Estimating the duration and overlap of Escherichia coli contamination events in private groundwater supplies for quantitative risk assessment using a multiannual (2010-2017) provincial dataset

Approximately 1.6 million individuals in Ontario rely on private water wells. Private well water quality in Ontario remains the responsibility of the well owner, and due to the absence of regulation, quantitative microbial risk assessment (QMRA) likely represents the most effective approach to estimating and mitigating waterborne infection risk(s) from these supplies. Annual contamination duration (i.e., contaminated days per annum) rep-resents a central input for waterborne QMRA; however, it is typically based on laboratory studies or meta -analyses, thus representing an important limitation for risk assessment, as groundwater mesocosms cannot accurately replicate subsurface conditions. The present study sought to address these limitations using a large spatio-temporal in-situ groundwater quality dataset (>700,000 samples) to evaluate aquifer-specific E. coli die -off rates (CFU/100 mL per day decline), subsequent contamination sequence duration(s) and the likelihood of overlapping contamination events. Findings indicate median E. coli die-off rates of 0.38 CFU/100 mL per day and 0.64 CFU/100 mL per day, for private wells located in unconsolidated and consolidated aquifers, respectlvely, with mean calculated contamination sequence durations of 18 days (unconsolidated) and 11 days (consolidated). Study findings support and permit development of increasingly evidence-based, regionally-and temporally -specific quantitative waterborne risk assessment.

Automated summary

Approximately 1.6 million individuals in Ontario rely on private water wells. Due to the absence of regulation, quantitative microbial risk assessment (QMRA) is the most effective approach to estimate and mitigate waterborne infection risks. This study evaluated E. coli die-off rates, contamination sequence durations, and overlapping contamination events using a large groundwater quality dataset, providing support for evidence-based, region-specific waterborne risk assessment.