首页|Integrating principal component analysis and U-statistics for mapping polluted areas in mining districts
Integrating principal component analysis and U-statistics for mapping polluted areas in mining districts
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NSTL
Elsevier
? 2021 Elsevier B.V.Detecting metal contaminations and mapping the areas affected by pollutants surrounding the mining activities are essential issues in environmental geochemistry. Toxic metals can be broadly distributed around the mines owing to the effects of dust, water, and diverse anthropogenic activities. In this study, the effects of metal pollution from mining activities in Irankuh Pb-Zn mine, located in central Iran, on the surrounding environment have been investigated. Toxic metals, especially lead and zinc, dispersed in the agricultural area in the south of the Irankuh mine have been deemed environmental stressors. This study introduces a new combined methodology based on principal component analysis (PCA) and U-statistics modeling to recognize the zones afflicted by toxic metal pollution. The stepwise principal component analysis (SPCA) is a multi-stage dimension reduction technique applied to extract significant pollution sources of toxic metals around agricultural areas. After five stages of SPCA, the Pollution Principal Factor (PPF) was determined based on the rotated component matrix, and the toxic metals related to the pollution area were identified. For mapping the multivariate metal contamination, the U-statistics method was applied to the PPF for identification of toxic metal anomalies. The concentration-area fractal method was also utilized for separating and mapping different populations of Pb, Zn, As, Mn, Ba, and Cd. To apply this method, ordinary Kriging was used to grid and interpolate raw data. The U-values of PPF in the proposed method were delineated and compared to the results of the fractal method. The proposed approach displays more accurate results for pollution mapping. The methodology adopted here can be applied to other case studies for the source apportionment of toxic metals.
Fractal methodPollution mappingPollution principal factorToxic metalsU-spatial statistics
NSTL
Elsevier
