Indices and models of surface water quality assessment: Review and perspectives. (1st September 2022)

• Record Type: Journal Article
• Authors: Yan, Tao; Shen, Shui-Long; Zhou, Annan
• Is Part Of: Environmental pollution. Volume 308(2022)
• Publication Date: 2022-09-01
• Subjects: Influential factors -- Assessment indices -- Assessment models -- Management -- Surface water; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2022.119611
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Many technologies have been designed to monitor, evaluate, and improve surface water quality, as high-quality water is essential for human activities including agriculture, livestock, and industry. As such, in this study, we investigated water quality indices (WQIs), trophic status indices (TSIs), and heavy metal indices (HMIs) for assessing surface water quality. Based on these indices, we summarised and compared water assessment models using expert system (ES) and machine learning (ML) methods. We also discussed the current status and future perspectives of water quality management. The results of our analyses showed that assessment indices can be used in three aspects of surface water quality assessment: WQIs are aggregated from multiple parameters and commonly used in surface water quality classification; TSIs are calculated from the concentrations of different nutrients required for algae and bacteria, and employed to evaluate the eutrophication levels of lakes and reservoirs; HMIs are mainly applied for human health risk assessment and the analysis of correlation of heavy metal sources. ES- and ML-based assessment models have been developed to efficiently generate assessment indices and predict water quality status based on big data obtained from new techniques. By implementing dynamic monitoring and analysis of water quality, we designed a next-generation water quality management system based on the above indices and assessment models, which shows promise for improving the accuracy of water quality assessment. Graphical abstract: Image 1 Highlights: Indices of water quality, trophic status, heavy metal risk for surface water were reviewed. Water quality assessment models based on expert system and machine learning were discussed. Applications of assessment indices and models were demonstrated by cases. Perspectives for water quality management based on AI and IoT technology were proposed. Development of water quality monitoring methods and early warning system were suggested.