Using carbon-14 and carbon-13 measurements for source attribution of atmospheric methane in the Athabasca oil sands region

The rapidly expanding and energy-intensive production from the Canadian oil sands, one of the largest oil reserves globally, accounts for almost 12 % of Canada's greenhouse gas emissions according to inventories. Developing approaches for evaluating reported methane (CH₄) emission is crucial for developing effective mitigation policies, but only one study has characterized CH₄ sources in the Athabasca oil sands region (AOSR). We tested the use of ¹⁴C and ¹³C carbon isotope measurements in ambient CH₄ from the AOSR to estimate source contributions from key regional CH₄ sources: (1) tailings ponds, (2) surface mines and processing facilities, and (3) wetlands. The isotopic signatures of ambient CH₄ indicate that the CH₄ enrichments measured at the site were mainly influenced by fossil CH₄ emissions from surface mining and processing facilities (56 ± 18 %), followed by fossil CH₄ emissions from tailings ponds (34 ± 18 %) and to a lesser extent modern CH₄ emissions from wetlands (10 ± <1 %). Our results confirm the importance of tailings ponds in regional CH₄ emissions and show that this method can successfully distinguish wetland CH₄ emissions. In the future, the isotopic characterization of CH₄ sources and measurements from different seasons and wind directions are needed to provide a better source attribution in the AOSR.

1 Introduction

Methane (CH₄) is an important greenhouse gas that has 32 times the global warming potential (mass basis) of carbon dioxide (CO₂) on a 100-year timescale and which contributes to the production of ozone, water vapour (in the stratosphere), and CO₂ in the atmosphere (Myhre et al., 2013; Etminan et al., 2016). Global CH₄ concentration in the atmosphere has almost tripled compared to pre-industrial values (Rubino et al., 2019), largely due to increased anthropogenic activities that include fossil fuel production and use and agriculture (Jackson et al., 2020; Turner et al., 2019). Since most fossil fuel emissions originate from coal, oil, and natural gas exploitation, transportation, and use (Jackson et al., 2020; Saunois et al., 2020), mitigating CH₄ emissions from these activities is necessary to fulfil governmental CH₄ emissions reduction goals. Furthermore, a fast CH₄ mitigation from the oil and gas sector is projected to have a key role in slowing the rate of global warming over the next few decades (Ocko et al., 2021).

Canada contains approximately 10 % of the world's proven crude oil reserves, with 82 % of these reserves located in the Athabasca oil sands region (AOSR) in Alberta (Alberta Energy Regulator, 2015). Oil sand deposits, composed of a mixture of sand grains, water, bitumen, and clay minerals (Mossop, 1980; Takamura, 1982), are extracted through two methods. Shallow deposits (< 75 m) are recovered through surface mining, and the bitumen is subsequently separated from sands with alkaline warm water, concentrated, upgraded, and refined (Larter and Head, 2014). Residual water, solids, and diluents used to separate the bitumen are then stored in tailings, which depending on their age and composition emit volatile organic compounds (VOCs), reduced sulfur compounds, CO₂, and CH₄ (Small et al., 2015). In contrast, the recovery of deeper deposits requires the use of in situ techniques that involve lowering the viscosity of bitumen by injecting steam into the reservoir to extract it (Bergerson et al., 2012). Although only around 20 % of the oil sands deposits are recoverable using surface mining (Alberta Energy Regulator, 2015), surface mining accounts for 45 %–65 % of the annual crude oil production from oil sands (Holly et al., 2016). Each of these methods has greenhouse gas (GHG) emissions associated with it, and it is estimated that the oil sands account for 12 % of Canada's total GHG emissions (Government of Canada, 2018). In the AOSR, an aircraft-based study attributed CH₄ emissions to three main sources: microbial methanogenesis in tailings ponds (45 % of total CH₄ emissions), disturbance of mine faces in open-pit mines (50 % of total CH₄ emissions), and facility activities such as venting, cogeneration, and natural gas leakage (5 % of total emissions) (Baray et al., 2018).

Methane emissions from the oil sands are reported annually to Environment and Climate Change Canada (ECCC) through the Greenhouse Gas Reporting Program (GHGRP), based on inventories of facilities that emit more than 1×10⁷ kg CO₂-eq. yr⁻¹ (Government of Canada, 2018). The GHGRP and other inventory approaches have varying degrees of accuracy and are vulnerable to uncertainty in the “emission factors” used to calculate the GHG emission rates. Top-down approaches are used to verify inventory-based GHG emission estimates, and aircraft-based top-down estimates in the AOSR have shown that inventories underestimate GHG emissions (Liggio et al., 2019), with an aircraft-based estimate reporting 48 % higher CH₄ emissions than in the inventories (Baray et al., 2018). However, these aircraft measurements were limited to a short period of time (summer 2013), and there have not been other studies confirming and updating these findings. Given these limitations, additional measurements of CH₄ and source specific tracers are needed to reconcile differences amongst methods, to generate data at different times of the year, and to generate long-term data for monitoring the evolution of AOSR emissions.

We can use ¹³C and ¹⁴C carbon isotopes to determine the sources of CH₄ emissions because different CH₄ sources have distinct isotopic compositions (Sherwood et al., 2017; Whalen et al., 1989). δ¹³C denotes the ratio of ¹³C relative to ¹²C compared to the Vienna Pee Dee Belemnite (VPDB) standard and reported in parts per thousand. The δ¹³C of CH₄ depends strongly on how CH₄ is produced: by microbial activity (−61.7 ± 6.2 ‰), by the thermal breakdown of organic molecules (−44.8 ± 10.7 ‰), and by incomplete combustion (−26.2 ± 15 ‰) (Sherwood et al., 2017). Δ¹⁴C reports the ratio of ¹⁴C relative to ¹²C compared to a decay-corrected standard and normalized to a δ¹³C of −25 ‰ to account for fractionation (Stuiver and Polach, 1977). Fossil fuels, including CH₄ in natural gas, as well as CH₄ produced from fossil fuel precursors, lack ¹⁴C and have a Δ¹⁴C value of −1000 ‰. In contrast, CH₄ produced from other substrates has a Δ¹⁴C signal close to the contemporary atmospheric Δ¹⁴CO₂ value (Whalen et al., 1989), which was approximately −5 ‰ in 2019 in the Northern Hemisphere, estimated from trends reported by Hammer and Levin (2017). CH₄ produced from contemporary substrates does not approximate the atmospheric Δ¹⁴CH₄ value (estimated to be 340 ‰ from the available data), which is determined by the ratio of modern biogenic to fossil methane emissions, as well as the ¹⁴C enrichment due to global nuclear power plant ¹⁴CH₄ emissions (Lassey et al., 2007). The implication is that in the AOSR, δ¹³C can be used to separate thermogenic CH₄ from surface mine emissions, and microbial CH₄ from tailings ponds, local wetlands, and landfill emissions, while Δ¹⁴C can further separate the fossil microbial CH₄ from tailings ponds from the modern microbial CH₄ from landfills and wetlands.

Previous studies have shown that δ¹³C can be successfully used for regional CH₄ source attribution in urban, natural, and fossil fuel industrial settings (Eisma et al., 1994; Lowry et al., 2001; Fisher et al., 2011; Townsend-Small et al., 2012; Lopez et al., 2017; Maazallahi et al., 2020), and current instruments allow for relatively cheap and precise δ¹³C determinations in small atmospheric samples using gas-source mass spectrometers or cavity ring-down spectrometers. Conversely, Δ¹⁴C measurements have been successful in CO₂ source attribution (Lopez et al., 2013; Zimnoch et al., 2012; Turnbull et al., 2015b; Miller et al., 2020) but less successful in CH₄ source attribution (Eisma et al., 1994; Townsend-Small et al., 2012). Additionally, Δ¹⁴C measurements are rarely used as analyzing ¹⁴C requires larger samples than ¹³C analysis, a more demanding extraction of methane from air, and more expensive measurements using accelerator mass spectrometry. Furthermore, Δ¹⁴C regional source attribution can become complicated in places such as continental Europe where there is a large influence of nuclear power plants with poorly constrained ¹⁴CH₄ emissions (Eisma et al., 1994). Improvements in the atmospheric methane collection and processing are currently being developed, which could increase the use of ¹⁴CH₄ measurements in the near future (Zazzeri et al., 2021), and at the same time there have been improvements in constraining the influence of nuclear power in Δ¹⁴CH₄ measurements (Graven et al., 2019).

Figure 1 Satellite view of the Athabasca oil sands region (satellite image: Google Landsat/Copernicus) showing the location of oil sands mining and processing facilities and the FMS/AMS13 site from which samples described in this paper were collected (57^∘08^′57.54^′′ N, 111^∘38^′32.66^′′ W). The light green polygons show the approximate area of the forest–wetland complexes in the region (based on data from Golder Associates Ltd., 2002).

In this study, our main goal is to test the use of combined Δ¹⁴C and δ¹³C measurements in ambient CH₄ to estimate contributions from the largest CH₄ sources in the AOSR region including wetlands, surface mines, and tailings ponds. We expect to provide a new and practical proof-of-concept method for the long-term monitoring of key CH₄ emissions in regions with multiple CH₄ sources like the AOSR, which is crucial to developing effective CH₄ mitigation policies and, in the specific case study, to fulfil Canada's goal of reducing CH₄ emissions from the oil and gas sector by 40 %–45 % below 2012 levels by 2025 (Government of Canada, 2016).

2 Methods

2.1 Sampling campaign

The sampling campaign took place between 16 and 23 August 2019 at the Environment Canada atmospheric monitoring site Fort McKay South (FMS), adjacent to the Wood Buffalo Environmental Association Air Monitoring Station 13 (AMS13). The monitoring station is located in the AOSR (57^∘08^′57.54^′′ N, 111^∘38^′32.66^′′ W), surrounded to the east and west by boreal forest and wetland complexes and to the north and south by oil sands mining and processing facilities (Fig. 1). Air pollution levels at the site depend on the wind direction, and the principal wind directions in Fort McKay are northerly and southerly (Bari and Kindzierski, 2015).

To the north, facilities include the Canadian Natural Resources Limited (CNRL) Horizon Processing Plant and Mine and Muskeg River and Jackpine mines, the Fort Hills Oil Sands Mine, Syncrude Aurora North Mine Site, and the Imperial Oil Kearl Processing Plant and Mine (Government of Canada, 2017). CH₄ emissions from CNRL Horizon facilities, Muskeg River and Jackpine mines, and the Syncrude Aurora North Mine have been primarily attributed to open-pit mining (5200 ± 1200 kg h⁻¹), but significant CH₄ emissions originating from the CNRL Horizon main plant facility (1000 ± 300 kg h⁻¹) have also been detected (Baray et al., 2018). To the south, the main facilities are Syncrude Canada Mildred Lake and Suncor Energy Inc. oil sands (Government of Canada, 2017). CH₄ emissions from these two facilities have been mainly attributed to tailings ponds (8800 ± 1100 kg h⁻¹) followed by open mining (4600 ± 600 kg h⁻¹) (Baray et al., 2018).

We collected air samples in 70 L cylinder tanks by filling the tank for around 10 min to a pressure of 13.8 MPa using a Bauer PE-100 compressor with a magnesium perchlorate water trap. We aimed to sample CH₄ peaks coming from different wind directions. Before the field campaign, the new Bauer PE-100 compressor was tested at the ECCC laboratories and compared to an existing oil-free RIX compressor system, used to fill reference gases (“laboratory standards”) for ECCC. The difference in methane dry air mole fraction in the cylinders when using the Bauer PE-100 and RIX compressor was found to be within 10 ppb when consecutively filling tanks using ambient air. During the sampling campaign, we flushed the cylinders two times by filling the tank with air until it reached 13.8 MPa and subsequently purging the air by opening the tank valve before collecting the air sample.

We performed continuous measurements of methane (CH₄), carbon dioxide (CO₂), and carbon monoxide (CO) dry air mole fractions for the whole sampling campaign using a Picarro G2401 gas analyzer, which has a 5 min average precision of 1.5 ppb for CO, 20 ppb for CO₂, and 0.5 ppb for CH₄. Results were reported as 1 h averages of the dry air mole fractions. The intake lines of all the instruments were attached at the rooftop of the air monitoring station, approximately 3 m above ground level (m a.g.l.).

2.2 CH₄ isotopic analyses

Methane was extracted from the gas samples at the National Institute of Water and Atmospheric Research (NIWA) in Wellington, New Zealand, following the methods described in Lowe et al. (1991), with updates as described in the following. In summary, a mass flow controller set at 1 L min⁻¹ was connected to the tanks. Air was drawn from the tanks using a 170 L min⁻¹ rotary pump and pumped through two cryogenic traps to remove CO₂, H₂O, N₂O, and other specific hydrocarbons. Each of these cryogenic traps is made of four 350 mm long loops passing in and out of liquid nitrogen. The loops are made of 12 mm ID Pyrex tubing and are kept at pressures lower than 10 kPa. After these first two traps, the sample passed through a third trap containing a Sofnocat reagent (containing platinum and palladium on a tin oxide support) which acts as a catalyst in the conversion of CO to CO₂. This CO₂ was subsequently removed using two additional cryogenic traps. Next, CH₄ was combusted at 750 ^∘C to CO₂ and H₂O using an alumina-supported platinum catalyst. The resulting CO₂ was collected and purified in three additional cryogenic traps. Last, H₂O was removed using alcohol dry ice traps at −80 ^∘C, and CO₂ was vacuum distilled into glass vials or break seals for mass spectrometry. Separate extractions were carried out for each ¹³C and ¹⁴C analysis, processing 26 L of air for ¹³C and 230–290 L for ¹⁴C (depending on CH₄ content of the sample).

Analysis of ¹³C was performed on a Thermo MAT-253 isotope ratio mass spectrometer (IRMS) in dual inlet mode. Samples were analyzed against a pure CO₂ working reference gas derived from a ¹³C-depleted barium carbonate standard (NZCH). The standard deviation for a δ¹³C determination is 0.02 ‰. The results were reported relative to PDB-CO₂. For ¹⁴C analysis, the methane-derived CO₂ was reduced to graphite using H₂ and an iron catalyst at 550 ^∘C (Turnbull et al., 2015a) and measured for ¹⁴C content by accelerator mass spectrometry (Zondervan et al., 2015). The results were reported as fraction modern carbon and Δ¹⁴C age corrected to the date of sample collection following internationally agreed upon conventions (Stuiver and Polach, 1977, Donahue et al., 1990, Reimer et al., 2004). The measurement precision for this dataset is 2.2 ‰ to 2.6 ‰ in Δ¹⁴C.

2.3 Back-trajectory modelling using HYSPLIT-5

We generated hourly 12 h backward trajectories for the duration of the sampling campaign using HYSPLIT-5. HYSPLIT is a model for computing atmospheric transport and dispersion of air masses developed by NOAA's Air Resources Laboratory, and a more complete description of the system can be found in Stein et al. (2015). In this model, a back-trajectory is calculated from a particle that represents a gas being moved by the mean wind field. To calculate the concentration of the trace gas (air concentrations), a number of particles are released from the receptor, and dispersion equations are applied to the upwind trajectory calculation. Then, the mass of the computed particles is added and divided by the volume of their horizontal and vertical distribution. We configured the model to start 3 $\mathrm{m}\mathrm{a}.\mathrm{g}.\mathrm{l}.$ from the location of the FMS site and to use meteorological parameters from the NAM 12 km (hybrid sigma pressure US 2010-Present) database.

2.4 Estimating source contributions using keeling plots

The Keeling plot approach is based on the conservation of mass in the lower planetary boundary layer (Keeling, 1958, 1961). It assumes that the atmospheric CH₄ is the result of a simple mixing between two components, background CH₄ and the sum of all CH₄ sources, and that the isotope ratio of the two components does not change substantially over time, as in this study. As a result, the intercept of a linear regression between $\mathrm{1}/\left[{\mathrm{CH}}_{\mathrm{4}}\right]$ and δ¹³CH₄ or Δ¹⁴CH₄ from atmospheric samples is interpreted as the mean isotopic signature of the CH₄ sources (Eqs. 1 and 2). Here, we calculated the slope and intercept of the linear regression and their uncertainties after York et al. (2004).

$$\begin{array}{}\text{(1)}& {\displaystyle }\begin{array}{rl}\mathrm{\Delta }{}^{\mathrm{14}}{\mathrm{C}}_{\mathrm{air}}=& {\displaystyle \frac{{\mathrm{C}}_{\mathrm{background}}\left(\mathrm{\Delta }{}^{\mathrm{14}}{\mathrm{C}}_{\mathrm{background}}-\mathrm{\Delta }{}^{\mathrm{14}}{\mathrm{C}}_{\mathrm{source}}\right)}{{\mathrm{C}}_{\mathrm{air}}}}\\ & +\mathrm{\Delta }{}^{\mathrm{14}}{\mathrm{C}}_{\mathrm{source}}\end{array}\text{(2)}& {\displaystyle }\begin{array}{rl}\mathit{\delta }{}^{\mathrm{13}}{\mathrm{C}}_{\mathrm{air}}=& {\displaystyle \frac{{\mathrm{C}}_{\mathrm{background}}\left(\mathit{\delta }{}^{\mathrm{13}}{\mathrm{C}}_{\mathrm{background}}-\mathit{\delta }{}^{\mathrm{13}}{\mathrm{C}}_{\mathrm{source}}\right)}{{\mathrm{C}}_{\mathrm{air}}}}\\ & +\mathit{\delta }{}^{\mathrm{13}}{\mathrm{C}}_{\mathrm{source}}\end{array}\end{array}$$

Because the source isotopic signature represents the weighted sum of all the CH₄ sources, a mixing model can be used to determine the individual CH₄ source contributions from the mean CH₄ source isotopic signature if the individual source isotope signatures are known. We used MixSIAR, a Bayesian isotope mixing model framework implemented as an open-source R package (see Stock et al., 2018), to estimate the contribution of potential CH₄ sources to the “mixture mean” (mean source signature in air samples). The base of the MixSIAR framework is a mixing model in which the tracer value of the mixture (e.g., δ¹³C) is the sum of the mean tracer value of each source component multiplied by its proportional contribution to the mixture (p) as in Eq. (3). The assumptions for this model are that all the sources are known, tracers are conserved through the mixing process, tracer values do not vary over time, the tracer values differ between sources, and the sum of the proportional contributions (p) is 1 (Stock et al. 2018).

$$\begin{array}{}\text{(3)}& \mathit{\delta }{}^{\mathrm{13}}{\mathrm{C}}^{\mathrm{mixture}}=\sum _k\mathit{\delta }{}^{\mathrm{13}}{\mathrm{C}}_{\mathrm{k}}^{\mathrm{source}}{p}_k\end{array}$$

To account for source uncertainty, MixSIAR incorporates error structures using the summary statistics of the source isotopic values (mean, variance, and sample size), and source parameters are fitted as in Ward et al. (2010). The mixing system can then be solved analytically for multiple tracers simultaneously if the number of sources does not exceed the number of tracers plus one. In this case, we used two tracers, δ¹³C and Δ¹⁴C, and three sources. The source isotopic values used in the mixing model were derived from the literature and are described in the following section.

3 Results and discussion

3.1 Isotopic signature of CH₄ sources in the AOSR

To estimate the proportion of CH₄ emitted from different potential sources, the isotopic signatures of these potential sources must be known. However, specifying the δ¹³CH₄ from these sources can be especially challenging because δ¹³CH₄ signatures can have wide ranges and vary locally (Sherwood et al., 2017), and there are no studies isotopically characterizing CH₄ from different sources in the AOSR. Based on the previous aircraft source attribution study (Baray et al., 2018), we identified two main CH₄ source categories: CH₄ emissions related to the mining and processing of bitumen (e.g., leaking and venting) and tailings pond CH₄ emissions. Furthermore, we added wetlands as a third source of regional CH₄ emissions as they are estimated to cover approximately 60 % of the Athabasca oil sands region (Rooney et al., 2012), and the wetland CH₄ emissions in the province of Alberta have been estimated to be roughly half of the total anthropogenic emissions (Baray et al., 2021).

Table 1 Estimated values of δ¹³CH₄ and Δ¹⁴CH₄ for the three source categories used in the source attribution.

^a δ¹³CH₄ associated with heavily degraded oils from Head et al. (2003). ^b Hypolimnetic δ¹³CH₄ values from Base Mine Lake from Goad (2017). ^c Canadian boreal wetland δ¹³CH₄ from Ganesan et al. (2018). ^d Tailings pond substrate signature from Ahad and Pakdel (2013). ^e Range of atmospheric Δ¹⁴CO₂ values from 2010 to 2019 extrapolated from Hammer and Levin (2017).

Thermogenic CH₄ associated with Alberta's Lower Cretaceous oils varies between −42 ‰ and −48 ‰ (Jha, Gray and Strausz, 1979; Tilley and Muehlenbachs, 2007), but the prevalence of anaerobic biodegradation in shallow subsurface petroleum reservoirs changes the δ¹³CH₄ composition of heavily degraded oils to between −45 ‰ and −55 ‰, in particular by hydrogenotrophic CH₄ production (Head et al., 2003; Jones et al., 2008). This biogenically over-printed thermogenic CH₄ is present in the mined material of the AOSR, which is potentially released when oil sands are mined, but also during transport, ore preparation, and extraction of bitumen (Johnson et al., 2016). Thus, we used this δ¹³C range to represent CH₄ emissions derived from the bitumen mining and processing (Table 1).

Residual water generated from the surface mining process is stored in tailings ponds where aerobic and anaerobic degradation are mainly fuelled by certain naphtha components in the diluents, in specific short-chain n-alkanes (C₆ to C₁₀), BTEX compounds (i.e., toluene and xylenes), and long-chain n-alkanes (C₁₄ to C₁₈) (Siddique et al., 2006, 2007, 2011, 2012). Radiocarbon measurements of tailings pond components, including total organic carbon (TOC), total lipid extract (TLE), and phospholipid fatty acids (PLFAs), have yielded Δ¹⁴C signatures of approximately −995 ‰ (Ahad and Pakdel, 2013). We infer that CH₄ is most likely produced from these substrates and therefore has the same Δ¹⁴C signature (Table 1). The chemical composition of the tailings ponds – determined by mineralogy of the oil sands, extraction techniques and additives used, and age of the ponds – influences the microbial communities involved in the substrate degradation (Small et al., 2015), which are likely dominated by syntrophic communities as well as both acetoclastic methanogens, previously associated with short n-alkane degradation, and hydrogenotrophic methanogens, associated with the metabolism of long-chain alkanes and BTEX (Penner and Fogth, 2010; Shahimin et al., 2016; Siddique et al., 2012; Zhou et al., 2012). Measurements of the dissolved δ¹³CH₄ from the hypolimnion of Base Mine Lake, a dimictic end pit lake, range between −60 ‰ and −65 ‰ and to our knowledge are the only available δ¹³CH₄ measurements associated with oil sands lakes (Goad, 2017). However, variations in the microbial community composition between ponds result in variations in the rate of CH₄ production (Small et al., 2015) and might also result in differences in the δ¹³CH₄ due to different fractionation in acetoclastic and hydrogenotrophic methanogenesis (Whiticar, 1999; Whiticar et al., 1986). Moreover, the Base Mine Lake δ¹³CH₄ value should be regarded as a minimum, because methanotrophic communities are active in the surface of the tailings ponds, most likely shifting the δ¹³CH₄ towards more positive values during partial oxidation of methane before emission to the atmosphere (Saidi-Mehrabad et al., 2013).

Boreal wetland CH₄ emissions are estimated to have a mean δ¹³C value of −67.8 ‰, based on atmospheric measurements (Ganesan et al., 2018). In terms of Δ¹⁴C, wetland CH₄ emissions are most likely predominantly modern and close to the atmospheric Δ¹⁴CO₂ value, even in wetlands associated with permafrost collapse (Cooper et al., 2017; Estop-Aragonés et al., 2020). Because the residence time of carbon released as CH₄ in wetlands is likely decadal (Whalen et al., 1989; Chanton et al., 1995), we used a Δ¹⁴C signature ranging from approximately 40 ‰, corresponding to the atmospheric Δ¹⁴CO₂ value in the Northern Hemisphere in 2010 (Hammer and Levin, 2017), to approximately −10 ‰, which is the lower limit when using that same dataset to extrapolate for the atmospheric Δ¹⁴CO₂ value in 2019 (Table 1).

Additional CH₄ potential regional sources that were not included in this analysis to avoid having an underdetermined mixing model were forest fires and landfills, both of which would emit CH₄ with a modern Δ¹⁴CO₂ signature. Three major wildfire events occurred in 2019 in Alberta: the Battle complex (Peace River area), Chuckegg Creek wildfire (High Level area), and the McMillan complex (Slave Lake area). The three events started in May and were declared under control on 26 June, 1 July, and 18 August, respectively (MNP LLP 2020), with the third event briefly overlapping with some of the sampling dates (16 to 18 August). However, the event was 290 km southwest of the sampling site, while the air in the sampling site originated from the northwest (see Sect. 3.2), and therefore it is unlikely that this was a significant source of CH₄ in the air samples. In the case of the landfill, some back trajectories show air masses coming from the general Fort McMurray direction, where the municipal landfill is (Fig. 1). We speculated that between these two sources, wetlands are the most prominent CH₄ source because at a provincial level (Alberta), CH₄ wetland emissions are estimated to be 2.5 to 3.5 × 10⁹ kg a⁻¹ while solid waste disposal accounts for 5.2 × 10⁴ kg a⁻¹ (Baray et al., 2021; Environment Climate Change Canada, 2018). If we were to add a landfill component, assuming a δ¹³C value of −55 ‰ for landfills (Lopez et al., 2017), the revised estimation would result in a slightly larger contribution of microbial fossil CH₄ relative to thermogenic CH₄. For example, if 10 % of the microbial modern emissions were derived from landfills and 90 % from wetlands, our model estimate of the contribution from tailings ponds increases by 2 % (see Sect. 3.3).

Figure 2 (a) Hourly CH₄ dry air mole fraction measurements at the FMS13 station (Fort McKay South), with the CH₄ mole fraction of the collected air samples in red circles. (b) HYSPLIT 12 h back-trajectories associated with hourly measurements, with the colour scale representing CH₄ dry air mole fractions in both panels.

3.2 Isotopic signature of ambient CH₄

Analyses of the 12 h back trajectories for the 7 d sampling campaign showed that air masses arriving at the FMS station during this time period primarily originated from two general directions (Fig. 2b): from the northwest between 16 and 19 August and from the southwest and southeast between 20 and 23 August. The CH₄ mole fraction time series for this time period indicated that most CH₄ enrichments were associated with trajectories originating from the west and south, in particular from air masses that transit over the Syncrude Mildred Lake facilities and CNRL Horizon oil sands facilities (Fig. 2).

Figure 3 Keeling plots of (a) CH₄ and Δ¹⁴CH₄, (b) CH₄ and δ¹³CH₄, and (c) plot of δ¹³CH₄ and Δ¹⁴CH₄ in air samples collected from 20 to 23 August (south) in black (n=9) and from 16 to 20 August (north) in red (n=5). In (a) and (b), the intercept of the Keeling plot b indicates the isotopic signature of the CH₄ source. In panel (c), the intercept b is interpreted as the δ¹³C value of fossil CH₄.

The CH₄ mole fraction [CH₄], δ¹³CH₄, and Δ¹⁴CH₄ of the air samples are shown in Table 2. There were significant correlations between $\mathrm{1}/\left[{\mathrm{CH}}_{\mathrm{4}}\right]$ and Δ¹⁴CH₄ (r² =  0.99; black lines in Fig. 3a), between $\mathrm{1}/\left[{\mathrm{CH}}_{\mathrm{4}}\right]$ and δ¹³CH₄ (r² = 0.84; black lines in Fig. 3b), and between Δ¹⁴CH₄ and δ¹³CH₄ (r² = 0.8; black lines in Fig. 3c) in the air samples associated with back-trajectories originating from the south and southwest, corresponding to 20 to 23 August. The intercept of the Δ¹⁴C Keeling plot for these samples showed a source signature of −898 ± 9 ‰ (Fig. 3a), while the intercept of the δ¹³C Keeling plot yielded a source value of −56 ± 0.8 ‰ (Fig. 3b).

Table 2 Methane mole fraction [CH₄], δ¹³CH₄, and Δ¹⁴CH₄ of air samples collected in the Athabasca oil sands region in August 2019. Note that local time of sampling (mountain time, MDT) is 6 h behind UTC universal time.

There were also significant correlations between all variables in the samples associated with back-trajectories originating from the north, corresponding to 16 to 19 August (red lines in Fig. 3). However, there were only five data points, and four of them had very similar values, which could artificially strengthen the correlation. When building the Δ¹⁴C and a δ¹³CH₄ Keeling plot with these five samples, the intercepts yielded source values of Δ¹⁴C ≈ −1000 ‰ and δ¹³C = −35.1 ± 4.5 ‰, which points to a thermogenic source of CH₄ originating in the northern mines.

Figure 4 (a) δ¹³C and Δ¹⁴C signatures of potential CH₄ sources used to estimate source contribution using MixSIAR and mean δ¹³CH₄ and Δ¹⁴CH₄ source signatures of the samples associated with south trajectories derived from Keeling plots. (b) Boxplot of the estimated source contributions from microbial fossil CH₄ (tailings ponds), thermogenic CH₄ (surface mines and processing facilities), and microbial modern CH₄ (wetlands) for these samples. The line inside the boxes represents the median, boxes indicate the 25th and 75th percentiles, and whiskers show the 5th and 95th percentiles.

3.3 Source contributions

The approximate contributions from each source category to samples associated with back-trajectories originating from the south were calculated with MixSIAR and are shown in Fig. 4. The microbial and thermogenic fossil enrichment observed in the CH₄ air samples (∼ 90 %) indicates that most of the CH₄ enrichment observed at the site was influenced by CH₄ emissions from the oil sands mines and processing facilities. Specifically, the contribution from thermogenic CH₄ was estimated to be 56 ± 18 % while the contribution from fossil microbial CH₄ from tailings ponds was estimated to be 34 ± 18 %, with a large uncertainty associated with both estimates (Fig. 4b). The results also indicate an influence of approximately 10 ± < 1 % from modern microbial sources (Fig. 4b), most likely from wetlands. If most of the modern microbial enrichment is derived from wetlands, it is likely that the contribution from this source is near the annual maximum, as CH₄ wetland emissions typically peak in the summer (Baray et al., 2021).

Analyses of the back-trajectories indicated that the air masses from which these sample were collected originated from the south, and therefore the samples are likely predominantly influenced by the Syncrude and Suncor facilities and tailings ponds (Fig. 1). This would explain the substantial enrichment of fossil microbial CH₄ in our samples, as measurements of CH₄ emissions have shown that the largest CH₄-emitting tailings management areas are Syncrude's Mildred Lake settling basin and the Base Mine Lake (Small et al., 2015; You et al., 2021). In comparison to the oil sands facilities in the south (Syncrude Mildred Lake and Suncor), the facilities to the north of the air monitoring site have been shown to have much larger CH₄ contributions from surface mining and natural gas leaking and venting (Baray et al., 2018), as tailings pond emissions are minimal (below 0.1 $\mathrm{kg}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{a}}^{-\mathrm{1}}$) (Small et al., 2015). This was reflected in the few air samples originating from the north that show a δ¹³CH₄ of −35 ‰ and a Δ¹⁴CH₄ of −1000 ‰, which is consistent with the isotopic signature of thermogenic CH₄ (Fig. 3b).

Compared to the only previous CH₄ source attribution study available (Baray et al., 2018), our results implied a lower contribution from tailings ponds and a larger contribution from surface mines and processing facilities. Baray et al. (2018) estimated that 65 % of CH₄ emissions from the Syncrude Mildred Lake and Suncor mines and facilities originated from tailings ponds and 34 % from surface mines, but there have not been studies updating these estimates since this study was performed in summer 2013. We suggest that differences between studies can be attributed to changes in bitumen production in the different sites from 2013 and from the large uncertainties in our estimates. The uncertainty in our estimates is mainly due to the uncertainty in the δ¹³CH₄ signatures of CH₄ sources. For example, a change of 5 ‰ towards more positive values in the tailings pond δ¹³CH₄ signature due to microbial oxidation of CH₄ in the epilimnion would increase the calculated contribution from tailings ponds to 52 ± 23 % and decrease the thermogenic contribution to 38 ± 23 %. This example illustrates the need to reduce the uncertainty in the source isotopic signatures with an extensive δ¹³C characterization of CH₄ sources in the AOSR, in particular from tailings ponds and surface mines. Furthermore, the use of additional tracers such as methane $/$ ethane (${\mathrm{C}}_{\mathrm{2}}{\mathrm{H}}_{\mathrm{6}}/{\mathrm{CH}}_{\mathrm{4}}$) ratios and δ²H in CH₄ could help constrain emissions from source categories since biogenic and thermogenic processes yield distinctive ${\mathrm{CH}}_{\mathrm{4}}/{\mathrm{C}}_{\mathrm{2}}{\mathrm{H}}_{\mathrm{6}}$ ratios and δ²H in CH₄ (Townsend-Small et al., 2016; Lopez et al., 2017; Douglas et al., 2021).

While an exhaustive δ¹³C characterization of CH₄ sources is needed to improve source estimates using carbon isotopes, the clear correlations in our air samples show that this method is useful for estimating CH₄ source contributions in regions with multiple CH₄ sources like the AOSR. Moreover, the collection of air in cylinders is less costly and easier to do on a regular basis compared to techniques such as aircraft measurements and therefore is well suited for monitoring how source emissions change with time (seasonally and annually). The use of an instrument for continuous δ¹³CH₄ measurement such as a Picarro G2201-I isotope analyzer could make this process even easier and more evenly distributed through the year.

4 Summary and conclusions

We conducted a sampling campaign in the Athabasca oil sands region in summer 2019 with the objective of evaluating the potential of using combined Δ¹⁴C and δ¹³C measurements in ambient CH₄ for source attribution. While tracers such as δ¹³C, δD, and ${\mathrm{C}}_{\mathrm{2}}{\mathrm{H}}_{\mathrm{6}}/{\mathrm{CH}}_{\mathrm{4}}$ can separate thermogenic from microbially produced CH₄, the use of Δ¹⁴C indicates if CH₄ is produced from a fossil source regardless of the pathway of CH₄ formation. We demonstrated the use combined Δ¹⁴C and δ¹³C measurements for separating emissions from three sources: mines and processing facilities, tailings ponds, and regional wetlands. Our results confirm the importance of tailings ponds in regional CH₄ emissions (Baray et al., 2018), which we estimated to be approximately 34 % of all the emissions in the region. Furthermore, the addition of Δ¹⁴C in the measurements allowed us to separate wetland CH₄ emissions, which are a major provincial source of CH₄ (Baray et al., 2021) and therefore have the potential to interfere in the accuracy of top-down CH₄ estimates. In general, this method proved to be a suitable tool for CH₄ source attribution in the AOSR and potentially other oil-producing regions as there are clear correlations between δ¹³C and Δ¹⁴C, isotopic measurements are cheap relative to other approaches such as aircraft measurements, and the instrumentation set-up allows for continuous year-round measurements.

Although this study is one of the first to provide a conclusive source attribution using combined Δ¹⁴C and δ¹³C measurements in ambient CH₄, there are still large uncertainties associated with this method, mainly due to the lack of δ¹³C data from key CH₄ sources. These uncertainties can be addressed with a characterization of δ¹³C and Δ¹⁴C in the main CH₄ sources and using additional tracers such as methane-to-ethane ratios and δ²H signatures. Moreover, future work should focus on adding measurements at different times of the year and in consecutive years, as seasonal and annual variations in CH₄ emissions are currently not well constrained. At a seasonal scale, temperature changes in the winter probably reduce microbial methanogenesis, decreasing tailings pond and wetland emissions, and snow cover in open mining areas could affect CH₄ emissions. At an annual scale, changes in mine and processing facilities operations, the development of in situ mining over surface mining, and changes in the age-dependent tailings pond emission profile could also result in CH₄ emission variations. Consequently, implementing isotopic measurements for long-term CH₄ emission monitoring is essential to have a complete understanding of CH₄ emissions in the AOSR and for developing effective mitigation policies.