We present a novel airborne imaging differential optical absorption
spectroscopy (DOAS) instrument: the Ultraviolet Visible Hyperspectral Imaging
Spectrometer (UVHIS), which is developed for trace gas monitoring and
pollution mapping. Within a broad spectral range of 200 to 500 nm and
operating in three channels, the spectral resolution of UVHIS is better than
0.5 nm. The optical design of each channel comprises a fore-optics
with a field of view (FOV) of 40∘, an Offner imaging spectrometer and
a charge-coupled device (CCD) array detector of 1032×1072 pixels. A
first demonstration flight using UVHIS was conducted on 23 June 2018, above an
area of approximately 600 km2 in Feicheng, China, with a spatial
resolution of about 25m×22m. Measurements of nadir
backscattered solar radiation of channel 3 are used to retrieve tropospheric
vertical column densities (VCDs) of NO2 with a mean total error of
3.0×1015moleccm-2. The UVHIS instrument clearly
detected several emission plumes transporting from south to north, with a peak
value of 3×1016moleccm-2 in the dominant one. The
UVHIS NO2 vertical columns are consistent with the ground-based
mobile DOAS observations, with a correlation coefficient of 0.65 for all
co-located measurements, a correlation coefficient of 0.86 for the co-located
measurements that only circled the steel factory and a slight underestimation
for the polluted observations. This study demonstrates the capability of UVHIS
for NO2 local emission and transmission monitoring.
Introduction
Nitrogen oxides (NOx), the sum of nitrogen monoxide (NO) and nitrogen
dioxide (NO2), play a key role in the chemistry of the atmosphere,
such as the ozone destruction in the stratosphere (Solomon, 1999), and the
secondary aerosol formation in the troposphere (Seinfeld and Pandis, 2016). In
the troposphere, despite lightning, soil emissions and other natural
processes, the main sources of NOx are anthropogenic activities like
fossil fuel combustion by power plants, factories and road transportation,
especially in urban and polluted regions. As an indicator of anthropogenic
pollution which leads to negative effects both on the environment and human
health, the amounts and spatial distributions of NOx have attracted
significant attention. For example, China has become one of the largest
NOx emitters in the world due to its fast industrialisation; meanwhile,
China has also experienced a series of severe air pollution problems in recent
years (Crippa et al., 2018; An et al., 2019). Therefore, measuring the
NOx distribution by applying different techniques would benefit the
pollutant emission detection and the air quality trend forecast (Liu et al.,
2017; Zhang et al., 2019).
UVHIS instrument characteristics of three channels.
Compared to NO, nitrogen dioxide (NO2) is more stable in the
atmosphere. Based on the characteristic absorption structures of NO2
in the ultraviolet–visible spectral range, the differential optical absorption
spectroscopy (DOAS) technique has been applied to retrieve light path
integrated densities from different platforms (Platt and Stutz,
2008). Combined the imaging spectroscopy technique, imaging DOAS instruments
were developed in recent years to determine the temporal variation and the
two-dimensional distribution of trace gases. The global horizontal distribution of
tropospheric NO2 and other trace gases has been mapped and studied
by several space-borne sensors, including SCIAMACHY (Scanning Imaging
Absorption Spectrometer for Atmospheric CHartographY; Bovensmann et al.,
1999), GOME (Global Ozone Monitoring Experiment; Burrows et al., 1999), GOME-2
(Munro et al., 2016), OMI (Ozone Monitoring Instrument; Levelt et al., 2006)
and TROPOMI (TROpospheric Ozone Monitoring Instrument; Veefkind et al.,
2012). The Environmental trace gases Monitoring Instrument (EMI; Zhao et al.,
2018; Cheng et al., 2019; Zhang et al., 2020), as the first designed
space-borne trace gas sensor in China, was launched on 9 May 2018, aboard
the Chinese GaoFen-5 (GF5) satellite. The spatial resolution of most
space-borne sensors is coarser than 10km×10km, except for
that of TROPOMI, which is 3.5km×5.5km.
To achieve a spatial resolution higher than 100m×100m for
investigating the spatial distribution in urban areas and individual source
emissions, several researchers have applied imaging DOAS instruments on
airborne platforms. The airborne imaging DOAS measurement was first performed
by Heue et al. (2008) over the South African Highveld plateau. To retrieve
urban NO2 horizontal distribution, Popp et al. (2012), General
et al. (2014), Schönhardt et al. (2015), Lawrence et al. (2015), Nowlan
et al. (2016) and Lamsal et al. (2017), respectively, took measurements in
Zürich, Switzerland; Indianapolis and Utqiaġvik (formerly Barrow), USA; Ibbenbüren,
Germany; Leicester, England; Houston, USA; and Maryland and Washington DC,
USA. In 2013, an airborne measurement focusing on source emissions was taken
in China, over Tianjin, Tangshan and the Bohai Bay (Liu et al., 2015). An
intercomparison study of four airborne imaging DOAS instruments over Berlin,
Germany, suggests a good agreement between different sensors and the
effectiveness of imaging DOAS in revealing the fine-scale horizontal
variability in tropospheric NO2 in urban context (Tack et al.,
2019).
Here, we present a novel airborne imaging DOAS instrument: the Ultraviolet Visible
Hyperspectral Imaging Spectrometer (UVHIS), which was designed and developed
by Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences
(AIOFM, CAS). As a hyperspectral imaging sensor with a high spectral and
spatial resolution, UVHIS is designed for operation on an aircraft platform
for atmospheric trace gas measurements and pollution monitoring over large
areas in a relative short time frame. By using the DOAS technique and
georeferencing, the two-dimensional spatial distribution of tropospheric
NO2 of its first demonstration flight over Feicheng, China, is also
presented in this paper.
This paper is organised as follows: Sect. 2 presents a technical description
of the UVHIS system and its preflight calibration results. Section 3
introduces the detailed information of its first research flight over
Feicheng, China. Section 4 describes the developed algorithm for the retrieval
and geographical mapping of tropospheric NO2 vertical column
densities from hyperspectral data. Section 5 presents the retrieved
NO2 column densities, and Sect. 6 compares the airborne measurements
with the correlative ground-based data sets from a mobile DOAS system.
Instrument detailsUVHIS instrument
UVHIS is a hyperspectral instrument measuring nadir backscattered solar
radiation in the ultraviolet and visible wavelength region from 200 to
500 nm. The instrument is operated in three channels with the
wavelength ranges of 200–276 nm (channel 1), 276–380 nm
(channel 2) and 380–500 nm (channel 3) for minimal stray light
effects and highest spectral performance. The main characteristics of UVHIS
are summarised in Table 1.
Optical layout of UVHIS channel 3. The optical design of
channel 1 and channel 2 is similar.
Figure 1 shows the optical bench of channel 3, and those of the other two are
similar. The optical design of each channel comprises a telecentric
fore-optics, an Offner imaging spectrometer and a two-dimensional charge-coupled device (CCD) array
detector. The Offner imaging spectrometer consists of a concave mirror and a
convex grating. The backscattered light below the aircraft is collected by a
wide-field telescope with a field of view (FOV) of 40∘ in the across-track
dimension. After passing through a bandpass filter and a 12.5 mm long
entrance slit in the focal plane, the light is reflected and diffracted by a
concave mirror and a convex grating. The dispersed light is imaged onto a
frame transfer CCD detector which consists of 1032×1072 individual
pixels. For the alignment and slight adjustment of the spectrometer, only the
central 1000 rows of pixels are well illuminated in the across-track
dimension. In the wavelength dimension, the image covers the central 1024 columns
of pixels on the CCD detector, whilst the left and right edges are used to
monitor dark current. The spectral sampling and spectral resolution of all
three channels can be found in Table 1.
To reduce dark current and improve the signal-to-noise ratio (SNR) of the
instrument, the CCD detector is thermally stabilised at
-20 ∘C with a temperature stability of ±0.05∘C (Zhang et al., 2017). However, the optical bench is
not thermally controlled, because the instrument is mounted inside the
aircraft platform which has a constant temperature of
20 ∘C. The UVHIS is mounted on a Leica PAV-80 gyro-stabilised
platform that provides angular motion compensation. A high-grade Applanix
navigation system on board is used to receive position (i.e. latitude,
longitude and elevation) and orientation (i.e. pitch, roll and heading)
information, which is required for accurate georeferencing. The UVHIS
instrument telescope collects the solar radiation backscattered from the
surface and atmosphere through a fused silica window at the bottom of the
aircraft. In the case of NO2 measurement, all observations in this
study only use channel 3.
Preflight calibration
Spectral and radiometric calibration was performed in the laboratory prior to
the flights to reduce errors in spectral analysis.
For radiometric calibration, we used an integrating sphere with a tungsten
halogen lamp for channel 2 and channel 3. For channel 1, a diffuser plate with
a Newport xenon lamp was used for sufficient ultraviolet output. With the help
of a well-calibrated spectral radiometer to monitor the radiance of
calibration system, the digital numbers from the CCD detectors of the three
channels can be converted to radiance correctly. The uncertainty of absolute
radiance calibration of the UVHIS is 4.89 % for channel 1, 4.67 % for
channel 2 and 4.42 % for channel 3.
Preflight wavelength calibration results (full width at half maximum values; FWHMs) of UVHIS
channel 3 for nine viewing angles. Light sources used in the calibration are a
mercury–argon lamp and a tuneable laser. Slit function shapes are retrieved
by least square fitting of characteristic spectral lines, using a symmetric
Gaussian function.
Measured slit functions (dots) at 450.504 nm and
retrieved slit function shapes (lines) using a symmetric Gaussian function
for nine viewing angles.
Preflight wavelength calibration was also performed in the laboratory, using a
mercury–argon lamp and a tuneable laser as light sources. We modelled the
slit function of the UVHIS using a symmetric Gaussian function. Spectral
registration and slit function calibration were achieved by least square
fitting of the characteristic lines in the collected spectra. Table 2 lists
the retrieved full width at half maximum values (FWHMs) for channel 3.
Figure 2 shows the measured slit functions at 450.504 nm for nine
viewing angles (i.e. -20, -15, -10, -5, 0, 5, 10, 15, 20∘) and
the respective retrieved slit function shapes using a symmetric Gaussian
function. These Gaussian fit results suggest that a symmetric Gaussian
function is a reasonable assumption for the slit shape in all viewing
directions.
Research flight
The first demonstration flight over the city of Feicheng, the town of Shiheng and
neighbouring rural areas was conducted on 23 June 2018, aiming at producing
tropospheric NO2 field maps of a large area in a relatively short
time frame. Feicheng is a county-level city in the Shandong province,
approximately 410 km away from Beijing. Figure 3 shows the TROPOMI
NO2 tropospheric observation on 23 June 2018, with the background
Google map and the location of Feicheng. The flight area is located on the
south bank of the Yellow River, at the western foot of Mount Tai. The UVHIS
was operated from the Y-5 aircraft at an altitude of
3 km a.s.l., which is higher than the height of planetary boundary
layer (PBL), with an average aircraft ground speed of
50 ms-1. An
overview of the observation area and the flight lines is provided in
Fig. 4. The aircraft took off at local noon from the airfield in Pingyin
County, approximately 19 km northwest of the centre of the field. An
area of approximately 600 km2 was covered in 3 h, under
clean sunny and cloudless conditions with low-speed southerly winds.
TROPOMI observation of tropospheric NO2 over China
on 23 June 2018. The location of the UVHIS flight (Feicheng city) is also
plotted in the map.
Overview of the Feicheng demonstration flight on 23 June 2018. Flight lines are shown in blue. Two orange circles represent the
routes of the mobile DOAS system. White dots numbered from 1 to 8 represent the
major emission sources. Number 1: several carbon factories; number 2: a
power plant; numbers 3–6: individual emitters inside the steel factories,
while numbers 4 and 5 are inside the circle of one mobile DOAS route;
numbers 7–8: two cement factories. The dashed white box represents the reference
area.
The research flight included 13 parallel lines in the east–west direction,
starting from the lower left corner in Fig. 4. The distance between adjacent
lines was 1.5 km, whilst the swath width of each individual line was
approximately 2.2 km. Gapless coverage between adjacent lines can be
guaranteed in this pattern because of the adequate overlap. To validate the
NO2 column densities retrieved from the UVHIS by comparison to
ground measurements, mobile DOAS measurements were taken inside the research
area on the same day. As shown in Fig. 4, the measurements of the mobile DOAS
system circled around the steel factory and the power plant which are the
presumed major emission sources inside the observation area.
Data processing chain
The NO2 tropospheric vertical column density (VCD) retrieval
algorithm of the UVHIS consists of four major steps. First, some necessary
pre-processing procedures are required before any spectral analysis of the
UVHIS data. Next, the UVHIS spectral data after pre-processing are analysed in
a suitable wavelength region by applying the well-established DOAS
technique. Then, the air mass factors (AMFs) are calculated for every
observation based on the SCIATRAN radiative transfer model to convert the
slant column densities (SCDs) to tropospheric vertical column densities. In
the final step, the georeferenced NO2 VCDs are resampled and
overlaid onto Google satellite map layers.
Pre-processing
The pre-processing procedure before spectral analysis includes data selection,
georeferencing, dark current correction, spatial binning and in-flight
calibration. First, the spectral data acquired during aircraft U turns are
removed in the processing because of the large and changing orientation
angles. Furthermore, a radiance threshold of 12.8 µWcm-2sr-1nm-1 at 450 nm is set to neglect some
over-illuminated ground pixels inside the flight area, which are usually
caused by the presence of clouds or water mirror reflection. During the entire
flight, the Sun glinted on water several times in the southern part of the
flight area, especially above the river near the reference area. However,
clouds were not present due to the clean clear-sky weather condition.
Accurate georeferencing is essential for emission source locating and data
comparison, and can be achieved with the sensor position and orientation
information recorded by the navigation system and inertial measurement unit (IMU) on board.
Dark current correction is performed based on the measurement at the start of
the entire flight by blocking the fore-optics, which is necessary to improve
the instrument performance and reduce the analysis error in DOAS fit.
In order to increase the SNR of the instrument and the sensitivity to
NO2, the raw pixels of the imaging DOAS are usually aggregated in
the across- and along-track directions. According to photon statistics when
only shot noise is considered, the SNR should rise with the square root of the
number of binned spectra. However, this improved SNR of the instrument results
in reduced spatial resolution. In the data analysis of the Feicheng flight, we
use the binning of 10 pixels in the across-track direction, resulting in a
ground pixel size of approximately 25m×22m.
In-flight spectral calibration: (a) the spectral
resolution (FWHM); (b) the spectral shift on different across-track
positions. Results at three wavelengths are plotted: blue for 430 nm, green
for 450 nm and red for 470 nm.
Given that the wavelength-to-pixel registration and the slit function shape of
the UVHIS could change compared to laboratory calibration results, in-flight
wavelength calibration is essential for the next DOAS analysis. This
in-flight wavelength calibration is achieved by fitting the measured spectra
to a high-resolution solar reference (Chance and Kurucz, 2010) with slit
function convolution and wavelength shift. The nominal wavelength-to-pixel
registration determined in laboratory calibration is used as initial values
in the iteratively fitting procedure for convergence to the optimal
solution. The effective shifts and FWHMs of different across-track positions
are plotted in Fig. 5. The results at three wavelengths are presented as
follows: blue for 430 nm (the start of the analysis wavelength
region), green for 450 nm (the middle of the analysis wavelength
region) and red for 470 nm (the end of the analysis wavelength
region).
DOAS analysis
After pre-processing, the observed UVHIS spectra are analysed using the QDOAS
software (Danckaert et al., 2020) to retrieve the NO2 slant column
densities. The basic idea of the DOAS approach is to separate broadband
signals like surface reflectance and Rayleigh scattering, and narrow-band
signals like trace gas molecular absorption. The fit window is
430–470 nm, which is considered to contain strongly structured NO2
absorption features and with low interference of other trace gases such as
O3, O4 and water vapour. The absorption cross sections of
NO2 and other trace gases and a synthetic Ring spectrum are
simultaneously fitted to the logarithm of the ratio of the observed spectrum
to the reference spectrum. These cross sections are made by convolving the
high-resolution cross sections with the in-flight wavelength calibration
results for all across-track positions. Further details of the DOAS analysis
setting can be found in Table 3.
Main analysis parameters and absorption cross sections for
NO2 DOAS retrieval.
ParameterSettingsWavelength calibrationSolar atlas, Chance and Kurucz (2010)Fitting interval430–470 nmCross sectionsNO2298 K, Vandaele et al. (1998)O3223 K, Serdyuchenko et al. (2014)O4293 K, Thalman and Volkamer (2013)H2O293 K, Rothman et al. (2013)Ring effectChance and Spurr (1997)Polynomial termOrder 5OffsetOrder 1
For each analysed spectrum, the direct result of the DOAS fit is the
differential slant column density (dSCD), which is the NO2 integrated
concentration difference along the effective light path between the studied
spectrum and the selected reference spectrum (SCDref). Reference
spectra were acquired over a clean rural area upwind of the urban and factory
areas, as shown in the lower left corner of Fig. 4. In the quite homogeneous background
area, several spectra were averaged to increase the SNR of the reference
spectrum. To avoid across-track biases, a reference spectrum is required for
each across-track position because of its intrinsic spectral
response. According to the TROPOMI tropospheric NO2 product of the
reference area on the same day, the residual NO2 amount in the
background spectra is estimated to be 3×1015moleccm-2. Changes in the stratospheric NO2
could also propagate to the measured tropospheric columns of UVHIS. Under the
assumption of a constant stratosphere in time and space during the flight, the
changes in the solar zenith angle (SZA) impact the column difference between the measurement and
the reference. To correct the change in the stratospheric NO2 SCD,
we apply a geometric approximation of the stratospheric AMF with a
stratospheric VCD of 3.5×1015moleccm-2 from TROPOMI
product. The maximum change in the stratospheric SCD with respect to the
reference was 8×1014moleccm-2.
Sample DOAS fit result for NO2: (a) observed
(dashed black line) and fitted (blue line) optical depths from measured
spectra; (b) the remaining residuals of DOAS fit.
A sample NO2 DOAS fit result and the corresponding residual of UVHIS
spectra are illustrated in Fig. 6 with a dSCD of 4.95±0.34×1016moleccm-2 and a rms on the residuals of 4.27×10-3.
Air mass factor calculations
SCD is the integrated concentration along the effective light path of
observation, which is strongly dependent on the viewing geometry and the
properties that influence radiative transfer of light through the atmosphere.
VCD is the integrated concentration along a single vertical transect from the
Earth's surface to the top of the atmosphere, which is independent of the
changes in the light path length of the SCD.
VCDit=dSCDi+dSCDis+SCDreftAMFit1=dSCDi+dSCDis+VCDreft×AMFreftAMFit
As shown in Eq. (), the dSCDi from the DOAS fit can be
converted to tropospheric VCDit by dividing the
AMFit which accounts for the enhancements in the light
path (Solomon et al., 1987). The dSCDis is the
stratospheric SCD difference between the measurement and the reference; the
SCDreft, the
VCDreft and the
AMFreft are the tropospheric SCD, VCD and AMF
of the reference. In this study, tropospheric NO2 AMFs have been
computed using the SCIATRAN (Rozanov et al., 2014) radiative transfer model
(RTM). The SCIATRAN model numerically calculates AMFs based on a priori
information on the parameters that changes the effective light path, such as
Sun and viewing geometry, trace gas and aerosol vertical profiles and surface
reflectance.
Parameters in RTM
(1) During flight, the viewing geometry is retrieved from the orientation
information of the aircraft. The solar position defined by the SZA
and the solar azimuth angle (SAA), as well as the relative azimuth
angle (RAA), can be calculated based on the time information and the latitude
and longitude position of each observation. (2) Since the flight is performed
under a clear-sky condition, the effect of cloud presence can be ignored in
AMF computation. (3) The surface reflectance used in AMF calculation is the
product of the Landsat 8 Operational Land Imager (OLI) space-borne instrument
(Barsi et al., 2014). The coastal aerosol band (433 to 450 nm) is
selected because its bandwidth is relatively narrow, and this band is
basically inside the DOAS fitting window (Vermote et al., 2016). (4) Since no
accurate trace gas vertical profile is available during flight, a well-mixed
vertical distribution (box profile) of NO2 in the PBL is
assumed. However, accurate PBL height is also unavailable, so the typical
height of 2 km is a reasonable guess on a sunny summer day in the
midlatitude area in China. (5) The aerosol optical depth (AOD) information
used in AMF calculation is the MODIS AOD product MYD04 at 470 nm on
the same day with resampling for every ground UVHIS pixel (Remer et al.,
2005), because ground-based aerosol measurement is not performed and no Aerosol Robotic Network (AERONET)
station data near the flight area are available. The MODIS AOD measurements
inside the flight area range from 0.14 to 0.36. Like the NO2
profile, the aerosol extinction box profile is constructed from the PBL height
and the AOD. A single scattering albedo (SSA) is assumed to be 0.93, and an
asymmetry factor is assumed to be 0.68 for the aerosol extinction profile, based
on previous studies of typical urban/industrial aerosols (Li et al., 2018).
The Landsat 8 surface reflectance is retrieved through atmospheric correction,
using the Second Simulation of the Satellite Signal in the Solar Spectrum
Vectorial (6SV) model (Vermote et al., 1997). Since no overpass on the same
day existed inside the UVHIS research flight area, we selected the surface
reflectance product on 3 May 2018, considering the sunny weather conditions and
no cloud presence. The spatial resolution of Landsat is approximately
30 m, which is slightly larger than that of the UVHIS. A resampling of
the Landsat 8 surface reflectance product based on nearest neighbour
interpolation was performed for every UVHIS ground pixel.
Overview of the input parameters in the SCIATRAN RTM,
characterising the AMF LUT.
RTM parameterGrid settingsWavelength450 nmSensor altitude3 kmSurface reflectance0.01–0.4 (steps of 0.01)Solar zenith angle10–40∘ (steps of 10∘)Viewing zenith angle0–40∘ (steps of 10∘)Relative azimuth angle0–180∘ (steps of 30∘)Aerosol optical depth0–1 (steps of 0.1)Aerosol extinction profileBox of 2.0 kmNO2 profileBox of 2.0 km
The radiative transfer equation in SCIATRAN is solved in a pseudo-spherical
multiple scattering atmosphere, using the scalar discrete ordinate
technique. Simulations were performed for the sensor altitude of
3 km a.s.l. and the wavelength of the middle of the NO2
fitting windows, i.e. 450 nm. A NO2 AMF look-up table (LUT)
was computed, with the different RTM parameter settings provided in
Table 4. For each retrieved dSCD, an AMF was linearly interpolated from the
LUT based on the Sun geometry, the viewing geometry and the surface
reflectance.
RTM dependence studyAMF dependence on the surface reflectance
As shown in Fig. 7, a time series of computed AMFs is plotted for the
research flight on 23 June 2018, as well as the corresponding surface
reflectance, solar zenith angles and relative azimuth angles. Note that only
data of nadir observations are plotted for a clear display, and the time gaps
between adjacent flight lines can be observed. Despite the great degree of
varieties in viewing and Sun geometries, the AMFs strongly depend on the
surface reflectance. Previous studies reported by Lawrence et al. (2015),
Meier et al. (2017) and Tack et al. (2017) suggest a similar conclusion. A
sensitivity test was carried out to investigate the impact of surface
reflectance on the AMF calculations based on the SCIATRAN model, with varying
values of surface reflectance and the fixed values of other parameters. The
results of this test are shown in Fig. 8a and indicate that the relation
between the surface reflectance and the AMF is non-linear. Especially when the
surface reflectance is below 0.1, the AMF increases with the surface
reflectance rapidly.
Time series of NO2 AMF compared with (a) surface reflectance;
(b) SZA and RAA for the research flight on 23 June 2018, computed with SCIATRAN model based on the RTM parameters from the
UVHIS instrument. Only data of the nadir observations in each flight line
are plotted.
AMF dependence analysis results (a) on the surface
reflectance; (b) on the SZAs; (c) on the VZAs; (d) on the wavelength.
Generally speaking, the AMF should be higher in the case of a bright surface
reflectance because more sunlight is reflected from the ground back to the
atmosphere and then recorded by the airborne sensor. Compared to rural areas,
urban and industrial areas usually exhibit enhanced surface reflectance and a
subsequent increment in AMF. As shown in Fig. 9, the dependency of the AMF on
the surface reflectance is very strong. Moreover, a strong variability of the
surface reflectance and the AMF can be observed in these areas. Figure 9 also
shows several slight inconsistencies between the UVHIS measured radiance and
the Landsat 8 surface reflectance product. For example, the east–west main
road looks thinner in Fig. 9a compared to Fig. 9b and c. This could be
explained by the relatively higher spatial resolution performance of the UVHIS
and the resampling of Landsat 8 pixels.
(a) UVHIS measured radiance; (b) Landsat 8 surface reflectance; (c)
computed AMFs for one flight line of the
Feicheng data set. A strong dependency of the AMF on the surface reflectance
can be observed.
AMF dependence on profiles
Based on airborne UVHIS retrieval product, the horizontal distribution of
NO2 can be detected, but the vertical distribution of NO2
in the atmosphere is unavailable. The assumptions we made for the profile
shape of the trace gas and aerosol extinction do not consider the effective
variability during research flight which can be expected in an urban area.
Focusing on the impact of different profile shapes on the AMF computation,
sensitivity tests of two different NO2 profiles which are closer to
ground surface were performed: well-mixed NO2 box profiles of 0.5
and 1 km heights. Compared to the box profile of 2 km which is
near the estimated height of PBL, the AMFs decreased by an average of 13 %
in the case of a box profile of 1.0 km, whilst the AMFs decreased by
an average of 22 % in the case of a box profile of 0.5 km.
Depending on the relative position of the aerosol and trace gas layer, the
optical thickness and the scattering properties, aerosols can enhance or
reduce the AMF in different ways (Meier et al., 2017). If an aerosol layer is
located above the majority of the trace gas, the aerosols with high SSA have a
shielding effect as less scatter light passes through the trace gas layer,
leading to a shorter light path. On the other hand, if aerosols and the trace
gas are present in the same layer, the aerosols can lead to multiple
scattering effects which extend the light path and result in a larger
AMF. According to the simulations of a well-mixed aerosol box profile of
2 km and a pure Rayleigh atmosphere, AMFs are slightly higher (by
approximately 1 %) than those of the pure Rayleigh scenario.
AMF dependence on Sun and viewing geometries
Figure 7 shows that the effect of Sun and viewing geometries on AMFs is very
small. Based on a previous study by Tack et al. (2017), the changing SZA have
the greatest effect on the AMFs, compared to other Sun and viewing
geometries. In this study, we also performed an AMF dependence analysis on
SZAs and viewing zenith angles (VZAs). The SZA varied from 12.8 to 37.4∘ during the
3 h research flight, whilst the VZA ranged from 0 to
30∘ in most cases. As shown in Fig. 8b and c, the changes in
AMF were less than 10 % and 7 %, respectively, when other parameters
were set to the mean values. Generally, a larger SZA or a larger VZA could
result in a longer light path through the atmosphere and thus a larger AMF.
AMF dependence on the analysis wavelength
The dependence of AMF on the analysis wavelength is shown in Fig. 8d. The AMF
increases with the analysis wavelength. This could be explained by the
Rayleigh scattering characteristics. That is, photons at shorter wavelengths
are more likely to be scattered than photons at longer wavelengths, leading to
reduced sensitivity to AMF at shorter wavelengths. In the DOAS analysis
wavelength window of 430–470 nm, the increase in AMF is approximately
2 %.
Resampling and mapping
The georeferenced NO2 VCDs are gridded to combine overlapped
adjacent measurements, with a spatial resolution of 0.0003∘×0.0002∘. Corresponding to 27m×22m, the grid
size used is slightly larger than the effective spatial resolution of the
UVHIS to reduce the number of empty grid cells. All VCDs are assigned to a
grid cell based on its centre coordinates, and several VCDs in one grid cell
are an unweighted average. As shown in Fig. 10, the final NO2 VCD
distribution map is plotted over the satellite maps layers in QGIS 3.8
software (QGIS development team, 2020).
Tropospheric NO2 VCD map retrieved from UVHIS over
Feicheng on 23 June 2018. The major contributing NOx emission sources
are indicated by numbers 1 to 8.
Results
The tropospheric NO2 VCD two-dimensional distribution map is shown
in Fig. 10 for the research flight on 23 June 2018. With the high performance
of UVHIS in spectral and spatial resolution, Fig. 10 shows fine-scale
NO2 spatial variability to resolve individual emission sources. In
general, the NO2 distribution is dominated by several exhaust plumes
with enhanced NO2 concentration in the northwest part that share a
transportation pattern from south to north that is consistent with the wind
direction. These sources include a power plant, a steel factory, two cement
factories and several carbon factories. The largest plume, with peak values of
up to 3×1016moleccm-2, originated from an emitter
inside a steel factory (number 3 in Fig. 10). This dominant plume reaches its
peak value outside at a small valley approximately 1 km north of the
factory and was transporting at least 9 km and seems to be continuing
outside the flight region. This enhanced level of NO2 may be caused
by the terrain factor which contributes to the accumulation of pollution
gases.
Numbers 4 to 6 represent other emitters inside the steel factory, whilst the
exhaust plumes from numbers 4 and 5 merged with the dominant plume, the plume
from number 6 transported to north individually with a peak value of 1.4×1016moleccm-2. A plume with peak values of 1.5×1016moleccm-2 was also detected by UVHIS, which
seemed to originate from the power plant. Indicated by number 2 in Fig. 10,
this power plant is less than 2 km south of the steel factory. Number
1 in Fig. 10 indicates several carbon factories which are located on the left
side of the flight area. Several plumes with peak values of 1.6×1016moleccm-2, gradually merged during transportation
downwind. Numbers 7 and 8 in Fig. 10 represent two different cement
factories. The peak values of these two plumes are 1.5×1016 and
1.4×1016moleccm-2, respectively.
Compared to the industrial areas mentioned above, the pollution levels of the
rural areas are much lower due to the lack of contributing sources, ranging
from 2 to 6×1015moleccm-2. The urban area of
Feicheng city is located on the right side of the flight area. Figure 11 is an
enlarged map of the UVHIS NO2 observations over Feicheng city, with
a colour scale that only extends to 7×1015moleccm-2.
The two black lines in Fig. 11 represent the truck roads in this city. S104 is
a provincial highway that crosses Feicheng from north to south, whilst S330
crosses Feicheng from east to west.
Three flight lines that pass through the steel factory
at 13:26 (a), 14:57 (b) and 13:32 LT (c). Panels (a) and (b)
represent flight lines that cover the same area with a 1.5 h time gap;
(a) and (c) represent adjacent flight lines with a 6 min time gap.
Due to temporal discontinuity of the flight lines and the dynamic
characteristics of the tropospheric NO2 field, artefacts can be
observed between adjacent flight lines. Figure 12 shows three flight lines
that pass through the steel factory at 13:26 (a),
14:57 (b) and 13:32 LT (c).
Figure 12a, b represent the flight lines that cover the same
area with a 1.5 h time gap, and Fig. 12a, c represent adjacent
flight lines with a 6 min time gap. These flight lines can be divided
into three regions: region A covers no NO2 source but is affected by
the carbon factories approximately 3 km away; region B covers the
steel factory as the dominant NO2 source; region C covers no
NO2 source and is not affected by other sources. In these three
regions, only region C is temporally consistent with relatively low
NO2 columns, whilst a large temporal variety of NO2 VCDs
exists in region A and region B because of inconstant emission sources and
changing meteorology.
NO2 VCD assessmentUncertainty analysis
The total uncertainty on the retrieved tropospheric NO2 VCDs is
composed of three parts: (1) uncertainties in the retrieved dSCDs, (2)
uncertainties in reference column SCDref and (3) uncertainties in
computed AMFs. Assuming that these uncertainties originating from independent
steps are sufficiently uncorrelated, the total uncertainty of the tropospheric
NO2 VCD can be quantified as follows:
σVCDi2=σdSCDiAMFi2+σSCDrefAMFi2+SCDiAMFi2×σAMFi2.
The first uncertainty source, σdSCDi, originates from the
DOAS fit residuals and is a direct output in the QDOAS software. This dSCD
uncertainty is dominated by the shot noise from radiance, the electronic noise
from the instrument, the systematic uncertainties from the cross sections and
the errors from wavelength calibration. In this study, spatial binning of 10
pixels is performed to reduce these DOAS fit residuals, with a mean slant
error of 4.8×1015moleccm-2.
The second uncertainty source, σSCDref, is caused by the
NO2 residual amount in the reference spectra. Since we use the
TROPOMI tropospheric NO2 product of the clean reference area as the
background amount, the uncertainty of NO2 vertical column is
estimated to be 1×1015moleccm-2 directly from
TROPOMI product. A tropospheric AMF of 2.0 and a tropospheric AMF over the
reference spectra of 1.8, result in an uncertainty 9×1014moleccm-2 to the tropospheric vertical column.
The third uncertainty source, σAMFi, derives from the
uncertainties in the parameter assumptions of radiative transfer model
inputs. According to previous studies (Boersma et al., 2004; Pope et al.,
2015), σAMFi is treated as systematic and depends on the
surface albedo, the NO2 profile, the aerosol parameters and the
cloud fraction. (1) The cloud fraction is neglected in this case because the
research flight was under cloudless conditions. (2) The results of the
dependence tests in Sect. 4.3.2 suggest that the surface albedo has the most
significant effect on the AMF. According to Vermote et al. (2016), the
uncertainty of the Landsat 8 surface reflectance product of band 1 is 0.011.
(3) According to the sensitivity study performed in Sect. 4.3.2, the
uncertainty related to the a priori NO2 profile shape is lower than
22 %. (4) According to the performed simulations of a pure Rayleigh
atmosphere, the uncertainty related to the aerosol state is estimated to be
less than 1 %. (5) Because of the high accuracy of the viewing and Sun
geometries and their low impact on the AMF computation revealed in the
previous section, the uncertainty related to the viewing and Sun geometries is
expected to be negligible. Therefore, combining all the uncertainty sources in
the quadrature, a mean relative uncertainty of 24 % on the
σAMFi is obtained.
Based on the above discussion, the total uncertainties on the retrieved
tropospheric NO2 VCDs of all the observations of the research flight
are calculated, typically ranging from 1.5×1015 to 5.9×1015moleccm-2, with a mean value of 3.0×1015moleccm-2.
Comparison to mobile DOAS measurements
In order to compare the UVHIS NO2 VCDs to the ground-based
measurements, mobile DOAS observations were performed on 23 June 2018. This
mobile DOAS system is composed of a spectrum acquisition unit and a GPS
module. The spectrum collection unit consists of a spectrometer, a telescope,
an optical fibre and a workbench. The FOV of this telescope is 0.3∘,
and its focal length is 69 mm. The spectrometer used is a Maya 2000
Pro spectrometer, with a wavelength range of 290–420 nm and a
spectral resolution of 0.55 nm. The zenith-sky observations of the
mobile DOAS were adopted for minimal blocking of buildings and trees in this
research. The important properties of the mobile DOAS system and its
NO2 retrieval approach are shown in Table 5. It is worth noting that
the retrieval window in the mobile DOAS observations differs from the one used
for the airborne observations.
Properties of the mobile DOAS system and its NO2 fit.
ParameterSettingsElevation angleZenithFitting interval356–376 nmWavelength calibrationMercury lampCross sectionsNO2298 K, Vandaele et al. (1998)O3223 K, Serdyuchenko et al. (2014)O4293 K, Thalman and Volkamer (2013)Ring effectChance and Spurr (1997)Polynomial termOrder 5OffsetOrder 1
Overview of VCDs retrieved from the ground-based mobile DOAS
system (circle marks) and VCDs retrieved by UVHIS (background layer)
measured on 23 June 2018.
For better comparison with the UVHIS NO2 observations, assumptions
and parameters in the tropospheric NO2 retrieval method for the
mobile DOAS were similarly set to those of the UVHIS. For example, the
residual amount of NO2 in the reference spectra was set to 3×1015moleccm-2 with an error of 1×1015moleccm-2; the mobile DOAS observations only focused on
the tropospheric portion of the NO2 columns, assuming that the
difference in the stratospheric NO2 columns between the observed and
reference spectra is negligible; the vertical profiles of NO2 and
aerosol extinction, albedo and aerosol properties in the AMF calculation were
similarly set to those of the UVHIS.
Scatter plot and linear regression analysis of the
co-located NO2 VCDs, retrieved from the UVHIS and mobile DOAS system,
(a)
for all co-located measurements, with a time offset of 1 h, (b) for
co-located measurements that only circled the steel factory, with a time
offset of 15 min.
Like the uncertainty analysis of the UVHIS NO2 columns, the total
uncertainty on the retrieved mobile tropospheric VCD is composed of three
parts: (1) the mean uncertainty on the dSCD of the mobile DOAS is 1.4×1015moleccm-2; (2) the uncertainty of reference vertical
column is estimated to be 1×1015moleccm-2. In the
case that the tropospheric AMFs of the measured and reference spectra are very
close, this part results in an uncertainty of 1×1015moleccm-2 to the total uncertainty; (3) the mean
relative uncertainty on the AMF calculation is 22 % by the square root of
the quadratic sum of the individual uncertainties like UVHIS. Combining these
uncertainties together, the mean total uncertainties on the retrieved
tropospheric NO2 VCD is 2.1×1015moleccm-2.
Basically, the route of the mobile DOAS was designed to encircle the power
plant and the steel factory which are supposed to be predominant sources. For
comparison, the mobile DOAS observations are first gridded to the same
sampling of the UVHIS pixels. Then the VCD of the UVHIS NO2 results
is extracted for each co-located mobile measurement. An overview of the mobile
DOAS measurements over the UVHIS NO2 layer is shown in Fig. 13. The
NO2 distributions of the mobile DOAS system and the UVHIS exhibit
similar spatial characteristics; i.e. low values are in the south of the steel
factory and power plant, and high values are inside the plumes.
Figure 14a shows scatter plots with the VCDs retrieved by the UVHIS on the
x axis and the mobile DOAS VCDs on the y axis for all co-located
measurements. The corresponding results of the linear regression analysis are
also provided in Fig. 14a, with a correlation coefficient of 0.69, a slope of
1.30 and an intercept of -9.01×1014. The absolute time offset
between the mobile DOAS and airborne observations can be up to 1 h,
indicating that both instruments cannot sample the NO2 column at
certain geolocations simultaneously. As shown in Fig. 14b, when only comparing
UVHIS VCDs to mobile measurements that circled the steel factory, the
correlation coefficient improved to 0.86. In this case, all mobile
measurements occurred inside the swath of one flight line of aircraft, and the
time offset between two instruments is shortened to 15 min. In general,
an underestimation of the UVHIS VCDs of increased value can be observed in
Fig. 14a and b. Considering the variability in local emissions and
meteorology, it is reasonable that the differences between these two
instruments exist. A sensitivity test of the AMF on the NO2 profile
was performed for all co-located measurements, using a box profile of
500 m. Compared to the box profile of 2 km, the UVHIS AMFs
decreased by an average of 17 %, whilst the mobile DOAS AMFs decreased by
an average of 2.7 %. These results suggest that a more realistic profile
with the NO2 layer closer to the ground could improve the slope and bring it
closer to unity.
Conclusions
In this paper, we present the newly developed UVHIS instrument, with a broad
spectral region ranging from 200 to 500 nm and a high spectral
resolution better than 0.5 nm. The instrument is operated in three
channels at wavelengths of 200 to 276 nm (channel 1), 276 to
380 nm (channel 2) and 380 to 500 nm (channel 3) for minimal
stray light effects and the highest spectral performance. The optical design
of each channel consists of a fore-optics with a FOV of 40∘, an Offner
imaging spectrometer and a CCD array detector of 1032×1072 pixels.
We also present the first tropospheric NO2 retrieval results from
the UVHIS airborne observation in June 2018. The research flight over
Feicheng, China, covered an area of approximately 30km×20km within 3 h, with a high spatial resolution
approximately 25m×22m. We first retrieved the
differential NO2 slant column densities from nadir-observed spectra
by applying the DOAS technique to a mean reference spectra over a clean
area. Then we converted those NO2 slant columns to tropospheric
vertical columns using the air mass factors derived from the SCIATRAN model
with the Landsat 8 surface reflectance product. The total uncertainties of the
tropospheric NO2 vertical columns range from 1.5×1015 to
5.9×1015moleccm-2, with a mean value of 3.0×1015moleccm-2.
The two-dimensional distribution map of the tropospheric NO2 VCD
demonstrates that the UVHIS is adequate for trace gas pollution monitoring
over a large area in a relatively short time frame. With the high spatial
resolution of the UVHIS, different local emission sources can be
distinguished, fine-scale horizontal variability can be revealed, and trace
gas emission and transmission can be understood. For the flight on 23 June
2018, the NO2 distribution was dominated by several exhaust plumes
which exhibited the same south-to-north direction of transmission, with a peak
value of 3×1016moleccm-2 in the dominant plume. The
comparisons of the UVHIS NO2 vertical columns with the mobile DOAS
observations show a good overall agreement, with a correlation coefficient of
0.65 for all the co-located measurements and a correlation coefficient of
0.86 for the co-located measurements that only circled the steel factory.
However, an underestimation of the high NO2 columns of the UVHIS is
observed relative to the mobile DOAS measurements.
The high-resolution information about the NO2 horizontal
distribution generated from UVHIS airborne data is unique and valuable
compared to those from ground-based instruments and space-borne sensors. In
future studies, the UVHIS could be applied in the validation of satellite trace
gas instruments and in the connection between local point observations, air
quality models and global monitoring from space.
Data availability
The data sets in the present work are available from the corresponding
author upon reasonable request.
Author contributions
Conceptualisation of the paper was done by FS. YJ, HZ and XQ built the UVHIS instrument. XQ set up and operated the UVHIS instrument. ZC developed the measurement software. KZ built the mobile DOAS instrument. DY set up and operated the mobile DOAS instrument. LX performed the analysis of the UVHIS data, provided the figures and wrote the manuscript. FS provided the review and editing of the manuscript. All authors contributed to the final manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank Thomas Danckaert, Caroline Fayt and Michel
Van Roozendael for help on QDOAS software. We are thankful to the following
agencies for providing the satellite data: the Sentinel-5 Precursor TROPOMI
Level 2 NO2 product is developed by KNMI with funding from the
Netherlands Space Office (NSO) and processed with funding from the European
Space Agency (ESA). TROPOMI data can be downloaded from
https://s5phub.copernicus.eu (last access: 2 June 2020). Landsat 8 OLI data have been produced,
archived and distributed by the US Geological Survey (USGS). The original
Landsat surface reflectance algorithm was developed by Eric Vermote,
NASA Goddard Space Flight Center (GSFC). Landsat 8 OLI data are available at
https://earthexplorer.usgs.gov (last access: 2 June 2020).
Financial support
This research has been supported by the National Key Research and Development
Program of China (grant nos. 2019YFC0214702 and 2016YFC0200402).
Review statement
This paper was edited by Andreas Richter and reviewed by two anonymous referees.
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