Chemical composition and source apportionment of PM2.5 in Seoul during 2018-2020

Jeong, Min JaeHwang, Seung-OnYoo, Hee-JungOh, Sang MinJang, JunhyukLee, YounjunKim, TaeyunKim, Seongheon
Issue Date
Elsevier BV
Atmospheric Pollution Research, v.15, no.6
Air pollution, particularly high ambient particulate matter (PM) concentrations, poses a serious public health concern in South Korea. This study analyzed 139 daily PM2.5 samples collected from an urban site in Seoul between March 2018 and January 2020. This study aimed to quantify the chemical composition of PM2.5 and identify the source characteristics. The average PM2.5 mass concentration for the entire study period was 25.3 mu g m- 3. The chemical composition was dominated by ionic particles (13.2 mu g m- 3, 52%), carbonaceous particles (7.0 mu g m- 3, 28%), and trace elements (0.5 mu g m- 3, 2%). The Positive Matrix Factorization (PMF) model was employed to identify PM2.5 sources using quality-controlled sampling data after eliminating factors that could adversely affect the modeling. Seven sources were resolved through PMF, applying a trial-and-error method to determine the optimal number of sources. The PMF modeling results indicated that secondary sulfate (28%) was the largest contributor, followed by internal factors (23%), secondary nitrate (19%), soil (14%), biomass burning (9%), and aged sea salt (7%). As an additional case study, the analysis of backward trajectories for the winter of 2018, which had the highest PM2.5 concentrations, revealed that the air current carrying PM2.5 had passed from the northwest region of Seoul. Particulary, source identification for this winter case resulted in the highest secondary nitrate across all study periods. This more specific interpretation of the pollutant characteristics of PM2.5 in Seoul could aid in developing effective control strategies and policies for air pollution mitigation.
POSITIVE MATRIX FACTORIZATION; PMF; PARTICLES; MODELS; Particulate matter; PMF model; Chemical composition; Source identification
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