ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-10459-2018Emissions preparation and analysis for multiscale air quality modeling over
the Athabasca Oil Sands Region of Alberta, CanadaEmissions preparation and analysis for multiscale air quality modelingZhangJunhuajunhua.zhang@canada.caMoranMichael D.ZhengQiongMakarPaul A.BaratzadehPegahMarsonGeorgeLiuPeterLiShao-Menghttps://orcid.org/0000-0002-7628-6581Air Quality Research Division, Environment and Climate Change Canada,
4905 Dufferin Street, Toronto, ON, M3H 5T4, CanadaPollutant Inventories and Reporting Division, Environment and Climate
Change Canada, 4905 Dufferin Street, Toronto, ON, M3H 5T4, CanadaAir Quality Research Division, Environment and Climate Change Canada,
335 River Road, Ottawa, ON, K1A 0H3, CanadaJunhua Zhang (junhua.zhang@canada.ca)23July20181814104591048122December201713February20181June201826June2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/10459/2018/acp-18-10459-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/10459/2018/acp-18-10459-2018.pdf
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.
Introduction
Alberta's oil sands (OS: see Appendix A for a list of acronyms), which
consist of a mixture of bitumen, sand, clay, and water, are found in the
Athabasca, Cold Lake, and Peace River areas of northern Alberta. Together
these three areas cover 142 200 km2, about 21 % of the area of the
province of Alberta (Alberta Energy, 2018) or about the same area as Greece.
The Athabasca Oil Sands Region (AOSR) contributes the largest share of OS
bitumen production: 82 % in 2015 (Alberta Energy Regulator, 2018a). There
are two main methods used to produce oil from the bitumen, each of which has
associated atmospheric emissions. For bitumen deposits buried less than 60 m
or so below the surface, the oil sands are mined by open-pit mining methods,
in which large excavators dig up oil sand ore and transfer it to heavy-hauler
trucks for transport to crushers, where large ore lumps are broken up. The
crushed ore is then mixed with hot water and transported to an extraction
plant, where the bitumen is separated from the other components and then
transferred to either an on-site or a remote upgrader to create synthetic
crude oil. About 3 % of the OS area, mainly within the AOSR, can be
surface mined but it accounts for about 20 % of the recoverable OS oil
reserves. Oil sands in the remaining 97 % of the OS area are situated too
deep for surface mining and can only be recovered by in situ extraction
methods such as steam-assisted gravity drainage (Alberta Energy Regulator,
2018b). As of 2015, about 46 % of Alberta oil production from oil sands
comes from surface mines in the AOSR (Alberta Energy Regulator, 2018a).
According to the 2013 National Pollutant Release Inventory (NPRI; Canada's
legislated inventory of pollutant releases reported by industrial,
commercial, and institutional facilities that meet certain reporting
requirements), emissions from Alberta's OS sector account for 61, 34, and
14 % of the total reported VOC (volatile organic compound), SO2,
and NOx emissions, respectively, for the province, whose
NPRI total VOC, SO2, and NOx provincial emissions
are the highest of the Canadian provinces
(https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory.html,
last access: 15 July 2018). The OS industrial sector is also a significant
source of PM (particulate matter) and CO emissions. Due to the complex nature
of open-pit mining and the OS oil extraction processes, pollutants are mainly
emitted from the following five processes: (1) exhaust emissions from
off-road vehicles used for removal of the surface overburden and for
excavation and transportation of the OS ore to an extraction plant;
(2) pollutants emitted from processing taking place at the extraction and
upgrading plants; (3) fugitive VOC emissions from mine faces, tailings ponds,
and extraction plants; (4) fugitive dust emissions from surface disturbances
such as the passage of the large vehicles belonging to the off-road mine
fleets; and (5) wind-blown dust emissions from open surfaces such as mine
faces and tailings-pond “beaches”. The emissions of
criteria-air-contaminant (CAC) pollutants (NOx, VOC,
SO2, NH3, CO, PM2.5, and PM10) from in situ OS
extraction activities are believed to be lower currently than those of
open-pit mining facilities based on the emissions reported to NPRI by
facilities
(https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory.html,
last access: 15 July 2018).
To support air quality (AQ) modeling activities that are part of the
Governments of Canada and Alberta Joint Oil Sands Monitoring (JOSM) plan (see
JOSM, 2011), emissions input files were created over the past 6 years in
three successive phases for Environment and Climate Change Canada's (ECCC)
Global Environmental Multiscale–Modelling Air quality and CHemistry
(GEM-MACH) AQ modeling system, which was set up to conduct nested AQ
forecasts at model horizontal grid spacings of 10 and 2.5 km (see Fig. S1 in
the Supplement). The generation of emission input files was particularly
challenging for the inner 2.5 km grid because the AOSR surface mining and
processing facilities at the center of the grid are large, complex, and
unconventional industrial facilities that cannot be well represented by
standard emissions-processing approaches for point sources. At the beginning
of emissions-related work for the JOSM plan in 2012 (referred to as phase 1,
2012–2013), considerable effort was invested in reviewing a number of
available emissions inventories, compiling a hybrid emissions inventory, and
preparing GEM-MACH emissions input files for multiple model grids to support
AQ forecasting for an August–September 2013 AQ field campaign in the AOSR.
Particular attention was paid to the emissions input files for the inner
(2.5 km) model domain centered over the AOSR, since the model forecasts for
this grid were the primary numerical guidance used during the field campaign
period. Additional emissions input files were then developed for JOSM plan
post-campaign AQ modeling activities in the second phase (2014–2015) based
on new emissions-related information available after the field study and in
the third phase (2016–2017) with updated emissions inventories, as well as
new emissions estimates obtained from analysis of the 2013 field-study
measurements.
GEM-MACH emissions input files developed during the first two phases using
the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions-processing
system (https://www.cmascenter.org/smoke, last access: 15 July 2018) have been discussed in Zhang et al. (2015) and in
a joint report by ECCC and AEP (Alberta Environment and Parks; formerly
AESRD, Alberta Environment and Sustainable Resource Development) for the JOSM
project (ECCC & AEP, 2016: hereinafter referred to as the JOSM report).
This paper briefly summarizes the work of the first two phases but focuses on
the development of new emissions input files during the third phase for the
following GEM-MACH AQ modeling applications:
Base-case study for AQ forecasting and a long-term deposition study for
the region (Makar et al., 2018) and for improvements for NH3
predictions (Whaley et al., 2018).
Model sensitivity study on the use of CEMS (continuous emission
monitoring system) measurements of SO2, NOx, exit
temperature, and flow rate (Akingunola et al., 2018; Gordon et al., 2017).
Model sensitivity study on the impact of updated VOC and PM2.5
emissions and speciation derived from surface measurements and from
airborne measurements made during the 2013 field campaign (Stroud et al.,
2018).
Mercury modeling over North America and the OS area using updated
emissions (Fraser et al., 2018).
In the rest of this paper, Sect. 2 provides an overview of the various
emissions inventories considered to build the base-case model emissions for
all three phases. Challenges faced and approaches taken to compile a
best-available hybrid emissions inventory for each of the three phases are
discussed. Section 3 describes the emissions-processing methodology applied
in phase 3 to generate base-case emissions. A land-cover database was also
updated for biogenic emissions and for land-surface characterization to
account for the rapid change in land use over this region. Next, Sect. 4
describes the emissions data and emissions processing used for several
post-campaign emissions sensitivity studies. Lastly, Sect. 5 provides a
summary of this work and gives recommendations for future updates and
improvements of emissions for AOSR AQ modeling.
Emissions inventories used for the base-case emissionsReview of emissions inventories used for JOSM phases 1 and 2 AQ
modeling
In 2012, prior to the summer 2013 AOSR field study (Gordon et al., 2015;
Liggio et al., 2016; Li et al., 2017), the national emissions inventories
used to generate the emissions input files for ECCC's operational GEM-MACH AQ
forecast model consisted of the AQ modeling version of the 2006 Canadian
national Air Pollutant Emission Inventory (APEI) from ECCC, a projected 2012
United States National Emissions Inventory (NEI) from the U.S. Environmental
Protection Agency (EPA) based on version 4 of the 2005 United States NEI, and the 1999
Mexican inventory (Moran et al., 2013a, 2014). The 2006 Canadian APEI
represented a base year 7 years earlier than the field-study period, an
important consideration for the AOSR due to its rapid development. For
example, one of the five AOSR surface mining facilities in operation in 2012,
the Canadian Natural Resources Limited (CNRL) Horizon mine (see Fig. 1), only
began production in 2009. Hence, pollutant emissions from that mine were not
available in the 2006 APEI. Thus, while the 2006 APEI was being used as the
basis for national-scale operational AQ forecasting for Canada, it was not an
ideal choice for high-resolution AQ modeling for the AOSR field study. A
number of newer emissions inventories, however, had been developed for the
AOSR area or for the province of Alberta, albeit not always for the purpose
of supporting AQ modeling.
Location of six AOSR surface mining and processing facilities:
(a) Suncor Millenium and Steepbank, (b) Syncrude Mildred
Lake, (c) Syncrude Aurora North, (d) Shell Canada Muskeg
River and (e) Shell Canada Jackpine (reported to NPRI as one
facility), (f) Canadian Natural Resources Limited Horizon, and
(g) Imperial Oil Kearl (only started production in 2013, not
considered in earlier inventories). The city of Fort McMurray is located
about 10 km to the south.
After an intense review of 10 available national, provincial, and
sub-provincial emissions inventories in 2012 (AESRD, 2013; Marson, 2013), a
hybrid inventory was compiled for phase 1 and was used to prepare
GEM-MACH-ready emissions input files for near-real-time GEM-MACH forecasts
during the 2013 field study. Section S1 of the Supplement provides details
about the creation of the phase 1 emissions files. After the field study,
emissions were updated during the 2014–2015 period (phase 2) to incorporate
newly available emissions information, including new versions of national
inventories, measurements from CEMS attached to 17 smokestacks at four AOSR
mining facilities for the field-study months of August and September 2013,
and daily reports of SO2 emissions during a 1-week period in
August 2013 when the CNRL Horizon facility experienced abnormal operating
conditions. Details of the creation of the phase 2 emissions files are
summarized in Sect. S2 of the Supplement.
Comparison of annual facility-total VOC emissions (tonnes) between
2010 NPRI, 2010 CEMA, and versions 1 and 2 of the 2013 NPRI for the OS mining
facilities within the AOSR study area.
Emissions-processing phase1/23Facility name2010Original2010-NPRI-scaled20132013 NPRIAPEI/NPRI2010 CEMACEMAAPEI/NPRIversion 2Suncor Millenium and Steepbank28 94010 80828 01367689529Syncrude Mildred Lake8591766319 861829120 732Syncrude Aurora North51823319860225728268Shell Muskeg River and Jackpine14602813729126142614CNRL Horizon27 8532623679843284560Imperial Oil Kearl25462546Total72 02627 22670 56627 11948 249Inventory updates for the phase 3 hybrid emissions inventory
After the generation of the phase 2 emissions input files for GEM-MACH, five
important new sources of 2013-related emissions data became available:
The 2011 United States NEI version 1 from the U.S. EPA (Eyth et al.,
2013).
The 2013 Canadian APEI.
2013 Canadian APEI version 1 from ECCC for all sectors, including the
first version of reviewed, publicly available 2013 NPRI (released December
2014), except for on-road and off-road mobile source emissions (Sassi et al.,
2016).
Second version of reviewed, publicly available 2013 NPRI (released
December 2015).
The 2011 Canadian upstream oil and gas (UOG) point-source inventory for small
and medium UOG facilities (Clearstone Engineering Ltd., 2014a, b, c) and a
projected 2013 Canadian UOG inventory (created by ECCC as part of the 2013
APEI version 1).
CEMS measurements for all CEMS stacks with relatively large SO2
and/or NOx emissions in the province of Alberta for August
and September 2013 (from AEP).
Top-down aircraft-measurement-based estimates of VOC emissions during the
2013 field-study period for four of the six AOSR mining facilities (Li et
al., 2017) and aircraft-measurement-based size-resolved PM emissions for all
six facilities.
There were large differences noted between the 2011 United States NEI and the older
projected 2012 United States NEI (projected from the 2005 United States NEI) used in phases 1
and 2, despite the 1-year difference in base year. For example, the
projected 2012 NEI SO2 emissions from all sectors were reduced by
48 % in the 2011 NEI, but NO2 emissions increased in the latter by
8 %, due mainly to a 40 % increase in on-road NOx
emissions (Moran et al., 2015). Among the many reasons that may have
contributed to these large differences between the two inventories, one is
the change in the on-road emissions estimation tool used by the U.S. EPA from
MOBILE6.2 and MOVES2010 (U.S. EPA, 2010) to SMOKE–MOVES2014 (U.S. EPA, 2015;
Choi, 2016). Given that the 2011 United States NEI is a retrospective inventory based
on actual activity data and CEMS data for base year 2011, it was chosen to
replace the projected 2012 United States NEI used in phases 1 and 2 for the creation
of the phase 3 emissions input files for base year 2013. Note, however, that
the U.S. EPA's emissions trend data set suggests a reduction of
NOx emissions by 8 % and SO2 emissions by
23 % between 2011 and 2013
(https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data,
last access: 15 July 2018).
The first AQ modeling version (i.e., SMOKE-ready version) of the 2013
Canadian APEI (v1), which included point-source emissions from the first
version (v1) of the reviewed, publicly available 2013 NPRI (released in late
2014), became available in early 2016 for most sectors, with the exception of
the on-road and off-road mobile source sectors. There are significant
differences for some sectors between the modified 2010 APEI used in phase 2
(Table S3 in the Supplement) and the 2013 APEI. Figure 2 shows a comparison
of fugitive-dust PM2.5 emissions from four sectors for the province of
Alberta. PM2.5 emissions from construction more than doubled from 2010
to 2013 due to a combination of increased construction activities and changes
in the methodology used to estimate PM emissions for this sector (Environment
Canada, 2014). Table 1 provides a comparison of facility-total VOC emissions
for the six surface OS mining facilities used for phases 1 and 2 vs. phase 3. For
phases 1 and 2 these emissions were 2010-NPRI-scaled CEMA VOC emissions
(Tables S2 and S3), whereas for phase 3, version 2 (v2) of the 2013 NPRI,
which became available in late 2015, was used (Table 2). VOC emissions from
the Suncor Millenium and Steepbank facility were reduced from about
28 000 t yr-1 in phase 2 to 9500 t yr-1 in phase 3, a 64 % reduction; the Shell
Canada Muskeg River and Jackpine mine had a similar percentage reduction. One
additional complication is that facilities may submit modified reports to
NPRI for past reporting years based on updated information, as can be seen by
comparing the last two columns of Table 1, where reported total VOC emissions
increased for Suncor Millenium and Steepbank, Syncrude Mildred Lake, and Syncrude
Aurora North in the 2013 NPRI v2 (see also Li et al., 2017). One other
important change evident in Table 1 is the inclusion of emissions from the
Imperial Oil Kearl surface mine, which began production in 2013, in the two
2013 emission inventory versions.
Comparison of fugitive PM2.5 emissions for four sectors between
2010 APEI (used for phase 2) and 2013 APEI (used for phase 3) for the
province of Alberta.
Emissions from smokestacks that are released at high-volume flow rates and
high temperatures may rise much higher into the atmosphere than stack
releases with lower volume flow rates and temperatures. As a consequence, AQ
models such as GEM-MACH include specialized parameterizations to calculate
this plume rise (see Akingunola et al., 2018; Gordon et al., 2017). However,
the extent to which this information is reported depends on the regulatory
environment. One limitation of the 2013 NPRI is that only emissions from
stacks higher than 50 m must be reported separately. Emissions from all
other shorter stacks are aggregated together with surface-level fugitive
emissions and are treated as surface releases (ECCC, 2016). On the other
hand, the 2009–2010 CEMA inventory has separate emissions information for all
individual stacks. To allow plume rise to be calculated for stacks both above
and below the NPRI reporting threshold, facility-total NPRI aggregate stack
emissions were allocated proportionately to each stack in the CEMA inventory
based on the 2009–2010 CEMA stack emissions.
There are a variety of activities with pollutant releases to air within any
given facility's boundaries, and the type of activity may influence the type
and amount of VOCs being emitted at the facility. The extent to which these
activities can be identified to allow spatial allocation within a facility
once again depends on the regulatory environment and the reporting
requirements. Surface-level fugitive VOC emissions are reported to NPRI as
facility-total emissions without differentiation between source type (e.g.,
mine faces, tailings ponds, and extraction and upgrading plants). To distribute
2013 NPRI fugitive VOC emissions spatially within an OS mining facility,
process allocation factors calculated from the process-specific fugitive VOC
emissions in the 2009–2010 CEMA inventory for each AOSR mining facility were
used to allocate fugitive VOC emissions between mine faces, tailings ponds,
and plants (similar to the procedure used in phase 2; see ECCC & AEP,
2016). For the Imperial Oil Kearl mine, which was not operating in 2010, 2013
fugitive VOC emissions were differentiated based on process allocation
factors from the Shell Muskeg River and Jackpine facility given that both
facilities use paraffinic froth treatment (PFT) technology to produce diluted
bitumen, which is then transported through pipelines to off-site refineries
for further processing
(http://www.oilsandsmagazine.com/technical/mining/froth-treatment/paraffinic,
last access: 15 July 2018; Li et al., 2017).
However, due to the fact that the operation of a new mine during its first
months may be quite different than a mine that has been operating for years,
this was at best a necessary assumption with considerable uncertainty.
Summary of Canadian data sources used for generating JOSM phase 3
base-case emissions input files.
Data categoryData sourcesPoint and facility sources– 2013 NPRI v1 for the whole domain except for the OS facilities – 2013 NPRI v2 for the OS facilities, but 2009–2010 CEMA stack information usedOS off-road fleet– 2009–2010 CEMA inventoryFugitive dust from major facility– 2013 NPRI v1Tailings ponds, mines, and plant fugitives– Facility-total VOC emissions from 2013 NPRI v2 – Splitting factors for fugitive VOC emissions from tailings ponds, mines, and plants based on the 2009–2010 CEMA inventorySmall and medium UOG sources– 2013 APEI (projected from the 2011 Canadian UOG inventory)Non-mobile area sources– 2013 APEIOn-road and off-road mobile sources– 2010 APEI
The UOG emissions input files generated for phase 2 were based in part on a
year-2000 Canadian UOG inventory projected to 2010 (Table S3). After phase 2,
a 2011 Canadian UOG inventory that was compiled for ECCC became available
(Clearstone Engineering Ltd., 2014a, b, c). This new subinventory was then
projected by ECCC to 2013 for inclusion in the 2013 APEI based on activity
data and a methodology described in a letter report from Clearstone
Engineering Ltd. (2014d). Figure 3 shows the national-level differences
between the year-2000-based projected 2010 UOG inventory and the
year-2011-based projected 2013 UOG inventory for the seven CAC pollutants,
where about 95 % of the UOG facilities are located within the
high-resolution OS modeling domain. VOC, CO, and NOx
emissions are higher for the new subinventory by 27, 23, and 11 %,
respectively, while SO2 emissions are 11 % lower. Thus, the
projection of total UOG emissions from 2000 to 2010 that was used for phase 2
seems to have been reasonable in total. However, the number of UOG facilities
with CAC emissions increased from about 207 000 in the 2000 UOG inventory to
334 000 in the 2011 UOG inventory, a 61 % increase. Figure S2 shows the
locations of UOG facilities in the Ft. McMurray AOSR area for the 2000 and
2011 UOG inventories. We can see that some UOG facilities that existed in
2000 have been closed while many new facilities have opened since 2000.
Updating the UOG inventory to the 2011-based 2013 projected inventory might
thus be expected to have a significant impact on the spatial distribution of
UOG emissions.
Comparison of national CAC emissions between the year-2000-based
projected 2010 UOG inventory and the year-2011-based projected 2013 UOG
inventory.
Given the availability of these new emissions data sets, a synthesized
phase 3 hybrid emissions inventory was created from the inventories listed in
Table 2. As a complement to Table 1, which compared the VOC emissions from
the AOSR mines used for the three phases, Tables S4 to S6 compare the
facility-total emissions of other CAC species compiled for the three phases
from three main source types: CEMA off-road mobile emissions, facility
smokestack and area-source emissions, and road dust emissions. As described
in the next section, further improvements were also made to the
emissions-processing methodology before new phase 3 model-ready 2013 base-case
emissions files were generated from the phase 3 hybrid inventory. Additional
phase 3 emissions input files that were generated for GEM-MACH emissions
sensitivity runs using an expanded set of CEMS measurements and
aircraft-observation-based emissions estimates are then discussed in Sect. 4.
Phase 3 emissions processing for GEM-MACH 2013 base-case
simulations
The same overall emissions-processing methodology described in Zhang et
al. (2015) and the JOSM report (ECCC & AEP, 2016) was used in phase 3 to
generate gridded, hourly, model-ready emissions fields for GEM-MACH using the
SMOKE emissions-processing system. The three main steps required to process a
typical emissions inventory that contains monthly or annual CAC emissions
reported by jurisdiction for a small number of pollutants into gridded,
hourly, model-ready emissions input files are (a) spatial disaggregation,
(b) temporal disaggregation, and (c) chemical speciation (e.g., Dickson and
Oliver, 1991; Houyoux et al., 2000; Moran et al., 2013b). Note that before
spatial disaggregation (i.e., spatial allocation) can be performed, a set of
spatial surrogate fields must first be generated on the model grid of
interest for such proxy or surrogate fields as population, road density, and
agricultural land use. Different inventories are then processed separately,
often subinventory by subinventory (e.g., point sources, area sources,
off-road sources, on-road sources), and as a last step some of the resulting
gridded output fields may be merged.
PM10 size-bin ranges as Stokes diameter (µm) for the
12-bin version of GEM-MACH.
Key aspects of the emissions-processing methodology for phase 3 that were
specific to the AOSR emissions included the following:
Updated facility-specific and process-specific spatial surrogate fields
were used (similar to phase 2) for the 10 km North American grid and 2.5 km
western Canada grid based on GIS polygons of mine faces, tailings ponds, and
plants for the six AOSR mining facilities (Fig. 1) in order to spatially
allocate the surface area emissions from off-road fleet and fugitive sources
between mine faces, tailings ponds, and plants. Emissions from individual
smokestacks within these facilities, on the other hand, were treated as
point-source emissions and assigned to the specific grid cells in which the
stacks are located.
Facility-specific monthly temporal profiles for production-related
emissions, such as emissions from off-road mine fleets and extraction plants,
were generated based on facility-specific monthly production statistics for
2013 (Alberta Energy Regulator, 2014). Weekly and diurnal temporal profiles
were treated as constant (i.e., flat) as a default because the AOSR
mining facilities usually operate around the clock throughout the year (note,
however, the discussion on CEMS emissions in Sect. 4.1). Temperature-based
monthly temporal profiles were created for fugitive VOC emissions from mine
faces and tailing ponds, similar to the methodology that has been used in
past AOSR environmental impact assessment (EIA) submissions (e.g., Cenovus FCCL Ltd.,
2010; Imperial Oil, 2005).
Facility-specific and process-specific VOC speciation profiles were
created based on VOC speciation profiles compiled in the CEMA inventory
(Davies et al., 2012; Zhang et al., 2015).
PM speciation profiles from version 4.3 of the U.S. EPA SPECIATE database
(https://www.epa.gov/air-emissions-modeling/speciate-version-45-through-40,
last access: 15 July 2018; Reff et al., 2009)
were used to split PM emissions into six model chemical components: sulfate,
nitrate, ammonium, elemental carbon, primary organic matter, and crustal
material. Process-specific PM profiles were used for stack emissions based on
the Source Classification Code (SCC) assigned to the stacks in the CEMA
inventory (Davies et al., 2012). The “Unpaved Road Dust – Composite” PM
speciation profile from SPECIATE v4.3 was used to speciate fugitive dust
emissions from unpaved roads within each facility in the base-case emissions.
Another required emissions-processing step was to perform PM size
disaggregation. As discussed in Makar et al. (2018) GEM-MACH may be
configured to represent the PM size distribution with either 2 or 12 size
bins. Accordingly, the PM emissions were processed twice, once for each
representation of the PM size distribution. The two-bin version separates
PM10 emissions into two size bins, PM2.5 (fine bin) and PM coarse bin (equal to PM10–PM2.5), whereas the 12-bin version separates
PM10 emissions into the 10 size bins listed in Table 3, plus 2 larger
size bins for diameters greater than 10 µm (note that the base-case
emissions thus assumed no primary particulate emissions for sizes greater
than 10 µm diameter). For the 12-bin PM emissions, generic PM size
distribution profiles were applied for three broad source types (area,
mobile, and point) based on 10 source-specific particle size distributions
discussed in Eldering and Cass (1996). Figure 4 shows the distribution of the
eight PM2.5 bins for these three source types. Mobile source PM2.5
emissions have a normal size distribution centered around 0.16 µm in
diameter, but point-source and area-source PM2.5 emissions are skewed to
the smaller and larger size bins, respectively.
Fractional distribution of the eight PM2.5 size bins for the
12-bin version of GEM-MACH modeling for three broad types of emissions
sources.
(a) Leaf area index and (b) peak summer isoprene
emissions computed on the 2.5 km for a portion of the 2.5 km OS grid
centered on the AOSR study area from the original BELD3 database. The gray
lines indicate the cleared areas within the boundaries of the six AOSR mining
and processing facilities (see Fig. 1).
(a) Inland water coverage for a portion of the 2.5 km OS
grid centered on the six AOSR mining and processing facilities generated from
the original land-cover database (only natural lakes); and
(b) modified inland water coverage including tailings ponds and
rivers. The black and pink lines in panel (a) indicate the
cleared-land areas and the tailings ponds within the boundaries of the six
AOSR mining and processing facilities, whereas the blues lines in
panel (a) mark the boundaries of natural lakes and rivers.
(a) Modified biogenic isoprene emissions for a portion of
the 2.5 km OS grid centered on the AOSR study area and
(b) difference between the original and the modified isoprene
emissions (original – modified). The gray lines indicate the cleared-land
areas within the boundaries of the OS mining facilities. The location of Fort
McMurray is indicated by the diamond symbol.
In addition to anthropogenic emissions, GEM-MACH must also consider natural
emissions, including biogenic VOC emissions, which depend on local vegetation
type and light and/or temperature conditions. GEM-MACH calculates biogenic
emissions dynamically (that is, making use of the GEM meteorological model's
predictions of temperatures and light levels during a simulation combined
with vegetation-type-dependent biogenic emissions formulas from BEIS, Biogenic Emission Inventory System, v3.06). Vegetation type is described
using the BELD3 (Biogenic Emissions Landuse Database, version 3) database,
which contains 230 vegetation classes at 1 km resolution (Pierce et al.,
2000). However, by 2013 the vegetation fields in the BELD3 database, which is
based on early 1990s satellite imagery (Kinnee et al., 1997), were outdated
over the AOSR mining area – much of the area that was forested in the 1990s
was later cleared of forest cover during the construction of the AOSR mining
facilities. This is illustrated in Fig. 5, which shows mean leaf area index
(LAI) for the gridded vegetation and corresponding summer peak isoprene
emissions computed from the original BELD3 database. Except for some areas
within the two oldest AOSR mining facilities, Suncor Millenium–Steepbank and
Syncrude Mildred Lake, LAI values and isoprene emissions over the other
mining facilities as computed with the BELD3 database are erroneously high,
due to the fact that these areas, which by 2013 had been cleared for surface
mining, were still characterized in the database as forested. Furthermore,
the only water bodies contained in the land-cover database over this area are
natural lakes. The large artificial tailings ponds present in the mining
facilities are not characterized as water covered in the database (Fig. 6a)
even though in 2013 the tailings ponds in the AOSR covered an area of about
180 km2
(https://web.archive.org/web/20170727072144/http://www.energy.alberta.ca/OilSands/pdfs/FSTailings.pdf, last
access: 15 July 2018), the
equivalent of 29 grid cells on the OS 2.5 km grid. Tests of the GEM-MACH
model's meteorology for plume-rise algorithm analysis have shown that these
artificial water bodies can have a significant influence on local meteorology
and atmospheric vertical stability. In addition, an examination of the
default water-body field portion of the grid cells overlapping the Athabasca
River (center of Fig. 6a, flowing from south to north) showed that the river
was also not being treated as a body of water in the default meteorological
model database. The accuracy of the land-use database thus influences both
meteorological and biogenic emissions estimation accuracy.
The outdated land-cover characteristics over the AOSR area would thus have an
impact on GEM-MACH predictions, particularly at high spatial resolutions. To
improve the land-use and vegetation characterization of this area, masks for
cleared land and artificial water bodies were generated as GIS polygons based
on 2013 satellite images. Rivers were added using more detailed GIS
water-body data. Figure 7a shows the biogenic isoprene emissions over the
AOSR surface mining area after the modification (cf. Fig. 5b) and Fig. 7b
shows the difference between the original and modified isoprene emissions.
The modified inland water coverage is shown in Fig. 6b. By applying these
masks to update vegetation and land-cover data, GEM-MACH-calculated biogenic
emissions can be reduced by as much as 100 % for the cleared areas
related to mining activities. Meteorological fields are also affected. For
example, Fig. S3 shows that the predicted planetary boundary layer height
over the OS facilities can be a few hundred meters lower than the surrounding
areas, which is similar to the effect of natural lakes.
As an example of the emissions input files generated with the SMOKE
emissions-processing system from the phase 3 inventory, Fig. S4 shows gridded August
mean monthly emissions of six pollutant species for a portion of the 2.5 km
OS grid centered on the AOSR study area. Similar to Fig. 7b, the locations of
the six AOSR mining facilities can be seen clearly, but other emissions
sources are also evident, such as on-road vehicle emissions and emissions from
the city of Fort McMurray. GEM-MACH results from the use of the new phase 3
base-case emissions input files generated using these updated emissions
inventories (Table 2), updated AOSR facility- and process-specific spatial
surrogate fields, new AOSR facility-specific monthly temporal profiles and
VOC speciation profiles, and updated BELD3 vegetation and land-use data sets
are described in Makar et al. (2018).
Additional phase 3 emissions processing for GEM-MACH sensitivity and
scenario studies
In addition to the phase 3 base-case emissions input files described in
Sect. 3, additional GEM-MACH emissions input files were generated using four
special emissions data sets in order to examine the effects of specific
changes to the emissions data on model predictions. These four data sets were
(1) an expanded 2013 CEMS emissions data set, (2) 2013 OS field campaign
aircraft-measurement-based top-down VOC emissions estimates, (3) 2013 OS
field campaign aircraft-measurement-based top-down PM2.5 emissions
estimates, and (4) updated mercury emissions. These additional GEM-MACH
emissions input files were used for a number of phase 3 GEM-MACH sensitivity
studies that are referenced in this section and described in detail elsewhere
in this special issue.
Comparison of speciated annual ADOM-2 (Acid Deposition and Oxidant Model, version 2) model VOC species emissions
(t yr-1) between base-case emissions from the 2013 NPRI version 2
(bottom-up) and the aircraft-observation-based estimates (top-down). Note
that unknown or unreactive VOC species are not included. Suncor Millenium and Steepbank: Suncor – M/S; Syncrude Mildred
Lake: Syncrude
– ML; Shell Muskeg River and Jackpine: Shell – MR/J.
Suncor – M/S Syncrude – ML Shell – MR/J CNRL – Horizon SPECIESBase caseAircraftBase caseAircraftBase caseAircraftBase caseAircraftHigher alkenes60110388635133412191771657Higher alkanes563613 48812 34810 022169014 384265123 779Higher aldehydes150.0403016428100.0Higher aromatics1457156952731696746881125500Propane0.59530.015923.19550.01928Ethene8.00.015770.22903.50.0Formaldehyde3.82354.56470.70.00.70.0Isoprene0.322300.50.00.31430.11346Toluene486111280615396.872135393Methyl ethyl ketone0.00.00.02120.00.00.00.0Total VOC820820 62519 35016 600254517 180410229 603Expanded CEMS emissions data set
As noted in Sect. S2, CEMS-measured hourly SO2 and
NOx emissions from 17 stacks within four AOSR mining
facilities were used in phase 2 emissions processing for a GEM-MACH
sensitivity test (ECCC & AEP, 2016; Makar et al., 2015; Zhang et al.,
2015). This earlier work showed a relatively large impact of the better
time-resolved CEMS data on model results. Recall that in Canada regulatory
reporting at the national level requires only annual total emissions from
large stacks; hence, details on specific time periods within the year are
lost and calculations to reconstruct this time variation using each
facility's operating schedule for the emitting activities can only be
approximate at best. However, detailed CEMS records are reported to the
Alberta provincial government. For phase 3, CEMS measurements from about
100 stacks at 33 facilities with relatively large SO2 or
NOx emissions were obtained for the province of Alberta for
August and September 2013. A sensitivity study was designed to investigate
the impacts of both (i) CEMS-measured hourly SO2 and NOx
emissions and (ii) CEMS-measured stack volume flow rates and exit temperatures on
GEM-MACH predictions (Akingunola et al., 2018). For this study, the phase 3
base-case stack emissions (based on 2013 NPRI annual reporting of stack
emissions) were replaced with the corresponding CEMS hourly measurements. For
the phase 3 base-case emissions, the stack flow rate and exit temperature,
which are used to calculate plume rise, were assumed to be static at the
annual reported values. However, CEMS-measured stack exit temperature and
flow rate often display significant temporal variation as shown in Fig. S5
for one example; hence, these measured values were saved in model-ready form
for the 2-month period to evaluate their impact on model predictions.
Due to the NPRI reporting threshold that facility operators are not required
to report stack-specific emission from smokestacks shorter than 50 m
(Sect. 2.2), not all CEMS stacks could be matched to NPRI stacks. Overall, 38
of the 100 stacks in the expanded CEMS data set were matched with NPRI stacks
at 20 facilities. However, since the 38 matched stacks were de facto
all tall stacks with generally large emissions, emissions from the matched
stacks account for 77 and 43 % of total SO2 and
NOx emissions, respectively, from all NPRI point sources in
Alberta. Figures S6 and S7 show comparisons by facility of SO2 and
NOx emissions between the NPRI annual inventory and the
2-month CEMS measurements for SO2 and NOx, scaled
up to annual values. Overall, these scaled CEMS-based estimates agree well
with NPRI annual totals, in spite of the large short-term temporal variation
shown in the CEMS measurements. This is reasonable since facilities are
expected to base their reported annual stack emissions on CEMS measurements.
Over shorter time intervals, however, the stack emissions levels may vary by
up to several orders of magnitude, thus having a significant influence on
model predictions. In addition, the differences between CEMS volume flow rates
and exit temperatures and the annual reported values may also have a
significant influence on plume dispersion and transformation of SO2
and NOx emitted from tall stacks. Akingunola et al. (2018)
showed that model-predicted SO2 concentration could be changed by as
much as 50 % and the NOx concentration by about 10 %
using the CEMS-measured hourly stack flow rate and temperature. On the other
hand, the use of the more realistic CEMS-measured volume flow rates and
temperatures resulted in a slight degradation of model performance with a
new, improved plume-rise algorithm.
Aircraft-measurement-based top-down VOC emissions estimates for
AOSR mining facilities
Airborne measurements have recently been used to quantify emissions from
various oil and gas fields. For example, Karion et al. (2013) estimated
methane emissions over a western United States natural gas field, Peischl et
al. (2015) quantified methane emissions as well from three United States shale
production regions, and Li et al. (2017) estimated VOC emissions for four
AOSR facilities during the 2013 OS field campaign. As described in Li et
al. (2017), aircraft observations of VOC species concentrations were used to
estimate facility-total emissions of individual VOC species using a
mass-balance approach (Gordon et al., 2015) for the Suncor
Millenium and Steepbank, Syncrude Mildred Lake, Shell Canada Muskeg
River and Jackpine, and CNRL Horizon mining facilities (see Fig. 1). Comparisons
between the aircraft-observation-based top-down estimates of individual VOC
species emissions and the corresponding bottom-up emissions reported to NPRI
by these four facilities showed differences in terms of the magnitude of both
VOC species emissions and total VOC emissions (Li et al., 2017).
Comparisons of facility-specific VOC speciation profiles for
ADOM-2 mechanism for four AOSR mining facilities used for the base-case study
with facility-specific profiles derived from aircraft observations. Different
VOC speciation profiles for plants, mine faces, and tailings ponds were used
for the base-case study. The “composite” VOC speciation profile for the
base case is an emissions-weighted combination of the plant, mine-face, and
tailings-pond profiles for each facility to allow comparison with the
aircraft-based facility-specific VOC speciation profiles.
Some previous studies have shown that the use of aircraft-derived top-down
emissions improved model performance. For example, in an attempt to
understand high O3 events during winter time in a western United States oil
and gas region, Ahmadov et al. (2015) compared AQ model performance using
emissions from two different sources: (1) the U.S. EPA NEI (bottom-up) and
(2) emissions derived from aircraft observations (top-down). They found that
the top-down emissions improved model prediction of methane, other VOCs, and
NOx. The use of these top-down emissions also captured the
O3 episode better than using the bottom-up emissions. To assess the
impact of the suggested uncertainty of VOC emissions for these four OS
facilities on GEM-MACH predictions, emissions of the individual VOC species
estimated from the aircraft observations (top-down) were mapped to the model
VOC species used by GEM-MACH's ADOM-2 (Acid Deposition and Oxidant Model,
version 2) gas-phase chemistry mechanism (Makar et al., 2003; Stroud et al.,
2008) to replace the corresponding phase 3 base-case model VOC species
emissions (bottom-up) for the four facilities.
Table 4 shows a comparison of facility-total emissions of ADOM-2 model VOC
species between the phase 3 base-case emissions input files (bottom-up) and
the aircraft-observation-based emissions input files (top-down). The
aircraft-derived VOC emissions estimates shown in Table 4 were annualized by
scaling daily values with seasonal variation factors as discussed in Li et
al. (2017). Except for Syncrude Mildred Lake, the totals of the
aircraft-observation-based top-down VOC emissions for these facilities are
higher than the corresponding bottom-up base-case totals, ranging from a
factor of 2.5 for Suncor Millenium and Steepbank to 6.7 for Shell Canada Muskeg
River and Jackpine and 7.2 for CNRL Horizon. The relative rankings of the
emissions by model VOC species also differ for the two data sources. Figure 8
compares the process-specific VOC speciation profiles for these four
facilities that were used for the phase 3 base-case study based on the CEMA
inventory (Davies et al., 2012; Zhang et al., 2015). Figure 8 also compares
the inventory-based VOC speciation profiles (bottom-up) with the
aircraft-observation-based VOC speciation profiles (top-down) by facility. As
the emissions estimated from the aircraft observations corresponded to
facility-total emissions, an emissions-weighted, base-case “composite” VOC
speciation profile was created for each facility by combining the plant,
mine-face, and tailings-pond VOC speciation profiles based on the emissions
of each ADOM-2 model VOC species. Both the aircraft-observation-based VOC
speciation profiles and the composite VOC profiles vary from facility to
facility, but there are some differences between the two profile types.
Consistent with Li et al. (2017), for example, the aircraft-observation-based
VOC profiles have a higher propane emissions fraction and a much lower
higher-aromatic emissions fraction than the composite profiles for all four
facilities. The aircraft also measured significant amounts of isoprene
emissions likely originated from bitumen vapor emissions from the Suncor
Millenium and Steepbank and the CNRL Horizon facilities, which are not present in
the corresponding bottom-up base-case profiles. Further studies are needed to
confirm the source of non-biogenic isoprene emissions.
To generate model-ready emissions files, the aircraft-estimated top-down VOC
emissions were first split by process based on the process-specific VOC
emissions compiled for the base case and then spatially allocated within each
facility based on the process-specific and facility-specific surrogates.
Figure S8 shows spatial variations in the ratio of the gridded, model-ready,
aircraft-observation-based higher-alkane emissions (top-down) to
corresponding base-case emissions (bottom-up) for the GEM-MACH 2.5 km grid
over the AOSR study area. Consistent with Table 4, the ADOM-2 higher-alkane
emissions estimated from the top-down estimation are about 8 times higher
for the Shell Canada Muskeg River and Jackpine and the CNRL Horizon facilities than
corresponding bottom-up emissions from the 2013 NPRI but are closer for the
Suncor Millenium and Steepbank and the Syncrude Mildred Lake facilities. The
variations seen within individual facilities are due to different emission
rates and different VOC speciation profiles for plants, mine faces, and
tailings ponds. As expected there is no difference for areas outside of these
four facilities. The new GEM-MACH emissions input files generated using the
aircraft-observation-based VOC emissions have been used for a GEM-MACH
sensitivity test (see Stroud et al., 2018).
Aircraft-measurement-based top-down PM emissions estimates for AOSR
mining facilities
PM emissions from the AOSR mining facilities originate mainly from four major
source categories: (1) emissions from plant stacks; (2) tailpipe emissions
from the off-road mining fleet; (3) fugitive dust originating from various
activities, such as excavation of oil sand ore, loading and unloading trucks,
and wheel abrasion of surfaces by off-road vehicles; and (4) wind-blown dust.
As summarized in Table 2, PM emissions from plant stacks and fugitive dust
source categories were obtained from the 2013 NPRI, whereas emissions from
tailpipe emissions were provided by the 2009–2010 CEMA inventory. However,
none of the inventories included wind-blown dust emissions, and the estimates
of anthropogenic fugitive dust emissions are highly uncertain. In addition,
emissions of construction dust from one facility, the Imperial Oil Kearl
mine, a portion of which was still under construction during the aircraft
monitoring campaign, were expected to be large. In order to evaluate and
potentially to improve these emissions estimates, top-down estimates of
size-resolved PM emissions were also calculated based on aircraft
measurements of size-resolved PM concentrations made during the 2013 AOSR
field campaign for all six AOSR mining facilities.
Comparison of PM2.5 emissions between base-case annual
emissions and aircraft-observation-based estimates for the 2 summer months
(August and September) for the six AOSR mining facilities.
The 2013 aircraft campaign used a top-down mass-balance approach (Gordon et
al., 2015) to determine PM emissions from all six AOSR surface mining
facilities. For particles with a diameter in the range of 0.3 to
20 µm, a forward scattering spectrometer probe (FSSP) model 300 was
deployed from a wing-mounted pod (Baumgardner et al., 1989) to measure the
particle number-concentration size distribution in 30 size bins. An
ultra-high sensitivity aerosol spectrometer (UHSAS) was used inboard to
determine the number-concentration size distribution of particles with
diameter from 0.06 to 1.00 µm in 99 size bins. Volume-concentration
size distributions of particles were derived from these number-concentration
size distributions from 0.06 to 20 µm by combining both sets of
measurements from the two instruments. Size-dependent particle densities,
varying from 1.5 to 2.5 g cm-3, were used to convert the volume-concentration size distributions
to mass-concentration size distributions, based on the known mineralogy for
the supermicron particles for the topsoil in the region and the known
chemical composition for submicron particles from concurrent aerosol mass
spectrometer measurements (Liggio et al., 2016). The resulting particle mass
concentration size distributions were combined to match the 12-bin version of
the GEM-MACH model particle size distribution. The mass-balance emission
algorithm TERRA (Top-down Emission Rate Retrieval Algorithm) (Gordon et al.,
2015) was then applied to these particle size bins to determine the particle
mass emission rates for each bin. Uncertainties in the particle mass emission
rate from each facility determined this way were estimated at approximately
36 % for supermicron particles and 32 % for submicron particles.
Based on the aircraft observations, 68 % of the PM10 emissions are
in the coarse mode (2.5 to 10 µm).
Figure 9 shows a comparison of facility-level PM2.5 emissions between
the base-case inventory-based annual values (bottom-up) and the
aircraft-observation-based estimates (top-down) for the 2 summer months
(August and September) for the six AOSR facilities. Note that the latter were
calculated for this comparison simply by assuming constant daily emissions
throughout the 2 summer months. This avoided an annualization calculation,
for which it is difficult to account for modulation by snow cover, frozen
ground, or precipitation during wintertime. Moreover, the
aircraft-observation-based top-down estimates were only used in GEM-MACH for
summertime modeling. Except for the Imperial Oil Kearl facility, the
PM2.5 emissions estimated from the top-down aircraft observations, even
for just 2 summer months, were a factor of 1.5 to 5 larger than the
bottom-up 2013 APEI PM2.5 annual emissions used for the phase 3
base-case emissions processing. One reason for the difference is that
wind-blown dust is not included in the APEI inventory, which is compiled for
anthropogenic emissions only. For the base-case bottom-up inventory, total
PM2.5 emissions from off-road vehicle tailpipe emissions and stacks are
2272 t yr-1 (Tables S4 and S5) while road dust emissions are
4134 t yr-1 (Table S6). Thus, anthropogenic fugitive dust emissions
account for 65 % of total PM2.5 emissions from the AOSR mines.
Aircraft-observation-based estimated total PM2.5 emissions from all six
facilities are about 10 300 t for the 2 summer months. If we assume that
all of the unreported PM2.5 emissions come from natural wind-blown dust,
then fugitive dust emissions will dominate total PM2.5 emissions from
those facilities even more.
Figure 10 shows the observed size distribution of the first eight GEM-MACH
size bins, which correspond to the PM2.5 size range (see Table 3).
Although the size distribution of the PM2.5 emissions varies from
facility to facility, 65–95 % of PM2.5 emissions are in bin 8
(diameter range from 1.28 to 2.56 µm), implying that the majority of
the PM2.5 emissions are from fugitive-dust area sources (Eldering and
Cass, 1996), either from dust kicked up by off-road mining vehicles or from
wind-blown dust. Compared to the area-source PM size distribution profile
used by SMOKE to process the bottom-up base-case emissions (Fig. 4), a much
larger bin 8 mass fraction and smaller bin 1 to 7 (i.e.,
< 1.28 µm) mass fractions were observed by the aircraft for the
AOSR mining facilities.
PM2.5 size distribution derived from the aircraft observations
for the six AOSR mining facilities.
An AOSR-specific PM chemical speciation profile consisting of six chemical
components was also constructed for fugitive dust emissions from these
facilities to replace the “Unpaved Road Dust – Composite” profile from the
U.S. EPA SPECIATE v4.3 database (see Sect. 3). Wang et al. (2015) analyzed
soil samples collected from 17 AOSR facility sites and 10 forest sites. The
samples were further characterized as paved road dust, unpaved road dust,
tailings sands, and overburden soil. Their analysis showed that PM speciation
is clearly different between the dust collected from the facility sites and
from the forest sites. For this study, the new AOSR-specific fugitive-dust PM
speciation profile was compiled by averaging the site-specific profiles from
all 17 facility sites from Wang et al. (2015) to represent surface PM
speciation with the following three exceptions:
For the unpaved-road site S16, the elemental-carbon percentage seemed to
be too large, which might be an artifact due to dry deposition from
heavy-duty diesel exhaust (Wang et al., 2015). This site was excluded from
the facility profile average in their study and was excluded in this study
too.
The organic-carbon percentage for site S10 was much smaller and the
elemental-carbon percentage was larger than those of other facility sites.
That site was excluded from the organic-carbon range discussion in Wang et
al. (2015) and was excluded here as well.
S17 is located on Highway 63, so it was also excluded from the facility
average.
Figure 11 shows a comparison of the fugitive-dust PM speciation profile used
for the phase 3 base-case emissions processing, which is the standard
“Unpaved Road Dust – Composite” profile from the U.S. EPA SPECIATE v4.3
database, and the new profile described above. The organic-matter
(OM = organic carbon + particulate non-carbon organic matter)
percentage in the AOSR-specific PM speciation profile (21.8 %) is about
3 times larger than the fraction in the standard “Unpaved Road Dust –
Composite” profile (7.6 %), suggesting that soils in the AOSR facilities
contain more organic matter than soils in other areas. The crustal-material
percentage decreases correspondingly, from over 91 to 76 %. The
AOSR-specific PM speciation profile also has more sulfate and elemental
carbon, but the fractions are relatively small.
Comparison of the fugitive-dust PM speciation profile used for the
base-case study and the one compiled from soil analyses from Wang et
al. (2015) for the AOSR mining facilities.
Figure S9 shows spatial variations in the ratio of the gridded
aircraft-observation-based bin 8 OM emissions (top-down) to the corresponding
base-case emissions (bottom-up) for the GEM-MACH 2.5 km grid over the AOSR
study area. Except for the Imperial Oil Kearl facility, the top-down OM
emissions are more than 2 orders of magnitude larger than those for
the base-case study (bottom-up) due to the combination of higher PM emissions
(Fig. 9), larger bin 8 mass fraction (Figs. 4 and 10), and the larger OM mass
fraction (Fig. 11).
The new estimates of total fugitive dust emissions and the new PM
size distribution and speciation profiles were used for two GEM-MACH
sensitivity simulations. One of these simulations focussed on the impact of
the increases of VOC and primary OM emissions on total organic aerosol and
the formation of secondary organic aerosol (SOA; Stroud et al., 2018). The second
examined the impact of the increased crustal-material emissions on regional
acid deposition by making use of the Wang et al. (2015) PM speciation profile
to further speciate the model's crustal material into a base-cation fraction
(Makar et al., 2018). Similar to Ahmadov et al. (2015), Stroud et al. (2018)
demonstrated that the measurement-derived top-down emissions improved the
modeled VOC and organic aerosol (OA) concentration maxima in plumes. Bias
was also improved for OA predictions. Their study suggested that intermediate
volatile organic compound (IVOC) emissions need to be included as precursors
to SOA for further improvement of SOA predictions. In their examination of
acidifying deposition in the region, Makar et al. (2018) found that the new
aircraft-based top-down emissions improved the model fit to observations,
increasing correlation coefficients (R from 0.47 to 0.54) and improving
slopes of the model-to-observation best-fit line (slope changed from 0.051 to
0.73, correcting most of the large underestimate in predicted base-cation
deposition). The revised fugitive dust estimates from the aircraft study,
while resulting in greatly improved model performance relative to the
reported emissions, still resulted in an underestimate of base cations
relative to observations, implying the need for further improvements to these
emissions data.
Mercury emissions
Mercury emissions from the SMOKE-ready versions of the 2010 Canadian APEI and
version 1 of the 2011 United States NEI (NEIv1) were used in phase 2 for creating
gridded GEM-MACH-ready mercury emissions. In phase 3 these emissions input
files were updated with two AOSR-specific adjustments. First, annual total
mercury emissions to air from all NPRI facilities in the 2010 Canadian APEI,
including the six AOSR mining facilities, were 3429 kg yr-1. In
comparison, the annual total mercury emissions to air reported by all NPRI
facilities for 2013 were 2529 kg yr-1, of which only 61 kg were
emitted from the surface mining facilities. Thus, for the 2013 field study,
the 2013 NPRI reported values were used for the model Hg emissions. Second,
for the United States, mercury
emissions from off-road vehicles were only available for
the state of California in the SMOKE-ready version of the 2011 NEIv1
(https://www.epa.gov/air-emissions-modeling/2011-version-6-air-emissions-modeling-platforms,
last access: 15 July 2018), whereas the
original 2011 NEIv1
(https://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventory-nei-data,
last access: 15 July 2018) included
off-road-mobile mercury emissions for other states as well. The amount of
off-road-mobile mercury emissions for California was the same in the two
inventory versions. Based on the original 2011 NEIv1 inventory, total annual
off-road-mobile mercury emissions for the entire United States were
40.9 kg yr-1, of which 26.1 kg yr-1 was from California.
Although these off-road-mobile mercury emissions were relatively small
compared with other emissions sources (see Table 5) and more than 60 % of
the off-road-mobile mercury emissions were from California, the second
adjustment was to use off-road-mobile mercury emissions from the original
2011 NEIv1 to add in mercury emissions for the missing states in the
off-road-mobile subinventory of the SMOKE-ready version of the 2011 United States
NEIv1.
Sum of source-sector-specific mercury emissions (kg) for the 2011
United States inventory (version 1) and the 2010–2013 Canadian inventory.
Source category2011 United States2010–2013 CanadaPoint42 2022529Area43211803On-road3582.3Off-road410.0Total46 9224334
Spatial distribution of phase 3 elemental mercury emissions for
Canada and the United States for the 10 km continental model grid for 1 h in the afternoon
in August. Note logarithmic spacing of the emissions contour intervals;
white areas have emissions less than
10-10 g cell-1 s-1.
Table 5 presents a summary of source-specific anthropogenic mercury emissions
used for phase 3 for both the United States and Canada. Total 2011 United States annual mercury
emissions from all four broad categories were 46 992 kg, of which nearly
90 % was from point sources and the rest was mainly from area sources
(9 %). Mercury emissions from on-road and off-road vehicles accounted for
less than 1 % of total mercury emissions, and most of these vehicular
emissions (90 %) came from on-road vehicles. The summary of 2010–2013
Canadian mercury emissions shows that point sources were the largest
anthropogenic source of mercury emissions in Canada (58 %), followed by
area sources (42 %), and on-road and off-road vehicle emissions
contributed little. Total mercury emissions from Canada for 2010–2013 were
about 9 % of those emitted in the United States for 2011. The two adjustments made
for phase 3 reduced United States and Canadian anthropogenic mercury emissions by
885 kg yr-1 or less than 2 %. However, emissions of mercury from
forest fires were also recognized as a major source (Fraser et al., 2018).
Three mercury species (elemental, divalent gas, and particulate) are
considered in the mercury version of the GEM-MACH model (Fraser et al.,
2018). Mercury emissions for the Canadian 2013 NPRI point-source emissions
were pre-speciated based on the 2006 Canadian point-source emissions
inventory used for the 2008 mercury assessment (UNEP, 2008). For other
inventories, mercury emissions were reported as unspeciated totals in the
2010 Canadian APEI and the 2011 United States NEIv1. For these other inventories,
mercury speciation was carried out using speciation profiles for nine broad
source categories following the same methodology used in the U.S. EPA 2005
NEIv4.1 platform. The same profiles had also been used in the U.S. EPA 2002 v3
platform (see Tables 3–14 in U.S. EPA, 2011).
Figure 12 shows the spatial distribution of phase 3 elemental mercury
emissions for both Canada and the United States on the 10 km GEM-MACH continental
grid for 1 h in the afternoon in August. Most of the mercury emissions are from
populated and industrial areas. Figure S10 shows the domain-average
percentages of the three mercury species based on total emissions summed over
the nine source categories. About 50, 30, and 20 % of the total mercury
emissions are in the elemental, divalent gas, and particulate states,
respectively. Fraser et al. (2018) present some results from the use of these
phase 3 mercury emissions input files.
Summary and future work
A number of sets of model-ready emissions input files have been prepared over
the past 6 years in three successive phases for the GEM-MACH air quality
modeling system in support of the Governments of Canada and Alberta Joint
Oil Sands Monitoring (JOSM) plan. These emissions files were used by GEM-MACH
to conduct nested AQ forecasts in support of an Oil Sands field campaign
carried out in summer 2013 as well as ongoing experimental forecasts since
then and retrospective model simulations and analyses for the field-study
period. Two GEM-MACH grids were considered: a North American continental grid
with 10 km grid spacing and a high-resolution western Canada grid with
2.5 km grid spacing centered over the Athabasca Oil Sands Region (AOSR) of
northeastern Alberta, Canada.
Ten available emissions inventories covering the study area were reviewed in
phase 1 (2012–2013) and a detailed synthesized or hybrid AQ modelers'
emissions inventory was constructed. An important approach developed in
phase 1 was to treat three types of major emissions sources within each AOSR
mining facility – mine faces, tailings ponds, and extraction plants – as
area sources rather than point sources due to their large spatial extent by
developing and using three sets of facility-specific and process-specific
spatial surrogate fields based on a 2010 GIS shapefile describing the AOSR
mines. For phase 2 emissions processing from 2014 to 2015, more up-to-date
emissions inventories and other relevant emissions information became
available, including continuous emissions monitoring system (CEMS) data sets
for 2013 for 17 smokestacks in four AOSR mining facilities and updated
2013-specific AOSR shapefiles.
This paper focused on the phase 3 emissions processing that was carried out
from 2016 to 2017. Some of the gaps and recommendations raised in the JOSM
report (ECCC & AEP, 2016) were addressed during this phase. Newer Canadian
and US inventory compiled for, or close to, 2013 were used. An expanded CEMS
data set of hourly SO2 and NOx emissions and
smokestack operating characteristics for August–September 2013 was obtained
for the entire province of Alberta, increasing the provincial total coverage
of point-source SO2 and NOx emissions by CEMS
measurements from 31 and 3 % to 77 and 43 %, respectively. New VOC and PM
emissions estimates and chemical speciation profiles for the AOSR mining
facilities that had been derived from on-site surface observations and
aircraft observations made during the 2013 field campaign were processed for
several GEM-MACH sensitivity studies. The aircraft-observation-based top-down
VOC emissions were about 2 times larger than the bottom-up base-case
emissions from the 2013 NPRI (Li et al., 2017). For PM emissions, 2-month
PM emissions estimated from the top-down aircraft-observation-based emissions
were even larger than the bottom-up NPRI annual emissions for five of the six
facilities (Fig. 9). The VOC and PM chemical speciation profiles used to
speciate emissions from the AOSR mines were also noticeably different than
those used to process the phase 3 base-case emissions. A vegetation database
used to estimate biogenic emissions and a land-cover database used in the
parameterizations of land-surface processes and dry deposition were also
modified to account for the rapid change in vegetation cover and land use in
the AOSR region due to year-by-year changes in surface mining activities. In
addition to CAC emissions, mercury emissions were also processed to support
mercury modeling activities using newly available data sets.
This study also provides specific examples of some common issues related to
the preparation of emissions input files for AQ models. First, there is
always a time lag between a year of interest and the year in which an
emissions inventory becomes available for that year of interest. Second,
inventories are always subject to change due to reported corrections or to
changes in estimation methodology. Third, if multiple inventories are
available for the same region and the same base year, they are unlikely to
be in perfect agreement. Fourth, a synthesized or hybrid inventory can
provide a more accurate representation of emissions than any of its
component inventories. Fifth, extra effort and investigation related to the
specific year and region of interest can yield significant improvements over
standard emissions-processing methodologies. And sixth, top-down emissions,
such as those from aircraft observations, can be used to verify bottom-up
emissions and to improve AQ modeling performance, as demonstrated by the
companion AQ modeling papers in this special issue (Stroud et al., 2018;
Makar et al., 2018).
Nevertheless, although improved sets of emissions input files were generated
during phase 3 after a considerable effort to acquire and apply new sources
of emissions data representative of the 2013 AOSR field-study period, there
are still large uncertainties associated with these emissions. In the following, six
areas that still need further improvement are described.
Top-down emissions estimates from aircraft measurements made in late summer
2013 during the AOSR field study show that VOC and PM emissions reported to
the NPRI using currently accepted estimation methods might be underestimated
for the AOSR facilities (Li et al., 2017). However, these measurements were
made during a limited time period (4 weeks) and the mass-balance
calculations used to estimate emissions were only applied to a relatively
large area (Gordon et al., 2015; Li et al., 2017). Large variations in PM
emissions results were also seen from flight to flight for the same
facilities, probably related at least in part to the variation of mined
volume of oil sands from day to day or recent precipitation. There are thus
still issues with the spatial and temporal allocation of emissions to the
right location at the right time.
The aircraft measurements also indicated that the VOC speciation reported to
NPRI by individual AOSR mining facilities may need to be improved (Li et
al., 2017), and additional VOC speciation data should be collected to
improve speciation profiles. Moreover, these aircraft measurements were
carried out at the facility level, but within these very large facilities
the individual VOC species emitted from mine faces, tailings ponds, and
plants can be very different. More aircraft measurements, especially at
other times of year, and additional measurements of emissions at the
sub-facility level, from mine faces, tailings ponds, and plants for multiple
AOSR facilities, are needed to confirm and augment the findings of the 2013
field study and to further improve emissions factors, temporal profiles, and
chemical speciation profiles used for OS emissions inventories and emissions
processing (e.g., Small et al., 2015; Stantec Consulting Ltd. et al., 2016).
Given the above differences between field-study measurements and reports,
the AOSR mining facilities should also review the methodologies that they
employ to estimate and report VOC emissions to NPRI.
The off-road mining fleets in the six AOSR mining facilities are a large
source of NOx emissions, but large differences are seen in
the emissions estimates for this source sector between different inventories.
For example, the 2010 CEMA inventory lists 38 362 t of NOx
emissions for this sector, but the 2010 APEI for the same year lists
27 786 t. The 2013 APEI then reduced NOx emissions from
the OS off-road mining fleets to 12 370 t. Since mined oil sands increased
by 17 % between 2010 and 2013, the significant drop of
NOx emissions is probably due to different emissions factors
being used for these two inventory years (possibly due in part to the
introduction of cleaner heavy-hauler trucks: e.g., M.J. Bradley &
Associates LLC, 2008).
Additional sources of information are needed to reconcile the differences
amongst existing inventories. One possible data source is satellite remote
sensing. For example, a methodology has been developed recently to use
repeated satellite measurements of NO2 vertical column density over
the AOSR to estimate NOx emissions (McLinden et al., 2014,
2016). Preliminary top-down results from satellite remote sensing show that
area-source NOx emissions in the OS area, which are mainly
from the off-road fleets, are about 38 kt yr-1 for 2013, comparable to the bottom-up 2010 CEMA inventory. The 2010
CEMA inventory was also deemed to have the best estimation of off-road
emissions for the AOSR facilities (ECCC & AEP, 2016). Satellite remote
sensing (e.g., McLinden et al., 2014; Shephard et al., 2015; Sioris et al.,
2017) and ground-based remote sensing (e.g., Fioletov et al., 2016) should
thus be considered in the future for emissions estimation and verification.
There have been ongoing efforts to improve the spatial allocation of
emissions within the huge AOSR mining facilities using spatial surrogate
fields generated from the locations of mine faces, tailings ponds, and
extraction and upgrading plants. For example, the 2010 version of the shapefile
used for generating these surrogates was updated in phase 2 based on 2013
satellite images (Zhang et al., 2015). Further improvements, however, are
possible. As one example, the spatial surrogate used to allocate emissions
from the off-road mining fleet currently allocates all of the emissions to
the mine-face locations and does not account for the movement of the
heavy-hauler trucks between the mine faces and the extraction plants.
Year-specific shapefiles with locations of active mining areas and current
boundaries of tailing ponds as well as activity data sets for the actual or
average movement of mining vehicles and time spent at locations throughout
the mine should be obtained to improve the spatial allocation of off-road
emissions for the AOSR mining operations (ECCC & AEP, 2016)
Fugitive VOC emissions from tailing ponds and mine faces are currently
provided as annual totals in the inventory. A temperature-based monthly
temporal profile was used to allocate the annual emissions to each month
while weekly and diurnal temporal profiles were assumed to be constant,
which is likely not realistic. For example, nighttime emission rates over
the mine faces are likely lower than daytime rates due to lower surface
temperatures. In the future, model-predicted or locally measured hourly
temperature and wind speed may be used to estimate hourly fugitive VOC
emissions if the dependence of fugitive VOC emission rates on temperature
and wind speed can be parameterized (Li et al., 2017). Snow cover over the
mining areas and ice cover over the ponds during wintertime also affect
fugitive VOC emissions and need to be considered. A related issue is that
the tailings ponds are of different ages; some are receiving fresh tailings
while others have been inactive for years, which may mean lower emission
rates due to past off-gassing of more volatile components. Consideration
should thus be given to tailings-pond age when allocating VOC emissions
between different tailings ponds. A recently completed study (summer 2017)
of tailings-pond emissions conducted by ECCC is expected to lead to improved
estimates of emissions from these sources.
Top-down fugitive dust emissions estimates based on aircraft observations
suggest large underestimates in the reported inventory totals, and GEM-MACH
modeling suggests that even these revised estimates, or the fraction of
their mass which is composed of base cations, might be underestimated (Makar
et al., 2018). Further aircraft-based measurements of fugitive dust emissions
and their speciation are needed to improve the emissions inventories used
here. A parameterization of wind-blown dust emissions should also be added to
GEM-MACH.
For mercury emissions, although unspeciated mercury emissions were obtained
from inventories with base years close to 2013, chemical speciation was done
crudely using speciation profiles for nine broad source categories. This
methodology needs to be updated as more detailed speciation information
becomes available in the future.
The pre-phase 3 Canadian CAC (criteria air contaminant)
emissions inventory described in the JOSM report (ECCC & AEP, 2016) is
available from the ECCC web page at
http://donnees.ec.gc.ca/data/air/monitor/source-emissions-monitoring-oil-sands-region/source-emissions-oil-sands-region/?lang=en
(ECCC, 2018). The Canadian SMOKE-ready CAC emissions inventories compiled for
the phase 3 base case and sensitivity studies are available from the ECCC
weblink
http://collaboration.cmc.ec.gc.ca/cmc/arqi/ACP-2017-1215/CAC_inventory.tz
(last access: 18 July 2018). The SMOKE-ready mercury emissions used for this
study are available from the ECCC weblink
http://collaboration.cmc.ec.gc.ca/cmc/arqi/ACP-2017-1215/Mercury_inventory.tz
(last access: 18 July 2018). SMOKE-ready CAC emissions for the United States
are available from the U.S. EPA's website for their emissions modeling
platforms at
https://www.epa.gov/air-emissions-modeling/emissions-modeling-platforms
(EPA, 2018).
List of acronyms used in the paper
AcronymExpansionAAEIAlberta Air Emissions InventoryADOM-2Acid Deposition and Oxidant Model, version 2AEPAlberta Environment and Parks (formerly AESRD)AERAlberta Energy RegulatorAESRDAlberta Environment and Sustainable Resource Development (now AEP)AOSRAthabasca Oil Sands RegionAPEIAir Pollutant Emission InventoryAQair qualityBEISBiogenic Emission Inventory SystemBELDBiogenic Emissions Landuse DatabaseCACcriteria air contaminantsCEMACumulative Environmental Management AssociationCEMScontinuous emission monitoring systemCNRLCanadian Natural Resources LimitedECCCEnvironment and Climate Change CanadaEIAenvironmental impact assessmentEPAEnvironmental Protection Agency (United States)EPEAEnvironmental Protection and Enhancement Act (Alberta)FSSPforward scattering spectrometer probeGEM-MACHGlobal Environmental Multiscale–Modelling Air quality and CHemistryGISgeographic information systemJOSMJoint Oil Sands Monitoring planLAIleaf area indexLARPLower Athabasca Regional PlanNEINational Emissions InventoryNPRINational Pollutant Release InventoryOSoil sandsPFTparaffinic froth treatmentPMparticulate matterSCCSource Classification CodeSMOKESparse Matrix Operator Kernel EmissionsTERRATop-down Emission Rate Retrieval AlgorithmUHSASultra-high sensitivity aerosol spectrometerUOGupstream oil and gasVOCvolatile organic compoundWBEAWood Buffalo Environmental Association
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-10459-2018-supplement.
JZ performed the analysis and compilation of the emissions inventory, generated the air
quality model-ready emissions input files, and wrote and revised the
manuscript. MDM oversaw the compilation of the emissions inventory and
generation of emissions and wrote and revised the manuscript. QZ assisted
with the analysis and compilation of inventory and with the generation of air
quality model-ready emissions input files. PAM provided suggestions for
improvements of emissions for air quality modeling. PB assisted with
inventory analysis and preparation. GM performed the inventory analysis. PL
estimated PM emissions based on aircraft observations. SML estimated VOC
emissions based on aircraft observations.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Atmospheric
emissions from oil sands development and their transport, transformation and
deposition (ACP/AMT inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
Emissions inventories used in this study were provided by the Pollutant
Inventory and Reporting Division of ECCC, the Cumulative Environmental
Management Association, and the U.S. Environmental Protection Agency. The
Alberta CEMS data were provided by Marilyn Albert, Ewa Przybylo-Komar,
Katelyn Mackay, and Tara-Lynn Carmody of Data Management and Stewardship,
Corporate Services Division, Alberta Environment and Parks. We also
appreciate the information provided by Sunny Cho and Richard Melick of Albert
Environment and Parks about emissions in the AOSR and the province of
Alberta. We thank our colleagues in the Program Integration Division of ECCC,
Alicia Berthiaume and Anne Monette, and colleagues in the Pollutant Inventory
and Reporting Division of ECCC for their careful internal review of the
manuscript. We also appreciate the external review comments provided by
Canada's Oil Sands Innovation Alliance (COSIA). Finally, we are very grateful
for the insightful comments on the manuscript from two anonymous reviewers.
The readability and quality of the paper was greatly improved by addressing
their comments. This project was supported by the Climate Change and Air Quality Program
of ECCC and the Joint Oil Sands Monitoring (JOSM) program of ECCC and AEP. Edited by: Jan W.
Bottenheim Reviewed by: two anonymous referees
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