Modification of Hybrid Receptor Model for Atmospheric Fine Particles (PM2.5) in 2020 Daejeon, Korea, Using an ACERWT Model

Abstract

Hybrid receptor models overestimate the contribution of background areas (no specific emission sources), like the Yellow Sea in Korea. This study aimed to improve model performances using Advanced Concentration Emission and Retention Time Weighted Trajectory (ACERWT). ACERWT was combined with a positive matrix factorization (PMF), back trajectory, and Regional Emission Inventory in Asia (REAS). The PMF receptor model used one year of data from Korea’s Central Air Environment Research Center. In the PMF receptor model, eight sources (dust/soil, secondary nitrate, biomass burning, vehicles, secondary sulfate, industry, coal combustion and sea salt) influenced PM2.5 pollution at the receptor site (Daejeon, Korea). Secondary sulfate was the most dominant source, followed by secondary nitrate and vehicle sources. ACERWT results showed high contributions from China, Japan, and Korean regions, while the contribution from the Yellow Sea was significantly lower. Several regions, such as the eastern and south-eastern areas of China, the southern area of Taiwan, the western area of Tokyo, and the central area of Korea, showed high contributions due to large-scale emission facilities and industrial complexes. In this study, the ACERWT model significantly improved its performance regarding regional contributions to PM2.5 pollution at the receptor site.