Using regression model to identify and evaluate heavy metal pollution sources in an open pit coal mine area, Eastern Junggar, China

Abstract

This study aims at identifying multi-source heavy metal pollution from natural and anthropogenic sources using a regression model, principal component analysis, and five different indices (geo-accumulation index (I geo), the modified degree of contamination, pollution load index (PLI), enrichment factor, and ecological risk factor. Results revealed that: (1) although the average concentrations of soil heavy metals (Cu, Cr, Pb, Hg, As, Zn) were generally low, Hg, As, and Cr concentrations exceeded national standard values by approximately 0.91, 1.84, and 0.91 times with maximum concentrations up to 0.41, 78.6, and 175.2 μg/g, respectively; (2) PLI results showed that the industrial park and Wucaiwan open coal mining area were the most polluted (PLI of 1.98, 1.71). The potential ecological hazards index indicated that the E i r of three heavy metals (Cu, Hg, As) in the soil were relatively high, presenting potential ecological risk factors of 74.89, 16.71, 4.15%, respectively; (3) stepwise regression model and principal component analysis suggest that Cu and Zn were primarily effected by the natural geological condition and atmospheric dust fall. Cr, Hg, Pb are mainly derived from anthropogenic sources, particularly coal mining activities and industrial sources. Results of this research have some significant implications for heavy metal pollution prevention and the sustainable development of the economy and ecology of arid regions in China.