This work presents the source apportionment of fine aerosols in an urban site of Beijing by using a CMB model. Seven primary sources (industrial/residential coal burning, biomass burning, gasoline/diesel vehicles, cooking and vegetative detritus) explained an average of 75.7 % and 56.1 % of fine OC in winter and summer, respectively. CMB was found to resolve more primary OA sources than AMS-PMF but the latter apportioned more secondary OA sources.
This work presents the source apportionment of fine aerosols in an urban site of Beijing by...
1School of Geography Earth and Environmental Science, University of Birmingham, Birmingham, B15 2TT, UK
2School of Geology and Mineral Resources, China University of Geosciences Xueyuan Road 29, Beijing, 100083, China
3Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
4Institute of Surface-Earth System Science, Tianjin University, Tianjin, 300072, China
5State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
6State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
anow at: Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
bnow at: Faculty of Life Sciences & Medicine, King's College London, London, WC2R 2LS, UK
cnow at: Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, UMR8212, Gif-sur-Yvette, France
1School of Geography Earth and Environmental Science, University of Birmingham, Birmingham, B15 2TT, UK
2School of Geology and Mineral Resources, China University of Geosciences Xueyuan Road 29, Beijing, 100083, China
3Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
4Institute of Surface-Earth System Science, Tianjin University, Tianjin, 300072, China
5State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
6State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
anow at: Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
bnow at: Faculty of Life Sciences & Medicine, King's College London, London, WC2R 2LS, UK
cnow at: Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, UMR8212, Gif-sur-Yvette, France
Received: 30 Sep 2020 – Accepted for review: 02 Dec 2020 – Discussion started: 03 Dec 2020
Abstract. Fine particles were sampled from 9th November to 11th December 2016 and 22nd May to 24th June 2017 as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-China) field campaigns in urban Beijing, China. Inorganic ions, trace elements, OC, EC, and organic compounds including biomarkers, hopanes, PAHs, n-alkanes and fatty acids, were determined for source apportionment in this study. Carbonaceous components contributed on average 47.2 % and 35.2 % of total reconstructed PM2.5 during the winter and summer campaigns, respectively. Secondary inorganic ions (sulfate, nitrate, ammonium; SNA) accounted for 35.0 % and 45.2 % of total PM2.5 in winter and summer. Other components including inorganic ions (K+, Na+, Cl−), geological minerals, and trace metals only contributed 13.2 % and 12.4 % of PM2.5 during the winter and summer campaigns. Fine OC was explained by seven primary sources (industrial/residential coal burning, biomass burning, gasoline/diesel vehicles, cooking and vegetative detritus) based on a chemical mass balance (CMB) receptor model. It explained an average of 75.7 % and 56.1 % of fine OC in winter and summer, respectively. Other (unexplained) OC was compared with the secondary OC (SOC) estimated by the EC-tracer method, with correlation coefficients (R2) of 0.58 and 0.73, and slopes of 1.16 and 0.80 in winter and summer, respectively. This suggests that the unexplained OC by CMB was mostly associated with SOC. PM2.5 apportioned by CMB showed that the SNA and secondary organic matter were the highest two contributors to PM2.5. After these, coal combustion and biomass burning were also significant sources of PM2.5 in winter. The CMB results were also compared with results from Positive Matrix Factorization (PMF) analysis of co-located Aerosol Mass Spectrometer (AMS) data. The CMB was found to resolve more primary OA sources than AMS-PMF but the latter apportioned more secondary OA sources. The AMS-PMF results for major components, such as coal combustion OC and oxidized OC correlated well with the results from CMB. However, discrepancies and poor agreements were found for other OC sources, such as biomass burning and cooking, some of which were not identified in AMS-PMF factors.
This work presents the source apportionment of fine aerosols in an urban site of Beijing by using a CMB model. Seven primary sources (industrial/residential coal burning, biomass burning, gasoline/diesel vehicles, cooking and vegetative detritus) explained an average of 75.7 % and 56.1 % of fine OC in winter and summer, respectively. CMB was found to resolve more primary OA sources than AMS-PMF but the latter apportioned more secondary OA sources.
This work presents the source apportionment of fine aerosols in an urban site of Beijing by...