Enterococci Predictions from Partial Least Squares Regression Models in Conjunction with a Single-Sample Standard Improve the Efficacy of Beach Management Advisories

Hou, Deyi; Rabinovici, Sharyl J. M.; Boehm, Alexandria B.

doi:10.1021/es0515250

Cited by 57 publications

(43 citation statements)

References 35 publications

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“…At another Lake Michigan beach, the best-fit model contained measurements of winds, rainfall, solar radiation, lake level, water temperature, and turbidity (42). Because they expected different factors to influence Southern California ocean beaches on dry and wet days, Hou et al (43) developed separate models for these two conditions. The important variables in models were rainfall and stream discharge (wet days only), tides, water temperature, winds, visitor numbers, waves, and solar radiation (dry days only) (43).…”

Section: Discussionmentioning

confidence: 99%

“…Because they expected different factors to influence Southern California ocean beaches on dry and wet days, Hou et al (43) developed separate models for these two conditions. The important variables in models were rainfall and stream discharge (wet days only), tides, water temperature, winds, visitor numbers, waves, and solar radiation (dry days only) (43). In the present study, the day of the year, number of swimmers, wave height, discharge from a nearby stream, and water surface elevation were seldom used in models (Ͻ2 times).…”

Section: Discussionmentioning

confidence: 99%

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Predictive Models for Escherichia coli Concentrations at Inland Lake Beaches and Relationship of Model Variables to Pathogen Detection

Francy

Stelzer

Duris³

et al. 2013

Appl Environ Microbiol

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cPredictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predictive Models for Escherichia coli Concentrations at Inland Lake Beaches and Relationship of Model Variables to Pathogen Detection

Francy

Stelzer

Duris³

et al. 2013

Appl Environ Microbiol

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“…Predictive models (22,(33)(34)(35) may help to estimate average water quality given high frequency variability of measurements. These models use physical, chemical, and biological factors to predict concentrations of ENT.…”

Section: Discussionmentioning

confidence: 99%

Enterococci Concentrations in Diverse Coastal Environments Exhibit Extreme Variability

Boehm

2007

Environ. Sci. Technol.

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109

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Fecal indicator bacteria (FIB) concentrations in a single grab sample of water are used to notify the public about the safety of swimming in coastal waters. If concentrations are over a single-sample standard, waters are closed or placed under an advisory. Previous work has shown that notification errors occur often because FIB vary more quickly than monitoring results can be obtained (typically 24 h). Rapid detection technologies (such as quantitative polymerase chain reaction) that allow FIB quantification in hours have been suggested as a solution to notification errors. In the present study, I explore variability of enterococci (ENT) over time scales less than a day that might affect interpretation of FIB concentrations from a single grab sample, even if obtained rapidly. Five new data sets of ENT collected at 10 and 1 min periodicities for 24 and 1 h, respectively, are presented. Data sets are collected in diverse marine environments from a turbulent surf zone to a quiescent bay. ENT vary with solar and tidal cycles, as has been observed in previous studies. Over short time scales, ENT are extremely variable in each environment even the quiescent bay. Changes in ENT concentrations between consecutive samples (1 or 10 min apart) greater than the single-sample standard (104 most probable number per 100 mL) are not unusual. Variability, defined as the change in concentration between consecutive samples, is not distinct between environments. ENT change by 60% on average between consecutive samples, and by as much as 700%. Spectral analyses reveal no spectral peaks, but power-law decline of spectral density with frequency. Power-law exponents are close to 1 suggesting ENT time series share properties with 1/f noise and are fractal in nature. Since fractal time series have no characteristic time scale associated with them, it is not obvious how the fractal nature of ENT can be exploited for adaptive sampling or management. Policy makers, as well as scientists designing field campaigns for microbial source tracking and epidemiology studies, are cautioned that a single sample of water reveals little about the true water quality at a beach. Multiple samples must be taken to gain a snapshot into the patchy structure of microbial water quality and associated human health risk.

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“…Efforts to overcome these shortcomings have included the use of rapid enumeration methods such as qPCR (155), alternative indicators that are more specific to the contaminant sources (e.g., human-associated Bacteroides, Catellicoccus gull fecal markers, and Brevibacterium poultry fecal markers) (26,213,351,352), direct monitoring for potential pathogens and QMRA (see above) (13), and predictive modeling (164,249), but cost, ease of use, and sustainability as a monitoring program must all be considered in optimizing the application of any newer method or monitoring technology. More information on the sensitivities and specificities of individual tests are provided in other sections.…”

Section: Rapid Testing Methodsmentioning

confidence: 99%

Enterococci in the Environment

Byappanahalli

Nevers

Korajkic

et al. 2012

Microbiol Mol Biol Rev

538

383

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SUMMARYEnterococci are common, commensal members of gut communities in mammals and birds, yet they are also opportunistic pathogens that cause millions of human and animal infections annually. Because they are shed in human and animal feces, are readily culturable, and predict human health risks from exposure to polluted recreational waters, they are used as surrogates for waterborne pathogens and as fecal indicator bacteria (FIB) in research and in water quality testing throughout the world. Evidence from several decades of research demonstrates, however, that enterococci may be present in high densities in the absence of obvious fecal sources and that environmental reservoirs of these FIB are important sources and sinks, with the potential to impact water quality. This review focuses on the distribution and microbial ecology of enterococci in environmental (secondary) habitats, including the effect of environmental stressors; an outline of their known and apparent sources, sinks, and fluxes; and an overview of the use of enterococci as FIB. Finally, the significance of emerging methodologies, such as microbial source tracking (MST) and empirical predictive models, as tools in water quality monitoring is addressed. The mounting evidence for widespread extraenteric sources and reservoirs of enterococci demonstrates the versatility of the genusEnterococcusand argues for the necessity of a better understanding of their ecology in natural environments, as well as their roles as opportunistic pathogens and indicators of human pathogens.

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Enterococci Predictions from Partial Least Squares Regression Models in Conjunction with a Single-Sample Standard Improve the Efficacy of Beach Management Advisories

Cited by 57 publications

References 35 publications

Predictive Models for Escherichia coli Concentrations at Inland Lake Beaches and Relationship of Model Variables to Pathogen Detection

Predictive Models for Escherichia coli Concentrations at Inland Lake Beaches and Relationship of Model Variables to Pathogen Detection

Enterococci Concentrations in Diverse Coastal Environments Exhibit Extreme Variability

Enterococci in the Environment

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