Rapid and effective decision-making is crucial during drinking water contamination events to ensure public safety. This paper examines a case study where a water utility, responding to customer complaints, suspected wastewater contamination in its network. We compare the traditional expert judgement approach to a model-based approach using the PathoINVEST tool. The tool performs simulations of contamination events informed by sensor measurements, identifies contamination sources using sampling results, and suggests optimal valve closures for mitigation. Our findings show that the model-based approach significantly enhances response efficiency and accuracy. It identified the contamination source with four samples in 1.3 h, compared to 11 samples in 3.7 h for the traditional approach, and resulted in a lower infection risk (12% versus 20%) at the time of source identification. Regarding valve closure, the model-based approach performed better, resulting in a 3%-point reduction in infection risk compared to the traditional approach. Modeling uncertainty is addressed by considering valve settings uncertainty; despite a 0.7% discrepancy in valve settings compared to the model, the tool accurately pinpointed the contamination vicinity 75% of the time. These findings support the claim that integrating modeling and sensor tools into emergency response protocols for drinking water contamination events can improve early identification and mitigation, potentially safeguarding public health in urban water supply systems.

Safe drinking water is critical for public health, yet microbial contamination remains a significant global challenge. We conducted a systematic review to update World Health Organization guidance on water disinfection technologies by synthesizing peer-reviewed literature from 1997 to 2021 on the performance of free chlorine, chlorine dioxide, ozone, and ultraviolet (UV) light against bacteria, viruses, and protozoa. Following PRISMA guidelines, we analyzed log10 reduction values (LRVs) and contact times (Ct) or fluence (for UV) from laboratory and field studies. We included studies from multiple databases and expert-recommended studies. Results show mean Cts for 2 LRV of non-opportunistic bacteria as 6.0 (free chlorine), 0.4 (chlorine dioxide), and 1.2 (ozone) mg/L*min, and a mean UV fluence of 8.2 mJ/cm² (all bacteria). Viruses required lower Cts, except for UV-resistant adenoviruses, while protozoa required higher Cts or fluences. Opportunistic bacteria required significantly higher Cts than non-opportunistic bacteria for free chlorine and chlorine dioxide. Temperature and pH effects were inconsistent, highlighting data variability and gaps in field studies. These findings support global guidance on water treatment and may be used alongside other context-specific data to understand the roles these technologies play in reducing waterborne exposures. We recommend standardized reporting from performance studies to enable straightforward synthesis of evidence.

A comparative risk scenario analysis was conducted using quantitative microbial risk assessment (QMRA) for urine-derived fertiliser use, considering membrane filtration and urine storage as treatment options. The risk scenarios examined included accidental ingestion through unwashed, uncooked home-grown crops; occupational exposure of workers handling and mixing urine fertiliser; and recreational exposure of toddlers playing in green areas where the fertiliser had been recently applied. The primary source of contamination originates from fecal displacement into urine during source separation in the toilet. Campylobacter jejuni and rotavirus were selected as reference pathogens. As no guidelines currently specify benchmarks for urine reuse, the annual infection risk and the DALY were estimated and evaluated against existing wastewater reuse benchmarks. The analysis shows that membrane treatment can greatly reduce health risks associated with urine-derived fertiliser use: the risk was sufficiently low to meet annual, and DALY targets for bacterial and viral risks. The results also indicate that inactivation of both bacteria and viruses through urine storage is unlikely to meet safety targets without extended storage durations. The infection risks were higher during accidental ingestion of urine fertiliser on food crops compared to indirect exposure due to occupational and recreational activities. Nevertheless, the residual risk is sufficiently low to be effectively managed with additional, simple mitigation measures communicated through user guidance on product labels. In conclusion, membrane treatment technology could mitigate exposure to microbial hazards potentially present in urine-derived fertiliser and may be satisfactory to reassure health regulators of its safety, thereby supporting progress toward sustainability goals.

Disinfection is key to controlling the infection risk caused by viral contamination. Current disinfection guidelines often refer to a single virus resistant to the disinfectant of interest, despite the large variation in sensitivity to disinfectants among viruses or even among strains within the same species. Here, we demonstrate a statistical framework that integrates multiple experimental data sets and model the variation in sensitivity to disinfectants across different virus species using a parametric distribution termed the disinfectant sensitivity distribution. To illustrate this framework, we used 37, 9, and 28 species-dependent inactivation rate constants for ultraviolet (UV), ozone, and free chlorine, respectively, from systematic reviews. We estimated the sensitivity distributions of these disinfectants by incorporating the uncertainty in the individual inactivation rate constants using a Bayesian framework. The estimated sensitivity distributions suggested that it should be possible to achieve 4-log inactivation of 93.0% (95% credible interval (CrI): 84.2%-97.5%), 99.4% (95% CrI: 86.7%-100%), and 95.0% (95% CrI: 85.5%-98.8%) of the examined virus species using UV, ozone, and free chlorine, respectively, if the disinfectant dose complies with the values recommended by the US EPA. The proposed approach provides a reasonable extrapolation of observed inactivation kinetics to untested viruses and tools for more transparent risk assessments.

Access to safe drinking water is crucial for public health necessitating the use of effective water treatment processes. We conducted a systematic literature review on microorganism removal by physical treatment processes used in drinking water treatment systems with the aim of providing current summary data to update the World Health Organization's Guidelines for Drinking Water Quality (GDWQ) and to reflect on the data available for comparison of treatment technologies. We reviewed peer-reviewed articles reporting original data that were published between 1997 and March 2022 on the following physical treatment technologies: roughing filters, storage reservoirs, bank filtration, conventional and high-rate clarification, dissolved air flotation, lime softening, granular media filtration, slow sand filtration, precoat filtration, membrane filtration, granular activated carbon, ceramic membrane filtration, and soil aquifer treatment. The literature search was conducted in several databases including Web of Science and PubMed. Data from 165 articles were included in the analysis and used to calculate Log Reduction Values (LRVs) for each technology by microbial contaminant type (bacteria, virus, or protozoa). The quantity and quality of data ranged widely for each technology. We found granular media, membranes (microfiltration (MF), ultrafiltration (UF), and reverse osmosis (RO)), and precoat filtration to remove the most protozoa with average LRVs of 3.0 (95% CI 2.8-3.3), 5.7 (95% CI 5.4-6.0), and 4.4 (95% CI 4.1-4.7), respectively. Bacteria was removed most effectively by membrane filtration (MF, UF, RO) with average LRVs of 4.5 (95% CI 3.9-5.1) and moderately by dissolved air flotation, lime softening, and soil aquifer treatment with average LRVs of 2.7, 2.6, and 2.4 respectively. Viruses were removed most effectively by reverse osmosis membrane filtration with an average LRV of 4.9 (95% CI 4.0-5.7). This data provides valuable information on pathogen reduction and areas of needed research. The variation in results underscores the importance of further consideration when selecting technologies to use and the need for standardized reporting in both lab and field studies. It is important to consider variables in water quality and technology operation that may impact treatment effectiveness when selecting treatment options for use. The findings contribute to ongoing efforts to revise the WHO's GDWQ, offering updated insights into LRVs for different water treatment technologies.

In a desktop exercise, a water utility’s emergency response to suspected wastewater contamination in a drinking water network was compared with a model-based approach using PathoINVEST. This tool simulates contamination scenarios and assists with locating the source of contamination using sampling results. The sampling procedure used a portable sensor that offers rapid (20 min time-to-result) screening of fecal contamination. Preliminary results show that the model-based approach is able to find the contamination source faster and with fewer samples than current practices. Integrating modeling and rapid sensor tools in emergency responses improves decision-making and public health protection in drinking water networks.

Pathogen intrusion in drinking water systems can pose severe health risks. To better prepare in planning and responding to such events, computational models that capture the intrusion and health impact dynamics are needed. This study presents a novel benchmark testbed that integrates current knowledge on pathogen transport and fate in chlorinated systems and can assess infection risk from contamination events. The model considers organic matter degradation, chlorine decay mechanisms, pathogen inactivation kinetics, as well as stochastic water demands. We studied modeling of wastewater intrusion events that can occur anywhere within a chlorinated and non- chlorinated network. We applied the Quantitative Microbial Risk Assessment framework focusing on three pathogens: enterovirus, Campylobacter, and Cryptosporidium, and their respective dose-response models. Synthetic household-level water demand time series were used to model the individual water consumption timing and calculate the infection risk (exposure via ingestion). Model outcomes indicate that while chlorination aids mitigation, larger contaminations can still lead to infections due to chlorine resistance (for Cryptosporidium) and chlorine depletion at the contamination point. In our example scenarios, chlorine-susceptible pathogens infected 0.78 -26.6 % of the downstream population, while chlorine-resistant ones infected the entire downstream population. Enterovirus infection risk is higher, despite the concentrations in the contamination source being lower, due to the lower susceptibility to chlorine than Campylobacter. In non-chlorinated networks, the modeled wastewater contamination events led to 11 -46 % infection risk in the total population, depending on the contamination location. Hydraulic uncertainty had a limited influence on infection risk. Furthermore, Campylobacter's infection risk is more sensitive to the initial concentration in the contamination source whereas enterovirus infection risk to the inactivation rate. The model further indicates that the time window for effective mitigation of the magnitude of a waterborne outbreak is short (within hours).

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Water safety plans address both routine operations and incident responses to support risk management in drinking water utilities. Their use and relevance in facing the challenges of the Covid-19 crisis were investigated via a survey distributed to water utilities and health or environmental agencies across the globe. Responses from 86 respondents from 38 countries were analysed to identify the water safety challenges faced and responses. Water safety plans appear to provide some preparedness and organizational advantages to utilities in facing the Covid-19 crisis, including stronger communication links between utilities and governing agencies. Guidance for future water safety planning is provided.

Effect-based methods (EBM) have great potential for water quality monitoring as they can detect the mixture effects of all active known and unknown chemicals in a sample, which cannot be addressed by chemical analysis alone. To date, EBM have primarily been applied in a research context, with a lower level of uptake by the water sector and regulators. This is partly due to concerns regarding the reliability and interpretation of EBM. Using evidence from the peer-reviewed literature, this work aims to answer frequently asked questions about EBM. The questions were identified through consultation with the water industry and regulators and cover topics related to the basis for using EBM, practical considerations regarding reliability, sampling for EBM and quality control, and what to do with the information provided by EBM. The information provided in this work aims to give confidence to regulators and the water sector to stimulate the application of EBM for water quality monitoring.

Emergencies and disasters (such as earthquakes and floods), may contaminate drinking water systems with pathogens, that can affect the health of both First Responders and Citizens. As part of the Horizon 2020 “Pathogen Contamination Emergency Response Technologies” (PathoCERT) project, we are developing a Digital Twin tool (PathoINVEST) to assist First Responders and Water Authorities in investigating and responding efficiently to drinking water contamination events. In this paper, we present preliminary work on PathoINVEST, its architecture, and how it operates with the PathoCERT ecosystem of technologies. Moreover, using an illustrative case study, we demonstrate how PathoINVEST will process data and produce useful insights for the First Responders during a realistic contamination event. This work demonstrates how different research results can be integrated into a holistic water contamination emergency management system, in accordance with the needs of First Responders who need to make decisions within a limited time frame and to reduce the impact of a contamination event.

Water safety plans (WSPs) are intended to assure safe drinking water (DW). WSPs involve assessing and managing risks associated with microbial, chemical, physical and radiological hazards from the catchment to the consumer. Currently, chemical hazards in WSPs are assessed by targeted chemical analysis, but this approach fails to account for the mixture effects of the many chemicals potentially present in water supplies and omits the possible effects of non-targeted chemicals. Consequently, effect-based monitoring (EBM) using in vitro bioassays and well plate-based in vivo assays are proposed as a complementary tool to targeted chemical analysis to support risk analysis, risk management and water quality verification within the WSP framework. EBM is frequently applied to DW and surface water and can be utilised in all defined monitoring categories within the WSP framework (including 'system assessment', 'validation', 'operational' and 'verification'). Examples of how EBM can be applied within the different WSP modules are provided, along with guidance on where to apply EBM and how frequently. Since this is a new area, guidance documents, standard operating procedures (SOPs) and decision-making frameworks are required for both bioassay operators and WSP teams to facilitate the integration of EBM into WSPs, with these resources being developed currently.

Health risk assessment of environmental exposure to pathogens requires complete and up to date knowledge. With the rapid growth of scientific publications and the protocolization of literature reviews, an automated approach based on Artificial Intelligence (AI) techniques could help extract meaningful information from the literature and make literature reviews more efficient. The objective of this research was to determine whether it is feasible to extract both qualitative and quantitative information from scientific publications about the waterborne pathogen Legionella on PubMed, using Deep Learning and Natural Language Processing techniques. The model effectively extracted the qualitative and quantitative characteristics with high precision, recall and F -score of 0.91, 0.80, and 0.85 respectively. The AI extraction yielded results that were comparable to manual information extraction. Overall, AI could reliably extract both qualitative and quantitative information about Legionella from scientific literature. Our study paved the way for a better understanding of the information extraction processes and is a first step towards harnessing AI to collect meaningful information on pathogen characteristics from environmental microbiology publications.

Water, Sanitation and Hygiene (WASH) practices are important factors in preventing diarrhoea. The objectives of this study were to assess the behaviour of the mothers of under-five children with regard to WASH practices, water quality, incidence rate of diarrhoea and the WASH predictors responsible for diarrhoea and water contamination, using multivariate regression analysis. The present study was conducted in households (n=55) having under-five children (n=88) based in an urban slum pocket of Mumbai city, India. Key satisfactory practices included (percent household following them in parenthesis) boiling of water (63.6%) and daily cleaning of storage containers (74.5%). Households followed unsatisfactory practices during water transfer (72.7%), handwashing (58.2%), defaecation location (96.4%) and disposal of children's faeces (98.2%). The incidence rate of diarrhoea among <5-year-old children was 4.7 diarrhoeal episodes/100 child months. 86 and 39.7% of untreated and boiled drinking water samples had coliforms, while 12.5 and 5.1% had E. coli, respectively. Untreated drinking water and water sourced from shared taps were significantly associated with the incidence of diarrhoea (p<0.05), and inconsistent in-house treatment of water was significantly associated with the presence of coliforms in drinking water (p<0.05). The study results suggest that WASH advocacy and an improvement in sanitation-related infrastructure for the slum population can reduce diarrhoeal incidence.

The occurrence and hazard risks of mixtures of Contaminants of Emerging Concern (CECs) in drinking water (DW) lead to serious consideration regarding the possible impacts on public health. Consequently, there is ongoing research, development and empowerment of risk assessment procedures to get more toxicological insight. For instance, alkylphenols and phthalates have been frequently reported to be present both in bottled and tap water, affecting different human endpoints. Currently, deterministic chemical risk assessment (CRA) is used to evaluate the compounds’ mixture health risk. However, CRA deals just qualitatively with sources of uncertainty, which may lead to erroneous assessment of risks. Here, a new procedure for quantitative chemical risk assessment of CEC mixtures (QCRAMIX) is proposed. Its potential is illustrated by a case study where the risks related to the presence of mixtures of alkylphenols or phthalates in tap versus bottled DW are compared. Uncertainties in both exposure and hazard assessment steps of the procedure are included to calculate a probabilistic mixture Benchmark Quotient (BQMIX). The QCRAMIX procedure highlighted the non-negligible health risks posed by those compounds in both DW sources based on overall water consumption. In fact, DW consumers’ behaviour in 13 different countries, in terms of total DW consumption and fraction of bottled and tap water consumed, were considered to evaluate the influence on health risk. For alkylphenols, the total water consumption was found to be the most relevant factor in increasing the health risk, while for phthalates the risk was found to be mainly influenced by the percentage of bottled water consumed. Hence, the proposed QCRAMIX procedure can be a valuable tool for prioritization of CECs to be included in DW regulations which aim to minimize the overall risk, accounting for actual DW consumption.

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.

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.

Water reuse has the potential to substantially reduce the demand on groundwater and surface water. This study presents a method to evaluate the potential of water reuse schemes in a regional context and demonstrates how water reuse propagates through the water system and potentially reduces pressure on groundwater resources. The use of Sankey diagram visualisation provides a valuable tool to explore and evaluate regional application of water reuse, its potential to reduce groundwater and surface water demand, and the possible synergies and trade-offs between sectors. The approach is demonstrated for the Dutch anthropogenic water system in the current situation and for a future scenario with increased water demand and reduced water availability due to climate change. Four types of water reuse are evaluated by theoretically upscaling local or regional water reuse schemes based on local reuse examples currently in operation in the Netherlands or Flanders: municipal and industrial wastewater effluent reuse for irrigation, effluent reuse for industrial applications, and reuse for groundwater replenishment. In all cases, water reuse has the potential to significantly reduce groundwater extraction volume, and thus to alleviate the pressure on the groundwater system. The water-quantity based analysis is placed in the context of water quality demands, health and safety aspects, technological requirements, regulations, public perception, and its net impact on the environment. This integrative context is essential for a successful implementation of water reuse in practice.

The uncertainties on the occurrence, fate and hazard of Contaminants of Emerging Concern (CECs) increasingly challenge drinking water (DW) utilities whether additional measures should be taken to reduce the health risk. This has led to the development and evaluation of risk-based approaches by the scientific community. DW guideline values are commonly derived based on deterministic chemical risk assessment (CRA). Here, we propose a new probabilistic procedure, that is a quantitative chemical risk assessment (QCRA), to assess potential health risk related to the occurrence of CECs in DW. The QCRA includes uncertainties in risk calculation in both exposure and hazard assessments. To quantify the health risk in terms of the benchmark quotient probabilistic distribution, the QCRA estimates the probabilistic distribution of CECs concentration in DW based on their concentration in source water and simulating the breakthrough curves of a granular activated carbon (GAC) treatment process. The model inputs and output uncertainties were evaluated by sensitivity and uncertainty analyses for each step of the risk assessment to identify the most relevant factors affecting risk estimation. Dominant factors resulted to be the concentration of CECs in water sources, GAC isotherm parameters and toxicological data. To stress the potential of this new QCRA approach, several case studies are considered with focus on bisphenol A as an example CEC and various GAC management options. QCRA quantifies the probabilistic risk, providing more insight compared to CRA. QCRA proved to be more effective in supporting the intervention prioritization for treatment optimization to pursue health risk minimization.

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. beta-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. (C) 2021 Elsevier Ltd. All rights reserved.