Quantifying methane emissions from Queensland’s coal seam gas producing Surat Basin using inventory data and a regional Bayesian inversion

Methane (CH4) is a potent greenhouse gas and a key precursor of tropospheric ozone, itself a powerful greenhouse 10 gas and air pollutant. 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. Using an inverse modelling approach coupled with a bottom-up inventory, we quantify methane emissions from this area. The inventory suggests that the total emission is 173.2 × 106 kg CH4 yr-1, with grazing cattle contributing about half of that, cattle 15 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-intime configuration of the forward regional transport model, a posterior sampling scheme, and the hourly methane observations 20 and a derived methane background. The inferred emissions obtained from one of the inverse model setups that uses a Gaussian prior whose averages are identical to 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 of 165.8 × 106 kg CH4 yr-1, slightly smaller than the inventory total. However, in a subdomain covering the CSG development areas, the 25 inferred emissions are 63.6 × 106 kg CH4 yr-1, 33% larger than those from the inventory. We also infer seasonal variation of methane emissions and examine its correlation with climatological rainfall in the area.


S1.1 Filtering for nearby cattle
Our standard data processing, using the software package GCWerks (http://www.gcwerks.com/), involves manual checks on a number of instrument parameters, but also some automatic flagging procedures based on very conservative thresholds for removing spurious data. In particular, GCWerks removes outlying points in the high frequency data if they lie more than 10 standard deviations from the mean, as calculated over a two-minute moving average window. By judiciously modifying the 30 default parameters in the GCWerks' statistical filter, we are able to remove the signal due to the local cattle, without modifying the underlying signal from more distant sources. We find the optimal filter parameters for filtering nearby cattle signal to be a standard deviation of 2.5, with a moving average over a ten-minute window.
To give an example, Figure S1 shows the measured time series of minutely mean CO 2 , CH 4 and CO at Burncluith for 10 January 2017. Particularly from 0100-0900 UTC (1100( -1900, there is a series of sharp spikes in the CH 4 record. At 35 this time, there were light winds from an east-south-easterly direction, and the landholder confirmed that there were cows loitering to the east of the intake line. While the 'tight' or cattle filter removes most of these sharp peaks in the minutely data, it retains the underlying rise in CH 4 between 0400 and 0600 UTC which mirrors the rise in CO, suggestive of biomass burning signal being transported from further afield.
The same cattle filter was applied to the Ironbark data, for consistency, although cattle are fewer and further away at Ironbark 40 and have much less impact on the methane measurements.

S1.2 Diurnal and low wind filtering
Nocturnal low wind conditions can be approximately defined as those with a wind speed at 10-m above ground of less than 2-3 m s -1 , which corresponds to a Richardson number (a stability parameter that is the ratio of buoyant suppression of turbulence to shear generation of turbulence in the lower atmosphere) greater than 0.2 − 0.3 (Luhar et al., 2009). There are considerable 45 occurrences of high methane concentrations at the two sites under such conditions. This is mostly because under these conditions the atmosphere near the ground is typically characterised by strong stable stratification with a very shallow inversion layer so that even small local sources near the ground can lead to very large enhancements in the local methane concentration due to very little vertical atmospheric mixing. Despite being of considerable practical interest, however, these are some of the most difficult conditions to simulate by a flow and dispersion model, particularly at a regional or mesoscale. Thus, one option 50 to circumvent the issue of modelling generally not being able to properly simulate strong inversion conditions at night is to consider, for each site, daytime hours (1000-1700 h) irrespective of wind speed, and the remaining hours for which the observed wind speed is greater than 3 m s -1 . The daytime window typically corresponds to periods of strong mixing dominated by convective motions resulting from the solar heating of the ground. For data selection for the two sites, the respective measured wind speeds were used. 55 3 S1.3 Filtering for biomass burning events Both methane and CO are emitted from biomass burning. CO is not present in other large methane sources of interest, including those compiled in the bottom-up emissions inventory. Methane emissions from power stations, domestic wood heating and vehicles on the other hand would contain CO, but the modelled CH 4 signals for these sources are predicted to be virtually undetectable at Burncluith. The majority (89%) of CSG methane source emissions in the bottom-up inventory are not from 60 combustion. Importantly, emissions from the less well known migratory or seepage sources, which also do not originate from combustion, would not be screened out by a CO filter. Thus, comparisons of observed CH 4 with model simulations are more accurately made by excluding hourly periods with large CO enhancements above the background CO concentration.
A plot of the measured CO vs CH 4 concentrations at Burncluith after applying the above cattle and low wind filtering is shown in in Figure S2 (orange circles). Two distinct groupings are apparent; the data group with high magnitudes of CO concentration 65 likely represents dominant contributions from combustion sources. Enhancements of CO above background at Burncluith are mostly observed during north-westerly and easterly winds, consistent with the locations of the occasional forest burn offs and the wood fire in the dwelling adjacent to the monitoring station, respectively. (The background CO concentration was calculated using the same methodology as the background CH 4 (Section 4.2). To filter out such events, we chose an hourly mean CO enhancement of 10 ppb (above the background) as a cut off, which is about twice the one standard-deviation 70 uncertainty in the observed CO around the estimated background CO variation without considering the CO enhancement periods. The hourly mean data points after removing the data points with CO values greater than 10 ppb above background concentrations are shown as blue dots in Figure S2. This CO filter further removed about 22% of the filtered Burncluith data.

S2. Bottom-up methane emission inventory (full details in S6)
The following is a brief account of how the bottom-up methane emissions from the various sectors for the year 2015 were 75 compiled. Full details are given in the attached report "Surat Basin Methane Inventory 2015 -Summary Report" by Katestone (2018).

S2.1 Grazing cattle
The information used to estimate methane emissions for grazing cattle included: • Total cattle livestock and grazing area based on agricultural commodities for Australia for 2014-15 (ABS, 2015a) and 80 Land Management and Farming in Australia for 2014-15 (ABS, 2015b).
• Methane emission factor for grazing cattle based on direct measurements (Harper et al., 1999).

4
The number of grazing cattle were calculated in each NRM region and was distributed uniformly across the region. This was then multiplied by the emission factor to give the corresponding methane emissions. There were 1,086,059 grazing cattle in 85 the study area.

S2.2 Feedlots
The following information was used to compute methane emissions for cattle in feedlots: • National Pollutant Inventory (NPI) data for the 2014/15 reporting year with the ANZSIC (Australian and New Zealand Standard Industrial Classification) description "Beef Cattle Feedlots (Specialised)". 90 • Queensland Government datasets including Lot and plan boundaries (Property boundaries Queensland cadastral dataset) and Locations and standard cattle unit numbers contained in NRM regions (Department of Agriculture and Fisheries).
• Methane emission factor for "enteric fermentation" and "manure management" for non-dairy cattle from the Food and Agriculture Organization (FAO) of the United Nations (FAOSTAT, 2016). A total emission factor combines the 95 above two methane sources at a feedlot.
There were 235 cattle feedlots in the study area. The number of cattle per feedlot was estimated. The methane emissions were calculated by multiplying the number of cattle per feedlot by the emission factor.

S2.3 Coal Seam Gas (CSG) activities
The locations of CSG wells and processing facilities were based on data available through DNRM and methane emissions data 100 and calculations were provided by the operators.
The information used to calculate the CSG methane emissions included: locations of CSG wells and processing facilities, methane emissions data and reporting prepared for the National Greenhouse and Energy Reporting (NGER) program and directly from the operators, quantity of gas combusted and the volume of produced water.
The calculation methods are consistent with the NGER program where methane is classified as a greenhouse gas and is 105 quantified and reported in terms of carbon dioxide equivalents.
Combustion: Emissions of methane due to incomplete combustion of CSG (including flaring) and diesel were calculated as the product of the quantity of fuel type, the energy content of fuel type, an appropriated emission factor and the GWP of methane.

Fugitive emissions (venting of CSG):
Estimates of the quantities of gas vented are based on methods prescribed by the 110 Compendium of Greenhouse Gas Emissions Methodologies for the Oil and Natural Gas Industry (API Compendium) (API, 5 2009). Emissions were estimated based on the direct measurement of gas released, if this information was unavailable industry standard factors were applied (API, 2009).
Methane from produced water is a component of both CSG production and processing is an important source. It was included under venting and was calculated at 1.63 × 10 6 kg yr -1 (∼ 10% of the total CSG emissions) using an emission factor of 0.036 115 tonnes CH 4 per 1000 m 3 of produced water based on API (2009) ( Table 5-11, page 5-57).
Fugitive emissions (other than venting or flaring): These fugitive methane emissions include emissions from (NGER Determination 2008, Section 3.70 (Clean Energy Regulator, 2016): a gas wellhead through to the inlet of a gas processing plant, a gas wellhead through to the tie-in points on gas transmission systems (if processing of natural gas is not required), gas processing plants, well servicing, gas gathering, gas processing and associated waste water disposal. The emissions are 120 calculated as the product of the total quantity of natural gas, the appropriate emission factor and the GWP of methane.

S2.4 Coal mining
Methane emissions for four coal mines in the study were calculated from the following information (only the dominant coal extraction process was included): • DNRM Mining lease surface areas.
• Fugitive methane emission factor for extraction of coal in Queensland of 0.02 tonnes CO 2 -e per tonne of raw coal (DoE, 2016).
• Methane Global Warming Potential (GWP) of 25 (DoE, 2016). 130 The methane emissions for each mine were calculated by multiplying the amount of ROM coal by the appropriate emission factor. They were allocated uniformly across the mining lease areas associated with each mine as identified from the DNRM dataset.

S3. Estimating the background methane concentration
Data from the measured concentration time series were retained if they occurred between 1200 -1500 h local time (typically 135 the time of highest boundary layer height and maximum trace gas homogeneity during the diurnal cycle) and the hourly standard deviation of concentration was less than or equal to 1 ppb, indicating very well mixed conditions. This filtered dataset was then used to derive a smooth curve. Based on the method described by Thoning et al. (1989), the filtered dataset was fitted with a function consisting of a cubic polynomial and three harmonics. This fit is then subtracted from the filtered data and the residuals further filtered with a band-pass filter of 80 days. The original function fit is then added back to the filtered residuals 140 6 to give a smooth fit through the data. These operations are performed iteratively (with hours lying outside twice the standard deviation around the fit excluded) until the fit converges. An interpolation routine then produced the fitted background CH 4 concentrations at each of the hourly timestamps of the original measured data. Figure S3 presents wind roses for Burncluith and Ironbark constructed using the TAPM generated hourly vector winds and the 145 observed hourly vector winds for all hours (i.e. without filtering, with sample size 12855 and 13198, respectively) for the period August 2015 to December 2016. The TAPM winds are from the inner-nest model output at a height of 10 m (i.e. the lowest model level). Only those modelled hours for which there are wind data are considered. The modelled winds are qualitatively similar to those observed, with the most frequent modelled wind direction also from the north-east quadrant, and winds from the south-west quadrant modelled at a relatively smaller frequency in agreement with the observations. The 150 modelled winds at Burncluith and Ironbark are more similar than those observed. At Burncluith the model underestimates the frequency of low wind speed events (< 2 m s -1 ), which mostly occurs at night, and overestimates the frequency of higher wind speed events (> 4 m s -1 ) from the north-east sector. The wind speed distribution at Ironbark is better modelled than that at Burncluith-one reason for this could be that Burncluith has several tall trees in the vicinity which may weaken the flow field and whose influence is not properly accounted for in the model. There is also a difference in the height at which winds are 155 given: the model height is 10 m whereas, it is 7.6 m at Burncluith and 5.8 m at Ironbark. Generally, in the surface layer, winds get stronger with height, and, therefore, one factor in the modelled winds being stronger than the observation could be the height difference. Figure S4 is the same as Figure S3, except that only the filtered hours (i.e. the daytime hours 1000-1700 h irrespective of wind speed and the remaining hours for which the observed wind speed is greater than 3 m s -1 ) are used. The sample size after 160 filtering is 5452 and 7841 for Burncluith and Ironbark, respectively. These two sets of plots are qualitatively similar, except that, as expected, the low wind underprediction by the model has reduced after filtering, particularly for Burncluith, and the frequency of flow from between 0°-45° has reduced for this site.

S4. Model performance for meteorology
Values of some commonly used model performance statistics are given in Table S1 for the prediction of wind speed (WS, m s -1 7 The IOA and r values in Table S1 suggest that with the filtering the mean wind speed (WS) is predicted slightly worse, but the wind components U and V are predicted better, which implies that there is an improvement in the estimation of wind direction with filtering. As judged from the IOA values, the overall TAPM performance for meteorology for the Surat Basin is satisfactory and comparable to those in other studies (e.g., Luhar and Hurley, 2003;Hurley et al., 2005) (also see papers in TAPM citation database https://scholar.google.com.au/scholar?oi=bibs&hl=en&cites=13876071272134760358).
180 Figure S1. Burncluith minutely-mean data for 10 January 2017. Blue curve represents the default filtering, red curve the cow filtering ("tight"). Where the two curves cannot be seen separately, they overlap.
185 Figure S2. Hourly mean concentrations of CO versus CH 4 measured at Burncluith selected for 1000-1700 for all wind speeds and for 1800-0900 for wind speed greater than 3 m s -1 (orange circles). The data group with high magnitudes of CO concentration likely represents dominant contributions from combustion sources. The data marked with blue dots are the measurements when hourly mean CO concentrations are within 10 ppb of the background CO concentration at the time of measurement and are selected to represent contributions from non-combustion sources. 190

S5. Sensitivity of the inversion to an alternate background methane concentration
The background concentration calculation methodology used in the paper and described in Section S3 assumes that under vigorous atmospheric mixing conditions in the daytime, the measured concentrations within study domain represent methane levels both within and outside the domain boundaries, so that the measured concentrations can be taken to represent the background (i.e. the concentration outside the study domain boundaries) under such conditions. In our study, because the 205 background concentration is calculated from the measurements within the source region under study, it may represent an upper limit on the magnitude of the background. Below, as a sensitivity test, we try an alternate methane background time series in our inversion.
The Cape Grim Baseline Air Pollution Station, located on the north-west tip of Tasmania (40.7ºS,144.7ºE), is a baseline air pollution station (https://research.csiro.au/acc/capabilities/cape-grim-baseline-air-pollution-station/; https://capegrim.csiro.au) 210 that measures atmospheric composition, including continuous, in-situ measurements of methane ( Figure S5). It is part of the World Meteorological Organization-Global Atmosphere Watch (WMO-GAW) network (https://community.wmo.int/gawstations-network-and-other-measurements; data from https://gaw.kishou.go.jp). The measurements from the Station are filtered for the marine baseline air in southern mid latitudes, with the baseline (or clean air) sector as defined in Figure S5. The baseline methane thus represents concentration levels without the direct influence of the continental sources. 215 Figure S5. The Cape Grim Baseline Air Pollution Station, with the clean air sector used for baseline composition measurement (https://capegrim.csiro.au). The Surat Basin region as used in our study is shown as a red square.

220
The hourly-averaged Cape Grim baseline methane for the study period is shown in Figure S6 (red line). Also shown is the background methane used in the present paper (green line -which is the average of the background times series calculated from the Ironbark and Burncluith data). On average, the Cape Grim marine baseline is 8.4 ppb (by volume) lower than the background we used in the model (green line). It is thus reasonable to assume an alternate background that lies between the Surat Basin background as used in our study and the Cape Grim marine baseline (i.e. between the two bounds). The 8.4 ppb 225 gradient reflects the impact of methane emissions over the land surface in the fetch between the coast and the Surat measurement sites. So how much of this total gradient is due to the inland sources within the model domain (350 km × 350 km) and those beyond? The fetch of the latter from the coast varies with direction and is on average much greater than our regional domain. However, plumes from these sources would be much more diffused arriving in the study domain than sources within the study domain. Here we make a rather crude assumption that 2/3 rd of the gradient (i.e. 5.6 ppb on average) is due to 230 sources from the outer parts of the model domain (due to larger fetches and hence larger integrative surface source areas) and 1/3 rd (i.e. 2.8 ppb on average) due to sources within the model domain (in other words, on average our background may have been overestimated by 2.8 ppb). Hence, our alternate hourly background ( , ) is as follows: where is the hourly background as used in our study and is the hourly Cape Grim marine baseline concentration. Figure S6 shows the alternate background based on Eq. (S1) as a blue line. On average, the alternate background is 2.8 ppb 235 lower than the original background and 5.6 ppb higher than the Cape Grim baseline. (Obviously, it is possible that, at a given time, the background concentration may vary spatially along the study domain boundaries, and as such it is not possible to investigate this with the modelling tools and measurements we have at present.) To examine how the alternate background influences the inferred emissions, we performed an inversion using the alternate background methane with all other inversion settings the same as finalised Case 3c (i.e., a Gaussian prior with the bottom-up 240 inventory emission as the mean (q p ) and standard deviation = 3% of the mean value (q p ) for each of the 11 × 11 sources). hourly-averaged Cape Grim marine baseline methane is shown as a red line. The alternate background (blue line) is as calculated from the Cape Grim baseline methane (red line) and the background concentration (green line) using Eq. (S1), and is used for an inversion sensitivity test here.
The inversion results in Table S2 show that compared to the inferred emissions obtained using the original background methane 250 the alternate background gives total emissions that are 6.8% higher, while the increase is smaller at 3.9% in the CSG subdomain and larger at 8.5% in the non-CSG region. The overall increase is expected because the increase in the measured concentrations by 2.8 ppb as a result of the use of the alternate background needs to be accounted for by the inversion by enhancing the amount of inferred emissions.
We also find that the amount of increase in the inferred emissions with the alternate background is almost uniformly spread 255 through the study domain relative to the total emission, and that there are no significant spatial distributional shifts in the inferred emissions with the two background choices. This means that if these emissions were to be used in a forward model simulation, they would lift the modelled concentrations throughout the region by a very similar amount (likely by 2.8 ppb). 260 Table S2: Inferred emissions (×10 6 kg yr -1 ) obtained using the original methane background variation used in the paper (Case 3c, with the bottom-up inventory as a Gaussian prior with σp = 3% qp) and those obtained using the alternate methane background variation calculated in this section using Eq. (S1). The values in the parentheses are % change over the original inferred    Table 13 Emission factors and energy content for methane emissions from combustion and flaring of CSG19  Figure 3 Spatial Population data (

INTRODUCTION
The Surat Basin is a geological region in central Queensland that has seen large scale development of coal seam gas (CSG) extraction and processing operations in the past five years. The main driver of CSG development in the Surat Basin is the recent technology advancement in unconventional gas (CSG) extraction techniques and specifically advanced drilling capabilities. This technology advancement has made unconventional gas extraction projects commercially viable and, coupled with the extensive CSG resources in Surat Basin, has led to large scale development.
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) commissioned Katestone Environmental Pty Ltd (Katestone) to develop an inventory of activities that generate emissions of methane within the Surat Basin and quantify their associated annual emission rates (Surat Basin Methane Inventory).
The Surat Basin Methane Inventory was provided to CSIRO as a series of datafiles. This report provides a summary of the activities that generate methane emissions and describes the methodologies that were used to generate the Surat Basin Methane Inventory.

Project background
The datafiles provided to CSIRO for the Surat Basin Methane Inventory include the following industry sectors and activities: • Large industry (power stations, coal mines, coal seam gas processing and production) • Agriculture (feedlots, grazing cattle, piggeries and poultry farms) • Domestic wood heating

Motor vehicles
• Miscellaneous sources (landfills, sewage treatment plants, river seeps and geological seeps).

Study area
The study area for the Surat Basin Methane Inventory was defined by CSIRO. It extends from Toowoomba in the southeast to Emerald in the northwest. The study area is 344 km by 345 km and is shown in Figure 1. The study area was divided into a network of evenly-spaced grid cells each 1 km by 1 km.

Scope of works
The scope of works includes the following elements: • Identification and mapping of sources in the Surat Basin with emissions of methane for the year 2015 • Quantification of annual methane emission rates from each identified source for 2015 • Provision of a methane emissions dataset for each source type in a format suitable for use by CSIRO • Provision of a summary report detailing all the methods and sources used to create the inventory.

Exclusions and missing data
The scope of works does not include methane emissions from the following activities: • Land clearing • Biomass burning • Wetlands • Registered ground water wells • Fuel usage and material handling associated with mining activities. Methane emissions from these aspects of mining operations are expected to be minimal and significantly less than the aspects of mining that have been quantified, namely: open cut coal mining and coal off gassing.

SUMMARY OF THE SURAT BASIN METHANE INVENTORY
A summary of total methane emissions from the industry sectors and sources included in the Surat Basin Inventory is presented in Table 1 and shown in Figure 2. Emission rates of methane aggregated by each 1 km by 1 km grid cell across the study area is presented in Figure 3. A detailed breakdown of the Surat Basin Methane Inventory by industry sector or source, including a description of the methane emissions calculation methodology, is provided in the following sections.

Overview
This chapter details the agricultural activities included in the Surat Basin Methane Inventory and summarises their associated emission rates of methane. The methods used to calculate methane emissions, the data sources and assumptions are provided.

Emission sources
There are 235 beef cattle feedlots, 1,086,059 grazing cattle, 114 piggeries and 18 poultry farms in the study area for which methane emissions have been estimated for 2015.
The locations of the agricultural facilities (feedlots, poultry farms and piggeries) included in the Surat Basin Methane Inventory are shown in Figure 4. The National Resource Management (NRM) region boundaries within the study area are also shown in Figure 4 (blue lines).

Feedlots
The following information was used to calculate methane emissions from cattle feedlots: • Methane emission factor for enteric fermentation and manure management for non-dairy cattle from the Food and Agriculture Organization (FAO) of the United Nations (FAOSTAT, 2016). A total emission factor of 62 kg CH4/SCU was used, which combines the two key methane sources at a feedlot (Table 2).

Grazing cattle
The following information was used to calculate methane emissions for grazing cattle: • Methane emission factor for grazing cattle of 0.23 kg CH4/animal/day based on direct measurements (Harper et al., 1999).

Poultry farms
The following information was used to calculate methane emissions from poultry farms: • NPI for the 2014/15 reporting year for facilities in the study area with the ANZSIC description "Poultry farming (Eggs)" and "Poultry Farming (Meat)." There were three records returned for the filter "Poultry farming (Eggs)." There were no records returned for "Poultry Farming (Meat)." • Approvals documentation publicly available via council's PD (Property Development) online services.
• Google Earth imagery to determine approximate floor areas of some poultry farm sheds.
• Distribution of broiler chickens and layer chickens for Australia (Table 3) from the FAO (FAOSTAT, 2016).
• Methane emission factor for manure management (Table 4) for broiler chickens and layer chickens from the FAO (FAOSTAT, 2016). Emission factor for unknown poultry farm types derived from combined emissions for broiler and layer chickens, using a weighted average based on population distribution.
• Stocking densities for different farm types (Table 5) based on industry literature (Sustainable Table, 2015; Queensland Government, 2013) and industry experience.

Piggeries
The following information was used to calculate methane emissions from piggeries: • Emissions of ammonia reported to the NPI for the 2014/15 reporting year for facilities in the study area with the ANZSIC description "Pig Farming".

•
The NPI reporting threshold of 10,000 kg/year of ammonia emissions for facilities that do not report to the NPI. •

NRM's and DAF's datasets including:
o Lot and plan boundaries contained in the Property boundaries Queensland cadastral dataset o Locations of piggeries contained in the Agricultural land audit -current piggeries -Queensland dataset.
• Distribution of breeding swine and market swine for Australia (Table 6) from the FAO (FAOSTAT, 2016).
• Methane emission factor for enteric fermentation and manure management (Table 7) for breeding swine and market swine from the FAO (FAOSTAT, 2016). A total emission factor of 15.6 kg CH4/SPU (standard pig unit) (default type) was used, which combines the two key methane sources at a piggery.

Data checks
The calculation of the methane emissions for agriculture involved obtaining information from a range of sources (as described in Section 3.3.1). To ensure that activities were not double counted, a number of checks were conducted.

Feedlots
Several feedlots were contained in both the NPI and DAF datasets. To avoid double counting of feedlot emissions, the following checks were conducted: • Lot and plan numbers were identified for all feedlots in both datasets.
• For each feedlot in the NPI dataset, the nearest feedlot in the DAF's dataset was identified.
• Any feedlots with matching plan numbers were considered to be the same. The coordinates of the feedlots identified to be within both datasets varied by no more than 70 m.

Poultry farms
Locations of poultry farms contained in the NPI and DAF datasets were overlaid on aerial imagery to identify farms that were contained in both datasets. The checks found that the three poultry farms that reported to the NPI were also found in the DAF dataset.

Piggeries
Some piggeries, but not all, were contained in both the NPI and DAF datasets. To avoid double counting, the following steps were taken: • Lot and plan numbers were identified for all piggeries in both datasets.
• For each piggery in the NPI dataset, the nearest piggery in the DAF dataset was identified.
• Any piggeries with matching lot or plan numbers in both datasets were considered to be the same.

Poultry farms
The DAF dataset identified 12 poultry farms within the study area.
The comparison of the NPI database and DAF dataset indicated that all poultry farms that reported to the NPI were found in the DAF dataset.
The capacity of each poultry farm was determined from publicly available approval documents, where available. Where no publicly available data could be found, capacity was estimated from the area of poultry sheds evident in aerial imagery and stocking densities presented in Table 5.
The DAF dataset reported poultry farm capacity as a range. In these cases, a mid-point was selected. For example, for poultry farms with a reported capacity of 1,000 -200,000, emissions were calculated using a capacity of 100,000.
The type of poultry farm was determined from approval documents or publicly available information, where available. Where no information could be found, the default emission factors were used.
For poultry farms of unknown type, a stocking density for breeder farms of 14 birds/m2 was used as required to calculate poultry farm capacity.
Methane emissions due to poultry were calculated as:

Piggeries
The DAF dataset included 114 piggeries within the study area. Six of these facilities reported to the NPI during the 2014/15 period.
For facilities that reported to the NPI, piggery capacity was estimated based on the reported ammonia emissions for 2014/15 and the ammonia emission factor of 6.8 kg NH3/SPU (Katestone, 2016). Where piggeries did not report to the NPI, the capacity was estimated as the number of pigs that would produce ammonia emissions equal to 70% of the reporting threshold of 10,000 kg NH3/year.
There is no detailed information on specific farm types. Therefore, the default factor of 15.6 kg CH4/SPU, estimated using pig population distribution (Table 6) and emission factors for enteric formation and manure management (Table 7), was used.
Methane emissions due to piggeries were calculated as:

Emission rates
Total methane emissions from agriculture (feedlots, grazing cattle, poultry farms and piggeries) included in the Surat Basin Methane Inventory are summarised in Table 8.

Overview
This chapter details the coal mines included in the Surat Basin Methane Inventory and summarises their associated emissions of methane. The methods used to calculate emissions, data sources and assumptions are provided.

Emission sources
Four coal mines within the study area were identified as in operation during 2015. Table 9 details Run-of-mine (ROM) tonnages for each mine for 2014/15 (DNRM, 2016) and Figure 5 shows the mine locations. There is no emission factor for methane emissions associated with coal exploration. Therefore, emissions of methane from this activity could not be quantified and have been excluded for the inventory. Notwithstanding this, methane emissions associated with coal exploration are likely to be relatively small.
There is also no publicly available information regarding material handling and fuel usage at the coal mines identified in the study area. Emissions of methane from these activities are expected to be relatively small compared to extraction of coal, consequently, emissions of methane from these sources have been excluded from the inventory.

Information
The following information was used to estimate methane emissions from coal mining: • DNRM Mining lease surface areas.
• Fugitive methane emission factor for extraction of coal in Queensland of 0.020 tonnes CO2-e / tonne raw coal (DoE, 2016).

Coal extraction
Methane emissions from extraction of coal were calculated as: Amount of run-of-mine coal (kg/year)

CH4_EF
Methane emission factor of 0.8 kg/tonne coal/year (equivalent to 0.020 tonnes CO2-e/tonne coal) Methane emissions were allocated uniformly across the mining lease areas associated with each mine as identified from the DNRM dataset.

Emission rates
Methane emissions from coal mining activities included in the Surat Basin Methane Inventory are summarised in Table 10.

Overview
This chapter details the coal seam gas (CSG) activities that were considered by the Surat Basin Methane Inventory. The methods used to calculate emissions, data sources and assumptions are provided.

Emission sources
CSG is primarily composed of methane. CSG production and processing can lead to the release of methane into the atmosphere. Methane emission from CSG operations can occur for a number of reasons, with some sources emitting continuously while others are more intermittent in nature. A summary of the emission sources considered in this inventory is provided in Table 11. Collection and storage of produced water X The CSG operators active in the study area are as follows: • Origin • QGC • Arrow • Santos • APT Petroleum Pipelines (Kogan North Processing Facility). Table 12 provides a summary of the operations.  Methane emissions due to two of the five operators have not been included in the inventory due to insufficient detail being available at the time of this report. Based on published Queensland government CSG production data, it has been determined that these operators account for approximately 1.5% of emissions associated with CSG activities. This is small compared to the overall uncertainty associated with the estimated methane emissions from CSG operations (see Section 13). As a result, omission of these small operators from the inventory is unlikely to affect the reliability of the CSG methane inventory.

Methodology
The methane emissions resulting from CSG activities in the Surat Basin have been estimated in consultation with the relevant operators.

Information
Information sources used in the assessment include: • Location of CSG well and processing facilities based on data available through DNRM.
• Methane emissions data and calculations provided by operators.
Where possible, all information provided by operators has been subject to a comprehensive review process. This has included consideration of the calculation methods and emission factors as well as the magnitude and intensity of methane emissions associated with production and processing activities.

Calculation methodology
The calculation methods used to estimate methane emissions from CSG activities are consistent with the NGER program. Methane is classified as a GHG under the NGER program and is quantified and reported in terms of carbon dioxide equivalents (CO2-e). CO2-e is calculated by multiplying the mass rate of methane by its Global Warming Potential (GWP), which for methane (GWPCH4) is 25. It provides an indication of the contribution of methane to global warming relative to CO2 (where CO2 GWP=1). In simple terms the quantity of methane emissions can be calculated by dividing the quantity of methane emissions estimated in CO2-e by the GWP for methane: The general equations used to estimate methane emissions from CSG activities are described in the following sections.

Combustion
Emissions due to combustion of CSG (including flaring) and diesel were calculated based on the following equation: Emissions of methane in kilograms due to the combustion of fuel type (i).
Quantity of fuel type (i) combusted measured in cubic metres (CSG combustion), tonnes (CSG flaring), kilolitres (diesel) or gigajoules (energy content is not applied in this instance).
Energy content of fuel type (i) measures in gigajoules per cubic metre (CSG) or gigajoules per kiloliter (diesel).

4
Emission factor for methane (CH4) measured in kilograms CO2-e/gigajoule (CSG combustion and diesel), tonnes CO2-e/tonnes of gas flared (CSG flaring).  however, if this information is unavailable industry standard factors can be applied. Table 14 provides a list of the relevant sections of the API Compendium.

Emissions process API Compendium section
Gas treatment processes Section 5.1 Cold process vents Section 5.3 Other venting sources-gas driven pneumatic devices Section 5.6.1 Other venting sources-gas driven chemical injection pumps Section 5.6.2 Other venting sources-coal seam exploratory drilling, well testing and mud degassing Section 5.6.3 and 5.6.6 Non-routine activities-production related non-routine emissions Section 5.7.1 or 5.7.2 Non-routine activities-gas processing related non-routine emissions Section 5.7.1 or 5.7.3

Fugitive emissions -other than venting or flaring
Fugitive methane emissions, other than emissions that are vented of flared (also referred to as leaks), include emissions from (NGER Determination 2008, Section 3.70): • a gas wellhead through to the inlet of gas processing plants; and • a gas wellhead through to the tie-in points on gas transmission systems, if processing of natural gas is not required; and • gas processing plants; and • well servicing; and • gas gathering; and • gas processing and associated waste water disposal.
Fugitive methane emissions other than emission that are vented or flared have been calculated based on the following equation: Emissions of methane other than emissions that are vented or flared from production and processing of natural gas in kilograms.
the total quantity of natural gas that passes through the natural gas.
production and processing measured in tonnes. 4 1.2 x 10 -3 , which is the emission factor for methane from general leaks in the natural gas production and processing, measured in CO2-e tonnes per tonne of natural gas that passes through the natural gas production and processing.

Emission Rates
Methane emissions associated with CSG activities have been estimated based on the data provided together with supplementary calculations using the methodology described above. Table 15 provides a summary of methane emissions from CSG activities in the Surat Basin. 6. LANDFILLS

Overview
This chapter details the landfills that were included in the Surat Basin Methane Inventory and summarises their estimated methane emissions. The methods used to calculate emissions, data sources and assumptions are provided.

Emission sources
There are 41 landfills in the Surat Basin Methane Inventory study area for which methane emissions have been calculated, as shown in Figure 7.

Figure 7
Location of landfills in the study area 6.3 Methodology

Information
The following information was used to generate the methane emissions for landfills in the study area: • Locations of landfills was based on contact details and site information for public waste and recycling facilities in Queensland (EHP, 2016) • Population data for 2011 (ASGS, 2011) from residential mesh blocks, approximately 30-60 dwellings designed to be small enough to aggregate accurately to a wide range of spatial units

Calculation methodology
Locations of each landfill in the study area were identified from the EHP (2016) dataset. Population data for the study area was sourced from ASGS (2011) for residential mesh blocks.
The total waste per mesh block was calculated using the estimated waste per person for Queensland, detailed in Table 16.  The population and total waste calculated for each region are summarised in Table 17.

Emission rates
Methane emissions due to landfills included the Surat Basin Methane Inventory are summarised in Table 18.

Overview
This chapter details the power stations that were included in the Surat Basin Methane Inventory. The methods used to calculate emissions, data sources and assumptions are provided.

Emission sources
There are eight power stations in the study area for which methane emissions have been calculated. The power stations detailed in Table 19 and Figure 8 are: Methane emissions from the above power stations are related to the combustion of coal or natural gas.

Information
The following information was used to generate the power stations methane emissions: • Actual electricity generation for each power station from the Australian Energy Market Operator (AEMO) for 2015 at 5-minute intervals • Stack characteristics sourced from Australia Pacific LNG Project Volume 5: Attachments Attachment 28: Air Quality Impact Assessment -Gas Fields (Katestone, 2010b).
Emission intensity for each power station changes with load. However, this detailed information was not available. Therefore, it was assumed that the emission intensity remained constant throughout the year.

Calculation methodology
Methane emissions from the power stations were calculated as follows:

Emission rates
Methane emissions due to power stations included in the Surat Basin Methane Inventory are summarised in Table   21.

Overview
This chapter details the wastewater treatment facilities that were included in the Surat Basin Methane Inventory and summarises the estimated methane emissions. The methods used to calculate emissions, data sources and assumptions are provided.

Emission sources
There are 20 wastewater treatment facilities in the Surat Basin study area for which methane emissions have been calculated.
The location of the wastewater treatment facilities in the study area are shown in Figure 9.

Figure 9
Wastewater treatment facilities in the study area

Information
The following information was used to generate the wastewater treatment facility methane emissions: • Locations of wastewater treatment facilities based on national database (Geoscience Australia, 2012) • Population data for 2011 (ASGS, 2011) from residential mesh blocks, approximately 30-60 dwellings designed to be small enough to aggregate accurately to a wide range of spatial units

Calculation methodology
The location of each wastewater treatment facility in the study area was identified from the Geoscience Australia (2012) dataset. Population data within the study area were sourced from ASGS (2011), consistent with population data used in the development of the landfill and motor vehicle methane inventory.
The total wastewater per mesh block was calculated using the estimated wastewater factor of 0.0585 tonnes wastewater/person (DoE, 2016 The population and total wastewater treated in the study area is summarised in Table 22.

Emission rates
Methane emissions from wastewater treatment facilities included in the Surat Basin Methane inventory are summarised in Table 23.

Overview
This chapter summarises the methane emissions due to river seeps included in the Surat Basin Methane Inventory. The methods used to calculate emissions, data sources and assumptions are provided.

Information
The emissions of methane from river seeps in the study area is based on advice from Stuart Day from CSIRO who advised that a total methane flux of 1000L/min should be used for the four main seeps in the Condamine River.
The location of river seeps in the study area are shown in Figure 10 and correspond to locations provided by CSIRO in the file "Condamine River Seeps.kmz".

Figure 10
Location of river seeps included in the Surat Basin Methane Inventory

Calculation methodology
The emission rate of methane from river seeps was calculated using the following formula:

CH4_ER
Emission rate of methane g/s Methane flux Methane flux in L/min (1000) nsites Number of sites giving methane flux (4 sites) density Density of methane (0.716 g/L)

Emission rates
Methane emissions due to river seeps included in the Surat Basin Methane Inventory are summarised in Table 24.

Overview
This chapter summarises the methane emissions due to ground seeps included in the Surat Basin Methane Inventory. The methods used to calculate emissions, data sources and assumptions are provided.

Information
The emissions of methane from ground seeps in the study area is based on work conducted by CSIRO and reported in the report Characterisation of Regional Fluxes of Methane in the Surat Basin, Queensland, Phase 2: A Pilot Study of Methodology to Detect and Quantify Methane Sources (CSIRO, 2015).
The locations of ground seeps in the study area are shown in Figure 11 and correspond to locations provided in CSIRO (2015).

Figure 11
Location of ground seeps in the study area Table 3.5 of CSIRO (2015) summarised the measured methane emission rate for 10 sites using a combination of measurement methods (traverse and flux chamber). The measured emission rates are reproduced in Table 25.
The maximum emission at each site has been used in the Surat Basin Methane Inventory.

Emission rates
Methane emissions due to ground seeps included in the Surat Basin Methane Inventory are summarised in Table 26.

Overview
This chapter details the domestic wood heating sources included in the Surat Basin Methane Inventory. The methods used to calculate emissions, data sources and assumptions are provided.

Emission sources
The types of domestic wood heaters included in the Surat Basin Methane Inventory were based on the NSW EPA GMR Inventory 2008 (NSW EPA, 2012a) and include the following: • Slow combustion heaters with compliance plates

Information
The following information was used to generate the domestic wood heating methane emissions:

Calculation methodology
The following approach was taken to assign domestic wood heating methane emissions spatially: • The average ownership of solid fuel heaters and the average consumption of fuel per heater were taken from the NSW EPA GMR Inventory 2008 and used to determine a consumption factor for each heater type per dwelling

•
The consumption factor was then applied to estimate the total annual consumption of fuel within each ABS mesh block • The estimated consumption rates were assigned to individual grids within the study area.
Diurnal, weekly and monthly profiles were constructed from information presented in the NSW EPA GMR Inventory 2008.
All dwellings in the study area were assumed to fall into the 'Separate house' category.
The methane emission factors for the different types of wood heaters are based on the NSW EPA GMR Inventory 2008 as presented in Table 27.

Emission rates
Methane emissions due to wood-fired heaters included in the Surat Basin Methane Inventory are summarised in Table 28.

Overview
This chapter details the methane emissions from motor vehicles included in the Surat Basin Methane Inventory. The methods used to calculate emissions, data sources and assumptions are provided.

Emission sources
Roads within the study area that were included in the Surat Basin Methane Inventory are shown in Figure 13. The following scenarios were used to develop methane emissions from motor vehicles in the study area: • Hot running base emissions • Speed correction.

Information
The following information was used to generate the motor vehicle inventory: •

Data checks
The following data checks were conducted on the motor vehicle information: •

Continuity of AADT data in segments
• Estimates of AADT were compared with estimated grid emissions • Missing data were interpolated from existing data • Overlay of detailed road data with imagery • Visual comparison and inspection of AADT road geospatial information and detailed road information.

Calculation methodology
Methane emissions attributed to motor vehicles were calculated using the base equation: Methane emissions were derived using the speciation of organic compounds in vehicle exhausts by fuel type. Aggregate methane emission factors for each vehicle type are summarised in Table 29. Diesel light duty vehicles are a subset of diesel light vehicles on all road types except for off-road.

Emission rates
Methane emissions due to motor vehicles included in the Surat Basin Methane Inventory are summarised in Table 30.  Table 31 provides an indication of the uncertainty associated with the emission source categories defined for the Surat Basin Methane Inventory.