U.S. Recreational Water Quality Criteria: A Vision for the Future

Fujioka, Roger S.; Solo‐Gabriele, Helena M.; Byappanahalli, Muruleedhara N.; Kirs, Marek

doi:10.3390/ijerph120707752

Cited by 77 publications

(63 citation statements)

References 100 publications

(113 reference statements)

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“…Fate and transport of FIB can also differ greatly from that of human pathogens, particularly human viruses [27]. As the regulatory framework shifts from fixed numeric criteria for FIB to a risk-based framework that allows for the establishment of alternative criteria based on equivalent public health protection [28], microbial water quality regulations may embrace alternative indicators and even direct measurement of pathogens [26]. Indeed, coliphages (viruses that infect E. coli) are being considered as a viral fecal indicator because bacteria viruses are expected to mimic fate and transport of human viruses better than FIB [29].…”

Section: Conceptual Model For Removal Of Microbial Contaminantsmentioning

confidence: 99%

“…FIB can originate from non-human or even non-fecal sources and multiply in environmental habitats (perhaps even in biofilters) under a wide range of climatic conditions [26]. In some biofilters, negative removal efficiency has been reported for E. coli, possibly due to re-growth of this indicator [7].…”

Section: Conceptual Model For Removal Of Microbial Contaminantsmentioning

confidence: 99%

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Indicator and Pathogen Removal by Low Impact Development Best Management Practices

Peng¹,

Cao

Rippy

et al. 2016

Water

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Microbial contamination in urban stormwater is one of the most widespread and challenging water quality issues in developed countries. Low impact development (LID) best management practices (BMPs) restore pre-urban hydrology by treating and/or harvesting urban runoff and stormwater, and can be designed to remove many contaminants including pathogens. One particular type of LID BMP, stormwater biofilters (i.e., vegetated media filters, also known as bioinfiltration, bioretention, or rain gardens), is becoming increasingly popular in urban environments due to its multiple co-benefits (e.g., improved hydrology, water quality, local climate and aesthetics). However, increased understanding of the factors influencing microbial removal in biofilters is needed to effectively design and implement biofilters for microbial water quality improvement. This paper aims to provide a holistic view of microbial removal in biofilter systems, and reviews the effects of various design choices such as filter media, vegetation, infauna, submerged zones, and hydraulic retention time on microbial removal. Limitations in current knowledge and recommendations for future research are also discussed.

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Section: Conceptual Model For Removal Of Microbial Contaminantsmentioning

confidence: 99%

Section: Conceptual Model For Removal Of Microbial Contaminantsmentioning

confidence: 99%

Indicator and Pathogen Removal by Low Impact Development Best Management Practices

Peng¹,

Cao

Rippy

et al. 2016

Water

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“…They suggested, however, that their results may be site‐specific and influenced by the lack of human sources and negative detection of enteric viruses. Fujioka et al (2015) noted that the 2012 Recreational Water Quality Criteria did not improve strategies to assess bathers’ health risks in all types for recreational waters. Additionally, questions remain concerning human health risks associated with nonpoint sources of fecal pollution (Field and Samadpour, 2007; Yates, 2007).…”

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confidence: 99%

Canonical Variable Selection for Ecological Modeling of Fecal Indicators

et al. 2018

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More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as Escherichia coli are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict E. coli impairment and F+‐somatic bacteriophage detections. Models were validated using a bootstrapping cross‐validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial E. coli model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. Escherichia coli impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation. Core Ideas Maxent models were developed to infer source and transport of two fecal indicators. Canonical correlation analysis was applied to select parameters for Maxent models. Canonical selection and sensitivity analysis improved E. coli models. Elevated concentrations of E. coli are associated with hardness and heterotrophic activity. Bacteriophage detection is inhibited by sediment coliforms and enzyme activity.

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“…However, major limitations in the implementation of the current recreational water quality standards are related to the fact that the approved FIB measurement methods do not determine their sources, and only proven sewage sources of FIB have been shown to reliably predict the risk of disease transmission to swimmers (2). Moreover, many studies have provided evidence that in many situations, FIB are poor indicators of human health risk.…”

mentioning

confidence: 99%

Human-Associated Bacteroides spp. and Human Polyomaviruses as Microbial Source Tracking Markers in Hawaii

Kirs

Caffaro-Filho

Wong

et al. 2016

Appl Environ Microbiol

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Identification of sources of fecal contaminants is needed to (i) determine the health risk associated with recreational water use and (ii) implement appropriate management practices to mitigate this risk and protect the environment. This study evaluated human-associated Bacteroides spp. (HF183TaqMan) and human polyomavirus (HPyV) markers for host sensitivity and specificity using human and animal fecal samples collected in Hawaii. The decay rates of those markers and indicator bacteria were identified in marine and freshwater microcosms exposed and not exposed to sunlight, followed by field testing of the usability of the molecular markers. Both markers were strongly associated with sewage, although the cross-reactivity of the HF183TaqMan (also present in 82% of canine [n ‫؍‬ 11], 30% of mongoose [n ‫؍‬ 10], and 10% of feline [n ‫؍‬ 10] samples) needs to be considered. Concentrations of HF183TaqMan in human fecal samples exceeded those in cross-reactive animals at least 1,000-fold. In the absence of sunlight, the decay rates of both markers were comparable to the die-off rates of enterococci in experimental freshwater and marine water microcosms. However, in sunlight, the decay rates of both markers were significantly lower than the decay rate of enterococci. While both markers have their individual limitations in terms of sensitivity and specificity, these limitations can be mitigated by using both markers simultaneously; ergo, this study supports the concurrent use of HF183TaqMan and HPyV markers for the detection of sewage contamination in coastal and inland waters in Hawaii. IMPORTANCEThis study represents an in-depth characterization of microbial source tracking (MST) markers in Hawaii. The distribution and concentrations of HF183TaqMan and HPyV markers in human and animal fecal samples and in wastewater, coupled with decay data obtained from sunlight-exposed and unexposed microcosms, support the concurrent application of HF183TaqMan and HPyV markers for sewage contamination detection in Hawaii waters. Both markers are more conservative and more specific markers of sewage than fecal indicator bacteria (enterococci and Escherichia coli). Analysis of HF183TaqMan (or newer derivatives) is recommended for inclusion in future epidemiological studies concerned with beach water quality, while better concentration techniques are needed for HPyV. Such epidemiological studies can be used to develop new recreational water quality criteria, which will provide direct information on the absence or presence of sewage contamination in water samples as well as reliable measurements of the risk of waterborne disease transmission to swimmers. T he recently revised recreational water quality criteria (1) continue to use concentrations of fecal indicator bacteria (FIB), such as enterococci and Escherichia coli, to determine risks to swimmers and to establish recreational water quality standards. However, major limitations in the implementation of the current recreational water quality standards are related to the fact t...

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U.S. Recreational Water Quality Criteria: A Vision for the Future

Cited by 77 publications

References 100 publications

Indicator and Pathogen Removal by Low Impact Development Best Management Practices

Indicator and Pathogen Removal by Low Impact Development Best Management Practices

Canonical Variable Selection for Ecological Modeling of Fecal Indicators

Human-Associated Bacteroides spp. and Human Polyomaviruses as Microbial Source Tracking Markers in Hawaii

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