Spatial Distribution Characteristics and Analysis of PM2.5 in South Korea: A Geographically Weighted Regression Analysis

Abstract

PM2.5, a critical air pollutant, requires health-conscious management, with concentrations varying across regions due to diverse sources. This study, conducted in South Korea in 2021, employed the geographically weighted regression model to analyze the spatiotemporal correlations of PM2.5 with O3 and the normalized difference vegetation index (NDVI). Regional differences in the correlation between PM2.5 and O3 were observed, influenced by common precursors (SOx, NOx, and volatile organic compounds (VOCs)), seasonal temperature variations, and solar radiation differences. Notably, PM2.5 and O3 exhibited a heightened regression coefficient in summer, emphasizing the need for specific management targeting VOCs and NO2. The interplay between PM2.5 and NDVI revealed a negative overall impact but a positive effect in the central region of Korea, suggesting vegetation’s role in the PM2.5 concentration increase due to atmospheric stagnation caused by mountain ranges. These findings enhance our understanding of PM2.5 distribution mechanisms, highlighting the need for tailored policies in each region for effective concentration reductions.