Articles | Volume 25, issue 22
https://doi.org/10.5194/acp-25-15631-2025
© 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/acp-25-15631-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Nov 2025
Research article |
| 17 Nov 2025
| 17 Nov 2025
Development of a parametrised atmospheric NOx chemistry scheme to help quantify fossil fuel CO2 emission estimates
School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom
School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom
National Centre for Earth Observation, Leicester, United Kingdom
Auke Visser
European Centre for Medium-Range Weather Forecasts, Bonn, Germany
Nicolas Bousserez
European Centre for Medium-Range Weather Forecasts, Bonn, Germany
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Alice Drinkwater, Paul I. Palmer, Liang Feng, Tim Arnold, Xin Lan, Sylvia E. Michel, Robert Parker, and Hartmut Boesch
Atmos. Chem. Phys., 23, 8429–8452, https://doi.org/10.5194/acp-23-8429-2023, https://doi.org/10.5194/acp-23-8429-2023, 2023
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Liang Feng, Paul I. Palmer, Robert J. Parker, Mark F. Lunt, and Hartmut Bösch
Atmos. Chem. Phys., 23, 4863–4880, https://doi.org/10.5194/acp-23-4863-2023, https://doi.org/10.5194/acp-23-4863-2023, 2023
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Anna Agustí-Panareda, Jérôme Barré, Sébastien Massart, Antje Inness, Ilse Aben, Melanie Ades, Bianca C. Baier, Gianpaolo Balsamo, Tobias Borsdorff, Nicolas Bousserez, Souhail Boussetta, Michael Buchwitz, Luca Cantarello, Cyril Crevoisier, Richard Engelen, Henk Eskes, Johannes Flemming, Sébastien Garrigues, Otto Hasekamp, Vincent Huijnen, Luke Jones, Zak Kipling, Bavo Langerock, Joe McNorton, Nicolas Meilhac, Stefan Noël, Mark Parrington, Vincent-Henri Peuch, Michel Ramonet, Miha Razinger, Maximilian Reuter, Roberto Ribas, Martin Suttie, Colm Sweeney, Jérôme Tarniewicz, and Lianghai Wu
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Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
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This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Kai Wu, Paul I. Palmer, Dien Wu, Denis Jouglet, Liang Feng, and Tom Oda
Atmos. Meas. Tech., 16, 581–602, https://doi.org/10.5194/amt-16-581-2023, https://doi.org/10.5194/amt-16-581-2023, 2023
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We evaluate the theoretical ability of the upcoming MicroCarb satellite to estimate urban CO2 emissions over Paris and London. We explore the relative performance of alternative two-sweep and three-sweep city observing modes and take into account the impacts of cloud cover and urban biological CO2 fluxes. Our results find both the two-sweep and three-sweep observing modes are able to reduce prior flux errors by 20 %–40 % depending on the prevailing wind direction and cloud coverage.
Robert J. Parker, Chris Wilson, Edward Comyn-Platt, Garry Hayman, Toby R. Marthews, A. Anthony Bloom, Mark F. Lunt, Nicola Gedney, Simon J. Dadson, Joe McNorton, Neil Humpage, Hartmut Boesch, Martyn P. Chipperfield, Paul I. Palmer, and Dai Yamazaki
Biogeosciences, 19, 5779–5805, https://doi.org/10.5194/bg-19-5779-2022, https://doi.org/10.5194/bg-19-5779-2022, 2022
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Wetlands are the largest natural source of methane, one of the most important climate gases. The JULES land surface model simulates these emissions. We use satellite data to evaluate how well JULES reproduces the methane seasonal cycle over different tropical wetlands. It performs well for most regions; however, it struggles for some African wetlands influenced heavily by river flooding. We explain the reasons for these deficiencies and highlight how future development will improve these areas.
Sebastien Garrigues, Samuel Remy, Julien Chimot, Melanie Ades, Antje Inness, Johannes Flemming, Zak Kipling, Istvan Laszlo, Angela Benedetti, Roberto Ribas, Soheila Jafariserajehlou, Bertrand Fougnie, Shobha Kondragunta, Richard Engelen, Vincent-Henri Peuch, Mark Parrington, Nicolas Bousserez, Margarita Vazquez Navarro, and Anna Agusti-Panareda
Atmos. Chem. Phys., 22, 14657–14692, https://doi.org/10.5194/acp-22-14657-2022, https://doi.org/10.5194/acp-22-14657-2022, 2022
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The Copernicus Atmosphere Monitoring Service (CAMS) provides global monitoring of aerosols using the ECMWF forecast model constrained by the assimilation of satellite aerosol optical depth (AOD). This work aims at evaluating two new satellite AODs to enhance the CAMS aerosol global forecast. It highlights the spatial and temporal differences between the satellite AOD products at the model spatial resolution, which is essential information to design multi-satellite AOD data assimilation schemes.
Dien Wu, Junjie Liu, Paul O. Wennberg, Paul I. Palmer, Robert R. Nelson, Matthäus Kiel, and Annmarie Eldering
Atmos. Chem. Phys., 22, 14547–14570, https://doi.org/10.5194/acp-22-14547-2022, https://doi.org/10.5194/acp-22-14547-2022, 2022
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Prior studies have derived the combustion efficiency for a region/city using observed CO2 and CO. We further zoomed into the urban domain and accounted for factors affecting the calculation of spatially resolved combustion efficiency from two satellites. The intra-city variability in combustion efficiency was linked to heavy industry within Shanghai and LA without relying on emission inventories. Such an approach can be applied when analyzing data from future geostationary satellites.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
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The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Selena Georgiou, Edward T. A. Mitchard, Bart Crezee, Paul I. Palmer, Greta C. Dargie, Sofie Sjögersten, Corneille E. N. Ewango, Ovide B. Emba, Joseph T. Kanyama, Pierre Bola, Jean-Bosco N. Ndjango, Nicholas T. Girkin, Yannick E. Bocko, Suspense A. Ifo, and Simon L. Lewis
EGUsphere, https://doi.org/10.5194/egusphere-2022-580, https://doi.org/10.5194/egusphere-2022-580, 2022
Preprint archived
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Two major vegetation types, hardwood trees and palms, overlay the Central Congo Basin peatland complex, each dominant in different locations. We investigated the influence of terrain and climatological variables on their distribution, using a regression model, and found elevation and seasonal rainfall and temperature contribute significantly. There are indications of an optimal range of net water input for palm swamp to dominate, above and below which hardwood swamp dominates.
Joe McNorton, Nicolas Bousserez, Anna Agustí-Panareda, Gianpaolo Balsamo, Luca Cantarello, Richard Engelen, Vincent Huijnen, Antje Inness, Zak Kipling, Mark Parrington, and Roberto Ribas
Atmos. Chem. Phys., 22, 5961–5981, https://doi.org/10.5194/acp-22-5961-2022, https://doi.org/10.5194/acp-22-5961-2022, 2022
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Concentrations of atmospheric methane continue to grow, in recent years at an increasing rate, for unknown reasons. Using newly available satellite observations and a state-of-the-art weather prediction model we perform global estimates of emissions from hotspots at high resolution. Results show that the system can accurately report on biases in national inventories and is used to conclude that the early COVID-19 slowdown period (March–June 2020) had little impact on global methane emissions.
Carlos Alberti, Frank Hase, Matthias Frey, Darko Dubravica, Thomas Blumenstock, Angelika Dehn, Paolo Castracane, Gregor Surawicz, Roland Harig, Bianca C. Baier, Caroline Bès, Jianrong Bi, Hartmut Boesch, André Butz, Zhaonan Cai, Jia Chen, Sean M. Crowell, Nicholas M. Deutscher, Dragos Ene, Jonathan E. Franklin, Omaira García, David Griffith, Bruno Grouiez, Michel Grutter, Abdelhamid Hamdouni, Sander Houweling, Neil Humpage, Nicole Jacobs, Sujong Jeong, Lilian Joly, Nicholas B. Jones, Denis Jouglet, Rigel Kivi, Ralph Kleinschek, Morgan Lopez, Diogo J. Medeiros, Isamu Morino, Nasrin Mostafavipak, Astrid Müller, Hirofumi Ohyama, Paul I. Palmer, Mahesh Pathakoti, David F. Pollard, Uwe Raffalski, Michel Ramonet, Robbie Ramsay, Mahesh Kumar Sha, Kei Shiomi, William Simpson, Wolfgang Stremme, Youwen Sun, Hiroshi Tanimoto, Yao Té, Gizaw Mengistu Tsidu, Voltaire A. Velazco, Felix Vogel, Masataka Watanabe, Chong Wei, Debra Wunch, Marcia Yamasoe, Lu Zhang, and Johannes Orphal
Atmos. Meas. Tech., 15, 2433–2463, https://doi.org/10.5194/amt-15-2433-2022, https://doi.org/10.5194/amt-15-2433-2022, 2022
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Space-borne greenhouse gas missions require ground-based validation networks capable of providing fiducial reference measurements. Here, considerable refinements of the calibration procedures for the COllaborative Carbon Column Observing Network (COCCON) are presented. Laboratory and solar side-by-side procedures for the characterization of the spectrometers have been refined and extended. Revised calibration factors for XCO2, XCO and XCH4 are provided, incorporating 47 new spectrometers.
Zhu Deng, Philippe Ciais, Zitely A. Tzompa-Sosa, Marielle Saunois, Chunjing Qiu, Chang Tan, Taochun Sun, Piyu Ke, Yanan Cui, Katsumasa Tanaka, Xin Lin, Rona L. Thompson, Hanqin Tian, Yuanzhi Yao, Yuanyuan Huang, Ronny Lauerwald, Atul K. Jain, Xiaoming Xu, Ana Bastos, Stephen Sitch, Paul I. Palmer, Thomas Lauvaux, Alexandre d'Aspremont, Clément Giron, Antoine Benoit, Benjamin Poulter, Jinfeng Chang, Ana Maria Roxana Petrescu, Steven J. Davis, Zhu Liu, Giacomo Grassi, Clément Albergel, Francesco N. Tubiello, Lucia Perugini, Wouter Peters, and Frédéric Chevallier
Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, https://doi.org/10.5194/essd-14-1639-2022, 2022
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In support of the global stocktake of the Paris Agreement on climate change, we proposed a method for reconciling the results of global atmospheric inversions with data from UNFCCC national greenhouse gas inventories (NGHGIs). Here, based on a new global harmonized database that we compiled from the UNFCCC NGHGIs and a comprehensive framework presented in this study to process the results of inversions, we compared their results of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).
Douglas P. Finch, Paul I. Palmer, and Tianran Zhang
Atmos. Meas. Tech., 15, 721–733, https://doi.org/10.5194/amt-15-721-2022, https://doi.org/10.5194/amt-15-721-2022, 2022
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We developed a machine learning model to detect plumes of nitrogen dioxide satellite observations over 2 years. We find over 310 000 plumes, mainly over cities, industrial regions, and areas of oil and gas production. Our model performs well in comparison to other datasets and in some cases finds emissions that are not included in other datasets. This method could be used to help locate and measure emission hotspots across the globe and help inform climate policies.
Thomas E. Taylor, Christopher W. O'Dell, David Crisp, Akhiko Kuze, Hannakaisa Lindqvist, Paul O. Wennberg, Abhishek Chatterjee, Michael Gunson, Annmarie Eldering, Brendan Fisher, Matthäus Kiel, Robert R. Nelson, Aronne Merrelli, Greg Osterman, Frédéric Chevallier, Paul I. Palmer, Liang Feng, Nicholas M. Deutscher, Manvendra K. Dubey, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Frank Hase, Laura T. Iraci, Rigel Kivi, Cheng Liu, Martine De Mazière, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Matthias Schneider, Coleen M. Roehl, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, and Debra Wunch
Earth Syst. Sci. Data, 14, 325–360, https://doi.org/10.5194/essd-14-325-2022, https://doi.org/10.5194/essd-14-325-2022, 2022
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We provide an analysis of an 11-year record of atmospheric carbon dioxide (CO2) concentrations derived using an optimal estimation retrieval algorithm on measurements made by the GOSAT satellite. The new product (version 9) shows improvement over the previous version (v7.3) as evaluated against independent estimates of CO2 from ground-based sensors and atmospheric inversion systems. We also compare the new GOSAT CO2 values to collocated estimates from NASA's Orbiting Carbon Observatory-2.
Auke J. Visser, Laurens N. Ganzeveld, Ignacio Goded, Maarten C. Krol, Ivan Mammarella, Giovanni Manca, and K. Folkert Boersma
Atmos. Chem. Phys., 21, 18393–18411, https://doi.org/10.5194/acp-21-18393-2021, https://doi.org/10.5194/acp-21-18393-2021, 2021
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Dry deposition is an important sink for tropospheric ozone that affects ecosystem carbon uptake, but process understanding remains incomplete. We apply a common deposition representation in atmospheric chemistry models and a multi-layer canopy model to multi-year ozone deposition observations. The multi-layer canopy model performs better on diurnal timescales compared to the common approach, leading to a substantially improved simulation of ozone deposition and vegetation ozone impact metrics.
Margarita Choulga, Greet Janssens-Maenhout, Ingrid Super, Efisio Solazzo, Anna Agusti-Panareda, Gianpaolo Balsamo, Nicolas Bousserez, Monica Crippa, Hugo Denier van der Gon, Richard Engelen, Diego Guizzardi, Jeroen Kuenen, Joe McNorton, Gabriel Oreggioni, and Antoon Visschedijk
Earth Syst. Sci. Data, 13, 5311–5335, https://doi.org/10.5194/essd-13-5311-2021, https://doi.org/10.5194/essd-13-5311-2021, 2021
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People worry that growing man-made carbon dioxide (CO2) concentrations lead to climate change. Global models, use of observations, and datasets can help us better understand behaviour of CO2. Here a tool to compute uncertainty in man-made CO2 sources per country per year and month is presented. An example of all sources separated into seven groups (intensive and average energy, industry, humans, ground and air transport, others) is presented. Results will be used to predict CO2 concentrations.
Mark F. Lunt, Alistair J. Manning, Grant Allen, Tim Arnold, Stéphane J.-B. Bauguitte, Hartmut Boesch, Anita L. Ganesan, Aoife Grant, Carole Helfter, Eiko Nemitz, Simon J. O'Doherty, Paul I. Palmer, Joseph R. Pitt, Chris Rennick, Daniel Say, Kieran M. Stanley, Ann R. Stavert, Dickon Young, and Matt Rigby
Atmos. Chem. Phys., 21, 16257–16276, https://doi.org/10.5194/acp-21-16257-2021, https://doi.org/10.5194/acp-21-16257-2021, 2021
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We present an evaluation of the UK's methane emissions between 2013 and 2020 using a network of tall tower measurement sites. We find emissions that are consistent in both magnitude and trend with the UK's reported emissions, with a declining trend driven by a decrease in emissions from England. The impact of various components of the modelling set-up on these findings are explored through a number of sensitivity studies.
Mehliyar Sadiq, Paul I. Palmer, Mark F. Lunt, Liang Feng, Ingrid Super, Stijn N. C. Dellaert, and Hugo A. C. Denier van der Gon
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-816, https://doi.org/10.5194/acp-2021-816, 2021
Publication in ACP not foreseen
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We make use of high-resolution emission inventory of CO2 and co-emitted tracers, satellite measurements, together with nested atmospheric transport model simulation, to investigate how reactive trace gases such as nitrogen dioxide and carbon monoxide can be used as proxies to determine the combustion contribution to atmospheric CO2 over Europe. We find stronger correlation in ratios of nitrogen dioxide and carbon dioxide between emission and satellite observed and modelled column concentration.
Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao
Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, https://doi.org/10.5194/gmd-14-5331-2021, 2021
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We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source Python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
Caterina Mogno, Paul I. Palmer, Christoph Knote, Fei Yao, and Timothy J. Wallington
Atmos. Chem. Phys., 21, 10881–10909, https://doi.org/10.5194/acp-21-10881-2021, https://doi.org/10.5194/acp-21-10881-2021, 2021
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We use a 3-D atmospheric chemistry model to investigate how seasonal emissions sources and meteorological conditions affect the surface distribution of fine particulate matter (PM2.5) and organic aerosol (OA) over the Indo-Gangetic Plain. We find that all seasonal mean values of PM2.5 still exceed safe air quality levels, with human emissions contributing to PM2.5 all year round, open fires during post- and pre-monsoon, and biogenic emissions during monsoon. OA contributes up to 30 % to PM2.5.
Jérôme Barré, Ilse Aben, Anna Agustí-Panareda, Gianpaolo Balsamo, Nicolas Bousserez, Peter Dueben, Richard Engelen, Antje Inness, Alba Lorente, Joe McNorton, Vincent-Henri Peuch, Gabor Radnoti, and Roberto Ribas
Atmos. Chem. Phys., 21, 5117–5136, https://doi.org/10.5194/acp-21-5117-2021, https://doi.org/10.5194/acp-21-5117-2021, 2021
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This study presents a new approach to the systematic global detection of anomalous local CH4 concentration anomalies caused by rapid changes in anthropogenic emission levels. The approach utilises both satellite measurements and model simulations, and applies novel data analysis techniques (such as filtering and classification) to automatically detect anomalous emissions from point sources and small areas, such as oil and gas drilling sites, pipelines and facility leaks.
Cited articles
Andrew, R. M.: A comparison of estimates of global carbon dioxide emissions from fossil carbon sources, Earth Syst. Sci. Data, 12, 1437–1465, https://doi.org/10.5194/essd-12-1437-2020, 2020. a
Berezin, E. V., Konovalov, I. B., Ciais, P., Richter, A., Tao, S., Janssens-Maenhout, G., Beekmann, M., and Schulze, E.-D.: Multiannual changes of CO2 emissions in China: indirect estimates derived from satellite measurements of tropospheric NO2 columns, Atmos. Chem. Phys., 13, 9415–9438, https://doi.org/10.5194/acp-13-9415-2013, 2013. a, b
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, Journal of Geophysical Research: Atmospheres, 106, 23073–23095, 2001. a
Breiman, L.: Random Forests, Machine Learning, 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001. a
Cooper, M. J., Martin, R. V., Hammer, M. S., Levelt, P. F., Veefkind, P., Lamsal, L. N., Krotkov, N. A., Brook, J. R., and McLinden, C. A.: Global fine-scale changes in ambient NO2 during COVID-19 lockdowns, Nature, 601, 380–387, https://doi.org/10.1038/s41586-021-04229-0, 2022. a
European Space Agency: TROPOMI NO2 tropospheric column (S5P Level 2, product version 2.2.0, processing version 1.6.0), Copernicus Sentinel-5P, Copernicus Data Space Ecosystem [data set], https://doi.org/10.5270/S5P-9bnp8q8, 2021. a
Feng, L., Palmer, P. I., Bösch, H., and Dance, S.: Estimating surface CO2 fluxes from space-borne CO2 dry air mole fraction observations using an ensemble Kalman Filter, Atmos. Chem. Phys., 9, 2619–2633, https://doi.org/10.5194/acp-9-2619-2009, 2009. a, b
Feng, L., Palmer, P. I., Parker, R. J., Lunt, M. F., and Bösch, H.: Methane emissions are predominantly responsible for record-breaking atmospheric methane growth rates in 2020 and 2021, Atmos. Chem. Phys., 23, 4863–4880, https://doi.org/10.5194/acp-23-4863-2023, 2023. a
Goldberg, D. L., Lu, Z., Oda, T., Lamsal, L. N., Liu, F., Griffin, D., McLinden, C. A., Krotkov, N. A., Duncan, B. N., and Streets, D. G.: Exploiting OMI NO2 satellite observations to infer fossil-fuel CO2 emissions from U.S. megacities, The Science of the Total Environment, 695, 133805–133805, 2019. a, b
Hoesly, R. M., Smith, S. J., Feng, L., and Bond, T. C.: Community Emissions Data System (CEDS): Historical emissions (1750–2014), Zenodo [data set], https://doi.org/10.5281/zenodo.1188083, 2018a. a
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018b. a, b
Inness, A., Baier, F., Benedetti, A., Bouarar, I., Chabrillat, S., Clark, H., Clerbaux, C., Coheur, P., Engelen, R. J., Errera, Q., Flemming, J., George, M., Granier, C., Hadji-Lazaro, J., Huijnen, V., Hurtmans, D., Jones, L., Kaiser, J. W., Kapsomenakis, J., Lefever, K., Leitão, J., Razinger, M., Richter, A., Schultz, M. G., Simmons, A. J., Suttie, M., Stein, O., Thépaut, J.-N., Thouret, V., Vrekoussis, M., Zerefos, C., and the MACC team: The MACC reanalysis: an 8 yr data set of atmospheric composition, Atmos. Chem. Phys., 13, 4073–4109, https://doi.org/10.5194/acp-13-4073-2013, 2013. a
Jacob, D.: Introduction to atmospheric chemistry, Princeton University Press, Princeton, NJ, ISBN 978-0-691-00185-2, 1999. a
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., Bergamaschi, P., Pagliari, V., Olivier, J. G. J., Peters, J. A. H. W., van Aardenne, J. A., Monni, S., Doering, U., Petrescu, A. M. R., Solazzo, E., and Oreggioni, G. D.: EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012, Earth Syst. Sci. Data, 11, 959–1002, https://doi.org/10.5194/essd-11-959-2019, 2019. a
Jiang, X., Huang, X., Liu, J., and Han, X.: NOx emission of fine-and superfine-pulverized coal combustion in O2/CO2 atmosphere, Energy & Fuels, 24, 6307–6313, 2010. a
Kemball-Cook, S., Yarwood, G., Johnson, J., Dornblaser, B., and Estes, M.: Evaluating NOx emission inventories for regulatory air quality modeling using satellite and air quality model data, Atmospheric Environment (1994), 117, 1–8, 2015. a
Kuenen, J. J. P., Visschedijk, A. J. H., Jozwicka, M., and Denier van der Gon, H. A. C.: TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling, Atmos. Chem. Phys., 14, 10963–10976, https://doi.org/10.5194/acp-14-10963-2014, 2014. a
Liu, X., Ou, J., Wang, S., Li, X., Yan, Y., Jiao, L., and Liu, Y.: Estimating spatiotemporal variations of city-level energy-related CO2 emissions: An improved disaggregating model based on vegetation adjusted nighttime light data, Journal of Cleaner Production, 177, 101–114, 2018. a
Lopez, M., Schmidt, M., Delmotte, M., Colomb, A., Gros, V., Janssen, C., Lehman, S. J., Mondelain, D., Perrussel, O., Ramonet, M., Xueref-Remy, I., and Bousquet, P.: CO, NOx and 13CO2 as tracers for fossil fuel CO2: results from a pilot study in Paris during winter 2010, Atmos. Chem. Phys., 13, 7343–7358, https://doi.org/10.5194/acp-13-7343-2013, 2013. a, b, c
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Miyazaki, K., Bowman, K., Sekiya, T., Takigawa, M., Neu, J. L., Sudo, K., Osterman, G., and Eskes, H.: Global tropospheric ozone responses to reduced NOx emissions linked to the COVID-19 worldwide lockdowns, Science Advances, 7, https://doi.org/10.1126/sciadv.abf7460, 2021. a
Napelenok, S. L., Pinder, R. W., Gilliland, A. B., Marin, R. V., Miranda, A. I., Borrego, C., Miranda, A., and Borrego, C.: Developing a Method for Resolving NOx Emission Inventory Biases Using Discrete Kalman Filter Inversion, Direct Sensitivities, and Satellite-Based NO2 Columns, in: AIR POLLUTION MODELING AND ITS APPLICATION XIX, NATO Science for Peace and Security Series Series C: Environmental Security, Springer Netherlands, Dordrecht, 322–330, ISBN 9781402084522, 2008. a
Nayagam, L., Maksyutov, S., Oda, T., Janardanan, R., Trisolino, P., Zeng, J., Kaiser, J. W., and Matsunaga, T.: A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks, Environmental Research Letters, 19, 014031, https://doi.org/10.1088/1748-9326/ad0f74, 2023. a
Nguyen, D.-H., Lin, C., Vu, C.-T., Cheruiyot, N. K., Nguyen, M. K., Le, T. H., Lukkhasorn, W., Vo, T.-D.-H., and Bui, X.-T.: Tropospheric ozone and NOx: A review of worldwide variation and meteorological influences, Environmental Technology & Innovation, 28, 102809, https://doi.org/10.1016/j.eti.2022.102809, 2022. a
Oda, T., Feng, L., Palmer, P. I., Baker, D. F., and Ott, L. E.: Assumptions about prior fossil fuel inventories impact our ability to estimate posterior net CO2 fluxes that are needed for verifying national inventories, Environmental Research Letters, 18, 124030, https://doi.org/10.1088/1748-9326/ad059b, 2023. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825–2830, 2011. a
Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki, T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van der Laan-Luijkx, I. T., and Zhang, X.: Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions, Biogeosciences, 10, 6699–6720, https://doi.org/10.5194/bg-10-6699-2013, 2013. a
Randerson, J., van Der Werf, G., Giglio, L., Collatz, G., and Kasibhatla, P.: Global Fire Emissions Database, Version 4.1 (GFEDv4), ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/1293, 2017. a
Shu, Y. and Lam, N. S.: Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model, Atmospheric Environment, 45, 634–640, 2011. a
Simone, N., Stettler, M., Eastham, S., and Barrett, S.: Aviation Emissions Inventory Code (AEIC), Zenodo [code], https://doi.org/10.5281/zenodo.6461767, 2013. a
Super, I., Denier van der Gon, H. A. C., van der Molen, M. K., Dellaert, S. N. C., and Peters, W.: Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2, Geosci. Model Dev., 13, 2695–2721, https://doi.org/10.5194/gmd-13-2695-2020, 2020. a, b, c
Super, I., Scarpelli, T., Droste, A., and Palmer, P. I.: Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5), Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, 2024. a
Wang, S., Cohen, J. B., Guan, L., Lu, L., Tiwari, P., and Qin, K.: Observationally constrained global NOx and CO emissions variability reveals sources which contribute significantly to CO2 emissions, npj Climate and Atmospheric Science, 8, 87, https://doi.org/10.1038/s41612-025-00977-2, 2025. a, b
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Short summary
This study presents a new method to estimate fossil fuel CO2 (ffCO2) emissions by modelling NOx chemistry. Our regression models predict NOx chemical rates and NO2 : NO ratios with R² values above 0.95 using meteorological inputs. Incorporating these regressions reduces computational time compared to traditional methods and enables integration into model inversion frameworks. This scalable approach supports global emissions monitoring and climate change mitigation efforts.
This study presents a new method to estimate fossil fuel CO2 (ffCO2) emissions by modelling NOx...
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