Department of Environmental Science and Engineering, School of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
International Joint Research Center of Shaanxi Province for Pollutant Exposure and Eco-Environmental Health, Xi'an, 710062, People's Republic of China.
Arch Environ Contam Toxicol. 2019 Nov;77(4):575-586. doi: 10.1007/s00244-019-00651-8. Epub 2019 Jul 8.
Sixty-two topsoil samples were collected within the third ring road of Xi'an City in Northwest China and analyzed by X-ray fluorescence spectrometry for the concentrations of As, Ba, Co, Cr, Cu, Mn, Ni, Pb, V, and Zn. The pollution levels of trace metals were assessed by pollution index (PI) and Nemerow pollution index (NPI). Meanwhile, the sources of trace metals were apportioned by receptor models, including positive matrix factorization (PMF), UNMIX, and principal component analysis-multiple linear regression (PCA-MLR). The average concentrations of the trace metals analyzed in the urban soil exceeded the corresponding soil element background values of Shaanxi Province, especially for Co, which was 2.38 times higher than the corresponding background value. The mean of PI was 2.38 for Co, reflecting a moderate pollution level, and ranged from 1.07 to 1.72 for other trace metals, presenting slight pollution levels. The NPI of trace metals varied between 1.20 and 3.50 with an average of 2.00, indicating that trace metals presented slight pollution in 62.90% of soil samples, moderate pollution in 30.65% of soil samples, and heavy pollution in 6.45% of soil samples, respectively. Three sources of trace metals apportioned by the three receptor models were mixed nature and anthropogenic source, traffic exhaust, and industrial emissions. The contributions of them were 38.58%, 32.72%, and 28.70% from the PMF, 65.36%, 17.76%, and 16.88% through the UNMIX and 49.16%, 38.90%, and 11.94% via the PCA-MLR, respectively. Meanwhile, the study results suggested that the combined usage of multiple receptor models is a good method to apportion the source compositions and contributions of trace metals in urban soil.
在中国西北部的西安市三环内采集了 62 个表层土壤样本,并用 X 射线荧光光谱法分析了这些样本中 As、Ba、Co、Cr、Cu、Mn、Ni、Pb、V 和 Zn 的浓度。通过污染指数(PI)和内梅罗污染指数(NPI)评估了痕量金属的污染水平。同时,利用受体模型(包括正矩阵因子分解(PMF)、UNMIX 和主成分分析-多元线性回归(PCA-MLR))分配痕量金属的来源。城市土壤中分析的痕量金属的平均浓度超过了陕西省相应的土壤元素背景值,特别是 Co,其值比相应的背景值高 2.38 倍。Co 的 PI 平均值为 2.38,反映出中度污染水平,而其他痕量金属的 PI 平均值在 1.07 到 1.72 之间,呈现出轻微污染水平。痕量金属的 NPI 介于 1.20 和 3.50 之间,平均值为 2.00,表明 62.90%的土壤样本中痕量金属呈现出轻微污染,30.65%的土壤样本中呈现出中度污染,6.45%的土壤样本中呈现出重度污染。三种受体模型分配的三种痕量金属源为混合性质和人为源、交通尾气和工业排放。它们的贡献率分别为 PMF 中的 38.58%、32.72%和 28.70%,UNMIX 中的 65.36%、17.76%和 16.88%,以及 PCA-MLR 中的 49.16%、38.90%和 11.94%。同时,研究结果表明,多受体模型的联合使用是分配城市土壤中痕量金属源组成和贡献的一种好方法。