Efforts to improve public health protection in recreational swimming waters have focused on obtaining real-time estimates of water quality. Current monitoring techniques rely on the time-intensive culturing of fecal indicator bacteria (FIB) from water samples, but rapidly changing FIB concentrations result in management errors that lead to the public being exposed to high FIB concentrations (type II error) or beaches being closed despite acceptable water quality (type I error). Empirical predictive models may provide a rapid solution, but their effectiveness at improving health protection has not been adequately assessed. We sought to determine if emerging monitoring approaches could effectively reduce risk of illness exposure by minimizing management errors. We examined four monitoring approaches (inactive, current protocol, a single predictive model for all beaches, and individual models for each beach) with increasing refinement at 14 Chicago beaches using historical monitoring and hydrometeorological data and compared management outcomes using different standards for decision-making. Predictability (R(2)) of FIB concentration improved with model refinement at all beaches but one. Predictive models did not always reduce the number of management errors and therefore the overall illness burden. Use of a Chicago-specific single-sample standard-rather than the default 235 E. coli CFU/100 ml widely used-together with predictive modeling resulted in the greatest number of open beach days without any increase in public health risk. These results emphasize that emerging monitoring approaches such as empirical models are not equally applicable at all beaches, and combining monitoring approaches may expand beach access.
"Increasingly, efforts to develop a variety of water quality criteria have focused on the use of field data to develop relationships between biological responses and their stressors, and then to identify levels of the stressors that preserve the desired biological conditions. Examples of stressors other than nutrients that have been investigated in this way include river sediments, pathogens, metals, and specific conductance (Shine et al. 2003; Paul and McDonald 2005; Cormier et al. 2008, 2013; Hollister et al. 2008; Nevers and Whitman 2011; USEPA 2011). "
"These steps can hinder the process of emergency preparedness and prevention, particularly for remote areas and regions having numerous water bodies such as Canada. Further, the concentration values of microbial indicators can change rapidly and result in management errors regarding the exposure of the public to higher concentrations of bacteria or beach closures despite an acceptable water quality (Nevers & Whitman, 2011). The assessment and monitoring of microbiological quality of surface water requires standardized measurements and systematic observations, in a systemic approach integrating microbiological indicators, environmental determinants (land cover and use, topography, climate, extreme weather events, etc.) and socioeconomic factors, at various scales of water governance such as the watershed (Fig. 1), "
[Show abstract][Hide abstract] ABSTRACT: Contaminated surface water poses a risk to human populations and is a challenge for public health authorities. Climatic change, intensification of agriculture, urban development of coastal areas, and declining freshwater sources may contribute significantly to the risk of surface water contamination and increase incidence of waterborne diseases. Monitoring of surface water quality requires early detection of problems in order to minimize any negative impact on public health. Tele-epidemiology uses remote sensing and geospatial technologies to characterize the spatial and temporal variability of environmental determinants involved in the epidemiology of some diseases. By offering a systematic and integrated approach to water and risk management in public health, tele-epidemiology can be an efficient tool to assess surface water quality and any associated health risks.
"Predictive modeling of water quality based on environmental factors is being explored as an alternative to the current monitoring techniques which can take days to complete. Such models can predict the FIB levels using observed environmental conditions with varying degrees of success (Nevers & Whitman, 2011). This approach allows proactive beach closure for recreational use as soon as the conditions for contamination are met, rather than waiting for the water sample results. "
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