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摘要
This study took a typical lead-zinc mining area in Yangshuo county, Guangxi as the research object, and analyzed the contents of 10 heavy metal elements (Cr, Mn, Ni, Cu, Zn, As, Cd, Sb, Hg, and Pb) in the surface soil. The absolute principal component scores-multiple linear regression (APCS-MLR) model and positive definite matrix factorization (PMF) model were comprehensively used to identify and quantitatively analyze pollution sources and their contribution. The results showed that the means content of Pb, Zn, Hg, Cd, Mn, and Cu were higher than their corresponding local background values by approximately 3.29~13.08 times, Cr, Ni, As, and Sb also exceeded the background value in some areas, indicated that heavy metal pollution existed in the study area. The 10 heavy metals were mainly distributed in strips and spots at various depths, and the high content of Mn, Cu, Zn, As, Cd, Sb, and Pb were mainly distributed in the left bank of the Side river and southeast of the study area. The high contents of Cr, Ni, and Hg were mainly distributed in strip highlands of the central and western of study area. The source apportionment results of the APCS-MLR model and the PMF model were relatively consistent in terms of pollution sources. The metal pollution sources in the surface soil were jointly affected by mining activities, natural sources (such as, soil parent materials, rainfall erosion, etc), and mixed source of mining activities and agricultural activities. There were differences in the contribution rate of the APCS- MLR model and the PMF model. For the APCS-MLR model, the contribution rate of pollution sources in the order of mixed sources of mining activities and agricultural activities (30.95%), mining activities (22.39%), natural sources (15.79%), and unidentified sources (8.35%). The PMF model extracted the contribution of pollution sources in the order of mining activities (35.16%), tailings and waste (28.21%), mixed sources of mining activities and agricultural activities (20.89%), and natural sources (15.74%). The reasons for the difference in the pollution sources apportionment of the APCS-MLR model and PMF model may be attributed to different factor extraction methods, orthogonality constraint of the APCS-MLR model, and the uncertainty consideration and the non-negative constraint of the PMF model.
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