4.7 Article

Source apportionment of heavy metals in agricultural soil based on PMF: A case study in Hexi Corridor, northwest China

Journal

CHEMOSPHERE
Volume 193, Issue -, Pages 189-197

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chemosphere.2017.10.151

Keywords

Heavy metal; Farmland; PMF; Hexi corridor; Pollution sources

Funding

  1. National Natural Science Foundation of China [41671188]

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Hexi Corridor is the most important base of commodity grain and producing area for cash crops. However, the rapid development of agriculture and industry has inevitably led to heavy metal contamination in the soils. Multivariate statistical analysis, GIS-based geostatistical methods and Positive Matrix Factorization (PMF) receptor modeling techniques were used to understand the levels of heavy metals and their source apportionment for agricultural soil in Hexi Corridor. The results showed that the average concentrations of Cr, Cu, Ni, Pb and Zn were lower than the secondary standard of soil environmental quality; however, the concentrations of eight metals (Cr, Cu, Mn, Ni, Pb, Ti, V and Zn) were higher than background values, and their corresponding enrichment factor values were significantly greater than 1. Different degrees of heavy metal pollution occurred in the agricultural soils; specifically, Ni had the most potential for impacting human health. The results from the multivariate statistical analysis and GIS-based geostatistical methods indicated both natural sources (Co and W) and anthropogenic sources (Cr, Cu, Mn, Ni, Pb, Ti, V and Zn). To better identify pollution sources of heavy metals in the agricultural soils, the PMF model was applied. Further source apportionment revealed that enrichments of Pb and Zn were attributed to traffic sources; Cr and Ni were closely related to industrial activities, including mining, smelting, coal combustion, iron and steel production and metal processing; Zn and Cu originated from agricultural activities; and V, Ti and Mn were derived from oil- and coal-related activities. (C) 2017 Elsevier Ltd. All rights reserved.

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