生态与农村环境学报2025，Vol.41Issue(9)：1143-1157,15.DOI:10.19741/j.issn.1673-4831.2025.0330
人工智能技术驱动的水环境污染物溯源研究进展
Research Progress on Source Tracing of Aquatic Environmental Pollutants Driven by Artificial Intelligence Tech-nology
摘要
Abstract
Aquatic pollutants source tracing is fundamental to effective pollution control and ecological remediation,provi-ding critical technical support for basin-scale ecosystem management and socio-ecological governance.Conventional meth-ods,constrained by insufficient detection sensitivity,oversimplified models,and inadequate cross-domain data integration,fail to support dynamic full-chain(source-pathway-sink)analysis of pollutant behavior.This review synthesizes recent ad-vances in aquatic pollutants source tracing methods and emerging trends driven by artificial intelligence(AI)technologies.Bibliometric analysis and case studies identify three converging research frontiers intelligent source recognition,dynamic transport modeling,and sink-related risk early warning.AI technologies,high-dimensional data processing,nonlinear re-lationship mining,and dynamic simulation,are pollutant tracing frameworks across multiple dimensions.In source appor-tionment,deep learning architectures integrated with transfer learning frameworks demonstrate exceptional performance in multi-scale source fingerprinting under heterogeneous conditions via multimodal data fusion.For transport modeling,phys-ics-informed neural networks and hybrid time-series prediction models enable precise prediction of cross-media(water-at-mosphere-soil)pollutant trajectories.Advanced sink-risk assessment integrates multi-scale early-warning systems with mechanistic frameworks to elucidate sink formation dynamics and ecological risk propagation pathways.Future research should prioritize cross-basin generalizability,explainability of physical and chemical mechanisms,and lightweight deploy-ment.Equally critical are collaborative platforms enabling comprehensive environmental perception and intelligent deci-sion-making,alongside technical system standardization and ethical governance frameworks.关键词
人工智能/溯源/水环境/污染物/源-径-汇过程Key words
artificial intelligence/source tracing/aquatic environment/pollutants/source-pathway-sink process分类
资源环境引用本文复制引用
吴欣怡,朱梦圆,孙佩佩,王秦玲,常梦杰,陈玲,吴兵..人工智能技术驱动的水环境污染物溯源研究进展[J].生态与农村环境学报,2025,41(9):1143-1157,15.基金项目
国家自然科学基金长江水科学研究联合基金重点支持项目(U2340202) （U2340202）
