Atmospheric methane isotopes identify inventory knowledge gaps in the Surat Basin, Australia, coal seam gas and agricultural regions

In-flight measurements of atmospheric methane (CH_4(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 CH_4(a) mole fraction readings to one or more emission sources is difficult.

The primary aims of this study were to use the CH_4(a) isotopic composition (δ¹³C) 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 (δ¹³C) 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 CH_4(b) and δ¹³C).

For IFAA samples collected from 250–350 m a.g.l. altitude, the best-fit δ¹³C signatures compare well with the ground observation: CSG δ¹³C of −55.4 ‰ (confidence interval (CI) 95 % ± 13.7 ‰) versus δ¹³C of −56.7 ‰ to −45.6 ‰; grazing cattle δ¹³C of −60.5 ‰ (CI 95 % ± 15.6 ‰) versus −61.7 ‰ to −57.5 ‰. For cattle feedlots, the derived δ¹³C (−69.6 ‰, CI 95 % ± 22.6 ‰), was isotopically lighter than the ground-based study (δ¹³C 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 δ¹³C 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 δ¹³C signatures. For the 100–200 m a.g.l. set collected over grazing cattle districts the δ¹³C signature (−53.8 ‰, CI 95 % ± 17.4 ‰) was heavier than expected from the BU inventory. An isotopically light set had a low δ¹³C signature of −80.2 ‰ (CI 95 % ± 4.7 ‰). A CH₄ source with this low δ¹³C 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 δ¹³C measurements used in conjunction with endmember mixing modelling of CH₄ sources are powerful tools for BU inventory verification.