Articles | Volume 13, issue 3
https://doi.org/10.5194/amt-13-1315-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-1315-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
S5P TROPOMI NO2 slant column retrieval: method, stability, uncertainties and comparisons with OMI
Satellite Observations Department, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
K. Folkert Boersma
Satellite Observations Department, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Meteorology and Air Quality Group, Wageningen University (WUR), Wageningen, the Netherlands
Henk Eskes
Satellite Observations Department, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Maarten Sneep
Satellite Observations Department, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Mark ter Linden
Satellite Observations Department, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Science and Technology Corporation (S[&]T), Delft, the Netherlands
Marina Zara
Satellite Observations Department, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Meteorology and Air Quality Group, Wageningen University (WUR), Wageningen, the Netherlands
J. Pepijn Veefkind
Satellite Observations Department, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Faculty of Civil Engineering and Geosciences, Delft University of Technology (TUDelft), Delft, the Netherlands
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- A satellite-data-driven framework to rapidly quantify air-basin-scale NO<sub><i>x</i></sub> emissions and its application to the Po Valley during the COVID-19 pandemic K. Sun et al. 10.5194/acp-21-13311-2021
- Analysis of NO2 and O3 Total Columns from DOAS Zenith-Sky Measurements in South Italy P. Pettinari et al. 10.3390/rs14215541
- Global enhanced vegetation photosynthesis in urban environment and its drivers revealed by satellite solar-induced chlorophyll fluorescence data S. Wang et al. 10.1016/j.agrformet.2023.109622
- Global fine-scale changes in ambient NO2 during COVID-19 lockdowns M. Cooper et al. 10.1038/s41586-021-04229-0
- Assessment of Spatial and Temporal Variation in NO2 Levels over Tourist Reception Areas in Poland D. Mochocki & W. Zgłobicki 10.3390/app13169477
- Long-term observations of NO2, SO2, HCHO, and CHOCHO over the Himalayan foothills: Insights from MAX-DOAS, TROPOMI, and GOME-2 P. Rawat et al. 10.1016/j.atmosenv.2024.120746
- Detection of NO2 pollution plumes from individual ships with the TROPOMI/S5P satellite sensor A. Georgoulias et al. 10.1088/1748-9326/abc445
- Atmospheric NO2 Distribution Characteristics and Influencing Factors in Yangtze River Economic Belt: Analysis of the NO2 Product of TROPOMI/Sentinel-5P X. Liu et al. 10.3390/atmos12091142
- High-resolution modeling of the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source–receptor model (GNAQPMS-SM) Q. Ye et al. 10.5194/gmd-14-7573-2021
- Revised estimates of NO2 reductions during the COVID-19 lockdowns using updated TROPOMI NO2 retrievals and model simulations B. Fisher et al. 10.1016/j.atmosenv.2024.120459
- Impacts of TROPOMI-Derived NOX Emissions on NO2 and O3 Simulations in the NCP during COVID-19 Y. Zhu et al. 10.1021/acsenvironau.2c00013
- Impact of the COVID-19 pandemic related to lockdown measures on tropospheric NO<sub>2</sub> columns over Île-de-France A. Pazmiño et al. 10.5194/acp-21-18303-2021
- Discrete-wavelength DOAS NO<sub>2</sub> slant column retrievals from OMI and TROPOMI C. Ruiz Villena et al. 10.5194/amt-13-1735-2020
- Understanding temporary reduction in atmospheric pollution and its impacts on coastal aquatic system during COVID-19 lockdown: a case study of South Asia M. Shafeeque et al. 10.1080/19475705.2021.1885503
- Meteorological Drivers of Permian Basin Methane Anomalies Derived from TROPOMI E. Crosman 10.3390/rs13050896
- Quantifying the diurnal variation in atmospheric NO2 from Geostationary Environment Monitoring Spectrometer (GEMS) observations D. Edwards et al. 10.5194/acp-24-8943-2024
- Impacts of Ship Emissions on Air Quality in Southern China: Opportunistic Insights from the Abrupt Emission Changes in Early 2020 X. Feng et al. 10.1021/acs.est.3c04155
- 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. 10.1029/2023GL104109
- 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
- The impact of COVID-19-induced lockdowns during spring 2020 on nitrogen dioxide levels over major American counties J. Poetzscher & R. Isaifan 10.1525/elementa.2021.00002
- War Impact on Air Quality in Ukraine R. Zalakeviciute et al. 10.3390/su142113832
- 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
- Impact of urbanization on air quality in the Yangtze River Delta during the COVID-19 lockdown in China L. Shen et al. 10.1016/j.jclepro.2021.126561
- Simultaneous decreases in NO2 levels and disparities in California during the COVID-19 pandemic H. Lee et al. 10.1016/j.atmosenv.2023.120214
- 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
- Different Response Mechanisms of N‐Bearing Components to Emission Reduction Across China During COVID‐19 Lockdown Period R. Li et al. 10.1029/2023JD039496
- Consistent weekly cycles of atmospheric NO2, CO, and CO2 in a North American megacity from ground-based, mountaintop, and satellite measurements H. Wang et al. 10.1016/j.atmosenv.2021.118809
- 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
- Hybrid deep learning models for mapping surface NO2 across China: One complicated model, many simple models, or many complicated models? X. Liu et al. 10.1016/j.atmosres.2022.106339
- Examining TROPOMI formaldehyde to nitrogen dioxide ratios in the Lake Michigan region: implications for ozone exceedances J. Acdan et al. 10.5194/acp-23-7867-2023
- Direct Retrieval of NO 2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network C. Li et al. 10.34133/2022/9817134
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- Transformation of urban mobility during COVID-19 pandemic – Lessons for transportation planning S. Rahman et al. 10.1016/j.jth.2021.101257
- Carbon Monoxide in Optically Thick Wildfire Smoke: Evaluating TROPOMI Using CU Airborne SOF Column Observations J. Rowe et al. 10.1021/acsearthspacechem.2c00048
- Global Observations of Tropospheric Bromine Monoxide (BrO) Columns From TROPOMI Y. Chen et al. 10.1029/2023JD039091
- Two years of satellite-based carbon dioxide emission quantification at the world's largest coal-fired power plants D. Cusworth et al. 10.5194/acp-23-14577-2023
- Global Significant Changes in Formaldehyde (HCHO) Columns Observed From Space at the Early Stage of the COVID‐19 Pandemic W. Sun et al. 10.1029/2020GL091265
- Evaluating Phoenix Metropolitan Area Ozone Behavior Using Ground-Based Sampling, Modeling, and Satellite Retrievals J. Miech et al. 10.3390/atmos15050555
- Assessment of environmental consequences of hostilities: Tropospheric NO2 vertical column amounts in the atmosphere over Ukraine in 2019–2022 L. Malytska et al. 10.1016/j.atmosenv.2023.120281
- A Satellite-Based Indicator for Diagnosing Particulate Nitrate Sensitivity to Precursor Emissions: Application to East Asia, Europe, and North America R. Dang et al. 10.1021/acs.est.4c08082
- Inferring vertical variability and diurnal evolution of O3 formation sensitivity based on the vertical distribution of summertime HCHO and NO2 in Guangzhou, China Q. Hong et al. 10.1016/j.scitotenv.2022.154045
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- A novel physics-based cloud retrieval algorithm based on neural networks (CRANN) from hyperspectral measurements in the O2-O2 band W. Wang et al. 10.1016/j.rse.2024.114267
- Sensitivities of Ozone Air Pollution in the Beijing–Tianjin–Hebei Area to Local and Upwind Precursor Emissions Using Adjoint Modeling X. Wang et al. 10.1021/acs.est.1c00131
- Estimating 2013–2019 NO2 exposure with high spatiotemporal resolution in China using an ensemble model C. Huang et al. 10.1016/j.envpol.2021.118285
- Observations of Lightning NOx Production From Tropospheric Monitoring Instrument Case Studies Over the United States D. Allen et al. 10.1029/2020JD034174
- Diagnosing the Sensitivity of Particulate Nitrate to Precursor Emissions Using Satellite Observations of Ammonia and Nitrogen Dioxide R. Dang et al. 10.1029/2023GL105761
- Towards a New MAX-DOAS Measurement Site in the Po Valley: NO2 Total VCDs P. Pettinari et al. 10.3390/rs14163881
- Anthropogenic NO x emissions of China, the U.S. and Europe from 2019 to 2022 inferred from TROPOMI observations Y. Mao et al. 10.1088/1748-9326/ad3cf9
- Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact C. Zhang et al. 10.1016/j.envpol.2022.119510
Latest update: 21 Nov 2024
Short summary
The Tropospheric Monitoring Instrument (TROPOMI) provides atmospheric trace gase and cloud and aerosol property measurements at unprecedented spatial resolution. This study focusses on the TROPOMI NO2 slant column density (SCD) retrieval: the retrieval method used, the stability of and uncertainties in the SCDs, and a comparison with Ozone Monitoring Instrument (OMI) NO2 SCDs. TROPOMI shows a superior performance compared to OMI/QA4ECV and operates as anticipated from instrument specifications.
The Tropospheric Monitoring Instrument (TROPOMI) provides atmospheric trace gase and cloud and...