Heavy metal contamination of agricultural soils affected by mining activities around the Ganxi River in Chenzhou, Southern China

• Published in: Environmental monitoring and assessment Vol. 187; no. 12; pp. 731 - 9
• Main Authors: Ma, Li, Sun, Jing, Yang, Zhaoguang, Wang, Lin
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2015; Springer Nature B.V
• Subjects: Acid mine drainage; Agricultural land; Agricultural pollution; agricultural soils; Agriculture; anthropogenic activities; Anthropogenic factors; Atmospheric Protection/Air Quality Control/Air Pollution; atomic absorption spectrometry; China; Cities; Cluster analysis; Correlation analysis; Crops; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental Monitoring; Environmental Pollution - analysis; Environmental Pollution - statistics & numerical data; Heavy metals; lead; Mass spectrometry; Metal concentrations; Metals; Metals, Heavy - analysis; Minerals; Mining; Monitoring/Environmental Analysis; multivariate analysis; Pollutants; Pollution; Polyethylene; principal component analysis; Principal components analysis; risk; Rivers; Rivers - chemistry; Sediments; Soil - chemistry; Soil Pollutants - analysis; Statistical methods; Toxicity; variance; Water analysis; Watershed management; Watersheds; Zinc; Zinc - analysis; China; China, People's Rep; Agricultural soil; Multivariate analysis; Risk assessment; Heavy metals
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-015-4966-8

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.