ReviewEnvironmental science & technology2026
Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges.
Review in Environmental science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
28 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.