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| Investigating factors influencing the PMF model: A case study of source apportionment of heavy metals in farmland soils near a lead-zinc ore |
| Received:April 13, 2018 Revised:July 20, 2018 |
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| KeyWord:heavy metals;source apportionment;receptor model;crustal elements;anomalous data |
| Author Name | Affiliation | E-mail | | WEI Ying-hui | Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
University of Chinese Academy of Sciences, Beijing 100049, China | | | LI Guo-chen | Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China | ligc@iae.ac.cn | | WANG Yan-hong | Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
Liaoning Province Engineering Research Center for Agro-products Environment and Quality Control Technology, Shenyang 110016, China | wangyh@iae.ac.cn | | ZHANG Qi | Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
University of Chinese Academy of Sciences, Beijing 100049, China | | | LI Bo | Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China | | | WANG Shi-cheng | Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
Liaoning Province Engineering Research Center for Agro-products Environment and Quality Control Technology, Shenyang 110016, China | | | CUI Jie-hua | Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China | | | ZHANG Hong | Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China | | | ZHOU Qiang | Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China | |
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| Abstract: |
| Positive matrix factorization (PMF)is widely used to apportion the sources of heavy metals in soils even when the sources of the pollution are unknown. However, PMF is sensitive to the data of receptor samples; thus, the results may vary significantly. To evaluate the availability of PMF in classifying heavy metal sources in soil, this study investigated two factors:The composition of elements and anomalous data-using soil samples collected from Shuikoushan lead-zinc ore farmland in Hunan Province. By changing the composition of the elements (whether crustal elements were added or not)and the composition of samples (whether the anomalous data were removed or not), four datasets were produced and used to compare the differences in the results. When two samples were removed from the dataset based on the detection of anomalies, the source profiles did not change but the contribution rates of each source to each element varied significantly. After six species of crustal elements of the samples were added to the statistical analysis, both the source profiles and contribution rates changed. The anomalous data had a much smaller influence, and the results of PMF were more stable and easy to explain when the crustal elements were included. Therefore, this study suggested that crustal elements, in addition to the eight species of heavy metals, should be determined for soil samples. Based on the documents and investigations on site, five sources were identified:Pb, Zn, Cd, and Sb came mainly from industrial activities, such as lead-zinc ore beneficiation and smelting (contribution rate of 26.81%); As and Hg were mainly from agricultural activities, such as sewage irrigation and chemical fertilizer application (14.68%); Cr, Ni, Co, and Mo were found mainly in soil parent material (24.41%); Mn and Fe came mainly from iron ore mining and transportation (16.39%); and Al and Ca were mainly from the weathering of ore (17.72%). |
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