Spatiotemporal variability of the PM2.5 distribution and weather anomalies during severe pollution events: Observations from 462 air quality monitoring stations across South Korea

Abstract

We investigated the nationwide spatiotemporal variability of PM2.5 during 49 severe PM2.5 pollution events based on observations from 462 air quality monitoring stations (AQMSs) across South Korea during 2015-2020. We calculated the coefficient of divergence (COD) and Pearson correlation coefficient (R) for the entire nationwide AQMS network with respect to the time series of average PM2.5 in the Seoul area. We identified three distinct spatial COD and R distribution patterns (Cases I-III) among 49 pollution events that can help to determine locations for additional AQMSs. We present the spatial distributions of time lag associated with PM2.5 pollution events at each monitoring station relative to the Seoul area, clarifying the inter-district transport of PM2.5. When the time lag was adjusted, the average R values at all AQMSs were greater than 0.74 for all cases (compared to 0.52-0.57 before adjustment), suggesting that characteristic weather patterns over East Asia drive nationwide severe PM2.5 pollution. Analyses of composite anomalies of meteorological variables (compared to climatology for 1991-2020) suggest that weakened surface winds accompanied by strong positive temperature anomalies in East Asia play an important role in the onset of severe PM2.5 pollution in Korea (for all classified cases). Our findings have elucidated the weather patterns that lead to severe PM2.5 pollution, variations of spatiotemporal PM2.5 pollution patterns over Korea on the regional scale with weather patterns, and pollution transport across air quality districts (inter-air-quality-district transport patterns).