Analysis of Weather Patterns Related to Wintertime Particulate Matter Concentration in Seoul and a CMIP6-Based Air Quality Projection

Abstract

This study analyzes the relationship between various atmospheric fields and the observed PM10 concentrations in the Seoul metropolitan area, South Korea, during the winters of the 2001–2014 period to find suitable atmospheric indices for predicting high PM10 episodes in the region. The analysis shows that PM10 concentration in the metropolitan area is mainly affected by the intensity of horizontal ventilation and the 500 hPa high-pressure system over the Korean peninsula. The modified Korea particulate matter index (MKPI) is proposed based on a 10 m wind speed for surface ventilation and 500 hPa zonal wind for the intensity of a 500 hPa high-pressure system over the Korean peninsula. It is found that a positive MKPI value is closely correlated with the occurrence of high PM10 concentration episodes, and hence, can be used as a predictor for high PM10 episodes in the area. A future projection of the MKPI using two three-member general circulation model (GCM) ensembles with four shared socioeconomic pathway (SSP) scenarios in Coupled Model Intercomparison Project Phase 6 (CMIP6) shows that positive MKPI events and high PM10 episodes are expected to increase by 5.4%−16.4% depending on the SSP scenarios in the 2081−2100 period from the present-day period of 1995−2014.

Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions

“…The mean values at all available air pollutants and meteorological stations were used ( Teng et al 2018 ), as the focus of the study is to find the overall relationship among them across Taiwan despite the different numbers of air quality (n = 69) and meteorological stations (n = 224). Similar approaches were also reported by He et al (2020) , Kwon et al (2020) and Zhu et al (2021) . Therefore, the representative air pollutant concentration and meteorological parameter value were defined by averaging the monthly mean concentration/value of all the available monitoring stations from 2018 to 2020.…”