@article { ISI:000439427800005,
	title = {Emissions preparation and analysis for multiscale air quality modeling over the Athabasca Oil Sands Region of Alberta, Canada},
	journal = {ATMOSPHERIC CHEMISTRY AND PHYSICS},
	volume = {18},
	number = {14},
	year = {2018},
	month = {JUL 23},
	pages = {10459-10481},
	abstract = {The oil sands (OS) of Alberta, Canada, which are classified as unconventional oil, are the third-largest oil reserves in the world. We describe here a 6-year effort to improve the emissions data used for air quality (AQ) modeling of the roughly 100 km x 100 km oil extraction and processing industrial complex operating in the Athabasca Oil Sands Region (AOSR) of northeastern Alberta. This paper reviews the national, provincial, and sub-provincial emissions inventories that were available during the three phases of the study, supplemented by hourly SO2 and NOx emissions and stack characteristics for larger point sources measured by a continuous emission monitoring system (CEMS), as well as daily reports of SO2 from one AOSR facility for a 1-week period during a 2013 field campaign when the facility experienced upset conditions. Next it describes the creation of several detailed hybrid emissions inventories and the generation of model-ready emissions input files for the Global Environmental Multiscale-Modelling Air quality and CHemistry (GEM-MACH) AQ modeling system that were used during the 2013 field study and for various post-campaign GEM-MACH sensitivity studies, in particular for a high-resolution model domain with 2.5 km grid spacing covering much of western Canada and centered over the AOSR. Lastly, it compares inventory-based bottom-up emissions with aircraft-observation-based top-down emissions estimates. Results show that emissions values obtained from different data sources can differ significantly, such as a possible 10-fold difference in PM2.5 emissions and approximately 40 and 20\% differences for total VOC (volatile organic compound) and SO2 emissions. A novel emissions-processing approach was also employed to allocate emissions spatially within six large AOSR mining facilities in order to address the urban-scale spatial extent of the facilities and the high-resolution 2.5 km model grid. Gridded facility-and process-specific spatial surrogate fields that were generated using spatial information from GIS (geographic information system) shapefiles and satellite images were used to allocate non-smokestack emissions for each facility to multiple grid cells instead of treating these emissions as point sources and allocating them to a single grid cell as is normally done. Facility-and process-specific temporal profiles and VOC speciation profiles were also developed. The pre-2013 vegetation and land-use databases normally used to estimate biogenic emissions and meteorological surface properties were modified to account for the rapid change in 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 in addition to the seven criteria-air-contaminant (CAC) species (NO x, VOC, SO2, NH3, CO, PM2.5, and PM10) to support AOSR mercury modeling activities. Six GEM-MACH modeling papers in this special issue used some of these new sets of emissions and land-use input files.},
	issn = {1680-7316},
	doi = {10.5194/acp-18-10459-2018},
	author = {Zhang, Junhua and Moran, Michael D. and Zheng, Qiong and Makar, Paul A. and Baratzadeh, Pegah and Marson, George and Liu, Peter and Li, Shao-Meng}
}
