Introduction
Nitrogen oxides
(NOx≡NO+NO2) are a group of reactive trace gases that
are toxic to human health and can be converted in the atmosphere into other
noxious chemical species. In the presence of abundant volatile organic
compounds (VOCs) and strong sunlight, NOx can participate in a
series of chemical reactions to generate a net accumulation of O3,
another toxic air pollutant with a longer atmospheric lifetime.
NOx also participates in a series of reactions to create
HNO3, a key contributor to acid rain, and particulate nitrate
(NO3-), a component of fine particulate matter
(PM2.5), an additional health hazard. There are some natural
emissions of NOx (e.g., lightning), but the majority of the
NOx emissions are from anthropogenic sources (van Vuuren et
al., 2011).
There is a rich legacy of NO2 measurements by remote sensing
instruments (Burrows et al., 1999). One of these instruments is the
Dutch–Finnish ozone monitoring instrument (OMI), which measures the
absorption of solar backscatter in the UV–visible spectral range.
NO2 can be observed from space because it has strong absorption
features within the 400–465 nm wavelength region (Vandaele et al., 1998).
By comparing observed spectra with a reference spectrum, the amount of
NO2 in the atmosphere between the instrument in low-Earth orbit and
the surface can be derived; this technique is called differential optical
absorption spectroscopy (DOAS) (Platt, 1994).
Tropospheric NO2 column contents from OMI have been used to
estimate NOx emissions from various areas around the globe
(Streets et al., 2013; Miyazaki et al., 2017) including North America
(Boersma et al., 2008; Lu et al., 2015), Asia (Zhang et al., 2008; Han et
al., 2015; Kuhlmann et al., 2015; Liu et al., 2017), the Middle East (Beirle
et al., 2011), and Europe (Huijnen et al., 2010; Curier et al., 2014). It has
also been used to produce and validate NOx emissions
estimates from sources such as soil (Hudman et al., 2010; Vinken et al.,
2014a; Rasool et al., 2016), lightning (Allen et al., 2012; Liaskos et al.,
2015; Pickering et al., 2016; Nault et al., 2017), power plants (de Foy et
al., 2015), aircraft (Pujadas et al., 2011), marine vessels (Vinken et al.,
2014b; Boersma et al., 2015), and urban centers (Lu et al., 2015; Canty et
al., 2015; Souri et al., 2016).
With a pixel resolution varying from 13 km × 24 km to 26 km × 128 km, the OMI sensor was developed for global- to regional-scale
studies rather than for individual urban areas. Even at the highest spatial
resolution of 13 km × 24 km, the sensor has difficulty observing
the fine structure of NO2 plumes at or near the surface (e.g.,
highways, power plants, factories) (Chen et al., 2009; Ma et al., 2013;
Flynn et al., 2014), which are often less than 10 km in width (Heue et al.,
2008). This can lead to a
spatial averaging of pollution (Hilboll et al., 2013). A temporary remedy,
until higher spatial resolution satellite instruments are operational, is to
use a regional air quality simulation to estimate the subpixel variability
of OMI pixels. Kim et al. (2016) utilize the spatial variability in a
regional air quality model to spatially downscale OMI NO2
measurements using a spatial averaging kernel. The spatial averaging kernel
technique has been shown to increase the OMI NO2 signal within urban
areas, which is in better agreement with observations in these regions
(Goldberg et al., 2017).
Furthermore, the air mass factor and surface reflectance used in obtaining
the global OMI NO2 retrievals are at a coarse spatial resolution
(Lorente et al., 2017; Kleipool et al., 2008). While appropriate for a global
operational retrieval, this is known to cause an underestimate in the OMI
NO2 signal in urban regions (Russell et al., 2011). The air mass
factors in the operational OMI NO2 retrieval are calculated using
NO2 profile shapes that are provided at a
1.25∘ × 1∘ spatial resolution in the NASA product
(Krotkov et al., 2017) and 2∘ × 3∘ spatial
resolution in the DOMINO product (Boersma et al., 2011). Developers of the
NASA product provide scattering weights and additional auxiliary information
so that users can develop their own tropospheric vertical column product a
posteriori (Lamsal et al., 2015). Several users have recalculated the air
mass factor using a regional air quality model (Russell et al., 2011;
Kuhlmann et al., 2015; Lin et al., 2015; Goldberg et al., 2017; Laughner et
al., 2019), which can better capture the NO2 profile shapes in
urban regions. Other techniques to improve the air mass factor involve
correcting for the surface pressure in mountainous terrain (Zhou et al.,
2009) and accounting for small-scale heterogeneities in surface reflectance
(Zhou et al., 2010; Vasilkov et al., 2017). These a posteriori products have
better agreement with ground-based spectrometers measuring tropospheric
vertical column contents (Goldberg et al., 2017). When available,
observations from aircraft can constrain the NO2 profile shapes
used in the air mass factor calculation (Goldberg et al., 2017).
In this paper, we apply both techniques (the spatial averaging kernel and an
air mass factor adjustment) to develop a regional OMI NO2 product
for South Korea. We then use the regional product with only the air mass
factor adjustment to derive NOx emissions estimates for the
Seoul metropolitan area using a statistical fit to an exponentially modified
Gaussian (EMG) function (Beirle et al., 2011; Valin et al., 2013; de Foy et
al., 2014; Lu et al., 2015); the methodology is described in depth in
Sect. 2.5.
Methods
OMI NO2
OMI has been operational on NASA's Earth Observing System Aura satellite since
October 2004 (Levelt et al., 2006). The satellite follows a sun-synchronous,
low-Earth (705 km) orbit with an Equator overpass time of approximately
13:45 local time. OMI measures total column amounts in a 2600 km swath
divided into 60 unequal area “fields of view”, or pixels. At nadir (center
of the swath), pixel size is 13 km × 24 km, but at the swath
edges, pixels can be as large as 26 km × 128 km. In a single
orbit, OMI measures approximately 1650 swaths and achieves daily global
coverage over 14–15 orbits (99 min per orbit). Since June 2007, there has
been a partial blockage of the detector's full field of view, which has
limited the number of valid measurements by blocking consistent rows of data;
this is known in the community as the row anomaly (Dobber et al., 2008):
http://projects.knmi.nl/omi/research/product/rowanomaly-background.php
(last access: 1 February 2019).
OMI measures radiance data between the instrument's detector and the Earth's
surface. Comparison of these measurements with a reference spectrum (i.e.,
DOAS technique) enables the calculation of the total slant column density
(SCD), which represents an integrated trace gas abundance from the sun to the
surface and back to the instrument's detector, passing through the atmosphere
twice. For tropospheric air quality studies, vertical column density (VCD)
data are more relevant. This is done by subtracting the stratospheric slant
column from the total (tropospheric + stratospheric) slant column and
dividing by the tropospheric air mass factor (AMF), which is defined as the
ratio of the SCD to the VCD, as shown in Eq. (1):
VCDtrop=SCDtotal-SCDstratAMFtrop,whereAMFtrop=SCDtropVCDtrop.
The tropospheric AMF has been derived to be a function of the optical
atmospheric and surface properties (viewing and solar angles, surface reflectivity,
cloud radiance fraction, and cloud height) and a priori profile shape (Palmer
et al., 2001; Martin et al., 2002) and can be calculated as follows (Lamsal
et al., 2014) in Eq. (2):
AMFtrop=∑n=surfacetropopauseSWn×xn∑n=surfacetropopausexn,
where x is the partial column. The optical atmospheric and surface
properties in the NASA retrieval are characterized by the scattering weight, which is calculated by the TOMRAD forward radiative transfer model.
The scattering weights are first output as a lookup table and then
adjusted to real time depending on observed viewing angles, surface albedo,
cloud radiance fraction, and cloud pressure.
We follow previous studies (e.g., Palmer et al., 2001; Martin et al., 2002;
Boersma et al., 2011; Bucsela et al., 2013) and assume that scattering
weights and trace gas profile shapes are independent. The a priori trace gas
profile shapes (xa) must be provided by a model simulation. In an
operational setting, NASA uses a monthly averaged and year-specific Global
Model Initiative (GMI) global simulation with a spatial resolution of
1.25∘ long × 1∘ lat (∼110 km × 110 km in the midlatitudes) to provide the a priori profile shapes. For
this study, we derive tropospheric VCDs using a priori NO2 profile
shapes from a regional WRF-Chem simulation. A full description of this
methodology can be found in Goldberg et al. (2017); it is also described in
brief in Sect. 2.1.1. We filter the level 2 OMI NO2 data to ensure
only valid pixels are used. Daily pixels with solar zenith angles ≥80∘, cloud radiance fractions ≥0.5, or surface albedo ≥0.3
are removed as well as the five largest pixels at the swath edges (i.e.,
pixel numbers 1–5 and 56–60). Finally, we remove any pixel flagged by NASA
including pixels with missing values, “XTrackQualityFlags” ≠0 or 255
(row anomaly flag), or “VcdQualityFlags” >0 and least significant bit ≠0 (ground
pixel flag).
OMI–WRF-Chem NO2
We modify the air mass factor in the OMI NO2 retrieval based on the
vertical profiles from a high spatial (4 km × 4 km) resolution
WRF-Chem simulation. The vertical profiles are scaled based on a comparison
with in situ aircraft observations; this accounts for any consistent biases
in the model simulation. We use a campaign mean comparison over all
land-based areas (34–38∘ N, 126–130∘ E) and scale all
modeled profiles in this box by this ratio; there are not enough measurements
in any one grid box to scale each individual model grid cell differently. For
example, if the aircraft observations during the campaign show that mean
NO2 concentrations between 0 and 500 m are low by 50 %, then
we scale all of the modeled NO2 in this altitude bin by this same
amount. To recalculate the air mass factor for each OMI pixel, we first
compute subpixel air mass factors for each WRF-Chem model grid cell, using
the same method as outlined in Goldberg et al. (2017). The subpixel air mass
factor for each WRF-Chem grid cell is a function of the modeled NO2
profile shape and the scattering weight calculated by a radiative transfer
model. We then average all subpixel air mass factors within an OMI pixel
(usually 10–100) to generate a single tropospheric air mass factor for each
individual OMI pixel. This new air mass factor is used to convert the total
slant column into a total vertical column using Eq. (1). Model outputs were
sampled at the local time of OMI overpass. For May 2016, we used daily
NO2 profiles and terrain pressures (e.g., Zhou et al., 2009;
Laughner et al., 2016) to recalculate the AMF. For other months and years, we
used May 2016 monthly mean values of NO2 and tropopause pressures
for the a priori profiles, which are used in the calculation of the AMF.
Once the tropospheric vertical column of each OMI pixel was recalculated, the
product was oversampled (de Foy et al., 2009; Russell et al., 2010) for
April–June over a 3-year period (2015–2017; 9 months total). During this
time frame, there are approximately 9 valid OMI NO2 pixels per
month over any given location on the Korean Peninsula. In the top-down
emissions derivation, we use all 9 months of OMI data for the analysis.
NO2 observations during KORUS-AQ
We use in situ NO2 observations from the KORUS-AQ field campaign to
test the regional satellite product. KORUS-AQ was a joint South
Korea–United States field experiment designed
to better understand the trace gas and aerosol composition above the Korean
Peninsula using aircrafts, ground station networks, and satellites. The
campaign took place between 1 May and 15 June 2016 and measurements were
primarily focused in the Seoul metropolitan area. In this paper, we utilize
data acquired by the ground-based Pandora spectrometer network, the thermally
dissociated laser-induced fluorescence NO2 instrument on DC-8
aircraft, and the chemiluminescence NOy instrument on the
DC-8 aircraft (NOy=NO+NO2+HNO3+2×N2O5 + peroxy
nitrates + alkyl nitrates + …). KORUS-AQ observations were
retrieved from the online data archive:
http://www-air.larc.nasa.gov/cgi-bin/ArcView/korusaq (last access:
1 February 2019). A further description of this field campaign can be found
in the KORUS-AQ White Paper
(https://espo.nasa.gov/korus-aq/content/KORUS-AQ_Science_Overview_0,
last access: 1 February 2019).
Pandora NO2 data
Measurements of total column NO2 from the Pandora instrument
(Herman et al., 2009) are used to evaluate the OMI NO2 satellite
products. The Pandora instrument is a stationary, ground-based, sun-tracking
spectrometer, which measures direct sunlight in the UV–visible spectral range
(280–525 nm) with a sampling period of 90 s. The Pandora spectrometer
measures total column NO2 using a DOAS technique similar to OMI. A
distinct advantage of the Pandora instrument is that it does not require
complex assumptions for converting slant columns into vertical columns,
compared to zenith sky measurements (e.g., MAX-DOAS). The spatial and
temporal variation of NO2 columns in South Korea as observed by Pandora
has been documented elsewhere (Chong et al., 2018; Herman et al., 2018).
In our comparison, valid OMI NO2 pixels are matched spatially and
temporally to Pandora total column NO2 observations. To smooth the
data and eliminate brief small-scale plumes that would be undetectable by a
satellite, we average the Pandora observations over a 2 h period (±1 h
of the overpass time) before matching to the OMI NO2 data (Goldberg
et al., 2017). During May 2016, there were seven Pandora NO2
spectrometers operating during the experiment (five instruments were situated
within the Seoul metropolitan area and their locations are shown in Fig. 5);
this corresponded to 50 instances in which valid Pandora NO2
observations matched valid OMI NO2 column data.
DC-8 aircraft data
We compare the model simulation to in situ NO2 data gathered by the
UC-Berkeley Cohen Group (Thornton et al., 2000; Day et al., 2002) on the DC-8
aircraft. The instrument quantifies NO2 via laser-induced
fluorescence at 585 nm. This instrument does not have the same positive bias
as chemiluminescence NO2 detectors, so there is no need to modify
NO2 concentrations by applying an empirical equation (e.g., Lamsal
et al., 2008). We also compare the model simulation to chemiluminescence
NOy data gathered by the NCAR Weinheimer group (Ridley et
al., 2004).
We utilize 1 min averaged DC-8 data from all 26 flights during
May–June 2016. A typical flight path included several low-altitude spirals
over the Seoul metropolitan area and a long-distance transect over the Korean
Peninsula or the Yellow Sea. The 1 min averaged data are already
pregenerated in the data archive. Hourly output from the model simulation is
spatially and temporally matched to the observations. We then bin the data
into different altitude ranges for our comparison.
WRF-Chem model simulation
For the high-resolution OMI NO2 product, we use a regional
simulation of the Weather Research and Forecasting model (Skamarock et al.,
2008) coupled to Chemistry (WRF-Chem) (Grell et al., 2005) in forecast mode
prepared for flight planning during the KORUS-AQ field campaign. The forecast
simulations were performed daily and used National Centers for Environmental
Prediction Global Forecast System
(https://rda.ucar.edu/datasets/ds084.6/, last access: 1 February 2019)
meteorological initial and boundary conditions from the 06:00 UTC cycle.
Initial conditions for aerosols and gases were obtained from the previous
forecasting cycle, while the Copernicus Atmosphere Monitoring Service (Inness
et al., 2015) forecasts were used as boundary conditions. WRF-Chem was
configured with two domains, with 20 and 4 km grid spacing. The 20 km
domain included the major sources for transboundary pollution impacting the
Korean Peninsula (deserts in China and Mongolia, wildfires in Siberia and
anthropogenic sources from China). The 4 km domain provided a
high-resolution simulation where detailed local sources could be modeled and
where the KORUS-AQ flight tracks were contained. The inner domain was started
18 h after the outer domain and was simulated for 33 h (00:00 UTC from
day 1 to 09:00 UTC of day 2 of the forecast); output was saved hourly. The
last 24 h of each inner domain daily forecast over the course of KORUS-AQ
was selected to allow spinup from the outer domain and was used in the
analysis presented here.
WRF-Chem was configured with four-bin MOSAIC aerosols (Zaveri et al., 2008), a
reduced hydrocarbon trace gas chemical mechanism (Pfister et al., 2014)
including simplified secondary organic aerosol formation (Hodzic and
Jimenez, 2011), and with capabilities to assimilate satellite aerosol
optical depth both from low-Earth-orbiting and geostationary satellites
(Saide et al., 2013, 2014).
Emissions inventory
The WRF-Chem simulation was driven by emissions developed by Konkuk
University. Monthly emissions for South Korea were developed using the
projected 2015 South Korean national emissions inventory, the Clean Air
Policy Support System (CAPSS), provided by the National Institute of
Environmental Research of South Korea and with enhancements by Konkuk
University, which primarily include the addition of new power plants. The
projected CAPSS 2015 emissions were estimated based on CAPSS 2012 and 3-year
growth factors. Since the base year of the inventory is 2012, observed
emissions from the post-2013 large point source inventory were not
included. Emissions from China and North Korea were taken from the
Comprehensive Regional Emissions for Atmospheric Transport Experiments
(CREATE) v3.0 emissions inventory. In order to project the year 2010
emissions to 2015, the latest energy statistics from the International Energy
Agency (http://www.iea.org/weo2017/, last access: 1 February 2019) and
the China Statistical Yearbook 2016
(http://www.stats.gov.cn/tjsj/ndsj/2016/indexeh.htm, last access:
1 February 2019) were used to update the growth of fuel activities. In
addition, the new emissions control policies in China, which were compiled by
the International Institute for Applied Systems Analysis, were applied to
consider efficiencies of emissions control (van der A et al., 2017).
Emissions were first processed to the monthly timescale at a spatial
resolution of 3 km in South Korea and 0.1∘ for the rest of Asia
using SMOKE-Asia (Woo et al., 2012). Information from GIS, such as
population, road network, and land cover, was applied to generate gridded
emissions from the region-based (17 metropolitan and provincial boundaries of
South Korea) emissions. The GIS-based population and regional boundary data
compiled by the Ministry of Interior and Safety
(https://www.mois.go.kr/, last access: 1 February 2019) and land cover
data compiled by the Ministry of Environment (https://egis.me.go.kr/,
last access: 1 February 2019) were used to generate population- and
land-cover-based spatial surrogates. The Road and Railroad network data
compiled by The Korea Transport Institute were used to generate spatial
surrogates for on-road and non-road emissions (https://www.koti.re.kr/,
last access: 1 February 2019). The emissions were downscaled temporally from
monthly to hourly and spatially reallocated to 4 km over South Korea and
20 km over the rest of East Asia using the University of Iowa emissions
preprocessor (EPRES).
Biogenic emissions are included using the on-line Model of Emissions of Gases
and Aerosols from Nature (MEGAN) version 2; there are no
NOx emissions from MEGAN. For this simulation, the lightning
NOx parameterization was turned off. For wildfires we used
the Quick Fire Emissions Dataset (QFED2), but there were only isolated, small
fires in South Korea during this time frame.
Exponentially modified Gaussian fitting method
An EMG function is fit to
a collection of NO2 plumes observed from OMI in order to determine
the NO2 burden and lifetime from the Seoul metropolitan area. The
original methodology, proposed by Beirle et al. (2011), involves the fitting
of OMI NO2 line densities to an EMG function. OMI NO2
line densities are the integral of OMI NO2 retrieval perpendicular
to the path of the plume; the units are mass per distance. We define
integration length scale as the across-plume width. The across-plume width is
dependent on the NO2 plume size and can vary between 10 km (for
small point sources) and 240 km (for large urban areas). Visual inspection
of the rotated oversampled OMI NO2 plumes is the best way to
determine the spatial extent of the emissions sources (Lu et al., 2015).
The EMG model is expressed as Eq. (3):
OMINO2linedensity=α1xoexpμxo+σ22xo2-xxoΦx-μσ-σxo+β,
where α is the total number of NO2 molecules observed near
the hotspot, excluding the effect of background NO2, β;
xo is the e-folding distance downwind, representing the length scale
of the NO2 decay; μ is the location of the apparent source
relative to the city center; σ is the standard deviation of the
Gaussian function, representing the Gaussian smoothing length scale; and Φ
is the cumulative distribution function. Using the “curvefit” function in
IDL, we determine the five unknown parameters, α, xo, σ,
μ, and β, based on the independent (distance; x) and dependent (OMI
NO2 line density) variables.
Using the mean zonal wind speed, w, of the NO2 line density
domain, the mean effective NO2 lifetime τeffective
and the mean NOx emissions can be calculated from the fitted
parameters xo and α. The wind speed and direction are obtained
from the ERA-Interim reanalysis project (Dee et al., 2011), instead of the
WRF simulation because the WRF simulation is a forecast. We use the averaged
wind fields of the bottom eight levels of the reanalysis (i.e., from the
surface to ∼500 m). Only days in which the wind speeds are >3 m s-1 are included in this analysis, because NO2 decay
under this condition is dominated by chemical removal, not variability in the
winds (de Foy et al., 2014). The factor of 1.33 is the mean column-averaged
NOx/NO2 ratio in the WRF-Chem model simulation for the
Seoul metropolitan area during the midafternoon. The NOx/NO2 ratio is time-dependent, spatially varying and is primarily a
function of the localized j(NO2) and O3 concentration.
NOxemissions=1.33ατeffective,whereτeffective=xow
The NO2 plume concentration is a function of the emissions source
strength, wind speed, and wind direction. Originally, the method separated
all NO2 plumes by wind direction and fit an EMG function to
NO2 in eight wind directions (Beirle et al., 2011; Ialongo et al.,
2014; Liu et al., 2016). Newer methodologies rotate the plumes so that all
plumes are in the same direction (Valin et al., 2013; de Foy et al., 2014; Lu
et al., 2015). This process increases the signal-to-noise ratio and generates
a more robust fit. In this work, we filter OMI NO2 data and rotate
the NO2 plumes, as described in Lu et al. (2015), so that all
plumes are decaying in the same direction. We rotate the retrieval based on
the reanalyzed 0–500 m wind speed direction from the ERA-Interim
reanalysis. In doing so, we develop a regridded satellite product in an
x–y coordinate system, in which the urban plume is aligned along the
x axis. Following de Foy et al. (2014) and Lu et al. (2015), we only use
days in which the ERA-Interim wind speeds are >3 m s-1 because
there is more direct plume transport and less plume meandering on days with
stronger winds; this yields more robust NOx emissions
estimates. We fit an EMG function to the line density as a function of the
horizontal distance. This yields a single value at each point along the
x direction.
Results
In this section, we describe the regional high-resolution satellite product
and our validation efforts. All OMI NO2 results presented here are
vertical column densities. First, we show a continental snapshot of OMI
NO2 (OMI-standard) over
East Asia using the standard NASA product. Then, we show a regional NASA OMI
NO2 satellite product (OMI-regional) using AMFs generated from the
WRF-Chem a priori NO2 profiles. We compare the OMI-regional product
with NO2 VCDs from the original WRF-Chem simulation. We evaluate
the OMI-regional product by comparing to KORUS-AQ observations. Finally, we
use the OMI-standard and OMI-regional products to estimate
NOx emissions from the Seoul metropolitan area.
OMI NO2 in East Asia
Oversampled OMI NO2 for May–September 2015–2017 (15 months total)
in East Asia and the 4 km WRF-Chem model domain are shown in Fig. 1. The OMI
NO2 signals in East Asia over major metropolitan areas are 3 to 5
times larger than over similarly sized cities in the US (Krotkov et al.,
2016). This is in spite of recent NOx reductions in China
since 2011 (de Foy et al., 2016; Souri et al., 2017; Zheng et al., 2018). OMI
has observed a recent decrease in the NO2 burden in the immediate
Seoul metropolitan area in South Korea, but an increase in areas just outside
the city center (Duncan et al., 2016). Oversampled values greater than 8×1015 molecules cm-2 are still consistently seen in East
Asia, while they are non-existent in the US during the warm season.
Warm-season averaged (May–September) NO2 tropospheric
vertical column content using the OMI-standard NO2 product for the
years 2015–2017 in East Asia. The 4 km × 4 km WRF-Chem domain is
outlined over the Korean Peninsula.
(a) OMI-standard NO2 product averaged over a
9-month period, April–June 2015–2017, (b) the OMI-regional
NO2 product with only the air mass factor adjustment averaged over
the same time frame, and (c) the ratio between the two products:
(b)/(a). (d) Same as (a),
(e) the OMI-regional NO2 product with the air mass factor
adjustment and spatial kernel averaged over the same time frame and
(f) the ratio between the two products:
(e)/(d).
(a) The OMI-regional NO2 product with the air
mass factor adjustment and spatial kernel averaged during the month of May
2016, (b) the WRF-Chem model simulation showing only days with valid
OMI measurements, and (c) the ratio between the two products. On
average, there are only nine valid OMI pixels per month observed at any given
location on the Korean Peninsula during May 2016.
Calculation of new OMI tropospheric column NO2
In Fig. 2, we plot the OMI-standard and OMI-regional products over South
Korea. The left panels are identical and show the OMI-standard product for
April–June 2015–2017. Figure 2b shows a regional product in which only the
air mass factor (AMF) correction is applied. Figure 2e shows a regional product in which the air mass factor
correction and spatial averaging kernel (AMF + SK) are applied. The
regional product yields larger OMI NO2 values throughout the
majority of the Korean Peninsula. Areas near major cities (e.g., Seoul),
power plants, steel mills, and cement kilns have OMI NO2 values
that are >1.25 times larger in the regional AMF product and >2 times
larger in the regional AMF + SK product. There are two reasons for the
larger OMI NO2 signals: the air mass factors in polluted regions
are now smaller (Russell et al., 2011; Goldberg et al., 2017) and the spatial
weighting kernel allocates a large portion of the OMI NO2 signal
into a smaller region (Kim et al., 2016).
The diurnal profile of NOx emissions rates
processed from the bottom-up inventory. (a) The diurnal profile of
NOx emissions rates during a weekday in New York City during
July 2011 using SMOKE as the emissions preprocessor (Goldberg et al., 2016).
(b) The diurnal profile of emissions rates during a weekday in Seoul
during May 2016 using EPRES as the emissions preprocessor. Emissions profiles
in the right panel were used in the WRF-Chem simulation.
OMI-regional vs. WRF-Chem
We now compare the OMI-regional product to tropospheric vertical columns from
the WRF-Chem model simulation directly. In Fig. 3, we compare the regional
satellite product (AMF + SK) to the WRF-Chem simulation over the Korean
Peninsula. In most areas, the modeled tropospheric column NO2 is of
smaller magnitude than inferred by the satellite. In the area within 40 km
of the Seoul city center, modeled tropospheric vertical columns are 44 %
smaller than observed tropospheric vertical column in the regional
AMF + SK product. We posit four reasons as to why the model simulation
calculates columns that are consistently smaller. First, our WRF-Chem
simulation uses a reduced hydrocarbon gas-phase chemical mechanism. This
fast-calculating mechanism implemented in WRF-Chem for regional climate
assessments (Pfister et al., 2014) and used during KORUS-AQ for forecasting
does not quickly recycle alkyl nitrates back to NO2; this will
cause NO2 to be too low. While an underestimate of the chemical
conversion to NO2 in WRF-Chem is a contributor to the
underestimate, it likely does not account for the entire discrepancy; Canty
et al. (2015) suggest that by shortening the lifetime of alkyl nitrates in
the chemical mechanism, NO2 will increase by roughly 3 % in
urban areas and 18 % in rural areas. Second, an underestimate in VOC emissions would have
an impact on peroxyacyl and alkyl nitrate formation, and should enhance the
effective NOx lifetime (Romer et al., 2016). Third, the
temporal allocation of bottom-up emissions inventories can be a very
significant source of uncertainty (Mues et al., 2014). The temporal
allocation of the bottom-up South Korean NOx emissions is
such that the early afternoon rate during the OMI overpass time (between
12:00 and 14:00 local time) is approximately equal to the 24 h averaged rate
(Fig. 4). For comparison, in the eastern US, the early afternoon emissions
rate is 1.35 larger than the 24 h averaged emissions rate. Thus, there are
scenarios in which the temporal allocation can be up to 35 % different in
the midafternoon during the OMI overpass time. We are not suggesting that the
South Korean emissions inventory should have the diurnal profile of the US or
vice versa, but instead that there are scenarios in which the temporal
allocation can vary widely. This substantiates the future use of
geostationary satellites to better constrain this temporal allocation
uncertainty. Lastly, the remaining difference will likely be due to an
underestimate in the emissions inventory.
Measurements from the DC-8 aircraft binned by altitude in black.
Co-located WRF-Chem observations within the same altitude bin as the aircraft
observations are plotted above in red. Square dots represent the median
values. Boxes represent the 25th and 75th percentiles, while whiskers
represent the 5th and 95th percentiles. (a) Comparison of
NO2 in the Seoul plume (SW corner: 37.1∘ N,
127.05∘ E; NE corner: 37.75∘ N, 127.85∘ E),
(b) comparison of NOy in the Seoul plume,
(c) comparison of NO2 in areas outside of the Seoul
metropolitan area on the Korean Peninsula (SW corner: 34.0∘ N,
126.4∘ E; NE corner: 37.1∘ N, 130.0∘ E), and
(d) comparison of NOy in areas outside of the Seoul
metropolitan area on the Korean Peninsula.
Comparing WRF-Chem to aircraft measurements
When comparing the model simulation to in situ observations from the
UC-Berkeley NO2 instrument aboard the aircraft, we find that
NO2 concentrations are substantially larger than the model when
spatially and temporally collocated in the immediate Seoul metropolitan area
(Fig. 5). The comparison isolates the NO2 within the lowermost
boundary layer as the primary contributor to the tropospheric column
underestimate. When comparing aircraft NO2 to modeled NO2
in other areas of the Korean Peninsula, the underestimate is smaller.
When comparing the model simulation of NOy to observations
of the same quantity observed from the aircraft, we find a similarly large
underestimate. NOy observed on the aircraft is roughly a
factor of 2 larger at all altitudes below 2 km. This suggests that errors in
NO2 recycling (NO2↔NOy)
are not the main cause of the NO2 discrepancies seen in the
satellite and aircraft comparison (also see Fig. 9). Instead, there must be
errors in the NOy production (i.e., NOx
emissions rates are too low) or removal rates (i.e., NOy
deposition rates are too slow).
Comparison of OMI NO2 to Pandora NO2
To quantify the skill of the regional OMI NO2 product, we compare
the new total NO2 vertical columns from the satellite product to
the same quantities observed by the Pandora instruments. In Fig. 6, monthly
averaged observations during May 2016 from the Pandora instrument are
overlaid onto the monthly average of the three OMI NO2 satellite
products. The two regional OMI NO2 products capture the magnitude
and spatial variability of monthly averaged NO2 within the
metropolitan region better.
(a) Total vertical column contents from the OMI-standard
NO2 product for May 2016, (b) same quantities from the
OMI-regional product with only the air mass factor adjustment (AMF) during
the same time frame, (c) same quantities from the OMI-regional
product with the air mass factor adjustment and spatial kernel (AMF + SK)
during the same time frame, and (d) a comparison between total column
contents from the three OMI NO2 products and Pandora NO2
during May 2016. An average of Pandora 2 h means co-located to valid daily
OMI overpasses are overlaid in the spatial plots.
(a) Bottom-up NOx emissions inventory
compiled for the KORUS-AQ field campaign, (b) the oversampled
NO2 plume rotated based on wind direction for Seoul, South Korea,
from WRF-Chem (4 km × 4 km) for May 2016, and
(c) NO2 line densities integrating over the 240 km
across-plume width (-120 to 120 km along the y axis) and the
corresponding EMG fit. NOx emissions estimates are shown in
units of kt yr-1 NO2 equivalent and represent the
midafternoon emissions rate.
We then compare daily Pandora observations to each daily OMI NO2
value spatially and temporally collocated with the Pandora instrument
(Fig. 6). The Pandora observation is a 2 h mean centered on the
midafternoon OMI overpass. The slope of the linear best fit of the standard
product is 0.58, indicating that there is a consistent low bias in the
satellite product when the Pandora instrument observes large values. A
similar result was also found by Herman et al. (2018). The best-fit slope of
the OMI-regional product with only the air mass factor adjustment (AMF) is
0.76, and the OMI-regional product with the air mass factor adjustment and
spatial kernel (AMF + SK) is 1.07, indicating that the regional products
capture the polluted-to-clean spatial gradients best. The correlation of
daily observations to the satellite retrievals does not improve between
retrievals (OMI-standard: r2=0.57, OMI-regional (AMF): r2=0.57,
and OMI-regional (AMF + SK): r2=0.58). The lack of improvement in
the correlation suggests that the forecasted WRF-Chem simulation is unable to
capture the daily variability of NO2 plumes better than a
longer-term average.
Estimating NOx emissions from Seoul
To estimate NOx emissions from the Seoul metropolitan area
using a top-down satellite-based approach, we follow the exponentially
modified Gaussian fitting methodology outlined in Sect. 2.5. When fit
using the EMG method, the photochemical lifetime and OMI NO2 burden
can be derived. Using this information, a NOx emissions rate
can be inferred.
Validating the EMG method using WRF-Chem
The WRF-Chem simulation can serve as a test bed to assess the accuracy of the
EMG method, since the bottom-up emissions used for the simulation are known.
For this study, we find that for Seoul, an across-plume width of 160 km
encompasses the entire NO2 downwind plume. Using the NO2
lifetime, NO2 burden, and a 160 km across-plume width, we
calculate the top-down NOx emissions rate in the WRF-Chem
simulation from the Seoul metropolitan area during the early afternoon
(Fig. 7). We find the effective NO2 photochemical lifetime to be
3.1±1.3 h and the emissions rate to be 227±94 kt yr-1
NO2 equivalent. Uncertainties in the top-down NOx
emissions are the square root of the sum of the squares of the
NOx/NO2 ratio (10 %), the OMI NO2
vertical columns (25 %), the across-plume width (10 %), and the wind
fields (30 %) (Lu et al., 2015). Only the latter three terms are used to
calculate the uncertainty of the NO2 lifetime (Lu et al., 2015).
Panels (a) and (b) represent the oversampled
(4 km × 4 km) OMI NO2 plume from Seoul rotated based on
wind direction over a 9-month period, April–June 2015–2017, centered on
May 2016. Panels (c) and (d) represent the OMI
NO2 line densities integrating over the 240 km across-plume width
(-120 to 120 km along the y axis of a, b) and the corresponding
EMG fit. Panels (a) and (c) are from the OMI-standard
NO2 product and (b) and (d) are from the
OMI-regional NO2 product. NOx emissions estimates
are shown in units of kt yr-1 NO2 equivalent and represent
the midafternoon emissions rate.
The NOx bottom-up emissions inventory calculated using a
40 km radius from the Seoul city center is 198 kt yr-1 NO2
equivalent. We use a 40 km radius in lieu of a larger radius because an
assumption in the EMG method is that the emissions must be clustered around a
single point (in this case, the city center). Therefore, the calculated
emissions rate from the EMG fit is only measuring the magnitude of the
perturbing emissions source, and not of smaller sources that are further from
the city center. Previous studies (de Foy et al., 2014, 2015) suggest that
the background level calculated by the EMG fit accounts for emissions outside
the plume that are more regional and diffuse in nature. The agreement between
the top-down (227 kt yr-1) and bottom-up (198 kt yr-1)
approaches demonstrates the accuracy and effectiveness of the EMG method in
estimating the emissions rate.
Deriving emissions using OMI NO2
We now calculate the top-down NOx emissions rate from the
satellite data from the Seoul metropolitan area during the early afternoon
(Fig. 8). Here we use the OMI standard product and the OMI NO2
retrieval without the spatial averaging kernel; only the new air mass factor
is applied to this retrieval. We do not use the retrieval with the spatial
averaging kernel when calculating top-down NOx emissions
because the spatial averaging is strongly dependent on the wind fields in the
WRF-Chem simulation, which are forecasted. Errors in the winds can greatly
affect the estimate using this top-down approach (Valin et al., 2013; de Foy
et al., 2014).
Measurements from the DC-8 aircraft binned by altitude in black.
Co-located WRF-Chem within the same altitude bin as the aircraft observations
are plotted above in red for the original and in orange for the 2.13×NOx emissions simulation. Square dots represent the median
values. Boxes represent the 25th and 75th percentiles, while whiskers
represent the 5th and 95th percentiles. (a) Comparison of
NO2 in the Seoul plume (SW corner: 37.1∘ N,
127.05∘ E; NE corner: 37.75∘ N, 127.85∘ E),
(b) comparison of NOy in the Seoul plume,
(c) comparison of the NO2–NOy ratio in
the Seoul plume when coincident NO2 and NOy
measurements are available.
Same as Fig. 3, but now showing the WRF-Chem simulation with
NOx emissions in the Seoul metropolitan area increased by a
factor of 2.13 in panel (b).
(a) The OMI-standard product during the month of May 2016,
(b) the OMI-regional NO2 product with the WRF-Chem air
mass factor adjustment and spatial kernel during the same period,
(c) same as (b) but using WRF-Chem NO2 profiles
scaled based on the aircraft comparison, and (d) same as
(b) but using the WRF-Chem simulation with NOx in
the Seoul metropolitan area emissions increased by a factor of 2.13.
For the standard product, the effective NO2 photochemical lifetime
is 4.2±1.7 h, while in the regional product, the effective lifetime is
3.4±1.4 h. In the standard product, we derive the NOx
emissions rate to be 353±146 kt yr-1 NO2 equivalent,
while in the regional product it is 484±201 kt yr-1
NO2 equivalent. Emissions
estimates using satellite products with coarse-resolution air mass factors
will yield top-down emissions estimates that are lower than in reality. In this case, the regional satellite product yields
NOx emissions rates that are 37 % higher; we would
expect similar results from other metropolitan regions. The top-down approach
for the model simulation yielded a NOx emissions rate of
227 kt yr-1, while the top-down approach using the satellite data
yielded a 484 kt yr-1 NOx emissions rate: a 53 %
underestimate in the emissions inventory.
It should be noted that the NO2 photochemical lifetime derived here
is a fundamentally different quantity than the NO2 lifetime
observed by in situ measurements (de Foy et al., 2014; Lu et al., 2015) or
derived by model simulations (Lamsal et al., 2010). This is because the
lifetime calculation is extremely sensitive to the accuracy of the wind
direction (de Foy et al., 2014) and spatial pattern of the emissions.
Inaccuracies in the wind fields introduce noise that shortens the tail of the
fit. As a result, NO2 photochemical lifetimes derived here are
considered “effective” photochemical lifetimes and are generally shorter
than the tropospheric column NO2 lifetimes derived by model
simulations (Lamsal et al., 2010). NOx sources at the outer
portions of urban areas will lead to an artificially longer NO2
lifetime. This partially compensates for the bias introduced by the wind
direction. The heterogeneous topography and oscillating thermally driven wind
flows (such as the Yellow Sea breeze) in the Seoul metropolitan area are
effects that may bias the effective photochemical lifetime calculation. We
partially account for this bias by only selecting days with strong winds (>3 m s-1); on days with faster winds speeds, the sea and mountain
breeze effects are secondary to the synoptic flow.
Model simulation with increased NOx emissions
To test whether an increase in the NOx emissions rate is
appropriate for the Seoul metropolitan area, we conduct a simulation with
NOx emissions in the Seoul metropolitan area (within a
40 km radius of the city center), which increased by a factor of 2.13, and
we analyze the results for May 2016. The 2.13 increase is
representative of the change suggested by the top-down method (OMI-regional:
484 kt yr-1 vs. WRF-Chem original: 227 kt yr-1). This
simulation was performed slightly differently than the original simulation in
that it was a continuous month-long simulation and the outer domain was
nudged to the reanalysis.
When comparing the new model simulation to in situ observations from the
UC-Berkeley NO2 and NCAR NOy instruments aboard
the DC-8 aircraft, we find that NO2 concentrations are a bit high,
but NOy concentrations are in good agreement with WRF-Chem
in the boundary layer when spatially and temporally collocated in the
immediate Seoul metropolitan area (Fig. 9). The
NO2–NOy partitioning is captured well by both
model simulations, and there is no significant change in the
NO2–NOy ratio when using increased
NOx emissions.
When comparing the new WRF-Chem simulation to the OMI-regional product for
May 2016 (Fig. 10), we now find no significant biases in the Seoul
metropolitan area. In the area within 40 km of the Seoul city center,
NO2 columns are now only 11 % smaller in the new model
simulation. The better agreement in NO2 and NOy
from a combination of aircraft and satellite data suggests that an increase
in NOx emissions by a factor of 2.13 is appropriate.
Finally, we reprocess the air mass factors for May 2016 using the newest
WRF-Chem simulation. In Fig. 11, we show the OMI-standard product, the
OMI-regional product with no scaling of the a priori profiles from the
original WRF-Chem simulation, the OMI-regional product with scaling of the
original a priori profiles, and the OMI-regional product with a priori
profiles from the new WRF-Chem simulation. While using the new a priori
profiles increases the OMI NO2 retrieval further by 8 %, this
change is much smaller than the 37 % increase associated with switching
models and model resolution (i.e., standard vs. regional product).
Conclusions and discussion
In this work, we use a high-resolution (4 km × 4 km) WRF-Chem
model simulation to recalculate satellite NO2 air mass factors
over South Korea. We also apply a spatial averaging kernel to better account
for the subpixel variability that cannot be observed by OMI. The regional
OMI NO2 retrieval yields increased tropospheric columns in city
centers and near large industrial areas. In the area within 40 km of the
Seoul city center, OMI NO2 values are 1.37 larger in the regional
product. Areas near large industrial sources have OMI NO2 values
that are >2 times larger. The increase in remotely sensed tropospheric
vertical column contents in the Seoul metropolitan area is in better
agreement with the Pandora NO2 spectrometer measurements acquired
during the KORUS-AQ field campaign.
Using the regional OMI NO2 product with only the air mass factor
correction applied, we derive the NOx emissions rate from
the Seoul metropolitan area to be 484±201 kt yr-1, while the
standard NASA OMI NO2 product gives an emissions rate of 353±146 kt yr-1. The WRF-Chem simulation yields a midafternoon
NOx emissions rate of 227±94 kt yr-1. This
suggests an underestimate in the bottom-up NOx emissions
from Seoul metropolitan area by 53 %, when compared to the
484 kt yr-1 emissions rate from our top-down method. When comparing
observed OMI NO2 to the WRF-Chem model simulation, we find similar
underestimates of NO2 in the Seoul metropolitan area. The effective
photochemical lifetime derived in the Seoul plume is 4.2±1.7 h using
the standard OMI NO2 product and 3.4±1.4 h using the
regional product. The regional product yields shorter NO2
lifetimes, although it is not a statistically significant difference.
Finally, we show that a WRF-Chem simulation with an increase in the
NOx emissions by a factor of 2.13 yields a better comparison
with aircraft observations of NO2 and NOy and is
in better agreement with the OMI-regional NO2 product developed
herein.
It should be noted that the Seoul metropolitan area has complex geographical
features, which adds further uncertainty to this analysis. The area has large
topographical changes over short distances, including many hills (>500 m)
within the metropolitan area. Furthermore, the city is in close proximity to
the Yellow Sea, which causes the area to be affected by sea breeze fronts,
especially in the springtime, which is our period of focus. The localized
mountain and sea breezes may not be fully captured by our 4 km × 4 km WRF-Chem simulation used to derive the OMI-regional product or
the ERA-Interim dataset used to calculate top-down NOx
emissions. The effects of these features on local air quality have been
documented elsewhere in the literature (Kim and Ghim, 2002; Lee et al., 2008;
Ryu et al., 2013). Nevertheless, the 4 km × 4 km simulation will
capture topography and mesoscale phenomena better than a coarse global model
and further supports the benefits of WRF-Chem over a global model to derive
NO2 vertical column contents.
We hypothesize that the temporal allocation of NOx emissions
in the bottom-up emissions inventory is a large remaining uncertainty. The
satellite-derived emissions rates are instantaneous rates at the time of the
OMI overpass (∼13:45 local time). This is a different quantity
than a bottom-up NOx emissions inventory, which is often a
daily averaged or monthly averaged emissions rate. For this study, we only
attempt to derive a midafternoon NOx emissions rate.
Subsequently, we make sure to compare this to the midafternoon
NOx emissions rate from WRF-Chem. While bottom-up studies
provide estimates of the diurnal variability of NOx
emissions, these are very difficult to confirm from top-down approaches. Due
to a consistent midafternoon overpass time, OMI or TROPOMI cannot address
this issue. Due to boundary layer dynamics, this is also very difficult to
constrain from ground-based and aircraft measurements. In the future,
observations from a geostationary satellite instruments such as the
Geostationary Environment Monitoring Spectrometer (GEMS) and Tropospheric
Emissions: Monitoring Pollution (TEMPO) will be integral in constraining the
ratio of the midafternoon emissions rate to the 24 h averaged emissions
rate.