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Similarities and Differences in Mass Spectral Signatures of Primary Organic Aerosol From Biomass Burning, Cooking, Coal Combustion, and Vehicle Exhaust

  • Jie Tian
  • , Qiyuan Wang
  • , Li Li
  • , Weikang Ran
  • , Yong Zhang
  • , Yaqing Zhou
  • , Huikun Liu
  • , Miao Gao
  • , Yichen Wang
  • , Haiyan Ni
  • , Chong Wei
  • , Nan Ma
  • , Yongming Han
  • , Junji Cao
  • CAS - Institute of Earth Environment
  • Guanzhong Plain Ecological Environment Change and Comprehensive Treatment National Observation and Research Station
  • Jinan University
  • Xi'an Jiaotong University
  • Xi'an University of Architecture and Technology
  • CAS - Shanghai Advanced Research Institute
  • CAS - Institute of Atmospheric Physics

科研成果: 期刊稿件文章同行评审

摘要

A comprehensive understanding of mass spectra of primary organic aerosols (OA) from anthropogenic sources is crucial for source apportionment of ambient OA measured by aerosol mass spectrometer. However, localized mass spectral data in China are still scarce. This study conducted a series of source emission experiments using a quadrupole aerosol chemical speciation monitor (ACSM) to characterize mass spectra of OA from crop residue burning (CRBOA), wood burning (WBOA), cooking (COA), coal combustion (CCOA), and vehicle exhaust (VEOA). The extracted marker fragments and diagnostic ratios from mass spectra were investigated, revealing their representativeness and effectiveness in distinguishing OA sources. Distinct differences were generally found between OA mass spectra from various anthropogenic sources. Additionally, subtypes of COA and CCOA mass spectra showed notable differences, allowing for finer OA source apportionment. Cluster analysis of existing 57 OA mass spectra further demonstrated their efficacy in identifying OA sources, and revealed certain sources were grouped into distinct clusters. This highlights the importance of using localized mass spectra for refined OA source classification. Our mass spectral characterization of primary OA from anthropogenic sources offers a practical data set for ACSM-based source apportionment, which can support more accurate identification of ambient OA sources in China.

源语言英语
期刊论文编号e2025JD044129
期刊Journal of Geophysical Research: Atmospheres
130
19
DOI
出版状态已出版 - 16 10月 2025
已对外发布

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