Articles | Volume 26, issue 14
https://doi.org/10.5194/acp-26-10629-2026
© Author(s) 2026. 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-26-10629-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jul 2026
Research article |
| 30 Jul 2026
| 30 Jul 2026
Quantifying national, state, and oil/gas field methane emissions and trends in the US (2019–2024) through high resolution inversion of satellite observations
School of Engineering and Applied Science, Harvard University, Cambridge, 02134 MA, USA
Daniel J. Jacob
School of Engineering and Applied Science, Harvard University, Cambridge, 02134 MA, USA
School of Engineering and Applied Science, Harvard University, Cambridge, 02134 MA, USA
James D. East
School of Engineering and Applied Science, Harvard University, Cambridge, 02134 MA, USA
International Methane Emissions Observatory (IMEO), United Nations Environment Programme (UNEP), Paris, France
Daniel J. Varon
Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, 02139 MA, USA
Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Cambridge, 02139 MA, USA
Nicholas Balasus
School of Engineering and Applied Science, Harvard University, Cambridge, 02134 MA, USA
Sarah E. Hancock
School of Engineering and Applied Science, Harvard University, Cambridge, 02134 MA, USA
Melissa Sulprizio
School of Engineering and Applied Science, Harvard University, Cambridge, 02134 MA, USA
Kevin W. Bowman
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, 91011 CA, USA
John R. Worden
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, 91011 CA, USA
Emily Reidy
ExxonMobil Technology and Engineering Company, Spring, 02138 TX, USA
Benjamin R. K. Runkle
Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville, 72701 AR, USA
Related authors
Sarah E. Hancock, Daniel J. Jacob, Rodrigo Jimenez, Andrés Ardila, Luis Morales-Rincon, Néstor Rojas, Lucas A. Estrada, Nicholas Balasus, James D. East, Melissa P. Sulprizio, Xiaolin Wang, James L. France, Lauren Potyk, Elise Penn, Zichong Chen, Daniel J. Varon, Christian Frankenberg, Marci Baranski, Andreea Calcan, and Robert J. Parker
Atmos. Chem. Phys., 26, 10455–10476, https://doi.org/10.5194/acp-26-10455-2026, https://doi.org/10.5194/acp-26-10455-2026, 2026
Short summary
Short summary
We combined satellite observations with detailed national data to improve estimates of Colombia’s methane emissions. Our results show that total methane emissions are higher than reported, with the biggest differences from fossil fuels. This approach demonstrates how satellite data can make national greenhouse gas reporting more accurate and transparent under the Paris Agreement.
John Worden, Sudhanshu Pandey, Hannah Nesser, Kevin Bowman, Colin Harkins, Congmeng Lyu, Joannes D. Maasakkers, Deborah Gordon, Daniel Jacob, Lucas Estrada, Daniel J. Varon, James D. East, Lauren Schmeisser, and Zhen Qu
Atmos. Chem. Phys., 26, 8855–8873, https://doi.org/10.5194/acp-26-8855-2026, https://doi.org/10.5194/acp-26-8855-2026, 2026
Short summary
Short summary
To support methane reduction, we compared three widely used maps of methane emissions in the United States with estimates derived from a methane-measuring satellite from 2012 to 2020. The satellite indicates higher emissions from oil and gas production and from livestock in several regions, while other sources were harder to pin down. Neither approach shows a clear change over time. The results point to where better measurements can most improve emissions reporting and guide mitigation.
Nicholas Balasus, Daniel J. Jacob, A. Anthony Bloom, James D. East, Lucas A. Estrada, Sarah E. Hancock, Megan He, Todd A. Mooring, Alexander J. Turner, and John R. Worden
Atmos. Chem. Phys., 26, 4601–4617, https://doi.org/10.5194/acp-26-4601-2026, https://doi.org/10.5194/acp-26-4601-2026, 2026
Short summary
Short summary
We use satellite observations of methane to determine the amount of methane coming from Africa and relate this to the physical processes responsible, with relevance for climate change given methane's potency as a greenhouse gas. We find that the amount of methane coming from Africa has increased by a third across 2019–2024, driven primarily by steady increases in emissions from livestock, in addition to irregular surges from wetlands.
Srijana Lama, Joannes D. Maasakkers, Xin Zhang, Marianne Girard, Swarna Dutt, Suyash Nandgaonkar, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Nicholas Balasus, Robert J. Parker, Yukio Terao, and Ilse Aben
EGUsphere, https://doi.org/10.5194/egusphere-2025-6528, https://doi.org/10.5194/egusphere-2025-6528, 2026
Short summary
Short summary
Methane is the second most important anthropogenic greenhouse gas in terms of radiative forcing, with a global warming potential 27–30 times that of CO2 over a 100-year timescale. Effective mitigation requires a detailed understanding of the spatial distribution and magnitude of emissions. Here, we quantify India’s 2021 methane emissions at a resolution of up to 0.25° × 0.3125° using TROPOMI observations within the Bayesian Integrated Methane Inversion framework.
Jack H. Bruno, Daniel J. Jacob, Xiaolin Wang, Melissa P. Sulprizio, Lucas A. Estrada, Daniel J. Varon, Steven C. Wofsy, Mark Omara, and Ritesh Gautam
EGUsphere, https://doi.org/10.5194/egusphere-2025-4626, https://doi.org/10.5194/egusphere-2025-4626, 2025
Short summary
Short summary
We use data from both aircraft (MethaneAIR) and satellite (TROPOMI) based measurements of methane in the atmosphere to better understand methane emissions from fossil fuel extraction. Data from these instruments are combined with a computer model of the atmosphere to improve estimates of methane emissions. We find that combining data from multiple sources provides more information than either source on its own. The tools and data we use are freely available.
Drew C. Pendergrass, Daniel J. Jacob, Nicholas Balasus, Lucas Estrada, Daniel J. Varon, James D. East, Megan He, Todd A. Mooring, Elise Penn, Hannah Nesser, and John R. Worden
Atmos. Chem. Phys., 25, 14353–14369, https://doi.org/10.5194/acp-25-14353-2025, https://doi.org/10.5194/acp-25-14353-2025, 2025
Short summary
Short summary
We use satellite observations of atmospheric methane, a greenhouse gas, to calculate emissions from both human and natural sources. We find that methane emissions surged in 2020–2021 before declining in 2022–2023. We attribute the surge in large part to emissions from eastern Africa, which experienced large methane-generating floods. Wetland models underestimate emissions in that region, which has led some previous work to incorrectly attribute the African surge in methane emissions to livestock.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Sarah E. Hancock, Daniel J. Jacob, Zichong Chen, Hannah Nesser, Aaron Davitt, Daniel J. Varon, Melissa P. Sulprizio, Nicholas Balasus, Lucas A. Estrada, María Cazorla, Laura Dawidowski, Sebastián Diez, James D. East, Elise Penn, Cynthia A. Randles, John Worden, Ilse Aben, Robert J. Parker, and Joannes D. Maasakkers
Atmos. Chem. Phys., 25, 797–817, https://doi.org/10.5194/acp-25-797-2025, https://doi.org/10.5194/acp-25-797-2025, 2025
Short summary
Short summary
We quantify 2021 methane emissions in South America at up to 25 km × 25 km resolution using satellite methane observations. We find a 55 % upward adjustment to anthropogenic emission inventories, including those reported to the UN Framework Convention on Climate Change under the Paris Agreement. Our estimates match inventories for Brazil, Bolivia, and Paraguay but are much higher for other countries. Livestock emissions (65 % of anthropogenic emissions) show the largest discrepancies.
Brian Nathan, Joannes D. Maasakkers, Stijn Naus, Ritesh Gautam, Mark Omara, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Alba Lorente, Tobias Borsdorff, Robert J. Parker, and Ilse Aben
Atmos. Chem. Phys., 24, 6845–6863, https://doi.org/10.5194/acp-24-6845-2024, https://doi.org/10.5194/acp-24-6845-2024, 2024
Short summary
Short summary
Venezuela's Lake Maracaibo region is notoriously hard to observe from space and features intensive oil exploitation, although production has strongly decreased in recent years. We estimate methane emissions using 2018–2020 TROPOMI satellite observations with national and regional transport models. Despite the production decrease, we find relatively constant emissions from Lake Maracaibo between 2018 and 2020, indicating that there could be large emissions from abandoned infrastructure.
Randall V. Martin, Sebastian D. Eastham, Liam Bindle, Elizabeth W. Lundgren, Thomas L. Clune, Christoph A. Keller, William Downs, Dandan Zhang, Robert A. Lucchesi, Melissa P. Sulprizio, Robert M. Yantosca, Yanshun Li, Lucas Estrada, William M. Putman, Benjamin M. Auer, Atanas L. Trayanov, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 15, 8731–8748, https://doi.org/10.5194/gmd-15-8731-2022, https://doi.org/10.5194/gmd-15-8731-2022, 2022
Short summary
Short summary
Atmospheric chemistry models must be able to operate both online as components of Earth system models and offline as standalone models. The widely used GEOS-Chem model operates both online and offline, but the classic offline version is not suitable for massively parallel simulations. We describe a new generation of the offline high-performance GEOS-Chem (GCHP) that enables high-resolution simulations on thousands of cores, including on the cloud, with improved access, performance, and accuracy.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
Short summary
Short summary
Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Sarah E. Hancock, Daniel J. Jacob, Rodrigo Jimenez, Andrés Ardila, Luis Morales-Rincon, Néstor Rojas, Lucas A. Estrada, Nicholas Balasus, James D. East, Melissa P. Sulprizio, Xiaolin Wang, James L. France, Lauren Potyk, Elise Penn, Zichong Chen, Daniel J. Varon, Christian Frankenberg, Marci Baranski, Andreea Calcan, and Robert J. Parker
Atmos. Chem. Phys., 26, 10455–10476, https://doi.org/10.5194/acp-26-10455-2026, https://doi.org/10.5194/acp-26-10455-2026, 2026
Short summary
Short summary
We combined satellite observations with detailed national data to improve estimates of Colombia’s methane emissions. Our results show that total methane emissions are higher than reported, with the biggest differences from fossil fuels. This approach demonstrates how satellite data can make national greenhouse gas reporting more accurate and transparent under the Paris Agreement.
John Worden, Sudhanshu Pandey, Hannah Nesser, Kevin Bowman, Colin Harkins, Congmeng Lyu, Joannes D. Maasakkers, Deborah Gordon, Daniel Jacob, Lucas Estrada, Daniel J. Varon, James D. East, Lauren Schmeisser, and Zhen Qu
Atmos. Chem. Phys., 26, 8855–8873, https://doi.org/10.5194/acp-26-8855-2026, https://doi.org/10.5194/acp-26-8855-2026, 2026
Short summary
Short summary
To support methane reduction, we compared three widely used maps of methane emissions in the United States with estimates derived from a methane-measuring satellite from 2012 to 2020. The satellite indicates higher emissions from oil and gas production and from livestock in several regions, while other sources were harder to pin down. Neither approach shows a clear change over time. The results point to where better measurements can most improve emissions reporting and guide mitigation.
Xiaolin Wang, Melissa P. Sulprizio, Yuyao Zhuge, Randall V. Martin, and Daniel J. Jacob
Atmos. Chem. Phys., 26, 6857–6867, https://doi.org/10.5194/acp-26-6857-2026, https://doi.org/10.5194/acp-26-6857-2026, 2026
Short summary
Short summary
We implement a new 12-km global nested simulation capability in GEOS-Chem, an open-source global 3-D model of atmospheric chemistry. Compared with the standard 25-km simulation, the 12-km simulation features stronger vertical transport due to better resolved horizontal convergence, along with improved representation of urban NO2 and ozone titration. Application to methane emission inversion yields higher information content and resolves finer spatial structure in emission sectors.
Frank Werner, Kevin W. Bowman, Seungwon Lee, Joshua L. Laughner, Vivienne H. Payne, and James L. McDuffie
Atmos. Meas. Tech., 19, 3095–3109, https://doi.org/10.5194/amt-19-3095-2026, https://doi.org/10.5194/amt-19-3095-2026, 2026
Short summary
Short summary
We developed a hybrid machine learning-optimal estimation retrieval system that efficiently and accurately mimics operational retrieval results. Crucially, this algorithm also predicts critical diagnostic variables including observation operators needed for comparison with independent data and ingestion into downstream chemical data assimilation models.
Zhaojun Tang, Panpan Yang, Kazuyuki Miyazaki, John Worden, Helen Worden, Daven K. Henze, Dylan B. A. Jones, and Zhe Jiang
Atmos. Chem. Phys., 26, 5531–5551, https://doi.org/10.5194/acp-26-5531-2026, https://doi.org/10.5194/acp-26-5531-2026, 2026
Short summary
Short summary
We provide a quantitative analysis of global CO emissions and drivers of atmospheric CO trends in 2003–2022, using GEOS-Chem adjoint model constrained by satellite CO measurements. Our results indicate a substantial decline in global anthropogenic CO emissions of 14 %–17 % over the two-decade period, as well as an important offsetting effect from biomass burning emissions on atmospheric CO decline driven by anthropogenic reductions, primarily attributable to extreme wildfire events in 2021.
Nicholas Balasus, Daniel J. Jacob, A. Anthony Bloom, James D. East, Lucas A. Estrada, Sarah E. Hancock, Megan He, Todd A. Mooring, Alexander J. Turner, and John R. Worden
Atmos. Chem. Phys., 26, 4601–4617, https://doi.org/10.5194/acp-26-4601-2026, https://doi.org/10.5194/acp-26-4601-2026, 2026
Short summary
Short summary
We use satellite observations of methane to determine the amount of methane coming from Africa and relate this to the physical processes responsible, with relevance for climate change given methane's potency as a greenhouse gas. We find that the amount of methane coming from Africa has increased by a third across 2019–2024, driven primarily by steady increases in emissions from livestock, in addition to irregular surges from wetlands.
Yifan Li, Drew Pendergrass, Daniel Jacob, Yunxiao Tang, Jiaxin Qiu, and Bo Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-6284, https://doi.org/10.5194/egusphere-2025-6284, 2026
Short summary
Short summary
We tracked China’s weekly methane emissions from 2019 to 2024 by constructing a satellite-based regional atmospheric inversion framework. Over the period, annual methane emissions in China rose from 61.1 to 66.8 million tonnes, with nearly half of the growth driven by livestock, alongside additional contributions from waste and oil and gas. Emissions growth shifted toward previously low-emitting northwest and northeast China, revealing new priorities for targeted methane mitigation.
Nadia K. Colombi, Daniel J. Jacob, Xingpei Ye, Robert M. Yantosca, Kelvin H. Bates, Drew C. Pendergrass, Laura Hyesung Yang, Ke Li, and Hong Liao
Atmos. Chem. Phys., 26, 2623–2633, https://doi.org/10.5194/acp-26-2623-2026, https://doi.org/10.5194/acp-26-2623-2026, 2026
Short summary
Short summary
Surface ozone pollution in East Asia is among the highest in the world and has risen steadily over the past two decades. Using aircraft observations and a global 3-D chemical transport model, we show that ozone in the lower atmosphere in East Asia has risen in part due to intensified transport from the upper atmosphere. This rising natural background limits the effectiveness of local pollution controls, with major implications for air quality policy.
Srijana Lama, Joannes D. Maasakkers, Xin Zhang, Marianne Girard, Swarna Dutt, Suyash Nandgaonkar, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Nicholas Balasus, Robert J. Parker, Yukio Terao, and Ilse Aben
EGUsphere, https://doi.org/10.5194/egusphere-2025-6528, https://doi.org/10.5194/egusphere-2025-6528, 2026
Short summary
Short summary
Methane is the second most important anthropogenic greenhouse gas in terms of radiative forcing, with a global warming potential 27–30 times that of CO2 over a 100-year timescale. Effective mitigation requires a detailed understanding of the spatial distribution and magnitude of emissions. Here, we quantify India’s 2021 methane emissions at a resolution of up to 0.25° × 0.3125° using TROPOMI observations within the Bayesian Integrated Methane Inversion framework.
Raphaël Savelli, Dustin Carroll, Dimitris Menemenlis, Jonathan M. Lauderdale, Clément Bertin, Stephanie Dutkiewicz, Manfredi Manizza, A. Anthony Bloom, Karel Castro-Morales, Charles E. Miller, Marc Simard, Kevin W. Bowman, and Hong Zhang
Geosci. Model Dev., 19, 867–885, https://doi.org/10.5194/gmd-19-867-2026, https://doi.org/10.5194/gmd-19-867-2026, 2026
Short summary
Short summary
Accounting for carbon and nutrients in rivers is essential for resolving carbon dioxide (CO2) exchanges between the ocean and the atmosphere. In this study, we add the effect of present-day rivers to a pioneering global-ocean biogeochemistry model. This study highlights the challenge for global ocean numerical models to cover the complexity of the flow of water and carbon across the Land-to-Ocean Aquatic Continuum.
Jean-Francois Lamarque, Pierre Friedlingstein, Brian Osias, Steve Strongin, Venkatramani Balaji, Kevin W. Bowman, Josep G. Canadell, Philippe Ciais, Heidi Cullen, Kenneth J. Davis, Scott C. Doney, Kevin R. Gurney, Alicia R. Karspeck, Charles D. Koven, Galen McKinley, Glen P. Peters, Julia Pongratz, Britt Stephens, and Colm Sweeney
EGUsphere, https://doi.org/10.5194/egusphere-2025-6457, https://doi.org/10.5194/egusphere-2025-6457, 2026
Short summary
Short summary
This Perspective highlights requirements to scale the carbon credit market and enable the growth in climate solutions funded through such market. The requirements are on the understanding of the value of the proposed carbon credit projects, and on the availability of a verification system. This verification becomes particularly relevant as the carbon credit market scales to significant impacts on CO2 (or other greenhouse gases), such that attribution to collective actions can be identified.
Tia R. Scarpelli, Elfie Roy, Daniel J. Jacob, Melissa P. Sulprizio, Ryan D. Tate, and Daniel H. Cusworth
Earth Syst. Sci. Data, 17, 7019–7033, https://doi.org/10.5194/essd-17-7019-2025, https://doi.org/10.5194/essd-17-7019-2025, 2025
Short summary
Short summary
We present an update of the Global Fuel Exploitation Inventory (GFEI), a global inventory of methane emissions from oil, gas, and coal exploitation. GFEI v3 uses emissions as reported by countries in national inventories for 2020, and new infrastructure information, including a new dataset on coal mine locations. The goal of updating GFEI is to provide a more accurate spatial representation of the country-reported national inventories, allowing comparison with methane monitoring data.
Hannah Nesser, Kevin W. Bowman, Matthew D. Thill, Daniel J. Varon, Cynthia A. Randles, Ashutosh Tewari, Felipe J. Cardoso-Saldaña, Emily Reidy, Joannes D. Maasakkers, and Daniel J. Jacob
Geosci. Model Dev., 18, 9279–9291, https://doi.org/10.5194/gmd-18-9279-2025, https://doi.org/10.5194/gmd-18-9279-2025, 2025
Short summary
Short summary
Regional analyses of atmospheric trace gases can improve knowledge of fluxes at high resolution but rely on specified boundary conditions (BCs) at the domain edges. Biases in the often-uncertain BCs propagate to the inferred fluxes. We develop a framework to explain how errors in the BCs influence the optimized fluxes, derive two metrics to estimate this influence, and compare two methods to correct for the biases. We demonstrate correcting BCs directly is more effective at reducing bias.
Jack H. Bruno, Daniel J. Jacob, Xiaolin Wang, Melissa P. Sulprizio, Lucas A. Estrada, Daniel J. Varon, Steven C. Wofsy, Mark Omara, and Ritesh Gautam
EGUsphere, https://doi.org/10.5194/egusphere-2025-4626, https://doi.org/10.5194/egusphere-2025-4626, 2025
Short summary
Short summary
We use data from both aircraft (MethaneAIR) and satellite (TROPOMI) based measurements of methane in the atmosphere to better understand methane emissions from fossil fuel extraction. Data from these instruments are combined with a computer model of the atmosphere to improve estimates of methane emissions. We find that combining data from multiple sources provides more information than either source on its own. The tools and data we use are freely available.
Jeewoo Lee, Jhoon Kim, Seoyoung Lee, Myungje Choi, Jaehwa Lee, Daniel J. Jacob, Su Keun Kuk, and Young-Je Park
Earth Syst. Sci. Data, 17, 5761–5782, https://doi.org/10.5194/essd-17-5761-2025, https://doi.org/10.5194/essd-17-5761-2025, 2025
Short summary
Short summary
Atmospheric aerosols adversely affect human health, with East Asia recognized as one of the most impacted regions. This study presents a long-term (2011–2021), high spatiotemporal resolution aerosol optical depth dataset retrieved from a geostationary satellite over East Asia. The high-resolution data capture subtle aerosol gradients and land-ocean boundaries, providing valuable input for various fields such as aerosol-cloud interaction, climate change, ocean optics, and air quality studies.
Drew C. Pendergrass, Daniel J. Jacob, Nicholas Balasus, Lucas Estrada, Daniel J. Varon, James D. East, Megan He, Todd A. Mooring, Elise Penn, Hannah Nesser, and John R. Worden
Atmos. Chem. Phys., 25, 14353–14369, https://doi.org/10.5194/acp-25-14353-2025, https://doi.org/10.5194/acp-25-14353-2025, 2025
Short summary
Short summary
We use satellite observations of atmospheric methane, a greenhouse gas, to calculate emissions from both human and natural sources. We find that methane emissions surged in 2020–2021 before declining in 2022–2023. We attribute the surge in large part to emissions from eastern Africa, which experienced large methane-generating floods. Wetland models underestimate emissions in that region, which has led some previous work to incorrectly attribute the African surge in methane emissions to livestock.
Anne Boynard, Catherine Wespes, Juliette Hadji-Lazaro, Selviga Sinnathamby, Daniel Hurtmans, Pierre-François Coheur, Marie Doutriaux-Boucher, Jacobus Onderwaater, Wolfgang Steinbrecht, Elyse A. Pennington, Kevin Bowman, and Cathy Clerbaux
Atmos. Chem. Phys., 25, 11719–11755, https://doi.org/10.5194/acp-25-11719-2025, https://doi.org/10.5194/acp-25-11719-2025, 2025
Short summary
Short summary
This study analyzes 16 years of global ozone data to assess its impact on air quality and climate. Using satellite measurements, we observed a global decrease in tropospheric ozone, particularly in tropical and European regions. The drop became more noticeable around 2020, partly due to reduced emissions during the pandemic. The study highlights the need for better data processing and integration to more accurately monitor ozone changes over time.
Elyse A. Pennington, Gregory B. Osterman, Vivienne H. Payne, Kazuyuki Miyazaki, Kevin W. Bowman, and Jessica L. Neu
Atmos. Chem. Phys., 25, 8533–8552, https://doi.org/10.5194/acp-25-8533-2025, https://doi.org/10.5194/acp-25-8533-2025, 2025
Short summary
Short summary
Tropospheric ozone is a harmful pollutant and powerful greenhouse gas. For satellite products to accurately quantify trends in tropospheric ozone, they must have a low bias compared to a reliable source of data. This study compares three NASA satellite products to ozonesonde data. They have low global measurement bias and thus can be used to detect global tropospheric ozone trends, but the measurement bias should be considered in certain regions and time periods.
Kazuyuki Miyazaki, Yuliya Marchetti, James Montgomery, Steven Lu, and Kevin Bowman
Atmos. Chem. Phys., 25, 8507–8532, https://doi.org/10.5194/acp-25-8507-2025, https://doi.org/10.5194/acp-25-8507-2025, 2025
Short summary
Short summary
This study employs explainable machine learning to analyze the causes of significant biases in surface ozone estimates from chemical reanalysis. By analyzing global observations and chemical reanalysis outputs, key bias drivers, such as meteorological conditions and precursor emissions, were identified. This provides actionable insights to improve chemical transport models, observation systems, and emissions inventories, ultimately enhancing ozone reanalysis for better air pollution management.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Edward Malina, Jure Brence, Jennifer Adams, Jovan Tanevski, Sašo Džeroski, Valentin Kantchev, and Kevin W. Bowman
Atmos. Meas. Tech., 18, 1689–1715, https://doi.org/10.5194/amt-18-1689-2025, https://doi.org/10.5194/amt-18-1689-2025, 2025
Short summary
Short summary
The large fleet of Earth observation satellites in orbit currently generate huge volumes of data, requiring significant computational resources to process these data in a timely manner. We present a method for predicting poor-quality measurements using machine learning. We find that machine learning methods can accurately predict poor-quality measurements and remove them from the processing chain, saving time and computational resources.
Shutao Zhao, Yuzhong Zhang, Shuang Zhao, Xinlu Wang, and Daniel J. Varon
Atmos. Chem. Phys., 25, 4035–4052, https://doi.org/10.5194/acp-25-4035-2025, https://doi.org/10.5194/acp-25-4035-2025, 2025
Short summary
Short summary
We target the challenge of detecting methane super-emitters in oil and gas fields, which is critical for mitigating climate change. Traditional satellite-based detectors struggle due to interference from complex surfaces. We developed a novel method using deep transfer learning that improves detection efficiency and accuracy by reducing artifacts and adapting methane knowledge to different regions. Application revealed significant methane emissions, demonstrating the potential of our method.
Yujin J. Oak, Daniel J. Jacob, Drew C. Pendergrass, Ruijun Dang, Nadia K. Colombi, Heesung Chong, Seoyoung Lee, Su Keun Kuk, and Jhoon Kim
Atmos. Chem. Phys., 25, 3233–3252, https://doi.org/10.5194/acp-25-3233-2025, https://doi.org/10.5194/acp-25-3233-2025, 2025
Short summary
Short summary
We analyze 2015–2023 air quality trends in South Korea using surface and satellite observations. Primary pollutants have decreased, consistent with emissions reductions. Surface O3 continues to increase and PM2.5 has decreased overall, but the nitrate component has not. O3 and PM2.5 nitrate depend on nonlinear responses from precursor emissions. Satellite data indicate a recent shift to NOx-sensitive O3 and nitrate formation, where further NOx reductions will benefit both O3 and PM2.5 pollution.
Elise Penn, Daniel J. Jacob, Zichong Chen, James D. East, Melissa P. Sulprizio, Lori Bruhwiler, Joannes D. Maasakkers, Hannah Nesser, Zhen Qu, Yuzhong Zhang, and John Worden
Atmos. Chem. Phys., 25, 2947–2965, https://doi.org/10.5194/acp-25-2947-2025, https://doi.org/10.5194/acp-25-2947-2025, 2025
Short summary
Short summary
The hydroxyl radical (OH) destroys many air pollutants, including methane. Global-mean OH cannot be directly measured, and thus it is inferred with the methyl chloroform (MCF) proxy. MCF is decreasing, and a replacement is needed. We use satellite observations of methane in two spectral ranges as a proxy for OH. We find shortwave infrared observations can characterize yearly OH and its seasonality but not the latitudinal distribution. Thermal infrared observations add little information.
Min Huang, Gregory R. Carmichael, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
Atmos. Chem. Phys., 25, 1449–1476, https://doi.org/10.5194/acp-25-1449-2025, https://doi.org/10.5194/acp-25-1449-2025, 2025
Short summary
Short summary
We use model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutant budgets in this area as local anthropogenic emissions drop.
Sarah E. Hancock, Daniel J. Jacob, Zichong Chen, Hannah Nesser, Aaron Davitt, Daniel J. Varon, Melissa P. Sulprizio, Nicholas Balasus, Lucas A. Estrada, María Cazorla, Laura Dawidowski, Sebastián Diez, James D. East, Elise Penn, Cynthia A. Randles, John Worden, Ilse Aben, Robert J. Parker, and Joannes D. Maasakkers
Atmos. Chem. Phys., 25, 797–817, https://doi.org/10.5194/acp-25-797-2025, https://doi.org/10.5194/acp-25-797-2025, 2025
Short summary
Short summary
We quantify 2021 methane emissions in South America at up to 25 km × 25 km resolution using satellite methane observations. We find a 55 % upward adjustment to anthropogenic emission inventories, including those reported to the UN Framework Convention on Climate Change under the Paris Agreement. Our estimates match inventories for Brazil, Bolivia, and Paraguay but are much higher for other countries. Livestock emissions (65 % of anthropogenic emissions) show the largest discrepancies.
Amey Tilak, Alina Premrov, Ruchita Ingle, Nigel Roulet, Benjamin R. K. Runkle, Matthew Saunders, Avni Malhotra, and Kenneth Byrne
EGUsphere, https://doi.org/10.5194/egusphere-2024-3852, https://doi.org/10.5194/egusphere-2024-3852, 2024
Preprint archived
Short summary
Short summary
For the future model users, 16 peatland and wetland models reviewed to identify individual model operational scale (spatial and temporal), stabilization timeframes of different carbon pools, model specific advantages and limitations, common and specific model driving inputs, critical inputs of individual models impacting CH4 plant mediated, CH4 diffusion and CH4 ebullition. Finally, we qualitatively ranked the process representations in each model for CH4 production, oxidation and transport.
Edward Malina, Kevin W. Bowman, Valentin Kantchev, Le Kuai, Thomas P. Kurosu, Kazuyuki Miyazaki, Vijay Natraj, Gregory B. Osterman, Fabiano Oyafuso, and Matthew D. Thill
Atmos. Meas. Tech., 17, 5341–5371, https://doi.org/10.5194/amt-17-5341-2024, https://doi.org/10.5194/amt-17-5341-2024, 2024
Short summary
Short summary
Characterizing the distribution of ozone in the atmosphere is a challenging problem, with current Earth observation satellites using either thermal infrared (TIR) or ultraviolet (UV) instruments, sensitive to different portions of the atmosphere, making it difficult to gain a full picture. In this work, we combine measurements from the TIR and UV instruments Suomi NPP CrIS and Sentinel-5P/TROPOMI to improve sensitivity through the whole atmosphere and improve knowledge of ozone distribution.
Yujin J. Oak, Daniel J. Jacob, Nicholas Balasus, Laura H. Yang, Heesung Chong, Junsung Park, Hanlim Lee, Gitaek T. Lee, Eunjo S. Ha, Rokjin J. Park, Hyeong-Ahn Kwon, and Jhoon Kim
Atmos. Meas. Tech., 17, 5147–5159, https://doi.org/10.5194/amt-17-5147-2024, https://doi.org/10.5194/amt-17-5147-2024, 2024
Short summary
Short summary
We present an improved NO2 product from GEMS by calibrating it to TROPOMI using machine learning and by reprocessing both satellite products to adopt common NO2 profiles. Our corrected GEMS product combines the high data density of GEMS with the accuracy of TROPOMI, supporting the combined use for analyses of East Asia air quality including emissions and chemistry. This method can be extended to other species and geostationary satellites including TEMPO and Sentinel-4.
Haipeng Lin, Louisa K. Emmons, Elizabeth W. Lundgren, Laura Hyesung Yang, Xu Feng, Ruijun Dang, Shixian Zhai, Yunxiao Tang, Makoto M. Kelp, Nadia K. Colombi, Sebastian D. Eastham, Thibaud M. Fritz, and Daniel J. Jacob
Atmos. Chem. Phys., 24, 8607–8624, https://doi.org/10.5194/acp-24-8607-2024, https://doi.org/10.5194/acp-24-8607-2024, 2024
Short summary
Short summary
Tropospheric ozone is a major air pollutant, a greenhouse gas, and a major indicator of model skill. Global atmospheric chemistry models show large differences in simulations of tropospheric ozone, but isolating sources of differences is complicated by different model environments. By implementing the GEOS-Chem model side by side to CAM-chem within a common Earth system model, we identify and evaluate specific differences between the two models and their impacts on key chemical species.
Laura Hyesung Yang, Daniel J. Jacob, Ruijun Dang, Yujin J. Oak, Haipeng Lin, Jhoon Kim, Shixian Zhai, Nadia K. Colombi, Drew C. Pendergrass, Ellie Beaudry, Viral Shah, Xu Feng, Robert M. Yantosca, Heesung Chong, Junsung Park, Hanlim Lee, Won-Jin Lee, Soontae Kim, Eunhye Kim, Katherine R. Travis, James H. Crawford, and Hong Liao
Atmos. Chem. Phys., 24, 7027–7039, https://doi.org/10.5194/acp-24-7027-2024, https://doi.org/10.5194/acp-24-7027-2024, 2024
Short summary
Short summary
The Geostationary Environment Monitoring Spectrometer (GEMS) provides hourly measurements of NO2. We use the chemical transport model to find how emissions, chemistry, and transport drive the changes in NO2 observed by GEMS at different times of the day. In winter, the chemistry plays a minor role, and high daytime emissions dominate the diurnal variation in NO2, balanced by transport. In summer, emissions, chemistry, and transport play an important role in shaping the diurnal variation in NO2.
Brian Nathan, Joannes D. Maasakkers, Stijn Naus, Ritesh Gautam, Mark Omara, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Alba Lorente, Tobias Borsdorff, Robert J. Parker, and Ilse Aben
Atmos. Chem. Phys., 24, 6845–6863, https://doi.org/10.5194/acp-24-6845-2024, https://doi.org/10.5194/acp-24-6845-2024, 2024
Short summary
Short summary
Venezuela's Lake Maracaibo region is notoriously hard to observe from space and features intensive oil exploitation, although production has strongly decreased in recent years. We estimate methane emissions using 2018–2020 TROPOMI satellite observations with national and regional transport models. Despite the production decrease, we find relatively constant emissions from Lake Maracaibo between 2018 and 2020, indicating that there could be large emissions from abandoned infrastructure.
Drew C. Pendergrass, Daniel J. Jacob, Yujin J. Oak, Jeewoo Lee, Minseok Kim, Jhoon Kim, Seoyoung Lee, Shixian Zhai, Hitoshi Irie, and Hong Liao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-172, https://doi.org/10.5194/essd-2024-172, 2024
Preprint withdrawn
Short summary
Short summary
Fine particles suspended in the atmosphere are a major form of air pollution and an important public health burden. However, measurements of particulate matter are sparse in space and in places like East Asia monitors are established after regulatory policies to improve pollution have changed. In this paper, we use machine learning to fill in the gaps. We train an algorithm to predict pollution at the surface from the atmosphere’s opacity, then produce high resolution maps of data without gaps.
Matthieu Dogniaux, Joannes D. Maasakkers, Daniel J. Varon, and Ilse Aben
Atmos. Meas. Tech., 17, 2777–2787, https://doi.org/10.5194/amt-17-2777-2024, https://doi.org/10.5194/amt-17-2777-2024, 2024
Short summary
Short summary
We analyze Landsat 8 (L8) and Sentinel-2B (S-2B) observations of the 2022 Nord Stream 2 methane leak and show how challenging this case is for usual data analysis methods. We provide customized calibrations for this Nord Stream 2 case and assess that no firm conclusion can be drawn from L8 or S-2B single overpasses. However, if we opportunistically assume that L8 and S-2B results are independent, we find an averaged L8 and S-2B combined methane leak rate of 502 ± 464 t h−1.
Jack H. Bruno, Dylan Jervis, Daniel J. Varon, and Daniel J. Jacob
Atmos. Meas. Tech., 17, 2625–2636, https://doi.org/10.5194/amt-17-2625-2024, https://doi.org/10.5194/amt-17-2625-2024, 2024
Short summary
Short summary
Methane is a potent greenhouse gas and a current high-priority target for short- to mid-term climate change mitigation. Detection of individual methane emitters from space has become possible in recent years, and the volume of data for this task has been rapidly growing, outpacing processing capabilities. We introduce an automated approach, U-Plume, which can detect and quantify emissions from individual methane sources in high-spatial-resolution satellite data.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Alba Lorente, Zichong Chen, Xiao Lu, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Margaux Winter, Shuang Ma, A. Anthony Bloom, John R. Worden, Robert N. Stavins, and Cynthia A. Randles
Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024, https://doi.org/10.5194/acp-24-5069-2024, 2024
Short summary
Short summary
We quantify 2019 methane emissions in the contiguous US (CONUS) at a ≈ 25 km × 25 km resolution using satellite methane observations. We find a 13 % upward correction to the 2023 US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) for 2019, with large corrections to individual states, urban areas, and landfills. This may present a challenge for US climate policies and goals, many of which target significant reductions in methane emissions.
Sebastian D. Eastham, Guillaume P. Chossière, Raymond L. Speth, Daniel J. Jacob, and Steven R. H. Barrett
Atmos. Chem. Phys., 24, 2687–2703, https://doi.org/10.5194/acp-24-2687-2024, https://doi.org/10.5194/acp-24-2687-2024, 2024
Short summary
Short summary
Emissions from aircraft are known to cause air quality impacts worldwide, but the scale and mechanisms of this impact are not well understood. This work uses high-resolution computational modeling of the atmosphere to show that air pollution changes from aviation are mostly the result of emissions during cruise (high-altitude) operations, that these impacts are related to how much non-aviation pollution is present, and that prior regional assessments have underestimated these impacts.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
Short summary
Short summary
Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
Berend J. Schuit, Joannes D. Maasakkers, Pieter Bijl, Gourav Mahapatra, Anne-Wil van den Berg, Sudhanshu Pandey, Alba Lorente, Tobias Borsdorff, Sander Houweling, Daniel J. Varon, Jason McKeever, Dylan Jervis, Marianne Girard, Itziar Irakulis-Loitxate, Javier Gorroño, Luis Guanter, Daniel H. Cusworth, and Ilse Aben
Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023, https://doi.org/10.5194/acp-23-9071-2023, 2023
Short summary
Short summary
Using two machine learning models, which were trained on TROPOMI methane satellite data, we detect 2974 methane plumes, so-called super-emitters, in 2021. We detect methane emissions globally related to urban areas or landfills, coal mining, and oil and gas production. Using our monitoring system, we identify 94 regions with frequent emissions. For 12 locations, we target high-resolution satellite instruments to enlarge and identify the exact infrastructure responsible for the emissions.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
Short summary
Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Nicholas Balasus, Daniel J. Jacob, Alba Lorente, Joannes D. Maasakkers, Robert J. Parker, Hartmut Boesch, Zichong Chen, Makoto M. Kelp, Hannah Nesser, and Daniel J. Varon
Atmos. Meas. Tech., 16, 3787–3807, https://doi.org/10.5194/amt-16-3787-2023, https://doi.org/10.5194/amt-16-3787-2023, 2023
Short summary
Short summary
We use machine learning to remove biases in TROPOMI satellite observations of atmospheric methane, with GOSAT observations serving as a reference. We find that the TROPOMI biases relative to GOSAT are related to the presence of aerosols and clouds, the surface brightness, and the specific detector that makes the observation aboard TROPOMI. The resulting blended TROPOMI+GOSAT product is more reliable for quantifying methane emissions.
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023, https://doi.org/10.5194/acp-23-7503-2023, 2023
Short summary
Short summary
We use TROPOMI satellite observations to quantify weekly methane emissions from the US Permian oil and gas basin from May 2018 to October 2020. We find that Permian emissions are highly variable, with diverse economic and activity drivers. The most important drivers during our study period were new well development and natural gas price. Permian methane intensity averaged 4.6 % and decreased by 1 % per year.
Ruijun Dang, Daniel J. Jacob, Viral Shah, Sebastian D. Eastham, Thibaud M. Fritz, Loretta J. Mickley, Tianjia Liu, Yi Wang, and Jun Wang
Atmos. Chem. Phys., 23, 6271–6284, https://doi.org/10.5194/acp-23-6271-2023, https://doi.org/10.5194/acp-23-6271-2023, 2023
Short summary
Short summary
We use the GEOS-Chem model to better understand the magnitude and trend in free tropospheric NO2 over the contiguous US. Model underestimate of background NO2 is largely corrected by considering aerosol nitrate photolysis. Increase in aircraft emissions affects satellite retrievals by altering the NO2 shape factor, and this effect is expected to increase in future. We show the importance of properly accounting for the free tropospheric background in interpreting NO2 observations from space.
Zichong Chen, Daniel J. Jacob, Ritesh Gautam, Mark Omara, Robert N. Stavins, Robert C. Stowe, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Drew C. Pendergrass, and Sarah Hancock
Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, https://doi.org/10.5194/acp-23-5945-2023, 2023
Short summary
Short summary
We quantify methane emissions from individual countries in the Middle East and North Africa by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We show that the ability to simply relate oil/gas emissions to activity metrics is compromised by stochastic nature of local infrastructure and management practices. We find that the industry target for oil/gas methane intensity is achievable through associated gas capture, modern infrastructure, and centralized operations.
Shixian Zhai, Daniel J. Jacob, Drew C. Pendergrass, Nadia K. Colombi, Viral Shah, Laura Hyesung Yang, Qiang Zhang, Shuxiao Wang, Hwajin Kim, Yele Sun, Jin-Soo Choi, Jin-Soo Park, Gan Luo, Fangqun Yu, Jung-Hun Woo, Younha Kim, Jack E. Dibb, Taehyoung Lee, Jin-Seok Han, Bruce E. Anderson, Ke Li, and Hong Liao
Atmos. Chem. Phys., 23, 4271–4281, https://doi.org/10.5194/acp-23-4271-2023, https://doi.org/10.5194/acp-23-4271-2023, 2023
Short summary
Short summary
Anthropogenic fugitive dust in East Asia not only causes severe coarse particulate matter air pollution problems, but also affects fine particulate nitrate. Due to emission control efforts, coarse PM decreased steadily. We find that the decrease of coarse PM is a major driver for a lack of decrease of fine particulate nitrate, as it allows more nitric acid to form fine particulate nitrate. The continuing decrease of coarse PM requires more stringent ammonia and nitrogen oxides emission controls.
Nadia K. Colombi, Daniel J. Jacob, Laura Hyesung Yang, Shixian Zhai, Viral Shah, Stuart K. Grange, Robert M. Yantosca, Soontae Kim, and Hong Liao
Atmos. Chem. Phys., 23, 4031–4044, https://doi.org/10.5194/acp-23-4031-2023, https://doi.org/10.5194/acp-23-4031-2023, 2023
Short summary
Short summary
Surface ozone, detrimental to human and ecosystem health, is very high and increasing in South Korea. Using a global model of the atmosphere, we found that emissions from South Korea and China contribute equally to the high ozone observed. We found that in the absence of all anthropogenic emissions over East Asia, ozone is still very high, implying that the air quality standard in South Korea is not practically achievable unless this background external to East Asia can be decreased.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Laura Hyesung Yang, Daniel J. Jacob, Nadia K. Colombi, Shixian Zhai, Kelvin H. Bates, Viral Shah, Ellie Beaudry, Robert M. Yantosca, Haipeng Lin, Jared F. Brewer, Heesung Chong, Katherine R. Travis, James H. Crawford, Lok N. Lamsal, Ja-Ho Koo, and Jhoon Kim
Atmos. Chem. Phys., 23, 2465–2481, https://doi.org/10.5194/acp-23-2465-2023, https://doi.org/10.5194/acp-23-2465-2023, 2023
Short summary
Short summary
A geostationary satellite can now provide hourly NO2 vertical columns, and obtaining the NO2 vertical columns from space relies on NO2 vertical distribution from the chemical transport model (CTM). In this work, we update the CTM to better represent the chemistry environment so that the CTM can accurately provide NO2 vertical distribution. We also find that the changes in NO2 vertical distribution driven by a change in mixing depth play an important role in the NO2 column's diurnal variation.
Viral Shah, Daniel J. Jacob, Ruijun Dang, Lok N. Lamsal, Sarah A. Strode, Stephen D. Steenrod, K. Folkert Boersma, Sebastian D. Eastham, Thibaud M. Fritz, Chelsea Thompson, Jeff Peischl, Ilann Bourgeois, Ilana B. Pollack, Benjamin A. Nault, Ronald C. Cohen, Pedro Campuzano-Jost, Jose L. Jimenez, Simone T. Andersen, Lucy J. Carpenter, Tomás Sherwen, and Mat J. Evans
Atmos. Chem. Phys., 23, 1227–1257, https://doi.org/10.5194/acp-23-1227-2023, https://doi.org/10.5194/acp-23-1227-2023, 2023
Short summary
Short summary
NOx in the free troposphere (above 2 km) affects global tropospheric chemistry and the retrieval and interpretation of satellite NO2 measurements. We evaluate free tropospheric NOx in global atmospheric chemistry models and find that recycling NOx from its reservoirs over the oceans is faster than that simulated in the models, resulting in increases in simulated tropospheric ozone and OH. Over the U.S., free tropospheric NO2 contributes the majority of the tropospheric NO2 column in summer.
Javier Gorroño, Daniel J. Varon, Itziar Irakulis-Loitxate, and Luis Guanter
Atmos. Meas. Tech., 16, 89–107, https://doi.org/10.5194/amt-16-89-2023, https://doi.org/10.5194/amt-16-89-2023, 2023
Short summary
Short summary
We present a methane flux rate retrieval methodology using the Sentinel-2 mission, validating the algorithm for different scenes and plumes. The detection limit is 1000–2000 kg h−1 for homogeneous scenes and temporally invariant surfaces and above 5000 kg h−1 for heterogeneous ones. Dominant quantification errors are wind-related or plume mask-related. For heterogeneous scenes, the surface structure underlying the methane plume can become a dominant source of uncertainty.
James D. East, Barron H. Henderson, Sergey L. Napelenok, Shannon N. Koplitz, Golam Sarwar, Robert Gilliam, Allen Lenzen, Daniel Q. Tong, R. Bradley Pierce, and Fernando Garcia-Menendez
Atmos. Chem. Phys., 22, 15981–16001, https://doi.org/10.5194/acp-22-15981-2022, https://doi.org/10.5194/acp-22-15981-2022, 2022
Short summary
Short summary
We present a framework that uses a computer model of air quality, along with air pollution data from satellite instruments, to estimate emissions of nitrogen oxides (NOx) across the Northern Hemisphere. The framework, which advances current methods to infer emissions from satellite observations, provides observationally constrained NOx estimates, including in regions of the world where emissions are highly uncertain, and can improve simulations of air pollutants relevant for health and policy.
Zhan Zhang, Evan D. Sherwin, Daniel J. Varon, and Adam R. Brandt
Atmos. Meas. Tech., 15, 7155–7169, https://doi.org/10.5194/amt-15-7155-2022, https://doi.org/10.5194/amt-15-7155-2022, 2022
Short summary
Short summary
This work developed a multi-band–multi-pass–multi-comparison-date Sentinel-2 methane retrieval algorithm, and the method was calibrated by data from a controlled release test. To our knowledge, this is the first study that validates the performance of a Sentinel-2 methane detection algorithm by calibration with a ground-truth testing. It illustrates the potential for additional validation with systematic future experiments wherein algorithms can be tuned to meet different detection expectations.
Randall V. Martin, Sebastian D. Eastham, Liam Bindle, Elizabeth W. Lundgren, Thomas L. Clune, Christoph A. Keller, William Downs, Dandan Zhang, Robert A. Lucchesi, Melissa P. Sulprizio, Robert M. Yantosca, Yanshun Li, Lucas Estrada, William M. Putman, Benjamin M. Auer, Atanas L. Trayanov, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 15, 8731–8748, https://doi.org/10.5194/gmd-15-8731-2022, https://doi.org/10.5194/gmd-15-8731-2022, 2022
Short summary
Short summary
Atmospheric chemistry models must be able to operate both online as components of Earth system models and offline as standalone models. The widely used GEOS-Chem model operates both online and offline, but the classic offline version is not suitable for massively parallel simulations. We describe a new generation of the offline high-performance GEOS-Chem (GCHP) that enables high-resolution simulations on thousands of cores, including on the cloud, with improved access, performance, and accuracy.
Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Haipeng Lin, Elizabeth W. Lundgren, Steve Goldhaber, Steven R. H. Barrett, and Daniel J. Jacob
Geosci. Model Dev., 15, 8669–8704, https://doi.org/10.5194/gmd-15-8669-2022, https://doi.org/10.5194/gmd-15-8669-2022, 2022
Short summary
Short summary
We bring the state-of-the-science chemistry module GEOS-Chem into the Community Earth System Model (CESM). We show that some known differences between results from GEOS-Chem and CESM's CAM-chem chemistry module may be due to the configuration of model meteorology rather than inherent differences in the model chemistry. This is a significant step towards a truly modular Earth system model and allows two strong but currently separate research communities to benefit from each other's advances.
Tai-Long He, Dylan B. A. Jones, Kazuyuki Miyazaki, Kevin W. Bowman, Zhe Jiang, Xiaokang Chen, Rui Li, Yuxiang Zhang, and Kunna Li
Atmos. Chem. Phys., 22, 14059–14074, https://doi.org/10.5194/acp-22-14059-2022, https://doi.org/10.5194/acp-22-14059-2022, 2022
Short summary
Short summary
We use a deep-learning (DL) model to estimate Chinese NOx emissions by combining satellite analysis and in situ measurements. Our results are consistent with conventional analyses of Chinese NOx emissions. Comparison with mobility data shows that the DL model has a better capability to capture changes in NOx. We analyse Chinese NOx emissions during the COVID-19 pandemic lockdown period. Our results illustrate the potential use of DL as a complementary tool for conventional air quality studies.
Haolin Wang, Xiao Lu, Daniel J. Jacob, Owen R. Cooper, Kai-Lan Chang, Ke Li, Meng Gao, Yiming Liu, Bosi Sheng, Kai Wu, Tongwen Wu, Jie Zhang, Bastien Sauvage, Philippe Nédélec, Romain Blot, and Shaojia Fan
Atmos. Chem. Phys., 22, 13753–13782, https://doi.org/10.5194/acp-22-13753-2022, https://doi.org/10.5194/acp-22-13753-2022, 2022
Short summary
Short summary
We report significant global tropospheric ozone increases in 1995–2017 based on extensive aircraft and ozonesonde observations. Using GEOS-Chem (Goddard Earth Observing System chemistry model) multi-decadal global simulations, we find that changes in global anthropogenic emissions, in particular the rapid increases in aircraft emissions, contribute significantly to the increases in tropospheric ozone and resulting radiative impact.
Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
Biogeosciences, 19, 4779–4799, https://doi.org/10.5194/bg-19-4779-2022, https://doi.org/10.5194/bg-19-4779-2022, 2022
Short summary
Short summary
Plants draw CO2 from the atmosphere during the growing season, while respiration releases CO2 to the atmosphere throughout the year, driving seasonal variations in atmospheric CO2 that can be observed by satellites, such as the Orbiting Carbon Observatory 2 (OCO-2). Using OCO-2 XCO2 data and space-based constraints on plant growth, we show that permafrost-rich northeast Eurasia has a strong seasonal release of CO2 during the autumn, hinting at an unexpectedly large respiration signal from soils.
Katherine L. Hayden, Shao-Meng Li, John Liggio, Michael J. Wheeler, Jeremy J. B. Wentzell, Amy Leithead, Peter Brickell, Richard L. Mittermeier, Zachary Oldham, Cristian M. Mihele, Ralf M. Staebler, Samar G. Moussa, Andrea Darlington, Mengistu Wolde, Daniel Thompson, Jack Chen, Debora Griffin, Ellen Eckert, Jenna C. Ditto, Megan He, and Drew R. Gentner
Atmos. Chem. Phys., 22, 12493–12523, https://doi.org/10.5194/acp-22-12493-2022, https://doi.org/10.5194/acp-22-12493-2022, 2022
Short summary
Short summary
In this study, airborne measurements provided the most detailed characterization, to date, of boreal forest wildfire emissions. Measurements showed a large diversity of air pollutants expanding the volatility range typically reported. A large portion of organic species was unidentified, likely comprised of complex organic compounds. Aircraft-derived emissions improve wildfire chemical speciation and can support reliable model predictions of pollution from boreal forest wildfires.
Helen M. Worden, Gene L. Francis, Susan S. Kulawik, Kevin W. Bowman, Karen Cady-Pereira, Dejian Fu, Jennifer D. Hegarty, Valentin Kantchev, Ming Luo, Vivienne H. Payne, John R. Worden, Róisín Commane, and Kathryn McKain
Atmos. Meas. Tech., 15, 5383–5398, https://doi.org/10.5194/amt-15-5383-2022, https://doi.org/10.5194/amt-15-5383-2022, 2022
Short summary
Short summary
Satellite observations of global carbon monoxide (CO) are essential for understanding atmospheric chemistry and pollution sources. This paper describes a new data product using radiance measurements from the Cross-track Infrared Sounder (CrIS) instrument on the Suomi National Polar-orbiting Partnership (SNPP) satellite that provides vertical profiles of CO from single-field-of-view observations. We show how these satellite CO profiles compare to aircraft observations and evaluate their biases.
Lu Shen, Ritesh Gautam, Mark Omara, Daniel Zavala-Araiza, Joannes D. Maasakkers, Tia R. Scarpelli, Alba Lorente, David Lyon, Jianxiong Sheng, Daniel J. Varon, Hannah Nesser, Zhen Qu, Xiao Lu, Melissa P. Sulprizio, Steven P. Hamburg, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, https://doi.org/10.5194/acp-22-11203-2022, 2022
Short summary
Short summary
We use 22 months of TROPOMI satellite observations to quantity methane emissions from the oil (O) and natural gas (G) sector in the US and Canada at the scale of both individual basins as well as country-wide aggregates. We find that O/G-related methane emissions are underestimated in these inventories by 80 % for the US and 40 % for Canada, and 70 % of the underestimate in the US is from five O/G basins, including Permian, Haynesville, Anadarko, Eagle Ford, and Barnett.
Zichong Chen, Daniel J. Jacob, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Elise Penn, and Xueying Yu
Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, https://doi.org/10.5194/acp-22-10809-2022, 2022
Short summary
Short summary
We quantify methane emissions in China and contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We find that anthropogenic methane emissions for China are underestimated in the national inventory. Our estimate of emissions indicates a small life-cycle loss rate, implying net climate benefits from the current
coal-to-gasenergy transition in China. However, this small loss rate can be misleading given China's high gas imports.
Lutz Beckebanze, Benjamin R. K. Runkle, Josefine Walz, Christian Wille, David Holl, Manuel Helbig, Julia Boike, Torsten Sachs, and Lars Kutzbach
Biogeosciences, 19, 3863–3876, https://doi.org/10.5194/bg-19-3863-2022, https://doi.org/10.5194/bg-19-3863-2022, 2022
Short summary
Short summary
In this study, we present observations of lateral and vertical carbon fluxes from a permafrost-affected study site in the Russian Arctic. From this dataset we estimate the net ecosystem carbon balance for this study site. We show that lateral carbon export has a low impact on the net ecosystem carbon balance during the complete study period (3 months). Nevertheless, our results also show that lateral carbon export can exceed vertical carbon uptake at the beginning of the growing season.
Daniel J. Jacob, Daniel J. Varon, Daniel H. Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, John Kelley, Jason McKeever, Lesley E. Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, and Riley M. Duren
Atmos. Chem. Phys., 22, 9617–9646, https://doi.org/10.5194/acp-22-9617-2022, https://doi.org/10.5194/acp-22-9617-2022, 2022
Short summary
Short summary
We review the capability of satellite observations of atmospheric methane to quantify methane emissions on all scales. We cover retrieval methods, precision requirements, inverse methods for inferring emissions, source detection thresholds, and observations of system completeness. We show that current instruments already enable quantification of regional and national emissions including contributions from large point sources. Coverage and resolution will increase significantly in coming years.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
Short summary
Short summary
Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022, https://doi.org/10.5194/acp-22-7461-2022, 2022
Short summary
Short summary
This study demonstrates that ozone dry-deposition modeling can be improved by revising the model's dry-deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
Vivienne H. Payne, Susan S. Kulawik, Emily V. Fischer, Jared F. Brewer, L. Gregory Huey, Kazuyuki Miyazaki, John R. Worden, Kevin W. Bowman, Eric J. Hintsa, Fred Moore, James W. Elkins, and Julieta Juncosa Calahorrano
Atmos. Meas. Tech., 15, 3497–3511, https://doi.org/10.5194/amt-15-3497-2022, https://doi.org/10.5194/amt-15-3497-2022, 2022
Short summary
Short summary
We compare new satellite measurements of peroxyacetyl nitrate (PAN) with reference aircraft measurements from two different instruments flown on the same platform. While there is a systematic difference between the two aircraft datasets, both show the same large-scale distribution of PAN and the discrepancy between aircraft datasets is small compared to the satellite uncertainties. The satellite measurements show skill in capturing large-scale variations in PAN.
John R. Worden, Daniel H. Cusworth, Zhen Qu, Yi Yin, Yuzhong Zhang, A. Anthony Bloom, Shuang Ma, Brendan K. Byrne, Tia Scarpelli, Joannes D. Maasakkers, David Crisp, Riley Duren, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022, https://doi.org/10.5194/acp-22-6811-2022, 2022
Short summary
Short summary
This paper is intended to accomplish two goals: 1) describe a new algorithm by which remotely sensed measurements of methane or other tracers can be used to not just quantify methane fluxes, but also attribute these fluxes to specific sources and regions and characterize their uncertainties, and 2) use this new algorithm to provide methane emissions by sector and country in support of the global stock take.
Elena Sánchez-García, Javier Gorroño, Itziar Irakulis-Loitxate, Daniel J. Varon, and Luis Guanter
Atmos. Meas. Tech., 15, 1657–1674, https://doi.org/10.5194/amt-15-1657-2022, https://doi.org/10.5194/amt-15-1657-2022, 2022
Short summary
Short summary
This study seeks to present the as-yet-unknown potential use of WorldView-3 for the mapping of methane point source emissions. The proposed retrieval methodology is based on the idea that the spectral channels not affected by methane can be used to predict the methane-affected band through regression analysis. The results show the precise location of 26 independent point emissions over different methane hotspot regions worldwide, which prove the game-changing potential that this mission entails.
Tia R. Scarpelli, Daniel J. Jacob, Shayna Grossman, Xiao Lu, Zhen Qu, Melissa P. Sulprizio, Yuzhong Zhang, Frances Reuland, Deborah Gordon, and John R. Worden
Atmos. Chem. Phys., 22, 3235–3249, https://doi.org/10.5194/acp-22-3235-2022, https://doi.org/10.5194/acp-22-3235-2022, 2022
Short summary
Short summary
We present a spatially explicit version of the national inventories of oil, gas, and coal methane emissions as submitted by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. We then use atmospheric modeling to compare our inventory emissions to atmospheric methane observations with the goal of identifying potential under- and overestimates of oil–gas methane emissions in the national inventories.
Drew C. Pendergrass, Shixian Zhai, Jhoon Kim, Ja-Ho Koo, Seoyoung Lee, Minah Bae, Soontae Kim, Hong Liao, and Daniel J. Jacob
Atmos. Meas. Tech., 15, 1075–1091, https://doi.org/10.5194/amt-15-1075-2022, https://doi.org/10.5194/amt-15-1075-2022, 2022
Short summary
Short summary
This paper uses a machine learning algorithm to infer high-resolution maps of particulate air quality in eastern China, Japan, and the Korean peninsula, using data from a geostationary satellite along with meteorology. We then perform an extensive evaluation of this inferred air quality and use it to diagnose trends in the region. We hope this paper and the associated data will be valuable to other scientists interested in epidemiology, air quality, remote sensing, and machine learning.
Lu Shen, Daniel J. Jacob, Mauricio Santillana, Kelvin Bates, Jiawei Zhuang, and Wei Chen
Geosci. Model Dev., 15, 1677–1687, https://doi.org/10.5194/gmd-15-1677-2022, https://doi.org/10.5194/gmd-15-1677-2022, 2022
Short summary
Short summary
The high computational cost of chemical integration is a long-standing limitation in global atmospheric chemistry models. Here we present an adaptive and efficient algorithm that can reduce the computational time of atmospheric chemistry by 50 % and maintain the error below 2 % for important species, inspired by machine learning clustering techniques and traditional asymptotic analysis ideas.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
Short summary
Short summary
We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
Kelvin H. Bates, Daniel J. Jacob, Ke Li, Peter D. Ivatt, Mat J. Evans, Yingying Yan, and Jintai Lin
Atmos. Chem. Phys., 21, 18351–18374, https://doi.org/10.5194/acp-21-18351-2021, https://doi.org/10.5194/acp-21-18351-2021, 2021
Short summary
Short summary
Simple aromatic compounds (benzene, toluene, xylene) have complex gas-phase chemistry that is inconsistently represented in atmospheric models. We compile recent experimental and theoretical insights to develop a new mechanism for gas-phase aromatic oxidation that is sufficiently compact for use in multiscale models. We compare our new mechanism to chamber experiments and other mechanisms, and implement it in a global model to quantify the impacts of aromatic oxidation on tropospheric chemistry.
Sabour Baray, Daniel J. Jacob, Joannes D. Maasakkers, Jian-Xiong Sheng, Melissa P. Sulprizio, Dylan B. A. Jones, A. Anthony Bloom, and Robert McLaren
Atmos. Chem. Phys., 21, 18101–18121, https://doi.org/10.5194/acp-21-18101-2021, https://doi.org/10.5194/acp-21-18101-2021, 2021
Short summary
Short summary
We use 2010–2015 surface and satellite observations to disentangle methane from anthropogenic and natural sources in Canada. Using a chemical transport model (GEOS-Chem), the mismatch between modelled and observed methane concentrations can be used to infer emissions according to Bayesian statistics. Compared to prior knowledge, we show higher anthropogenic emissions attributed to energy and/or agriculture in Western Canada and lower natural emissions from Boreal wetlands.
Shixian Zhai, Daniel J. Jacob, Jared F. Brewer, Ke Li, Jonathan M. Moch, Jhoon Kim, Seoyoung Lee, Hyunkwang Lim, Hyun Chul Lee, Su Keun Kuk, Rokjin J. Park, Jaein I. Jeong, Xuan Wang, Pengfei Liu, Gan Luo, Fangqun Yu, Jun Meng, Randall V. Martin, Katherine R. Travis, Johnathan W. Hair, Bruce E. Anderson, Jack E. Dibb, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jung-Hun Woo, Younha Kim, Qiang Zhang, and Hong Liao
Atmos. Chem. Phys., 21, 16775–16791, https://doi.org/10.5194/acp-21-16775-2021, https://doi.org/10.5194/acp-21-16775-2021, 2021
Short summary
Short summary
Geostationary satellite aerosol optical depth (AOD) has tremendous potential for monitoring surface fine particulate matter (PM2.5). Our study explored the physical relationship between AOD and PM2.5 by integrating data from surface networks, aircraft, and satellites with the GEOS-Chem chemical transport model. We quantitatively showed that accurate simulation of aerosol size distributions, boundary layer depths, relative humidity, coarse particles, and diurnal variations in PM2.5 are essential.
Liam Bindle, Randall V. Martin, Matthew J. Cooper, Elizabeth W. Lundgren, Sebastian D. Eastham, Benjamin M. Auer, Thomas L. Clune, Hongjian Weng, Jintai Lin, Lee T. Murray, Jun Meng, Christoph A. Keller, William M. Putman, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 14, 5977–5997, https://doi.org/10.5194/gmd-14-5977-2021, https://doi.org/10.5194/gmd-14-5977-2021, 2021
Short summary
Short summary
Atmospheric chemistry models like GEOS-Chem are versatile tools widely used in air pollution and climate studies. The simulations used in such studies can be very computationally demanding, and thus it is useful if the model can simulate a specific geographic region at a higher resolution than the rest of the globe. Here, we implement, test, and demonstrate a new variable-resolution capability in GEOS-Chem that is suitable for simulations conducted on supercomputers.
Lee T. Murray, Eric M. Leibensperger, Clara Orbe, Loretta J. Mickley, and Melissa Sulprizio
Geosci. Model Dev., 14, 5789–5823, https://doi.org/10.5194/gmd-14-5789-2021, https://doi.org/10.5194/gmd-14-5789-2021, 2021
Short summary
Short summary
Chemical-transport models are tools used to study air pollution and inform public policy. However, they are limited by the availability of archived meteorology. Here, we describe how the GEOS-Chem chemical-transport model may now be driven by meteorology archived from a state-of-the-art general circulation model for past and future climates, allowing it to be used to explore the impact of climate change on air pollution and atmospheric composition.
Zhen Qu, Daniel J. Jacob, Lu Shen, Xiao Lu, Yuzhong Zhang, Tia R. Scarpelli, Hannah Nesser, Melissa P. Sulprizio, Joannes D. Maasakkers, A. Anthony Bloom, John R. Worden, Robert J. Parker, and Alba L. Delgado
Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, https://doi.org/10.5194/acp-21-14159-2021, 2021
Short summary
Short summary
The recent launch of TROPOMI offers an unprecedented opportunity to quantify the methane budget from a top-down perspective. We use TROPOMI and the more mature GOSAT methane observations to estimate methane emissions and get consistent global budgets. However, TROPOMI shows biases over regions where surface albedo is small and provides less information for the coarse-resolution inversion due to the larger error correlations and spatial variations in the number of observations.
Xuan Wang, Daniel J. Jacob, William Downs, Shuting Zhai, Lei Zhu, Viral Shah, Christopher D. Holmes, Tomás Sherwen, Becky Alexander, Mathew J. Evans, Sebastian D. Eastham, J. Andrew Neuman, Patrick R. Veres, Theodore K. Koenig, Rainer Volkamer, L. Gregory Huey, Thomas J. Bannan, Carl J. Percival, Ben H. Lee, and Joel A. Thornton
Atmos. Chem. Phys., 21, 13973–13996, https://doi.org/10.5194/acp-21-13973-2021, https://doi.org/10.5194/acp-21-13973-2021, 2021
Short summary
Short summary
Halogen radicals have a broad range of implications for tropospheric chemistry, air quality, and climate. We present a new mechanistic description and comprehensive simulation of tropospheric halogens in a global 3-D model and compare the model results with surface and aircraft measurements. We find that halogen chemistry decreases the global tropospheric burden of ozone by 11 %, NOx by 6 %, and OH by 4 %.
Haipeng Lin, Daniel J. Jacob, Elizabeth W. Lundgren, Melissa P. Sulprizio, Christoph A. Keller, Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Patrick C. Campbell, Barry Baker, Rick D. Saylor, and Raffaele Montuoro
Geosci. Model Dev., 14, 5487–5506, https://doi.org/10.5194/gmd-14-5487-2021, https://doi.org/10.5194/gmd-14-5487-2021, 2021
Short summary
Short summary
Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to select, add, and scale emissions from different sources through a configuration file with no change to the model source code. We demonstrate the implementation of HEMCO in several models, all sharing the same HEMCO core code and database library.
Yi Yin, Frederic Chevallier, Philippe Ciais, Philippe Bousquet, Marielle Saunois, Bo Zheng, John Worden, A. Anthony Bloom, Robert J. Parker, Daniel J. Jacob, Edward J. Dlugokencky, and Christian Frankenberg
Atmos. Chem. Phys., 21, 12631–12647, https://doi.org/10.5194/acp-21-12631-2021, https://doi.org/10.5194/acp-21-12631-2021, 2021
Short summary
Short summary
The growth of methane, the second-most important anthropogenic greenhouse gas after carbon dioxide, has been accelerating in recent years. Using an ensemble of multi-tracer atmospheric inversions constrained by surface or satellite observations, we show that global methane emissions increased by nearly 1 % per year from 2010–2017, with leading contributions from the tropics and East Asia.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Tia R. Scarpelli, Melissa P. Sulprizio, Yuzhong Zhang, and Chris H. Rycroft
Atmos. Meas. Tech., 14, 5521–5534, https://doi.org/10.5194/amt-14-5521-2021, https://doi.org/10.5194/amt-14-5521-2021, 2021
Short summary
Short summary
Analytical inversions of satellite observations of atmospheric composition can improve emissions estimates and quantify errors but are computationally expensive at high resolutions. We propose two methods to decrease this cost. The methods reproduce a high-resolution inversion at a quarter of the cost. The reduced-dimension method creates a multiscale grid. The reduced-rank method solves the inversion where information content is highest.
Cited articles
Allen, D., Tullos, E., Chen, Q., Kimura, Y., Lyon, D., Patsche, L., and Thompson, E.: Greenhouse Gas Inventory for Texas, Texas Commission on Environmental Quality, https://www.tceq.texas.gov/downloads/agency/climate-pollution-reduction-grants/texas-cap-v5-appendix-b-ghg-inventory.pdf (last access: 1 July 2026), 2025.
Alvarez, R. A., Zavala-Araiza, D., Lyon, D. R., Allen, D. T., Barkley, Z. R., Brandt, A. R., Davis, K. J., Herndon, S. C., Jacob, D. J., Karion, A., Kort, E. A., Lamb, B. K., Lauvaux, T., Maasakkers, J. D., Marchese, A. J., Omara, M., Pacala, S. W., Peischl, J., Robinson, A. L., Shepson, P. B., Sweeney, C., Townsend-Small, A., Wofsy, S. C., and Hamburg, S. P.: Assessment of methane emissions from the U.S. oil and gas supply chain, Science, 361, 186–188, https://doi.org/10.1126/science.aar7204, 2018.
Appuhamy, R. and Kebreab, E.: Characterizing California-specific Cattle Feed Rations and Improve Modeling of Enteric Fermentation for California's Greenhouse Gas Inventory, California Environmental Protection Agency, Air Resources Board, Research Division, 52 pp., https://ww2.arb.ca.gov/sites/default/files/classic/research/apr/past/16rd001.pdf (last access: 4 December 2025), 2018.
Balasus, N., Jacob, D. J., Lorente, A., Maasakkers, J. D., Parker, R. J., Boesch, H., Chen, Z., Kelp, M. M., Nesser, H., and Varon, D. J.: A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases, Atmos. Meas. Tech., 16, 3787–3807, https://doi.org/10.5194/amt-16-3787-2023, 2023.
Balasus, N., Jacob, D. J., Maxemin, G., Jenks, C., Nesser, H., Maasakkers, J. D., Cusworth, D. H., Scarpelli, T. R., Varon, D. J., and Wang, X.: Satellite monitoring of annual US landfill methane emissions and trends, Environ. Res. Lett., 20, 024007, https://doi.org/10.1088/1748-9326/ada2b1, 2025.
Barré, J., Aben, I., Agustí-Panareda, A., Balsamo, G., Bousserez, N., Dueben, P., Engelen, R., Inness, A., Lorente, A., McNorton, J., Peuch, V.-H., Radnoti, G., and Ribas, R.: Systematic detection of local CH4 anomalies by combining satellite measurements with high-resolution forecasts, Atmos. Chem. Phys., 21, 5117–5136, https://doi.org/10.5194/acp-21-5117-2021, 2021.
Bharadwaj, S., Clark, T., Loken, R., and Trivedi, D.: New Mexico Greenhouse Gas Emissions Inventory and Forecast, New Mexico Environment Department, https://cloud.env.nm.gov/resources/_translator.php/YjYxOGY4Y WZhMDUwMWI3YmQ4MDQ1OGI5NF8yMDMxNjc~.pdf (last access: 25 November 2025), 2024.
Bloom, A. A., Bowman, K. W., Lee, M., Turner, A. J., Schroeder, R., Worden, J. R., Weidner, R. J., Mcdonald, K. C., and Jacob, D. J.: CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.3.1), ORNL DAAC, https://doi.org/10.3334/ORNLDAAC/1915, 2021.
Brasseur, G. P. and Jacob, D. J.: Modeling of Atmospheric Chemistry, Cambridge University Press, Cambridge, https://doi.org/10.1017/9781316544754, 2017.
Bruhwiler, L. M., Basu, S., Bergamaschi, P., Bousquet, P., Dlugokencky, E., Houweling, S., Ishizawa, M., Kim, H.-S., Locatelli, R., Maksyutov, S., Montzka, S., Pandey, S., Patra, P. K., Petron, G., Saunois, M., Sweeney, C., Schwietzke, S., Tans, P., and Weatherhead, E. C.: U.S. CH4 emissions from oil and gas production: Have recent large increases been detected?, J. Geophys. Res.-Atmos., 122, 4070–4083, https://doi.org/10.1002/2016JD026157, 2017.
Buchwitz, M., Schneising, O., Reuter, M., Heymann, J., Krautwurst, S., Bovensmann, H., Burrows, J. P., Boesch, H., Parker, R. J., Somkuti, P., Detmers, R. G., Hasekamp, O. P., Aben, I., Butz, A., Frankenberg, C., and Turner, A. J.: Satellite-derived methane hotspot emission estimates using a fast data-driven method, Atmos. Chem. Phys., 17, 5751–5774, https://doi.org/10.5194/acp-17-5751-2017, 2017.
California Air Resources Board (CARB): Greenhouse Gas Emission Standards for Crude Oil and Natural Gas Facilities, California Code of Regulations, Title 17, Div. 3, Ch. 1, Subch. 10, Art. 4, Subart. 13, https://ww2.arb.ca.gov/sites/default/files/2020-03/2017 Final Reg Orders GHG Emission Standards.pdf (last access: 25 September 2025), 2017.
California Air Resources Board (CARB): California Greenhouse Gas Emissions from 2000 to 2023: Trends of Emissions and Other Indicators, California Air Resources Board, California Air Resources Board, https://ww2.arb.ca.gov/sites/default/files/2025-11/nc-2000_2023_ghg_inventory_trends.pdf (last access: 19 September 2025), 2025.
California Legislature: Senate Bill 1383 (Short-Lived Climate Pollutant Reduction Law), California Health and Safety Code, § 39730.5 et seq., https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201520160SB1383 (last access: 25 September 2025), 2016.
Cardoso-Saldaña, F. J. and Allen, D. T.: Projecting the Temporal Evolution of Methane Emissions from Oil and Gas Production Basins, Environ. Sci. Technol., 55, 2811–2819, https://doi.org/10.1021/acs.est.0c04224, 2021.
Chen, Y., Sherwin, E. D., Berman, E. S. F., Jones, B. B., Gordon, M. P., Wetherley, E. B., Kort, E. A., and Brandt, A. R.: Quantifying Regional Methane Emissions in the New Mexico Permian Basin with a Comprehensive Aerial Survey, Environ. Sci. Technol., 56, 4317–4323, https://doi.org/10.1021/acs.est.1c06458, 2022a.
Chen, Z., Jacob, D. J., Nesser, H., Sulprizio, M. P., Lorente, A., Varon, D. J., Lu, X., Shen, L., Qu, Z., Penn, E., and Yu, X.: Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations, Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, 2022b.
Chen, Z., Jacob, D. J., Gautam, R., Omara, M., Stavins, R. N., Stowe, R. C., Nesser, H., Sulprizio, M. P., Lorente, A., Varon, D. J., Lu, X., Shen, L., Qu, Z., Pendergrass, D. C., and Hancock, S.: Satellite quantification of methane emissions and oil–gas methane intensities from individual countries in the Middle East and North Africa: implications for climate action, Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, 2023.
Colorado Department of Public Health and Environment (CDPHE): Regulation Number 7: Control of Emissions from Oil and Gas Operations, Code of Colorado Regulations, 5 CCR 1001-9, Colorado Secretary of State, https://www.sos.state.co.us/CCR/DisplayRule.do?action=ruleinfo&ruleId=2341&deptID=16&agencyID=7&deptName=Department of Public Health and Environment&agencyName=Air Quality Control Commission&seriesNum=5 CCR 1001-9 (last access: 9 September 2025), 2025.
Colorado Department of Public Health and Environment (CDPHE): 2025 Colorado Statewide Inventory of Greenhouse Gas Emissions and Sinks, https://cdphe.colorado.gov/apcd/greenhouse-gas-inventory (last access: 1 July 2026), 2026.
Colorado General Assembly: Climate Action Plan to Reduce Pollution Act, Colorado Revised Statutes, C.R.S. § 25-7-102 and related provisions, Colorado General Assembly, https://leg.colorado.gov/bills/hb19-1261 (last access: 9 September 2025), 2019.
Crippa, M., Guizzardi, D., Pagani, F., Schiavina, M., Melchiorri, M., Pisoni, E., Graziosi, F., Muntean, M., Maes, J., Dijkstra, L., Van Damme, M., Clarisse, L., and Coheur, P.: Insights into the spatial distribution of global, national, and subnational greenhouse gas emissions in the Emissions Database for Global Atmospheric Research (EDGAR v8.0), Earth Syst. Sci. Data, 16, 2811–2830, https://doi.org/10.5194/essd-16-2811-2024, 2024.
Cui, Y. Y., Henze, D. K., Brioude, J., Angevine, W. M., Liu, Z., Bousserez, N., Guerrette, J., McKeen, S. A., Peischl, J., Yuan, B., Ryerson, T., Frost, G., and Trainer, M.: Inversion Estimates of Lognormally Distributed Methane Emission Rates From the Haynesville-Bossier Oil and Gas Production Region Using Airborne Measurements, J. Geophys. Res.-Atmos., 124, 3520–3531, https://doi.org/10.1029/2018JD029489, 2019.
Delwiche, K. B., Harrison, J. A., Maasakkers, J. D., Sulprizio, M. P., Worden, J., Jacob, D. J., and Sunderland, E. M.: Estimating Drivers and Pathways for Hydroelectric Reservoir Methane Emissions Using a New Mechanistic Model, J. Geophys. Res.-Biogeosci., 127, e2022JG006908, https://doi.org/10.1029/2022JG006908, 2022.
Desai, M., Camobreco, V., Hedger, T., Irving, W., Rewcastle, K., Steller, J., Barbieri, L., Weitz, M., Murumkar, T., Fawcett, A., Lou, J., Cui, R., and Hultman, N.: Greenhouse Gas Inventory and Analysis for the United States: 1990-2024, https://doi.org/10.13016/g1xw-qftw, 2026.
Dismukes, D. E.: Louisiana 2021 Greenhouse Gas Inventory, Louisiana State University Center for Energy Studies, https://www.lsu.edu/ces/publications/2021/louisiana-2021-greehouse-gas-inventory-df-rev_reduced.pdf (last access: 9 September 2025), 2021.
Donahue, C. P., Oberoi, K., Dillon, J. W., Hengst, V., Kennedy, B., Kearney, W., Lennox, J., Rehbein, E., Sykes, R., Dudiak, C. D., Altamura, D. T., Doherty, G., Roos, P. A., Brasseur, J. K., and Thorpe, M. J.: Aerial LiDAR-Based, Source-Resolved Methane Emissions Inventory: Permian Basin Case Study for Benchmarking U.S. Emissions, Environ. Sci. Technol., 60, 13968–13979, https://doi.org/10.1021/acs.est.5c15184, 2026.
East, J. D., Jacob, D. J., Jervis, D., Balasus, N., Estrada, L. A., Hancock, S. E., Sulprizio, M. P., Thomas, J., Wang, X., Chen, Z., Varon, D. J., and Worden, J. R.: Worldwide inference of national methane emissions by inversion of satellite observations with UNFCCC prior estimates, Nat. Commun., 16, 11004, https://doi.org/10.1038/s41467-025-67122-8, 2025.
Enverus: PRISM platform, https://prism.enverus.com/prism/home, last access: 5 September 2025.
EPA: AP-42: Compilation of Air Emissions Factors from Stationary Sources, Environmental Protection Agency, https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources (last access: 30 June 2025), 1998.
EPA: EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2022, EPA 430-R-24-004. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-andsinks-1990-2022 (last access: 5 July 2025), 2024.
EPA: Learn more about Official State Greenhouse Gas Inventories: https://www.epa.gov/ghgemissions/learn-more-about-official-state-greenhouse-gas-inventories (last access: 21 November 2025), 2025.
Eskes, H. J., Velthoven, P. F. J. V., Valks, P. J. M., and Kelder, H. M.: Assimilation of GOME total-ozone satellite observations in a three-dimensional tracer-transport model, Q. J. Roy. Meteorol. Soc., 129, 1663–1681, https://doi.org/10.1256/qj.02.14, 2003.
Estrada, L. A., Varon, D. J., Sulprizio, M., Nesser, H., Chen, Z., Balasus, N., Hancock, S. E., He, M., East, J. D., Mooring, T. A., Oort Alonso, A., Maasakkers, J. D., Aben, I., Baray, S., Bowman, K. W., Worden, J. R., Cardoso-Saldaña, F. J., Reidy, E., and Jacob, D. J.: Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations, Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, 2025.
Estrada, L. A., djvaron, Sulprizio, M., Downs, W., nicholasbalasus, He, M., East, J., JohnThomasSEAS, Chatzimichelakis, S., Yantosca, B., Zhuang, J., Baray, S., and Ivatt, P.: laestrada/CONUS_project_imi: Publication code, Version 1.0.0, Zenodo [computer software], https://doi.org/10.5281/zenodo.21383461, 2026.
Fung, I., John, J., Lerner, J., Matthews, E., Prather, M., Steele, L. P., and Fraser, P. J.: Three-dimensional model synthesis of the global methane cycle, J. Geophys. Res.-Atmos., 96, 13033–13065, https://doi.org/10.1029/91JD01247, 1991.
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., and Zhang, H.: The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity, in: Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021.
GHGRP – Greenhouse Gas Reporting Program : Greenhouse Gas Reporting Program (GHGRP), Environmental Protection Agency, https://www.epa.gov/ghgreporting (last access: 4 December 2025), 2025.
Hancock, S. E., Jacob, D. J., Chen, Z., Nesser, H., Davitt, A., Varon, D. J., Sulprizio, M. P., Balasus, N., Estrada, L. A., Cazorla, M., Dawidowski, L., Diez, S., East, J. D., Penn, E., Randles, C. A., Worden, J., Aben, I., Parker, R. J., and Maasakkers, J. D.: Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations, Atmos. Chem. Phys., 25, 797–817, https://doi.org/10.5194/acp-25-797-2025, 2025.
Hancock, S. E., Jacob, D. J., Jimenez, R., Ardila, A., Morales-Rincon, L., Rojas, N., Estrada, L. A., Balasus, N., East, J. D., Sulprizio, M. P., Wang, X., France, J. L., Potyk, L., Penn, E., Chen, Z., Varon, D. J., Frankenberg, C., Baranski, M., Calcan, A., and Parker, R. J.: Applying satellite observations to improve bottom-up national emission inventories for methane: application to Colombia, Atmos. Chem. Phys., 26, 10455–10476, https://doi.org/10.5194/acp-26-10455-2026, 2026.
Hardke, J., Sha, X., and Bateman, N.: B.R. Wells Arkansas Rice Research Studies 2021, Arkansas Agricultural Experiment Station Research Series, 9–17, https://scholarworks.uark.edu/aaesser/237/ (last access: 30 December 2025), 2024.
He, M., Jacob, D. J., Estrada, L. A., Varon, D. J., Sulprizio, M., Balasus, N., East, J. D., Penn, E., Pendergrass, D. C., Chen, Z., Mooring, T. A., Maasakkers, J. D., Brodrick, P. G., Frankenberg, C., Bowman, K. W., and Bruhwiler, L.: Attributing 2019–2024 methane growth using TROPOMI satellite observations, Sci. Adv., 12, eadz9007, https://doi.org/10.1126/sciadv.adz9007, 2026.
Heald, C. L., Jacob, D. J., Jones, D. B. A., Palmer, P. I., Logan, J. A., Streets, D. G., Sachse, G. W., Gille, J. C., Hoffman, R. N., and Nehrkorn, T.: Comparative inverse analysis of satellite (MOPITT) and aircraft (TRACE-P) observations to estimate Asian sources of carbon monoxide, J. Geophys. Res.-Atmos., 109, https://doi.org/10.1029/2004JD005185, 2004.
Hmiel, B., Petrenko, V. V., Dyonisius, M. N., Buizert, C., Smith, A. M., Place, P. F., Harth, C., Beaudette, R., Hua, Q., Yang, B., Vimont, I., Michel, S. E., Severinghaus, J. P., Etheridge, D., Bromley, T., Schmitt, J., Faïn, X., Weiss, R. F., and Dlugokencky, E.: Preindustrial 14CH4 indicates greater anthropogenic fossil CH4 emissions, Nature, 578, 409–412, https://doi.org/10.1038/s41586-020-1991-8, 2020.
Houweling, S., Bergamaschi, P., Chevallier, F., Heimann, M., Kaminski, T., Krol, M., Michalak, A. M., and Patra, P.: Global inverse modeling of CH4 sources and sinks: an overview of methods, Atmos. Chem. Phys., 17, 235–256, https://doi.org/10.5194/acp-17-235-2017, 2017.
Jacob, D. J., Turner, A. J., Maasakkers, J. D., Sheng, J., Sun, K., Liu, X., Chance, K., Aben, I., McKeever, J., and Frankenberg, C.: Satellite observations of atmospheric methane and their value for quantifying methane emissions, Atmos. Chem. Phys., 16, 14371–14396, https://doi.org/10.5194/acp-16-14371-2016, 2016.
Jacob, D. J., Varon, D. J., Cusworth, D. H., Dennison, P. E., Frankenberg, C., Gautam, R., Guanter, L., Kelley, J., McKeever, J., Ott, L. E., Poulter, B., Qu, Z., Thorpe, A. K., Worden, J. R., and Duren, R. M.: Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane, Atmos. Chem. Phys., 22, 9617–9646, https://doi.org/10.5194/acp-22-9617-2022, 2022.
Karion, A., Sweeney, C., Pétron, G., Frost, G., Michael Hardesty, R., Kofler, J., Miller, B. R., Newberger, T., Wolter, S., Banta, R., Brewer, A., Dlugokencky, E., Lang, P., Montzka, S. A., Schnell, R., Tans, P., Trainer, M., Zamora, R., and Conley, S.: Methane emissions estimate from airborne measurements over a western United States natural gas field, Geophys. Res. Lett., 40, 4393–4397, https://doi.org/10.1002/grl.50811, 2013.
Karki, S., Adviento-Borbe, M. A. A., Massey, J. H., and Reba, M. L.: Assessing Seasonal Methane and Nitrous Oxide Emissions from Furrow-Irrigated Rice with Cover Crops, Agriculture, 11, 261, https://doi.org/10.3390/agriculture11030261, 2021.
Kim, P. S., Jacob, D. J., Fisher, J. A., Travis, K., Yu, K., Zhu, L., Yantosca, R. M., Sulprizio, M. P., Jimenez, J. L., Campuzano-Jost, P., Froyd, K. D., Liao, J., Hair, J. W., Fenn, M. A., Butler, C. F., Wagner, N. L., Gordon, T. D., Welti, A., Wennberg, P. O., Crounse, J. D., St. Clair, J. M., Teng, A. P., Millet, D. B., Schwarz, J. P., Markovic, M. Z., and Perring, A. E.: Sources, seasonality, and trends of southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport model, Atmos. Chem. Phys., 15, 10411–10433, https://doi.org/10.5194/acp-15-10411-2015, 2015.
Kort, E. A., Eluszkiewicz, J., Stephens, B. B., Miller, J. B., Gerbig, C., Nehrkorn, T., Daube, B. C., Kaplan, J. O., Houweling, S., and Wofsy, S. C.: Emissions of CH4 and N2O over the United States and Canada based on a receptor-oriented modeling framework and COBRA-NA atmospheric observations, Geophys. Res. Lett., 35, https://doi.org/10.1029/2008GL034031, 2008.
Kort, E. A., Frankenberg, C., Costigan, K. R., Lindenmaier, R., Dubey, M. K., and Wunch, D.: Four corners: The largest US methane anomaly viewed from space, Geophys. Res. Lett., 41, 6898–6903, https://doi.org/10.1002/2014GL061503, 2014.
Lan, X., Tans, P., Sweeney, C., Andrews, A., Dlugokencky, E., Schwietzke, S., Kofler, J., McKain, K., Thoning, K., Crotwell, M., Montzka, S., Miller, B. R., and Biraud, S. C.: Long-Term Measurements Show Little Evidence for Large Increases in Total U.S. Methane Emissions Over the Past Decade, Geophys. Res. Lett., 46, 4991–4999, https://doi.org/10.1029/2018GL081731, 2019.
Liu, M., van der A, R., van Weele, M., Eskes, H., Lu, X., Veefkind, P., de Laat, J., Kong, H., Wang, J., Sun, J., Ding, J., Zhao, Y., and Weng, H.: A New Divergence Method to Quantify Methane Emissions Using Observations of Sentinel-5P TROPOMI, Geophys. Res. Lett., 48, e2021GL094151, https://doi.org/10.1029/2021GL094151, 2021.
Lorente, A., Borsdorff, T., Butz, A., Hasekamp, O., aan de Brugh, J., Schneider, A., Wu, L., Hase, F., Kivi, R., Wunch, D., Pollard, D. F., Shiomi, K., Deutscher, N. M., Velazco, V. A., Roehl, C. M., Wennberg, P. O., Warneke, T., and Landgraf, J.: Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements, Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, 2021.
Lu, X., Jacob, D. J., Zhang, Y., Maasakkers, J. D., Sulprizio, M. P., Shen, L., Qu, Z., Scarpelli, T. R., Nesser, H., Yantosca, R. M., Sheng, J., Andrews, A., Parker, R. J., Boesch, H., Bloom, A. A., and Ma, S.: Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations, Atmos. Chem. Phys., 21, 4637–4657, https://doi.org/10.5194/acp-21-4637-2021, 2021.
Lu, X., Jacob, D. J., Wang, H., Maasakkers, J. D., Zhang, Y., Scarpelli, T. R., Shen, L., Qu, Z., Sulprizio, M. P., Nesser, H., Bloom, A. A., Ma, S., Worden, J. R., Fan, S., Parker, R. J., Boesch, H., Gautam, R., Gordon, D., Moran, M. D., Reuland, F., Villasana, C. A. O., and Andrews, A.: Methane emissions in the United States, Canada, and Mexico: evaluation of national methane emission inventories and 2010–2017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric observations, Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, 2022.
Lu, X., Jacob, D. J., Zhang, Y., Shen, L., Sulprizio, M. P., Maasakkers, J. D., Varon, D. J., Qu, Z., Chen, Z., Hmiel, B., Parker, R. J., Boesch, H., Wang, H., He, C., and Fan, S.: Observation-derived 2010-2019 trends in methane emissions and intensities from US oil and gas fields tied to activity metrics, P. Natl. Acad. Sci. USA, 120, e2217900120, https://doi.org/10.1073/pnas.2217900120, 2023.
Lucchesi, R.: File Specification for GEOS-5 FP. GMAO Office Note No. 4, Version 1.1, 61 pp., http://gmao.gsfc.nasa.gov/pubs/office_notes (last access: 3 January 2025), 2017.
Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Turner, A. J., Weitz, M., Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad, L., Bloom, A. A., Bowman, K. W., Jeong, S., and Fischer, M. L.: Gridded National Inventory of U.S. Methane Emissions, Environ. Sci. Technol., 50, 13123–13133, https://doi.org/10.1021/acs.est.6b02878, 2016.
Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J.-X., Zhang, Y., Hersher, M., Bloom, A. A., Bowman, K. W., Worden, J. R., Janssens-Maenhout, G., and Parker, R. J.: Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015, Atmos. Chem. Phys., 19, 7859–7881, https://doi.org/10.5194/acp-19-7859-2019, 2019.
Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J., Zhang, Y., Lu, X., Bloom, A. A., Bowman, K. W., Worden, J. R., and Parker, R. J.: 2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane, Atmos. Chem. Phys., 21, 4339–4356, https://doi.org/10.5194/acp-21-4339-2021, 2021.
Maasakkers, J. D., McDuffie, E. E., Sulprizio, M. P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, D. J., Jeong, S., Irving, B., and Weitz, M.: A Gridded Inventory of Annual 2012–2018 U.S. Anthropogenic Methane Emissions, Environ. Sci. Technol., 57, 16276–16288, https://doi.org/10.1021/acs.est.3c05138, 2023.
Miller, S. M., Wofsy, S. C., Michalak, A. M., Kort, E. A., Andrews, A. E., Biraud, S. C., Dlugokencky, E. J., Eluszkiewicz, J., Fischer, M. L., Janssens-Maenhout, G., Miller, B. R., Miller, J. B., Montzka, S. A., Nehrkorn, T., and Sweeney, C.: Anthropogenic emissions of methane in the United States, P. Natl. Acad. Sci. USA, 110, 20018–20022, https://doi.org/10.1073/pnas.1314392110, 2013.
National Centers for Environmental Information (NCEI): Climate at a Glance | Statewide Time Series, https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/statewide/time-series/3/pcp/3/8/2019-2025, last access: 30 December 2025.
Nesser, H., Jacob, D. J., Maasakkers, J. D., Lorente, A., Chen, Z., Lu, X., Shen, L., Qu, Z., Sulprizio, M. P., Winter, M., Ma, S., Bloom, A. A., Worden, J. R., Stavins, R. N., and Randles, C. A.: High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills, Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024, 2024.
New Mexico Oil Conservation Commission: NM Natural Gas Waste Rule, New Mexico Administrative Code, 19.15.27.8 NMAC, https://www.srca.nm.gov/parts/title19/19.015.0027.html (last access: 25 September 2025), 2021.
Omara, M., Himmelberger, A., MacKay, K., Williams, J. P., Benmergui, J., Sargent, M., Wofsy, S. C., and Gautam, R.: Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021), Earth Syst. Sci. Data, 16, 3973–3991, https://doi.org/10.5194/essd-16-3973-2024, 2024.
Parker, R. J., Webb, A., Boesch, H., Somkuti, P., Barrio Guillo, R., Di Noia, A., Kalaitzi, N., Anand, J. S., Bergamaschi, P., Chevallier, F., Palmer, P. I., Feng, L., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Kivi, R., Morino, I., Notholt, J., Oh, Y.-S., Ohyama, H., Petri, C., Pollard, D. F., Roehl, C., Sha, M. K., Shiomi, K., Strong, K., Sussmann, R., Té, Y., Velazco, V. A., Warneke, T., Wennberg, P. O., and Wunch, D.: A decade of GOSAT Proxy satellite CH4 observations, Earth Syst. Sci. Data, 12, 3383–3412, https://doi.org/10.5194/essd-12-3383-2020, 2020.
Pendergrass, D. C., Jacob, D. J., Balasus, N., Estrada, L., Varon, D. J., East, J. D., He, M., Mooring, T. A., Penn, E., Nesser, H., and Worden, J. R.: Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations, Atmos. Chem. Phys., 25, 14353–14369, https://doi.org/10.5194/acp-25-14353-2025, 2025.
Penn, E., Jacob, D. J., Bon, D. M., Howell, K., O’Neill, K., Scarpelli, T. R., Chen, Z., Field, R. A., Karacan, C. Ö., Roy, E., and Cusworth, D. H.: Remote sensing enables basin-scale inventories of coal mine methane, Environ. Sci. Technol., 60, 14924–14933, https://doi.org/10.1021/acs.est.5c14976, 2026.
Pennsylvania DEP: 2025 Pennsylvania Greenhouse Gas Inventory Report, Pennsylvania Department of Environmental Protection, https://www.pa.gov/agencies/dep/residents/climate-change/ghg-inventory (last access: 24 November 2025), 2025.
Randerson, J. T., Van Der Werf, G. R., Giglio, L., Collatz, G. J., and Kasibhatla, P. S.: Global Fire Emissions Database, Version 4.1 (GFEDv4), ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/1293, 2017.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and Practice, WORLD SCIENTIFIC, https://doi.org/10.1142/3171, 2000.
Runkle, B. R. K., Suvočarev, K., Reba, M. L., Reavis, C. W., Smith, S. F., Chiu, Y.-L., and Fong, B.: Methane Emission Reductions from the Alternate Wetting and Drying of Rice Fields Detected Using the Eddy Covariance Method, Environ. Sci. Technol., 53, 671–681, https://doi.org/10.1021/acs.est.8b05535, 2019.
Saunois, M., Martinez, A., Poulter, B., Zhang, Z., Raymond, P. A., Regnier, P., Canadell, J. G., Jackson, R. B., Patra, P. K., Bousquet, P., Ciais, P., Dlugokencky, E. J., Lan, X., Allen, G. H., Bastviken, D., Beerling, D. J., Belikov, D. A., Blake, D. R., Castaldi, S., Crippa, M., Deemer, B. R., Dennison, F., Etiope, G., Gedney, N., Höglund-Isaksson, L., Holgerson, M. A., Hopcroft, P. O., Hugelius, G., Ito, A., Jain, A. K., Janardanan, R., Johnson, M. S., Kleinen, T., Krummel, P. B., Lauerwald, R., Li, T., Liu, X., McDonald, K. C., Melton, J. R., Mühle, J., Müller, J., Murguia-Flores, F., Niwa, Y., Noce, S., Pan, S., Parker, R. J., Peng, C., Ramonet, M., Riley, W. J., Rocher-Ros, G., Rosentreter, J. A., Sasakawa, M., Segers, A., Smith, S. J., Stanley, E. H., Thanwerdas, J., Tian, H., Tsuruta, A., Tubiello, F. N., Weber, T. S., van der Werf, G. R., Worthy, D. E. J., Xi, Y., Yoshida, Y., Zhang, W., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: Global Methane Budget 2000–2020, Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, 2025.
Scarpelli, T. R., Jacob, D. J., Villasana, C. A. O., Hernández, I. F. R., Moreno, P. R. C., Alfaro, E. A. C., García, M. Á. G., and Zavala-Araiza, D.: A gridded inventory of anthropogenic methane emissions from Mexico based on Mexico's national inventory of greenhouse gases and compounds, Environ. Res. Lett., 15, 105015, https://doi.org/10.1088/1748-9326/abb42b, 2020.
Scarpelli, T. R., Jacob, D. J., Moran, M., Reuland, F., and Gordon, D.: A gridded inventory of Canada's anthropogenic methane emissions, Environ. Res. Lett., 17, 014007, https://doi.org/10.1088/1748-9326/ac40b1, 2022.
Schneising, O., Burrows, J. P., Dickerson, R. R., Buchwitz, M., Reuter, M., and Bovensmann, H.: Remote sensing of fugitive methane emissions from oil and gas production in North American tight geologic formations, Earth's Future, 2, 548–558, https://doi.org/10.1002/2014EF000265, 2014.
Schneising, O., Buchwitz, M., Reuter, M., Vanselow, S., Bovensmann, H., and Burrows, J. P.: Remote sensing of methane leakage from natural gas and petroleum systems revisited, Atmos. Chem. Phys., 20, 9169–9182, https://doi.org/10.5194/acp-20-9169-2020, 2020.
Schuldt, K. N., Aalto, T., Andrade, M., et al.: Multi-laboratory compilation of atmospheric methane data for the period 1981–2024, NOAA Earth System Research Laboratory, Global Monitoring Laboratory, https://doi.org/10.25925/20241001, 2024.
Shen, L., Gautam, R., Omara, M., Zavala-Araiza, D., Maasakkers, J. D., Scarpelli, T. R., Lorente, A., Lyon, D., Sheng, J., Varon, D. J., Nesser, H., Qu, Z., Lu, X., Sulprizio, M. P., Hamburg, S. P., and Jacob, D. J.: Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins, Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, 2022.
Shen, L., Jacob, D. J., Gautam, R., Omara, M., Scarpelli, T. R., Lorente, A., Zavala-Araiza, D., Lu, X., Chen, Z., and Lin, J.: National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations, Nat. Commun., 14, 4948, https://doi.org/10.1038/s41467-023-40671-6, 2023.
Sheng, J.-X., Jacob, D. J., Turner, A. J., Maasakkers, J. D., Benmergui, J., Bloom, A. A., Arndt, C., Gautam, R., Zavala-Araiza, D., Boesch, H., and Parker, R. J.: 2010–2016 methane trends over Canada, the United States, and Mexico observed by the GOSAT satellite: contributions from different source sectors, Atmos. Chem. Phys., 18, 12257–12267, https://doi.org/10.5194/acp-18-12257-2018, 2018.
Somkuti, P., McGarragh, G., O'Dell, C., Di Noia, A., Vogel, L., Crowell, S., Ott, L. E., and Bösch, H.: Surface reflectance biases in XCH4 retrievals from the 2.3 µm band are enhanced in the presence of aerosols, Atmos. Meas. Tech., 18, 4647–4663, https://doi.org/10.5194/amt-18-4647-2025, 2025.
The International GEOS-Chem User Community: geoschem/GCClassic: GCClassic 14.4.1 (Version 14.4.1), Zenodo [computer software], https://doi.org/10.5281/zenodo.12584192, 2024.
Turner, A. J., Jacob, D. J., Wecht, K. J., Maasakkers, J. D., Lundgren, E., Andrews, A. E., Biraud, S. C., Boesch, H., Bowman, K. W., Deutscher, N. M., Dubey, M. K., Griffith, D. W. T., Hase, F., Kuze, A., Notholt, J., Ohyama, H., Parker, R., Payne, V. H., Sussmann, R., Sweeney, C., Velazco, V. A., Warneke, T., Wennberg, P. O., and Wunch, D.: Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data, Atmos. Chem. Phys., 15, 7049–7069, https://doi.org/10.5194/acp-15-7049-2015, 2015.
Turner, A. J., Jacob, D. J., Benmergui, J., Wofsy, S. C., Maasakkers, J. D., Butz, A., Hasekamp, O., and Biraud, S. C.: A large increase in U.S. methane emissions over the past decade inferred from satellite data and surface observations, Geophys. Res. Lett., 43, 2218–2224, https://doi.org/10.1002/2016GL067987, 2016.
Twyman, M., Hill, S., Banta, K., and Billingsley, R.: 2023 Colorado Statewide Inventory of Greenhouse Gas Emissions and Sinks, Colorado Department of Public Health and Environment, https://cdphe.colorado.gov/apcd/greenhouse-gas-inventory (last access: 24 November 2025), 2024.
U.S. EIA: Natural gas gross withdrawals and production, U.S. Energy Information Administration, https://www.eia.gov/dnav/ng/ng_prod_sum_a_epg0_vgm_mmcf_a.htm (last access: 3 September 2025), 2025.
U.S. Greenhouse Gas (GHC) Center: https://earth.gov/ghgcenter, last access: 2 May 2025.
USDA NASS: Quick Stats Database, https://quickstats.nass.usda.gov/ (last access: 2 September 2025), 2025.
Varon, D. J., Jacob, D. J., Sulprizio, M., Estrada, L. A., Downs, W. B., Shen, L., Hancock, S. E., Nesser, H., Qu, Z., Penn, E., Chen, Z., Lu, X., Lorente, A., Tewari, A., and Randles, C. A.: Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations, Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, 2022.
Varon, D. J., Jacob, D. J., Hmiel, B., Gautam, R., Lyon, D. R., Omara, M., Sulprizio, M., Shen, L., Pendergrass, D., Nesser, H., Qu, Z., Barkley, Z. R., Miles, N. L., Richardson, S. J., Davis, K. J., Pandey, S., Lu, X., Lorente, A., Borsdorff, T., Maasakkers, J. D., and Aben, I.: Continuous weekly monitoring of methane emissions from the Permian Basin by inversion of TROPOMI satellite observations, Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023, 2023.
Varon, D. J., Jacob, D. J., Estrada, L. A., Balasus, N., East, J. D., Pendergrass, D. C., Chen, Z., Sulprizio, M., Omara, M., Gautam, R., Barkley, Z. R., Saldaña, F. J. C., Reidy, E. K., Kamdar, H., Sherwin, E. D., Biraud, S. C., Jervis, D., Pandey, S., Worden, J. R., Bowman, K. W., Maasakkers, J. D., and Kleinberg, R. L.: Seasonality and Declining Intensity of Methane Emissions from the Permian and Nearby US Oil and Gas Basins, Environ. Sci. Technol., https://doi.org/10.1021/acs.est.5c08745, 2025.
Wang, X., Jacob, D. J., Nesser, H., Balasus, N., Estrada, L., Sulprizio, M., Cusworth, D. H., Scarpelli, T. R., Chen, Z., East, J. D., and Varon, D. J.: Quantifying urban and landfill methane emissions in the United States using TROPOMI satellite data, arXiv [preprint], https://doi.org/10.48550/arXiv.2505.10835, 2025.
Worden, J. R., Cusworth, D. H., Qu, Z., Yin, Y., Zhang, Y., Bloom, A. A., Ma, S., Byrne, B. K., Scarpelli, T., Maasakkers, J. D., Crisp, D., Duren, R., and Jacob, D. J.: The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates, Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022, 2022.
Yuan, B., Kaser, L., Karl, T., Graus, M., Peischl, J., Campos, T. L., Shertz, S., Apel, E. C., Hornbrook, R. S., Hills, A., Gilman, J. B., Lerner, B. M., Warneke, C., Flocke, F. M., Ryerson, T. B., Guenther, A. B., and de Gouw, J. A.: Airborne flux measurements of methane and volatile organic compounds over the Haynesville and Marcellus shale gas production regions, J. Geophys. Res.-Atmos., 120, 6271–6289, https://doi.org/10.1002/2015JD023242, 2015.
Zhang, L., Liu, L., Zhao, Y., Gong, S., Zhang, X., Henze, D. K., Capps, S. L., Fu, T.-M., Zhang, Q., and Wang, Y.: Source attribution of particulate matter pollution over North China with the adjoint method, Environ. Res. Lett., 10, 084011, https://doi.org/10.1088/1748-9326/10/8/084011, 2015.
Zhang, Y., Gautam, R., Pandey, S., Omara, M., Maasakkers, J. D., Sadavarte, P., Lyon, D., Nesser, H., Sulprizio, M. P., Varon, D. J., Zhang, R., Houweling, S., Zavala-Araiza, D., Alvarez, R. A., Lorente, A., Hamburg, S. P., Aben, I., and Jacob, D. J.: Quantifying methane emissions from the largest oil-producing basin in the United States from space, Sci. Adv., 6, eaaz5120, https://doi.org/10.1126/sciadv.aaz5120, 2020.
Zhao, C., Andrews, A. E., Bianco, L., Eluszkiewicz, J., Hirsch, A., MacDonald, C., Nehrkorn, T., and Fischer, M. L.: Atmospheric inverse estimates of methane emissions from Central California, J. Geophys. Res.-Atmos., 114, https://doi.org/10.1029/2008JD011671, 2009.
Short summary
We use satellite observations to track US methane emissions from 2019–2024 at national, state, and oil and gas field scales. While total emissions remain stable, oil and gas emissions decline even as production rises, indicating progress in emissions management for the oil and gas sector.
We use satellite observations to track US methane emissions from 2019–2024 at national, state,...
Altmetrics
Final-revised paper
Preprint