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Risk assessment of heavy metals in soil of Tongnan District (Southwest China): evidence from multiple indices with high-spatial-resolution sampling

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Abstract

Assessment of heavy metal (HM) pollution in soil is critical for human health, ecological remediation, and soil conservation. In this study, statistical analyses and geochemical approaches such as enrichment factor (EF), the index of geoaccumulation (Igeo), and potential ecological risk index (RI) were used for characterization and risk assessment of soil HMs through a high-spatial-resolution 385 samples from Tongnan District, an important agricultural practice area in Chongqing Municipality in Southwest China. Igeo and EF indicated that Hg and Cd could be considered as low and moderate polluted, respectively, and others HMs were not a major concern. Comprehensive ecological risk information further demonstrated that the HMs have caused a moderate risk. Principal component analysis (PCA) extracted two principal components (PCs) with eigenvalue >1 explaining about 66.1% of the total variance in the HM data sets, demonstrating major source of anthropogenic activity, phosphate fertilizers, vehicle, and pesticides. These multi-index methods have the capacity of HM assessment in soil, which are useful for soil conservation and ecological remediation.

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Acknowledgements

This study was financially supported by “the Hundred-Talent Program” of the Chinese Academy of Sciences (granted to Dr. SY Li), the National Natural Science Foundation of China (NSFC 31670473), and West Light Foundation of the Chinese Academy of Sciences (granted to Dr. Yongmei Ma). Data are from the National Soil Investigation Program. Special thanks are given to Editor Prof. Zhihong Xu and two anonymous reviewers for their constructive suggestions for improving the manuscript.

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Correspondence to Siyue Li.

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Responsible editor: Zhihong Xu

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Ma, Y., Jia, Z. & Li, S. Risk assessment of heavy metals in soil of Tongnan District (Southwest China): evidence from multiple indices with high-spatial-resolution sampling. Environ Sci Pollut Res 24, 20282–20290 (2017). https://doi.org/10.1007/s11356-017-9707-9

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