&EPA
United States
Environmental Protection
Agency
Great Lakes
National Program Office
77 West Jackson Boulevard
Chicago, Illinois 60604
EPA 905-B94-002
August 1994
Assessment and
Remediation
Of Contaminated Sediments
(ARCS) Program
ASSESSMENT GUIDANCE
DOCUMENT
United States Areas of Concern
ARCS Priority Areas of Concern
printed on recycled paper
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FINAL
ASSESSMENT OUTDANCE DOCUMENT
Submitted to
U.S. ENVIRONMENTAL PROTECTION AGENCY
OCEANS AND COASTAL PROTECTION DIVISION
and
GREAT LAKES NATIONAL PROGRAM OFFICE
EPA Contract No. 68-C2-0134
Work Assignment No. 1-3
July 1994
Edited by
PTI Environmental Services
15375 SE 30th Place, Suite 250
Bellevue, Washington 98007
(206) 643-9803
v? Under Contract to
5
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^ 397 Washington Street
^ Duxbury, Massachusetts 02332
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U.S. Environmental Protection Agency
Region 5, Library (PL-12J)
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ASSESSMENT AND REMEDIATION OF CONTAMINATED SEDIMENTS
(ARCS) PROGRAM
ASSESSMENT GUIDANCE DOCUMENT
Great Lakes National Program Office
U.S. Environmental Protection Agency
77 West Jackson Boulevard
Chicago, Illinois 60604-3590
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DISCLAIMER
The information in this document has been funded wholly
or in part by the U.S. Environmental Protection Agency
(USEPA). Mention of trade names or commercial products
does not constitute endorsement or recommendation for use
by USEPA.
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A CKNO WLEDGMENTS
This report was prepared by the Toxicity/Chemistry Work Group as part of the
Assessment and Remediation of Contaminated Sediments (ARCS) Program administered
by U.S. Environmental Protection Agency's (USEPA) Great Lakes National Program
Office (GLNPO) in Chicago, Illinois. Editing of individual chapters was performed by
Mr. Rick Fox of GLNPO and PTI Environmental Services. Dr. Philippe Ross of The
Citadel, Charleston, South Carolina, and Mr. Rick Fox served as chairmen of the
Toxicity/Chemistry Work Group. Mr. David Cowgill and Dr. Marc Tuchman of
GLNPO served as the ARCS Program managers.
Contributors to this report included:
Chapter 1. Rick Fox, USEPA, GLNPO, Chicago, Illinois
Peter Landrum, National Oceanic and Atmospheric Administration, Ann
Arbor, Michigan
Lawrence McCrone, PTI Environmental Services, Bellevue, Washington
Chapter 2. Brian Schumacher, USEPA, Environmental Monitoring Systems Labora-
tory, Las Vegas, Nevada
Rick Fox, USEPA, GLNPO, Chicago, Illinois
J. C. Filkins, USEPA, Environmental Research Laboratory, Large Lakes
Research Station, Grosse He, Michigan
Bob Barrick, PTI Environmental Services, Bellevue, Washington
Chapter 3. V.E. Smith and S.G. Rood, AScI Corporation, Dearborn, Michigan
Chapter 4. J.E. Rathbun, L.L. Huellmantel, M. Tracy, and K.A. Ahlgren, AScI
Corporation, Dearborn, Michigan
Chapter 5. Eric Crecelius, Brenda Lasorsa, Lisa Lefkovitz, Battelle, Pacific North-
west Division, Marine Sciences Laboratory, Sequim, Washington
Peter Landrum, National Oceanic and Atmospheric Administration, Ann
Arbor, Michigan
Bob Barrick, PTI Environmental Services, Bellevue, Washington
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Chapter 6. G. Allen Burton, Jr., Wright State University, Dayton, Ohio
Christopher G. Ingersoll, National Biological Survey, Columbia,
Missouri
Chapter 7. Timothy Canfield, National Biological Survey, Columbia, Missouri
Thomas La Point, Clemson University, Clemson, South Carolina
Michael Swift, University of Minnesota, Monticello, Minnesota
Chapter 8. Mary Ellen Mueller, National Biological Survey, Washington, DC
Michael Mac, National Biological Survey, Office of Research Support,
Washington, DC
Chapter 9. S. G. Rood and V.E. Smith, AScI Corporation, Dearborn, Michigan
J.C. Filkins, USEPA, Environmental Research Laboratory, Large Lakes
and Rivers Research Branch, Grosse He, Michigan
Mark L. Wildhaber and C.J. Schmitt, National Biological Survey,
Columbia, Missouri
Chapter 10. Lawrence McCrone, PTI Environmental Services, Bellevue, Washington
This report was edited and produced by PTI Environmental Services for Battelle Ocean
Sciences under USEPA Contract No. 68-C2-0134.
IV
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ABSTRACT
This document provides guidance on procedures for assessing the nature and extent of
sediment contamination as applied to areas in the Great Lakes region. The document was
prepared by the Toxicity/Chemistry Work Group as part of the Assessment and Remedia-
tion of Contaminated Sediments (ARCS) Program, administered by the U.S. Environ-
mental Protection Agency's (USEPA) Great Lakes National Program Office (GLNPO),
in Chicago, Illinois.
Assessment of sediment contamination is intended to determine whether chemical concen-
trations hi the sediments are sufficient to cause adverse effects on either aquatic
organisms or organisms higher in the food chain, including humans. One of the main
goals of the Toxicity/Chemistry Work Group was the selection of scientifically sound
methods for assessing sediment quality. The selected sediment assessment methods were
then applied hi demonstration studies at several of the Great Lakes Areas of Concern
(AOCs).
The sediment assessment methods described in this document include an integration of
physical, chemical, and biological information. Decisions regarding the possible need
for sediment remediation could therefore be made on the basis of a preponderance of
evidence.
The chapters of this guidance document focus on various topics related to the assessment
of contaminated sediments. Included is guidance on the necessary elements of a quality
assurance and quality control (QA/QC) program, considerations for the conduct of field
surveys, screening-level analyses (i.e., relatively rapid, low-cost tests to focus subsequent
comprehensive analyses on the more contaminated sediments), chemical analyses, toxicity
tests for assessing biological impacts, assessments of benthic invertebrate community
structure, surveys of fish tumors and abnormalities, and data presentation and interpreta-
tion techniques. In addition to descriptions of the available options within each chapter,
recommendations are made to guide the selection of appropriate sediment assessment
methods, using the experience gained by the Toxicity/Chemistry Work Group to illustrate
key issues. It is intended that the guidance on appropriate sediment assessment methods
provided herein may be applied to other Great Lakes AOCs as they undergo investigation
by Great Lakes Remedial Action Plan (RAP) personnel at the Federal, State, and local
levels.
This report should be cited as follows:
U.S. Environmental Protection Agency. 1994. "ARCS Assessment Guidance Docu-
ment." EPA-905-B94-002. Great Lakes National Program Office, Chicago, IL.
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CONTENTS
Page
DISCLAIMER ii
ACKNOWLEDGMENTS iii
ABSTRACT v
LIST OF FIGURES xiii
LIST OF TABLES xv
ACRONYMS AND ABBREVIATIONS xviii
1. INTRODUCTION 1
BACKGROUND 1
OVERVIEW OF SEDIMENT ASSESSMENT METHODS 2
OVERVIEW OF THE ASSESSMENT GUIDANCE DOCUMENT 8
2. QUALITY ASSURANCE AND QUALITY CONTROL 10
QUALITY ASSURANCE PROGRAM 10
DEVELOPMENT OF DATA QUALITY OBJECTIVES AND
MEASUREMENT QUALITY OBJECTIVES 12
Data Quality Objectives 12
Measurement Quality Objectives 16
QUALITY ASSURANCE AND QUALITY CONTROL SAMPLES 21
Replicate Samples 22
Blank Samples 23
Reference Materials 24
Matrix Spikes 25
VI
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Page
Surrogate Spikes 26
Initial Instrument Calibration Standards 27
Ongoing Calibration Check Samples 27
Control Charts 28
PREPARATION OF QUALITY ASSURANCE PLANS 28
DEVELOPMENT OF A LABORATORY AUDIT PROGRAM 29
Evaluation Samples 29
Laboratory Performance and System Audits 29
DATABASE REQUIREMENTS AND DATA VERIFICATION/
VALIDATION METHODS 30
CONCLUSIONS 31
3. SEDIMENT SAMPLING SURVEYS 33
SEDIMENT SAMPLING VESSEL 35
Sampling Vessel Used in the ARCS Program 36
FIELD POSITIONING METHODS 38
Advantages and Disadvantages of Available Positioning Systems 39
Field Positioning System Used in the ARCS Program 41
SEDIMENT SAMPLING PROCEDURES 41
Grab Samplers 42
Sediment Corers 44
Sediment Samplers and Procedures Used in the ARCS Program 44
Conclusions 46
FIELD PROCESSING OF SEDIMENT SAMPLES FOR PHYSICAL AND
CHEMICAL ANALYSES 47
FIELD PROCESSING OF SEDIMENT SAMPLES FOR BENTHIC
COMMUNITY ANALYSES 52
FIELD PROCESSING OF SEDIMENT SAMPLES FOR TOXICITY
TESTING 53
VII
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SEDIMENT CHARACTERIZATION BY REMOTE SENSING 53
Acoustic Subbottom Profiling 54
Electrical Resistivity (Conductivity) Profiling 56
Conclusions 56
4. SCREENING-LEVEL ANALYSES 57
SUMMARY OF SCREENING-LEVEL METHODS 58
Total PAHs by Fluorometry 58
Total PCBs, Chlorinated Pesticides, and Other Organic Chemicals by
Enzyme Immunoassay 58
Total Petroleum Hydrocarbons by Infrared Spectroscopy 59
Semivolatile Organic Compounds by Thin-Layer Chromatography 60
Metals by X-ray Fluorescence 60
Rapid Toxicity Tests 60
INDICATOR ANALYSES 61
Results for Core Samples Analyzed During the ARCS Program 61
Correlation Between Indicator and Comprehensive Analyses 65
CONCLUSIONS 67
5. CHEMICAL ANALYSES 69
METHOD SELECTION (GENERAL OVERVIEW) 71
CONVENTIONAL VARIABLES 71
Sediments 71
Tissues 73
Conclusions 73
ORGANIC COMPOUNDS 74
Nonchlorinated Semivolatile Organic Compounds 74
PCBs and Chlorinated Pesticides 77
PCDDs and PCDFs 80
VIII
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Page
ORGANOMETALLIC COMPOUNDS 81
Methylmercury 81
Butyltin Compounds 82
METALS 82
Sediments 82
Tissues 84
Elutriate and Pore Water 84
CONCLUSIONS 84
6. EVALUATION OF SEDIMENT TOXICITY 86
OVERVIEW 86
INTRODUCTION 88
EXPERIMENTAL DESIGN 92
METHODS FOR SAMPLE COLLECTION AND EXPOSURE 94
Sediment Manipulation and Characterization: The Importance of
Maintaining Sediment Integrity 94
General Exposure Procedures for Sediment Toxicity Tests 97
Quality Control and Quality Assurance for Sediment Toxicity Tests 98
DATA ANALYSIS 99
EVALUATION OF SEDIMENT TOXICITY TESTS IN THE ARCS
PROGRAM 100
Toxicity Test Methods 101
Data Analysis Approach 106
Sensitivity 107
Discriminatory Ability 111
Combined Sensitivity and Discriminatory Abilities 115
Similarities in Measured Endpoint Responses 115
Correlations Between Toxicity Test Endpoint Responses 117
Comparisons of Acute and Chronic Toxicity Testing with Whole
Sediments 120
IX
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Page
EVALUATION OF TOP-RANKED TOXICITY TESTS 122
CONCLUSIONS AND RECOMMENDATIONS 124
Criteria for Selection of Individual Toxicity Tests 124
Recommended Toxicity Tests 126
7. ASSESSMENT OF BENTHIC INVERTEBRATE COMMUNITY
STRUCTURE 131
INTRODUCTION 131
EXPERIMENTAL DESIGN 132
METHODS FOR SAMPLE COLLECTION 133
Grab Samplers 134
Artificial Substrate Samplers 134
DATA ANALYSIS 135
THE ARCS APPROACH 136
Site Description 136
Methods 137
Quality Assurance and Quality Control for Benthic Invertebrate
Community Analysis 140
Statistical Analysis 140
SUMMARY AND RECOMMENDATIONS FOR FUTURE STUDIES 173
8. FISH TUMORS AND ABNORMALITIES 176
INTRODUCTION 176
ROLE OF FISH TUMOR SURVEYS IN ASSESSING SEDIMENT
CONTAMINATION 176
USE OF FISH TUMOR SURVEYS TO INFER CAUSE-AND-EFFECT
LINKAGES 176
HISTOPATHOLOGY AS A SENSITIVE ASSESSMENT TOOL 177
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METHODS AND MATERIALS 178
Fish Collection 178
Fish Processing 178
Evaluation of Tissue Samples 179
Quality Assurance and Quality Control 180
THE ASHTABULA RIVER AOC TUMOR SURVEY 181
External Abnormalities 181
Histological Findings 182
Biological Correlations 182
Contaminant Correlations 183
DISCUSSION 185
Limitations of Results 185
Recommendations 186
9. DATA PRESENTATION AND INTERPRETATION 189
SEDIMENT QUALITY DESCRIPTION AND MAPPING 190
Preparing Base Maps 191
Data Set Mapping 192
SEDIMENT CLASSIFICATION METHODS 201
Whole Sediment Toxicity Testing 201
Spiked Sediment Toxicity Testing 202
Interstitial Water Toxicity Identification Evaluation 202
Equilibrium Partitioning 202
Tissue Residues 203
Benthic Macroinvertebrate Community Structure 203
Sediment Quality Triad 204
Apparent Effects Threshold 204
National Status and Trends Program Effects-Based Approach 207
Use of the Sediment Classification Approaches 208
XI
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NUMERICAL RANKING OF HAZARDOUS SEDIMENTS TO
PRIORITIZE SITES FOR REMEDIAL ACTION 210
Ranking Sites Based on Toxicity Estimated from Chemistry Data 210
Ranking Sites Based on Toxicity as Measured by Laboratory Toxicity
Tests 212
Ranking Sites Based on Toxicity as Measured by Benthic Community
Structure 213
Final Ranking 214
CONCLUSIONS AND RECOMMENDATIONS 215
10. CONCLUSIONS 216
11. REFERENCES 222
XII
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LIST OF FIGURES
Page
Figure 1-1. Buffalo River Area of Concern, New York 3
Figure 1-2. Indiana Harbor Area of Concern, Indiana 4
Figure 1-3. Saginaw River Area of Concern, Michigan 5
Figure 2-1. Steps in the data quality objectives process 13
Figure 3-1. R/V Mudpuppy 37
Figure 3-2. Example sample numbering system used in the ARCS
Program 48
Figure 3-3. Diagram of acoustic subbottom profiling 55
Figure 7-1. Artificial substrate samplers used to collect aquatic
invertebrates in the ARCS Program 139
Figure 7-2. Total concentration of simultaneously extracted metals (Cd,
Cr, Cu, Ni, Pb, Zn) vs. mean total invertebrate abundance
at three priority AOCs 162
Figure 7-3. Total PAH concentration vs. mean total invertebrate
abundance at three priority AOCs 163
Figure 7-4. Total PCB concentration vs. mean total invertebrate
abundance at three priority AOCs 164
Figure 7-5. Composition of the invertebrate taxa collected using an
artificial substrate sampler and a Ponar grab sampler at the
Buffalo River AOC 168
Figure 7-6. Composition of the invertebrate taxa collected using an
artificial substrate sampler and a Ponar grab sampler at the
Indiana Harbor AOC 169
XIII
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Figure 7-7. Composition of the invertebrate taxa collected using an
artificial substrate sampler and a Ponar grab sampler at the
Saginaw River AOC 170
Figure 9-1. Examples of single-value point maps 193
Figure 9-2. Example use of icons to plot the value of a single
parameter 194
Figure 9-3. Example use of icons to plot the values of multiple
parameters 195
Figure 9-4. Example use of icons to plot both quantitative (copper
concentrations) and qualitative (sediment type) parameters 196
Figure 9-5. Examples of different contouring algorithms applied to the
same data set 197
Figure 9-6. Example of a pseudo 3-dimensional surface model
generated from a 2-dimensional contour map 199
Figure 9-7. Example of a 2-dimensional contour map "draped" over a
pseudo 3-dimensional surface model 200
Figure 9-8. Use of triaxial graphs to plot sediment quality triad data 206
XIV
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LIST OF TABLES
Jage
Table 2-1. Examples of the measurement quality objectives for
inorganic and organic chemistry analyses of sediment used
by the ARCS Program 17
Table 3-1. Comparison of positioning systems 39
Table 3-2. Advantages and disadvantages of various sediment samplers 43
Table 3-3. Recommended sample sizes, containers, preservation
techniques, and holding times 50
Table 4-1. Indicator analysis descriptions and citations 62
Table 4-2. Mean values for selected indicator variables in core
samples 64
Table 4-3. Comparison between predicted and measured Microtox®
EC50 values 66
Table 5-1. Approximate costs for chemical analyses 72
Table 6-1. Rating of selection criteria for selected whole sediment
toxicity test organisms 90
Table 6-2. Sediment toxicity tests evaluated in the ARCS Program 91
Table 6-3. Advantages, disadvantages, and routine uses of sediment
phases in laboratory toxicity tests 95
Table 6-4. Ranking of toxicity test endpoints by sensitivity over four
AOC surveys 108
Table 6-5. Ranking of toxicity test endpoints by discriminatory ability
over four AOC surveys 112
xv
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Table 6-6. Combined ranking of ARCS toxicity tests: sensitivity +
discriminatory ability 116
Table 6-7. Factor analysis of ARCS sediment toxicity test data 118
Table 6-8. Toxicity test endpoints with the highest average r2 and
lowest average P values 119
Table 6-9. Percentage of significant correlations between benthic and
nonbenthic endpoint responses 120
Table 6-10. Optimal toxicity test battery groupings 127
Table 6-11. Toxicity test selection approach 128
Table 6-12. Example of selection of toxicity tests based on study
objectives 129
Table 7-1. Percent contribution of major taxa to the total number of
taxa collected in grab samples from the Buffalo River in
October 1989 143
Table 7-2. Percent contribution of major taxa to the total number of
taxa collected in grab samples from the Indiana Harbor in
August 1989 144
Table 7-3. Percent contribution of major taxa to the total number of
taxa collected in grab samples from the Saginaw River hi
December 1989 145
Table 7-4. Percent contribution of major taxa to the total number of
taxa collected in grab samples from the Saginaw River in
June 1990 146
Table 7-5. Mean abundance (number/m2) of oligochaetes collected in
grab samples from the Buffalo River in October 1989 148
Table 7-6. Mean abundance (number/m2) of oligochaetes collected in
grab samples from Indiana Harbor in August 1989 149
Table 7-7. Mean abundance (number/m2) of oligochaetes collected in
grab samples from the Saginaw River in December 1989 150
XVI
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Table 7-8. Mean abundance (number/m2) of oligochaetes collected in
grab samples from the Saginaw River in June 1990 151
Table 7-9. Mean abundance (number/m2) of chironomids collected in
grab samples from the Buffalo River in October 1989 153
Table 7-10. Mean abundance (number/m2) of chironomids collected in
grab samples from the Saginaw River in December 1989 154
Table 7-11. Mean abundance (number/m2) of chironomids collected in
grab samples from the Saginaw River in June 1990 155
Table 7-12. Mean abundance (number/m2) of molluscs collected in grab
samples from the Buffalo River in October 1989 156
Table 7-13. Mean abundance (number/m2) of molluscs collected in grab
samples from the Saginaw River in December 1989 158
Table 7-14. Mean abundance (number/m2) of molluscs collected in grab
samples from the Saginaw River in June 1990 159
Table 7-15. Comparison of absolute and relative abundances of
oligochaetes and chironomids for each Area of Concern 160
Table 7-16. Prevalences of larval chironomid mouthpart deformities
from Buffalo River, Indiana Harbor, and Saginaw River
AOCs 166
Table 7-17. Percentage of total variance of benthic invertebrate abun-
dance estimates partitioned among various sources 171
Table 8-1. Selected mean concentrations of polynuclear aromatic
hydrocarbons in sediments and the prevalence of liver
tumors in brown bullheads from the Black, Cuyahoga,
Ashtabula, and Huron rivers 184
Table 9-1. Possible conclusions resulting from use of the sediment
quality triad approach 205
Table 10-1. ARCS Toxicity/Chemistry Work Group 217
XVII
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ACRONYMS AND ABBREVIATIONS
AET
ANOVA
AOC
ARCS
ASTM
AVS
AWQC
CAB
the Corps
CRM
CVAA
CVAF
DEC
DCB
DGPS
DQO
BCD
ER-L
ER-M
FID
GC/MS
GFAA
GIS
GLNPO
GMP
GPC
GPS
HPLC
HRMS
HSI
ICP/AES
ICP/MS
IDL
IUPAC
LaMP
LLRS
LOQ
MDL
MQO
apparent effects threshold
analysis of variance
Area of Concern
Assessment and Remediation of Contaminated Sediments
American Society for Testing and Materials
acid-volatile sulfide
ambient water quality criteria
cellulose acetate butyrate
U.S. Army Corps of Engineers
certified reference material
cold vapor atomic absorption
cold vapor atomic fluorescence
dibutylchlorendate
decachlorobiphenyl
differential global positioning system
data quality objective
electron capture detection
effects range-low
effects range-median
flame ionization detection
gas chromatography/mass spectrometry
graphite furnace atomic absorption spectroscopy
geographic information system
Great Lakes National Program Office
geologic modeling program
gel permeation chromatography
global positioning system
high-pressure liquid chromatography
high-resolution mass spectrometry
hepatosomatic index
inductively coupled plasma-atomic emission spectroscopy
inductively coupled plasma-mass spectrometry
instrument detection limit
International Union of Pure and Applied Chemistry
Lakewide Management Plan
Large Lakes Research Station
limit of quantification
method detection limit
measurement quality objective
XVIII
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NFCRC
NOAA
NOEL
OCN
%RSD
PAH
PCA
PCB
PCDD
PCDF
PEL
QA/QC
QAMP
QAPP
RAP
RPD
RRF
SIM
SQV
SRM
TBT
TCDD
TCMX
TDL
TEL
TIE
TLC
TOC
TPH
U.S. NIST
USEPA
USGS
XRF
National Fisheries Contaminant Research Center
National Oceanic and Atmospheric Administration
no-observed-effect-level
octachloronaphthalene
percent relative standard deviation
polynuclear aromatic hydrocarbon
principal component analysis
polychlorinated biphenyl
polychlorinated dibenzo-p-dioxin
polychlorinated dibenzofuran
probable effects level
quality assurance and quality control
quality assurance management plan
quality assurance project plan
Remedial Action Plan (for Great Lakes AOCs)
relative percent difference
relative response factor
selected ion monitoring
sediment quality value
standard reference material
tributyltin
2,3,7,8-tetrachlorodibenzo-p-dioxin
tetrachloro-m-xylene
target detection limit
threshold effects level
toxicity identification evaluation
thin-layer chromatography
total organic carbon
total petroleum hydrocarbon
U.S. National Institute of Standards Technology
U.S. Environmental Protection Agency
U.S. Geological Survey
x-ray fluorescence
XIX
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. INTRODUCTION
This document provides guidance on procedures for assessing the nature and extent of
sediment contamination as applied to areas in the Great Lakes region. It was prepared
under the Assessment and Remediation of Contaminated Sediments (ARCS) Program,
administered by the U.S. Environmental Protection Agency's (USEPA) Great Lakes
National Program Office (GLNPO) in Chicago, Illinois.
BACKGROUND
Although toxic discharges into the Great Lakes and elsewhere have been reduced in the
last 20 years, persistent contaminants in sediments continue to pose a potential risk to
human health and the environment. Elevated concentrations of contaminants in bottom
sediments and associated adverse effects have been found throughout the Great Lakes and
connecting channels. The extent of sediment contamination and its associated adverse
effects have been the subject of considerable concern and study in the Great Lakes
community and elsewhere. For example, contaminated sediments can have direct toxic
effects on aquatic life, such as the development of cancerous tumors in bottom-feeding
fish exposed to polynuclear aromatic hydrocarbons (PAHs) in sediments (Myers et al.
1990). In addition, the bioaccumulation of toxic contaminants in the food chain can also
pose a risk to humans, wildlife, and aquatic organisms. As a result, advisories against
consumption of fish are in place in many areas of the Great Lakes. These advisories
have had a negative economic impact on the affected areas.
To address concerns about the adverse effects of contaminated sediments in the Great
Lakes, Annex 14 of the Great Lakes Water Quality Agreement (1978) between the Uni-
ted States and Canada (as amended by the 1987 Protocol) stipulates that the cooperating
parties will identify the nature and extent of sediment contamination in the Great Lakes,
develop methods to assess impacts, and evaluate the technological capability of programs
to remedy such contamination. The 1987 amendments to the Clear Water Act, in
§ 118(c)(3), authorized GLNPO to coordinate and conduct a 5-year study and demonstra-
tion projects relating to the appropriate treatment of toxic contaminants in bottom
sediments. Five areas were specified in the Act as requiring priority consideration hi
conducting demonstration projects: Saginaw Bay, Michigan; Sheboygan Harbor, Wiscon-
sin; Grand Calumet River, Indiana; Ashtabula River, Ohio; and Buffalo River, New
York. To fulfill the requirements of the Act, GLNPO initiated the ARCS Program. In
addition, the Great Lakes Critical Programs Act of 1990 amended the section, now
§ 118(c)(7), by extending the program by 1 year and specifying completion dates for
certain interim activities. ARCS is an integrated program for the development and
testing of assessment techniques and remedial action alternatives for contaminated
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Chapter 1. Introduction
sediments. Information from ARCS Program activities will help address contaminated
sediment concerns in the development of Remedial Action Plans (RAPs) for all 43 Great
Lakes Areas of Concern (AOCs, as identified by the United States and Canadian govern-
ments), as well as similar concerns in the development of Lakewide Management Plans
(LaMPs).
To accomplish the ARCS Program objectives, the following work groups were estab-
lished:
• The Toxicity/Chemistry Work Group was responsible for assessing the
current nature and extent of contaminated sediments in three of the five
priority AOCs (i.e., Buffalo River, Indiana Harbor Canal, and Saginaw
River; Figures 1-1, 1-2, and 1-3, respectively) by studying the chemical,
physical, and biological characteristics of contaminated sediments, and for
demonstrating cost-effective assessment techniques that can be used at
other Great Lakes AOCs and elsewhere. Superfund activities have provi-
ded good characterizations of Ashtabula River and Sheboygan Harbor, so
the ARCS Program focused the assessment activities on the other three
priority AOCs.
• The Risk Assessment/Modeling Work Group was responsible for assessing
the current and future risks presented by contaminated sediments to human
and ecological receptors under various remedial alternatives (including the
no-action alternative).
• The Engineering/Technology Work Group was responsible for evaluating
and testing available removal and remedial technologies for contaminated
sediments, for selecting promising technologies for further testing, and for
performing field demonstrations at each of the five priority AOCs.
• The Communication/Liaison Work Group was responsible for facilitating
the flow of information from the technical work groups and the overall
ARCS Program to the interested public and for providing feedback from
the public to the ARCS Program on needs, expectations, and perceived
problems.
OVERVIEW OF SEDIMENT ASSESSMENT METHODS
Sediments are associated with impairment of beneficial uses at 42 of the 43 Great Lakes
AOCs. Prior to addressing the potential need for remediation of those sediments, it is
necessary to answer the following questions:
• Are the sediments sufficiently "contaminated" to warrant consideration for
remediation? In this context, "contaminated" refers to the presence of
chemicals in the sediments that have the potential to cause adverse effects
in humans or ecological receptors.
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Lake
Erie
- Snipping channel boundary
Figure 1 -1. Buffalo River Area of Concern, New York.
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Scale = 1:20,000
kfcmeters
5 1
15
trite
.75
Figure 1-2. Indiana Harbor Area of Concern, Indiana.
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SAGINAW BAY
Shipping channel boundary
htensive sorting (Tea
Figure 1-3. Saginaw River Area of Concern, Michigan.
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Chapter 1. Introduction
• Is there evidence indicating that existing concentrations of sediment con-
taminants are adversely affecting ecological receptors? In other words,
can it be shown that the presence of contaminants in the sediments is caus-
ing adverse effects in organisms, either organisms naturally occurring in
the environment, or those exposed to sediments in controlled, laboratory
toxicity tests?
• Are ecological receptors exposed to the sediments bioaccumulating chemi-
cal contaminants to the extent that the resultant body burdens are adversely
affecting the organisms themselves or other organisms higher in the food
chain, including humans?
• If the sediments are judged to be sufficiently contaminated to be causing
such effects, what is the spatial extent (i.e., both horizontal and vertical)
of the contamination, and what are the implications of the distribution of
contaminants on possible remedial alternatives?
One of the main goals of the Toxicity/Chemistry Work Group was the selection of
methods for answering these sediment assessment questions. Early in the ARCS Pro-
gram, it was recognized that the current state of sediment assessment methods was rap-
idly evolving. Whereas in the past the focus had been primarily on measuring physical
and chemical characteristics of the sediments, the emphasis over the last decade has been
on the development of a suite of assessment methods that also incorporate a number of
biological measures and indicators of sediment quality. The sediment assessment
methods currently available consider a wide variety of endpoints and effects, which differ
in their suitability and sensitivity for investigating sediment contamination. It is therefore
vitally important that the assessment methods selected reflect site- and program-specific
objectives of the study being conducted.
It was not the intent of the Toxicity/Chemistry Work Group to develop new sediment
assessment methods, but rather to survey existing methods and select those methods that
show the most promise for addressing the aforementioned questions at the Great Lakes
AOCs. The selected sediment assessment methods were then applied in demonstration
studies at several of the Great Lakes AOCs. There was a consensus among the work
group members that the sediment assessment methods selected for demonstration should
include an integration of physical, chemical, and biological information. This consensus
reflects the common thinking of the scientific and regulatory communities that is
succinctly summarized in the USEPA's Sediment Classification Methods Compendium
(USEPA 1992) as follows:
Unfortunately, there simply is no single method that will measure all contami-
nated sediment impacts at all times and to all biological organisms. This is the
result of a number of factors, including environmental heterogeneity and
associated sampling problems, variability in the laboratory exposures, analytical
variability, differing sensitivities of different organisms to different types of
contaminants, the confounding effects caused by the presence of unmeasured
contaminants, the synergistic and antagonistic effects of contaminants, and the
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Chapter 1. Introduction
physical processes of sediments. While one method will suffice for some cir-
cumstances, it is often advisable to use several complementary methods rather
than a single one. When several of these approaches are used together, they
can provide additional insights into the nature and degree of sediment contami-
nation problems. The use of complementary assessment methods can provide
a kind of independent verification of the degree of sediment contamination if
the conclusions of the different approaches agree. If the conclusions differ, that
difference indicates a need for caution in interpreting the data since some
unusual site-specific circumstances may be at work. The importance of this
type of verification increases with the significance of the decisions that must be
made using the information obtained.
The integrated application of different sediment assessment methods is therefore valuable
because decisions can be made on the basis of a preponderance of evidence.
As noted by USEPA (1992), there may be a regulatory requirement for the application
of specific sediment testing procedures (e.g., the Toxicity Characteristic Leaching
Procedure under the Resource Conservation and Recovery Act; the analysis of
poly chlorinated biphenyls [PCBs] under the Toxic Substances Control Act), criteria (e.g.,
the limitations in the London Dumping Convention), and evaluation procedures (e.g.,
risk assessment guidance under the Comprehensive Environmental Response, Compensa-
tion and Liability Act). It is not the intent of this document to describe the sediment
assessment methods that might be required under such specific regulatory programs.
Instead, this document describes sediment assessment methods that might be applied more
generally in investigations of the nature and extent of sediment contamination. While the
methods described herein are based on the experience of the ARCS Program and are
intended primarily for application in the Great Lakes AOCs, they may be applicable in
other aquatic environments as well. Some of the methods described (e.g., the sediment
toxicity tests) are applicable only to freshwater environments, while others are more
generally applicable.
Sediment assessment methods may be categorized as either numeric or descriptive
(USEPA 1992). Numeric methods are chemical-specific and can be used to generate
numerical sediment quality criteria for individual chemicals. Descriptive methods are not
chemical-specific, but may be used to directly assess the overall impact of all chemicals
that may be present in the sediment (e.g., through the use of sediment toxicity tests).
A disadvantage of most numeric methods is that they cannot be used to predict the com-
bined effect of several chemicals. The toxic units approach, however, does predict the
combined effects of chemicals (Enserink et al. 1991). Descriptive methods, on the other
hand, have the disadvantage that they cannot be used alone to generate numerical sedi-
ment quality criteria for specific chemicals.
Assessments of the nature and extent of sediment contamination focus on the measure-
ment of the concentrations of chemicals of concern in the sediments, on the measurement
of biological impacts, or, more commonly, on a combination of the two. Ultimately, an
understanding of the causes of biological impacts can only come through synoptic surveys
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Chapter 1. Introduction
that include measurement of chemical and biological parameters on the same sediment
samples.
OVERVIEW OF THE ASSESSMENT GUIDANCE DOCUMENT
The remaining chapters of this guidance document address specific topics pertaining to
the assessment of contaminated sediments. These chapters include:
• Chapter 2. Quality Assurance and Quality Control—Provides guidance
on the necessary elements of a quality assurance and quality control
(QA/QC) program, including the development of data quality objectives
(DQOs) and measurement quality objectives (MQOs), the use of QA/QC
samples, contents of quality assurance plans, development of a laboratory
audit program, database requirements, and data verification/validation
methods.
• Chapter 3. Sediment Sampling Surveys—Describes methodology for con-
ducting field surveys of contaminated sediments, including the design of
sediment sampling vessels, field positioning methods, sediment sampling
procedures, field processing of sediment samples, and sediment character-
ization by remote sensing.
• Chapter 4. Screening-Level Analyses—Describes the use of relatively
rapid, low-cost assays that can be applied either in the field or in the
laboratory to focus comprehensive analyses on "hot spots" likely to require
remediation or on "grey" areas where the integrated sediment assessment
approach should be applied to evaluate the need for remediation.
• Chapter 5. Chemical Analyses—Provides guidance on the selection of
appropriate chemical analytical techniques for sediment samples, including
methods for conventional sediment variables, organic compounds, organo-
metallic compounds, and metals.
• Chapter 6. Evaluation of Sediment Toxicity—Provides guidance on
the selection of appropriate toxicity tests for assessing the biological
impacts of sediment contamination.
• Chapter 7. Assessment of Benthic Invertebrate Community Structure—
Provides guidance on the use of assessments of benthic invertebrate
community structure as an indicator of in situ biological impacts of
contaminated sediments.
• Chapter 8. Fish Tumors and Abnormalities—Describes the use of surveys
of fish tumors and abnormalities as indicators of in situ biological impacts
of contaminated sediments.
• Chapter 9. Data Presentation and Interpretation—Provides guidance on the
application of several different methods of interpreting sediment quality
8
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Chapter 1. Introduction
data, including procedures for mapping sediment quality data, sediment
classification methods, and approaches to numerical ranking of contami-
nated sediments to prioritize sites for remedial action.
• Chapter 10. Conclusions—Provides an overall summary of this document.
In addition to describing usable alternatives within each chapter, recommendations are
made for selecting appropriate sediment assessment methods, using the experience gained
by the ARCS Toxicity/Chemistry Work Group to illustrate key issues. It is intended that
the guidance on appropriate sediment assessment methods provided herein may be applied
to other Great Lakes AOCs.
9
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2. QUALITY ASSURANCE AND QUALITY
CONTROL
It is USEPA policy that all environmental sampling and testing be conducted in accor-
dance with a formalized quality assurance program. Quality assurance has been defined
as "those operations and procedures which are undertaken to provide measurement data
of stated quality with a stated probability of being right" (Taylor 1987). The purpose
of the quality assurance program is to specify the policies, organization, objectives, and
QA/QC activities needed to achieve the data quality requirements of the program. These
specifications are used to assess and control measurement errors that may enter the
system at various phases of the project, such as during sampling, preparation, and
analysis. Therefore, QA/QC procedures implemented in any program should be designed
to ensure that the best possible data are collected and that the quality of the resulting data
can be evaluated and documented. Adherence to an overall quality assurance program
is essential for large, multiparticipant programs, such as the ARCS Program, to ensure
that the data collected by individual investigators will be comparable and congruous.
Some of the QA/QC considerations specific to sediment toxicity tests are discussed in
Chapter 6.
QUALITY ASSURANCE PROGRAM
USEPA currently recognizes four categories of quality assurance programs. These cate-
gories differ according to the end use of the data. The following definitions of the four
categories are modified from Simes (1989):
Category I Projects that produce results that can stand alone. These projects
are of sufficient scope and substance that their results could be
used directly, without additional support, for compliance or other
litigation. Such projects are of critical importance to USEPA
goals and must be able to withstand legal challenge. Accor-
dingly, the quality assurance requirements for these projects will
be the most rigorous and detailed to ensure that such goals are
met.
Category n Projects that produce results that complement information from
other projects. These projects are of sufficient scope and
substance that their results could be combined with the results of
other projects of similar scope to produce narratives that would
be used for making rules, regulations, or policies. In addition,
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Chapter 2. Quality Assurance and Quality Control
projects that do not fit this pattern, but have high public visibil-
ity, would also be included in this category.
Category HI Projects that produce results for the purpose of evaluating and
selecting basic options, or performing feasibility studies or
reconnaissance of unexplored areas that might lead to further
work.
Category IV Projects that produce intermediate results used in testing assump-
tions.
Each program, or individual project within a program, should be categorized at its
inception. The quality assurance category selected for the program will have a dramatic
effect on the complexity of the quality assurance program as well as the writing
requirements for the quality assurance project plans (QAPPs) that must be prepared (see
Preparation of Quality Assurance Plans). Category I projects involve the most stringent
data acceptance criteria, the most expansive quality assurance approach, and the most
detailed QAPP, whereas Category IV projects involve the least stringent requirements
for data acceptance, perhaps the fewest number of QA/QC samples, and the least number
of issues to be addressed in the QAPP. Categories II and III fall progressively between
these two categories. The various projects completed during the ARCS Program were
Category II and III projects. Generally, Category II or III projects are recommended for
integrated sediment assessments in the Great Lakes. However, when developing DQOs,
it is imperative to consider all potential uses of the data. For example, if potential data
uses might support enforcement, a Category I project is recommended.
The general components of a good quality assurance program for any level of effort,
ranging from the individual laboratory through the nationwide program level, should
address the following issues:
1) Development of the DQOs and MQOs
2) Preparation of the quality assurance plans
3) Development of a laboratory audit program
4) Development of the database requirements and data verification/validation
methods.
These issues should be addressed and in place prior to any sampling; however, the
quality assurance program should be flexible enough to allow for changes during the
study. Each of these issues is discussed in more detail in the following sections.
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Chapter 2. Quality Assurance and Quality Control
DEVELOPMENT OF DA TA QUALITY OBJECTIVES AND
MEASUREMENT QUALITY OBJECTIVES
Data Quality Objectives
One of the initial activities in any environmental assessment program is the development
of DQOs. DQOs are used to focus the initial design of the field and laboratory studies
to provide the necessary data to guide selection of remedial alternatives (if necessary).
The DQO process also provides a logical, objective, and quantitative framework for
finding an appropriate balance between the time and resources that will be used to
generate the data and the quality of the resulting data (Neptune et al. 1990). DQOs may
be defined as the "qualitative and quantitative statements of the overall level of
uncertainty that a decision-maker is willing to accept in results or decisions derived from
environmental data" (USEPA 1987a). DQOs result from an iterative process of logical
interaction between the decision-makers and the technical team involved in a given
project.
The development of the DQOs can be divided into seven steps (Figure 2-1). The seven
steps presented do not include all of the individual operational processes that may be
involved at each step in the DQO process, but do provide guidance on the overall
development of the program or laboratory DQOs. In Step 1, the problems that need to
be resolved or studied are defined and the overall objectives for the program are
formulated. For example, one of the questions to be answered in the ARCS Program
was, "What is the nature and extent of bottom sediment contamination at the selected
Great Lakes AOCs?"
The next step in the DQO process is to define the specific decisions to be made or
questions to be answered based on the data collected (Step 2, Figure 2-1). For example,
in the ARCS Program, one of the decisions that needed to be made to guide the selection
of remedial alternatives for the Buffalo River AOC was, "Are the sediments contami-
nated with organic compounds?"
In Step 3 (Figure 2-1), the types of data required to make decisions, how the data will
be obtained, and the use(s) of the collected data are defined. The types of data that may
be required include, but are not limited to, physical, chemical, biological, and toxicologi-
cal properties of the site. Data may be obtained by sampling and laboratory analysis,
physical testing, or modeling studies. Potential uses of the data encompass site
screenings and evaluations, human health and ecological risk assessments, regulatory
compliance or violation assessments using predefined action limits, modeling efforts, and
the determination of remedial process effectiveness and efficiency. To answer the
example question identified in Step 2, sediment chemical data would be required for a
variety of persistent organic compound classes such as PCBs, chlorinated pesticides,
poly chlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and
PAHs (specific compound names should be listed in the QAPP). The chemical data
12
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STEP1
State the Problem
What are the problems that
need to be studied or resolved
and the overall objectives of
the project?
STEP 2
Identify the Decision or Question
What specific decisions need
to be made or questions need
to be answered based on the
data collected?
STEP 3
Describe Inputs to the Decision
What types of data are required
(e.g., physical, chemical,
biological), how will the data be
obtained and managed, and how
will the data be used to make
decisions?
STEP 4
Define the Boundaries of the Study Area
What are the spatial boundaries
of the study area, considering
also temporal and demographic
information?
STEPS
Develop a Decision Rule
How will data collected be
summarized and used to make
decisions?
STEP 6
Specify Limits on Uncertainties
What are the constraints or
levels of uncertainty in the data
that will be considered
acceptable?
STEP 7
Optimize the Study Design
What is the most cost-effective
design that, is expected to meet
the data quality objectives?
Figure 2-1. Steps in the data quality objectives process.
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Chapter 2. Quality Assurance and Quality Control
should be generated from recently collected sediment samples to accurately represent
current conditions in the Buffalo River.
It is during this third step in the DQO process that the database manager(s) should
become involved. Three general phases of database design and implementation are
1) initial design, 2) initial concepts, and 3) detailed design. During the initial design
process the "structure" of the project and all potential data users should be identified.
The "structure" is defined by site and sample identifiers, original data specifications, and
user-identified data requirements. The structure of the project addresses the experimental
design in both the field and laboratory, the volume of data to be collected, and the
required turn-around time for data entry and manipulations. In the initial concepts phase,
sample tracking, data management (computer systems and/or hardcopy data recording and
subsequent data entry), data collation methods, quality assurance checking (electronic vs.
manual), and quality control data requirements (batch-wide vs. sample-specific quality
control) should be addressed. Detailed design considerations include: where the data
will be stored, the development of a system if an appropriate storage system is not avail-
able, the specific format in which the data should be reported, which data are necessary
during the data reporting, which data need to be maintained in the database, how the data
will be retrieved (hardcopy, PC-based systems, or mainframe computers), and which sta-
tistical tests will be available for subsequent data analysis. Further information on
system design and database development can be found in the USEPA systems design and
development guidance documents (USEPA 1989).
Defining the boundaries of the study area (Step 4, Figure 2-1) is necessary to limit
studies to a manageable area, without excluding any areas of significant interest identified
from historical or other ongoing studies. This boundary definition incorporates not only
spatial but temporal and demographic considerations based on past and present land use.
For example, in the ARCS Program, the boundary of the Buffalo River AOC was
defined to extend from the mouth of the river upstream to just above the confluence of
the Buffalo River and Cazenovia Creek. Thus, the boundary of this AOC was restricted
to the stretch of river in which a majority of the industrial outflows exist (or existed, if
the companies no longer operate) that could have contributed to the potential contamina-
tion of the Buffalo River sediments.
The development of decision rules is the next important step (Step 5, Figure 2-1) in the
DQO process. A decision rule is a restatement of the decision to be made that clearly
indicates how the data to be collected will influence the outcome of the decision. The
decision rule is typically formulated as an if-then statement, showing all the possible
outcomes. For example, "If . . . " may specify exceedance of some criterion or action
level and "then ..." would state the action to be taken. These decision rules help
decision-makers bring the study into sharper focus. An example of a decision rule
developed for the ARCS Program was, "If concentrations of total PCBs exceed a particu-
lar level in sediments, as determined by USEPA SW-846 Method 8080 (USEPA 1986b),
then the Buffalo River sediments will be classified as toxic and considered for
remediation by the Engineering/Technology Work Group." A similar decision rule for
biological analyses could be, "If exposure to whole sediment significantly (P<0.05)
14
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Chapter 2. Quality Assurance and Quality Control
reduces survival or growth of test organisms relative to appropriate reference conditions,
then the sediment will be classified as toxic and considered for remediation by the
Engineering/Technology Work Group." The use of either or both example decision rules
may be appropriate in a program, depending on an assessment of uncertainty and cost
addressed in the following two steps (as discussed in Step 7 below, a combination of
these decision rules is recommended).
The sixth step in the DQO process (Figure 2-1) is to specify the constraints or levels of
uncertainty that are acceptable in addressing the issues defined in Step 1. Uncertainty
levels can be both qualitative and quantitative. It is in this step that the MQOs are
established (a more complete discussion of the components of the MQOs is provided in
the next section). These objectives determine how many samples to collect, where to
sample in the AOC, what methodologies will be used for all phases of the program
(including field sampling, sample preparation, and analysis), how reliable the resultant
analyses need to be in terms of accuracy (bias and precision), and how to assemble the
data to present the desired results. The effects of potential false positives (e.g., sample
is uncontaminated yet chemical or biological results indicate contamination) and false
negatives (e.g., sample is contaminated yet chemical or biological results indicate no
contamination) should also be assessed during this DQO step.
An example of a constraint that was applied to sediment chemistry data in the ARCS
Program is "Triplicate analyses of sediment samples analyzed for PCBs following
USEPA SW-846 Method 8080 (USEPA 1986b) should have a precision, measured as
percent relative standard deviation (%RSD), of less than or equal to 20 percent." The
%RSD is the standard deviation of multiple (three or more) measurements divided by the
mean of the measurements and multiplied by 100. The uncertainty associated with this
constraint affects the ability to use the decision rule identified hi Step 5 for sediment
chemistry. Similarly, constraints applied to biological data will affect use of the decision
rule developed in Step 5 for biological analyses.
Generally, it is during this assessment of uncertainty in the DQO process that budgetary
constraints that limit the number of samples and analyses to be performed should be
taken into account. If all the analyses cannot be performed under the budgetary
constraints, the number of samples to be collected or the number of analyses to be
performed on a given sample should be reevaluated, keeping in mind how the elimination
of a given test affects uncertainty and the researcher's ability to answer critical questions
(Step 2) as determined in the logic statements developed in Step 5.
The final step in the DQO process is to optimize the study design so that the most cost-
effective decision rules with an acceptable degree of uncertainty are selected to meet the
specified DQOs. For example, in the ARCS Program, biological testing (i.e., Micro-
tox®) was determined to be a cost-effective way to screen hot spots (e.g., areas of high
acute toxicity) and clean areas that exhibit no statistically significant (P<0.05) response.
Intermediate areas that exhibit moderate toxicity or conflicting toxicity results using well-
accepted tests have sufficient uncertainty to warrant chemical analysis and further
evaluation by the integrated sediment assessment approach.
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Chapter 2. Quality Assurance and Quality Control
In addition, chemical analyses are needed in follow-up assessments of biological hot spots
to identify the specific composition of chemicals that may be contributing to the observed
toxicity as well as potential sources of the chemicals. Therefore, the decision rule
concerning potential remediation requires the assessment of both sediment toxicity and
chemical concentration data.
Measurement Quality Objectives
MQOs are specific goals defined by the data users that clearly describe the data quality
that is sought for the project phase. The quality assurance program should focus on the
definition, implementation, and assessment of MQOs that are specified for the sampling,
analysis, and verification phases of the project. The MQOs should be defined according
to the following six quality assurance objectives and attributes:
• Detection Limit—The lowest concentration of an analyte that a specified
analytical procedure can reliably detect
• Bias—The difference between an observed value and the "true" value (or
known concentration) of the parameter being measured; bias is the first
component of accuracy, which is the ability to obtain precisely a nonbiased
(true) value
• Precision—The level of agreement among multiple measurements of the
same characteristic; precision is the second component of accuracy
• Representativeness—The degree to which the data collected accurately
represent the population of interest (e.g., contaminant concentrations)
• Comparability—The similarity of data from different sources included
within individual or multiple data sets; the similarity of analytical methods
and data from related projects across AOCs
• Completeness—The quantity of data that is successfully collected with
respect to the amount intended in the experimental design.
Each of these objectives and attributes will be discussed separately in the following text.
A list of MQOs for the ARCS Program is provided in Table 2-1.
Detection Limits
All analytical laboratories should be required to determine the instrument detection limit
(IDL) prior to any analysis of the routine samples. The IDLs serve as a statistical
estimate of the lowest concentration of an analyte that an instrument could reliably
distinguish between the background noise and the signal. The target detection limit
(TDL) is the concentration at which the presence of an analyte must be detected to
properly be able to assess and satisfy the DQOs. Method detection limits (MDLs) are
16
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TABLE 2-1. EXAMPLES OF THE MEASUREMENT QUALITY OBJECTIVES
FOR INORGANIC AND ORGANIC CHEMISTRY ANALYSES OF
SEDIMENT USED BY THE ARCS PROGRAM
Parameter
Total organic carbon
Oil and grease
PH
Acid-volatile sulfides
Organohalogens6
Total sulfur
Total solids
Volatile solids
Particle size'
MDLa
(//g/kg) Accuracy6
0.03% ±20 percent
10,000 ±20 percent
N/A ±0.1 unit
1 ,000 N/A
30 ng ±20 percent
10,000 ±20 percent
0.001 g N/A
0.002 g N/A
0.001 g windows
Solvent extractable residue 0.001 g +20 percent
Moisture content
PAHs
Pesticides
PCB/congener
PCB/Aroclor®
PCDDs/PCDFs
Methylmercury
Tributyltin
Metals9
Except:
Arsenic
Cadmium
Mercury
Note: ARCS
MDL
N/A
PAH
PCB
0.001 g N/A
200 ± 20 percent
10 ±20 percent
0.5 ±20 percent
20 ±20 percent
0.002 ±20 percent
10 ±20 percent
10 ±20 percent
2,000 ±20 percent
100 ±20 percent
100 ±20 percent
100 ±20 percent
Frequency
1 /batchd
1 /batch
1 /batch
N/A
1 /batch
1 /batch
N/A
N/A
1 /batch
1 /batch
N/A
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
Precision0
<20 percent
<20 percent
±0.1 unit
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
<20 percent
Frequency
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
1 /batch
- Assessment and Remediation of Contaminated Sediments
- method detection limit
- not applicable
- polynuclear aromatic hydrocarbon
- polychlorinated biphenyl
PCDDs/PCDFs - polychlorinated dibenzo-p-dioxins/polychlorinated dibenzofurans
a Units presented in the subheading are applicable to all parameters unless otherwise noted.
b Accuracy is determined from a certified reference material, standard reference material, or
standard and is measured from the known concentration.
(footnotes continued on following page!
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TABLE 2-1. (cont.)
c Precision is calculated as percent relative standard deviation. Precision requirements listed here
are for analytical replicates only; field duplicates are required to have a relative percent difference
<30 percent.
d A batch is a sample group (usually 10-20 samples) that is carried through the analytical scheme
simultaneously.
e The MDL for chlorine and bromine is 30 ng, while the MDL for iodine is 10 ng.
f A soil sample with acceptance windows per size fraction was provided for use as an accuracy
standard.
9 Metals include arsenic, cadmium, chromium, copper, iron, lead, manganese, mercury, nickel,
selenium, silver, and zinc. Exceptions are noted where different methodologies are used during
the metals quantification.
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Chapter 2. Quality Assurance and Quality Control
based on a method's ability to determine the presence, qualitatively or quantitatively, of
an analyte in a sample matrix, regardless of its source of origin (Glaser et al. 1981).
MDLs may be determined by making repeated measurements (a minimum of seven) over
several days of either a calibration blank (a blank consisting solely of the reagents mixed
in the same proportions as those to be used during routine sample extraction/digestion)
or a low-level standard with a concentration within 1-5 times the IDL. The MDL is
calculated, at the 95 percent confidence level, as 3 tunes the standard deviation of the
measured sample concentrations. Generally, the conditions for acceptance of a labora-
tory's ability to determine small quantities of various analytes is that the MDL is less
than or equal to the TDL. The detectability attribute is generally only applicable for
quantitative physical and chemical analyses.
The advantage of determining MDLs by the analysis of spiked uncontaminated field
samples is that the concentration of the analyte can be in the optimum range for
quantification and the variance caused by the sample matrix and sample processing will
be reflected in the MDLs. MDLs are affected by both matrix interferences and highly
contaminated samples. The MDLs for highly contaminated samples will often be much
greater than those for relatively "clean" samples.
In addition to MDLs, a second limit commonly associated with detectability in a sample
matrix is the limit of quantification (LOQ). This limit is often arbitrarily defined as
5-10 times the standard deviation of the measured low-level standard or blank sample
concentration. At the higher end of this concentration range, the relative confidence in
the measured value is about +30 percent at the 95 percent probability level (Taylor
1987).
Bias
Bias is the degree of agreement of a measured value with the true or expected value, and
is the first component of accuracy. A highly biased value has low accuracy. Bias, for
physical and chemical measurements, is commonly assessed through the use of certified
reference materials (CRMs; a reference material certified by a technically competent
organization), standard reference materials (SRMs; a reference material certified by the
U.S. National Institute of Standards Technology [U.S. NIST]), or other standards (either
created internally by the laboratory or provided by another laboratory). In the absence
of CRMs or SRMs, matrix spikes can be used to determine bias. Bias can be determined
by comparing the analytical results to the known value of the reference material, plus or
minus an established acceptance range either provided with the reference material or
agreed upon as part of the DQO process. For example, in the ARCS Program, accep-
table recovery values should be within 85-115 percent of the spiked values for metals
and 70-130 percent for organic and organometallic compounds. Control samples for
assessing bias should be analyzed at a rate of 1 per 20 environmental samples.
For toxicity tests, bias can be assessed through the use of a control sediment (a "clean"
sediment that contains only background quantities of the analyte[s] of interest), reference
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Chapter 2. Quality Assurance and Quality Control
toxicants, and long-term monitoring of the coefficients of variance among reference
toxicants used for a given toxicity test. The assessment of organism response from the
use of the control sediment and reference toxicant establishes the test validity and is
similar to that of the physical and chemical testing in that the results should be within the
bias window (e.g., mean plus or minus acceptance range, percent survival greater than
a set limit, given number of young produced by the third brood) established for that
reference material. Long-term monitoring of the coefficient of variation of the use of a
given reference toxicant provides the researcher with an assessment of the degree of
temporal change in the test organism.
Precision
Precision is the degree of agreement among repeated independent measurements under
specified conditions. Precision is the second component of accuracy; a measurement
with poor precision (high variability) can only sometimes be accurate. Alternatively,
measurement systems that have both low bias and good precision are always accurate.
Precision is assessed through the use of replicate samples and determining the statistical
relationship among the results compared to the mean of the results. Commonly, the
coefficient of variation (standard deviation divided by the mean) for triplicate or greater
replication, or the relative percent difference (RPD) for duplicate samples (the absolute
difference between two duplicate measurements divided by the mean, and multiplied by
100), is calculated to rapidly assess the precision of a set of measurements. Precision
is deemed acceptable when the obtained precision result is less than or equal to some
defined value agreed upon during the DQO process. For example, in the ARCS Pro-
gram, precision was based on analytical duplicates or triplicates analyzed at a rate of
1 per 20 samples; the acceptable coefficient of variation was <20 percent.
Representativeness
Representativeness in the quality assurance program should be defined for both the field
sampling and laboratory analysis aspects of the program. Representativeness may be
defined as the degree to which the sampling data properly characterize the study
environment. In the field sampling and characterization phase of a program, representa-
tiveness should be maintained by the collection of samples throughout the entire AOC (to
address the spatial variability of the area) or at the locations identified by the decision-
makers and technical work group members during their initial establishment of the
program DQOs. In the analytical phase of the program, representativeness considerations
include proper sample storage and preservation conditions (to ensure that the sample does
not substantially change from the time of sampling until the time of analysis) and sample
homogenization (to ensure that the subsample taken for analysis is no different from any
other subsample).
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Chapter 2. Quality Assurance and Quality Control
Comparability
Comparability is an important component of the MQOs because it states the confidence
with which one data set can be compared to another. If data are not comparable, then
conclusions drawn from the combination of two data sets will have an increased level of
uncertainty. Comparability is enhanced by the consistent use of standardized sampling
methods and specified protocols for the sampling phase and through the use of standard
documented methodologies (e.g., USEPA, American Society for Testing and Materials
[ASTM], U.S. Army Corps of Engineers [the Corps]) for analyte determinations. If a
standard method is not available, the method selected should be clearly documented by
reference or provided as a written standard operating procedure in the QAPP (to be
discussed in a later section). Any deviations from the standardized, selected methods or
protocols should be clearly documented because these changes may significantly affect
the resultant data.
One issue that should be considered when evaluating comparability is the influence of
temporal variation, especially if resampling events are planned in the same AOC. The
influence of short-term discrete disturbances (e.g., storm events) and long-term changes
(e.g., seasonal variations) can markedly change the sediment contaminant concentrations,
biological communities, and toxicity of the sediments in the system. Therefore, temporal
variability can play an important role when data are evaluated and compared between
sampling events.
Completeness
Completeness levels should be established during the DQO process. These levels state
the minimum number of samples that must be obtained during the field sampling phase
and the minimum amount of acceptable data (i.e., data that must meet and pass the
QA/QC requirements of the program) that must be generated to be able to confidently
resolve the identified program issues. Completeness is generally expressed as the amount
of data actually obtained divided by the amount of data expected to be obtained, on a
percentage basis. The ARCS Program used a 90 percent level of completeness.
QUALITY ASSURANCE AND QUALITY CONTROL SAMPLES
To achieve the DQOs and MQOs, various types of measurement samples can be used to
quantitatively assess and control the error associated with the results. These samples fall
into two categories, QA and QC samples.
Quality assurance samples are samples incorporated into batches during sample collection
or preparation. These samples provide data users with a means of independently
assessing the quality of the data generated at a given analytical laboratory. These
samples can be either double-blind samples (sample identity and analyte concentration are
unknown to the laboratory) or single-blind samples (sample identity is known but analyte
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Chapter 2. Quality Assurance and Quality Control
concentration is unknown to the analytical laboratory). Double-blind samples are
preferable to single-blind samples. Examples of typical quality assurance samples can
include reference materials, field replicates, field-prepared blanks (e.g., trip blanks), and
preparation laboratory replicates, if sample preparation is performed at a separate
laboratory.
Quality control samples are those samples prepared at the analytical laboratory, and
hence the sample identity and analyte concentration, if applicable, are known to the
laboratory personnel. These samples enable the laboratory to control measurement error
and meet the program MQO requirements. Typically, quality control samples include
blanks, controls, ongoing calibration check standards, analytical replicates, matrix spikes,
and surrogate spikes.
The QA/QC samples should be analyzed with the routine sample analysis. Each QA/QC
sample should have specifications to be met before the data are considered acceptable.
These specifications include acceptance limits and required frequency of use (i.e., 1
blank per 20 routine samples with an acceptable measured concentration below the
MDL). The use of QA/QC samples and their required frequency of use should be
balanced with the data quality needs of the program.
The following sections briefly describe the types and uses of the various QA/QC
samples.
Replicate Samples
A replicate sample may be used to assess the precision MQO. Replicates of samples can
be obtained from the field, preparation laboratory (if separate from the analytical
laboratory), and analytical laboratory. The most common form of replicates is the
analytical replicates. These samples are created at the analytical laboratory by obtaining
two or more subsamples from a single routine sample and analyzing them as separate
individual samples. The results from the analytical replicates can be used to demonstrate
or confirm that the analytical precision MQOs are being satisfied.
Field replicate samples are collected during the sampling phase of the program. A field
replicate sample may be obtained by collecting two unique individual samples from the
same location that will be treated as separate samples throughout the rest of the sample
preparation and analysis phases. These samples are generally submitted to the prepara-
tion and/or analytical laboratory as blind samples (identities of the replicates are unknown
to the laboratory personnel). The individual sets of samples are used to assess the overall
(laboratory plus field) precision. Interpretation of the results of the field replicates can
be difficult due to the fact that significant variability may exist in the field. Generally,
a failure to meet the MQOs for the field replicates would result in only a minor concern,
indicating the existence of minor uncertainty in the data (assuming that the laboratory
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replicates show no major problem with analytical variability). Such concerns should not
be used in isolation to disqualify data from the sample or sample batch (Papp et al.
1989).
Field split samples represent an additional form of replicate sample commonly collected
in the field. The field split samples are similar to the field replicate samples except that
the two samples are subsampled from a single collected sediment sample and not from
two separately collected samples. These samples can be used to assess the same error
components (or variances) as the field replicate samples but on a smaller spatial scale.
Preparation laboratory replicates can be created at the preparation laboratory if it is
separate from the laboratories responsible for sampling and parameter analysis. These
samples are prepared by splitting a randomly selected routine sample into two represen-
tative halves (i.e., after the sample is homogenized). Each half is then treated as a
separate sample at the analytical laboratory. Preparation laboratory replicates can be
used to assess the preparation laboratory within-batch precision.
Precision acceptance limits for the analytical replicates should be tighter (smaller
allowable variability) than the precision limits for the field replicates and field splits.
Preparation laboratory replicates should be expected to have a precision variability
somewhere between those for the analytical replicates (i.e., split of one sample) and field
replicates (i.e., two samples from the same location). For example, in the ARCS Pro-
gram, the precision requirement for the field replicates is an RPD of < 30 percent, while
for the analytical replicates is a %RSD of <20 percent for acceptance. It should be
noted that the field and preparation laboratory replicates are forms of quality assurance
samples and can only be checked by the data user. In contrast, the analytical replicates
are quality control samples that the laboratory can use to immediately check the precision
of their measurement system.
Blank Samples
Blank samples are quality control samples that can be used for two purposes in a quality
assurance program, as a calibration check and as a check for potential contamination of
the measurement system. A calibration blank is defined as a zero mg/L or jiig/L standard
and contains only the solvent or acid used to dilute the calibration standards without any
analyte present (Papp et al. 1989). The calibration blank is analyzed periodically to
check for significant instrument baseline drift and should have results that are below the
MDL.
To assess whether outside contamination has entered the measurement system, various
blank samples can be used. Perhaps the two most common forms of blank samples are
field and reagent blanks. Reagent blanks may be defined as a sample composed of all
the reagents, in the same quantities, used to prepare an actual routine sample for analy-
sis. A field blank generally consists of either "clean" water or reagents brought from
the laboratory to the field and passed through all the sampling equipment used to obtain
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Chapter 2. Quality Assurance and Quality Control
the routine samples. Both field and reagent blanks should undergo the same preparation
and analysis procedures as an actual routine sample. Similar to the calibration blanks,
these blanks should have measured concentrations that are below the MDL (Appendix B
of 40 CFR Part 136).
For toxicity tests, the blank is better known as the control sample or the negative control.
This sample simply consists of the water or sediment in which the organisms had either
been cultured or raised. The negative control sample in these tests is used to assess
organism health during the given toxicity test period and the influence of the "clean"
water or sediment on the organism. The response of the organisms in the control
samples should be required to equal or exceed a specified response limit (e.g., 90-percent
survival, if survival is the toxicity test endpoint) that was determined during the DQO
process.
Reference Materials
Reference materials are analyzed to assess the bias of measurements being made at the
analytical laboratories. These samples can be used either as quality assurance or quality
control samples. The three major forms of reference materials commonly used are
CRMs, SRMs, and standards. CRMs are those materials that have one or more of their
property values established by a technically valid procedure and are accompanied by or
traceable to a certificate or other documentation issued by the certifying body. For
example, reference materials produced by USEPA, the U.S. Geological Survey (USGS),
or the Canadian Centre for Mineral and Energy Technology are considered CRMs.
SRMs are CRMs produced by the U.S. NIST and characterized for absolute analyte
content independent of the analytical method. If reference materials, either certified or
standard, are not available for a given analyte, the laboratory can assess bias by using
a standard of known concentration created by the quality assurance officer or other
member of the quality assurance staff at the analytical laboratory, or using a standard
provided by a different laboratory. The standards should be submitted as at least single-
blind samples to the analyst, if possible. Bias can be determined by comparing the
analytical results to the known value of the reference material, plus or minus an
established acceptance range either provided with the reference material or agreed upon
as part of the DQO process. For the ARCS Program, the accuracy requirement for bias
in either SRMs or CRMs is that the measured value must be within +20 percent of the
known concentration. The reference materials are used to control bias and reduce
between-batch components of the measurement uncertainty.
For toxicity tests, two forms of reference materials can be used to assess the bias of
organism responses. The first is to expose the organism to a reference toxicant that has
a known and quantifiable response in the organism. The reference toxicants can be used
to test organism sensitivity to waterborne or sediment-associated contaminants. The
reference toxicants can also be used to control bias and to assess the within- and
between-batch components of the measurement uncertainty.
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Chapter 2. Quality Assurance and Quality Control
The second reference material for assessing the bias of toxicity tests is the control sedi-
ment (see Blank Samples above). The control sediment is a "clean" sediment that con-
tains only background quantities of the analytes of interest and that has been routinely
used to assess the acceptability of the test. The control sediment exposes the organism
to a matrix similar to the sediments being assayed without elevated concentrations of con-
taminants. The acceptability of the toxicity tests can be assessed by the response (e.g.,
survival or growth) of the control organisms to the control sediment. The acceptance
criteria for control sediment toxicity tests should be determined during the DQO process.
Matrix Spikes
Matrix spike samples are quality control samples used to assess the efficiency of the
extraction technique and as a form of accuracy testing. These samples are prepared by
adding the spiking analyte to the routine sample prior to extraction or digestion and
ensuring that the spiking solution is thoroughly mixed with the sample matrix. If no
matrix is present and the spike is added to the reagents only, this is called a spiked
reagent blank. The spike concentration should be approximately equal to the expected
concentration (if known or can be reasonably estimated) of the analyte in the environmen-
tal sample or 10 times the detection limit, whichever is larger (Papp et al. 1989). The
volume of the added spike should be negligible (i.e., < 1 percent of the sample aliquot
volume) to avoid any dilution effects. Matrix spikes are analyzed in conjunction with
an unspiked routine sample. Matrix spike analyses are generally reported as the percent
spike recovery of the known quantity added to the sample for each analyte and calculated
as follows:
% Recovery = 100 X
where:
S = measured concentration hi the spiked aliquot
U = measured concentration in the unspiked aliquot
C = actual concentration of spike added.
The MQOs for matrix spike recoveries should be 100 percent, plus or minus the accep-
tance range. For example, in the ARCS Program, the acceptance criterion for inorganic
matrix spikes was limited to a percent recovery range of between 85 and 115 percent
(100 ± 15 percent).
Matrix spikes are used for all studies, even when SRMs or CRMs are used, because they
can help determine potential, site-specific matrix problems.
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Chapter 2. Quality Assurance and Quality Control
Surrogate Spikes
Surrogate spike analyses are only applicable to the organic analyses, such as for PCBs,
chlorinated pesticides, PCDDs and PCDFs, and PAHs. A surrogate spike may be
defined as an added organic compound that is similar to the analytes of interest in
chemical composition, extraction, and chromatography, but that is not normally found
in the environmental sample (USEPA 1986b). These compounds are spiked into blanks,
standards, reference materials, routine samples, and matrix spike samples prior to
extraction. Percent recoveries are calculated for each surrogate compound. For the
ARCS Program, acceptable surrogate spike recoveries were set at 100 + 30 percent.
Surrogate spikes are used to assess the efficiency of the extraction technique and as a
form of accuracy testing, but without the confounding influence of the analyte of interest
already present in the sample.
Surrogate spike compounds may be target compounds labeled with stable isotopes of
carbon (i.e., C13) or hydrogen (i.e., deuterium, H2), or other compounds that are physi-
cally and chemically similar to the chemicals of interest but that do not typically occur
in nature. For example, dibromooctafluorobiphenyl is sometimes used as a surrogate for
PCBs, although this compound is not identical in structure to a PCB. Analyses for semi-
volatile organic compounds typically include the spiking of three neutral compounds
(e.g., naphthalene-d8), two organic acid compounds (e.g., phenol-d5), and sometimes
two organic base compounds (e.g., n-nitrosodiphenylamine-d6).
Compound-specific recovery corrections in each sample analyzed can be accomplished
for organic analyses using the isotope dilution technique (e.g., USEPA Method 1625C
for solids). This technique is appropriate only when sample results will be quantified
using gas chromatography /mass spectrometry (GC/MS) analysis. Sample processing is
identical whether this technique is used or not, except that a large number of isotopically
labeled compounds (available in kits) are spiked into the sample matrix prior to extraction
instead of the three to five surrogate spike compounds normally used. Ideally, there
should be an isotopically labeled compound that matches each (unlabeled) target com-
pound that will be quantified. Rather than acting simply as indicators of analytical
recovery for the sample (as do surrogate spike compounds), these labeled compounds are
used as analytical "recovery standards" for their unlabeled counterparts. Therefore, the
final concentration calculated by the GC/MS system for the target compounds can incor-
porate a correction for the analytical recovery experienced by the corresponding isotopi-
cally labeled compound.
The isotope dilution technique has been routinely used for years in USEPA methods for
the quantification of PCDDs and PCDFs (e.g., USEPA Methods 8280 and 8290), and
is now an option for other semivolatile (and volatile) organic compounds in hazardous
waste samples analyzed under USEPA's Contract Laboratory Program. This technique
is designed to increase the accuracy of chemical analyses and the comparability of results
among laboratories. In addition, the technique increases the confidence in the validity
of reported detection limits for undetected target compounds. By forcing a search for
every recovery standard in each sample extract, the technique also increases the
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Chapter 2. Quality Assurance and Quality Control
efficiency of detection and reporting frequency of compounds that otherwise may be
overlooked in complex extracts.
A potential disadvantage of this technique is that the addition of a large number of
isotopically labeled compounds complicates some automated machine searches for new
or unknown compounds (i.e., tentatively identified compounds), although the labeled
compounds can also serve as markers to help identify and locate unknown compounds.
Also, not all laboratories are familiar with the isotope dilution technique, which requires
additional computer programming and can have a higher analysis price than routine
GC/MS analyses.
Initial Instrument Calibration Standards
Initial instrument calibration should be performed for all analytical instruments immedi-
ately prior to analysis of any samples. The initial calibration should be completed using
a minimum of a three-point calibration curve (five-point calibration for semivolatile and
volatile organic compound analyses) or following the instrument manufacturer's instruc-
tions for special analyses. For metals run by atomic absorption, these calibration
standards should be analyzed as standard additions to the matrix. The standard concen-
trations tested should encompass the range of expected sample concentrations, including
one standard near the LOQ. The acceptance criterion for the initial calibration curve is
that all points used in the determination of the calibration curve should have a calculated
coefficient of determination (r2) of some fixed value determined during the establishment
of the MQOs for the project or program. For the ARCS Program, an r2 of >0.97 was
required for the determination of a properly calibrated instrument (in practice the values
are generally better than 0.99). In addition, the %RSD of the relative response factor
(RRF) obtained for each standard in the initial calibration should not exceed 30 percent.
The RRF is the ratio of the response measured by the mass spectrometer to a known
amount (mass) of an analyte relative to that of a known amount (mass) of an internal
standard.
Ongoing Calibration Check Samples
The ongoing calibration check samples should be analyzed to verify the calibration curve
before, during, and after any routine sample analyses to check for instrument drift. The
ongoing calibration check sample is a standard prepared by the laboratory that has a
concentration about mid-calibration range for the given analyte. The MQO for the
ongoing calibration check samples should be the known concentration, plus or minus the
acceptance range defined during the DQO process. The MQO for ongoing calibration
check samples in the ARCS Program was set at ± 10 percent of the known concentration
of the analyte. In addition, the RRF determined for PCBs, chlorinated pesticides, and
selected semivolatile and volatile compounds should be within 25 percent difference of
the RRF for those compounds in the initial calibration. Specific semivolatile and volatile
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Chapter 2. Quality Assurance and Quality Control
compounds that should meet this requirement are listed in USEPA Contract Laboratory
Program guidance.
Control Charts
Control charts, while not actually a type of QA/QC sample, are extremely useful for the
monitoring of long-term bias within the measurement system. Control charts are
generally constructed by plotting the individual analytical results from a quality assurance
or quality control sample against the mean value with ±2 and 3 tunes the standard
deviation plotted as warning and action limits, respectively. Control charts can be
created for accuracy samples, ongoing calibration check samples, replicate samples where
individual values are plotted during the long-term use of replicates from the same source,
reagent blanks, reference toxicants, cumulative mean LC50s, and control sediments.
Ideally, control charts are updated after each day of analysis. Bias is indicated by the
occurrence of seven or more consecutive points on one side of the cumulative mean.
PREPARA TION OF QUALITY ASSURANCE PLANS
After the DQOs and MQOs have been determined and to meet the Federally mandated
USEPA policy that all environmental sampling and testing programs have a formalized
quality assurance program, a program-wide quality assurance management plan (QAMP)
and laboratory-based QAPPs must be prepared (Costle 1979a,b). A program-wide
QAMP was prepared for the ARCS Program (Schumacher 1991). The QAMP encom-
passes all of the quality assurance activities that will occur at each of the laboratories
participating in the program. These activities include all data generation phases of the
program from sample collection and mapping through the final sample analysis, as well
as database verification and database management activities. A QAPP should be pre-
pared by each laboratory and needs to address only the QA/QC concerns for the work
that will be performed by that individual laboratory. It is in these documents that the
DQOs and MQOs are clearly defined for the program.
USEPA Quality Assurance Management Staff guidelines (Stanley and Verner 1985) state
that the QAMP and QAPPs should address in detail or by reference, each of the
following 16 items:
1) Title page with provisions for approval signatures
2) Table of contents
3) Project description
4) Project organization and responsibilities
5) Quality assurance objectives for measurement data in terms of precision,
accuracy, completeness, representativeness, and comparability
6) Sampling procedures
7) Sample custody
8) Calibration procedures and frequency
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Chapter 2. Quality Assurance and Quality Control
9) Analytical procedures and calibration
10) Data reduction, validation, and reporting
11) Internal quality control checks
12) Performance and system audits
13) Preventive maintenance procedures
14) Calculation of data quality indicators
15) Corrective actions
16) QA/QC reports to management.
The preparation and approvals of the QAMP and QAPPs should take place prior to the
initiation of any sample or data collection processes within the program. More specific
information on the various requirements of the QAPP may be found in shortened format
in the pocket guide titled Preparing Perfect Project Plans (Simes 1989) or in expanded
format in the Preparation Aids for the Development of Category X Quality Assurance
Project Plans, where X refers to Category I, II, III, or IV projects defined by Simes
(1991).
DEVELOPMENT OF A LABORA TORY AUDIT PROGRAM
A laboratory audit program is essential for the monitoring of all data generation phases
of any project or program. The audit program should include the submittal of evaluation
samples to each participating laboratory and the execution of laboratory performance and
system audits by the funding agency. Each of these parts of the audit program is
discussed in the following sections.
Evaluation Samples
The submittal of evaluation or audit samples, if available, is an extremely useful
technique for determining a laboratory's ability to successfully perform the required
analyses of the program. Ideally, an initial set of "pre-award" evaluation samples should
be sent to each participating laboratory prior to the awarding of a contract. The results
from these samples will allow for the evaluation of the timeliness of sample analysis, the
ability of the laboratory to follow the required quality assurance measures implemented
in the program, and the ability of the laboratory to accurately perform the required
analyses. Once the contract is awarded, a set of evaluation samples should be submitted
with the routine samples to ensure that the laboratory is maintaining control of the
analyses being performed. These samples will also allow for problem identification/
resolution to occur during the analytical phase of the program.
Laboratory Performance and System Audits
Performance audits essentially consist of reviewing the ongoing quality assessment
program of a laboratory (Taylor 1987). The objective of this form of audit is to evaluate
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Chapter 2. Quality Assurance and Quality Control
the accuracy of the data being generated at the laboratory. The review should include
data checks of the QA/QC samples as well as the examination of control charts for
potential laboratory bias.
System audits should be conducted to ensure that all personnel are adhering to the proto-
cols specified in the QAPP in a consistent manner. System audits should be conducted
during both the field sampling and analytical laboratory phases of the data generation
process.
On an individual laboratory basis, an internal performance and system audit should be
performed at least once during the analysis of samples for each project or phase of a
given project. An external combined performance/system audit should be conducted at
least once (more frequently if major problems are identified) during a large multi-
laboratory program.
DA TABASE REQUIREMENTS AND DA TA VER1FICA TION/VALIDA TION
METHODS
During the initial phases of a program's development, consideration should be given to
exactly what data should be received from each laboratory, which format is to be used
for formal submittal of the data, which data acquisition methods are to be used (on-line
computer feed vs. manual data entry), which methods of verification and/or validation
are going to be performed on the submitted data, how the data are going to be analyzed,
and where the data are going to be stored during and at the end of the program. These
questions should be addressed at the beginning of the program to avoid confusion at the
laboratory during the later phases of sample analysis and data report generation. These
points should be clearly delineated in the program QAMP and in each participating labo-
ratory's QAPP in the data reduction, validation, and reporting section.
It is suggested that all data generated in conjunction with the project (i.e., data from
routine samples, QA/QC samples, instrument calibration, etc.) be obtained by the
funding agency. The purpose of collecting all the data is to verify the calculations and
final results, to check for transcription errors, to ensure that all the QA/QC requirements
were addressed, and so that if any problems or questions arise concerning the data in the
future, it will be possible to resolve issues without having to contact the original
analytical laboratory. Collecting and storing laboratory data that supplement the results
(i.e., meta-data) will improve the long-term viability of the data and will allow more
secondary use of the data by those outside of the project. The data submitted from the
laboratory should be in both hard-copy and computer-readable formats.
Whenever possible, a list of acceptable values should be developed for certain data ele-
ments. For example, the list for the chemical parameters might include methylmercury,
total PCBs, benzo[a]pyrene, etc. Using such lists to simplify the task of data quality
control can greatly reduce inconsistencies (spelling, synonyms, and data format) in both
data reporting and in data entry.
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Chapter 2. Quality Assurance and Quality Control
Data verification should consist of those analyses or checks on the submitted data to
assess the degree of success that a laboratory obtained in meeting the MQOs specified
for their project. Data verification should be performed on both the field sampling and
characterization data as well as the analytical laboratory data. Field data should be
examined for consistency, relative accuracy, and completeness of the submitted data (as
defined for the DQOs). For this discussion, consistency is defined as the use of the same
descriptive terms, reporting units, and station coordinates (i.e., latitude and longitude)
throughout the field database. Relative accuracy is defined as consistency within the
reported measurements. If deficiencies are identified in the field data, the laboratories
should be contacted and requested to provide missing data or correct erroneous data.
Verification of the analytical laboratory data should be performed to check all calcula-
tions and to check for missing data, proper use of QA/QC samples, proper sample
identification, data transmittal errors, internal consistency, intralaboratory comparability
(if similar analyses were performed on the same sample), and even temporal and spatial
consistency. Statistical checking for outliers may be appropriate during data verification/
validation.
CONCLUSIONS
A quality assurance program is an integrated system of activities involving planning,
quality control, quality assessment, and reporting to ensure that the data generated in a
program meet defined standards of quality with a stated level of confidence. Adherence
to a well-defined quality assurance program is essential to ensure that the data collected
will be of known and acceptable quality as well as comparable among laboratories.
The first step in the development of a good quality assurance program is to define the
DQOs. The DQOs are clear, concise statements that delineate all aspects of a program.
DQOs ensure that all parties understand the goals of the program and the "route" the
program will take to meet the goals. Properly established DQOs help to eliminate
unnecessary waste of time and money. Further, the DQOs allow for the upfront planning
of the level of data quality needed to meet the program's goals.
There are many forms of QA/QC samples and measures that can be used to assess and
control the sources of error throughout the sample processing stream. QA/QC samples
used during chemical and physical analyses can identify system contamination, method
extraction efficiency, and the accuracy (bias and precision) of the measurements. The
health, sensitivity, and influence of "clean" water or sediments on test organisms, as well
as the bias and test reproducibility, can be assessed through the use of quality control
samples during sediment toxicity tests and bioaccumulation studies. In addition to these
quality control samples prepared by the laboratory, samples can be incorporated by the
data user into the study design to independently assess the quality of data generated by
a laboratory. The selection and use of the QA/QC samples should be balanced in terms
of quantity and acceptance limits to meet the needs of the program, as defined by the
DQOs.
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Chapter 2. Quality Assurance and Quality Control
Upon completion of the DQO process and determination of which QA/QC samples are
to be used, all the decisions need to be documented in a QAPP. It is USEPA policy that
all environmental programs be performed in accordance with a formalized quality
assurance program, and the QAPP is the formalized written statement of that program.
The QAPP describes the management policies, objectives, principles, organizational
authority, responsibilities, and implementation plan of the program (or laboratory) for
ensuring the quality of the data.
A laboratory audit program is an integral part of a good quality assurance program. The
use of evaluation or audit samples can provide valuable information on a laboratory's
capabilities prior to awarding a contract and can be used to assess laboratory performance
during the program. Laboratory system or performance audits should be conducted to
ensure that the laboratory is adhering to the quality assurance program specified in the
QAPP and to provide an assessment of the quality of the data being generated during the
program.
If data are to be stored in an electronic database, procedures should be established to
document all data sources and any changes made to the values over time. In addition,
verification of any hand-entered data should be performed (by a second individual).
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3. SEDIMENT SAMPLING SURVEYS
An accurate assessment of the physical, chemical, and biological characteristics of
sediments is highly dependent on the collection of representative samples of the
sediments. Any sediment samples collected for analysis represent but a small fraction
of the total sediments of interest. Careful consideration must therefore be given to
ensuring that those samples accurately reflect the characteristics of the sediments in the
area they were collected.
In general, contaminants tend to be associated more with fine-grained sediments (e.g.,
silt or clay) of high organic content than with coarse-grained sediments (e.g., sand or
gravel). Fine-grained sediments originate in part from suspended organic particles that
adsorb various contaminants from the water column. Once they settle and are buried
over time by newer sediments, the original link with contaminant sources and water
quality in general may be broken. A recent USEPA document (USEPA 1990) makes this
important point:
It is worth noting that sediment contamination problems need not be connected
to poor water quality. The ability of sediments to retain contaminants over time
makes it possible for sediments to remain contaminated while water column
contaminant concentrations remain below applicable water quality standards.
The distribution of chemical contaminants in sediments depends not only on local
contaminant sources, both past and present, but also on natural and anthropogenic
processes that redistribute contaminated sediments. In most urban-industrial harbors, like
those studied in the ARCS Program, the distribution of chemical contaminants in
sediments may be highly variable and "patchy" both horizontally and vertically. In
shipping channels, or wherever navigational dredging occurs frequently, contaminated
sediment deposits are likely to be relatively thin unless contaminants are mixed to greater
depths by vessel propeller scour. However, in areas where dredging was once practiced
and then ceased years ago, relatively thick layers of contaminated sediments may have
accumulated. Sediment quality in these depositional areas can reflect a complex history
of pollution events that have occurred over a span of decades. In this situation, surveys
that are limited to collection of a few grab samples of surficial sediment (i.e., a few
centimeters) will not produce results that accurately represent the sediment quality. The
number, location, and type of sediment samples (e.g., grab samples or sediment cores)
must be carefully planned to ensure an accurate assessment of sediment quality.
Field surveys are generally conducted to provide data on sediment contamination that will
be used to make decisions on the need for and extent of sediment remediation. The
design of field surveys should consider the following factors:
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Chapter 3. Sediment Sampling Surveys
• Historical chemical and physical data on sediments in the area
• The magnitude of currents in the area and their potential effect on sedi-
ment accumulation or erosion (i.e., where currents are strong and the area
is likely to be erosional; where currents are weak and the area is likely to
be depositional; or where the area is susceptible to periodic, high flow
events [e.g., floods] that can cause erosion in an otherwise depositional
environment)
• The history of dredging in the area
• Bathymetric surveys of the area
• The future need for navigational maintenance dredging or other dredging
associated with construction projects.
Historical sediment chemistry data may identify areas likely to require more intensive
investigation or chemicals of particular interest. Historical data on physical properties
(e.g., grain size) of the sediments may provide an indication of whether a given area is
more likely to represent an erosional or a depositional environment.
In erosional environments where bottom sediments generally consist of coarse sand or
gravel, there is a lower likelihood of sediment contamination, and sampling by routine
methods (e.g., grab samplers or sediment corers) may be precluded.
In depositional environments where there is a very low likelihood of sediment resuspen-
sion associated with high flow events or dredging, or where transport of contaminants
by groundwater is unlikely, the potential need for sediment remediation can be assessed
by sampling only surface sediments. Humans, aquatic organisms, and wildlife will
generally only be exposed to sediment contamination in the uppermost "active" layer of
the sediment deposit. Unless groundwater transport is significant, release of contami-
nants to the water column will only occur from the sediments in contact with the water.
Hence, contaminated sediments separated from the overlying water by a surface layer of
relatively clean sediments may not represent an ongoing risk to humans, aquatic
organisms, or wildlife. In such cases, the best remedial alternative may be no action,
allowing additional deposition and accumulation of cleaner sediments to further isolate
the contaminated sediments.
If surface sediments in a depositional environment are sufficiently contaminated to
require evaluation of remedial alternatives, it will then be necessary to sample subsurface
sediments as well. This sampling should be designed to provide information that can be
used to define the vertical extent of sediments that may need to be dredged, to investigate
remedial alternatives for those sediments, and to characterize the sediment that will be
left in place and exposed once the overlying contaminated sediments are removed.
It will also be necessary to sample subsurface sediments in areas subject to periodic, high
flow events, which can cause erosion in an otherwise depositional environment, as is
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Chapter 3. Sediment Sampling Surveys
often the case in Great Lakes AOCs. The depth to which sampling must extend should
be based on an analysis of the likelihood of erosion extending to a given depth during an
event of a predictable magnitude.
It is difficult to provide generic guidance on the depth of subsurface sediments that will
need to be sampled. Historical dredging records or bathymetric surveys may be used to
estimate sediment accumulation rates in a given area, which may in turn be used to esti-
mate the depth of sediments that may have accumulated since some historical event of
importance (e.g., the initiation of a specific point-source discharge in the local area).
In many cases, it may be necessary to conduct a survey in several phases. If the results
of early phases indicate sediment contamination extending to the maximum depths
sampled, further sampling will likely be required at greater depths. Often, the depth of
sampling is determined by equipment limitations.
In areas subject to navigational maintenance dredging or other planned dredging projects,
it will likely be necessary to sample sediments over the entire depth to be dredged. In
addition, sediment samples should be collected from just below that depth to characterize
the sediment that will be left in place and exposed once the overlying contaminated
sediments are removed.
ARCS Program field surveys were designed to conduct representative sampling of thick
(up to 6 m deep) deposits of contaminated sediments and to provide data for 3-dimen-
sional mapping of sediment quality. It was considered important to characterize
sediments with depth, because contaminant concentrations in surface sediments were
sufficiently high that remediation was considered likely to be required. It was therefore
necessary to establish the vertical extent of sediment contamination.
This chapter describes procedures for collecting sediment samples for an integrated
sediment assessment (including physical, chemical, and biological characterization of the
sediments). Topics discussed include the sediment sampling vessel; field positioning
methods; sediment sampling procedures; field processing of sediment samples for
physical and chemical analyses, benthic community analyses, and toxicity testing; and
sediment characterization by remote sensing. As appropriate for each topic, various
options or objectives are discussed, the procedures used in the ARCS Program are
described, and recommendations are made for procedures to use in future sediment
surveys of other Great Lakes AOCs.
SEDIMENT SAMPLING VESSEL
Many types of vessels can be used to sample sediments in Great Lakes AOCs. Several
issues that need to be considered when choosing the type of vessel include, but are not
limited to:
• Trailerability
• Required lifting capacity (including required buoyancy, balance, and
winches)
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Chapter 3. Sediment Sampling Surveys
• Whether sample processing should be conducted on board
• Need for a protective cabin
• Vessel draft (ability to operate in 0.5 m of water)
• Need for precise location, depth, and other electronic equipment.
Samp/ing Vessel Used in the ARCS Program
A vessel operating in protected waters such as rivers and harbors does not require a
design suitable for operation in heavy weather. The boat constructed for the ARCS
Program, the R/V Mudpuppy (Figure 3-1), is a monohull aluminum barge with an overall
length of 30 ft (9.2 m), an 8-ft (2.4-m) beam, and a draft of 1.5 ft (0.5 m). The hull
consists of four sealed compartments and is flat-bottomed, with a 5° V-shaped bow and
a square foredeck. This design provides the maximum forward buoyancy needed for
sample collection. A lifting boom is mounted on the bow, and a recessed cabin is
located at the stern. The vessel is powered by twin outboard engines, which are mounted
on an extended bracket to minimize loss of deck space. Electrical power for onboard
instrumentation and cabin air conditioning is provided by a diesel generator located on
the aft deck. Continuous electrical power for the ship's lights, communications, and
winches is provided by two 12-volt batteries. Electronic instruments used in vessel
operations include a marine radio, a fathometer, a global positioning system (GPS),
computers for data logging and navigation, and a Loran-C receiver, which serves as a
backup for the ship's positioning system.
A trailerable vessel was considered necessary to provide the most cost-effective means
of moving between study sites. Because the R/V Mudpuppy has a maximum 8-ft (2.4-m)
beam, it meets the maximum allowed trailer width for highways. A greater beam would
require trailering as a "wide load," and would require additional safety measures and
towing experience.
A work boat's hull needs to be rugged and able to resist the dents and scratches that
plague boats working hi industrial marine areas, such as Great Lakes AOCs. Wood and
fiberglass do not provide the protection from hull damage offered by aluminum or steel.
For the R/V Mudpuppy, aluminum was chosen as the hull material. Although steel
provides more strength, aluminum provides a strong but light vessel that is more easily
trailered.
The deck layout confines all sampling activities to the forward 14 x 8-ft (4.3 x 2.4-m)
deck, providing the vessel operator with a clear view of all sampling operations. Bow-
mounted catwalks fold down forward providing additional work deck around the coring
operations. To provide maximum buoyancy and minimum heel, all sediment coring is
conducted from the bow using a 13-ft (4-m) long, rectangular lifting boom with a
2,000-lb (900-kg) lift capacity. The bow-mounted boom also allows sediment cores to
36
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Co
XI
(Photograph by D. Marklund)
Figure 3-1. R/V Mudpuppy.
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Chapter 3. Sediment Sampling Surveys
be collected near the shore or obstructions while preventing possible damage to the
engine propellers. Two electric lifting winches, one 110-volt AC and one 12-volt DC,
each wound with 0.25-in. (0.64-cm)-diameter, stainless-steel cable, lift the sampling gear
and sediment cores (see Sediment Sampling Procedures). When the vessel is trailered,
the sampling boom is lowered aft over the cabin roof, allowing the total height of the
trailered vessel to meet highway height restrictions.
The R/V Mudpuppy has worked well, providing a rugged, trailerable, shallow-draft work
platform. It is capable of operating in confined areas, providing both a stable platform
for sample collection and a climate-controlled cabin for instrumentation.
FIELD POSITIONING METHODS
Accurate positioning of sampling stations is essential for field investigations of sediment
characteristics. Because of navigational dredging activities and hydrology in Great Lakes
AOCs, contaminated sediments frequently occur in narrow bands along shorelines or in
localized pockets. To accurately map such areas, positioning of high accuracy is needed.
In addition, contour mapping algorithms depend on accurate measurements of distances
between sampled and estimated points. Inaccurately determined sampling locations will
introduce mapping error. Often, additional sampling is needed at previously sampled sta-
tions, requiring accurate positioning to relocate at the same station.
Although relative positions can be determined by measuring bearings and distances from
reference points on shore, absolute positioning is required to link positions to a
geographic coordinate system (e.g., latitude/longitude, state plane coordinates).
Requirements for an ideal positioning system are:
• Minimum accuracy of < 1 m
• No required shore access to reference points (to eliminate the need to gain
access to privately held industrial property bordering many rivers and
harbors)
• Capability for real-time position determination
• Positions output in geographic coordinates (e.g., latitude/longitude) to
facilitate conversion to other coordinate systems (e.g., state plane coordi-
nates)
• Minimal operator involvement
• Ability to coordinate position data with other simultaneously collected data
(e.g., seismic data)
• Position updating at intervals of no more than 3 seconds
• Ability to log positions for later review and/or processing
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Chapter 3. Sediment Sampling Surveys
• No interference from waterside structures and natural features
• No "black out" periods, during which the system does not function
• Low cost.
Advantages and Disadvantages of Available Positioning Systems
A number of different positioning systems were investigated as part of the ARCS
Program. Selected capabilities and features of the various systems are summarized in
Table 3-1 and in the subsections that follow.
TABLE 3-1. COMPARISON OF POSITIONING SYSTEMS
Absolute accuracy
Repeatable accuracy
Shore access required
Real-time capability
Frequency of fixes
Cost
LORAN-C
400 m
10-15 m
No
Yes
Continuous
Low
MICROWAVE
3 m
3 m
Yes
Yes
Continuous
High
GPS
<1-5 ma
<1-5 ma
No
Yes
Continuous
Medium
a Assumes differential global positioning system, and varies with the manufac-
turer of the receiver.
Loran-C
Loran-C is a land-based radio navigation system that calculates positions based on time
differences between master/slave transmitter pairs. Although the cost of Loran-C
receivers is low and the repeatable accuracy is good, they have less than acceptable
absolute accuracy (0.25 miles or 400 m) and are prone to interference by bridges and
other large metal structures commonly found along industrialized rivers and harbors.
Microwave Ranging Systems
Microwave ranging systems calculate positions by determining distances and bearings to
previously established transponders onshore. Although these systems are capable of
excellent absolute accuracy, they have many disadvantages, including high cost, daily
initialization and/or calibration of the system, the necessity for each transponder to be
placed over an accurately surveyed point onshore, and the requirement that transponders
be moved to remain in line of sight with the sampling vessel.
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Chapter 3. Sediment Sampling Surveys
Global Positioning System (NAVSTAR GPS)
GPS represents a rapidly evolving technology that calculates positions by triangulating
on three or more very high-orbit satellites. GPS currently provides nearly continuous
2- and 3-dimensional coverage.
Differential GPS (DGPS) is a variation of standard GPS in which a reference receiver
is used to greatly enhance the accuracy of standard GPS. The reference receiver is
placed at a precisely known location where GPS data are simultaneously collected and
compared with that of a remote receiver, such as on the survey vessel. The reference
unit calculates correction factors that are then transmitted to the remote receiver(s).
DGPS can be operated in either a real-time mode or in a post-survey mode. In the real-
time mode, the differentially corrected position information is available to the operator
of the system instantaneously. This feature is a necessity if it is important for sampling
to occur at an accurately known, predetermined location. In the post-survey mode, posi-
tion information is logged by the equipment, but the correction algorithm is not applied
to the data until after the survey. The latter mode may be adequate if it is not important
to know the precise location at the time of sampling, but only to be able to accurately
locate the sampling stations after the fact.
To control the quality of position data obtainable by users, the U.S. Department of
Defense, for national security reasons, has the ability to activate a feature referred to as
"selective availability." This feature allows the intentional degradation of GPS signals
produced by transmitting slightly erroneous data. Selective availability was activated on
a continuing basis on March 25, 1990. The standard GPS signal with selective availabil-
ity provides accuracies of +100 m. DGPS corrects for the effects of selective availabil-
ity and is capable of absolute accuracies of < 1 m.
GPS systems also have the ability to obtain accurate altitude and tune information. The
time feature is useful for linking geographic positions to other computer-logged data,
such as seismic or bathymetric survey data.
There are many advantages of the DGPS, including very good, absolute accuracy
(< 1 m); no required shore access (except for establishing a reference station that can be
anywhere from 20 to 100 miles [160 km] away); minimal operator involvement; ability
to coordinate positions with simultaneously collected data using time as the integrating
element; capability for real-tune position determination; and the ability to log data for
later processing to improve the accuracy.
The disadvantages of DGPS may include the need to buy an additional receiver to act as
a reference station, and the requirement that receivers retain line of site with the satellites
being used for position determinations, although this is seldom a problem.
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Chapter 3. Sediment Sampling Surveys
Field Positioning System Used in the ARCS Program
After investigating the various positioning technologies available, DGPS was determined
to be the most appropriate for establishing sampling locations in the ARCS Program.
Both real-time and post-survey DGPS were used for position determinations during
ARCS Program sediment sampling surveys. Because the primary role of the positioning
system was to determine where samples had been collected, post-survey DGPS was the
predominant method used. Real-time DGPS was used only when it was necessary to
position the survey vessel at an accurately known, predetermined position. The
procedures for collecting data were similar for both real-time and post-survey DGPS,
with the exception of the required maintenance of the radio data link for real-tune DGPS.
Post-survey data processing involved analyzing the logged GPS data with differential cor-
rection software, which, along with data filtering and smoothing, used the reference sta-
tion data to calculate corrected position data for the mobile station. After processing, the
corrected position data were available in a standard latitude/longitude format.
DGPS proved to be a very accurate and efficient means of collecting positional data for
the ARCS Program. Although the incomplete satellite constellation caused some schedu-
ling problems early on, the launching of additional satellites soon solved this problem.
The current satellite constellation supports 24-hour, 2- and 3-dimensional positioning.
The ability to acquire position fixes in areas of massive shore structures was an unexpec-
ted benefit. This ability can be attributed to the number of satellites available and their
varied and changing positions. There are some areas where satellite signal acquisition
is not possible, such as under bridges; positions for these obstructed points can be deter-
mined by calculating a relative position from a nearby absolute position and using geome-
try to calculate the offset of the relative point from the absolute point. When collecting
continuous data, GPS navigational software uses sophisticated data processing techniques
involving a Kalman filter to handle signal dropout.
As DGPS receiver prices, size, and power requirements continue to decrease and features
such as reference station services and geographic information system (GlS)-compatible
data formats become more available, the advantages of DGPS will continue to increase.
DGPS is therefore recommended for field surveys in other Great Lakes AOCs.
SEDIMENT SAMPLING PROCEDURES
The field practices and methods by which sediment samples are collected form the
foundation for the quality of the sediment study being conducted. Prior to commencing
field operations, thought must be given to the DQOs of the project (see Chapter 2). Per-
tinent considerations include the type(s) of sediment samples that will be required and
how they will be analyzed.
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Chapter 3. Sediment Sampling Surveys
In all field operations, the primary goal of sediment sampling is to collect a sample that
accurately represents the sediment condition in situ. Specific sediment collection and
preservation requirements will depend on the study. For example, benthic community
analyses require different sediment collection and preservation methods than those for
chemical analyses.
The type of sediment collection technique chosen will depend on several considerations,
including the study objectives, the numbers and types of analyses required, the available
sampling vessel, weather conditions, the type(s) of sediment being collected, and the
depth to which sediment is to be sampled.
There are two general types of sediment samplers, grab samplers and sediment corers.
Grab samplers are routinely used to collect surficial sediment samples, as are usually
required for physical and chemical analyses and benthic invertebrate characterization.
Sediment corers can pro vide less disturbed samples and profiles of subsurface sediments,
in which in situ conditions are preserved, although the surface layer may be disturbed
from compaction or being eroded immediately prior to impact by the water pushed ahead
of the coring unit. Distortion of the sediment core can also occur, caused by compaction
or stretching of the sediment during collection. Sediment corers are most often used for
assessment of environmental contaminants in subsurface sediments, for evaluation of
sediment for dredging and disposal, and in geochemical surveys.
The advantages and disadvantages of various recommended sediment samplers are
summarized in Table 3-2. In-depth discussions of sediment samplers can be found in
Baudo et al. (1990), Burton (1992b), Mudroch and MacKnight (1991), APHA (1989),
and ASTM (1990).
Grab Samplers
Grab samplers are usually designed as a box with a set of jaws, or a rotating bucket, that
takes a wedge-shaped bite out of the surface sediment. These samplers allow the collec-
tion of small or large sample volumes and can be effective over a wide range of surficial
sediment types. They are easy to use, and the smaller grab samplers allow hand deploy-
ment and retrieval from a small sampling platform. Disadvantages of the grab sampler
include the uncertainty of the depth of sediment penetration and the loss of sample integ-
rity when the sampler is open. Grab samplers also do not disturb the surface sediment
significantly unless they overpenetrate. Penetration depth of grab samplers can be highly
variable, depending on sampler design and sediment composition.
When selecting a grab sampler, the method of retrieval, the type of sediment, the
required sample volume, and the strength of currents at the site should be considered.
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TABLE 3-2. ADVANTAGES AND DISADVANTAGES OF VARIOUS SEDIMENT SAMPLERS
Sampler
Advantages
Disadvantages
Hand and gravity corers
Box corer
Vibrocorer
Ekman or box dredge
Ponar grab
Maintain sediment layering of the inner core.
Fine surficial sediments retained by hand
corer. Replicate samples efficiently obtain-
ed. Removable liners. Inert liners may be
used. Quantitative sampling allowed.
Maintains sediment layering of large volume
of sediment. Surficial fine sediments re-
tained relatively well. Quantitative sampling
allowed. Excellent control of depth of pene-
tration.
Samples deep sediment for historical analy-
ses. Samples consolidated sediments.
Relatively large volume of sediment may be
obtained. May be subsampled through lid.
Lid design reduces loss of surficial sediments
as compared to many dredges. Usable in
moderately compacted sediments of varying
grain sizes.
Commonly used. Large volume of sediment
obtained. Adequate on most substrates.
Weight allows use in deep waters. Good
sediment penetration.
Small sample volume. Gravity corer may result
in loss of fine surficial sediments. Liner removal
required for repetitive sampling. Not suitable in
coarse-grain or consolidated sediments.
Size and weight require power winch; difficult to
handle and transport. Not suitable in consoli-
dated sediment.
Expensive and requires winch. Outer core integ-
rity slightly disrupted.
Loss of fine sediments may occur during sam-
pling. Incomplete jaw closure occurs in coarse-
grain sediments or with large debris. Sediment
integrity disrupted. Not an inert surface.
Loss of fine sediments and sediment integrity
occurs. Incomplete jaw closure occurs occa-
sionally. Not an inert surface. Heavy and
requires a winch.
van Veen or Young grab
Petersen grab
Orange-peel grab
Shipek grab
Useful in deep water and on most substra-
tes. Young grab coated with inert polymer.
Large sediment volume obtained.
Large sediment volume obtained from most
substrates in deep waters.
Large sediment volume obtained from most
substrates. Efficient closure.
Adequate on most substrates.
Loss of fine sediments and sediment integrity
occurs. Incomplete jaw closure possible, van
Veen grab has metal surface. Young grab is
expensive. Both may require a winch.
Loss of fine sediments and sediment integrity.
Not an inert surface. Incomplete jaw closure
may occur. May require winch.
Loss of fine sediments and sediment integrity.
Not an inert surface. Requires winch.
Small volume. Loss of fine sediments and sedi-
ment integrity. Not an inert surface.
Source: Adapted from Burton (1992).
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Chapter 3. Sediment Sampling Surveys
Sediment Carers
Sediment coring is generally accomplished by inserting a rectangular or cylindrical tube
into the sediment and withdrawing a sediment core. Sediment corers range in size and
complexity. Small push corers and small gravity corers can be retrieved by hand and
used from a small boat. Larger and more complicated corers such as piston and vibro-
corers require a lifting boom, a winch, larger sampling vessels, and more field crew.
Problems in sediment coring are often associated with inadequate sediment penetration,
core distortion, or inadequate core retention during corer retrieval. Heavy weights or
vibrations applied to the core tube can improve penetration in dense sediments. Various
types of core "catchers" installed at the lower end of the core tube can prevent sample
loss in uncompacted sediments; however, these catchers can also impede penetration in
compacted sediment. Corer deployment can also be difficult under certain conditions.
The vessel should be 3-way anchored to maintain a steady position while the corer pene-
trates into the sediment. Trying to core in a strong current or wind, even with the vessel
properly anchored, can result in the corer entering the sediment at an angle or core tubes
being bent during retrieval.
Sediment Samplers and Procedures Used in the ARCS Program
During the ARCS Program, Ponar and van Veen grab samplers were used to collect
surface sediments. When sampling for benthic invertebrates, it was important to collect
these benthic samples before collecting other samples to minimize disturbance to the
benthic community prior to collection of the sample. The 0.05-m2 Ponar grab sampler
(23 cm X 23 cm, 529 cm2, 23 kg; Wildlife Supply Company, Saginaw, Michigan) was
designed to penetrate the sediment by weight alone and to sample about the same amount
of sediment with each cast. In the ARCS Program, a sediment penetration depth of
10-20 cm was desired. The Ponar grab sampler was designed for use in lakes, reser-
voirs, rivers, and estuaries with soft or hard sediments. It is equipped with No. 30 mesh
brass screens on the open ends of its jaws to minimize loss of material. The van Veen
grab sampler (Kahl Scientific Instrument Corporation, El Cajon, California) samples a
surface area of 1 ft2 (0.1 m2) and has a capacity of 5 gal (20 L). The Ponar grab samp-
ler was easier to handle than the van Veen grab sampler, but collected smaller samples.
The van Veen grab sampler proved to be much more efficient for collecting large vol-
umes of sediment, although it requires a power winch to operate safely. The van Veen
grab sampler penetrates to a greater depth than the Ponar grab sampler.
Where collection of large volumes of sediment is not as important, it may be possible to
use the petite Ponar grab sampler. The petite Ponar grab sampler is essentially the same
as the full-sized Ponar grab sampler, but it is smaller (15 cm X 15 cm, 225 cm2,
6.8 kg). The advantages and limitations of the petite Ponar grab sampler are the same
as those for the full-sized Ponar grab sampler, with the exceptions that the petite sampler
is considerably lighter than the full-sized sampler, does not penetrate clay substrates as
well, and is not as efficient in flowing water with a velocity of > 1 m/sec (Klemm et al.
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Chapter 3. Sediment Sampling Surveys
1990). The petite Ponar grab sampler is designed to penetrate the sediment to depths of
5-15 cm. The main advantage of using this sampler is that it can be operated without
a boat or winch.
For the collection of sediment grab samples during the ARCS Program, the vessel was
first anchored and its position determined. As multiple grab sampler casts were made,
the vessel was slightly repositioned as necessary to ensure that only surficial sediments
were collected.
Collection of sediment core samples during the ARCS Program was preceded by drop-
ping a grab sampler or probing the bottom near the area where sample collection was
desired, to determine whether soft sediments were present. The vessel was then securely
anchored by triple anchoring. Next, the water depth was measured to determine the
approximate depth at which the corer encountered the sediments. For vibrocoring, the
core unit was allowed to penetrate until the tube no longer penetrated the sediments (i.e.,
until refusal) or until the vibrocore head was near the sediment surface.
Two vibrocoring devices were used in the ARCS Program. The first, assembled by the
staff of the Large Lakes Research Station (LLRS), used a Wacker® Model M3000,
3-horsepower, electro-mechanical vibrator (Wacker Corporation, Milwaukee, Wisconsin).
Its flexible shaft was attached to a custom-made, stainless-steel core head with a
Model H45 vibrator head. The core head accepted 3-in. (7.6-cm)-diameter core tubes.
This unit, although fairly light (the vibrator head weighed 18 Ib [8.1 kg]) and portable,
was not sufficiently powerful to collect cores more than 5-6 ft (1.8-2 m) long.
The second vibrocoring unit was a Rossfelder® Model P-4 Vibrocorer (Rossfelder Corpo-
ration, La Jolla, California). The vibrating head consists of two, 2-horsepower electric
(3-phase) motors in a water-tight housing, and it produces a centrifugal force of 7,000 Ibs
(15.7 kiloNewtons) and a mono-directional frequency of 3,400 vibrations per minute.
An aluminum core tube (4 in. [10 cm] in diameter; up to 20 ft [6 m] long) is inserted
into the vibrating head, and the entire assembly is lowered into the water. The
Model P-4 unit is heavy (i.e., 250 Ibs; 113 kg); therefore, a vessel like the R/V Mud-
puppy must be used to maintain vessel balance and provide adequate lift to break the
corer out of the mud and retrieve it. Vessels like the R/V Mudpuppy require experienced
crew for safe, efficient operation of the boat and equipment.
The Model P-4 Vibrocorer proved powerful enough to collect cores more than 16 ft
(5 m) in length, even when they included several feet of clay. However, no cores much
longer than 16 ft were collected, even when the 20-ft core tube fully penetrated the
bottom. One obvious reason for not collecting samples throughout the entire depth of
penetration of the core tube was that the cross-sectional area inside the core nose was
about 10 percent less than the cross-sectional area of the core tube itself, thereby
reducing the collected sediment volume by that much. Another reason may be that fric-
tion inside the core tube can exceed the bearing strength of soft sediments, resulting in
a plugged core tube that continues to penetrate without collecting more sediment.
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Chapter 3. Sediment Sampling Surveys
Finally, sediments can compress when cored, but this is less of a problem with large-
diameter vibrocorers than with gravity or piston corers.
Hard surface and subsurface debris (e.g., rock, pavement) can prevent collection of a
core or even damage or destroy the core tube. These materials are not uncommon in the
industrialized rivers and harbors of the Great Lakes.
Conclusions
For the collection of surface sediment samples, either the Ponar or van Veen grab
sampler is recommended. The van Veen grab sampler may be more appropriate when
large volumes of sediment are needed for analysis.
Vibrocoring is a versatile and efficient method for collecting long sediment cores
throughout Great Lakes AOCs or similar harbors. Although rotary drilling methods
could yield longer cores even in hard-bottom areas, they were not considered to be a
feasible alternative to vibrocoring for several reasons. One reason is the greater cost of
the drilling rig and barge support. Another is that vibrocoring is more mobile and
practical for close-quarters sampling in shallow areas. A vessel similar to the R/V
Mudpuppy is a relatively small craft that provides better access to congested harbor sites.
Although the Model P-4 Vibrocorer worked extremely well and appears to be the only
unit capable of consistently collecting long cores, it was not without limitations, which
included:
• Its 3-phase, 230-volt power, which required the use of a special generator
• Its weight (the vibrating head weighs 250 Ib [113 kg], and with a 15-ft
[5 m] core tube full of sediments the entire assembly weighs over 400 Ib
[180 kg]).
A rigid tube core liner (e.g., cellulose acetate butyrate [CAB]) should be used to easily
retrieve and store core sections. Prior to commencing sampling operations, a plan for
subsampling the cores should be developed indicating the desired sampling intervals;
however, flexibility must be maintained to allow for plan modifications in the field as
dictated by observed core strata.
Future refinements to sediment coring include the application of suction to the upper end
of the core tube during coring, which may result in the retrieval of longer cores, and the
development of in-field, real-time analyses (i.e., screening-level analyses, see Chapter 4)
that will provide data to guide subsequent sampling.
Aside from vibrocorers, few coring devices were considered suitable for use in the ARCS
Program. Box cores are generally heavy (>500 Ib [230 kg]) and their cores are usually
< 1.5 ft (0.5 m) long. Gravity or hand-held corers will seldom penetrate greater than
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Chapter 3. Sediment Sampling Surveys
3 ft (1 m). Piston corers are awkward to deploy in shallow waters, such as those found
in Great Lakes AOCs, and are less able to penetrate clay or gravel layers.
Proper identification of individual cores and associated subsamples is especially important
for sediment projects because of the potential for collection of large numbers of samples,
especially when various laboratories will be analyzing splits of those samples. Each
sample should be assigned a unique sample number. Figure 3-2 shows an example
sample numbering system used during the ARCS Program. Individual laboratories often
assign in-house numbers to their samples as necessary, but all interlaboratory data
transfers should use the original sample number.
The visual characteristics of each sediment core, total length, position of layers within
the core, and the color, texture, and composition of the material should be recorded in
a core observation log immediately upon collection. Much of this information is qualita-
tive or subjective and could vary from one observer to another; for consistency, only one
or two workers should describe the cores during any one survey.
Describing the cores is relatively simple; however, several cautions and techniques that
were learned during the ARCS Program can be used to improve the quality of this
information. Although polarized sunglasses are often worn when working on the water,
they can influence color vision; therefore, all core descriptions should be conducted
without them. To determine the color of sediments, a standard color scale can be used.
A typical set of Munsell color charts was tried, but none depicted the colors found in
these aquatic sediments. However, other Munsell pages are available and could be used
to describe the colors of Great Lakes sediments. Monitoring sediment odor, although
useful for detecting petroleum hydrocarbons, poses an unacceptable risk of inhalation
exposure, and should not be performed. Descriptions of sediment texture and composi-
tion were improved when the "ribbon test" (a texture-by-feel test) was applied to distin-
guish between clay and compressed silt (Brady 1974). To conduct the ribbon test, a
small piece of suspected clay is rolled between the fingers while wearing protective
gloves. If it easily rolls into a ribbon (or rod) over 1 in. (2.5 cm) long, it is clay; if it
breaks apart, it is silt.
During the ARCS Program, the core description process was initially videotaped, with
the intent to later inspect the videotape if questions or data anomalies arose. Later, color
photographs were used to record the core appearance in overlapping frames at approxi-
mately 30-cm intervals. This provided a visual record of core zonation.
FIELD PROCESSING OF SEDIMENT SAMPLES FOR PHYSICAL AND
CHEMICAL ANALYSES
Sample processing (i.e., sectioning, subsampling, and packaging samples for shipment
to the laboratory) can be conducted onboard the vessel or from a shore-based sampling
area. The advantages of onboard processing are 1) it takes less crew to perform a
sampling survey and 2) the excess sediment can be dumped back into the water after the
47
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Field Identification Number
oo
i HJ.O r,o ^,c.^,.o
<
<
<
i
<
i
1,
i Sample Fraction:
Surface grab - 00
Core intervals - 01 , 02, 03, etc.
> Sample Replicate:
Single sample - 1
Replicate No. - 2, 3, 4, etc.
• Sample Type:
G - grab (box core, dredge)
C - core (piston, gravity, vibra-core)
F - fish
B - benthos
• Station Number: Sequential on each transect
• Transect Number: Sequential at each site
• Survey Number: Sequential at each
> Site code:
site
IH - Indiana Harbor
BR - Buffalo River
SR - Saginaw River
Note: Supplemental information is recorded on the field data sheets.
Figure 3-2. Example sample numbering system used in the ARCS Program.
-------
Chapter 3. Sediment Sampling Surveys
subsampling is complete. Dumping the sediment back into the water body lessens the
costs for transportation, storage, and disposal of samples. The main advantage of shore-
based processing is that it allows more samples to be collected during a sampling day,
because time is not taken for sample processing by the sampling crew. However, shore-
based processing requires disposal of excess sediments (i.e., returning the excess sedi-
ments to the water may not be an option). During the ARCS Program, buckets were
used to transport bulk sediments to shore, and plastic bag liners were used to minimize
the need for cleaning the buckets between samples. Cores were transported in capped
sections of the CAB liner.
Sample processing requirements for a project are specific to the goals of the study and
should be described in a sampling plan. Factors to be addressed include the sample size
to be collected, the number and type of subsamples to be collected from each sample, the
types of analyses to be conducted, the analytical resources available, and sample storage
requirements and preservation techniques. Required container types, preservation tech-
niques, and holding times for sediment samples should follow recommendations in
40 CFR 136.3, Table II. A summary of the container types, preservation techniques,
and holding times appropriate for commonly measured sediment parameters is provided
in Table 3-3. However, 40 CFR § 136.3 does not allow for freezing or freeze-drying.
In addition, the sampling plan should include specific information as to which samples
will be associated with various quality assurance samples (e.g., if a matrix spike will be
performed on a sample, double the normal sample volume may be required).
When subsampling cores, two methods are commonly used. The first method is to
subsample discrete layers. For example, during the ARCS Program, the cores were
either cut into 2-ft sections or subsampled by visual strata. Each section was homoge-
nized and subsampled from the homogenate. The second subsampling method is to split
the core longitudinally (either as a whole or cut into subsections). Subsamples are then
selected by homogenizing or collecting entire visually homogeneous layers. Subsampling
the homogenate from 1- to 3-ft intervals is generally recommended, because most reme-
diation scenarios will involve dredging, and dredging accuracy is approximately 1-3 ft.
However, it may make sense to define important boundaries accurately if dredging is
warranted. The second method of subsampling cores should be employed if the intent
is to characterize visually distinct layers throughout the core. In some areas, however,
this can lead to a tremendous number of samples per core.
During collection of sediment samples, homogenization is generally necessary to obtain
a representative sample and to provide a sufficient volume of sediment for required
testing. Homogenization is performed by mixing sediments in a clean, stainless-steel
bowl with a stainless-steel spoon until visually homogeneous. The mixing time varies,
increasing inversely with sediment water content. Care must be taken to minimize con-
tamination, both in the field and laboratory, and to reduce exposure to oxygen if acid-
volatile sulfide (AVS) is a parameter of interest. Due to sample volume requirements
of the ARCS Program, however, large sediment composite samples were homogenized
in the field without protecting the sediment from oxidation. Nonetheless, there was
generally far more AVS on a molar basis than the divalent metals (cadmium, copper,
49
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TABLE 3-3. RECOMMENDED SAMPLE SIZES, CONTAINERS,
PRESERVATION TECHNIQUES, AND HOLDING TIMES
Media/Analyte
Sediments
Particle size
Total solids
Total volatile solids
Total organic
carbon
Oil and grease
Acid-volatile
sulfides
Total sulfides
Semivolatile
organic compounds
Pesticides and
PCBs
Mercury
Methylmercury
Butyltin
compounds
Metals (except
mercury)
Tissues (whole)
Semivolatile
organic compounds
Pesticides and
PCBs
Mercury
Methylmercury
Metals (except
mercury)
Sample
Size
(g)a
100-150d
50
50
50
100
15
50
50-100
50-100
1e
20
50-100
50e
_.g
-.9
..9
-.9
-.9
Contained
P,G
P,G
P,G
P,G
G
P,G
P,G
G
G
P,G
P,G
G
P,G
A
A
A
A
A
Preservation
Technique
Cool, 4°C
Freeze
Freeze
Freeze
Cool, 4°C,
HCI; freeze
Cool, 4°C
Cool, 4°C,
1N zinc ace-
tate
Freeze
Freeze
Freeze
Freeze-driedf
Freeze
Freeze
Freeze
Freeze
Freeze
Freeze
Freeze
Freeze
Maximum
Holding
Timec
6 months
1 year
1 year
1 year
28 days
6 months
1 4 days
7 days
1 year
1 year
28 days
2 yearsf
28 days
1 year
2 years
1 year
1 year
28 days
28 days
2 years
Maximum
Extract
Holding
Time
-
-
-
—
—
—
—
40 days
40 days
—
-
40 days
~
40 days
40 days
-
-
-
50
-------
TABLE 3-3. (cont.)
Media/Analyte
Sample
Size
(g)a
Maximum
Maximum Extract
Preservation Holding Holding
Container15 Technique Timec Time
Tissues (after
resection)
Semivolatile
organic compounds
Pesticides and
PCBs
25
25
G,T
G,T
Freeze
Freeze
1 year 40 days
1 year
40 days
Mercury
Methylmercury
Metals (except
mercury)
0.2e
20
6e
P,G
P,G
P,G
Freeze;
freeze-driedf
Freeze
Freeze
28 days
2 years'
28 days
2 years
a Recommended field sample sizes for one laboratory analysis. If additional laboratory
analyses are required (e.g., replicates), the field sample size should be adjusted accordingly
(i.e., multiply the sample size by 4 to account for laboratory quality control samples). For
tissue samples (after resection), studies using specific organs may require more tissue.
b A - wrapped in aluminum foil
G - glass with Teflon®; pre-cleaned jars can be purchased
P - polyethylene
T - PTFE (Teflon®).
c Suggested holding times; no USEPA criteria exist for these variables in these media. The
holding time of 1 year for semivolatile organic compounds exceeds the USEPA criterion of 14
days; every effort should be made to analyze the sample as soon as possible.
d Sandier sediments require larger sample sizes than do muddier sediments.
e Wet weight.
f Standard reference materials prepared by the U.S. National Institute for Standards and
Technology (U.S. NIST) are freeze-dried and can be stored for at least 2 years. It should,
therefore, be acceptable to freeze-dry these samples and hold them for a similar period
(Crecelius 1994, pers. comm.)
9 Whole tissues are not generally recommended for analysis.
51
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Chapter 3. Sediment Sampling Surveys
lead, mercury, nickel, silver, and zinc) that precipitate as sulfides (Ingersoll et al. 1993).
AVS is an important parameter for determining the bioavailability of divalent metals (see
additional discussion in Chapter 5). AVS is also subject to destruction if samples are
improperly handled; therefore, efforts should be made to maintain the in situ AVS con-
centrations. Ideally, the sediment sampling and homogenization process could be con-
ducted in the field in a nitrogen-filled glove box or glove bag to reduce sulfide oxidation.
However, this method may be impractical. An alternative recommended method for
sampling sediments for AVS is to transfer an unhomogenized aliquot of the sediment
from the sampling device to a glass jar with minimum disturbance and contact with air.
The jar should be filled to the brim to exclude air and then capped and stored at 4°C
(freezing may break the jar). The AVS analysis should be completed within 2 weeks.
Similar procedures should be followed for collecting unhomogenized aliquots of sediment
for analyses of total sulfides and volatile organic compounds.
Sample jars should be double wrapped in plastic bags prior to shipment and packed in
such a manner as to prevent jar breakage. Ice chests should be used to store and ship
the samples. By changing freezer packs once a day, it is generally possible to keep the
samples cooler than ambient temperatures until shipment by overnight express. Samples
should be kept at 4°C until arrival at the laboratory. Methods employed might include
ice, freezer packs, or dry ice. Freezer packs have the advantage of not creating a water
problem with melting as ice does. When using dry ice, the samples should be insulated
from the dry ice using paper or plastic bubble wrap to prevent sample freezing. It is also
necessary to be cognizant of possible transport restrictions regarding the use of dry ice,
especially when transporting by air.
When transporting samples, all U.S. Department of Transportation packaging regulations
should be followed. If field crews are transporting the samples to the laboratory, the
driver should be provided with a manifest listing all samples and the preservation
methods. If any hazardous chemicals are used to preserve the samples, the driver should
also be provided with Material Safety Data Sheets.
Chain-of-custody forms should accompany the samples at all times. As sediment samples
are received from the field, they should be immediately logged into a sample tracking
system and the chain-of-custody forms checked against the actual contents of the coolers.
The samples should then be placed in a continuously monitored cold storage room until
subsampled for analysis. See Chapter 2 for additional details on QA/QC for chemical
analyses.
FIELD PROCESSING OF SEDIMENT SAMPLES FOR BENTHIC
COMMUNITY ANAL YSES
During the ARCS Program, benthos samples were collected using a Ponar or van Veen
grab sampler, sieved aboard the R/V Mudpuppy, and then shipped to the laboratory for
taxonomic analysis. (See Chapter 7 for detailed discussion and recommendations on
sampling methods and study objectives.) Each sample was sieved through a 500-jum
52
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Chapter 3. Sediment Sampling Surveys
brass screen, using station water to wash the material. This operation was greatly
facilitated by the wash table on the deck of the R/V Mudpuppy. Material retained by the
sieve was transferred to a 500-mL glass jar and preserved with 10-percent buffered
formalin. Sample jars were double-wrapped in plastic bags before shipment; neverthe-
less, a few jars broke during shipping.
Ancillary information collected in the field included percent fullness of the Ponar sampler
and water chemistry information (dissolved oxygen, conductivity, temperature, and
oxidation/reduction potential) measured with a Hydrolab® sonde positioned 1 m above
the bottom.
FIELD PROCESSING OF SEDIMENT SAMPLES FOR TOXICITY TESTING
In general, the sediments collected for toxicity testing will be subsamples of homogenized
sediment samples that are also chemically analyzed. During the ARCS Program, the
sediments intended for toxicity testing were processed by the same methods described
above for sediment samples subjected to physical and chemical analyses. The volume
of sediment collected for toxicity testing was a function of the number and type of tests
to be conducted. The sediment was placed in high-density polyethylene jars, labeled, and
transferred in ice chests to the toxicity testing laboratory by overnight express. Sedi-
ments intended for toxicity testing should not be frozen.
SEDIMENT CHARACTERIZA TION BY REMOTE SENSING
To maximize the effectiveness of the sampling design, it is recommended that a prelimi-
nary survey be conducted to determine where fine-grained sediment deposits are located.
Contaminants tend to be associated more with fine-grained sediments (e.g., silts and
clays) than with coarser-grained materials (e.g., sands and gravels). Fine-grained
sediments can often be found by probing the bottom at specific locations in shallow
areas, or by taking small grab samples. In a more systematic way, remote sensing tech-
nology may be used for cost-effective characterization and mapping of sediment types
over broad areas in harbors and rivers, as it is currently used in offshore waters. How-
ever, these techniques need more development for work in the shallow waters typical of
Great Lakes rivers and some harbors.
The objectives of remote sensing sediment characterization in the ARCS Program were:
• To map the spatial extent and thickness of post-glacial bottom sediments
• To qualitatively characterize mapped sediments in terms of their clay con-
tent
• To qualitatively characterize the sediments in terms of their degree of com-
paction or hardness
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Chapter 3. Sediment Sampling Surveys
• To provide a database of qualitative sediment characteristics to assist in the
selection of sediment coring sites.
The goals associated with these objectives were:
• To ensure that the locations of principal sediment types would be directly
sampled for chemical analysis
• To determine whether sediment contamination is associated primarily or
entirely with selected sediment deposits that have been geophysically
mapped, or whether the sediment contamination is distributed indepen-
dently of the mapped sediment deposits.
Acoustic subbottom profiling and electrical resistivity are two geophysical profiling
techniques that can be used for remote sensing sediment characterization, although these
techniques require further development before they can be routinely implemented in
investigations of Great Lakes AOCs.
Acoustic Subbottom Profiling
Acoustic subbottom profiling of sediments makes use of reflected sound waves from
different subsurface sediment layers (Figure 3-3). These layers, which exhibit interfaces
of different elasticity and density, can sometimes be distinguished as distinct layers within
the profile trace. Uncompacted, fine-grained sediments demonstrate high porosity and
are poor acoustical reflectors. Coarse-grained sediments exhibit lower porosity and tend
to be good reflectors (Guigne et al. 1991).
Interpretation of the seismic profile is accomplished by "ground truthing" using sediment
cores collected at selected points along the ship's track followed during the seismic
survey. The visual description of core stratigraphy is compared to the seismic profile
for that position. A comparison of the core profile to the seismic profile allows
interpretation of seismic reflectors (layers) as sediment types, such as gravel, sand, silt,
and clay. The characterization of sediment stratigraphy between cores is mapped using
the interpreted seismic profiles, providing a complete picture of sediment distribution in
the study area.
Acoustic subbottom profiling has limitations. In shallow water, multiple echoes from the
water and bottom surface may obscure echoes from deeper sediments. The gas content
of sediment also reduces the effectiveness of acoustic subbottom profiling by prohibiting
acoustic signal penetration, absorbing or scattering most of the acoustic energy back to
the surface. During the ARCS Program, acoustic subbottom profiling was unsuccessful,
probably due to multiple reflectors (see Figure 3-3) and attenuation and scattering of the
sound waves by gases contained in the sediments.
54
-------
Source
Receiver
Ol
Ol
Reflector 2
LEGEND
Multiple
First return
Sound produced at the source reflects off the sea bed surface, reflector 1, and reflector 2,
which are areas of rapid density change. The receiver captures the reflected sound waves.
A multiple sound wave is received at the same time as the reflector 2 sound wave, obscuring
reflector 2 on the seismic record.
Figure 3-3. Diagram of acoustic subbottom profiling.
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Chapter 3. Sediment Sampling Surveys
Electrical Resistivity (Conductivity) Profiling
Electrical resistivity (conductivity) profiling is a common geophysical technique used in
pollution-related studies on land. The electrical resistivity of sediments is primarily a
function of their porosity and pore fluid chemistry. For clay-rich sediments, the clay
mineralogy is also a significant factor. It is generally not possible to separate the effects
of porosity, pore fluid chemistry, or mineralogy on resistivity measurements.
The objectives of electrical resistivity or conductivity surveys are the same: a lateral and
vertical mapping of sediments with similar electrical properties. Comparison of the elec-
trical properties of the sediments with actual cores provides a basis for associating the
electrical properties with specific sediment types to assess sediment deposit hardness.
Resistivities of approximately 10 to 40 ohm-meters are generally associated with wet
clays, while resistivities in the range of 100 to 200 ohm-meters are generally associated
with wet clean sand (Telford et al. 1976).
While not as useful by themselves, electrical resistivity surveys could be used to supple-
ment acoustical subbottom profiling.
Conclusions
Acoustically turbid sediments (i.e., sediment with acoustically unresolvable layering)
were found at all three ARCS priority AOCs where acoustic subbottom profiling was
applied, preventing demonstration of this technique for remote sensing sediment
characterization. Other forms of remote sensing such as ground penetrating radar or
induced conductivity still need to be explored. A suite of remote sensing techniques,
including acoustic subbottom profiling and electrical resistivity, may be needed to
perform reliable mapping of sediment deposits in harbors and rivers, although more
research needs to be conducted.
Surface hardness classification using acoustical first return amplitudes was shown to be
a promising remote sensing technique, but needs further development. This method
should be refined with algorithms developed to allow classification of rock, sand, silt,
and clay sediment types, although this will only classify surface sediments. Research in
acoustical sediment classification continues (Schock et al. 1986; Guigne et al. 1991;
Sjostrom et al. 1992) and may yet prove useful in other Great Lakes AOCS.
56
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4. SCREENING-LEVEL ANALYSES
Most of the Great Lakes rivers and harbors that have been designated as AOCs have been
industrialized for decades. These AOCs have been subject to a large number of altering
and often degrading forces, including discharges of metals, oils, halogenated organic
compounds, domestic wastes, and other pollutants; altered sedimentation patterns due to
watershed deforestation; and artificial rearrangement of their bedded sediments from
dredging, ship traffic, and shoreline construction. One result is that the ecosystems of
the Great Lakes AOCs, and sediments in particular, possess a mosaic of chemical and
physical characteristics that reflect a multitude of historical anthropogenic alterations.
The chemical and physical characteristics of Great Lakes AOCs are sufficiently complex
that conducting even a general inventory is difficult. For many of these AOCs, there are
no prior surveys of contaminated sediments except for routine navigational dredging stud-
ies performed by the Corps. The available historical studies of contaminated sediments
usually included a limited number of chemical and even fewer toxicity tests performed
primarily on surficial samples. Detailed chemical and biological tests are expensive,
time-consuming, and require relatively large volumes of sediments. Few studies have
had the resources to analyze enough samples to create meaningful contour maps of conta-
minant distributions for significant portions of a river or other water body.
In situations where there were insufficient resources to conduct enough detailed assays
to adequately characterize sites, two methods have the potential to fill hi data gaps at
relatively low cost: indicator (performed during the ARCS Program) and screening-level
(the recommended approach) analyses. The analysis approach used in the ARCS Pro-
gram was designed to test the efficacy of a two-phased sampling design: 1) a set of
quick, less expensive assays ("indicator analyses") performed at a large number of sta-
tions where sediment cores were collected, and 2) detailed chemical and toxicological
assays performed at a limited number of surface sediment stations throughout the study
area. Multivariate statistics were then used to explore the potential relationships between
indicator analyses and more detailed assays, although the results were often inconclusive
(see Indicator Analyses section below).
More recent research has shown that the screening-level analyses discussed in the follow-
ing section are also quick and less expensive, and may be more comparable from site to
site than the indicator analyses tested in the ARCS Program. The screening-level
analyses discussed below are appropriate for in-field use, as well as in the laboratory.
In the field, screening-level analyses can be very useful for guiding sampling later in the
survey, for example, to better delineate an area of high contamination.
57
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Chapter 4. Screening-Level Analyses
Indicator analyses are the field-level analyses performed during the ARCS Program.
They are inexpensive surrogates for more expensive parameters. Screening-level analy-
ses are those described in the next section.
SUMMARY OF SCREENING-LEVEL METHODS
Screening-level analyses are relatively rapid, inexpensive assays that can be readily
applied to sediment assessment. These analyses can be completed in the field or in the
laboratory. Due to their relatively low cost, screening-level analyses should be used to
focus comprehensive analyses on hot spots where remediation is most likely to occur and
on "grey" areas (i.e., areas of intermediate contamination) where the integrated sediment
assessment approach is necessary to make a proper evaluation. They can also be used
to conduct a preliminary survey of a large area to locate suspected hot spots. Data from
these analyses are useful for providing a sufficient number of data points for proper
mapping of sediment conditions. Currently used screening-level analyses include fluor-
ometry for PAHs; immunoassays for PCBs, chlorinated pesticides, and PAHs; infrared
spectroscopy for petroleum hydrocarbons; thin-layer chromatography (TLC) for semi-
volatile organic compounds; x-ray fluorescence (XRF) for metals; and rapid toxicity
tests. These screening-level analyses are described below.
Total PAHs by Fluorometry
The total PAH assay using fluorometry was developed and tested in surveys along the
Buffalo River, and has proven to be fairly rapid (20 samples/day), inexpensive, and
strongly correlated with GC/MS and high-pressure liquid chromatography (HPLC)
analyses (Friocourt et al. 1985). This assay is particularly sensitive to compounds
containing aromatic rings, such as PAHs, which strongly fluoresce at specific wave-
lengths that can be set on the instrument (e.g., 280-nm for excitation spectra). This
sensitivity is enhanced by the specificity of the assay, which reduces the need for
complicated separation techniques during sample preparation. Fluorometry does not
respond to a wide range of organic compounds found in sediments, such as aliphatic
hydrocarbons from oils, fatty acid methyl esters from natural and anthropogenic sources,
and phthalate esters, all of which are common interferences in gas chromatographic
analyses.
Total PCBs, Chlorinated Pesticides, and Other Organic Chemicals
by Enzyme Immunoassay
Enzyme immunoassays are biochemical procedures that rely on the binding of specific
chemicals in a sample (plus an enzyme-labeled version of the chemical) to antibodies
provided in a test kit; the bound chemicals can then be separated from the rest of the
sample and associated interferences by simple washing; then the labeled component is
detected by adding a color indicator (Schrynemeeckers 1993). Immunoassay kits are
58
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Chapter 4. Screening-Level Analyses
available from several manufacturers. For sediments, a small sample is quickly extracted
and purified. The extract is then tested with the immunoassay kit.
Enzyme immunoassays are inherently free of most chemical interferences (Vanderlaan
et al. 1991) and are available for several aromatic compounds besides PCBs and pesti-
cides (e.g., petroleum hydrocarbons, PAHs, trinitrotoluene, benzene). These assays are
typically used either to indicate the presence/absence at some predetermined concentra-
tion for chemical mixtures such as total PCBs, or to provide order-of-magnitude
estimates of concentrations of individual chemicals. The detection limit for total PCBs
in sediments for various test kits ranges from less than 0.1 ppm to approximately 5 ppm,
and the immunoassay results can be strongly correlated with gas chromatography results
(Huellmantel et al. 1992). The most confident use of hnmunoassays is in determining
when assay-specific contaminants are below levels of concern because of the low poten-
tial for false negative readings.
Enzyme hnmunoassays can also be performed for PAHs. Production rates and costs are
similar to the rates and costs for PCB immunoassays. Other recent field tests of PCB-
contaminated soil using hnmunoassays resulted in an approximate 40-percent decrease
in field time and a 44-percent decrease in overall costs compared with more traditional
sampling and laboratory analyses (Scallen et al. 1992). Such cost savings will be
realized most readily at sites that have a limited number of contaminants of concern,
where only a single immunoassay is required. Several different immunoassays would be
required to fully characterize sediments containing a wide range of compound classes of
concern, because each kit is sensitive to only one of the compounds or classes of com-
pounds described above.
Total Petroleum Hydrocarbons by Infrared Spectroscopy
The infrared assay is intended to be a field version of the extractable residue analysis by
USEPA Method 418.1. This analysis is useful for measuring total petroleum hydrocar-
bons (TPHs) because responses in relatively narrow ranges of the entire infrared
spectrum correspond to the presence of specific groups of atoms regardless of the
structure of the remaining molecule. For example, a variety of hydrocarbon structures
are simultaneously detected in a sample by characteristic changes in carbon-hydrogen or
carbon-carbon bonds (e.g., stretching and bending vibrations) that are induced by
exposure to infrared radiation. Detection limits are typically in the range of 1-10 ppm.
The infrared method has the advantage of providing a rapid, quantitative determination
of TPH concentrations, but also has some limitations that can produce either negative or
positive analytical bias (Douglas and Uhler 1993). As a result, this screening method
may be less accurate than other techniques for measuring hydrocarbons such as field gas
chromatography, which has the disadvantage of a higher cost, or TLC.
59
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Chapter 4. Screening-Level Analyses
Semivolatile Organic Compounds by Thin-Layer Chromatography
The TLC field method, developed by Friedman & Bruya, Inc. (Seattle, Washington) and
reported by Newborn and Preston (1990, 1991), can be used for a wide range of semi-
volatile organic compounds with detection limits of approximately 10 ppm (lower detec-
tion limits to approximately 1 ppm are feasible for some compounds). The TLC method
was not used in the ARCS Program.
This method involves placing a drop of sample extract near the bottom of a silica gel-
coated glass plate. The end of the plate is immersed in an appropriate solvent. As the
solvent front moves upward on the plate, the compounds of interest are separated out of
the mixture based on their mobility in the solvent-solid phase system, and can then be
identified both qualitatively and quantitatively, using ultraviolet light or iodine to
visualize the separated chemicals.
Metals by X-ray Fluorescence
Field portable XRF units have been used to analyze soils at Superfund sites (e.g., Fri-
bush 1992; Driscoll et al. 1991) and have been shown to provide rapid (<5 minutes/
dried sample) quantification of more than 20 elements at a time. Detection limits for
portable units have typically been reported in the 100-1,000 ppm range for most metals,
while laboratory-based XRF units have greater resolution and are capable of lower detec-
tion limits in the range of 2-25 ppm (Grupp et al. 1989; Fribush 1992). Laboratory
XRF units have a somewhat longer analysis time (20 minutes). XRF analyses, unlike
other metal analyses that rely on digestion of samples with various acids, do not destroy
the sample and require only a small amount of material. XRF results have been found
to be correlated strongly with conventional atomic absorption and inductively coupled
plasma (ICP) spectroscopy results (Kuharic et al. 1993).
Rapid Toxicity Tests
The Microtox® test is a rapid, sensitive method of toxicity testing based on light emission
by the luminescent bacterium Photobacterium phosphoreum in the presence and absence
of aqueous toxicants. The emitted light is a product of the bacterial electron transport
system and thus directly reflects the metabolic state of the cells. Accordingly, decreased
luminescence following exposure to chemical contaminants provides a quantitative
measure of toxicity. The test was developed for use in freshwater habitats to assess the
toxicity of waterborne pollutants (Bulich et al. 1981). Recently, a solid-phase modifica-
tion of the Microtox® test was developed. See Chapter 6, Evaluation of Sediment
Toxicity, for a more detailed discussion of the Microtox® test.
In addition to Microtox®, other rapid toxicity tests, such as Daphnia IQ® or the 2-day
rotifer toxicity test (Snell and Moffat 1992), can be used for screening-level analyses.
60
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Chapter 4. Screening-Level Analyses
INDICA TOR ANAL YSES
The indicator analyses used in the ARCS Program (Table 4-1) are rapid, low-cost analy-
ses chosen with several considerations in mind, including environmental relevance,
probable correlation with conventional analyses, analysis time, analytical cost, and
regulatory relevance. The chosen analyses may be divided into two categories: those
that produce a direct measure of sediment composition or contamination, and those that
produce an indirect measure of sediment quality that may be related to other variables
of environmental or regulatory importance.
The metals (cadmium, chromium, copper, iron, lead, nickel, and zinc), total and volatile
solids, total organic carbon (TOC), grain size, ammonia, and the Microtox® test (also
a screening-level analysis technique) fall into the first category. TOC is an important
factor in determining the environmental availability of hydrophobic organic contaminants
and metals. Grain size data are useful in determining the geographic extent of sediment
deposition zones and layers and perhaps their origin, and provide a quick method to
make initial determinations as to the potential contaminant load of the sediments, with
finer-grained sediments typically more contaminated than the sandy, coarser-grained
fraction. Ammonia is often present in toxic concentrations in harbors and tributaries of
the Great Lakes (Ankley et al. 1990). Microtox® has been employed in previous
sediment quality investigations as a toxicity screening tool (e.g., Giesy et al. 1988b).
The remaining indicator assays (conductivity, pH, extractable residue, and the organo-
halogens) may be related to other variables of environmental importance (e.g., bioavail-
ability, organic contamination). Extractable residue is a measure of the solvent-
extractable materials (oils and other petroleum-related products) in sediment, some
components of which are known toxins (e.g., PAHs), and which have also been shown
to influence the availability of many organic contaminants (Neff 1985). The organo-
halogen analysis is a rapid, inexpensive measure of total halogenated compounds. While
the organohalogen analysis is unable to distinguish highly toxic organochlorine com-
pounds (e.g., PCDDs and PCDFs) from other high-concentration, less toxic compounds
(e.g., non-planar PCBs), it does give an estimate of the total concentration of these
compounds present.
Results for Core Samples Analyzed During the ARCS Program
The following three sample matrices were used in the ARCS Program to assess the indi-
cator analysis approach for core samples collected from the Saginaw River, Buffalo
River, and Indiana Harbor AOCs: whole sediment for analysis of grain size, total and
volatile solids, metals, solvent extractable residue, organohalogens, and TOC; sediment
elutriate for analysis of ammonia and Microtox®; and sediment pore water for analysis
of conductivity. The elutriate procedure is designed to mimic the rapid desorption of
contaminants from sediments resulting from the open-water disposal of dredged materials
(Plumb 1981). (See Chapter 6, Evaluation of Sediment Toxicity, and Burton [1992a] for
further discussion of the use of elutriates and pore water.)
61
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TABLE 4-1. INDICATOR ANALYSIS DESCRIPTIONS AND CITATIONS
Analysis
Method Description
Citation3
Ammonia
Conductivity
pH
Total solids
Volatile solids
Grain size
Microtox®
Total organic carbon
Metals (cadmium, chromium,
copper, iron, lead, nickel, zinc)
Solvent extractable residue
Organohalogens (chlorine and
bromine)
Selective ion electrode [1]
Wheatstone Bridge-B platinum electrode [2]
Selective ion electrode [3]
Oven drying at 60°C [3]
Ashed at 500°C [3]
Wet sieving [4]
Bacterial luminescence [5]
Combustion/thermal conductivity [6]
Inductively coupled emission spectro- [7]
scopy
Solvent extraction/gravimetric [8]
Solvent extraction/neutron activation [9]
[1] Instruction manual for Orion ammonia electrode, with modifications from Methods of
Soil Analysis, Part 2. Chemical and Microbiological Properties. 1982. 2nd Edition.
A. Page (ed). American Society of Agronomy, Madison, Wl. 1,159pp.
[2] ASTM. 1989. Standard methods for the examination of water and wastewater.
Method 2510. 19th Edition. American Society for Testing and Materials, Philadelphia,
PA.
[3] Plumb, R. 1981. Procedures for handling and chemical analysis of sediment and water
samples. Technical Report EPA/CE-81-1. U.S. Army Corps of Engineers, Vicksburg,
MS.
[4] Instruction manual for Gilson Model WV-2 wet siever.
[5] Instruction manual for Microtox® analyzer.
[6] Perkin-Elmer 2400 CHN elemental analyzer instructions. Instruction manual 0993-
7147, revised 10/88. Perkin-Elmer Corporation, Norwalk, CT.
[7] Instruction manual for Perkin-Elmer Model 40 plasma emission spectrophotometer.
[8] Tecator Soxtec System HT6 Manual No. 1000 1590. Tecator AB, Hoganas, Sweden.
20pp.
[9] Robertson, D.E., and R. Carpenter. 1974. Neutron activation techniques for the
measurement of trace metals in environmental samples. Report of the Subcommittee
on Radiochemistry, National Academy of Sciences—National Research Council, Wash-
ington, DC. 78 pp.
62
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Chapter 4. Screening-Level Analyses
The ARCS Program indicator assay data indicate that the three AOCs examined exhibit
a wide range of sediment contamination, with Saginaw River the least contaminated and
Indiana Harbor the most contaminated (Table 4-2). Only pore water conductivity failed
to show this pattern.
Some specific observations drawn from Table 4-2 include:
• The mean ammonia concentration in the sediment elutriates from Indiana
Harbor, 24 mg/L, was observed to cause significant mortality in fathead
minnow (Pimephalespromelas) and cladoceran (Ceriodaphnia dubid) toxi-
city tests of sediment pore water from the lower Fox River/Green Bay
watershed in Wisconsin (Ankley et al. 1990).
• The greatly elevated TOC and extractable residue values found at Indiana
Harbor are indicative of the extreme amounts of oil found in these sedi-
ments. In fact, some volatile solid samples from this area ignited when
combusted in a muffle furnace.
• The Microtox® test results suggest that sediments from Indiana Harbor
were substantially more toxic than sediments from the other study areas.
In general, the Microtox® results reflected the chemical results; increasing
chemical contamination was associated with increasing toxicity.
The core sampling, while fairly costly and time-consuming, yielded information unavail-
able through traditional surficial grab sampling. Indicator analysis data from many cores
showed increasing contamination and toxicity with depth, with highly contaminated and
toxic material up to 4 m beneath the surface of the sediment. For example, in the Buf-
falo River, 50 percent of the cores ended in what was visually characterized as black,
oily silt. This material contained elevated concentrations of metals (e.g., maximum lead
and copper concentrations of 1,586 pig/g and 951 /zg/g dry weight, respectively) and
volatile solids (maximum of 19.8 percent dry weight), and was also toxic (50 percent of
the deepest samples had an EC50 < 50 percent). Sediment cores from the Saginaw River
at Station 6, near the wastewater treatment plant discharge, contained an 8- to 20-in.
layer of black, oily silt beneath 1 ft or so of cleaner surficial sand. This oily silt layer
exhibited metals concentrations 3-15 times higher than those in the sand above (e.g.,
cadmium = 19 vs. 1.3 /ug/g dry weight; chromium = 590 vs. 40 /jg/g dry weight; lead
= 180 vs. 32 /xg/g dry weight). The most highly contaminated sediments in most of the
cores from the Saginaw and Buffalo Rivers were found well below the surficial sedi-
ments. This distribution will often be the case in Great Lakes AOCs when there has
been recent success in controlling contaminants from point sources.
In contrast, Indiana Harbor typically exhibited the most highly contaminated sediments
at the surface, probably indicating continuing contaminant inputs.
63
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TABLE 4-2. MEAN VALUES FOR SELECTED INDICATOR
VARIABLES IN CORE SAMPLES
Survey
Variable
Ammonia (mg/L)
Conductivity (//S/cm)
PH
Total organic carbon
(% dry weight)
Cadmium (//g/g dry weight)
Chromium (//g/g dry weight)
Copper (//g/g dry weight)
Iron (% dry weight)
Lead (//g/g dry weight)
Nickel (//g/g dry weight)
Zinc (//g/g dry weight)
Microtox® (EC50)
Extractable residue
(//g/g dry weight)
Saginaw River
#2a
11
2,200
7.13
2.0
1.3
48
42
1.3
44
21
134
98
750
Saginaw River
#3b
11
2,200
7.12
2.2
2.2
64
47
1.4
41
28
150
96
1,000
Buffalo River
#3C
19
3,000
7.24
2.4
4.6
210
162
4.8
300
42
650
72
4,600
Indiana Harbor
#2d
24
2,500
7.27
9.1
10
450
261
12
790
79
3,300
38
20,000
Note: EC - effective concentration
a Lower river.
b Bay City wastewater treatment plant area.
c Lower river, including Buffalo Color Peninsula.
d Indiana Harbor Canal.
64
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Chapter 4. Screening-Level Analyses
Correlation Between Indicator and Comprehensive Analyses
As stated earlier, the time and resource requirements of many comprehensive chemical
analyses (Chapter 5) and biological analyses (Chapters 6 and 7) limited their application
in the ARCS Program to only a few master stations in each demonstration AOC. The
quicker and less expensive indicator analyses were performed for a much larger number
of stations and samples in order to test whether these indicator analyses could be
considered to be both stand-alone measures of sediment contamination and possible
correlates with the more detailed master station data. The indicator analysis approach
was successful in showing chemical and toxicity concerns at a much lower cost than
could be achieved with a comprehensive survey. Therefore, these analyses can be used
to focus resources in a follow-up phase of work at a specific site.
Multivariate statistics were also applied to the two data sets to attempt to create pre-
dictive, correlative equations for the comprehensive analyses as a function of indicator
analysis results. Although significant correlations were often found, the resulting
predictive equations were not readily interpretable. For example, in Indiana Harbor, the
concentration of total PCBs in surficial sediment was found to be significantly correlated
with the concentrations of zinc and cadmium:
PCBs = -58,700 + (11.6xZn) - (2,020xCd)
(r2 = 0.9442; P = 0.0031)
where:
PCBs = concentration of total PCBs in surficial sediment, /ig/kg dry weight
Zn = concentration of zinc in surficial sediment, mg/kg dry weight
Cd = concentration of cadmium in surficial sediment, mg/kg dry weight.
However, there is no apparent reason why the concentration of total PCBs should
increase with increasing concentrations of zinc, but decrease with increasing concentra-
tions of cadmium. In most cases, the resulting predictive equations were not universally
applicable between AOCs, probably as the result of site-specific variations among the
study areas.
Because Microtox® was performed as both an indicator assay and a comprehensive analy-
sis assay (along with many other analyses), the prediction of Microtox® toxicity could
be evaluated. Predictive equations for the Microtox® test were calculated from the indi-
cator analyses of the master stations, and then used to calculate EC50 values for each of
the many reconnaissance station samples. For evaluation purposes, the calculated EC50
values were divided into three categories: low toxicity (> 70 percent elutriate),
intermediate toxicity (30-70 percent elutriate), and high toxicity (< 30 percent elutriate),
and comparisons were made between the calculated and measured values from each
reconnaissance station. The comparison between the predicted Microtox® EC50 values
65
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Chapter 4. Screening-Level Analyses
produced by the correlation equations and the measured EC50 values is presented in
Table 4-3. The high percentage of "false positive" results (especially for the Buffalo
River and Indiana Harbor study areas) suggests that the data do not provide sufficient
resolution for predicting toxic and nontoxic conditions. Furthermore, the variability in
regression equations among areas suggests that any one equation would have limited
applicability. For example, the predictive equations for the Microtox® test endpoint in
the three AOCs were:
Buffalo River
Saginaw River
Indiana Harbor
where:
Organobromine
Pb
Cd
Cu
Microtox® EC50 = 119 - (587 x organobromine) - (0.127 x Pb)
(r2 = 0.9700; P = 0.0001)
Microtox® EC50 = 106 - (10.5 x Cd)
(r2 = 0.9943; P = 0.0001)
Microtox® EC50 = 59.3 - (0.117 x Cu) - (0.008 x Pb)
(r2 = 0.9432; P = 0.0032)
= concentration of organobromine in surficial sediment, mg/kg dry weight
= concentration of lead in surficial sediment, mg/kg dry weight
= concentration of cadmium in surficial sediment, mg/kg dry weight
= concentration of copper in surficial sediment, mg/kg dry weight.
Hence, the predictive equation developed for one AOC would not enable accurate predic-
tions to be made for the other AOCs. It should be noted that Microtox® also had a high
number of significant correlations with other toxicity tests (Chapter 6).
TABLE 4-3. COMPARISON BETWEEN PREDICTED AND
MEASURED MICROTOX® EC50 VALUES
Site
Percent of
Calculated Values
in Same Category
Percent of Percent of
Calculated Values Calculated Values
in Higher Category in Lower Category
(False Positive) (False Negative)
Buffalo River
Saginaw River
Indiana Harbor
47.3
71.6
58.9
47.3
11.2
35.8
5.4
17.2
5.3
Categories: Low toxicity
Intermediate toxicity
High toxicity
= EC50 >70 percent
= 30-70 percent
= <30 percent
66
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Chapter 4. Screening-Level Analyses
Regression equations for other indicator variables were also highly variable among AOCs
suggesting that site-specific differences and the strong potential for coincidental corre-
lations severely limit the use of these predictive tools in decision-making. The regression
equations are indicative of correlative rather than causative relationships among the
variables.
CONCLUSIONS
In conclusion, each of the indicator analyses conducted in the ARCS Program was simple
to perform and, except for metals by ICP-atomic emission spectroscopy (ICP/AES),
required only one technician to operate. The TOC analysis, which was performed by
several ARCS laboratories independently (LLRS, Battelle, and the National Oceanic and
Atmospheric Administration [NOAA] Great Lakes Environmental Research Laboratory),
yielded comparable data despite some differences in analytical procedure. Discussion
with other laboratories suggests that TOC protocols can differ substantially, however,
especially in the ratio of sediment:acid, strength of the acid, and oxidation tune and
conditions. In some cases, these differences may call interlaboratory data comparisons
into question, which will have implications for risk assessments and sediment quality
criteria development.
The sediment cores collected in the ARCS Program yielded information that would not
have been available from surficial grab samples. Subsurface sediment layers, up to 13 ft
beneath the sediment-water surface, exhibited high toxicity and high concentrations of
metals and oils. Sediment buried at these depths may not pose substantial environmental
or human health risks of exposure, but future sediment surveys should investigate the
3-dimensional distribution of contaminated sediments to provide reasonable estimates of
the potential vertical extent of any remedial activities that may be required to address
surface sediment contamination.
The indicator analysis approach provides a useful means of focusing the time-consuming
and costly comprehensive chemical analyses and toxicity tests on selected samples that
are most likely to be of concern from a study area. The interpretation of indicator
analysis results, however, appears to be site-specific and without a firm scientific basis
for making meaningful predictions for the outcome of more comprehensive assays. The
combined indicator analyses, however, do provide a preponderance of evidence that can
be used to make a decision on the existence or lack of concern about a specific study
area. Further, some of the indicator analyses (e.g., Microtox®, metals by ICP, TOC)
provide valuable, direct information.
The indicator analyses used in the ARCS Program, however, are more labor-intensive
and have an overall higher cost than the screening-level analyses discussed in Summary
of Screening-Level Methods. These screening-level analyses are efficient and provide
interpretable data that can be used to make appropriate and timely decisions on how to
focus resources on areas most likely to be remediated, as well as to identify less certain
areas where comprehensive analyses are needed in a subsequent phase of investigation
67
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Chapter 4. Screening-Level Analyses
to make a final sediment assessment. Screening-level analyses show much promise for
evaluating parameter concentrations (e.g., PAHs, PCBs, metals, toxicity) on many
samples more quickly and less expensively than comprehensive chemical and biological
analyses (Chapters 5,6, and 7). Also, screening-level analyses are recommended over
most indicator parameters because it is preferable to directly measure something rather
than to model it. However, most of these screening-level analyses provide less selective
and accurate results and should not be used to make decisions regarding the need for
sediment remediation without confirmation by comprehensive chemical and biological
analyses.
68
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5. CHEMICAL ANAL YSES
The assessment of risks associated with contaminated sediments at a specific site is
limited by the available database. A comprehensive assessment of contaminated sedi-
ments requires both evaluation of the biological component (e.g., toxicity tests, lesions,
developmental abnormalities, benthic community surveys) and chemical characterization
at the study site. In many studies, either the biological or the chemical component is
absent, limiting the quality of professional judgment that may be applied to assess risks.
One goal of the ARCS Program was to generate a large body of chemical data to comple-
ment extensive biological studies at the demonstration AOCs. It was recognized that
ideally the sampling scheme should be complete spatially (horizontally as well as with
depth) and that the analysis scheme should include the full range of chemicals that might
be present at the site. Assessment of the depth of contamination was extremely important
to ensure that any contamination that could be uncovered during site remediation would
be handled properly.
To develop a strategy for sample collection and analysis, investigators should study the
site history and gather relevant data that may indicate the identity and location of poten-
tial contaminants in sediments. Ideally, the contaminant history should be gleaned both
from past environmental monitoring studies and a history of chemical loading to the
drainage basin (e.g., based on agricultural, urban, industrial land use practices). Local
experts may be able to provide additional information regarding potential contamination
at a particular site. Selection of the appropriate analytical variables should also take into
consideration available analytical methods and whether the new data will be comparable
with historical data (assuming that the historical data meet current DQOs). Often practi-
cal considerations, such as use of the current methods for USEPA's priority pollutant list
compounds, limit the selection of chemicals that may be evaluated.
Typical Great Lakes sites present multiple contamination problems that may have been
explored to some degree during previous studies. For the five ARCS priority AOCs,
historical data were gathered by the Corps (Brandon et al. 1991; Skogerboe et al. 1991;
Lee et al. 1991; Simmers et al. 1991; Tatem et al. 1991). While these reports were not
complete at the time that the list of potential contaminants of concern was developed,
sufficient information was available to select the chemicals to be analyzed. Local experts
were, and should always be, consulted regarding the identity and locations of sediment
contaminants, as well as potential sources of those contaminants.
An exploratory screening-level study (see Chapter 4) should be completed first whenever
possible to better target analyses of samples in cases where the historical database and
69
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Chapter 5. Chemical Analyses
knowledge of the contaminants released to the system are limited. Screening-level analy-
ses can help target areas requiring detailed assessments. When possible, screening-level
studies should be initiated well in advance of more detailed assessment studies so that any
major class of contaminants found in the system can be incorporated into the detailed
study design. An exploratory screening-level study may also save money by narrowing
the list of contaminants and areas to sample for the detailed assessment.
A detailed assessment is usually needed to determine appropriate remedial alternatives
that may be cost effective for a given site. Spending too many project dollars for the
detailed assessment, however, can leave too few dollars for remediation and post-
remediation monitoring. Because remediation of contaminated sediments is often very
costly, a great deal of money can be saved through accurate, comprehensive assessment
activities. A tiered approach makes maximum use of available funds by quickly identi-
fying potential concerns using relatively low-cost screening analyses and then focusing
higher-cost, detailed analyses on high-priority sites. Sampling costs can often be mini-
mized in such a program by collecting and archiving samples from all stations sampled
in a screening survey, and then conducting detailed analyses on selected stations after
concerns are better defined.
Factors that affect the bioavailability of contaminants should also be considered when
developing the list of chemicals to be analyzed. In general, several sediment characteris-
tics have been identified as major factors that will alter contaminant bioavailability
(Landrum and Robbins 1990). Among these are the TOC and AVS content of sediments.
The TOC concentration is used to estimate the partitioning of nonionic organic com-
pounds between sediment solid fractions and pore water. The AVS theory of metals bio-
availability in sediment is that sulfide can form an insoluble compound with many dival-
ent metals (e.g., cadmium, copper, lead, nickel, zinc), thereby reducing the concentration
of these metals that may be available to exposed organisms (Di Toro et al. 1990). Both
TOC and AVS analyses are recommended, although there is still scientific debate about
the use of AVS normalization in predicting the toxicity of metals. Additional analyses
to characterize the bioavailability of sediment-associated contaminants may need to be
considered in future assessments as new data become available from ongoing national
research on bioavailability.
Chemicals not typically analyzed for may also be associated with sediment toxicity.
Exploratory analyses for tentatively identified compounds should also be considered.
These techniques employ use of GC/MS, liquid chromatography/mass spectrometry, nuc-
lear magnetic resonance spectroscopy, and infrared spectroscopy. They will not be
discussed here.
In some cases, chemical analyses will be performed not only on sediment samples, but
also on tissue, elutriate, or pore water samples. The contaminant concentrations in tissue
may be used in human health or ecological risk assessments, while the contaminant con-
centrations in elutriate or pore water samples may provide a better estimate of the con-
taminant concentrations to which benthic organisms are exposed.
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Chapter 5. Chemical Analyses
METHOD SELECTION (GENERAL OVERVIEW)
Analytical techniques should be selected that produce reliable data, have adequate sensi-
tivity to meet the required detection limits, and are cost effective. In addition, standard
techniques such as those in Test Methods for Evaluating Solid Waste: Physical/Chemical
Methods (USEPA 1986b) should be used, when possible, to ensure that the data to be
collected will be comparable with historical data. Other methods are needed for analyses
of PCB congeners, methylmercury, and tributyltin (TBT), which are not addressed in
standard USEPA methods. Recommended analytical procedures are discussed in the fol-
lowing sections. Approximate costs (in 1993) for common chemical analyses are pro-
vided in Table 5-1.
CONVENTIONAL VARIABLES
This section describes recommended analytical methods for the measurement of conven-
tional (noncontaminant) variables in sediment and tissue samples.
Sediments
Total solids, grain-size distribution, and TOC are common analyses that are conducted
to characterize sediments or to provide data used to interpret specific chemical analyses.
Additional information on the use of these analyses to characterize sediments can be
found in Chapter 4, Screening-Level Analyses, or in Plumb (1981).
The total solids content of sediments can be determined by oven-drying the sample at
105°C or by freeze-drying a subsample and calculating the ratio of dry to wet weight of
the sediment. Grain-size distribution (e.g., the percent gravel [ >2-mm diameter], sand
[2 mm-62.5 /mi], silt [62.5 /mi-3.9 /mi], and clay [<3.9 /mi] content) of a sediment
sample can be determined using a nest of sieves and pipette analysis or hydrometer.
After treating the sediment with hydrochloric acid (non-oxidizing acid) to remove carbo-
nates, organic carbon can be determined as total carbon by combusting the sample at
800-1,000°C in an oxygen atmosphere and transferring the evolved CO2 directly into a
gas analyzer with either a thermal conductivity or infrared spectroscopy detector.
In addition to these more common analyses, AVS can be determined in sediments as
described in Cutter and Oatts (1987) or Allen et al. (1991). These methods involve
generation of hydrogen sulfide from sediment in IN HC1, trapping the hydrogen sulfide,
and quantifying by a number of possible techniques.
71
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TABLE 5-1. APPROXIMATE COSTS FOR CHEMICAL ANALYSES
Parameter
Total solids
Acid-volatile sulfides
Grain size
Total organic carbon
Lipid content
Moisture content
Semivolatile organic
compounds
Polynuclear aromatic
hydrocarbons
Matrix
Sediment
Sediment
Sediment
Sediment
Tissue
Tissue
Sediment
Tissue
Water or elutriates
Sediment
Tissue
Methods
Gravimetric
GC/PID
Sieve and pipette
Combustion
Gravimetric
Gravimetric
GC/MS
GC/MS, SIM
HPLC
Cost3
(1993)
$15
$75
$65-$100
$50-$70
$25-$75
$25
$300-$700
$450
$175
PCB Aroclors® and chlorinated
pesticides
PCB congeners
PCB coplanars
PCDDs/PCDFs
Methylmercury
Water or elutriates
Sediment
Tissue
Water or elutriates
Sediment
Tissue
Water or elutriates
Sediment
Tissue
Water or elutriates
Sediment
Tissue
Water or elutriates
Sediment
Tissue
Water or elutriates
GC/ECD
GC/ECD
GC/ECD, HRGC/HRMS
HRGC/HRMS
Ethylation, CVAF
$200-$300
$300-$500
$900-$1,500
$900-$1,500
$225
Butyltin compounds Sediment
Metals
Note: AES -
CVAA -
CVAF -
ECD -
FPD -
GC
GFAA -
HPLC -
Tissue
Water or elutriates
Sediment
Tissue
Water or elutriates
atomic emission spectroscopy
cold vapor atomic absorption
spectroscopy
cold vapor atomic fluorescence
electron capture detection
flame photometric detection
gas chromatography
graphite furnace atomic absorption
spectroscopy
high-pressure liquid chromatography
Derivatization, $400-$500
GC/FPD
GFAA, ICP/MS, $300-$400
ICP/AES, CVAA or
CVAF (mercury only).
XRF $70
HRGC - high-resolution gas chromatography
HRMS - high-resolution mass spectrometry
ICP - inductively coupled plasma
MS - mass spectrometry
PCB - polychlorinated biphenyl
PCDD - polychlorinated dibenzo-p-dioxin
PCDF - polychlorinated dibenzofuran
PID - photoionization detector
SIM - selective ion monitoring
XRF - x-ray fluorescence
Cost per sample. Costs per sample generally go down as more samples are analyzed.
72
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Chapter 5. Chemical Analyses
Tissues
Moisture content in biological tissue can be determined gravimetrically by oven- or
freeze-drying the samples and determining the ratio of dry to wet weight of tissue. Lipid
content can be determined gravimetrically following a method derived from Bligh and
Dyer (1959). A subsample of each tissue sample is extracted with a chloroform-
methanol solution (a nonpolar-polar solvent combination) and centrifuged, and then the
chloroform layer is drawn off and filtered. Care must be taken to ensure that the filter
is rinsed with solvent so that lipids are not adsorbed. The organic filtrate is evaporated
and the remaining residue is dried at 103 °C. The method should be performed on a sub-
sample of the same tissue homogenate used for organic chemical analyses to avoid intro-
ducing sampling variability into lipid-normalized concentrations of organic compounds.
Alternatively, the gravimetric weight of solvent-extractable organic material in tissue
samples (i.e., lipid content) can be determined directly from the same extract used for
analysis of semivolatile organic compounds, assuming that a combination of polar and
nonpolar solvents is used in the extraction (e.g., acetone-dichloromethane or methanol-
dichloromethane). Following separation of the organic and aqueous fractions of the
tissue extract using a separatory funnel, and prior to additional extract cleanup (e.g., gel
permeation chromatography [GPC]), a subsample not exceeding l/40th of the total
extract should be transferred to a pre-weighed aluminum dish, evaporated gently, and
weighed.
Use of nonpolar solvents alone in the tissue extraction process will not extract the more
polar lipids such as phospholipids. Further, the partitioning of nonpolar contaminants
associated with the more polar lipids appears to be similar to that for nonpolar lipids
(Gardner et al. 1990). Thus, if a completely nonpolar extraction is employed, the lipid
content will be underestimated while the measured contaminant concentrations will be
fairly complete, creating a positive bias in lipid-normalized concentrations. Overall
extraction efficiency will also decrease because of the creation of emulsions between the
nonpolar solvent and water in the tissue sample. To avoid these concerns, lipids in tissue
samples should always be determined using a procedure that incorporates both polar and
nonpolar extraction solvents.
Because lipid content may be calculated on a dry-weight basis by some researchers, the
wet- to dry-weight ratio should be provided so that users of the data can convert between
a wet- and dry-weight basis as required. The units used to report percent lipids content
(wet or dry weight) should be clearly indicated on the data table.
Conclusions
The analysis of total solids, grain-size distribution, and TOC content should be required
for all sediment samples. AVS analyses for sediment samples are optional but recom-
mended. Other sediment analyses such as total volatile solids and ammonia content may
73
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Chapter 5. Chemical Analyses
be appropriate as screening tests for sites that are expected to have substantial concerns
with anoxia. All tissue samples should be analyzed for moisture content and percent
lipid content.
ORGANIC COMPOUNDS
The three groups of organic chemicals that are frequently quantified in sediment samples
include 1) nonchlorinated semivolatile organic compounds, which include PAHs; 2) PCBs
and chlorinated pesticides; and 3) PCDDs and PCDFs. The usual sequence for analysis
includes extraction with solvent, purification (cleanup) and separation by column chroma-
tography or HPLC, and quantification by capillary column gas chromatography with
detection by electron capture detection (BCD), mass spectrometry, or flame ionization
detection (FID).
Nonchlorinated Semivolatile Organic Compounds
Nearly 200 nonchlorinated semivolatile organic compounds can be routinely analyzed by
environmental laboratories, including phenols, phthalate esters, and PAH compounds.
Among these compounds, those that appear to pose the greatest health risk are a number
of the PAH compounds classified as B2 carcinogens by the USEPA (1993b) (e.g., ben-
zo[a]pyrene, benz[a]anthracene, benzo[b]fluoranthene, chrysene, dibenz[a,h]anthracene,
and indeno[l,2,3-cd]pyrene). For this reason, the ARCS Program focused primarily on
analysis of PAH compounds.
The most widely used method for the analysis of semivolatile organic compounds is the
USEPA Method 8270 described in USEPA (1986b). In addition, other USEPA methods
for analyzing more specific groups of semivolatile compounds such as PAHs (USEPA
SW-846 Methods 8100 and 8310) and phenols (USEPA SW-846 Method 8040) are
designed to achieve lower detection limits than USEPA SW-846 Method 8270. NOAA
also has a widely accepted set of methods for analyzing PAH compounds as part of the
National Status and Trends Program (NOAA 1993).
Extraction
Extraction of sediment samples for the entire range of acid, base, and neutral semivolatile
organic compounds is best conducted using a mixture of nonpolar and polar solvents
(e.g., dichloromethane-memanol, dichloromethane-acetone, hexane-methanol) and some-
tunes in sequential extraction steps. The goal is to extract as completely as possible all
compounds of interest while preserving their chemical structure for analysis. Polar sol-
vents are necessary to extract polar (acid and base) compounds and to aid in removing
water from the sediment matrix, which can interfere with the proper extraction of non-
polar compounds. Some extraction methods make use of anhydrous sodium sulfate as
a drying agent to remove water from the sample prior to and during the extraction step.
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Chapter 5. Chemical Analyses
USEPA SW-846 provides specific extraction procedures, including sonication (USEPA
SW-846 Method 3550) and Soxhlet (USEPA SW-846 Method 3540) extraction. A
sequential cold extraction technique on a roller table is specified by NOAA's National
Status and Trends Program (NO A A 1989). Other extraction techniques may include
derivatization to make compounds of interest detectable by more sensitive instruments,
to minimize losses of relatively unstable compounds, and to eliminate potential matrix
effects (e.g., interference from co-eluting nontarget analytes). Selected phenols, for
example, can be extracted and derivatized to allow the use of analytical techniques that
provide greater sensitivity (USEPA SW-846 Method 8040).
Tissue samples for semivolatile analysis are macerated prior to extraction using an appro-
priate tool such as a Tekmar Tissuemizer® or a stainless-steel blender. Samples are often
thoroughly mixed with a drying agent such as anhydrous sodium sulfate and then extrac-
ted with either dichloromethane-acetone or dichloromethane as described above. Care
should be taken to avoid caking of the tissue/desiccant mixture, which may hinder com-
plete extraction.
To assess the efficiency of extraction and cleanup procedures, surrogate compounds are
added to all samples and blanks prior to the extraction step. The surrogate compounds
are either compounds that are similar in chemistry to the analytes of interest or deutera-
ted analogs of the compounds of interest. The concentration of the compounds of interest
can then be corrected for the recovery of the surrogate compounds. The laboratory
should be clearly instructed either to provide analytical results that are recovery-corrected
and report the recovery of the surrogate compounds for informational purposes, or to
report the recovery of the surrogate compounds. Generally, recovery corrections are
only applied when all of the major chemicals of interest have a directly analogous
surrogate compound. Minimum requirements for use of surrogate compounds are listed
in the USEPA methods; these compounds can be purchased through many major
chemical suppliers.
Cleanup
Exhaustive extraction of sediment or tissue samples also brings into the sample extract
organic and inorganic constituents other than those of interest. These constituents can
interfere with the analysis being performed, but often can be removed or minimized
through subsequent cleanup steps. If the entire range of polar and nonionic semivolatile
organic compounds is of interest, then cleanup steps must be chosen with caution to
avoid losing some of the compounds while removing interfering constituents. For PAH
analyses, cleanup is usually accomplished by column chromatography using alumina and/
or silica gels (USEPA SW-846 Methods 3611, 3630) or GPC (USEPA Method 3640),
which will remove many pigments and macromolecules such as lipids, polymers, and
proteins. Of these procedures, only GPC also minimizes loss of certain acid or base
compounds that would be of interest for semivolatile organic compound analysis. The
NOAA National Status and Trends Program (Krahn et al. 1988; NOAA 1989) uses an
HPLC procedure as a final cleanup step for neutral organic compounds. This procedure
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Chapter 5. Chemical Analyses
is somewhat more effective in removing interferences than column chromatography , espe-
cially for tissue samples that contain large amounts of lipids. The HPLC method also
has the advantage of using far less solvent than is required for column chromatography.
Therefore, the use of HPLC is recommended, when practical.
Other interferences, such as elemental sulfur, can be removed or reduced in a sediment
sample extract by the addition of activated copper (NO A A 1989), tetrabutylammonium-
sulfite (USEPA SW-846 Method 3660 A), or mercury (USEPA SW-846 Method 3660A).
After the final cleanup step, extracts are reduced in volume to approximately 500
(depending on the detection limits required and the nature of the sample). Reduction of
solvent volume can be performed using various techniques. The most common and prob-
ably the most reliable technique for removing 5-500 mL of solvent is the use of the
Kuderna-Danish apparatus with Snyder columns. Zymark® is another tool that is avail-
able for reducing large volumes of solvent; however, losses of some semivolatile organic
compounds have been found when using this technique. Final reduction of solvent to
small volumes (i.e., 1-5 mL) can be achieved by using micro Snyder columns followed
by a nitrogen-blowdown using a carefully controlled stream of nitrogen gas. Additional
internal standards should be added at this point to assess any losses or variability due to
the analytical quantification technique employed.
Analysis
Many commercial laboratories screen extracts prior to quantitative analysis by using
GC/FID to assess the approximate concentration range of the extract. This procedure
avoids contaminating sensitive instruments with high-concentration extracts that should
be diluted prior to quantitative analysis.
Quantification of semivolatile organic compounds can be performed using a number of
different techniques depending on the sensitivity and selectivity required. The method
most commonly used is GC/MS in the full-scan mode. Detection limits using this
method range from approximately 0.1 to 10 mg/kg (ppm). Alternatively, a selected
group of compounds can be analyzed using selected ion monitoring (SIM), and sensitivity
can be improved by up to 2 orders of magnitude, with detection limits for individual
PAH compounds, for example, ranging from 1 to 10 /*g/kg (ppb). GC/MS is a selective
technique that makes positive identification of the chemical possible based on both
structural and retention time characteristics.
Another option for the analysis of PAH compounds is HPLC (e.g., USEPA SW-846
Method 8310). This method provides increased sensitivity, with detection limits ranging
from approximately 0.01 to 10 /xg/kg (ppb). This HPLC technique is very cost effective
when PAH compounds are the only constituents of interest and is subject to fewer chemi-
cal interferences than GC/MS analyses. HPLC and GC/MS provide comparable quantita-
tive results for extracts that have been subjected to appropriate cleanup procedures (Prahl
and Carpenter 1979).
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Chapter 5. Chemical Analyses
Recommenda tions
GC/MS analyses (or specialized GC/MS-SIM analyses) are recommended for analyzing
semivolatile organic compounds to minimize the influence of interfering substances that
may remain after extract cleanup. Any of the analytical methods described in the pre-
vious section can be used to determine PAH compounds, although separate HPLC analy-
ses may not be cost effective or needed if GC/MS analyses are used to quantify other
semivolatile organic compounds.
PCBs and Chlorinated Pesticides
During the ARCS Program, discussion of PCB analyses focused on current understanding
of the toxicity of specific PCB congeners relative to the PCB mixture as a whole. PCBs
are a set of 209 different compounds—congeners, all of the possible combinations and
variations of the biphenyl molecule substituted with one or more chlorine atoms. Only
about 80-120 of these congeners occur to any significant extent in the environment. The
toxicity of the individual congeners depends on the number and the placement of the
chlorine atoms on the biphenyl. When neither of the phenyl rings contains a bulky
chlorine atom on the ortho positions (adjacent to the other phenyl ring), the molecule can
become planar—the rings are said to be coplanar. These coplanar congeners (Interna-
tional Union of Pure and Applied Chemistry [IUPAC] Nos. 77, 126, and 169) are partic-
ularly toxic. In addition, congeners with one ortho-chlorine can also become coplanar.
While not as inherently toxic as the non-ortho-chlorine congeners, the much higher
amount of these mono-ortho congeners means that the presence of these congeners may
present a greater health hazard in the environment.
The following section describes the standard USEPA methods for extraction, cleanup,
and analysis of PCB Aroclor® mixtures in sediment samples. Aroclor® was a trade name
used by Monsanto Company for mixtures of PCBs with varying degrees of chlorination
(e.g., 1242 represents 42 percent chlorine by weight). Quantification of individual
congeners is also discussed.
Extraction
Chlorinated pesticides and PCBs may be extracted using either sonication (USEPA
SW-846 Method 3550) or Soxhlet extraction (USEPA SW-846 Method 3540) procedures.
This extraction can be performed simultaneously with that for nonchlorinated semivolatile
organic compounds. When chlorinated pesticides and PCBs are the only chemicals of
interest, however, a solvent mixture of hexane-acetone is often preferred in the Soxhlet
extraction.
As with semivolatile organic compounds, surrogate compounds are added prior to extrac-
tion of chlorinated pesticides and PCBs to assess overall analytical efficiency. A number
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Chapter 5. Chemical Analyses
of different surrogate compounds can be used; however, USEPA SW-846 Method 8080
recommends the use of dibutylchlorendate (DEC) (which can break down at high gas
chromatography injector temperatures), decachlorobiphenyl (DCB), octachloronaphth-
alene (OCN), and tetrachloro-m-xylene (TCMX). At a minimum, one early eluting sur-
rogate (e.g., OCN or TCMX) and one late eluting compound (e.g., DCB or DBC)
should be used. When performing PCB congener-specific analyses, surrogate compounds
should include PCB congeners that do not occur in environmental samples (e.g., IUPAC
Nos. 103, 198, and 204). If appropriate, nonchlorinated semivolatile and PAH surrogate
compounds may be added, this extract may also be used for analysis of those compounds,
thus saving a separate extraction step.
Tissue samples for PCB and/or pesticide analysis should be treated identically to those
for the analysis of nonchlorinated semivolatile organic compounds by first macerating the
sample, drying the sample with anhydrous sodium sulfate or equivalent, and then extrac-
ting. Care should be taken to avoid caking of the tissue/drying agent mixture, which
may hinder complete extraction.
Cleanup
Standard cleanup procedures that can be used for PCB/pesticide analysis include Florisil®
column chromatography (USEPA SW-846 Method 3620) and the other HPLC, alumina/
silica gel, and GPC cleanup techniques described above for nonchlorinated semivolatile
organic compounds. Sulfur cleanup is particularly important for analyses of chlorinated
compounds because the electron capture detector used for analysis of chlorinated hydro-
carbons is sensitive to small amounts of elemental sulfur (USEPA SW-846
Method 3660). USEPA SW-846 Method 8080 provides further guidance on cleanup pro-
cedures to be used when analyzing for chlorinated pesticides, because some of these com-
pounds are more polar than most PCB congeners and different cleanup methods may be
needed to separate the pesticides from the PCBs. HPLC cleanup as described by Krahn
et al. (1988) can also be used, but addition of a different surrogate compound (e.g.,
dibromooctafluorobiphenyl) is needed prior to this step to assess any loss to the HPLC
system.
Some sediment samples from highly contaminated areas contain oils (hydrocarbons) that
interfere with the quantification of pesticides or PCBs. A relatively rigorous cleanup can
be achieved by using sulfuric acid to extract the hydrocarbons (USEPA 1981). This step
will also degrade many pesticide compounds and, therefore, should be used only when
analyzing for PCBs.
Analysis
Historically, the most common method used to quantify PCBs has been to analyze for
PCB Aroclor® mixtures. PCBs as Aroclors®, as well as chlorinated pesticides, may be
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Chapter 5. Chemical Analyses
quantified using USEPA SW-846 Method 8080. This method involves the use of
capillary column gas chromatography/ECD.
Aroclor® analysis includes not only chromatographic requirements for quantification
(e.g., correct retention times, peak shape) but pattern matching as well. Pattern match-
ing is the comparison of the heights of dominant peaks in samples relative to the heights
of the same peaks in an Aroclor® standard. These requirements can introduce a signifi-
cant amount of uncertainty into the quantification because environmental samples exhibit
"weathering" of the original Aroclor® pattern. This weathering is a result of selective
degradation or other loss of congeners based on their physical and chemical characteris-
tics.
Low molecular weight chlorinated compounds, for example, have higher vapor pressures
and may evaporate from sediment or partition into aqueous media, resulting in a pattern
that has a higher proportion of more chlorinated congeners as compared to an Aroclor®
standard mixture. In these cases, analyst judgment is often used in determining a final
concentration. In severely weathered samples, the total PCB concentration is less accu-
rate, and interlaboratory variability is higher. Methods for computer-based multiple lin-
ear regression pattern matching have produced good total PCB results on weathered
samples (Burkhard and Weininger 1987).
Analysis of individual PCB congeners alleviates the need for pattern recognition, because
individual compounds are being quantified. A method using known amounts of up to
80 congeners in a specific combination of three Aroclor® mixtures (Mullin et al. 1984)
was used for the ARCS Program to quantify a large number of PCB congeners. Total
PCB concentrations obtained from the sum of the concentrations of PCB congeners deter-
mined by this method and the total PCB concentration determined by Aroclor® analysis
were found by Mullin et al. (1984) to be comparable for all types of samples. This
method is somewhat cumbersome, however, and the degree of confidence is reduced
when there are substantial matrix interferences (such as might be encountered in Great
Lakes AOCs). When such interferences are of concern, pattern matching methods can
be applied to the data, and confirmation with a second capillary GC column can be
added. Analyzing for a subset of congeners may be a more advantageous route.
A subset of 20 PCB congeners, chosen for their potential toxicity and frequency of
occurrence in the environment, has been recommended by NOAA for continued analysis
in the National Status and Trends Program (NOAA 1993). All 209 congeners are avail-
able from at least some chemical suppliers (e.g., AccuStandard, Inc.). Specific mixtures,
which make quantification more reliable, can be ordered. However, this method does
not allow for calculating total PCB concentrations. This is a problem for some
regulatory programs and for comparing PCB concentrations to historical data.
Analysis of all 209 congeners is problematic because of the difficulty in separating many
of the individual compounds during chromatography. The coplanar congeners co-elute
with other congeners that are generally present in significantly higher proportions and,
therefore, mask the quantification of the more toxic congeners. A special separation step
using carbon, and analysis using high-resolution mass spectrometry (HRMS), allows for
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Chapter 5. Chemical Analyses
isolation of these compounds. This analysis procedure is similar to that used for PCDDs
and PCDFs (USEPA SW-846 Method 8290) and is often performed at the same time.
However, the cost of this analysis is high and unless PCDD and PCDF data are required,
the cost is usually prohibitive for analysis of coplanar PCB congeners alone. New
separation techniques, such as the use of polymeric C18 phases to separate congeners
based on molecular shape (Sander et al. 1991) or polystyrene divinylbenzene bonded to
C60/70 fullerenes (Stalling et al. 1993) to enrich coplanar PCB congeners from sample
extracts, may allow for a relatively simple analysis by liquid or gas chromatography.
These methods are still under development.
The primary obstacle to analysis of PCB congeners, especially the more toxic coplanar
PCBs, is the resolution of the individual compounds from other interferences as well as
from each other. Currently, coplanar congeners are analyzed using a method similar to
that used for analysis of PCDDs and PCDFs (USEPA SW-846 Method 8290), which
employs high-resolution GC/MS. This method is costly and precludes analysis of most
samples for the more toxic PCBs. More complex methods have been employed where
the sample extract is chromatographed twice using tandem gas chromatographs (Duinker
et al. 1988). Other methods involving reverse-phase separations of the extract on special
carbon columns are currently under investigation (Tanabe et al. 1987). Because of the
high cost of coplanar PCB analyses using HRMS, it was recognized that not all ARCS
samples could be analyzed to resolve co-eluting coplanar congeners. As a result, the
ARCS Program analyzed for PCB congeners in all samples but only conducted the more
costly analyses to resolve co-eluting coplanar congeners in selected samples.
For chlorinated pesticides, a dual-column analysis (e.g., DB-5 and DB-608 or equivalent)
is performed simultaneously and the results from both columns are compared. Pesticide
results from the two columns should be within 50 percent of each other to be reliably
reported.
Conclusions
Congener-specific analysis using NOAA's procedure (NOAA 1989) is recommended for
routine PCB analyses of both sediment and tissue samples. This procedure can also be
used to quantify concentrations of chlorinated pesticides. Additional analyses to resolve
co-eluting coplanar congeners should be conducted on selected samples, if warranted by
concerns at the site and if funding is available.
PCDDs and PCDFs
Extraction and cleanup of sediment samples for PCDDs and PCDFs can be accomplished
using the isotope dilution method (USEPA SW-846 Method 8290; USEPA 1986b).
Stable, isotopically labeled PCDDs and PCDFs are added prior to extraction as specified
in USEPA SW-846 Method 8290. These compounds include one carbon-13 labeled
isomer from each PCDD and PCDF homolog group. All PCDD and PCDF congeners
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Chapter 5. Chemical Analyses
within these homolog groups are actually quantified based on the recovery of the stable,
isotopically labeled compounds. The isotope dilution technique can be a very accurate
method of quantification.
Samples are extracted with benzene for 18 hours using a Soxhlet extractor. Extracts then
undergo an extensive cleanup procedure to remove interferences. This procedure
involves three separate column chromatography steps using acidified silica gel, alumina,
and AX-21 activated carbon on silica gel. Deuterium-labeled 2,3,7,8-tetrachlorodibenzo-
/j-dioxin (TCDD) is added prior to these enrichment steps to assess process efficiency.
Two additional recovery internal standards are added after extract cleanup but prior to
the final concentration of the extract.
PCDDs and PCDFs are quantified using capillary column high-resolution gas chromatog-
raphy/HRMS, which enables detection limits of approximately 1-5 ng/kg (parts per
trillion) for individual congeners. The data are acquired by SIM analysis of the groups
of ion masses described in USEPA SW-846 Method 8290. Low-resolution mass spec-
trometry is often used (e.g., USEPA SW-846 Method 8280), but detection limits attained
using this method (i.e., approximately 100-2,000 ng/kg [parts per trillion]) are higher
than concentrations thought to be environmentally hazardous. Therefore, the recom-
mended method for analysis of PCDDs and PCDFs is USEPA SW-846 Method 8290,
which is the only standard method with adequately low detection limits needed for risk
assessment.
ORGANOMETALLIC COMPOUNDS
Methylmercury
Bacteria in sediments can transform inorganic mercury into the more bioavailable form
of methylmercury, which can then enter the aquatic food chain. Methylmercury concen-
trations can be determined in sediment and tissue using the method hi Bloom (1989).
This method is currently the most sensitive and reliable technique available.
Homogenized sediment samples are digested in a potassium hydroxide-methanol solution
by heating at 60°C for 2-4 hours. Samples are allowed to cool, additional methanol is
added, and the samples are mixed well by shaking. Undissolved solids are allowed to
settle completely prior to analysis. An alkylating agent (sodium tetraethylborate) is added
to the digestate to form a volatile methyl-ethylmercury derivative, which is purged onto
graphitized carbon traps for preconcentration and the removal of interferences. The
sample components are then separated on a cryogenic gas chromatography column, and
the eluting mercury species are pyrolytically broken down to elemental mercury. The
mercury is detected and quantified using a cold vapor atomic fluorescence (CVAF)
technique, which is based on the emission of 254 nm radiation by excited Hg° atoms in
an inert gas stream (Bloom 1989).
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Chapter 5. Chemical Analyses
Butyltfn Compounds
Butyltin compounds, primarily TBT, have been used as the active ingredient in
antifouling bottom paint for boat hulls for the last two decades. Sediments in marinas
and near boat or ship maintenance facilities are frequently contaminated with TBT and
its breakdown products, monobutyltin and dibutyltin. The analytical method most com-
monly used involves solvent extraction and chemical derivatization prior to analysis.
Sediment samples are extracted with 0.2 percent tropolone in dichloromethane. The
resulting extract is filtered through glass wool. The filtrates are derivatized using a
Grignard's reagent (hexyl magnesium bromide) and purified using a Florisil® column.
Quantification is accomplished using gas chromatography/flame photometric detection
(Unger et al. 1986).
METALS
Procedures for analyzing metals in sediment, tissue, and elutriate/pore water samples are
summarized in the following sections.
Sediments
To determine metals concentrations in sediment samples (except for mercury, discussed
below), the sample matrix must be digested prior to qualitative and quantitative analysis.
There are two options for digestion of the sediment sample: total acid digestion and
strong acid digestion. Total acid digestion may be performed using either a combination
of nitric, perchloric, and hydrofluoric acids (Method 200.4; USEPA 1983) or a combina-
tion of hydrofluoric acid and aqua regia (Rantala and Loring 1975). Although both total
acid digestion methods result in the release of all mineral-bound metals (including those
in crustal minerals) into solution, the method of Rantala and Loring (1975) is preferred
by some laboratories because it does not require the special fume hood necessary for the
use of perchloric acid as in Method 200.4 (USEPA 1983).
Strong acid digestion uses nitric acid and hydrogen peroxide (USEPA SW-846
Method 3050), but, unlike total acid digestion, does not break down all mineral (matrix)
components. Therefore, the total acid digestion method is recommended for the analysis
of sediment samples for the following reasons:
• Comparability among data sets is improved with total
acid digestion (i.e., variable extraction efficiency due
to variable grain size or sediment matrix effects is
eliminated).
• The results using total acid digestion are more repro-
ducible among different analytical laboratories.
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Chapter 5. Chemical Analyses
• The total acid digestion procedure is consistent with
the methods used by NOAA in the National Status and
Trends Program.
• SRMs can be included as an element of quality assur-
ance (not possible with strong acid digestion because
the metal extraction is incomplete). Standard refer-
ence sediments are certified only for total metals.
• The potential loss of volatile metals during digestion
is minimized by using an enclosed digestion chamber.
The strong acid digestion method does have three distinct advantages, however:
• Matrix interference during atomic absorption analysis
is less of a problem using strong acid digestion than it
is using total acid digestion.
• Laboratory safety is improved because the digestion
bombs and hydrofluoric acid used in total acid diges-
tion are not used in strong acid digestion.
• Lower limits of detection may be achieved with strong
acid digestion because of matrix interference problems
and method-imposed sample size limitations'for total
acid digestion.
Following digestion of the sediment sample, the metals (with the exception of mercury)
in the resulting solution are analyzed by ICP/AES, ICP-mass spectrometry (ICP/MS),
or graphite furnace atomic absorption spectroscopy (GFAA).
As an alternative to total acid digestion, the total metals content can be analyzed by
freeze-drying a sediment sample, ball milling it to approximately 120 mesh, pelletizing
it, and analyzing the sample using XRF (Nielson and Sanders 1983). Typically, detec-
tion limits achievable with XRF are higher than those achievable with the digestion
methods and analysis by GFAA and are lower than those obtained by ICP.
In most cases, the appropriate analysis method for metals is chosen by considering both
its ability to obtain the desired detection limit and the time and cost efficiency of the
method. In general, XRF is the most time- and cost-efficient method because all metals
are quantified from the same easily prepared subsample. XRF is also a nondestructive
analysis. However, the detection limit for certain metals is occasionally unacceptable
using XRF (e.g., cadmium and silver in both sediments and tissues; chromium, nickel,
and lead in tissues only; selenium in sediments only). For these metals, the digested
sample may be analyzed by GFAA or ICP/MS.
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Chapter 5. Chemical Analyses
The analysis of mercury in sediments requires a separate digestion procedure using
potassium permanganate as the oxidizing agent, with analysis by cold vapor atomic
absorption spectroscopy (CVAA; USEPA SW-846 Method 7470).
If analyses for AVS are conducted to determine the bioavailability of metals in the
sediment, then metals concentrations in the aqueous portion of the stillbottom should be
determined after AVS distillation is complete. Current theories for metals bioavailability
hold that these simultaneously extracted metals more accurately reflect the concentrations
of metals that can form metal sulfides with AVS. The expense of this additional analysis
may not yet be warranted, however, until the applicability of AVS measurements is con-
firmed.
Tissues
Tissue samples may be freeze-dried without loss of trace metals. Dried tissue may be
analyzed by XRF, similar to sediments, for metals at concentrations greater than approxi-
mately 2 fig/g dry weight. For analysis of metals in tissue by GFAA, ICP/AES and
ICP/MS, the tissue must be dissolved. Tissue digestion with nitric acid conducted in a
sealed Teflon® container at elevated temperature and pressure is effective at dissolving
metals without significant contamination.
Elutriate and Pore Water
With the exception of mercury, elutriates may be analyzed by ICP/AES, ICP/MS, or
GFAA without any sample preparation. Zinc in the elutriates may be quantified using
flame atomic absorption spectroscopy because the concentrations are often quite high.
Mercury may be analyzed using CVAF with gold amalgamation (USEPA Method 245.1)
to provide detection limits at the sub-ng/L level. The mercury procedure employed for
the ARCS Program included a bromine monochloride/UV oxidation procedure to oxidize
the organic compounds prevalent in many of the Great Lakes samples (Bloom and
Crecelius 1983). For pore water analyses, a preconcentration step with ammonium
pyrrolidinedithiocarbamate (Bloom and Crecelius 1984) may be used prior to analysis by
GFAA or ICP/MS to improve the detection limits for cadmium, copper, lead, nickel, and
silver.
CONCLUSIONS
The ARCS Program conducted both chemical analyses and a number of toxicity tests on
sediment samples. The chemical analyses were focused on employing the best currently
available methods. Use of this approach resulted in several recommendations that may
serve to improve the quality and information content of the chemical data for future
monitoring and assessment studies.
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Chapter 5. Chemical Analyses
Significant analytical problems occurred during the analysis of organic compounds in
sediment samples that were heavily contaminated with hydrocarbons. During the prepa-
ration of some solvent extracts, some material precipitated when these sediment extracts
were concentrated below a volume of 5 mL. Also, the high concentrations of hydrocar-
bons in extracts caused degradation of the HPLC cleanup columns and changed the
properties of the carbon cleanup column used to process extracts for PCDDs and PCDFs.
One option to avoid problems caused by high concentrations of hydrocarbons is to dilute
the extracts for the initial gas chromatography analysis; however, this can significantly
increase detection limits. A second option for PAHs and other semivolatile organic
compounds is to use a secondary cleanup technique such as reverse phase C-18 columns
in addition to GPC and prior to instrument analysis (Ozretich and Schroeder 1986).
Additional cleanup using concentrated sulfuric acid to oxidize interfering compounds is
helpful for PCB analyses only.
The recommended organic and inorganic analyses provide total concentrations of each
contaminant in a matrix. Supplemental analyses that provide a better representation of
the biologically available fraction of chemicals in a matrix, particularly the simultaneous
extraction of metals during the extraction of AVS, may provide data that are more suit-
able for performing risk assessments. Additional research is required, however, before
such analyses are recommended for routine use.
The level and complexity of chemical analyses necessary to complement the biological
assessment component may vary from situation to situation, depending on the particular
questions that need to be addressed. Improved analytical methods may make the choices
simpler and more meaningful, from a toxicological perspective, but much development
is still required. In general, the available chemical data have often been inadequate for
risk assessment purposes. In particular, exploratory surveys that could be used to test
for a wide array of lexicologically important compounds at a site have rarely been con-
ducted. It is recommended that the selection of analytes be based on a complete survey
of the literature for both previous monitoring and exploratory studies, as well as on
available data regarding municipal and industrial discharges in the drainage basin for the
site. This information, in combination with an exploratory study and best professional
judgment, will provide the basis for selecting the appropriate contaminants and analytical
methods.
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6. EVAL UA TION OF SEDIMENT TOXICITY
OVERVIEW
This chapter reviews methods commonly used to evaluate the toxicity of freshwater
sediments and summarizes experiences from the toxicity tests conducted as part of the
ARCS Program (Burton 1994; Ingersoll et al. 1993). Laboratory sediment toxicity tests
described in Burton (1994) include elutriate and whole-sediment toxicity tests with
various organisms including bacteria, algae, macrophytes, rotifers, cladocerans,
amphipods, mayflies, and fish. Laboratory sediment toxicity tests described in Ingersoll
et al. (1993) include exposures with elutriates (algae [Hall et al. 1993]; cladocerans and
Microtox® bacteria [Coyle et al. 1993]) and whole-sediment samples (amphipods and
chironomids [Nelson et al. 1993]). Up to 12 stations were sampled from each of three
AOCs (Buffalo River, New York [Figure 1-1]; Indiana Harbor, Indiana [Figure 1-2]; and
Saginaw River, Michigan [Figure 1-3]) and evaluated for toxicity to selected test
organisms.
Selected results from the ARCS Program that are described in this chapter include
1) ranking of toxicity tests by their sensitivity and discriminatory power, 2) response
similarity and correlations among toxicity tests, and 3) comparison of responses of the
amphipod Hyalella azteca in acute and chronic exposures to whole sediments.
Conclusions and recommendations for sediment testing in this chapter include:
• For most applications, a battery consisting of two to three sediment toxi-
city tests should be used. Testing multiple species reduces uncertainty and
limits the probability of false positive or false negative results. The
importance of testing multiple species increases with the level of ecosystem
protection desired and the need to define "significant" contamination in the
"grey" zone (marginally contaminated sites).
• At least two test organisms, comprising at least three measured responses
(i.e., survival, growth, or reproduction) for a total of three tests, should
be used in integrated assessments of sediment contamination. Behavior as
a measured response is a fourth possible endpoint that can be considered,
but tests incorporating this endpoint are less well developed. Integrative
studies should use both water column and benthic species in whole-sedi-
ment exposures as resources permit.
• In the ARCS Program, the testing of survival and growth endpoints in the
Hyalella azteca exposures (14- to 28-day) was the most efficient approach
because each endpoint in this toxicity test produced unique information that
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Chapter 6. Evaluation of Sediment Toxicity
was correlated with other toxicity test responses. Additional toxicity tests
that ranked highest in their sensitivity, discriminatory power, and ability
to produce unique information included the midge Chironomus riparius
(14-day, survival and growth), the cladocerans Ceriodaphnia dubia (7-day,
survival and reproduction) and Daphnia magna (7-day, survival and repro-
duction), the fathead minnow Pimephales promelas (7-day, larval survival
and growth), the amphipod Diporeia spp. (formerly Pontoporeia hoyi) (5-
day, avoidance/preference), and the mayfly Hexagenia bilineata (10-day,
survival and molting frequency). The latter two toxicity tests require field
collection of test organisms and therefore have a more limited use than the
other toxicity tests.
Sediment preference and avoidance endpoints with Diporeia spp. were the
most sensitive endpoints overall. However, this toxicity test is one of the
least developed. Because Diporeia spp. is of critical importance in the
Great Lakes, this toxicity test should be given high priority for additional
methods development and testing.
The Microtox® test (elutriate pluse) is a useful tool for quickly processing
large numbers of samples in reconnaissance surveys based on its ease of
use, low cost, sensitivity, discriminatory power, and high correlation with
other toxicity test responses.
Interpretations of toxicity test data with the alga Selenastrum capricorn-
utum were complicated by variable nutrient and inorganic carbon concen-
trations in the elutriate samples. The algal medium needs to be modified
before this test can be used to evaluate toxicity in environmental samples
with high nutrients.
Whole sediment toxicity tests were very sensitive and provided the most
realistic exposure system. Exposures using only interstitial (pore) waters
may be subject to misinterpretation due to alteration of physical or
chemical gradients, which modifies exposure routes. Elutriate tests are
more appropriate for evaluation of the effects of suspended sediments
(e.g., dredged material evaluations) to assess effects within the water
column, but are not appropriate for assessing the in situ toxicity of
sediments.
The duration of the exposure can have an influence on the response of
organisms in sediment toxicity tests. For example, exposures of 28 days
with Hyalella azteca have identified toxic sediment samples that were not
toxic in exposures of 2 to 14 days.
Further method development is needed on culturing and chronic sediment
testing procedures for additional infaunal species with a variety of feeding
habits, including suspension and deposit feeders. Results of chronic tests
should be used to help correlate the structure and function of benthic
communities to the presence of contaminants.
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Chapter 6. Evaluation of Sediment Toxicity
An integrated sediment assessment evaluation using toxicity testing,
measures of benthic community structure, andphysicochemical characteris-
tics is necessary for accurate evaluation of the degree of sediment contami-
nation. Identification of cause-and-effect relationships for specific
chemical contaminants requires further evaluation through the use of
spiked sediment toxicity tests (see Lamberson and Swartz 1992) or
Toxicity Identification Evaluation (TIE) procedures (Ankley and Thomas
1992).
INTRODUCTION
Sediment toxicity testing is a relatively new approach used in ecological risk assessments.
The first sediment tests were developed because of concerns in the late 1960s and early
1970s over dredged material contamination and its suitability for open-water disposal by
the Corps (USEPA-USACOE 1977). There was relatively little testing until the 1980s,
with a dramatic increase in the past 5-10 years (Burton 1991). The science has
progressed at a relatively fast rate because of the similarities to, and the earlier develop-
ment of, the water column and effluent toxicity tests. The USEPA is developing
approaches for managing contaminated sediments and method standardization that will
undoubtedly result in an even greater amount of sediment testing and research in the near
future (Southerland et al. 1992; USEPA 1994).
Historically, the assessment of sediment quality was often limited to chemical charac-
terizations. However, quantifying contaminant concentrations alone cannot provide
enough information to adequately evaluate the potential adverse effects, interactions
among chemicals, or the time-dependent availability of these materials to aquatic organ-
isms. Because relationships between total concentrations of contaminants in sediment and
bioavailable concentrations are poorly understood, determination of the effects of con-
taminated sediment on aquatic organisms requires controlled laboratory toxicity and
bioaccumulation tests.
The objective of a sediment toxicity test is to determine whether sediment is potentially
harmful to aquatic organisms. Because these tests measure biological responses directly,
they account for interactive toxic effects of complex contaminant mixtures in sediment.
These tests do not require knowledge of specific pathways of interactions among sedi-
ment and test organisms (Kemp and Swartz 1988). Toxicity testing of sediment can be
used to 1) determine the relationship between toxic effects and bioavailability, 2) investi-
gate interactions among contaminants, 3) determine the spatial and temporal distribution
of toxicity, 4) evaluate hazards of dredged material, 5) rank areas for cleanup, and
6) monitor the effectiveness of remediation and management actions. Toxicity tests on
sediments spiked with known concentrations of contaminants can be used to establish
cause-and-effect relationships between chemicals and responses, but the behavior of
contaminants in spiked sediments cannot necessarily be equated with that in field-
contaminated sediments.
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Chapter 6. Evaluation of Sediment Toxicity
Test organisms that have been used to evaluate the toxicity of freshwater sediments
include 1) microbial enzyme systems and bacteria, 2) algae, 3) macrophytes, 4) amphi-
pods, 5) midges, 6) mayflies, 7) cladocerans, 8) oligochaetes, and 9) fish (Burton 1991).
The choice of the test organism has a major influence on the ecological relevance, suc-
cess, and interpretation of the test. Furthermore, no one species is best suited for all
applications over the wide range of sediment characteristics. ASTM E 1525 and USEPA
(1994) outline the following criteria to consider when selecting an organism for sediment
testing (see also Table 6-1):
• A toxicity database exists to evaluate the relative sensitivity of the
organism
• The organism lives hi contact with the sediment
• The organism can be cultured in the laboratory
• The organism can be maintained in the laboratory under test conditions
• Taxonomic identification of the organism presents no problems
• The organism is ecologically important
• The geographical distribution of the organism includes the area of interest
• The organism is tolerant of a wide range of natural sediment physico-
chemical conditions
• The organism is tolerant of a wide range of water quality conditions
• Round-robin laboratory studies have been conducted
• The test using that organism has been peer reviewed
• The test using that organism has been field validated.
Various methods have been developed to evaluate sediment toxicity. These procedures
range in complexity from short-term lethality tests that measure effects of individual
contaminants on single species to long-term tests that determine the effects of chemical
mixtures on the structure and function of communities. The sediment phase tested may
include whole sediment, suspended sediment, elutriates, or sediment extracts (Lamberson
et al. 1992; Burton 1991). Burton (1992b) provided a comprehensive review of sediment
toxicity test methods, their advantages and disadvantages, and considerations related to
sampling and testing of sediments.
The ARCS Program evaluated 20 single-species and 5 community toxicity tests com-
prising a total of 55 endpoints (Table 6-2). Species used in the tests included bacteria,
algae, macrophytes, rotifers, cladocerans, chironomids, amphipods, mayflies, and fish.
Together, these species represent many of the major trophic groups in aquatic ecosystems
(Table 6-2). The toxicity tests evaluated have been used successfully in previous studies
of sediment contamination.
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TABLE 6-1. RATING OF SELECTION CRITERIA FOR SELECTED WHOLE SEDIMENT
TOXICITY TEST ORGANISMS
Criterion
Hyalella Chironomus Chironomus Lumbriculus
azteca tentans riparius variegatus
Hexagenia sp.
Daphnia sp.
Ceriodaphnia sp.
Pimephales
promelas
Relative sensitivity toxicity
database
Contact with sediment
Laboratory culture
Maintain in laboratory
Taxonomic identification
Ecological importance
Geographical distribution
Sediment physico-chemical
tolerance
Peer reviewed
Round-robin studies con-
ducted
Field validated
Endpoints monitored
I
Survival
Growth
Survival
Growth
Survival
Growth
Bioaccumulation
Survival
Survival
Growth
NA
Survival
Growth
Reproduction
NA
Survival
Growth
Terata
Note: A + or - rating indicates a positive or negative attribute and NA is not applicable.
a Large database for water-only testing.
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TABLE 6-2. SEDIMENT TOXICITY TESTS EVALUATED IN THE ARCS PROGRAM
Biological Level Test Organism/Community
Fish
Zooplankton
Benthic
Invertebrate
Phytoplankton
Macrophyte
Microbial
Note: E
S
Pimephales promelas
Daphnia rnagna
Ceriodaphnia dubia
Brachionus sp.
Hyalella azteca
Diporeia spp.
Chironomus tentans
Chironomus riparius
Hexagenia bilineata
Rapid Bioassessment III (artificial
substrates)
Selenastrum capricornutum
Lemna minor
Hydrilla verticillata
Microtox® (Photobacterium phosphoreum)
Alkaline phosphatase (sediment
community)
Dehydrogenase (sediment community)
(5-Galactosidase (sediment community)
Glucosidase (sediment community)
elutriate
whole sediment
Duration
7 day
7 day
48 hour
7 day
7 day
24 hour
7 day
14, 28 day
28 day
5 day
10 day
14 day
10 day
48, 96 hour
24 hour
4 day
10 day
Endpoint(s)
Larval survival/weight
Embryo-larval survival,
length, terata
Survival
Survival/reproduction
(3 brood)
Survival/reproduction
(3 brood)
Survival
Survival
Survival, length,
antenna segment
number, sexual
maturation
Survival
Preference/avoidance,
survival
Survival, length/weight
Survival, length/weight
Survival
Molting frequency
Community indices
(10)
Growth
14C uptake
Growth (frond number)
Chlorophyll a
Biomass (wet weight)
Chlorophyll a
Dehydrogenase activity
Shoot length
Root length
Peroxidase
Luminescence
Enzyme activity
Enzyme activity
Enzyme activity
Enzyme activity
Phase
S
S
S, E
S
S, E
E
E
S
S
S
S
S
S, E
S, E
S
E
E
S
S
S
S
S
S
S
S
E
S
S
S
S
Summary: Total toxicity test types
Single-species tests
Community tests
Total endpoints
Single-species endpoints
Community test endpoints
- .25
- 20
- 5
- 55 (duplicate endpoints
- 41
- 14
in solid and elutriate phases, counted as one)
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Chapter 6. Evaluation of Sediment Toxicity
EXPERIMENTAL DESIGN
The specific experimental design of a sediment toxicity assessment depends on the
objectives of the study. Therefore, it is essential that the study objectives be sufficiently
detailed to adequately guide a sediment toxicity evaluation. In turn, the experimental
design determines the success or failure of a testing program. If a study is not designed
properly, the best field collection protocols, laboratory methods, and data analysis
techniques may not provide an adequate assessment of sediment toxicity. Additional
design specifications that are related to the study objectives include the general assess-
ment strategy, the kind of toxicity tests to use, the number of sampling stations, the
number of replicates, and the collection of ancillary information.
The strategy for a toxicity evaluation may include a tiered assessment plan. In a tiered
approach, a sensitive screening evaluation precedes one or more detailed, definitive
evaluations. For example, the definitive evaluations would be conducted only at stations
where the screening evaluation has indicated the likelihood of significant sediment
contamination. The tiered approach can focus most of the evaluation effort on a subset
of high priority stations, thereby reducing the cost of the overall evaluation.
Within a tier, effects within an AOC may be evaluated by a reference area approach or
a gradient approach. For the reference area approach, effects are evaluated by statistical-
ly comparing the toxicity results for test sediments from an AOC with those from a
reference area or to a control sediment. In a gradient approach, three or more stations
are located along a suspected gradient of contamination, such as at increasing distances
from a discharge point. Data analysis for the gradient approach may include graphical
or statistical correlation analysis.
Some key considerations for selecting a toxicity test or battery of tests include the test
species, the life stage tested, the test endpoints, the exposure period, and the reliability,
ecological relevance, exposure relevance, and availability of the test. These criteria were
used to evaluate the toxicity tests examined in the ARCS Program (Table 6-1). Available
site-specific data on chemical and physical properties of the sediments can be useful in
selecting test species that are sensitive to the presence of the contaminants of concern yet
have minimal interferences from other properties of the sediment (e.g., grain size).
Knowing what aquatic organisms would be expected to inhabit the study area can aid in
selecting appropriate species. Other important information that should be assembled
includes regional water quality data, habitat types, and seasonal patterns in biological or
physical/chemical characteristics.
If the tests are to be conducted as part of a regulatory program, the selection of sediment
toxicity tests should be based on thorough understanding of the applicable regulatory
requirements. These factors can include specifications for lethal or sublethal tests, expo-
sure duration, seasons for testing, the battery of species for testing, and DQOs.
Guidelines for selecting toxicity tests can also be included as part of regulatory programs.
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Chapter 6. Evaluation of Sediment Toxicity
Variables that need to be considered in the experimental design include the number of
treatments and replicates, the number and type of control and reference sediments, and
water quality characteristics (ASTM 1993). If the purpose of the study is to conduct a
reconnaissance field survey to identify toxic stations for further investigation, experimen-
tal design might include only one composite sediment sample from each station to allow
for maximum spatial coverage. Although composite sampling may be better than collec-
ting one grab sample, compositing over a large area can dilute high contaminant concen-
trations and may produce false negatives. In a reconnaissance survey, the lack of repli-
cation usually limits statistical comparisons, but these surveys can be used to identify
toxic stations for further study or can be used in correlation analyses.
The number of replicates per station should be based on the need for sensitivity or statis-
tical power. For example, the purpose of the study might be to conduct a detailed quan-
titative sediment survey to determine statistically significant differences between effects
of several test sediments, control, and reference sediments. In such a survey, replicates
(separate samples from different grab samples collected at the same station) would need
to be collected at each station. Sediment chemistry and physical characterizations would
need to be performed on each of the grab samples. Separate subsamples might be used
to determine within-sample variability (precision) or for comparisons of test procedures
(e.g., comparative sensitivity among test species), but these subsamples cannot be consid-
ered to be true replicates for statistical comparisons among stations (ASTM 1993;
USEPA 1994).
The application and interpretation of sediment toxicity tests can be limited by the pres-
ence of substances or conditions other than elevated concentrations of contaminants of
concern (e.g., skewed sediment grain size distributions) that vary naturally and thereby
interfere with the toxicity results. Information that may assist in the interpretation of the
toxicity test results and in the selection of reference areas include analyses of sediment
conventional variables (e.g., organic carbon and grain size composition), sediment
chemical concentrations, and in situ biological effects.
Laboratory sediment toxicity tests generally include the use of control and reference sedi-
ment samples. A control sediment is a sediment that is essentially free of contamination
and is used routinely to assess the acceptability of a test, although control sediment is not
necessarily collected near the site of concern (USEPA-USACOE 1991). Any contami-
nants in control sediment may originate from the global spread of chemicals from both
natural and synthetic sources and do not reflect any substantial input from local point or
non-point sources. In addition, a control sediment may consist of formulated compo-
nents, such as clay, sand, and organic matter (USEPA 1994). A control sediment pro-
vides a measure of test acceptability, evidence of test organism health, and is one basis
for interpreting data obtained from the test sediments. In contrast, a reference sediment
is collected near a study site and is used to assess sediment conditions exclusive of the
contaminant material(s) of interest (USEPA-USACOE 1991). Testing a reference sedi-
ment provides a site-specific basis for evaluating toxicity. Selection of a reference
material is not trivial. If the physico-chemical characteristics of the test sediment exceed
the tolerance range of the test organism, a reference or control sediment encompassing
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Chapter 6. Evaluation of Sediment Toxidty
these characteristics should be evaluated (DeWitt et al. 1988) or another test organism
should be chosen. Selection of an inappropriate reference sediment, which may result
in a reduction in the ability to statistically determine the effects of the test sediments, can
be a problem in the assessment of highly contaminated sites.
METHODS FOR SAMPLE COLLECTION AND EXPOSURE
Sediment Manipulation and Characterization: The Importance of
Maintaining Sediment Integrity
Sediments are a semi-solid media comprised of minerals, organic material, interstitial
water, and a myriad of physico-chemical and biological components. The ASTM
Standard E 1391-90 (ASTM 1991) provides guidance on methods for collection, storage,
and manipulation of sediments for toxicity testing. The following paragraphs summarize
methods outlined in this ASTM guide.
Sediments cannot be collected in the field, transported to the laboratory, stored, and then
tested for toxicity without some alteration to their original structure. Some methods of
sample collection and testing are more disruptive than others. For example, use of a
sediment grab sampler (e.g., Ponar, Ekman, van Veen, Shipek, Peterson) is more disrup-
tive than a sediment core sampler. A standard core sampler is more disruptive than a
box core sampler.
The advantages and disadvantages of elutriate, interstitial-water (pore-water), and whole-
sediment toxicity tests are listed in Table 6-3. Toxicity tests of sediment interstitial water
were developed for evaluating the potential in situ effects of contaminated sediment on
aquatic organisms (Ankley et al. 1991). For many benthic invertebrates, the toxicity and
bioaccumulation of sediment-associated contaminants such as metals and nonionic organic
contaminants have been correlated with concentrations of these chemicals in interstitial
water (Di Toro et al. 1991; USEPA 1994). Interstitial water may be an important route
of exposure for many infaunal benthic invertebrates in contaminated sediments. How-
ever, interstitial water may not be the relevant route of exposure for evaluations of
organisms that ingest sediment.
Testing of the elutriate (water-extractable) fraction of the sediment is a commonly used
technique. The elutriate test was developed for evaluating the potential short-term effects
(hours or days) of open-water disposal of dredged material. Tests with elutriate samples
measure the potential effects of the release of water-soluble constituents from sediment
to the water column during the disposal of dredged material. Advantages of testing
elutriates are similar to those for interstitial water because the test method is similar to
water column testing and is easy to perform. Elutriate samples are generally less toxic
than either whole-sediment or interstitial water samples (Sasson-Brickson and Burton
1991; Ankley et al. 1991).
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TABLE 6-3. ADVANTAGES, DISADVANTAGES, AND ROUTINE USES OF SEDIMENT PHASES IN LABORATORY TOXICITY TESTS
Phase
Advantages
Disadvantages
Routine Uses
Extractable phase (XP)
(solutes vary)
Elutriate phase (EP)
(water extractable)
Interstitial water (IW)
CO
Ol
Whole sediment (WS)
In situ3 (NS)
Use with all sediment types
Sequentially extract different degrees of
bioavailable fractions
Greater variety of available test endpoints
Determine dose response
Use with all sediment types
Readily available fraction
Mimics anoxic toxic environmental process
Large variety of available test endpoints
Methods relatively standardized
Determine dose response
Direct route of uptake for some species
Semidirect exposure phase for some species
Large variety of available test endpoints
Methods of exposure relatively standardized
Determine dose response
Sediment quality criteria
Use with all sediment
Relative realism high
Determine dose response
Holistic (whole) versus reductionist toxicity
approach (water, IW, EP, and XP)
Sediment quality criteria may be determined
Use site or reconstituted water to isolate WS
toxicity
Real measure integrating all key components,
eliminating extraneous influences
Sediment quality criteria may be determined
Resuspension/suspended solids effects assessed
Ecosystem realism:
chemical alteration
Bioavailability unknown,
Ecosystem realism: Only one oxidizing
condition used; only one solidiwater ratio;
exposure for extended period of one-phase
condition that never occurs in situ or never
occurs in equilibrium in situ.
Extract conditions vary with investigator
Filtration affects response, sometimes used
Cannot collect IW from some sediments
Limited volumes can be collected efficiently
Optimal collection method unknown,
constituents altered when isolated from WS
Exposure phase altered chemically and
physically when isolated from WS
Flux between overlying water and sediment
unknown
Relationship to and between some organisms
uncertain: burrowers, epibenthic, water column
species, filter feeders, selective filtering, life
cycle versus pore water exposure
Some physical/chemical/ microbiological
alteration from field collection
Dose-response methods tentative
Testing more difficult with some species and
some sediments
Few standard methods
Indigenous biota may be present in sample
Few methods and endpoints
Not as rapid as some test systems
Mesocosms variable
Predation by indigenous biota
Rapid screen
Unique endpoints, so component
of test battery
Rapid screen
Endpoints not possible with WS
Dredging evaluations
Rapid screen
Endpoints not possible with WS
Initial surveys
Sediment criteria
Rapid screen
Chronic studies
Initial surveys
Sediment criteria
• Resuspension effects
• Intensive system monitoring
• Sediment criteria
Source: Burton (1991)
a Organisms exposed in situ in natural systems, pond/stream mesocosms, or lake limnocorrals.
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Chapter 6. Evaluation of Sediment Toxicity
Whole-sediment toxicity tests are most appropriate for organisms that live directly in or
on the sediments and ingest sediment particles. Use of whole sediments for toxicity tests
also requires less manipulation of the original sample and preparation of special sample
phases for testing. Whole-sediment toxicity tests with field-collected sediments are of
limited use for establishing cause-and-effect relationships, although spiking of clean sedi-
ments with individual chemicals can be useful for this purpose.
Manipulation or storage of whole-sediment samples can alter the bioavailability of
contaminants in sediment; however, the alterations that occur may not substantially affect
toxicity. Storage of field-collected sediment samples for several months at 4°C did not
result in significant changes in chemistry or toxicity (Ankley 1994; pers. comm.);
however, others have demonstrated changes in spiked sediment within days to weeks
(e.g., Burton 1991; Stemmer et al. 1990). Sediments contaminated primarily with non-
ionic, semivolatile organic compounds will probably change little during storage at 4°C
because of their relative resistance to biodegradation and sorption to solids. However,
metals and metalloids may be affected by changing redox, oxidation, or microbial
metabolism (such as with arsenic, selenium, mercury, lead, and tin; all of which are
methylated by various bacteria and fungi). Metal-contaminated sediments may need to
be tested relatively soon after collection with as little manipulation as possible.
Given that the contaminants of concern and the influencing sediment characteristics are
not always known a priori, it is desirable to hold sediments in the dark at 4°C and start
toxicity tests soon after collection from the field. Recommended sediment holding tune
ranges from less than two (ASTM 1993) to less than 8 weeks (USEPA-USACOE 1993).
If whole-sediment toxicity tests are started more than 2 weeks after collection, it is
desirable to conduct additional characterizations of sediment to evaluate possible effects
of storage on sediment. For example, concentrations of contaminants of concern could
be measured in pore water (extracted from a subsample of the sediment separate from
that used in the toxicity test) within 2 weeks of sediment collection and in pore water
from a second subsample (again separate from that used in the toxicity test) at the start
of the test (Kemble et al. 1993). Ingersoll et al. (1993) recommend conducting a toxicity
test with pore water within 2 weeks of sediment collection. Freezing and longer term
storage might further change sediment properties such as grain size or partitioning and
should be avoided (ASTM 1990; Schuytema et al. 1989; Day et al. 1994). Sediment
should be stored with no air over the sealed samples (no head space) at 4°C before the
start of a test (Shuba et al. 1978; ASTM 1990). Sediment may be stored in containers
constructed of suitable materials, as outlined in Chapter 3.
Characterization of sediment should include factors known to control the availability of
contaminants in sediment because bulk chemical concentrations alone cannot be used to
evaluate bioavailability (Di Toro et al. 1991). These measures should include sediment
organic carbon, ammonia, percent water, and grain size (e.g., percent sand, silt, and
clay). Depending on the experimental design, other analyses might include inorganic
carbon, AVS, biochemical/sediment oxygen demand, chemical oxygen demand, dissolved
organic carbon, pH, cation exchange capacity, oxidation-reduction potential, total volatile
solids, metals, organosilicates, synthetic organic compounds, oil and grease, petroleum
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Chapter 6. Evaluation of Sediment Toxicity
hydrocarbons, and chemical analysis of interstitial water (ASTM 1993). These
characteristics should be measured in split samples related to those used for toxicity
testing. For additional guidance on chemical analyses, see Chapter 5.
General Exposure Procedures for Sediment Toxicity Tests
Currently, there are ASTM standards for several of the test species used in the ARCS
Program (ASTM 1993). In addition, the USEPA is in the process of standardizing
toxicity test methods for Hyalella azteca and Chironomus tentans, and the bioaccumula-
tion assay using Lumbriculus variegatus (USEPA 1994). The Corps and the USEPA are
also developing guidance for conducting dredged material evaluations USEPA-USACOE
(1993). These standard test procedures may vary slightly from those used hi the ARCS
Program. The most appropriate methods for meeting a specific program's objectives
should be selected before starting any field sampling.
Water for culturing organisms and testing should be acceptable to the test organisms and
uniform in quality. Acceptable water quality allows satisfactory survival, growth, and
behavior of test organisms. Natural overlying water should be uncontaminated and of
constant quality as specified by ASTM (1993). For certain applications, the experimental
design might require water from the same site as the sediment.
The day before the test starts, sediment is generally mixed in the storage container and
a subsample of the whole sediment is added to each test chamber. Sediment depth in the
test chambers is dependent on experimental design and the test organism. Overlying
water is then gently poured along the side of the test chambers to minimize the
resuspension of sediment. Gentle aeration is started and the test chambers are left to
equilibrate overnight in a water bath (ASTM 1993).
The pH, alkalinity, hardness, dissolved oxygen, conductivity, and ammonia of the over-
lying water samples should be measured at the beginning, end, and at least weekly during
the test in each sediment treatment. Toxicity tests are typically conducted at 23 °C
(USEPA 1994). If the study objectives warrant monitoring changes in Interstitial water
or whole sediment during the test, separate test chambers should be set up and destruct-
ively sampled during the exposure (ASTM 1993).
In static tests, the volume of overlying water sampled for water quality determinations
should be minimized and replaced with fresh overlying water. In static tests, the over-
lying water may have to be aerated throughout the exposure period. Evaporated water
should be replaced at least weekly with deionized water.
In water-renewal tests with additions of one to four volumes of overlying water per day,
water quality characteristics generally remain similar to the inflowing water (Ingersoll
and Nelson 1990; Ankley et al. 1993). In static tests, however, water quality may
change profoundly during the exposure (Ingersoll and Nelson 1990). Although contam-
inant concentrations are reduced in the overlying water in water-renewal tests, organisms
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Chapter 6. Evaluation of Sediment Toxicity
in direct contact with sediment generally receive a substantial proportion of a contaminant
dose directly from either the whole sediment or from the interstitial water.
Test animals should be handled as little as possible and should be introduced into the
overlying water below the air-water interface. During the test, all chambers should be
checked daily and observations should be made to assess test organism behavior such as
sediment avoidance or reproductive behavior. Monitoring the behavior of burrowing test
organisms is difficult because the animals are not normally visible during the exposure.
At the end of an exposure, test organisms are typically removed from the chambers by
wet-sieving the sediment.
Quality Control and Quality Assurance for Sediment Toxicity Tests
General QA/QC considerations for sediment assessment programs are discussed in Chap-
ter 2. QA/QC considerations for sediment toxicity tests are discussed in this section.
Before a toxicity test is conducted in a new facility, "non-contaminant" tests should be
conducted in which all test chambers contain a control sediment and overlying water.
This information is used to demonstrate that the facility, control sediment, water, and
handling procedures provide acceptable species-specific responses. The within- and
between-replicate variance should be determined and the statistical precision of the test
should also be evaluated in relation to sample size (ASTM 1993). Performance-based
criteria have been recommended for use in judging the quality of the culture and the test
(USEPA 1994). For example, different culturing procedures would be acceptable if con-
sistent organisms are produced for testing. Performance could be evaluated using criteria
such as control survival and growth, and reference toxicant control charts.
It is the responsibility of a laboratory to demonstrate its ability to obtain precise results
with reference toxicants before it performs toxicity tests. Intralaboratory precision,
expressed as a coefficient of variation, of the range for each type of test to be used in
a laboratory should be determined by performing five or more tests with different batches
of test organisms, using the same reference toxicant, at the same concentrations, with the
same test conditions (e.g., the same test duration, type of water, age of test organisms,
feeding), and same data analysis methods. A reference toxicant concentration series (0.5
or higher) should be selected that will consistently provide partial mortalities at two or
more concentrations of the test chemical (USEPA 1994).
Before conducting toxicity tests with contaminated sediment, the laboratory should
demonstrate its ability to conduct tests by conducting five exposures in control sediment.
It is recommended that these five exposures with control sediment be conducted
concurrently with the five reference toxicity tests (USEPA 1994).
The quality of test organisms obtained from an outside source must be verified by
conducting a reference toxicity test concurrently with the sediment test. The supplier
should provide data with the shipment describing the history of the sensitivity of
98
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Chapter 6. Evaluation of Sediment Toxicity
organisms from the same source culture. If the supplier has not conducted five reference
toxicity tests with the test organism, it is the responsibility of the testing laboratory to
conduct five reference toxicity tests before starting a sediment test (USEPA 1994).
It is desirable to conduct reference toxicant toxicity tests in conjunction with sediment
tests to evaluate the condition of the test species (Lee 1980). Deviations outside an
established normal range (e.g., ±2 standard deviations) may indicate a change in the
condition of the test organism population or a change in laboratory procedures. Results
of reference toxicant tests also enable inter-laboratory comparisons of test responses.
Reference toxicant tests are most often acute lethality tests performed in the absence of
sediment (USEPA-USACOE 1991). Sediment spiked with a reference toxicant might
also be included as a positive control for the sediment toxicity test. Many chemicals
have been used as reference toxicants, including sodium chloride, potassium chloride,
cadmium, copper, chromium, sodium lauryl sulfate, and phenol. No one reference
toxicant can be used to measure the condition of test organisms with respect to another
toxicant with a different mode of action. However, it is unrealistic to routinely test more
than one reference toxicant.
DATA ANALYSIS
The data analysis approach should be developed in conjunction with the study design
specifications. Data analysis methods can then be tailored to the objectives and the level
of detail in the assessment.
When developing a statistical approach, the first decision is whether to use parametric
or nonparametric statistical methods. Typically, it is desirable to use parametric methods
because they generally are more powerful than nonparametric methods in detecting signi-
ficant differences. However, the assumptions that must be met by the data are generally
stricter for parametric tests. Therefore, it is important that those assumptions be evalu-
ated for each data set. If one or more parametric assumptions are not met, the data can
be transformed and the assumptions can then be reevaluated for the transformed data.
If the data still do not satisfy the assumptions, nonparametric methods should generally
be used to evaluate the untransformed data.
The kind of statistical test to be used is usually determined by the study objectives. If
the objective is to compare the toxicity results between test sites within an AOC or
between each test site and a reference area, analysis of variance (ANOVA) can be used
to conduct the evaluation. If the objective is to evaluate whether a gradient of toxicity
exists with distance from a potential problem area, a correlation analysis or multivariate
analysis approach can be used. For details of potential statistical approaches, refer to
Gilbert (1987), Green (1979), and USEPA (1994).
USEPA (1994) provides the following guidance on statistical analysis of toxicity test
data:
99
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Chapter 6. Evaluation of Sediment Toxicity
As the minimum difference between treatments that the test is required or
designed to detect decreases, the number of replicates required to meet a given
significance level and power increases. Because no consensus currently exists
on what constitutes a biologically acceptable difference, the appropriate
statistical minimum significant difference should be a DQO established by the
individual user based on their data requirements, the logistics and economics
of test design, and the ultimate use of the data.
Three replicates per treatment or control are the absolute minimum number of
replicates for a sediment toxicity test. Eight replicates are recommended for
each control or experimental treatment. It is always prudent to include as many
replicates in the test design as economically and logistically possible.
Statistical tests of hypotheses can be designed to control for the chances of
making incorrect decisions. Alpha (a) represents the probability of making a
Type I statistical error. A Type I statistical error in this testing situation results
from the false conclusion that the treated sample is toxic or contains chemical
residues not found in the control or reference sample. Beta (j8) represents the
probability of making a Type II statistical error, or the likelihood that one
erroneously concludes there are no differences among the mean responses in the
treatment, control, or reference samples. Traditionally, acceptable values for
a have ranged from 0.1 to 0.01, with 0.05 (or 5 percent) used most commonly.
This choice should depend upon the consequences of making a Type I error.
Historically, having chosen a, environmental researchers have ignored /3 and
the associated power of the test (1-/3).
The consequences of a Type II statistical error in environmental studies should
never be ignored and may in fact be the most important criteria to consider in
experimental designs and data analyses which include statistical hypothesis
testing. The critical components of the experimental design associated with the
test of the hypothesis are 1) the required minimum detectable difference
between the treatment and control or reference responses, 2) the variance
among treatment and control replicate experimental units, 3) the number of
replicate units for the treatment and control samples, 4) the number of animals
exposed within a replicate exposure chamber, and 5) the selected probabilities
of Type I (a) and Type II (j8) errors.
EVALUA TION OF SEDIMENT TOXICITY TESTS IN THE
ARCS PROGRAM
In the ARCS Program, sediment toxicity tests were conducted with species or biotic
communities representative of the major trophic levels in freshwater aquatic ecosystems
(Table 6-2) in order to evaluate toxic effects of the sediments. Secondary objectives of
the toxicity testing conducted as part of the ARCS Program were to:
100
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Chapter 6. Evaluation of Sediment Toxicity
• Evaluate the relative sensitivities of the various toxicity tests to sediment
contaminants
• Evaluate the abilities of the various toxicity tests to discriminate between
different degrees of sediment contamination
• Evaluate the degree of correlation between responses of the various
toxicity tests and their redundancy
• Recommend toxicity tests for use hi future studies of sediment contami-
nation in the Great Lakes.
By conducting all laboratory toxicity tests on split sediment samples that were collected
and processed in the same manner and by generally initiating testing within a 2-week
period, the results of the various toxicity tests should be directly comparable.
The toxicity test methods are briefly described below. For a detailed description of the
toxicity test methods used, see Burton (1994) and Ingersoll et al. (1993). Sediment
samples for toxicity testing were collected from a number of stations in three of the
priority AOCs: Buffalo River, New York (Figure 1-1); Indiana Harbor, Illinois
(Figure 1-2); and Saginaw River, Michigan (Figure 1-3) (two separate sampling surveys
in the Saginaw River AOC).
Toxicity Test Methods
Toxicity tests were conducted with 1) fathead minnows (Pimephales promelas, whole
sediment), 2) cladocerans (Daphnia magna and Ceriodaphnia dubia, elutriates or whole
sediment), 3) amphipods (Hyalella azteca andDiporeia spp. [formerly Pontoporeia hoyi],
whole sediment), 4) midges (Chironomus riparius and Chironomus tentans, whole sedi-
ment), 5) mayflies (Hexagenia bilineata, elutriates and whole sediment), 6) duckweed
(Lemna minor, whole sediment), 7) macrophytes (Hydrilla verticillata, whole sediment),
8) rotifers (Brachionus caldflorus, elutriates), 9) microbial enzymes (whole sediment,
elutriates) and Microtox® (elutriates), and 10) algae (Selenastrum capricornutwn, elutri-
ates). In situ colonization of artificial substrates by benthic invertebrates was also eval-
uated at each AOC (see also Chapter 7).
Ideally, toxicity tests with liquid-phase exposures should be conducted with interstitial
water. Toxicity tests with interstitial water are preferable to tests with elutriates for
evaluating the potential in situ effects of contaminated sediment on aquatic organisms
(Ankley et al. 1991). Elutriate tests are most appropriately used in the evaluation of
dredged material. However, because of the large water volumes required for conducting
this test battery and the difficulty of collecting sufficient undisturbed interstitial water,
the decision was made to test elutriates instead of interstitial water. Elutriate samples are
generally less toxic than either whole-sediment or interstitial-water samples (Sasson-
Brickson and Burton 1991; Ankley et al. 1991). The various advantages and disadvan-
tages of each test phase are listed in Table 6-3.
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Chapter 6. Evaluation of Sediment Toxidty
Sediment toxicity tests were conducted with macrobenthic organisms in static or water-
renewal systems at temperatures of 20 to 25 °C. Sediments were placed in the test
chambers and overlying laboratory water was gently added. Test organisms were
randomly added within 24 hours and the test was started. Numbers of replicates ranged
from 3 to 10 depending on the toxicity test. Exposure water was moderately hard
(hardness 134 mg/L as CaCO3; alkalinity 60 to 65 mg/L as CaCO3; pH 7.8 to 8.0; con-
ductivity 300 jwmhos/cm; sulfate 72 mg/L). Dissolved oxygen, temperature, alkalinity,
pH, conductivity, and hardness were measured in the surface water either daily or at the
start and end of the test, depending on the parameter. See Burton (1994) and Ingersoll
et al. (1993) for further details on toxicity test protocols using macrobenthic organisms.
Several indigenous microbial enzyme systems have been used to measure cycling of key
elements and degradation of organic matter (Griffiths et al. 1982). The usefulness of
microbial tests in evaluations of contaminant effects is well established (Stotzky 1980;
Babich and Stotzky 1983). Shifts in hydrolase activity (e.g., protease, amylase) can be
construed as resulting from chemical exposure (Griffiths and Morita 1981).
The Microtox® test measures luminescence of the marine bacterium Photobacterium
phosphoreum. Inhibition of this luminescence is considered a toxic response because it
results from disruption of cellular energy transfer. Results of Microtox® tests have been
compared to those of standard toxicity tests with rainbow trout (Oncorhynchus mykiss),
fathead minnow (Pimephalespromelas), bluegill (Lepomis macrochims), sheepshead min-
now (Cyprinidon variegatus), and cladoceran (Daphnia magna) for a variety of pure
compounds and complex environmental samples. In most cases, Microtox® results
showed similar sensitivity to the compounds tested (Bulich et al. 1981; Curtis et al. 1982;
Qureshi et al. 1982).
The Selenastrum capricornutum test measures effects on photosynthesis by following cell
growth or uptake of radioactively-labeled carbon (as bicarbonate). Inhibition or
stimulation of photosynthesis is considered an abnormal response due to toxicant or
nutrient presence. Some studies have shown the algal growth test to be more sensitive
than other traditionally used surrogate species (DeZwart and Sloof 1983; LeBlanc 1984).
Rooted aquatic vascular plants (e.g., Hydrilla verticillatd) occupy a unique niche in
aquatic ecosystems. A major contributor to primary productivity in some systems, these
plants are in direct contact and dynamic interaction with both the overlying water and the
interstitial water of sediment. Thus, rooted aquatic macrophytes can be used to evaluate
the entire aquatic system, not just the sediments or the water column.
Hyalella azteca and Diporeia spp. are two amphipods that have been used successfully
to evaluate freshwater and estuarine sediments. These organisms play a dominant role
in many aquatic ecosystems, assisting with the processing of organic matter (detritus),
and represent a primary food source for many benthic-feeding fish species (Pennak
1989). Toxicity tests with H. azteca generally start with immature animals (less than
2 weeks old) and can be conducted for up to 4 weeks through reproductive maturation
(ASTM 1993; USEPA 1994). Toxicity tests with Diporeia spp. are initiated with field-
702
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Chapter 6. Evaluation of Sediment Toxidty
collected juveniles and can continue for up to 4 weeks (ASTM 1993, draft Annex #7).
Endpoints measured in toxicity tests with amphipods include survival, growth, behavior,
or reproductive maturation.
Chironomids (midges) are also important benthic macroinvertebrate species in many
aquatic systems. They tend to be the dominant benthic macroinvertebrate taxon in
systems where there is an ample supply of organic material associated with fine- to
medium-grained sediments. In the past, midges were considered to be relatively
insensitive in toxicity assessments (Ingersoll and Nelson 1990). This conclusion was
based on the practice of conducting short-term toxicity tests with fourth instar larvae in
water-only exposures, a procedure that may underestimate the sensitivity of midges to
toxicants. The first and second instar larvae are more sensitive to contaminants than are
the third or fourth instar larvae. For example, first instar Chironomus tentans larvae
were 6 to 27 times more sensitive than fourth instar larvae to acute copper exposure
(Nebeker et al. 1984; Gauss et al. 1985), and first instar Chironomus riparius larvae
were 127 tunes more sensitive than second instar larvae to acute cadmium exposure
(Williams et al. 1986). Endpoints typically measured in sediment toxicity tests with C.
riparius and C. tentans include growth and survival.
Mayflies (e.g., Hexagenia bilineatd) are an important component of fish and waterfowl
diets. They are also important as an indicator of overall ecosystem health and provide
a critical ecological link in the conversion process of changing organic detritus into a
readily available food source for aquatic microbial communities. Sediment toxicity tests
with mayflies are generally conducted for up to 10 days (Bahnick et al. 1980; Nebeker
et al. 1984). Survival, growth, or molting frequency are the toxicity endpoints measured
in the mayfly tests. Unfortunately, few laboratories have been successful at routinely
culturing or maintaining these species, and testing often requires use of field-collected
organisms.
Cladocerans represent a major group in many zooplankton communities. There is a large
database that exists from chemical-specific, effluent, and water quality testing with the
cladocerans Daphnia and Ceriodaphnia. Survival, growth, or reproduction are typically
measured in the cladoceran tests. Although cladocerans do not live in continuous contact
with sediment, they are frequently in contact with the sediment surface and are exposed
to both water-soluble contaminants in the overlying water and particulate-bound
contaminants at the sediment surface (ASTM 1993). Cladocerans are also one of the
more sensitive groups of organisms used in toxicity testing (Mayer and Ellersieck 1986).
Oligochaetes, like chironomids, are often associated with aquatic systems rich in organic
matter. They also play a major role in the processing of organic material and as a food
source for benthic feeding fish. Most oligochaetes are relatively tolerant of many classes
of chemical contaminants; however, this tolerance may be a positive attribute for assess-
ing bioaccumulation or the toxicity of severely contaminated sites (Phipps et al. 1993).
Due to their relative insensitivity to chemical contaminant toxicity, they were not inclu-
ded in the ARCS Program. The most frequently described sediment testing methods for
oligochaetes are acute toxicity tests (Keilty et al. 1988a), although Wiederholm et al.
703
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Chapter 6. Evaluation of Sediment Toxicity
(1987) described methods for conducting 500-day oligochaete exposures that measure
effects of sediment on growth and reproduction. Recently, Reynoldson et al. (1991) and
ASTM (1993) described a 28-day test starting with sexually mature Tubifex tubifex. In
this shorter test, effects on growth and reproduction are monitored and the duration of
the exposure makes the test more useful for routine assessments of sediment toxicity.
Phipps et al. (1993) outlined testing methods for Lumbriculus variegatus to assess lethal
and sublethal toxicity and bioaccumulation of sediment contaminants in 10- to 28-day
exposures.
In addition to the aforementioned toxicity tests, an investigation of the bioaccumulation
potential of sediment-associated contaminants was also conducted under the ARCS Pro-
gram by exposing the fathead minnow Pimephales promelas to contaminated sediments
in the laboratory. Sediment samples were collected from three predetermined stations
in the Saginaw River, Michigan in June, 1990 and from three predetermined stations in
the Buffalo River, New York in August, 1990. The sediment samples were placed in
laboratory aquaria with flow-through water systems. The fathead minnows were exposed
in these aquaria for 10 days according to the methods of Mueller et al. (1992). Pre-
exposure samples of the minnows were analyzed for PCBs, chlorinated pesticides, and
metals. After the 10-day exposure, the exposed minnows were also analyzed for the
same contaminants. An assessment of bioaccumulation was attempted by comparing the
post-exposure contaminant concentrations in the fish with both the pre-exposure contami-
nant concentrations in the fish and the contaminant concentrations in fish exposed to a
clean reference sediment under similar conditions. The results of these bioaccumulation
bioassays were varied. While there were indications of significant bioaccumulation of
several metals, the assessment of bioaccumulation of PCBs was confounded by apparent
contamination of the test organisms before their arrival in the laboratory. Several pesti-
cides detected hi the sediments were also found in low concentrations in the tissue
samples. In addition, the test sediments did not exhibit the expected high concentrations
of the analytes of interest. Although such bioaccumulation bioassays are considered
feasible, further research and development work will be required before they can be
recommended for routine application. Therefore, these bioaccumulation bioassays are
not discussed further in this document.
Other species of organisms have been suggested for possible use in studies of chemical
bioaccumulation from aquatic sediments. Several criteria should be considered before
a species is adopted for routine use (Ankley et al. 1992a; Call et al. 1993; USEPA
1994). These criteria include 1) availability of organisms throughout the year, 2) known
chemical exposure history, 3) adequate tissue mass for chemical analyses, 4) ease of
handling, 5) tolerance of a wide range of sediment physico-chemical characteristics (e.g.,
particle size), 6) low sensitivity to contaminants associated with sediment (e.g., metals,
organics), 7) amenability to long-term exposures without adding food, and 8) ability to
accurately reflect concentrations of contaminants in field-exposed organisms (e.g.,
exposure is realistic). With these criteria in mind, the advantages and disadvantages of
several potential freshwater taxa for bioaccumulation testing are discussed below. See
USEPA (1994) for additional detail.
704
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Chapter 6. Evaluation of Sediment Toxicity
Freshwater fingernail clams provide an adequate tissue mass, are easily handled, and can
be used in long-term exposures. However, few freshwater clam species are available for
testing. Exposure of clams is uncertain because of valve closure. Chironomids can be
readily cultured, are easy to handle, and reflect appropriate routes of exposure.
However, their rapid life-cycle makes it difficult to perform long-term exposures with
hydrophobic compounds that equilibrate slowly between sediment, pore water, and tissue.
Further, chironomids are capable of biotransforming PAHs (Leversee et al. 1982).
Larval mayflies reflect appropriate routes of exposure, have adequate tissue mass for
residue analysis, and can be used in long-term tests. However, mayflies cannot be
continuously cultured in the laboratory and consequently are not always available for
testing. Furthermore, the background concentrations of contaminants and the health of
field-collected individuals may be uncertain. Amphipods (e.g., Hyalella aztecd) can be
cultured in the laboratory, are easy to handle, and reflect appropriate routes of exposure.
However, their size may be insufficient for residue analysis, and H. azteca are sensitive
to contaminants in sediment. Fish (e.g., fathead minnows) provide an adequate tissue
mass, are readily available, are easy to handle, and can be used hi long-term exposures.
However, the routes of exposure are not appropriate for evaluating the bioavailability of
sediment-associated contaminants to benthic organisms.
Oligochaetes are infaunal benthic organisms that meet many of the test criteria listed
above. Certain oligochaete species are easily handled and cultured, provide reasonable
biomass for residue analyses, and are tolerant of varying sediment physical and chemical
characteristics. Oligochaetes are exposed to contaminants via all appropriate routes of
exposure, including pore water and ingestion of sediment particles. Oligochaetes do not
need to be fed during long-term bioaccumulation exposures (Phipps et al. 1993). Various
oligochaete species have been used in toxicity and bioaccumulation evaluations (Chapman
et al. 1982a,b; Wiederholm et al. 1987; Keilty et al. 1988a,b; Mac et al. 1990; Phipps
et al. 1993), and field populations have been used as indicators of pollution of aquatic
sediments (Brinkhurst 1980; Spencer 1980; Oliver 1984; Lauritsen et al. 1985; Robbins
et al. 1989; Ankley et al. 1992b; Branson et al. 1994).
USEPA (1994) describes methods for 28-day bioaccumulation tests with the oligochaete
Lumbriculus variegatus. The use of L. variegatus in laboratory bioaccumulation studies
has been field validated with natural populations of Oligochaetes. Total PCB concentra-
tions in laboratory-exposed L. variegatus were similar to concentrations measured hi
field-collected Oligochaetes from the same sites (Ankley et al. 1992b). PCB homolog
patterns also were similar between laboratory-exposed and field-collected Oligochaetes.
The more highly chlorinated PCBs tended to have greater bioaccumulation in the field-
collected organisms. In contrast, total PCBs hi laboratory-exposed (Pimephales
promelas) and field-collected (Ictalurus melas) fish revealed poor agreement in bioaccum-
ulation relative to sediment concentrations at the same sites (Ankley et al. 1992b).
However, laboratory exposures supply PCBs to organisms from test sediments, while
field exposures can potentially supply PCBs from sediments, diet, and water. Brunson
et al. (1994) also compared bioaccumulation of laboratory-exposed L. variegatus and
105
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Chapter 6. Evaluation of Sediment Toxicity
field-collected oligochaetes from the same sites. Select PAH and DDT peak concentra-
tions were similar in field-collected oligochaetes and L. variegatus exposed for 28 days
in the laboratory.
Data Analysis Approach
The toxicity test responses were evaluated and compared by several methods, as
described below:
• Sensitivity—Sensitivity was evaluated by comparison of the toxicity test
responses to the control response (only applicable for laboratory sediment
toxicity tests where a control sediment was also evaluated). Test responses
were considered to be indicative of effects if they were 20 percent or more
above the control response. Test responses indicative of effects were then
grouped into two categories, 1) 20-50 percent difference and 2) greater
than 50 percent difference from the control. Tests with responses in the
first category were judged to be relatively insensitive; tests with responses
in the second group were judged to be more sensitive. The numbers of
responses within each category were used to rank the relative sensitivity
among tests within each of the four surveys. In general, the most sensitive
toxicity test endpoints were considered to be those associated with the
highest percentage of the stations exhibiting responses of 20 percent or
more above the control response. In cases where more than one toxicity
test endpoint exhibited the same percentage of stations with responses of
20 percent or more above the control response, the toxicity test endpoint
with a higher percentage of responses in the more sensitive group (i.e.,
those exhibiting responses of 50 percent or more above the control) was
considered to be more sensitive.
• Discrimination—Discrimination is the ability of the toxicity test to detect
differing degrees of toxicity among samples. It is important when defining
the spatial extent of contamination to be able to ascertain whether sediment
samples vary in toxicity. A nonparametric statistical test (Kruskal-Wallis)
was conducted to determine whether the toxicities of the sediment samples
from each station within an AOC (e.g., within the Buffalo River AOC)
were different from the control. The lower the P value was for the statis-
tical comparisons between stations, the more discriminatory the toxicity
test was considered to be. The average P value, the range of P values,
and the number of AOC surveys (one to four) for which this discrimina-
tion analysis was conducted were all considered in the relative ranking of
their toxicity tests by their discriminatory power. It is misleading, in some
cases, to only consider the average P value, if it only came from one AOC
survey or if highly significant P values (e.g., P = 0.0001) for some
station comparisons were offset by very high P values (e.g., P = 0.9) for
other station comparisons.
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Chapter 6. Evaluation of Sediment Toxidty
• Redundancy—The degree of similarity between toxicity test responses was
evaluated using correlation analyses (both parametric and nonparametric)
and by grouping the test responses into patterns through factor analysis.
A high degree of correlation or pattern (grouping) similarity implies that
the toxicity tests were responding in a similar manner. These analyses
were conducted across all AOC surveys to better meet the study objective
of determining which toxicity tests were best (in terms of predictive
power) for Great Lakes studies. If a group of toxicity tests are producing
similar information, then it is less important that each toxicity test be
conducted, unless a weight-of-evidence assessment approach is being used.
It is, perhaps, of greater importance that a range of toxicity tests be used
that respond differently to varying types of sediment contamination (i.e.,
that show different response patterns and groups). This approach will
increase the likelihood that any detrimental effects on the aquatic eco-
system will be detected.
Data analyses included parametric or nonparametric correlation and mean comparison
analyses. Correlation analyses, sensitivity analyses, discriminatory analyses, and
principal component analysis (PCA) were generated using a Statistical Analysis Systems
computer package. Because sediments from each of the stations sampled were not
analyzed with all of the toxicity tests, a weight-of-evidence approach was applied to
interpret the results and identify trends in test responses. Conclusions from the results
of these AOC surveys may change with testing of additional contaminated sites.
Sediment toxicity test raw data and summary statistics are presented in Burton (1994),
Nelson et al. (1993), Hall et al. (1993), and Coyle et al. (1993). The data have also
been entered into the USEPA's Ocean Data Evaluation System (ODES) database and
have received a quality assurance validation from the USEPA (see Chapter 2).
Sensitivity
A total of 11 toxicity tests, comprising 43 endpoints, were ranked for sensitivity
(Table 6-4). The remainder of the toxicity tests and endpoints were deleted from this
ranking either because there were insufficient data or because the controls were not
appropriate for the sensitivity calculation used in the ranking process (e.g, microbial
enzymes or artificial substrate colonization).
Several benthic test species were very sensitive to sediment contamination. Preference
behavior by Diporeia spp. was the most sensitive endpoint, exhibiting responses of
20 percent or more above the control in 90 percent of the samples. Behavior would be
expected to be a responsive sublethal measure, but the ecological significance of
behavioral responses is difficult to interpret. Diporeia spp. is a clearwater species and
may exhibit behavioral responses in the test exposures as a result of factors other than
sediment contaminants. Although Hexagenia bilineata test endpoints were among the
most sensitive responses, the small data set for this species precluded use of the results
707
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TABLE 6-4. RANKING OF TOXICITY TEST ENDPOINTS BY SENSITIVITY OVER FOUR AOC SURVEYS
Toxicity Test (Endpoint)3
Diporeia spp. (5-day preference)
Hexagenia bilineata (elutriate, 1 0-day
survival)
Hydrilla verticillata (10-day root length)
Hexagenia bilineata (elutriate, 1 0-day
molting frequency)
Daphnia magna (7-day reproduction)
Hyalella azteca (14-day sexual
maturation)
Pimephales promelas (7-day larval
weight)
Microtox® (45-percent dilution.
5 minute)
Hyalella azteca (7-day survival)
Microtox® (45-percent dilution,
1 5 minute)
Chironomus tentans (10-day survival)
Ceriodaphnia dubia (7-day reproduction)
Hexagenia bilineata (10-day survival)
Chironomus riparius (14-day survival)
Hexagenia bilineata (10-day molting
frequency)
Diporeia spp. (28-day survival)
Hyalella azteca (14-day survival)
Chironomus tentans (10-day length)
Overall
Sensitivity
Rankb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Effect Level0
20-100%
90
75
75 (10)e
69
64
60 (23)
58
54
51
50
50
50
50
47
44
43
35
31
50-100%
84
75
40
56
20
58
24
42
35
46
38
37
31
37
38
16
26
0
20-50%
6
0
35
13
44
2
34
12
16
4
12
13
19
10
6
27
9
31
IH
1 =
1 =
1 =
1 =
6
•»
3
2
1 =
-1
1 =
10 =
1 =
1 =
1 =
14
1 =
5
Surveyd Ranks
BR
3
1
11 =
4
7
2
8
9
5
10
26 =
18
17
6
11 =
14
13
12
SR1
5
1
3
2
6
17
8
23 =
10
23 =
-
4
19 =
15
19 =
7
19
-
SR3
1
20 =
2
20 =
8
7
10
.
17 =
.
4
3
6
19 =
20 =
9
20 =
20 =
Average
Survey Rank
(Range)
2.5 (1-5)
5.75 (1-20)
4.25 (1-11)
6.75 (1-20)
6.75 (6-8)
6.75 (1-17)
7.75 (3-10)
11.3 (2-23)
8.25 (1-17)
11.3 (1-23)
10.3 (1-26)
8.75 (3-18)
10.75 (1-19)
10.25 (1-19)
12.75 (1-20)
11.6 (7-14)
13.25 (1-20)
12.3 (5-20)
-------
TABLE 6-4. (cont.)
Toxicity Test (Endpoint)3
Ceriodaphnia dubia (elutriate, 7-day
reproduction)
Pimephales promelas (7-day embryo
larval terata)
Hydrilla verticillata (10-day shoot
length)
Ceriodaphnia dubia (7-day survival)
Pimephales promelas (7-day embryo
larval survival)
Daphnia magna (48-hour survival)
Ceriodaphnia dubia (100-percent
elutriate, 7-day survival)
Hydrilla verticillata (10-day
dehydrogenase)
Hyalella azteca (28-day survival)
Hydrilla verticillata (10-day chlorophyll)
Microtox® (100 percent, 5 minute)
Pimephales promelas (7-day larval
survival)
Hyalella azteca (14-day length)
Lemna minor (4-day biomass)
Microtox® (100 percent, 15 minute)
Hyalella azteca (28-day sexual
maturation)
Hyalella azteca (28-day length)
Daphnia magna (7-day survival)
Overall
Sensitivity
Rankb
20
19
22
21
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Effect Level0
20-100%
31
31
29 (52)
26
21
19
18
1 7 (70)
15
15 (53)
14.8
14.5
13
1 2 (34)
11
10 (2.5)
10
8
50-100%
28
10
2
26
8
10
18
12
10
4
7.4
5.3
0
11
7
5
2.5
8
20-50%
3
21
27
0
13
9
0
5
5
11
7.4
9.2
13
1
4
5
7.5
0
IH
7
4
9
16
8
12
17
21 =
-
20 =
-
13
10 =
11
-
-
-
18
Survey6
BR
26 =
17
23
21
22
26 =
24
16
-
20
26 =
19
15
21
26 =
-
-
25
Ranks
SR1
14
11
13
20
27 =
12
16
25
19 =
9
28 =
28 =
25
28 =
27 =
20
18
24
SR3
14
20 =
15
11
17 =
20 =
16
5
18
20 =
12
19 =
20 =
20 =
13
19 =
20 =
19 =
Average
Survey Rank
(Range)
15.25 (7-26)
13.0 (4-20)
12.5 (5-23)
17.0 (11-21)
18.5 (8-27)
17.5 (12-23)
18.25(16-24)
16.75 (5-25)
18.5 (18-19)
17.75 (9-20)
22.0 (12-28)
19.75 (13-28)
17.5 (10-25)
20.0 (11-28)
22.0(13-27)
19.5 (19-20)
19.0 (18-20)
21.5 (18-25)
-------
TABLE 6-4. (cont.)
Toxicity Test (Endpoint)3
Pimephales promelas (7-day embryo
larval length)
Chironomus riparius (14-day length)
Hyalella azteca (28-day antenna
segment number)
Lemna minor (4-day frond number)
Lemna minor (4-day chlorophyll a)
Hyalella azteca (14-day antenna
segment number)
Hydrilla verticillata (10-day peroxidase)
Overall
Sensitivity
Rank"
37
38
39
40
41
42
43
Effect Level0
20-100%
7
6.6 (16.7)
5
3 (36)
2 (25)
0
0 (87.5)
50-100%
0
3.3
0
0
0
0
0
20-50%
7
3.3
5
3
2
0
0
IH
15
-
-
19 =
20 =
21 =
21 =
Surveyd Ranks
BR
26 =
26 =
-
26 =
26 =
26 =
26 =
SR1
28 =
17
21
28 =
28 =
27 =
28 =
SR3
20 =
20 =
20 =
20 =
20 =
20 =
20 =
Average
Survey Rank
(Range)
22.25 (15-28)
21.0 (17-26)
20.5 (20-21)
23.25 (19-28)
23.25 (20-28)
23.5 (20-27)
23.75 (20-28)
a Some endpoints were not included due to lack of true control values for determining sensitivity (Response - Control/Control) or data were
too limited. All toxicity tests were conducted with whole sediment unless indicated otherwise.
b Overall sensitivity ranks based on the numbers of responses within each category of test responses relative to the control (see text).
; Percentage of values showing effects ranging from 50 to 100 percent or 20 to 50 percent of the control response
Response - Control
x 100 .
Control
d IH, Indiana Harbor; BR, Buffalo River; SR1, Saginaw River Survey No. 1; SR3, Saginaw River Survey No. 3. Based on mean value of station
replicates.
e (x) = additional percentage of responses stimulated greater than 20 percent over the control response. Value not considered in ranking.
-------
Chapter 6. Evaluation of Sediment Toxidty
in the final relative ranking. Sediment samples were also stored for prolonged periods
(up to 6 months) before the H. bilineata tests were started.
Discriminatory Ability
The discriminatory ability of a toxicity test measures how well the response detects vary-
ing levels of sediment toxicity. This ability was evaluated using levels of statistical
significance, or P values. The smaller the P value, the greater the capacity to detect sta-
tistical differences between samples/stations. A total of 53 endpoints were ranked for
their discriminatory ability (Table 6-5). Some toxicity test data were not available or
could not be analyzed by this procedure, so discriminatory ability was not determined for
all endpoints for all four AOC surveys.
The photosynthetic and indigenous microbial endpoints would be expected to be good
discriminators because they can exhibit both inhibitory and stimulatory responses, giving
them a wider range of response than just 0 to 100 percent, as with conventional toxicity
test responses. Indeed, the Selenastrum capricornutum growth at 48 h (average P value
of 0.0213) and at 96 h (average P value of 0.0150) were among the best discriminatory
toxicity tests for the four AOC surveys. However, of the other photosynthetic endpoints,
only Lemna minor chlorophyll a production showed significant differences for three AOC
surveys. Lemna minor frond number and biomass showed significant differences for
only one AOC survey and Hydrilla verticillata endpoints did not detect any significant
differences.
The indigenous microbial endpoints were better discriminators than these latter two pho-
tosynthetic surrogate endpoints, with significant differences observed for two or three of
the AOC surveys. These endpoints ranked from high to low discriminatory ability, in
order, as: dehydrogenase, glucosidase, galactosidase, and alkaline phosphatase.
Several of the benthic macroinvertebrate community indices, sampled using the artificial
substrates, were good discriminators. The top two listed in Table 6-5 (hydra numbers
and macroinvertebrate biomass) cannot be reliably evaluated because they were only
analyzed or determined for one AOC survey. The Family Biotic Index, however, was
highly discriminatory (P = 0.0291 to 0.0319) for all three AOC surveys where it was
evaluated. The second best discriminator in this group of endpoints was percent flat-
worm composition, showing significant differences for two of the three AOC surveys
where it was evaluated. Two other endpoints showing this level of discrimination, but
with slightly lower P values, were percent contributing dominant family and oligochaete
number.
Among the other toxicity tests evaluated, several benthic species endpoints were good
discriminators. Survival of Hyalella azteca, Chironomus riparius, mdDiporeia spp. did
not rank high in discriminatory ability for any of the four AOC surveys. However,
chronic endpoints of length and sexual maturation were highly discriminatory for a
minimum of one AOC survey. The C. riparius length (average P value of 0.0116) and
777
-------
TABLE 6-5. RANKING OF TOXICITY TEST ENDPOINTS BY DISCRIMINATORY ABILITY OVER FOUR AOC SURVEYS
Toxicity Test (Endpoint)3
Hydra (numerical percent, artificial substrate)
Saginaw River macroinvertebrates (biomass, artificial
substrate)
Brachionus sp. (50-percent elutriate, 24-hour survival)
Ceriodaphnia dubia (100-percent elutriate, 7-day
reproduction)
Chironomus riparius (14-day length)
Se/enastrum capricornutum (100-percent elutriate,
96-hour growth)
Sediment microbial community (dehydrogenase activity)
Pimephales promelas (7-day larval weight)
Selenastrum capricornutum (100-percent elutriate,
48-hour growth)
Rapid Bioassessment Protocol Phase II (Family Biotic
Index, artificial substrate)
Hyalella azteca (28-day length)
Sediment microbial community (glucosidase activity)
Selenastrum capricornutum (50-percent elutriate,
24-hour 14C uptake)
Daphnia magna (7-day reproduction)
Sediment microbial community (galactosidase activity)
Flatworms (numerical percent, artificial substrate)
Lemna minor (4-day chlorophyll a)
Amphipods (numerical percent, artificial substrate)
Discriminatory
Rankb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Average P
Value0
0.0069
0.0069
0.0071
0.0083
0.0116
0.0150
0.0152
0.0198
0.0213
0.0240
0.0298
0.0331
0.0507
0.0570
0.0647
0.0675
0.0676
0.0707
Standard
Deviation
0.0000
0.0000
0.0048
0.0110
0.0050
0.0097
0.0170
0.0180
0.0155
0.0113
0.0176
0.0314
0.0736
0.1136
0.0529
0.0726
0.0547
0.0488
Significant
Surveysd
1/1
1/1
4/4
4/4
3/3
4/4
2/2
4/4
3/3
3/3
3/3
2/3
3/4
3/4
2/2
2/3
3/4
1/3
Range of P
Values
-
_
0.0018-0.0134
0.0001-0.0233
0.0063-0.0162
0.0037-0.0273
0.0032-0.0273
0.0061-0.0463
0.0084-0.0329
0.0291-0.0319
0.0129-0.0481
0.0050-0.0670
0.0013-0.1581
0.0001-0.2274
0.0273-0.1021
0.0223-0.1513
0.0086-0.1266
0.0284-0.1223
-------
TABLE 6-5. (cont.)
Co
Toxicity Test (Endpoint)3
Pimephales promelas (7-day embryo larval terata)
Rapid Bioassessment Protocol Phase II (percent
contributing dominant family, artificial substrate)
Microtox® (50-percent dilution, 15 minute)
Hyalella azteca (14-day survival)
Hydrilla verticillata (10-day peroxidase)
Hyalella azteca (28-day survival)
Hydrilla verticillata (10-day shoot length)
Oligochaetes (number, artificial substrate)
Rapid Bioassessment Protocol Phase II (taxa richness,
artificial substrate)
Ceriodaphnia dubia (7-day survival)
Hyalella azteca (28-day sexual maturation)
Sediment microbial community (alkaline phosphatase
activity)
Hyalella azteca (28-day antenna segment number)
Hyalella azteca (14-day length)
Pimephales promelas (7-day embryo larval length)
Ceriodaphnia dubia (7-day reproduction)
Ceriodaphnia dubia (elutriate, 7-day survival)
Lemna minor (4-day biomass)
Chironomus riparius (14-day survival)
Daphnia magna (7-day survival)
Diporeia spp. (5-day preference)
Daphnia magna (48-hour survival)
Discriminatory Average P
Rankb Value0
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
0.0826
0.0870
0.0890
0.1049
0.1051
0.1098
0.1182
0.1397
0.1407
0.1452
0.1639
0.1712
0.1726
0.1805
0.1808
0.1914
0.1930
0.2017
0.2091
0.2441
0.2671
0.2764
Standard Significant
Deviation Surveys'1
0.1404
0.0965
0.0060
0.1441
0.0000
0.1137
0.0270
0.2051
0.0977
0.1698
0.2102
0.2671
0.2391
0.1372
0.1928
0.3613
0.2234
0.2509
0.3290
0.2865
0.2441
0.2212
3/4
2/3
0/3
2/3
0/1
1/3
0/4
2/3
1/3
2/4
1/3
2/3
2/3
1/3
2/4
3/4
2/4
1/4
3/4
2/4
2/4
1/4
Range of P
Values
0.0020-0.2929
0.0302-0.1984
0.0833-0.1017
0.0173-0.2712
-
0.0169-0.2366
0.0922-0.1479
0.0116-0.3763
0.0290-0.2107
0.0001-0.3402
0.0463-0.5296
0.0032-0.4712
0.0219-0.4483
0.0277-0.2930
0.0183-0.3766
0.0002-0.7329
0.0001-0.4060
0.0452-0.5743
0.0245-0.7017
0.0001-0.5527
0.0539-0.8296
0.0272-0.4060
-------
TABLE 6-5. (cont.)
Discriminatory Average P
Toxicity Test (Endpoint)3 Rankb Value0
Hyalella azteca (14-day sexual maturation)
Hyalella azteca (14-day antenna segment number)
Pimephales promelas (7-day larval survival)
Diporeia spp. (28-day survival)
Hydrilla verticillata (10-day chlorophyll)
Chironomids (numerical percent, artificial substrate)
Pimephales promelas (7-day larval survival)
Lemna minor (4-day frond number)
Rapid Bioassessment Protocol Phase II (EPT/Chironomidae,
artificial substrate)
Hyalella azteca (7-day survival)
Hydrilla verticillata (10-day dehydrogenase)
Zebra mussels (numbers, artificial substrate)
Hydrilla verticillata (10-day root length)
41
42
43
44
45
46
47
48
49
50
51
52
53
0.2765
0.3120
0.3172
0.3182
0.3720
0.3633
0.3815
0.3824
0.4191
0.4742
0.4988
0.5080
0.5826
Standard Significant
Deviation Surveysd
0.2425
0.3849
0.4498
0.4398
0.1783
0.4987
0.3122
0.3065
0.5078
0.0000
0.3647
0.6958
0.3152
1/3
1/3
2/4
2/4
0/4
1/2
1/4
1/4
0/3
0/4
0/3
1/2
0/4
Range of P
Values
0.0463-0.5296
0.0262-0.7496
0.0161-0.9716
0.0233-0.9576
0.1931-0.6110
0.0105-0.7161
0.0159-0.7580
0.0427-0.7863
0.0593-1.0000
0.0713-1.0000
0.1125-0.8371
0.0160-1.0000
0.2521-0.8769
a All toxicity tests were conducted with whole sediment unless indicated otherwise. Some endpoints lacked adequate data for ranking.
b Discriminatory ranks based on the average P value for pairwise statistical comparisons of all station responses with the control response.
c Average of P values for the AOC surveys analyzed.
d Number of AOC surveys with significant Kruskal-Wallis P value (P<0.05) per total number of AOC surveys where that endpoint was
analyzed.
-------
Chapter 6. Evaluation of Sediment Toxidty
H. azteca 28-day length (average P value of 0.0298) were significant for all three AOC
surveys where they were evaluated. The most discriminatory nonbenthic invertebrate
endpoints were ranked as follows: Brachionus sp. survival, Ceriodaphnia dubia reproduc-
tion (elutriate), and Pimephales promelas larval weight, each showing significant differ-
ences for all four AOC surveys. Although the Brachionus sp. test showed significant
discrimination for all four AOC surveys, the data are questionable, for comparison pur-
poses, due to storage of sediment for 12 months before testing. Five endpoints had signi-
ficant P values for three of the four AOC surveys, including Selenastrum capricornutum
14C-uptake, Daphnia magna reproduction, P. promelas embryo-larval terata, C. dubia
reproduction (whole sediment), and C. riparius survival. Some other endpoints (e.g.,
C. dubia survival [whole sediment], P. promelas embryo larval length, C. dubia repro-
duction [whole sediment] and survival [elutriate], and D. magna survival [whole sedi-
ment]) showed highly significant P values for two of the four AOC surveys, but had high
P values for the other AOC surveys.
In summary, there were several toxicity test endpoints that proved to be highly discrimi-
natory of degrees of sediment toxicity. This is a critically important trait for toxicity
tests when attempting to define the spatial extent of site contamination. The nonbenthic
toxicity tests tended to be more discriminatory than the benthic toxicity tests, and
therefore should be included in any test battery.
Combined Sensitivity and Discriminatory Abilities
The rankings developed for sensitivity and discriminatory ability were combined to
provide a comprehensive rank over all four AOC surveys (Table 6-6). It is evident in
this table that there is a wide range in ranks for each characteristic, ranging from ranks
of 1 to 25 for the toxicity tests with the top 10 combined ranks. The Daphnia magna 7-
day reproduction test ranked first, while the Pimephales promelas 7-day larval weight test
was second. All of the top five combined rank test endpoints were nonbenthic, tending
to have more discriminatory ability than the benthic test endpoints. The Daphnia magna
(7-day reproduction) test had ranks of 5 for both sensitivity and discriminatory ability.
The Microtox® (45 percent, 5 minute and 15 minute) tests were the next most consistent
tests between the two characteristics of sensitivity and discriminatory ability, ranking 8
or 10 for each characteristic. The high combined ranking of Microtox® at 3 and 4, and
the high degree of correlation with other responses (as discussed below), illustrates the
usefulness of Microtox® in reconnaissance surveys.
Similarities in Measured Endpoint Responses
A PC A was conducted to determine if there were meaningful groupings of toxicity tests
that could be used to further refine a list of recommended tests. In the PCA, the data
undergo a transformation to generate factors that remain independent of each other. The
results of the analysis are presented as separate factors, each of which explains one
aspect of the variability among test responses. In the ARCS Program, these factors were
115
-------
TABLE 6-6. COMBINED RANKING OF ARCS TOXICITY TESTS:
SENSITIVITY + DISCRIMINATORY ABILITY
Combined
Rank3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Toxicity Test (Endpoint)
Daphnia magna (7-day reproduction)
Pimephales promelas (7-day larval weight)
Microtox® (45 percent, 5 minute)
Microtox® (45 percent, 1 5 minute)
Ceriodaphnia dubia (elutriate, 7-day reproduction)
Diporeia spp. (5-day preference)
Hyalella azteca (14-day survival)
Pimephales promelas (7-day embryo larval terata)
Ceriodaphnia dubia (7-day reproduction)
Hyalella azteca (14-day sexual maturation)
Hydrilla verticillata (10-day shoot length)
Ceriodaphnia dubia (7-day survival)
Chironomus riparius (14-day survival)
Hydrilla verticillata (10-day root length)
Microtox® (100 percent, 5 minute)
Hyalella azteca (28-day survival)
Hyalella azteca (28-day length)
Microtox® (100 percent, 15 minute)
Chironomus riparius (14-day length)
Hyalella azteca (7-day survival)
Sensitivity
Rankb
5
7
8
10
19
1
17
20
12
6
21
22
14
3
29
27
35
33
38
9
Discriminatory
Rank0
5
3
8
8
1
23
9
7
18
25
12
13
21
34
8
11
4
6
2
32
a Combined rank based on the sum of the sensitivity and discriminatory ranks.
b Sensitivity ranks from Table 6-4.
0 Discriminatory ranks initially from Table 6-5. Ranks in Table 6-5 were modified, however, by
deleting those endpoints for which sensitivity was not also ranked (Table 6-4). In addition, the
Microtox® 45-percent and 100-percent endpoints were not included in Table 6-5 because of data
limitations. These Microtox® endpoints were ranked relative to the other endpoints using an
alternative procedure.
116
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Chapter 6. Evaluation of Sediment Toxicity
evaluated to determine if they could be interpreted as different response patterns. The
percent contribution of each variable (test response) to each factor is listed in Table 6-7.
Test responses for similar endpoints (e.g., growth) that contribute similarly to a factor
may represent redundant tests. There can be no missing data for any variable; that is,
the number of data points must be equal. There were only 20 endpoints (Table 6-7) that
met these data requirements.
The results of the correlation analysis indicated that a large number of endpoints were
significantly related. These similarities are also observed in the results of the factor
analysis (Table 6-7), which shows several endpoints contributing to Factors 1-3. These
findings suggest that responses within each factor are producing similar and redundant
information. If a test battery were to be selected that detected each type of toxicity
response pattern (Factors 1-4), one toxicity test consisting of two or more endpoints
could provide unique information for multiple groupings. For example, the Hyalella
azteca 14-day test consisting of survival, length, antenna segment number, and sexual
maturation endpoints is representative of three unique response patterns, while only
Hexagenia bilineata describes the fourth pattern. Both the Ceriodaphnia dubia and
Chironomus riparius tests can be used to explain Factors 1 and 2. Use of these toxicity
tests would enable each unique response pattern to be covered with fewer organism
types.
Correlations Between Toxicity Test Endpoint Responses
Correlating the endpoint responses (both laboratory toxicity tests and community struc-
ture analyses) to detect similar response patterns is another useful method to evaluate data
redundancy and provide field validation of toxicity tests. All 93 measured endpoints
were correlated with each other (Spearman rank correlation) and the top 10 correlations
for each toxicity test were further evaluated based on the resulting r2 and P values.
The numbers of significant correlations between endpoint responses varied with the
degree of site contamination. Indiana Harbor was the most contaminated (Nelson et al.
1993) and most toxic of the three AOCs surveyed. Indiana Harbor had the highest
number of significant (P < 0.05) correlations. The Buffalo River samples exhibited less
contamination and toxicity compared to the other two AOCs and had the fewest
significant correlations. The Saginaw River No. 1 survey had a moderate level of
toxicity. The response patterns among toxicity tests were similar for sediments collected
from the Indiana Harbor and Saginaw River No. 1 surveys. There were only three
samples collected in the Saginaw River No. 1 survey, and therefore correlations would
be similar, particularly because one sample (Station No. 6) was very toxic. There was
little toxicity observed in the Saginaw River No. 3 sediment samples, and consequently
there were fewer significant correlations in that survey.
Seventy-two percent of the endpoints had more than 10 significant correlations and
77 percent had endpoint correlations with r2 greater than 0.80. Endpoints with the fewest
significant correlations included Hydrilla verticillata root and shoot length (no significant
777
-------
TABLE 6-7. FACTOR ANALYSIS OF ARCS SEDIMENT TOXICITY TEST DATA
Factor
Toxicity Test (Endpoint) 1
Chironomus riparius (14-day survival) 0.97
Chironomus tentans (10-day length) 0.96
Hyalella azteca (28-day antenna segment number) 0.95
Hyalella azteca (28-day length) 0.94
Hyalella azteca (14-day antenna segment number) 0.94
Daphnia magna (7-day reproduction) 0.92
Lemna minor (4-day frond number) 0.90
Hyalella azteca (14-day length) 0.86
Hyalella azteca (28-day survival) 0.63
Ceriodaphnia dubia (7-day reproduction) -0.74
Hyalella azteca (28-day sexual maturation) -0.94
Ceriodaphnia dubia (7-day survival) 0.85
Chironomus riparius (14-day length) 0.83
Hyalella azteca (14-day sexual maturation) -0.70
Pimephales promelas (7-day larval weight) -0.71
Hydrilla verticillata (10-day root length) 0.92
Diporeia spp. (5-day preference) 0.78
Hyalella azteca (14-day survival) -0.60
Hexagenia bilineata (10-day survival) 0.91
Hexagenia bilineata (10-day molting frequency) 0.72
118
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Chapter 6. Evaluation of Sediment Toxicity
correlations), Lemna minor biomass and benthic taxa richness (2 correlations each), per-
cent flatworms and microbial galactosidase activity (3 correlations), Daphnia magna
7-day survival (4 correlations), and L. minor chlorophyll a (5 correlations).
The endpoints with the highest average correlation (r2 value) were (in rank order)
Microtox®, Chironomus teutons length, and percent chironomids and percent tolerant
species in the artificial substrate samples (Table 6-8). The high number of significant
correlations between laboratory toxicity test endpoints and some artificial substrate
benthic macroinvertebrate endpoints (e.g., percent chironomids and percent tolerant
species) provides a high degree of field validation for the laboratory tests.
TABLE 6-8. TOXICITY TEST ENDPOINTS WITH THE HIGHEST AVERAGE r2
AND LOWEST AVERAGE P VALUES8
Endpoint
Microtox®
Chironomus tentans length
Percent chironomids
Percent tolerant species
Average
r2 Value
0.86
0.83
0.82
0.81
Endpoint
Percent tolerant species
Percent chironomids
Microtox®
Chironomus tentans length
Average
P Value
0.0003
0.002
0.003
0.009
a Based on average values from top 10 Spearman rank correlations for each endpoint.
When assessing sediment toxicity, it is important to consider effects on both benthic and
nonbenthic species, because there may be interactions between the sediment and the over-
lying water and between benthic and nonbenthic species. Of the nonbenthic species, the
Pimephales promelas and cladoceran toxicity tests are the most commonly used in
sediment testing. Fish and cladocerans feed on the sediment surface during whole
sediment exposures, which increases their exposure. When toxicity response patterns
were compared between benthic and nonbenthic species, there were many significant
correlations (Table 6-9). The 7-day toxicity tests with Ceriodaphnia dubia, Daphnia
magna, and Pimephales promelas larval growth were significantly correlated with 10 to
70 percent of the benthic responses. The various endpoint responses of Hyalella azteca
were significantly correlated with up to 80 percent of the nonbenthic endpoint responses.
Chironomus tentans and Chironomus riparius endpoint responses were significantly
correlated with greater than 60 and 70 percent of the nonbenthic endpoint responses,
respectively. The indigenous sediment microbial enzyme activities were significantly
correlated with up to 70 percent of the nonbenthic endpoint responses.
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Chapter 6. Evaluation of Sediment Toxicity
TABLE 6-9. PERCENTAGE OF SIGNIFICANT CORRELATIONS BETWEEN
BENTHIC AND NONBENTHIC ENDPOINT RESPONSES3
Benthic Test Percentage Nonbenthic Test Percentage
Chironomus riparius 70 + Ceriodaphnia dubia (100-percent 70
elutriate, 7-day survival)
Chironomus tentans 60+ Ceriodaphnia dubia (7-day 50
reproduction)
Hya/el/a azteca
Hexagenia bilineata
Microbial enzyme
activities
Hydrilla verticillata
10-80
20-60
10-70
0-50
Daphnia magna (7-day reproduction)
Daphnia magna (48-hour survival)
Lemna minor
Microtox®
Se/enastrum capricornutum
Pimephales promelas
50
50
10-70
30-60
30
10-70
Based on significant correlations with top 10 endpoints.
Comparisons of Acute and Chronic Toxicity Testing with
Whole Sediments
Ideally, a sediment toxicity test should be rapid, simple, and inexpensive if the objective
of the study is to screen a large number of samples. Acute lethality tests are useful in
identifying "hot spots" of sediment contamination, but these tests cannot be used to
evaluate moderately contaminated areas where only chronic effects may occur.
Concentrations of contaminants in sediments may not be lethal, but may interfere with
the ability of an animal to develop, grow, or reproduce. A better understanding of the
sublethal effects of chemicals in sediment is needed to identify areas with moderate
contamination and evaluate chemicals that do not elicit acutely lethal responses.
Many benthic organisms continuously inhabit sediment. Extrapolations from a 10-day
lethality test conducted in the laboratory to a lifetime of exposure in the field may
underestimate effects from long-term exposures to benthic organisms. Desorption of
contaminants from sediment into interstitial water may be kinetically limited. Therefore,
long-term exposures should be used to better evaluate moderate levels of contamination
where subtle effects are more difficult to discern.
Estimates of sublethal effects of contaminated sediment are typically based on exposures
of 10 days or less with midges, amphipods, or cladocerans (e.g., Burton 1991). These
partial life-cycle exposures may not always include the most sensitive life stage(s) of the
test species. Testing sensitive life stages in longer-term exposures may provide a more
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Chapter 6. Evaluation of Sediment Toxicity
subtle measure of chemical toxicity (Breteler et al. 1989; Ingersoll and Nelson 1990;
Kemble et al. 1993; Nelson et al. 1993).
Procedures for conducting whole-sediment toxicity tests for up to 29 days with Hyalella
azteca have been recently reported (Borgmann and Munawar 1989; Ingersoll and Nelson
1990; Nelson et al. 1993; Kemble et al. 1993). Endpoints monitored at the end of these
exposures include survival, growth, or sexual maturation. Supplemental food is typically
added to the chambers during exposures, with daily renewal of water overlying the
sediment.
The toxicity to Hyalella azteca of sediment contaminated with PAHs and PCBs was
evaluated after exposures of 2, 10, and 29 days in static and water-renewal exposures
(Ingersoll and Nelson 1990). Survival of amphipods was not reduced after a 2-day
exposure, was reduced by about 50 percent after a 10-day exposure, and was reduced by
about 70 to 90 percent after a 29-day exposure. Body length of amphipods was only
reduced in the 29-day exposure.
The toxicity to Hyalella azteca of contaminated Great Lakes sediment was evaluated after
7-, 14-, or 28-day exposures (Burton 1994; Nelson et al. 1993). Survival and length
endpoints were more discriminatory compared to sexual maturation. Effects after 28
days of exposure were often more severe than effects after 7 or 14 days of exposure.
For example, only one station in the first survey of the Saginaw River was toxic to
amphipods after 14 days of exposure (reduced survival but not length with exposure to
sediment from Station SR-6). After 28 days of exposure, Station SR-6 sediment was still
the only sample that reduced survival. Sexual maturation did not identify any additional
toxic samples. However, length of amphipods was reduced in all of the exposures to
Saginaw River sediments after 28 days.
The toxicity of metal-contaminated sediment to Hyalella azteca was evaluated after 28-
day exposures (Kemble et al. 1993). Length was a more sensitive endpoint compared
to survival or sexual maturation. Only 7 percent of the samples reduced survival and
23 percent of the samples reduced sexual maturation. However, 62 percent of the
samples reduced length of the amphipods after 28 days of exposure. Reduction in length
of amphipods was correlated to metal concentration in the whole sediment and in the
interstitial water. Amphipod length and benthic community evaluations both provided
complementary evidence of metal-induced degradation to aquatic communities at study
sites in the Milltown Reservoir and Clark Fork River in Montana (Kemble et al. 1993).
In summary, the duration of the exposure can have a profound influence on the response
of organisms in sediment toxicity tests. Extended exposures (i.e., 14-28 days) with Hya-
lella azteca may exhibit toxicity for sediment samples that do not exhibit toxicity in
exposures of 2 to 7 days. In addition, assessment of sublethal endpoints such as length
may detect subtle effects for sediment samples that do not reduce survival in 14- or 28-
day exposures. Additional method development is needed on culturing and chronic
sediment testing procedures for other benthic infaunal species with a variety of feeding
habits including suspension and deposit feeders. Potential depletion of contaminants or
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Chapter 6. Evaluation of Sediment Toxidty
changes in sediment during exposures may be a problem when conducting long-term
tests. Effects of natural physico-chemical characteristics of sediment (e.g., grain size)
or indigenous animals (e.g., predators) may also be exacerbated in chronic exposures
(Reynoldson et al. 1994). Despite these limitations, sublethal responses of benthic
organisms need to be evaluated in sediment assessments. Long-term exposures should
be used to provide data on growth and reproduction of organisms inhabiting sediment.
Results of these chronic exposures can be used to better evaluate the structure and
function of benthic communities in moderately contaminated areas.
EVALUATION OF TOP-RANKED TOXICITY TESTS
Several promising test species for which an adequate database exists for use in sediment
toxicity testing are listed in Table 6-1 with a subjective ranking of selection criteria for
sediment testing. The primary advantages and disadvantages of each of the test species
used in the ARCS Program are discussed in this section.
While the Diporeia spp. preference and avoidance endpoints were the most sensitive
overall, this toxicity test is one of the least developed (Gossiaux et al. 1993). The
survival endpoint for this organism was relatively insensitive (sensitivity ranks from 7 to
14 in the four AOC surveys) and Diporeia spp. must be collected from the field for
testing. The ecological significance of behavioral endpoints, such as avoidance/
preference, is difficult to evaluate at this tune. However, Diporeia spp. is of critical
importance in the Great Lakes. This characteristic alone indicates this toxicity test
should be given high priority for additional methods development and testing.
Hexagenia bilineata endpoints exhibited relatively sensitive responses for most of the
AOC surveys. However, the Kruskal-Wallis test could not be run with this data set.
Previous discriminatory analysis using a different procedure (whereby the geometric
mean is divided by the arithmetic mean) indicated that the molting endpoint was rela-
tively discriminatory (rank = 5); however, survival was not discriminatory (rank = 21).
Surprisingly, the elutriate exposures were, for H. bilineata, more sensitive than the
whole-sediment toxicity tests. The sensitivity of H. bilineata exhibited in the ARCS
Program may have resulted from the prolonged storage of sediment before testing. The
validity of the data comparisons with this toxicity test are compromised due to the differ-
ent storage periods. The inability to continuously culture mayflies in the laboratory has
limited their routine use in sediment testing. Mayflies may also be sensitive to sediment
grain size in whole-sediment exposures (ASTM 1993).
The rotifer Brachionus sp. survival toxicity test (Snell and Persoone 1989) had to be
conducted after prolonged sediment storage (up to 12 months). As with the Hexagenia
bilineata toxicity test, comparison of sediment effects on rotifers to the other toxicity
tests is tenuous because of potential toxicity artifacts caused by prolonged sediment
storage. The rotifer was insensitive, but was discriminatory in elutriate exposures.
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Chapter 6. Evaluation of Sediment Toxidty
Hyalella azteca responses were highly variable, depending on the length of exposure
(7 to 28 days) and the endpoint measured, with sensitivity ranks ranging from 1 to 27 for
the four AOC surveys. The advantages of conducting sediment toxicity tests with
H. azteca are 1) the animals can be cultured in the laboratory, 2) testing and culturing
methods have been standardized, 3) effects on survival, growth, or sexual maturation can
be monitored hi 7- to 28-day exposures, 4) H. azteca are insensitive to grain size of the
sediment (Ankley et al. 1994), 5) H. azteca had a combined rank of 4 for sensitivity and
discriminatory ability for 14-day survival, and 6) H. azteca endpoints correlated well
with other toxicity test endpoints.
As with H. azteca, the midges Chironomus tentans and Chironomus riparius exhibited
a wide range of sensitivity and discriminatory ability over the four AOC surveys, but
ranked relatively high overall. Control survival for the midges was typically lower than
for the other test species. The advantages of conducting sediment tests with midges are
1) the animals can be cultured in the laboratory, 2) testing and culturing methods have
been standardized, and 3) effects on survival and growth can be monitored in 10- to
14-day exposures.
Toxicity tests with the aquatic macrophyte Hydrilla verticillata have been conducted by
very few laboratories. Some of the measured endpoints used in this test proved to be
sensitive (root length, sensitivity ranks of 1-11 for the four AOC surveys), but the
endpoints were not discriminatory. H. verticillata represents a unique level of biological
organization and should be considered in future assessments if adequate resources are
available for testing. The Lemna minor (duckweed) toxicity test also measures a unique
biological level of organization that is of importance to ecosystem functioning. By
design, this test cannot be highly sensitive to sediment contaminants because the plants
float on the surface of the water. Therefore, the only exposure is to contaminants that
are water soluble or associated with suspended colloidal particles.
Hall et al. (1993) reported problems conducting elutriate toxicity tests using the 24-hour,
14C-assimilation with Selenastrum capricomutum. Interpretations of toxicity using
S. capricomutum were complicated by variable nutrient and inorganic carbon concentra-
tions in the elutriate samples. All of the elutriate samples tested stimulated carbon
assimilation by 5. capricomutum in one or more of the dilutions. Attempts to modify
the algal medium to provide unlimited nutrients were not successful. An algal medium
that supports greater growth potential should be developed in order to evaluate the
toxicity of environmental samples with high concentrations of algal nutrients.
The Microtox® test response was relatively sensitive (overall sensitivity rank of 8). Its
discriminatory ability was moderate (Table 6-6) and was well correlated with other
toxicity test responses (Table 6-8). Other advantages of the Microtox® test are rapid
response, small volume requirements, and standardized testing procedures.
The indigenous tests included the benthic macroinvertebrate indices from artificial sub-
strates and the microbial enzyme activities of the sediment samples. These data could
not be analyzed for sensitivity with the above data sets because of the lack of controls
723
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Chapter 6. Evaluation of Sediment Toxicity
for comparisons. Several endpoints for these tests proved to be highly discriminatory
(Table 6-5). The percent tolerant species and percent chironomid composition indices
were highly correlated with toxicity test responses. Both indices represent unique levels
of biological organization. Microbial enzyme and benthic colonization tests evaluate
indigenous organisms, not surrogate species, and therefore there is reduced uncertainty
in data extrapolations. See Chapter 7 for more complete analyses of the benthic macro-
invertebrate data.
CONCLUSIONS AND RECOMMENDA T/ONS
A wide range of sediment toxicity tests covering multiple levels of biological organization
and trophic levels should be used to effectively assess sediment toxicity. Each toxicity
test provides information that is unique to that species and the life process measured
(e.g., survival, growth). Use of a battery of toxicity tests allows a "weight-of-evidence"
assessment approach and yields stronger conclusions because false negatives or false
positives from individual tests can be interpreted in light of results of the entire battery.
Nevertheless, combinations of tests that provide redundant information should be avoided
to be more cost effective and allow greater spatial coverage of a site (i.e., allowing more
samples to be tested).
Criteria for Selection of Individual Toxicity Tests
Selection of the appropriate toxicity test(s) depends on the characteristics of the site, the
resources available, and the objectives of the study. Criteria for selecting toxicity tests
are listed in Table 6-1. Two critical factors to consider are relative abilities at detecting
sediment toxicity (i.e., sensitivity) and measuring level of toxicity (i.e., discrimination).
Sediment toxicity appeared to correlate with the relative degree of chemical contamina-
tion at the ARCS priority AOCs. Further relationships between biological and chemical
variables could be developed using detailed analyses (described in Chapter 9) based on
the Apparent Effects Threshold (AET), Sediment Quality Triad, TIE procedures, and
sediment spiking studies (Ingersoll et al., in prep.). Nevertheless, the present results are
based on the most comprehensive study of its kind (7,600 data points). Toxicity tests
that were relatively sensitive or discriminatory for three or four of the AOC surveys in
the ARCS Program would probably be sensitive or discriminatory at other sites. The
toxicity tests recommended here are similar to those recommended in studies by the IJC
(1988), Giesy and Hoke (1990), Giesy et al. (1988a, 1989), Kemble et al. (1993), and
Burton et al. (1989).
Ecological significance of the measured endpoints is not directly addressed with
laboratory toxicity tests alone. The most sensitive toxicity endpoint in the ARCS
Program was the avoidance or preference behavior oiDiporeia spp., a common amphi-
pod in the Great Lakes. Behavior is often a sensitive indicator of sublethal responses.
What is not known; however, is whether the preference of organisms for one sediment
over another would alter the population, community, or ecosystem to any degree that
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Chapter 6. Evaluation of Sediment Toxicity
constitutes short- or long-term impairment. These issues are best resolved using a
"weight-of-evidence" assessment approach in which other toxicity endpoints and com-
munity analyses are considered along with chemical and physical characteristics. As
discussed above, there were many significant correlations between laboratory toxicity test
responses and benthic community structure patterns in the field.
The process of selecting the optimal toxicity test(s) for use in an ecosystem assessment
is not simple or straightforward. The optimal toxicity test can only be selected when the
objectives of the study and associated DQOs have been defined (see Chapter 2) and there
is a reasonable understanding of the physical, chemical, and biological characteristics of
the study site. This information must be combined with an understanding of the strengths
and weaknesses of the various sediment toxicity tests that are available (Table 6-1).
No one toxicity test is superior to all others. A number of useful toxicity tests have been
evaluated in freshwater and marine studies (Burgess and Scott 1992; Burton 1991;
Lamberson et al. 1992; Burton and Scott 1992). To reduce uncertainty and reduce the
chance of obtaining false positive or false negative results, it is important to test more
than one species. The importance of testing multiple species increases with the
importance of protecting the ecosystem and the need to define "significant" contamina-
tion in the "grey" (marginally contaminated) zone.
For most applications, a battery consisting of two to three toxicity tests should be
evaluated. These recommendations are for waters in the United States and are based on
the above characteristics and on comparison studies where multiple species have been
used simultaneously in sediment contamination investigations (Burton 1991; Burton et al.
1992b; Burton and Scott 1992; Giesy et al. 1988a; Giesy and Hoke 1990; Hoke et al.
1990; Ingersoll et al. 1993; Kemble et al. 1993; Chapman et al. 1992; Long and
Buchman 1989).
The choice of the appropriate endpoint (response) to measure is important to the
assessment process. All toxicants do not affect the same metabolic processes and result
in the same effects because they have differing modes of action and target receptors.
Some toxicants may interfere with processes essential for reproduction or growth.
Relative species sensitivity frequently varies among contaminants. For example, Reish
(1988) reported the relative toxicity of six metals (arsenic, cadmium, chromium, copper,
mercury, and zinc) to marine crustaceans, polychaetes, pelecypods, and fishes, and
concluded that no one species or group of organisms was the most sensitive to all of the
metals. Contaminants may also stimulate a process due to interruption of a feed-back
mechanism, or contaminants may be essential nutrients at low concentrations (e.g.,
selenium). Stimulation at low concentrations of toxicant exposure (hormesis) is often
reported in the literature (Stebbing 1982; Burton and Stemmer 1988; Burton et al. 1989).
Some responses are much more sensitive than others (e.g., enzyme inhibition vs.
lethality), and should not necessarily be weighted equally in evaluating the importance
of effects.
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Chapter 6. Evaluation of Sediment Toxicity
The duration of the exposure can have a profound influence on the response of organisms
in sediment toxicity tests. Extended exposures of up to 28 days with Hyalella azteca can
be used to identify sublethal responses for sediment samples that are not acutely toxic in
exposures of 2 to 7 days. Additional method development is needed on culturing and
chronic sediment testing procedures for additional infaunal species with a variety of feed-
ing habits, including suspension and deposit feeders. Results of chronic exposures should
be used to better evaluate the structure and function of benthic communities in moder-
ately contaminated areas. The USEPA is currently developing standardized acute toxicity
test methods for sediments using Hyalella azteca 10-day survival and Chironomus tentans
10-day survival and growth endpoints (USEPA 1994). These methods should become
final in 1994 and should be strongly considered for use in any studies of contaminated
sediments.
It appears from the ARCS Program data that several measured endpoints would be useful
for routine sediment contamination assessments. Results from the statistical analyses
indicate two test species (with 4 measured endpoints) could be used to describe the 3
major toxicity response patterns observed at the ARCS AOCs. The endpoints that could
be selected vary in their sensitivity, discrimination of toxicity, relationship to other
toxicity test responses and benthic community indices, and other advantages and
disadvantages (Tables 6-1, 6-6, 6-7, and 6-8). Selection of the appropriate toxicity test
depends on the characteristics of the site, the resources available, and the objectives of
the study.
Recommended Toxicity Tests
The following recommendations for selection of optimal toxicity tests in future assess-
ments of contaminated Great Lakes sediment are based on sensitivity, discrimination, and
similarity analyses, and on the advantages and disadvantages of the selection criteria
listed in Table 6-1. It is evident that the optimal toxicity tests vary between sites and this
variation cannot be confirmed a priori. Factor analysis provides an approach for
selection of toxicity tests to be included in a test battery. Species can be chosen with
endpoints representing each of the major response pattern groups identified in Table 6-7,
to better ensure that the many varied and potentially adverse species responses are being
evaluated. Many of the toxicity tests that appeared best in the factor analysis and in the
sensitivity and discriminatory analyses have also been demonstrated to be good indicators
of sediment toxicity in previous assessments (Burton 1991). The minimal test battery
recommended for Great Lakes sediment toxicity studies should consist of 2 species, 4
measurement endpoints, and represent 3 of the 4 major response pattern groups
(Table 6-10a). This enables some flexibility in the choice of the test species, which may
be based on other decision criteria, such as resource requirements, laboratory expertise
or organism availability, need for sensitivity or discriminatory power, or other character-
istics (Table 6-11). Some examples of different study objectives that may be important
are shown in Tables 6-11 and 6-12, with recommended test species.
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TABLE 6-10. OPTIMAL TOXICITY TEST BATTERY GROUPINGS3
(a)
Minimal Size Recommended: Two test species and four measurement endpoints encompassing three
groupings.
Group A: Chironomus riparius 14-day survival; Chironomus tentans 10-day length; Hyalella azteca
28-day survival, length, sexual maturation, and antenna segment number; H. azteca
14-day survival; Daphnia magna 7-day reproduction; Lemna minor 4-day frond growth; and
Ceriodaphnia dubia 7-day reproduction.
Group B: C. dubia 7-day survival; C. riparius 14-day length; H. azteca 14-day sexual maturation; and
Pimephales promelas 7-day larval weight.
Group C: Hydrilla verticillata 10-day root growth; Diporeia spp. 5-day avoidance/preference; and
H. azteca 14-day survival.
Group D: Hexagenia bilineata 10-day survival and molting frequency.
(b)
Minimal Groupings With The Recommended Size Limitsb (All endpoints should be measured for each
test):
Option 1: a. H. azteca (14-day)
b. C. dubia, C. riparius, D. magna, P. promelas, Diporeia spp. or H. bilineata.
Option 2: a. C. dubia or C. riparius
b. Diporeia spp. or H. bilineata
Option 3: a. D. magna
b. P. promelas
c. Diporeia spp. or H. bilineata
a Selected from Principal Components Analysis (all assays were whole-sediment exposures) and from
Correlation Analysis.
b The Microtox® test should be used in reconnaissance surveys due to its high degree of correlation
with the above toxicity test responses.
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TABLE 6-11. TOXICITY TEST SELECTION APPROACH
Assay Selection Criteria
Study Criteria
Objectives Weighting
Examples
1. Organism sensitivity (reduces likelihood of 2,3
false negatives)
2. Organism discriminatory (define contami- 3
nant zone)
3. Standardized methods (USEPA>ASTM> 2,3
peer-reviewed)
4. Response patterns (indicators of differing 1-3
toxicant sensitivities)
5. Laboratory expertise (USEPA recommends 1-3
5 reference toxicant tests and 5 control
sediment tests with test species)
6. Organism availability 2,3
7. Bioaccumulation potential (contaminants 1-3
such as mercury, PCBs, and dioxins
present)
8. Characteristics of benthos (reference 2,3
areas characterized with amphipods or
midges/worms)
9. Fisheries (proximity to sport or 2,3
commercial fisheries)11
10. Hydrodynamics (tendency for flushing and 2,3
transport to open-lake)
1a H. azteca, Diporeia spp.b
1 C. dubia, P. promelas,
C. r/parius, H. azteca
1 H. azteca, C. ten tans
1 See Table 6-7
2 All species
Hexagenia bilineatac,
Diporeia spp.c
Lumbriculus
3 H. azteca, Diporeia spp.
vs. Chironomids
3 P. promelas, Lumbriculus
3 Benthos vs. Water-column
species
Study Objectives: 1) Reconnaissance
2) Initial Survey
3) Definitive Study
a Weighting criteria indicate relative importance in typical assessment: 1 > 2 > 3.
b Diporeia spp. are important Great Lakes benthic organisms; however, the draft ASTM test method
used in the ARCS Program is not used extensively or well developed and should be used with caution.
0 Require field collection.
d The fathead minnow test is an indicator of toxicity only. Bioaccumulation potential should be assayed
using Lumbriculus variegatus (USEPA 1994) and indigenous fish or invertebrate sampling.
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TABLE 6-12. EXAMPLE OF SELECTION OF TOXICITY TESTS
BASED ON STUDY OBJECTIVES
Example 1: Bioaccumulation of Chlorinated Compounds
If the site is contaminated with nonionic compounds, such as PCBs and dioxins, which tend
to bioaccumulate through the food-chain, then short-term toxicity testing may be an inadequate
indicator of contaminant bioavailability. Bioaccumulation of contaminants from whole
sediments should be evaluated using the 28-day Lumbriculus variegatus assay (USEPA 1994).
In addition, resident species should be collected and tissues analyzed for contaminants.
Example 2: Sport/Commercial Fishery
Fisheries impact should be evaluated using example 1 guidance for bioaccumulation testing and
also use toxicity tests with the fathead minnow (Pimephales promelas). Many fish species are
highly sensitive to ammonia toxicity which may be a contaminant in sediments receiving
nutrient loadings from point and nonpoint sources. The fathead minnow short-term chronic
tests are superior to other tests at detecting ammonia toxicity.
Example 3: Use of Data for Litigation
It may be advantageous, in studies where data may be used for litigation purposes, to use
toxicity tests that have been standardized. Currently, the only methods standardized by the
EPA for testing sediments are the 10-day Hyalella azteca and Chironomus tentans toxicity tests
and the 28-day bioaccumulation assay with Lumbriculus variegatus (USEPA 1994).
Example 4: Defining the Spatial Extent of Significant Ecosystem Contamination
Species vary widely in their sensitivity to contaminants. In areas where contaminant concen-
trations are not acutely toxic, it is more difficult to define the zone of significant sediment
contamination. Uncertainty is reduced by testing additional species that tend to be highly
discriminatory in nature (show significantly different responses to differing levels of contamina-
tion - see Table 6-5).
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Chapter 6. Evaluation of Sediment Toxicity
Based on the response patterns (Table 6-7), sensitivity, and discriminatory patterns, the
following toxicity test combinations are recommended. However, other decision criteria
(as discussed above) should also be considered in the selection process. A number of test
battery options are outlined in Table 6-1 Ob. One test battery option could consist of two
species. The only toxicity test whose endpoints characterized three of the four response
patterns was the Hyalella azteca 14-day test, consisting of survival, length, and sexual
maturation endpoints. Unfortunately, to measure organism length accurately requires use
of digitizing microscope equipment, which is not common in most testing laboratories.
It is possible that dry weight could be measured instead of length (USEPA 1994).
Furthermore, antenna segment number was a good predictor of organism length (ASTM
1993). In combination with this amphipod, any of five different toxicity tests should be
tested, including Ceriodaphnia dubia 7-day survival and reproduction, Chironomus
riparius 14-day survival and length, Daphnia magna 7-day survival and reproduction,
Pimephalespromelas 7-day larval growth, Diporeia spp. 5-day preference, or Hexagenia
bilineata 10-day survival and molting test.
Another test battery option could consist of either C. dubia or C. riparius, and either
Diporeia spp. or H. bilineata (Table 6-1 Ob).
A third option for a test battery could consist of three species: D. magna, P. promelas,
and either Diporeia spp. or H. bilineata (Table 6-1 Ob).
The Microtox® test is superior to the others tested for use in reconnaissance surveys.
The ease of operation, cost, correlation with other toxicity tests, and sensitivity and
discriminatory ability of the Microtox® test make it a useful tool for quickly processing
large numbers of samples.
There is no perfect toxicity test. Each of these toxicity tests has advantages and
disadvantages. Many of the toxicity tests that ranked high in the ARCS Program have
been used successfully in other studies of sediment toxicity. Evaluations of sediment
using laboratory toxicity tests and benthic community structure indices, combined with
physico-chemical characterization of the test site, will allow for an integrated "weight-of-
evidence" assessment approach that can be used to provide evidence of contaminant-
induced degradation to aquatic communities.
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7. A SSESSMENT OF BENTHIC IN VERTEBRA TE
COMMUNITY STRUCTURE
INTRODUCTION
Contaminated sediments are a major source of pollution in the United States and
represent a potential threat to all components of aquatic ecosystems (Sorensen et al.
1977; Landrum and Robbins 1990). Sediments are a repository for organic and inorganic
contaminants that can accumulate to high concentrations (Shimp et al. 1971; Oschwald
1972; Medine and McCutcheon 1989). Benthic invertebrates are closely associated with
surficial sediments and therefore are continuously exposed to contaminants in the sedi-
ments.
Since aquatic ecosystems are composed of interdependent trophic levels, it generally is
not appropriate to study individual components of an ecosystem when making assess-
ments of sediment toxicity (Burton 1991). Complete ecological assessments of sediment
toxicity usually require the use of resident biota as indicators of sediment quality. For
the assessment to be successful, closely integrated biological, chemical, and physical data
are required. Since sediments tend to integrate historical water quality conditions, the
spatial and temporal distribution of resident organisms can reflect the degree to which
chemicals in the sediments are toxic. Field surveys of benthic invertebrates provide an
essential component of biological assessments of the toxicity associated with contaminated
sediments. These surveys have several advantages: 1) indigenous benthic organisms
complete all or most of their life cycles in the aquatic environment and serve as continu-
ous monitors of sediment quality, 2) many benthic invertebrates living in sediments are
relatively sedentary and are therefore representative of local conditions, 3) macroinverte-
brates are relatively easy to collect and are generally abundant across a broad array of
sediment types, 4) a field assessment of natural populations can provide a screening-level
evaluation of potential sediment contamination, and 5) results of an assessment of indige-
nous populations are usually biologically interpretable, which allows resource injuries to
be quantified in a manner more easily understood by managers, regulators, and the gen-
eral public (Cook 1976; Pratt and Coler 1976; Davis and Lathrop 1989).
This chapter reviews the methods used to evaluate the response of benthic invertebrate
communities to contaminated sediments and makes recommendations based on informa-
tion gained during quantitative benthic invertebrate surveys conducted simultaneously
with sediment evaluations under the ARCS Program (Canfield et al. 1993). The objec-
tive of the surveys was to describe the species distributions and relative abundances of
benthic invertebrates and to interpret these in light of the chemical and physical charac-
teristics of the sediments. This information, when analyzed in conjunction with the
results of sediment toxicity tests (see Chapter 6), will provide a more complete
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Chapter 7. Assessment of Benthic Invertebrate Community Structure
representation of the effects of in situ contaminants on benthic invertebrate communities.
As many as 10 stations were sampled from each of three priority AOCs: the Buffalo
River in New York (Figure 1-1), Indiana Harbor in Indiana (Figure 1-2), and the Sagi-
naw River in Michigan (Figure 1-3). Benthic community evaluations described in
Canfield et al. (1993) include descriptions of estimated abundances for the total benthic
community and individuals identified to the lowest possible taxon at each AOC,
comparisons between the characteristics of benthic communities and the concentrations
of sediment contaminants, evaluation of the prevalence of deformities in chironomids
(midges), and analyses of the sources of variability in collecting representative benthic
samples with a Ponar grab sampler.
Selected ARCS Program data sets from Canfield et al. (1993) are used in this chapter to
evaluate 1) the usefulness of different indices for characterizing benthic invertebrate
communities in contaminated sediments, 2) the numbers of samples needed to achieve
a confidence level of 95 percent for estimated sample means, 3) the usefulness of statis-
tical tests for evaluating the effects of sediment contamination on benthic invertebrate
communities, and 4) key considerations for conducting future studies of benthic inverte-
brate communities in contaminated sediments. Data from Swift et al. (in prep.) are also
used to evaluate the usefulness of artificial substrates for evaluating the effects of
contaminants on benthic invertebrate communities.
EXPERIMENTAL DESIGN
The first step in conducting an evaluation of benthic invertebrate communities is the
development of an appropriate experimental design. An inappropriate experimental
design can be a major source of error hi the resulting data (Thornton et al. 1982; La
Point and Fairchild 1992). There are many factors that need to be considered when
sampling contaminated sediments for benthic invertebrates that differ from the con-
siderations required for sampling sediments for toxicity testing. Benthic invertebrate
distributions are strongly influenced by abiotic factors in the absence of contaminants
(Resh 1979; Pettigrove 1990; La Point et al. 1984; La Point and Fairchild 1992) and, in
some cases, the effects of contaminants can be masked by effects due to abiotic factors.
Important abiotic characteristics (e.g., sediment grain size, sediment organic content,
sediment nutrient content, water quality, current velocity, water depth) at a study site
should therefore be evaluated so that the potential confounding effects of these character-
istics can be accounted for when the data are analyzed and interpreted. This holds true
whether the intent of the project is to make comparisons between upstream and down-
stream areas, between different aquatic systems (i.e., different rivers or lakes), or
between seasons.
When assessing benthic invertebrate communities for changes in community structure,
it is critical to select appropriate reference sites with which the benthic invertebrate
communities at study sites can be compared (Davis and Lathrop 1989; La Point and Fair-
child 1992). Ideally, a reference site should be unaffected or minimally affected by
anthropogenic influences. Since a completely unaffected system is difficult or impossible
732
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Chapter 7. Assessment of Benthic Invertebrate Community Structure
to find, it is usually considered acceptable to use sites that are considerably less contami-
nated than the study site (Chapman 1986). However, because low contaminant concen-
trations in water and sediment can sometimes affect benthic invertebrate communities,
caution should be used when comparing results with a reference site that has contaminant
concentrations higher than in pristine areas. A reference site should also have physical
and chemical characteristics of both water and sediment that are similar to the study site
to account for the potential effects of those characteristics on benthic communities at the
study site.
Many studies have evaluated the number of replicate samples required to provide ade-
quate assessments of benthic invertebrate communities and to allow cause-and-effect
predictions to be made from the data (Elliott 1977; Green 1979; Resh 1979; Barton
1989). Many of these studies suggest that a sufficient number of replicate samples
should be taken so that the among-sample coefficient of variation for all invertebrates is
less than 50 percent (Davis and Lathrop 1989). To determine the number of benthic
invertebrate samples that should be collected, it is recommended that a preliminary sur-
vey be conducted of the study areas. This is done to qualitatively identify the taxa that
will be encountered and the relative abundances of those taxa at each station. Depending
on the types of taxa collected, the methods used to collect benthic invertebrates may need
to be modified to more effectively sample the benthos.
Although the optimum number of samples may be determined when designing a particu-
lar study, 3-5 replicate samples per station are usually collected in most studies. The
reason for collecting 3-5 replicate samples is based primarily on funding and personnel
constraints, which limit the processing of large numbers of samples. Although the col-
lection of a smaller number of replicate samples may not invalidate the benthic inverte-
brate data for making community assessments, investigators should interpret such data
with caution if the sample replicates are heterogeneous (i.e., abundance estimates will
have high variance).
There are several books and articles pertaining to the proper design and conduct of
benthic invertebrate surveys (Davis and Lathrop 1989; Plafkin et al. 1989; Hurlbert
1984; APHA 1985; Elliott 1977; Resh 1979; La Point and Fairchild 1992; Merritt and
Cummins 1984; Klemm et al. 1990). These sources should be consulted during the plan-
ning stage of a benthic invertebrate survey to develop the optimum study design within
the constraints of the financial limitations of the study.
METHODS FOR SAMPLE COLLECTION
The collection methods for benthic invertebrate samples depend on the type of habitat to
be sampled (e.g., rocky substrate, fine-grained substrate, heavily vegetated areas) and
the type of system (e.g., flowing or standing water) from which the samples are to be
taken. Many of the benefits and limitations of different sampling devices have been
described in previous publications (Resh 1979; Downing 1984; Klemm et al. 1990). In
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Chapter 7. Assessment ofBenthic Invertebrate Community Structure
the ARCS Program, the sampling devices used were the 0.05-m2 Ponar grab sampler
(Powers and Robertson 1967) and an artificial substrate sampler (Stauffer et al. 1976).
Grab Samplers
Grab samplers are designed to take discrete "bites" of the sediments that are representa-
tive of a fixed area and are therefore the preferred method for collecting sediments for
the quantitative assessment of benthic infauna. The advantages and disadvantages of
various grab samplers are discussed in Chapter 3, Sediment Sampling Surveys. Ponar
grab samplers, both full-sized and petite, are among the best all-purpose samplers avail-
able for sampling unconsolidated sediments (Downing 1984). The Ponar grab sampler
was chosen for the collection of sediment for the analysis of benthic invertebrates in the
ARCS Program. It is recommended that a Ponar grab sampler be used for the collection
of sediment in future studies of benthic invertebrate communities in the Great Lakes,
because the benefits of this type of sampler outweigh any limitations.
Artificial Substrate Samplers
Artificial substrate samplers have a long history of use in studies of benthic invertebrate
communities in aquatic ecosystems. Artificial substrate samplers are designed to mimic
natural substrates (e.g., gravel, cobble, small spaces) and to provide an easily quantified
sampling unit. As with grab samplers, artificial substrate samplers can provide both
qualitative and quantitative samples of benthic macroinvertebrates. Cairns (1982) and
Klemm et al. (1990) review the advantages and limitations of artificial substrate samp-
lers. General descriptions of the use of artificial substrate samplers in ecological and
hazard assessments include those of Rosenberg and Resh (1982), Isom (1986), Ohio EPA
(1987), and Klemm et al. (1990). Stauffer et al. (1976) and Swift (1985) describe the
use of mesh-filled chicken baskets as artificial substrates.
In general, artificial substrate samplers primarily sample the epifaunal community,
whereas grab samplers primarily sample the infaunal community. Artificial substrate
samplers made of mesh are particularly good at collecting large numbers of animals
because of the large number of interstitial spaces. Mesh artificial substrate samplers are
a good alternative to grab samplers when collecting animals for tissue residue analyses.
Multiplate samplers (e.g., Hester-Dendy samplers) are designed to provide small spaces
for benthic organisms to hide in. They are easy to make and readily available from com-
mercial suppliers (e.g., Wildco). They have been used extensively in shallow water
stream studies but less often in studies conducted in standing water. Like the mesh
samplers, Hester-Dendy samplers tend to sample the epifaunal community.
Selection of the most appropriate samplers should be based on the objectives of the study
as well as the depth of water at the site. The mesh samplers can be used easily in both
shallow water and in deeper waters, whereas the Hester-Dendy samplers are primarily
734
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Chapter 7. Assessment of Benthic Invertebrate Community Structure
designed for use in shallower waters where wading is possible. Hester-Dendy samplers
can be used in deeper waters, but they require the use of a heavy platform (i.e., steel
plate or cement tile) as an attachment surface for the samplers to sit in the proper upright
orientation on the bottom at the sampling site.
DATA ANALYSIS
To determine which indices to evaluate in studying the effects of contaminants on the
benthic invertebrate communities, it is necessary to determine the kind of information
required for the planned data analysis. Some analytical methods are only amenable to
a qualitative assessment of benthic invertebrate communities. However, the more
desirable goal of a study is the quantitative assessment of the effects of the contaminants
on benthic invertebrate communities. Several metrics are available for qualitative and
quantitative assessments of benthic invertebrate communities (Merritt and Cummins 1984;
Pennak 1989; Plafkin et al. 1989; Klemm et al. 1990). Community structure measure-
ments can be divided into four broad categories: numbers of individuals and standing
crop, multivariate analyses, diversity and similarity indices, and indicator organisms (IJC
1988). Benthic community health can be assessed by determining the structure (e.g.,
taxa richness), community balance (e.g., percent dominant taxa), and functional feeding
group (e.g., percent scrapers, percent filter feeders) composition of the macroinvertebrate
community (Plafkin et al. 1989) Most of these metrics are quantitative, although the use
of indicator organisms tends to be more qualitative in nature. The most frequently used
and simplest metrics are numerical abundance, percent composition of dominant taxa, and
taxa richness (i.e., the number of taxa present). These indices have the advantage of
being easily measured and are highly sensitive to contaminants and other anthropogenic
perturbations (Sheehan and Winner 1984; IJC 1988; Van Hassel et al. 1988).
Multivariate analyses are frequently used for measuring patterns associated with benthic
invertebrate distribution and relative abundance. Multivariate analyses typically fall into
two categories: clustering and ordination methods (IJC 1988). In addition, other com-
monly used multivariate techniques include PCA, multiple regression analysis, and mul-
tiple ANOVA. There is considerable literature describing these multivariate techniques
(Blackith and Reyment 1971; Poole 1974; Elliott 1977; Green 1979, 1980; IJC 1988).
Multivariate approaches are used to answer questions relating to when and where a conta-
minant is affecting benthic communities. In highly contaminated areas, the large num-
bers of samples with zero abundances for many taxa often preclude the use of multivari-
ate statistics.
Diversity and similarity indices have been widely used for assessing the impacts of
contaminants (Peet 1974; Pielou 1977; Green 1979; Sheehan 1984; Klemm etal. 1990).
In theory, communities that are unaffected by contaminants have higher diversity and
communities affected by contaminants have lower diversity. The primary advantage of
using diversity indices is that a large amount of data is reduced to a single number
representing an entire community. However, the use of a single number can result in
the loss of information that would be retained by the use of other statistical methods (IJC
735
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Chapter 7. Assessment of Benthic Invertebrate Community Structure
1988). In addition, the reliance on a single index can produce misleading results. For
example, use of the Shannon-Wiener diversity index for samples having few individuals
and a relatively even distribution among several species could yield a high diversity value
even if the study site is extremely contaminated (Green 1979). The use of diversity indi-
ces has decreased in recent years in favor of other indices such as numerical abundance,
biomass, taxa richness, or composite or multimetric indices (Ohio EPA 1989; Plafkin et
al. 1989). There is considerable literature describing diversity and similarity indices
(Cairnes et al. 1982; Washington 1984; IJC 1988; Davis and Lathrop 1989; Plafkin et
al. 1989; Klemm et al 1990).
Indicator species or communities are primarily qualitative indices. Some of the indices
most widely used are the modified Hilsenhoff Biotic Index, the Ephemeroptera +
Plecoptera + Trichoptera Index, the oligochaete/chironomid ratio, the percent contribu-
tion of dominant taxa, the community loss index, the Jaccard coefficient of community
similarity, and the ratio of shredders to total abundance (IJC 1988; Plafkin et al. 1989;
Klemm et al. 1990). The use of indicator species is based on a prior knowledge of the
contaminant tolerances of various taxonomic groups. Some invertebrate taxa are known
to be relatively tolerant to organic enrichment and chemical contamination (e.g., certain
oligochaetes and chironomids), whereas other groups are characteristically intolerant
(e.g., certain mayflies and stoneflies). Within these broad groups, it is important to be
able to identify individuals to at least the generic level, because there are both tolerant
and intolerant taxa within most of these groups (Resh and Unzicker 1975; Plafkin et al.
1989; Klemm et al. 1990). For example, within the family Chironomidae, the genus
Chironomus is generally more tolerant of organic enrichment than the genus Polypedilum
(Klemm et al. 1990). Within the genus Chironomus, there are differences in the
tolerances of Chironomus riparius, which is considered extremely tolerant of organic
enrichment, and Chironomus tuxis, which is considered intolerant of organic enrichment
(Klemm et al. 1990).
THE ARCS APPROACH
The purpose of this section is to provide "lessons-learned" information using data from
the ARCS Program. A general overview and the goals of the ARCS Program are
described in detail in Chapter 1, Introduction, and Chapter 6, Evaluation of Sediment
Toxicity. Additional details pertaining to the specifics of the benthic community survey
are presented in this chapter. This section identifies the positive lessons learned from
the benthic invertebrate surveys conducted in the ARCS Program, as well as those
aspects of the studies that can be strengthened for future work.
Site Description
Assessments of benthic invertebrate communities were conducted as part of the ARCS
Program in three priority AOCs in the Great Lakes: the Buffalo River in New York
(Figure 1-1), Indiana Harbor in Indiana (Figure 1-2), and the Saginaw River in Michigan
136
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Chapter 7. Assessment of Benthic Invertebrate Community Structure
(Figure 1-3). All three priority AOCs receive municipal and industrial wastewater dis-
charges that contain a variety of organic and inorganic contaminants.
Methods
In the ARCS Program, benthic invertebrate samples were collected from each of the
three priority AOCs. A total of 155 benthic grab samples (about 5 grabs per station and
10 stations per AOC) were collected. Artificial substrate samplers were deployed at five
stations in the Buffalo River, four stations in Indiana Harbor, and six stations hi the
Saginaw River. To minimize potential disturbance of the sediments and associated inver-
tebrates, all 155 benthic grab samples were collected before sediment samples were col-
lected for chemistry and toxicity evaluations. The five replicate benthic grab samples at
each station were collected within a 100-m2 area. Each benthic grab sample was sieved
through a 500-jnm brass screen; site water was used for rinsing. Material retained by the
sieve was rinsed into 500-mL glass jars and preserved with 10-percent buffered formalin.
Before sorting, samples were rinsed thoroughly with tap water to remove formalin and
excess silt or mud. The rinsed samples were drained of excess water, returned to their
original jars, and allowed to soak in 95-percent ethanol for at least 24 hours to facilitate
extraction of any volatile chemicals. After the 24-hour soaking period, each sample was
rinsed again with tap water to remove the ethanol and volatile chemicals. Each sample
(except the samples from Indiana Harbor Station 10) was placed in a 4-L wide-mouth jar
and agitated with tap water so that the invertebrates and lighter detrital material floated
while the snails, clams, and heavier material remained on the bottom of the jar. Aliquots
of the sample were removed from the jar to sort the benthic invertebrates from the
debris. Aliquots were removed until the entire sample had been sorted. In the case of
the samples from Indiana Harbor Station 10, only a portion (50 mL) of each sample was
sorted and enumerated, because the abundance of oligochaetes was so high that enumera-
tion of the entire sample could not be conducted in a timely manner. Sorting times
ranged from approximately 3 to 20 hours per sample.
A binocular dissecting microscope with a magnifying power of 4x-12x was used to sort
the samples. Organisms were sorted and enumerated into the following orders or fami-
lies: Oligochaeta, Chironomidae, Bivalvia, Gastropoda, Ephemeroptera, Odonata, Ple-
coptera, Hemiptera, Megaloptera, Trichoptera, Coleoptera, Diptera (other than Chirono-
midae), Hirudinea, and Amphipoda. These samples were used to estimate macroinverte-
brate numerical abundance (individuals/m2), species composition, and taxa richness.
Taxonomic identifications were made by National Fisheries Contaminant Research Center
(NFCRC) personnel using published taxonomic keys (Wiederholm 1983; Merritt and
Cummins 1984; Pennak 1989; Thorp and Covich 1991). Oligochaetes and chironomids
were mounted on slides for identification. Oligochaetes were identified to genus and
species (when possible), and chironomids were identified to genus. Molluscs were iden-
tified to genus and species, and all other taxa were identified to the lowest practical taxon
(usually genus and species).
737
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Chapter 7. Assessment of Benthic Invertebrate Community Structure
Chironomid larvae were also examined for deformities in mouthpart structures. These
deformities consisted of various types of asymmetry, missing teeth, extra teeth, fusion
among various teeth, and labial separation, as described by several investigators (Saether
1970; Hamilton and Saether 1971; Hare and Carter 1976; Warwick et al. 1987; Warwick
1989). Individual chironomid larvae were mounted on slides and examined for
deformities in the mentum (Orthocladinae and Chironominae) and ligula (Tanypodinae).
The prevalence of mouthpart deformities was calculated as a proportion of the total
number of chironomid larvae found at each station.
Artificial substrate samplers were constructed from 3M® synthetic mesh and stainless-
steel wire rotisserie chicken baskets (Stauffer et al. 1976). Each substrate consisted of
five pieces of mesh (20 X 20 cm) folded in half and placed beside each other hi a basket.
The baskets were 26 cm in length, 17 cm in diameter, and 53 cm in circumference
(Figure 7-1). The baskets were wired shut, and three baskets were wired to a cinder
block at each sampling station. The baskets were connected to the cinder block with
2-m wires and were placed horizontally on the bottom near the cinder blocks. One end
of the wire was attached to the cinder block and the other end was connected to a recog-
nizable landmark on shore to facilitate retrieval of the artificial substrate samplers.
Artificial substrate samplers were deployed at five stations in the Buffalo River (October
1989), four stations in Indiana Harbor (August 1991), and six stations in the Saginaw
River (June 1990), with deployment at each station lasting for 30 days. Each substrate
sampler was placed on the surface of the sediment. Upon retrieval, the samplers were
lifted carefully to the water surface and placed in plastic dish pans. The mesh substrate
material was removed, placed hi 4-L wide-mouth jars, and preserved in 4-percent buf-
fered formalin. The water and sediments remaining in the dish pans were poured
through a 250-jwm mesh sieve, and the retained material was preserved with the synthetic
mesh.
The artificial substrate samples were rinsed in the laboratory through a 250-/im mesh
sieve, and each piece of mesh was then unraveled under water in a dish pan. Sediment
and organisms were retained in the water, sieved (250-pm mesh), and stored in
70-percent ethanol. The mesh was discarded. The entire preserved sample was placed
in a pan for sorting at 4X-12X magnification. A subsample of 100 organisms was
removed from each sample. Organisms were subsampled in approximate proportion to
their relative abundance in the sample following the Rapid Bioassessment Protocols
(Plafkin et al. 1989). At Indiana Harbor Station 3, each sample contained thousands of
small (<500 /mi), recently settled zebra mussels (Dreissena sp.). A subsample of
100 invertebrates other than zebra mussels was picked to evaluate the other organisms
present at that station. At all stations, organisms that were too rare to be included hi the
subsamples were recorded qualitatively.
735
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ro
a>
a
Cinder block
Three artificial
substrate samplers
Figure 7-1. Artificial substrate samplers used to collect aquatic invertebrates
in the ARCs Program.
735
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Chapter 7. Assessment ofBenthic Invertebrate Community Structure
Quality Assurance and Quality Control for Benthic Invertebrate
Community Analysis
Benthic invertebrate samples should be collected in a manner that provides the best pos-
sible estimate of benthic invertebrate community structure. To minimize potential dis-
turbance of the sediments and associated invertebrates, all 155 benthic grab samples for
the ARCS Program were collected before sediment samples were collected for chemistry
and toxicity evaluations.
Samples were sorted in the laboratory by a number of technicians. To ensure that all
samples were sorted with a similar efficiency, 1 of every 10 samples was randomly selec-
ted and resorted by a supervisor to confirm that the sample was sorted completely. If
the number of invertebrates found during resorting was >5 percent of the total number
of invertebrates in the sample, all 10 samples in the lot were resorted in their entirety.
Two major elements of benthic invertebrate surveys can contribute to the variability asso-
ciated with estimates of species distribution and abundance. The first is the variability
associated with different field collection methods, and the second stems from inaccuracies
in taxonomic identification. Although benthic community structure cannot be assessed
for accuracy, precision was monitored. The precision associated with the collection of
benthic invertebrate samples was evaluated by examining the five replicate grab samples
collected at each station. The replicate samples were collected within a 100-m2 area at
each station. The variance associated with field collection was evaluated using an
ANOVA to identify the sources of variability.
The accuracy of taxonomic identifications was evaluated by having independent taxo-
nomic experts outside of the NFCRC verify the identifications made by NFCRC person-
nel.
Statistical Analysis
As described by Canfield et al. (1993), data were analyzed using appropriate parametric
and nonparametric statistical tests (Snedecor and Cochran 1982; Statistical Analysis Sys-
tem 1988). Statistical analysis was performed using the Statistical Analysis System com-
puter package for personal computers (Statistical Analysis System 1988). Relationships
among the abundances of benthic invertebrates within and among AOCs were evaluated
using a nested ANOVA (Snedecor and Cochran 1982). Comparisons between benthic
invertebrate abundances and physical and chemical data were conducted using correlation
and multivariate regression analyses. Unless otherwise specified, statements of statistical
significance refer to significance at P< 0.05.
Raw data and summary statistics are presented in Canfield et al. (1993), Coyle et al.
(1993), and Nelson et al. (1993). The data have also been entered into USEPA's Ocean
Data Evaluation System database and have received quality assurance validation from the
140
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Chapter 7. Assessment ofBenthic Invertebrate Community Structure
USEPA (Chapter 2). Selected data sets from the ARCS Program were used to evaluate
the following questions:
1. What is the benthic invertebrate community composition at each station?
2. What is the benthic invertebrate community composition at each AOC?
3. Is there a significant correlation between sediment contamination and
benthic invertebrate community structure?
4. Is there a relationship between larval chironomid mouthpart deformities,
benthic invertebrate community composition, and physical and chemical
sediment characteristics?
5. What value do artificial substrate samplers have in the assessment of ben-
thic invertebrate communities in relation to contaminated sediments?
6. What are the sources of variability in the benthic invertebrate community
analyses?
7. What value do analyses of benthic invertebrate community structure have
in the evaluation of contaminated sediment sites for potential remediation?
8. How should evaluations of benthic invertebrate community structure be
used in future Great Lakes studies?
Although a number of different metrics were used to evaluate the data collected for the
ARCS Program, only the metrics that showed the best ability to discriminate among sites
were used to evaluate the data. Those metrics were percent contribution of major taxa,
comparisons between numerical abundances and species composition, comparisons
between numerical abundances and sediment chemistry, prevalences of mouthpart defor-
mities in larval chironomids, percent of total variance in abundance estimates, and eval-
uations of chironomid genera richness.
Benthic Invertebrate Numerical Abundance and Community Structure
in Grab Samples
The estimates of the numerical abundances of benthic invertebrates in the ARCS Program
were probably conservative (i.e., biased low) because of the sampling device used to
collect the invertebrates (Resh 1979) and the size of the mesh used for sieving (Brink-
hurst 1974; Resh 1979; Heushelle 1982). Ponar grab samplers are relatively heavy so
that they can penetrate the sediment surface evenly and efficiently. Nevertheless, if the
sediments are very soft, Ponar grab samplers can overpenetrate. However, a problem
can occur as the sampler nears the sediment. A shock wave of displaced water can
impact the sediment just before the sampler makes contact, causing small surface-
141
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Chapter 7. Assessment of Benthic Invertebrate Community Structure
dwelling animals to be pushed out of the way of the sampler (Flannagan 1970; How-
miller 1971; Howmiller and Beeton 1971; Milbrink and Wiederholm 1973). The effect
of this type of disturbance is not easily quantified and was not addressed in these studies.
However, any bias caused by this type of sampler should have been consistent across
stations and AOCs.
All sediment grab samples were sieved through a 500-ptm mesh brass screen after field
collection. Although this mesh size is adequate for separating benthic invertebrates from
the sediments, many smaller organisms, such as the naidid worms and early life stages
of midge larvae, pass through the sieve (Brinkhurst 1974; Mason et al. 1975; Resh 1979;
Heushelle 1982). In retrospect, it is now considered more appropriate to sieve samples
sequentially through 500-jLim and 250-jum mesh screens to capture organisms that pass
through a 500-/im mesh screen (Burt et al. 1991; Brinkhurst 1992, pers. comm.). The
500-/mi mesh screen allows quick and efficient separation of larger organisms and debris
from the smaller organisms and fine-grained sediments. If the 250-jum mesh screen were
used alone, the screen would tend to clog and sieving would be slowed considerably.
Therefore, sequential sieving through 500-jwm and 250-^im mesh screens is recommen-
ded for future studies of benthic invertebrates in Great Lakes sediments.
Comparisons Among AOCs: Benthic Invertebrate Numerical Abundance
and Species Composition in Grab Samples
Benthic invertebrate samples from the Buffalo River exhibited a wide range of total
numerical abundance across all stations (Table 7-1). Although oligochaetes were the
most abundant organisms, several stations had a large number of chironomids, bivalves,
and gastropods. Representatives from the orders Ephemeroptera, Odonata, Hemiptera,
Trichoptera, Coleoptera, Diptera (other than Chironomidae), Hirudinea, and Amphipoda
were rarely collected.
Indiana Harbor had a depauperate benthic invertebrate community. Except for two indi-
vidual chironomids collected at Station IH-01-10, no other insects were present in the
grab samples from Indiana Harbor (Table 7-2). Bivalve molluscs were rare, occurring
only at three stations (IH-01-03, IH-01-04, IH-01-07). The bivalve genera (Musculium,
Pisidium, and Sphaeriwri) found in Indiana Harbor are considered tolerant of organic
enrichment (Carr and Hiltunen 1965; Fuller 1974; Bode 1988).
The benthic invertebrate samples from the Saginaw River exhibited a fairly narrow range
of total numerical abundance (Tables 7-3 and 7-4). As with Indiana Harbor and the
Buffalo River, oligochaetes were the most abundant organisms in the grab samples at all
stations. Oligochaetes accounted for a higher percentage of the invertebrate communities
across stations during the Saginaw River survey conducted in December 1989 than hi the
June 1990 survey. Chironomids were more abundant in the June 1990 survey than in the
December 1989 survey. Although the oligochaetes and chironomids are present through-
out the year in these sediments, it is likely that there are seasonal fluctuations in the
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TABLE 7-1. PERCENT CONTRIBUTION OF MAJOR TAXA TO THE TOTAL
NUMBER OF TAXA COLLECTED IN GRAB SAMPLES FROM
THE BUFFALO RIVER IN OCTOBER 1989
Station
Taxon
Oligochaeta
Chironomidae
Bivalvia
Gastropoda
01
90
7
2
<1
02
98
2
--
—
03
94
2
3
<1
04
89
4
<1
4
05
99
<1
<1
<1
06
99
<1
<1
<1
07 08 09
99 98 87
<1 1 12
<1 <1 <1
< 1 < 1
10
51
45
-
—
Ephemeroptera
Odonata
Pelcoptera
Hemiptera
Megaloptera
Trichoptera
Coleoptera
Diptera
Hirudinea
Amphipoda
Mean Abundance
(number/m2) of
all Benthic
Organisms 3,013 7,530 7,536 9,461 6,445 19,418 14,70816,4732,294 6,067
743
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TABLE 7-2. PERCENT CONTRIBUTION OF MAJOR TAXA TO THE TOTAL
NUMBER OF TAXA COLLECTED IN GRAB SAMPLES FROM
INDIANA HARBOR IN AUGUST 1989
Station
Taxon 03 04 05 06 07 08
Oligochaeta 99 98 98 100 91 >99
Chironomidae
Bivalvia <1 <1 -- -- 1
Gastropoda
Ephemeroptera
Odonata
Pelcoptera
Hemiptera
Megaloptera
Trichoptera
Coleoptera
Diptera
Hirudinea <1 1 2 -- 8 <1
Amphipoda
10
>99
Mean Abundance
(number/m2) of
all Benthic
Organisms
3,791 6,025 5,307 1,501 609 2,907 493,917
744
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TABLE 7-3. PERCENT CONTRIBUTION OF MAJOR TAXA TO THE
TOTAL NUMBER OF TAXA COLLECTED IN GRAB SAMPLES
FROM THE SAGINAW RIVER IN DECEMBER 1989
STATION
Taxon
Oligochaeta
Chironomidae
Bivalvia
02
96
4
_.
03
98
2
<1
04
98
1
<1
06
98
1
1
07
98
1
09
80
19
__
10
92
T
Gastropoda
Ephemeroptera
Odonata - -- - - ~ . —
Pelcoptera
Hemiptera
Megaloptera
Trichoptera -- -- -- -- .... < -j
Coleoptera - - - -- - -- <1
Diptera <1 <1 <1 - <1 <1 <1
Hfrudinea
Amphipoda - -- - -- <1 <1
Mean Abundance
(number/m2) of all
Benthic Organisms 6,664 7,129 3,686 1,709 6,056 1,890 888
745
-------
TABLE 7-4. PERCENT CONTRIBUTION OF MAJOR TAXA TO THE
TOTAL NUMBER OF TAXA COLLECTED IN GRAB SAMPLES
FROM THE SAGINAW RIVER IN JUNE 1990
Station
Taxon
Oligochaeta
Chironomidae
01
69
31
02
99
<1
05
94
5
06
95
5
08
69
30
16
90
9
24
86
13
Bivalvia
Gastropoda
Ephemeroptera
Odonata
Pelcoptera
Hemiptera
Megaloptera
Trichoptera
Coleoptera
Diptera
Hirudinea
Amphipoda
Mean Abundance
(number/m2) of all
Benthic Organisms 7,152 3,780 3,047 321 1,157 3,977 941
146
-------
Chapter 7. Assessment of Benthic Invertebrate Community Structure
abundance of both groups of organisms. These potential seasonal fluctuations should be
considered when comparing the abundances of benthic invertebrate communities among
different seasons.
Oligochaeta—Invertebrate communities dominated by the tubificid oligochaetes,
to the exclusion of other invertebrate groups, are often indicative of organic enrichment
(Brinkhurst et al. 1972; Brinkhurst and Cook 1974; Cook and Johnson 1974; Burt et al.
1991). In the Buffalo River, the oligochaetes were dominated by tubificids, including
species (e.g., Limnodrilus hoffmeisteri) that are generally considered tolerant of organic
enrichment and metal contamination (Table 7-5; Kennedy 1965; Brinkhurst et al. 1972;
Burt et al. 1991). Limnodrilus hoffmeisteri was the most abundant oligochaete in the
grab samples at all stations except Stations BR-01-01 and BR-01-10. The reasons for the
lower abundances of this oligochaete species at those two stations are not clear, but may
be a result of food or habitat preferences not being met there (Verdonschot 1989).
All of the tubificid genera present in Indiana Harbor are known to be tolerant to organic
enrichment (Kennedy 1965; Brinkhurst et al. 1972). Limnodrilus hoffmeisteri, one of the
most tolerant oligochaete species, was the most abundant species in the grab samples at
all stations (Table 7-6).
The abundance of oligochaetes was lowest at Indiana Harbor Stations IH-01-07 (junction
of Lake George Branch and Grand Calumet Branch) and IH-01-06 (main channel), and
highest at Station IH-01-10. Sediments from Station IH-01-07 generally had the highest
concentrations of metals and organic contaminants (Nelson et al. 1993), and Sta-
tion IH-01-06 had the second highest concentrations of metals and organic contaminants.
This indicates that the combined inputs of metals and organic contaminants from the
Grand Calumet Branch and the Lake George Branch are affecting even the relatively
contaminant-tolerant oligochaetes at these stations. Samples from these stations were also
found to be among the most toxic sediments evaluated by the sediment toxicity tests
(Coyle et al. 1993; Nelson et al. 1993). High concentrations of metals may reduce the
abundance of oligochaetes by reducing the abundance of bacteria on which they feed.
The abundance of oligochaetes was extremely high at Station IH-01-10, approaching
1,000,000 individuals/m2 in individual grab samples. Although metals concentrations at
that station were the second lowest of any of the stations sampled, the primary reason
for the higher abundances of oligochaetes may be the high density of aquatic vegetation
present at Station IH-01-10. Large amounts of vegetation were most likely present
because Station IH-01-10 is on the upstream side of a low-clearance bridge, which tends
to minimize the amount of disturbance caused by boat traffic. This vegetation probably
retains decaying plant material, which enhances bacterial abundances.
In the Saginaw River, as in Indiana Harbor and the Buffalo River, the oligochaetes were
dominated by the tolerant tubificids (Tables 7-7 and 7-8). Limnodrilus hoffmeisteri was
more dominant in the samples from the December 1989 survey than in the samples from
the June 1990 survey, in which the relative abundances of oligochaetes were distributed
747
-------
TABLE 7-5. MEAN ABUNDANCE (NUMBER/m2) OF OLIGOCHAETES COLLECTED
IN GRAB SAMPLES FROM THE BUFFALO RIVER IN OCTOBER 1989
do
Taxon
Naididae
Dero digitate
UNa
Tubificidae
Aulodrilus pigueti
Aulodrilus limnobius
Aulodrilus pluriseta
llyodrilus templetoni
Limnodrilus cervix
Limnodrilus cervix variant
Limnodrilus claparedianus
Limnodrilus clap.-cerv. complex
Limnodrilus hoffmeisteri
Limnodrilus maumeensis
Limnodrilus udekemianus
Limnodrilus sp.
Quistadrilus multisetosus "I"
Quistadrilus multisetosus "m"
Tubifex tubifex
UIW/OCCb
UW/CC0
Station
01 02 03 04 05 06 07
16
—
30
—
—
-
100
100 -- - - - 77 58
-
30
370 1,884 1,224 160 1,743 2,305
--
—
43 111 494 588 -- 747 449
100 30 219 261
28 34
--
2,249 6,766 4,377 5,212 6,172 16,590 11,698
73 81 56 67 70
08 09
—
„
—
—
54
—
—
1,233 125
—
—
277 26
277 10
20
65
14,322 1,706
130 46
10
—
.
—
62
—
—
49
37
12
—
2,829
99
a Unidentifiable naidid.
b Unidentifiable, without capilliform chaetae (mostly Tubificidae).
c Unidentifiable, with capilliform chaetae (mostly Tubificidae).
-------
TABLE 7-6. MEAN ABUNDANCE (NUMBER/m2) OF OLIGOCHAETES COLLECTED
IN GRAB SAMPLES FROM INDIANA HARBOR IN AUGUST 1989
Station
Taxon 03 04 05 06 07 08 10
Naididae
Dero digitata
UNa
Tubificidae
Aulodrilus pigueti
Aulodrilus limnobius -- 77
Aulodrilus pluriseta 113
llyodrilus templetoni - -- -- --15 - 1,975
Limnodrilus cervix 128
Limnodrilus cervix variant 72 101 - -- 6 38
Limnodrilus claparedianus
Limnodrilus clap.-cerv. com-
plex
Limnodrilus hoffmeisteri 926 1,056 790 102 238 1,112 35,560
Limnodrilus maumeensis
Limnodrilus udekemianus 72 53 ~ --
Limnodrilus sp. 166 -- - 21 10 52
Quistadrilus multisetosus "I" 15 30
Quistadrilus multisetosus "m" -- 178 858 102 6 38
Tubifex tubifex — — — — — 6
UIW/OCCb 2,191 4,445 3,524 1,246 258 1,646 419,310
UW/CCC 30 - 26 30 15 - 37,535
a Unidentifiable naidid.
b Unidentifiable, without capilliform chaetae (mostly Tubificidae).
c Unidentifiable, with capilliform chaetae (mostly Tubificidae).
149
-------
TABLE 7-7. MEAN ABUNDANCE (NUMBER/m2) OF OLIGOCHAETES COLLECTED
IN GRAB SAMPLES FROM THE SAGINAW RIVER IN DECEMBER 1989
Taxon
Naididae
Dero digitate
UNa
Tubificidae
Aulodrilus pigueti
Aulodrilus limnobius
Aulodrilus pluriseta
llyodrilus templetoni
Limnodrilus cervix
Limnodrilus cervix variant
Limnodrilus claparedianus
Limnodrilus clap.-cerv. complex
Limnodrilus hoffmeisteri
Limnodrilus maumeensis
Limnodrilus udekemianus
Limnodrilus sp.
Quistadrilus multisetosus "I"
Quistadrilus multisetosus "m"
Tubifex tubifex
UIW/OCCb
UW/CCC
Station
02 03 04 06 07
-
-
-
-
-
32 98 - 34 30
32 265 174 59 101
90 70 109 34 30
19
~
809 1,318 391 232 536
22
-
122 133 221 86 286
22 8
--
8
5,295 5,056 2,680 1,189 4,972
32 35 - 8
09 10
15
-
-
-
--
15
62 109
21
..
-
268 35
7
-
109 71
-
--
„
998 585
13
3 Unidentifiable naidid.
b Unidentifiable, without capilliform chaetae (mostly Tubificidae).
0 Unidentifiable, with capilliform chaetae (mostly Tubificidae).
750
-------
TABLE 7-8. MEAN ABUNDANCE (NUMBER/m2) OF OLIGOCHAETES COLLECTED
IN GRAB SAMPLES FROM THE SAGINAW RIVER IN JUNE 1990
Station
Taxon
01
02 05 06 08
16
24
Naididae
Dero digitate
UNa
Tubificidae
Aulodrilus pigueti
Aulodrilus limnobius
Aulodrilus pluriseta
llyodrilus templetoni
Limnodrilus cervix
Limnodrilus cervix variant
Limnodrilus claparedianus
Limnodrilus clap.-cerv. complex
Limnodrilus hoffmeisteri
Limnodrilus maumeensis
Limnodrilus udekemianus
Limnodrilus sp.
Quistadrilus multisetosus "I"
Quistadrilus multisetosus "m"
Tubifex tubifex
UIW/OCCb
UW/CC0
14
158 243 257 68
124 34 106 23
460 209 92 5
20
59
3,505 2,633 1,634 121
180 34 69
98
71
11
14
--
20
138
282
19
82
190
291
14
160
69
446
--
-
18
68
~
--
21
92
—
60
198
308
—
-
30
181
240
120
120
188
42
12
527 2,487
352
a Unidentifiable naidid.
b Unidentifiable, without capilliform chaetae (mostly Tubificidae).
c Unidentifiable, with capilliform chaetae (mostly Tubificidae).
757
-------
Chapter 7. Assessment of Benthic Invertebrate Community Structure
more evenly across several other species. These differences may be attributed to sea-
sonal variability, spatial variability, or differing food sources.
Chironomidae—Tae chironomid community in the Buffalo River (Table 7-9)
consisted primarily of genera known to be tolerant of organic enrichment (Hilsenhoff
1982, 1987; Beck 1977; Bode 1988; Klemm et al. 1990). The exception to this
generalization was Tanytarsus at Station BR-01-10, which reportedly prefers less
organically enriched environments (Krieger 1984). Chironomus, Prodadius, Crypto-
chironomus, and Cncotopus are generally considered to be the most abundant chironomid
genera in heavily contaminated environments (Cook and Johnson 1974; Krieger 1984).
The first three of these genera were generally the most abundant chironomids collected
from Buffalo River sediments.
Only two individual chironomids were collected in grab samples from Indiana Harbor.
The larvae were identified as members of the genus Cncotopus, which is generally consi-
dered tolerant of organic enrichment and metals contamination.
The chironomid community in the Saginaw River was also comprised predominantly of
tolerant genera (i.e., Chironomus, Cryptochironomus, Prodadius) (Tables 7-10 and 7-11;
Hilsenhoff 1982, 1987; Bode 1988). The exception was Tanytarsus at Station SR-03-08,
which was present but in very low abundance. Prodadius and Chironomus were the
most frequently collected chironomid genera and are reported to be tolerant of organic
enrichment and metals contamination (Cook and Johnson 1974; Krieger 1984; Klemm
et al. 1990).
Mollusca—Several genera and species of Bivalvia (6 genera, 6 species) and
gastropods (4 genera, 5 species) were present in the Buffalo River (Table 7-12). Two
genera, Musculium and Pisidium, have been reported from organically enriched environ-
ments (Carr and Hiltunen 1965; Fuller 1974). Species of the genus Sphaerium may be
somewhat tolerant to organic enrichment by virtue of being in the family Sphaeridae,
which reportedly is tolerant of organic enrichment (Bode 1988; Plafkin et al. 1989).
Representatives from the family Unionidae were collected at only 4 of 10 stations in the
Buffalo River and their abundances were always low. Although this pattern could
indicate that these organisms are less tolerant than some of the Sphaeridae, members of
the family Unionidae tend to be large and the Ponar grabs may not sample them
efficiently.
Representatives from the genera Valvata and Bithynia have been reported to be somewhat
tolerant of organic enrichment (Carr and Hiltunen 1965; Krieger 1984; Klemm et al.
1990). Even so, the occurrence of these genera was limited to 6 of 10 stations in the
Buffalo River, and the abundances of these genera were usually low at most stations.
The low abundances of those genera may be due to the toxic effects of metals and
organic contaminants, the absence of sufficient grazing material, the lack of suitable
752
-------
TABLE 7-9. MEAN ABUNDANCE (NUMBER/m2) OF CHIRONOMIDS COLLECTED
IN GRAB SAMPLES FROM THE BUFFALO RIVER IN OCTOBER 1989
STATION
Taxon 01 02 03 04 05 06 07 08 09 10
Tanypodinae
Coelotanypus - - 28 26 4
Procladius 45 8 71 325 11 110 26 174 57 261
Tanypus - - - - - - - - - 11
Tanytarsini
Tany tarsus — — - — — — — - -- 404
Chironomini
Chironomus 76 53 - - -- 64 355
Cladopelma 4 34 -- - 15 4
Cryptochironomus 30 15 52 34 - 4 - 4 140 284
Dicrotendipes 4 - -- - - - - - - 155
Glyptotendipes 34
Microchironomus - - - - - - - - 4
Polypedilum 4 -- - - 8 1297
Orthocladinae
Cricotopus
Total Abundance8 197 116 151 386 11 114 26 181 287 2771
Taxa Richness 7433 121368
a Reported total abundance may include numbers of chironomids that were unidentified because of
incomplete specimens, and therefore are not listed above.
-------
TABLE 7-10. MEAN ABUNDANCE (NUMBER/m2) OF CHIRONOMIDS COLLECTED
IN GRAB SAMPLES FROM THE SAGINAW RIVER IN DECEMBER 1989
STATION
Taxon 02 03 04 06 07 09 10
Tanypodinae
Coelotanypus
Procladius 227 121 45 -- 83 280 38
7any pus
Tanytarsini
Tanytarsus
Chironomini
Chironomus 4 -- -- - - 79
Cladopelma
Cryptochironomus - - --11 - 4 24
Dicrotendipes 4
Glyptotendipes -- - - - 4
Microchironomus
Polypedilum
Orthocladinae
Cricotopus 4
Total Abundance8 238 121 45 11 87 363 61
Taxa Richness 4111 23 2
a Reported total abundance may include numbers of chironomids that were unidentified because
of incomplete specimens, and therefore are not listed above.
154
-------
TABLE 7-11. MEAN ABUNDANCE (NUMBER/m2) OF CHIRONOMIDS COLLECTED
IN GRAB SAMPLES FROM THE SAGINAW RIVER IN JUNE 1990
Station
Taxon 01 02 05 06 08 16 24
Tanypodinae
Coelotanypus - - - - 8
Procladius 144 15 125 4 147 234 87
Tanypus - -- - 4 8
Tanytarsini
Tanytarsus — - — — 8
Chironomini
Chironomus 2045 4 19 4 178 34 26
Cladopelma
Cryptochironomus - 4 -8 4113 4
Dicrotendipes
Glyptotendipes
Microchironomus -- - - 11 8
Polypedilum
Orthocladinae
Cricotopus
Total Abundance8 2204 23 144 15 360 389 125
Taxa Richness 23237 4 4
a Reported total abundance may include numbers of chironomids that were unidentified because
of incomplete specimens, and therefore are not listed above.
155
-------
TABLE 7-12. MEAN ABUNDANCE (NUMBER/m2) OF MOLLUSCS COLLECTED
IN GRAB SAMPLES FROM THE BUFFALO RIVER IN OCTOBER 1989
Taxon
Gastropoda
Valvatidae
Valvata lewisi
Valvata tricarinata
Bithyniidae
Bithynia tentaculata
Ancylidae
Laevapex fucus
Hydrobiidae
Cincinnatia cincinnatiensis
Total Abundance
Taxa Richness
Bivalvia
Sphaeriidae
Musculium sp.
Pisidium sp.
Sphaerium sp.
Sp. unidentified
Unionidae
Anodonta imbecillis
Anodonta grandis
Eliptio complanata
Total Abundance
Taxa Richness
01 02 03
23 -- 5
5
4
5
57
27 0 72
204
11 - 70
30 - 109
33
4
4 -- 5
-
49 0 217
404
04
163
23
4
185
375
4
11
45
15
-
4
4
79
5
Station
05 06 07 08 09 10
- 57 23
4
4 57 23 0 0 0
111000
8 15 4 8
-- 53 57 30 8
4 19 19 30
- 4
4
12 87 84 68 12 0
234320
756
-------
Chapter 7. Assessment ofBenthic Invertebrate Community Structure
habitats, or a combination of the above factors. Interestingly, two gastropod species,
Valvata lewisi and Cincinnatia cincinnatiensis, which are reportedly uncommon in New
York State (Jokinen 1992, pers. comm.), were the most abundant gastropod species in
the grab samples from the Buffalo River stations. Other reported occurrences of these
species in New York are in Oneida Lake, Seneca Lake, and several tributaries in the
Oswego drainage basin. All of these areas are subject to relatively high levels of organic
enrichment. This may indicate a tolerance for organic enrichment that allows these
species to displace other gastropods and survive in such areas.
The only molluscs present in the grab samples in Indiana Harbor were fingernail clams
(Sphaeridae). Musculium sp., Pisidium sp., and Sphaerium sp., were present at
extremely low abundances (4/m2). Many of the grab samples from Indiana Harbor had
large numbers of gastropod and bivalve shells. The affected stations were located in
depositional areas and the shells may have been carried into those areas from upstream
locations. However, it is also possible that molluscs once inhabited those areas but have
died.
As in Indiana Harbor, the only molluscs in the grab samples from the Saginaw River
were fingernail clams (Sphaeridae) (Tables 7-13 and 7-14). Musculium sp., Pisidium sp,
and Sphaerium sp. were collected during the December 1989 survey, but only Musculium
sp. was present in the June 1990 survey. The reason for the difference in species com-
position between seasons is unknown. Perhaps these genera were present at very low
abundances during the June 1990 survey and were missed when the samples were collec-
ted, or perhaps they were not present at any of the stations during the June 1990 survey
because of changes in available habitat or increased contamination in these areas.
Comparisons among the three AOCs indicated that Indiana Harbor (Station 10 excluded)
may have been the most toxic AOC for benthic invertebrates, and the Buffalo River may
have been the least toxic AOC (Table 7-15). This conclusion is based on the overall
numerical abundances (BR>SR>IH) and total number of species (BR=33>SR=20>
IH=14) present at each AOC. The Buffalo River had many genera that were not present
or were present in low numbers in Indiana Harbor and Saginaw River. Varying degrees
of contamination at these AOCs could influence the abundance and species composition
of benthic communities, but the influence of differences in substrate quality (particle size)
and the amount and quality of food sources cannot be discounted. Regression analysis
indicated no significant relationship between benthic communities and any single
contaminant or physical variable at any of the stations. Given the complex mixtures of
both organic and inorganic contaminants found at most of the Great Lakes AOCs, it is
not surprising that such simple relationships are difficult to discern. A large bed of
submerged aquatic vegetation at Indiana Harbor Station 10 provided structural support
above the contaminated sediments and most likely a place for relatively clean organic
material and associated bacteria to accumulate. As a result, extremely high abundances
of oligochaetes were found at Indiana Harbor Station 10.
Percent contribution of major taxon, abundance, and species composition proved to be
good discriminators of benthic community responses within and among AOCs. By
157
-------
TABLE 7-13. MEAN ABUNDANCE (NUMBER/m2) OF MOLLUSCS COLLECTED
IN GRAB SAMPLES FROM THE SAGINAW RIVER IN DECEMBER 1989
Taxon
Station
02
03
04 06
07
09
10
Gastropoda
Valvatidae
Valvata lewisi
Valvata tricarihata
Bithyniidae
B/thynia tentaculata
Ancylidae
Laevapex fucus
Hydrobiidae
Cincinnatia cincinnatiensis
Total Abundance
Taxa Richness
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Bivalvia
Sphaeriidae
Musculium sp.
Pisidium sp.
Sphaerium sp.
Sp. unidentified
Unionidae
Anodonta imbecillis
Anodonta grandis
Eliptio complanata
Total Abundance
Taxa Richness
4
12
8
0
0
4
1
8
1
16
2
0
0
0
0
0
0
755
-------
TABLE 7-14. MEAN ABUNDANCE (NUMBER/m2) OF MOLLUSCS COLLECTED
IN GRAB SAMPLES FROM THE SAGINAW RIVER IN JUNE 1990
Station
Taxon 01 02 05 06 08 16 24
Gastropoda
Valvatidae
Valvata lewisi
Valvata tricarinata
Bithyniidae
Bithynia tentaculata
Ancylidae
Laevapex fucus
Hydrobiidae
Cincinnatia cincinnatiensis
Total Abundance 000000 0
Taxa Richness 000000 0
Bivalvia
Sphaeriidae
Musculium sp. - 8 11 — - 4
Pisidium sp.
Sphaerium sp. -
Sp. unidentified
Unionidae
Anodonta imbecillis
Anodonta grandis
Eliptio compfanata
Total Abundance 0811 0 0 4 0
Taxa Richness 01 1001 0
159
-------
TABLE 7-15. COMPARISON OF ABSOLUTE AND RELATIVE ABUNDANCES OF
OLIGOCHAETES AND CHIRONOMIDS FOR EACH AREA OF CONCERN
Taxa
Buffalo River
October 1989
(n = 49)
Indiana Harbor
August 1989
(n = 35)
Study3
Indiana Harborb Saginaw River
August 1989 December 1989
(n = 30) (n = 34)
Saginaw River
June 1990
(n = 32)
Absolute Abundance (number Im2)
Oligochaeta
Mean
Median
Range
Std. Dev.
Chironomidae
Mean
Median
Range
Std. Dev.
Total Number of
Mean
Median
Range
Std. Dev.
8,726
7,333
1 70-28,047
6,669
426
132
0-9,148
1,325
73,391
3,364
132-970,704
222,599
1
0
0-38
6.4
3,309
1,994
132-14,062
3,563
0
0
0
0
3,938
3,572
0-8,505
2,596
135
95
0-548
152
2,429
2,325
0-7,163
1,909
465
170
0-3,553
793
Benthic Organisms
9,323
8,108
189-28,558
6,680
73,437
3,515
283-970,704
222,592
3,357
2,003
284-14,099
3,591
4,094
3,695
0-9,072
2,623
2,783
2,476
0-10,716
2,519
Relative Abundance (percent)
Oligochaeta
Mean
Median
Range
Std Dev.
Chironomidae
Mean
Median
Range
Std. Dev.
91
95
39-100
13
7
2
0-60
12
97
99
37-100
11
0.0001
0
0-0.0045
0.0008
96
99
37-100
12
0
0
0
0
95
98
69-100
8
5
2
0-30
7
86
90
47-100
13
14
10
0-53
13
a n = number of replicate samples collected from each AOC.
b Station 10 not included in analysis.
750
-------
Chapter 7. Assessment ofBenthic Invertebrate Community Structure
examining the percent contribution of each taxon to the overall community, it is readily
apparent that among the three AOCs, Indiana Harbor was dominated by oligochaetes with
very few other benthic taxa present. This result is also apparent from the numerical
abundances and the species composition of the various taxa of invertebrates. All three
of these measures are easily obtained, and statistical comparisons among numerical abun-
dance estimates are readily comparable. Overall, the benthic invertebrate communities
showed a graded response from low total numerical abundance in Indiana Harbor (Sta-
tion 10 excluded) to high total numerical abundance in the Buffalo River. Results of
sediment chemistry analyses and laboratory toxicity tests indicated that Indiana Harbor
was the most toxic AOC, while Saginaw River was the least contaminated. The three
measures of sediment contamination (i.e., benthic invertebrate community, laboratory
sediment toxicity testing, and sediment chemistry) do not always agree, and the reasons
for these discrepancies need to be identified. However, the integrated sediment assess-
ment approach will help reduce the Type I and Type II errors when assessing contamina-
ted sites.
Benthos-Chemistry Comparisons
Comparisons between the concentrations of simultaneously extracted metals, total PAHs,
and total PCBs and invertebrate abundances demonstrated a consistent pattern of
decreasing abundance with increasing contamination (Figures 7-2 to 7-4). Regardless of
the contaminant examined, there seemed to be a concentration below which abundance
was independent of contaminant concentrations. There also was generally a concentration
above which abundance was consistently reduced. This suggests that a threshold concen-
tration of contamination exists, below which invertebrate abundance is more strongly
controlled by other factors and above which the influence of the contaminant is more
pronounced. Therefore, additional research is needed to evaluate specific contaminant,
biotic, and abiotic factors that control invertebrate abundance and benthic community
structure in contaminated sediments.
The value of this threshold level, regardless of the contaminant examined, seems to be
consistently higher for the oligochaetes compared with other benthic invertebrates. This
is consistent with the identification of oligochaetes as among the most contaminant-
tolerant of benthic invertebrates (Hilsenhoff 1982, 1987; Bode 1988). Relationships
between benthic invertebrates and sediment chemistry might have been stronger if the
sediment samples for chemistry and physical measurements had corresponded more
closely with the benthic invertebrate samples. The benthic grab samples were collected
at each station before sediment samples were collected for analytical chemistry, physical
characterization, and toxicity testing. Benthic invertebrates often exhibit patchy
distributions (Elliott 1977) and typically have a high variability associated with cor-
responding abundance estimates (Winner et al. 1980; Luoma and Carter 1992). Sediment
physical and chemical characteristics may also be variable (Burton 1991). By not pairing
each benthic sample with the chemical and physical samples, the chance for conflicting
results was increased.
767
-------
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Ul
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Q
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Sji 10,000
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Figure 7-2. Total concentration of simultaneously extracted metals (Cd, Cr, Cu, Ni, Pb, Zn) vs. mean
total invertebrate abundance at three priority AOCs.
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Figure 7-4. Total PCB concentration vs. mean total invertebrate abundance at three priority AOCs.
-------
Chapter 7. Assessment of Benthic Invertebrate Community Structure
Deformities in Chironomids
Chironomid genera exhibit different tolerances to sediment contaminants (Hamilton and
Saether 1971; Hare and Carter 1976; Warwick 1985, 1988; Wiederholm 1984). Some
genera are intolerant, and low contaminant concentrations eliminate them from the
benthic community. On the other hand, genera such as Procladius, Chironomus, and
Cryptochironomus, are more tolerant (Warwick 1985; Bode 1988). A relationship
between increased sediment contamination and the presence of deformities in chironomid
larvae has been documented by many investigators (Hamilton and Saether 1971; Warwick
1980, 1985; Tennessen and Gottfried 1983; Cushman 1984; Wiederholm 1984). Some
of the reported deformities are thickening of the exoskeleton, enlargement and darkening
of the head capsule, asymmetry in mouthparts, missing or fused lateral teeth, and
deformed antennae.
The mentum (Orthocladinae and Chironominae) and ligula (Tanypodinae) of chironomid
larvae were examined for deformities at all three AOCs. The specimens had various
mouthpart deformities, including missing lateral and central teeth, asymmetry in the
mentum, badly deformed and twisted lateral teeth on the mentum, and missing teeth on
the ligula (Procladius). None of the specimens exhibited deformed antennae. Most
deformities in this study were found among larvae of the genera Procladius and Chirono-
mus. Even when other chironomid genera were present, they rarely displayed mouthpart
deformities. The reasons for this are not clear, but may be the result of individuals
dying before they can exhibit abnormalities.
In unimpacted areas, the prevalence of deformities in chironomids is generally less than
1 percent (Wiederholm 1984; Warwick et al. 1987). Several investigators have suggested
that deformity prevalences of 5-25 percent or greater are indicative of moderate to severe
sediment contamination (Wiederholm 1984; Warwick et al. 1987). Given this criterion,
chironomid deformity prevalences at the three AOCs were in the ranges found for moder-
ately to severely contaminated environments (Table 7-16). The stations at which no chi-
ronomids were found with deformities were also the areas at which few or no chirono-
mids were collected. Only two individual chironomids were collected from Indiana Har-
bor (Station 10) and both had deformities.
As with the abundance data, the prevalence of mouthpart deformities in the Buffalo River
chironomids indicates that conditions there are less toxic compared with conditions in
Indiana Harbor or the Saginaw River. The prevalence of mouthpart deformities was con-
sistently high at the Saginaw River stations, indicating that contaminant concentrations
were high enough to affect the chironomid community at most stations. This would seem
contrary to the expected result based on laboratory sediment toxicity tests and sediment
chemical analyses, which indicate that the Saginaw River samples were less toxic than
sediment samples from Indiana Harbor or the Buffalo River. It is possible that the con-
taminants in the Saginaw River were present in a more available form than they were in
the Buffalo River, thereby causing more mutagenic effects than were observed in the Buf-
falo River.
165
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TABLE 7-16. PREVALENCES OF LARVAL CHIRONOMID
MOUTHPART DEFORMITIES FROM BUFFALO RIVER,
INDIANA HARBOR, AND SAGINAW RIVER AOCs
Study/Station na
Prevalence of
Mouthpart
Deformities
(percent)
Buffalo River, October 1 989
BR-01-01
BR-01-02
BR-01-03
BR-01-04
BR-01-05
BR-01-06
BR-01-07
BR-01-08
BR-01-09
BR-01-10
MEAN
Indiana Harbor,
IH-01-10
Saginaw River,
SR-01-02
SR-01-03
SR-01-04
SR-01-06
SR-01-07
SR-01-09
SR-01-10
MEAN
Saginaw River,
SR-03-01
SR-03-02
SR-03-05
SR-03-06
SR-03-08
SR-03-16
SR-03-24
MEAN
52
29
32
102
3
30
7
48
76
210
-
August 1989
2
December 1989
63
32
12
3
23
96
6
~
June 1990
334
6
38
4
94
103
33
--
8
7
6
5
0
7
14
4
17
7
7
100
14
15
25
0
13
16
6
13
2
14
23
25
16
21
16
17
n = number of larval chironomids examined.
166
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Chapter 7. Assessment ofBenthic Invertebrate Community Structure
The occurrence of deformities among chironomids at the three AOCs may be related to
the degree of sediment contamination. However, the limited number of samples made
it difficult to evaluate these potential relationships. The prevalences of deformities
exhibited by chironomids exposed in laboratory sediment toxicity tests (Nelson et al.
1993) should be compared with the prevalences of deformities in chironomids collected
from the field. A more specific studfy is needed, which is designed to elucidate the
relationships between particular contaminants and chironomid mouthpart deformities and
to encompass a broad range of contaminated and uncontaminated areas. With a mini-
mum of training, chironomid larvae can be mounted on slides and mouthpart deformities
can be noted. Further study is needed to determine the usefulness of chironomid larvae
mouthpart deformities for identifying sediment contamination. Preliminary results from
the ARCS Program indicate that the use of larval chironomid mouthpart deformities in
future sediment assessment studies would be useful.
Artificial Substrates vs. Grab Samples
Several investigators have examined the advantages and disadvantages of using artificial
substrate samplers to assess benthic invertebrate communities (e.g., Cairns 1982). The
artificial substrate samplers used in the ARCS Program were modified from those used
by Stauffer et al. (1976). These samplers were easy to work with, and the synthetic
mesh was easily pulled apart, allowing easy access to the invertebrates, with a minimum
of specimen damage.
Artificial substrate samplers can be deployed at any site to provide a standardized habitat.
In the ARCS Program, artificial substrate samplers were deployed at each site for
30 days, and the invertebrate samples were processed following the methods outlined in
the Rapid Bioassessment Protocols (Plafkin et al. 1989). At Indiana Harbor Station 03,
each artificial substrate sample contained thousands of newly settled (< 500 /xm) zebra
mussels (Dreissena sp.). To assess the presence of other benthic organisms at this sta-
tion, a 100-organism subsample of invertebrates other than zebra mussels was picked
from each artificial substrate sample. The invertebrates were then enumerated into major
orders and families.
The artificial substrate samplers used in the ARCS Program collected a broader spectrum
of benthic invertebrates than the benthic grab samplers (Figures 7-5 to 7-7). This finding
indicates that there were more benthic invertebrates in the areas sampled than would be
collected in the benthic grab samples alone. The absence of certain benthic invertebrates
from the sediments collected with the Ponar grab sampler does not necessarily indicate
effects of sediment contaminants. Furthermore, LaPoint and Fairchild (1992) caution
that colonization of artificial substrates is a function of habitat availability and may not
necessarily reflect sediment exposure. The artificial substrates may simply act as a focal
point for colonization by invertebrates in areas where other suitable substrates are
unavailable. Planktonic larvae or mobile benthic invertebrate species may be present in
the water column and settle on the artificial substrate samplers, but not settle on the sedi-
ments.
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Chapter 7. Assessment ofBenthic Invertebrate Community Structure
Alternatively, the difference in faunal composition observed between the artificial
substrate samples and the benthic grab samples may provide useful supplementary
information for benthic community assessments. Benthic grab samples may not
effectively sample all taxa that could potentially influence benthic community structure
(i.e., through competition or predation) and may therefore suggest a severely impacted
community based on the low diversity of taxa. Additional invertebrate taxa may be
present in an area that are not collected in the benthic grab sample. Based on the results
of the artificial substrate sampling, this information becomes extremely important if
potential food chain bioaccumulation estimates are being considered. Therefore, depend-
ing upon study objectives, the use of artificial substrate samplers may be warranted
because they collect the epibenthic community more readily available to vertebrate preda-
tors. Future studies should consider the use of both benthic grab samplers and artificial
substrate samplers to make estimates of the total benthic invertebrate community struc-
ture.
Variation in Benthic Invertebrate Sampling Using Ponar Grab Samplers
It is important to identify sources of variability in ecological studies so that a meaningful
interpretation of the data can be made (Collins and Sprules 1983) and future studies can
be designed to address the variability. The results of the variance partitioning in the
ARCS Program indicate that among-station and among-replicate variability accounted for
most of the explained variability in the abundance estimates (Table 7-17). It is not
uncommon for among-station variability to account for a considerable amount of the
explained variability in abundance estimates for invertebrates (Lewis 1978; Threlkeld
1983). The variability among stations may be due to 1) heterogeneity of chemical
concentrations in sediments, 2) stations being located at variable distances from conta-
minant sources, 3) differences in substrate characteristics among stations that could influ-
ence colonization by the invertebrates, or 4) different station depths that could influence
benthic communities.
TABLE 7-17. PERCENTAGE OF TOTAL VARIANCE OF BENTHIC INVERTEBRATE
ABUNDANCE ESTIMATES PARTITIONED AMONG VARIOUS SOURCES
Taxon
Oligochaetes
Chironomids
Bivalves
Gastropods
AOCb
26
1
19
4
Source of Variability
Station0
49
37
25
59
(percent)3
Samplingd
25
62
55
37
a Variance estimates are based on samples collected using a 0.05-m2 Ponar grab
sampler. Variability was evaluated using a nested ANOVA.
b Variability among AOCs.
c Variability among stations within an AOC.
d Variability among replicate benthic samples at a station within an AOC.
171
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Chapter 7. Assessment of Benthic Invertebrate Community Structure
The variability among AOCs was relatively high for oligochaetes and bivalves, but
minimal for chironomids and gastropods. The overall numerical abundances of benthic
invertebrates were similar among the three AOCs. This is not unexpected considering
that all three AOCs have received substantial amounts of contaminants from industrial
and municipal sources, and the majority of the benthic community consists of oligo-
chaetes. Future assessment studies would likely benefit from the inclusion of a relatively
uncontaminated reference area.
The relatively high among-replicate variability in abundance estimates was likely due in
part to the patchy spatial distributions that most benthic invertebrates typically exhibit
(Elliott 1977). For most studies of benthic invertebrates, this source of variability can
be reduced by collecting additional replicate samples at each station.
The partitioning of the variance into different components indicated that future studies
might provide better data if 1) sample replication was increased, perhaps in conjunction
with the use of a smaller grab sampler, and 2) additional stations were sampled to better
represent the entire range of sediment contamination within each AOC. The main rea-
sons for limiting the number of grab samples taken from each AOC are the time and cost
of processing the samples. By taking smaller individual samples while still sampling the
same overall area, the overall processing time for each station would not change substan-
tially, but the estimates of invertebrate abundances among replicate grab samples should
have a lower variance (Frederickson 1992, pers. comm.).
Value of Benthic Community Structure Analyses for Assessing
Contaminated Sediments
Analyses of benthic community structure provide important information regarding the in
situ effects of contaminants on resident biota. By evaluating community structure and
the abundances of the genera and species of benthic invertebrates at a site, an assessment
can be made of the extent of contamination. This is a useful tool for conducting recon-
naissance surveys to determine if a problem may exist at a site. The structure of the
benthic invertebrate community can be examined relatively quickly at a reasonable cost
to provide a qualitative assessment of how the benthic invertebrate community may be
affected by contaminants. Although benthic invertebrate community studies provide
evidence of sediment contamination, they cannot identify the contaminants or even
families of contaminants that are responsible for adverse effects.
The structure of benthic invertebrate communities should be evaluated in future
assessments of Great Lakes sediments. A tiered approach using benthic invertebrates
may be warranted. The first tier should be a qualitative assessment of the benthic
invertebrate community to 1) determine if the community structure shows signs of
alterations relative to the community structure in unaffected areas; 2) evaluate whether
there are differences in benthic invertebrate communities across spatial gradients that may
identify potential hot spots of contamination; 3) determine if representatives from several
772
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Chapter 7. Assessment of Benthic Invertebrate Community Structure
orders are present, and determine if the community is skewed toward one or a few orders
such that a second tier is unwarranted; and 4) determine the number of samples required
in the second tier of the assessment.
The benefit of using a first-tier assessment to examine the resident benthic invertebrates
in a contaminated area is that decisions can then be made on the best methods for samp-
ling, based on the organisms that are present. The first-tier assessment can help
determine how detailed the sampling plan should be, or even if benthic sampling should
be conducted at all in a second-tier assessment. For example, in the ARCS Program, the
majority of the benthic organisms collected were oligochaetes and chironomids. Some
of the oligochaetes and chironomids are very small and would pass through a 500-^m
mesh sieve. A first-tier assessment would have indicated that the community was
primarily oligochaetes and chironomids, with very few other benthic invertebrates.
Therefore, a 250-jum mesh sieve could have been used instead of a 500-jiim mesh sieve
when collecting the invertebrates in a second-tier assessment. Considering the low
diversity of benthic invertebrates, the decision may also have been made that a second-
tier assessment was unwarranted.
The second-tier assessment, if warranted, should involve a more quantitative analysis,
if the qualitative analyses in the first tier warranted further evaluations of the sites.
Based on the first-tier assessment, the decision as to whether a second-tier assessment is
warranted will be different for each project, considering the objectives of the project and
the funding constraints. For instance, in the ARCS Program, oligochaetes and chirono-
mids comprised over 90 percent of the benthic invertebrate community at all of the
stations examined. At this point, a decision could be made that the species composition
of these two orders is not sufficiently important, so further analysis would not be
warranted. However, a decision could also be made that knowledge of the species
composition is important, so that the success of any remediation activities can later be
evaluated. Quantitative measurements, including statistical analyses of various commu-
nity measures, would then be appropriate in a second-tier assessment of the benthic
invertebrate communities.
Benthic invertebrates can be used to monitor the success of remediation activities. A
sampling regime could be designed to monitor the long-term recovery of the benthic
invertebrate communities in the remediated areas. This type of monitoring is important
because the recovery of benthic invertebrate communities may affect the whole aquatic
ecosystem. By comparing the structure of benthic invertebrate communities at remedi-
ated sites with the communities at undisturbed reference sites, the rates and effectiveness
of recovery can be quantified and monitored.
SUMMARY AND RECOMMENDATIONS FOR FUTURE STUDIES
Oligochaetes and chironomids accounted for more than 90 percent of the benthic inverte-
brate community collected using the Ponar grab sampler in the three AOCs. The domi-
nance by these two taxonomic groups is indicative of disturbed benthic invertebrate
173
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Chapter 7. Assessment of Benthic Invertebrate Community Structure
communities. Indiana Harbor was the most toxic AOC for benthic invertebrates. The
Buffalo River had the largest number of genera and species (n=33), followed by the
Saginaw River (n=20) and Indiana Harbor (n= 14). This result contrasts with laboratory
toxicity tests and sediment chemistry evaluations, which predicted the Saginaw River as
the least contaminated AOC.
Comparisons between concentrations of simultaneously extracted metals, total PAHs, and
total PCBs and benthic invertebrate abundances demonstrated a consistent pattern of
decreasing abundance with increasing contamination. These data suggest that a threshold
concentration exists below which abundance may be controlled by factors other than con-
taminant concentrations and above which the influence of contaminants is pronounced.
However, direct cause-and-effect relationships between invertebrate abundances and indi-
vidual contaminants were not demonstrated.
The prevalence of larval chironomid mouthpart deformities was relatively high in all
three AOCs. The Buffalo River had a lower prevalence of deformities than the Saginaw
River. The two individual chironomids collected from Indiana Harbor were deformed.
Overall, the prevalences of deformities in the three AOCs indicate that these areas are
moderately to severely polluted.
The Ponar grab samples and artificial substrate samples indicated the presence of
different numbers and taxa of benthic invertebrates. While the grab samples were
predominantly comprised of oligochaetes and chironomids, the artificial substrate samples
were comprised predominantly of amphipods, isopods, turbellarians, and zebra mussels
(Dreissena sp.). The Ponar grab sampler may not have sampled a considerable number
of benthic invertebrates. The incorporation of artificial substrate sampling into benthic
invertebrate surveys may enhance the accuracy of estimates of the total benthic inverte-
brate community composition and the potential for recruitment to uncontaminated
sediments.
The results of a variance partitioning analysis indicated that among-station and among-
replicate variability accounted for most of the explained variability in the estimates of
invertebrate abundances from grab samples. The variability associated with differences
among AOCs was relatively high for oligochaete and bivalve abundance estimates, but
minimal for chironomids and gastropods.
Based on the information presented in this chapter, the following conclusions and
recommendations can be made:
• Benthic community evaluations provide empirical information on the
effects of contaminated sediments on resident biota that are not addressed
directly by laboratory toxicity tests or sediment chemical analyses.
Changes in benthic communities are likely the result of long-term expo-
sures to chemical contaminants and are, therefore, indicative of chronic
effects. Benthic community evaluations should be used as part of an inte-
grated assessment of contaminated sediments.
174
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Chapter 7. Assessment ofBenthic Invertebrate Community Structure
Measurements of chemical and physical variables should be made on sub-
samples of the sediments from which the invertebrates are collected to
avoid the potential problems associated with heterogeneous distributions of
organisms and contaminants.
If possible, a preliminary survey of each study area should be conducted
to identify the resident benthic taxa and to determine the number of
invertebrate samples that need to be collected.
It is useful to compare the prevalence of mouthpart deformities in larval
chironomids exposed to contaminated sediments in laboratory toxicity tests
with the prevalence of mouthpart deformities observed hi larval chirono-
mids collected from the field.
Comparisons between the concentrations of certain contaminants and inver-
tebrate abundances suggest a threshold concentration of contamination
below which invertebrate abundance is more strongly controlled by other
factors and above which the influence of the contaminant is more pro-
nounced. Therefore, additional research is needed to evaluate the specific
contaminant, biotic, and abiotic factors that control invertebrate abundance
and community structure in contaminated sediments.
Benthic community assessments should consider using both artificial sub-
strate samplers and grab samplers.
Because most of the variance in abundance estimates of benthic inverte-
brates appears to be associated with differences among stations and among
replicates, future studies should sample more stations and collect more
replicate samples, perhaps using a smaller grab sampler.
775
-------
8. FISH TUMORS AND ABNORMALITIES
INTRODUCTION
The purpose of this chapter is to provide guidelines for performing a survey of resident
fish for the presence of liver tumors (using the Ashtabula River AOC as a demonstration
site) and to provide information useful for interpreting the data obtained in such surveys.
Additional detailed information on the use of histopathological surveys for environmental
assessments is provided by USEPA (1987b).
ROLE OF FISH TUMOR SURVEYS IN ASSESSING SEDIMENT
CONTAMINATION
Laboratory toxicity tests (as discussed in Chapter 6) are effective methods for assessing
the toxicity of contaminated bottom sediments. These tests measure changes in survival,
growth, reproduction, or other endpoints and can be used in concert with chemical eval-
uations to evaluate the biological effects of sediment contamination.
Some biological effects that result from exposure to environmental contaminants take a
long time to develop and cannot be evaluated using short-term toxicity tests. Carcino-
genesis is a prime example. A long time is usually needed to develop pathologic lesions
in tissue that can be identified as cancer. The methods available to determine this type
of effect include laboratory toxicity tests with long exposure and grow-out times; how-
ever, these tests are costly and have been used with only a few species. Currently, the
most effective method for assessing the potential carcinogenicity of contaminated sedi-
ments is to survey resident organisms, particularly species that are known to be sensitive
to the development of cancer. However, because fish move, it is not possible to deter-
mine whether specific locations of contaminated sediments are responsible for the
observed tumors or abnormalities. Although mutagenicity assays of sediment extracts
also provide information on potential carcinogenicity, these assays cannot address
questions of availability and do not recognize nongenotoxic carcinogens (Mac and John-
son 1989).
USE OF FISH TUMOR SURVEYS TO INFER CAUSE-AND-EFFECT
LINKAGES
The role of contaminated sediments in inducing liver cancer in wild fishes has become
better understood in the last decade. In a survey of hepatic neoplasms (i.e., liver tumors)
in fishes from North America, 14 species from 41 geographic regions were found to have
176
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Chapter 8. Fish Tumors and Abnormalities
tumors that were related to environmental contamination (Harshbarger and Clark 1990).
Most of these fishes were benthic-dwelling bottom feeders. In a study on the etiology
of hepatic neoplasms in wild English sole (Parophrys vetulus) from Puget Sound,
Washington, Myers et al. (1990) reported evidence for a link between exposure to
sediment-associated contaminants (mainly PAHs) and the development of liver lesions,
including neoplasms. Hepatic lesions that are suspected of being induced by contami-
nants have also been found in fishes from Great Lakes tributaries such as the Niagara
River (New York) (Hickey et al. 1990); the Buffalo River (New York), Cuyahoga River
(Ohio), and Black River (Ohio) (Baumann et al. 1991; Couch and Harshbarger 1985);
the Detroit River (Michigan) (Kreis et al. 1989); and the Fox River (Illinois) (Brown et
al. 1973, 1977); and from areas such as Torch Lake (Michigan) and Boston Harbor
(Massachusetts) (Couch and Harshbarger 1985).
Liver tumors have been induced in fishes by exposure to contaminated sediments in the
laboratory (Black 1983; Myers et al. 1990). Laboratory exposure to contaminants from
sediments has also caused skin cancers and other hyperplastic abnormalities (Black 1982).
Many of the parent chemical compounds found in the sediments are not necessarily the
carcinogens found in the fish themselves because of metabolic transformations within the
fish. Often, parent compounds (such as PAHs) are metabolized into carcinogenic meta-
bolites that can form DNA-aromatic adducts. Adduct formation is indicative of the initia-
tion phase of carcinogenesis (Dunn et al. 1987; Varanasi et al. 1987). Findings such as
these help to substantiate a causal relationship between cancer and sediment contami-
nants.
Bottom-dwelling fishes are particularly susceptible to sediment-associated carcinogens by
virtue of direct contact with the sediments and direct absorption through the skin or gills,
or by exposure via dietary routes through ingestion of contaminated sediments and detri-
tus or benthic invertebrates that have body burdens of carcinogenic compounds. In a
study evaluating the neoplasms in bottom-dwelling flatfishes and highly migratory salmon
from the same study area, a much higher prevalence of neoplasms was found in the flat-
fishes, presumably as a result of their direct exposure to contaminated sediments (Couch
and Harshbarger 1985). Surveys of neoplasms in bottom-dwelling fishes are particularly
effective in providing tangible evidence of damage to resident organisms from exposure
to contaminated sediments.
HISTOPATHOLOGYAS A SENSITIVE ASSESSMENT TOOL
While tumor surveys may be an effective means of determining damage to aquatic orga-
nisms from exposure to contaminated sediments, gross examination of fish for tumors
during autopsies is not sufficient for accurately determining the prevalence of tumors.
Although large nodules that are easily detectable to the naked eye are often tumors,
histopathological examination is critical to determine the origin of the lesion and the type
of neoplasm. Furthermore, some lesions and nodules have other etiologies such as para-
sitic infestations, which often resemble neoplastic nodules on gross examination. In addi-
tion, precancerous lesions or small neoplasms such as those seen in the liver are only
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Chapter 8. Fish Tumors and Abnormalities
detectable by microscopic examination of the liver. Also, the amount of liver tissue
examined influences the chances of detecting lesions. Studies need to be conducted to
determine the amount of tissue that should be examined from a liver to achieve an accep-
table level of confidence. This information would enhance the sensitivity and interpreta-
tion of the surveys. In a study of brown bullheads (Ameims nebulosus) taken from the
Black River, Ohio, fish less than or equal to 2 years old had a prevalence of grossly
observable neoplasms of 33 percent. Subsequent sampling of brown bullheads from the
same area during the same year revealed an 80-percent prevalence of neoplasms when
livers were examined histologically (Couch and Harshbarger 1985). Histopathological
examination is the most sensitive tool for evaluation of tissue damage resulting from
exposure to contaminated sediments.
METHODS AND MA TERIALS
This section describes recommended methods for fish collection, fish processing, and
evaluation of tissue samples, as well as recommended QA/QC procedures for fish tumor
surveys.
Fish Collection
Although numerous methods can be used to collect fishes for a tumor survey, it is critical
that fish are collected alive with the least amount of physical damage. Electroshocking
or trap netting meet these criteria. Electroshocking is preferable to trap netting because
fish often sustain physical damage in trap nets. This is especially true with bullheads,
because fighting between captured individuals leads to external wounds. However, in
some areas electroshocking will be ineffective due to physical limitations (e.g., depth,
water hardness). If destructive sampling gear such as gill nets must be used, frequent
tending of the nets is necessary to minimize physical damage to the fish. Once fish are
caught, they should be held in a manner that will keep them alive until processing. It
is preferable to use a live well or other kind of tank for holding individuals prior to
processing.
Fish Processing
Before beginning the external examination and general autopsy of captured fish, fish must
be sacrificed using a humane method that minimizes trauma to the tissues. The preferred
method is an overdose of an anesthetic. Fish are sacrificed individually to minimize any
post-mortem tissue changes that may confound or interfere with histopathological analy-
ses. Immediately following death, fish length and weight should be measured and a care-
ful examination of the external body surface should be made. Abnormalities often asso-
ciated with contaminated sediments, such as lip papillomas and stubbed barbels of bull-
heads, should be recorded. The skin should also be examined for any changes in thick-
ness or coloration. Melanomas or skin tumors are common in fish from contaminated
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Chapter 8. Fish Tumors and Abnormalities
areas. Spines (from scaleless fish), scales, or otoliths should be taken to determine the
age of the fish (Blouin and Hall 1990).
Upon completion of the external examination, a ventral incision on the midline running
from the anus to just above the pectoral fins should be made, keeping the scalpel just
under the skin so internal organs are not damaged. The incision should then be opened
to expose the internal organs for examination, and to allow the excision of appropriate
tissue samples for later histopathological examination. The liver should first be removed
and weighed, and then grossly examined for the presence of any abnormalities such as
swelling or nodules. If these abnormalities are present, tissue slices should be taken
from the nodule, taking care to include "normal appearing" tissue in the same slice. If
the liver appears normal, a 1-cm slice should be taken diagonally from top to bottom of
the entire liver. Additional tissue samples can be taken at this time. Slices should not
exceed 1 cm in thickness to allow for maximum penetration of the tissue fixative. Tis-
sues should be placed in individual labeled jars with a volume of fixative at least 3 times
the volume of the tissue. Labels should include a sample identification number reflecting
the nature of the project, date, and individual fish number.
Various tissue fixation procedures can be used depending on the type of microscopic pro-
cedures used for histopathological analysis (Yevich and Barszcz 1981). For tumor
surveys, the most common analytical procedure is fixation with neutral buffered formalin
(10-percent solution) or Bouin's fluid (a picric acid-formalin mixture) and examination
by light microscopy. It is critical that tissues be placed in the fixation medium as soon
as possible after death of the fish to minimize post-mortem changes. Caution should be
exercised when using picric acid (explosion potential) and formalin (carcinogen). After
a fixation period of 24 hours, samples should be transferred to a 70-percent ethanol solu-
tion for storage until processing. Tissues should be processed using routine paraffin
embedment procedures (Humason 1979), cut at 6 /mi, and stained with hematoxylin and
eosin.
Evaluation of Tissue Samples
Histopathological evaluation of tissues is relatively subjective and agreement among path-
ologists is sometimes variable because of different interpretations about the fate of speci-
fic kinds of lesions. Because most of our knowledge about tumor progression is based
on mammalian models, there is much speculation about the significance of similar lesions
in fish. For tumor surveys, the primary goal is to determine the presence or absence of
tumors, and to evaluate whether any observed tumors are cancerous or non-cancerous.
The determination of the exact origin and morphologic description of the various lesions
is less critical. Some researchers use morphologic classification schemes for diagnosis
(Myers et al. 1987), while others may rely on two main categories of lesions: preneo-
plastic and neoplastic (Baumann et al. 1991). Preneoplastic lesions infer a potential for
neoplasia, and neoplastic lesions indicate a cancerous condition. There are many kinds
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Chapter 8. Fish Tumors and Abnormalities
of lesions that fall under each category. Establishing specific criteria for lesion classifi-
cation is critical because it assists other pathologists hi understanding the exact nature of
the diagnosis and facilitates validation.
When evaluating livers for tumors, it is generally too costly to microscopically examine
entire livers. It is recommended that a sample be composed of at least four different
tissue subsamples (four separate slides) from the same fish for histological examination.
Each slide should be examined and all abnormalities recorded on a data sheet. Often,
one section of a liver may exhibit more than one type of lesion or abnormality. All
lesions should be recorded, even though the most serious lesion is considered to be the
definitive diagnosis and is usually the one used in data analysis.
Quality Assurance and Quality Control
USEPA quality assurance policy stipulates that every monitoring and measurement pro-
ject must have a written and approved QAPP. Guidance on preparation of a QAPP is
presented in Chapter 2. The six primary topics covered in a QAPP include detection
limit, bias, precision, representativeness, comparability, and completeness. The
following sections discuss the latter five topics with regard to fish tumor surveys.
Bias
The bias associated with a tumor survey is controlled by sample size (number of fish
examined histologically). Sample sizes should be large enough to be able to detect one
tumor-bearing fish (at a 95-percent level of confidence) if the tumor prevalence in the
population is at least 2 percent. Tumor prevalences < 2 percent have been estimated as
the expected values for bullhead populations in unimpacted areas (Hartig and Mikol
1992). The minimum number of fish needed to achieve this criterion is 85 individuals
from each individual sampling site. If fewer than 85 fish are examined, the level of
confidence in sampling at least one tumor-bearing fish must be reduced accordingly.
Precision
Precision is a function of the histopathological diagnosis, which is a relatively subjective
determination. Because there are no manuals for diagnosing fish tumors, acceptable
criteria should be established. Precision can be evaluated by having the diagnoses for
at least 10 percent of the slides validated by another pathologist. When different diagno-
ses occur, both pathologists should confer until they arrive at a common diagnosis. If
disagreements occur for more than 25 percent of the samples checked, the original
pathologist should reevaluate all samples after consultation with the other pathologist.
Criteria should also be established for determining the number of liver sections that
should be evaluated to provide a representative assessment of the entire organ. Although
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Chapter 8. Fish Tumors and Abnormalities
there are currently no commonly accepted guidelines on this issue, four sections were
used in the Ashtabula River survey and are recommended for similar surveys elsewhere.
Although selection of the appropriate number of sections is somewhat arbitrary, four
represents a balance between the need to assess a representative portion of the organ and
the cost of evaluating individual sections.
Representativeness
Because fish move, it is not possible to determine how long they have resided in any one
location. Representativeness is therefore difficult to demonstrate. Despite this
uncertainty, fish should be collected from areas within, or areas believed to be represen-
tative of, the study area.
Comparability
In studies involving multiple sample sites, tumor survey data should be obtained in the
same manner at all sites. In addition, because of the subjectiveness of the histopathologi-
cal diagnoses, it is preferable if one pathologist evaluates samples from all sites.
Completeness
If the target number of fish is 85, a minimum of 50 fish (59 percent) should be consi-
dered adequate for estimating tumor prevalence, although this estimate will have a
reduced confidence level. If 50 fish are not available, use of an alternative species
should be evaluated.
The following section describes a case study of a tumor survey conducted in the Ashta-
bula River AOC under the ARCS Program.
THE ASHTABULA RIVER AOC TUMOR SURVEY
A total of 98 brown bullheads were collected by electroshocking in three areas of the
Ashtabula River AOC—the harbor, breakwater, and river. The sample consisted of
40 males and 57 females (1 unknown sex) ranging in age from 3 to 7 years old.
Attempts to collect sufficient numbers of fish for similar surveys in the Indiana Harbor
and Saginaw River AOCs were unsuccessful.
External Abnormalities
External abnormalities such as skin discolorations, stubbed barbels, and lip papillomas
were found in brown bullheads from all three areas. Skin discolorations, confirmed
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Chapter 8. Fish Tumors and Abnormalities
through histological examination to be pigmented nevi, were observed in 41 percent of
the captured fish. Stubbed barbels, an abnormality thought to result from intimate
contact with contaminated sediments, were found in 35 percent of the fish. Lip papil-
lomas, common in fish from contaminated areas, were present in 16 percent of the fish.
Histological Findings
Liver lesions were generally classified as either "preneoplastic," inferring a potential for
neoplasia; "neoplastic," indicating a cancerous condition; or non-neoplastic. In counting
the number of fish with each class of lesions, the more severe class was the determinant.
Hence, a fish with both neoplastic and preneoplastic lesions was counted in the neoplastic
category to avoid double counting. Complete morphological descriptions and classifica-
tions were made for each liver sample. "Preneoplastic" lesions included areas of hepato-
cellular alteration, hepatocellular adenoma, and cholangioma, whereas "neoplastic"
lesions included hepatocellular carcinomas and cholangiocarcinomas.
Approximately 20 percent (20 of 97 fish for which the sex could be determined) of the
total sample of bullheads from the Ashtabula River AOC (all three areas combined) had
preneoplastic liver lesions; most of these lesions were areas of hepatocellular alteration.
Of the three areas sampled in the Ashtabula River system, the prevalence of preneoplastic
lesions (as a percentage of the total catch from each area) was highest in the river
(64 percent, 9 of 14 fish), followed by the breakwater (14 percent, 6 of 44 fish) and
harbor (13 percent, 5 of 39 fish). Given the relatively small numbers of fish examined
from each of these areas, such differences in prevalences may be indicative of trends but
should not be considered statistically rigorous. Larger numbers of fish would need to
be collected from each of the three areas (river, harbor, and breakwater) if statistical
comparisons were to be made, especially if the prevalences had been much lower.
Neoplastic lesions were observed in four fish (4 percent of the total sample of bullheads
from the Ashtabula River AOC). Three of these fish were from the river and one fish
was from the breakwater. Three fish had hepatocellular carcinomas while the third indi-
vidual had a mixed hepatocellular and cholangiohepatocellular carcinoma. All of these
neoplastic lesions were considerably advanced in development.
Biological Correlations
All biological comparisons in this section were made using chi-square analysis of contin-
gency tables.
Hepatosomatic Index
A hepatosomatic index (HSI) was derived for 82 of the fish by dividing the liver wet
weight by the total body wet weight and multiplying by 100. (Livers of the remaining
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Chapter 8. Fish Tumors and Abnormalities
14 fish were chemically analyzed and not weighed.) This index is often related to the
presence of liver lesions because the density or mass of affected livers is often increased
in response to preneoplastic or neoplastic conditions. In addition, enlarged livers can be
caused by increased activity of mixed-function oxidase enzymes as a result of exposure
to certain contaminants. In the Ashtabula River sample, the mean HSI (2.31) was signi-
ficantly higher (P<0.05) for fish with preneoplastic and neoplastic lesions relative to fish
without these lesions (mean HSI of 1.85), and was significantly higher (P<0.05) in
females compared to males.
Age
The bullheads collected ranged from 3 to 7 years old. Analysis of the prevalence of pre-
neoplastic lesions for each age group showed a general trend of increasing prevalence
with increasing age. In addition, neoplastic lesions were only found in older fish (i.e.,
ages 5-6 years old). This pattern is consistent with the hypothesis of chemical causation,
in which a latent period between initiation and tumor development is expected (Baumann
et al. 1990). Older fish have a longer period for exposure and development of lesions
than do younger fish.
Sex
There was no correlation between most external abnormalities or liver lesions and sex.
Approximately the same numbers of fish were collected for both sexes (i.e., 40 males
and 57 females). There were no significant differences (P>0.05) in the prevalence of
liver lesions, stubbed barbels, or skin discolorations between the sexes. However, there
was a significantly higher (P<0.05) prevalence of lip papillomas in males than in
females.
Internal and External Abnormalities
There were no significant differences (P^O.05) in the prevalence of external abnormali-
ties between fish with and without liver lesions (either neoplastic or preneoplastic).
Contaminant Correlations
The suspected relationship between liver lesions in fish and contaminated sediments is
based on four main lines of evidence:
• A chemical etiology of the lesions
• The presence of contaminants in the sediments
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Chapter 8. Fish Tumors and Abnormalities
Evidence of contaminants being bioaccumulated by the fish
Evidence of a higher prevalence of lesions in the contaminated area,
relative to a reference area.
Evidence for Chemical Etiology
Much evidence exists in the literature to support the hypothesis of chemical causation of
liver lesions. Studies of wild English sole from Puget Sound have shown a relationship
between PAHs in the sediments and elevated prevalences of hepatic lesions (Myers et al.
1990). These findings were further validated by laboratory studies in which PAH-
enriched extracts from contaminated sediments were injected into English sole and
induced lesions identical to those found in fish from the environment. Similar relation-
ships have been reported by Baumann et al. (1991) for brown bullheads taken from the
Cuyahoga River and the Black River (Baumann et al. 1990) and for flatfish from other
areas of Puget Sound (Malms et al. 1984).
Presence of Contaminants in the Sediments
Sediment samples collected from the Ashtabula River AOC showed the presence of
PAHs, PCBs, metals, and other chemicals (Ohio EPA 1991). It should be noted that the
sediment samples were collected at a different time than the fish samples. When com-
pared to USEPA guidelines for determining the extent of contamination, results of the
sediment analyses indicate that four stations were characterized by low to moderate
concentrations of metals, while PCB and PAH concentrations were lower than the con-
centrations in other areas (the Black and Cuyahoga Rivers) where epizootics of liver
tumors in fish have been found (Ohio EPA 1991) (Table 8-1).
TABLE 8-1. SELECTED MEAN CONCENTRATIONS OF POLYNUCLEAR AROMATIC
HYDROCARBONS IN SEDIMENTS AND THE PREVALENCE OF LIVER TUMORS IN BROWN
BULLHEADS FROM THE BLACK, CUYAHOGA, ASHTABULA, AND HURON RIVERS
Parameter
PAH (mg/kg dry weight)
Benz[a]anthracene
Benzo[b]fluoranthene
Benzo[a]pyrene
Tumor Prevalence (percent)d
Black
River3
11.00
15.00
8.80
51
Cuyahoga
River8
2.20
4.60
2.60
35
Ashtabula
Riverb
1.26
1.35
1.08
23
Huron
River0
0.08
Trace
0.01
4
a Baumann et al. (1991).
bOhio EPA (1991).
c Smith et al. (In press).
d The prevalence of liver tumors is for both preneoplastic and neoplastic lesions.
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Chapter 8. Fish Tumors and Abnormalities
Bioaccumulation of Contaminants by Brown Bullhead
There is little information on bioaccumulation of contaminants in brown bullheads from
the Ashtabula River because only one composite sample of 10 fish was analyzed, and few
chemicals were analyzed. The only relevant concentration available is for total PCBs
(0.7 /xg/g) (Ohio EPA 1991). Most parent PAH compounds would not be expected to
be found in fish tissue because they would be metabolized soon after uptake.
Tumor Prevalence
The prevalence of preneoplastic and neoplastic liver lesions in brown bullheads from the
Ashtabula River AOC was 23 percent. This prevalence is much higher than would be
expected from an unpolluted site such as the Huron River, Ohio, in which the prevalence
was only 4 percent (Table 8-1). Compared to other areas in which there were unusually
high incidences of tumors in bullheads, the prevalence observed for the Ashtabula River
is not quite as high (Table 8-1). Within the Ashtabula River system, however, there was
a significantly higher (P<0.05) prevalence of liver tumors in samples taken from the
river, compared to samples from the harbor or breakwater.
DISCUSSION
Limitations of Results
Using the fish tumor survey of the Ashtabula River AOC study as an example, it is evi-
dent that the brown bullhead population is suffering adverse effects as a result of some
environmental factor(s). A fish tumor survey is one method of establishing a link
between these demonstrable effects and possible causes. While conclusions have been
drawn based on the available data, there are limitations of the results that must be con-
sidered. Although it is clear that the fish in this area have liver abnormalities, his-
tological diagnoses are somewhat subjective and open to a variety of interpretations rela-
tive to the biological significance to the fish.
The two most relevant areas of uncertainty are in assigning a diagnosis and predicting
an outcome based on the kind of lesion. Although pathologists may differ in the detailed
morphologic description and identification of each lesion, there is relatively good agree-
ment in assigning lesions to the more general categories of either cancerous or precan-
cerous. These major categories of lesions, neoplastic and preneoplastic, imply ir-
reparable damage with possibly fatal consequences. Therefore, for tumor surveys
concerned with the presence or absence of injury, discrepancies in diagnoses are not
likely to affect the overall evaluation of impact to fish populations. However, these
surveys do not address the significance of the tumors to the fish (e.g., whether the
presence of liver lesions affects the ability of a fish to reproduce and survive). Although
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Chapter 8. Fish Tumors and Abnormalities
the survey found fish with liver tumors, there appeared to be a stable resident population
of reproducing fish. Very few older fish were found, suggesting that exposure to
environmental factors may ultimately be lethal. To lend credence to this suggestion, it
would be necessary to compare the age structure of the population with that in a
reference area, but this was not done. Nevertheless, the presence of environmentally
induced tumors in wild fish populations may serve as a warning system for the environ-
mental health of all animals, including humans.
Another important factor to consider is the limits inherent in the sampling methods.
While the condition of bottom-dwelling fish may be indicative of exposure to sediment
contaminants, the extent of exposure is difficult to determine. Movement of the fish
makes correlations with sediment contamination difficult. In many studies, sediment
samples have been collected simultaneously with fish samples, and, in these cases,
contaminant concentrations may be representative of actual exposure. However, many
fish collections are made after sediment samples have been collected and an area is
determined to have a sediment contamination problem. In the Ashtabula River survey,
the observed prevalence of tumors is indicative of a highly contaminated area; however,
supporting data on sediment chemistry are lacking. This may be a result of the fish
visiting more contaminated areas or the collection of sediment samples from areas that
were not representative of the entire AOC. Correlations of tumor prevalence and
chemical contamination can be strengthened by conducting supporting laboratory studies
that determine the direct effect of sediment extracts on fish by producing lesions identical
to those observed in field-collected specimens (Myers et al. 1990). Injection of sediment
extracts may be preferable to exposing fish directly to contaminated sediments because
of the difficulties in holding fish in laboratory exposures for sufficiently long periods for
contaminant uptake to occur. Additional evidence can be provided by conducting surveys
several years after remediation of sediment contamination and determining whether tumor
prevalence declined in response to remediation.
For the Ashtabula River survey, the apparent relationship between liver lesions in fish
and contaminated sediments supports a hypothesis of chemical causation of the lesions.
However, these correlations are not enough to prove chemical etiology. Instead, they
provide a body of evidence that is consistent with, but not proof of, the hypothesis of
chemical causation.
Recommendations
Improvements in Study Design
Statistical studies should be conducted to determine the amount of liver tissue that should
be examined to ensure a high probability of detecting lesions. In the Ashtabula River
survey, liver tissue that was removed for pathology included both routine tissue sub-
samples and any tissue that appeared abnormal. For histological examination, four slides
were prepared for each liver. This number was selected based on the recommendations
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Chapter 8. Fish Tumors and Abnormalities
of other pathologists and cost constraints; however, no information is available on the
number needed to ensure adequate confidence that the sections are representative of the
entire liver.
Establishing Links Between Chemical Contamination and Tumors
Significant progress has been made during the last decade in determining strong correla-
tions between some chemicals and certain tumors (particularly liver tumors). However,
in assessment and remediation studies, the information available for a particular site is
often inadequate to make definitive associations between tumor prevalences and specific
chemicals. Laboratory studies in which wild fish are exposed to sediment extracts to
induce lesions similar to those observed in the field are effective in providing the neces-
sary evidence to make strong correlations between sediment contamination and tumors.
Studies to determine the effect of exposure to chemical fractions of sediment extracts are
also useful. Other types of studies that would provide similar information include cages
positioned along a suspected sediment contamination gradient, laboratory testing using
surrogate species that have a relatively short latency period for tumor development (e.g.,
Japanese medaka), and in vitro testing of sediment extracts for genotoxic and non-geno-
toxic potential.
Species Sensitivity
Studies have shown that certain species of bottom-dwelling fish are more sensitive to the
effects of contaminated sediments than are other species. The nature of this selective
sensitivity should be evaluated to determine if it is a function of the unique physiology
of the sensitive species (e.g., immune system, nutrition, metabolic pathways, lifespan,
behavior). This information would be critical for elucidating mechanisms of toxicity and
for determining the appropriate remedial measures for reestablishing healthy, self-
sustaining fish populations.
Species Movement
Inherent to tumor surveys that are coupled with evaluations of sediment contamination
is the assumption that the fish observed with abnormalities are exposed to the con-
taminants at the site from which the fish were collected. Little information is available
describing the movement patterns of many of these bottom-dwelling fish. Tagging stud-
ies to address the potential migratory behavior of these fish would improve the confi-
dence in determining location and duration of contaminant exposure.
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Chapter 8. Fish Tumors and Abnormalities
Post-Remediation Monitoring
Long-term monitoring is essential to any sediment remediation project. Tumor surveys
are useful methods for assessing the effectiveness of remediation. Comparisons can be
made between tumor prevalences in specific year classes of fish evaluated before and
after sediment remediation. Because tumor prevalence should decline in the absence of
sediment contamination, surveys conducted 2-3 years after remediation should reflect
improved conditions or identify a recurring problem.
Manual on Liver Tumors
An atlas describing the various liver lesions would greatly expand the application of
tumor surveys for assessment and remediation purposes. A manual standardizing the
classification and interpretation of liver tumors would reduce the subjectivity of diagnoses
and allow for preliminary screening of liver samples by less specialized personnel.
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9. DA TA PRESENTA TION AND
INTERPRETATION
The interpretation of sediment quality data is an iterative process that proceeds through-
out the course of a lengthy sediment assessment program. Ideally, it begins with the
compilation, review, and synthesis of available information on the AOC. The rationale
for such interpretation of existing data was recently summarized by USEPA (1990):
Before full-scale, potentially costly sediment assessment programs are begun,
the initial identification of areas containing probable contamination problems
should be attempted. The contamination of sediments is a process influenced
by a number of variables including contaminant source, contaminant type, sedi-
mentary and hydrologic environment, sediment grain size distribution and com-
position, presence and type of aquatic life, and historical influences. The likeli-
hood of there being a sediment contamination problem at a particular site needs
to be appraised based on readily available information. Such information may
be available from ongoing monitoring or regulatory programs, previous site
characterizations, dredging records, discharge permits, area maps, fishing advi-
sories, reports of spills, fish kills and beach closings, etc.
This early data interpretation effort should be designed to identify the chemicals of
concern for a given AOC, and any data gaps that must be filled to provide a more
complete characterization of any sediment quality problems. This should serve to focus
further assessment efforts and make the best possible use of available resources.
Subsequent data collection efforts may be tiered, and each tier may be followed by a
separate data interpretation effort. The purpose of each tier of field sampling and
laboratory analyses should be to refine and fine-tune the understanding of sediment
contamination problems within the AOC. The overall goals of a sediment assessment
program can generally be summarized as answering three questions:
• What is the nature and spatial extent of chemical contaminants in sedi-
ments relative to appropriate reference conditions?
• What sediments have sufficiently high concentrations of chemical contami-
nants so as to present unacceptable risks to humans or aquatic biota, and
therefore must be considered for remediation?
• How should priorities for remediation be assigned to various sites within
an overall AOC?
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Chapter 9. Data Presentation and Interpretation
The first question above is addressed by collecting representative sediment samples,
analyzing them for the chemicals of concern, and then accurately recording the resultant
data in an easily interpreted form. The first section below describes a number of consi-
derations and recommendations for describing and mapping sediment quality data in
easily readable forms that enable the data user to interpret the 3-dimensional distribution
of important sediment quality parameters.
The second question above is addressed through the collection of data for an integrated
sediment assessment, encompassing sediment chemistry, physical characteristics, and bio-
logical effects data. The second section below discusses various approaches that are
potentially applicable to the interpretation of data resulting from an integrated sediment
assessment. Human health and ecological risk assessments are not addressed in this guid-
ance document, but are also considered to be of vital importance in answering the ques-
tion of whether sediments present unacceptable risks. Additional information on these
topics is discussed in the ARCS Risk Assessment and Modeling Overview Document
(USEPA 1993a).
The last question is especially important, given the high cost of dredging and other forms
of sediment remediation. Cost considerations may well limit future remediation activities
to only the most hazardous sediments, however they may be defined. It is therefore
vitally important that a detailed, accurate characterization of the area be performed to
focus remedial efforts where they will most efficiently lower risks posed by the most haz-
ardous sediments. The third section below summarizes a strategy that is potentially
applicable to prioritizing sites within an overall AOC for remediation.
It is impossible to recommend a single set of data presentation and interpretation tech-
niques that would be applicable in all cases. The approach to be taken will necessarily
be a function of both the types of data collected and the specifics of the AOC under con-
sideration. Nevertheless, the discussion that follows is intended to give the reader an
overview of potentially applicable data presentation and interpretation techniques that may
be useful for individual sediment assessment programs.
SEDIMENT QUALITY DESCRIPTION AND MAPPING
Sediment quality is often highly variable in all three dimensions; representing this
variability through sampling and other means is a key component of the overall sediment
assessment. Highly contaminated sediment deposits can be quite localized, as noted in
a national overview of sediment quality data (Lyman et al. 1987):
The combined effect of varied source locations, and variable hydrology and
sediment characteristics, has led to large variability in the concentrations of in-
place [sediment] pollutants within a water course or water body. The more
contaminated spots are often referred to as "hot spots."
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Chapter 9. Data Presentation and Interpretation
The report continues:
An important qualification ... is that in each location, the actual areas of high
contamination may be extremely localized. These localized areas with high
levels are often related to the location of the sources of contamination, e.g., at
the end of a sewage or industrial outfall. In general, however, they are diffi-
cult to identify and pinpoint. Their locations appear to vary due to the move-
ments of currents and other disturbances, e.g., ship traffic or dredging. The
high mobility of sediments in some water bodies is a complicating issue. Pollu-
tants discharged in the upper reaches of a watershed may travel tens or hun-
dreds of miles before finding a relatively permanent "home" in an open harbor,
lake or bay. Even here, however, permanent or episodic (e.g., storm genera-
ted) currents can result in significant sediment redistribution. In some areas,
older contaminated sediments may become buried by cleaner material as part
of the natural sedimentation processes.
Even so, much of the reported information about contaminated sediments, which is based
on grab samples of surficial sediments, gives the impression that this is largely a two-
dimensional problem. In fact, as ARCS and other coring studies have shown, the most
highly contaminated sediments may be located well below the sediment surface (i.e., in
older sediments). Consequently, it is essential to have some means of representing con-
taminant distributions in three dimensions.
In general, sediment quality data are more easily interpreted when presented in map
form, because the goal is to understand how sediment contaminants and toxicity are dis-
tributed within a particular AOC. Quantitative mapping provides valuable insights on
the extent and variability of contaminant zones. It also aids in providing some basis for
prioritizing or ranking sites within an AOC.
Preparing Base Maps
The cartographic representation of sediment quality data begins with the selection of a
suitable base map upon which to plot the data. The base map should be of such a scale
as to balance the need for detail with the area to be covered and of an accuracy commen-
surate with the intended use of the final map product. For example, smaller scale maps
are more appropriate for detailed data analysis (e.g., hot-spot mapping). Potential
sources of base maps include USGS 1:24,000 and 1:100,000 topographic quadrangles,
NO A A nautical charts, and, for rivers and harbors where navigational dredging takes
place, Corps project maps. The latter, used by the Corps to plan navigational dredging,
may provide the most useful base maps for sediment mapping (see Data Set Mapping
below). In some cases, it may be useful to indicate historical information, such as
former industrial sites or effluent sources, on the base maps. Base maps considered
useful for the ARCS priority AOCs included USGS 7-1/2 minute quadrangles (for water-
shed information), NOAA nautical charts (for navigation and harbor surroundings), city
maps (for local road access), and Corps project maps.
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Chapter 9. Data Presentation and Interpretation
Maps generated using computer mapping software require a plane coordinate reference
grid to plot information. The two most common plane coordinate systems are the State
Plane Coordinate System and the Universal Transverse Mercator Grid System. Regard-
less of which grid system is chosen, all position data collected in the field using the
spherical geographic coordinate system (i.e., latitude/longitude) need to be converted to
the appropriate plane coordinate system before they can be plotted on a map. A variety
of coordinate conversion programs are available, and many computer mapping programs
have built-in conversion capabilities. The locations of sampling stations in the ARCS
field surveys were determined accurately using DGPS (see Chapter 3) and plotted on
digitized Corps project maps in a state plane coordinate system.
It is sometimes useful to include bathymetric data, normalized to a common datum (i.e.,
water depth relative to low water datum), on maps because such data establish the con-
tours of the upper surface of the sediments. Bathymetric data may already be available
from the Corps and NO A A charts, or measured with sounding instruments (fathometers)
in the field.
Data Set Mapping
The distribution of sediment quality parameters can be represented on maps in various
ways. Single-value quantitative point symbol maps can be used to represent the values
of a single parameter at various locations. A simple geometric symbol, such as a circle,
can be placed at the desired location, and its size, color, color intensity, or texture can
be varied to indicate the magnitude of the parameter. This type of mapping lends itself
well to samples collected from a particular depth horizon, such as surface grab samples
(Figure 9-1).
Sediment core data, which represent multiple values at a given point, can be graphically
depicted using icons that are diagrammatic representations of data. The advantage of
icons is that they can express large amounts of information concisely in a small space
and, if carefully designed, are easily understood. The apparent simplicity of icons,
however, belies the fact that their generation can be extremely labor intensive. Icons can
be used to show multiple values of a single parameter (Figure 9-2), multiple values of
many parameters (Figure 9-3), or both qualitative and quantitative data for multiple
values for one or many parameters (Figure 9-4).
Contour mapping or surface modeling is a tool that can be used to predict continuous,
2-dimensional distributions of data from discrete point data. A number of contouring
software programs exist with each package usually containing several different contouring
algorithms. As Figure 9-5 shows, different contouring algorithms applied to the same
data set can result in different data distribution patterns. A more thorough description
of contour mapping and alternative algorithms is provided by Baudo (1990).
One option available with many contour mapping software packages is the ability to
create pseudo 3-dimensional surface representations. These surface representations lend
752
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Nickel concentrations
in surficial sediments
(Values plotted using symbol size)
CO
CO
Ni concentration
("9/9)
0-30
>30-60
>60-90
>90-120
Pollution classification of surficial
sediment by nickel concentration
(Values plotted using symbol shading)
Unpolluted
Moderately polluted
Highly polluted
miles
meters
400
800
Figure 9-1. Examples of single-value point maps.
-------
200-1
- High
— Intermediate
— Low
0.25 0.5
miles
meters
400 800
13
Survival of Chironomus riparlus
in 14-day sediment toxicity assays
25
Figure 9-2. Example use of icons to plot the value of a single parameter.
-------
Cadmium, chromium, and
copper concentrations in sediment cores
CO
i. o
_l
^ 25
LJ 50-
STATION IH21201
Cadmium Chromium Copper
- 75-
UJ
DC
O
; 100-
W/A
° 0 10 20 0 4008000 240480
ug/g ug/g ug/g
STATION IH21001
•?• Cadmium Chromium Copper
=• 0
oc
UJ
25-
50
75-
100
0 10 20 0 4008000 240480
ug/g ug/g ug/g
STATION IH21102
— Cadmium Chromium Copper
—. 0-
0 10 20 0 4008000 240480
ug/g ug/g ug/g
250
500
feet
meters
Columbus Drive
— "c 0
STATION IH21202
Cadmium Chromium Copper
25-
50-
75-
100-
0 10 20 0 4008000 240480
ug/g ug/g ug/g
STATION IH21101
--, Cadmium Chromium Copper
=. 0
i
0 10 20 0 4008000 240480
ug/g ug/g ug/g
Figure 9-3. Example use of icons to plot the values of multiple parameters.
-------
CO
05
Copper concentrations and
sediment type in sediment cores
1302
LEGEND
0 150 300
CONCENTRATION
("9/9)
Sediment type
[lj| Gravel
^ Black gravel/slag
[[:] Sand
^ Brown silt
^ Black oily silt
(JJ] Red/brown clay
B Black/brown clay
250
500
750
feet
meters
150
300
100
200
= -10
M4
300
Figure 9-4. Example use of icons to plot both quantitative (copper concentrations) and qualitative
(sediment type) parameters.
-------
study area
Original Data Set
+ Station
52 Lead concentration (ug/g)
Inverse distance
weighted
Contouring Algorithms
Kriged
Inverse distance
weighted with
biharmonic cubic spline
0 100 200 300
meters
Figure 9-5. Examples of different contouring algorithms applied to the
same data set.
197
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Chapter 9. Data Presentation and Interpretation
themselves extremely well to the visualization of sediment topography (Figure 9-6). In
addition, some contouring packages allow contour maps of different parameters to be
draped over these generated surfaces (Figure 9-7). With some contouring packages, it
is also possible to estimate sediment volumes.
Contour mapping is a powerful tool for visualizing the spatial distribution of sediment
quality data, but it is important to recognize that the resulting contour map is only a
model of the actual surface distribution based on interpolation and extrapolation of values
at selected sampling points. The more accurately the sampling points represent the con-
centration range and distribution of the parameter of interest, the more accurate will be
the contour map. Consequently, an effort should be made to incorporate sampling points
that anticipate the distribution and range of the target parameter into the sampling stra-
tegy. For example, selecting a single sampling point upstream or downstream of a
known point source might bias the resulting contour map in the area of that point source,
without representing the true spatial variability. By sampling both upstream and down-
stream of the point source, an abrupt change in parameter values will be constrained to
the appropriate area.
The producer of contour maps must exercise some discretion in deciding what can be
reasonably contoured and how it should be done. Contouring large areas with few data
points may yield maps that are useful for planning purposes but are too inaccurate for
other purposes (e.g., for making remedial decisions). Producing contour maps with data
collected from a linear array of stations along the shore will reflect variability along the
shore but will not give an accurate representation of variability outward from the shore,
especially in areas where navigational dredging has occurred.
A GIS combines cartographic display, data management, and spatial analysis capabilities
in one software package. In addition to producing maps using the techniques discussed
above, a GIS allows the spatial analysis of existing maps using various analytical tools
to produce new maps with new or enhanced information.
Options for true 3-dimensional mapping of sediment quality data include the geologic
modeling program (GMP, Dynamic Graphics Corporation, California), which runs on
a Personal Iris 4D/2D graphics workstation (Silicon Graphics Corporation, Mountain
View, California). On the workstation, sediment data representing specified intervals of
coring data can be interpolated onto a 3-dimensional grid, concentration ranges can be
color-coded, and the resulting data model (contour map) of contaminant zones can be dis-
played on an outline map of the site. Once displayed, the model can be manipulated on
the screen: it can be scaled up or down, stretched vertically, rotated on three axes,
viewed transparently, "peeled" away zone by zone, and sectioned along different planes.
GMP can also be queried for point concentration values and volume calculations from
the various displays of contaminant zones. This is a useful way to simulate different
dredging scenarios and estimate their costs.
198
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to
CO
2-Dimensional map
Pseudo 3-dimensional surface model
Figure 9-6. Example of a pseudo 3-dimensional surface model generated from a 2-dimensional contour map.
-------
2-dimensional contour plot of lead
concentrations (ug/g) in surficial sediments
Pseudo 3-dimensional representation
of sediment topography
Contour plot of lead values "draped"
over sediment topography
Figure 9-7. Example of a 2-dimensional contour map "draped" over a pseudo
3-dimensional surface model.
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Chapter 9. Data Presentation and Interpretation
SEDIMENT CLASSIFICA TION METHODS
The ultimate goal of the sediment assessment techniques discussed in this guidance docu-
ment is to assess whether and to what extent sediments are "contaminated" or have the
potential to adversely affect the environment. Chemical analyses of sediment samples
can demonstrate whether chemical concentrations in a specific area of interest are ele-
vated relative to a reference or background area. However, elevated chemical concentra-
tions alone are insufficient to demonstrate adverse environmental effects. The focus of
attempting to classify sediments as "contaminated" or "uncontaminated" may be on the
protection of ecological receptors, human receptors, or, more typically, both. The sedi-
ment assessment techniques described in this document can be used together to help inter-
pret integrated sediment assessment data (i.e., combining measurements of sediment
chemistry, physical characteristics, and various indicators of biological effects). As
indicated earlier, human health and ecological risk assessment procedures are not
addressed in this guidance document, but are discussed in the ARCS Risk Assessment and
Modeling Overview Document (USEPA 1993a).
A number of different approaches are potentially applicable to the assessment of the
adverse effects of sediment contamination on ecological receptors. The following
approaches, recently reviewed and evaluated in the USEPA's Sediment Classification
Methods Compendium (USEPA 1992), are summarized here for potential application to
sediment assessments in Great Lakes AOCs.
Whole Sediment Toxicity Testing
Whole sediment toxicity testing can be used to predict whether sediments can have
adverse effects on benthic biota (USEPA 1992; see also Chapter 6). Test organisms are
exposed in the laboratory under controlled conditions to field-collected sediments. To
measure toxicity, a specific biological endpoint (e.g., mortality, reductions in growth or
reproduction) is used to assess the response of the organisms to contaminants in the sedi-
ments. It is assumed that the toxicity of chemicals measured in the test sediments is sim-
ilar to that in natural in situ sediments. One of the benefits of whole sediment toxicity
testing is that it integrates the effects of all sediment contaminants. That is, the inter-
actions (e.g., synergism, additivity, antagonism) of various chemicals can be taken into
account without the need to measure their concentrations in the sediments, and without
any a priori knowledge of specific pathways of interaction between sediments and test
organisms. Although whole sediment toxicity testing can be used to demonstrate adverse
effects, such biological testing cannot be used alone to identify the chemical contami-
nant^) responsible for the observed effects or to generate sediment quality values (SQVs)
for individual chemical contaminants.
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Chapter 9. Data Presentation and Interpretation
Spiked Sediment Toxicity Testing
Spiked sediment toxicity testing can be used to predict the concentrations of specific
chemicals that would be expected to be harmful to resident biota under field conditions
(USEPA 1992). Test organisms are exposed in the laboratory under controlled condi-
tions to uncontaminated sediments that are spiked with known concentrations of specific
chemical contaminants. The results are evaluated to establish cause-and-effect relation-
ships between chemicals and specific adverse effects (e.g., mortality, reductions in
growth or reproduction). The results can also be evaluated to establish dose-response
relationships and to generate SQVs for individual chemicals. While it is theoretically
possible to evaluate the interactions (e.g., synergism, additivity, antagonism) among
various chemicals by combining those chemicals in the spiked sediment samples, it would
rarely be possible to mimic the complex mixtures of chemicals typically found in natural
sediments. Another difficulty with this approach is that the site-specific factors that
affect the bioavailability of chemical contaminants are not always known and would be
difficult to simulate in the laboratory, especially for a wide variety of field-collected
sediments (e.g., varying gram sizes, TOC content). It is also difficult to determine
whether the contaminants are at equilibrium with the sediments. Evaluation of a large
number of chemical contaminants by this method would also be very expensive.
Interstitial Water Toxicity Identification Evaluation
The interstitial water toxicity approach is a multiphase procedure for assessing sediment
toxicity using interstitial (pore) water separated from field-collected sediment samples
(USEPA 1992). Interstitial water is used because of the supposition that it more accu-
rately represents the contaminant concentrations that an organism is exposed to in the
environment. The toxicity of the pore water is first quantified in laboratory toxicity
tests, and then TIE procedures are used to identify and quantify the chemical constituents
of the interstitial water responsible for the sediment toxicity. The TIE procedures are
implemented in three phases to characterize the nature of the interstitial water toxicant(s),
identify the suspected toxicant(s), and confirm identification of the suspected toxicant(s).
These procedures, developed primarily for the evaluation of municipal and industrial
effluents, are not as readily applied to sediments because of the difficulty in collecting
sufficient volumes of interstitial water for toxicity testing. Typically, the TIE approach
has only been used to assess the acute toxicity of sediment samples (e.g., <4-day tests).
Equilibrium Partitioning
The equilibrium partitioning approach focuses on predicting the chemical interactions
among sediments, interstitial water, and contaminants, and assumes that the chemical
contaminant concentrations in interstitial water are acceptable predictors of adverse bio-
logical effects (USEPA 1992). Based on equilibrium partitioning theory, the chemical
contaminant concentrations in interstitial water are predicted from the bulk sediment
202
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Chapter 9. Data Presentation and Interpretation
chemical contaminant concentrations. If the predicted chemical contaminant concentra-
tions in interstitial water exceed applicable water quality criteria or any other effect
concentration, the sediment is predicted to have adverse biological effects. Many other
sediment contaminants may have toxic effects that cannot be predicted using this
approach. The equilibrium partitioning approach can be used to generate SQVs for indi-
vidual chemicals. The USEPA is currently developing specific regulatory uses of SQVs
based on this approach; however, the widespread application of the approach will be
dependent on the development of water quality criteria for many more potentially toxic
chemicals and an appropriate determination of uncertainty for site-specific applications.
The equilibrium partitioning approach is also not capable of evaluating the synergistic,
additive, or antagonistic effects of mixtures of sediment contaminants, such as those
found in most naturally occurring sediments.
Tissue Residues
In the tissue residue approach, sediment chemical concentrations are determined that
would result in unacceptable residues in the tissues of organisms of concern (i.e., either
ecological or human receptors) (USEPA 1992). The chemical concentrations that repre-
sent unacceptable tissue residues may be derived from toxicity tests performed during
generation of chronic water quality criteria, from bioconcentration factors derived from
the literature or generated by experimentation, or by comparison with human health risk
criteria associated with consumption of aquatic organisms. The tissue residue approach
can be used to generate SQVs, and is most applicable for nonionic organic and organo-
metallic compounds. However, this approach can also be used to evaluate metals and
polar organic compounds. The approach has recently been applied to the calculation of
the sediment concentration of TCDD that would be necessary to attain acceptable concen-
trations of TCDD in fish in Lake Ontario (Cook et al. 1990). The acceptable TCDD
concentration in sediment is being used as the criterion for determining the remedial
action necessary to reduce incremental loading of TCDD to the lake from a Superfund
site (Carey et al. 1989).
Benthic Macroinvertebrate Community Structure
Documentation of the structure of benthic macroinvertebrate communities through the
taxonomic identification and enumeration of field-collected organisms may be used to
assess sediment quality (USEPA 1992; see also Chapter 7). Benthic macroinvertebrates
are relatively sedentary organisms that inhabit or depend on the sedimentary environment
for their various life functions. Therefore, they may be sensitive to both long-term and
short-term changes in habitat, sediment, and water quality. Unlike laboratory toxicity
tests, assessments of the structure of benthic macroinvertebrate communities provide
direct evidence of the effects of sediment contaminants on naturally occurring communi-
ties. Deviations from expected community characteristics (such as may be demonstrated
by statistical comparisons with reference area conditions) may be attributable to the
presence of chemical contaminants. However, they may also be attributable to other
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Chapter 9. Data Presentation and Interpretation
factors (e.g., sediment grain size, organic content) unrelated to chemical contamination.
Therefore, it is generally considered essential to make comparisons with the benthic
macroinvertebrate communities in reference areas with similar sediment characteristics
except for the presence of chemical contaminants. Evaluations of benthic macroinverte-
brate community structure cannot be used alone to generate SQVs, but may be an impor-
tant part of an integrated sediment assessment.
Sediment Quality Triad
The Sediment Quality Triad approach is an effects-based approach to describing sediment
quality that incorporates measures of sediment chemistry, sediment toxicity, and benthic
macroinvertebrate community structure (Chapman 1986, 1989; Chapman et al. 1992;
USEPA 1992). All three measures are evaluated for samples of field-collected sediments
from the same location. The Sediment Quality Triad can provide strong, complementary
evidence for the degree of contamination-induced degradation in aquatic communities.
The Sediment Quality Triad also provides a direct assessment of sediment quality and can
be applied to all chemicals of concern, although it does not prove a cause-and-effect rela-
tionship between the concentrations of individual chemicals and adverse biological
effects. This approach is most commonly used to describe sediment characteristics
qualitatively.
The results of the three measures can be arrayed in a matrix to facilitate interpretation
of the results (Table 9-1). Sediment Quality Triad data can also be plotted on triaxial
graphs (Figure 9-8) to provide a visual representation of the data (Chapman et al. 1991).
The data for each individual measure are first scaled proportionally between 1 and 100
(with 100 being the greatest effect; i.e., highest concentration of chemical contaminants,
highest toxicity, or most altered benthic macroinvertebrate community) to keep the rela-
tive magnitude of the differences consistent for the three measures. Relative sediment
quality can be evaluated by the sizes and shapes of the triangles. Large triangles are
indicative of more contaminated or more impacted sites. More equilateral triangles indi-
cate that the data from the three measures agree.
Apparent Effects Threshold
The AET approach employs synoptically collected sediment samples that are analyzed
for both sediment chemistry and biological effects (Barrick et al. 1988; USEPA 1992).
The biological effects used to date in the generation of AET values have included both
assessments of benthic community structure and several different whole sediment toxicity
tests. The significance of adverse biological effects is assessed by statistical comparisons
with suitable reference or control sediments. The biological effects data are then consi-
dered in conjunction with the paired sediment chemistry data. For a given data set, the
AET value for a given chemical contaminant is the sediment concentration above which
a particular adverse biological effect has always been found to be statistically significant
relative to reference conditions.
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TABLE 9-1. POSSIBLE CONCLUSIONS RESULTING FROM USE OF THE
SEDIMENT QUALITY TRIAD APPROACH
Sediment Quality Triad Variables
Benthic
Sediment Sediment Community
Contamination Toxicity Alteration
Possible Conclusions
+ Strong evidence for pollution-induced degradation
— Strong evidence for absence of pollution-induced
degradation
- Contaminants are not bioavailable
— Unmeasured chemicals or conditions exist that have
the potential to cause degradation
+ Alteration is probably not due to toxic chemical
contamination
- Toxic chemicals are stressing the system
+ Unmeasured toxic chemicals are causing degradation
+ Chemicals are not bioavailable or alteration is not due
to toxic chemicals
Source: U.S. EPA (1992).
Note:
measured difference between test and control or reference conditions
no measurable difference between test and control or reference conditions
205
-------
Ni
O
Note:
Sediment toxicity, sediment chemistry,
and benthic macroinvertebrate community
structure data are all scaled proportionally
between 1 and 100 (with 100 being the
greatest effect; i.e., highest concentration
of chemical contaminants, highest toxicity,
or most altered benthic macroinvertebrate
community).
Sampling stations
Sediment
Toxicity
T 100
Sediment
Chemistry
Benthic
Macroinvertebrate
Community Structure
Figure 9-8. Use of triaxial graphs to plot sediment quality triad data.
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Chapter 9. Data Presentation and Interpretation
The AET values can be used as predictors of adverse biological effects for sediment
samples where only sediment chemistry data are available. If the concentration of any
chemical in a given sediment sample exceeds its AET value for a particular biological
indicator, an adverse biological effect is predicted for that indicator. If the concentra-
tions of all chemicals in a given sediment sample are below their respective AET values
for a particular biological indicator, then no adverse effect is predicted for that biological
indicator. The AET approach does not prove a cause-and-effect relationship between the
concentrations of individual chemicals and adverse biological effects, but it provides a
valuable tool for screening out samples where there is only a low likelihood of such
effects.
To ensure the reliability of the AET values generated using this approach, a relatively
large database (generally more than 30, and preferably at least 50 stations) is recom-
mended, spanning a wide range of chemical contaminant mixtures and concentrations
(Barrick et al. 1988). The AET values generated using this approach should appropri-
ately only be applied within the geographic region where the AET database was collec-
ted. To date, the AET approach has been used in the State of Washington for the gen-
eration of marine SQVs used in sediment regulatory programs, and has been initially
examined for similar use by the State of California.
National Status and Trends Program Effects-Based Approach
NO A A has employed this approach to develop "informal, effects-based guidelines" for
the assessment of sediment quality (USEPA 1992). It involves the identification of
ranges in sediment chemical concentrations associated with biological effects based on
a weight of evidence from many studies. In this approach, the data for many individual
chemicals are assembled from modeling, laboratory, and field studies to determine ranges
in chemical concentrations that are rarely, sometimes, and usually associated with
adverse biological effects (e.g., toxicity). The approach has been used to calculate,
based on the statistical distribution of a large amount of effects-based data, a "no-effects
range," a "possible effects range," and a "probable effects range" of sediment contami-
nant concentrations for individual chemicals. Two slightly different methods have been
used to determine these ranges.
Long and Morgan (1990) initially assembled a large database that included both data
demonstrating biological effects and data demonstrating no biological effects. Included
were field and laboratory data for both freshwater and saltwater organisms. Long and
Morgan (1990) defined an Effects Range-Low (ER-L) value as the lower 10th percentile
concentration for those sediment chemical contaminant concentrations associated with
biological effects. Sediment chemical contaminant concentrations below the ER-L value
were considered to represent the "no effects range." An Effects Range-Median (ER-M)
value was defined as the 50th percentile concentration for those sediment chemical
contaminant concentrations associated with biological effects. Sediment chemical
contaminant concentrations between the ER-L and the ER-M values were considered to
represent the "possible effects range" (i.e., at concentrations above the ER-L value,
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Chapter 9. Data Presentation and Interpretation
adverse effects may begin or are predicted to occur among sensitive lifestages or species
as determined by sublethal tests). Sediment chemical contaminant concentrations above
the ER-M value were considered to represent the "probable effects range" (i.e., at
concentrations above the ER-M value, adverse effects are frequently or always observed
or predicted to occur among most species).
Long and Morgan (1990) indicated that the ER-L and ER-M values were intended "only
for use by NOAA as general guidance in evaluating the NS&T [NOAA's National Status
& Trends] Program data." They also cautioned that "there is no intent expressed or
implied that these values represent official NOAA standards." Nevertheless, others have
attempted to use the ER-L and ER-M values as SQVs in other applications and in ways
not intended by Long and Morgan (1990). Such uses should be attempted with caution.
More recently, MacDonald (1992) and Long et al. (in press) have refined the application
of the Long and Morgan (1990) approach. MacDonald (1992) segregated saltwater data
from freshwater data, and identified the three effects ranges with a method that used both
the concentrations associated with biological effects and those associated with no
observed effects. Based on statistical manipulations of the chemical contaminant con-
centration data, MacDonald (1992) then defined a no-observed-effect-level (NOEL), a
threshold effects level (TEL), and a probable effects level (PEL). The "effects" and "no
effects" databases still contain a wide variety of biological tests.
Long et al. (in press) have applied similar refinements to the approach and have expan-
ded the original Long and Morgan (1990) database. The revised Long et al. (in press)
database is limited to saltwater and estuarine data, however, and is therefore not appli-
cable to the Great Lakes region.
Although different in their approach, the ER-L values defined by Long and Morgan
(1990) are roughly equivalent to the NOEL values defined by MacDonald (1992), while
the ER-M values are roughly equivalent to the PEL values. Just as for the AET values,
neither the ER-L and ER-M values developed by Long and Morgan (1990) nor the TEL
and PEL values developed by MacDonald (1992) prove a cause-and-effect relationship
between the concentrations of individual chemicals and adverse biological effects.
Nevertheless, they may be useful for screening sediment samples to determine the
likelihood of such effects. Neither the ER-L and ER-M values nor the TEL and PEL
values should be used alone as SQVs for establishing whether a given sediment is
"contaminated" or "uncontaminated."
Use of the Sediment Classification Approaches
For an extensive discussion of the sediment classification approaches described above,
including their applicability, advantages, disadvantages, and level of acceptance, as well
as for additional references to pertinent source documents on these approaches (to 1992),
the reader is referred to USEPA (1992). Although not discussed by USEPA (1992), it
should be noted that USEPA and the Corps are jointly developing guidelines for the
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Chapter 9. Data Presentation and Interpretation
evaluation of dredged material from inland waters (USEPA-USACOE 1993), but these
guidelines are not yet final.
The various approaches described above for classifying sediments as "contaminated" or
"uncontaminated" can generally be categorized as numeric, descriptive, or a combination
of numeric and descriptive approaches (USEPA 1992). Numeric methods (e.g., spiked
sediment toxicity testing, interstitial water TIE, equilibrium partitioning, tissue residues)
can be used to derive chemical-specific SQVs. Descriptive methods (e.g., whole sedi-
ment toxicity testing, benthic community structure) cannot be used alone to generate
numerical SQVs for individual chemicals but do provide important information on ecolo-
gical effects. Although both numeric and descriptive approaches can be used in assessing
sediment quality, none of these approaches alone is considered adequate for a comprehen-
sive sediment assessment. An integration of several methods using a weight-of-evidence
approach is needed to assess the effects of chemical contaminants associated with sedi-
ment. The approaches that integrate data from whole sediment toxicity testing, chemical
analyses, and benthic community assessments (e.g., the Sediment Quality Triad or AET
approaches) provide strong complementary evidence of the degree of contaminant-
induced degradation in aquatic communities and are therefore recommended for future
studies of Great Lakes AOCs.
Under the ARCS Program, the integrated sediment assessment approach developed by
the Toxicity/Chemistry Work Group included chemical analyses of sediments (see Chap-
ter 5), whole sediment toxicity testing (see Chapter 6), and analyses of benthic commu-
nity structure (see Chapter 7). Some toxicity tests were conducted using interstitial water
and elutriate samples collected from the sediment samples, but they were not conducted
in a phased manner with TIE procedures to identify and quantify the chemical constitu-
ents responsible for observed adverse effects. Because SQVs and equilibrium partitioning
are tools for manipulating and interpreting the results of chemical analyses rather than
for generation of chemical data directly, they were applied during data interpretation,
classification of sediments as "contaminated" or "uncontaminated," and for intra-site
ranking. Methods for developing SQVs based on tissue residues were investigated by
the ARCS Risk Assessment and Modeling Work Group, and are discussed hi the ARCS
Risk Assessment and Modeling Overview Document (USEPA 1993a).
Two other important types of information collected under the ARCS Program were data
on fish tumors and abnormalities (see Chapter 8) and on bioaccumulation in fishes.
While neither is strictly part of the integrated sediment assessment approach, both
provide important complementary information on the health of ecological communities
that may potentially be related to sediment contamination.
Efforts to interpret the ARCS Program's integrated sediment assessment data using the
various sediment classification approaches are continuing and are not yet ready for publi-
cation.
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Chapter 9. Data Presentation and Interpretation
NUMERICAL RANKING OF HAZARDOUS SEDIMENTS TO PRIORITIZE
SITES FOR REMEDIAL ACTION
One goal of the ARCS Program was to develop a ranking method by which the relative
risks associated with contaminated sediment from different sites can be compared within
AOCs, among AOCs, or both. A general method of ranking contaminated sediment sites
based on whole sediment chemistry was proposed by Kreis (1989), which put all sedi-
ment chemical concentration variables on the same scale so that they could be compared
and combined. The numerical ranking system developed by Kreis (1989) was intended
for use by managers in regulatory and remediation decision-making for contaminated
Great Lakes sediments. Kreis (1988) had previously shown that the ranking process can
be an effective tool for determining which sites, of a range of contaminated sites, need
the most immediate attention. Thus, the results of the ranking process can be used to
prioritize sites for remediation, which is desirable because of the high cost of sediment
remediation. As resources become available, the sediments needing remediation could
each be "cleaned-up" in the order of their ranking. However, other important factors
that are not included in this ranking scheme (e.g., human health, economic factors) must
also be considered. The actual remediation technology or combination of remediation
technologies chosen is site-specific and would depend on ecological, chemical, economic,
and engineering considerations that are independent of the site ranking process.
The Kreis (1989) methodology was modified for use in the ARCS Program by incorpora-
ting estimates of contaminant bioavailability, toxicity, and potential for effects on benthic
community structure for the sediment contaminants of concern (Wildhaber, in press).
The basic elements of this ranking method are described in the following sections. It
should be recognized that the method is still undergoing development and is not yet ready
for routine application. Nevertheless, it introduces some of the concepts considered
desirable in any contaminated sediment site-ranking method that may ultimately be
selected.
Ranking Sites Based on Toxicity Estimated from Chemistry Data
In the ranking system proposed by Kreis (1989) for sediment chemistry, each chemical
or group of chemicals (e.g., metals, dioxins) analyzed is ranked independently of each
other. The measured concentrations of each chemical or group of chemicals for each site
under consideration are scaled from 1 to 100, relative to each other; the lowest contami-
nant concentration for a given chemical or group of chemicals becomes 1 and the highest
concentration for that chemical or group of chemicals becomes 100. The equation used
to calculate the ranks for each chemical or group of chemicals is:
r, , , f Site Value - Minimum Value 1 .. nn
Rank = 1 + X 99
I Maximum Value - Minimum ValueJ
The ranks calculated for each chemical or group of chemicals are then averaged (arithme-
tic mean) for each site. The result is an average rank for each site based on all measured
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Chapter 9. Data Presentation and Interpretation
chemicals for which ranks were assigned. One problem with this ranking process is that
the chemicals analyzed are scaled relative to each other based only on the concentrations
present; it does not scale those chemicals based on a true measure of concern, such as
their toxicity and availability to aquatic organisms. Another problem is that the ranks
for each chemical or group of chemicals at a given site are not necessarily independent
of one another, especially if the chemicals have a common source or sources.
The alternative approach considered under the ARCS Program differs substantially from
that proposed by Kreis (1989) in that it uses toxicological and ecological information as
well as estimated contaminant bioavailability to scale the chemicals before then- ranks are
combined. In this approach, each chemical or group of chemicals analyzed is not inde-
pendently ranked. Instead, all the chemicals are put on a common toxicity scale and
totaled among chemicals for each site; this total toxicity is then ranked. The result is a
relative ranking of the sites under investigation based on what is known about the toxicity
and potential bioavailability of the compounds found in the sediments.
Before the data for different chemicals can be combined, they must be put on the same
toxicity scale, which is achieved through the use of individual bioavailability and toxicity
estimates for each chemical measured in the sediments. The toxicity of chemicals in
sediments is believed to be at least in part a function of how tightly bound the chemicals
are to the sediments, or, conversely, how readily the chemicals can dissolve in the pore
water. Different sediments with the same total quantities of individual toxic chemicals
may exhibit varying toxicities because other sediment properties may influence the extent
to which the chemicals are bound to the sediments.
For nonionic organic chemicals, the organic carbon content of the sediments is believed
to be a primary determinant of the distribution of the chemicals between the solid and
aqueous phases. Hence, the pore water concentration of nonionic organic chemicals can
be estimated based on the whole sediment concentration of the chemical, the TOC con-
tent of the sediment, the partition coefficient for sediment organic carbon, and the
assumption of equilibrium partitioning (Di Toro et al. 1991). There are, of course, many
situations where the sediment and pore water may not be hi equilibrium, but for the pur-
poses of this estimation, the assumption is necessary.
The sorption of metals to sediments is potentially more complex, and may be influenced
by the presence of oxides of iron and manganese, organic carbon, and sulfides. Di Toro
et al. (1990) have suggested that the solubility (and therefore bioavailability and toxicity)
of divalent metals may be primarily determined by the AVS phase (i.e., the solid-phase
sediment sulfides that are soluble in cold acid). If there is more metal present on a molar
basis than sulfides on a molar basis, then the metal may exist in the aqueous phase and
be available in pore water. If the reverse is true, all of the metal may be present as a
solid metal sulfide.
Once estimates have been made of the pore water concentrations of nonionic organic
compounds and divalent metals, it may then be possible to estimate the relative toxicities
of different sediment samples. The relative toxicity of each analyte in a sediment sample
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Chapter 9. Data Presentation and Interpretation
can be defined as the ratio (expressed in "toxic units") of the estimated equilibrium pore
water concentration of the analyte to the ambient water quality criterion (AWQC) for
aquatic life (USEPA 1986a) for that analyte:
„, . TT .. Pore Water Concentration
T°K1C Ua" ' AWQC
Toxic units for those chemicals with AWQCs are easily estimated. For those chemicals
without AWQCs, it is necessary to use relative comparisons of toxicity (e.g., toxic equi-
valency factors [TEFs]; Safe 1990) to those chemicals with AWQCs.
Once toxic units have been estimated for each analyte at each site, the toxic units for
each site are totaled over all analytes. Kreis' (1989) ranking process, as described
above, is then used to rank the sites based on their total toxic units. The result is a
relative toxicity ranking for the group of sites under investigation based on total estimated
potential toxicity at each site.
Ranking Sites Based on Toxicity as Measured by
Laboratory Toxicity Tests
The data from laboratory sediment toxicity tests and multiple endpoints measured within
some tests must be put on a common risk scale before they can be combined (arithmetic
mean). To accomplish this, the different measured responses associated with each of the
tests is divided by the response observed for the control or reference sediment. Adjust-
ing each response for the control response not only puts each measure on the same scale
(i.e., proportion of the control), but it also adjusts each measured response for the
laboratory conditions at the time of the test. Adjusting for the conditions at the time of
the test is necessary to account for variations in test methods resulting from tests being
run at different tunes, in different locations, by different investigators, or combinations
of these factors. Analysis of control sediments alone does not take into account differ-
ences in toxicity test responses that may be attributable to differences in physical (e.g.,
sediment grain size) or other factors (e.g., ammonia, TOC content) between sediments.
Analysis of reference sediment samples appropriately matched with the test sediment
samples is necessary to take such factors into account.
Calculation of the proportional laboratory toxicity response for each measured value is
as follows:
Proportional Laboratory _ Endpoint Value for Test Sediment
Toxicity Response ~ Endpoint Value for Control or Reference Sediment
The estimates of risk for each toxicity test measure are then averaged over all measured
endpoints at each site to estimate the average (arithmetic mean) risk at a site based on
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Chapter 9. Data Presentation and Interpretation
laboratory toxicity. Again, it is this average estimated risk that is ranked among sites.
For this approach to be effective, the endpoints measured at each site should be similar,
if not identical.
Ranking Sites Based on Toxicity as Measured by Benthic
Community Structure
As for laboratory toxicity tests, the different measures of benthic community structure
should ideally be put on one scale for evaluation. For the ARCS Program, the different
measurements of benthic community structure were the percentages of the benthic com-
munity (i.e., as percentages of the total number of organisms) represented by each inver-
tebrate order observed among all the sites. The use of the full list of observed orders
is appropriate as long as the potential list of orders for the set of sites under consideration
is similar among sites.
Since all values of these benthic community structure variables are percentages, they are
already on the same general scale. To put the observed percentages for each invertebrate
order on a relative risk scale, it is desirable to adjust their abundances by their relative
tolerance to contamination. There is an implicit assumption in this approach that
differences in the abundances of the various invertebrate orders are attributable to
differences in sediment contamination, and not to differences in physical or other factors
between sites. Future refinements to this ranking method may need to take this fact into
account. Adjusting each order's percentage of the benthic community by its tolerance
to contamination ensures that the presence of less tolerant orders (i.e., that may therefore
be present in relatively low abundances) still influences a site's ranking.
Several different indices of tolerance to contamination have been proposed. Hilsenhoff
(1987) proposed a biotic index for aquatic invertebrates in Wisconsin streams that was
related to their tolerance of organic enrichment. Lenat (1993) proposed a biotic index
for aquatic invertebrates in North Carolina streams that was related to their tolerance of
chemical contamination. Although Lenat (1993) cautioned against using his biotic index
outside its intended geographic range (i.e., southeastern United States stream environ-
ments), this index is currently the only index of tolerance to chemical contamination
available. Until an index such as Lenat's (1993) index is developed for the Great Lakes,
it remains the best measure of chemical contamination tolerance for benthic organisms.
The use of such a biotic index to adjust the abundances of the various invertebrate orders
would be as follows:
Tolerance-adjusted Benthic _ % Benthic Community for an Order
Community Response ~ Contamination Tolerance of that Order
The index of contamination tolerance is structured such that the more tolerant orders
receive a higher value, thereby adjusting their abundances downward in relationship to
those of less tolerant orders. The estimates of risk generated by the tolerance-adjusted
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Chapter 9. Data Presentation and Interpretation
benthic community response for each invertebrate order would then be averaged (arith-
metic mean) over all orders for each site. This average contamination tolerance-adjusted
benthic community response represents an estimate of the relative risk at each site based
on the assumed toxicity of the sediments to the benthic community. Again, it is this
average estimated risk that would then be ranked among all sites under consideration.
Final Ranking
The rankings that result from the different types of information discussed (i.e., sediment
chemistry, laboratory toxicity tests, and benthic community structure) can be combined
to produce an overall ranking for each site. At this point, each type of information is
on a scale from 1 to 100. The estimate of relative risk for the sites under investigation,
based on all three types of information, is just the average (arithmetic mean) of the three
ranks. A simple average of these three ranks implicitly assumes that a range in values
of 1 to 100 has the same meaning for each variable. This may represent a gross simplifi-
cation, but still should allow a comparison of overall risk among sites.
The only requirement necessary before the three different rankings (i.e., sediment chem-
istry, laboratory toxicity tests, and benthic community structure) can be combined is that
all three ranks must order the sites in the same manner (i.e., 1 = least toxic and 100 =
most toxic). The chemistry rank already ranks the sites in the appropriate manner, but
the laboratory toxicity tests and benthic community structure ranks must be reversed.
To reverse any of the ranks, the following equation is used:
Site Rank = 1 + l - x 99
The purpose of the described ranking process is to allow different types of data,
measured on different scales, to be combined into one overall estimate of relative risk
for the set of contaminated sites under investigation. The scaling done to each class of
data (i.e., sediment chemistry, laboratory toxicity tests, benthic community) allows for
the incorporation into the estimates of relative risk as much information as is currently
available in the scientific literature. The result is the current best estimate of relative risk
associated with sediment contamination for the sites under investigation. This approach
enables the comparison and combination of sediment contamination information,
measured on different scales, on one relative scale that has a foundation hi environmental
chemistry, toxicology, and ecology.
The ranking process is dynamic; as more information becomes available about sediment
processes, chemical fates, toxicity, etc., new information can be incorporated into the
ranking model. Thus, the estimates of relative risk become more robust as the base of
knowledge increases.
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Chapter 9. Data Presentation and Interpretation
One very important set of assumptions associated with this process is that each measure
within each class of data is considered independent of all the other measures in its class
(i.e., effects are strictly additive). This is not necessarily the case for all the measures
(e.g., lead and zinc; Schmitt et al. 1993). As information becomes available that contra-
dicts these assumptions, the interactions that are present can also be incorporated into the
ranking method.
Finally, the process does not have to be limited to the types of data described above.
Other, less scientifically based classes of data (e.g., aesthetics, recreational potential)
could potentially be incorporated into the ranking method. The ranking method could
potentially also be extended to other quantifiable risks, such as carcinogenicity.
CONCLUSIONS AND RECOMMENDATIONS
This chapter provides an overview of potentially applicable data interpretation techniques
that may be useful for individual sediment assessment programs. Included are examples
of techniques for mapping sediment quality data, classifying sediments as "contaminated"
or "uncontaminated," and ranking of sites for consideration for remediation. It is not
possible to recommend specific data interpretation techniques for each and every sedi-
ment assessment program. The data interpretation techniques selected for a given sedi-
ment assessment program will be a function of the program under which the assessment
is being conducted, as well as of the types of data collected and the specifics of the AOC
under consideration.
It is important that the reader understand that efforts to interpret the sediment quality data
collected under the ARCS Program are continuing, and that more is yet to be learned
about the most appropriate ways of analyzing these data. It was not the intent of the
ARCS Program to select specific sediment classification methods for application in the
Great Lakes AOCs, but the ARCS Program has considered how these methods could be
applied. Similarly, it was not the intent of the ARCS Program to select specific methods
for the ranking of contaminated sediment sites, but the ARCS Program is continuing to
explore application of such methods in an effort to show how they might be applied in
other programs.
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10. CONCLUSIONS
This document represents the culmination of several years of work, which was designed
to investigate scientifically sound methods of assessing sediment contamination in Great
Lakes AOCs. This work is the result of the combined efforts of the ARCS Toxicity/
Chemistry Work Group, whose members represent a broad spectrum of expertise
(Table 10-1). The assessment methods described in this document are intended to assist
Great Lakes RAP personnel and others in answering the following questions:
• Are the sediments sufficiently "contaminated" to warrant consideration of
the need for remediation? In this context, "contaminated" refers to the
presence of chemicals in the sediments that have the potential to cause
adverse effects in humans or ecological receptors.
• Is there evidence indicating that existing concentrations of sediment
contaminants are adversely affecting ecological receptors? In other words,
can it be shown that the presence of contaminants in the sediments is
causing adverse effects in organisms, either those naturally occurring in
the environment, or those exposed to sediments in controlled, laboratory
toxicity tests?
• Are ecological receptors exposed to the sediments bioaccumulating
contaminants to the extent that the resultant body burdens are adversely
affecting the organisms themselves, or humans or other organisms higher
in the food chain?
• If the sediments are judged to be sufficiently contaminated to be causing
such effects, what is the spatial extent (i.e., both horizontal and vertical)
of the contamination, and what are the implications of the distribution of
contaminants on possible remedial alternatives?
The Toxicity/Chemistry Work Group surveyed the field of existing sediment assessment
methods and identified those methods that showed the most promise for addressing these
questions, and then demonstrated their use in studies of several Great Lakes AOCs. The
selected methods integrate physical, chemical, and biological information to achieve an
overall assessment of sediment contamination that is based on a preponderance of evi-
dence from independent measurements (or observations).
The guidance provided in this document is intended to address not only the physical,
chemical, and biological assessment methods themselves, but also related topics such as
QA/QC considerations, the design of sediment sampling surveys, and data interpretation
methods. The described assessment methods are not those required under specific
regulatory programs, but are instead more generally applicable in investigations of the
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TABLE 10-1. ARCS TOXICITY/CHEMISTRY WORK GROUP
Name
Affiliation
Gerald Ankley
Bruce Baker
Frederick Brown
G. Allen Burton, Jr.
Timothy Canfield
William Creal
Eric Crecelius
David Dabertin
J.C. Filkins
Rick Fox
John Giesy
Edward J. Hanlon
Darveen Adams
Christopher G. Ingersoll
Peter Landrum
Julie Letterhos
Michael Mac
John McMahon
Mary Ellen Mueller
Thomas Murphy
J.E. Rathbun
Philippe Ross
Brian Schumacher
Griff Sherbin
V.E. Smith
Frank Snitz
Henry Tatem
Mark L. Wildhaber
USEPA, Environmental Research Laboratory, Duluth, Minnesota
Wisconsin Department of Natural Resources, Madison, Wisconsin
Great Lakes United, Midland, Michigan
Wright State University, Dayton, Ohio
National Biological Survey, Columbia, Missouri
Michigan Department of Natural Resources, Lansing, Michigan
Battelle Marine Science Laboratory, Sequim, Washington
Indiana Department of Environmental Management, Gary, Indiana
USEPA, Environmental Research Laboratory, Large Lakes Research Station,
Grosse lie, Michigan
USEPA, Great Lakes National Program Office, Chicago, Illinois
Michigan State University, Department of Fisheries, East Lansing, Michigan
USEPA, Region V, Chicago, Illinois
USEPA, Region II, Edison, New Jersey
National Biological Survey, Columbia, Missouri
National Oceanic and Atmospheric Administration, Ann Arbor, Michigan
Ohio EPA, Columbus, Ohio
National Biological Survey, Washington, DC
New York State Department of Environmental Conservation, Buffalo, New York
National Biological Survey, Washington, DC
DePaul University, Chemistry Department, Chicago, Illinois
AScI Corporation, Dearborn, Michigan
The Citadel, Charleston, South Carolina
USEPA, Environmental Monitoring Systems Laboratory, Las Vegas, Nevada
Environment Canada, Toronto, Ontario
AScI Corporation, Dearborn, Michigan
U.S. Army Corps of Engineers, Detroit District, Detroit, Michigan
U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg,
Mississippi
National Biological Survey, Columbia, Missouri
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Chapter 10. Conclusions
nature and extent of sediment contamination. Although intended for application in the
Great Lakes AOCs, they may be applicable in other environments as well. Some of the
methods described (e.g., the sediment toxicity tests) are applicable only in freshwater
environments, while others are more generally applicable.
It is absolutely essential that any data to be collected in a sediment assessment program
be of high quality when those data are likely to be used in decisions about the potential
need for sediment remediation. Chapter 2 provides guidance on the essential elements
of a QA/QC program. DQOs should be defined early in the planning for a sediment
assessment program to ensure that all parties understand the goals of the program, to
eliminate unnecessary waste of tune and money, and to establish the level of data quality
necessary to meet the program's goals. MQOs should then be defined in terms of
detection limits, bias, precision, representativeness, comparability, and completeness.
Any sediment assessment program that includes the field collection and laboratory
analysis of sediment samples should include various QA/QC samples to quantitatively
assess and control the error associated with the results. DQOs and MQOs should be
defined in a project-specific QAPP developed prior to sample collection. Other
important aspects of the QA/QC program discussed in Chapter 2 include the development
of a laboratory audit program, database requirements, and data verification/validation
methods.
Given that any sediment samples collected for analysis will represent but a small fraction
of the total sediments of interest, it is critical that sufficient consideration be given to
ensuring that those samples accurately reflect the characteristics of the sediments in the
area in which they were collected. The design of field surveys of contaminated sedi-
ments is highly site-specific, and therefore detailed guidance is beyond the scope of this
document. Nevertheless, Chapter 3 provides an overview of the general issues that
should be considered in the design of such field surveys. Chapter 3 also describes the
desirable features for sampling vessels to be used in sediment surveys and the advantages
and disadvantages of available field positioning methods. The ARCS Program demon-
strated the use of both sediment grab samplers and vibrocorers for the collection of
sediment samples; Chapter 3 describes the advantages and disadvantages of several dif-
ferent types of each of these sediment samplers. Field processing methods for sediment
samples are then briefly discussed, followed by a brief description of available remote
sensing equipment that may provide important supplementary information for sediment
surveys.
In areas where there is a paucity of data on sediment characteristics, there is often a need
for a low-cost, screening-level investigation to determine whether there is sufficient
sediment contamination to be of concern, and to identify areas where more detailed
investigations are warranted (Chapter 4). The ARCS Program explored the efficacy of
a two-phased sampling design: a set of quick, less expensive assays ("indicator analy-
ses") was performed on the sediments collected from a large number of sediment coring
stations, while detailed chemical analyses and toxicity tests were performed at a limited
number of surface sediment stations throughout the study area. The indicator analyses
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Chapter 10. Conclusions
included both those that produce a direct measure of sediment composition or contamina-
tion (i.e., metals, total and volatile solids, TOC, grain size, ammonia, and the Microtox®
test) and those that produce an indirect measure of sediment quality (i.e., conductivity,
pH, extractable residue, and organohalogens) that may be related to other variables of
environmental or regulatory importance. Attempts were made to define relationships
between the indicator analyses and the more detailed toxicity tests conducted at a limited
number of stations so that the latter could be predicted from the former, but the results
were site-specific. More recent research has suggested that other screening-level analyses
(e.g., fluorometry for PAHs; immunoassays for PCBs, chlorinated pesticides, and PAHs;
infrared spectroscopy for petroleum hydrocarbons; TLC for semivolatile organic
compounds; XRF for metals; rapid toxicity tests) are also quick and relatively inexpen-
sive, can sometimes be performed in the field, and may be more comparable from site
to site than the indicator analyses tested in the ARCS Program. These screening-level
analyses may be very useful in delineating areas of high contamination that warrant more
detailed investigation, while eliminating areas likely to be relatively uncontaminated.
Chemical analyses conducted under the ARCS Program were focused on application of
the best currently available analytical methods (Chapter 5). The sediment samples
collected for analysis presented significant problems, such as high levels of hydrocarbon
contamination. This resulted in a series of recommendations for additional sample
cleanup steps to overcome such analytical challenges. Routine organic and inorganic
chemical analyses provide total concentrations of each contaminant in a matrix.
Supplemental analyses that provide a better representation of the biologically available
fraction of chemicals in a matrix may provide data that are more suitable for interpreting
the risk to aquatic organisms. For example, the simultaneous extraction of metals during
the extraction of AVS holds promise, but more research is required before such analyses
are recommended for routine use. It is impossible to provide detailed guidance on the
selection of appropriate analytes and analytical methods for all sediment assessment
programs because each situation generally presents a unique combination of factors. It
is recommended that the selection of analytes be based on a complete survey of the
literature for previous monitoring and exploratory studies in an area of interest, as well
as on available data concerning treated and untreated wastewater discharges in the drain-
age basin for the site. Consideration should also be given to exploratory chemical
analyses. This information, in combination with the results of a screening-level investi-
gation and best professional judgment, should provide the basis for selecting the approp-
riate analytes and analytical methods.
A wide variety of laboratory sediment toxicity tests were performed under the ARCS
Program on samples collected from three AOCs (Chapter 6). Included were both elutri-
ate and whole-sediment toxicity tests using various organisms (e.g., bacteria, algae,
macrophytes, rotifers, cladocerans, amphipods, mayflies, and fish) and a range of acute
and chronic endpoints. Sensitivity, discriminatory power, and redundancy were deter-
mined for the various tests. Based on the experience with toxicity tests in the ARCS
Program, it is recommended that future sediment assessment programs include a battery
of two to three toxicity tests. The use of more than one species is recommended because
it reduces uncertainty and limits the probability of false positive or false negative results.
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Chapter 10. Conclusions
At least three measured responses (i.e., survival, growth, or reproduction) should be
used in integrated assessments of sediment contamination; behavior as a measured
response is a fourth possible endpoint that can be considered, but tests incorporating this
endpoint are less well developed. Whole sediment toxicity tests were shown to be very
sensitive and provided the most realistic exposure system; exposures using elutriate
samples are not recommended for routine sediment assessments. Sediment toxicity
testing complements analyses of benthic community structure and physicochemical
characteristics of the sediments, and is recommended for an integrated assessment of the
degree of sediment contamination. Chapter 6 includes additional guidance on the selec-
tion of an appropriate battery of sediment toxicity tests from those shown to produce the
most reliable and interpretable results in the ARCS Program.
Field surveys of freshwater benthic invertebrate community structure (Chapter 7) repre-
sent the third component (along with sediment chemical analyses and laboratory toxicity
tests) of an integrated assessment of sediment contamination. Quantitative surveys of
benthic invertebrates were conducted under the ARCS Program in three AOCs. The data
were evaluated to provide guidance on the conduct of similar surveys in future sediment
assessment programs for other AOCs. Based on the ARCS Program data, the following
recommendations can be made: 1) benthic community evaluations provide an important
complement to laboratory toxicity tests because changes in benthic communities are likely
the result of long-term exposures not adequately simulated in the laboratory; 2) measure-
ments of chemical and physical variables should be made on subsamples of the sediments
from which the invertebrates are collected; 3) preliminary benthic community surveys
enable an assessment to be made of the species likely to be present, and assist in the
design of subsequent more detailed investigations; 4) consideration should be given to
sampling with artificial substrates as well as with sediment grab samplers because of the
different fauna sampled; 5) the variance in abundance estimates might be reduced by col-
lecting and analyzing more replicate samples than used in the ARCS Program, perhaps
using a smaller grab sampler; and 6) additional research is needed to evaluate the specific
contaminant, biotic, and abiotic factors that control invertebrate abundance and commu-
nity structure in contaminated sediments.
Although not as frequently included in assessments of sediment contamination as are
investigations of sediment chemistry, sediment toxicity, and benthic invertebrate
community structure, fish tumor surveys (Chapter 8) can provide valuable complementary
information about biological effects of sediment contamination. Laboratory sediment
toxicity tests (Chapter 6) typically focus on biological effects that are manifested within
several weeks of exposure to contaminated sediments. Other biological effects, such as
carcinogenesis, take a long time to develop and cannot be evaluated using short-term
toxicity tests. Although it is feasible to conduct long-term tests in the laboratory that are
designed to induce the development of lesions, such tests are usually prohibitively
expensive. In lieu of such long-term laboratory tests, surveys of liver lesions in bottom-
dwelling fishes have been shown to provide valuable evidence of damage to resident
organisms potentially resulting from exposure to contaminated sediments. Chapter 8
provides guidance on the conduct of fish tumor surveys, based on the experience gained
in a survey of the Ashtabula River AOC conducted under the ARCS Program. Histo-
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Chapter 10. Conclusions
pathological examination of the fish by trained specialists is required to achieve accurate
estimates of lesion prevalence. If there is an increased prevalence of liver lesions in
bottom-dwelling fish from a specific area as compared to a reference area, there is a
strong suggestion of potential adverse effects resulting from exposure to contaminated
sediments. It should be recognized that movement of the fish, about which little is
generally known, complicates the interpretation of exposure. In the absence of suppor-
ting laboratory studies designed to examine the effect of exposure to contaminated sedi-
ments or sediment extracts in producing lesions, apparent relationships between lesions
in fish and the presence of contaminated sediments provide a body of evidence that is
consistent with, but not proof of, the hypothesis of chemical causation of the lesions.
Chapter 9 provides an overview of potentially applicable data presentation and interpreta-
tion techniques that may be useful for individual sediment assessment programs. Inclu-
ded are examples of techniques for mapping sediment quality data, classifying sediments
as "contaminated" or "uncontaminated," and ranking of sites for consideration for reme-
diation. It is not possible to recommend specific data interpretation techniques for each
and every sediment assessment program. The data interpretation techniques selected for
a given sediment assessment program may be a function of the program under which the
assessment is being conducted, as well as of the types of data collected and the specifics
of the AOC under consideration.
The ARCS Program was working with the state-of-the-science throughout these studies.
Sediment assessment is a rapidly evolving science, and advances have taken place since
the field and laboratory studies described in this document were completed. Further,
several valuable techniques were omitted from these studies due to budgetary concerns.
Overall, this document incorporates the state-of-the-science at the present time. How-
ever, users should be aware that newer techniques and assays may supplant the recom-
mended tests. The multidisciplinary approach described in this document will remain
sound, but the latest technologies should be adopted as appropriate.
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Ankley, G., and N. Thomas. 1992. Interstitial water toxicity identification evaluation
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