{"title":"首尔特大城市 PM2.5 与 PM1.0 和 PM10 的质量关系特征","authors":"Jihyun Han, Seahee Lim, Meehye Lee, Young Jae Lee, Gangwoong Lee, Changsub Shim, Lim-Seok Chang","doi":"10.5572/ajae.2021.124","DOIUrl":null,"url":null,"abstract":"<div><p>This study examines the PM<sub>2.5</sub> characteristics in Seoul in relation to those of PM<sub>1.0</sub> and PM<sub>10</sub>. Samples were typically collected daily on filters and a few hours sampling were conducted during a few haze events (March 2007 to June 2008). Mean mass concentrations of PM<sub>1.0</sub>, PM<sub>2.5</sub>, and PM<sub>10</sub> were 19.7 μg/m<sup>3</sup>, 26.0 μg/m<sup>3</sup>, and 48.2 μg/m<sup>3</sup>, respectively, and PM<sub>2.5</sub> was reasonably correlated with PM<sub>1.0</sub> (γ=0.79) and PM<sub>10</sub> (γ=0.52). Three mass group types were mainly distinguished. Group 1 (31%): linear increase of PM<sub>1.0</sub> with PM<sub>10</sub> and high OC and NO<sub>3</sub><sup>−</sup>; Group 2 (17%): PM<sub>10</sub> considerably higher than PM<sub>1.0</sub> and high Ca<sup>2+</sup> and SO<sub>4</sub><sup>2−</sup>; Group 3 (52%): PM<sub>1.0</sub> relatively more enhanced than PM<sub>10</sub> and highest carbonaceous fraction against mass. The fine mode fraction was lowest (highest) in Group 2 (Group 3). Haze and dust episodes relating to Chinese outflows were mostly evident in Groups 1 and 2, respectively; average PM<sub>2.5</sub> concentrations were visibly higher than in Group 3. Non-Negative Matrix Factorization analysis demonstrated that traffic-related urban primary (28%) and coal-fired industry (27%) emissions equally contributed to the PM<sub>2.5</sub> mass, followed by aged urban secondary (19%), soil mineral (16%), and biomass combustion (10%) sources. Seasonal variations were apparent in air mass trajectories. Urban primary and coal-fired industry factors were predominant in Group 3 under stagnant conditions in the warm season and under a strong northerly wind in the cold season, respectively. However, contributions of the other three factors were higher in Groups 1 and 2. This study shows that the PM<sub>2.5</sub> mass in Seoul is largely dependent on high concentration episodes occurring mostly in cold seasons. It also shows that local emissions contribute considerably during warm months, while the influence of Chinese outflow predominates during cold months.</p></div>","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"16 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.5572/ajae.2021.124.pdf","citationCount":"0","resultStr":"{\"title\":\"Characterization of PM2.5 Mass in Relation to PM1.0 and PM10 in Megacity Seoul\",\"authors\":\"Jihyun Han, Seahee Lim, Meehye Lee, Young Jae Lee, Gangwoong Lee, Changsub Shim, Lim-Seok Chang\",\"doi\":\"10.5572/ajae.2021.124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study examines the PM<sub>2.5</sub> characteristics in Seoul in relation to those of PM<sub>1.0</sub> and PM<sub>10</sub>. Samples were typically collected daily on filters and a few hours sampling were conducted during a few haze events (March 2007 to June 2008). Mean mass concentrations of PM<sub>1.0</sub>, PM<sub>2.5</sub>, and PM<sub>10</sub> were 19.7 μg/m<sup>3</sup>, 26.0 μg/m<sup>3</sup>, and 48.2 μg/m<sup>3</sup>, respectively, and PM<sub>2.5</sub> was reasonably correlated with PM<sub>1.0</sub> (γ=0.79) and PM<sub>10</sub> (γ=0.52). Three mass group types were mainly distinguished. Group 1 (31%): linear increase of PM<sub>1.0</sub> with PM<sub>10</sub> and high OC and NO<sub>3</sub><sup>−</sup>; Group 2 (17%): PM<sub>10</sub> considerably higher than PM<sub>1.0</sub> and high Ca<sup>2+</sup> and SO<sub>4</sub><sup>2−</sup>; Group 3 (52%): PM<sub>1.0</sub> relatively more enhanced than PM<sub>10</sub> and highest carbonaceous fraction against mass. The fine mode fraction was lowest (highest) in Group 2 (Group 3). Haze and dust episodes relating to Chinese outflows were mostly evident in Groups 1 and 2, respectively; average PM<sub>2.5</sub> concentrations were visibly higher than in Group 3. Non-Negative Matrix Factorization analysis demonstrated that traffic-related urban primary (28%) and coal-fired industry (27%) emissions equally contributed to the PM<sub>2.5</sub> mass, followed by aged urban secondary (19%), soil mineral (16%), and biomass combustion (10%) sources. Seasonal variations were apparent in air mass trajectories. Urban primary and coal-fired industry factors were predominant in Group 3 under stagnant conditions in the warm season and under a strong northerly wind in the cold season, respectively. However, contributions of the other three factors were higher in Groups 1 and 2. This study shows that the PM<sub>2.5</sub> mass in Seoul is largely dependent on high concentration episodes occurring mostly in cold seasons. It also shows that local emissions contribute considerably during warm months, while the influence of Chinese outflow predominates during cold months.</p></div>\",\"PeriodicalId\":45358,\"journal\":{\"name\":\"Asian Journal of Atmospheric Environment\",\"volume\":\"16 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.5572/ajae.2021.124.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asian Journal of Atmospheric Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.5572/ajae.2021.124\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Atmospheric Environment","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.5572/ajae.2021.124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Characterization of PM2.5 Mass in Relation to PM1.0 and PM10 in Megacity Seoul
This study examines the PM2.5 characteristics in Seoul in relation to those of PM1.0 and PM10. Samples were typically collected daily on filters and a few hours sampling were conducted during a few haze events (March 2007 to June 2008). Mean mass concentrations of PM1.0, PM2.5, and PM10 were 19.7 μg/m3, 26.0 μg/m3, and 48.2 μg/m3, respectively, and PM2.5 was reasonably correlated with PM1.0 (γ=0.79) and PM10 (γ=0.52). Three mass group types were mainly distinguished. Group 1 (31%): linear increase of PM1.0 with PM10 and high OC and NO3−; Group 2 (17%): PM10 considerably higher than PM1.0 and high Ca2+ and SO42−; Group 3 (52%): PM1.0 relatively more enhanced than PM10 and highest carbonaceous fraction against mass. The fine mode fraction was lowest (highest) in Group 2 (Group 3). Haze and dust episodes relating to Chinese outflows were mostly evident in Groups 1 and 2, respectively; average PM2.5 concentrations were visibly higher than in Group 3. Non-Negative Matrix Factorization analysis demonstrated that traffic-related urban primary (28%) and coal-fired industry (27%) emissions equally contributed to the PM2.5 mass, followed by aged urban secondary (19%), soil mineral (16%), and biomass combustion (10%) sources. Seasonal variations were apparent in air mass trajectories. Urban primary and coal-fired industry factors were predominant in Group 3 under stagnant conditions in the warm season and under a strong northerly wind in the cold season, respectively. However, contributions of the other three factors were higher in Groups 1 and 2. This study shows that the PM2.5 mass in Seoul is largely dependent on high concentration episodes occurring mostly in cold seasons. It also shows that local emissions contribute considerably during warm months, while the influence of Chinese outflow predominates during cold months.