Introduction
Ammonia (NH3) is a short-lived pollutant that is receiving
global attention because of its increasing concentrations. Emissions of
NH3 – which are in large part from agricultural fertilizer, livestock
, and biomass burning –
have not been regulated to the same extent as other nitrogen species. NH3
is the only aerosol precursor whose global emissions are projected to rise
throughout the next century .
NH3 has an atmospheric lifetime of hours to a day
. It is a base that reacts in the atmosphere with
sulphuric acid (H2SO4) and nitric acid (HNO3) to form crystalline
sulphate, nitrate salts (e.g., (NH4)2SO4, NH4HSO4,
NH4NO3) and aqueous ions (SO42-, HSO4-, NO3-)
, which are significant components of fine particulate
matter (PM2.5; e.g., , ;
, ), thus having health
and climate impacts . A large portion of NH3 is readily
deposited in the first 4–5 km from its source, but when in fine particulate
form (as NH4+), its lifetime is days to several weeks
, and it can be transported
hundreds of kilometres . Deposition of
NH3 and these aerosols can lead to nitrogen eutrophication and soil
acidification . NH3 is
listed as a “criteria air contaminant” in order to help address
air quality issues such as smog and acid rain.
Modelling can be used to better understand NH3 processes. Recent NH3
models have focused on improving bidirectional flux processes and impacts of
livestock. Measurements of NH3 bidirectional flux include those in
; and , with indirect support
for bidirectional flux also in . Thus, these studies were the
motivation for the recent design of parameterizations to describe this
important process .
Additionally, satellite observations are providing valuable insight into
ammonia concentrations and emissions both on regional and global scales
.
The Athabasca Oil Sands region (AOSR), located in the north-eastern part of
the province of Alberta, Canada, is a large source of pollution to air
and ecosystems ,
as well as a source of greenhouse gases due to mining
and processing by the oil industry. While NH3 volume mixing ratios (VMRs)
surrounding the AOSR in northern Alberta and Saskatchewan remain relatively
low – around 0.6 to 1.2 ppbv background (this study and
, ) – due to low population
and lack of agriculture, the northern Alberta and Saskatchewan ecosystems are
sensitive to nitrogen deposition
, and the modelled
background NH3 must be correct in order to understand the relative impacts
of the oil sand operations. It is important to understand if the AOSR
facilities or other sources (e.g., fires, re-emissions) are causing NH3 to
reach levels that cause ecosystem damage. A monitoring study from 2005 to
2008 found NH3 VMRs near Fort McMurray and Fort McKay (population centers
in the vicinity of the oil sand facilities) to be highly variable in space
and time with a range of 1.1 to 8.8 ppbv (where the upper end corresponds to
NH3 levels found in agricultural regions of Canada and the U.S.), with
NH3 concentrations 1.5–3 times higher than HNO3 concentrations
. also found NH3 concentrations in the
AOSR to be much higher than HNO3, NO3-, and NH4+ concentrations
(by 5, 23, and 1.8 times, respectively). Thus, NH3 may contribute the
largest fraction of deposited nitrogen in the AOSR compared to other nitrogen
species. Estimates of deposition of nitrogen compounds in the AOSR are
described in ; however, they did not include NH3
bidirectional flux or forest fires in their model simulations.
In a previous study by , it was found that the GEM-MACH air
quality forecasting model ,
using a domain covering the Canadian provinces of Alberta and Saskatchewan
at 2.5 km resolution, under-predicted summertime tropospheric ammonia VMRs by
0.4–0.6 ppbv (which is 36–100 % depending on altitude – see Fig. 16 in
, ) in the AOSR when compared
to the Tropospheric Emission Spectrometer (TES) satellite measurements and
aircraft measurements. Having too much modelled NHx deposition is a
cause that was ruled out when showed that GEM-MACH actually
underestimates NHx deposition. Underestimating anthropogenic and
agricultural emissions was also ruled out as a cause since the GEM-MACH model
performs well in southern Canada and the U.S. when compared to the U.S.
Ambient Ammonia Monitoring Network (AMoN). NH3 sources known to be missing
from the GEM-MACH model were forest fire emissions and re-emission of
deposited NH3 from soils and plants (the latter referred to as
bidirectional flux, hereafter), which would have the greatest impact in
background areas, such as northern Alberta and Saskatchewan. Therefore, these
two sources were added to an updated version of GEM-MACH and model
simulations were repeated for a 2013 summer period (12 August to 7 September
2013), during which an intensive measurement campaign occurred. We utilize
ground, aircraft, and satellite measurements of NH3 and related species to
evaluate the model and to quantify the impacts of the different sources on
atmospheric NH3 and its deposition.
Section provides the model description. Section provides a brief description of ammonia measurements during
the campaign. Section presents the evaluation of
three model scenarios against three different types of measurements (surface,
aircraft, and satellite), and Sect. presents our
quantitative assessment on the impacts of different sources of NH3 to
ambient VMRs and NHx fluxes in the region. Our conclusions appear in
Sect. .
GEM-MACH model description
GEM-MACH (Global Environment Multiscale-Modelling Air
quality and CHemistry) is an online chemical transport model, which is
embedded in GEM, Environment and Climate Change Canada's (ECCC) numerical
weather prediction model . This means that the chemical
processes of the model (gas-phase chemistry, plume rise emissions
distribution, vertical diffusion and surface fluxes of tracers, and a
particle chemistry package including particle microphysics, cloud processes,
and inorganic heterogeneous chemistry) are imbedded within GEM's physics
package, which in turn is imbedded within GEM's dynamics package, the latter
handling chemical tracer advection. A detailed description of the process
representation of GEM-MACH, and an evaluation of its performance for
pollutants such as ozone and particulate matter (PM) appears in
; and .
GEM-MACHv2 is used operationally to issue twice-daily, 48 h public
forecasts of criteria air pollutants (ozone, nitrogen oxides, PM), as well as
the Air Quality Health Index (https://ec.gc.ca/cas-aqhi/). Any
improvements to NH3 in the model may result in better Air Quality Health Index predictions,
since NH3 is a major precursor of PM2.5, as mentioned in the
introduction. We start with a similar research version of GEM-MACHv2
(rev2285) to make the bidirectional flux modifications. The key differences
between this and older versions are the use of a more recent meteorological
package (GEMv4.8), the capability to nest in the vertical dimension as well
as the horizontal dimension, and improvements to the treatment of fluxes,
vertical diffusion, and advection.
GEM-MACH can be run for many different spatial domains, at various spatial
resolutions, and in 2-bin or 12-bin aerosol-size-distribution modes. For this
study, we run the model in the 2-bin mode (for computational efficiency),
using a nested set of domains. The outer domain at 10 km resolution covers
North America and the inner domain at 2.5 km resolution covers the provinces
of Alberta and Saskatchewan. The latter is referred to as the 2.5 km oil
sands domain. This setup, along with the emissions described in the next
section, is hereafter called our “base” simulation.
Emissions
The emissions of 25 species (SO2, SO4 gas,
sulphate, nitrate, NH4+, NO, NO2, NH3, CO, nitrous acid, benzene,
propane, higher alkanes, higher alkenes, ethene, toluene, aromatics,
formaldehyde, aldehydes, methyl ethyl ketone, creosol, isoprene, crustal
material, elemental carbon, and primary carbon) used in GEM-MACH (base case)
come from Canadian and U.S. emissions inventories: the 2011 National Emissions
Inventory (known as NEI) version 1 for U.S. emissions and the Air Pollutant Emission
Inventory (APEI) 2013 for Canadian emissions (2010 for on-road and off-road
emissions). Emissions were processed with SMOKE (Sparse Matrix Operator
Kernel Emissions, https://www.cmascenter.org/smoke/) to convert the
inventories into model-ready gridded hourly emissions files for modelling,
separated into major point emissions (typically industrial emissions from
stacks, emitted into the model layers that correspond to the stack height at
the reported temperature and velocity in the inventory's stack parameters),
and area emissions (emissions from spread-out sources, such as transportation
and agriculture, emitted into the first model layer). For more details about
these emissions, see and .
The emissions data for NH3 from oil sand sources are reported to the
Canadian National Pollutant Release Inventory (NPRI) on a “total annual
emissions per facility” basis. NH3 emissions are generally more uncertain
than SO2 and NOx emissions because NH3 emissions are not
measured to the same extent as those two. The oil sands represent only 1 %
of total Alberta NH3 emissions, at approximately 1438 t in 2013. For
comparison, about 18 times more NOx and 57 times more SO2 was
emitted from the oil sand facilities that year
(http://www.ec.gc.ca/inrp-npri/donnees-data/index.cfm?lang=En).
However, we found an issue with NH3 in this inventory that impacted our
model evaluation in the region, which we describe below.
If stack parameters (e.g., stack height and diameter, volume flow rates,
temperatures, etc.) are included as part of those NPRI data, then the
emissions are allocated to large stacks in our configuration of the SMOKE
emissions processing system. In the absence of this information, SMOKE will
assign default stack parameters based on its source category code. For the
Syncrude Canada Ltd. – Mildred Lake plant site, NPRI ID 2274 (a facility in
the AOSR), the default stack parameters were the following: 18.90 m for the stack height
(which is within the first model layer), 0.24 m for the stack diameter, 320.0
K for the exhaust temperature, and 0.58 m s-1 for the exhaust velocity.
However, when these defaults were applied to NH3 emissions in initial
model simulations, they were found to result in erroneous short-term plume
events with simulated surface NH3 levels up to 2 orders of magnitude
higher than ground observations, and modelled VMRs aloft too low compared to
aircraft measurements. Conversely, for species such as SO2, for which
stack parameters were reported, the model was able to correctly place the
SO2 enhancements in space and time, relative to observations. When we
applied those same stack parameters for NH3 emissions as well (stack
height = 183 m, stack diameter = 7.9 m, exit temperature = 513 K, exit
velocity = 23.9 m s-1, from the NPRI website), the simulation of surface NH3
was greatly improved. All subsequent simulations reported here make use of
this correction, and we advise the reporting of stack parameters for all
species for future inventories in order to avoid this kind of error for
models.
Ammonia bidirectional flux parameterization
NH3 can be both deposited from the atmosphere to the ground and
re-emitted from soils and plants back to the atmosphere. The two taken
together are called bidirectional flux, since the flux of NH3 can go both
up and down. The sources of NH3 available for re-emissions are from the
accumulated NHx in the soil and stomatal water, which can arise from
increased deposition from anthropogenic sources, as well as from organic
nitrogen decomposition , N2-fixation , and
natural microbial action .
The bidirectional flux scheme of was applied within the
GEM-MACHv2 model, replacing the original deposition velocity for NH3 only
(deposition velocity of other gas species follows a scheme based on a
multiple resistance approach and a single-layer “big leaf” approach;
). The bidirectional flux
scheme is described in detail in , but we summarize it here.
Bidirectional exchange occurs between air–soil and air–stomata interfaces.
The bidirectional flux (Ft) equation is
Ft=-Ca-CcRa+Rb,
where Ra and Rb are the aerodynamic and quasi-laminar resistances,
respectively. Ca is the NH3 concentration in the air, and Cc is the
canopy compensation point concentration, given by Eq. ().
Cc=CaRa+Rb+CstRst+CgRac+Rg(Ra+Rb)-1+(Rst)-1+(Rac+Rg)-1+(Rcut)-1,
where Cst and Cg are the stomatal and ground compensation points, respectively, and
Ri are the resistances in s m-1 of the ground/soil (Rg), stomata
(Rst), cuticle (Rcut), and in-canopy aerodynamic (Rac). All
resistance formulas can be found in .
Stomata (st) and ground (g) compensation points are both calculated using Eq. ():
Cst,g=ATst,gexp(-BTst,g)Γst,g.
A and B are constants derived from the equilibria constants for NH3(g)
in leaves' stomatal cavities to NH4+ and OH- in the water contained in
the apoplast within the leaf and in the soil where NH3(g) in the soil pore
air space is in equilibrium with the NH4+ and OH- dissolved in soil
water . A= 16 1500 mol K L-1 , or 2.7457 × 1015 µg K m-3
for NH3 for both stomata and soil.
B= 10 380 K . Γst,g is the emission potential of the
stomata and ground, respectively and, in theory, is equal to the NH4+
concentration over the H+ concentration in the apoplast water of the
canopy leaves or soil water:
Γst,g=[NH4+]st,g[H+]st,g.
However, since there are no modelled NH4+ and H+ apoplast water
concentrations to use, we use Γst,g from , which is
based on long-term empirical averages. gives a range of values
for emission potentials for 26 land use categories (LUCs), and we use the
low end of the values in our model with the following exceptions: we further
lower the Γg for agriculture LUCs to 800, and increase Γst
of boreal forest LUCs to 3000, all of which were necessary in order to
achieve realistic NH3 concentrations (e.g., compared to reported AMoN
values), while staying consistent with Γ findings from the literature.
This version of the model, which we call GEM-MACH-Bidi (or just “bidi”
hereafter) was quite sensitive to the selection of these emission potentials,
which are themselves highly uncertain . GEM-MACH-Bidi uses the
exact same emissions as in the base case, described in the previous section.
However, when the sign of Ft in Eq. () becomes positive
(that is, when Ca<Cc), the bidirectional flux acts effectively as an
additional source of NH3 gas, releasing stored NH3 until and unless the
ambient concentration rises to the compensation point concentration. When the
flux is negative, net deposition of NH3 occurs.
It is important to note that Cst,g values are exponentially dependent on
temperature (Fig. shows an example of this relationship for
the dominant LUCs in the northern part of the domain), and the higher the
compensation point, the greater the likelihood there will be upward flux. The
lower the Cst,g, the more likely there will be deposition. Since our
simulation period was August and September 2013, when the average temperature
in the AOSR was about 18 ∘C
(http://agriculture.alberta.ca/acis/alberta-weather-data-viewer.jsp), we
expect to have more NH3 re-emission than at other times of the year.
During the rest of the year (e.g., the preceding winter and spring), the
compensation point would be much lower, greatly increasing the likelihood to
have net deposition, even in northern Alberta and Saskatchewan where ambient
NH3 concentrations are low. Other meteorological factors affect the
magnitude of bidirectional flux via the resistance terms. For example, canopy
compensation points have been observed to decrease with decreasing wind
velocity and increased precipitation
. In other words, we expect more
re-emission during higher winds and drier conditions.
Compensation point (Cg) relationship to temperature;
Cg for evergreen needleleaf LUC shown as example.
![]()
Other chemical transport models, such as GEOS-Chem and CMAQ, use a similar
method as ; however, instead of the constant average soil
emission potentials used here, they utilize a CMAQ–agroecosystem coupled
simulation to calculate a soil pool from which to estimate Γg
. In this case, the emission potential will vary
and can go to zero if the NH4+ in the pool is depleted. However, it was
shown in that their Γst,g worked well during the same
time of year as this investigation (August and September). This time of year
was also shown in to not have a large effect on emissions from
the NH4+ pool. Additionally, calculated the
approximate relative abundances of NHx in the boundary layer versus
NH4+ in the soil pool to assess whether surface-to-air fluxes were
sustainable. They found that soil NH4+ concentrations were much greater
than boundary layer NHx (by over 2 orders of magnitude), further
supporting the assumption made here. In addition, the turnover time for soil
NH4+ is on the order of 1 day, hence it is unlikely that NH3
bidirectional fluxes would significantly deplete or enhance soil NH4+
pools. Finally, given that GEM-MACH is used for real-time air quality
forecasts at ECCC, it is not desirable for our bidirectional flux scheme to
have to rely in advance on another model's output. Therefore, we use this
simplified version, and assess whether its results provide an improvement
(smaller biases and better correlations to measurements) to simulated NH3.
Addition of forest fire emissions
Our third model scenario (called “fire + bidi” hereafter) uses the
GEM-MACH-Bidi model, and the exact same area emissions and anthropogenic
major point emissions as the base and bidi scenarios. However, in addition,
we add hourly North American forest fire emissions for all species to the
major point emissions. The forest fire emissions system for GEM-MACH (called
“FireWork is described in detail in . Briefly, to
calculate the fire emissions for input to FireWork, biomass burning areas are
first identified in near-real-time by the Canadian Wildland Fire Information
System (CWFIS), which is operated by the Canadian Forest Service
(http://cwfis.cfs.nrcan.gc.ca/home). CWFIS uses fire hotspots detected
by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and
NOAA's Advanced Very High Resolution Radiometer and Visible Infrared
Imaging Radiometer Suite imagery as inputs. Daily total emissions per hotspot
are then estimated by the Fire Emission Production Simulator module of the
BlueSky modelling framework . SMOKE was then used to prepare
model-ready hourly emissions of several species (including NH3) in a
point-source format for model input.
In ECCC's operational forest fire forecasts, these emissions are used at
10 km resolution for the domain encompassing North America, with forest fires
being treated as point sources with specific plume rise .
We have added 2013 forest fire emissions which were originally created for
the 2013 FireWork forecasts to the anthropogenic point-source emissions used
in the base case simulation, and have modified the GEM-MACH model to be able
to accommodate the changing number of major point sources each day (as the
number of fires changes daily). Fire plume rise is an ongoing area of
investigation (e.g., , ;
, ); smoldering emissions tend to be
emitted directly at the surface, whereas flaming emissions can inject plumes
to the upper troposphere. Here, we have set all fire emissions to be
distributed evenly throughout the boundary layer, which is a simplification
but one that averages out smouldering and flaming plume heights. Different
parameterizations of fire plume rise are currently under development in
GEM-MACH. The FireWork fire emissions are described in detail in
, and this study represents the first time they have been used
at a 2.5 km horizontal resolution.
Model setup for three scenarios
The base, bidi, and fire + bidi models were all run in the
following way: each scenario was run from 1 August to 7 September 2013,
where the first 11 days were “spin up” in order to allow chemical
concentrations to stabilize, and are not used in our evaluation. This is a
sufficient amount of spinup time, given that the atmospheric lifetime of
NH3 is typically up to 1 day , and given that it
is close to the transport time of air crossing the larger North American
domain. The time period from 12 August to 7 September was chosen to coincide
with the intensive measurement campaign described in Sect. .
Map of 10 km resolution continental piloting model domain
(green) and 2.5 km resolution nested model domain (purple).
![]()
The model was run in a nested setup, whereby the North American domain was
run at 10 km resolution using “climatological” chemical initial and boundary
conditions from a 1-year MOZART simulation for all pollutants
. The nested oil sands region (which covers most of Alberta
and Saskatchewan) was run at 2.5 km horizontal resolution, using the initial
and boundary conditions from the 10 km North American model run. Figure shows the two model domains.
The model simulations for the pilot and nested domains were not run as a
continuous multi-day forecast but rather following the operational air
quality forecast process, where the meteorological values are updated
regularly with new analyses (products of meteorological data assimilation
which provide optimized initial conditions for the 12 UTC hour of each day).
The analyses were obtained from ECCC archives
(), in order to prevent chaotic drift of
the model meteorology from observations. Consequently, our simulation setup
comprises simulations on the North American domain in 30 h cycles starting
at 12:00 UTC, and the oil sands domain in 24 h cycles starting at 18:00 UTC (the
6 h lag being required to allow meteorological spinup of the lower
resolution model). The next cycle uses the chemical mass mixing ratios from
the end of the last cycle as initial conditions for the next 24–30 h.
This system of staggered meteorological driving forecasts with a continuous
chemical record continues until the full time period completes.
We run GEM-MACH in the 2-bin particle mode, which means that particles fall
in either fine mode (diameter 0–2.5 µm) or coarse mode (diameter 2.5–10 µm), for computational
efficiency (although sub-binning is used in some
particle microphysics processes in order to ensure an accurate representation
of particle microphysics; ), and in order to follow the setup
used for the operational 10 km resolution GEM-MACH forecast.
Measurements
Our three model simulations (base, bidi, and fire + bidi) are evaluated with
surface, aircraft, and Cross-track Infrared Sounder (CrIS) satellite
measurements. We briefly describe each of these observation datasets below.
AMS13 ground measurements
An extensive suite of instrumentation was deployed at monitoring site AMS13
(57.1492∘ N, 111.6422∘ W; 270 m a.s.l.; Fig. ) from 7 August 2013 until 12 September 2013. Mining
operations and bitumen upgrading facilities are 5 km to the south and
north of the site, which is surrounded by boreal forest with dominant winds
from the west averaging 1.9 m s-1 throughout the year. The average
temperatures in the region for August are highs between 20–25 ∘C
and lows around 10 ∘C, which is warm enough to make upward NH3
flux more likely (recall Fig. ). However, temperatures drop rapidly
at the end of August, into September, where the September highs average
around 15 ∘C and lows around 5 ∘C. The skies are the clearest
during August, with at least partly clear skies 50 % of the time. That
said, the warm season (May through September) is the wetter season (average
of 20 % chance of precipitation daily), with more precipitation than during
the cold season (when there is an average of 7 % chance of precipitation
daily). However, year-round precipitation, as well as relative humidity, are both
relatively low in the AOSR. During the cold season (November through
February), the average temperatures range from -21 to
-5 ∘C, when the forest and soils are more likely to be a deposition
sink for NH3. During November to April, it is also much cloudier, with
February having cloudy conditions 77 % of the time. (All weather data cited
here are from the annual report at Fort McMurray:
https://weatherspark.com/y/2795/Average-Weather-in-Fort-McMurray-Canada-Year-Round).
Flight path on 13 August 2013, where elevation (in metres) is
denoted by the colour scale and the AMS13 site is indicated by a black circle.
![]()
NH3, fine particulate ammonium and nitrate, and other species were
measured by an ambient ion monitor ion chromatograph via an inlet
4.55 m off the ground. The uncertainty of these measurements is ±15 %.
These measurements are described in more detail in .
Data gaps sometimes appeared in the surface NH3 time series for the
following reasons: instrument zero (14–15 and 17–18 August), instrument
maintenance (19 August), and a power outage (27–28 August).
Aircraft measurements
During the oil sands monitoring intensive campaign, there were a total of 22
flights spanning 13 August to 7 September 2013. These measurements are
described in detail in ; and , and are
summarized here. Aircraft NH3 measurements were conducted with a dual
quantum cascade laser (QCL) trace gas monitor (Aerodyne Inc., Billerica, MA,
USA; McManus et al., 2008), collecting data every 1 s. Outside air was
sampled through a heated Teflon inlet tube shared with a high-resolution
time-of-flight chemical ionization mass spectrometer (HR-ToF-CIMS); the flow
rate through the QCL was 10.8 L min-1. The 1σ uncertainty for
each measurement was estimated to be ±0.3 ppbv (±35 %;
).
Particulate NH4+ (0 to <1 µm in diameter) was measured by the
Aerodyne high-resolution time-of-flight aerosol mass spectrometer
(HR-ToF-AMS) instrument on board the same flights, which collected data every
10 s. The ambient air was drawn through a forward-facing shrouded isokinetic
particle inlet from which the HR-ToF-AMS sub-sampled. The total residence
time in the inlet and associated tubing was approximately 1 s. The error
on these measurements is ±9 %.
Figure shows a sample flight path from the campaign from
13 August 2013 – one of the 13 flights with valid NH3 measurements.
The others took place on 15–17, 19 (two this day), 22–24, 26, 28 August, and
5–6 September 2013. NH3 data on the other nine flights were invalidated
due to instrument issues (those on 14, 20–21, 29, 31 August, and 2–4
September 2013), but were successful for the NH4+ measurements.
CrIS satellite measurements
CrIS was launched in late October 2011 on board the Suomi NPP platform. CrIS
follows a sun-synchronous orbit with a daytime overpass time at 13:30 LT
(local time, ascending) and a night time equator overpass at 01:30 LT (descending). The instrument scans along a 2200 km swath using a 3 × 3 array of
circular pixels with a diameter of 14 km at nadir for each pixel. The CrIS
“fast physical retrieval” described by is used to
perform satellite profile retrievals of NH3 VMR given the infrared
emission spectrum from the atmosphere. This retrieval uses an optimal
estimation approach that provides the satellite vertical
sensitivity (averaging kernels) and an estimate of the total errors (error
covariance matrix).
We take the CrIS retrieved profile and match it up with the closest model
profile in both distance and time, compute the distance between the CrIS
pixel and model field for each time step, and then select the time step that
best matches the satellite overpass time. Since the model time steps are
every hour with a 10 km spatial resolution they are always matched up to
better than half an hour, and within 5 km.
Model evaluation
An older version of GEM-MACH (v1.5.1) has been compared to TES satellite and
aircraft measurements of ammonia over the AOSR .
Simulations with that version of the model were shown to be biased low, by
about -0.5 ppbv throughout the lower-tropospheric vertical profile. This
represented a substantial deficit in the model predicted sources of NH3,
prompting the current work. We now compare our three GEM-MACHv2 simulations
(base, bidi, and fire + bidi) against surface point measurements at the
measurement site near an oil sands facility (AMS13), aircraft measurements
over the broader AOSR, and satellite measurements over the Alberta and
Saskatchewan area. We will discuss which simulation agrees best with
measurements and where there may still be room for additional model
improvement.
Surface daily average VMR of (a) NH3, and concentrations of
(b) fine particulate NH4+, (c) NO3-, and (d) SO42- at the AMS13 ground
site in the AOSR. Measurements (Meas) in orange, base model in green, bidirectional flux model (Bidi) in blue, and fire + bidi model (Fire) in red.
![]()
At the AMS13 ground site
Figure shows the time series of the daily average (for
clarity) VMRs of NH3 and concentrations of fine-particulate NH4+,
NO3-, and SO42- at the AMS13 oil sands ground site for the
observations and three model simulations. The hourly data were also studied,
but are not shown in the time series.
We first note that the NH3 VMRs in the measured time series are relatively
low with mean, median, and maximum of 0.6, 0.426, and 2.98 ppbv,
respectively, in the hourly data, which are lower than the 1–8 ppbv range in
and the 2.7 ppbv summertime mean given in
. However, this may be due to the different time periods and
locations measured. Our mean measured values at the AMS13 site are similar to
the VMRs found at U.S. AMoN background sites
(http://nadp.sws.uiuc.edu/amon/).
Hourly model–measurement bias in surface (a) NH3 VMR, and
(b) NH4+, (c) NO3-, and (d) SO42- concentrations at the AMS13 ground site in the AOSR.
![]()
Figure a shows that the base model (green) background VMRs
of NH3 are very low (nearly 0 ppbv when there is no plume influence)
compared to the measurements (orange). Only during the spike on 3–4 September
does the base model exceed the measured values, probably indicating a
local plume event fumigating to a lesser extent in the observations than was
predicted by the model. The NH3 VMRs of the base case are biased low
compared to the surface measurements by a median of -0.35 ppbv (Fig. a) over the time period of the campaign.
In Fig. , the bidi model (blue line) and fire + bidi model (red line)
show a significant improvement to the NH3 VMRs compared to the base model
(green line). Unfortunately, during some time periods, these two versions of
the model overestimate NH3: during 13 August, the model adds a
significant level of NH3 due to fire emissions; however, the surface
in situ observations show no evidence of fire impact. During other
time periods (e.g., 30 August to 3 September, and 4–7 September), the bidi
model appears to have put too much NH3 into the system. Therefore, the
bidi model bias (Fig. a) is now 0.30 ppbv too high
(median), and the fire + bidi bias is 0.32 ppbv high (median) over the time
period of the campaign, resulting in an overall improvement of only 0.03 ppbv
in the model bias.
Hourly modelled vs. measured surface (a) NH3 VMR, and
(b) NH4+, (c) NO3-, and (d) SO42- concentrations at the AMS13 ground
site in the AOSR. Base model is in grey, bidirectional flux model in blue, and fire + bidi model in red.
![]()
While the bias improvement is small, the bidi and fire + bidi both have greatly
improved correlation coefficients (from R=0.1 to 0.4) and slopes much closer
to 1 (from 0.1 to 0.7), showing that those added sources are important to
improve model results (Fig. a). Additionally, the
diurnal cycle (not shown) was improved in the bidi simulation, with both it
and the measurements shaped like a sine curve with a minimum at
03:00–04:00 LT, and a maximum at noon local time, although the amplitude of the
cycle was underestimated. Whereas, the base model diurnal cycle was flat from
midnight to noon local time, and spiky from noon to midnight.
While Figs. a to a show that the
addition of bidirectional flux improves the model correlation coefficient,
slope, and bias, there is still room for further improvement. Paired t-test
results indicate that the fire + bidi and measurements are still significantly
different (see Table for comparison statistics of
all three simulations). While inherent limitations from model resolution and
uncertainties may be responsible for the remaining bias, it is likely that
(a) the emission potentials for the LUCs in the region
may be causing too much re-emission of NH3, and need refinement, and (b)
the fire emissions of NH3 are not properly distributed in the vertical,
placing too much NH3 near the surface and/or the fire emission factors for
NH3 are too high.
Refinement needed for the emission potentials and LUCs may be a significant
cause of the bidi and fire + bidi model biases. have shown
that about 64 % of the AOSR are wetlands (fens, bogs, and marshes), which
should be mapped to the swamp LUC. However, our model currently assigns the
AOSR landscape to evergreen needleleaf trees, deciduous broadleaf trees,
inland lake, mixed shrubs, and mixed forests (and none of the region to
swamp). This would lead to an overestimation of re-emission given that bogs
are fairly acidic and our swamp emission potential is lower than the
aforementioned LUCs. Other evidence for these two explanations will be
presented below in Sect. .
For NH4+, NO3- and SO42- the time series, model biases, and model-vs.-measured correlations are shown in
Figs. , , and ,
respectively. There is very little change in NH4+ (Figs. b, b,
and b) and SO42- (Figs. d, d, and d) despite the increase in NH3 that
the bidirectional flux yields. The bias is very small for all three model
scenarios, and the correlation coefficients are all relatively poor. So while
there is an improvement to modelled NH3 with bidirectional flux, there is
a neutral affect on fine particulate NH4+. This may be because the charge
of NH4+ in the particles is already enough in the base model to balance
the charge of 2 × SO42-+ NO3- in the aerosols, thus causing
any additional NH3 (from bidi and fires) to remain in the gas phase.
Alternatively, the minimal change in NH4+ could be due to additional wet
scavenging of the additional NH3, which will be discussed in Sect. .
The change in NH3 VMR has no effect on SO42-
since particulate SO42- is not sensitive to the amount of
NH3/NH4+ available, and is dominated by anthropogenic and fire
emissions. For NO3- (Fig. c), the base model bias was quite small at 0.01 µg m-3; however, the addition of bidi and fire + bidi further reduced that
bias to 0.0011 and 0.0004 µg m-3, respectively, which is a significant
improvement. The correlation coefficient for NO3- also improved from
about 0.1 to 0.3 (Fig. c).
NH3 VMRs aloft (colour scale) over the oil sands region during the 13 August 2013
flight. (a) Measurements, (b) base model, (c) fire + bidi model, and (d) bidi model.
![]()
Along the oil sands campaign flight paths
There were 13 flights during the oil sands campaign that had
valid (above detection limit, and no instrument error) NH3 measurements,
and 22 flights that had valid NH4+ (0–1 µm diameter) measurements.
The flight path of the first flight, which occurred on 13 August 2013, is
shown in Fig. ; chosen as an example because this flight
sampled mainly background NH3 (rather than facility plumes).
Figure shows the NH3 VMRs along this flight path over
time. Here the hourly model output is interpolated to the same time frequency
as the measurements. The model output also has spatial resolution limits when
comparing to the aircraft. However, we clearly see that for this flight, the
bidirectional flux has increased NH3 VMRs, bringing them closer to the
measured values (median biases for this flight are -1.38, 0.68, and 0.69 ppbv
in the base, bidi, and fire + bidi simulations, respectively). There is little change when
fires are added (Fig. d vs. c) because this flight did not
pass through a fire plume.
Hourly averages along all flight paths over the oil sands region during the summer 2013
campaign: model–measurement bias in (a) NH3 and (b) NH4+. Modelled vs. measured
(c) NH3 VMR and (d) NH4+ concentrations aloft. Base model is in grey, bidirectional
flux model in blue, and fire + bidi model in red.
![]()
Figure shows the model–measurement differences and
the model vs. measurement scatter plots for the combined set of all flight
paths for hourly average NH3 and NH4+. For NH3 the median base
model bias is -0.75 ppbv, comparable to the bias observed in
, , with the bidi model bias
improving to -0.24 ppbv and the fire + bidi bias improving to -0.23 ppbv. Also, the best
correlation coefficient and slope is achieved by the fire + bidi scenario. The
use of the bidirectional flux has thus reduced the model bias relative to the
aircraft observations by a factor of 3. The fire + bidi simulation has the
best statistics compared to measurements, as summarized in Table .
Again, the NH4+ results show little change despite the increase in
NH3. The small bias from the base case gets insignificantly smaller, and
the slope and correlation coefficients are all negligibly changed.
In the vertical profiles across the region
The CrIS satellite has many observations over North America during the 2013
oil sands campaign. We have evaluated the model with these observations in two ways:
All daytime data from 12 August–7 September 2013, with model–measurement comparisons
over a large region encompassing Alberta and Saskatchewan
(latitude range: 48–60∘ N, longitude range: 100–122∘ W), which contains agricultural areas, a number of cities, the northern
boreal forest, and oil sand facilities.
Case studies where we attempt to isolate fire emissions and non-fire conditions to evaluate both new components (fires and bidi) of the model.
The latitude and longitude ranges of our model–measurement pairs are given in
Table . The satellite passes over these regions at
approximately 13:00 LT and 01:00 LT.
Latitude and longitude ranges that the model was evaluated over with the CrIS satellite measurements.
Domain
Date (in 2013)
Lat. range
Long. range
Alberta and Saskatchewan large domain
12 Aug to 7 Sep
48 to 60∘ N
-122.0 to -100.0∘ W
Northern, no-fire case study
03 Sep
55 to 60∘ N
-120.0 to -110.0∘ W
Southern, no-fire case study
01 Sep
49 to 53.5∘ N
-117.0 to -106.0∘ W
Northern, fire case study
12 Aug
56.5 to 60∘ N
-110.0 to -104.4∘ W
(a) (i) NH3 vertical profiles as measured by CrIS satellite from
12 August to 7 September 2013; difference between measurement and (ii) base model, (iii) bidi model, and
(iv) fire + bidi model; and (v) averaging kernel (labelled AK) of CrIS satellite for NH3 retrieval. (b) Average
(12 August–7 September 2013) surface NH3 VMRs given by the (i) base model, (ii) fire + bidi model, and (iii) CrIS satellite.
![]()
There were over 60 000 model–measurement pairs between the model and the CrIS
satellite over the model domain during 12 August to 7 September 2013.
Figure a presents model biases for the entire dataset
in a box and whiskers plot of the vertical NH3 profiles at five vertical
levels. The left-most panel (panel i) shows the NH3 VMRs measured by CrIS, and
the right-most panel (v) shows the diagonal elements of the CrIS averaging
kernels, illustrating the sensitivity of the satellite measurements to each
vertical level. The NH3 VMRs over Alberta and Saskatchewan measured by
CrIS are very similar to those found by TES in the study
for the AOSR region.
The middle panels (Fig. a, ii–iv) show the model
biases from the three simulations. The base model has a very similar bias to
CrIS as the older version of GEM-MACH (v.1.5.1) had compared to TES
observations in the study – thus showing that the
negative NH3 biases were not improved with the use of the newer GEM-MACH
version (v2) itself. The fire + bidi model has the smallest bias in the highest
three layers, but the bidi model has the smallest bias in the two lowest
layers. In those lower layers, the fire + bidi model increases NH3 VMRs too
far (though still a smaller absolute bias compared to the base case; Fig. a). The fire + bidi positive bias could be due to an
overestimate of the bidirectional flux re-emissions or of the fire emissions,
or to an underestimate of the altitude of the fire emissions, or a
combination of all three factors. In order to distinguish between these
possibilities, two case studies were examined further in the next section.
The statistics from the model–CrIS comparison can be found in Table . That summary shows that the fire + bidi simulation
performs better than the base and the bidi simulations.
The spatial distribution of modelled NH3 can also be evaluated with CrIS
measurements, as shown in Fig. b. These are maps of
the average surface NH3 from the base model, the fire + bidi model, and the
CrIS satellite. The fire + bidi model over-predicts the effect of fires in the
middle of northern Saskatchewan, but appears to be missing fires in
north-western Manitoba. Other than fire influence, the spatial distribution
in the fire + bidi model is the same as that of the base model, but with
significant increases in overall VMR. The spatial distribution of the model
simulations is different from the spatial distribution that CrIS measures.
For example, the model predicts much higher NH3 near the city of Edmonton
than CrIS shows. That said, the addition of bidirectional flux has greatly
improved the NH3 simulation in the northern part of the province, where it
was almost zero in the base model.
Model–measurement NH3 comparison statistics from 12 August to 7 September 2013: R is correlation coefficient; slope
is of the line-of-best-fit between model vs. measurement; p and t are from a paired t test between model and measurement data pairs
(p> 0.05 and |t|<1 means that the model is statistically indistinguishable from measurements); the median model bias;
RMSE is the root mean square error; and FE is the fractional error of the models.
CrIS (troposphere) results are for the entire model domain at all tropospheric levels shown in Fig. (top),
and CrIS (surface) results are for the lowest retrieval level (both are during mid-day satellite overpass times); aircraft results
are from the 12 flight paths over the oil sand facilities, hourly averages during the daytime; and AMS13 results are from hourly
data (day and night) at the one ground station.
R
Slope
p
t
Bias (ppbv)
RMSE (ppbv)
FE
CrIS (troposphere)
base
0.248
0.076
<2E-16
-247.5
-0.430
2.02
-5.3E-6
bidi
0.302
0.205
<2E-16
-77.4
-0.176
1.93
-1.2E-6
fire + bidi
0.338
0.425
<2E-16
36.2
-0.126
2.45
5.9E-7
CrIS (surface)
base
0.272
0.118
<2E-16
-19.0
-1.11
5.72
-1.6E-3
bidi
0.289
0.162
<2E-16
-12.8
-0.66
5.32
-8.9E-4
fire + bidi
0.566
1.195
1.4E-06
4.9
-0.19
8.67
3.7E-4
Aircraft (hourly)
base
0.368
0.114
8.5E-14
-10.3
-0.751
1.14
-2.5E-3
bidi
0.549
0.503
0.0026
-3.2
-0.244
0.69
-5.0E-4
fire + bidi
0.560
0.519
0.0052
-2.9
-0.233
0.68
-4.5E-4
AMS13 (hourly)
base
0.103
0.116
<2E-16
-12.4
-0.35
0.92
-1.6E-3
bidi
0.413
0.652
<2E-16
12.1
-0.30
0.95
8.0E-4
fire + bidi
0.403
0.691
<2E-16
13.1
0.32
1.04
9.0E-4
We selected three sample days (12 August and 1 and 3 September 2013) that we
use for the case studies. The measured surface NH3 and sample Aqua MODIS
true colour composite maps for those days are shown (Fig. ). The four boxed regions on those maps indicate where
model–measurement pairs were sampled for this study. The cyan and black boxes
(in Fig. a and b) are the regions where we sample
clear-sky and no-fire conditions on 3 and 1 September 2013, respectively. The magenta box
in Fig. c is the region where we isolated our fire case
study on 12 August 2013. The blue box is the region we discussed above, which
we analysed for the full time period simulated (12 August–7 September 2013; Fig. a).
Images of the Alberta and Saskatchewan region with clouds and fire hotspots
from MODIS (upper panels). Maps of CrIS-measured surface NH3 VMRs, with coloured boxes
showing the regions where model and satellite measurements were sampled (lower panels). These three
examples are for (a) northern bidi case study (cyan), (b) southern bidi case study (black), and
(c) fire case study (magenta), discussed in Sect. , and the blue box is the region of our overall comparison.
![]()
Case study 1: clear-sky days with little fire influence – evaluating bidi
In order to evaluate the bidirectional flux component separately from the
fire component, we selected 3 September (northern, boreal forest and AOSR
region – cyan box in Fig. a) and 1 September (southern,
agricultural region – black box in Fig. b), where the
MODIS maps (EOSDIS NASA Worldview map; https://worldview.earthdata.nasa.gov/)
showed very little hotspots from fires and conditions that were relatively
cloud and smoke free, which yield the most CrIS observations (see Table for the latitude and longitude ranges).
Figure also shows the surface NH3 VMRs as observed by CrIS on
each of those days. Figure a shows that in the
north, the bidi model improves the bias from -0.84 to -0.07 ppbv in the
lowest vertical level, and smaller, but still significant, improvements to
the bias at the other levels. The fire + bidi model has a nearly identical
impact as the bidi model, which is expected in a fire-free zone. Therefore,
the GEM-MACH-Bidi model performs very well in northern Alberta and
Saskatchewan where there is mainly boreal forest, and background-level
NH3. This also implies that the LUC assignment discussed in Sect. may only apply to a small region around the AOSR, and
not to the overall large region we have defined here.
As in Fig. a, but for our (a) northern “bidi-only” case study
(3 September 2013), (b) southern “bidi-only” case study (1 September 2013), and (c) northern
“fire-only”
case study (12 August 2013). These regions are shown in Fig. a (cyan),
b (black), and c (magenta) boxes, respectively.
![]()
In the southern region (Fig. b), the addition of
bidirectional flux moves the bias from near-zero to +1.02 ppbv in the lowest
level. In this case, the base model with no bidirectional flux appears to be
the most accurate model in areas dominated by agricultural sources. There are
two possible explanations: (a) agricultural emissions are too high in the base
model, and the addition of the bidirectional flux leads to an overestimation
of the NH3 amounts or (b) re-emissions from bidirectional flux from crops
are not significant. The literature
indicates that crops do indeed re-emit NH3, therefore (a) is the more
likely explanation. The agriculture NH3 emission inventory we used was
created by the NAESI (National Agri-Environmental Standards Initiative)
project and has about 30–200 %
uncertainty associated with it . Therefore, with
improved national NH3 emission inventories, GEM-MACH-Bidi is likely to
improve model results across the domain.
Case study 2: a clear-sky day with significant fire influence – evaluating fires
In order to evaluate the fire component separately from the bidirectional
flux, we selected 12 August (a northern region with little-to-no
agricultural contributions) where the MODIS map shows numerous hotspots from
fires and smokey conditions (Fig. c; magenta box). The
base and bidi models underestimate NH3 VMRs (Fig. c) by -6.22 and -5.84 ppbv, respectively (in the
lowest vertical layer), but the fire + bidi model overestimates NH3 by +4.06 ppbv. The fire + bidi version of the model still
has the lowest bias of the
three simulations; however, either (a) the fire + bidi model does not
distribute the fire emissions properly in the vertical, (b) the fire
emissions of NH3 are too high, or (c) the model's oxidation rate of
NO2 and SO2 in the fire may be underestimated, resulting in less
sulfate and nitrate to convert NH3 to NH4+. It is potentially a
combination of all three explanations, as both fire-plume rise and fire
emission factors are ongoing areas of study, and we further elaborate below.
suggest that fire plumes are Gaussian-distributed in a
thin layer aloft, which is not how our current fire-emissions module
distributes the fire plume. In our simulation, the fire emissions are
distributed evenly throughout the boundary layer (the first 3–4 layers in
Fig. c). However, we do not believe our
parameterization of plume distribution causes the fire + bidi bias since the
positive bias extends throughout the first three vertical layers and does not
go negative in any layer (Fig. c), as would be
expected if mass redistribution of the plume was the cause of the biases. We
also know that the plume heights for most of the Fort McMurray fires of 2016
reached only up to 3–4 km altitude range based on the NASA Cloud-Aerosol
Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Multi-angle
Imaging Spectroradiometer (MISR) satellite observations. Therefore, the fire
plumes are not located above the altitudes we studied.
Unfortunately, there were no flights that captured the fine structure of the
fire plumes during the 2013 monitoring intensive campaign that can be used to
further corroborate the vertical distribution of the fire plumes. There will, however,
be flight observations of fires during the planned 2018 AOSR
measurement campaign.
Explanation (b) seems the most likely, as the uncertainty in emission factors
for NH3 from wildfires is very large (e.g., ±50–100 % depending on
the fuel type; , ), and could
easily be overestimated. The NOx and SO2 fire emission factors
have smaller uncertainties of ±10–40 % . Therefore,
the model may be further improved with reduced NH3 emission factors for
fires.
Impacts of bidirectional flux and forest fires on NH<inline-formula><mml:math id="M414" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> VMRs
Effect on ambient ammonia
Given that the overall fire + bidi model agrees best with measurements in the
greater Alberta/Saskatchewan region (discussed throughout Sect. , and Table ) and contains
all known missing sources of NH3, we can use the model to answer one of
our key questions: what percent contributions to total ambient NH3 VMRs
came from bidirectional flux versus from forest fires during the study time
period? We do so by subtracting the bidi model output from the fire + bidi
model output to get the forest fire component, and subtracting the base model
output from the bidi model output to get the bidi component. The absolute
differences are calculated as follows:
bidicomponent=NH3bidi-NH3base,firecomponent=NH3fire+bidi-NH3bidi,
which tell us how many ppbv of NH3 on average is associated with
re-emissions of NH3 (upward component of bidirectional flux) versus fire
emissions.
The percent differences are calculated as follows:
bidipercent=NH3bidi-NH3baseNH3fire+bidi×100%,firepercent=NH3fire+bidi-NH3bidiNH3fire+bidi×100%,
which tell us what percent of total NH3 VMRs on average comes from
re-emissions of NH3 and from fire emissions, assuming the NH3 from our
fire + bidi simulation is the true total NH3.
Maps of the modelled (a) absolute bidirectional flux contribution, (b) absolute fire
contribution, (c) percent bidirectional flux contribution, and (d) percent fire contribution
to surface NH3. These are averages over 12 August to 7 September 2013.
![]()
We perform this calculation on the averaged model output (12 August to 7
September 2013) over the 2.5 km model domain, and get an average of 20.3 %
(or 0.42 ppbv) and a median of 10.4 % for ambient surface NH3 VMRs that
come from forest fires (Fig. b and d). The mean and median
are so different because fires are sporadic, large contributions to NH3
VMRs, and the mean value is more sensitive to the big outliers. We get an
average of 56.6 % (or 1.24 ppbv) from bidirectional flux (56.3 %
median; Fig. a and c), and the remaining 23.1 % average (33.3 % median) comes from direct emissions from anthropogenic sources
(agriculture, fossil fuel combustion, oil sands industry, etc.). These numbers
are summarized in Table . The increase in NH3 due
to the bidirectional flux scheme is of the same order of magnitude as that
found in the study using the GEOS-Chem model, during the month
of July in the United States (where they found ∼1 ppbv increase in surface
VMRs due to bidirectional flux). It is also similar to values found in Europe
in the study.
Average source contributions to ambient NH3 VMRs over the Alberta and Saskatchewan model domain during 12 August to 7 September 2013.
Source
Median
Median
Average
Average
(ppbv)
(%)
(ppbv)
(%)
Total surface NH3
1.60
100
2.53
100
From fires to surface
0.25
10.4
0.42
20.3
From bidi to surface
0.97
56.3
1.24
56.6
From anthro. to surface
0.38
33.3
0.87
23.1
Total column NH3
18.8
100
25.6
100
From fires to total column
6.1
27.7
8.1
30.5
From bidi to total column
8.8
48.1
11.15
50.0
From anthro. to total column
3.9
24.2
6.35
19.5
Over the model domain, the minimum bidi influence on surface NH3 is just
north of Edmonton, where only 1 % of NH3 comes from bidi. Similarly, two
AOSR facilities north of Fort McMurray stand out as having small bidi
influence (12–40 %, surrounded by values greater than 90 %; Fig. d).
Also, any remote region with fire emissions will have
a small percentage contribution from bidirectional flux during the fires, as
they are in northern Saskatchewan (Fig. d). This is
expected given that the average VMRs in cities and near large sources are
very close to, or exceed, the compensation point. The absolute maximum in the
bidi component map is 4.5 ppbv in the lower right corner (an agricultural
region with high NH3 emissions), and the minimum is 0 ppbv (Fig. b).
This means that nowhere in the domain did the
bidirectional flux formula result in more net deposition than the base model
calculated via the Welesley–Robichaud–Zhang scheme. The maximum fire
contribution to surface NH3 is 27.9 ppbv where large fires occurred in
northern Saskatchewan (Fig. c).
Maps of the modelled average NHx deposition for (a) base, (b) bidi, and
(c) fire + bidi models. In all maps, red/positive represents upward flux and blue/negative
represents downward flux. These are daily amounts averaged over 12 August to 7 September 2013.
![]()
Effect on deposition
Similar to our analysis from the previous section, we can use the model to
determine how bidirectional flux and fires impact daily NHx
deposition (which equals the dry deposition of NH3 + the wet deposition of
NH4+). Figure shows the average daily net deposition
(or net flux) of NHx from the base, bidi, and fire + bidi models.
Negative (or blue) indicates net deposition (downward flux), and positive (or
red) net emission (upward flux). The base model (Fig. a)
had no re-emission (upward flux) option, thus NH3 was always net dry
deposited in that scenario. The bidi (Fig. b) and fire + bidi
(Fig. c) maps show that most of the Alberta and Saskatchewan
area has net deposition (e.g., near the cities, agriculture, and forest
fires), but that some regions (with low atmospheric NH3 VMRs) have net
emission of NHx. The dry NH3 flux is net positive over the
domain; however, when the increase in wet NH4+ is accounted for, the net
flux of NHx is still negative (downward). This is very similar to
what found in a 2007 study in Europe; a reduction in dry
NH3 compensated by an increase in wet NH4+ deposition.
Note that the assumption of an infinite soil pool of NH4+ in our
bidirectional flux scheme has not caused an overwhelming upward flux of
NHx. In fact, the average results across the domain actually have
more deposition in the fire + bidi scenario than in our base scenario. Table shows the mean and median net NHx flux for each
scenario (presented as deposition, so negative signs removed). That said,
following the soil pool approach , the soil pool of
NH4+ may eventually get depleted. However, we believe this to be very
unlikely for the following reasons: (1) deposition of NHx throughout
the year continually replenishes the soil pool – especially when
temperatures are cooler in winter, spring, and fall since the compensation
point is exponentially dependent on temperature. (2) The short time frame of
this study would not be long enough to deplete the soil pool. For example,
needed to spin up their model for three months in order to get
the NH4 soil pool stable, implying both a large pool and a long time
required for it to empty.
Average NHx deposition (downward flux) over the Alberta and Saskatchewan model domain during 12 August to 7 September 2013.
Net flux
Base
Bidi
Fire + bidi
(moles m-2 day-1)
Mean
3.025 × 10-5
1.811 × 10-5
3.765 × 10-5
Median
2.061 × 10-5
1.299 × 10-5
2.843 × 10-5
In the AOSR near Fort McMurray, we can compare our NH3 dry deposition
results to those calculated in . Their values for nitrogen range from 0.7 to
1.25 kg ha-1 yr-1 (or 1.13 to 2.01 × 10-5 moles m-2 day-1), while
ours are 10 times lower at around 0.13 kg ha-1 yr-1 (scaled up to a year,
from 2.12 × 10-6 moles m-2 day-1) near Fort McMurray, and do not
vary much among our three model scenarios. These differences in deposition
estimates are likely due to the fact that our study is only during a very
warm time of the year, when deposition will be at a minimum, whereas the
study covered both winter and summer time periods for multiple
years. The differences may also be partially due to the fact that our
modelled ambient NH3 VMRs are also low compared to those measured in
near Fort McMurray. They measured an average of 1.55 ± 0.6 ppbv (1.9 µg m-3) at Fort McMurray,
whereas our fire + bidi model has an
average of 1.01 ppbv there (0.73 ppbv in bidi, and 0.39 in base). There may
also be differences in that our model has more of the NHx deposition
coming down as NH4+ rather than as NH3.
Our fire + bidi NHx deposition values (Table ) are
well in line with reported NH3 deposition in , who report
satellite-derived NH3 deposition of about 2.1 to 7.0 × 10-5 moles m-2 day-1
in Alberta, and are at the low end of NH3 deposition
values reported within .
The difference in deposition between the fire + bidi and bidi cases – which is
the contribution of fires to the total NHx flux – showed that the
fires increased downward flux or deposition over large swaths of the domain
(e.g., difference between Fig. c and b). The fires
contributed an average of 1.954 × 10-5 moles m-2 day-1 of NHx deposition across the domain.
Although the atmospheric concentrations of particulate NH4+ did not change
much in our three simulations (see Sect. and ), the wet deposition of NH4+ increased significantly
going from the base to bidi to fire + bidi models. This is in contrast to what
found, which was little change to NH4+ wet deposition due
to bidirectional flux. However, that could be due to other parameters, such as
the meteorological conditions, scavenging parameters, and/or gas-particle
partitioning of NHx. It would seem that in GEM-MACH-Bidi, the
increased NH3 is scavenged by precipitation. The average NH4+
deposition from the three simulations had a nearly threefold increase in the
NH4+ deposition due to the increased NH3 that the fire + bidi simulation
yields. The average NH4+ wet deposition for our fire + bidi simulation is
5.86 × 10-5 moles m-2 day-1, which is in between values reported in
the U.S. in (where they found an average of
1.9 × 10-5 moles m-2 day-1 over the country), and in Japan in
(where they found an average of 10.3 × 10-5 moles m-2 day-1 over the country).
In the three scenarios, the average daily relative ratio of dry : wet
deposition was 0.43 for base, -0.77 for bidi, and -0.51 for fire + bidi (the
negative value for the bidi and fire + bidi cases are because of the average
upward direction of NH3). Since all average ratios are less than 1, this
means that most of the removal process is from wet deposition rather than
dry deposition (even for the base case that had no re-emission of NH3).
Therefore, increased monitoring of wet deposition in the region would be
useful. These results may also be useful for Alberta Environment and Parks (AEP) terrestrial or aquatic
scientists interested in nitrogen eutrophication. Maps of these ratios can be
found in the Supplement.
Conclusions
The GEM-MACHv2 air quality forecasting model was altered to include both the
bidirectional flux scheme for NH3 and forest fire
emissions of all species. This new “fire + bidi” model improves the simulated
NH3 in the modelled oil sands domain at 2.5 km resolution when compared to
independent in situ measurements at the ground (at the AMS13 oil
sands monitoring site) and aloft (aircraft measurements), as well as at 10 km
resolution when compared to cutting-edge satellite measurements from the CrIS
instrument in Alberta and Saskatchewan. Almost all comparison statistics are
best with our fire + bidi simulation. This suggests that the fire + bidi model
shows promise for improving NH3 model predictions elsewhere and during
other time periods. However, more work is required to validate the model in
other regions of the continent (e.g., with the Wood Buffalo Environmental
Association, WBEA; the AMoN; and further CrIS satellite measurements), and for different time
periods (e.g., springtime fertilizer season, cooler conditions, etc.). We
have also shown that for further improvements in the Alberta and Saskatchewan
region, the NH3 emission factors for fires, and the NH3 emissions from
agriculture, likely need to be reduced.
Despite the significant increase in atmospheric NH3 VMRs with these
additional sources, the impact on its byproduct, NH4+, was minuscule – as
was the change to SO42- concentrations (0.02 µg m-3 for each).
The model bias for those species was not significantly changed in either
direction. This is probably because of the extra NH4+ wet scavenging by
precipitation, and the NH3 VMRs were already high enough (before adding
the extra sources) to charge balance the SO42- and NO3- in the
aerosols. Thus, any additional NH3 would remain in the gas phase. That
said, the model bias for NO3- at the AMS13 ground station was essentially
removed with the fire + bidi model.
By running the base, bidi, and fire + bidi model scenarios, and taking the
fire + bidi results as “true”, we were able to calculate their differences and
determine the average contributions from each source. We found that, on
average, during the 12 August to 7 September 2013 time period in the
Alberta and Saskatchewan model domain, 23.1 % of surface NH3 comes from
direct anthropogenic emissions, 56.6 % of surface NH3 comes from
bidirectional flux (re-emission from soils and plants), and 20.3 % of
NH3 comes from forest fires. Possible sources of error that remain in the
bidi and fire + bidi simulations are the agricultural and fire emissions of
NH3, as well as the emission potentials for different LUCs.
The fraction of NH3 from fires is highly variable depending on the time
periods and spatial domain analysed; on average from 12 August to 7 September
2013, the largest impact was in northern Saskatchewan. We also expect the
re-emission source to be near the highest at this time of year because of the
high temperatures, and this source should be much lower during the cold
season when deposition is expected to dominate the bidirectional flux
process.
The bidirectional flux process has decreased NHx deposition on
average across the domain, with some areas having a net emission of NH3.
However, that upward flux is due to the low atmospheric concentrations and
high temperatures, and does not exceed the amount of NHx deposition
that occurs during the cooler winter and spring times. When fires are also
taken into account, the net NHx deposition is greater, on average
across the domain, compared to the base model.