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Comparative analysis of organic chemical compositions in airborne particulate matter from Ulaanbaatar, Beijing, and Seoul using UPLC-FT-ICR-MS and artificial neural network.
Son, Seungwoo; Park, Moonhee; Jang, Kyoung-Soon; Lee, Ji Yi; Wu, Zhijun; Natsagdorj, Amgalan; Kim, Young Hwan; Kim, Sunghwan.
Afiliação
  • Son S; Department of Chemistry, Kyungpook National University, Daegu 41566, Republic of Korea.
  • Park M; Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju 28119, Republic of Korea.
  • Jang KS; Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju 28119, Republic of Korea; Department of Bio-Analytical Science, University of Science and Technology, Daejeon 34113, Republic of Korea.
  • Lee JY; Department of Environmental Science and Engineering, Ewha Womans University, Seoul 03760, Republic of Korea.
  • Wu Z; State Joint Key Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
  • Natsagdorj A; Department of Chemistry, School of Arts and Sciences, National University of Mongolia, Ulaanbaatar 14201, Mongolia.
  • Kim YH; Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju 28119, Republic of Korea; Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon 34134, Republic of Korea. Electronic address: yhkim@kbsi.re.kr.
  • Kim S; Department of Chemistry, Kyungpook National University, Daegu 41566, Republic of Korea; Mass Spectrometry Convergence Research Center and Green-Nano Materials Research Center, Daegu 41566, Republic of Korea. Electronic address: sunghwank@knu.ac.kr.
Sci Total Environ ; 901: 165917, 2023 Nov 25.
Article em En | MEDLINE | ID: mdl-37527716
ABSTRACT
This paper presents comparative study on the composition and sources of PM2.5 in Ulaanbaatar, Beijing, and Seoul. Ultrahigh performance liquid chromatography (UPLC) combined with ultrahigh resolution mass spectrometry (UHR-MS) were employed to analyze 85 samples collected in winter. The obtained 340 spectra were interpreted with artificial neural network (ANN). PM2.5 mass concentrations in Ulaanbaatar were significantly higher than those in Beijing and Seoul. ANN based interpretation of UPLC UHR-MS data showed that aliphatic/lipid derived organo­sulfur compounds, polycyclic aromatic and organo­oxygen compounds were characteristic to Ulaanbaatar. Whereas, aliphatic/lipid-derived organo­oxygen compounds were major components in Beijing and Seoul. Aromatic organo­nitrogen compounds were the main contributors to differentiating the spectra obtained from Beijing from the other cities. Based on two-dimensional gas chromatography/high resolution mass spectrometric (GCxGC/HRMS) data, it was determined that the concentrations of the polycyclic aromatic hydrocarbon (PAH) and polycyclic aromatic sulfur heterocycle (PASH) containing sulfur were highest in Ulaanbaatar, followed by Beijing and Seoul. Coal/biomass combustion was identified as the primary source of contamination in Ulaanbaatar, while petroleum combustion was the main contributor to PM2.5 in Beijing and Seoul. The conclusion that diesel-powered heavy-duty trucks and buses are the main contributors to NOx emissions in Beijing is consistent with previous reports. This study provides a more comprehensive understanding of the composition and sources of PM2.5 in the three cities, with a focus on the differences in their atmospheric pollution profiles based on the UPLC UHR-MS and ANN analysis. It is notable that this study is the first to utilize this method on a large-scale sample set, providing a more detailed and molecular-level understanding of the compositional differences among PM2.5. Overall, the study contributes to a better understanding of the sources and composition of PM2.5 in Northeast Asia, which is essential for developing effective strategies to reduce air pollution and improve public health.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Total Environ Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Total Environ Ano de publicação: 2023 Tipo de documento: Article