Feng Shuzhen, Zhang Chaokai, Yan Jiaheng, Ren Ke, Peng Ningbo, Jiang Wei, Liu Shouhua
Nanjing Vocational Institute of Railway Technology, Nanjing, 210031, China.
Faculty of Architecture and Civil Engineering, Huaiyin Institute of Technology, Huaian, 223003, China.
Sci Rep. 2025 Jun 5;15(1):19763. doi: 10.1038/s41598-025-02964-2.
The Huaihe River Basin (HRB) is an important water system in eastern China, and its water quality has received widespread attention. This study explored the latest spatial variation patterns of surface water quality in the HRB to cope with the increasingly severe challenges of water resource management. By integrating multidimensional water quality data from surface water monitoring stations, including dissolved oxygen (DO), chemical oxygen demand (COD and COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH-N), total phosphorus (TP), and total nitrogen (TN), this study utilized a cluster analysis technique to categorize the water quality data and reveal changes in the geographic variability of water quality. Among the 382 monitoring stations in the HRB, 258 stations had TN content lower than Class V, which was the highest among all monitoring indicators. The entropy weight method used to assess the comprehensive water quality showed that there were 157 and 163 monitoring stations belonging to Class III and IV, respectively, and stations with poor water quality were distributed downstream in the river network and estuary area. Correlation and cluster analyses indicated that agricultural and organic matter pollution were the two main factors affecting water quality in the HRB, particularly in the downstream area, and the high loading of nutrient salts such as TP and NH-N reflected the significant influence of agricultural activities. In addition, the study examined the potential driving role of factors such as topography, geomorphology, and human activities on water quality changes and visualized the relationship between water quality class and cluster categories through spatial distribution maps and Sankey diagrams to clarify the regional patterns of water quality problems.
淮河流域是中国东部重要的水系，其水质受到广泛关注。本研究探索了淮河流域地表水水质的最新空间变化模式，以应对水资源管理日益严峻的挑战。通过整合地表水监测站的多维水质数据，包括溶解氧（DO）、化学需氧量（COD和COD）、生化需氧量（BOD）、氨氮（NH-N）、总磷（TP）和总氮（TN），本研究利用聚类分析技术对水质数据进行分类，并揭示水质地理变异性的变化。在淮河流域的382个监测站中，有258个站的总氮含量低于Ⅴ类，这在所有监测指标中是最高的。用于评估综合水质的熵权法表明，分别有157个和163个监测站属于Ⅲ类和Ⅳ类，水质较差的站点分布在河网下游和河口地区。相关性和聚类分析表明，农业和有机物污染是影响淮河流域水质的两个主要因素，特别是在下游地区，总磷和氨氮等营养盐的高负荷反映了农业活动的重大影响。此外，该研究考察了地形、地貌和人类活动等因素对水质变化的潜在驱动作用，并通过空间分布图和桑基图直观展示了水质类别与聚类类别的关系，以阐明水质问题的区域模式。
