| 引用本文: | 王沁茹,邱瑞,寇旭,陈阳,郭洪光.中国流域富营养化时空评价与智慧驱动因素解析[J].哈尔滨工业大学学报,2026,58(6):56.DOI:10.11918/202509053 |
| WANG Qinru,QIU Rui,KOU Xu,CHEN Yang,GUO Hongguang.Spatial-temporal assessment of eutrophication in China′s river basins and intelligent identification of driving factors[J].Journal of Harbin Institute of Technology,2026,58(6):56.DOI:10.11918/202509053 |
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| 中国流域富营养化时空评价与智慧驱动因素解析 |
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王沁茹1,2,邱瑞3,寇旭1,2,陈阳1,2,郭洪光1,2
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(1.四川大学 建筑与环境学院,成都 610065; 2.深地工程智能建造与健康运维国家重点实验室(四川大学), 成都 610065; 3.四川大学 商学院,成都 610065)
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| 摘要: |
| 富营养化是目前地表水面临的最具挑战性的环境问题之一,现有研究多聚焦于区域尺度分析。为揭示大范围流域的富营养化格局及其复杂驱动机制,对中国范围内流域的富营养化状况进行了评估与等级划分,并结合机器学习和SHapley加性解释（SHAP）的可解释人工智能方法探究影响富营养状况变化的潜在驱动因素。结果显示:富营养现象在大多数流域中普遍存在,中国东部、东北部和中部地区的富营养化问题较西部地区严重,且呈现出显著的时空聚集特征；总磷和总氮是关键的水质因子,贡献度分别为20.6%和75.4%,二者与流域富营养化存在显著的空间正相关关系；轻量级梯度提升机（LightGBM）模型在所有模型中表现最优,准确率达91%。SHAP分析表明,相比人为因素,自然驱动因素在解释流域整体富营养状况变化中起着更为重要的作用,其中,地形条件控制营养物质的积累和流失,降水则起到调节作用。然而,随着富营养化程度的加剧,人口密度、国内生产总值和污水处理能力等人为因素的影响逐渐凸显。经济发展若能伴随基础设施的提高,能够在一定程度上缓解富营养化。鉴于内部营养负荷和外部流域特征对富营养化的协同作用,建议实施分区分类精准管控以有效应对这一环境挑战。本研究结合人工智能方法深化了对流域尺度富营养化驱动机制的理论理解,也为水环境治理提供了定量化、可解释的科学证据。 |
| 关键词: 富营养 水质 流域 机器学习 |
| DOI：10.11918/202509053 |
| 分类号:X824；TP181 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2023YFC3210100)；国家自然科学基金(52470107) |
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| Spatial-temporal assessment of eutrophication in China′s river basins and intelligent identification of driving factors |
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WANG Qinru1,2,QIU Rui3,KOU Xu1,2,CHEN Yang1,2,GUO Hongguang1,2
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(1.College of Architecture & Environment, Sichuan University, Chengdu 610065, China; 2.State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering (Sichuan University), Chengdu 610065, China; 3.Business School, Sichuan University, Chengdu 610065, China)
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| Abstract: |
| Eutrophication is one of the most challenging environmental problems facing surface water at present, and existing studies mostly focus on regional-scale analysis. To reveal the eutrophication patterns and their complex driving mechanisms in large-scale river basins, this paper evaluated and classified the eutrophication status of river basins across China and explored the potential driving factors affecting the changes in eutrophication status by combining machine learning and the explainable artificial intelligence method of SHapley Additive exPlanations (SHAP). The results show that eutrophication is widespread in most river basins, and the eutrophication problem in eastern, northeastern, and central China is more severe than that in western regions, exhibiting significant spatial-temporal clustering characteristics; total phosphorus and total nitrogen are key water quality factors, with contributions of 20.6% and 75.4%, respectively, and both have a significant positive spatial correlation with river basin eutrophication; the Light Gradient Boosting Machine (LightGBM) model performs best among all models, with an accuracy of 91%. SHAP analysis reveals that compared with anthropogenic factors, natural driving factors play a more important role in explaining the overall changes in the eutrophication status of river basins, among which topographic conditions control the accumulation and loss of nutrients, while precipitation plays a regulatory role. However, with the aggravation of eutrophication, the impact of anthropogenic factors such as population density, gross domestic product, and wastewater treatment capacity gradually becomes prominent. If economic development is accompanied by the improvement of infrastructure, eutrophication can be mitigated to a certain extent. Given the synergistic effect of internal nutrient loads and external river basin characteristics on eutrophication, it is recommended to implement precise management and control based on spatial zoning and classification to effectively address this environmental challenge. This study deepens the theoretical understanding of the driving mechanism of river basin-scale eutrophication by combining artificial intelligence methods and provides quantitative and interpretable scientific evidence for water environment governance. |
| Key words: eutrophication water quality river basin machine learning |
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