AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-3829-2018Airborne lidar measurements of aerosol and ozone above the
Canadian oil sands regionAirborne lidar measurements of aerosol and ozoneAggarwalMonikaWhitewayJameswhiteway@yorku.caSeabrookJeffreyhttps://orcid.org/0000-0002-2163-7276GrayLawrenceStrawbridgeKevinLiuPeterO'BrienJasonLiShao-Menghttps://orcid.org/0000-0002-7628-6581McLarenRoberthttps://orcid.org/0000-0003-1941-5567York University, Centre for Research in Earth and Space Science,
Toronto, ON, M3J 1P3, CanadaEnvironment and Climate Change Canada, Air Quality Processes Research
Section, Toronto, ON, M3H 5T4, CanadaYork University, Centre for Atmospheric Chemistry, Toronto, ON, M3J 1P3,
CanadaJames Whiteway (whiteway@yorku.ca)28June20181163829384931October201722November201718May201829May2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/3829/2018/amt-11-3829-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/3829/2018/amt-11-3829-2018.pdf
Aircraft-based lidar measurements of atmospheric aerosol and ozone were
conducted to study air pollution from the oil sands extraction industry in
northern Alberta. Significant amounts of aerosol were observed in the
polluted air within the surface boundary layer, up to heights of 1 to 1.6 km above ground. The ozone mixing ratio measured in the polluted boundary
layer air directly above the oil sands industry was equal to or less than the
background ozone mixing ratio. On one of the flights, the lidar measurements
detected a layer of forest fire smoke above the surface boundary layer in
which the ozone mixing ratio was substantially greater than the background.
Measurements of the linear depolarization ratio in the aerosol backscatter
were obtained with a ground-based lidar and this aided in the discrimination
between the separate emission sources from industry and forest fires. The
retrieval of ozone abundance from the lidar measurements required the
development of a method to account for the interference from the substantial
aerosol content within the polluted boundary layer.
Introduction
The oil sands mining and upgrading facilities in northern Alberta are a
known source of air pollution (Simpson et al., 2010; Liggio et al., 2016).
The Joint Canada–Alberta Implementation Plan for Oil Sands Monitoring (JOSM)
was organized by Environment and Climate Change Canada (ECCC) to monitor the
impact of oil sands emissions on the quality of air, water, land, and
wildlife (Abbatt et al., 2011). This paper concerns the measurement
technique and results of airborne lidar measurements of aerosol and ozone
that were contributed to the JOSM field campaign during August 2013. A lidar
instrument from York University was installed on a Twin Otter aircraft in
order to complement the main research aircraft for the JOSM flight campaign,
the Convair-580 from the National Research Council of Canada (Gordon et al.,
2015; Liggio et al., 2016).
Emissions from the oil sands facility stacks and mining operations include
nitrogen oxides (NO and NO2), sulfur dioxide (SO2), sulfate
(SO4), methane (CH4), carbon dioxide (CO2), carbon monoxide
(CO), particulate matter, volatile organic compounds (VOCs), and secondary
organic aerosol (SOA) (Simpson et al., 2010; Davies, 2012; Howell et al.,
2014; Liggio et al., 2016; Li et al., 2017; Baray et al., 2018). Large
amounts of NOx and SO2 emissions within the oil sands region
originate from the upgrading processes and high-temperature combustion of
oil, gasoline, and coal. The facilities are surrounded by boreal forests,
which are a natural source of biogenic VOC emissions and also smoke
emissions from forest fires. Enhancements in ozone are well known to occur
in urban air pollution due to the photochemical reactions involving NO,
NO2, VOCs, and sunlight (Crutzen, 1979, Banta et al., 1998, 2005;
Valente, 1998; Langford et al., 2010a; Senff et al., 2010). The exposure to
O3 levels higher than the background can result in damage to biological
tissue in crops and living organisms (Haagen-Smit, 1952) and decrease the
rate of photosynthesis in plants (Morgan et al., 2003).
The airborne lidar measurements were carried out to assess whether a
substantial amount of ozone was generated from the oil sands industrial
emissions. The field campaign with the lidar on board a Twin Otter aircraft
consisted of five flights out of Fort McMurray during the period between
22 and 26 August 2013. The ozone mixing ratio was measured in the
unpolluted air upwind, in the polluted air directly above the oil sands
operations, and as far as 150 km downwind. The only observed enhancement in
ozone occurred in a layer above the polluted boundary layer and air
trajectory analysis linked this to forest fire emissions. The measurement
technique, analysis methods, results, and interpretation are described in
the following sections.
Measurement technique
The differential absorption lidar instrument shown in Figs. 1 and 2 was
installed on a Twin Otter aircraft and viewed downward for vertical profile
measurements of ozone and aerosol. The lidar transmitter consisted of a
Q-switched Nd:YAG laser with second and fourth harmonic generation for
emitting pulsed light with wavelengths of 532 and 266 nm at a repetition
rate of 20 Hz. The pulsed light with a wavelength of 266 nm was focused into
a cell filled with CO2, at a pressure of 965 kPa, to generate
light at wavelengths of 276.2, 287.2, and 299.1 nm by stimulated Raman
scattering (Nakazato et al., 2007). The maximum laser pulse energy was 80 mJ
at a wavelength of 266 nm and the Raman conversion efficiency was about 29 % with pulse energies of 10, 8, and 5 mJ for the 276,
287,
and 299 nm wavelengths respectively. The fourth harmonic generation was
quite sensitive to temperature and the 266 nm output from the laser was
typically in the range of 60 to 80 mJ. The Raman conversion thus varied as
well in a nonlinear fashion. The UV wavelengths were directed into the
atmosphere along the nadir from the Twin Otter aircraft. The laser light at
a wavelength of 532 nm was directed independently into the atmosphere along
the nadir.
A 15 cm diameter off-axis parabolic mirror with a focal length of 500 mm was
used to collect the light that was scattered back from molecules and aerosol
particles. Two separate optical fibers were positioned behind field stop
apertures with diameters of 0.5 and 1.0 mm that determine fields of view
of 1.0 mrad for the 532 nm and 2.0 mrad for the UV backscatter signals. The
four UV wavelengths (266, 276.2, 287.2, and 299.1 nm) were separated in the
receiver using the transmittance and reflectance from interference filters
having a bandwidth of 1 nm and tilted at an angle of 7.5∘.
Photomultiplier tubes (Hamamatsu R9880) were used to detect the
backscattered light. Photon counting was used to record the weak signals
from distances greater than 1.5 km and analog to digital conversion was used
for strong signals in the near range. The vertical range bin was 3.75 m for
the 532 nm signal and 7.5 m for the UV signals, while the in-flight temporal
averaging was 10 s for both the UV and 532 nm signals.
Schematic diagram of the differential absorption lidar system used
for measurement of ozone and aerosol during the oil sands field campaign.
The lidar system installed (a) in the aircraft rack and (b) on the
Twin Otter aircraft.
All of the components shown in Fig. 1 were integrated within an aircraft
rack (Fig. 2) with internal vibration isolation and thermal control. The
rack is shipped to field sites as a single unit with a separate power
supply. The lidar system was developed over several years between field
campaigns on the Canadian research icebreaker CCGS Amundsen (Seabrook et al.,
2011), the AWI Polar 5 (Bassler BT-67) modernized DC-3 aircraft (Seabrook et
al., 2013), and the Twin Otter.
An in-flight visualization tool was used for preliminary real-time data
analysis and this informed decisions on the flight track. The program
provided contour plots of the 532 nm aerosol scattering ratio and the
O3 mixing ratio as measurements were being collected. Line plots of
aerosol backscatter and O3 mixing ratio were displayed at the location
the cursor was placed on the contour plot for profile-by-profile analysis
during the flight.
Analysis of lidar measurements
The backscattered optical power as a function of range z is described by the
lidar equation as
Pλz=Cz2βλ,zexp-2∫0zσλNO3z+αλ,zdz.
The constant C includes the emitted pulse energy, sampling interval,
telescope area, optical throughput, and detection efficiency. The
backscatter coefficient, βλ,z is the
fraction of the laser light pulse scattered back to the receiver per unit
length through the atmosphere and per unit solid angle. The exponential term
represents the round-trip transmittance of the laser pulse between the lidar
and range z. The extinction coefficient, αλ,z, is the fractional decrease in the laser pulse intensity per unit
length in the atmosphere due to scattering. For this experiment, the
molecule under investigation is O3 with an absorption cross section of
σλ. The number density of O3 molecules in the
atmosphere is represented by NO3z in Eq. (1) and the
product σλNO3z represents the
fractional decrease in the laser pulse intensity per unit length due to the
absorption by O3.
(a) Lidar backscatter signals for the analog (AN) and photon
counting (PC) measurements. Altitude is above sea level (a.s.l.). (b) The
O3 mixing ratio derived by using the AN:276/299 and PC:266/299
wavelength pairs. The shading represents the sum of the relative statistical
uncertainty (from ±1 standard deviation in photon counting or shot
noise in analog detection) and the possible range in the aerosol correction
shown in Fig. 4. The measurements shown here were collected on 23 August
2013 at 13:15 local time (UTC-6 h) as the Twin Otter flew directly above
the oil sands industry.
The differential absorption lidar technique was used to determine the mixing
ratio of O3. It is based on the difference between the absorption cross
sections of two transmitted wavelengths. The measurement wavelength with
larger ozone absorption cross section is referred to as the ON wavelength,
λON, and the wavelength with smaller absorption cross section as
the OFF wavelength, λOFF. Starting from the ratio of separate
lidar equations for the ON and OFF wavelengths, the number density of ozone
can be extracted by taking the natural logarithm and differentiating with
height
[A]NO3z=-12σO3,ON-σO3,OFFddzlnPλONzPλOFFz[B][C]-ddzlnβλON,zβλOFF,z-αλON,z-αλOFF,zσO3,ON-σO3,OFF[D]-σSO2,ON-σSO2,OFFσO3,ON-σO3,OFFNSO2z.
Term A in Eq. (2) represents the O3 number density calculated directly
from the lidar signals. The terms B and C in Eq. (2) are corrections for the
wavelength dependence in the backscatter and extinction coefficients and
term D is a correction for other gaseous molecules that absorb UV radiation,
such as SO2. The number density of SO2 molecules in the atmosphere
is represented in Eq. (2) by NSO2(z). The O3 mixing ratio could
be derived from six different ON–OFF wavelength pair combinations from the
four UV wavelengths that were transmitted by the lidar.
Temperature-dependent O3 absorption cross sections were obtained from
the HITRAN 2012 database (Rothman et al., 2013).
The recorded signals were smoothed in the vertical with a running (or
sliding) boxcar average over five points (18.75 m) for the 532 nm signal and
five
points (37.5 m) for the UV signals. In order to reduce the uncertainty in
the O3 measurements, temporal averaging was applied over 1.5 min,
corresponding to a distance of about 7.5 km along the path of the aircraft.
The vertical derivative in term A of Eq. (2) was calculated using a running
(or sliding) linear least squares fit over 19 points with the ozone value
assigned to the midpoint. The distance between entirely independent ozone
values was 180 m. Complete overlap between the transmitted laser pulses and
the field of view of the telescope occurred at distances greater than 300 m and signals recorded nearer to the aircraft were not used in the analysis.
Measurements within clouds have also not been used for retrieving ozone
density.
Figure 3a shows an example of backscatter signals (after averaging) recorded
from below the aircraft at wavelengths of 266, 276, and 299 nm and Fig. 3b
shows the corresponding derived O3 volume mixing ratio. This was
determined by dividing the derived O3 number density by the total
atmospheric molecular number density, which was calculated using temperature
and pressure data from radiosondes launched in the city of Edmonton (430 km
south of Fort McMurray). For the O3 measurements presented in this
paper, analog signal acquisition with the wavelength pair 276/299 nm
was used above the top of the boundary layer, while photon counting
data acquisition with the wavelength pair 266/299 nm was used within the
polluted boundary layer. The height of transition was always a minimum of
300 m above the top of the boundary layer. The 266/299 and 276/299 ozone
profiles were calculated separately and were within the margin of
uncertainty where overlapping in height above the boundary layer (e.g. Fig. 3b). The transition was at a single height.
Correction for aerosol interference in the lidar ozone retrieval
When the amount of aerosol in the atmosphere is insignificant, term B in Eq. (2) is very small and term C is straightforward to calculate since it
includes only molecular scattering. When there is a significant amount of
aerosol present, terms B and C in Eq. (2) can result in large uncertainties
if the aerosol contributions to the backscatter and extinction coefficients
are not accounted for. Previously applied methods for correcting the
interference of aerosol in the O3 lidar retrieval were based on the use
of a power law wavelength dependence in the extinction and backscatter
coefficients with an assumed Ångström exponent (e.g. Browell et al., 1985;
Alvarez et al., 1998), iteration (Alvarez II et al., 2011), or use of three
wavelengths (Eisele and Trickl, 2005). For the study reported here, aircraft-based in situ measurements of aerosol particles size distribution were
available and these data were used to determine the UV backscatter and
extinction coefficients for calculating the aerosol correction terms in Eq. (2). There was no need to estimate the wavelength dependence with a power
law Ångström exponent.
The aerosol backscatter and extinction coefficients at the UV wavelengths
were derived by making use of the lidar signal at a wavelength of 532 nm and
in situ measurements of aerosol size distribution obtained on the NRC
Convair-580 aircraft that was operated for ECCC as part of the JOSM program.
Particles with diameters in the range 0.03 to 1 µm were sampled with
the UHSAS instrument (Cai et al., 2008) and larger particles ranging in
diameter from 0.3 to 20 µm were measured with the FSSP-300 instrument
(Baumgardner et al., 1992).
The steps involved in accounting for aerosol in the derivation of ozone from
the lidar measurements are summarized as follows.
(A) Aerosol optical properties derived from the lidar backscatter signal at a wavelength of 532 nm
The profile of extinction and backscatter coefficients was derived from the
recorded lidar backscatter signal at the wavelength of 532 nm. The
absorption by ozone is not significant at this wavelength so the backscatter
and extinction coefficients can be derived from the data independent of
the ozone measurement. The method of Fernald (1984) was employed and this
required a reference value for the extinction coefficient at a particular
height and also the ratio of extinction to backscatter coefficients (the
lidar ratio). The reference height was a minimum of 200 m above the top of
the boundary layer. The aerosol extinction and backscatter coefficients at
the reference height were estimated by using in situ measurements of
particle size distribution, N(r), during a Convair flight on 23 August 2013
above the boundary layer and integrating over all particle sizes as follows:
αaerosolλ532=∫0∞πr2Qextm,r,λ532Nrdr,βaerosolλ532=∫0∞πr2Qbackm,r,λ532Nrdr.
The aerosol extinction and backscatter efficiencies (Qext and
Qback) were determined from the in situ size distribution measurements
with calculations based on the theory of Mie scattering for spherical
particles (Bohren and Huffman, 1983). A software module was used to evaluate
Maxwell's equations for scattering and absorption of light by homogenous
spherical particles. The calculation required a value of the complex
refractive index of particles, m, at the measurement wavelength in order to
compute the extinction and backscatter efficiencies.
Refractive index for particle compositions used in aerosol
correction calculations.
CompositionRefractive indexUVRefractive indexvisibleReferenceKaolinite1.68+0.04i1.572+0.006iArakawa et al. (1997)Ammonium sulfate1.507+0.005i (at λ=355 nm)1.504+0iMichel Flores et al. (2012)Sulfuric acid1.383+0i (at λ=360 nm)1.366+0i (at λ=556 nm)Palmer and Williams (1975)Toluene secondaryorganic aerosol1.58+0i (at λ=300 nm)1.50+0iNakayama et al. (2013) (visible) Kozma et al. (2005) (UV)Diesel soot1.62+0.55i (at λ=400 nm)1.68+0.56i (at λ=550 nm)Marley et al. (2001)Forest fire smoke1.47+0.01i (at 440 nm)1.61+0.06iWandinger et al. (2002)(visible) Dubovik et al.(2001) (UV)
It was assumed that within the boundary layer the particles large enough to
contribute significantly to the lidar backscatter signal were mineral dust
lofted by mining operations. The particle refractive index corresponding to
the mineral kaolinite was used (Table 1). Kaolinite is a clay mineral
composed of aluminum silicate. Studies done by Cloutis et al. (1995),
Omotoso and Mikula (2004), and Mercier et al. (2008) have found kaolinite
to be the prominent clay particle in the oil sands region.
The in situ particle size distribution measurements acquired directly over
the oil sands region within the boundary layer, at an altitude of 785 m a.s.l.,
were used to calculate the value of the lidar ratio as 31 sr at a wavelength
of 532 nm. This was used for the Fernald algorithm to derive the extinction
and backscatter coefficient height profiles at a wavelength 532 nm. It was
assumed that the lidar ratio was independent of height, which corresponds to
an assumption that changes in the optical extinction and backscatter
coefficients are attributed to variation in aerosol concentration and not
changes in the particle size distribution.
(B) Height profile of aerosol UV backscatter and extinction coefficients from combined lidar and in situ measurements
The lidar 532 nm aerosol extinction coefficient was combined with the in
situ measurements of aerosol properties to derive the height profile of
aerosol number density as follows:
NaerosolzN0=αaerosolλ532,z∫0∞πr2Qextm,r,λ532Nrdr.
The numerator on the right side of the equation is the extinction
coefficient derived from the lidar measurements at the wavelength of 532 nm.
The denominator is the extinction coefficient calculated from the in situ
measurements on the Convair aircraft at a single height. As before, N(r) is
the measured number density for particles with radius in the range between
r and r+dr, and N0 represents the total particle number density
determined from integrating the size distribution.
The backscatter and extinction coefficients as a function of height at the
UV wavelengths can be derived as
αaerosolλUV,z=NaerosolzN0∫0∞πr2Qext(m,r,λUV)Nrdr,βaerosolλUV,z=Naerosol(z)N0∫0∞πr2Qbackm,r,λUVNrdr.
It was assumed that the shape of the aerosol size distribution from the in
situ measurement is applicable throughout the boundary layer. The ratio
Naerosol(z)/No provides a scaling factor that accounts for changes in
the total number density.
(C) Aerosol correction in the ozone retrieval
The calculated aerosol extinction and backscatter coefficients at the UV
wavelengths were substituted into terms B and C in Eq. (2). Figure 4b, c,
and d show the computed values for the first three terms in Eq. (2) for the
measurements taken on 23 August 2013, directly over the oil sands industry
where significant aerosol was observed within the boundary layer.
In order to assess the amount of uncertainty due to assumptions concerning
the aerosol properties, calculations were carried out with a range of
parameters. For example, the measured aerosol particle size distribution had
an equivalent area effective radius (square root of average particle cross
sectional area divided by π) that varied between 0.06 and 0.08 µm along a flight leg of the Convair through the polluted air in the
boundary layer. The aerosol correction terms in Eq. (2) were computed for
particle size distributions corresponding to the effective radii of 0.06
and 0.08 µm. This was done for a variety of aerosol compositions
that included kaolinite, diesel soot, ammonium sulfate, sulfuric acid, and
secondary organic aerosol from photooxidation of toluene (toluene-SOA). The
refractive indices used for the calculations are provided in Table 1. The
red shaded area in Fig. 4c, d, and e represents the range of aerosol
correction values computed with the various particle compositions and size
distributions. This represents an estimate of the amount of uncertainty in the
aerosol correction.
For the case presented in Figs. 3 and 4 there is significant aerosol loading,
as indicated with the profile of aerosol optical extinction coefficient
(Fig. 4a). Term B in Eq. (2) (Fig. 4c) contributed a maximum correction of
-5 to -8 ppbv at a height of 1.1 km where there was a gradient in the
aerosol content at the top of the boundary layer. At lower heights within
the boundary layer where there was a smaller gradient in the amount of
aerosol (altitudes below 0.8 km in Fig. 4a) the contribution of term B in
Eq. (2) was much less. The aerosol contribution to the differential
extinction, term C in Eq. (2), shown in Fig. 4d, was proportional to the
aerosol extinction coefficient and had a maximum contribution with a range
of 1 to 6 ppb near the ground. So in this case the total amount of
correction due to aerosol ranged from -5 to -10 ppbv at the top of the
boundary layer and -1 to -6 ppbv near the ground. The uncertainty associated
with the aerosol correction within the polluted boundary layer was ±2.5 ppbv in this case. The relative uncertainty due to statistical
variations in photon counting or shot noise in the analog detection is
indicated as the grey shading in the uncorrected profile in Fig. 4b and e.
This reached a maximum of ±4 ppbv near the ground. The sum of the
statistical uncertainty and aerosol correction uncertainty is shown as the
shaded region in the profiles in Fig. 3. In cases further downwind from the
oil sands extraction facilities, the aerosol loading was much smaller, as
was the aerosol correction and the associated uncertainty. Examples of
profiles with smaller aerosol correction are shown in Fig. 15.
O3 retrieval and aerosol correction for the AN:276/299
(altitude above 1.65 km) and PC:266/299 (below 1.65 km) wavelength pairs
shown in Fig. 3. (a) The aerosol optical extinction coefficient derived
using the lidar measurement at a wavelength of 532 nm. Calculation of term A
(b), term B (c), and term C (d) from Eq. (2). (e) The addition
of the first three terms in Eq. (2). The shading in red represents the range
of O3 values due to the uncertainty in the aerosol correction method.
The grey shading represents the statistical uncertainty in the detection.
The measurements shown here were collected on 23 August 2013 at 13:15
local time (UTC-6 h) as the Twin Otter flew directly above the oil sands
industry.
The magnitude of the aerosol correction in the retrieved ozone mixing ratio
(Fig. 4) was within the range of what has been reported from previous ozone
lidar studies that employ different methods for calculating the
aerosol extinction and backscatter coefficients at UV wavelengths (Alvarez
et al., 1998; Eisele and Trickl, 2005). As reported in the literature, the
aerosol correction was small (< 3 ppbv) in regions of low aerosol
loading or in regions where the vertical gradient in the backscatter
coefficient was small (Sullivan et al., 2014). Greater aerosol corrections
of 15 to 35 ppbv have been calculated due to the presence of large aerosol
gradients at the top of the boundary layer (e.g. Alvarez et al., 1998;
Eisele and Trickl, 2005).
Forest fire smoke was observed in the lidar measurements for the flight on
24 August 2013 and the results are shown in Sect. 5.2. The correction in the
lidar ozone retrieval due to the interference of forest fire aerosol was
carried out in a similar manner to that described above. In situ particle
size distribution measurements in a layer of forest fire smoke were acquired
on the Convair-580 aircraft during an ascent above the boundary layer
approximately an hour before the time that the lidar observed the layer of
smoke at the same height. The complex refractive index of forest fire
aerosol that was used in this correction method is listed in Table 1. The
lidar ratio at a wavelength of 532 nm was found to be 65 sr from Mie
scattering calculations with the in situ particle size distribution and
refractive index for smoke particles. It was found that the correction due
to the interference of forest fire aerosol decreased the ozone measurement
by a maximum of 7 ppbv. There was some uncertainty since the
Convair in situ measurements used in the correction method were taken 1 h before the time the lidar observed the layer of forest fire smoke.
Different aerosol size distributions from previous studies of biomass
burning (Chakrabarty et al., 2006; Pirjola et al.,
2015) were also used to assess
the uncertainty in the aerosol correction and a maximum correction of -5 ppbv was calculated.
Interference from SO2
Term D in Eq. (2) represents the interference by SO2, which absorbs
with varying optical cross section within the range of UV wavelengths used
for the ozone retrieval. For the measurements presented in this paper, the
266/299 wavelength pair was used to retrieve ozone concentration within the
boundary layer since the interference from SO2 was a minimum in
comparison to the other wavelength pairs. The value of the absorption
coefficients found in the literature for a given wavelength are different
for each reported study. For example, the bias in the derived ozone due to
SO2 for the wavelength pair 266/299 nm is 0.2 % of the SO2
concentration using the values from the HITRAN database (Rothman et al.,
2013), 0.6 % using the values from Vandaele et al (2009), and 1.1 %
using the values reported by Bogumil et al. (2003). In situ SO2 measurements were acquired on the Convair-580 aircraft and mixing ratios
typically ranged between 30 and 150 ppbv within the boundary layer. An
SO2 concentration of 150 ppbv would result in a change in derived ozone
mixing ratio ranging from 0.3 to 1.65 ppbv with the 266/299 pair,
depending the source for absorption cross sections. Corrections for SO2
were not applied to the lidar ozone retrieval since the actual concentration
of SO2 along the Twin Otter flight path was not measured and the
magnitude of the correction was small.
Corrections for detection nonlinearity and signal-induced
background
For weak signals measured with the photomultiplier detectors the photon
count rate is proportional to the incident optical power. At high count
rates (greater than 10 MHz), the photon counting signal does not respond
linearly to the optical power due to overlapping pulses from the detector
being counted erroneously as a single pulse. The detection system is unable
to count separate individual pulses accurately within a certain time period
commonly referred to as dead time td (Donovan et al., 1993). A dead time
correction was applied to the raw photon counting data by calculating the
true count rate NT, in terms of the measured count rate Nm, as
NT=Nm/(1-Nm×td). The true count rate was found by using a value
of dead time for which the ratio of NT to the recorded analog signal
was
constant up to a count rate of 100 MHz. Unique photon counting dead times
were determined for each of the five individual photomultiplier detectors
and the values ranged from 4.5 to 7.5 ns. The optimal dead time correction
at each wavelength was determined from lidar measurements acquired along one
leg of the Twin Otter flight on 22 August 2013 and these dead time
corrections were applied to the measurements collected on all subsequent
flights. The value of dead time correction at each wavelength was consistent
throughout the campaign for each detector.
Another correction was applied to the UV signals in order to remove a
residual decaying signal in the far range. Strong UV signals in the near
range can introduce a residual decaying signal in the far range, most
commonly referred to as the signal-induced noise or signal-induced
background. The cause of this signal-induced noise or background is likely UV
fluorescence from the detector (Zhao, 1999) and this occurs only in the UV
wavelength range. This residual signal had an amplitude that was
proportional to the relatively large signal amplitude at near range. It can
be modelled by an exponential function (Sunesson et al., 1994). The residual
signal was corrected by fitting an exponential decay function to the signal
recorded at distances beyond the ground, where there is no backscatter
signal. The exponential fit was then subtracted from the lidar backscatter
signal.
Air trajectory calculations
The Hybrid Single Particle Integrated Trajectory (HYSPLIT) model (Stein et
al., 2015) was used to predict the trajectory of the emissions released from
the oil sands in both the forward and backward directions. Forward air
trajectories from the oil sands locations were used for flight planning and
backward air trajectory calculations were used in the analysis to
reconstruct the past motion of air parcels. The HYSPLIT model was accessed
through the NOAA ARL READY website (http://www.arl.noaa.gov/HYSPLIT.php, last access: October 2017) and
the trajectories were computed by using the Global Data Assimilation System
(GDAS) meteorological dataset with a resolution of 1∘. The uncertainty
in the trajectory calculation from the HYSPLIT model has been assessed to be
less than 20 % of the travel distance over a travel time of 48 h
(Baumann and Stohl, 1997). In this paper, air trajectories were used in the
interpretation of the lidar measurements. The largest travel time for a
backward trajectory used in the analysis was 10 h. The uncertainty in
the air trajectory related to a travel time of 10 h did not affect the
final interpretation or conclusions based on the use of these trajectories.
Observations
The Twin Otter flight segments were straight and usually oriented either
parallel or perpendicular to the wind direction. The goal was to obtain
measurements in regions upwind and downwind from the oil sands pollution
emission sources. The path of the Twin Otter aircraft for each of the five
flights during the campaign is shown in Fig. 5. The wind direction used for
flight planning was from a forecast, but the wind direction indicated on
each map was obtained from the back-trajectory analysis (GDAS dataset). The
conditions during each flight were clear with occasional fair weather
cumulous, ground temperatures not greater than 20 ∘C, and a few
showers at the most northern flight legs during the flight on 24 August
2013.
Flight track of the Twin Otter aircraft displayed on Google Earth
for (a) 22 August, (b–c) 23 August, (d) 24 August, and (e) 26 August
2013. The direction of wind at the start of each flight is represented by
the arrow. The oil sands industry surface operations area is contained
within the white box in each panel.
The flight segment G–H on 23 August 2013 (13:04 to 13:26 local
time (UTC-6 h) displayed on Google Earth. Point G represents the starting
position of the flight leg and point H the ending position. The oil sands
industry was contained within the regions outlined in white. The average
direction of the wind at height of 800 m a.s.l. on 23 August 2013 at the
measurement time is indicated by the arrow. Inset photo shows the area that
was flown over, including the flue-gas desulfurization (FGD) stack.
(a) The aerosol optical extinction coefficient derived from the
lidar measurements at a wavelength of 532 nm and (b) the O3 mixing
ratio derived from the UV lidar measurements for the flight segment G–H on
23 August 2013. The lidar measurements were collected between 13:04 and 13:26 local time (UTC-6 h). The height is above sea level (a.s.l.) and the
distance is along the flight segment in Fig. 6. The measurements contained
within the vertical dashed lines represent the part of the flight segment
directly above the oil sands surface operations contained within the regions
outlined in white in Fig. 6.
Industrial pollution
A typical flight leg from 23 August 2013 that covered areas upwind, above,
and downwind from the oil sands industry is shown in Fig. 6. The altitude of
the aircraft was 2.95 km above sea level (a.s.l.). The aerosol optical
extinction coefficient and the ozone mixing ratio derived from the lidar
measurements along this flight leg are shown in Fig. 7. Measurements
collected upwind of the industry (within 20 km from the start of the flight
leg) in Fig. 7a show small amounts of background aerosol present in this
region. Directly above the oil sands industry (distances of 25 to 75 km
along the flight track) significant amounts of aerosol were observed to be
mixed to heights of up to 1.5 km a.s.l. (about 1 km above ground). Downwind of
the industry (distances of 90 to 120 km along the flight leg) the aerosol
was dispersed to heights of up to 2.3 km a.s.l. (or 1.6 km above ground). The
depth of the boundary layer was apparent from the vertical range over which
the aerosol was mixed. A flue-gas desulfurization (FGD) stack was
intersected at 57∘03′ N and 111∘39′ W in Fig. 6. The
vertical plume from the stack is clearly seen in the lidar aerosol
measurement in Fig. 7a as the presence of a vertical extension in the depth
of the aerosol layer at the distances of 55 to 60 km from the start of the
flight leg.
Figure 7b shows the corresponding ozone mixing ratio along the flight
segment. Regions of reduced ozone mixing ratio between 17 and 32 ppbv were
measured in the polluted boundary layer directly above the oil sands
industry (distances from 40 to 60 km along the flight leg). The ozone
mixing ratio upwind and downwind from the industry varied between 27 and 40 ppbv.
The flight segment I–J on 22 August 2013 is represented by the
yellow line. Point “I” represents the starting position of the flight leg
and point “J” is the ending position. Backward air trajectories initiated
from an altitude of 1000 m a.s.l. on 22 August 2013 at 14:00
local time (UTC-6 h) are shown in red and marked by the points *a to *f.
(a) The aerosol optical extinction coefficient measured at a
wavelength of 532 nm and (b) the O3 mixing ratio derived from the UV
lidar measurements for the flight segment I–J on 22 August 2013. The lidar
measurements were collected between 13:52 and 14:20 local time (UTC-6 h).
Points *a to *f represent the location of backward trajectories along the
measurement path and are marked on the flight segment in Fig. 8.
(a) The aerosol optical extinction coefficient and (b) the
O3 mixing ratio as a function of time since the air had passed over the
industry to reach the measurement point along all Twin Otter flight tracks.
The measurements shown here were collected between 22 and 26 August 2013.
The flight segment A–B on 24 August 2013. The lidar measurements
were collected between 14:48 and 15:18 local time (UTC-6 h). The red
section along A–B represents the region where forest fire smoke was
observed. The forest fire locations are depicted by red fire symbols. A
backward air trajectory was initiated from an altitude of 2.0 km a.s.l. at a
time of 15:00 local time on 24 August 2013. The round marks along the
trajectory represent a time interval of 1 h. The location of the ECCC
ground-based lidar is indicated as AMS 13.
A case is presented in Figs. 8 and 9 with a segment of the flight on 22 August
2013 that was oriented transverse to the path of the air that passed
over the oil sands industry, approximately 100 km downwind. The flight
segment was south along longitude 110∘ W between positions I and J
(Fig. 8), and the corresponding lidar measurements are shown in Fig. 9. The
optical extinction coefficient derived from the lidar measurements (Fig. 9a)
indicated that the depth over which the aerosol was mixed from the ground
ranged from 1.5 to 2 km a.s.l. (1.0 to 1.5 km above ground). The O3 mixing
ratio (Fig. 9b) within the boundary layer varied between 28 and 42 ppbv
throughout the flight segment. Ozone mixing ratios greater than the
background were not observed.
Backward air trajectories were calculated from locations along all of the
Twin Otter flight tracks during the campaign. For each measurement location,
the time was determined for the air to travel between the oil sands industry
and the position of the lidar measurement from the Twin Otter. Only the
trajectories that passed over the oil sands industry were selected for this
analysis. Figure 10 shows lidar measurements of the aerosol optical
extinction coefficient and O3 mixing ratio as a function of time taken
by the air to travel between the oil sands industry and the measurement
point. The measurements were averaged within the height range of 500 to
800 m a.s.l. As expected, Fig. 10a shows that larger values of aerosol optical
extinction coefficient occur closer to the pollution source area and
decrease moving away from the source. The observed O3 mixing ratios in
Fig. 10b showed no trend with time or distance from the industry. There is
no evidence of increasing O3 for up to 10 h downwind of the oil
sands industrial areas. The only deviation from the background were
instances of smaller ozone mixing ratios.
Forest fire emissions
A case with air pollution from both natural and industrial sources is
described in this section. Airborne lidar measurements to the north of Fort
McMurray were made on 24 August 2013 and the corresponding flight path is
shown in Fig. 11. In this flight leg, the Twin Otter started at point A, a
distance of ∼ 90 km to the east of the oil sands industry and
travelled westbound to point B along constant latitude of 57∘06′ N.
In the contour plot of optical extinction coefficient (Fig. 12a) there was a
layer of aerosol in the altitude range 1.5 to 2.5 km a.s.l. at distances of 0
to 90 km from point B, and this layer was separated from the industrial
pollution in the surface boundary layer. The aerosol from the oil sands
industrial emissions was confined within the depth of the boundary layer,
below an altitude of 1.2 km a.s.l. at distances of 20 to 60 km from point B.
It was observed visually from the aircraft that the separated aerosol layer
above the boundary layer originated from forest fires to the southwest. This
was consistent with the approximate locations of forest fires provided from
the Alberta Forestry and Emergency Response Division as indicated in Fig. 11. A backward trajectory is shown in Fig. 11 that was initiated from an
altitude of 2.0 km at the time and location of the lidar measurement of the
aerosol layer that was observed to be separated above the boundary layer.
The air containing the separated aerosol layer had passed over the vicinity
of the forest fires southwest of the flight track. This aerosol layer is
considered to be forest fire smoke.
(a) The aerosol extinction coefficient measured at a wavelength
of 532 nm and (b) the O3 mixing ratio derived from the lidar
measurements for the flight segment A–B on 24 August 2013. The lidar
measurements were collected between 14:48 and 15:18 local time (UTC-6 h).
The height is above sea level (a.s.l.). Distance is from point B along the
flight segment in Fig. 11. The blank section in this figure represents a
region where clouds interfered with the lidar measurements.
Ground-based lidar depolarization ratio measurements acquired
with the ECCC ground-based lidar system located at 57.14∘ N,
111.6∘ W (AMS 13 in Fig. 11).
Flight tracks of the Twin Otter aircraft (yellow lines) and
locations the Convair-580 aircraft spiral ascents for vertical profiles of
in situ measurements (red stars). Backward trajectories coloured in blue and
pink show the air coming from unpolluted and polluted areas respectively.
Points labelled as A and B correspond to cases (a) and (b) in Fig. 15.
A comparison between the in situ and the lidar-derived O3
mixing ratio for the measurements taken in (a) polluted air and (b)
unpolluted air. Grey shading represents the sum of the random uncertainty in
the lidar signal detection and the range of possible aerosol corrections.
The locations of the measurements are indicated in Fig. 14 as points A and B
for panels (a) and (b) respectively.
Histograms of in situ and lidar-derived O3 mixing ratio for
the measurements taken within the boundary layer in (a) unpolluted air and
(b) polluted air.
A section of the Convair flight on 23 August 2013 is shown in
pink. In situ measurements were collected from west to east between 11:27
and 11:37 local time (UTC-6 h) on 23 August 2013. The triangular marks
indicate the positions of the dashed lines in Fig. 18.
In situ measurements of O3, NO, and NO2 taken from a
section of the Convair-580 flight on 23 August 2013 along the east–west
direction over the oil sands industry and within the surface boundary layer.
The in situ measurements were collected between 11:27 and 11:37 local
time (UTC-6 h). The corresponding flight path is shown in Fig. 17. The
dashed lines represent the positions of the blue triangles in Fig. 17.
Backward air trajectories initiated from an altitude of 700 m above sea level on 22 to 26 August 2013 at 13:00 local time (UTC-6 h). The round marks along each trajectory represent the distance travelled
over time intervals of 4 h.
The O3 mixing ratio measured with the lidar along this flight segment
is shown in Fig. 12b. The O3 mixing ratio reached a maximum of 70 ppbv
at an altitude of 1.9 km, within the separated aerosol layer (distances 15
to 40 km from point B) that originated from the region of forest fires.
In the pollution from the oil sands industry (below the forest fire smoke
layer), significant amounts of aerosol were observed in which the O3
mixing ratio varied between 10 and 33 ppbv. In the eastern half of the
flight leg the O3 mixing ratios were consistent with background values
(23–32 ppbv).
Ground-based lidar polarization measurements
A ground-based lidar was operated by ECCC at the location AMS 13, which was approximately 5 km north of the
Twin Otter flight track as indicated in Fig. 11. The ECCC lidar observed a
distribution of aerosol that was similar to the airborne lidar measurements
in Fig. 12a. It detected the same layer of forest fire smoke in the height
range of 1.5 to 2.5 km a.s.l. The ECCC lidar had an additional capability for
measuring the polarization in the backscatter signal. The ratio of the
perpendicular to parallel components of polarization, relative to the
transmitted polarization, in the aerosol backscatter signals (the
depolarization ratio) provides an approximate method for discriminating
between particles of different sizes and shapes. For example, hexagonal ice
crystals can cause a depolarization ratio greater than 0.5, and spherical
water droplets result in a small (almost zero) depolarization ratio (Sassen
et al., 1991). For this case, the depolarization ratio was used to
discriminate between the aerosol from the oil sands operations and forest
fire smoke.
The difference in the depolarization ratio between the industrial pollution
and forest fire smoke is clearly seen in Fig. 13. The depolarization ratio
throughout the forest fire smoke layer (altitudes of 1.5 to 2.3 km) was
measured to be 5–6 % and this value is consistent with previous
depolarization lidar measurements of forest fire aerosol (Mattis et al.,
2004; Murayama et al., 2004; Pereira et al., 2014). The small values of the
depolarization ratio measured in the smoke layer reveal a more spherical
shape of forest fire particles. The depolarization ratio within the polluted
boundary layer at altitudes below 1.2 km a.s.l. had larger values in the range
7–10 % due to the mixture of mineral dust and other emissions from the
oil sands facilities. This provides further evidence that the separated
layer at heights of 1.5 to 2.5 km with the enhanced O3 mixing ratio
had originated from forest fires rather than industrial pollution.
Comparison between lidar and in situ measurements
The Twin Otter and Convair aircraft were not coordinated to fly along the
same flight tracks. The airborne lidar measurements were taken over long and
straight flight segments, while the in situ measurements were concentrating
on specific emission sources during the period when both aircraft were
operating. It was not possible to have a direct comparison between the lidar
and in situ measurements with both aircraft simultaneously measuring the
same volume of air. There were a few cases when the location where the
Convair carried out a spiral ascent or descent could be linked to the
location of the Twin Otter lidar measurements since the air trajectory
passed through both locations.
Two cases are presented here where vertical profiles of O3 mixing ratio
derived from lidar measurements were compared with the in situ O3
measurements that were acquired on the Convair-580 aircraft during spiral
ascents on 23 August 2013. Locations are indicated in Fig. 14 as “A-In situ” and “B-In
situ”
(designated by a star-shaped symbol) where in situ measurements of the
vertical profile of O3 were collected during two spiral ascents with
the Convair-580 aircraft. The Convair spiral ascent at the position labelled
as “A-In situ” in Fig. 14 was carried out in polluted air above the industry. The Twin
Otter did not pass directly over this point, but the lidar measurements used
for comparison were obtained 2.5 h later at a position along the Twin
Otter flight track, labelled “A-Lidar” in Fig. 14, where the back trajectory of the
air passed over position “A-In situ”. The in situ and lidar measurements of the
vertical profiles of ozone mixing ratio are shown in Fig. 15a. The two
separate measurements were within the limits of measurement uncertainty
throughout most of the overlapping height range (0.8 to 1.2 km), where
the O3 mixing ratio was in the range 20–30 ppbv in the polluted air.
At position “B-Lidar” in Fig. 14, the Twin Otter aircraft collected measurements at a
distance of 12 km away from the location of a Convair spiral ascent at the
position labelled “B-In situ” in Fig. 14. The air back trajectories did not pass over
any pollution sources in the upwind direction and this is considered to be
unpolluted air. The lidar and in situ measured O3 mixing ratios shown
in Fig. 15b were within the limits of uncertainty throughout the overlapping
height range.
The statistical distribution of O3 mixing ratio was also used for
comparing the lidar and in situ measurements. Backward trajectories were
computed from an altitude of 800 m a.s.l. along all the Twin Otter and Convair
flight tracks during the 22 to 26 August time period. The
trajectories were separated into two categories. The air trajectories that
did not pass over the oil sands industry were categorized as unpolluted air
and the trajectories that passed over the industry were categorized as
polluted air. In situ measurements of O3 were acquired at a single
altitude between 550 and 900 m a.s.l. and the lidar measurements were averaged
between altitudes of 550 and 900 m a.s.l. for this analysis. The in situ and
lidar measurements were both averaged for 1.5 min along the flight
tracks. Figure 16a shows the histograms of the O3 measurements in
unpolluted air for both the lidar and in situ measurements collected within
the surface boundary layer. The lidar and in situ histograms for the
unpolluted air are very similar with a peak occurrence at around 30 ppbv,
and with values distributed between 12 and 45 ppbv. For the polluted air,
the O3 measurements were distributed between 10 and 50 ppbv, but the in
situ histogram was not as symmetrical, with peaks at about 25 and 35 ppbv compared to a more normal distribution with a peak at 32 ppbv for the
lidar measurements. The in situ measurements were almost always obtained
directly above the region of oil sands surface operations, but the lidar
polluted air measurements were also obtained downwind to distances of up to
150 km from the oil sands emissions. In both the vertical profile (Fig. 15)
and statistical (Fig. 16) comparisons the lidar and in situ measurements
were consistent with the general result that the O3 mixing ratio in the
polluted air was smaller than or equal to the background O3 abundance.
Discussion
A general result from the lidar measurements was that the amount of O3
in the polluted air directly above the oil sands operations was smaller than
or equal to the amount in the background unpolluted air. The in situ
measurements of NO, NO2, and O3 from the Convair-580 aircraft were
used to investigate the reason for the cases in which the O3 abundance
was less than the background. A flight segment of the Convair aircraft on
23 August 2013 (between 11:27 and 11:37 local time) that travelled
eastward at a constant altitude of 650 m a.s.l. while intersecting the oil
sands industry is shown in Fig. 17. Figure 18 shows the in situ measurements
of O3, NO, and NO2 mixing ratio along the Convair flight segment
shown in Fig. 17. In regions upwind and further downwind of the oil sands
industry, the amount of NO was measured to be less than 2 ppbv while the
O3 mixing ratio ranged between 25 and 30 ppbv. Directly over the oil
sands industry (the region in Fig. 18 contained within the dashed lines),
the mixing ratio of NO increased to 25 ppbv while the O3 mixing ratio
decreased from 30 to 13 ppbv. The reduction in O3 over the
industry is consistent with NO titration: NO+O3→NO2+O2. The sum of the NO2 and O3 mixing ratios in Fig. 18
remained relatively constant, such that the decrease in O3 was
approximately compensated by an increase in NO2.
Another way to describe this is in terms of odd oxygen, Ox (O3+NO2) (Brown et al., 2006). The Ox mixing ratio (shown in Fig. 18)
remained relatively constant along the flight track, with a slight increase
within the industrialized portion of the track despite the significant
decrease in O3. The decrease in O3 was slightly more than
compensated by the increase in NO2, which is entirely consistent with a
titration of O3 in a combustion plume where the NOx composition of
the source is mostly NO (e.g. ∼ 90 % NO, 10 % NO2),
with negligible photochemical formation of O3 close to the source.
It was observed that O3 mixing ratios at distances as far as 150 km
downwind of the pollution sources, and up to 10 h since emission, were
in the range of the regional background levels. There were no observed
enhancements in O3 and this is consistent with previous measurements of
O3 in the Fort McMurray oil sands region (Rudolph, 2004). This result
was different from what has normally been observed in polluted air. For
example, previous studies of air pollution surrounding the power plants,
refineries, and petrochemical industry near Houston, Texas, have observed
that O3 mixing ratios greatly exceeded the regional background (Banta
et al., 2005; Senff et al., 2010; Langford et al., 2010b). One obvious
difference in comparing the Fort McMurray region to Houston is the
temperature. The air temperatures measured from the Convair aircraft over
the Fort McMurray oil sands region during late August 2013 were less than
20 ∘C. The surface air temperatures in the vicinity of Houston
reported by Senff et al. (2010) were in the range of 30 to
42 ∘C. It is well known and documented that episodes of
substantial ozone generation due to pollution occur in hot and stagnant
conditions (e.g. Banta et al., 1998; Valente et al., 1998; Jacob et al.,
1993; Jacob and Winner, 2009; Lin et al., 2001; Camalier et al., 2007; Coates et al., 2016;
Shen et al., 2016).
In addition to the relatively cold temperatures, the conditions could not be
characterized as stagnant (Camalier et al., 2007) for most of the flights
above the oil sands region. Figure 19 shows the 24 h back trajectory for
each day of the Twin Otter flight campaign at midday. With the exception of
25 August 2017, the air had travelled at least 200 km in the 12 h prior to passing over the oil sands region. An increase in the aerosol
layer depth downwind of the oil sands emissions (e.g. Fig. 7) provided
evidence for vertical mixing. As the pollutants mixed with the clean
background air, O3 mixing ratios downwind of the industry gradually
increased to between 25 and 40 ppbv and were consistent with background
levels.
The absence of enhanced O3 in pollution downwind of the industry is
interpreted here as being a result of meteorological conditions that were
not favourable for the generation of ozone. These factors include (A)
ambient temperatures not greater than 20 ∘C, (B) no regional
stagnation of air, and (C) vertical mixing of the polluted air with clean
background air.
An enhancement in the O3 abundance was detected in the forest fire
emissions that were encountered above the boundary layer (Fig. 12b). It has
been established previously that forest fire emissions include the
precursors for generation of ozone (e.g. Jaffe and Wigder, 2012). In this
case the environmental conditions were also more favourable for generation
of ozone. The layer remained well defined and separated from the turbulent
boundary layer. It was not subjected to ventilation from the boundary layer
convection cells, and the mixing with background air was weak enough that
ozone generation could proceed in the middle of the layer.
Conclusions
A lidar instrument for measurements of atmospheric aerosol and ozone was
developed for field deployment. It was installed on a Twin Otter aircraft
for a flight campaign to study the impact of air pollution from the oil
sands extraction industry in northern Alberta. A correction for the
interference of aerosol was required for the retrieval of ozone
concentration from the UV differential absorption lidar measurements in
polluted air. An aerosol correction method was developed that made use of in
situ measurements of the aerosol size distribution in combination with lidar
measurements. It was found that the abundance of ozone in the pollution
directly above the oil sands operations was reduced from what was measured
in the background air. In situ measurements of NO, NO2, and O3 on
board the separate NRC Convair-580 aircraft were used to show that the
reduction in ozone abundance was consistent with NO titration. It was also
found that there was no increase in ozone abundance in the industrial
pollution, compared to background levels, as it was transported downwind to
distances of 150 km and times of up to 10 h. The lack of substantial
ozone generation was attributed to conditions that were not favourable for
the generation of ozone: low temperatures, lack of stagnation, and vertical
mixing with clean background air. A layer of forest fire emissions that was
separated from the turbulent boundary layer was observed to contain an
increase in ozone abundance to 70 ppbv. This was consistent with the
conditions being more favourable for ozone generation within the forest fire
emissions since there was less turbulent mixing above the boundary layer.
Data are available from the corresponding author and will also be archived by Environment and Climate Change
Canada at http://donnees.ec.gc.ca/data/air/monitor/ambient-air-quality-oil-sands-region/pollutant-transformation-summer-2013-aircraft-intensive-multi-parameters-oil-sands-region/?lang=en.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Atmospheric
emissions from oil sands development and their transport, transformation and
deposition (ACP/AMT inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
Financial support for this study was provided by FedDev Ontario (through
Communitech Corp.), Environment and Climate Change Canada (ECCC), the
Natural Sciences and Engineering Research Council of Canada (NSERC), and the
Canadian Foundation for Innovation (CFI). Data from the ECCC radiosonde
measurements were obtained from the Department of
Atmospheric Science, University of Wyoming, website. The authors would like to thank Mr. Cordy
Tymstra from the Forestry and Emergency Response Division (Environment and
Sustainable Resource Development of Alberta) for providing records of forest
fire locations.
Edited by: Randall Martin
Reviewed by: two anonymous referees
ReferencesAbbatt, J., Aherne, J., Austin, C., Banic, C., Blanchard, P., Charland, J.
P., Kelly, E., Li, S. M., Makar, P., Martin, R., McCullum, K., McDonald, K.,
McLinden, C., Mihele, C., Percy, K., Rideout, G., Rudolph, J., Savard, M.,
Spink, D., Vet, R., and Watson, J.: Integrated Monitoring Plan for the Oil
Sands: Air Quality Component, 2011, retrieved from:
http://publications.gc.ca/site/eng/394253/publication.html (last access: 1
June 2017).
Alvarez, R. J., Senff, C. J., Hardesty, R. M., Parrish, D. D., Luke, W. T.,
Watson, T. B., Daum, P. H., and Gillani, N.: Comparisons of airborne lidar
measurements of ozone with airborne in situ measurements during the 1995
Southern Oxidants Study, J. Geophys. Res., 103, 31155–31171, 1998.Alvarez II, R. J., Senff, C. J., Langford, A. O., Weickmann, A. M., Law, D.
C., Machol, J. L., Merritt, D. A., Marchbanks, R. D., Sandberg, S. P.,
Brewer, W. A., Hardesty, R. M., and Banta, R. M.: Development and
application of a compact, tunable, solid-state airborne ozone lidar system
for boundary layer profiling, J. Atmos. Ocean. Tech., 28, 1258–1272,
10.1175/JTECH-D-10-05044.1, 2011.
Arakawa, E. T., Tuminello, P. S., Khare, B. N., Milham, M. E., Authier, S.,
and Pierce, J.: Measurement of optical properties of small particles, in:
1997 Scientific Conference on Obscuration and Aerosol Research, Aberdeen
Proving Ground, Maryland, 23–26 June 1997, 1–30, 1997.
Banta, R. M., Senff, C. J., White, A. B., Trainer, M., McNider, R. T.,
Valente, R. J., Mayor, S. D., Alvarez, R. J., Hardesty, R. M., Parrish, D.,
and
Fehsenfeld, F. C.: Daytime buildup and nighttime transport of urban ozone in
the boundary layer during a stagnation episode, J. Geophys. Res., 103, 22519–22544, 1998.Banta, R. M., Senff, C. J., Nielsen-Gammon, J., Darby, L. S., Ryerson, T.
B., Alvarez, R. J., Sandberg, S. P., Williams, E. J., and Trainer, M.: A bad
air day in Houston, B. Am. Meteorol. Soc., 86, 657–669,
10.1175/BAMS-86-5-657, 2005.Baray, S., Darlington, A., Gordon, M., Hayden, K. L., Leithead, A., Li,
S.-M., Liu, P. S. K., Mittermeier, R. L., Moussa, S. G., O'Brien, J.,
Staebler, R., Wolde, M., Worthy, D., and McLaren, R.: Quantification of
methane sources in the Athabasca Oil Sands Region of Alberta by aircraft mass
balance, Atmos. Chem. Phys., 18, 7361–7378,
10.5194/acp-18-7361-2018, 2018.
Baumann, K. and Stohl, A.: Validation of a long-range trajectory model using
gas balloon tracks from the Gordon Bennett Cup 95, J. Appl. Meteorol., 36,
711–720, 1997.
Baumgardner, D, Dye, J. E., Gandrud, B. W., and Knollenburg, R. G.:
Interpretation of measurements made by the Forward Scattering Spectrometer
Probe (FSSP-300) during the Airborne Arctic Stratospheric Expedition, J.
Geophys. Res., 97, 8035–8046, 1992.
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of light by
small particles, John Wiley & Sons, Inc., New York, 1983.
Bogumil, K., Orphal, J., Homann, T., Voigt, S., Spietz, P., Fleischmann, O.
C., Vogel, A., Hartmann, Kromminga, H., M., Bovensmann, H., Frerick, J., and
Burrows, J. P.: Measurements of molecular absorption spectra with the
SCIAMACHY pre-flight model: Instrument characterization and reference data
for atmospheric remote sensing in the 230–2380 nm region, J. Photochem.
Photobiol. A: Chem., 157, 167–184, 2003.Browell, E. V., Ismail, S., and Shipley, S. T.: Ultraviolet DIAL measurements
of O3 profiles in regions of spatially inhomogeneous aerosols,
Appl. Opt., 24, 2827–2836, 1985.Brown, S. S., Neuman, J. A., Ryerson, T. B., Trainer, M., Dubé, W. P.,
Holloway, J. S., Warneke, C., de Gouw, J. A., Donnelly, S. G., Atlas, E.,
Matthew, B., Middlebrook, A. M., Peltier, R., Weber, R. J., Stohl, A.,
Meagher, J. F., Fehsenfeld, F. C., and Ravishankara, A. R.: Nocturnal
odd-oxygen budget and its implications for ozone loss in the lower
troposphere, Geophys. Res. Lett., 33, L08801, 10.1029/2006GL025900,
2006.
Cai, Y., Montague, D., Mooiweer-Bryan, W., and Deshler, T.: Performance
characteristics of the ultra high sensitivity aerosol spectrometer for
particles between 55 and 800 nm: Laboratory and field studies, J. Aerosol
Sci., 39, 759–769, 2008.Camalier, L., Cox, W., and Dolwick, P.: The effects of meteorology on ozone
in urban areas and their use in assessing ozone trends, Atmos. Environ., 41,
7127–7137, 10.1016/j.atmosenv.2007.04.061, 2007.Chakrabarty, R. K., Moosmuller, H., Garro, M. A., Arnott, W. P., Walker, J.,
Susott, R. A., Babbitt, R. E., Wold, C. E., Lincoln, E. N., and Hao, W. M.:
Emissions from the laboratory combustion of wildland fuels: Particle
morphology and size, J. Geophys. Res., 111, 1–16, 10.1029/2005JD006659,
2006.
Cloutis, E. A., Gaffey, M. J., and Moslow, T. F.: Characterization of
minerals in oil sands by reflectance spectroscopy, Fuel, 74, 874–879, 1995.Coates, J., Mar, K. A., Ojha, N., and Butler, T. M.: The influence of
temperature on ozone production under varying NOx conditions – a
modelling study, Atmos. Chem. Phys., 16, 11601–11615,
10.5194/acp-16-11601-2016, 2016.Crutzen, P. J.: The role of NO and NO2 in the chemistry of the
troposphere and stratosphere, Annu. Rev. Earth Pl. Sc., 7, 443–472, 1979.Davies, M. J. E.: Air quality modeling in the Athabasca Oil Sands Region,
in: Alberta Oil Sands: Energy, Industry and the Environment, 1st ed., edited
by: Percy, K. E., Elsevier Ltd., Oxford, UK, 267–309, 2012.
Donovan, D. P., Whiteway, J. A., and Carswell, A. I.: Correction for
nonlinear photon-counting effects in lidar systems, Appl. Opt., 32,
6742–6753, 1993.
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M.
D., Tanre, D., and Slutsker, I.: Variability of absorption and optical
properties of key aerosol types observed in worldwide locations, J. Atmos.
Sci., 59, 590–608, 2001.
Eisele, H. and Trickl, T.: Improvements of the aerosol algorithm in ozone
lidar data processing by use of evolutionary strategies, Appl. Opt., 44,
2638–2651, 2005.
Fernald, F. G.: Analysis of atmospheric lidar observations: some comments,
Appl. Opt., 23, 652–653, 1984.Gordon, M., Li, S.-M., Staebler, R., Darlington, A., Hayden, K., O'Brien, J.,
and Wolde, M.: Determining air pollutant emission rates based on mass balance
using airborne measurement data over the Alberta oil sands operations, Atmos.
Meas. Tech., 8, 3745–3765, 10.5194/amt-8-3745-2015, 2015.
Haagen-Smit, A. J.: Chemistry and physiology of Los Angeles Smog, Ind. Eng.
Chem., 44, 1342–1346, 1952.Howell, S. G., Clarke, A. D., Freitag, S., McNaughton, C. S., Kapustin, V.,
Brekovskikh, V., Jimenez, J.-L., and Cubison, M. J.: An airborne assessment
of atmospheric particulate emissions from the processing of Athabasca oil
sands, Atmos. Chem. Phys., 14, 5073–5087,
10.5194/acp-14-5073-2014, 2014.Jacob, D. J. and Winner, D. A.: Effect of climate change on air quality,
Atmos. Environ., 43, 51–63, 10.1016/j.atmosenv.2008.09.051, 2009.
Jacob, D. J., Logan, J. A., Gardner, G. M., Yevich, R. M., Spivakovsky, C.
M., and Wofsy, S. C.: Factors regulating ozone over the United States and its
export to the global atmosphere, J. Geophys. Res., 98, 14817–14826, 1993.
Jaffe, D. A. and Wigder, N. L.: Ozone production from wildfires: A critical
review, Atmos. Environ., 51, 1–10, 2012.
Kozma, I. Z., Krok, P., and Riedle, E.: Direct measurement of the
group-velocity mismatch and derivation of the refractive-index dispersion for
a variety of solvents in the ultraviolet, J. Opt. Soc. Am. B, 22,
1479–1485, 2005.Langford, A. O., Senff, C. J., Banta, R. A., Alvarez, J., and Hardesty, R.
M.: Long range transport of ozone from the Los Angeles basin: A case study,
Geophys. Res. Lett., 37, L06807, 10.1029/2010GL042507, 2010a.Langford, A. O., Tucker, S. C., Senff, C. J., Banta R. M., Brewer, W. A.,
Alvarez II, J., Hardesty, R. M., Lerner, M. N., and Williams, E. J.:
Convective venting and surface ozone in Houston during TEXAQS 2006, J.
Geophys. Res., 115, D16305, 10.1029/2009JD013301, 2010b.Li, S.-M., Leithead, A., Moussa, S. G., Liggio, J., Moran, M. D., Wang, D.,
Hayden, K., Darlington, A., Gordon, M., Staebler, R., Makar, P. A., Stroud,
C. A., McLaren, R., Liu, P. S. K., O'Brien, J., Mittermeier, R. L., Zhang,
J., Marson, G., Cober, S. G., Wolde, M., and Wentzell, J. J. B.: Differences
between measured and reported volatile organic compound emissions from oil
sands facilities in Alberta, Canada, P. Natl. Acad. Sci., 114, E3756–E3765
10.1073/pnas.1617862114, 2017.Liggio, J., Li, S.-M., Hayden, K., Taha, Y. M., Stroud, C., Darlington, A.,
Drollette, B. D., Gordon, M., Lee, P., Liu, P., Leithead, A., Moussa, S. G.,
Wang, D., O'Brien, J., Mittermeier, R. L., Brook, J. R., Lu, G., Staebler,
R. M., Han, Y., Tokarek, T. W., Osthoff, H. D., Makar, P. A., Zhang, J.,
Plata, D. L., and Gentner, D. R.: Oil sands operations as a large source of
secondary organic aerosols, Nature, 534, 91–94, 10.1038/nature17646,
2016.
Lin, C.-Y., Jacob, D. J., and Fiore, A. M.: Trends in exceedances of the
ozone air quality standard in the continental United States, 1980–1998,
Atmos. Environ., 35, 3217–3228, 2001.
Marley, N. A., Gaffney, J. S., Baird, J. C., Blazer, C. A., Drayton, P. J.,
and Frederick, J. E.: An empirical method for the determination of the
complex refractive index of size-fractionated atmospheric aerosols for
radiative transfer calculations, Aerosol Sci. Tech., 34, 535–549, 2001.
Mattis, I., Müller, D., Ansmann, A., Wandinger, U., Murayama, T., and
Damoah, R.: Siberian Forest-Fire smoke observed over central Europe in
spring/summer 2003 in the framework of Earlinet, in: Proceeding of the 22nd
International Laser Radar Conference, Matera, Italy, 12–16 July 2004,
857–860, 2004.Mercier, P. H. J., Le Page, Y., Tu, Y., and Kotlyar, L.: Powder X-ray
Diffraction Determination of Phyllosilicate Mass and Area versus Particle
Thickness Distributions for Clays from the Athabasca Oil Sands, Pet. Sci.
Technol., 26, 307–321, 10.1080/10916460600806069, 2008.Michel Flores, J., Bar-Or, R. Z., Bluvshtein, N., Abo-Riziq, A., Kostinski,
A., Borrmann, S., Koren, I., Koren, I., and Rudich, Y.: Absorbing aerosols at
high relative humidity: linking hygroscopic growth to optical properties,
Atmos. Chem. Phys., 12, 5511–5521, 10.5194/acp-12-5511-2012,
2012.
Morgan, P. B., Ainsworth, E. A., and Long, S. P.: How does elevated ozone
impact soybean? A meta-analysis of photosynthesis, growth and yield, Plant
Cell Environ., 26, 1317–1328, 2003.Murayama, T., Müller, D., Wada, K., Shimizu, A., Sekiguchi, M., and
Tsukamoto, T.: Characterization of Asian dust and Siberian smoke with
multiwavelength Raman lidar over Tokyo, Japan in spring 2003, Geophys. Res.
Lett., 31, 1–5, 10.1029/2004GL021105, 2004.Nakayama, T., Sato, K., Matsumi, Y., Imamura, T., Yamazaki, A., and Uchiyama,
A.: Wavelength and NOx dependent complex refractive index of SOAs
generated from the photooxidation of toluene, Atmos. Chem. Phys., 13,
531–545, 10.5194/acp-13-531-2013, 2013.
Nakazato, M., Nagai, T., Sakai, T., and Hirose, Y.: Tropospheric ozone
differential-absorption lidar using stimulated Raman scattering in carbon
dioxide, Appl. Opt., 46, 2269–2279, 2007.Omotoso, O. E. and Mikula, R. J.: High surface areas caused by smectitic
interstratification of kaolinite and illite in Athabasca oil sands, Appl.
Clay Sci., 25, 37–47, 10.1016/j.clay.2003.08.002, 2004.
Palmer, K. F. and Williams, D.: Optical constants of sulphuric acid;
Application to the clouds of Venus?, Appl. Opt., 14, 208–219, 1975.Pereira, S. N., Preibler, J., Guerrero-Rascado, J. L., Silva, A. M., and
Wagner F.: Forest fire smoke layers observed in the free troposphere over
Portugal with a multiwavelength Raman lidar: Optical and microphysical
properties, The Scientific World Journal, 2014, 1–11,
10.1155/2014/421838, 2014.
Pirjola, L., Virkkula, A., Petäjä, T., Levula, J., Kukkonen, J., and
Kulmala, M.: Mobile ground-based measurements of aerosol and trace gases
during a prescribed burning experiment in boreal forest in Finland, Boreal
Environ. Res., 20, 105–119, 2015.Rothman, L. S., Gordon, I. E., Babikov, Y., Barbe, A., Chris Benner, D.,
Bernath, P. F., Birk, M., Bizzocchi, L., Boudon, V., Brown, L. R., Campargue,
A., Chance, K., Cohen, E. A., Coudert, L. H., Devi, V. M., Drouin, B. J.,
Fayt, A., Flaud, J.-M., Gamache, R. R., Harrison, J. J., Hartmann, J.-M.,
Hill, C., Hodges, J. T., Jacquemart, D., Jolly, A., Lamouroux, J., Le Roy, R.
J., Li, G., Long, D. A., Lyulin, O. M., Mackie, C. J., Massie, S. T.,
Mikhailenko, S., Müller, H. S. P., Naumenko, O. V., Nikitin, A. V.,
Orphal, J., Perevalov, V., Perrin, A., Polovtseva, E. R., Richard, C., Smith,
M. A. H., Starikova, E, Sung, K., Tashkun, S., Tennyson, J., Toon, G. C.
Tyuterev, V. G., and Wagner, G.: The HITRAN2012 molecular spectroscopic
database, J. Quant. Spectrosc. Ra., 130, 4–50,
10.1016/j.jqsrt.2013.07.002, 2013.
Rudolph, R.: Analysis of airborne ozone and ozone precursor measurements in
the oil sands summer 2001 and 2002, Cumulative Environmental Management
Association, Fort McMurray, Alberta, Document ID: 1023971, 536 pp., 2004.
Sassen, K.: The polarization lidar technique for cloud research: A review and
current assessment, B. Am. Meteorol. Soc., 72, 1848–1866, 1991.Seabrook, J., Whiteway, J., Staebler, R., Bottenheim, J., Komguem, L., Gray,
L., Barber, D., and Asplin, M.: LIDAR measurements of Arctic boundary layer ozone depletion events over the frozen Arctic Ocean, J. Geophys. Res.,
116, D00S02, 10.1029/2011JD016335, 2011.Seabrook, J. A., Whiteway, J. A., Gray, L. H., Staebler, R., and Herber, A.:
Airborne lidar measurements of surface ozone depletion over Arctic sea ice,
Atmos. Chem. Phys., 13, 6023–6029, 10.5194/acp-13-6023-2013,
2013.Senff, C. J., Alvarez, R. J., Hardesty, R. M., Banta, R. M., and Langford, A.
O.: Airborne lidar measurements of ozone flux downwind of Houston and Dallas,
J. Geophys. Res., 115, D20307, 10.1029/2009JD013689, 2010.Shen, L., Mickley, L. J., and Gilleland, E.: Impact of increasing heat waves
on U.S. ozone episodes in the 2050s: Results from a multimodel analysis
using extreme value theory, Geophys. Res. Lett., 43, 1–9,
10.1002/2016GL068432, 2016.
Simpson, I. J., Blake, N. J., Barletta, B., Diskin, G. S., Fuelberg, H. E.,
Gorham, K., Huey, L. G., Meinardi, S., Rowland, F. S., Vay, S. A.,
Weinheimer, A. J., Yang, M., and Blake, D. R.: Characterization of trace
gases measured over Alberta oil sands mining operations: 76 speciated
C2–C10
volatile organic compounds (VOCs), CO2, CH4, CO, NO, NO2, NOy,
O3 and SO2, Atmos. Chem. Phys., 10, 11931–11954,
10.5194/acp-10-11931-2010, 2010.Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J. B., Cohen, M. D.,
and Ngan, F.: NOAA's HYSPLIT atmospheric transport and dispersion modeling
system, B. Am. Meteorol. Soc., 96, 2059–2077, 10.1175/BAMS-D-14-00110.1,
2015.Sullivan, J. T., McGee, T. J., Sumnicht, G. K., Twigg, L. W., and Hoff, R.
M.: A mobile differential absorption lidar to measure sub-hourly fluctuation
of tropospheric ozone profiles in the Baltimore-Washington, D.C. region,
Atmos. Meas. Tech., 7, 3529–3548, 10.5194/amt-7-3529-2014,
2014.
Sunesson, J. A., Apituley, A., and Swart, D. P.: Differential absorption
lidar system for routine monitoring of tropospheric ozone, Appl. Opt., 33,
7045–7058, 1994.
Valente, R. J., Imhoff, R. E., Tanner, R. L., Meagher, J. F., Daum, P. H.,
Hardesty, R. M., Banta,R. M., Alvarez, R. J., McNider, R. T., and Gillani, N.
V.: Ozone production during an urban air stagnation episode over Nashville,
Tennessee, J. Geophys. Res., 103, 22555–22568, 1998.Vandaele, A. C., Hermans, C., and Fally, S.: fourier transform measurements
of SO2 absorption cross sections: II. Temperature dependence in the
29,000 to 44,000 cm-1 (227–345 nm) region, J. Quant. Spectrosc.
Ra., 110, 2115–2126, 2009.Wandinger, U., Müller, D., Böckmann, C., Althausen, D., Matthias, V.,
Bösenberg, J., Weib, V., Fiebig, M., Wendisch, M., Stohl, A., and
Ansmann, A.: Optical and microphysical characterization of biomass-burning
and industrial-pollution aerosols from multiwavelength lidar and aircraft
measurements, J. Geophys. Res., 107, 8125, 10.1029/2000JD000202,
2002.
Zhao, Y.: Signal-induced flourescence in photomultipliers in differential
absorption lidar systems, Appl. Opt., 38, 4639–4648, 1999.