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https://doi.org/10.5194/egusphere-2026-772
© 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/egusphere-2026-772
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
09 Mar 2026
| 09 Mar 2026
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Evaluation of coal mine methane inventory methods using aircraft-based approaches in the Bowen Basin, Australia
Abstract. Australia uses a blend of IPCC Tier 2 and 3 bottom-up approaches to estimate and report fugitive coal mine methane (CH4) emissions. To date, Tier 3 reporting for underground coal mines, which predominantly relies on direct measurements of ventilated air, has not been systematically assessed against top-down atmospheric measurements. Tier 2 coal core-based emission factors and Tier 3 model guidelines for estimating surface (open-cut) mine emissions similarly lack verification.
Here, two aircraft-based approaches were used to quantify the rate of CH4 emissions from 17 coal mines in the Bowen Basin, Australia. When compared to bottom-up mean annual reported estimates, airborne estimates from underground mines showed a non-significant mean positive bias of 0.28 t hr-1 (p = 0.28, n = 8 estimates) and good agreement (normalised root mean squared error (NRMSE) = 0.20). When aggregated, top-down measured emissions from all underground mines were within 8 % of bottom-up reported totals. In contrast, aircraft-based estimates from surface mines showed a significant mean positive bias of 3.7 t hr-1 CH4 (p = 0.001, n = 10 estimates) and poor agreement (NRMSE = 0.86). In aggregate, top-down emissions from all surface mines were 3.6 times the bottom-up totals.
These results demonstrate for Australian coal mines, direct monitoring approaches to quantify underground mine emissions are fit for purpose, but bottom-up surface mine emission estimation methods require review. Given that surface mines in the Basin alone account for ~38 % of national production, the contribution of coal mining to Australia’s CH4 emissions may exceed the reported ~19 %.
How to cite. Harris, S. J., Krautwurst, S., Hacker, J., Lunt, M., Jakob, B., Bai, M., Boesch, H., Brain, T., Burrows, J. P., Chakravarty, S., Field, R. A., Fisher, R. E., France, J. L., Gerilowski, K., Huhs, O., Junkermann, W., Kelly, B. F. J., Kumm, M., Lanoisellé, M., Lieff, W., McGrath, A., Murphy, A., Röckmann, T., Salmon, Z., Schindewolf, J., Thoböll, J., van der Veen, C., and Bovensmann, H.: Evaluation of coal mine methane inventory methods using aircraft-based approaches in the Bowen Basin, Australia, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-772, 2026.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Stephen J. Harris
CORRESPONDING AUTHOR
UNEP's International Methane Emissions Observatory (IMEO), Paris, France
School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, Australia
Sven Krautwurst
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Jorg Hacker
Airborne Research Australia, Parafield, SA, Australia
College of Science and Engineering, Flinders University, Adelaide, SA, Australia
Mark Lunt
Environmental Defense Fund, Perth, WA, Australia
Borchardt Jakob
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Airborne Research Australia, Parafield, SA, Australia
The University of Melbourne, School of Agriculture, Food and Ecosystem Sciences, Parkville, VIC, Australia
Hartmut Boesch
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Tarra Brain
School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, Australia
John Philip Burrows
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Shakti Chakravarty
Airborne Research Australia, Parafield, SA, Australia
Robert A. Field
UNEP's International Methane Emissions Observatory (IMEO), Paris, France
Rebecca E. Fisher
Department of Earth Sciences, Royal Holloway, University of London, Egham, TW20 0EX, UK
James L. France
Environmental Defense Fund, Perth, WA, Australia
Department of Earth Sciences, Royal Holloway, University of London, Egham, TW20 0EX, UK
Konstantin Gerilowski
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Wolfgang Junkermann
Airborne Research Australia, Parafield, SA, Australia
Karlsruhe Institute of Technology, Garmisch Partenkirchen, Germany
Bryce F. J. Kelly
School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, Australia
Martin Kumm
Jade University of Applied Sciences, Wilhelmshaven, Germany
Mathias Lanoisellé
Department of Earth Sciences, Royal Holloway, University of London, Egham, TW20 0EX, UK
Wolfgang Lieff
Airborne Research Australia, Parafield, SA, Australia
Andrew McGrath
Airborne Research Australia, Parafield, SA, Australia
College of Science and Engineering, Flinders University, Adelaide, SA, Australia
Adrian Murphy
Airborne Research Australia, Parafield, SA, Australia
Thomas Röckmann
Institute for Marine and Atmospheric Research, Faculty of Science, Utrecht University, Utrecht, 3584 CC, the Netherlands
Zoe Salmon
School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW, Australia
Josua Schindewolf
Alfred Wegener Institute - Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Jakob Thoböll
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Carina van der Veen
Institute for Marine and Atmospheric Research, Faculty of Science, Utrecht University, Utrecht, 3584 CC, the Netherlands
Heinrich Bovensmann
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Short summary
The accuracy of methods used to estimate and report fugitive methane emissions from Australian coal mines remains unclear. This study compares airborne emission rate estimates with reported estimates from 17 coal mines in the Bowen Basin, a region accounting for 45 % of national coal production. Results show good agreement for underground coal mines, but poor agreement for surface coal mines, suggesting improvements to surface coal mine reporting methods is needed to improve inventory reporting.
The accuracy of methods used to estimate and report fugitive methane emissions from Australian...
Review of Harris et al., 2026 - Evaluation of coal mine methane inventory methods using aircraft-based approaches in the Bowen Basin, Australia
Summary:
This very well written, detailed, and rigorous study by Harris et al., describes a comparison between aircraft (top down) methane flux measurements and bottom-up reported emissions for 17 coal mines in Australia. A combination of in situ (ABB UGGA) and remote sensing (MAMAP2D) instruments were used on ARA’s Dimona aircraft. A well-established mass balance approach was used to derive top down fluxes (and uncertainty). The paper finds that reported bottom-up emissions are underestimated by factors of two or more (i.e accounting for less than half of real measured emissions). Much larger differences are found for some types. This is a significant finding, and a not uncommon one from similar measurements of methane emissions from many human sources all over the world. The factor 2 seems to pop up everywhere (cities, landfills etc etc). One must wonder if something fundamentally incorrect is happening with bottom up accounting, like an incorrectly assumed molecular mass or something common to all inventory accounting methods (given that CH4 molecular mas is around half that of dry air...). Anyway, I digress... This was a really interesting read – thank you. It is a very detailed and rigorous study. Every clarification of method and detail I would expect to see is there. It is presented clearly, and well, and I have little doubt in the quality and accuracy of the results and conclusions in the paper. It is highly relevant to readers of ACP and consistent with the quality and impact of similar work in the journal. I recommend what amount to only technical revisions that mostly concern clarity of presentation in a few small areas (see below). I congratulate the team on some excellent fieldwork and a neat piece of analysis. I hope that the results lead to better Australian coal mine inventories.
Specific Comments
Figure 3.3d – page 10 – the caption says this is a “representation”. Is this real data that are shown, or some kind of conceptual illustration? I suspect it’s the former. If so, please add which date/flight this was to the caption and make clear it “represents” real data.
Line 285 and figure 4 – I appreciate the text says that the MAMPA2D emissions method is described in detail elsewhere, but I would recommend adding a brief summary of it here such that the figures and narrative can stand alone better in this paper. What is the column anomaly relative to? I.e. what is the background column (and column units) that the percentages in figure 4 deviate from?
Line 318 – Hysplit has been used as a guide to the footprint of sources. I think this is fair enough, but it’s not the best air history model around and relies on some sketchy meteorological inputs available compared to e.g. WRF-STILT or FLEXPART, or NAME. I’m not suggesting those models must be used here as this seems like a quick and ready sense check. But did you check for any wild divergence in footprint if you try trajectories further up or down in the mixing layer to check consistency of the assumed footprint, or to constrain any potential uncertainty?
Table 1 – is really long.... Can you summarise and highlight/colour/bold some of the more salient results you want readers to take from it? And, again, the caption is very long – move some of this to the methods narrative as it contains lots of info on data filtering that should not be in a caption.
Figure 5 – caption does not say what the solid black line is? Guessing this is a line of best fit to all data? Least square fit?
Technical Comments:
Line 167 – space needed between quantity and unit (Mt). Several other instances to check.
Figure 2 caption – this is a very long and descriptive caption. Can some of it be moved to the narrative (esp. for what is not shown in the figure)?
Figure 2 right panel – only coal mines register on the y-scale. Would a log-10 y axis help to resolve the other sources better?