**3. NCCA method highlights**

*Water Quality - Science, Assessments and Policy*

The approaches used to communicate survey results in summary reports reflect

how the coastal survey approach evolved and innovated. Early regional EMAP surveys were essentially data reports prepared for a technical audience of monitoring practitioners. These terse reports emphasized methodology and reported results in tables, weighted-CDF plots, and bar plots e.g., [11]. While invaluable to technical staff and managers, these statistical summary reports attracted little public attention. In contrast, the national reports summarizing the EMAP-NCA surveys—the National Coastal Condition Reports NCCR I–IV [16–19]—were primarily prepared to be informative and understandable to the general public. These attractive and sizable documents were organized by region, featured highlights about local issues and showcased abundant photos and illustrations, as well as were available in hardcopy. In particular, NCCR-II and NCCR-III presented maps with site conditions portrayed by color-coded symbols. The NCCR reports' use of pie charts conveyed assessment results concisely and intuitively, but without adequate expression of uncertainty. Beginning with the NARS-NCCA 2010, the reporting strategy changed substantially to accommodate the approach of conducting relatively standardized assessments on a regular schedule. The reports focused on delivering assessment results

*Examples of coastal survey summary graphics from NCCA national reports highlighting national status in* 

*2010 (A), trends 1999 to 2010 (B), and "dashboard" approach of reporting results (C).*

**2.4 Communicating results**

**138**

**Figure 3.**

In this section we take a closer look at the methods used to assess the major components of coastal ecosystems—the water column, sediment, and benthic and fish communities. One issue was recognized early in the NCA program when national-scale surveys were undertaken—the U.S. coastal regions are extraordinarily diverse. The northeastern states reflect relatively late deglaciation, featuring minimal run-off from small watersheds into well-mixed coastal waters. Large drowned-river estuaries dominate the mid-Atlantic states, where environmental conditions are heavily influenced by the densely populated coastal communities. Estuaries along the southeastern states and the Gulf of Mexico reflect interaction with large, flat watersheds; these regions are subject to distinct sub-tropical biophysical processes. In contrast, there are far fewer estuaries along the Pacific coast because of the absence of a coastal plain, and coastal processes there are uniquely affected by strong ocean currents and upwelling of cold, nutrient-rich water. How should surveys account for such diversity and differentiate natural from anthropogenic sources and responses?

In response to these challenges, survey planners initially relied on the advice of regional estuarine experts convened to suggest assessment indicators and provide benchmark values used to distinguish good, fair, and poor conditions. In reports we emphasized that these cut-points were appropriate for the surveys only, and generally distinct from regulatory thresholds. For each component assessment, several indicators of condition were evaluated separately and then combined into an overall index. In some cases, as is described below, the initial suite of indicators, indices, and benchmark values were modified and refined based on lessons learned. For instance, local benthic indices were replaced with a single index applicable nationwide; the fish community index was refashioned to better reflect ecological rather than human health conditions; and several human-health indicators were introduced. In the following sections, we describe the indicators and thresholds currently specifically employed in the NCCA surveys while highlight lessons learned from 30 years of experimenting and refining techniques.

#### **3.1 Assessing water quality**

The water column is a notoriously dynamic environment. Physical and biological process interact to create rapid and highly localized interactions of light, nutrients, algal growth and predation, and a host of quickly changing abiotic factors. Despite these challenges, deepening concerns regarding cultural eutrophication in coastal waters motivated survey planners to devise a strategy for assessing coastal water quality. Cultural eutrophication is the detrimental degradation of water quality often associated with nutrient over-enrichment [28, 29]. The NCCA assessment

approach consisted of employing indicators that measure eutrophication-related symptoms and problems such as nutrient over-enrichment, excessive algal blooms, hypoxia or anoxia, low water transparency, etc. To moderate the inherent variability of such measures, the indicators were then combined into an index that is less dynamic than the individual components.

**Table 2** lists the five core indicators and thresholds used in recent NCCA surveys to assess water quality in estuaries and the Great Lakes. Nutrient and chlorophyll concentrations were measured in surface water, dissolved oxygen levels were determined in bottom water, and water clarity was established at each site. These measures were then combined into a water quality index (WQI) that captured conditions likely to be indicative of problematic eutrophication regardless of when in summer sampling occurred [17]. For instance, the WQI might record excessive dissolved nutrients in early season, excessive algal production and poor water clarity in mid-season or hypoxic, turbid conditions in late season. Essentially, the WQI reflects a "preponderance of evidence"; the index is a more robust indicator of problematic eutrophication symptoms than the core indicators.

Thresholds generally varied by region. For instance, less stringent nutrient thresholds were specified for the West coast region, which experiences natural upwelling in summer [30], and more conservative guidelines were applied when assessing the tropical waters of southern Florida to protect submerged aquatic vegetation (SAV) beds. Assessment methods differed slightly when evaluating nutrients and water clarity in estuaries and the Great Lakes, recognizing the distinct ecologies and assessment histories in these environments. See details of the coastal water quality approach at pp. 11–15 of reference [19].

The NCCA approach of assessing nutrient status in estuaries continues to evolve. Early surveys measured nutrients as dissolved inorganic nitrogen and phosphorus (DIN and DIP). While DIN and DIP concentrations are valid indicators of nutrient enrichment status, they are unreliable measures of nutrient availability later in the season because they are generally assimilated into algal biomass in spring and early summer [19]. This is particularly problematic for NCCA surveys, which sample throughout the summer index period. In contrast, total nitrogen and phosphorus (TN and TP) are less variable and are related to chlorophyll concentrations [31]. Consequently, TN and TP were added as core indicators beginning in 2010 and were used to evaluate nutrient status in subsequent NCCA surveys. Since regional TN and TP thresholds have not yet been established, TN and TP were treated as exploratory indicators, rated as low, moderate, high, and very high based on the 25th, 50th, and 75th quartile values of the measured 2010 TN and TP values. The water quality index (WQI) continued to be calculated with DIN and DIP as described in **Table 2**, reflecting the key role of dissolved nutrients in eutrophication processes [17, 31].

#### **3.2 Assessing sediment quality**

Contaminants from agricultural, industrial, and nonpoint sources find their way to coastal waters where they may adsorb onto suspended particles and settle to the sediment. There, metals and organic pollutants are ingested by benthic-dwelling organisms and may become concentrated throughout the food web and adversely affect fish, pelagic mammals, and human consumers of aquatic organisms. To monitor sediment contamination, all EPA coastal assessments since the 1990s followed the approach of NOAA's Status and Trends program [32] and collected sediment grab samples and measured a suite of 74 metal, PAH, PCB, and pesticide contaminants in surficial sediment samples (**Table 1**). The impacts of the pollutants on benthic organisms were evaluated against the effects-based sediment quality guidelines, ERL (effects range low) and ERM (effects range median) [33].

**141**

**Table 2.**

assays [34, 35].

*Lessons Learned from 30 Years of Assessing U.S. Coastal Water*

**Water quality indicators Estuary thresholds Great Lakes thresholds**

*Fair, Fair/Poor* NE/SE/Gulf: 0.1, 0.5 West: 0.35, 0.5 Tropical: 0.05, 0.1 *2015 TN Interim Thresholds* 

*(mgN/L):*

*Fair, Fair/Poor* NE/SE/Gulf: 0.01, 0.05 West: 0.07, 1.0 Tropical: 0.005, 0.01 *2015 TP Interim Thresholds (mgN/L):* 25th, 50th, 75th percentiles of NCCA 2010 TP

values.

*Chla Thresholds (μg/L): Good/Fair, Fair/Poor* NE/SE/Gulf/West: 5.0, 20.0

*DO Thresholds (mg/L): Good/Fair,* 

*Transmissivity\* @ 1m(%)*: *Good/*

Naturally turbid waters: 10%, 5% Normally turbid waters: 20%,

SAV restoration priority: 40%,

**Good**: a maximum of one metric is rated as fair, and no metrics are

**Fair**: one metric is rated as poor, or two are rated as fair **Poor**: two or more metrics are

**Missing**: two metrics are missing, and available metrics do not suggest fair or poor ratings

All coastal regions: 5.0, 2.0

Tropical: 0.5, 1.0

*Fair/Poor*

10%

20%

*Thresholds*

rated as poor

rated as poor

*intensity Iz/Io vs depth z, i.e., Beer's law: Iz/Io = exp(−Kd\*z).*

*Fair, Fair/Poor*

*DIN Thresholds (mgN/L): Good/*

Nitrogen not assessed in the Great

*TP Thresholds (mgP/L): Good/Fair, Fair/Poor/*Superior/

*Chla Thresholds (μg/L): Good/Fair, Fair/Poor/*Superior/

*DO Thresholds (mg/L): Good/Fair, Fair/Poor* All lakes and basins: 5.0, 2.0

Michigan: 6.7, 5.3 Saginaw/West Erie: 3.9, 2.1 Mid & East Erie/Ontario: 5.3, 3.9

*Thresholds*

rated as poor

as poor

two are rated as fair

fair or poor ratings

*Secchi Thresholds* (m): *Good/Fair, Fair/Poor/*Superior/Huron: 8.0, 5.3

**Good**: a maximum of one metric is rated as fair, and no metrics are

**Fair**: one metric is rated as poor, or

**Poor**: two or more metrics are rated

**Missing**: two metrics are missing, and available metrics do not suggest

Huron: 1.3, 2.6 Michigan: 1.8, 2.6 Saginaw/Erie West: 3.6, 6.0 Mid & East Erie/Ontario: 2.6, 3.6

Huron: 0.005, 0.01 Michigan: 0.007, 0.01 Saginaw/West Erie: 0.015, 0.032 Mid & East Erie/Ontario: 0.01,

0.015

Lakes

25th, 50th, 75th percentiles of NCCA 2010 TN values

*DIP Thresholds (mgP/L): Good/*

ERL values are the concentration levels below which adverse bioeffects are unlikely, and ERM values signify the concentration above which adverse effects are likely. Sediments were also characterized by measuring grain size and total organic carbon (TOC) concentrations and were further tested for toxicity arising from either natural or anthropogenic sources by exposing amphipods to sediments in laboratory

*Indicators and thresholds employed in the NCCA 2010 to assess water quality in estuaries and the Great Lakes.*

**\****Trans = exp(−Kd); where Kd is PAR extinction factor, calculated via regression of exponential attenuation of PAR* 

*DOI: http://dx.doi.org/10.5772/intechopen.92326*

*Nitrogen status*

*Phosphorus status* DIP & TP in surface water DIP used to assess P status in estuaries prior to 2015; TP used thereafter. Water Quality Index (WQI) constructed with DIP in estuaries; with TP in the GL

*Algal biomass*

*Oxygen status*

bottom water

*Water clarity*

depth

water

Chlorophyll *a* in surface

Dissolved oxygen (DO) in

*Estuaries*: Rated by fraction of PAR transmitted through 1 m. Thresholds vary by turbidity category *Great Lakes*: Rated by Secchi

*Water Quality Index (WQI)* Constructed based on the ratings of the measured component WQ metrics (five metrics in estuaries, including DIN & DIP; four metrics in the Great Lakes)

DIN & TN in surface water DIN used to assess N status in estuaries prior to 2015; TN used thereafter. Water Quality Index (WQI) constructed with DIN in estuaries; with TN in the GL *Water Quality - Science, Assessments and Policy*

dynamic than the individual components.

problematic eutrophication symptoms than the core indicators.

water quality approach at pp. 11–15 of reference [19].

**3.2 Assessing sediment quality**

approach consisted of employing indicators that measure eutrophication-related symptoms and problems such as nutrient over-enrichment, excessive algal blooms, hypoxia or anoxia, low water transparency, etc. To moderate the inherent variability of such measures, the indicators were then combined into an index that is less

**Table 2** lists the five core indicators and thresholds used in recent NCCA surveys to assess water quality in estuaries and the Great Lakes. Nutrient and chlorophyll concentrations were measured in surface water, dissolved oxygen levels were determined in bottom water, and water clarity was established at each site. These measures were then combined into a water quality index (WQI) that captured conditions likely to be indicative of problematic eutrophication regardless of when in summer sampling occurred [17]. For instance, the WQI might record excessive dissolved nutrients in early season, excessive algal production and poor water clarity in mid-season or hypoxic, turbid conditions in late season. Essentially, the WQI reflects a "preponderance of evidence"; the index is a more robust indicator of

Thresholds generally varied by region. For instance, less stringent nutrient thresholds were specified for the West coast region, which experiences natural upwelling in summer [30], and more conservative guidelines were applied when assessing the tropical waters of southern Florida to protect submerged aquatic vegetation (SAV) beds. Assessment methods differed slightly when evaluating nutrients and water clarity in estuaries and the Great Lakes, recognizing the distinct ecologies and assessment histories in these environments. See details of the coastal

The NCCA approach of assessing nutrient status in estuaries continues to evolve. Early surveys measured nutrients as dissolved inorganic nitrogen and phosphorus (DIN and DIP). While DIN and DIP concentrations are valid indicators of nutrient enrichment status, they are unreliable measures of nutrient availability later in the season because they are generally assimilated into algal biomass in spring and early summer [19]. This is particularly problematic for NCCA surveys, which sample throughout the summer index period. In contrast, total nitrogen and phosphorus (TN and TP) are less variable and are related to chlorophyll concentrations [31]. Consequently, TN and TP were added as core indicators beginning in 2010 and were used to evaluate nutrient status in subsequent NCCA surveys. Since regional TN and TP thresholds have not yet been established, TN and TP were treated as exploratory indicators, rated as low, moderate, high, and very high based on the 25th, 50th, and 75th quartile values of the measured 2010 TN and TP values. The water quality index (WQI) continued to be calculated with DIN and DIP as described in **Table 2**, reflecting the key role of dissolved nutrients in eutrophication processes [17, 31].

Contaminants from agricultural, industrial, and nonpoint sources find their way to coastal waters where they may adsorb onto suspended particles and settle to the sediment. There, metals and organic pollutants are ingested by benthic-dwelling organisms and may become concentrated throughout the food web and adversely affect fish, pelagic mammals, and human consumers of aquatic organisms. To monitor sediment contamination, all EPA coastal assessments since the 1990s followed the approach of NOAA's Status and Trends program [32] and collected sediment grab samples and measured a suite of 74 metal, PAH, PCB, and pesticide contaminants in surficial sediment samples (**Table 1**). The impacts of the pollutants on benthic organisms were evaluated against the effects-based sediment quality guidelines, ERL (effects range low) and ERM (effects range median) [33].

**140**


**\****Trans = exp(−Kd); where Kd is PAR extinction factor, calculated via regression of exponential attenuation of PAR intensity Iz/Io vs depth z, i.e., Beer's law: Iz/Io = exp(−Kd\*z).*

#### **Table 2.**

*Indicators and thresholds employed in the NCCA 2010 to assess water quality in estuaries and the Great Lakes.*

ERL values are the concentration levels below which adverse bioeffects are unlikely, and ERM values signify the concentration above which adverse effects are likely. Sediments were also characterized by measuring grain size and total organic carbon (TOC) concentrations and were further tested for toxicity arising from either natural or anthropogenic sources by exposing amphipods to sediments in laboratory assays [34, 35].

Prior to the NCCA 2010, estuarine surveys evaluated sediment quality based on three core metrics: (1) sediment contaminants were evaluated as good, fair, or poor based on the number of ERL or ERM exceedances evident at a site; (2) toxicity was rated as good or poor if the survival rate of the amphipod *Ampelisca abdita* exceeded or was less than 80%, respectively; and (3) TOC was rated against concentration thresholds of 2 and 5%. A sediment quality index (SQI) was then calculated reflecting the ratings of the individual core components. Details are further explained in the National Coastal Condition Report IV [19].

Several modifications were introduced into NCCA surveys conducted in 2010 and later. Pollutant levels in estuaries were expressed as mean ERM quotients (mERMQ )—the ratio of a contaminant concentration to its ERM value, designated as mERMQ [36, 37]. Estuarine sediment contaminants were evaluated in a more nuanced manner, using the mERMQ and a logistic regression model approach [38] to better estimate the adverse effects of pollutants on benthic organisms. Estuarine sediment toxicity tests were primarily conducted using the amphipod species


*a ERM-Q, conc/ERM for estuarine sites only, for 28 analytes with ERM values; PEC-Q, conc/PEC for Great Lakes only, for 9 analytes with PEC values (As, Cd, Cr, Cu, Pb, Ni, Zn, PAHs, PCBs); mean ERM-Q, ∑ERM-Q/n; mean PEC-Q = ∑PEC-Q/n; where n = number or analytes.*

*b LRM factor calculated as follows, for 36 analytes with fitting factors B0 and B1 (Field et al., 2002). LRM Pmax = 0.11 + 0.33\*LRMmax + 0.4\*LRMmax2 .*

#### **Table 3.**

*Indicators and thresholds employed in the NCCA 2010 to assess sediment quality in estuaries and the Great Lakes.*

**143**

gradient [49].

**3.4 Assessing fish tissue contaminants**

*Lessons Learned from 30 Years of Assessing U.S. Coastal Water*

*Leptocheirus plumulosus* and *Eohaustorius estuarius* (in California), which could be cultured in the laboratory and gave more consistent results than *Ampelisca abdita*. TOC was no longer used to assess sediment quality because it could have positive and negative impacts on organisms, complicating interpretation. The sediment quality index (SQI) was constructed from the remaining two core metrics to summarize overall sediment condition. Similar to the estuarine approach, the Great Lakes sediment quality index utilizes a mean sediment quality guideline quotient method and a toxicity test. The mean Probable Effect Concentration Quotient (mPEC-Q ), rather than the mERM-Q, is used in the Great Lakes [39], along with using the freshwater amphipod *Hyalella azteca* to assess toxicity (**Table 3**). Further details concerning the evolution and calculation of methods are available in the

All EPA estuarine surveys since the 1990s collected sediment grab samples of benthic macroinvertebrate communities for assessment of ecological condition based on measures of diversity, species richness, and dominance. The benthos is a key component of estuarine ecosystems, serving as important food source for higher trophic levels and maintaining sediment and water quality. Benthic communities respond to contaminant concentrations, dissolved oxygen stress, salinity fluctuations and physical disturbance, and are relatively immobile and therefore

Separate regional, benthic-community condition indices were developed during the EMAP programs of the 1990s, including for the Virginian [41], Carolinian [42] and Louisianan [43, 44] biogeographic provinces. Later, an index was created for the Acadian Province (Maine through Cape Cod waters) [45]. No specific index was developed for the Pacific coast; rather, sites were assessed based on observed vs. expected species richness [30]. These benthic indices were used in estuarine assessments prior to NCCA 2015. Benthic communities in the Great Lakes were evaluated using an oligochaete trophic index (OTI) based on the classification of oligochaete species by their tolerance to organic enrichment [46, 47]. **Table 4** presents a summary of these regional benthic indices. The NCCA 2010 Technical Appendix [40] provides further detail regarding the development and calculation of the indices. While the separate estuarine indices performed well in the region for which they were developed, they were developed using different statistical models and metrics. Because the different indices might not be comparable, combining the separate indices into a nationwide evaluation tool was problematic. In response, a nationalscale index called M-AMBI (multivariate-AZTI marine biotic index) was adapted to provide a single index applicable to all U.S. estuarine waters [48, 49]. This index is based on benthic indices that were successfully deployed in Europe and elsewhere [50, 51]. AMBI is an abundance-weighted tolerance index, while M-AMBI combines AMBI, species richness and species diversity together using factor analysis calculated for a given habitat. The resulting index was shown to be comparable to several local indices [49] and was better correlated with land use variables [52]. The resulting scores are based on comparison of a sites' position along a pollution

Many aquatic organisms in coastal regions are inadvertent inheritors of a legacy

of disturbances often associated with human practices. For instance, chemical pollutants from farms and cities delivered to coastal waters enter the food web

integrate the effect of adverse conditions over months and years.

*DOI: http://dx.doi.org/10.5772/intechopen.92326*

NCCA 2010 technical report [40].

**3.3 Assessing benthic community condition**

*Lessons Learned from 30 Years of Assessing U.S. Coastal Water DOI: http://dx.doi.org/10.5772/intechopen.92326*

*Water Quality - Science, Assessments and Policy*

*Sediment contamination* **Mean contaminant quotients**<sup>a</sup> For estuaries: calculate ERM-Q and mean ERM-Q (mERM-Q ); For Great Lakes: calculate PEC-Q and mean PEC-Q (mPEC-Q ). **Logistic regression model** 

For estuaries only: (1) calculate LRM factor for each of 36 analytes with fitting parameters; (2) select largest factor LRMmax; (3) calculate LRM Pmax

% Survival of amphipods after 10-day exposure to site sediment, compared with survival in clean

Amphipods tested for estuarine sediments: *Leptocheirus plumulosus* or *Eohaustorius estuarius*; for Great Lakes sediments: *Hyalella azteca*

*Sediment Quality Index (SQI)* Constructed based on the ratings of sediment contaminant and sediment toxicity metrics The assessment criteria are the same for estuarine and Great Lakes

*PEC-Q = ∑PEC-Q/n; where n = number or analytes.*

*LRM Pmax = 0.11 + 0.33\*LRMmax + 0.4\*LRMmax2*

**(LRM)**<sup>b</sup>

*Sediment toxicity*

control sediment

Prior to the NCCA 2010, estuarine surveys evaluated sediment quality based on three core metrics: (1) sediment contaminants were evaluated as good, fair, or poor based on the number of ERL or ERM exceedances evident at a site; (2) toxicity was rated as good or poor if the survival rate of the amphipod *Ampelisca abdita* exceeded or was less than 80%, respectively; and (3) TOC was rated against concentration thresholds of 2 and 5%. A sediment quality index (SQI) was then calculated reflecting the ratings of the individual core components. Details are

Several modifications were introduced into NCCA surveys conducted in 2010 and later. Pollutant levels in estuaries were expressed as mean ERM quotients (mERMQ )—the ratio of a contaminant concentration to its ERM value, designated as mERMQ [36, 37]. Estuarine sediment contaminants were evaluated in a more nuanced manner, using the mERMQ and a logistic regression model approach [38] to better estimate the adverse effects of pollutants on benthic organisms. Estuarine sediment toxicity tests were primarily conducted using the amphipod species

**Sediment quality indicators Estuary thresholds Great Lakes thresholds**

mERM-Q > 0.1 & ≤ 0.5 or LRM Pmax > 0.5 & ≤ 0.75

mERM-Q ≤ 0.1 & LRM Pmax ≤ 0.05

**Good**: mPEC-Q ≤ 0.1 **Fair**:

**Poor**:

mPEC-Q > 0.1 and ≤ 0.6

**Good**: control corrected survival ≥90% **Fair**: control corrected survival ≥75 and <90% **Poor**: ontrol corrected survival <75%

**Same assessment criteria**

mean PEC-Q > 0.6

mERM-Q > 0.5 or LRM Pmax > 0.5

**Good**: test results not significantly different from control (p > 0.05) and ≥ 80% control-corrected survival **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-

**Poor**: test results significantly different from control (p < 0.05) and <80% control-corrected survival

**Good**: both sediment contaminant and sediment toxicity metrics are rated

**Fair**: neither metric is rated poor and at least one metric is rated fair **Poor**: at least one metric is rated poor

*ERM-Q, conc/ERM for estuarine sites only, for 28 analytes with ERM values; PEC-Q, conc/PEC for Great Lakes only, for 9 analytes with PEC values (As, Cd, Cr, Cu, Pb, Ni, Zn, PAHs, PCBs); mean ERM-Q, ∑ERM-Q/n; mean* 

*Indicators and thresholds employed in the NCCA 2010 to assess sediment quality in estuaries and the Great* 

corrected survival

good

*LRM factor calculated as follows, for 36 analytes with fitting factors B0 and B1 (Field et al., 2002).*

*.*

further explained in the National Coastal Condition Report IV [19].

**Good**:

**Fair**:

**Poor**:

**142**

sites

*a*

*b*

**Table 3.**

*Lakes.*

*Leptocheirus plumulosus* and *Eohaustorius estuarius* (in California), which could be cultured in the laboratory and gave more consistent results than *Ampelisca abdita*. TOC was no longer used to assess sediment quality because it could have positive and negative impacts on organisms, complicating interpretation. The sediment quality index (SQI) was constructed from the remaining two core metrics to summarize overall sediment condition. Similar to the estuarine approach, the Great Lakes sediment quality index utilizes a mean sediment quality guideline quotient method and a toxicity test. The mean Probable Effect Concentration Quotient (mPEC-Q ), rather than the mERM-Q, is used in the Great Lakes [39], along with using the freshwater amphipod *Hyalella azteca* to assess toxicity (**Table 3**). Further details concerning the evolution and calculation of methods are available in the NCCA 2010 technical report [40].

#### **3.3 Assessing benthic community condition**

All EPA estuarine surveys since the 1990s collected sediment grab samples of benthic macroinvertebrate communities for assessment of ecological condition based on measures of diversity, species richness, and dominance. The benthos is a key component of estuarine ecosystems, serving as important food source for higher trophic levels and maintaining sediment and water quality. Benthic communities respond to contaminant concentrations, dissolved oxygen stress, salinity fluctuations and physical disturbance, and are relatively immobile and therefore integrate the effect of adverse conditions over months and years.

Separate regional, benthic-community condition indices were developed during the EMAP programs of the 1990s, including for the Virginian [41], Carolinian [42] and Louisianan [43, 44] biogeographic provinces. Later, an index was created for the Acadian Province (Maine through Cape Cod waters) [45]. No specific index was developed for the Pacific coast; rather, sites were assessed based on observed vs. expected species richness [30]. These benthic indices were used in estuarine assessments prior to NCCA 2015. Benthic communities in the Great Lakes were evaluated using an oligochaete trophic index (OTI) based on the classification of oligochaete species by their tolerance to organic enrichment [46, 47]. **Table 4** presents a summary of these regional benthic indices. The NCCA 2010 Technical Appendix [40] provides further detail regarding the development and calculation of the indices.

While the separate estuarine indices performed well in the region for which they were developed, they were developed using different statistical models and metrics. Because the different indices might not be comparable, combining the separate indices into a nationwide evaluation tool was problematic. In response, a nationalscale index called M-AMBI (multivariate-AZTI marine biotic index) was adapted to provide a single index applicable to all U.S. estuarine waters [48, 49]. This index is based on benthic indices that were successfully deployed in Europe and elsewhere [50, 51]. AMBI is an abundance-weighted tolerance index, while M-AMBI combines AMBI, species richness and species diversity together using factor analysis calculated for a given habitat. The resulting index was shown to be comparable to several local indices [49] and was better correlated with land use variables [52]. The resulting scores are based on comparison of a sites' position along a pollution gradient [49].

#### **3.4 Assessing fish tissue contaminants**

Many aquatic organisms in coastal regions are inadvertent inheritors of a legacy of disturbances often associated with human practices. For instance, chemical pollutants from farms and cities delivered to coastal waters enter the food web


#### **Table 4.**

*Summary of methods, metrics, and thresholds used to construct regional benthic indices used to evaluate assess coastal waters.*

and accumulate, threatening fish and higher trophic-level communities, humans included. To assess the ecological danger to aquatic communities, EPA's coastal surveys since the 1990s have measured concentrations of metals, PCBs, PAHs, and pesticides (**Table 1**) in demersal and pelagic fish collected at sampling stations. Prior to the NCCA 2010 survey, sites were evaluated by comparing contaminant concentrations against human health fish-consumption advisory thresholds as a surrogate for ecologically-relevant benchmarks [53]. When both humans and wildlife were similarly sensitive to specific contaminant exposures, the surrogate used for the assessment was meaningful. Beginning with the NCCA 2010 survey, an *ecological* risk-based approach using wildlife endpoints was incorporated to better align with the ecosystem focus of the NCCA surveys.

The ecological risk approach assessed contaminant levels in whole-body fish tissue following the methods of EPA's ecological risk assessment [54]. The primary goal of this NCCA index, therefore, was to evaluate the potential risk that consuming contaminated fish poses to predators other than humans. Because such "wild" predators consume the entire fish, the NCCA protocol measured contaminant concentrations in the entire fish collected in the survey, rather than measuring contaminant levels in just the fillet—the protocol formerly used when human health was the focus. Operationally, the process first identified mammalian, avian, and piscivorous "receptors," i.e., predator species that consume coastal fish and could be adversely affected by contaminants in the prey-fish. **Table 5** lists the freshwater and marine receptors selected for analysis based on their diet (predominantly fish) and availability of data in the literature. The literature studies were reviewed to identify the Lowest Observed Adverse Effects Level or LOAEL for each receptor, that is,

**145**

**Table 6.**

*Lessons Learned from 30 Years of Assessing U.S. Coastal Water*

**mammalian receptors**

Osprey Mink Bottlenose

**Rating criteria for ecological fish tissue contaminant index**

No contaminant concentration exceeds a LOAEL for any receptor group

**Good Fair Poor**

*Whole-body tissue contaminant LOAEL concentrations (μg/dry g) by receptor group.*

At least one contaminant concentration exceeds a LOAEL for one receptor group

the contaminant concentration likely to elicit toxicological effects. The minimum contaminant LOAEL found for any member of a receptor group was designated as an impairment threshold, and was used to rate survey sites as good, fair, or poor (**Table 6**). Because of the very different methods used in the human-health and ecological-risk approaches, the NCCA assessments cannot be directly compared

> **Marine mammalian receptors**

Dolphin

*Higher trophic-level piscivores potentially at risk from consuming contaminated prey fish.*

Great Blue Heron River Otter Harbor Seal Largemouth Bass Bluefin Tuna

Bald Eagle — Walrus Muskellunge Shortfin Mako Herring Gull — — Snakehead Mackerel Tuna Belted Kingfisher — — Lake Walleye Swordfish Brown Pelican — — — —

**Contaminant Whole-body tissue concentration (μg/dry g) by receptor group**

Arsenic (inorganic) 3.8 9.2 0.7 Cadmium 32.1 14.0 3828 Mercury (methyl) 1.1 0.1 1.4 Selenium 2.3 0.6 33.6 Chlordane 55.4 2.9 — DDTs 28.0 1.6 7.1 Dieldrin 1.2 0.3 1.6 Endosulfan 42.8 43.2 0.003 Endrin 5.6 0.1 3.9 Heptachlor epoxide 7.5 6.3 81.1 Hexachlorobenzene 14.0 0.6 0.04 Lindane 280 2.4 376 Mirex 4.6 0.7 9.9 Toxaphene 280 3.6 0.03 PCBs 3.9 1.3 2.0

**Freshwater fish receptors**

**Lowest observed adverse effect level (LOAEL)**

**Mammal Avian Fish**

At least one contaminant concentration exceeds a LOAEL for two or more receptor groups

**Marine fish receptors**

Florida Gar Yellowfin Tuna

*DOI: http://dx.doi.org/10.5772/intechopen.92326*

**Avian receptors Freshwater** 

**Table 5.**

## *Lessons Learned from 30 Years of Assessing U.S. Coastal Water DOI: http://dx.doi.org/10.5772/intechopen.92326*

the contaminant concentration likely to elicit toxicological effects. The minimum contaminant LOAEL found for any member of a receptor group was designated as an impairment threshold, and was used to rate survey sites as good, fair, or poor (**Table 6**). Because of the very different methods used in the human-health and ecological-risk approaches, the NCCA assessments cannot be directly compared


**Table 5.**

*Water Quality - Science, Assessments and Policy*

Gulf/Louisianian Discriminant

Great Lakes Abundance-

Logistic regression analysis

analysis

weighted tolerance equation

Discriminant analysis

B-IBI approach Mean abundance

Northeast/ Acadian

Northeast/ Virginian

Southeast/ Carolinian

National estuarine

**Table 4.**

*coastal waters.*

and accumulate, threatening fish and higher trophic-level communities, humans included. To assess the ecological danger to aquatic communities, EPA's coastal surveys since the 1990s have measured concentrations of metals, PCBs, PAHs, and pesticides (**Table 1**) in demersal and pelagic fish collected at sampling stations. Prior to the NCCA 2010 survey, sites were evaluated by comparing contaminant concentrations against human health fish-consumption advisory thresholds as a surrogate for ecologically-relevant benchmarks [53]. When both humans and wildlife were similarly sensitive to specific contaminant exposures, the surrogate used for the assessment was meaningful. Beginning with the NCCA 2010 survey, an *ecological* risk-based approach using wildlife endpoints was incorporated to better

**Region/province Method Component metrics References**

(species tolerance index)

% Spionids (abundance)

Mean number of taxa

% Capitellids % Bivalves % Amphipods

Pacific coast Regression Observed vs expected species richness [11, 27]

Factor analysis Shannon H' (diversity)

*The national M-AMBI index was developed for the NCCA 2015 and future surveys.*

organic enrichment)

Species richness

tolerance)

*Summary of methods, metrics, and thresholds used to construct regional benthic indices used to evaluate assess* 

Shannon H' (diversity) MN\_ES(50)0.05

[39]

[35]

[36]

[37, 38]

[40, 41]

[42, 43]

% Capitellid polychaetes (abundance)

Salinity adjusted Gleason D (diversity) Salinity adjusted % tubificid (abundance)

100% abundance of 2 dominant taxa % Abundance of pollution sensitive taxa

% Expected diversity (Shannon H') Mean abundance of tubificids

Oligochaete tolerance scores (based on

AMBI (abundance-weighted pollution

The ecological risk approach assessed contaminant levels in whole-body fish tissue following the methods of EPA's ecological risk assessment [54]. The primary goal of this NCCA index, therefore, was to evaluate the potential risk that consuming contaminated fish poses to predators other than humans. Because such "wild" predators consume the entire fish, the NCCA protocol measured contaminant concentrations in the entire fish collected in the survey, rather than measuring contaminant levels in just the fillet—the protocol formerly used when human health was the focus. Operationally, the process first identified mammalian, avian, and piscivorous "receptors," i.e., predator species that consume coastal fish and could be adversely affected by contaminants in the prey-fish. **Table 5** lists the freshwater and marine receptors selected for analysis based on their diet (predominantly fish) and availability of data in the literature. The literature studies were reviewed to identify the Lowest Observed Adverse Effects Level or LOAEL for each receptor, that is,

align with the ecosystem focus of the NCCA surveys.

**144**

*Higher trophic-level piscivores potentially at risk from consuming contaminated prey fish.*


#### **Table 6.**

*Whole-body tissue contaminant LOAEL concentrations (μg/dry g) by receptor group.*

with earlier survey results and cannot be used to inform human consumption advisories. Refer to the NCCA 2010 technical appendix for further details [34].

#### **3.5 Addressing human-health concerns and emerging issues**

Along with evaluating the ecological condition of several major ecological compartments of coastal ecosystems, the NCCA also addressed several matters regarding human health and emerging issues. For instance, in the Great Lakes with the support of the Great Lakes Restoration Initiative (GLRI) [55], the concentrations in fish tissue of the contaminants mercury, polychlorinated biphenyls (PCBs), flame retardant polybrominated diphenyl ethers (PBDEs), and perfluorinated compounds (PFCs) were measured in the Great Lakes NCCA surveys and evaluated against human health screening values [40]. The NCCA also initiated a surveywide monitoring program quantifying aqueous concentrations of the algal toxins microcystin and cylindrospermopsin, as well as mercury in fish muscle. Several exploratory studies were also undertaken to address important issues such as ocean acidification and the distribution of micro-plastics in coastal water. Newer assessment techniques are also under investigation, such as exploring the use of underwater cameras and environmental genetic screening to monitor the expansion of invasive organisms in the Great Lakes.

## **4. Conclusion**

In retrospect, the mandate issued to the U.S. EPA by the Clean Water Act in 1972 to compile a national assessment of water quality was a bold and challenging directive. No blueprint was available to indicate the best approach of conducting a large-scale assessment program. Tactics regarding monitoring designs, sampling strategies, indicators, thresholds, assessment protocols, etc. all needed to be developed from scratch. The EPA adopted a pragmatic approach to assessing coastal regions, exploring and testing methodologies regionally, and then gradually building a national program based on the best practices learned over 30 years of experimentation. While the NARS-coastal surveys and assessments are not perfect, they represent the first nationally consistent effort, based on current practices, to assess the Nation's coastal waters through time. The data and results represent information available for evaluating national policy and a basis for the scientific community to evaluate coastal waters from many perspectives.

The evolution of methodologies and approaches for the NCCA is an ongoing process. Future surveys will continue the practices of adapting current methods to the latest best practices and the adaptation of new strategies, while striving to strike a balance between consistency and creative exploration. The continued importance of partnerships among federal, state and tribal agencies cannot be over-emphasized in achieving the aims of the monitoring program. Such cooperation has proven to be both efficient and productive, and the enhanced capacity of states and tribes to conduct assessments independently is particularly valuable in assuring a sustainable monitoring program. Particularly striking has been the deep commitment of many individuals, research scientists, program planners, crew members, information managers, analysts, communicators, and partners, who have offered feedback and criticism to continuously improve the coastal assessment process. Finally, the development and evolution of coastal assessment expertise described in this chapter is similarly evident in sister NARS programs that assess lakes, rivers and streams, and wetlands. Descriptions of these programs are presented elsewhere in this book.

**147**

*Lessons Learned from 30 Years of Assessing U.S. Coastal Water*

project managers, quality control officers, and reviewers.

The authors declare no conflict of interest.

These coastal assessments were conducted in partnership with many state and tribal agencies, the National Oceanic and Administration (NOAA), the National Park Service (NPS), as well as other federal agencies. EPA offices included the Office of Research and Development, the Office of Water, Great Lakes National Program Office, and EPA Regional Monitoring Coordinators from Regions 1, 2, 3, 4, 5, 6, 9, and 10. We note the particular efforts of EPA colleagues who contributed to the conception, administration and operation of the coastal surveys, including Kevin Summers, John Paul, Virginia Hansen, Treda Grayson, Sarah Lehmann, and Greg Colianni and many others too numerous to name. Thanks also to EPA colleagues Don Cobb, Charlie Strobel, and Hal Walker who reviewed the chapter. We especially wish to acknowledge the truly invaluable assistance of the hundreds of participants who conducted the assessments, including the field crews, biologists, taxonomists, statisticians, analysts, program administrators, regional coordinators,

The views expressed in this chapter are those of the authors and do not necessarily represent the views or policies of the United States Environmental Protection

*DOI: http://dx.doi.org/10.5772/intechopen.92326*

**Acknowledgements**

**Conflict of interest**

**Disclaimer**

Agency.
