Introduction
Sulfur dioxide (SO2) is a designated criteria air pollutant that enters
the atmosphere through anthropogenic (e.g., combustion of sulfur-containing
fuels, oil refining processes, metal ore smelting operations) and natural
processes (e.g., volcanic eruptions and degassing). Over the past 3
decades both the US and Canada have taken measures to reduce atmospheric
emissions of SO2 in order to combat acidification of the ecosystem
(e.g., acid rain) and fine particulate matter. As a result, between 1990 and
2012, reported emissions of SO2 declined by 78 % in the United
States and 58 % in Canada (IJC, 2014). In this study, we
examined how well the changes in the reported emissions agree with the
SO2 changes in North America observed by satellite and surface
instruments.
Ground-based networks such as the US Clean Air Status and Trends Network
(CASTNet) and Canadian Air and Precipitation Monitoring Network (CAPMoN) are
specifically designed to monitor long-term trends of gaseous pollutants in
rural areas away from major pollution emission sources
(Baumgardner
et al., 1999; Park et al., 2004; Schwede et al., 2011). Their measurements
show that over the eastern US, reductions in regional SO2 emissions
have led to significant reductions in monitored SO2 concentrations
(Sickles
II and Shadwick, 2015; Xing et al., 2013).
Satellites provide global measurements of SO2 vertical column densities
(VCDs): the total number of molecules or total mass per unit area
(Krotkov
et al., 2008; Li et al., 2013; Theys et al., 2015). They have been
previously used to study the evolution of SO2 VCDs over large regions
such as Europe (Krotkov et al., 2016), China
(Jiang
et al., 2012; Koukouli et al., 2016; Li et al., 2010; Witte et al., 2009),
India (Lu et al., 2013), and the US
(Fioletov et al., 2011). Satellite
instruments can detect anthropogenic SO2 signals from large individual
point sources such as copper and nickel smelters, power plants, oil and gas
refineries, and other sources
(Bauduin
et al., 2014, 2016; Carn et al., 2004, 2007; Fioletov et al., 2013; de Foy
et al., 2009; Lee et al., 2009; McLinden et al., 2012, 2014; Nowlan et al.,
2011; Thomas et al., 2005). An 11-year-long record of satellite SO2
data over different regions of the globe, including the eastern US and
southeastern Canada, was examined recently
(Krotkov et al., 2016). The analysis shows a
substantial (up to 80 %) decline in the observed VCD values over that
region.
These satellite measurements can also be used as an independent source to
verify reported changes in emissions. Methods for emission estimates from
satellite measurements have been recently reviewed by
Streets et al. (2013). One such method that does not require the use of atmospheric
chemistry models has been commonly used in recent years. By first merging
observations from the Ozone Monitoring Instrument (OMI) with wind
information, the downwind decay of several pollutants can be analyzed, and
in so doing estimates of the total SO2 (or NO2) mass (α)
near the source and its lifetime or, more accurately, decay time (τ)
can be derived
(Fioletov
et al., 2011, 2015; de Foy et al., 2015; Lu et al., 2013, 2015; Wang et al.,
2015). The emission strength (E) can be obtained using the expression
E=α/τ if we assume a steady state for these quantities. The
mass can be derived directly from satellite measurements, while the lifetime
can be either prescribed using known emissions
(Fioletov et al., 2013) or
estimated from the measurements based on the rate of decay of VCD with
distance downwind
(Beirle
et al., 2014; Carn et al., 2013; de Foy et al., 2015). Model-based
comparisons of different methods to estimate E and τ demonstrate that
such methods can produce accurate estimates of τ (de
Foy et al., 2014). In our previous study
(Fioletov et al., 2015), values of α and τ for anthropogenic point sources were derived from OMI
measurements by fitting a 3-D function of the geographic
coordinates and wind speed.
These methods, however, are applicable to individual point sources. When
this condition is not met, as is the case for multiple sources, either the sources
can be combined together if they are close
(Fioletov et al., 2015) or the fitting
domain is split and the sources are fit separately
(Wang et al., 2015). Both approaches have
their limitations. In this study, we derive a general relationship between
emissions and VCDs that can be used for the estimation of emissions from
multiple sources. Moreover, the approach can be used in reverse: that is,
VCDs can be estimated directly from reported emission data, thus making it
possible to study the link between VCDs and surface concentrations even for
the period before satellite measurements became available. This study is
focused on the eastern US and southeastern Canada, where the majority of
large North American SO2 emission sources (mainly coal-burning power
plants) are located, where the changes in both reported emissions and
measured VCDs are particularly large, and where emissions are measured
directly at the stack for most sources. In this region, there is also a
network with long-term records of uniform SO2 surface concentration
measurements. All of this makes it possible to study consistency between the
measurements of emissions, VCDs, and surface concentrations. Once the link
between these measurements is verified, it is possible to estimate one
measured quantity from another. As an illustration, we demonstrate how
European SO2 emissions can be estimated from OMI VCD data.
Data sets
Satellite SO2 VCD data
OMI, a Dutch–Finnish UV–visible wide field-of-view nadir-viewing
spectrometer flying on NASA's Aura spacecraft
(Schoeberl et al., 2006),
provides daily global coverage at high spatial resolution
(Levelt et al., 2006). OMI has the
highest spatial resolution and is the most sensitive to SO2 sources
among the satellite instruments of its class
(Fioletov et al., 2013).
Operational OMI planetary boundary layer SO2 data produced with
the principal component analysis (PCA) algorithm (Li et al.,
2013) for the period 2005–2015 were used in this study. Retrieved SO2
VCD values are given in Dobson units (DU; 1 DU =2.69×1026 molec km-2).
OMI SO2 VCD data are retrieved for 60 cross-track positions (or rows).
In order to use only data with the highest spatial resolution, we excluded
data from the first 10 and last 10 cross-track positions from the analysis
to limit the across-track pixel width from 24 km to about 40 km, while the
along-track pixel length was about 15 km
(de Graaf et al., 2016). In other
words, a single OMI measurement represents an SO2 VCD value averaged
over a 350–500 km2 area.
Measurements with snow on the ground were excluded from the analysis as the
OMI PCA algorithm presently does not account for the effects of snow albedo.
Only clear-sky data, defined as having a cloud radiance fraction (across
each pixel) less than 20 %, and only measurements taken at solar zenith
angles less than 70∘ were used. Beginning in 2007, up to half of
all rows were affected by field-of-view blockage and stray light (the
so-called “row anomaly”) and those affected pixels were also excluded.
Additional information on the OMI PCA SO2 product can be found in other
publications (Krotkov
et al., 2016; McLinden et al., 2015).
SO2 VCD data from the Ozone Mapping Profiler Suite (OMPS) Nadir Mapper
on board the Suomi National Polar-orbiting Partnership (or Suomi NPP)
satellite operated by NASA/NOAA and launched in October 2011 were also used
in the study to verify a potential bias in some OMI data (see the Supplement,
Sect. S1). OMPS data were processed with the same PCA
algorithm as OMI data (Li et al., 2013; Zhang et al., 2017). OMPS has a lower
spatial resolution than OMI, 50 km by 50 km but better signal-to-noise
characteristics.
To eliminate cases of transient volcanic SO2, periods when SO2
values observed over the eastern US were affected by volcanic emissions;
we determined and excluded such cases from the analysis. The range of
analyzed SO2 VCD values was limited to a maximum of 3 DU. Since the
average SO2 value over the largest SO2 source in the US is about 1
DU and the standard deviation of individual measurements is 0.5 DU, such a
limit corresponds to the 4 standard deviations level even over even the
largest sources. Of the SO2 values over the eastern US and southern
Canada considered here, the years 2008 and 2009 are particularly problematic
due to the eruptions of Kasatochi (Aleutian Islands, Alaska, August 2008,
52∘ N) and Sarychev (Kuril Islands, Eastern Russia, June 2009,
48∘ N). High volcanic SO2 values were also observed on
several days in 2011. In addition to the filtering based on SO2 values,
five time intervals were explicitly removed from the analysis to avoid
misinterpretation of volcanic SO2 as anthropogenic pollution. The
intervals are 7 –23 July 2008, 8 August –8 September 2008,
23 March–10 April 2009, 16 June–5 July 2009, and 22 May–9 June 2011. To
remove volcanic SO2 in the case of Europe, the analyzed data were
divided into 5∘ by 5∘ cells, and for each cell the days
with the 90th percentile above a 5 DU limit were excluded from the
analysis. Only about 1.5 % of all data were removed by this screening.
Annual mean OMI SO2 VCDs from PCA algorithm (column I), mean
OMI SO2 VCDs with a large-scale bias removed (column II), results of
the fitting of OMI data by the set of functions that represent VCDs near
emission sources using estimated emissions (see text) (column III), and
SO2 VCDs calculated using the same set of functions but using reported
emission values (column IV). Point sources that emitted 20 kt yr-1 at
least once in the period 2005–2015 were included in the fit (they are shown
as the black dots). Results of the fitting of OMI data by the set of
functions that represent “sources” as 0.5∘ by 0.5∘
grid cells (shown as the black dots) using estimated emissions (see text)
are shown in column V. The maps are smoothed by the pixel averaging
technique with a 30 km radius (Fioletov
et al., 2011). Averages for four multi-year periods – 2005–2006, 2007–2009,
2010–2012, and 2013–2015 – over the area 32.5 to 43∘ N
and 75 to 89∘ W are shown.
Wind data
As in several previous studies
(Fioletov et al., 2015; McLinden
et al., 2016), wind-speed and direction data for each satellite pixel were
required for the analysis methods applied. European Centre for Medium-Range
Weather Forecasts (ECMWF) reanalysis data
(Dee et al.,
2011; http://apps.ecmwf.int/datasets/) were merged with OMI measurements.
Wind profiles are available every 6 h on a 0.75∘ horizontal
grid and are interpolated in time and space to the location of each OMI
pixel centre. U and V (west–east and south–north, respectively) wind-speed
components were first averaged in the vertical between 0 and 1 km where the
majority of the SO2 mass resides. The wind components were then
interpolated spatially and temporally to the location and overpass time of
each OMI pixel.
Note that to reconstruct annual mean VCD maps based on annual emissions
(Sect. 3.4), it is not necessary to have the actual year-specific
meteorological information, as annual mean wind characteristics do not vary
much from year to year (see Sect. S6), and so for
convenience we simply used wind data from 2005 for all years prior to 2005.
(a–c) Examples of reported and estimated seasonal emissions (in
kt yr-1) for three 1∘ by 1∘ grid cells as labelled on
the plots. (d) Reported and estimated seasonal point-source emission rates
for the entire eastern US and southeastern Canada (the region shown in
Fig. 1) for spring, summer, and autumn. Estimated emissions are shown for
the statistical model based on the actual source location (blue lines) and
on a 0.5∘ by 0.5∘ regular grid (red lines). Note that
the seasonal emission values are scaled to give annual emission rates.
Winter data are not shown due to high uncertainties of OMI measurements.
SO2 emission inventories
Monthly or annual emissions from individual US point sources available from
the US Environmental Protection Agency (EPA) National Emissions Inventory
(https://www.epa.gov/air-emissions-inventories) for the period 1980–2015
were examined in this study. US EPA national emission inventories are
available from 1980, although at that time they contained just annual values
and were updated only every 5 years. Regular annual emission data for
consecutive years first became available in 1995 and US emission data
with higher temporal resolution (monthly, daily, and hourly) are only
available after 2004. Note that the inventory data for these sources after
the early 1990s were based on direct stack measurements by Continuous
Emissions Monitoring Systems as mandated by Title IV of the 1990 US Clean
Air Act Amendments (Public Law 101-549; e.g.,
https://www.epa.gov/clean-air-act-overview). The Canadian SO2 annual
point-source emission data were obtained from the National Pollutant
Release Inventory (NPRI), http://open.canada.ca/data/en/dataset/). Canadian
annual point-source emission data sets are available back to 2002 and we
used the 2002 emission data for the 1980–2001 period. For Canadian sites,
only annual emissions are available and seasonal values were calculated by
dividing annual emissions by 4. This study is based on point-source
emissions only, but point sources have contributed a large majority
(> 90 % in the early 2000s and > 70 % in the
recent years) of North American SO2 emissions.
The scatter plots between the reconstructed from emission-based
VCDs and the three OMI-based data sets shown in Fig. 1: (a) mean OMI SO2
VCDs, (b) mean OMI SO2 VCDs with a large-scale bias removed, and
(c) results of the fitting of OMI data by the set of functions that represent
VCDs near emission sources using estimated emissions (the first term of
Eq. A2). Each symbol on the plot represents the annual mean SO2
VCD value averaged over one 1∘ by 1∘ grid cell and all
cells within the domain area shown in Fig. 1 are included in the plot.
Different colours represent different years. The correlation coefficients
between the two data sets on each plot are also shown.
Information about point-source emissions from the European Union (EU)
countries from the European Pollutant Release and Transfer Register (E-PRTR)
for 2004–2014 is available from
http://www.eea.europa.eu/data-and-maps/data/lcp-1 and was used for the
analysis for Europe. For non-EU European countries, spatially distributed
2005–2014 TNO-MACC-III emission data for air pollutants from the MACC
project were used (Kuenen et al.,
2014; Monitoring Atmospheric Composition and Climate; see
http://www.gmes-atmosphere.eu/) prepared by TNO. When E-PRTR data are not
available, proxy data are used by TNO, such as for power plants from the
World Electric Power Plants Database (WEPP; see
http://www.platts.com/products/world-electric-power-plants-database). WEPP
provides no emission data, only listing unit characteristics, so emissions
are allocated to individual plant units based on the reported thermal
capacity, configuration and generic interpretations of reported fuel
type(s), and installed emission control technologies. Site-specific
parameters not provided by WEPP, such as exact fuel sulfur content,
achieved pollutant removal efficiencies, and load fluctuations, are not
taken into account when emissions are allocated. Therefore, the MACC-III
point-source emission data should be regarded as estimates that may differ
considerably from the actual emissions.
SO2 surface concentration data
In situ SO2 ground-level measurements from the US Clean Air Status
and Trend Network (CASTNet; Baumgardner
et al., 1999; Park et al., 2004; Schwede et al., 2011), operated by the US
EPA (http://www.epa.gov/castnet), and the Canadian Air and Precipitation
Monitoring Network (CAPMoN:
http://www.ec.gc.ca/rs-mn/default.asp?lang=En&n=752CE271-1; Schwede et al., 2011),
operated by Environment and Climate Change Canada (ECCC), were used in this
study. Both networks were established to assess regional trends in pollutant
concentrations, atmospheric deposition, and ecological effects due to
changes in air pollutant emissions. CASTNet started operations in 1987 and
CAPMoN started in the late 1970s. Both networks employ filter packs to
measure SO2, although CASTNet uses a 1-week sampling period vs. a
1-day sampling period for CAPMoN. It is important to note that the
monitoring sites belonging to these networks are located in relatively
remote areas, so that direct impacts of local pollution sources on the
measurements are minimal. Annual mean SO2 values in µg m-3
were used in this study.
Left: mean OMI SO2 VCDs grouped by wind speed with a
large-scale bias removed. Right: results of the fitting of OMI data by the
set of functions that represent VCDs near emission sources using estimated
emissions. While the fitting was done using all data, the results of the
fitting are grouped by wind speed. Averages for 2005–2007 binned by the wind
speed (0–5, 5–15, and 15–45 km h-1) are shown.
Sources that emitted 20 kt yr-1 at least once in 2005–2015 were
included in the fit (they are shown as black dots).
Linking satellite SO2 VCDs and SO2 emissions
The method for linking OMI SO2 VCDs to SO2 emissions is based on a
fit of OMI VCDs to an empirical plume model developed to describe the
SO2 spatial distribution (as seen by OMI) near emission point sources
(Fioletov et al., 2015), but unlike the
previous studies it is not limited to a single point source. The plume
model assumes that the SO2 concentrations emitted from a point source
decline exponentially with time and that they are affected by turbulent
diffusion that can be described by a 2-D Gaussian function. The overall
behaviour can be described as a combination of exponential and Gaussian
random variables, also known as an exponentially modified Gaussian function
(see the Appendix for details). Each satellite measurement (or pixel) is fit
by a sum of plumes from all point sources. The distribution of SO2
emanating from each source is described by the plume model based on a known
plume function Ω(θ,φ,ω,s,θi,φi) dependent on the satellite pixel coordinates (θ,φ),
pixel wind direction and speed (ω,s), and source coordinates
(θi,φi) scaled by an unknown parameter (αi) representing the total SO2 mass from the source i. These
unknown parameters are then estimated from the best fit of the OMI
measurements. The emission rate for source i is E=αi/τ, where
τ is a prescribed SO2 decay time. In other words, the method finds
the emission rates that produce the best agreement with the observed OMI
SO2 VCD values. The detailed formulas and prescribed seasonal decay
times are given in the Appendix.
Thus, the fitting procedure allows for the isolation of the
emission-related “signal” in the data from known sources and can be used
to check existing point-source emission inventories. If all sources are
included in the fit, it can be expected that the difference between the OMI
data and the fit is within the noise level and the estimated emission rates
E should agree with the reported emissions. We used OMI observations and
emission data for the eastern US and southeastern Canada to confirm this
expectation. Sources that are not included in the fit would appear as
“hotspots” on the maps of the difference between OMI VCDs that could be
used for source detection. Furthermore, emissions from such sources could
then be derived by adding their coordinates to the source list in the
fitting procedure. The suggested method can thus be used as a source of
independent emission estimates in regions where emission values have large
uncertainties.
The method requires information about the point-source locations. We used
source location data available from the US and Canadian emission inventories
mentioned in Sect. 2.3. As discussed by Fioletov et al. (2015), sources that emit
30 kt yr-1 or more can be detected by OMI. Since multiple smaller
sources located in a close proximity can also be seen as a hotspot in
OMI data, we lowered the minimum limit and included all SO2 point
sources that reported emissions of 20 kt yr-1 or more at least once
in the period 2005–2015. It should be noted that while the method does not
improve the level of source detectability, it gives more accurate emission
estimates for clusters of small sources where the point-source algorithm is
not really applicable.
Earlier versions of the OMI SO2 data product have some large-scale
biases (Fioletov et al., 2011) that were
largely removed in the present PCA version. However, we found that even the
PCA version has some local biases that may interfere with the regression
fit. The local bias can be accounted for by introducing functions that
change slowly (compared to signal from emission sources) with latitude and
longitude. We used Legendre polynomials of latitude and longitude and their
products that are orthogonal over the analyzed domain, as discussed in the
Appendix.
The OMI data with and without the bias and the fitting results for four
multi-year intervals are shown in the columns I and II of Fig. 1. The
additional plots of the bias itself and the residuals are available from the
Supplement, Figs. S1–S4. Figure 1 is based on the annual estimates
averaged over 2- and 3-year periods. Figure 1 confirms that there was
a large decline in SO2 VCD over the eastern US and southeastern
Canada in the period 2005–2015 (Krotkov et al.,
2016). In contrast, the bias estimated from the fitting procedure appears to
be fairly constant over time (Fig. S1), which suggests that it may be an
artifact from the retrieval. The lack of this feature in OMPS observations
further suggests it is a bias in OMI PCA data as discussed in the Supplement
(Sect. S1 and Fig. S2).
It should be mentioned that the use of an empirical plume model is
appropriate when atmospheric advection/diffusion can be considered to be the
dominant process and meteorological conditions can be assumed to be
quasi-steady. This is a reasonable assumption for short time periods and
transport distances and when chemical transformation and surface removal of
SO2 can be well represented as simple first-order loss. The consistent
mid-day overpass time for OMI means that the vast majority of the satellite
measurements will be associated with a well-developed quasi-steady planetary
boundary layer. A 3-D atmospheric chemistry model, in contrast, would
be more appropriate for longer time periods and transport distances and for
emissions occurring at all times of day, but that is not the case for this
analysis.
Analysis
SO2 emission estimates from OMI data
The functions Ω(θ,φ,ω,s,θi,φi) decline very rapidly with distance from the source located at
θi,φi. For an isolated point source (θi,φi) where other sources are located 100 km away or
more, Ω(θ,φ,ω,s,θi,φi) is not correlated with any other Ω(θ,φ,ω,s,θj,φj,), where i≠j, and the regression model
(1) or (2) can be simply split into two parts: a model for point-source
emission estimates for source i and a model for all other point sources. Then
the estimate of αi is independent from estimates for all other
sources. If, however, there is another source j located at (θj,φj) that is closer to source i than ∼ 100 km, then the
functions Ω(θ,φ,ω,s,θi,φi) and Ω(θ,φ,ω,s,θj,φj,)
become correlated, as do their estimates of αi and
αj. As the two Ω functions depend on the wind, the
correlation coefficients also depend on the wind distribution and the
locations of the sources relative to the prevailing wind direction and to
each other, but the separation distance is the dominant factor. Typical
absolute values for the correlation coefficients are about 0.2, 0.6, and 0.8
for distances between sources of 100, 50, and 25 km, respectively. A
high correlation means that, in practice, emission estimates for sources
located in close proximity have large uncertainties as we may have
difficulty separating signals from the individual sources. However, if
sources i and j are located in close proximity to each other but far from all
other sources, then their combined emissions can still be estimated
accurately. Thus, such sources can be grouped into clusters, where the
member sources are located in close proximity (20–40 km) but the clusters
themselves are well separated and total emissions from each cluster can be
estimated from satellite data.
Another way of grouping sources into clusters is to establish a grid over
the analysis region and then sum up estimated emissions (Ei) from all
sources within each grid cell. Of course, this does not prevent situations
in which two sources are in close proximity but are located in adjacent grid
cells. Such cases would lead to larger uncertainties in the cell values, but
they are uncommon. Figure 2a–c show examples of such estimated total
emissions for three 1∘ by 1∘ cells. Seasonal emission
estimates scaled to annual values were used for this plot and winter data
are not shown in this plot due to much higher uncertainties of OMI data. The
estimated emissions agree reasonably well with the emissions calculated by
summing up reported SO2 emissions from the point sources in each cell.
The standard deviation of the difference between the emission estimates for
all 1∘ by 1∘ cells within the domain area shown in
Fig. 1 and reported SO2 emissions for the same cells are 112, 39, 28,
and 41 kt yr-1 for winter, spring, summer, and autumn, respectively. The
standard deviations of the difference are 25 and 37 kt yr-1 for annual emissions without and with winter data (not shown),
respectively. Finally, total point-source emissions for the entire region
can be estimated by summing over all individual point sources. Such a plot
is shown in Fig. 2d. The estimated SO2 emissions in Fig. 2d follow
the trend in the reported emissions well, and the correlation coefficient
between the two data sets is 0.98. The agreement is particularly good in
summer. Large discrepancies are observed only in autumn months after 2007,
when relatively high measurement noise combined with the reduction of data
due to the row anomaly. In addition, the 2008 and 2009 satellite data were
affected by SO2 emitted from volcanic eruptions
(McLinden et al., 2015). More information
on the autumn data is available from Sects. S2 and S3.
This grid-based approach can be potentially used for area sources or when
the locations of sources are not well known. For illustration, we used VCD
measurements over the same area but assumed that it is an area source
with no individual point sources. If we set a regular grid and assume that
each grid point is a “source”, we can estimate emissions from such
“sources” as described above. VCD can then be calculated using these
estimated emissions. Such reconstruction for a 0.5∘ by
0.5∘ grid is also shown in Fig. 1 (column V) and demonstrates a
good agreement with the measured VCD values. Note that the grid spacing
should not be too large or else the areal emissions will be underestimated.
Likewise, if it is too fine adjacent grid cells will be highly correlated
and may result in artificial structure. As Fig. 1 (column IV) shows, the
fitting results based on emissions are very close to the OMI fitted data
(column II). We used the OMI data with local bias removed because, with this
approach, any instrumental local bias will be interpreted as an area source,
resulting in overestimation of emissions.
Emissions estimated by this gridded method are also shown in Fig. 2. Their
uncertainties are higher than for the case of known source locations but are
still reasonable. The standard deviation of the difference between the
emission estimates for all 1∘ by 1∘ cells within the
domain area shown in Fig. 1 and reported SO2 emissions for the same
cells are 54, 37, and 56 kt yr-1 for spring, summer, and autumn,
respectively. High measurement errors and data gaps prevent estimation of
the emissions for winter.
The uncertainties of satellite-based emission estimates have been discussed
in our previous studies
(Fioletov et
al., 2015, 2016). They can be as high as 50 %, but the two largest
contributors to this uncertainty, the air mass factor (AMF; determined by the
assumed vertical profile, surface reflectivity, and viewing geometry) and
the prescribed lifetime, are related to site-specific conditions and can be
considered primarily as systematic. They introduce a scaling factor in
estimated emissions that affects absolute values but not relative
year-to-year changes in emissions. Moreover, the constant, effective AMF
embedded in the OMI SO2 product is based on measurements taken in the
eastern US, and the lifetime estimates used here are based on data from the
US power plants as well, so these errors are minimal for this region. To
further support this claim, AMF values were recalculated for all SO2
observations used in Fioletov et al. (2016) and its impact on these sources
was found to minimal, typically less than 5 %.
SO2 VCDs estimated from reported emissions
The equation that links emissions and VCDs (A1) can also be used for forward
calculations: if coefficients αi are known, then SO2 VCDs
can be calculated for any location for given wind conditions and these daily
VCDs can be averaged to give annual or seasonal means for the analyzed area.
Since αi=Ei⋅τ, and τ is prescribed
in our calculations, the available emission inventories that contain
Ei can be used to calculate αi. In this case, there is no
need to do any fitting or to use any OMI measurements to calculate VCDs. In
practice, we can simply use the reported emission data and available OMI
pixel locations merged with the wind information and calculate VCDs for each
OMI pixel based on its centre coordinates. OMI provides daily near-global
coverage, and of course no pixel screening is required for such forward
calculations, so it would be essentially a reconstruction of daily VCD maps
with spatial resolution of about 15 by 35 km (approximately the average
size of the OMI pixel used in this study) assuming a constant emission rate.
Figure 1 (column IV) also shows the result of such annual reconstructions
averaged over 2- to 3-year periods. Annual point-source emissions from the
EPA and NPRI inventories were used as inputs. The agreement of the
reconstructed VCDs with OMI data (with the bias removed) is very good, and
the agreement with the OMI data fitting results is truly remarkable. To
characterize the overall agreement with the OMI data, fitting results, and
reconstructed emission-based VCDs, a 1∘ by 1∘ grid was
established and various statistical characteristics were calculated for the
gridded data. The standard deviation of the residuals ε for
this grid is 0.025 DU, i.e., about 20 times less than the uncertainty of
individual OMI measurements. The standard deviation of the difference
between the OMI-fitted and the reconstructed emission-based VCDs is 0.016 DU.
Figure 3 shows the scatter plots between the annual VCDs reconstructed from
emissions and the three OMI-based data sets shown in Fig. 1 for all years.
The correlation between the VCDs reconstructed from emissions with the
actual OMI data is 0.75, but it rises to 0.91 after the local bias is
removed and to 0.97 after the emission-related signal is extracted from the
OMI data by the fitting procedure (the first term of Eq. A4). Moreover,
values of the latter correlation coefficient are above 0.88 for all seasonal
averages (excluding winter) and they are substantially higher than the
correlation coefficients with the actual seasonal OMI data. This result
could be used to extract an emission-related SO2 signal from the OMI
data when the signal is weak compared to the noise level but the source
locations are known. Additional information is available from Sect. S2, including a figure of the difference between the
fitted VCDs and the reconstructed VCDs as well as seasonal and annual
statistics.
Figure 1 shows the fitting results in geographical coordinates; i.e., the
first term of Eq. (A4) from the Appendix was calculated for each OMI
pixel without any stratification by the wind speed and direction. However,
the fitting itself is done in a four-dimensional space where the wind speed
and direction are the other two coordinates. To illustrate the fitting
results for different wind speeds, Fig. 4 shows the original mean OMI
SO2 values (with the bias removed) and the fitting results when the
data are binned by the wind speed. Note that the fitting parameter estimate
was done using data for all wind speeds and the binning applies only to the
fitting outputs. In other words, the first term of Eq. (A4) from the
Appendix was calculated using only OMI pixels where the wind speed was
within the selected range. The calculations were done for three wind-speed
bins for the 2005–2007 period when the SO2 emissions were the highest
and the measurements were not affected by the “row anomaly”. The
wind-speed modal value is about 10 km h-1, and the first bin represent
calm conditions, the second bin contains measurements taken within ±5 km h-1 from the modal value, and the last bin corresponds to relatively
high wind speeds. As Fig. 4 demonstrates the fitting results are able to
capture the changes in SO2 distribution at different wind-speed bins.
When the wind speed is low, SO2 values are high over the sources, while
the plume spreads out over a larger area when the wind speed is high. The
figure also shows that SO2 VCD values measured over the sources, or
integrated over a small area around the source, are not good proxies for the
emissions because they depend on the wind speed.
The same as Fig. 1, columns I–IV, but for the part of Europe
where the majority of SO2 point sources are located. Point sources that
emitted 10 kt yr-1 at least once in the period 2005–2014 were
included in the fit (they are shown as the black dots). High SO2 values
related to the Mt. Etna volcano in Sicily are excluded from the OMI plots.
The area 35.6 to 56.6∘ N and 10∘ W to
28.4∘ E is shown.
Applications for other regions
Direct SO2 emission measurements are not available for many regions of
the globe. The described method can be used to verify or even estimate
SO2 emissions for other regions. To test this method further, we
applied it to the European region using E-PRTR and TNO-MACC emission inventory data (see Sect. 2.3).
Figure 5 is similar to Fig. 1, but for a part of Europe where the
majority of the SO2 sources are located. Sources that emitted more than
10 kt in any year between 2005 and 2014 are shown on the map as black dots.
The limit was lowered to 10 kt yr-1 from the 20 kt yr-1 value used
for North America since clusters of small sources are common in Europe. When
the coordinates of the sources were included in the fitting procedure there
appeared to be some large-scale local biases particularly over Spain and the
Balkan region.
Figure 5 also shows a good general agreement between the OMI data and VCDs
estimated from emissions. Both show a substantial SO2 VCD decline over
most regions, with SO2 values the highest at the beginning of the
analyzed period (Spain, Romania, Bulgaria, Greece). No major changes are
observed by OMI for power plants in Serbia and in Bosnia and Herzegovina,
and they are now producing the highest SO2 VCD values over the domain
shown. As their emissions are not in the E-PRTR database, TNO-MACC emission
inventory data were used instead.
OMI-based (blue bars) and reported/estimated (black lines)
emissions for different European countries. E-PRTR reported emissions were
used for all countries except Serbia and Bosnia and Herzegovina, where
TNO-MACC estimates (Kuenen et al.,
2014) were used (see Supplement). The error bars
represent 2 standard errors of the annual mean calculated by averaging three
seasonal (spring, summer, autumn) OMI-based emission estimates.
The method produces estimates for individual sources that can be further
grouped in different ways. Estimated and reported annual emissions for the
period 2005–2014 were grouped by nation for nine countries with large
SO2 emissions, as shown in Fig. 6. There is good agreement
qualitatively between the reported and estimated emissions. Some differences
in absolute values are expected due to possible multiplicative biases in
OMI-based estimates (from the air mass factor and potential errors in τ). In some cases, however, a possible deficiency in the reported emissions
cannot be ruled out. For example, OMI-based values for Romania show nearly
constant emissions up to 2012 and then a 50 % drop, whereas the reported
emissions suggest a steady decline between 2005 and 2013. The uncertainty
level of the OMI-based emissions is illustrated in Fig. 6 by the panel for
Hungary: the total emissions from three sources there are below the sensitivity
of OMI-based estimates. Figure 6 also shows OMI-based and inventory
emissions for Serbia and for Bosnia and Herzegovina. Their inventory
emission data are available as estimates based on reported thermal capacity,
configuration, and generic interpretations of reported fuel type and may not
be accurate. OMI-based estimates provide an independent source for their
verification. For example, the inventory estimates for the copper smelter at
Bor, Serbia, are about 4.5 kt yr-1, i.e, well below the OMI sensitivity
level. However, OMI sees this source clearly and the OMI-based mean emission
estimate for 2005–2016 is about 70 kt yr-1, a value in line with high
SO2 levels observed there (Serbula et
al., 2014). See also Fig. S7.
Another clear benefit of the satellite-based method of emission estimates is
that such estimates are available with almost no delay. At the time of this
study (February 2017), we were able to estimate OMI-based emission for the
period including 2016, while the E-PRTR inventory only reached until 2014.
Annual mean SO2 VCD calculated using the plume model applied
to the reported emission data. Annual emission data from ∼ 380 SO2 sources (black dots) that emitted 1 kt yr-1 at least once
in 2005–2015 were included in the calculations. The area 30 to
48∘ N and 70 to 90∘ W is shown.
Reconstruction of the past VCD distribution
If detailed emission data are available, it is also possible to calculate
emission-based VCD maps using Eq. (A3) for years before the launch of
OMI. Figure 7 shows the annual mean VCD maps over the eastern US and
southeastern Canada reconstructed from the emission inventories available
since 1980. All point sources (shown by black dots) that emitted more than
1 kt of SO2 in at least 1 year during the 1980–2015 period were
included in the calculations for a total about 380 sources. Note that we
slightly expanded the domain area in all directions to include sources that
emitted large SO2 amounts prior to the OMI launch. There are two major
periods of dramatic changes in SO2 VCD values: first, in the early
1990s, corresponding to the implementation of the US Acid Rain Program
(ARP), established under Title IV of the 1990 Clean Air Act (CAA) Amendments
(IJC, 2014). Then beginning in 2009–2010 there are further large
reductions attributable to the installation of additional flue-gas
desulfurization units (or “scrubbers”) at many US power plants to meet
stricter emission limits introduced by the Clean Air Interstate Rule. The
overall decline of total SO2 point-source emissions from the domain
area shown in Fig. 5 between 1980 and 2015 is 86 %.
Annual mean surface SO2 concentrations in µg m-3
for different periods calculated using data from the CASTNet and CAPMoN
surface monitoring networks. The area 30 to 48∘ N and
70 to 90∘ W is shown.
SO2 surface concentrations and VCDs
Multi-year mean surface SO2 concentrations at stations belonging to the
CASTNet and CAPMoN networks (see Sect. 2.4) were compared to the estimated
VCD values. Maps of multi-year mean surface SO2 concentrations at
stations belonging to the CASTNet and CAPMoN networks are shown in Fig. 8.
The colour scheme of Fig. 8 was chosen to be comparable to that used in
Fig. 1. The main features of the VCD and surface concentration
distributions are very similar. Both sets of maps portray a strong decline
from the 1980s to 2010s with the highest values observed along the Ohio
River, where many coal-fired power plants are located. However, the spatial
gradients in the VCD distribution appear to be sharper than in the surface
concentration distribution and elevated surface concentrations are spread
out over larger areas. For example, SO2 VCDs over Virginia were much
lower compared to West Virginia, while SO2 surface concentrations were
similar.
There are 50 network sites within the domain area shown in Fig. 7 that
have 15 or more years of observations in the period 1980–2015. A scatter
plot of annual mean SO2 surface concentration at all of these sites
versus emission-based SO2 VCD values is shown in Fig. 9a for all
available years. While there is a clear correlation between the two
quantities that reflects similar spatial distributions and temporal trends,
the correlation coefficient is not very high (0.83). However, correlation
coefficients calculated separately for individual measurement sites are
higher, ranging between 0.87 and 0.99. This is illustrated in Fig. 9b,
where a subset of the scatter plot from Fig. 9a for eight sites is shown
using different colours for each site.
(a) A scatter plot of annual mean surface SO2 from CASTNet
and CAPMoN vs. VCDs calculated from EPA and NPRI point-source emission
inventories. The correlation coefficient between the two data sets is 0.83.
(b) A subset of the scatter plot from panel (a) for eight sites (shown by
different colours). The correlation coefficients for individual sites are
between 0.96 and 0.99. (c) The same plot as (b), but for mean SO2 VCDs
multiplied by a site-specific surface-concentration-to-column ratio. The
correlation coefficient is 0.98. (d) The site-specific
surface-concentration-to-column ratio as a function of the 1980–2015 mean
SO2 VCD. Each dot represents one site. Only the 50 regional surface
SO2 sites with 15 or more years of data between 1980 and 2015 were used
in this figure.
Figure 9b also shows that the slopes of the regression lines vary from site
to site. If we calculate the slope of the individual regression line for
each site (it is essentially the surface-concentration-to-VCD ratio) and
then multiply the emission-based VCDs by that ratio, then we obtain a very
good correlation as illustrated by Fig. 9c (R= 0.986 for the eight sites
shown in Fig. 9b and R= 0.983 for all data points). The regression-line
y intercepts have also been analyzed. A positive intercept means that the
surface concentration could be non-zero even in the absence of any regional
point-source emissions. The estimated intercepts are within ±1.5 µg m-3 for all sites except one where the intercepts
is 3.5 µg m-3. The exception is the CASTNet Horton Station site, located in
Virginia 18 km east of the Glen Lyn power plant, whose emissions were about
10 in 2008 and 6.5 kt yr1 in 2011. However, its
emission information was largely missing for the period 2009–2015 and this
affected our VCD calculations.
The surface-concentration-to-VCD ratio ultimately depends on the shape of
the SO2 vertical profile. The shape could be affected by boundary layer
height, site elevation, and perhaps some local conditions. There are,
however, some common features in the ratio distribution. As shown in Fig. 9d, the ratio is low in areas of high emission-based VCDs and low in areas
where emission-based VCDs are low. Of course, it is not the mean VCD value
itself that affects the ratio but proximity to emission sources. Figure 9d
is based on VCDs derived from emissions, but the same analysis for
OMI-measured VCD demonstrates similar results (Sect. S5).
It may be possible to reconstruct surface concentration distribution from
VCDs and additional information such as the planetary boundary layer height
(Knepp et al., 2015), but such
estimates are outside of the scope of this study.
Summary and discussion
Fitting OMI SO2 VCD data by a linear combination of functions, where
each function represents the plume from an individual source, makes it
possible to estimate emission from these sources or groups of sources. If
the location of all sources is known, it is expected that the fitting
results and the actual OMI data will agree within the noise level as was
found to be the case for the eastern US and southeastern Canada. The same
agreement is also observed for this region if the reported emissions are
used to calculate VCDs. This suggests a simple way of interpreting satellite
SO2 VCD data: they should agree with VCD estimates based on available
emission inventories or the fitting results based on known source locations.
By applying a statistical plume model (developed from satellite SO2
measurements) to US and Canadian annual SO2 point-source emission
inventories, we were able to reconstruct past annual mean VCDs for the
period 1980–2015. High correlation coefficients between the reconstructed
VCDs and the OMI-based values (0.91 for OMI data with local bias removed)
for the period 2005–2015 gives us confidence in both data sets. It also
demonstrates that the reported changes in SO2 point-source emissions
are reflected by OMI measurements for the period 2005–2015. Moreover, the
annual surface SO2 concentrations at the CASTNet and CAPMoN sites also
show high correlation coefficients (0.87–0.99) with SO2 VCDs
reconstructed from reported emissions. All of these comparisons suggest a
high degree of consistency between the reported SO2 point-source emissions and measured SO2 values over the entire 1980–2015 period.
The approach described in this study can be used in several ways. The
derived emissions can serve as an independent data source for inventory
verification (both point source and gridded) by comparing OMI-estimated
SO2 emissions with the inventories or by comparing VCDs calculated from
emission inventories to the OMI VCD measurements. It can also provide
emission information for regions where there are no other information
sources available. Unreported point and area sources can be detected and
emissions from them can be estimates by subtracting VCDs calculated from
available emission inventories from satellite VCD measurements, although
emission inventories with good spatial resolution would be required for such
an analysis. While this study is focused on SO2, the methods can be
applied to other species with relatively short lifetimes measured from
space, particularly to NO2 and NH3.
We have also applied the method to Europe. The results strikingly illustrate
the positive impact of EU legislation; the countries where no decreasing
trends are observed are non-EU member states surrounded by EU countries with
decreasing emissions. In general, the satellite-based results confirm the
trends in reported SO2 emissions from EU member states over the period
2004–2014, but some discrepancies were found that deserve further attention.
In one case, for example, it seems that reported emissions already take into
account certain planned or foreseen measures, but real-world
(satellite-observation) estimates suggest that implementation of these
measures was delayed by several years. Moreover, although the trend is
clearly followed, the absolute emission levels suggested by the OMI SO2
VCD fitting method are sometimes substantially above the reported emission
levels for recent years (Fig. 6). Whether these differences are due to
underreporting or to methodological issues requires further study.
There are certain limitations to the suggested methods. Satellite SO2
VCD data may still contain local biases that will interfere with emission
estimates or will themselves be interpreted as a source. As the OMI and OMPS
data show, these biases could be different from instrument to instrument.
Moreover, data from the same OMI instrument could have different biases if
processed by different algorithms (Fioletov et al., 2016; their Fig. 3).
Although the biases could be partially removed using, for example, a
constant (for a small fitting area) or polynomial (for larger areas) fit,
further improvement of retrieval algorithms is required to eliminate the
bias problem. The biases could be particularly large over regions of high
SO2 VCD values such as the Persian Gulf and China, so the method should
be applied there with caution. The method is also based on the assumption
that all SO2 is located near the surface, which determines the wind
data used for the fitting. This may not always be the case for very large
sources where SO2 can be lifted into the free troposphere. Finally, the
plume model itself may not be optimal in some cases.