This paper presents the algorithm for the operational near real time retrieval of total and tropospheric NO₂ columns from the Global Ozone Monitoring Experiment (GOME-2). The retrieval is performed with the GOME Data Processor (GDP) version 4.4 as used by the EUMETSAT Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF). The differential optical absorption spectroscopy (DOAS) method is used to determine NO₂ slant columns from GOME-2 (ir)radiance data in the 425–450 nm range. Initial total NO₂ columns are computed using stratospheric air mass factors, and GOME-2 derived cloud properties are used to calculate the air mass factors for scenarios in the presence of clouds. To obtain the stratospheric NO₂ component, a spatial filtering approach is used, which is shown to be an improvement on the Pacific reference sector method. Tropospheric air mass factors are computed using monthly averaged NO₂ profiles from the MOZART-2 chemistry transport model. An error analysis shows that the random error in the GOME-2 NO₂ slant columns is approximately 0.45 × 10¹⁵ molec cm⁻². As a result of the improved quartz diffuser plate used in the GOME-2 instrument, the systematic error in the slant columns is strongly reduced compared to GOME/ERS-2. The estimated uncertainty in the GOME-2 tropospheric NO₂ column for polluted conditions ranges from 40 to 80 %. An end-to-end ground-based validation approach for the GOME-2 NO₂ columns is illustrated based on multi-axis MAXDOAS measurements at the Observatoire de Haute Provence (OHP). The GOME-2 stratospheric NO₂ columns are found to be in good overall agreement with coincident ground-based measurements at OHP. A time series of the MAXDOAS and the GOME-2 tropospheric NO₂ columns shows that pollution episodes at OHP are well captured by GOME-2. Monthly mean tropospheric columns are in very good agreement, with differences generally within 0.5 × 10¹⁵ molec cm⁻².

In this paper, we present the estimation of surface NO${}_2$ concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO${}_2$ vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO${}_2$ concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO${}_2$ concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO${}_2$ concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO${}_2$ concentrations over Germany from 2018 to 2020. Estimated surface NO${}_2$ concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO${}_2$ in Germany. The estimated surface NO${}_2$ concentrations provide comprehensive information of NO${}_2$ spatial distribution which is very useful for exposure estimation. We estimated the annual average NO${}_2$ exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µ$\mathsf{\mu }$g/m${}^3$, respectively. While the annual average NO${}_2$ concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µ$\mathsf{\mu }$g/m${}^3$. In addition, we used the surface NO${}_2$ data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO${}_2$ levels in Germany. In general, 10–30% lower surface NO${}_2$ concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.

Onboard the Chinese GaoFen-5 (GF5) satellite, the Environmental trace gases Monitoring Instrument (EMI) is a nadir-viewing wide-field spectrometer that was launched on May 9, 2018. EMI measures the back-scattered earthshine solar radiance in the ultraviolet and visible spectral range. By using the differential optical absorption spectrometry (DOAS) method and the EMI measurements in the VIS1 band (405–465 nm), we performed retrievals of NO₂. Some first retrieval results of NO₂ from EMI and a comparison with OMI and TROPOMI products are presented in this paper. The monthly mean total vertical column densities (VCD) of NO₂ show similar spatial distributions to OMI and TROPOMI (r > 0.88) and their difference is less than 27%. A comparison of the daily total VCD shows that EMI could detect the NO₂ patterns in good agreement with OMI (r = 0.93) and TROPOMI (r = 0.95). However, the slant column density (SCD) uncertainty (0.79 × 10¹⁵ molec cm⁻²) of the current EMI algorithm is relatively larger than OMI. The daily variation pattern of NO₂ from EMI in Beijing in January 2019 is consistent with TROPOMI (r = 0.96). The spatial distribution correlation of the tropospheric NO₂ VCD of EMI with OMI and TROPOMI is 0.88 and 0.89, respectively, but shows an overestimate compared to OMI (15%) and TROPOMI (23%), respectively. This study demonstrates the capability of using EMI for global NO₂ monitoring.

In recent decades, there has been an increasing interest in making use of satellite measurements for identifying trends in atmospheric composition and climate. Instruments like GOME-2 and TROPOMI are dedicated to air-quality and global trace gas monitoring. For the accurate retrieval of columnar information of the trace gases, cloud correction is necessary. This work is meant to examine the quality of the GOME-2 operational cloud product from AC SAF and to propose enhancements of the current dataset to improve the retrieval of the NO₂ and HCHO tropospheric gases.