The atmospheric lifetimes of pollutants determine their impacts on human
health, ecosystems and climate, and yet, pollutant lifetimes due to dry deposition over large regions have not been determined from measurements.
Here, a new methodology based on aircraft observations is used to determine
the lifetimes of oxidized sulfur and nitrogen due to dry deposition over
(3-6)×103 km2 of boreal forest in Canada. Dry deposition
fluxes decreased exponentially with distance from the Athabasca oil sands
sources, located in northern Alberta, resulting in lifetimes of 2.2–26 h. Fluxes were 2–14 and 1–18 times higher than model estimates for
oxidized sulfur and nitrogen, respectively, indicating dry deposition
velocities which were 1.2–5.4 times higher than those computed for models. A
Monte Carlo analysis with five commonly used inferential dry deposition algorithms indicates that such model underestimates of dry deposition
velocity are typical. These findings indicate that deposition to vegetation
surfaces is likely underestimated in regional and global chemical transport models regardless of the model algorithm used. The
model–observation gaps may be reduced if surface pH and quasi-laminar and aerodynamic resistances in algorithms are optimized as shown in the
Monte Carlo analysis. Assessing the air quality and climate impacts of atmospheric pollutants on regional and global scales requires improved
measurement-based understanding of atmospheric lifetimes of these
pollutants.
Introduction
Deposition represents the terminating process for most air pollutants and
the starting point for ecosystem impacts. Understanding deposition is
critical in determining the atmospheric lifetimes and spatial scales of atmospheric transport of pollutants, which in turn dictates their ecosystem
(WHO, 2016; Solomon et al., 2007) and climate (Samset et al., 2014) impacts.
In particular, atmospheric lifetimes (τ) of
oxidized sulfur and nitrogen compounds influence their concentrations and
column burdens in air, which affect air quality and hence human exposure
(WHO, 2016). Furthermore, the lifetime of these species affects their
contributions to atmospheric aerosols, with a consequent influence on
climate via changes to radiative transfer through scattering and cloud
formation (Solomon et al., 2007). In addition, their deposition can exceed
critical load thresholds, causing aquatic and terrestrial acidification and eutrophication in the case of nitrogen deposition (Howarth, 2008; Bobbink et
al., 2010; Doney, 2010; Vet et al., 2014; Wright et al., 2018). Quantifying
τ and deposition thus provides a crucial assessment
of these regional and global impacts.
Deposition occurs through wet and dry processes. While wet deposition fluxes
can be measured directly (Vet et al., 2014), there are few validated methods
for dry deposition fluxes (Wesley and Hicks, 2000) and none which estimates deposition over large regions. Dry deposition fluxes (F)
may be obtained using micrometeorological measurements for pollutants for
which fast-response instruments are available. However, these results are only valid for the footprints of the observation sites, typically hundreds
of metres (Aubinet et al., 2012), and their extrapolation to larger regions may suffer from representativeness issues. As a result, atmospheric
lifetimes τ with respect to dry deposition have not
been determined through direct observations. On a regional scale, dry
deposition fluxes are typically derived using an inferential approach by
multiplying network-measured or model-predicted air concentrations with dry
deposition velocities (Vd) (Sickles and
Shadwick, 2015; Fowler et al., 2009; Meyers et al., 1991), which are derived
using resistance-based inferential dry deposition algorithms (Wu et al.,
2018) and compared with limited micrometeorological flux measurements (Wesley and Hicks, 2000; Wu et al., 2018; Finkelstein et al., 2000; Matsuda
et al., 2006; Makar et al., 2018) for validation. When applied to a regional
scale, an inferential-algorithm-derived Vd may have significant uncertainties (Wesley and Hicks, 2000; Aubinet et al.,
2012; Wu et al., 2018; Finkelstein et al., 2000; Matsuda et al., 2006; Makar
et al., 2018; Brook et al., 1997). For example, inferred
Vd for SO2, despite being the most
studied and best estimated, may be underestimated by 35 % for forest
canopies (Finkelstein et al., 2000). Underestimated
Vd for SO2 and nitrogen oxides can
contribute to model overprediction of regional and global SO2 concentrations (Solomon et al., 2007; Christian et al., 2015; Chin et al.,
2000) or underprediction of global oxidized nitrogen dry deposition fluxes (Paulot et al., 2018; Dentener et al., 2006).
Here, a new approach is presented to determine τ
with respect to dry deposition and F for total oxidized
sulfur (TOS, the sulfur mass in SO2 and particle SO4 – pSO4) and total reactive oxidized nitrogen (TON, the nitrogen mass in NO, NO2, and others designated as NOz) on a
spatial scale of (3-6)×103 km2, using aircraft measurements. This
approach provides a unique methodology to determine τ and F over a large region. Coupled with analyses for
chemical reaction rates (for TOS compounds), the average
Vd for TOS and TON over
the same spatial scale was also determined. The airborne measurements were obtained during an intensive campaign from August to September 2013 in the
Athabasca Oil Sands Region (AOSR) (Gordon et al., 2015; Liggio et al., 2016;
Li et al., 2017; Baray et al., 2018; Liggio et al., 2019) in northern
Alberta, Canada. Direct comparisons with modelled dry deposition estimates
are made to assess their uncertainties and the spatial–temporal scales of air pollutant impacts.
MethodsLagrangian flight design
Details of the airborne measurement program have been described elsewhere
(Gordon et al., 2015; Liggio et al., 2016; Li et al., 2017; Liggio et al.,
2019; Baray et al., 2018). Briefly, an instrumented National Research
Council of Canada Convair-580 research aircraft was flown over the AOSR in Alberta, Canada, from 13 August to 7 September 2013. The flights were
designed to determine emissions from mining activities in the AOSR, assess
their atmospheric transformation processes and gather data for satellite and
numerical model validation. Three flights were flown to study transformation
and deposition processes by flying a Lagrangian pattern so that the same
pollutant air mass was sampled at different time intervals downwind of
emission sources for a total of 4–5 h and up to 107–135 km downwind of
the AOSR sources. Flights 7 (F7, 19 August), 19 (F19, 4 September) and 20 (F20, 5 September) took place during the afternoon when the boundary layer was well
established. The flights were conducted in clear-sky conditions, so wet deposition processes were insignificant. As shown in Fig. 1, the aircraft
flew tracks perpendicular to the oil sands plume at multiple altitudes
between 150 and 1400 m a.g.l. and multiple intercepts of the same plume downwind. Vertical profiles conducted as spirals were flown at the centre of
the plume which provided information on the boundary layer height and extent
of plume mixing. The flight tracks closest to the AOSR intercepted the main
emissions from the oil sands operations; there were no other anthropogenic
sources as the aircraft flew further downwind of the AOSR.
A comprehensive suite of detailed gas- and particle-phase measurements was made from the aircraft. Measurements pertaining to the analysis in this
paper are discussed below.
SO2and NOy. Ambient
air was drawn in through a 6.35 mm (1/4”) diameter perfluoroalkoxy (PFA) sampling line taken from a rear-facing inlet located on the roof towards the rear of the aircraft. The inlet was pressure-controlled to 770 mm Hg using a combination
of a MKS pressure controller and a Teflon pump. Ambient air from the
pressure-controlled inlet was fed to instrumentation for measuring SO2
and NOy. The total sample flow rate was measured at 4988 cm3 min-1, of which SO2 and NOy were 429 and 1085 cm3 min-1, respectively. SO2 was detected via pulsed fluorescence with
a Thermo 43iTLE (Thermo Fisher Scientific, Franklin, MA, USA). NOy
(also denoted as TON) was measured by passing ambient air across a heated (325 ∘C) molybdenum converter that reduces reactive nitrogen oxide species to NO. NO was then detected
through chemiluminescence with a modified Thermo 42iTL (Thermo Fisher
Scientific, Franklin, MA, USA) run in NOy mode. An inlet filter was
used for SO2 to exclude particles, but NOy was not filtered prior
to the molybdenum converter. NOy includes NO, NO2, HNO3 and
other oxides of nitrogen such as peroxy acetyl nitrate and organic nitrates
(Dunlea et al., 2007; Williams et al., 1998). Although there was no filter
on the NOy inlet to exclude particles, the inlet was not designed to
sample particles (i.e. rear-facing PFA tubing). As a result, pNO3 was
not included as part of NOy (TON). The conversion efficiency of the heated molybdenum converter and inlet transmission was evaluated with
NO2 and HNO3 and found to be near 100 % and >90 %, respectively. Previous studies conducted by Williams et al. (1998)
showed similar molybdenum converter efficiencies, including that of n-propyl nitrate near 100 %. Interferences from alkenes or NH3 were assumed to
be negligible (Williams et al., 1998; Dunlea et al., 2007). Species like
NO3 radical and N2O5 are expected to be low in concentration
as they photolyse quickly during daytime. Zeroes were performed three to five times per flight for both the SO2 and NOy instruments by passing ambient air through an in-line Koby King Jr. cartridge for ∼5 min. For
the NOy measurements pre-reactor zeroes (dynamic instrument zero) were
also obtained periodically throughout each flight using either ambient air
or a Koby King Jr. air purifier. Multiple calibrations were conducted
before, during and after the study using National Institute Standards and
Technology reference standards. Data were recorded at a time resolution of 1 s and corrected for a sampling time delay of 1–3 s depending on
the instrument. Detection limits were determined as 2 times the standard
deviation of the values acquired during zeroes; NOy was 0.09 ppbv and
SO2 was 0.70 ppbv (Table S1).
Aerosols. Multiple aerosol instruments sub-sampled from a forward-facing, shrouded, isokinetic particle inlet (Droplet Measurement
Technologies, Boulder, CO, USA). A Time-of-Flight High Resolution Aerosol
Mass Spectrometer (AMS) (Aerodyne Research Inc.) was used to measure
non-refractory submicron aerosol components, including pSO4, pNO3, pNH4, and p organics. Details of the AMS and its operations have been
published elsewhere (DeCarlo et al., 2006). The instrument was operated in
mass spectrometry V mode with a sampling time resolution of 10 s. Filtered measurements were taken four to five times per flight to determine background signals. Detection limits of 0.048, 0.036, 0.235 and 0.236 µg m-3 for pSO4, pNO3, pNH4 and p organics were determined using 3 times the standard deviation of the average of filtered time periods
for all flights (Table S1). Ionization efficiency calibrations using
monodisperse ammonium nitrate were performed during the study with an
uncertainty of ±9 %. Data were corrected for a sampling time delay
of 10 s by comparing with faster response instruments, e.g. a wing-mounted Forward Scattering Spectrometer Probe Model 300 (FSSP-300) and
an in-board Ultra High Sensitivity Absorption Spectrometer (UHSAS) (both
from Droplet Measurement Technologies). The FSSP and UHSAS instruments
measure particle diameters that range from 300 nm to 20 µm and from 50 nm to 1 µm, respectively. The AMS data were processed using AMS data analysis software (Squirrel, version 1.51H and PIKA, version 1.10H). The particle
collection efficiency (CE) of the AMS was determined through comparisons of
the total AMS-derived mass with the mass estimated from the size
distribution measurements of the UHSAS assuming a density based on the
chemical composition. The CE for F7 and F20 was 0.5 for both flights, and
for F19 it was 1.0. The CE was applied to all AMS species for the duration
of each flight (Fig. S1). Since the AMS measures only particle mass
<1µm (PM1) in diameter, the mass of SO4 formed
through OH oxidation was scaled upward to account for all particle sizes
that H2SO4 vapour could potentially condense on. The scaling factor was determined using the surface area ratio of PM1/ PM20 from the
aircraft particle measurements, assuming that the condensation process is
approximately proportional to the surface area. PM1 measurements were
from the UHSAS, and for PM20 they were from the FSSP300. As the ratio did not vary significantly in the plumes, one single value was used between each set
of screens; in F19 the ratio between screens ranged from 0.6 to 0.8, in F20
the ratio ranged from 0.8 to 0.9, and in F7 the ratio ranged from 0.7 to 0.9
(Liggio et al., 2016).
Measurements are discussed in terms of TOS, the sulfur mass in SO2 from the Thermo SO2 instrument,
pSO4 from the AMS instrument and TON, the nitrogen mass in reactive oxidized nitrogen species, from the Thermo NOy instrument, often denoted as NOy.
Volatile organic compounds (VOCs). Selected VOCs were used to estimate the OH concentrations used for determining oxidation rates for
SO2. VOCs were measured with a proton transfer reaction time-of-flight
mass spectrometer (PTR-ToF-MS, Ionicon Analytik GmbH, Austria) as well as
with discrete canister grab samples. The PTR-ToF-MS and its operation, along with the details of the canister sampling and lab analyses during the
study, were described in detail previously (Li et al., 2017). Briefly, the PTR-ToF-MS used chemical ionization with H3O+ as the primary
reagent ion. Gases with a proton affinity greater than that of water were
protonated in the drift tube. The pressure and temperature of the drift tube
region were maintained at a constant 2.15 mbar and 60 ∘C,
respectively, for an E/N of 141 Td (Townsend, 1 Td = 10-17 V cm2). E/N refers to the reduced electric field parameter in the drift tube; E is
the electric field and N is the number density of the gas in the drift tube.
The E/N ratio can affect the reagent ion distribution in the drift tube and
VOC fragmentation (de Gouw and Warneke, 2007). The protonated gases were
detected using a high-resolution time-of-flight mass spectrometer at a time resolution of 2 s. Instrumental backgrounds were performed in flight
using a custom-built zero-air generating unit. The unit contained a
catalytic converter heated to 350 ∘C with a continuous flow of
ambient air at a flow rate of one litre per minute. The data were processed
using Tofware software (Tofwerk AG). Calibrations were performed on the
ground using gas standard mixtures from Ionicon, Apel-Reimer and
Scott-Marrin for 22 compounds. The canister samples were collected in
pre-cleaned and passivated 3 L stainless steel canisters that were
subsequently sent to an analytical laboratory for GC-FID/MS analyses for a
suite of 150 hydrocarbon compounds.
Meteorology and aircraft state parameters. Meteorological
measurements have been described elsewhere (Gordon et al., 2015). In brief,
3-D wind speed and temperature were measured with a Rosemount 858 probe. Dew
point was measured with an Edgetech hygrometer and pressure was measured
with a DigiQuartz sensor. Aircraft state parameters including positions and
altitudes were measured with GPS and a Honeywell HG1700 unit. All
meteorological measurements and aircraft state parameters were measured at a
1 s time resolution.
Mass transfer rates in the atmosphere
Mass transfer rates (T) across flight screens (Fig. 1)
were determined using an extension of the Top-down Emission Rate Retrieval
Algorithm (TERRA) developed for emission rate determination using aircraft
measurements (Gordon et al., 2015). Briefly, at each plume interception
location, the level flight tracks were stacked to create a virtual screen.
Background subtracted pollutant concentrations and horizontal wind speeds
normal to the screen were interpolated using kriging. The background for
SO2 was ∼0 ppb, and pSO4 was 0.2–0.3 µg m-3, which was subtracted from the pSO4 measurements before mass transfer rates were calculated (Liggio et al., 2016). Integration of the
horizontal fluxes across the plume extent on the screen yields the transfer
rate T in units of t h-1. Using SO2 as an
example,
TSO2=∫s1s2∫z1z2Cs,zuns,zdsdz,
where Cs,z is the background subtracted concentration at
screen coordinates s and z, which represent the horizontal and vertical axes of the screen. The uns,z is the horizontal wind speed
normal to the screen at the same coordinates.
Since the lowest flight altitude was 150 m a.g.l., it was necessary to
extrapolate the data to the surface as per the procedures described
previously (Gordon et al., 2015). Extrapolation to the surface methods was compared and differences were included in the uncertainty estimates. The
main sources of SO2 were from elevated facility stacks associated with
the desulfurization of the raw bitumen (Zhang et al., 2018). The stacks with
the biggest SO2 emissions range in height from 76.2 to 183.0 m. Since
the main source of SO2 is from the elevated facility stacks, the
uncertainty for a single screen is estimated at 4 % (Gordon et al., 2015).
NOy was also extrapolated linearly to the surface, and the mass transfer rates were similarly compared to other extrapolation methods. NOy
sources include the elevated facility stacks and surface sources such as the
heavy hauler trucks operating in the surface mines. The uncertainty in the
resulting transfer rate T for a single screen is estimated
to be larger at 8 %, as a larger fraction of the NOy mass may be
below the lowest measurement altitude (Gordon et al., 2015). Sulfur and
nitrogen data were also extrapolated linearly to background values from the
highest-altitude flight tracks upwards to the mixed-layer height, which was determined from vertical profiles of pollutant mixing ratios, temperature and dew point (Table 1).
Average observed meteorological conditions and facility
emission rates of TOS (ETOS) and
TON (ETON) (determined from extrapolated (to distance = 0) transfer rates; Fig. 1) for TOS and TON during the F7, F19 and F20 flights. SP: southern plume; NP: northern plume.
Changes in the mass transfer rate T (denoted
ΔT) in units of t h-1 were then calculated as the differences in T between
pairs of virtual screens. The uncertainty in ΔT was estimated as 8 % for TOS and 26 %
for TON, as supported by emission rate uncertainties determined for box flights (Gordon et al., 2015). The uncertainty analysis for box flights
is applicable to ΔT here, as
both account for uncertainties with an upwind and a downwind screen. The
ΔT uncertainties were propagated
through subsequent calculations.
Knowing the change in mass transfer rate ΔT and accounting for the net rates of chemical loss and formation between screens for SO2 and pSO4, the deposition rates
(and subsequently the deposition flux in tonnes S (or N) km-2 h-1, Sect. 2.4) were determined for the sulfur compounds as follows:
2ΔTSO2=TSO2t2-TSO2t1=XSO2-DSO2,3ΔTpSO4=TpSO4t2-TpSO4t1=XpSO4-DpSO4,4ΔTTOS=TTOSt2-TTOSt1=-DTOS,
where XSO2 is the rate of chemical reaction
loss of sulfur mass in SO2, XpSO4 is
the rate of chemical formation of sulfur mass as pSO4,
DSO2 and
DpSO4 are deposition rates of sulfur mass in
SO2 and pSO4, respectively, and t1 and t2 are plume interception times at Screen 1 and Screen 2, respectively. Note that the
chemical loss rate of SO2 is set to be equivalent to the formation rate
of pSO4, i.e. XSO2=XpSO4. Equation (4) for TOS can also similarly be written as shown in Eq. (5).
ΔTTOS=ΔTSO2+ΔTpSO4=-DSO2-DpSO4
Units in Eqs. (2) to (5) are all in t h-1. Reaction with the OH radical was considered to be the most significant chemical loss of SO2 and the
most significant path for the formation of pSO4. XSO2 and XpSO4 were determined using estimated OH radical
concentrations, which were estimated using the methodology described in Supplement Sect. S4. Although TON encompasses a range of different N species with
expected differences in their deposition rates, it was not possible to
quantitatively separate their chemical formation/losses from their
deposition rates with this method. For total oxidized sulfur TOS
(i.e. sulfur in SO2+pSO4) and total oxidized nitrogen TON (i.e. nitrogen in NOy), the chemistry term is not relevant, and thus the dry deposition rate
DTOS was directly determined from
ΔTTOS using Eq. (4) and, respectively, for TON.
Dry deposition fluxes and dry deposition velocities
Average dry deposition fluxes (F) for TOS and
TON were obtained by dividing the deposition rates
D in t h-1 by the footprint surface area of the plume between two adjacent screens (Fig. 1 grey-shaded regions), as shown in Eq. (6) for the dry deposition flux FTOS of
TOS (in t S km-2 h-1):
FTOS=DTOSArea,
where the surface area, Area, was identified as the geographic area under the plume extending to the edges of the plume where concentrations fell to
background levels (i.e. SO2 to ∼0 ppb; SO4∼0.2µg m-3). This approach was similarly used to derive
deposition fluxes from an air quality model, Global Environmental Multiscale
– Modelling Air-quality and Chemistry (GEM-MaCH) (Moran et al., 2014; also
see Supplement Sect. S5 for details). The geographic surface area uncertainty is
estimated at 5 %. Dry deposition fluxes between the sources and the first
screen were also estimated using change in mass transfer rate
ΔT based on the extrapolated
transfer rates back to the source region (“extended” region). The surface
area boundaries for these “extended” regions were determined using latitude
and longitude coordinates that were weighted by emissions. This was done by
first using the average wind direction from Screen 1 and creating a set of
parallel back trajectories (∼20) starting at different parts
of Screen 1 back across the source region. For TON, the NOx
emission sources along each back trajectory were weighted by their NOx
emissions to obtain an emissions-weighted centre location with latitude and longitude coordinates for each back trajectory. The line connecting these
emissions-weighted centre locations formed the boundary of the extended surface area. The extended surface area was similarly determined for
TOS based upon the known locations of the major SO2 point
sources. The uncertainty of the “extended” regions is estimated at 10 %
based on repeated optimizations of the geographical area. Surface areas are
visualized as grey-shaded regions between screens in Fig. 1 and tabulated in Supplement Table S1.
Spatially averaged dry deposition velocities, Vd, based on the aircraft measurements were
determined over the surface area between screens using average plume
concentrations across pairs of screens at about 40 m above the ground
for SO2 and TON (e.g. Eq. 7 for SO2 in units of cm s-1). Although TOS includes the S in both SO2 and
pSO4, only SO2 is used in the calculation of
Vd since the deposition behaviour of gases
and particles differs substantially, and particles additionally have size-dependent deposition rates (Emerson et al., 2020). As the dominant form
of TOS is SO2 (>92 %), the deposition behaviour of TOS is expected to be largely driven by that of SO2. The measured TON does not
include pNO3.
Vd=FSO2[SO2]
The largest source of uncertainty in Vd
calculated this way was the determination of concentration at 40 m
above the surface as the measurements were extrapolated from the lowest
aircraft altitude to the surface and interpolated concentrations were used.
The measurement-derived Vd are compared
with those from the air quality model GEM-MACH, which uses inferential methods.
Monte Carlo simulations of dry deposition velocities using multiple resistance-based parameterizations
Parameterization of dry deposition in inferential algorithms is commonly
based on a resistance approach with dry deposition velocity depending on
three main resistance terms as below:
Vd=1Ra+Rb+Rc,
where Ra, Rb and Rc represent the aerodynamic, quasi-laminar
sublayer and bulk surface resistances, respectively. Although these resistance terms are common among many regional air quality models (Wu et
al., 2018), the formulae used (and inputs into these formulae) to calculate the individual resistance terms differ significantly among the inferential
deposition algorithms. To assess the potential for a general underestimation
of Vd across different inferential
deposition algorithms and to compare with the aircraft-derived Vd, five different inferential deposition
algorithms, including that used in the GEM-MACH model for calculating
Vd (Wu et al., 2018), were incorporated into a Monte Carlo simulation for Vd for SO2. NOy was not considered
here, as its measurement includes multiple reactive nitrogen oxide species
with different individual deposition velocities. We note that many of the
inferential algorithms are based on observations of SO2 and O3
deposition made at single sites, and the extent to which a chemical is
similar to SO2 or O3 features in its Vd calculation – the comparison thus has
relevance for species aside from SO2. The five deposition algorithms
considered are denoted ZHANG, NOAH-GEM, C5DRY, WESLEY and GEM-MACH and are
compared in Wu et al. (2018) (except the algorithm in GEM-MACH). The five
algorithms all use a big-leaf approach for calculating
Vd; i.e. Vd is based on the resistance-analogy approach for calculating dry deposition
velocity, where Vd is the reciprocal sum of three resistance terms Ra, Rb and Rc. Although the approach
is similar, the formulations of Ra, Rb and Rc between the
algorithms are substantially different (Table 1 in Wu et al., 2018). Results
from Wu et al. (2018) suggest that the differences in Ra+Rb
between different models would cause a difference in their
Vd values of the order of 10 %–30 % for most chemical species (including SO2 and NO2), although the differences
can be much larger for species with near-zero Rc such as HNO3.
To perform the simulations, formulae for the first four algorithms were
taken from Wu et al. (2018) and for GEM-MACH taken from Makar et al. (2018).
The stomatal resistance in the ZHANG algorithm was from Zhang et al. (2002).
The GEM-MACH formula (Eq. 8.7 in the Supplement of Makar et al., 2018) for
mesophyll resistance Rmx contained a typo (missing the Leaf Area Index
– LAI) and was corrected for as follows.
Rmx=LAIH∗/3000+100f0-1
Prescribed input values were constrained by the range of possible values consistent with the conditions during the aircraft flights and are shown in
Supplement Table S3 with associated references. Calculations for the Ra term
were based on unstable and dry conditions as observed during the aircraft
flights. The Monte Carlo simulation generated a distribution of possible Vd values, based on randomly generated
values of the input variables to each algorithm and selected from Gaussian
distributions with a range of 3σ for all input parameters. All
simulations were performed with the same input values that were common
between the algorithms.
Results and discussionMeteorological and emissions conditions during the transformation flights
Three aircraft flights, Flights 7 (F7), 19 (F19) and 20 (F20), were conducted in Lagrangian patterns where the same plume emitted from oil sands
activities was repeatedly sampled for a 4–5 h period and up to 107–135 km
downwind of the AOSR. The first screen of each flight captured the main
emissions from the oil sands operations with no additional anthropogenic
sources between subsequent screens downwind. The main sources of nitrogen
oxides were from exhaust emissions from off-road vehicles used in open pit
mining activities and sulfur and nitrogen oxides from the elevated facility
stack emissions associated with the desulfurization of raw bitumen (Zhang et
al., 2018). As depicted in Fig. 1, F7 and F19 captured a plume that
contained both sulfur and nitrogen oxides. The westerly wind direction and
orientation of the aircraft tracks on F20 resulted in the measurement of two
distinct plumes: one plume exhibited increased levels of sulfur and nitrogen oxides mainly from the facility stacks and the other plume contained
elevated levels of nitrogen oxides, mainly from the open pit mining
activities, and no SO2.
During the experiments, the dry deposition rates (D) (t h-1) were quantified under different meteorological conditions and
emissions levels of TOS and TON
(ETOS and
ETON) for the three flights (see Table 1).
These differences played important roles in the observed pollutant
concentrations and resulting dry deposition fluxes for F7, F19 and F20.
Mixed-layer heights (MLHs) were derived from aircraft vertical profiles that were conducted in the centre of the plume at each downwind set of transects.
The profiles of temperature, dew point temperature, relative humidity and
pollutant mixing ratios were inspected for vertical gradients, indicating a contiguous layer connected to the surface. The highest MLH was determined
for F7 at 2500 m a.g.l., whereas F19 had the lowest MLH at 1200 m a.g.l. (Table 1). In F20, the MLH was 2100 m a.g.l. The combination of a high MLH in F7 with the highest wind speeds resulted in the lowest pollutant concentrations of the
three flights. In F19, lower wind speeds and the lowest mixed-layer heights led to the highest pollutant levels. F20 had emissions and meteorological
conditions that were in between F7 and F19, resulting in pollutant concentrations between those of F7 and F19.
Emission rates of SO2 and NOx (designated as
ETOS and
ETON) from the main sources in the AOSR were
estimated from the aircraft measurements and varied significantly between
the 3 flight days. The measurement-based emission rates of ETOS and
ETON were taken from the mass
transfer rates of TSO2 and
TNOy (described in Methods) by extrapolating
backwards to the source locations in the AOSR using exponential functions
(Fig. 2, Sect. 3.2). For TOS, the source location was set at
57.017∘ N, -111.466∘ W, where the main stacks for SO2 emissions are
located. For TON, the source locations were determined from
geographically weighted locations. Emission rates
ETOS and ETON for each flight are shown in Table 1.
TERRA-derived transfer rates of (a) TOS and (b)
TON for F7, F19 and F20. The vertical bars indicate the propagated
uncertainties. The model emission rates ETOS
and ETON are shown by the open symbols.
Model-based ETOS and
ETON were also obtained from the 2.5 km × 2.5 km gridded emissions fields that were specifically developed for model
simulations of the large AOSR surface mining facilities (Zhang et al., 2018), i.e. Suncor Millennium, Syncrude Mildred Lake, Syncrude Aurora North, Shell Canada Muskeg River Mine & Muskeg River Mine Expansion, CNRL Horizon
Project and Imperial Kearl Mine. The emissions fields have been used in
GEM-MACH (described in Supplement Sect. S5) to carry out a number of model
simulations (Zhang et al., 2018; Makar et al., 2018), including for the present study. In this work, emissions were summed from various sources,
including off-road, point (continuous emissions monitoring – CEMS), and point (non-CEMS), for the surface mines to obtain total AOSR hourly emission rates for the flight time periods of interest (Table 2). The standard deviations
reflect the emissions variations during the simulated flight.
Model average meteorological conditions and facility
emission rates of TOS (ETOS) and
TON (ETON) during the F7, F19 and
F20 flights as described above. SP: southern plume; NP: northern plume.
The mass transfer rates T (in t h-1) across the
virtual flight screens for all three flights are shown for TOS and
TON in Fig. 1 and plotted in Fig. 2. In F20, two distinct
TON plumes were observed, allowing separate T
calculations for TON. Monotonic decreases in T
were observed for both TOS and TON during transport
downwind in all flights, clearly showing dry depositional losses. The
deposition rate D (Methods, Sect. 2.3) was used to
estimate the cumulative deposition of TOS and TON as a
fraction of ETOS or
ETON and is shown in Fig. 3 for F7, F19 and
F20 for transport distances of up to 107–135 km downwind of the sources.
Curves were fitted to the TOS and TON dry deposition
cumulative percentages from which d1/e and
τ were determined (Supplement Table S1). The transport
e-folding distance (d1/e) was determined
where 63.2 % of ETOS (or
ETON) was dry deposited, i.e. ∑d=0d1/eD(d)=0.368ETOS. The atmospheric lifetimes
(τ) were derived as τ=d1/e/u, where
u was the average wind speed across the distance
d1/e. These estimates were compared with
predictions from the regional air quality model GEM-MACH (Makar et al.,
2018; Moran et al., 2014; Supplement Sect. S5) using facility emission rates (Table 2). For TOS during F19 (Fig. 3b, e), the observed cumulative deposition at the maximum distance accounted for 74 ± 5 % vs. the
modelled 21 % of ETOS. The measurements
indicate that the cumulative deposition of TOS was due mostly to SO2
dry deposition, where SO2 was ∼100 % of TOS closest to the oil sands sources, decreasing to 94 % farthest downwind. Although the modelled cumulative deposition of TOS was significantly lower than the
observations, the fractional deposition of SO2 was similar, decreasing
from ∼100 % to 95 % of TOS. Fitting a curve to
D and interpolating the cumulative deposition fraction to
the 63.2 % ETOS loss leads to a d1/e of 71 ± 1 km vs. 500 km for the model prediction. Under the prevailing wind conditions, the observed distance indicates a τ for TOS of
approximately 2.2 h, whereas the model prediction indicated 16 h.
Large observation-based values and model prediction differences in lifetime
were also evident for the other flights (Supplement Table S1). Clearly, the model
predictions significantly underestimated deposition and vastly overestimated
d1/e and τ. The
observation-based values for τ are also lower than
average lifetimes of 1–2 d for SO2 and 2–9 d for pSO4
derived from global models (Chin et al., 2000; Benkovitz et al., 2004;
Berglen et al., 2004), which include the effects of wet deposition and
chemical conversion for SO2, thus making their implicit residence times
with respect to dry deposition even longer.
Cumulative dry deposition as a percentage of emissions
ETOS (a to f) or
ETON (g to n) for F7, F19 and F20
measurements with corresponding GEM-MACH model predictions. The bars show
the dry deposition due to SO2 and pSO4. The curves were fitted to
the TOS and TON dry deposition percentages from which
d1/e and τ were
determined.
For TON in F19 (Fig. 3h, l), the observed cumulative deposition
accounted for 49 ± 11 % of ETON at
the maximum flight distance vs. 19 % predicted by the model. Similar model underestimates for cumulative deposition fractions were found for F7
and F20. Extrapolating to the 63.2 % cumulative deposition fraction,
d1/e was estimated to be 190 ± 7 km for
F19 vs. a predicted 650 km from the model, implying a τ of approximately 5.6 h for the
measurement-based results and 23 h for the model prediction. Again,
analogous differences for F7 and F20 were found (Supplement Table S1). Similar to
TOS, the measurement-based d1/e and
τ values for TON were significantly smaller than
commonly accepted lifetimes of a few days for nitrogen oxides in the
boundary layer (Munger et al., 1998).
Dry deposition fluxes F
Using the deposition rate D (in tonnes S or N h-1),
the average dry deposition fluxes, F (in tonnes S or N km-2 h-1), were calculated by dividing D by the
plume footprint surface areas estimated by extending to the plume edges
where the concentrations fell to background levels (Methods, Sect. 2.4).
These footprints are shown as the grey-shaded geographic areas in Fig. 1, totalling 3500, 5700, and 4200 km2 for the F7, F19, and F20 plumes, respectively; see Supplement Table S1 for TON plume areas). Figure 4a shows
FTOS values for all three flights,
exhibiting exponential decreases with increasing distance away from the
sources and showing e-folding distances for
FTOS of 18, 27, and 55 km for F7, F19, and
F20, respectively. More than 90 % of the decreases in
FTOS were accounted for by
FSO2. Similarly,
FTON decreased exponentially with increasing
transport distances in all flights (Fig. 4c), exhibiting e-folding distances
of 18 and 33 km for F7 and F19 and 55 and 189 km for the southern and northern TON plumes during F20, respectively. These e-folding distances were
similar to those for FTOS, indicating
similar rates of decreases in FTON with
transport distances.
Dry deposition fluxes
FTOS and
FTON (in t km-2 h-1) determined from
measurements (solid symbols) and GEM-MACH model predictions (open symbols).
(a)FTOS, (b) ratios of measurement-to-model normalized emissions
FTOS/ETOS,
(c)FTON, and (d) ratios of measurement-to-model normalized emissions
FTON/ETON.
The potential for other processes to contribute to the derived TOS and TON
fluxes were considered, including losses from the boundary layer to the free troposphere and re-emission of TOS or TON species from the surface back to
the gas phase. Two different approaches, a finite-jump model and a gradient flux approach (Stull, 1988; Degrazia et al., 2015), were used to estimate
the potential upward loss across the interface between the boundary layer
and the free troposphere for sulfur and nitrogen. In both approaches, the
upward S flux was a minor loss at <45 g km-2 h-1, about
3 orders of magnitude lower than the several to many kg km-2 h-1
horizontal advectional transport that was determined using TERRA. For N, the upward flux was estimated to be ∼570 g km-2 h-1, so although a larger flux than S, it is about factor of 18 lower
than the TON fluxes derived from observations.
As expected from the τ and transport e-folding
distance d1/e comparisons, the GEM-MACH
model FTOS was significantly lower than the measurement-based FTOS results (Fig. 4a),
with the model FTOSe-folding distances
usually large: 133, 797, and 57 km for F7, F19, and F20, respectively, or
7.4, 29.5, and 1.1 times longer than the corresponding measurement results.
Part of the differences between model and measurement
FTOS could be explained by differences in
actual vs. model emissions, ETOS (Tables 1 vs. 2). To remove the influence of emissions, an emission-normalized flux (=FTOS/ETOS and
FTON/ETON)
was calculated for both measurement and model (Supplement Fig. S2). Figure 4b shows the
ratios of measurement-to-model normalized emissions for TOS. The model emission-normalized fluxes
FTOS/ETOS
were lower than the measurement-based values by factors of 2.5–14, 1.8–3.4,
and 2.0–3.0 for F7, F19, and F20, respectively, decreasing with increased
transport distances. However, they coalesce to a factor of 2 at the furthest
distances sampled by the aircraft, indicating that the model
FTOS estimates were biased low by similar
factors. The decreasing trends suggest that at distances further downwind,
model fluxes may exceed measurement-based fluxes, albeit at magnitudes lower
than those shown in Fig. 4a, which is consistent with earlier study results
(Makar et al., 2018). For FTON, the
model-predicted values were also lower than the measurement results,
especially near the sources (Fig. 4c), and showed little variation with
transport distances from the oil sands sources for all flights, in strong
contrast to the exponential decays observed from the aircraft. However, the
emission-normalized fluxes
(=FTON/ETON)
for the model approached those from measurements within maximum flying
distances for F19 and F20, although still significantly lower for F7
(>10×) (Fig. 4d).
Dry deposition velocities Vd
The shorter d1/e and τ and larger deposition fluxes F near the sources determined from the aircraft measurements compared to predictions by the
GEM-MACH model indicate that the model dry deposition velocities
Vd were underestimated. Gas-phase Vd in the model is predicted with a standard
inferential “resistance” algorithm (Wesley, 1989; Jarvis, 1976), with
resistance to deposition calculated for multiple parameters, including aerodynamic, quasi-laminar sublayer and bulk surface resistances (Baldocchi et al.,
1987). To demonstrate the model underestimation in
Vd, comparisons between the
measurement-based and model Vd were made
where an evaluation of Vd for TOS
and TON was possible. All
FSO2 were converted into
Vd-SO2 by dividing
FSO2 by interpolated SO2 concentrations
at 40 m above ground, averaging 1.2 ± 0.5, 2.4 ± 0.4, and
3.4± 0.6 cm s-1 for F7, F19, and F20, respectively, across the plume footprints (Methods Sect. 2.4 and Supplement Table S2). The corresponding model Vd-SO2 derived in the same way as the
observations was 0.72, 0.63, and 0.58 cm s-1, 1.7–5.4 times lower than
observations (Supplement Sect. S5; Supplement Table S2). Interestingly, the median
Vd for SO2 of 4.1 cm s-1
determined using eddy covariance/vertical gradient measurements from a tower
in the AOSR is higher than the mass-balanced method, showing an even larger discrepancy compared to the model (Supplement Sect. S3; Fig. S5). Similarly, derived
Vd-TON averaged 2.8 ± 0.8, 1.6 ± 0.5, 4.7 ± 1.4, and 2.2 ± 0.7 cm s-1 for the F7, F19, and F20 southern plumes and F20 northern plume, respectively (Supplement Table S2), 1.2–5.2 times higher than
the corresponding modelled Vd-TON of 1.4,
1.3, 0.92, and 0.90 cm s-1.
Using the observations, it was not possible to derive individual TON
deposition rates separate from their chemical formation/losses. In previous
modelling work, Makar et al. (2018) use the GEM-MACH model and describe the relative contributions of different TOS and TON species towards total S and
N deposition in the AOSR. TON was dominated by dry NO2 (g) dry deposition fluxes close to the sources (>70 % of total N close to the
sources), and dry HNO3 (g) dry deposition increases with increasing distance from the sources (remaining <30 % of total N) and other sources of TON having minor contributions to deposition (<10 %). Although TON encompasses a range of different N species with expected
differences in their deposition rates, comparisons of Vd-TON with the
model show, nevertheless, that overall large differences do exist.
Monte Carlo simulations of Vd for SO2
To further demonstrate observation–model differences, Vd distributions of SO2 from five
common inferential dry deposition algorithms (Wu et al., 2018; Makar et al.,
2018) were determined for the conditions encountered during the flights
using a Monte Carlo approach as described in Methods Sect. 2.5. Results for the Vd simulation algorithms are shown in Fig. 5a. Histograms for all five algorithms have peak Vd values at ∼1 cm s-1
or lower. Probability distributions for the individual resistance terms Ra, Rb, and Rc showed that the dominant resistance driving
Vd was the Rc term (Supplement Fig. S3). Also
shown in Fig. 5a are the measurement-derived
Vd for Flights 7, 19 and 20 and that from the Oski-ôtin ground site. The observed
Vd values are larger than the
Vd values for most of the simulations, with
the exception of Flight 7, where the Zhang et al. (2002), NOAH-GEM (Wu et
al., 2018) and C5DRY (Wu et al., 2018) algorithms' distributions agree with
the observations. All algorithms are biased low relative to the observations
for the remaining flights and the Oski-ôtin ground site. It is noted that the ground-site observations that were derived using a standard flux
tower methodology (Supplement Sect. S3) at a single site appeared to be higher than all other Vd; nevertheless, these
observations are closer to the aircraft values than the algorithm estimates.
These results indicate that an underestimation of
Vd relative to both aircraft and ground-based measurements in the AOSR is not unique to the GEM-MACH model or its
dry deposition algorithm; similar results would occur with the other
algorithms included in the Monte Carlo simulations, all of which are used within other regional models.
(a) Distributions of Vd
for SO2 from Monte Carlo simulations using five different deposition parameterizations (Wu et al., 2018; Makar et al., 2018) and (b) Monte Carlo
simulations for the GEM-MACH algorithm using a pH = 8 and using a pH = 8
plus replacing the GEM-MACH algorithm Ra and Rb formulae with that
from Zhang et al. (2002) and NOAH-GEM (Wu et al., 2018), respectively.
Aircraft-derived Vd for F7, F19 and F20 as
well as the median value for the Oski-ôtin ground site (Supplement Fig. S5) are
shown in both (a) and (b) for comparison.
To investigate the possible reasons behind the low model
Vd relative to the observations, a series of
sensitivity tests using SO2 were conducted. Differences in model
Vd have been shown to be mainly due to
differences in the calculated Rc (Wu et al., 2018), and sensitivity
tests here indicated that Rc is particularly sensitive to the cuticular
resistance Rcut. Hence, factors causing Rcut to change can have
significant impact on model Vd. In some of
the algorithms, Rcut and other resistance terms are dependent on the
effective Henry's law constant KH∗ for SO2. The Monte Carlo simulations for Fig. 5 assumed a surface pH = 6.68 resulting in a KH∗ of 1 × 105 for SO2. Additional Monte Carlo
simulations were performed for the GEM-MACH dry deposition algorithm by
adjusting KH∗ assuming different pH with small variations from
a pH = 6.68 significantly changing Rc, Rcut, and
Vd (Supplement Fig. S4). In Fig. 5b
– red dashed line – with a surface pH change from 6.68 to 8, consistent with possible alkaline surfaces in the AOSR (Makar et al., 2018), in the GEM-MACH
simulation, the Vd
distribution is moved to larger values, with its peak value shifting from 0.6 to 1.4 cm s-1. These results show that model
Vd may be highly sensitive to assumed
surface pH, at least when using some inferential dry deposition algorithms
which are pH-dependent. However, Fig. 5b shows that this pH-associated
increase in Vd is still insufficient to
encompass the range of measurement-derived
Vd. Increasing pH to 8 for the GEM-MACH
simulation reduces Rcut, hence Rc, to values much smaller than
Ra and Rb, suggesting that model
Vd cannot further increase without
reductions in both Ra and Rb. In other words, Ra and Rb
were probably overestimated in the current deposition velocity algorithms.
By using the Zhang et al. (2002) Ra and the NOAH-GEM (Wu et al., 2018)
Rb parameterizations in the GEM-MACH algorithm, a further shift of the
GEM-MACH Vd distribution to larger
values was found, with the range encompassing most of the observations (Fig. 5b, pink dashed line). Using the Zhang and NOAH-GEM parameterizations,
rather than the GEM-MACH parameterization, would decrease the Ra and
Rb for the momentum, heat and moisture fluxes as well but still remain within the range of what is expected based on published parameterizations
(Wu et al., 2018, and references therein).
The potential for re-emission of TOS and TON species was also considered.
Fulgham et al. (2020) report that the bidirectional fluxes of volatile
organic acids are driven by an equilibrium partitioning between surface
wetness and the atmosphere. The observations presented here represent the
net flux of all processes including the effects of deposition and any
potential re-emissions of TOS and TON compounds should this process occur.
As the results show a net downward flux (i.e. net deposition), if any
re-emission was occurring, it would be smaller than the deposition fluxes observed here, which are themselves higher than shown by currently available
deposition algorithms. This implies that the deposition part of the flux
must be even larger than the net observed flux, and the measured net fluxes presented here should then be considered minimum values. The current
deposition algorithms do not include bidirectional fluxes for inorganics,
and adjustments related to pH in some situations may not be sufficient to
parameterize deposition fluxes. A bidirectional approach may be needed that
would include not only [H+], but also surface heterogeneous reactions, to determine near-surface equilibrium concentrations of co-depositing gases
such as ammonia and nitric acid.
It is clear that from the Monte Carlo simulations for SO2Vd comparisons, inferential dry deposition algorithms as used in regional and global chemical transport models need to
be further validated and improved, especially over large geographic regions.
Here, the role of pH was identified for improvement in some algorithms along
with possible improvement in aerodynamic and quasi-laminar sublayer
resistance parameters. Yet for other algorithms and for TON compounds, the model low biases in Vd remain to be investigated.
The underestimates suggest that the applications of these algorithms in
regional or global models may significantly underestimate predictions of
TOS dry depositional loss from the atmosphere. Underestimates in
Vd are the result of a combination of
uncertainties in the parameterizations of each algorithm. In the case of the
algorithm used in GEM-MACH, by adjusting the assumed surface pH from 6.68 to
8 (justifiable given the considerable dust emissions in the region: Zhang et al., 2018), the model Vd moved closer to
the aircraft-derived values (Fig. 5b), reducing the model–observation gap by approximately two-thirds. In addition, substituting the aerodynamic resistance and quasi-laminar sublayer resistance parameterizations in the GEM-MACH
algorithm with that from Zhang et al. (2002) and NOAH-GEM (Wu et al., 2018),
respectively, resulted in a further increase in the model
Vd distribution that encompasses most
of the observations (Fig. 5b). Clearly, different algorithms respond
differently to changes in the parameterizations, and validation and
adjustment to each algorithm need measurement-based results over large regions such as derived here.
Conclusions
The atmospheric transport distances and lifetimes
d1/e and τ
determined from the aircraft measurements are substantially shorter than the
GEM-MACH model predictions, and the dry deposition fluxes
F and velocities and Vd
near sources are larger compared to the predictions by GEM-MACH and five
inferential dry deposition velocity algorithms, respectively. There are
important implications for these measurement–model discrepancies. Such discrepancies indicate that regional or global chemical transport models
using these algorithms are biased low for local deposition and high for
long-range transport and deposition, and TOS and TON losses from the atmosphere are significantly underpredicted, resulting in overestimated lifetimes. While the measurements took place over a relatively
short time period, these results indicate that TOS and TON may be removed
from the atmosphere at about twice the rate as predicted by current
atmospheric deposition algorithms. This, in turn, implies a potentially
significant impact on deposition over longer timescales (potentially weeks to months) and relevance towards cumulative environmental exposure metrics
such as critical loads and their exceedance. A faster near-source deposition
velocity for emitted reactive gases may imply less S and N mass being
available for long-range transport, reducing concentrations and deposition further downwind. The near-source higher deposition velocity thus has the
important implication of a reduction in more distant and longer timescale
deposition for locations further from the sources. Moreover, emissions
assessed through network measurements or budget analysis of atmospheric
TOS and TON (Sickles and Shadwick, 2015; Paulot et al.,
2018; Berglen et al., 2004) may be underestimated due to lower
Vd used in these estimates and may require reassessment of the effectiveness of control policies. Shorter
τ for TOS and TON reduces their
atmospheric spatial scale and intensity of smog episodes, potentially
reducing human exposures (Moran et al., 2014). Importantly, shorter
τ for TOS and TON reduces their
contribution to atmospheric aerosols; consequently, the negative direct and
indirect radiative forcing from these sulfur and nitrogen aerosols is reduced, reducing their cooling effects on climate (Solomon et al., 2007).
These impacts suggest that more measurements to determine
τ and F for these pollutants
across large geographic scales and different surface types are necessary for better quantifying their climate and environmental impacts in support of
policy. While in the past such determination was difficult and/or
impossible, the present study provides a viable methodology to achieve such
a goal.
Code availability
All the computer code associated with the TERRA algorithm, including for the
kriging of pollutant data, a demonstration dataset and associated
documentation are freely available upon request. The authors request that future publications which make use of the TERRA algorithm cite this paper,
Gordon et al. (2015), Liggio et al. (2016), or Li et al. (2017) as appropriate.
Data availability
All data used in this publication are freely available on the Canada-Alberta
Oil Sands Environmental Monitoring Information Portal:
https://www.canada.ca/en/environment-climate-change/services/oil-sands-monitoring/monitoring-air-quality-alberta-oil-sands.html (Government of Canada, 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-8377-2021-supplement.
Author contributions
KH, SML, JL, SGM, RM, RMS, JO'B and MW all contributed to the collection of aircraft observations in the field. KH, RM and JO'B made the SO2, NOy and pSO4 measurements and carried out subsequent QA/QC of data. RM analysed canister VOCs and provided OH concentration estimates. RMS made and provided the ground-site deposition velocity measurements. AD contributed to the development of TERRA. JL wrote the Monte Carlo code. PM and AA ran the model and provided model analyses. JZ provided emissions data. LZ and RMS provided deposition algorithm parameters. KH and SML wrote the paper input from all the co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank the National Research Council of Canada flight crew of the
Convair-580, the Air Quality Research Division technical support staff,
Julie Narayan for in-field data management support, and Stewart Cober for
the management of the study.
Financial support
The project was funded by the Air Quality program of Environment and Climate Change Canada and the Oil Sands Monitoring (OSM) program. It is independent of any position of the OSM program.
Review statement
This paper was edited by Barbara Ervens and reviewed by two anonymous referees.
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