AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-1495-2017Continuation of long-term global SO2 pollution monitoring from OMI to
OMPSZhangYanyan.zhang@nasa.govLiCanKrotkovNickolay A.https://orcid.org/0000-0001-6170-6750JoinerJoannaFioletovVitalihttps://orcid.org/0000-0002-2731-5956McLindenChrishttps://orcid.org/0000-0001-5054-1380Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USANASA Goddard Space Flight Center, Greenbelt, MD 20771, USAAir Quality Research Division, Environment Canada, Toronto, ON, CanadaYan Zhang (yan.zhang@nasa.gov)20April2017104149515095July20162November201621March201723March2017This 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/10/1495/2017/amt-10-1495-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/1495/2017/amt-10-1495-2017.pdf
Over the past 20 years, advances in satellite remote sensing of
pollution-relevant species have made space-borne observations an increasingly
important part of atmospheric chemistry research and air quality management.
This progress has been facilitated by advanced UV–vis spectrometers, such as
the Ozone Monitoring Instrument (OMI) on board the NASA Earth Observing
System (EOS) Aura satellite, and continues with new instruments, such as the
Ozone Mapping and Profiler Suite (OMPS) on board the NASA–NOAA Suomi National
Polar-orbiting Partnership (SNPP) satellite. In this study, we demonstrate
that it is possible, using our state-of-the-art principal component analysis
(PCA) retrieval technique, to continue the long-term global SO2
pollution monitoring started by OMI with the current and future OMPS
instruments that will fly on the NOAA Joint Polar Satellite System (JPSS) 1,
2, 3, and 4 satellites in addition to SNPP, with a very good consistency of
retrievals from these instruments. Since OMI SO2 data have been
primarily used for (1) providing regional context on air pollution and
long-range transport on a daily basis and (2) providing information on point
emission sources on an annual basis after data averaging, we focused on these
two aspects in our OMI–OMPS comparisons. Four years of retrievals
(2012–2015) have been compared for three regions: eastern China, Mexico, and
South Africa. In general, the comparisons show relatively high correlations
(r=0.79–0.96) of daily regional averaged SO2 mass between the two
instruments and near-unity regression slopes (0.76–0.97). The annual
averaged SO2 loading differences between OMI and OMPS are small
(< 0.03 Dobson unit (DU) over South Africa and up to 0.1 DU over
eastern China). We also found a very good correlation (r=0.92–0.97) in
the spatial distribution of annual averaged SO2 between OMI and OMPS
over the three regions during 2012–2015. The emissions from ∼ 400
SO2 sources calculated with the two instruments also show a very good
correlation (r=∼ 0.9) in each year during 2012–2015. OMPS-detected
SO2 point source emissions are slightly lower than those from OMI, but
OMI–OMPS differences decrease with increasing strength of source. The
OMI–OMPS SO2 mass differences on a pixel by pixel (daily) basis in each
region can show substantial differences. The two instruments have a spatial
correlation coefficient of 0.7 or better on < ∼ 50 % of the days. It is worth noting that consistent SO2
retrievals were achieved without any explicit adjustments to OMI or OMPS
radiance data and that the retrieval agreement may be further improved by
introducing a more comprehensive Jacobian lookup table than is currently
used.
Introduction
Sulfur dioxide (SO2) is an important pollutant gas that has significant
impacts on the environment and climate at global, regional, and local
scales. It oxidizes to form sulfate aerosols that reduce visibility, affect
cloud formation, and lead to acid rain and deposition. Anthropogenic sources
of SO2, consisting primarily of fossil fuel burning
(Fioletov et al., 2015; Li et al., 2010a, b), metal smelting (Carn et al., 2007), and oil
and gas refining (McLinden et al.,
2014), contribute roughly 70 % of global SO2 emissions (Smith et al., 2011). The
remainder of SO2 emissions come from natural sources, e.g., volcanic
eruptions and degassing and sea spray (Faloona et al.,
2010).
Space-based SO2 retrievals were first demonstrated for the El Chichón
volcanic eruption using the Total Ozone Mapping Spectrometer (TOMS)
(Krueger, 1983). Since then, satellite
retrievals of global SO2 pollution have undergone substantial
improvements. Satellite remote sensing using spectral fitting techniques in
the ultraviolet (UV) has been employed for global retrievals of SO2
total columns (Eisinger
and Burrows, 1998; Fioletov et al., 2013; Krotkov et al., 2016; Li et al.,
2013; Theys et al., 2015). Measurements of anthropogenic SO2 have been
demonstrated using several hyperspectral UV spectrometers such as the Global
Ozone Monitoring Experiment (GOME) (e.g., Eisinger and
Burrows, 1998), GOME-2 (Nowlan et al., 2011),
SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY
(SCIAMACHY) (Lee et
al., 2009), Ozone Monitoring Instrument (OMI) (Krotkov
et al., 2006, 2008, 2016; Li et al., 2010a, b; Fioletov et al., 2015;
McLinden et al., 2014, 2016a), and the nadir mapper of the Ozone Mapping and
Profiler Suite (OMPS) (Yang et
al., 2013). However, it is challenging to build consistent, multi-satellite
datasets necessary for long-term monitoring and trend studies, as different
characteristics between satellite instruments must be accounted for;
relatively small inconsistencies in satellite radiance measurements and
calibration may introduce large retrieval biases. Previous studies also
suggested that the spatial resolution of a satellite instrument is the main
limiting factor in detection of SO2 emissions from point sources
(Fioletov et al., 2013,
2015). This is because the coarse spatial resolution will dilute the derived
SO2 columns or masses for point sources as compared with fine spatial
resolution. This causes additional measurement differences in SO2
loading from different instruments. Stitching together satellite SO2
retrievals from different instruments and processed with various algorithms
therefore usually requires empirical SO2 bias corrections
(Fioletov et al., 2013).
Recently, a principal component analysis (PCA) SO2 algorithm was
developed and applied to OMI (Li et al., 2013, 2017). This approach greatly reduces
the noise and bias compared with the previous band residual difference (BRD)
OMI SO2 algorithm (Krotkov et al., 2006), allows smaller sources to be
detected from space (Fioletov et al., 2015, 2016; McLinden et al., 2016b),
and enables trends to be studied for more regions. One of the strengths of
the PCA technique is that it does not require instrument-specific, explicit
corrections to satellite-measured radiance data. This makes it relatively
straightforward to adapt to other instruments and reduces the chance of
introducing retrieval biases between different instruments. In this paper we
apply the PCA technique to OMPS measurements (2012–2015) to examine the
feasibility of continuing the OMI anthropogenic SO2 dataset with OMPS.
OMI and OMPS SO2 dataOMI operational PCA planetary boundary layer (PBL) SO2
OMI is a nadir-viewing UV–visible
spectrometer (Levelt et al., 2006a) on board NASA's Aura
satellite launched in 2004 (Schoeberl et al.,
2006). It measures sunlight backscattered from the Earth and solar
irradiance covering the wavelength range from 270 to 500 nm at approximately
0.5–0.6 nm spectral resolutions. The nominal pixel size of OMI is
∼ 13 km by 24 km at nadir and ∼ 28 km by 150 km
at the swath edges. The swath is ∼ 2600 km wide and contains
60 cross-track binned field of views (FOVs or “rows” on a two-dimensional
CCD detector). The current local Equator crossing time is about 13:38 local time (LT). OMI
measurement of SO2 is one of the key objectives of the OMI mission (Levelt et al., 2006b).
This study focuses on anthropogenic SO2 that is mainly distributed
within the PBL near source regions. Therefore we
use the OMI operational PCA PBL SO2 product (OMSO2 v1.2.0). It employs
a PCA technique applied to OMI radiances between 310.5 and 340 nm to derive
principal spectral features from the full spectral content. The principal
components (PCs) are used to represent various interfering processes in spectral
fitting. This greatly reduces the OMI SO2 spatially dependent biases as
compared with the original OMI PBL product (Krotkov et al., 2006) and
decreases retrieval noise by a factor of 2 (Li et al.,
2013). Details of the PCA algorithm and the OMI PBL SO2 data quality
are provided in Li et al. (2013) and Krotkov et al. (2016). The product 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). It contains SO2 vertical column densities (VCDs) in
Dobson units (1 DU =2.69×1016 molecules cm-2).
Beginning in 2007, some OMI cross-track positions have been affected by FOV
blockage and scattered light (i.e., the so-called the “row anomaly”)
presumably caused by material associated with the satellite outside the
instrument. We exclude pixels with nonzero values in the XTrackQualityFlag
data field in the L1B data to avoid influence of the row anomaly. We also
exclude pixels with large FOVs at the edges of the swath (rows < 5
or > 54, zero-based).
OMPS SO2 data
The mapping component of the OMPS is a
nadir-viewing UV spectrometer. The first model has been flying on board the
NASA–NOAA Suomi National Polar-orbiting Partnership (SNPP) spacecraft since
2011 (Dittman
et al., 2002; Flynn et al., 2014; Seftor et al., 2014). SNPP continues some
of the long-term record of climate quality observations established by
NASA's Earth Observation System (EOS) satellites. It crosses the Equator
each afternoon at about 13:30 LT, ∼ 10 min ahead
of the Aura Equator crossing time. OMPS measures backscattered UV radiance
spectra from the Earth and solar irradiance in the 300–380 nm wavelength
range at a spectral resolution of ∼ 1 nm. It has a
∼ 2800 km cross-track swath (110∘ FOV) with a nadir
pixel size of 50 km × 50 km in the nominal observation mode.
Although it has coarser spectral and spatial resolutions and expected higher
detection thresholds for emissions from point sources as compared with OMI
(Fioletov et al., 2013), it is still suitable for monitoring large
anthropogenic SO2 pollution sources (Yang et al., 2013; Krotkov et al.,
2016).
Here, we apply the same PCA retrieval technique described above to OMPS in
order to obtain the total PBL SO2 VCDs. The main difference between the
OMPS and OMI PCA algorithms is that for the current OMI operational product,
we only retrieve SO2 for pixels with slant column ozone (O3)
< 1500 DU, while for OMPS we retrieve SO2 for all pixels with
solar zenith angle (SZA) < 75∘ in order to obtain better
spatial coverage at high latitudes in winter (particularly near the edge of
the swath). We have tested OMI retrievals using the same SZA threshold as
OMPS and found results to be very similar to the operational product.
Similar to the processing of OMI data, we also exclude OMPS pixels with
large FOVs at the edges of the swath (rows < 2 or > 33
zero-based).
Another difference is that in the spectral fitting for the operational OMI
product, up to 20 PCs derived from radiance data are
used. For OMPS, we use up to 15 PCs. We found that fewer PCs are required in
OMPS retrievals to achieve a background bias reduction similar to that for
OMI. Both OMI and OMPS algorithms employ a simplified fixed SO2
Jacobian table, calculated assuming the same surface albedo (0.05), surface
pressure (1013.25 hPa), fixed SZA (30∘), nadir-viewing zenith angle
(0∘), and O3 and temperature profiles representative of typical
midlatitude conditions (Krotkov et al., 2008). In the future, we plan to
enhance the lookup table for SO2 Jacobians to more accurately
account for different measurement conditions.
OMI and OMPS data filtering and gridding
In order to account for different FOV sizes, we average both OMI and OMPS
SO2 pixels (level 2) to the same 0.5∘ latitude by 0.5∘
longitude grid daily from 2012 to 2015. Only mostly clear sky data, defined
as pixels with effective cloud radiance fraction < 30 %, are used.
The effective cloud radiance fraction is defined at each pixel as the
fraction of the measured radiance that is scattered by clouds. The values
are calculated and reported in the OMI and OMPS total ozone product. We also
exclude large negative outliers in the data gridding (< -1 DU for
OMI and < -0.5 DU for OMPS). The use of different thresholds
accounts for the fact that the standard deviation of OMI retrievals over the
presumably SO2-free equatorial Pacific (∼ 0.5 DU) is
greater than that of OMPS (∼ 0.2–0.3 DU). The small
systematical differences between retrievals from the two satellite
instruments are mainly due to differences in how the simplified air mass
factors (AMFs) relate to the true AMFs. In this paper, no empirical bias
corrections are applied to the gridded SO2 data. We plan to explore the
use of empirical corrections to further improve the retrievals in the
future.
Annual SO2 loading (unit: DU) in 2012 for OMI (a) and
OMPS (b). Both OMI and OMPS SO2 maps are gridded to
0.5∘× 0.5∘ grid cells. The three black boxes are
regions for eastern China, Mexico, and South Africa, respectively, that will
be examined in more detail below. The grey shaded area shows the area
affected by the South Atlantic Anomaly.
Emission from OMI and OMPS
Emissions in this paper are estimated statistically using a “bottom-up”
approach by fitting a parametric model to the average oversampled satellite
SO2 spatial patterns in the vicinity of each point source using a
reference frame aligned with wind direction (Fioletov et al., 2015). Here,
u and v wind components from European Centre for Medium-Range Weather
Forecasts (ECMWF) reanalysis data
(http://data-portal.ecmwf.int/data/d/interim_full_daily) are matched to
each OMI FOV. All OMI data are rotated about the known source location in
order to align with their wind vectors. This allows all OMI observations over
a given period (1 year in this case) to be analyzed together in order to
derive emissions by following the downwind decay of SO2 plumes. These
rotated OMI data along with wind speed are then fit to a three-parameter
plume-like function that describes the crosswind distribution using a
Gaussian and the downwind distribution using an exponentially modified
Gaussian. A decay constant, representing an effective lifetime, and width
parameter are specified, rather than fit, in order to improve the stability
of the fitting (Fioletov et al., 2016). Values for these were derived by
considering dozens of well-behaved SO2 sources and found to exhibit a
variability of less than 50 %. Uncertainties of estimated annual
emissions from individual sources are about 50 %. However, comparisons
with emissions measured directly at power plants stacks in the eastern US
indicate better agreement (∼ 20 %). Local AMF corrections are
applied to the OMI SO2 data in the vicinity of each emission source as
outlined in McLinden et al. (2016b). The OMI
emissions used in the comparison below are taken directly from the global OMI
SO2 emission catalogue (Fioletov et al., 2016). OMPS emissions are
calculated using a similar approach.
OMI and OMPS SO2 spatial and temporal comparison
In this section, we compute two types of correlations. The first is the
correlation between OMI and OMPS annual averaged SO2 loading, which is
computed for each pixel within a region. We refer to this as the OMI–OMPS
“spatial correlation”. We also compute the correlation between daily OMI and
OMPS SO2 masses in specific regions over an extended time period; we
refer to this as the OMI–OMPS “temporal correlation”.
Annual/regional average SO2
In Fig. 1, we show that global annual average (2012) SO2 columns from
OMPS and OMI are generally consistent. Both OMI and OMPS PCA SO2 data
show regions with major anthropogenic pollution sources including eastern
China, South Africa, Mexico, the Persian Gulf, and India, as well as a
number of degassing and eruptive volcanoes (e.g., Mount Etna). For the
regional comparisons, we focus on eastern China, Mexico, and South Africa.
These are the regions affected by anthropogenic SO2 pollution due to
extensive emissions from coal-fired power plants and industrial processes
(Krotkov et al., 2016). Mexico also has
substantial volcanic SO2 emissions from Popocatépetl volcano
south of Mexico City (de Foy et al., 2009).
The regions are situated in different latitude bands/climate zones and have
different SO2 loadings. This allows us to evaluate OMI and OMPS
retrieval performance under a broad range of conditions. The three regions
are outlined as black boxes in Fig. 1, and the coordinates are provided in
Table 1. In the South Atlantic Anomaly (SAA) region, SO2 data are
screened by removing SO2 columns greater than 0.3 DU. In this region,
Earth's magnetic field traps high-energy charged particles. These particles
can cause higher-than-normal irradiance to a low-Earth-orbiting satellite
detector (e.g., OMI) and decrease the quality of measurements, notably in
the UV.
The coordinates of each region.
LatitudeLongitudeEastern China30∘ N, 42∘ N110∘ E, 122∘ EMexico14∘ N, 25∘ N105∘ W, 92∘ WSouth Africa30∘ S, 20∘ S25∘ E, 35∘ E
Annual SO2 loading (unit: DU) over eastern China (left), Mexico
(middle), and South Africa (right) for OMI (top), OMPS (center), and
differences between OMPS and OMI (bottom, Diff = OMPS-OMI) in 2012.
SO2 columns amounts are gridded to 0.5∘× 0.5∘
grid cells.
Figure 2 shows that both OMPS and OMI capture the details of the annual
average spatial distribution of the SO2 pollution over the three
regions examined in 2012. The average SO2 pollution total columns over
eastern China and Mexico are higher than over South Africa. The OMPS data
show slightly higher SO2 loading over eastern China and lower SO2
loading over Mexico and South Africa as compared with OMI products. The
annual regional averaged SO2 columns over eastern China are 0.79 and
0.69 DUs for OMPS and OMI, respectively. On an annual basis the OMI–OMPS
spatial correlations are high. They are 0.96, 0.94, and 0.95 for eastern
China, Mexico, and South Africa, respectively. Particularly over Mexico, the
spatial patterns of high SO2 (> ∼ 1 DU) from
OMPS and OMI are similar. Regional annual average SO2 loadings are 0.58
and 0.51 DU for OMPS and OMI, respectively, and the spatial correlation
coefficient is 0.94. South Africa shows the smallest SO2 loading and
the best overall agreement between OMI and OMPS as compared with the other
two regions. The regional annual average SO2 loading from OMPS is 0.29 DU and from OMI is 0.28 DU. The spatial correlation coefficient for SO2
loading between the two instruments in this region is 0.95. Three distinct
“hot” spots (SO2 loading > 0.53 DU) are captured by both
OMPS and OMI in South Africa. These correspond to clusters of coal-fired
power plants also detected in OMI NO2
data (Duncan et al.,
2016). We find that peak SO2 columns from OMPS are smaller than from
OMI, possibly due to the lower OMPS spatial resolution. This is less of an
issue for eastern China, where the regional loading of SO2 pollution
is much higher and more homogeneous due to the numerous sources.
The averaged SO2 loading (unit: DU) in 2012–2015 from OMI and
OMPS and their spatial correlations, r, for three regions: eastern China,
Mexico, and South Africa.
Eastern China Mexico South Africa 201220132014201520122013201420152012201320142015OMI0.690.610.500.370.510.420.320.420.280.280.270.38OMPS0.790.670.520.360.580.450.340.410.290.290.280.35r0.960.960.950.920.940.970.950.940.950.960.970.95
The differences in the spatial distributions of annual mean SO2 between
OMPS and OMI over these regions in 2012 are also presented in Fig. 2.
Larger differences between the two instruments are found in areas with the
strongest SO2 sources. The maximum SO2 differences between OMPS
and OMI are 0.64 DU (29 %), -2.0 DU (-61 %), and -0.54 DU (-41 %) over
eastern China, Mexico, and South Africa, respectively. For eastern China,
the SO2 loading is relatively high for the entire region due to the
large cluster of point and area sources. The higher loading in OMPS
retrievals may be due to the minor differences in algorithm implementation
(see Sect. 2.2) and the different sampling between the two instruments. As
for Mexico and South Africa, the SO2 sources (and distributions) are
more local. The negative bias of OMPS as compared with OMI may reflect the
effect of different spatial resolutions between the two instruments and
their capabilities of resolving point sources. In addition, the retrievals
over Mexico are strongly affected by emissions from the Popocatépetl
volcano (elevation 5426 m above sea level) and likely biased high since the
volcanic plume is elevated while our retrievals assume a boundary layer
profile. The elevated volcanic plume may be transported relatively quickly
and the difference in sampling time between OMI and OMPS may cause
relatively large differences in the spatial distributions. As a result, the
difference between the two instruments may be exacerbated by the retrieval
assumption of a boundary layer profile. The OMPS pixel size is about the
same size as the 0.5∘ grid boxes used for comparisons. Emissions from a
point SO2 source could therefore alias into nearby grid boxes when we
grid OMPS pixels. This may produce differences in gridded SO2 data
between OMPS and OMI. The difference is the largest for 2012, when
Popocatépetl was most active with approximately 2 times the emissions
of 2013 and 2014 (Fioletov et al., 2016). For these 2 latter years, the
OMPS–OMI maximum differences are -0.69 and -0.68 DU, respectively.
The total number of days with valid SO2 for both OMI and OMPS
for each year. Number of samples within ±50 and ±25 % agreement
and percentage of the total sample, temporal correlation coefficient (r),
and the slopes and intercepts from reduced major axis fitting and ordinary
least-squares fitting for each year and all years. X represents OMI SO2
and Y represents OMPS SO2.
Eastern ChinaMexicoSouth Africa2012Total days:143141163Reduced major axis:Y=0.97X+0.75Y=0.99X+0.41Y=0.96X+0.32Ordinary least squares:Y=0.83X+2.56Y=0.95X+0.72Y=0.86X+0.78Days within ±50 %:129 (90 %)121 (86 %)145 (89 %)Days within ±25 %:75 (52 %)88 (62 %)96 (59 %)r:0.850.960.902013Total days:213144193Reduced major axis:Y=0.98X+0.93Y=0.99X+0.86Y=0.96X+0.25Ordinary least squares:Y=0.86X+2.27Y=0.94X+1.18Y=0.81X+0.93Days within ±50 %:189 (89 %)120 (83 %)168 (87 %)Days within ±25 %:109 (51 %)79 (55 %)108 (56 %)r:0.880.960.842014Total days:159133186Reduced major axis:Y=0.86X+1.40Y=0.91X+0.65Y=0.89X+0.28Ordinary least squares:Y=0.68X+3.00Y=0.83X+1.05Y=0.78X+0.77Days within ±50 %:134 (84 %)109 (82 %)164 (88 %)Days within ±25 %:71 (45 %)66 (50 %)101 (54 %)r:0.790.910.882015Total days:142126199Reduced major axis:Y=0.91X+0.41Y=0.95X+0.68Y=0.76X+0.93Ordinary least squares:Y=0.72X+1.64Y=0.89X+1.05Y=0.71X+1.22Days within ±50 %:120 (85 %)106 (84 %)181 (91 %)Days within ±25 %:78 (55 %)75 (60 %)116 (58 %)r:0.790.940.942012–2015Total days:657544741Reduced major axis:Y=0.96X+0.68Y=0.97X+0.60Y=0.80X+0.82Ordinary least squares:Y=0.82X+2.04Y=0.92X+0.93Y=0.73X+1.16Days within ±50 %:572 (87 %)456 (84 %)658 (89 %)Days within ±25 %:333 (51 %)308 (57 %)421 (57 %)r:0.860.950.91
Table 2 presents annual average SO2 loading for each region and the
OMI–OMPS spatial correlation for each year between 2012 and 2015. Over
eastern China, the average SO2 loading decreased significantly in 2015
as compared with 2012 (from ∼ 0.69 DU in 2012 to
∼ 0.37 DU in 2015 for OMI), in agreement with
Krotkov et al. (2016). We note that the OMI–OMPS
spatial correlation also decreases with reductions in the average SO2
loading, possibly due to reductions in the SO2 variability and thus a
decrease in signal as compared with the noise.
(a–d) Eastern China regional daily SO2 mass (unit:
kt) for OMI and OMPS for the years 2012, 2013, 2014, and 2015. Red solid line
is the 1 : 1 line and dashed lines are ±25 %. Black line is the reduced major axis fitting of
OMI and OMPS SO2 masses. r is the temporal correlation coefficient.
The number of samples within ±25 % is also presented here.
(e) Time series of daily SO2 mass in the eastern China region
from 2012 to 2015 with valid data for both OMI and OMPS.
Regional daily SO2
In this section we compare regional SO2 masses on a daily basis derived
from the two instruments. Daily regional SO2 masses are calculated as a
sum of the SO2 masses from the grid cells
(0.5∘× 0.5∘) that satisfy our filtering criteria
(see Sect. 2.3). We only consider grid cells that have valid SO2
retrievals from both instruments. This ensures consistent spatial sampling
between the two instruments. We consider days only with the number of
nonempty grid cells > 25 % of total grids cells in each
region for both OMI and OMPS. Temporal correlations (r) between OMI and
OMPS in Table 3 are calculated based on daily SO2 masses from the two
instruments that satisfy the above criteria. In Table 3, we show results of
linear regression analyses using reduced major axis fitting that accounts for
the uncertainties in both OMI and OMPS data. Results of the ordinary least-squares linear regression analyses are also provided in Table 3.
Figure 3 compares OMPS and OMI daily regional SO2 masses over the
eastern China domain from 2012 to 2015. The year 2013 has the best sampling
(more than 200 days) and the best temporal correlation and slope between the
instruments (r=0.88 and the regression slope is 0.98). The other 3
years, despite reduced sampling, also have good temporal correlations (r=0.79–0.85) and linear regression slopes close to unity (0.86 to 0.98).
Although the SO2 columns over the region remain the world's highest,
the decreasing trend is also significant. Annual averaged OMI SO2 masses in this region were 8.4, 8.8, 6.2, and 4.1 kt (kiloton,
103 metric ton) (Table 5) in 2012, 2013, 2014, and 2015, respectively. This is
in line with a ∼ 50 % decrease over the North China Plain
region also derived from OMI (Krotkov et al.,
2016). Overall, OMPS SO2 masses are slightly higher as compared with
OMI. The temporal correlation between OMI and OMPS reduces from r=0.85–0.88 in 2012–2013 to r∼ 0.79 in 2014–2015. The correlation
decreases can be explained by reduced SO2 emissions and pollution
levels that bring SO2 columns close to the OMI–OMPS detection limit and
therefore scatter around a remaining offset. The eastern China area is
located in the midlatitudes. High values of column O3 in cold season
are a major interfering species in SO2 retrievals. This, together with
higher solar zenith angles and possible snow events, leads to relatively
large noise and potential biases in retrieved SO2 in winter months.
When we restrict our analysis to the warm season (April–October), the temporal
correlation and regression slope between the two instruments improves
especially for 2014 and 2015 (r=0.82–0.87 and slope is 0.92–1.01; see
Table 4).
Same as Fig. 3 but for the Mexico region.
The Mexico region is located in the tropics where the SO2 retrievals
from the PCA algorithm are less influenced by weather patterns and the total
O3 columns are less variable as compared with middle- and high-latitude
regions. Due to the high frequency of cloud occurrence in this region, the
number of days with valid SO2 retrievals for each year is less than
that from eastern China. Figure 4 shows that OMI and OMPS retrieved
consistent SO2 masses in all 4 years. The temporal correlation
between the instruments is also the highest (r=0.91–0.96) and regression
line slopes are 0.91–0.99; i.e., this indicates that OMPS shows a relatively
small multiplicative low bias as compared with OMI. As mentioned above, the
SO2 loading in Mexico region is subject to Popocatépetl eruptions.
The increased sensitivity of the satellite instruments to SO2 at higher
altitudes possibly contributes to the OMI–OMPS SO2 agreement over the
Mexico region.
Same as Table 3 but for the warm season (April–October) over
eastern China.
2012Total days:105Reduced major axis:Y=0.92X+1.00Ordinary least squares:Y=0.80X+2.21r:0.872013Total days:132Reduced major axis:Y=0.95X+1.62Ordinary least squares:Y=0.83X+2.67r:0.872014Total days:101Reduced major axis:Y=0.98X+1.12Ordinary least squares:Y=0.83X+2.09r:0.852015Total days:96Reduced major axis:Y=1.01X+0.46Ordinary least squares:Y=0.83X+1.34r:0.82
Averaged SO2 mass (unit: kt) over eastern China, Mexico, and
South Africa in 2012, 2013, 2014, and 2015 for both OMI and OMPS.
Probability distribution functions (PDFs) of daily spatial
correlations over eastern China (a), Mexico (b), and South
Africa (c) from 2012 to 2015.
Compared with eastern China and Mexico, averaged SO2 masses in South
Africa are much smaller. The maximum SO2 mass is less than 20 kt in
2012–2014 as shown in Fig. 5. The SO2 mass exceeding 30 kt in
April–May 2015 resulted from the passage of a volcanic SO2 plume from
the April 2015 Calbuco eruption in Chile (http://so2.gsfc.nasa.gov/pix/special/2015/calbuco/Calbuco_20150427_omiomps_1.html). After removing those
days (total of 3 days), the linear regression slope increases from 0.76 to
0.83. The 2015 averaged SO2 mass in the South Africa region decreases
from 4.6 to 3.6 kt for OMPS and from 4.3 to 3.4 kt for OMI (see Table 5). Overall, SO2 masses in the South Africa region from the two
instruments are in good agreement.
We also investigated the correlation between the spatial distributions of the
OMI and OMPS PCA retrievals on a daily basis as shown in Fig. 6. We excluded
SO2 masses < 2.5 kt over the area since, for those unpolluted
days, OMI and OMPS retrievals are near their noise levels. Mexico shows the
best correlation among the three regions; 82 % of the days have spatial
correlation coefficient r > 0.6. The other two regions also
have more than half of all qualified days with daily spatial correlation
coefficients r > 0.6. These comparisons over the three regions
suggest that the daily spatial distributions of SO2 from OMI and OMPS
PCA retrievals are correlated for even moderately polluted days. All three
regions show that less than ∼ 50 % of the days have spatial
correlation coefficient r > 0.7. The discrepancy between the
two instruments is probably a result of different spatial resolutions. OMPS
large pixels can effectively cause SO2 to spill out into the adjacent
areas when averaging over our grid boxes. Li et al. (2017) used a volcanic
case to demonstrate how the OMPS low spatial resolution produced lower
SO2 columns as compared with OMI (Li et al., 2017, their Fig. S8).
Instrument performance and trends
Instrument degradation may affect SO2 retrievals. We examined the trends
in spatial standard deviation (SD) and standard errors (SE = SD divided
by the square root of the number of daily observations) of the daily SO2
noise over three clean regions in the Pacific (150–120∘ W).
Figure 7 shows the median and 25th and 75th percentiles of daily SO2 SD
and SE in August in each year. In addition to the tropical Pacific region
between 10∘ S and 10∘ N, we selected the same latitudes as
those of our eastern China and South Africa regions (called north and south
Pacific regions, respectively) and similar filtering has been applied to the
data. Over these background regions, the SO2 levels are below satellite
detection limits and, as expected, the medians of daily averaged SO2
columns were statistically equal to zero for the regions (-0.06–0.04 DU
for OMI and 0.07–0.1 DU for OMPS). The OMI SDs increased by
∼ 10 % from 2005 to 2015 over the north Pacific and tropical
Pacific regions, which can be explained by increased CCD detector noise after
12 years of continuous operation in space. As expected the OMPS SDs do not
show significant changes during its first 4 years in space. We note that OMPS
SDs (∼ 0.3 DU) are roughly half the OMI values
(∼ 0.5 =-0.7 DU), which can be explained in part by the larger
OMPS FOV that results in higher signal-to-noise ratio as well as OMI
long-term degradation. OMPS large FOVs may also reduce errors generated by
variability in observation conditions (by smoothing them out) that affect our
simple fixed AMF assumptions, e.g., geometry, cloudiness, and surface
conditions. We plan to re-examine this issue with future versions of the PCA
algorithm that will have more detailed AMF calculations. OMI SEs in 2005 are
actually smaller than OMPS in 2012, which may be explained by higher OMI
spatial resolution and a resulting larger number of measurements over the
same region. However, the OMI SEs increased after 2008 due to the row anomaly
that decreased the number of available observations. OMI and OMPS SEs became
comparable in recent years. In other months, SDs and SEs show similar trends
as those shown for August. But winter months (October–February) show more
interannual variations compared to other months (see Supplement) because
winter months are more likely influenced by pollution transport from other
regions as compared with other months.
Standard deviation (SD, a) and standard error (SE,
b) of SO2 noise (DU) averaged over clean background regions in
the Pacific (150–120∘ W), in the north between 30 and
42∘ N (Red), in the tropics between 10∘ S and
10∘ N (black), and in the south between 30 and 20∘S (blue)
in August of each year from 2005 to 2015. Solid lines are medians of daily
SO2 SD from each month (31 days), and dashed lines are 25 and 75 %
of daily SO2 SD from each month, respectively. OMI data start in 2005
and OMPS data start in 2012. The OMI SD peak in 2008 over north Pacific
results from the Okmok and Kasatochi eruptions (Krotkov et al., 2010).
Emission comparison of SO2 emissions estimated from OMI and OMPS PCA
retrievals
In this section, we evaluate OMPS' ability to continue space-based monitoring
of SO2 emissions from large point sources. It has been recently
demonstrated that, by combining wind data and OMI PCA PBL SO2
retrievals, one can quantify emissions from more than 490 anthropogenic and
volcanic sources around the globe (Fioletov et al., 2015, 2016). This
top-down approach is independent of the conventional bottom-up method and has
helped to uncover a number of SO2 sources that are missing or
underreported in some leading emission inventories using OMI data (McLinden
et al., 2016b). Here we apply the same method to OMPS SO2 data to
estimate emissions for the same point sources and compare the OMPS-based
emission estimates with those from OMI. It should be noted that the
OMPS-based results presented in this section are preliminary, as the emission
derivation method has been developed and optimized for use with OMI. In
particular, the method includes a step in which local bias is estimated and
removed from satellite data. Since OMPS has a much larger footprint and far
less pixels as compared with OMI, its local bias has to be estimated from a
much larger domain. Nonetheless, the comparison in this section should offer
some insights into the performance of SNPP OMPS in SO2 emission
monitoring.
Figure 8a shows the locations of ∼ 400 large point sources and
their average annual SO2 emissions during 2012–2015 estimated from OMI
retrievals. OMPS-based emission estimates for these sources (Fig. 8b) show
a generally similar spatial distribution, with numerous large anthropogenic
sources in China, India, and the Middle East, as well as a number of active
degassing volcanoes around the Pacific Ocean. This similarity is not
surprising given that the annual mean SO2 loadings derived with the
PCA algorithm is largely consistent between the two instruments (Fig. 1).
However, one may notice that OMPS-based emission estimates tend to
be smaller than OMI. A linear regression analysis (Fig. 9) indicates that
for all 4 years in our study period, the emissions estimated using OMPS
and OMI data are highly correlated, with correlation coefficient of
0.91–0.97. The slope ranges between 0.88 and 0.97, suggesting that OMPS
underestimates emissions as compared with OMI. The OMI- and OMPS-based annual
emissions agree to within ±25 % of each other for about one third
of the large 403 sources (i.e., 132–149 for different years). If we exclude
volcanic sources from the regression analysis, the OMI and OMPS-based
emission estimates for anthropogenic sources are still in relatively good
agreement, with correlation coefficients of 0.93–0.94 and slopes of
0.85–0.98 for all years.
(a) Average annual SO2 emissions during 2012–2015
estimated from the operational OMI PCA PBL SO2 retrievals for 403 point
sources worldwide. (b) Same as panel (a) but with emissions
estimated from OMPS retrievals. Color represents the magnitude of the
estimated emissions while source types are marked with different symbols.
Scatterplots comparing the annual SO2 emissions estimated using
OMPS and OMI retrievals for 403 sources (see Fig. 8 for their locations)
indicate generally good agreement between the two datasets, with correlation
coefficients between 0.91 and 0.97 and slopes between 0.88 and 0.97.
It has been shown that point sources emitting more than ∼ 30–40 kt
SO2 each year can be detected from OMI PCA retrievals (Fioletov et al.,
2015). Detection of smaller sources may be possible in some cases but is more
uncertain. Indeed, if we require that, for a source to be considered as being
detected, its estimated annual emissions must be greater than or equal to
twice the associated emission uncertainty (estimated by the emission
estimation algorithm), only ∼ 20 of those sources detected by OMI in
2012 have emissions below 30 kt yr-1 (Fig. 10a). Based on this
criterion, OMPS is only capable of detecting a fraction of these OMI-detected
sources (Fig. 10a). This fraction increases with the strength of emissions
(Fig. 10b). It is generally below 50 % for sources of
10–50 kt yr-1, but it grows to ∼ 60–80 % for sources of
60–130 kt yr-1. For even larger sources, this fraction is close to
100 %.
(a) Red (blue) bars: the number of SO2 sources within
different emission bins detected using OMI (OMPS) PCA retrievals for 2012.
For a source to be counted as a successful detection, its estimated annual
emissions have to be at least twice the associated uncertainty;
(b) the percentage of OMI-detected sources within each bin that is
also detected by OMPS in different years.
Overall, our comparison between SO2 emissions derived from the two
instruments suggests that OMI- and OMPS-based emissions are highly
correlated and that OMPS-based emissions are slightly smaller, probably
reflecting its reduced sensitivity to anthropogenic sources due to coarser
spatial resolution. The OMPS detection limit for point sources is probably
∼ 80–100 kt yr-1, greater than the previously estimated OMI
detection limit of 30–40 kt yr-1 (Fioletov et al., 2016). Despite these
differences, OMPS is capable of detecting the majority of point sources
detected by OMI as compared with only 30–40 sources that are detectable with
GOME-2 and SCIAMACHY (Fioletov et al., 2013). This is due in part to the
relatively low noise level of OMPS that partially compensates for its larger
footprint; unlike OMI, OMPS does not have significantly more pixels in the
fitting area as compared with GOME-2 or SCIAMACHY. However, the uncertainty
in derived SO2 emissions tends to be smaller for OMPS as compared with
GOME-2 or SCIAMACHY. Global total anthropogenic emissions (for the 403 sources) are 24 Tg yr-1 from OMPS and 30 Tg yr-1 from OMI; bottom-up inventories
indicate total anthropogenic emissions of 100–110 Tg yr-1, albeit for an
earlier period (Janssens-Maenhout
et al., 2015; Klimont et al., 2013). This suggests OMPS is able to detect
roughly 25 % of the total anthropogenic source.
Conclusions
Taking advantage of the 4-year overlap between OMI and OMPS local afternoon
measurements and applying the same PCA algorithm to retrieve SO2, we
demonstrate that OMI and OMPS SO2 retrievals are highly consistent for
the world's most polluted regions from 2012 through 2015. The annually
averaged OMI–OMPS spatial correlation coefficients of SO2 loading over
eastern China, Mexico, and South Africa are greater than ∼ 0.9 in each
year. The daily regional SO2 temporal correlation coefficients are 0.86,
0.95, and 0.91 for eastern China, Mexico, and South Africa, respectively. The
difference of regional averaged SO2 mass is less than 10 % between
the two instruments for the three regions in each year except over Mexico in
2013, when the difference is 14 %. The comparison of ∼ 400 global
anthropogenic and volcanic SO2 emissions, derived from OMI and OMPS
retrievals using a top-down approach, indicates that the correlations between
OMI and OMPS annual emissions are high (r≳ 0.9). OMPS is capable of
detecting sources about 50 % of sources with emissions of
10–50 kt yr-1 that are detected with OMI and close to 100 % of
sources larger than 130 kt yr-1 detected with OMI. Good consistency
between the two instruments provides confidence that the OMPS nadir mapper
currently flying on board the SNPP satellite and similar future instruments
planned for the follow-up JPSS 1, 2, 3, and 4 NOAA operational satellites with
improved spatial resolution similar to OMI, can be used to continue long-term
OMI SO2 record started in 2004.
The OMI PBL SO2 product (OMSO2 v1.2.0) 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;
NASA GES DISC, 2017).
The OMPS PBL monthly SO2 product is publicly available from NASA/GSFC
Aura Validation Data Center (AVDC)
(http://avdc.gsfc.nasa.gov/index.php?site=1868800100; NASA/GSFC AVDC,
2017).
The Supplement related to this article is available online at doi:10.5194/amt-10-1495-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
The authors acknowledge the NASA Earth Science Division (ESD) Aura Science
Team program for funding of OMI product development and analysis. The Dutch-
and Finnish-built OMI instrument is part of the NASA's Earth Observing
System (EOS) Aura satellite payload. The OMI project is managed by the Royal
Meteorological Institute of the Netherlands (KNMI) and the Netherlands Space
Agency (NSO). Can Li acknowledges partial support from NASA's Earth Science New
Investigator program in developing the OMPS SO2 algorithm (grant no. NNX14AI02G).
The authors would like to thank the NASA OMPS ozone Product Evaluation and
Test Element (PEATE) team for updating the OMPS calibration and producing
the OMPS Level 1b data used in this analysis. We thank the OMI calibration
team, led by KNMI, for the calibrated OMI level 1b data used here and the
Atmospheric Chemistry Processing System (ACPS) team for processing the OMI
data.
Edited by: A. Richter
Reviewed by: three anonymous referees
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