Emissions preparation and analysis for multiscale air quality modeling over the Athabasca Oil Sands Region of Alberta, Canada
Authors: Zhang Junhua , Moran Michael D. , Zheng Qiong , Makar Paul A. , Baratzadeh Pegah , Marson George , Liu Peter , Li Shao-Meng 
Published: July 2018

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 × 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 (NOx,
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.