Eunhwa Choi  1, Kwonho Jeon2, Young Su Lee  3, Jongbae Heo4, Ilhan Ryoo5, Taeyeon Kim5, Chuanlong Zhou6, Philip K. Hopke7,8, Seung-Muk Yi  This email address is being protected from spambots. You need JavaScript enabled to view it.5

1 Research Institute of Industrial Science & Technology, Gyeongsangbuk-do 37673, Korea
2 Climate and Air Quality Research Department Global Environment Research Division, National Institute of Environmental Research, Incheon, Korea
3 Department of Energy and Environmental Engineering, Soonchunhyang University, Asan, Korea
4 Busan Development Institute, Busan 47210, Korea
5 Department of Environmental Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
6 Laboratory for Sciences of Climate and Environment, Gif-sur-Yvette, France
7 Center for Air Resources Engineering and Science, Clarkson University, Potsdam, New York 13699, USA
8 Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, USA

Received: January 31, 2024
Revised: May 16, 2024
Accepted: May 18, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Cite this article:

Choi, E., Jeon, K., Lee, Y.S., Heo, J., Ryoo, I., Kim, T., Zhou, C., Hopke, P.K., Yi, S.M. (2024). Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China. 24, 240031. Aerosol Air Qual. Res.


  • PM2.5 sources in Ulsan, Korea and Dalian, China were apportioned and located.
  • Higher contributions of secondary sulfate were resolved in Ulsan.
  • Higher residential burning were estimated in Dalian.
  • Correlations of sources common to two cites were higher during heating period.
  • Sulfate during the non-heating period came from different locations.


PM2.5 mass and its constituent species were analyzed in two coastal cities (Ulsan, South Korea, and Dalian, China) between July 13, 2018, and September 20, 2019. Ten and nine sources were identified in Ulsan and Dalian, respectively, using positive matrix factorization (PMF). In Ulsan, three sources (secondary nitrate [SN], secondary sulfate [SS], and traffic) contributed ~83.0% of the PM2.5 mass concentration (23.7 µg m–3) during the heating period. In Dalian, four sources (SN, SS, traffic, and residential burning) accounted for ~84.3% of the total PM2.5 mass concentration (47.8 µg m–3). Higher contributions of residential burning in Dalian (11.7 µg m–3) than biomass burning in Ulsan (0.22 µg m–3) were resolved during the heating period as was a higher proportion of SS contributions in Ulsan (6.28 µg m–3, 41.6%) than in Dalian (6.42 µg m–3, 21.2%) during non-heating period. Squared correlation coefficients (r2) of sources common to the two cities were examined for lag times from –2 days to +4 days from Dalian to Ulsan. The largest r2 of PM2.5 mass concentrations during the heating period was 0.34 on Lag day 1. The same day, largest r2 during the non-heating period was 0.14 indicating, stronger, lagged PM2.5 correlations during the heating period. The SN, SS, soil, and oil combustion sources, with r2 values of 0.25, 0.20, 0.41, and 0.25, respectively, show fair correlations between the cities for these sources during the heating period. Probable source locations were identified by simplified quantitative transport bias analysis (SQTBA) and potential source contribution function (PSCF) as a multiple site approach and a single site approach, respectively. Weaker correlations of SN (r2 = 0.15) and SS (r2 < 0.1) during the non-heating period were supported by the different probable source locations. This study identified the sources requiring individual national and/or joint international efforts to reduce ambient PM2.5 in these neighboring countries.

Keywords: PM2.5, Source apportionment, Positive matrix factorization, Potential source contribution function, Simplified quantitative transport bias analysis

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