[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2430":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":60},2430,"Pollution-driven surface water quality improvement and expanding population benefits in karst regions of China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs43247-026-04006-9","Karst regions in China have high geological permeability, fragmented river networks, and strong surface–groundwater interactions, making water quality management challenging. Despite their ecological and societal importance, long-term patterns of surface water quality remain poorly understood. Here we provide a nationwide, multi-decadal evaluation of surface water quality in karst regions using remote sensing and machine learning. The results show that surface water quality improved markedly, with the proportion of water bodies classified as the highest quality increasing from 22.7% to 35.9%. Attribution analysis indicates that pollution-related factors account for the dominant contribution across basins (> 65%), whereas climate effects are secondary and land-use contributions are generally small (\u003C 5%). The number of people benefiting from high-quality water rose to 4.86 million by 2020, although 1.36 million people remained exposed to polluted water near expanding settlements. Future scenario projections indicate that these improvements may be reversed without sustained pollution control. These findings highlight the importance of protecting surface water in karst regions for ecosystem and human well-being. Surface water quality in China’s karst regions improved substantially, with top-quality waters increasing by up to 36 percent and 4.86 million people benefiting, but gains may reverse without continued controls, according to remote sensing and machine learning.","中国喀斯特地区地质渗透性强、河网破碎、地表水与地下水相互作用强烈，水质管理面临较大挑战。尽管这些区域具有重要的生态和社会意义，但其地表水水质的长期变化规律仍缺乏系统认识。本研究基于遥感和机器学习，对中国喀斯特地区地表水水质开展了全国尺度、跨数十年的评估。结果表明，地表水水质显著改善，水质类别为最高等级的水体比例从22.7%上升至35.9%。归因分析显示，污染相关因素在各流域中占主导贡献（>65%），气候影响次之，土地利用贡献总体较小（\u003C5%）。到2020年，受益于优质水的人口增至486万，但仍有136万人暴露于不断扩张的居民点附近的污染水体中。未来情景预测表明，若缺乏持续的污染控制，上述改善可能发生逆转。这些发现凸显了保护喀斯特地区地表水对生态系统和人类福祉的重要性。基于遥感和机器学习的研究表明，中国喀斯特地区地表水水质大幅改善，最高等级水体比例增加达36%，486万人因此受益，但若缺乏持续管控，这一改善可能逆转。",null,"Communications Earth & Environment","2026-09-11T00:00:00Z","论文",10,false,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,14,7,1,"基于遥感与机器学习的全国性喀斯特地区地表水质多年代评估，数据规模大、结论有新意，对农业用水与乡村生态保护有参考价值，但主题偏生态环境而非农业信息化核心，故未达每日精选顶级门槛。",[25],{"name":10,"url":6},[27,28,29,30,31],"机器学习","生态保护","遥感监测","水质评价","喀斯特地区",0,"10.1038\u002Fs43247-026-04006-9",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":51,"card":52,"direction":58,"ingested_from":59},"W7212291140",[37,39,41,44,46,48],{"name":38,"orcid":9},"Mingxia He",{"name":40,"orcid":9},"Jie Niu",{"name":42,"orcid":43},"Chuanhao Wu","https:\u002F\u002Forcid.org\u002F0000-0003-4855-7716",{"name":45,"orcid":9},"Dongdong Liu",{"name":47,"orcid":9},"Pan Wu",{"name":49,"orcid":50},"Bill X. Hu","https:\u002F\u002Forcid.org\u002F0000-0003-4490-5250","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43247-026-04006-9_reference.pdf",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"用遥感与机器学习评估中国喀斯特地区地表水水质长期变化及受益人口。","遥感与机器学习，多年代全国尺度水质分类与归因分析。","水质显著改善，污染治理主导，486万人受益，但控制放松可能逆转。","农业绿色发展与碳","可探究农业面源污染在喀斯特水质改善中的具体贡献及持续控制策略。","农业遥感与作物表型","openalex","2026-09-14T23:30:27.091671Z"]