Effects of Seasonal Management Programs on PM2.5 in Seoul, Korea Using Dispersion Normalized PMF
ILHAN RYOO, Jieun Park, Songkang Kim, Hyejin Shin, Sunghwan Shim, Sujung Han, Taeyeon Kim, Yeonseung Cheong, Kwon Ho Jeon, Hyeog Ki Chae, Kyungmi Lee, Ju Gyo Lee, Seung-Muk Yi,
Seoul National University Abstract Number: 160
Working Group: Source Apportionment
AbstractParticulate matter with aerodynamic diameter equal to or less than 2.5 μm (PM2.5) is classified as class 1 carcinogen by the WHO (World Health Organization) in 2013.
South Korea is a one of the East Asian countries with severe air pollution such as PM2.5, and the government of South Korea has been implementing several policies to reduce PM2.5. The relevant actions were initiated with the forecasting/warning of fine dust in 2014 and the Comprehensive Action Plan on Fine Dust was implemented in 2017. Additionally, the Seasonal Particulate Matter Management System was introduced in December 2019. Since then, this seasonal management system has been enforced from December through March.
Therefore, the present study aims to identify and apportion the PM2.5 sources in Seoul, Korea using Dispersion Normalized Positive Matrix Factorization (DN-PMF) based on South Korea and China joint research program, and to extensively evaluate the effects of seasonal management policy for South Korea by comparing PM2.5 source contributions of the different periods.
Samples using a three-channel low-volume air sampler in Seoul were collected three times from January to December 2019 (before implementation policy period), and from September 2020 to May 2021 (after implementation policy, 1st study period), and from July 2021 to March 2022 (after implementation policy, 2nd study period).
The samples were analyzed for trace elements (fifteen species, Al, Si, Ca, etc.), carbonaceous species (Organic Carbon, Elemental Carbon), and ionic species (six species, SO42-, NO3-, NH4+, etc.). PM2.5 mass concentration and its chemical constituents were applied to DN-PMF for source apportionment. Subsequently, directional analysis of PM2.5 sources was performed using CBPF (Conditional Bivariate Probability Function).
Our results can be used as basic data to establish more effective air pollution control and management policies to reduce PM2.5 in ambient air.