A124-01
Air Quality Model Simulations for the Canadian Oil Sands

Friday, 11 December 2020: 05:30
Virtual
Paul Makar1, John Liggio1, Ayodeji Akingunola1, Ralf M Staebler1, Craig Stroud1, Wanmin Gong1, Roghayeh (Roya) Ghahremaninezhad2, Jack Chen3, Junhua Zhang4, Michael D Moran1, Qiong Zheng1, Katherine Lynne Hayden1, Kerry Anderson5, Sepehr Fathi6, Jacob Sommers7, E. Boutzis1, Balbir Pabla3 and Shao-Meng Li8, (1)Environment and Climate Change Canada, Air Quality Research Division, Toronto, ON, Canada, (2)University of Calgary, Calgary, AB, Canada, (3)Environment Canada Dorval, Air Quality Research Division, Dorval, QC, Canada, (4)Environment Canada, Toronto, ON, Canada, (5)Northern Forestry Centre, Edmonton, AB, Canada, (6)York University, Physics and Astronomy, Toronto, ON, Canada, (7)Université de Montreal, Department of Chemistry, Montreal, QC, Canada, (8)Peking University, College of Environmental Sciences and Engineering, Beijing, China
Recorded Presentation
Abstract:
Environment and Climate Change Canada recently led two measurement studies in the Alberta Oil Sands Region (AOSR), in the summers of 2017 and 2018, respectively. The first of these was a focused study on emissions from a tailings pond within one of the AOSR facilities, the second an instrumented aircraft study with observations being taken under late winter (April) and summer (June) conditions. These data, combined with ongoing supersite observations at the Oski-Otin ground observation station, provide a detailed picture of the emissions, transformation and fate of AOSR pollutants, and in turn represent a unique dataset for testing and improving air-quality models. In this work we provide and overview of air-quality modelling for this project, focusing on improvements to the Global Environmental Multi-scale – Modelling Air-quality and CHemistry (GEM-MACH model), making use of and comparing to these two measurement datasets as well as other surface monitoring network data. We describe a suite of improvements to the 2.5km grid cell resolution model including updated gas chemistry and particle formation parameterizations, aerosol direct and indirect effect representation, model emissions inventory inputs, model winter chemistry representation, model deposition algorithms, model forest fire emissions algorithms, and model turbulence and vertical transport algorithms. Simulations will be carried out relative to a base case model lacking these new parameterizations and compared to both airborne, supersite, and tailings pond observations.

The revised version of the model was then used to aid in the interpretation of the observations, and to help determine (1) the likely sources/causes of observed events in the measurement record (e.g. high concentration plumes); (2) the deposition flux of both organic and inorganic pollutants to the ecosystems downwind of the oil sands; (3) the relative contribution of anthropogenic and natural (forest fire) sources of emissions towards air-quality, and (4) the relative impact of emissions estimated from observations versus inventory reported values on model predictions.

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