AI could transform how scientists detect and understand microplastic pollution

Microplastics are now found across oceans, soils, food chains, and even human biological samples. Yet scientists still face a major challenge: these tiny particles are difficult to detect, track, and link to health risks using traditional laboratory methods alone. A new perspective article argues that artificial intelligence could help change that.

Published in Artificial Intelligence & Environment, the article, “Revisiting the detection, fate, and health risks of microplastics in the environment through artificial intelligence,” examines how AI can support the full research chain of microplastics, from detection and environmental tracking to health risk assessment and policy decision-making.

Microplastics, generally defined as plastic particles smaller than 5 millimeters, are part of a wider environmental crisis involving pollution, climate change, and biodiversity loss. They can move through water, soil, air, and food webs, while also carrying or interacting with other pollutants. Their behavior depends on many factors, including size, shape, polymer type, aging, biofilm formation, hydrodynamics, and local environmental conditions.

Traditional tools such as Fourier transform infrared spectroscopy and Raman spectroscopy remain important, but they can be slow and labor-intensive when applied to large numbers of complex environmental samples. According to the authors, AI can help overcome these barriers by recognizing subtle patterns in high-dimensional data. Machine learning can improve the speed and accuracy of microplastic identification, while deep learning can support rapid classification of very small particles.

“Artificial intelligence gives us a new way to see microplastics not as isolated particles, but as part of a connected environmental and health system,” said corresponding author Xiangang Hu of Nankai University. “By integrating monitoring data, toxicological evidence, climate information, and human exposure data, AI can help us move from fragmented observations toward more predictive and actionable science.”

The article highlights emerging applications such as AI-assisted spectroscopy, microfluidic detection, portable field sensors, satellite and drone monitoring, interpretable machine learning for transport modeling, and digital twins for testing environmental management strategies before they are implemented.

The authors also propose a “Pan-microplastic AI Framework” for One Health governance. This framework aims to connect environmental, ecological, and human health data to better understand how microplastics interact with climate change, biodiversity loss, and other emerging contaminants.

However, the authors caution that AI is not a magic solution. High-quality data, standardized methods, interpretable models, and responsible “Green AI” practices are essential. With careful development, they argue, AI could become a powerful tool for building more quantitative, systematic, and intelligent responses to microplastic pollution.

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Journal reference: 

Hu XG; Wang RQ; Liu CH. Revisiting the detection, fate, and health risks of microplastics in the environment through artificial intelligence. AI Environ. 2026, 1(1): 33-39. DOI: 10.66178/aie-0026-0005  

https://www.the-newpress.com/aie/article/doi/10.66178/aie-0026-0005  

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About the Journal: 

Artificial Intelligence & Environment is an international multidisciplinary platform for communicating advances in fundamental and applied research on the intersection of environmental science and artificial intelligence (AI). It is dedicated to serving as an innovative, efficient and professional platform for researchers in the cross-discipline fields of earth and environmental sciences, big data science and AI around the world to deliver findings from this rapidly expanding field of science. It is a peer-reviewed, open-access journal that publishes critical review, original research, rapid communication, view-point, commentary and perspective papers.

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