AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-2961-2016Sulfur dioxide (SO2) vertical column density measurements by Pandora
spectrometer over the Canadian oil sandsFioletovVitali E.vitali.fioletov@outlook.comvitali.fioletov@canada.cahttps://orcid.org/0000-0002-2731-5956McLindenChris A.https://orcid.org/0000-0001-5054-1380CedeAlexanderDaviesJonathanMiheleCristianNetchevaStoykaLiShao-Menghttps://orcid.org/0000-0002-7628-6581O'BrienJasonEnvironment Canada and Climate Change Canada, Toronto, ON, CanadaNASA Goddard Space Flight Center, Greenbelt, MD, USALuftBlick, Kreith, AustriaVitali E. Fioletov (vitali.fioletov@outlook.com, vitali.fioletov@canada.ca)14July2016972961297618February20161April20168June201620June2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/2961/2016/amt-9-2961-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/2961/2016/amt-9-2961-2016.pdf
Vertical column densities (VCDs) of SO2 retrieved by
a Pandora spectral sun photometer at Fort McKay, Alberta, Canada, from
2013 to 2015 were analysed. The Fort McKay site is located in the Canadian oil
sands region, approximately 20 km north of two major SO2 sources
(upgraders), with total emission of about 45 kt yr-1. Elevated SO2
VCD values were frequently recorded by the instrument, with the highest
values of about 9 Dobson Units (DU; DU =2.69×1016 molecules cm-2). Comparisons with co-located in situ
measurements demonstrated that there was a very good correlation between
VCDs and surface concentrations in some cases, while in other cases, elevated
VCDs did not correspond to high surface concentrations, suggesting the plume
was above the ground. Elevated VCDs and surface concentrations were observed
when the wind direction was from south to southeast, i.e. from the direction
of the two local SO2 sources. The precision of the SO2
measurements, estimated from parallel measurements by two Pandora
instruments at Toronto, is 0.17 DU. The total uncertainty of Pandora
SO2 VCD, estimated using measurements when the wind direction was away
from the sources, is less than 0.26 DU (1σ). Comparisons with
integrated SO2 profiles from concurrent aircraft measurements support
these estimates.
Introduction
Sulfur dioxide (SO2) plays an important role in Earth's atmospheric
chemistry and climate. It forms sulfate aerosols that influence weather and
climate and leads to acid deposition through the formation of sulfuric acid
(H2SO4)
(Hutchinson and Whitby,
1977). It is also a designated criteria air pollutant in many countries that
poses a direct hazard to public health
(Longo et al., 2010; Pope and
Dockery, 2006). Active volcanoes are the primary natural source of SO2,
while coal-burning power plants, smelters, and oil refineries are the
primary anthropogenic emitters of SO2 into the atmosphere. In Canada,
the majority of SO2 emissions come from three sources: 32 % from
smelting and refining for non-ferrous metals, 22 % from coal-fired
electricity generation, and 21 % from the petroleum industry
(Wood, 2012). One of the largest sources of atmospheric
pollutants, including SO2, in Canada is the oil sands operations.
Located in the province of Alberta, the oil sands contain vast deposits of
bitumen oil mixed with sand, clay, and water. Environmental and health
concerns associated with the oil sands operations, including air quality and
acid deposition, are well known (Kelly et al.,
2010). Highly elevated levels of SO2 over the oil sands area have been
detected there (Simpson et al., 2010) and
are a subject of concern. Due to the large size of the oil sands operation
area, satellite measurements are an appealing approach for air pollution
monitoring in this region.
The applications of satellites for monitoring SO2 have been progressing
rapidly during the last 2 decades. They are widely used to monitor
volcanic
(Carn
et al., 2003, 2013; Fioletov et al., 2013; Krueger et al., 2000; Rix et al.,
2012; Theys et al., 2013) and anthropogenic sources
(Carn
et al., 2007; Clarisse et al., 2011; Eisinger and Burrows, 1998; Fioletov et
al., 2011, 2013; de Foy et al., 2009; Georgoulias et al., 2009; Lee et al.,
2011; Nowlan et al., 2011; Thomas et al., 2005, Krotkov et al., 2016). More
recently, satellites have been used for monitoring SO2 (and NO2)
emissions and trends in the oil sands region
(McLinden
et al., 2015, 2012, 2014). Many satellite instruments provide vertical
column densities (VCDs) once a day, but derivation of surface concentrations
from them is not straightforward (Knepp
et al., 2015). Time-resolved ground-based measurements of the same quantity,
VCD, help both in the validation of satellite measurements and the facilitation of a
better interpretation of satellite data and their links to surface
concentration (Richter et al., 2013).
Ground-based observations of SO2 VCDs were first made by Brewer
spectrophotometers, using the strong absorption features of SO2 in the
ultraviolet (UV) part of the spectrum (Kerr et al.,
1988). The Brewer spectrophotometer operating in the direct-sun (DS) mode
measures UV at five wavelengths between 306 and 320 nm to retrieve column ozone
and SO2. Such measurements have been used to monitor volcanic SO2
(Krueger et al., 1995) and
anthropogenic SO2 in extreme pollution events
(De Backer and Muer, 1991; Zerefos et al.,
2000). The uncertainty of the Brewer DS SO2 measurements is about 1–2
Dobson Units (DU, where 1 DU is equal to 2.69×1016 molecules cm-2) and is typically insufficient for air
quality applications. A Brewer spectrometer can also measure global spectral UV
irradiance on a horizontal surface in the spectral range 290–325 or
286–363 nm (depending on the type) with a 0.5 nm increment. Pronounced
SO2 absorption features were seen in these global UV spectra when
SO2 amounts were high (Bais et al., 1993).
SO2 VCD can be even derived from such global spectral UV measurements
(Fioletov et al., 1998). However this method of SO2
VCD retrieval is less sensitive than the Brewer DS method and has the
detection limit of about 5 DU. A more accurate method (with an uncertainty
as low as 0.13 DU) based on Brewer “group-scan” spectral direct-sun
radiation measurements at 45 wavelengths from 306 to 324 nm was developed
(Kerr, 2002), but not widely implemented due to its complexity.
The ground-based multi-axis differential optical absorption spectroscopy
(MAX-DOAS) method is another technique used for ground-based SO2
retrievals. The method is based on scattered sunlight measured in the UV
part of the spectrum at different elevation angles with data analysed using
the DOAS technique (Platt and Stutz, 2008). In addition to
total VCD, the method can provide some information on the vertical profile.
It was widely used for measurements of volcanic SO2 under the Network
for Observation of Volcanic and Atmospheric Change (NOVAC) project
(Galle et al., 2010). Only a few studies have focused on
MAX-DOAS measurements of anthropogenic SO2 (Wang et al., 2014;
Wu et al., 2013; Theys et al., 2015). The uncertainty of the retrieved
SO2 estimated from the spectral fitting error is about 0.1 DU
(Wang et al., 2014).
Pandora (SciGlob, http://www.sciglob.com/) is a recently developed
instrument for UV and visible spectral measurements, which was primarily
designed for direct-sun observations, but is also capable of conducting
zenith sky (ZS) and MAX-DOAS measurements. Similar to the Brewer instrument,
it tracks the sun, but as it measures the entire spectrum at once, a
DOAS-type algorithm can be applied. The instruments were used in several
NASA Deriving Information on Surface Conditions from Column and Vertically
Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaigns.
DISCOVER-AQ is a series of field missions with an overarching objective to
improve the interpretation of satellite observations to diagnose
near-surface conditions relating to air quality (Reed
et al., 2015). It has been demonstrated that Pandora can successfully
measure total column ozone and NO2
(Herman et al., 2009, 2015; Tzortziou et
al., 2012, 2015). The instrument characteristics are suitable for total
column SO2 measurements, but available information on Pandora SO2
measurements is very limited (Knepp
et al., 2015). This is probably a result of most Pandora instruments being
deployed away from major SO2 sources. In 2013, a Pandora instrument,
along with other instrumentation, was deployed to the oil sands region where
relatively high SO2 column amounts are common
(McLinden et al., 2012; Simpson et al., 2010). This made it possible to
establish optimal retrieval procedures for the Pandora SO2
measurements, estimate uncertainties, and study the relationship between
total columns and surface concentrations.
Instruments and locations
The Pandora spectrometer system is based on an Avantes symmetric
Czerny-Turner optical bench design with a 2048 CCD linear array detector.
The Pandora spectrometer system consists of an optical head sensor, mounted
on a computer controlled sun-tracker and sky-scanner, and connected to an
Avantes array spectrometer by means of a 400-micron core diameter single
strand multi-mode optical fibre. It operates in the 280–530 nm spectral
range with a 0.6 nm slit function width (full width at half maximum). A
detailed description of the instrument is given by Herman et
al. (2009). Wavelength calibration and slit functions for Pandora
instruments were determined by the manufacturer from lamp emission lines
(Hg, Cd, Cu, In, Mg, Zn). Wavelength stability is validated during
instrument operation by an analysis of the solar Fraunhofer line structures.
Stray light in the 304 to 330 nm range (used for SO2 retrievals) is
reduced by utilizing a U340 (280–380 nm) band pass filter with a cut-off limit at
380 nm. Further stray light correction by the Pandora processing software is
obtained from pixels corresponding to 280 to 285 nm, which contain almost
zero direct illumination. We used the instrument in DS and ZS modes but will
limit this study to DS measurements only. To allow for the detection of
different absorbers, the instrument periodically measures UV spectra with
the U340 filter on and off, with an interval of about 90 s.
In 2013, Environment Canada (EC) acquired two Pandora sun spectrometers from
SciGlob. One of them, with Serial Number 104, after a 3-week testing period
at Toronto, was deployed to Fort McKay (57.184∘ N,
111.64∘ W) in the oil sands region, where it began operations on
15 August 2013. The instrument head was mounted on a tripod, and the
spectrometer and computer were installed in waterproof plastic housing
(Fig. 1, left). The head and the housing were connected by 10 m long
optical, data, and power cables. Such configuration provided a rapid
deployment of the instrument, but it could not withstand a cold Alberta
winter. The instrument was shipped back to Toronto on 3 December 2013. It
was then redeployed on 21 August 2014 in a different configuration with the
instrument head installed on the roof of the EC instrument trailer, while
the spectrometer and computer were installed inside the trailer in a
temperature-controlled environment (Fig. 1, right).
Fort McKay is a small town (population of 600) surrounded by five oil sands
surface mining facilities to the north and south. There are two major
SO2 sources located south of Fort McKay: the Syncrude Mildred Lake
plant is located 16 km to the south of Fort McKay and the Suncor Millenium
Plant 23 km south–south-east. According to the National Pollutant Release
Inventory (NPRI, http://www.ec.gc.ca/inrp-npri), the 2014 annual emissions
were about 26 and 17 kt of SO2 per year from the Syncrude and Suncor
facilities, respectively. There is also a source (Horizon Oil Sands
processing plant and mine) 18 km north, but the emission from that source
are 4 kt per year. There are hundreds of kilometres of pristine boreal
forests to the west and east from Fort McKay with no SO2 sources. Thus,
the pollution level at Fort McKay is largely dependent on the wind
direction.
Another Pandora instrument (S/N 103) was installed on 14 October, 2013 on the
roof of the EC building at Downsview (43.782∘ N, 79.47∘ W), a typical urban location in Toronto. The instrument head was mounted on
a tripod, but the spectrometer and computer were located inside the building
and connected to the head by a 10 m cables. The same configuration was used
for Pandora 104 when the instrument was in Toronto. The two instruments were
run side-by-side from January to June 2014, making a direct comparison
possible. According to NPRI, there are no large SO2 emission point
sources in the vicinity of the EC building in Toronto, but there are
multiple sources with emissions under 10 kt per year in the area. The
largest source with total emissions of about 12 kt per year is a cluster of
plants on the west coast of Lake Ontario, about 60 km from the site.
Pandora instruments at Fort McKay in (left) 2013 and
(right) 2014–2015. In 2013, the instrument's optical head was installed on a
white Brewer tripod and the spectrometer and computer were inside the black
box to the right. From 2014, the head was installed on the roof of a trailer
with the spectrometer and computer located inside the trailer.
Pandora's SO2 algorithm and data
The Pandora Operation and Analysis Software Suite (Version 1.6, available
from http://avdc.gsfc.nasa.gov/pub/tools/Pandora/install/) was used for data
processing and retrieval. The software can be configured to include a
specific set of retrieved species and to use an extraterrestrial reference
spectrum. The information about the retrieval algorithm and configuration
settings is available from the software manual. In particular, the software
allows for the selection of a spectral fitting window. MAX-DOAS retrievals
tend to use narrow spectral windows to avoid differences in the air mass
factors (μ) for individual wavelengths within the window caused by
differences in optical paths due to scattering. This factor is far less
important for DS measurements.
The first step in the Pandora spectral fitting algorithm is to subtract the
irradiance logarithm of the measured spectra from the irradiance logarithm
of the reference spectrum. Then, a simultaneous least-squares fit is
applied to the difference between measured and reference spectra (see
Herman et al., 2009 for details) as illustrated by Fig. 2. The fitted
functions are a low-order polynomial, the absorption spectra of SO2, ozone, and other atmospheric absorbers, and wavelength shift and squeeze
functions.
The Pandora standard algorithm uses a SO2 absorption cross section at
295 K (Vandaele et al., 1994) in the retrieval. As the
SO2 absorption cross section possesses very distinct absorption
features (Fig. 3a), a wider fitting window, including more spectral lines,
should reduce the uncertainties of the retrievals. Figure 3 also shows
SO2 values retrieved for the 304–311, 311–330, 304–330, and
306–330 nm spectral windows based on measurements taken at Fort McKay on
14 September 2013 (Fig. 3c), and at Downsview on 16 March 2014 for both
instruments (Fig. 3b and d). It is evident from the plot that the first
two windows produce SO2 values that have a much larger scattering than
the values retrieved based on the entire 304–330 nm spectral window. The
SO2 absorption spectrum has strong peaks in the 304–311 nm spectral
window, but the absolute signal is weaker and the stray light is higher than
in the 311–330 nm spectral window. However the weaker SO2 absorption
features in 311–330 nm result in increased sensitivity to the influence of
ozone and possibly other absorbers.
Fitted Pandora slant column optical depths (blue) for a
measurement at Fort McKay on 2 August 2015 at 07:42 MST (red) when some of
the highest SO2 values were observed. The synthetic reference spectrum
was used in the fitting (see text for details). The negative O3 slant
column means that there was more O3 in the reference than in the
measurements at 07:42 MST. Note that NO2 and HCHO are fitted in this
fitting windows, but only to improve the results for SO2. The measured
(red) lines for each gas were obtained from the measured irradiance
logarithm by subtracting all other fitting functions except the absorption
spectrum-based fit for that gas.
Examples of SO2 VCD retrieved using different
spectral windows as indicated on the plot. Data from Pandora 104 at Fort
McKay, 14 September 2013, and Downsview, 16 March 2014, are shown.
Different colours represent different spectral windows used for the fitting.
Grey bars connect SO2 values retrieved from 304–311 and 311–330 nm
spectral windows.
The 306–330 nm spectral window was used in this study. When SO2 is
high, the values retrieved using all four spectral windows are very similar.
The retrievals using the 306–330 nm spectral window are nearly identical to
those obtained for the 304–330 nm window, but produce slightly lower
uncertainties. The software also has the option to use a prescribed
extraterrestrial spectrum or to generate it from the measurements
(“synthetic reference spectrum”). Both options were tested and the latter
option was used as it produces slightly smaller uncertainties. The synthetic
reference spectrum is derived from an average of the logarithms of about 100
low integration time spectra measured near noon in Toronto. Then, the slant
column amount for each trace gas in this spectrum was estimated and added to
the spectrum to get an “absorption-free” reference spectrum (see Herman
et al., 2009 for details). Note that the synthetic reference spectrum was
established from clear sky measurements in Toronto before each instrument
deployment to Fort McKay and then used to process all subsequent data.
SO2 VCDs at Toronto are low, so the synthetic reference spectrum is
close to an SO2 absorption-free reference spectrum. The calibration
procedure discussed below removes any biases caused by remaining SO2 signals in the reference spectrum.
Several criteria were used for data filtering. The instrument integration
time varies from 4 to 4000 ms depending on the signal strength. To exclude
measurements when the sun was covered by clouds, a 500 ms cut-off limit was
used as a quality control tool: measurements that required more than 500 ms
integration time were rejected. Furthermore, for high sun elevations, lower
cut-off limits were used: 300 ms for air mass (μ) value μ< 3 and 100 ms for μ< 2. Measurements taken at
μ>5 were not used. In addition to SO2 vertical
column calculation, the Pandora data processing software calculates its
statistical uncertainty. After the filtration by the integration time, the
statistical fitting uncertainties are less than 0.05 DU for 60 %, and
between 0.05 and 0.15 DU for about 30 % of all retrieved VCD values.
The remaining 10 % have higher uncertainties and are related to
measurements at low sun position or under very thin clouds, but some of them
can still be useful. Only data with a fitting uncertainty of SO2 VCD < 0.35 DU were used.
Top: slant column densities derived from 306 to 330 nm
spectral fitting of Pandora 103 data at Fort McKay in August 2013. Bottom:
vertical column densities derived from the same data using the fit based on
25th percentile (see text for details). The lines corresponding to
5th, 10th, 25th, and 50th percentiles are also shown.
All data were processed with the both reference spectra and with several
spectral windows. Then all retrieved SO2 values for each measurement
were compared. We encountered one practical problem related to the fitting
algorithm that appears when the SO2 amount is close to zero and the
measurements are relatively noisy (e.g. due to thin clouds). In general, the
306–330 nm spectral window and the synthetic spectrum give the best results,
but on some occasions, that fitting algorithm finds the best fit with a
small wavelength offset and an artificially elevated SO2 value (by as
much as 1 DU compared to the values for the other reference spectrum or for
the same spectrum, but different fitting windows). Only 2–3 % of the data
are affected by this error, but they are very noticeable on time series
plots. A simple filtering by the standard error of retrieved SO2
(< 0.2 DU) and/or by the wavelength offset removes these erroneous
data, but it can also remove some valid observations. As mentioned, there
are two options for the reference spectrum: an independently measured
extraterrestrial spectrum (“prescribed reference spectrum”) or generated
from the measurements themselves (“synthetic reference spectrum”). Such
problematic cases were processed with the prescribed spectrum that appear to
give better results in this situation than processing with the synthetic
reference spectrum used for all other retrievals.
The calibration procedure for NO2 DS DOAS measurements (determining the
absolute slant column amount in the reference spectrum) is described by
Herman et al. (2009). It is based on a linear fit of the lowest 2 % (or
so) slant column values as a function of the air mass. Unlike NO2 measurements with a precision of 0.01 DU (Herman et al., 2009), retrievals
of SO2 have to deal with relatively high noise in individual
measurements, their precision is about 0.17 DU (shown later). This means
that the 2nd percentile will be determined largely by the noise and not the
lowest VCD values. Additionally, unlike stratospheric NO2 that is always
present in any NO2 VCD retrieval, we can assume that there is a certain
fraction of measurements without any measurable SO2 presence in the
atmosphere. We modified the NO2 calibration approach to make it more
suitable for SO2 by considering higher percentiles and then adjusting
the “baseline” level using known characteristics of the noise distribution
function as described below.
The scatter plot of coincidental SO2 measurements at
Downsview by Pandora 103 and 104 in January–July 2014. The data were grouped
into 10 min bins and only bins with between five and seven individual
measurements were used for the comparison. The total number of bins was
1844. The correlation coefficient between the plotted data is 0.7.
Figure 4 (top) shows slant column SO2 measured at Fort McKay in
August–September 2013 as a function of the air mass factor μ. In the
absence of any SO2, the slant column values should be scattered around
the zero line, so the mean and median values as a function μ of should
be equal to zero. If, however, the reference spectrum is not absolutely
correct, it may contain structures that the fitting procedure interprets as
a contribution from SO2 absorption. Similarly, if the ozone absorption
is not accounted for completely, the fitting procedure could produce a
residual SO2 signal that depends on μ. Thus, even in the case
with no SO2 in the atmosphere, the mean (or median) values of slant
column are not constant, but can be described as a linear function (in the
simplest case) of μ. That function could be used as a reference
corresponding to zero SO2 slant column values in the SO2-free
atmosphere. If we assume that the noise is Gaussian with a known standard
deviation, then the mean can be also calculated from any percentile value
because the Gaussian distribution is determined by only two parameters. This
can be used to determine the “no SO2” atmosphere mean value in the
case of real atmosphere.
If we assume that SO2 is present even in a fraction of all observations
at an otherwise SO2-free site, then the mean value will be elevated
compared to the SO2-free “clean” atmosphere and therefore it cannot
be used as a reference in the calibration procedure. However, the low
percentiles (e.g. 5th, 10th, or 25th) of slant column values
are close to the same percentiles for clean conditions as they
correspond to conditions with no SO2 in the atmosphere (e.g. when the
wind is from the west where there is no sources). This makes them suitable
for determining the absolute slant column amount in the reference spectrum
as the mean value for the no SO2 atmosphere. There are two
potential complications: first, the low percentiles could be biased low
relative to zero SO2 conditions due to random errors, thereby resulting
in scattering of data points. The lower the percentile value, the larger
that bias. Second, the standard deviation of the scattering increases with
the air mass factor as the signal strength declines, resulting in higher
errors and lower values in the low percentiles.
The intercept (a) and slope (b) of different percentile lines were determined
by a method that is based on quintile regressions (Koenker and
Bassett, 1978). Once the slope and intercept of the reference line are
known, the VCD values can be derived from slant column density (SCD) as VCD
= (SCD – a)/μ-b. This selection of a particular percentile line as a
reference introduces a bias that can be corrected using the information
about measurement uncertainties as discussed below. We used the 25th
percentile in our calculations. Note that the slopes of the percentile lines
are very similar, and the difference between the 25th percentile
intercept and 50th or 10th percentiles intercepts for VCDs is
+0.16 and -0.12 DU respectively.
The fact that the slope of the regression line for the SCD vs. μ in
the absence of SO2 is negative may seem counterintuitive, but the
spectral fitting procedure is dominated by the ozone signal. A small
imperfection in accounting for ozone absorption can be compensated by a
biased SO2 signal. As the shape of the ozone profile and the
temperature of the ozone layer change throughout the year, this
calibration procedure was applied on a monthly basis. The correction to the
VCD (that is equal to -a/μ-b) introduced by this calibration procedure
is not negligible and changes throughout the year. For μ=3, the
correction value ranges from -0.15 DU in October to 0.55 DU in May.
Estimation of the instrument precision would be an easy task if we could
install the instrument in an SO2-free location, but this was not the
case for our sites. However, the side-by-side operation of two instruments
at Toronto made it possible to study the precision of Pandora's SO2
measurements. Figure 5 shows a scatter plot of the SO2 values by the
two instruments from 16 January 2014 to 31 July 2014 when both instruments
were at the Downsview site. Typically, the SO2 VCD values in Toronto
are relatively small, less than 1.7 DU. The correlation coefficient between
the measurements of the two instruments shown in Fig. 5 is 0.7. Using
Grubbs estimation method (see Appendix A for details) and assuming that
there is no multiplicative bias between measurements, we can calculate that
the standard deviations of instrument errors are 0.18 and 0.16 DU for
Pandora 103 and 104 respectively.
This information about the instrument errors can be used to reduce the
effect of an arbitrary selection of the percentile line as a reference used
by the calibration procedure. If we assume that the instrumental errors have
a Gaussian distribution with a 0.17 DU standard deviation, then in the
absence of any SO2, the 25th and 10th percentile values would
be -0.11 and -0.22 DU respectively. These values represent the biases
introduced by the calibration procedure and should be added back to the
retrieved values. Note that the lower the percentile value, the less the
calibration procedure is affected by non-zero SO2 values. However, the
lower the percentile value, the higher the uncertainty of the percentile
estimate and the higher the impact of the uncertainty of the instrumental
error estimate can have on the size of the bias. In our case, either the
25th or 10th percentile seems to be optimal. As the distance
between them from the Gaussian distribution with σ=0.17 DU is
-0.11 DU, very close to the -0.12 DU value from the Fig. 4 (bottom)
estimates, there is no impact on the results regardless of what percentile
value to use.
The precision of Pandora instruments is high and the two instruments track
each other reasonably well, but there could be systematic errors related to
the retrieval algorithm itself. Figure 3b and d show an extreme example
when both instruments reported fluctuations around the zero line with
negative values as low as -0.4 DU. The spectral fitting algorithm includes
ozone absorption spectrum as one of the main fitted functions. The ozone
optical depth is about 2 orders of magnitude higher than the SO2
optical depth (for 1DU of SO2 and the 311–330 nm spectral window). Even
a small imperfection in accounting for ozone absorption could cause a
substantial error in retrieved SO2. Column ozone was high on that day
(420 DU) and the incomplete removal of ozone absorption features by the
spectral fitting algorithm could be a possible explanation for such a large
negative value. The SO2 values retrieved using the 304–311 nm spectral
window, where the influence of other absorbers is smaller due to the
stronger SO2 absorbing features, are close to the zero line. Similar
fluctuations with positive and negative values at Fort McKay were observed
on days when surface concentrations were close to zero with the site at
upwind of the sources, indicating these fluctuations were not related to
real changes in SO2 VCD. Further improvement of the retrieval algorithm
could increase the accuracy of Pandora's SO2 measurements.
SO2 measurements at Fort McKay
The site at Fort McKay is located 20 km from the major emission sources and
SO2 values as high as 9 DU are observed from time to time. Time series
of SO2 measurements at Fort McKay in August 2013–October 2015 are
shown in Fig. 6. Different colours and symbols represent different
uncertainties of the retrieved values as estimated by the Pandora processing
software.
The first days of the Pandora operation at Fort McKay revealed that a
combination of strong emissions from the industrial sources to the south
with southern winds result in “pollution events” characterized with high
VCD values of SO2 and NO2. An example of such an event on 23 August
2013 is shown in Fig 7a. During that event, the wind direction was
from the south or southeast, i.e. from the direction of the major pollution
sources.
Time series of SO2 measurements at Fort McKay from
August 2013 to October 2015. Different symbols and colours represent
different standard errors estimated by the fitting algorithm. Data from
December 2014–January 2015 are missing because of low sun elevation (μ>5).
Vertical column density (DU) measured by the Pandora
spectrometer at Fort McKay and in situ SO2 concentration (ppbv)
measured at the same location for 6 days, which illustrate fluctuations of
VCDs and surface concentrations. Note that the vertical scales are the same
for all six plots.
VCD and surface concentration SO2 data binned by
(left) surface concentration and (right) VCD values. Binned VCD and surface
concentration data show a clear link between the two parameters, but binning
by Pandora's values gives a different result from the binning by in situ
data. When the surface concentration is elevated, vertical columns are also
elevated. However, in about 25 % cases, surface concentrations are close
to zero even when the vertical column values are elevated. The bottom and
top edges of the box are located at the sample 25th and 75th
percentiles; the whiskers correspond to the 5th and 95th
percentiles. The centre horizontal line is drawn at the median.
Although the VCD and surface concentration are different qualities, they are
often affected by the same plume and therefore correlated. In situ
measurements on 23 August 2013 demonstrated the same behaviour of surface
SO2 and VCDs as illustrated by Fig. 7a, with a relationship where each
10 ppbv at the surface corresponds to about 1 DU in total column. However,
this column–surface relationship is different from event to event. Figure 7b and c show examples of pollution events on 4 and 5 November 2013, when
1 DU corresponds to about 5 and 20 ppbv respectively. On some
occasions, the VCD and surface concentration show a similar behaviour for
many hours (Fig. 7d), while on other days the changes in surface
concentration follow the VCD changes with some time lag (Fig. 7e), probably
due to the shape of the plume, or do not really show any good correlation
(Fig. 7f). In the case of 2 August 2015 (Fig. 7f), the SO2 plume
was probably above the ground early in the morning and was not detected by
in situ instruments.
The mean and 90th percentile of in situ SO2
concentration (a) and Pandora SO2 VCD (b) at Fort McKay as a function
of the wind direction in 2013–2015. Both Pandora and in situ data show
similar patterns: high SO2 values are associated with south-east winds
(c). The mean values from the plots (a) and (b) are overlaid with Landsat
images of the surface mining area
(http://earthobservatory.nasa.gov/Features/WorldOfChange/athabasca.php?all=y).
The plots are based on simultaneous Pandora and in situ measurements
averaged over 10 min intervals. The 0, 90,
180, and 270∘ azimuths correspond to the northern,
eastern, southern, and western wind directions respectively. The two red
squares indicate the major SO2 emission sources. Data for this plot
were binned into 10∘ bins by the wind direction.
High surface concentrations should contribute to elevated vertical columns.
The examples presented above indicate that the opposite may not be always
true: elevated vertical column densities may be related to plumes that are
above the ground and therefore may not produce high surface concentrations.
This is further illustrated by Fig. 8 where scatter plots of surface
concentrations binned by VCD values and VCDs binned by surface
concentrations are shown.
Surface wind data were used to study the dependence of VCDs and surface
concentrations on wind directions. The main SO2 emission sources are
located within about 20 km to the south of Fort McKay, and the winds from
the south generally produce elevated SO2 values. Figure 9a confirms
that surface SO2 concentrations are the highest when the wind direction
is south–southeasterly. This can be seen for both the mean values and for
extremes (90th percentile).
As discussed in Sect. 3, the calibration procedure is based on the
assumption that a certain fraction of all measurements corresponds to
clean conditions. As Fig. 9a shows, surface concentrations are very
low for most wind directions except south and south-east. This can be used
to refine the calibration-related bias in Pandora data mentioned in Sect. 3. A minimum in SO2 of about zero was found for winds from the west as
expected from the location of industrial sources. We can assume that this
direction represents clean conditions and this gives us an additional
confidence in the suggested calibration procedure. Figure 9b shows Pandora
SO2 VCD as a function of the wind direction with this bias removed. As
expected, it is very similar to the distribution of surface concentrations
from Fig. 9a.
It should be noted that the 90th percentile values for the direction
from west are 0.25–0.3 DU (Fig. 9b). This can be interpreted as yet
another estimate of the overall uncertainty of Pandora's SO2 data. If
we assume the Gaussian distribution of the errors, then the 0.3 DU value of
the 90th percentile corresponds to 0.23 DU value of the standard
deviation. The standard deviation as calculated directly is between 0.22 and
0.26 DU for six 10∘ bins corresponding to clean western directions.
These values are higher than the previously estimated 0.17 DU value of the
instrument precision, but they also include the errors related to
imperfection of the retrieval algorithm.
SO2 VCD calculated from measurements of seven aircraft flights,
from Pandora 104 measurements and surface SO2 concentrations.
Date and time (MST)Min. aircraftMax. aircraftPandoraSurfaceVCD (DU)VCD (DU)VCD (DU)(ppbv)23 Aug 2013 10:490.040.040.160.1623 Aug 2013 12:200.630.880.385.9323 Aug 2013 13:591.081.311.109.4924 Aug 2013 11:280.180.410.273.0424 Aug 2013 13:040.150.390.729.3602 Sep 2013 10:360.020.02-0.2103 Sep 2013 16:090.260.511.06
The measured vertical profiles of SO2 mixing ratios
in ppbv (the black lines) with 1σ uncertainties as a function of
altitude for seven spiral flights around Fort McKay in August–September 2013.
The black dots indicate mixing ratios at the surface. The red dots represent
the wind directions as labelled on the top axis. Note that the two major
SO2 sources are located to the south and south–south east of Fort McKay.
The dates and times of the flights and the mean distances from Fort McKay
are also shown.
SO2 VCD (DU) measured by the Pandora spectrometer (red)
at Fort McKay and calculated from integrated aircraft SO2 profile
measurements (blue), as well as in situ SO2 concentration in ppbv
(green). The vertical blue bars represent the range of SO2 VCDs
calculated from two assumptions: for 0 mixing below the lowest aircraft
flight height and a constant mixing ratio that corresponds to the mixing
ratio at the lowest altitude where SO2 was measured.
Aircraft-based measurements of air pollutants from sources in the Canadian
oil sands were made in support of the Joint Canada–Alberta Implementation
Plan for Oil Sands Monitoring during a summer intensive field campaign
between 13 August and 7 September 2013 (Gordon et al.,
2015). The SO2 measurements from these flights were used to examine the
accuracy of the Pandora VCDs. Specifically, seven spirals were flown within 4 km
of Fort McKay in which vertical profiles were made with a Thermo Scientific
43i-TLE analyser. The measured vertical profiles of SO2 mixing ratios
and the wind directions as a function of altitude for these seven flights are
shown in Fig. 10. Integrated SO2 columns were compared with Pandora
VCDs, and the results are shown in Fig. 11 and Table 1. The minimum
altitude range flown by the aircraft used to construct a profile was
100–1500 m, with most profiles exceeding 2000 m. The integrals were
calculated for three scenarios for SO2 concentrations below the lowest
aircraft flight altitude of about 100–150 m: (1) assuming a zero mixing
ratio between the ground and the lowest aircraft altitude, (2) assuming that
the mixing ratio was constant as at the lowest aircraft altitude, and (3)
with the mixing ratio linearly extrapolated below the lowest aircraft
altitude. For most spirals an SO2 value from the surface was available
to “anchor” the profile, and it was found to be consistent with the constant
mixing ratio scenario. In five flights, the integrated SO2 was very close
to the Pandora values with differences within 0.25 DU. The Pandora
instrument was able to track the increase in VCD spanning three spirals,
capturing the onset on 23 August 2013 of a large pollution event. On two
occasions where the aircraft measurements showed near-zero SO2
concentrations, Pandora's values were 0.12 DU and -0.2 DU, i.e. within
the 1σ uncertainties of the measurements. Only for the final spiral
was the aircraft VCD lower than the Pandora value by more than 0.5 DU. On
that flight, both aircraft and Pandora data demonstrated elevated SO2
values. The aircraft data showed a thin layer of SO2 below 600 m with a
maximum at 200 m (Fig. 10). That may be an indication of the edge of the
plume, and the plume could be thicker along the Pandora optical path.
Nevertheless, the average difference between the integrated aircraft values
and Pandora VCDs was about 0.1 DU, with standard deviations of 0.37 or
0.29 DU if the last flight was excluded.
The Pandora SO2 VCDs were also compared with measurements from the
Dutch–Finnish Ozone Monitoring Instrument (OMI) on board NASA Aura satellite
(Levelt et al., 2006). The recent
version of the operational OMI SO2 data set, based on the principal
component analysis algorithm (Li et al., 2013) with
additional adjustment based on modelled SO2 profiles over the oil sands
region (McLinden et al., 2014), was used in
this comparison. We also limited the comparison to pixel sizes less than 40 km
(track positions 11–50) centred within 15 km from Fort McKay. Only OMI
measurements taken under snow-free and cloud-free (cloud fraction < 0.2) conditions were used.
The Pandora values were averaged within ±0.5 h from the OMI overpass time. A scatter plot of all 51 coincident
OMI and Pandora measurements that satisfy these criteria is shown in Fig. 12. The correlation coefficient between the two data sets is only about 0.2
and this is not surprising given a large pixel size and high uncertainties
of OMI measurements (0.5 DU at 1σ level) relative to the range of
SO2 levels. We simulated OMI and Pandora measurements using the EC GEM-
MACH (Global Environmental Multi-scale – Modelling Air quality and
Chemistry)
(Makar
et al., 2015a, b) model at 2.5 km resolution, accounting for the size of
the OMI pixel and the 15 km coincidence criteria, and considering the addition
of realistic measurement noise. A detailed discussion of this simulation is
beyond the scope of this study, but we found that the correlation
coefficient between such simulated Pandora and OMI data was only about 0.3.
The model study indicates that the main culprits in the degradation of the
correlation were the measurement noise (primarily in the OMI) and the OMI
horizontal resolution. This example demonstrates that a simple scatter
plot comparison of satellite and ground-based VCD measurements is not very
informative, and a proper account for satellite viewing geometry and
measurement uncertainties is necessary.
A scatter plot of SO2 VCD values measured
by the OMI satellite instrument and Pandora. The correlation coefficient
between the two data sets is 0.17 and the slope of the regression line is
0.23. The error bars represent 2σ intervals.
Summary and discussion
In order to study variability and changes of VCDs of major pollutants such
as SO2 in the Canadian oil sands region, a Pandora sun photometer was
installed at Fort McKay in August 2013. We found that the instrument is
suitable for SO2 monitoring and reliable enough to operate in remote
areas. Originally, the instrument was deployed in a configuration with the
instrument head mounted on a tripod and the spectrometer and computer
installed in waterproof plastic housing. In August 2014, the instrument
spectrometer was redeployed with the spectrometer and computer installed
indoors in a temperature-controlled environment and the instrument optical
head connected by an optical cable, located on the roof of the instrument
trailer. In this modified configuration, the instrument demonstrated that it
can operate for extended periods under cold conditions.
The instrument is sensitive enough to measure anthropogenic SO2 from
two major SO2 sources, with total emissions of about 45 kt yr-1,
located approximately 20 km from the observation site. As expected, elevated
VCD (up to 9 DU) and surface concentrations of SO2 are observed when
the wind is from the south and south-east, where the emission sources are
located. With no industrial sources located to the west, VCD and surface
concentrations are about zero for westerly winds.
The calibration procedure applied in this study was similar to that
developed by Herman et al. (2009) for NO2, but it was modified to
account for a much higher noise level of SO2 VCD measurements (0.01 DU
for NO2 vs. 0.17 DU for SO2). This calibration procedure is based
on the 25th (or 10th) percentile and makes the assumption that a
sizable fraction of all measurements corresponds to SO2-free
conditions. Fort McKay measurements stratified by the wind direction
confirmed the validity of this approach: the average SO2 values after
the calibration procedure were close to zero for winds from the west where
no SO2 sources are found. This 25th (or 10th)
percentile-based calibration approach is optimal when clean conditions
occur frequently, e.g. for sites located in a vicinity of emission point
sources where SO2 VCDs depend on the wind direction. However, lower
percentiles such as the 10th or smaller may be required in regions with
persistent high SO2 values, e.g. in eastern China.
Various sources of measurement uncertainties were examined. The statistical
standard error of SO2 VCD calculated by the spectral fitting algorithm
is under 0.05 DU for 60 % and under 0.15 DU for 90 % of all direct-sun
observations. The instrument precision, calculated from parallel
measurements by two instruments, is about 0.17 DU (1σ). The
spectral fitting procedure and the accuracy of the retrieved SO2 values
largely depend on properly accounting for ozone absorption and the reference
spectrum. Based on some examples, we estimate that these factors can
introduce errors up to 0.4 DU. Further development of the retrieval
algorithms could improve the instrument accuracy. Measurements at Fort
McKay, when the wind was from clean sectors, demonstrated that the overall
Pandora uncertainty is under 0.26 DU (1σ). The Pandora measurements
were further validated using seven aircraft profile measurements that
demonstrated a bias within 0.1 DU and the standard deviation of the
difference under 0.3 DU for all but one of the aircraft profiles.
When used for satellite validation, simple scatter plot comparisons of
coincident satellite and ground-based VCD measurements are less informative
for a localized industrial source such as the oil sands, and their
interpretation requires a proper accounting of satellite viewing geometry
and measurement uncertainties. The comparison of Pandora and OMI VCDs over
Fort McKay demonstrated a low correlation, with a correlation coefficient of
about 0.2. This is not surprising given a large pixel size, high
uncertainties of OMI measurements (0.5 DU at 1σ level) relative to
the range of SO2 variations, and heterogeneity of the SO2 spatial
distribution at this location. The use of models to account for the
difference in spatial and temporal resolution between ground-based and
satellite measurements would greatly facilitate satellite SO2 VCDs
validation.
The comparison of SO2 VCDs and surface concentrations at Fort McKay
suggests that there is no simple link between these two quantities. High
surface concentrations contribute to the column values. A simple statistical
relationship suggests that, on average, each 10 ppbv of surface
concentration roughly corresponds to 1 DU of total column (similar to what one
would calculate for 10 ppbv spread through a 1 km surface layer). However,
elevated VCDs may be related to plumes that are above the ground with little
or no fumigation, and therefore may not produce elevated surface
concentrations.
Data availability
Data from Pandora instruments are available through Environment and Climate Change
Canada (contact Dr. Vitali Fioletov, vitali.fioletov@canada.ca).
The data will also be available from a centralized Pandora data archive when such an archive is
established.
Information about the natural variability of measured parameter and
measurement uncertainties can be derived from the measurements themselves.
This approach, also known as the Grubbs estimation method
(Grubbs, 1948; Toohey and Strong, 2007), is
often used to estimate the precision of measurements. For readers'
convenience, we present the method here as it was described by Fioletov
et al. (2006).
The result of a measurement (M) is the sum of the true measured value (X) and
an error (e). Let us consider two instruments that measure the same parameter
X, but with different errors e1 and e2. The results of their
measurements (M1 and M2) can be used to estimate the variances of
X, e1, and e2, as follows. If we assume that the measured value and
the errors are independent, then the variance of M is the sum of variances of
X and ei:
σ2(Mi)=σ2(X)+σ2(ei),i=1,2.
The difference of M1 and M2 does not depend on X. If, in addition, we
assume that the errors of different instruments are not correlated, then the
variance of the difference is equal to the sum of σ2(ei)
and σ2(ei):
σ2(M1-M2)=σ2(e1)+σ2(e2).
The values of σ2(M1), σ2(M2), and σ2(M1-M2) can be estimated from a set of parallel measurements.
The three resulting equations can be solved for σ2(X), σ2(e1), and σ2(e2):
σ2(X)=1/2(σ2(M1)+σ2(M2)-σ2(M1-M2)),σ2(e1)=1/2(σ2(M1)-σ2(M2)+σ2(M1-M2)),σ2(e2)=1/2(σ2(M2)-σ2(M1)+σ2(M1-M2)).
Equation (3) was used to estimate the standard deviation (SD) of
instrument errors (we will refer to it as to standard instrument
uncertainty) and the SD of variability.
In reality, we do not actually know the variances σ2(Mi) and σ2(M1-M2); we can only estimate
them, with a certain error, from the available measurements. The α-level confidence interval for the variance σ2 depends on the
estimated variance value itself and the number of data points, n:
(n-1)s2χ1-α/22(n-1)<σ2<(n-1)s2χα/22(n-1),
where s2 is the sample variance and χ2(n-1) is the chi-square
distribution with n-1 degrees of freedom. The error of the variance estimate
depends on the variance itself. All three variances, σ2(M1), σ2(M2), and σ2(M1-M2),
determine σ2(X), σ2(e1), and σ2(e2) in Eq. (3). Therefore, the errors in the σ2(X),
σ2(e1), and σ2(e2) estimates depend on the
sum of all three variances σ2(M1), σ2(M2), and σ2(M1-M2), and can be high even if
the estimated variance itself is low (but one or more of the variances
σ2(M1), σ2(M2), or σ2(M1-M2) are high). The estimates are thus only as accurate as
the least accurate of these parameters. The variance estimates can be
improved by increasing the number of data points.
Acknowledgements
The authors wish to thank the NRC-FRL flight crew
of the Convair 580 for making the airborne study possible. Funding for the
airborne study over the oil sands region was provided in part by Environment
Canada's Clean Air Regulatory Agenda (CARA). We acknowledge the NASA Earth
Science Division for funding of OMI SO2 product development and
analysis. The Dutch–Finnish-built OMI instrument is part of the NASA's EOS
Aura satellite payload. We thank systems engineering, instrument calibration,
and satellite integration teams for making this mission a success. The OMI
project is managed by KNMI and the Netherlands Space Agency (NSO).
Edited by: M. Weber
ReferencesBais, A. F., Zerefos, C. S., Meleti, C., Ziomas, I. C., and Tourpali, K.:
Spectral measurements of solar UVB radiation and its relation to total
ozone, SO2, and clouds, J. Geophys. Res., 98, 5199–5204, 1993.
Carn, S. A., Krueger, A. J., Bluth, G. S. J., Schaefer, S. J., Krotkov, N.
A., Watson, I. M., and Datta, S.: Volcanic eruption detection by the Total
Ozone Mapping Spectrometer (TOMS) instruments: A 22-year record of sulfur
dioxide and ash emissions, in: Volcanic Degassing, Special Publication of the
Geological Society of London, edited by: Oppenheimer, C., Pyle, D. M., and
Barclay, J., 177–202, Geological Society, London, UK, 2003.Carn, S. A., Krueger, A. J., Krotkov, N. A., Yang, K., and Levelt, P. F.:
Sulfur dioxide emissions from Peruvian copper smelters detected by the Ozone
Monitoring Instrument, Geophys. Res. Lett., 34, L09801,
10.1029/2006GL029020, 2007.Carn, S. A., Krotkov, N. A., Yang, K., and Krueger, A. J.: Measuring global
volcanic degassing with the Ozone Monitoring Instrument (OMI), Geol. Soc.
London, Spec. Publ., 380, 229–257, 10.1144/SP380.12, 2013.Clarisse, L., Fromm, M., Ngadi, Y., Emmons, L., Clerbaux, C., Hurtmans, D.,
and Coheur, P.-F.: Intercontinental transport of anthropogenic sulfur
dioxide and other pollutants: An infrared remote sensing case study,
Geophys. Res. Lett., 38, L19806, 10.1029/2011GL048976, 2011.
De Backer, H. and Muer, D. De: Intercomparison of total ozone data measured
with Dobson and Brewer spectrophotometers at Uccle (Belgium) from January
1984 to March 1991, including zenith sky observations, J. Geophys. Res., 96,
20711–20719, 1991.de Foy, B., Krotkov, N. A., Bei, N., Herndon, S. C., Huey, L. G.,
Martínez, A.-P., Ruiz-Suárez, L. G., Wood, E. C., Zavala, M., and
Molina, L. T.: Hit from both sides: tracking industrial and volcanic plumes
in Mexico City with surface measurements and OMI SO2 retrievals during the
MILAGRO field campaign, Atmos. Chem. Phys., 9, 9599–9617,
10.5194/acp-9-9599-2009, 2009.
Eisinger, M. and Burrows, J. P.: Tropospheric sulfur dioxide observed by the
ERS-2 GOME instrument, Geophys. Res. Lett., 25, 4177–4180, 1998.Fioletov, V. E., Griffioen, E., Kerr, J. B., Wardle, D. I., and Uchino, O.:
Influence of volcanic sulfur dioxide on spectral UV irradiance as measured
by Brewer Spectrophotometers, Geophys. Res. Lett., 25, 1665–1668,
10.1029/98GL51305, 1998.Fioletov, V. E., Tarasick, D. W., and Petropavlovskikh, I.: Estimating ozone
variability and instrument uncertainties from SBUV(/2), ozonesonde, Umkehr,
and SAGE II measurements: Short-term variations, J. Geophys. Res., 111,
D02305, 10.1029/2005JD006340, 2006.Fioletov, V. E., McLinden, C. A., Krotkov, N., Moran, M. D., and Yang, K.:
Estimation of SO2 emissions using OMI retrievals, Geophys. Res. Lett.,
38, L21811, 10.1029/2011GL049402, 2011.Fioletov, V. E., McLinden, C. A., Krotkov, N., Yang, K., Loyola, D. G.,
Valks, P., Theys, N., Van Roozendael, M., Nowlan, C. R., Chance, K., Liu,
X., Lee, C., and Martin, R. V.: Application of OMI, SCIAMACHY, and GOME-2
satellite SO2 retrievals for detection of large emission sources, J.
Geophys. Res.-Atmos., 118, 11399–11418, 10.1002/jgrd.50826, 2013.Galle, B., Johansson, M., Rivera, C., Zhang, Y., Kihlman, M., Kern, C.,
Lehmann, T., Platt, U., Arellano, S., and Hidalgo, S.: Network for
Observation of Volcanic and Atmospheric Change (NOVAC) – A global network
for volcanic gas monitoring: Network layout and instrument description, J.
Geophys. Res., 115, D05304, 10.1029/2009JD011823, 2010.Georgoulias, A. K., Balis, D., Koukouli, M. E., Meleti, C., Bais, A., and
Zerefos, C.: A study of the total atmospheric sulfur dioxide load using
ground-based measurements and the satellite derived Sulfur Dioxide Index,
Atmos. Environ., 43, 1693–1701, 10.1016/j.atmosenv.2008.12.012, 2009.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.
Grubbs, F. E.: On estimating precision of measuring instruments and product
variability, J. Am. Stat. Assoc., 242, 243–264, 1948.Herman, J., Cede, A., Spinei, E., Mount, G., Tzortziou, M., and Abuhassan,
N.: NO2 column amounts from ground-based Pandora and MFDOAS spectrometers
using the direct-sun DOAS technique: Intercomparisons and application to OMI
validation, J. Geophys. Res., 114, D13307, 10.1029/2009JD011848, 2009.Herman, J., Evans, R., Cede, A., Abuhassan, N., Petropavlovskikh, I., and
McConville, G.: Comparison of ozone retrievals from the Pandora spectrometer
system and Dobson spectrophotometer in Boulder, Colorado, Atmos. Meas. Tech.,
8, 3407–3418, 10.5194/amt-8-3407-2015, 2015.Hutchinson, T. C. and Whitby, L. M.: The effects of acid rainfall and heavy
metal particulates on a boreal Forest ecosystem near the sudbury smelting
region of Canada, Water. Air. Soil Pollut., 7, 421–438,
10.1007/BF00285542, 1977.Kelly, E. N., Schindler, D. W., Hodson, P. V, Short, J. W., Radmanovich, R.,
and Nielsen, C. C.: Oil sands development contributes elements toxic at low
concentrations to the Athabasca River and its tributaries, P. Natl. Acad.
Sci. USA, 107, 16178–16183, 10.1073/pnas.1008754107, 2010.Kerr, J. B.: New methodology for deriving total ozone and other atmospheric
variables from Brewer spectrophotometer direct sun spectra, J. Geophys. Res.,
107, 4731, 10.1029/2001JD001227, 2002.Kerr, J. B., Asbridge, I. A., and Evans, W. F. J.: Intercomparison of total
ozone measured by the Brewer and Dobson spectrophotometers at Toronto, J.
Geophys. Res., 93, 11129–11140, 10.1029/JD093iD09p11129, 1988.Knepp, T., Pippin, M., Crawford, J., Chen, G., Szykman, J., Long, R., Cowen,
L., Cede, A., Abuhassan, N., Herman, J., Delgado, R., Compton, J., Berkoff,
T., Fishman, J., Martins, D., Stauffer, R., Thompson, A. M., Weinheimer, A.,
Knapp, D., Montzka, D., Lenschow, D., and Neil, D.: Estimating surface NO2
and SO2 mixing ratios from fast-response total column observations and
potential application to geostationary missions, J. Atmos. Chem.,
72, 261–286,
10.1007/s10874-013-9257-6, 2015.
Koenker, R. and Bassett, G. W.: Regression Quantiles, Econometrica, 46,
33–50, 1978.Krotkov, N. A., McLinden, C. A., Li, C., Lamsal, L. N., Celarier, E. A.,
Marchenko, S. V., Swartz, W. H., Bucsela, E. J., Joiner, J., Duncan, B. N.,
Boersma, K. F., Veefkind, J. P., Levelt, P. F., Fioletov, V. E., Dickerson,
R. R., He, H., Lu, Z., and Streets, D. G.: Aura OMI observations of regional
SO2 and NO2 pollution changes from 2005 to 2015, Atmos. Chem. Phys.,
16, 4605–4629, 10.5194/acp-16-4605-2016, 2016.Krueger, A. J., Walter, L. S., Bhartia, P. K., Schnetzler, C. C., Krotkov, N.
A., Sprod, I., and Bluth, G. J. S.: Volcanic sulfur dioxide measurements from
the Total Ozone Mapping Spectrometer instruments, J. Geophys. Res., 100,
14057–14076, 10.1029/95JD01222, 1995.
Krueger, A. J., Schaefer, S. J., Krotkov, N., Bluth, G., and Barker, S.:
Ultraviolet remote sensing of volcanic emissions, in: Remote Sensing of
Active Volcanism, vol. 116, edited by: Mouginis, M. and Crisp, J., 25–43,
American Geophysical Union, 2000.Lee, C., Martin, R. V., Van Donkelaar, A., Lee, H., Dickerson, R. R., Hains,
J. C., Krotkov, N., Richter, A., Vinnikov, K., and Schwab, J. J.: SO2
emissions and lifetimes: Estimates from inverse modeling using in situ and
global, space-based (SCIAMACHY and OMI) observations, J. Geophys. Res., 116,
D06304, 10.1029/2010JD014758, 2011.Levelt, P. F., van den Oord, G. H. J., Dobber, M. R., Malkki, A., Stammes,
P., Lundell, J. O. V., and Saari, H.: The Ozone Monitoring Instrument, IEEE
T. Geosci. Remote Sens., 44, 1093–1101, 10.1109/TGRS.2006.872333, 2006.Li, C., Joiner, J., Krotkov, N. A., and Bhartia, P. K.: A fast and sensitive
new satellite SO2 retrieval algorithm based on principal component
analysis: Application to the ozone monitoring instrument, Geophys. Res.
Lett., 40, 6314–6318, 10.1002/2013GL058134, 2013.Longo, B. M., Yang, W., Green, J. B., Crosby, F. L., and Crosby, V. L.: Acute
health effects associated with exposure to volcanic air pollution (vog) from
increased activity at Kilauea Volcano in 2008, J. Toxicol. Environ. Health.
A, 73, 1370–1381, 10.1080/15287394.2010.497440, 2010.Makar, P. A., Gong, W., Hogrefe, C., Zhang, Y., Curci, G., Zabkar, R.,
Milbrandt, J., Im, U., Balzarini, A., Baro, R., Bianconi, R., Cheung, P.,
Forkel, R., Gravel, S., Hirtl, H., Honzak, L., Hou, A., Jimenz-Guerrero, P.,
Langer, M., Moran, M. D., Pabla, B., Perez, J. L., Pirovano, G., San Jose,
R., Tuccella, P., Werhahn, J., Zhang, J., and Galmarini, S.: Feedbacks
between air pollution and weather, part 2: Effects on chemistry, Atmos.
Environ., 115, 499–526, 10.1016/j.atmosenv.2014.10.021, 2015a.Makar, P. A., Gong, W., Milbrandt, J., Hogrefe, C., Zhang, Y., Curci, G.,
Zabkar, R., Im, U., Balzarini, A., Baro, R., Bianconi, R., Cheung, P.,
Forkel, R., Gravel, S., Hirtl, H., Honzak, L., Hou, A., Jimenz-Guerrero, P.,
Langer, M., Moran, M. D., Pabla, B., Perez, J. L., Pirovano, G., San Jose,
R., Tuccella, P., Werhahn, J., Zhang, J., and Galmarini, S.: Feedbacks
between air pollution and weather, part 1: Effects on weather, Atmos.
Environ., 115, 442–469, 10.1016/j.atmosenv.2014.12.003, 2015b.McLinden, C. A., Fioletov, V., Boersma, K. F., Krotkov, N., Sioris, C. E.,
Veefkind, J. P., and Yang, K.: Air quality over the Canadian oil sands: A
first assessment using satellite observations, Geophys. Res. Lett., 39, 1–8,
10.1029/2011GL050273, 2012.McLinden, C. A., Fioletov, V., Boersma, K. F., Kharol, S. K., Krotkov, N.,
Lamsal, L., Makar, P. A., Martin, R. V., Veefkind, J. P., and Yang, K.:
Improved satellite retrievals of NO2 and SO2 over the Canadian oil
sands and comparisons with surface measurements, Atmos. Chem. Phys., 14,
3637–3656, 10.5194/acp-14-3637-2014, 2014.McLinden, C., Fioletov, V., Krotkov, N. A., Li, C., Boersma, K. F., and
Adams, C.: A decade of change in NO2 and SO2 over the Canadian oil
sands as seen from space, Environ. Sci. Technol., 50, 331–337,
10.1021/acs.est.5b04985, 2015.Nowlan, C. R., Liu, X., Chance, K., Cai, Z., Kurosu, T. P., Lee, C., and
Martin, R. V.: Retrievals of sulfur dioxide from the Global Ozone Monitoring
Experiment 2 (GOME-2) using an optimal estimation approach: Algorithm and
initial validation, J. Geophys. Res., 116, D18301, 10.1029/2011JD015808,
2011.
Platt, U. and Stutz, J.: Differential Optical Absorption Spectroscopy,
Springer, Berlin, Heidelberg, 2008.
Pope, C. A. and Dockery, D. W.: Health effects of fine particulate air
pollution: Lines that connect, J. Air Waste Manag. Assoc., 56, 709–742,
2006.Reed, A. J., Thompson, A. M., Kollonige, D. E., Martins, D. K., Tzortziou, M.
A., Herman, J. R., Berkoff, T. A., Abuhassan, N. K., and Cede, A.: Effects of
local meteorology and aerosols on ozone and nitrogen dioxide retrievals from
OMI and pandora spectrometers in Maryland, USA during DISCOVER-AQ 2011, J.
Atmos. Chem., 72, 455–482, 10.1007/s10874-013-9254-9, 2013.Richter, A., Weber, M., Burrows, J. P., Lambert, J.-C., and van Gijsel, A.:
Validation strategy for satellite observations of tropospheric reactive
gases, Ann. Geophys., 56, 1–10, 10.4401/ag-6335, 2013.Rix, M., Valks, P., Hao, N., Loyola, D., Schlager, H., Huntrieser, H.,
Flemming, J., Koehler, U., Schumann, U., and Inness, A.: Volcanic SO2 ,
BrO and plume height estimations using GOME-2 satellite measurements during
the eruption of Eyjafjallajökull in May 2010, J. Geophys. Res.-Atmos.,
117, D00U19, 10.1029/2011JD016718, 2012.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.Theys, N., Campion, R., Clarisse, L., Brenot, H., van Gent, J., Dils, B.,
Corradini, S., Merucci, L., Coheur, P.-F., Van Roozendael, M., Hurtmans, D.,
Clerbaux, C., Tait, S., and Ferrucci, F.: Volcanic SO2 fluxes derived from
satellite data: a survey using OMI, GOME-2, IASI and MODIS, Atmos. Chem.
Phys., 13, 5945–5968, 10.5194/acp-13-5945-2013, 2013.Theys, N., Smedt, I. De, Gent, J. Van, Danckaert, T., Wang, T., Hendrick, F.,
Stavrakou, T., Bauduin, S., Clarisse, L., Li, C., Krotkov, N., Yu, H.,
Brenot, H., and Roozendael, M. Van: Sulphur dioxide vertical column DOAS
retrievals from the Ozone Monitoring Instrument: Global observations and
comparison to ground-based and satellite data, J. Geophys. Res., 120,
2470–2491, 10.1002/2014JD022657, 2015.Thomas, W., Erbertseder, T., Ruppert, T., Roozendael, M. Van, Verdebout, J.,
Balis, D., Meleti, C., and Zerefos, C.: On the retrieval of volcanic sulfur
dioxide emissions from GOME backscatter measurements, J. Atmos. Chem., 50,
295–320, 10.1007/s10874-005-5544-1, 2005.Toohey, M. and Strong, K.: Estimating biases and error variances through the
comparison of coincident satellite measurements, J. Geophys. Res.-Atmos.,
112, 1–12, D13306, 10.1029/2006jd008192, 2007.Tzortziou, M., Herman, J. R., Cede, A., and Abuhassan, N.: High precision,
absolute total column ozone measurements from the Pandora spectrometer
system: Comparisons with data from a Brewer double monochromator and Aura
OMI, J. Geophys. Res., 117, D16303, 10.1029/2012JD017814, 2012.Tzortziou, M., Herman, J. R., Cede, A., Loughner, C. P., Abuhassan, N., and
Naik, S.: Spatial and temporal variability of ozone and nitrogen dioxide over
a major urban estuarine ecosystem, J. Atmos. Chem., 72, 287–309,
10.1007/s10874-013-9255-8, 2013.
Vandaele, A. C., Simon, P. C., Guilmot, J. M., Carleer, M., and Colin, R.:
SO2 absorption cross section measurement in the UV using a Fourier
transform spectrometer, J. Geophys. Res., 99, 25599–25605, 1994.Wang, T., Hendrick, F., Wang, P., Tang, G., Clémer, K., Yu, H., Fayt, C.,
Hermans, C., Gielen, C., Müller, J.-F., Pinardi, G., Theys, N., Brenot, H.,
and Van Roozendael, M.: Evaluation of tropospheric SO2 retrieved from
MAX-DOAS measurements in Xianghe, China, Atmos. Chem. Phys., 14,
11149–11164, 10.5194/acp-14-11149-2014, 2014.
Wood, J.: Environmental Policy Indicators – Air Quality, Studies in
Environmental Policy, Fraser Institute., 2012.Wu, F. C., Xie, P. H., Li, A., Chan, K. L., Hartl, A., Wang, Y., Si, F. Q.,
Zeng, Y., Qin, M., Xu, J., Liu, J. G., Liu, W. Q., and Wenig, M.:
Observations of SO2 and NO2 by mobile DOAS in the Guangzhou eastern
area during the Asian Games 2010, Atmos. Meas. Tech., 6, 2277–2292,
10.5194/amt-6-2277-2013, 2013.Zerefos, C., Ganev, K., Kourtidis, K., Tzortziou, M., Vasaras, A., and
Syrakov, E.: On the origin of SO2 above Northern Greece, Geophys. Res.
Lett., 27, 365–368, 10.1029/1999GL010799, 2000.