期刊
ENVIRONMENTAL SCIENCE & TECHNOLOGY
卷 55, 期 8, 页码 4494-4503出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.est.0c08604
关键词
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资金
- JSPS KAKENHI [19H04253]
- National Natural Science Foundation of China [41625014]
- China Scholarship Council
- Grants-in-Aid for Scientific Research [19H04253] Funding Source: KAKEN
Understanding the sources and characteristics of atmospheric organic aerosols in Beijing is crucial for assessing their impact and implementing effective control strategies. This study used a combination of chemical fractionation and statistical grouping approaches to determine the sources of different organic aerosol fractions, providing valuable insights for future source apportionment studies. Additionally, the study highlighted the association between mass spectral-based PMF factors and specific molecular substructures.
Understanding how the sources of an atmospheric organic aerosol (OA) govern its burden is crucial for assessing its impact on the environment and adopting proper control strategies. In this study, the sources of OA over Beijing were assessed year-around based on the combination of two separation approaches for OA, one from chemical fractionation into the high-polarity fraction of water-soluble organic matter (HP-WSOM), humic-like substances (HULIS), and water-insoluble organic matter (WISOM), and the other from statistical grouping using positive matrix factorization (PMF) of high-resolution aerosol mass spectra. Among the three OA fractions, HP-WSOM has the highest O/C ratio (1.36), followed by HULIS (0.56) and WISOM (0.17). The major sources of different OA fractions were distinct: HP-WSOM was dominated by more oxidized oxygenated OA (96%); HULIS by cooking-like OA (40%), less oxidized oxygenated OA (27%), and biomass burning OA (21%); and WISOM by fossil fuel OA (77%). In addition, our results provide evidence that mass spectral-based PMF factors are associated with specific substructures in molecules. These structures are further discussed in the context of the FT-IR results. This study presents an overall relationship of OA groups monitored by chemical and statistical approaches for the first time, providing insights for future source apportionment studies.
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