National Coastal Condition
Assessment 2015
Technical Support
Document
U.S. Environmental Protection Agency
Office of Wetlands, Oceans and Watersheds
Office of Research and Development
Washington, DC 20460
July 2021
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National Coastal Condition Assessment 2015 Technical Support Document
U.S. Environmental Protection Agency. 2020. National Coastal Condition Assessment 2015
Technical Support Document. EPA-841-R-20-002. Office of Water and Office of Research and
Development. Washington, D.C. https:/ /www.epa.gov/national-aquatic-resource-surveys/ticca
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National Coastal Condition Assessment 2015 Technical Support Document
Table of Contents
List of Figures 8
List of Tables 9
List of Acronyms 12
1 Introduction 14
1.1 Additional Resources for Survey Operations 14
1.2 Additional Report Materials 15
1.3 References 16
2 Quality Assurance 17
2.1 Introduction 17
2.2 Survey Design 18
2.2.1 Statistical Design 18
2.2.2 Completeness 18
2.2.3 Comparability 20
2.3 Quality Assurance in Field Operations 21
2.3.1 Field Method PilotTesting 21
2.3.2 Training of Field Trainers and Assistance Visitors 21
2.3.3 Field Crew Training 21
2.3.4 Field Assistance Visits 22
2.3.5 Revisits of Selected Field Sites 22
2.4 Laboratory Quality Assurance And Quality Control 22
2.4.1 Basic Capabilities 23
2.4.2 Benthic Macroinvertebrate Identifications 23
2.4.3 Chemical Analyses 25
2.4.4 Sediment Toxicity Analyses 25
2.5 Data Management And Review 26
2.6 NCCA 2015 Report 26
2.7 References 27
3 Selection of Probability Sites 29
3.1 Objectives 29
3.2 Estuarine Design 29
3.2.1 Target Population 29
3.2.2 Sample Frame 30
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3.2.3 Survey Design 30
3.2.4 Stratification 30
3.2.5 Unequal Probability Categories 32
3.2.6 Panels 32
3.2.7 Expected Sample Size 32
3.2.8 Site Usage and Replacement 34
3.3 Great Lakes Nearshore Design 34
3.3.1 Nearshore Target Population 34
3.3.2 Nearshore Sample Frame 34
3.3.3 Survey Design 35
3.3.4 Stratification 35
3.3.5 Unequal Probability Categories 35
3.3.6 Panels 35
3.3.7 Expected Sample Size 35
3.3.8 Site Usage and Replacement 36
3.4 Great Lakes Embayment Design 37
3.4.1 Embayment Target Population 37
3.4.2 Embayment Sample Frame 37
3.4.3 Embayment Survey Design 37
3.4.4 Stratification 38
3.4.5 Unequal Probability Categories 38
3.4.6 Panels 38
3.4.7 Expected Embayment Sample Size 38
3.5 Evaluation Process 39
3.6 Statistical Analysis 41
3.7 References 42
4 Benthic Macro in vertebrates 43
4.1 Overview 43
4.2 Field Collection 44
4.3 Data Preparation 45
4.4 Data Analysis 45
4.4.1 Estuarine Samples 45
4.4.2 Great Lakes Samples 48
4.5 References 51
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5 Eutrophication Index 53
5.1 Background 53
5.2 Field Collection 54
5.3 Laboratory Methods 54
5.4 Index Calculation 55
5.4.1 Estuarine Sites 55
5.4.2 Great Lakes Sites 60
5.5 References 65
6 Sediment Quality Index 67
6.1 Background 67
6.2 Field Collection 68
6.3 Laboratory Analyses 68
6.3.1 Sediment Contamination 68
6.3.2 Sediment Toxicity 68
6.4 Sediment Contaminant Index Calculations 69
6.4.1 Data Preparation 71
6.4.2 Estuarine Contaminant Index Calculations 72
6.4.3 Great Lakes Contaminant Index Calculations 72
6.5 Sediment toxicity Index Calculations 74
6.5.1 Data Preparation 74
6.5.2 Control-corrected survival 74
6.5.3 Significance tests (estuarine only) 75
6.6 Sediment Quality Index Calculations 76
6.7 References 77
7 Ecological Fish Tissue Contaminant Index 80
7.1 Overview 80
7.2 Field collection and laboratory analysis 81
7.3 Data preparation 81
7.4 Screening Values 82
7.4.1 Receptors of Concern 82
7.4.2 Ecological Risk Assessment Based Approach for Deriving Screening Values 83
7.4.3 Receptor Characteristics: Body Weight and Food Ingestion Rate 84
7.4.4 Wildlife Toxicity Reference Value (TRV) Calculations 85
7.4.5 Screening Values 87
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7.4.6 EPA Tissue-Based Criteria Deriving Selenium Screening Values for Fish 87
7.5 Application of Screening Values for the EFTCI 89
7.6 References 90
8 Human Health Fish Tissue Indicator 92
8.1 Field Fish Collection 92
8.1.1 Whole Fish Samples for Chemical Analyses of Homogenized Fillet Tissue Composite
Samples 92
8.1.2 Fish Tissue Plug Samples for Mercury Analysis 93
8.2 Mercury Analysis And Human Health Fish Tissue Benchmark 93
8.3 PCB Analysis And Human Health Fish Tissue Benchmarks 98
8.4 PFAS Analysis And Human Health Fish Tissue Benchmark 99
8.5 References 101
9 Enterococci Indicator 103
9.1 Field Collection 103
9.2 Lab Methods 103
9.2.1 Calibration 104
9.3 Analysis of Enterococci Concentrations 104
9.4 References 104
10 Microcystins 105
10.1 Field and Laboratory Methods 105
10.2 Analysis of Microcystin Concentrations 105
10.3 References 105
11 From Analyses to Results 107
11.1 Extent Estimation And Assessment 107
11.1.1 Condition Classes 107
11.1.2 Estimating the Extent for Each Condition 107
11.2 Analyses 108
11.3 Trend Analysis 108
11.4 References 108
APPENDIX A. Ecological Fish Tissue Contaminant Index Background Information 109
A.l Laboratory Endpoints for NCCA EFTCI Contaminants of Concern 109
A.l.l Arsenic, Inorganic 109
A. 1.2 Cadmium 109
A. 1.3 Chlordane, Total 110
A. 1.4 DDT, Total 110
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A.1.5 Dieldrin 110
A. 1.6 Endrin, Total Ill
A. 1.7 Endosulfan, Total Ill
A. 1.8 Heptachlor 112
A. 1.9 Hexachlorobenzene 112
A.1.10 Lindane 112
A. 1.11 Mercury (Methylmercury) 113
A.1.12 Mirex 113
A.1.13 Polychlorinated Biphenyls (PCBs), Total 114
A.1.14 Selenium 114
A.2 Uncertainties/Limitations 135
A.2.1 Body Weight 135
A.2.2 High Food Ingestion Rate 135
A.2.3 Ingestion TRVs 135
A.2.4 Contaminant Exposure 135
A.2.5 Constituent Mixtures 136
A.2.6 Chlordane Dietary Exposure to Fish 136
A.3 Generalized Receptor of Concern Groupings 137
A.4 Fish Species Analyzed for Contaminants 138
A. 5 References 141
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List of Figures
Figure 3.1 Site Evaluation Questions 40
Figure 4.1 Points from M-AMBI factor analysis for each site are projected onto a pollution gradient
based upon reference (High) and degraded (Bad) anchor points to obtain an M-AMBI Score
ranging from 0 to 1 for each site 47
Figure 5.1 Summary of eutrophication index components 53
Figure 5.2 Turbidity classes used for water clarity condition rating 58
Figure 5.3 Basin boundaries of the Great Lakes 61
Figure 5.5 Determining Water Clarity in the Great Lakes 63
Figure 6.1 Summary of sediment quality index components 67
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List of Tables
Table 1.1 NCCA 2015 data files available on NARS and other EPA websites 15
Table 2.1 Sample collection success (percentage of expected sites sampled) in estuaries and the Great
Lakes in 2010 and 2015 20
Table 2.2 Benthic taxonomy performance measure, by NCCA Region 24
Table 3.1 Strata by State 31
Table 3.2 Site Selection Summary by State and Type of Site for the Estuarine Survey 33
Table 3.3 Number of Sites by NCCA Reporting Region 33
Table 3.4 Site Selection Summary by State and Type of Site for Great Lakes Survey. Number of
nearshore sites by state for base sample: 36
Table 3.5 Nearshore Site Selection Distribution by Great Lake 36
Table 3.6 Embayment Site Selection Summary by State and Type of Site 39
Table 3.7 Evaluation Status of Dropped Sites 41
Table 4.1 Sediment grab sampler type, surface area and location used 44
Table 4.2 M-AMBI salinity zones 45
Table 4.3 Reference (High) and degraded (Bad) benchmarks for each salinity zone/grab area
combination used in factor analysis to calculate M-AMBI scores 47
Table 4.4 Benchmarks for NCCA estuarine benthic index (M-AMBI) 48
Table 4.5 M-AMBI contingency table 48
Table 4.6 Trophic classifications of oligochaete species1 49
Table 4.7 Benchmarks for NCCA Great Lakes benthic index (OTI) 50
Table 4.8 OTI contingency table 50
Table 5.1 Laboratory methods for water chemistry analyses 54
Table 5.2 Estuarine indicator benchmarks for Dissolved Inorganic Phosphorus (DIP) 56
Table 5.3 Estuarine indicator benchmarks for Dissolved Inorganic Nitrogen (DIN) 56
Table 5.4 Estuarine indicator benchmarks for Chlorophyll a (CHLA) 56
Table 5.5 Estuarine indicator benchmarks for Dissolved Oxygen (DO) 56
Table 5.6 Estuarine indicator benchmarks for water clarity 58
Table 5.7 Rules for determining Eutrophication index condition at estuarine sites 59
Table 5.8 Eutrophication index contingency table (estuaries) 59
Table 5.9 Estuarine benchmarks for total nutrients derived from 2010 concentrations 60
Table 5.10 Great Lakes indicator benchmarks for Total Phosphorus (TP) 61
Table 5.11 Great Lakes indicator benchmarks for Chlorophyll a (CHLA) 62
Table 5.12 Great Lakes indicator benchmarks for Dissolved Oxygen (DO) 62
Table 5.13 Great Lakes indicator benchmarks for water clarity 62
Table 5.14 Rules for determining Eutrophication Index condition at Great Lakes sites 64
Table 5.15 Great Lakes eutrophication index contingency table 64
Table 5.16 Great Lakes quartile-based benchmarks for total nutrients, derived from ranked 2010
concentrations 65
Table 6.1 Laboratory methods for sediment analyses 69
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Table 6.2 Estuarine Sediment Quality Guidelines used in calculating the mERM-Q and LRM Pmax. .70
Table 6.3 Great Lake sediment quality guidelines used in calculating the mPEC-Q 71
Table 6.4 Benchmarks for NCCA sediment contaminant index (SCI) 73
Table 6.5 Estuarine sediment contaminant index contingency table 73
Table 6.6 Great Lakes sediment contaminant index contingency table 74
Table 6.7 Summary of excluded sediment toxicity test results 74
Table 6.8 Benchmarks for NCCA sediment toxicity index (STI) 75
Table 6.9 Estuarine sediment toxicity index contingency table 76
Table 6.10 Great Lakes sediment toxicity contingency table 76
Table 6.11 Benchmarks for NCCA sediment quality index 76
Table 6.12 Estuarine sediment quality index contingency table 76
Table 6.13 Great Lakes sediment quality index contingency table 77
Table 7.1 Laboratory methods for fish tissue contaminant analysis Error! Bookmark not defined.
Table 7.2 Summary of generalized receptor body weights and daily food ingestion rates used to
calculate screening fish tissue values 84
Table 7.3 NOAELtest values (for use in Equation 7-3 to calculate TRVwiiaufe) for contaminants or
contaminant classes calculated for each generalized ROC 85
Table 7.4 Wildlife TRVs for each contaminant and generalized ROC, based upon NOAELS reported
in Table 7.3. (for use in Equation 7-4 to calculate screening values) 86
Table 7.5 EPA-derived trophic transfer factor (TTF) values presented in the Aquatic Life Ambient
Water Quality Criterion for Selenium — Freshwater (USEPA 2016b; see Table 3.11) and used
to derive the median TTF for piscivorous fish 88
Table 7.6 NCCA ecological risk-based screening values for receptors of concern 89
Table 7.7 Application of SV in the NCCA ecological fish tissue contaminant assessment. The result is
an EFTCI for each site surveyed 90
Table 8.1 NCCA 2015 Great Lakes Human Health Fish Composite Sample Species for Homogenized
Fillet Analyses (All species were appropriate for human health objectives) 94
Table 8.2 NCCA 2015 Fish Plug Species for Mercury Analysis 94
Table 8.3 Percentages of Total PCB Human Health Fish Tissue Benchmark Exceedances 99
Table 8.4 Percentages of PFOS Human Health Fish Tissue Benchmark Exceedances 100
Table 8.5 2015 Great Lakes Human Health Fish Fillet Tissue Study PFAS Fillet Composite Data ....101
Table A. 1.1 Summary of literature values for arsenic, inorganic 115
Table A. 1.2 Summary of literature values for cadmium 116
Table A. 1.3 Summary of literature values for chlordane, total 117
Table A. 1.4 Summary of literature values for DDT, total 118
Table A.1.5 Summary of literature values for dieldrin 120
Table A.1.6 Summary of literature values for endrin, total 122
Table A.1.7 Summary of literature values for endosulfan, total 124
Table A.1.8 Summary of literature values for hexachlorobenzene 126
Table A.1.9 Summary of literature values for lindane 128
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Table A.1.10 Summary of literature values for mercury (methylmercury) 129
Table A. 1.11 Summary of literature values for mirex 131
Table A. 1.12 Summary of literature values for polychlorinated biphenyls (PCBs) 133
Table A.1.13 Summary of literature values for selenium 134
Table A.3.1 Minimum and maximum body weights and derived food ingestion rates for select
receptors of concern commonly used in ecological risk assessments 137
Table A.4.1 Fish species analyzed for contaminants from estuarine sites. Number of sites from which
each species was submitted, by NCCA region 138
Table A.4.2 Fish species analyzed for contaminants from the Great Lakes 140
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List of Acronyms
AUF
Area Use Factor used to calculate exposure concentration in EFTCI
AV
Assistance Visit
BW
Body Weight
CCE
Calibrator Cell Equivalent
CHLA
Chlorophyll a
DIN
Dissolved Inorganic Nitrogen
DIP
Dissolved Inorganic Phosphorus
DO
Dissolved Oxygen
EFTCI
Ecological Fish Tissue Contaminant Index
EPA
Environmental Protection Agency
FIR
Food Ingestion Rate
FOM
Field Operations Manual
CRTS
Generalized Random Tessellation Stratified
HQ
Hazard Quotient
IAC
Internal Amplification Control
IM
Information Management
LOAEL
Lowest Observed Adverse Effect Level
LOM
Laboratory Operations Manual
LRM
Logistic Regression Model
M-AMBI
multivariate AZTI Marine Biotic Index
MDL
Method Detection Limit
mERM-Q
mean Effects Range-Median Quotient
mPEC-Q
mean Probable Effects Concentrations Quotient
MQOs
Measurement Quality Objectives
NARS
National Aquatic Resource Surveys
NCA
National Coastal Assessment
NCCA
National Coastal Condition Assessment
NOAEL
No Observed Adverse Effect Level
OTI
Oligochaete Trophic Index
PAHs
Polycyclic Aromatic Hydrocarbons
PAR
Photosynthetically Active Radiation
PBS
Phosphate Buffer Solution
PCBs
Polychlorinated Biphenyls
PDE
Percent Difference in Enumeration
PFAS
Per- and Polyfluoroalkyl Substances
PTD
Percent Taxonomic Disagreement
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QA
Quality Assurance
QAPP
Quality Assurance Project Plan
QC
Quality Control
qPCR
quantitative Polymerase Chain Reaction
QRG
Quick Reference Guide
ROC
Receptors of Concern
SAV
Submerged Aquatic Vegetation
S:N
Signal to Noise ratio
SCI
Sediment Contaminant Index
SEG
Site Evaluation Guidelines
SPC
Sample Processing Control
SQG
Sediment Quality Guideline
SQI
Sediment Quality Index
STI
Sediment Toxicity Index
sv
Screening Value
TN
Total Nitrogen
TP
Total Phosphorus
TRV
Estimated Wildlife Toxicity Reference Value
TSC
Target Sequence Copies
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1 Introduction
The National Coastal Condition Assessment 2015 Report (USEPA 2021) presents an overview and
results of the sampling effort undertaken by the U.S. Environmental Protection Agency (USEPA)
and its state partners during the National Coastal Condition Assessment (NCCA) 2015. NCCA
provides information on the ecological condition of the nation's estuaries1 and the nearshore waters
of the Laurentian Great Lakes, both on a national and a regional scale. It summarizes change in
conditions in estuaries from the precursor National Coastal Assessment (NCA) conducted from
2004-2006 and the NCCA 2010, and changes in conditions in the nearshore waters of the Great
Lakes from the NCCA 2010. This technical support document provides details on the quality
assurance measures and analyses techniques for the survey. The objectives of the NCCA are to
determine:
• Condition of Coastal Waters. What is the condition of the nation's estuarine and Great Lakes
nearshore waters?
o Estimate, with a margin of error of ± 5%, the proportion of area of the nation's
estuarine waters in good, fair or poor conditions, with 95% confidence,
o Estimate, with a margin of error of ± 5%, the proportion of all Great Lakes nearshore
waters in good, fair or poor conditions, with 95% confidence,
o Estimate with a margin of error of ± 15% the proportion of NCCA regional estuarine
waters in good, fair or poor conditions, with 95% confidence,
o Estimate with a margin of error of ± 15%, the proportion of each Great Lake
nearshore waters in good, fair or poor conditions, with 95% confidence.
• Change over time. Are conditions in our coastal waters getting better, worse or staying the
same?
• Impact of stressors on aquatic and estuarine life. How widespread are major pollutants and
other stressors that affect estuarine and Great Lakes nearshore waters?
1.1 Additional Resources for Survey Operations
A series of protocols were used to ensure consistency throughout the survey operations. The
following documents provide the field sampling methods, laboratory procedures, quality assurance
measures, and site selection guidelines for the NCCA 2015.
• U.S. EPA. 2015. National Coastal Condition Assessment: Field Operations Manual. EPA-
841-R-14-007. Washington, D.C. (FOM, USEPA 2015a)
• U.S. EPA. 2015. National Coastal Condition Assessment: Laboratory Operations Methods
Manual. EPA-841-R-14-008. Washington, D.C. (LOM, USEPA 2015b)
• U.S. EPA. 2015. National Coastal Condition Assessment: Quality Assurance Project Plan.
EPA-841-R-14-005. Washington, D.C. (QAPP)
1 While areas where riverine water meets the Great Lakes are referred to as freshwater estuaries, the National Coastal
Condition Assessment uses "estuary" to refer exclusively to areas where rivers meet saltwater.
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• U.S. EPA. 2015. National Coastal Condition Assessment: Site Evaluation Guidelines. EPA-
841-R-14-006. Washington, D.C. (SEG, USEPA 2015d)
1.2 Additional Report Materials
Data collected during the NCCA 2015 (Table 1.1) are available to download from the National
Aquatic Resource Surveys (NARS) website (https://www.epa.gov/national-aauatic-resource-
surveys/data-national-aauatic-resource-surveys). Data collected in conjunction with the NCCA
2015 as part of the Great Lakes Human Health Fish Fillet Tissue Study are available to download
from the study's website (https://www.epa.gov/fish-tech/2015-great-lakes-human-health-fish-
tissue-studv). Underwater video files recorded in the Great Lakes during the NCCA 2015 are
available online fgtspub.epa.gov/NCCA/). Condition results for the estuarine study area, the Great
Lakes study area, and additional subpopulations are available to view in the NCCA online data
dashboard (https: / /coastalcotiditioti.epa.gov /).
Table 1.1 NCCA 2015 data files available on NARS and other EPA websites
ncca2015_algx_data
ncca2015_bentCnt_data
ncca2015 benthicTaxa data
Algal toxin data
Benthic invertebrate count data
Benthic invertebrate taxonomy data
ncca2015_ente_data
ncca2015_fplg_data
ncca2015_ftis_data
ncca2015_hydroprofile_data
ncca2015_indicesCondition_data
ncca2015_micx_data
ncca2015_secchi_data
ncca2015_sedChem_data
ncca2015_sedtoxControlRep_data
ncca2015_sedtoxControlSummary_data
ncca2015_sedtoxSampleRep_data
ncca2015_sedtoxSampleSummary_data
ncca2015_sitedata_data
ncca2015_waterChem_data
ncca2015 wide fishcollection data
Enterococci data
Mercury concentration in fish fillet plug data
Contaminant concentration in whole fish data
Hydrographic profile data
Indicator condition data
Microcystin data
Secchi depth data
Sediment contaminant data
Sediment toxicity control replicate data
Sediment toxicity control data summary
Sediment toxicity sample replicate data
Sediment toxicity sample data summary
Site data
Water nutrient and chlorophyll a data
Fish collection data
ncca2015_GreatLakes_Phytoplankton_data.xlsx Great Lakes phytoplankton data
2015 Great Lakes Human Health Fish Fillet
Tissue Study data
Mercury data, PCB Data PFAS data,
Dioxin/Furan data, Fatty Acids data,
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1.3 References
U.S. Environmental Protection Agency (USEPA). 2021. National Coastal Condition Assessment 2015
Report. Office of Water and Office of Research and Development. EPA-841-R-21-001.
Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015a. National Coastal Condition Assessment:
Field Operations Manual. EPA- 841-R-l4-007. Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015b. National Coastal Condition Assessment:
Laboratory Operations Methods Manual. EPA-841-R-l4-008. Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015c. National Coastal Condition Assessment:
Quality Assurance Project Plan. EPA-841-R-14-005. Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015d. National Coastal Condition Assessment:
Site Evaluation Guidelines. EPA-841-R-14-006. Washington, D.C.
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2 Quality Assurance
NCCA implemented measures to assess the quality of its operations and data throughout the
survey. This chapter documents NCCA's adherence to the requirements of EPA's quality system
described below. The following sections describe quality assurance for the statistical survey design,
field operations, laboratory measurements, data management, and report preparation. These quality
assurance measures are vital to conducting a national scale survey and allow for comparable data to
be collected across the country.
2.1 Introduction
The EPA quality system incorporates a national consensus standard for quality systems authorized
by the American National Standards Institute (ANSI) and developed by the American Society for
Quality Control (ASQC, ANSI/ASQ E4-2004, Quality Systems for Environmental Data and
Technology Programs — Requirements with Guidance for Use). EPA Order CIO 2105.0, dated May
5, 2000, requires all of its component organizations to participate in an agency-wide quality system.
The EPA Order also requires quality assurance project plans or "equivalent documents" for all
projects and tasks involving environmental data.
In accordance with the EPA order, the Office of Water (OW) developed the Office of Water
Quality Management Plan (QMP; USEPA 2015e) to describe OW's quality system that applies to all
water programs and activities collecting or using environmental data. As required by the EPA Order
and OW QMP, NCCA developed and abided by its Quality Assurance Project Plan (QAPP;
USEPA 2015c) throughout the survey. The NCCA QAPP contains elements of the overall project
management, data quality objectives, measurement and data acquisition, and information
management. Any data excluded for not meeting QC requirements are noted in each indicator
section of this document.
The QAPP and its companion documents (Field Operation Manual (USEPA 2015a), Laboratory
Operations Manual (USEPA 2015b) and Site Evaluation Guidelines (USEPA 2015d) describe
detailed procedures for implementing the field and lab work for the survey (see Section 1.1):
The four documents together address all aspects of NCCA's data acquisition and evaluation. The
Laboratory Operations Manual (LOM; USEPA 2015b) also lists measurement quality objectives
(MQOs) used to evaluate the level of quality attainment for individual survey metrics.
Every person involved in NCCA was responsible for abiding by the QAPP (USEPA 2015c) and
adhering to the procedures specified in its companion document in order for comparable data to be
collected by different field and laboratory personnel. Moreover, every NCCA participant was
trained in the requirements applicable to the person's role in the survey. For example, field crews
were trained in the Field Operations Manual (FOM; USEPA 2015a) procedures and applicable
QAPP requirements by attending a combined classroom and hands-on training in field procedures.
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2.2 Survey Design
The NCCA survey design was based upon statistical concepts that are well accepted by the
scientific community. As described in the following sections, the survey design quality objectives
were met by requirements of the statistical design, completeness of implementing the design, and
consistency with established procedures. By applying the statistical concepts of this design, the
survey was able to meet the following overarching data quality objectives:
• Estimate, with a margin of error of ± 5%, the proportion of the nation's estuarine waters in
good, fair or poor conditions, with 95% confidence.
• Estimate, with a margin of error of ± 5%, the proportion of all Great Lakes nearshore waters
in good, fair or poor conditions, with 95% confidence.
• Estimate with a margin of error of ± 15% the proportion of NCCA regional estuarine waters
in good, fair or poor conditions, with 95% confidence.
• Estimate with a margin of error of ± 15%, the proportion of each Great Lake nearshore waters
in good, fair or poor conditions, with 95% confidence.
2.2.1 Statistical Design
The population surveyed for NCCA is the area of estuarine and Great Lakes nearshore waters of
the contiguous United States. Surveying a population of this size presents logistical and resource
challenges that are overcome by using a probabilistic survey design. An extensive body of statistical
literature supports making statements about large populations by sampling representative sites
(Kish 1965). Sample surveys have been used in a variety of fields (e.g., monthly labor estimates) to
determine the status of populations of interest, especially if the population is too numerous for a
complete census or if a census is unnecessary to reach the level of precision desired for describing
the population's status. In natural resource fields, probability sampling surveys have often been
used to estimate the conditions of the entire population. For example, the National Agricultural
Statistics Survey conducted by the U.S. Department of Agriculture and the Forest Inventory
Analysis conducted by the Forest Service (Bickford et al. 1963, Hazard and Law 1989) both use
probability-based sampling to monitor and estimate the condition and productivity of agricultural
and forest resources. To select the sites for the survey, NCCA used a peer-reviewed (Stevens 1994,
Stevens and Olsen 1999) probability design based on the fundamental requirement of an explicitly
defined regional resource population, wherein the sample is constrained to reflect the spatial
dispersion of the population.
2.2.2 Completeness
To ensure that the implementation of the NCCA sample design resulted in adequate measurements,
the survey included completeness requirements for field sampling and laboratory analyses. The
QAPP requires that valid data for individual indicators be acquired from enough sites to make
subpopulation estimates with a specified level of confidence or sampling precision (QAPP estimate
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was 90% of planned sampling locations (or X-sites)). Samples were successfully collected at most
sites. See Table 2.1
Crews were not able to collect some sample types at all sites for various reasons. For example,
sediment contaminant and toxicity, as well as benthic macroinvertebrate sample collection rates
may have been hampered by bedrock substrates without sediment (for which no sample was
possible) or that were too hard or too soft to obtain a successful grab. In the Great Lakes, presence
of invasive mussel beds may have also prevented successful sediment or benthic macroinvertebrate
sample collection. Fish tissue sample collection success was subject to the movement of fish and
availability of suitable fish habitat surrounding the X-site. Mercury in fish fillet sample collection
success was lower than ecological fish tissue sample collection success because the human health
target species list was more restrictive and subject to a minimum size requirement in order to be
used for analysis. EPA identified ways to improve sampling success including emphasizing the
importance of collecting all samples during field crew training, increasing the radius around the
designated X-sites from which samples may be collected (e.g., for fish tissue), and requiring crews
to attempt to sample more times at a site and document reasons for missing samples. These
improvements in the 2015 NCCA sampling efforts led to across-the-board increases in sampling
success in both estuaries and the Great Lakes. While collection success for some samples didn't
reach 90% of planned sites, enough samples were collected to achieve statistical significance in
making population estimates. Missing samples contribute to the area estimated as "unassessed" for
each indicator.
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Table 2.1 Sample collection success (percentage of expected sites sampled) in estuaries and
the Great Lakes in 2010 and 2015
Design
Sample Type
2010
2015
Estuaries
Benthic macroinvertebrates
94%
97%
Dissolved inorganic nutrients
99%
100%
Enterococci*
100%
Ecological fish tissue samples
80%
87%
Fish fillet samples for Hg*
83%
Microcystin in water*
100%
Sediment contaminant
93%
97%
Sediment toxicity
93%
97%
Chlorophyll a
99%
100%
Total N & P
99%
100%
Great
Lakes
Benthic macroinvertebrates
79%
81%
Dissolved inorganic nutrients
98%
100%
Enterococci*
100%
Ecological fish tissue samples
68%
85%
Fish fillet plug samples for Hg*
81%
Microcystin in water*
100%
Sediment contaminants
78%
81%
Sediment toxicity
73%
80%
UW video footage**
75%
97%
Chlorophyll a
98%
100%
Phytoplankton* *
96%
100%
Total N & P
98%
100%
Fish homogenized fillet samples for Hg, PCBs and PFAS
100%
100%
* Enterococci, microcystin and Hg analysis in fish fillets were introduced to the NCCA in 2015.
** Underwater video footage and phytoplankton are collected in the Great Lakes only.
2.2.3 Comparability
Comparability is defined as the confidence with which one data set can be compared to another
(Stanley and Verner, 1985; Smith et al., 1988). For all indicators, NCCA ensured comparability by
the use of standardized sampling procedures, sampling equipment and analytical methodologies by
all sampling crews and laboratories. For all measurements, reporting units and format are specified,
incorporated into standardized data recording forms, and securely transferred into a centralized
information management system. Because EPA used the same comparable methods measures to
collect data in the NCA in 2005-06 and the first NCCA in 2010, the data can be compared across
those studies. The following sections on field and laboratory operations describe additional
measures to ensure consistency in NCCA.
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2.3 Quality Assurance in Field Operations
The requirements and methods presented in the Field Operations Manual (FOM) ensured that
quality objectives were attainable and survey activities were manageable. As described below,
NCCA tested its FOM, trained crews using the FOM and visited crews during the field season.
2.3.1 Field Method Pilot Testing
Representatives from the NCCA team, logistics and data management contractors, and state
partners tested sampling methods, paper and electronic field forms, and field equipment described
in the FOM. The test run assessed the accuracy and clarity of the FOM's instructions for executing
the procedures and quality assurance practices. The test run also evaluated sampling logistics,
sample preparation, and sample shipping instructions. As a result of lessons learned during the test
run, NCCA staff amended and improved the FOM prior to field crew training.
2.3.2 Training of Field Trainers and Assistance Visitors
Before training field crews, members of the NCCA team, oversight staff, contractor trainers, and
other experts tested the training materials during intensive classroom and hands on training
sessions. This "train-the-trainer" event served two primary purposes. First, the event was designed
to make sure that all trainers understood the methods and provided consistent instruction to field
crews. Second, it provided another opportunity to ensure that the field documents and forms were
clear and accurate. During this training event, the attendees tested the materials to ensure that the
instructions were correct and easy to execute, and they practiced training the methods. The training
materials included the FOM, Quick Reference Guide (QRG), field forms and PowerPoint
presentations. As a result of the training, practice training sessions and expert discussions, NCCA
staff amended and improved training materials, the FOM and the QRG before the field crew
training.
2.3.3 Field Crew Training
To ensure consistency across field crews, all field crews leads and their alternates were required to
attend a 2-3-day training session prior to visiting any field site. Led by NCCA trainers, regional field
crew training consisted of classroom and field-based sessions. The session topics included
conducting site reconnaissance; recording field observations and in situ water quality measurements;
collecting field samples; preparing, packing and shipping sample containers; and use of the
standardized field forms. The field crew leaders were taught to review every form and verify that all
hand-entered data were complete and correct.
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2.3.4 Field Assistance Visits
To further assist the crews in correctly implementing the field procedures and quality steps, a
NCCA team member or contractor trainer visited every NCCA field crew during the field season.
These visits, known as assistance visits (AV), provided an opportunity to observe field crews in the
normal course of a field day, assist in correctly applying the procedures, and document the crew's
adherence to sampling procedures. If circumstances were noted where a field crew was not
conducting a procedure properly, the observer recorded the deficiency, reviewed the appropriate
procedure with field team, and assisted the field crew until their technique conformed with
expectations.
2.3.5 Revisits of Selected Field Sites
Useful metrics and indicators tend to have high repeatability. That is, among-site variability will be
greater than sampling variability based on repeat sampling at a subset of sites. To evaluate within-
year sampling variability, the NCCA design required crews to revisit 10 percent of the sites. These
sites were sampled twice during the NCCA index period. To quantify repeatability between first and
second visits, NARS uses one of two metrics, either signaknoise (S:N) or contingency tables.
Signaknoise is defined as the ratio of variance associated with different sites (signal) to the variance
associated with repeated visits to the same site (noise) (Kaufmann et ah 1999). It is used to
determine the repeatability of parameters or indices that produce a continuous numerical result. For
indices that produce a categorical result (i.e., Good, Fair or Poor), contingency tables are used to
visualize agreement between condition ratings for the first and second visits. When calculating the
S:N ratio, all sites are included in the signal, whereas only the second visit to revisit sites contribute
to the noise component. Metrics with high S:N are more likely to show consistent results.
Contingency tables provide a visual representation of the number of sites that were rated good, fair
or poor for both visits, as well as the sites that showed disagreement between sites, and the
magnitude of that difference (i.e., sites rated good for one visit and poor for the other showed
greater disagreement than those that were either good for one visit and fair for the other or fair for
one visit and poor for the other). Signaknoise ratios and contingency tables are not used to look at
variance for indicators that have primarily non-detects for results. Where applicable, S:N and
contingency tables, are presented in this document with each of the indicators.
2.4 Laboratory Quality Assurance And Quality Control
The NCCA laboratories used standard methods and/or followed the requirements (e.g.,
performance-based objectives) in the Laboratory Operations Manual (LOM). The QAPP identified
the overall quality requirements and the LOM provided methods that could be used to achieve the
quality requirements. If a laboratory used a different method, it still had to meet the QA
requirements as described in the QAPP.
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2.4.1 Basic Capabilities
All laboratories were required to submit documentation of their analytical capabilities prior to
analyzing any NCCA samples. NCCA team members reviewed documentation to ensure that the
laboratories could meet required measurement quality objectives (MQOs; e.g., reporting limits,
detection limits, etc.). National Environmental Laboratory Accreditation Conference (NELAC)
certification, satisfactory participation in round-robin or other quality assurance assessments were
considered acceptable capabilities documentation.
2.4.2 Benthic Macroinvertebrate Identifications
For benthic macroinvertebrate taxonomy, laboratories were required to use the same taxa lists,
conduct regular internal QA checks, and participate in an independent quality check. All
participating laboratories identified organisms using the most appropriate technical literature that
was accepted by the taxonomic discipline and reflected the accepted nomenclature at the time of
the survey. The Integrated Taxonomic Information System (ITIS, https: //www.itis.gov/) was also
used to verify nomenclatural validity and reporting for freshwater species. The World Register of
Marine Species (WoRMS, http: / /www.marinespecies.org/) was used for marine species.
Taxonomic accuracy is evaluated by comparing identifications of the same organisms by
independent primary and secondary laboratories. Each primary laboratory provided the organisms
from 10 percent of its samples (with a minimum of three samples per lab), to a secondary
laboratory for an independent evaluation. EPA, supported by an expert contractor, assessed the
primary and secondary identifications and then held reconciliation calls to allow the taxonomists to
discuss organisms that were identified differently. As part of this process, recommendations and
corrective actions were identified to address inaccurate taxonomic identification, and measurement
objectives were established to ensure the data were of sufficient quality for the NCCA.
The NCCA 2015 resulted in the collection of 1,269 benthic samples, of which 775 were from
estuarine waters and 494 were collected in the Great Lakes. The majority (1,214) of the samples
were processed by EPA's primary contract lab. The remainder were processed by labs contracted to
the states of Maryland and Virginia. The rate of taxonomic error in the NCCA 2015 benthic dataset
was minor, and the data are acceptable for additional analyses. Results of QC analyses are detailed
in the following paragraph.
As approximately 10% of the overall dataset, 127 samples were randomly selected for quality
control re-identification by the secondary laboratory. Comparison of the results of whole sample re-
identifications provided a Percent Taxonomic Disagreement, a measure of taxonomic precision
wherein the number of agreements in identification between a primary taxonomist and a quality
control taxonomist are compared to the number of specimens in a sample (PTD; Equation 2-1,
below). The majority of Great Lakes and estuarine samples were analyzed by the same laboratory;
therefore, the overall mean PTD (10.1%) reflects samples from both populations combined. The
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actual PTD was better than the NCCA measurement objective identified in the QAPP, which
allowed a PTD of 15 percent. Comparison of counts was quantified by calculation of percent
difference in enumeration, a relative measure of count precision within a sample, wherein the
difference in specimen counts in a sample between a primary and QC taxonomist is compared to
the sum of the two counts (PDE; Equation 2-2, below). The overall PDE was 1.5 percent, which
was better than the NCCA measurement objective of 5 percent as identified in the QAPP. See
Table 2.2 for a breakout of PTD and PDE by NCCA Region.
Table 2.2 Benthic taxonomy performance measure, by NCCA Region
PTD PDE
Coastal Region n
Avg
SD
Avg
SD
Estuarine Overall
78
10.4%
10.6%
1.9%
3.7%
Northeast
21
10.5%
8.2%
1.7%
1.4%
Mid-Atlantic
6
3.9%
3.3%
1.6%
1.9%
Southeast
13
7.4%
14.9%
2.6%
6.8%
Gulf of Mexico
25
13.3%
11.6%
2.3%
4.1%
West
13
10.7%
7.3%
0.8%
0.8%
Great Lakes
49
9.6%
11.4%
1.0%
1.3%
PTD = [l - (''omPr°s)] * 100 Equation 2-1
Where compPos is the number of agreements, and N is the total number of individuals in the larger
of the two counts.
pDE = f\Labl-Lab2\\ ^ 1Q() Equation 2-2
V Labl+Lab2 ) n
Even when the measurement objectives were met, laboratories implemented recommendations and
corrective steps. If, for example, it was evident that empty mollusk shells were being identified and
recorded in one or more of the QC samples, the laboratories needed to verify that they had not
counted empty mollusk shells in their other samples.
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2.4.3 Chemical Analyses
For quality assurance of chemical analyses, laboratories used QC samples which are similar in
analyte concentration range to samples being measured. QC samples provide estimates of precision
and bias that are applicable to sample measurements. To ensure the ongoing quality of data during
analyses, every water sample analysis batch was required to include QA samples to verify the
precision and accuracy of the equipment, reagent quality, and other quality measures. These checks
were completed by analyzing blanks or samples spiked with known quantities of reference materials,
duplicate analyses of the same samples, or other appropriate evaluations. The laboratories reported
quality assurance results along with each batch of sample results to the NCCA QA Coordinator for
review for compliance with the data quality objectives in the QAPP. Excursions from the limits of
the data quality objectives were marked or "flagged" for further investigation. In addition,
laboratories reported holding times. Holding time requirements for analyses ensure analytical results
are representative of conditions at the time of sampling. The NCCA team reviewed the data and
noted any quality failures in the data files. The data analysts used the information about quality to
determine whether to include or exclude data from the assessment. QA data for all NCCA data are
stored in the NARS Information Management database and are available for review upon request.
2.4.4 Sediment Toxicity Analyses
Sediment toxicity data were reviewed, and replicates were removed from the analysis if any of the
following situations were met:
• Presence of predatory organisms in a replicate and the replicate percent survival was below
100% (for marine samples only). For freshwater samples, survival is not typically impacted
by predators so percent survival for freshwater samples with predators were accepted for
analysis.
• Large particle size in a replicate and the replicate test percent survival appeared impacted (at
least 50% less than the mean of the other replicates within the sample).
• Additional organisms were present, no organisms were loaded within a test replicate, and/or
the incorrect species was used as the test organism for a sample.
• The laboratory provided mean test percent survival exceeding 100% for a replicate (and
insufficient information on the number of organisms loaded in the replicate).
Note that when a replicate was removed from analysis due to QA/QC concerns, the data associated
with that replicate were not used in calculating control-adjusted survival for the sample, nor were
they used in the significance tests for marine samples. When a sample was removed from analysis
due to QA/QC concerns that impacted the entire sample (or there were an insufficient number of
replicates for that sample), the condition category for the NCCA sediment toxicity index for the site
was set to "Not Assessed."
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2.5 Data Management And Review
Information management (IM) is integral to all aspects of the NCCA from initial selection of
sampling sites through dissemination and reporting of final, validated data. Quality measures
implemented for the IM system are aimed at preventing corruption of data at the time of their
initial incorporation into the system and maintaining the integrity of data and information after
incorporation into the system through reporting and publication of results.
Reconnaissance, field observation and laboratory analysis data were transferred from NCCA survey
participants and collected and managed by the NARS IM center. Data and information were
managed using a tiered approach. First, all data transferred from a field team or laboratory were
physically organized (e.g., system folders) and stored in their original state. Next, NARS IM created
a synthesized and standardized version of the data to populate a database that represented the
primary source for all subsequent data requests, uses and needs. All samples were tracked from
collection to the laboratory to ensure completeness and provide quality assurance for the survey.
The IM staff applied an iterative process in reviewing the database for completeness, transcription
errors, formatting compatibility, consistency issues and other quality control-related topics. This
first-line data review was a joint exercise by NARS IM and the NCCA team. A second-phase data
quality review consisted of evaluating the quality of data based on MQOs as described in the
QAPP. This QA review was performed by the NCCA team using a variety of qualitative and
quantitative analytical and visualization approaches. Data that met the MQOs were used without
restriction. Data that did not meet the MQOs were qualified and further evaluated to determine the
extent to which quality control results deviated from the target MQOs. Minor deviations were
noted and qualified but did not prevent data from being used in analyses. Major deviations were
also noted and qualified, but data were excluded from the analyses. Data quality flags are included in
the data files. Data not used for analyses because of quality control concerns account for a subset of
the missing data for each indicator analysis. The missing data add to the uncertainty in condition
estimates and contribute (along with "missing" data where samples were not collected or for some
other reason couldn't be analyzed) to the "Not Assessed" category in the report.
2.6 NCCA 2015 Report
The NCCA 2015 Report provides a summary of the results from the NCCA. In addition to being
extensively reviewed in-house by the NCCA team, its partners, and other EPA experts, the report
underwent external peer review. This review was the final step in ensuring that the main report and
its findings met the quality requirements of the QAPP. EPA contracted with an outside firm to
conduct an Independent External Peer Review of the main report. The firm selected three peer
reviewers who were experts in water resource monitoring, biological and ecosystem assessments,
and one who is an expert in ecotoxicology. The firm provided the reviewers with a copy of the
main report and the technical report, links to the NCCA Dashboard and a charge that solicited
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comments specifically on the technical content, completeness and clarity, and scientific integrity of
the main report. EPA used the comments from the peer reviewers to refine and review the main
report.
2.7 References
American National Standards Institute and American Society for Quality Control (ANSI/ASQC).
2004. Quality Systems for Environmental Data Collection and Environmental Technology
Programs: Collection and Evaluation of Environmental Data. E4-2004. Milwaukee, WI.
Bickford, C.A., C.E. Mayer, and K.D. Water. 1963. An Efficient Sampling Design for Forest
Inventory: The Northeast Forest Resurvey. Journal of Forestry. 61: 826-833.
Kaufmann PR, Levine P, Robison EG, Seeliger C, Peck DV. 1999. Quantifying Physical Habitat in
Wadable Streams. EPA/620/R_99/003. US Environmental Protection Agency,
Washington, DC.
Kish, L. 1965. Survey Sampling. John Wiley & Sons. New York. 643 pp.
Smith, F., S. Kulkarni, L. E. Myers, and M. J. Messner. 1988. Evaluating and presenting quality
assurance data. Pages 157-68 in L.H. Keith, ed. ACS Professional Reference Book.
Principles of Environmental Sampling. American Chemical Society, Washington, D.C.
Stanley, T.W., and S.S. Verner. 1986. The U.S. Environmental Protections Agency's quality
assurance program, pp. 12-19 IN: J.K. Taylor and T.W. Stanley (eds.). Quality Assurance for
Environmental Measurements. ASTM STP 867, American Society for Testing and
Materials, Philadelphia, Pennsylvania.
Stevens Jr., D. L. 1994. Implementation of a National Monitoring Program. Journal Environmental
Management 42:1-29.
Stevens Jr., D. L., and A. R. Olsen. 1999. Spatially restricted surveys over time for aquatic resources.
Journal of Agricultural, Biological, and Environmental Statistics 4:415-428.
Stoddard JL, Herlihy AT, Peck DV, Hughes RM, Whittier TR, Tarquinio E. 2008. A process for
creating multimetric indices for large-scale aquatic surveys. Journal of North American
Benthological Society 27: 878-891.
Stribling, J.B., S.R. Moulton, and G.T. Lester. 2003. Determining the quality of taxonomic data.
Journal of the North American Benthological Society 22(4):621-631.
U.S. Environmental Protection Agency (USEPA). May 2000. Order CIO 2105.0, Policy and
Program Requirements for the Mandatory Agency-wide Quality System.
U.S. Environmental Protection Agency (USEPA). 2015a. National Coastal Condition Assessment:
Field Operations Manual. EPA- 841-R-l4-007. Washington, D.C.
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U.S. Environmental Protection Agency (USEPA). 2015b. National Coastal Condition Assessment:
Laboratory Operations Methods Manual. EPA-841-R-14-008. Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015c. National Coastal Condition Assessment:
Quality Assurance Project Plan. EPA-841-R-14-005. Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015d. National Coastal Condition Assessment:
Site Evaluation Guidelines. EPA-841-R-14-006. Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015e. Office of Water Quality Management
Plan. EPA-821-F-15-009. Revision 4.0. U.S. Environmental Protection Agency, Office of
Water. Washington, DC.
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3 Selection of Probability Sites
During the summer of 2015, field crews sampled 1,060 probability sites (699 sites in estuaries and
361 sites in Great Lakes nearshore) across the country representing approximately a total of
34,597square miles (27,479 square miles in estuaries and 7,118 square miles in the Great Lakes). A
subset (106) of these probability sites were sampled twice during the index period. Using
standardized field methods, crews sampled estuaries as large as the Chesapeake Bay in the Mid-
Atlantic region and as small as Morro Bay in California during the survey index period (June
through September). Sites were selected using a random sampling technique that uses a probability-
based design that is described in this chapter. The following sections describe the statistical
objectives, target population, sample frame, survey design, evaluation, and statistical analysis.
Details for each site are included in the site information file available to download from the NARS
data webpage https: / /www.epa.gov /nationai-aquatic-resource-surveys /data-national-aauatic-
res on r c e - s u rv ev s.
3.1 Objectives
• Condition of Coastal Waters. What is the condition of the nation's estuarine and Great Lakes
nearshore waters?
o Estimate, with a margin of error of ± 5%, the proportion of the nation's estuarine
waters in good, fair or poor conditions, with 95% confidence,
o Estimate, with a margin of error of ± 5%, the proportion of all Great Lakes nearshore
waters in good, fair or poor conditions, with 95% confidence,
o Estimate with a margin of error of ± 15% the proportion of NCCA regional estuarine
waters in good, fair or poor conditions, with 95% confidence,
o Estimate with a margin of error of ± 15%, the proportion of each Great Lake
nearshore waters in good, fair or poor conditions, with 95% confidence.
• Change over time. Are conditions in our coastal waters getting better, worse or staying the
same?
• Extent of stressors. How widespread are major pollutants and other stressors that affect the
aquatic life in estuarine and Great Lakes Nearshore Waters?
3.2 Estuarine Design
3.2.1 Target Population
The estuarine survey was designed to assess the target population of coastal waters of the United
States from the head-of-salt (0.5 parts per thousand) to confluence with ocean, including inland
waterways and major embayments such as Florida Bay, Cape Cod Bay and San Francisco Bay.
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3.2.2 Sample Frame
The NCCA 2015 sample frame (the GIS construct that is used to represent the target population)
was derived from the prior National Coastal Assessment sample frame developed by the EPA
Office of Research and Development (ORD) Gulf Ecosystem Measurement and Modeling
Division (GEMMD; Formerly Gulf Ecology Division). The GEMMD sample frame was enhanced
as part of the National Coastal Monitoring Network design by including information from NOAA's
Coastal Assessment Framework, boundaries of National Estuary Programs and identification of
major coastal systems. Information on salinity zones for the NCCA 2010 was obtained from
NOAA (Nelson and Monaco 2004). In addition, the NCA sample frames for Delaware Bay,
Chesapeake Bay, Puget Sound, and the state of South Carolina were replaced by GIS layers
provided by organizations within whose jurisdictions they are found. The updated sample frame
ensured that no prior areas in NCA were excluded and any differences were clearly identified in the
new NCCA 2010 sample frame. For the Californian Province excluding San Francisco Bay, the
GEMMD sample frame was changed to match the sample frame used for the NCA 2004 study. In
2015, the sample frame was updated to include information related to 1999-20012 and 2005-2006
NCA sample frames in order to provide the information required to estimate change between these
periods, 2010 and 2015.
3.2.3 Survey Design
The NCCA 2015 estuarine survey design consisted of two independent designs. One design re-
sampled sites sampled during NCCA 2010. The other design selected new sites using essentially the
same survey design used for NCCA 2010. Both survey designs were a stratified design with unequal
probability of selection based on area within each stratum. A Generalized Random Tessellation
Stratified (GRTS) survey design for an area resource was used. The details are given below.
3.2.4 Stratification
The population was first divided into subgroups before sites were selected. Stratification was by
major estuary based on the NOAA Coastal Assessment Framework; NEP estuaries and state. The
strata, listed by state, were:
2 The 1999-2001 data are not included in change estimates because differences in sample frame definitions incorporated for
the 2005-2006 survey reduced the overall area for which comparisons can be made.
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National Coastal Condition Assessment 2015 Technical Report
Table 3.1 Strata by State
State
Maine
New Hampshire
Massachusetts
Rhode Island
Connecticut
New York & New Jersey
Delaware
Maryland & Virginia
North Carolina
South Carolina
Georgia
Florida
Alabama
Mississippi
Louisiana
Texas
California
Oregon
Washington
Stratum5
AP_Casco_Bav, AP_Pcnobscot_Bav, AP_( )thcr_MJ
AP_New_Hampshire_Estuaries
AP_Buzzards_Bay, AP_Massachusetts_Bay, AP_Other_MA
VP_Narragansett_Bay, VP_Other_RI
VP_Long_Island_Sound,
VP_NY_NJ_I I arbor, VP_Pcconic_Bav, VP_()thcr_NY,
VP_NJ_Barnegat_Inland_Bays
VP_l)<.i.i\vaiv_Bav. VIM )thcr_DI i
VIM^hcsapcake_Bav, VIM )thcr_MD, VIM )thcr_V A
CarP_Albemarle_Pamlico_Sounds, CarP_Other_NC
(>arP_S( ]_()PI (>arP_S( ]_(]RI U Us.
CarP_Other_GA
CarP_Indian_River, CarP_Other_FL, WIP_Biscayne_Bay,
WIP_Charlotte_Harbor, WIP_Florida_Bay, WIP_Tampa_Bay,
WIP_Other_FL, LP_Apalachee_Bay, LP_Apalachicola_Bay,
] T_Pensacola_Bav, 1 .P_( )ther_l;],
] ,P_Mc)bilc_Biiv, 1 .P_( )ther_/\],
] ,1M )thcr_MS
LP_West_Mississippi_Sound, LP_Atchafalaya_Vermilion_Bay,
LP_Barataria_Terrabonne, LP_Breton_Chandeleur_Sound,
] T_Mississippi_Rivcr, ] .P_( )ther_] ,A
LP_Coastal_Bend_Bays, LP_Galveston_Bay,
LP_Matagorda_Bay, LP_San_Antonio_Bay, LP_Other_TX
CalP_San_Francisco_Bay, CalP_Other_CA, ColP_Other_CA
ColP_Lower_Columbia_River, ColP_Other_OR
ColP_Puget_Sound, ColP_Other_WA
3 The prefix in each stratum name represent the oceanic province in which the stratum is located: AP = Acadian Province;
VP = Virginian Province; CarP = Carolinian Province; WIP = West Indian Province; LP = Louisianian Province; ColP=
Columbian Province; CalP = Californian Province
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Sites in major estuaries that occur in two states (e.g., Chesapeake Bay, Delaware Bay, and Lower
Columbia River) are not evenly divided between the states. Rather, the sites were assigned to the
state in which they occur. Long Island Sound was assigned to New York as the major polygon was
divided into the portion within each state. Consequently, most Long Island Sound sites were
assigned to New York.
3.2.5 Unequal Probability Categories
Unequal probability categories were created based on area of polygons that subdivide each major
estuary. The number of size categories within a major estuary ranged from 3 to 7. The categories
were used to ensure that sites were selected in the smaller polygons.
Within each stratum, the sample frame for the coastal waters consisted of multiple polygons
associated with subregions of the stratum that are typically smaller estuaries, coastal water regions
or main bays within the stratum. The smaller estuaries are either subregions of large estuaries or
separate small estuaries within the stratum. These subregions (polygons) were categorized by area
and the number of sites within the categories were assigned to ensure that sites were selected in the
smaller subregions. The number of size categories within a stratum ranged from 3 to 7.
3.2.6 Panels
The combined designs have the following panels:
1. Basel0_RVT2: Sites that were sampled in NCCA 2010 that were sampled twice in 2015
2. BaselO: Sites that were sampled in NCCA 2010 that were sampled once in 2015
3. Basel5: New sites that were sampled once in 2015
4. BaselO_OverSamp: Sites from NCCA 2010 that were oversample sites that were only used if
any Basel0_RVT2 or BaselO sites could not be sampled in 2015
5. Basel5_OverSamp: New sites that were oversample sites that were used if any Basel 5 site
could not be sampled in 2015
3.2.7 Expected Sample Size
The planned sample size for NCCA 2015 was 684 unique sites for the conterminous 21 coastal
states. The planned total number of site visits was 750 where 66 sites were sampled twice in 2015.
Of the 684 unique sites, 336 sites were sites that were sampled in NCCA 2010 and 348 were new
sites selected for NCCA 2015. Oversample sites were drawn to be used for replacing sites that were
nontarget (did not meet the definition of target waters) or were not sampleable (e.g., site was unsafe
to sample).
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Table 3.2 Site Selection Summary by State and Type of Site for the Estuarine Survey
Number of
Number of Unique Sites Over Sample
Sites
2010
: 2010 Sites
; Sites i
New
2010
2015
Total
Sampled
: Sampled :
Sites
Total
Nvimber
Over ;
Over
Number
Twice in
• Once in
for
• Unique
of Site
Sample :
Sample
of Sites
State
2015
2015
2015
: Sites
Visits
Sites ;
Sites
Available
AL
2
5
10
17
19
10
13
40
CA
4
26
21
51
55
29
29
109
CT
1
3
0
4
5
4
3
n
DE
0
4
10
14
14
6
9
29
FL
9
37
45
91
100
47
45
183
GA
0
3
5
: 8
8 '
4
4
16
LA
11
i 32 f
43
86
97
43
48
177
MA
5
n
20
36
41
17
21
74
MD
1
15
14
30
31
15
15
60
ME
3
17
20
40
43
23
16
79
MS
0
4
4
8
8
6
6
20
NC
4
15
18
[ 37
41
17
22
76
NH
; 2
4
6
12
14
6
5
23
NJ
3
i 8 :
12
F 23
26
17
7
47
NY
5
14
20
39
44
16
20
75
OR
2
10
7
19
21
9
5
33
RI
2
5
9
16
18
7
7
30
SC
2
: 8
12
22
24
10
12
44
TX
5
20
30
55
60
28
31
114
VA
2
9
11
22
24
12
18
52
WA
3
20
31
54
57
29
31
114
Sum
66
270
348
684
750
355
367
1406
Table 3.3 Number of Sites by NCCA Reporting Region
NCCA Report Region # Base Sites „ ~ Total
Sample Sites
i East Coast 322 336 658
| Gulf Coast 238 254 492
West Const 124 132 256
Total 684 722 1406
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National Coastal Condition Assessment 2015 Technical Report
3.2.8 Site Usage and Replacement
When a "Base" site could not be sampled for any reason, the site was replaced using the following
rules:
1. Basel0_RVT2: When a site in this category could not be sampled it was replaced by the next
available site in the BaselO_OverSamp list within the same state and STRATUM_15 (where
sites are in numerical SITEID_15 order within the state and stratum) and the replacement site
was sampled twice in 2015.
2. BaselO: When a site in this category could not be sampled it would be replaced by the next
available site in the BaselO_OverSamp list within the same state and STRATUM_15 (where
sites are in numerical SITEID_15 order within the stratum).
3. Basel 5: When a site in this category could not be sampled it was replaced by the next available
site in the Basel 5_OverSamp list within the same state and STRATUM_15 (where sites are in
numerical SITEID_15 order within the stratum)
3.3 Great Lakes Nearshore Design
3.3.1 Nearshore Target Population
The Great Lakes survey was designed to assess conditions in nearshore waters of the Great Lakes
of the United States and Canada. However, the 2015 NCCA Great Lakes assessment was restricted
to the United States portion so only sites drawn in the United States were evaluated and sampled.
The nearshore zone is defined as the region from shoreline to 30 m depth within 5 km from
shoreline. The Great Lakes include Lake Superior, Lake Michigan, Lake Huron, Lake Erie, and
Lake Ontario.
3.3.2 Nearshore Sample Frame
The Great Lakes nearshore sample frame was first developed for the 2010 NCCA from existing
standard GIS vector shoreline coverage from NOAA (USEPA 2015d; Kelly et al. 2015). That
coverage was modified to include a coverage extension 500 m upstream into river mouths and to
add embayment areas missing from the existing shoreline coverage.
The 2015 Great Lakes NCCA nearshore sample frame was developed by the USEPA's Office of
Research and Development (ORD) Great Lakes Toxicology and Ecology Division (GLTED;
formerly Mid-Continent Ecology Division; MED). The nearshore includes river mouths and
estuaries, embayments, and open waters adjacent to the US shorelines. It does not include the
connecting channels of the Great Lakes (water bodies between lakes plus the upper St. Lawrence
River).
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National Coastal Condition Assessment 2015 Technical Report
3.3.3 Survey Design
The survey design consists of two independent designs. One design re-sampled sites sampled
during NCCA 2010 Great Lakes assessment. The other design selects new sites using the same
survey design used for NCCA 2010. Both designs use a Generalized Random Tessellation Stratified
(GRTS) survey design for an area resource.
3.3.4 Stratification
Both designs were stratified by Great Lake and country.
3.3.5 Unequal Probability Categories
Both designs use unequal probability categories where the categories are based on states or province
within each Great Lake and the expected sample size is proportional to state shoreline length within
each stratum.
3.3.6 Panels
The combined designs had the following panels:
1. Basel0_RVT2_FT: Sites sampled in NCCA 2010 thatwere sampled twice in 2015 and once
for Fish Tissue study
2. Basel0_FT: Sites sampled in NCCA 2010 thatwere sampled once in 2015 and for Fish Tissue
study
3. BaselO: Sites sampled in NCCA 2010 thatwere sampled once in 2015 and not for Fish Tissue
study
4. Basel5_RVT2: New sites in Canadian4 portion of the design thatwere to be sampled twice in
2015
5. Basel5_FT: New sites thatwere sampled once in 2015 and for Fish Tissue study
6. Basel5: New sites thatwere sampled once in 2015 and not for Fish Tissue study
7. BaselO_OverSamp: Sites from NCCA 2010 thatwere oversample sites thatwere only used if
any Basel0_RVT2 or BaselO sites could not be sampled in 2015
8. Basel5_OverSamp: New sites thatwere oversample sites thatwere only used if any Basel 5 site
could not be sampled in 2015
3.3.7 Expected Sample Size
The base sample design assigned 45 sites to the United States portion of nearshore waters of each
of the five Great Lakes for a total of 225 sites (Table 3.4, Table 3.5). Samples in each lake were
4 While sites were drawn in Canada, they were not sampled and the NCCA 2015 estimates are exclusive to waters within
U.S. jurisdiction.
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National Coastal Condition Assessment 2015 Technical Report
allocated among bordering states' waters proportionally by shoreline length. Five sites in each Great
Lake were to be sampled twice in 2015 for a total of 250 site visits. All sites that were intended to
be sampled twice in 2015 are sites that were sampled in 2010 and in most cases were sampled twice
in 2010. Approximately 50% of the sites were sampled in NCCA 2010 and re-sampled in 2015 and
50% were new sites.
Table 3.4 Site Selection Summary by State and Type of Site for Great Lakes Survey. Number of
nearshore sites by state for base sample:
:2010 Sites
Sampled
State
Twice in
2015
(BaselO_
VRVT2)
IL
o
IN
o
MI
n
MN
2
NY
1 7
OH
2
PA
o
WI
3
Svim
25
Nvimber of Unique Sites
2010 Sites
Sampled
Once in
2015
(Base 10)
0
1
45
1
21
10
1
6
85
Nvimber Nvimber
of Unique of Site
Sites visits
Oversample Sites
Total
nvimber
of Sites
Available
New ;
Sites for i
2010 Over
Sample Sites :
2015 Over
Sample
Sites
(Basel5_()
ver Samp)
2015
(Basel 5) :
(Basel()_()v
: er Samp) ;
o
o
1 o
o
1
1
2
3
3
3
1
7
55
111
122
52
52
215
3
6
8
4
5
15
28 r
56
63
27 :
29
112
14
26
f 28
13
13
52
2
3
3
3
1
7
11
20
23
21
13
54
115
225
250
123
115
463
Table 3.5 Nearshore Site Selection Distribution by Great Lake
# Base
# Revisit
Total Site
Great .Lake
Sites
Sites
Visits
! Lake Superior :
45
5
50
Lake Huron
45
5
50
Lake Michigan i
45
5
50
i Lake Erie
45
5
50
! Lake Ontario i
45
5
50
Svim
225
25
250
3.3.8 Site Usage and Replacement
When a "base" site could not be sampled for any reason, the site was replaced using the following
rules:
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National Coastal Condition Assessment 2015 Technical Report
1. Basel0_RVT2_FT: When a site in this category could not be sampled it was replaced by the
next available site in the BaselO_OverSamp list within the same Great Lake and state (where
sites are in SITEID_15 order within the Great Lake and state) and the replacement site should
be sampled twice in 2015. The oversample site was sampled for the fish tissue study.
2. BaselO_FT: When a site in this category could not be sampled it was replaced by the next
available site in the BaselO_OverSamp list within the same Great Lake and state (where sites
are in SITEID_15 order within the Great Lake and state). The oversample site was sampled for
the fish tissue study
3. BaselO: When a site in this category could not be sampled it was replaced by the next available
site in the Basel 0_OverSamp list within the same Great Lake and state (where sites are in
SITEID_15 order within the Great Lake and state).
4. Basel 5_RVT2: When a site in this category could not be sampled it was replaced by the next
available site in the BaselO_OverSamp list within the same Great Lake (where sites are in
SITEID_15 order within the Great Lake)
5. Basel5_FT: When a site in this category could not be sampled it was replaced by the next
available site in the Basel 5_OverSamp list within the same Great Lake and state (where sites
are in SITEID_15 order within the Great Lake and state). The oversample site was sampled for
the fish tissue study
6. Basel 5: When a site in this category could not be sampled it was replaced by the next available
site in the Basel 5_OverSamp list within the same Great Lake and state (where sites are in
SITEID_15 order within the Great Lake and state)
3.4 Great Lakes Embayment Design
3.4.1 Embayment Target Population
The target population was embayments within the nearshore waters of the Great Lakes of the
United States.
3.4.2 Embayment Sample Frame
Embayments were defined as indentations of the shoreline for which the width from a line across
the opening of the indentation to the furthest inland point is greater than the width of the opening
and having an area at least as large as that of a semicircle with a diameter equivalent to the width of
the opening (Kelly et al., 2015).
3.4.3 Embayment Survey Design
The survey design consisted of two independent designs. Both designs used a Generalized Random
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National Coastal Condition Assessment 2015 Technical Report
Tessellation Stratified (GRTS) survey design for an area resource. One design re-sampled sites
sampled during NCCA 2010 Great Lakes embayment assessment. The other design selected
additional new sites using the same survey design used for NCCA 2010.
3.4.4 Stratification
A single Great Lake embayment stratum was used.
3.4.5 Unequal Probability Categories
Both designs used unequal probability categories. These unequal probability categories were based
on area of embayments. These categories are represented as (x,y] where the parenthesis indicates
that x is not included, and the bracket indicates that y is included. For example, for 2010 the
categories were (0,50], (50,75] and (75,100] where the area is in square kilometers. For 2015 the
categories were (0,20], (20,30] and (30,40] where the area is in square miles and were selected to
approximately match the 2010 categories. The latter two categories identify two large embayments
while the first category includes the remaining embayments.
3.4.6 Panels
The combined designs had the following panels:
1. Basel0_RVT2: Sites from NCCA 2010 that were re-sampled twice in 2015
2. BaselO: Sites from NCCA 2010 that were re-sampled once in 2015
3. Basel5: New sites that were sampled once in 2015
4. BaselO_OverSamp: Sites from NCCA 210 that were oversample sites that were only used if
any Basel0_RVT2 or BaselO sites could not be sampled in 2015
5. Basel5_OverSamp: New sites that were oversample sites that were only used if any Basel 5 site
could not be sampled in 2015
3.4.7 Expected Embayment Sample Size
The Embayment design expected sample size was 150 sites. Fourteen sites from 2010 Embayment
assessment were sampled twice in 2015. Fifty-four sites from 2010 Embayment assessment were
sampled once in 2015. Sixty-eight new sites were sampled in 2015. This resulted in 136 unique sites
(Table 3.6).
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National Coastal Condition Assessment 2015 Technical Report
Table 3.6 Embayment Site Selection Summary by State and Type of Site
Nvimber of Embayment Sites by state
xt , Nvimber of XT ,
Nvimber ol TT . Nvimber ol Site „ ,
TT . „. Uniqvie . . Oversample Sites
Unique Sites g. visits
Total
nvimber
of
Available
Sites
State
Basel0_
I Base
Base i
Basel0_
Basel 5_
RVT2
i io
15 1
OverSamp
: OverSamp ;
IL
0
: 0
1
1
1
1
0
2
IN "
0
1
1
2
2
3
1
6
MI
10
: 33
39
82
92
: 48
42
172
MN
0
r "2
' 3 1
5
5
0
2 ['
7
NY
1
7
7
15
16
n
9
35
OH
0
1 2
3 ['
5
5
1 2
4
n
PA
1
1
2 :
4
5
3
1
8
WI
2
!' 8
12
22
24
15
9
46
Svim
14
54
68
136
150
83
68
287
3.5 Evaluation Process
To achieve the planned sample size, sites that could not be sampled were replaced with sites from
oversample panels as described in Section 3.4.6. Evaluation Status (EvalStatus) was initially set to
Not Evaluated (NotEval) to indicate that the site had yet to be evaluated for sampling. When a site
was evaluated for sampling, then the EvalStatus for the site was changed to indicate it was
sampleable or, if unsampleable, indicated using a category as listed in Table 3.7. Figure 3.1 shows
the questions addressed during the site evaluation process and acceptable answers. For NCCA
2015, 1,171 design sites were evaluated (799 in estuaries and 372 in the Great Lakes). Of these 1,060
were classified as target (see 3.2.1, 3.3.1, and 3.4.1 for definitions of target waters) and sampled (699
in estuaries and 361 in the Great Lakes), with 106 sites sampled twice (67 in estuaries and 39 in the
Great Lakes). The remaining 111 sites were dropped and replaced for various reasons (Table 3.7).
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National Coastal Condition Assessment 2015 Technical Report
Question 1: Does the site meet the requirements of a target site?
1.
Yes, Target
2.
Maybe, requires on-site evaluation
3.
Maybe, tide too low (return at appropriate time in tidal cycle)
4.
Maybe, mudflat at certain times (return at appropriate time in tidal cycle)
5.
Unable to access site, but clearly is target (e.g., in shipping channel)
6.
Unable to access site, but probably target (e.g., site map indicates target)
7.
Unable to access site, and unable to determine if target
8.
No, Dry
9.
No, Mudflat (permanent)
10.
No, Wetland
11.
No, Great Lakes site is outside of an embayment, greater than 30 m deep, or greater than
5 km from shore.
12.
No, Marine site has salinity <0.5 PPT (freshwater is out of scope except within Great
Lakes)
13.
No, Map Error (X-site is clearly not target, for example: parking lot)
14.
No, Other (explain in comments)
Question 2: Is the site accessible and safe to sample?
Note that responses to the second question reference whether the site
would be sampleable if landowner permission is granted.
1.
Yes, Sampleable
2.
Maybe, Temporarily inaccessible (try again later)
3.
Maybe, Unable to access site; available sources are insufficient to determine if target
4.
No, Equipment related unsampleable (e.g., less than 1 meter in depth).
5.
No, Permanently inaccessible (unable/unsafe to reach site)
6.
No, EPA concurred that site could be dropped because access would require extreme
efforts
Question
3: Has landowner granted permission to access the site?
1.
N/A, public access available
2.
Yes, Landowner granted permission
3.
No, Landowner denied permission
Figure 3.1 Site Evaluation Questions
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National Coastal Condition Assessment 2015 Technical Report
Table 3.7 Evaluation Status of Dropped Sites
Number of sites dropped
Evaluation Category
Reason for Dropping
Estuaries
Great L
Target vs. NonTarget
: Depth_Too_Deep
1
2
Target vs. NonTarget
: Depth_Too_Shallow
12
1
Target vs. NonTarget
Map_Error
7
1
Accessibility and Safety
' No_Access ;
18
4
Target vs. NonTarget
i NonTarget_Other
32
o
Target vs. NonTarget
Target_Other
4
0
Target vs. NonTarget
Target_Presumed
2
0
Accessibility and Safet
Unsafe
22
0
Target vs. NonTarget
i Wetland
2
3
Total Dropped Sites
100
n
3.6 Statistical Analysis
Statistical analysis of the data must incorporate information about the monitoring survey design.
The survey weights in the design file assumed that the survey was implemented as designed. At the
end of the sampling season, EPA statisticians adjusted the weights to account for changes due to
dropping and replacing sites. This weight adjustment process required the statisticians to know
which sites were sampled, which sites were dropped, and if they were dropped, the reason why
(Section 3.5). The NCCA statistical analyses, which were completed using the R package spsurvey
(Kincaid, et al., 2019), accounted for the site weights that are based upon stratifications and unequal
probability selection in the design. The weights are equivalent to the area of the system represented
by each site. Procedures for developing the survey design are available from the Aquatic Resource
Monitoring Web page (https: / /archive.epa.gov /nheerl/arm /web /html /index.html). A statistical
analysis library of functions to do common population estimates in the statistical software
environment R is also available from the webpage. In the NCCA 2015 Site Information data file
(available to download from https: / /www.epa.gov /national-aquattc-resource-surveys /data-
national-aquatic-resource-surveys). the adjusted weights used to calculate national condition
estimates are in the column "WGT SP".
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National Coastal Condition Assessment 2015 Technical Report
3.7 References
Diaz-Ramos, S., D. L. Stevens, Jr, and A. R. Olsen. 1996. EMAP Statistical Methods Manual.
EPA/620/R-96/002, U.S. Environmental Protection Agency, Office of Research and
Development, NHEERL-Western Ecology Division, Corvallis, Oregon.
Horn, C.R. and W.M. Grayman.1993. Water-quality modeling with EPA reach file system. Journal of
Water Resources Planning and Management, 119, 262-74.
Kelly, J. R., P.M. Yurista, M. Starry, J. Scharold, W. Bartsch, and A. Cotter. 2015. Exploration of
spatial variability in nearshore water quality using the first Great Lakes National Coastal
Condition Assessment survey. Journal of Great Lakes Research, 41(4), 1060-1074.
Kincaid, T.M., A.R. Olsen and M.H. Weber. 2019. Spsurvey: Spatial Survey Design and Analysis. R
package version 4.10.
Stevens, D.L., Jr. 1997. Variable density grid-based sampling designs for continuous spatial
populations. Environmetrics, 8:167-95.
Nelson, D.M., and M.E. Monaco. 2000. National overview and evolution of NOAA's Estuarine
Living Marine Resources (ELMR) Program. NOAA Tech. Memo. NOS NCCOS CCMA-
144. 60 p. Available at https://coastalscietice.tioaa.gov/project/estuarine-species-database-
noaa-estuarttie-ltvttig-mart tie-resources-program/
Stevens, D.L., Jr. and A.R. Olsen. 1999. Spatially restricted surveys over time for aquatic resources.
Journal of Agricultural, Biological, and Environmental Statistics, 4:415-428
Stevens, D. L., Jr., and A. R. Olsen. 2003. Variance estimation for spatially balanced samples of
environmental resources. Environmetrics 14:593-610.
Stevens, D. L., Jr., and A. R. Olsen. 2004. Spatially-balanced sampling of natural resources in the
presence of frame imperfections. Journal of American Statistical Association-. 99:262-278.
U.S. Environmental Protection Agency (USEPA). 2015d. National Coastal Condition Assessment:
Site Evaluation Guidelines. EPA-841-R-14-006. Washington, D.C.
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National Coastal Condition Assessment 2015 Technical Report
4 Benthic Macroinvertebrates
4.1 Overview
The NCCA estimates biological condition by assessing the condition of estuarine and Great Lakes
benthic communities.
For estuarine sites in 2015, the NCCA adopted the multivariate AMBI (M-AMBI; Pelletier et al.,
2018). M-AMBI is a new national benthic index that is applicable to estuarine sites across the
country and improves upon the assessment of low salinity environments. M-AMBI was also used to
recalculate biological condition estimates for data collected in the 2005-2006 and 2010 surveys in
order to evaluate change in benthic condition between surveys. The M-AMBI integrates three
metrics of environmental condition: AMBI (AZTI Marine Biotic Index; Borja et al. 2000), the
Shannon Wiener diversity index, and species richness5. AMBI is an abundance-weighted, tolerance
value index that assesses habitat condition based upon the relative abundance of taxa in different
tolerance value groups, similar in concept to the Hilsenhoff Biotic Index (Hilsenhoff 1977) or the
Southern California Benthic Response Index (Smith et al. 2001). M-AMBI uses factor analysis to
combine the three metrics of environmental condition into a single index value. Index values range
from 0 to 1 with lower scores indicating degraded conditions and higher scores indicating good
conditions. M-AMBI is designed to reflect changes in benthic community diversity and the
abundance of pollution-tolerant and pollution-sensitive species. Good sites have a wide variety of
species, including low proportions of pollution-tolerant species and high proportions of pollution-
sensitive species, while poor sites are less diverse and are populated by more pollution-tolerant
species and fewer pollution-sensitive species.
In the Great Lakes, the NCCA assesses benthic community condition using an oligochaete trophic
index (OTI) that is used by State of the Lakes Ecosystem Conference (SOLEC 2007; ECCC and
USEPA 2017). It is based on Howmiller and Scott's (1977) index with subsequent modifications by
Milbrink (1983) and Lauritsen et al. (1985). The OTI is a weighted index based on the classification
of oligochaete species by their known tolerance to organic enrichment (Environment Canada and
USEPA 2014; ECCC and USEPA 2017). OTI scores range from 0 to 3 with lower scores indicating
oligotrophic conditions and higher scores indicating eutrophic conditions. In the NCCA 2015
report, oligotrophic equates to good condition, and eutrophic equates to poor condition.
5 In tidal freshwater habitat, percent oligochaetes is substituted for species richness in the calculation of M-AMBI.
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National Coastal Condition Assessment 2015 Technical Report
Summary of Biological quality components
Benthic index:
Estuarine
• Multivariate AMBI (M-AMBI):
o AMBI
o Shannon diversity 0-T)
o Species richness (or % oligochaetes in tidal freshwater
habitat)*
Great Lakes
• Oligochaete trophic index (OTI)
Figure 4.1 Summary of indices used to estimate biological quality
4.2 Field Collection
Sediment samples were collected using different sediment grab apparatus, as shown in Table 4.1.
Crews sieved the sediment through a 0.5 mm screen, retaining macroinvertebrates, which were
preserved and distributed to laboratories for identification (to the lowest practical taxonomic level)
and enumerated.
Table 4.1 Sediment grab sampler type, surface area and location used
Grab type
Small van Veen or Young-modified
van Veen
Large van Veen
Standard Ponar
Petite Ponar
Ekman Grab
Grab area (m2) Location
0.04
0.1
0.052
0.023*
0.02*
CT, DL, PL, GA, LA, MA, Ml), NC, Ni l,
Nj, NY, RI, VA
CA, ML, OR, WA
AL, IN, IL, MI, MN, MS, NC, NY, OH, PA,
RI, SC. I X. WA, \\ I
IL. I X. VA
TX
Diver-collected
0.063
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National Coastal Condition Assessment 2015 Technical Report
4.3 Data Preparation
Because state crews used various grab apparatus to collect sediment samples (Table 4.1), it was
necessary to standardize the raw count of organism abundance by grab area for each sample.
Standardization for both estuarine and Great Lakes samples used the following formula:
.. , .7 Abundance/grab „
Abundance /m* = — Equation 4-1
grab area * number of grabs 1
4.4 Data Analysis
4.4.1 Estuarine Samples
Estuarine benthic index scores are based on the expectations of best and worst condition in distinct
salinity zones (Table 4.2). Bottom water salinity measurements (from the hydrographic profile data)
were merged with the estuarine benthos dataset. If salinity data were not available for a sample, M-
AMBI could not be calculated and the sample was designated as 'Not Assessed.' All taxa in the
dataset were also matched with M-AMBI tolerance values, hereafter referred to as Ecological
Groups (EG; Gillett et al. 2015; Appendix A https://ars.els-cdn.com/content/image/l-s2.0-
S1470160.X14005287-mmcl .xlsx). For those species without an EG classification, the genus EG
classification, if available, was applied.).
Table 4.2 M-AMBI salinity zones
Salinity zone
Salinity range (ppt)
Tidal freshwater
< 0.5
Oligohaline
> 0.5 and < 5
Mesohaline
> 5 and < 18
Polyhaline
> 18 and < 30
Euhaline
> 30 and < 40
Hyperhaline
> 40
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National Coastal Condition Assessment 2015 Technical Report
4.4.1.1 Multivariate-AMBl (M-AMBI) Index calculations
First, standard benthic community metrics, including total abundance, Shannon Wiener diversity
(H') and species richness (the number of unique species)6, were calculated for each sample.
Species diversity (H') was calculated as follows:
H' = £ pi * In (pj) Equation 4-2
where
Pi = ni/N Equation 4-3
where n is the number of individuals of a given species, i, and N is the total number of species.
Next, the percentage (P) of taxa in each EG was calculated. AMBI was calculated as follows:
AMBI = 0 * Pegi "I" 1- 5 * Pegu "I" 3 * Pegiii "I" 4. 5 * Pegiv "I" 6 * Pegv Equation 4-4
The percentage of uncategorized (i.e., organisms that did not correspond to established EGs) was
also calculated for each sample. If the value of uncategorized taxa exceeded 50%, AMBI (and M-
AMBI) were not calculated and the sample was designated as 'Not Assessed.'
The above metrics were compiled into a .csv file for input into R (R Core Team, 2017). The M-
AMBI factor analysis based on benchmarks in Table 4.3 was calculated using R scripts from
Sigovini et al. (2013). Reference (High) and highly degraded (Bad) anchor points for each salinity
zone/grab size (Table 4.3) are included in the factor analysis and used to create a pollution
gradient (Figure 4.1). The Bad benchmark was the worst possible value for that metric (e.g. AMBI
score of 6, diversity score of 0). The High benchmark was based on the 95th percentile of the data
for a metric that was higher at unimpacted sites (richness, diversity), and the 5th percentile for a
metric that was higher at impacted sites (AMBI, % oligochaetes7). The station values from factor
analysis are projected onto the pollution gradient in Euclidean space (Figure 4.1), producing the
index score of the sample (Muxika et al. 2007). Because the factor analysis is calculated separately
based on habitat and grab size, it allows for the interpretation of benthic samples relative to local-
specific expectations of condition (Pelletier et al. 2018).
6 In the tidal freshwater habitat, percent oligochaetes, the number of oligochaetes divided by the total number of organisms in the sample
multiplied by 100, was substituted for species richness in the calculation of M-AMBI.
7 Based upon data from the 1999 through 2006 National Coastal Assessment.
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National Coastal Condition Assessment 2015 Technical Report
Factor 3
V
n V.
• T% Good =1
factor 2 ¦
\
S
V
\
V
V
'v
\
* Factor 1
V>""'
\
•v
1
i
1
1
i •
i
i
Y
\
V
• V
Hr Bad = 0
\
Pollution Gradient
Figure 4.1 Points from M-AMBI factor analysis for each site are projected onto a pollution
gradient based upon reference (High) and degraded (Bad) anchor points to obtain an M-
AMBI Score ranging from 0 to 1 for each site.
Table 4.3 Reference (High) and degraded (Bad) benchmarks for each salinity zone/grab area
combination used in factor analysis to calculate M-AMBI scores.
Salinity Zone
Final grab
area (m2)
Scale
AMBI
Species
Richness
Diversity
(H1)
Percent
oligochaetes
All
All
Bad
6
0
0
100
i Tidal Freshwater ;
All
High
0.15
1.93
0
: Oligohaline
All
High
0.53
16
2.12
: Mesohaline
All
High
0.85
26
2.48
i Polyhaline
0.03-0.06
High
0.72
44
2.96
: Polyhaline
0.08-0.10
High
0.18
77
3.30
: Euhaline
0.03-0.06
High
0.56
61
3.29
: Euhaline
0.08-0.10
High
0.66
92
3.62
i Hyperhaline
All
High
0.32
55
3.45
47
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National Coastal Condition Assessment 2015 Technical Report
The M-AMBI score output files for each salinity/grab area group were imported into Excel, and
samples were designated as Good, Fair or Poor based on M-AMBI values based on Borja et al.
2007 and Borja et al. 2012; Table 4. 4). These benchmarks were developed and refined through an
extensive process by European Water Framework Directive intercalibration exercises in order to
provide consistent and accurate condition assessment. For NCCA, M-AMBI index benchmarks
were assessed for classification accuracy based on sediment contaminant data, amphipod toxicity,
total organic carbon, and dissolved oxygen concentrations from regional validation datasets (see
Pelletier et al. 2018 for more details).
Table 4.4 Benchmarks for NCCA estuarine benthic index (M-AMBI)
Benthic Index Condition
Condition Estuarine
Good M-AMBI > 0.53
I'air M-AMBI > 0.39 and < 0.53
Poor M-AMBI < 0.39
Variance in the M-AMBI results was evaluated by calculating the signal to noise ratio as described
in Section 2.3.5 and resulted in S:N of 2.970. In addition, the contingency table for the good, fair
and poor rating derived from the M-AMBI scores showed agreement between visits 1 and 2 in 35
of 52 sites. See Table 4.5
Table 4.5 M-AMBI contingency table
Visit 1
Good
Fair
Poor
Good
28
3
1
Visit 2
Fair
9
6
1
Poor
1
2
1
4.4.2 Great Lakes Samples
4.4.2.1 Oligochaete Trophic Index (OTI) Calculations
For Great Lakes samples, benthic community condition was assessed using the oligochaete trophic
index (OTI). All oligochaetes were classified into five groups — the four classes listed below (Table
4.6) and those that were unidentified.
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National Coastal Condition Assessment 2015 Technical Report
Table 4.6 Trophic classifications of oligochaete species1
Group 0
Group 1
Group 2
Group 3
Umnodrilus profundicola
Arcteonais lomondi2
Aulodrilus pluriseta
Umnodrilus hoffmeisteri
Umbriculidae3
Aulodrilus americanus
Umnodrilus angustipenis
Tubifex tubifex4
Rhyacodrilus coccineus
Aulodrilus limnobius
Umnodrilus cervix
Rhyacodrilus montana
Aulodrilus pigueti
Umnodrilus claparedianus
Rhjacodrilus sp.
Dero digitatd
Umnodrilus maumeensis
Spirosperma nikolskyi
Ilyodrilus templetoni
Umnodrilus udekemianus
Stylodrilus heringianus
Isochaetides freji
Votamothrix bedoti
Trasserkidrilus superiorensis
Slavina appendicular
Potamothrix moldaviensis
Trasserkidrilus americanus
Spirosperma ferox
Votamothrix vejdovskji
Tubifex tubifex4
Uncinais uncinate?
Quistadrilus multisetosus
^ased on Environment Canada and the U.S. Environmental Protection Agency (2014). Only species in the
families Naididae and lMmbriculidae are included.
2Species added due to taxonomic reclassification
3A11 immature lMmbriculidae were classified by SOLEC as Stjlodrilus heringianus, so all lMmbriculidae were
classified as Group 0.
^Tubifex tubifex was, assigned to Group 0 or Group 3 according to the relative abundance of Groups 0 and 3,
or the value of c.
The abundance of oligochaete species in each group was calculated for each site, and the OTI was
calculated as:
i2>o+2ni+22n2+32>3
0T' = C' z-o+Z^+Z'i+Z; Equation 4-5
where no, «/, ri2, refer to the total abundance of species in Group 0, 1, 2, 3, respectively, and c
adjusts the ratio to the total abundance of tubificid and lumbriculid oligochaetes (n — number per
m2) as follows:
1
when
n > 3600
0.75
when
1200 < n < 3600
0.5
when
400
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National Coastal Condition Assessment 2015 Technical Report
Tubifex tubifex^j-A?, assigned to Group 0 or Group 3 according to the following rules:
if no/ti3< 0.75 then classified as Group 3
if no /ti3> 1.25 then classified as Group 0;
\£n0/n3 = 0.75-1.25
and if c < 0.5, then classified as Group 0,
or if c > 0.5 then classified as Group 3;
if = 0
and if no is relatively high and/or c is low8, then classified as Group 0,
otherwise classified as Group 3.
The OTI values were classified into Good, Fair, and Poor categories based on benchmarks
developed and validated in Milbrink 1983 and adopted for the State of the Great Lakes reporting
(SOLEC 2007; ECCC and USEPA 2017; See Table 4.7).
Table 4.7 Benchmarks for NCCA Great Lakes benthic index (OTI)
Benthic Index Condition
Condition
Great Lakes
Good
OTI < 0.6
Fair
OTI > 0.6 and < 1
Poor
OTI > 1
Variance in the OTI was evaluated by calculating the signal to noise ratio as described in Section 3.4.5
and resulted in S:N of 4.420. In addition, the contingency table illustrates agreement among 18 good,
fair and poor ratings between the first and second visits at 24 revisit sites (See Table 4.8)
Table 4.8 OTI contingency table
Visit 1
Good
Fair
Poor
Good
6
2
1
Visit 2
Fair
1
3
1
Poor
1
9
8 Note that 'relatively high' no was operationally defined as greater than the average of Group 0 abundance, and 'low* c was defined as
0.25.
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National Coastal Condition Assessment 2015 Technical Report
4.5 References
Borja, A, J. Franco, and V. Perez. 2000. A marine biotic index to establish the ecological quality of
soft-bottom benthos within European estuarine and coastal environments. Marine Pollution
Bulletin AO (12): 1100-1114.
Borja, A., A.B. Josefson, A. Miles, I. Muxika, F. Olsgard, G. Phillips, J. German Rodriguez, and B.
Rygg. 2007. An approach to the intercalibration of benthic ecological status assessment in
the North Atlantic ecoregion, according to the European Water Framework Directive.
Marine Pollution Bulletin 55, 42-52.
Borja, A., J. Mader, and I. Muxika, I. 2012. Instructions for the use of the AMBI software (Version
5.0). ^Levista de investigation Marina. AZTI-Tech. 19, 71—82.
Environment Canada and the U.S. Environmental Protection Agency (USEPA). 2014. State of the
Great Lakes 2011. Cat No. Enl61-3/l-2011E-PDF. EPA 950-R-13-002. Available at
http: / /binational.net
Environment and Climate Change Canada and the U.S. Environmental Protection Agency (ECCC
and USEPA). 2017. State of the Great Lakes 2017 Technical Report. Cat No. Enl61-3/IE-
PDF. EPA 905-R-17-001.
https: / /binational.net/wpcotitent/uploads /2017 /09/S< '017 Technical Report-
EN.pdf
Gillett, D.J., S.B. Weisberg, T. Grayson, A. Hamilton, V. Hansen, E.W. Leppo, M.C. Pelletier, A.
Borja, D. Cadien, D. Dauer, R. Diaz, M. Dutch, J.L. Hyland, M. Kellogg, P.F. Larsen, J.S.
Levinton, R. Llanso, L.L. Lovell, P.A. Montagna, D. Pasko, C.A. Phillips, C. Rakocinski,
J.A. Ranasinghe, D.M Sanger, H. Teixeira, R.F. VanDolah, R.G. Velarde, and K.I. Welch.
2015. Effect of ecological group classification schemes on performance of the AMBI
benthic index in US coastal waters. Ecological Indicators 50: 99-107.
Hilsenhoff, W.L., 1977. Use of Arthropods to Evaluate Water Quality of Streams. Wisconsin
Department of Natural Resources Technical Bulletin, No 100. pp. 1—15.
Howmiller, R.P., and M.A. Scott. 1977. An environmental index based on relative abundance of
oligochaete species. Journal of the Water Pollution ControlFederation 49:809-815.
Lauritsen, D.D., S.C. Mozley, and D.S. White. 1985. Distribution of oligochaetes in Lake Michigan
and comments on their use as indices of pollution. Journal of Great Lakes Research 11:67-76.
Milbrink, G. 1983. An improved environmental index based on the relative abundance of
oligochaete species. Hydrobiologia 102:89-97.
Muxika, I., A. Borja, and J. Bald, 2007. Using historical data, expert judgement and multivariate
analysis in assessing reference conditions and benthic ecological status, according to the
European water framework directive. Marine Pollution Bulletin 55, 16—29.
51
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National Coastal Condition Assessment 2015 Technical Report
Pelletier M.C., D.J. Gillett, A. Hamilton, T. Grayson, V. Hansen, E.W. Leppo, S.B. Weisberg, and
A. Borja. 2018. Adaptation and application of multivariate AMBI (M-AMBI) in US coastal
waters. Ecological Indicators 89:818-827.
R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Sigovini M., E. Keppel, and D. Tagliapietra. 2013. M-AMBI revisited: looking inside a widely-used
benthic index. Hydrobiologia 717:41-51.
Smith, R.W., M. Bergen, S.B. Weisberg, D. Cadien, A. Dalkey, D. Montagne, J.K Stull, and R.G.
Velarde. 2001. Benthic response index for assessing infaunal communities on the southern
California mainland shelf. Ecological Applications 11:1073-1087.
SOLEC (State of the Lakes Ecosystem Conference). 2007. State of the Great Lakes Draft, Indicator
#104. https://tiepis.epa.go' ()()41H,T.PDF?Dockey=P10041H,T.PDF
52
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5 Eutrophication Index
5.1 Background
NCCA 2015 used the same approaches that were used in previous surveys to calculate the estuarine and
Great Lakes Eutrophication Indices9. At both estuarine and Great Lakes sites, surface nutrients, surface
chlorophyll-^ (CHLA), bottom water dissolved oxygen (DO) and water clarity were measured.
However, the specific nutrient parameters and water clarity metrics that were integrated into the overall
Eutrophication Index were different between estuarine and Great Lakes sites (See Figure 5.1). In
addition to the nutrient parameters contributing to the index, the NCCA has also adopted surface total
nitrogen (TN) and total phosphorus (TP) as measures of nutrient enrichment in estuaries and the Great
Lakes.
Summary of Eutrophication index components
Estuarine
• Surface dissolved inorganic phosphorus, PO4 (DIP)
• Surface dissolved inorganic nitrogen (DIN; NO3+NO2+NH4)
• Surface chlorophyll-a (CHLA)
• Bottom water dissolved oxygen (DO)
• Water clarity as transmittance at 1 m calculated from Photosynthetically Active
Radiation (PAR)*
Great Lakes
• Surface total phosphorus (TP)
• Surface chlorophyll a (CHLA)
• Bottom water dissolved oxygen (DO)
• Water clarity as Secchi depth*
Additional metrics: Total nutrients quartile rankings**
Estuarine
• Surface total nitrogen (TN)
• Surface total phosphorus (TP)
Great Lakes
• Surface total nitrogen (TN)
• Surface total phosphorus (TP)
*Secchi depth was used at some estuarine sites if PAR measurements were missing; PAR measurements were
used at some Great Lakes sites if Secchi depth was missing.
**Quartile rankings are based on total nutrients quartiles from 2010 and are included in the online data
dashboard (https: / /coastakoiiditioii.epa.gov/)
Figure 5.1 Summary of eutrophication index components
9 Formerly called the "Water Quality Index". In 2015, the name was changed to "Eutrophication Index" to reflect the focus on
eutrophication related parameters and not other "water quality" parameters such as pathogens, contaminants, pH or
temperature.
53
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5.2 Field Collection
Field crews used similar methods to collect water samples in estuaries and the Great Lakes. Dissolved
oxygen (DO) downcast and upcast profile data were collected at regular intervals from the surface to
0.5 meters from the bottom, using a calibrated multi-parameter water quality meter (or sonde). The DO
value used for the Eutrophication Index is the average of the downcast and upcast values measured
0.5 m above the bottom sediment. Water clarity was measured both with a 20 cm Secchi disk and a
Photosynthetically Active Radiation (PAR) meter. Water samples were collected 0.5 m below the
surface using either a pumped system or a water sampling bottle such as a Niskin, Van Dorn, or
Kemmerer bottle and then transferred to a rinsed 250 mL amber Nalgene bottle for total nutrients and
a 2 L amber HDPE bottle for chlorophyll-^ and dissolved nutrients. The CHLA and dissolved nutrient
sample was filtered using a Whatman GF/F 47 mm 0.7-micron filter. The filter was analyzed for CHLA
content and the filtrate was used for dissolved nutrients analyses. Refer to the NCCA 2015 Field
Operations Manual for detailed descriptions of sample collection and analysis protocols (USEPA
2015a).
5.3 Laboratory Methods
Eutrophication Index parameters were analyzed using methods that are the same as or equivalent to
those listed in Table 5.1. Refer to the NCCA 2015 Laboratory Operations Manual for detailed
descriptions of sample analysis protocols (USEPA 2015b).
Table 5.1 Laboratory methods for water chemistry analyses
Dataset
Estviarine
Great Lakes
Parameter
Symbol
Method*
Chlorophyll-^
CIILA
EPA 445.0i_
Total phosphorus
TP
! APHA Standard Method 4500-P.E
Total nitrogen
TN
: APHA Standard Method 4500-N.C
Dissolved inorganic nitrogen
DIN
| (calculated)
Ammonia
NIL,
EPA 350.1i_
Nitrate
NO.,
EPA 353.2
Nitrite
no2
EPA 353.2; I'SGS 12540-85
Nitrate + Nitrite
NOAO,
EPA 353.2; ASTM 7781
Dissolved Inorganic
DIP
; APHA Standard Method 4500-P.E
phosphorus; Orthophosphate
(filtered before analysis)
Chlorophyll-^
CI 11,A
EPA 446.0 1
Total phosphorus
TP
APHA Standard Method 4500-P.E
Total nitrogen
TN
i APHA Standard Method 4500-N.C
54
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^Multiple state and federal laboratories participate in the survey. Any acceptable method that meets the required data quality
objectives may be used. See dataset for exact method used for any specific sample.
t EPA's Willamette Research Station (WRS) Laboratory has modified some procedures to lower the method detection limits for
samples sent to the national lab.
For estuarine samples, EPA's WRS Lab analyzed nitrate and nitrite together and modified EPA Method 353.2 to be performed on
a Flow Injection Analyzer.
5.4 Index Calculation
5.4.1 Estuarine Sites
Five metrics contributed to the estuarine Eutrophication Index: surface DIN, DIP, and CHLA
concentrations; bottom water DO; and water clarity (% transmittance at 1 m). Note that DIN is a
derived parameter, calculated as the sum of nitrate (NO3), nitrite (NO2), and ammonium (NH4)
concentrations. Some labs reported nitrate (NO3) and nitrite (NO2) concentrations separately; others
reported these analytes as the sum of nitrate and nitrite (NO3 + NO2).
DIN, DIP, and CHLA concentrations were evaluated as good, fair or poor relative to benchmarks
listed in Table 5.2 - Table 5.4. The benchmarks were set according to NCCA reporting regions:
• Northeast: Coasts of Maine through Virginia
• Southeast: Southern Atlantic seaboard from North Carolina to Florida
• Gulf: Gulf of Mexico from coastline Florida through Texas
• West: Coasts of California, Oregon, and Washington
• Tropics: Florida Bay, Biscayne Bay and waters of the Florida Keys
The nutrient and chlorophyll-^ benchmarks were established by a consensus of experts including
academic scientists, state and federal government scientists, and others after evaluation of literature,
best professional judgement, and expert opinions. Information and long-term data were systematically
compiled from over 300 regional experts on estuarine eutrophication across the country during the
National Estuarine Eutrophication Assessment (Bricker et al. 1999). The benchmarks developed were
designed to characterize eutrophication conditions on a national basis. For NCCA, adjustments in
benchmark values for different regions were made to account for regional differences in background
nutrient concentrations during the NCCA summer index sampling period based on comments from
peer reviewers and consultations with state water quality managers. (USEPA 2004; USEPA 2012).
DO was evaluated as good, fair or poor relative to benchmarks listed in Table 5.5. DO benchmarks
reflect levels that are shown to disrupt estuarine communities (Diaz and Rosenberg 1995; USEPA
2000) and are often used as state regulatory DO limits.
55
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Table 5.2 Estuarine indicator benchmarks for Dissolved Inorganic Phosphorus (DIP)
DIP (mg/L) Condition
Condition
Good
Fair
Poor
Northeast
< 0.01
> 0.01 and <
0.05
> 0.05
Southeast
< 0.01
> 0.01 and <
0.05
> 0.05
Gulf
< 0.01
> 0.01 and <
0.05
> 0.05
West
< 0.07
> 0.07 and <
0.1
> 0.1
(South
Florida)
< 0.005
> 0.005 and <
0.01
> 0.01
Table 5.3 Estuarine indicator benchmarks for Dissolved Inorganic Nitrogen (DIN)
DIN (mg/L) Condition
Condition Northeast Southeast Gulf West (South Florida)
Good < 0.1 < 0.1 < 0.1 < 0.35 < 0.05
i Fair ! > 0.1 and < 0.5 ; > 0.1 and < 0.5 ; > 0.1 and < 0.5 * > 0.35 and < 0.5 : > 0.05 and < 0.1
Poor > 0.5 > 0.5 > 0.5 > 0.5 > 0.1
Table 5.4 Estuarine indicator benchmarks for Chlorophyll a (CHLA)
CHLA (iig/L) Condition
Condition Northeast Sovitheast Gulf West (South Florida)
Good <5 <5 <5 <5 < 0.5
I Fair > 5 and <20 >5 and < 20 ; >5 and <20 >5 and <20 > 0.5 and < 1
Poor > 20 > 20 > 20 > 20 > 1
Table 5.5 Estuarine indicator benchmarks for Dissolved Oxygen (DO)
DO (mg/L) Condition
Condition All regions
Good > 5
I Fair < 5 and > 2
Poor < 2
10 For the NCCA 2015 Report, "South Florida" benchmarks were used to assess to waters of Florida Bay, Biscayne Bay and the
Florida Keys.
56
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Water clarity in estuaries was characterized as transmittance, the percent of photosynthetically active
radiation (PAR) transmitted through 1 m of water. PAR attenuation was measured using two PAR
sensors: one sensor was lowered through the water column, measuring PAR intensity (1^) at depths z
(m), and a second sensor remained in the air measuring incident PAR intensity (X). The normalized
PAR attenuation is assumed to follow Beer's law, i.e., light intensity decreases exponentially with
distance:
j- = e~Kd*z Equation 5-1
'0
where Kj is the PAR attenuation coefficient; larger Kj magnitudes indicate greater attenuation, or poorer
water clarity. Equation 5-1 (above) is equivalently expressed as follows, highlighting that intensity
ln(J^/J0) is linearly proportional to depth:
In y- = —Kd * z Equation 5-2
'O
At each site, Kj is calculated as the negative slope of the regression ofln(Iz/I0) on the y-axis vs. depth on
the x-axis using the downcast measurements11. Once Kj values were calculated, % transmittance at 1 m
(i.e. IjL&t £ = 1) was calculated as:
% Trans @ 1 m = e~Kd * 100 Equation 5-3
The water clarity condition at each site (good, fair, or poor) was determined by %Trans @ 1 m values
relative to benchmarks in Table 5.6. These transmittance benchmarks vary depending on the turbidity
level or status of submerged aquatic vegetation (SAV) at each site. Benchmarks for naturally turbid
regions allow for reduced clarity, while those for waters supporting SAV on the Atlantic and Gulf
Coasts (either naturally occurring or due to ongoing restoration efforts) support a higher degree of
clarity.
Regional delineations of turbidity classes (Figure 5.2) for the 2010 and 2015 NCCA reports are
described in Smith et al. (2006). Naturally turbid regions consisted of waters in Alabama, Louisiana,
Mississippi, South Carolina, Georgia, and Delaware Bay. Regions supporting SAV included Laguna
Madre (TX), the entire west coast of Florida, Biscayne Bay (FL), the Indian River lagoon (FL), and
portions of Chesapeake Bay (VA). Sites on the West Coast and all other Atlantic and Gulf Coast sites
were considered to exhibit normal turbidity. During review, EPA received comments that there are
additional areas around the country that should be classified as supporting SAV restoration and should
be subject to more stringent water clarity benchmarks. EPA will review turbidity classification and
apply updates in future assessments.
11 In the unlikely event that a downcast value was suspect for any water quality parameter, and there was greater confidence in
the upcast value at that corresponding depth, the upcast value may have been substituted for the downcast value.
57
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Table 5.6 Estuarine indicator benchmarks for water clarity
Water Clarity (% Transmittance at 1 m) Condition
Condition Naturally Turbid Normal Turbidity SAV Restoration
| Good > 10% > 20% > 40%
j Fair < 10% and > 5% < 20% and > 10% < 40% and > 20%
| Poor <5% < 10% < 20%
c-values* 1 1.4 1.7
*If PAR is not measured, IQ is estimated from Secchi depth using c-values: IQ — c/Secchi depth
f
&
r
NCCA Turbidity Classes:
Natural!},' turbiil
_ Normal
SAV
Figure 5.2 Turbidity classes used for water clarity condition rating
In the instance where PAR data were not available for a site, Kj was estimated from Secchi depth as:
= secM Lptnm Eiua
-------
Atkins 1929). If neither PAR data nor Secchi depth were available, the condition at the site was set to
"missing".
The Eutrophication Index for each estuarine site was based on the condition of the component
metrics, evaluated according to the rules in Table 5.7.
Table 5.7 Rules for determining Eutrophication index condition at estuarine sites
Eutrophication Index
Condition Benchmarks
Good A maximum of 1 indicator is rated fair; no indicators are rated poor
! Fair : 1 of the indicators is rated poor; or > 2 indicators are rated fair
i Poor ; > 2 of the 5 indicators are rated poor
; Missing 2 indicators are missing, and the available indicators do not suggest a fair/poor rating
Variance in the components used in the estuarine eutrophication index was evaluated by calculating the
signal to noise ratio as described in Section 2.3.5 and resulted in S:N of 1.945 for DIN, 7.429 for DIP,
2.486 for CHLA, 2.199 for DO and 5.445 for Transmissivity. In addition, the contingency table for the
overall eutrophication index illustrates the agreement of good, fair and poor ratings between the first and
second visits at 55 of 67 revisit sites (See Table 5.8)
Table 5.8 Eutrophication index contingency table (estuaries)
Visit 1
Good
Fair
Poor
Good
21
3
Visit 2
Fair
2
24
5
Poor
2
10
5.4.1.1 Total nitrogen (TN) and total phosphorus (TP)
TN and TP concentrations were characterized as low, moderate, high, or very high based on the 25th,
50th, and 75th percentile TN or TP values at sites in estuaries in the 2010 NCCA survey (Table 5.9).
Quartile results are reported in the online data dashboard at https: / /coastalcondition.epa.gov/.
59
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Table 5.9 Estuarine benchmarks for total nutrients derived from 2010 concentrations
Total Nutrients
High
Very High
Condition
Low
Moderate
TN (mg/L)
< 0.31
> 0.31 to < 0.48
> 0.48 to < 0.68
< 0.037
> 0.037 to < 0.062
> 0.062 to < 0.101
TP (mg/L)
> 0.68
> 0.101
5.4.2 Great Lakes Sites
Four metrics were employed in assessing Great Lakes water quality: TP and CHLA concentrations in
surface water; DO at the bottom; and Secchi depth as a measure of water clarity. TN was measured in
the Great Lakes but was not included in the Eutrophication Index because nitrogen has generally not
been considered a limiting nutrient in this system so there are no published benchmarks for nitrogen
suitable to be used in the Great Lakes.
The International Joint Commission (IJC) Phosphorus Management Strategies Task Force (PMSTF;
IJC 1980) developed total phosphorus, chlorophyll a, and Secchi depth benchmarks for each Great
Lake and each basin of Lake Erie based on expected trophic status (Figure 5.3). The benchmarks were
developed for "open waters", but data used to generate the benchmarks included nearshore samples
(Gregor and Rast 1979), so they were considered relevant to the nearshore and embayment sites for the
Great Lakes assessment. The PMSTF only identified a single benchmark based on the trophic status for
each lake (fair to good), so the lower benchmark (fair to poor) was defined for the NCCA report as the
value indicative of crossing into the next more nutrient-enriched trophic status. The NCCA analysts
and partners used IJC study results (Gregor and Rast 1979) to identify trophic status benchmarks for
selected basins (i.e., Saginaw Bay in Lake Huron and western, central, and eastern basins of Lake Erie),
that were not specified in the 1980 PMSTF report. Table 5.9- Table 5.13 list the benchmarks used to
evaluate conditions in Great Lakes coastal waters. Note that benchmarks vary by lake and basin. DO
benchmarks were the same as those used in estuaries and the 2 mg/L is used to define a hypoxic
condition in the Great Lakes (Diaz and Rosenberg 1995; USEPA 2000).
60
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L
r
Kustcm
Figure 5.3 Basin boundaries of the Great Lakes
Table 5.10 Great Lakes indicator benchmarks for Total Phosphorus (TP)
TP (iig/L) Condition
Condi
tion
Good
Fair
Poor
Lake
Lake
Lake
Saginaw
Western
Central
Eastern
Lake
Superior
Michigan
Huron
Bay
Lake Erie
Lake Erie
Lake Erie
Ontario
< 5
< 7
< 5
< 15
< 15
< 10
< 10
< 10
> 5 and
: > 7 and < ;
> 5 and
15 and ;
>15 and <
: > 10 and < ;
> 10 and <
10.md
< 10
10
< 10
< 32
32
15
15
< 15
> 10
> 10
> 10
> 32
> 32
> 15
> 15
> 15
61
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Table 5.11 Great Lakes indicator benchmarks for Chlorophyll a (CHLA)
CHLA (iig/L) Condition
Condi
tion
Lake
Superior
Lake
Michigan
Lake
Huron
Sagina
w Bay
Western
Lake Erie
Central
Lake Erie
Eastern
Lake Erie
Lake
Ontario
Good
< 1.3
< 1.8
< 1.3
< 3.6
< 3.6
< 2.6
< 2.6
< 2.6
Fair
>1.3 and
< 2.6
>1.8 and
< 2.6
>1.3 and
< 2.6
> 3.6
and < 6
> 3.6 and <
6
> 2.6 and <
3.6
> 2.6 and <
3.6
> 2.6 and
< 3.6
Poor
> 2.6
> 2.6
> 2.6
> 6
> 6
> 3.6
> 3.6
> 3.6
Table 5.12 Great Lakes indicator benchmarks for Dissolved Oxygen (DO)
DO (mg/L) Condition
Condition All regions
Good > 5
| Fair < 5 and > 2
Poor < 2
Table 5.13 Great Lakes indicator benchmarks for water clarity
Water Clarity (Secchi depth in m) Condition
Condi
tion
Lake
Superior
Lake
Michigan
Lake
Huron
Saginaw
Bay
Western
Lake Erie
Central
Lake Erie
Eastern
Lake Erie
Lake
Ontario
Good
> 8
> 6.7
> 8
> 3.9
> 3.9
> 5.3
> 5.3
> 5.3
Fair
< 8 and
> 5.3
< 6.7 and
> 5.3
< 8 and
> 5.3
< 3.9 and
> 2.1
< 3.9 and >
2.1
< 5.3 and >
3.9
< 5.3 and >
3.9
< 5.3 and
> 3.9
Poor
< 5.3
< 5.3
< 5.3
< 2.1
< 2.1
< 3.9
< 3.9
< 3.9
Water clarity was characterized in the Great Lakes primarily by Secchi depth, and secondarily by Secchi
depth estimated from PAR attenuation at sites lacking Secchi data or at sites where the Secchi disk was
visible clear to the bottom. To assign a Secchi depth condition class to a clear-to-bottom site (CTB),
site depth was considered first. If site depth was greater than the good/fair Secchi depth benchmark for
that waterbody (Table 5.12), then the Secchi depth condition class was rated "good". If site depth was
less than or equal to the good/fair benchmark, then a condition class could not be unambiguously
assigned as fair or poor. At those sites, the missing Secchi depth could be estimated using the site's kj.
62
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If neither PAR data nor Secchi depth were available and site depth was less than the good/fair
benchmark, then Secchi depth and its condition class, were considered "missing".
To predict Secchi depth at clear-to-bottom sites where site depth was less than the good/fair
benchmark for the water body and to assign those sites a condition class, a power function was used to
model the relationship between Secchi depth and kj. The Secchi depth — kj model is derived for each
NCCA cycle based on that year's dataset. The relationship between Secchi depth and depends on a
combination of site-specific factors like chlorophyll a, suspended solids, colored dissolved organic
matter (e.g. Brezonik et al. 2019). If these factors change across the Great Lakes over time, this
relationship may also change. By basing estimated Secchi depth on a model derived based on that years'
data, each NCCA cycle may differ in its kj. value.
For 2015 sites located in the Great Lakes, the Secchi depth estimation model was based on all sites
located in the lakes, including multiple site visits and enhancement sites (embayments, Lake Erie
enhancement; 298 sites total) and was described as:
Secchi depthest = 1. 3891 * kd° 983 r2 = 0.90 Equation 5-5
Figure 5.4 Determining Water Clarity in the Great Lakes
The Eutrophication index for Great Lakes sites was then determined based on the condition of the
component metrics, evaluated according to the rules in Table 5.13.
63
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Table 5.14 Rules for determining Eutrophication Index condition at Great Lakes sites
Eutrophication Index
Condition Benchmarks
Good A maximum of 1 indicator is rated fair; no indicators are rated poor
Fair 1 of the indicators is rated poor; or > 2 indicators are rated fair
Poor ! > 2 of the 5 indicators are rated poor
Missing 2 indicators are missing, and the available indicators do not lead to a fair/poor rating
Variance in the components used in the Great Lakes eutrophication index was evaluated by calculating
the signal to noise ratio as described in Section 2.3.5 and resulted in S:N ratios of 1.84912 for total
phosphorus (PTL), 15.37512 for CHLA, 1.752 for DO and 5.369 for mean Secchi depth. In addition a
contingency table for the Great Lakes eutrophication index illustrates the agreement of good, fair and
poor ratings between the first and second visits at 39 revisit sites (See Table 5.15).
Table 5.15 Great Lakes eutrophication index contingency table
Visit 1
Good
Fair
Poor
Visit 2
Good
21
2
Fair
2
2
4
Poor
8
5.4.2.1 Total nitrogen (TN) and total phosphorus (TP)
TN and TP concentrations are characterized as low, moderate, high, or very high based on the 25th,
50th, and 75th percentile TN or TP values at sites in the Great Lakes in the 2010 NCCA survey (Table
5.16). Results are reported in the online data dashboard at https: / /coastaicondition.epa.gov /.
12 One second visit sampling event in western Lake Erie occurred during a record-breaking algal bloom. The chlorophyll a
concentration for that site was extremely high. Due to the unusual nature of this event, the reported S:N ratios for the
eutrophication index were calculated twice. S:N ratios reported in text do not include results from the second visit sampled
during this algal bloom. Appreciably different S:N ratios when extreme second visit values are included in the calculation are
listed below:
• Total phosphorus:-0.469;
• Chlorophyll a. 0.713
64
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Table 5.16 Great Lakes quartile-based benchmarks for total nutrients, derived from ranked 2010
concentrations
Total Nutrients
Condition TN (mg/L) TP (mg/L)
Low < 0.36375 < 0.0028
! Moderate > 0.36375 to < 0.4025 > 0.0028 to < 0.00522
High > 0.4025 to < 0.48 > 0.00522 to < 0.00988375
i Very High > 0.48 > 0.00988375
5.5 References
Brezonik, P. L., R.W. Bouchard Jr, J.C. Finlay, C.G. Griffin, L.G. Olmanson, J.P Anderson, and R.
Hozalski. 2019. Color, chlorophyll a and suspended solids effects on Secchi depth in lakes:
implications for trophic state assessment. Ecological Applications, e01871.
Bricker, S.B., C.G. Clement, D.E. Pirhalla, S.P. Orlando, and D.R.G. Farrow. 1999. National Estuarine
Eutrophication Assessment: Effects of Nutrient Enrichment in the Nation's Estuaries.
Prepared for the U.S. Department of Commerce, National Oceanic and Atmospheric
Administration, National Ocean Service, Special Projects Office and the National Centers for
Coastal Ocean Science, Silver Spring,
Diaz, R.J., and R. Rosenberg. 1995. Marine benthic hypoxia: A review of its ecological effects and the
behavioral responses of benthic macrofauna. Oceanography and Marine Biology Annual Review
33:245—303.
Gregor D.J., and W. Rast. 1979. Trophic Characterization of the US and Canadian Nearshore Zones of
the Great Lakes. International Reference Group on Great Lakes Pollution from Land Use
Activities. International Joint Commission, (http://www.iic.org/files/publication; 3.pdf)
International Join Commission (IJC). 1980. Phosphorus Management Strategies Task Force.
Phosphorus Management for the Great Lakes. Final Report to the Great Lakes Water Quality
Board and Great Lakes Science Advisory Board. Windsor, Ontario, July 1980, 129 pp.
Poole, H.H and W.R.G Atkins. 1929. Photo-electric measurements of submarine illumination through
out the year. J. Mar. Biol. Assoc. UK, 16, 297-324
Smith, L.M., V.D. Engle, and J.K. Summers. 2006. Assessing water clarity as a component of water
quality in Gulf of Mexico estuaries. Environmental Monitoring and Assessment 115:291—305.
U.S. Environmental Protection Agency (USEPA). 2000. Ambient Water Quality Criteria for Dissolved
0>ygen (Saltwater): Cape Cod to Cape Hatteras. EPA/822-R-00-012. U.S. Environmental Protection
Agency, Office of Water, Washington, DC.
65
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U.S. Environmental Protection Agency (USEPA). 2004. National Coastal Condition Report II. EPA-
620-R-03-002. U.S. Environmental Protection Agency, Office of Research and
Development/Office of Water. Washington, DC.
U.S. Environmental Protection Agency (USEPA). 2012. National Coastal Condition Report IV. EPA-
842-R-l 0-003. U.S. Environmental Protection Agency, Office of Water. Washington, DC.
U.S. Environmental Protection Agency (USEPA). 2015a. National Coastal Condition Assessment: Field
Operations Manual. EPA- 841-R-14-007. Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015b. National Coastal Condition Assessment:
Laboratory Operations Methods Manual. EPA-841-R-14-008. Washington, D.C.
66
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6 Sediment Quality Index
6.1 Background
The NCCA 2015 used the same approach as the NCCA 2010 for sediment collection, analysis and
interpretation. Surficial sediment was collected in the field using a grab sampler and composited to be
analyzed for sediment contaminants, sediment toxicity, total organic carbon and grain size. The NCCA
assesses sediment for possible adverse effects on the benthic community using a two-component
sediment quality index (SQI). The SQI consists of a sediment contaminant index (SCI) and a sediment
toxicity index (STI). For NCCA 2015, 677 sediment samples were collected at the estuarine visit one
sites and 294 sediment samples were collected at Great Lakes visit one sites.
The SCI uses literature-based sediment quality guidelines (SQG) and is calculated into a quotient. For
estuarine sites, the SCI uses both the Effects Range Median (ERM) to calculate the SQG quotient and a
logistic regression model. For Great Lakes sites, the SCI uses the Probable Effect Concentration (PEC)
to calculate the SQG quotient. For both estuarine and Great Lakes sites, the STI uses the results of a
10-day amphipod toxicity test. The organisms are exposed to collected sediments, capturing responses
to a broader range of sediment properties that might contribute to overall toxicity. In the estuarine
samples, sediment toxicity tests use the amphipod L,eptocheirusplumulosus, while Great Lakes tests use the
amphipod Hyalella a^teca. The toxicity indices were primarily based on control-corrected survival
(statistical significance was included for estuarine sites only).
Sediment contaminant index (SCI):
Estuarine
• Mean effects range median quotient (mean ERM-Q)
• Logistic regression model (LRM) maximum probability (Pmax)
Freshwater (Great Lakes only)
• Mean probable effects concentration quotient (mean PEC-Q)
Sediment toxicity index (STI):
Estuarine
• Control-corrected survival of amphipods (Leptochirus plumulosus)
• Statistical significance (one-sided t-test) between control and test
survivals
Freshwater (Great Lakes only)
• Control-corrected survival of amphipods (Hyalella azteca)
Figure 6.1 Summary of sediment quality index components
67
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6.2 Field Collection
Field crews collected the top 2 cm of surface sediments at predetermined probabilistic sites as
prescribed in the Field Operations Manual (USEPA 2015a). Estuarine crews used assorted stainless
steel grab samplers, (e.g., Young-modified Van Veen, ponar or Eckman; See Table 4.1), whereas Great
Lakes crews used a stainless-steel standard Ponar sampler. Crews composited the surface sediments
from multiple grab samples to collect approximately 2 liters of sediment—the total sediment volume
required for analysis.
6.3 Laboratory Analyses
6.3.1 Sediment Contamination
Samples were analyzed for contaminant concentrations of metals (including mercury), polycyclic
aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and organochlorine pesticides using
a variety of spectrometry methods (Table 6.1 and see LOM (USEPA 2015b)). Total organic carbon,
grain size and percent moisture were measured to provide supplementary information but were not
included in index calculations and condition assessment.
6.3.2 Sediment Toxicity
Sediment toxicity tests were performed to determine the percent survival of laboratory amphipods
(estuarine species: L,eptocheirusplumulosus\ freshwater species: Hyalella a^teca) following 10 days of
exposure to sample sediments (Table 6.2 and see LOM (USEPA 2015b; ASTM 1990; ASTM 2005).
Control tests were run in parallel with the sample tests. Control tests were run using reference
sediments, organisms from the same batch as used in sample tests and water from the same sources.
6.3.2.1 Estuarine
The estuarine test was run using a static water method with 5 replicate chambers and 20 organisms
exposed in each chamber. A minimum of 90% survival of control organisms was required to meet test
acceptability criteria (USEPA 2001; USEPA 2015b; ASTM 1990).
6.3.2.2 Freshwater
The freshwater toxicity test was run using a flow-through method with 4 replicate chambers and 10
organisms in each chamber; a minimum of 80% survival of control organisms was required to meet test
acceptability criteria (USEPA 2000; USEPA 2015b; ASTM 2005).
68
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Table 6.1 Laboratory methods for sediment analyses
Dataset
Sediment contaminants
Parameter
Method
Sediment Toxicity
Sediment Characteristics
Metals (excluding Mercury)
Mercury
PAI Is, PCBs, pesticides
Marine amphipod 10-day acute
toxicity test
Freshwater amphipod 10-day
acute toxicity test
Total organic carbon
Percent solids
Grain size
HP A 8270D
HP A 9060
Test Method 100.1 in EPA
600/R-99/064
Test Method 100.4 in ASTM
HI 367-03
HPA 6020
HP A 245.7
SM 2540 B
SM 2560
6.4 Sediment Contaminant Index Calculations
Sediment quality guidelines (SQGs) identify concentrations of individual contaminants that may be
associated with adverse effects to benthic organisms (Long 2006). While SQGs are adequate for
assessing individual contaminant levels in sediments, they do not address combinations of contaminants
typically found in a sample. Therefore, the NCCA used mean SQG quotients to produce overall
unitless assessments of contamination to predict aggregate toxicity due to multiple contaminants (Fairey
et al. 2001; Long et al. 2006). The mean effects-range median quotient (mERM-Q; Long et al. 1995)
was used to assess contamination in estuarine sediments while the mean probable effects
concentrations quotient (mPEC-Q; MacDonald et al. 2000; Ingersoll et al. 2001) was used for
freshwater sediments.
A logistic regression model (LRM) was used in addition to the mERM-Q to assess contamination in
estuarine sediments. The LRM is based upon modeled relationships between concentrations of
individual contaminants and their documented effects on benthic organisms (Field et al. 2002; USEPA
2005). The individual LRM models were combined to generate the maximum probability (Pmax) of
toxicity due to contamination.
69
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Table 6.2 Estuarine Sediment Quality Guidelines used in calculating the mERM-Q and LRM
Pmax.
Sediment Contaminant
Included in
mean Effects
Range Median
Quotient
(mERM-Q)
Effects
Range
Low (ERL)
Effects
Range
Median
(ERM)
Included in
Logistic
Regression
Model (LRM)
LRM b0
LRM b1
LRM T25
Metals (ug/g dry weight)
Antimony
X
-0.9005
2.4111
0.83
Arsenic
X
8.2
70
X
-4.1407
3.1674
9.13
Cadmium
X
f 1.2
9.6
X
-0.34
2.5073
0.5
Chromium
X
81
370
X
-6.4395 ;
2.9952
60.69
Copper
X
34
270
X
-5.7878 1
2.9325
39.72
Lead
X
46.7
218
X
-5.4523
2.7662
37.49
Mercury
X
0.15
0.71
X
0.8041
25461
0.18
Nickel
20.9
51.6
X
-4.6119
2.7658
18.63
Silver
X
1
3.7
X
-0.1117
19684
0.32
Zinc
X
150
410
X
-7.9834
3.342
114.84
Organic pollutants (ng/g dry weight)
Acenaphthene
X
16
500
X
-3.6165
1.7532
27.3
Acenaphthylene
X
44
640
X
-2.962
1.3797
22.42
Anthracene
X
85.3
1100
X
: -3.6574
1.4854
52.8
Benz(a)anthracene
X
261
1600
X
-4.2013
1.5747
93.4
Benzo(b)fluoranthene
X
-4.5409 :
1.4916
203.13
Benzo(k)fluoranthene
X
-4.2781
1.5669
106.94
Benzo(a)pyrene
X
430
1600
X
: -4.3005 ;
1.5832
105.3
Biphenyl
X
-4.1144
2.2085
23.2
Chrysene
X
384
2800
X
7 -4.3241
1.5372
125.4
Dibenz(a,h)anthracene
X
63.4
260
X
-a6308
1.7692
26.99
2,6-dimethylnapthalene
X
: -4.0456 ;
1.904
35.3
Fluoranthene
X
600
5100
X
-4.4574 i
14787
186.83
Fluorene
X
19
540
X
; -3.7146 ;
1.8071
28.03
lndeno(1,2,3-c,d)pyrene
X
-4.3674
1.6245
102.84
1-methylnapthalene
X
; -4.1405
2.0961
28.26
2-methylnapthalene
X
70
670
X
j -3.7579
17833
30.99
1-methylphenanthrene
X
: -3.5884 1
1.7501
26.46
Napthalene
X
160
2100
X
-3.7753
16152
45.41
Perylene
X
: -4.6827 :
1.7632
107.82
Phenanthrene
X
240
1500
X
] -44576
1.6768
100.74
Pyrene
X
665
2600
X
-4.708
1.5854
189.08
Total PCB congeners
X
22.7
180
X
I -3.4613
1.3488
56.45
4.4-DDD
X
-1.8983 ;
1.4913
344
4.4-DDE
' 2.2
27
X
-1.8392
0.9129
6.48
4,4'-DDT
X
' -1.7705
1.6786
2.51
Total DDT
Dieldrin
1.6
46.1
Total PCBs included the following congeners: 8, 18, 28, 44, 52, 66, 77, 101, 105, 110
209
"Total DDT represents the sum of 4,4'-DDT; 2,4'-DDT; 4,4'-DDE; 2,4'-DDE; 4,4'-DDD; 2,4'-DDD
Sources: Long et al. 1995; Field et al. 2002
-1.1728
118, 126, 128, 138, 153,
2.558 1.07
170, 180, 187. 195. 206.
70
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Table 6.3 Great Lake sediment quality guidelines used in calculating the mPEC-Q.
_ .. t r t ' t Consensus-based Benchmark Consensus-based Probable Effects
seaimeni uoniaminani Effects Concentration (TEC) Values Concentration (PEC) Values
Metals (ug/g dry weight)
; Arsenic 9.79 33
I Cadmium 0.99 4.98
: Chromium 43.4 111
| Copper 31.6 149
Lead 35.8 128
Nickel 22.7 48.6
Zinc 121 459
Organic pollutants (ng/g dry weight)
I Total PAHs* 1610 22800
! Total PCB congeners 60 676
; 'Total PAHs represents the sum of low molecular weight PAHs Acenaphtherte, Acenaphthylerte, Anthracene,
: Fluorene, 2-methylnaphthalene, Naphthalene, Phenanthrene, and high molecular weight PAHs
; Benz(a)anthracene, Benzo(a)pyrene, Chrysene, Dibenz(a,h)anthracene, Fluoranthene, Pyrene
I "Total PCBs included the following congeners: 8, 18, 28, 44, 52, 66, 77, 101, 105, 110, 118, 126,128, 138, 153, ;
M70,180,187,195,206,209
; Sources: CCME 1999; MacDonald et al. 2000; Crane and Hennes 2007; Crane et al. 2002
6.4.1 Data Preparation
For any given contaminant, results were excluded from the sediment contaminant calculations if the
associated laboratory method detection limits (MDLs) exceeded the corresponding effects range low
(ERL) and LRM T25 values for estuarine sediments (Table 6.2; Field and Norton 2014), or Threshold
Effect Concentration (TEC) values for Great Lakes sediments (Table 6.3).
Sample results reported as nondetects (values less than the MDL) were substituted with one-half of the
MDL. Total contaminant classes were calculated as the sum of concentrations of individual
contaminants in each class:
• Total PAHs (calculated for Great Lakes sites only):
o low molecular weight PAHs Acenaphthene, Acenaphthylene, Anthracene, Fluorene, 2-
methylnaphthalene, Naphthalene, Phenanthrene, plus
o high molecular weight P \I Is Benz(a)anthracene, Benzo(a)pyrene, Chrysene,
Dibenz(a,h)anthracene, Fluoranthene, Pyrene),
• Total PCBs: congeners 8, 18, 28, 44, 52, 66, 77, 101, 105, 110, 118, 126, 128, 138, 153, 170, 180,
187, 195, 206, and 209
• Total DD I s: 4,4'-DDT; 2,4'-DDI'; 4,4'-DDE; 2,4'-DDE; 4,4'-DDD; 2,4'-DDD.
71
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6.4.2 Estuarine Contaminant Index Calculations
6.4.2.1 Mean effects range median quotient (mean ERM-Q)
To calculate an individual ERM-Q, each sample contaminant result (cone) was divided by the ERM SQG
(Table 6.2):
Individual ERM-Q = Equation 6-1
* ERM n
To calculate the mean ERM quotient for each sample, individual ERM quotients were averaged:
Mean ERM-Q = ERM-^senic+ERM-Qchr^nium+ ...ERM-QTotalPCBs Equation 6-2
where n is the number of analytes included in the analysis (n = 23; see Table 6.2).
6.4.2.2 Logistic regression model (LRM) maximum probability (Pmax)13
Individual LRM probabilities were calculated as:
ef>0 + (fil*loglOconc)
V = 1+e»o+(»i*ioeio cone) Equation 6-3
wherep is the probability of observing a toxic effect; bo and bi values are provided in Table 6.2.
Pmax is derived from the maximump (pmax[miim) result from a sample and calculated as:
Pmax ~ 0- H (0- * Pmaximum) (0.4 * Vmaximum ) Equation 6-4
Nickel was excluded from LRM Pmax calculations for West Coast samples due to naturally high
background levels of nickel in the region (Lauenstein et al. 2000; Long et al. 1995; and Nelson 2008).
6.4.3 Great Lakes Contaminant Index Calculations
6.4.3.1 Mean Probable Effects Concentration Quotient (mean PEC-Q)
Individual PEC-Qs were calculated for metals, total P AI Is and total PCBs as the contaminant
concentration result divided by the PEC SQG (Table 6.3):
Individual PEC-Q = Equation 6-5
pec n
The mean PEC-Q for metals was calculated as:
13 ERMQ and PEC-Q look at a range of priority pollutants and calculate the mean probability of adverse effects. In contrast,
LRM uses a single contaminant (the highest) at each station to predict impairment.
72
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n/i n t? r rt Y,Individal PEC-Qs
Mean PEC-Qmetais = - Equation 6-6
where n is the number of metals included in the analysis (n — 7). Only metals with reliable PECs (i.e.:
arsenic, cadmium, chromium, copper, lead, nickel, and zinc) were included.
The mean PEC-Q was calculated as the average of the above PEC-Qs:
Mean PEC-Q = ^PBC-Qrnetals+PEC-QTotalPAHs+PEC.QTotalPCBs)
Equation 6-7
Where n is the number of contaminant classes (n — 3).
Once the index is calculated, benchmarks are applied to categorize results in to good, fair and poor
condition. Sediment contaminant benchmarks are based on literature review and best professional
judgement and are intended to represent the probability of toxicity (Table 6.4). Mean SQG quotients
were developed from prior studies relating guideline exceedances and observed toxicity levels (Ingersoll
et al. 2001; Crane et al. 2002; Field et al. 1999; Field et al. 2002; Long et al. 1998). The benchmarks for
good correspond to low incidence of toxicity, fair with less known incidence of toxicity and poor with
a higher incidence of toxicity.
Table 6.4 Benchmarks for NCCA sediment contaminant index (SCI)
Sediment Contaminant Index (SCI) Condition
Condition Estuarine Great Lakes
n A mean ERM-Q <0.1;
Good iu\fi> i i i /
Fair n>\fn i i i r ^ mean PEC-Q > 0.1 and < 0.6
LRM Pmax benchmark >0.5-<0.75
mean ERM-Q >0.5;
i LRM Pmax benchmark >0.75 mean PEC"Q °"6
There was very little variance in the estuarine sediment contaminant index results. 58 of 63 sites showed
agreement between good and fair ratings between visit 1 and 2 (See Table 6.5). Only one site was rated
poor at visit 2.
Table 6.5 Estuarine sediment contaminant index contingency table
Visit 1
Good
Fair
Poor
Good
58
1
Visit 2
Fair
1
2
Poor
1
There was also very little variance in the Great Lakes sediment contaminant index. 30 of 33 sites showed
agreement between good and fair ratings between visit 1 and 2 (See Table 6.6). Zero sites were rated poor.
73
-------
Table 6.6 Great Lakes sediment contaminant index contingency table
Visit 1
Good
Fair
Poor
Visit 2
Good
26
Fair
3
4
Poor
6.5 Sediment toxicity Index Calculations
Sediment toxicity tests provide a second line of evidence used for assessing whether sediments can support
a healthy benthic ecosystem. They compare the ability of test organisms to survive in samples collected in
the field to samples that come from an area of known clean sediment. Control corrected survival is used to
assess both estuarine and Great Lakes sediment samples, statistically significant differences between field
and control samples are only assessed for estuarine samples.
6.5.1 Data Preparation
NCCA sediment toxicity testing data underwent quality checks to ensure that the results were suitable for
use in the sediment toxicity index. In some cases, one or two replicates from a sample were excluded from
analysis, while in others entire samples were excluded. When a replicate is removed from analysis due to
QA/QC concerns, the data associated with that replicate are neither used in calculating control-adjusted
survival for the sample, nor are they used in the significance tests for marine samples. When a sample is
removed from analysis due to QA/QC concerns or there are an insufficient number of replicates for that
sample, the condition category for the sediment toxicity index at the site will be considered missing.
Sediment toxicity results excluded from analysis are deprecated from the sediment toxicity data published
on the NARS website. See Table 6.7 for a summary of excluded sediment toxicity test results.
Table 6.7 Summary of excluded sediment toxicity test results
Number of results removed
Estuarine
Great Lakes
Reason
Replicate
Sample
Replicate
Sample
Predators present in test chamber
7
2
0
0
Large particle size impacted test
organism survival
1
0
0
0
Laboratory misload: 0 organisms loaded
1
0
0
0
Laboratory misload: unknown number
1
0
1
0
of organisms greater than protocol
number
6.5.2 Control-corrected survival
Control-corrected survival for each sample was calculated for both estuarine and freshwater sediments.
Sample mean percent survival, or average survival across sample replicates, was divided by control
mean percent survival, or average survival across corresponding control replicates, as follows:
74
-------
_ ^ , , , sample mean percent survival ^ „
Control-corrected survival = - - Equation 6-8
control mean percent survival
6.5.3 Significance tests (estuarine only)
For each sample, mean field sample survival was compared to mean control survival. First, normality of
the raw sample replicate data was assessed via Shapiro-Wilkes tests (data were considered normal ifp-
value > 0.10). If raw data were not normally distributed, the arc-sine square root transformation was
applied, and normality of transformed values was assessed again. For normally distributed data (either
raw or transformed), one-sided t-tests with equal or unequal variances (homogeneity of variance was
assessed via Bartlett tests; variances were considered equal ifp-value > 0.10) were performed for each
sample and the associated control batch. For data that were not normally distributed (even after
transformation), one-sided Wilcoxon tests were performed. Sample replicate survival was considered
significantly less than control replicate survival if p-value < 0.05 for both t-tests and Wilcoxon tests.
Once the control-corrected survival and significance tests are calculated, benchmarks are applied to
categorize results into good, fair and poor condition. Sediment toxicity index benchmarks are based on
literature values for estuarine samples (Long et al. 1998; Greenstein et al. 2011) and Great Lakes
samples (USEPA 2004; Norberg-King et al. 2006; See Table 6.8).
Table 6.8 Benchmarks for NCCA sediment toxicity index (STI)
Sediment Toxicity Index (STI) Condition
Condition Estuarine Great Lakes
control-corrected survival > 90%
control-corrected survival > 7 5%
and < 90%
control-corrected survival < 7 5%
There was a large amount of disagreement in the estuarine sediment toxicity index. Of the 60 sites that
were visited twice, 33 agreed in good or fair ratings. Most of the disagreement was from sites that were
rated fair for the first visit and good for the second visit. There was no agreement in poor ratings (See
Table 6.9)
Good
Fair
Poor
control-corrected survival > 80% and not statistically
significantly less than control (p > 0.05)
control-corrected survival > 80% and statistically
significantly less than control (p < 0.05) or
control-corrected survival < 80% and not significantly
less than control (p > 0.05)
control-corrected survival < 80% and statistically
significantly less than control (p < 0.05)
75
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Table 6.9 Estuarine sediment toxicity index contingency table
Visit 1
Good
Fair
Poor
Good
32
18
1
Visit 2
Fair
5
1
1
Poor
1
1
In contrast to the estuarine sediment toxicity index, the Great Lakes showed very little variance and
agreed on good rating for 28 of the 32 revisit sites. There were no sites rated poor at visit 1 or visit 2, and
no sites rated fair at visit 1 (See Table 6.10).
Table 6.10 Great Lakes sediment toxicity contingency table
Visit 1
Good
Fair
Poor
Visit 2
Good
28
Fair
4
Poor
6.6 Sediment Quality Index Calculations
The sediment contaminant indices and sediment toxicity indices contribute equally to the sediment
quality index (Table 6.11). For a site to be rated good, both component indices must be rated good,
and a site will be rated poor if either of the component indices are rated poor.
Table 6.11 Benchmarks for NCCA sediment quality index
Sediment Quality Index Condition
! Good : SCI = good and STI = good
i Fair i SCI = fair and/or STI = fair (SCI i1 poor; STI 7^ poor)
| Poor : SCI = poor and/or STI = poor
Driven by the sediment toxicity index, the estuarine sediment quality index shows very little agreement
between visits 1 and 2 (See Table 6.12).
Table 6.12 Estuarine sediment quality index contingency table
Visit 1
Good
Fair
Poor
Good
31
18
1
Visit 2
Fair
5
4
1
Poor
1
2
76
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The overall Great Lakes sediment quality index showed agreement with the exception of a few sites rated
fair at visit 1 or 2. No sites were rated poor (See Table 6.13).
Table 6.13 Great Lakes sediment quality index contingency table
Visit 1
Good
Fair
Poor
Visit 2
Good
22
Fair
7
4
Poor
6.7 References
ASTM. 1990. Guide for conducting 10-day static sediment toxicity tests with marine and estuarine
amphipods. American Testing and Materials ASTM Standard Methods. Section 11.04, methods
number E-1367-90. American Society for Testing and Materials, Philadelphia, PA.
ASTM. 2005. Standard Test Method for Measuring the Toxicity of Sediment-Associated Contaminants
with Freshwater Invertebrates. American Society for Testing and Materials ASTM Standard
Methods. Section 11.06, methods number E-1706-19 American Society for Testing and
Materials, Philadelphia, PA.
Crane, J.L., D.D. MacDonald, C.G. Ingersoll, D.E. Smorong, R.A. Lindskoog, C.G. Severn, T.A.
Berger, and L.J. Field. 2002. Evaluation of numerical sediment quality targets for the St. Louis
River Area of Concern. Arch. Environ. Contam. Toxicol. 43:1-10.
Crane, J.L. and S. Hennes. 2007. Guidance for the use and application of sediment quality targets for
the protection of sediment-dwelling organisms in Minnesota. Minnesota Pollution Control
Agency, St. Paul, MN. MPCA Doc. No. tdr-gl-04.
(Iittp: / /www.pca.state.mn.us/index.php/view-documetit.htm1?gid=9163)
Fairey, E., R. Long, C.A. Roberts, B.S. Anderson, B.M. Phillips, J.W. Hunt, H.R. Puckett, and C.J.
Wilson. 2001. An evaluation of methods for calculating mean sediment quality guideline
quotients as indicators of contamination and acute toxicity to amphipods by chemical mixtures.
Environ. Toxic. <& Chem. 20 (10): 2236-2286.
Field J.F., D.D. MacDonald, S.B. Norton, C.G. Severn, and C.G. Ingersoll. 1999. Evaluating sediment
chemistry and toxicity data using logistic regression modeling. Environ. Toxicol. Chem. 18
(6) :1311—1322.
Field, L.J., D.D. MacDonald, S.B. Norton, C.G. Ingersoll, C.G. Severn, D. Smorong, and R. Lindskoog.
2002. Predicting amphipod toxicity from sediment chemistry using logistic regression models.
Environ. Toxicol. Chem. 21:1993-2005.
Field, L.J. and S.B. Norton. 2014. Regional models for sediment toxicity assessment. Environ. Toxicol.
Chem. 33:708-717.
77
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Greenstein, D.J. and S.M. Bay. 2011. Selection of methods for assessing sediment toxicity in California
bays and estuaries. Integr. Environ. Assess. Manage. 8:625-637.
Ingersoll, C.G., D.D. MacDonald, N. Wang, J.L. Crane., L.J. Field, P.S. Haverland, N.E. Kemble, R.A
Lindskoog, C. Severn, and D.E. Smorong. 2001. Predictions of sediment toxicity using
consensus-based freshwater sediment quality guidelines. Arch. Environ. Contam. Toxicol. 41:8-21.
Lauenstein, G.G., E.A. Crecelius, and A.Y. Cantillo. 2000. Baseline metal concentrations of the U.S.
West Coast and their use in evaluating sediment contamination. Presented at 21st Ann. Soc.
Environ. Toxicology and Chemistry meeting, November 12 - 15, 2000, Nashville Tennessee.
Long, E.R., D.D. MacDonald, S.L. Smith, and F.D. Calder. 1995. Incidence of adverse biological
effects within ranges of chemical concentrations in marine and estuarine sediments. Environ.
Manage. 19:81—97.
Long, E.R., L.J. Field, and D.D. MacDonald. 1998. Predicting toxicity in marine sediments with
numerical sediment quality guidelines. Environ. Toxicol. Chem. 17:714—727.
Long, E.R., C.G. Ingersoll and D.D. MacDonald. 2006. Calculation and uses of mean sediment quality
guideline quotients: A critical review. Environmental Science Technology 40:1726-1736.
MacDonald, D.D., C.G. Ingersoll, and T.A. Berger. 2000. Development and evaluation of consensus-
based sediment quality guidelines for freshwater ecosystems. Arch. Environ. Contam. Toxicol.
39:20-31.
Nelson W.G., J.L. Hyland, H. Lee II, C.L. Cooksey, J.O. Lamberson, F.A. Cole, and P.J. Clinton. 2008.
Ecological Condition of Coastal Ocean Waters along the U.S. Western Continental Shelf: 2003.
EPA 620/R-08/001, U.S. EPA, Office of Research and Development, National Health and
Environmental Effects Research Laboratory, Western Ecology Division, Newport OR, 97365;
and NOAA Technical Memorandum NOS NCCOS 79, NOAA National Ocean Service,
Charleston, SC 29412-9110. 137 p.
Norberg-King, T.J., P.K. Sibley, G.A. Burton, C.G. Ingersoll, N.E. Kemble, S. Ireland, D.R. Mount,
and C.D. Rowland. 2006. Interlaboratory Evaluation of Hjalella a^teca and Chironomus tentans
Short-term and Long-term Sediment Toxicity Tests. Environmental Toxicology and Chemistiy, Vol
25, No. 10. Pp. 2662-2674. Setae Press
U.S. Environmental Protection Agency (USEPA). 2000. Methods for measuring the toxicity and
bioaccumulation of sediment-associated contaminants with freshwater invertebrates. Second
Edition. EPA 600-R-99-064. U.S. Environmental Protection Agency. Washington, DC.
U.S. Environmental Protection Agency (USEPA). 2001. Method for assessing the chronic toxicity of
marine and estuarine sediment-associated contaminants with the amphipod Leptocheirus
plumulosus. EPA 600-R-01-020. U.S. Environmental Protection Agency. Washington, DC.
U.S. Environmental Protection Agency (USEPA). 2004. The incidence and severity of sediment
contamination in surface waters of the United States, National Sediment Quality Survey: Second
Edition. EPA-823-R-04-007. U.S. Environmental Protection Agency, Office of Science and
Technology. Washington, DC
78
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U.S. Environmental Protection Agency (USEPA). 2005. Predicting toxicity to amphipods from
sediment chemistry. EPA/600/R-04/030. U.S. Environmental Protection Agency, Office of
Research and Development. Washington, DC
U.S. Environmental Protection Agency (USEPA). 2015a. National Coastal Condition Assessment: Field
Operations Manual. EPA- 841-R-14-007. Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015b. National Coastal Condition Assessment:
Laboratory Operations Methods Manual. EPA-841-R-14-008. Washington, D.C.
79
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7 Ecological Fish Tissue Contaminant Index
7.1 Overview
The NCCA is designed to characterize ecological conditions in near-shore marine and freshwater
coastal resources at regional and continental scales. The ecological fish tissue contaminant index
(EFTCI) is a generalized approach that accommodates the wide variety of species, climate zones,
geographies, salinity regimes and sampling methods that comprise the target populations of this
national assessment. This approach provides a nationally consistent way to screen a wide variety of fish
species in diverse ecosystems for contaminants that may lead to adverse ecological effects within the
food web.
Contaminant concentrations in fish provide a time-integrated measurement of chemical bioavailability,
fate and distribution. The NCCA measures concentrations of select contaminants in whole-fish tissues
to assess the biologically available contaminant levels in the Nation's coastal waters. Tissue chemistry
results are compared to a suite of contaminant screening values to evaluate whether potential exposure
may lead to adverse effects for predatory wildlife that depend on fish as a primary food source (or
"piscivorous" wildlife). This analysis culminates with the EFTCI that creates ratings of good, fair or
poor based upon the degree to which contaminants are found in fish composite samples.
Wildlife, such as fish, birds and mammals, can be exposed to contaminants in several ways (e.g.,
ingestion, dermal contact and inhalation). Ingestion is a common mode of wildlife exposure that
typically occurs by incidental consumption of soils or sediments associated with the food source;
drinking contaminated surface water; or eating prey that have accumulated contaminants in their
bodies. This approach specifically assesses the potential that piscivorous wildlife may experience
adverse effects when exposed to ingested contaminants that have accumulated in the tissues of target
fish caught during the NCCA survey. To assess contaminant levels in tissues, the EFTCI is calculated
using an adaptation of EPA's ecological risk assessment guidelines (USEPA 1997) based on a wildlife
exposure framework (USEPA 1993). The NCCA analyzes whole-body fish samples for a broad list of
legacy environmental contaminants, including metals and metalloids, some pesticides, and other
persistent organic pollutants such as PCBs. For a full list of the analytes analyzed for in whole fish
samples, see the NCCA 2015 LOM (USEPA 2015b). For a list of species used for analyses in each
NCCA Region, please see Appendix A.5.
The EFTCI relies on development of screening values14 that can be summarized to characterize the
potential for multiple contaminant exceedances in a fish tissue composite from each site. Screening
values derived for each of the NCCA contaminants of interest in relation to broad categories of
piscivorous wildlife (i.e., fish, birds and mammals) are used to account for the challenges of assessing
the ecological relevance of fish tissue contaminants at a national scale. Information on the development
14 In contrast to other contaminants in the EFTCI that do not have published screening values, the EPA has published a final
national aquatic life criterion recommendation for selenium in freshwater (USEPA 2016b). The selenium screening values used
in the EFTCI are described in Section 7.4.6.
80
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of these screening values and EFTCI was first prepared for US EPA Region 6 by Tetra Tech, Inc.
(Tetra Tech 2012) and subsequently presented in the NCCA 2010 Technical Report (USEPA 2016a).
Updated information for 2015, including revised equations15, are described in detail below.
7.2 Field collection and laboratory analysis
Composited whole-body fish samples of select forage-size fish species were analyzed for a suite of
metals, metalloids, and organic pollutants (Table 7.1; USEPA 2015a; 2015b).
Table 7.1 Laboratory methods for fish tissue contaminant analysis
Parameter Method
| Metals (excluding mercury) • EPA 6020 •
Mcrcurv I '.PA 245.7
PCBs, pesticides I '.PA 8270D
7.3 Data preparation
In cases where the laboratory reported values that were non-detect (i.e., below the method detection
limit), results were set to zero. Result values were then converted to a dry weight (mg/kg dw)
concentration by dividing by a constant (0.28) that approximates the proportion of solids in wet fish
tissue composite samples (USEPA 1993). The inorganic fraction of arsenic was estimated as 10% of the
total arsenic concentration reported (USEPA 2003). Total mercury was assumed to consist primarily of
methylmercury and was not adjusted to remove the non-methylated fraction (Wagemann et al. 1997).
Total organic contaminant groups were calculated by summing the concentration of components in the
fish tissue composite samples (See Table 7.2)
15 The EFTCI used for the NCCA 2010 Report has been updated for the NCCA 2015 Report with revised formulae. The
updated formulae have also been used to revise results for the NCCA 2010 EFTCI, which is reflected in an addendum to the
NCCA 2010 Report (Tittps://www.epa.eov/national-aquatic-resource-sufveys/national-coastal-condition-assessment-2010-
report) and NCCA Dashboard (https://coastalcondition.epa.gov/).
81
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Table 7.2 Organic Contaminant groups used for calculating the EFTCI.
Organic Contaminant Group Components
Total DDT
2,4'-DDD; 2,4'-DDH; 2,4'-DDT; 4, 4'-DDl); 4,4'-DDi:; 4,4'-DDT
Alpha-chlordane, cis-nonachlor, gamma-chlordane, heptachlor,
heptachlor epoxide, oxychlordane, trans-nonachlor
Endosulfan sulfate, Endosulfan I, Endosulfan II
Total Chlordane
Total Endosulfan
Total Endrin
Endrin, Endrin Ketone, Endrin Aldehyde
PCB8, 18, 28, 44, 52, 66, 77, 101, 105, 110, 1 18, 126, 128, 138, 153,
170, 180, 187, 195, 206, 209
Total PCB
7.4 Screening Values
There are few published ecologically based fish contaminant assessment approaches that are
appropriate in the context of the NCCA, wherein a single composite sample is collected and analyzed
for contamination once every five years from a probabilistically selected site. However, the evaluation
of risk using food webs for contaminant exposure through dietary uptake is well documented (ODEQ
2007; US ARMY 2006; CCME 1998; Sample et al. 1996; Newell et al. 1987). For its Superfund program
under RCRA (USEPA 1997; 1998; 1999), EPA suggests the use of a tiered approach, including a
screening-level analysis (such as used for the NCCA EFTCI), as a part of determining the level of effort
needed for ecological risk assessments. Methods described in the Wildlife Exposure Factors Handbook
(USEPA 1993) serves as the analytic framework for conducting screening-level risk assessments for
common wildlife species. These screening-level risk assessments may be used for several purposes,
including: to assess potential effects of environmental contamination on wildlife species and to support
site-specific decisions (e.g., for hazardous waste sites); to support the development of water-quality or
other media-specific criteria for limiting environmental levels of toxic substances to protect wildlife
species; or to focus research and monitoring efforts. A screening-type approach is a cost-effective first step in
conducting wildlife exposure assessments, and is suitable for use in the NCCA to characterize the
potential for contaminants in fish to adversely affect predators.
For the EFTCI, ecologically relevant screening values were calculated to evaluate whether the
concentrations of metals, metalloids and organic contaminants measured in whole-body fish tissue
potentially lead to adverse effects when consumed by predatory fish and piscivorous wildlife. See
Appendix A for the laboratory-based endpoints based on surrogates for each group of receptors.
7.4.1 Receptors of Concern
Receptors of concern (ROCs) are typically those animals that are exposed to contaminants through
ingestion, dermal contact, and/or inhalation (USEPA 1997). The exposure of ROCs to contaminants
by ingestion is through either incidental media uptake (i.e., eating soil or sediment that is associated
with prey items), drinking contaminated surface water, or through the ingestion of prey organisms that
82
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have accumulated contaminants in their tissues. The EFTCI considers upper trophic level organisms
including birds, fish and mammals to be ROCs. For NCCA, contaminant concentrations were
measured in whole-body forage fish tissue composite samples and risk assessment considerations were
evaluated based solely on the uptake of contaminants that have been accumulated in the tissues of prey
items consumed by groups of ROCs.
Generalized ROC groupings (See Table A.3.1) informed development of exposure-based screening
values because fish composite samples were collected in both freshwater and marine waters across all
US coastal resources. These classes include freshwater predatory fish, marine predatory fish,
piscivorous birds, piscivorous freshwater mammals and piscivorous marine mammals. Receptors were
chosen based on having predominantly fish diets and the availability of contaminant exposure data in
the literature. Species that comprise receptors of concern groupings for NCCA evaluations represent
those aquatic dependent organisms that are commonly included in ecological risk assessments
(Appendix A.l).
7.4.2 Ecological Risk Assessment Based Approach for Deriving Screening Values
Under EPA (1997) guidelines, risk is defined as a ratio of exposure concentration of contaminant to a
concentration that is known to have toxicological effects (toxicity reference value) in specific biological
species. In a typical application, risk potential is derived by calculating a hazard quotient (HQ), which is
a ratio expressed by dividing exposure concentration by a reference concentration (or toxicity reference
value) known to elicit adverse toxicological effect (Low Observed Adverse Effects Level or LOAEL,
Equation 7.1, below). Similarly, a HQ can be calculated for more conservative contaminant exposure
concentrations that are known to not elicit toxicological effects by substituting the No Observed
Adverse Effect Level or NOAEL as the toxicity reference value. Risk can be expressed as:
... , . , Exposure Concentration ^ _ ,
HQ (risk) = — — Equation 7-1
Toxicity Reference Value
Thus, when the exposure concentration of a contaminant is greater than the concentration known to
elicit toxic effects, the HQ is greater than 1.0, and the receptor is at risk.
Following through, the exposure concentration can be represented by Equation 7-2, below:
_ ..... FIRx[fish]xAUF ^ .
Exposure Concentration = Equation 7-2
Where:
FIR = food ingestion rate (kg food/d)
[fish] = concentration in fish tissue (mg/kg)
AUF = area use factor
BW = body weight of receptor
83
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If the concentration known to elicit toxic effects can be estimated for different ROCs, then the
exposure concentration equation can be rearranged to produce screening values representing an HQ =
1 for each receptor and contaminant combination. See Section 7.5 Application of Screening Values for the
EFTCI for specific usage. The following sections describe the methods for parameterizing receptor and
contaminant characteristics such as body weight, food ingestion rate, and toxicity reference values
(TRVs), which are components needed to quantify NCCA fish tissue contaminant screening values. To
be most protective, the AUF is set to 1.0 indicating all foraging, resting, breeding and other activities
are expected to occur within the exposure area of concern.
7.4.3 Receptor Characteristics: Body Weight and Food Ingestion Rate
Because food ingestion for birds and mammals and daily ration for fish are based on the metabolism of
the animal, smaller individuals generally consume more food per unit of body weight than larger
receptors (USEPA 2016a; See Table 7.3).
Food ingestion rates were available for marine fish (Maldeniya 1996) and freshwater fish (Carlander
1969). In the absence of FIR in the literature, we used allometric regression models based on metabolic
rate and expressed in terms of body weight (g) for birds and mammals (Nagy et al. 1987). These models
are described in more detail in Sample et al (1996) and Sample and Arenal (1999). To be most
protective, the food ingestion rates were calculated based on the minimum body weight of the receptor.
The receptor exhibiting the lowest body weight and highest ingestion rate was selected to represent
each respective receptor group (See Table 7.3). The body weight and food ingestion rate each of the
generalized receptors were used to calculate toxicity reference values (Equation 7-3) and screening
values (Equation 7.4) for each contaminant. While the body weights and food ingestion rates for
freshwater and marine mammals and fish are listed in Table 7.3, the freshwater organisms were used in
this generalized screening because they have higher food ingestion rates per body weight and are
therefore contribute to more protective TRVs and subsequent screening values.
Table 7.3 Summary of generalized receptor body weights and daily food ingestion rates used to
calculate screening fish tissue values.
Receptor Group
Body Weight Food Ingestion Rate Daily Food Ingestion
(kg) (kg food/kg BW/d) Rate(kg/d)
Birds
('Megaceiyle algoti)
Freshwater Mammals
(Nemion vison)
Marine Mammals
0.13
0.55
58.8
0.033
0.076
0.118
0.0420
0.0156
1.956
(Phoca vitulina)
Freshwater Fish
(Esox masquinongf)
Marine Fish
(Thunnus alhacares)
23.42
0.34
0.064
0.023
0.02176
0.539
84
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7.4.4 Wildlife Toxicity Reference Value (TRV) Calculations
For NCCA, toxicity is defined by a toxicity reference value (TRV), which is a derived from no
observable adverse effects level (NOAEL) values generated from laboratory-based experimental
studies. NOAELs represents the contaminant concentration above which ecologically relevant adverse
effects might occur in wildlife populations after long-term dietary exposure. Literature-based NOAEL
values used to derive the NCCA TRVs are based on laboratory surrogate species (e.g., chickens, quail,
duck, rat mouse, rainbow trout and Japanese medaka). For some contaminants and receptors of
concern, laboratory-based tests used to develop TRVs may not have resulted in endpoints that were
protective for chronic exposure. In such cases, a chronic exposure endpoint was determined from the
reported endpoint using a conversion factor (Sample, et al. 1996). Fish TRVs were extracted from
existing literature (See Appendix A.l). Avian and mammal TRVs were acquired from reported
laboratory tests, then scaled to the food ingestion rates16 and body weights of the selected NCCA
ROCs using the interspecies allometric model introduced in Sample and Arenal (1999). NOAEL
concentrations for the contaminants evaluated for the NCCA for each generalized receptor of concern
is shown in Table 7.4. See Appendix A.l for sources of each NOAEL value.
Table 7.4 NOAELtest values (for use in Equation 7-3 to calculate TRVwiiaiife) for contaminants or
contaminant classes calculated for each generalized ROC.
Analyte
Avian NOAELtc,t
FW Mammal NOAELt t
FW Fish NOAELi,
1 (mg/kg-bw/d)
i (mg/kg-bw/d)
i (mg/kg-bw/d)
Arsenic, Inorganic
5.1
0.126
0.02563
Cadmium
1.45
j 0.75
! 20
Chlordane, Total
' 0.8
4.58
NA
DDT, Total
| 0.3
| 0.8
0.143
Dieldrin
; 0.08
; 0.028
0.024
Endrin, Total
| 0.02
| 0.18
0.04
Endosulfan, Total
10
1
! 0.0002393
H exachlorobenz ene
0.11
1
I 0.000685
Lindane
0.56
: 8
10
Mercury
! 0.03
! 0.032
| 0.06768
Mirex
0.01
0.07
: 0.3
PCB, Total
0.18
| 0.068
0.05
16 Food ingestion rates for fish were found in Carlander 1969. Avian and mammalian food ingestion rates were calculated using
regression equations derived from Nagy (1987).
85
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TRY,
wildlife
Where:
= NOAEL
TRVwnaiife
NOAELtest
BWtest
BWwildlife
X
x ( BWtest \
test VBWwildlifcJ
(1-x)
Equation 7-3
toxicity reference value for wildlife species (See Table 7.4)
no observed adverse effect level for test species
body weight for test species (See Appendix A)
body weight for wildlife species
scaling factor17
Table 7.5 Wildlife TRVs for each contaminant and generalized ROC, based upon NOAELS
reported in Table 7.3. (for use in Equation 7-4 to calculate screening values).
Analyte
Avian
TRVwildlife
FW Mammal
TRVwildlife
FW Fish
TRVwildlife
(mg/kg-bw/d)
(mg/kg-bw/d)
(mg/kg-bw/d)
Arsenic, Inorganic
3.391242502
0.105822023
0.025846448
Cadmium
0.9371 10516
0.892564279
72.0589905
Chlordane, Total
0.531959608
3.846546545
NA
DDT, Total
0.147013886
0.778596251
0.26802071
Dieldrin
0.061867004
0.0333224
0.06181342
Endrin, Total
0.018739766
0.151174318
0.147421822
Endosulfan, Total
7.986871041
1.190085705
0.000254478
Hexachlorobenzene
0.106896406
0.973245314
0.001665614
Lindane
0.544199885
7.785962511
14.14703843
Mercury
0.019948485
0.031 14385
0.134918462
Mirex
0.006649495
0.063889156
0.372954577
PCB, Total
0.119690912
0.054557547
0.073362105
Selenium18
0.265979804
0.194649063
17 Scaling factors presented by Sample and Arenal (1999) indicate that mammalian sensitivity increases with increased body
weight, and avian sensitivity increases with decreased body weight. Scaling factors were unavailable for fish receptors but, like
avian receptors, an increase in sensitivity with decreased body weight was reported (Buhler and Shanks, 1970). A scaling factor
of 0.94 is used for mammalian receptors (Sample and Arenal, 1999) and a scaling factor of 1.2 is used for avian receptors
(Sample and Arenal, 1999) and fish receptors (Buhler and Shanks, 1970).
18 TRVs are the best available science for developing screening values for most elemental and organic contaminants analyzed for
this index. In 2016b, EPA developed an aquatic life criterion for selenium, which was used to derive the screening values for Se.
See Section 7.4.6.
86
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7.4.5 Screening Values
Using the proxy TRVs (Table 7.5), and the body weights and daily food ingestion rates from Table 7.3,
adverse dietary exposure concentrations in the fish that NCCA ROCs may eat can be estimated.
Screening values for NCCA tissue contaminants of interest were derived using the following equation:
SV =
(TRVwil dl if e x ^^wildl if e )
FIRwildlife
Equation 7-4
Where:
SV
TRV =
BW =
FIR =
screening value concentration in fish tissue (mg/kg) for specific analyte
estimated wildlife toxicity reference value (mg/kg bw/d)
body weight (kg) of wildlife ROC used represent receptor group
daily food ingestion rate (kg food/d) of wildlife ROC used to represent
receptor group
Each screening value represents an estimated contaminant concentration in fish tissue composite
samples that could result in the minimum risk for exposure to each receptor group. Minimum potential
exposure risks (HQ=1.0) were calculated for each analyte and receptor group combination to serve as
screening benchmarks.
7.4.6 EPA Tissue-Based Criteria Deriving Selenium Screening Values for Fish
In 2016, the EPA published the final national aquatic life criterion recommendation for selenium in
freshwater (USEPA 2016b).19 The EPA selenium criterion recommendation is composed of four
elements, including two criterion elements based on the concentration of selenium in fish tissue (i.e.,
egg-ovary element and whole-body or muscle element). This criterion represents the best available
science on the toxicity of selenium to freshwater aquatic life; therefore, the EFTCI utilized the whole-
body chronic selenium criterion element to derive the selenium screening value for fish ROCs.
The methods used to derive the EPA selenium criterion recommendation differ from the TRV
methods described in previous sections. The EPA derived the selenium criterion recommendation
using the procedure outlined in EPA's Guidelines for Deriving Numerical National Water Quality Criteria for
the Protection of Aquatic Organisms and Their Uses (Stephan et al. 1985). The dataset used to derive the
19 The 2016 "Aquatic Life Ambient Water Quality Criterion for Selenium — Freshwater, 2016," is a chronic criterion that is
composed of four elements. All elements are protective against chronic selenium effects. Two elements are based on the
concentration of selenium in fish tissue and two elements are based on the concentration of selenium in the water column. The
recommended elements are: (1) a fish egg-ovary element; (2) a fish whole-body and/or muscle element; (3) a water column
element (one value for lentic and one value for lotic aquatic systems); and (4) a water column intermittent element to account
for potential chronic effects from short-term exposures (one value for lentic and one value for lotic aquatic systems).
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tissue-based criterion consists primarily of fish species: 12 fish species representing 10 genera and 7
families. Reproductive toxicity tests using dietary exposures yielded effects endpoints (e.g., larval
mortality and deformities) that were set to the 10% effect concentration (ECio) for use in the derivation
of a selenium criterion. An ECio is generally of similar magnitude to a NOAEL (Iwasaki et al. 2015),
which is the TRV effect level used for other contaminants making it appropriate for use in the EFTCI.
Whole body reproductive chronic values were calculated directly from whole body tissue
concentrations measured in the study or by applying an egg-ovary (EO) to whole-body (WB)
conversion factor, detailed in Section 3.2.2.2 of Aquatic Ufe Ambient Water Quality Criterion for Selenium —
Freshwater2016 (EPA, 2016b). The final criteria recommendation is derived from the species sensitivity
distribution and represents the concentration of selenium in egg-ovary, whole body, or muscle tissue
that would be protective against selenium effects on fish reproduction.
EFTCI screening values are set as dietary concentrations in prey species rather than the selenium body-
burden of the receptor. Therefore, we translated the whole-body selenium criterion element (8.5 mg/kg
dw) to a dietary endpoint by accounting for the trophic transfer of selenium through the dietary
pathway with trophic transfer factor (TTF) values. TTFs quantify the degree of bioaccumulation
between trophic levels. The EPA derived TTF values for invertebrates and a wide range of fish based
on taxonomic relationships that do not directly align with the EFTCI fish ROC (EPA 2016b; see
Appendix B). To account for the expected trophic transfer of selenium for the EFTC fish ROCs, we
used the median TTF value for fish species that are piscivorous as adults (median = 1.41, see Table
7.6). The median TTF was then applied to the whole-body fish tissue criterion of 8.5 mg/kg dry weight
resulting in a selenium SV of 6.05 mg/kg dry weight for fish ROCs.
Table 7.6 EPA-derived trophic transfer factor (TTF) values presented in the Aquatic Life Ambient
Water Quality Criterion for Selenium - Freshwater (USEPA 2016b; see Table 3.11) and used to
derive the median TTF for piscivorous fish.
Order
Species
TTF
Esociformes
Northern pike
1.78
Perciformes
Largemouth bass
1.39
Perciformes
; Smallmouth bass
: 0.86
Perciformes
Striped bass
1.48
Perciformes
Walleye
1.6
Perciformes
i Yellow perch
1.42
Salmoniformes
Brown trout
! 1.38
Salmoniformes
I Rainbow trout
1.07
Median TTF 1.41
88
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7.5 Application of Screening Values for the EFTCI
The ROC with the lowest body weight and highest ingestion rate was selected to represent a sensitive
species in each wildlife receptor group. The receptor group and ROC species combinations used for
this report are: mammals — mink (Neovison vison) \ birds — belted kingfisher (Megaceryle altjon) \ and
predatory fish — muskellunge (Esox masquinongy).
NCCA whole-body fish tissue contaminant analysis results were compared to these calculated
benchmarks to determine if piscivorous fish and wildlife may be at risk due to the consumption of fish.
In Table 7.7, screening values for each group of receptors are summarized.
Table 7.7 NCCA ecological risk-based screening values for receptors of concern
NCCA Whole-Body Fish Tissue Contaminant Screening Values
Contaminant (mg/kSdw) (NOAEL-based)
MAMMAL1
AVIAN
FISHb
Arsenic (inorg)
1.3849
28.5892
0.4039
Cadmium
11.6807
7.9001
1125.9217
Mercury
0.4076
0.1682
2.1081
Selenium
2.5473
2.2423
6.05c
Dieldrin
0.4361
0.5216
0.9658
Total Endosulfan
15.5742
67.3318
0.004
Total Endrin
1.978
0.158
2.3035
Total Chlordane
50.3383
4.4846
NA
Hexachlorobenzene
12.736
0.9012
0.026
Lindane
101.892
4.5878
221.0475
Mirex
0.836
0.0561
5.8274
Total DDTs
10.1892
1.2394
4.1878
Total PCBs
0.714
1.009
1.1463
"Two mammal receptor group screening values calculated. The more sensitive freshwater mammal group was used in the
EFTCI.
bTwo fish receptor group screening values calculated. The more sensitive freshwater fish group was used in the EFTCI.
c Selenium screening value derived using the median EPA trophic transfer factor value of piscivorous species.
Table 7.8 describes how screening values were translated to good, fair and poor ratings for each
NCCA site.
89
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Table 7.8 Application of SV in the NCCA ecological fish tissue contaminant assessment. The
result is an EFTCI for each site surveyed.
Ecological Fish Tissue Contaminant Index Condition
j Good : All of the measured contaminant concentrations < screening value for all receptor group,
i Fair I At least one measured contaminant concentration > screening value for one receptor group.
j pQOr At least one measured contaminant concentration > screening value for two or more receptor
: groups.
7.6 References
Bowersox, M., P. Siwinski, and J. Diamond. 2012. Development of § 303(d) Listing Mechanism, TMDL
Targets and National Coastal Conditions Assessment (NCCA) Benchmarks for Selected
Bioaccumulative Pollutants. Tetra Tech, Inc. Prepared for US EPA Region 6 (EP-C-07-068
Work Assignments 3-08 and 4-08).
Buhler, D.R., and W.E. Shanks. 1970. Influence of body weight on chronic oral DDT toxicity in coho
salmon. J Fish Res Bd Can 27: 347-356.
Carlander, K.D. 1969. Handbook of freshwater fishery biology, volume 1. The Iowa State University
Press, Ames. Iowa.
Nagy, K. A. 1987. Field metabolic rate and food requirement scaling in mammals and birds. Ecol.
Monogr. 57: 111-128.
Sample, B.E., D.M. Opresko, and G.W. Suter II. 1996. Toxicological benchmarks for wildlife: 1996
revision. Environmental Restoration Division, ORNL Environmental Restoration Program.
ES/ER/TM-86/R3.
Sample, B.E. and C.A. Arenal. 1999. Allometric models for interspecies extrapolation of wildlife toxicity
data. Bull. Environ. Contam. Toxicol. 62:653-663.
U.S. Environmental Protection Agency (USEPA). 1993. Wildlife Exposure Factors Handbook, Vol I of
II. EPA/600/R-93/187a.
U.S. Environmental Protection Agency (USEPA). 1997. Ecological Risk Assessment Guidance for
Superfund: Process for Designing and Conducting Ecological Risk Assessments. Interim Final.
EPA/540/R-97/006.
U.S. Environmental Protection Agency (USEPA). 2003. Technical summary of information available
on the bioaccumulation of arsenic in aquatic organisms. EPA-822-R-03-032. Office of Science
and Technology Office of Water. Washington, DC. pp. 42.
U.S. Environmental Protection Agency (USEPA). 2015a. National Coastal Condition Assessment: Field
Operations Manual. EPA-841-R-14-007. Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015b. National Coastal Condition Assessment:
Laboratory Operations Methods Manual. EPA-841-R-14-008. Washington, D.C.
90
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U.S. Environmental Protection Agency (USEPA). 2016a. NCCA 2010 Technical Report. Washington,
D.C. Available at: https:/ /www.epa.gov/national-aquatic-resource-surveys/national-coastal-
condition-assessment-2010-technical-report.
U.S. Environmental Protection Agency (USEPA). 2016b. Aquatic Life Ambient Water Quality
Criterion for Selenium - Freshwater. EPA-822-R-16-006. Office of Science and Technology
Office of Water. Washington, D.C.
Wagemann, R., E. Trebacz, R. Hunt, and G. Boila. 1997. Percent methylmercury and organic mercury
in tissues of marine mammals and fish using different experimental and calculation methods.
'Environmental Toxicology and Chemistiy 16(9), 1859-1866.
91
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8 Human Health Fish Tissue Indicator
Fish are time-integrating indicators of persistent pollutants, and contaminant bioaccumulation in fish
tissue has important human health implications. Contaminants in fish pose various health risks (e.g.,
cancer risks, and noncancer risks such as reproduction or neurological development impacts) to human
consumers. The NCCA 2015 human health fish tissue indicator consists of collection and analysis of
two types of fish composite samples, including fish fillet plug samples and whole fish samples for
homogenized fillet analyses. Collectively, these samples provide information on the distribution of
selected persistent, bioaccumulative, and toxic (PBT) chemical residues (e.g., mercury, polychlorinated
biphenyls or PCBs, and per- and polyfluoroalkyl substances or PFAS). The fish fillet plug samples were
collected from both the marine and Great Lakes sites and analyzed for mercury only. The whole fish
samples for homogenized fillet composite analysis were collected from Great Lakes nearshore sites only
and analyzed for mercury, PCBs, and PFAS.20 Table 8.5 for a summary of PFAS results.
Field and analysis procedures for the Great Lakes human health homogenized fillet tissue indicator
described below were based on EPA's National Study of Chemical Residues in Lake Fish Tissue
(USEPA 2009) and EPA's Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories,
Volume 1, third edition (USEPA 2000). Fish were scaled and filleted in the laboratory where muscle
fillets from both sides of each fish were prepared with skin on and the belly flap attached, and fillets
from all of the individual specimens that comprise a composite sample from a site were homogenized
together.
8.1 Field Fish Collection
8.1.1 Whole Fish Samples for Chemical Analyses of Homogenized Fillet Tissue Composite
Samples
The NCCA 2015 crews collected whole fish samples for chemical analyses of homogenized fillet
composite samples from Great Lakes sites only. The 152 fish samples collected for this Great Lakes
human health fish tissue indicator consisted of a composite of fish (i.e., typically five similarly sized
individuals of one target species) from each site. The fish had to be large enough to provide sufficient
tissue for analysis and for archiving (when possible) (i.e., 155 grams of fillets for analysis and 330 g for
archive, collectively). Additional criteria for each fish composite sample included fish that were:
• All of the same species (for each site);
• Harvestable size per legal requirements or of consumable size if there were no harvest limits; and
• Similar size so that the smallest individual in the composite from a site was no less than 75% of
the total length of the largest individual in the composite.
20 For the NCCA 2015 survey, a composite sample was formed by combining fillet: tissue from up to five adu the same
siwiw finrl similar size from the same site. Use of cornposite sampling for screening studies is a cost-effectiv' estimate
ritaimriant' concentrations while also ensuring that there is sufficient' fish tissue to analyze for all co its of
92
-------
Crews were provided with a recommended list of target fish species and a list of alternative species in
the field operations manual (USEPA 2015a); however, if none of the recommended fish species were
available, crews chose an appropriate substitute. Table 8.1 provides a list of the fish species successfully
collected for this human health fish tissue indicator and identifies the number of samples collected for
each fish species.
8.1.2 Fish Tissue Plug Samples for Mercury Analysis
The NCCA crews removed fish fillet tissue plugs (taken from dorsal muscle) from whole fish that were
collected for the ecological fish tissue indicator if they were also on the target list for mercury analysis
(See Table 8.2). They attempted to collect fish fillet plug samples from all marine and Great Lakes
sites. To form a fillet plug sample, the crews collected fillet tissue plugs from two fish of the same
species (one plug per fish). Crews collected each fillet tissue plug by inserting a biopsy punch into a de-
scaled thicker area of dorsal muscle section of a fish. After plug sample collection from live fish, they
placed antibiotic salve over the wound and released the fish. The crews were provided with a
recommended list of target fish species for fish plug sample collection in the field operations manual
(USEPA 2015a); however, if none of the recommended fish were available, crews chose an appropriate
substitute. Table 8.2 provides a list of the fish species collected for fillet plug sample removal by
geographic area and identifies the number of fillet plug samples collected from each fish species in a
geographic area.
8.2 Mercury Analysis And Human Health Fish Tissue Benchmark
All fish tissue samples (both homogenized fillet composite tissue and fillet tissue plug samples) were
analyzed for total mercury. The samples were prepared using EPA Method 163IB, Appendix A
(USEPA 2001a) and analyzed using EPA Method 1631E (USEPA 2002), which utilizes approximately
1 g of fillet tissue for analysis. In screening-level studies of fish contamination, EPA guidance
recommends monitoring for total mercury rather than methylmercury (an organic form of mercury)
because most mercury in adult fish is in the toxic form of methylmercury, which will be captured during
an analysis for total mercury. Applying the conservative assumption that all mercury is present in fish
tissue as methylmercury is also more protective of human health (USEPA 2001b and Bloom 1992). The
human health benchmark used to interpret mercury concentrations in fillet tissue is 0.3 milligrams (mg)
of methylmercury per kilogram (kg) of tissue (wet weight) or 300 parts per billion (ppb), which is EPA's
tissue-based water quality criterion for methylmercury (USEPA 2001b). This human health fish tissue
benchmark represents the chemical concentration in fish tissue that, if exceeded, may adversely impact
human health. NCCA fish tissue collection data were screened to exclude samples where non-target
species were collected (i.e., species that are not typically consumed by humans) or the average fish
length was less than 190 mm. All of the Great Lakes fish collected for homogenized fillet analysis were
species that are commonly consumed by humans (Table 8.1). In contrast, some fillet plug samples
were not analyzed because they were from fish that were inappropriate for human health objectives
based on size or species (Table 8.2). Application of the mercury human health fish tissue benchmark to
the homogenized fillet composite data from this study identifies the number and percentage of square
93
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miles in the nearshore Great Lakes sampled population that contained fish composite samples with
mercury fillet concentrations that are above the mercury human health fish tissue benchmark. Mercury
concentration data from analysis of homogenized fish fillet samples are available to download from the
NCCA Great Lakes Fish Tissue Studies webpage - https://www.epa.gov/fish-tech/national-coastal-
condition-assessment-great-lakes-human-health-fish-tissue-studies. Mercury concentration data from
fish fillet tissue plugs are available to download from the NARS data webpage -
https: / /www.epa.gov/ national-aquatic-resource-surveys/data-national-aquatic-resource-surveys.
Table 8.1 NCCA 2015 Great Lakes Human Health Fish Composite Sample Species for
Homogenized Fillet Analyses (All species were appropriate for human health objectives).
Scientific Name
(>0111111011 Name
Clival Lakes
1'illel Samples
Aplodinotus grunniens
Freshwater Drum
11
Catostomus catostomus
Longnose Sucker
10
Catostomus commersonii
White Sucker
9
Coregonus clupeaformis
Lake Whitefish
17
Esox ludus
Northern Pike
1
Ictalurus punctatus
Channel Catfish
3
J jQtci lota
Burbot
2
Microptems dolomieu
Smallmouth Bass
11
Morone americana
White Perch
4
Morone chryscps
White Bass
4
Oncorhynchus kisutch
Coho Salmon
3
Oncorhynchus rnykiss
Rainbow Trout
9
Oncorhynchus tshawytscha
Chinook Salmon
6
Perca flavescens
Yellow Perch
19
Salmo trutta
Brown Trout
4
Salvelinus namaycush
Lake Trout
26
Sander vitreus
Walleye
13
Table 8.2 NCCA 2015 Fish Plug Species for Mercury Analysis. Checkmarks indicate species that
are not appropriate for human health objectives and were not analyzed for mercury.
Scieniilic Name
(>0111111011 Name
Numl
1 vast
Coast
ht Caug
Great
Lakes
111 Per |{
( 1 ul 1"
C< >ast
egion
Wr-I
Coast
Inappropriate
for I Iiinian
I Ieallli
Objectives
A losa pseudoharengus
Alewife
5
94
-------
Scientific Name
Common Name
Numl
1 vast
Coast
>er ("au«
Great
Lakes
In Per |{
( i ul 1"
C< >ast
ejjio11
Wr-I
Const
Inappropriate
for I Inman
I Icallli
Objectives
Amhloplites rupestris
Rock Bass
8
Ameiurus catus
White Catfish
1
Ameiurus nebulosus
Brown Bullhead
1
Anguilla rostrata
American Eel
3
Aplodinotus grunniens
Freshwater Drum
39
Ariopsis felis
Hardhead Catfish
9
96
Bagre marinus
Gafftopsail Catfish
50
Bairdiella chiysoura
Silver Perch
5
1
Brevoortia smithi
Yellowfin
Menhaden
2
yA
Brevoortia tyrannus
Atlantic Menhaden
3
yA
Caranx crysos
Blue Runner
1
Caranx hippos
Crevalle Jack
2
Catostomus catostomus
Longnose Sucker
15
Catostomus commersonii
White Sucker
1
25
Centroprostis striata
Black Sea Bass
8
1
Cheilotrema saturnum
Black Croaker
1
Citharichthys sordidus
Pacific Sanddab
7
Citharichthys stigmaeus
Speckled Sanddab
3
Clupea harengus
Atlantic Herring
1
Coregonus artedi
Cisco
1
Coregonus clupeaformis
Lake Whitefish
35
Cymatogaster aggregata
Shiner Perch
10
Cynoscion arenarius
Sand Seatrout
7
Cynoscion nebulosus
Spotted Seatrout
1
Cynoscion regalis
Weakfish
7
Cyprinus capio
Common Carp
11
Diplectrum formosum
Sand Perch
2
Diplodus holbrooki
Spottail Pinfish
1
Dorosoma cepedianum
Gizzard Shad
3
yB
Elops saurus
Ladyfish
2
1
Embiotoca lateralis
Striped Seaperch
2
Esox ludus
Northern Pike
3
E ucin ostomus gula*
Silver Jenny
1
Fundulus majalis
Striped Killifish
1
yc
95
-------
Scientific Name
Common Name
Numl
1 vast
Coast
jcr ("au«
Great
Lakes
In Per |{
( i ul 1"
C< >ast
ejjio11
Wr-I
Const
Inappropriate
for I Inman
I Icallli
Objectives
(,enyonemus lineatus
White Croaker
6
Haemulon plumierii
White Grunt
1
3
Haemulon sciurus
Bluestriped Grunt
1
Ictalurus punctatus
Channel Catfish
2
6
Lagodon rhomboides
Pinfish
9
12
yc
Leiostomus xanthurus
Spot
25
19
l^epidopsetta bilineata
Rock Sole
3
Lepisosteus osseus
Longnose Gar
1
l^epomis gibbosus
Pumpkinseed
1
Leptocottus armatus
Pacific Staghorn
Sculpin
9
yB
Umandaferruginea
Yellowtail
Flounder
1
J jQtci lota
Burbot
2
lMtjanus campechanus
Red Snapper
1
Lutjanus griseus
Gray Snapper
1
5
1
lMtjanus synagris
Lane Snapper
4
Luxilus cornutus
Common Shiner
1
yc
Menidia menidia
Atlantic Silverside
10
yc
Menticirrhus americanus
Southern Kingfish
11
Menticirrhus littoralis
Gulf Kingfish
1
Menticirrhus saxatilis
Northern Kingfish
3
Merlucdus bilinearis
Silver Hake
1
Micropogonias undulatus
Atlantic Croaker
13
18
Microptems dolomieu
Smallmouth Bass
35
Microptems salmoides
Largemouth Bass
3
Morone americana
White Perch
20
9
Morone chryscps
White Bass
7
Morone saxatilis
Striped Bass
7
Moxostoma anisurum
Silver Redhorse
1
Moxostoma
macrolepidotum
Shorthead
Redhorse
11
Mustelus canis
Smooth Dogfish
1
Neogobius melanostomus
Round Goby
1
yc
Oncorhynchus kisutch
Coho Salmon
2
96
-------
Scientific Name
Common Name
Numl
1 vast
Coast
>er ("au«
Great
Lakes
In Per |{
( i ul 1"
C< >ast
egion
Wr-I
Const
Inappropriate
for I Iuman
I Ieallli
Objectives
< >¦ vrhynchus my kiss
Rainbow Trout
1
Opsanus tau
Oyster Toadfish
1
yB
Orthcpristis chiysoptera*
Pigfish
2
Osmerus mordax
Rainbow Smelt
1
Paralabrax
maculatofasciatus
Spotted Sand Bass
4
Paralabrax nebulifer
Barred Sand Bass
4
Paralichthjs californicus
California Halibut
21
Paralichthys dentatus
Summer Flounder
14
Paralichthjs lethostigma
Southern Flounder
1
2
Perca flavescens
Yellow Perch
57
Platichthjs stellatus
Starry Flounder
3
Pleuronectes glacialis*
Arctic Flounder
1
Pleuronichthys guttulatus
Diamond Turbot
1
Pogonias cromis
Black Drum
2
Pollachius virens
Pollock
1
Pomatomus saltatrix
Bluefish
10
Pomoxis nigromaculatus
Black Crappie
1
Prionotus carolinus
Northern Searobin
2
Prionotus scitulus*
Leopard Searobin
1
Proscpium
cylindraceum**
Round Whitefish
5
Pseudcpleuronectes
americanus
Winter Flounder
26
Salmo salar
Atlantic Salmon
1
Salvelinus namaycush
Lake Trout
9
Sander vitreus
Walleye
29
Sciaenops ocellatus
Red Drum
2
Scomber scombms
Atlantic Mackerel
9
Sccphthalmus aquosus
Windowpane
1
Stenotomus chiysops
Scup
45
Tautogolabrus adspersus
Cunner
6
Trinectes maculatus
Hogchoker
2
yc
Urophycis chuss
Red Hake
1
Zoarces americanus
Ocean Pout
1
97
-------
* Although small, may be eaten by humans.
**Although not typically targeted, may be eaten by humans.
AUsed commercially for fish meal, fish oil, or bait; typically, not directly consumed by humans.
B Consumption by humans extremely rare.
c Small forage (and/or bait) species, not consumed by humans.
8.3 PCB Analysis And Human Health Fish Tissue Benchmarks
Fish fillet tissue samples prepared from the 152 fish composite samples collected at Great Lakes
nearshore sites were analyzed for PCBs using EPA Method 1668C (USEPA 2010). This method utilizes
approximately 10 g of fillet tissue for analysis and provides results for the full set of 209 PCB congeners.
The total PCB concentration for each sample was determined by summing the results for any of the 209
congeners that were detected, using zero for any congeners that were not detected in the sample.
EPA used a 49 ppb human health benchmark for total PCB noncancer effects and a 12 ppb human
health benchmark for total PCB cancer effects to report 2015 Great Lakes Human Health Fish Fillet
Tissue Study data in the NCCA 2015 Final Report. Both of these benchmarks were derived using a fish
consumption rate of 32 g/day21. This nutrition-based fish consumption rate of 32 g/day better reflects
the role and purpose of fish advisory programs because it does not include data for non-consumers and is
also consistent with the rate used in fish advisory programs across the Great Lakes. EPA acknowledges
this rate does not reflect "high frequency consumers" such as subsistence fishers or those who eat several
meals of fish per week, which often includes individuals in underserved communities. In an effort to
provide information to state, territorial, or tribal programs with populations of frequent fish consumers,
EPA has provided Table 8.3 that includes estimated benchmark exceedances for PCBs using fish
consumption rates that are more typical of these populations. This table also includes results for the
human health benchmarks based on a 32 g/day fish consumption rate for comparison.
Application of the PCB benchmarks representing average fish consumers for the Great Lakes area and
two other sets of PCB benchmarks, described below for high frequency fish consumers, to the total PCB
fillet data identifies the number and percentage of square miles in the Great Lakes nearshore sampled
population containing fish with total PCB fillet concentrations that are above each PCB human health
fish tissue benchmark. Data on exceedances of the PCB human health benchmarks are provided in Table
8.3. In addition to the benchmarks representing average fish consumers, the first set of PCB benchmarks
for "high frequency consumers" is based on a fish consumption rate of 142 g/day, which is described in
the EPA 2000 Human Health Methodology (USEPA 2000b). The second set of PCB benchmarks for
"high frequency consumers" is based on a fish consumption rate of 175 g/day, which has been used by
EPA and some states for high frequency consumers or subsistence fishers in the Pacific Northwest.
21 Since EPA does not currently have a fish tissue-based water quality criterion for PCBs, EPA has selected to use the equations
found in its Guidance for Assessing Chemical Contaminant Datafor Use in Fish Advisories (USEPA 2000) with updated body weights in
EPA's Exposure Factors Handbook (USEPA 2011) and a nutritionally focused fish consumption rate consistent with the U.S.
Department of Agriculture and Department of Health and Human Services' Dietary GuideRnesfor Americans, 2020-2025 of 32
grams/day (equivalent to one eight-ounce meal of fish and shellfish per week).
98
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Table 8.3 Percentages of Total PCB Human Health Fish Tissue Benchmark Exceedances
Chemical-Specific Human
Health Fish Tissue (HH)
Benchmarks
% Assessed Nearshore Area
with Fish Fillet
Concentrations Above Total
PCB Noncancer
HH Benchmarks
% Assessed Nearshore Area
with Fish Fillet
Concentrations Above Total
PCB Cancer
HH Benchmarks
Total PCB Noncancer 49 ppb
HH Benchmark (32 g/day FCR*)
53%
Total PCB Noncancer 11 ppb
HH Benchmark (142 g/day FCR)
81%
Total PCB Noncancer 9.1 ppb
HH Benchmark (175 g/day FCR)
.o
00
00
Total PCB Cancer 12 ppb HH
Benchmark (32 g/day FCR)
79%
Total PCB Cancer 2.8 ppb HH
Benchmark (142 g/day FCR)
100%
Total PCB Cancer 2.3 ppb HH
Benchmark (175 g/day FCR)
100%
FCR — Fish consumption rate
PCB concentration data from analysis of homogenized fish fillet samples are available to download from
the NCCA Great Lakes Fish Tissue Studies webpage - https://www.epa.gov/fish-tech/national-coastal-
condition-assessment-great-lakes-human-health-fish-tissue-studies.
8.4 PFAS Analysis And Human Health Fish Tissue Benchmark
Fish fillet tissue samples prepared from the 152 fish composite samples collected at Great Lakes
nearshore sites were analyzed for 13 per- and polyfluoroalkyl substances (PFAS), including
perfluorooctane sulfonate or PFOS, which is the most commonly detected PFAS in freshwater fish.
There are no standard EPA methods for PFAS analysis of tissue samples, so the samples were analyzed
by SGS AXYS Analytical Services, Ltd. using a proprietary procedure developed by their laboratory in
Sidney, British Columbia, Canada. That procedure, which utilizes approximately 2 g of fillet tissue for
analysis, uses high performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS)
and applies the technique known as isotope dilution to determine the concentration of each of the 13
PFAS.
EPA used a 46 ppb human health benchmark for PFOS to report 2015 Great Lakes Human Health Fish
Fillet Tissue Study data in the NCCA 2015 Final Report. This benchmark was derived using a fish
99
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consumption rate of 32 g/day22. This nutrition-based fish consumption rate of 32 g/day better reflects
the role and purpose of fish advisory programs because it does not include data for non-consumers and is
also consistent with the rate used in fish advisory programs across the Great Lakes. EPA acknowledges
this rate does not reflect "high frequency consumers" such as subsistence fishers or those who eat several
meals of fish per week, which often includes individuals in underserved communities. In an effort to
provide information to state, territorial, or tribal programs with populations of frequent fish consumers,
EPA has provided Table 8.4 that includes estimated benchmark exceedances for PFOS using fish
consumption rates that are more typical of these populations. This table also includes results for the
human health benchmark based on a 32 g/day fish consumption rate for comparison.
Application of the PFOS benchmark representing average fish consumers for the Great Lakes area and
two other PFOS benchmarks, described below for frequent fish consumers, to the PFOS fillet data
identifies the number and percentage of square miles in the Great Lakes nearshore sampled population
containing fish with PFOS fillet concentrations that are above each PFOS human health fish tissue
benchmark. Data on the exceedance of this human health benchmark for average fish consumers are
provided in Table 8.4. In addition to the benchmark representing average fish consumers, the first PFOS
benchmark for "high frequency consumers" is based on a fish consumption rate of 142 g/day, which is
described in the EPA 2000 Human Health Methodology (USEPA 2000b). The second PFOS benchmark
for "high frequency consumers" is based on a fish consumption rate of 175 g/day, which has been used
by EPA and some states for high frequency consumers or subsistence fishers in the Pacific Northwest.
Table 8.4 Percentages of PFOS Human Health Fish Tissue Benchmark Exceedances
Chemical-Specific Human Health Fish Tissue
(HH) Benchmarks
% Assessed Great Lakes Nearshore Area
with Fish Fillet Concentrations
Above PFOS HH Benchmarks
PFOS 46 ppb HH Benchmark (32 g/day FCR*)
5%
PFOS 11 ppb HH Benchmark (142 g/day FCR)
52%
PFOS 8.6 ppb HH Benchmark (175 g/day FCR)
67%
FCR — Fish consumption rate
Summary statistics, including the number of detections for each of the 13 PFAS analyzed for the 2015
Great Lakes Human Health Fish Fillet Tissue Study, are provided in Table 8.5. PFAS concentration data
from analysis of homogenized fish fillet samples are available to download from the NCCA Great Lakes
Fish Tissue Studies webpage - https://www.epa.gov/fish-tech/national-coastal-condition-assessment-
great-lakes-human-health-fish-tissue-studies.
22 Since EPA does not currently have a fish tissue-based water quality criterion for PFOS, EPA has selected to use the equations
found in its Guidance for Assessing Chemical Contaminant Datafor Use in Fish Advisories (USEPA 2000) with updated body weights in
EPA's Exposure Factors Handbook (USEPA 2011) and a nutritionally focused fish consumption rate consistent with the U.S.
Department of Agriculture and Department of Health and Human Services' Dietary GuideRnesfor Americans, 2020-2025 of 32
grams/day (equivalent to one eight-ounce meal of fish and shellfish per week).
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Table 8.5 2015 Great Lakes Human Health Fish Fillet Tissue Study PFAS Fillet Composite Data
Chemical
Number
Detection
MDLs
Measured
Weighted
Measured
of
Frequency
(PPb)
Minimum
Median
Maximum
Detections
(%)
Concentration
(PPb) a
Concentration
(ppb) b
Concentration
(ppb)a
PFBA
0
0
0.21
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U.S. Environmental Protection Agency (USEPA). 2002. Method 1631, Revision E: Mercury in Water
by Oxidation, Purge and Trap, and Cold Vapor Atomic Fluorescence Spectrometry. EPA-821-
R-02-019. August 2002. U.S. Environmental Protection Agency, Office of Water, Washington,
DC.
U.S. Environmental Protection Agency (USEPA). 2009. The National Study of Chemical Residues in
Lake Fish Tissue. EPA-823-R-09-006. U.S. Environmental Protection Agency, Office of Water,
Washington, DC.
U.S. Environmental Protection Agency (USEPA). 2010. Method 1668C, Chlorinated Biphenyl
Congeners in Water, Soil, Sediment, Biosolids, and Tissue by HRGC/HRMS, April 2010. U.S.
Environmental Protection Agency, Office of Water, Washington, DC.
U.S. Environmental Protection Agency (USEPA). 2015a. National Coastal Condition Assessment: Field
Operations Manual. EPA-841-R-14-007. Washington, D.C.
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9 Enterococci Indicator
The EPA developed and validated a molecular testing method employing quantitative polymerase
chain reaction (qPCR) as a rapid approach for the detection of enterococci in recreational water
(USEPA 2015). NCCA used this method to estimate the presence and quantity of these fecal
indicator bacteria in the nation's coastal area. The statistical benchmark value of 1280 calibrator cell
equivalents (CCE)/100 mL from EPA's 2012 Recreational Water Quality Criteria document
(RWQC) was then applied to the enterococci data to assess the recreational condition of coastal
waters.
9.1 Field Collection
To collect enterococci samples, field crews took a water sample with a gloved hand or a pole-dipper
at a depth of 0.5 m using a sterile 250 mL bottle. In addition to collecting the sample, crews looked
for signs of disturbance that would contribute to the presence of fecal contamination to the
waterbody. Following collection, crews added sodium thiosulfate and placed the sample in a cooler
on wet ice. Within 6 hours of collection, two 50 mL volumes were filtered and the filters were
frozen and shipped to the lab on dry ice. A sterile phosphate buffer solution (PBS) blank was also
filtered at revisit sites during visit 1 and visit 2.
9.2 Lab Methods
The sample collections and the laboratory method followed EPA's Enterococcus qPCR Method
1609.1 (USEPA 2015; available on-line at https: / / www.epa.gov / cwa-methods / other-clean-water-
act-test-methods-microbiological). Method 1609.1 describes a quantitative polymerase chain
reaction (qPCR) procedure for the detection of DNA from enterococci bacteria in ambient water
matrices based on the amplification and detection of a specific region of the large subunit ribosomal
RNA gene (lsrRNA, 23S rRNA) from these organisms. This method uses an arithmetic formula
(the comparative cycle benchmark (CT) method; Applied Biosystems, 1997) to calculate the ratio of
Enterococcus lsrRNA gene target sequence copies (TSC) recovered in total DNA extracts from the
water samples relative to those recovered from similarly prepared extracts of calibrator samples
containing a consistent, pre-determined quantity of Enterococcus cells. Mean estimates of the
absolute quantities of TSC recovered from the calibrator sample extracts were then used to
determine the quantities of TSC in the water samples and then converted to CCE values as
described in the section below. To normalize results for potential differences in DNA recovery,
monitor signal inhibition or fluorescence quenching of the PCR analysis caused by a sample matrix
component, or detect possible technical error, CT measurements of sample processing control
(SPC) and internal amplification control (IAC) target sequences were performed as described in
Method 1609.1. The qPCR method is appropriate for both marine and freshwater environments as
described in the 2012 Recreational Water Quality Criteria guidelines.
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9.2.1 Calibration
Estimates of absolute TSC recoveries from the calibrator samples were determined from standard
curves using EPA-developed plasmid DNA standards of known TSC concentrations as described
in Method 1609.1. Estimates of TSC recovered from the test samples were determined by the
comparative cycle benchmark (CT) method, as also described in Method 1609.1. Before applying
the EPA benchmarks to the qPCR data, it was necessary to convert the TSC estimates to CCE
values. The standardized approach developed for this conversion is to assume 15 TSC/CCE
(USEPA 2015). This approach allows the CCE values to be directly compared to the EPA RWQC
values (Haugland et al. 2015).
9.3 Analysis of Enterococci Concentrations
For the data analysis of the enterococci measurements determined by Method 1609.1, EPA used
benchmarks as defined and outlined in the 2012 RWQC document (USEPA 2012). The document
contains the EPA's ambient water quality criteria recommendations for protecting human health in
marine and freshwaters. Enterococci CCE/100 mL values were compared to the EPA statistical
benchmark value of 1280 CCE/100 mL23 (USEPA 2012). The enterococci concentration data are
available to download from the NARS data webpage - https: / /www.epa.gov /national-aauatic-
resource-survevs/data-na tio nal-aqua tic-resource-sun,? evs.
9.4 References
Applied Biosystems (1997) User Bulletin #2. ABI PRISM 7700 Sequence Detection System.
Applied Biosystems Corporation, Foster City, CA.
Haugland, R.A., S.D. Siefring, M. Varma, A.P. Dufour, K.P. Brenner, T.J. Wade, E. Sams, S.
Cochran, S. Braun, and M. Sivaganesan. 2015. Standardization of enterococci density
estimates by EPA qPCR methods and comparison of beach action value exceedances in
river waters with culture methods./. Microbiol. Methods. 105, 59-66.
U.S. Environmental Protection Agency (USEPA). 2012. Recreational Water Quality Criteria. EPA
820-F-12-058. Washington, D.C.
U.S. Environmental Protection Agency (USEPA). 2015. Method 1609.1: Enterococci in water by
TaqMan® quantitative polymerase chain reaction (qPCR) assay with internal amplification
control (IAC) assay. EPA-820-R-15-009. US Environmental Protection Agency, Office of
Water, Washington, D.C.
23 Estimated Illness Rate (NGI): 32/1000 primary contact recreators. See USEPA 2012 for more information on additional
NGI statistical threshold values for the qPCR method.
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10 Microcystes
Microcystins comprise a group of toxins produced by various cyanobacteria, or blue-green algae.
Microcystin exposure risk to humans is elevated when an overabundance of cyanobacteria occurs
in recreational surface water, especially during algal bloom events. Human exposure to
microcystins and associated cyanobacterial toxins may produce adverse symptoms ranging in
severity from nausea, diarrhea, weakness, to liver and kidney failure, potentially cancer, and even
death in severe cases (Chorus and Bartram 1999; Giannuzzi et al. 2011; Meneely and Elliott
2013). For NCCA, microcystin concentrations were evaluated against the EPA recommended
swimming advisory level of 8 ng/L (USEPA 2019).
10.1 Field and Laboratory Methods
Water samples were collected at a depth of 0.5 m using a water collection device (e.g., a Niskin
bottle) for microcystin analysis from all estuarine and freshwater sites. Water was transferred to a
500 mL bottle, kept on ice, and then stored frozen until analysis.
Samples were lysed by three freeze-thaw cycles and filtered with 0.45-micron syringe filters, then
analyzed using the Abraxis Microcystins-ADDA ELISA Kit. Brackish water (salinity greater than
3.5%) samples underwent further extraction to remove salts and eliminate adverse performance
effects on the immunoassay (per the Abraxis Bulletin R(. .Microcystins in Brackish Water
or Seawater Sample Preparation). For freshwater samples, the procedure's reporting limit is
0.15 [J.g/L, although, theoretically, the procedure can detect, but not quantify, microcystins
concentrations as low as 0.10 [J.g/L. For brackish samples (samples with greater than 3.5 ppt
salinity), the procedure's reporting limit is 0.263 [J.g/L, although, theoretically, the procedure can
detect, but not quantify, microcystins concentrations as low as 0.175 [J.g/L.
10.2 Analysis of Microcystin Concentrations
Microcystin concentrations were evaluated against the EPA recommended swimming advisory
level of 8 |Jg/L (USEPA 2019). Microcystin concentration data are available to download from
the NARS data webpage: https: / /www.epa.gov/national-aquatic-resource-surveys /data-national-
aquatic-resource-survevs.
10.3 References
Abraxis, "Microcystins-ADDA ELISA (Microtiter Plate)," Product 520011, R021412, Undated.
Retrieved January 2015 from
http://www.abraxiskits.com/uploads/products/docfiles/278 Microcystin%20PL%20A
DDA%20i.isers%20R120214.ndf.
Abraxis, "Microcystin-ADDA ELISA Kit, Detailed Procedure," Undated. Retrieved January
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National Coastal Condition Assessment 2015 Technical Report
2015 from
http://www.abraxiskits.com/uploads/products/docfiles/253 PN520011 FLOW.pdf
Chorus, I. and J. Bartram. (Eds.). 1999. Toxic cyanobacteria in water. A. guide to theirpublic health
consequences, monitoring and management. World Health Organization and E&FN Spon Press,
London, UK.
Giannuzzi, L., D. Sedan, R. Echenique, and D. Andrinolo. 2011. An acute case of intoxication
with cyanobacteria and cyanotoxins in recreational water in Salto Grande Dam,
Argentina. Marine Drugs 9:2164-2175.
James, R., A. Gregg, A. Dindal, and J. McKernan. 2010. "Environmental Technology Verification
Report: Abraxis Microcystin Test Kits: ADDA ELISA Test Kit; DM ELISA Test Kit;
Strip Test Kit," in Environmental Technology Verification System Center. Retrieved
March 2015 from http://tiepis.epa.gov/Adol: OOFJ/SB.ndf
Maneely, J.P., and C.T. Elliot. 2013. Microcystins: measuring human exposure and the impact on
human health. Biomarkers 18(8):639-49.
U.S. Environmental Protection Agency (USEPA). 2019. Recommended Human Health
Recreational Ambient Water Quality Criteria or Swimming Advisories for Microcystins
and Cylindrospermopsin. EPA 822-R-19-001. U.S. Environmental Protection Agency,
Office of Water, Washington, DC.
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11 From Analyses to Results
11.1 Extent Estimation And Assessment
A major goal of the National Aquatic Resource Surveys is to assess the condition of the nation's
waters and changes over time. The following discussion describes the condition class assignments
and calculations used in EPA's assessments. In the main report, results were calculated for the
Great Lakes population and the Estuarine population separately.
11.1.1 Condition Classes
Condition classes were assigned to each site for each indicator based on the analysis described in
the individual indicator chapters. The condition classes were then used to estimate the extent,
change, and trend in condition across the sampled population. Only sites that were included in
the probability design and were evaluated as "Target_Sampled" were used to calculate statistics.
If sites were visited twice during NCCA 2015, only data from one site visit24 were used to
calculate condition estimates.
11.1.2 Estimating the Extent for Each Condition
The estimated extent E measures the prevalence of a particular condition k (good, fair, or poor).
For each Y indicator, E provides an estimate of the square miles of coast in that condition.
The extent is estimated in two steps for each condition. The first step classifies each statistically
selected site into one of the three conditions for each Y. The second step estimates the miles
using the estimated survey weights Wi for each site i, classified into condition k. Applying weights
to the data allows inferences to be made about all coastal areas in the target population, not just
the sites from which physical samples were collected. Each sampled site is assigned an estimated
weight for the number of square miles that it represents. For example, one site might represent
200 square miles of coastal area in the entire target population, and thus, its sample weight
was Wyki = 200. Equation 11-1 (below) shows the estimation of extent (c^ ) for condition class k
for each Y.
Eyk — YiiWYk. Equation 11-1
24 For all but one or two sites, "Visit 1", which is denoted as VISIT_NO = "1" in the data files, was used to calculate
condition estimates. If quality purposes required use of VISIT_NO = "2" data for condition estimates, that information
is noted in the data files.
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11.2 Analyses
Estimates of each condition category were computed using spsurvey (Kincaid and Olsen, 2016).
The margin of error for national estimates was +/- 5% and for ecoregional estimates was +/-
15% with 95% confidence, meeting the objectives outlined in Section 3.1.
One of the objectives of the NCCA is to track changes over time. Previously, EPA and partners
reported on the condition of coastal area in the NCCA 2010 and in the National Coastal
Assessment (NCA) in 2005-2006 and 1991-2001. The 2015 report presents the difference in
percentage points of coastal square miles in "good," "fair," and "poor" condition between the
NCA 2005-2006 (data from 2005 and 2006 were combined into the 2005 time frame for the 2015
report), NCCA 2010 and NCCA 2015. Comparisons with earlier years can be viewed in the
online data dashboard (https:/ /coastalcondition.epa.gov/).
Benchmarks and analyses that were modified in NCCA 2015 (i.e. M-AMBI, Ecological Fish
Tissue Contaminants) were applied to previous survey datasets in order for data to be directly
comparable for the change analyses. Change analysis was not conducted for mercury in fish
plugs, enterococci, or microcystin because these indicators were not included in the 2005 or 2010
surveys.
Change analysis was conducted through the use of the spsurvey 3.3 package in R (Kincaid and
Olsen, 2016). Within the GRTS (Generalized Random Tessellation Stratified) survey design,
change analysis can be conducted on continuous or categorical variables. When using categorical
variables, change is estimated by the difference in category estimates from the two surveys.
Category estimates were defined as the estimated proportion of values in each category (i.e. good,
fair, and poor categories). Change between the two years was statistically significant when the
resulting error bars around the change estimate did not cross zero.
11.3 Trend Analysis
Trend estimates for "good" condition were calculated for the estuarine population using linear
regressions. Values of 2005, 2010, and 2015 were used to represent the three design cycles,
respectively, which provided an equally spaced set of values. Trend estimates for good condition
from 2005 to 2015 can be viewed in online data dashboard (https://coastalcondition.epa.gov/).
11.4 References
Kincaid, T.M., and A.R. Olsen. 2016. spsurvey: Spatial Survey Design and Analysis. R package
version 3.3.
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APPENDIX A. Ecological Fish Tissue Contaminant Index
Background Information
The following sub-sections summarize the laboratory-based endpoints for each group of
receptors chosen for each contaminant. The section describes the conversion factor applied if
necessary as well as the body weight scaling for each group of receptors using the formula and
scaling factors presented above. The laboratory-based endpoints presented below were those
chosen to be used in the derivation of the fish tissue screening values. Tables A.1.1 through
A. 1.13 contain all laboratory endpoints extracted from the available scientific literature (search:
2011-2012) for each contaminant of concern measured for the NCCA.
A.l Laboratory Endpoints for NCCA EFTCI Contaminants of Concern
A.1.1 Arsenic, Inorganic
USFWS (1964) reported a chronic NOAEL and LOAEL for mallard mortality of 5.1 and 12.8
mg/kg-bw/d which were converted to wildlife avian NOAEL and LOAEL of 3.39 and 8.51
mg/kg-bw/d. A laboratory chronic NOAEL for mouse reproduction of 0.126 mg/kg-bw/d was
reported (Sample et al., 1996). A conversion factor of 5 was applied to extrapolate a LOAEL of
0.63 mg/kg-bw/d. The wildlife mammalian TRVs were calculated using these values and
resulted in a freshwater mammalian TRV NOAEL and LOAEL of 0.11 and 0.53 mg/kg-bw/d,
respectively, and a marine mammalian NOAEL and LOAEL of 0.080 and 0.40 mg/kg-bw/d,
respectively. Pedlar et al. (2002) reported a sub-chronic NOEC of 119.6 mg/kg food for growth
of lake whitefish. Lake whitefish weighing 326 g, were fed 0.5% of their body weight or 1.63 g
food. As reported, 1 kg of food contained 119.6 mg arsenic, each fish was fed 0.20 mg/0.326 kg-
bw/d, or 0.60 mg/kg-bw/d. The fish were fed three times a week, so the daily dosage was 0.26
mg/kg-bw/d. The sub-chronic NOAEL was extrapolated to a chronic NOAEL by applying a
conversion factor of 0.1. The NOAEL, 0.026 mg/kg-bw/d was used to extrapolate a LOAEL
by applying a conversion factor of 5, resulting in a LOAEL of 0.13 mg/kg-bw/d. The laboratory
TRVs were converted to wildlife TRVs of 0.027 and 0.14 mg/kg-bw/d for freshwater fish and
0.060 and 0.30 mg/kg-bw/d for marine species. See Table A.1.1.
A.l.2 Cadmium
A laboratory chronic NOAEL and LOAEL of 1.45 and 20 mg/kg-bw/d, respectively, were
reported by Sample et al. (1996) for mallard reproduction. The laboratory TRVs were
extrapolated to avian wildlife TRVs of 0.94 and 12.93 mg/kg-bw/d, respectively. ATSDR (2008)
reported a cadmium NOAEL of 0.75 mg/kg-bw/d for reproduction in the dog. A conversion
factor of 5 was applied to the NOAEL to extrapolated a LOAEL of 3.75 mg/kg-bw/d. These
values were converted to mammalian wildlife TRVs of 0.89 mg/kg-bw/d and 4.46 mg/kg-bw/d
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for freshwater mammals and 0.67 mg/kg-bw/d and 3.37 mg/kg-bw/d for marine mammals.
Szczerbik et al. (2006) reported a chronic NOAEL and LOAEL of 1 and 10 mg cadmium/g
food, respectively, for growth in the carp. As reported 0.56 g carp were fed 2% of their body
weight per day, which is equal to 0.0112 g food containing the reported concentrations of
cadmium. Therefore, the NOAEL was 20 mg/kg-bw/d and 200 mg/kg-bw/d was the LOAEL.
The laboratory TRVs were converted to wildlife TRVs of 76.34 and 763.49 mg/kg-bw/d for
freshwater fish and 168.0 and 1680.0 mg/kg-bw/d for marine species. See Table A.1.2.
A.1.3 Chlordane, Total
Wiemeyer (1996) reported a chronic NOAEL and LOAEL for mallard reproduction of 0.8 and
4.0 mg/kg-bw/d which were converted to wildlife avian NOAEL and LOAEL of 0.53 and 2.66
mg/kg-bw/d. A laboratory chronic NOAEL and LOAEL for mouse reproduction of 4.58 and
9.16 mg/kg-bw/d, respectively, were reported (Sample et al., 1996). The wildlife mammalian
TRVs were calculated using these values and resulted in a freshwater mammalian TRV NOAEL
and LOAEL of 3.85 and 7.69 mg/kg-bw/d, respectively, and a marine mammalian TRV
NOAEL and LOAEL of 2.91 and 5.81 mg/kg-bw/d, respectively. Dietary exposure of fish to
chlordane was not available in the literature and therefore represents an uncertainty. See Table
A.1.3.
A.1.4 DDT, Total
A laboratory chronic NOAEL and LOAEL of 0.3 and 3.0 mg/kg-bw/d, respectively, were
reported by USEPA (1995) for reproduction in the bald eagle. The laboratory TRVs were
extrapolated to avian wildlife TRVs of 0.15 and 1.47 mg/kg-bw/d. Sample et al. (1996) reported
a DDT NOAEL of 0.8 mg/kg-bw/d and a LOAEL of 4.0 mg/kg-bw/d for reproduction in the
rat. These values were converted to mammalian wildlife TRVs of 0.78 mg/kg-bw/d and 3.89
mg/kg-bw/d for freshwater mammals and 0.59 mg/kg-bw/d and 2.94 mg/kg-bw/d for marine
mammals. A chronic NOEC of 1 mg/kg-bw/week for the rainbow trout was reported (Macek et
al., 1970) and converted to a daily dosage of 0.143 mg/kg-bw/d. A conversion factor of 5 was
applied to derive the LOAEL, 0.715 mg/kg-bw/d. The laboratory NOAEL and LOAEL were
converted to a freshwater and marine fish NOAEL and LOAEL of 0.28 and 1.42 mg/kg-bw/d,
respectively, for freshwater fish and 0.62 and 3.12 mg/kg-bw/d, respectively, for marine fish,
respectively. See Table A.1.4.
A.1.5 Dieldrin
Sample et al. (1996) reported a chronic NOAEL and LOAEL for the barn owl of 0.08 and 0.39
mg/kg-bw/d which were converted to wildlife avian NOAEL and LOAEL of 0.062 and 0.30
mg/kg-bw/d. A laboratory chronic LOAEL for the dog of 0.14 mg/kg-bw/d reported by
ATSDR (2002b) was used to convert a chronic NOAEL by applying a conversion factor of 0.2,
resulting in a laboratory mammalian NOAEL of 0.028 mg/kg-bw/d. The wildlife mammalian
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TRVs were calculated using these values and resulted in a freshwater mammalian TRV NOAEL
and LOAEL of 0.033 and 0.17 mg/kg-bw/d, respectively, and a marine mammalian TRV
NOAEL and LOAEL of 0.025 and 0.13 mg/kg-bw/d, respectively. Argyle et al. (1975) reported
a laboratory NOAEL of 0.8 |LXg Dieldrin/g food. As reported, 3.0 g fish were fed 4.2% of their
body weight/day for a total of 0.12 kg food containing 0.8 |LXg Dieldrin/g. This is equivalent to
0.0336 mg/kg-bw/d. Because the fish were fed only 5 days a week, the laboratory chronic
NOAEL was calculated as 0.024 mg/kg-bw/d. The reported LOAEL was 4 |LXg Dieldrin/g food
which was also converted to 0.12 mg/kg-bw/d. Extrapolation of fish TRVs resulted in
freshwater fish NOAEL and LOAEL of 0.065 and 0.33 mg/kg-bw/d, respectively, and marine
fish NOAEL and LOAEL of 0.14 and 0.72 mg/kg-bw/d, respectively for wildlife species. See
Table A.1.5.
A.1.6 Endrin, Total
Sample et al. (1996) reported a chronic NOAEL and LOAEL of 0.02 and 0.1 mg/kg/d for
reproduction in the screech owl, respectively. The laboratory TRVs were converted to avian
wildlife TRVs of 0.019 and 0.099 mg/kg/d. A chronic NOAEL and LOAEL of 0.18 and 0.92
mg/kg-bw/d were reported for reproduction in the mouse (Sample et al., 1996). These values
were converted to mammalian wildlife TRVs of 0.15 and 0.77 mg/kg-bw/d for freshwater
species. For marine mammals, wildlife TRVs were calculated to be 0.11 and 0.58 mg/kg-bw/d.
A chronic NOAEL of 0.04 mg/kg-bw/d was reported by Argyle et al. (1973). A conversion
factor of 5 was applied to calculate a chronic LOAEL of 0.2 mg/kg-bw/d. The calculated
wildlife TRVs for freshwater fish were 0.16 and 0.78 mg/kg-bw/d. The calculated wildlife TRVs
for marine species were 0.34 and 1.72 mg/kg-bw/d. See Table A.1.6.
A.1.7 Endosulfan, Total
A laboratory chronic NOAEL and LOAEL of 10 and 50 mg/kg-bw/d, respectively, were
reported by Sample et al. (1996) for reproduction in the gray partridge. The laboratory TRVs
were extrapolated to avian wildlife TRVs of 7.99 and 39.93 mg/kg-bw/d. ATSDR (2000)
reported NOAEL of 1.0 mg/kg-bw/d and a LOAEL of 5.0 mg/kg-bw/d for systemic effects of
endosulfan in dogs. These values were converted to mammalian wildlife TRVs of 1.19 mg/kg-
bw/ d NOAEL and 5.95 mg/kg-bw/d LOAEL for freshwater mammals and 0.90 mg/kg-bw/d
NOAEL and 4.50 mg/kg-bw/d LOAEL for marine mammals. A chronic NOAEL of 0.24
|j.g/kg-bw/d and a chronic LOAEL of 0.5 |ag/kg-bw/d for the Atlantic salmon was reported
(Lundebye et al., 2010). The reported Atlantic salmon NOAEL/LOAEL were converted to a
freshwater and marine fish NOAEL/LOAEL of 0.26 and 0.60 |ag/kg-bw/d for freshwater fish
and 0.60 and 1.31 |ag/kg-bw/d for marine fish, respectively. See Table A.1.7.
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A.1.8 Heptachlor
The LD50 for survival in the bobwhite quail was reported to be 125 mg/kg (USEPA, 1972). A
conversion factor of 0.01 was applied to calculate a chronic NOAEL of 1.25 mg/kg-bw/d. A
conversion factor of 5 was applied to the NOAEL to calculate a chronic LOAEL of 6.25 mg/kg-
bw/d. The laboratory TRVs were converted to avian wildlife TRVs of 1.16 and 5.79 mg/kg-
bw/d. Sample et al. (1996) reported a chronic NOAEL and LOAEL for reproduction in the
mink of 0.2 and 1 mg/kg-bw/d, respectively. The laboratory TRVs were converted to
mammalian wildlife TRVs of 0.21 and 1.037 mg/kg-bw/d for freshwater species. For marine
mammals, wildlife TRVs were calculated to be 0.16 and 0.78 mg/kg-bw/d. Andrews et al. (1996)
reported a laboratory NOAEL of 3.57 mg/kg-bw/d and a chronic LOAEL of 7.14 mg/kg-bw/d.
Extrapolation of fish TRVs resulted in freshwater fish NOAEL and LOAEL of 8.09 and 16.2
mg/kg-bw/d, respectively, and marine fish NOAEL and LOAEL of 17.8 and 35.6 mg/kg-bw/d,
respectively for wildlife species. See Table A.1.8.
A.1.9 Hexachlorobenzene
The chronic NOAEL and LOAEL for reproduction in the Japanese quail were reported as 0.11
and 0.57 mg/kg-bw/d (Coulston and Kolbye, 1994; Terretox, 2002). The laboratory TRVs were
extrapolated to avian wildlife TRVs of 0.11 and 0.55 mg/kg-bw/d. Laboratory TRVs of 1 and 2
mg/kg-bw/d were reported by ATSDR (2002a) for reproduction in the rat. The calculated
wildlife chronic NOAEL and LOAEL for freshwater mammals were 0.97 and 1.95 mg/kg-bw/d,
respectively. For marine mammalian species, the calculated TRVs were 0.74 and 1.47 mg/kg-
bw/ d. Woodburn et al. (2008) reported a subchronic NOAEL of 327 ng HCB/g food for
growth in the channel catfish. As reported, 4.0 g catfish were fed 2.1% of their body weight per
day for a total of 0.084 g of food containing 0.000327 mg HCB per day. Therefore, the sub-
chronic NOAEL was 0.00685 mg/kg/d. This value was converted to a chronic NOAEL of
0.00069 mg/kg/d using a conversion factor of 0.1. By applying a conversion factor of 5 to the
NOAEL, the chronic LOAEL was calculated to be 0.0034 mg/kg/d. The laboratory TRVs were
converted to wildlife TRVs of 0.0018 and 0.0088 mg/kg-bw/d for freshwater fish and 0.0039
and 0.019 mg/kg-bw/d for marine species. SeeTable A.1.9
A.1.10 Lindane
The chronic NOAEL and LOAEL for reproduction in the Japanese quail were reported as 0.56
and 2.25 mg/kg-bw/d, respectively (Sample et al., 1996). These values were converted to an
avian wildlife chronic NOAEL and LOAEL of 0.54 and 2.19 mg/kg-bw/d, respectively. Sample
et al. (1996) reported chronic endpoints of 8 and 40 mg/kg/d for reproduction in the rat. These
values were converted to a mammalian wildlife chronic NOAEL and LOAEL of 7.79 and 38.93
mg/kg-bw/d, respectively, for freshwater species. The calculated wildlife TRVs for marine
mammals were 5.88 and 29.41 mg/kg-bw/d. Cossarini-Dunier et al. (1987) reported chronic
NOAEL of 1.0 g lindane/kg food for immune response in the carp. As reported, 60 g carp were
112
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National Coastal Condition Assessment 2015 Technical Report
fed 1% of their body weight each day for a total of 0.0006 kg food containing 1.0 mg lindane/kg.
Therefore, the endpointwas 0.6 mg/0.06 kg-bw/d, resulting in a calculated laboratory chronic
NOAEL of 10 mg/kg-bw/d. A conversion factor of 5 was applied to extrapolate a chronic
LOAEL of 50 mg/kg-bw/d. The laboratory TRVs were converted to wildlife TRVs of 14.99
and 74.95 mg/kg-bw/d for freshwater fish, and 32.98 and 164.91 mg/kg-bw/d for marine fish.
See Table A.1.10.
A.l.ll Mercury (Methylmercury)
Heinz and Locke (1976) reported a chronic NOAEL and LOAEL for mallard reproduction of
0.03 and 0.18 mg/kg-bw/d which were converted to wildlife avian NOAEL and LOAEL of
0.020 and 0.12 mg/kg-bw/d. A laboratory chronic NOAEL and LOAEL for rat reproduction of
0.032 and 0.16 mg/kg-bw/d were reported (Sample et al., 1996). The wildlife mammalian TRVs
were calculated using these values and resulted in a freshwater mammalian TRV NOAEL and
LOAEL of 0.031 and 0.16 mg/kg-bw/d, respectively, and a marine mammalian NOAEL and
LOAEL of 0.024 and 0.12 mg/kg-bw/d, respectively. Berntssen et al. (2003) reported a chronic
NOAEL and LOAEL of 4.23 and 8.31 mg methylmercury chloride/kg food for brain pathology
in the Atlantic salmon. As reported, 10.8g Atlantic salmon were fed 1.6% of their body weight
per day. Therefore, the endpoint was 0.068 mg/kg/d NOAEL and 0.13 mg/kg-bw/d LOAEL.
The laboratory TRVs were converted to wildlife TRVs of 0.14 and 0.28 mg/kg-bw/d for
freshwater fish and 0.31 and 0.62 mg/kg-bw/d for marine species. See Table A.l.ll.
A.1.12 Mirex
Hyde et al. (1973) reported chronic NOEC and LOEC of 1 and 100 mg mirex/kg food for
reproduction in the mallard. A reference body weight of 1 kg and a reference food ingestion rate
of 100 g/d (Sample et al., 1996) were used to convert the dietary concentrations to units of
mg/kg-bw/d. Therefore, the chronic NOAEL and LOAEL for reproduction in the mallard
duck are 0.01 and 1 mg/kg-bw/d, respectively. The laboratory TRVs were converted to avian
wildlife TRVs of 0.0066 and 0.66 mg/kg-bw/d. A mammalian chronic NOAEL and LOAEL of
0.07 and 0.7 mg/kg-bw/d, respectively, were reported by NTP (1990) for liver and thyroid
effects in the rat. These laboratory TRVs were converted to freshwater mammalian wildlife
TRVs of 0.064 and 0.64 mg/kg-bw/d and marine mammalian wildlife TRVs of 0.048 and 0.48
mg/kg-bw/d. A chronic NOAEL of 0.3 mg/kg-bw/d for growth in the brook trout was
reported by Skea et al. (1981). A conversion factor of 5 was applied to this value to obtain a
chronic LOAEL of 1.5 mg/kg-bw/d. These values were converted to wildlife NOAELs and
LOAELs of 0.40 and 1.98 mg/kg-bw/d for freshwater fish, respectively, and 0.87 and 4.35
mg/kg-bw/d, respectively for marine fish. See Table A.1.12.
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National Coastal Condition Assessment 2015 Technical Report
A.1.13 Polychlorinated Biphenyls (PCBs), Total
Polychlorinated biphenyls typically exist as conglomerates of multiple aroclors (i.e., Aroclor 1242,
-1248, -1254, -1260, etc.). The aroclor number with respect to PCBs is an indication of the
percent of chlorination (i.e., Aroclor-1254 has 54% chlorination). Using the toxic effects of
Aroclor-1254 as a surrogate for PCBs should yield a conservative estimate because toxic effects
are thought to be related to the degree of chlorination (Exponent, 2010). A laboratory chronic
NOAEL and LOAEL of 0.18 and 1.8 mg/kg-bw/d, respectively, were reported by Sample et al.
(1996) for Aroclor-1254 and ring-necked pheasant reproduction. The laboratory TRVs were
extrapolated to avian wildlife TRVs of NOAEL = 0.12 and LOAEL= 1.20 mg/kg/d. Sample et
al. (1996) reported an aroclor-1254 NOAEL of 0.068 mg/kg-bw/d and a LOAEL of 0.68
mg/kg-bw/d for reproduction in the oldfield mouse. These values were converted to
mammalian wildlife TRVs of 0.055 mg/kg-bw/d NOAEL and 0.55 mg/kg-bw/d LOAEL for
freshwater mammals and 0.041 mg/kg-bw/d NOAEL and 0.41 mg/kg-bw/d LOAEL for
marine mammals. Leatherland and Sonstegard (1980) reported a subchronic LOEC of 50 mg/kg
food for liver and thyroid effects in rainbow trout. As reported, 50 g trout were fed 2% of their
body weight per day, or a dosage of 1 mg/kg-dw/d. The sub-chronic LOAEL was converted to
a chronic NOAEL by applying a conversion factor of 0.05 for NOAEL = 0.05 mg/kg-bw/d. A
conversion factor of 5 was applied to get a chronic LOAEL of 0.25 mg/kg-bw/d. The
laboratory TRVs were converted to wildlife TRVs of 0.078 and 0.39 mg/kg-bw/d for freshwater
fish and 0.17 and 0.86 mg/kg-bw/d for marine species. See Table A.1.13.
A.1.14 Selenium
A laboratory chronic NOAEL and LOAEL of 0.4 and 0.8 mg/kg-bw/d, respectively, were
reported by Sample et al. (1996) for mallard reproduction. The laboratory TRVs were
extrapolated to avian wildlife TRVs of NOAEL = 0.27 and LOAEL= 0.53 mg/kg-bw/d.
Sample et al. (1996) reported a selenium NOAEL of 0.2 mg/kg-bw/d and a LOAEL of 0.33
mg/kg-bw/d for reproduction in the rat. These values were converted to mammalian wildlife
TRVs of 0.19 mg/kg-bw/d NOAEL and 0.32 mg/kg-bw/d LOAEL for freshwater mammals
and 0.15 mg/kg-bw/d NOAEL and 0.24 mg/kg-bw/d LOAEL for marine mammals. A chronic
NOAEL of 0.91 and LOAEL of 1.22 mg/kg-bw/d for the fathead minnow were reported (Ogle
and Knight, 1989). The reported NOAEL and LOAEL were converted to a freshwater and
marine fish NOAEL and LOAEL of 5.02 and 6.70 mg/kg-bw/d for freshwater fish and 11.04
and 14.75 mg/kg-bw/d for marine fish, respectively.
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National Coastal Condition Assessment 2015 Technical Report
Table A.l.l Summary of literature values for arsenic, inorganic
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-d
USFWS 1964
Avian
Mallard
1
Mortality
128 days
Chronic
NOAEL
5.1
5.1
USFWS 1964
Avian
Mallard
1
Mortality
128 days
Chronic
LOAEL
12.8
12.8
Pedlar et al.
2002
Fish
Lake
whitefish
0.326
Growth
64 days
Subchronic
NOEC
0.25631
mg/kg/ d
0.1
0.02563
5
0.12815
USEPA 2005
Mammal
Dog
10.1
Biochemical
8 weeks
Chronic
NOAEL
1.04
1.04
USEPA 2005
Mammal
Dog
10.1
Biochemical
8 weeks
Chronic
LOAEL
1.66
1.66
ATSDR 1993d
Mammal
Dog
10
Systemic
2 years
Chronic
NOAEL
1.2
mg/kg/ d
1.2
5
6
Sample et al.
1996
Mammal
Mouse
0.03
Reproduction
3 generations
Chronic
NOAEL
0.126
mg/kg/ d
0.126
5
0.63
a - NOEC was 119.6 ug/g for 3 day/week feeding at 0.5% BW/tank with 6 326g fish/tank. Converted to a daily dosage.
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National Coastal Condition Assessment 2015 Technical Report
Table A. 1.2 Summary of literature values for cadmium
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Sample et al.
1996
Avian
Mallard
1.153
Reproduction
90 days
Chronic
NOAEL
1.45
mg/kg/ d
1.45
Sample et al.
1996
Avian
Mallard
1.153
Reproduction
90 days
Chronic
LOAEL
20
mg/kg/ d
20
Chowdhury et
al. 2004
Fish
Rainbow
Trout
0.1654
Survival
45 days
Subchronic
NOEC
6.9
mg/kg/ d
0.1
0.69
5
3.45
Szczerbik et al.
2006
Fish
Prussian
Carp
0.00056
Growth
3 years
Chronic
NOEC
20*
mg/kg/ d
20
Szczerbik et al.
2006
Fish
Prussian
Carp
0.00056
Growth
3 years
Chronic
LOEC
200b
mg/kg/ d
200
Sample et al.
1996
Mammal
Rat
0.303
Reproduction
6 weeks
w/gestation
Chronic
NOAEL
1
mg/kg/ d
1
Sample et al.
1996
Mammal
Rat
0.303
Reproduction
6 weeks
w/gestation
Chronic
LOAEL
10
mg/kg/ d
10
ATSDR 2008
Mammal
Dog
10
Reproduction
3 months
Chronic
NOAEL
0.75
mg/kg/ d
0.75
5
3.75
a - Carp at 2% BW/d, or 0.0112g. NOEC conc. Was 1 mg cd/g food, so daily dose was 20 mg/kg/d.
b - Carp at 2% BW/d, or 0.0112g. LOEC conc. Was 10 mg cd/g food, so daily dose was 200 mg/kg/d.
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National Coastal Condition Assessment 2015 Technical Report
Table A. 1.3 Summary of literature values for chlordane, total
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Sample et al.
1996
Avian
Red-
winged
blackbird
0.06
Survival
84 days
Chronic
NO ART,
2.14
mg/kg/ d
2.14
Sample et al.
1996
Avian
Red-
winged
blackbird
0.06
Survival
84 days
Chronic
LOARL
10.7
mg/kg/ d
10.7
Wiemeyer 1996
Avian
Northern
bobwhite
0.19
Reproduction
Not Specified
Chronic
NOARL
1.19
mg/kg/ d
1.19
Wiemeyer 1996
Avian
Northern
bobwhite
0.19
Reproduction
Not Specified
Chronic
LOARL
5.95
mg/kg/ d
5.95
Wiemeyer 1996
Avian
Mallard
1
Reproduction
Not Specified
Chronic
NOARL
0.8
mg/kg/ d
0.8
Wiemeyer 1996
Avian
Mallard
1
Reproduction
Not Specified
Chronic
LOARL
4
mg/kg/ d
4
Hudson et al.
1984
Avian
Mallard
l1
Survival
14 days
LD50
1200
mg/kg
0.01
12
5
60
Hudson et al.
1984
Avian
California
quail
1b
Survival
14 days
LD50
14.1
mg/kg
0.01
1.41
5
7.05
Hudson et al.
1984
Avian
Pheasant
1b
Survival
14 days
LD50
24
mg/kg
0.01
0.24
5
1.2
Sample et al.
1996
Mammal
Mouse
0.03
Reproduction
6 generations
Chronic
NOARL
4.58
mg/kg/ d
4.58
Sample et al.
1996
Mammal
Mouse
0.03
Reproduction
6 generations
Chronic
LOARL
9.16
mg/kg/ d
9.16
USEPA 1976
Mammal
Rat
0.351
Survival
Not Specified
LD50
335
mg/kg
0.01
3.35
5
16.75
a - Reference BW from Sample et al., 1996.
b - Extrapolated from LD50 because the unit is mg chemical/kg body weight.
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National Coastal Condition Assessment 2015 Technical Report
Table A. 1.4 Summary of literature values for DDT, total
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
USEPA 1995
Avian
Japanese
quail
0.11
Reproduction
3 generations
Chronic
NOAEL
0.5
mg/kg/ d
0.5
USEPA 1995
Avian
Japanese
quail
0.11
Reproduction
3 generations
Chronic
LOAEL
5
mg/kg/ d
5
USEPA 1995
Avian
Mallard
1
Reproduction
2 years
Chronic
NOAEL
0.6
mg/kg/ d
0.6
USEPA 1995
Avian
Mallard
1
Reproduction
2 years
Chronic
LOAEL
1.5
mg/kg/ d
1.5
USEPA 1995
Avian
Bald Eagle
4.6
Reproduction
112 days
Chronic
NOAEL
0.3
mg/kg/ d
0.3
USEPA 1995
Avian
Bald Eagle
4.6
Reproduction
112 days
Chronic
LOAEL
3
mg/kg/ d
3
Hudson et al.
1984
Avian
Mallard
1*
Survival
14 days
LD50
>2240
mg/kg
0.01
>22.4
5
>112
Hudson et al.
1984
Avian
California
quail
1*
Survival
14 days
LD50
595
mg/kg
0.01
5.95
5
29.75
Hudson et al.
1984
Avian
Japanese
quail
0.15b
Survival
14 days
LD50
841
mg/kg
0.01
8.41
5
42.05
Hudson et al.
1984
Avian
Pheasant
1*
Survival
14 days
LD50
1334
mg/kg
0.01
13.34
5
66.7
Hudson et al.
1984
Avian
Sandhill
crane
1*
Survival
14 days
LD50
>1200
mg/kg
0.01
>12
5
>60
Hudson et al.
1984
Avian
Rock dove
1*
Survival
14 days
LD50
>4000
mg/kg
0.01
>40
5
>200
Hudson et al.
1984
Avian
Mallard
lb
Survival
30 days
EMLD
(empirical
minimum
lethal dosage)
50
md/kg/d
Macek et al.
1970
Fish
Rainbow
trout
0.0147
Growth
140 days
Chronic
NOEC
1
mg/kg/w
0.143
5
0.715
Sample et al.
1996
Mammal
Rat
0.35
Reproduction
2 years
Chronic
NOAEL
0.8
mg/kg/ d
0.8
Sample et al.
1996
Mammal
Rat
0.35
Reproduction
2 years
Chronic
LOAEL
4
mg/kg/ d
4
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National Coastal Condition Assessment 2015 Technical Report
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
ATSDR 2002c
Mammal
Dog
10
Reproduction
2 generations
Chronic
NOAEL
1
mg/kg/ d
1
5
5
USEPA 1976
Mammal
Rat
0.35 b
Survival
Not Specified
LD50
113
mg/kg
0.01
1.13
5
5.65
EXTOXNET
1996
Mammal
Rat
0.35 b
Reproduction
15-19 days
Chronic
NOAEL
38
mg/kg/ d
38
5
190
EXTOXNET
1996
Mammal
Monkey/
Hamster
1*
Unspecified
3.5-7 years
Chronic
NOAEL
8 to 20
mg/kg/ d
8 to 20
5
40-100
Macek 1968
Fish
Brook
trout
0.162
Growth
156 days
Chronic
NOEC
2
mg/kg/w
0.286
5
1.43
a - Extrapolated from LD50 because the unit is mg chemical/kg body weight,
b - Reference BW from Sample et al., 1996.
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National Coastal Condition Assessment 2015 Technical Report
Table A.1.5 Summary of literature values for dieldrin
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Sample et al.
1996
Avian
Barn Owl
0.47
Reproduction
2 years
Chronic
NO ART,
0.08
mg/kg/ d
0.08
Sample et al.
1996
Avian
Barn Owl
0.47
Reproduction
2 years
Chronic
LOARL
0.39
mg/kg/ d
0.39
Hudson et al.
1984
Avian
Canada
Goose
V
Survival
14 days
LD50
<141
mg/kg
0.01
<1.41
5
<7.05
Hudson et al.
1984
Avian
Mallard
lb
Survival
14 days
LD50
381
mg/kg
0.01
3.81
5
19.05
Hudson et al.
1984
Avian
California
quail
1*
Survival
14 days
LD50
8.78
mg/kg
0.01
0.0878
5
0.439
Hudson et al.
1984
Avian
Japanese
quail
0.15b
Survival
14 days
LD50
69.7
mg/kg
0.01
0.697
5
3.485
Hudson et al.
1984
Avian
Pheasant
1*
Survival
14 days
LD50
79
mg/kg
0.01
0.79
5
3.95
Hudson et al.
1984
Avian
Chukar
l1
Survival
14 days
LD50
25.3
mg/kg
0.01
0.253
5
1.265
Hudson et al.
1984
Avian
Rock dove
l1
Survival
14 days
LD50
26.6
mg/kg
0.01
0.266
5
1.33
Hudson et al.
1984
Avian
House
sparrow
l1
Survival
14 days
LD50
47.6
mg/kg
0.01
0.476
5
2.38
Hudson et al.
1984
Avian
Fulvous
whistling
duck
l1
Survival
14 days
LD50
100
mg/kg
0.01
1
5
5
Hudson et al.
1984
Avian
Mallard
1b
Survival
30 days
RMLD
(empirical
minimum
lethal dosage)
5
mg/kg/ d
Hudson et al.
1984
Avian
Fulvous
whistling
duck
1*
Survival
30 days
RMLD
2.5
mg/kg/ d
Hudson et al.
1984
Avian
Gray
partridge
1*
Survival
30 days
RMLD
1.25
mg/kg/ d
Argyle et al.
1975
Fish
Channel
Catfish
0.003
Growth
210 days
Chronic
NORC
0.024c
mg/kg/ d
0.024
Macek et al.
Fish
Rainbow
0.0147
Growth
140 days
Chronic
1
mg/kg/w
0.143
5
0.715
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National Coastal Condition Assessment 2015 Technical Report
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
1970
trout
NOEC
eek
Argyle et al.
1975
Fish
Channel
Catfish
0.003
Growth
210 days
Chronic
LOEC
0.12d
mg/kg/ d
0.12
Sample et al.
1996
Mammal
Rat
0.35
Reproduction
3 generations
Chronic
NO ART,
0.04
mg/kg/ d
0.04
Sample et al.
1996
Mammal
Rat
0.35
Reproduction
3 generations
Chronic
LOARL
0.2
mg/kg/ d
0.2
ASTDR 2002b
Mammal
Dog
10
Systemic
15.7 months
Chronic
LOARL
0.14
mg/kg/ d
0.2
0.028
0.14
Hudson et al.
1984
Mammal
Mule deer
1*
Survival
14 days
LD50
75
mg/kg
0.01
0.75
5
3.75
Hudson et al.
1984
Mammal
Domestic
goat
1*
Survival
14 days
LD50
100
mg/kg
0.01
1
5
5
USEPA 1976
Mammal
Rat
0.35
Survival
Not Specified
LD50
46
mg/kg
0.01
0.46
5
2.3
a - Extrapolated from LD50 because the unit is mg chemical/kg body weight,
b - Reference BW from Sample et al., 1996.
c - Treatment of 0.8 ug/g food, 3g BW, 4.2%BW feeding rate, 5 days a week was converted to mg/kg/d.
d - Treatment of 4 ug/g food, 3g BW, 4.2%BW feeding rate, 5 days a week was converted to mg/kg/d.
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National Coastal Condition Assessment 2015 Technical Report
Table A.1.6 Summary of literature values for endrin, total
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Sample et al.
1996
Avian
Mallard
1.15
Reproduction
>200 days
Chronic
NOAEL
0.3
mg/kg/ d
0.3
Sample et al.
1996
Avian
Mallard
1.15
Reproduction
>200 days
Chronic
LOAEL
1.5
mg/kg/ d
1.5
Sample et al.
1996
Avian
Screech
Owl
0.18
Reproduction
>83 days
Chronic
NOAEL
0.02
mg/kg/ d
0.02
Sample et al.
1996
Avian
Screech
Owl
0.18
Reproduction
>83 days
Chronic
LOAEL
0.1
mg/kg/ d
0.1
Hudson et al.
1984
Avian
Mallard
V
Survival
14 days
LD50
5.64
mg/kg
0.01
0.0564
5
0.282
Hudson et al.
1984
Avian
Sharp-
tailed
grouse
lb
Survival
14 days
LD50
1.06
mg/kg
0.01
0.0106
5
0.053
Hudson et al.
1984
Avian
California
quail
lb
Survival
14 days
LD50
1.19
mg/kg
0.01
0.0119
5
0.0595
Hudson et al.
1984
Avian
Pheasant
lb
Survival
14 days
LD50
1.78
mg/kg
0.01
0.0178
5
0.089
Hudson et al.
1984
Avian
Rock dove
lb
Survival
14 days
LD50
2
mg/kg
0.01
0.02
5
0.1
Hudson et al.
1984
Avian
Mallard
1*
Survival
30 days
EMLD
(empirical
minimum
lethal dosage)
0.25
mg/kg/ d
IPCS 1992
Avian
Pigeon
lb
Survival
Not specified
LD50
2
mg/kg
0.01
0.02
5
0.1
IPCS 1992
Avian
Redwinged
blackbird
lb
Survival
Not specified
LD50
2.37
mg/kg
0.01
0.0237
5
0.1185
IPCS 1992
Avian
Quail
lb
Survival
Not specified
LD50
4.22
mg/kg
0.01
0.0422
5
0.211
Grant and
Mehrle 1970
Fish
Goldfish
0.0152
Growth
157 days
Chronic
NOEC
0.143
mg/kg/ d
0.143
Grant and
Mehrle 1970
Fish
Goldfish
0.0137
Growth
157 days
Chronic
LOEC
0.43
mg/kg/ d
0.43
Grant and
Mehrle 1973
Fish
Rainbow
trout
0.129
Growth
163 days
Chronic
NOEC
0.043
mg/kg/ d
0.043
-------
National Coastal Condition Assessment 2015 Technical Report
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Grant and
Mehrle 1973
Fish
Rainbow
trout
0.134
Growth
163 days
Chronic
LOEC
0.145
mg/kg/ d
0.145
Argyle et al.
1973
Fish
Channel
catfish
0.0005
Growth
198 days
Chronic
NOEC
0.04
mg/kg/ d
0.04
5
0.2
Sample et al.
1996
Mammal
Mouse
0.03
Reproduction
120 Days
Chronic
NOAEL
0.18
mg/kg/ d
0.18
Sample et al.
1996
Mammal
Mouse
0.03
Reproduction
120 Days
Chronic
LOAEL
0.92
mg/kg/ d
0.92
Hudson et al.
1984
Mammal
Mule deer
lb
Survival
14 days
LD50
6.25
mg/kg
0.01
0.0625
5
0.3125
Hudson et al.
1984
Mammal
Domestic
goat
lb
Survival
14 days
LD50
25
mg/kg
0.01
0.25
5
1.25
USEPA 1976
Mammal
Rat
0.351
Survival
Not specified
LD50
8
mg/kg
0.01
0.08
5
0.4
IPCS 1992
Mammal
Big brown
bat
lb
Survival
Not specified
LD50
5
mg/kg
0.01
0.05
5
0.25
a - Reference BW from Sample et al., 1996.
b - Extrapolated from LD50 because the unit is mg chemical/kg body weight.
-------
National Coastal Condition Assessment 2015 Technical Report
Table A.1.7 Summary of literature values for endosulfan, total
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Sample et al.
1996
Avian
Gray
Partridge
0.4
Reproduction
28 days
Chronic
NOAEL
10
mg/kg/ d
10
Sample et al.
1996
Avian
Gray
Partridge
0.4
Reproduction
28 days
Chronic
LOAEL
50
mg/kg/ d
50
Hudson et al.
1984
Avian
Mallard
V
Survival
14 days
LD50
33
mg/kg
0.01
0.33
5
1.65
Hudson et al.
1984
Avian
Mallard
1*
Survival
14 days
LD50
45
mg/kg
0.01
0.45
5
2.25
Hudson et al.
1984
Avian
Mallard
1*
Survival
14 days
LD50
31.2
mg/kg
0.01
0.312
5
1.56
Hudson et al.
1984
Avian
Pheasant
lb
Survival
14 days
LD50
80
mg/kg
0.01
0.8
5
4
Hudson et al.
1984
Avian
Pheasant
1b
Survival
14 days
LD50
190
mg/kg
0.01
1.9
5
9.5
Hudson et al.
1984
Avian
Pheasant
1b
Survival
14 days
LD50
>320
mg/kg
0.01
>3.2
5
>16
Lundebye et al.
2010
Fish
Atlantic
Salmon
0.25
Lipid
digestibility
95 days
Chronic
NOEC
0.0002393
mg/kg/ d
0.0002393
Lundebye et al.
2010
Fish
Atlantic
Salmon
0.25
Lipid
digestibility
95 days
Chronic
LOEC
0.0005286
mg/kg/ d
0.0005286
Petri et al. 2006
Fish
Atlantic
Salmon
0.0387
Condition
Factor
49 days
Subchronic
NOEC
0.000758
mg/kg/ d
0.1
0.0000758
5
0.000379
Petri et al. 2006
Fish
Atlantic
Salmon
0.0387
Condition
Factor
49 days
Subchronic
LOEC
0.010621
mg/kg/ d
0.05
0.00053105
5
0.00265525
Bemtssen et al.
2008
Fish
Atlantic
Salmon
0.148
Growth
92 days
Chronic
NOEC
0.005792
mg/kg/ d
0.005792
5
0.02896
Coimbra et al.
2007
Fish
Nile Tilapia
0.09105
Liver
Pathology
35 days
Subchronic
LOEC
0.0000197
mg/kg/ d
0.05
0.000000985
5
0.000004925
Sample et al.
1996
Mammal
Rat
0.35
Fertility
30 days
Chronic
NOAEL
1.5
mg/kg/ d
1.5
Sample et al.
1996
Mammal
Rat
0.35
Fertility
30 days
Chronic
LOAEL
7.5
mg/kg/ d
7.5
ASTDR 2000
Mammal
Dog
10
Systemic
2 years
Chronic
NOAEL
1
mg/kg/ d
1
-------
National Coastal Condition Assessment 2015 Technical Report
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
ASTDR 2000
Mammal
Dog
10
Systemic
2 years
Chronic
LOAEL
5
mg/kg/ d
5
EXTOXNET
1996
Mammal
Rat
0.351
Reproduction
Three
generations
Chronic
NOAEL
2.5
mg/kg/ d
2.5
5
12.5
a - Reference BW from Sample et al., 1996.
b - Extrapolated from LD50 because the unit is mg chemical/kg body weight.
-------
National Coastal Condition Assessment 2015 Technical Report
Table A.1.8 Summary of literature values for hexachlorobenzene
Source
(Author, Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Coulston and
Kolbye 1994;
Terretox 2002
Avian
Japanese
Quail
0.15
Reproduction
90 days
Chronic
NOAEL
0.11
mg/kg/ d
0.11
Coulston and
Kolbye 1994;
Terretox 2002
Avian
Japanese
Quail
0.15
Reproduction
90 days
Chronic
LOAEL
0.57
mg/kg/ d
0.57
EXTOXNET
1996
Avian
Bobwhite
0.191
Survival
Not Specified
LD50
575
mg/kg
0.01
5.75
5
28.75
EXTOXNET
1996
Avian
Mallard
lb
Survival
Not Specified
LD50
1450
mg/kg
0.01
14.5
5
Niimi and Cho
1980
Fish
Rainbow
Trout
0.09
Growth
57 days
Subchronic
NOEL
0.0234d
mg/kg/ d
0.0234
5
0.117
Woodburn et
al. 2008
Fish
Channel
catfish
0.004
Growth
28 days
Subchronic
NOEL
0.00685e
mg/kg/ d
0.1
0.000685
5
0.003425
ATSDR 2002a
Mammal
Rat
0.35
Reproduction
4 generations
Chronic
NOAEL
1
mg/kg/ d
1
ATSDR 2002a
Mammal
Rat
0.35
Reproduction
4 generations
Chronic
LOAEL
2
mg/kg/ d
2
ATSDR 2002a
Mammal
Dog
10
Systemic
1 Year
Chronic
NOAEL
1.2
mg/kg/ d
1.2
ATSDR 2002a
Mammal
Dog
10
Systemic
1 Year
Chronic
LOAEL
12
mg/kg/ d
12
EXTOXNET
1996
Mammal
Rat
0.35 b
Survival
Not Specified
LD50
3500
mg/kg
0.01
35
5
175
EXTOXNET
1996
Mammal
Mouse
0.03 b
Survival
Not Specified
LD50
4000
mg/kg
0.01
40
5
200
EXTOXNET
1996
Mammal
Rabbit
lc
Survival
Not Specified
LD50
2600
mg/kg
0.01
26
5
130
EXTOXNET
1996
Mammal
Cat
lc
Survival
Not Specified
LD50
1700
mg/kg
0.01
17
5
85
Arnold et al.
1985
Mammal
Rat
0.35 b
Liver Effects
130 weeks
Chronic
NOAEL
0.08
mg/kg/ d
0.08
Arnold et al.
1985
Mammal
Rat
0.35 b
Liver Effects
130 weeks
Chronic
LOAEL
0.29
mg/kg/ d
0.29
a - Reference BW from Wildlife Exposure Factors Handbook.
-------
National Coastal Condition Assessment 2015 Technical Report
b - Reference BW from Sample et al., 1996.
c - Extrapolated from LD50 because the unit is mg chemical/kg body weight.
d - 90g trout consumed 3% BW/d, which is .0027 kg food. Highest conc. Was 780 ugHCB/kg food. 0.002106 mgHCB/ .09 kgBW/ d = 0.0234 mgHCB/kg BW/d
e - 4g fish were fed 327 ngHCB/g food and ate 2.1% BW/d = 0.084g food/d. 0.084g food x 0.000327mg HCB/g food = 0.0000274 mg/HCB/4g BW/d. 0.0000274 mg HCB/0.004kgBW = 0.00685
mg/kg/d.
-------
National Coastal Condition Assessment 2015 Technical Report
Table A.1.9 Summary of literature values for lindane
Source
(Author,
Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body Weight
(kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Sample et al.
1996
Avian
Mallard
1
Reproduction
8 weeks
Chronic
NOAEL
4
mg/kg/ d
4
Sample et al.
1996
Avian
Mallard
1
Reproduction
8 weeks
Chronic
LOAEL
20
mg/kg/ d
20
Sample et al.
1996
Avian
Japanese
quail
0.15
Reproduction
90 days
Chronic
NOAEL
0.56
mg/kg/ d
0.56
Sample et al.
1996
Avian
Japanese
quail
0.15
Reproduction
90 days
Chronic
LOAEL
2.25
mg/kg/ d
2.25
Hudson et al.
1984
Avian
Mallard
V
Survival
14 days
LD50
>2000
mg/kg
0.01
>20
Hudson et al.
1984
Avian
Mallard
1*
Survival
30 days
EMLD
(empirical
minimum
lethal dosage)
30
mg/kg/ d
Cossarini-
Dunier et al
1987
Fish
Carp
0.06
Immune
Response
109 days
Chronic
NOEC
10c
mg/kg/ d
10
5
50
Sample et al.
1996
Mammal
Rat
0.35
Reproduction
3 generations
Chronic
NOAEL
8
mg/kg/ d
8
Sample et al.
1996
Mammal
Rat
0.35
Reproduction
3 generations
Chronic
LOAEL
40
mg/kg/ d
40
EXTOXNET
1996
Mammal
Rat
0.351
Survival
Not Specified
LD50
88
mg/kg
0.01
0.88
5
4.4
EXTOXNET
1996
Mammal
Mouse
0.031
Survival
Not Specified
LD50
59
mg/kg
0.01
0.59
5
2.95
EXTOXNET
1996
Mammal
Guinea pig
lb
Survival
Not Specified
LD50
100
mg/kg
0.01
1
5
5
EXTOXNET
1996
Mammal
Rabbit
lb
Survival
Not Specified
LD50
200
mg/kg
0.01
2
5
10
EXTOXNET
1996
Mammal
Mice, Rats,
Dogs
lb
Chronic
2 years
Chronic
NOAEL
1.25
mg/kg/ d
1.25
5
6.25
EXTOXNET
1996
Mammal
Rat
0.351
Reproduction
138 days
Chronic
NOAEL
5
mg/kg/ d
5
5
25
a - Reference BW from Sample et al., 1996.
b - Extrapolated from LD50 because the unit is mg chemical/kg body weight.
c - 60gcarp fed l%BW/d = 0.6gfood/d. lOOOmg lindane/kg food = 0.6mg lindane/0.06kg BW/d = lOmg/kg/d.
-------
National Coastal Condition Assessment 2015 Technical Report
Table A. 1.10 Summary of literature values for mercury (methylmercury)
Source
(Author,
Year)
ROC
Effects Endpoint
Study
Species
Study Species
Body Weight
(kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Heinz and
Locke 1976
Avian
Mallard
1
Reproduction
Chronic
NOAEL
0.03
0.03
Heinz and
Locke 1976
Avian
Mallard
1
1.5 years
Chronic
LOAEL
0.18
0.18
Sample et al.
1996
Avian
Japanese
quail
0.15
Reproduction
1 year
Chronic
NOAEL
0.45
mg/kg/ d
0.45
Sample et al.
1996
Avian
Japanese
quail
0.15
Reproduction
1 year
Chronic
LOAEL
0.9
mg/kg/ d
0.9
USEPA 1995
Avian
Red-tailed
Hawk
1.1
Survival/
Neurological
12 weeks
Chronic
NOAEL
0.49
mg/kg/ d
0.49
USEPA 1995
Avian
Red-tailed
Hawk
1.1
Survival/
Neurological
12 weeks
Chronic
LOAEL
1.2
mg/kg/ d
1.2
USEPA 1997
Avian
Mallard
1
Reproduction
3 generations
Chronic
NOAEL
0.026
mg/kg/ d
0.026
USEPA 1997
Avian
Mallard
1
Reproduction
3 generations
Chronic
LOAEL
0.078
mg/kg/ d
0.078
Lee et al.
2011
Fish
Green
Sturgeon
0.028
Survival and
Growth
8 weeks
Subchronic
NOEC
0.6251
mg/kg/ d
0.1
0.0625
Lee et al.
2011
Fish
Green
Sturgeon
0.028
Survival and
Growth
8 weeks
Subchronic
LOEC
1.25b
mg/kg/ d
0.05
0.0625
5
0.3125
Lee et al.
2011
Fish
White
Sturgeon
0.028
Survival and
Growth
8 weeks
Subchronic
NOEC
1.25 b
mg/kg/ d
0.1
0.125
Lee et al.
2011
Fish
White
Sturgeon
0.028
Survival and
Growth
8 weeks
Subchronic
LOEC
2.5c
mg/kg/ d
0.05
0.125
5
0.625
Berntssen et
al. 2003
Fish
Atlantic
Salmon
0.0105
Brain
Pathology
4 months
Chronic
NOEC
0.13776 d
mg/kg/ d
0.13776
Berntssen et
al. 2003
Fish
Atlantic
Salmon
0.0105
Brain
Pathology
4 months
Chronic
LOEC
1.59456e
mg/kg/ d
1.59456
Berntssen et
al. 2003
Fish
Atlantic
Salmon
0.0108
Brain
Pathology
4 months
Chronic
NOEC
0.06768f
mg/kg/ d
0.06768
Berntssen et
al. 2003
Fish
Atlantic
Salmon
0.0108
Brain
Pathology
4 months
Chronic
LOEC
0.13296 e
mg/kg/ d
0.13296
Fuyuta et al.
1978
Mammal
Rat
0.428
Development
Chronic
LOAEL
4
0.2
0.8
4
-------
National Coastal Condition Assessment 2015 Technical Report
Source
(Author,
Year)
ROC
Effects Endpoint
Study
Species
Study Species
Body Weight
(kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Khera and
T abacova
1973
Mammal
R
0.1875
Reproduction
122 days
Chronic
NOAEL
0.25
0.25
5
1.25
Sample et al.
1996
Mammal
Rat
0.35
Reproduction
3 generations
Chronic
NO ART,
0.032
mg/kg/ d
0.032
Sample et al.
1996
Mammal
Rat
0.35
Reproduction
3 generations
Chronic
LOARL
0.16
mg/kg/ d
0.16
Sample et al.
1996
Mammal
Mink
1
Survival/
Weight loss
93 days
Chronic
NO ART,
0.25
mg/kg/ d
0.25
Sample et al.
1996
Mammal
Mink
1
Survival/
Weight loss
93 days
Chronic
LOARL
0.15
mg/kg/ d
0.15
a - Avg. daily ration of 2.5%. Treatment was 25 mg/kg food, converted to a daily dose,
b - Avg. daily ration of 2.5%. Treatment was 50 mg/kg food, converted to a daily dose,
c - Avg. daily ration of 2.5%. Treatment was 100 mg/kg food, converted to a daily dose,
d - Ration was 1.6% BW/d, treatment was 8.61 mg/kg feed converted to a daily dose,
e - Ration was 1.6% BW/d, treatment was 99.66 mg/kg feed converted to a daily dose,
f - Ration was 1.6% BW/d, treatment was 4.23 mg/kg feed converted to a daily dose,
g - Ration was 1.6% BW/d, treatment was 8.31 mg/kg feed converted to a daily dose.
-------
National Coastal Condition Assessment 2015 Technical Report
Table A. 1.11 Summary of literature values for mirex
Effects Endpoint
Source
(Author,
Year)
ROC
Study
Species
Study Species
Body Weight
(kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Hudson et al.
1984
Avian
Mallard
Survival
14 days
LD50
>2400
mg/kg
0.01
>24
5
>120
Hudson et al.
1984
Avian
Pheasant
1 b
Survival
14 days
LD50
>2000
mg/kg
0.01
>20
5
>100
Hyde et al.
1973
Avian
Mallard
1
Reproduction
25 weeks
Chronic
NOEC
0.01c
mg/kg/ d
0.01
Hyde et al.
1973
Avian
Mallard
1
Reproduction
25 weeks
Chronic
LOEC
Id
mg/kg/ d
1
USEPA 1986
Fish
Bluegill
1b
Growth
168 days
Chronic
NOAEL
3
mg/kg
3
5
15
Van Valin et
al. 1968
Fish
Bluegill
0.0132
Growth
168 days
Chronic
NOEL
2.14e
mg/kg/ d
2.14
Van Valin et
al. 1968
Fish
Bluegill
0.0136
Growth
168 days
Chronic
LOEL
3.57f
mg/kg/ d
3.57
Skea et al.
1981
Fish
Brook
Trout
0.1145
Growth
104 days
Chronic
NOEC
0.3 s
mg/kg/ d
0.3
5
1.5
Leatherland
& Sonstegard
1980
Fish
Rainbow
trout
0.05
Liver and
Thyroid
Effects
1 month
Subchronic
NOEC
lh
mg/kg/ d
0.1
0.1
5
0.5
WHO 1984
Mammal
Rat
0.351
Survival
Not Specified
LD50
600
mg/kg
0.01
6
5
30
WHO 1984
Mammal
Rat
0.351
Survival
Not Specified
LD50
365
mg/kg
0.01
3.65
5
18.25
WHO 1984
Mammal
Hamster
lb
Survival
Not Specified
LD50
125
mg/kg
0.01
1.25
5
6.25
WHO 1984
Mammal
Dog
lb
Survival
Not Specified
LD50
1000
mg/kg
0.01
10
5
50
USEPA 1986
Mammal
Rat
0.351
Chronic
90 days
Chronic
LOAEL
6.2
mg/kg/ d
0.2
1.24
NTP 1990
Mammal
Rat
0.12
Liver and
Thyroid
Effects
104 weeks
Chronic
NOAEL
0.07
mg/kg/ d
0.07
NTP 1990
Mammal
Rat
0.12
Liver and
Thyroid
Effects
104 weeks
Chronic
LOAEL
0.7
mg/kg/ d
0.7
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National Coastal Condition Assessment 2015 Technical Report
a - Reference BW from Sample et al., 1996.
b - Extrapolated from LD50 because the unit is mg chemical/kg body weight.
c - Value reported was 1 ppm. Used reference values of 1 kg BW and 100 g/d food intake from Sample et al to convert to mg/kg/d.
d - Value reported was 100 ppm. Used reference values of 1 kg BW and 100 g/d food intake from Sample et al to convert to mg/kg/d.
e - Treatment of 3 mg/kg 5 days a week converted to a daily dosage of 2.14 mg/kg.
f - Treatment of 5 mg/kg five days a week converted to a daily dosage of 3.57 mg/kg.
g - Treatment of .7 mg/kg three times a week converted to daily dosage of .3 mg/kg.
h - 50g trout at 2% BW/d at 50mg/kg food = 0.001kg food/d x 50mgMirex/kg food = 0.05mg/0.05kg/d = 1 mg/kg/d.
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National Coastal Condition Assessment 2015 Technical Report
Table A.1.12 Summary of literature values for polychlorinated biphenyls (PCBs)
Source
(Author,
Year)
ROC
Effects End
point
Study
Species
Study
Species
Body
Weight (kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-d
Hudson et
al. 1984
Avian
Mallard
V
Survival
14 days
LD50
>2000
mg/kg
0.01
>20
5
100
Hudson et
al. 1984
Avian
Bobwhite
0.19b
Survival
14 days
LD50
>2000
mg/kg
0.01
>20
5
100
Biessmann
1982
Avian
Japanese
quail
0.0721
Reproduction
3 weeks
Chronic
LOEC
7.29d
mg/kg/ d
0.2
1.458
7.29
Sample et
al. 1996
Avian
Ring-necked
pheasant
1
Reproduction
17 weeks
Chronic
NO ART,
0.18
mg/kg/ d
0.18
Sample et
al. 1996
Avian
Ring-necked
Pheasant
1
Reproduction
17 weeks
Chronic
LOARL
1.8
mg/kg/ d
1.8
Leatherlan
d and
Sonstegard
1980
Fish
Rainbow
Trout
0.05
Liver and
Thyroid
Effects
1 month
Subchronic
LORC
le
mg/kg/ d
0.05
0.05
5
0.25
Nakayama
2004
Fish
Japanese
medaka
0.0003c
Reproduction
3 weeks
Subchronic
NORC
1
mg/kg/ d
0.1
0.1
5
0.5
Hudson et
al. 1984
Mammal
Albino rat
0.351
Survival
14 days
LD50
841
mg/kg
0.01
8.41
5
42.05
Hudson et
al. 1984
Mammal
Albino rat
0.351
Survival
14 days
LD50
2000
mg/kg
0.01
20
5
100
Sample et
al. 1996
Mammal
Oldfield
mouse
0.014
Reproduction
12 months
Chronic
NO ART,
0.068
mg/kg/ d
0.068
Sample et
al. 1996
Mammal
Oldfield
mouse
0.014
Reproduction
12 months
Chronic
LOARL
0.68
mg/kg/ d
0.68
Sample et
al. 1996
Mammal
Mink
1
Reproduction
4.5 months
Chronic
NO ART,
0.14
mg/kg/ d
0.14
Sample et
al. 1996
Mammal
Mink
1
Reproduction
4.5 months
Chronic
LOARL
0.69
mg/kg/ d
0.69
a - Reference BW from Sample et al., 1996.
b - Reference BW from Wildlife Exposure Factors Handbook.
c - This was the BW before a 1 month acclimation prior to test initiation (Leatherland and Sonstegard, 1980).
d - Delayed egg laying was observed at 50 ppm, ingestion rate was 0.1458 kg food/kg bw/d according to Nagy equation, so daily dose was 7.29 mg/kg/d
e - 50g trout at 2% BW/d at 50mg/kg food = 0.001kg food/d x 50mg PCB/kg food = 0.05mg/0.05kg/d = 1 mg/kg/d.
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National Coastal Condition Assessment 2015 Technical Report
Table A. 1.13 Summary of literature values for selenium
Source
(Author,
Year)
ROC
Effects Endpoint
Study
Species
Study
Species
Body Weight
(kg)
Study
Endpoint
Study
Duration
Study
Endpoint
Type
Reported
Endpoint
Units
UF
Chronic
NOAEL/
NOEC
mg/kg-day
UF
Chronic
LOAEL/
LOEC
mg/kg-day
Sample et
al. 1996
Avian
Mallard
1
Reproduction
100 days
Chronic
LOAEL
0.8
mg/kg/ d
0.8
Sample et
al. 1996
Avian
Mallard
1
Reproduction
100 days
Chronic
NO ART,
0.4
mg/kg/ d
0.4
Sample et
al. 1996
Avian
Black-
crowned
night-heron
0.88
Reproduction
94 days
Chronic
NOARL
1.8
mg/kg/ d
1.8
Sample et
al. 1996
Avian
Black-
crowned
night-heron
0.88
Reproduction
94 days
Chronic
LOARL
9
mg/kg/ d
9
Sample et
al. 1996
Avian
Screech owl
0.2
Reproduction
13.7 weeks
Chronic
NO ART,
0.44
mg/kg/ d
0.44
Sample et
al. 1996
Avian
Screech owl
0.2
Reproduction
13.7 weeks
Chronic
LOARL
1.45
mg/kg/ d
1.45
Wang et
al. 2007
Fish
Crucian carp
0.01367
Survival
30 days
Subchronic
NORC
0.01651
mg/kg/ d
0.1
0.00165
5
0.00825
Ogle and
Knight
1989
Fish
Fathead
minnow
0.00009
Growth
98 days
Chronic
NORC
0.912
mg/kg/ d
0.912
Ogle and
Knight
1989
Fish
Fathead
minnow
0.00009
Growth
98 days
Chronic
LORC
1.218
mg/kg/ d
1.218
Sample et
al. 1996
Mammal
Rat
0.35
Reproduction
1 year
Chronic
NO ART,
0.2
mg/kg/ d
0.2
Sample et
al. 1996
Mammal
Rat
0.35
Reproduction
1 year
Chronic
LOARL
0.33
mg/kg/ d
0.33
a - Fed 3% BW/d of 0.55 mg selenium/kg diet, converted to 0.0165 mg/kg/d
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National Coastal Condition Assessment 2015 Technical Report
A.2 Uncertainties/Limitations
A.2.1 Body Weight
The use of minimum adult body weights may overestimate the risk to the receptor population
that are typically heavier than the minimum reported weight. The use of the minimum body
weight may also under-estimate the risk to juveniles within each population. The use of the
minimum body weight is a typical conservative assumption in risk estimate (USEPA, 1997).
A.2.2 High Food Ingestion Rate
The formulae presented in Nagy (1987) calculate food ingestion rate based on body weight.
Because the food ingestion (birds and mammals) and daily ration (fish) are based on metabolism
of the receptor, the smaller individuals generally consume more food than larger receptors based
on body weight. This uncertainty may over- or under-estimate the calculated fish tissue
concentration depending on whether a receptors food ingestion rate is higher or lower than what
is calculated.
A.2.3 Ingestion TRVs
Data on the toxicity of many of the contaminants to wildlife receptors were sparse or lacking,
requiring the extrapolation of data from laboratory studies with non-wildlife species. This is a
typical extrapolation for ecological risk assessments because, so few wildlife species have been
tested directly for most constituents. The uncertainties associated with toxicity extrapolation
were minimized through the selection of the most appropriate test species for which suitable
toxicity data were available. The factors considered in selecting a test species to represent a
receptor group included taxonomic relatedness, trophic level, and available dietary toxicity data.
A.2.4 Contaminant Exposure
The screening fish tissue concentration calculated accounts for the risk to upper trophic level
receptors from each contaminant due to the uptake through the diet only. Receptors are not only
exposed to contaminants through diet but may be exposed through incidental uptake of
inorganic media (i.e., surface water, sediment, or soil), dermal contact, and via respiration. These
additional exposure pathways are typically evaluated in ecological risk assessments but were not
in the calculation of the screening fish tissue concentrations. Therefore, the risk to upper trophic
level receptors based on the fish tissue screening value may under-estimated the overall risk to
each receptor group from the contaminants of concern.
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A.2.5 Constituent Mixtures
Information on the ecotoxicological effects of constituent interactions is generally lacking,
although it is required (as is standard for ERAs) that the constituents be evaluated on a
constituent-by-constituent basis in comparison to TRVs. This could result in an underestimation
of risk (if there are additive or synergistic effects among constituents) or an overestimation of
risks (if there are antagonistic effects among constituents).
A.2.6 Chlordane Dietary Exposure to Fish
Toxicity data for dietary exposure of chlordane to fish was not available in the scientific literature
and represents an uncertainty.
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A.3 Generalized Receptor of Concern Groupings
Generalized receptor of concern ROC groupings used as endpoints for the NCCA ecological fish
tissue contaminant index. The most sensitive receptor in each group was determined by the highest
food ingestion rate per body weight (highlighted in yellow in the following table). American mink
and muskellunge were selected, respectively as the generalized mammalian and fish ROCs.
Table A.3.1 Minimum and maximum body weights and derived food ingestion rates for
select receptors of concern commonly used in ecological risk assessments.
Group
Receptors
Body Weight (kg)
Min/Ave
Max
Ref.
Food Ingestion Rate
(kg food/kg BW/d)
Min/Ave BW
Max BW
Great Blue Heron
1.47
2.99
0.051
0.040
Western Osprey
1.22
1.95
0.054
0.046
Avian1
Bald Eagle
3.00
4.50
0.040
0.034
Herring Gull
0.83
1.62
0.062
0.049
Belted Kingfisher
0.13
0.22
0.120
0.100
Brown Pelican
3.00
3.50
0.040
0.038
River Otter
5.00
15.00
0.052
0.042
Freshwater Mammals1
American Mink
0.55
0.076
0.060
Harbor Seal
58.80
124.00
0.033
0.029
Marine Mammals1
Bottlenose Dolphin
150.00
490.00
0.028
0.023
Adantic Walrus
900.00
1400.00
0.020
0.019
Bluefin Tuna
32.00
219.00
0.044
0.016
Yellowfin Tuna
23.42
52.45
0.023
0.010
Shortfin Mako
63.50
Marine Fish2
Sandbar Shark
34.00
Mackerel Tuna
34.55
Swordfish
58.00
Brown Trout
0.91
Freshwater Fish2
Muskellunge
0.34
Largemouth Bass
0.45
0.046
0.009
0.022
0.016
0.0095
0.064
0.024
1 Avian and mammalian food ingestion rates were calculated using equations derived from Nagy (1987).
2 Food ingestion rates for fish were calculated based on daily rations. Daily rations were converted from percent body weight/day to
kg food/ kg body weight/day in order to estimate food ingestion rates that are comparable to the avian and mammalian values. Data
for the shortfin mako, sandbar shark, mackerel tuna, and swordfish are based on average body weight and daily ration as opposed to
minimum and maximum body weight.
d — Bom et al., 2003
a — USEPA 1993 b - Schreiber, 1976
e — Aguado-Gimenez and Garcia-Garcia, 2005
h — Stillwell and Kohler, 1993
k — Becker, 1983 1 — Carlander, 1969
c — Kastelein et aL, 2002
f—Maldeniya, 1996
i — Giffiths et al., 2009
m — Carlander, 1977
g - Wood et al., 2009
j — Stillwell and Kohler, 1985
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A.4 Fish Species Analyzed for Contaminants
Table A.4.1 Fish species analyzed for contaminants from estuarine sites. Number of sites
from which each species was submitted, by NCCA region.
Genus
Species
Gulf
NCCA Region
Northeast Southeast
West
A nguilla
rostrata
2
Ariopsis
felis
94
9
Bagre
marinus
44
Bairdiella
chysoura
1
4
Brevoortia
smithi
4
Brevoortia
yrannus
2
1
Caranx hippos
2
Centrcpristis striata
1
10
Cheilotrema
saturnum
1
Chriodorus
atherinoides
1
Citharichthys
sordidus
6
Citharichthys
stigmaeus
3
Clupea
harengus
1
Cymatogaster aggregata
15
Cynoscion
arenarius
4
Cynoscion
nebulosus
1
Cynoscion
regalis
9
1
Diplectrum
formosum
2
Diplodus
holbrookii
1
Elops
saurus
1
Embiotoca
lateralis
1
Eucinostomus gula
1
Fundulus
heteroclitus
1
Fundulus
majalis
1
Genyonemus
lineatus
6
ELaemulon
plumierii
3
1
ELaemulon
sciurus
1
Hypsopsetta guttulata
1
Ictalurus
punctatus
2
1
Eagodon
rhomboides
16
10
Eeiostomus
xanthurus
17
7
16
Epidcpsetta bilineata
3
Eptocottus
armatus
27
Eimanda
ferruginea
1
Eutjanus
campechanus
1
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National Coastal Condition Assessment 2015 Technical Report
NCCA Region
Genus
Species
Gulf
Northeast
Southeast
West
l^utjanus
griseus
5
1
l^utjanus
synagris
4
Menidia menidia
12
Mentidrrhus
americanus
4
9
Mentidrrhus
littoralis
1
Mentidrrhus
saxatilis
2
Merlucdus
bilinearis
1
Micrcpogonias
undulatus
34
7
6
Morone
americana
18
2
Morone
saxatilis
6
Mustelus
canis
4
Opsanus
tau
2
Orthopristis
chiysoptera
2
Paralabrax
clathratus
1
Paralabrax
maculatofasaatus
4
Paralabrax
nebulifer
3
Paralichthys
californicus
22
Paralichthys
dentatus
17
Peprilus
triacanthus
1
Platichthys
stellatus
3
Pollachius
virens
1
Pomatomus
saltatrix
5
2
Prionotus
carolinus
2
Prionotus
evolans
1
Prionotus
sdtulus
1
Pseudopleuronectes
americanus
25
Saaenops
ocellatus
1
Scomber
scombrus
9
Scophthalmus
aquosus
1
Sphoeroides maculatus
1
Stenotomus
chysops
49
Tautogolabrus
adpersus
6
Urcphyas
chuss
1
Zoarces
americanus
1
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Table A.4.2 Fish species analyzed for contaminants from the Great Lakes.
Genus
species
Great Lakes
Alosa
pseudoharengus
6
Amblcplites
rupestris
2
Ameiurus
nebulosus
1
Aplodinotus
grunniens
22
Catostomus
catostomus
24
Catostomus
commersonii
22
Coregonus
artedi
1
Coregonus
clupeaformis
54
Cyprinus
capio
4
Dorosoma
cepedianum
8
Esox
lucius
2
Ictalurus
punctatus
6
Epomis gibbosus
1
Eota
lota
3
Euxilus
cornutus
1
Micropterus
dolomieu
24
Micropterus
salmoides
3
Morone
americana
6
Morone
chysops
5
Moxostoma
carinatum
1
Moxostoma
macrolepidotum
1
Neogobius
melanostomus
22
Oncorhynchus kisutch
2
Oncorhynchus rnykiss
1
Osmerus
mordax
1
Perca
flavescens
50
Pomoxis
nigromaculatus
1
Prosopium
ylindraceum
5
Salvelinus
namaycush
5
Sander
vitreus
23
Species not reported
1
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National Coastal Condition Assessment 2015 Technical Report
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