Application of the Self-Organizing Map (SOM) Algorithm to Identify Hydrochemical Characteristics and Genetic Mechanism of High-Nitrate Groundwater in Baoding Area, North China Plain

Nitrate pollution poses a pervasive environmental issue for groundwater systems worldwide, which is particularly pronounced in the agricultural heartlands of the North China Plain. Combining hydrochemical analysis, the Self-Organizing Map algorithm, and Human Health Risk Assessment, 91 shallow groundwater samples were collected to identify the hydrochemical characteristic and the genetic mechanisms of high NO3− concentration groundwater. The SOM analysis identified three distinct hydrochemical clusters. Cluster 1, with the hydrochemical characteristic of HCO3-Ca and HCO3-Mg, is severely contaminated, showing the highest NO3−, Ca2+, and TDS. In contrast, the majority of samples fell into Cluster 3, which is characterized by the lowest ion concentrations and an HCO3-Ca type. Cluster 2, characterized by HCO3-Ca/Mg, exhibits an intermediate chemical signature with elevated Na+, Mg2+, and HCO3−. Nitrate concentrations varied widely, with 30.43% of collected samples exceeding the anthropogenic pollution threshold. Agricultural activities are identified as the primary nitrate source, with domestic sewage as a secondary contributor. The Human Health Risk Assessment further reveals that long-term exposure poses non-carcinogenic health risks, particularly for children, who are found to be the most vulnerable group. This study provides a hydrogeochemical perspective on nitrogen pollution in shallow groundwater and offers scientific support for sustainable groundwater management in typical agricultural regions worldwide.