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Beach Monitoring Criteria: Reading the Fine Print Meredith B. Nevers* and Richard L. Whitman U.S. Geological Survey, Great Lakes Science Center, Lake Michigan Ecological Research Station, 1100 N. Mineral Springs Road, Porter, Indiana 46304, United States ABSTRACT: Beach monitoring programs aim to decrease swimming-related illnesses resulting from exposure to harmful microbes in recreational waters, while providing maximum beach access. Managers are advised by the U.S. EPA to estimate microbiological water quality based on a 5-day geometric mean of fecal indicator bacteria (FIB) concentrations or on a jurisdiction-specific single-sample maximum; however, most opt instead to apply a default single-sample maximum to ease application. We examined whether re-evaluation of the U.S. EPA ambient water quality criteria (AWQC) and the epidemiological studies on which they are based could increase public beach access without affecting presumed health risk. Single-sample maxima were calculated using historic monitoring data for 50 beaches along coastal Lake Michigan on various temporal and spatial groupings to assess flexibility in the application of the AWQC. No calculation on either scale was as low as the default maximum (235 CFU/100 mL) that managers typically use, indicating that current applications may be more conservative than the outlined AWQC. It was notable that beaches subject to point source FIB contamination had lower variation, highlighting the bias in the standards for these beaches. Until new water quality standards are promulgated, more site-specific application of the AWQC may benefit beach managers by allowing swimmers greater access to beaches. This issue will be an important consideration in addressing the forthcoming beach monitoring standards.
’ INTRODUCTION Passage of the U.S. BEACH Act1 required that all coastal recreational waters be monitored for fecal indicator bacteria (FIB) starting in 2004. With the initiation of numerous programs and expansion of existing programs, a wealth of data has been generated by monitoring agencies from coastal beaches, including Great Lakes beaches. With the increase in data generation, more instances of high FIB concentrations have been detected, resulting in a higher overall number of beach closures and the impression that beach water quality is universally declining,2 despite a lack of supporting information. Negative publicity, combined with known limitations of using FIB as an indicator—i.e., lengthy analysis time, natural sources3—has been a likely disincentive to expand beach monitoring beyond minimal requirements. While the existing ambient water quality criteria (AWQC) originally developed in 1986 are under revision by U.S. EPA, beach managers are obliged to monitor their beaches using the currently accepted FIB standards until new standards are promulgated; however, there is underused flexibility already integrated into the existing AWQC that could benefit the public and management. The AWQC developed by the U.S. EPA for freshwater were derived from epidemiological studies conducted in 19791982 at four beaches on two lakes directly influenced by point source contamination.4 The criteria define an acceptable illness rate of 8/1000 swimmers and an FIB standard; water with FIB concentrations in This article not subject to U.S. Copyright. Published 2011 by the American Chemical Society
excess of the criteria are out of compliance, and beach managers typically close the beach to swimming or issue a swimming advisory. The AWQC primarily recommend that beach management decisions be based on a geometric mean calculation of water quality of at least five samples collected over the previous 30-day period to estimate the steady-state mean; this is to prevent unnecessary closures/advisories due to day to day fluctuations in FIB. Decisions about implementing the AWQC standards are the responsibility of the individual states, and most states opt to use a single sample maximum limit (ssmax) (e.g., refs 510), also presented in the AWQC, rather than the five-day mean despite the document’s urging that such a decision “may be erroneous”.11 Further, most states use the default ssmax (ssmaxEPA) of 235 colony-forming units (CFU) E. coli/100 mL water calculated by EPA and based on standard deviations derived in the epidemiological studies, disregarding the AWQC recommendation that “each jurisdiction should establish its own standard deviation for its conditions which would then vary the single sample limit”.11 FIB may exhibit high spatial-temporal variation in beach waters, the extent of which varies among beaches.1214 For this reason, Received: July 25, 2011 Accepted: November 7, 2011 Revised: October 27, 2011 Published: November 07, 2011 10315
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Environmental Science & Technology the AWQC provide flexibility in the form of establishing jurisdiction-specific ssmax (ssmaxCALC). An understanding of variation in local bacteria densities would benefit swimmers and beach managers because a monitoring standard can be developed that may be as protective of public health risk as the default criteria outlined in the AWQC. In this study, we examine historical beach monitoring data for beaches along southern Lake Michigan, extending from Chicago through coastal Indiana, and we calculate ssmaxCALC, partitioning data in a number of different spatial and temporal scales to highlight the potential variation in monitoring outcomes. Further, we explore the implications of this assessment on beach monitoring outcomes, expectation of health protection, and beach access. The results of this study emphasize typically overlooked details included in the AWQC and the adaptability of monitoring protocols, with implications for both the current and forthcoming ambient water quality criteria.
’ METHODS Beach monitoring data were obtained from several existing databases maintained by the Chicago Park District, the Indiana Department of Environmental Management, and the Indiana Dunes National Lakeshore;15,16 included in the analysis were 50 beaches that cover the majority of the Great Lakes coasts of Illinois and Indiana. These beaches are monitored for E. coli, one of the fecal indicator bacteria, as recommended for freshwater beaches in the AWQC. Frequency of monitoring at a given beach ranged from 1 to 7 days a week, and the available historical monitoring data ranged from 6 to 21 years. Beach monitoring is under the jurisdiction of numerous regulatory agencies, which use different sampling replication and averaging protocols and have different regulations regarding swimming restrictions when water quality is out of compliance.3 Although sampling depth, location, and time can have a significant impact on E. coli results outcome,3 as well as analytical method (defined substrate or membrane filtration),17 we evaluate the data “as is”; that is, as submitted to regulatory agencies and U.S. EPA to satisfy requirements of the BEACH Act and therefore considered adequate for monitoring recreational water quality. For the time scale analysis, data from five beaches in Indiana were used that were collected during a period of 21 years (1990 2010). These beaches are directly influenced by the outfall of the Little Calumet River (Burns Ditch): from west (closest to the river outfall) to east, the beaches include Ogden Dunes (Ogden), West, Wells Street (Wells), Marquette Park (Marquette), and Lake Street (Lake) Beaches. In further analyses of spatial patterns, data collected from 2004 to 2010 at 50 subject beaches were used for more intensive analyses because of the higher collection frequency and presumed better characterization of overall water quality. Beaches in this region are affected in different ways by point and nonpoint sources of fecal contamination. Spatial analysis of Lake Michigan beaches included grouping the 50 beaches into 5 geographic regions: Chicago (22 beaches in Chicago, IL); Lake (7 beaches in western Lake County, IN); Burns Ditch (5 beaches influenced by the Burns Ditch outfall of the Little Calumet River in IN); Indiana Dunes (8 beaches in the Indiana Dunes State Park and National Lakeshore); and LaPorte (8 beaches in LaPorte County, IN). These designations are based jointly on geographic location, management jurisdiction, and specific point source influence.
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Single-sample maxima (ssmax) for freshwater were calculated based on the AWQC11 developed in epidemiological studies conducted at two locations in the United States:4 ssmax ¼ 10∧ ðlog10 GM þ fZ SDgÞ or ssmax ¼ GM 10∧ ðZ SDÞ where ssmax = the single sample maximum limit; GM = 126, geometric mean E. coli concentration developed in the epidemiological study for acceptable illness rate of 8 per 1000; Z = 0.675, the 75% calculated one-sided confidence level for a designated beach area; and SD is the calculated standard deviation of the singlesample log10 E.coli concentrations. In the AWQC, a geometric mean E. coli concentration for a minimum of 5 samples collected over a 30-day period in excess of 126 CFU/100 mL is considered to be out of compliance (GM = 126) and corresponds to an acceptable swimming-associated illness rate of 8 per 1000 swimmers. The AWQC also consider a single sample with a concentration in excess of 235 CFU/100 mL to be out of compliance. This is based on a control chart approach, with the upper control limit being the 75th percentile for a geometric mean of 126, and using a standard deviation of 0.4 log, as calculated from the epidemiological studies.4 With the recommendation of a beach- or jurisdiction-specific calculation, a beach that has a higher standard deviation in E. coli concentrations, indicative of higher variation overall, would have a higher ssmax, regardless of the water quality. Similarly, a beach with a lower standard deviation and lower variation in E. coli concentrations would have a lower ssmax. Data were analyzed using Systat 12.018 and SPSS 12.019 software. Overall FIB concentrations were compared across beaches in the spatial variance section using analysis of variance with P < 0.05. Standard deviation for monitoring data was calculated by bootstrapping a subset of the entire 20042010 data set. A total of 100 calculations of standard deviation were made using 100 randomly selected monitoring results without replacement, after this sample size was determined to be adequate to represent confidence limits in a power analysis. The standard deviation and 95% confidence interval are reported. The number of instances of FIB concentration exceeding the ssmaxCALC vs the ssmaxEPA were compared using the nonparametric McNemar test or the Fisher exact test where data distribution was uneven (P < 0.05).
’ RESULTS Temporal Variation. A review of data across time for a group of five Indiana beaches directly influenced by a point source (Burns Ditch beaches) indicates that variation in the ssmaxCALC depends on time range considered in the calculation. E. coli means and standard deviations calculated from all available monitoring data were highly variable year to year and did not follow a general trend, so ssmaxCALC fluctuated from a low of 253 in 1991 to a high of 430 in 2003 (Figure 1). Calculating across a 4-year, moving average smoothed the variation, resulting in a lower range of ssmaxCALC (296387; Figure 1). Cumulative calculation over the 21 years further smoothed variation, resulting in a narrower range but higher overall ssmaxCALC for any given year (304353; Figure 1). Regardless of calculation method, standard deviation and therefore ssmaxCALC for these beaches was consistently 10316
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Environmental Science & Technology
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Figure 1. Single-sample maxima (ssmaxCALC), as calculated from the EPA ambient water quality criteria,5 for five Indiana beaches along Lake Michigan using three methods of subdividing the data. Annual is an ssmaxCALC for each individual year; cumulative is the ssmaxCALC for each year using the target and all previous years of available data; 4 yr-running is the ssmaxCALC using the target year and previous three. The ssmaxEPA is the default 235 ssmax provided in the AWQC and is provided for reference. N for each individual year ranged from 33 (in 1991) to 383 (in 2004).
Figure 2. Calculated single-sample maxima (ssmaxCALC), as defined in the EPA ambient water quality criteria5 for individual beaches along southern Lake Michigan and the entire area. N = 100 for each beach; box and lines indicate the ssmaxCALC with 95% confidence limit.
higher than the ssmaxEPA currently used in the local beach monitoring programs. Spatial Variation. The increase in monitoring frequency in 2004 significantly increased the number of samples considered, therefore, our examination of spatial calculations of ssmaxCALC incorporated monitoring data from 2004 to 2010. Analysis of data across a range of spatial scales also showed high variation in E. coli concentrations and ssmaxCALC (Figure 2). Of the 50 beaches studied, four stood out with significantly higher E. coli concentrations: Jeorse Park, 63rd Street, Washington Park, and Buffington beaches (F = 64.694, df = 49, P < 0.01), all of which have a history of frequently elevated E. coli events.2,20 Only Washington Park, however, is situated immediately adjacent to a point source outfall; FIB sources at any of these beaches have not been definitively identified. For individual beaches, ssmaxCALC included a wide range: 261 CFU/100 mL at Washington Park, IN to 407
CFU/100 mL at Buffington Harbor, IN (Figure 2). Beaches with lower standard deviations/ssmaxCALC often included locations directly influenced by a point source outfall (Washington Park, Ogden, Lake). For a subset of beaches known to be directly influenced by a point source outfall,21 Burns Ditch, there was relatively less variation between ssmaxCALC for individual beaches (range 287302 CFU/100 mL). E. coli concentrations in general were significantly higher at Lake and Marquette than at the other three beaches (impacted by Burns Ditch) according to an ANOVA (F = 40.373, df = 4, P < 0.01). Lake had the lowest standard deviation and therefore the lowest ssmaxCALC: 285 CFU/100 mL; the highest ssmaxCALC was associated with West (300 CFU/100 mL). When beaches were divided into five groupings, analysis of variance revealed a significant difference across regions (F = 110.973, df = 4, P < 0.01), with Lake County and Chicago beaches having 10317
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Environmental Science & Technology
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Table 1. Values of ssmaxCALC for Each of Five Designated Regions along Southern Lake Michigan, Using Monitoring Data Collected 20042010a region Chicago
N
region ssmaxCALC
% exceeding ssmaxEPA
% exceeding region ssmaxCALC
difference in number of beach days
11248
394
17.4
11.3
681
Lake Burns Ditch
3951 2217
401 304
24.2 7.9
17.9 6.0
248 43
Indiana Dunes
2003
318
11.5
7.9
73
LaPorte
3149
328
9.2
5.9
102
a
Percent of beach days exceeding the default ssmaxEPA and exceeding ssmaxCALC specifically for a given region are provided. Using a region-specific ssmaxCALC would result in a higher number of beach days meeting the AWQC.
Figure 3. Outline of southern Lake Michigan beach locations showing variation in the standard deviation of E. coli concentrations (color gradations) and % difference in number of days exceeding the E. coli concentration ssmaxEPA vs ssmaxCALC (size gradations).
significantly higher means (Figure 2). The lowest standard deviation and ssmaxCALC were associated with the Burns Ditch beaches (303 CFU/100 mL); this characteristic supports the application of the monitoring standard at point source locations, although the standard deviation was notably higher (0.567) than the 0.4 used in the AWQC. The highest standard deviation and ssmaxCALC was associated with the Lake County beaches (400 CFU/100 mL); these beaches are subject to frequently elevated E. coli concentrations and high fluctuations overall, although no source of contamination has been adequately identified. Using data from the entire 50 beach data set, the ssmaxCALC for the entire southern Lake Michigan crescent was 376 CFU/100 mL (SD = 0.703).
Effect of Alternate ssmax on Number of Beach Advisories. Use of an ssmax based on local water quality would have resulted in an increase in beach access (open beaches) because in all instances ssmaxCALC was higher than the default 235 CFU/ 100 mL in the AWQC. Results of a nonparametric McNemar test indicated that use of ssmaxCALC for all regions (P < 0.01) and the southern Lake Michigan region (McNemar chi-squared = 1125.0, df = 1, P < 0.01) resulted in significantly fewer instances of beaches exceeding the ssmax (Table 1). Results for individual beaches also indicated that the higher ssmaxCALC resulted in significantly fewer out-of-compliance events (P < 0.01) (Figure 3), according to the McNemar test. Beaches 10318
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Environmental Science & Technology associated with natural, undeveloped areas without a nearby point source were more likely not to exhibit a significant difference and included Central, Dunbar, Kemil, Long Beach, and Porter. Kemil had no samples collected with an E. coli count that exceeded either standard. Based on these historical monitoring data, use of a regional ssmaxCALC decreased the percent of instances when the beach water was out of compliance, when compared to the ssmaxEPA. This decrease ranged from 2 to 6%, which was the equivalent of 43681 beach days (Table 1). Using ssmaxCALC for individual beaches, the number of days out of compliance decreased in a range from 0 to 11% (Figure 3).
’ DISCUSSION Regardless of how the data were partitioned, temporally or spatially, all ssmaxCALC were higher than the standard ssmaxEPA currently applied by beach managers in southern Lake Michigan to determine when swimming water is out of compliance, according to the AWQC. This indicates that many current monitoring applications in this region and perhaps nationwide may be overly conservative relative to the recommendations of the AWQC. The AWQC are currently under revision by the U.S. EPA; until any upcoming changes are promulgated, beach managers are likely to monitor their beaches using the currently accepted FIB standards since retrospective analyses and applications for revisions are cumbersome. The AWQC, as we highlight, provide allowances for the wide range of waters subject to assessment; these could benefit practical beach management and should be carefully considered in development of the new criteria. The great discrepancy between ssmaxEPA and ssmaxCALC may stem from the calculation used in epidemiological studies.4 The epidemiological studies were conducted at four freshwater beaches over two years, plus one additional beach/year, and included the collection of an unidentified number of days of indicator bacteria data along with interviews from thousands of beachgoers.4 These data were simplified to a single mean E. coli concentration and number of illnesses for each beach/year, and from the resulting nine data pairs, regression analysis was conducted.4 Further, the standard deviation from which the ssmaxEPA is calculated is based on the standard deviation of the log10 mean E. coli concentrations. This type of averaging will reduce variation, resulting in a lower standard deviation, and perhaps explaining the difference between those results and the analyses presented here. Calculation of ssmaxCALC must consider the temporal and spatial extent over which data are averaged. Neither of these factors are specifically mentioned in the AWQC, but Chawla and Hunter22 recommended periods of 34 years; this was subsequently included in the European Union directive.23 Using this approach, we calculated a 4-year running average, and it was notable that in the earlier years the standard deviation was higher (0.530.79) and the ssmaxCALC range was wider (335387 CFU/100 mL), prior to the added influx of data starting in 2004 (range SD 0.510.66; ssmaxCALC 296324 CFU/100 mL). The increase in number of observations, in general, increases confidence in the estimate of overall water quality.11 Use of a single year of data could result in highly variable ssmaxCALC due to interyear variation in FIB concentrations resulting from rainfall, sewer overflows, differences in sources, and hydrometeorological effects.
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Basing overall FIB estimates of variation on sources or similar geographic areas may provide a better estimate of overall variation in FIB. Research has indicated that background FIB fluctuations are similar across beach regions as long as 35 km,20,24 but variation within and between beaches may warrant use of shorter lengths of coast to account for local sources and circulation patterns.13,14 In each beach group in this analysis, the ssmaxCALC was higher than 300 CFU/100 mL. Simplifying across the entire 50 beaches resulted in an ssmaxCALC well above the default 235 CFU/100 mL generally used (Figure 3). Use of an ssmaxCALC for specific regions resulted in increased access (i.e., fewer days out of compliance) to beaches at all regional divisions, with an increase, for example, of as many as 681 days of beach access overall for Chicago beaches. One of the five designated regions directly downcurrent of a point source (Burns Ditch) had the least overall benefit because it had the lowest ssmaxCALC. The higher ssmaxCALC for all regions, even an ssmaxCALC for the entire southern Lake Michigan region, would allow for more beach access days. Previous estimates of regionspecific ssmaxCALC also indicated improvements in prediction success using empirical predictive models that can potentially provide results in less than an hour compared to current analysis techniques that can take 2448 h.25 Because of the high variation in E. coli among beaches, use of the default ssmaxEPA despite the recommendation for a sitespecific ssmaxCALC results in the application of differential illness risk across beaches. Two components of the ssmaxEPA are the acceptable illness rate set at 8/1000 and the 75% confidence limits (around the geometric mean of 126 CFU/100 mL); use of ssmaxEPA when the standard deviation of a water body is different from 0.4 affects both of these assumptions. If the confidence limits are controlled at 75%, use of ssmaxEPA in a situation where ssmaxEPA < ssmaxCALC results in the application of a lower level of risk tolerance, that is, management is “overprotective”. For example, at Buffington, where logSD = 0.755 and ssmaxCALC = 407 CFU/100 mL, use of ssmaxEPA (235 CFU/100 mL) for beach management would effectively protect for an acceptable illness rate of 5.74/1000. Alternately, if the acceptable illness rate is controlled at 8/1000, use of ssmaxEPA when ssmaxEPA < ssmaxCALC results in a reduction of the confidence limit; from 75% in the AWQC standards to 64% in the case of Buffington. The implications of the violation of these assumptions will need to be considered in the development of new standards because of the potential for uneven human health protection across beaches, regions, or states. Accuracy of risk estimates in the AWQC are hindered by the lack of data collected that correspond to high illness rates: because illness rate must be projected beyond a certain level, there is a high rate of error in estimates at higher bacteria/illness levels. Therefore, accuracy in presumptions of illness risk decreases at higher levels. Interpretation of higher illness rates and higher calculations of ssmaxCALC should take this into consideration. For this reason, studies that estimate risk based on concentrations of known pathogens (e.g, quantitative microbial risk assessment, QMRA26) may be a viable solution for more accurately estimating acceptable water quality. These evaluations determine the public health outcome with exposure to water contaminated with fecal pathogens (e.g., Norovirus, Cryptosporidium, Giardia). This technique may allow estimates of illness rates at higher levels of indicator bacteria, without extensive epidemiological studies. Although not considered in this analysis, the original AWQC also outlined the use of wider confidence intervals for swimming 10319
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Environmental Science & Technology areas with lower use, although use is not quantified in the criteria. The beaches considered in this analysis have a wide range of use, so those allowances may apply to some of these waters. Numerous beaches in Great Lakes locations could further consider these wider confidence intervals in calculating and using an ssmaxCALC for monitoring. Use of the current FIB monitoring standard has been questioned due to inconsistencies in development and application: specifically, use of data from rain-free days, time period averaging, and the presence of a point source sewage impact. The low ssmaxCALC at the Burns Ditch beaches is reflected in a related study of E. coli variation conducted by U.S. EPA at one of these beaches (West);27 here, the ssmaxCALC, based on the standard deviation (0.538), was comparable to the results presented here: 291 CFU/100 mL. At the other freshwater beach studied by Wymer et al.,27 Belle Isle in Michigan, the resulting ssmaxCALC was 255 CFU/100 mL (SD = 0.453). The lowest ssmaxCALC for an individual beach in this study was Washington Park (261 CFU/100 mL; SD = 0.469), which is also affected by a point source, Trail Creek. These comparisons support the strength of this statistic for point source impacted beaches. It should be noted, however, that all coastal beaches are required to incorporate these standards into a beach monitoring program, regardless of whether a point source is present. Our study illustrates that data averaging spatially and temporally is an important consideration in the development and implementation of new water quality criteria for both fresh and marine waters. Others have suggested the use of a more flexible system of standards with more gradations to designate water quality,28 as opposed to the binary system inherent in the AWQC. Even with the adoption of new standards, however, implementation will likely take some time, and beach managers may re-evaluate the variable application of the current AWQC to allow greater access to beaches presumably without additional public health risk.
’ AUTHOR INFORMATION Corresponding Author
*Phone: 219-926-8336 ext. 425; fax: 219-929-5792; e-mail:
[email protected].
’ ACKNOWLEDGMENT This research was funded by the USGS Ocean Research Priorities Plan and the Great Lakes Restoration Initiative through USGS. Engaging discussions with Shannon Briggs (Michigan Department of Environmental Quality) and reviews by Samir Elmir (Florida Department of Health), Jean Adams (USGS), and anonymous reviewers helped us to improve this manuscript. Photo of North Avenue Beach, Chicago, Illinois; credit: Antonio Vernon. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This article is Contribution 1670 of the USGS Great Lakes Science Center. ’ REFERENCES (1) Beaches Environmental Assessment and Coastal Health Act, 33 USC 1251. Public Law 106284, 114 Stat. 870877, 2000; Vol. 33 USC 1251. (2) Dorfman, M.; Rosselot, K. S. Testing the Waters: A Guide to Water Quality at Vacation Beaches, 19th ed.; Natural Resources Defense Council: New York, 2009.
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(3) Nevers, M. B.; Whitman, R. L. Policies and practices of beach monitoring in the Great Lakes, USA: A critical review. J. Environ. Monit. 2010, 12 (3), 581–590. (4) Dufour, A. P. Health Effects Criteria for Fresh Recreational Waters; EPA-600/1-84-004; U.S. EPA: Cincinnati, OH, 1984. (5) New York State Department of Health. Public Health Law. Subpart 62 Bathing Beaches, Section 225 62.15, 2004. (6) State of Illinois, Swimming Pool and Bathing Beach Act 210 ILCS 125. 77: Public Health Section 820.400 minimum Sanitary Requirements for Bathing Beaches; Illinois General Assembly, 2004. (7) State of Indiana. Water Quality Standards. 327 IAC 21-6, Indiana Administrative Code, 2008. (8) State of Michigan. Michigan Natural Resources and Environmental Protection Act. Part 31, Water Quality Standard, Part 4 Rules, Rule 323.1062 (1), 1997; Vol. PA 451. (9) State of Minnesota. Minnesota Administrative Rule 7050.0222. Minnesota Office of the Revisor Statutes, 2008. (10) Wisconsin Department of Natural Resources. Water Quality Standards for Wisconsin Surface Waters; Department of Natural Resources: Madison, WI, 2001. (11) U.S. EPA. Ambient Water Quality Criteria for Bacteria; EPA 440/5-84-002; Office of Water Regulations and Standards: Washington, DC, 1986. (12) Boehm, A. B.; Grant, S. B.; Kim, J. H.; Mowbray, S. L.; McGee, C. D.; Clark, C. D.; Foley, D. M.; Wellman, D. E. Decadal and shorter period variability of surf zone water quality at Huntington Beach, California. Environ. Sci. Technol. 2002, 36 (18), 3885–3892. (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. Escherichia coli sampling reliability at a frequently closed Chicago beach: Monitoring and management implications. Environ. Sci. Technol. 2004, 38 (16), 4241–4246. (15) Indiana Department of Environmental Management. Accessed Sept. 15, 2011; https://extranet.idem.in.gov/beachguard/. (16) Illinois Department of Public Health. Accessed Sept. 15, 2011. http://app.idph.state.il.us/envhealth/ilbeaches/public/. (17) Eckner, K. F. Comparison of membrane filtration and multipletube fermentation by the Colilert and Enterolert methods for detection of waterborne coliform bacteria, Escherichia coli, and enterococci used in drinking water and bathing water quality monitoring in southern Sweden. Appl. Environ. Microbiol. 1998, 64, 3079–3083. (18) Systat 12.0; SYSTAT Software, Inc.: Chicago, IL, 2007. (19) SPSS version 12; SPSS Inc.: Chicago, IL, 2003. (20) Whitman, R. L.; Nevers, M. B. Summer E. coli patterns and responses along 23 Chicago beaches. Environ. Sci. Technol. 2008, 42 (24), 9217–9224. (21) 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. (22) 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. (23) EC (Commission of the European Communities). Directive 2006/7/EC of the European Parliament and of the council of 16 February 2006 concerning the management of bathing water quality and repealing Directive 76/160/EEC. Commission of the European Communities, 2006. (24) Nevers, M. B.; Whitman, R. L. Coastal strategies to predict Escherichia coli concentrations for beaches along a 35 km stretch of southern Lake Michigan. Environ. Sci. Technol. 2008, 42 (12), 4454– 4460. (25) 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. (26) Schoen, M. E.; Ashbolt, N. J. Assessing pathogen risk to swimmers at non-sewage impacted recreational beaches. Environ. Sci. Technol. 2010, 44 (7), 2286–2291. 10320
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(27) Wymer, L. J.; Brenner, K. P.; Martinson, J. W.; Stutts, W. R.; Schaub, S. A.; Dufour, A. P. The EMPACT Beaches Project: Results from a Study on the Microbiological Monitoring of Recreational Waters; EPA 600/ R-04/023; U.S. EPA, Office of Research and Development: Cincinnati, OH, 2005. (28) Kim, J. H.; Grant, S. B. Public mis-notification of coastal water quality: A probabilistic evaluation of posting errors at Huntington Beach, California. Environ. Sci. Technol. 2004, 38 (9), 2497–2504.
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