Revealing the drivers and genesis of NO3-N pollution classification in shallow groundwater of the Shaying River Basin by explainable machine learning and pathway analysis method

Abstract

Nitrate (NO3-N), as one of the ubiquitous contaminants in groundwater worldwide, has posed a serious threat to public health and the ecological environment. Despite extensive research on its genesis, little is known about the differences in the genesis of NO3-N pollution across different concentrations. Herein, a study of NO3-N pollution concentration classification was conducted using the Shaying River Basin as a typical area, followed by examining the genesis differences across different pollution classifications. Results demonstrated that three classifications (0–9.98 mg/L, 10.14–27.44 mg/L, and 28.34–136.30 mg/L) were effectively identified for NO3-N pollution using Jenks natural breaks method. Random forest exhibited superior performance in describing NO3-N pollution and was thereby affirmed as the optimal explanatory method. With this method coupling SEMs, the genesis of different NO3-N pollution classifications was proven to be significantly different. Specifically, strongly reducing conditions represented by Mn2+, Eh, and NO2-N played a dominant role in causing residual NO3-N at low levels. Manure and sewage (represented by Cl−) leaching into groundwater through precipitation is mainly responsible for NO3-N in the 10–30 mg/L classification, with a cumulative contribution rate exceeding 80 %. NO3-N concentrations >30 mg/L are primarily caused by the anthropogenic loads stemming from manure, sewage, and agricultural fertilization (represented by Cl− and K+) infiltrating under precipitation in vulnerable hydrogeological conditions. Pathway analysis based on standardized effect and significance further confirmed the rationality and reliability of the above results. The findings will provide more accurate information for policymakers in groundwater resource management to implement effective strategies to mitigate NO3-N pollution.