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Decoding the PlasticPatch: Exploring the Global MicroplasticDistribution in the Surface Layers of Marine Regions with InterpretableMachine Learning
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Researchers applied four interpretable machine learning algorithms to a calibrated global marine microplastic dataset to construct a predictive model of surface-layer microplastic distribution, finding that biogeochemical and anthropogenic factors are the dominant drivers of global marine microplastic pollution patterns.
The marine environment is grappling with microplastic (MP) pollution, necessitating an understanding of its distribution patterns, influencing factors, and potential ecological risks. However, the vast area of the ocean and budgetary constraints make conducting comprehensive surveys to assess MP pollution impractical. Interpretable machine learning (ML) offers an effective solution. Herein, we used four ML algorithms based on MP data calibrated to the size range of 20–5000 μm and considered various factors to construct a robust predictive ML model of marine MP distribution. Interpretation of the ML model indicated that biogeochemical and anthropogenic factors substantially influence global marine MP pollution, while atmospheric and physical factors exert lesser effects. However, the extent of the influence of each factor may vary within specific marine regions and their underlying mechanisms may differ across regions. The predicted results indicated that the global marine MP concentrations ranged from 0.176 to 27.055 particles/m3 and that MPs in the 20–5000-μm size range did not pose a potential ecological risk. The interpretable ML framework developed in this study covered MP data preprocessing, MP distribution prediction, and interpretation of the influencing factors of MPs, providing an essential reference for marine MP pollution management and decision making.
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