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

doi:10.5194/acp-2020-337

GSTDTAP > 地球科学

DOI	10.5194/acp-2020-337
	Quantifying methane emissions from Queensland's coal seam gas producing Surat Basin using inventory data and an efficient regional Bayesian inversion
	Ashok K. Luhar, David M. Etheridge, Zoë M. Loh, Julie Noonan, Darren Spencer, Lisa Smith, and Cindy Ong
	2020-05-15
发表期刊	Atmospheric Chemistry and Physics
出版年	2020
英文摘要	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 × 350 km². Coupling bottom-up inventory and inverse modelling approaches, we quantify methane emissions from this area. The inventory suggests that the total emission is 173 × 10⁶ kg CH₄/yr, 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. The inferred emissions obtained from one of the inverse model setups that uses a Gaussian prior whose averages are identical 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 that is very similar to the total inventory emission. However, in a subdomain covering the CSG development areas, the inferred emissions are 33 % larger than those from the inventory.
领域	地球科学
URL	查看原文
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/267575
专题	地球科学
推荐引用方式 GB/T 7714	Ashok K. Luhar, David M. Etheridge, Zoë M. Loh, Julie Noonan, Darren Spencer, Lisa Smith, and Cindy Ong. Quantifying methane emissions from Queensland's coal seam gas producing Surat Basin using inventory data and an efficient regional Bayesian inversion[J]. Atmospheric Chemistry and Physics,2020.
APA	Ashok K. Luhar, David M. Etheridge, Zoë M. Loh, Julie Noonan, Darren Spencer, Lisa Smith, and Cindy Ong.(2020).Quantifying methane emissions from Queensland's coal seam gas producing Surat Basin using inventory data and an efficient regional Bayesian inversion.Atmospheric Chemistry and Physics.
MLA	Ashok K. Luhar, David M. Etheridge, Zoë M. Loh, Julie Noonan, Darren Spencer, Lisa Smith, and Cindy Ong."Quantifying methane emissions from Queensland's coal seam gas producing Surat Basin using inventory data and an efficient regional Bayesian inversion".Atmospheric Chemistry and Physics (2020).