The global daily High Spatial–Temporal Coverage Merged tropospheric NO₂ dataset (HSTCM-NO₂) from 2007 to 2022 based on OMI and GOME-2

Abstract

Remote sensing based on satellites can provide long-term, consistent, and global coverage of NO2 (an important atmospheric air pollutant) as well as other trace gases. However, satellites often miss data due to factors including but not limited to clouds, surface features, and aerosols. Moreover, as one of the longest continuous observational platforms of NO2, the Ozone Monitoring Instrument (OMI) has suffered from missing data over certain rows since 2007, significantly reducing its spatial coverage. This work uses the OMI-based tropospheric NO2 (OMNO2) product as well as a NO2 product from the Global Ozone Monitoring Experiment-2 (GOME-2) in combination with machine learning (eXtreme Gradient Boosting – XGBoost) and spatial interpolation (data-interpolating empirical orthogonal function – DINEOF) methods to produce the 16-year global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2; https://doi.org/10.5281/zenodo.10968462; Qin et al., 2024), which increases the average global spatial coverage of NO2 from 39.5 % to 99.1 %. The HSTCM-NO2 dataset is validated using upward-looking observations of NO2 (multi-axis differential optical absorption spectroscopy – MAX-DOAS), other satellites (the Tropospheric Monitoring Instrument – TROPOMI), and reanalysis products. The comparisons show that HSTCM-NO2 maintains a good correlation with the magnitudes of other observational datasets, except for under heavily polluted conditions (> 6 × 1015 molec.cm-2). This work also introduces a new validation technique to validate coherent spatial and temporal signals (empirical orthogonal function – EOF) and confirms that HSTCM-NO2 is not only consistent with the original OMNO2 data but in some parts of the world also effectively fills in missing gaps and yields a superior result when analyzing long-range atmospheric transport of NO2. The few differences are also reported to be related to areas in which the original OMNO2 signal was very low, which has been shown elsewhere but not from this perspective, further confirming that applying a minimum cutoff to retrieved NO2 data is essential. The reconstructed data product can effectively extend the utilization value of the original OMNO2 data, and the data quality of HSTCM-NO2 can meet the needs of scientific research.