Assessing spatial variability and source identification of heavy metals in agricultural soils: A geostatistical and multivariate analysis of coastal eastern Zhejiang, China
Jingwen Ji, Xiangyuan Wu
TL;DR
This study analyzes heavy metal pollution in agricultural soils in Zhejiang, China, using geostatistics and multivariate analysis to identify sources and spatial patterns.
Contribution
The integration of geostatistical and multivariate methods provides a comprehensive framework for identifying pollution sources and spatial variability in heavy metals.
Findings
Pollution levels are lower in southern regions and higher in northern areas of the study site.
Mobile sources like traffic contribute the most (52.5%) to heavy metal pollution, followed by industrial and agricultural sources.
Cd, Hg, and As are concentrated in agricultural plains, while Pb is more widely distributed.
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…
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Taxonomy
TopicsHeavy metals in environment · Soil Geostatistics and Mapping · Geochemistry and Geologic Mapping