{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T21:11:18Z","timestamp":1783977078116,"version":"3.55.0"},"reference-count":75,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008383","name":"Bundesministerium f\u00fcr Verkehr und Digitale Infrastruktur","doi-asserted-by":"publisher","award":["19F2065"],"award-info":[{"award-number":["19F2065"]}],"id":[{"id":"10.13039\/100008383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 \u00b5\u00b5g\/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 \u00b5\u00b5g\/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\u201330% 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.<\/jats:p>","DOI":"10.3390\/rs13050969","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:39:07Z","timestamp":1614904747000},"page":"969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method"],"prefix":"10.3390","volume":"13","author":[{"given":"Ka Lok","family":"Chan","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ehsan","family":"Khorsandi","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center, German Aerospace Center (DLR), 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Liu","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3425-6309","authenticated-orcid":false,"given":"Frank","family":"Baier","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center, German Aerospace Center (DLR), 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pieter","family":"Valks","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1002\/qj.49709640815","article-title":"The influence of nitrogen oxides on the atmospheric ozone content","volume":"96","author":"Crutzen","year":"1970","journal-title":"Q. J. R. Meteorolog. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3626","DOI":"10.1021\/es010676+","article-title":"Characterization of Secondary Aerosol from the Photo oxidation of Toluene in the Presence of NOx and 1-Propene","volume":"35","author":"Jang","year":"2001","journal-title":"Environ. Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"27701","DOI":"10.1029\/2000JD000191","article-title":"NOx production by lightning over the continental United States","volume":"106","author":"Bond","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1073\/pnas.252763799","article-title":"Impacts of anthropogenic and natural NOx sources over the U.S. on tropospheric chemistry","volume":"100","author":"Zhang","year":"2003","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2969","DOI":"10.5194\/amt-10-2969-2017","article-title":"Mixing layer height as an indicator for urban air quality?","volume":"10","author":"Wiegner","year":"2017","journal-title":"Atmos. Meas. Tech."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4499","DOI":"10.5194\/amt-13-4499-2020","article-title":"MAX-DOAS measurements of tropospheric NO2 and HCHO in Munich and the comparison to OMI and TROPOMI satellite observations","volume":"13","author":"Chan","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1175\/1520-0469(1999)056<0151:TGOMEG>2.0.CO;2","article-title":"The global ozone monitoring experiment (GOME): Mission concept and first scientific results","volume":"56","author":"Burrows","year":"1999","journal-title":"J. Atmos. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1175\/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2","article-title":"SCIAMACHY: Mission objectives and measurement modes","volume":"56","author":"Bovensmann","year":"1999","journal-title":"J. Atmos. Sci."},{"key":"ref_9","first-page":"28","article-title":"GOME-2-Metop\u2019s second-generation sensor for operational ozone monitoring","volume":"102","author":"Callies","year":"2000","journal-title":"ESA Bull."},{"key":"ref_10","first-page":"65","article-title":"Overview of the nadir sensor and algorithms for the NPOESS Ozone Mapping and Profiler Suite (OMPS)","volume":"4891","author":"Rodriguez","year":"2003","journal-title":"SPIE"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/s41377-020-0306-z","article-title":"First observation of tropospheric nitrogen dioxide from the Environmental Trace Gases Monitoring Instrument onboard the GaoFen-5 satellite","volume":"9","author":"Zhang","year":"2020","journal-title":"Light Sci. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1109\/TGRS.2006.872333","article-title":"The Ozone Monitoring Instrument","volume":"44","author":"Levelt","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2011.09.027","article-title":"TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications","volume":"120","author":"Veefkind","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.atmosenv.2012.02.041","article-title":"Real-time air quality forecasting, part II: State of the science, current research needs, and future prospects","volume":"60","author":"Zhang","year":"2012","journal-title":"Atmos. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5325","DOI":"10.5194\/acp-15-5325-2015","article-title":"Data assimilation in atmospheric chemistry models: Current status and future prospects for coupled chemistry meteorology models","volume":"15","author":"Bocquet","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_16","unstructured":"Mak, H.W.L. (2019). Improved Remote Sensing Algorithms and Data Assimilation Approaches in Solving Environmental Retrieval Problems. [Ph.D. Thesis, Hong Kong University of Science and Technology]."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lamsal, L.N., Martin, R.V., van Donkelaar, A., Steinbacher, M., Celarier, E.A., Bucsela, E., Dunlea, E.J., and Pinto, J.P. (2008). Ground-level nitrogen dioxide concentrations inferred from the satellite-borne Ozone Monitoring Instrument. J. Geophys. Res. Atmos., 113.","DOI":"10.1029\/2007JD009235"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.atmosenv.2015.08.011","article-title":"Assessment of the magnitude and recent trends in satellite-derived ground-level nitrogen dioxide over North America","volume":"118","author":"Kharol","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105578","DOI":"10.1016\/j.envint.2020.105578","article-title":"Bayesian geostatistical modelling of high-resolution NO2 exposure in Europe combining data from monitors, satellites and chemical transport models","volume":"138","author":"Beloconi","year":"2020","journal-title":"Environ. Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104013","DOI":"10.1088\/1748-9326\/aba3a5","article-title":"Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument","volume":"15","author":"Cooper","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"13555","DOI":"10.1021\/es403089q","article-title":"Western European Land Use Regression Incorporating Satellite- and Ground-Based Measurements of NO2 and PM10","volume":"47","author":"Vienneau","year":"2013","journal-title":"Environ. Sci. Technol."},{"key":"ref_22","first-page":"2305","article-title":"Daily Ambient NO2 Concentration Predictions Using Satellite Ozone Monitoring Instrument NO2 Data and Land Use Regression","volume":"48","author":"Lee","year":"2014","journal-title":"Environ. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.atmosenv.2015.01.053","article-title":"Satellite NO2 data improve national land use regression models for ambient NO2 in a small densely populated country","volume":"105","author":"Hoek","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Qin, K., Rao, L., Xu, J., Bai, Y., Zou, J., Hao, N., Li, S., and Yu, C. (2017). Estimating Ground Level NO2 Concentrations over Central-Eastern China Using a Satellite-Based Geographically and Temporally Weighted Regression Model. Remote Sens., 9.","DOI":"10.3390\/rs9090950"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, D., Lee, H., Hong, H., Choi, W., Lee, Y.G., and Park, J. (2017). Estimation of Surface NO2 Volume Mixing Ratio in Four Metropolitan Cities in Korea Using Multiple Regression Models with OMI and AIRS Data. Remote Sens., 9.","DOI":"10.3390\/rs9060627"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11985","DOI":"10.1002\/2017GL075710","article-title":"Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach","volume":"44","author":"Li","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.scitotenv.2018.04.251","article-title":"A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information","volume":"636","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10279","DOI":"10.1021\/acs.est.9b03107","article-title":"Predicting Fine-Scale Daily NO2 for 2005\u20132016 Incorporating OMI Satellite Data Across Switzerland","volume":"53","author":"Saucy","year":"2019","journal-title":"Environ. Sci. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"117322","DOI":"10.1016\/j.atmosenv.2020.117322","article-title":"Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data","volume":"224","author":"Qin","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_30","unstructured":"Statistisches Bundesamt (2020, November 30). Population\u2014Statistisches Bundesamt. Available online: https:\/\/www.destatis.de\/EN\/Themes\/Society-Environment\/Population\/Current-Population\/Tables\/liste-current-population.html."},{"key":"ref_31","unstructured":"International Monetary Fund (2020). Research Dept. World Economic Outlook, October 2020, International Monetary Fund."},{"key":"ref_32","unstructured":"Umweltbundesamt (2020, December 14). Nitrogen Dioxide Loads in Germany Down Slightly in 2018. Available online: https:\/\/www.umweltbundesamt.de\/en\/press\/pressinformation\/nitrogen-dioxide-loads-in-germany-down-slightly-in."},{"key":"ref_33","unstructured":"Platt, U., and Stutz, J. (2008). Differential Optical Absorption Spectroscopy\u2014Principles and Applications, Springer."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8311","DOI":"10.1029\/JD092iD07p08311","article-title":"On the interpretation of zenith sky absorption measurements","volume":"92","author":"Solomon","year":"1987","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"721","DOI":"10.5194\/gmd-10-721-2017","article-title":"The high-resolution version of TM5-MP for optimized satellite retrievals: Description and validation","volume":"10","author":"Williams","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kleipool, Q.L., Dobber, M.R., de Haan, J.F., and Levelt, P.F. (2008). Earth surface reflectance climatology from 3 years of OMI data. J. Geophys. Res. Atmos., 113.","DOI":"10.1029\/2008JD010290"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.5194\/amt-9-2357-2016","article-title":"OCRA radiometric cloud fractions for GOME-2 on MetOp-A\/B","volume":"9","author":"Lutz","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"409","DOI":"10.5194\/amt-11-409-2018","article-title":"The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor","volume":"11","author":"Loyola","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.5194\/amt-9-2753-2016","article-title":"The STRatospheric Estimation Algorithm from Mainz (STREAM): Estimating stratospheric NO2 from nadir-viewing satellites by weighted convolution","volume":"9","author":"Beirle","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_40","unstructured":"Liu, S., Valks, P., Pinardi, G., Xu, J., Chan, K.L., Argyrouli, A., Lutz, R., Beirle, S., Khorsandi, E., and Baier, F. (2021). An improved tropospheric NO2 column retrieval algorithm for TROPOMI over Europe. Atmos. Meas. Techniques, 1\u201343."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The Shuttle Radar Topography Mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Abrams, M., Crippen, R., and Fujisada, H. (2020). ASTER Global Digital Elevation Model (GDEM) and ASTER Global Water Body Dataset (ASTWBD). Remote Sens., 12.","DOI":"10.3390\/rs12071156"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1080\/23754931.2015.1014272","article-title":"Taking Advantage of the Improved Availability of Census Data: A First Look at the Gridded Population of the World, Version 4","volume":"1","author":"MacManus","year":"2015","journal-title":"Pap. Appl. Geogr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5479","DOI":"10.5194\/acp-7-5479-2007","article-title":"Technical Note: The air quality modeling system Polyphemus","volume":"7","author":"Mallet","year":"2007","journal-title":"Atmos. Chem. Phys."},{"key":"ref_46","unstructured":"Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.Y., Wang, W., and Powers, J.G. (2008). A Description of the Advanced Research WRF Version 3, University Corporation for Atmospheric Research. NCAR Tech. Note NCAR\/TN-475+ STR."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1504\/IJEP.2004.005474","article-title":"Development and validation of a fully modular platform for numerical modelling of air pollution: POLAIR","volume":"22","author":"Boutahar","year":"2004","journal-title":"Int. J. Environ. Pollut."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"25847","DOI":"10.1029\/97JD00849","article-title":"A new mechanism for regional atmospheric chemistry modeling","volume":"102","author":"Stockwell","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"28275","DOI":"10.1029\/2001JD000384","article-title":"Modeling the formation of secondary organic aerosol within a comprehensive air quality model system","volume":"106","author":"Schell","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.5194\/acp-7-1537-2007","article-title":"Technical Note: A new SIze REsolved Aerosol Model (SIREAM)","volume":"7","author":"Debry","year":"2007","journal-title":"Atmos. Chem. Phys."},{"key":"ref_51","unstructured":"Spee, E.J. (1998). Numerical Methods in Global Transport-Chemistry Models. [Ph.D. Thesis, University of Amsterdam]."},{"key":"ref_52","first-page":"107","article-title":"Numerical time integration for air pollution models","volume":"10","author":"Verwer","year":"2002","journal-title":"Surv. Math. Ind."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"43","DOI":"10.5194\/gmd-3-43-2010","article-title":"Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4)","volume":"3","author":"Emmons","year":"2010","journal-title":"Geosci. Model Dev."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/BF00122760","article-title":"A simple model of the atmospheric boundary layer; sensitivity to surface evaporation","volume":"37","author":"Troen","year":"1986","journal-title":"Bound.-Layer Meteorol."},{"key":"ref_55","unstructured":"Denier van der Gon, H., Visschedijk, A., Van der Brugh, H., and Dr\u00f6ge, R. (2020, December 14). A High ResolutionEuropean Emission Data Base for the Year 2005, A Contribution to UBA-Projekt PAREST: Particle Reduction Strategies. Available online: https:\/\/www.umweltbundesamt.de\/sites\/default\/files\/medien\/461\/publikationen\/texte_41_2013_appelhans_e03_komplett_0.pdf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"10963","DOI":"10.5194\/acp-14-10963-2014","article-title":"TNO-MACC_II emission inventory; A multi-year (2003\u20132009) consistent high-resolution European emission inventory for air quality modelling","volume":"14","author":"Kuenen","year":"2014","journal-title":"Atmos. Chem. Phys."},{"key":"ref_57","unstructured":"Erbertseder, T. (2020, December 14). Final Report\u2014PASODOBLE (Promote Air Quality Services Integrating Observations\u2013Development of Basic Localised Information for Europe). Available online: https:\/\/cordis.europa.eu\/docs\/results\/241557\/final1-pasodoble-final-publishable-summary-report.pdf."},{"key":"ref_58","first-page":"3","article-title":"Estimation and causes of uncertainty of air quality forecasts for the Blackforest region","volume":"49","author":"Bergemann","year":"2012","journal-title":"Wiss. Mitteilungen Aus Dem Inst. F\u00fcr Meteorol. Der Univ. Leipz."},{"key":"ref_59","unstructured":"Erbertseder, T., and Loyola, D. (2020, December 14). Despite Weather Influence\u2013Corona Effect Now Indisputable. Available online: https:\/\/www.dlr.de\/eoc\/en\/desktopdefault.aspx\/tabid-14195\/24618_read-64626."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"D16S45","DOI":"10.1029\/2007JD008988","article-title":"Validation of OMI tropospheric NO2 column densities using direct-Sun mode Brewer measurements at NASA Goddard Space Flight Center","volume":"113","author":"Wenig","year":"2008","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"901","DOI":"10.5194\/amt-5-901-2012","article-title":"NO2 measurements in Hong Kong using LED based long path differential optical absorption spectroscopy","volume":"5","author":"Chan","year":"2012","journal-title":"Atmos. Meas. Tech."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"10313","DOI":"10.5194\/acp-16-10313-2016","article-title":"Evaluation of European air quality modelled by CAMx including the volatility basis set scheme","volume":"16","author":"Ciarelli","year":"2016","journal-title":"Atmos. Chem. Phys."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4087","DOI":"10.1016\/j.asoc.2013.05.007","article-title":"Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels","volume":"13","author":"Wahid","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2012.05.008","article-title":"Biases in long-term NO2 averages inferred from satellite observations due to cloud selection criteria","volume":"124","author":"Geddes","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3015","DOI":"10.1016\/j.atmosenv.2010.05.009","article-title":"An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations","volume":"44","author":"Cobourn","year":"2010","journal-title":"Atmos. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1016\/j.atmosenv.2010.11.051","article-title":"Quantifying the influence of local meteorology on air quality using generalized additive models","volume":"45","author":"Pearce","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.apr.2016.08.001","article-title":"Developing a statistical based approach for predicting local air quality in complex terrain area","volume":"8","author":"Kwok","year":"2017","journal-title":"Atmos. Pollut. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1126\/science.186.4168.1037","article-title":"Sunday and Workday Variations in Photochemical Air Pollutants in New Jersey and New York","volume":"186","author":"Cleveland","year":"1974","journal-title":"Science"},{"key":"ref_69","unstructured":"Tedros, A.G. (2020, December 14). WHO Director-General\u2019s Opening Remarks at the Media Briefing on COVID-19\u201411 March 2020. Available online: https:\/\/www.who.int\/director-general\/speeches\/detail\/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19\u201411-march-2020."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"140353","DOI":"10.1016\/j.scitotenv.2020.140353","article-title":"COVID-19 lockdown effects on air quality by NO2 in the cities of Barcelona and Madrid (Spain)","volume":"741","author":"Baldasano","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"139864","DOI":"10.1016\/j.scitotenv.2020.139864","article-title":"Changes in U.S. air pollution during the COVID-19 pandemic","volume":"739","author":"Berman","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"110129","DOI":"10.1016\/j.envres.2020.110129","article-title":"The role of air pollution (PM and NO2) in COVID-19 spread and lethality: A systematic review","volume":"191","author":"Copat","year":"2020","journal-title":"Environ. Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"138605","DOI":"10.1016\/j.scitotenv.2020.138605","article-title":"Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality","volume":"726","author":"Ogen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"18984","DOI":"10.1073\/pnas.2006853117","article-title":"COVID-19 lockdowns cause global air pollution declines","volume":"117","author":"Venter","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"eabd4049","DOI":"10.1126\/sciadv.abd4049","article-title":"Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis","volume":"6","author":"Wu","year":"2020","journal-title":"Sci. Adv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/969\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:32:50Z","timestamp":1760160770000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,4]]},"references-count":75,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13050969"],"URL":"https:\/\/doi.org\/10.3390\/rs13050969","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,4]]}}}