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Article

Apportionment and Spatial Pattern Analysis of Soil Heavy Metal Pollution Sources Related to Industries of Concern in a County in Southwestern China

1
Center for Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(12), 7421; https://doi.org/10.3390/ijerph19127421
Submission received: 21 March 2022 / Revised: 5 June 2022 / Accepted: 9 June 2022 / Published: 16 June 2022
(This article belongs to the Special Issue Soil Degradation, Soil Pollution and Ecological Restoration)

Abstract

Soil heavy metal pollution is frequent around areas with a high concentration of heavy industry enterprises. The integration of geostatistical and chemometric methods has been used to identify sources and the spatial patterns of soil heavy metals. Taking a county in southwestern China as an example, two subregions were analyzed. Subregion R1 mainly contained nonferrous mining, and subregion R2 was affected by smelting. Two factors (R1F1 and R1F2) associated with industry in R1 were extracted through positive matrix factorization (PMF) to obtain contributions to the soil As (64.62%), Cd (77.77%), Cu (53.10%), Pb (75.76%), Zn (59.59%), and Sb (32.66%); two factors (R2F1 and R2F2) also related to industry in R2 were extracted to obtain contributions to the As (53.35%), Cd (32.99%), Cu (53.10%), Pb (56.08%), Zn (67.61%), and Sb (42.79%). Combined with PMF results, cokriging (CK) was applied, and the z-score and root-mean square error were reduced by 11.04% on average due to the homology of heavy metals. Furthermore, a prevention distance of approximately 1800 m for the industries of concern was proposed based on locally weighted regression (LWR). It is concluded that it is necessary to define subregions for apportionment in area with different industries, and CK and LWR analyses could be used to analyze prevention distance.
Keywords: heavy metals; industries of concern; source apportionment; spatial patterns; prevention distance heavy metals; industries of concern; source apportionment; spatial patterns; prevention distance

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MDPI and ACS Style

Chen, X.; Lei, M.; Zhang, S.; Zhang, D.; Guo, G.; Zhao, X. Apportionment and Spatial Pattern Analysis of Soil Heavy Metal Pollution Sources Related to Industries of Concern in a County in Southwestern China. Int. J. Environ. Res. Public Health 2022, 19, 7421. https://doi.org/10.3390/ijerph19127421

AMA Style

Chen X, Lei M, Zhang S, Zhang D, Guo G, Zhao X. Apportionment and Spatial Pattern Analysis of Soil Heavy Metal Pollution Sources Related to Industries of Concern in a County in Southwestern China. International Journal of Environmental Research and Public Health. 2022; 19(12):7421. https://doi.org/10.3390/ijerph19127421

Chicago/Turabian Style

Chen, Xiaohui, Mei Lei, Shiwen Zhang, Degang Zhang, Guanghui Guo, and Xiaofeng Zhao. 2022. "Apportionment and Spatial Pattern Analysis of Soil Heavy Metal Pollution Sources Related to Industries of Concern in a County in Southwestern China" International Journal of Environmental Research and Public Health 19, no. 12: 7421. https://doi.org/10.3390/ijerph19127421

APA Style

Chen, X., Lei, M., Zhang, S., Zhang, D., Guo, G., & Zhao, X. (2022). Apportionment and Spatial Pattern Analysis of Soil Heavy Metal Pollution Sources Related to Industries of Concern in a County in Southwestern China. International Journal of Environmental Research and Public Health, 19(12), 7421. https://doi.org/10.3390/ijerph19127421

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