Nitrogen dioxide (NO₂), the main component of nitrogen oxides (NO_x), is an important air pollutant that can adversely affect human health and the environment. Satellite remote sensing monitoring offers near-real-time, continuous, and large-scale monitoring of atmospheric NO₂. The geostationary environmental monitoring spectrometer (GEMS) aboard GK2B, launched in February 2020, is the world’s first satellite payload capable of monitoring atmospheric trace gases on an hourly scale. It provides tropospheric NO₂ column densities in Asia and the Pacific during the daytime. In this study, we validate GEMS tropospheric NO₂ column density products using observations from the TROPOspheric Monitoring Instrument (TROPOMI) and ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) to obtain more comprehensive results. These steps are essential prerequisites for applying quantitative remote sensing products. Furthermore, since satellite data coverage can be influenced by various factors, including cloud cover, which can drastically reduce the spatial coverage of the GEMS dataset after quality control, applying the data interpolating empirical orthogonal functions (DINEOF) method to the quality-controlled GEMS dataset significantly improves spatial coverage. This enables a more comprehensive assessment of tropospheric NO₂ concentrations across the study area.

The datasets we used are satellite-based data from GEMS and TROPOMI and ground-based data from MAX-DOAS (Xuzhou). In the data preprocessing phase, the satellite data are first screened by parameters such as cloud fraction to ensure the state and quality of the inversion results. Then, the bilinear interpolation method is applied to resample both GEMS and TROPOMI observation data into a 0.05°×0.05° grid. In the comparison with TROPOMI, the TROPOMI data are first averaged on a daily basis. Subsequently, the data from GEMS at 12:45 and 13:45 (Beijing time) are selected for averaging, and the integrated dataset is used for correlation analysis. For the comparison with MAX-DOAS, the data corresponding to the grid in GEMS are initially filtered based on station coordinates. Then, hourly averages of MAX-DOAS data are calculated based on the actual transit time of GEMS, with the analysis limited to the first and second half hours. We analyze metrics such as data volume (N), correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and normalized mean bias (NMB) for validation. In the reconstruction of the quality-controlled dataset, the DINEOF algorithm initializes all missing data to an identical predicted value at the beginning of the reconstruction. Subsequently, the dataset undergoes iterative cross-validation using the EOF method to achieve optimal reconstruction results.

GEMS tropospheric NO₂ data products are compared and validated before and after quality control using TROPOMI and MAX-DOAS (Xuzhou) (Fig. 1). After quality control, the R-values are 0.88 and 0.85 respectively (P<0.05), indicating a high correlation between GEMS and both datasets. Numerically, GEMS data show similarities with MAX-DOAS and significantly higher values than TROPOMI. The number of products changes in a phased pattern, consistent with the designed observation schedule (Fig. 2). From the perspective of mean values, tropospheric NO₂ column densities in East China generally exhibit an increasing trend from morning to noon followed by a decrease (Fig. 3). On a daily basis, normalized NO₂ mass concentrations observed by ground stations in Shanghai display a pattern similar to satellite monitoring data, albeit with a relative lag [Fig. 4(b)]. The overall high NO₂ column densities derived from GEMS inversion are also prominently visible [Fig. 4(c)]. Cloud fraction is the most influential factor affecting GEMS data volume during quality control. The data product coverage stabilizes at a high level when transitioning to full central (FC) and full west (FW) modes. Spatially, observation coverage in the southern to central parts of East China is generally lower compared to that in the northern regions. The distribution of cloud fraction generally follows a pattern of high in the south and low in the north (Fig. 5). Data reconstruction markedly increases the coverage of GEMS tropospheric NO₂ products [Fig. 6(a)]. The validation of the reconstructed dataset using satellite-based and ground-based observations yields R-values of 0.85 and 0.64 respectively [Figs. 6(b) and (c)]. Therefore, the reconstructed dataset maintains high reliability.

1) GEMS provides 6?10 observations per day, which enables the study of hourly distribution of tropospheric NO₂ concentrations. 2) GEMS products demonstrate good agreement with both TROPOMI and MAX-DOAS observations during validation. After quality control, the R-values can reach 0.88 and 0.85 respectively. 3) Numerically, GEMS shows noticeably higher values than TROPOMI and similar values to MAX-DOAS. The inversion results from GEMS generally indicate higher overall concentrations. 4) Due to influences such as cloud fraction, there has been a notable reduction in the volume of GEMS tropospheric NO₂ data after quality control. Spatial-temporal reconstruction using the DINEOF method effectively improves the spatial coverage of GEMS data. The reconstructed dataset maintains a high level of reliability.