Modelling pathogen log10 reduction values achieved by activated sludge treatment using naïve and semi naïve Bayes network models

Adsorption of uranyl ion on hexagonal boron nitride for remediation of real U-contaminated soil and its interpretation using random forest

Acid leaching has been widely applied to treat contaminated soil, however, it contains several inorganic pollutants. The decommissioning of nuclear power plants introduces radioactive and soluble U(VI), a substance posing chemical toxicity to humans. Our investigation sought to ascertain the efficacy of hexagonal boron nitride (h-BN), an highly efficient adsorbent, in treating U(VI) in wastewater. The adsorption equilibrium of U(VI) by h-BN reached saturation within a mere 2 h. The adsorption of U(VI) by h-BN appears to be facilitated through electrostatic attraction, as evidenced by the observed impact of pH variations, acidic agents (i.e., HCl or H2SO4), and the presence of background ions on the adsorption performance. A reusability test demonstrated the successful completion of five cycles of adsorption/desorption, relying on the surface characteristics of h-BN as influenced by solution pH. Based on the experimental variables of initial U(VI) concentration, exposure time, temperature, pH, and the presence of background ions/organic matter, a feature importance analysis using random forest (RF) was carried out to evaluate the correlation between performances and conditions. To the best of our knowledge, this study is the first attempt to conduct the adsorption of U(VI) generated from real contaminated soil by h-BN, followed by interpretation of the correlation between performance and conditions using RF. Lastly, a. plausible adsorption mechanism between U(VI) and h-BN was explained based on the experimental results, characterizations, and a. comparison with previous adsorption studies on the removal of heavy metals by h-BN.

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.

Influence of resampling techniques on Bayesian network performance in predicting increased algal activity

Early warning of increased algal activity is important to mitigate potential impacts on aquatic life and human health. While many methods have been developed to predict increased algal activity, an ongoing issue is that severe algal blooms often occur with low frequency in water bodies. This results in imbalanced data sets available for model specification, leading to poor predictions of the frequency of increased algal activity. One approach to address this is to resample data sets of increased algal activity to increase the prevalence of higher than normal algal activity in calibration data and ultimately improve model predictions. This study aims to investigate the use of resampling techniques to address the imbalanced dataset and determine if such methods can improve the prediction of increased algal activity. Three techniques were investigated, Kmeans under-sampling (US_Kmeans), synthetic minority over-sampling technique (SMOTE), and 'SMOTE and cluster-based under-sampling technique' (SCUT). The resampling methods were applied to a Bayesian network (BN) model of Lake Burragorang in New South Wales, Australia. The model was developed to predict chlorophyll-a (chl-a) using a range of water quality parameters as predictors. The original data and each of the balanced datasets were used for BN structures and parameter learning. The results showed that the best graphical structure was obtained by adding synthetic data from SMOTE with the highest true positive rate (TPR) and area under the curve (AUC). When compared using a fixed graphical structure for the BN, all resampling techniques increased the ability of the BN to detect events with higher probability of increased algal activity. The resampling model results can also be used to better understand the most important influences on high chl-a concentrations and suggest future data collection and model development priorities.

A review of the application of machine learning in water quality evaluation

With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.

Integrated approach for legionellosis risk analysis in touristic-recreational facilities

Legionellosis is a severe pneumonia caused by the inhalation of aerosols containing Legionella, Gram-negative bacteria present in the water systems of touristic-recreational facilities. The purpose of this study was to develop a scoring tool to predict the risk of both environmental contamination and Legionnaires' disease cases in such facilities in the Apulia region of southern Italy. We analyzed 47 structural and management parameters/risk factors related to the buildings, water systems, and air conditioning at the facilities. A Poisson regression model was used to compute an overall risk score for each facility with respect to three outcomes: water samples positive for Legionella (risk score range: 7-54), water samples positive for Legionella with an average load exceeding 1000 colony-forming units per liter (CFU/L) (risk score range: 22-179,871), and clinical cases of Legionnaire's disease (risk score range: 6-31). The cut-off values for three outcomes were determined by receiver operating characteristic curves (first outcome, samples positive for Legionella in a touristic-recreational facility: 19; second outcome, samples positive for Legionella in a touristic-recreational facility with an average load exceeding 1000 CFU/L: 2062; third outcome, clinical cases of Legionnaire's disease in a touristic-recreational facility: 22). Above these values, there was a significant probability of observing the outcome. We constructed this predictive model using 70% of a large dataset (18 years of clinical and environmental surveillance) and tested the model on the remaining 30% of the dataset to demonstrate its reliability. Our model enables the assessment of risk for a touristic facility and the creation of a conceptual framework to link the risk analysis with prevention measures.

Graphical Trajectory Comparison to Identify Errors in Data of COVID-19: A Cross-Country Analysis

Data from the early stage of a novel infectious disease outbreak provide vital information in risk assessment, prediction, and precise disease management. Since the first reported case of COVID-19, the pattern of the novel coronavirus transmission in Wuhan has become the interest of researchers in epidemiology and public health. To thoroughly map the mechanism of viral spreading, we used the patterns of data at the early onset of COVID-19 from seven countries to estimate the time lag between peak days of cases and deaths. This study compared these data with those of Wuhan and estimated the natural history of disease across the infected population and the time lag. The findings suggest that comparative analyses of data from different regions and countries reveal the differences between peaks of cases and deaths caused by COVID-19 and the incomplete and underestimated cases in Wuhan. Different countries may show different patterns of cases peak days, deaths peak days, and peak periods. Error in the early COVID-19 statistics in Brazil was identified. This study provides sound evidence for policymakers to understand the local circumstances in diagnosing the health of a population and propose precise and timely public health interventions to control and prevent infectious diseases.

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.

A multivariate Bayesian network analysis of water quality factors influencing trihalomethanes formation in drinking water distribution systems

Controlling disinfection by-products formation while ensuring effective drinking water disinfection is important for protecting public health. However, understanding and predicting disinfection by-product formation under a variety of conditions in drinking water distribution systems remains challenging as disinfection by-product formation is a multifactorial phenomenon. This study aimed to assess the application of Bayesian Network models to predict the concentration of trihalomethanes, the dominant halogenated disinfection by-product class, using various water quality parameters. Naïve Bayesian and semi-naïve Bayesian models were constructed from Sydney and South East Queensland datasets across 15 drinking water distribution systems in Australia. The targeted variable, total trihalomethanes concentration, was discretised into 3 bins (<0.1 mg L^-1, 0.1 - 0.2 mg L^-1 and >0.2 mg L^-1). The Bayesian network structures were built using water quality parameters including concentrations of individual and total trihalomethanes, disinfectant species (free chlorine, monochloramine, dichloramine, total chlorine), nitrogen species (free ammonia, total ammonia, nitrate, nitrite), and other physical/chemical parameters (temperature, pH, dissolved organic carbon, total dissolved solids, conductivity and turbidity). Seven performance parameters, including predictive accuracy and the rates of true and false positive and negative results, were used to assess the accuracy and precision of the Bayesian network models. After evaluating the model performance, the optimum models were selected to be Bayesian network augmented naïve models. These were observed to have the highest predictive accuracies for Sydney (78%) and South East Queensland (94%). Although disinfectant residuals are among the key variables that lead to trihalomethanes formation, potential concentrations of trihalomethanes in distribution systems can be more confidently predicted, in terms of probability associated with a wider range of water quality variables, using Bayesian networks. The modelling procedure developed in this work can now be applied to develop system-specific Bayesian network models for trihalomethanes prediction in other drinking water distribution systems.

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.

Spatiotemporal prediction of Escherichia coli and Enterococci for the Commonwealth Games triathlon event using Bayesian Networks

A number of Bayesian Networks were developed in order to nowcast and forecast, up to 4 days ahead and in different locations, the likelihood of water quality within the 2018 Commonwealth Games Triathlon swim course exceeding the critical limits for Enterococci and Escherichia coli. The models are data-driven, but the identification of potential inputs and optimal model structure was performed through the parallel contribution of several stakeholders and experts, consulted through workshops. The models, whose main nodes were discretised with a customised discretisation algorithm, were validated over a test set of data and deployed in real-time during the Commonwealth Games in support to a traditional water quality monitoring program. The proposed modelling framework proved to be cost-effective and less time-consuming than process-based models while still achieving high accuracy; in addition, the added value of a continuous stakeholder engagement guarantees a shared understanding of the model outputs and its future deployment.

Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China

Microcystis spp., which occur as colonies of different sizes under natural conditions, have expanded in temperate and tropical freshwater ecosystems and caused seriously environmental and ecological problems. In the current study, a Bayesian network (BN) framework was developed to access the probability of microcystins (MCs) risk in large shallow eutrophic lakes in China, namely, Taihu Lake, Chaohu Lake, and Dianchi Lake. By means of a knowledge-supported way, physicochemical factors, Microcystis morphospecies, and MCs were integrated into different network structures. The sensitive analysis illustrated that Microcystis aeruginosa biomass was overall the best predictor of MCs risk, and its high biomass relied on the combined condition that water temperature exceeded 24 °C and total phosphorus was above 0.2 mg/L. Simulated scenarios suggested that the probability of hazardous MCs (≥1.0 μg/L) was higher under interactive effect of temperature increase and nutrients (nitrogen and phosphorus) imbalance than that of warming alone. Likewise, data-driven model development using a naïve Bayes classifier and equal frequency discretization resulted in a substantial technical performance (CCI = 0.83, K = 0.60), but the performance significantly decreased when model excluded species-specific biomasses from input variables (CCI = 0.76, K = 0.40). The BN framework provided a useful screening tool to evaluate cyanotoxin in three studied lakes in China, and it can also be used in other lakes suffering from cyanobacterial blooms dominated by Microcystis.

Evaluating the influential priority of the factors on insurance loss of public transit

Understanding correlation between influential factors and insurance losses is beneficial for insurers to accurately price and modify the bonus-malus system. Although there have been a certain number of achievements in insurance losses and claims modeling, limited efforts focus on exploring the relative role of accidents characteristics in insurance losses. The primary objective of this study is to evaluate the influential priority of transit accidents attributes, such as the time, location and type of accidents. Based on the dataset from Washington State Transit Insurance Pool (WSTIP) in USA, we implement several key algorithms to achieve the objectives. First, K-means algorithm contributes to cluster the insurance loss data into 6 intervals; second, Grey Relational Analysis (GCA) model is applied to calculate grey relational grades of the influential factors in each interval; in addition, we implement Naive Bayes model to compute the posterior probability of factors values falling in each interval. The results show that the time, location and type of accidents significantly influence the insurance loss in the first five intervals, but their grey relational grades show no significantly difference. In the last interval which represents the highest insurance loss, the grey relational grade of the time is significant higher than that of the location and type of accidents. For each value of the time and location, the insurance loss most likely falls in the first and second intervals which refers to the lower loss. However, for accidents between buses and non-motorized road users, the probability of insurance loss falling in the interval 6 tends to be highest.

Bayesian belief network modelling of chlorine disinfection for human pathogenic viruses in municipal wastewater

Chlorine disinfection of biologically treated wastewater is practiced in many locations prior to environmental discharge or beneficial reuse. The effectiveness of chlorine disinfection processes may be influenced by several factors, such as pH, temperature, ionic strength, organic carbon concentration, and suspended solids. We investigated the use of Bayesian multilayer perceptron (BMLP) models as efficient and practical tools for compiling and analysing free chlorine and monochloramine virus disinfection performance as a multivariate problem. Corresponding to their relative susceptibility, Adenovirus 2 was used to assess disinfection by monochloramine and Coxsackievirus B5 was used for free chlorine. A BMLP model was constructed to relate key disinfection conditions (CT, pH, turbidity) to observed Log Reduction Values (LRVs) for these viruses at constant temperature. The models proved to be valuable for incorporating uncertainty in the chlor(am)ination performance estimation and interpolating between operating conditions. Various types of queries could be performed with this model including the identification of target CT for a particular combination of LRV, pH and turbidity. Similarly, it was possible to derive achievable LRVs for combinations of CT, pH and turbidity. These queries yielded probability density functions for the target variable reflecting the uncertainty in the model parameters and variability of the input variables. The disinfection efficacy was greatly impacted by pH and to a lesser extent by turbidity for both types of disinfections. Non-linear relationships were observed between pH and target CT, and turbidity and target CT, with compound effects on target CT also evidenced. This work demonstrated that the use of BMLP models had considerable ability to improve the resolution and understanding of the multivariate relationships between operational parameters and disinfection outcomes for wastewater treatment.