Choices in Recreational Water Quality Monitoring: New Opportunities

Policy Analysis pubs.acs.org/est

Choices in Recreational Water Quality Monitoring: New Opportunities and Health Risk Trade-Offs Meredith B. Nevers,* Muruleedhara N. Byappanahalli, and Richard L. Whitman U.S. Geological Survey, Great Lakes Science Center, 1100 N. Mineral Springs Road, Porter, Indiana 46304, United States ABSTRACT: With the recent release of new recreational water quality monitoring criteria, there are more options for regulatory agencies seeking to protect beachgoers from waterborne pathogens. Included are methods that can reduce analytical time, providing timelier estimates of water quality, but the application of these methods has not been examined at most beaches for expectation of health risk and management decisions. In this analysis, we explore health and monitoring outcomes expected at Lake Michigan beaches using protocols for indicator bacteria including culturable Escherichia coli (E. coli; EC), culturable enterococci (ENT), and enterococci as analyzed by qPCR (QENT). Correlations between method results were generally high, except at beaches with historically high concentrations of EC. The “beach action value” was exceeded most often when using EC or ENT as the target indicator; QENT exceeded the limit far less frequently. Measured water quality between years was varied. Although methods with equivalent health expectation have been established, the lack of relationship among method outcomes and annual changes in mean indicator bacteria concentrations complicates the decision-making process. The monitoring approach selected by beach managers may be a combination of available tools that maximizes timely health protection, cost efficiency, and collaboration among beach jurisdictions.

■

INTRODUCTION With increasing incidence of outbreaks of waterborne illnesses1 and attention garnered by recent legislation outlining the need for additional recreational water quality monitoring,2 beaches have been at the forefront of much recent research on human health risk. Recent revisions of monitoring criteria in both the European Union (EU) and the United States (U.S.) have resulted in numerous assessments of analytical methods,3 statistical methods,4,5 monitoring efficiency,6 and implications for health risk7,8 to develop water quality criteria that meet the needs of regulators, managers, and the public. Fecal indicator bacteria (FIB) (e.g., E. coli and enterococci), as measured by membrane filtration, have been the gold standard for recreational water quality monitoring because they meet the criteria of a suitable indicator, in particular the relationship between FIB and pathogens and analytical simplicity.9 Research has highlighted potential problems associated with this standard, including the survival time of the target bacteria, the abundance of natural sources of these bacteria,10−12 and the lengthy assay timeoften in excess of changing water quality.13,14 As a result of these findings, efforts have been directed at determining a better approach to monitoring, including a more suitable indicator or a more rapid test for pathogens or their surrogates. Among the most promising, cost-effective technologies that have been heavily pursued for beach monitoring purposes is the rapid quantification of enterococci through polymerase chain reaction (qPCR).15,16 This article not subject to U.S. Copyright. Published 2013 by the American Chemical Society

The end point for determining the usefulness of an indicator is actual cases of waterborne illness, as typically derived from epidemiological studies. Epidemiological studies conducted in the 1970s and 1980s by the U.S. Environmental Protection Agency (U.S. EPA) were the basis for the use of culturable E. coli and enterococci in the associated ambient water quality criteria.17 A positive association between concentrations of these fecal indicator bacteria and highly credible gastrointestinal illness (HCGI) confirmed the use of E. coli and enterococci (freshwaters) or enterococci (marine waters) for monitoring recreational waters impacted by known contaminant sources. More recent epidemiological studies conducted by U.S. EPA have confirmed the relationship between concentrations of culturable enterococci and NGI (a revised definition of HCGI) as well as enterococci as measured by qPCR, with very similar results outcomes.16,18 In the 2012 recreational water quality criteria (RWQC), epidemiological findings from both the 1970s and 1980s studies as well as the National Epidemiological and Environmental Assessment of Recreational Water (NEEAR) study of the early 2000s were combined.19 Data were reassessed to develop more accurate estimates of health risk and to compare fresh and marine waters across different analytical methods. Additional flexibility for beach managers was incorporated into the 2012 Received: Revised: Accepted: Published: 3073

October 29, 2012 January 25, 2013 March 5, 2013 March 5, 2013 dx.doi.org/10.1021/es304408y | Environ. Sci. Technol. 2013, 47, 3073−3081

Environmental Science & Technology

Policy Analysis

Figure 1. Established relationship between rates of highly credible gastrointestinal illness (HCGI)/1000 recreators and indicator bacteria in fresh water, as determined in epidemiological studies for culturable E. coli and enterococci17 and enterococci by qPCR.19 Points highlight the magnitude geometric mean associated with the acceptable illness rate. To compare all three methods on the same scale, qPCR results were transformed from expected NGI/1000 recreators to HCGI/1000 recreators. A rate of 8/1000 HCGI is equivalent to 36/1000 NGI.19.

million people in a given summer.22 In Chicago, four beaches were selected with variable ranges of FIB (i.e., E. coli) concentrations. Beaches at 63rd Street and Montrose are among those with frequent high E. coli concentrations in Chicago (geometric mean: 63rd, 140 CFU/100 mL, Montrose, 76.7 CFU/100 mL for years 2000−2008), followed by Calumet (65.9 CFU/100 mL) and then Foster (59.4 CFU/100 mL).23 Typically, percentage of beach days with a swimming advisory or swim ban due to high FIB concentration ranges 19−40%. Jeorse Park Beach is located in urban East Chicago, Indiana and often has the highest rate of closed beach days in the Great Lakes due to high E. coli concentrations,24 with a rate as high as 75% in 2010.24,25 Water samples were collected in 45-cm deep water at established monitoring locations designed to characterize the most highly used swimming water. Triplicate samples were collected at two beaches (Montrose and 63rd Street) in 2009 and five beaches (Foster, Montrose, 63rd Street, Calumet, and Jeorse) in 2010. Samples were collected seven days a week for four weeks in 2009 and three days a week for ten weeks in 2010. After collection, samples were held at 4 °C until analysis, typically within 4−6 h of collection. Samples were analyzed for culturable E. coli using Colilert-18 (IDEXX Laboratories, Westbrook, Maine) and for culturable enterococci using membrane filtration, EPA Method 1600.26 Enterococci qPCR assays were performed using procedures previously described elsewhere.27 Briefly, aliquots of lake (usually 100 mL) water were filtered through 47-mm, 0.4μm-pore-size polycarbonate filters (GE Osmonics Labstore, Minnetonka, MN). The filters were then folded and transferred into 2-mL semiconical microcentrifuge tubes containing 0.3 g of acid-washed glass beads and stored at −80 °C until used. Genomic DNA from filters was extracted using 600 μL of AE Buffer (Qiagen, Valencia, CA), containing 0.2 μg/mL salmon testes DNA (Sigma-Aldrich, St. Louis, MO) as an internal positive control and reference. The tubes containing filters plus AE buffer were placed in a bead beater (Biospec products, Inc., Bartlesville, Oklahoma) for 60 s at maximum speed; the tubes were then centrifuged, and the resultant supernatants were

RWQC, including a choice of indicators, the use of predictive models, and the option to establish site-specific criteria based on quantitative microbial risk assessment.19 With the option for monitoring analytical method left to state regulators and local beach managers and little historical data available for making comparisons, the implications of using newly revised criteria for monitoring has not been adequately examined in terms of expectations of health risk and rates of exceeding water quality criteria. This has implications for meeting both beach notification requirements and long-term limits associated with attainment regulations under the Clean Water Act.20 Both the 1986 criteria and the 2012 RWQC were developed from epidemiological studies at point source impacted beaches: those beaches that are regularly or periodically exposed to discharge from wastewater treatment plants. While this targets the most susceptible populations, based on increased potential for exposure to harmful pathogens, it results in a monitoring model that may be more conservative than is likely necessary at beaches with diffuse or limited sources of contamination. Health risks may differ among locations and depend on the source of contamination, as has been highlighted in several recent studies.21,22 In this analysis we examined the results of beach monitoring data collected and analyzed using three different indicator/ methods to compare expected health outcomes under each monitoring choice. Comparisons between concentrations of indicator bacteria, associated calculated health risk, and number of beach visitors were made. In the analysis we include a range of beach types with variations in source of contamination, mean concentration of indicator bacteria, and visitor use. We sought to illuminate the potential for active use of rapid methods and the implications on beach management and human health risk.

■

MATERIALS AND METHODS Study Locations, Sample Collection, and Analysis. Beach water quality was analyzed at five Lake Michigan beaches extending from Chicago, Illinois into northern Indiana during the summers of 2009 and 2010. Several of the study beaches are high-use beaches; Chicago’s 24 beaches host upward of 20 3074

dx.doi.org/10.1021/es304408y | Environ. Sci. Technol. 2013, 47, 3073−3081


Policy Analysis

broader 90th percentile newly presented as part of the 2012 RWQC. Both have been established based on an acceptable illness rate of 36 NGI/1000 recreators, which is roughly comparable to the 8 HCGI/1000 recreators in the 1986 criteria. As in previous studies, we are interested in the action value used to determine whether recreators are permitted access to the beach water. For this reason, we are using the BAV of 235 CFU/100 mL for culturable E. coli, 70 CFU/100 mL for culturable enterococci, and 1000 CCE/100 mL for qPCRmeasured enterococci. Estimates of illness rates were calculated using equations developed by the U.S. EPA in their extensive epidemiological studies (Figure 1).17,19 For culturable E. coli17

aseptically transferred to clean 1.6-mL centrifuge tubes and stored at −80 °C until used. Primers and hybridization probe sequences for the enterococci and the positive control salmon DNA assays are provided elsewhere.15 All primers and probes for enterococci and salmon DNA assays were obtained from Integrated DNA Technologies (Coralville, Iowa). Reactions were performed in Eppendorf 96-well PCR plates using an Eppendorf Mastercycler ep realplex2 instrument, with a reaction volume of 25 μL containing the following components: 12.5 μL of TaqMan Universal Master Mix, a 2× concentrated proprietary mixture of AmpliTaq Gold DNA polymerase, AmpErase UNG, DNTPs with UTP, passive reference dye, and optimized buffer components (Applied Biosystems, Foster City, California); 5 μL of a mixture containing forward and reverse primers (5 μM each) and 400 nM TaqMan probe; 2.5 μL of 2 mg/mL bovine serum albumin (FisherBiotech) and 5 μL DNA extracts from the samples and Enterococcus faecalis ATCC 29212 calibrators (diluted from 5-fold in AE buffer). The thermal cycling conditions consisted of 2 min at 50 °C, 10 min at 95 °C, followed by 45 cycles of 15 s at 95 °C, and 2 min at 60 °C. Enterococci in test samples were quantified as calibrator cell equivalents (CCE)15 expressed as CCE/100 mL. Suitable positive- and negative-controlsE. faecalis ATCC 29212 and no template, respectivelywere included in all qPCR assays. Review of Epidemiology and Calculation of Water Quality Criteria. The epidemiological studies conducted by U.S. EPA to develop the 1986 ambient water quality criteria compared concentrations of culturable E. coli and enterococci in water with resulting incidents of illness in recreators, e.g., gastrointestinal illness, skin rashes, and respiratory illness.17,28 Further studies were conducted in the early 2000s (NEEAR) by the U.S. EPA to develop the new 2012 RWQC, which compared culturable enterococci and also enterococci as measured by qPCR with a similar range of illnesses.16,18 In the first set of studies, a regression of the results (Figure 1) was used to establish a fresh water quality standard based on an acceptable illness rate, in this case 8/1000 for both E. coli and enterococci. A similar illness rate was used to establish comparable water quality criteria for enterococci by qPCR.19 Several changes were made between the 1986 criteria and the newly released 2012 RWQC that provide a wider range of options for following U.S. EPA’s criteria recommendations. Among these, the definition of the gastrointestinal end point was revised from HCGI to NGI (NEEAR gastrointestinal illness), and all subsequent calculations in the 2012 RWQC were normalized to the new definition, which more broadly includes several illnesses without the required co-occurrence of a fever. This effectively changes the acceptable illness rate by a factor of 4.5.19 Second, one water quality criteria value was established for culturable enterococci to be applied to both marine and freshwater, based on review of the two sets of epidemiological findings. Third, a water quality value was presented for enterococci as measured by qPCR to be used sitespecifically. Also, the use of RWQC for Clean Water Act use attainment and daily beach use are distinguished, with an optional Beach Action Value (BAV)related to the 1986 criteria and subsequent analyses for swimming waters suggested as a more conservative tool for beach notification decisions. For this analysis, we are using the BAV. The BAV is the more conservative value, being based on a 75th percentile around the geometric mean that links FIB to health effects rather than the

Y = − 11.74 + 9.397log10(EC)

(1)

where Y = the rate of HCGI/1000 recreators, and EC = E. coli CFU/100 mL water. The 2012 RWQC criteria based on these epidemiological studies recommend a magnitude geometric mean of 126 E. coli CFU/100 mL, which equates to an HCGI rate of 8/1000 recreators or an NGI rate of 36/1000 recreators. A BAV of 235 CFU/100 mL is also provided, which is the upper 75th percentile of the calculated geometric mean, given a standard deviation of log10 0.4. For enterococci as measured by qPCR19 Y = −27.31 + 23.73log10(QENT )

(2)

where Y = the rate of NGI/1000 recreators, and QENT = enterococci CCE/100 mL water. The 2012 RWQC criteria based on these epidemiological studies recommend a magnitude geometric mean of 470 enterococci CCE/100 mL, which equates to an NGI rate of 36/1000 recreators. A BAV of 1000 CCE/100 mL is also provided, which is the upper 75th percentile of the calculated geometric mean, using combined results for fresh and marine waters, given a standard deviation of log10 0.49. Calculation of the criteria for culturable enterococci is based on a series of approaches that compare the epidemiological findings from the 1970s and 1980s with the NEEAR results of the early 2000s and also combine marine and freshwater criteria. As a result, the recommended RWQC are not associated with a simple regression but rather a series of calculations that support findings from the 1986 criteria. The recommended criteria include a magnitude geometric mean of 35 CFU/100 mL, which is comparable to an HCGI of 8/1000 recreators and an NGI of 36/1000 recreators and is the same as the marine criteria value for 1986. A BAV of 70 CFU/100 mL is also provided, which is the upper 75th percentile of the calculated geometric mean, given a standard deviation of log10 0.44. Given the high level of similarity in results for the two sets of epidemiological studies,19 the regression equation used for the 1986 freshwater criteria17 was used in our analysis. This allowed the assessment of illness rates along a continuum: Y = − 6.28 + 9.397log10(ENT )

(3)

where Y = the rate of HCGI/1000 recreators, and ENT = enterococci CFU/100 mL water. Data Analysis. Data comparisons were made only using sampling events when all three methods were analyzed. Triplicate results were averaged, and all values were log10transformed to meet assumptions of normal distribution. The 3075



Policy Analysis

two years of data for Montrose and 63rd were analyzed separately to maintain N among beaches and because there was a significant difference in FIB counts between years at 63rd Street. Comparisons among beaches and affected populations were made using analysis of variance, followed by Duncan posthoc test. Correlations between monitoring results were performed using Pearson correlation, and results are presented as Pearson R correlation coefficient, a measure of linear relationship, and associated P-values, indicating significance level. 29 In this manuscript, “EC” will be used to refer to E. coli as measured by defined substrate methods;30 “ENT” will be used to refer to enterococci as measured with culturing methods (Method 1600A),26 and “QENT” will be used to refer to enterococci as measured using qPCR.15

■

RESULTS

Comparisons among Beaches. In an analysis of variance, mean log10 EC was significantly higher at Jeorse Park (mean 2.714 MPN/100 mL ± standard deviation 0.562) and 63rd Street in 2009 (2.901 MPN/100 mL ± 0.530) (df = 6, F = 21.468, P < 0.01), and lowest at Foster (1.425 MPN/100 mL ± 0.733). Mean log10 ENT followed a somewhat similar pattern, with significantly higher means at 63rd Street in 2009 (2.723 CFU/100 mL ± 0.665) (df = 6, F = 17.419, P < 0.01), and significantly lower at Foster (1.194 CFU/100 mL ± 0.589) and 63rd Street in 2010 (1.366 CFU/100 mL ± 0.655). Finally, results for log10 QENT followed a pattern similar to ENT results, with significantly higher mean at 63rd Street in 2009 (3.132 CCE/100 mL ± 0.632) (df = 6, F = 19.486, P < 0.01) and lower mean at Foster (1.618 CCE/100 mL ± 0.852) and 63rd Street in 2010 (1.622 CCE/100 mL ± 0.589), but Jeorse Park (2.314 CCE/100 mL ± 0.878) was paired with Montrose 2009 (2.514 CCE/100 mL ± 0.621) and Montrose 2010 (2.129 CCE/100 mL ± 0.666). When the beaches were all grouped together, variation and mean were highest for QENT (2.207 CCE/100 mL ± 0.835) followed by EC (2.061 MPN/100 mL ± 0.829) and ENT (1.748 CFU/100 mL ± 0.817). Differences in variation were high within each method and between beaches (Figure 2). Overall, the results followed a trend of highest means for QENT, followed by EC and then ENT. Indicator Relationships. Results of the three methods were correlated overall (Pearson R = 0.24−0.723), but the breakdown by beach showed different relationships between methods depending on location (Table 1). All three methods were highly correlated at 63rd Street Beach in 2009 (0.634− 0.920, P < 0.01), but those correlation coefficients decreased in 2010. The relationship between EC vs ENT was generally the most highly correlated, followed by ENT vs QENT. Lowest correlations were found at Montrose in 2010, with a particularly low Pearson R for EC vs QENT: 0.239 (P = 0.195). The relationships between methods at Jeorse Park were unusual; correlation coefficients for EC vs ENT (0.257, P = 0.156) and EC vs QENT (0.344, P = 0.063) were quite low, but ENT vs QENT had one of the highest correlation coefficients among the beaches: 0.697 (P < 0.01). For the two beaches with multiple years of data, the difference between years was notable. For Montrose, there was a decrease in mean FIB for all three methods between 2009 and 2010, and correlations between results also decreased between years. At 63rd Street, means for all three FIB also decreased

Figure 2. Comparison of EC, ENT, and QENT method results over single swimming seasons. Box length shows the range of the central 50% of values. The box edges indicate the first and third quartiles. Whiskers extend to values falling within 1.5 times the interquartile range. Asterisks are values considered “outside” and open circles are considered “far outside”.29

Table 1. Pearson Correlation Coefficients between Pairs of Analytical Methods for Each Beach/Yeara Foster Montrose 2009 Montrose 2010 63rd 2009 63rd 2010 Calumet Jeorse overall

EC vs ENT

EC vs QENT

ENT vs QENT

0.340 (0.057) 0.843** 0.480** 0.920** 0.576** 0.534** 0.257 (0.156) 0.638**

0.435* 0.415* 0.239 (0.195) 0.634** 0.356* 0.558** 0.344 (0.063) 0.540**

0.630** 0.510** 0.440* 0.716** 0.579** 0.694** 0.697** 0.724**

Significance of the correlation is indicated by ** (P < 0.01), * (P < 0.05), or specific P-value

a

between years, significantly, and the correlations between methods results also decreased. Expected Illness Outcomes. Average expected illness rates were calculated for each day for all three methods (Figure 3), based on the published epidemiology findings, and therefore three separate regression models (Figure 1). Overall, the highest mean expected illness rate was using ENT (51 NGI/ 1000; range: 0.04−161), followed by EC (47 NGI/1000; range: 0.5−114) and QENT (29 NGI/1000; range: 0.5−76). When divided by beach, the same pattern was evident, except at Jeorse Park. At Jeorse Park, the highest mean number of expected illnesses was using EC (64 NGI/1000), followed by ENT (54 NGI/1000), and then QENT (32 NGI/1000). Because it is directly linked to mean concentration of FIB, the beaches exhibit the same geographic trend, with higher illness rates associated with beaches that have higher FIB concentrations. Differences in Monitoring Strategies. A comparison of the three sampling methods in terms of meeting the BAV reveals that using QENT would result in the lowest number of instances when the sample exceeds the associated BAV (Table 2). For the two culturing tests, there were differences among beaches: Foster, Jeorse, and 63rd Street in 2010 had a higher 3076



Policy Analysis

Population Affected by Health Risk. Within a visitation rate, as described previously,22 expected cases of swimmer gastrointestinal illnesses were determined based on each method’s calculated health risk, using U.S. EPA’s epidemiological studies (Figure 4). Under the same water conditions, the

Figure 3. Comparison of estimates of expected illness rate, using EC, ENT, or QENT. Illness rates are based on epidemiological studies that regressed actual highly credible gastrointestinal illnesses (HCGI) or NEEAR gastrointestinal illness (NGI) with FIB concentrations; all results have been converted to NGI. Box length shows the range of the central 50% of values. The box edges indicate the first and third quartiles. Whiskers extend to values falling within 1.5 times the interquartile range. Asterisks are values considered “outside” and open circles are considered “far outside”.29

Figure 4. Estimated average number of NGI illnesses per day at four Chicago beaches, depending on monitoring method used. Calculation is based on published epidemiological studies19,28 and previous estimates of visitor numbers.22 Lines indicate +1 standard error.

percentage of BAV exceedances using EC, while Calumet, Montrose, and 63rd Street in 2009 had a higher number of BAV exceedances using ENT. Interestingly, the pattern changed between years at 63rd Street, with a remarkable decrease in BAV exceedances, overall, between 2009 and 2010. Most notably, the high percentage of BAV exceedances in 2009 using QENT decreased to 0 in 2010. Also notable was the difference between years at Montrose, where the number of BAV exceedances using EC and ENT increased between years, but using QENT, decreased. Montrose and 63rd were the only two beaches with multiple years considered in the analysis. In terms of the three methods matching the binomial decision for below or exceeding the BAV, all three methods resulted in concentrations below the BAV (44%) on most days. All three methods had measured concentrations exceeding the BAV 13% of the days. For the remaining days, one of the three methods predicted a concentration exceeding the BAV 27% of the time, and two methods measured a concentration exceeding the BAV 16% of the time.

predicted number of individuals experiencing NGI illness would be estimated as significantly higher using ENT and significantly lower using QENT (df = 2, F = 41.487, p < 0.01). At Montrose, the beach with the highest number of visitors (average of 11 800 swimmers/day22), this amounts to a difference in expected illness rate of 349 individuals/day (range 5−824) using QENT to 596 individuals/day (range 89− 1373) using ENT, assuming annual visitors are equally spaced over a 100-day swimming season. Beach visitor counts were not available for Jeorse Park.

■

DISCUSSION The need for a more rapid estimate of water quality has been repeatedly emphasized by beach mangers, public health officials, and the scientific community.13,14,31,32 As a result, U.S. EPA’s recently released 2012 RWQC include the option of monitoring for QENT, a rapid, molecular test for enterococci that can be completed in 4−6 h.19 Several studies have explored

Table 2. Comparison of the Percent of Samples Exceeding a Beach Action Value (BAV) for Three Analytical Methods Tested, Using the Same Water Samplea Calumet 2010 Foster Montrose 2009 Montrose 2010 63rd 2009 63rd 2010 Jeorse 2010

EC %exceeding

ENT %exceeding

QENT %exceeding

N

25 13 18 27 76 24 78

27 12 42 55 97 21 44

3 3 12 6 64 0 26

33 33 33 33 33 33 32

a

The BAV was established in epidemiological studies and corresponds to the 75th percentile limit about a geometric mean of FIB associated with an illness rate of 8 HCGI/1000 or 36 NGI/1000.17,19 3077



Policy Analysis

specific discrepancies, stating that use of QENT should be tested at a beach before being fully implemented for monitoring.19 Implications for Selection of Monitoring Method. With the difference in variation across locations and methods, the selection of beach monitoring method directly influences the expectation of health protection and beach access. Consistently, there were fewer instances of exceeding the BAV associated with the use of QENT; this was true across all beaches and years. And consistently, fewer individuals would be affected by illness, using the regression developed for QENT. Both swimming advisories/beach closures and illness rates could be minimized using QENT, even though the same water would predict more beach closures and higher illness rates if a different indicator was used. One of the problematic issues with the equations used for estimating illness rates is the reduction of errors by averaging. In both early and subsequent epidemiological studies,17,19 large amounts of data were condensed across spatial and temporal scales, thereby reducing variation in the relationship between indicator and illness rate. While this makes sense for setting a national standard across many water types, it also confines local beach management to very restrictive criteria.42 Further, the relationship between methods varies by individual location, as shown in this analysis. As recommended in the 2012 RWQC, beach managers may use epidemiological studies, quantitative microbial risk assessment, or sanitary surveys19 at their beaches in order to adequately assess true risk to human health. For the two study beaches with two years of data, there were significant changes in results between years. A surprising decrease in instances of high FIB across all methods was apparent at 63rd Street Beach between 2009 and 2010. Anecdotally, in the summer of 2010, 63rd Street beach experimentally used canines to decrease gull use of the beach (C. Breitenbach, personal communication, Chicago Park District), a treatment that has since been proven effective for reducing FIB in beach water.43 In that study, both ENT and QENT decreased as a result of gull removal from the beach,43 which happens to be located approximately 120 km from the beaches studied here. Without a control, it is difficult to determine if the gull harassment alone was responsible for this decrease at 63rd Street Beach. The less pronounced difference at Montrose was not unexpected. Previous surveys of multiple year FIB data have indicated some fluctuations between years,42 but without remediation, certain beaches are prone to higher E. coli concentrations, as a result of persistent sources or physical situation. Real-Time Water Monitoring. At the forefront of decision-making is the need for real-time estimates of water quality, a need not met by traditional culturing methods currently widely used in beach monitoring programs but potentially met in QENT. The EC and ENT results presented here are calculated based on real-time concentrations, which is not possible using the methods by which most beaches are currently regulated; the lapse in time between sample collection and culturing results means most beaches are regulated based on old results, a situation that severely undermines public health protection due to rapidly changing FIB concentrations.13,44 Studies of variation in EC and ENT have shown their high temporal variation,13,44 but an analysis of predictive errors in the present data emphasizes the need for real-time estimates. Given that QENT results can be generated in a few hours, the

the sample analytical inhibition and related problems associated with the qPCR analysis and have expressed some concern about its relationship to standard culturable indicators, its cost, and its practicality for monitoring programs.3,33 Only a few studies, however, have explored the human health risk end point in relation to the monitoring options available.34,35 The epidemiological studies conducted as part of establishing the 2012 RWQC found a link between QENT concentrations and gastrointestinal illness,16,18,36 but these studies were all conducted at point source impacted beaches. In studies of marine beaches impacted by nonpoint sources, comparisons have been made of analytical methods, including ENT and QENT, with human health effects; in one a dose−response was only found with ENT35 and in another, no statistical relationship was found between any traditional indicators and illness.8 While human illnesses were not the end point, a study of freshwater beaches impacted by nonpoint sources found a correlation between ENT and QENT with protozoan parasites (Cryptosporidium and Giardia), indicating a human health risk.37 Further evaluation of QENT and health risk would strengthen monitoring applications. Effect of Method on Health Risk Projections. The beaches included in this study have been extensively researched, and both 63rd Street and Jeorse Park beaches are among the Great Lakes beaches historically experiencing high E. coli concentrations throughout the summer swimming season. The results presented here support historical findings for EC as well as the other two methods examined. Where these two high-FIB beaches diverge is in the relationship between the analytical methods: there was a high correlation among all three methods at 63rd Street, but the correlations were quite low for Jeorse Park. So while there is a consistent pattern in scale of methods between beaches, likely because the methods have different targetsi. e., culturable organisms vs DNA of both culturable and unculturable/dead cellsthe relationships between methods vary within beaches. This may be directly related to bacteria source. Multiple sources have been implicated at the study beaches. In particular, shore birds (e.g., gulls and geese) have been identified as a significant source of FIB at 63rd Street Beach and Jeorse Park.12,38 Source of contamination subsequently affects health risk. While the U.S. EPA epidemiological studies examined point source contaminated beaches, the study beaches experience more diffuse sources of FIB contamination. Studies have shown little relationship between ENT and QENT and health risk at nonpoint source contaminated beaches8 but a health risk if the source is stormwater runoff, in marine water.7 The complication between source of contamination and outcomes for these methods has been previously examined in some detail. It was determined that the relationship between ENT and QENT was much more variable at lower concentrations and that proximity to a point source influenced the relationship.33 Analysis of results from another Great Lakes beach indicated that the relationship between the two methods required a corrective factor for site-specific variation attributed to contamination source and local forcing factors (e.g., rainfall, waves).3 Source of contamination directly affects human health risk because human pathogens are targeted in beach monitoring programs, but FIB can come from a variety of nonhuman sources.39−41 Further, all beaches must be monitored using the same guidelines, regardless of FIB source. Relative to the use of QENT, the 2012 RWQC from the U.S. EPA acknowledges site3078



Policy Analysis

elimination of significant sources of FIB and related pathogens, which could minimize the need for extensive monitoring. QENT also offers the advantage of the ability to analyze multiple targets (including indicators and pathogens) in a reasonably short time. Ideally, it would be more useful to apply culture-dependent (i.e., living organisms) methods to determine beach water quality impairments; however, culture-based methods for infectivity measurements are not practical for many pathogens, especially viruses. Thus, QENT can be a good alternative. Ultimately, the choice will be made by managers based on a trade-off of these attributes, with substantial subjective impact. As in a risk−reward model, higher risk (human illnesses) may allow for higher reward (swimmability), and managers and regulators will have to choose a reasonable compromise between the desired outcomes. With public health protection at the forefront of decision-making, the available tools can be effectively used to increase timeliness, accuracy, and effectiveness of beach monitoring.

likelihood of errors between actual and predicted water quality is assumed to be low, although some studies have found decay potentially associated with solar inactivation.45 However, the 24-h time lag between sample collection for EC and ENT and results availability introduces a considerable amount of error. According to the binomial BAV decision-making, there would be 25 type I errors (15%) and 29 type II errors (17%), using EC, and there would be 33 type I errors (19%) and 33 type II errors (19%) using ENT, resulting solely from the time lapse between sample collection and management action. Type II errors are directly related to increased health effects: these errors occur when the water quality is assumed to be below BAV but in fact FIB concentrations exceed the BAV. Any increase in type II errors might have a concomitant increase in health risk. The timeliness of QENT could minimize this type of error. Another rapid method that has been heavily investigated is empirical predictive modeling.46−48 These models rely on hydrometeorological conditions to predict FIB in real time, and all three indicators presented in this analysis have been investigated in predictive modeling efforts. In an analysis using data collected at West Beach, Indiana, Telech et al.49 compared QENT and ENT with physical conditions at the beaches to develop empirical predictive models. They found similar parameters (e.g., turbidity, wave height, rainfall, number of boats) could predict the two method outcomes, but the parameters explained more variation in ENT, both individually and overall. Despite this, sensitivity and specificity about theoretical thresholds were similar between QENT and ENT, as were numbers of type I (0−9%) and type II (1−15%) errors.49 Another analysis of predictive modeling on these beaches had very similar findings27 and pointed out the difference in environmental influences on the two targets and the potential for high variation in QENT. A comparison of error results between culturable FIB and predictive models indicates that predictive modeling generally reduces management errors, but site and source of contamination are important confounding factors.50 Selecting a Monitoring Approach. The options available for beach monitoring have been expanded, allowing beach managers to assess water quality in real time with more accuracy. Few beaches have been able to assess the implications of changing monitoring protocols and the effect it would have on both management and public health protection. Results presented here indicate that the use of QENT would permit more swimming access, but the cost to public health protection is unclear. The three methods assessed measure different target indicators and use different analytical approaches, so while standard illness rates can be calculated across all three, relationships among them are not linear. Further, rates diverge at higher and lower concentrations, complicating direct comparisons. It is possible that targeting the days on which all three methods have high concentrations could give insight as to the conditions associated with higher health risk, overall, but with all three methods, error in the estimate increases at higher FIB concentrations.28,51 The 2012 RWQC afford beach managers with an array of monitoring choices, each with its own level of precision, accuracy, protection, cost, and timeliness. The approach that beach managers pursue could be a combination of methods, including empirical predictive modeling of traditional FIB at suitable beaches, QENT where the high costs are offset by cross-jurisdiction collaboration, and also the location and

■

AUTHOR INFORMATION

Corresponding Author

*Phone: 219-926-8336 ext. 425; fax: 219-929-5792; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This article is Contribution 1740 of the USGS Great Lakes Science Center. Thank you to Sharon Napier, U.S. EPA, for her review and consultation. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

■

REFERENCES

(1) Yoder, J. S.; Hlavsa, M. C.; Craun, G. F.; Hill, V.; Roberts, V.; Yu, P. A.; Hicks, L. A.; Alexander, N. T.; Calderon, R. L.; Roy, S. L.; Beach, M. J. Surveillance for waterborne disease and outbreaks associated with recreational water use and other aquatic facility-associated health events–United States, 2005−2006. MMWR Surveill. Summ. 2008, 57 (9), 1−29. (2) Beaches Environmental Assessment and Coastal Health Act, 33 USC 1251. 2000. (3) Lavender, J. S.; Kinzelman, J. L. A cross comparison of QPCR to agar-based or defined substrate test methods for the determination of Escherichia coli and enterococci in municipal water quality monitoring programs. Water Res. 2009, 43 (19), 4967−4979. (4) Chawla, R.; Hunter, P. R. Classification of bathing water quality based on the parametric calculation of percentiles is unsound. Water Res. 2005, 39, 4552−4558. (5) López Martínez, I.; Á lvarez Díaz, C.; Gil Díaz, J. L.; Revilla Cortezón, J. A.; Juanes, J. A. The European bathing water directive: Application and consequences in quality monitoring programs. J. Environ. Monit. 2010, 12 (1), 369−376. (6) Shibata, T.; Solo-Gabriele, H. M.; Sinigalliano, C. D.; Gidley, M. L.; Plano, L. R. W.; Fleisher, J. M.; Wang, J. D.; Elmir, S. M.; He, G.; Wright, M. E.; Abdelzaher, A. M.; Ortega, C.; Wanless, D.; Garza, A. C.; Kish, J.; Scott, T.; Hollenbeck, J.; Backer, L. C.; Fleming, L. E. Evaluation of conventional and alternative monitoring methods for a recreational marine beach with nonpoint source of fecal contamination. Environ. Sci. Technol. 2010, 44 (21), 8175−8181. (7) Colford, J. M.; Schiff, K. C.; Griffith, J. F.; Yau, V.; Arnold, B. F.; Wright, C. C.; Gruber, J. S.; Wade, T. J.; Burns, S.; Hayes, J.; McGee, C.; Gold, M.; Cao, Y.; Noble, R. T.; Haugland, R.; Weisberg, S. B. Using rapid indicators for Enterococcus to assess the risk of illness 3079



Policy Analysis

after exposure to urban runoff contaminated marine water. Water Res. 2012, 46 (7), 2176−2186. (8) Colford, J. M., Jr.; Wade, T. J.; Schiff, K. C.; Wright, C. C.; Griffith, J. F.; Sandhu, S. K.; Burns, S.; Sobsey, M.; Lovelace, G.; Weisberg, S. B. Water quality indicators and the risk of illness at beaches with nonpoint sources of fecal contamination. Epidemiology 2007, 18 (1), 27−35. (9) Dukta, B. J. Coliforms are an inadequate index of water quality. J. Environ. Health 1973, 36, 39−46. (10) Byappanahalli, M.; Fowler, M.; Shively, D.; Whitman, R. Ubiquity and persistence of Escherichia coli in a midwestern stream. Appl. Environ. Microbiol. 2003, 69 (8), 4549−4555. (11) Solo-Gabriele, H. M.; Wolfert, M. A.; Desmarais, T. R.; Palmer, C. J. Sources of Escherichia coli in a coastal subtropical environment. Appl. Environ. Microbiol. 2000, 66 (1), 230−237. (12) Whitman, R. L.; Nevers, M. B. Foreshore sand as a source of Escherichia coli in nearshore water of a Lake Michigan beach. Appl. Environ. Microbiol. 2003, 69 (9), 5555−5562. (13) Boehm, A. B. Enterococci concentrations in diverse coastal environments exhibit extreme variability. Environ. Sci. Technol. 2007, 41 (24), 8227−8232. (14) Whitman, R. L.; Nevers, M. B.; Gerovac, P. J. Interaction of ambient conditions and fecal coliform bacteria in southern Lake Michigan waters: Monitoring program implications. Nat. Areas J. 1999, 19, 166−171. (15) Haugland, R. A.; Siefring, S. C.; Wymer, L. J.; Brenner, K. P.; Dufour, A. P. Comparison of Enterococcus measurements in freshwater at two recreational beaches by quantitative polymerase chain reaction and membrane filter culture analysis. Water Res. 2005, 39 (4), 559−568. (16) Wade, T. J.; Calderon, R. L.; Sams, E.; Beach, M.; Brenner, K. P.; Williams, A. H.; Dufour, A. P. Rapidly measured indicators of recreational water quality are predictive of swimming associated gastrointestinal illness. Environ. Health Perspect. 2006, 114 (1), 24−28. (17) U.S. EPA. Ambient Water Quality Criteria for Bacteria 1986; EPA 440/5-84-002; Office of Water Regulations and Standards: Washington, DC, 1986. (18) Wade, T. J.; Calderon, R. L.; Brenner, K. P.; Sams, E.; Beach, M.; Haugland, R.; Wymer, L.; Dufour, A. P. High sensitivity of children to swimming-associated gastrointestinal illness: Results using a rapid assay of recreational water quality. Epidemiology 2008, 19 (3), 375−383. (19) U.S. EPA. Recreational Water Quality Criteria; 820-D-11-02; Office of Water: Washington, DC, 2012. (20) Federal Water Pollution Control Amendments, Clean Water Act. 1972. 92nd U.S. Congress, 33 U.S.C. § 1251, P.L. 92-500, 86 Stat. 816 (1972). (21) Calderon, R. L.; Mood, E. W.; Dufour, A. P. Health effects of swimmers and nonpoint sources of contaminated water. Int. J. Environ. Health Res. 1991, 1, 21−31. (22) Nevers, M. B.; Whitman, R. L. Efficacy of monitoring and empirical predictive modeling at improving public health protection at Chicago beaches. Water Res. 2011, 45 (4), 1659−1668. (23) Whitman, R. L.; Nevers, M. B. Summer E. coli patterns and responses along 23 Chicago beaches. Environ. Sci. Technol. 2008, 42 (24), 9217−9224. (24) Dorfman, M.; Rosselot, K. S. Testing the Waters: A Guide to Water Quality at Vacation Beaches, 21st Annual Report; National Resources Defense Council, 2011. (25) Indiana Department of Environmental Management. https:// extranet.idem.in.gov/beachguard/. (09/13/12), (26) U.S. EPA. Method 1600--Enterococci in Water by Membrane Filtration Using Membrane-Enterococcus Indoxyl-B-D-Glucoside Agar (mEI); EOA-821-R-02-022; Washington, D.C., 2002. (27) Byappanahalli, M. N.; Whitman, R. L.; Shively, D. A.; Nevers, M. B. Linking non-culturable (qPCR) and culturable enterococci densities with hydrometeorological conditions. Sci. Total Environ. 2010, 408 (16), 3096−3101.

(28) Dufour, A. P. Health Effects Criteria for Fresh Recreational Waters; EPA Report EPA-600/1-84-004; Cincinnati, OH, 1984. (29) SYSTAT. Systat 13.0; SYSTAT Software, Inc.: Chicago, IL, 2009. (30) Francy, D. S.; Darner, R. A. Comparison of methods for determining Escherichia coli concentrations in recreational waters. Water Res. 2000, 34 (10), 2770−2778. (31) Morgan, R.; Morris, C.; Livzey, K.; Hogan, J.; Buttigieg, N.; Pollner, R.; Kacian, D.; Weeks, I. Rapid tests for detection and quantitation of Enterococcus contamination in recreational waters. J. Environ. Monit. 2007, 9 (5), 424−426. (32) U.S. EPA. Action Plan for Beaches and Recreational Waters; EPA/ 600/R-98/079; Washington, DC, 1999. (33) Whitman, R. L.; Ge, Z.; Nevers, M. B.; Boehm, A. B.; Chern, E.; Lukasik, A. M.; Przybyla-Kelly, K.; Shively, D. A.; White, E.; Haugland, R.; Zepp, R. G.; Byappanahalli, M. N. Relationship and variation of qPCR and culturable enterococci in ambient surface waters are predictable. Environ. Sci. Technol. 2010, 44 (13), 5049−5054. (34) Fleisher, J. M.; Fleming, L. E.; Solo-Gabriele, H. M.; Kish, J. K.; Sinigalliano, C. D.; Plano, L.; Elmir, S. M.; Wang, J. D.; Withum, K.; Shibata, T.; Gidley, M. L.; Abdelzaher, A.; He, G.; Ortega, C.; Zhu, X.; Wright, M.; Hollenbeck, J.; Backer, L. C. The BEACHES Study: Health effects and exposures from non-point source microbial contaminants in subtropical recreational marine waters. Int. J. Epidemiol. 2010, 39 (5), 1291−1298. (35) Sinigalliano, C. D.; Fleisher, J. M.; Gidley, M. L.; Solo-Gabriele, H. M.; Shibata, T.; Plano, L. R. W.; Elmir, S. M.; Wanless, D.; Bartkowiak, J.; Boiteau, R.; Withum, K.; Abdelzaher, A. M.; He, G.; Ortega, C.; Zhu, X.; Wright, M. E.; Kish, J.; Hollenbeck, J.; Scott, T.; Backer, L. C.; Fleming, L. E. Traditional and molecular analyses for fecal indicator bacteria in nonpoint source subtropical recreational marine waters. Water Res. 2010, 44 (10), 3763−3772. (36) Wade, T. J.; Sams, E.; Brenner, K. P.; Haugland, R.; Chern, E.; Beach, M.; Wymer, L.; Rankin, C. C.; Love, D.; Li, Q.; Noble, R.; Dufour, A. P. Rapidly measured indicators of recreational water quality and swimming-associated illness at marine beaches: A prospective cohort study. Environ. Health: A Global Access Sci. Source 2010, 9, (1). (37) Dorevitch, S.; Doi, M.; Hsu, F. C.; Lin, K. T.; Roberts, J. D.; Liu, L. C.; Gladding, R.; Vannoy, E.; Li, H.; Javor, M.; Scheff, P. A. A comparison of rapid and conventional measures of indicator bacteria as predictors of waterborne protozoan pathogen presence and density. J. Environ. Monit. 2011, 13 (9), 2427−2435. (38) Byappanahalli, M. N.; Nevers, M. B.; Shively, D. A.; PrzybylaKelly, K.; Lukasik, A. M.; Whitman, R. L. An Investigation of E. coli Sources in Beach Water at Jeorse Park. In Report to the Indiana Department of Environmental Management; U.S. Geological Survey, 2011; p 28. (39) Badgley, B. D.; Nayak, B. S.; Harwood, V. J. The importance of sediment and submerged aquatic vegetation as potential habitats for persistent strains of enterococci in a subtropical watershed. Water Res. 2010, 44 (20), 5857−5866. (40) Edge, T. A.; Hill, S. Multiple lines of evidence to identify the sources of fecal pollution at a freshwater beach in Hamilton Harbour, Lake Ontario. Water Res. 2007, 41 (16), 3585−3594. (41) Ishii, S.; Sadowsky, M. J. Escherichia coli in the environment: Implications for water quality and human health. Microbes Environ. 2008, 23 (2), 101−108. (42) Nevers, M. B.; Whitman, R. L. Beach monitoring criteria: Reading the fine print. Environ. Sci. Technol. 2011, 45 (24), 10315− 10321. (43) Converse, R. R.; Kinzelman, J. L.; Sams, E. A.; Hudgens, E.; Dufour, A. P.; Ryu, H.; Santo-Domingo, J. W.; Kelty, C. A.; Shanks, O. C.; Siefring, S. D.; Haugland, R. A.; Wade, T. J. Dramatic improvements in beach water quality following gull removal. Environ. Sci. Technol. 2012, 46 (18), 10206−10213. (44) Whitman, R. L.; Nevers, M. B. Escherichia coli sampling reliability at a frequently closed Chicago beach: Monitoring and management implications. Environ. Sci. Technol. 2004, 38 (16), 4241− 4246. 3080



Policy Analysis

(45) Boehm, A. B.; Yamahara, K. M.; Love, D. C.; Peterson, B. M.; McNeill, K.; Nelson, K. L. Covariation and photoinactivation of traditional, rapid, and novel indicator organisms and human viruses at a sewage-impacted marine beach. Environ. Sci. Technol. 2009, 43 (21), 8046−8052. (46) Francy, D. S. Use of predictive models and rapid methods to nowcast bacteria levels at coastal beaches. Aquat. Ecosyst. Health Manage. 2009, 12 (2), 177−182. (47) Nevers, M. B.; Whitman, R. L. Nowcast modeling of Escherichia coli concentrations at multiple urban beaches of southern Lake Michigan. Water Res. 2005, 39 (20), 5250−5260. (48) Nevers, M. B.; Whitman, R. L.; Frick, W. A.; Ge, Z. Interaction and influence of two creeks on E. coli concentrations of nearby beaches: Exploration of predictability and mechanisms. J. Environ. Qual. 2007, 36 (5), 1338−1345. (49) Telech, J. W.; Brenner, K. P.; Haugland, R.; Sams, E.; Dufour, A. P.; Wymer, L.; Wade, T. J. Modeling Enterococcus densities measured by quantitative polymerase chain reaction and membrane filtration using environmental conditions at four Great Lakes beaches. Water Res. 2009, 43 (19), 4947−4955. (50) Nevers, M. B.; Boehm, A. B., Modeling fate and transport of fecal bacteria in surface water. In The Fecal Bacteria, Sadowsky, M. J., Whitman, R. L., Eds.; ASM Press: Washington, DC, 2011; pp 165− 188. (51) Cabelli, V. J. Health Effects Criteria for Marine Recreational Waters; EPA-600/1-80-031; U.S. Environmental Protection Agency: Research Triangle Park, NC, 1983.

3081


Choices in Recreational Water Quality Monitoring: New Opportunities

Recommend Documents