Quantifying methane emissions from Queensland's coal seam gas producing Surat Basin using inventory data and a regional Bayesian inversion

• Published in: Atmospheric chemistry and physics Vol. 20; no. 23; pp. 15487 - 15511
• Main Authors: Luhar, Ashok K, Etheridge, David M, Loh, Zoë M, Noonan, Julie, Spencer, Darren, Smith, Lisa, Ong, Cindy
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 11.12.2020; Copernicus Publications
• Subjects: Air pollution; Analysis; Bayesian analysis; Carbon; Cattle; Coal; Coal mining; Coalbed methane; Domains; Emission; Emission inventories; Emissions; Fossil fuels; Greenhouse effect; Greenhouse gases; Intensive farming; Methane; Methane emissions; Monitoring systems; Ozone; Pollutants; Probability theory; Rain; Rainfall; Receptors; Regional development; Sampling; Seasonal variation; Seasonal variations; Stations; Transport; Tropospheric ozone; Uncertainty; Australia; Queensland Australia; Surat Basin
• ISSN: 1680-7324; 1680-7316; 1680-7324
• DOI: 10.5194/acp-20-15487-2020

Methane (CH4) is a potent greenhouse gas and a key precursor of tropospheric ozone, itself a powerful greenhouse gas and air pollutant. Methane emissions across Queensland's Surat Basin, Australia, result from a mix of activities, including the production and processing of coal seam gas (CSG). We measured methane concentrations over 1.5 years from two monitoring stations established 80 km apart on either side of the main CSG belt located within a study area of 350 km × 350 km. Using an inverse modelling approach coupled with a bottom-up inventory, we quantify methane emissions from this area. The inventory suggests that the total emission is 173.2 × 106 kg CH4 yr−1, with grazing cattle contributing about half of that, cattle feedlots ∼ 25 %, and CSG processing ∼ 8 %. Using the inventory emissions in a forward regional transport model indicates that the above sources are significant contributors to methane at both monitors. However, the model underestimates approximately the highest 15 % of the observed methane concentrations, suggesting underestimated or missing emissions. An efficient regional Bayesian inverse model is developed, incorporating an hourly source–receptor relationship based on a backward-in-time configuration of the forward regional transport model, a posterior sampling scheme, and the hourly methane observations and a derived methane background. The inferred emissions obtained from one of the inverse model setups that uses a Gaussian prior whose averages are identical to the gridded bottom-up inventory emissions across the domain with an uncertainty of 3 % of the averages best describes the observed methane. Having only two stations is not adequate at sampling distant source areas of the study domain, and this necessitates a small prior uncertainty. This inverse setup yields a total emission of (165.8 ± 8.5) × 106 kg CH4 yr−1, slightly smaller than the inventory total. However, in a subdomain covering the CSG development areas, the inferred emissions are (63.6 ± 4.7) × 106 kg CH4 yr−1, 33 % larger than those from the inventory. We also infer seasonal variation of methane emissions and examine its correlation with climatological rainfall in the area.