Seasonal characteristics of PM1 in Seoul, Korea, measured using HR-ToF-Aerosol Mass Spectrometer in 2018

In Korea, the characteristics of aerosols vary according to the four distinct seasons (spring, summer, fall, and winter); the seasonal variations are influenced by different air mass inflows of each season. Therefore, research is required to analyze the composition and characteristics of aerosols in different seasons. In this study, high-resolution time-of-flight aerosol mass spectrometry (HR-ToF-AMS) and positive matrix factorization (PMF) analyses were conducted in 2018 to examine the seasonal characteristics of aerosols with focus on organic aerosol in Seoul metropolitan area of South Korea. The results indicated that the seasonal non-refractory submicron particle (NR-PM₁) concentration in Seoul metropolitan area ranged from 6.3 to 41.0 μg/m³, which was lower than those in other metropolitan areas of Northeast Asia and slightly higher than those in suburban and rural areas of South Asia and Europe. The organic aerosol concentration, which was the highest among PM₁ components, ranged 3.5–14.24 μg/m³. In addition, a large increase in nitrate content was observed in high-concentration episodes. In all seasons, the O:C ratio and oxidation state were higher than those in previous studies, and it was estimated that the oxidation state was higher than those in other countries due to the influx of aged aerosols by long-range transport. In summer, the odd oxygen (Ox) concentration tends to be proportional to temperature and inversely proportional to humidity, indicating that the formation of fresh organic aerosol (OA) by active photochemical reaction is the main process of organic aerosol generation. In spring, fall, and winter, the Van Krevelen graph showed a slope of −0.68 ~ –0.99, indicating that carboxylic acid production was an important process for OA production. In addition, the causes of the high concentration episodes observed in spring and winter were estimated through the oxidation state of organic aerosols, PMF factor, and backward trajectory analyses.