Quantifying the Impact of Synoptic Weather Systems on High PM2.5 Episodes in the Seoul Metropolitan Area, Korea

Abstract

AbstractVariations in concentrations of PM2.5, particulate matters of diameters below 2.5 μm, vary following both meteorological conditions and emissions controls. Meteorological conditions particularly affect short‐term high PM2.5 episodes through accumulations, transports, and secondary formations. This study quantifies the meteorological impacts on high PM2.5 episodes in the Seoul Metropolitan Area (SMA), Korea, for the period 2016–2018 using empirical and statistical methods. Synoptic weather maps of 77 high PM2.5 episodes in 2016 are grouped into two synoptic types: onshore winds associated with migratory pressure systems over the SMA and offshore winds from continental high pressure extending toward the SMA. We applied principal component analysis and regression to extract the dominant synoptic types controlling PM2.5 variability. It identifies two major principal components (PCs) from 12 surface and upper‐air meteorological variables for 2017–2018. Weather patterns in 49 examples of the high‐positive PCs show that the two PCs are capable of reproducing the synoptic weather patterns relevant for high PM2.5 episodes. To quantify the relationship between the synoptic weather patterns and PM2.5 levels, the two PCs are further classified into four groups according to their signs. Positive‐ and negative‐PC groups are associated with about 82% and 73% of high‐ and low‐PM2.5 episodes, respectively, suggesting that most of the high/low PM2.5 episodes in the SMA can occur under the two PCs‐dominant weather conditions. The results can be utilized as a reference for daily predictions of high PM2.5 episodes, as well as for quantitative analysis of the climatic influence on the long‐term PM2.5 variability.