Article pubs.acs.org/est
Sunny with a Chance of Gastroenteritis: Predicting Swimmer Risk at California Beaches W. Thoe,*,† M. Gold,‡ A. Griesbach,§ M. Grimmer,§ M. L. Taggart,§ and A. B. Boehm† †
Department of Civil and Environmental Engineering, Environmental and Water Studies, Stanford University, Stanford, California 94305, United States ‡ Institute of the Environment and Sustainability, University of California, Los Angeles, California 90095, United States § Heal the Bay, Santa Monica, California 90401, United States S Supporting Information *
ABSTRACT: Traditional beach management that uses concentrations of cultivatable fecal indicator bacteria (FIB) may lead to delayed notification of unsafe swimming conditions. Predictive, nowcast models of beach water quality may help reduce beach management errors and enhance protection of public health. This study compares performances of five different types of statistical, data-driven predictive models: multiple linear regression model, binary logistic regression model, partial least-squares regression model, artificial neural network, and classification tree, in predicting advisories due to FIB contamination at 25 beaches along the California coastline. Classification tree and the binary logistic regression model with threshold tuning are consistently the best performing model types for California beaches. Beaches with good performing models usually have a rainfall/flow related dominating factor affecting beach water quality, while beaches having a deteriorating water quality trend or low FIB exceedance rates are less likely to have a good performing model. This study identifies circumstances when predictive models are the most effective, and suggests that using predictive models for public notification of unsafe swimming conditions may improve public health protection at California beaches relative to current practices.
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correct beach management decisions.11−21 Common modeling tools include multiple linear regression, partial least-squares regression, artificial neural networks, and decision trees. Despite the advantages of using predictive models in reducing management errors and protecting public health, there are few examples where predictive models have been implemented for actual beach management. These include the Great Lakes beaches in the United States22 and coastal beaches in Scotland.23 A pilot beach water quality prediction system has also been developed for Hong Kong coastal beaches.24 Model application for beach management has been hindered perhaps due to a lack of studies at diverse beaches with different geographic and pollution characteristics. Little work has assessed systematically the strengths and weaknesses of different types of predictive models, and clear guidelines for choosing the best model type for different beaches are lacking. Moreover, previous studies mainly focused on successful application of predictive modeling, without discussion of model limitations and circumstances when predictive models
INTRODUCTION Swimming in fecal polluted waters may result in gastrointestinal and respiratory diseases.1−3 As a result, bathing beaches are typically monitored for fecal indicator bacteria (FIB). It takes 18−24 h to detect FIB using traditional methods, or 4−6 h using rapid detection methods such as qPCR.4 Public notification of unsafe swimming conditions due to FIB measured in the early morning is at best available at around noon if qPCR is used, and the next day if culture-based detection methods are used. However, water quality can change dynamically with hydro-meteorological conditions over time scales from minutes to days;5 consequently, unsafe swimming conditions cannot be identified using current monitoring methods based purely on water sampling.6,7 The use of beach water quality predictive models for public notification of unsafe swimming conditions was first introduced by the World Health Organization (WHO),8 and was incorporated by the European Union in the Bathing Water Directive in 2006.9 In 2012, the U.S. Environmental Protection Agency (USEPA) recommended beach water quality predictive modeling as a rapid and inexpensive tool to address the problem of management errors due to the time lag in FIB measurement.10 Predictive models have been shown to generally outperform traditional beach monitoring in providing © 2014 American Chemical Society
Received: Revised: Accepted: Published: 423
September 24, 2014 December 5, 2014 December 9, 2014 December 9, 2014 dx.doi.org/10.1021/es504701j | Environ. Sci. Technol. 2015, 49, 423−431
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Figure 1. Twenty-five study beaches in California. Details of each beach are listed in Table S1.
that receive freshwater discharges from storm drains or rivers/ creeks, 12 beaches), pier (beaches where a municipal pier is located in close proximity, 5 beaches), or open (beaches that are exposed to the ocean without storm drains or piers, 2 beaches). Northern and Southern California have oceanic and Mediterranean climates, respectively.26 With reference to the weather stations at different California airports (http://www. ncdc.noaa.gov/), air temperature ranges from 15 to 25 °C in the summer and 5−15 °C in the winter. Annual rainfall is ∼700 mm in San Francisco County, with a decreasing trend from north to south and is ∼260 mm in San Diego County. Winter (November−March) is the wet season, and summer (April− October) is the dry season when little to no rainfall occurs. Data. The 25 beaches chosen for this study are monitored for total coliform (TC), fecal coliform (FC) and enterococci (ENT), pursuant to state law (Assembly Bill 41127). A beach is posted with a swimming advisory if any of the FIB concentrations exceed their single sample standard (SSS): 10 000, 400, and 104 most probable number (MPN)/100 mL for TC, FC and ENT, respectively. In addition, the single sample concentration of TC should not exceed 1000 MPN/100 mL when TC-FC ratio is smaller than 10 (TC ratio standard). The three FIB concentrations and the exact water sampling time at the 25 beaches from April 2006 to July 2012 were obtained from their corresponding monitoring agencies (Table S2). Some beaches were sampled nearly daily while others weekly. The number of FIB measurements over the entire study period ranges from 239 (Avalon Beach) to 1670 (Mothers Beach). Standard, EPA-approved FIB detection methods were used (Table S3, detection limits in Table S4). If a concentration was reported as below the lower limit of detection or above the upper limit of detection, then it was replaced with the corresponding detection limit. FIB
may be less effective. A framework to evaluate performances of different types of beach water quality models was recently developed and illustrated at a single marine beach.25 The goal of this study was to determine whether statistical, data-driven models can be used to predict daily water quality (FIB concentrations) at California beaches. California beaches are unique among other coastal beaches due to their popularity, and the bathing season (April−October) corresponds to the dry season.25 The specific objectives of this study were to (1) develop models of enterococci, fecal coliform and total coliform in summer (dry season) and winter (wet season) at 25 beaches along the California coast, (2) assess the performance of five different model types including multiple least-squares regression (MLR), binary logistical regression (BLR), partial leastsquares regression (PLS), artificial neural network (ANN), and classification tree (CT), and (3) compare model performance against the current method of using a previous sample to assess whether the beach should be open or posted with a swimming advisory. In total, over 700 models were created and tested. The large quantity of models developed in this study facilitates the discussion of various issues. The best performing model types are identified. Independent variables that are useful in model development are discussed. Beaches where predictive models are the most effective, and reasons that some models cannot achieve validation goals are also discussed.
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MATERIALS AND METHODS Study Beaches. Twenty-five beaches along the California coastline were selected to develop water quality predictive models (Figure 1 and Table S1 in the Supporting Information (SI)). Four of the beaches are in Northern and Central California, and 21 in Southern California. Beaches were classified as enclosed (beaches that are enclosed or semienclosed in harbor or bay, 6 beaches), storm drain (beaches 424
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output ranges from 0 to 100%). The default interpretation of BLR models by SPSS is if the predicted chance of SSS exceedance (% exceedance) >50%, the beach is predicted to be posted with a beach advisory. We also varied the % exceedance to maximize model performance in capturing more beach postings; BLR model after “tuning” the % exceedance is abbreviated as BLR-T.25 CT models have binary dependent and output variables (1: posting, 0: no posting). Models of each type were developed for each of the 144 beach-scenarios, and a total of 720 models were developed. Model Evaluation Criteria. Predictive model performances were compared with the “current method”, which assumes that the water quality at the present time can be adequately estimated from water quality obtained during the most recent measurementusually the previous day for frequently sampled beaches, or one-week old for other beaches. For example, if a water quality measurement indicates that a particular FIB exceeds the SSS, the current method indicates that the beach will be posted as unfit for swimming (hereafter refer to as “beach posting”) starting from the following day (allow for 24 h to detect FIB concentrations) until a new sampling result which shows a SSS compliance is available. While the standards are stipulated in California law, the implementation of the standards varies among beach managers. The scenario described here is typically implemented along the California coast. The models were compared in their ability to predict correct SSS exceedance or compliance for ENT, FC, and TC (104, 400, and 10 000 MPN/100 mL, respectively) on days when FIB concentrations are available. Model sensitivity and specificity were used to assess model performance and were defined as 1. Sensitivity (%): percentage of SSS exceedance days that can be predicted by the model; 2. Specificity (%): percentage of SSS compliance days that can be predicted by the model. Criteria were used to define whether a model is ready for management application based on its ability to provide accurate beach management decisions. From the public health protection perspective, the models were deemed effective for beach management, and were considered passing calibration or validation when model sensitivity was higher than 30% and 10% higher than that of the current method.25 Hereafter, these criteria are referred to as the “sensitivity criteria”. A similar set of criteria is used in the Great Lakes.22 A future decision to change these criteria can be readily implemented if different thresholds are deemed appropriate. The sensitivity criterion of 30% was chosen because the median sensitivity of the current method across all beaches in the calibration period is ∼30%. The 10% criterion was chosen to ensure that the model produced better results than the current method. Model specificity was not included as a passing criterion because model specificity was found to be consistently high (>90%, see below), therefore more focus was given to model sensitivity.
concentrations were log10-transformed to reduce skewness and variance for more stable estimators.28 Types of independent variables used to develop the predictive models include: past FIB concentrations (last sample, rolling geometric mean of the past 30 and 60 days, all log10-transformed), rainfall (daily and cumulative rainfall in the past 7 days, and cumulative rainfall in the past 30 days, all log10-transformed), number of dry days before a rain event (product of a binary variable indicating the presence (1) or absence (0) of rain on the sampling day × number of antecedent dry days), tide level (tide level during the sampling time, maximum and minimum tide level and tidal range on the sampling day, number of hours since last high tide), solar radiation, cloud cover, wind speed (onshore and alongshore), air pressure, upwelling index, air and water temperature, wave (height, period and alongshore current), streamflow (log10transformed), and storm drain condition. Variables were described in more detail previously25 with the exception of log10-transformed daily averaged streamflow from U.S. Geological Survey (http://www.usgs.gov); observed wave height at five beaches were also obtained from Surfline (http://www. surfline.com). Table S5 lists all the independent variables used in this study and their corresponding data sources and Table S6 lists the types of data available at the 25 beaches. These data were readily available for model development from data archives and no new data were collected for the study. Predictive Models. Individual models of each of the 25 beaches were developed for six different “scenarios”: three FIB ENT, FC, and TC as dependent variables in the summer (S: April−October) and winter (W: November−March). The model for ENT at a beach in the summer period is a scenario and is abbreviated as “ENT-S”. Models were developed using data from a calibration period (years 2006−2010) and then tested using data from a validation period (years 2011−2012). Calibration and validation periods for Rincon were years 2006− 2009 and 2010−2012, respectively, as there were no FIB exceedances in 2011−2012. Winter models for Rincon and Avalon were not developed because sampling was not conducted in the winter. A total of 144 beach-scenarios (beach × FIB × season) were considered in this study. The five types of predictive models used in this study include: multiple linear regression (MLR), binary logistic regression (BLR), partial least-squares regression (PLS), artificial neural networks (ANN), and classification tree (CT). Detailed descriptions of the models can be found elsewhere.25 Briefly, MLR models were developed using SPSS Statistics (version 20, IBM, Chicago, IL), and the stepwise regression algorithm was adopted for variable selection while considering Variable Inflation Factors to avoid variable collinearity.29 BLR models were also developed using SPSS, and “Forward: conditional algorithm” was adopted for variable selection. PLS models were developed using MATLAB (version R2012b, Natick, MA); all independent variables were transformed into principle components for model development, and the stepwise regression algorithm was used to select the principle components. ANN models were developed using the MATLAB “Neural Network” Toolbox; only variables included in the MLR model for that particular FIB were used to develop the ANN model. CT models were developed using the MATLAB “Classification Tree” Toolbox. MLR, PLS and ANN models are continuous models and predict FIB concentrations. BLR models have a binary dependent variable (1: posting, 0: no posting) and predict the chance of a SSS exceedance (model
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RESULTS Beach Water Quality during the Study Period. The composite summer-time and winter-time exceedance rates, defined as the exceedance rate of all FIB SSS, were calculated for the 25 beaches (Tables S7 and S8, exceedance rates due to individual FIB are also in the tables). The composite exceedance rate ranges from 3% (Huntington City) to 52% (Poche) in the summer, and 6% (Seal) to 87% (Doheny State) 425
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Figure 2. Boxplots of sensitivity and specificity for different model types in the calibration and validation periods (all beach-scenarios, N = 144). In the sensitivity boxplots, a dotted line is plotted at the 30% sensitivity criterion for reference.
Storm drain condition (an ordinal variable representing high, medium, low, or no flow) is important at all 3 beaches with available data. Important variable types for BLR models are shown in Table S10 and the first independent variable selected in CT models (representing the variable classifies the maximum number of correct beach management decisions) is shown in Table S11. Similar to the MLR models, these model types also show past FIB, rainfall and tide variables as the dominating factors affecting beach water quality in California, and are thus not described here in detail. Model Sensitivity and Specificity. Figure 2 shows boxplots of sensitivities (left panel) and specificities (right panel) achieved by different model types for different beachscenarios (N = 144) in the calibration (upper panel) and validation (lower panel) periods. The sensitivities and specificities obtained by the BLR-T models were based on a beach-specific % exceedance threshold, which was obtained by varying the threshold by 5% intervals from 0 to 100% to maximize model sensitivity without excessive reduction in specificity (Table S12). The adopted % exceedance threshold for different BLR-T models has a median and mode of 40% and 50%, respectively; there are occasions when a threshold as low as 10% is used. Sensitivities range widely (0−100%) for all model types. In the calibration period, sensitivities of the current method, MLR, BLR, BLR-T, PLS, ANN, and CT have medians of 27%, 20%, 39%, 57%, 25%, 30%, and 62%, respectively. In the validation period, the medians are 25%, 13%, 19%, 50%, 14%, 30%, and 50%, respectively. The medians are significantly different (p < 0.05, Kruskal−Wallis31) between model types, and this result also holds when the sensitivities are subdivided into groups of beaches with frequent sampling (daily, 9 beaches, N = 54) and weekly sampling (16 beaches, N = 90). The CT and BLR-T
in the winter. Most study beaches have worse water quality in the winter than in the summer except Cowell, Pismo, and Poche. Six beaches have a year-round composite exceedance rate 90%, and are usually slightly higher than that achieved by the current method. This is a direct consequence of SSS compliance being more frequent than SSS exceedance at California beaches. Of the five types of models tested, CT and BLR-T are consistently the best performing models in terms of predicting beach advisories based on the single sample standards. The BLR-T model is the binary logistic regression model with threshold tuning. A BLR model is similar to an ordinary leastsquares regression model, but the output is a number between 0 and 100% and describes the chance that an FIB concentration will be above the single sample standard. A model prediction of 50% or greater is used by default in many statistical packages, including SPSS, to indicate a positive result (beach posting in this case); however, in the tuning version of the model, we allowed that percentage to change to increase the sensitivity of the model, while not appreciably changing the specificity.
(a) calibration period MLR
BLR
2 6 1 3 2 4 18
4 11 4 7 10 11 47
MLR
BLR
1 9 3 4 2 2 21
3 9 2 3 3 4 24
ENT-S ENT-W FC-S FC-W TC-S TC-W total
ENT-S ENT-W FC-S FC-W TC-S TC-W total
BLR-T
ANN
CT
3 14 3 6 2 4 32 period
5 13 4 6 2 6 36
20 23 17 21 17 19 117
BLR-T
PLS
ANN
CT
12 14 10 11 8 9 64
4 11 4 4 1 2 26
5 13 8 8 5 4 43
16 13 11 10 12 8 70
14 19 18 19 17 18 105 (b) validation
PLS
during calibration period (Table S13), and 83 of 125 (66%) beach-scenarios pass during the validation period (Table S14). Overall, there are 17, 15, 11, 11, 11, and 9 beaches that have a BLR-T and/or CT model that passes the sensitivity criteria in both calibration and validation periods for scenarios ENT-S,
Table 2. Summary of Beach-Scenarios That Have at Least One BLR-T or CT Model Passing the Sensitivity Criteria (>30% and 10%> Current Method) In Both Calibration and Validation Periodsa−d beach
ENT-S
Candlestick Cowell Capitola Pismo Arroyo Burro East Rincon Kiddie Surfrider Topanga Santa Monica Mothers Dockweiler Manhattan Redondo Pier Redondo State Cabrillo Avalon Belmont Alamitos Bay Seal Huntington City Doheny State Poche Imperial subtotal
C+B
C B C C /
ENT-W B C+B C+B C+B C / C+B
FC-S
FC-W
TC-S
C+B C+B
C+B
C / / / C C+B C / C
C+B B C+B C+B / /
C C B C+B B
C+B C C+B B
C+B C+B C+B
C+B C+B C+B B /
C C C C
/ C+B C C+B
C+B B C+B 17
C B C C+B
/ C+B C+B /
C+B C+B / / C / C / C
C C 15
C+B 11
11
C+B 11
TC-W C / / B / C+B B C+B C+B C+B
/ / C+B C+B / / / / 9
subtotal 5 2 2 3 5 4 2 2 1 2 4 4 6 4 2 1 2 1 6 4 2 3 2 1 4 74
a
C = CT model passes in both calibration and validation. bB = BLR-T model passes in both calibration and validation. cC+B = Both CT and BLR-T models pass in both calibration and validation. d/ = the model has not been developed for this scenario, or there is no exceedance during calibration and/or validation. 428
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turbidity, respectively, are important predictors of FIB concentrations.19,22 These sorts of data were not available for California beaches so future efforts to collect these may allow for model improvement. Given the results of our work, a one-size fits all model does not seem plausiblebeach specific models are needed. Beaches with similar characteristics (i.e., storm drains, piers, enclosed) or which were close together geographically did not yield models that were similar in construct or that performed similarly. If a beach is to be modeled, then a beach-specific effort must be undertaken. However, our work does indicate that regardless of beach, CT and BLR-T are the best performing model types among those considered in this study. Factors Affecting Model Performance. There are 6 beaches with both scenarios ENT-S and FC-S (the two most important scenarios in terms of public health protection because summer represents the primary bathing season, and ENT and FC exceed the standards more frequently than TC) passing the sensitivity criteria using the same model type (CT and/or BLR-T) in both calibration and validation periods: 4 of 12 storm drain beaches (Arroyo Burro, Dockweiler, Manhattan, Imperial), 1 of 6 enclosed beaches (Candlestick) and 1 of 5 pier beaches (Seal). Additionally, Belmont (pier beach) has a CT model passing for scenario ENT-S and BLR-T model passing for scenario FC-S. Most of these beaches have MLR models with higher-than-median adjusted R2, even if their MLR models achieve low sensitivity. This is due to the existence of one dominating factor affecting beach water quality (based on variables included in MLR models), and is consistently rainfall/ flow related: 0−2 days rainfall (4 beaches), >3 days rainfall (2 beaches), and streamflow (1 beach). As summer is the dry season, rainfall in the summer is usually due to trace rainfall events due to the passing of the monsoonal storms.38 The list includes beaches that are frequently sampled (Dockweiler and Manhattan) and beaches that are only sampled once a week, implying sampling frequency does not seem to affect the predictive model performances, although these models were developed with data of considerable different sample sizes. However, as predictive models can provide daily predictions, the use of predictive models at beaches that are monitored only once a week, the majority of beaches in California and around the world, can be a vast improvement over the current monitoring system, because the current system only allows for a weekly update of beach management decisions. It is expected that models will perform worse in validation than calibration as during validation, they are being tested with data with which they were not trained. By studying the 42 unvalidated beach-scenarios that fail to achieve the sensitivity criteria during validation, we found two major reasons that may lead to a beach-scenario achieving the sensitivity criteria in calibration but not validation. First, a model may perform worse in validation if the modeled FIB during that particular season have a deteriorating water quality trend (i.e., exceedance rate in the validation period is >8% higher than that in the calibration period, see SI). The percentage of beach-scenarios having a deteriorating water quality trend that achieve the sensitivity criteria in the validation period is lower than beach-scenarios not having a deteriorating water quality trend (p < 0.05, Wilson test39). When there are systematic differences between water quality in the “past” and the “present”, possibly caused by changes in pollution conditions, models developed using historical data may not be able to capture present water quality trends. Therefore, it is important to continue collecting and
Further adjustment to the percentage is possible based on beach management strategies to protect public health (accurate beach advisories) versus maximize beach access (no incorrect beach advisories). The CT model is a nonlinear, visual model. The model consists of “nodes” (boxes) and “branches” (lines) that terminate in a classification of whether the beach should be open or posted, which may be easily understood by bathers. However, in the present study, the CT model structure tends to become more complicated when the modeled beach is more polluted; the number of end nodes in a CT model is significantly and positively correlated with the number of exceedances (correlation coefficient = 0.80, Figure S7). This is possibly due to an increased number of variable interactions that affect water quality at polluted beaches. CT models have consistently the highest sensitivities among different model types (median sensitivities are 35% and 25% higher than the current method in calibration and validation, respectively), and are closely followed by BLR-T models. Although a sensitivity threshold of 30% is adopted for the sensitivity criteria, half of the CT and BLR-T models achieve sensitivity of 50% or higher in the validation period. The number of CT and BLR-T models that pass the sensitivity criteria in both calibration and validation periods is also higher than other model types. In particular, 17 of 24 beaches have CT and/or BLR-T models that pass the sensitivity criteria for ENT in the summer time during both calibration and validation periods. The summer period represents the required FIB monitoring period for the State of California27 and also the period of time when the most people visit the beaches, and ENT is the FIB that exceeds the SSS most frequently. Comparatively, MLR, BLR, PLS, and ANN models pass the sensitivity criteria in fewer beach-scenarios compared to CT and BLR-T because these model types consistently underpredict FIB concentrations. The most commonly used model type in practicethe MLR model22,24has the lowest number of beach-scenarios that pass the sensitivity criteria at California beaches, although the medians of the MLR adjusted R2 (0.23− 0.41) fall in a similar range with other marine beach models.24,30 A “tuning” of a decision threshold, as the BLR-T models considered in this study, may help improve sensitivity for these continuous models.32 Critical Factors Affecting California Beach Water Quality. The most important predictors of FIB concentrations or posting status at California beaches are rainfall and flow related variables, tide-related variables, and past FIB concentrations. These variables appear most often in the five different types of models. It is not surprising that rainfall and flow from a nearby river or storm drain are important variables as it is well understood that runoff is a source of FIB to coastal waters.33 The rising and falling tide can mobilize contaminants on the beach34 as well as sediments which have FIB associated with them,35 and modulate the flow of contaminated groundwater36 and surface water37 into the ocean. The importance of past FIB concentrations suggests that although previous FIB measurements are poor predictors for current water quality conditions, previous measurements do provide important information to the model that improves predictions. Previous FIB measurements are also included in Hong Kong beach management models.24 The results indicate that continual efforts to measure flow, storm drain condition, and berm status is important for managers, and that FIB measurements must be continually taken, even if a model is used for public notification. In other locations like Hong Kong and the Great Lakes, salinity and 429
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document do not necessarily reflect the views and policies of the State Water Resources Control Board, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.
analyzing water samples to monitor any changes in beach pollution conditions, as well as continually update predictive models to reflect the most up-to-date beach conditions. Second, predictive models for beach-scenarios with low FIB exceedance rates are less likely to pass the sensitivity criteria in validation. Such models are usually calibrated against data with low exceedance rates, which may lead to a lower predictive power of exceedances and low sensitivities in calibration. A longer period of data for model calibration and validation may be necessary for beaches with low FIB exceedance rates, so that more exceedance events are available for the model to identify their pattern of occurrence (see SI). Considerations for Future Work and Model Applications for Beach Management. The models developed herein predict FIB concentrations collected at a specific timeof-day. The time-of-day sampling occurs affects the values of several tide- and rainfall-related variables. FIB are measured once during a day at daily to weekly intervals around the globe to assess beach water quality, and a single daily FIB measurement is often what has been used in epidemiology studies to assess the relationship between FIB concentrations and daily swimmer risk of recreational waterborne illness.40 Thus, a daily FIB concentration is the most relevant management-specific modeling target. However, FIB concentrations vary at smaller than daily periods5 and it is possible that swimmer health risk also varies at smaller periods. High frequency (higher than once per day) FIB variation is not taken into account in beach management in the U.S. and this remains a challenge that should be undertaken in the future. Until then, it is unclear how useful models that predict FIB more frequently than once per day will be for beach managers. There are also analytical uncertainties associated with FIB measurements41 that are not considered in our modeling approach or in beach management in California. For example, although analytically a measurement of 106 MPN/100 mL may not be statistically different from 103 MPN/100 mL at the 95% confidence level, the former will result in a beach posting for ENT, and the latter will not. Until this challenge is addressed fully through the development of standards and beach management protocols, it is unclear how one should consider analytical uncertainty in predictive modeling applications.
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(1) Prüss, A. Review of epidemiological studies on health. Int. J. Epidemiol. 1998, 27 (1), 1−9. (2) Haile, R. W.; Witte, J. S.; Gold, M.; Cressey, R.; McGee, C.; Millikan, R. C.; Glasser, A.; Harawa, N.; Ervin, C.; Harmon, P.; Harper, J.; Dermand, J.; Alamillo, J.; Barrett, K.; Nides, M.; et al. The health effects of swimming in ocean water contaminated by storm drain runoff. Epidemiology 1999, 10 (4), 355−363. (3) Colford, J. M. J.; 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.; et al. Using rapid indicators for Enterococcus to assess the risk of illness after exposure to urban runoff contaminated marine water. Water Res. 2012, 46 (7), 2176−2186. (4) Noble, R. T.; Blackwood, A. D.; Griffith, J. F.; McGee, C. D.; Weisberg, S. B. Comparison of rapid quantitative PCR-based and conventional culture-based methods for enumeration of Enterococcus spp. and Escherichia coli in recreational waters. Appl. Environ. Microbiol. 2010, 72 (2), 1604−1612. (5) 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. (6) Kim, J. H.; Grant, S. B. Public mis-notification of coastal water quality: A probabilistic analysis of posting errors at Huntington Beach, California. Environ. Sci. Technol. 2004, 38 (9), 2497−2505. (7) 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. (8) Guidelines for Safe Recreational Water Environments, Vol. 1: Coastal and Freshwaters; World Health Organization: Geneva, Switzerland, 2003. (9) European Parliament. Directive 2006/7/EC of the European Parliament, concerning the management of bathing water quality and repealing Directive 76/160EEC. Off. J. Eur. Union 2006, 64, 37−51. (10) Recreational Water Quality Criteria; U.S. Environmental Protection Agency: Washington, DC, 820-F-12−058, 2011. (11) Crowther, J.; Kay, D.; Wyer, M. D. Relationships between microbial water quality and environmental conditions in coastal recreational waters: the Fylde Coast, UK. Water Res. 2001, 35 (17), 4029−4038. (12) Lin, B.; Kashefipour, S. M.; Falconer, R. A. Predicting near-shore coliform bacteria concentrations using ANNS. Water Sci. Technol. 2003, 48 (10), 225−232. (13) Olyphant, G. A.; Whitman, R. L. Elements of a predictive model for determining beach closures on a real time basis: the case of 63rd Street Beach Chicago. Environ. Monit. Assess. 2004, 98 (1−3), 175− 190. (14) Parkhurst, D. F.; Brenner, K. P.; Dufour, A. P.; Wymer, L. J. Indicator bacteria at five swimming beachesanalysis using random forests. Water Res. 2005, 39 (7), 1354−1360. (15) Boehm, A. B.; Whitman, R. L.; Nevers, M. B.; Weisberg, S. B. Statistical Framework for Water Quality Criteria and Monitoring; Wymer and Dufour Eds.; John Wiley & Sons, Ltd.: Chichester, West Sussex, England, 2007. (16) He, L. M.; He, Z. L. Water quality prediction of marine recreational beaches receiving watershed baseflow and stormwater runoff in southern California, USA. Water Res. 2008, 42 (10−11), 2563−2573. (17) Bae, H. K.; Olson, B. H.; Hsu, K. L.; Sorooshian, S. Classification and regression tree (CART) analysis for indicator bacterial concentration prediction for a California coastal area. Water Sci. Technol. 2010, 61 (2), 545−553.
ASSOCIATED CONTENT
S Supporting Information *
Additional discussion; adjusted R2 and scatter plots (Figures S1−S7); and list of study beaches, monitoring agencies, FIB detection methods, independent variables, data used for model development, exceedance rates, types of independent variables, and summaries of beach-scenarios (Tables S1−S15). This material is available free of charge via the Internet at http:// pubs.acs.org/.
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REFERENCES
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS Funding for this project has been provided through the California State Water Resources Control Board. Any opinions in this paper are those of the authors. The contents of this 430
dx.doi.org/10.1021/es504701j | Environ. Sci. Technol. 2015, 49, 423−431
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