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.

The latest epidemiological studies have revealed that the adverse health effects of PM_2.5 have impacts beyond respiratory and cardio-vascular diseases and also affect the development of the brain and metabolic diseases. The need for accurate and spatio-temporally resolved PM_2.5 data has thus been substantiated. While the selective information provided by station measurements is mostly insufficient for area-wide monitoring, satellite data have been increasingly applied to comprehensively monitor PM_2.5 distributions. Although the accuracy and reliability of satellite-based PM_2.5 estimations have increased, most studies still rely on a single sensor. However, several datasets have become available in the meantime, which raises the need for a systematic analysis. This study presents the first systematic evaluation of four satellite-based AOD datasets obtained from different sensors and retrieval methodologies to derive ground-level PM_2.5 concentrations. We apply a random forest approach and analyze the effect of the resolution and coverage of the satellite data and the impact of proxy data on the performance. We examine AOD data from the Moderate resolution Imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites, including Dark Target (DT) algorithm products and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. Additionally, we explore more recent datasets from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3a and from the Tropospheric Monitoring Instrument (TROPOMI) operating on the Sentinel-5 precursor (S5p). The method is demonstrated for Germany and the year 2018, where a dense in situ measurement network and relevant proxy data are available. Overall, the model performance is satisfactory for all four datasets with cross-validated R² values ranging from 0.68 to 0.77 and excellent for MODIS AOD reaching correlations of almost 0.9. We find a strong dependency of the model performance on the coverage and resolution of the AOD training data. Feature importance rankings show that AOD has less weight compared to proxy data for SLSTR and TROPOMI.

MIPAS observations are assimilated using a modified version of the chemistry‐transport model ROSE to derive consistent global chemical analyses of the stratosphere. Due to different retrieval schemes applied, available MIPAS datasets are expected to differ in quality and coverage. In this study we investigate the sensitivity of the data assimilation scheme to two different sample datasets. ENVISAT/MIPAS baseline observations of H₂O,O₃,HNO₃,CH₄,N₂O and NO₂, covering October–November 2003, are considered. Sequential assimilation is performed using an optimum interpolation scheme with error propagation. It is shown that all assimilated model species benefit significantly from observations. Results are analysed using observation minus first‐guess error statistics and are additionally compared to UARS/HALOE data. Optimized assimilation parameters are derived using χ² diagnostics. Two different MIPAS data products are examined: the European Space Agency operational product and the Institute for Meteorology Karlsruhe (IMK) scientific product. Assimilation results show some significant differences with respect to the dataset type used. For example, regions with increased stratospheric H₂O concentrations near the tropical tropopause are only present when IMK data are applied. Both datasets are found to be well suited for global assimilation experiments to study the chemistry and dynamics of the middle atmosphere. Copyright © 2005 Royal Meteorological Society

Urban populations are significantly affected by air pollution, which poses a major threat to public health. However, standardized and public mobility data, essential for an exposure assessment, are frequently unavailable. Earth Observation-derived and model datasets can support large-scale health studies especially in remote areas with limited data availability. This study investigates the use of a globally-derivable variable from remote sensing data to estimate the static versus dynamic population exposure difference. A health risk assessment using a higher and a lower-resolution air pollution data was performed. This was achieved by examining air pollution concentrations in two European regions, Lombardy, in Italy, and Germany, incorporating commuting datasets. Accordingly, a retrospective long-term exposure assessment to particulate matter less than 2.5 microns (PM2.5), nitrogen dioxide (NO2), and ozone (O3) was performed from 2013 to 2022. The study evaluates the difference between the resident and the dynamic population exposed to concentrations exceeding the new limits set by the World Health Organization (WHO). The relation between pollutants concentration and the Fraction of Settlement Area (FSA), a proxy of urbanization levels, derived from the global World Settlement Footprint dataset was explored. Two pollution datasets were used: with European, and global coverage. The analysis decouples daytime and nighttime hours. For each region and pollutant specific FSA thresholds were identified, that maximize the population exposure gap. Our findings highlight the impact of air pollution on population health, revealing widespread exposure exceeding WHO limits, particularly for PM2.5, and emphasizing the importance of considering diurnal exposure variations in health risk assessments.

In most countries, freight is predominantly transported by road cargo trucks. We present a new satellite remote sensing method for detecting moving trucks on roads using Sentinel-2 data. The method exploits a temporal sensing offset of the Sentinel-2 multispectral instrument, causing spatially and spectrally distorted signatures of moving objects. A random forest classifier was trained (overall accuracy: 84%) on visual-near-infrared-spectra of 2500 globally labelled targets. Based on the classification, the target objects were extracted using a developed recursive neighbourhood search. The speed and the heading of the objects were approximated. Detections were validated by employing 350 globally labelled target boxes (mean $F_1$ score: 0.74). The lowest $F_1$ score was achieved in Kenya (0.36), the highest in Poland (0.88). Furthermore, validated at 26 traffic count stations in Germany on in sum 390 dates, the truck detections correlate spatio-temporally with station figures (Pearson r-value: 0.82, RMSE: 43.7). Absolute counts were underestimated on 81% of the dates. The detection performance may differ by season and road condition. Hence, the method is only suitable for approximating the relative truck traffic abundance rather than providing accurate absolute counts. However, existing road cargo monitoring methods that rely on traffic count stations or very high resolution remote sensing data have limited global availability. The proposed moving truck detection method could fill this gap, particularly where other information on road cargo traffic are sparse by employing globally and freely available Sentinel-2 data. It is inferior to the accuracy and the temporal detail of station counts, but superior in terms of spatial coverage.

Observing system simulation experiments (OSSEs) are performed to examine the impact of ozonesonde observations on a data assimilation system during a simulated satellite data gap during February 2003. Using the four-dimensional variational chemical data assimilation system SACADA, the relative influence of launch rates and station coverage is investigated. Starting with the VINTERSOL/MATCH campaign, different network and sounding configurations are evaluated. To quantify the performance of assimilation experiments in the lower stratosphere, analysis skill and linear pattern correlation with respect to ERA-Interim reference data are assessed for the 20 km altitude level. Using first-guess and analysis minus observation error statistics, a priori error settings are tuned to optimise the assimilation of simulated and real-world ozone soundings. In summary, it is found that, during satellite data gaps, ozonesonde data can have a significant positive impact on the mean analysis skill depending both on the number of observations and the network layout. A better distributed network based on the GAW system, with 28 stations and three soundings bi-weekly, proves clearly superior to VINTERSOL/MATCH, showing a positive gain in skill of 0.26 compared to a free-running model.