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The Role of Artificial Intelligence in Microplastic Pollution Studies and Management
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This review explores how artificial intelligence is transforming microplastic research, from automating detection in microscopy images and spectral analysis to predicting how plastics interact with pollutants and living organisms. AI-powered sensors and real-time monitoring systems are also being integrated into wastewater treatment to reduce microplastic release, making the technology a powerful tool for both understanding and managing plastic pollution.
Artificial intelligence (AI) is reshaping microplastic research by enabling faster, more accurate, and scalable detection, characterization, and modeling. Deep learning automates the identification and classification of microplastics from microscopy images, while machine learning accelerates the recognition of polymers from Raman and infrared spectra. AI-based clustering and segmentation improve the analysis of complex samples, and source-apportionment models learn morphological and chemical features to trace emissions from various activities and land uses. AI also enhances predictions of microplastic interactions and impacts, modeling pollutant adsorption, leaching behaviors, and biological toxicity responses. Large language models are increasingly used to streamline quality assurance/control (QA/QC) and support exposure and risk assessments. Emerging AI-enabled sensors and real-time control systems can be integrated into manufacturing and wastewater treatment processes, enabling continuous monitoring and adaptive process adjustments to reduce microplastic release. Collectively, AI provides powerful tools for advancing microplastic detection, understanding their ecological and health risks, and supporting proactive pollution mitigation.
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This review explored how artificial intelligence is being used to improve the detection and removal of microplastics from water and the environment. Researchers found that machine learning techniques can enhance the identification of microplastic particles and optimize treatment processes like filtration and coagulation. The study suggests that AI-driven approaches could overcome many of the efficiency and cost limitations of conventional microplastic removal methods.
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