Integrated probabilistic-fuzzy synthetic evaluation of drinking water quality in rural and remote communities

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water quality from 2020 to 2023 were collected including nine biophysical and chemical indicators in seventeen rivers in Yancheng and Nantong, two coastal cities in Jiangsu Province, China, adjacent to the Yellow Sea. Linear regression and seven machine learning models (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) and Stochastic Gradient Boosting (SGB)) were developed to predict WQI using different groups of input variables based on correlation analysis. The results indicated that water quality improved from 2020 to 2022 but deteriorated in 2023, with inland stations exhibiting better conditions than coastal ones, particularly in terms of turbidity and nutrients. The water environment was comparatively better in Nantong than in Yancheng, with mean WQI values of approximately 55.3-72.0 and 56.4-67.3, respectively. The classifications "Good" and "Medium" accounted for 80 % of the records, with no instances of "Excellent" and 2 % classified as "Bad". The performance of all prediction models, except for SOM, improved with the addition of input variables, achieving R2 values higher than 0.99 in models such as SVM, RF, XGB, and SGB. The most reliable models were RF and XGB with key parameters of total phosphorus (TP), ammonia nitrogen (AN), and dissolved oxygen (DO) (R2 = 0.98 and 0.91 for training and testing phase) for predicting WQI values, and RF using TP and AN (accuracy higher than 85 %) for WQI grades. The prediction accuracy for "Medium" and "Low" water quality grades was highest at 90 %, followed by the "Good" level at 70 %. The model results could contribute to efficient water quality evaluation by identifying key water parameters and facilitating effective water quality management in river basins.

A review of drinking water quality issues in remote and indigenous communities in rich nations with special emphasis on Australia

This review paper examines the drinking water quality issues in remote and Indigenous communities, with a specific emphasis on Australia. Access to clean and safe drinking water is vital for the well-being of Indigenous communities worldwide, yet numerous challenges hinder their ability to obtain and maintain water security. This review focuses on the drinking water-related issues faced by Indigenous populations in countries such as the United States, Canada, New Zealand, and Australia. In the Australian context, remote and Indigenous communities encounter complex challenges related to water quality, including microbial and chemical contamination, exacerbated by climate change effects. Analysis of water quality trends in Queensland, New South Wales, Western Australia, and the Northern Territory reveals concerns regarding various pollutants with very high concentrations in the source water leading to levels exceeding recommended drinking water limits such as hardness, turbidity, fluoride, iron, and manganese levels after limited treatment facilities available in these communities. Inadequate water quality and quantity contribute to adverse health effects, particularly among Indigenous populations who may resort to sugary beverages. Addressing these challenges requires comprehensive approaches encompassing testing, funding, governance, appropriate and sustainable treatment technologies, and cultural considerations. Collaborative efforts, risk-based approaches, and improved infrastructure are essential to ensure equitable access to clean and safe drinking water for remote and Indigenous communities, ultimately improving health outcomes and promoting social equity.

Drinking water management strategies for distribution networks: An integrated performance assessment framework

Due to rapid population growth, urbanization, water contamination, and climate change, global water resources are under increasing pressure. Water utilities apply drinking water management strategies (DWMS) to ensure that water is safe for drinking. However, in recent years, due to increased inclination towards climate change, environmental emissions, and sustainable development goals; the environmental and economic performance of DWMSs is getting attention. An integrated framework combining life cycle thinking and water quality assessment techniques was developed in this study to evaluate the DWMSs' performance in terms of water quality, environment, and economics. Six DWMSs were assessed using the integrated framework as a case study. The environmental impacts in terms of human health, ecosystem, and resource use ranged from 1.46E-06 to 4.01E-06 DALY, 9.35E-10 to 3.80E-09 species.yr, and 0.0025-0.0071 USD-$, respectively. Pollution water index (PWI) and cost-benefit analysis (CBA) were used as decision-making techniques to assess the overall performance and suitability of DWMSs under given settings. The DWMSs with surface water as a source or ones providing relatively more degree of treatment have a relatively high PWI score (i.e., ≈0.31), reflective of high environmental impacts and water pollution compared to other alternatives. The CBA scores of selected alternatives ranged between 0.22 and 1.0. Furthermore, it was identified that DWMSs applied on relatively bigger water distribution systems can outweigh their costs (i.e., environmental and economic impacts). The proposed framework and approaches are flexible as they can incorporate different criteria in evaluating the performance and applicability of DWMSs.

Selection of green remediation alternatives for chemical industrial sites: An integrated life cycle assessment and fuzzy synthetic evaluation approach

The concept of green site remediation calls for a model that can consider environmental impacts in the selection of site remediation alternatives. In this study, an integrated life cycle assessment (LCA)-fuzzy synthetic evaluation (FSE) model is developed to help practitioners select the optimal site remediation plan by incorporating life cycle impacts into the comprehensive suitability evaluation. The LCA module quantifies environmental and economic impacts using ReCiPe and Input-Output LCA methods, respectively. The impacts are evaluated along with other suitability considerations, presented in 32 indicators under ten criteria, by practitioners through a questionnaire survey. FSE is used to process the collected subjective judgments and generate a suitability index for informed selection. The integrated model is applied to a case study of an abandoned chemical industrial site contaminated by various organic chemicals and mercury. Four remediation alternatives, designed as the combined uses of ex-situ thermal desorption, in-situ thermal desorption, and in-situ containment, are evaluated. The LCA results show that the alternative with extensive use (treating 93.8 % of the contaminated soil) of in-situ thermal desorption is associated with the highest environmental and economic impacts, followed by the alternative with less extensive use (6.2 %) of in-situ thermal desorption. The FSE results show that the economic, technical, and environmental impact considerations are the top three important criteria. The integrated LCA-FSE results indicate that the alternative with mixed use of ex-situ thermal desorption and in-situ containment could be the optimal plan. Excluding LCA results could alter the suitability ranks of the alternatives.

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions.

Risk-Based Evaluation of Improvements in Drinking Water Treatment Using Cost-Benefit Analysis

Reliable and safe drinking water supply requires adequate risk management. Decision support models can aid decisionmakers to effectively evaluate risk mitigation measures and allocate societal resources. Here, a Swedish case study illustrates how the installation of ultrafiltration membranes can be evaluated by combining risk assessment and cost-benefit analysis. Quantitative microbial risk assessment was used to assess several contamination sources and estimate the achieved risk reduction from waterborne pathogens using Campylobacter, Norovirus, and Cryptosporidium as reference pathogens. The societal value of the improved water quality was estimated in the cost-benefit analysis by monetising the gained quality adjusted life years and aesthetic water quality improvements. The calculated net present value (mean of 7 MEUR) indicated that the installation of the ultrafiltration membranes was a sound investment from a societal economic perspective. The ultrafiltration membranes reduced the annual probability of infection from 3 × 10−2 to 10−7, well below the U.S. EPA’s acceptable level, as well as improving the aesthetic quality of the drinking water. The results provide a novel example of the importance for water distributors to consider not only health-related metrics when evaluating treatment options or monitoring the drinking water quality, but to also consider the aesthetic quality of the drinking water.