Daniel Choi, Hyo-Jung Lee, L. Chang, Hyun-Young Jo, Yu-Jin Jo, Shin-Young Park, Geum-Hee Yang, Cheol-Hee Kim
{"title":"与韩国首尔高PM2.5事件相关的独特气象模式","authors":"Daniel Choi, Hyo-Jung Lee, L. Chang, Hyun-Young Jo, Yu-Jin Jo, Shin-Young Park, Geum-Hee Yang, Cheol-Hee Kim","doi":"10.1175/jamc-d-23-0016.1","DOIUrl":null,"url":null,"abstract":"\nIn this study, high particulate matter (PM2.5) pollution episodes were examined in Seoul, the capital city of South Korea, which, based on the episode characteristics, were influenced by a distinct meteorological mode, long-range transport (LRT), from two-level meteorological observations: surface and 850-500 hPa level. We performed two-step statistical analysis including principal component (PC) analysis of meteorological variables based on the observation data, followed by multiple linear regression (MLR). The meteorological variables included surface temperature (Tsfc), wind speed (WSsfc), and the east–west (usfc) and north– south (vsfc) components of wind speed, as well as wind components at 850 hPa geopotential height (u850 and v850, respectively) and the vertical temperature gradient between 850 and 500 hPa. Our two-step analysis of data collected during 2018–2019 revealed that the dominant factors influencing high-PM2.5 days in Seoul (129 days) were upper wind characteristics in winter, including positive u850 and negative v850, that were controlled by the presence of continental anticyclones that increased the likelihood of LRT of PM2.5 pollutants. Regional-scale meteorological variables, including surface and upper meteorological variables on normal and high-PM2.5 days, showed distinct covariation over Seoul, a megacity in the eastern part of northeast Asia with large anthropogenic emissions. Although this study examined only two atmospheric layers (surface and 500-850 hPa), our results clearly detected high-PM2.5 episodes with LRT characteristics, suggesting the importance of considering both geographical distinctiveness and seasonal meteorological covariability when scaling down continental-to-local response to emission reduction.","PeriodicalId":15027,"journal":{"name":"Journal of Applied Meteorology and Climatology","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distinct Meteorological Mode associated with High PM2.5 Episodes in Seoul, South Korea\",\"authors\":\"Daniel Choi, Hyo-Jung Lee, L. Chang, Hyun-Young Jo, Yu-Jin Jo, Shin-Young Park, Geum-Hee Yang, Cheol-Hee Kim\",\"doi\":\"10.1175/jamc-d-23-0016.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nIn this study, high particulate matter (PM2.5) pollution episodes were examined in Seoul, the capital city of South Korea, which, based on the episode characteristics, were influenced by a distinct meteorological mode, long-range transport (LRT), from two-level meteorological observations: surface and 850-500 hPa level. We performed two-step statistical analysis including principal component (PC) analysis of meteorological variables based on the observation data, followed by multiple linear regression (MLR). The meteorological variables included surface temperature (Tsfc), wind speed (WSsfc), and the east–west (usfc) and north– south (vsfc) components of wind speed, as well as wind components at 850 hPa geopotential height (u850 and v850, respectively) and the vertical temperature gradient between 850 and 500 hPa. Our two-step analysis of data collected during 2018–2019 revealed that the dominant factors influencing high-PM2.5 days in Seoul (129 days) were upper wind characteristics in winter, including positive u850 and negative v850, that were controlled by the presence of continental anticyclones that increased the likelihood of LRT of PM2.5 pollutants. Regional-scale meteorological variables, including surface and upper meteorological variables on normal and high-PM2.5 days, showed distinct covariation over Seoul, a megacity in the eastern part of northeast Asia with large anthropogenic emissions. Although this study examined only two atmospheric layers (surface and 500-850 hPa), our results clearly detected high-PM2.5 episodes with LRT characteristics, suggesting the importance of considering both geographical distinctiveness and seasonal meteorological covariability when scaling down continental-to-local response to emission reduction.\",\"PeriodicalId\":15027,\"journal\":{\"name\":\"Journal of Applied Meteorology and Climatology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Meteorology and Climatology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1175/jamc-d-23-0016.1\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Meteorology and Climatology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/jamc-d-23-0016.1","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Distinct Meteorological Mode associated with High PM2.5 Episodes in Seoul, South Korea
In this study, high particulate matter (PM2.5) pollution episodes were examined in Seoul, the capital city of South Korea, which, based on the episode characteristics, were influenced by a distinct meteorological mode, long-range transport (LRT), from two-level meteorological observations: surface and 850-500 hPa level. We performed two-step statistical analysis including principal component (PC) analysis of meteorological variables based on the observation data, followed by multiple linear regression (MLR). The meteorological variables included surface temperature (Tsfc), wind speed (WSsfc), and the east–west (usfc) and north– south (vsfc) components of wind speed, as well as wind components at 850 hPa geopotential height (u850 and v850, respectively) and the vertical temperature gradient between 850 and 500 hPa. Our two-step analysis of data collected during 2018–2019 revealed that the dominant factors influencing high-PM2.5 days in Seoul (129 days) were upper wind characteristics in winter, including positive u850 and negative v850, that were controlled by the presence of continental anticyclones that increased the likelihood of LRT of PM2.5 pollutants. Regional-scale meteorological variables, including surface and upper meteorological variables on normal and high-PM2.5 days, showed distinct covariation over Seoul, a megacity in the eastern part of northeast Asia with large anthropogenic emissions. Although this study examined only two atmospheric layers (surface and 500-850 hPa), our results clearly detected high-PM2.5 episodes with LRT characteristics, suggesting the importance of considering both geographical distinctiveness and seasonal meteorological covariability when scaling down continental-to-local response to emission reduction.
期刊介绍:
The Journal of Applied Meteorology and Climatology (JAMC) (ISSN: 1558-8424; eISSN: 1558-8432) publishes applied research on meteorology and climatology. Examples of meteorological research include topics such as weather modification, satellite meteorology, radar meteorology, boundary layer processes, physical meteorology, air pollution meteorology (including dispersion and chemical processes), agricultural and forest meteorology, mountain meteorology, and applied meteorological numerical models. Examples of climatological research include the use of climate information in impact assessments, dynamical and statistical downscaling, seasonal climate forecast applications and verification, climate risk and vulnerability, development of climate monitoring tools, and urban and local climates.