NCCA 2010 TECHNICAL
REPORT
National Coastal Condition Assessment 2010
January 2016
U. S. Environmental Protection Agency
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Table of Contents
Contents
Section 1: Survey Design 1
Section 2: Assessing Benthic Condition (NCCA 2010) 3
Section 3: Assessing Water Quality (NCCA 2010) 11
Section 4: Assessing Sediment Quality (NCCA 2010) 19
Section 5: Assessing Ecological Fish Tissue Contaminants (NCCA 2010) 28
Section 6: Quality Assurance and Quality Control (NCCA 2010) 43
This document provides supplemental technical information on the background and development of
the Benthic Index, Water Quality Index, Sediment Quality Index, and Ecological Fish Tissue
Contaminants Index used in the National Coastal Condition (NCCA) 2010 Report. It was developed by
EPA to provide technical information to readers of the NCCA 2010.
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Section 1: Survey Design
The National Coastal Condition Assessment uses a probability-based survey design to select sites within the
target population. This type of survey design allows for spatially-balanced sampling wherein each point has a
known probability of being included in the draw. The design also ensures that no points in the target: population
are too far from a sampled point, while reducing the clumping of points that are close together. The target:
population is divided (or "stratified") into unequal probability categories allowing for adequate representation
of varying characteristics within the sample frame.
Estuarine Design
Target population: All coastal waters of the United States from the head-of-salt to confluence with ocean
including inland waterways and major embayments such as Florida Bay and Cape Cod Bay.
Survey Design: A Generalized Random Tessellation Stratified (GRTS) survey design for an area resource is used.
The survey design is a stratified design with unequal probability of selection based on area within each stratum.
The details are given below.
Stratification: Stratification is based on major estuaries based on NOAA Coastal Assessment framework and
National Estuaries Program estuaries.
Multi-density categories: Unequal probability categories were created based on area of polygons within each
major estuary. The number of categories ranged from 3 to 7. The categories were used to ensure that sites were
selected in the smaller polygons.
Expected sample size: The expected sample size is 682 sites for conterminous coastal states and 45 sites for
Hawaii and Puerto Rico. The maximum number of sites for a major estuary was 46 (Chesapeake Bay). In total,
the estuarine design contains 682 sites. Of these 68 were revisited, for a total of 750 total visits.
Great Lakes Design
Target population: Near shore waters of the Great Lakes of the United States and Canada. Near shore zone is
defined as up to 30m depth and a maximum distance of 5 km from shoreline. Great Lakes include Lake Superior,
Lake Michigan, Lake Huron, Lake Erie, and Lake Ontario. The NARS Great Lakes survey will be restricted to the
United States portion. It does not include the connecting channels of the Great Lakes (between lakes and the St.
Lawrence River outlet).
Survey Design: A Generalized Random Tessellation Stratified (GRTS) survey design for an area resource is used.
The survey design is stratified by Lake and country with unequal probability of selection based on state shoreline
length within each stratum.
Stratification: Stratification is based on Great Lake and country.
Multi-density categories: Unequal probability categories are states within each Great Lake based on proportion
of state shoreline length within each stratum.
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Expected sample size: Expected sample size of 45 sites in Near Shore zone for each Great Lake and country
combination for a total of 405 sites. Sample sizes were allocated proportional to shoreline length by state within
each Great Lake.
Additional Sites: Additional sites that followed the above design frames as well as sample collection methods for
special studies were included in this assessment. An example of this is the embayment study which added 150
sites into the Great Lakes assessment.
Site Weights
Each site has an associated weighting factor equal to the surface area represented by the site. As in previous
assessments, the status of the nation and each region for each of the indices used in this assessment, is reported
as the percent area in good, fair, poor, or missing condition. The percent area in each condition is calculated as
the sum of weighting factors (areas) of sites in a condition category, divided by the sum of weights (total area) of
all sites in the region. For instance, for the Northeast region, the percent area in good condition is calculated as
the sum of the weighting factors (areas) of sites rated as good, divided by the sum of all NE weighting factors.
Results were reported in this manner for the component metrics and the overall indices.
Data availability
All data used in the 2010 survey are available from the NARS web site (http://www.epa.gov/national-aquatic-
resource-survevs/ncca). In particular, "NCCA 2010 Assessed [indicator name] - Data (CSV)" data files contain
only sites and data used to develop the assessments in this report. The data files also contain any auxiliary
parameters necessary to calculate all report metrics and indices.
Survey Design References
Diaz-Ramos, S., Stevens, D. L., Jr, & Olsen, A. R. (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.
Olsen, T. (2010, January). USEPA. National Coastal Assessment 2010 Great Lakes Embayment Survey Design.
Olsen, T. (2009, January). USEPA. National Coastal Assessment 2010 Great Lakes Survey Design.
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Section 2: Assessing Benthic Condition (NCCA 2010)
The worms, mollusks, crustaceans, and other invertebrates that inhabit the bottom substrates of coastal waters
are collectively called benthic macroinvertebrates, or benthos. These organisms play a vital role in monitoring
water quality and provide an important food source for bottom-feeding fish; shrimp; ducks; and marsh birds.
Benthos are often used as indicators of disturbance in coastal environments because they are not very mobile
and thus cannot avoid environmental problems. Benthic populations and communities serve as reliable
indicators of coastal environmental quality because they are sensitive to chemical-contaminant and dissolved-
oxygen stresses, salinity fluctuations, and sediment disturbance.
To assess the ecological condition of benthic communities, EMAP and the NCA developed regional benthic
indices of environmental condition for the Southeast (Van Dolah et al., 1999), Northeast (Paul et al., 2001; Hale
and Heltshe, 2008), and Gulf coasts (Engle et al., 1994; Engle and Summers, 1999). Each index was developed
independently for a specific biogeographical region, used different statistical methods, and incorporated
different metrics of benthic community condition (Table B-l). In general, however, all of the benthic indices
reflect changes in benthic community diversity and the abundance of pollution-tolerant and pollution-sensitive
species. A good benthic index rating for benthos means that the benthic habitats contain a wide variety of
species, including low proportions of pollution-tolerant species and high proportions of pollution-sensitive
species. A poor benthic index rating indicates that the benthic communities are less diverse than expected and
are populated by more pollution-tolerant species and fewer pollution-sensitive species than expected.
Table B-l. NCA Benthic Indices
Region/
Province
Data Source
Statistical
Method
Component Metrics
Index Condition Scale
Source
Good
Fair
Poor
Northeast/
Acadian
NCA
2000-2001
Logistic
Regression
Analysis
Diversity (Shannon H')
Pollution Tolerant Taxa
Proportion Capitellids
>5
4-5
<4
Hale & Heltsche
2008
Northeast/
Virginian
EMAP
1990-1993
Discriminant
Analysis
Diversity (Gleason D)
Abundance Tubificids
Abundance Spionids
>0
n/a
<0
Paul et al. 2001
Southeast/
Carolinian
EMAP
1993-1994
Cluster
Analysis
Abundance
Species Richness
Dominance
Pollution Sensitive Taxa
>2.5
2-2.5
<2
Van Dolah et al.
1999
Gulf/
Louisianian
EMAP
1991-1992
Discriminant
Analysis
Diversity (Shannon H')
Abundance Tubificids
Proportion Capitellids
Proportion Bivalves
Proportion Amphipods
>5
3-5
<3
Engle et al. 1994;
Engle & Summers
1999
No regional benthic index has been developed for the West Coast, although several local benthic indices have
been developed (e.g., Smith et al. 2001; Ranasinghe et al. 2007). In the West Coast region benthic species
richness was used as a surrogate for a regional benthic index. Values for species richness were compared with
salinity regionally to determine if a significant relationship existed. For West Coast estuaries, a highly significant
(p < 0.0001) linear regression between log species richness and salinity was found for the region, although
variability was high (R2 = 0.33). A surrogate benthic index was calculated by determining the expected species
richness from the statistical relationship to salinity and then calculating the ratio of observed to expected
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species richness. Poor benthic condition was defined as observed species richness less than 75% of the lower
95% confidence interval of the regression for expected benthic species richness at a particular salinity (Table 2).
Good benthic condition was defined as observed species richness greater than 90% of the lower 95% confidence
interval of the regression for expected benthic species richness at a particular salinity (Table 2).
In the Great Lakes, the State of the Lakes Ecosystem Conference (SOLEC) assesses benthic community condition
using an oligochaete trophic index (OTI) based on Howmiller & Scott's (1977) index with subsequent
modifications by Milbrink (1983) and Lauritsen et al. (1985). The OTI is based on the classification of oligochaete
species by their known tolerance to organic enrichment (Environment Canada & USEPA 2014). The OTI ranges
from 0 to 3 where scores less than 0.6 indicate oligotrophic conditions, scores between 0.6 and 1.0 indicate
mesotrophic conditions, and scores > 1.0 indicate eutrophic conditions (Table B-2). In this report, oligotrophic
equates to good condition, mesotrophic equates to fair condition, and eutrophic equates to poor condition.
Table B-2. Thresholds for Assessing Benthic Condition
Region
Good
Fair
Poor
Northeast
Acadian
Province
Benthic index score is
greater than 5.0.
Benthic index score is
between 4.0 and 5.0.
Benthic index score is less than
4.0.
Virginian
Province
Benthic index score is
greater than 0.0.
NAa
Benthic index score is less than
or equal to 0.0.
Southeast
Benthic index score is
greater than 2.5.
Benthic index score is
between 2.0 and 2.5.
Benthic index score is less than
2.0.
Gulf
Benthic index score is
greater than 5.0.
Benthic index score is
between 3.0 and 5.0.
Benthic index score is less than
3.0.
West
Observed species richness is
more than 90% of the lower
95% confidence interval of
expected species richness for
a specific salinity.
Observed species richness is
between 75% and 90% of
the lower 95% confidence
interval of expected species
richness for a specific
salinity.
Observed species richness is
less than 75% of the lower 95%
confidence interval of
expected species richness for a
specific salinity.
Great Lakes
Oligochaete trophic index
score is less than 0.6
Oligochaete trophic index
score is between 0.6 and 1.0
Oligochaete trophic index
score is greater than 1.0
aBy design, the Virginian Province index discriminates between good and poor conditions only.
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Detailed Methods for Calculating Benthic Indices
Preparation of NCCA Benthic Data
Sediment samples were collected using sediment grab apparatus as shown in Table B-3. Crews sieved the
sediment*, retained macroinvertebrates, preserved them and sent them to benthic taxonomy laboratories for
taxonomic identification and organism counts. Because States used different grabs to collect sediment samples,
it is necessary to standardize raw counts of benthic abundance by using the grab areas in Table B-3 (i.e., convert
number/grab to number/m2):
Number m-2 = Number grab-1 / (grab size in m2 x number of grabs) (Formula B-l)
Table B-3. Benthic grab types, surface area, and states where each grab was used.
Grab Type
Grab Area
(m2)
States where used
Small van Veen
0.04
CT, DE, FL, GA, LA, MD, MS, NC,
NH, NJ, NY, Rl, VA
Large van Veen
0.1
CA, OR, WA
Young-modified
van Veen
0.04
SC, VA
Standard Ponar
0.052
AL, CA, IL, IN, MA, ME, Ml, MN,
NY, OH, PA, Wl
Petite Ponar
0.023**
FL, VA
Ekman
0.046
TX
Modified Post-hole
Digger
0.1
OR, WA
6-inch Corer
0.182
FL
* A I sediment grabs but those collected on the West Coast were sieved using 0.5 mm mesh; the West
Coast grabs were sieved using 1.0 mm mesh.
** Two benthic grabs composited for grabs smaller than 0.03 m2.
Different laboratories were used for benthic taxonomy, which required standardization of taxonomic names.
The World Register of Marine Species (WoRMS) was used to standardize taxonomic nomenclature for marine
species [http://www.marinespecies.org/] and the Integrated Taxonomic Information System (ITIS) was used to
standardize freshwater species [http://www.itis.gov/], Taxa that were not considered to be benthic
macroinvertebrate infauna were removed from the data (i.e., Phylum Nematoda, Phylum Bryozoa, Class
Ostracoda, Class Maxillopoda, and Class Arachnida).
Standard benthic community metrics, including total abundance, species richness, and Shannon's diversity (H')
were calculated for the benthic data at all stations. Bottom salinity measures were also added to the database
for all stations.
Gulf of Mexico Benthic Index
The Gulf of Mexico benthic index is based on a benthic index originally developed by Engle et al. (1999) and
revised by Engle & Summers (1999) for the Louisianian biogeographic province. This index was developed from
EMAP-Estuaries data collected from 1991-1992 in the Louisianian Province (Texas/Mexico border to Anclote
Key, FL). Reference and degraded sites were selected based on criteria for dissolved oxygen, sediment
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contaminants, and sediment toxicity. Discriminant analysis was performed on a set of benthic community
metrics to determine those metrics that best distinguished between reference and degraded sites. The benthic
index included the following metrics: Proportion of Expected Shannon's H' Diversity (based on salinity), Mean
Abundance of Family Tubificidae, Percent Abundance of Family Capitellidae, Percent Abundance of Class
Bivalvia, and Percent Abundance of Order Amphipoda.
Proportion of Expected Shannon's H' Diversity (based on salinity) is calculated as:
H'iog2 = X log2 (Formula B-2)
Expected H' = 2.7095 + (0.0367 x Salinity) + (0.0015 x Salinity2) —
(0.000033 x Salinity3) (Formula B-3)
Proportion of Expected Diversity = —H 1092—- (Formula B-4)
Mean Abundance of Family Tubificidae is transformed using logio and Percent Abundance of Family Capitellidae,
Percent Abundance of Class Bivalvia, and Percent Abundance of Order Amphipoda are transformed using
arcsine. All parameters are standardized to mean=l and standard deviation=0. The Gulf of Mexico Benthic
Index is then calculated as;
GulfBI =(((Proportion of Expected Diversity x 1.51038682) +
(Mean Abundance of Family Tubificidae x -1.033492089) +
(Percen t Abundance of Family Capitellidae x -0.560706007) +
(Percent Abundance of Class Bivalvia x -0.446995840) +
(Percen t Abundance of Order Amphipoda x 0.502344732)) + 3.2059424) x 1.3325
—(Formula B-5)
Southeast Benthic Index
The Southeast benthic index is based on a benthic index originally developed by Van Dolah et al. (1999) for the
Carolinian biogeographic province. This index was developed from EMAP-Estuaries data collected from 1993-
1994 in the Carolinian Province (Cape Henry, VA to St. Lucie Inlet, FL). Reference and degraded sites were
selected based on criteria for dissolved oxygen, sediment contaminants, and sediment toxicity. Sites were also
grouped by habitat type (Oligohaline-mesohaline stations (< 18 psu) from all latitudes; Polyhaline-euhaline
stations (> 18 psu) from northern latitudes (> 34.5° N); Polyhaline-euhaline stations from middle latitudes (30-
34.5° N); and Polyhaline-euhaline stations from southern latitudes (< 30° N). Classification cluster analysis was
performed on a set of benthic community metrics to determine those metrics that best distinguished between
reference and degraded sites within each habitat type. The final benthic index included four metrics: Mean
Abundance per grab, Mean number of taxa per grab, 100% minus percent abundance of two most dominant
taxa, and % Pollution-sensitive taxa (Group C - Ampeliscidae, Haustoriidae, Tellinidae, Lucinidae, Hesionidae,
Cirratulidae, Cyathura polita, Cyathura burbancki.).
Scoring criteria for each metric were developed based on the distribution of values at the non-degraded
(reference) sites in the 1994 development data set. A score of 1 was used if the value of the metric for the
station being evaluated was in the lower 10th percentile of corresponding reference values. A score of 3 was
used if the value of the metric for the station was in the lower 10-50th percentile of reference values. A score of
5 was used if the value of the metric for the station was in the upper 50th percentile of reference values.
Individual metric scores were then averaged for each site. Scoring criteria were determined separately for each
metric and habitat type using the threshold values provided in Table B-4.
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Table B-4. Scoring criteria percentile breakpoints for metrics used In the Southeast ienthic Index {Van Dolah et at. 1999)
Metric
Oligohaline-
mesohaline
All latitudes
Polyhaline-
euhaline
Northern latitudes
Polyhaline-
euhaline
Middle latitudes
Polyhaline-
euhaline
Southern latitudes
10th
50th
10th
50th
10th
50th
10th
50th
Mean abundance
per 0.04 m2
53.50
93.00
26.00
109.75
18.50
255.50
112.50
301.00
Mean number of
taxa per 0.04 m2
7.00
8.50
7.50
17.00
6.25
23.00
26.50
35.00
100% of two most
dominant taxa
9.62
25.45
28.94
51.53
17.36
52.04
52.89
61.19
% Pollution-
sensitive taxa
0.61
5.04
0.00
12.83
1.61
12.23
0.71
2.22
West Coast Benthic Index
Since no regional benthic index has been developed for the West Coast, benthic species richness was used as a
surrogate for a regional benthic index. Species richness was first logio-transformed. A highly significant (p <
0.0001) linear regression between log species richness and salinity was found for the region, although variability
was high (R2 = 0.26). A surrogate benthic index was calculated by determining the lower 95th confidence limit for
expected species richness from the linear regression with salinity and then calculating a ratio by dividing
observed species richness by the lower 95th confidence limit.
Poor benthic condition was defined as observed species richness less than 75% of the lower 95% confidence
interval of the regression for expected benthic species richness at a particular salinity. Good benthic condition
was defined as observed species richness greater than 90% of the lower 95% confidence interval of the
regression for expected benthic species richness at a particular salinity.
Great Lakes Benthic Index
In the Great Lakes, benthic community condition is assessed using an oligochaete trophic index (OTI) based on
Howmiller & Scott's (1977) index with subsequent modifications by Milbrink (1983) and Lauritsen et al. (1985).
The OTI is based on the classification of oligochaete species by their known tolerance to organic enrichment
(Environment Canada & USEPA 2014). Table 4 shows the oligochaete species that were assigned to four trophic
groups as well as those that could not be assigned to a group. The abundance of oligochaete species in each
group is calculated for each site, and the OTI is calculated as:
!£»«>+£»,+ 2£)!2+32>:<
OTI = c —————— Formula B-6)
£»(> + £ »i + £ »2 +£»:<
where n0, n1, n2, n3 refer to the total abundance of species in Group 0, 1, 2, 3 and c
adjusts the ratio to the total abundance of tubificid and lumbriculid oligochaetes (n = number per m2) as follows:
c = 1 when n > 3600
c = 0.75 when 1200 < n < 3600
c = 0.5 when 400 < n < 1200
c = 0.25 when 130 < n < 400
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c = 0 when n < 130
The OTI ranges from 0 to 3, where scores less than 0.6 indicate oligotrophic conditions, scores between 0.6 and
1.0 indicate mesotrophic conditions, and scores > 1.0 indicate eutrophic conditions. In this report, oligotrophic
equates to good condition, mesotrophic equates to fair condition, and eutrophic equates to poor condition.
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Tabie IS-5, Trophic Classification of Oligochaete Species in NCCA 2010 Great Lakes data11'
Group 0
Limnodrilus profundicola
Rhyacodrilus coccineus
Rhyacodrilus montana
Rhyacodrilus sp.
Spirosperma nikolskyi
Stylodrilus heringianus
Lumbriculidae3
Trasserkidrilus superiorensis
Trasserkidrilus americanus
Tubifex tubifex*
Group 1
Arcteonais lomondi2
Aulodrilus americanus
Aulodrilus limnobius
Aulodrilus pigueti
Dero digitata2
llyodrilus templetoni
Isochaetidesfreyi
Slavina appendiculata2
Spirosperma ferox
Uncinais uncinate2
Group 2
Aulodrilus pluriseta
Limnodrilus angustipenis
Limnodrilus cervix
Limnodrilus claparedianus
Limnodrilus maumeensis
Limnodrilus udekemianus
Potamothrix bedoti
Potamothrix moldaviensis
Potamothrix vejdovskyi
Quistadrilus multisetosus
Group 3
Limnodrilus hoffmeisteri
Tubifex tubifex*
Unassigned4
Branchiura sowerbyi (2)
Chaetogaster diaphanus (2)
Dero sp. (2)
llyodrilus frantzi
Naidinae
Nais sp.
Nais bretscheri
Ophidonais serpentina (2)
Paranais grandis
Paranais litoralis
Piguetiella sp.
Piguetiella blanci (2)
Specaria
Stylaria lacustris (2)
Tubificinae
Varichaetadrilus
Vejdovskyella intermedia (1)
f Species in bold above were not reported from NCCA 2010 Great Lakes samples
*Tubifex tubifex is assigned to Group 0 or Group 3 according to the following rules:
- if n0: n3 < 0.75 then Group 0;
- if n0: n3 > 1.25 then Group 3;
-if n0:n3 = 0.75 - 1.25 then Group 0 if c < 0.5 or Group 3 if c > 0.5;
- if n3= 0 then Group 0 if n0 is relatively high and/or c is low; otherwise Group 3
1 from State of the Great Lakes 2012 - Draft - Benthic Diversity and Abundance Table 1. [Classifications are from Howmiller and
Scott (1977), Milbrink (1983), Kreiger (1984), and Lauritsen et al (1985)]. Only species in the families, Naididae (formerly Tubificidae
and Lumbriculidae were included.
2 These species were not included in SOLEC 2011 list presumably because they were thought to be in the family Naididae, not
Tubificidae, although they were included in group 2 in earlier publications. However, recent taxonomy changes have reclassified
Tubificidae to Naididae which has several subfamilies Naidinae within Tubificinae, so they were included in Group 1.
3 SOLEC classified all immature Lumbriculidae as Stylodrilus heringianus. Therefore taxa in NCCA 2010 GL samples that were
identified as Lumbriculidae are assigned Group=0.
4Taxa with numbers are group assignments recommended by Kurt Schmude, Univ. of Wisconsin - Superior
Benthic Condition References
Engle, V.D., and J.K. Summers. 1999. Refinement, validation, and application of a benthic condition index for
northern Gulf of Mexico estuaries. Estuaries 22(3A):624-635.
Engle, VD., J.K. Summers, and G.R. Gaston. 1994. A benthic index of environmental condition of Gulf of Mexico
estuaries. Estuaries 17:372-384.
Environment Canada and the U.S. Environmental Protection Agency. 2014. State of the Great
Lakes 2011. Cat No. Enl61-3/l-2011E-PDF. EPA 950-R-13-002. Available at http://binational.net
Hale, S.S., and J.F. Heltshe. 2008. Signals from the benthos: Development and evaluation of a benthic index for
the nearshore Gulf of Maine. Ecological Indicators 8:338-350.
Howmiller, R.P., and Scott, M.A. 1977. An environmental index based on relative abundance of oligochaete
species. J. Wat. Poll. Cont. Fed. 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. J. Great Lakes Res. 11(1): 67-76.
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Milbrink, G. 1983. An improved environmental index based on the relative abundance of oligochaete species.
Hydrobiologia 102:89-97.
Paul, JF, KJ Scott, DE Campbell, et al. 2001. Developing and applying a benthic index of estuarine condition for
the Virginian Biogeographic Province. Ecological Indicators 1:83-99.
Ranasinghe, J A, SB Weisberg, RW Smith, et al. 2009. Calibration and evaluation of five indicators of benthic
community condition in two California bay and estuary habitats. Marine Pollution Bulletin. 59:5-13.
Smith, RW, M Bergen, SB Weisberg, et al. 2001. Benthic response index for assessing infaunal communities on
the southern California mainland shelf. Ecological Applications 11:1073-1087.
Van Dolah, R.F., J.L. Hyland, A.F. Holland, J.S. Rosen, and T.T. Snoots. 1999. A benthic index of biological integrity
for assessing habitat quality in estuaries of the southeastern USA. Marine Environmental Research
48:(4-5):269-283.
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Section 3: Assessing Water Quality (NCCA 2010)
This section outlines the methods used in assessing water quality in coastal estuaries and the Great Lakes in the
2010 National Coastal Condition Assessment (NCCA). Estuaries were assessed using the same approach used in
previous 2000-2006 National Coastal Assessment (NCA) surveys. The Great Lakes were included for the first time
in the 2010 coastal survey, and water quality was assessed similar to estuaries, but with several notable
differences. In both water types, nutrients, chlorophyll, dissolved oxygen (DO), and water clarity were measured
at each site and then combined into an overall Water Quality Index. But different nutrient and water clarity
measures were employed in the saline and freshwater cases, as is highlighted in Table WQ-1. Details regarding
assessment in estuaries and in the Great Lakes are presented separately below.
Table WQ-1. Indicators used to assess water quality in estuaries and the Great Lakes
Specific to
Specific to
Metric
Coastal Estuaries
the Great Lakes
Surface Phosphorus
DIP (mg P/L) a
TP (mg P/L) b
Surface Nitrogen
DIN (mg N/L)c
Not used in analysis
Surface Chlorophyll a
Chla (ug/L)
Chla (ug/L)
Bottom Dissolved
Oxygen
DO (mg/L)
DO (mg/L)
Water Clarity
Transmittance @lm d
Secchi depth (m)
a DIP: Dissolved Inorganic Phosphorus; P04
bTP: Total Phosphorus
c DIN: Dissolved Inorganic Nitrogen; Sum of N03, N02 and NH4
d Calculated from PAR vs. depth profiles or Secchi depth
Water samples were collected similarly in estuaries and Great Lakes. Dissolved oxygen data was collected using
a calibrated multi-parameter water quality meter (or sonde). The downcast dissolved oxygen values, measured
0.5 meters from the bottom, were used in the assessment. Water clarity was measured using both a 20 cm
Secchi disk and a Photosynthetically Active Radiation (PAR) meter. The nutrients and chlorophyll samples were
collected 0.5 meters 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. The
chlorophyll and dissolved nutrients were filtered with a Whatman GF/F 47 mm 0.7 micron filter. Refer to online
manuals for detailed descriptions of methods used to collect and analyze samples in the 2010 NCCA survey
(USEPA 2010a-d).
Assessment Procedure for Water Quality in Coastal Estuaries.
Table WQ-1 indicates that five metrics were employed in assessing estuaries: DIN, DIP, and chlorophyll
concentrations in surface water; DO in bottom water; and PAR attenuation (transmittance) as a measure of
water clarity. Assessments of the first four metrics were straightforward; assessing water clarity was more
involved. Discrete surface nutrient and chlorophyll samples were collected from 0.5 meter below the water
surface and analyzed by multiple laboratories. Labs were free to select analysis methods as long as acceptance
criteria were met (USEPA 2010b). A quality assurance review of results did not reveal any sign of bias by lab.
Note that DIN is a derived parameter, calculated as the sum of nitrate, nitrite, and ammonium concentrations.
Some labs reported nitrate and nitrite concentrations separately; others reported these analytes as the sum of
nitrate and nitrite. Bottom water dissolved oxygen was measured by DO probe 0.5 meter above the sediment
surface.
I I
-------
Table WQ-2. Thresholds used to calculate water quality condition at estuarine sites
Surface DIP
Surface DIN
Surface CHLA
Bottom DO
(mg P/L)
(mg N/L)
(ug/L)
(mg/L)
TH1
TH2
TH1
TH2
TH1
TH2
TH1
TH2
Northeast
0.01
0.05
0.1
0.5
5
20
2
5
Southeast
0.01
0.05
0.1
0.5
5
20
2
5
Gulf
0.01
0.05
0.1
0.5
5
20
2
5
West
0.07
0.1
0.35
0.5
5
20
2
5
Tropics
0.005
0.01
0.05
0.1
0.5
1
2
5
Nutrient, chlorophyll a, and DO measurements in estuaries were evaluated as good, fair, or poor relative to
thresholds listed in the Tables WQ-2. The thresholds for nutrients and chlorophyll vary by region. The Northeast
encompasses the coasts of Maine through Virginia; the Southeast includes the remaining southern Atlantic
seaboard; the Gulf refers to the Gulf of Mexico coastline Florida through Texas; and the West pertains to the
coasts of California, Oregon, and Washington. While the tropics included various low latitude tropical locations
in previous NCA surveys, the classification is limited to Florida Bay and Biscayne in this report. The nutrient and
chlorophyll thresholds were set by consensus of regional experts at the beginning of the NCA program and
maintained through all surveys (including this assessment) to maintain continuity. Dissolved oxygen thresholds
reflect documented limits of disruption to estuarine communities (Diaz and Rosenberg, 1995; USEPA, 2000) and
regulatory limits set by some states. Conditions for DIN, DIP, and CHLA were calculated as: good TH2; and for DO as: good > TH2, fair < TH2, and poor < TH1.
Water clarity in estuaries was characterized primarily as Transmittance, defined as the percent of
photosynthetically active radiation (PAR) transmitted through one meter of water, calculated as follows. PAR
attenuation was measured using two PAR sensors. One sensor was lowered through the water column,
measuring PAR intensity (Iz) at depths z. A second sensor in air reported varying incident PAR intensity (lo)
arising, for instance, from changing cloud cover. The normalized PAR attenuation (Iz/lo) is assumed to follow
Beer's law, i.e., light intensity decreasing exponentially with distance:
Iz/lo = exp(-Kd*z) (Formula WQ-1)
Where Kd is the PAR attenuation coefficient; larger Kd magnitudes indicate greater attenuation, i.e., poorer
water clarity. Equation WQ-1 is equivalently expressed as follows, highlighting the fact that the decreasing
intensity ln(lz/lo) is linearly proportional to depth:
In (Iz/lo) = -Kd*z (Formula WQ-2)
Operationally, Kd is calculated as the negative slope of a regression of In(Iz/lo) vs depth. PAR intensities and
depth measurements are reported in a "hydrolab" data file available at the NARS website
(http://water.epa.gov/type/watersheds/monitoring/aquaticsurvev index.cfm). An Excel spreadsheet was
devised to quickly review the regression plots for every site in order to identify, flag, and remove errant data
values used in the regression calculation—a necessary step, as errant values were common.
Once reliable Kd values are obtained, % transmittance at one meter (i.e., Iz/lo at one meter) was calculated from
Formula WQ-1 as:
12
-------
% Trans @ lm = exp(-Kd)*100
(Formula WQ-3)
The water clarity condition at a site (good, fair, or poor) was then determined by evaluating Transmittance
relative to the thresholds in Table WQ-3. These transmittance thresholds vary depending on the turbidity level
or SAV restoration status of the site. Less stringent thresholds hold for naturally turbid regions, and more
stringent thresholds apply for waters supporting SAV restoration. To proceed with the analysis, sites must be
categorized as to their turbidity status.
For consistency with previous NCA reports, the same regional delineations of turbidity classes were used for this
report (Smith et al., 2006). Naturally turbid regions consisted of waters in Alabama, Louisiana, Mississippi, South
Carolina, Georgia, and Delaware Bay. Regions supporting SAV restoration included Laguna Madre, the Big Bend
region of Florida, the coast from Tampa Bay to Florida Bay, the Indian River lagoon, and portions of Chesapeake
Bay. All other sites were considered to exhibit normal turbidity. The turbidity class assignments for sites
measured in 2010 are indicated in Figure WQ-1. Water clarity conditions were calculated as: good > TH2, fair <
TH2, and poor
-------
turbidity Clans
e Naturally' Turbid
\ '
-------
Historical perspective regarding water quality assessment in estuaries. Prior to writing this report, an advisory
committee was assembled to review the NCA approach of assessing water quality. The committee largely found
the original approach sound, but suggested using total nitrogen and total phosphorus rather than DIN and DIP as
nutrient indicators, and recommended considering adjusting the regional thresholds for nutrients and
chlorophyll to better bracket historical ranges of measured values (particularly in the case of DIP). NCCA
program managers decided to retain the original NCA approach entirely, primarily to maintain continuity with
earlier surveys and also because of an absence of any peer-reviewed alternate thresholds. TN and TP may be
adopted for the 2015 survey if a review of relationships between total and dissolved measures of nutrients in
2010 suggest thresholds for TN and TP that would permit reliable comparison with earlier survey findings.
Assessment Procedure for Water Quality in the Great Lakes
Water Quality of the Great Lakes nearshore waters were assessed for the first time in 2010 as part of the
National Assessment Resource Survey (NARS). Prior to writing this report, an advisory committee was convened
to recommend methods for evaluating the Great Lakes that were compatible with methods used to assess water
quality in estuaries. The committee found the general estuarine approach of basing the assessment on measures
of nutrients, chlorophyll a, dissolved oxygen, and water clarity, applicable but recommended several changes
appropriate for assessing a fresh water system.
Table WQ-1 outlines the recommended approach for assessing water quality along the Great Lakes coastline.
Changes from the estuarine approach included: 1) assessing TP rather than DIP to characterize freshwater
nutrient status; 2) excluding nitrogen from the assessment, following historical precedent and because of an
absence of documented evaluation thresholds; and 3) using Secchi depth as the primary indicator of water
clarity in the current assessment (rather than PAR attenuation) to ease comparison with prior Great Lakes
assessments and to make use of established Secchi depth evaluation thresholds. Importantly, this
recommended approach was based on existing International Joint Commission studies (IJC 1979 and IJC, 1980).
Although the 1980 IJC guidelines were intended for open water, some of the 1979 IJC guidelines focused on
nearshore waters and overlap with some of the 2010 design frame. The United States and Canada, under Annex
4 of the Great Lakes Water Quality Agreement (GLWQA of 2012) are currently reviewing and negotiating new
guidelines, and the committee strongly advised against introducing new NCCA assessment methods or
thresholds at this time. The advisory committee was open to including TN and PAR attenuation in future
assessments following a careful review of 2010 data and release of new GLWQA guidelines.
In the report the overall water quality condition of the Great Lakes is presented, based on assessment by basin
and lake. Figure WQ-2 below shows the different basin categories used in the assessment. These categories are
based on the expected trophic status of the basin. Lake Huron, Michigan and Superior are considered
oligotrophic basins whereas most of Lake Erie and Ontario are oligomesotrophic. Saginaw Bay and western
basin of Lake Erie are mesotrophic.
15
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Figure WQ-2. Water body designations used in describing Great Lakes water quality in this report.
Table WQ-5 lists the thresholds used to evaluate conditions along the Great Lakes coast. Thresholds for TP,
chlorophyll a, and Secchi depth are as specified in the current IJC guidelines (IJC 1979, IJC 1980). Note that
thresholds vary by lake and basin. Dissolved oxygen thresholds were the same as those used in estuaries (Diaz
and Rosenberg, 1995; USEPA, 2000). Conditions for TP and CHLA were calculated as: good < TH1, fair < TH2, and
poor > TH2; and for Secchi depth and DO as: good > TH2, fair < TH2, and poor < THl.
Table WQ-5. Thresholds used to calculate water quality condition at Great Lakes sites
Surface TP
(ugP/L)
TH1 TH2
Surface Chla
(ug/L)
THl TH2
Secchi Depth
(m)
THl TH2
Bottom DO
(mg/L)
THl TH2
Lake Superior
5
10
1.3
2.6
5.3
8
2
5
Lake Michigan
7
10
1.8
2.6
5.3
6.7
2
5
Lake Huron
5
10
1.3
2.6
5.3
8
2
5
Saginaw Bay
15
32
3.6
6
2.1
3.9
2
5
Western Lake Erie
15
32
3.6
6
2.1
3.9
2
5
Central Lake Erie
10
15
2.6
3.6
3.9
5.3
2
5
Eastern Lake Erie
10
15
2.6
3.6
3.9
5.3
2
5
Lake Ontario
10
15
2.6
3.6
3.9
5.3
2
5
-------
Discrete surface TP and chlorophyll samples were collected from one meter below the water surface, and
analyzed by multiple labs using methods of their selection (as long as acceptance criteria were met; USEPA
2010b). 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. The procedure used to estimate Secchi depth
from PAR attenuations is as follows (refer to discussion above regarding measuring water clarity in estuaries):
Normalized PAR intensity was recorded as a sensor was lowered through the water column, and an attenuation
coefficient Kd was calculated from a regression of PAR vs. depth (equations WQ-1 through WQ-3 above). A best-
fit relationship was then determined between Secchi depths and Kd from sites where both measurements were
available. For the 2010 survey, this relationship was:
Secchi depth (estimated) = 1.31 * Kd'0'91 (Formula WQ-5)
This relationship was then used to estimate Secchi depths at sites where only Kd values were available. If neither
Kd or Secchi depth was available, the condition at the site was set to "missing".
A Water Quality Index (WQI) for a Great Lakes site was then determined based on the condition of the four
component metrics, evaluated according to the rules in Table WQ-6 (which is very similar to the WQI calculated
for estuarine sites, as expressed in Table WQ-4).
Table WQ-6 Rules for determining the Water Quality Index at Great Lakes sites
Rating
Thresholds
Good
A maximum of one indicator is rated fair, and no indicators are rated poor.
Fair
One of the indicators is rated poor, or two or more indicators are rated fair.
Poor
Two or more of the four indicators are rated poor.
Missing
Two component indicators are missing, and the available indicators do not suggest a fair or
poor rating.
References for Water Quality
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
55:245-303.
IJC 1979 http://ijc.org/files/publications/ID530.pdf
IJC. 1980. International Joint Commission. 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.
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.
USEPA. 2000. Ambient Water Quality Criteria for Dissolved Oxygen (Saltwater): Cape Cod to Cape Hatteras.
EPA/822-R-00-012. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
17
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USEPA. 2010a. National Coastal Condition Assessment: Field Operations Manual. EPA-841-R-09-003. U.S.
Environmental Protection Agency, Office of Water. Washington, DC.
USEPA. 2010b. National coastal condition assessment: Laboratory methods manual. EPA 841-R-09-002. U.S.
Environmental Protection Agency, Office of Water. Washington, DC.
USEPA. 2010c. National Coastal Condition Assessment: Quality Assurance Project Plan 2008-2012. EPA/841-R-
09-004. U.S. Environmental Protection Agency, Office of Water. Washington, D.C.
USEPA. 2010d. National Coastal Condition Assessment: Site Evaluation Guidelines. U.S. Environmental
Protection Agency, Office of Water. Washington, DC.
USEPA. 2012. National Coastal Condition Report IV. EPA-842-R-10-003. U.S. Environmental Protection Agency,
Office of Water. Washington, DC.
Costantini, Marco; Kolesar, Sarah; Ludsin, Stuart A; Mason, Doran M.; Rae, Christopher M.; Zhang, Hongyan.
2011. Does hypoxia reduce habitat quality for Lake Erie walleye (Sander vitreus)? A bioenergetics
perspective. Canadian Journal of Fisheries and Aquatic Sciences, Volume 68, Number 5, pp. 857-879(23).
http://www.ingentacoiiiiect.com/content/iirc/cifas/2011/00000068/00000005/art00011
Ohio Lake Erie Commission Report "Nearshore Hypoxia as a New Lake Erie Metric"
http://lakeerie.ohio.gov/Portals/0/Closed%20Grants/small%20grants/SG%20334-
07%20Final%20Report.pdf
18
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Section 4: Assessing Sediment Quality (NCCA 2010)
The National Coastal Condition Assessment (NCCA) program uses sediment chemistry and sediment toxicity data
to assess the sediment quality of the Nation's nearshore coastal waters. Field crews collect comparable
sediment samples that are analyzed to determine concentrations of a suite of contaminants and subjected to
sediment toxicity tests (USEPA 2010a; 2010b). All of these studies are conducted with a high level of quality
assurance/quality control procedures (USEPA 2010c).
The NCCA program reports integrate sediment chemistry and sediment toxicity data into a Sediment Quality
Index (SQJ) to designate the percentage of the Nation's coastal waters that are in good, fair, and poor condition.
The NCCA has determined changes are needed for how this index is calculated. Better techniques are now
available for calculating the index for estuarine sediments. In addition, the NCCA field surveys in 2010 were
expanded to include freshwater nearshore areas along the Great Lakes, and this provided a new opportunity to
develop a SQJ specific to freshwater sites. This section describes how the Sediment Quality Index was
calculatedfor nearshore estuarine and freshwater areas.
Sediment Quality Index in the NCCA 2010 Survey
What's New for the Sediment Quality Index?
• Sediment Contaminant condition
o Estuarine
¦ mean Effects Range Median quotient (ERM-Q)
¦ Logistic regression models (LRM)
o Freshwater (Great Lakes)
¦ mean probable Effects Concentration quotient (PEC-Q)
• Sediment Toxicity condition
o Estuarine
¦ Control-corrected survival of amphipods and statistical
significance of test vs. control survival
¦ leptochirus plumulosus or Eohaustorius estuarus (California
only).
o Freshwater (Great Lakes)
¦ Control-corrected survival of amphipods
¦ Hyalella azteca
• Total Organic Carbon (TOC)-
o No longer part of the indices for estuarine sediment assessment,
o Data are collected and maintained for ancillary purposes.
Sediment Collection
The NCCA field crews collected surficial sediment samples during the summer of 2010 from nearshore coastal
areas of the Great Lakes and contiguous United States. Sediment was collected using a variety of grab apparatus
(see Table B-3). Samples were collected as close to a predetermined probabilistic site as possible. If sediment
was not found at the site and within 37 meters (anchor swing), crews moved outward, attempting collection
-------
within a 100 meter radius of the index site in estuarine waters or within a 500 meter radius in the Great Lakes.
At each site, the top two centimeters of sediment were composited from multiple grab samples to obtain the
volumes necessary to analyze for concentrations of chemical constituents, sediment toxicity, TOC, and grain size
(USEPA 2010a; 2010b).
Assessing Sediment Chemistry
With the exception of South Carolina and California, who used in-state labs, all sediment chemistry samples
were collected and sent to one contracted laboratory to determine the concentrations of metals, mercury,
PAHs, PCBs, organochlorine pesticides and TOC (USEPA 2010a; 2010b). Laboratory results were transmitted to
the NARS database and collated in a single database. Values for total PAHs (i.e., Sum of LMW PAH
(Acenaphthene, Acenaphthylene, Anthracene, Fluorene, 2-methylnaphthalene, Naphthalene, Phenanthrene)
and HMW PAH (Benz(a)anthracene, Benzo(a)pyrene, Chrysene, Dibenz(a,h)anthracene, Fluoranthene, Pyrene),
total PCBs, and total DDTs (i.e., p,p'-DDT, o,p'-DDT, p,p'-DDE, o,p'-DDE, p,p'-DDD, o,p'-DDD) were calculated as
the sum of concentrations of individual chemicals in each class. Detection limits all had to be below or at the
Effects Range Low (ERL) or Threshold Effect Concentration (TEC) or T25 to be included in calculation of their
respective methods. See Table S-4 for these values. Where concentrations were reported as non-detects,
concentrations were converted to one-half the method detection limit (0.5*MDL).
Sediment quality guidelines (SQGs) identify concentrations of individual contaminants that may be associated
with adverse effects on benthic organisms (Long et al. 2006). Two such SQGs are the effects-range median
(ERM; Long et al. 1995) developed for marine waters, and the probable effects concentrations (PEC; MacDonald
et al. 2000; Ingersoll et al. 2001) developed for freshwater. While these SQGs are adequate for assessing
individual contaminants in sediment, contaminants rarely occur alone; rather, they are almost always present as
complex mixtures. Therefore, researchers
use mean SQG quotients that consider the composition of the mixture to assess the relative degree of
contamination and corresponding probability of toxicity to benthic organisms (Long et al. 2006). Details about
the use of SQGs in estuarine and freshwater samples can be found below.
In addition to SQGs, a logistic regression model (LRM) approach was also used in estuarine waters to evaluate
relationships between contaminant concentrations and adverse effects of select contaminants (Field et al. 2002;
USEPA 2005). The model provides information on chemical concentrations associated with particular levels of
sediment toxicity to benthic invertebrates. An LRM type of model does not exist for assessing freshwater
sediments in the Great Lakes.
Estuarine Samples
The mean ERM quotient (mERM-Q) SQG approach was used in combination with the LRM to provide multiple
lines of evidence to interpret the sediment chemistry collected at estuarine coastal sites.
The mERM-Q approach calculates the degree to which concentrations of various chemical contaminants in a
sample exceed corresponding ERM SQG values (Table S-4). To avoid redundancy, 4,4'-DDE, total PAHs, and
summed low or high molecular weight PAHs were excluded from this calculation. Nickel was also excluded due
to the unreliability of its ERM guideline (Long et al. 1998). To calculate mERM-Q, each chemical concentration is
divided by its corresponding ERM value. The mERM-Q is the average of the resulting ratios for a sample:
Individual ERM-Q = chemical concentration (dry wt.)/corresponding ERM value
(Formula S-l)
Mean ERM-Q = (ERM-Qarsenic + ERM-Qchromium ••• ERI\/l~CltotaipcBs)/n (Formula S~2J
20
-------
The LRM approach evaluates relationships between contaminant concentrations and adverse effects of select
contaminants (Field et al. 2005; USEPA 2005). For the LRM approach, nickel was excluded from West Coast
samples due to naturally high levels of nickel in sediments. Contaminants in any sample with a method detection
limit (MDL) greater than T25 (Table S-4) were also excluded to avoid having non-detects that exceeded the 25%
probability of toxicity (Field and Norton 2014). The Pmax is the calculation of maximum probability of observing
sediment toxicity taken from the set of probabilities that were calculated for each chemical in a sample. The
LRM value for each chemical is calculated as
(Fo^ula S-3)
where b0 and bi are from Table S-4.
The maximum LRM value for each sample was determined and the Pmax value was calculated as
Pmax = 0.11 + (0.33 * LRM max) + (0.4*LRM max2) (Formula S-4)
Table S-4 Sediment quality guideline values used to calculate components of the sediment chemistry index.
21
-------
Sediment Chemicals
ERL/ERM
Used to
Used In LRM - Pm»
LRM
LRM
Consensus
Consensus
analyzed for NCCA 2010
Values
calculate
calculation
based
Based
mERM-Q
TEC/PEC
mPECq
(Metals in |ig/g; PAHs,
Bo
Bi
T25
T75
Values
Pesticides and PCBs in ng/g)
Aluminum
Antimony
-0.9005
2.4111
0.83
6.75
Arsenic
8.2/70
X
-4.1407
3.1674
9.13
45.10
9.79/33
X
Cadmium
1.2/9.6
X
-0.3400
2.5073
0.50
3.75
.99/4.98
X
Chromium
81/370
X
-6.4395
2.9952
60.69
328.65
43.4/111
X
Copper
34/270
X
-5.7878
2.9325
39.72
223.00
31.6/149
X
Iron
Lead
46.7/218
X
-5.4523
2.7662
37.49
233.45
35.8/128
X
Manganese
Mercury
.15/. 71
X
0.8041
2.5461
0.18
1.31
.18/1.06
Nickel
20.9/51.6
-4.6119
2.7658
18.63
116.06
22.7/48.6
X
Selenium
Silver
1/3.7
X
-0.1117
1.9684
0.32
4.12
Tin
Zinc
150/410
X
-7.9834
3.3420
114.84
521.84
121/459
X
Acenaphthene
16/500
X
-3.6165
1.7532
27.30
489.14
6.7/89
Acenaphthylene
44/640
X
-2.9620
1.3797
22.42
877.23
5.9/130
Anthracene
85.3/1100
X
-3.6574
1.4854
52.80
1591.62
57.2/845
Benz(a)anthracene
261/1600
X
-4.2013
1.5747
93.40
2320.94
108/1050
Benzo(b)fluoranthene
-4.5409
1.4916
203.13
6037.37
Benzo(e)pyrene
Benzo(k)fluoranthene
-4.2781
1.5669
106.94
2700.43
Benzo(ghi)perylene
101.00
2444.30
Benzo(a)pyrene
430/1600
X
-4.3005
1.5832
105.30
2571.89
150/1450
Biphenyl
-4.1144
2.2085
23.20
229.31
Chrysene
384/2800
X
-4.3241
1.5372
125.40
3370.20
166/1290
Dibenz(a,h)anthracene
63.4/260
X
-3.6308
1.7692
26.99
471.19
Dibenzothiophene
2,6-dimethylnapthalene
-4.0456
1.9040
35.30
503.26
Fluoranthene
600/5100
X
-4.4574
1.4787
186.83
5719.56
423/2230
Fluorene
19/540
X
-3.7146
1.8071
28.03
460.79
77.4/536
lndeno(l,2,3-c,d)pyrene
-4.3674
1.6245
102.84
2315.98
1-methylnapthalene
-4.1405
2.0961
28.26
315.83
2-methylnapthalene
70/670
X
-3.7579
1.7833
30.99
528.85
20.2/200
1-methylphenanthrene
-3.5884
1.7501
26.46
476.58
Napthalene
160/2100
X
-3.7753
1.6152
45.41
1041.19
176/561
Perylene
-4.6827
1.7632
107.82
1900.53
Phenanthrene
240/1500
X
-4.4576
1.6768
100.74
2058.64
204/1170
Pyrene
665/2600
X
-4.7080
1.5854
189.08
4597.84
195/1520
2,3,5-trimethylnapthalene
LMWPAH
552/3160
HMWPAH
1700/9600
Total PAHs*
4020/44800
1610/22800
X
Total PCB congeners
22.7/180
X
-3.4613
1.3488
56.45
2402.80
60/676
X
Aldrin
Alpha-Chlordane
Lindane
2.37/4.99
2,4'DDD
4,4'DDD
-1.8983
1.4913
3.44
102.23
2,4'DDE
4,4'DDE
2.2/27
-1.8392
0.9129
6.48
1652.38
2,4'DDT
4,4'DDT
-1.7705
1.6786
2.51
51.20
Total DDT
1.6/46.1
X
5.28/572
Dieldrin
-1.1728
2.5580
1.07
7.73
1.9/61.8
Endosulfan 1
Endosulfan II
Endosulfan sulfate
Endrin
2.2/207
Heptachlor
Heptachlor epoxide
2.5/16
22
-------
Heptachlorobenzene
Mirex
Trans-Nonachlor
Sources: Field et al. 2002; Long 1995; McDonalc
et al. 2000; Crane et al. 2002, Crane and Hennes 2007)
Freshwater Samples
The freshwater consensus-based PEC values were derived from an aggregation of several different empirically
derived sediment quality guidelines having similar narrative intent (MacDonald et al. 2000). Similar to the
mERM-Q approach, the mPEC-Q distills data from a mixture of contaminants into one unitless index which can
be compared to incidence of sediment toxicity. The mean PEC quotient is calculated using the average of three
PEC-Qs using only those contaminants with reliable PECs: 1) mean PEC-Qfor metals; 2) PEC-Qfor total PAHs;
and 3) PEC-Q for total PCBs. Total PAHs are used instead of summing the PEC-Qs of individual PAHs (Table S-4).
Individual PEC-Qs are calculated as follows:
Individual PEC-Q = chemical concentration (dry w.t)/corresponding PEC value
(Formula S-5)
Next, the mPEC-Q for the metals with reliable PECs (i.e., arsenic, cadmium, chromium, copper, lead, nickel, and
zinc) is calculated as follows:
mPEC-Qmetais = ^individual metal PEC-Qs/n (Formula S-6)
where n is the number of metals with reliable PECs for which sediment chemistry data are available. Finally, the
mPEC-Q for the main classes of chemicals with reliable PECs is calculated as follows:
mPEC-Q = (mPEC-Qmetais + PEC-Q,0,ai pahs + PEC-QtotaipcBs)/n (Formula S-7)
Where n = number of classes of chemicals for which sediment chemistry data are available (i.e., 1 to 3).
Thresholds
Thresholds were selected based on the probability of toxic effects and do not represent values for which
adverse effects are always observed or not observed. They are based on literature review, best professional
judgment and statistical analysis of historic data. The thresholds for mERMq are based on a study that used a
national dataset (Long et al. 1998) and the mPECq thresholds are based on several studies (Ingersoll et al. 2001,
Crane et al. 2002, Crane and Hennes 2007). The LRM thresholds were selected as 0.75 and 0.50, however, the
LRM model is designed to determine continuous estimates of risk so the application can match the degree of
risk as defined by the user and their objective (Field et al. 1999, Field et al. 2002, EPA 2005). The thresholds for
rating sediment chemistry based on the mERM-Q and LRM approaches for estuarine sites and the mPEC-Q
approach for Great Lakes sites are shown in Table S-5.
23
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Table S-5. Thresholds for sediment chemistry used inNCCA2010.
Ecological Condition by Site
Rank
Estuarine
Great Lakes
Good
mERM-Q <0.1 and LRM Pmax < 0.5
mPEC-Q < 0.1
Fair
mERM-Q >0.1 - <0.5 or LRM Pmax >0.5 -
<0.75
mPEC-Q >0.1 -<0.6
Poor
mERM-Q >0.5 or LRM Pmax >0.75
mPEC-Q >0.6
Sediment Toxicity
Sediment toxicity was assessed by measuring the survival of estuarine amphipods, Leptocheirus plumulosus (or
Eohaustorius estuarius in San Francisco Bay, CA), and the freshwater amphipod, Hyalella azteca, after a 10-day
exposure to the estuarine and freshwater sediments, respectively, under laboratory conditions (USEPA 2010b).
With the exception of samples collected in California and South Carolina (who used in-state labs), all sediment
samples were sent to three contract labs for toxicity testing.
The estuarine toxicity test used a static water approach with 5 (minimum of 4) replicate chambers per sample
with 20 organisms in each chamber. A minimum 90% survival of the control organisms was required to meet test
acceptability criteria. The freshwater toxicity test used a flow through approach with 4 replicate chambers per
sample loaded with 10 organisms in each. A minimum 80% survival of the control organisms was required to
meet test acceptability criteria. The control sediments for both tests were field-collected reference sediments.
The methods used for the survey were based on published methods (USEPA 2000; USEPA 2001; USEPA 2010b).
In estuarine sediments, toxicity was assessed as good, fair, or poor based on thresholds for control-corrected
survival (U.S. EPA 2004; Thursby et al. 1997) and a statistical test of significant differences between control and
test survivals (Thursby et al. 1997; Greenstein and Bay 2011). In freshwater sediments, only thresholds for
control-corrected survival were used to assess toxicity (USEPA 2004). The thresholds for rating sediment toxicity
based on amphipod survival and significance tests for each sampling site and for a region are shown in Table S-6.
The thresholds for freshwater and marine sediment toxicity tests are different but were selected with the
intention that the assessments would be comparable.
Table S-6. Thresholds for sediment toxicity used in NCCA 2010.
Ecological Condition by Site
Rank
Estuarine
Great Lakes
Good
Test results not significantly
different from control (p>0.05) and
>80% control-corrected survival
>90% control-
corrected survival
24
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Ecological Condition by Site
Rank
Estuarine
Great Lakes
Fair
Test results significantly different
from control (p<0.05) and >80%
control-corrected survival or Test
not significantly different from
control (p>0.05) and <80% control-
corrected survival
75-<90% control-
corrected survival
Poor
Test results significantly different
from control (p<0.05) and <80%
control-corrected survival
<75% control-
corrected survival
Sediment Quality Index
The NCCA 2010 calculates a sediment quality index (SQI) from the component indicators. The SQI relies on
sediment chemistry and toxicity to suggest whether a site is highly likely or not likely to cause adverse effects to
benthic organisms. For instance, the SQI at a site is rated poor when either of the component metrics are poor.
Table S-7 summarizes the rules used in assessing sediment quality conditions for both marine and Great Lakes
coastal regions. The sediment chemistry and sediment toxicity thresholds do not address variations in
bioavailability due to geochemical factors or differences in the nature of chemical mixtures between sites or
regions. The thresholds and index are not intended for regulatory or site-specific interpretations.
TableS- 2. Thresholds for the sediment quality index used in NCCA 2010.
Rank
Ecological Condition by Site
Good
Both sediment chemistry index and sediment toxicity index are rated good.
Fair
Neither sediment chemistry index nor sediment toxicity index are rated
poor and at least one index is rated fair
Poor
Either sediment chemistry index or sediment toxicity index are rated poor
References for Sediment Quality
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. (http://www.pca.state.rnn.us/index.php/view-document.html?gid=9163)
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
Fairey, E. R. Long, C. A. Roberts, B. S. Anderson, B. M. Phillips, J. W. Hunt, H. R. Puckett, C. J. Wilson. 2001. An
evaluation of methods for calculating mean sediment quality guideline quotients as indicators of
25
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contamination and acute toxicity to amphipods by chemical mixtures. Environmental Toxicology &
Chemistry 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.
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.
Hyland,J, L. Balthis, I. Karakassis, P. Magni, A. Petrov, J. Shine,O. Vesterg aard, R. Warwick. 2005. "Organic
carbon content of sediments as an indicator of stress in the marine benthos" Marine Ecology Progress
Series.
Vol. 295: 91-103, 2005 Published June 23
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. Con tarn. Toxicol. 41:8-21.
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., D.D. MacDonald, C.G. Severn and C.B. Hong. 2000. Classifying the probabilities of acute toxicity in
marine sediments with empirically derived sediment quality guidelines. Environ.
Toxicol. Chem. 19:2598-2601.
Long, E. R., C. G. Ingersoll, D. D. MacDonald. 2005. Calculation and uses of mean sediment quality guideline
quotients: A critical review. Environmental Science and Technology 40 (6): 1726-1736.
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.
McCready, S., G. F. Birch, E. R. Long, G. Spyrakis, C. R. Greely. 2006. Predictive abilities of numerical sediment
quality guidelines in Sydney Harbour, Australia, and vicinity. Environment International 32: 638-649
Thursby G.B., J. Heltshe and K.J. Scott. 1997. Revised approach to toxicity test acceptability criteria using a
statistical performance assessment. Environ. Toxicol. Chem. 16:1322-1329.
26
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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.
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.
USEPA. 2002. A guidance manual to support the assessment of contaminated sediments in freshwater
ecosystems. Volume III - Interpretation of the results of sediment quality investigations. EPA-905-B02-
001-C. U.S. Environmental Protection Agency, Great Lakes National Program Office, Chicago, IL.
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.
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.
USEPA. 2010a. National Coastal Condition Assessment: Field Operations Manual. EPA-841-R-09-003. U.S.
Environmental Protection Agency, Office of Water. Washington, DC.
USEPA. 2010b. National coastal condition assessment: Laboratory methods manual. EPA 841-R-09-002. U.S.
Environmental Protection Agency, Office of Water. Washington, DC.
USEPA. 2010c. National Coastal Condition Assessment: Quality Assurance Project Plan 2008-2012. EPA/841-R-
09-004. U.S. Environmental Protection Agency, Office of Water. Washington, D.C.
USEPA. 2010d. National Coastal Condition Assessment: Site Evaluation Guidelines. U.S. Environmental
Protection Agency, Office of Water. Washington, DC.
USEPA. 2012. National Coastal Condition Report IV. EPA-842-R-10-003. U.S. Environmental Protection Agency,
Office of Water. Washington, DC.
27
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Section 5: Assessing Ecological Fish Tissue Contaminants (NCCA 2010)
Contaminant concentrations in biotic tissues provide a time integrated assessment of bioavailability and
information on chemical fate and distribution. The National Coastal Condition Assessment (NCCA) program uses
whole-body fish tissue data to assess the biologically available contaminant conditions in the Nation's nearshore
coastal waters. Statistically-based field surveys are designed to collect fish samples of selected species that are
analyzed for a suite of contaminants (USEPA 2010a; 2010b). All of these studies are conducted with a high level
of quality assurance/quality control procedures (USEPA 2010c) that ensure data collected from a subset of
sampled sites can be applied to broader coastal regions.
Tissue chemistry results provide the basis for calculating an Ecological Fish Tissue Contaminant Index (EFTCI). For
the current report, NCCA has determined that index calculation changes were needed in order to better
represent ecological relevance. This appendix describes how the EFTCI was calculated in previous assessments,
the rationale for updating it, and the new procedure for calculating the index for nearshore estuarine and Great
Lakes coastal areas. Review and approach development for this effort was prepared for US EPA, Region 6 by
Tetra Tech, Inc. (Tetra Tech, 2012).
Ecological Fish Tissue Contaminant Index for the NCCA 2010 Survey
The evaluation of risk using food webs for contaminant exposure through dietary uptake has been well
documented (USEPA, 1997; US ARMY 2006; Sample et al., 1996). USEPA has established risk assessment
guidelines primarily for its Superfund program under the Resource Conservation and Recovery Act (RCRA)
(USEPA, 1997; 1998; 1999). These guidelines evaluate whether environmental concentrations of contaminants
(i.e., soil, sediment, water, and tissue) potentially pose risk to nonhuman receptors of concern. The guidelines
governing the evaluation of ecological risk derivation are well documented and have been used in many
programs (Newell et al., 1987; USEPA, 1997; CCME, 1998; US Army, 2006; and ODEQ, 2007).
Field crews collected selected fish specimens (USEPA 2010a) from over 800 sampling locations randomly located
within continental US nearshore marine and estuarine areas as well as throughout the Great Lakes nearshore
coastal areas. Whole fish tissue samples of predominantly forage-size fish were analyzed for measurable
concentrations of multiple contaminants of concern (USEPA 2010b). Analytical results were compared with
updated ecological fish tissue contaminant screening values that were developed to evaluate risk to upper-
trophic level fish and wildlife, including birds and mammals.
Using an ecological risk assessment approach (USEPA, 1997), risk was defined by developing a ratio of exposure
concentration compared to a concentration that is known to have toxicological effects. The exposure
concentration is developed based on known characteristics of each of the receptors of concern (i.e., fish, birds
and mammals) including body weight, food ingestion rate, and home range (i.e., natural range of receptor with
respect to foraging, breeding, and other activities). The concentration of contaminant that is known to elicit
toxicological effects (i.e., toxicological reference value or TRV), is reported in the literature for certain species for
each contaminant. Using an ecological risk assessment framework, a ratio greater than 1.0 indicates that
exposure concentration is greater than the toxicological reference value. By using the minimum risk level of 1.0,
the fish tissue concentration that would indicate this minimum risk can be calculated.
Methods for Developing Ecological Fish Tissue Contaminant Threshold Values
28
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Risk potential was derived by calculating a hazard quotient (HQ) or the ratio of exposure concentration divided
by a concentration known to elicit toxicological effects (Low Observed Adverse Effects Level or LOAEL) or known
not to elicit toxicological effects (No Observed Adverse Effect Level or NOAEL). Risk can be expressed as:
Risk (HQ) = Exposure Concentration/Toxicity (Formula EFTC-1)
Thus, when the exposure concentration is greater than the concentration known to elicit toxic effects, the HQ is
greater than 1.0, and the receptor is at risk.
The derivation of the exposure concentration was specific for each receptor and dependent on known
characteristics for each receptor including body weight, food ingestion rate, exposure area relative to the
amount of time the organism spends in the area (or Area Use Factor, AUF), and fish tissue concentration. The
exposure concentration can be represented by the formula:
Exposure Concentration = FI*^Fl^*AUF (Formula EFTC-2)
Where:
Fl = food ingestion (kg/kg bw/d)
[Fish] = concentration in fish tissue (mg/kg)
AUF = area use factor
BW = body weight of receptor (kg-bw)
For added conservativeness the AUF was set to 1.0 indicating all foraging, resting, breeding and other activities
are expected to occur within the exposure area of concern. Toxicity was quantified as toxicity reference values
(TRVs). Toxicity reference values are established from the available scientific literature. For the 2010 NCCA
survey, the NOAEL and LOAEL served as the basis for establishing threshold contaminant values. Toxicity
reference values are typically established for each receptor of concern or group of receptors (i.e., avian,
freshwater and marine fish and mammals, etc.).
Receptors of Concern
For NCCA, upper trophic level organisms including birds, fish and mammals are considered receptors of concern
(ROCs). ROCs are typically those animals that are exposed to contaminants through ingestion, dermal contact,
and/or inhalation. 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 items which have accumulated contaminants in their tissues. For NCCA, data evaluated
were whole-body forage fish tissue concentrations; therefore the only pathway of exposure evaluated for the
assessment focused on the uptake of contaminants that have been accumulated in the tissues of prey items
(i.e., fish).
Classes of receptors were created to develop potential exposure-based screening values since data consisted of
both freshwater and marine fish tissues. These classes include: freshwater predatory fish, marine predatory
29
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fish, piscivorous birds, piscivorous freshwater mammals and piscivorous marine mammals. Receptors were
chosen based on their diet (predominantly fish) and the availability of data in the literature. Potential receptors
evaluated for NCCA represent those species that are typically included in ecological risk assessments (Table
EFTC-1).
Table EFTC-1. Potential receptors of concern often evaluated in ecological risk assessments.
Avian Receptor
Freshwater
Marine
Freshwater Fish
Marine Fish
Mammalian
Mammalian
Receptor
Receptor
Receptor
Receptor
Great Blue Heron
River Otter
Harbor Seal
Largemouth Bass
Bluefin Tuna
Osprey
Mink
Bottlenose
Dolphin
Florida Gar
Yellowfin Tuna
Bald Eagle
Walrus
Muskellunge
Shortfin Mako
Herring Gull
Snakehead
Sandbar Shark
Belted Kingfisher
Lake Walleye
Mackerel Tuna
Brown Pelican
Swordfish
The list summarized in Table EFTC-1 may not be representative of potential receptor species at all sampling
locations. To account for this limitation, generalized body weights and food ingestion rates for freshwater and
marine fish, birds, and mammals were estimated from the receptor species listed. To be most protective, the
lowest body weight and highest food ingestion rate where chosen for each receptor category for calculating
dosage estimates. Table EFTC-2 summarizes the minimum and maximum receptor factors considered in
determining weight and ingestion rate constants applied in the developing the threshold values. Table EFTC-3
describes the "generalized" receptor factors used to derive the new NCCA threshold values.
Table EFTC-2. Minimum and Maximum Body Weights and Derived Food Ingestion Rates for Selected Receptors of Concern.
Group
Receptors
Body Weight (kg)
Ref.
Food Ingestion Rate
(kg food/kg BW/d)
Min/Ave
Max
Min/Ave BW
Max BW
Avian1
Great Blue Heron
1.47
2.99
a
0.051
0.040
Osprey
1.22
1.95
0.054
0.046
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
30
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Group
Receptors
Body Weight (kg)
Food Ingestion Rate
(kg food/kg BW/d)
Min/Ave
Max
Ref.
Min/Ave BW
Max BW
Brown Pelican
3.00
3.50
b
0.040
0.038
Freshwater
River Otter
5.00
15.00
0.052
0.042
Mammals1
Mink
0.55
2.08
a
0.076
0.060
Harbor Seal
58.80
124.00
0.033
0.029
Marine Mammals1
Bottlenose Dolphin
150.00
490.00
c
0.028
0.023
Walrus
900.00
1400.00
d
0.020
0.019
Bluefin Tuna
32.00
219.00
e
0.044
0.016
Yellowfin Tuna
23.42
52.45
f
0.023
0.010
Marine Fish2
Shortfin Mako
63.50
g
0.046
Sandbar Shark
34.00
h
0.009
Mackerel Tuna
34.55
i
0.022
Swordfish
58.00
j
0.016
Brown Trout
0.91
3.63
k
0.0095
Freshwater Fish2
Muskellunge
0.34
31.64
1
0.064
Largemouth Bass
0.45
4.50
m
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.
a - USEPA 1993 b - Schreiber, 1976
e - Aguado-Gimenez and Garcia-Garcia, 2005
h - StillwelI and Kohler, 1993
k-Becker, 1983 I - Carlander, 1969
c - Kastelein et al., 2002
f- Maldeniya, 1996
i- Giffiths et al., 2009
m-Carlander, 1977
d - Born et al., 2003
g-Wood et al., 2009
j-Stillwell and Kohler, 1985
Table EFTC-3. Summary of generalized receptor body weights and food ingestion rates used to calculate screening fish tissue
values.
Receptor Group
Body Weight (kg)
Food Ingestion Rate (kg
food/kg BW/d)
Birds
0.13
0.1203
Freshwater Mammals
0.55
0.0764
Marine Mammals
58.8
0.0333
Freshwater Fish
0.34
0.0640
Marine Fish
23.42
0.023
31
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Toxicity Reference Values
Literature based toxicological data typically used to derive reference values are based on laboratory species. The
laboratory based tests used to develop TRVs may not have resulted in an endpoint that is protective of chronic
exposure. A chronic exposure endpoint was extrapolated from the reported endpoint using a conversion factor
(CF). CFs have been used for various extrapolations, and their applications reflect policy to provide conservative
estimates of risk (Chapman et al., 1998). Table EFTC-4 summarizes conversion factors applied to laboratory-
based endpoints to estimate chronic NOAEL or no observable effects concentration (NOEC) (Wentsel et al.,
1996).
Table EFTC-4 Conversion factors to estimate chronic NOAELs or NOECs (Wentsel et aL, 1996).
Convert From
Convert To
Multiply By
Chronic NOAEL or NOEC
Chronic NOAEL or NOEC
1.0
Chronic LOAEL or LOEC
Chronic NOAEL or NOEC
0.2
Subchronic NOAEL or NOEC
Chronic NOAEL or NOEC
0.1
Subchronic LOAEL or LOEC
Chronic NOAEL or NOEC
0.05
Acute NOAEL or NOEC
Chronic NOAEL or NOEC
0.033
Acute LOAEL or LOEC
Chronic NOAEL or NOEC
0.02
LD50 or LC50
Chronic NOAEL or NOEC
0.01
Durations are defined as follows (USEPA, 1999; Sample et al., 1996):
Acute: <14 days (fish, birds, mammals)
Subchronic: 14-90 days (fish, birds, mammals)
Chronic: >90 days or during critical life stage (fish, birds, mammals)
Generally, reference values were developed from laboratory tests using non-wildlife species (e.g., chickens,
quail, duck, rat, mouse, rainbow trout, and Japanese medaka). Using the reported body weights of laboratory
test species and wildlife receptors, laboratory based endpoints were normalized to wildlife receptors using
formulae developed by Sample and Arenal (1999). TRVs were calculated using the following equation:
Where:
TRVwildlife
NOAELtest
BWtest
BWwildlife
X
TRVwildlife — {BWtest/BWwiidufe)
(1-x)
toxicity reference value for wildlife species
no observed adverse effect level for test species
body weight for test species
body weight for wildlife species
scaling factor
(Formula EFTC-3)
32
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Scaling factors presented by Sample and Arenal (1999) indicated 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 was used for mammalian receptors (Sample and
Arenal, 1999) and a scaling factor of 1.2 was used for avian (Sample and Arenal, 1999) and fish receptors (Buhler
and Shanks, 1970). Table EFTC-5 shows calculated TRVs for each NCCA analyte of interest that was used for
estimating threshold values.
Table EFTC-5. Calculated toxicity reference values (TRVs) based cited literature and estimation methods.
Calculated Wildlife TRVs
Constituent
TRV Type
Avian
Mammal
Fish
Freshwater
Marine
Freshwater
Marine
TRV
Ref.
TRV
Ref.
TRV
Ref.
TRV
Ref.
TRV
Ref.
Arsenic
NOAEL
3.39
z
0.11
b
0.08
b
0.027
aa
0.06
aa
LOAEL
8.51
0.53
0.4
0.14
0.3
Cadmium
NOAEL
0.94
b
0.89
X
0.67
X
76.34
168
LOAEL
12.93
4.46
3.37
763.49
y
1680
y
Mercury (methyl)
NOAEL
0.02
V
0.31
b
0.024
b
0.14
w
0.31
w
LOAEL
0.12
0.16
0.12
0.28
0.62
Selenium
NOAEL
0.27
b
0.19
b
0.15
b
5.02
u
11.04
u
LOAEL
0.53
0.32
0.24
6.7
14.75
Chlordane
NOAEL
0.53
a
3.85
b
2.91
b
NA
NA
NA
NA
LOAEL
2.66
7.69
5.81
NA
NA
DDTs
NOAEL
0.15
a
0.78
b
0.59
b
0.28
t
0.62
t
LOAEL
1.47
3.89
2.94
1.42
3.12
Dieldrin
NOAEL
0.08
b
0.033
0.025
0.065
r
0.14
r
LOAEL
0.39
0.17
q
0.13
q
0.33
0.72
Endosulfan
NOAEL
7.99
b
1.19
0
0.9
0
0.26
0.6
LOAEL
39.93
5.95
4.5
0.6
P
1.31
P
Endrin
NOAEL
0.019
b
0.15
b
0.11
b
0.16
n
0.34
n
LOAEL
0.099
0.77
0.58
0.78
1.72
Heptachlor epoxide
NOAEL
1.16
1
0.21
b
0.16
b
8.09
m
17.8
m
LOAEL
5.79
1.037
0.78
16.2
35.6
Hexachlorobenzene
NOAEL
0.11
hj
0.97
j
0.74
j
0.0018
k
0.003
9
k
LOAEL
0.56
1.95
1.47
0.0088
0.019
NOAEL
0.54
7.79
5.88
14.99
32.98
Lindane
LOAEL
2.19
b
38.93
b
29.41
b
74.95
g
164.9
1
g
Mirex
NOAEL
0.0066
d
0.064
e
0.048
e
0.4
f
0.87
f
LOAEL
0.66
0.64
0.048
1.98
4.35
Toxaphene
NOAEL
0.66
a
7.79
b
5.88
b
0.0011
c
0.002
4
c
33
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Constituent
TRV Type
Calculated Wildlife TRVs
Avian
Mammal
Fish
Freshwater
Marine
Freshwater
Marine
TRV
Ref.
TRV
Ref.
TRV
Ref.
TRV
Ref.
TRV
Ref.
LOAEL
3.32
38.93
29.41
0.0056
0.012
PCBs
(Arochlor 1254)
NOAEL
0.12
b
0.055
b
0.041
b
0.078
bb
0.17
bb
LOAEL
1.2
0.55
0.41
0.39
0.86
High Molecular Weight
PAHs
NOAEL
4.35
ii
0.58
jj
0.44
jj
0.55
kk
1.21
kk
LOAEL
21.77
2.92
2.21
2.76
6.07
Low Molecular Weight
PAHs
NOAEL
15.16
II
2.97
mm
2.24
mm
NA
NA
NA
NA
LOAEL
151.6
297
224.4
NA
NA
a-Wiemeyer 1996
b - Sample et al. 1996
c - Fabraeus-Van Ree and
Payne (1997)
d-Hyde etal. 1973
e - NTP 1990
f-Skea etal. 1981
g - Cossarini-Dunier et al. 1987
h - Coulston and
Kolbye 1994
i-Terretox 2002
j-ATSDR 2002a
k-Woodburn et al.
2008
l-USEPA 1972
m-Andrews etal.
1996
n - Argyle et al. 1973
o - ATS DR 2000
p - Lunebye et al. 2010
q-ATSDR 2002b
r-Argyle et al. 1975
s-USEPA 1995
t- Macek et al. 1970
u - Ogle and Knight
1989
v- Heinz and Locke
1976
w - Berntssen et al.
2003
x - ATSDR 2008
y-Szczerbiketal. 2006
z-USFWS 1964
aa - Pedlar et al. 2002
bb - Leatherland and
Sonstegard 1980
cc- Giesy et al. 2002
dd - USEPA 2008
ee - Nakamaya 2004
Calculating Ecological Fish Tissue Contaminant Threshold Values
The tissue contaminant concentration threshold values for the suite of NCCA analytes was derived using the
following equation:
[Fish] = (TRV*BW)/FI (Formula EFTC-4)
Where:
[Fish] = threshold concentration in fish tissue (mg/kg) for a specific analyte
TRV = related estimated toxicity reference value
BW = generalized body weight of receptor (kg-bw)
Thus, using the toxicity reference values plus estimated body weights and food ingestion rates, the
concentration value of a selected analyte measured in fish tissues that presented a minimum exposure risk
(HQ=1.0) was calculated for each group of receptors. The calculated fish tissue concentrations can be used to
screen fish tissue data to determine if piscivorous fish and wildlife may be at risk due to the consumption offish.
A fish tissue concentration for each receptor group was calculated and can be used individually to screen for the
potential risk to each receptor group. The lowest calculated fish tissue concentration can be used to screen
34
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tissue concentration for risk to any receptor group regardless of the source of equation terms. In Table EFTC-6,
the results for each group of receptors used for the NCCA Ecological Fish Tissue Contaminant Index are
summarized.
35
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References for Ecological Fish Tissue Contaminants
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Hyde, K.M., J.B. Graves, A.B. Watts, and F.L. Bonner. 1973. Reproductive Success of Mallard Ducks Fed
Mirex. J. Wildl. Manage. 37 (4): 479-484.
Kastelein, R.A., N. Vaughan, S. Walton, and P.R. Wiepkema. 2002. Food intake and body measurements
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53: 199-218.
Kim, S.G., D.K. Park, S.W. Jang, J.S. Lee, S.S. Kim, and M.H. Chung. 2008. Effects of Dietary
Benzo(a)pyrene on Growth and Hematological Parameters in Juvenile Rockfish, Sebates schlegeli
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38
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Leatherland, J.F. and R.A. Sonstegard. 1980. Effect of dietary Mirex and PCB's in combination with food
deprivation and testosterone administration on thyroid activity and bioaccumulation of
organochlorines in rainbow trout Salmo gairdeneri Richardson. Journal of Fish Diseases 3: 115-
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Macek, K.J., C.R. Rodgers, D.L. Stalling, and S. Korn. 1970. The Uptake, Distribution and Elimination of
Dietary 14C-DDT and 14C-Dieldrin in Rainbow Trout. Transactions of the American Fisheries
Society 4: 689-695.
MacKenzie, K.M., and D.M. Angevine. 1981. "Infertility in Mice Exposed in Utero to Benzo(a)pyrene."
Biology of Reproduction. Volume 24. Pages 183-191.
Maldeniya, R. 1996. Food consumption of yellowfin tuna, Thunnus albacares, in Sri Lankan waters.
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Nagy, K. A. 1987. Field metabolic rate and food requirement scaling in mammals and birds. Ecol.
Monogr. 57: 111-128.
Nakayama, K., Y. Oshima, T. Yamaguchi, Y. Tsuruda, I.J. Kang, M. Kobayashi, N. Imada, and T. Honjo.
2004. Fertilization success and sexual behavior in male medaka, Oryzias latipes, exposed to
tributyltin. Chemosphere 55: 1331-1337.
Newell, A.J., D.W. Johnson, and L.K. Allen. 1987. Niagra River biota contamination project: Fish flesh
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Ogle, S.E. and A.W. Knight. 1989. Effects of Elevated Foodborne Selenium on Growth and Reproduction
of the Fathead Minnow (Pimephales promelas). Arch. Environ. Contam. Toxicol. 18: 795-803.
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Chemicals of Concern in Sediment. Portland, OR.
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selected compounds following prenatal expsoures in the mouse: naphthalene, pnitrophenol,
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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.
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Sample, B. E. and C. A. Arenal. 1999. Allometric models for interspecies extrapolation of wildlife toxicity
data. Bull. Environ. Contam. Toxicol. 62:653-663.
Schreiber, R.W. 1976. Growth and development of nestling brown pelicans. Bird-Band. 47 (1): 19-39.
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U.S. EPA (Environmental Protection Agency). 2000. Guidance for Assessing Chemical Contaminant Data
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00-008. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
U.S. Environmental Protection Agency (USEPA). 2010a. National Coastal Condition Assessment: Field
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U.S. Environmental Protection Agency (USEPA). 2010b. National coastal condition assessment:
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(Salmo salar) to dietborne endosulfan assessed by haematology, biochemistry, histology and
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Neal, J. and R. H. Rigdon. 1967. Gastic tumors in mice fed benzo(a)pyrene: a quantitative study. Texas
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Section 6: Quality Assurance and Quality Control (NCCA 2010)
This section of the Technical Report documents the procedures for managing and assessing the quality
of data used for the NCCA 2010 Report.
The National Coastal Condition Assessment (NCCA) program follows the guidance of the EPA Office
Water Quality Management Plan (USEPA 2009) to integrate quality assurance and quality control
(QA/QC) into every aspect of the survey. This QA/QC effort involves a team of personnel who are
responsible for ensuring data quality (e.g., the NCCA QA Team):
• NCCA Program Quality Assurance Coordinator (QAC) - Responsible for ensuring that a QA
program is in place and is being followed, the quality of data used in the assessment is evaluated
and documented, and identifying data that do not meet the quality requirements of the NCCA,
as specified by the Quality Assurance Project Plan (USEPA 2010a).
• Quality Assurance Advisors - EPA staff from the Office of Research and Development who
provide advice to the QAC about specific aspects of QA/QC for individual indicators or
parameters.
• National Aquatic Resource Surveys Information Management Center (NARS IM) staff - Contract
staff who manage the NCCA survey data and information. NARS IM staff add record qualifiers
that document potential quality issues, make corrective changes to the data and disseminate,
after review by the QAC, data-related information as requested.
Approach for Implementing the NCCA Quality Assurance Strategy
The NCCA Program employs several key elements to assure the quality of the data used in the
assessments. Each element is briefly described in the following paragraphs.
Quality Assurance Project Plan
The NCCA Quality Assurance Project Plan (QAPP) outlines the program's quality objective requirements.
The QAPP addresses multiple levels of the program ranging from sample collection in the field and
laboratory processing of samples to review of results data sets. The QAPP establishes target Data Quality
Objectives (DQO) for assessing the status of condition indicators for the NCCA population of coastal
waters (USEPA 2010a), as follows:
• For each indicator of condition, estimate the proportion of the nation's estuaries and combined
area of the Great Lakes in degraded condition with a ± 5% margin of error and with 95%
confidence.
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• For each indicator of condition, estimate the proportion of regional estuarine resources
(Northeast, Southeast, Gulf of Mexico, West Coast and Great Lakes) in degraded condition with
a ± 15% margin of error and with 95% confidence.
Field Operations and Laboratory Methods Manuals
The Field Operations Manual (FOM, USEPA 2010b) and Laboratory Methods Manual (LMM, USEPA
2010c) provide an interpretation of the QAPP that guide the activities of NCCA participants in a manner
that meets quality requirements. The FOM and the LMM help ensure that quality objectives are
attainable and survey activities are more tractable. Every NCCA participant (e.g., field crews and
laboratories) is provided training and expected to comply with the procedures published in the FOM and
the LMM. The LMM and FOM also list measurement quality objectives (MQOs). MQOs allow NCCA
quality staff to evaluate the level of quality attainment for individual survey metrics.
Field Method Pilot Testing
A representative group of the NCCA steering and oversight staff pilot tested sampling methods and
documentation requirements (e.g., field forms) described in the FOM. The purpose of this activity was
three-fold. First, the pilot period ensures that instructions are clear. Second, NCCA staff have the
opportunity to evaluate the capacity of the FOM for adequately supporting and documenting the quality
objectives. Finally, the pilot period allows time to test the feasibility of sampling logistics, sample
preparation and sample shipping instructions. Any deficiencies noted in the FOM during pilot testing are
corrected prior to field crew training.
Field Crew Training
As a nationwide survey, the NCCA requires that all crews use the same methods. To ensure data
comparability, all field crews must attend training prior to sampling. For the 2010 survey, NCCA trainers
led seven regional field crew training sessions consisting of classroom and field based lessons. These
ranged from how to conduct site reconnaissance and record field observations and in situ data, to
sample collection, shipping, reporting and troubleshooting. The field crew leaders were taught to review
every form and verify that all hand-entered data were complete and correct.
Field Assistance Visits
In addition to attending training sessions, an EPA employee or contractor visited every NCCA field crew
during the 2010 field season. These visits, known as assistance visits or AVs, provided an opportunity to
observe field crews in the normal course of a field day and document adherence to sampling
procedures. If circumstances are noted where a field crew was not conducting a procedure properly, the
observer recorded the deficiency, reviewed appropriate procedure with field team as a preemptory
intervention and assisted the field crew until the procedure was completed correctly.
Laboratory Quality Assurance and Quality Control
All laboratories were required to submit documentation of their analytical capabilities prior to analyzing
any 2010 NCCA sample. EPA NCCA Team members reviewed documentation to ensure that the labs
could meet required MQOs (e.g., reporting limits, detection limits, etc.). National Environmental
Laboratory Accreditation Conference certification, satisfactory participation in round-robin or other
usual and customary types of evaluations were considered acceptable capabilities documentation. For
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biological analyses (i.e. benthic invertebrate taxonomy) labs were required to use the same taxa lists,
conduct regular internal QC checks, as well as participate an independent quality check of 10% of all
samples. Reconciliation calls were held to allow all taxonomists involved in benthic analyses to come to
consensus when organism identification was in question. The NCCA program allowed chemical analyses
to be completed using performance-based methodology. That is, differing analysis methods were
allowed as long as the methods met the MQOs for the indicators. To ensure the ongoing quality of data
during analyses, every batch of samples was required to include QA samples to verify the precision and
accuracy of the equipment, reagent quality, etc. These "checks" could have been completed by
analyzing blanks or samples spiked with known or unknown quantities of reference materials, duplicate
analyses of the same samples, blank analyses, etc. The laboratories reported quality assurance results
along with each batch of sample results. Labs sent electronic data deliverables to the NARS IM center for
upload in to the NCCA database.
Data Management and Review
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 are 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.
An iterative process was used to review the database content (e.g., data) for completeness,
transcription errors, formatting compatibility, consistency issues and other quality control-related
topics. This first-line data review was performed primarily by NARS IM in consultation with the NCCA QA
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 QA 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 not used for analyses because of quality
control concerns account for a subset of the "missing" data for each indicator analysis and add to the
uncertainty in condition estimates.
It is the responsibility of the end data user to become familiar with the QA codes used in the NCCA 2010
assessment (NCCA_QA_Codes.csv) and review the "QA_CODES" column in each dataset to determine
whether the data meet quality objectives for specific uses.
During the NCCA 2010 survey, other related but independent sampling activities were running
concurrently. The NARS IM team developed a mechanism to easily and robustly sequester data by
activity. Once established, this technique reduced all of the NCCA 2010 survey information into a single
data source, thus minimizing the coordination of multiple data sets while maximizing the utility of a
database and improving data version control. Using readily available database tools, the IM team was
able to quickly and consistently provide all relevant and QA'd data for completing the national coastal
resource assessments. Table QA-1 briefly describes the data offerings for the NCCA 2010.
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Table QA-1. List of NCCA 2010 database tables and/or groupings.
Name
Description
Benthic
Benthic invertebrate data
Comments
Compiled comments from field forms
Fish info
Fish collection information
HHFish tissue
Human health fish tissue data
Hydroprofile
Hydrographic profile data
SedTox
Sediment toxicity data
SedChem
Sediment chemistry data
Tissue chemistry
Ecological fish tissue contaminant data
Water chemistry
Nutrients and chlorophyll a data
Sitelnfo
Site identification, location and weighting
information
References for Quality Assurance and Control
USEPA. 2009. Quality Management Plan. EPA 821-R-09-001. U.S. Environmental Protection Agency,
Office of Water. Washington, DC.
USEPA. 2010a. National Coastal Condition Assessment: Quality Assurance Project Plan 2008-2012.
EPA/841-R-09-004. U.S. Environmental Protection Agency, Office of Water. Washington, D.C.
USEPA. 2010b. National Coastal Condition Assessment: Field Operations Manual. EPA-841-R-09-003. U.S.
Environmental Protection Agency, Office of Water. Washington, DC.
USEPA. 2010c. National Coastal Condition Assessment: Laboratory Methods Manual. EPA 841-R-09-002.
U.S. Environmental Protection Agency, Office of Water. Washington, DC.
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