ABSTRACT
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 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 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. [ABSTRACT FROM AUTHOR] Copyright of Remote Sensing is the property of MDPI Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)