Systematic review of predictive models of microbial water quality at freshwater recreational beaches

Predicting recreational water quality and public health safety in urban estuaries using Bayesian Networks

To support the reactivation of urban rivers and estuaries for bathing while ensuring public safety, it is critical to have access to real-time information on microbial water quality and associated health risks. Predictive modelling can provide this information, though challenges concerning the optimal size of training data, model transferability, and communication of uncertainty still need attention. Further, urban estuaries undergo distinctive hydrological variations requiring tailored modelling approaches. This study assessed the use of Bayesian Networks (BNs) for the prediction of enterococci exceedances and extrapolation of health risks at planned bathing sites in an urban estuary in Sydney, Australia. The transferability of network structures between sites was assessed. Models were validated using a novel application of the k-fold walk-forward validation procedure and further tested using independent compliance and event-based sampling datasets. Learning curves indicated the model's sensitivity reached a minimum performance threshold of 0.8 once training data included ≥ 400 observations. It was demonstrated that Semi-Naïve BN structures can be transferred while maintaining stable predictive performance. In all sites, salinity and solar exposure had the greatest influence on Posterior Probability Distributions (PPDs), when combined with antecedent rainfall. The BNs provided a novel and transparent framework to quantify and visualise enterococci, stormwater impact, health risks, and associated uncertainty under varying environmental conditions. This study has advanced the application of BNs in predicting recreational water quality and providing decision support in urban estuarine settings, proposed for bathing, where uncertainty is high.

Sunlight Inactivation of Enveloped Viruses in Clear Water

Enveloped virus fate in the environment is not well understood; there are no quantitative data on sunlight inactivation of enveloped viruses in water. Herein, we measured the sunlight inactivation of two enveloped viruses (Phi6 and murine hepatitis virus, MHV) and a nonenveloped virus (MS2) over time in clear water with simulated sunlight exposure. We attenuated UV sunlight wavelengths using long-pass 50% cutoff filters at 280, 305, and 320 nm. With the lowest UV attenuation tested, all decay rate constants (corrected for UV light screening, k̂) were significantly different from dark controls; the MS2 k̂ was equal to 4.5 m2/MJ, compared to 16 m2/MJ for Phi6 and 52 m2/MJ for MHV. With the highest UV attenuation tested, only k̂ for MHV (6.1 m2/MJ) was different from the dark control. Results indicate that the two enveloped viruses decay faster than the nonenveloped virus studied, and k̂ are significantly impacted by UV attenuation. Differences in k̂ may be due to the presence of viral envelopes but may also be related to other differing intrinsic properties of the viruses, including genome length and composition. Reported k̂ values can inform strategies to reduce the risk from exposure to enveloped viruses in the environment.

Scientific mapping of the research in microbial and chemical contamination of potable water in Bangladesh: A review of literature

Drinking water contamination is one of the most pressing concerns for the people of Bangladesh as they rely on groundwater to meet their water needs. The existing water sources of Bangladesh are losing potability due to natural, anthropogenic, and geogenic factors, resulting in acute to severe health consequences. To address the issue of safe drinking water, researchers are constantly examining potential sources that cause the pollution of drinking water. Through bibliometric and systematic research, the current work seeks to review the past research on microbiological and chemical contamination of drinkable water in Bangladesh. The bibliometric review provides insights into the research trends, notable authors, countries, and institutions, whereas the systematic review unfolds the key research areas, the contamination process, and the strategies used to mitigate the contamination process. The results show that arsenic and various coliform bacteria are the most commonly reported sources of chemical and microbiological contaminants that degrade water quality. The study demonstrates that the most crucial factors influencing arsenic mobilization include microbial decomposition of organic matter (biologically available organic matter, for example, peat), arsenic adsorption by metal-oxyhydroxides, Fe-Mn oxyhydroxide, chemical fertilizers, pond excavation, and altering of groundwater hydrology. The studies also indicated the sources that contribute to the microbiological quality decline. The current work has addressed the scope of future research.

Assessment of a Multifunctional River Using Fuzzy Comprehensive Evaluation Model in Xiaoqing River, Eastern China

Rivers are beneficial to humans due to their multiple functions. However, human meddling substantially degrades the functions of rivers and constitutes a threat to river health. Therefore, it is vital to assess and maintain river function. This study used the Xiaoqing River in Shandong Province, China, as a case study and established a multilayered multifunctional river evaluation indicator system consisting of environmental function, ecological function, social function, and economic function. The weights of indicators were calculated using the analytic hierarchy process (AHP) and the entropy method. Furthermore, a fuzzy comprehensive evaluation model based on the Cauchy distribution function was developed to assess the operation status of each function in each river segment. The results of the indicator and criterion layers in different river sections varied. The multifunctionality of the river decreased from upstream to downstream. The Jinan section was the most multifunctional, followed by the Binzhou, Zibo, and Dongying sections, and finally the Weifang section. Through additional analysis, this study determined the constraint indicators and functions of each river section. Overall, the results reveal that the idea of a "multifunctional river" can advance the theoretical understanding of a river's function, and the fuzzy comprehensive evaluation model is demonstrated to provide fresh perspectives for evaluating river function.

Recreational water illness in Canada: a changing risk landscape in the context of climate change

Swimming and other recreational water activities at public beaches are popular outdoor leisure activities among Canadians. However, these activities can lead to increased risks of acquiring acute gastrointestinal illness and other illnesses among beachgoers. Young children have much higher rates of exposure and illness than other age groups. These illnesses have a significant health and economic burden on society. Climate change is expected to influence both the risk of exposure and illness. A warming climate in Canada, including more severe summer heatwave events, will likely lead to increased recreational water use. Warmer temperatures will also contribute to the growth and increased range of harmful algal blooms and other climate-sensitive pathogens. Increased precipitation and heavy rainfall events will contribute to fecal and nutrient contamination of beach waters, increasing risks of gastrointestinal illness and harmful algal bloom events. There is a need to enhance recreational water research and surveillance in Canada to prepare for and adapt to these changing risks. Key research and policy needs are suggested and discussed, including evaluating and monitoring risks of recreational water illness in Canadian contexts, improving timely reporting of recreational water quality conditions, and enhancing approaches for routine beach water surveillance.

Interpretable tree-based ensemble model for predicting beach water quality

Tree-based machine learning models based on environmental features offer low-cost and timely solutions for predicting microbial fecal contamination in beach water to inform the public of the health risk. However, many of these models are black boxes that are difficult for humans to understand, which may cause severe consequences such as unexplained decisions and failure in accountability. To develop interpretable predictive models for beach water quality, we evaluate five tree-based models, namely classification tree, random forest, CatBoost, XGBoost, and LightGBM, and employ a state-of-the-art explanation method SHAP to explain the models. When tested on the Escherichia coli (E. coli) concentration data collected from three beach sites along Lake Erie shores, LightGBM, followed by XGBoost, achieves the highest averaged precision and recall scores. For all three sites, both models suggest lake turbidity as the most important predictor, and elucidate the crucial role of accurate local data of wave height and rainfall in the model development. Local SHAP values further reveal the robustness of the importance of lake turbidity as its SHAP value increases nearly monotonically with its value and is minimally affected by other environmental factors. Moreover, we found an intriguing interaction between lake turbidity and day-of-year. This work suggests that the combination of LightGBM and SHAP has a promising potential to develop interpretable models for predicting microbial water quality in freshwater lakes.

Region-Specific Associations between Environmental Factors and Escherichia coli in Freshwater Beaches in Toronto and Niagara Region, Canada

Poor freshwater beach quality, measured by Escherichia coli (E. coli) levels, poses a risk of recreational water illness. This study linked environmental data to E. coli geometric means collected at 18 beaches in Toronto (2008-2019) and the Niagara Region (2011-2019) to examine the environmental predictors of E. coli. We developed region-specific models using mixed effects models to examine E. coli as a continuous variable and recommended thresholds of E. coli concentration (100 CFU/100 mL and 200 CFU/100 mL). Substantial clustering of E. coli values at the beach level was observed in Toronto, while minimal clustering was seen in Niagara, suggesting an important beach-specific effect in Toronto beaches. Air temperature and turbidity (measured directly or visually observed) were positively associated with E. coli in all models in both regions. In Toronto, waterfowl counts, rainfall, stream discharge and water temperature were positively associated with E. coli levels, while solar irradiance and water level were negatively associated. In Niagara, wave height and water level had a positive association with E. coli, while rainfall was negatively associated. The differences in regional models suggest the importance of a region-specific approach to addressing beach water quality. The results can guide beach monitoring and management practices, including predictive modelling.