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Application of a Combined Model to Study the Source Apportionment of PM10 in Taiyuan, China

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Volume: 10 | Issue: 2 | Pages: 177-184
DOI: 10.4209/aaqr.2009.09.0058
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Fang Zeng1, Guo-Liang Shi1, Xiang Li1,2, Yin-Chang Feng 1, Xiao-Hui Bi1, Jian-Hui Wu1, Yong-Hua Xue1

  • 1 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
  • 2 Department of Computer Science, University of Georgia, Athens, GA, USA

Abstract

Twenty four-hour averaged concentrations of ambient PM10 were collected in Taiyuan, China from April 2001 to January 2002. A sum of 14 chemical species in PM10 was analyzed and a combined receptor model (PCA/MLR-CMB) was applied to this speciation data to determine the contributions of major source categories. On stage 1, two factors were extracted by Principle Component Analysis/Multiple Linear Regression (PCA/MLR), while some unknown sources were excluded from the model as un-extracted factors. Each factor might contain more than one actual source categories and was identified as extracted complex source (EC-source). The actual source categories contained in each EC-source were investigated according to the factor loadings and emission inventory. On stage 2, the two EC-sources were separately used as new receptors, and their corresponding actual sources were apportioned by Chemical Mass Balance (CMB). Although near colinearity existing in some source profiles, a total of eight sources were still well estimated: resuspended dust (about 26%), coal combustion (about 18%), cement dust (about 5%), steel manufacture (about 12%), soil dust (about 7%), vehicle exhaust (about 13%), secondary sulfate (about 12%) and secondary nitrate (about 4%). The combined model resolved 97% of PM10 mass concentrations and the evaluation analysis showed the results obtained by the combined model were reasonable.

Keywords

Receptor model PCA/MLR-CMB PM10 Source appointment Coefficient of divergence


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