Intrinsic atmospheric circulation patterns associated with high PM2.5 concentration days in South Korea during the cold season

Yong Cheol Jeong, Sang Wook Yeh, Jaein I. Jeong, Rokjin J. Park, Changhyun Yoo, Jin Ho Yoon

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Based on observation data and a novel K-mean clustering method, we investigated whether intrinsic atmospheric circulation patterns are related with the occurrence of high particulate matter (PM) concentration days (diameters less than or equal to 2.5 μm (PM2.5)), in Seoul, South Korea, during the cold season (December to March). A simple composite map shows that weak horizontal and vertical ventilation over the Korean Peninsula can cause high PM2.5 concentration (High_PM2.5) days. Also, atmospheric circulations are quite different between one day of High_PM2.5 and periods longer than two days. We also found that two intrinsic atmospheric circulation patterns in Asia, which were obtained by adopting K-mean clustering to the daily 850 hPa geopotential height anomalies for 2005–2020, were associated with High_PM2.5 days. These results indicate that High_PM2.5 days in Seoul, South Korea, occur as a result of intrinsic atmospheric circulation patterns, therefore, they are unavoidable unless the anthropogenic emission sources over the Korean Peninsula, East Asia, or both are reduced. In addition, these two intrinsic atmospheric circulation patterns are more prominent for periods longer than two days while there are no favorable intrinsic atmospheric circulation patterns to induce one day of High_PM2.5, which indicates that a single day of High_PM2.5 tends to occur by a stochastic atmospheric circulation rather than the intrinsic atmospheric circulation patterns.

Original languageEnglish
Article number160878
JournalScience of the Total Environment
Volume863
DOIs
StatePublished - 10 Mar 2023

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Intrinsic atmospheric circulation
  • K-mean clustering
  • Particulate matter
  • Stochastic

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