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Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges
Edoardo Borgomeo1, Luke A Holmes2, Camilla G Billari1
1Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.
Environmental Science & Technology
|March 28, 2026
Summary
Artificial intelligence (AI) offers potential for managing water quality risks. Further development is needed to align AI with user needs and institutional frameworks for effective decision-making.
Area of Science:
- Environmental Science
- Public Health
- Computer Science
Background:
- Water pollution poses global risks to public health, environment, and economy.
- Challenges include emerging pollutants, climate change, and evolving regulations.
- Current water quality management requires innovative solutions.
Purpose of the Study:
- To explore artificial intelligence (AI) potential in addressing water quality challenges.
- To assess AI's role in water quality regulation and decision-making.
- To evaluate the maturity of current AI applications in this domain.
Main Methods:
- A system-oriented approach to define an AI-informed water quality decision pipeline.
- Literature review and a stakeholder workshop in England.
- Critical assessment of AI maturity against water quality priorities.
Main Results:
- AI research is mature in operational efficiency, modeling, and prediction.
- Less attention has been given to aligning AI with user needs, trustworthiness, and explainability.
- Current AI applications show limited integration with organizational constraints.
Conclusions:
- Realizing AI's full potential requires clear institutional processes and accountability.
- Development of AI-ready datasets is crucial for wider adoption.
- Open-source examples of AI in water quality can support regulators and stakeholders.
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