A multifaceted approach to explain short- and long-term PM2.5 concentration changes in Northeast Asia in the month of January during 2016-2021.

Changes in PM2.5 concentrations are influenced by interwoven impacts of key drivers (e.g., meteorology, local emissions, and regional emissions). However, it is challenging to quantitatively disentangle their impacts individually at once. Therefore, we introduced a multifaceted approach (i.e., meteorology vs. emissions and self-contribution vs. long-range transport) to analyze the effects of major drivers for long- and short-term PM2.5 concentration changes based on observation and simulation in the month of January during 2016-2021 in Northeast Asia. For the simulations, we conducted modeling with the WRF-CMAQ system. The observed PM2.5 concentrations in China and South Korea in January 2021 decreased by 13.7 and 9.8 µg/m3, respectively, compared to those in January 2016. Emission change was the dominant factor to reduce PM2.5 concentrations in China (-115%) and South Korea (-74%) for the 6 years. However, the short-term changes in PM2.5 concentrations between January of 2020-2021 were mainly driven by meteorological conditions in China (-73%) and South Korea (-68%). At the same time, in South Korea located in downwind area, the impact of long-range transport from upwind area (LTI) decreased by 55% (9.6 µg/m3) over the 6 years whereas the impact of local emissions increased (+2.9 µg/m3/year) during 2016-2019 but decreased (-4.5 µg/m3/year) during 2019-2021. Additionally, PM2.5 concentrations in the upwind area showed a positive relationship with LTIs. However, for the days when westerly winds became weak in the downwind area, high PM2.5 concentrations in upwind area did not lead to high LTIs. These results imply that the decline of PM2.5 concentrations in South Korea was significantly affected by a combination of emission reduction in upwind area and meteorological conditions that hinder long-range transport. The proposed multifaceted approach can identify the main drivers of PM2.5 concentration change in a region by considering the regional characteristics.