Quantification of methane sources in the Athabasca Oil Sands Region of Alberta by aircraft mass balance

Abstract

Abstract. Aircraft-based measurements of methane (CH 4 ) and other air pollutants in the Athabasca Oil Sands Region (AOSR) were made during a summer intensive field campaign between 13 August and 7 September 2013 in support of the Joint Canada-Alberta Implementation Plan for Oil Sands Monitoring. Chemical signatures were used to identify CH 4 sources from tailings ponds (BTEX VOCs), open pit surface mines (NO y and rBC) and elevated plumes from bitumen upgrading facilities (SO 2 and NO y ). Emission rates of CH 4 were determined for the five primary surface mining facilities in the region using two mass-balance methods. Emission rates from source categories within each facility were estimated when plumes from the sources were spatially separable. Tailings ponds accounted for 45 % of total CH 4 emissions measured from the major surface mining facilities in the region, while emissions from operations in the open pit mines accounted for ∼ 50 %. The average open pit surface mining emission rates ranged from 1.2 to 2.8 t of CH 4 h −1 for different facilities in the AOSR. Amongst the 19 tailings ponds, Mildred Lake Settling Basin, the oldest pond in the region, was found to be responsible for the majority of tailings ponds emissions of CH 4 (> 70 %). The sum of measured emission rates of CH 4 from the five major facilities, 19.2 ± 1.1 t CH 4 h −1 , was similar to a single mass-balance determination of CH 4 from all major sources in the AOSR determined from a single flight downwind of the facilities, 23.7 ± 3.7 t CH 4 h −1 . The measured hourly CH 4 emission rate from all facilities in the AOSR is 48 ± 8 % higher than that extracted for 2013 from the Canadian Greenhouse Gas Reporting Program, a legislated facility-reported emissions inventory, converted to hourly units. The measured emissions correspond to an emissions rate of 0.17 ± 0.01 Tg CH 4 yr −1 if the emissions are assumed as temporally constant, which is an uncertain assumption. The emission rates reported here are relevant for the summer season. In the future, effort should be devoted to measurements in different seasons to further our understanding of the seasonal parameters impacting fugitive emissions of CH 4 and to allow for better estimates of annual emissions and year-to-year variability.

Estimation of NO<sub><i>x</i></sub> and SO<sub>2</sub> emissions from Sarnia, Ontario, using a mobile MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) and a NO<sub><i>x</i></sub> analyzer

“…Various lifetimes of NO x , τ , have been used in previous mobile MAX-DOAS studies for the calculation of NO x emissions from NO 2 measurements: 6 h in Germany (Ibrahim et al, 2010), 5 h in Delhi (Shaiganfar et al, 2011), 5 h in China (Wu et al, 2017), and 3 h in summer and 12 h in winter in Paris (Shaiganfar et al, 2017). In Beirle et al (2011), the daytime lifetime of NO x was quantified by analyzing the downwind patterns of NO 2 measured by satellite instruments and shown to vary from ∼ 4 h in low-to midlatitude locations (e.g., Riyadh, Saudia Arabia) to ∼ 8 h in northern locations in wintertime (e.g., Moscow, Russia). In a follow-up study, Valin et al (2013) showed that one cannot assume that τ is independent of wind speed and derived values of τ from the satellite observations over Riyadh to be 5.5 to 8 h, corresponding to OH levels of 5-8 × 10 6 molec.…”

“…3.3. Bias in the emission estimates from an incorrect lifetime could be avoided by determining NO x lifetimes from photochemical modelling or, for large cities, satellite observations (Beirle et al, 2011) but taking into account wind speeds (Valin et al, 2013).…”

Quantification of CO<sub>2</sub> and CH<sub>4</sub> emissions over Sacramento, California, based on divergence theorem using aircraft measurements

“…The commonly used bottom-up inventories derive estimates of direct and indirect emissions of greenhouse gases based on an understanding of emission factors from the constituent sectors (Andres et al, 1999;Marland et al, 1985;Boden et al, 2010;California Air Resources Board, 2015;US EPA, 2016). These estimates rely on monthly or quarterly statistical averages of emission activities and often time-invariant emission factors, which mask behavioral patterns.…”

“…Tadić et al (2017) and Conley et al (2017) accomplished emission estimates by flying a cylinder pattern around an emission source to measure GHGs both upwind and downwind for analysis based on the divergence theorem. More recently, Baray et al (2018) used both a screen flight and box flight approach around oil sands facilities and showed that each flight pattern could be preferred, depending on the types of emissions and spatial characteristics.…”

Profiling of CH<sub>4</sub> background mixing ratio in the lower troposphere with Raman lidar: a feasibility experiment

“…Methane is currently the second most important greenhouse gas (after carbon dioxide) of anthropogenic origin (IPCC, 2013). Methane is emitted from a variety of natural and anthropogenic sources (e.g., Baray et al, 2018;Kavitha et al, 2016, and references therein) and on a per-molecule basis, methane is about 30 times more effective a greenhouse gas than carbon dioxide (Etminan et al, 2016). Global information about the CH 4 column concentration is available from satellite observations with, for example, the SCIA-MACHY sensor on board the ENVISAT satellite (Bovensmann et al, 1999) or the TANSO-FTS sensor on board the GOSAT satellite (Kuze et al, 2009).…”