Continental-scale assessment of soil heavy metal contamination is complicated by the contrasting environmental behaviour of individual metals and by spatial autocorrelation, which can lead to overly optimistic model evaluation. This study assessed As, Cu, Hg and Cd contamination in European topsoils using a harmonized 500 m dataset integrating soil properties, hydroclimatic and topographic conditions, socioeconomic indicators and anthropogenic emission sources. A graph-based mixture-of-experts (GMoE) model was used to jointly learn shared and metal-specific contamination patterns, while spatial-block hold-out testing evaluated its transferability to geographically independent regions. The model achieved the highest accuracy and macro-F1 among the evaluated approaches for all four metals, with the clearest improvements for As and Cu. Accuracy reached 76.96% for As and 75.43% for Cu, exceeding multi-gate mixture-of-experts (MMoE) by 4.79 and 2.86 percentage points, respectively. Routing diagnostics indicated broad but differentiated expert participation rather than reliance on a single expert. Spatial attribution further revealed that soil properties and hydroclimatic conditions strongly influenced contamination heterogeneity, whereas anthropogenic-source signals were more evident for As and Cd. These findings show that European topsoil contamination reflects both environmental filtering and source-related inputs, and demonstrate the value of combining spatially transferable multi-metal prediction with driver attribution for large-scale soil contamination assessment.
Niu et al. (Tue,) studied this question.