Inland recreational waters—state of the science substantially within the category of IWs and could account for more variability in FIB lev- els in IWs than in CWs. hTe decoupling of indicator and pathogen densities in IWs. Human pathogenic viruses and protozoan parasites reproduce in the cells of their hosts, whereas as noted above, sedi- ments can provide favorable conditions for the persistence and re-growth of FIB. A con- cern among workshop participants was that a decoupling (meaning a significantly weaker association) of indicators and pathogens may occur in IWs. The basis for this concern is the combination of the known growth of FIB in sediments and the influence that sediment is thought to have on IW FIB. This decou- pling is likely to result in different indicator- pathogen relationships at coastal and inland systems, as well as differences across IWs with varying hydrologic characteristics. As a result of this decoupling, FIB may overesti- mate pathogen densities and expected illness rates among IW recreators. In part because of this putative decoupling, the application of coastal-derived FIB criteria to inland set- tings should result in rates of sporadic illness (although not necessarily outbreaks of severe illness) that are at least as protective in IW as they are in CW. Challenges at Both CWs and IWs Rapid testing methods. Recent epidemiologic studies have used quantitative real time poly- merase chain reaction (qPCR) measurements of enterococci (Colford et al. 2007; Wade et al. 2006, 2008), and this method may be endorsed in the new 2012 AWQC. Currently, qPCR and other rapid methods of measur- ing FIB are in various stages of development (Griffith et al. 2009; Noble and Weisberg 2005). If the necessary monitoring protocols and communications systems were in place, these methods could provide same-day water quality information in IWs. One broad concern with the qPCR approach is method performance, mean- ing the precision, accuracy, sensitivity, and interlaboratory variability. A second concern is the interpretation of qPCR output to esti- mate health risks. In epidemiologic studies, enterococci qPCR results are being directly calibrated against rates of illness. That calibra- tion is being performed at coastal beaches impacted by treated human sewage. Humic and fulvic acids found in sediment (CWs and IWs) can inhibit PCR analyses (Rutjes et al. 2006; Tsai and Olson 1992). Because of the importance of sediment in determining sur- face water quality in IWs, sediment may result in more inhibition than occurs in CW. The qPCR methods detect viable, nonviable, and cell-free DNA of FIB, whereas conventional methods detect culturable microbes only. The distribution of these components of the qPCR signal (Nocker et al. 2007) could be different in IW compared with CW, particularly at sites impacted by treated wastewater. IW pol- lutant sources and sediments may result in qPCR-illness rate relationships that are differ- ent from those described (Wade et al. 2006, 2008) in CW. Predictive modeling: opportunities and uncertainties in CWs and IWs. Modeling approaches offer alternatives to epidemiologic studies or extensive microbial monitoring. Simple regression modeling of FIB densities use real-time information such as meteorolog- ical and physical parameters, such as turbid- ity, to produce a timely and, in the long-run, a lower-cost alternative to microbiological monitoring (Boehm et al. 2007; Frick et al. 2008; Olyphant and Whitman 2004). Regression models are used to issue beach notification at three Great Lakes locations and on the Schuylkill River (Philly RiverCast 2009). Mechanistic models, which make use of microbial loading, dilution, decay, trans- port, and other parameters to predict loca- tion-specific densities of FIB (Boehm et al. 2005; Steets and Holden 2003), are probably best used for evaluating management practices in watersheds. These approaches model FIB levels; it is unknown whether they predict pathogen presence or illness rates. The above approaches predict water qual- ity, whereas quantitative microbial risk assess- ment (QMRA) is used to predict health risks in populations (Haas et al. 1999). The inputs to QMRA models include readily obtainable demographic and water quality data. Health risks are predicted using estimates of pathogen densities, water exposure, and dose response (number of units of pathogens ingested as a predictor of illness probability). Conversely, a desired water quality target can be modeled to meet a health risk target. QMRA allows eval- uations of relative risks across a range of site- specific contamination scenarios. Few studies have directly compared QMRA projections with epidemiologic observations (Ashbolt et al. 1997), although the NEEAR study site in Boqueron, Puerto Rico (http://www.epa. gov/NHEERL/neear/) will prospectively col- lect information needed to compare modeled (QMRA) and observed risk. Predictive mod- els of water quality and QMRA can be only as accurate as their inputs; some input data, such as dose response, remain limited. Sensitivity analyses can evaluate sources of uncertainty in model estimates to allow prioritization of additional data collection (such as spatiotem- poral variability in indicators and pathogens). Policy and implementation challenges. To protect the public from waterborne illness at IWs, criteria will have to be established based on a targeted level of risk. In the absence of stakeholder input, such a targeted risk could Environmental Health Perspectives • volume 118 | number 6 | June 2010 not yet be considered an acceptable risk. The unadjusted rate of illness attributable to swim- ming at Great Lakes point source–impacted bathing beaches appears to be about 20–25 cases of gastrointestinal illness per 1,000 swim- mers (Wade et al. 2008). In addition to the rate of illness, the severity of illness attributable to water recreation is also an important con- sideration in characterizing risk. The U.S. epi- demiologic studies (set in CW) have described rates of gastrointestinal illness, generally thought to be mild and self-limited. By con- trast, reported disease outbreaks in IWs have included rare but potentially life-threatening infections, likely due to limited dilution and proximity to fecal sources (including other bathers). Once elements of acceptable risk, rate, and severity have been defined, specific values of FIB can be evaluated with a goal of keeping risk below those levels. AWQC developed to protect recreational water users are applied to other Clean Water Act (CWA) programs, such as the listing of impaired waters and discharge limits (under Section 303d of the CWA). A better under- standing of wastewater treatment effects on the components of the qPCR signal (viable bacteria, nonviable bacteria, and cell-free DNA) will help define the value of qPCR monitoring for these other CWA purposes. Translation factors or a refinement of qPCR assays to identify viable cells (Nocker et al. 2007) may be needed to determine, for exam- ple, if treated wastewater effluent met dis- charge standards. The costs of implementing qPCR requirements of new AWQC for both CWs and IWs will be significant and could result in the allocation of local funds away from other water quality programs. Same- day measures of FIB have limited benefits beyond beach notification. Continued use of culture-based methods in those contexts is reasonable. A potential advantage of qPCR is the ability to differentiate human from non- human sources of FIB. Work published after the workshop evaluated numerous promis- ing approaches for both rapidly evaluating microbe density and differentiating human from nonhuman sources (Griffith et al. 2009). However, limitations of specific qPCR approaches for differentiating sources has been described (Stapleton et al. 2009). New monitoring requirements for recre- ational waters may encompass the vast num- ber of inland lakes and miles of rivers within individual states. The application of predic- tive models, sanitary surveys, and QMRA to develop site-specific standards, particularly where the dominant source of fecal pollution is nonhuman, is of interest to the regulated community. It remains an open question whether these alternatives to epidemiologic studies would provide accurate and sufficiently precise projections of FIB and health risk. 873