Zong-shuang Wang 1,2,3, Ting Wu4, Guo-liang Shi 5, Xiao Fu1, Ying-ze Tian5, Yin-chang Feng5, Xue-fang Wu2, Gang Wu1, Zhi-peng Bai2,5, Wen-jie Zhang2

  • 1 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
  • 2 Chinese Research Academy of Environmental Sciences, Beijing 100012, China
  • 3 Graduate University of Chinese Academy of Sciences, Beijing 100049, China
  • 4 Publicity and Education Center, Tianjin Environmental Protection Bureau, Tianjin, China
  • 5 State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China

Received: December 29, 2016
Revised: December 29, 2016
Accepted: December 29, 2016
Download Citation: ||https://doi.org/10.4209/aaqr.2011.04.0045  

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Cite this article:
Wang, Z.s., Wu, T., Shi, G.l., Fu, X., Tian, Y.z., Feng, Y.c., Wu, X.f., Wu, G., Bai, Z.p. and Zhang, W.j. (2012). Potential Source Analysis for PM10 and PM2.5 in Autumn in a Northern City in China. Aerosol Air Qual. Res. 12: 39-48. https://doi.org/10.4209/aaqr.2011.04.0045



In this study, PM10 and PM2.5 samples were obtained in a northern city in China. The 12-h averaged concentrations of particulate matter and species were analyzed. A PCA-MLR model was applied to identify the potential source categories and to estimate the source contributions for the PM10 and PM2.5 datasets. Five factors were extracted for the PM10 samples, and their percentage contributions were estimated as follows: crustal dust—39.87%; vehicle exhaust—30.16%; secondary sulfate and nitrate—14.42%; metal emission source—6.77%; and residual oil combustion source—1.82%. Four factors were resolved for the PM2.5 dataset, and their contributions were obtained: crustal dust—35.81%; vehicle exhaust—22.67%; secondary sulfate and nitrate—32.35%; and metal emission and residual oil combustion sources—4.57%. In addition, a Potential Source Contribution Function (PSCF) was used to investigate the possible locations of the major sources. The PSCF results showed that for each source category, PM10 and PM2.5 had similar potential source areas.

Keywords: Sources; Potential source contribution function; PM10; PM2.5

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