Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6733-2020
© 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-13-6733-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
15 Dec 2020
Research article | Highlight paper |
| 15 Dec 2020
| 15 Dec 2020
Quantifying CO2 emissions of a city with the Copernicus Anthropogenic CO2 Monitoring satellite mission
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
Grégoire Broquet
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
Yasjka Meijer
European Space Agency (ESA), ESTEC, Noordwijk, the Netherlands
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26 citations as recorded by crossref.
- Determination of the emission rates of CO<sub>2</sub> point sources with airborne lidar S. Wolff et al. 10.5194/amt-14-2717-2021
- Analyzing Local Carbon Dioxide and Nitrogen Oxide Emissions From Space Using the Divergence Method: An Application to the Synthetic SMARTCARB Dataset J. Hakkarainen et al. 10.3389/frsen.2022.878731
- Theoretical assessment of the ability of the MicroCarb satellite city-scan observing mode to estimate urban CO2 emissions K. Wu et al. 10.5194/amt-16-581-2023
- Assessing the Impact of Atmospheric CO2 and NO2 Measurements From Space on Estimating City-Scale Fossil Fuel CO2 Emissions in a Data Assimilation System T. Kaminski et al. 10.3389/frsen.2022.887456
- Analyzing nitrogen dioxide to nitrogen oxide scaling factors for data-driven satellite-based emission estimation methods: A case study of Matimba/Medupi power stations in South Africa J. Hakkarainen et al. 10.1016/j.apr.2024.102171
- Automated detection of atmospheric NO<sub>2</sub> plumes from satellite data: a tool to help infer anthropogenic combustion emissions D. Finch et al. 10.5194/amt-15-721-2022
- XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees X. Li et al. 10.3390/atmos15040440
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. 10.5194/gmd-17-1995-2024
- Evaluating NOx stack plume emissions using a high-resolution atmospheric chemistry model and satellite-derived NO2 columns M. Krol et al. 10.5194/acp-24-8243-2024
- A fibre-based 2D-slit homogenizer concept for high-precision space-based spectrometer missions T. Hummel et al. 10.1007/s12567-021-00419-8
- A lightweight NO2-to-NOx conversion model for quantifying NOx emissions of point sources from NO2 satellite observations S. Meier et al. 10.5194/acp-24-7667-2024
- Estimating anthropogenic CO2 emissions from China's Yangtze River Delta using OCO-2 observations and WRF-Chem simulations M. Sheng et al. 10.1016/j.rse.2024.114515
- Using Space‐Based CO2 and NO2 Observations to Estimate Urban CO2 Emissions E. Yang et al. 10.1029/2022JD037736
- Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants J. Dumont Le Brazidec et al. 10.5194/gmd-16-3997-2023
- The CO2 Human Emissions (CHE) Project: First Steps Towards a European Operational Capacity to Monitor Anthropogenic CO2 Emissions G. Balsamo et al. 10.3389/frsen.2021.707247
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. 10.5194/gmd-17-4773-2024
- Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1 M. Goudar et al. 10.5194/gmd-16-4835-2023
- Impact of the horizontal resolution of GEOS-Chem on land‒ocean and tropic‒extratropic partitioning and seasonal cycle in CO2 inversion Z. Liu et al. 10.1088/1748-9326/ad7870
- Evaluating the Ability of the Pre-Launch TanSat-2 Satellite to Quantify Urban CO2 Emissions K. Wu et al. 10.3390/rs15204904
- Quantifying CO2 Emissions of Power Plants With CO2 and NO2 Imaging Satellites G. Kuhlmann et al. 10.3389/frsen.2021.689838
- Complementing XCO2 imagery with ground-based CO2 and 14CO2 measurements to monitor CO2 emissions from fossil fuels on a regional to local scale E. Potier et al. 10.5194/amt-15-5261-2022
- Integrated energy carbon emission monitoring and digital management system for smart cities J. Liu & Z. Zhang 10.3389/fenrg.2023.1221345
- Accounting for meteorological biases in simulated plumes using smarter metrics P. Vanderbecken et al. 10.5194/amt-16-1745-2023
- Do State‐Of‐The‐Art Atmospheric CO2 Inverse Models Capture Drought Impacts on the European Land Carbon Uptake? W. He et al. 10.1029/2022MS003150
- A dynamic planning method for satellite imaging mission based on improved genetic algorithm D. Zhao et al. 10.2478/amns-2024-1526
- Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement A. Agustí-Panareda et al. 10.1038/s41597-022-01228-2
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Short summary
The European CO2M mission is a proposed constellation of CO2 imaging satellites expected to monitor CO2 emissions of large cities. Using synthetic observations, we show that a constellation of two or more satellites should be able to quantify Berlin's annual emissions with 10–20 % accuracy, even when considering atmospheric transport model errors. We therefore expect that CO2M will make an important contribution to the monitoring and verification of CO2 emissions from cities worldwide.
The European CO2M mission is a proposed constellation of CO2 imaging satellites expected to...
