Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China
Abstract 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, thre...
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| Format: | Article |
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Springer
2024-05-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.240031 |
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| author | Eunhwa Choi Kwonho Jeon Young Su Lee Jongbae Heo Ilhan Ryoo Taeyeon Kim Chuanlong Zhou Philip K. Hopke Seung-Muk Yi |
| author_facet | Eunhwa Choi Kwonho Jeon Young Su Lee Jongbae Heo Ilhan Ryoo Taeyeon Kim Chuanlong Zhou Philip K. Hopke Seung-Muk Yi |
| author_sort | Eunhwa Choi |
| collection | DOAJ |
| description | Abstract 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 (r 2) of sources common to the two cities were examined for lag times from –2 days to +4 days from Dalian to Ulsan. The largest r 2 of PM2.5 mass concentrations during the heating period was 0.34 on Lag day 1. The same day, largest r 2 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 r 2 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 (r 2 = 0.15) and SS (r 2 < 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. |
| format | Article |
| id | doaj-art-801e031b57d54984a948efd53830ee88 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Springer |
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| series | Aerosol and Air Quality Research |
| spelling | doaj-art-801e031b57d54984a948efd53830ee882025-02-09T12:24:31ZengSpringerAerosol and Air Quality Research1680-85842071-14092024-05-0124812110.4209/aaqr.240031Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in ChinaEunhwa Choi0Kwonho Jeon1Young Su Lee2Jongbae Heo3Ilhan Ryoo4Taeyeon Kim5Chuanlong Zhou6Philip K. Hopke7Seung-Muk Yi8Research Institute of Industrial Science & TechnologyClimate and Air Quality Research Department Global Environment Research Division, National Institute of Environmental ResearchDepartment of Energy and Environmental Engineering, Soonchunhyang UniversityBusan Development InstituteDepartment of Environmental Health Sciences, Graduate School of Public Health, Seoul National UniversityDepartment of Environmental Health Sciences, Graduate School of Public Health, Seoul National UniversityLaboratory for Sciences of Climate and EnvironmentCenter for Air Resources Engineering and Science, Clarkson UniversityDepartment of Environmental Health Sciences, Graduate School of Public Health, Seoul National UniversityAbstract 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 (r 2) of sources common to the two cities were examined for lag times from –2 days to +4 days from Dalian to Ulsan. The largest r 2 of PM2.5 mass concentrations during the heating period was 0.34 on Lag day 1. The same day, largest r 2 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 r 2 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 (r 2 = 0.15) and SS (r 2 < 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.https://doi.org/10.4209/aaqr.240031PM2.5Source apportionmentPositive matrix factorizationPotential source contribution functionSimplified quantitative transport bias analysis |
| spellingShingle | Eunhwa Choi Kwonho Jeon Young Su Lee Jongbae Heo Ilhan Ryoo Taeyeon Kim Chuanlong Zhou Philip K. Hopke Seung-Muk Yi Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China Aerosol and Air Quality Research PM2.5 Source apportionment Positive matrix factorization Potential source contribution function Simplified quantitative transport bias analysis |
| title | Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China |
| title_full | Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China |
| title_fullStr | Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China |
| title_full_unstemmed | Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China |
| title_short | Apportioning and Locating PM2.5 Sources Affecting Coastal Cities: Ulsan in South Korea and Dalian in China |
| title_sort | apportioning and locating pm2 5 sources affecting coastal cities ulsan in south korea and dalian in china |
| topic | PM2.5 Source apportionment Positive matrix factorization Potential source contribution function Simplified quantitative transport bias analysis |
| url | https://doi.org/10.4209/aaqr.240031 |
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