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.

An improved algorithm for the retrieval of total and tropospheric nitrogen dioxide (NO₂) columns from the Global Ozone Monitoring Experiment-2 (GOME-2) is presented. The refined retrieval will be implemented in a future version of the GOME Data Processor (GDP) as used by the EUMETSAT Satellite Application Facility on Atmospheric Composition and UV Radiation (AC-SAF). The first main improvement is the application of an extended 425–497 nm wavelength fitting window in the differential optical absorption spectroscopy (DOAS) retrieval of the NO₂ slant column density, based on which initial total NO₂ columns are computed using stratospheric air mass factors (AMFs). Updated absorption cross sections and a linear offset correction are used for the large fitting window. An improved slit function treatment is applied to compensate for both long-term and in-orbit drift of the GOME-2 slit function. Compared to the current operational (GDP 4.8) dataset, the use of these new features increases the NO₂ columns by ∼1–3×10¹⁴ molec cm² and reduces the slant column error by ∼24 %. In addition, the bias between GOME-2A and GOME-2B measurements is largely reduced by adopting a new level 1b data version in the DOAS retrieval. The retrieved NO₂ slant columns show good consistency with the Quality Assurance for Essential Climate Variables (QA4ECV) retrieval with a good overall quality. Second, the STRatospheric Estimation Algorithm from Mainz (STREAM), which was originally developed for the TROPOspheric Monitoring Instrument (TROPOMI) instrument, was optimised for GOME-2 measurements to determine the stratospheric NO₂ column density. Applied to synthetic GOME-2 data, the estimated stratospheric NO₂ columns from STREAM shows good agreement with the a priori truth. An improved latitudinal correction is introduced in STREAM to reduce the biases over the subtropics. Applied to GOME-2 measurements, STREAM largely reduces the overestimation of stratospheric NO₂ columns over polluted regions in the GDP 4.8 dataset. Third, the calculation of AMF applies an updated box-air-mass factor (box-AMF) look-up table (LUT) calculated using the latest version 2.7 of the Vector-LInearized Discrete Ordinate Radiative Transfer (VLIDORT) model with an increased number of reference points and vertical layers, a new GOME-2 surface albedo climatology, and improved a priori NO₂ profiles obtained from the TM5-MP chemistry transport model. A large effect (mainly enhancement in summer and reduction in winter) on the retrieved tropospheric NO₂ columns by more than 10 % is found over polluted regions. To evaluate the GOME-2 tropospheric NO₂ columns, an end-to-end validation is performed using ground-based multiple-axis DOAS (MAXDOAS) measurements. The validation is illustrated for six stations covering urban, suburban, and background situations. Compared to the GDP 4.8 product, the new dataset presents improved agreement with the MAXDOAS measurements for all the stations.

An improved tropospheric nitrogen dioxide (NO₂) retrieval algorithm from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument based on air mass factor (AMF) calculations performed with more realistic model parameters is presented. The viewing angle dependency of surface albedo is taken into account by improving the GOME-2 Lambertian-equivalent reflectivity (LER) climatology with a directionally dependent LER (DLER) dataset over land and an ocean surface albedo parameterisation over water. A priori NO₂ profiles with higher spatial and temporal resolutions are obtained from the IFS (CB05BASCOE) chemistry transport model based on recent emission inventories. A more realistic cloud treatment is provided by a clouds-as-layers (CAL) approach, which treats the clouds as uniform layers of water droplets, instead of the current clouds-as-reflecting-boundaries (CRB) model, which assumes that the clouds are Lambertian reflectors. On average, improvements in the AMF calculation affect the tropospheric NO₂ columns by ±15 % in winter and ±5 % in summer over largely polluted regions. In addition, the impact of aerosols on our tropospheric NO₂ retrieval is investigated by comparing the concurrent retrievals based on ground-based aerosol measurements (explicit aerosol correction) and the aerosol-induced cloud parameters (implicit aerosol correction). Compared with the implicit aerosol correction utilising the CRB cloud parameters, the use of the CAL approach reduces the AMF errors by more than 10 %. Finally, to evaluate the improved GOME-2 tropospheric NO₂ columns, a validation is performed using ground-based multi-axis differential optical absorption spectroscopy (MAXDOAS) measurements at different BIRA-IASB stations. At the suburban Xianghe station, the improved tropospheric NO₂ dataset shows better agreement with coincident ground-based measurements with a correlation coefficient of 0.94.

Inversion models for retrieving the total and tropospheric nitrogen dioxide ( ${\mathrm{NO}}_2$ ) columns from spaceborne remote sensing data are presented. For total column retrieval, we propose the so-called differential radiance models with internal and external closure and solve the underlying nonlinear equations by using the method of Tikhonov regularization and the iteratively regularized Gauss–Newton method. For tropospheric column retrieval, we design a nonlinear and a linear model by using the results of the total column retrieval and the value of the stratospheric ${\mathrm{NO}}_2$ column delivered by a stratosphere–troposphere separation method. We also analyze the fundamentals of the commonly used differential optical absorption spectroscopy (DOAS) model and outline its relationship to the proposed inversion models. By a numerical analysis, we analyze the accuracy of the inversion models to retrieve total and tropospheric ${\mathrm{NO}}_2$ columns.

An improved algorithm for the retrieval of total and tropospheric nitrogen dioxide (NO₂) columns from the Global Ozone Monitoring Experiment-2 (GOME-2) is presented. The refined retrieval will be implemented in a future version of the GOME Data Processor (GDP) as used by the EUMETSAT Satellite Application Facility on Atmospheric Composition and UV Radiation (AC-SAF). The first main improvement is the application of an extended 425–497 nm wavelength fitting window in the differential optical absorption spectroscopy (DOAS) retrieval of the NO₂ slant column density. Updated absorption cross-sections and a linear offset correction are used for the large fitting window. An improved slit function treatment is applied to compensate for both long-term and in-orbit drift of the GOME-2 slit function. Compared to the current operational (GDP 4.8) dataset, the use of these new features increases the NO₂ columns by 1–3 × 10¹⁴ molec/cm² and reduces the slant column error by ∼ 24 %. In addition, the bias between GOME-2A and GOME-2B measurements is largely reduced by adopting a new level 1b data version in the DOAS retrieval. The retrieved NO₂ slant columns show good consistency with the Quality Assurance for Essential Climate Variables (QA4ECV) retrieval with a good overall quality. Second, the STRatospheric Estimation Algorithm from Mainz (STREAM), which was originally developed for the TROPOspheric Monitoring Instrument (TROPOMI) instrument, was optimized for GOME-2 measurements to determine the stratospheric NO₂ column density. Applied to synthetic GOME-2 data, the estimated stratospheric NO₂ columns from STREAM shows a good agreement with the a priori truth. An improved latitudinal correction is introduced in STREAM to reduce the biases over the subtropics. Applied to GOME-2 measurements, STREAM largely reduces the overestimation of stratospheric NO₂ columns over polluted regions in the GDP 4.8 dataset. Third, the calculation of AMF applies an updated box-air mass factor (box-AMF) look-up table (LUT) calculated using the latest version of VLIDORT model with an increased number of reference points and vertical layers, a new GOME-2 surface albedo climatology, improved a priori NO₂ profiles obtained from the TM5-MP chemistry transport model, and improved GOME-2 cloud parameters. A large effect on the retrieved tropospheric NO₂ columns (more than 10 %) is found over polluted regions. To evaluate the GOME-2 tropospheric NO₂ columns, an end-to-end validation is performed using ground-based multiple-axis DOAS (MAXDOAS) measurements. The validation is illustrated for 6 stations covering urban, suburban, and background situations. Compared to the GDP 4.8 product, the new dataset presents an improved agreement with the MAXDOAS measurements for all the stations.