Articles | Volume 11, issue 2
https://doi.org/10.5194/essd-11-687-2019
© Author(s) 2019. 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-11-687-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
17 May 2019
| 17 May 2019
A global map of emission clumps for future monitoring of fossil fuel CO2 emissions from space
Yilong Wang
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX, France
Philippe Ciais
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX, France
Grégoire Broquet
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX, France
François-Marie Bréon
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX, France
Tomohiro Oda
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, MD, USA
Franck Lespinas
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX, France
Yasjka Meijer
European Space Agency (ESA), Noordwijk, the Netherlands
Armin Loescher
European Space Agency (ESA), Noordwijk, the Netherlands
Greet Janssens-Maenhout
European Commission, Joint Research Centre, Directorate Sustainable Resources, via E. Fermi 2749 (T.P. 123), 21027 Ispra, Italy
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX, France
Haoran Xu
Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
Shu Tao
Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
Kevin R. Gurney
School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Geoffrey Roest
School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Diego Santaren
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ- Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX, France
Yongxian Su
Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application,
Guangzhou Institute of Geography, Guangzhou 510070, China
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- Quantifying CO2 emissions of a city with the Copernicus Anthropogenic CO2 Monitoring satellite mission G. Kuhlmann et al. https://doi.org/10.5194/amt-13-6733-2020
- Biofuel burning and human respiration bias on satellite estimates of fossil fuel CO2 emissions P. Ciais et al. https://doi.org/10.1088/1748-9326/ab7835
- Optimal selection of satellite XCO2 images for urban CO2 emission monitoring A. Danjou et al. https://doi.org/10.5194/amt-18-533-2025
- Quantifying CO2 Emissions of Power Plants With CO2 and NO2 Imaging Satellites G. Kuhlmann et al. https://doi.org/10.3389/frsen.2021.689838
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- A city-level comparison of fossil-fuel and industry processes-induced CO2 emissions over the Beijing-Tianjin-Hebei region from eight emission inventories P. Han et al. https://doi.org/10.1186/s13021-020-00163-2
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- Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities A. Kuze et al. https://doi.org/10.1016/j.rse.2022.112966
- PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data Y. Wang et al. https://doi.org/10.5194/gmd-13-5813-2020
- Estimating Global Anthropogenic CO2 Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model Y. Zhang et al. https://doi.org/10.3390/rs14163899
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- Exploring spatiotemporal variation characteristics of China’s industrial carbon emissions on the basis of multi-source data Y. Fu et al. https://doi.org/10.1007/s11356-021-13092-5
- Urban Compactivity Models: Screening City Trends for the Urgency of Social and Environmental Sustainability N. Lobner et al. https://doi.org/10.3390/urbansci5040083
- Toward a satellite-based monitoring system for urban CO2 emissions in support of global collective climate mitigation actions T. Wilmot et al. https://doi.org/10.1088/1748-9326/ad6017
- The Space Carbon Observatory (SCARBO) concept: assessment of XCO2 and XCH4 retrieval performance M. Dogniaux et al. https://doi.org/10.5194/amt-15-4835-2022
- A local- to national-scale inverse modeling system to assess the potential of spaceborne CO2 measurements for the monitoring of anthropogenic emissions D. Santaren et al. https://doi.org/10.5194/amt-14-403-2021
- Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization G. Li et al. https://doi.org/10.3390/pr12020402
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- A Novel POP-Ni Catalyst Derived from PBTP for Ambient Fixation of CO2 into Cyclic Carbonates F. Wei et al. https://doi.org/10.3390/ma16062132
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23 citations as recorded by crossref.
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review H. Iyer et al. https://doi.org/10.1007/s40572-025-00497-4
- Quantifying CO2 emissions of a city with the Copernicus Anthropogenic CO2 Monitoring satellite mission G. Kuhlmann et al. https://doi.org/10.5194/amt-13-6733-2020
- Biofuel burning and human respiration bias on satellite estimates of fossil fuel CO2 emissions P. Ciais et al. https://doi.org/10.1088/1748-9326/ab7835
- Optimal selection of satellite XCO2 images for urban CO2 emission monitoring A. Danjou et al. https://doi.org/10.5194/amt-18-533-2025
- Quantifying CO2 Emissions of Power Plants With CO2 and NO2 Imaging Satellites G. Kuhlmann et al. https://doi.org/10.3389/frsen.2021.689838
- Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space Z. Wang et al. https://doi.org/10.1016/j.rse.2026.115260
- A city-level comparison of fossil-fuel and industry processes-induced CO2 emissions over the Beijing-Tianjin-Hebei region from eight emission inventories P. Han et al. https://doi.org/10.1186/s13021-020-00163-2
- Towards the next generation of Geospatial Artificial Intelligence G. Mai et al. https://doi.org/10.1016/j.jag.2025.104368
- Satellite-Based Emission Inversion for Air Pollutants and Greenhouse Gases: A Review Z. Jiang et al. https://doi.org/10.1007/s13351-025-4914-7
- Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities A. Kuze et al. https://doi.org/10.1016/j.rse.2022.112966
- PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data Y. Wang et al. https://doi.org/10.5194/gmd-13-5813-2020
- Estimating Global Anthropogenic CO2 Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model Y. Zhang et al. https://doi.org/10.3390/rs14163899
- Attributed radiative forcing of air pollutants from biomass and fossil burning emissions K. Jiang et al. https://doi.org/10.1016/j.envpol.2022.119378
- Exploring spatiotemporal variation characteristics of China’s industrial carbon emissions on the basis of multi-source data Y. Fu et al. https://doi.org/10.1007/s11356-021-13092-5
- Urban Compactivity Models: Screening City Trends for the Urgency of Social and Environmental Sustainability N. Lobner et al. https://doi.org/10.3390/urbansci5040083
- Toward a satellite-based monitoring system for urban CO2 emissions in support of global collective climate mitigation actions T. Wilmot et al. https://doi.org/10.1088/1748-9326/ad6017
- The Space Carbon Observatory (SCARBO) concept: assessment of XCO2 and XCH4 retrieval performance M. Dogniaux et al. https://doi.org/10.5194/amt-15-4835-2022
- A local- to national-scale inverse modeling system to assess the potential of spaceborne CO2 measurements for the monitoring of anthropogenic emissions D. Santaren et al. https://doi.org/10.5194/amt-14-403-2021
- Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization G. Li et al. https://doi.org/10.3390/pr12020402
- The potential of a constellation of low earth orbit satellite imagers to monitor worldwide fossil fuel CO2 emissions from large cities and point sources F. Lespinas et al. https://doi.org/10.1186/s13021-020-00153-4
- A Novel POP-Ni Catalyst Derived from PBTP for Ambient Fixation of CO2 into Cyclic Carbonates F. Wei et al. https://doi.org/10.3390/ma16062132
- Multi-scale assessment of energy transition and emission patterns in industrial coastal regions Y. Teng et al. https://doi.org/10.1016/j.fuel.2025.137069
- Automated detection of atmospheric NO2 plumes from satellite data: a tool to help infer anthropogenic combustion emissions D. Finch et al. https://doi.org/10.5194/amt-15-721-2022
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
We address the question of the global characterization of fossil fuel CO2 emission hotspots that may cause coherent XCO2 plumes in space-borne CO2 images, based on the ODIAC global high-resolution 1 km fossil fuel emission data product. For space imagery with 0.5 ppm precision for a single XCO2 measurement, a total of 11 314 hotspots are identified, covering 72 % of the global emissions. These hotspots define the targets for the purpose of monitoring fossil fuel CO2 emissions from space.
We address the question of the global characterization of fossil fuel CO2 emission hotspots that...
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