147 citations as recorded by crossref.

1. Spatiotemporal inhomogeneity of total column NO2 in a polluted urban area inferred from TROPOMI and Pandora intercomparisons J. Park et al. 10.1080/15481603.2022.2026640
2. Comparison of S5P/TROPOMI Inferred NO2 Surface Concentrations with In Situ Measurements over Central Europe A. Pseftogkas et al. 10.3390/rs14194886
3. Evaluation of TROPOMI and OMI Tropospheric NO2 Products Using Measurements from MAX-DOAS and State-Controlled Stations in the Jiangsu Province of China K. Cai et al. 10.3390/atmos13060886
4. Estimation of OH in urban plumes using TROPOMI-inferred NO2 ∕ CO S. Lama et al. 10.5194/acp-22-16053-2022
5. The Dynamics of Air Pollution in the Southwestern Part of the Caspian Sea Basin (Based on the Analysis of Sentinel-5 Satellite Data Utilizing the Google Earth Engine Cloud-Computing Platform) V. Tabunshchik et al. 10.3390/atmos15111371
6. Environmental dust effect phenomenon on the sustainability of urban areas using remote sensing data in GEE V. Isazade et al. 10.1007/s42797-022-00067-z
7. Machine learning-based estimation of ground-level NO2 concentrations over China Y. Chi et al. 10.1016/j.scitotenv.2021.150721
8. Environmental impact of COVID-19 led lockdown: A satellite data-based assessment of air quality in Indian megacities S. Prakash et al. 10.1016/j.uclim.2021.100900
9. A new method for inferring city emissions and lifetimes of nitrogen oxides from high-resolution nitrogen dioxide observations: a model study F. Liu et al. 10.5194/acp-22-1333-2022
10. Evaluation of the LOTOS-EUROS NO<sub>2</sub> simulations using ground-based measurements and S5P/TROPOMI observations over Greece I. Skoulidou et al. 10.5194/acp-21-5269-2021
11. Evaluation of the Potential of Sentinel-5P TROPOMI and AIS Marine Traffic Data for the Monitoring of Anthropogenic Activity and Maritime Transport NOx-Emissions in Canary Islands Waters M. Rodriguez Valido et al. 10.3390/su15054632
12. Assessment of Tropospheric Concentrations of NO2 from the TROPOMI/Sentinel-5 Precursor for the Estimation of Long-Term Exposure to Surface NO2 over South Korea U. Jeong & H. Hong 10.3390/rs13101877
13. Analyzing COVID-19 Impacts on Vehicle Travels and Daily Nitrogen Dioxide (NO2) Levels among Florida Counties A. Karaer et al. 10.3390/en13226044
14. Assessing Nitrogen Dioxide in the Highveld Troposphere: Pandora Insights and TROPOMI Sentinel-5P Evaluation R. Kai-Sikhakhane et al. 10.3390/atmos15101187
15. Effect of vegetation and land surface temperature on NO2 concentration: A Google Earth Engine-based remote sensing approach S. Rahaman et al. 10.1016/j.uclim.2022.101336
16. Improvement in air quality and its impact on land surface temperature in major urban areas across India during the first lockdown of the pandemic B. Parida et al. 10.1016/j.envres.2021.111280
17. Tropospheric NO2 and O3 Response to COVID‐19 Lockdown Restrictions at the National and Urban Scales in Germany V. Balamurugan et al. 10.1029/2021JD035440
18. Air Quality Response in China Linked to the 2019 Novel Coronavirus (COVID‐19) Lockdown K. Miyazaki et al. 10.1029/2020GL089252
19. A machine learning-based approach for fusing measurements from standard sites, low-cost sensors, and satellite retrievals: Application to NO2 pollution hotspot identification J. Fu et al. 10.1016/j.atmosenv.2023.119756
20. COVID-19 lockdown air quality change implications for solar energy generation over China K. Choi & H. Brindley 10.1088/1748-9326/abd42f
21. Assessing the impact of COVID-19 lockdown on fine-scale air quality across a heavy-pollution city using low-cost sensors W. Sun & R. Li 10.1016/j.atmosenv.2023.120275
22. Air quality trends and implications pre and post Covid-19 restrictions A. Cardito et al. 10.1016/j.scitotenv.2023.162833
23. Spatiotemporal estimation of surface NO2 concentrations in the Pearl River Delta region based on TROPOMI data and machine learning Q. Wei et al. 10.1016/j.apr.2024.102353
24. The Development and Application of Machine Learning in Atmospheric Environment Studies L. Zheng et al. 10.3390/rs13234839
25. Sudden changes in nitrogen dioxide emissions over Greece due to lockdown after the outbreak of COVID-19 M. Koukouli et al. 10.5194/acp-21-1759-2021
26. Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models N. Zaman et al. 10.1016/j.jclepro.2024.141559
27. Unveiling urban air quality dynamics during COVID-19: a Sentinel-5P TROPOMI hotspot analysis A. Mathew et al. 10.1038/s41598-024-72276-4
28. TROPOMI NO2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO2 Concentrations D. Goldberg et al. 10.1029/2020EF001665
29. Emissions of nitrogen dioxide in the northeast U.S. during the 2020 COVID-19 lockdown S. Azad & M. Ghandehari 10.1016/j.jenvman.2022.114902
30. Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants A. Alnaim et al. 10.3390/rs14030729
31. Peculiar COVID-19 effects in the Greater Tokyo Area revealed by spatiotemporal variabilities of tropospheric gases and light-absorbing aerosols A. Damiani et al. 10.5194/acp-22-12705-2022
32. Characterization of Nitrogen Dioxide Variability Using Ground-Based and Satellite Remote Sensing and In Situ Measurements in the Tiber Valley (Lazio, Italy) C. Bassani et al. 10.3390/rs15153703
33. Direct estimates of biomass burning NO<sub><i>x</i></sub> emissions and lifetimes using daily observations from TROPOMI X. Jin et al. 10.5194/acp-21-15569-2021
34. Observing Nitrogen Dioxide Air Pollution Inequality Using High-Spatial-Resolution Remote Sensing Measurements in Houston, Texas M. Demetillo et al. 10.1021/acs.est.0c01864
35. Spatiotemporal variability/stability analysis of NO 2 , CO, and land surface temperature (LST) during COVID-19 lockdown in Amman city, Jordan A. Almagbile & K. Hazaymeh 10.1080/10095020.2022.2066575
36. High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data C. Liu et al. 10.3390/rs15153878
37. Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument M. Cooper et al. 10.1088/1748-9326/aba3a5
38. Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine M. Kazemi Garajeh et al. 10.3390/pollutants3020019
39. Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine A. Saim & M. Aly 10.1016/j.apr.2024.102226
40. Green space coverage versus air pollution: a cloud-based remote sensing data analysis in Sichuan, Western China A. Naboureh et al. 10.1080/17538947.2024.2383454
41. Spatial-Temporal analysis of urban environmental variables using building height features M. Kakooei & Y. Baleghi 10.1016/j.uclim.2023.101736
42. Sentinel satellite data monitoring of air pollutants with interpolation methods in Guayaquil, Ecuador D. Mejía C. et al. 10.1016/j.rsase.2023.100990
43. Assessment of Airport-Related Emissions and Their Impact on Air Quality in Atlanta, GA, Using CMAQ and TROPOMI A. Lawal et al. 10.1021/acs.est.1c03388
44. Air Quality Improvement Following COVID-19 Lockdown Measures and Projected Benefits for Environmental Health Y. Liou et al. 10.3390/rs15020530
45. Assessment of tropospheric NO2 concentrations over greater Doha using Sentinel-5 TROPOspheric monitoring instrument (TROPOMI) satellite data: Temporal analysis, 2018–2023 Y. Mohieldeen et al. 10.1016/j.envpol.2024.124995
46. Air quality changes in Ukraine during the April 2020 wildfire event M. Savenets et al. 10.5937/gp24-27436
47. CLASP: CLustering of Atmospheric Satellite Products and Its Applications in Feature Detection of Atmospheric Trace Gases T. Lee & Y. Wang 10.1029/2023JD038887
48. Nitrogen Dioxide (NO2) Pollution Monitoring with Sentinel-5P Satellite Imagery over Europe during the Coronavirus Pandemic Outbreak M. Vîrghileanu et al. 10.3390/rs12213575
49. Assessing vulnerability of densely populated areas to air pollution using Sentinel-5P imageries: a case study of the Nile Delta, Egypt M. Hassaan et al. 10.1038/s41598-023-44186-4
50. Assessment of air quality and damage caused after an anthropogenic disaster in the city of Beirut S. Jayalakshmi et al. 10.1051/matecconf/202338401003
51. On the influence of vertical mixing, boundary layer schemes, and temporal emission profiles on tropospheric NO2 in WRF-Chem – comparisons to in situ, satellite, and MAX-DOAS observations L. Kuhn et al. 10.5194/acp-24-185-2024
52. Decoding seasonal variability of air pollutants with climate factors: A geostatistical approach using multimodal regression models for informed climate change mitigation S. Morshed et al. 10.1016/j.envpol.2024.123463
53. NO2 emissions from oil refineries in the Mississippi Delta M. Filonchyk & M. Peterson 10.1016/j.scitotenv.2023.165569
54. Scalable data processing and visualization service of Sentinel 5P for Earth Observations Data Cubes H. Astsatryan et al. 10.1007/s12517-023-11672-y
55. Declines and peaks in NO<sub>2</sub> pollution during the multiple waves of the COVID-19 pandemic in the New York metropolitan area M. Tzortziou et al. 10.5194/acp-22-2399-2022
56. Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI P. Levelt et al. 10.5194/acp-22-10319-2022
57. The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2 K. Qin et al. 10.5194/essd-16-5287-2024
58. Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula H. Petetin et al. 10.5194/acp-23-3905-2023
59. Evaluating Sentinel-5P TROPOMI tropospheric NO<sub>2</sub> column densities with airborne and Pandora spectrometers near New York City and Long Island Sound L. Judd et al. 10.5194/amt-13-6113-2020
60. Examining the status of improved air quality in world cities due to COVID-19 led temporary reduction in anthropogenic emissions S. Sannigrahi et al. 10.1016/j.envres.2021.110927
61. Quantifying urban, industrial, and background changes in NO<sub>2</sub> during the COVID-19 lockdown period based on TROPOMI satellite observations V. Fioletov et al. 10.5194/acp-22-4201-2022
62. Spatiotemporal mapping and assessment of daily ground NO2 concentrations in China using high-resolution TROPOMI retrievals S. Wu et al. 10.1016/j.envpol.2021.116456
63. Nitrogen dioxide reductions from satellite and surface observations during COVID-19 mitigation in Rome (Italy) C. Bassani et al. 10.1007/s11356-020-12141-9
64. Air pollution in Vietnam during the COVID-19 social isolation, evidence of reduction in human activities T. Ngo et al. 10.1080/01431161.2021.1934911
65. Assessment of the TROPOMI tropospheric NO<sub>2</sub> product based on airborne APEX observations F. Tack et al. 10.5194/amt-14-615-2021
66. Estimation of Surface-Level NO2 Using Satellite Remote Sensing and Machine Learning: A review M. Siddique et al. 10.1109/MGRS.2024.3398434
67. Assessment of the quality of TROPOMI high-spatial-resolution NO<sub>2</sub> data products in the Greater Toronto Area X. Zhao et al. 10.5194/amt-13-2131-2020
68. Non-uniform tropospheric NO2 level changes in European Union caused by governmental COVID-19 restrictions and geography G. Varga et al. 10.1016/j.cacint.2024.100145
69. Assessment of Spatial and Temporal Variation in NO2 Levels over Tourist Reception Areas in Poland D. Mochocki & W. Zgłobicki 10.3390/app13169477
70. Direct measurements of ozone response to emissions perturbations in California S. Wu et al. 10.5194/acp-22-4929-2022
71. New Insights Into the Role of Atmospheric Transport and Mixing on Column and Surface Concentrations of NO2 at a Coastal Urban Site T. Adams et al. 10.1029/2022JD038237
72. Significant annual variations of firework-impacted aerosols in Northeast China: Implications for rethinking the firework bans Y. Cheng et al. 10.1016/j.atmosenv.2024.120914
73. Satellite-detected tropospheric nitrogen dioxide and spread of SARS-CoV-2 infection in Northern Italy T. Filippini et al. 10.1016/j.scitotenv.2020.140278
74. Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO<sub>2</sub> measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks T. Verhoelst et al. 10.5194/amt-14-481-2021
75. Sensitivity of total column NO2 at a marine site within the Chesapeake Bay during OWLETS-2 A. Kotsakis et al. 10.1016/j.atmosenv.2022.119063
76. Air Pollution Patterns Mapping of SO2, NO2, and CO Derived from TROPOMI over Central-East Europe B. Wieczorek 10.3390/rs15061565
77. Evaluation of the coupled high-resolution atmospheric chemistry model system MECO(n) using in situ and MAX-DOAS NO<sub>2</sub> measurements V. Kumar et al. 10.5194/amt-14-5241-2021
78. Nitrogen dioxide spatiotemporal variations in the complex urban environment of Athens, Greece T. Drosoglou et al. 10.1016/j.atmosenv.2023.120115
79. Informing Near-Airport Satellite NO2 Retrievals Using Pandora Sky-Scanning Observations A. Mouat et al. 10.1021/acsestair.4c00158
80. Estimations of the Ground-Level NO2 Concentrations Based on the Sentinel-5P NO2 Tropospheric Column Number Density Product P. Grzybowski et al. 10.3390/rs15020378
81. A satellite-based, near real-time, street-level resolution air pollutants monitoring system using machine learning for personalised skin health applications R. Temple et al. 10.1007/s11869-024-01577-4
82. Disentangling the Impact of the COVID‐19 Lockdowns on Urban NO2 From Natural Variability D. Goldberg et al. 10.1029/2020GL089269
83. New observations of NO<sub>2</sub> in the upper troposphere from TROPOMI E. Marais et al. 10.5194/amt-14-2389-2021
84. Unveiling Air Pollution in Crimean Mountain Rivers: Analysis of Sentinel-5 Satellite Images Using Google Earth Engine (GEE) V. Tabunschik et al. 10.3390/rs15133364
85. Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future A. Andries et al. 10.3390/su14031191
86. Google Earth Engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing F. Ghasempour et al. 10.1016/j.jclepro.2021.128599
87. Trends of CO and NO2 Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth Engine S. Shami et al. 10.3390/pollutants2020012
88. Comparison of TROPOMI NO2, CO, HCHO, and SO2 data against ground‐level measurements in close proximity to large anthropogenic emission sources in the example of Ukraine M. Savenets et al. 10.1002/met.2108
89. Variability of nitrogen oxide emission fluxes and lifetimes estimated from Sentinel-5P TROPOMI observations K. Lange et al. 10.5194/acp-22-2745-2022
90. Comparison and Validation of TROPOMI and OMI NO2 Observations over China C. Wang et al. 10.3390/atmos11060636
91. Synergizing google earth engine and earth observations for potential impact of land use/ land cover on air quality K. Jodhani et al. 10.1016/j.rineng.2024.102039
92. Coupling Remote Sensing Insights With Vegetation Dynamics and to Analyze NO2 Concentrations: A Google Earth Engine-Driven Investigation X. Zheng et al. 10.1109/JSTARS.2024.3397496
93. Grid-stretching capability for the GEOS-Chem 13.0.0 atmospheric chemistry model L. Bindle et al. 10.5194/gmd-14-5977-2021
94. Spatiotemporal Investigations of Multi-Sensor Air Pollution Data over Bangladesh during COVID-19 Lockdown Z. Qiu et al. 10.3390/rs13050877
95. Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique Q. Tu et al. 10.5194/amt-16-2237-2023
96. Validation of TROPOMI tropospheric NO2 columns using dual-scan multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in Uccle, Brussels E. Dimitropoulou et al. 10.5194/amt-13-5165-2020
97. Environmental impacts of shifts in energy, emissions, and urban heat island during the COVID-19 lockdown across Pakistan G. Ali et al. 10.1016/j.jclepro.2021.125806
98. Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO D. Goldberg et al. 10.5194/acp-22-10875-2022
99. NO2 retrievals from NOAA-20 OMPS: Algorithm, evaluation, and observations of drastic changes during COVID-19 X. Huang et al. 10.1016/j.atmosenv.2022.119367
100. Air Quality Monitoring Using Sentinel-5p TROPOMI—A Case Study of Pune City S. Shah et al. 10.1007/s42979-024-03500-1
101. Assessment of NO2 population exposure from 2005 to 2020 in China Z. Huang et al. 10.1007/s11356-022-21420-6
102. A Land Use Regression Model to Estimate Ambient Concentrations of PM10 and SO2 in İzmit, Turkey E. Yücer et al. 10.1007/s12524-023-01704-1
103. Reliability Analysis Based on Air Quality Characteristics in East Asia Using Primary Data from the Test Operation of Geostationary Environment Monitoring Spectrometer (GEMS) W. Choi et al. 10.3390/atmos14091458
104. Evaluation of the first year of Pandora NO2 measurements over Beijing and application to satellite validation O. Liu et al. 10.5194/amt-17-377-2024
105. Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador D. Mejía C et al. 10.1016/j.heliyon.2024.e28152
106. To new heights by flying low: comparison of aircraft vertical NO2 profiles to model simulations and implications for TROPOMI NO2 retrievals T. Riess et al. 10.5194/amt-16-5287-2023
107. Intimately tracking NO2 pollution over the New York City - Long Island Sound land-water continuum: An integration of shipboard, airborne, satellite observations, and models M. Tzortziou et al. 10.1016/j.scitotenv.2023.165144
108. Estimating surface-level nitrogen dioxide concentrations from Sentinel-5P/TROPOMI observations in Finland H. Virta et al. 10.1016/j.atmosenv.2023.119989
109. Comprehensive Insights Into O3 Changes During the COVID‐19 From O3 Formation Regime and Atmospheric Oxidation Capacity S. Zhu et al. 10.1029/2021GL093668
110. Monitoring the Impacts of Human Activities on Urban Ecosystems Based on the Enhanced UCCLN (EUCCLN) Model N. Abbaszadeh Tehrani et al. 10.3390/ijgi12040170
111. Satellite-based estimates of nitrogen oxide and methane emissions from gas flaring and oil production activities in Sakha Republic, Russia I. Ialongo et al. 10.1016/j.aeaoa.2021.100114
112. TROPOMI NO2 Tropospheric Column Data: Regridding to 1 km Grid-Resolution and Assessment of their Consistency with In Situ Surface Observations A. Cersosimo et al. 10.3390/rs12142212
113. Estimating daily ground-level NO2 concentrations over China based on TROPOMI observations and machine learning approach S. Long et al. 10.1016/j.atmosenv.2022.119310
114. Air pollution impacts of COVID-19–related containment measures G. Chossière et al. 10.1126/sciadv.abe1178
115. Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS regional air quality ensemble J. Douros et al. 10.5194/gmd-16-509-2023
116. Ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of NO2 and H2CO at Kinshasa and comparisons with TROPOMI observations R. Yombo Phaka et al. 10.5194/amt-16-5029-2023
117. High-resolution modeling of gaseous air pollutants over Tehran and validation with surface and satellite data N. Shahrokhishahraki et al. 10.1016/j.atmosenv.2021.118881
118. NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations L. Kuhn et al. 10.5194/amt-17-6485-2024
119. Examining land surface temperature and relations with the major air pollutants: A remote sensing research in case of Tehran K. Fuladlu & H. Altan 10.1016/j.uclim.2021.100958
120. Investigating effect of COVID-19 on NO2 density using remote sensing products (case study: Tehran province) N. Abbaszadeh Tehrani et al. 10.1007/s41324-022-00449-2
121. An improved TROPOMI tropospheric NO<sub>2</sub> research product over Europe S. Liu et al. 10.5194/amt-14-7297-2021
122. The Temporal–Spatial Characteristics of Column NO2 Concentration and Influence Factors in Xinjiang of Northwestern Arid Region in China Z. Yu & X. Li 10.3390/atmos13101533
123. Assessing the Impact of Corona-Virus-19 on Nitrogen Dioxide Levels over Southern Ontario, Canada D. Griffin et al. 10.3390/rs12244112
124. Quantifying instantaneous nitrogen oxides emissions from power plants based on space observations T. Tang et al. 10.1016/j.scitotenv.2024.173479
125. Spatial and Temporal Variation of NO2 Vertical Column Densities (VCDs) over Poland: Comparison of the Sentinel-5P TROPOMI Observations and the GEM-AQ Model Simulations M. Kawka et al. 10.3390/atmos12070896
126. Air quality estimation using remote sensing and GIS-spatial technologies along Al-Shamal train pathway, Al-Qurayyat City in Saudi Arabia S. Al-Alola et al. 10.1016/j.indic.2022.100184
127. Changes in Power Plant NOx Emissions over Northwest Greece Using a Data Assimilation Technique I. Skoulidou et al. 10.3390/atmos12070900
128. An integrated analysis of air pollution from US coal-fired power plants M. Filonchyk & M. Peterson 10.1016/j.gsf.2022.101498
129. Markers of economic activity in satellite aerosol optical depth data S. Kondragunta et al. 10.1088/1748-9326/ace466
130. Shipborne MAX-DOAS measurements for validation of TROPOMI NO<sub>2</sub> products P. Wang et al. 10.5194/amt-13-1413-2020
131. Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium C. Poraicu et al. 10.5194/gmd-16-479-2023
132. National ground-level NO2 predictions via satellite imagery driven convolutional neural networks E. Cao 10.3389/fenvs.2023.1285471
133. Meteorological Drivers of Permian Basin Methane Anomalies Derived from TROPOMI E. Crosman 10.3390/rs13050896
134. Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak A. Rudke et al. 10.1016/j.rse.2023.113514
135. A Simple and Effective Random Forest Refit to Map the Spatial Distribution of NO2 Concentrations Y. Chi & Y. Zhan 10.3390/atmos13111832
136. Estimating surface NO2 concentrations over Europe using Sentinel-5P TROPOMI observations and Machine Learning S. Shetty et al. 10.1016/j.rse.2024.114321
137. Association between satellite-detected tropospheric nitrogen dioxide and acute respiratory infections in children under age five in Senegal: spatio-temporal analysis A. Kawano et al. 10.1186/s12889-022-12577-3
138. Air Quality in Two Northern Greek Cities Revealed by Their Tropospheric NO2 Levels M. Koukouli et al. 10.3390/atmos13050840
139. The Impact of Changes in Anthropogenic Activity Caused by COVID-19 Lockdown on Reducing Nitrogen Dioxide Levels in Thailand Using Nighttime Light Intensity N. Thongrueang et al. 10.3390/su15054296
140. High-resolution mapping of nitrogen oxide emissions in large US cities from TROPOMI retrievals of tropospheric nitrogen dioxide columns F. Liu et al. 10.5194/acp-24-3717-2024
141. COVID‐19 Induced Fingerprints of a New Normal Urban Air Quality in the United States S. Kondragunta et al. 10.1029/2021JD034797
142. Assessment of the Performance of TROPOMI NO2 and SO2 Data Products in the North China Plain: Comparison, Correction and Application C. Wang et al. 10.3390/rs14010214
143. Horizontal distribution of tropospheric NO2 and aerosols derived by dual-scan multi-wavelength multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in Uccle, Belgium E. Dimitropoulou et al. 10.5194/amt-15-4503-2022
144. Improving machine-learned surface NO2 concentration mapping models with domain knowledge from data science perspective M. Hu et al. 10.1016/j.atmosenv.2024.120372
145. A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology W. Wang et al. 10.1016/j.envint.2021.106917
146. Space-borne air pollution observation from Sentinel-5p Tropomi: relationship between pollutants, geographical and demographic data G. KAPLAN & Z. YİGİT AVDAN 10.26833/ijeg.644089
147. A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide F. Liu et al. 10.5194/acp-20-99-2020