Researchers have used artificial intelligence to identify viable action plans for complex environmental issues, screening materials for pollution treatment and predicting health risks across water, air, waste, soil and health sectors.
The team at Tohoku University focused on five major fields: water pollution treatment, air pollution control, solid waste disposal, soil remediation and environmental health. The study was published in Environment International on 12 September 2025.
“Our study reveals the breakthrough value of technologies such as machine learning in material screening, performance prediction, real-time prediction, global distribution simulation of pollutants, and health risk management,” explains Professor Hao Li (WPI-AIMR).
The AI can develop strategies to enhance water treatment techniques and predict which materials most effectively remove pollutants, such as greenhouse gases, from the air. AI-driven material screening and process optimisation can reduce pollution treatment costs, improve resource recycling efficiency and enhance immediate surroundings.
The technology addresses issues too complex for humans to solve alone. Some pollutants may be more or less toxic to humans depending on their interactions, making predictions far from simple, Li explained.
The analysis provides support for formulating public health policies and ensuring food and drinking water safety. However, bottlenecks to wide adoption exist, including data scarcity, overfitting of small-sample models and uneven geographical distribution of observational data.
The team proposes a shared Digital Catalysis Platform that integrates cross-media data processing with domain-specific prior knowledge. The researchers plan to build a cross-media environmental database, develop solutions for overfitting with small samples and collaborate with global institutions to establish a standardised data collection and sharing platform.
