Collage of Geography and Environmental Sciences, Northwest Normal University, Lanzhou 730070, China.
Collage of Geography and Environmental Sciences, Northwest Normal University, Lanzhou 730070, China; Lanzhou Center for Disease Control and Prevention, Lanzhou 730030, China.
Ecotoxicol Environ Saf. 2021 Jun 1;215:112150. doi: 10.1016/j.ecoenv.2021.112150. Epub 2021 Mar 21.
To estimate spatial distribution, source analysis and uncertainty of heavy metals (Pb, Cd, Cr, Hg, As, Cu, Zn, and Ni) based on geographic information system (GIS), positive matrix factorization model (PMF) and bootstrap (BS) using 382 soil samples collected from cultivated soils in Lanzhou. The mean contents of Cd, Hg, Cu, Zn and Ni were high as 1.7,1.7, 2.1, 1.5 and 1.3 times local background values, mean contents of Pb, Cr and As were lower than local background values. However, the mean contents of eight heavy metals were lower environmental quality risk control standard for soil contamination of agricultural soil. Proportions of four sources were identified: Cr was predominantly contributed by natural sources (29.14%), Cu, Zn and Ni was primarily from industrial sources (25.26%), Hg and As were mainly of agricultural sources (27.49%), Pb and Cd mainly came from traffic source and smelting-related activities (18.09%). Uncertainties analysis contained three aspects: bootstrap runs, factor contributions in the PMF solution, and coefficient of variation (CV) values. By combining the four pollution source factors with bootstrap runs, the accuracy of the four pollution source factors were reliable based on PMF model. The median values in the BS runs was considered the most true factor contribution, and the 5th-95th quartile interval represents the variability of each factor, Factor 4 (traffic source) R was 0.70 and lower variability. The highest CV value usually means a significantly deviation degree. In this study, the CV values of Cr in Factor 1, Cu, Zn, and Ni in Factor 2, Hg, and As in Factor 3, Pb, and Cd in Factor 4 were lower, indicates a lower deviation degree. and with the lowest content among heavy metals usually was also with the greatest uncertainties. In this study improves understanding of the reduction of heavy metal pollution in cultivated soil, and also serves as reference for pollution source apportionment in other regions.
基于地理信息系统（GIS）、正矩阵因子分解模型（PMF）和自举（BS），利用 382 个取自兰州耕地土壤的样本，对重金属（Pb、Cd、Cr、Hg、As、Cu、Zn 和 Ni）的空间分布、来源分析和不确定性进行了评估。Cd、Hg、Cu、Zn 和 Ni 的平均含量分别为当地背景值的 1.7、1.7、2.1、1.5 和 1.3 倍，Pb、Cr 和 As 的平均含量低于当地背景值。然而，八种重金属的平均含量均低于农业土壤环境污染质量风险控制标准。鉴定出了四个来源的比例：Cr 主要来自自然源（29.14%），Cu、Zn 和 Ni 主要来自工业源（25.26%），Hg 和 As 主要来自农业源（27.49%），Pb 和 Cd 主要来自交通源和与冶炼相关的活动（18.09%）。不确定性分析包括三个方面：自举运行、PMF 解决方案中的因子贡献以及变异系数（CV）值。通过将四个污染源因子与自举运行相结合，基于 PMF 模型，四个污染源因子的准确性是可靠的。BS 运行中的中位数被认为是最真实的因子贡献，第 5 个四分位至第 95 个四分位区间表示每个因子的变异性，因子 4（交通源）的 R 值为 0.70，变异性较低。CV 值越高通常意味着偏离程度越大。在本研究中，因子 1 中 Cr、因子 2 中 Cu、Zn 和 Ni、因子 3 中 Hg 和 As、因子 4 中 Pb 和 Cd 的 CV 值较低，表明偏离程度较低。并且重金属中含量最低的通常也具有最大的不确定性。本研究提高了对耕地土壤中重金属污染减少的认识，也为其他地区的污染源分配提供了参考。
