Modeling and interpreting the probability of soil heavy metal contamination in China

Heavy metal contamination in soil poses a serious threat to human health. Traditional geostatistical methods struggle to predict contamination over large regions due to the high cost of soil sampling and their limited capacity to analyze complex contamination mechanisms. This study integrates soil contamination data from literature reviews with contamination probability prediction and mechanism analysis using the maximum entropy (Maxent) model. It proposes a predictive framework for assessing soil heavy metal contamination probability (CP) on a large scale. Using China as a case study, the framework predicts the spatial distribution of CP for heavy metals As, Cd, Cr, Cu, Hg, Pb, and Zn in soil, examines influencing factors and city-level CP averages, and provides targeted countermeasures and recommendations. The results indicate that the concentrations of seven heavy metals are strongly associated with regional development levels and mineral resource abundance. High-CP areas are primarily located in developed regions of China, exhibiting planar or clustered distribution patterns. Anthropogenic factors, including population density, GDP (Gross Domestic Product), industrial and mining enterprise density, road density, and land use, are the primary drivers of soil heavy metal contamination. Additionally, industrial upgrading in developed megacities indirectly increases soil heavy metal CP in their satellite cities.