Articles | Volume 12, issue 9
https://doi.org/10.5194/amt-12-4659-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-12-4659-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment
School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia
Andrew Zammit-Mangion
School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia
Sangeeta Bhatia
Centre for Research in Mathematics, Western Sydney University, Parramatta, Australia
currently at: School of Public Health, Imperial College London, London, UK
Ivan Schroder
Geoscience Australia, Canberra, Australia
Frances Phillips
Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, Australia
Trevor Coates
School of Agriculture and Food, University of Melbourne, Melbourne, Australia
Karita Negandhi
Department of Earth and Planetary Sciences, Macquarie University, Sydney, Australia
currently at: Office of Environment and Heritage, Parramatta, Australia
Travis Naylor
Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, Australia
Martin Kennedy
Department of Earth and Planetary Sciences, Macquarie University, Sydney, Australia
Steve Zegelin
CSIRO Oceans and Atmosphere, Canberra, Australia
Nick Wokker
Department of Industry, Innovation and Science, Canberra, Australia
Nicholas M. Deutscher
Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, Australia
Andrew Feitz
Geoscience Australia, Canberra, Australia
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Cited
10 citations as recorded by crossref.
- Rational Design of PdPt Nanoalloys Sensitized Mesoporous SnO2 for High‐Performance Methane Sensing Applications C. Yuan et al. https://doi.org/10.1002/admt.202500222
- Performance of an open-path near-infrared measurement system for measurements of CO2 and CH4 during extended field trials N. Deutscher et al. https://doi.org/10.5194/amt-14-3119-2021
- Dynamic quantification of methane emissions at facility scale using laser tomography: demonstration of a farm deployment K. Scheel et al. https://doi.org/10.5194/amt-19-2343-2026
- Probabilistic inversion modeling of gas emissions: A gradient-based MCMC estimation of Gaussian plume parameters T. Newman et al. https://doi.org/10.1214/25-AOAS2101
- Mobile atmospheric measurements and local-scale inverse estimation of the location and rates of brief CH4 and CO2 releases from point sources P. Kumar et al. https://doi.org/10.5194/amt-14-5987-2021
- Intercomparison of Three Continuous Monitoring Systems on Operating Oil and Gas Sites W. Daniels et al. https://doi.org/10.1021/acsestair.4c00298
- Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. https://doi.org/10.1002/env.2754
- Greenhouse gas emission mapping and quantification based on 3D transport modeling and Bayesian state estimation E. Vänskä et al. https://doi.org/10.1088/1361-6420/adfb29
- The Otway shallow fault experiment: Insights from surface monitoring C. Patterson et al. https://doi.org/10.1016/j.ijggc.2026.104707
- A Bayesian hierarchical model for methane emission source apportionment W. Daniels et al. https://doi.org/10.1214/26-AOAS2202
10 citations as recorded by crossref.
- Rational Design of PdPt Nanoalloys Sensitized Mesoporous SnO2 for High‐Performance Methane Sensing Applications C. Yuan et al. https://doi.org/10.1002/admt.202500222
- Performance of an open-path near-infrared measurement system for measurements of CO2 and CH4 during extended field trials N. Deutscher et al. https://doi.org/10.5194/amt-14-3119-2021
- Dynamic quantification of methane emissions at facility scale using laser tomography: demonstration of a farm deployment K. Scheel et al. https://doi.org/10.5194/amt-19-2343-2026
- Probabilistic inversion modeling of gas emissions: A gradient-based MCMC estimation of Gaussian plume parameters T. Newman et al. https://doi.org/10.1214/25-AOAS2101
- Mobile atmospheric measurements and local-scale inverse estimation of the location and rates of brief CH4 and CO2 releases from point sources P. Kumar et al. https://doi.org/10.5194/amt-14-5987-2021
- Intercomparison of Three Continuous Monitoring Systems on Operating Oil and Gas Sites W. Daniels et al. https://doi.org/10.1021/acsestair.4c00298
- Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. https://doi.org/10.1002/env.2754
- Greenhouse gas emission mapping and quantification based on 3D transport modeling and Bayesian state estimation E. Vänskä et al. https://doi.org/10.1088/1361-6420/adfb29
- The Otway shallow fault experiment: Insights from surface monitoring C. Patterson et al. https://doi.org/10.1016/j.ijggc.2026.104707
- A Bayesian hierarchical model for methane emission source apportionment W. Daniels et al. https://doi.org/10.1214/26-AOAS2202
Saved (final revised paper)
Latest update: 05 Nov 2026
Short summary
Despite extensive research, emission detection and quantification of greenhouse gases (GHGs) remain an open problem. This article presents a novel statistical framework for detecting and quantifying methane emissions and showcases its efficacy on data collected from different instruments in the 2015 Ginninderra controlled-release experiment. The developed techniques can be used to aid GHG emission reduction schemes by, for example, detecting and quantifying leaks from carbon storage facilities.
Despite extensive research, emission detection and quantification of greenhouse gases (GHGs)...
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