186 citations as recorded by crossref.

1. Top‐Down Constraints on Methane Point Source Emissions From Animal Agriculture and Waste Based on New Airborne Measurements in the U.S. Upper Midwest X. Yu et al. 10.1029/2019JG005429
2. Investigating high methane emissions from urban areas detected by TROPOMI and their association with untreated wastewater B. de Foy et al. 10.1088/1748-9326/acc118
3. Development of a continuous UAV-mounted air sampler and application to the quantification of CO2 and CH4 emissions from a major coking plant T. Han et al. 10.5194/amt-17-677-2024
4. Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach M. Hemati et al. 10.1038/s41598-024-58995-8
5. Revised methane emissions factors and spatially distributed annual carbon fluxes for global livestock J. Wolf et al. 10.1186/s13021-017-0084-y
6. A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0) A. Bloom et al. 10.5194/gmd-10-2141-2017
7. Deep-Learning-Based Gas Leak Source Localization From Sparse Sensor Data D. Badawi et al. 10.1109/JSEN.2022.3202134
8. Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions Z. Deng et al. 10.5194/essd-14-1639-2022
9. Methane Emissions from Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite Observations P. Sadavarte et al. 10.1021/acs.est.1c03976
10. Balancing aggregation and smoothing errors in inverse models A. Turner & D. Jacob 10.5194/acp-15-7039-2015
11. Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations R. Janardanan et al. 10.3390/rs12030375
12. Estimating 2010–2015 anthropogenic and natural methane emissions in Canada using ECCC surface and GOSAT satellite observations S. Baray et al. 10.5194/acp-21-18101-2021
13. 2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane J. Maasakkers et al. 10.5194/acp-21-4339-2021
14. Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications M. Reuter et al. 10.5194/amt-13-789-2020
15. Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling S. Miller et al. 10.5194/bg-13-1329-2016
16. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. 10.3390/rs14020371
17. Minimum detection limits of the TROPOMI satellite sensor across North America and their implications for measuring oil and gas methane emissions L. Dubey et al. 10.1016/j.scitotenv.2023.162222
18. Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG A. Thorpe et al. 10.5194/amt-10-3833-2017
19. Detection of Methane Emission from a Local Source Using GOSAT Target Observations A. Kuze et al. 10.3390/rs12020267
20. Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories F. Wang et al. 10.3390/rs11212489
21. A high-resolution (0.1° × 0.1°) inventory of methane emissions from Canadian and Mexican oil and gas systems J. Sheng et al. 10.1016/j.atmosenv.2017.02.036
22. Large Methane Emission Fluxes Observed From Tropical Wetlands in Zambia J. Shaw et al. 10.1029/2021GB007261
23. Underestimates of methane from intensively raised animals could undermine goals of sustainable development M. Hayek & S. Miller 10.1088/1748-9326/ac02ef
24. Atmospheric Carbon and Transport – America (ACT‐America) Data Sets: Description, Management, and Delivery Y. Wei et al. 10.1029/2020EA001634
25. A large increase in U.S. methane emissions over the past decade inferred from satellite data and surface observations A. Turner et al. 10.1002/2016GL067987
26. Assimilation of satellite NO<sub>2</sub> observations at high spatial resolution using OSSEs X. Liu et al. 10.5194/acp-17-7067-2017
27. Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors Z. Lv et al. 10.3390/atmos11030231
28. Emission Monitoring Mobile Experiment (EMME): an overview and first results of the St. Petersburg megacity campaign 2019 M. Makarova et al. 10.5194/amt-14-1047-2021
29. A 15-year record of CO emissions constrained by MOPITT CO observations Z. Jiang et al. 10.5194/acp-17-4565-2017
30. Comparing mass balance and adjoint methods for inverse modeling of nitrogen dioxide columns for global nitrogen oxide emissions M. Cooper et al. 10.1002/2016JD025985
31. Satellite Constraints on the Latitudinal Distribution and Temperature Sensitivity of Wetland Methane Emissions S. Ma et al. 10.1029/2021AV000408
32. A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases N. Balasus et al. 10.5194/amt-16-3787-2023
33. How well can inverse analyses of high-resolution satellite data resolve heterogeneous methane fluxes? Observing system simulation experiments with the GEOS-Chem adjoint model (v35) X. Yu et al. 10.5194/gmd-14-7775-2021
34. Inverse modeling of GOSAT-retrieved ratios of total column CH<sub>4</sub> and CO<sub>2</sub> for 2009 and 2010 S. Pandey et al. 10.5194/acp-16-5043-2016
35. Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? Z. Tan et al. 10.5194/acp-16-12649-2016
36. Influences of Uncertainties in the STT Flux on Modeled Tropospheric Methane Z. Wang 10.1029/2023JD039107
37. Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. 10.5194/acp-16-14371-2016
38. Contribution of oil and natural gas production to renewed increase in atmospheric methane (2007–2014): top–down estimate from ethane and methane column observations P. Hausmann et al. 10.5194/acp-16-3227-2016
39. Atmospheric CO and CH<sub>4</sub> time series and seasonal variations on Reunion Island from ground-based in situ and FTIR (NDACC and TCCON) measurements M. Zhou et al. 10.5194/acp-18-13881-2018
40. Mineralization of organic matter in boreal lake sediments: rates, pathways, and nature of the fermenting substrates F. Clayer et al. 10.5194/bg-17-4571-2020
41. Constraints on methane emissions in North America from future geostationary remote-sensing measurements N. Bousserez et al. 10.5194/acp-16-6175-2016
42. Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone B. Duncan et al. 10.1029/2019RG000652
43. An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data M. Lunt et al. 10.5194/acp-19-14721-2019
44. Comparison of Methane Detection Using Shortwave and Longwave Infrared Hyperspectral Sensors Under Varying Environmental Conditions L. Zimmerman & J. Kerekes 10.1109/JSTARS.2023.3247246
45. High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills H. Nesser et al. 10.5194/acp-24-5069-2024
46. Comparative analysis of low-Earth orbit (TROPOMI) and geostationary (GeoCARB, GEO-CAPE) satellite instruments for constraining methane emissions on fine regional scales: application to the Southeast US J. Sheng et al. 10.5194/amt-11-6379-2018
47. Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources R. Bun et al. 10.1007/s11027-018-9791-2
48. What are the greenhouse gas observing system requirements for reducing fundamental biogeochemical process uncertainty? Amazon wetland CH<sub>4</sub> emissions as a case study A. Bloom et al. 10.5194/acp-16-15199-2016
49. The challenge of reconciling bottom-up agricultural methane emissions inventories with top-down measurements R. Desjardins et al. 10.1016/j.agrformet.2017.09.003
50. Methyl, Ethyl, and Propyl Nitrates: Global Distribution and Impacts on Reactive Nitrogen in Remote Marine Environments J. Fisher et al. 10.1029/2018JD029046
51. Rain-fed pulses of methane from East Africa during 2018–2019 contributed to atmospheric growth rate M. Lunt et al. 10.1088/1748-9326/abd8fa
52. Detecting high-emitting methane sources in oil/gas fields using satellite observations D. Cusworth et al. 10.5194/acp-18-16885-2018
53. Bias Correction of the Ratio of Total Column CH4 to CO2 Retrieved from GOSAT Spectra H. Oshio et al. 10.3390/rs12193155
54. Using Multiscale Ethane/Methane Observations to Attribute Coal Mine Vent Emissions in the San Juan Basin From 2013 to 2021 A. Meyer et al. 10.1029/2022JD037092
55. Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model I. Stanevich et al. 10.5194/gmd-13-3839-2020
56. A multiyear estimate of methane fluxes in Alaska from CARVE atmospheric observations S. Miller et al. 10.1002/2016GB005419
57. Evaluation of single-footprint AIRS CH<sub>4</sub> profile retrieval uncertainties using aircraft profile measurements S. Kulawik et al. 10.5194/amt-14-335-2021
58. An integrated analysis of contemporary methane emissions and concentration trends over China using in situ and satellite observations and model simulations H. Tan et al. 10.5194/acp-22-1229-2022
59. 2010–2016 methane trends over Canada, the United States, and Mexico observed by the GOSAT satellite: contributions from different source sectors J. Sheng et al. 10.5194/acp-18-12257-2018
60. On Methane Leaks from Pipelines in Bryan and College Station, Texas, USA K. Koch et al. 10.2174/1874282301610010056
61. The recent increase of atmospheric methane from 10 years of ground-based NDACC FTIR observations since 2005 W. Bader et al. 10.5194/acp-17-2255-2017
62. Assessment of Anthropogenic Methane Emissions over Large Regions Based on GOSAT Observations and High Resolution Transport Modeling R. Janardanan et al. 10.3390/rs9090941
63. Methane emissions are predominantly responsible for record-breaking atmospheric methane growth rates in 2020 and 2021 L. Feng et al. 10.5194/acp-23-4863-2023
64. Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales A. Turner et al. 10.5194/acp-18-8265-2018
65. National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations L. Shen et al. 10.1038/s41467-023-40671-6
66. Spatial and temporal change patterns of near-surface CO2 and CH4 concentrations in different permafrost regions on the Mongolian Plateau from 2010 to 2017 S. Adiya et al. 10.1016/j.scitotenv.2021.149433
67. Quantification of CH<sub>4</sub> coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO<sub>2</sub> and Methane (CoMet) campaign S. Krautwurst et al. 10.5194/acp-21-17345-2021
68. Recovery of sparse urban greenhouse gas emissions B. Zanger et al. 10.5194/gmd-15-7533-2022
69. Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale S. Jongaramrungruang et al. 10.5194/amt-14-7999-2021
70. Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments Z. Qu et al. 10.5194/acp-21-14159-2021
71. Characterizing model errors in chemical transport modeling of methane: using GOSAT XCH<sub>4</sub> data with weak-constraint four-dimensional variational data assimilation I. Stanevich et al. 10.5194/acp-21-9545-2021
72. A high-resolution satellite-based map of global methane emissions reveals missing wetland, fossil fuel, and monsoon sources X. Yu et al. 10.5194/acp-23-3325-2023
73. A critical review of green growth indicators in G7 economies from 1990 to 2019 K. Herman et al. 10.1007/s11625-023-01397-y
74. Characterisation of methane variability and trends from near-infrared solar spectra over Hefei, China Y. Tian et al. 10.1016/j.atmosenv.2017.11.001
75. Retrieval of Metop-A/IASI N2O Profiles and Validation with NDACC FTIR Data B. Barret et al. 10.3390/atmos12020219
76. Conversion of surface CH4 concentrations from GOSAT satellite observations using XGBoost algorithm Y. Wan et al. 10.1016/j.atmosenv.2023.119694
77. Simultaneous shipborne measurements of CO<sub>2</sub>, CH<sub>4</sub> and CO and their application to improving greenhouse-gas flux estimates in Australia B. Bukosa et al. 10.5194/acp-19-7055-2019
78. Methane emissions from dairies in the Los Angeles Basin C. Viatte et al. 10.5194/acp-17-7509-2017
79. MethaNet – An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery S. Jongaramrungruang et al. 10.1016/j.rse.2021.112809
80. A gridded inventory of anthropogenic methane emissions from Mexico based on Mexico’s national inventory of greenhouse gases and compounds T. Scarpelli et al. 10.1088/1748-9326/abb42b
81. Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate L. Feng et al. 10.1038/s41467-022-28989-z
82. Investigation of Spectral Bands and Sensor Parameters for Methane Emission Detection Imaging Spectrometer H. Varchand et al. 10.1016/j.jqsrt.2024.109100
83. Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use F. Wang et al. 10.1038/s41598-022-19462-4
84. Tropospheric emissions: Monitoring of pollution (TEMPO) P. Zoogman et al. 10.1016/j.jqsrt.2016.05.008
85. Revisiting global fossil fuel and biofuel emissions of ethane Z. Tzompa‐Sosa et al. 10.1002/2016JD025767
86. Methane emissions from underground gas storage in California A. Thorpe et al. 10.1088/1748-9326/ab751d
87. Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases E. Solazzo et al. 10.5194/acp-21-5655-2021
88. Interpreting contemporary trends in atmospheric methane A. Turner et al. 10.1073/pnas.1814297116
89. Monitoring global tropospheric OH concentrations using satellite observations of atmospheric methane Y. Zhang et al. 10.5194/acp-18-15959-2018
90. Cattle, climate and complexity: food security, quality and sustainability of the Australian cattle industries I. Lean & P. Moate 10.1111/avj.13072
91. Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations D. Varon et al. 10.5194/gmd-15-5787-2022
92. A simple and quick sensitivity analysis method for methane isotopologues detection with GOSAT-TANSO-FTS E. Malina et al. 10.14324/111.444/ucloe.000013
93. Hydrocarbon Tracers Suggest Methane Emissions from Fossil Sources Occur Predominately Before Gas Processing and That Petroleum Plays Are a Significant Source A. Tribby et al. 10.1021/acs.est.2c00927
94. Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities A. Kuze et al. 10.1016/j.rse.2022.112966
95. Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions X. Yu et al. 10.5194/acp-21-951-2021
96. Large XCH<sub>4</sub> anomaly in summer 2013 over northeast Asia observed by GOSAT M. Ishizawa et al. 10.5194/acp-16-9149-2016
97. Methane emissions from a waste treatment site: Numerical analysis of aircraft-based data Y. Cai et al. 10.1016/j.agrformet.2020.108102
98. Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations Z. Qu et al. 10.1088/1748-9326/ac8754
99. Assessment of the climate commitments and additional mitigation policies of the United States J. Greenblatt & M. Wei 10.1038/nclimate3125
100. High-resolution inversion of methane emissions in the Southeast US using SEAC<sup>4</sup>RS aircraft observations of atmospheric methane: anthropogenic and wetland sources J. Sheng et al. 10.5194/acp-18-6483-2018
101. Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space A. Kuze et al. 10.5194/amt-9-2445-2016
102. Dairy Methane Emissions in California's San Joaquin Valley Inferred With Ground‐Based Remote Sensing Observations in the Summer and Winter S. Heerah et al. 10.1029/2021JD034785
103. Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ S. Voshtani et al. 10.3390/atmos14040758
104. Atmospheric methane isotopes identify inventory knowledge gaps in the Surat Basin, Australia, coal seam gas and agricultural regions B. Kelly et al. 10.5194/acp-22-15527-2022
105. Evaluating urban methane emissions from space using TROPOMI methane and carbon monoxide observations G. Plant et al. 10.1016/j.rse.2021.112756
106. Quantifying Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite Observations D. Varon et al. 10.1021/acs.est.0c01213
107. Assessing the Iterative Finite Difference Mass Balance and 4D‐Var Methods to Derive Ammonia Emissions Over North America Using Synthetic Observations C. Li et al. 10.1029/2018JD030183
108. CH4 Fluxes Derived from Assimilation of TROPOMI XCH4 in CarbonTracker Europe-CH4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes A. Tsuruta et al. 10.3390/rs15061620
109. A Coupled CH4, CO and CO2 Simulation for Improved Chemical Source Modeling B. Bukosa et al. 10.3390/atmos14050764
110. Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
111. Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution S. Zhu et al. 10.1007/s00376-022-1434-6
112. Artificial Intelligence Approach for Estimating Dairy Methane Emissions S. Jeong et al. 10.1021/acs.est.1c08802
113. Improvements of a low-cost CO2 commercial nondispersive near-infrared (NDIR) sensor for unmanned aerial vehicle (UAV) atmospheric mapping applications Y. Liu et al. 10.5194/amt-15-4431-2022
114. Seasonal variability of stratospheric methane: implications for constraining tropospheric methane budgets using total column observations K. Saad et al. 10.5194/acp-16-14003-2016
115. Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics S. Voshtani et al. 10.3390/rs14020375
116. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps G. Hurtt et al. 10.1088/1748-9326/ac7407
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118. Emerging reporting and verification needs under the Paris Agreement: How can the research community effectively contribute? L. Perugini et al. 10.1016/j.envsci.2021.04.012
119. Evaluating year-to-year anomalies in tropical wetland methane emissions using satellite CH4 observations R. Parker et al. 10.1016/j.rse.2018.02.011
120. Estimating methane emissions in California's urban and rural regions using multitower observations S. Jeong et al. 10.1002/2016JD025404
121. A gridded inventory of Canada’s anthropogenic methane emissions T. Scarpelli et al. 10.1088/1748-9326/ac40b1
122. The CHRONOS mission: capability for sub-hourly synoptic observations of carbon monoxide and methane to quantify emissions and transport of air pollution D. Edwards et al. 10.5194/amt-11-1061-2018
123. Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations F. Wang et al. 10.1088/1748-9326/abd352
124. The added value of satellite observations of methane forunderstanding the contemporary methane budget P. Palmer et al. 10.1098/rsta.2021.0106
125. Implications of Generation Efficiencies and Supply Chain Leaks for the Life Cycle Greenhouse Gas Emissions of Natural Gas-Fired Electricity in the United States S. Tavakkoli et al. 10.1021/acs.est.1c05246
126. Emissions of methane in Europe inferred by total column measurements D. Wunch et al. 10.5194/acp-19-3963-2019
127. U.S. CH4 emissions from oil and gas production: Have recent large increases been detected? L. Bruhwiler et al. 10.1002/2016JD026157
128. Methane emission reduction in China's natural gas industry: Construction of technology inventory and selection of optimal technology programs N. Li et al. 10.1016/j.spc.2023.12.002
129. Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method Y. Wang et al. 10.5194/essd-15-3597-2023
130. Quantifying lower tropospheric methane concentrations using GOSAT near-IR and TES thermal IR measurements J. Worden et al. 10.5194/amt-8-3433-2015
131. Global inverse modeling of CH<sub>4</sub> sources and sinks: an overview of methods S. Houweling et al. 10.5194/acp-17-235-2017
132. Estimating methane emissions from biological and fossil‐fuel sources in the San Francisco Bay Area S. Jeong et al. 10.1002/2016GL071794
133. Source Partitioning of Methane Emissions and its Seasonality in the U.S. Midwest Z. Chen et al. 10.1002/2017JG004356
134. Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations Y. Zhang et al. 10.5194/acp-21-3643-2021
135. Evaluation of column-averaged methane in models and TCCON with a focus on the stratosphere A. Ostler et al. 10.5194/amt-9-4843-2016
136. Quantifying sources of Brazil's CH<sub>4</sub> emissions between 2010 and 2018 from satellite data R. Tunnicliffe et al. 10.5194/acp-20-13041-2020
137. Atmospheric observations consistent with reported decline in the UK's methane emissions (2013–2020) M. Lunt et al. 10.5194/acp-21-16257-2021
138. Rates and pathways of sedimentary organic matter mineralization in two basins of a boreal lake: Emphasis on methanogenesis and methanotrophy F. Clayer et al. 10.1002/lno.10323
139. Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources K. Qin et al. 10.5194/acp-24-3009-2024
140. Contributions of the troposphere and stratosphere to CH<sub>4</sub> model biases Z. Wang et al. 10.5194/acp-17-13283-2017
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142. Gridded National Inventory of U.S. Methane Emissions J. Maasakkers et al. 10.1021/acs.est.6b02878
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144. Atmospheric observation-based estimation of fossil fuel CO2 emissions from regions of central and southern California X. Cui et al. 10.1016/j.scitotenv.2019.01.081
145. Methane variability associated with natural and anthropogenic sources in an Australian context M. Hatch et al. 10.1080/08120099.2018.1471004
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148. Bottom-Up Estimates of Coal Mine Methane Emissions in China: A Gridded Inventory, Emission Factors, and Trends J. Sheng et al. 10.1021/acs.estlett.9b00294
149. Comparison of Satellite-Observed XCO2 from GOSAT, OCO-2, and Ground-Based TCCON A. Liang et al. 10.3390/rs9101033
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151. China’s coal mine methane regulations have not curbed growing emissions S. Miller et al. 10.1038/s41467-018-07891-7
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153. Biogenic link to the recent increase in atmospheric methane over India A. Singh et al. 10.1016/j.jenvman.2021.112526
154. Fuel cycle emissions and life cycle costs of alternative fuel vehicle policy options for the City of Houston municipal fleet S. Sengupta & D. Cohan 10.1016/j.trd.2017.04.039
155. Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins L. Shen et al. 10.5194/acp-22-11203-2022
156. Methane, carbon dioxide, hydrogen sulfide, and isotopic ratios of methane observations from the Permian Basin tower network V. Monteiro et al. 10.5194/essd-14-2401-2022
157. Top-down constraints on global N<sub>2</sub>O emissions at optimal resolution: application of a new dimension reduction technique K. Wells et al. 10.5194/acp-18-735-2018
158. Landfill gas collection efficiency: Categorization of data from existing in-situ measurements C. Giordano et al. 10.1016/j.wasman.2023.12.042
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164. Observations of XCO<sub>2</sub> and XCH<sub>4</sub> with ground-based high-resolution FTS at Saga, Japan, and comparisons with GOSAT products H. Ohyama et al. 10.5194/amt-8-5263-2015
165. Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set M. Buchwitz et al. 10.1016/j.rse.2016.12.027
166. Satellite and ground-based measurements of XCO<sub>2</sub> in a remote semiarid region of Australia V. Velazco et al. 10.5194/essd-11-935-2019
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