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Impact of Hydrometeorological Events for the Selection of Parametric Models for Protozoan Pathogens in Drinking‐Water Sources
Author(s) -
Sylvestre Émile,
Burnet JeanBaptiste,
Dorner Sarah,
Smeets Patrick,
Medema Gertjan,
Villion Manuela,
Hachad Mounia,
Prévost Michèle
Publication year - 2021
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.13612
Subject(s) - hydrometeorology , environmental science , cryptosporidium , snowmelt , sampling (signal processing) , poisson distribution , hydrology (agriculture) , atmospheric sciences , statistics , ecology , mathematics , biology , precipitation , meteorology , geography , geotechnical engineering , filter (signal processing) , geology , computer science , surface runoff , engineering , computer vision , feces
Temporal variations in concentrations of pathogenic microorganisms in surface waters are well known to be influenced by hydrometeorological events. Reasonable methods for accounting for microbial peaks in the quantification of drinking water treatment requirements need to be addressed. Here, we applied a novel method for data collection and model validation to explicitly account for weather events (rainfall, snowmelt) when concentrations of pathogens are estimated in source water. Online in situ β ‐ d ‐glucuronidase activity measurements were used to trigger sequential grab sampling of source water to quantify Cryptosporidium and Giardia concentrations during rainfall and snowmelt events at an urban and an agricultural drinking water treatment plant in Quebec, Canada. We then evaluate if mixed Poisson distributions fitted to monthly sampling data ( n = 30 samples) could accurately predict daily mean concentrations during these events. We found that using the gamma distribution underestimated high Cryptosporidium and Giardia concentrations measured with routine or event‐based monitoring. However, the log‐normal distribution accurately predicted these high concentrations. The selection of a log‐normal distribution in preference to a gamma distribution increased the annual mean concentration by less than 0.1‐log but increased the upper bound of the 95% credibility interval on the annual mean by about 0.5‐log. Therefore, considering parametric uncertainty in an exposure assessment is essential to account for microbial peaks in risk assessment.