Preprints
https://doi.org/10.5194/acp-2020-1017
https://doi.org/10.5194/acp-2020-1017

  07 Apr 2021

07 Apr 2021

Review status: this preprint is currently under review for the journal ACP.

Insight into PM2.5 Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Deepchandra Srivastava1, Jingsha Xu1, Tuan V. Vu1,a, Di Liu1,2, Linjie Li2, Pingqing Fu3, Siqi Hou1, Zongbo Shi1, and Roy M. Harrison1,b Deepchandra Srivastava et al.
  • 1School of Geography Earth and Environmental Science, University of Birmingham, Birmingham, B15 2TT United Kingdom
  • 2Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing, 100029, China
  • 3Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China
  • anow at: School of Public Health, Imperial College London, London United Kingdom
  • balso at: Department of Environmental Sciences/Centre of Excellence in Environmental Studies, King Abdulaziz University, PO Box 80203, Jeddah, 21589, Saudi Arabia

Abstract. This study presents the source apportionment of PM2.5 performed by PMF on data collected at an urban (Institute of Atmospheric Physics – IAP) and a rural site (Pinggu-PG) in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9th November to 11th December 2016 and 22nd May to 24th June 2017. The PMF included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted to be traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (22–24 %), biomass burning (30–36 %), and coal combustion (20–21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %) and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry (AMS)) and showed a good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2 = 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS, and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.

Deepchandra Srivastava et al.

Status: open (until 02 Jun 2021)

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Deepchandra Srivastava et al.

Deepchandra Srivastava et al.

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Short summary
This study presents the source apportionment of PM2.5 performed by PMF at an urban and a rural site in Beijing. These factors are interpreted to be traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion and secondary inorganics. The PMF failed to resolve some sources identified by CMB and AMS, and appears to overestimate the dust sources. A comparison with earlier PMF studies from the Beijing area highlights inconsistent findings using this method.
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