The urban soils suffered seriously from heavy metal pollutions with rapid industrialization and urbanization in China. In this study, 54 urban soil samples were collected from Changsha, a mine-impacted city located in Southern China. The concentrations of heavy metals (As, Cd, Co, Cu, Mn, Ni, Pb, and Zn) were determined by ICP-MS. The pollution sources of heavy metals were discriminated and identified by the combination of multivariate statistical and geostatistical methods. Four main sources were identified according to the results of hierarchical cluster analysis (HCA), principal component analysis (PCA), and spatial distribution patterns. Co and Mn were primarily derived from soil parent material. Cu, Pb, and Zn with significant positive relationships were associated with mining activities and traffic emissions. Cd and Ni might be affected by commercial activities and industrial discharges. As isolated into a single group was considered to have correlation with coal combustion and waste incineration. Risk assessment of heavy metals in urban soils indicated an overall moderate potential ecological risk in the urban region of Changsha.
Heavy metal contamination attracted a wide spread attention due to their strong toxicity and persistence. The Ganxi River, located in Chenzhou City, Southern China, has been severely polluted by lead/zinc ore mining activities. This work investigated the heavy metal pollution in agricultural soils around the Ganxi River. The total concentrations of heavy metals were determined by inductively coupled plasma-mass spectrometry. The potential risk associated with the heavy metals in soil was assessed by Nemerow comprehensive index and potential ecological risk index. In both methods, the study area was rated as very high risk. Multivariate statistical methods including Pearson's correlation analysis, hierarchical cluster analysis, and principal component analysis were employed to evaluate the relationships between heavy metals, as well as the correlation between heavy metals and pH, to identify the metal sources. Three distinct clusters have been observed by hierarchical cluster analysis. In principal component analysis, a total of two components were extracted to explain over 90% of the total variance, both of which were associated with anthropogenic sources.
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