Instruments for air quality observations on geostationary satellites provide multiple observations per day and allow for the analysis of the diurnal variation in important air pollutants such as nitrogen dioxide (NO₂). The South Korean instrument GEMS (Geostationary Environmental Monitoring Spectrometer), launched in February 2020, is the first geostationary instrument that is able to observe the diurnal variation in NO₂. The measurements have a spatial resolution of 3.5 km × 8 km and cover a large part of Asia. This study compares 1 year of tropospheric NO₂ vertical column density (VCD) observations from the operational GEMS L2 product, the scientific GEMS IUP-UB (Institute of Environmental Physics at the University of Bremen) product, the operational TROPOspheric Monitoring Instrument (TROPOMI) product, and ground-based differential optical absorption spectroscopy (DOAS) measurements in South Korea. The GEMS L2 tropospheric NO₂ VCDs overestimate the ground-based tropospheric NO₂ VCDs with a median relative difference of +61 % and a correlation coefficient of 0.76. The median relative difference is −2 % for the GEMS IUP-UB product and −16 % for the TROPOMI product, with correlation coefficients of 0.83 and 0.89, respectively. The scatter in the GEMS products can be reduced when observations are limited to the TROPOMI overpass time. Diurnal variations in tropospheric NO₂ VCDs differ by the pollution level of the analyzed site but with good agreement between the GEMS IUP-UB and ground-based observations. Low-pollution sites show weak or almost no diurnal variation. In summer, the polluted sites show a minimum around noon, indicating the large influence of photochemical loss. Most variation is seen in spring and autumn, with increasing NO₂ in the morning, a maximum close to noon, and a decrease towards the afternoon. Winter observations show rather flat or slightly decreasing NO₂ throughout the day. Winter observations under low-wind-speed conditions at high-pollution sites show enhancements of NO₂ throughout the day. This indicates that under calm conditions, dilution and the less effective chemical loss in winter do not balance the accumulating emissions. Diurnal variation observed at a low-pollution site follows seasonal wind patterns. A weekday–weekend effect analysis shows good agreement between the different products. However, the GEMS L2 product, while agreeing with the other data sets on weekdays, shows significantly less reduction on weekends. The influence of the stratospheric contribution and the surface reflectivity product on the satellite tropospheric NO₂ VCD products is investigated. While the TM5 model's stratospheric VCDs, used in the TROPOMI product, are too high, resulting in tropospheric NO₂ VCDs that are too low and even negative, when used in the GEMS IUP-UB retrieval, the GEMS L2 stratospheric VCD is too low. Surface reflectivity comparisons indicate that the GEMS L2 reflectivity makes a large contribution to the observed overestimation and scatter.

In this study, the spatio-temporal variability of aerosol optical depth (AOD), total column ozone (TCO), and total column NO₂ (TCN) was identified over East Asia using long-term datasets from ground-based and satellite observations. Based on the statistical results, optimized spatio-temporal ranges for the validation study were determined with respect to the target materials. To determine both spatial and temporal ranges for the validation study, we confirmed that the observed datasets can be statistically considered as the same quantity within the ranges. Based on the thresholds of R²>0.95 (temporal) and R>0.95 (spatial), the basic ranges for spatial and temporal scales for AOD validation was within 30 km and 30 min, respectively. Furthermore, the spatial scales for AOD validation showed seasonal variation, which expanded the range to 40 km in summer and autumn. Because of the seasonal change of latitudinal gradient of the TCO, the seasonal variation of the north-south range is a considerable point. For the TCO validation, the north-south range is varied from 0.87° in spring to 1.05° in summer. The spatio-temporal range for TCN validation was 20 min (temporal) and 20–50 km (spatial). However, the nearest value of satellite data was used in the validation because the spatio-temporal variation of TCN is large in summer and autumn. Estimation of the spatio-temporal variability for respective pollutants may contribute to improving the validation of satellite products.

Prior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Observing System Simulation Experiments (OSSE) is to assess the potential impact of new observations on the quality of the current monitoring or forecasting systems, thereby making this framework valuable. This study introduces the overall OSSE framework established to support air quality forecasting and the details of its individual components. Furthermore, it shows case study results from Northeast Asia and the potential benefits of the new observation data scenarios on the PM_2.5 forecasting skills, including the PM data from 200 virtual monitoring sites in the Gobi Desert and North Korean non-forest areas (NEWPM) and the aerosol optical depths (AOD) data from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS AOD). Performance statistics suggest that the concurrent assimilation of the NEWPM and the PM data from current monitoring sites in China and South Korea can improve the PM_2.5 concentration forecasts in South Korea by 66.4% on average for October 2017 and 95.1% on average for February 2018. Assimilating the GEMS AOD improved the performance of the PM_2.5 forecasts in South Korea for October 2017 by approximately 68.4% (~78.9% for February 2018). This OSSE framework is expected to be continuously implemented to verify its utilization potential for various air quality observation systems and data scenarios. Hopefully, this kind of application result will aid environmental researchers and decision-makers in performing additional in-depth studies for the improvement of PM air quality forecasts.

High-spatial-resolution NO₂ vertical column densities (VCDs) were retrieved from airborne observations using the low-cost hyperspectral imaging sensor (HIS) at three industrial areas (i.e., Chungnam, Jecheon, and Pohang) in South Korea, where point sources (i.e., power plant, petrochemical complex, steel yard, and cement kiln) with significant NO₂ emissions are located. An innovative and versatile approach for NO₂ VCD retrieval, hereafter referred to as the modified wavelength pair (MWP) method, was developed to overcome the excessively variable radiometric and spectral characteristics of the HIS attributed to the absence of temperature control during the flight. The newly developed MWP method was designed to be insensitive to broadband spectral features, including the spectral dependency of surface and aerosol reflectivity, and can be applied to observations with relatively low spectral resolutions. Moreover, the MWP method can be implemented without requiring precise radiometric calibration of the instrument (i.e., HIS) by utilizing clean-pixel data for non-uniformity corrections and is also less sensitive to the optical properties of the instrument and offers computational cost competitiveness. In the experimental flights using the HIS, NO₂ plumes emitted from steel yards were particularly conspicuous among the various NO₂ point sources, with peak NO₂ VCDs of 2.0 DU (Dobson unit) at Chungnam and 1.8 DU at Pohang. Typical NO₂ VCD uncertainties ranged between 0.025–0.075 DU over the land surface and 0.10–0.15 DU over the ocean surface, and the discrepancy can be attributable to the lower signal-to-noise ratio over the ocean and higher sensitivity of the MWP method to surface reflectance uncertainties under low-albedo conditions. The NO₂ VCDs retrieved from the HIS with the MWP method showed a good correlation with the collocated Tropospheric Monitoring Instrument (TROPOMI) data (r=0.73, mean absolute error equals 0.106 DU). However, the temporal disparities between the HIS frames and the TROPOMI overpass, their spatial mismatch, and their different observation geometries could limit the correlation. The comparison of TROPOMI and HIS NO₂ VCDs further demonstrated that the satellite sub-grid variability could be intensified near the point sources, with more than a 3-fold increase in HIS NO₂ VCD variability (e.g., difference between 25th and 75th quantiles) over the TROPOMI footprints with NO₂ VCD values exceeding 0.8 DU compared to footprints with NO₂ VCD values below 0.6 DU.