Research gaps and priorities for quantitative microbial risk assessment (QMRA).

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.

Application of Effect-Based Methods to Water Quality Monitoring: Answering Frequently Asked Questions by Water Quality Managers, Regulators, and Policy Makers.

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.

Effect-based monitoring to integrate the mixture hazards of chemicals into water safety plans.

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.

Using Artificial Intelligence to extract information on pathogen characteristics from scientific publications.

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.

Quantitative chemical risk assessment for mixtures: Application to alkylphenol mixtures and phthalate mixtures in tap and bottled water.

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.

Changes in Escherichia coli to enteric protozoa ratios in rivers: Implications for risk-based assessment of drinking water treatment requirements.

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.

Using surrogate data to assess risks associated with microbial peak events in source water at drinking water treatment plants.

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. ß-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.

Demonstrating the reduction of enteric viruses by drinking water treatment during snowmelt episodes in urban areas.

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 ß-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 ß-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.

Development of a quantitative chemical risk assessment (QCRA) procedure for contaminants of emerging concern in drinking water supply.

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.

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

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 ß-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.

Importance of Distributional Forms for the Assessment of Protozoan Pathogens Concentrations in Drinking-Water Sources.

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 ( n<30 ). 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.

Can routine monitoring of E. coli fully account for peak event concentrations at drinking water intakes in agricultural and urban rivers?

In several jurisdictions, the arithmetic mean of Escherichia coli concentrations in raw water serves as the metric to set minimal treatment requirements by drinking water treatment plants (DWTPs). An accurate and precise estimation of this mean is therefore critical to define adequate requirements. Distributions of E. coli concentrations in surface water can be heavily skewed and require statistical methods capable of characterizing uncertainty. We present four simple parametric models with different upper tail behaviors (gamma, log-normal, Lomax, mixture of two log-normal distributions) to explicitly account for the influence of peak events on the mean concentration. The performance of these models was tested using large E. coli data sets (200-1800 samples) from raw water regulatory monitoring at six DWTPs located in urban and agricultural catchments. Critical seasons of contamination and hydrometeorological factors leading to peak events were identified. Event-based samples were collected at an urban DWTP intake during two hydrometeorological events using online ß-d-glucuronidase activity monitoring as a trigger. Results from event-based sampling were used to verify whether selected parametric distributions predicted targeted peak events. We found that the upper tail of the log-normal and the Lomax distributions better predicted large concentrations than the upper tail of the gamma distribution. Weekly sampling for two years in urban catchments and for four years in agricultural catchments generated reasonable estimates of the average raw water E. coli concentrations. The proposed methodology can be easily used to inform the development of sampling strategies and statistical indices to set site-specific treatment requirements.

Optimizing the performance of conventional water treatment system using quantitative microbial risk assessment, Tehran, Iran.

The performance of conventional drinking water treatment plants (WTPs) can be improved using quantitative microbial risk assessment (QMRA). A QMRA study on Cryptosporidium using actual pathogen density was conducted to examine the performance of Jalaliyeh WTP in Tehran, Iran. The infection risk and the burden of disease attributed to the parasite presence in finished water were estimated incorporating physical and chemical log reduction values (LRVs), using stochastic modeling and disinfection profiling. The risk and burden of disease were compared with health-based targets, i.e. one case of infection per 10,000 people or 10-6 DALYs per person per year. The parasite's LRVs were 2.31 and 0.034 log provided by physico-chemical treatment and disinfection processes, respectively. The mean of estimated risk (111 cases per 104 people per year) and the burden of disease (11.7 DALYs per 106 people per year) both exceeded the targets. To control the excess risk, three QMRA-based disinfection scenarios were examined including: (1) employing chlorine dioxide (ClO2) instead of chlorine (2) ozonation with a concentration of 0.75 mg/L (Ct = 22.5 min mg/L) and (3) UV irradiation with a dose of 10 mJ/cm2. The LRV of parasite may be increased to 3.0, 5.1 and 4.9 log by employing ClO2, ozonation and UV irradiation, respectively. The use of ozone or UV as alternative disinfectants, could enhance the disinfection efficacy and provide sufficient additional treatment against the excess risk of parasite. QMRA could make it easier applying appropriate improvement to conventional WTPs in order to increase the system performance in terms of health-based measures.

Using QMRA-based regulation as a water quality management tool in the water security challenge: experience from the Netherlands and Australia.

Innovation in the water sector is at play when addressing the global water security challenge. This paper highlights an emerging role for Quantitative Microbial Risk Assessment (QMRA) and health-based targets in the design and application of robust and flexible water quality regulation to protect public health. This role is especially critical as traditional supply sources are subject to increased contamination, and recycled wastewater and stormwater become a crucial contribution to integrated water supply strategies. Benefits and weaknesses of QMRA-based regulation are likely to be perceived differently by the multiple stakeholders involved. The goal of the current study is to evaluate the experience of QMRA-based regulation implementation in the Netherlands and Australia, and to draw some lessons learned for regulators, policy makers, the industry and scientists. Water experts from regulatory bodies, government, water utilities, and scientists were interviewed in both countries. This paper explores how QMRA-based regulation has helped decision-making in the Netherlands in drinking water safety management over the past decade. Implementation is more recent in Australia: an analysis of current institutional barriers to nationally harmonized implementation for water recycling regulation is presented. This in-depth retrospective analysis of experiences and perceptions highlights the benefits of QMRA-based regulation and the challenges of implementation. QMRA provides a better assessment of water safety than the absence of indicators. Setting a health target addresses the balance between investments and public safety, and helps understand risks from alternative water sources. Challenges lie in efficient monitoring, institutional support for utilities, interpretation of uncertainty by regulators, and risk communication to consumers.