Emissions Preparation and Analysis for Multiscale AirQuality Modelling over the Athabasca Oil Sands Region ofAlberta, Canada

Abstract. The oil sands of Alberta, Canada are classified as unconventional oil, but they are also the third-largest oil reserves in the world, behind only Venezuela and Saudi Arabia. We describe here a six-year effort to improve the emissions data used for air quality (AQ) modelling of the roughly 100 km x 100 km oil extraction and processing industrial complex operating in the Athabasca Oil Sands Region (AOSR) of north-eastern Alberta. The objective of this work was to review the available emissions data, provide information for comparison with observation-based emissions estimates, and generate model-ready emissions files for the Global Environmental Multiscale–Modelling Air-quality and CHemistry (GEM-MACH) AQ modelling system for application to the AOSR. GEM-MACH was used to produce nested AQ forecasts during an AQ field study carried out in the AOSR in summer 2013 as well as ongoing experimental forecasts since then and retrospective model simulations and analyses for the field-study period. This paper discusses the generation of GEM-MACH emissions input files, in particular for a high-resolution model domain with 2.5-km grid spacing covering much of western Canada and centred over the AOSR. Prior to the field study, ten pre-2013 national, provincial, or sub-provincial emissions inventories for up to seven criteria-air-contaminant species (NOx, VOC, SO2, NH3, CO, PM2.5, and PM10) that covered the AOSR study area and that had been compiled for various purposes were reviewed, and then a detailed hybrid emissions inventory was created by combining the best available emissions data from some of these ten inventories. After the field study, additional sources of emissions-related data became available, including 2013 hourly SO2 and NOx emissions and stack characteristics for large point sources measured by Continuous Emission Monitoring Systems, 2013-specific national inventories, daily reports of SO2 emissions from one AOSR facility for a one-week period during the field campaign when that facility experienced upset conditions, aircraft measurements of VOC and PM2.5 concentrations from the 2013 field campaign and derived estimates of their emissions, and measurements of chemical composition of dust collected from various AOSR sites. These new data were used to generate updated emissions input files for various post-campaign GEM-MACH sensitivity studies. Their inclusion resulted in some significant emissions revisions, including a reduction in total VOC and SO2 emissions from surface mining facilities of about 40 % and 20 %, respectively, and a ten-fold increase in PM2.5 emissions based on aircraft observations. In addition, standard emissions processing approaches could not provide an accurate representation of emissions from such large, unconventional emissions sources as AOSR surface mines. In order to generate more accurate high-resolution, model-ready emissions files, AOSR-specific improvements were made to the emissions processing methodology. To account for the urban-scale spatial extent of the AOSR mining facilities and the high-resolution 2.5-km model grid, novel facility-specific gridded spatial surrogate fields were generated using spatial information from GIS (geographic information system) shapefiles and satellite images to allocate emissions spatially within each mining facility. Facility- and process-specific temporal profiles and VOC speciation profiles were also developed. The pre-2013 vegetation and land-use data bases normally used to estimate biogenic emissions and meteorological surface properties were modified to account for the rapid change of land use in the study area due to marked, year-by-year changes in surface mining activities, including the 2013 opening of a new mine. Lastly, mercury emissions data were also processed to support AOSR mercury modelling activities. The combination of emissions inventory updates and methodological improvements to emissions processing has resulted in a more representative and more accurate set of emissions input files to support AQ modelling to predict the ecosystem impacts of AOSR air pollutant emissions. Seven other papers in this special issue used some of these new sets of emissions input files.

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