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Research Progress on the Application of Machine Learning in New Pollutants Studies
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This review examines how machine learning is being applied to study new pollutants including microplastics, pharmaceuticals, and PFAS, covering detection, migration modeling, risk assessment, and remediation design. The authors identify that deep learning and ensemble methods have shown the strongest performance across these applications.
New Pollutants (NP) are widely distributed and frequently detected in oceans, soil, the atmosphere, and drinking water. With their persistent accumulation and diffusion in the environment, the potential ecological and health risks of NP have become increasingly prominent, drawing significant attention from both academia and the public. Currently, researchers worldwide have conducted extensive systematic studies on the identification and detection of NP, their migration and transformation patterns, ecotoxicological effects, and efficient removal technologies.Machine Learning, as an advanced data modeling and analysis method, can significantly enhance the timeliness and predictive accuracy of NP research through big data-driven forecasting. This review summarizes the current applications of ML in the identification, distribution, and toxicological effects of persistent organic pollutants, endocrine disrupting chemicals, antibiotics and microplastics. Additionally, it provides insights into future research directions, such as exploring interpretable ML models, establishing data-sharing platforms, enabling real-time pollutant monitoring and establishing an association model between traditional and new pollutants.
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This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
Research Progress and Challenges of Machine Learning in Environmental Risk Identification and Control of Emerging Contaminants
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This Chinese review examines advances and challenges in applying machine learning to identify environmental risks from emerging contaminants, covering how AI-driven models can accelerate hazard screening, source tracing, and pollution control for novel pollutants including microplastics.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
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This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
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This review examines how machine learning approaches — including deep neural networks, hybrid physics-data models, and reinforcement learning — can be applied to detect and predict emerging chemical pollutants such as microplastics and recycling byproducts in water quality monitoring systems.
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
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Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.