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Author:

Wu, J. (Wu, J..) | Teng, Y.-G. (Teng, Y.-G..) | Li, J. (Li, J..) | Chen, H.-Y. (Chen, H.-Y..)

Indexed by:

Scopus PKU CSCD

Abstract:

To explore the impacts of variables on receptor model results in source apportionment for soil pollutants, the sampling data set of soil heavy metals in the middle and upper reaches of Le'an River was used as the input data set for the typical receptor model (PMF model). After obtaining the results of basic scenarios by PMF model, local sensitivity analysis method was introduced to study the sensitivity of variables on PMF diagnosis and source identification. The six-factor scenario was the best result for the simulation of PMF base model, Cu, Mo, Na2O, As, Mn and Cd in the soil were the sensitive variables and also the main loading elements in each factor profile (i.e. the typical pollutants of each source). There was a significant difference on the sensitivity for these variables: the total sensitivity of Cu and Mo are much higher than that of the other variables, reaching 12.1 and 8.2 respectively. Therefore, it revealed that the sensitive variables should be the specific pollutants when applying the receptor model for source apportionment, and the data quality was an important factors affecting of the reliability of source apportionment. © 2019, Editorial Board of China Environmental Science. All right reserved.

Keyword:

Input variables; Positive matrix factorization method; Sensitivity analysis; Soil heavy metal; Source apportionment

Author Community:

  • [ 1 ] [Wu, J.]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Teng, Y.-G.]College of Water Sciences, Beijing Normal University, Beijing, 100875, China
  • [ 3 ] [Li, J.]Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
  • [ 4 ] [Chen, H.-Y.]College of Water Sciences, Beijing Normal University, Beijing, 100875, China

Reprint Author's Address:

  • [Li, J.]Chinese Research Academy of Environmental SciencesChina

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Source :

China Environmental Science

ISSN: 1000-6923

Year: 2019

Issue: 7

Volume: 39

Page: 2960-2969

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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