Inversion Analysis of Heavy Metal Pollution in Soil in Mining Disturbed Areas Based on Remote Sensing Data: A Case Study of Lanping Zn-Pb Mining Area

Xiu-Li Fu, Ting-Ting Song, Yu Chen, Yong-Ming Wei, Qin-Jun Wang

Abstract


Jinding Zn-Pb mining area is a super large multi-metal deposit in Lanping county, Yunnan province, China. The development of mining industry has led to severe soil and water pollution, which made it an ideal place for studying the geological problems of mineral environment. This paper is based on systemically conducted experiments about a test of the contents of plumbum (Pb) and zinc (Zn) in soil samples and spectral response. Spectral characteristics of soil samples were analyzed in detail. The measured data were resampled to ASTER image. Partial least squares models (PLS) were constructed between heavy metal content and measured resampling spectra of the soil. And these models were applied into Zn-Pb mining area in Lanping, generating distribution map of heavy metal content. The results demonstrate that: (1) The contents of the two kinds of heavy metal elements (Zn, Pb) were beyond the soil standard, and there was a significant correlationship between them. (2) The partial least squares model between heavy metal content and fitting ASTER data was of higher accuracy, with R higher than 0.86. (3) The inversion results of ASTER showed that the Pb and Zn content centered the Lanping Zn-Pb mining area, and decreased when spreading around. The results of this study may provide technical support for predicting heavy metal content of large scale soil and for mapping heavy metal pollution in soil by satellite or aerial remote sensing technology.


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