257 citations as recorded by crossref.

1. Air Pollution Patterns Mapping of SO2, NO2, and CO Derived from TROPOMI over Central-East Europe B. Wieczorek https://doi.org/10.3390/rs15061565
2. Soil Emissions of Reactive Oxidized Nitrogen Reduce the Effectiveness of Anthropogenic Source Control in China Y. Wang et al. https://doi.org/10.1021/acs.est.4c08526
3. Quantitative reconstruction of long-term spatiotemporal patterns of high-resolution ground-level NO2 concentrations in mainland China using fusion techniques and a machine learning framework Z. Li et al. https://doi.org/10.1016/j.envint.2025.109672
4. Integrated satellite-derived assessment of multi-pollutant spatio-temporal variability across Saudi Arabia (2005–2024): urban environmental implications and pathways toward sustainable development goals A. Ayek et al. https://doi.org/10.1007/s11869-026-02142-x
5. Monitoring European anthropogenic NOx emissions from space R. van der A et al. https://doi.org/10.5194/acp-24-7523-2024
6. Deep learning approach for reconstructing three-dimensional distribution of NO2 on an urban scale Z. Zhang et al. https://doi.org/10.1016/j.rse.2025.114678
7. Assessment of surface ozone production in Qinghai, China with satellite-constrained VOCs and NOx emissions W. Li et al. https://doi.org/10.1016/j.scitotenv.2023.166602
8. A lightweight NO2-to-NOx conversion model for quantifying NOx emissions of point sources from NO2 satellite observations S. Meier et al. https://doi.org/10.5194/acp-24-7667-2024
9. Impacts of multi-source data assimilation and model resolution on anthropogenic NOₓ emission inversions C. Wu et al. https://doi.org/10.1016/j.atmosres.2026.109105
10. Quantification and evaluation of atmospheric emissions from crop residue burning constrained by satellite observations in China during 2016–2020 X. Hong et al. https://doi.org/10.1016/j.scitotenv.2022.161237
11. 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. https://doi.org/10.5194/amt-16-2237-2023
12. Study on the method of reconstructing the vertical plane distribution of SO2 using IDOAS M. Zhong et al. https://doi.org/10.1007/s11869-024-01628-w
13. Improvements in aerosol layer height retrievals from TROPOMI oxygen A-band measurements by surface albedo fitting in optimal estimation M. de Graaf et al. https://doi.org/10.5194/amt-18-2553-2025
14. The worldwide COVID-19 lockdown impacts on global secondary inorganic aerosols and radiative budget T. Sekiya et al. https://doi.org/10.1126/sciadv.adh2688
15. Influence of Refined Soil NO Emissions on Summertime Ozone Modeling and Mitigation Efficacy: A Case Study in China X. Wang et al. https://doi.org/10.1021/acsenvironau.6c00118
16. Nitrogen dioxide spatiotemporal variations in the complex urban environment of Athens, Greece T. Drosoglou et al. https://doi.org/10.1016/j.atmosenv.2023.120115
17. TROPOMI NO2 Shows a Fast Recovery of China’s Economy in the First Quarter of 2023 H. Li & B. Zheng https://doi.org/10.1021/acs.estlett.3c00386
18. Air pollution exposure and hotspots of CO, NO2, SO2, PM2.5, and aerosols in Iraq A. Onojeghuo et al. https://doi.org/10.1007/s10661-025-14571-9
19. Characterizing global air pollution seasonality and trends through integrated satellite and meteorological analytics M. Naveed et al. https://doi.org/10.1016/j.envpol.2026.127728
20. Spatiotemporal optimization of NOx and VOC emissions using a hybrid inversion framework and implications for ozone sensitivity-regime diagnosis J. Moon et al. https://doi.org/10.5194/acp-26-10379-2026
21. Estimations of the Ground-Level NO2 Concentrations Based on the Sentinel-5P NO2 Tropospheric Column Number Density Product P. Grzybowski et al. https://doi.org/10.3390/rs15020378
22. Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques C. Zhang et al. https://doi.org/10.5194/acp-25-759-2025
23. From Earth Observation to Landscape Units: A Framework for Urban Cooling Assessment in Polish Functional Urban Area A. Markowska et al. https://doi.org/10.3390/rs18193390
24. Harmonized cloud datasets for the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) using the O2–O2 477 nm absorption band H. Yu et al. https://doi.org/10.5194/amt-18-4131-2025
25. Global retrieval of TROPOMI tropospheric HCHO and NO2 columns with improved consistency based on the updated Peking University OMI NO2 algorithm Y. Zhang et al. https://doi.org/10.5194/amt-18-1561-2025
26. Harmonizing satellite and ground NO2 observations in China: A multi-sensor framework for scenario-specific calibration J. Gu et al. https://doi.org/10.1016/j.isprsjprs.2025.09.028
27. Global NO2 changes between 2019 and 2024 as observed by TROPOMI in urban areas and emerging hotspots D. Huber et al. https://doi.org/10.5194/acp-26-3783-2026
28. Satellite-Based Estimates of Individual Ship NOx Emissions Z. Luo et al. https://doi.org/10.1021/acsestair.5c00237
29. Constraining the budget of NOx and volatile organic compounds at a remote tropical island using multi-platform observations and WRF-Chem model simulations C. Poraicu et al. https://doi.org/10.5194/acp-25-6903-2025
30. Global gridded NOx emissions using TROPOMI observations A. Rey-Pommier et al. https://doi.org/10.5194/essd-17-3329-2025
31. Local scale air quality impacts in the Los Angeles Basin from increased port activity during 2021 supply chain disruptions T. Skipper et al. https://doi.org/10.1039/D3EA00166K
32. Dynamic assessment of NO2 plume rise and pollution contributions from steel industry point sources in an energy-intensive region of China C. Gao et al. https://doi.org/10.1016/j.apr.2026.102921
33. 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. https://doi.org/10.5194/amt-16-5029-2023
34. S‐5P/TROPOMI‐Derived NOxEmissions From Copper/Cobalt Mining and Other Industrial Activities in the Copperbelt (Democratic Republic of Congo and Zambia) S. Martínez‐Alonso et al. https://doi.org/10.1029/2023GL104109
35. Ground-based MAX-DOAS observations of tropospheric formaldehyde and nitrogen dioxide: Insights into ozone formation sensitivity Y. Qian et al. https://doi.org/10.1016/j.apr.2024.102285
36. Conflict-induced ship traffic disruptions constrain cloud sensitivity to stricter marine pollution regulations M. Diamond & L. Boss https://doi.org/10.5194/acp-25-16401-2025
37. Advances and challenges of machine learning in satellite-based atmospheric NO2 monitoring R. Zhang et al. https://doi.org/10.1016/j.apr.2026.103066
38. Validation of Sentinel-5P TROPOMI tropospheric NO2 products by comparison with NO2 measurements from airborne imaging DOAS, ground-based stationary DOAS, and mobile car DOAS measurements during the S5P-VAL-DE-Ruhr campaign K. Lange et al. https://doi.org/10.5194/amt-16-1357-2023
39. Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO D. Goldberg et al. https://doi.org/10.5194/acp-22-10875-2022
40. Satellite reveals a steep decline in China’s CO 2 emissions in early 2022 H. Li et al. https://doi.org/10.1126/sciadv.adg7429
41. Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to TROPOMI L. Cheng et al. https://doi.org/10.3390/rs16214087
42. Disentangling the Impacts of Air Pollution on Vegetation Photosynthesis Over China Using an Explainable Machine Learning Approach L. Liu et al. https://doi.org/10.1109/TGRS.2026.3743776
43. Two Air Quality Regimes in Total Column NO2 Over the Gulf of Mexico in May 2019: Shipboard and Satellite Views A. Thompson et al. https://doi.org/10.1029/2022EA002473
44. Chemical Consistency of GNN-Inferred High-Resolution NO2-O3 Fields: Surface Oxidant Partitioning in the Seoul Metropolitan Area-Gangwon Region P. Jeong et al. https://doi.org/10.5572/KOSAE.2026.42.2.342
45. Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method Y. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.175177
46. Detecting nitrogen oxide emissions in Qatar and quantifying emission factors of gas-fired power plants – a 4-year study A. Rey-Pommier et al. https://doi.org/10.5194/acp-23-13565-2023
47. Temporal variability of NOx emissions from power plants: a comparison of satellite- and inventory-based estimates G. Kuhlmann et al. https://doi.org/10.5194/acp-26-4405-2026
48. Gas Alert A Satellite Surveillance Framework for Multi-GasIndustrial Emission Detection, and Flux Quantification in Arid Environments T. Moustafa et al. https://doi.org/10.66279/p8fwd350
49. A review of satellite remote sensing for pollution control and carbon reduction in China S. Zhao et al. https://doi.org/10.1016/j.jag.2026.105109
50. The Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky (GOBLEU): a mission overview, instrument description, and results from the first flight H. Suto et al. https://doi.org/10.1186/s13021-024-00273-1
51. Active CO2 lidar extends satellite monitoring of urban fossil-fuel CO2 emissions to high-aerosol cities Y. Huang et al. https://doi.org/10.1016/j.jag.2026.105606
52. Feasibility of robust estimates of ozone production rates using a synergy of satellite observations, ground-based remote sensing, and models A. Souri et al. https://doi.org/10.5194/acp-25-2061-2025
53. Carbon–Transformer: Near–real–time monthly tracking of global anthropogenic CO2 emissions constrained by satellite NO2 observations X. Chen et al. https://doi.org/10.1016/j.isprsjprs.2026.10.024
54. Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium C. Poraicu et al. https://doi.org/10.5194/gmd-16-479-2023
55. 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. https://doi.org/10.5194/amt-16-5287-2023
56. Ethnoracial Disparities in Nitrogen Dioxide Pollution in the United States: Comparing Data Sets from Satellites, Models, and Monitors G. Kerr et al. https://doi.org/10.1021/acs.est.3c03999
57. Tracking daily NOx emissions from an urban agglomeration based on TROPOMI NO2 and a local ensemble transform Kalman filter Y. Kong et al. https://doi.org/10.5194/acp-25-5959-2025
58. Validation of TROPOMI and WRF-Chem NO2 across seasons using SWING+ and surface observations over Bucharest A. Pasternak et al. https://doi.org/10.5194/acp-26-5185-2026
59. The greenhouse gas observation mission with Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW): objectives, conceptual framework and scientific contributions H. Tanimoto et al. https://doi.org/10.1186/s40645-025-00684-9
60. Evaluation of the optimal schemes for O3-NOx-VOC sensitivity utilizing regridding TROPOMI observations Y. Mo et al. https://doi.org/10.1016/j.ecoenv.2026.119802
61. Remote Sensing Measurements at a Rural Site in China: Implications for Satellite NO2 and HCHO Measurement Uncertainty and Emissions From Fires K. Chong et al. https://doi.org/10.1029/2023JD039310
62. Applications of Sentinel-5P TROPOMI Satellite Sensor: A Review A. Reshi et al. https://doi.org/10.1109/JSEN.2024.3355714
63. Enhancing spatiotemporal coverage of satellite-derived high-resolution NO2 data with a super-resolution model M. Zhang et al. https://doi.org/10.1016/j.rse.2025.114897
64. Evaluating the Concentration of Air Pollutants in Different Land-Use Patterns in Nairobi M. Nyanchoka et al. https://doi.org/10.4236/ojap.2026.152004
65. Efficient Ozone Production within Indochina Biomass Burning Plumes: From Near-Source to Downwind Regions Z. Liao et al. https://doi.org/10.1038/s44407-026-00101-5
66. Space-based assessment of NOx emissions from global oil and gas fields: Bridging the gap in current emission inventories P. Patel et al. https://doi.org/10.1016/j.rse.2025.115229
67. Air quality monitoring in Ukraine during 2022 military conflict using Sentinel-5P imagery M. Mehrabi et al. https://doi.org/10.1007/s11869-023-01488-w
68. 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. https://doi.org/10.1016/j.scitotenv.2023.165144
69. GEMS逐小时对流层NO2产品数据质量评价与提升：以华东地区为例 高. Gao Hongrui et al. https://doi.org/10.3788/AOS240826
70. High-resolution mapping of NO2 population exposure in China from satellite observations J. Gu et al. https://doi.org/10.1016/j.envdev.2025.101238
71. Spatiotemporal estimation of surface NO2 concentrations in the Pearl River Delta region based on TROPOMI data and machine learning Q. Wei et al. https://doi.org/10.1016/j.apr.2024.102353
72. Impacts of Russia-Ukraine War on air pollution over the Black Sea Region due to changes in maritime transport M. Onay et al. https://doi.org/10.1016/j.apr.2025.102613
73. Identifying missing sources and reducing NOx emissions uncertainty over China using daily satellite data and a mass-conserving method L. Lu et al. https://doi.org/10.5194/acp-25-2291-2025
74. Maritime sector contributions on NO2 surface concentrations in major ports of the Mediterranean Basin A. Pseftogkas et al. https://doi.org/10.1016/j.apr.2024.102228
75. Comparison of GEMS v3.0 tropospheric NO 2 columns with ground-based DOAS instruments in Ulsan K. Bae et al. https://doi.org/10.1080/15481603.2025.2597604
76. Observational evidence of reduced Bay of Bengal lightning since 2020 linked to cloud responses to shipping emission regulations Q. Jin et al. https://doi.org/10.1038/s41612-025-01256-w
77. Examining Ozone Production in the Permian Oil and Gas Basin Using Carlsbad Caverns National Park Measurements and Satellite Data A. Marsavin et al. https://doi.org/10.1021/acsestair.6c00159
78. Direct sun total NO2 column measurements at Thessaloniki, Greece with two DOAS systems and comparisons with S5P/TROPOMI D. Nikolis et al. https://doi.org/10.1016/j.atmosenv.2025.121161
79. Assessing nitrogen dioxide monitoring techniques: a comparative analysis of Sentinel-5 Precursor satellite and ground measurements in Catalonia D. Garcia Santos & M. Parés https://doi.org/10.5194/amt-18-5569-2025
80. Influence of convection on the upper-tropospheric O3 and NOx budget in southeastern China X. Zhang et al. https://doi.org/10.5194/acp-22-5925-2022
81. A new method for estimating megacity NOx emissions and lifetimes from satellite observations S. Beirle & T. Wagner https://doi.org/10.5194/amt-17-3439-2024
82. Assessing Nitrogen Dioxide in the Highveld Troposphere: Pandora Insights and TROPOMI Sentinel-5P Evaluation R. Kai-Sikhakhane et al. https://doi.org/10.3390/atmos15101187
83. 卫星观测港口CO2排放间接估算：以日照港为例 陆. Lu Lingxiao et al. https://doi.org/10.3788/AOS250851
84. Spatiotemporal analysis of atmospheric pollutant trends near the Sigus Cement Plant region: a GEE-based and remote sensing approach N. Khenchouche et al. https://doi.org/10.1108/WJE-05-2026-0388
85. Evaluating the suitability of sentinel-5P satellite data for air quality monitoring in Tehran A. Kafi et al. https://doi.org/10.1016/j.scitotenv.2026.182069
86. Satellite data-driven estimates of NO x and co-emitted CO2 from coal-fired power plants in China (2021–2024) L. Chen et al. https://doi.org/10.1088/1748-9326/ae84e9
87. Refining NO 2 air-mass factors with scattering clouds: TROPOMI-based assessment in TANSO-3/GOSAT-GW data processing H. Lim et al. https://doi.org/10.1080/2150704X.2026.2673539
88. NOx emissions in France in 2019–2021 as estimated by the high-spatial-resolution assimilation of TROPOMI NO2 observations R. Plauchu et al. https://doi.org/10.5194/acp-24-8139-2024
89. A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx D. Wu et al. https://doi.org/10.5194/gmd-16-6161-2023
90. 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. https://doi.org/10.5194/acp-23-3905-2023
91. Natural emissions of VOC and NOx over Africa constrained by TROPOMI HCHO and NO2 data using the MAGRITTEv1.1 model B. Opacka et al. https://doi.org/10.5194/acp-25-2863-2025
92. NO2 Data Analytics and Visualization Service: Improving Air Quality Insights in Armenia and Belarus R. Abrahamyan et al. https://doi.org/10.1134/S0361768823090025
93. Tracking the path to cleaner cities using global urban NO2 monitoring from space A. Hassani et al. https://doi.org/10.1088/1748-9326/ae5e97
94. Spaceborne Observations of Lightning NO2 in the Arctic X. Zhang et al. https://doi.org/10.1021/acs.est.2c07988
95. 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. https://doi.org/10.5194/amt-15-4503-2022
96. Evolution of global O3-NOx-VOCs sensitivity before and after the COVID-19 from the ratio of formaldehyde to NO2 from satellite observations D. Wang et al. https://doi.org/10.1016/j.jes.2024.07.029
97. Cloud Property Retrieval Based on DISAMAR Using Oxygen-A and Oxygen-B Band Measurements by TROPOMI on Sentinel-5P X. Zhang et al. https://doi.org/10.1109/TGRS.2025.3643150
98. Developing a Machine Learning Approach for Estimating Urban NO2 Concentrations Using Multi-Source Remote Sensing Data M. Kukavica et al. https://doi.org/10.3390/smartcities9100166
99. Satellite air quality monitoring: an interactive and accessible tool for spatiotemporal analysis using Google Earth Engine A. Ayek et al. https://doi.org/10.1080/10095020.2025.2537352
100. Nitrogen oxides emissions from selected cities in North America, Europe, and East Asia observed by the TROPOspheric Monitoring Instrument (TROPOMI) before and after the COVID-19 pandemic C. Lonsdale & K. Sun https://doi.org/10.5194/acp-23-8727-2023
101. Predicting nitrogen dioxide column density from the International Wealth Index with a convolutional neural network in Africa F. Grieco et al. https://doi.org/10.1038/s41598-026-73707-0
102. Air Quality Improvement Following COVID-19 Lockdown Measures and Projected Benefits for Environmental Health Y. Liou et al. https://doi.org/10.3390/rs15020530
103. Improved catalog of NOx point source emissions (version 2) S. Beirle et al. https://doi.org/10.5194/essd-15-3051-2023
104. Detailed analysis of air pollution in the Canadian prairie region: A step toward net-zero emission A. Ghahremanlou & D. Ghahremanlou https://doi.org/10.1016/j.scitotenv.2025.178492
105. Estimation of biomass burning emission of NO2 and CO from 2019–2020 Australia fires based on satellite observations N. Wan et al. https://doi.org/10.5194/acp-23-711-2023
106. Cement and brick factories contribute elevated levels of NO2 pollution in Nepal: Evidence of high-resolution view from space M. Gyawali et al. https://doi.org/10.1016/j.aeaoa.2025.100324
107. TANGO CO2 and NO2 observations: synergistic usage to improve emission quantification and characterize atmospheric chemistry T. Borsdorff et al. https://doi.org/10.5194/amt-19-4617-2026
108. Transboundary Impacts of NO2 on Soil Nitrogen Fixation and Their Effects on Crop Yields in China J. Xie et al. https://doi.org/10.3390/agriculture15020208
109. NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations L. Kuhn et al. https://doi.org/10.5194/amt-17-6485-2024
110. Top-down estimates of anthropogenic NOx emissions over China through a new zone-stratified RF machine learning model Y. Huang et al. https://doi.org/10.1016/j.envpol.2026.128202
111. Spatiotemporal patterns of nitrogen dioxide (NO2) in an arid industrial region: integrating satellite and ground observations (2018–2023) S. Nematollahi et al. https://doi.org/10.1007/s13762-026-07264-4
112. New Perspective on Using Observational Uncertainty to Improve Reliability of NO x Emissions Over Northern China L. Lu et al. https://doi.org/10.1109/TGRS.2025.3620116
113. Integrating MODIS and TROPOMI Atmospheric Products into TUV for High-Accuracy Surface UV Irradiance Modeling in the Mountainous Southwest USA N. Taylor et al. https://doi.org/10.3390/atmos17100945
114. Underappreciated Soil NOx Emissions Amplify Background O3 Pollution During Compound Dry-Hot Extremes in North China Z. Yang et al. https://doi.org/10.1021/acs.est.6c06361
115. Relation Between Air Quality and Wind Speed in the Eastern Anatolia Region of Türkiye M. Çelik et al. https://doi.org/10.1007/s00024-026-03994-1
116. A Hybrid Regression–Kriging–Machine Learning Framework for Imputing Missing TROPOMI NO2 Data over Taiwan A. Valerio et al. https://doi.org/10.3390/rs17122084
117. Remote sensing of rural clean heating impacts on air quality: An STL-Q3 framework in Henan Province, China Y. Yang et al. https://doi.org/10.1016/j.rsase.2026.102126
118. An assessment of the spatiotemporal dynamics and seasonal trends in NO₂ concentrations across India using advanced statistical analysis A. Srivastava et al. https://doi.org/10.1016/j.rsase.2025.101490
119. Trends and drivers of anthropogenic NO emissions in China since 2020 H. Li et al. https://doi.org/10.1016/j.ese.2024.100425
120. 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. https://doi.org/10.1002/met.2108
121. Global seasonal urban, industrial, and background NO2 estimated from TROPOMI satellite observations V. Fioletov et al. https://doi.org/10.5194/acp-25-575-2025
122. Satellite observations showed a negligible reduction in NO2 pollution due to COVID-19 lockdown over Poland E. Ugboma et al. https://doi.org/10.3389/fenvs.2023.1172753
123. Synergistic use of satellite and in-situ data for policy-relevant air quality information: A case study on Belgium T. Verhoelst et al. https://doi.org/10.1016/j.atmosenv.2025.121447
124. Spatial–temporal variations of NO2 pollution in Shandong Province based on Sentinel-5P satellite data and influencing factors S. Zeng et al. https://doi.org/10.1515/geo-2025-0833
125. Spatio-Temporal Modelling of Air Pollution Using Earth Observation and Deep Learning Techniques: A Case Study of Nairobi Metropolitan Area S. Odoyo et al. https://doi.org/10.4236/gep.2026.145010
126. High-resolution mapping of urban NO2 concentrations using Retina v2: a case study on data assimilation of surface and satellite observations in Madrid B. Mijling et al. https://doi.org/10.5194/gmd-18-6439-2025
127. 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. https://doi.org/10.1016/j.atmosenv.2023.119756
128. Analysis of Ozone Formation Sensitivity in Chinese Representative Regions Using Satellite and Ground-Based Data Y. Li et al. https://doi.org/10.3390/rs16020316
129. Uncertainty assessment of TROPOMI NO2 over Europe using ground-based remote sensing observations F. Cifuentes et al. https://doi.org/10.5194/amt-19-2437-2026
130. Introducing new lightning schemes into the CHASER (MIROC) chemistry–climate model Y. He et al. https://doi.org/10.5194/gmd-15-5627-2022
131. Cross-regional NO2 transport over the Tibetan Plateau (2005–2024): bidirectional flux dynamics, seasonal drivers, and environmental implications Z. Sun et al. https://doi.org/10.5194/acp-26-11027-2026
132. Twin-satellite constraints from GF-5B and DQ-1 enable hourly near-surface NO2 estimation across China C. Zhang et al. https://doi.org/10.1016/j.rse.2026.115740
133. Hybrid transformer and physics-informed neural operator for correcting TEMPO NO2 biases over North America S. Kayastha et al. https://doi.org/10.1038/s44407-026-00056-7
134. Extreme wildfires over Northern Greece during Summer 2023 – Part B. Adverse effects on regional air quality M. Koukouli et al. https://doi.org/10.1016/j.atmosres.2025.108034
135. Improved NO2 spectral fits for TROPOMI and OMI by removing wavelengths around 430 nm J. van Geffen et al. https://doi.org/10.5194/amt-19-4233-2026
136. Quantifying the impacts of the COVID-19 pandemic lockdown and the armed conflict with Russia on Sentinel 5P TROPOMI NO 2 changes in Ukraine A. Phan & H. Fukui https://doi.org/10.1080/20964471.2023.2265105
137. A review of machine learning for modeling air quality: Overlooked but important issues D. Tang et al. https://doi.org/10.1016/j.atmosres.2024.107261
138. Impact of Lockdowns on Air Pollution: Case Studies of Two Periods in 2022 in Guangzhou, China X. Zhao et al. https://doi.org/10.3390/atmos15091144
139. Clear-sky and cloudy-sky differences in NO2 concentrations over the United States: implications for satellite measurement applications D. Goldberg et al. https://doi.org/10.5194/acp-25-16287-2025
140. Air pollution and sectoral anthropogenic emissions in Pakistan: a comprehensive review M. Rasheed et al. https://doi.org/10.1007/s11869-026-02132-z
141. Evaluation of GEMS NO2 Retrieval Algorithm Version 2.0 and 3.0 Using TROPOMI and Pandora Observations Y. Jung et al. https://doi.org/10.7780/kjrs.2025.41.5.8
142. Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. https://doi.org/10.1007/s11783-025-1922-x
143. Intercomparison of Sentinel-5P TROPOMI cloud products for tropospheric trace gas retrievals M. Latsch et al. https://doi.org/10.5194/amt-15-6257-2022
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145. Assessing the ability to quantify the decrease in NOx anthropogenic emissions in 2019 compared to 2005 using OMI and TROPOMI satellite observations A. Fortems-Cheiney et al. https://doi.org/10.5194/acp-25-6047-2025
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164. Enhancing ozone nowcasting over East Asia using a data-to-data translation approach with observations from a geostationary environment monitoring spectrometer S. Oh et al. https://doi.org/10.1016/j.apr.2024.102054
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166. Improved constraints on ammonia emissions and deposition from co-assimilating NH3 and NO2 satellite observations over the Netherlands T. Wizenberg et al. https://doi.org/10.5194/acp-26-9589-2026
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172. Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO2) with hyperspectral imagers and reduce noise in spectral fitting J. Joiner et al. https://doi.org/10.5194/amt-16-481-2023
173. Solar FTIR measurements of NOx vertical distributions – Part 1: First observational evidence of a seasonal variation in the diurnal increasing rates of stratospheric NO2 and NO P. Nürnberg et al. https://doi.org/10.5194/acp-24-3743-2024
174. Associations Between Hydroclimatic Extremes and Tropospheric NO2 Anomalies Across Land-Use Backgrounds in Northeast China’s Black Soil Region K. Yang et al. https://doi.org/10.3390/agronomy16202051
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181. Toward monitoring daily anthropogenic CO2 emissions with air pollution sensors from space H. Li & B. Zheng https://doi.org/10.1016/j.oneear.2024.08.019
182. Ground-based monitoring of nitrogen dioxide in Kumasi, Ghana, and its comparison with satellite observations B. Mijling et al. https://doi.org/10.5194/amt-19-3063-2026
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184. Reconciliation of asynchronous satellite-based NO2 and XCO2 enhancements with mesoscale modeling over two urban landscapes R. Lei et al. https://doi.org/10.1016/j.rse.2022.113241
185. Utilizing machine learning for high resolution NO2 total columns from PACE OCI Z. Fasnacht et al. https://doi.org/10.1088/1748-9326/addfef
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187. Deep learning-based reconstruction of high-resolution satellite-derived NO2 columns over the Bohai Sea Z. Li et al. https://doi.org/10.1016/j.atmosenv.2026.122243
188. Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)? E. Dammers et al. https://doi.org/10.5194/gmd-17-4983-2024
189. Air-pollution-satellite-based CO2 emission inversion: system evaluation, sensitivity analysis, and future research direction H. Li et al. https://doi.org/10.5194/acp-25-1949-2025
190. Constraining NOx emissions with satellite NO2 data to improve modeling of 2023 wildfire air quality J. Kumm & Z. Qu https://doi.org/10.1016/j.atmosenv.2026.121985
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197. A detailed comparison of the Dutch emission inventory with satellite-derived NOx emissions H. Witt et al. https://doi.org/10.5194/acp-26-5237-2026
198. Contrasting Biogenic Isoprene Emission Responses to La Niña and El Niño Driven by Temperature: Insights from HCHO-Based Global Inversion H. Li et al. https://doi.org/10.1021/acs.est.5c12927
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200. Ozone impacts caused by shipping NO x emissions and implications for an integrated prevention strategy: atmospheric modeling and insights from natural experiments Z. Han et al. https://doi.org/10.1088/1748-9326/ae8f5b
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204. Nitrogen oxides in the free troposphere: implications for tropospheric oxidants and the interpretation of satellite NO2 measurements V. Shah et al. https://doi.org/10.5194/acp-23-1227-2023
205. Assessment of the impacts of anthropogenic ozone precursors on domestic and transboundary ozone concentrations using a high-resolution global chemistry model D. Goto et al. https://doi.org/10.1186/s40645-025-00791-7
206. Los Angeles Wildfires 2025: Satellite-Based Emissions Monitoring and Air-Quality Impacts K. Michailidis et al. https://doi.org/10.3390/atmos17010050
207. Deriving regional and point source nitrogen oxides emissions in China from TROPOMI using the directional derivative approach with nonlinear chemical lifetime fitting L. Chen et al. https://doi.org/10.5194/essd-18-2749-2026
208. Evaluating NOx stack plume emissions using a high-resolution atmospheric chemistry model and satellite-derived NO2 columns M. Krol et al. https://doi.org/10.5194/acp-24-8243-2024
209. Satellite Evidence for Glyoxal Depletion in Elevated Fire Plumes C. Lerot et al. https://doi.org/10.1029/2022GL102195
210. Analysis of Spatio-Temporal Distribution of Air Quality Index in Nairobi City County: Observations from Sentinel-5P TROPOMI M. Nyanchoka et al. https://doi.org/10.4236/ojap.2026.153009
211. Examining TROPOMI formaldehyde to nitrogen dioxide ratios in the Lake Michigan region: implications for ozone exceedances J. Acdan et al. https://doi.org/10.5194/acp-23-7867-2023
212. Measurement report: MAX-DOAS measurements characterise Central London ozone pollution episodes during 2022 heatwaves R. Ryan et al. https://doi.org/10.5194/acp-23-7121-2023
213. Geospatial association between aerosols and its short-lived precursors over South Asia H. Baranwal et al. https://doi.org/10.1016/j.rsase.2026.101962
214. Evaluating the impact of aerosol treatments in TROPOMI NO2 retrievals on top-down NOx emission inversions over Eastern China W. Dong et al. https://doi.org/10.1016/j.atmosres.2026.109391
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218. Observational operator for fair model evaluation with ground NO2 measurements L. Fang et al. https://doi.org/10.5194/gmd-17-8267-2024
219. Quantifying daily NOx and CO2 emissions from Wuhan using satellite observations from TROPOMI and OCO-2 Q. Zhang et al. https://doi.org/10.5194/acp-23-551-2023
220. Active and passive satellite observations coupled with carbon–nitrogen synergy for urban fossil fuel CO2 emissions monitoring J. Yi et al. https://doi.org/10.5194/acp-26-8475-2026
221. Modelling stratospheric composition for the Copernicus Atmosphere Monitoring Service: multi-species evaluation of IFS-COMPO Cy49 S. Chabrillat et al. https://doi.org/10.5194/gmd-18-8973-2025
222. Retrieval of NO2 profiles from 3 years of Pandora MAX-DOAS measurements in Toronto, Canada R. Alwarda et al. https://doi.org/10.5194/amt-18-2397-2025
223. Localized pollutant emission increases in China due to COVID-19 lockdowns F. Zhao et al. https://doi.org/10.1039/D5EA00095E
224. TROPOMI Level 3 tropospheric NO2 dataset with advanced uncertainty analysis from the ESA CCI+ ECV precursor project I. Glissenaar et al. https://doi.org/10.5194/essd-17-4627-2025
225. China’s air pollution: Remarkable progress and ongoing challenges under new standards from combined surface-satellite observations (2015–2024) Y. Chen et al. https://doi.org/10.1016/j.jhazmat.2026.142461
226. Lightning‐Produced Nitrogen Oxides Per Flash Length Obtained by Using TROPOMI Observations and the Ebro Lightning Mapping Array F. Pérez‐Invernón et al. https://doi.org/10.1029/2023GL104699
227. The Antarctic stratospheric nitrogen hole: Southern Hemisphere and Antarctic springtime total nitrogen dioxide and total ozone variability as observed by Sentinel-5p TROPOMI A. de Laat et al. https://doi.org/10.5194/acp-24-4511-2024
228. Comparison of S5P/TROPOMI Inferred NO2 Surface Concentrations with In Situ Measurements over Central Europe A. Pseftogkas et al. https://doi.org/10.3390/rs14194886
229. Satellite-Based Seasonal Monitoring of PM2.5-Related Trace Gases and Aerosol Loading over the Lazio Region F. Fois et al. https://doi.org/10.3390/rs18193265
230. Vertical profiles of global tropospheric nitrogen dioxide (NO2) obtained by cloud slicing the TROPOspheric Monitoring Instrument (TROPOMI) R. Horner et al. https://doi.org/10.5194/acp-24-13047-2024
231. Monitoring and quantifying CO2emissions of isolated power plants from space X. Lin et al. https://doi.org/10.5194/acp-23-6599-2023
232. Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS regional air quality ensemble J. Douros et al. https://doi.org/10.5194/gmd-16-509-2023
233. Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes F. Cifuentes et al. https://doi.org/10.5194/gmd-18-621-2025
234. Characterizing lightning NOx production in the Ebro Valley with meteosat 12 LI and LMA observations F. Pérez-Invernón et al. https://doi.org/10.5194/acp-26-14277-2026
235. Limitations of Polar-Orbiting Satellite Observations in Capturing the Diurnal Variability of Tropospheric NO2: A Case Study Using TROPOMI, GOME-2C, and Pandora Data Y. Li et al. https://doi.org/10.3390/rs17162846
236. Diurnal NO emission underestimation constrained using overlapping TROPOMI swaths Q. He et al. https://doi.org/10.1016/j.atmosenv.2025.121354
237. Feasibility and Optimization Analysis of Discrete-Wavelength DOAS for NO2 Retrieval Based on TROPOMI and EMI-II Observations R. Song et al. https://doi.org/10.3390/rs18030481
238. Leveraging High-Resolution Satellite-Derived NO2 Estimates to Evaluate NO2 Exposure Representativeness and Socioeconomic Disparities N. Kim & H. Lee https://doi.org/10.1021/acs.est.4c10996
239. Contrasting air pollution responses to hourly varying anthropogenic NOx emissions in the contiguous United States M. Tao et al. https://doi.org/10.5194/acp-26-6683-2026
240. Characterization of Pandora Sky-Scan Modes from the Japan Pandora Network and Evaluation of Their Use in Validating Satellite NO2 and HCHO Retrievals N. Chi et al. https://doi.org/10.1021/acsestair.5c00223
241. A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration Z. Ji et al. https://doi.org/10.3390/atmos15030323
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243. Satellite-Based Ground-Level NO2 Estimation and Population Exposure Assessment Across the Marmara Region Using Tree-Based Machine Learning K. Yurt & H. Gündüz https://doi.org/10.3390/app16104935
244. Revised estimates of NO2 reductions during the COVID-19 lockdowns using updated TROPOMI NO2 retrievals and model simulations B. Fisher et al. https://doi.org/10.1016/j.atmosenv.2024.120459
245. Estimating surface-level nitrogen dioxide concentrations from Sentinel-5P/TROPOMI observations in Finland H. Virta et al. https://doi.org/10.1016/j.atmosenv.2023.119989
246. Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion X. Zheng et al. https://doi.org/10.1016/j.scitotenv.2024.175073
247. Development of a city level nitrogen dioxide exposure (CLeNE) intelligence platform for West Africa P. Asilevi et al. https://doi.org/10.1007/s44274-026-01108-1
248. Quantification of lightning-produced NOx over the Pyrenees and the Ebro Valley by using different TROPOMI-NO2 and cloud research products F. Pérez-Invernón et al. https://doi.org/10.5194/amt-15-3329-2022
249. Assessment of variations in air quality in cities of Ecuador in relation to the lockdown due to the COVID-19 pandemic O. Atiaga et al. https://doi.org/10.1016/j.heliyon.2023.e17033
250. An Unprecedented Ozone Reduction at a Coastal Background Site in South China: Drivers and Implications R. Zhou et al. https://doi.org/10.1021/acsestair.5c00386
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252. An intercomparison of satellite, airborne, and ground-level observations with WRF–CAMx simulations of NO2 columns over Houston, Texas, during the September 2021 TRACER-AQ campaign M. Nawaz et al. https://doi.org/10.5194/acp-24-6719-2024
253. Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation P. Rijsdijk et al. https://doi.org/10.5194/gmd-18-483-2025
254. Towards routine shipborne measurements of columnar CO2, CH4, CO, and NO2: a case study for tracking regional-scale emission patterns V. Enders et al. https://doi.org/10.5194/amt-19-2633-2026
255. Flux measurements of NOx, O3 and Ox: Emission characteristics, inventory validation and titration effects X. Zhang et al. https://doi.org/10.1016/j.envpol.2025.127213
256. Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models N. Zaman et al. https://doi.org/10.1016/j.jclepro.2024.141559
257. Anthropogenic NO x emissions of China, the U.S. and Europe from 2019 to 2022 inferred from TROPOMI observations Y. Mao et al. https://doi.org/10.1088/1748-9326/ad3cf9