Abstract
Nitrogen dioxide (NO2) is one of the most important trace gases in the atmosphere, mainly produced from the combustion of fossil fuels, thermal power plants, transportation activities, and natural sources. Short-term exposure to high concentrations of NO2 in the atmosphere can be problematic as it can cause adverse effects on human health, such as respiratory diseases, and exacerbate the symptoms of those already suffering from lung or heart conditions. The TROPOspheric Monitoring Instrument (TROPOMI) has limitations in tracking diurnal variation. TROPOMI scans South Korea only once daily. On the other hand, the Geostationary Environment Monitoring Spectrometer (GEMS) onboard the GEO-KOMPSAT 2B satellite was designed to continuously observe air pollutants, including NO2, SO2, HCHO, O3, and aerosols. The spatiotemporal pattern of total NO2 vertical column density (VCD) from GEMS shows spatial variability and the diurnal cycle of NO2. In this study, monthly averaged data were generated to compare GEMS, TROPOMI, and ground observation data. The research results showed that the monthly total NO2 VCD from GEMS and surface NO2 mixing ratio exhibited greater temporal variations compared to the total NO2 VCD from TROPOMI. Additionally, the monthly NO2 values were higher in spring and winter, while lower in summer and autumn. GEMS effectively detected the characteristics of NO2 in South Korea, including the distinct weekday-weekend effect, which is similar to ground observations. In the analysis of diurnal variations, GEMS exhibited a continuous increase in NO2 values from 9:45 to 14:45 KST for January. In contrast, other months showed a diurnal cycle. The comparison between GEMS and ground data showed a moderate level of correlation (R=0.77), while TROPOMI exhibited a higher correlation (R=0.81). However, the slope of GEMS was closer to the 1:1 line. GEMS demonstrated a good correlation, particularly in urban observation sites where total NO2 VCD was relatively high throughout the year. However, it showed a lower correlation in port observation sites.
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