High-resolution U.S. methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills
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J. Maasakkers | M. Sulprizio | H. Nesser | A. Lorente | C. Randles | Lu Shen | D. Jacob | A. A. Bloom | Z. Qu | Zichong Chen | Xiao Lu | Margaux Winter | Robert Stavins | Shuang Ma | R. Worden
[1] M. Omara,et al. Continuous weekly monitoring of methane emissions from the Permian Basin by inversion of TROPOMI satellite observations , 2023, Atmospheric Chemistry and Physics.
[2] J. Maasakkers,et al. Observation-derived 2010-2019 trends in methane emissions and intensities from US oil and gas fields tied to activity metrics , 2023, Proceedings of the National Academy of Sciences of the United States of America.
[3] T. Borsdorff,et al. Investigating high methane emissions from urban areas detected by TROPOMI and their association with untreated wastewater , 2023, Environmental Research Letters.
[4] Mark M. Falinski,et al. Underestimation of Sector-Wide Methane Emissions from United States Wastewater Treatment , 2023, Environmental science & technology.
[5] M. Zondlo,et al. Methane Emissions from Municipal Wastewater Collection and Treatment Systems , 2023, Environmental science & technology.
[6] R. Weiss,et al. A declining trend of methane emissions in the Los Angeles basin from 2015 to 2020 , 2023, Environmental Research Letters.
[7] J. Sheng,et al. Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins , 2022, Atmospheric Chemistry and Physics.
[8] M. Sulprizio,et al. Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations , 2022, Atmospheric Chemistry and Physics.
[9] M. J. Schwab,et al. Hydrogen Sulfide Emission Properties from Two Large Landfills in New York State , 2022, Atmosphere.
[10] R. Gautam,et al. Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane , 2022, Atmospheric Chemistry and Physics.
[11] Evan D. Sherwin,et al. Quantifying Regional Methane Emissions in the New Mexico Permian Basin with a Comprehensive Aerial Survey. , 2022, Environmental science & technology.
[12] R. Gautam,et al. 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 , 2022, Atmospheric Chemistry and Physics.
[13] G. Balsamo,et al. Quantification of methane emissions from hotspots and during COVID-19 using a global atmospheric inversion , 2022, Atmospheric Chemistry and Physics.
[14] J. Maasakkers,et al. Evaluating urban methane emissions from space using TROPOMI methane and carbon monoxide observations , 2022, Remote Sensing of Environment.
[15] D. Jacob,et al. A gridded inventory of Canada’s anthropogenic methane emissions , 2021, Environmental Research Letters.
[16] J. Maasakkers,et al. The 2019 Methane Budget And Uncertainties At 1 Degree Resolution And Each Country Through Bayesian Integration Of GOSAT Total Column Methane Data And A Priori Inventory Estimates , 2021, Atmospheric Chemistry and Physics.
[17] S. Wofsy,et al. Majority of US urban natural gas emissions unaccounted for in inventories , 2021, Proceedings of the National Academy of Sciences.
[18] H. Eskes,et al. A New Divergence Method to Quantify Methane Emissions Using Observations of Sentinel‐5P TROPOMI , 2021, Geophysical Research Letters.
[19] J. Sheng,et al. AGU Advances Authors’ Response to Peer Review Comments on Satellite constraints on the latitudinal distribution and temperature sensitivity of wetland methane emissions , 2021 .
[20] Atul K. Jain,et al. Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions , 2021, Earth System Science Data.
[21] Chris H. Rycroft,et al. Reduced-cost construction of Jacobian matrices for high-resolution inversions of satellite observations of atmospheric composition , 2021, Atmospheric Measurement Techniques.
[22] J. Maasakkers,et al. Supplementary material to "Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments" , 2021, Atmospheric Chemistry and Physics.
[23] I. Aben,et al. Systematic detection of local CH4 anomalies by combining satellite measurements with high-resolution forecasts , 2021, Atmospheric Chemistry and Physics.
[24] J. Sheng,et al. 2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane , 2021 .
[25] T. Borsdorff,et al. Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements , 2021 .
[26] P. Shepson,et al. Relative flux measurements of biogenic and natural gas-derived methane for seven U.S. cities , 2021, Elementa: Science of the Anthropocene.
[27] Chris H. Rycroft,et al. Reduced-Cost Construction of Jacobian Matrices for High-Resolution Inversions of Satellite Observations of Atmospheric Composition , 2020 .
[28] J. Sheng,et al. Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations , 2020, Atmospheric Chemistry and Physics.
[29] D. Jacob,et al. A gridded inventory of anthropogenic methane emissions from Mexico based on Mexico’s national inventory of greenhouse gases and compounds , 2020, Environmental Research Letters.
[30] E. Kort,et al. Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions , 2020, Atmospheric chemistry and physics.
[31] Z. Barkley,et al. Background heterogeneity and other uncertainties in estimating urban methane flux: results from the Indianapolis Flux Experiment (INFLUX) , 2020 .
[32] O. Schneising,et al. Remote sensing of methane leakage from natural gas and petroleum systems revisited , 2020, Atmospheric Chemistry and Physics.
[33] M. Omara,et al. Quantifying methane emissions from the largest oil-producing basin in the United States from space , 2020, Science Advances.
[34] R. Duren,et al. Synthesis of Methane Observations Across Scales: Strategies for Deploying a Multitiered Observing Network , 2020, Geophysical Research Letters.
[35] John C. Lin,et al. Daily Satellite Observations of Methane from Oil and Gas Production Regions in the United States , 2020, Scientific Reports.
[36] Brian D. Bue,et al. California’s methane super-emitters , 2019, Nature.
[37] E. Kort,et al. Large Fugitive Methane Emissions From Urban Centers Along the U.S. East Coast , 2019, Geophysical research letters.
[38] R. Weiss,et al. Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity , 2019, Journal of Geophysical Research: Atmospheres.
[39] D. Blake,et al. Source Apportionment of Ambient Methane Enhancements in Los Angeles, California, To Evaluate Emission Inventory Estimates. , 2019, Environmental science & technology.
[40] J. Sheng,et al. Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane , 2018, Atmospheric Chemistry and Physics.
[41] Haili Hu,et al. Toward Global Mapping of Methane With TROPOMI: First Results and Intersatellite Comparison to GOSAT , 2018 .
[42] C. Sweeney,et al. Source Partitioning of Methane Emissions and its Seasonality in the U.S. Midwest , 2018, Journal of geophysical research. Biogeosciences.
[43] Peter Bergamaschi,et al. EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012 , 2017, Earth System Science Data.
[44] D. Jacob,et al. A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0) , 2017 .
[45] E. Kort,et al. Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The San Joaquin Valley , 2017 .
[46] R. Weiss,et al. Estimating methane emissions in California's urban and rural regions using multitower observations , 2016 .
[47] J. Maasakkers,et al. Gridded National Inventory of U.S. Methane Emissions. , 2016, Environmental science & technology.
[48] Geoffrey C. Toon,et al. Quantifying the loss of processed natural gas within California's South Coast Air Basin using long-term measurements of ethane and methane , 2016 .
[49] Daven K. Henze,et al. Optimal and scalable methods to approximate the solutions of large‐scale Bayesian problems: theory and application to atmospheric inversion and data assimilation , 2016, ArXiv.
[50] Riley M. Duren,et al. Mitigation of methane emissions in cities: How new measurements and partnerships can contribute to emissions reduction strategies , 2016 .
[51] Karen C. Seto,et al. Climate change: Track urban emissions on a human scale , 2015, Nature.
[52] E. Kort,et al. Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The South Coast Air Basin , 2015 .
[53] Gabrielle Pétron,et al. Aircraft-Based Estimate of Total Methane Emissions from the Barnett Shale Region. , 2015, Environmental science & technology.
[54] Hartmut Boesch,et al. Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data , 2015 .
[55] J. Bogner,et al. From California dreaming to California data: Challenging historic models for landfill CH4 emissions , 2015 .
[56] Kevin R. Gurney,et al. Quantification and source apportionment of the methane emission flux from the city of Indianapolis , 2015 .
[57] John R. Worden,et al. Spatially resolving methane emissions in California: constraints from the CalNex aircraft campaign and from present (GOSAT, TES) and future (TROPOMI, geostationary) satellite observations , 2014 .
[58] Zhe Jiang,et al. Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data , 2014 .
[59] E. Kort,et al. Mapping CH 4 : CO 2 ratios in Los Angeles with CLARS-FTS from Mount Wilson, California , 2014 .
[60] Scot M. Miller,et al. Anthropogenic emissions of methane in the United States , 2013, Proceedings of the National Academy of Sciences.
[61] Peter Bergamaschi,et al. A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements , 2013 .
[62] R. Martin,et al. Emissions estimation from satellite retrievals: A review of current capability , 2013 .
[63] R. Lucchesi,et al. File Specification for GEOS-5 FP (Forward Processing) , 2013 .
[64] M. Wall. Bay Area Air Quality Management District (BAAQMD) Naturally Occurring Asbestos Regulations and Enforcement Protocols , 2012 .
[65] K. Seto,et al. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools , 2012, Proceedings of the National Academy of Sciences.
[66] Henk Eskes,et al. TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications , 2012 .
[67] Rebecca Castano,et al. A method for evaluating bias in global measurements of CO 2 total columns from space , 2011 .
[68] Frédéric Chevallier,et al. Impact of correlated observation errors on inverted CO2 surface fluxes from OCO measurements , 2007 .
[69] Y. Calisesi,et al. Regridding of remote soundings: Formulation and application to ozone profile comparison , 2005 .
[70] John C. Gille,et al. Comparative inverse analysis of satellite (MOPITT) and aircraft (TRACE-P) observations to estimate Asian sources of carbon monoxide , 2004 .
[71] Clive D Rodgers,et al. Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .
[72] William Lane Austin,et al. The Census of Agriculture , 1930 .
[73] J. Maasakkers,et al. A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases , 2023, Atmospheric Measurement Techniques.
[74] O. Hasekamp,et al. Algorithm Theoretical Baseline Document for Sentinel-5 Precursor Methane Retrieval[1cm] Otto Hasekamp, Alba Lorente, Haili Hu, Andre Butz, Joost aan de Brugh, Jochen Landgraf , 2019 .
[75] J. Sheng,et al. Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015 , 2019 .
[76] S. Houweling,et al. Algorithm Theoretical Baseline Document for Sentinel-5 Precursor: Carbon Monoxide Total Column Retrieval[1cm] Jochen Landgraf, Joost aan de Brugh, Remco A. Scheepmaker, [1mm] Tobias Borsdorff, Sander Houweling, Otto P. Hasekamp , 2018 .