U.S. Environmental Protection Agency



## EVALUATION OF MULTIPLE INDICATOR COMBINATIONS TO DEVELOP QUANTIF IABLE RELATIONSHIPS

U.S. Environmental Protection Agency

Office of Water December 2010 EPA 822-R-10-004U.S. Environmental Protection Agency

Table of Contents

Executive Summary.............................................................................................................. .........1 1 Introduction ............................................................................................................................3 1.1 Purpose, Objectives, and Importance................................................................................3 1.2 Approaches for Developing Water Quality Measures Corresponding to Equivalent Risks..................................................................................................................................4 1.2.1 Risk Link................................................................................................................ .....4 1.2.2 Water Quality Link .....................................................................................................6 2 Background.............................................................................................................................8 2.1 Review and Assessment of Relevant Epidemiology Studies and Datasets.......................8 2.1.1 Prospective Cohort Studies.........................................................................................8 2.1.2 Randomized Control Trials.........................................................................................9 2.1.3 Studies that have Generated Health Effects Relationships.........................................9 2.1.4 Factors that Preclude Direct Co mparison of Epidemiology Studies ........................10 2.2 Differences in Indicator Performance for Indicators as Measured by CultureBased and qPCR-Based Methods ...................................................................................11 2.2.1 General Method Performance...................................................................................11 2.2.2 Factors Impacting Indica tor/Method Performance ...................................................16 2.2.3 Uncertainty of Indicator/Method Combinations at Low Density .............................20 3 Analyses.................................................................................................................................21 3.1 Risk Link.................................................................................................................. .......21 3.1.1 Review of NEEAR Study and Other Health Effect s Relationships for qPCR and Culturable FIB....................................................................................................22 3.1.2 Direct Application of the Risk Link Approach: Linking Bacteroidales Measured by qPCR to Enterococcus Measured by qPCR........................................24 3.1.3 Linking E. coli Densities as Measured by MF via Health Effects Curves from Different Epidemiology Studies ...............................................................................24 3.1.4 Application of the Risk Link Approach for Health Effects Curves Based on Different Illness Definitions .....................................................................................27 3.2 Water Quality Link Approach.........................................................................................29 3.2.1 Datasets Used in Water Quality Link Demonstrations.............................................29 3.2.2 Linear Models of Log-Transformed Indicator Data .................................................30 3.2.3 Demonstration of the Water Quality Linkage Approach using Linear Regression Models....................................................................................................35 3.2.4 Broken Stick Models of Log-Transformed Indicator Data.......................................38 4 Discussion..............................................................................................................................45 5 References ..................................................................................................................... ........46

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List of Tables

Table 1. Epidemiology study differences that limit direct comparis on of health effects curves ................................................................................................................................6 Table 2. Epidemiology studies th at have establishe d relationships between FIB density and excess GI illness due to swimming.................................................................................10 Table 3. Summary of factors a ffecting culture-based and/or qPCR data for fecal indicator bacteria....................................................................................................................... .....11 Table 4. Summary of the target organisms and primer sets used for a selection of qPCR assays ..............................................................................................................................15 Table 5. Summary of health effects relations hips from USEPA NEEA R studies and marine studies conducted to support de velopment of the current RWQC..................................23 Table 6. Risk-linked Bacteroidales and Enterococcus densities..................................................24 Table 7. Results from ANCOVA comparing data from the USEPA (1984) and Marion et al. (2010) epidemiology studies..................................................................................26 Table 8. Equivalent Criteria in Terms of HCGI and NGI Definitions of Illness..........................28 Table 9. Examples of equivalent criteria va lues for marine waters via the statistical linkages approach............................................................................................................29 Table 10. Results of ANCOVAs for sets of data for Huntington Beach with different collection times ............................................................................................................... 34 Table 11. Summary of models resulting from re tention of data pairs with culture counts above three thresholds.....................................................................................................44

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iv List of Figures

Figure 1. Illustration of the risk link approach .............................................................................. .5 Figure 2. Illustration of the Wa ter Quality Linkage approach........................................................7 Figure 3. Paired qPCR and culture data for Enterococcus : Huntington Beach............................12 Figure 6. Correlation of qPCR Enterococcus densities from replicate samples ..........................14 Figure 7. Trend lines for dilutions of laboratory cultures of E. coli and C. jejuni enumerated using qPCR and culture methods....................................................................................17 Figure 8. Comparison of bacterial data from culture-based and multiplex real-time PCR methods, and statistical sample description....................................................................18 Figure 9. Health Effects Data and Tre nd Lines from the USEPA and Marion Studies................25 Figure 10. Typical plot of paired Enterococcus densities as measured by qPCR and culturebased methods.................................................................................................................31 Figure 11. Paired qPCR and culture Enterococcus data, all NEEAR study Great Lakes beaches............................................................................................................................32 Figure 12. Paired culture and qPCR Enterococcus data, all samples from Huntington Beach ....33 Figure 13. Paired qPCR and culture Enterococcus data, Huntington Beach: 8:00 AM samples only................................................................................................................... .33 Figure 14. Paired qPCR and culture Enterococcus data, Huntington Beach: 3:00 PM samples only................................................................................................................... .34 Figure 15. Linear model, data fr om all NEEAR freshwater beaches ...........................................36 Figure 16. Linear model, da ta from Huntington Beach................................................................37 Figure 17. Linear model, 8:00 AM data from Huntington Beach ................................................38 Figure 18. Huntington Beach broken stick mode l fits for all data and 8:00 AM data..................39 Figure 19. West Beach broken stick model fits for all data and 8:00 AM data............................40 Figure 20. Silver Beach broken stick mode l fits for all data and 8:00 AM data ..........................40 Figure 21. Washington Park Beach broken stick model fits for all data and 8:00 AM data ........41 Figure 22. Model resulting from retention of all data above the “kink” in the broken stick........42 Figure 23. Model resulting from retenti on of all data above 80 CFU/100 mL.............................43 Figure 24. Model resulting from retenti on of all data above 104 CFU/100 mL...........................43U.S. Environmental Protection Agency



### Executive Summary
This report was written to meet the EPA Critical Path Science Plan 1 element P15. The objectives of this work were to compare the fecal indicator bacteria and health effect relationships for multiple indicator/method combin ations used in epidemiology studies and to evaluate multiple indicator/method combinations to develop quantifiable relationships. To meet those objectives, two approaches were employe d to relate the different indicator/method combinations to gastrointestinal (GI) illness risks, namely the Risk Link and Water Quality Link.

In the Risk Link approach, indi cator-method combinations are li nked via health effects curves 2 generated by epidemiology studies. Three demo nstrations of the Risk Link approach are presented. In the first demonstr ation, linkages are established between indicator densities for multiple indicators used in the same epidem iology studies. This demonstration is a straightforward implementation of the Risk Link approach and entails linkage of Enterococcus density as measured by qPCR with Bacteroidales density as measured by qPCR. The second demonstration illustrates an assessment of di fferences between epidemiology studies conducted at different places or times, using an analysis of covariance (ANCOVA) to compare data from (Marion et al. 3 ) and (USEPA 4 ) EPA recreational water epidemiol ogy studies in freshwater. The analyses indicate that although the two sets of studies were conducted at different times, the slopes of their health effects curves are statistically similar.

The third Risk Link was performed using health e ffects relations from epidemiology studies with Enterococcus and GI relationships, measured by either qPCR or culture-based method. The EPA recreational water epidemiology study in marine waters 5 and the EPA’s National Epidemiological and Environmental Assessmen t of Recreational (NEEAR) Water Studies (marine) were linked to determine comparable Enterococcus culture and qPCR indicator densities at various GI health risk levels. To enable the calculation of comparable Enterococcus densities for the two studies, results from the 1983 USEPA study were first translated such that the GI illness definition matched that of the NEEAR GI illness (NGI) definition. Although numeric qPCR-based water quality cr iteria have not been establis hed at this time, assumptions about the way those criteria will likely be developed can be used to demonstrate potential qPCR and culture-based criteria that are consistent with the same level of risk (table below).

1 USEPA. 2007. Critical Path Science Plan for the Development of New or Revised Recreational Water Quality Criteria . U.S. Environmental Protection Ag ency, Offices of Water and Resear ch and Development, Washington, DC.
2 A health effects curve refers to a mathematical relati onship between fecal indicator bacteria (FIB) density and observed illness in epidemiology studies of recreational waters . All health effects curves referenced in this report relate the incidence of gastrointestinal (GI) illness to the log-transformed FIB density
3 Marion, J.W., Lee, J., Lemeshow, S., Buckley, T.J. 2010. Association of illness and recreational water exposure during advisory and non-advisory co nditions at an inland U.S. beach. Water Research 44 (16): 4796-4804.

4 USEPA 1984. Health Effects Criteria for Fresh Recreational Water. EPA-600/1-84-004. Research Triangle Park, NC: USEPA.

5 USEPA 1983. Health Effects Criteria for Marine Recreational Waters . EPA-600/1-80-031. Research Triangle Park, NC: USEPA.

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Tolerable attributable illness level (as HCGI ‡ per 1000 swimmers) Tolerable attributable illness level (as

NGI * per 1000

swimmers) Hypothetical qPCR Enterococcus density (CCE § /100 mL) Hypothetical geometric mean membrane filtration Enterococcus density (CFU/100 mL) 75 th percentile

value † (CFU/100 mL)

8 35 427 11 33

10 43 610 14 42

19 82 3460 35 104

† The 75 th percentile value for Enterococcus density based on the calculated geometric mean Enterococcus density, assuming Enterococcus densities are log-normally distributed, and assuming a typical standard deviation of log-transformed Enterococcus density for marine sites of 0.7. ‡ Highly Credible Gastrointestinal Illness, as defined in the epidemiology studies conducted in support of the 1986 water quality criteria

* NEEAR study GI illness (NGI, per the definit ion used in the NEEAR epidemiology studies) § Calibration cell equivalent; using a calibration sample containing a known concentration of the target sequence, CCEs are the normalized va lues of the test sample cell equivalents

The Water Quality Link approach links an indi cator-method combination for which there is no health effects relation to an i ndicator-method combination with a health effects relation via a quantifiable relationship between th eir measured fecal indicator bact eria densities. In short, the Water Quality Link Approach uses paired water qua lity data, rather than the linkage of health effects curves alone, as the basis for an alternative method for estab lishing culture-based criteria. Demonstrations of this approach were made using densities of Enterococcus enumerated by qPCR and by membrane filtration (MF) and linked to the health effects relationship from EPA’s NEEAR freshwater studies. It was found that wh ile the Water Quality Link may be useful on a site specific basis, in this particular demonstration the relationships between paired Enterococcus data, measured by culture and qPCR, were not consistent among N EEAR study freshwater beaches when simple linear and broken stick (segmented) regression models were employed. Further, the regression fits to this dataset exhibited heterosked asticity (uneven distribution of residuals) and both the simple lin ear regression and broken-stick m odels resulted in comparable Enterococcus culture criteria values far in excess of the current criteria.

The exploration of the Risk Link and the Wate r Quality Link approaches provides a proof of concept with the currently available data. It is likely that new health effects data or improved models of the co-occurrence of indicators will allow for additional or improved linkages to be established.

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### 1 Introduction

1.1 Purpose, Objectives, and Importance Pathogens within fecal matter ar ising from human and animal s ources that enter coastal and inland waters pose risks to recreationa l swimmers. Because it is not feasible to monitor for all pathogens that may occur in ambient waters, monitoring strategies currently involve the detection of indicator organisms that are present in fecal materi al in greater numbers than the pathogenic organisms (NRC 2004). For decades, EPA has relied on the use of epidemiology studies to assess the risk of gastrointestinal (GI) illness in swimmers exposed to increasing densities of fecal indi cator bacteria (FIB). 6 Previous epidemiology studies used only culturebased methods for enumerating FIB. More recent epidemiology studies have evaluated associations between GI illness and a wider rang e of indicator organisms, using both improved culture techniques and molecu lar-based (rapid) methods.

The primary purpose of this report is to help meet one of the elements (Project P15) in the U.S. Environmental Protection Agency (EPA or the Agency) Critical Path Science Plan for Development of New or Revised Re creational Water Quality Criteria (CPSP or science plan) (USEPA 2007). The science plan is a key comp onent of EPA’s overall process to develop new or revised Section 304(a) Ambient Water Quality Criteria (RWQC) for r ecreational waters that will be used by States, Territories, and Tribes to develop their own water quality standards (WQS 7 ).

The objectives of CPSP Proj ect P15 are as follows:

 To compare the GI illness response to expos ure relationship for multiple fecal indicator organism/method combinations; and

 To develop quantifiable relationships between the results from the various indicator/method combinations. More generally, P15 commissions scientific studi es to explore correlations and linkages between available indicator organisms (FIB) and methods. These linkages are established to ensure that equal protection is provided for any future criteria associated w ith each indicator-method combination.

Section 2 provides a background review and asse ssment of relevant epidemiology studies, as well as a review of differences in FIB perfor mance for indicators e numerated culture and quantitative polymerase chain reaction (qPCR). The background supports the analyses described in Section 3 that analyze avai lable epidemiology datasets using both the “Risk Link” and the

6 “Traditional” FIB include culturable total co liforms, fecal (thermotolerant) coliforms, Escherichia coli (an important member of the coliform group), and Enterococcus (enterococci). Although the presence of FIB indicates the presence of fecal matter, and the potential presence of (enteric) pathog ens, FIB are not pathogenic (NRC 2004; WHO 2003).

7 Under the Clean Water Act (CWA) States, Territories, an d Tribes are required to adopt new or revised WQS for those pathogens and pathogen indicators for which EPA’s new or revised criteria have been developed. Once approved by EPA, WQS are used for various CWA purposes an d programs and are the effective (enforceable) standards to protect waters for specified designa ted uses, such as “primary contact recreation.”

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“Water Quality Link” approaches. Both of thes e approaches are described in detail in later sections of this report. Section 4 provides a discussion of the analyses , including limitations and assumptions.

1.2 Approaches for Developing Water Quality Measures Corresponding to Equivalent Risks At the core of RWQC is a quantitative relations hip between a measure of fecal pollution in water and the risk of adverse health outcomes (health effects curv es) arising from primary recreational contact (e.g., swimming) in the polluted water. Although the expense, complexities, and time associated with conducting epidemiology studies are significant, several studies have been completed and are ongoing in the U.S. and abro ad since the issuance of the current (1986) RWQC. Many of those studies we re conducted in direct support of the development of RWQC, standards, and guidance. In a ddition, new technologies, such as rapid molecular-based detection methods, have been developed for identifying and enumerating FIB and other (alternate) indicator organisms that are fundamentally diffe rent from traditional culture-based methods. None of these approaches (new methods, alternat ive indicators, or alternate risk assessment methods) involves direct measurement of the pa thogen(s) suspected of causing illness in recreational water users. Theref ore, linkages are made between the indicators that are being measured and the adverse health effects observed in relevant and available epidemiology studies.

The health effects curves from some of the epidemiology studies may not be compared without prior analysis and transformations. Differences that necessitate transf ormations include, for example, differences in study design (prospec tive cohort [PC] vs. randomized control trial ([RCT]) (see more below), differences in definitions of GI illness, and differences in the settings (marine vs. freshwater environments). Ongoing epidemiology studies may yield health effects curves for additional indicator/method combinations and these health curves may be suitable for use in developing linkages between indicator/method combinations.

Further, incorporation of molecular-based methods into RWQC based on culture methods (or incorporation of culturable met hods into criteria based on molecular methods) is complicated by the differences in their targets. Because di fferent methods measure different targets with different abundances in environmental samples, it is not a given that hea lth effect relationships developed for one particular indicator-method combination are valid for another indicatormethod combination. This report places emphasis on the linkages between (1) Enterococcus measured by qPCR and Bacteroidales measured by qPCR for publicly owned (sewage) treatment works (POTW)-impacted marine settings; (2) E. coli measured by membrane filtration (MF) for studies conducted at diffe rent times and in diffe rent settings; and (3) Enterococcus measured by qPCR and Enterococcus measured by MF for studies of ma rine beaches conducted at different times.

1.2.1 Risk Link

1.2.1.1 Approach Description The most direct route to meeting the CPSP P15 ob jectives is through use of health effect-FIB density curves to determ ine qua ntifiable relationships for indi cator/method combinations that correspond to the same risk level for swimmers. This approach, termed the Risk Link, is illustrated in Figure 1 . For this approach to be feasib le, all indicator-method combinations

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should have an associated relationship between in dicator density and health effects (the solid lines in both plots in Figure 1 ). At present, all such health effects curves have been developed based on epidemiology studies of surface water recr eation sites. Future relationships may be developed using QMRA, watershed modeling approaches, or other methods developed specifically for that purpose. A ssuming the indicator densities and illness rates for the standard and alternative indicator health curves are comp arable (and this assumption is addressed below), the indicator density corresponding to a selected acceptable level of risk is calculated using the health effects relationship for each of the indicator/illness combinations.

Figure 1. Illustration of the risk link approach
The rationale for using a Risk Link approach is that it maximizes use of direct measurements of health effects from relevant and available epid emiology studies. Over the recreational season, the pathogens to which swimmers are exposed and the densities of fecal i ndicators are variable. Further, the characteristic pa thogens and FIB densities at di fferent beaches likely differ, depending on the alignment of the beach with fe cal pollution sources and other factors such as climate and rainfall. Given this variability, m easurement of the association of adverse health effects with FIB through epidemio logy studies appears to be the mo st direct and reliable means for relating water quality to health effects.

1.2.1.2 Harmonizing Data from Disparate Studies For health effects curves to be comparable, they should relate to risks for the same illness. Further, the statistical measure used for characterizing water qua lity (e.g., geometric mean of multiple samples on a single day vs. the water quality from a single sample taken in a zone and at a time where swimming occurs) should be taken in to account. An extreme example of curves that would not be comparable is curves that correspond to excess GI illness in swimmers and excess respiratory infection in non-swimmers. A summary of potentia l differences between epidemiology studies that could pr event direct comparis on of health effects curves is presented in Table 1

.

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Table 1. Epidemiology study differences that limit direct comparison of health effects curves

Cause Differences in data construct
Different epidemiology study design (PC vs. RCT)  Exposures in prospective cohort (PC) studies are not prescribed and have a much wider range of durations and ingestions ty pical of recreation ev ents; exposures in randomized control trial (RCT) studies are controlled and have more consistent ingestion volume, but less variability in exposure.

 RCT studies control the location of exposu re far more than in PC studies; thus, the impact of this element of study design is site specific.

 Water quality associated with illness incidence in PC studies is based on an average indicator (usually FIB) density fo r the recreation site and over the entire study day; water quality associated with ill ness incidence in RCT studies is based on the indicator density for a sample taken at the same time and location as swimmer exposure. Proponents of RCT desig ns state that this feature reduces bias, but this claim may not be true depending on the magnitude of short-term variability. Different definition of human health outcome Epidemiology studies differ in their choices in health endpoints and their definitions of those endpoints, including GI illness.

Different settings Indicators may be associated wi th different risks at sites with different

 fecal pollution sources;

 level of treatment of fecal pollution;

 loading characteristics (continuous vs. event); and

 proportion of FIB resuspended in sediments.

Examples of pairs of studies that may have significant setting-related effects include

 studies conducted in inland and coastal waters,

 studies conducted in the United States and Europe, and

 studies conducted at sites in (sub)tropical and temperate climates. Regulation changes and technological advances Regulations and advances in technology have changed wastewat er (including POTW) treatment practices and the resulting indicato r and pathogen loads to recreation sites. The most important of these changes are disinfection of wastewater, development of improved animal waste treatment technologies, and regulations on the land application of biosolids (treated wastewater sludge) and animal wastes.

1.2.2 Water Quality Link Many indicator/method combinations are not, at present, associated with health effects curves or have not been applied directly in epidemiology st udies. In such cases, a less direct approach for linking indicator/me thod combinations can be ap plied. The Water Quality Linkage approach relates paired data from two indicator/method combinations and links the method/indicator combinations to each other through their relations hip, rather than through a direct linkage to health effects curves. This approach is illustrated in Figure 2 , where in the top graph, a criterion density for a standard indicator/method combination is established based on a selected level of tolerable risk and a health effects curve. A quantifiable relationshi p for a n alternative method/indicator combination is established thr ough a model relating the density of the standard indicator/method combination to the density of the alternative method/indicator combination.

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Figure 2. Illustration of the Water Quality Linkage approach

As with the Risk Link approach, the Water Qual ity Link should be employed when paired water quality data are from comparable studies. Furthe r, a Water Quality Linkage at one site may not be applicable to another site—even if the site s have the same primary fecal pollution impact. A critical component in the appli cation of the Water Quality Link is the development of a useful statistical model that relates FIB-method combinations. At present, models relating culturable and qPCR indicator counts are ei ther site-specific (e.g., Byappana halli et al. 2010; Lavender and Kinzelman 2009) or have been developed based on pooling of datasets under the assumption that datasets are similar and may be pooled (Hauglan d et al. 2005; Whitman et al. 2010). In this report, linear, and “broken stick” models for re lating log-transformed culture and qPCR indicator densities are explored. It is po ssible that other statistical models linking these types of analytical methods will be proposed and evaluated in the future.

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### 2 Background
Ordinarily, FIB themselves do not cause illness, but their densities can provide estimates overall levels of fecal contamination. As a result, their densities cannot usually be used to directly estimate health risks through a risk assessment approach. The relationship between indicators and health risks are best and most commonl y established by epidemiology studies (NRC 2004; WERF 2009). Epidemiology studies (1) establis h microbial water qual ity, typically via FIB density measurements, with a sufficient num ber and timing of samples to establish a characteristic indicator density during swimming; and (2) asso ciate the FIB density with the adverse health effects observed among the population swimming compared to a non-swimming control population. This section includes a review and assessment of relevant and available epidemiology studies, as well as a review of di fferences in FIB performance for culture and qPCR indicators.

2.1 Review and Assessment of Relevant Epidemiology Studies and Datasets Since the 1950s, numerous epidemiology studies ha ve been conducted in the United States and abroad, m ost commonly at beaches impacted by sewage/wastewater effluent (e.g., POTWs), to evaluate the association between recreational water quality and adverse health outcomes. In these studies, attempts were made to relate a qua ntitative microbial indicator of water quality to health effects (usually some form of GI illness) using log-transformed data or a geometric mean to characterize exposure to water quality indicators. However, ey e infections; skin irritations; ear, nose, and throat infections; a nd respiratory illness have also been evaluated. Many of these recreational water epidemiology studies are reviewed in one or more of the metaanalyses/systematic reviews of Prüss (1998), Wade et al. (2003), and Zmirou et al. (2003). More recent studies are reviewed in WERF (2009), wh ich notes that all recreational epidemiology studies identified higher rates of at least some se lf-reported health end points (usually GI illness) in relation to water exposure (usu ally swimmers vs. non-swimmers). That is, recreational water contact by its nature is associated with increased risk of adverse health effects—even if the excess risk is not correlated with in creases in fecal indicator organisms.

Both PC and RCT (also called prospective rando mized exposure studies [Fleisher et al. 2010]) epidemiology study designs have been used to evaluate recreational waters. The primary difference between RCT and PC design is that in RCT studies the vol unteers are randomly assigned to swim in a selected area where th e water quality is measured during the timed swimming exposure. A brief overview of each design is provided below.

2.1.1 Prospective Cohort Studies EPA’s current (1986) recr eational water quality are based on the observed occurrence of GI illness associated with s wimming in fresh or marine recreational waters receiving point sources of effluent from POTWs as determined through several PC studies conducted in the 1970s through the early 1980s. Over the past severa l years, the EPA conducted a PC study called National Epidemiological and Environmental Assessment of Recreational (NEEAR) Water Study. This series of epidemiology studies eval uated sites at four POTW-impacted Great Lakes beaches and five marine recreational beach es (four POTW-impacted and one non-POTWimpacted). The results of the freshwater and ma rine studies have been published (e.g., Wade et

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al. 2006, 2008, 2010). Further, there have been relatively few studies of inland (non-Great Lakes) waters or of nonpoint sour ce-impacted recr eational sites.

2.1.2 Randomi zed Control Trials The other major epidemiology study design used fo r evaluating health risks associated with recreational exposures is the RCT. This design has been used extensively in Europe, and more

recen tly in the United States to determine the association between microbial water quality and increased risk of adverse health effects in sw immers vs. non-swimmers in both marine and fresh recreational waters. The WHO Guidelines for Safe Recreational Water Environments (WHO, 2003) apply to both fresh and re creational waters and are la rgely based on RCT studies conducted in the late 1980s and early 1990s at POTW-impacted marine recreational beaches in the United Kingdom (UK) (Fleisher et al. 1996; Kay et al. 1994). In 2006, the European Union adopted a new directive for the management of bath ing water quality that is also based, in large part, on the same UK marine recr eational water studies. It also includes the results of a more recent RCT study of POTW- and nonpoint source-imp acted fresh recreational waters in Germany (Wiedenmann et al. 2006). Epibathe (2009a, 2009b) describes the results of a series of RCT studies conducted at marine a nd fresh recreational waters in Europe in 2006 and 2007. Finally, the results of an RCT study of a non-POTW-impacted marine beach in Miami, Florida have been recently published (Fleisher et al. 2010).

2.1.3 Studies that have Generated Health Effects Relationships A number of epidemiology studies have establis hed a significant relatio nship between indicator organism density and increased GI illness ( Table 2 ; see also WERF, 2009). Highlighted rows in Table 2 indicate relevant and availabl e epidem iological datasets that were obtained and evaluated in this report (see Section 3). It is beyond the scope of this repo rt to list all epidem iology studies and datasets of recreational wa ters that have been conducted.

Among the studies listed in Table 2 , three have established relati onships for inland waters, and all of those studies were of waters that are likely predomin antly impacted by hum an fecal pollution sources. A number of the unique features for the studies that resulted in health effects relations are the following:

 the USEPA (1983, 1984) studies rela ted illness rates to season -averaged culture indicator densities;

 the USEPA (1984) study health effects rela tionship was developed using pooled data collected for both inland and Great Lakes beaches;

 the USEPA (1983, 1984) studies used different definitions of GI illness than the NEEAR studies;

 the study by Marion et al. (2010) was conducte d on a relatively small inland lake, though as shown in Section 3.1, the health effects obs erved in that study were similar to those observed by USEPA (1984).
In contrast to studies of wate rs potentially impacted primarily by POTW effluent, epidemiology studies of marine and freshwat er non-POTW impacted-recreationa l waters tend to yield weak associations between increased dens ities of traditional indicators ( Enterococcus and E. coli ) and health risk. Calderon (1991) found no association of indicator density with incidence of adverse

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health effects at a non-POTW-impacted pond wi th no known hum an fecal pollution effects. Similar results were reported by Colford et al. (2007) for a California coastal beach suspected to be affected primarily by birds, and for a subtr opical coastal marine beach most likely affected by dog and human nonpoint sources of fecal pollution (A bdelzaher et al. 2010; Fleisher et al. 2010; Sinigalliano et al. 2010). Results from a study of stormwater impacts on GI illness rates are difficult to interpret (Haile et al. 1999). It is possible that densities of alternative (nontraditional) indicators might be associated with risk and that relationships may be established as a result of ongoing epidemiology studies.

Table 2. Epidemiology studies that have established relationships between FIB density and excess GI illness due to swimming

Study(s) Indicator and primary detection method Setting and sources Study type

Marion et al. (2010) E. coli culture U.S. impounded freshwater beaches, human and other sources PC

NEEAR Density of Bacteroidales measured by qPCR U.S. mari ne beaches, POTWs PC

NEEAR (Wade et al. 2006, 2008) Density of Enterococcus spp. measured by qPCR US freshwater beaches (Great Lakes), POTWs PC

NEEAR (Wade et al. 2006, 2008) Density of Enterococcus spp. measured by qPCR U.S. marine beaches, POTW PC

Wiedenmann et al. (2006) Enterococcus and E. coli chromogenic substrate German, freshwater beaches with one of more point- (including POTWs) and nonpoint sources RCT

Fleisher et al. (1996) and Kay et al. (1994) Enterococcus culture U.K. marine beaches, POTWs RCT

USEPA (1984) Enterococcus and E. coli culture U.S. Great Lakes and inland freshwater beaches, POTWs PC

USEPA (1983) Enterococcus culture U.S. marine beaches, POTWs PC

2.1.4 Factors that Preclude Direct Co mparison of Epidemiology Studies Although epidemiology studies may be able to id entify a general association between a given fecal pollution source an d indicator organism densities by estimating incidence of disease, a major and ongoing concern is that their results may be limited to describing risk only for beaches similar to those evaluated in the epidemiology studi es (e.g., similar fecal sour ces). The ability to conduct valid comparisons between PC and RCT epidemiology studies depends on several key and potentially interrelated factor s. These factors rela te to several critical differences in the details of RCT and PC studies , and include the following:

 the manner in which microbial water quality is associated with illnesses;

 the specific exposure that swimmers experience;

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 the definition of (GI) illness a nd the duration of follow-up;

 the age and make up (e.g., tourists vs. locals) of the subject pool;

 the source of fecal indicators and p athogens in the recreational water, their distribution, and temporal-spatial variability of those distributions; and

 the methods used to enumerate the indi cators and the corresponding relationships between the indicators and the pathog ens, as measured by those methods.

2.2 Differences in Indicator Performance for I ndicators as Measured by Culture-Based and qPCR-Based Methods Cell counts obtained by qPCR are ge nerally greater, often by orde rs of magnitude, than those provided by MF analyses of the same samp les (e.g., as observed by He and Jiang, 2005). This section begins with a review of select studies illustrating the differences between qPCR and MF methods. Common causes of variations in FIB c ounts via the different methods, as shown in the literature, are listed below by category and subcat egory and are discussed subsequently. Results from studies on paired compar isons of different indicator/m ethod combinations are also highlighted throughout the section.

2.2.1 General Method Performance The performance of selected analytical methods and the fate and transport of cells and genomic material can be influenced by a numbe r of factors, as summ arized in Table 3

.
Table 3. Summary of factors affecting culture-b ased and/or qPCR data for fecal indicator bacteria
Influencing factor Variables Outcome Reference(s) Time of day for sample collection Exposure to sunlight (UV radiation) Diurnal variation in FIB density Haugland et al. (2005)

Variations in method performance  qPCR amplification efficiency

 Limits of detection

 Inter-laboratory

variability

 Choice of target

gene(s)

 Sample preparation

 Concentration

factor Inconsistencies in data generation and interpretation Haugland et al. (2005) Khan et al. (2007) Kinzelman et al. (2010; preliminary report)

Bacterial viability Enumeration of live, dead, and/or VNBC FIB Under-representation ofVNBC FIB using MF; overrepresentation of viable bacteria using qPCR Nocker and Camper (2006) Nocker et al. (2006) Bae and Wuertz (2009) Varma et al. (2009)

With regards to the sampling approach, the time of day at which samples are collected has been shown to influence the relationship between cu lture-based and qPCR data (e.g., Haugland et al. 2005). During EPA’s NEEAR studies conducted at fr eshwater sites in the Great Lakes, water samples were tested for Enterococcus by qPCR and MF. Samples were collected three times per day (8:00 AM, 11:00 AM, and 3:00 PM), at multip le depths (waist, shin, and knee), and along three transects at all four beach es being studied. A model relating enterococci qPCR and culture data can be developed using results from all the sites and times, or using data that are selected

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because they appear to have similar underlying tr ends. All samples collected from one of these beaches are shown in Figure 3 , along with a 45° line showing perfect agreement. Although one may observe a general trend at high FIB density , at densities below ~500 CFU/100 mL, no trend is apparent and there is no obvious choice for th e form of a model relati ng enterococci density derived from qPCR me thods to culturable enterococci densit y. The influence of sample collection time on the relationship between cultur e and qPCR FIB density is explored in Section 3.2.1.

Figure 3. Paired qPCR and culture data for Enterococcus : Huntington Beach

Variations in the data because of the perf ormance of the analytical method have been demonstrated in a number of studies. The factor s affecting the analytical performance include the efficiency of the qPCR method, the influenc e of detection limits, a nd variations in interlaboratory performance. Haugland et al. ( 2005) compared a qPCR-ba sed enumeration method for Enterococcus to EPA Method 1600 (MF enumeration). The qPCR method employed in that NEEAR-related study demonstrated very high amplification efficiency (0.99, or 99% of amplicons doubling with each cycle) for DNA in dilu tion water. A modest level of false positive results from qPCR (19% of 217 negative control samples) was speculated to be the result of aerosolized DNA contamination that occurred in analytical laboratories. DNA recoveries in calibration samples (from seeded filters) from the two beaches sampled in the study were 82% and 51% in beach samples, respectively. Re sults of qPCR and MF enumerations were reasonably well correlated ( R 2 = 0.68) and log-normal distribu tions described the densities of Enterococcus for samples collected at each beach and for both qPCR and MF enumerations. The authors performed linear regression of qPCR results (as cell equiva lents) against MF results (as CFU) for samples collected on multiple days and at multiple locations on two beaches. The resulting relation:

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##    CFU CE 10 10 

### log53.056.1 log  

##  [1]

was proposed to describe the variation in qPCR cell counts with those from MF.

Khan et al. (2007) evaluated qPCR enumeration of E. coli in waters of agriculture-dominated watersheds against MF methods, finding as di d Haugland et al. (2005), that (1) qPCR-based enumerations yielded consistently higher estimates of density than culture methods (attributed to lack of discrimination between DNA from live and dead cells); and (2) that standard curves (in this case, based on both dilution water and autoclav ed agricultural water) ha d high coefficients of variation. Khan and colleagues noted that the det ection limit in agricultural waters was 10 cells, whereas in dilution water it was 1 cell, and that sample preparation ha d a profound impact on the viability of the qPCR method. Overall, qPCR enum erations were found to be consistently higher than those from MF methods—the cell counts fo r all samples analyzed via MF ranged from 1.0 to 2800 CFU/100 mL, whereas the range for qP CR counts was 15 to 9900 cells/100 mL. The authors concluded that qPCR results were less variable than MF re sults; that qPCR could be used effectively in diverse agricu ltural watersheds; and that qPCR is more rapid, producing meaningful results far faster than culture- based indicator methods. Application of qPCR methods and interpretation of qPCR results may require considerati on of the source of indicator organisms as well as the physiological status of FIB at the time of sampling. For example, the selection of target gene play s an important role in qPCR en umeration. The number of target genes per bacteria cell is depe ndent upon physiological state of the cell and, for example, during the bacterial logarithmic-growth phase up to 36 rrn genes/cell (range: 12-36 copies/cell) have been reported for E. coli (Bremer and Dennis 1996).

Inter-laboratory variability was assessed by Kinz elman et al. (2010; prel iminary report) through the analysis of replicate samples in multiple labs to assess the uncertainty and recovery of qPCR. Archived (MF) filters were used to develop replicate samples and qPCR was used to determine Enterococcus calibrator cell equivalents (CCEs) in the replicate samples. Multiple laboratories conducted the study and all reported similar findings. A typical plot of the paired samples is presented in Figure 4 . In all cases there was very little difference between replicate samples at high indicator density and higher sc atter at lower densitie s. The scatter at low densities appears to be Poisson distributed (analysis not shown) and attributable to sampling uncertainty. The relatively tight distribution of points around the 45 ° line indicates a consis tent and relatively high
recovery.

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Figure 4. Correlation of qPCR Enterococcus densities from replicate samples (Source: Kinzelman et al. 2010)
Comparing some of the key papers used to review general method performance, it is important that the choice of target gene (s) and reaction conditions for the qPCR assays be accounted for when comparing research approaches. As shown in Table 4 , there are a variety of bacteria targeted in these key papers, some targeting the genus as a whole (e.g., Ente rococcus spp.), others targeting a particular species (e.g., E. coli ), and others targeting a particular strain of a species (e.g., E. coli O157:H7).

Accounting for bacterial viability is an importa nt consideration when comparing culture-based methods with qPCR assays. Because of its sensitivity, qPCR will amplify DNA regardless of whether or not it is contained within an intact cell. However, successf ully culturing bacteria requires the organism to be intact. There is an intermediate phase termed viable but nonculturable (VBNC), where ba cteria appear to not be culturable but retain the ability to reproduce under appropriate conditions. By their nature, cu lture-based methods will discriminate against free DNA and VNBC bacteria, whereas qPCR methods will quantify genetic material from cells in all states of existence. In the case of detecti on of DNA from dead cells, this ability is a significant and widely recognized shortcoming of qPCR methods a nd a potential cause of false positives (NRC 2004).

Several researchers have expl ored PCR techniques that en able discrimination between DNA from live and dead cells. For example, Nocker and Camper (2006) used a chemical reaction of DNA from dead cells with ethidi um monoazide (EMA) to preven t reaction of DNA from dead cells with PCR reagents. Thus, prior to qPCR de termination, EMA was added to sample water. EMA, which bonds strongly with DNA and is inactivat ed in water, can ente r only bacterial cells with compromised cell walls. Following this st ep, DNA was extracted from the live cells using conventional techniques and qPCR was performed for determination of E. coli O157:H7 and

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Table 4. Summary of the target organisms and primer sets used for a selection of qPCR assays

Reference Target organism Target gene Primers

ECST748F Haugland et al. (2005) Enterococcus 23R rRNA ENC854R

IEC-UP Khan et al. (2007) E. coli Internal transcribed spacer (ITS) region between the 16S–23S rRNA IEC-DN

stx1-forward E. coli O157:H7 Shiga toxin 1 stx1-reverse

invA2-F Nocker and Camper (2006) Salmonella enterica invA gene invA2-R

stx1-forward Nocker et al. (2006) E. coli O157:H7 Shiga toxin 1 stx1-reverse

BacUni-520f

BacUni-690r1

BacUni-690r2
BacHum-160f Bae and Wuertz (2009) Bacteroidales 16S rRNA

BacHum-241r
ECST748F Enterococcus spp. 16S RNA ENC854R Varma et al. (2009)

Bacteroidales 16S rRNA In Siefring et al. (2008)

Salmonella . The authors suggested that use of EM A with DNA-based methods, if refined, could be a viable alternativ e to the use of more complicated RNA methods (RNA degrades rapidly after cell death) in developing cel l density estimates that exclude dead cells. In a different study, Nocker et al. (2006) suggested th at propidium monoazide (PMA) may be a better reagent for use in preventing DNA from dead cells from being amplified in the PCR reaction. In that study, EMA was found to penetrate live ce lls of some microbiological sp ecies, preventing amplification of DNA from the penetrated cells and reducing the accuracy of qP CR estimates of cell density. In contrast, PMA was observed to be selective only for dead cells for the organisms tested in that study.

Similarly, Bae and Wuertz (2009) used PMA to i nhibit amplification of DNA from dead cells. The use of PMA (rather than EMA) was suggest ed because, and as noted by Nocker et al. (2006), EMA is believed to cause degrada tion of some DNA from viable cells for E. coli O157:H7, Campylobacter jejuni

, Listeria monocytogenes , and perhaps other pathogenic and indicator bacteria. Bae a nd Wuertz (2009) determined PMA reaction conditions (PMA concentration and light exposure time) that opt imized removal of target DNA (host-specific Bacteroidales genetic markers) from dead cells in wast ewater plant influent and effluent. For samples from the wastewater plant effluent, ge ne copies from qPCR with PMA were only 30%

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of those from qPCR without PMA. The diffe rence between qPCR with and without PMA was greater than two orders of magnitude fo r sam ples of wastewater plant effluent.

Varma et al. (2009) investigated the differe nce between PMA-qPCR and qPCR without PMA for Enterococcus and Bacteroidales in wastewater. Objectives of the study included assessment of water matrix effects on the efficiency of the PMA reaction and explorat ion of use of qPCR for analysis of wastewater treatment plant operation. Some of the conclusions from that study were the following:

 high levels of biomass or suspended solids in water samples appeared to interfere with the ability of the PMA-qPCR method to specifically detect live cells, and

 standard POTW chlorine disinfection practices resulted in substantia lly greater reductions in fecal indicator bacteria CFU densities th an those observed for PMA-qPCR detectable target sequences. When reviewing general method performance, the in fluence of variability in the data generated from different methods and their influence on hea lth effect relations has also been described. Similar to Haugland et al. (2005), albeit for groundw ater samples, Lleo et al. (2005) found that qPCR estimates of E. coli and Enterococcus faecalis density were significan tly and consistently higher than those obtained using MF methods. The authors ascribed the difference to the ability of qPCR to detect VBNC cells and suggested that the qPCR method provides estimates of FIB density that are more protective of human hea lth—particularly given th e potential for VBNC cells to resuscitate unde r appropriate conditions.

In the course of EPA’s NEEAR studies conducte d at Great Lakes beaches, Wade et al. (2008) observed that, in contrast to counts from cultu re methods, qPCR counts of enterococci were relatively constant during the day. In contrast, counts of enterococci from culture methods show a distinctive diurnal variation, w ith densities for early morning samples being as much as two orders of magnitude higher than for late afternoon samples. In addition to observing less hourly variation in indicator density measurements us ing qPCR compared to culturable methods, Wade and colleagues also noted that increasing qPCR de nsities (expressed as ce ll equivalents) were associated with excess risk of GI illness in swimmers vs. non-swimmers.

2.2.2 Factors Impacting Indica tor/Method Performance

2.2.2.1 Choice of T arget Organism Although this report focuses on the methods used to detect and quantify Enterococcus in relation to health effects, it is important to consider how different FIB compar e to one another when detected in recreational waters by various methods. Differences in the comparison of qPCR and culture signal exist for different microorganisms, as demonstrated by Rothrock et al. (2009). In that study, triplicate analyses were performed on samples inoculated with dilutions of laboratory cultures of E. coli and C. jejuni . Plots of qPCR counts vs. MF counts for the two organisms are presented in Figure 5 , where the dashed line is a 45° line (exact concordance between methods). In general, the qPCR and culture counts are 1:1, with better agreement at the higher counts (lower dilutions) than at lo wer counts. The curve for C. jejuni has a slope of 1:1, but is shifted above the 45° line. This shift is potentially due to low recoveries typical for this bacterium via culture methods. As suggested in Figure 5 , the impact of difference in recovery between the methods is manifested as a shift of the trend line above or below the hypot hetical line of perfect

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agreement. Differences in recovery among MF counts may results from damage or shock to bacteria during sample preparation, clumping of cells, or difficulty in maintaining growth conditions for some organisms. Differences in recovery among qPCR counts may arise from the presence of substances inhibiting the PCR reaction and differences in specificity, efficiency, and sensitivity for PCR assays targ eting different microorganisms.

Figure 5. Trend lines for dilutions of laboratory cultures of E. coli and C. jejuni enumerated using qPCR and culture methods (SOURCE: Rothrock et al. 2009)

Simultaneous detection of Enterococcus and Bacteroidales in recreational marine water was undertaken by Elmir et al. (2009) using MF and qPCR detection methods. Two pools, one large and one small, were filled with local offshore marine water and recreational users (adults and toddlers) were required to be in the water for pre-determined periods. Using three analytical methods, Enterococcus and Bacteroidales were enumerated from the water samples before and after users were in contact with the water. Mi crobial concentrations in the source water were generally low (MF = 5 [std. dev. ± 7] CFU, qP CR = 29 [± 49] genomic equivalent units). Bacteroidales was detected only using qPCR human markers (UCD a nd HF8), yielding genomic equivalent units (GEU) of 45 (± 18 3) a nd 3 (± 10) GEU/100 mL, respectively. The concentrations of Enterococcus and Bacteroidales in both pools were variable following recreational use. Enterococcus densities calculated us ing MF ranged from 1.8  10 4 to 2.0  10 6

, whereas values calculated using qPCR ranged from 3.8  10 5 to 5.5  10 6

. Bacteroidales values ranged from below the limit of detection (1.4  10 3 GEU) to 1.3  10 6 . The authors concluded that the bathers appeared to release significant am ounts of FIB via shedding from their bodies and into the water column. For this study, an a dded significance of the st udy is that culture and qPCR counts for enterococci from bathers were generally in the same range. By comparison, effluent from POTWs employing chlorination ma y have culture and qPCR counts that differ by many orders of magnitude (e.g., He and Jiang 2005, Bolster et al. 2005) and qPCR and culture

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counts in POTW-imp acted coastal waters may differ by smaller margins than observed in chlorinate POTW effluent (Byappanahalli et al. 2010).

A suite of FIB from brackish water was analy zed by Ortega et al. ( 2009). Eighteen sampling events were conducted over five sampling trips in the St. Lucie River Estuary, South Florida. Enterococci were enumerated using MF, whereas E. coli were assayed using the most probable number (MPN) method. The values for enterococci and E. coli were similar (10 1 to 10 2 CFU or MPN/100 mL) and an R 2 value of 0.53 was reported when corr elating the two fecal indicators.

Finally, Agudel et al. (2010) used MF and multiplex real time PCR to compare the concentrations of Bacteroides spp., E. coli , and enterococci in a variety of environmental matrices in Barcelona, Spain. Se venty-four samples from rivers , wells, urban groundwater, and wastewater were analyzed for FIB counts using standardized MF methods for E. coli , total coliforms and enterococci, and qPCR assays for total Bacteroides spp. and enterococci. Based on the authors’ statistical analyses, Figure 6 summarizes the enumeration of the fecal indicators. The authors concluded that bact erial quantification data were more homogeneous when using PCR than when using conventional culture microbiology.

Figure 6. Comparison of bacterial data from culture-based and multiplex real-time PCR methods, and statistical sample description (SOURCE: Agudel et al. 2010)

2.2.2.2 Water Matrix Inhibition of the qPCR amplification reaction is an important factor to consider when comparing performance of qPCR with the Enterococcus MF technique. Duprey et al. (1997) hypothesized possible seawater inhibition of the qPCR assay through secretion of substances by phytoplankton and zooplankton, while Haugland et al. (2005) also noted freque nt inhibition of qPCR in undiluted Great Lakes water. However, such in hibition is reduced or eliminated by performing serial dilutions of the sample (Ahmed et al. 2009).

Characteristics of the water matrix also impact quantification assays. Sint on et al. (2002) showed that salinity increased bacterial cells rate of decay, which would widen the already demonstrated

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gap between culture-based and qP CR results in marine waters. Duprey et al. (1997) noted in their seawater study that DNA pe rsistence was lower during the summer for free and dead cell DNA, and could extend up to 55 days in the winter for dead cell DNA. The persistences of naked DNA and a sm all inoculum of dead cell DNA we re the lowest at two days. Walters et al. (2009) reported that in sewage microc osms exposed to sunlight, culturable Enterococcus concentration fell below detection limit within 5 days while qPCR signal persisted for at least 28 days. These observations are consistent with the general observati on of higher qPCR-based bacterial densities (enumerated in cell equivale nts) than culture-base d counterparts in both marine and freshwaters (Byappanahalli et al. 2010; Bower et al. 2005; Haugland et al. 2005; Lavender and Kinzelman 2009; Morisson et al. 2008; Whitman et al. 2010)—except for Elmir et al. (2009) who found comparable densities from bot h techniques in a marine setting and Noble et al. (2010) who found underestimation of bacter ial density via qPCR fo r marine settings.

The two studies by Noble et al. (2010) were in terpreted by the authors as demonstrating an underestimation of Enterococcus by qPCR with regards to cult ure-based methods in marine water. However, on this point there are several concerns in interpretation that need to be considered. The primer set used was not specifically described in the study. In the discussion it was indicated that the primers used were designed for high specificity for E. faecalis and E. faecium (which would detect a narrower population of Enterococcus ) in this study th an in studies that used the EPA method. The use of a single sp ecies calibrator standard could also result in disagreement between the techni ques, as different species of enterococci may have different numbers of the target gene. Finally, the author s pointed that the relati ve quantification method with a calibrator and a salmon testes DNA contro l used by other teams expressing results in CCEs could explain the observed discrepancy between underestimation and overestimation. They discarded the possibility of error or iginating from PCR ch emistry by showing no significant difference between the Taqman and Scorpion assays for Enterococcus . However, this team confirmed the previously published findi ngs of significant correlation between qPCR and culture-based assays for both Enterococcus and E. coli , with a stronger agreement for E.coli than for Enterococcus in terms of beach management decisions (88% vs. 94%). With regards to technique implementation, the authors showed that microbiologists w ith little qPCR training produced similar results as their experien ced counterparts, while finding DNA extraction complex and time-consuming. This surprising finding was attributed to a simplified PCR preparation protocol.

The forgoing findings illustrate the complexities of applying qPCR methods , particularly since this technique can amplify all DNA present in a sa mple, regardless of its viability state. This implies that ambient background DNA (i.e., na ked and dead cell DNA; see Lavender and Kinzelman 2009) needs to be assessed when em ploying qPCR methods, regardless of the water matrix. Lavender and Kinzelman (2009) successfully demonstrated the use of a site-specific corrective factor to correlate culture and qPCR counts and high lighted its importance for the prediction of beach m icrobial water quality status.

2.2.2.3 Primary Fecal Pollution Source on Indicator/Method Performance Fecal pollution sources can include point- and nonpoint sources. Common point sources are POTW effluents and stormwater outfalls, whil e nonpoint sources incl ude wildlife, human shedding and soils and sediments. Although most U.S. POTW effluents are disinfected, some are not, and in some facilities seasonal disinf ection is employed. Disinfection usually yields

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minima l to null culture-based results for FIB, while qPCR results can still remain high, accounting for the VBNC and background fractions of enterococci and other FIB. However, POTW monitoring results typically represent human-derived en terococci, with direct relevance to public health. Comparatively, in dicators in stormwater outfalls can originate from a variety of sources, in which case qPCR primer s are chosen based on an understanding of the origin of the fecal pollution. Such human/nonhuman speciation would be especially necessary in rural settings with agricultura lly developed watersheds but could be recommended at all recreational sites due to potential contribution of native Enterococcus to beach advisories (Yamahara et al. 2009). Simultaneous probing with Enterococcus and Bacteriodales primer sets can provide such speciation (Converse et al. 2009; El mir et al. 2009; Shanks et al. 2009). The human shedding of enterococci from bathers was considered minimal in marine waters during in itial load or bathing cycle (about 4%) and proportional to the body’s surface area (Elmir et al. 2009). These researchers noted that additional bathing cycles could increase the predominance of human shedding of FIB.

2.2.2.4 Treatment and Environmental Factors Lavender and Kinzelman (2009) and Varma et al. ( 2009) investigated the e ffects of wastewater treatment on culture-based and qPCR results for en terococci. Their similar findings showed that culture-based numbers are strongly reduced (2 to 5 orders of magnitude), especially by secondary treatment and disinfection, while qP CR numbers experience sm aller reductions or remain unchanged. As noted previously, this is probably due to the strong impact of disinfection on culturable cells. Environmenta l factors (e.g., rain events, UV light) can also significantly affect qPCR and MF results differently. Lavender and Kinzelman showed that culture-based Enterococcus densities at stormwater outfalls were si gnificantly different depending on rain events and location, while no significant differen ces were observed for qPCR. These researchers also showed that ambient background DNA appear ed insignificant during stormwater events. Varma et al. (2009) also found that qPCR results were virtually unaffected between normal and stormflow operations. The contribution of FIB from undeveloped watersheds to reference beaches studied by Griffith et al. (2010) implies that native sands are at l east partially responsible for some FIB exceedances of water quality thre sholds. Furthermore, Walters et al. (2009) showed that sunlight seems to inhibit the cultiv ability of cells and accele rate their degradation, reducing persistence of enterococci by half for cu lture-based results. Neither dead cell nor naked DNA (ambient background DNA) appeared to be a ffected by sunlight. Given the nature of qPCR and the relative small fraction of viable a nd culturable FIB, qPCR are not greatly affected by insolation while culture-based methods e xperience strong reduction by photoinactivation during sunlight exposure.

2.2.3 Uncertainty of Indicator/Method Combinations at Low Density As noted previously, qPCR measurements of FI B exhibit higher variability at low densities (Haugland et al. 2005). This trend is probabl y explained by the presen ce of am bient background DNA, the abundance of which is site-specific. Lavender and Kinzelman (2009) proposed to reconcile qPCR counts with culture counts by introduc ing a site-specific corre ctive factor in the calculation of qPCR-derived de nsities. Bae and Wuertz (200 9) proposed a more universal solution by modifying the traditional qPCR appr oach with the introduction of PMA, allowing qPCR to distinguish viable from non-viable cells. This approach was found to be promising and was used successfully by Varma et al. (2009).

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### 3 Analyses
This section describes and demonstrates analys es comparing the illne ss response to exposure relationship for multiple indicator/method comb inations used in epidemiology studies and evaluating multiple indicator/method combinations to develop quantifiable relationships. The approach taken to meet the repo rt’s objectives is thr ough the use of available data to provide a proof of concept for the applic ation of various statistical t echniques to develop equivalence between indicator densities for different indicat or-method combinations. These initial analyses illustrate the most direct approaches for linking indicator-method combinations and the steps that can be undertaken to effect meaningful comp arison of results across disparate datasets. Two approaches for developing quantifiable relationships for “linking” indicator/method combinations, the Risk Link approach and Water Quality Link approach, are demonstrated in this section.

The Risk Link approach is evalua ted first using three different st atistical linkage analyses. The first is a straight-f orward linkage of Enterococcus density as measured by qPCR to Bacteroidales density as measured by qPCR. The second demons tration shows analyses that can be conducted prior to linkage of indicators using curves from different studies. Th at demonstration links curves (Marion et al. 2010 and USEPA 1984) ba sed on the same indicator-method combination ( E. coli by MF), but from epidemiologi cal studies conducted in differe nt settings, with different samples sizes, and at different times. The third demonstration illustrates the linkage of Enterococcus enumerated by MF to Enterococcus enumerated by qPCR, both from studies conduted in POTW-impacted marine waters. That demonstration also illustrates the translation of GI definitions between studies, prior to c onducting the Risk Link analysis. Comparisons beyond the three Risk Links demonstrated here are possible, and additional linkages may be conducted as alternative stat istical analyses or datasets become available.

Secondly, the Water Quality Linkage approach is evaluated using two statistical models, simple linear regression and broken stick (segmented) re gression. Using paired water quality data from the NEEAR freshwater dataset, the culture-qPCR relationships appear to vary significantly among beaches. The linkage re sults in transl ated culture Enterococcus criteria values generally significantly higher than the existing culture crite ria for those specific beaches. Other sitespecific models, or models that incorporate data beyond paired indica tor-method data, may be developed to overcome the limitations of the evaluated regression models in the future.

3.1 Risk Link Several Risk Link demonstrations are provided in this section. The simplest and perhaps strongest ap plication of the Risk Link approach is the linkage of de nsities of indicators for multiple indicator-method combinations via heal th effects curves generated in the same epidemiology study. In this comparison, illness definitions, sampling strategies, statistical interpretations and fecal pollution sources are the same, and no conversions or intermediate data analyses are necessary. However, Risk Linkages can also be made using health effects curves from different epidemiology studies. In that ev ent, analyses must be conducted prior to the linkage of indicator densities, re sulting in linkages that are less direct than those established using multiple health effects curves from a single epidemiology study.

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The Risk Linkages demonstrated in this section i llustrate both the direct application of the Risk Link using health effects relations hips from the same study, and th e less direct Risk Link, that requires preliminarily conversions or analyses. Th e first dem onstration illus trates the linkage of Bacteroidales as measured by qPCR and Enterococcus as measured by qPCR. Health effects curves for both of those indicator-method comb inations were develope d in NEEAR study marine beach epidemiology studies in which both indicato r densities were measured concurrently and associated with the same illness levels. The second demonstration illustrates techniques for assessing the differences in hea lth effects associations with indicator densities for a common indicator-method combination ( E. coli by MF) for epidemiology studies with similar designs, but conducted at different times and in different settings. The two studies from which health effects curves were drawn are a recent study of illness-in dicator density associations at a small inland lake (Marion et al. 2010), and th e studies conducted as a prelude to establishment of the current RWQC (USEPA 1984). The third demonstration illu strates the harmonization of health effects curves, from two epidemiology studies using differe nt illness definitions, prior to the application of the Risk Link approach. In that study, qPCR-based Enterococcus health effects curves from the NEEAR study marine beaches are linke d to health effects curves based on Enterococcus as measured by MF in the USEPA (1983) epidemiology studies. Implicit in the third demonstration is an assumption that the conditi ons at the beaches studied in th e two epidemiology studies were sufficiently similar to allow meaningful co mparison of their health effects curves.

3.1.1 Review of NEEAR Study and Other Health Effects Relationships for qPCR and Culturable FIB The NEEAR studies provide the best current as sociation between water quality measures and illness rates (i.e., health eff ects curves), as they are the most recent, largest, and most carefully designed epidemiology studies conducted by EPA. In this section, health effects relations from the NEEAR studies are presented. Those relati onships may be used in development of new qPCR-based criteria. Note that the results presen ted in this report are only a small portion of the findings of the NEEAR studies. Mo re detailed results and full desc riptions of the studies can be found in Wade et al. (2006, 2008, 2010).

To date, EPA’s NEEAR recreational water epid emiology studies have employed both qPCR and culture-based methods in determining FIB densit y. Further, the studies have yielded health effects relationships between Enterococcus density as measured by qPCR, Bacteroidales density as measured by qPCR, and health effects (GI il lness) for both marine and Great Lakes POTWimpacted sites. The development of associ ations of illnesses with those indicator-method combinations is particularly significant because qPCR is a rapid method. Health effects curves generated from observed indicator densities in the NEEAR study marine beaches and for the USEPA (1983) study are presented in Table 5 . Candidate NEEAR study health effects relations for marine beaches were provided in a pe rsonal comm unication from T. Wade (2010).

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Table 5. Summary of health effects relationshi ps from USEPA NEEAR stud ies and marine studies conducted to support development of the current RWQC
Setting Health effects relationship Indicator and method Reference

Great Lakes 

##   00918.0 log0214.01000 10   ENT C N Enterococcus measured by qPCR (CE/100 mL) Wade et al. (2006)

Marine beaches 

##   101.0 log0517.01000 10   ENT C N Enterococcus measured by qPCR (CCE/100 mL) T. Wade, personal communication, unpublished data, 2010

Marine beaches 

##   0144.0 log00406.01000 10   ENT C N Enterococcus measured by MF (CFU/100 mL) T. Wade, personal communication, unpublished data, 2010

Marine beaches 

##   0962.0 log0368.01000 10   BAC C N Bacteroidales measured by qPCR (CCE/100 mL) T. Wade, personal communication, unpublished data, 2010

Marine beaches 

##   01485.0 log0219.01000 10   ENT C N Enterococcus measured by MF (CFU/100 mL) USEPA (1983)

In the EPA study of marine beaches impacted by PO TW effluent, Wade et al. (T. Wade, personal communication, unpublished data, 2010) found that sw immers experienced more GI illness than non-swimmers on days when Enterococcus density (as measured by MF) exceeded the current geometric mean guidelines. However, the association among swimmers was not statistically significant. The authors contrast this lack of associat ion with those for Enterococcus and Bacteroidales enumerated by qPCR, both of which were associated with a consistent trend of excess GI illness among swimmers. No te that a weak association of Enterococcus density as measured by MF with health effects (GI illne ss) was also observed in EPA’s Great Lakes epidemiology studies (Wade et al. 2006, 2008).

For a rate of swimming-associated GI illness of 35 per 1000 swimmers (risk levels similar to those underlying the USEPA1986 criteria) the Great Lakes health effects curve indicates an indicator density of 116 qPCR Enterococcus CCE/100 mL. For a swimming-associated GI illness rate of 35 per 1000 swimmers, the marine health effects curve based on qPCR enumeration of enterococci yields a co rresponding indicator density of 427 qPCR Enterococcus CCE/100 mL. The culture-based N EEAR study health effects curv e for marine waters produces a high FIB density corresponding to a swimmi ng-associated illness rate of 35 per 1000 swimmers.

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3.1.2 Direct Application of the Risk Link Approach: Linking Bacteroidales Measured by qPCR to Enterococcus Measured by qPCR Application of the Risk Link appr oach to determin e densities of Enterococcus measured by qPCR and Bacteroidales measured by qPCR that are consistent with com parable risks is straightforward. The Enterococcus and Bacteroidales health effects curves presented in Table 5 were generated using water quality and attributable illness rate data collected concurrently at the same beaches and from the same cohorts. Thus the health effects cu rves ( Table 5 ) may be used without prior analyses to establish risk-linked indi cator densities. Risk-linked Enterococcus and Bacteroidales densities for marine waters at three risk levels of interest are presented in Table 6

Table 6. Risk-linked Bacteroidales and Enterococcus densities

Tolerable attributable illness level (as NGI † per 1000 swimmers) Enterococcus density, qPCR enumeration (CCE/100 mL) Bacteroidales density, qPCR enumeration (CCE/100 mL)

35 427 3680

43 610 6060

82 3460 69,600

† NEEAR study definition of GI illness

3.1.3 Linking E. coli Densities as Measured by MF via Health Effects Curves from Different Epidemiology Studies In the second application of the Risk Link appro ach, health effects curves based on densities of E. coli measured by MF are used to demonstrate the viability of comparing results from studies conducted at different times and in different settings. The analyses performed in this demonstration are intended to illustrate tec hniques for assessing whether epidemiology study findings differ between studies a nd provide insights regarding th e combinability of data from multiple studies. The demonstrat ion relates to the P15 objectives in that it illustrates methods for comparing the GI illness response to exposure re lationships, though in this case the relationships are for the same indicator-method combination. Note that the analyses pres ented are not the only ones that could be performed. Other meta-analyses that have explored the combination of health effects data from disparate epidemiology studies are summarized in a text box concluding this section.

A recent epidemiology study conducted by Marion et al . (2010) resulted in a health effects curve relating E. coli density via MF to both HCGI and GI illness. Limitations of this study’s use in developing general, quantifiabl e relations are that the study was relatively small (minimum number of swimmers on a study day was 11 a nd the maximum was 88); the study was conducted for an inland waterbody (impounded reservoir); the dominant fecal pollution source was not clearly POTW discharge; and E. coli by MF was the sole indicator/ method investigated. Despite these limitations, the analyses seek to evaluate wh ether the health effects curves generated in the Marion study are comparable to those genera ted in the epidemiology studies conducted by USEPA (1984) on freshwaters in support of the 1986 cr iteria. Although not conclusive, the comparison indicates that for the beaches st udied by USEPA (1984) a nd Marion et al. (2010), FIB densities are associat ed with similar risks.

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The Marion et al. (2010) and USEPA (1984) studies differ in the following respects:

 the decade in which the study was conducted,

 the manner in which water quality and health effects are characterized, and

 the settings at which data were collected.
Plots of health effects vs. indicator dens ities for the two studies are presented in Figure 7 . Data from the USEPA studies are blue diamonds and the regression line through the data is blue. The data from Marion and colleagues ar e presented in two different ways . In that study, illness rates are provided for ranges of FIB densities. Those da ta are plotted as purple crosses. Data for each study day were provided by the author in a personal corresponde nce (Marion, personal communication, 2010) and those data and associated regression line are show n as red circles and a red line. Individual study day data from the Marion et al. (2010) study show wide scatter, which is not surprising given the overall low rates of attributable illness and the relatively small number of swimmers on each study day. Overall, th e ranges of attributable illness rates for the two sets of studies are similar (based on the binn ed data from the Marion study) and the slopes of the health effects curves also appear similar.

Figure 7. Health Effects Data and Trend Lines from the USEPA and Marion Studies

To test whether the data from the two studies indi cated the same or different trends, an analysis of covariance (ANCOVA) for the individual stu dy day and USEPA datasets was performed. The ANCOVA tests the hypothesis that the slope and intercept estimates for regression lines are the

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same, accounting for covariation of the slopes and intercepts. The results of the ANCOVA are presented in Table 7 . In ANCOVA analyses, the p-value is the probability that the par ameter is the same for both models and a p-value above a va lue of 0.05 indicates that the null hypothesis is accepted. For the two epidemiological datasets th e p-values for both the slopes and intercept are significantly above 0.05. Thus, it is concluded that the health e ffects curves are not different from each other and that the data may be pooled.

Table 7. Results from ANCOVA comparing data from the USEPA (1984) and Marion et a l. (2010) epidemiology studies

Parameter P-value for co mparison between models Slope 0.85 Intercept 0.10

The finding that the health effects models fo r the USEPA and Marion epidemiology studies are not significantly different has two ramifications . First, the study by Ma rion et al. (2010) was conducted at an inland lake (impounded reservoir) with mixed fecal pollution sources, one of which was discharge from small POTWs into tri butaries to the lake. The studies conducted by USEPA (1984) took place on Lake Erie and at a freshwater inland lake (Keystone Lake, Oklahoma). While not conclusive, the ability to pool health effects data from these sites indicates that health effects at POTW-impactedinland waters are similar to those for POTWimpacted coastal waters. Second, the ANCOVA s upports health effects relations for the two studies being similar despite the long time pe riod between the studies . This finding cannot, however, be used to assert that water quality at all sites has not changed in the intervening years between the studies. However, the similarity in the health effects curve for the Marion et al. (2010) and USEPA (1984) studies may indicate that the earlier relationship is indicative of indicator-health effects relations hips that may be observed for so me types of current recreational sites.

Again, it is noted that the comparison of the US EPA (1984) and Marion et al. (2010) studies is conducted to demonstrate techniques for establishing Risk Links. In this case, the analyses are conducted to establish that indicator densities are associated with similar illness levels in different studies. Analyses such as these ma y be required to establis h linkages in the event straightforward linkages with data from a si ngle study or set of studi es are not possible.

Finally, we note that meta-analyses of epidemiol ogy studies have evaluated either implicitly or explicitly the similarities and differences in observed health effects and in health effects associations with indicator dens ities for different beaches and times. The objectives of those meta-analyses are in some respects similar to th e analyses conducted to es tablish that the USEPA (1983) health effects curves are generally applic able today. Brief descriptions of other metaanalyses that have been performed are provided in the text box below.

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Prüss (1998) conducted a systematic review following discussions between the WHO Regional Office for Europe and WHO Headquarters to initiate devel opment of new guidelines for recreational use of the water environment. The comprehensive review of 22 published studies on sewage pollution of recreational water and health outcomes concluded that there was a causal association between GI illness symptoms and increased bacterial indicator d ensity (i.e., enterococci for marine, enterococci and E. coli for fresh) in recreational waters. A meta-analysis of 18 published studies (Zmir ou et al. 2003) provided a scientific basis for establishing new standards for the microbial quality of marine and fresh recreational waters to replace the 30 year-old European Union bathing water qua lity guidelines. The researchers provided four major results: (1) increased concentrations of fecal coliforms or E. coli and enterococci in both fresh and marine recreational waters are associated with increased risks of acute GI illness, with enterococci eliciting four to eight times gr eater excess risks than fecal coliforms or E. coli at the same concentrations; (2) GI illness risks associated with enterococci occur at lowe r concentration in marine versus fresh recreational waters; (3) increased conc entrations of total coliforms have little or no association with GI illness risk; an d (4) no evidence exists of a threshold of indicator density below which there would be no GI illness risk to bathers. Wade et al. (2003) conducted a systematic review and meta-analysis of 27 published studies to evaluate the evidence linking specific microbial indi cators of recreational water quality to specific health outcomes under non-outbreak (endemic) conditions. Secondary goals included identifying and describing critical study design issues and evaluati ng the potential for health effects at or below the current regulatory criteria (USEPA, 1986). The researchers conc luded that (1) enterococci and to a lesser extent E. coli are adequate indicators (predictors) of GI illness in marine recreational waters, but fecal coliforms are not; (2) the risk of GI illness is considerably lower in studies with enterococci and E. coli densities below those established by EPA (1986), thus providing support for their regulatory use; (3) E. coli is a more reliable and consistent predi ctor of GI illness than enterococci or other indicators in fresh recreational waters; an d (4) based on heterogeneity analyses, studies that used a non-swimming control group and that focu sed on children found elevated GI illness risks.

3.1.4 Application of the Risk Link Approach for Health Effects Curves Based on Different Illness Definitions Two steps are required to demons trate the Risk Li nkage between Enterococcus measured by MF in studies of POTW-impacted marine sites conducted by USEPA (1983) and Enterococcus measured by qPCR in NEEAR marine water studies:

 illness rates from the USEPA (1983) studies are translated to equivalent rates reflecting different illness definition used in NEEAR studies, and

 demonstration that the associa tion of indicator densities and health effects observed in the two studies are similar. Direct comparison of health risks associated with water quality publishe d in 1986 and the health, water quality relationships developed by Wade (2009 to 2010) cannot be done because the case definitions underlying the relationships are not the same. Wymer (L. Wymer, personal communication, 2010) suggested a tr anslation algorithm for converting the health criteria developed in 1986, based on a HCGI case definition, to an equivalent health criteria that uses NEEAR study data based on a NGI case definition. The translation would be independent of water quality data and uses health data from the 1972 to 1981 and 2002 to 2009 EPA studies.

Conversion of the 1986 criteria to potential new criteria requir es only the illness rates (using their respective case definitions ) from the non-swimming populati ons from both study periods

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and the 1986 criteria value. The al gorithm uses the HCGI illness rate ( IR ) in non-swimmers and the acceptable HCGI illn ess rate from the cr iterion, and calculates a relative risk ( RR ) value as:

1986 1986 1986 1986 swimmer-Non swimmer-Non acceptable Criteria

IR IR IR RR 

 [2]

An equivalent criterion value (ECV) can be calc ulated by multiplying the relative risk from the 1986 data by the 2002-2009 non-swimmer illness ra te (NGI) and subtra cting the 2002-2009 nonswimmer illness rate (NGI) from that value. 

##  
2009 2009 1986 swimmer-Non swimmer-Non IR IR RRECV

  [3]

Non-swimmer illness rates obser ved in the epidemiology studies conducted to support the1986 criteria and in the NEEAR studies we re 13.6/1000 swimmers and 59/1000 swimmers, respectively (USEPA 1986). Based on the transl ation and observed non-swimmer illness rates, equivalent criteria values for three ri sk levels of interest are provided in Table 8

.
Table 8. Equivalent Criteria in Terms of HCGI and NGI Definitions of Illness
HCGI criterion (per 1000 swimmers) ECV (NGI equivalent) (per 1000 swimmers)

8 35

10 43

19 82

At present, EPA has not selected new RW QC based on qPCR or MF measurement of Enterococcus . Therefore, this section concludes with il lustration of the risk linkage approach for a range of risk levels and appro aches for selecting criteria. Reas onable levels of risk for EPA to evaluate when selecting new cr iteria are the tolerable illne ss rates from the 1986 RWQC for recreation in freshwaters (8 HCGI attributable illnesses per 1000 swimmers), the tolerable illness rate for recreation in marine wa ters (19 HCGI attributable illne sses per 1000 swimmers), or some intermediate risk level (e.g., 10 HCGI attributable illnesses per 1000 swimmers).

The FIB densities used in the USEPA (1983) he alth effects relationship are geometric mean values for indicator densities taken over enti re recreation seasons. Recognizing that water quality varies over the recreation season, current criteria for single sample maximums for designated beaches are based on the 75 th percentile of a log-normal distribution whose geometric mean corresponds to the indicator density from the USEPA health effect s relationship and whose log-transformed standard deviation is 0.7. New or revised criteria for Enterococcus using MF methods can be based on the 75 th percentile value or other va lues selected based on improved characterization of the te mporal variability of Enterococcus densities in typical marine waters.

Considering all of these and other factors, ex ample criteria values for qPCR and culture-based datacorresponding to equivalent risk levels are presented in Table 9 . The qPCR example criteria were calculated using the NEEAR study health effects relationship for all marine POTW28U.S. Environmental Protection Agency

impacted beaches combined. Note that all alterna tives to th e current culture-based criteria value for designated beaches (104 CFU/100 mL) are significantly lower than the current RWQC.

Table 9. Examples of equivalent criteria values for marine waters via the statistical linkages approach

Tolerable attributable illness level (as HCGI per 1000 swimmers) Tolerable attributable illness Level (as

NGI per 1000 swimmers) Enterococcus density measured by qPCR (CCE/100 mL) Geometric mean Enterococcus density measured

by MF (CFU/100 mL) 75 th percentile value † (CFU/100 mL)

8 35 427 11 33

10 43 610 14 42

19 82 3460 35 104

† The 75 th percentile value for Enterococcus density based on the calculated geometric mean density and assuming a typical standard deviation of log-transformed density for marine sites of 0.7.

3.2 Water Quality Link Approach The Water Quality Link approach r equires two steps: (1) development of a model relating indicator density as measured by one indicato r-method combination to indicator density as measured by another and (2) establ ishment of equivalent values of indicator densities based on two indicator-method combinations using the resu lting model and a health effects curve for one of the indicator-method combinations. As noted in Section 2, approaches for establishing models relating indicator-method pairs are in development. Recently published studies have included models based on simple linear regressi on (e.g., Noble et al. 2010; Whitman et al. 2010) and suggested correction factors that account for site- or time-specific co nditions influencing the ratio of indicator counts by different methods (e.g., La vender and Kinzelman 2009).

This section demonstrates both the modeling com ponent and the linkage component of the Water Quality Link approach. First, general features of the paired data used in the Water Quality Linkage are presented. Next, de velopment of the statistical m odels (functional form of the relationship between the densities by the different methods and data used to determine the model parameters) for linking indicator-method density pairs is demonstrated. The demonstration includes techniques for determining which data to use in generating the model and alternative model forms for NEEAR study fres hwater beach data pairs for Enterococcus density as measured by qPCR and Enterococcus density as measured by MF. ANCOVA (described in Section 3.1.3) is suggested as a potential means for screening data prior to model development. Simple linear regression and broke n stick (segmented) regression m odels are fit to the data and compared. The broken stick regression models were investigated because simple linear regression models of the NEEAR study data exhibite d distributions of resi duals that indicate the simple linear models may not adequately describe the relationship between the indicator method combinations.

3.2.1 Datasets Used in Water Quality Link Demonstrations The NEEAR study freshwater data are used in demonstrating the Water Quality Lin k approach. Briefly, the NEEAR study water quality data include paired data for Enterococcus as measured

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by MF, and Enterococcus as measured by qPCR for samples collec ted at multiple transects at four beaches and at three sample collection times for each sample day. The four beaches included Huntington Beach (Lake Erie), Washingt on Park Beach (Lake Michigan), Silver Beach (Lake Michigan) and West Beach (Lake Michigan). Samples were collected along multiple transects on each sample day at 8:00 AM, 11:00 AM, and 3:00 PM and at shin and waist depths. Other water quality and environmental data were collected during the N EEAR studies, but those data were not used in the current demonstration.

3.2.2 Linear Models of Log-Tr ansformed Indicator Data

The most critica l component of the Water Qual ity Linkage approach is establishing a model relating the densities of pair ed water quality samples fo r different indicator/method combinations. Several researchers have propos ed linear models for relating log-transformed Enterococcus qPCR densities and culture counts (e.g., Ha ugland et al. 2005; Noble et al. 2010; Whitman et al. 2010;). This section descri bes a process by which a linear model of logtransformed data may be developed. Model de velopment includes regression of data to find model parameters (slope and intercept). Addi tionally, the data under go a selection process by which similar data are included, and dissim ilar data are excluded, in the analysis.

To introduce the Water Quality Link approach a nd modeling of paired indicator density data, a plot of paired data ( Enterococcus density as measured by MF and Enterococcus density as measured by qPCR, Figure 8 ) is presented and described. Th e data and fits shown in this example provide background for development of the linear regression model and orient the reader to general features of th e relationship between densities of Enterococcus m easured by MF and Enterococcus measured by qPCR. The data shown in Figure 8 were collected and analyzed by Ferretti et al. (2008) in a study of multiple beac hes along the New Jersey shore. They show a general trend toward a 1:1 co rrespondence between cell equiva lents (CE) and CFUs at high indicator densities, with signifi cantly greater scatter, and possi bly a different trend, at low indicator densities. These trends are both expect ed and, as described in Section 2, are likely due to the following:

 the association of each live, culturable cell with genetic material measured via qPCR;

 high uncertainty in qPCR at low densities due to analysis of very small samples volumes (in terms of original sample volume);

 divergence in the culture and qPCR signals as organisms age; and

 the difference in the culture to qPCR ratio arising from uneven loading of culture and qPCR targets to recreational sites.

In Figure 8 , paired Enterococcus densities for two New Jersey beaches, in Monm outh County (red circles) and Ocean County (blue circles), ar e illustrated. A dashed green regression line shows the best fit of a linear model to the raw data (not log-transforme d) while a dashed red regression line shows a linear regr ession fit to the log-transformed data. The black 45° line is a hypothetical line showing perf ect correspondence between Enterococcus densities when measured via the two methods. The paired data for these two beaches are similar to those reported for other beaches—high variability and un certainty in the qPCR counts at low density, with a correlation approaching a 1:1 ratio at higher indicator density. In th is example, it is not obvious whether the data from the two beaches are sufficiently similar to be pooled and used to

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develop a single regression model for linking the i ndicator/method combinati ons. It is also not clear which water quality model is the best for fi tting the data, though both have the potential for application. These two observations (i.e., uncer tainty regarding data pooling and uncertainty regarding model form ) indicate that statistical tests should be used to evaluate the similarity between datasets prior to their inclusion in th e model, and that alte rnative functional forms should be evaluated for the model relating Enterococcus density as measured by culture (MF) methods to Enterococcus density as measured by qPCR.

Figure 8. Typical plot of paired Enterococcus densities as measured by qPCR and culture-based methods
ANCOVA, a statistical method for assessing differences in model sl opes and intercepts for data corresponding to different factors (e.g., different beaches or different times of day), can be used to assess whether trends observed at different locations or times are similar, and to help ensure that only related data are used in model development. In the event that paired water quality data are determined to be similar, the data may be pooled and model parameters may be estimated using the pooled dataset. If models using datase ts that are determined to be dissimilar, it is assumed that there are differences in the system s that generated the data and those differences are not accounted for in the model relating the two datasets. The use of ANCOVA for developing datasets is illustrated with the following example.

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As part of EPA’s NEEAR studies conducted in the Great Lakes, Enterococcus densities as measured by MF and by qPCR were collected (W ade et al. 2006, 2008). Samples were collected three times per day (8 :00 AM, 11:00 AM, and 3:00 PM), at multiple depths (waist, shin, and knee), and along three transects at each of four beaches studied. A model relating the paired Enterococcus densities as measured by both qPCR and MF can be developed from all the sites and times, or from data that are selected because they appear to have similar underlying trends. All the paired water quality data collected are shown in Figure 9 , along with a 45° line showing perfect agreement. Although one may observe a general trend at high in dicator densities, no trend is apparent at densities below roughly 500 CFU/100 mL. Additionally, there is no obvious choice for the form (equation relating Enterococcus density as measured by qPCR and as measured by membrane filtration) of a model rela ting the data from the tw o analytical methods. Figure 10 shows paired water quality data from H untington Beach only, one of the four Great Lakes beaches studied. Again, the FIB densities appear linearly related at high densities and scattered at low densities, though th e scatter is considerab ly less than that observed when data for all the beach es are plotted together.

Figure 9. Paired qPCR and culture Enterococcus data, all NEEAR study Great Lakes beaches

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Figure 10. Paired culture and qPCR Enterococcus data, all samples from Huntington Beach
Figure 11 and Figure 12 show data for only 8:00 AM and 3: 00 PM, respectively, along with 45 ° lines and regression fits d eveloped via linear regression of the log-transformed densities. Note that the trend lines for the 8: 00 AM and 3:00 PM samples are markedly different and that segregation of data by time of the day ma rkedly reduces the scatter in the plots.

Figure 11. Paired qPCR and culture Enterococcus data, Huntington Beach: 8:00 AM samples only

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Figure 12. Paired qPCR and culture Enterococcus data, Huntington Beach: 3:00 PM samples only

ANCOVAs can be used to assess whether the differences in the re gression lines observed for the 8:00 AM and 3:00 PM data are statistically si gnificant. ANCOVAs were run to compare the regression lines for sets of data corresponding to each of the collecti on times at Huntington Beach. As shown in Table 10 , the ANCOVA indicates that there are significant differences in the models for the 8:00 AM and 11:00 AM datase t, and for the 8:00 AM and 3:00 PM datasets, but not for the 11:00 AM and 3:00 PM datasets. This finding is not surprisi ng given that the 8:00 AM and 3:00 PM samples do not correspond to the same m odel, and because solar radiation is known to reduce culture counts for afternoon sa mples while qPCR counts remain relatively constant throughout the day.

Table 10. Results of ANCOVAs for sets of data for Huntington Beach with different collection times

P-value
Collection times Intercepts Slopes

8:00 AM and 11:00 AM <0.0001 0.002

8:00 AM and 3:00 PM <0.0001 0.01

11:00 AM and 3:00 PM 0.52 0.60

Additional ANCOVAs were performed to compar e models for all of the NEEAR freshwater study datasets and to determine whether sample water depth, time of day, and beach location altered the model rela ting qPCR and culture-based datasets. Findings from the ANCOVAs are as follows:

 Samples corresponding to different samp le collection depths can be pooled.

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 Models for different times of day differ for two beaches (Huntington and West Beach) but do not differ for the other two beaches (S ilver Beach and Washington Park Beach). Agreement between models for different tim es of day for Silver Beach and Washington Park Beach might be the result of extreme scatter in the data for those two beaches. Based on these findings and knowledge of th e variation in culture and qPCR signals during the day, 8:00 AM Enterococcus measured by MF are fit by different models than samples from other times of day..

 For these data, models for different beaches are dissimilar and pooling of the data may be inconsistent with simple linear models.

3.2.3 Demonstration of the Water Quality Linkage Approach using Linear Regression Models
In this section, the W ater Quality Link appr oach is demonstrated using NEEAR study Great Lakes Enterococcus density data (as measured by both MF and qPCR), the NEEAR study freshwater health effects curve (presented in equation form in Table 5 ), and linear models relating log-transformed data of Enterococcus density as m easured by qPCR and MF. As noted previously, these linear models may not adequately characterize the correlation between qPCR and culture Enterococcus density for some datasets. Therefore, the illustration presented below may be used for developing an understanding of th e Water Quality Linkage approach but not for developing specific equivalenc es between densities of Enterococcus as measured by MF and as measured by qPCR. The Water Qu ality Link demonstration entails

1. selection of an Enterococcus density based on the NEEAR study freshwater beaches health effects curve;

2. use of linear regression to develop severa l candidate models relating density of
Enterococcus as measured by qPCR to Enterococcus density as measured by MF; and

3. use of the linear relationships develo ped in step 2 to establish candidate Enterococcus densities as measured by MF to the Enterococcus density selected in step 1.
After the Water Quality Linkage approach is demons trated the results are critically evaluated and used to suggest alternative Wa ter Quality Linkage approaches.

The NEEAR study health effects curve for Great Lakes beaches (see Table 5 ) indicates that an Enterococcus density of about 125 qPCR CE/100 mL rela tes to a risk of about 45 NGI illnesses in 1000 swimmers. The rate 45 NGI illnesses in 1000 is approximately equivalent to a risk of 8 HCGI illnesses in 1000 swimmers—the level at which current (1986) fr eshwater criteria are based (see Section 3.1.4). Thus for this demonstr ation, the qPCR level of interest is selected to be 125 qPCR CE. This selection does not imply th at new or revised crite ria will be based on the Great Lakes health effects curve or on a risk level of 45 NGI illnesses in 1000 swimmers. It is selected for demonstration purposes only and because it is based on risk in a range consistent with current (USEPA 1986) criteria.

The choice of a model relating the Enterococcus density data from qPCR to density data from MF has a profound influence on the outcome of a Water Quality Linkage analysis. This is illustrated using the NEEAR study data. When data from all the NEEAR study freshwater

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beaches are used to develop a model relating Enterococcus qPCR and culture densities (the purple line in Figure 13 ), the regression line has a slope <1 and falls significantly below the 45° line, for the culture-based dens ity above about 100 CFU/100 mL . The model mean value for CFU equivalent to CE for 45 illnesses/1000 sw immers is 1212 CFU/100 mL and the confidence interval for CFU equivalent to CE for 45 illnesses/1000 swimmers is <519 CFU/100 mL, 4628 CFU/100mL>. Thus, these Enterococcus densities, measured by MF, are clearly very high and inconsistent with the selected level of tolerable risk.

Figure 13. Linear model, data from all NEEAR freshwater beaches
When only Huntington Beach data are used, the re sults appear to be more reasonable. The model line (the purple line in Figure 14 ) still has a slope <1, which is inconsistent with expected trends at high indicator density, but the slope is st eeper than that of the model for data from all beaches. The model mean value for CFU equiva lent to CE for 45 illnesses/1000 swimmers is 10.8 CFU/100 mL, and the confidence interval around that value is <7.1 CFU/100mL, 15.4 CFU/100mL>. These equivalent values appear unrealistically low, t hough more reasonable than the equivalent values derived using the model for data from all beaches.

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Figure 14. Linear model, da ta from Huntington Beach
The most reasonable equivalent culture densities result from a model developed using only 8:00 AM data from Huntington Beach. The model, equi valent value, and confidence interval are all shown in Figure 15 . The model mean value for CFU e quivalent to CE for 45 illnesses/1000 swimmers is 29.4 CFU/100 mL and the confid ence interval around that value is <17.8 CFU/100mL, 45.9 CFU/ 100mL>.

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Figure 15. Linear model, 8:00 AM data from Huntington Beach

Based on the analysis of paired water quality NEEAR study Enterococcus density data measured by qPCR and MF and using simple linear model of log-transformed variables, several observations may be made, though the extent to which these may appl y to datasets other than the ones studied is unknown. First, model selection (i nclusive of data sele ction) profoundly impacts the Water Quality Linkage approach. For this da taset, when all data are pooled and a water quality model is generated, the resulting densities of Enterococcus as measured by MF that are equivalent to the density of Enterococcus as measured by qPCR appear very high in comparison to the current criteria. When only data from morning samples at a single beach are used to develop the linear model, the resulting equivalent Enterococcus densities for MF are closer to the current criteria. The non-robust performance of linear models of log-transformed indicator densities in this demonstration s uggests exploration of alternative models that may be capable of modeling more complex phenomena and providing an adequate fit to a larger set of data.

3.2.4 Broken Stick Models of Log-Transformed Indicator Data The relationship between paired Enterococcus densities as measured by MF and Enterococcus densities as measured by qPCR data mi ght not be fit best by a simple lin ear regression model of log-transformed data. An alternative model eval uated in the development of the Water Quality Linkage approach is the broke n stick regression model, also known as the segmented model (Draper and Smith 1998). In broke n stick regression models, the data are divided in two parts, each of which is fit with a different linear model. The two linear models meet at a “kink” in the regression line. Broken stick regression models have four parameters (two slopes and the coordinates of the point where th e segments of the broken stick m eet) that may be determined via

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optimization, whereas simple linear r egressi on models have two parameters (slope and intercept). The broken stick model appears a good choice for analyzing paired densities of Enterococcus as measured by MF and Enterococcus as measured by qPCR because the data appear to behave differently at low and high indicator densities.

Broken stick regression models were developed for each of the four NEEAR Great Lakes study beaches separately. Two models were develope d for each beach—one for all data for that beach pooled and one for only data from samples colle cted at 8:00 AM. The resulting curves are shown in Figure 16 to Figure 19

.

Figure 16. Huntington Beach broken stick m odel fits for all data and 8:00 AM data

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Figure 17. West Beach broken stick model fits for all data and 8:00 AM data

Figure 18. Silver Beach broken stick model fits for all data and 8:00 AM data

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Figure 19. Washington Park Beach broken stick model fits for all data and 8:00 AM data
Broken stick regression models of Huntington and West Beach data both exhibit slopes closer to 1.0 in the high FIB density portion of the broken s tick, compared to the low density portion. For the Washington Park and Silver Beach datasets, the presence of water quality data pairs with relatively high Enterococcus culture density and very low Enterococcus qPCR density appear to exert an undue influence on the slope portion of th e broken stick in the high culture density data range. For the four beaches, th e location of the “kink” in the stick varies within the range 12– 100 CFU/100 mL for models of data including all sample times, and in the range 28–62 CFU/100 mL for the 8:00 AM samples of the Huntington and West Beach data.

Given the empirical and theoretical support for different correlation of qPCR and culture counts at high and low densities, the broken stick model o ffers advantages over models of simple linear regression of log-transformed densities, including

 better fits to data,

 reduction in heteroskedasticity of regression fits, and

 consistency with observed relationships between qPCR and culture densities. Regression analysis of correlations between th e qPCR and culture densities, as well as the literature, support different relati onships between the two water qua lity measures at low and high indicator counts. At low indicat or densities, the relationship of the culture indicator with the fecal pollution source becomes tenuous and the qPCR counts become highly uncertain. At higher densities, the abundance in targets for the two methods is correlated. These findings both support and suggest performing regression analysis only for those data pairs for which culture densities exceed a threshold value.

Three regression analyses were performed to evaluate whether using only data above such threshold values yielded robust models for estima ting culture densities th at are equivalent to qPCR densities. In all of the analyses, only FIB density data collected in the morning (8:00 AM

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sample time) were included, due to potential di screpancies between enumer ation m ethods at later times. In the first analysis, only points above the “kink” in the stick were re tained for analysis for each of the four Great Lakes beaches at which EPA’s NEEAR epidemiology studies were conducted. The data retained for each beach were determined by the “kink” location for each of the four beaches. In the second and third an alyses, a threshold common to all beaches was chosen as the basis for retaining data. Two thresholds were explor ed—80 CFU/100mL and 104 CFU/100 mL. The value 104 CFU/100 mL was sele cted based on the current single sample maximum for designated beaches; 80 CFU/100 mL was selected because it lies between the current criteria and the geometric mean on which it is based (35 CFU/100 mL).

Plots showing the resulting three regression models and the culture equivalent to a qPCR density of 126 CE/100 mL are shown in Figure 20 (data above the kink in the stick), Figure 21 (data above 80 CFU/100 mL), and Figure 22 (data above 104 CFU/100 mL). The qPCR density chosen for this illustration (126 CE/100 m L) is a plausible value for qPCR-based criteria ( Table 9 ). In all cases, the new regr ession models and qPCR value of interest re sult in equivalent culture criteria ex ceeding the current single sample maximum value for infrequently-used fresh waters of 151 CFU enterococci / 100 mL. Regre ssion model lines and culture equivalents to a potential qPCR criterion of 126 CE/100 mL are provided in Table 11

.

Figure 20. Model resulting from retention of all data above the “kink” in the broken stick

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Figure 21. Model resulting from retention of all data above 80 CFU/100 mL

Figure 22. Model resulting from retention of all data above 104 CFU/100 mL

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Table 11. Summary of models resulting from rete ntion of data pairs with culture counts above three thresholds

Treatment Regression line Equivalent culture density of interest (CFU/100 mL)
Retain data for which CFU count is above the break in the stick 

#    
CFU10 qPCR 10 log759.0393.0 log C C   178

Retain data for which CFU count is above 80 CFU/100 mL 

#    
CFU10 qPCR 10 log944.0056.0 log C C 

  192

Retain data for which CFU count is above 126 CFU/100 mL 

#    
CFU10 qPCR 10 log358.1289.1 log C C 

  246

Several observations can be made regarding development of a regression model based on data only above thresholds. First, the model results appear quite sensitiv e to the selection of the break point above which data are retained. In this rega rd, there does not appear to be a widely accepted methodology for selecting the appropriate break point. Both Lavender and Kinzelman (2009) and Byappanahalli et al. (2010) determined that the qPCR-culture relationship varies with environmental conditions which, in turn, vary differ ently at different sites. Second, in all cases, data pairs with much lower qPCR densities than culture densities appear to have a pronounced effect on the regression models. At present, we have no explanation for the occurrence of those data pairs. Because the pairs oc cur above the cutoff value for culture densities, it is unlikely that sampling error (i.e., small qPCR sample size in terms of original sample volume) is the cause. In summary, regression models based on the broken s tick or threshold approach were dependent on a cut-off value, the choice of which could be viewed as arbitrary. Further, culture densities equivalent to potential qPCR dens ities were higher than the curre nt single sample maximum for infrequently-used freshwater beaches when the Water Quality Link approach is applied to the NEEAR study freshwater indicator dataset, using a broken stic k regression model to relate densities of Enterococcus as measured by MF and qPCR.

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### 4 Discussion
Two approaches for linking indicator-method co mbinations to a common risk level were demonstrated—the Risk Link approach and the Water Quality Link approach. Using currently available data and techniques, the exploration of these methods meets both the objective of developing quantifiable relationships between indicator-method combinations and provides practical experience in the complexities underlying the two approaches.

Applying the Risk Link approach can be relativ ely straightforward when multiple indicator health relationships data are available from a single epidemiology study, as demonstrated in the Bacteroidales (measured by qPCR) and Enterococcus (measured by qPCR) Risk Link comparison. However, when directly comparable data are not available, additional steps are added to the Risk Link approach. These st eps may include, but are not limited to, the harmonization of GI definitions used in two ep idemiology studies or the generality of health effects relations established for sites w ith specific sources of fecal pollution.

Regarding the Water Quality Link approach, generating a relationship between indicator densities, as measured by qPCR and culturable me thods, is more complex than simple linear or broken stick regression models. In our demonstration, the rela tionship, between paired water quality data corresponding to di fferent indicator-method combinat ions, appears to vary from beach to beach. The Water Quality Link may be a useful tool for the development of sitespecific standards by states; but as formulated in this report, is not necessarily useful for the development of National criteria.

It is possible that additional analyses or datasets might a llow for alternative quantifiable relationships to be explored using both Link appr oaches. For example, if statistical techniques are developed that allow for the direct compar ison of results between RCT and PC epidemiology study designs, additional health e ffects relationships, such as t hose from the Epibathe studies, could be evaluated.

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### 5 References
Abdelzaher, A., M. Wright, C. Ortega, H. Solo-G abriele, G. Miller, S. Elmir, X. Newman, P. Shih, T.D. Bonilla, C.J. Palmer, T. Scott, J. Lukasik, V.J. Harwood, S. McQuaig, C. Sinigalliano, M. Gidley, L. Plano, X. Zhu, J.D. Wang, and L. Fleming. 2010. Presence of pathogens and indicator microbes at a non-poi nt source subtropical recreational marine beach. Applied and Environmen tal Microbiology 76(3): 724-732. Agudel, R.M., Codony, F., Adrado, B., Fittipaldi M., Peñuela, G., and J. Morató. 2010 Monitoring bacterial faecal contamination in waters using multiplex real-time PCR assay for Bacteroides spp. and faecal enterococci. Water SA, 36(1): 127-132. Ahmed, W., S. Sawant, F. Huygens, A. Goonetill eke, and T. Gardner. 2009. Prevalence and occurrence of zoonotic bacteria l pathogens in surface waters determined by quantitative PCR. Water Research 43: 4918-4928. Bae, S., and S. Wuertz 2009. Discrimination of vi able and dead fecal bacteroidales bacteria by quantitative PCR with propidium monoazide. Applied and Environmental Microbiology 75(9): 2940-2944. Bolster, C.H., J.M. Bromley, and S.H. J ones. 2005. Recovery of chlorine-exposed Escherichia coli in estuarine microcosms. Environmen tal Science and Technology 39(9):3083-3089 Bower, P.A., C.O. Scopel, E.T. Jensen, M.M. Depas, and S.L. McLellan. 2005. Detection of genetic markers of fecal indica tor bacteria in Lake Michigan and determination of their relationship to Escherichia coli densities using standard microbiological methods. Applied and Environmental Microbiology 71(12): 8305-8313. Bremer, H. and Dennis, P. P. 1996. Modulation of chemical composition and other parameters of the cell growth rate. In Escherichia coli and Salmonella : Cellular and Molecular Biology, 2 nd ed., pp. 1553–1568. Edited by F. C. Neidha rdt and others. Washington DC: American Society for Microbiology.Byappanahalli, M.N., R.L. Whitman, D.A. Shively, and M.B. Nevers. 2010. Linking nonculturable (qPCR) and culturable enteroco cci densities with hydrometeorological conditions. Science of the Total Environment 408: 3096-3101. Calderon, R.L., E.W. Mood, and A.P. Dufour . 1991. Health effects of swimmers and nonpoint sources of contaminated water. Internationa l of Envionmental Health Research 1: 21-31. Colford, J.M.J., T.J. Wade, K.C. Schiff, C.C. Wright, J.F. Gri ffith, S.K. Sandhu, S. Burns, M. Sobsey, G. Lovelace, and S.B. Weisberg. 2007. Water quality indicators and the risk of illness at beaches with nonpoint sources of fecal contamination. Epidemiology 18(1): 27-35. Converse, R., A. Blackwood, M. Kirs, J. Griff ith, and R. Noble. 2009. Rapid QPCR-based assay for fecal Bacteroides spp. as a tool for assessing fecal contamination in recreational waters. Water Research 43(19): 4828-4837. Draper, N.R., and Smith, H. 1998. Applied Regression Analysis, 3 rd ed. John Wiley and Sons, NY. Duprey, E., M.P. Caprais, A. Derrien, and P. Fach, 1997. Salmonella DNA persistence in natural seawaters using PCR analysis. Journal of Applied Microbiology 82: 507-510. European Commission (Epibathe consortium). 2009a. Accessible Report to Commission Staff Responsible for Reporting to the Parliament and to the Council Under Article 14 of the Bathing Water Directive of 15th February 2006. Assessment of Human Health Effects

46U.S. Environmental Protection Agency

Caused by Bathing Waters (EpiBathe proj ect). European Union Sixth Frame work Programme, Scientific Support to Po licies, FP6 Project Reference 022618. European Commission (Epibathe consortiu m). 2009b. Appendix 2 Section 1. Risk of Gastroenteritis from Recreational Bathing Wa ter: Analysis of Epibathe data from Hungary and Spain. Elmir S.M., T. Shibata, H.M. Solo Gabriele, C.D. Sinigalliano, M.L. Gidley, G. Miller, L.R.W. Plano, J. Kish, K. Withum, and L.E. Fleming. 2009. Quantitative evaluation of enterococci and Bacteroidales released by adults and toddl ers in marine water. Water Research 43(18): 4610-4616. Ferretti, J.A., Tran, H.V., Loftin, V.K., Cosgr ove, B., and Protonentis, J. 2008. Comparison of Enterococcus Measurements in Marine Beach an d Bay Samples by Quantitative (RealTime) Polymerase Chain Reaction, Membrane Filtration, and Enterolert. Edison, NJ: US EPA Region 2. Fleisher, J.M., D. Kay, R.L. Salmon, F. Jone s, M.D. Wyer, and A.F. Godfree. 1996. Marine waters contaminated with domestic sewage: nonenteric illnesses associated with bather exposure in the United Kingdom. American J ournal of Public Health 86(9): 1228-1234. Fleisher, J.M., L.E. Fleming, H.M. Solo-Gabriele, J.K. Kish, C.D. Sinigalliano, L. Plano, S.M. Elmir, J.D. Wang, K. Withum, T. Shibata, M.L. Gidley, A. A bdelzaher, G. He, C. Ortega, X. Zhu, M. Wright, J. Hollenbeck, and L.C. Backer. 2010. The BEACHES Study: health effects and exposures from non-point source microbial contaminants in subtropical recreational marine waters. Internatio nal Journal of Epidemiology 39(5): 1291-1298. Griffith, J., K.C. Schiff, G.S. Lyon, and J.A. Fuhrman. 2010. Microbiologi cal water quality at non-human influenced reference beaches in southern California during wet weather. Marine Pollution Bulletin 60(4): 500-508. Haile, R.W., J.S. Witte, M. Gold, R. Cressey, C. McGee, R.C. Millikan, A. Glasser, N. Harawa, C. Ervin, P. Harmon, J. Harper, J. Dermand, J. Alamillo, K. Barrett, M. Nides, and G. Wang. 1999. The health effects of swimming in ocean water contaminated by storm drain runoff. Epidemiology 10(4): 355-363. Haugland, R.A., S.C. Siefring, L.J. Wymer, K. P. Brenner, and A.P. Dufour. 2005. Comparison of Enterococcus measurements in freshwater at tw o recreational beaches by quantitative polymerase chain reaction and membrane filter culture analysis. Water Research 39(4): 559-568. He, J.-W., and S. Jiang. 2005. Quantification of enterococci and human adenoviruses in environmental samples by real-time PCR. Applied and Environmental Microbiology 71(5): 2250-2255. Khan, I.U.H., V. Gannon, R. Kent , W. Koning, D.R. Lapen, J. Mi ller, N. Neumann, R. Phillips, W. Robertson, E. Topp, E. van Bochove, and T.A. Edge. 2007. Development of a rapid quantitative PCR assay for di rect detection and quantific ation of culturable and nonculturable Escherichia coli from agriculture watersheds. Journal of Microbiological Methods 69(3): 480-488. Kinzelman, J.L., R.N. Bushon, R.T. Noble, and S.E. Dorevitch. 2010. WERF - PHASE I Progress Report. Comparative Evaluation of Molecular and Culture Methods for Fecal Indicator Bacteria for use in In land Recreational Waters. In: WERF. Lavender, J.S., and J.L. Kinzelman. 2009. A cross co mparison of QPCR to agar-based or defined substrate methods for determination of Escherichia coli and enterococci in municipal water quality monitoring program s. Water Research 43: 4967-4979.

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Lleo, M.M., B. Bonato1, M.C. Tafi1, C. Si gnoretto1, C. Pruzzo2 and P. Canepari. 2005. Molecular vs culture methods for the detec tion of bacterial fae cal indicators in groundwater for huma n use Letter s in Applied Microbiology 40:289–294 Marion, J.W., J. Lee, S. Lemeshow, and T. J. Buckley. 2010. Association of illness and recreational water exposure dur ing advisory and non-advisory conditions at an inland U.S. beach. Water Research 44(16): 4796-4804. Morrison, C.R., D.S. Bachoon, and K.W. Gate s 2008. Quantification of enterococci and bifidobacteria in Georgia estuaries using conventional and molecular methods. Water Research 42(14): 4001-4009. Nocker, A., and A.K. Camper. 2006. Selective re moval of DNA from dead cells of mixed bacterial communities by use of ethidium monoazide. Applied and Environmental Microbiology 72(3): 1997-2004. Nocker, A., C.Y. Cheung, and A.K. Camper. 2006. Comparison of propidium monoazide with ethidium monoazide for differe ntiation of live vs. dead bacteria by selective removal of DNA from dead cells. Journal of Microbiological Methods 67: 310-320. NRC (National Research Council). 2004. Indicators for Waterborne Pathogens . Washington DC: National Academies Press. Ortega, C., M. Helena, S. Gabriele, A. Abde lzaher, M. Wright, Y. Deng, and L.M. Stark 2009. Correlations between microbial indicators, pa thogens, and environmental factors in a subtropical estuary. Marine Po llution Bulletin 58(9): 1374-1381. Petterson, S.R., R.S. Signor, and N.J. As hbolt. 2007. Incorporating method recovery uncertainties in stochastic estimates of ra w water protozoan concentrations for QMRA. Journal of Water and Health 5(Supplement 1): 51-65. Prüss, A. 1998. Review of epidemiological st udies on health effects from exposure to recreational water. Inte rnational Journal of Epidemiology 27(1): 1-9. Rothrock, M. J. J., K. L. Cook and K. H. Bolster 2009. Comparative quantification of Campylobacter jejuni from environmental samples us ing traditional and molecular techniques. Canadian Journal of Microbiology 55(6):633-641. Siefring, S.C., M. Varma, E. Atikovic, L.J. Wymer, and R.A. Haugland. 2008. Improved realtime PCR assays for the detection of fecal indicator bacteria in surface waters with different instrument and reagent systems. Journal of Water and Health 6(2): 225-237. Shanks, O., C.A. Kelty, M. Sivaganesan, M. Varma, and R. Haugland. 2009. Quantitative PCR for genetic markers of human fecal pollution. Applied and Environmental Microbiology 75(17): 5507-5513. Sinigalliano, C.D., J.M. Fleisher, M.L. Gidley, H.M. Solo-Gabriele, T. Shibata, L. Plano, S.M. Elmir, D. Wanless, J. Bartkowiak, R. Boitea u, K. Withum, A.M. Abdelzaher, G. He, C. Ortega, X. Zhu, M.E. Wright, J. Kish, J. Ho llenbeck, T. Scott, L.C. Backer, and L.E. Fleming. 2010. Traditional and molecular anal yses for fecal indi cator bacteria in nonpoint source subtropical recreational mari ne waters. Water Research 44: 3763-3772. Sinton, L.W., C.H. Hall, P.A. Lynch, and R.J. Davies-Colley. 2002. Sun light inactivation of fecal indicator bacteria and bacteriophages fr om waste stabilization pond effluent in fresh and saline waters. Applied and Envi ronmental Microbiology 68(3): 1122-1131. USEPA (U.S. Environmental Protection Agency). 1983. Health Effects Criteria for Marine Recreational Waters . EPA-600/1-80-031. Research Tr iangle Park, NC: USEPA. USEPA (U.S. Environmental Protection Agency). 1984. Health Effects Criteria for Fresh Recreational Water . EPA-600/1-84-004. Research Tr iangle Park, NC: USEPA.

48U.S. Environmental Protection Agency

USEPA (U.S. Environmental Protection Agency). 1986. Ambient water quality for bacteria - 1986 . EPA440/5-84-002. USEPA (U.S. Environmental P rotection Agency ). 2000. Improved Enumeration Methods for the Recreational Water Quality Indicators: Enterococci and Escherichia coli

. USEPA (U.S. Environmental Protection Agency). 2007. Critical Path Science Plan for the Development of New or Revised Recreation Water Criteria . Washington DC: Office of Water and Office of Research and Development, 823-R-08-002 Varma, M., R. Field, M. Stinson, B. Rukovets , L. Wymer, and R. Haughland. 2009. Quantitative real-time PCR analysis of total and propidium monozide-resistant feca l indicator bacteria in wastewater. Water Research (early publication online ahead of print). Wade, T.J., N. Pai, J.N. Eisenberg, and J.M. Colford. 2003. Do U.S. Environmental Protection Agency water quality guidelines for recreationa l waters prevent gastrointestinal illness? A systematic review and meta-analysis. E nvironmental Health Perspectives 111(8): 1102- 1109. Wade, T.J., R.L. Calderon, E. Sams, M. Beach, K. P. Brenner, A.H. Williams, and A.P. Dufour. 2006. Rapidly measured indicators of recr eational water quality are predictive of swimming-associated gastrointestinal illness. Environmental Health Perspectives 114(1): 24-28. Wade, T.J., R.L. Calderon, K.P. Brenner, E. Sa ms, M. Beach, R. Haugland, L. Wymer, and A.P. Dufour. 2008. High sensitivity of children to swimming-associated gastrointestinal illness – results using a rapid assay of r ecreational water quality. Epidemiology 19(3): 375-383. Wade, T.J., E. Sams, K.P Brenne r, R. Haugland, E. Chern, M. Beach, L. Wymer, C.C. Rankin, D. Love, Q. Li, R. Noble, and A.P. Du four. 2010. Rapidly measured indicators or recreational water quality a nd swimming-associated illn ess at marine beaches: a prospective cohort study. E nvironmental Health 9: 66. http://www.ehjournal.net/contents/9/1/66 Walters, S.P., K.M. Yamahara, and A.B. Boehm. 2009. Persistence of nucleic acid markers of health-relevant organisms in sewater micr ocosms. Water Research (early publication online ahead of print). WERF (Water Environment Research Federation). 2009. Report on the Experts Scientific Workshop on Critical Research and Science Needs for the Development of Recreational Water Quality Criteria for Inland Waters. Dallas/Ft. Worth, Texas, February 18-20, 2009. PATH4W0. Alexandria, VA. Whitman, R.L., Z. Ge, M.B. Nevers, A.B. Boeh m, E.C. Chern, R.A. Haugland, A.M. Lukasik, M. Molina, K. Przybyla-Kelly, D.A. Shively, E.M. White, R.G. Zepp, and M.N. Byappanahalli. 2010. Relationship and varia tion of qPCR and culturable enterococci estimates in ambient surface waters are predictable. Environmental Science and Technology 44: 5049-5054. WHO (World Health Organization). 2003. Guidelines for Safe Recreational Water Environments. Volume 1 Coastal and Fresh Waters . World Health Organization. Geneva, Switzerland. Wiedenmann, A., P. Kruger, K. Dietz, J. L opez-Pila, R. Szewzyk, and K. Botzenhart. 2006. A randomized controlled trial asse ssing infectious disease ri sks from bathing in fresh recreational waters in relation to the concentration of Escherichia coli , intestinal enterococci , Clostridium perfringens , and somatic coliphages. Environmental Health Perspectives 114:228-236.

49U.S. Environmental Protection Agency

50 Wymer, L., K.P. Brenner, J.W. Martinson, W. R. Stutts, S.A. Schaub, and A.P. Dufour 2005. The EMPACT Beaches Project: Results from a Study on Microbiological Monitoring in Recreational Waters . EPA 600/R-04/023 USEPA, Office of Research and Development, National Exposure Research Laboratory. Yamahara, K.M., S.P. Walters, and A.B. Boehm. 2009. Growth of enterococci in unaltered, unseeded beach sands subjected to tidal wetting. Applied and Environmental Microbiology 75: 1517-1524. Zmirou, D., L. Pena, M. Ledrans, and A. Letertre. 2003. Risks associated with the microbiological quality of bodi es of fresh and marine water used for recreational purposes: summary estimates based on publishe d epidemiological studies. Archives of Environmental Health 58(11): 703-771.