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[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.
Microplastics are an emerging contaminant that can persist in the environment for extended periods， posing risks to ecological systems. Recently， microplastic pollution has emerged as a major global environmental problem. In order to ensure accurate and scientific evaluation of the ecological risks associated with microplastic pollution， it is of paramount importance to improve the simplicity and reliability of microplastic identification， systematically analyze the pollution characteristics of microplastics in various environmental media， and clarify their environmental impacts. Machine learning technology has gained widespread attention in microplastic research by learning and analyzing large volumes of data to establish result evaluation or prediction models. The use of machine learning can enhance the automation and identification efficiency of visual and spectral identification of microplastics， provide scientific support for tracing the sources of microplastic pollution， and help reveal the complex environmental effects of microplastics. This review provides a summary of the application characteristics and limitations of machine learning in the aforementioned areas by reviewing the progress made in research that employs machine learning technology in microplastic identification and environmental risk assessment. Furthermore， the findings of the review will provide suggestions and prospects for the development and application of machine learning in related areas.
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