基于APCS-MLR和PMF模型的煤矸山周边耕地土壤重金属污染特征及源解析

以重庆市南川区某煤矸山周边耕地土壤为研究对象，运用内梅罗指数法和地累积指数法分析土壤重金属污染水平和分布特征，并采用绝对因子得分-多元线性回归(APCS-MLR)和正定矩阵因子分解(PMF)模型，探析研究区土壤重金属来源及其贡献率.结果表明，下游区土壤中8项重金属均值含量均高于上游区，其中Cu、 Ni和Zn含量显著高于上游区(P<0.05).内梅罗综合污染指数表现为：下游区(1.22)>上游区(0.95),重金属污染程度由大到小表现为：Cd>Cu>Hg、 As、 Pb、 Cr、 Ni和Zn.地累积指数由大到小表现为：Cd>As>Cu=Hg>Ni>Zn=Cr>Pb.源解析表明，研究区土壤中Cu、 Ni和Zn主要受煤矸山堆存影响，APCS-MLR模型的贡献率分别为49.8%、 94.5%和73.2%,PMF模型的贡献率分别为62.8%、 62.2%和63.1%; Cd、 Hg和As主要受农业和交通混合源影响，APCS-MLR模型的贡献率分别为49.8%、 94.5%和73.2%,PMF模型的贡献率分别为62.8%、 62.2%和63.1...

Pollution Characteristics and Source Apportionment of Heavy Metals in Farmland Soils Around the Gangue Heap of Coal Mine Based on APCS-MLR and PMF Receptor Model

To analyze the pollution characteristics and source apportionment of heavy metal pollution in soil of farmland surrounding the Gangue Heap of Coal Mine in Nanchuan, Chongqing, the Nemerow pollution index and Muller index were used. Meanwhile, to investigate the sources and contribution rate of heavy metals in the soil, absolute principal component score-multiple linear regression receptor modeling (APCS-MLR) and positive matrix factorization (PMF) were employed. The results showed higher amounts of Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn in the downstream area than those in the upstream area, and only Cu, Ni, and Zn showed significantly higher amounts in the downstream area than those in upstream area (P<0.05). The comprehensive Nemerow pollution index was as follows:downstream area (1.22)>upstream area (0.95), and the degree of heavy metal pollution was:Cd>Cu>Hg, As, Pb, Cr, Ni, and Zn. The Muller pollution index showed:Cd>As>Cu=Hg>Ni>Zn=Cr>Pb. The pollution source analysis showed that Cu, Ni, and Zn were mainly affected by mining activities such as long-term accumulation of the gangue heap of coal mine, with the contribution rates of APCS-MLR being 49.8%, 94.5%, and 73.2%, respectively. Additionally, PMF contribution rates were 62.8%, 62.2%, and 63.1%, respectively. Cd, Hg, and As were mainly affected by agricultural activities and transportation activities, with APCS-MLR contribution rates of 49.8%, 94.5%, and 73.2% and PMF contribution rates of 62.8%, 62.2%, and 63.1%, respectively. Further, Pb and Cr were mainly affected by natural factors, with APCS-MLR contribution rates of 66.4% and 94.7% and PMF contribution rates of 42.7% and 47.7%, respectively. The results of source analysis were basically consistent between the APCS-MLR and PMF receptor models.