 7 Station: WP-07
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
benzo(a)anthracene
benzo(a)pyrene
benzo(ghi)perylene
indeno(l,2,3-cd)pyrene
pyrene
di-n-octyl phthalate
4,4'-ODT
manganes
High molecular wt. PAH
Concentration DW
1757.0
1622.0
2568.0
2432.0
2838
500.0
10.0
460
17028.0
Group: 7 Station: WP-08
Toxicity code: 0 Benthic code: 1
Microtox code 0
Benthic
manganes
Concentration DW
630
Group: 7 Station: WP-09
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
manganes
nickel
Concentration DW
1000
40.0
B-112
I
-------
Group: 7 Station: WP-10
Toxicity code: 0 Benthic code: 1 Mlcrotox code 0
Benthic
Concentration DW
benzo(ghi)perylene
di-n-octyl phthalate
manganes
944.0
3571
360
Group: 7 Station: WP-11
Toxicity code: 0 Benthic code: 1
Microtox code 0
Benthic
acenaphthylene
anthracene
phenanthrene
fluorene
fluoranthene
benzo(a)anthracene
benzo(a)pyrene
Total benzofluoranthenes
chrysene
benzo(ghi)perylene
dibenzo(a.h)anthracene
indeno(l,2,3-cd)pyrene
pyrene
Low molecular wt. PAH
High molecular wt. PAH
Concentration DW
643.0 *
1273.0
3150
643.0
6299
4462.0
6824.0
8005
6693.0
5381.0
1155
5249.0
7349
6139.0
51417.0
Group: 7 Station: WP-12
Toxicity code: 0 Benthic code:
1 Microtox code 0
Benthic
Concentration DW
Iron
manganes
FINES
32000
460
.97
B-113
-------
Group: 7 Station: WP-13
Toxicity code: 0 Benthic code: 1 Microtox code 0
Benthic
Concentration DW
di-n-octyl phthalate
iron
manganes
nickel
Total phthalates
2605
29000
380
42.0
7123.0
Group: 7 Station: WP-14
Toxicity code: 0 Benthic code:
1 Microtox code 0
Benthic
N-n i trosod i phenyl ami ne
fluoranthene
di-n-octyl phthalate
iron
manganes
nickel
silver
mercury
Total phthalates
FINES
Concentration DW
63.0
2375
68602
30000
380
40.0 *
.61
.88
69723.0
.91
Group: 7 Station: WP-15
Toxicity code: 0 Benthic code:
1 Microtox code 0
Benthic
di-n-octyl phthalate
4,4'-DDT
iron
manganes
silver
FINES
Concentration DW
1264
11.0
30000
450
.58
.93
B-114
-------
Group: 7 Station: WP-16
Toxicity code: 0 Benthic code: 1
Microtox code 0
Benthic
iron
manganes
nickel
silver
FINES
Group: 8 Station: EV-20
Toxicity code: 1 Benthic code: 0
Amphipod
acenaphthene
Group: 9 Station: DR-10
Toxicity code: 3 Benthic code: 0
Amphipod
total PCBs
Group: 9 Station: DR-11
Toxicity code: 3 Benthic code: 0
Amphipod
total PCBs
Group: 9 Station: DR-14
Toxicity code: 1 Benthic code: 0
Amphipod
lead
Concentration DW
30000
500
40.0 *
.58
.93
Microtox code 0
Concentration OW
558.0
Microtox code 0
Concentration DW
5400.0
Microtox code 0
Concentration OW
530.0
Microtox code 0
Concentration DW
700
8-115
-------
Group: 9 Station: DR-23
Toxicity code: 1 Benthic code: 0
Microtox code 0
Amphipod Concentration DW
total PCBs 1800.0
Group: 9 Station: DR-25
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 790.0
zinc 523.0
Group: 9 Station: DR-26
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
zinc 1211.0
Group: 9 Station: DR-27
Toxicity code: 3 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
cadmium 10.40
zinc 2600.0
mercury 2.30
Group: 9 Station: DR-28
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 2500.0
8-116
-------
Group: 9 Station: 0R-29
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 2200.0
Group: 9 Station: DR-30
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 650.0
Group: 9 Station: DR-31
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 560.0
Group: 9 Station: DR-33
Toxicity code: I Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 1200.0
Group: 9 Station: DR-34
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 1300.0
8*117
-------
Group: 9 Station: DR-35
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 620.0
Group: 9 Station: DR-36
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 1500.0
Group: 9 Station: DR-38
Toxicity code: 1 Benthic code: 0 Microtox code 0
Amphipod Concentration DW
total PCBs 1400.0
B-118
-------
TABLE B-6A. COMMENCEMENT BAY AET SEDIMENT QUALITY VALUES3
(ug/kg dry weight for organics; mg/kg dry weight for metals)
Benthic
Amphipod
Chemical
AET
AET
Low molecular weight PAH
5,200
5,200
naphthalene
2,100
2,100
acenaphthylene
>560
>560
acenaphthene
500
500
fluorene
540
540
phenanthrene
1,500
1,500
anthracene
960
960
High molecular weight PAH
18,000
12,000
fluoranthene
3,900
1,900
pyrene
4,300
2,600
benzo(a)anthracene
1,600
1,300
chrysene
2,800
2,300
benzof1uoranthenes
3,700
3,000
benzo(a)pyrene
2,400
1,600
indeno(1,2,3-c,d)pyrene
690
690
dibenzo(a,h)anthracene
260
260
benzo(g,h,i jperylene
740
740
Total PCBs
420
1,100
Total chlorinated benzenes
670
400
1,3-dichlorobenzene
>170
>170
1,4-di chlorobenzene
260
120
1,2-di chlorobenzene
>350
50
1,2,4-trichlorobenzene
51
64
hexachlorobenzene (HCB)
130
230
Total phthalates
>5,100
>5,100
dimethyl phthalate
160
160
diethyl phthalate
>73
>73
di-n-butyl phthalate
>5,100
>5,100
butyl benzyl phthalate
>470
>470
bis(2-ethylhexyl)phthalate
>3,100
1,900
di-n-octyl phthalate
>420
>420
B-U9
-------
TABLE B-6A. (Continued)
Amphi pod
Benthic
Chemical
AET
AET
Pesticides
p.p'-DDE
p,p'-DDD
p.p'-DDT
3.9
>5.8
aldrin
chlordane
dieldrin
heptachlor
gamma-HCH (lindane)
Phenols
Phenol
500
1,200
2-methylphenol
63
>72
4-methylphenol
1,200
670
2,4-dimethylphenol
>50
29
pentachlorophenol
>140
>140
Miscellaneous extractables
hexachloroethane
_ _
hexachlorobutadiene
290
270
1-methylphenanthrene
310
370
1-methylnaphthalene
670
670
biphenyl
260
270
dibenzothiophene
240
250
dibenzofuran
540
540
benzyl alcohol
73
73
benzoic acid
>690
650
N-ni trosodi phenyl ami ne
220
28
Volatile organics
trichloroethene
tetrachloroethene
>210
140
ethyl benzene
>50
37
total xylenes
>160
120
Metals (mg/kg dry weight)
antimony
5.3
3.1
arsenic
93
85
beryl 1ium
—
—
cadmium
6.7
5.8
chromi um
—
—
copper
310
310
iron
27,000
26,000
lead
660
300
manganese
230
200
mercury
1.1
0.52
B-120
-------
TABLE B-6A. (Continued)
nickel 39 39
selenium
silver >0.56 >0.56
thallium 0.24 0.24
zinc 490 260
Conventional variables
total organic carbon 15% 15%
total volatile solids 22% 22%
percent fine-grained >89% >89%
">" in AET columns indicate that a definite AET could not be established because
there were no impacted stations with chemical concentrations above the highest con-
centration among nonimpacted stations.
B-121
-------
APPENDIX C
SUMMARY OF REVIEW OF DATA FOR INCLUSION
IN PUGET SOUND DATABASE
-------
TABLE C-l.
SUMMARY
OF REVIEW OF
PUGET SOUND
DATA
Chemistry
Benthic Infauna
Toxicology
Study (References)*
Analytical
Techniques
Detection
Llalts
Scope of
Cheaicals
Synoptic?
Saapli ng/
Subsaapling
Replicat ion
Reference
sites available?
Synoptic?
Frozen?
Accepted
Technique?
Alkl Extension (9,15)
OK
OK
OK
Yes
OK
OK (S rep)
Yes
N.A.
N.A.
N.A.
Coaaenceaent Bay (14)
OK
high for
pesticides
Halted
volatiles
Yes
OK
OK (4 rep)
Yes
Yes
No
Yes
OuMWlsh Head (12)
OK
OK
OK
Yes
OK
Excluded'
—9
Yes
Excluded*!
—9
Duwaalsh River I, II (2)
OK
OK
no volatlles,
acids
N.A.d
N.A.
N.A.
N.A.
Yes
No
Yes
Eight Bay (1)
OK
high for OK
aany organics
Yes
Excluded*
—9
—9
Yes
No
Yes
Everett Harbor (16) cores
c
OK
aostly PAH
N.A.
N.A.
N.A.
N.A.
Yes
No
Yes
(16) grabs
c
OK
aostly PAH
Yes
OK
OK (5 rep)
Excluded11
N.A.
N.A.
N.A.
OHPA 2, 19 (6,7)
OK
OK
no volatlles,
polars
Yes
Excluded*
—9
—9
N.A.
N.A.
N.A.
Seahurst (4,5,8,ll,13,17)b
TPPS (3,10) Phase IIIA
Phase 11 IB
OK
OK
OK
OK
no volatiles
no volatiles
Yes
Yes
OK
OK
OK (4 rep)
OK (4 rep)
No for 17/26 sta.
No for 6/26 sta.
Yes
Excluded'
Excluded-!
—g
—9,k
—g
-------
a References:
1.
Battelle (1985a)
2.
Chan et al. (1985a,b)
3.
Comiskey et al. (1984)
4.
Dinnell et al. (1984)
5.
Landolt et al. (1984)
6.
Mai ins et al. (1980)
7.
Malins et al. (1982)
8.
Nevissi et al. (1984)
9.
Osborn et al. (1985)
10.
Romberg et al. (1984)
11.
Stober et al. (1983)
12.
Stober et al. 1984a)
13.
Stober et al. 1984b)
14.
Tetra Tech, Inc. (1985)
15.
Trial et al. (1985)
16.
U.S. Department of the Navy (1985)
17.
Word et al. (1984).
b The chemical and biological data for the Seahurst study are not currently
in a form that allows for easy compilation, although infaunal data are
of good quality. Sampling efforts were not always focused on the same
stations for biological and chemical tasks of the seven sampling phases
of this study. A station-by-station correlation of all data is not currently
available. Furthermore, sediments used for benthic infaunal analyses were
collected up to 6 months before sediments collected for chemical analyses
(Matsuda, B., 20 September 1985, personal communication). This time lag
may not be critical for stations resampled at the identical location, but
it adds uncertainty. Because of the intractability of the data and uncertain-
ties regarding how many of the data are synoptic, Seahurst infaunal data
are not recommended for use in this project. Sediments used for amphipod
bioassays were not collected synoptical ly with sediments used for chemical
analyses.
c The selected organic priority pollutants reported in this study (almost
exclusively PAH) were analyzed by procedures subject to interferences by
fatty acid methyl esters and related compounds. The relatively nonspecific
GC detection system used for PAH (flame ionization detection, as opposed
to mass spectrometry) and the co-elution of interfering substances with
PAH resulted in an inability to confirm all compound identities and their
quantification. In addition, a confirmation column was not used to verify
peaks identified as PCBs. However, the data were accepted after a careful
review.
d Not applicable (i.e., this biological indicator was not tested).
e These infaunal data were excluded from the database because of the subsampling
procedure used. Grab samples were subsampled with cores after retrieval.
As noted in "Recommended protocols for sampling and analyzing subtidal
benthic macroinvertebrate assemblages in Puget Sound" [Puget Sound Estuary
Program; Tetra Tech (1986) draft]: "Subsamples are not recommended for
benthic infaunal analyses because it is unknown what effect the sampling
C-2
-------
process has on the spatial distribution of motile organisms. For example,
surface-dwelling organisms may move to the edges of the sample as the grab
is being retrieved. If the sampling process disrupts the natural spatial
patterns of the organisms, collection of a representative subsample for
infaunal analysis may not be possible."
f Only one or two replicates were available for infaunal stations in this
study. Such levels of replication could not provide an estimate of variance
that would be comparable to infaunal data from the other studies considered
(these studies had either four or five replicates).
9 Dashes indicate that the data from this study have already been excluded
because of another review criterion.
^ These infaunal samples were not sieved prior to analysis. No appropriate
unsieved reference samples were available for statistical comparisons.
i Bioassay and chemistry samples were not taken from the same sediment
homogenate. They were collected at different times.
J Freezing may alter sediment properties (e.g., effective particle size)
and is therefore not recommended (Swartz et al. 1984). Samples that were
stored at 4° C are not excluded. Sediments used for microtox bioassays
in the Commencement Bay Remedial Investigation were stored for less than
3 wk at 4° C in test tubes that were flushed with nitrogen and then sealed.
Under these inert atmospheric conditions, the storage time is not expected
to have an effect on the results (see Appendix H).
k Uncertainties about the reliability of the Phase IIIA bioassays are described
in TPPS documents (Comiskey et al. 1984). The bioassay method used in
Phase IIIA was not consistent with methods used in the other studies considered.
C-3
-------
APPENDIX D
EVALUATION OF STATISTICAL RELATIONSHIPS AMONG CHEMICAL AND
BIOLOGICAL VARIABLES USING PATTERN RECOGNITION TECHNIQUES
by
Tetra Tech, Inc. / G.A. Erickson & Associates
prepared for
Resource Planning Associates
for
Puget Sound Dredged Disposal Analysis
and
Puget Sound Estuary Program
-------
CONTENTS
Page
TABLE OF CONTENTS D-ii
LIST OF FIGURES D-iv
LIST OF TABLES D-v
INTRODUCTION D-l
OBJECTIVES D-I
APPROACH TO EXPLORATORY MULTIVARIATE DATA ANALYSIS D-l
ANALYTICAL SCOPE D-2
DISCUSSION OF MAJOR RESULTS D-7
GENERAL APPROACH D-7
CHEMICAL FACTORS D-10
BIOLOGICAL FACTORS D-10
BIOLOGICAL-CHEMICAL RELATIONSHIPS D-l 1
UTILITY OF ORGANIC CARBON OR GRAIN-SIZE NORMALIZATIONS D-27
EFFECTS OF STATION LOCATION ON FACTOR LOADINGS D-28
SUMMARY D-32
MAJOR STATISTICAL RELATIONSHIPS D-32
RECOMMENDATIONS FOR DEVELOPING SEDIMENT QUALITY VALUES D-35
ADDITIONAL ANALYSES RECOMMENDED TO REFINE OR VERIFY RESULTS D-35
EXHIBIT D-l - TECHNICAL BACKGROUND ON PATTERN RECOGNITION TECHNIQUES D-38
D-ii
-------
CONTENTS - EXHIBIT D-l
Page
OVERVIEW OF MULTIVARIATE DATA ANALYSIS METHODS D-39
PREPROCESSING D-39
DISPLAY 0-39
UNSUPERVISED LEARNING D-40
SUPERVISED LEARNING D-42
TYPICAL SEQUENCE OF METHODS D-43
PATTERN RECOGNITION TECHNIQUES APPLIED TO COMMENCEMENT BAY DATA D-46
DATA SETS EVALUATED D-46
DATA ENTRY AND VALIDATION D-55
CHEMICAL EVALUATIONS D-55
BIOLOGICAL EVALUATIONS D-56
DOCUMENTATION D-b6
METHODS D-57
DATA PREPARATION AND DATA SET CREATION D-57
COMPUTER RUNS D-58
SPECIFIC RESULTS FROM INDIVIDUAL AND COMPARED COMPUTER RUNS D-58
INTERACTIVE SCREEN LOG FOR RUN #17 D-68
D-iii
-------
FIGURES
Number Page
1 Area map of Commencement Bay and Carr Inlet D-3
2 Scatterplot of abundance of Praxi11 el 1 a graci1is and
sediment concentration of pyrene D-l9
3 Scatterplot of a major "biological" factor and a
"bioassay/conventional chemical" factor D-21
4 Scatterplot of a major "biological" factor and a
"chlorinated butadiene" factor D~23
5 Scatterplot of a "biological" factor and an
"organic enrichment/sediment toxicity" factor D-24
6 Hierarchial classification analysis using 5 factors
from factor analysis of 64 numerically dominant
infaunal species 0-26
7 Scatterplot of a "chlorinated organics" factor and a
"PAH" factor D~30
8 Scatterplot of a "chlorinated organics" factor and a
"metals" factor 0-31
D-iv
-------
TABLES
Number Page
1 Frequent significant correlations among chemical or
conventional variables and biological variables D-15
2 Number of occurrences of anti-correlated variables in a
mixed chemical-biological factor from factor analyses of
data subsets D-17
3 Critical concentrations of chemicals indicated by
sensitive species D-20
D-l Variables in CHEMBIO data set, combined chemical and
biological data for 56 stations in the Commencement
Bay area d-47
D-Z Variables in MSQSGVAL data set (data set contains 144
sediment samples in the Commencement Bay area) D-50
0-3 Variables contained in CB2.DAT extended benthic data set D-53
D-4 Pattern recognition analysis - computer runs D-59
D-5 Comparison of means and standard deviations for variables
in MSQSGVAL (144 stations) and CHEMBIO (56 stations) D-61
D-6 Outlier variables from pattern recognition analysis 0-62
D-7 Chemical factor differences, Run #5-IMSQ2 vs Run #10-IMSQ7 0-65
D-8 Chemical variables with >1 significant (P<0.05) correla-
tions with biological variables D-67
D-v
-------
INTRODUCTION
OBJECTIVES
The main findings of the application of a pattern recognition software
system (ARTHUR) to sediment chemistry and biological data from 56 Commencement
Bay and Carr Inlet stations are presented in this appendix. As specified
in the Sediment Quality Values Work Plan (August 1985), the objectives
of the pattern recognition analysis task were:
• To identify statistical relationships among sediment contaminants
and biological effects
• To identify relationships that may be useful in developing
sediment quality values
• To summarize additional analyses that may be needed in the
future to refine or verify the apparent relationships.
These analyses were not intended to be used to establish sediment quality
values, but apparent associations identified by the analyses provided guidance
for the appropriate application of approaches to develop chemical-specific
sediment quality values. All objectives were realized. The pattern recognition
analyses were successful in:
• Providing corroboration of trends among chemical variables
in a Commencement Bay data set and an independent Puget Sound
chemical data set that had been previously analyzed using ARTHUR
(Quinlin et al. 1984; more limited site-specific bioeffects
information was available for this earlier study)
• Confirming chemical-biological trends that had been previously
identified in the Commencement Bay data set using alternative
data analysis techniques (Tetra Tech 1985a)
• Identifying new relationships among chemical and biological
indicators (e.g., apparent "sensitive species" to certain
chemical contaminants) that warrant additional investigation
• Providing evidence that dry-weight as well as organic carbon
normalization of chemical data resulted in interpretable trends
with respect to biological effects.
APPROACH TO EXPLORATORY MULTIVARIATE DATA ANALYSIS
The problem represented by the Commencement Bay data is the potential
complexity of relationships expected among toxic materials, physical/chemi-
cal parameters of the system, and biological communities. The purpose
of exploratory analysis is to use multivariate techniques to quickly determine
D-l
-------
unbiased relationships among the samples or among the 64 benthic abundance
variables, 15 taxonomic fijroup variables, 3 bioassay variables, 1 total
species count variable, 10 conventional chemistry and grain size variables,
and 100 chemical variables.
Exploratory analyses do not require an^ assumptions concerning the distri-
bution of variables because no statistical hypothesis testing is conducted.
Two principal techniques, factor analysis and cluster analysis, were used
in these analyses. Factor analysis helps to define linear relationships
among the measured variables that may reveal more fundamental physical,
chemical, or biological forces and processes affecting the samples. Cluster
analysis helps to define relationships among the samples that may reveal
natural grouping or categorization that can be interpreted based on factor
analysis results or other fundamental influences on the samples. Additional
discussion of these techniques is presented in the Methods section (see
Exhibit D-l).
In defining relationships among samples and variables, factor analysis
and cluster analysis were also useful for identifying potential "anomalies."
The term "anomalies" indicates data values identified in an early stage
of statistical analysis as exhibiting unusual characteristics relative
to other data values. A stepwise analysis was important in evaluating
the effects of these anomalies on interpretations of the data. For the
most part, these data were associated with samples collected adjacent to
major pollution sources. Later analyses that excluded these values were
used to evaluate trends observed in initial analyses that included anomalies,
and to explore underlying trends that may have been masked by the anomalies.
ANALYTICAL SCOPE
The data analyzed are a subset of stations in the database compiled for
development of chemical-specific sediment quality values for Puget Sound (see
Commencement Bay Figure A-l in Appendix A). An additional 88 Commencement
Bay stations analyzed for chemical concentrations only were included in the
evaluation of chemical-chemical relationships (i.e., a total of 144 chemistry
stations; most are shown in Figure 1). The Work Plan limited the scope of
analyses to the Commencement Bay/Carr Inlet stations because of three factors:
1. Considerable data manipulation would have been required to
compile and enter biological data from other data sets (e.g.,
benthic species-level abundance data, percent toxicity response)
in addition to the chemical data already being entered, which
would have delayed the overall project schedule. The only
biological data required to determine sediment quality values
(see Section 5 of the main report) were codes in the database
that specify whether each biological indicator was or was
not significant at each station.
2. A combination of all appropriate data sets is not recommended
until the large chemical and biological data set generated for
the Elliott Bay Toxics Action Plan is available. This combined
0-2
-------
C3
I
CO
HYLEBOS
mjvmm hw
• C8-M
COMMENCEMENT
BAY
HY43
HY-42
HY-30
HY-26
BL*27
Sl-t3
HY23
BL-23
84-12
HY-22
RS-11
SMI
HY-19
SM3
BL-24
BL-21
BL-20
ST. PAUL
BC-V4
HY-15
BL-15
BL-13
BL-t2
MD-11
m
CM8-
Ct-17
CM5 -
CMS
4000
—| METERS
1000
CH3
Figure 1. Locations of Commencement Bay stations sampled
for surficial sediment chemistry.
CHI
-------
• RS-22
RUl
RUSTON
COMMENCEMENT
BAY
• RS-W
TACOMA
4000
J I «£T
1 METERS
1000
Figure 1. (Continued).
-------
data set may be subjected to the most efficient data reduction
and analysis techniques determined in this task.
3. Of the data sets identified for the sediment quality values
project (see Appendix C), only the Commencement Bay/Carr Inlet
data set contained a full complement of paired chemistry,
toxicity (amphipod, oyster larvae, Microtox bioassays), and
benthic infaunal data for analysis of the possible relation-
ship of sediment chemistry to site-specific biological effects.
The pattern recognition analyses enabled a more detailed investiga-
tion of these data than was possible under the constraints
of the earlier Superfund investigation (e.g., use of species
abundances rather than total taxa abundances in multivariate
analyses of chemical-biological relationships; Tetra Tech
1985a).
Four caveats were recognized in the application of pattern recognition
techniques to this project. First, the pattern recognition results were
used only to identify potential relationships among variables as supple-
mental information for the development of sediment quality values, not
to derive quantitative equations such as may be required for specifying
a predictive model for sediment quality. The relationships discussed in
this appendix can be investigated further by a suitable experimental design,
further sampling and analysis, and appropriate statistical tests. As directed
in the Work Plan, suggested analyses to refine or verify the relationships
are summarized at the end of this report.
Second, no statistical confirmation (hypothesis) tests were required
for the exploratory tests conducted on the data, and data uncertainties
were not employed. These analyses have produced findings that suggest
possible chemical-biological relationships. Confirmation that these relation-
ships occur with a defined statistical confidence would require hypothesis
testing. As discussed in the previous section, pattern recognition techniques
do not require any statistical assumptions about variable distributions
when used in an exploratory mode.
For example, the chemical data used in the analysis were first autoscaled
(i.e., by a transformation similar to a z-score transformation). Autoscaling
is a one-to-one mapping of the values of a variable from one reference
system to another. The mapping preserves the shape of the variable distri-
bution, zero-centers the distribution, and uniformly scales the variance.
Also, factor analysis (used as part of the pattern recognition procedure)
does not require variables to be normally distributed, especially where
the approach is being applied for information compression (i.e., a Karhunen-
Loeve transformation; see for example Watanabe 1973) as was done in this work.
Hence, data transformations and knowledge of the data distributions become
important only if multivariate hypothesis testing were to be conducted.
The ARTHUR program can incorporate data uncertainties in its statistical
analyses, but these capabilities were not applied in this study because
of resource constraints. It was assumed that, for the trends observed,
D-5
-------
environmental variability exceeded the expected analytical variability
of the measurements. As a test of this assumption, additional pattern
recognition tests were performed with subsets of the data to verify the
repeatability of some findings (e.g., the identification of potentially
sensitive benthic species). The conclusions reached in this report are
based on consideration of these additional tests.
Third, the data set is biased in the greater number of stations from
an area that is generally recognized as polluted. Only 5 of the 56 biological
stations were located outside the defined Commencement Bay Remedial Investi-
gation area: four in Carr Inlet and a fifth in deep water outside Hylebos
Waterway. This constraint is not considered severe because sediments sampled
within the Commencement Bay system showed a substantial range in concentra-
tions for the chemicals measured, and many chemicals were undetected at
several of the stations. Also, statistically significant bioeffects (e.g.,
toxicity or depressed benthic infaunal abundances) occurred at only 29
of the 52 Commencement Bay stations.
Therefore, although there are substantially more stations from Commencement
Bay than from a defined reference area, conditions within Commencement
Bay range from low pollution and effects to high pollution and effects.
Therefore, a potential for identification of effects on pollution-sensitive
species remains. The data are especially useful for identifying potential
relationships among chemicals and species that are not extremely sensitive
to pollutants.
Fourth, a complete discovery of relationships in the data set is not
guaranteed with pattern recognition analyses. Although the data analyses
techniques used in this project provide a reasonably comprehensive multivariate
analysis of the data, it is not expected that all relationships have been
identified. Nonlinear relationships among variables are hard to determine.
The results indicate some additional experiments and analyses that should
be performed. Although the findings may be used as guidelines for the
appropriate development of sediment quality values or for regulatory actions,
such actions must be consistent with the general biogeochemical understand-
ing of the environmental systems under study.
D-6
-------
DISCUSSION OF MAJOR RESULTS
GENERAL APPROACH
The combination of pattern recognition techniques in ARTHUR were used
to confirm appropriate groupings of chemicals and normalizations of chemical
data, identify sensitive benthic species and system-wide relationships among
chemicals and bioeffects, and determine the importance of area-specific contami-
nation on the ability to discern potential chemical-bioeffects relationships.
The approach used in this study combined statistical methods in sequences that
are typical of exploratory evaluation of complex data sets. The sequence of
statistical manipulations (including treatment of undetected chemicals) are
described in detail in Exhibit D-l. The general sequence of steps was:
1. Autoscale the data to put the variables on a common footing
of "unit variance", and list univariate statistical parameters
2. Apply factor analysis techniques to calculate factors represent-
ing more efficient dimensions of variation in the data
3. Interpret the factors for underlying chemical or biological
meaning
4. Project the scaled measurement values onto the new factor
axes and plot the data according to these "scores"
5. Evaluate the factor plots for indications of anomalies (i.e.,
unusual values) or structure and relationships among the stations
6. Apply cluster analysis techniques to explore for intrinsic
group association between the stations
7. Apply other selected techniques, as appropriate, to calculate
interfeature correlations, variable variance weights between
groups of samples, or variable selection to look for important
variables.
Data Description
Data analyzed from Commencement Bay/Carr Inlet included:
• Chemical data for 144 stations (0-2 cm sediment samples; most
stations are shown in Figure 1)
• Chemical, bioassay, and benthic infaunal data for a subset
of 56 stations (Figure A-l in Appendix A; benthic data were
missing for two of these stations).
D-7
-------
The latter chemical-biological data set incorporated 192 variables, including:
• Bioeffects data for 64 benthic abundance variables, 15 taxonomic
group abundance variables, 3 bioassay variables, and a "species
richness" variable representing the total number of unique benthic
infauna species at each station
• Chemical concentration data for 100 organic compounds, metals,
and metalloids
• Conventional chemical data for 10 variables (e.g., grain size,
total organic carbon, total sulfides).
Separate pattern recognition analyses were conducted using chemical variables
normalized to (1) sediment dry weight, (2) total organic carbon content
of the sediment, and (3) total percent fine-grained material in each sample.
These different data normalizations were used to determine chemical-chemical
relationships (i.e., groups of chemicals with covarying distributions in
the environment), and chemical-biological effects relationships. The importance
of each kind of data normalization is briefly summarized in the following
sections.
Dry Weight Normalization—
Most sedimentary contaminants are associated predominantly with the
solid material in bulk sediments, not with the interstitial water. Thus,
dry-weight contaminant concentrations are preferred to wet-weight concentra-
tions. Use of dry-weight concentrations precludes the possibility that
variations in sedimentary moisture content will obscure informative trends
in chemical data. Pattern recognition analyses were also conducted using
biological effects data and chemical concentration data normalized to sediment
dry weight to determine whether a relationship existed between biological
effects and the total mass of chemical In a given volume of sample (i.e.,
represented by the dry weight concentration).
Total Organic Carbon Normalization-
Chemical concentration gradients, particularly of nonpolar, nonionic
organic compounds, have been observed to correlate positively with sedimentary
organic carbon content (e..g., Choi and Chen 1976). This observation is
commonly interpreted in one of two ways: (1) organic matter is the "active
fraction" of sediment and serves as a sorptive sink for neutral, and possibly
polar or metallic, compounds, or (2) carbon-rich particles may be an important
transport medium for contaminants [e.g., PAH may tend to be associated
with soot particles (Prahl and Carpenter 1983)]. Also, if organic matter
is a sorptive sink for contaminants, toxic biological effects from exposure
to contaminated sediments should decrease with increasing organic carbon
content (see Appendix H for more detailed discussion). Hence, pattern
recognition analyses were conducted with biological effects data and chemical
concentrations normalized to organic carbon content to examine whether
increases in toxicity or biological effects correspond to increased contaminant
0-8
-------
concentrations relative to total organic carbon content. Total organic
carbon was also used as a variable in the analysis of dry-weight normalized
chemical data.
Normalization to Percent Fine-Grained (<63um) Particles—
On a limited spatial basis, contaminant concentrations are often inversely
correlated with particle size. Thus, contaminants (especially metals)
may be concentrated in the fine-grained particles of bulk sediments. This
observation is often explained in terms of surface area, in that finer
particles have greater specific surface area, and thus greater sorption
capacity, than larger particles. Because organic carbon content also tends
to vary inversely with particles size, normalizing to percent fines may
be effectively equivalent to normalizing to organic carbon content. The
percent fine-grained material in each sample was also included as a variable
in dry-weight and TOC normalized analyses.
Statistical Procedures
Statistical procedures applied to these data included factor analysis,
cluster analysis, and category classification. Major chemical and biological
factors derived from the total data set are briefly described in the next
two sections, followed by a presentation of biological-chemical relationships,
the influences of different normalizations of sediment chemistry, and the
importance of small-scale geographic effects. Most of the discussion of
benthic infauna-chemistry results focuses on individual species, which
were not examined in detail in previous studies. Preliminary analysis
of the statistical results by G.A. Erickson and Associates was performed
without specific knowledge of the results of the Commencement Bay Remedial
Investigation (Tetra Tech 1985a).
Factors derived in factor analysis are mathematical (usually linear)
combinations of individual variables, and represent different aspects of
the data set. The goal of factor analysis is to explain as much of the
variation in the data with as few dimensions (factors) as possible. In
the current study, up to 10 factors were extracted from the data set and
documented. The 5 factors that accounted for the greatest amount of variability
in the data set were examined in detail. Each of these 5 factors accounted
for at least 5 percent of the total variability and, in combination, accounted
for approximately 65-75 percent of the total variability in the data set.
The variables making up a factor may have either a positive or negative
loading (i.e., influence) on the total value of the factor. When several vari-
ables load strongly in either the positive or negative direction onto a factor,
the factor may serve as a replacement for this combination of variables,
thus reducing the number of variables in the system. If these factors
are interpretable as physical, chemical, or biological influences on a
system, they provide additional insight on the relationships between the
variables making up the factor.
D-9
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CHEMICAL FACTORS
Each of the following four chemical factors were extracted from the
chemical data set as linear combinations of the concentrations of related
chemicals. Chemicals for each factor are listed in decreasing order of
the strength of their loading:
1. A phenols and light aromatic hydrocarbon factor: 4-methylphenol,
isopimaradiene (a diterpene), 2-methoxyphenol (guaiacol), an alkyl-
ated benzene isomer (tentatively identified as a cymene isomer),
and phenol.
2. A metals factor: nickel, iron, barium, zinc, total metals (the
sum of U.S. EPA priority pollutant metals), selenium, arsenic,
manganese, beryllium, lead, antimony, copper, cadmium.
3. A chlorinated compound factor: hexachloroethane, 1,2,4-trichloro-
benzene, hexachlorobenzene, hexachlorobutadiene, other tri-, tetra-,
and pentachlorinated butadienes, total PCBs [often joined by some
polynuclear aromatic hydrocarbon (PAH), e.g., dibenzo(a,h)anthra-
cene or indeno(l,2,3-cd)pyrene].
4. A high molecular weight PAH (HPAH) factor: total HPAH, dibenzo-
(a,h)anthracene, indeno(l,2,3-cd)pyrene, total benzof 1 uoranthenes ,
methylpyrenes, benzo(a)pyrene, chrysene, benzo(a)anthracene.
These factors indicated groups of chemicals with similar geographic
distributions. For example, the metals factor was strongly influenced
by the concentrations of metals at stations near the ASARCO smelter along
the Ruston-Pt. Oefiance Shoreline (Figure 1); the chlorinated compounds
factor was strongly influenced by concentrations of chlorinated compounds
at stations toward the mouth of Hylebos Waterway (Figure 1). Concentrations
of HPAH near the Kaiser Ditch toward the head of Hylebos Waterway strongly
influenced the HPAH factor.
Similarly interpretable factors were derived from several NOAA data
sets on other areas of Puget Sound (Quinlin et al. 1984). These previous
factors included HPAH, low molecular weight PAH (LPAH), metals, and DDTs
and high molecular weight chlorinated hydrocarbons (e.g., PCBs). The similarity
of these derived factors in different data sets from around Puget Sound
suggest similar chemical-chemical relationships in each area (at least
for these select chemicals). Because of this covariance, it is recommended
that sediment quality values be derived for the sum of these variables
as well as for the individual chemicals.
BIOLOGICAL FACTORS
In a manner similar to the derivation of chemical factors, several
factors dominated by biological variables emerged from an evaluation of
the benthic infauna and conventional chemical data set (e.g., grain size,
organic carbon). These data were evaluated separately from the toxic chemical
D-10
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data to examine features that might suggest community relationships among
benthic infauna. The resultant factors sometimes included significant
contributions from grain size variables (e.g., percent sand and silt content),
but not from other conventional chemical variables (e.g., TOC, sulfides,
total volatile solids, oil and grease). The inclusion of conventional
chemistry variables in this analysis enabled some interpretation of habitat
type as an important characteristic of benthic communities. For example,
the following 3 factors contained combinations of biological variables
that could be interpreted from an environmental perspective (run #17, Table
D-4 in Exhibit 0—1; variables listed for each factor are in decreasing
order of their loading on varimax rotated factors; benthic infauna are
in terms of abundance):
1. A factor composed primarily of molluscs, ostracods, and decapods,
some species of which are pollution-tolerant and most of which
are abundant in Commencement Bay waterways): This factor includes
Total molluscs, Axinopsida spp., Axinopsida serricata, Nucula tenuis,
Euphi lomedes spp., Euphi lomedes producta, Total abundance, Macoma
spp., Macoma carlottensis. Eteone Tonga. Pinni xa spp., Lumbri neri s
spp.
2. A factor composed primarily of benthic infauna associated with
fine-grained sediment types: includes Thar.yx mul tifilis, Tharyx
spp., Total polychaetes, Total abundance, [-Sand], Lumbrineris
spp., [+ Silt], Lumbrineris sp. group 1, [+ Clay], Glycera capitata,
Leitoscoloplos pugettensis, Macoma elimata
3. A factor composed primarily of benthic infauna associated with
sandy sediment types: includes Prionospio steenstrupi , Prionos-
pio spp., Odostomi a spp., Mysel1 a tumida, Mitrel la gouldi, Total
crustaceans, Mediomastus spp., Leptochelia dubia, [+ Sand], [- Silt],
- Lumbrineris sp. group 1.
Variables preceded by a minus sign (-) in these lists have a negative
loading on the factor (i.e., are inversely correlated with variables having
a positive loading). Non-biological variables (i.e., sand and silt content)
are shown in brackets. Sand, silt, and clay were the only non-biological
variables included in the analysis that loaded strongly on these 3 factors
(e.g., organic carbon content did not load strongly on these factors).
The first two factors showed a high degree of correlation in most of
the Commencement Bay study areas. The third factor had a stronger influence
from Carr Inlet stations as well as selected Commencement Bay stations
(e.g., St. Paul Waterway stations near a pulp and paper discharge).
BI0L06ICAL-CHEMICAL RELATIONSHIPS
Statistical relationships among biological and chemical variables were
examined to ensure that the prediction of sediment quality values would
reflect known empirical trends. Many studies have documented that the
presence of toxic substances can result in decreased abundances of, or
sublethal effects on, affected organisms (e.g., Gary 1979; Boesch and Rosenberg
D-ll
-------
1981; Eagle 1981; Gray 1982; Wolfe et al. 1982). In cases where opportunistic
or pollution-tolerant species have shown an initial increase in abundance
after an exposure to toxic chemicals (e.g., Capitella capitata at the West
Falmouth oil spill site), high abundances of those taxa have usually been
attributed to their abilities to become established in a disturbed or polluted
environment and in the absence of competition for resources. Thus, there
is little or no documentation of a significant enhancement of benthic organisms
as a direct response to a toxic chemical, although such a response is theoreti-
cally possible. There is also no evidence that enhancement occurs for
one species or taxonomic group in the presence of toxic chemicals without
a significant depression being observed in the abundance of another species
or group.
Hence, the development of sediment quality values has generally assumed
that increasing concentration of certain toxic chemicals results in an
increase in biological effects. In the current pattern recognition study,
no a priori assumption was made by the statistician concerning the direction
of population change in response to a toxic chemical effect. All significant
correlations among chemical and biological variables, whether positive
or negative, were examined using scatterplots of the data distributions.
Examination of scatterplots was also used to prevent blind acceptance of
apparent positive or negative trends between two variables based on summary
statistical results.
Approach
A series of analyses was used to identify apparent biological-chemical
relationships. Results of these analyses are provided in the following
sections. Briefly, the steps followed are:
1. Examine correlations among variables as a preliminary check
for linear relationships
2. Identify sensitive species using factor analysis:
Conduct factor analyses using all chemical-biological
stations in the data set to identify factors to which
chemical and biological variables contributed in opposite
directions (i.e., are inversely correlated)
Check the stability (i.e., reproducibility) of these
factors, and the possible existence of additional mixed
biological-chemical factors by re-running factor analyses
on subsets of the whole data sets (i.e., subsets were
defined geographically according to the known distribu-
tion of different types of major chemical sources in
the study area)
Using data from all chemical-biological stations, examine
scatterplots of the individual chemical and biological
variables contributing to the mixed biological-chemical
factors to verify their implied inverse relationships.
D-12
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3. Interpret scatterplots of the primarily chemical and primarily
biological factors (discussed in previous sections) to identify
possible relationships between combinations of chemicals and
combinations of biological variables (note: because factor
analysis attempts to derive independent factors character-
istic of the data set, a correspondence between these factors
is not necessarily expected; there is an implied correspondence
only among variables that load onto a common factor).
4. Perform cluster analyses using the factors to define groups
of stations according to similarities in the species compositions
and abundances of infaunal organisms; check to see if differences
among these clusters can be attributed to differences in the
associated sediment chemistry or sediment toxicity.
Significant Correlations Among Variables
To derive chemical-chemical variable coefficients, biological-biological
variable coefficients, and biological-chemical coefficients, correlation
coefficients were calculated for the 192 variables of the various data
sets. Correlation coefficients give an indication of the degree to which
two variables are related linearly. In multivariate data sets, care must
be taken in interpreting correlation coefficients where multiple correlations
are present among variables. For example, copper may correlate strongly
with the amount of clay present in samples, and the mere presence of a
large percentage of clay may also be unsuitable for some benthic organism.
A negative correlation between copper concentrations and the abundance
of the benthic organism could be misinterpreted unless the effects of the
covarying clay have been removed (e.g., by normalizing copper concentrations
to percent clay content).
Correlation matrices were scanned for coefficients that were significant
for pairs of variables (i.e., at least at the 95 percent confidence interval;
P<0.05). At this stage of the analysis, data for all stations were included
to determine if there were system-wide correlations among variables. Very
strong correlations were observed in the 144 station chemical data set
for several chemical-chemical variable pairs (e.g., significant coefficients
of r>0.8 were found for a number of variables associated with LPAH and HPAH).
Only a few of the linear chemical-biological correlations exceeded a
coefficient of r=0.7 (r2=0.5; i.e., a linear relationship between the variables
accounts for approximately 50 percent of the variability):
• "Other" taxa versus aniline (r=+0.986), 2,4,5-trichlorophenol
(r=+0.780), and isophorone (r=+0.697)
• Nematoda versus aniline (r=+0.985), 2,4,5-trichlorophenol
(r=+0.781), and isophorone (r=+0.700).
In both cases, these results appeared to be driven by a single unusual
data value. The "Other" taxa are dominated by Nematoda, which are present
D-13
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in very high abundance at Station CI-11 at the head of City Waterway.
Aniline, 2,4,5-trichlorophenol, and isophorone were detected at this station,
but were either undetected or present at lower concentrations at all other
stations.
At smaller correlation coefficients (e.g., r>0.4 to 0.7), some additional
biological variables exhibited significant correlations (P<0.001) with
chemical variables. Of these biological variables, bioassay responses
(i.e., percent amphipod mortality and oyster abnormality) were significantly
correlated with the largest number of chemicals (P<0.001; r>0.4). These
bioassay response variables were also negatively correlated with the numbers
of unique species at each station [i.e., amphipod mortality (r= -0.468)
and oyster abnormality (r= -0.537)]. This inverse relationship between
numbers of unique species and bioassay responses suggests that this measure
of species richness may be sensitive to toxicological responses of indicator
organi sms.
Of 51 biological variables retained for the combined chemical-biologi-
cal evaluations, 37 had correlations significant at the 95 percent confidence
level (P<0.05) with at least one organic/inorganic chemical or conventional
(e.g., grain size) variable. Ten chemical or conventional variables that
were significantly correlated with >10 percent (i.e., >4) of these bio-
logical variables are listed in Table 1. The number of times each variable
was positively and negatively correlated with biological variables is also
indicated.
All of the individual chemicals in Table 1 that were negatively correlated
with more than one biological variables are either crustal elements that
derive predominantly from natural sources (e.g., nickel, beryllium, and
chromium) or compounds that could derive as natural biological products [e.g.,
9-hexadecenoic acid methyl ester; fatty acid methyl esters are possibly derived
from microbial methylation of naturally occurring fatty acids (Ehrhardt et al.
1980)]. The suggested interrelationships between metals and biological vari-
ables (Table 1) may simply reflect silt/sand correlations with biological
variables because the metals also have significant positive correlations
with percent silt content and negative correlations with percent sand content.
A similar grain size dependency was not observed for organic compounds.
Overall, when data from all 56 chemical-biological stations in Commence-
ment Bay/Carr Inlet were combined in a simple correlation analysis, no
strong evidence was found for a chemical-benthic species relationship that
could be interpreted as an adverse effect of a pollution source common
to the entire area. This lack of obvious linear relationships between
pairs of chemical-biological variables suggested the need for more complex
factor analysis involving combinations of several variables.
Identification of Potentially Sensitive Species
Factor analysis and factor plots (i.e., scatter plots where at least
one of the axes is a derived factor from factor analysis) were performed using
the combined chemical-biological data set (56 stations). As a test of
0-14
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TABLE 1. FREQUENT SIGNIFICANT CORRELATIONS AMONG CHEMICAL
OR CONVENTIONAL VARIABLES AND BIOLOGICAL VARIABLES a
Number of Significant Correlations
Conventional/Chemical Variable Negative Positive Total
Silt
9
8
17
Sand
10
7
17
Nickel
6
0
6
9-Hexadecenoic acid methyl ester
3
2
5
Total organic carbon
3
2
5
Benzo(ghi)perylene
0
4
4
Beryl 1ium
2
2
4
Chromium
2
2
4
Isophorone
I
3
4
Benzyl alcohol
0
4
4
a Significant correlations (P<0.05) based on all 56 biological stations
throughout Commencment Bay/Carr Inlet.
D-15
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the stability of the relationships indicated by factors derived with the
entire chemical-biological data set, stations were assigned to one of the
following five groups and subjected to additional factor analysis. These
subset analyses were used to separate the influence of grossly different
known sources of chemicals, because similar toxicity and benthic infaunal
responses could be expected from completely different chemical exposures:
1 - All biological stations in the relatively low contaminated Blair
and Milwaukee Waterways, and reference stations in Carr Inlet (Figure
A-l in Appendix A)
2 - All biological stations in the adjacent Middle and City Waterways
(Figure A-l) (with a mixture of contaminants)
3 - All biological stations in upper Hylebos Waterway (Figure A-l;
exposed to major sources discharging some chlorinated compounds
but mainly metals and PAH)
4 - All biological stations in lower Hylebos Waterway (Figure A-l;
exposed to major sources discharging primarily chlorinated compounds)
5 - All biological stations in Sitcum Waterway and along the Ruston-Pt.
Defiance Shoreline (Figure A-l; evidence of significant metals
contamination in both areas)
6 - All biological stations in St. Paul Waterway (Figure A-l; exposed
to the discharge of a major pulp and paper facility including high
concentrations of phenolic substances).
In the five subset analyses conducted, stations in groups 2 through 6
were tested in combination with group 1 stations, believed to be the least
contaminated. This pairing of groups ensured a contrast between stations
with low contamination and stations with higher concentrations of chemicals
in each subset analysis. The factor analysis results were then examined
to determine which chemical and biological variables loaded onto the same
factor. Variables chosen for further study were those with the highest
(absolute value) loading on each factor, down to a level that explained
over 50 percent of the total variance contained by the factor, or to where
the loadings decreased noticeably in magnitude.
Most of the factors in these analyses appeared to be primarily chemical
or primarily biological factors. A single mixed chemical-biological factor
appeared frequently in the different subset analyses. Biological and chemical
variables on this mixed factor were loaded with opposite signs. The frequencies
with which variables were prominent on this factor are listed in Table 2.
Frequently appearing taxa were Praxi 11 el 1 a graci 1 is (Polychaeta), Euclyme-
ninae (Polychaeta), Euphi1omedes producta (Ostracoda), and Nucula tenuis
(Pelecypoda). Frequently appearing chemicals were naphthalene, anthracene,
benzo(ghi)perylene, pyrene, 2-methylnaphthalene, total organic carbon,
9-hexadecenoic acid methyl ester (tentative identification), and retene
(probable identification). This factor was present in the factor analysis
of all 56 stations, and its persistence and the repeated appearance of
0-16
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TABLE 2. NUMBER OF OCCURRENCES OF ANTI-CORRELATED VARIABLES
IN A MIXED CHEMICAL-BIOLOGICAL FACTOR FROM
FACTOR ANALYSES OF DATA SUBSETS
Chemical
Variable
Number of
Occurrences*
Biological
Variable
Number of
Occurrences*
9-Hexadecenoic
acid methyl ester 5
Benzo(ghi)perylene 4
Beryllium 4
Acenaphthalene 3
Total organic carbon 3
Retene 3
Unidentified diterpeneb 3
2-Methylnaphthalene 2
Anthracene 2
Pyrene 2
Naphthalene 2
Euclymeninae 6
Praxillella gracilis 5
Nucula tenuis 4
Euphilomedes producta 3
Nemocardium centifilosum 4
Mitrella gouldi 2
Phyllochaetropterus
pro!i fica 2
Axinopsida serricata 2
Total molluscs 2
Macoma elimata 2
Euphilomedes 2
Callianassa spp. 2
a The variables listed were present in a mixed biological-chemical factor
in repeated factor analyses of the entire data set and subsets of the
data (separated geographically as discussed in text). The number of
occurrences indicates the number of factor analyses in which the variable
appeared in the factor; these chemical and biological variables were
loaded on the factor in opposite directions (i.e., were anti-correlated).
b Tentatively identified (with low confidence) as kaur-16-ene.
D-17
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the variables in Table 2 in different subset analyses (including analyses
normalized to organic carbon or fine-grained material) strongly suggests
that the factor is not an artifact.
These results led to the direct examination of potential relationships
between the biological and chemical variables that frequently appeared
in this mixed chemical-biological factor. Scatterplots of these variables
demonstrated several instances where selected benthic infaunal taxa showed
strong tendencies for lower abundances at higher chemical concentrations.
An example of this inverse correlation behavior is shown in Figure 2.
Praxi11 el 1 a gracilis (Polychaeta) ranged in abundance up to 387 individuals/m2
at stations where pyrene concentrations were less than about 1900 ug/kg
dry weight. At higher pyrene concentrations found at 8 stations, the abundance
of this polychaete never exceeded 16 individuals/m2. Approximate chemical
concentrations above which abundances of benthic taxa were consistently
low in these scatterplots are summarized in Table 3.
These taxa were not the most abundant species identified, but were
moderately abundant. As a result, their possible chemical sensitivity
was not readily apparent using traditional statistical techniques. With
the exception of Euphi1omedes producta, none of these species have been
identified previously as possible indicator organisms. Their potential
use as potential sensitive indicators of chemical contamination should
be explored further. Euphi 1 omedes spp. often exhibit enhanced abundances
in response to moderate organic enrichment of the sediments (Word 1978,
1980).
Inverse Relationships Among Chemical Factors and Biological Factors
Several factor plots indicated an inverse relationship between predomi-
nantly taxonomic factors and predominantly chemical factors, or factors
that combined chemicals or conventional sediment characteristics (e.g.,
organic carbon content) with measures of sediment toxicity. As discussed
previously, an inverse relationship may be most characteristic of a direct
toxic response of an organism to chemical contamination. For example,
in pattern recognition analyses run with onl y the biological effects data
and conventional sediment variables (i.e., excluding toxic chemicals),
higher species abundances appear to be associated with areas low in organic
enrichment and sediment toxicity as shown in Figure 3.
Figure 3 represents a fa'ctor projection scatter plot of the stations
as they relate to a biological factor (vertical axis; composed primarily
of taxa ) and a conventional-bioassay factor influenced primarily by volatile
solids content, TOC and toxicity response. The upper right portion of
the plot contains no stations, indicating that high infaunal abundances
are not coincident with organically-enriched sediments that are toxic in
laboratory tests.
Similar, but sometimes less obvious relationships were observed for
individual chemicals. This is not surprising because the composition of
equally toxic sediments can vary widely over the entire study area. Hence,
general variables such as organic carbon content or bioassay response may
D-18
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Ui
o
z
<
Q
z
D
ffl
<
S3
O E
2;
\
Ul
"J
4
1
•TRANSITION POINT
~ 1900 ppb
• • • •
I M« • • •
—r
1000
—I—
2000
—I—
1000
—I—
MOO
11
MOO
PYRENE
(ppb, dry wt.)
Figure 2. Scatterplot of the abundance of Praxi11 el 1 a gracilis
and sediment concentration of pyrene.
D-19
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TABLE 3. CRITICAL CONCENTRATIONS OF CHEMICALS
INDICATED BY SENSITIVE SPECIES
Chemical
Low Abundance Threshold Concentration by Species a
Praxillella Euclymeninae Euphilomedes Nucula
gracilis producta tenui s
Naphthalene 1100
2-Methyl naphthalene 380
Anthracene 560
Pyrene 1900
Benzo(ghi)perylene 400
Retene 270
9-Hexadecenoic acid
Methyl Ester 560
Total organic carbon 3
1100
390
560
1900
450
510
560
3 %
1300
380
560
1900
270
1800
7 %
1400
565
3300
570
1700
4 %
a Above these concentrations, abundances of the species indicated were
uniformly low; concentrations in ug/kg dry weight (ppb) unless otherwise
indicated.
D-20
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LOW VALUES HK3H VALUES'
BIOASSAY AND CONVENTIONAL CHEMICAL
LOADING FACTOR
Figure 3. Scatterplot of a major "biological" factor and a
"bioassay/conventional chemical" factor.
D-21
-------
show stronger relationships with the distribution of benthic effects in
the overall data set than any one or group of chemicals.
In several computer runs, it was clear that higher population abundances
indicated by biological factors occurred only within a restricted, low
value range of the chemical factor (e.g., see Figure 4). This behavior
was also observed in factor plots based only on conventional, bioassay,
and benthic variables (e.g., see Figure 5). The relationships observed
indicate that abundances of benthic organisms do respond to chemical contami-
nation and in a manner that parallels (but does not perfectly duplicate)
the responses observed in bioassays.
Infaunal Classification Analyses
Numerical classification analyses were performed on the benthic infaunal
data to define groups of stations (i.e., clusters) based on the similarity
of taxonomic composition of infaunal organisms. The resulting station
clusters were evaluated to determine if they could be distinguished on
the basis of observed differences in biological/toxicity effects or chemical
measurements. Euclidean distance (standardized to the maximum similarity)
was used as a similarity measure, and a hierarchial clustering strategy
was used to generate the station groups (see Appendix A).
The similarity measure used is sensitive to the absence of species as
well as to the number of individuals of species that are present. The
Euclidean distance can result in high resemblance between stations that
do not have many attributes in common, but whose attribute scores are low
(Boesch 1977). Such resemblances are expected among heavily impacted stations
although different taxa may be present at the stations. This similarity
would not be expected to be found using the Bray-Curtis similarity measure
as previously applied with these data (Tetra Tech 1985a).
Clusters of stations were determined based on their similarity in species
composition and abundance as defined by five discrete factors generated
in a factor analysis of the 64 numerically dominant infaunal species.
Several factor analyses were conducted during a stepwise series of tests
with intervening technical review. Preliminary factor analysis indicated
five "anomalous" stations that were removed prior to the final factor and
cluster analyses. As previously discussed, were primarily associated with
samples collected adjacent to major pollutant sources. These data were
excluded from subsequent analyses for three reasons:
• The effect of these particular data was clear from the preliminary
analyses
• The preliminary trends observed required reexamination without
the effects of the anomalies
• Any underlying trends that may have been "masked" by the anomalous
data points needed to be understood.
D-22
-------
<*» ~
»l*j
°ali
2?3.?
ffle
• •.
LOW CONCENTRATION
HIGH CONCENTRATION
CHLORINATED BUTADIENE FACTOR
FACTOR ¦ ~ PENCBD ~ KNTACHL * TITCBD * TRJCBD -
Figure 4. Scatterplot of a major "biological" factor and a
"chlorinated butadiene" factor.
D-23
-------
low VALUES HIGH VALUES
FACTOR LOADING (INTERPRETED AS ORGANIC
ENRICHMENT AND SEDIMENT TOXICITY)
FACTOR ¦ ~ 0.3M VSOUDS ~ OJSS ABNORM ~ 04S2 TOC-OJ39 SOLIDS
~ OJOt MORT ~0.272 MTROOEN ~ 0.114 CHANOE - 0.176 SAND -
Figure 5. Scatterplot of a "biological" factor and an "organic
enrichment/sediment toxicity factor.
D-24
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Because the Commencement Bay study design was intentionally focused around
potential sources to determine concentration gradients, such anomalies
were expected. Concern regarding treatment of anomalies is higher and
more critical in experimental designs based on random sampling (either
spatially or temporally). Thus, the treatment of anomalies in the staged
analysis conducted for this project was considered appropriate.
A dendogram showing the grouping of stations at different dissimilarity
values is shown in Figure 6. Stations with statistically significant sediment
toxicity or statistically significant depressed abundances of major taxonomic
groups (PC0.05; Tetra Tech 1985a) are also indicated.
Seven groups of stations were defined at inter-group dissimilarities
ranging generally from 30 to 40 percent (Figure 6). Group VII displayed
the highest intra-group dissimilarity as well as the highest dissimilarity
to other groups. The station clusters are characterized by no or few bioeffects
in Clusters V, VI, and VII (i.e., effects indicated only by a single toxicity
bioassay or, even less frequently, by a single major taxonomic infaunal
indicator) to a group almost entirely composed of stations with multiple
indications of biological effects (i.e., Cluster II). Cluster II contained
all of the stations where sediments exhibited the most severe effects (e.g.,
>50 percent mortality or abnormality in toxicity tests, and almost complete
absence of benthic infaunal organisms).
Factor projection plots (i.e., scatterplots of pairs of factors) were
examined to determine the major factors contributing to the definition
of these clusters. The factor that clearly distinguished the low biological
effects clusters from Cluster II (high biological effects) had the following
composition of variables (in order of the loading by each variable):
Factor = -0.354 Praxi11 el 1 a graci1i s - 0.320 Axinopsida serricata -
0.318 Nucula tenuis - 0.283 Euphilomedes producta
- 0.270 Eteone 1onga - 0.263 Pholoe minuta - 0.238 Euchone
incolor - 0.234 Euclymeninae - 0.208 Lumrineris sp. gr. 1...
Stations in Cluster II had uniformly high values for this factor (i.e.,
low abundances of the indicated species). Several of these species are
the same as reported in Table 2, which listed apparently sensitive species
that appeared to be inversely correlated with selected chemicals in factor
analyses of the complete chemical-toxicity-infauna data set. Axinopsida
serricata was the dominant mollusc found in the waterways, and Euphi lomedes
spp. was the dominant crustacean taxon. The other four factors from factor
analysis either did not contribute to the separation of these particular
clusters or had a minimal effect. Hence, stations that appear to be strongly
impacted on the basis of statistically significant (PC0.05) bioassay responses
and depressions in major taxonomic groups, have similar low abundances
of individual species that may be sensitive to chemical contamination.
Sediments from three stations in Cluster II did not exhibit statistically
significant effects in previous studies (Tetra Tech 1985a). Of these stations,
sediments from Station MD-12 had high concentrations of several
D—25
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PERCENT DISSIMILARITY
100.0
HT-17
HY-14
HY-24
HY-12
RS-20
HY-J7
11-21
Sr-16
RS-24
MO-12
CR-14
RS-I*
HY-44
CI -16
CM1
SP-IS
OA MC
NY-22
OA HC
HY-2 J
OA H FT
SP-14
OA NCPT
OA. JOT
ltS-12
HY-47
HY-41
C*-I2
SI-IS
AMI
ftL-li
IL-2S
81*21
WY -28
I 1 ' k' i 1 I I I I' "i " i
100.9 M-0 80.0 70.0 60.0 SO.O W.O 30.0 20.0 10.0 0.00
PERCENT DISSIMILARITY
COOCS FOR SIGNIFICANT EFFECT OISERVEO ARC:
SIGNIFICANT OTSTER LARVAE IIOASSA*
SIGNIFICANT AWMIPOO BIOASSAY
SIGNIFICANT DEPRESSION IN TOTAL MOLLUSC AflUNOANCE
SIGNIFICANT OEPRESSION IN TOTAL CRUSTACEAN ABUNDANCE
SIGNIFICANT OEPRESSION IN TOTAL POLTCNAETE AIUNOANCE
SIGNIFICANT OEPRESSION IN TOTAL ABUNDANCE '
Figure 6. Hierarchial classification analysis using 5 factors
from factor analysis of 64 numerically dominant
infaunal species.
D-26
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chemicals at or near the level above which effects were always found at
other stations, and Stations CR-14 and HY-44 had coarse sediments (i.e.,
>75 percent rocks and sand) that are not expected to support high abundances
of organisms. Hence, when only benthic species data comprise the clustering
factors, the factors do not necessarily distinguish between sediments with
potential major chemical impacts and all sediments that may have low abundances
of sensitive species because of presumed natural factors.
The most frequent significant indicator in the no-effects to low-effects
groups of stations was a significant amphipod bioassay response. A significant
amphipod bioassay in the absence of other indicators of bioeffects occurred
only at stations with sediments containing greater than 80 percent fine-grained
material. This response has been interpreted as possibly indicating a
grain size effect in the bioassay rather than necessarily a toxic effect,
although not all sediments with high percentages of fine-grained material
are significantly toxic by this bioassay (Tetra Tech 1985a). A toxic effect
cannot be ruled out; it is also possible that the amphipod bioassay may
be more sensitive to some forms of contamination than are other indicators.
In general, the cluster results suggest that laboratory bioassay results
and determinations of significant depressions in major taxonomic groups
are reasonably sensitive to changes in the structure of benthic communities.
They further suggest that a high degree of concordance may be expected
among these indicator variables.
Preliminary analyses were conducted to determine if the sediment concen-
tration of some toxic chemical factor was higher in a group of stations
having high toxicity compared with a group of stations with lower toxicity,
but similar benthic cluster assignments. No clear relationships were found
with specific chemicals, likely because the stations composing each group
had diverse chemical sources. Additional analyses are warranted after
controlling for gross differences in chemical composition within a particular
benthic cluster. These analyses were not conducted because the current
data set contains only a few stations that have closely related benthic
assemblages and similar chemical sources, which limits the confidence with
which a statistical analysis can be made. Solutions to this problem are
discussed in the summary (see Additional Analyses Recommended to Refine
or Verify Results).
UTILITY OF ORGANIC CARBON OR GRAIN-SIZE NORMALIZATIONS
The grain-size dependency suggested for metals in a previous section
(i.e., Significant Correlations Among Variables) may indicate a need to
normalize chemical concentrations for the content of fine-grained materials
in the sediments to distinguish any independent effect of the chemicals.
Likewise, a normalization of organic compounds to total organic carbon
may be appropriate because of the known correlation between these variables.
Exploratory runs were made with both of these normalizations. In terms
of the biological relationships, few new observations emerged. The results
tend to corroborate the findings determined with the dry-weight normalized
data, especially the general patterns of decreased abundances of sensitive
species with selected combined chemical factors.
D-27
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Normalization to percent fine-grained material seemed to produce results
in factor analysis that were nearly identical to dry-weiqht calculations.
Normalization to TOC was also consistent in indicating potentially sensitive
species, with the possible suggestion of additional species that may decline
in abundance with increased concentration of certain chemicals The apparently
sensitive species indicated in all three types of runs were:
• Axinopsida serricata
• Total mollusc abundance
t Nucula tenuis
• Euphilomedes producta
• Euclymeninae
• Praxillella gracilis.
Additional sensitive species suggested by the TOC normalized analyses were:
• Macoma elimata
• Nemocardium centifilosum
• Lumbrineris spp.
• Euchone sp. A
• Nepht.ys cornuta.
Generally, it appears that analyzing the variables without normalizing
to fines or organic carbon content provides the majority of interpretable
information. This fact may indicate that the primary influence of chemicals
in sediments on the biological systems and individual species is related
to the total volume concentration (mass) that is present for a chemical.
Second order effects may be related to the relative content to fines and
organic carbon. However, until the actual mechanisms of chemical-organism
interactions are determined (e.g., in laboratory studies), all data sets
should be analyzed with and without normalization to these "master" variables,
to confirm the results seen in these calculations.
EFFECTS OF STATION LOCATION ON FACTOR LOADINGS
A more distinctive effect than chemical concentration normalizations,
and hence of more general concern, were the substantial differences observed
between individual study areas (e.g., waterways) represented in the data
set. These differences indicate the importance of intensively sampling
small regions, in addition to larger-scale sampling to yield an integrated
picture of Puget Sound sediment systems.
Distinctive Basin Behavior in Chemical Factors
The most extreme chemical variations observed were in the Hylebos
Waterway (chlorinated organics and HPAH) and in the Ruston area (metals).
These major chemical differences are not unlike those that may be observed
throughout Puget Sound (e.g., Eagle Harbor creosote contamination, or lead
contamination around Harbor Island).
D-28
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A factor plot based on analysis of chemistry data for 144 Commencement
Bay/Carr Inlet stations is shown in Figure 7. The results clearly identify
the wel1-known geographic variations in chemical concentrations in this
system The factor plotted on the horizontal axis is strongly influenced
by HP AH compounds. The factor plotted on the vertical axis is primarily
composed of chlorinated organic compounds. In both factors, the highest
concentrations are represented by stations from Hylebos Waterway. Within
Hylebos Waterway, sediments near the head of the waterway have the highest
PAH concentrations. Sediments toward the mouth of the waterway have the
highest chlorinated organic compound concentrations. A similar plot for
a metals factor and a chlorinated organics factor demonstrates the relative
differences between Hylebos Waterway and the Ruston-Pt. Defiance Shoreline
(Figure 8). Analyses were performed with and without these "anomalous
samples to verify the stability of statistical trends.
Because of these substantial chemical differences, the relatively
small size of the biological data set (56 stations), and the large number
of variables in the problem, relationships between sensitive species and
chemical concentration were substantially blurred when all of the data
were analyzed together. By analyzing subsets of the data (e.g., two water
ways at a time), combined chemical-biological factors emerged (see section
on Identification of Sensitive Species).
D—29
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t
STATIONS FROM
MOUTH OF HYLEBOS
ALL ENCLOSED STATIONS ARE
FROM HYLEBOS WATERWAYS
-STATIONS FROM HEAD
OF HYLEBOS
• LOW OCNCENTR/kTION
HIGH MOLECULAR WEIGHT POLYCYCLIC
AROMATIC HYDROCARBONS FACTOR
FACTOR ¦ 0J99 PB79 ~ 0.2M PB78 » 0.280 TWIANTH ~ 0.774 METH2PYR
~ 0JS« HMWPAH ~ 0.2M HEXAOECS « 0.316 PM4 ~ 0,21 S PM3
« 0.207 PBM ~ 0.1 M PB72 * C.1H M7t * 0.177 PB73 _
HIGH CONCENTRATION
Figure 7. Scatterplot of a "chlorinated organics" factor and
a "PAH" factor.
D-30
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/
• •
¦ STATIONS FROM
MOUTH OF
HYLEBOS
•RUSTON
STATIONS
¦ LOW CONCENTRATIONS
HIGH CONCENTRATIONS ¦
METALS FACTOR
FACTOR ¦ • 0.32S NICKEL • 0.318 IRON - 0.263 ZINC - 0MB COPPER
- 0.253 BARIUM - 0.247 TOTVET • 0.244 ICOORP1 • 0.243 SELENIUM
• 0.242 ARSENIC • 0.231 MANGANESE • 0.1AS BERYLLIUM- 0.186 LEAD
• 0.146 CADMIUM ¦ 0.136 ANTIMONY • 0.132 CHROMIUM- 0.105 MRCURY.
Figure 8. Scatterplot of a "chlorinated organics" factor and
a "metals" factor.
D-31
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SUMMARY
Many of the pattern recognition results have corroborated previous
results obtained with traditional statistical techniques (Tetra Tech 1985a)
and have not been reported in detail here. This corroboration indicates
that traditional techniques have been useful in obtaining the majority
of interpretable information contained in the data set, and satisfies one
of the major objectives of conducting the pattern recognition analyses
(i.e., to validate historical findings). The results of this study are
also generally supportive of other previous Puget Sound analyses of chemical-
chemical interrelationships using ARTHUR (e.g., Quinlin et al. 1984; Chapman
et al. 1984).
MAJOR STATISTICAL RELATIONSHIPS
Major statistical relationships among sediment contaminants and biological
effects that were identified using pattern recognition techniques include:
Chemical Factors
Four chemical factors were interpretable from analysis of the complete
set of 144 chemistry stations: (1) a phenols and light aromatic hydrocarbon
factor; (2) a metals factor; (3) a chlorinated compound factor; and (4)
a high molecular weight PAH factor. Previous studies identified similar
groups of significantly correlated chemicals (e.g., groups of hydrocarbons,
metals, and chlorinated compounds). These results suggest that these major
pollutant groups have approximately similar compositions throughout Puget
Sound, although local variations certainly exist. Assuming that the composition
of chemicals is important in the type and magnitude of bioeffects produced,
this similarity implies that sediment quality values for these chemicals
may be applicable to much of Puget Sound. Given that different combinations
of chemical sources occur in different areas of Puget Sound, there is still
a concern that different synergistic effects may occur in these different
areas. This concern cannot be resolved by extrapolation of the results
in this report, except to note that a multitude of different chemical sources
found over a reasonably large geographic area is already represented in
the Commencement Bay/Carr Inlet data set.
Biological Factors
Three biological factors were interpretable from analysis of the 54
benthic infauna stations:
1) A factor composed primarily of molluscs, ostracods, and decapods,
some species of which are pollution-tolerant and most of which
are abundant in Commencement Bay waterways
2) A factor composed primarily of benthic infauna associated
with fine-grained sediment types
D-32
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3) A factor composed primarily of benthic infauna associated
with sandy sediment types.
These factors showed little correlation with chemical factors, and appeared
to be most strongly influenced by natural conditions (e.g., variations
in sand and silt content).
Biological-Chemical ftelationships
Factor analysis provided a means to calculate factors that contained
contributions from both chemical and biological variables (i.e., there
was an apparent relationship among these variables). One factor identified
in the overall data set, and verified in reanalyses with subsets of the
data, contained chemical variables that were inversely correlated with
benthic infauna variables on the same factor. These results were interpreted
to indicate potential sensitive species to chemical contamination in Commence-
ment Bay. These relationships were further analyzed using individual scatter
plots of the species abundance for the chemicals of interest. Several
of these species had not previously been reported as potentially sensitive
indicators of contaminated sediments, including two polychaetes (Praxillella
gracilis, and Euclymeninae), and a clam (Nucula tenuis). A fourth species,
Euphilomedes producta, has been recognized as a potential indicator organisms
for moderate organic enrichment (showing enhanced abundances), but has
not previously been shown to exhibit a negative correlation with increasing
pollutant concentrations.
Some of the factor plots displayed a general reduction in infaunal abundances
where higher values of selected chemicals (e.g., metals, HPAH), bioassay
responses, or organic enrichment were observed. Three important points
were apparent after review of the results:
t No one chemical or chemical group accounted for all toxicity or
benthic effects on a system-wide basis
• When analyzed at the system-wide level, the most apparent species-chemi-
cal relationships did not always predict the most severe bioeffects
observed in localized "hotspots" in Commencement Bay (i.e., off
major discharges; however, these stations were distinguished in
a cluster analysis on infaunal data)
• Associations between bioeffects and chemical concentrations can
be observed in the most biologically impacted areas, but generally
only when the data set has been geographically segmented.
Factor and cluster analyses on the benthic infaunal species data separated
a cluster that was almost entirely comprised of stations with sediments
exhibiting both statistically significant sediment bioassay responses and
depressions in the abundance of several major taxonomic groups. Sediments
from stations in three other clusters typically had none of these significant
effects, or less frequently, exhibited only a single significant effect
(e.g., often an amphipod bioassay response only). The factor that distinguished
the low- to no-effect clusters from the highly impacted cluster was strongly
0-33
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influenced by potentially sensitive species summarized above. Overall,
these analyses indicated that laboratory bioassay results and determina-
tions of significant depressions in major taxonomic groups are reasonably
sensitive to changes in the structure of benthic communities. The results
also suggest that in Commencement Bay and Carr Inlet, a high degree of
concordance may be expected among those indicator variables (especially
the oyster larvae bioassay and benthic depressions). This concordance
was indicated by the following items in this study and the previous remedial
investigation (Tetra Tech 1985a):
• "Impact" versus "no impact" designations made by benthic and
bioassay indicators agreed at 67-79 percent of the 48 stations
in the Commencement Bay Remedial Investigation (and at 83-
100 percent of the 6 stations in a separate dredging study
conducted concurrently with identical methods in Blair Waterway
and included in the ARTHUR analyses)
9 A significant depression in the abundance of at least one
major taxonomic group was observed in 6 of 7 cases (86 percent)
that also exhibited significant toxicity in both the amphipod
and oyster larvae bioassays
• Eighty-nine percent of the cases exhibiting a significant
depression in the abundance of at least two major taxonomic
groups occurred in a similarity cluster (assigned on the basis
of species-level benthic data) that contained 75 percent of
the cases exhibiting significant toxicity in both amphipod
and oyster larvae toxicity
• All 6 cases exhibiting a significant depression in the abundance
of at least three major taxonomic groups occurred in this
same similarity cluster; 83 percent of these cases exhibited
toxicity in the oyster larvae bioassay, 50 percent exhibited
toxicity in the amphipod bioassay.
The amphipod bioassay results for Commencement Bay showed the least agreement
in comparison with the benthic infauna results. This lower degree of con-
cordance may result from a sensitivity of the amphipod bioassay to fine-
grained sediments. However, in sediments containing <70 percent fine-grained
material, significant benthic depressions were observed in 100 percent
(6 cases) of the sediments exhibiting significant amphipod mortality (no
benthic data were available for a seventh station).
Appropriate Normalization of Chemical Data
There does not appear to be any justification based on pattern recognition
analyses to recommend one normalization technique over others for exploring
potential biological-chemical relationship; normalization of chemical data
to either organic carbon or a grain-size variable (i.e., percent fine-grained
material) tended to produce the same results in factor analysis as chemical
data normalized to dry weight of sediment. The abundance of a few additional
species not apparent using dry-weight normalized data may decrease with
D-34
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increased concentrations of chemicals relative to organic carbon content.
These species include: Macoma elimata, Nemocardium centifilosum, Lumbrineris
spp., Euchone sp. A, and Nepht.ys cornuta.
RECOMMENDATIONS FOR DEVELOPING SEDIMENT QUALITY VALUES
Results of the pattern recognition analyses recommended for consideration
in developing sediment quality values include:
1. Given that the total concentration for some chemical groups
(e.g., HPAH) correlates well with the concentrations of all
individual components of that group, it may not be necessary
to set sediment quality values for each individual chemical
in the group. Correlations established by pattern recognition
analyses were used to support the definition of appropriate
chemical groups for the derivation of sediment quality values.
2. Benthic species that appear to have a predictable and significant
response to contaminants or conventional sediment variables
(e.g., apparent PAH-sensitive species, or species sensitive
to organic enrichment) may be useful as indicators for assessing
benthic effects. The agreement between benthic effects assessed
according to major taxonomic groups (e.g., Polychaeta, Crustacea,
and Mollusca) and those assessed by potential sensitive species
(e.g., Praxillella gracilis) identified by pattern recognition
techniques was examined in the development of sediment quality
values.
3. Hyperbolic, or inverse, relationships between the response
of selected biological indicators and contaminant concentrations
were observed. This evidence is supportive of a critical
assumption in most sediment quality approaches that a threshold
concentration exists, above which a chemical can be expected
to elicit a negative biological response.
ADDITIONAL ANALYSES RECOMMENDED TO REFINE OR VERIFY RESULTS
An attempt to include associated fish histopathology data in previous
ARTHUR analyses (Quinlin et al. 1984) was inconclusive with respect to
chemical influence on the biological systems. This lack of correspondence
may result from the mobile nature of fish, their integrative feeding patterns,
and their higher positions in the food chain. Because of these reasons and
resource constraints, a multivariate testing of Cofrmencement Bay hi stopathol ogy
data with sediment chemistry data was not conducted. Unless additional evidence
becomes available that would prompt an analysis, it is recoomended that further
analyses focus on the influence of chemicals on biological systems in intimate
contact with the sediments. Additional analyses are recommended below.
Data Set Expansion
The data sets for which chemical and associated biological effects
data are available for the same sediment sample, or identical sampling
D-35
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location are still too small. This is especially true given the number
of variables that are potentially of interest and the apparent chemical
complexity of the Puget Sound system (typically requiring some form of
segmentation to derive interpretable results). Data acquisition is recommended
in two directions.
More Intensive Sampling—
First, given the extensive variation in urban embayments such as Commence-
ment Bay, further data gathering should intensively sample small geographic
regions to supplement more dispersed sampling over the entire Puget Sound.
Specifically, one-third or more sampling stations should have 2-4 additional
sampling stations chosen in close proximity (e.g., 50-100 yd). This sampling
strategy will help evaluate the microscale variation that seems to be present
in chemical concentrations and biological effects. A major problem with
multivariate techniques apparent in this application is the difficulty
in distinguishing a particular localized chemical effect when the biological
effect produced by one chemical is similar to the effect produced by another
chemical in a different part of the system under study. Hence, large data
sets combining multiple areas with completely different chemical influences
will likely be difficult to interpret, but large data sets covering a small
area (exposed to some combination of sources) will make the most efficient
use of a multivariate approach.
More "Pristine" Sampling-
Second, more samples from less affected (reference) areas must be
included. All data sets analyzed to date have been biased toward polluted
areas. Experimental designs containing 20-33 percent of the sampling stations
(and samples) in areas believed to be less affected by anthropogenic influences
are recommended.
Data Analysis
The suggestions above are for further data gathering, regardless of
which data analysis methods are used. The exploratory approach applied
in this project viewed the chemical, toxicity, and biological variables
as descriptors of the sediment stations. This approach could be supplemented
by some other analytical approaches and methods to resolve some of the
difficulties in multivariate analysis encountered in this study. These
approaches include path modeling (canonical correlation) and classification
methods.
Path Modeling (Canonical Correlation) Analysis—
Path modeling is a new technique that is an advance of regression
analysis. The technique allows variables to be assigned to sectors of
influence in a manner that approaches modeling the system. Canonical correla-
tion is a method that calculates the degree of correlation between factors
in the independent variables with factors in the dependent variables.
A simple two-block path modeling calculation is similar to a canonical
correlation analysis.
D-36
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Both techniques could be applied to sediment chemical and biological
data to partially overcome the complexity of separate influences from different
chemical groups (or sources). In a combined data set, such as Commencement
Bay or Elliott Bay, this approach may reveal distinct factor relationships
without having to break the data set into appropriate subsets (e.g., geographic
units). Although analysis of data subsets (as conducted in this project)
get to the same relationships, this approach requires substantial insight
or intuition on the part of the research team.
Classification Modeling for Screening—
Separate from the above recommendation, classification techniques such
as available in the ARTHUR system could be used as an interim decision
system, based on the existing data sets. For example, embayment data can
be grouped into stations that are above or below some accepted sediment
quality value for biological effects. Once these designations have been
made, classification methods can be "trained" to decide which group status
to assign to new sediment samples based on values of selected chemical
variables. Part of the classification analysis would pinpoint which variables
are key to the decision-making process.
An initial approach would look at the classification accuracy achieved
when a few of the most influential factor variables are used. Other techniques
can be used to evaluate the importance of each chemical variable. This
approach could complement sediment quality values for specific chemical
concentrations and may allow screening analysis of sediments based on a
few simple measurements (e.g., bioassays with selected chemical measure-
ments).
D—37
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EXHIBIT D-l
TECHNICAL BACKGROUND ON PATTERN RECOGNITION TECHNIQUES
D—38
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OVERVIEW OF MULTIVARIATE DATA ANALYSIS METHODS
A general overview of multivariate methods is presented in this section
followed by a description of specific tasks performed in the exploratory
analysis performed for this project. ARTHUR is a system consisting of
about 70 separate routines for data preprocessing, display, unsupervised
learning, and supervised learning operations for general pattern recognition.
It is used for exploratory data analysis (factor and cluster analysis),
anomaly evaluation, selected feature-time plots, feature distribution statis-
tics and interfeature correlations, and classification/ prediction model
development.
PREPROCESSING
Preprocessing includes transformation of variables and manipulation
of samples. Transformation of variables can include scaling (this operation
preserves the shape of the original variable distribution), mathematical
functional operations (eg., logarithmic), and linear combinations of variables.
Manipulation of samples can include changing property/category value (re-
stratifying), random deletion, and specific deletion and assignment to
test sets.
The types and combinations of preprocessing steps that can be applied
to data are possibly infinite in number. The most common preprocessing
steps can be applied using either a combination of or individual methods
from 16 routines in the ARTHUR system. These steps include feature scaling,
linear combinations of features, feature ratios, feature selection, weighting,
category changes, merge sets, split sets, random subsets, and missing data
filling.
Feature/variable scaling is commonly employed to eliminate dependence
on units of measure and to put all variables on a common footing of relative
(within feature) variance. The autoscaling option in ARTHUR method SCALE
was used in this project. This transformation subtracts the mean value
of the variable from each measured value and divides by a term proportional
to the variance. This transformation is similar to the standard normal
variable transformation (zAtransform), and is a one-to-one mapping of the
values of a variable from one reference system to another.
DISPLAY
Display routines are provided in the ARTHUR system to allow plots of
data versus selected axes of information, and to allow other graphical
information representations. Several of the methods have line-printer
graphical output to portray performance results (eg., regression methods
plots of residual errors, hierarchical cluster analysis dendograms).
Plots of the data versus selected pairs of variables, eigenvectors or
factors can be done on line-printer, Calcomp, or Tektronix terminal output.
D—39
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The line-printer routine is the method VARVAR (Variable by Variable plot-
ting). A method for nonlinear mapping of higher dimensional information
to 2-space is also provided (NLM).
UNSUPERVISED LEARNING
Unsupervised learning consists of applying exploratory techniques,
in an unbiased manner, to search for relationships among the samples (stations)
and among the variables measured for the samples. The main techniques
include factor analysis, cluster analysis, and data plots.
Factor Analysis
Factor analysis is a mathematical data analysis approach that is aimed
at representing the variation (or information) in the data in the fewest
dimensions. The data are initially represented by their measured values
for the original variables. This is usually described as the distribution
of samples in the measurement space (or NVAR-space, where NVAR = the number
of original variables). The first step in factor analysis is usually calcula-
tion of principal component (pc) axes which fit the distribution of samples
in an ordered manner; the first axis lies along the direction of the greatest
variation in the data, the next lies orthogonal (perpendicular) to the
first, along the next greatest variation in the data, etc. The samples
can be projected onto these axes and plotted. The next steps in factor
analysis are usually retention of a subset of the pc-axes, rotation by
small amounts and reorthogonalization of the axes, and interpretation according
to the variables that have the highest loadings onto the axes (or factors).
Factor analysis was typically done using the following sequence of methods
in the ARTHUR system: KAPRIN, KAVECT, KAVARI, KAVECT, KAORTH, and KAVECT
(optional CHSUB followed by the above sequence again). KAPRIN performs
principal component extraction. KAVARI performs Varimax rotation on the
various pc. KAORTH performs reorthogonalization of the rotated vectors.
KAVECT prints out detailed information on the pc/factors, including variance
retained and factor loadings of each variable. CHSUB was used to randomly
keep 80 percent of the samples in each category before recalculating pc's
and factors.
The linear principal component extraction step in KAPRIN is invariant
with respect to the number of pc's calculated (up to the limit of the original
number of variables) as long as the samples and variables in the data remain
unchanged. Deletion of a few anomalous samples can have major effects
on the calculated pc's. Varimax rotation and reorthogonalization are steps
that can be useful for interpretation of principal components such as physical,
chemical, or other factors. These steps, along with randomly calculated
subsets, were used to help identify, interpret, and ascertain which were
the major factors and the most strongly contributing variables in the data.
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Cluster Analysis
Cluster analysis was done on the samples using the hierarchical clus-
tering method (HIER) in the ARTHUR system. Both single-link and complete-link
connection dendograms were calculated and printed on line-printer plots.
Single-link connection similarity calculations start with the two
samples in the data that lie closest in the n-dimensional space of the
measurements, where n is equal to the number of measurements made on each
sample (or station). This distance is the simple Euclidean distance, and
corresponds to the calculated similarity between the two samples. The
similarity measure for each sample Xi and Xj is defined as:
Si j = 1 - d-j j/MAX(di j)
where
M 7 0 5
dij - c I (Xik - Xjk)2 r-5
k=l
and
MAX(dij) is the largest interpoint distance.
The most unlike samples give Sjj=0, and identical objects give S-jj=l.
The initial two samples selected with tnis algorithm are defined as a cluster.
The method then looks for the next smallest distance in the data set.
If this involves one of the previous samples, the new sample is connected
to the first cluster with a similarity value representing its distance
from the nearest sample already in the cluster. If the next smallest distance
involves two samples, neither of which is already in a cluster, then a
new cluster is begun using these two samples, and the method moves on to
look for the next smallest distance. These steps are repeated until all
samples are connected.
Complete-link similarity calculations start the same way as single-link
for the first two samples. Other clusters are built up by linking samples
previously not in a cluster by distance-similarity and by graphing samples
to clusters by maximum distance to the samples in the cluster.
Interpretation of cluster analysis results is similar for both single-
and complete-link methods. Groups of samples linked at higher similarity
values must be examined using external knowledge to identify whether some
common basis is obvious for explaining why the samples appear to form a
cluster. If a basis is not obvious, classification methods can be used
to determine if classification accuracies into the apparent groups are
high and which are the important variables. The variables that are important
can point toward a possible explanation for the observed clusters.
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Anomaly Evaluation
Potential sample anomalies were evaluated using a combination of methods
contained in the ARTHUR system. The two approaches most frequently used
in this study employed factor plots and cluster analysis.
The factor analysis approach uses the following sequence of methods
in the ARTHUR system: KAPRIN,KATRAN,VARVAR,KAVARI,KATRAN,VAR\/AR. This
sequence calculates the principal component or factor axes (KAPRIN followed
by KAVARI), projects the data onto the new axes (KATRAN), and plots the
data versus selected factor pairs as axes (VARVAR). Any bad samples in
the data tend to adversely influence the calculation of the factors. As
a result, they show up as wel1-separated points (anomalies) on the data
plots, even when only the most significant factor pairs are used as axes.
The cluster analysis approach uses the standard output information
from hierarchical cluster analysis to identify potential anomalies. The
method sequence in the ARTHUR system is 01ST,HIER. The output information
is in the form of a connection dendogram. Potential anomalous samples
appear with very low connection similarity values in the dendogram, indicating
that they are quite different from all of the other samples in the data
set.
SUPERVISED LEARNING
Supervised Learning consists of methods to develop classification
or prediction models, developed on a "training set" of samples that have
known category/property values, testing of the models on the training set
and any available "test-set" samples, evaluation of the variables important
to the models, and the physical/chemical implications of the models.
Category Classification
Category classification is aimed at developing models that accurately
classify samples into discreet groups or into continuous categories (eg.,
low, middle, and high concentration groups). The methods include K-Nearest
Neighbor (KNN), principal component modeling (SIMCA), discriminant analysis
(LEDISC and REGRESS), and Bayesian Probability (BAYES).
KNN uses a committee vote of nearest neighbors to classify. The under-
lying philosophy is that the closer two samples are in the multidimensional
measurement space, the more similar they should be. SIMCA (SIPRIN + SICLAS)
uses principal components (eigenvectors) for each group to define the posi-
tion and distribution of the group in the measurement space. Classifi-
cation of a sample is based on the smallest distance between the sample
and the pc-models for the categories. Discriminant analysis uses a linear
function to separate two categories, based on positioning the separation
by a minimization of squared-error. BAYES classification is most suited
to large data sets with many samples, where the distribution histograms
for each variable can be accurately characterized for each category. Classifi-
cation is made on the basis of a summation of probabilities that the sample
D-42
-------
belongs to each category weighted by the variable-category probabilities
for the measurement values of the sample.
TYPICAL SEQUENCE OF METHODS
Appendix A-l is the interactive dialogue from computer run #17 (output
code INMC9). It illustrates the typical sequence of methods applied to
the data for exploratory analysis.
Univariate Distribution Parameters
Autoscaling of the variables was one of the first methods applied to
the data sets, under almost all circumstances. Using the method SCALE
in ARTHUR, each variable was transformed to a new feature with zero mean
and unit variance. It removes the units of measure from the variables,
and puts them all on a common footing of variance (this transformation
does not change the original shape of the variable distribution). In addition,
the method SCALE calculates distribution parameters for each variable over
the data set, consisting of mean, standard deviation, normalized standard
deviation, minimum value, maximum, range, and coefficients of skewness
and kurtosis.
Feature Correlations
The correlation matrix of all interfeature correlations was calculated
once on the autoscaled data for each data set. This step provides complemen-
tary information to the principal component and factor analysis calculations.
The method CORREL in ARTHUR calculates the correlation coefficient, the
low and high values of the 95 percent confidence interval about the coefficient,
and the probability that the correlation would be calculated if the sample
were drawn from a random parent population. The correlation matrix was
scanned after each run for values with a probability <0.001 and coefficients
above r=0.500.
Basic Exploratory Analysis
Below is a listing of methods and option parameters that would be typi-
cal of those used to do initial exploratory work with the data.
ARTHUR
NMC9
(title)
INPUT,!,...
INFILL,1,1$
SCALE,1,2$
CORREL,2$
KAPRIN,2,4,0,0,10$
KAVECT$
KATRAN,2,3,4,5
VAX level command to start ARTHUR program
four-character code to identify run output
unique title to identify the run
read data from file named in response to
ARTHUR prompt, according to parameters
specified to define number of variables, data
format, etc.
missing data fill by means for categories
autoscale variables, list univariate statistics
calculate interfeature correlations
principal component (pc) extraction of 10 pc's
lists detailed eigenvector information
data projection onto five pc axes
D-43
-------
VARVAR,3,0,-1,1,0,1$ plots of data versus pairs of pc's
STATUS,-1 lists status of files in use by ARTHUR
SAVE,3 saves eigenvector projected data
SAVE,7 saves ARTHUR run lineprinter output file
EXIT end ARTHUR run
Infill shows how much data is missing and what feature values are mis-
sing for each sample. SCALE autoscales the data and prints out univariate
statistical information on the distribution parameters of each variable
(feature) over the entire data set. CQRREL calculates and prints the inter-
feature correlation matrix. The correlation matrix lists the 95 percent
confidence interval values about the correlation coefficient and the probability
(in parentheses) that the correlation could have come from a parent population
with zero interfeature correlation for the two variables.
Methods KAPRIN and KAVECT calculate the linear principal components
(eigenvectors) and list the detailed information on the eigenvectors, includ-
ing variance retained by each, feature loadings and communalities. Methods
KATRAN and VARVAR project the data onto principal component axes and plot
the data in two dimensions versus pairs of the first five pc's in the above
menu. Sample index and category plots are all generated. Structure (grouping
in the plots) and anomalies should be examined.
After applying the above menu of methods to get an initial view of
data structure and relationships between variables, a more extended series
of methods, below, can be applied to give more information on relationships
between the samples and between the variables:
Additional Exploratory Analysis
The following menu of methods is a list of typical methods that would
provide a more detailed look at the relationships between variables, data
plots, and cluster analysis relationships. The sequence starts after several
initial steps have been applied, including autoscaling the data and putting
the scaled features into file 2.
KAPRIN,2,4,0,0,10$
KAVECT$
KAVARI,4,4,0,0,5$
KAVECT$
KA0RTH,4,4$
KATRAN,2,3,4,5$
VARVAR,3,0,-1,1,0,1$
DIST,2,3$
HIER,3,0,1$
TUTRAN,2,3$
DIST,3,3$
HIER ,3,0,1$
extract and print 10 eigenvectors
list information on all 10 pc's
Varimax rotation of 5 pc's
list info on 5 rotated pc's
orthogonalize rotated pc's
project data onto rotated axes
plot data for rotated axes
calculate intersample distance matrix
cluster analysis (complete link)
transpose the scaled data matrix
add distance (over sample space)
cluster analysis on features
The above sequence starts with extraction of linear principal components
(eigenvectors) in KAPRIN, and listing detailed information about the pc's
and the variables that contribute to each, in KAVECT. Additional information
D-44
-------
on underlying relationships between the variables is obtained by applying
Varimax rotation and reorthogonal i zation to the pc's to calculate factors
that may be more easily interpreted, chemically or biologically. Interpretation
is based on each variable's contribution (and loading) on each pc given
in KAVECT and the HIER dendogram to see which features/variables are most
similar over the samples. The interpretation will need the knowledge and
intuition of the scientists/analysts working on the problem as to whether
the variable associations (factors) make sense biologically, chemically,
or physically. The data plots for the rotated eigenvectors can supplement
the previous plots.
D-45
-------
PATTERN RECOGNITION TECHNIQUES APPLIED TO COMMENCEMENT BAY DATA
The pattern recognition tasks used in this project were divided into
four areas: (1) data entry and validation, (2) analysis of chemical information
for the sampling stations, (3) analysis of relationships among biological
and chemical variables, and (4) documentation. These tasks are described
in the following sections.
DATA SETS EVALUATED
Three data sets were studied. The data sets are briefly described
in the following paragraphs. The variables in each data set are listed
in Tables D-l, D-2, and D-3. All data sets were received as ASCII files
on IBM-PC compatible floppy diskettes, accompanied by descriptive material
and hard-copy printouts for verification.
Set 1 -- CHEMBIO
This data set consisted of 56 station samples characterized by 116
variables and parameters listed in Table D-l. The stations were located
in nine waterways/areas: Blair (12 stations), Carr Inlet (4), Milwaukee (3),
City (6), Middle (1), Hylebos (14), Ruston (8), Sitcom (3), and St. Paul (5).
The variables included chemical and biological measures.
Set 2 — MSQSGVAL
This set consisted of 144 stations in the above waterways/areas charac-
terized by 114 chemical variables and parameters. The data set did not
include any biological/benthic variables and was only used for detailed
chemical factor analysis to complement factor analysis on the data sets
that had fewer stations.
Set 3 — CB2
This set consisted of 54 of the 56 CHEMBIO stations characterized
by abundance measures for 64 benthic infauna variables. A measure of total
individual species counted at each station was later included with this
data set. After exploratory data analysis, a subset of the variables from
this set were added to the CHEMBIO set for later analyses on chemical-biological
relationships.
The pattern recognition tasks applied in this project are divided
into four areas: 1) data entry and validation, 2) analysis of chemical
information for the sampling stations, 3) analysis of relationships between
biological variables and chemical information, and 4) documentation. These
tasks are described in the following sections.
D-46
-------
TABLE D-l. VARIABLES IN CHEMBIO DATA SET, COMBINED CHEMICAL
AND BIOLOGICAL DATA FOR 56 STATIONS IN THE COMMENCEMENT BAY AREA
Variable
Number
Complete Name
PA65
1
phenol
PA34
2
2,4-dimethylphenol
PA21
3
2,4,6-trichlorophenol
PA64
4
pentachlorophenol
PB36
5
2,6-dinitrotoluene
PB37
6
1,2-diphenylhydrazine
PB62
7
N-nitrosodipheny1amine
PBOl
8
acenaphthene
PB55
9
naphthalene
PB77
10
acenaphthalene
PB78
11
anthracene
PB81
12
phenanthrene
PB80
13
fluorene
PB39
14
fluoranthene
PB72
15
benzo(a)anthracene
PB73
16
benzo(a)pyrene
TBFLANTH
17
Total benzofluoranthenes
PB74
18
benzo(b)fluoranthene
PB75
19
benzo(k)fluoranthene
PB76
20
chrysene
PB79
21
benzo(ghi)perylene
PB82
22
dibenzo(a.h)anthracene
PB83
23
indeno(l,2,3-cd)pyrene
PB84
24
pyrene
PB08
25
1,2,4-trichlorobenzene
PB09
26
hexachlorobenzene
PB25
27
1,2-dichlorobenzene
PB26
28
l»3-dichlorobenzene
PB27
29
1,4-dichlorobenzene
PB52
30
hexachlorobutadiene
PB12
31
hexachloroethane
PB53
32
hexachlorocyclopentadiene
PB66
33
bis(2-ethylhexyl)phthalate
PB67
34
butyl benzyl phthalate
PB68
35
di-n-butyl phthalate
PB69
36
di-n-octyl phthalate
PB70
37
diethyl phthalate
PB71
38
dimethyl phthalate
PB54
39
isophorone
PP92
40
4,4'-DDT
PV23
41
chloroform
PV85
42
tetrachloroethene
PV87
43
trichloroethylene
PV38
44
ethyl benzene
D-47
-------
TABLE D-l. (Continued)
TPCBS
45
Total PCB's
A65850
46
benzoic acid
A95487
47
2-methylphenol
A108394
48
4-methylphenol
A95954
49
2,4,5-trichlorophenol
B132649
50
dibenzofuran
B62533
51
ani1ine
B100516
52
benzyl alcohol
B91576
53
2-methylnaphthalene
TRICBD
54
Total trichlorinated butadienes
TETCBD
55
Total tetrachlorinated butadienes
PENCBD
56
Total pentachlorinated butadienes
TXYLENES
57
Total xylenes
ANTIMONY
58
antimony
ARSENIC
59
arsenic
BARIUM
60
barium
BERYLLIU
61
beryllium
CADMIUM
62
cadmium
CHROMIUM
63
chromium
COPPER
64
copper
IRON
65
iron
LEAD
66
lead
MANGANES
67
manganese
NICKEL
68
nickel
SELENIUM
69
selenium
SILVER
70
silver
THALLIUM
71
thai 1ium
ZINC
72
zinc
MERCURY
73
mercury
SEG
74
station waterway segment (location code)
SOLIDS
75
% dry weight (dry wt/wet wt)
VSOLIDS
76
volatile solids (total organic content)
TOC
77
total organic carbon
NITROGEN
78
organic nitrogen
SULFIDE
79
free sulfide
GREASE
80
oil and grease (freon extractable portion
ROCKS
81
coarse fraction of sediment size
SAND
82
sand fraction of sediment size
SILT
83
fine fraction of sediment size
CLAY
84
very fine fraction of sediment size
POLYCH
85
Total polychaete abundance
OLIGO
86
Total oligochaete abundance
MOLLUSC
87
Total mollusc abundance
CRUSTA
88
Total crustacea abundance
ECHINO
89
Total echinoderm abundance
OTHER
90
Misc species abundance
TOTAL
91
Total abundance
THARYX
92
Tharyx multifilis abundance
PRIONOSP
93
Prionospio spp. abundance
LUMBRI
94
Lumbrineris spp. abundance
D-48
-------
TABLE D-l. (Continued)
AXINOPS
95
Axinopsida spp. abundance
MACOMA
96
Macoma carlottensis abundance
PSEPHIQ
97
Psephidia lourdi abundance
AMPHIPOD
98
Total amphipod abundance
EUPHILO
99
Euphilomedes spp. abundance
MORT
100
% Amphipod mortality
ABNORM
101
% Oyster larvae abnormality
CHANGE
102
% Change in luminescence (microtox)
METHYLET
103
1-methy 1 -2-(1-methyl ethyl)benzene
MOXYPHEN
104
2-methoxyphenol
PENTACHL
105
pentachlorocyclopentane
BIPHENYL
106
1,11-biphenyl
01BENZOT
107
dibenzothiophene
METHYL2P
108
2-methylphenanthrene
METHYL1P
109
1-methylphenanthrene
HEXADEC9
110
9-hexadecenoic acid methyl ester
ISOPIMAR
111
isopimaradiene
KAUR16EN
112
unidentified diterpenoid hydrocarbon
METHYLPY
113
1-methylpyrene
RETENE
114
retene
METH2PYR
115
2-methylpyrene
COPROSTA
116
coprostanol
0-49
-------
TABLE D-2. VARIABLES IN MSQSGVAL DATA SET (DATA SET
CONTAINS 144 SEDIMENT SAMPLES IN THE COMMENCEMENT BAY AREA)
Var #
Var Name
Complete Variable Name
1.
PA65
phenol
2.
PA34
2,4-dimethyIphenol
3.
PA21
2,4,6-trichlorophenol
4.
PA64
pentachlorophenol
5.
PB36
2,6-dinitrotoluene
6.
PB37
1,2-diphenylhydrazine
7.
PB62
N-nitrosodiphenylamine
8.
PBOl
acenaphthene
9.
PB55
naphthalene
10.
PB77
acenaphthalene
11.
PB78
anthracene
12.
PB81
phenanthrene
13.
PB80
fluorene
14.
PB39
fluoranthene
15.
PB72
benzo(a)anthracene
16.
PB73
benzo(a)pyrene
17.
TBFLANTH
Total benzofluoranthenes
18.
PB74
benzo(b)fluoranthene
19.
PB75
benzo(k)fluoranthene
20.
PB76
chrysene
21.
PB79
benzo(ghi}perylene
22.
PB82
dibenzo(a,h)anthracene
23.
PB83
indeno(l,2,3-cd)pyrene
24.
PB84
pyrene
25.
PB08
1,2,4-trichlorobenzene
26.
PB09
hexachlorobenzene
27.
PB25
1,2-dichlorobenzene
28.
PB26
1,3-dichlorobenzene
29.
PB27
1,4-dichlorobenzene
30.
PB52
hexachlorobutadiene
31.
PB12
hexachloroethane
32.
PB53
hexachlorocyclopentadiene
33.
PB66
bis(2-ethylhexyl)phthalate
34.
PB67
butyl benzyl phthalate
35.
PB68
di-n-butyl phthalate
36.
PB69
di-n-octyl phthalate
37.
PB70
diethyl phthalate
38.
PB71
dimethyl phthalate
39.
PB54
isophorone
4,4'-DDT
40.
PP92
41.
PV23
chloroform
42.
PV85
tetrachloroethene
43.
PV87
trichloroethylene
44.
PV38
ethyl benzene
45.
PP106
PCB-1242
0-50
-------
TABLE D-2. (Continued)
46.
PP107
PCB-1254
47.
PP110
PCB-1248
48.
PP111
PCB-1260
49.
TPCBS
Total PCB's
50.
A65850
benzoic acid
51.
A95487
2-methylphenol
52.
A108394
4-methylphenol
53.
A95954
2,4,5-trichlorophenol
54.
B132649
dibenzofuran
55.
B62533
ani1ine
56.
B100516
benzyl alcohol
57.
B91576
2-methy1 naphthalene
58.
TRICBD
Total trichlorinated butadienes
59.
TETCBD
Total tetrachlorinated butadienes
60.
PENCBD
Total pentachlorinated butadienes
61.
TXYLENES
Total xylenes
62.
BA
Basin Code
63.
ANTIMONY
antimony
64.
ARSENIC
arsenic
65.
BARIUM
barium
66.
BERYLLIU
beryl lium
67.
CADMIUM
cadmi um
68.
CHROMIUM
chromium
69.
COPPER
copper
70.
IRON
iron
71.
LEAD
lead
72.
MANGANES
manganese
73.
NICKEL
nickel
74.
SELENIUM
selenium
75.
SILVER
si 1ver
76.
THALLIUM
thailium
77.
ZINC
zinc
78.
MERCURY
mercury
79.
SEG
station waterway segment (location code)
80.
SOLIDS
% dry weight (dry wt/wet wt)
81.
VSOLIDS
volatile solids (total organic content)
82.
TOC
total organic carbon
83.
NITROGEN
organic nitrogen
84.
SULFIDE
free sulfide
85.
GREASE
oil and grease (freon extractable portion)
86.
ROCKS
coarse fraction of sediment size
87.
SAND
sand fraction of sediment size
88.
SILT
fine fraction of sediment size
89.
CLAY
very fine fraction of sediment size
90.
CODE
station type designation
91.
TIO
92.
METHYLET
1-methy1-2-(1-methylethyl)benzene
93.
MOXYPHEN
2-methoxyphenol
94.
PENTACHL
pentachlorocyclopentane
95.
BIPHENYL
1,1'-biphenyl
D-51
-------
TABLE D-2. (Continued)
96.
DIBENZOT
dibenzothiophene
97.
METHYL2P
2-methylphenanthrene
98.
METHYL1P
1-methylphenanthrene
99.
HEXADEC9
9-hexadecenoic acid methyl ester
100.
ISOPIMAR
isopimaradiene
101.
KAUR16EN
unidentified diterpenoid hydrocarbon
102.
METHYLPY
1-methylpyrene
103.
RETENE
retene
104.
METH2PYR
2-methylpyrene
105.
COPROSTA
coprostanol
106.
LMWPAH
Light Molecular Weight Hydrocarbons
107.
HMWPAH
Heavy Molecular Weight Hydrocarbons
108.
IC0GRP1
elemental metals group 1
109.
TOTCBD
Total chlorinated butadienes
110.
PHTHAL
Total phthalates
111.
CLBENZ
Total chlorinated benzenes
112.
FINES
Silt + Clay sediment content
113.
TOTMET
Total elemental metals
114.
TOTORG
Total organics
D-52
-------
TABLE 0-3. VARIABLES CONTAINED IN CB2.DAT EXTENDED BENTHIC OATA SET
Var# Name Taxonomic Name NODC Taxonomic Code
1.
16Nemert
Nemertea
43
2.
20Nemato
Nematoda
47
3.
37Pholoe
Pholoe minuta
5001060101
4.
53Eteone
Eteone longa
5001130205
5.
72Microp
Micropodarke dubia
5001210801
6.
98P1atyn
Platynereis bicanaliculata
5001240501
7.
lOlNepht
Nephtys cornuta
5001250104
8.
102Nepht
Nephtys cornuta franciscana
500125010498
9.
106Nepht
Nephtys ferruginea
5001250111
10.
114Glyce
Glycera capitata
5001270101
11.
129Lumbr
Lumbrineris spp
50013101
12.
135Lumbr
Lumbrineris sp. gr. 4
5001310194
13.
136Lumbr
Lumbrineris sp. gr. 3
5001310195
14.
138Lumbr
Lumbrineris sp. gr. 1
5001310197
15.
151Sch i s
Schistomeringos rudolphi
5001360598
16.
154Leito
Leitoscoloplos pugettensis
5001400102
17.
172Po1yd
Polydora spp
50014304
18.
185Pri on
Prionospio cirrifera
5001430502
19.
186Prion
Prionospio steenstrupi(= P.malmgreni) 5001430506
20.
187Prion
Prionospio (Minuspio) multibranchiata 5001430599
21.
197Sp i op
Spiophanes berkelyorum
5001431004
22.
206Phyl1
Phyllochaetopterus prolifica
5001490202
23.
207Spioc
Spiochaetopterus costarum
5001490302
24.
210Cirra
Cirratulus cirratus
5001500101
25.
214Thary
Tharyx multifilis
5001500302
26.
215Chaet
Chaetozone spp
50015004
27.
216Cossu
Cossura spp
50015201
28.
221Scali
Sealibregma inflatum
5001570101
29.
223Arman
Armandia brevis
5001580202
30.
231Capit
Capitella capitata
5001600101
31.
236Notom
Notomastus tenuis
5001600302
32.
238Medio
Mediomastus spp.
50016004
33.
252Praxi
Praxillella gracilis
5001630901
34.
256Eucly
Euclymeninae
5001631
35.
297Pista
Pista cristata
5001680701
36.
323Eucho
Euchone incolor
5001700204
37.
324Eucho
Euchone sp. A
5001700299
38.
338Tubif
Tubificidae
500902
39.
348Mitre
Mitrella gouldi
5105030204
40.
3520dost
Odostomia spp
51080101
41.
353Turbo
Turbonilla spp
51080102
42.
373Nucu1
Nucula tenuis
5502020201
43.
386Megac
Megacrenella columbiana
5507010301
44.
391Parvi
Parvilucina tenuisculpta
551501010
45.
394Axino
Axinopsida serricata
5515020201
46.
399Mysel
Mysella tumida
5515100102
47.
407Nemoc
Nemocardium centifilosum
5515220301
D-53
-------
TABLE D-3. (Continued)
48.
411Macom
Macoma elimata
5515310102
49.
412Macom
Macoma obliqua
5515310106
50.
415Macom
Macoma carlottensis
5515310112
51.
416Macom
Macoma nasuta
5515310114
52.
421Telli
Tellina modesta
5515310204
53.
429Pseph
Psephidia lordi
5515470501
54.
455Euphi
Euphilomedes carcharodonta
6111070301
55.
456Euphi
Euphilomedes producta
6111070303
56.
4728alan
Balanus crenatus
6134020104
57.
479Eudor
Eudorella pacifica
6154040202
58.
496Lepto
Leptochelia dubia
6157020103
59.
544Photi
Photis brevipes
6169260201
60.
599Capre
Caprellidae
617101
61.
619Cal11
Callianassa spp.
61830402
62.
639Pinni
Pinnixa spp
61890604
63.
663Amphi
Amphiuridae
812903
64.
665Amphi
Amphiodia urtica
8129030104
Variable Added to Benthic Data After Exploratory Results
65. DIVERSTY Total Different Species Counted at Station
D-54
-------
DATA ENTRY AND VALIDATION
Data receipt, entry and validation was a more time-consuming process
than originally estimated. Three data sets were incorporated into the
data analysis.
Receive Data
Data were obtained on PC floppy diskettes. The data were in ASCII formatted
files, with explanatory material and hard copy printouts for verification.
Read and Load Diskettes—
The ASCII files were read from the diskettes into SYMPHONY database
or Microsoft WORD wordprocessing programs. Within these programs, the
files were converted to ARTHUR format and rewritten to ASCII output files.
These files were then uploaded on a mainframe computer system.
Data Validation
The data were printed using ARTHUR UTILIT after loading and were scanned
for correspondence with the hard copy information at randomly selected
data points throughout the set. ASCII files on diskettes were also scanned
during wordprocessing or database conversion to ARTHUR format.
The remaining validation steps utilized the ability of various combina-
tions of methods to pinpoint atypical values or samples. Potential sample
anomalies were most frequently identified using a combination of factor
analysis, plus data plots, and cluster analysis. Constant or redundant
variables were identified using the method INDUMP in ARTHUR. Missing data
were filled with the mean value for the variable over the appropriate waterway
category to which the station was assigned.
CHEMICAL EVALUATIONS
After the data were entered into ARTHUR, they were analyzed by the combina-
tion of methods previously described to search for relationships among
the variables, among the samples, and between the variables and character-
istics of the samples. The primary aim was to identify fundamental chemical
factors based on relationships among the chemical variables and to determine
distinctions between the sampling stations in their assigned category,
if present. Steps involved in chemical analysis of the data included:
1. Calculation and interpretation of factors
2. Evaluation of interfeature correlations
3. Examination of data plots versus selected factors
4. Examination of cluster analysis results for indications of
natural groups, based on interpretable associations
D-55
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5. Stratification of samples into test groups to determine the
degree of differentiation and the relevance between the groups.
BIOLOGICAL EVALUATIONS
The relationships between chemical variables and variables characteriz-
ing biological aspects of the samples were evaluated using the same explora-
tory data analysis techniques employed in the evaluation of the chemical
variables. Two types of biological variables considered were bioassay
results that characterized stations as "active" or "inactive" in creating
a significant effect on test tissue or organisms, and benthic infauna abundances
for selected individual species and summary taxonomic groups.
The bioassay data were also used as the basis for sample stratification
into subsets. The aim was to determine if chemical variables could distinguish
between the bioassay groups and if so, were logically interpretable by
the scientists on the team. The data were stratified into active and inactive
groups, according to the bioassay results and accounting for major chemical
differences between various waterways. These sets were then analyzed with
variance weighting of variables, k-nearest neighbor classification, and
principal component and factor plots.
The benthic abundance data were evaluated separately to examine biologi-
cal factors that might suggest community relationships. The benthic data
were then analyzed in combination with the chemical data to look for sugges-
tions of influence by chemistry variations. The results of these evaluations
are presented in the summary report.
DOCUMENTATION
Documentation for the study included an interim outline report covering
initial exploratory results and presented to U.S. COE and U.S. EPA project
review staff in mid-November. Results of interest, upon completion of
all computer runs and interpretation of results, were documented in a separate
summary report.
The specific detailed results are not reported for each computer run.
The increase in the number of data sets included in the study and the larger
number of preliminary exploratory runs that evolved precluded detailed
reporting of all results. The focus of the reporting of results is on
those runs that culminate a series of exploratory and preceding steps.
D-56
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METHODS
DATA PREPARATION AND DATA SET CREATION
The following discussion is a description of the steps involved in trans-
forming the three data sets for use in the ARTHUR system.
CHEMBIO Data
The CHEMBIO data set was received on floppy diskette on 15 October,
1985. Several conversion steps were applied to the CHEMBIO data to prepare
it for exploratory analysis. Data coded as below detection limit were
replaced by a random value between zero and the specified detection limit.
A conversion program in dBase III changed all missing data to the missing
data flag specific to ARTHUR.
The computer runs done with the CHEMBIO data are coded with NMC charac-
ters included in the Print Code. At later stages in the study, these codes
also include benthic infauna abundance data (from CB2). Runs were also
made with chemical variables normalized to fines and total organic carbon.
MSQSGVAL Data
After initial exploratory runs on CHEMBIO, concern was raised over
whether the chemical factors would be representative of the data contained
in a larger set of stations. To address this concern the MSQSGVAL data
was received on 25 October. The data were comprised of 144 samples (stations)
and 110 chemical variables. Two files were contained on the diskette:
MSQSGVAL.LIS containing the values and MSQSGVAL.POR containing the variable
names. The data were ARTHUR formatted, with the category number corres-
ponding to station waterway groups comparable to CHEMBIO.
An initial exploratory data analysis was performed on the full MSQSGVAL
data. Several anomalies were spotted; after interpretation of their effect
on the entire data set those samples were removed, and the exploratory
data analysis was repeated. Also, 12 variables had extensively missing
data and the set was reduced to 98 variables for subsequent analysis.
CB2 Benthic Data
After initial exploratory results using CHEMBIO and MSQSGVAL were
evaluated, it was decided to include an extended benthic infauna data set
containing abundance values for 64 taxa at 54 of the 56 CHEMBIO stations.
The CB2 data, in two files, were obtained on floppy diskette and uploaded
to the mainframe computer on 10 December. Values for an additional variable,
total individual species counted at each station, were added from hardcopy
obtained from Tetra Tech.
D-57
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The CB2 data were analyzed separately and then joined to the CHEMBIO
set for analysis combined with conventional and previous biological variables.
Following analysis of these results, the extended data set was trimmed
to 142 variables and analyzed to provide chemical-biological results.
COMPUTER RUNS
Although 8 to 10 computer runs were anticipated at the start of the
study, the analysis of the three data sets prompted a greater number of
exploratory runs. Twenty-seven computer runs were made for the study.
The runs and a quick description of each, in the order in which they
were done are listed in Table D-4. Reporting of results from these runs
concentrates on #5-IMSQ2, #10-IMSQ7ZD, #12-IMSQ8, #15-INMC7, #16-INMC8,
#17-INMC9, #18a-INC10ZD,and #23-IABC4.
SPECIFIC RESULTS FROM INDIVIDUAL AND COMPARED COMPUTER RUNS
In the following paragraphs, specific results from the individual compu-
ter runs or comparison between a set of runs are listed.
INMC2 and IMSQ2 Univariate Distribution Statistics and Differences
Statistics for mean, standard deviation, range, maximum and minimum
values, and higher moments were obtained for each variable over the CHEMBIO
and MSQSGVAL data sets. Examination of these statistics indicated substantial
differences between the two data sets in several of the chemical variables.
These differences, listed in Table D-5, indicate that some caution should
be observed in accepting the results that suggest chemical-biological relation-
ships may be present, based on CHEMBIO calculations. Because of its smaller
sample size (56 versus 144 stations), CHEMBIO is less representative of
Commencement Bay chemical variation than MSQSGVAL.
Valid "Anomalies"
During the Commencement Bay Superfund project, considerable effort
was taken to validate the data in the data sets for this study. This quality
assurance/quality control effort was aimed at correcting gross analytical
errors in the data set prior to data interpretation.
Analysis using pattern recognition techniques indicated that apparent
anomalies remained in the data sets. These anomalies were highly unusual,
but nonetheless real and valid data (see Table D-6). In most cases, the
unusual values derived from highly contaminated samples collected at stations
closest to pollution outfalls. Interpretation of pattern recognition results
was performed with and without these anomalies to ensure that their effect
on the data set was understood.
D-58
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TABLE D-4. PATTERN RECOGNITION ANALYSIS -- COMPUTER RUNS
Run # Print Code Data Set Description
1
2
3
4
5
6
7
8
9
10
11
12
13
14a
14b
IGAEN CHEMBIO/
NOMISSCR
IGAEM MSQSGVL2
INMC2
INMC3
IMSQ2
IMSQ3
IMSQ4
IMSQ5
CIS51
IMSQ8
INM32
INMC5
INMC6
CHEMBIO/
NOMISSCR
CHEMBIO/
NOMISSCR
MSQSGVL2
MSQSGVL2/
0MSQ2
MSQSGVL2/
0MSQ2
MSQSGVL2/
0MSQ2
MSQSGVL2
IMSQ7ZD MSQSGVL2
INMC4ZT 0NMC3 +
INM42ZD 0CB22
0MSQ4
VNMC3 +
VNMC2
NMC +
0CB22
CNMC5
initial exploratory run on combined
chem & bio data -- 56 station set
initial exploratory run on 144 station
chemistry variable set
2nd exploratory run on 56 station set
chemistry variables only
3rd exploratory run on 56 station set
biological variables only
2nd exploratory run on 144 station set
98 chemistry variables retained
attempted replot run of KAORTH V2-V3 w/o
major trend samples
exploratory run using TOC normalized
chemical variables -- 144 station set
exploratory run using FINES normalized
chemical variables -- 144 station set
outliers removed, 139 station chem vars,
exploratory run
outlier removed, 98 chem vars, short
exploratory run
combined benthic/bio vars exploratory
run (miscombined data)
generate chem factor scores for 144 station
set (PMSQ8ZD & VMSQ8ZD data saved)
listing of factor scores
CHEMBIO + BENTHIC exploratory analysis -
created CNMC5.DAT
BIO-BENTH-CONVENTIALS Vars, 56 stations,
restratified, exploratory run
D-59
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TABLE D-4. (Continued)
15
INMC7
CNMC5
reduced to 66 BIO-BENTH-CONVENTIALS Vars,
56 stations, created CNMC7.DAT
16
INMC8
CNMC7
66 Vars, 51 stations, exploratory run
17
INMC9
CNMC7
66 Vars, outlier removed (51 stations),
exploratory run (comp. INMC7 and INMC8)
18a
INC10
CNMC5
reduced combined set to 142 variables,
56 stations, chem-bio-benth exploratory
18b
INM11
& DVRS
DVRS +
CNMC5
input of diversity data and joining to
CNM5, saved as ACBBB.DAT
19
IACB1 &
IACB2
ACBBB
subset factor analysis, saved 136 Var data
set as BCBBB.DAT (Var 115 wrong)
20
IABC1
BCBBB +
ACBBB
created BioBenth Cluster Stratification &
analyzed (WEIG.SELE.DIST/KNN), saved ABCD
21
IABC2
ABCO
created Impact Category Stratification &
analyzed similar to previous run
22
IABC3 &
IAB32
ABCO
outliers removed, subset factor analysis
recalculated for Waterway Stratification
23
IABC4
ABCD
combined bioassay, waterway, benthcluster
stratification analysis
24
IABC5
ABCO
Variable by Variable scatter plots
25
IABC6
ABCD
Waterway stratified subset analysis with
TOC normalization
26
IABC7
ABCD
Waterway stratified subset analysis with
SILT normalization
27
IUTI1
ABCD
Utility listing of values for selected
variables and species abundance plots
versus SILT and SAND
D-60
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TABLE D-5. COMPARISON OF MEANS AND STANDARD DEVIATIONS FOR
VARIABLES IN MSQSGVAL (144 STATIONS) AND CHEMBIO (56 STATIONS)a
Variable
1
MSQSGVAL
CHEMBIO
Abbrev.
Mean
Std.Dev.
Mean
Std.Dev.
PA34
13.96
19.77
7.17
8.18
PA21
10.25
13.24
5.88
5.83
PA64
62.24
107.4
28.91
27.79
PB37
14.84
102.1
3.34
4.83
PB26
23.73
39.21
14.39
27.43
PB52
64.48
130.0
48.16
112.6
PB12
65.00
231.1
75.36
371.3
PB67
63.21
124.3
34.09
69.71
PB71
82.46
149.2
43.39
51.20
PP92
31.18
15.34
16.47
12.35
A65850
203.2
686.7
96.97
161.5
A108394
870.6
7989.
1957.
12,800.
A95954
12.44
12.68
6.43
7.29
B100516
35.78
53.68
22.28
26.73
SULFIDE
25.56
86.06
15.48
43.59
METHYLET
290.1
691.2
409.8
996.7
MOXYPHEN
96.66
362.4
176.9
566.3
PENTACHL
11.46
31.37
6.41
16.72
COPROSTA
133.0
285.3
85.84
379.1
a These variables had substantially different means and standard deviations
between the two data sets; abbreviations are defined in Tables A-l
through A-3.
D-61
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TABLE D-6. OUTLIER VARIABLES FROM PATTERN RECOGNITION ANALYSIS
Run # 3 INMC2 CHEMBIO 2nd exploratory run on 56 station set
chemistry variables only
* KAPRIN Plots
Index# StationID Outlier in Eigenvector/Factor.
38 RS-18 VI(high), V2(low)
20 HY-22 Vl(mod-high), V2(high), V3(mod-high), V5(high)
48 SP-14 V3(low), V4(mod-high)
7 CI-11 V4(low)
* KAORTH Plots
Index# StationID Outlier in Eigenvector/Factor.
38 RS-18 VI(high)
20 HY-22 V2(high)
48 SP-14 V3(low)
7 CI-11 V4(1ow)
Run # 5 IMSQ2 MSQSGVL2 2nd exploratory run on 144 station set
98 chemistry variables retained
* KAPRIN Plots
Index# StationID Outlier in Eigenvector/Factor
113 SP-14 V4(low), V5(mod-high)
* KAORTH Plots
Index# StationID Outlier in Eigenvector/Factor...
(48) HY-16 VI(mod-high); end of trend in stations 45-58
99 RS-18 V4(h i gh)
102 RS-21 V4(hi gh)
113 SP-14 V5(low)
Run # 7 IMSQ4 MSQSGVL2/ exploratory run using TOC normalized
0MSQ2 chemical variables -- 144 station set
* KAPRIN Plots
Index# StationID Outlier in Eigenvector/Factor...
78 HY-46 VI(low), V3(high), V4(high)
131 RS-03 V5(hi gh)
* KAORTH Plots
Index# StationID Outlier in Eigenvector/Factor...
78 HY-46 V2(hi gh)
131 RS-03 V5(hi gh)
0-62
-------
TABLE D-6. (Continued)
Run # 8 IMSQ5 MSQSGVL2/ exploratory run using FINES normalized
0MSQ2 chemical variables — 144 station set
* KAPRIN Plots
Index# StationID Outlier in Eigenvector/Factor...
103 RS-22 V2(low - opposite of 102 & 99)
* KAORTH Plots
Index# StationID Outlier in Eigenvector/Factor...
103 RS-22 VI(low)
113 SP-14 V5(low - slight hint of trend)
D-63
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Chemical Factor Changes with Anomaly Removal
After initial interpretation using all data was complete, five anomalous
values were removed from the MSQSGVAL data set and exploratory steps were
recalculated on the remaining 139 stations. The five stations left out
in this stage of the analysis were #48-HY-6, #78HY-46, #99RS-18, #102RS-21,
and #113SP—14. Factor analysis results were compared for the run (#10-IMSQ7ZD)
versus the original analysis containing all 144 stations (#5-IMSQ2). Three
of the first five factors showed some correspondence in the variables having
the greatest loadings. The correspondence list is shown in Table D-7,
with common variables underlined.
Combined Chemical-Biological Results, Run #18a-INC10
After exploratory runs were made on the chemical and biological varia-
bles separately, they were combined in an analysis of potential chemical-
biological variable relationships. The CHEMBIO data set was used and 38
benthic abundance variables were added from the CB2 data set after explora-
tory runs on those data alone. The resultant analysis focused on 142 chemical
and biological variables.
Results from the principal component analysis are briefly summarized
below, including the highest loading variables in order of their contribution:
#1. Heavy organics and metals (strongly influenced by station RS-18):
PB81, B132649, PB01, NICKEL, PB39, DIBENZOT, PB80, METHYL2P, LEAD,
PB72, CADMIUM, COPPER, MERCURY, SELENIUM, ANTIMONY, ARSENIC, THALLIUM,
PB76, PB84, ZINC, PB62, B91576, PB78, METH2PYR, PB73, MANGANESE,
#2. Benthic infauna associated with sandy sediment types: SAND, -CLAY,
-SILT, 154Leito, AMPHIPOD, 98Platyn, 106Nepht, 238Medio, -THARYX,
412Macoma, 207Spioc, -214Tharyx, 816Prion, 421Telli, -BERYLLIUM,
CRUSTA, -138Lumbri, 496Lepto, PRIONOSP, LUMBRI, ...
#3. Mixed chemical-biological factor: MOLLUSC, 394Axino, AXINOPS,
-PA21, -SULFIDE, -KAUR16EN, 456Euphi, -TOC, ANTIMONY, ARSENIC,
373Nucul , MERCURY, -PA65, -MOXYPHEN, SELENIUM, CADMIUM, -231Capit,
THALLIUM, COPPER, -VSOLIDS, -OTHER, -20Nemato,...
#4. Chlorinated organics and HPAH: PB82, PB52, PB09, PB08, PB83, TPCBS,
-MOXYPHEN, PB12, TBFLANTH, -ISOPIMAR, -A108394, PB73, ...
#5. Benthic infauna associated with silty sediment types (and including
some organic compounds): 238Medio, TOTAL, EUPHILO, POLYCH, 207Spioc,
D-64
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TABLE D-7. CHEMICAL FACTOR DIFFERENCES, Run #5-IMSQ2 vs Run #10-IMSQ7
IMSQ2 (144 stations)
Factor & Variables
IMSQ7 (139 stations)
Factor & Variables
1. PAH and some metals
LMWPAH, PB80, PB81, PB01,
B132649, PB39, LEAD,
DIBENZOT, CADMIUM, PB78,
METHYL2P, BIPHENYL,~FB72,
ANTIMONY, HMWPAH, IC0GRP1
2. Metals
ANTIMONY, ARSENIC, TOTMET,
COPPER, CADMIUM, MERCURY,
SELENIUM, IC0GRP1, -PB83,
SOLIDS, LEAD, -PB79,
3. Chlorinated Organics
PB52, PB08, PENCBD, TETCBD,
TOTCBDTTR'ICBD, PB09, PENTACHL
CLBENZ, ...
4. Other Organics
MOXYPHEN, A108394, PB55,
KAUR16EN, METHYLET, -PB76,
-TBFLANTH, RETENE, -HMWPAH,
PB77, -PB83, -PB79, -PB82,
ISOPIMAR, B91576, ...
5. Pulp Organics/ & Mn
ISOPIMAR, KAUR16EN, A108394,
METHYLET, MOXYPHEN, MANGANESE,
A95487
1. PAH and other organics
TOTORG, HPAH, PB84, LPAH,
PB39, PB72, PB81, PB73, PB78,
TbFlanth, pbSTTpb76, pbSTT
-SOLIDS, VSOLIDS, PB80, B91576,
PB01, PB79, TOC, B132649, PB55,
DIBENZOT
2. Metals
ANTIMONY, TOTMET, ICOGRP1, COPPER,
ARSENIC, CADMIUM, MERCURY,
SELENIUM, LEAD, ZINC, IRON,
NICKEL, BARIUM, MANGANESE,
3. Chlorinated Organics
PB52, PENCBD, PB09, TETCBD,
TQTTBD, PB08, TETCBD, PENTACHL,
BERYLLIUM, -A95487, TPCB$, ...
4. Mixture
-CHROMIUM, -IRON, PB08, PB52,
PENCBD, B132649, TOTCBD, -SULFIDE
TETCBD, -MANGANESE, PB92, -CLAY,
TRICBD, RETENE, CLBENZ. PB36.
-PB76, ...
5. Grain Size Related
SILT, FINES, -SAND, CLAY,
-METHYLPY, -PB12, -PB82, -SOLIDS
D-65
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CRUSTA, 455Euphi, A95487, 16Nemert, MOLLUSC, VSOLIDS, PB55, 421Telli
PB77, -SOLIDS, B91576,...
A scatter plot of the stations versus factors 2 and 5 showed inversely
correlated behavior for the two factors with the exception of stations
RS-13 and RS-14, which were high in scores for both factors.
Conventional Chemical and Biological Results — Run #17-INMC9
Prior to the complete chemical-biological run (#18a, above), the 56
CHEMBIO stations were evaluated for only conventional chemical, bioassay
score, and biological/benthic variables. Factors mainly reflected biologi-
cal loadings, with the exception of one factor loaded negatively with TOC,
VSOLIDS and bioassay score variables, and positively with large grain size
and biological variables. Results of the principal components analysis
included the following factors:
#1. TOTAL, AXINOPS, MOLLUSC, 394Axino, LUMBRI, 138Lumbri, THARYX, SILT,
456Euphi, -SAND, POLYCH, 214Tharyx, 114Glyce, MACOMA, 415Macoma,
#2. -TOC, -ABNORM, SOLIDS, -VSOLIDS, CRUSTA, -MORT, 186Prionosp, 106Nepht,
-CLAY, SAND, -CHANGE, PRIONOSP, 455Euphi, 238Medio, ...
#3. 102Nepht, -SOLIDS, 223Arman, -236Notom, 399Mysel, 421Telli, 3520dost,
-411Macoma, SULFIDE, -20Nemato, 348Mitre, -CHANGE
#4. -342Eucho, 214Tharyx, THARYX, 16Nemert, 102Nepht, POLYCH, 207Spioc,
-348Mitre, 411Macoma, -373Nucul, 353Turbo, -53Eteone, ...
Scatterplots of the stations versus the first two factors indicated
that Sitcom and Pt. Defiance-Ruston stations had different behavior, even
though they had been grouped in the same waterway category for analysis
because of metals contamination in both areas. Also, Carr Inlet stations
behaved considerably different than the Blair and Milwaukee stations with
which they had been grouped. The upper Hylebos stations were in a tight
group and the lower Hylebos stations were in a moderately tight group except
for #28HY-44 and #30HY-50.
Scatter plots for factor 2 versus factor 4 indicated the possibility
that the two benthic compositions represented species of different habitat
orientations. Where one group was high, the other was low.
Correlations Among Variables
Linear Pearson correlation coefficients were calculated for the chemical
and biological data. The biological variables were searched for chemical
variable correlation coefficients that had a calculated probability of less
than 0.05 of being drawn from a parent population with a truly random rela-
tionship (zero correlation). These were listed and the frequencies for
which the chemical variables were observed were tabulated. These are listed
in Table D-8, for both positive and negative r-values, with the total
D-66
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TABLE D-8. CHEMICAL VARIABLES WITH >.1 SIGNIFICANT (P<0.05)
CORRELATIONS WITH BIOLOGICAL VARIABLES
Chemical*
Number of
Observances (@ P < 0.05)
's Positive r's Total Obs
Negative r1
MORT
1
1
ABNORM
2
1 3
NICKEL
6
6
MANGANESE
2
2
RETENE
-
2 2
PB77
-
3 3
A95487
-
3 3
SILT
9
8 17
SAND
10
7 17
PB27
1
2 3
HEXADEC9
3
2 5
TOC
3
2 5
CHANGE
1
1
PB36
3
3
PB68
-
3 3
METHYLPY
-
1 1
PB79
-
4 4
PB76
-
2 2
PB84
-
1 1
BERYLLIUM
2
2 4
PB26
-
2 2
PENCBD
-
1 1
PENTACHL
-
1 1
PB72
-
1 1
SOLIDS
-
1 1
PB67
-
1 1
CHROMIUM
2
2 4
PB66
1
1 2
PA64
1
1 2
PP92
-
1 1
COPROSTA
-
3 3
B62533
-
3 3
A95954
-
3 3
PB54
1*
3 4
PA21
-
2 2
B100516
-
4 4
PB37
-
3 3
PA65
-
2 2
PB70
-
1 1
a Codes for variables are explained in Tables A-l through A-3
D-67
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number of observances out of 51 biological variables (13 taxonomic groups
and 38 benthic species).
INTERACTIVE SCREEN LOG FOR RUN # 17 — INMC9 OUTPUT CODE
An example of the screen log for a typical run on ARTHUR is recreated
in this section. The run consisted of a second stage exploratory analysis
of 66 conventional chemical, biological and benthic variables after removing
five anomalous stations.
(Comments in quotation marks added to explain intent of following steps)
Version 4.0 (Released 1 January 1985).
AAA RRRR TTTTT H H U U RRR
AARR T HHUURR
A-jmw-A RRR T HHHH U U RRR
A ARR T HHUURR
A ARR T HHUURR
General Pattern Recognition
***************************
Multivariate Data Analysis System
* **********************************
**************************************************
* Copyright (c) Infometrix, Inc. 15 August 1984 *
* All rights reserved. *
**************************************************
Use of this program implies that the individual
has read and agrees to the terms and conditions of
the Infometrix Software Program License Agreement
for ARTHUR and the Limited Warranty contained
therein.
Set terminal to UPPER case and enter to continue:
Please enter 4 character identification code:
NMC9
Enter session title:
WEIGHT/FACTOR ANALYSIS OF CNMC7 W/ 5 ANOMALIES REMOVED
Option? "Note - following steps brought data set CNMC7.DAT into file
1"
LET
Option LET called at 21:22:44 on 17-DEC-85.
Enter destination file# for copy of data (1 thru 6):
1
Let file #1 contain data from file named?
CNMC7
D-68
-------
Option? "Following sequence with CHDATA deleted 5 anomalous stations"
CHDATA,1,1,-1
Option CHOA called at 21:23:05 on 17-DEC-85.
Enter the pattern-changes in form
TRAIN/TEST, 1st inc. sam. indx, last sam. indx, cat# (end 0$)
TRAIN,1,16,1
TRAIN,18,19,1
TRAIN,21,22,2
TRAIN,24,26,2
TRAIN,27,32,3
TRAIN,33,40,4
TRAIN,41,41,5
TRAIN,44,51,5
TRAIN,52,56,6
Option?
CHCAT,1,1,0
Option CHCA called at 21:24:51 on 17-DEC-85.
Number Data Vectors In Training Set 51
Number Data Vectors In Test/evaluation Set... 0
Number of Categories... 6
Category Members
1 18
2 5
3 6
4 8
5 9
6 5
Option?
UT HIT ,1,-1
Option UTIL called at 21:25:15 on 17-DEC-85.
Option? "Next step autoscales the data and prints univariate statistics"
SCALE,1,2
Option SCAL called at 21:25:24 on 17-DEC-85.
Option? "Next step calculates variance weights between categories"
WEIGHT,2,3,-1,-1,3
Option WEIG called at 21:25:39 on 17-DEC-85.
D-69
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Option? "Next 8 steps generate factor analysis and factor plots"
KAPR,2,4,0,0,10
Option KAPR called
at 21:26:08 on
Eigenvalue
Var. Preserved
Each Total
1 1.205E+01
25.8 25.8
2 8.365E+00
17.9 43.8
3 5.221E+00
11.2 55.0
4 4.437E+00
9.5 64.5
5 3.435E+00
7.4 71.9
Option?
KAVE,0,0
Option KAVE called
at 21:26:35 on
Option?
KATR.2,3,4,5
Option KATR called
at 21:26:53 on
Option?
VARV,3,0,-1,1,0,1
Option VARV called
at 21:27:15 on
Option?
KAVA,4,4,0,0,8
Option KAVA called
at 21:28:08 on
Eigenvalue
Var. Preserved
Each Total
1 8.366E+00 19.9 19.9
2 6.511E+00 15.5 35.4
3 6.296E+00 15.0 50.4
4 5.015E+00 11.9 62.4
5 4.776E+00 11.4 73.7
Option?
KAVE,0,0
Option KAVE called at 21:28:30 on 17-DEC-85.
Option?
KATR,2,3,4,6
D-70
-------
Option KATR called at 21:28:44 on 17-DEC-85.
Option?
VARV,3,0,-1,1,0,1
Option VARV called at 21:29:04 on 17-DEC-85.
Option? "Next step calculates the distance matrix using 6 factor scores"
DIST,3,4
Option DIST called at 21:29:48 on 17-DEC-85.
Option? "Next step calculates hierarchical cluster analysis ..."
HIER,4,0,1 " ... dendogram using the factor distance matrix"
Option HIER called at 21:29:59 on 17-DEC-85.
Option?
STATUS,-1
Option STAT called at 21:30:27 on 17-DEC-85.
File § File Specification File Purpose
1 0NMC9ZD a data f
le
2 CNMC9ZD a data file
3 VNMC9ZD a data file
4 FNMC9ZD a data file
5 PNMC9ZD a data file
6 RNMC9ZD a data file
7 INMC9ZD the printer file
8 ANMC9ZD the interactive output file
9 BNMC9ZD the ASCII data output file
Option? "Run completed, the next step saves the printer file on disk"
SAVE,7
Option SAVE called at 21:30:57 on 17-DEC-85.
Option? "next step saves the factor projection data to disk"
SAVE,3
Option SAVE called at 21:33:34 on 17-DEC-85.
Option? "All steps complete, so quit ARTHUR"
EXIT
FORTRAN STOP
$ LO
C0USE2 logged out at 17-DEC-1985 21:34:29.85}
D-7I
-------
APPENDIX E
RECOMMENDED CONTAMINANTS OF CONCERN
FOR MANAGEMENT OF DREDGED MATERIAL
-------
RECOMMENDED CONTAMINANTS OF CONCERN
Several previous reports (e.g., Konasewich et al. 1982) have discussed
criteria for the selection of contaminants of concern present in Puget
Sound. In general, chemicals of concern have the following attributes:
• A demonstrated or suspected effect on ecology or human health
(i.e., the focus of chemical concerns is on ultimate biological
effects); toxic effects of chemicals are typically characterized
by laboratory tests of acute or chronic toxicity to marine
benthic organisms or tests of carcinogenicity or mutagenicity
• Environmental persistence of the parent compounds or of toxic
metabolites
t A potential for remaining in a toxic form for a long time
in the environment
• One or more present or historical sources of sufficient magnitude
to be of concern (i.e., widespread distribution and high concentra-
tion relative to Puget Sound reference sediments).
Contaminants of concern recommended in this section were selected based on
consideration of the above attributes, existing lists of contaminants of
concern in Puget Sound [i.e., Konasewich et al. 1982; Quinlan et al. 1985;
Jones and Stokes 1983], results of PSDDA/PSEP workshops held to establish
procedures for environmental analysis of metals and organic contaminants (e.g.,
Tetra Tech 1986b, 1986c), and chemical data from a wide range of Puget
Sound studies and sampling areas [e.g., Metro TPPS study (Romberg et al. 1984),
Commencement Bay Remedial Investigation (Tetra Tech 1985), NOAA Technical
Memorandum OMPA-19 (Malins et al., 1982), Eight Bay study (Battel 1e 1985b)].
Inorganic and organic contaminants of concern in dredged materials are
listed in Tables E-l and E-2, respectively. The lists comprise many U.S. EPA
priority pollutants with several noteworthy additions and deletions summarized
in the tables. Although the total number of contaminants in Tables E-l
and E-2 is fairly large, it is not recommended that different subsets of
this list be used for different geographic areas of Puget Sound. Such
a recommendation would have to be based on the assumption that certain
chemicals are unlikely to occur in certain regions of Puget Sound. The
a priori exclusion of chemicals is not advised. Potential pollution sources
(e.g., highly populated and industrialized areas, isolated industrial facili-
ties, agricultural runoff) are located in many regions of Puget Sound,
and estuarine and atmospheric circulation can transport contaminants throughout
most of Puget Sound. Justification for deletion of a particular chemical
class (e.g., priority pollutant acid compounds) from the list of contaminants
of concern in a particular geographic area should always be based on representa-
tive field analyses that confirm the absence (or acceptably low concentration)
of the chemical class.
E-2
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TABLE E-l. INORGANIC CONTAMINANTS OF POTENTIAL CONCERN
IN PUGET SOUND SEDIMENTS3
Antimony
Arsenic"
Cadmium
Copper^
Chromium
Leadb
Mercury^
Nickel
Si 1 verb
Zinc
Cyanide
a The first group of elements consists of 10 of the 13 U.S. EPA priority
pollutant metals and cyanide. The remaining 3 priority pollutant
metals not recommended are beryllium, thallium, and selenium. Chromium
may only be of local concern in areas where chromium-rich wastes are
being discharged (e.g., chrome plating industries)
Beryllium and thallium are toxic but have not been found at concentrations
that exceed reference levels in Puget Sound (see Tetra Tech 1986c).
High selenium concentrations have been reported in a single Puget
Sound study; these values are considered to be elevated most likely
because of spectral interferences during the particular instrumental
analysis used (see Appendix A in Tetra Tech 1986c). Other studies
using alternative techniques have not found levels of selenium in
excess of reference conditions.
b These elements have been suggested previously as contaminants of concern
in Puget Sound (see Konasewich et al. 1982; Jones and Stokes 1983).
Note: Three non-priority pollutant metals (aluminum, iron, and manganese)
are not of toxicological concern, but are useful for normalization of other
metals data and as tracers of natural terrigenous material (see Appendix A
in Tetra Tech 1986c).
E-3
-------
TABLE E-2. ORGANIC CONTAMINANTS OF POTENTIAL CONCERN
IN PUGET SOUND SEDIMENTS®
Phenols arid Substituted Phenols (organic acids)
phenol 2,4-dimethylphenol
2-methyl phenol j>»c pentachlorophenold
4-methylphenolD»c
Miscellaneous Organic Acids (selected samples only)e
2-methoxyphenol
3.4.5-trichloroguaiacol
4.5.6-trichloroguaiacol
tetrachloroguai acol
mono- and di- chlorodehydroabietic acids
Low Molecular Weight Aromatic Hydrocarbons (neutrals)1'
naphthalene fluorene
acenaphthylene phenanthrene
acenaphthene anthracene
Alkylated Low Molecular Weight Aromatic Hydrocarbons (neutrals)d»f
2-methylnaphthal ene 1-methylnaphthal ene
1-, 2-, and 3-methyl phenanthrenes
High Molecular Weight PAH (neutrals)
f1uoranthene benzo(k)f1uoranthene
pyrene benzo(a)pyrene
benzo(a)anthracene indeno(l,2,3-c,d)pyrene
chrysene dibenzo(a,h)anthracene
benzo(b)f1uranthene benzo(g,h,i)perylene
Chlorinated Aromatic Hydrocarbons (neutrals)
1.3-dichlorobenzene 1,2,4-trichlorobenzene
1.4-dichlorobenzene hexachlorobenzene (HCB)
1,2-di chlorobenzene
Total PCBs (mono- through decachlorobiphenyls)
E-4
-------
TABLE E-2 (continued).
Chlorinated Aliphatic Hydrocarbons (neutrals)
trichlorobutadiene isomersd»9 hexach1orobutadiened»9
tetrachlorobutadiene 1somersd»9
pentachlorobutadiene isomersd»9
Phthalate Esters (neutrals)d
dimethyl phthalate butyl benzyl phthalate
diethyl phthalate bis(2-ethylhexylJphthalate
di-n-butyl phthalate di-n-octyl phthalate
Miscellaneous oxygenated compounds (neutrals)
isophorone polychlorodibenzofuransd»*
benzyl alcoholD.n polychlorodlbenzodloxins*
benzoic acidj.p coprostanolJ
dibenzofuranD»"
Organonitrogen Compounds (organic bases)k
N-ni trosodi phenyl ami ne
nitrogen heterocycles [e.g., 9(H)-carbazole]1
Pesticides (neutrals)m
p,p'-DDEd endrind
p,p'-DDD° heptachlor
p,p'-DDTd alpha-HCH
aldrin<« beta-HCH
dieldrin" delta-HCH
alpha-chlordane gamma-HCH
Volatile Halogenated Alkenes (neutrals)
trichloroethene tetrachloroethene
Volatile Aromatic and Chlorinated Aromatic Hydrocarbons (neutrals)
benzene styrene (ethenylbenzene)
toluene total xylenes
ethyl benzene chlorobenzene
E-5
-------
TABLE E-2 (continued).
NOTE: Compounds not recommended from the priority pollutant list include:
• Halogenated ethers (two volatile and five semivolatile compounds)
are rarely reported in Puget Sound and are not expected
to persist in sediments.
• Hexachlorocyclopentadiene has not been confirmed to be present
in Puget Sound sediments, is easily degraded during laboratory
analysis, and has no suspected sources in Puget Sound.
• Acrolein and acrylonitri1e have not been detected in Puget
Sound sediments and are difficult to analyze for in routine
volatile analyses.
• Other priority pollutants not recommended are indicated
in the following footnotes and in Table E-3.
a Additional compounds are listed in Table E-3. These additional compounds
have been rarely or never detected in Puget Sound, but can be analyzed
relatively easily with the compounds on this list.
b Indicates U.S. Hazardous Substance List (HSL) compound that is not
also on the U.S. EPA priority pollutant list.
c 2-Methyl phenol is an HSL compound and is a known component of krafft
pulp effluents. 4-Methylphenol is an HSL compound that was reported
at high concentration in numerous areas of Commencement Bay. There
are few data available for this compound but it has been found in
pulp mill effluent, and could derive from degradation of other substances.
The occurrence of 4-methylphenol was highly correlated with sediment
toxicity and effects on benthic biota in a problem area near a pulp
and paper operation in Commencement Bay. The compound may also be
derived as a groundwater contaminant in other areas.
d Compound or group of compounds has been designated previously as a
contaminant of concern in Puget Sound (Jones and Stokes 1983; Konasewich
et al. 1982; Quinlan et al. 1985).
e These compounds are recommended only for areas adjacent to pulp mills.
Guaiacol was repored in Commencement Bay and is useful as an indicator
of pulp mill effluent (both kraft and sulfite mills). Chlorinated
guaiacols are toxic, persistent, and are excellent indicators of chlori-
nated pulp mill effluents (e.g., bleached kraft mills). Analytical
recoveries of chlorinated guaiacols will probably be low (as is the
case of chlorinated phenols) unless analytical procedures are modified
to stabilize the compounds (e.g., by derivitization techniques). Chlori-
nated dehydroabietic acids are also good indicators of chlorinated pulp
E-6
-------
TABLE E-2 (continued).
effluent and are toxic and persistent (based on studies of unchlorinated
dehydroabietic acid). Modified analytical procedures (e.g., derivitiza-
tion) will also be needed to recover resin acids.
f These non-priority pollutant compounds are often detected in Puget
Sound sediments. Although this is not an exhaustive list of alkylated
aromatic compounds, the compounds shown are accessible as analytical
standards and are useful for determining alkylated/non-alkylated ratios
for indicating PAH sources.
9 Tri-, tetra-, and pentachlorobutadienes are non-priority pollutants
that have been detected at highly elevated levels in certain areas
of Puget Sound (e.g., Hylebos Waterway in Commencement Bay). Because
standards are generally unavailable for these compounds, they are
recommended for analysis only where chlorinated butadienes are suspected
to have a major source (standards are available for hexachlorobutadiene).
h Dibenzofuran, benzyl alcohol, and benzoic acid are HSL compounds and
have been detected frequently in Commencement Bay.
^ Chlorinated dibenzofurans and dioxins are recommended as special analyses
only, as determined by specific project goals. Both classes of compounds
are of concern because of their severe toxic effects on higher organisms
[only 2,3,7,8-tetrachlorodibenzodiox1n (TCDD) is a U.S. EPA priority
pollutant]. Few analyses have been conducted for these compounds
in Puget Sound in the past. Recent data suggest that higher molecular
weight isomers of the dibenzofurans and dioxins are relatively common
(e.g., the much less toxic hexa- and octachlorinated forms), but 2,3,7,8-
TCDD has not been detected in Puget Sound samples.
i Not a U.S. EPA priority pollutant, and not known to be toxic but is
useful as a source indicator of sewage and agricultural wastes.
^ The remaining 7 priority pollutant organic bases are seldom detecteed
in Puget Sound and often present analytical problems (e.g., benzidine
and 3,3-dichlorobenzidine). N-ni trosodiphenylamine can probably be
recovered well with the PSEP organics full-scan procedure even though
the procedure is not designed for high recovery of organic bases (i.e.,
by back-extracting rinse waters at pH>12)
I 9(H)-carbazole is a component of creosote and coal tar and has been
reported in Puget Sound regions with these sources (although not exclus-
ively with these sources)
II Toxaphene, a priority pollutant pesticide mixture, has not been reconmended
because it has not been reported 1n Puget Sound and requires some
additional analytical work.
E-7
-------
Detection of the chemicals in Tables E-l and E-2 will require at least
three different analytical procedures (for metals, volatile organics, and
semi volatile organics). The lists in these tables are appropriate for
dredged materials; a reduced list of contaminants may be appropriate for
other matrices (e.g., biological tissues).
Organic priority pollutants that are not strongly recommended as contaminants
of concern are listed in Table E-3. These compounds have been detected
infrequently (or not at all) in Puget Sound, but are relatively easy to
analyze along with chemically similar compounds listed in Table E-2 (e.q.
P!?eu?ls/o0m Tab1e E~3 cou1d analyzed along with pentachl'oro-
phenol listed in Table E-2). The analysis of the entire set of chemicals
kUI not"e"ssari11|y be more costly than analysis of a reduced set of chemicals
because full scan analyses for metals, volatile organics, and semivolatile
organics can be used to detect a wide range of chemicals with a single
analysis for each of the three chemical groups.
. -1" J!1® evaluation of dredged material, contaminants of concern must
be identified relative to their proposed disposal environment (i.e., aquatic
near-shore, or upland disposal). The specific contaminants of concern in
a given sediment may be different at the dredged site and the disposal
site because of differences in their environmental mobility and route of
exposure to biological organisms. For example, physicochemical conditions
of sediments are substantially altered in the transfer of material from
the subtidal environment to an upland or nearshore environment. These
changes can favor the leaching of acid soluble metals from otherwise stable
sol id matrices, or volatilization of some organic compounds (including
various PCB congeners). As a result, substantially greater metals bioaccumula-
tion or other loss from a terrestrial site could occur relative to that
expected at the original marine site.
The general criteria listed above do not explicitly take into account
these differences in disposal environments. An evaluation of dredged material
should include consideration of the following additional factors (in order
of decreasing importance):
Available regulatory limits or other guidelines relating contami-
nant concentrations to biological effects relevant to the
disposal site
• Concentrations of contaminants relative to reference conditions
appropriate to the disposal site.
Although attention is usually focused on contaminant effects at the
disposal site, contaminant release can occur at the dredging site during
dredging operations. Dredged material destined for upland and nearshore
disposal may require evaluation for contaminants of concern in both aquatic
and terrestrial environments. Finally, contaminants of concern may vary
geographically within one type of environment. Some contaminants may be
of general concern, but such a list should not be used as the sole guide.
Data required for evaluation of contaminants of concern in a dredged material
E-8
-------
TABLE E-3. ADDITIONAL ORGANIC CONTAMINANTS OF LIMITED CONCERN3
Substituted Phenols (acids)
2-chlorophenol 2,4,5-trichlorophenol
2,4-dichlorophenol pentachlorophenol
4-chloro-3-methylphenol 2-ni trophenol
2,4,6-trichlorophenol 2,4-dintrophenol
4,6-dinitro-o-cresol
Chlorinated Hydrocarbons (neutrals)
2-chloronaphthalene hexachloroethane
Pesticides (neutrals)
alpha-endosulfan endrin aldehyde
beta-endosulfan heptachlor epoxide
endosulfan sulfate
Volatile Halogenated Alkanes (neutrals)
chloromethane carbon tetrachloride
bromomethane bromodichloromethane
chloroethane 1,2-dichloropropane
dichloromethane chlorodibromomethane
1»1'-d1chloroethane 1,1,1-trichloroethane
chloroform bromoform
1,2-dichloroethane 1,1,2,2-tetrachloroethane
1,1,1-trichloroethane
Volatile Halogenated Alkenes (neutrals)
vinyl chloride cis-l,3-dich1oropropene
l,r-dichloroethene trans-l,3-dichloropropene
trans-l,2-dichloroethene
Volatile Aromatic and Chlorinated Aromatic Hydrocarbons (neutrals)
styrene (etheny1benzene)b chlorobenzene
a These priority pollutant compounds (except as noted) are not strongly
recommended because they are seldom, if ever, detected in Puget Sound.
However, these compounds can be analyzed relatively easily with the
other chemicals in their class listed in Table E-2.
b Indicates U.S. EPA Hazardous Substance List (HSL) compound that is
not also on the U.S. EPA priority pollutant list.
E-9
-------
should inc 1 tide at least one analysis for a broad range of contaminants
(subject to waiver for small dredging projects in areas away from suspected
sources).
CONTAMINANTS OF CONCERN FOR AQUATIC DISPOSAL
Most Puget Sound studies have focused attention on contaminants of concern
relative to the aquatic (marine) environment. Two NOAA studies have summarized
evaluations for 463 contaminants in Puget Sound sediments: (1) Konasewich
al. (1982) presented a rationale for the inclusion of 15 contaminants
or groups of contaminants based on a review of evidence for 230 inorganic
and organic pollutants; (2) Quinlan et al. (1985) updated the previous
NOAA report and reviewed data for an additional 233 pollutants. The latter
report did not identify any new contaminants of concern, and argued for
the deemphasis of one contaminant (i.e., mercury) on the basis of recent
bioaccumulation data. Data suggesting the importance of some contaminants
not addressed in the NOAA studies (e.g., alkylated phenols) have recently
been released (Tetra Tech 1985). These more recent data also suggested
that elevated mercury concentrations in some surface sediments may be associated
with observed sediment toxicity and depressed abundances of benthic infauna.
CONTAMINANTS OF CONCERN FOR NEARSHORE AND UPLAND DISPOSAL
in ^nQear!.-0re.andu U?l\nd disP°sa1' chemical concentrations measured
in marine sediments should be comparable with those measured in terrestrial
"ence, contaminants of concern for terrestrial disposal sites may
be identified through a review of available U.S. FDA limits for cropland
soils, or by comparison with the overall crustal abundance (i.e., averaqe
content in soils of all types) of trace constituents. The concern for
a given level of contamination relative to biological effects in terrestrial
environments should be evaluated using terrestrial biological indicators
E-10
-------
APPENDIX F
RECOMMENDED ANALYTICAL DETECTION LIMITS
-------
RECOMMENDED ANALYTICAL DETECTION LIMITS
Appropriate detection limits are a critical consideration for sediment
quality management. For example:
t Sediment quality approaches that rely on observations of biological
effects require chemical analyses that can detect low concentra-
tions of potent toxic contaminants.
• Detection limits for samples must be considerably lower than
the established sediment quality values against which they
are tested.
• The reference approach requires sensitive chemical analyses
for relatively uncontaminated reference sites. Analyses with
high detection limits will give regulators very limited data
upon which to base sediment quality values.
APPROACHES TO DETECTION LIMITS
Environmental analytical chemists have not universally agreed upon a
convention for determining and reporting the lower detection limits of
analytical procedures. Values reported as lower detection limits are commonly
based on instrumental sensitivity, levels of blank contamination, and/or
matrix interferences and have various levels of statistical significance.
The American Chemical Society's Committee on Environmental Improvement
(CEI) defined the following types of detection limits in an effort to standard-
ize the reporting procedures of environmental laboratories (Keith et al. 1983):
• Instrument Detection Limit (IDL) — the smallest signal above
background noise that an instrument can detect reliably.
• Limit of Detection (LOD) — the lowest concentration level
that can be determined to be statistically different from
the blank. The recommended value for LOD is 3 , where is
the standard deviation of the blank in replicate analyses.
• Limit of Quantitation (LOQ) — the level above which quantitative
results may be obtained with a specified degree of confidence.
The recommended value for LOQ is 10 , where is the standard
deviation of blanks in replicate analyses.
• Method Detection Limit (MDL) — the minimum concentration
of a substance that can be identified, measured, and reported
with 99 percent confidence that the analyte concentration
is greater than zero. The MDL is determined from seven replicate
analyses of a sample of a given matrix containing the analyte
(Glaser et al. 1981).
F-2
-------
The CEI recommended that results below 3 should be reported as "not detected"
(ND) and that the detection limit (or LOD) be given in parentheses. In
addition, if the results are near the detection limit (3 to 10 , which
is the "region of less-certain quantitation"), the results should be reported
as detections with the limit of detection given in parentheses.
The CEI definitions are useful for establishing a conceptual framework
for detection limits, but are somewhat limited in a practical sense. The
IDL does not address possible blank contaminants or matrix interferences
and is not a good standard for complex environmental matrices. The LOQ
accounts for blank contamination, but not specifically for matrix complexity
and interferences. The high 10 level specified for LOQ helps to preclude
false positive findings, but may also necessitate the rejection of valid
data. The MDL is the only operationally defined detection limit and provides
a high statistical confidence level but, like the LOQ, may be too stringent
and necessitate the rejection of valid data.
Metals
The LOO (3 ) detection limit is appropriate for metals data. Inter-
ferences are a major determinant of attainable detection limits and are
not accounted for in the LOD calculation. However, the LOD is appropriate
because matrix and interelement interferences can be corrected for during
instrumental analysis (e.g., by matrix matching and background corrections.)
Organic Analytes
Interferences are also a major determinant of detection limits of organic
analytes, but cannot be corrected for easily during instrumental analysis.
An alternative detection limit, the lower limit of detection (LLD), is
recommended for data generated by gas chromatography-mass spectrometry
(GC/MS). LLD are established by analysts based on their experience with
the instrumentation and with interferences in the sample matrix being analyzed.
LLD are greater than instrumental detection limits because they take into
account sample interferences. To estimate LLD, the noise level should
be determined in the retention window for the quantitation mass of repre-
sentative analytes. These determinations should be made for at least three
field samples in the sample set under analysis. The signal required to
exceed the average noise level by at least a factor of two should then
be estimated. This signal is the minimum response required to identify
a potential signal for quantification. The LLD is the concentration corre-
sponding to the level of this signal based on calibrated response factors.
Based on best professional judgment, this LLD would then be applied to
samples in the set with comparable or lower interference. Samples with
much higher interferences (e.g., at least a factor of two higher) should
be assigned LLD at a multiple of the original LLD.
FACTORS AFFECTING DETECTION LIMITS
The following factors influence the attainable detection limits for
metal and organic analytes:
F-3
-------
0 Physical sample size available - In most cases, the more sample
that is available for analysis, the better the detection levels
that can be achieved. Thus, for a given method, larger samples
will have lower detection limits than smaller samples.
• Presence of interfering substances - For example, the presence
of elemental sulfur in a solvent extract to be analyzed by
gas chromatography-electron capture detection (GC/ECD) will
potentially obscure analyte peaks and raise the amount of
analyte required for detection.
• Range of pollutants to be analyzed - For example, if only
one compound is of interest, a method can be optimized for
that compound without regard to potential effects on other
compounds. Dedicated protocols can yield far lower detection
limits than full-scan protocols.
• Instrumental sensitivity - For detection of most priority
pollutant metals, inductively coupled plasma (ICP) emission
spectrometry is less sensitive than graphite furnace atomic
absorption (GFAA) spectrophotometry. Thus, GFAA detection
allows for lower detection limits. However, simultaneous
multi-element analyses are possible for ICP but not GFAA.
• Level of confirmation of results - For example, GC/ECD is
more sensitive than GC/MS for chlorinated pesticide analysis.
However, a single GC/ECD analysis does not provide positive
identification of a compound, whereas GC/MS provides more
information for molecular confirmation.
• Level of pollutant found in the field and in analytical blanks
- For example, due to bottle preparation procedures, analytical
blanks are often contaminated with varying concentrations
of methylene chloride. This variation in contaminant level
often precludes sensitive detection levels in tissue.
Organic Compounds
The choice of analytical procedures can affect the attainable detection
limits of semivolatile organic compounds. Removal of interferences from
extracts (e.g., removal of elemental sulfur by treatment with metallic
mercury, removal of biological macromolecules by gel permeation chromatography)
can significantly reduce the detection limits of many organic analytes.
The separation and dedicated analysis of chemically distinct fractions
can also reduce detection limits, but requires greater laboratory effort
and expense than a full-scan analysis.
RECOMMENDED DETECTION LIMITS - LOW LEVEL
Certain program goals will require sensitive detection limits. For
example, low detection limits will be required to assess pollutant concen-
trations in relatively uncontaminated reference areas. High detection
F-4
-------
limits could yield undetected values, which would be of little use in estab-
lishing sediment quality values. Low detection limits would also be appropriate
for programs that must determine pollutant concentrations corresponding
to biological effects. In such cases, the LOD (for metals) and LLD (for
organic compounds) would be more likely than LOQ to allow for reports of
detected values. Whereas LOQ would provide more statistical confidence
than LOD or LLD, they would be far more likely to result in undetected
values. For samples that are to be evaluated with sediment quality values,
detection limits should be less than half, preferably less than one tenth,
the sediment quality values.
The detection limits recommended 1n this report are considered to be
typically attainable values based on the best professional judgment and
experience of analytical chemists who considered the Instrumental sensi-
tivity of affordable equipment, common problems with blank contamination
and matrix interferences, and reasonable levels of laboratory analytical
effort. The recommended values are not absolute, as analytical procedures
and laboratory precision can affect attainable detection levels. State-
of-the-art instrumentation and dedicated analytical procedures will enable
laboratories to attain lower detection limits.
The following recommended limits are based on a 5-g (wet) sediment sample
in a 100-mL dlgestate:
Instrumental techniques are major determinants of the attainable detection
limits for metals. With the exception of mercury, all of the above metals
can be detected at the specified levels with graphite furnace atomic absorp-
tion. Mercury should be detected by cold vapor atomic absorption. Other
instrumental techniques can also be used to attain the specified levels
(e.g., hydride generation atomic absorption can be used for arsenic, antimony,
and selenium). Several metals (e.g., zinc) may occur at relatively high
levels even in uncontaminated sediments. If Instrumentation 1s available,
it could be cost-effective to screen samples by ICP and then analyze undetected
compounds with the more sensitive GFAA.
Organic Compounds
Attainable detection limits for organic analytes can vary considerably
depending on extraction, extract cleanup, and Instrumental detection techniques
used. For a sample size of approximately 5 g (wet) and GC/MS detection,
detection limits for most volatile organic analytes of Interest should
be in the range of 1-15 ppb (dry weight).
The detection limits of most semivolatlle analytes should be 1n the
range of 1-50 ppb (dry weight), assuming a 100-g (wet weight) sediment
Metals
0.01 ppm (dry weight)
0.02 ppm (dry weight)
0.1 ppm (dry weight)
0.2 ppm (dry weight)
Hg
Be
Sb, As, Cd, Cr, Cu, Pb, Se, N1, Ag, T1
Zn
F-5
-------
sample size and GC/MS detection. If pesticides and PCBs are analyzed by
a more sensitive instrumental technique, GC/ECD, detection limits for single-
component pesticides should fall in the 0.1-5 ppb range and PCBs should
be detectable at 5-20 ppb. To attain these detection limits for typically
complex sedimentary extracts, extract cleanup steps will be required (e.g.,
adsorption chromatography and sulfur cleanup are necessary for extracts
to be analyzed by GC/ECD).
RECOMMENDED DETECTION LIMITS - MODERATE LEVEL
Some projects involving dredged materials may not require very sensitive
detection limits. Dredged materials to be disposed of at established disposal
sites may be tested against fairly high sediment quality values. However,
disposal in relatively uncontaminated areas may require analyses with low
detection limits.
Metals
When samples are being evaluated in comparison to high sediment quality
values, it is recommended that detection limits be at least 10 times lower
than than the sediment quality values. In such cases, multielement ICP
analyses may be sufficient and cost-effective (except for mercury, which
requires cold vapor atomic absorption). Detection limits consistent with
ICP analyses could also be appropriate when sediments are being screened
for gross contamination.
Organic Compounds
The Contract Laboratory Program (CLP) has designed analyses to meet
approximately 1,000 ppb (dry weight) or approximately 300-600 ppb (wet
weight) detection limits. CLP detection limits could be appropriate for
screening of samples for gross contamination. Lower detection limits (100
ppb dry weight) have been recommended for evaluation of dredged materials
for the Fourmile Rock disposal site (U.S. Environmental Protection Agency/
Washington Department of Ecology 1984). In a recent roundtable discussion
among Puget Sound chemists sponsored by PSEP/PSDDA, LLD in the range of
1-50 ppb dry weight were considered appropriate for all uses except screening-
level analyses.
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APPENDIX G
RECOMMENDATIONS FOR
ANCILLARY SEDIMENT VARIABLES
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RECOMMENDATIONS FOR ANCILLARY SEDIMENT VARIABLES
Variables other than organic and metal contaminants can provide useful
data for assessing sediment quality or for facilitating development of
chemical-specific sediment quality values. The physical and chemical ancillary
variables considered to be of greatest use are:
• Total solids (dry wt/wet wt expressed as a percent)
• Total organic carbon content (as percent of dry wt sediment)
• Percent fine-grained (<63 um) particles (as percent of dry
wt sediment)
• Iron and manganese content (ppm dry wt).
Recommendations for the use of these variables are discussed in the following
sections. Other variables (e.g., sulfides, chemical oxygen demand, oil
and grease) have use as gross indicators of sediment quality, but have
limited use in the development of chemical-specific sediment quality values.
These latter variables should not be used as replacements for such sediment
quality values because substantial chemical contamination can be found
in sediments that appear "acceptable" according to conventional measurements.
TOTAL SOLIDS
Total solids data are essential as they allow for calculation of contam-
inant concentrations on a dry-weight basis. Most sedimentary contaminants
are associated predominantly with the solid material in bulk sediments,
not with the interstitial water. Thus, dry-weight contaminant concentrations
are preferred to wet-weight concentrations. Use of dry-weight concentrations
precludes the possibility that variations in sedimentary moisture content
will obscure informative trends in chemical data.
TOTAL ORGANIC CARBON
Chemical concentration gradients, particularly of nonpolar, nonionic
organic compounds, have been observed to correlate positively with sedimentary
organic carbon content. This observation is commonly interpreted in one
of two ways: (1) organic matter is the "active fraction" of sediment and
serves as a sorptive sink for neutral, and possibly polar or metallic,
compounds (see Section 2.3 of the main report and Appendix H), or (2) carbon-
rich particles may be an important transport medium for contaminants (e.g.,
PAH may tend to be associated with soot particles; Prahl and Carpenter
1983). Laboratory and field investigations suggesting a strong relationship
between organic carbon content and nonionic organic compounds (e.g., geochemical
studies and bioaccumulation studies) have led to the preferential use of
organic carbon normalization for the equilibrium partitioning approach
(JRB Associates 1984b) and the Screening Level Concentration approach (Battelle
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1986; as discussed in Section 8.6 of the main report, this approach does
not require use of organic carbon normalization).
Total volatile solids (TVS) is sometimes measured instead of total organic
carbon (TOC). TVS only crudely approximates TOC because (1) organic matter
can be volatilized during the total solids determination (which precedes
TVS determination), (2) inorganic substances (e.g., carbonates) can be
lost during high-temperature combustion (e.g., 550° C), and (3) TVS is
a rough measure of organic matter content, not organic carbon content.
Thus, a conversion constant for organic matter to organic carbon must be
empirically derived and applied to TVS data.
PERCENT FINE-GRAINED (<63 urn) PARTICLES
On a limited spatial basis, contaminant concentrations are often inversely
correlated with particle size. Thus, contaminants may be concentrated
in the fine-grained particles of bulk sediments. This observation is often
explained in terms of surface area, in that finer particles have greater
specific surface area, and thus greater sorption capacity, than larger
particles. However, organic carbon content also tends to vary inversely
with particle size in natural sediments (Choi and Chen 1976; JRB Associates
1984b). Thus, normalizing to percent fines may be effectively equivalent
to organic carbon normalization.
Grain size, independent of its correlation with contaminant concentration,
is an environmentally significant variable. It may play a role in sediment
toxicity during bioassays (Tetra Tech 1985; Ott 1985) and affects benthic
ecological structure. Thus, grouping of biological data according to sediment
grain size could be a useful way to factor out natural environmental effects
from contaminant-related effects. Grain size distribution should be an
important factor when choosing reference samples for bioassays or benthic
infaunal abundance assessments.
MANGANESE OR IRON CONTENT
Trace metals can be selectively enriched in iron and manganese oxides
and hydrous oxides under oxidizing conditions (e.g., Jenne and Luoma 1975;
Brannon et al. 1976). In such cases, normalization of metal concentrations
to manganese and/or iron can reduce the effect of dilution of chemically
enriched oxide and hydrous oxide phases with variable amounts of unrelated
material in sediments. This normalization is not highly reliable, as site-
specific and compound-specific differences affect the significance of oxide-
and hydrous oxide-metal associations.
USE OF CONVENTIONAL VARIABLES IN SEDIMENT MANAGEMENT
In the present project, conventional variables were evaluated as tools
for sediment management in two ways (1) conventional variables were used
to normalize chemical concentrations when generating AET and SLC values
(see Sections 5.3 and 5.4 of the main report), and (2) conventional variables
were themselves used as indicators of sediment quality (e.g., a total organic
carbon AET was developed, potentially as an indicator of organic enrichment).
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Other uses (e.g., as geochemical indicators or as normalizing variables
for analyses of chemical gradients) are valuable, but were not considered
in this project.
For both the AET and SLC approaches, use of chemicals normalized to
dry weight was consistently more accurate (by measures defined in Section 7.1)
than use of chemicals normalized to organic carbon content or percent fine-
grained sediment (see Tables 13 and 17 in Sections 7.1.1 and 7.1.4, respec-
tively). AET values for organic carbon content, total volatile solids,
and percent fine-grained material did not identify biologically impacted
stations that were not identified by chemical variables (see Section 8.7).
Thus, dry weight normalization is recommended for field-based approaches
(i.e., AET, SLC) based on its greater predictive success relative to normaliza-
tion to organic carbon content or percent fine-grained material.
The lower predictive success of sediment quality values for chemicals
normalized to organic carbon content or percent fine-grained material is
not considered to be a good basis for precluding further use of these vari-
ables. The available results suggest the need for further testing of the
toxicological aspects of organic carbon normalization theory (e.g., how
does organic carbon affect sediment toxicity and is this effect consistent
with different kinds of organic matter and different test organisms).
Such testing is currently being conducted by Battelle (1985b) and U.S. EPA
(Newport).
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APPENDIX H
RESPONSE TO COMMENTS ON DRAFT REPORTS
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INTRODUCTION
(Prepared by the sponsoring agencies)
The preliminary result of the PSDDA/PSEP study on sediment quality
values were initially presented in a series of four separate draft reports
(Table H-l). These reports were distributed to a variety of technical
and management experts at a number of agencies and consulting firms for
their review. The draft reports generated significant interest and controversy,
and numerous comments were received.
The comments submitted covered a variety of topics, ranging from correction
of typographical errors to complete report reorganization. It is apparent
that many of the technical and editorial comments could have been avoided
had the reader been provided the entire series of draft reports at one
time. With only partial information, it was difficult to put the different
parts of the sediment quality study into perspective. The final sediment
quality values report represents the revision and synthesis of the four
draft reports into a single document. We hope that this reorganization
will help put the overall Puget Sound sediment quality values effort into
better focus.
The agencies sponsoring this study regard all comments received on
the draft reports as important, useful, and constructive, and have seriously
considered all concerns in preparation of the final report. In addition,
in order to facilitate review and a better understanding of how the final
report responds to the issues and concerns raised by reviewers, the agencies,
with the assistance of Tetra Tech, Inc., have prepared the following response
to comments section. This section generally addresses the majority of
the technical comments received. Because specific comments were submitted
based on review of separate draft reports, the response to comments section
is divided accordingly. Where possible, however, an attempt has been made
to identify the location in the final report where the reader may find
additional information if desired. We hope that this section, combined
with the specific changes made in the report itself, will be useful to
you.
The development of sediment quality values for use in Puget Sound
is an ongoing effort, with interim values currently being identified for
use in Puget Sound by PSDDA and PSEP technical committees. If you have
additional comments on the final sediment quality values report, or questions
about companion efforts, please contact either Keith Phillips (U.S. Army
Corps of Engineers, 764-3624) or Catherine Krueger (U.S. EPA, 442-1287).
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TABLE H-l. LIST OF RELEVANT DRAFT REPORTS AND PREPARATION DATES
TASK 1: Evaluation of Puget Sound data sets for the development
of sediment quality values (October 1985)
TASK 2: Evaluation of statistical relationships among chemical
and biological variables using pattern recognition
techniques (February 1986)
TASK 3: Evaluation of approaches for the development of sediment
quality values for Puget Sound (October 1985)
TASK 4 and 5a: Application of selected sediment quality values approaches
to Puget Sound data (March 1986)
[A fifth draft report (Task 5b) concerning an approach to risk exposure
assessment has been finalized as a separate report (Tetra Tech 1986a)]
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RESPONSE TO COMMENTS ON DRAFT SEDIMENT QUALITY VALUES REPORTS
TASK 1 REPORT: EVALUATION OF PUGET SOUND DATA SETS FOR THE DEVELOPMENT
OF SEDIMENT QUALITY VALUES
1. It was unclear what criteria were used for review/selection of data.
Clearly define the criteria for data selection and the rationale for
using these criteria (e.g., why were only synoptic data sets considered
appropriate for use?).
Selection of site-specific chemical/biological data for the compiled
Puget Sound database was carried out in three basic steps:
• Identify synoptic data sets (see Appendix C of the final
report): Available data sets were reviewed for synoptic
collection of data and only synoptically collected chemical
and biological data were considered further. (Note: a
synoptic data set is one for which toxicity data are col-
lected on the same sediment homogenate used for sediment
chemistry and benthic infaunal samples are collected at
the identical station locations and at the same time, or
at nearly the same time, as sediment chemistry samples.)
• Review quality assurance information (see Section 5.1.1
of the final report): Potential data sets were reviewed
for documentation of quality assurance (QA) methods and
summaries of QA review (such documentation was typically
provided in the reports in which the data were presented).
• Review data comparability (see Section 5.1.1 and Appendix C
of the final report): Available data were also subjected
to a more detailed review that focused on issues related
to data comparability.
Synoptically collected data were used to maximize the probability
of detecting trends among biological and chemical variables. Differences
between independent chemical and biological samples collected at a "station"
could hamper attempts at establishing correlations between chemical concentra-
tions and biological effects. For this reason, it was considered essential
that chemical and biological data be collected from nearly identical subsamples
from a given location. For example, acceptable toxicity measurements were
only those made on a subsample of the same sediment homogenate used for
chemical analysis. Because such homogenization and subsampling may compromise
the integrity of benthic samples (e.g., through loss of motile benthic
organisms), benthic and chemistry samples could not be taken from the same
homogenized sample. Instead, acceptable benthic infaunal analyses were
those conducted on replicate sediment samples from the same station sampled
for chemical analyses. Acceptable replicate samples were those from studies
H-4
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with clear documentation of multiple positioning techniques used for station
location. Reliable positioning is important for correcting for vessel
drift between collections of replicate grab samples or for returning to
the same station within a few days to complete chemical and biological
components of a survey.
A detailed QA review of all data that were considered for inclusion in
the database was beyond the scope of this project. However, the chemical
and biological methods were reviewed for every data set considered in an
attempt to ensure comparability of chemical, bioassay, and benthic infaunal
data from all studies. Items reviewed for chemical data were analytical
techniques, detection limits, and the chemical scope of pollutants analyzed
(e.g., polar and nonpolar semiyolatile organic compounds, metals, volatile
organic compounds). The latter item was considered important to document
for sediment quality value approaches that are based on field data (e.g.,
the toxicity endpoint and AET approaches), because the availability of
a wide diversity of chemical data enhances the probability that toxic agents
(or chemicals that covary with toxic agents) can be identified in sediments
with observed biological impacts. No data sets were excluded from the
database as a result of the review of chemical data.
The OA review of benthic infaunal data focused on sampling methods,
and in particular, on subsampling techniques (e.g., cores taken from grab
samples), and on the level of replication. The OA review of toxicological
data focused on sediment storage (fresh vs. frozen) and on the general
acceptance of bioassay methods used.
[see Appendix C and Section 5.1.1 for discussion relevant to comment #1]
2. Reviewers were concerned that this effort apparently focused on U.S. EPA
priority pollutants and did not address other chlorinated compounds
such as those present in Commencement Bay samples.
Data for several compounds that are not U.S. EPA priority pollutants
were used in the sediment quality values project. The most comprehensive
data set made available for this effort was from the Commencement Bay Remedial
Investigation. Some of these samples did, indeed, contain a large number
of chlorinated compounds that were only identified qualitatively. It was
not within the economic scope of the investigation to quantify the several
hundred compounds present in each sample. However, attempts were made
to quantify chlorinated compounds considered to be representative of the
major components.
The complex mixture of unidentified chlorinated compounds observed
in Hylebos Waterway by several investigators tended to follow the distribution
of one or more of the following identified chemicals. Chlorinated compounds
detected and quantified included tri-, tetra-, and pentachlorobutadienes,
hexachlorobutadiene, six different chlorinated phenols, five different
chlorinated benzenes (including hexachlorobenzene, a major chlorinated
compound in Hylebos Waterway), hexachloroethane, three chlorinated ethers
(low concentrations only), PCBs, chloroform, three chlorinated ethenes,
and a pentachlorocyclopentane isomer (tentative identification). While
H-5
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these compounds were not all of the chlorinated compounds quantitatively
noted in the extracts, they were considered to be representative of the
major components.
In addition, Commencement Bay data made available for the sediment
quality values effort included blank-corrected analyses for the 13 U.S. EPA
priority pollutant metals, 3 additional metals (including iron and manganese
used as natural indicators), 78 extractable U.S. EPA priority pollutant
compounds, 12 additional U.S. EPA Hazardous Substance List compounds, and
selected tentatively identified compounds for which all samples were analyzed.
Twenty two of the samples were also analyzed for the 31 U.S. EPA volatile
priority pollutants and/or 2,3,7,8-tetrachlorodibenzodioxin. Over 550
tentatively identified compounds were identified in a preliminary survey
of 17 stations, from which 14 compounds were selected for scanning and
quantification in the remainder of the Commencement Bay project based on
their use as potential source markers, routinely high concentrations, or
occasional extreme concentration.
As noted in the final Commencement Bay report (Tetra Tech 1985a; p.
3.71), there were no chemicals detected [and quantified] in historical
Commencement Bay studies that were not also found in the study conducted
for the remedial investigation.
[comment #2 applies to data presented in Appendix A]
3. Several reviewers were concerned that data sets that did not include
volatiles and polar compounds were excluded from analyses. If so,
what was the rationale for their exclusion?
Data sets were not included or excluded based on whether these compounds
had been analyzed for in a particular project. For the final recommended
data sets listed in the Task 1 draft report, and in Appendix C of the final
report, the absence of data for these compounds was simply documented.
This documentation served only to identify a limitation in the use of the
data set for generating or validating sediment quality values for those
particular compounds (including volatiles for a portion of the Commencement
Bay data set). In one case, a data set that did not include either volatiles
or polar compounds, and also did not include bioassay or benthic infauna
data, was still recommended for use with associated fish histopathological
data (should sediment quality values for this type of data be developed).
[see Appendix C for discussion relevant to comment #3]
TASK 2 REPORT: EVALUATION OF STATISTICAL RELATIONSHIPS AMONG CHEMICAL
AND BIOLOGICAL VARIABLES USING PATTERN RECOGNITION TECHNIQUES
4. It was not clear to some reviewers what the objective of the ARTHUR
analysis was, whether the objective was realized, and how the results
of the analyses influenced development/recommendation of specific
sediment quality values.
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The ARTHUR statistical routine was used to perform exploratory analyses
on a data subset to be included in the sediment quality values database.
ARTHUR is a system comprised of approximately 70 different procedures that
can be used for data processing, display, and pattern recognition analysis.
These pattern recognition techniques can be used to quickly extract important
information from complex data sets (192 chemical and biological variables
were considered in this project). The statistical techniques incorporated
into the pattern recognition system can also reveal relationships among
variables that might be obscured in a preliminary nonstatistical analysis.
Therefore, the objective of the this pattern recognition task was to investigate
underlying trends among variables that might be useful in later development
of sediment quality values. This objective was realized. The analysis
was successful in:
• Providing corroboration of trends among chemical variables
in the Commencement Bay data set and an independent Puget
Sound chemical data set that had been previously analyzed
using ARTHUR (e.g., see discussion on p. 34 of the draft
report concerning groups of significantly correlated chemicals)
• Confirming trends that had been previously identified in
the data set using alternative data analysis techniques
(e.g., confirming the need for subset analysis by geographic
region to establish chemical-biological relationships, and
providing supportive evidence of a critical assumption in
most sediment quality value approaches that a threshold
concentration exists, above which a chemical can be expected
to elicit a negative biological response)
• Identifying new relationships among chemical and biological
indicators (e.g., apparent "sensitive species" to certain
chemical contaminants; these preliminary results were then
subjected to evaluation during the application of sediment
quality value approaches)
• Providing evidence that normalization of chemical concentrations
to total organic carbon or percent fine-grained material
produces results in factor analysis that are nearly identical
to those based on dry-weight normalized data. Hence, these
results demonstrated that nearly all of the interpretable
trends with respect to the chemical-biological effects data
from Commencement Bay/Carr Inlet can be derived using dry-weight
concentrations.
This latter result, in consideration of accuracy results for different
normalization techniques from the Task 4 application of sediment quality
value approaches (see Section 7 of the final report), suggests that dry-weight
normalized data may be the most useful for identifying stations with known
biological impacts (see additional discussion in comment #19/23).
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These and other results described on p. 37 of the draft report were used
to guide development of sediment quality values.
[see Appendix D for discussion relevant to comment #4]
5. Although outliers are often considered to carry as much or more information
than data which fit a given model, it appears that in some cases data
were ignored or eliminated to gain refinement or to investigate subset
trends. What procedures were applied to data that appeared to be
outliers and why? How might the practice employed have affected the
conclusions drawn?
Use of the term "outlier" in the discussion of the results may have
caused some confusion. In the final report, the term "anomaly" has been
substituted. There was no attempt or plan to summarily throw out or ignore
any data in the data set. The conclusions drawn in the study were made
in consideration of analyses conducted with and without anomalous values
because the ARTHUR analysis was conducted as a stepwise series of statistical
tests with intervening technical review. All data were analyzed initially.
The term "outlier" was intended to indicate data values identified in the
early stage of statistical analysis as exhibiting unusual characteristics
relative to other data values. For the most part, these data were associated
with samples collected adjacent to major pollution sources. Further analyses
were then applied to examine the behavior of the remainder of the data
set without these unusual values, so that the initial trends might be checked
and other trends might be more apparent (e.g., less likely to be masked
by samples collected close to particular sources). This staged analysis
was important in enabling a more complete interpretation of the available
data to be incorporated into the final recommendations and conclusions
(e.g., see discussion on p. 12; 16; 30-32 of the draft report).
For statistical analysis, the concern regarding treatment of anomalies
is higher and more critical in experimental designs based on random sampling
(either spatially or temporally). The Commencement Bay study was spatially
biased because there were more samples concentrated around potential sources
to determine concentration gradients. Thus, the treatment of anomalies
described above was considered appropriate.
[see Appendix D for discussion relevant to comment #5]
6. Assumptions were made throughout the report that the demonstration
of chemical effects requires decreases in animal abundance with increasing
contamination concentrations. Several reviewers commented that some
species may initially show increased abundance with increased contamination
(i.e., if contamination eliminates more pollution-sensitive species
thereby allowing more pollution-tolerant species to increase in abundance
as a result of decreased competition). How much uncertainty might
this consideration add to the conclusion drawn?
There was no a priori assumption made by the statistician that the
only change expected to occur was "population decrease = chemical effect".
H-8
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Changes in the species composition of benthic populations were also considered,
although they were not directly observed in the analyses that were run.
Many studies have documented that the presence of toxic chemicals can result
in decreased abundances of, or sublethal effects on, affected organisms
(see Gray 1979; Boesch and Rosenberg 1981; Eagle 1981; Gray 1982; Wolfe
et al. 1982). In cases reported in the literature where opportunistic
or pollution-tolerant species have shown an initial increase in abundance
after an exposure to toxic chemicals (e.g., Capitella capitata at the West
Falmouth oil spill site), high abundances of those taxa have usually been
attributed to their abilities to become established in a disturbed environment
and in the absence of competition for resources.
To our knowledge, a significant enhancement of benthic organisms as
a direct response to a toxic chemical has never been demonstrated, although
such a response is theoretically possible. There is also no evidence that
enhancement occurs for one species or taxonomic group in the presence of
toxic chemicals unless a significant depression occurs in another species
or group. Any station exhibiting a significant enhancement of one taxonomic
group in association with a significant depression for another taxonomic
group was always defined as "impacted". Hence, the conclusions drawn from
the ARTHUR analysis are not expected to be affected by the phenomena described
in the comment.
It should also be noted that as a further check on the ARTHUR results
reported, scatterplots of data distributions were produced and evaluated
to prevent blind acceptance of apparent positive or negative trends between
two variables based on summary statistical results.
7. If half of the stations with significant amphipod bioassay responses
showed no benthic depression in Comnenceinent Bay, how can the report
claim a high degree of concordance among bioassay results and benthic
depressions? (see p. 28, para. 2-3 of draft report).
The amphipod bioassay results for Commencement Bay did show the least
agreement in comparison with the benthic infaunal results. There was neither
a significant depression in the abundance of a major taxonomic group nor
a significant response in the oyster larvae bioassay at 7 of the 16 stations
that exhibited a significant amphipod bioassay response. As discussed
in the report, a possible (but not conclusive) explanation may be that
the high percentages of fine-grained material at these stations contributes
to the amphipod bioassay response (e.g., Ott 1985; Tetra Tech 1985a).
However, a high concordance between combined bioassay results (i.e., oyster
larvae and amphipod discussed in the report) and benthic infaunal results
in general was indicated by the following items;
• "Impact" vs. "no impact" designations made by benthic and
bioassay indicators agreed at 67-79 percent of the 48 stations
in the Commencement Bay Remedial Investigation (and at 83-100
percent of the 6 stations in a separate dredging study conducted
concurrently with identical methods in Blair Waterway and
included in the ARTHUR analyses)
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0 A significant depression in the abundance of at least one
major taxonomic group was observed in 6 of 7 cases (86 percent)
that also exhibited significant toxicity in both the amphipod
and oyster larvae bioassays
• Eighty-nine percent of the cases exhibiting a significant
depression in the abundance of at least two major taxonomic
groups occurred in a single similarity cluster (assigned
on the basis of species-level benthic data). This similarity
cluster also contained 75 percent of the cases exhibiting
significant toxicity in both amphipod and oyster larvae
bioassays
• All six of the cases exhibiting a significant depression
in the abundance of at least three major taxonomic groups
occurred in this same similarity cluster; 83 percent of
these cases exhibited toxicity in oyster larvae bioassay,
50 percent exhibited toxicity in the amphipod bioassay.
The final report stresses that the high concordance between bioassay
and benthic infauna results is best supported by the oyster larvae bioassay.
Overall, a significant depression in the abundance of at least one major
taxonomic group was observed in 79 percent of the cases (11 of 14) that
exhibited significant toxicity in the oyster larvae bioassay. However,
in sediments containing <70 percent fine-grained material, significant
benthic depressions were also observed in 100 percent of the cases (6 of 6)
of the sediments exhibiting a toxic response in the amphipod bioassay (no
benthic data were available for a seventh station). As discussed in the
response for comment #24/19, impacted or nonimpacted designations made
by benthic and either of the bioassay indicators agreed at 67-79 percent
of the 48 Commencement Bay stations evaluated. This level of agreement
is significant (PC0.05, binomial test), and suggests that benthic comparisons
based on higher taxa were as sensitive in the Commencement Bay study as
the bioassays in identifying problem sediments, although different organisms
may differ widely in their sensitivity to individual chemicals present
as a complex mixture of chemicals in contaminated sediments.
[see Appendix D for discussion relevant to comment #7]
8. The report was confusing as to whether the interpretation of biological/
chemical relationships was based on data that included habitats in
which certain taxa are not commonly found.
The data set comprised the 64 numerically dominant species in the
Commencement Bay and Carr Inlet samples. Sediments that exhibited low
abundances of organisms and high concentrations of certain chemicals exhibited
a range of grain size. Because the abundance of organisms depends on grain
size and water depth, these samples could not all be compared with the
same reference samples (a similar problem does not exist for dry-weight
chemical measurements; see Tetra Tech 1985a). Major habitat features were
taken into account before determining the significance of benthic effects
in these samples relative to reference conditions by matching groups of
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reference and study site stations of similar depth and grain size. Hence,
the relationships observed between certain chemicals and benthic effects
did not appear to be explained solely by habitat. These procedures were
also used in the development of sediment quality values from benthic data
in Task 4 of the project so that the effect of natural habitat factors
would be minimized.
9. Several reviewers requested clarification regarding the statistical
assumptions used in analysis (e.g., normality of variables distributions).
Was data transformed to induce normality and if so, in what way?
It is true that the choice of data transformations and knowledge of
the data distributions are critically important to multivariate hypothesis
testing. However, multivariate hypothesis testing was not the purpose
of the analyses conducted. The exploratory analyses conducted using ARTHUR
required no statistical assumptions about variable distributions (i.e.,
pattern recognition analysis as applied is useful regardless of the underlying
distribution of the data). Specifically, the factor analyses conducted
as part of the ARTHUR analysis does not require variables to be normally
distributed, especially where the approach is being applied for information
compression (i.e., a Karhunen-Loeve transformation; see for example Watanabe
1973) as was done in this project.
In the pattern recognition analysis, the chemical data were first
autoscaled (i.e., by a transformation similar to the z-score transformation).
Autoscaling is a one-to-one mapping of the values of a variable from one
reference system to another. The mapping preserves the shape of the variable
distribution (regardless of the specific distribution), by simply zero-centering
the distribution, and uniformly scaling the variance. This transformation
makes some aspects of the analysis (e.g., data display) easier, but results
in a data set that yields the same information as before the transformation.
[see Appendix D and comments #11 and #12 for discussion relevant to comment #9]
10. It is unclear as to why and how the data were "normalized" with respect
to total organic carbon (TOC) content, and why TOC may not have been
used as a factor in the analysis.
To normalize chemical data to total organic carbon (TOC) content,
the dry-weight concentration in a sample is divided by the decimal percent
TOC. As one means of interpreting environmental trends, concentrations
are normalized in this manner because many chemicals tend to be concen-
trated in organically-enriched fractions of bulk sediments. Hence, normaliza-
tion of dry-weight concentrations to TOC content can help to dampen variations
caused by patchiness or other depositional factors that could mask trends
among samples of dissimilar texture. Theoretical and some experimental
evidence also suggests that the chemical-TOC association may affect the
bioavailability of certain chemicals (see discussion for comment #22).
Total organic carbon (TOC) was used as an independent variable in
the analysis of dry-weight normalized chemical data (see last paragraph
on p. 7; Tables 2 and 3, and Figure 5 of the draft report). Statistical
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analyses using TOC-normalized data did not include TOC as a variable because
its contribution had already been analyzed in the dry-weight analyses.
TOC normalization of the data resulted in few additional trends that
were not already evident based upon dry-weight normalized data. As noted
in the draft report (p. 30), all data sets should continue to be analyzed
with and without normalization to "master" variables such as TOC to confirm
the results seen in this project, because the actual mechanism of chemical-
organism interactions have not been firmly established in laboratory studies.
[see Appendix G and Section 8.8 for discussion relevant to comment #10]
11. When one autoscales data, one is essentially rendering all variables
equally important (i.e., very rare taxa have equal importance to those
that numerically dominate the community). To what extent could the
influence of autoscaling the ARTHUR analyses have affected the inter-
pretation of analyses results or led to misinterpretation of results?
The process indicated in the comment is incorrect. Autoscaling does
not affect the shape of the variable distribution, and retains all of the
variable-variable relationships represented by the original data (Green
1979). Therefore, this transformation has no effect on the interpretation
of results. See additional discussion in comment #9.
[see Appendix D for discussion relevant to comment #11]
12. In autoscaling the data, the authors have used a standard z-score
transformation. This transformation is only appropriate when the
data are normally distributed. Most of the biological data used was
in the form of abundance estimates, which are notoriously Poisson-
distributed. What effect could the skewed distribution of many of
the biological, and probably some of the chemical, data have on the
results of the factor analysis?
The autoscaling technique is similar to a standard Z-score transformation
because it generates a zero-centered distribution with uniformly scaled
variance. This kind of transformation simplifies data processing and review
(e.g., see SPSS 1975), but does not by itself affect the data distribution
(see comment #11). When this tranformation is applied to data that are
normally distributed, the resulting value (i.e., the Z-score) can then,
and only then, also be interpreted according to certain statistical criteria
(i.e., the distribution defines a characteristic probability density function
of the normal distribution; see for example Crow et al. 1960). Such interpreta-
tions were not made and were not necessary for pattern recognition analyses.
As noted in the response to comment #9, the exploratory analyses conducted
for this project required no assumptions or corrections for data distributions
in order to yield useful results.
[see Appendix D for discussion relevant to comment #12]
13. At least one reviewer was concerned that canonical correlation analysis
is the only multivariate technique appropriate for comparing major
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trends in several different data sets, and that factor analysis is
a technique that is only appropriate for deriving trends in a single
data set. What is the rationale for selecting factor analysis as
an appropriate technique for use in this study? To what extent did
the use of multiple databases collected at different locations and
times and the use of the analytical procedure employed influence the
results of the factor analysis and possibly limit the validity of
the inferences drawn?
This comment suggests a highly restrictive approach that is similar
to biological/social modeling problems. The pattern recognition approach
used in this project views the chemical, toxicity, and biological variables
as descriptors of the sediment stations. These data for an individual
station were collected simultaneously during the Commencement Bay study
(i.e., chemical and toxicity data were from the same sediment homogenate,
and benthic data were from replicate grab samples collected at the same
location and time as the chemistry/toxicity samples). While it certainly
can be appropriate to analyze these data sets separately, it is also appropriate
to analyze the combined data in an exploratory mode to determine if inter-
pretable results can be derived. Canonical correlation is a subset of
path modeling that was recommended in this report for potential future
analyses.
[see Appendix D for discussion relevant to comment #13]
14. Are there "generally accepted" rules regarding the number of samples
required for given numbers of variables? If so, were they followed
in the factor analysis presented in the report? If not, why and how
will this affect the results?
A general rule of having three to four times as many samples as variables
is important before applying many analytical techniques (e.g., especially regres-
sion analysis). This rule is not applicable when factor analysis is being used
as a Karhunen-Loeve transformation for information compression (i.e., to reduce
the number of dimensions that represent the data set; see Watanabe 1973).
The value of this application of factor analysis was in identifying key
variables from a large list of variables that were most useful in describing
trends within the data set.
TASK 3 REPORT: EVALUATION OF APPROACHES FOR THE DEVELOPMENT OF SEDIMENT
QUALITY VALUES FOR PUGET SOUND
15. Different approaches or sediment quality values may be needed for
application in different situations (i.e., sediment quality values
for dredged material may need to be different than sediment quality
values for problem identification. Discuss the different potential
uses of sediment quality values and whether different sets of values
could be appropriate for different uses.
Potential uses of sediment quality values were addressed in the Task 4
and 5a report (p. 48-55; Recommended Uses of Sediment Quality Values).
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Different sets of values could be appropriate for different uses but likely
will require project-specific interpretation. A comment given at a joint
PSDDA/PSEP sediment criteria workshop on Jaunary 8, 1986 highlighted the
need to provide sediment quality values for more than one approach to enable
program managers some latitude in determining appropriate values for individual
programs based on administrative and technical factors. In response to
this comment, sediment quality values were summarized in the draft report
for three approaches (and several indicators for the empirical approaches).
Also percentile data for the distribution of chemical values in approximately
ZOO Puget Sound sediment samples were summarized for comparison with the
range of sediment quality values (see Table 10 in Section 6.3 of the final
report).
Decisions concerning the appropriate use of different sediment quality
values (e.g., use specific values, a combination of values, or values modified
by some "safety factor") are policy decisions of management agencies, and
are outside of the scope of this report.
16. What criteria/rationale was used for not including other methods (i.e.,
bioassay approach, reference area approach, etc.) in the discussion
and evaluation of approaches to setting recommended sediment quality
values? Concern was expressed that the bioassay approach (spiking
sediments in the lab with known concentrations of contaminants and
measuring toxicity) wasn't given fair consideration. The reviewers
claim that while the bioassay approach has the benefit of the ability
to allow identification and experimental control of physical, chemical,
and biological variables, the AET approach cannot distinguish patterns
of natural variability from those indicating pollution impacts. Given
its merits, why was the bioassay approach eliminated from further
consideration?
The reasons for excluding the water quality and bioassay approaches
from the discussion and evaluation section are noted in Section 3.0 of
the final report (and p. 34 of the Task 3 draft report). The reference
area approach was not excluded from the discussion and evaluation section
(see Section 3.1.1 of the final report).
The treatment of the spiked bioassay approach requires further discussion.
An important (albeit not clearly stated) criterion for detailed evaluation of
approaches in the Task 3 report was that they be able to provide chemical-
specific sediment quality values for testing at the present time. The
spiked bioassay approach is a powerful and systematic way to establish
dose-response relationships for benthic organisms, but it has thus far been used
to generate data for very few chemicals and for a few different organisms. A
considerable amount of time and effort would be required to generate sediment
quality values for a wide range of chemicals (as can be addressed by the
AET and SLC approaches using Puget Sound data) and for a wide range of
organisms (to account for possible effects on more sensitive organisms).
The spiked bioassay approach, unlike other approaches considered, can quantita-
tively assess additive, synergistic, and antagonistic effects, but this
would be a formidable task considering all possible combinations of contaminants
and their relative concentrations.
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The possible role of spiked bioassays in establishing and verifying
sediment quality values was discussed in the Task 4 and 5a report ("Prioritiza-
tion of Laboratory Cause-Effect Studies" section, p. 53). Spiked bioassays
are distinguished from (and treated separately from) field bioassays in
the final report. This modification should alleviate confusion regarding
the treatment of bioassays in this project.
Finally, the AET approach attempts to distinguish patterns of natural
variability from those indicating pollution impacts by statistically comparing
sample responses to reference benthic and bioassay samples that have similar
grain size distributions and are collected at similar water depths. This
statistical comparison reduces the potential for habitat-related factors
to confound the results or mask apparent relationships (see response to
comment #8).
17. Why is the Screening Level Concentration (toxicity endpoint) approach
limited to nonpolar organic contaminants?
The developers of the screening level concentration (toxicity endpoint)
approach have suggested that it be used for screening nonpolar organic
compounds. The restriction to this class of compounds is related to organic
carbon normalization theory, which assumes that interstitial water is the
primary biological uptake route for sedimentary contaminants and that
sedimentary organic matter is the predominant factor controlling compound
distribution in sediment-interstitial water systems (e.g., Battelle 1986).
Because of this assumption, organic carbon normalization is not appropriate
for metals and polar organic compounds (see pp. 13-14 of Task 3 draft).
In the present evaluation of SLC values, dry-weight normalization
was tested along with organic carbon normalization and demonstrated consistently
better "efficiency" in correctly identifying only stations with biological
impacts for the 3 chemicals evaluated. Similar results were observed in
the AET accuracy evaluation based on a larger number of chemicals. Dry-
weight normalization enables one to establish SSLC (species screening level
concentrations) for a wide range of compounds, including metals and polar
organic compounds. Thus, if dry-weight normalization is considered appropriate
(e.g., if the mass loading of a contaminant in sediment is considered to
be a more important influence on toxicity than the attenuating effects
of organic carbon), then the SLC approach need not be limited to nonpolar
organic compounds. The accuracy of SLC for additional chemicals must be
evaluated before a final recommendation can be made on this issue.
[see Section 2.6.3 and comment #10 for discussion relevant to comment #17]
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18. A reviewer commented that an incorrect statement was made that samples
in which organisms were not identified were or should be included
in calculations of screening level concentrations (toxicity endpoint
values). According to a representative of Battelle, the firm developing
the method, the stations should not be included. Were such samples
included in the Tetra Tech analyses, and if so, do the sediment quality
values change if these stations are removed?
Samples in which organisms of a given species were not present were
not included in the calculations of screening level concentrations (see
Section 5.4.2 of the final report). The term "presence/absence" in the
Task 3 draft referred to the initial data selection process used in the
approach (i.e., chemical data at stations would be included if a given
species of interest were present or disregarded if the species were absent).
The term "presence/absence" was used to highlight the contrast between
the toxicity endpoint approach, which uses benthic infaunal data but does
not rely on absolute abundance, and other approaches that rely on assessments
based on absolute infaunal abundances (e.g., AET).
19. Several readers expressed concern that the report's claim that the
AET approach does not require normalization to organic carbon content
goes against all that has been learned from experimental sediment
bioassays. What hypothesis can be forwarded to explain the weaker
showing of organic carbon normalization relative to dry weight? Could
this be an artifact of the particular database used?
Simply stated, organic carbon normalization theory assumes that inter-
stitial water in the primary source of nonpolar organic contaminants to
biota, and that, under equilibrium conditions, the distribution of nonpolar
contaminants between sedimentary organic matter and water (Koc) is constant
(and predictable). Organic carbon tends to act as a sink for nonpolar
contaminants (i.e., organic carbon content and sediment toxicity should
be inversely related). Hence, as sediment organic carbon content increases,
toxicity "threshold" values expressed per gram of bulk sediment should
decrease. If contaminant concentrations are normalized to organic carbon
content, threshold values should be constant for that contaminant in all
sediments.
Dry-weight normalization simply assumes that mass loading of a contaminant
in sediment is a predominant factor influencing toxicity to benthic organisms
(although organic carbon interactions may be a secondary factor). The
(empirical) AET approach does not favor one of these mechanistic explanations
over the other, but can operate whether one, a combination of the two,
or alternative assumptions are appropriate. The results from this approach
suggest that further research is required to confirm the underlying mechanism,
but that dry-weight normalization is the most accurate of the three normaliza-
tions tested.
For contaminated sediments in the environment, organic carbon normalization
could be less predictive than dry-weight normalization if sediment/interstitial
water systems are not at equilibrium (a key assumption of the organic carbon
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normalization model), if all sediment organic matter does not have uniform
affinity for hydrophobic pollutants, or if interstitial water is not the
predominant route of contaminant uptake. For example, it is quite plausible
that the equilibrium assumption could be violated in the environment by
kinetic aspects of sorption/desorption processes. Equilibrium requires
fairly rapid transfer of a contaminant between various phases in a system.
Studies of sorption/desorption kinetics have demonstrated that equilibration
of a nonpolar organic compound between sediment and aqueous phases could
take days to months, or longer (Karickhoff and Morris 1985a,b). In part,
slow rates of equilibration could be caused by entrainment or trapping
of contaminants within refractory (stable) organic matter (e.g., humic
substances or fecal pellets) (Freeman and Cheung 1981; Karickhoff and Morris
1985a). Prahl and Carpenter (1983) observed that PAH were disproportionately
concentrated in certain fractions of refractory sedimentary organic matter
(e.g., charcoal fragments and vascular plant detritus, such as lignin).
This disproportionality indicates that PAH may not have been at equilibrium
in the sediment phase or, alternatively, that different kinds of organic
matter may have different affinities for PAH. This latter possibility
is supported by studies of dissolved and solid humic materials and their
associations with hydrophobic organic pollutants (Carter and Suffet 1985;
Oiachenko 1981).
If sediment-water equilibrium is not often attained in the environment,
or various types of sediment organic matter have differing affinities for
hydrophobic pollutants, or interstitial water is not the primary route
of exposure for organisms, then the relationship between sediment organic
carbon content and bioavailable portions of nonpolar organic compound loadings
in sediment may not be consistent in environmental samples. Yet a consistent,
quantitative relationship is the basis for organic carbon normalization
theory.
To our knowledge, only two studies in the open literature address
the relationship between sediment bioassay results and sediment organic
carbon content [Adams et al. (1984) and Swartz et al. (1986)]. The study
by Adams et al. (1984) argues for organic carbon normalization of nonpolar
organic compounds. Adams et al. (1984) conducted a series of bioassays
in which freshwater midges (Chironomus tentans) were exposed to water,
sediments (with various levels of organic carbon content), and food contaminated
with Kepone (a relatively nonpolar ketone insecticide). No-effect concentra-
tions based on total sediment Kepone concentrations increased in proportion
to total organic carbon content of sediments, whereas no-effect levels
based on interstitial water Kepone concentrations were fairly constant
regardless of sediment concentration. The authors suggested that no-effect
concentrations should be based on sediment organic carbon content, not
on bulk sediment weight.
One possible reason for the success of organic carbon normalization
in the Adams et al. (1984) study is that sediments were spiked in the laboratory
with a carrier solvent containing Kepone. It is plausible that such spiking
procedures will promote homogeneous distribution of target compounds in
sediments and will preclude some factors that could impede equilibrium
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in the environment (e.g., incorporation into fecal pellets and other refractory
organic matter).
Swartz et al . (1986), in a study involving amphipod bioassays, found
that slight enhancement of total volatile solids content of sediment (resulting
from addition of small amounts of sewage sludge or fine particles enriched
in organic matter) reduced the toxicity of cadmium spiked into sediment.
These results do not provide a strong argument for organic carbon normalization
because the experiment did not distinguish between the effects of organic
matter "binding" of cadmium vs. the effects of fine particle "binding"
of cadmium. For example, it is possible that addition of fine iron oxide
particles (not enriched with organic matter) could have reduced toxicity
as well. In general, the applicability of organic carbon normalization
to metals and polar, ionizable organic compounds is limited because of
the variety of environmental factors other than TOC (e.g., pH, redox potential,
presence of Fe/Mn oxides and hydroxides) that could strongly influence
the sedimentary associations of these chemicals (see Task 3 draft, pp. 13-14).
It is unlikely that the database used for evaluation of AET influenced
the success of organic carbon versus dry weight AET (see response to comment
#10). The amphipod bioassay and benthic infaunal stations were compiled
from numerous studies and study areas. The oyster larvae and Microtox
bioassay samples were taken from the Commencement Bay study only. However,
evidence of the better predictive success of dry-weight AET relative to
organic carbon AET does not consist solely of oyster larvae and Microtox
bioassay AET (although they support the trends observed for amphipod bioassay
and benthic AET).
[This discussion for comment #19 is also presented in Section 8.6]
20. Concern was expressed that none of the approaches considered may adequately
address interactive effects. Hom would chemical interactiveness affect
uncertainty associated with sediment quality values developed using
different approaches? Discussion should include factors that could
overestimate or underestimate possible adverse effects and the general
likelihood/frequency/probability of these effects occurring.
The frequency that interactive effects occur in environmental settings
cannot be confidently determined using existing data. Additive, synergistic
(i.e., supra-additi ve), and antagonistic effects of contaminants are not
well understood but can be expected to have variable effects on the uncertainty
of sediment quality values generated by different approaches. If interactive
effects occurred on a major scale and the effects were not accounted for
by one of the approaches, the ability of the approach to correctly identify
problem sediments should be reduced. For all approaches based on field
data, collection of representative samples over a wide concentration range
and sediment type should help in addressing these concerns.
The only systematic approach to identifying and quantifying interactive
effects is the spiked laboratory bioassay (see paragraph 2, p. 23, Task 3
draft). This approach allows for control of contaminant mixtures, type of
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test organism, and various other test conditions. It should be noted that
while such tests are feasible, they would require considerable effort (i.e.,
years or decades) to be applicable to the numerous possible contaminant
mixtures in the environment. Interim measures are needed to address the
expressed need by agencies for application of sediment quality values as
a regulatory or investigative tool.
The equilibrium partitioning sediment-water approach and the related
Water Quality Criteria approach are based on toxicological data for chemicals
tested individually. Thus, the approaches are not designed to address
interactive effects of contaminants (a safety factor is built into the
water quality criteria used in the approaches, but this factor is not based
upon knowledge of interactive effects of contaminants). To the extent
that interactive effects occur in the environment and to the extent that
they alter the absolute threshold concentrations of individual contaminants,
these effects will increase the uncertainty of equilibrium partitioning values.
The uncertainty of the screening level concentration (SLC) approach
is potentially increased by the existence of interactive effects; the increase
in uncertainty will be less pronounced when large data sets collected from
diverse areas are used to generate sediment quality values with this approach.
Additivity and synergism can produce a comparatively low SSLC (species
screening level concentration) for a given chemical by causing species
absence at concentrations that would not eliminate a species in the absence
of these interactive effects. This would reduce the pool of "non-impacted"
stations used to generate an SSLC. If a large database is used such that
chemicals occur over a wide range of concentrations at stations where additivity
and synergism are not operative, then the SSLC will be not be biased by
these effects. Antagonism could potentially increase sediment quality
values set by the SLC approach by allowing a species to survive in a sample
at a concentration (of a given chemical) that would normally eliminate
the species. With a large database and the 90-percent safety factor in
SSLC values, such cases would probably have little effect.
Similar to the SLC approach, the uncertainty of the AET approach is
increased by the possibility of interactive effects; the increase in uncertainty
will be less pronounced when large data sets collected from diverse areas
are used to generate AET. Additivity and synergism can produce a comparatively
low AET for a given chemical by causing impacts at concentrations that
would not cause impacts in the absence of these interactive effects. This
would effectively reduce the pool of nonimpacted stations used to generate
AET. This effect is reduced if a large database is used such that chemicals
occur over a wide range of concentrations at stations where additivity
and synergism are not operative. Anatagonism will produce comparatively
high AET if the AET is established at a station where antagonism occurs.
A large database could not rectify this elevation of AET because the station
at which antagonism occurred would tend to be the non-impacted station
with the highest concentration.
[see Sections 2.6.4, 2.7.4 for discussion relevant to comment #20]
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21. Concern was expressed that the Apparent Effects Threshold (AET) approach
is limited to the number of species for which testing information
is available. National water quality criteria are based on eight
families of organisms, while sediment quality values developed using
the AET approach may not necessarily account for effects of some contami-
nants on other aquatic animals. Can AET really tell us anything other
than how one species responds to a given test? What confidence do
we have that sediment quality values based on AET developed using
one species will be appropriate (and protective) for use in making
decisions that could affect other species?
AET were generated for four biological indicators in this project
[i.e., amphipod bioassays, oyster larvae bioassays, Microtox bioassays,
and benthic infaunal abundances (at the major taxon rather than species
level)]. Other AET would have been generated if synoptic data had been
available because the AET approach is not inherently limited to any specific
indicators. The AET approach itself is simply a procedure for classifying
and ranking synoptic biological and chemical data and could be applied
to biological effects data (e.g., bioassays) for as many diverse species
as were used to develop U.S. EPA water quality criteria. Also, AET derived
from benthic infaunal abundance data take the responses of a variety of
benthic species into account. Benthic AET at the major taxon level (as
presented in this report) do not provide the "resolution" that a species-level
benthic AET could provide. Development of species-level AET was beyond
the scope of this work, but is recommended in the final report for future
work.
Contaminant concentrations below the AET developed for the four indicators
used in this project could potentially be harmful to untested aquatic species
(and in some cases to the species tested). The same could be true for
U.S. EPA water quality criteria, as toxicological data for all aquatic
species have not been incorporated into these criteria. Because the species
(and life stages) of bioassay organisms used in the present project are
thought to be relatively sensitive, there is reason to believe that sediment
quality values based on AET from these bioassays are representative of
a wide range of organisms. Greater confidence would result from application
of the potential effects threshold (i.e., the concentration of a contaminant
below which no statistically significant effects were observed in any sample)
as a sediment quality value, but this concentration is often below reference
conditions and is not recommended (see Section 2.7.2 discussion of the
AET approach). Considering the limited availability of field toxicological
data for various species, the best approaches to ensuring protection of
a wide range of species with AET are as follows:
• Develop AET based on species-specific benthic infaunal abundance
data (and as an environmentally protective measure, use
the most apparently sensitive of these could be used for
decision-making)
• Rely on bioassay data for species known to be sensitive
to contaminants.
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Development of AET for a wide range of species should help to better define
the sensitivity of individual species. This analysis could ultimately
enable AET to be used in a modified SLC approach, in which sediment quality
values are set to protect some percentage of species as indicated by the
lowest AET for a range of species. In such an approach, the AET would
not by themselves be considered "protective".
22. Concern was expressed that selection of study sites can greatly affect
AET because thresholds may depend on each specific mix of contaminants.
What evidence/confidence do we have that AET generated using one database
can adequately and consistently identify known or suspected impacted
stations from another database?
An additional accuracy analysis has been included in the final report
to address two issues: the applicability of AET generated from one study
area to another, and the potential bias that could result from using the
same data for establishing and then evaluating AET. AET (dry-weight normalized)
generated from the 56 Commencement Bay Remedial Investigation samples were
evaluated for accuracy with the remaining data in the compiled Puget Sound
database (134 samples, excluding Commencement Bay samples). The analysis
was carried out as before:
• The chemical database (from the 134 non-Commencement Bay
samples) was subdivided into groups of stations tested for
the same biological indicators (either amphipod bioassay
or benthic infaunal analysis; Microtox and oyster larvae
bioassays were not performed for these samples)
• The stations of each group were classified as "impacted"
(and "severely impacted") or "non-impacted" (i.e., without
significant effects relative to reference conditions)
• Using only Commencement Bay data, AET were generated for
all appropriate chemicals and were used to predict problem
stations from independent chemical concentration data for
the non-Commencement Bay stations (the predicted problem
stations were non-Commencement Bay stations with one or
more chemicals exceeding the Commencement Bay AET, i.e.,
those stations predicted to have biological effects)
• Measurements of accuracy ("sensitivity" and "efficiency,"
as defined in the final report) were calculated for each
subgroup of stations as:
sensitivity
(impacted)
predicted problem stations with impacts
all stations with impacts
sensitivity
(severely
impacted)
predicted problem stations with severe impacts
all stations with severe impacts
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efficiency
predicted problem stations with impacts
all potential problem stations
The results are tabulated below:
Sensitivity
("Impacted")
Sensitivity
("Severely Impacted") Efficiency
Amphipod 72 (18/25)
Benthic 90 ( 9/10)
100 (8/8)
100 (4/4)
37 (18/49)
31 ( 9/29)
Thus, AET based on the chemical mixtures represented by a range of
stations in one study area (Commencement Bay) appear to be fairly successful
at predicting biological impacts in diverse areas of Puget Sound. Furthermore,
these accuracy results support (and even slightly exceed) results obtained
by generating and evaluating AET with the same database (see Table 10 in
the Task 4 and 5a draft). However, the efficiency of Commencement Bay
AET for both biological indicators (31 and 37 percent) is a more reliable
estimate of true performance than the 100-percent efficiency reported in
Table 10 (as footnoted in Table 10, the 100-percent values were a result
of the way in which AET are generated). Efficiency may be better if two
larger, independent databases are used in such analyses.
23. Concern was expressed that the AET approach optimistically dismisses
the possibility of effects being caused by non-quantified (covarying)
chemicals. Several reviewers were of the opinion that a range of
concentrations must be tested empirically in the lab on a compound-
by-compound basis before criteria can be established. Is it possible
that the AET approach really only has utility in establishing for
later lab testing possible ranges of contamination that induce biological
effects, and not for setting recommended sediment quality values?
Unmeasured, covarying chemicals would not be expected to substantially
decrease the ability of AET to predict biologically impacted stations (excluding
interactive effects; see response #8). If an unmeasured chemical (or group
of chemicals) varies consistently in the environment with a measured chemical
(e.g., concentrations of certain alkylated PAH often correlate well with
those of their unalkylated priority pollutant counterparts), then the AET
established for the measured contaminant will (indirectly) apply to, or
result in management of, the unmeasured contaminant. In such cases, a
measured contaminant would be used as an "indicator" for an unmeasured
contaminant (or group of unmeasured contaminants). Because all potential
contaminants cannot be measured routinely, management schemes must rely
to some extent on "indicator" chemicals.
If an unmeasured chemical (or group of chemicals) covaries with a
measured chemical in some cases but not in others (e.g., if a certain industrial
process releases an unusual mixture of contaminants), the effect should
be discerned if a sufficiently large data set is used to establish AET.
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Use of a large data set comprising samples from a variety of areas with
wide-ranging chemical concentrations would decrease the likelihood that
an unrealistical ly low AET would be set. Because AET are set by the highest
concentrations in samples without observed biological impacts, AET will
not be affected by less contaminated samples in which unmeasured contaminants
cause impacts.
If an unmeasured toxic chemical does not co-occur with any measured
chemicals, it is likely that the AET approach will not predict impacts
at stations where the chemical is inducing toxic effects. This was one
of the sources of uncertainty addressed by the accuracy (sensitivity) evalua-
tion. In that evaluation, AET proved fairly successful at predicting impacted
stations (e.g., 54-94 percent accuracy in predicting impacted stations
depending on the biological indicator, and 92-100 percent accurate in predicting
"severely" impacted stations based on the same indicators). Note that,
like the AET approach, the spiked bioassay approach cannot be expected
to predict impacts in environmental samples in which unmeasured toxic chemicals
do not co-occur with measured chemicals. For this reason, chemical-specific
sediment quality values are recommended as one tool that can be used with
other tools (e.g., direct biological testing) for the management of sediments.
Nonetheless, the use of laboratory (e.g., spiked bioassay) studies
for confirming or "fine-tuning" AET values is desirable and will better
define the uncertainty of AET. This recommendation is discussed in the
"Prioritization of Laboratory Cause-Effect Studies" section in the Task 4
and 5a draft report.
24. What was the rationale for using the total number of individuals in
a class or phylum as opposed to finer levels of classification as
the measure of community disturbance?
Higher level taxa were used to set AET values for two major reasons.
First, because the AET approach is based on pair-wise statistical comparisons
with reference conditions, the benthic taxa must either be abundant enough
or have a low enough variance to allow major depressions to be discriminated
statistically. If these criteria are not met, it may be very difficult
to discriminate a depression and, in some cases, complete absence of a
taxon may not be indicated statistically as a significant impact. Therefore,
use of taxa that are either rare or highly variable may not result in a
useful indicator of environmental impact.
In developing the AET approach for the Commencement Bay Superfund
Study, it was found that almost all species, except the four or five most
abundant ones, were either too rare or too variable to be used as sensitive
indicators of impacts. By contrast, higher level taxa such as total taxa,
Polychaeta, Mollusca, and Crustacea were found to be variable, but abundant
enough to statistically discriminate major depressions in numbers of indivi-
duals. Echinodermata, a fifth higher taxon, was found to be both rare
and variable, so that depressions rarely could be discriminated. It therefore
was decided that total taxa, Polychaeta, Mollusca, and Crustacea were the
best available taxa for pair-wise statistical comparisons with reference
conditions. An alternate strategy might have been to use the four to five
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most abundant species, but it was decided that an index based on a greater
number of species considered as an aggregate might be more representative.
It should be noted, however, that the four to five most abundant species
dominated most of the higher taxa, and therefore exerted considerable influence
as to whether or not a depression was discriminated.
The second major reason for using higher taxa was that comparisons
with bioassay results (i.e., amphipod mortality and oyster larvae abnormality)
as part of the Commencement Bay Superfund Study showed that impacted or
non-impacted designations made by benthic and bioassay indicators agreed
at 67-79 percent of the 48 stations evaluated. This level of agreement
is significant (PC0.05, binomial test), and suggests that benthic comparisons
based on higher taxa were as sensitive in the Commencement Bay study as
the bioassays in identifying problem sediments, although different organisms
may differ widely in their sensitivity to individual chemicals present
as a complex mixture of chemicals in contaminated sediments. This independent
corroboration of the use of higher level taxa contributed to its acceptance
for setting benthic AET in Commencement Bay.
TASK 4 AND 5a REPORT: APPLICATION OF SELECTED SEDIMENT QUALITY VALUE APPROACHES
TO PUGET SOUND DATA
25. Concern was expressed that reliance on acute responses may generate
sediment quality values that are not protective of human health or
of chronic impacts on aquatic organisms, such as demersal fishes.
In order to be protective, one reviewer was of the opinion that sediment
quality values should be generated based on sediment concentrations
necessary to maintain contaminant levels in edible seafoods below
proposed tissue criterion.
Reliance on acute responses (i.e., acute toxicity bioassays) may indeed
generate sediment quality values that are not protective of human health
or against chronic health impacts to aquatic organisms (e.g., demersal
fishes). Some of the sediment quality values generated in this project
incorporated chronic effects data [e.g., equilibrium partitioning values
based on chronic water quality criteria, or AET based on benthic infaunal
abundances (the latter analyses would incorporate chronic toxicity, for
example, if samples were not taken during or shortly after a large contaminant
influx)]. However, these criteria do not directly address human health
or health of benthic biota and demersal fishes.
If human health (with regard to seafood consumption) or health of
demersal fishes are primary management objectives, both the equilibrium
partitioning or AET approaches could be oriented toward those objectives.
The sediment-biota equilibrium partitioning approach (discussed in Task
3) would be applicable for nonpolar organic compounds in shellfish if appro-
priate tissue criteria and bioaccumulation factors were available, and
if various assumptions were not violated (see Task 3). The approach would
involve greater uncertainty for bioaccumulation in demersal fishes than
for shellfish because of the more complex equilibrium relationships and
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bioaccumulation factors that apply to fish-sediment systems relative to
systems consisting of relatively immobile benthic infauna and sediments.
The AET approach could also be used to focus on human health and chronic
effects in fish. AET could be based on shellfish bioaccumulation ("impacted"
stations would be defined by shellfish tissue concentrations above an estab-
lished human dietary criterion), fish bioaccumulation ("impacted" areas
or groups of stations within a trawl area would be defined by fish tissue
concentrations above an established human dietary criterion), or fish histo-
pathology ("impacted" areas or groups of stations within a trawl area would
be defined by the frequency of certain histopathological conditions in
fish). The mobility of fish relative to shellfish or other benthic organisms
would preclude synoptic collection of chemical and biological data, thus
increasing the uncertainty of AET developed for mobile species. A fish
trawl (unlike a benthic infaunal sample or a sediment sample used for bioassays)
cannot be related to a single sediment chemistry sample. Instead, chemical
concentrations for multiple sediment samples in a trawl area must be averaged,
which will incorporate environmental variability of contamination into AET.
AET could be developed based on results of chronic laboratory tests.
Any sediment quality value could be modified (e.g., using a safety factor)
in an attempt to protect against chronic effects. Such sediment quality
values could be used in conjunction with direct bioassessments in a two-part
decision-making approach.
26. Concern was expressed regarding an apparent apples/oranges comparison
of sediment quality values generated by different approaches. Reviewers
were concerned that numbers were not directly comparable because sediment
quality values were generated by different approaches using different
databases and types of calculations and normalizations. Are these
concerns warranted? If the comparisons included in the report are
appropriate, discuss why.
Because the generation of sediment quality values by different approaches
(inherently involving different kinds of data and calculations) was one
of the major objectives of this project, it is assumed that this comment
refers to comparisons made in the accuracy section. The accuracy measurements
in Table 10 (based on comparison of potential problem stations and truly
impacted stations) were calculated identically for the equilibrium partitioning
and AET approaches. Direct comparisons of the predictive success (accuracy)
of both approaches were considered appropriate as a simulation of their
relative performance when applied in Puget Sound. The evaluation process
consisted of the following steps (pp. 25-26):
• "The chemical database was subdivided into groups of stations
that were tested for the same biological effects indicators"
(this was necessary because all stations were not tested
for all indicators)
• "The stations of each group were classified as 'impacted'
or 'nonimpacted' based on the appropriate statistical cri-
teria. .
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• "For each approach, sediment quality values for all applicable
chemicals... were compared with the corresponding chemical
data for each station."
• Sediment quality values normalized to a given variable (e.g.,
to organic carbon content, as for equilibrium partitioning
values) were compared to chemical data from the Puget Sound
database that were normalized to the same variable. "When
one or more chemicals exceeded the appropriate sediment
quality values at a given station, that station was considered
to be indicated as a potential problem station." (In essence,
potential problem stations are predicted to have biological
impacts.)
Accuracy of the toxicity endpoint approach was evaluated less thoroughly
than for the equilibrium partitioning and AET approaches for reasons explained
in the first paragraph of the draft section entitled "Preliminary Evaluation
of the Toxicity Endpoint Approach": "The evaluation of the toxicity endpoint
approach could not be conducted as thoroughly as that of the equilibrium
partitioning and AET approaches because PNEL [SLC] values were not developed
for all appropriate chemicals and did not incorporate data from a large number
of stations." These accuracy results were presented in a different section
than "Evaluation of Equilibrium Partitioning and AET Sediment Quality Values"
to preclude direct comparisons of the more thorough (equilibrium partitioning
and AET) and less thorough (toxicity endpoint) accuracy evaluations.
It was considered unreasonable to calculate the accuracy of toxicity
endpoint values in predicting problem stations because the values were
generated for only three chemicals (or chemical groups): naphthalene,
high molecular weight PAH (HPAH), and mercury. These three chemicals (or
chemical groups) could not be expected to account for all biological impacts
in Puget Sound that could be associated with other chemicals. Therefore,
the following measure of efficiency was used:
ff. . = predicted problem stations (based on chemical x) with impacts
e e y predicted problem stations (based on chemical x)
where:
chemical x = naphthalene, HPAH, or mercury.
This measure of efficiency is described in Section 7.1 and Figure 13 of
the final report.
Z7. Comment on the appropriateness of quantitatively comparing the sediment
quality values (Table 3 in the draft report) based on different normaliza-
tion factors if assumptions regarding these factors are required for
comparison (i.e., assuming 1-percent organic carbon content).
Equilibrium partitioning values were presented on a dry-weight bas^
in Table 3 (Table 6 of the final report) because this is the form of d'"
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typically reported in environmental studies and is familiar to most readers.
The equilibrium partitioning dry weight values were not used in the accuracy
evaluation of the equilibrium partitioning approach; however, they were
considered useful for giving readers an idea of the magnitude of the values
relative to other sediment quality values (e.g., Fourmile Rock Interim
Sediment Criteria), which are reported as dry-weight concentrations.
As footnoted in Table 3, a 1-percent organic carbon content is assumed
for equilibrium partitioning values, but "to adjust the value for a different
organic carbon content, multiply by the percent organic carbon." Thus,
the dry-weight equilibrium partitioning values can be easily adjusted for
any organic carbon content. Because the mean and median organic carbon
content of the compiled Puget Sound data set are 2.0 and 1.3 respectively,
it might be most representative to increase equilibrium partitioning values
by these factors.
Use of any of the organic carbon values in the Puget Sound database
would not invalidate the general observations made about relative magnitudes
of AET and equilibrium partitioning values in the Task 4 and 5a draft
These observations are further supported by Table 4, which is an unqualified
comparison of equilibrium partitioning and AET values based on organic
carbon normalization.
28. Criteria should be presented to show how the 66 chemicals were selected.
Were the data screened, and if so, how? Basis for selection of normal-
ization parameters should also be presented.
Because AET can be established for any chemical, there was no selection
scheme based on chemical type used to pare down the chemicals in the compiled
Puget Sound database. However, as noted in the draft Task 4 and 5a report
(p. 9), the frequency of occurrence and range of detected concentrations
of chemicals limit their appropriateness for establishing AET. Chemicals
seldom detected in the Puget Sound data set (e.g., hexachloroethane, heptachlor)
were not used because they did not cover a wide range of concentrations "
Beryllium and chromium are initially discussed and then dismissed in the
final report (Section 5.3.2) because their range of concentrations do not
exceed those found in nine different Puget Sound reference areas [as summarized
in Tetra Tech (1985a)].
The normalization variables were discussed in the draft Task 3 report
(Ancillary Sediment Variables; see Appendix G of the final report) and
are commonly used by environmental scientists. Organic carbon normalization
also enabled a direct comparison of AET and equilibrium partitioning sediment
quality values (Table 4 of the draft Task 4a and 5 report; Table 7 of the
final report).
29. To what extent do AET address other compounds not identified or measured
In sediment samples?
See discussion for comment #23. Aside from expanding the chemical
database to include unusual compounds that do not co-occur with measured
chemicals, direct biological testing is the only means to provide additional
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information beyond what can be provided by sediment quality values based
on AET or other available approaches.
30. Discuss the appropriateness (or lack thereof) of comparing AET generated
by using liquid (i.e., oyster larvae, Microtox) and non-liquid tests
(i.e., benthic community, amphipod bioassay).
A broadly based toxicity index that is based on multiple indicators
and encompasses a wider range of sediment toxicity than would be evident
from a single testing procedure is desirable for the development of sediment
quality values. The Apparent Effects Threshold (AET) is an example of
such an index because it identifies, for each data set and kind of bioassay,
the sediment concentration above which significant toxic responses were
always observed. Thus, the range of AET values for the different bioassay
(and benthic infaunal) indicators provides an index of the range of biological
variability (e.g., differences in organism sensitivity and route of exposure)
normally encountered in multiple species toxicity testing.
Comparability of the Microtox, oyster embryo, and amphipod bioassys used
to characterize toxicity of Commencement Bay sediments is evaluated by Williams
et al. (1986). Correlation analyses indicated a high level of agreement among
the three bioassays (Kendall's coefficient of concordance = 0.64, P<0.001).
Pair-wise comparisons using Pearson's correlation also indicated a high
level of agreement:
• Oyster embryo vs. amphipod (R = 0.86, P<0.001)
• Oyster embryo vs. Microtox (R = 0.62, P<0.001)
• Amphipod vs. Microtox (R = 0.48, P<0.001).
The magnitude of individual correlations suggests considerable variability
among the three bioassays, which may be partially attributable to differences
in exposure routes inherent in the experimental design of each bioassay.
An additional source of variabi 1 ity is interspecific differences in sensitivity
to the kinds of contaminants in the various sediment samples.
Differences among the bioassays in duration of exposure (i.e., 15 min
vs. 48 h vs. 10 day), and exposure medium (i.e., saline extract vs. sediment
slurry vs. whole sediments) may affect comparability of results. Most impor-
tantly, differences in sediment manipulation and exposure medium may affect the
relative proportions of polar, nonpolar, and sediment-bound contaminants in
each experimental system. It is not surprising that the Microtox and amphipod
bioassay results showed the lowest level of agreement, although the agreement
was still significant. These two bioassays are at opposite ends of the aqueous-
whole sediment exposure spectrum. Nevertheless, the three bioassays showed a
significant (PC0.05) level of concordance that indicates a robustness to with-
stand much of the variability in bioassay sensitivity, sediment heterogeneity,
and experimental exposure bias. Sediment bioassays results showed significant
agreement (67-79 percent; P<0.05) with the presence or absence of benthic
infaunal depressions in Commencement Bay (Tetra Tech 1985a).
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31. What do we know about the relative sensitivity of AET species compared
to screening level concentration species? Discuss the extent to which
species sensitivity is or is not addressed by the AET approach.
By the design of the screening level concentration (SLC) approach,
the most sensitive species of those evaluated are species that establish
the critical concentrations of chemical contaminants. The AET approach,
by contrast, can establish values for any single species, regardless of
its sensitivity. As currently applied, the AET approach applied to benthic
infauna data is based on higher level taxa. As such, current benthic AET
values probably are high estimates (i.e., not protective of all sensitive
species) of critical concentrations of chemical contaminants because less
sensitive species may be included in the analyses. However, to generate
lower (i.e., presumably more protective) estimates, the AET approach can
be applied to sensitive species alone, (see additional discussion for corrment
#25).
32. Provide scientific reasoning for selection of 80-percent depression
as Indication of sensitivity for use in the Screening Level Concentra-
tion (toxicity endpoint) approach. Is this truly a sensitive indicator?
Use of an 80-percent depression is as arbitrary as using P=0.05 as
the critical level of statistical significance. There is no scientific
basis for either. Instead, each should be set by a consensus of knowledgeable
parties. A critical level of 80 percent was used as a first-cut test level
for the screening level concentration (SLC) approach because 33 of 37 stations
exhibiting statistically significant (P<0.05) depressions in higher-level
benthic taxa in the Commencement Bay Superfund Study also exhibited a >80-
percent depression in abundance relative to reference conditions. If the
SLC approach is to be evaluated further in the future, it is highly desirable
to determine how different (higher and lower) critical depression levels
influence the results. Only then can a more informed decision be made
as to what critical depression level provides results that are "adequately"
sensitive. Based on the limited comparison made in this project (see Section
6.2.1 of the final report), SLC values based on absence/presence exceeded
those based on an 80 percent depression criterion by 12-56 percent. Hence,
SLC based on the 80 percent depression criterion are potentially more sensitive
indicators of contamination.
33. Circularity - Concern was expressed that use of the same data to define
and then evaluate the accuracy of AET and toxicity endpoint-generated
sediment quality values will bias the results.
The reason for using the entire Puget Sound database to generate AET
(Tables 3-5 of the Task 4 and 5a draft report; Tables 6-8 of the final
report) was that the reliability of AET is expected to be greater when
larger databases are used. This in turn necessitated that accuracy of
the AET was assessed with the same database from which AET were generated.
This issue is also addressed in response #22. As discussed in that
response, the accuracy of AET established from one database (Commencement
Bay/Carr Inlet) and evaluated with an independent database supported (and
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even slightly exceeded) the results presented in Table 10 of the draft
report (Table 13 of the final report, in which AET were evaluated with
the same data that were used to generate the AET). The results of the
two accuracy evaluations (tabulated below) indicate that the original accuracy
analysis was not biased. However, the efficiency of AET can only be realis-
tically evaluated with independent databases.
Sensitivity Sensitivity
AET Tested ("Impacted") ("Severely Impacted") Efficiency
Amphipod (DW)a 54 92 100*5
Benthic (DW)a 82 92 10Qb
Amphipod (DW)C 72 100 37
Benthic (DW)C 90 100 31
a From Table 13 of the final report (Table 10 of the draft Task 4 report),
AET generated and evaluated with the same data set.
b As noted in Table 13, efficiency is 100 percent by definition.
c AET generated with data from one data set (Commencement Bay, 56 samples)
and evaluated with data from several independent studies (Puget Sound data
in the compiled database, excluding Commencement Bay; 134 samples).
There is no reason to suspect circularity in the preliminary accuracy
evaluation of toxicity endpoint values because these values were generated
with a subset of the Commencement Bay database but were evaluated with
the entire compiled Puget Sound database.
34. There is more to uncertainty than technical variance. The weight
of evidence supporting certain numbers or the number of assumptions/steps
removed from the observation directly affects the "confidence" in
the use of the numbers. How does the report deal with this aspect
of uncertainty in evaluating the sediment quality values developed
from the different approaches?
Uncertainty was evaluated in this project with estimations of accuracy
(success at predicting biological impacts) and precision (an approximation
of "technical variance").
The accuracy evaluation was considered the best way to evaluate the
overall ability of each approach to predict biological impacts. The accuracy
analysis could not quantify various elements of uncertainty in each approach,
but instead provided an estimate of how the combined uncertainties of an
approach would affect its ultimate predictive success. This estimate was
considered particularly useful because numerous factors that affected the
uncertainty of the AET and equilibrium partitioning approaches were not
quantifiable, including factors that may have resulted in partially offsetting
effects.
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The precision analysis was an attempt to quantify the expected variability
of sediment quality values given the particular constraints in the design
and use of an approach. The equilibrium partitioning approach, which is
theoretically based, requires a number of estimations and assumptions (e.g.,
estimation of Koc values from K0w values, assumption of thermodynamic equili-
brium) to derive a sediment quality value. Quantifiable and unquantifiable
factors relating to the uncertainty of equilibrium partitioning values
were discussed in "Estimated Minimum Confidence Limits for Equilibrium
Partitioning Values." The precision of equilibrium partitioning values
could only be estimated for those chemicals with established chronic water
quality criteria; the uncertainty associated with estimated water quality
criteria was not possible to quantify.
For AET values, the effect of "weight of evidence" was not addressed
directly in the Task 4 and 5a draft, but was incorporated in the approach
used in "Estimated Confidence Intervals for AET Values" (see Section 7.2.2
of the final report). Unquestionably, there is less uncertainty for an
AET based on many observations than for an AET based on few observations.
(That is the reason that larger databases with wide-ranging chemical concentra-
tions are required for generating reliable AET.) Confidence limits for
AET were defined as the concentration range from two or three (non-impacted)
stations below the AET to one station above the AET (based on statistical
classification arguments). The number of stations used to establish an
AET (i.e., weight of evidence) would be expected to have a marked effect
on these confidence limits, because small data sets would tend to have
less continuous distributions of chemical concentrations than large data
sets. That is, small data sets would tend to have larger concentration
gaps between stations (and correspondingly wider confidence limits) than
larger data sets. Discussion of this concept has been reinforced in the
final report (Section 7.2.2).
35. Concern was expressed that Microtox data were derived from stored
sedinents. Is this correct? If so, how might storage have affected
the resulting AET? If storage is not a problem, couldn't other data
sets which were excluded have been included in the database?
Sediments used for Microtox bioassays in the Commencement Bay Remedial
Investigation were stored for less than 3 wk at 4° C in test tubes that
were flushed with nitrogen and then sealed. Under these inert atmospheric
conditions, the storage time is not expected to have a significant effect
on the results. With respect to chemical changes, U.S. EPA Contract Laboratory
Program guidelines allow 2 wk storage of refrigerated sediments without
any controls on the overlying atmosphere, and 40 day storage of refrigerated
sediment extracts. Up to a 4 wk storage period (under nitrogen) has been
recommended by the Evaluation Procedures Work Group of the Puget Sound
Dredged Disposal Analyis program (PSDDA) for Microtox bioassays; a 2 wk
period is recommended by the Puget Sound Estuary Program (PSEP; Tetra Tech
1986d). Synoptic bioassay data were excluded from the Puget Sound database
for this project if they were frozen because of concern by some investigators
that freezing may alter the toxicity of sediments (Task 1 draft). However,
samples that were stored at 4° C (with or without nitrogen) were not excluded.
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36. Does the recommendation that detection limits be set at 1/2 AET represent
any method difficulties given current protocols? Which AET did the
recommendation refer to (amphipod, oyster larvae, microtox, or benthic
community)?
The Puget Sound Estuary Program draft protocol for organic compounds
recommends detection limits of 1-50 ug/kg (dry weight) for analysis of
semi-vol ati 1 e organic compounds in sediments by gas chromatography/mass
spectroscopy (GC/MS) (Tetra Tech 1986b). These detection limits were agreed
upon at a workshop of regional experts. Recommended detection limits for gas
chromatography/ electron capture detection (GC/ECD) analysis are 0.1-5 ug/kg
for pesticides and 5-20 ug/kg for PCBs. Comparison of these values with
AET in Table 3 of the final report reveals that detection limits of 1/2
AET levels (e.g., for the lowest AET, usually Microtox) are reasonable.
The most serious problems would probably be presented by p,p'-DDT, 2-methyl-
phenol, 2,4-dimethylphenol, N-nitrosodiphenylamine, and benzyl alcohol.
Detection limits of 1/2 lowest AET for chlorinated benzenes (1,2-dichloro-
and 1,2,4-trichlorobenzene) could also be difficult to attain, but are
technically feasible with existing procedures.
Required detection limits of 1/2 AET for volatile organic compounds
and metals/metalloids should not present analytical problems.
37. What scientific reasoning can be provided for using the "Criterion
Maximum Concentration" value as the final chronic value instead of
the "Criterion Continuous Concentration (CCC)?" Wouldn't the longest
term CCC be more appropriate for setting a final chronic value?
The procedure that was used in estimating water quality criteria for
the equilibrium partitioning application was discussed on pages 7-8 (and
noted in Table 1, footnote 2) of the draft Task 4 and 5a report (see Section
5.2.3 and Table 4, footnote 2 of the final report). Criterion Maximum
Concentrations were not used as final chronic values instead of Criterion
Continuous Concentrations), criteria were selected based on preference
and availability. Chronic criteria (now referred to as Criterion Continuous
Concentrations by U.S. EPA) were preferred and used when available. In
the absence of U.S. EPA determined chronic criteria, chronic criteria were
estimated from the lowest concentration observed to induce chronic toxicity
in saltwater organisms [based on data in U.S. EPA (1980)]. Acute criteria
(Criterion Maximum Concentrations) or estimations of acute criteria were
used only if chronic criteria or chronic data were unavailable. Final
Chronic Values could not be calculated from acute criteria (or from estimated
acute criteria) because Final Acute:Chronic Ratios were unavailable.
Although estimates of water quality criteria add unquantifiable uncertainty
to equilibrium partitioning sediment quality values, they were necessary
to evaluate the method with the widest possible range of nonpolar organic
pollutants. If the equilibrium partitioning application was limited to
chemicals with established chronic water quality criteria, only p,p'-DDT,
PCBs, chlordane, dieldrin, and heptachlor could have been used. These
chemicals alone could not be expected to be useful for predicting biologically
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impacted stations in Puget Sound. The predictive success (expressed as
"efficiency") of equilibrium partitioning sediment quality values for individual
chemicals with established chronic water quality criteria was addressed
in the Task 4 and 5a draft (p. 43) and in more detail in the final report
(see Section 7.1.1).
38. Discuss/identify evidence of analyses on the underlying distribution
of the data, and the basis for the transformations used throughout
the report. Parametric statistics implicitly require a normal distribution
to be valid. According to several reviewers, if this distribution
analysis was not performed prior to the additional statistical analysis,
the results could be invalid.
The primary test used to evaluate bioassay and benthic impacts was
the t-test, a test that is mathematically equivalent to the single-classifi-
cation ANOVA based on two groups (Sokal and Rohlf 1981). Although one
assumption of ANCA/A is that the data are distributed normally, the consequences
of non-normality are not too serious and only very skewed distributions
have a marked effect on test results (Snedecor and Cochran 1967: Zar 1974:
Sokal and Rohlf 1981).
For the bioassay results, there was no reason to expect distributions
to be markedly skewed, as the five replicate values for each test were
generated from subsamples of a homogeneous composite under carefully controlled
conditions. These test conditions suggest that the values of all five
replicates should be very similar and that the random error encountered
among replicates should be relatively small. Transformation of the bioassay
results therefore were not considered necessary.
For the benthic results, there was considerable reason to believe
that the abundance data were strongly skewed, as that pattern is typical
of benthic infaunal assemblages (Gray 1981). Accordingly, abundances of
infauna were 1 ogiQ-transformed before statistical analyses were conducted
(see Section 5.3.4).
39. Will the utility of the equilibrium partitioning and/or Screening
Concentration Level (toxicity endpoint) approaches increase with a
growing Puget Sound database?
As a general rule, approaches based on field data (e.g., the toxicity
endpoint approach) are expected to generate more reliable sediment quality
values when based on large data sets (e.g., data sets with wide-ranging
chemical concentrations including different contamination sources, and including
data for a variety of organisms). Hence, addition of more species-specific
data (with synoptic chemistry data) to the Puget Sound database will enable
the generation of more reliable SLC values than were generated in the limited
application in this project (see Section 8.8 of the final report).
Sediment quality values for the equilibrium partitioning approach are
not based on field data. Hence, expansion of the Puget Sound database would
not affect current values. However, new field data can be used to further
evaluate the ability of the approach to predict biological impacts. The
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establishment of more chronic saltwater water quality criteria by U.S. EPA
may enhance the "precision" of equilibrium partitioning sediment quality values
for chemicals for which only estimated criteria could be determined. Thus,
an increase in the U.S. EPA toxicological database should increase the utility
of the equilibrium partitioning approach for nonpolar organic compounds.
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