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Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

Ka Lok Chan, +4 more
- 04 Mar 2021 - 
- Vol. 13, Iss: 5, pp 969
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TLDR
In this paper, a machine learning approach is used to estimate surface NO2 concentrations over Germany using satellite data and several meteorological parameters, which is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations.
Abstract
In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 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 NO2 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 NO2 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 NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µµg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µµg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 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.

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Variability of PM2.5 and O3 concentrations and their driving forces over Chinese megacities during 2018-2020.

TL;DR: Wang et al. as mentioned in this paper used generalized additive models (GAMs) to quantify the contribution of individual meteorological factors and their gas precursors on PM2.5 and O3.
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Stereoscopic hyperspectral remote sensing of the atmospheric environment: Innovation and prospects

TL;DR: In this paper , the authors systematically review the recent advances in satellite and ground-based hyperspectral remote sensing techniques, including China's first HRS satellite GF-5, hardware, algorithms, and applications.
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Machine learning-based estimation of ground-level NO2 concentrations over China.

TL;DR: Zhang et al. as mentioned in this paper proposed a machine learning estimation method for retrieving the ground-level NO2 concentrations throughout China based on the tropospheric NO2 column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020.
Journal ArticleDOI

Machine learning-based estimation of ground-level NO2 concentrations over China

TL;DR: Zhang et al. as discussed by the authors proposed a machine learning estimation method for retrieving ground-level NO2 concentrations throughout China based on the tropospheric NO2 column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020.
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Spatiotemporal Patterns in Data Availability of the Sentinel-5P NO2 Product over Urban Areas in Norway

TL;DR: In this paper, the authors evaluate the spatiotemporal patterns in the availability of valid data from the operational TROPOMI tropospheric nitrogen dioxide (NO2) product over five urban areas (Oslo, Bergen, Trondheim, Stavanger, and Kristiansand) and a 2.5 year period from July 2018 through November 2020.
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