Data-Driven Risk Identification and Prevention–Control Optimization of Groundwater Nitrate Contamination

Abstract

Groundwater nitrate contamination is delayed, spatially heterogeneous and difficult to screen with sparse monitoring, especially when exceedance probability and exposure concern must be considered together. We analysed 157 shallow groundwater samples from Handan City, China, collected in 2023, and 14 environmental predictors to map NO3− exceedance above 50 mg/L. Random forest, support vector machine (SVM) and XGBoost classifiers were evaluated by repeated stratified testing and spatial block cross-validation, with SHAP used to interpret model responses. Ordinary kriging and the USEPA non-carcinogenic risk framework estimated adult and child hazard quotients (HQs), and exceedance probability was cross-classified with child HQ in a probability–HQ matrix. Thirty-one samples (19.7%) exceeded the threshold. Although SVM achieved the highest ROC-AUC, XGBoost was retained for early-warning mapping because it better controlled missed exceedances. High-probability zones were concentrated in central-western and urban-fringe Handan. SHAP responses indicated that hydroclimatic, hydrogeological, land-use, terrain and soil factors jointly shaped model discrimination, without implying source attribution. Children had higher HQs than adults. At P = 0.5, general protection, exceedance-warning, concentration-verification and priority-intervention zones occupied 81.26%, 18.40%, 0.01% and 0.33% of the area, respectively. The framework supports targeted monitoring and drinking-water verification rather than fixed contamination boundaries.