Preprints
https://doi.org/10.5194/acp-2021-1007
https://doi.org/10.5194/acp-2021-1007
 
08 Feb 2022
08 Feb 2022
Status: this preprint was under review for the journal ACP but the revision was not accepted.

PM2.5 Source Apportionment using Organic Marker-based CMB Modeling: Influence of Inorganic Markers and Sensitivity to Source Profiles

Yingze Tian1, Xiaoning Wang1, Peng Zhao1, Zongbo Shi2, and Roy M. Harrison2,3 Yingze Tian et al.
  • 1State 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
  • 2School of Geography Earth and Environmental Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
  • 3Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, PO Box 80203, Jeddah, 21589, Saudi Arabia

Abstract. Chemical mass balance (CMB) is one of the most popular methods to apportion the sources of PM2.5. However, the source apportionment results are dependent on the choices of measured chemical species and the source profiles. Here, we explore the sensitivity of CMB results to source profiles by comparing CMB modeling based on organic markers only (OM-CMB) with a combination of organic and inorganic markers (IOM-CMB), using organic and inorganic markers in PM2.5 samples collected in the Chinese megacity of Chengdu. OM-CMB results show that gasoline vehicles, diesel vehicles, industrial coal combustion, resuspended dust, biomass burning, cooking, vegetation detritus, SOA, sulphate, and nitrate contributed to 4 %, 10 %, 15 %, 12 %, 5 %, 3 %, 4 %, 9 %, 10 %, and 20 %, in comparison to 4 %, 11 %, 15 %, 17 %, 6 %, 2 %, 5 %, 10 %, 7 %, and 18 % from IOM-CMB modelling. The temporal variations of PM2.5 contributions from sulphate, nitrate, SOA, gasoline vehicles, and biomass burning, characterized by unique markers and low collinearity, were in good agreement between the OM-CMB and IOM-CMB results. However, resuspended dust estimates from OM-CMB had a poor correlation with that from IOM-CMB, due to the different tracers used. When replacing the source profile for industrial coal combustion with that for from residential sources, the contributions of resuspended dust and residential coal combustion were overestimated because the residential coal combustion profile contained a higher concentration of OC and organic compounds but lower crustal elements. Different source profiles for gasoline vehicles were also evaluated. Our results confirm the superiority of combining inorganic and organic tracers and using up-to-date locally-relevant source profiles in source apportionment of PM.

Yingze Tian et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-1007', Anonymous Referee #1, 14 Feb 2022
  • RC2: 'Comment on acp-2021-1007', Anonymous Referee #2, 01 Apr 2022
  • RC3: 'Comment on acp-2021-1007', Anonymous Referee #3, 12 Apr 2022
  • AC1: 'Authors response to Comment on acp-2021-1007', Roy M. Harrison, 30 May 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-1007', Anonymous Referee #1, 14 Feb 2022
  • RC2: 'Comment on acp-2021-1007', Anonymous Referee #2, 01 Apr 2022
  • RC3: 'Comment on acp-2021-1007', Anonymous Referee #3, 12 Apr 2022
  • AC1: 'Authors response to Comment on acp-2021-1007', Roy M. Harrison, 30 May 2022

Yingze Tian et al.

Data sets

Organic components in PM2.5 of a Chinese megacity Roy M. Harrison and Yingze Tian https://doi.org/10.25500/edata.bham.00000745

Yingze Tian et al.

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Short summary
Chemical mass balance (CMB) is a widely used method to apportion the sources of PM2.5. We explore the sensitivity of CMB results to input data of organic markers only (OM-CMB) with a combination of organic and inorganic markers (IOM-CMB), as well as using different chemical profiles for sources. Our results indicate the superiority of combining inorganic and organic tracers and using locally-relevant source profiles in source apportionment of PM.
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