Decoding the Plastic Patch: Exploring the Global Microplastic Distribution in the Surface Layers of Marine Regions with Interpretable Machine Learning.

Journal: Environmental science & technology
PMID: 40226856

Abstract

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/m 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.

Authors

  • Linjie Zhang
    Shanghai Engineering Research Center of Biotransformation on Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China.
  • Wenyue Wang
    Shanghai Engineering Research Center of Biotransformation on Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China.
  • Feng Wang
    Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
  • Dong Wu
  • Yinglong Su
    Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China; Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing, 401120, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China. Electronic address: ylsu@des.ecnu.edu.cn.
  • Min Zhan
    2 University of Maryland School of Medicine, Baltimore, MD, USA.
  • Kaiyi Li
    Shanghai Engineering Research Center of Biotransformation on Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China.
  • Huahong Shi
    State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China.
  • Bing Xie
    Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Forensic Identification Center of Hebei Medical University, College of Forensic Medicine, Hebei Medical University, Shijiazhuang 050017, China.

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