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

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 (13CCH4(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 (13CCH4(s)) and to identify mitigation opportunities. IFAA samples were collected between 100-350m above ground level (ma.g.l.) over a 2-week period in September 2018. For each IFAA sample the 2h back-trajectory footprint area was determined using the NOAA HYSPLIT atmospheric trajectory modelling application. IFAA samples were gathered into sets, where the 2h 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 13CCH4(b)). For IFAA samples collected from 250-350ma.g.l. altitude, the best-fit 13CCH4(s) signatures compare well with the ground observation: CSG 13CCH4(s) of -55.4‰ (confidence interval (CI) 95%±13.7‰) versus 13CCH4(s) of -56.7‰ to -45.6‰; grazing cattle 13CCH4(s) of -60.5‰ (CI 95%±15.6‰) versus -61.7‰ to -57.5‰. For cattle feedlots, the derived 13CCH4(s) (-69.6‰, CI 95%±22.6‰), was isotopically lighter than the ground-based study (13CCH4(s) from -65.2‰ to -60.3‰) but within agreement given the large uncertainty for this source. For IFAA samples collected between 100-200ma.g.l. the 13CCH4(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 13CCH4(s) signatures. For the 100-200ma.g.l. set collected over grazing cattle districts the 13CCH4(s) signature (-53.8‰, CI 95%±17.4‰) was heavier than expected from the BU inventory. An isotopically light set had a low 13CCH4(s) signature of -80.2‰ (CI 95%±4.7‰). A CH4 source with this low 13CCH4(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 13CCH4(a) measurements used in conjunction with endmember mixing modelling of CH4 sources are powerful tools for BU inventory verification

Influence of Alluvial Morphology on Upscaled Hydraulic Conductivity

The hydraulic conductivity of aquifers is a key parameter controlling the interactions between resource exploitation activities, such as unconventional gas production and natural groundwater systems. Furthermore, this parameter is often poorly constrained by typical data used for regional groundwater modeling and calibration studies performed as part of impact assessments. In this study, a systematic investigation is performed to understand the correspondence between the lithological descriptions of channel-type formation and the bulk effective hydraulic conductivities at a larger scale (Kxeff, Kyeff, and Kzeff in the direction of channel cross section, along the channel and in the vertical directions, respectively). This will inform decisions on what additional data gathering and modeling of the geological system can be performed to allow the critical bulk properties to be more accurately predicted. The systems studied are conceptualized as stacked meandering channels formed in an alluvial plain, and are represented as two facies. Such systems are often studied using very detailed numerical models. The main factors that may influence Kxeff, Kyeff, and Kzeff are the proportion of the facies representing connected channels, the aspect ratio of the channels, and the difference in hydraulic conductivity between facies. Our results show that in most cases, Kzeff is only weakly dependent on the orientations of channelized structures, with the main effects coming from channel aspect ratio and facies proportion

Isotopic signatures of major methane sources in the coal seam gas fields and adjacent agricultural districts, Queensland, Australia

In regions where there are multiple sources of methane (CH4) in close proximity, it can be difficult to apportion the CH4 measured in the atmosphere to the appropriate sources. In the Surat Basin, Queensland, Australia, coal seam gas (CSG) developments are surrounded by cattle feedlots, grazing cattle, piggeries, coal mines, urban centres and natural sources of CH4. The characterization of carbon (δ13C) and hydrogen (δD) stable isotopic composition of CH4 can help distinguish between specific emitters of CH4. However, in Australia there is a paucity of data on the various isotopic signatures of the different source types. This research examines whether dual isotopic signatures of CH4 can be used to distinguish between sources of CH4 in the Surat Basin. We also highlight the benefits of sampling at nighttime. During two campaigns in 2018 and 2019, a mobile CH4 monitoring system was used to detect CH4 plumes. Sixteen plumes immediately downwind from known CH4 sources (or individual facilities) were sampled and analysed for their CH4 mole fraction and δ13CCH4 and δDCH4 signatures. The isotopic signatures of the CH4 sources were determined using the Keeling plot method. These new source signatures were then compared to values documented in reports and peer-reviewed journal articles. In the Surat Basin, CSG sources have δ13CCH4 signatures between -55.6ĝ€¯‰ and -50.9ĝ€¯‰ and δDCH4 signatures between -207.1ĝ€¯‰ and -193.8ĝ€¯‰. Emissions from an open-cut coal mine have δ13CCH4 and δDCH4 signatures of -60.0±0.6ĝ€¯‰ and -209.7±1.8ĝ€¯‰ respectively. Emissions from two ground seeps (abandoned coal exploration wells) have δ13CCH4 signatures of -59.9±0.3ĝ€¯‰ and -60.5±0.2ĝ€¯‰ and δDCH4 signatures of -185.0±3.1ĝ€¯‰ and -190.2±1.4ĝ€¯‰. A river seep had a δ13CCH4 signature of -61.2±1.4ĝ€¯‰ and a δDCH4 signature of -225.1±2.9ĝ€¯‰. Three dominant agricultural sources were analysed. The δ13CCH4 and δDCH4 signatures of a cattle feedlot are -62.9±1.3ĝ€¯‰ and -310.5±4.6ĝ€¯‰ respectively, grazing (pasture) cattle have δ13CCH4 and δDCH4 signatures of -59.7±1.0ĝ€¯‰ and -290.5±3.1ĝ€¯‰ respectively, and a piggery sampled had δ13CCH4 and δDCH4 signatures of -47.6±0.2ĝ€¯‰ and -300.1±2.6ĝ€¯‰ respectively, which reflects emissions from animal waste. An export abattoir (meat works and processing) had δ13CCH4 and δDCH4 signatures of -44.5±0.2ĝ€¯‰ and -314.6±1.8ĝ€¯‰ respectively. A plume from a wastewater treatment plant had δ13CCH4 and δDCH4 signatures of -47.6±0.2ĝ€¯‰ and -177.3±2.3ĝ€¯‰ respectively. In the Surat Basin, source attribution is possible when both δ13CCH4 and δDCH4 are measured for the key categories of CSG, cattle, waste from feedlots and piggeries, and water treatment plants. Under most field situations using δ13CCH4 alone will not enable clear source attribution. It is common in the Surat Basin for CSG and feedlot facilities to be co-located. Measurement of both δ13CCH4 and δDCH4 will assist in source apportionment where the plumes from two such sources are mixed

A nation that rebuilds its soils rebuilds itself- an engineer's perspective

Nations can build and rebuild degraded soils to help address climate change and potentially improve the nutritional content of food if we change policies that allow the addition of safe mineral and organic wastes to soil. We present a framework that facilitates the transition from intensive conventional to more regenerative farming practices by considering soil's natural cycle. Our paper is presented in three parts. Firstly, we consider that 'soil is living'; just like humans, the soil biome needs a balanced diet of macro and micronutrients as well as a nurturing environment. We simplify the soil science and take a systems approach which focuses on restoring soil's natural cycle to benefit both health (by increasing micronutrients in soil) and wealth (through climate change adaptation and mitigation). Secondly, we consider the scale of the problem of soil degradation and the timescales involved in rebuilding soils and barriers to implementation. Thirdly, we propose a potential framework which enables communities to identify what might be missing from soil's natural cycle. This framework helps communities consider how they might change soil texture by addition and manipulation of both minerals and organic matter. We present an educational tool, ‘soil in a jar’ based on a narrative of nurturing soil which is designed to engage and inspire society to get their hands dirty. Communities can use the framework to produce locally specific solutions to restore their soil's natural cycle and rebuild their local and national economies

Isotopic signatures of major methane sources in the coal seam gas fields and adjacent agricultural districts, Queensland, Australia

In regions where there are multiple sources of methane (CH4) in close proximity, it can be difficult to apportion the CH4 measured in the atmosphere to the appropriate sources. In the Surat Basin, Queensland, Australia, coal seam gas (CSG) developments are surrounded by cattle feedlots, grazing cattle, piggeries, coal mines, urban centres and natural sources of CH4. The characterization of carbon (δ13C) and hydrogen (δD) stable isotopic composition of CH4 can help distinguish between specific emitters of CH4. However, in Australia there is a paucity of data on the various isotopic signatures of the different source types. This research examines whether dual isotopic signatures of CH4 can be used to distinguish between sources of CH4 in the Surat Basin. We also highlight the benefits of sampling at nighttime. During two campaigns in 2018 and 2019, a mobile CH4 monitoring system was used to detect CH4 plumes. Sixteen plumes immediately downwind from known CH4 sources (or individual facilities) were sampled and analysed for their CH4 mole fraction and δ13CCH4 and δDCH4 signatures. The isotopic signatures of the CH4 sources were determined using the Keeling plot method. These new source signatures were then compared to values documented in reports and peer-reviewed journal articles. In the Surat Basin, CSG sources have δ13CCH4 signatures between -55.6ĝ€¯‰ and -50.9ĝ€¯‰ and δDCH4 signatures between -207.1ĝ€¯‰ and -193.8ĝ€¯‰. Emissions from an open-cut coal mine have δ13CCH4 and δDCH4 signatures of -60.0±0.6ĝ€¯‰ and -209.7±1.8ĝ€¯‰ respectively. Emissions from two ground seeps (abandoned coal exploration wells) have δ13CCH4 signatures of -59.9±0.3ĝ€¯‰ and -60.5±0.2ĝ€¯‰ and δDCH4 signatures of -185.0±3.1ĝ€¯‰ and -190.2±1.4ĝ€¯‰. A river seep had a δ13CCH4 signature of -61.2±1.4ĝ€¯‰ and a δDCH4 signature of -225.1±2.9ĝ€¯‰. Three dominant agricultural sources were analysed. The δ13CCH4 and δDCH4 signatures of a cattle feedlot are -62.9±1.3ĝ€¯‰ and -310.5±4.6ĝ€¯‰ respectively, grazing (pasture) cattle have δ13CCH4 and δDCH4 signatures of -59.7±1.0ĝ€¯‰ and -290.5±3.1ĝ€¯‰ respectively, and a piggery sampled had δ13CCH4 and δDCH4 signatures of -47.6±0.2ĝ€¯‰ and -300.1±2.6ĝ€¯‰ respectively, which reflects emissions from animal waste. An export abattoir (meat works and processing) had δ13CCH4 and δDCH4 signatures of -44.5±0.2ĝ€¯‰ and -314.6±1.8ĝ€¯‰ respectively. A plume from a wastewater treatment plant had δ13CCH4 and δDCH4 signatures of -47.6±0.2ĝ€¯‰ and -177.3±2.3ĝ€¯‰ respectively. In the Surat Basin, source attribution is possible when both δ13CCH4 and δDCH4 are measured for the key categories of CSG, cattle, waste from feedlots and piggeries, and water treatment plants. Under most field situations using δ13CCH4 alone will not enable clear source attribution. It is common in the Surat Basin for CSG and feedlot facilities to be co-located. Measurement of both δ13CCH4 and δDCH4 will assist in source apportionment where the plumes from two such sources are mixed

Isotopic signatures of major methane sources in the coal seam gas fields and adjacent agricultural districts, Queensland, Australia

In regions where there are multiple sources of methane (CH4) in close proximity, it can be difficult to apportion the CH4 measured in the atmosphere to the appropriate sources. In the Surat Basin, Queensland, Australia, coal seam gas (CSG) developments are surrounded by cattle feedlots, grazing cattle, piggeries, coal mines, urban centres and natural sources of CH4. The characterization of carbon (δ13C) and hydrogen (δD) stable isotopic composition of CH4 can help distinguish between specific emitters of CH4. However, in Australia there is a paucity of data on the various isotopic signatures of the different source types. This research examines whether dual isotopic signatures of CH4 can be used to distinguish between sources of CH4 in the Surat Basin. We also highlight the benefits of sampling at nighttime. During two campaigns in 2018 and 2019, a mobile CH4 monitoring system was used to detect CH4 plumes. Sixteen plumes immediately downwind from known CH4 sources (or individual facilities) were sampled and analysed for their CH4 mole fraction and δ13CCH4 and δDCH4 signatures. The isotopic signatures of the CH4 sources were determined using the Keeling plot method. These new source signatures were then compared to values documented in reports and peer-reviewed journal articles. In the Surat Basin, CSG sources have δ13CCH4 signatures between −55.6 ‰ and −50.9 ‰ and δDCH4 signatures between −207.1 ‰ and −193.8 ‰. Emissions from an open-cut coal mine have δ13CCH4 and δDCH4 signatures of −60.0±0.6 ‰ and −209.7±1.8 ‰ respectively. Emissions from two ground seeps (abandoned coal exploration wells) have δ13CCH4 signatures of −59.9±0.3 ‰ and −60.5±0.2 ‰ and δDCH4 signatures of −185.0±3.1 ‰ and −190.2±1.4 ‰. A river seep had a δ13CCH4 signature of −61.2±1.4 ‰ and a δDCH4 signature of −225.1±2.9 ‰. Three dominant agricultural sources were analysed. The δ13CCH4 and δDCH4 signatures of a cattle feedlot are −62.9±1.3 ‰ and −310.5±4.6 ‰ respectively, grazing (pasture) cattle have δ13CCH4 and δDCH4 signatures of −59.7±1.0 ‰ and −290.5±3.1 ‰ respectively, and a piggery sampled had δ13CCH4 and δDCH4 signatures of −47.6±0.2 ‰ and −300.1±2.6 ‰ respectively, which reflects emissions from animal waste. An export abattoir (meat works and processing) had δ13CCH4 and δDCH4 signatures of −44.5±0.2 ‰ and −314.6±1.8 ‰ respectively. A plume from a wastewater treatment plant had δ13CCH4 and δDCH4 signatures of −47.6±0.2 ‰ and −177.3±2.3 ‰ respectively. In the Surat Basin, source attribution is possible when both δ13CCH4 and δDCH4 are measured for the key categories of CSG, cattle, waste from feedlots and piggeries, and water treatment plants. Under most field situations using δ13CCH4 alone will not enable clear source attribution. It is common in the Surat Basin for CSG and feedlot facilities to be co-located. Measurement of both δ13CCH4 and δDCH4 will assist in source apportionment where the plumes from two such sources are mixed