Quantitative polymerase chain reaction (qPCR) offers a rapid, automated, and potentially on-site method for quantifying L. pneumophila in building potable water systems, complementing and potentially replacing traditional culture-based techniques. However, its application in assessing human health risks is complicated by a tendency to overestimate risks due to the detection of genomic copies unassociated with viable, infectious bacteria. This study examines the relationship between L. pneumophila measurements via qPCR and culture-based methods, aiming to establish qPCR-to-culture concentration ratios needed to inform associated health risks. Eligible studies collected quantitative data on L. pneumophila concentrations using molecular and culture-based methods within paired water samples. We developed a Poisson lognormal ratio model and a random-effects meta-analysis model to analyze variations in qPCR-to-culture ratios within and across sites. Of the 17 studies in the systematic review, seven, including 23 site-specific data sets, were used for meta-analysis. Our findings indicate these ratios typically vary from 1:1 to 100:1, with ratios close to 1:1 predicted at all sites. Consequently, adopting a default 1:1 conversion factor appears necessary as a cautious approach to convert qPCR concentrations to culturable concentrations for use in health risk models, such as quantitative microbial risk assessment (QMRA). Where this approach may be too conservative, viability-qPCR could improve the accuracy of qPCR-based QMRA. Standardizing qPCR and culture-based methods and reporting site-specific environmental factors affecting L. pneumophila culturability would improve understanding of the relationship between the two methods. The ratio model introduced here advances beyond simple correlation analyses, facilitating investigations of temporal and spatial heterogeneities in the relationship. This analysis is a step forward in the integration of QMRA and molecular biology, and the framework demonstrated for L. pneumophila is applicable to other pathogens monitored in the environment.

Inhalation of aerosols produced during showering exposes people to chemical and microbial contaminants present in the water. To improve quantitative estimates of exposure and to inform the efficacy of potential interventions to reduce exposures, the number and size distributions of aerosols generated during showering events were monitored and a mass balance model of the generated aerosols was developed. The aerosol generation rates were calculated through calibrating the model with the measured aerosol data. Specifically, aerosol count concentrations and size distributions were measured with an aerodynamic particle sizer over the duration of mock showering events under various conditions, including different water temperatures and different showerhead types (conventional and rain showers). The empirical data were then used to fit a mass balance model to obtain aerosol generation rates and decay rates for each aerosol size class through least square fitting. An initial high peak concentration of aerosols was observed under hot water conditions relative to cold water conditions which resulted in a rapid increase in aerosol exposure during the first 1–2 min of showering. This suggests that people showering in hot water conditions will have a potentially increased exposure during the first 1–2 min. The model-fitted values suggest large inter-experiment variation in estimated aerosol generation and decay rates, even among triplicates of the same showering conditions. Current exposure assessment approaches assume constant aerosol concentrations during showers which might lead to miscalculated cumulative risk for microbial hazards because of their uneven distribution in building plumbing systems and biofilm detachment process during flushing. Thus, considering aerosol dynamics is beneficial during shower exposure assessments to inform risk management interventions. The data set and associated modeling results provided can support this, as they can be readily integrated into microbial risk assessments for waterborne pathogens such as Legionella spp., nontuberculous Mycobacteria (NTM) and Pseudomonas aeruginosa.

To protect human health, limits for the concentrations of per- and polyfluoroalkyl substances (PFAS) in drinking water are decreasing in many countries. However, the required treatment to achieve these lower concentrations is more resource and energy intensive than conventional drinking water treatment. Consequently, this intensified water treatment has an indirect negative impact on human health. For example, treatment with granular activated carbon (GAC), commonly used for PFAS removal, can lead to particulate matter emissions and additional global warming. These negative impacts partly off-set the health benefit achieved by lower PFAS exposure via drinking water. In this study, we quantified health impacts of both the increased treatment and the reduced PFAS exposure in disability-adjusted life years (DALYs), to assess whether PFAS removal from drinking water to specified targets with GAC results in a net health benefit. We selected the prospective Dutch drinking water guideline for PFAS of 4.4 ng PFOA-equivalent (PEQ) L−1, as this guideline is amongst the more conservative concentration targets globally. We first conducted a Life Cycle Assessment (LCA) to quantify the health cost associated with the increased reactivation frequency of an existing GAC system in the Netherlands, required to achieve PFAS concentrations below 4.4 ng PEQ L−1. Then, we quantified the health benefit obtained by the corresponding lower PFAS exposure, using pharmacokinetic modelling combined with published dose–response relationships. For the treatment plant investigated in the current study, which uses reactivated wood-based GAC, increasing the reactivation frequency to remove more PFAS was found to result in a net health benefit of 6.9–300 DALYs per 106 persons per year. However, when single-use rather than reactivated GAC would be used for PFAS treatment, the health losses from the GAC production were in the same range as the health benefits from lower PFAS exposure. Overall, the negative health impacts associated with more intensive water treatment should be considered when developing strategies to reduce PFAS exposure.

Source Water Protection in Canada is regulated primarily by provincial governments, leading to a variety of approaches for characterizing threats to drinking water. This paper compares the key elements of vulnerability and threat assessments for microbial contaminants for two Canadian provinces. Drinking water intakes of two municipalities in Quebec and Ontario, Canada, located on opposite sides of a large transboundary river impacted by Combined Sewer Overflow (CSO) discharges were used as a case study to evaluate the two provincial approaches. Québec’s vulnerability classification for microbial contaminants is data driven based on regulatory monitoring (concentrations of Escherichia coli) at the drinking water intake) while that of Ontario’s is model driven and dependent on the physical and hydraulic characteristics of zones around an intake. To establish a quantitative criterion to compare these two threat assessment frameworks, the impacts of a series of CSO events upstream of the drinking water intakes were simulated using a calibrated hydrodynamic and water quality model. Corresponding enteric pathogen concentrations in the intakes were estimated and used as input for Quantitative Microbial Risk Assessment (QMRA) to calculate treatment requirement levels to meet human health targets. Unlike Ontario’s threat assessment approach, Quebec’s approach provides an opportunity to investigate the effectiveness of risk reduction strategies such as an adjustment of the frequency of CSO events or corrective actions to improve treatment. Considering the influence of CSO events on log removal requirements to remain compliant with human health targets permitted the differentiation of CSO risk levels for threat prioritization.

The disease burden from Legionella spp. infections has been increasing in many industrialized countries and, despite decades of scientific advances, ranks amongst the highest for waterborne diseases. We review here several key research areas from a multidisciplinary perspective and list critical research needs to address some of the challenges of Legionella spp. management in engineered environments. These include: (i) a consideration of Legionella species diversity and cooccurrence, beyond Legionella pneumophila only; (ii) an assessment of their environmental prevalence and clinical relevance, and how that may affect legislation, management, and intervention prioritization; (iii) a consideration of Legionella spp. sources, their definition and prioritization; (iv) the factors affecting Legionnaires' disease seasonality, how they link to sources, Legionella spp. proliferation and ecology, and how these may be affected by climate change; (v) the challenge of saving energy in buildings while controlling Legionella spp. with high water temperatures and chemical disinfection; and (vi) the ecological interactions of Legionella spp. with other microbes, and their potential as a biological control strategy. Ultimately, we call for increased interdisciplinary collaboration between multiple research domains, as well as transdisciplinary engagement and collaboration across government, industry, and science as the way toward controlling and reducing Legionella-derived infections.

Legionellosis is generally attributed to the inhalation of aerosolized Legionella pneumophila from engineered water systems and/or soils. However, aspiration of contaminated water─a known cause of aspiration pneumonia─is seldom modeled in L. pneumophila risk assessment. Here, we develop a quantitative microbial risk assessment model to estimate the risks associated with aspiration exposures. Monte Carlo simulations, incorporating the aspiration water volume, the aspiration frequency, and the alveolar deposition fraction, reveal that low L. pneumophila concentrations in water can yield an appreciable infection risk in populations prone to aspiration. Under equal L. pneumophila concentrations, we find that an aspiration event can pose a higher infection risk than aerosol inhalation from showers or faucets. Sensitivity analyses identify the aspiration volume as a driver of risk. Our findings highlight the need for risk management strategies that address not only aerosol generation but also the aspiration of contaminated water.

Cooling towers are critical engineered water systems for air conditioning and refrigeration but can create favorable conditions for Legionella pneumophila growth and aerosolization. Human exposure to L. pneumophila-contaminated aerosols can cause Legionnaire's disease. Routine monitoring of L. pneumophila in cooling towers offers possibilities to develop quantitative microbial risk assessment (QMRA) models to guide system design, operation, control, and maintenance. Here, we used the regulatory monitoring database from Quebec, Canada, to develop statistical models for predicting L. pneumophila concentration variability in cooling towers and integrate these models into a screening-level QMRA model to predict human health risks. Analysis of 105,463 monthly L. pneumophila test results revealed that the exceedance rate of the 104 colony-forming unit (CFU) per liter threshold was constant at 10 % from 2016 to 2020, emphasizing the need to better validate the efficacy of corrective measures following the threshold exceedances. Among 2852 cooling towers, 51.2 % reported no detections, 38.5 % had up to nine positives, and 10.2 % over ten. The gamma or the lognormal distributions adequately described site-specific variations in L. pneumophila concentrations, but parametric uncertainty was very high for the lognormal distribution. We showed that rigorous model comparison is essential to predict peak concentrations accurately. Using QMRA, we found that an average L. pneumophila concentration below 1.4 × 104 CFU L−1 should be maintained in cooling towers to meet a health-based target of 10−6 DALY/pers.-year for clinical severity infections. We identified 137 cooling towers at risk of exceeding this limit, primarily due to the observation or prediction of rare peak concentrations above 105 CFU L−1. Effective mitigation of those peaks is critical to controlling public health risks associated with L. pneumophila.

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.

Greywater reuse is a strategy to address water scarcity, necessitating the selection of treatment processes that balance cost-efficiency and human health risks. A key aspect in evaluating these risks is understanding pathogen contamination levels in greywater, a complex task due to intermittent pathogen occurrences. To address this, faecal indicator organisms like E. coli are often monitored as proxies to evaluate faecal contamination levels and infer pathogen concentrations. However, the wide variability in faecal indicator concentrations poses challenges in their modelling for quantitative microbial risk assessment (QMRA). Our study critically assesses the adequacy of parametric models in predicting the variability in E. coli concentrations in greywater. We found that models that build on summary statistics, like medians and standard deviations, can substantially underestimate the variability in E. coli concentrations. More appropriate models may provide more accurate estimations of, and uncertainty around, peak E. coli concentrations. To demonstrate this, a Poisson lognormal distribution model is fit to a data set of E. coli concentrations measured in shower and laundry greywater sources. This model estimated arithmetic mean E. coli concentrations in laundry waters at approximately 1.0E + 06 MPN 100 mL−1. These results are around 2.0 log10 units higher than estimations from a previously used hierarchical lognormal model based on aggregated summary data from multiple studies. Such differences are considerable when assessing human health risks and setting pathogen reduction targets for greywater reuse. This research highlights the importance of making raw monitoring data available for more accurate statistical evaluations than those based on summary statistics. It also emphasizes the crucial role of model comparison, selection, and validation to inform policy-relevant outcomes.

Antimicrobial resistance (AMR) poses a global health threat, causing millions of deaths annually, with expectations of increased impact in the future. Wastewater surveillance offers a cost-effective, non-invasive tool to understand AMR carriage trends within a population. We monitored extended-spectrum β-lactamase producing Escherichia coli (ESBL-E. coli) weekly in influent wastewater from six wastewater treatment plants (WWTPs) in Switzerland (November 2021 to November 2022) to investigate spatio-temporal variations, explore correlations with environmental variables, develop a predictive model for ESBL-E. coli carriage in the community, and detect the most prevalent ESBL-genes. We cultured total and ESBL-E. coli in 300 wastewater samples to quantify daily loads and percentage of ESBL-E. coli. Additionally, we screened 234 ESBL-E. coli isolates using molecular methods for the presence of 18 ESBL-gene families. We found a population-weighted mean percentage of ESBL-E. coli of 1.9% (95% confidence interval: 1.8–2%) across all sites and weeks, which can inform ESBL-E. coli carriage. Concentrations of ESBL-E. coli varied across WWTPs and time, with higher values observed in WWTPs serving larger populations. Recent precipitations (previous 24/96 h) showed no significant association with ESBL-E. coli, while temperature occasionally had a moderate impact (P < 0.05, correlation coefficients approximately 0.40) in some locations. We identified blaCTX-M-1, blaCTX-M-9, and blaTEM as the predominant ESBL-gene families. Our study demonstrates that wastewater-based surveillance of culturable ESBL-E. coli provides insights into AMR trends in Switzerland and may also inform resistance. These findings establish a foundation for long term, nationally established monitoring protocols and provide information that may help inform targeted public health interventions.

In light of increasingly diverse greywater reuse applications, this study proposes risk-based log-removal targets (LRTs) to aid the selection of treatment trains for greywater recycling at different collection scales, including appliance-scale reuse of individual greywater streams. An epidemiology-based model was used to simulate the concentrations of prevalent and treatment-resistant reference pathogens (protozoa: Giardia and Cryptosporidium spp., bacteria: Salmonella and Campylobacter spp., viruses: rotavirus, norovirus, adenovirus, and Coxsackievirus B5) in the greywater streams for collection scales of 5-, 100-, and a 1000-people. Using quantitative microbial risk assessment (QMRA), we calculated LRTs to meet a health benchmark of 10^–4 infections per person per year over 10′000 Monte Carlo iterations. LRTs were highest for norovirus at the 5-people scale and for adenovirus at the 100- and 1000-people scales. Example treatment trains were designed to meet the 95 % quantiles of LRTs. Treatment trains consisted of an aerated membrane bioreactor, chlorination, and, if required, UV disinfection. In most cases, rotavirus, norovirus, adenovirus and Cryptosporidium spp. determined the overall treatment train requirements. Norovirus was most often critical to dimension the chlorination (concentration × time values) and adenovirus determined the required UV dose. Smaller collection scales did not generally allow for simpler treatment trains due to the high LRTs associated with viruses, with the exception of recirculating washing machines and handwashing stations. Similarly, treating greywater sources individually resulted in lower LRTs, but the lower required LRTs nevertheless did not generally allow for simpler treatment trains. For instance, LRTs for a recirculating washing machine were around 3-log units lower compared to LRTs for indoor reuse of combined greywater (1000-people scale), but both scenarios necessitated treatment with a membrane bioreactor, chlorination and UV disinfection. However, simpler treatment trains may be feasible for small-scale and application-scale reuse if: (i) less conservative health benchmarks are used for household-based systems, considering the reduced relative importance of treated greywater in pathogen transmission in households, and (ii) higher log-removal values (LRVs) can be validated for unit processes, enabling simpler treatment trains for a larger number of appliance-scale reuse systems.

Preventing failures of water treatment barriers can play an important role in meeting the increasing demand for microbiologically safe water. The development and integration of failure prevention strategies into quantitative microbial risk assessment (QMRA) offer opportunities to support the design and operation of treatment trains. This study presents existing failure models and extends them to guide the development of risk-based operational monitoring strategies. For barriers with rapid performance loss, results show that a failure of 15 s should be reliably detected to verify a log reduction value (LRV) of 6.0; thus, detecting and remediating these failures may be beyond current technology. For chemical disinfection with a residual, failure durations in order of minutes should be reliably detected to verify a LRV of 6.0. Short-term failures are buffered because the disinfectant residual concentration sustains a partial reduction performance. Therefore, increasing the contact time and hydraulic mixing reduces the impact of failures. These findings demonstrate the importance of defining precise frequencies to monitor barrier performances during operation. Overall, this study highlights the utility of process-specific models for developing failure prevention strategies for water safety management.

Waterborne enteric viruses in lakes, especially at recreational water sites, may have a negative impact on human health. However, their fate and transport in lakes are poorly understood. In this study, we propose a coupled water quality and quantitative microbial risk assessment (QMRA) model to study the transport, fate and infection risk of four common waterborne viruses (adenovirus, enterovirus, norovirus and rotavirus), using Lake Geneva as a study site. The measured virus load in raw sewage entering the lake was used as the source term in the water quality simulations for a hypothetical scenario of discharging raw wastewater at the lake surface. After discharge into the lake, virus inactivation was modeled as a function of water temperature and solar irradiance that varied both spatially and temporally during transport throughout the lake. Finally, the probability of infection, while swimming at a popular beach, was quantified and compared among the four viruses. Norovirus was found to be the most abundant virus that causes an infection probability that is at least 10 times greater than the other viruses studied. Furthermore, environmental inactivation was found to be an essential determinant in the infection risks posed by viruses to recreational water users. We determined that infection risks by enterovirus and rotavirus could be up to 1000 times lower when virus inactivation by environmental stressors was accounted for compared with the scenarios considering hydrodynamic transport only. Finally, the model highlighted the role of the wind field in conveying the contamination plume and hence in determining infection probability. Our simulations revealed that for beaches located west of the sewage discharge, the infection probability under eastward wind was 43% lower than that under westward wind conditions. This study highlights the potential of combining water quality simulation and virus-specific risk assessment for a safe water resources usage and management.

Enteric pathogen infections are a leading cause of morbidity and mortality globally, with the highest disease burden in low-income countries. Hands act as intermediaries in enteric pathogen transmission, transferring enteric pathogens between people and the environment through contact with fomites, food, water, and soil. In this study, we conducted a systematic review of prevalence and concentrations of fecal indicator microorganisms (i.e., E. coli, fecal coliform) and enteric pathogens on hands. We identified 84 studies, reporting 35,440 observations of hand contamination of people in community or household settings. The studies investigated 44 unique microorganisms, of which the most commonly reported indicators were E. coli and fecal coliforms. Hand contamination with 12 unique enteric pathogens was reported, with adenovirus and norovirus as the most frequent. Mean E. coli prevalence on hands was 62% [95% CI 40%–82%] and mean fecal coliform prevalence was 66% [95% CI 22%–100%]. Hands were more likely to be contaminated with E. coli in low/lower-middle-income countries (prevalence: 69% [95% CI 48%–88%]) than in upper-middle/high-income countries (6% [95% CI 2%–12%]). The Review also highlights the importance of standardizing hand sampling methods, as hand rinsing was associated with greater fecal contamination compared to other sampling methods.

Legionella are natural inhabitants of building plumbing biofilms, where interactions with other microorganisms influence their survival, proliferation, and death. Here, we investigated the associations of Legionella with bacterial and eukaryotic microbiomes in biofilm samples extracted from 85 shower hoses of a multiunit residential building. Legionella spp. relative abundance in the biofilms ranged between 0–7.8%, of which only 0–0.46% was L. pneumophila. Our data suggest that some microbiome members were associated with high (e.g. Chthonomonas, Vrihiamoeba) or low (e.g. Aquabacterium, Vannella) Legionella relative abundance. The correlations of the different Legionella variants (30 Zero-Radius OTUs detected) showed distinct patterns, suggesting separate ecological niches occupied by different Legionella species. This study provides insights into the ecology of Legionella with respect to: (i) the colonization of a high number of real shower hoses biofilm samples; (ii) the ecological meaning of associations between Legionella and co-occurring bacterial/eukaryotic organisms; (iii) critical points and future directions of microbial-interaction-based-ecological-investigations.

Minimum treatment requirements are set in response to established or anticipated levels of enteric pathogens in the source water of drinking water treatment plants (DWTPs). For surface water, contamination can be determined directly by monitoring reference pathogens or indirectly by measuring fecal indicators such as Escherichia coli (E. coli). In the latter case, a quantitative interpretation of E. coli for estimating reference pathogen concentrations could be used to define treatment requirements. This study presents the statistical analysis of paired E. coli and reference protozoa (Cryptosporidium, Giardia) data collected monthly for two years in source water from 27 DWTPs supplied by rivers in Canada. E. coli/Cryptosporidium and E. coli/Giardia ratios in source water were modeled as the ratio of two correlated lognormal variables. To evaluate the potential of E. coli for defining protozoa treatment requirements, risk-based critical mean protozoa concentrations in source water were determined with a reverse quantitative microbial risk assessment (QMRA) model. Model assumptions were selected to be consistent with the World Health Organization (WHO) Guidelines for drinking-water quality. The sensitivity of mean E. coli concentration trigger levels to identify these critical concentrations in source water was then evaluated. Results showed no proportionalities between the log of mean E. coli concentrations and the log of mean protozoa concentrations. E. coli/protozoa ratios at DWTPs supplied by small rivers in agricultural and forested areas were typically 1.0 to 2.0-log lower than at DWTPs supplied by large rivers in urban areas. The seasonal variations analysis revealed that these differences were related to low mean E. coli concentrations during winter in small rivers. To achieve the WHO target of 10⁻⁶ disability-adjusted life year (DALY) per person per year, a minimum reduction of 4.0-log of Cryptosporidium would be required for 20 DWTPs, and a minimum reduction of 4.0-log of Giardia would be needed for all DWTPs. A mean E. coli trigger level of 50 CFU 100 mL⁻¹ would be a sensitive threshold to identify critical mean concentrations for Cryptosporidium but not for Giardia. Treatment requirements higher than 3.0-log would be needed at DWTPs with mean E. coli concentrations as low as 30 CFU 100 mL⁻¹ for Cryptosporidium and 3 CFU 100 mL⁻¹ for Giardia. Therefore, an E. coli trigger level would have limited value for defining health-based treatment requirements for protozoa at DWTPs supplied by small rivers in rural areas.

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.

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.

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.

The identification of appropriately conservative statistical distributions is needed to predict microbial peak events in drinking water sources explicitly. In this study, Poisson and mixed Poisson distributions with different upper tail behaviors were used for modeling source water Cryptosporidium and Giardia data from 30 drinking water treatment plants. Small differences (<0.5-log) were found between the “best” estimates of the mean Cryptosporidium and Giardia concentrations with the Poisson–gamma and Poisson–log-normal models. However, the upper bound of the 95% credibility interval on the mean Cryptosporidium concentrations of the Poisson–log-normal model was considerably higher (>0.5-log) than that of the Poisson–gamma model at four sites. The improper choice of a model may, therefore, mislead the assessment of treatment requirements and health risks associated with the water supply. Discrimination between models using the marginal deviance information criterion (mDIC) was unachievable because differences in upper tail behaviors were not well characterized with available data sets ((Formula presented.)). Therefore, the gamma and the log-normal distributions fit the data equally well but may predict different risk estimates when they are used as an input distribution in an exposure assessment. The collection of event-based monitoring data and the modeling of larger routine monitoring data sets are recommended to identify appropriately conservative distributions to predict microbial peak events.