Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 611756, China.
Nanjing Innowater Environmental Technology Co., Ltd, Nanjing, 210000, China.
Environ Sci Pollut Res Int. 2024 Mar;31(13):19815-19830. doi: 10.1007/s11356-024-32427-6. Epub 2024 Feb 17.
Against the backdrop of ecological conservation and high-quality development in the Yangtze River Basin, there is an increasing demand for enhanced water pollution prevention and control in small watersheds. To delve deeper into the intricate relationship between pollutants and environmental features, as well as explore the key factors triggering pollution and their corresponding warning thresholds, this study was conducted along the Jiuqu River, a strategically managed unit in the upstream region of the Yangtze River, between 2022 and 2023. A total of seven monitoring sites were established, from which 161 valid water samples were collected. The k-nearest neighbors mutual information (KNN-MI) technique indicated that water temperature (WT) and relative humidity (RH) were the main environmental factors. The principal component analysis (PCA) of ten water quality parameters and three environmental factors unveiled the distinguishing characteristics of the primary pollution sources. Consequently, the pollution sources were categorized as treated wastewater > groundwater runoff > phytoplankton growth > abstersion wastewater > agricultural drainage. Furthermore, the regression decision tree (RDT) algorithm was used to explore the combined effects between pollutants and environmental factors, and to provide visual decision-making process and quantitative results for understanding the triggering mechanism of organic pollution in Jiuqu River. It conclusively identifies total phosphorus (TP) as the predominant triggering parameter with the threshold of 0.138 mg/L. The study is helpful to deal with potential water pollution problems preventatively and shows the interpretability and predictive performance of the RDT algorithm in water pollution prevention.
在长江流域生态保护和高质量发展的背景下，对小流域水污染防治提出了更高的要求。为了深入研究污染物与环境特征之间的复杂关系，探索引发污染的关键因素及其相应的预警阈值，本研究以长江上游战略管理单元九曲河流域为研究对象，于 2022 年至 2023 年开展了相关工作。共设置 7 个监测点，采集了 161 个有效水样。基于 k-最近邻互信息（KNN-MI）技术，确定水温（WT）和相对湿度（RH）是主要的环境因素。通过对 10 个水质参数和 3 个环境因素的主成分分析（PCA），揭示了主要污染源的特征。结果表明，污染源依次为处理后的废水>地下水径流>浮游植物生长>洗车废水>农业排水。进一步采用回归决策树（RDT）算法，探讨了污染物与环境因素的联合作用，为理解九曲河有机污染的触发机制提供了可视化的决策过程和定量结果。研究发现总磷（TP）是主要的触发参数，其阈值为 0.138mg/L。该研究有助于对潜在的水污染问题进行预防性处理，并展示了 RDT 算法在水污染防治中的可解释性和预测性能。
