Assessing spatial variability and source identification of heavy metals in agricultural soils: A geostatistical and multivariate analysis of coastal eastern Zhejiang, China

Abstract

Heavy metal pollution in coastal agricultural soils poses significant threats to food security, human health, and marine ecosystems. Effective prevention and control require systematic analysis of their spatial distribution and sources. This study integrated geostatistics, principal component analysis (PCA), positive matrix factorization (PMF), and finite mixture modeling (FMM) to comprehensively analyze the spatial variability and sources of five heavy metals (Cr, Pb, Cd, Hg, As) across 877 sampling sites in the coastal area of eastern Zhejiang. The results indicate that overall soil quality is good, though enrichment occurs at some sites due to anthropogenic activities. Pollution displays a spatial pattern of lower levels in the south and higher levels in the north. Pb is widely distributed, while Cd, Hg, and As are concentrated in agricultural plain areas. PMF-based source apportionment revealed that mobile sources (traffic) contributed the most (52.5%), followed by industrial sources (30.4%) and agricultural sources (17.1%). The consistency of multi-model results validated the reliability of source identification. By implementing precise management strategies based on pollution source contributions, it is expected to effectively curb the further deterioration of heavy metal pollution in agricultural soils in Zhejiang Province, gradually improve soil environmental quality, and ensure the safety of agricultural products and the sustainable development of agriculture.