96 citations as recorded by crossref.

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4. Assessment of Eco-friendly Gases for Electrical Insulation to Replace the Most Potent Industrial Greenhouse Gas SF6 M. Rabie & C. Franck 10.1021/acs.est.7b03465
5. Nitrous Oxide Emissions Estimated With the CarbonTracker‐Lagrange North American Regional Inversion Framework C. Nevison et al. 10.1002/2017GB005759
6. Uncertainty quantification of pollutant source retrieval: comparison of Bayesian methods with application to the Chernobyl and Fukushima Daiichi accidental releases of radionuclides Y. Liu et al. 10.1002/qj.3138
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8. Objective inversion of the continuous atmospheric 137Cs release following the Fukushima accident X. Dong et al. 10.1016/j.jhazmat.2023.130786
9. Sustained growth of sulfur hexafluoride emissions in China inferred from atmospheric observations M. An et al. 10.1038/s41467-024-46084-3
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11. Marine Nitrous Oxide Emissions From Three Eastern Boundary Upwelling Systems Inferred From Atmospheric Observations A. Ganesan et al. 10.1029/2020GL087822
12. In Situ Observations of Halogenated Gases at the Shangdianzi Background Station and Emission Estimates for Northern China L. Yi et al. 10.1021/acs.est.3c00695
13. Changes in net ecosystem exchange over Europe during the 2018 drought based on atmospheric observations R. Thompson et al. 10.1098/rstb.2019.0512
14. Estimates of North African Methane Emissions from 2010 to 2017 Using GOSAT Observations L. Western et al. 10.1021/acs.estlett.1c00327
15. Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion A. Zammit-Mangion et al. 10.1016/j.chemolab.2015.09.006
16. Spatiotemporally resolved ambient particulate matter concentration by fusing observational data and ensemble chemical transport model simulations E. Chianese et al. 10.1016/j.ecolmodel.2018.07.019
17. Rising Perfluorocyclobutane (PFC-318, c-C4F8) Emissions in China from 2011 to 2020 Inferred from Atmospheric Observations Y. Wang et al. 10.1021/acs.est.3c10325
18. Atmospheric transport and chemistry of trace gases in LMDz5B: evaluation and implications for inverse modelling R. Locatelli et al. 10.5194/gmd-8-129-2015
19. Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement A. Ganesan et al. 10.1029/2018GB006065
20. A pragmatic protocol for characterising errors in atmospheric inversions of methane emissions over Europe B. Szénási et al. 10.1080/16000889.2021.1914989
21. Joint release rate estimation and measurement-by-measurement model correction for atmospheric radionuclide emission in nuclear accidents: An application to wind tunnel experiments X. Li et al. 10.1016/j.jhazmat.2017.09.051
22. Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011 O. Tichý et al. 10.5194/acp-17-12677-2017
23. Atmospheric observations consistent with reported decline in the UK's methane emissions (2013–2020) M. Lunt et al. 10.5194/acp-21-16257-2021
24. 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
25. Abundance and sources of atmospheric halocarbons in the Eastern Mediterranean F. Schoenenberger et al. 10.5194/acp-18-4069-2018
26. Reconciling reported and unreported HFC emissions with atmospheric observations M. Lunt et al. 10.1073/pnas.1420247112
27. Increase in CFC-11 emissions from eastern China based on atmospheric observations M. Rigby et al. 10.1038/s41586-019-1193-4
28. Technical Note: Atmospheric CO<sub>2</sub> inversions on the mesoscale using data-driven prior uncertainties: methodology and system evaluation P. Kountouris et al. 10.5194/acp-18-3027-2018
29. The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
30. Continued Emissions of the Ozone‐Depleting Substance Carbon Tetrachloride From Eastern Asia M. Lunt et al. 10.1029/2018GL079500
31. Diagnosing spatial error structures in CO<sub>2</sub> mole fractions and XCO<sub>2</sub> column mole fractions from atmospheric transport T. Lauvaux et al. 10.5194/acp-19-12007-2019
32. Uncertainty characterization in the retrieval of an atmospheric point release S. Singh et al. 10.1016/j.atmosenv.2016.12.016
33. Nitrogen Trifluoride Emissions in China from 2017 to 2021 Derived from Atmospheric Observations W. Weng et al. 10.1021/acs.estlett.4c00527
34. Regional atmospheric CO2 inversion reveals seasonal and geographic differences in Amazon net biome exchange C. Alden et al. 10.1111/gcb.13305
35. Substantial increase in perfluorocarbons CF 4 (PFC-14) and C 2 F 6 (PFC-116) emissions in China M. An et al. 10.1073/pnas.2400168121
36. U.S. emissions of HFC‐134a derived for 2008–2012 from an extensive flask‐air sampling network L. Hu et al. 10.1002/2014JD022617
37. Source estimation of an unexpected release of Ruthenium-106 in 2017 using an inverse modelling approach L. Western et al. 10.1016/j.jenvrad.2020.106304
38. Impact of transport model resolution and a priori assumptions on inverse modeling of Swiss F-gas emissions I. Katharopoulos et al. 10.5194/acp-23-14159-2023
39. Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. 10.5194/acp-16-14371-2016
40. Comparison of four inverse modelling systems applied to the estimation of HFC-125, HFC-134a, and SF<sub>6</sub> emissions over Europe D. Brunner et al. 10.5194/acp-17-10651-2017
41. An atmospheric inversion over the city of Cape Town: sensitivity analyses A. Nickless et al. 10.5194/acp-19-7789-2019
42. The regional European atmospheric transport inversion comparison, EUROCOM: first results on European-wide terrestrial carbon fluxes for the period 2006–2015 G. Monteil et al. 10.5194/acp-20-12063-2020
43. Development of an urban greenhouse gas modelling system to support a London monitoring network T. Hoare et al. 10.1002/wea.3795
44. Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion A. Zammit-Mangion et al. 10.1016/j.spasta.2016.06.005
45. Multivariate spatial covariance models: a conditional approach N. Cressie & A. Zammit-Mangion 10.1093/biomet/asw045
46. Estimating methane emissions in California's urban and rural regions using multitower observations S. Jeong et al. 10.1002/2016JD025404
47. Western European emission estimates of CFC-11, CFC-12 and CCl4 derived from atmospheric measurements from 2008 to 2021 A. Redington et al. 10.5194/acp-23-7383-2023
48. LS-APC v1.0: a tuning-free method for the linear inverse problem and its application to source-term determination O. Tichý et al. 10.5194/gmd-9-4297-2016
49. Automated release rate inversion and plume bias correction for atmospheric radionuclide leaks: A robust and general remediation to imperfect radionuclide transport modeling S. Fang et al. 10.1016/j.scitotenv.2020.142140
50. A Co-Location Study of 87 Low-Cost Environmental Monitors: Assessing Outliers, Variability, and Uncertainty S. Manu & A. Rysanek 10.3390/buildings14092965
51. A measurement-based verification framework for UK greenhouse gas emissions: an overview of the Greenhouse gAs Uk and Global Emissions (GAUGE) project P. Palmer et al. 10.5194/acp-18-11753-2018
52. Large Methane Emission Fluxes Observed From Tropical Wetlands in Zambia J. Shaw et al. 10.1029/2021GB007261
53. A renewed rise in global HCFC-141b emissions between 2017–2021 L. Western et al. 10.5194/acp-22-9601-2022
54. Rapid increase in ozone-depleting chloroform emissions from China X. Fang et al. 10.1038/s41561-018-0278-2
55. Multi-scenario validation of the robust inversion method with biased plume range and values X. Dong et al. 10.1016/j.jenvrad.2023.107363
56. Very large fluxes of methane measured above Bolivian seasonal wetlands J. France et al. 10.1073/pnas.2206345119
57. On the tuning of atmospheric inverse methods: comparisons with the European Tracer Experiment (ETEX) and Chernobyl datasets using the atmospheric transport model FLEXPART O. Tichý et al. 10.5194/gmd-13-5917-2020
58. Comparative study on gradient-free optimization methods for inverse source-term estimation of radioactive dispersion from nuclear accidents S. Jang et al. 10.1016/j.jhazmat.2023.132519
59. Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling S. Henne et al. 10.5194/acp-16-3683-2016
60. Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment L. Cartwright et al. 10.5194/amt-12-4659-2019
61. Sustained methane emissions from China after 2012 despite declining coal production and rice-cultivated area J. Sheng et al. 10.1088/1748-9326/ac24d1
62. Estimating methane emissions from biological and fossil‐fuel sources in the San Francisco Bay Area S. Jeong et al. 10.1002/2016GL071794
63. Emissions and Marine Boundary Layer Concentrations of Unregulated Chlorocarbons Measured at Cape Point, South Africa D. Say et al. 10.1021/acs.est.0c02057
64. Enhanced Methane Emissions during Amazonian Drought by Biomass Burning M. Saito et al. 10.1371/journal.pone.0166039
65. 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
66. Hierarchical Bayesian calibration of nitrous oxide (N2O) and nitrogen monoxide (NO) flux module of an agro-ecosystem model: ECOSSE X. Li et al. 10.1016/j.ecolmodel.2015.07.020
67. Site-specific (Multi-scenario) validation of ensemble Kalman filter-based source inversion through multi-direction wind tunnel experiments S. Sun et al. 10.1016/j.jenvrad.2018.12.005
68. Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite S. Miller et al. 10.5194/gmd-13-1771-2020
69. Atmospheric observations show accurate reporting and little growth in India’s methane emissions A. Ganesan et al. 10.1038/s41467-017-00994-7
70. Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling A. Nickless et al. 10.5194/acp-18-4765-2018
71. Anthropogenic Chloroform Emissions from China Drive Changes in Global Emissions M. An et al. 10.1021/acs.est.3c01898
72. Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. 10.5194/gmd-15-5547-2022
73. Quantifying methane and nitrous oxide emissions from the UK and Ireland using a national-scale monitoring network A. Ganesan et al. 10.5194/acp-15-6393-2015
74. Data assimilation using an ensemble of models: a hierarchical approach P. Rayner 10.5194/acp-20-3725-2020
75. impulseest: A Python package for non-parametric impulse response estimation with input–output data L. Fiorio et al. 10.1016/j.softx.2021.100761
76. Sources and fate of atmospheric microplastics revealed from inverse and dispersion modelling: From global emissions to deposition N. Evangeliou et al. 10.1016/j.jhazmat.2022.128585
77. Emissions of halocarbons from India inferred through atmospheric measurements D. Say et al. 10.5194/acp-19-9865-2019
78. Comparative interpretations of renormalization inversion technique for reconstructing unknown emissions from measured atmospheric concentrations S. Singh et al. 10.1002/2016EA000247
79. Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion A. Stell et al. 10.5194/acp-22-12945-2022
80. WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. 10.5194/gmd-15-45-2022
81. Bayesian inversion of HFC-134a emissions in southern China from a new AGAGE site: Results from an observing system simulation experiment J. Li et al. 10.1016/j.atmosenv.2024.120715
82. History of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gases Experiment (AGAGE) R. Prinn et al. 10.5194/essd-10-985-2018
83. Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions S. Jeong et al. 10.1029/2017JD028166
84. Diagnostic methods for atmospheric inversions of long-lived greenhouse gases A. Michalak et al. 10.5194/acp-17-7405-2017
85. Natural and anthropogenic methane fluxes in Eurasia: a mesoscale quantification by generalized atmospheric inversion A. Berchet et al. 10.5194/bg-12-5393-2015
86. Role of atmospheric oxidation in recent methane growth M. Rigby et al. 10.1073/pnas.1616426114
87. Quantifying fossil fuel methane emissions using observations of atmospheric ethane and an uncertain emission ratio A. Ramsden et al. 10.5194/acp-22-3911-2022
88. A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME E. Fillola et al. 10.5194/gmd-16-1997-2023
89. Source term determination with elastic plume bias correction O. Tichý et al. 10.1016/j.jhazmat.2021.127776
90. A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions M. Vojta et al. 10.5194/gmd-15-8295-2022
91. Global and regional emissions estimates of 1,1-difluoroethane (HFC-152a, CH<sub>3</sub>CHF<sub>2</sub>) from in situ and air archive observations P. Simmonds et al. 10.5194/acp-16-365-2016
92. Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network E. White et al. 10.5194/acp-19-4345-2019
93. Atmospheric HCFC-22, HFC-125, and HFC-152a at Cape Point, South Africa B. Kuyper et al. 10.1021/acs.est.9b01612
94. Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. 10.5194/acp-19-13911-2019
95. Perfluorocyclobutane (PFC-318, <i>c</i>-C<sub>4</sub>F<sub>8</sub>) in the global atmosphere J. Mühle et al. 10.5194/acp-19-10335-2019
96. 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