Utility of Bayesian networks in QMRA-based evaluation of risk reduction options for recycled water

Mitigating risks and maximizing sustainability of treated wastewater reuse for irrigation

Scarcity of freshwater for agriculture has led to increased utilization of treated wastewater (TWW), establishing it as a significant and reliable source of irrigation water. However, years of research indicate that if not managed adequately, TWW may deleteriously affect soil functioning and plant productivity, and pose a hazard to human and environmental health. This review leverages the experience of researchers, stakeholders, and policymakers from Israel, the United-States, and Europe to present a holistic, multidisciplinary perspective on maximizing the benefits from municipal TWW use for irrigation. We specifically draw on the extensive knowledge gained in Israel, a world leader in agricultural TWW implementation. The first two sections of the work set the foundation for understanding current challenges involved with the use of TWW, detailing known and emerging agronomic and environmental issues (such as salinity and phytotoxicity) and public health risks (such as contaminants of emerging concern and pathogens). The work then presents solutions to address these challenges, including technological and agronomic management-based solutions as well as source control policies. The concluding section presents suggestions for the path forward, emphasizing the importance of improving links between research and policy, and better outreach to the public and agricultural practitioners. We use this platform as a call for action, to form a global harmonized data system that will centralize scientific findings on agronomic, environmental and public health effects of TWW irrigation. Insights from such global collaboration will help to mitigate risks, and facilitate more sustainable use of TWW for food production in the future.

A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic

COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.

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.

A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria

Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.

Sunlight advanced oxidation processes vs ozonation for wastewater disinfection and safe reclamation

Solar processes (sunlight/H₂O_2, solar photo-Fenton with EDDS at neutral pH) were compared to a consolidated technology (ozonation) in the inactivation of target bacteria (E. coli, Salmonella spp. and Enterococcus spp.) under realistic conditions (real secondary treated urban wastewater (WW), pilot scale reactors, natural sunlight) to evaluate their possible industrial application. The highest bacteria inactivation rate (all the target pathogens were inactivated below the detection limit (DL) (100 CFU/100 mL) within 45 min treatment) was observed for ozonation (83 mgO₃/L h). Similar inactivation behavior for all bacteria was observed for sunlight/H₂O₂ (50 mg/L) and solar photo-Fenton (SPF) with EDDS (1:1 molar ratio, 0.1 mM of Fe and 50 mg/L of H₂O₂). Although the DL was not reached, faster inactivation kinetics (0.007, 0.013 and 0.002 1/min for E. coli, Salmonella spp. and Enterococcus spp., respectively) and lower bacterial concentration after a 180 min treatment were observed for sunlight/H₂O₂ process compared to SPF (0.005, 0.01 1/min and no inactivation, respectively), Enterococcus spp. being the higher resistance microorganism. The negative effect of carbonates on disinfection performance was also evaluated. Quantitative microbial risk assessment for the ingestion of lettuce irrigated with untreated and treated WW was estimated. Disinfection by ozonation and sunlight/H₂O₂ processes were found to drastically decrease the associated microbiological risk (the mean risk of illness decreased from 0.10 (untreated) to 1.35 × 10^-4 (treated) for E. coli and from 0.03 to 2.21 × 10^-6 for Salmonella).

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.

A novel, complex systems approach to modelling risk of psychological distress in young adolescents

Adolescence is a period of significant anatomical and functional brain changes, and complex interactions occur between mental health risk factors. The Longitudinal Adolescent Brain Study commenced in 2018, to monitor environmental and psychosocial factors influencing mental health in 500 adolescents, for 5 years. Participants are recruited at age 12 from the community in Australia's Sunshine Coast region. In this baseline, cross-sectional study of N = 64 participants, we draw on the network perspective, conceptualising mental disorders as causal systems of interacting entities, to propose a Bayesian network (BN) model of lifestyle and psychosocial variables influencing chances of individuals being psychologically well or experiencing psychological distress. Sensitivity analysis of network priors revealed that psychological distress (Kessler-10) was most affected by eating behaviour. Unhealthy eating increased the chance of moderate psychological distress by 600%. Low social connectedness increased the chance of severe psychological disorder by 200%. Certainty for psychological wellness required 33% decrease in unhealthy eating behaviours, 11% decrease in low social connectedness, and 9% reduction in less physical activity. BN can augment clinician judgement in mental disorders as probabilistic decision support systems. The full potential of BN methodology in a complex systems approach to psychopathology has yet to be realised.

Developing Bayesian networks in managing the risk of Legionella colonisation of groundwater aeration systems

An Australian water utility has developed a Legionella High Level Risk Assessment (LHLRA) which provides a semi-qualitative assessment of the risk of Legionella proliferation and human exposure in engineered water systems using a combination of empirical observation and expert knowledge. Expanding on this LHLRA, we propose two iterative Bayesian network (BN) models to reduce uncertainty and allow for a probabilistic representation of the mechanistic interaction of the variables, built using data from 25 groundwater treatment plants. The risk of Legionella exposure in groundwater aeration units was quantified as a function of five critical areas including hydraulic conditions, nutrient availability and growth, water quality, system design (and maintenance), and location and access. First, the mechanistic relationship of the variables was conceptually mapped into a fishbone diagram, parameterised deterministically using an expert elicited weighted scoring system and translated into BN. The "sensitivity to findings" analysis of the BN indicated that system design was the most influential variable while elemental accumulation thresholds were the least influential variable for Legionella exposure. The diagnostic inference was used in high and low-risk scenarios to demonstrate the capabilities of the BNs to examine probable causes for diverse conditions. Subsequently, the causal relationship of Legionella growth and human exposure were improved through a conceptual bowtie representation. Finally, an improved model developed the predictors of Legionella growth and the risk of human exposure through the interaction of operational, water quality monitoring, operational parameters, and asset conditions. The use of BNs modelling based on risk estimation and improved functional decision outputs offer a complementary and more transparent alternative approach to quantitative analysis of uncertainties than the current LHLRA.

Evaluation of pathogen risks using QMRA to explore wastewater reuse options: A case study from New Delhi in India

Selecting appropriate reuse for treated wastewater is a challenge. The current investigation outlines the utilization of quantitative microbial risk assessment (QMRA) to assist Effluent Treatment Plant (ETP) management to determine the best-possible reuse of treated wastewater from 11 ETPs in Delhi. Four representative pathogens: pathogenic Escherichia coli spp., Salmonella spp., Cryptosporidium spp. and Giardia spp. were selected to characterize microbial water quality. Reuse options selected based on the survey and interaction with ETP managers include crop irrigation, garden irrigation, toilet flush and industrial applications. The probability of infection was characterized for two exposure groups: workers and children. Water quality monitoring indicates the occurrence of pathogenic E. coli spp. (100%), Salmonella spp. (63%), Cryptosporidium spp. (81%) and Giardia spp. (45%) in the treated wastewater. QMRA reveals the annual median-probability of infection above acceptable limits for pathogenic E. coli spp., Cryptosporidium spp. and Salmonella spp. The probabilities of Giardia-associated infections were low. Adults showed a 1.24 times higher probability of infection compared to children. Sensitivity analysis indicated pathogen concentration as the most critical factor. The study highlights that the existing plans for chlorination-based treatment technology may prove insufficient in reducing the risk for selected reuse options; but, alternate on-site control measures and up-grading water reuse protocol may be effective.

Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens

(1) Background: The high use of antibiotics has made the issue of antimicrobial resistance (AMR) increasingly serious, which poses a substantial threat to the health of animals and humans. However, there remains a certain gap in the AMR system and risk assessment models between China and the advanced world level. Therefore, this paper aims to provide advanced means for the monitoring of antibiotic use and AMR data, and take piglets as an example to evaluate the risk and highlight the seriousness of AMR in China. (2) Methods: Based on the principal component analysis method, a drug resistance index model of anti-E. coli drugs was established to evaluate the antibiotic risk status in China. Additionally, based on the second-order Monte Carlo methods, a disease risk assessment model for piglets was established to predict the probability of E. coli disease within 30 days of taking florfenicol. Finally, a browser/server architecture-based visualization database system for animal-derived pathogens was developed. (3) Results: The risk of E. coli in the main area was assessed and Hohhot was the highest risk area in China. Compared with the true disease risk probability of 4.1%, the result of the disease risk assessment model is 7.174%, and the absolute error was 3.074%. Conclusions: Taking E. coli as an example, this paper provides an innovative method for rapid and accurate risk assessment of drug resistance. Additionally, the established system and assessment models have potential value for the monitoring and evaluating AMR, highlight the seriousness of antimicrobial resistance, advocate the prudent use of antibiotics, and ensure the safety of animal-derived foods and human health.

Impact of sludge bulking on receiving environment using quantitative microbial risk assessment (QMRA)-based management for full-scale wastewater treatment plants

During sludge bulking in wastewater treatment plants (WWTPs), high amounts of potentially pathogenic bacteria would release into the environment, causing various human-health risks. This is the first study attempting to assess the microbial infections associated with the reuse of WWTP effluents under various bulking conditions. Three common waterborne pathogens, viz., E. coli O157:H7, Salmonella, and Mycobacterium, were quantified from full-scale WWTPs using DNA extraction and qPCR at different sludge volume indices (SVIs). The detected pathogens were incorporated into a quantitative microbial risk assessment (QMRA) model to determine the applicability of WWTP discharge for recreational (bathing) activities and agricultural practices. The QMRA exposures were children, women, and men during swimming, and farmers and vegetable consumers during irrigation. Bacterial abundance in the treated wastewater increased in response to SVIs, and the QMRA values at all bulking events exceeded the tolerable risk of one case of infection per 10,000 people per year. Hence, various disinfection scenarios (chlorination, ultraviolet, and ozonation) were hypothetically tested to control the risks associated with pathogenic bacteria, allowing for safe disposal and reuse of the treated effluent. The ultraviolet application provided the highest ability to inactivate the pathogenic bacteria, except for the case of children exposed to Salmonella infection during swimming. The reduction of Mycobacterium infection risks with either chlorination or ozonation showed inefficient results. This study would be helpful for the management of human health risks associated with effluent wastewater containing pathogens, i.e., particularly concerning the case of sludge bulking.

A Bayesian Probability Model Can Simulate the Knowledge of Soybean Rust Researchers to Optimize the Application of Fungicides

Asian rust is the main soybean disease in Brazil, causing up to 80% of yield reduction. The use of fungicides is the main form of control; however, due to farmer's concern with outbreaks many unnecessary applications are performed. The present study aims to verify the usefulness of a probability model to estimate the timing and the number of fungicides sprays required to control Asian soybean rust, using Bayesian networks and knowledge engineering. The model was developed through interviews with rust researchers and a literature review. The Bayesian network was constructed with the GeNIe 2.0 software. The validation process was performed by 42 farmers and 10 rust researchers, using 28 test cases. Among the 28 tested cases, generated by the system, the agreement with the model was 47.5% for the farmers and 89.3% for the rust researchers. In general, the farmers overestimate the number. The results showed that the Bayesian network has accurately represented the knowledge of the expert, and also could help the farmers to avoid the unnecessary applications.

Stakeholder evaluations of risk interventions for non-potable recycled water schemes: A case study

Non-potable recycled water schemes can benefit sustainable urban water management through reducing demand for drinking water and mitigating environmental loadings through the provision of advanced wastewater treatment. However, scheme feasibility can be diminished by high capital and operating costs which can be elevated by perceptions of health risks and subsequently overly cautious risk reduction measures. Conversely, a failure to anticipate the risk management expectations of stakeholders can undermine scheme feasibility through insufficient demand for recycled water. The aim of this study was to explore how stakeholders' perceptions and preferences for risk management and recycled water end-uses might influence scheme design. Using a case study scheme in London, four risk management intervention scenarios and six alternative end uses were evaluated using a stochastic PROMETHEE-based method that incorporated quantitative microbial risk assessment and stakeholder criteria weights together with an attitudinal survey of stakeholders' risk perceptions. Through pair-wise criteria judgements, results showed that stakeholders prioritised health risk reductions which led to the more conservative management intervention of adding water treatment processes being ranked the highest. In contrast, responses to the attitudinal survey indicated that the stakeholders favoured maintaining the case study's existing levels of risk control but with more stakeholder engagement. The findings highlighted potential benefits of understanding risk perceptions associated with different design options and contrasting these with multi-criteria model results. Extrapolating from these findings, future research could explore potential challenges and benefits of providing flexibility in scheme designs to appeal to a wider range of stakeholder needs as well as being more adaptable to future social, environmental or economic challenges. The study concludes that contemporary risk management guidance would benefit from more explicitly outlining constructive ways to engage stakeholders in scheme evaluation.

Quantitative Microbial Risk Assessment and Infectious Disease Transmission Modeling of Waterborne Enteric Pathogens

PURPOSE OF REVIEW

Waterborne enteric pathogens remain a global health threat. Increasingly, quantitative microbial risk assessment (QMRA) and infectious disease transmission modeling (IDTM) are used to assess waterborne pathogen risks and evaluate mitigation. These modeling efforts, however, have largely been conducted independently for different purposes and in different settings. In this review, we examine the settings where each modeling strategy is employed.

RECENT FINDINGS

QMRA research has focused on food contamination and recreational water in high-income countries (HICs) and drinking water and wastewater in low- and middle-income countries (LMICs). IDTM research has focused on large outbreaks (predominately LMICs) and vaccine-preventable diseases (LMICs and HICs). Human ecology determines the niches that pathogens exploit, leading researchers to focus on different risk assessment research strategies in different settings. To enhance risk modeling, QMRA and IDTM approaches should be integrated to include dynamics of pathogens in the environment and pathogen transmission through populations.

Specific Interactions between Human Norovirus and Environmental Matrices: Effects on the Virus Ecology

Human norovirus is the major cause of non-bacterial epidemic gastroenteritis. Human norovirus binds to environmental solids via specific and non-specific interactions, and several specific receptors for human norovirus have been reported. Among them, histo-blood group antigens (HBGA) are the most studied specific receptor. Studies have identified the presence of HBGA-like substances in the extracellular polymeric substances (EPS) and lipopolysaccharides (LPS) of human enteric bacteria present in aquatic environments, gastrointestinal cells, gills, and palps of shellfish, and cell walls, leaves, and veins of lettuce. These HBGA-like substances also interact with human norovirus in a genotype-dependent manner. Specific interactions between human norovirus and environmental matrices can affect norovirus removal, infectivity, inactivation, persistence, and circulation. This review summarizes the current knowledge and future directions related to the specific interactions between human norovirus and HBGA-like substances in environmental matrices and their possible effects on the fate and circulation of human norovirus.

Epidemiological Evidence and Health Risks Associated With Agricultural Reuse of Partially Treated and Untreated Wastewater: A Review

The use of partially treated and untreated wastewater for irrigation is beneficial in agriculture but may be associated with human health risks. Reports from different locations globally have linked microbial outbreaks with agricultural reuse of wastewater. This article reviews the epidemiological evidence and health risks associated with this practice, aiming toward evidence-based conclusions. Exposure pathways that were addressed in this review included those relevant to agricultural workers and their families, consumers of crops, and residents close to areas irrigated with wastewater (partially treated or untreated). A meta-analysis gave an overall odds ratio of 1.65 (95% CI: 1.31, 2.06) for diarrheal disease and 5.49 (95% CI: 2.49, 12.10) for helminth infections for exposed agricultural workers and family members. The risks were higher among children and immunocompromised individuals than in immunocompetent adults. Predominantly skin and intestinal infections were prevalent among individuals infected mainly via occupational exposure and ingestion. Food-borne outbreaks as a result of crops (fruits and vegetables) irrigated with partially or untreated wastewater have been widely reported. Contamination of crops with enteric viruses, fecal coliforms, and bacterial pathogens, parasites including soil-transmitted helminthes (STHs), as well as occurrence of antibiotic residues and antibiotic resistance genes (ARGs) have also been evidenced. The antibiotic residues and ARGs may get internalized in crops along with pathogens and may select for antibiotic resistance, exert ecotoxicity, and lead to bioaccumulation in aquatic organisms with high risk quotient (RQ). Appropriate mitigation lies in adhering to existing guidelines such as the World Health Organization wastewater reuse guidelines and to Sanitation Safety Plans (SSPs). Additionally, improvement in hygiene practices will also provide measures against adverse health impacts.

Hydrology-oriented forest management trade-offs. A modeling framework coupling field data, simulation results and Bayesian Networks

Hydrology-oriented forest management sets water as key factor of the forest management for adaptation due to water is the most limiting factor in the Mediterranean forest ecosystems. The aim of this study was to apply Bayesian Network modeling to assess potential indirect effects and trade-offs when hydrology-oriented forest management is applied to a real Mediterranean forest ecosystem. Water, carbon and nitrogen cycles, and forest fire risk were included in the modeling framework. Field data from experimental plots were employed to calibrate and validate the mechanistic Biome-BGCMuSo model that simulates the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere. Many other 50-year long scenarios with different conditions to the ones measured in the field experiment were simulated and the outcomes employed to build the Bayesian Network in a linked chain of models. Hydrology-oriented forest management was very positive insofar as more water was made available to the stand because of an interception reduction. This resource was made available to the stand, which increased the evapotranspiration and its components, the soil water content and a slightly increase of deep percolation. Conversely, Stemflow was drastically reduced. No effect was observed on Runof due to the thinning treatment. The soil organic carbon content was also increased which in turn caused a greater respiration. The long-term effect of the thinning treatment on the LAI was very positive. This was undoubtedly due to the increased vigor generated by the greater availability of water and nutrients for the stand and the reduction of competence between trees. This greater activity resulted in an increase in GPP and vegetation carbon, and therefore, we would expect a higher carbon sequestration. It is worth emphasizing that this extra amount of water and nutrients was taken up by the stand and did not entail any loss of nutrients.

Evaluating a Bayesian modelling approach (INLA-SPDE) for environmental mapping

Understanding the uncertainty in spatial modelling of environmental variables is important because it provides the end-users with the reliability of the maps. Over the past decades, Bayesian statistics has been successfully used. However, the conventional simulation-based Markov Chain Monte Carlo (MCMC) approaches are often computationally intensive. In this study, the performance of a novel Bayesian inference approach called Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation (INLA-SPDE) was evaluated using independent calibration and validation datasets of various skewed and non-skewed soil properties and was compared with a linear mixed model estimated by residual maximum likelihood (REML-LMM). It was found that INLA-SPDE was equivalent to REML-LMM in terms of the model performance and was similarly robust with sparse datasets (i.e. 40-60 samples). In comparison, INLA-SPDE was able to estimate the posterior marginal distributions of the model parameters without extensive simulations. It was concluded that INLA-SPDE had the potential to map the spatial distribution of environmental variables along with their posterior marginal distributions for environmental management. Some drawbacks were identified with INLA-SPDE, including artefacts of model response due to the use of triangle meshes and a longer computational time when dealing with non-Gaussian likelihood families.