Systematic review of predictive models of microbial water quality at freshwater recreational beaches

Abstract

Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18–24 hours to obtain a result. To support real-time notifications of water quality, models using environmental variables have been created to predict indicator bacteria levels on the day of sampling. We conducted a systematic review of predictive models of fecal indicator bacteria at freshwater recreational sites in temperate climates to identify and describe the existing approaches, trends, and their performance to inform beach water management policies. We conducted a comprehensive search strategy, including five databases and grey literature, screened abstracts for relevance, and extracted data using structured forms. Data were descriptively summarized. A total of 53 relevant studies were identified. Most studies (n = 44, 83%) were conducted in the United States and evaluated water quality using E. coli as fecal indicator bacteria (n = 46, 87%). Studies were primarily conducted in lakes (n = 40, 75%) compared to rivers (n = 13, 25%). The most commonly reported predictive model-building method was multiple linear regression (n = 37, 70%). Frequently used predictors in best-fitting models included rainfall (n = 39, 74%), turbidity (n = 31, 58%), wave height (n = 24, 45%), and wind speed and direction (n = 25, 47%, and n = 23, 43%, respectively). Of the 19 (36%) studies that measured accuracy, predictive models averaged an 81.0% accuracy, and all but one were more accurate than traditional methods. Limitations identifed by risk-of-bias assessment included not validating models (n = 21, 40%), limited reporting of whether modelling assumptions were met (n = 40, 75%), and lack of reporting on handling of missing data (n = 37, 70%). Additional research is warranted on the utility and accuracy of more advanced predictive modelling methods, such as Bayesian networks and artificial neural networks, which were investigated in comparatively fewer studies and creating risk of bias tools for non-medical predictive modelling.

Region-Specific Associations between Environmental Factors and Escherichia coli in Freshwater Beaches in Toronto and Niagara Region, Canada

“…In the Toronto and Niagara regions in Ontario, the management of beach water quality is of significant public health importance, with the implementation of an extensive beach monitoring program for the many popular beaches [ 11 ]. While there have been studies conducted in several Great Lakes and across North America, few have been conducted in Ontario beaches specifically or in the Canadian context in general [ 12 ]. We aim to contribute to this research by examining recreational water quality in these two Southern Ontario regions to identify the environmental predictors associated with E. coli concentration at 18 beaches.…”

Assessment of Regional and Local Sources of Contamination at Urban Beaches Using Hydrodynamic Models and Field-Based Monitoring

“…Statistical modeling of E. coli levels has had mixed success, − and determinist models combined with source tracking methods could help identify conditions that result in beach water impairments that pose serious human health risk. Disentangling pollution signals due to shoreline local sources from more widespread watershed discharges likely cannot be accomplished with assessments of a single indicator.…”

Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management

“…By following the traditional approach, the analysis of indicator bacteria can achieve results after 18-24 hours, however using modern analysis tools, results can be achieved in 3-4 hours. To achieve faster results using different approaches, a predictive model is used with parameters such as temperature and turbidity [177]. the combination of geomatics, in-situ and satellite-derived variables is used to analyze fungi bacteria [182].…”