Introduction
Sulfur dioxide (SO2) and nitrogen dioxide (NO2) are reactive,
short-lived atmospheric trace gases with both anthropogenic and natural
sources. Major sources of NOx (NOx = NO + NO2) include
fossil fuel combustion, biomass burning, soil emissions (Vinken et al.,
2014b), and lightning (Schumann and Huntrieser, 2007). NO2 participates
in the nitrogen cascade of air, water, and soil (EPA, 2011; Galloway et al.,
2013), affects atmospheric oxidation rates (Valin et al., 2013), and
contributes to surface ozone production (Duncan et al., 2010; Seinfeld and
Pandis, 2006). The principal sources of SO2 are volcanic and
anthropogenic emissions from burning sulfur-contaminated fossil fuels and
the refinement of sulfide ores. Volcanic SO2 is often injected into the
atmosphere at high altitudes above the planetary boundary layer (PBL), while
anthropogenic SO2 emissions are predominantly in or slightly above the
PBL. Chemical reactions in the PBL involving SO2 and NO2 lead to
the production of sulfate and nitrate aerosols, and tropospheric ozone
(Seinfeld and Pandis, 2006). Volatile organic compounds (VOCs) oxidize in
the presence of NOx and sunlight to form ozone (O3), a major
tropospheric pollutant and greenhouse gas (EPA, 2013), and the oxidation
product of NO2, nitric acid (HNO3), reacts with ammonia (NH3)
to form ammonium nitrate aerosols. SO2 is oxidized in gas-phase
reactions with the hydroxyl radical (OH) or in aqueous-phase reactions with
O3 or hydrogen peroxide (H2O2) to form sulfate aerosols.
Sulfate and nitrate aerosols contribute to fine particulate matter pollution
with aerodynamic diameters less than 2.5 µm (PM2.5). PM2.5 poses serious health concerns (Lee et al., 2015; Liu et al., 2015),
degrades visibility, causes acidification of water and the biosphere with
adverse effects on plants and soil, and impacts weather and climate through
direct radiative forcing and indirectly modifying cloud formation and
optical properties (IPCC Working Group 1 et al., 2013; Twohy, 2005).
SO2, NO2, and their oxidation products, O3 and PM2.5,
are designated “criteria pollutants” (European Commission, 2015; US EPA,
2016). Space-based characterization of these pollutants enables global,
consistent monitoring, which is independent from ground-based measuring
networks.
The first space-based quantitative data on SO2 mass in volcanic clouds
after major eruptions of the El Chichón volcano in March–April 1982 were obtained from NASA's
Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) (Krueger, 1983). The TOMS
SO2 detection sensitivity was limited by the instrument's six narrow
wavelength bands. In practice, only exceptionally strong anthropogenic
SO2 signals could be detected, such as those produced by Norilsk
smelting plants in Russia or from an accidental combustion of elemental
sulfur (S) at the Al-Mishraq state sulfur mine plant in Iraq (Carn et al.,
2004; US Department of Veterans Affairs, 2015). Greatly improved sensitivity
was demonstrated through detection of SO2 emissions from coal-fired
power plants using ESA's Global Ozone Monitoring Experiment (GOME,
1995–2005) (Burrows et al., 1999; Eisinger and Burrows, 1998) and SCanning
Imaging Absorption spectrometer for Atmospheric CHartographY, (SCIAMACHY,
2002–2012) (Bovensmann et al., 1999) hyperspectral UV spectrometers. The
first tropospheric NO2 quantification was demonstrated using GOME and
SCIAMACHY visible data (Leue et al., 2001; Martin et al., 2002; Richter and
Burrows, 2002; Richter et al., 2005). These sensors needed several days to
acquire a contiguous global map. The Ozone Monitoring Instrument (OMI) is
the first satellite hyperspectral UV/Visible spectrometer with a push broom
CCD detector and a 2600 km wide swath (Levelt et al., 2006b), enabling
daily, global contiguous mapping of ozone and other trace gases, including
SO2 and NO2 (Levelt et al., 2006a). OMI was launched in July 2004
on NASA's Aura sun-synchronous afternoon equator-crossing polar satellite
(Schoeberl et al., 2006) and continues measurements through its 12th
year, providing the longest data record currently available. NO2 and
SO2 observations are also made by two GOME-2 instruments on EUMETSAT's
MetOp-A (2006) and B (2012) operational polar satellites (Callies et al.,
2000; Richter et al., 2011; Rix et al., 2012; Valks et al., 2011) and the Ozone
Mapping and Profiler Suite (OMPS) onboard the NASA–NOAA Suomi NPP satellite
(Dittman et al., 2002; Flynn et al., 2014; Seftor et al., 2014), which have
coarser spatial resolutions and higher detection thresholds for emissions
from point sources (Fioletov et al., 2013). ESA's next-generation Sentinel
series will provide higher spatial resolution and greater sensitivity to
SO2 and NO2 sources (Ingmann et al., 2012; Veefkind et al.,
2012).
In the PBL, both SO2 and NO2 have short lifetimes (< 1
day during the warm season) and are concentrated near their emission
sources. This facilitates space-based detection of SO2 and NO2
sources and global characterization of their spatiotemporal variability (van
der A et al., 2006, 2008; Burrows et al., 1999; Castellanos and Boersma,
2012; Eisinger and Burrows, 1998; Fioletov et al., 2013; de Foy et al.,
2009; Hayn et al., 2009; He et al., 2012; Hilboll et al., 2013; Huang et
al., 2013; Khokhar et al., 2005; Kim et al., 2009; Krotkov et al., 2008;
Martin, 2008; Martin et al., 2002; Mijling et al., 2009; Richter et al.,
2005; Russell et al., 2012; Schneider and Van Der A, 2012; Theys et al.,
2015; Valks et al., 2011; Zhou et al., 2009, 2012) and near-surface
concentrations (Duncan et al., 2014; Lamsal et al., 2008, 2010, 2015;
McLinden et al., 2014, 2016). Furthermore, over polluted regions, satellite-observable SO2 and NO2 vertically integrated number density
profiles (columns) are highly correlated with underlying emissions, allowing
space-based (i.e., “top-down”) inference of spatial and temporal changes
in emissions (van der A et al., 2008; Boersma et al., 2008, 2015; Carn et
al., 2007; Ding et al., 2015; Duncan et al., 2013; Fioletov et al., 2011,
2015; de Foy et al., 2014, 2015; Frost et al., 2006; Ghude et al., 2010,
2013; Hayn et al., 2009; He et al., 2012; Kim et al., 2009; Konovalov et
al., 2006, 2010; Lamsal et al., 2011; Lee et al., 2011; Li et al., 2010; Lu
et al., 2013, 2015; Martin, 2008; McLinden et al., 2012, 2014; Miyazaki et
al., 2012; Napelenok et al., 2008; Reuter et al., 2014; Stavrakou et al.,
2008; Streets et al., 2013; Vinken et al., 2014a, b; Zhang et al.,
2007), lifetime (Beirle et al., 2011; Fioletov et al., 2011, 2015; de Foy et
al., 2015; McLinden et al., 2012), physicochemical conversion (Duncan et
al., 2010; Valin et al., 2013), and deposition of these species (Nowlan et
al., 2014). OMI has been at the forefront of these rapid advances.
Previous OMI studies focused on specific species, emission sources and
regions (van der A et al., 2008; Ahmad et al., 2007; Beirle et al., 2011;
Boersma et al., 2011, 2015; Castellanos et al., 2014; Ding et al., 2015;
Duncan et al., 2013; Fioletov et al., 2015, 2011; de Foy et al., 2009, 2015;
Ghude et al., 2013; Lamsal et al., 2008, 2011, 2015; Lelieveld et al., 2015;
Lu et al., 2013; McLinden et al., 2014, 2016; Mebust and Cohen, 2014; Mijling
and Van Der A, 2012; Mijling et al., 2009; Russell et al., 2012; Valin et
al., 2013; Vinken et al., 2014a, b; Zhou et al., 2012). While NO2
and SO2 are both dominated by anthropogenic emissions in polluted
regions, the origin of their anthropogenic sources differs, as well as the
cost and efficacy of their respective emission control techniques. The often
different regional trends and abundances of NO2 and SO2 offer
valuable insights into energy infrastructures as well as pollution control
policies (Li et al., 2010; McLinden et al., 2014). In this paper, we examine
changes in both SO2 and NO2 over the world's most polluted regions
during the first decade of OMI observations. Section 2 briefly summarizes the OMI SO2 and NO2 algorithms and products. Section 3 describes
regional SO2 and NO2 changes for the world's industrial regions
with large SO2 emissions from coal burning power plants and industries
(Fig. 1). For these regions we update the previously published OMI trend
studies (Duncan et al., 2013; Fioletov et al., 2011; Lu et al., 2013;
Russell et al., 2012) and provide a context for a more detailed analysis of
individual sources (Duncan et al., 2016; Fioletov et al., 2016; Lu et al.,
2015).
OMI standard SO2 and NO2 products
OMI is the result of a partnership between NASA and the Dutch and Finnish
meteorological institutes and space agencies (Levelt et al., 2006b) and
flies on the NASA EOS-Aura satellite (Schoeberl et al., 2006). It measures
sunlight backscattered from the Earth over a wide range of Ultraviolet (UV)
and visible (Vis) wavelengths to derive abundances of ozone and other trace
gases important for air quality and climate. The measurements of SO2
and NO2 are both explicit objectives of the Aura OMI mission (Levelt et
al., 2006a) that are aimed at advancing our understanding of the sources and
transformation processes of these pollutants and enabling the application of
OMI data to inform public policy (Streets et al., 2013). Compared with other
satellite UV-Vis instruments, OMI has the highest spatial resolution, least
degradation and the longest record, allowing improved space-borne estimation
of NO2 and SO2 emissions and the study of their temporal behavior
(Carn et al., 2007; Castellanos and Boersma, 2012; Duncan et al., 2013;
Fioletov et al., 2011, 2013; de Foy et al., 2009; Lamsal et al., 2015; Lu et
al., 2013; McLinden et al., 2012; Zhou et al., 2012).
OMI-derived maps of PBL SO2 in Dobson units (DUs) (a) and
tropospheric NO2 columns in [1015 molecules cm-2] (b) for 2005–2007 show
enhanced pollution levels around major cities and industrial centers, seen
also in the “Earth at Night” (city lights) map (c), courtesy of the Aura EPO
team.
Aura has a local equator-crossing time of approximately 13:45 in the
ascending node and provides nearly global coverage each day. The OMI
detector is a 2-D charge-coupled device (CCD) array. The
instrument optics are designed such that the spatial dimension of the
detector is oriented across the orbit track, with an 115∘ field of
view, while the other dimension records spectral information. Three separate
detectors (Dobber et al., 2006; Levelt et al., 2006b), designated UV-1,
UV-2, and Vis, have spectral coverage (full performance) in the ranges of
270–310 nm (spectral resolution, full width at half maximum (FWHM), of
0.63 nm), 310–365 nm (0.45 nm), and 365–500 nm (0.63 nm), respectively. The OMI
SO2 product uses spectral measurements between 310.5 and 340 nm in
the UV-2 (Li et al., 2013) and the NO2 product uses spectral
measurements between 405 and 465 nm in the Vis region (Boersma et al.,
2011; Bucsela et al., 2013). The spatial dimension of both detectors is
divided into 60 cross-track fields of view (FOV) corresponding to the
specific binned CCD detector rows, such that rows 1 and 60 correspond to the
western and eastern edges of the swath, respectively. Spectral measurements
are made over 2-second exposure intervals. This results in along-track
coverage of 13 km and cross-track coverage of 24 km for the near-nadir FOVs
(CCD rows about 30). During each orbit, a total of about 1640 exposures are
recorded on the sunlit side of the Earth. The width of the swath (2600 km) is
such that 14–15 orbits per day are required to observe the entire surface of
the Earth, although with increased FOV size at the swath edges. Beginning in
2007, some cross-track positions of the OMI swath were affected by FOV
blockage and scattered light, also known as the “row anomaly” (KNMI,
2012). Here we use only unaffected OMI cross track FOVs throughout the
entire mission, also excluding large FOVs at the edge of the swath, thus
considering only the values for CCD rows 6–23.
Retrieval of PBL SO2
The original OMI PBL SO2 product employed the band residual difference
(BRD) algorithm, which used only 4 discrete wavelengths (Krotkov et al.,
2006). The BRD product is sensitive to the large SO2 point sources, but
has a high noise level (Krotkov et al., 2008) and systematic artifacts that
required empirical corrections (Fioletov et al., 2011; Lee et al., 2009). In
2014, a new PBL SO2 product was released, in which SO2 is
retrieved with a new algorithm that employs a principal component analysis
(PCA) technique applied to OMI radiances (Li et al., 2013). Using a clear-sky air mass factor (AMF) similar to the previous SO2 product, but with
the full spectral content between 310.5 and 340 nm, the PCA algorithm
reduces retrieval noise by a factor of 2 (Li et al., 2013). Recently, the
Differential Optical Absorption Spectroscopy (DOAS) SO2 algorithm
developed for the Sentinel 5 Precursor (TROPOMI) has been applied to the OMI
radiances and compared with the operational PCA product (Theys et al.,
2015). The two products compare well, which lends confidence in the OMI SO2
data. The estimated SO2 noise is similar between PCA and DOAS
algorithms, when using similar assumptions for AMF calculation for pollution
SO2. However, the DOAS SO2 algorithm requires empirical
corrections to remove background bias.
In this study we will use the OMI operational PCA PBL SO2 product,
which contains the vertical column density (VCD) in Dobson units (1 DU =
2.69 × 1016 molecules cm-2). The 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). For background areas the estimated 1σ noise is
∼ 0.5 DU over
tropical oceanic areas (Li et al., 2013). If we assume that the noise is
random and that there are about 100 cloud-free samples per year, the
detection limit over low latitudes is estimated to be 4 times the mean
error: ∼ 0.2 DU for the annual mean. For a single retrieval
over polluted areas, random error due to instrument noise is typically on
the order of 50–100 %. The systematic uncertainties due to our use of
fixed Jacobians are 50–100 % for cloud-free scenes. The total error for a
single retrieval is 70–150 %. For an annual average the uncertainties due
to the retrieval noise are reduced to the level of 10–15 % of the actual
signal, and become insignificant relative to the systematic errors. The
systematic errors could be further reduced to the level of 20 % applying
improved local Jacobians (McLinden et al., 2014, 2016). An important
advantage of the PCA algorithm is that the bias over background regions
(where SO2 columns are below the OMI detection limit) is small enough (< 0.1 DU) to require no empirical background
correction, as applied in other satellite SO2 algorithms (e.g.,
Fioletov et al., 2013; Theys et al., 2015). The improved data quality,
combined with the pixel averaging and oversampling techniques (e.g., de Foy et
al., 2009; Fioletov et al., 2011, 2013, 2015; Lu et al., 2013; McLinden et
al., 2014, 2016), provides greatly enhanced sensitivity to anthropogenic
SO2 sources near the surface (Fioletov et al., 2016; McLinden et al.,
2014). It has been demonstrated that US SO2 point sources (e.g., power
plants, smelters) with emissions rates as low as ∼ 30–40 kt yr-1 can be detected and analyzed using the PCA OMI SO2
product (Fioletov et al., 2015). This limit is substantially lower than that
reported (70 kt yr-1) for the previous version OMI SO2 data
(Fioletov et al., 2011).
Retrieval of tropospheric NO2
There are two algorithms used operationally to determine tropospheric
NO2 VCDs: the NASA standard product (SP, version 2.1, http://avdc.gsfc.nasa.gov/pub/tmp/OMNO2D_HR/) (Bucsela et al., 2013; Lamsal et al., 2015) and the KNMI
Dutch-OMI-NO2 (DOMINO) algorithm (TM4NO2A, version 2, http://www.temis.nl/airpollution/no2.html) (Boersma et al., 2011). Both
products share a common DOAS spectral fitting of the OMI-measured,
sun-normalized backscattered radiances to laboratory-measured absorption
spectra of NO2, H2O, and O3, and a calculated ring pseudo
absorption spectrum (Chance and Spurr, 1997), to give NO2 slant column
densities (SCDs). The estimated 1σ noise is ∼ 1015 molecules cm-2 or ∼ 10 % of the measured SCD
over polluted regions (Boersma et al., 2011). The SCDs, after subtraction of
the stratospheric contribution are converted to tropospheric VCDs by
applying AMFs interpolated from the look-up tables (LUTs) with OMI-measured
input parameters, such as viewing geometry, climatological surface
reflectivity, cloud pressure and cloud radiance fraction, assuming a priori NO2
vertical profile shapes. The NASA and KNMI algorithms differ in how they
remove the stratospheric contribution and use different a priori tropospheric
NO2 profile shapes in the AMF calculation. DOMINO subtracts
stratospheric SCD as determined in a data assimilation system, in which the
measured SCDs are assimilated with the TM4 chemical transport model (Boersma
et al., 2011). The SP estimates stratospheric NO2 from OMI data without
using stratospheric chemical transport models directly. The AMFs are
calculated with a priori NO2 monthly mean vertical profile shapes from the
Global Modeling Initiative (GMI) model (Bucsela et al., 2013). Despite the differences, both algorithms
produce statistically similar regional trends (see Supplement
Fig. S1). Here we use the SP tropospheric NO2 VCD product version 2.1
publicly available from NASA GES DISC at
http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/omno2_v003.shtml. Over polluted areas the total errors in OMI tropospheric
NO2 VCDs are typically less than 20 % for cloud-free FOVs, as
confirmed by validation studies employing in situ and remotely sensed data (Bucsela
et al., 2013; Irie et al., 2012; Lamsal et al., 2015; Oetjen et al., 2013).
Postprocessing of NO2 and SO2 data
For this study, level 2 (L2) tropospheric NO2 and PBL SO2 VCDs are
gridded at different ground resolutions after excluding FOVs possibly
affected by the (1) row anomaly; (2) snow; (3) transient volcanic SO2
clouds (Appendix A); (4) cloudy scenes with cloud radiance fraction, CRF
> 0.5 for NO2 or CRF > 0.2 for SO2. We note
that the CRF is approximately twice as large as the effective cloud
fraction derived assuming a mixed Lambert-equivalent reflectivity (MLER) cloud
model (Boersma et al., 2011; Bucsela et al., 2013; Stammes et al., 2008).
Given the very small CRF thresholds, the remaining cloud related errors were
estimated to be less than 20 % (Lee et al., 2009; McLinden et al., 2014).
However, by selecting mostly clear-sky conditions, our sampling of the OMI
data may introduce a bias relative to all-sky conditions (Geddes et al.,
2012; McLinden et al., 2014). Clouds are also associated with certain
weather conditions, which in turn may affect the level of pollution. These
factors may introduce biases in our derived trends in SO2 and/or
NO2, but only if there is a significant, long-term shift in weather
regimes. However, for polluted regions in Fig. 1 satellite derived regional
trends in cloud reflectivity (less than ±2 % per decade; Herman et
al., 2013)
are much smaller than those caused by changes in emissions (see
Sect. 3).
The standard gridded (0.25∘ × 0.25∘) level 3 (L3),
filtered, monthly regional mean values are used in time series analyses
following Lamsal et al. (2015) (Appendix B). The L3 data are publicly
available from NASA GES DISC archive at http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI. We also use L2
(pixel level) data oversampled at higher resolutions (0.01∘ × 0.01∘
for NO2 and 0.02∘ × 0.02∘ for SO2)
to create global and regional maps that highlight point pollution sources.
The regional maps are created directly from pre-filtered L2 data by
averaging all OMI pixels within a 20 km smoothing radius (30 km for SO2)
for 3 year time periods. Unlike previous studies (Lee et al., 2009; Fioletov
et al., 2011, 2013; Lu et al., 2013; McLinden et al., 2014), no empirical
background correction was applied to the PBL SO2 data.
Regional pollution changes and interpretation
Figure 1 shows SO2 and NO2 multi-year average maps at the
beginning of the OMI mission (2005–2007) over the northern hemisphere.
Regionally, population density (Lamsal et al., 2013), type of power
generation and fuel used, economic activity, and regulatory policies
determine average levels of air pollution. The SO2 map (Fig. 1a) shows
hotspots associated with major coal-fired power plants and industrial
activities, such as oil and gas refining and metal smelting. The highest
SO2 is found over industrialized and populated regions in eastern
China, as the world's second-largest economy relies on sulfur (S)-rich coal
for ∼ 70 % of its energy consumption (Klimont et al., 2009;
Zhang and Cheng, 2009; Wang et al., 2015). Based on bottom-up emission
inventories, SO2 emissions from China were the world's largest, at
∼ 33 Tg SO2 in 2005 (Lu et al., 2010, 2011). High S
coal-fired power plants are the major contributors to the SO2 over the
eastern US (SO2 emissions 14.5 Tg SO2 in 2005, US EPA, 2015),
eastern Europe and India (∼ 6.7 Tg SO2, Lu et al.,
2011). SO2 is undetectable over the western US and western Europe,
where emissions of SO2 have been relatively small due to a smaller
proportion of coal–fired power plants, the low S content of coal, and
installation of effective flue gas de-sulfurization devices (FDG) capable of
capturing more than 95 % of SO2 emissions (US EIA, 2010).
Large SO2 column amounts are also observed over the Persian Gulf, due
to emissions from the oil and gas industry, gas flaring and shipping in the
region. Based on a bottom-up SO2 emission inventory, the total SO2
emissions from the Middle East in 2005 were ∼ 6 Tg SO2 (Smith et al., 2011), less than those from India and the US. However,
OMI-observed SO2 columns over the Persian Gulf region are significantly
larger than over these two regions. This implies that real SO2
emissions from the Middle East (particularly in the Persian Gulf) are
significantly underestimated in current bottom-up emission estimates.
In addition to anthropogenic SO2, volcanic SO2 is frequently
observed over Kamchatka (Russian Federation), Japan, the South Pacific
(e.g., Anatahan volcano, Mariana Islands, Mauna Loa, Hawaii), Sicily (Etna),
Mexico (Popocatepetl volcano, south of Mexico City), Central America, and
Montserrat, West Indies. Although transient volcanic signals were filtered
from the PBL SO2 data (Table A1), the signals from frequently erupting
(e.g., Mt. Etna, Popocatepetl) or degassing volcanos remain. Except for Mt.
Etna, Iceland volcanoes (Ialongo et al., 2015; Schmidt et al., 2015), and
Mt. Popocatepetl (de Foy et al., 2009), most volcanic sources are located in
remote locations and do not contribute to the SO2 in industrial regions
considered here (see OMI daily SO2 maps for the world's volcanic
regions at http://so2.gsfc.nasa.gov).
The average OMI NO2 map (Fig. 1b) is correlated with the nighttime
lights map (Fig. 1c), used here as a proxy for population density and energy
production (Lamsal et al., 2013). For example, the highest NO2 levels
are observed over the world's most populated and industrialized regions,
including eastern China, western Europe, and the eastern US, where local
NO2 “hot spots” coincide with large urban agglomerations (Schneider
et al., 2015), power plants (Duncan et al., 2013; de Foy et al., 2015), and
industrial complexes. NO2 tropospheric columns over India and the
Middle East are significantly less than those over China, western Europe, and
the US. This can be explained by low NOx emissions, especially from
mobile sources, and, partly, by year-round high temperatures, leading to
shorter NO2 lifetimes (Beirle et al., 2011). For example, Indian
NOx emissions were relatively low, at 5.7 Tg NOx in 2005 (Lu and
Streets, 2012), whereas those from China and the US were 16.9 Tg NOx
(Klimont et al., 2009) and 20.4 Tg NOx (US EPA, 2015), respectively.
Relatively small, but significant, areal NO2 enhancements over west
African forest are caused by seasonal biomass burning NOx emissions
(Mebust and Cohen, 2014).
The differences between the spatial distributions of NO2 and SO2
over the large regions indicated as boxes in Fig. 1a and b are related to
economic activity, fuel types, combustion technology, and different
regulatory policies. The most abundant source of SO2 is pyrite
(FeS2) and organic S in lower-grade coal as well as liquid fuel, mostly
contained in heterocyclic aromatic compounds in oil, which largely accounts
for high SO2 levels over the Persian Gulf from gas flaring and oil
refining. Many developed countries have regulated the S content of fuels and
also required catalytic exhaust gas processing, resulting in decreased
mobile-source NOx and SO2 emissions in exhaust. Regulations are
also focused on stack emissions of NOx and SOx (SOx =
SO2 + SO3) at point sources, such as power plants and smelters.
This, in turn, has driven technological changes upstream to meet regulatory
requirements. For example, fluidized-bed combustion technology permits
burning at lower temperature, producing less NOx, and condensed phase
chemical capture of S, producing less gaseous SOx. Chemical loop
combustion technology uses catalytic oxygenation to oxidize the fuel largely
in the absence of N2, again resulting in greatly reduced NOx
leaving the combustion chamber. Stack scrubbers (i.e., flue gas
de-sulfurization devices, FDG) have been widely deployed in Europe and the
US, in particular, for existing plants, to remove SO2 and other
chemicals – notably mercury – from the flue gases, in order to meet
regulatory standards. However, these changes have yet to be widely
implemented in developing countries.
In addition to emissions, meteorology also plays an important role in
regional air pollution, particularly on relatively short time scales (days
to months). For midlatitude areas discussed in this study (the eastern US,
eastern China, and eastern Europe), the concentrations of SO2 and
NO2 often exhibit large day-to-day changes. They tend to increase under
the relatively stagnant conditions ahead of a cold front and decrease
dramatically after the cold front brings precipitation and strong winds into
the area (Li et al., 2007). On the interannual time scale, the frequency of
cold front passages may be influenced by large-scale circulation patterns
such as the position of the Siberian high for eastern China (Jia et al.,
2015), leading to interannual changes in SO2 and NO2. But
meteorology probably plays a lesser role in the longer-term trends that we
discuss in this study. For example, given the general trend of weakening
surface winds in the northern hemisphere (Vautard et al., 2010), one would
expect both SO2 and NO2 to increase over time in China, with
constant emissions. While OMI did initially observe growths in both SO2
and NO2 over China (Sect. 3.3), the different trends between the two
gases after 2007 imply that different emission control measures may play a
more significant role in OMI-observed trends. Similarly, the decreasing
pollution levels observed over the eastern US (Sect. 3.1) and eastern
Europe (Sect. 3.2) can only be explained by a reduction in emissions. As
for tropical areas such as India, the impact of year-to-year fluctuations in
OMI SO2 and NO2 data caused by meteorological variations is small
relative to the observed fast growth in emissions that occurred over areas
with newly built power plants and many cities (Sect. 3.4).
Another factor that can potentially affect derived long-term trends is
long-term changes in the vertical profile shape, because our a priori
profiles are constant for the entire mission. We believe that the impacts
are relatively minor for OMI measurements, as the boundary layer is often
thick and quite well mixed during OMI overpass time (in local afternoon).
Our previous aircraft measurements over northeastern China and the eastern
US show that the difference in AMF due to different SO2 profile shapes
over the two regions are very small (within a few percent, see Krotkov et
al. (2008) for more detailed discussion).
With this understanding of the influence of different factors on
anthropogenic NO2 and SO2 columns, we turn, in the remainder of
this section, to examining regional decadal trends as seen by OMI
measurements. We examine five regions indicated in Fig. 1: the eastern US,
eastern Europe and Turkey, eastern China, India, and the Middle East, which
all have SO2 and NO2 sources detectable by OMI. The regions are in
different phases of economic development and environmental regulations. We
can therefore compare and contrast the trends in SO2 and NO2 that
have different sources depending on the types of fuels burned, industrial
activity, and regulations.
Eastern US
Over the eastern US the highest levels of SO2 were observed in areas of
intense high-S coal combustion for industrial processes and electricity
generation, including the Ohio River valley and SW Pennsylvania (ORV, blue
box in Fig. 2). Concentrations are undetectable over the western US where
the local coal is intrinsically lower in S and emissions of SO2 have
been relatively small (US EIA, 2010). Prior investigations involving OMI
have reported a 40 % SO2 reduction near power plants in the eastern
US between 2005 and 2010 (Fioletov et al., 2011). More recent OMI
observations (Fig. 2) show that the SO2 levels continued to drop after
2010 due to both national (e.g., Clean Air Interstate Rule, CAIR, CAIR,
2009) and state regulations, such as 2005 Maryland Healthy Air Act (HAA)
(He et al., 2016). Currently, US regional SO2 levels are at or below
the OMI SO2 detection limit of ∼ 0.2 DU. The dramatic
decrease over the course of the first 11 years of the OMI mission (Fig. 2)
closely matches trends in reported SO2 emissions (US EPA, 2015) and
sulfate deposition (-5 % yr-1 decrease over the eastern US from
2000–2010, Hand et al., 2012; Solomon et al., 2014) and has also been
observed from surface and aircraft measurements (He et al., 2016). This
striking improvement in SO2 coincides with implementation of control
technology, such as flue gas de-sulfurization (FGD), closure of some of the
oldest coal power plants and fuel switching from coal to natural gas.
Reductions in SO2 emissions are required by the 1990 Clean Air Act
Amendments (CAAA, 1990) and other regulations. Substantial success has been
achieved through market-based cap and trade programs such as the Acid Rain
Program (ARP, 2010) and The Clean Air Interstate Rule (CAIR, 2009). These
allow electricity producers to pick the most economical emission control
methods. The conversion to natural gas with much less fuel S than coal has
also contributed to the reduction in SO2 pollution.
3-year average OMI SO2 (top) and tropospheric NO2
(bottom) regional maps over the eastern US for 3 periods: 2005–2007 (left),
2009–2011 (middle) and 2013–2015 (right). The blue box outlines the Ohio River
valley and SW Pennsylvania (ORV) region with the largest SO2 emissions from
coal-fired power plants. The red box outlines the megalopolis from Washington, DC to
New York along the I-95 interstate highway (I-95 corridor) with largest
NO2 from mobile sources.
Relative changes (compared to 2005) in OMI PBL SO2 columns
(left) and tropospheric NO2 columns (right) over the world's five most
polluted regions: (a) and (b): Ohio River valley and southwestern
Pennsylvania (ORV) in the eastern US (ORV – blue box in Fig. 2); (c) and (d):
the Maritsa Iztok power plants in Bulgaria (blue box in Fig. 4); (e) and (f):
North China Plain (NCP – blue box in Fig. 5); (g) and (h): NE India (blue box
in Fig. 6); (i) and (j): the Persian Gulf (blue box in Fig. 7). Gray circles
show de-seasonalized monthly columns (see details in Appendix B). Black
filled circles show annual means. Vertical bars show standard deviations.
Red diamonds show bottom-up emission estimates for power plants in ORV and
from coal-fired power plants in NE India (Chhattisgarh and Odisha region –
blue box in Fig. 6).
Same as Fig. 2, but for eastern Europe. The largest SO2
source in the domain is the Etna volcano in Sicily, Italy. The blue box is
centered on SO2 polluted area around Maritsa Iztok coal mining region
and the largest coal-fired power plants in southeastern Bulgaria.
Unlike SO2, which originates primarily from fuel-bound S, all
high-temperature combustion, including internal combustion engines, can
generate NOx. As expected, OMI NO2 columns peak over major cities
and highways, as well as over clusters of power plants. Chicago, Atlanta,
and the megalopolis from Washington, DC to New York, also called the I-95
corridor (red box in Fig. 2), stand out. At the beginning of the OMI mission
in 2005, a broad background of elevated NO2 was detected over rural
areas of the eastern US underlying the hot spots over large metropolitan
areas (Fig. 2). Since that time, NO2 has significantly decreased as a
result of emission regulations on power plants and cars (Duncan et al.,
2013; Lamsal et al., 2015; Lu et al., 2015; Russell et al., 2012). Decreases
in NO2 are evident in OMI NO2 data over all major cities (Lu et
al., 2015; Tong et al., 2015), especially over the I-95 corridor (red box in
Fig. 2 and Supplement Fig. S1). NO2 from clusters of power plants has also
decreased (e.g., ORV, blue box in Fig. 2). In general, downward trends in
OMI NO2 data near US power plants correlate well with trends in
NOx emissions from the Continuous Emissions Monitoring System (CEMS)
(Duncan et al., 2013) and with surface NO2 concentrations reported by
EPA Air Quality Systems (AQS) (Lamsal et al., 2015; Lu et al., 2015; Tong et
al., 2015). The NO2 reductions are due to selective catalytic reduction
(SCR) on point sources and three-way catalytic converters on vehicles
(Russell et al., 2012).
Figure 3 (upper row) compares year-to-year changes in the OMI SO2 and
NO2 annual columns and bottom-up emissions from power plants over the
ORV region (blue box in Fig. 2) with other heavily polluted regions
discussed later. Overall, between 2005 and 2015 the SO2 drop over ORV
was close to 80 %, while NO2 dropped by 40 %, the largest
reductions seen in this study. Previous studies demonstrate a linear
∼ 1:1 relationship between the percent change in NOx or
SO2 emissions from isolated power plants and the corresponding changes
in OMI columns (Fioletov et al., 2011, 2015; de Foy et al., 2015). However,
Duncan et al. (2013) show that most power plants, such as in the eastern US,
are co-located with mobile NOx sources, so that this relationship is
not always obvious. Indeed, OMI observed smaller drop in NO2 columns
(∼ 40 %) than would have been expected from ∼ 60 % reduction in NOx emissions from the power plants in the region
(Fig. 3).
The magnitude of the relative reduction in NO2 over the I-95 corridor
is similar to that over the ORV (Supplement
Fig. S1), suggesting similar reduction
in NOx emissions from cities and mobile sources. An independent analysis
of OMI NO2 data confirmed that NOx emissions of 35 major US urban
areas decreased by ∼ 50 % from 2006 to 2013 (Lu et al.,
2015). We also note the faster decline in NO2 levels before 2009
because of the installation of NOx emission control devices (ECDs) on
power plants and the impact of the economic recession in 2007–2009. Power plants
that were already operating ECDs during the ozone season began operating
them year-round (Lamsal et al., 2015). The annual reduction rate in NO2
has slowed since 2009 as the US economy has recovered from the recession and
the implementation of further pollution controls has slowed.
Although both SO2 and NO2 are criteria pollutants, and there
remain jurisdictions in the US in violation of the National Ambient Air
Quality Standards (NAAQS) for these primary pollutants, just as important is their role as precursors of key secondary air pollutants such as fine
particles (PM2.5) and ozone. The greatest numbers of Americans at risk for
harmful effects of air pollution are subject to exposure to these secondary
pollutants (Lee et al., 2015). By 2015, total US SO2 emissions fell to
about 1/6 of their 1970 peak, but NOx emissions only fell substantially
after 2000 and are now about 1/2 of their peak in 2000 (https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data). Because of these NOx
reductions, photochemical smog over the eastern US has improved significantly
over the same time period (Castellanos et al., 2011; Hogrefe et al., 2011;
Simon et al., 2015). The total deposition of oxidized N (the combination of
wet and dry deposition of species such as NO2 and NO3-) has
improved as well (Nowlan et al., 2014) indicating that the efforts to
control NOx emissions have been successful. As a result of larger
SO2 reductions, the SO2 / NO2 column ratio dropped over the ORV
region from its maximal values of ∼ 4–5 in 2005 to less than 2
in 2012 (Supplement Fig. S2). We expect a similar change in PM speciation with
increasing relative contribution of nitrate aerosols.
Eastern Europe
Europe experienced an ∼ 80 % reduction in SO2 emissions
between 1990 and 2011 (EEA, 2013). Particularly, in western Europe, after
significant reduction of SO2 emissions in the 1980s–1990s, the SO2
levels have dropped below the OMI detection limit of ∼ 0.2 DU.
There are, however, detectable SO2 sources in eastern Europe (Fig. 4).
The spatial distribution of the observed SO2 columns at the beginning
of OMI mission is consistent with the spatial pattern of SO2
concentrations derived from the surface monitoring stations for 2005 (Denby
et al., 2010). Notable anthropogenic SO2 sources include, for example,
the mining and industrial districts in Donbass region in eastern Ukraine,
large coal-fired thermal power plants around the Sea of Marmara and those
near Kahramanmaras in southern Turkey, as well as those near Galabovo in
Bulgaria, Gorj County in southwestern Romania, Belgrade in Serbia, and
Megalopolis in southern Greece (Fioletov et al., 2016). Most SO2 hot
spots are due to use of local high-S lignite (brown) coal for power
generation and incomplete SO2 removal from the flue gas. Figure 3 (second
row) shows interannual variations in SO2 and NO2 columns over the
Maritsa Iztok power complex in Stara Zagora, Bulgaria (see blue box in Fig. 4). Large SO2 reductions (∼ 50 %) between 2011 and
2015 are consistent with the installation of FGD, while NO2 remains
approximately constant, suggesting stable electricity production. Another
important source of SO2 in the region is the Mt. Etna volcano, in
Sicily. OMI SO2 retrievals indicate considerable decreases in SO2
over Megalopolis, Galabovo, and Gorj County, likely owing to more stringent
SO2 controlling measures on power plant emissions in response to
mandates by the European Union. SO2 emissions from Turkey, on the other
hand, have increased during the same period, particularly over
Kahramanmaras, where new power plants went into service in 2006 (see
http://globalenergyobservatory.org/geoid/42972). Increases in
SO2 over Serbia may reflect growth in energy consumption (mainly from
coal) as the country's economy recovers from wars in the 1990s.
Similar to Fig. 2 but for eastern China. The blue box outlines the North China Plain (NCP) also represented in Fig. 3, red box outlines Sichuan Basin (SB)
and black box outlinesa Yangtze River Delta (YRD). The boxes are also shown
in Supplement Figs. S1, S3, and S4.
Figure 4 (bottom row) gives the spatial distribution of OMI tropospheric
NO2 over eastern Europe, which shows enhanced columns in densely
populated and industrial areas. By far the largest NO2 was observed
over Moscow, Russian Federation, confirmed by in situ measurements at
different heights within PBL (Chubarova et al., 2009, 2016; Elansky et al.,
2007; Gorchakov et al., 2011). In Moscow maximal surface concentrations
exceed 100 ppb for NO2, but are less than 2 ppb for SO2 (Elansky
et al., 2007). OMI also observed large NO2 over industrial regions near
Katowice in south Poland, eastern Germany, and the northwestern Czech
Republic. Elevated NO2 columns are evident over large cities, such as
Istanbul, Prague, Warsaw, Vienna, Rome, Athens, and Budapest. These
enhancements correlate well with emissions source distribution
(Janssens-Maenhout et al., 2015). While road traffic is in general the most
important NOx source in Europe (EEA, 2013; Vestreng et al., 2009), in
some eastern European countries the power sector is the major contributor
(Zyrichidou et al., 2013). New construction and upgrades in capacity of
older power plants, as well as emission control measures affect NO2
columns (Castellanos and Boersma, 2012; Zhou et al., 2012). Several studies
based on bottom-up emissions and satellite observations have reported
substantial decreases in NOx emissions and NO2 columns in most western European countries due to stricter emission regulations
(Castellanos and Boersma, 2012; Curier et al., 2014; EEA, 2013; Lamsal et
al., 2011; Schneider et al., 2015; Vestreng et al., 2009; Zhou et al.,
2012). In contrast, changes in emissions are rather small in eastern Europe
(Zyrichidou et al., 2013). An increase in NOx emissions is reported
for those countries where implementation of the European Union (EU) air
quality standards is less effective (AQ_Environment_EC, 2015; Vestreng et al., 2009). OMI
measurements are consistent with previous studies, suggesting small or
insignificant NO2 column trends on a regional level. Changes appear to
be country-specific and likely depend on the socioeconomic and political
situation and legislative abatement measures of the country. The EU air
quality standards hold for all EU-countries (including Poland, Hungary,
Bulgaria, Croatia, the Baltic States, Slovenia, Slovakia), but not for
Serbia, Russia, Ukraine, Belarus, and Turkey. Some countries have asked for
a time extension to meet certain standards because several member states
have particular difficulties achieving compliance with the criteria for
PM and NO2.
Eastern China
The growth of the Chinese economy over the past two decades has been mainly
driven by rapid industrialization and urbanization (Huang et al., 2013) and
has been accompanied by large increases in both electricity generation
(mainly coal-fired power plants) and the number of vehicles on Chinese
roads. As evident in Figs. 1a and 5, China has the world's highest SO2
emissions, particularly over the high-S coal-rich, heavily industrial areas
in Hebei, Henan, and Shandong provinces in the North China Plain (NCP, blue
box in Fig. 5), Inner Mongolia (Li et al., 2010; Zhang et al., 2009), the
highly populated Sichuan Basin (SB, red box in Fig. 5), as well as the megacity clusters
around Shanghai (the Yangtze River Delta, YRD – black box in Fig. 5) and
Guangzhou–Hong Kong (the Pearl River Delta, PRD). Similarly, OMI retrievals
also reveal much greater NO2 over eastern China than other regions of
the world (Fig. 1b), especially over NCP, YRD, and PRD (Fig. 5). The NO2
levels are relatively low over SB, but higher over YRD and PRD. The
SO2 / NO2 column ratios were 8–10 over SB, 3–5 over NCP and
less than 2 over YRD and PRD in 2005 (Supplement Fig. S4). The ratios reflect to some
extent the level of modernization in the regions. The PRD and YRD have
relatively less coal-fired power plants but higher population and car
density, therefore greater NO2 relative to SO2.
The overall SO2 loading, although still at a relatively high level, has
decreased over the recent years (Fig. 5). This is more clearly shown in the
SO2 time series in Fig. 3e, which suggests that the SO2 loading
over the NCP peaked in 2007, and has since shown an overall decreasing trend
despite relatively large year-to-year variations. The reduction in SO2
during 2008–2010 may be attributed to both the economic recession and
emission control measures before the 2008 Beijing Olympic Games (Li et al.,
2010; Lu et al., 2011; Mijling et al., 2009; Witte et al., 2009). The
temporary rebound in 2011 may reflect a resurgence in the economy due to
stimulation by the government. This is followed by a dramatic
∼ 60 % reduction over the 4-year period during
2012–2015, which may be attributed to both stricter emission reduction
targets during the 12th Five-Year Plan (2010–2015) (Tian et al., 2013;
Zhao et al., 2013), more widespread use of FGD on coal-fired power plants
and industries (Wang et al., 2015), as well as a slowdown in the growth rate
of the Chinese economy. We confirmed the 2012–2015 SO2 reduction over
NCP applying our SO2 retrievals to the measurements from the Ozone
Mapping and Profiler Suite (OMPS) instrument onboard NASA–NOAA Suomi
National Polar Partnership (SNPP) satellite (Supplement Fig. S3). In relative terms, the
SO2 reduction in 2005–2015 was larger over YRD and SB regions compared
to NCP (Supplement Fig. S3).
NO2 over NCP, on the other hand, peaked in 2011 after dramatic
∼ 50 % increase since 2009 (Fig. 3) and decreased slightly
in 2012 and 2013 (Fig. 3). Temporary drop in 2008 can be attributed to
strict pollution reduction measures implemented before 2008 Olympic games
and economic recession. The reductions were strongest in Beijing, Tianjin,
and Shijiazhuang regions (Mijling et al., 2009; Witte et al., 2009). The
dramatic ∼ 40 % drop in NO2 in 2014–2015 is likely a
result of the slowest economic growth rate for China in nearly 25 years.
According to the National Bureau of Statistics, the electricity generation
by thermal power plants decreased by several percent in the second half of
2014 as compared with 2013. Similarly there is also a slowdown in
coal-intensive industrial sectors (Guay, 2015) and stricter emission control
policies (MEP, 2013). Independent satellite NO2 retrievals with
GOME-2A, GOME-2B, and OMI also confirm a large reduction in NO2 over
eastern China between 2013 and 2014 (Richter et al., 2015). Over SB and YRD,
NO2 columns peaked in 2010 and remained relatively constant afterwards
(Supplement Fig. S1). As a result of the different trends between SO2 and NO2, the SO2 to NO2 ratios dropped to their lowest values of
∼ 2–3, ∼ 1–2 and less than 1 over SB, NCP, and
YRD regions, respectively (Supplement Fig. S4).
Similar to Fig. 2 but for India. The blue box outlines the industrial
regions in Chhattisgarh and Odisha, which combine to represent one of India's most active areas in
terms of building new coal-fired power plants. The region is shown in Fig. 3.
India
Figure 6 shows 3-year mean OMI SO2 and NO2 maps over India. A
number of SO2 and NO2 hot spots are observed, and they match the
locations of large coal-fired power plants and major cities (Ghude et al.,
2011, 2013). This is because coal-fired power plants are the dominant
SO2 and NOx emission sources in India, and they are often built
near large cities where other anthropogenic emissions are also high. Figure 6
also shows that from 2005 to 2015, there was an increase in the OMI-observed
SO2 and NO2 columns over India, mainly reflecting the fast
expansion of the power sector driven by rapid economic growth. Based on an
updated unit-based coal-fired power sector database (Lu and Streets, 2012;
Lu et al., 2013), the total installed capacity, power generation, and fuel
consumption of Indian coal-fired power plants increased dramatically by 126, 91, and 93 %, respectively, during 2005–2014. The SO2
emissions from power plants are high, because S in local coal is mostly in
organic form and cannot be removed by physical cleaning methods (Lookman and
Rubin, 1998).
Unlike the US, Europe, and China, SO2 and NOx emitted from
coal-fired power plants are not regulated in India and the installation and
operation rates of SO2 and NOx emission control devices are very
low. FGD devices for SO2 were reported to be operating in only three
power plants at the beginning of OMI mission (Chikkatur et al., 2007).
NOx emissions by coal-fired power plants are also not regulated in
India. Although some new plants were reported to be equipped with
low-NOx burners (LNBs), the actual installation rate and performance of
these LNB devices are not known. Based on bottom-up emission inventories, we
estimate that SO2 and NOx emissions from Indian coal-fired power
plants increased by 103 and 94 %, respectively, during 2005–2014 (Lu
and Streets, 2012; Lu et al., 2013).
As shown in Fig. 3, the growth rates in OMI-observed SO2 (200 % ± 50 %) and NO2 (50 % ± 20 %) columns during
2005–2015 were particularly large over the industrial regions in
Chhattisgarh and Odisha (blue box in Fig. 6), one of India's most active
areas in terms of building new power plants. By the end of 2014, the total
installed capacity of coal-fired power plants in this region was 28 GW, 85 % of which (∼ 24 GW) was installed after 2005, accounting
for ∼ 26 % of the total newly installed capacity in India.
As a result, SO2 and NOx emissions from coal-fired power plants in
this region were both estimated to increase by ∼ 190 % from
2005 to 2014 (Lu and Streets, 2012; Lu et al., 2013), largely in line with
OMI SO2 observations (Fig. 3g). India's total annual SO2 emissions
almost doubled from 6.7 Tg in 2005 to estimated 12 Tg in 2014. In 2014,
India has not only surpassed the US to be the world's second largest
SO2 emitting country, but also has reached more than 40 % of the
SO2 emissions of the world's largest emitter, China.
During the last decade OMI observed much smaller NO2 increases
(∼ 50 %) than one would have expected from the increase in
NOx emissions from the coal-fired power plants (Fig. 3h). One possible
explanation for the discrepancy might be relatively high NO2 background
from other NOx emission sources. While coal-fired power plants may be
the single largest contributor to SO2 in this region, transportation is
a larger contributor to NOx, and the slower increase in transportation
emissions could have masked the sharp increase in coal-fired power plants
NOx emissions. In India, the prevalence of motorcycles with small,
two-stroke engines lead to high transportation emission factors for CO, VOC,
and PM, but produce only modest amounts of NOx (Dickerson et al.,
2002). Also, with a 3-fold increase in NOx emissions from the power
plants, there could be some non-linear effects in NOx chemistry,
changing the lifetime of NO2. Heavy loadings of soot may also remove
NO2 (Dickerson et al., 2002). The discrepancies will be addressed in
future studies.
Middle East
In the Middle East, abundant oil and gas deposits supply cheap and
relatively clean fuels for electricity generation, water desalination, and
industry. OMI detects the largest SO2 emissions over the Persian
Gulf. The sources for these emissions are apparently not included in current global emission inventories, such as the EDGAR-HTAP data set
(Janssens-Maenhout et al., 2015). Based on the most recent SO2 emission
inventory, the total SO2 emissions from the Middle East in 2005 were
∼ 6 Tg (Klimont et al., 2013; Smith et al., 2011), less than
those from India and the US. However, OMI observed SO2 columns over the
Gulf region are significantly larger than those over India and the US. That
suggests that the real SO2 emissions from the Middle East (particularly
in the Persian Gulf) may be several times higher than current bottom-up
emission estimates. This is consistent with independent OMI SO2
retrievals (Theys et al., 2015). Inverse modeling using OMI and SCIAMACHY
retrievals also suggests an underestimate of SO2 emissions from the
Persian Gulf (Lee et al., 2011).
Similar to Fig. 2 but for the Middle East. Blue box outlines
Persian Gulf region with high SO2 and NO2 levels due to oil and
gas operations.
Percent change in OMI annual average columns since 2005: SO2
(top) and NO2 (bottom) over the world's most polluted regions discussed
in this study.
In situ measurements of SO2 and other pollutants are rarely reported
for the region, but available data generally indicate significant SO2
loading over the Persian Gulf. For example, an aircraft campaign conducted
north of the United Arab Emirates during winter 2001 measured SO2
concentrations of up to 40 ppb (see
https://www.rap.ucar.edu/asr2002/i-precip_physics/precip_physics.htm), greater than what has been
previously observed over eastern China (Dickerson et al., 2007; He et al.,
2012). The largest hotspot observed by the aircraft, near Zirku Island, also
appears to be co-located with a hotspot in OMI retrievals. In another study,
passive sampling of SO2 at various locations on Khark Island near the
north end of the Gulf during 2003–2004 reported that the SO2 loading
was above the air quality standard (sometimes by several-fold) most of the
time (Pourzamani et al., 2012). These high SO2 columns over the Persian
Gulf are likely the result of gas flaring activities from offshore oil and
natural gas facilities, although shipping emissions and other sources may
also contribute to them. Gas flaring is used on offshore oil rigs to dispose
of gases such as hydrogen sulfide (H2S) for safety, operational, and
economic reasons and can have significant impacts on the local and regional
environment in the Middle East.
Middle East cities also show SO2 emissions due to both mobile and
stationary sources. Oil-burning boilers may constitute another important
source of SO2 in cities or population centers, as implied by the
relatively high sulfate (∼ 10 µg m-3) that is
closely associated with oil combustion tracers (e.g., vanadium), according
to an aerosol source apportionment study for Kuwait City (Alolayan et al.,
2013). The S content in gasoline and diesel is much higher in this region as
compared with others such as Europe, which enforces stricter emission
control measures (see http://www.unep.org/transport/pcfv/PDF/JordanWrkshp-MiddleEastFuelQuality.pdf).
Some of the largest point SO2 sources in the region coincide with
smelters or oil refineries, such as the Sarcheshmeh Copper Complex in Kerman
Province, Iran, which is the largest copper smelter in the Middle East. Figure 3 (bottom row) shows
interannual variations in observed SO2 and NO2
columns over the Persian Gulf (blue box in Fig. 7). Since 2010 SO2 columns
have steadily dropped by ∼ 20 % but increased again in
2014–2015 to 2005 levels. A recent study (Lelieveld et al., 2015) reported
that OMI SO2 over the Persian Gulf increased between 2005 and 2010 and
then decreased between 2010 and 2014. Their results are based on retrievals
using a different algorithm but are qualitatively consistent with this
study.
OMI-retrieved regional NO2 levels over the Middle East are much smaller
than over China (Fig. 5) and the US (Fig. 2). This may also be the results
of the short lifetime of NO2 in this hot and photochemically active
region (Beirle et al., 2011). NOx emissions in the region are
associated with power generation and mobile sources. Local NO2
enhancements coincide with heavily populated cities that have high car
densities, such as Jerusalem (Israel) and Cairo (Egypt) (Boersma et al.,
2009), Tehran (Iran), Kuwait City (Kuwait), Dubai (UAE), Riyadh and Jeddah
(Saudi Arabia). In terms of the regional trend over the Persian Gulf (blue box
in Fig. 7), NO2 columns increased by ∼ 20 % between
2005 and 2008 but remained approximately constant afterwards (Fig. 3). For
major metropolitan areas in the region, Lelieveld et al. (2015) focused on
the reversal of OMI NO2 trends due to recent air quality regulations
and domestic and international conflicts in the region. Their results are,
for the most part, qualitatively consistent with Fig. 7. For example, their
reported decrease of NO2 over Damascus, Syria since 2011 (due to civil
war) and increase over Baghdad, Iraq since 2007 are also visible in Fig. 7.