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

Chan, Ka Lok and Khorsandi, Ehsan and Liu, Song and Baier, Frank and Valks, Pieter (2021) Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method. Remote Sensing, 13 (5), pp. 1-23. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs13050969. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/13/5/969

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.

Item URL in elib:https://elib.dlr.de/141411/
Document Type:Article
Title:Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Chan, Ka LokKa.Chan (at) dlr.deUNSPECIFIED
Khorsandi, EhsanEhsan.Khorsandi (at) dlr.deUNSPECIFIED
Liu, SongSong.Liu (at) dlr.deUNSPECIFIED
Baier, FrankFrank.Baier (at) dlr.deUNSPECIFIED
Valks, PieterPieter.Valks (at) dlr.deUNSPECIFIED
Date:4 March 2021
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:13
DOI :10.3390/rs13050969
Page Range:pp. 1-23
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:NO2; surface concentration; TROPOMI; satellite; Germany; machine learning; exposure; COVID-19
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research, Vorhaben Spectroscopic Methods in Remote Sensing (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
German Remote Sensing Data Center > Atmosphere
Deposited By: Chan, Ka Lok
Deposited On:23 Mar 2021 10:33
Last Modified:16 Apr 2021 09:33

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