Supplementary file 1_Interpretable GNN–Residual Kriging for spatial prediction and investigation-priority zoning of soil mercury in a karst agricultural region.docx

Introduction<p>Soil mercury (Hg) in karst agricultural regions is commonly characterized by strong local heterogeneity, which limits the ability of conventional interpolation methods to delineate localized enrichment.</p>Methods<p>We developed an interpretable graph neural network–residual kriging (GNN-RK) framework for soil Hg prediction, uncertainty assessment, and investigation-priority zoning. The framework integrates 13 non-element environmental covariates, graph-based spatial learning, residual kriging, bootstrap uncertainty estimation, model-agnostic feature attribution, and GeoDetector analysis.

It was applied to 149 surface-soil samples from agricultural land in Zhijin County, Guizhou Province, Southwest China.</p>Results<p>Soil Hg ranged from 0.0256 to 10.5983 mg kg⁻¹ (mean 0.321; median 0.141 mg kg⁻¹). Under the pH‐specific threshold scenario for non‐paddy agricultural land in GB 15618-2018, 147 samples were below the risk-screening values and two exceeded the risk-control values. In label-masked transductive five-fold spatial block cross-validation, GNN‐RK achieved the highest pooled performance among the evaluated models (R² = 0.632, RMSE = 0.695 mg kg⁻¹, MAE = 0.201 mg kg⁻¹).