Intense rainfall and snowmelt events may affect the safety of drinking water, as large quantities of fecal material can be discharged from storm or sewage overflows or washed from the catchment into drinking water sources. This study used β-d-glucuronidase activity (GLUC) with microbial source tracking (MST) markers: human, bovine, porcine mitochondrial DNA markers (mtDNA) and human-associated Bacteroidales HF183 and chemical source tracking (CST) markers including caffeine, carbamazepine, theophylline and acetaminophen, pathogens (Giardia, Cryptosporidium, adenovirus, rotavirus and enterovirus), water quality indicators (Escherichia coli, turbidity) and hydrometeorological data (flowrate, precipitation) to assess the vulnerability of 3 drinking water intakes (DWIs) and identify sources of fecal contamination. Water samples were collected under baseline, snow and rain events conditions in urban and agricultural catchments (Québec, Canada). Dynamics of E. coli, HF183 and WWMPs were similar during contamination events, and concentrations generally varied over 1 order of magnitude during each event. Elevated human-associated marker levels during events demonstrated that urban DWIs were impacted by recent contamination from an upstream municipal water resource recovery facility (WRRF). In the agricultural catchment, mixed fecal pollution was observed with the occurrences and increases of enteric viruses, human bovine and porcine mtDNA during peak contaminating events. Bovine mtDNA qPCR concentrations were indicative of runoff of cattle-derived fecal pollutants to the DWI from diffuse sources following rain events. This study demonstrated that the suitability of a given MST or CST indicator depend on river and catchment characteristics. The sampling strategy using continuous online GLUC activity coupled with MST and CST markers analysis was a more reliable source indicator than turbidity to identify peak events at drinking water intakes.@en

A monitoring strategy was implemented at two drinking water treatment plants in Quebec, Canada, to evaluate microbial reduction performances of full-scale treatment processes under different source water conditions. β-D-glucuronidase activity in source water was automatically monitored in near-real-time to establish baseline and event conditions at each location. High-volume water samples (50–1,500 L) were collected at the inflow and the outflow of coagulation/flocculation, filtration, and UV disinfection processes and were analysed for two naturally occurring surrogate organisms: Escherichia coli and Clostridium perfringens. Source water Cryptosporidium data and full-scale C. perfringens reduction data were entered into a quantitative microbial risk assessment (QMRA) model to estimate daily infection risks associated with exposures to Cryptosporidium via consumption of treated drinking water. Daily mean E. coli and Cryptosporidium concentrations in source water under event conditions were in the top 5% (agricultural site) or in the top 15% (urban site) of what occurs through the year at these drinking water treatment plants. Reduction performances of up to 6.0-log for E. coli and 5.6-log for C. perfringens were measured by concentrating high-volume water samples throughout the treatment train. For both drinking water treatment plants, removal performances by coagulation/flocculation/sedimentation processes were at the high end of the range of those reported in the literature for bacteria and bacterial spores. Reductions of E. coli and C. perfringens by floc blanket clarification, ballasted clarification and rapid sand filtration did not deteriorate during two snowmelt/rainfall events. QMRA results suggested that daily infection risks were similar during two rainfall/snowmelt events than during baseline conditions. Additional studies investigating full-scale reductions would be desirable to improve the evaluation of differences in treatment performances under various source water conditions.

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This study investigates short-term fluctuations in virus concentrations in source water and their removal by full-scale drinking water treatment processes under different source water conditions. Transient peaks in raw water faecal contamination were identified using in situ online β-D-glucuronidase activity monitoring at two urban drinking water treatment plants. During these peaks, sequential grab samples were collected at the source and throughout the treatment train to evaluate concentrations of rotavirus, adenovirus, norovirus, enterovirus, JC virus, reovirus, astrovirus and sapovirus by reverse transcription and real-time quantitative PCR. Virus infectivity was assessed through viral culture by measurement of cytopathic effect and integrated cell culture qPCR. Virus concentrations increased by approximately 0.5-log during two snowmelt/rainfall episodes and approximately 1.0-log following a planned wastewater discharge upstream of the drinking water intake and during a β-D-glucuronidase activity peak in dry weather conditions. Increases in the removal of adenovirus and rotavirus by coagulation/flocculation processes were observed during peak virus concentrations in source water, suggesting that these processes do not operate under steady-state conditions but dynamic conditions in response to source water conditions. Rotavirus and enterovirus detected in raw and treated water samples were predominantly negative in viral culture. At one site, infectious adenoviruses were detected in raw water and water treated by a combination of ballasted clarification, ozonation, GAC filtration, and UV disinfection operated at a dose of 40 mJ cm⁻². The proposed sampling strategy can inform the understanding of the dynamics associated with virus concentrations at drinking water treatment plants susceptible to de facto wastewater reuse.

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Waterborne pathogens are heterogeneously distributed across various spatiotemporal scales in water resources, and representative sampling is therefore crucial for accurate risk assess-ment. Since regulatory monitoring of microbiological water quality is usually conducted at fixed time intervals, it can miss short-term fecal contamination episodes and underestimate underlying microbial risks. In the present paper, we developed a new automated sampling methodology based on near real-time measurement of a biochemical indicator of fecal pollution. Online monitoring of β-D-glucuronidase (GLUC) activity was used to trigger an automated sampler during fecal contamination events in a drinking water supply and at an urban beach. Significant increases in protozoan parasites, microbial source tracking markers and E. coli were measured during short-term (<24 h) fecal pollution episodes, emphasizing the intermittent nature of their occurrence in water. Synchro-nous triggering of the automated sampler with online GLUC activity measurements further re-vealed a tight association between the biochemical indicator and culturable E. coli. The proposed event sampling methodology is versatile and in addition to the two triggering modes validated here, others can be designed based on specific needs and local settings. In support to regulatory monitoring schemes, it should ultimately help gathering crucial data on waterborne pathogens more efficiently during episodic fecal pollution events.

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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 ((Formula presented.) = 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.

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