The Korea–United States Air Quality (KORUS-AQ) campaign
is a joint study between the United States National Aeronautics and Space
Administration (NASA) and the South Korea National Institute of
Environmental Research (NIER) to monitor megacity and transboundary air
pollution around the Korean Peninsula using airborne and ground-based
measurements. Here, tropospheric nitrogen dioxide (NO2) slant column
density (SCD) measurements were retrieved from Geostationary Trace and
Aerosol Sensor Optimization (GeoTASO) L1B data during the KORUS-AQ campaign
(2 May to 10 June 2016). The retrieved SCDs were converted to tropospheric
vertical column densities using the air mass factor (AMF) obtained from a
radiative transfer calculation with trace gas profiles and aerosol property
inputs simulated with the Community Multiscale Air Quality (CMAQ) model and
surface reflectance data obtained from the Moderate Resolution Imaging
Spectroradiometer (MODIS). For the first time, we examine highly resolved
(250 m × 250 m resolution) tropospheric NO2 over the Seoul and
Busan metropolitan regions and the industrial region of Anmyeon. We reveal
that the maximum NO2 vertical column densities (VCDs) were 4.94×1016 and 1.46×1017 molec. cm-2 at 09:00 and 15:00 LT over Seoul,
respectively, 6.86×1016 and 4.89×1016 molec. cm-2 in the morning and afternoon over Busan, respectively,
and 1.64×1016 molec. cm-2 over Anmyeon. The VCDs
retrieved from the GeoTASO airborne instrument were correlated with those
obtained from the Ozone Monitoring Instrument (OMI) (r=0.48), NASA's
Pandora Spectrometer System (r=0.91), and NO2 mixing ratios
obtained from in situ measurements (r=0.07 in the morning, r=0.26 in
the afternoon over the Seoul, and r>0.56 over Busan). Based on
our results, GeoTASO is useful for identifying NO2 hotspots and their
spatial distribution in highly populated cities and industrial areas.
Introduction
Nitrogen dioxide (NO2) is one of the most important atmospheric trace
gases and plays a key role in aerosol production and tropospheric ozone
photochemistry (Boersma et al., 2004; Richter et al., 2005). Furthermore,
high concentrations of NO2 in the atmosphere have adverse effects on
human health, such as respiratory infections and associated symptoms
(Brauer et al., 2002; Latza et al., 2009).
The main sources of NO2 in the atmosphere are fossil fuel combustion
from vehicles and thermal power plants, lightning, and biogenic soil
processes. Furthermore, NO2 concentrations are highly correlated with
population size (Lamsal et al., 2013). The implementation of emission
control technology and environmental regulation has led to a decrease in
surface NO2 concentrations in western Europe, the United States, and
Japan in the last few decades (Richter et al., 2005). The concentration of
NO2 in major metropolitan cities in South Korea and China is over 3
times larger than over similarly sized cities in Europe and the United States,
despite NO2 concentrations decreasing in China and South Korea (de Foy
et al., 2016; Choo et al., 2020).
To date, several low-orbit spaceborne sensors, such as the Global Ozone
Monitoring Experiment (GOME) (Burrows et al., 1999), the Scanning Imaging
Spectrometer for Atmospheric Cartography (SCIAMACHY) (Burrows et al., 1995),
the Ozone Monitoring Instrument (OMI) (Levelt et al., 2006), GOME-2
(Callies et al., 2000), and the Tropospheric Monitoring Instrument (TROPOMI)
(Veefkind et al., 2012), have monitored atmospheric ozone and its precursors,
including NO2 and formaldehyde (HCHO) as a proxy for volatile organic
compounds (VOCs). Furthermore, the Geostationary Environment Monitoring
Spectrometer (GEMS) (Choi et al., 2018; Kim et al., 2020), which was
launched on 18 February 2020, will form a constellation of geostationary
satellites, including the upcoming Tropospheric Emission: Monitoring of
Pollution (TEMPO) (Zoogman et al., 2017) and Sentinel-4 platforms, to
continuously observe the air quality of the Northern Hemisphere during the day.
NO2 retrievals from spaceborne hyperspectral measurements are
typically conducted using the differential optical absorption spectroscopy
(DOAS) method (Platt and Stutz, 2008) to first retrieve the view-dependent
slant column density (SCD), and then radiative transfer models are used to
determine the vertical column density (VCD) using an air mass factor (AMF)
correction. Previous and ongoing spaceborne instruments use various
radiative transfer codes and model input assumptions to calculate NO2
AMF values at coarse spatial resolution. Because AMF weighting has a large
impact on NO2 retrievals using the DOAS method, it is important to use
model input assumptions that most accurately match viewing and atmospheric
conditions. Several studies have demonstrated the sensitivity of AMF
calculations to inaccurate model input parameters (e.g., a priori NO2 vertical
profile and aerosol properties) and a priori data (cloud information and surface
reflectance) (Leitão et al., 2010; Hong et al., 2017; Lorente et al.,
2017; Boersma et al., 2018). NO2 retrievals have also been consistently
conducted based on surface remote sensing measurements, including the
Multi-Axis DOAS (MAX-DOAS), Système D'Analyse par Observations
Zènithales (SAOZ) spectrometer (Pastel et al., 2014), and Pandora
(Herman et al., 2009) systems. These ground-based measurements can be used
as validation references for both airborne and spaceborne measurements.
NO2 retrievals from airborne remote sensing instruments, such as the
Geostationary Coast and Air Pollution Event (GEO-CAPE) Airborne Simulator
(GCAS) (Kowalewski and Janz, 2014), the Heidelberg Airborne Imaging DOAS
Instrument (HAIDI) (General et al., 2014), the Geostationary Trace gas and
Aerosol Sensor Optimization (GeoTASO) (Leitch et al., 2014), the Airborne
Prism Experiment (APEX; Popp et al., 2012), the Airborne Imaging DOAS
instrument for Measurements of Atmospheric Pollution (AirMAP; Meier et al.,
2017; Schönhardt et al., 2015), the Small Whiskbroom Imager for
atmospheric compositioN monitorinG (SWING; Merlaud et al., 2018), and the
Spectrolite Breadboard Instrument (SBI; Vlemmix et al., 2017; Tack et al.,
2019), have also been performed to identify local emission sources and obtain
highly resolved horizontal NO2 distributions.
Observations using airborne measurements have an advantage as they enable
the observation of horizontal distributions of trace gases at resolutions
higher than those of space-based satellites and provide data over a wider
area than those of ground-based observations. For example, Nowlan et al. (2018) retrieved tropospheric NO2 VCDs over Houston, Texas, during the
Deriving Information on Surface Conditions from Column and Vertically
Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign and
identified a high correlation with data retrieved from Pandora. Popp et al. (2012) also presented the morning and afternoon NO2 spatial
distribution in Zurich, Switzerland, using APEX. Tack et al. (2017) have
conducted high-resolution mapping of NO2 over three Belgium cities
(Antwerp, Brussels, and Liège) using APEX, and Judd et al. (2020) and
Tack et al. (2021) compared NO2 VCDs retrieved from GCAS/GeoTASO and
APEX with those obtained from TROPOMI over New York City and over Antwerp and
Brussels, respectively. Merlaud et al. (2013) observed NO2 VCDs in
Turceni over Romania using SWING mounted on an uncrewed aerial vehicle (UAV)
during the Airborne Romanian Measurements of Aerosols and Trace gases
(AROMAT) campaign. These existing NO2 retrievals using airborne
measurements have been useful in constraining regional air quality models
due to the highly resolved source identification and the ability to tie
these results to ground-based observations.
This work focuses on airborne NO2 retrievals from GeoTASO. This
instrument was developed by Ball Aerospace to reduce mission risk for UV-Vis
air quality measurements from geostationary orbit for the GEMS and TEMPO
missions (Leitch et al., 2014). The retrieval of NO2, SO2, and
HCHO observed from GeoTASO L1B data using DOAS and principal component
analysis (PCA) (Wold et al., 1987) was conducted through the DISCOVER-AQ and
Korea–United States Air Quality (KORUS-AQ) campaigns (Nowlan et al., 2016;
Judd et al., 2018; Choi et al., 2020; Chong et al., 2020). The KORUS-AQ
campaign was a joint study organized from May to June 2016 between the National Institute of Environmental
Research (NIER) and National Aeronautics and Space Administration (NASA) to
monitor megacity air pollution and transboundary pollution and to prepare
for geostationary satellite (i.e., GEMS, TEMPO, and Sentinel-4) air quality
observability (of trace gases and aerosols).
Although surface NO2 concentrations in South Korea are the high due to
high population density, high traffic volumes, and many industrial complexes
and thermal power plants, and although NO2 retrieval studies using
airborne and ground measurements in North America, Europe, China, and Japan exist,
data for South Korea remain limited. The specific objectives of this study
are as follows: to retrieve tropospheric NO2 vertical column data using GeoTASO
measurements over polluted regions of the Seoul and Busan metropolitan areas
and the Anmyeon industrial region of the Korean Peninsula; to estimate NO2 VCD uncertainties using error propagation accounting
for spectral fitting errors and AMF uncertainties associated with input data
errors, including aerosol optical depth (AOD), single scattering albedo
(SSA), aerosol peak height (APH), and surface reflectance (SR); and
to compare NO2 VCDs retrieved from GeoTASO and those obtained from OMI
and ground-based Pandora instruments, as well as surface in situ
measurements.
KORUS-AQ campaign area, measurements, and model simulationCampaign area
The Korean Peninsula, located on the Asia-Pacific coast, has a complex
atmospheric environment due to local emissions and long-range transport under
appropriate weather conditions (Jeong et al., 2017; NIER and NASA, 2020;
Choo et al., 2020). Seoul, the capital of South Korea, and its metropolitan
area are densely populated, while power plants and industrial activities on
the northwestern coast emit relatively large amounts of
pollutants. The KORUS-AQ campaign conducted three-dimensional observations,
including ground-based remote, aircraft, and satellite observations, and air
quality modeling to understand the complex air quality and interpret the
observations of GEMS launched in 2020. The KORUS-AQ campaign period was from
2 May to 10 June 2016. During the KORUS-AQ campaign, air pollutants were
conducted using the GeoTASO on board the NASA Langley Research Center B200
aircraft to monitor air quality and long-range transport of pollutants over
the Korean Peninsula (NIER and NASA, 2020). The GeoTASO observations were
conducted 30 times in 23 d out of 40 d. Most observations were made once or
twice a day. Each flight was planned and conducted on a day when the weather
conditions were fine and flight hours were approximately 2–4 h. We show the
average values of GeoTASO flight information, such as flight time, altitude,
speed, solar zenith angle (SZA), and viewing zenith angle (VZA) for the
dates retrieved for NO2 VCD; aerosol properties (AOD, SSA) extracted
from the Community Multiscale Air Quality (CMAQ) model; and cloud fraction and SR extracted from the Moderate Resolution
Imaging Spectroradiometer (MODIS) in Table 1. Flight information about the date
of the aircraft observation can be found at
http://www-air.larc.nasa.gov/missions/korus-aq/docs/KORUS-AQ_Flight_Summaries_ID122.pdf (last access: 26 October 2022). Figure 1 indicates
the flight routes of B200 and the tropospheric NO2 VCD obtained from
the OMI during the campaign period. The observations were concentrated in
the metropolitan areas of Seoul and Busan and the industrial area of
Anmyeon, with an average flight altitude of ∼8.5 km during
KORUS-AQ.
Flight paths of the NASA LaRC B200 aircraft carrying GeoTASO and
the average tropospheric NO2 VCDs obtained from OMI gridded to a
0.25∘× 0.25∘ horizontal grid during the
KORUS-AQ campaign period. The color of each line represents flight height. In this
period, the GeoTASO observations focused on megacities (Seoul and Busan) and an
industrial complex area (Anmyeon) with high tropospheric NO2
concentrations. The reference spectrum for spectral fitting is obtained from
the radiation data over Jeju Island (marked with a red circle).
Summary of information on the dates when NO2 VCD was retrieved
during the KORUS-AQ period (LT = UTC+ 9 h). The average values of
GeoTASO data sets for flight characteristics, aerosol properties, geometric
information, and cloud information are given
Date5 Jun9 Jun morning9 Jun afternoon10 Jun morning10 Jun afternoonROIAnmyeonSeoul metropolitan Busan metropolitan Flight time (LT)13:11–17:2007:48–12:0013:46–17:5208:02–11:3813:05–15:19Flight altitude (km)8.68.48.58.68.5Flight speed (km h-1)117.0116.2117.6117.2117.1SZA (∘)39.236.145.335.933.0VZA (∘)11.912.612.812.111.8AOD0.270.400.210.130.09SSA0.9660.9800.9490.9810.968Surface reflectance0.070.090.090.060.06Cloud fraction0.080.310.550.160.20
As shown in Fig. 1, GeoTASO observations were conducted focusing on highly
NO2-polluted regions in the Seoul and Busan metropolitan areas and the
Anmyeon region during the KORUS-AQ campaign. The Seoul metropolitan area
(Seoul Special City, Gyeonggi Province, and Incheon City) is one of the most
densely populated areas worldwide, with a population of approximately 20
million in 2016. Busan is the second-largest city in South Korea, with a
population of approximately 3.4 million in 2016. Anmyeon is located
southwest of Seoul, with petrochemical complexes, steel mills, and thermal
power stations in the area. The background color in Fig. 1 represents the
average NO2 VCD obtained from the OMI during the KORUS-AQ campaign
period, showing over 1×1016 molec. cm-2 over the
Seoul metropolitan area. The OMI data were obtained with the Level 2.0 OMNO2
version 3.0 and downloaded from the NASA Earthdata search (http://search.earthdata.nasa.gov/search/, last access: 19 January 2023). We calculated the arithmetic
means of tropospheric NO2 VCDs, like Choo et al. (2020), to obtain the
grid data (0.25∘× 0.25∘) during the KORUS-AQ
period. The average tropospheric NO2 VCD data were excluded from 30 May to 9 Jun 2016, when the OMI data did not exist during the campaign period.
Pandora
NO2 VCDs retrieved from the GeoTASO were validated using those from
NASA's Pandora Spectrometer system. The Pandora spectrometer is a
hyper-spectrometer that can provide direct sun measurements of UV-Vis
spectra (280–525 nm with a full width at half maximum (FWHM) of 0.6 nm) for
observing atmospheric trace gases. During the KORUS-AQ, eight Pandora
instruments monitored NO2 and ozone (O3) VCD as depicted by plus
symbols in Fig. 1. The retrieved data are available on the KORUS-AQ pages of
NASA's Goddard Space Flight Center website
(https://avdc.gsfc.nasa.gov/pub/DSCOVR/Pandora/DATA/KORUS-AQ/, last access: 19 October 2022). We compared
NO2 VCDs obtained from five Pandora measurements (Busan university:
35.24∘ N, 129.08∘ E; Olympic park: 37.52∘ N, 127.13∘ E: Songchon: 37.41∘ N, 127.56∘ E; Yeoju: 37.34∘ N, 127.49∘ E; Yonsei University:
37.56∘ N, 126.93∘ E) within 0.05∘ and 30 min
with those from GeoTASO. Because NO2 has a short atmospheric lifetime,
especially during the summer (Shah et al., 2020), its spatial and temporal
distributions vary notably. A detailed description of Pandora's operation
during the KORUS-AQ campaign has previously been reported (Herman et al.,
2018; Spinei et al., 2018).
Ground-based in situ NO2 measurement
Although the basic physical quantity of VCD and the surface mixing ratio
from in situ measurements are different, comparison of their spatiotemporal
variations provides useful information for deriving surface air quality from
airborne instruments (e.g., Jeong and Hong, 2021a, b). In this study, we
compare the NO2 VCDs (molec. cm-2) retrieved from GeoTASO to
surface mixing ratios measured by ground-based in situ monitoring network
over South Korea (i.e., Air-Korea, a national real-time air quality network;
https://www.airkorea.or.kr/, last access: 19 January 2023). The instruments use the chemiluminescence
method (Kley and McFarland, 1980), and approximately 400 air quality
monitoring sites in South Korea are registered in the system, providing hourly
surface NO2 concentrations. We compared NO2 VCDs retrieved from
GeoTASO within 0.5 km and 30 min with NO2 concentrations obtained from
Air-Korea.
GeoTASO measurements
NO2 VCDs were retrieved from the L1B radiance dataset (version: V02y)
obtained using GeoTASO during the KORUS-AQ campaign. The NASA Goddard Space
Flight Center conducted the L1B radiance calibration, which included offset
and smear correction, gain matching, amplifier cross-talk correction, dark
rate correction, integration normalization, sensitivity derivation,
wavelength registration, geo-registration, nonlinearity correction, and
ground pixel geolocation (Kowalewski et al., 2017; Chong et al., 2020). The
detailed specifications of GeoTASO are listed in Table 2 (Nowlan et al., 2016).
Summary of the GeoTASO instrument and optical specification.
L1B versionV02yFull cross-track field of view45∘Single-pixel cross-track field of view0.046∘WavelengthUV: 290–400 nm VIS: 415–695 nmSpectral resolution (full width at half maximum, FWHM)UV: ∼0.39 nm VIS: ∼0.88 nmCCD1056 (wavelength) × 1033 (cross-track)Spatial resolution before binning∼35 m (along-track) × 7 m (cross-track)Spatial resolution after binning∼250 m (along-track) × 250 m (cross-track)
Flowchart of the algorithm for retrieving tropospheric NO2
data from GeoTASO.
NO2 slant column density retrieval
Figure 2 indicates the flowchart for retrieving the tropospheric NO2
VCD from the GeoTASO. We first retrieved NO2 SCDs using the DOAS method
(Platt, 1994). Nonlinear least-squares minimization was used to retrieve the
NO2 SCDs, which minimizes the difference between the measured optical
depth and the modeled value in QDOAS software (Eq. ; Danckaert et al.,
2012):
lnI(λ)lnI0(λ)=-∑j=1mρj×σj′λ+Bλ+Rλ+Aλ+Nλ),
where I(λ) is the measured earthshine radiance at wavelength
λ; I0 is the reference radiance from the reference sector
(ocean south of Jeju Island denoted with the red circle in Fig. 1;
32.983∘ N, 126.392∘ E) at 09:00 LT on 1 May 2016. The
Community Multiscale Air Quality (CMAQ) modeling system data indicated that
the NO2 VCD from the surface to 50 hPa over this reference sector on
this day was 6.75×1015 molec. cm-2, and the mean of
total NO2 VCD obtained from the OMI during the KOURS-AQ period was 4.77×1015 molec. cm-2 with a standard deviation of 1.33×1015 molec. cm-2. We also confirmed the stability of
NO2 distribution over this area using the TROPOMI offline data from
2019 to 2020. In this period, the NO2 VCD from the TROPOMI was 4.81×1015 molec. cm-2 with a standard deviation of 0.43×1015 molec. cm-2. The NO2 VCD used as a
reference sector obtained from CMAQ was mainly dominated by stratospheric
NO2 VCD. However, stratospheric NO2 VCD has a relatively lower value compared to tropospheric NO2 VCD. The ρj represents the SCD of each
species j; σj′(λ) represents the differential gas
phase absorption cross section convolved with the Gaussian distribution
function (GDF) with GeoTASO FWHM (the UV and VIS range were 0.34–0.49 nm
and 0.70–1.00 nm, respectively; Nowlan et al., 2016) at wavelength
λ of species j, respectively.
We used the measured radiances at the reference sector to calculate
differential slant column density (dSCD) over the whole domain of the
GetoTASO measurements. CMAQ calculation over the reference sector (i.e.,
6.75×1015 molec. cm-2) was adopted as the reference
SCD (SC0), which is added to all dSCD values to convert to the SCD. The
reference sector is known as a background area but is occasionally affected
by the long-range transport of NO2 from upwind areas. Considering the
standard deviation of the OMI measurements accounts for such effects during
the measurement period, we estimate the maximum uncertainties of the
SC0 can be calculated from this value (i.e., 1.33×1015 molec. cm-2) in addition to the difference in the mean values
between CMAQ and OMI (i.e., 1.98×1015 molec. cm-2). Therefore, our best estimate of the uncertainty of the SC0
is the root of the sum of squares of these values (i.e., 2.38×1015 molec. cm-2).
Residuals and NO2 SCD errors of 17 spectral fitting window
candidates (17 May 2016, with an across-track number of 15).
The spectral fitting window was selected based on the sensitivity test with
17 fitting window candidates from 420 to 480 nm with a
fitting window length from 25 to 60 nm. Spectral fitting residuals and NO2 SCD
errors have been investigated for 17 spectral fitting window candidates
(Fig. 3).
In terms of the residual, when the NO2 fitting window includes a
wavelength region less than 430 nm it has a larger residual compared to the
case where it does not. The higher residual can include the more noise
signals that cannot be calculated mathematically, which can become an
uncertainty for the NO2 SCD retrievals. Therefore, we excluded the
fitting window, which includes wavelengths less than 430 nm for the GeoTASO
NO2 retrievals during the KORUS-AQ campaign. In the case of the
NO2 SCD error, it was confirmed that the longer the fitting window
length was, the lower the NO2 SCD error appeared regardless of including
the wavelength region less than 430 nm. Therefore, for the stable NO2
SCD retrieval, an appropriate spectral fitting window needs to be selected
that can minimize the residual with a moderate length of the fitting
window. To find the optimal fitting window, we set the threshold value based
on the above results: residual <0.001, NO2 SCD error <1.4×1015 molec. cm-2, and the length of fitting
window >30 nm. Following this, the fitting window of 435–475 nm was
selected for the GeoTASO NO2 retrievals during the KORUS-AQ campaign.
To determine the wavelength registration more accurately in the narrow
fitting window, additional wavelength calibration of the spectra for each of
the 33 across-track pixels was performed using a high-resolution solar
reference spectrum (Kurucz solar spectrum) (Chance and Kurucz, 2010) with
the GDF. The absorption cross sections of NO2 (Vandaele et al., 1998),
O3 (Bogumil et al., 2000), H2O (Rothman et al., 2010), and the
Ring effect as pseudo-absorbers (Chance and Spurr, 1997) were used to
construct the model equation, while B(λ), R(λ), A(λ), and N(λ) are the broad absorption of trace gases, extinction by
Mie and Rayleigh scattering, variation in the spectral sensitivity of the
detector or spectrograph, and noise, respectively, which were accounted for
by an eighth-order polynomial. An example of the spectral fitting results
is presented in Fig. 4.
An example of the spectral fitting results of NO2 retrievals
from GeoTASO during the KORUS-AQ campaign (at Gangnam, Seoul on 9 June 2016). The red and black line in panel (a) represent the measured and reference
spectrum, respectively. Panels (b) to (h) depict examples of spectral
fitting results of (b) NO2, (c) O3 (293 K), (d) O3 (243 K), (e) Ring effect, (f) H2O, and (g) O4, where red and black lines are the
absorption cross section of the target species and the fitting residual plus the
absorption of the target species, respectively. Panel (h) indicates the
fitting residual of this example.
NO2 AMF calculation
AMF, the ratio of SCD to VCD, can be calculated using the scattering weight
(ω) and shape factor (S) (Palmer et al., 2001) in Eq. (2)–(5):
2AMF=SCDVCD,3AMF=AMFG∫z1z2ωzSzdz,4ωz=-1AMFG∂lnIB∂τ,5Sz=αzn(z)∫z1z2αznzdz,
where AMFG represents the geometric AMF, IB is the earthshine
radiance, τ is the optical depth, α is the absorption
cross section, and n is the number density of the absorber. NO2 AMF was
calculated using a linearized pseudo-spherical scalar and vector discrete
ordinate radiative transfer model (VLIDORT, version 2.6; Spurr and Christi,
2014). Aerosol properties, such as AOD, SSA, APH, and a priori NO2 vertical
profile information, were simulated using the CMAQ, and surface reflectivity
was obtained from MODIS (Collection 6). The SR products, MCD43A3, available
at a 500 m spatial resolution, provide an estimate of the surface spectral
reflectance including MODIS bands 1 through 7. Here, MODIS band 3 (459–479 nm) was used because this band is the closest the wavelength (455 nm) used
in the calculation of AMF in this study. APH was assumed to be the peak
height of the aerosol extinction coefficient simulated in CMAQ, and the
aerosol profile applied GDF based on APH (Hong et al., 2017). For pixels
without reflectance information, AMF was not calculated. The products were
corrected for atmospheric conditions, such as aerosol, gases, and Rayleigh
scattering. In previous studies (Lamsal et al., 2017; Nowlan et al., 2018;
Judd et al., 2019; Chong et al., 2020), an AMF was described for both above
and below aircraft altitude is used to convert NO2 SCDs to VCDs using
Eqs. (6)–(8).
6AMF↑=AMFG∫ZAZTOAωzSzdz,7AMF↓=AMFG∫Z0ZAωzSzdz,8NO2VCD↓=NO2SCD-AMF↑⋅NO2VCD↑AMF↓,
where AMF↑ and AMF↓ are AMF above and below aircraft,
respectively, and NO2 VCD↑ represents NO2 VCD above the
aircraft obtained from a chemical transport model (CTM). However, here we
calculated the NO2 VCD↓ by dividing NO2 SCDs by
AMF↓ as the CMAQ only simulates the troposphere (surface to 50 hPa). However, as the stratospheric and free tropospheric NO2
(NO2 VCD↑) column densities over megacities and industrial areas
are much lower than tropospheric NO2 column densities (Valks et al.,
2011), we assume that the uncertainties in the AMF without considering the
upper atmosphere are negligible in this study.
Chemical model description
Vertical profiles from CMAQ (Byun and Ching, 1999;
Byun and Schere, 2006), a CTM, were used to calculate AMFs. The CMAQ
simulations were conducted with a horizontal resolution of 15×15 km and had 27 vertical layers from the surface to 50 hPa. The meteorological
fields were prepared using the advanced research Weather Research and
Forecasting (WRF) Advanced Research WRF (ARW) Model
(Skamarock et al., 2008). Anthropogenic emissions were
generated based on the KORUS v5.0 model (Woo et al., 2012),
and biogenic emissions were simulated using the Model of Emissions of Gases
and Aerosols from Nature (MEGAN v2.1; Guenther
et al., 2006, 2012). Besides anthropogenic and biogenic emissions, the Fire
Inventory from NCAR (FINN; Wiedinmyer et al., 2006, 2011)
was used to update the pyrogenic emission fields.
The CMAQ AOD was calculated by integrating the aerosol extinction
coefficient (Qext), which is the sum of scattering (Qsca) and
absorption (Qabs) coefficients over all vertical layers (z) as
follows:
9AOD=∫Qextzdz=∫Qscaz+Qabszdz,10QabsMm-1=∑i∑j(1-ωij)⋅βij⋅fijRH⋅[C]ij,11QscaMm-1=∑i∑jωij⋅βij⋅fijRH⋅[C]ij,
where ωij indicates SSA of particulate species i for
the particulate mode (or size bin) j, βij denotes the
mass extinction efficiency, fijRH is the hygroscopicity
factor according to the relative humidity (RH), and [C]ij is the
concentration of particulate species. CMAQ SSA is defined as the ratio of
the integrated Qsca to AOD, and NO2 vertical profiles were
obtained from NO2 concentrations at each vertical layer by conducting
CMAQ simulations. Details of the model descriptions and calculations of
optical properties are given by Lee et al. (2020) and Malm and Hand (2007).
Results and discussionNO2 VCD retrievalSeoul metropolitan region
We show the final NO2 VCDs from 250 m spatial resolution. Because of
NO2 VCD, we selected the dates observed in both the morning and
afternoon during the KORUS-AQ period over the Seoul metropolitan area,
Busan, and Anmyeon. The retrieved dates for NO2 VCDs were 5, 9, and
10 June 2016.
The population of the Seoul metropolitan region is approximately 20 million,
which is approximately 40 % of the total population of South Korea. It is
rare to obtain high-resolution horizontal NO2 VCD distributions using
airborne measurements in the morning and afternoon, especially in Asian
megacities. Figure 5 indicates tropospheric NO2 VCDs over Seoul on 9 June 2016, at 09:00 and 15:00 LT. Because of an issue with the imaging
systems, enlarged views (Figs. 5–8) present a slightly stripy appearance
from the GeoTASO observation (Nowlan et al., 2016; Chong et al., 2020).
Tropospheric NO2 VCD in the Seoul metropolitan region on
9 June 2016 retrieved from GeoTASO (a) at 09:00 and (b) at 15:00 LT. The red
boxes represent expressways (counterclockwise from left to right, (1)
Gyeongin Expressway, (2) Seohaean Expressway, and (3) Gyeongbu Expressway),
the orange box indicates the industrial complex, and the blue boxes indicate
the major cities (Seoul, Incheon, Suwon, Bucheon, Anyang, Gunpo, Sungnam,
and Ansan) of the Seoul metropolitan region. The colors of the circles depict
the NO2 surface mixing ratio obtained from Air-Korea. The colored arrows
indicate the wind direction and speed at 1000 hPa over the Seoul metropolitan
region obtained via the Unified Model (UM) simulations (the background RGB
image is from Google Earth; https://www.google.com/maps/, last access: 19 January 2023).
In the morning, NO2 VCDs retrieved from GeoTASO were highly correlated
with expressways (red boxes in Fig. 5), such as the Gyeongin, Seohaean, and
Gyeongbu expressways, and over major cities with heavy traffic, such as
Seoul, Bucheon, Ansan, Anyang, and Suwon. GeoTASO observed NO2 VCD
values that were 3 times higher (>3×1016 molec. cm-2) in these areas compared to the surrounding rural areas. High
NO2 VCD values above 6×1016 molec. cm-2 were
observed above the Gyeongin Expressway, which has very heavy traffic in a
relatively short section, and the Gunpo Complex Logistics zone, where diesel
vehicle traffic is also high. The main NO2 source regions and the
regions where high NO2 VCD values were observed were highly consistent
at 09:00 LT because the wind speed at this time – as obtained from the Unified Model (UM)-based Regional Data Assimilation and Prediction System (RDAPS) of
the Korea Meteorological Administration (KMA) – was as low as 0.1 ms-1,
and the average wind direction was 84.7∘ at 1000 hPa over Seoul
metropolitan region. The average daily traffic volume of these expressways
exceeds 150 000 vehicles, and the total number of vehicles registered in
these major cities is >6 000 000, with an average daily mileage
per car per day of over 38 km. Detailed information on these cities and
expressways is listed in Tables 3 and 4. Based on the level of vehicular
traffic, combustion using gasoline and diesel engines leads to high overall
emissions of NO2 in the Seoul metropolitan region (Kendrick et al., 2015).
The population, number of registered vehicles, and average mileage
per car per day of the major cities in the Seoul and Busan metropolitan
region obtained from the Korean Statistical Information Service
(https://kosis.kr/eng, last access: 25 March 2022).
Daily average traffic volume on the Gyeongin, Gyeongbu, and
Seohaean expressways obtained using the Traffic Monitoring System
(https://www.road.re.kr, last access: 28 February 2022).
Compared to the data of the morning, the average wind speed and wind
direction were 1.7 ms-1 and 284.5∘ at 1000 hPa in the
afternoon, and the afternoon had extremely high tropospheric NO2 VCD
values (exceeding 5×1016 molec. cm-2) in most of the
Seoul metropolitan region, including rural areas, whereas the NO2
mixing ratio (MR) obtained from Air-Korea decreases in the afternoon.
According to Tzortziou et al. (2018), similar results were retrieved from
the Pandora site in Seoul, with higher afternoon NO2 VCDs than in the
morning. This result is because the amount of NO2 produced by chemical
conversion of nitric oxide (NO) by O3 and VOCs in the atmosphere, along
with NOx generated by regional emissions (traffic) in the Seoul metropolitan
region, is greater than the amount lost by photolysis and transport to
nearby areas (Herman et al., 2018). Furthermore, the increase in
tropospheric NO2 VCD in the afternoon is likely due to the accumulation
and dispersion of NO2 according to the height of the change in the
planetary boundary layer (Ma et al., 2013).
Industrial and power plant regions in Anmyeon
(a) Tropospheric NO2 VCD and (b) NO2 SCD retrieved from
GeoTASO and (c) NO2 AMF at native resolution (250 m) calculated using
VLIDORT over Anmyeon in South Korea on 5 June 2016. The colored arrows
indicate wind speed and wind direction at 850 hPa from the Unified Model
(UM) simulations. The red circles and rectangle in (a) represent the
major NO2 emission sources, such as steelworks and power plants
(background RGB image is from Google Earth; https://www.google.com/maps/, last access: 19 January 2023).
The high spatial resolution of the tropospheric NO2 VCD from GeoTASO
over the Anmyeon industrial region, where many industrial facilities and
several power plants are distributed, is shown in Fig. 6. Figure 6a and b indicate tropospheric NO2 VCD and NO2 SCD retrieved
from GeoTASO L1B data, respectively, between 13:00 and 17:00 LT on 5 June 2016. Figure 6c depicts the calculated AMF of NO2 from native resolution
over the domain. GeoTASO observations detected the following moderate and strong NO2
emission sources in this area: (1) Boryeong power plant, (2) Hyundai
integrated steelworks, (3) Dangjin power plant, (4) Daesan petrochemical
complex, and (5) Taean power plant. High NO2 VCD values (>5×1016 molec. cm-2) were observed over steel mill
works, petrochemical complexes, and power plants, whereas values were
comparatively low (<1×1016 molec. cm-2)
over small cities with populations
of less than 0.1 million, including Seosan, Dangjin, and Boryeong, and the Seohaean Expressway. In 2016, the annual
NOx emissions from the Hyundai steelworks and the Dangjin and Boryeong power
plants were approximately 10.3, 11.9, and 16.8 kt yr-1, respectively.
The NOx emission rates of major industrial facilities in the Anmyeon region
are shown in Table 5.
NOx emission rates in 2016 from major industrial facilities in the
Anmyeon region obtained from the Continuous Emission Monitoring System of
the Korea Environment Corporation
(https://www.stacknsky.or.kr/eng/index.html, last access: 25 March 2022).
NOx emissionIndustrial facilitiesrate (kg yr-1)Boryeong power plant16 788 438Hyundai integrated steelworks10 271 075Dangjin power plant11 852 972Daesan petrochemical complex3 397 939Taean power plant15 466 022
Figure 6 shows high NO2 concentrations of the main industrial
facilities in the Anmyeon region, where the combustion of fossil fuel in
factories and thermal power plants leads to high emissions (Prasad et al.,
2012). Due to relatively sparse distribution over rural areas, the Air-Korea
measurements did not detect the major NO2 plume as shown in Fig. 6a.
Thus, airborne remote sensing systems, such as GeoTASO, can effectively
complement ground-based networks for monitoring minor and major NOx
emissions, particularly over these remote industrial regions.
Enlarged view of GeoTASO tropospheric NO2 SCD observation
over (a) the Hyundai steel works, indicated by the red box in Fig. 6, and (b) the Boryeong power plant, indicated by the white box in Fig. 6. The arrows
represent the wind direction and speed at 850 hPa from the Unified Model
(UM) simulations (the background RGB image is from Google Earth;
https://www.google.com/maps/, last access: 19 January 2023).
The GeoTASO data captured not only NOx emissions from the chimneys of
steelworks and power plants but also its transport by the wind. Figure 7a
and b show enlarged views of tropospheric NO2 SCD retrieved using
GeoTASO over the Hyundai steelworks (red box in Fig. 6) and the Boryeong
power plant (white box in Fig. 6). The arrows in Fig. 7 represent the
prevailing wind direction and speed from RDAPS. NO2 emitted from the
chimneys of these sites was transported to the Yellow Sea, traveling
distances of over 26.5 km at speeds of approximately 6 ms-1. According
to Chong et al. (2020), similar results were found for SO2 emitted and
transported from these sites.
Busan metropolitan region
Tropospheric NO2 VCD in the Busan metropolitan region in the
(a) morning and (b) afternoon of 10 June 2016. The wind speed (color
scale) and wind direction (arrows) at 1000 hPa pressure level were obtained
from the Unified Model (UM) simulations. The white boxes represent the major
cities of Busan, Daegu, and Changwon. The orange box represents Busan
New Port (the background RGB image is from Google Earth; https://www.google.com/maps/, last access: 19 January 2023).
Figure 8a and b show tropospheric NO2 VCD retrieved from the GeoTASO
L1B data over the Busan metropolitan region on 10 June 2016 in the morning
(between 08:00 and 11:00 LT) and afternoon (between 13:00 and 16:00 LT),
respectively. The arrows in Fig. 8 indicate the wind speed and wind
direction at 1000 hPa obtained from the UM-RDAPS, with the average wind
speed and wind direction of 0.9 ms-1 and 55.4∘ and 1.9 ms-1 and 147.0∘, respectively, in the morning and afternoon.
High NO2 VCDs were observed above urban areas, ports, industrial
complexes, and the inter-city road between Busan and Changwon. Like the
Seoul metropolitan region, combustion using gasoline and diesel engines is
estimated to contribute to the high NOx emission. In the morning, NO2
VCDs were high (approximately 3×1016 molec. cm-2) in
the major cities and especially around Busan New Port, with values
exceeding 7×1016 molec. cm-2. In comparison, in the
mountainous regions between Daegu and Busan, the NO2 VCD values were
less than 1×1016 molec. cm-2 during the same period.
The spatial distribution of tropospheric NO2 VCDs was similar in the
Seoul metropolitan regions, with high values over major cities and roads
(compare Figs. 5 and 8). In Busan, fossil fuel combustion in use by both
road vehicles and ships is likely to contribute to the NOx emissions. In the
afternoon, unlike the Seoul metropolitan region, tropospheric NO2 VCD
over Busan decreased by over 3×1016 molec. cm-2,
which also corresponds with NO2 MR data obtained from the Air-Korea
sites. Detailed information on these cities is listed in Table 3.
Error estimation
The accuracy of the NO2 VCD retrieval using the DOAS method depends on
both the AMF calculation and the spectral fitting error of the SCD
retrieval. Retrieval errors of the NO2 VCD were estimated using error
propagation analysis as expressed in Eq. ():
εVCDVCD=(εSCDSCD)2+(εAMFAMF)2,
where εVCD is the total error of NO2 VCD. The error
of NO2 SCD (εSCD) is obtained from the spectral
fitting error of NO2 SCD via the DOAS spectral fitting. εAMF indicates the error of NO2 AMF caused by uncertainties in the
model input parameters for AMF calculation. Uncertainties in aerosol
properties (AOD, SSA, and APH) and SR for the radiative transfer model (RTM) calculations are the major
factors affecting NO2 AMF accuracy (Boersma et al., 2004; Leitão et
al., 2010; Hong et al., 2017). Therefore, in this present study we
quantified the NO2 AMF errors (εAMF) due to
uncertainties in the input parameters independent of each other using Eq. ():
εAMF=∂AMF∂AOD2σAOD2+∂AMF∂SSA2σSSA2+∂AMF∂ALH2σALH2+∂AMF∂SR2σSR2‾=∑i=14∂AMF∂χi2σχi2,
where ∂AMF∂χi are partial derivatives of
NO2 AMF regarding the input parameters (χi), σχi represents the uncertainty of the χi. The σ
of AOD, SSA, SR, and APH are assumed to be 30 % (Ahn et al., 2014), 0.04
(Jethva et al., 2014), 0.005 + 0.05 × SR (EOS Land Validation;
https://landval.gsfc.nasa.gov, last access: 22 July 2022), and 1 km (Fishman et al., 2012),
respectively, in this study. To derive (∂AMF∂χi)2, the true χi is input to the RTM to simulate “true”
NO2 AMF. For the AOD, SSA, APH, and SR, perturbed NO2 AMF was
simulated using RTM with χi±σχi.
∂χi denotes the difference between the “center” χi and χi±σχi, and ∂AMF
is the difference between the “center” NO2 AMF (AMFcentre)
simulated with “center” input values and the perturbed NO2 AMF
(AMFperturbed) simulated using the perturbed input parameters χi±σχi (i.e., the original input parameters
modified by the uncertainty). The simulation for calculating the
εAMF was conducted using the input parameters on 9 June 2016.
Total NO2 VCD caused by uncertainties in NO2 SCD and
NO2 AMF (the average for the flight on 9 June 2016).
Table 6 lists the estimated NO2 VCD error on 9 June 2016 for each
source based on the error propagation method. The error estimation was
conducted for the pixels where the root-mean-square residual is <0.001 and
NO2 VCD > 5 × 1015 molec. cm-2 since
NO2 SCD precision is reported to be highly decreased in low NO2
conditions (Hong et al., 2017). The total NO2 VCD error was 26.9 %,
with a high portion of NO2 AMF error. The NO2 SCD error was
calculated to be 11.7 %, showing the importance of accurate DOAS spectral
fitting for deriving NO2 SCD. The total AMF error due to uncertainties
in the input parameters was calculated to be 23.3 %. Among model input
parameters, the effect of APH on NO2 AMF becomes high (22.3 %),
indicating the importance of accurate aerosol profile information. APH
sensitivity affects NO2 AMF because aerosols lead to multiple scattering effects near the surface where trace gases
and aerosols are well mixed,
and the light absorption of trace gases is due to the increasing light path
(Castellanos et al., 2015; Hong et al., 2017). APH has the potential to be the
most important input parameter in the Asia region where high loadings of
aerosol plumes persist throughout the year. The NO2 AMF calculation
errors due to uncertainties in SSA and AOD were 4.1 % and 2.8 %,
respectively. The NO2 AMF calculation error due to uncertainties in
aerosol optical properties (SSA and AOD) appears to be smaller than those in
a previous study (Leitão et al., 2010). The smaller effect of the
aerosol properties can be explained by the moderate aerosol loading (AOD = 0.40) on the day of flight day. The NO2 AMF errors become larger under
high AOD conditions. The smallest effect of SR was found on NO2 AMF
calculation error, which was calculated based on the uncertainty of the SR
of the satellite-based product (MODIS). Therefore, it may be an unrealistic
number for the airborne NO2 AMF calculation. Once the uncertainty of
airborne-based SR is provided, considering its measurement geometry and
finer spatial resolution, more realistic airborne-based NO2 AMF
calculation error due to uncertainties in SR can be estimated. The a priori NO2 profile shape can also be a factor that causes a calculation error for the
NO2 AMF, as reported in previous studies (Leitão et al., 2010,
Meier et al., 2017; Hong et al., 2017). Therefore, it is necessary to
calculate the contribution of the shape of the a priori NO2 profile to the
accuracy of NO2 AMF in the future. Moreover, the resulting
uncertainties in input parameters of a GeoTASO ground pixel need to be
considered by combining the initial uncertainties of CTM and satellite-based
products and by using the variability of the parameters within the respective CTM
(AOD, SSA, and APH) and satellite (SR) grid box. If values such as SR are
assumed constant over larger areas, the fundamental spatial variability in
these data increases the uncertainty of the AMF and hence of the
determined NO2 VCD on the respective finer spatial scale. In addition,
the uncertainty from the assumption on the SC0 and the uncertainty from
ignoring the NO2 above the aircraft in the AMF calculations both need
to be considered in the error analysis. This analysis should be considered
in a further study.
AMFpercent_change=AMFperturbed-AMFcenterAMFcenter×100.
Percent change between AMF calculated using the CMAQ model
simulation and those using (a) 30 % lower AOD, (b) 30 % higher AOD, (c) 0.04 lower SSA, and (d) 1 km higher APH compared to the model outputs. The
percentage change for AMF calculated using MODIS data and those using (e)0.005+0.05× SR lower SR and (f) 0.005 + 0.05 × SR
higher SR are also shown (the background RGB image is from Google Earth; https://www.google.com/maps/, last access: 19 January 2023).
In this study, we also investigated the spatial distribution of AMF
calculation errors associated with uncertainties in aerosol properties (AOD,
SSA, and APH) and SR. The percent change in NO2 AMF
(AMFpercent_change) was calculated on each spatial pixel
using Eq. (). Figure 9a and b indicate the percentage change error
between the calculated AMFs using the CMAQ AOD data with 30 % lower (Fig. 9a) and 30 % higher (Fig. 9b) values, respectively. The AMF decreased and
increased by up to 10 % with decreasing and increasing AOD, respectively,
in the Seoul metropolitan region. We estimated that, under low aerosol
loading conditions, an increase in AOD near the surface leads to an increase
in the scattering probability within the surface layer with high NO2
concentrations. Figure 9c indicates the percent change error between the
calculated AMFs using CMAQ SSA data with a 0.04 lower value. The AMF
decreased with decreasing SSA because the absorption of light increased. APH
was also found to greatly affect the accuracy of the AMF calculations (Fig. 9d). The APH uncertainty of 1 km decreased the AMFs with an average
AMFpercent_change of -25 % on the flight day.
On the pixels where AOD > 0.6 in particular, the average
AMFpercent_change was found to be -26 %, whereas
this value was -27 % on the pixels where AOD > 0.4, showing the combined
effect of aerosol loading and aerosol profile shape on the NO2 AMF
calculations. Figure 9e and f indicate the percentage change error between
the calculated AMFs using the MODIS SR data with 0.005 + 0.05 × SR lower (Fig. 9e) and 0.005 + 0.05 × SR higher (Fig. 9f) values,
respectively. The AMF decreased by approximately 3 % when the SR
decreases, with the opposite occurring when it increased.
Validation of NO2 VCDs retrieved from GeoTASO
The tropospheric NO2 VCDs retrieved from GeoTASO L1B data (NO2,G)
were compared with those obtained from OMI total NO2 VCDs (NO2,O)
and Pandora (NO2,P). The NO2,O were only available for 10 June
during the campaign period. Therefore, we compared only 48 NO2,G and
NO2,O data points within a radius of 20 km and 30 min, which yielded a
correlation coefficient of 0.48 with a slope of 0.13 (Fig. 10a). To
validate this data, all NO2,G within a radius 20 km of the OMI center coordinate
were averaged.
The NO2 values are relatively low, as GeoTASO observation is conducted
in a region with low NO2 compared to the Seoul metropolitan region, and the
overpass time of OMI is approximately 13:30 LT when NO2 decreased. The
low slope value is because the OMI with low spatial resolution does not
reflect the spatial NO2 inhomogeneity in the pixel.
Scatter plots of (a) NO2 VCD retrieved from GeoTASO and total
NO2 VCD obtained from OMI and (b) total NO2 VCD obtained from
Pandora and NO2 VCD retrieved from GeoTASO, respectively.
To compare NO2,G data, we made a comparison with total NO2 VCD
obtained from the Pandora system (NO2,P) during the KORUS-AQ campaign
period. NO2,P obtained from Busan University, Olympic Park, Songchon,
Yeoju, and Yonsei University Pandora sites on 5, 9, and 10 June were used
for the GeoTASO validation (Fig. 1). NO2,G and NO2,P columns at
these sites are compared in Fig. 11. To compare NO2,G and NO2,P,
we used averaged NO2,G retrieved from 16 across-track runs with the
smallest viewing zenith angle possible and averaged 30 min NO2 data obtained from
Pandora measurements within a radius of approximately 0.05∘.
NO2,G and NO2,P were correlated (R=0.91, with a slope of
0.60); however, when NO2,P was lower than 1×1016 molec. cm-2, the correlation coefficient between NO2,G and
NO2,P was <0.1. The weak correlation at low NO2 levels
most likely reflects differences in viewing geometries and the horizontal
inhomogeneity of the measured NO2 between Pandora and GeoTASO.
Furthermore, Pandora and GeoTASO can be used for the NO2 validation of
geostationary satellites, such as GEMS. However, because the number of
Pandora measurements is limited in this campaign, we have difficulty validating NO2
retrieved from GeoTASO under various conditions. Many ground-based remote
sensing measurements are needed to validate GEMS under various conditions.
Scatter plot of the NO2 VCDs retrieved from GeoTASO and the
NO2 surface mixing ratio obtained from Air-Korea. The black and red
squares represent the NO2 data at 09:00 and 15:00 LT in the
Seoul metropolitan region, respectively. The black and red triangles
represent those in the morning and afternoon over Busan, respectively.
To compare the spatiotemporal distribution of NO2 VCDs retrieved from
GeoTASO, NO2,G was compared with surface spatial patterns and NO2,G was
compared with NO2,A for GeoTASO data within a radius of approximately
0.05 km and 30 min (Fig. 11). To compare NO2,G and NO2,A,
we used averaged NO2,G retrieved from 16 across-track runs and averaged 30 min within a radius of 0.05∘. Because in situ measurements
provide NO2 volume mixing ratio (VMR) (NO2,A) (ppmv) once per hour, NO2,A of the
nearest time is used to compare with NO2,G. The correlation coefficient
(R) between NO2,G (molec. cm-2) and NO2,A at 09:00 and 15:00 LT in the Seoul metropolitan region was 0.07 and 0.26, respectively. When
using only roadside station data from Air-Korea, the R value for the morning
increased to 0.72, which implies GeoTASO is more sensitive to emissions from
NO2 source areas, such as roadsides (Fig. 5). During the comparison
there were large differences in the morning and afternoon. These results
were identified because synoptic meteorology played an important role from
1 to 10 June 2016 (Choi et al., 2019). As described by Judd et al. (2018), the spatial distribution for NO2 VCDs appears to reflect the
emission source in local industrialized regions and transportation in the
morning with relatively weak winds. NO2 concentration often increases
in the late morning, indicating that the emission process proceeds faster
than the NO2 removal process. As the planetary boundary layer height
(PBLH) increases in the early afternoon and surface NO2 is mixed through a
deeper PBLH, the NO2 VCDs distribution showed a wider increase in most
of the Seoul metropolitan area, while the column amounts continue to increase
(Judd et al., 2018).
When comparing NO2 VCDs with surface NO2 concentrations, it should
be highlighted that it is a nonlinear relationship between NO2,G and
NO2,A. Although it may vary depending on weather conditions, high
NO2 VCDs from airborne observations can sometimes be detected with low
surface NO2 concentrations. When exhaust gases emitted from industrial
facilities occur at a certain altitude (stacks or chimneys), NO2,G shows
high NO2 VCDs, but NO2,A may be observed to have a low
concentration. Unfortunately, in the Anmyeon industrial region, NO2,G
and NO2,A could not be compared due to spatial restrictions because the
distribution of ground observation stations is concentrated in metropolitan
areas.
In the Busan metropolitan area, the R value of the NO2,G and NO2,A
data had a correlation coefficient greater than 0.56. This reflects the more
even horizontal distribution of NO2 in the afternoon, when diffusion
from the source areas had occurred. However, for a more accurate comparison,
NO2 VCD data should be converted to NO2 MR based on mixing layer
height, temperature, and pressure profile data (Kim et al., 2017; Qin et
al., 2017; Jeong and Hong, 2021a). However, because the number of Pandora
and satellite data are limited in this campaign, we had difficulties in
validating NO2 retrieved from GeoTASO under various conditions. Because
ground-based, airborne, and spaceborne remote sensing measurements have
their own advantages and disadvantages, it is recommended that a comprehensive
observation campaign involving all of ground-based, airborne, and spaceborne
measurements should be conducted continuously for the upcoming new era of
geostationary environmental satellites.
Conclusions
For the first time, we have retrieved NO2 VCD data using airborne
GeoTASO observations over the Seoul metropolitan region, one of the most
populous cities worldwide, the Busan metropolitan region, the
second-largest city in South Korea, and Anmyeon, an area with thermal power plants
and industrial complexes. By retrieving NO2 data using GeoTASO L1B
radiance, it was possible to observe the spatial distribution of NO2 in
these metropolitan and industrial regions. In the morning, tropospheric
NO2 VCD in Seoul showed a strong horizontal gradient between rural and
urban areas. In urban areas, tropospheric NO2 VCD was high, with values
exceeding 3×1016 molec. cm-2; in rural areas, values
were typically below 1×1016 molec. cm-2. Extremely
high values over 10×1016 molec. cm-2 were also
observed in both rural and urban areas. In Anmyeon, GeoTASO observations
showed that NO2 is mainly emitted from the chimneys of industrial
complexes and thermal power plants and subsequently transported by wind
approximately 26.5 km to the Yellow Sea on the western coast of the Korean
Peninsula. In the Busan metropolitan region, in the morning tropospheric
NO2 VCDs showed a pattern similar to the Seoul metropolitan region,
with high values above the inter-city road. However, unlike Seoul,
tropospheric NO2 VCDs in Busan decreased in the afternoon due to
different local weather conditions.
To compare the data retrieved from the GeoTASO system, we compared
NO2,G with NO2,O obtained from the OMI, NO2,A obtained from
Air-Korea, and NO2,P obtained from the Pandora observation system. When
the distance between two observations was below 20 km or 0.05∘
within 30 min, the correlation coefficients were relatively high (R=0.48, and 0.91, respectively). However, the correlation between NO2,G
and NO2,A over the Seoul metropolitan region was extremely weak (R=0.07) in the morning because of the more pronounced NO2 horizontal
gradient.
The GeoTASO system successfully observed NO2 VCDs with high horizontal
spatial resolution for both metropolitan and industrial regions. This
demonstrates that airborne remote sensing measurements from GeoTASO, similar
to GCAS, APEX, and others, can be an effective tool for the validation of
trace gases retrieved from environmental satellites, including the OMI,
TROPOMI, and GOME-2; these systems can obtain high-resolution measurements
over relatively wide areas. However, to validate geostationary environmental
satellites with higher spatiotemporal resolutions, such as the GEMS, TEMPO,
and Sentinel-4, additional validation strategies are needed. Based on error
estimation, it can be concluded that aerosol properties are relevant and
should be determined and that NO2 vertical profile retrievals should be performed
using, for example, Lidar, MAX-DOAS, and sondes. This is important because
the accuracy of aerosol properties, surface reflectance values, and the NO2
vertical profiles affects the accuracy of AMF calculations (Leitão et
al., 2010; Hong et al., 2017; Lorente et al., 2017; Boersma et al., 2018).
Furthermore, as we observed in the Seoul metropolitan area,
observations taken closer together using ground-based remote sensing systems and in situ
measurements are needed as NO2 displays large horizontal gradients,
especially in the morning.
Code availability
The whole code was developed in Interactive Data Language (IDL) environment. Code from this study (IDL scripts) is available from the corresponding author upon request to choo4616@korea.kr.
Data availability
KORUS-AQ campaign data are distributed from the NASA LaRC data archive, and they are available at https://www-air.larc.nasa.gov/cgi-bin/ArcView/korusaq (last access: 10 February 2022; NASA, 2022).
Author contributions
GHC and HH designed and implemented the research. KL provided the CTM data.
GHC developed the code for model runs and performed the RTM simulations.
HH and UJ contributed to the analysis of ground-based data. GHC and WC
conducted the sensitivity test. GHC, KL, HH, UJ, WC, and JJS revised and
edited the paper. HH, UJ, and WC provided constructive comments. All authors
contributed to this work.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Pandora data were obtained from the KORUS-AQ home page of NASA's Goddard
Space Flight Center (https://avdc.gsfc.nasa.gov/pub/DSCOVR/Pandora/DATA/KORUS-AQ/, last access: 22 July 2022).
Ground-based NO2 MR data were obtained from Air-Korea (http://www.airkorea.or.kr/web/detailViewDown?pMENU_NO=125/, last access: 16 February 2022). The authors would like to thank the KORUS-AQ campaign
team for providing the GeoTASO and Pandora data.
Financial support
This research has been supported by the National Institute of Environmental Research (grant no. NIER-2021-01-01-100).
Review statement
This paper was edited by Andreas Richter and reviewed by two anonymous referees.
References
Ahn, C., Torres, O., and Jethva, H., Assessment of OMI near‐UV aerosol optical depth over land, J. Geophys. Res.-Atmos., 119, 2457–2473, 2014.Boersma, K. F., Eskes, H. J., and Brinksma, E. J.: Error analysis for
tropospheric NO2 retrieval from space: ERROR ANALYSIS FOR TROPOSPHERIC
NO2, J. Geophys. Res., 109, D04311,
10.1029/2003JD003962, 2004.Boersma, K. F., Eskes, H. J., Richter, A., De Smedt, I., Lorente, A., Beirle, S., van Geffen, J. H. G. M., Zara, M., Peters, E., Van Roozendael, M., Wagner, T., Maasakkers, J. D., van der A, R. J., Nightingale, J., De Rudder, A., Irie, H., Pinardi, G., Lambert, J.-C., and Compernolle, S. C.: Improving algorithms and uncertainty estimates for satellite NO2 retrievals: results from the quality assurance for the essential climate variables (QA4ECV) project, Atmos. Meas. Tech., 11, 6651–6678, 10.5194/amt-11-6651-2018, 2018.Bogumil, K., Orphal, J., and Burrows, J. P.: Temperature dependent absorption cross sections of O3, NO2, and other atmospheric trace gases measured with the SCIAMACHY spectrometer, Proceedings of the ERS-Envisat-Symposium, Goteborg, Sweden, 2000.Brauer, M., Hoek, G., Van Vliet, P., Meliefste, K., Fischer, P. H., Wijga,
A., Koopman, L. P., Neijens, H. J., Gerritsen, J., Kerkhof, M., Heinrich,
J., Bellander, T., and Brunekreef, B.: Air Pollution from Traffic and the
Development of Respiratory Infections and Asthmatic and Allergic Symptoms in
Children, Am. J. Respir. Crit. Care Med., 166, 1092–1098,
10.1164/rccm.200108-007OC, 2002.
Burrows, J. P., Hölzle, E., Goede, A. P. H., Visser, H., and Fricke, W.:
SCIAMACHY – scanning imaging absorption spectrometer for atmospheric
chartography, Acta Astronaut., 35, 445–451,
10.1016/0094-5765(94)00278-T, 1995.Burrows, J. P., Weber, M., Buchwitz, M., Rozanov, V.,
Ladstätter-Weißenmayer, A., Richter, A., DeBeek, R., Hoogen, R.,
Bramstedt, K., Eichmann, K.-U., Eisinger, M., and Perner, D.: The Global
Ozone Monitoring Experiment (GOME): Mission Concept and First Scientific
Results, J. Atmos. Sci., 56, 151–175, 10.1175/1520-0469(1999)056<0151:TGOMEG>2.0.CO;2, 1999.
Byun, D. W. and Ching, J. K. S.: Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, U.S. EPA/600/R-99/030, 1999.Byun, D. and Schere, K. L.: Review of the Governing Equations, Computational
Algorithms, and Other Components of the Models-3 Community Multiscale Air
Quality (CMAQ) Modeling System, Appl. Mech. Rev., 59, 51,
10.1115/1.2128636, 2006.
Callies, J., Corpaccioli, E., Eisinger, M., Hahne, A., and Lefebvre, A.:
GOME-2-Metop's second-generation sensor for operational ozone monitoring,
ESA Bull, 102, 28–36, 2000.Castellanos, P., Boersma, K. F., Torres, O., and de Haan, J. F.: OMI tropospheric NO2 air mass factors over South America: effects of biomass burning aerosols, Atmos. Meas. Tech., 8, 3831–3849, 10.5194/amt-8-3831-2015, 2015.Chance, K. and Kurucz, R. L.: An improved high-resolution solar reference
spectrum for earth's atmosphere measurements in the ultraviolet, visible,
and near infrared, J. Quant. Spectrosc. Ra.
Trans., 111, 1289–1295, 10.1016/j.jqsrt.2010.01.036,
2010.Chance, K. V. and Spurr, R. J. D.: Ring effect studies: Rayleigh scattering,
including molecular parameters for rotational Raman scattering, and the
Fraunhofer spectrum, Appl. Opt., 36, 5224,
10.1364/AO.36.005224, 1997.Choi, S., Lamsal, L. N., Follette-Cook, M., Joiner, J., Krotkov, N. A., Swartz, W. H., Pickering, K. E., Loughner, C. P., Appel, W., Pfister, G., Saide, P. E., Cohen, R. C., Weinheimer, A. J., and Herman, J. R.: Assessment of NO2 observations during DISCOVER-AQ and KORUS-AQ field campaigns, Atmos. Meas. Tech., 13, 2523–2546, 10.5194/amt-13-2523-2020, 2020.Choi, W. J., Moon, K.-J., Yoon, J., Cho, A., Kim, S. K., Lee, S., Ko, D. H., Kim, J., Ahn, M. H., Kim, D.-R., Kim, S.-M., Kim, J.-Y., Nicks, D., and Kim, J.-S.: Introducing the geostationary environment monitoring
spectrometer, J. Appl. Remote Sens., 12, 1,
10.1117/1.JRS.12.044005, 2018.Choi, M., Lim, H., Kim, J., Lee, S., Eck, T. F., Holben, B. N., Garay, M. J., Hyer, E. J., Saide, P. E., and Liu, H.: Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign, Atmos. Meas. Tech., 12, 4619–4641, 10.5194/amt-12-4619-2019, 2019.Chong, H., Lee, S., Kim, J., Jeong, U., Li, C., Krotkov, N. A., Nowlan, C.
R., Al-Saadi, J. A., Janz, S. J., Kowalewski, M. G., Ahn, M.-H., Kang, M.,
Joiner, J., Haffner, D. P., Hu, L., Castellanos, P., Huey, L. G., Choi, M.,
Song, C. H., Han, K. M., and Koo, J.-H.: High-resolution mapping of SO2
using airborne observations from the GeoTASO instrument during the KORUS-AQ
field study: PCA-based vertical column retrievals, Remote Sens.
Environ., 241, 111725, 10.1016/j.rse.2020.111725, 2020.Choo, G.-H., Seo, J., Yoon, J., Kim, D.-R., and Lee, D.-W.: Analysis of
long-term (2005–2018) trends in tropospheric NO2 percentiles over
Northeast Asia, Atmos. Pollut. Res., 11, 1429–1440,
10.1016/j.apr.2020.05.012, 2020.
Danckaert, T., Fayt, C., Van Roozendael, M., De Smedt, I., Letocart, V.,
Merlaud, A., and Pinardi, G.: QDOAS Software user manual, Belgian Institute
for Space Aeronomy, 1–117, 2016.de Foy, B., Lu, Z., and Streets, D. G.: Satellite NO2 retrievals
suggest China has exceeded its NOx reduction goals from the twelfth
Five-Year Plan, Sci. Rep., 6, 35912, 10.1038/srep35912, 2016.
Fishman, J., Iraci, L., Al-Saadi, J., Chance, K., Chavez, F., Chin, M., Coble, P., Davis, C., DiGiacomo, P., Edwards, D., Eldering, L., Goes, J., Herman, J., Hu, C., Jacob, D. J., Jordan, C., Kawa, S. R., Key, R., Liu, X., Lohrenz, S., Mannino, A., Natraj, V., Neil, D., Neu, J., Newchruch, M., Pickering, K., Salisbury, J., Sosik, H., Subramaniam, A., Tzortziou, M., Wang, J., and Wang, M.: The United States’ next generation of atmospheric composition and coastal ecosystem measurements: NASA’s Geostationary Coastal and Air Pollution Events (GEO-CAPE) Mission, B. Am. Meteorol. Soc., 93, 1547–1566, 2012.General, S., Pöhler, D., Sihler, H., Bobrowski, N., Frieß+, U., Zielcke, J., Horbanski, M., Shepson, P. B., Stirm, B. H., Simpson, W. R., Weber, K., Fischer, C., and Platt, U.: The Heidelberg Airborne Imaging DOAS Instrument (HAIDI) – a novel imaging DOAS device for 2-D and 3-D imaging of trace gases and aerosols, Atmos. Meas. Tech., 7, 3459–3485, 10.5194/amt-7-3459-2014, 2014.Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, 10.5194/acp-6-3181-2006, 2006.Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, 10.5194/gmd-5-1471-2012, 2012.Herman, J., Cede, A., Spinei, E., Mount, G., Tzortziou, M., and Abuhassan,
N.: NO2 column amounts from ground-based Pandora and MFDOAS
spectrometers using the direct-sun DOAS technique: Intercomparisons and
application to OMI validation, J. Geophys. Res., 114, D13307,
10.1029/2009JD011848, 2009.Herman, J., Spinei, E., Fried, A., Kim, J., Kim, J., Kim, W., Cede, A., Abuhassan, N., and Segal-Rozenhaimer, M.: NO2 and HCHO measurements in Korea from 2012 to 2016 from Pandora spectrometer instruments compared with OMI retrievals and with aircraft measurements during the KORUS-AQ campaign, Atmos. Meas. Tech., 11, 4583–4603, 10.5194/amt-11-4583-2018, 2018.Hong, H., Lee, H., Kim, J., Jeong, U., Ryu, J., and Lee, D.: Investigation
of Simultaneous Effects of Aerosol Properties and Aerosol Peak Height on the
Air Mass Factors for Space-Borne NO2 Retrievals, Remote Sens., 9,
208, 10.3390/rs9030208, 2017.Jeong, U., and Hong, H.: Assessment of tropospheric concentrations of
NO2 from the TROPOMI/Sentinel-5 Precursor for the estimation of
long-term exposure to surface NO2 over South Korea, Remote Sens., 13,
1877, 10.3390/rs13101877, 2021a.Jeong, U. and Hong, H.: Comparison of total column and surface mixing ratio
of carbon monoxide derived from the TROPOMI/Sentinel-5 Precursor with
In-Situ measurements from extensive ground-based network over South Korea,
Remote Sens., 13, 3987, 10.3390/rs13193987,
2021b.Jeong, U., Kim, J., Lee, H., and Lee, Y. G.: Assessing the effect of long-range pollutant transportation on air quality in Seoul using the conditional potential source contribution function method, Atmos. Environ., 150, 33–44, 10.1016/j.atmosenv.2016.11.017, 2017.
Jethva, H., Torres, O., and Ahn, C., Global assessment of OMI aerosol single‐scattering albedo using ground‐based AERONET inversion, J. Geophys. Res.-Atmos., 119, 9020–9040, 2014.Judd, L. M., Al-Saadi, J. A., Valin, L. C., Pierce, R. B., Yang, K., Janz,
S. J., Kowalewski, M. G., Szykman, J. J., Tiefengraber, M., and Mueller, M.:
The Dawn of Geostationary Air Quality Monitoring: Case Studies From Seoul
and Los Angeles, Front. Environ. Sci., 6, 85,
10.3389/fenvs.2018.00085, 2018.Judd, L. M., Al-Saadi, J. A., Janz, S. J., Kowalewski, M. G., Pierce, R. B., Szykman, J. J., Valin, L. C., Swap, R., Cede, A., Mueller, M., Tiefengraber, M., Abuhassan, N., and Williams, D.: Evaluating the impact of spatial resolution on tropospheric NO2 column comparisons within urban areas using high-resolution airborne data, Atmos. Meas. Tech., 12, 6091–6111, 10.5194/amt-12-6091-2019, 2019.Judd, L. M., Al-Saadi, J. A., Szykman, J. J., Valin, L. C., Janz, S. J., Kowalewski, M. G., Eskes, H. J., Veefkind, J. P., Cede, A., Mueller, M., Gebetsberger, M., Swap, R., Pierce, R. B., Nowlan, C. R., Abad, G. G., Nehrir, A., and Williams, D.: Evaluating Sentinel-5P TROPOMI tropospheric NO2 column densities with airborne and Pandora spectrometers near New York City and Long Island Sound, Atmos. Meas. Tech., 13, 6113–6140, 10.5194/amt-13-6113-2020, 2020.Kendrick, C. M., Koonce, P., and George, L. A.: Diurnal and seasonal
variations of NO, NO2 and PM2.5 mass as a function of traffic
volumes alongside an urban arterial, Atmos. Environ., 122, 133–141,
10.1016/j.atmosenv.2015.09.019, 2015.Kim, D., Lee, H., Hong, H., Choi, W., Lee, Y., and Park, J.: Estimation of
Surface NO2 Volume Mixing Ratio in Four Metropolitan Cities in Korea
Using Multiple Regression Models with OMI and AIRS Data, Remote Sens., 9,
627, 10.3390/rs9060627, 2017.Kim, J., Jeong, U., Ahn, M.-H., Kim, J. H., Park, R. J., Lee, H., Song, C.
H., Choi, Y.-S., Lee, K.-H., Yoo, J.-M., Jeong, M.-J., Park, S. K., Lee,
K.-M., Song, C.-K., Kim, S.-W., Kim, Y. J., Kim, S.-W., Kim, M., Go, S.,
Liu, X., Chance, K., Chan Miller, C., Al-Saadi, J., Veihelmann, B., Bhartia,
P. K., Torres, O., Abad, G. G., Haffner, D. P., Ko, D. H., Lee, S. H., Woo,
J.-H., Chong, H., Park, S. S., Nicks, D., Choi, W. J., Moon, K.-J., Cho, A.,
Yoon, J., Kim, S., Hong, H., Lee, K., Lee, H., Lee, S., Choi, M., Veefkind,
P., Levelt, P. F., Edwards, D. P., Kang, M., Eo, M., Bak, J., Baek, K.,
Kwon, H.-A., Yang, J., Park, J., Han, K. M., Kim, B.-R., Shin, H.-W., Choi,
H., Lee, E., Chong, J., Cha, Y., Koo, J.-H., Irie, H., Hayashida, S., Kasai,
Y., Kanaya, Y., Liu, C., Lin, J., Crawford, J. H., Carmichael, G. R.,
Newchurch, M. J., Lefer, B. L., Herman, J. R., Swap, R. J., Lau, A. K. H.,
Kurosu, T. P., Jaross, G., Ahlers, B., Dobber, M., McElroy, C. T., and Choi,
Y.: New Era of Air Quality Monitoring from Space: Geostationary Environment
Monitoring Spectrometer (GEMS), B. Am. Meteorol. Soc., 101, E1–E22,
10.1175/BAMS-D-18-0013.1, 2020.Kley, D. and McFarland, M.: Chemiluminescence detector for NO and NO2,
Atmos. Technol., 12, 63–69, 1980.Kowalewski, M. G. and Janz, S. J.: Remote sensing capabilities of the
GEO-CAPE airborne simulator, SPIE Optical Engineering + Applications, San
Diego, California, United States, 92181I,
10.1117/12.2062058, 2014.
Kowalewski, M. G., Janz, S., Al-Saadi, J. A., Good, W., Ruppert, L., and Cole, J.:
GeoTASO instrument characterization and level1b radiance product generation,
in: Proceedings of the 1st KORUS-AQ Science Team Meeting, Jeju, South Korea,
27 February–3 March 2017, 13 pp., 2017.Lamsal, L. N., Martin, R. V., Parrish, D. D., and Krotkov, N. A.: Scaling
Relationship for NO2 Pollution and Urban Population Size: A Satellite
Perspective, Environ. Sci. Technol., 47, 7855–7861,
10.1021/es400744g, 2013.Lamsal, L. N., Janz, S. J., Krotkov, N. A., Pickering, K. E., Spurr, R. J.
D., Kowalewski, M. G., Loughner, C. P., Crawford, J. H., Swartz, W. H., and
Herman, J. R.: High-resolution NO2 observations from the Airborne
Compact Atmospheric Mapper: Retrieval and validation, J. Geophys. Res.-Atmos., 122, 1953–1970, 10.1002/2016JD025483, 2017.Latza, U., Gerdes, S., and Baur, X.: Effects of nitrogen dioxide on human
health: Systematic review of experimental and epidemiological studies
conducted between 2002 and 2006, Int. J. Hygiene
Environ. Health, 212, 271–287,
10.1016/j.ijheh.2008.06.003, 2009.Lee, K., Yu, J., Lee, S., Park, M., Hong, H., Park, S. Y., Choi, M., Kim, J., Kim, Y., Woo, J.-H., Kim, S.-W., and Song, C. H.: Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues, Geosci. Model Dev., 13, 1055–1073, 10.5194/gmd-13-1055-2020, 2020.Leitão, J., Richter, A., Vrekoussis, M., Kokhanovsky, A., Zhang, Q. J., Beekmann, M., and Burrows, J. P.: On the improvement of NO2 satellite retrievals – aerosol impact on the airmass factors, Atmos. Meas. Tech., 3, 475–493, 10.5194/amt-3-475-2010, 2010.Leitch, J. W., Delker, T., Good, W., Ruppert, L., Murcray, F., Chance, K.,
Liu, X., Nowlan, C., Janz, S. J., Krotkov, N. A., Pickering, K. E.,
Kowalewski, M., and Wang, J.: The GeoTASO airborne spectrometer project,
SPIE Optical Engineering + Applications, San Diego, California, United
States, 92181H, 10.1117/12.2063763, 2014.Levelt, P. F., van den Oord, G. H. J., Dobber, M. R., Malkki, A., Huib
Visser, Johan de Vries, Stammes, P., Lundell, J. O. V., and Saari, H.: The
ozone monitoring instrument, IEEE Trans. Geosci. Remote Sensing, 44,
1093–1101, 10.1109/TGRS.2006.872333, 2006.Lorente, A., Folkert Boersma, K., Yu, H., Dörner, S., Hilboll, A., Richter, A., Liu, M., Lamsal, L. N., Barkley, M., De Smedt, I., Van Roozendael, M., Wang, Y., Wagner, T., Beirle, S., Lin, J.-T., Krotkov, N., Stammes, P., Wang, P., Eskes, H. J., and Krol, M.: Structural uncertainty in air mass factor calculation for NO2 and HCHO satellite retrievals, Atmos. Meas. Tech., 10, 759–782, 10.5194/amt-10-759-2017, 2017.Ma, J. Z., Beirle, S., Jin, J. L., Shaiganfar, R., Yan, P., and Wagner, T.: Tropospheric NO2 vertical column densities over Beijing: results of the first three years of ground-based MAX-DOAS measurements (2008–2011) and satellite validation, Atmos. Chem. Phys., 13, 1547–1567, 10.5194/acp-13-1547-2013, 2013.Malm, W. C. and Hand J. L.: An examination of the physical and optical
properties of aerosols collected in the IMPROVE program, Atmos.
Environ., 41, 3407–3427, 10.1016/j.atmosenv.2006.12.012,
2007.
Merlaud, A., Constantin, D., Mingireanu, F., Mocanu, I., Maes, J., Fayt, C.,
Voiculescu, M., Murariu, G., Georgescu, L., and Van Roozendael, M.: Small
whiskbroom imager for atmospheric composition monitoring (SWING) from an
unmanned aerial vehicle (UAV), in: Proceedings of the 21st ESA Symposium on
European Rocket & Balloon Programmes and related Research, Thun,
Switzerland, 9–13, June, 2013.Meier, A. C., Schönhardt, A., Bösch, T., Richter, A., Seyler, A., Ruhtz, T., Constantin, D.-E., Shaiganfar, R., Wagner, T., Merlaud, A., Van Roozendael, M., Belegante, L., Nicolae, D., Georgescu, L., and Burrows, J. P.: High-resolution airborne imaging DOAS measurements of NO2 above Bucharest during AROMAT, Atmos. Meas. Tech., 10, 1831–1857, 10.5194/amt-10-1831-2017, 2017.Merlaud, A., Tack, F., Constantin, D., Georgescu, L., Maes, J., Fayt, C., Mingireanu, F., Schuettemeyer, D., Meier, A. C., Schönardt, A., Ruhtz, T., Bellegante, L., Nicolae, D., Den Hoed, M., Allaart, M., and Van Roozendael, M.: The Small Whiskbroom Imager for atmospheric compositioN monitorinG (SWING) and its operations from an unmanned aerial vehicle (UAV) during the AROMAT campaign, Atmos. Meas. Tech., 11, 551–567, 10.5194/amt-11-551-2018, 2018.NASA: Airborne Science Data for Atmosperic Composition, KORUSAQ_2016, NASA LaRC data archive [data set], https://www-air.larc.nasa.gov/cgi-bin/ArcView/korusaq, last access: 10 February 2022.National Institute of Environmental Research (NIER) and National Aeronautics
and Space Administration (NASA): KORUS-AQ Final Science Synthesis Report,
https://espo.nasa.gov/sites/default/files/documents/5858211.pdf (last
access: 27 June 2022), 2020.Nowlan, C. R., Liu, X., Leitch, J. W., Chance, K., González Abad, G., Liu, C., Zoogman, P., Cole, J., Delker, T., Good, W., Murcray, F., Ruppert, L., Soo, D., Follette-Cook, M. B., Janz, S. J., Kowalewski, M. G., Loughner, C. P., Pickering, K. E., Herman, J. R., Beaver, M. R., Long, R. W., Szykman, J. J., Judd, L. M., Kelley, P., Luke, W. T., Ren, X., and Al-Saadi, J. A.: Nitrogen dioxide observations from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument: Retrieval algorithm and measurements during DISCOVER-AQ Texas 2013, Atmos. Meas. Tech., 9, 2647–2668, 10.5194/amt-9-2647-2016, 2016.Nowlan, C. R., Liu, X., Janz, S. J., Kowalewski, M. G., Chance, K., Follette-Cook, M. B., Fried, A., González Abad, G., Herman, J. R., Judd, L. M., Kwon, H.-A., Loughner, C. P., Pickering, K. E., Richter, D., Spinei, E., Walega, J., Weibring, P., and Weinheimer, A. J.: Nitrogen dioxide and formaldehyde measurements from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator over Houston, Texas, Atmos. Meas. Tech., 11, 5941–5964, 10.5194/amt-11-5941-2018, 2018.Palmer, P. I., Jacob, D. J., Chance, K., Martin, R. V., Spurr, R. J. D.,
Kurosu, T. P., Bey, I., Yantosca, R., Fiore, A., and Li, Q.: Air mass factor
formulation for spectroscopic measurements from satellites: Application to
formaldehyde retrievals from the Global Ozone Monitoring Experiment, J.
Geophys. Res., 106, 14539–14550, 10.1029/2000JD900772,
2001.Pastel, M., Pommereau, J.-P., Goutail, F., Richter, A., Pazmiño, A., Ionov, D., and Portafaix, T.: Construction of merged satellite total O3 and NO2 time series in the tropics for trend studies and evaluation by comparison to NDACC SAOZ measurements, Atmos. Meas. Tech., 7, 3337–3354, 10.5194/amt-7-3337-2014, 2014.
Platt, U.: Differential absorption spectroscopy (DOAS), Chem. Anal. Series,
127, 27–83, 1994.
Platt, U. and Stutz, J.: Differential absorption spectroscopy, in: Differential
Optical Absorption Spectroscopy, Springer, Berlin, Heidelberg, 135–174, ISBN 978-3-540-21193-8.
2008.Popp, C., Brunner, D., Damm, A., Van Roozendael, M., Fayt, C., and Buchmann, B.: High-resolution NO2 remote sensing from the Airborne Prism EXperiment (APEX) imaging spectrometer, Atmos. Meas. Tech., 5, 2211–2225, 10.5194/amt-5-2211-2012, 2012.Prasad, A. K., Singh, R. P., and Kafatos, M.: Influence of coal-based
thermal power plants on the spatial–temporal variability of tropospheric
NO2 column over India, Environ. Monit. Assess., 184, 1891–1907,
10.1007/s10661-011-2087-6, 2012.Qin, K., Rao, L., Xu, J., Bai, Y., Zou, J., Hao, N., Li, S., and Yu, C.:
Estimating Ground Level NO2 Concentrations over Central-Eastern China
Using a Satellite-Based Geographically and Temporally Weighted Regression
Model, Remote Sens., 9, 950, 10.3390/rs9090950, 2017.Richter, A., Burrows, J. P., Nüß, H., Granier, C., and Niemeier, U.:
Increase in tropospheric nitrogen dioxide over China observed from space,
Nature, 437, 129–132, 10.1038/nature04092, 2005.
Rothman, L. S., Gordon, I. E., Barber, R. J., Dothe, H., Gamache, R. R.,
Goldman, A., Perevalov, V. I., Tashkun, S. A., and Tennyson, J.: HITEMP, the
high-temperature molecular spectroscopic database, J. Quant.
Spectrosc. Ra. Transf., 111, 2139–2150, 2010.Schönhardt, A., Altube, P., Gerilowski, K., Krautwurst, S., Hartmann, J., Meier, A. C., Richter, A., and Burrows, J. P.: A wide field-of-view imaging DOAS instrument for two-dimensional trace gas mapping from aircraft, Atmos. Meas. Tech., 8, 5113–5131, 10.5194/amt-8-5113-2015, 2015.Shah, V., Jacob, D. J., Li, K., Silvern, R. F., Zhai, S., Liu, M., Lin, J., and Zhang, Q.: Effect of changing NOx lifetime on the seasonality and long-term trends of satellite-observed tropospheric NO2 columns over China, Atmos. Chem. Phys., 20, 1483–1495, 10.5194/acp-20-1483-2020, 2020.Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Wang, W., Huang, X.-Y., and Duda, M.: A Description of the Advanced Research WRF Version 3, NCAR technical note: a description of the advanced research WRF version 3. Boulder, Colorado: National Center for Atmospheric Research, 10.5065/D68S4MVH, 2008.Spinei, E., Whitehill, A., Fried, A., Tiefengraber, M., Knepp, T. N., Herndon, S., Herman, J. R., Müller, M., Abuhassan, N., Cede, A., Richter, D., Walega, J., Crawford, J., Szykman, J., Valin, L., Williams, D. J., Long, R., Swap, R. J., Lee, Y., Nowak, N., and Poche, B.: The first evaluation of formaldehyde column observations by improved Pandora spectrometers during the KORUS-AQ field study, Atmos. Meas. Tech., 11, 4943–4961, 10.5194/amt-11-4943-2018, 2018.Spurr, R. and Christi, M.: On the generation of atmospheric property
Jacobians from the (V)LIDORT linearized radiative transfer models, J. Quant. Spectrosc. Ra. Transf., 142, 109–115,
10.1016/j.jqsrt.2014.03.011, 2014.Tack, F., Merlaud, A., Iordache, M.-D., Danckaert, T., Yu, H., Fayt, C., Meuleman, K., Deutsch, F., Fierens, F., and Van Roozendael, M.: High-resolution mapping of the NO2 spatial distribution over Belgian urban areas based on airborne APEX remote sensing, Atmos. Meas. Tech., 10, 1665–1688, 10.5194/amt-10-1665-2017, 2017.Tack, F., Merlaud, A., Meier, A. C., Vlemmix, T., Ruhtz, T., Iordache, M.-D., Ge, X., van der Wal, L., Schuettemeyer, D., Ardelean, M., Calcan, A., Constantin, D., Schönhardt, A., Meuleman, K., Richter, A., and Van Roozendael, M.: Intercomparison of four airborne imaging DOAS systems for tropospheric NO2 mapping – the AROMAPEX campaign, Atmos. Meas. Tech., 12, 211–236, 10.5194/amt-12-211-2019, 2019.Tack, F., Merlaud, A., Iordache, M.-D., Pinardi, G., Dimitropoulou, E., Eskes, H., Bomans, B., Veefkind, P., and Van Roozendael, M.: Assessment of the TROPOMI tropospheric NO2 product based on airborne APEX observations, Atmos. Meas. Tech., 14, 615–646, 10.5194/amt-14-615-2021, 2021.Tzortziou, M., Parker, O., Lamb, B., Herman, J., Lamsal, L., Stauffer, R.,
and Abuhassan, N.: Atmospheric Trace Gas (NO2 and O3) Variability
in South Korean Coastal Waters, and Implications for Remote Sensing of
Coastal Ocean Color Dynamics, Remote Sens., 10, 1587,
10.3390/rs10101587, 2018.Valks, P., Pinardi, G., Richter, A., Lambert, J.-C., Hao, N., Loyola, D., Van Roozendael, M., and Emmadi, S.: Operational total and tropospheric NO2 column retrieval for GOME-2, Atmos. Meas. Tech., 4, 1491–1514, 10.5194/amt-4-1491-2011, 2011.Vandaele, A. C., Hermans, C., Simon, P. C., Carleer, M., Colin, R., Fally,
S., Mérienne, M. F., Jenouvrier, A., and Coquart, B.: Measurements of
the NO2 absorption cross-section from 42 000 cm-1 to 10 000 cm-1 (238–1000 nm) at 220 K and 294 K, J. Quant.
Spectrosc. Ra. Transf., 59, 171–184,
10.1016/S0022-4073(97)00168-4, 1998.Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J.,
Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele,
M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann,
P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F.:
TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global
observations of the atmospheric composition for climate, air quality and
ozone layer applications, Remote Sens. Environ., 120, 70–83,
10.1016/j.rse.2011.09.027, 2012.Vlemmix, T., Ge, X. (., de Goeij, B. T. G., van der Wal, L. F., Otter, G. C. J., Stammes, P., Wang, P., Merlaud, A., Schüttemeyer, D., Meier, A. C., Veefkind, J. P., and Levelt, P. F.: Retrieval of tropospheric NO2 columns over Berlin from high-resolution airborne observations with the spectrolite breadboard instrument, Atmos. Meas. Tech. Discuss. [preprint], 10.5194/amt-2017-257, in review, 2017.Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, D., Zhang, X.,
O'Neill, S., and Wynne, K. K.: Estimating emissions from fires in North
America for air quality modeling, Atmos. Environ., 40, 3419–3432,
10.1016/j.atmosenv.2006.02.010, 2006.Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, 10.5194/gmd-4-625-2011, 2011.Wold, S., Esbensen, K., and Geladi, P.: Principal component analysis,
Chemometr. Intell. Lab., 2, 37–52,
10.1016/0169-7439(87)80084-9, 1987.Woo, J.-H., Choi, K.-C., Kim, H. K., Baek, B. H., Jang, M., Eum, J.-H.,
Song, C. H., Ma, Y.-I., Sunwoo, Y., Chang, L.-S., and Yoo, S. H.:
Development of an anthropogenic emission processing system for Asia using
SMOKE, Atmos. Environ., 58, 5–13,
10.1016/j.atmosenv.2011.10.042, 2012.
Zoogman, P., Liu, X., Suleiman, R. M., Pennington, W. F., Flittner, D. E.,
Al-Saadi, J. A., Hilton, B. B., Nicks, D. K., Newchurch, M. J., Carr, J. L.,
Janz, S. J., Andraschko, M. R., Arola, A., Baker, B. D., Canova, B. P., Chan
Miller, C., Cohen, R. C., Davis, J. E., Dussault, M. E., Edwards, D. P.,
Fishman, J., Ghulam, A., González Abad, G., Grutter, M., Herman, J. R.,
Houck, J., Jacob, D. J., Joiner, J., Kerridge, B. J., Kim, J., Krotkov, N.
A., Lamsal, L., Li, C., Lindfors, A., Martin, R. V., McElroy, C. T.,
McLinden, C., Natraj, V., Neil, D. O., Nowlan, C. R., O'Sullivan, E. J.,
Palmer, P. I., Pierce, R. B., Pippin, M. R., Saiz-Lopez, A., Spurr, R. J.
D., Szykman, J. J., Torres, O., Veefkind, J. P., Veihelmann, B., Wang, H.,
Wang, J., and Chance, K.: Tropospheric emissions: Monitoring of pollution
(TEMPO), J. Quant. Spectrosc. Ra. Transf., 186,
17–39, 10.1016/j.jqsrt.2016.05.008, 2017.