Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges

Journal article : Review

Managing risks from water pollution is central to public health, environmental quality, and economic prosperity worldwide. While improvements in water quality have been attained in some parts of the world, much remains to be done to deliver clean rivers, lakes, and seas in line with public interest, changing regulatory landscapes, increasing awareness of risks from pollutants of emerging concern, and climate change. This Perspective explores the potential for artificial intelligence (AI) to help tackle complex water quality challenges. We take a system-oriented approach to define a general pipeline of AI-informed water quality decisions and critically assess the potential of AI to contribute to regulation and decision-making in the context of water quality management. Building on insights obtained from the literature and through a workshop with academics, environmental regulators, industry, and civil society stakeholders in England, we assess the maturity of current AI applications to meet a range of priorities and challenges. While current AI research shows maturity in responding to operational efficiency and modeling and prediction challenges, far less attention has been paid to aligning algorithmic development with user needs and organizational constraints, including the need for trustworthiness and explainability. The full potential of AI to support water quality decisions could be realized through clear institutional processes and accountability frameworks for decision-making. Looking ahead, the development of AI-ready data sets and the availability of clear, open-source examples of AI applications in the water quality domain are potential avenues for supporting wider uptake by regulators and other stakeholders.

Authors: Borgomeo, E; Holmes, LA; Billari, CG; Bitsios, I; Brown, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Chemical Society
• Journal: Environmental Science & Technology
• Volume: 60
• Issue: 14
• Pages: 10405-10416
• Publication date: 2026-03-28
• Acceptance date: 2026-03-17
• DOI: 10.1021/acs.est.5c15991
• EISSN: 1520-5851
• ISSN: 0013936X, 0013-936X
• Language: English
• Keywords: AI; water pollution; water quality; drinking-water quality regulation; wastewater infrastructure
• Subtype: Review