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

Abstract

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.

Predicting Salmonella Populations from Biological, Chemical, and Physical Indicators in Florida Surface Waters

“…Some site models were validated by data collected from an independent year, while others were not able to be validated due to climatic changes (43). In the same study, no predictive models were generated for Salmonella, but the authors did note that water samples that were PCR positive for Salmonella had higher median concentrations of E. coli (43). The performance of our Salmonella predictive model was able to be evaluated by data collected during an independent year or from independent locations.…”

“…The model is limited to predictions with the range of E. coli concentrations used to develop the model, ranging from the limit of detection (Ϫ1 log MPN/100 ml) to the maximum possible enumeration level for E. coli (3.2 log MPN/100 ml). Predictive models (linear) for E. coli have been reported for individual sites for inland recreational lakes in Ohio; explanatory variables included rainfall, turbidity, wind, and water temperature (43). Some site models were validated by data collected from an independent year, while others were not able to be validated due to climatic changes (43).…”

“…Predictive models (linear) for E. coli have been reported for individual sites for inland recreational lakes in Ohio; explanatory variables included rainfall, turbidity, wind, and water temperature (43). Some site models were validated by data collected from an independent year, while others were not able to be validated due to climatic changes (43). In the same study, no predictive models were generated for Salmonella, but the authors did note that water samples that were PCR positive for Salmonella had higher median concentrations of E. coli (43).…”

Complex interactions between weather, and microbial and physiochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water

“…While some studies have identified an association between rainfall and microbial water quality (Francy et al, 2013; Pandey et al, 2012; Stocker et al, 2016; Wilkes et al, 2011; Won et al, 2013), other studies did not (Benjamin et al, 2013; McEgan et al, 2013a; Won et al, 2013). For instance, Won et al (Won et al, 2013) reported that E. coli levels in Ohio reservoirs were not associated with rainfall 0-1 d BSC, while E. coli levels in Ohio canals were higher on days when >2 cm of rain fell.…”

O uso de dados de precipitação e qualidade da água no gerenciamento de recursos hídricos com vistas à balneabilidade.

“…Trabalhos já realizados mostraram que, no mínimo, dois anos de dados são necessários no desenvolvimento de modelos estatísticos, e estes obtêm os melhores resultados em praias que sofrem impactos moderados . É importante que a coleta de dados, tanto para desenvolvimento como validação dos modelos, seja feita durante múltiplos anos, de forma a considerar variações no clima e nas condições de qualidade da água que ocorrem periodicamente (FRANCY, 2013 Os itens a seguir detalham alguns dos principais tipos de ferramentas de predição utilizados atualmente.…”