ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-5921-2017OMI satellite observations of decadal changes in ground-level sulfur dioxide
over North AmericaKharolShailesh K.shailesh.kharol@canada.caMcLindenChris A.https://orcid.org/0000-0001-5054-1380SiorisChristopher E.ShephardMark W.https://orcid.org/0000-0002-2867-9612FioletovVitalihttps://orcid.org/0000-0002-2731-5956van DonkelaarAaronPhilipSajeevMartinRandall V.Air Quality Research Division, Environment and Climate Change Canada,
Toronto, Ontario M3H 5T4, CanadaDepartment of Physics and Atmospheric Science, Dalhousie University,
Halifax, Nova Scotia, CanadaHarvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USAnow at: NASA Ames Research Center, Moffett Field, California, USAShailesh K. Kharol (shailesh.kharol@canada.ca)15May20171795921592929November20165December20169March201712April2017This 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://acp.copernicus.org/articles/17/5921/2017/acp-17-5921-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/5921/2017/acp-17-5921-2017.pdf
Sulfur dioxide (SO2) has a significant impact on the environment and
human health. We estimated ground-level sulfur dioxide (SO2)
concentrations from the Ozone Monitoring Instrument (OMI) using SO2
profiles from the Global Environmental Multi-scale – Modelling Air quality
and CHemistry (GEM-MACH) model over North America for the period of
2005–2015. OMI-derived ground-level SO2 concentrations (r=0.61)
and trends (r=0.74) correlated well with coincident in situ measurements
from air quality networks over North America. We found a strong decreasing
trend in coincidently sampled ground-level SO2 from OMI
(-81 ± 19 %) and in situ measurements (-86 ± 13 %)
over the eastern US for the period of 2005–2015, which reflects the
implementation of stricter pollution control laws, including flue-gas
desulfurization (FGD) devices in power plants. The spatially and temporally
contiguous OMI-derived ground-level SO2 concentrations can be used to
assess the impact of long-term exposure to SO2 on the health of
humans and the environment.
Introduction
Sulfur dioxide (SO2) is a short-lived atmospheric trace
gas emitted into the atmosphere from natural (e.g. volcanic eruption,
oxidation of dimethylsulfate (DMS) over oceans) and anthropogenic sources
(e.g. combustion of fossil fuels and smelting of sulfur-containing metal
ores), and plays a pivotal role in the global sulfur cycle. SO2 has a
short lifetime of hours to days, and it oxidizes quickly in the atmosphere to
produce sulfate aerosols that affect the climate (IPCC, 2013) and the
environment from local to regional and global scales. Sulfate aerosols are a
major contributor to PM2.5 (particulate matter with aerodynamic diameter
< 2.5 µm) chemical composition and account for 17 and
∼ 30 % of the annual mean PM2.5 mass globally and over the
eastern United States (Philip et al., 2014). Sulfate aerosol formation leads
to degradation in visibility and air quality (van Donkelaar et al., 2008) and
deposition of sulfuric acid (Dentener et al., 2006; Vet et al., 2014), and
poses a serious health hazard to the general population (Lee et al., 2015).
The increased risk of premature mortality associated with SO2 alone
or its secondary pollutants has been emphasized in several epidemiological
studies (Chinn et al., 1981; Derriennic et al., 1989; Hatzakis et al., 1986;
Krzyzanowski and Wojtyniak, 1982). Furthermore, it has been recently reported
by Lelieveld et al. (2015) using the EMAC (ECHAM5/MESSy Atmospheric
Chemistry) general circulation model that in the US, in addition to
agricultural emissions (an important source of ammonia, NH3),
emission from coal-fired power plants (an important source of SO2 and
nitrogen oxides, NOx) was the largest contributor to premature
mortality in 2010. Due to the adverse impact on the environment and human
health, SO2 and its oxidation products (i.e. fine particulate matter,
PM2.5) are considered designated criteria pollutants in the European
Union (European Commission,
http://ec.europa.eu/environment/air/quality/standards.htm), the United
States of America (US Environmental Protection Agency (EPA),
https://www.epa.gov/criteria-air-pollutants) and Canada
(https://www.ec.gc.ca/Air/default.asp?lang=En&n=7C43740B-1).
Globally, atmospheric SO2 is monitored regularly through a relatively
small number of measurement networks that produce accurate measurements but
over a limited spatial area. Satellite measurements have the advantage of
providing complete daily global coverage of SO2. Satellite
observations of SO2 vertical column density (VCD) began in the 1980s,
but the launch of the Ozone Monitoring Instrument (OMI) (Krotkov et
al., 2006; Yang et al., 2007) on the Aura satellite in 2004 has enabled large
point sources to be resolved with its higher spatial resolution (13×24km2 at nadir) (Fioletov et al., 2013). Satellite measurements of
SO2 have been used to identify and analyze emissions (Fioletov et
al., 2011, 2013, 2015, 2016; Lee et al., 2011; McLinden et al., 2016a), track
changes in total column density in various regions, including Canadian oil
sands, the eastern US, eastern Europe, eastern China, India and the Middle
East (McLinden et al., 2016b; Krotkov et al., 2016), and estimate dry
deposition flux (Nowlan et al., 2014). In previous studies (Lee et al., 2011;
Nowlan et al., 2011), ground-level SO2 concentrations were estimated
for only a 1-year period using satellite observations over North America.
However, multi-year spatial variations in ground-level SO2 have not
yet been assessed from the satellite observations. In contrast to total
column SO2, long-term records of ground-level SO2
concentrations from satellite observations will be directly useful to assess
air quality and associated health risks. Recently, a decreasing trend in
SO2 emissions and particulate sulfate has been reported by Hand et
al. (2012) over the United States from the early 1990s through 2010.
In this paper we first describe the OMI SO2 product, in situ
measurement network, the GEM-MACH (Global Environmental Multi-scale –
Modelling Air quality and Chemistry) air quality model, ground-based
SO2 estimation from the OMI and trend analysis. We then use these
data and this methodology to estimate ground-level SO2 from the OMI
and evaluate it with coincident in situ measurements over North America for
the period of 2005–2015. These results are then used to determine the trend
in ground-level SO2 from both OMI and collocated in situ
measurements.
Data sets and methodologyOMI
The OMI is a nadir-viewing UV-visible spectrometer boarded on the Aura
satellite that was launched in July 2004 and is part of the NASA A-train
constellation (Levelt et al., 2006). The Aura satellite overpasses the
Equator in the early afternoon (13:00–14:30 local time) in a
sun-synchronous ascending polar orbit. The OMI provides daily global coverage
of aerosols and trace gases, including SO2, with a variable ground
spatial resolution of 13km×24km at nadir to
140km×26km at swath edge. We use the OMI
operational principal component analysis (PCA) SO2 product (OMSO2
v1.2.0), which is publicly available from the NASA Goddard Earth Sciences
(GES) Data and Information Services Center (DISC)
(http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/omso2_v003.shtml).
The details of the PCA algorithm can be found elsewhere (Li et al., 2013). In
brief, this algorithm applies the PCA technique to OMI-measured radiances
between 310.5 and 340 nm to extract principal components from each
row of data on an orbital basis. The PCA algorithm replaced the band residual
difference (BRD) algorithm (Krotkov et al., 2006) as the operational
algorithm for the standard OMI SO2 data because only the latter
algorithm was biased (Fioletov et al., 2013; Krotkov et al., 2016). Also,
SO2 retrieval variability is reduced by a factor of 2 in the PCA
algorithm relative to the BRD algorithm (Li et al., 2013). Even though the
PCA algorithm directly estimates SO2 vertical column density in one
step using SO2 Jacobians, the air mass factor (AMF) is effectively
fixed at 0.36 (representing summertime conditions in the eastern USA),
similar to the BRD algorithm. A better estimation of AMFs is needed for
different regions to reduce these systematic errors that result from
conditions that do not match these. For this, we re-calculated the AMFs using
SO2 profile information from the high-resolution
(15km×15km) GEM-MACH air quality forecast model
(discussed in Sect. 2.3), monthly-varying surface reflectivity from the MODIS
satellite instruments, and an improved identification of snow. More details
on Environment Canada air mass factor calculation for SO2 are
discussed in McLinden et al. (2014, 2016b). Here, we exclude the cross-track
pixels affected by the row anomaly
(http://www.knmi.nl/omi/research/product/rowanomaly-background.php),
which was first noticed in the data in June 2007. We use OMI SO2
columns with cloud radiance fractions < 0.2, and solar zenith angles
< 65∘ following Nowlan et al. (2014). We exclude from the analysis
the OMI SO2 data affected by the largest northern mid-latitude
volcanic eruptions in the OMI time frame, namely Kasatochi (Aleutian Islands,
Alaska, August 2008, 52∘ N) and Sarychev (Kuril Islands, eastern
Russia, June 2009, 48∘ N). Here, we used the mean OMI values over a
32 km averaging radius (Fioletov et al., 2011) that is oversampled onto a
0.1∘×0.1∘ latitude–longitude grid.
SO2 monitoring networks
To evaluate the OMI-derived ground-level SO2 we use hourly in situ
SO2 measurements from the Air Quality System (AQS) network of the US
EPA
(http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm)
and Environment and Climate Change Canada's National Air Pollution
Surveillance (NAPS) network
(http://maps-cartes.ec.gc.ca/rnspa-naps/data.aspx) over the US and
Canada for the period of 2005–2015. US-EPA AQS site locations vary from
regional background to urban and industrial locations, and measure
SO2 using continuous gas monitors. The Canadian NAPS sites are
generally located in populated areas. The hourly in situ measurements are
averaged over a 2 h period (13:00–15:00 local time) to correspond to the
satellite observation times over North America.
Model information
We use the Global Environmental Multi-scale – Modelling Air quality and
CHemistry (GEM-MACH) model for the tropospheric SO2 profile to relate
the OMI SO2 column to ground-level concentrations. GEM-MACH is the
Canadian regional air quality forecast model used operationally to predict
the concentrations of O3, NO2, and PM2.5 over North
America (Moran et al., 2010; Gong et al., 2015). The GEM-MACH model utilizes
emissions inventories from US EPA and Environment Canada data for the year
2006. It uses detailed tropospheric processes for gas and particle chemistry
and microphysics originating in the offline AURAMS model (A Unified Regional
Air-quality Modelling System; Gong et al., 2006), and incorporates them
online into the Canadian weather forecast model (Global Environmental
Multiscale model, Côté et al., 1998). A detailed description of the
chemical processes found in AURAMS and GEM-MACH is provided elsewhere (Kelly
et al., 2012). The results used here are from archived forecasts from 2010 to
2011 for a domain covering North America at 15km×15km resolution. The lowest model layer, which is 20 m thick, is
taken as the ground-level concentration.
Estimation of ground-level SO2 from the OMI
The ground-level SO2 mixing ratio from the OMI is estimated using the
approach described by Lamsal et al. (2008) over North America for the period
of 2005–2015. The ground-level SO2 mixing ratio S is estimated
from the local OMI tropospheric SO2 column Ω as
SOMI=ΩOMI×SmodelΩmodel.
The subscript model represents the GEM-MACH model. More details on the
procedure are discussed in McLinden et al. (2014).
Trend analysis
We analyzed the trends in monthly ground-level SO2 over North America
from OMI and in situ measurements for the period of January 2005–December
2015. We applied a general least squares regression following Boys et
al. (2014) and Kharol et al. (2015) using the basic model
x=zβ+e,e∼N0,σ2V,
where, for a time series of n months, x is a
time series vector (n×1) containing SO2 surface mixing ratio
values; z is a design matrix (n×2) for the linear model;
β is a vector (2×1) containing the intercept and slope of
the linear model; e is an error vector (n×1) containing the
residuals which, for validity, should be approximately normally distributed
with zero mean, but which is permitted to covary with adjacent values
according to V – a positive definite, symmetric covariance
matrix, to accommodate possible autocorrelation between adjacent months.
Correlated errors between adjacent months are represented by a first-order
autoregressive model of e, which can be expressed as
et=∅et-1+wtt=1,…n,w∼N0,σ2I,
where the residual et for month t is a fraction ∅ of the
previous month's residual et-1 with a white noise component wt which,
for validity, should be approximately normally distributed with zero mean,
constant variance and be independent I. We deseasonalized the monthly time
series by subtracting the climatological monthly median prior to regression.
Note that the trend is more heavily weighted toward summer, when observations
are more frequent.
Results and discussion
Figure 1 shows the spatial distribution of mean OMI-derived ground-level
SO2 over North America for the periods of 2005–2007, 2008–2010,
2011–2015 and 2005–2015. The major SO2 hotspots (that is, locations
of high SO2 associated with a large nearby source) are primarily
located in the eastern US from coal-fired power plants and industrial
activities (Krotkov et al., 2016). There are far fewer sources in the western
US and Canada, with a few notable exceptions such as Flin Flon
(54.77∘ N, 101.88∘ W; copper smelter), Sudbury
(46.52∘ N, 80.95∘ W; copper and nickel smelter), Thompson
(55.74∘ N, 97.85∘ W; metal ore mining), Montreal
(45.50∘ N, 73.56∘ W), the oil sands region in northern
Alberta and power plants nearby Edmonton. The spatial distribution of annual
mean OMI-derived ground-level SO2 for each year is shown in
supporting information in Fig. S1 in the Supplement. A noticeable decrease in
OMI-derived ground-level SO2 is apparent from Fig. 1 during
2008–2010 and 2011–2015 compared to 2005–2007. These US reductions
correspond to the installation of flue-gas desulfurization (FGD) units at
many power plants to meet stricter emissions limits introduced by the Clean
Air Interstate Rule. The closure of Flin Flon (54.77∘ N,
101.88∘ W) copper smelter in June 2010 is also apparent in
OMI-derived ground-level SO2 during 2011–2015 (Fig. 1). The
OMI-derived ground-level SO2 concentrations over a large geographical
area could be useful to assess its impact on human health and environment. It
can also provide valuable information to policy makers where air quality
network measurements are not available.
Spatial distribution of the mean OMI-derived ground-level
SO2 mixing ratio over North America for the periods of 2005–2007,
2008–2010, 2011–2015 and 2005–2015.
Scatter plot of the annual mean OMI-derived ground-level SO2
versus collocated in situ measurements for the years of 2005–2015. Filled
black circles represent the original in situ values, and red circles
represent the comparison with spatially inhomogeneity adjusted in situ
values.
Spatial distribution of the OMI-derived surface SO2 trend at
0.1∘×0.1∘ over North America for the years of
2005–2015. Statistical significance is shown in the form of a two-sided
p value, tested against null being the zero trend.
To verify these satellite findings, we compared the OMI-derived ground-level
SO2 concentrations with in situ measurements over North America for
the period of 2005–2015. The original OMI-derived ground-level SO2
concentration (black circles) moderately correlates with collocated in situ
measurements (r=0.61), but has a significant difference in slope
(slope = 0.39) (Fig. 2). The departure from unity of the slope is a
common feature of virtually all satellite-surface comparisons of this kind
(Kharol et al., 2015), and can be a result of both the in situ monitor
placements (i.e. mainly located in the cities and close to pollution sources)
and differences in the spatial sampling of the two types of observations. To
quantify this inhomogeneity effect we utilized output from the GEM-MACH model
at high resolution (2.5km×2.5km; supporting
information in Fig. S2) over a region in central Canada. These
high-resolution GEM-MACH SO2 columns at the locations of the in situ
monitors were taken as representative of point (in situ) measurements. The
model SO2 columns were then progressively averaged up (smoothed) to
30km×30km, approximately representing the spatial
size of an OMI pixel. The smoothed columns are regressed against the
unsmoothed columns. The slope and correlation coefficient continue to
decrease from unity as the smoothing is increased. We used this estimate of
the spatial inhomogeneous sampling obtained from the original
(2.5 km) vs. smoothed (30 km) GEM-MACH SO2 column
(supporting information in Fig. S3) to derive a scaling factor (in situ
scaled = 0.52 × (in situ) +0.04, R=0.83) that is used to
adjust the in situ measurements to be representative of the OMI pixel size
over all of North America. We noticed an ∼ 92 % increase in slope
to 0.75 when comparing the spatial inhomogeneity adjusted in situ
measurements with the OMI ground-level SO2 (red circles in Fig. 2).
In comparison to previous studies, Lee et al. (2011), comparing ground-level
SO2 mixing ratios derived from SCIAMACHY and the OMI with in situ
measurements from US-EPA AQS and NAPS monitoring networks over the United
States and Canada for the year of 2006, reported slightly higher correlation
(r=0.86, slope = 0.91 for SCIAMACHY and r=0.80, slope = 0.79
for the OMI). In their study they used a 15 km coincidence criterion and
included only AQS sites measuring less than 6 ppbv at satellite
overpass times. Nowlan et al. (2011) estimated ground-level SO2 from
GOME-2 and compared with in situ measurements over North America from the
Clear Air Status and Trends Network (CASTNET; r=0.85) and US-EPA AQS and
NAPS (r=0.40) for 2008.
We determined the trend in ground-level SO2 from the OMI using the
monthly time series from January 2005 to December 2015. Figure 3 illustrates
the spatial distribution of the OMI-derived ground-level SO2 trend
over North America for the period of 2005–2015. We noticed a strong
decreasing trend in ground-level SO2 over the eastern US and Flin
Flon in Canada. The observed decrease in ground-level SO2
concentration in the eastern US corresponds to stricter pollution control
laws implemented to reduce SO2 emissions and the installation of FGD
devices in power plants (Fioletov et al., 2011; Krotkov et al., 2016).
Furthermore, we estimated the trend in ground-level SO2 at in situ
locations collocated with the OMI. Figure 4a and b show the trend in
ground-level SO2 from the OMI and collocated in situ measurements
over North America for the period of 2005–2015. Both in situ and OMI-derived
ground-level SO2 mixing ratios show a strong decreasing trend over
the eastern US mainly at locations close to power plants. Figure 4c shows the
scatter plot of trends in ground-level SO2 from collocated in situ
measurements and the OMI. The OMI-derived trends are significantly correlated
(r=0.74) with collocated in situ trends. As expected the slope of 0.43 is
similar to the absolute concentration slope (Fig. 2) and reveals the
difference in absolute trend.
Trends in ground-level SO2 for the period of 2005–2015.
Panels (a, b) show trends inferred from in situ measurements at the
OMI overpass and from the OMI for the period of 2005–2015. The filled circle
represents where trend p values < 0.05 and trend p values > 0.05
are shown as empty circles. Panel (c) contains scatter plots of
trends for the period of 2005–2015.
Percent change in the ground-level SO2 mixing ratio from
2005 over the eastern US and southern Ontario, Canada. The in situ and OMI
ground-level SO2 percent change are shown in black and red color,
respectively. Blue circles show changes in total SO2 emissions. The
locations of in situ measurement stations over the eastern US and southern
Ontario, Canada (blue box), are shown in the inset map. The error bars
represent the 1 standard error of the mean.
Time series of bottom-up annual SO2 emissions and
OMI-derived ground-level SO2 concentrations for Bowen power plant
(34.13∘ N, 84.92∘ W), USA, and Flin Flon copper smelter
(54.77∘ N, 101.88∘ W), Canada. The dashed orange line
represents the zero line in the Flin Flon, Canada, plot. Bottom-up
SO2 emissions data are not available after 2011 due to the
closure of Flin Flon copper
smelter. The error bars represent the 1 standard error of the mean.
Spatial distribution of (a) satellite-derived SO2
vertical column density (VCD) and (b) sulfate PM2.5 mass
concentration over the eastern US and southern Ontario, Canada. The power
plant locations overlaid on both panels are shown as circles. ECMWF
model-derived ground-level winds overlaid on the sulfate PM2.5 mass
concentration map are shown with arrows.
Figure 5 shows the percentage change compared to 2005 in annual mean
ground-level SO2 concentration from coincidently sampled OMI and
in situ measurements and total SO2 emissions from power plants over
the eastern US. The geographical locations of stations considered over the
eastern US are shown inside the blue color box within the inset map. Both OMI
and in situ measurements show -81 ± 19 % and
-86 ± 13 % decreases in ground-level SO2 over the eastern
US, respectively. Earlier OMI SO2 column studies reported 40 %
(Fioletov et al., 2011) and 80 % (Krotkov et al., 2016) decreases near
power plants in the eastern US and Ohio River Valley for the periods of
2005–2010 and 2005–2015, respectively. Furthermore, we derived a decrease
of 64±18 % from spatially averaged OMI-derived ground-level
SO2 (Fig. 3) over the eastern US from the entire domain (blue box in
Fig. 5). The observed decrease in ground-level SO2 from OMI and
in situ measurements is in agreement with the US EPA reported decrease of
about 70 % in total US SO2 emissions
(https://www3.epa.gov/airtrends/aqtrends.html). Figure 6 shows that
bottom-up SO2 emissions and OMI-derived ground-level SO2
concentrations are temporally correlated even for larger individual point
sources, namely the Bowen power plant (34.13∘ N, 84.92∘ W),
USA, and Flin Flon copper smelter (54.77∘ N, 101.88∘ W),
Canada. The bottom-up emissions data for these sites are obtained from the US
EPA (2016) and National Pollutant Release Inventory (NPRI, 2017),
respectively.
Recently Philip et al. (2014) analyzed the PM2.5 chemical composition
over North America from the satellite data and reported that sulfate aerosols
contribute ∼ 30 % in ground-level PM2.5 mass concentration
over the eastern US. Here, the ground-level sulfate PM2.5 mass
concentration is estimated by applying the sulfate fraction from Philip et
al. (2014) to the total PM2.5 mass concentration inferred using the
method of van Donkelaar et al. (2010), which uses information from
satellites, models and monitors. Figure 7 shows the spatial distribution of
OMI SO2 vertical column density (panel a) and sulfate PM2.5 mass
concentration (panel b) over the eastern US for the period of 2005–2008. The
locations of large (> 18.98 kt[SO2]yr-1 in 2006) power plants
(largest contributor to SO2 emissions) and 2005–2008 average
boundary-layer winds from an ECMWF (European Center for Medium range Weather
Forecasting) reanalysis (Dee et al., 2011) are overlaid on the plots as
circles and arrows, respectively. This demonstrates that SO2 VCD
influences air quality locally due to its shorter atmospheric lifetime.
However, sulfate PM2.5, with a longer atmospheric lifetime, influences
air quality locally as well as downwind through long-range transport. It is
evident from Fig. 7 that column SO2 and sulfate PM2.5 hotspots
are collocated around and downwind of power plant locations. There is only a
moderate spatial correlation (r=0.60) between OMI SO2 and sulfate
PM2.5, but given that sulfate is largely a secondary pollutant, this is
not surprising. It was also found that there is a saturation effect at high
SO2 VCDs (Fig. S4).
Conclusions
We examined the spatial and temporal characteristics of the
ground-level SO2 concentration from the OMI over North America during
the period from 2005 to 2015. OMI-derived ground-level SO2
concentrations and trends correlate well with in situ measurements (r=0.61 and 0.74, respectively), with a significant bias in slope. Once the
in situ observations are adjusted, based on nested GEM-MACH model results, to
account for the spatial sampling differences between the in situ and OMI
spatial resolution there is a notable increase (∼ 92 %) in slope to
a value of 0.75. The observed reduction in ground-level SO2
concentration from the OMI (-81 ± 19 %) is consistent with
in situ measurements (-86 ± 13 %) over the eastern US for the
period of 2005–2015. The observed decreasing trend in ground-level
SO2 could lead to considerable reduction in sulfate aerosols, and
thus play a major role in improving air quality, thereby minimizing its
deleterious health impact. The long-term spatial distribution maps of
ground-level SO2 from the OMI provide policy-makers with SO2
pollution monitoring at locations where ground measurements are not
available. Future satellite missions like TEMPO (Tropospheric Emissions:
Monitoring Pollution) will provide better coverage of SO2, and other
pollutants, as it will have higher spatial resolution and hourly frequency
over the North American continent during daytime (especially the USA and
parts of Canada). Also, the TROPOspheric Monitoring Instrument (TROPOMI) is
scheduled to launch in 2017 and will provide daily global coverage of
tropospheric SO2 and other pollutants, with a high spatial resolution
of 7km×7km.
The OMI operational principal component analysis (PCA)
SO2 product (OMSO2 v1.2.0) was obtained from the NASA Goddard Earth
Sciences (GES) Data and Information Services Center (DISC)
(http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/omso2_v003.shtml).
The in situ SO2 measurements were obtained from the Air Quality
System (AQS) network of the US EPA
(http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm)
and Environment and Climate Change Canada's National Air Pollution
Surveillance (NAPS) network
(http://maps-cartes.ec.gc.ca/rnspa-naps/data.aspx). The bottom-up
emissions data were obtained from the US EPA (2016)
(http://www.epa.gov/air-emissions-inventories)
and the National Pollutant Release
Inventory (NPRI, 2017)
(http://www.ec.gc.ca/inrp-npri/default.asp?lang=En&n=0EC58C98-). The
OMI-SO2 data used in this study can be made available on request (Shailesh
K. Kharol and Chris A. McLinden, Environment and Climate Change Canada,
Toronto, Ontario, Canada).
The Supplement related to this article is available online at doi:10.5194/acp-17-5921-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
We acknowledge the National Aeronautics and Space Administration (NASA) for
the availability of OMI SO2 tropospheric column data. We would like
to thank the Natural Sciences and Engineering Research Council of Canada
(NSERC) for funding support.
Edited by: A. Schmidt
Reviewed by: two anonymous referees
References
Boys, B. L., Martin, R. V., van Donlelaar, A., MacDonell, R. J., Hsu, N. C.,
Cooper, M. J., Yantosca, R. M., Lu, Z., Streets, D. G., Zhang, Q., and Wang,
S. W.: Fifteen-year global time series of satellite-derived fine particulate
matter, Environ. Sci. Technol., 48, 11109–11118, 2014.
Chinn, S., Florey, C. du V., Baldwin, I. G., and Gorgol, M.: The Relation of
Mortality in England and Wales 1969-73 to Measurements of Air Pollution,
J. Epidemiol. Commun. H., 35, 174–179, 1981.
Côté, J., Gravel, S., Méthot, A., Patoine, A., Roch, M., and
Staniforth, A.: The operational CMC-MRB Global Environmental Multiscale (GEM)
model. Part 1: Design considerations and formulation, Mon. Weather Rev., 126,
1373–1395, 1998.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy,
S. B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011.Dentener, F., Drevet, J., Lamarque, J. F., Bey, I., Eickhout, B., Fiore,
A. M., Hauglustaine, D., Horowitz, W. W., Krol, M., Kulshrestha, U. C.,
Lawrence, M., Galy-Lacaux, C., Rast, S., Shindell, D., Stevenson, D., Van
Noije, T., Atherton, C., Bell, N., Bergman, D., Butler, T., Cofala, J.,
Collins, B., Doherty, R., Ellingsen, K., Galloway, J., Gausee, M., Montanaro,
V., Müller, J. F., Pitari, G., Rodriguez, J., Sanderson, M., Solmon, F.,
Strahan, S., Schultz, M., Sudo, K., Szopa, S., and Wild, O.: Nitrogen and
sulfur deposition on regional and global scales: a multimodel evaluation,
Global Biogeochem. Cy., 20, GB4003, 10.1029/2005GB002672, 2006.
Derriennic, F., Richardson, S., Mollie, A., and Lellouch, J.: Short-term
effects of sulphur dioxide pollution on mortality in two French cities, Int.
J. Epidemiol., 18, 186–197, 1989.EPA: Air Emissions Inventories, U.S. Environmental Protection Agency, Durham,
NC, USA, available online at:
https://www.epa.gov/air-emissions-inventories, last access: 15 October
2016.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, 2013.Fioletov, V. E., McLinden, C. A., Krotkov, N., and Li, C.: Lifetimes and
emissions of SO2 from point sources estimated from OMI, Geophys. Res.
Lett., 42, 1969–1976, 2015.Fioletov, V. E., McLinden, C. A., Krotkov, N., Li, C., Joiner, J., Theys, N.,
Carn, S., and Moran, M. D.: A global catalogue of large SO2 sources
and emissions derived from the Ozone Monitoring Instrument, Atmos. Chem.
Phys., 16, 11497–11519, 10.5194/acp-16-11497-2016, 2016.
Gong, W., Dastoor, A. P., Bouchet, V. S., Gong, S.-L., Makar, P. A., Moran,
M. D., Pabla, B., Menard, S., Crevier, L.-P., Cousineau, S., and Venkatesh,
S.: Cloud processing of gases and aerosols in a regional air quality model
(AURAMS), Atmos. Res., 82, 248–275, 2006.
Gong, W., Makar, P. A., Zhang, J., Milbrandt, J., Gravel, S., Hayden, K. L.,
Macdonald, A. M., and Leaith, W. R.: Modelling aerosol-cloud-meteorology
interaction: a case study with a fully coupled air quality model (GEM-MACH),
Atmos. Environ., 115, 695–715, 2015.Hand, J. L., Schichtel, B. A., Malm, W. C., and Pitchford, M. L.: Particulate
sulfate ion concentration and SO2 emission trends in the United
States from the early 1990s through 2010, Atmos. Chem. Phys., 12,
10353–10365, 10.5194/acp-12-10353-2012, 2012.
Hatzakis, A., Katsouyanni, K., Kalandidi, A., Day, N., and Trichopoulos, D.:
Short-term effects of air pollution on mortality in Athens, Int.
J. Epidemiol., 15, 73–81, 1986.
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K.,
Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and
Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 1535 pp., 2013.Kelly, J., Makar, P. A., and Plummer, D. A.: Projections of mid-century
summer air-quality for North America: effects of changes in climate and
precursor emissions, Atmos. Chem. Phys., 12, 5367–5390,
10.5194/acp-12-5367-2012, 2012.
Kharol, S. K., Martin, R. V., Philip, S., Boys, B., Lamsal, L. N., Jerrett,
M., Brauer, M., Crouse, D. L., McLinden, C., and Burnett, R. T.: Assessment
of the magnitude and recent trends in satellite-derived ground-level nitrogen
dioxide over North America, Atmos. Environ., 118, 236–245, 2015.Krotkov, N. A., Carn, S. A., Krueger, A. J., Bhartia, P. K., and Yang, K.:
Band residual difference algorithm for retrieval of SO2 from the Aura
Ozone Monitoring Instrument (OMI), IEEE T. Geosci. Remote, 44, 1259–1266,
2006.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.
Krzyzanowski, M. and Wojtyniak, B.: Ten-year Mortality in Sample of an adult
Population in relation to air pollution, J. Epidemiol. Commun. H., 36,
262–268, 1982.Lamsal, L. N., Martin, R. V., van Donkelaar, A., Steinbacher, M., Celarier,
E. A., Bucsela, E., Dunlea, E. J., and Pinto, J. P.: Ground-level nitrogen
dioxide concentrations inferred from the satellite-borne Ozone Monitoring
Instrument, J. Geophys. Res., 113, D16308, 10.1029/2007JD009235, 2008.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.
Lee, C. J., Martin, R. V., Henze, D. K., Brauer, M., Cohen, A., and van
Donkelaar, A.: Response of global particulate-matter-related mortality to
changes in local precursor emissions, Environ. Sci. Technol., 49, 4335–4344,
2015.
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The
contribution of outdoor air pollution sources to premature mortality on a
global scale, Nature, 525, 367–371, 2015.
Levelt, P. F., van den Oord, G. H. J., Dobber, M. R., Malkki, A., Visser, H.,
de Vries, J., Stammes, P., Lundell, J. O. V., and Saari, H.: The Ozone
Monitoring Instrument, IEEE T. Geosci. Remote, 44, 1093–1101, 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.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. A., Fioletov, V., Shephard, M. W., Krotkov, N., Li, C., Martin,
R. V., Moran, M. D., and Joiner, J.: Space-based detection of missing sulfur
dioxide sources of global air pollution, Nat. Geosci., 9, 496–500, 2016a.McLinden, C. A., Fioletov, V. E., 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,
2016b.
Moran, M. D., Menard, S., Talbot, D., Huang, P., Makar, P. A., Gong, W.,
Landry, H., Gravel, S., Gong, S., Crevier, L.-P., Kallaur, A., and Sassi, M.:
Particulate-matter forecasting with GEM-MACH15, a new Canadian operational
air quality forecast model, in: Air Pollution Modelling and its Application
XX, edited by: Steyn, D. G. and Rao, S. T., Springer, Dordrecht, 289–293,
2010.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.Nowlan, C. R., Martin, R. V., Philip, S., Lamsal, L. N., Krotkov, N. A.,
Marais, E. A., Wang, S., and Zhang, Q.: Global dry deposition of nitrogen
dioxide and sulfur dioxide inferred from space-based measurements, Global
Biogeochem. Cy., 28, 1025–1043, 10.1002/2014GB004805, 2014.NPRI: National Pollutant Release Inventory datasets, Environment and Climate
Change Canada, Gatineau, QC, Canada, available at:
http://www.ec.gc.ca/inrp-npri/default.asp?lang=En&n=0EC58C98-, last
access: 1 March 2017.
Philip, S., Martin, R. V., van Donkelaar, A., Lo, J. W.-H., Wang, Y., Chen,
D., Zhang, L., Kasibhatla, P. S., Wang, S., Zhang, Q., Lu, Z., Streets,
D. G., Bittman, S., and Macdonald, D. J.: Global chemical composition of
ambient fine particulate matter for exposure assessment, Environ. Sci.
Technol., 48, 13060–13068, 2014.van Donkelaar, A., Martin, R. V., Leaitch, W. R., Macdonald, A. M., Walker,
T. W., Streets, D. G., Zhang, Q., Dunlea, E. J., Jimenez, J. L., Dibb, J. E.,
Huey, L. G., Weber, R., and Andreae, M. O.: Analysis of aircraft and
satellite measurements from the Intercontinental Chemical Transport
Experiment (INTEX-B) to quantify long-range transport of East Asian sulfur to
Canada, Atmos. Chem. Phys., 8, 2999–3014, 10.5194/acp-8-2999-2008,
2008.van Donkelaar, A., Martin, R. V., Brauer, M., Kahn, R. A., Levy, R. C.,
Verduzco, C., and Villeneuve, P. J.: Global estimates of ambient fine
particulate matter concentrations from satellite-based aerosol optical depth:
development and application, Environ. Health Persp., 118, 847–855,
10.1289/ehp.0901623, 2010.
Vet, R., Artz, R. S., Carou, S., Shaw, M., Ro, C.-U., Aas, W., Baker, A.,
Bowersox, V. C., Dentener, F., Galy-Lacaux, C., Hou, A., Pienaar, J. J.,
Gillett, R., Forti, M. C., Gromov, S., Hara, H., Khodzher, T., Mahowald,
N. M., Nickovic, S., Rao, P. S. P., and Reid, N. W.: A global assessment of
precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base
cations, organic acids, acidity and pH, and phosphorus, Atmos. Environ., 93,
3–100, 2014.Yang, K., Krotkov, N. A., Krueger, A. J., Carn, S. A., Bhartia, P. K., and
Levelt, P. F.: Retrieval of large volcanic SO2 columns from the Aura
Ozone Monitoring Instrument: Comparison and limitations, J. Geophys. Res.,
112, D24S43, 10.1029/2007JD008825, 2007.