Articles | Volume 23, issue 15
https://doi.org/10.5194/acp-23-8823-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-8823-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Aug 2023
Research article |
| 09 Aug 2023
| 09 Aug 2023
Can we use atmospheric CO2 measurements to verify emission trends reported by cities? Lessons from a 6-year atmospheric inversion over Paris
Origins.earth, SUEZ Group, Tour CB21, 16 Place de l'Iris, 92040 Paris La Défense CEDEX, France
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX, France
Thomas Lauvaux
Groupe de Spectrométrie Moléculaire et Atmosphérique (GSMA), Université de Reims-Champagne Ardenne, UMR CNRS 7331, Reims, France
Hervé Utard
Origins.earth, SUEZ Group, Tour CB21, 16 Place de l'Iris, 92040 Paris La Défense CEDEX, France
François-Marie Bréon
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX, France
Grégoire Broquet
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX, France
Michel Ramonet
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX, France
Olivier Laurent
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX, France
Ivonne Albarus
Origins.earth, SUEZ Group, Tour CB21, 16 Place de l'Iris, 92040 Paris La Défense CEDEX, France
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX, France
Mali Chariot
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX, France
Simone Kotthaus
Institut Pierre-Simon Laplace (IPSL), CNRS, École Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau CEDEX, France
Martial Haeffelin
Institut Pierre-Simon Laplace (IPSL), CNRS, École Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau CEDEX, France
Olivier Sanchez
AirParif, 7 rue Crillon, Paris, France
Olivier Perrussel
AirParif, 7 rue Crillon, Paris, France
Hugo Anne Denier van der Gon
Department of Climate, Air and Sustainability, TNO, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
Stijn Nicolaas Camiel Dellaert
Department of Climate, Air and Sustainability, TNO, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
Philippe Ciais
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette CEDEX, France
Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, 20 Konstantinou Kavafi Street, 2121, Nicosia, Cyprus
Viewed
Total article views: 7,710 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 04 Apr 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 5,611 | 1,902 | 197 | 7,710 | 607 | 253 | 316 |
- HTML: 5,611
- PDF: 1,902
- XML: 197
- Total: 7,710
- Supplement: 607
- BibTeX: 253
- EndNote: 316
Total article views: 5,260 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 Aug 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 4,219 | 925 | 116 | 5,260 | 301 | 160 | 181 |
- HTML: 4,219
- PDF: 925
- XML: 116
- Total: 5,260
- Supplement: 301
- BibTeX: 160
- EndNote: 181
Total article views: 2,450 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 04 Apr 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,392 | 977 | 81 | 2,450 | 306 | 93 | 135 |
- HTML: 1,392
- PDF: 977
- XML: 81
- Total: 2,450
- Supplement: 306
- BibTeX: 93
- EndNote: 135
Viewed (geographical distribution)
Total article views: 7,710 (including HTML, PDF, and XML)
Thereof 7,605 with geography defined
and 105 with unknown origin.
Total article views: 5,260 (including HTML, PDF, and XML)
Thereof 5,162 with geography defined
and 98 with unknown origin.
Total article views: 2,450 (including HTML, PDF, and XML)
Thereof 2,443 with geography defined
and 7 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
33 citations as recorded by crossref.
- Development and deployment of a mid-cost CO2 sensor monitoring network to support atmospheric inverse modeling for quantifying urban CO2 emissions in Paris J. Lian et al. https://doi.org/10.5194/amt-17-5821-2024
- Sustained Reductions of Bay Area CO2 Emissions 2018–2022 N. Asimow et al. https://doi.org/10.1021/acs.est.3c09642
- Quantifying facility-scale CO2 emissions using spaceborne hyperspectral imageries G. Han et al. https://doi.org/10.1016/j.rse.2026.115478
- From political pledges to quantitative mapping of climate mitigation plans: Comparison of two European cities I. Albarus et al. https://doi.org/10.1186/s13021-023-00236-y
- Evaluation of light atmospheric plume inversion methods using synthetic XCO2 satellite images to compute Paris CO2 emissions A. Danjou et al. https://doi.org/10.1016/j.rse.2023.113900
- Observing Anthropogenic and Biogenic CO2 Emissions in Los Angeles Using a Dense Sensor Network J. Kim et al. https://doi.org/10.1021/acs.est.4c11392
- Using urban-suburban difference of atmospheric CO2 to evaluate carbon neutrality capacity in Hangzhou, China B. Qi et al. https://doi.org/10.1016/j.jes.2024.10.027
- Constraining urban fossil fuel CO2 emissions in Seoul using combined ground and satellite observations with Bayesian inverse modelling S. Sim & S. Jeong https://doi.org/10.5194/acp-25-18509-2025
- A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale S. Annadate et al. https://doi.org/10.3390/atmos15010051
- Carbon sequestration in different urban vegetation types in Southern Finland L. Thölix et al. https://doi.org/10.5194/bg-22-725-2025
- Comparing local CO2 emission inventories with tall tower flux measurements in Vienna, Austria E. Fasano et al. https://doi.org/10.1016/j.aeaoa.2026.100443
- A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals L. Zhang et al. https://doi.org/10.3390/rs17030447
- 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
- Atmospheric CO2 dynamics in a coastal megacity: spatiotemporal patterns, sea–land breeze impacts, and anthropogenic–biogenic emission partitioning J. Zhang et al. https://doi.org/10.5194/acp-26-3253-2026
- Plume Detection and Emissions Quantification Potential Using a Dense Sensor Network M. Patel et al. https://doi.org/10.1021/acsestair.5c00069
- Measurement report: Eight years of greenhouse gas fluxes at Saclay, France, estimated with the Radon Tracer Method C. Yver-Kwok et al. https://doi.org/10.5194/acp-25-16085-2025
- A novel Koopman-based Assistant Features Network for long and short-term carbon emission prediction D. Cui et al. https://doi.org/10.1016/j.engappai.2025.110366
- High-Resolution Analysis of Temporal Variation and Driving Factors of CO2 Concentration in Nanning City in Spring 2024 J. Feng et al. https://doi.org/10.3390/atmos16040449
- Harmonised boundary layer wind profile dataset from six ground-based Doppler wind lidars in a transect across Paris, France W. Morrison et al. https://doi.org/10.5194/essd-17-6507-2025
- Monitoring CO2 in diverse European cities: highlighting needs and challenges through characterisation I. Storm et al. https://doi.org/10.5194/essd-17-6681-2025
- CO2 flux emissions at two urban sites in Beijing and responses to human activity H. Liu et al. https://doi.org/10.1016/j.pce.2024.103791
- Detection of local-scale changes in greenhouse gas emissions in urban environments using micrometeorological methods and comparison to a high-resolution inventory H. Kenion et al. https://doi.org/10.1525/elementa.2025.00036
- Estimation of CO2 fluxes in the cities of Zurich and Paris using the ICON-ART CTDAS inverse modelling framework N. Ponomarev et al. https://doi.org/10.5194/acp-26-547-2026
- Monitoring urban carbon dioxide emissions from the atmosphere: insights from vertical tower observations in Beijing, China K. Liu et al. https://doi.org/10.1016/j.atmosenv.2026.122166
- Carbon uptake of an urban green space inferred from carbonyl sulfide fluxes J. Soininen et al. https://doi.org/10.1038/s41612-025-00958-5
- 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
- Central-eastern China's chloroform emissions during 2021–2024 inferred from high-frequency in-situ atmospheric measurements and industrial sampling B. Li et al. https://doi.org/10.1016/j.scitotenv.2025.181275
- European pollen reanalysis, 1980–2022, for alder, birch, and olive M. Sofiev et al. https://doi.org/10.1038/s41597-024-03686-2
- A novel cluster-based learning scheme to design optimal networks for atmospheric greenhouse gas monitoring (CRO2A version 1.0) D. Matajira-Rueda et al. https://doi.org/10.5194/gmd-19-3757-2026
- High-resolution carbon modeling in dense urban areas: LiDAR-validated spatiotemporal mapping for neighborhood decarbonization Y. Wu et al. https://doi.org/10.1016/j.scs.2025.106981
- A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks L. Nayagam et al. https://doi.org/10.1088/1748-9326/ad0f74
- Real-time atmospheric CO2 and CH4 monitoring via a novel mobile sensing system J. Chen et al. https://doi.org/10.1016/j.rineng.2025.107676
- Advances in the design of urban CO2 emission monitoring networks: a review J. Li et al. https://doi.org/10.1007/s44246-025-00239-z
33 citations as recorded by crossref.
- Development and deployment of a mid-cost CO2 sensor monitoring network to support atmospheric inverse modeling for quantifying urban CO2 emissions in Paris J. Lian et al. https://doi.org/10.5194/amt-17-5821-2024
- Sustained Reductions of Bay Area CO2 Emissions 2018–2022 N. Asimow et al. https://doi.org/10.1021/acs.est.3c09642
- Quantifying facility-scale CO2 emissions using spaceborne hyperspectral imageries G. Han et al. https://doi.org/10.1016/j.rse.2026.115478
- From political pledges to quantitative mapping of climate mitigation plans: Comparison of two European cities I. Albarus et al. https://doi.org/10.1186/s13021-023-00236-y
- Evaluation of light atmospheric plume inversion methods using synthetic XCO2 satellite images to compute Paris CO2 emissions A. Danjou et al. https://doi.org/10.1016/j.rse.2023.113900
- Observing Anthropogenic and Biogenic CO2 Emissions in Los Angeles Using a Dense Sensor Network J. Kim et al. https://doi.org/10.1021/acs.est.4c11392
- Using urban-suburban difference of atmospheric CO2 to evaluate carbon neutrality capacity in Hangzhou, China B. Qi et al. https://doi.org/10.1016/j.jes.2024.10.027
- Constraining urban fossil fuel CO2 emissions in Seoul using combined ground and satellite observations with Bayesian inverse modelling S. Sim & S. Jeong https://doi.org/10.5194/acp-25-18509-2025
- A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale S. Annadate et al. https://doi.org/10.3390/atmos15010051
- Carbon sequestration in different urban vegetation types in Southern Finland L. Thölix et al. https://doi.org/10.5194/bg-22-725-2025
- Comparing local CO2 emission inventories with tall tower flux measurements in Vienna, Austria E. Fasano et al. https://doi.org/10.1016/j.aeaoa.2026.100443
- A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals L. Zhang et al. https://doi.org/10.3390/rs17030447
- 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
- Atmospheric CO2 dynamics in a coastal megacity: spatiotemporal patterns, sea–land breeze impacts, and anthropogenic–biogenic emission partitioning J. Zhang et al. https://doi.org/10.5194/acp-26-3253-2026
- Plume Detection and Emissions Quantification Potential Using a Dense Sensor Network M. Patel et al. https://doi.org/10.1021/acsestair.5c00069
- Measurement report: Eight years of greenhouse gas fluxes at Saclay, France, estimated with the Radon Tracer Method C. Yver-Kwok et al. https://doi.org/10.5194/acp-25-16085-2025
- A novel Koopman-based Assistant Features Network for long and short-term carbon emission prediction D. Cui et al. https://doi.org/10.1016/j.engappai.2025.110366
- High-Resolution Analysis of Temporal Variation and Driving Factors of CO2 Concentration in Nanning City in Spring 2024 J. Feng et al. https://doi.org/10.3390/atmos16040449
- Harmonised boundary layer wind profile dataset from six ground-based Doppler wind lidars in a transect across Paris, France W. Morrison et al. https://doi.org/10.5194/essd-17-6507-2025
- Monitoring CO2 in diverse European cities: highlighting needs and challenges through characterisation I. Storm et al. https://doi.org/10.5194/essd-17-6681-2025
- CO2 flux emissions at two urban sites in Beijing and responses to human activity H. Liu et al. https://doi.org/10.1016/j.pce.2024.103791
- Detection of local-scale changes in greenhouse gas emissions in urban environments using micrometeorological methods and comparison to a high-resolution inventory H. Kenion et al. https://doi.org/10.1525/elementa.2025.00036
- Estimation of CO2 fluxes in the cities of Zurich and Paris using the ICON-ART CTDAS inverse modelling framework N. Ponomarev et al. https://doi.org/10.5194/acp-26-547-2026
- Monitoring urban carbon dioxide emissions from the atmosphere: insights from vertical tower observations in Beijing, China K. Liu et al. https://doi.org/10.1016/j.atmosenv.2026.122166
- Carbon uptake of an urban green space inferred from carbonyl sulfide fluxes J. Soininen et al. https://doi.org/10.1038/s41612-025-00958-5
- 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
- Central-eastern China's chloroform emissions during 2021–2024 inferred from high-frequency in-situ atmospheric measurements and industrial sampling B. Li et al. https://doi.org/10.1016/j.scitotenv.2025.181275
- European pollen reanalysis, 1980–2022, for alder, birch, and olive M. Sofiev et al. https://doi.org/10.1038/s41597-024-03686-2
- A novel cluster-based learning scheme to design optimal networks for atmospheric greenhouse gas monitoring (CRO2A version 1.0) D. Matajira-Rueda et al. https://doi.org/10.5194/gmd-19-3757-2026
- High-resolution carbon modeling in dense urban areas: LiDAR-validated spatiotemporal mapping for neighborhood decarbonization Y. Wu et al. https://doi.org/10.1016/j.scs.2025.106981
- A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks L. Nayagam et al. https://doi.org/10.1088/1748-9326/ad0f74
- Real-time atmospheric CO2 and CH4 monitoring via a novel mobile sensing system J. Chen et al. https://doi.org/10.1016/j.rineng.2025.107676
- Advances in the design of urban CO2 emission monitoring networks: a review J. Li et al. https://doi.org/10.1007/s44246-025-00239-z
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
This study quantifies urban CO2 emissions via an atmospheric inversion for the Paris metropolitan area over a 6-year period from 2016 to 2021. Results show a long-term decreasing trend of about 2 % ± 0.6 % per year in the annual CO2 emissions over Paris. We conclude that our current capacity can deliver near-real-time CO2 emission estimates at the city scale in under a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.
This study quantifies urban CO2 emissions via an atmospheric inversion for the Paris...
Altmetrics
Final-revised paper
Preprint