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| Hauptverfasser: | , , , , , , |
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| Format: | Artículo científico |
| Sprache: | en |
| Veröffentlicht: |
Marine environmental research
2026
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| Schlagworte: | |
| Online-Zugang: | https://pubmed.ncbi.nlm.nih.gov/42143964/ |
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Inhaltsangabe:
- Transport and distribution patterns of floating marine litter: Numerical modeling and AI-empowered solutions. Yishan, Wei Junsen, Zeng Zirui, Zhao Hengxiang, Li Honglei, Jiang Kefu, Yu Xiangrong, Xu Artificial Intelligence Environmental Monitoring Models, Theoretical Water Pollutants Oceans and Seas Floating marine litter (FML) has emerged as a central priority in global ocean governance. Thoroughly deciphering the spatiotemporal evolutionary mechanisms of its "source-sink" system is fundamental to formulating scientifically robust management strategies, with numerical modeling acting as the core technical tool for quantifying this complex process. This study systematically reviews a decade of research on FML transport simulations, detailing the technical characteristics and applicable scenarios of Eulerian and Lagrangian methods. It clarifies the dynamic drivers and final fate characteristics of FML transport across all scales-from the global ocean and marginal seas to estuarine and coastal zones. It indicates that while phased breakthroughs have been made in existing studies, common bottlenecks persist-including the precise parameterization of complex physical processes, coupled sea-air-particle multiphase interactions, effective assimilation of sparse observational data, and the challenge of defining initial and boundary conditions related to FML sources. In light of these challenges, this study focuses on exploring pathways for the deep integration of artificial intelligence (AI) with traditional numerical models. Drawing on established AI applications in marine environmental forecasting, this study delves into the unique advantages of data-driven methods in capturing nonlinear characteristics of marine debris transport and enhancing computational efficiency. This research aims to provide critical technological guidance for developing a high-precision, intelligent monitoring and early warning system for FML.