Atmospheric methane isotopes identify inventory knowledge gaps in the Surat Basin, Australia, coal seam gas and agricultural regions
Authors: Kelly Bryce F. J. , Lu Xinyi , Harris Stephen J. , Neininger Bruno G. , Hacker Jorg M. , Schwietzke Stefan , Fisher Rebecca E. , France James L. , Nisbet Euan G. , Lowry David , van der Veen Carina , Menoud Malika , Röckmann Thomas 
Published: December 2022

Abstract. In-flight measurements of atmospheric methane (CH4(a)) and mass balance
flux quantification studies can assist with verification and improvement in the
UNFCCC National Inventory reported CH4 emissions. In the Surat Basin
gas fields, Queensland, Australia, coal seam gas (CSG) production and cattle
farming are two of the major sources of CH4 emissions into the
atmosphere. Because of the rapid mixing of adjacent plumes within the
convective boundary layer, spatially attributing CH4(a) mole fraction
readings to one or more emission sources is difficult. The primary aims of this study were to use the CH4(a) isotopic
composition (δ13CCH4(a)) of in-flight atmospheric air
(IFAA) samples to assess where the bottom–up (BU) inventory developed
specifically for the region was well characterised and to identify gaps in
the BU inventory (missing sources or over- and underestimated source
categories). Secondary aims were to investigate whether IFAA samples
collected downwind of predominantly similar inventory sources were useable
for characterising the isotopic signature of CH4 sources (δ13CCH4(s)) and to identify mitigation opportunities. IFAA samples were collected between 100–350 m above ground level (m a.g.l.)
over a 2-week period in September 2018. For each IFAA sample the 2 h back-trajectory footprint area was determined using the NOAA HYSPLIT atmospheric
trajectory modelling application. IFAA samples were gathered into sets,
where the 2 h upwind BU inventory had > 50 % attributable
to a single predominant CH4 source (CSG, grazing cattle, or cattle
feedlots). Keeling models were globally fitted to these sets using multiple
regression with shared parameters (background-air CH4(b) and δ13CCH4(b)). For IFAA samples collected from 250–350 m a.g.l. altitude, the best-fit δ13CCH4(s) signatures compare well with the ground observation:
CSG δ13CCH4(s) of −55.4 ‰ (confidence interval (CI) 95 % ± 13.7 ‰) versus δ13CCH4(s) of −56.7 ‰ to −45.6 ‰; grazing
cattle δ13CCH4(s) of −60.5 ‰ (CI 95 % ± 15.6 ‰) versus −61.7 ‰ to −57.5 ‰. For cattle
feedlots, the derived δ13CCH4(s) (−69.6 ‰, CI 95 % ± 22.6 ‰), was
isotopically lighter than the ground-based study (δ13CCH4(s) from −65.2 ‰ to −60.3 ‰) but within agreement given the large uncertainty
for this source. For IFAA samples collected between 100–200 m a.g.l. the
δ13CCH4(s) signature for the CSG set (−65.4 ‰, CI 95 % ± 13.3 ‰) was
isotopically lighter than expected, suggesting a BU inventory knowledge gap
or the need to extend the population statistics for CSG δ13CCH4(s) signatures. For the 100–200 m a.g.l. set collected over
grazing cattle districts the δ13CCH4(s) signature (−53.8 ‰, CI 95 % ± 17.4 ‰) was
heavier than expected from the BU inventory. An isotopically light set had a
low δ13CCH4(s) signature of −80.2 ‰ (CI 95 % ± 4.7 ‰). A CH4 source with
this low δ13CCH4(s) signature has not been incorporated
into existing BU inventories for the region. Possible sources include
termites and CSG brine ponds. If the excess emissions are from the brine
ponds, they can potentially be mitigated. It is concluded that in-flight
atmospheric δ13CCH4(a) measurements used in conjunction
with endmember mixing modelling of CH4 sources are powerful tools for
BU inventory verification.