Impact of Hydrometeorological Events for the Selection of Parametric Models for Protozoan Pathogens in Drinking‐Water Sources

• Published in: Risk analysis 2021-08, Vol.41 (8), p.1413-1426
• Main Authors: Sylvestre, Émile, Burnet, Jean‐Baptiste, Dorner, Sarah, Smeets, Patrick, Medema, Gertjan, Villion, Manuela, Hachad, Mounia, Prévost, Michèle
• Format: Article
• Language: English
• Subjects: Cities; Credibility; Cryptosporidiosis - parasitology; Cryptosporidiosis - prevention & control; Cryptosporidium; Data collection; Drinking water; Drinking Water - parasitology; Environmental Monitoring; Giardia; Giardiasis - parasitology; Giardiasis - prevention & control; Humans; Hydrometeorology; Measurement; Microorganisms; Normal distribution; Parametric statistics; Pathogens; Poisson distribution; Probability distribution functions; Protozoa; Quebec; Rain; Rainfall; Risk assessment; Risk Assessment - methods; Rivers; Sampling; Snow; Snowmelt; Statistical analysis; Surface water; Temporal variations; Treatment needs; Uncertainty; Upper bounds; Urban agriculture; Water Microbiology; Water Purification; Water treatment; Water treatment plants; Weather
• ISSN: 0272-4332; 1539-6924
• DOI: 10.1111/risa.13612

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.