Machine Learning Reveals Controls on Heavy Metal Distribution in War-Affected Soils of Eastern Ukraine

Armed conflict causes severe and lasting soil contamination, yet the relative contributions of military activities, geogenic factors and pre-war anthropogenic influences on heavy metal distributions in war-affected soils remain poorly understood. This study applies generalized additive models (GAM) and Random Forest (RF) algorithms to characterise the multi-factor controls on Cu, Pb, Ni and Zn concentrations in soils from eleven sites across Kharkiv Oblast, eastern Ukraine, affected by hostilities between March and November 2022. A total of 114 soil samples were collected from explosion craters, weapon-damaged fields and sites impacted by specific weapons. Predictor variables encompassed site disturbance category, ammunition type, soil type, land use, pH and soil organic carbon (SOC). Measured pseudo-total concentrations spanned wide ranges Cu 5.2-71.5, Pb 6.1-24.6, Ni 10.1-46.0 and Zn 0.43-114.2 mg kg^-1 with the highest values confined to specific disturbance categories. Under repeated cross-validation, Random Forest performed comparably to GAM for Cu, Ni and Zn (RF R² ≈ 0.82, 0.79 and 0.81, respectively) and significantly better only for Pb, improving mean R² from 0.56 to 0.67 and reducing root mean square error (RMSE) from 2.60 to 2.06 mg kg^-1; its overall advantage was modest and metal-specific.. Two contrasting behavioral groups were identified: Cu and Zn were predominantly associated with indicators of military activity, with ammunition type and helicopter crash site category as the dominant predictors, indicating localized contamination from military activity; Pb and Ni were primarily soil-controlled, governed by pH, organic carbon and soil type rather than disturbance intensity. Random Forest resolves the Cu/Zn point-source signal (spatially random residuals) while residual structure remains for Pb and Ni. These findings demonstrate that non-linear, multi-factor modelling approaches are essential for disentangling war-related geochemical signals from background variability, and that contamination patterns in active conflict zones are markedly more heterogeneous than those reported from military training grounds. The results have important implications for post-war land assessment and soil reclamation strategies in Ukraine.