Climate-driven QMRA model for selected water supply systems in Norway accounting for raw water sources and treatment processes

Machine learning approaches for predicting microplastic pollution in peatland areas

This study explored the potential for predicting the quantities of microplastics (MPs) from easily measurable parameters in peatland sediment samples. We first applied correlation and Bayesian network analysis to examine the associations between physicochemical variables and the number of MPs measured from three districts of the Long An province in Vietnam. Further, we trained and tested three machine learning models, namely Least-Square Support Vector Machines (LS-SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) to predict the composite quantities of MPs using physicochemical parameters and sediment characteristics as predictors. The results indicate that the quantity of MPs and characteristics such as color and shape in the samples were mostly influenced by pH, TOC, and salinity. All three predictive models demonstrated considerable accuracies when applied to the testing dataset. This study lays the groundwork for using basic physicochemical variables to predict MP pollution in peatland sediments and potentially locations and environments.

Risk assessment of Cryptosporidium intake in drinking water treatment plant by a combination of predictive models and event-tree and fault-tree techniques

Risk-informed decision making permits a more effective water safety management. In this framework, this article introduces the rationale and proposes a new approach to carry out a quantitative risk assessment along the water chain, from river source to tap water, by integrating predictive modelling combined with event-tree and fault-tree techniques. The model developed by this approach could not only account for normal but also for abnormal process conditions in the water treatment plant, as well as assess the real impact of the applied safety controls, such as turbidity control. A sensitivity study was conducted to determine the effect of considering a typical drinking water treatment plant (DWTP), i.e. coagulation, sedimentation and filtration with two turbidity controls (on intake and after filtration) on the risk of infection due to exposure to Cryptosporidium in tap water. The results showed that, with the current effectiveness of turbidity reduction in the DWTP, the first control did not minimise the annual risk of Cryptosporidium infection (3.6E-04) and only limiting turbidity after filtration to below 0.01NTU provided a clear reduction in risk (7.7E-05) at the cost of rejecting 60 % of the water after the control. The lowest risk was found when turbidity reduction was set at 4 logs (8.48E-06), although this means that the effectiveness of turbidity reduction should be greatly improved. It was therefore concluded that supplementing the current treatment with alternative barriers such as UV or ozone disinfection and/or implementing direct control of Cryptosporidium concentration should be considered.

Cross-connections in drinking water distribution networks: Quantitative microbial risk assessment in combination with fault tree analysis and hydraulic modelling

Deficiencies in drinking water distribution networks, such as cross-connections, may lead to contamination of the drinking water and pose a serious health risk to consumers. Cross-connections and backflows are considered among the most severe public health risks in distribution networks. The aim of this paper was to provide a framework for estimating the risk of infection from cross-connection and backflow events. Campylobacter, norovirus, and Cryptosporidium were chosen as reference pathogens for this study. The theoretical framework was constructed based on the fault tree analysis methodology. National aggregated cross-connection incident data was used to calculate the probability of a contamination event occurring in Swedish networks. Three risk cases were evaluated: endemic, elevated, and extreme. Quantitative microbial risk assessment (QMRA) was used to assess daily risk of infection for average national estimates. The framework was also evaluated using local data from the Gothenburg network. The daily risk of infection from cross-connection and backflow events in Swedish networks was generally above an acceptable target level of 10^-6 for all reference pathogens and modelled cases; the exception was for the Gothenburg system where the risk was lower than 10^-7. An outbreak case study was used to validate the framework results. For the outbreak case study, contaminant transport in the network was simulated using hydraulic modelling (EPANET), and risk estimates were calculated using QMRA. The outbreak simulation predicted between 97 and 148 symptomatic infections, while the epidemiological survey conducted during the outbreak reported 179 cases of illness. The fault tree analysis framework was successfully validated using an outbreak case study, though it was shown on the example of Gothenburg that local data is still needed for well-performing systems. The framework can help inform microbial risk assessments for drinking water suppliers, especially ones with limited resources and expertise in this area.

Systematic review of the relative concentrations of noroviruses and fecal indicator bacteria in wastewater: considerations for use in quantitative microbial risk assessment

Human noroviruses are a leading cause of food- and water-borne disease, which has led to an interest in quantifying norovirus health risks using quantitative microbial risk assessment (QMRA). Given the limited availability of quantitative norovirus data to input to QMRA models, some studies have applied a conversion factor to estimate norovirus exposure based on measured fecal indicator bacteria (FIB) concentrations. We conducted a review of peer-reviewed publications to identify the concentrations of noroviruses and FIB in raw, secondary-treated, and disinfected wastewater. A meta-analysis was performed to determine the ratios of norovirus-FIB pairs in each wastewater matrix and the variables that significantly impact these ratios. Norovirus-to-FIB ratios were found to be significantly impacted by the norovirus genotype, month of sample collection, geographic location, and the extent of wastewater treatment. Additionally, we evaluated the impact of using a FIB-to-virus conversion factor in QMRA and found that the choice of conversion ratio has a great impact on estimated health risks. For example, the use of a conversion ratio previously used in the World Health Organization Guidelines for the Safe Use of Wastewater, Excreta and Greywater predicted health risks that were significantly lower than those estimated with measured norovirus concentrations used as inputs. This work emphasizes the gold standard of using measured pathogen concentrations directly as inputs to exposure assessment in QMRA. While not encouraged, if one must use a FIB-to-virus conversion ratio to estimate norovirus dose, the ratio should be chosen carefully based on the target microorganisms (i.e., strain, genotype, or class), prevalence of disease, and extent of wastewater treatment.

The occurrence and control of waterborne viruses in drinking water treatment: A review

As the coronavirus disease 2019 continues to spread globally, its culprit, the severe acute respiratory syndrome coronavirus 2 has been brought under scrutiny. In addition to inhalation transmission, the possible fecal-oral viral transmission via water/wastewater has also been brought under the spotlight, necessitating a timely global review on the current knowledge about waterborne viruses in drinking water treatment system - the very barrier that intercepts waterborne pathogens to terminal water users. In this article we reviewed the occurrence, concentration methods, and control strategies, also, treatment performance on waterborne viruses during drinking water treatment were summarized. Additionally, we emphasized the potential of applying the quantitative microbial risk assessment to guide drinking water treatment to mitigate the viral exposure risks, especially when the unregulated novel viral pathogens are of concern. This review paves road for better control of viruses at drinking water treatment plants to protect public health.

Heavy weather events, water quality and gastroenteritis in Norway

Climate change will lead to more extreme weather events in Europe. In Norway, little is known about how this will affect drinking water quality and population's health due to waterborne diseases. The aim of our work was to generate new knowledge on the effect of extreme weather conditions and climate change on drinking water and waterborne disease. In this respect we studied the relationship between temperature, precipitation and runoff events, raw and treated water quality, and gastroenteritis consultations in Norway in 2006–2014 to anticipate the risk with changing climate conditions. The main findings are positive associations between extreme weather events and raw water quality, but only few with treated drinking water. Increase in maximum temperature was associated with an increase in risk of disease among all ages and 15–64 years olds for the whole year. Heavy rain and high runoff were associated with a decrease in risk of gastroenteritis for different age groups and time periods throughout the year. No evidence was found that increase in precipitation and runoff trigger increased gastroenteritis outbreaks. Large waterworks in Norway currently seem to manage extreme weather events in preventing waterborne disease. However, with more extreme weather in the future, this may change. Therefore, modelling future climate scenarios is necessary to assess the need for improved water treatment capacity in a future climate.

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Positive associations between extreme weather events and raw water quality.

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Increase in maximum temperature was associated with an increase in risk of disease.

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Heavy rain and high runoff were associated with a decrease in risk of gastroenteritis.

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Larger water works in Norway seem to cope with the extreme weather events in the current climate.

Quantitative microbial risk assessment of a non-membrane based indirect potable water reuse system using Bayesian networks

Risk-based approaches are used to define performance standards for water and wastewater treatment to meet health-based targets and to ensure safe and reliable water quality for desired end use. In this study, a screening level QMRA for a non-membrane based indirect potable reuse (IPR) system utilizing the sequential managed aquifer recharge technology (SMART) concept was conducted. Ambient removals of norovirus, Campylobacter and Cryptosporidium in advanced water treatment (AWT) steps were combined in a probabilistic QMRA utilizing Bayesian networks constructed in Netica. Results revealed that all pathogens complied with disease burden at the 95th percentile, and according to the assumptions taken about pathogen removal, Cryptosporidium was the pathogen with the greatest risk. Through systematic sensitivity analysis, targeted scenario analysis, and backwards inferencing, critical control points for each pathogen were determined, demonstrating the usefulness of Bayesian networks as a diagnostic tool in quantifying risk of water reuse treatment scenarios.

Potential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling

Waterborne disease outbreaks are a persistent and serious threat to public health according to reported incidents across the globe. Online drinking water quality monitoring technologies have evolved substantially and have become more accurate and accessible. However, using online measurements alone is unsuitable for detecting microbial regrowth, potentially including harmful species, ahead of time in the distribution systems. Alternatively, observational data could be collected periodically, e.g. once per week or once per month and it could include a representative set of variables: physicochemical water characteristics, disinfectant concentrations, and bacterial abundances, which would be a valuable source of knowledge for predictive modelling that aims to reveal pathogen-related threats. In this study, we utilised data collected from a pilot-scale drinking water distribution system. A data-driven random forest model was used for predictive modelling and was trained for nowcasting and forecasting abundances of bacterial groups. In all the experiments, we followed the realistic crossline scenario, which means that when training and testing the models the data is collected from different pipelines. In spite of the more accurate results of the nowcasting, the 1-week forecasting still provided accurate predictions of the most abundant bacteria, their rapid increase and decrease. In the future predictive modelling might be used as a tool in designing control measures for opportunistic pathogens which are able to multiply in the favourable conditions in drinking water distribution systems (DWDS). Eventually, the forecasting information will be able to produce practically helpful data for controlling the DWDS regrowth.

Implementation of quantitative microbial risk assessment (QMRA) for public drinking water supplies: Systematic review

In the more than 15 years since its introduction, quantitative microbial risk assessment (QMRA) has become a widely used technique for assessing population health risk posed by waterborne pathogens. However, the variation in approaches taken for QMRA in relation to drinking water supply is not well understood. This systematic review identifies, categorises, and critically synthesises peer-reviewed and academic case studies of QMRA implementation for existing distributed public drinking water supplies. Thirty-nine English-language, peer-reviewed and academic studies published from 2003 to 2019 were identified. Key findings were synthesised in narrative form. The overall designs of the included studies varied widely, as did the assumptions used in risk calculation, especially in relation to pathogen dose. There was also substantial variation in the degree to which the use of location-specific data weighed with the use of assumptions when performing risk calculation. In general, the included studies' complexity did not appear to be associated with greater result certainty. Factors relating to pathogen dose were commonly influential on risk estimates whereas dose-response parameters tended to be of low relative influence. In two of the included studies, use of the 'susceptible fraction' factor was inconsistent with recognised guidance and potentially led to the underestimation of risk. While approaches and assumptions used in QMRA need not be standardised, improvement in the reporting of QMRA results and uncertainties would be beneficial. It is recommended that future authors consider the water supply QMRA reporting checklist developed for the current review. Consideration of the broad types of uncertainty relevant to QMRA is also recommended. Policy-makers should consider emergent discussion on acute microbial health-based targets when setting normative guidelines. The continued representation of QMRA case studies within peer-reviewed and academic literature would also enhance future implementation. Further research is needed on the optimisation of QMRA resourcing given the application context.

Risk assessment of hybrid rain harvesting system and other small drinking water supply systems by game theory and fuzzy logic modeling

The complexity and uncertainties affecting drinking water supply systems and threatening hazards require a comprehensive and effective risk assessment to increase the reliability of drinking water safety, especially for small or household systems. This study presents a hierarchical structure risk assessment model based on fuzzy logic and game theory to assess the quantity, treatment technology, distribution systems, and storage at household level risks, for small systems in remote areas. Game theory combined with an analytical hierarchy process with entropy weight method is employed. The efficiency tradeoffs in risk from use of this model are examined in a case study which includes three types of small systems (i.e., rainwater harvesting, surface water combined with rainwater and groundwater) in Gansu Province, China. Fifteen risk factors are employed, with evaluation results showing that "Source water" is the most important factor. The hybrid (surface & rainwater) system in the driest year has "Medium" risk with the highest aggregate risk value as a result of source water availability and the distribution system is the most susceptible to failure. The groundwater system consistently has the lowest risk in the case study area. The utility of the model provides scientific support to decision-makers to plan for the most effective risk mitigation measures for water supply systems in remote areas. A cloud-based online-platform employing this methodology has been developed to facilitate the adoption of the methodology in remote areas with mobile or internet access.

A novel screening tool for the health risk in recreational waters near estuary: The Carrying Capacity indicator

The present study aims to provide a conceptual framework to help practitioners to improve the quality of recreational waters near estuary, which may be affected by untreated wastewater from Combined Sewer Overflows (CSOs). When CSOs are activated, the concentration of bacteria (e.g., Enterococci and E. coli) in estuary increases, thereby resulting in a potential health threat to swimmers. Here, the bacterial exposure is evaluated using physically-based stochastic model for contaminant transport, while human health risk is determined by Quantitative Microbial Risk Assessment (QMRA). Based on human health risk framework, we quantify the Carrying Capacity (CC) of the recreational water body. Such an indicator is defined as the number of swimming individuals that can be sustained in a beach resort with an acceptable risk threshold. The CC increases by dilution processes and by reduction of the source concentration, which in turn depends on the improvements in the sewage system. The presented approach can be a useful screening tool for policy-makers and other stakeholders, thereby providing a potential solution to the trade-off between economic development and the sustainable ecosystem in coastal areas.

DNA-based faecal source tracking of contaminated drinking water causing a large Campylobacter outbreak in Norway 2019

During June 2019, an outbreak of campylobacteriosis occurred in Askøy, an island northwest of Bergen, Norway. According to the publicly available records, over 2000 residents fell ill and 76 were hospitalised, and two deaths were suspected to be associated with Campylobacter infection. By investigating the epidemic pattern and scope, an old caved drinking water holding pool was identified that had been faecally contaminated as indicated by the presence of Escherichia coli (E. coli). Furthermore, Campylobacter bacteria were found at several points in the water distribution system. In the escalated water health crisis, tracking down the infectious source became pivotal for the local municipality in order to take prompt and appropriate action to control the epidemic. A major task was to identify the primary faecal pollution source, which could further assist in tracking down the epidemic origin. Water from the affected pool was analysed using quantitative microbial source tracking (QMST) applying host-specific Bacteroidales 16S rRNA genetic markers. In addition, Campylobacter jejuni, Enterococcus faecalis, Clostridium perfringens and Shiga toxin-producing E. coli were detected. The QMST outcomes revealed that non-human (zoogenic) sources accounted predominantly for faecal pollution. More precisely, 69% of the faecal water contamination originated from horses.