IPCC Expert Meeting on Use of Atmospheric OBSERVATION DATA IN EMISSION INVENTORIES 5-7 September 2022 Geneva, Switzerland INTERGOVERNMENTAL PANEL Task Force on National Greenhouse Gas Inventories (TFI) Estimating fugitive CH 4 emissions from the detection of plumes using aeroplane flyovers Associate Professor Bryce Kelly The University of New South Wales, Australia https://www.youtube.com/watch?v=89MtxxM-NTU
Active Methods LiDAR . How we used a regional mass balance emission estimate to “assess and critique” the bottom-up inventory methods for coal seam gas developments in Australia. . Why the use of isotopes for source attribution and identifying inventory knowledge gaps should be more commonly used. . Why aircraft-based atmospheric vertical profiles are critical for: . satellite verification . reducing emission estimate uncertainty and for identifying bias in national and global inverse models of in-situ and satellite observations. Example: Johnston et al. (2021) – controlled gas releases Hunter and Thorpe (2017) – oil and gas Passive Methods bsorption Spectro Example: Sherwin et al. (2021) - controlled gas releases Krautwurst et al. (2017) – landfills, spectral and mass balance In-Situ Methods aser Absorption Example: Hillier et al. (2014) - agricultural sources Alvarez et al. (2018) - oil and gas 2
. Hiller et al. (2014) did one of the earlier BU vs TD studies. This study highlights that multiple measurement methods should be used and/or multiple analysis methods applied. . They estimated emissions using two of the commonly applied methods: . Eddy Covariance, which considers vertical transport by turbulent eddies. This methods is often used for single long flights over wetlands, forests and agricultural regions. . Boundary Layer Budget, which quantifies the relative change in concentration into and out of the box (mass balance). This approach is used for many agricultural, urban, and oil and gas studies. . The results of the Reuss Valley study were: Inventory 0.4 < Eddy Covariance 0.62 < Boundary Layer Budget 1.02 (median µg CH , m -² s -¹) Flight tracks (red lines) from 7 April 2010 along the Reuss Valley. The yellow box indicates the box used in the boundary layer budget approach. The view is toward northwest.

UNEP: Methane Science Studies to Generate
Accurate, Public Data
In 2015, a series of methane science studies was initiated to improve the understanding of global methane emissions from the oil and gas industry. Through these peer-reviewed studies, scientists continue to investigate and measure methane emissions across the oil and gas supply chain, generating accurate, public information about where and how much methane is leaking. The results of ongoing and future studies will improve understanding of methane emissions at a global level, allowing industry, governments, and other stakeholders to prioritize actions to reduce methane emissions. Bryce Kelly Xinyi Lu (Lexie) Stephen Harris Rebecca Fisher Dave Lowry James France Thomas Röckmann Carina van der Veen Malika Menoud Jorg Hacker Airborne Research Australia Non-Profit Research Institute & Flinders University, Adelaide Bruno Neininger Stefan Schwietzke Switzerland
The Surat Basin is one of the largest coal seam gas (CSG) developments in the world and is located north-west of Brisbane. The subregion we surveyed covers an area of approximately 200 km x 200 km. It is anticipated that when fully developed there will be over 20,000 CGS wells and supporting infrastructure. In 2018 the total gas produced was 8912 Mm³·(data.qld.gov.au). At the start of the project there were limited emission data in the public domain: ' Katestone bottom-up inventory (appendix Luhar et al. 2018, 2020). Nonstandard methods and emission factors. CSIRO used the Katestone inventory as a prior for their two- tower inversion study (Luhar et al. 2018). Full details for the study are published in: Neininger BG, Kelly BFJ, Hacker JM, LU X, Schwietzke S. 2021 Coal seam gas industry methane emissions in the Surat Basin, Australia: comparing airborne measurements with inventories. Phil. Trans. R. Soc. A 379: 20200458. https://doi.org/10.1098/rsta.2020.0458 5
A tiny portion of the > 4000 wells in our region of study. Ground-based surveys in this region did not detect any gas leaks. EGU2020-12503; https://dororg/10.5194/egusphere-egu2020-12508; EGU General Assembly 2020; O: Attifor(s) 2020. This work is distributed under the Creative Commons Aftribution 4.0 License
There are 38 CSG facilities: gas compressor stations and processing plants. In addition to emissions from the CSG facilities, we have mapped emissions from the water pipeline high point vents and the raw water holding ponds. EGU2020-12508; https://doi.org/10.5194/egusphere-egu2020-12508; EGU General Assembly 2020; © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License
There are 55 cattle feedlots in the area. The largest feedlot can hold 70,000 animals. Many of the feedlots use the groundwater produced in association with extracting the gas. Throughout the region there are 0.5 million feedlot cattle and 1 million grazing cattle. EGU2020-12508; https://doi.org/10.5194/egusphere-egu2020-12508; EGU General Assembly 2020; © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License

Aircraft-Based Sampling
LGR Greenhouse Analyser (continuous mole fraction CH 4 and CO 2 ) Collection of atmospheric air samples in FlexFoil 3L Bags 2018 Sub-Basin Surveying 2018 and 2020 Facility Surveying Top-Down Methane Flux Estimate Sub-Region Mass Balance Emission Rate (kgh-1)
Plume Chemistry
Locate methane plumes using car- mounted LGR Greenhouse Gas Analyser Chemical Analyses CH 4 , 8¹³C_CH 4 , 8D_CH 4 Determine the Source Chemistry Using Keeling Plots Endmember Mixing Modelling In-Flight Atmospheric Air Samples TD vs BU
Inventory
Gas Production Data Agricultural Production Data Australian Census Research Papers Field Observations CSIRO Reports Company Reports Bottom-Up Annual Inventory Emission Rate Converted to kg h-1 9
In Australia, there is no block or facility-scale inventory in the public domain. We used public domain gas and agricultural, production data and worked closely with the national inventory team to produce the UNSW Inventory (it is like the National Inventory). The bottom-up inventory is less than the top- down estimate for the regions, but within the uncertainty bounds of the top-down 20% estimate. Emissions 101 Gg per year Total Top Down All Other Sources Agriculture (Animals) □ Coal Mass Balance Methane Emission Estimate 118 Gg per year ( ±20%)
Neininger BG, et al. (2021) https://doi.org/10.1098/rsta.2020.0458

l will discuss how we calculated our mass balance estimates of methane emissions from coal seam gas and agriculture. Emissions 101 Gg per year ■ Total Top Down All Other Sources Agriculture (Animals) ■  Coal Seam Gas Mass Balance Emission Estimate 118 Gg per year ( ±20%)
Neininger BG, et al. (2021) https://doi.org/10.1098/rsta.2020.0458

Emissions 101 Gg per year Total Top Down All Other Sources Agriculture (Animals) □ Coal Coal Seam Gas Mass Balance Methane Emission Estimate 118 Gg per year ( ±20%) . .
Neininger BG, et al. (2021) https://doi.org/10.1098/rsta.2020.0458

Does the bottom-up inventory estimate for coal seam gas = the top-down aircraft-based mass balance emission estimate ? . Australia’s National Inventory Report uses various approved methods for oil and gas, which are based on the best available science and use mostly accessible public data. . Summarising the methods: . Emissions from wells, gathering and boosting stations, and processing plants are a function of tonnes of gas throughput. . Emissions from the co-produced water are a function of how many megalitres of water are produced as part of the gas extraction operations. . There is also an emissions estimate for each kilometer of pipeline. . Coal emissions were estimated from tonnes of coal produced. . Waste and urban emissions from annual government reports.

For Livestock we used the country-specific tier 2 methods
Table 5.12    Implied EFs – enteric fermentation (kg CH 2 /head/year)
Source: (a) IPCC 2019
Table 5.17    Implied EFs – Methane manure management (kg/head/year) Source: IPCC 2006, CS = country specific; EF = emission factor; VS = volatile solids.
Challenges and Potential Improvements
. Where do you position 1 million grazing cattle in the landscape? . We only had access to yearly animal counts. Ideally, seasonal agricultural bottom-up emissions should be determined. . There are large agricultural areas in our study where we underestimated emissions. This could be due to emissions from feedlots being underestimated. . Ideally, inventory uncertainty analyses should be done using Bayesian and Monte Carlo methods. https://www.industry.gov.au/sites/default/files/2020-05/nga-national-inventory-report-2018-volume-1.pdf
Over 4600 wells have been drilled in the study region as part of exploration and development operations, and in 2018 there were approximately 1300 producing wells in the study area. 392 other methane point sources were identified from Google Earth, government data sets, listed companies' annual reports, and environmental impact statements.
Sources
t CSG wells CSG processing plants CSG compressor stations CSG raw water ponds | Coal mines Power stations Ground seeps of unknown origin Historical exploration wells seeps River gas seeps Cattle feedlots Cattle dairy Piggeries Poultry farms Landfills Wastewater treatment plants Domestic wood fires
Dryland Cropping and Grazing Orange Burnett Mary ABS area Dryland Cropping and Grazing Light yellow Condamine ABS area Dryland Cropping and Grazing Light Blue Condamine ABS area Irrigated Agriculture
. Within the UNSW inventory domain, 50 % of point sources have an emission rate of less than 4.5 kg h-¹. These point sources account for 59 % of the UNSW inventory total. . The top 10 % have an emission rate exceeding 113 kg h-1. . The 42 sources in the top 10 % account for 37.7 % of the UNSW inventory total. Point Source Methane Emission Rate (kg.h − 1 ) Cumulative probability distribution of CH 4 point source emission rates (kg.h -1 ) for the 392 point sources in the Surat Basin UNSW inventory domain (this excludes the CSG wells).
UNSW Inventory 2018 (top down domain)
. We did 13 measurement flights over 15 days. . Most flights were at 150 to 300 m above ground level and flown at a cruising speed of 150 to 170 km/h. . Vertical profiles from above the convective boundary layer to 150 m were done at the start and end of each session. MetPod with the 3D-turbulence probe and internally mounted LiCOR LI-7500 (CO 2 ) and MetOne particle counter mounted on one of the wing pylons LGR greenhouse CH 4 and CO 2 analyser and pump
The maximum methane enhancement above background recorded was 40 ppb. Even at this low level of methane enhancement the isotopic composition of methane proved to be a useful tool for verifying the upwind inventory and identifying inventory knowledge gaps. The cyan lines are the flight tracks. Having overlapping mass balance subregions is critical to reducing the uncertainty in the emission estimate.
The maximum methane enhancement above background recorded was 40 ppb. Even at this low level of methane enhancement the isotopic composition of methane proved to be a useful tool for verifying the upwind inventory and identifying inventory knowledge gaps. The cyan lines are the flight tracks. Having overlapping mass balance subregions is critical to reducing the uncertainty in the emission estimate.
Upwind Flight Turbulent Flux Exchange Upper Boundary Layer Downwind Flight IN Convective Boundary Layer (CBL) Flying heights: survey lines 150 m, 300 m, 500 m above ground level, plus CBL measurement surveys to >2 km Officential Sildes
The light blue lines are the flight tracks at 150 and 300 m above the ground. The coloured bands show the CH 4 emission estimates between the upwind and downwind tracks. Mass Balance Uncertainties: . Timing of the upwind and downwind tracks and alignment with an air mass, . Alignment of the upwind background CH 4 with the downwind enhanced CH 4 , Data smoothing intervals, . . Convective boundary layer stability or expansion between upwind and downwind tracks, . Temporal uniformity of emissions.
Mass distribution of CH 4 within the Convective Boundary Layer. This is calculated by forward modelling the emission rates (in kg/h) for each source specified in the UNSW bottom-up emission inventory.
This ‘patchwork’ covers the TD domain and shows the percentage of UNSW inventory mass balance (BU) / the airborne measurement mass balance (TD). The two CSG clusters are represented by the black polygons. The yellow and green areas (BU/TD = 90 to 130 %) overlap with the coal seam gas production regions. The poorer quality agreement between the BU and TD emission estimates mostly overlap with the agricultural districts. We are missing emissions from agriculture.
Daytime – yellow ribbon Nighttime – cyan ribbon Blue line = 1.8 ppm Green line = 2.0 ppm Red line = 3.0 ppm River Seeps Abandoned Wells Ground Seeps Feedlot Sewage Treatment Coal Mine EGU2020512503  https://doi.org/10.5194/egusphere-egu2020-12508; EGU General Assem | dresseryDate: 12/21/2018 lat -26.949885°  lon 150.253028°  elev - Om evealt 26514 km   (1)
The Talinga Gas Facility consists of 12 Waukesha Gas Engines coupled to Ariel Screw Compressors, which process first stage compression of raw coal seam gas from the surrounding gathering field network. www.valmec.com.au Lu et al. (2021) https://doi.org/10.5194/acp-21-10527-2021
The scatterplot of δD_CH₄ vs 8¹³C_CH₄ highlights good separation of sources based on the chemistry of the plume samples. It is clear in the graph that CSG activity emissions and cattle emissions form distinct clusters. When there is clear end-member chemistry we can use mixing models to apportion contributions. 1) Venting pipeline 2) Gas compression plant 3) CSG facility 5) Raw water pond (2019) 6) Coal mine 7) Ground seep A 8) Ground seep B 9) River seep 11) Abattoir B 12) Feedlot cattle 13) Grazing cattle 14) Piggery 16) WWTP Background atmosphere
The ethane content of most Australian gas is < 1%

Point Sources
CSG processing plants CSG compressor stations CSG raw water ponds Coal mines Power stations Ground seeps River gas seeps Cattle feedlots Cattle dairy Piggeries Poultry farms Landfills Wastewater treatment plants ↑ Domestic wood fires Kelly et al. (2022) under review acp-2022-552
Does the chemistry of the atmospheric air sample match what is expected from the upwind inventory? Kelly et al. (2022) under review acp-2022-552
For samples where one source type represented >50% of the upwind inventory we analysed the data using multi Keeling model regression with shared background air (CH4(b) and 813CcH4(b) )
Grazing cattle and forest districts >50 % of the upwind inventory Airborne samples δ 13Cch4(s −52.5 ‰ (Cl 95 ‰ ± 18.8 ‰) Expected from inventory 8¹³Cch4(s) −61.7 ‰ to −57.5 ‰ The relatively high permil value suggests CSG emissions entering from the northern western gas field.
2103 Downwind of CSG brine pond. No emissions from these ponds were included in the BU inventory. Is this due to the brine ponds or termites? Kelly et al. (2022) under review https://acp.copernicus.org/preprints/acp-2022-552/
https://ghginstitute.org/ipcc/ https://www.industry.gov.au/sites/defaul t/files/April%202021/document/national- inventory-report-2019-volume-1.pdf Satellite Measurements Continuous Measurements (cloud cover and column averaging will be a limitation) Aircraft Measurements Regional and Facility Scale Emission Estimates Satellite Verification Inventories Spatially and Temporally Varying Seasonal Inventories
Note the higher mole fractions for CH₄ and CO₂ above the boundary layer When analysing the satellite total atmospheric column data sets how do you account for the higher concentration of greenhouse gases in the higher altitude atmospheric layers?

The focus of aircraft-based campaigns
Aircraft-based in-situ measurements are helpful for reducing emission estimate uncertainty and for identifying bias in national and global inverse models. How do we facilitate greater use of aircraft-based measurements in inverse modelling studies? Tallying emission measurements from components within a facility will never equal the emissions from the complete facility. Could it be made a requirement that facility owners perform aircraft-based spot checks?
Kelly et al. (2022) under review https://acp.copernicus.org/preprints/acp-2022-552/

The focus of aircraft-based campaigns
There are no international standards for the use of tracer gas surveys for source attribution and to identify source knowledge gaps. Has the science matured enough to develop good practice guidelines? ' If there is to be increased use of satellite and aircraft-based methods in inventory development, don’t we need better data on non-reportable sources?
Aircraft-based methods are a fast way to locate unknown sources and verify inventory methods, from small-scale facilities to regions that cover thousands of square kilometers. Of high importance, the emission estimates give reassurance to governments, industries and the community. From large aircraft to drones, airborne surveys can be used to track Net Zero goals.
Methane Aircraft Methane UAV Methane Drone