Articles | Volume 17, issue 9
https://doi.org/10.5194/essd-17-4627-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-4627-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
TROPOMI Level 3 tropospheric NO2 dataset with advanced uncertainty analysis from the ESA CCI+ ECV precursor project
Isolde Glissenaar
Royal Netherlands Meteorological Institute (KNMI), Satellite Observations department, De Bilt, the Netherlands
University of Tromsø The Arctic University of Norway, Department of Physics and Technology, Tromsø, Norway
Klaas Folkert Boersma
CORRESPONDING AUTHOR
Royal Netherlands Meteorological Institute (KNMI), Satellite Observations department, De Bilt, the Netherlands
Wageningen University, Meteorology and Air Quality group, Wageningen, the Netherlands
Isidora Anglou
Royal Netherlands Meteorological Institute (KNMI), Satellite Observations department, De Bilt, the Netherlands
Pieter Rijsdijk
Royal Netherlands Meteorological Institute (KNMI), Satellite Observations department, De Bilt, the Netherlands
Vrije Universiteit, Department of Earth Sciences, Amsterdam, the Netherlands
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Tijl Verhoelst
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Steven Compernolle
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Gaia Pinardi
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Jean-Christopher Lambert
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Michel Van Roozendael
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Henk Eskes
Royal Netherlands Meteorological Institute (KNMI), Satellite Observations department, De Bilt, the Netherlands
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Cited
9 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- Comprehensive diurnal and nocturnal surface NO2 concentration retrieval with seamless temporal and spatial coverage Y. Zhang et al. https://doi.org/10.1016/j.atmosenv.2025.121764
- 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
- Station-Level Gap Filling of TROPOMI NO2 via Physics-Informed Shadow Manifold Reconstruction P. Trenchev et al. https://doi.org/10.3390/rs18142387
- Spatial Sampling Uncertainty for MODIS Terra Land Surface Temperature Retrievals C. Bulgin et al. https://doi.org/10.3390/rs17203435
- 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
9 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- Comprehensive diurnal and nocturnal surface NO2 concentration retrieval with seamless temporal and spatial coverage Y. Zhang et al. https://doi.org/10.1016/j.atmosenv.2025.121764
- 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
- Station-Level Gap Filling of TROPOMI NO2 via Physics-Informed Shadow Manifold Reconstruction P. Trenchev et al. https://doi.org/10.3390/rs18142387
- Spatial Sampling Uncertainty for MODIS Terra Land Surface Temperature Retrievals C. Bulgin et al. https://doi.org/10.3390/rs17203435
- 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
Saved (final revised paper)
Latest update: 24 Aug 2026
Short summary
We developed a new global dataset of nitrogen dioxide (NO2) levels in the lower atmosphere, using data from TROPOMI for 2018–2021. This dataset offers improved accuracy and detail compared to earlier versions, meeting high international standards for climate data. By refining how measurement errors are calculated and reduced over time and space, we provide clearer insights into pollution patterns. This work supports better air quality monitoring and informs actions to address pollution globally.
We developed a new global dataset of nitrogen dioxide (NO2) levels in the lower atmosphere,...
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