Dual-scan ground-based multi-axis differential optical absorption
spectroscopy (MAX-DOAS) measurements of tropospheric nitrogen dioxide
(NO2) and aerosols were carried out in Uccle (50.8∘ N, 4.35∘ E; Brussels region, Belgium) for 2 years from March 2018 to February 2020. The MAX-DOAS instrument operated in both UV and visible wavelength ranges in a dual-scan configuration consisting of two submodes: (1) an elevation scan in a fixed viewing azimuthal direction and (2) an azimuthal
scan in a fixed low elevation angle (2∘). By analyzing the O4 and
NO2 differential slant column
density (dSCD) at six different wavelength intervals along every azimuthal
direction and by applying a new optimal-estimation-based inversion approach
(the so-called mapping MAX-DOAS technique), the horizontal distribution of
the NO2 near-surface concentrations and vertical column densities
(VCDs) as well as the aerosol near-surface extinction coefficients are retrieved
along 10 azimuthal directions. The retrieved horizontal NO2
concentration profiles allow the identification of the main NO2
hotspots in the Brussels area. Correlative comparisons of the retrieved
horizontal NO2 distribution were conducted with airborne, mobile,
air quality model, and satellite datasets, and overall good agreement is
found. The comparison with TROPOMI observations from operational and
scientific data products reveals that the characterization of the
horizontal distribution of tropospheric NO2 VCDs by ground-based
measurements and an adequate a priori NO2 profile shape in TROPOMI
retrievals lead to better consistency between satellite and ground-based
datasets.
Introduction
Aerosols and nitrogen dioxide (NO2) play a crucial role in
tropospheric chemistry. NO2 is an important tropospheric pollutant
mainly emitted by combustion processes and nitrogen fertilizers used in
agriculture (Seinfeld and Pandis, 2016). Traffic, domestic heating,
industrial activities, and power plants are the largest NO2 emitters
(Tack et al., 2021). Beyond its harmful effects on human health (Chen et
al., 2007), NO2 participates in the formation of tropospheric ozone
(O3) by a nonlinear photochemical mechanism, which involves volatile
organic compounds (VOCs).
Aerosols with a small diameter can penetrate deeply into the lungs, causing
millions of premature deaths around the world per year (Khomenko et al., 2021). Additionally, aerosols influence the Earth's climate system by
changing its radiation budget by scattering and absorbing sunlight (Quaas et
al., 2008). In the boundary layer of urban regions, the horizontal
distribution of NO2 is highly heterogeneous given the fact that it is a
short-lived species (Beirle et al., 2003). For those reasons, the regional
and global monitoring of NO2 and aerosols at high spatial resolution is
crucial.
Since 1995, with the ERS-2 GOME (Global Ozone Monitoring Experiment)
instrument (Burrows et al., 1999), satellite nadir air quality measurements
of atmospheric backscattered sunlight in the UV–visible range have provided
daily global tropospheric column measurements of numerous trace gases, such
as NO2. Many satellite missions dedicated to air quality monitoring
followed over the next years with increasing spatial resolution. More
recently, the TROPOspheric Monitoring Instrument (TROPOMI) launched
on board the Sentinel-5P Precursor (S5P) platform in October 2017 reached an
initial spatial resolution of 7×3.5 km2 and was augmented on 6 August 2019 to 5.5×3.5 km2. Due to TROPOMI's fine spatial resolution, monitoring the horizontal distribution of NO2 in urban regions and identifying
specific emission sources are made easier than with previous satellite
missions, but TROPOMI still cannot fully capture the fine-scale
(sub-kilometer) structures in the effective NO2 field. Consequently,
TROPOMI requires further attention concerning its measurement validation.
Tropospheric vertical columns of many trace gases like NO2,
formaldehyde (HCHO), sulfur dioxide (SO2), nitrous acid (HONO), and
O3 can be retrieved by the multi-axis differential optical absorption
spectroscopy (MAX-DOAS) technique (Hönninger et al., 2004; Wittrock et
al., 2004; Pinardi et al., 2008, 2013; Clémer et al., 2010; Hendrick et
al., 2014; Irie et al., 2011, 2012; Sinreich et al., 2007; Wagner et al., 2011; Wang et al., 2018). In recent years, MAX-DOAS measurements have been
widely used as reference datasets for the validation of nadir airborne and
spaceborne air quality measurements. MAX-DOAS instruments measure the
scattered sunlight in the UV and visible spectral ranges at multiple
elevation angles above the horizon. For absorbers located close to the
surface, such as tropospheric NO2, higher sensitivity is achieved
for low MAX-DOAS elevation angles. During the last years, MAX-DOAS
measurements in more than one azimuthal direction have emerged (Ortega et
al., 2016; Wang et al., 2014; Chan et al., 2020; Schreier et al., 2021).
Multi-azimuthal MAX-DOAS measurements offer many possibilities for
air quality monitoring, such as a better characterization of the effective
NO2 field around the station. These ground-based datasets can be
valuable for validating satellite missions with fine spatial resolution in
regions where the NO2 horizontal distribution is heterogeneous, such as
urban and suburban areas.
In this study, a new aerosol and NO2 horizontal distribution inversion
approach based on 2 years (March 2018–February 2020) of dual-scan
multi-wavelength MAX-DOAS measurements in Uccle (Brussels-Capital Region,
Belgium) is presented. We refer to this new approach as the mapping MAX-DOAS
technique. In every azimuthal viewing direction, parameterized NO2
near-surface concentrations, NO2 tropospheric columns, and aerosol
extinctions measured at six different wavelengths are used as input in a new
horizontal distribution inversion approach. On this basis, the near-surface
aerosol extinction and NO2 horizontal distributions are retrieved at a
spatial resolution of about 3 km in a range of about 20 km around the
measurement site. These horizontal profiles are used to validate co-located
TROPOMI tropospheric NO2 columns. We use (i) the operational
TROPOMI data product, (ii) a so-called diagnostic dataset, which is a small
sample but with major updates compared to the operational product (van
Geffen et al., 2022), and (iii) a European TROPOMI product for which profiles from the CAMS (Copernicus Atmospheric Monitoring Service) regional chemistry
transport model (CTM) ensemble (S5P-CAMS) are used (Douros et al., 2022). One complete year of data (March 2018–March 2019) and two wavelength intervals (one in the UV and one in the visible) have already been used in Dimitropoulou et al. (2020). It is proven that multi-azimuthal (the
so-called dual-scan) MAX-DOAS measurements significantly improve the
agreement between ground-based and TROPOMI tropospheric NO2 column
observations over the Brussels area. By adding the multi-wavelength aspect,
the present work represents an extension of the former study.
The paper is organized into seven sections: in Sect. 2, the measurement
site with the MAX-DOAS experimental setup and the multi-wavelength DOAS
analysis are presented. In Sects. 3 and 4, the TROPOMI tropospheric NO2
measurements and ancillary measurements used in this study are described,
respectively. Section 5 is composed of two main parts: Sect. 5.1 is a
detailed description of the dual-scan multi-wavelength MAX-DOAS retrieval
method, and Sect. 5.2 is the horizontal aerosol and NO2 distribution
inversion approach (i.e., the mapping MAX-DOAS technique). In Sect. 6, main
results followed by correlative comparisons of the retrieved ground-based
and satellite horizontal NO2 distribution are presented. Finally, in
Sect. 7, conclusions and future perspectives are given.
Dual-scan multi-wavelength MAX-DOAS measurementsMeasurement site and experiment setup
The Brussels-Capital Region is the most densely populated area in Belgium, where
pollutant concentrations, such as NO2, are often high because of
anthropogenic activities (Tack et al., 2021).
A MAX-DOAS dual-scan instrument was operated by BIRA-IASB (Koninklijk
Belgisch Instituut voor Ruimte-Aeronomie – Institut Royal d'Aeronomie
Spatiale de Belgique) in Uccle from January 2017 to February 2020. Uccle is
located to the south of the city center of Brussels and to the west of a
large forested area (Bois de la Cambre). Therefore, it is an ideal site to
perform MAX-DOAS observations under moderate to high pollution
conditions. Additionally, the characterization of the horizontal
distribution of NO2 and aerosols at high spatial resolution is of great
interest here because of the heterogeneity of the pollution sources (car
traffic, national airport, power plant) in the capital region of Brussels.
The MAX-DOAS dual-scan instrument is composed of the following parts: the
optical head mounted on a sun tracker, two spectrometers (UV and visible)
inside a thermo-regulated box, and the data acquisition unit. The optical
head and the two spectrometers are connected with optical fibers. A more
detailed description of the BIRA-IASB MAX-DOAS dual-scan instrument can be
found in Dimitropoulou et al. (2020).
From March 2018 to February 2020, the MAX-DOAS instrument operated in a
dual-scan viewing mode. Two different submodes compose one complete
measurement scan (see Dimitropoulou et al., 2020): (1) a vertical scan in
nine different elevation angles (EAs) in one fixed telescope azimuth angle
(TAA; northeast direction, i.e., towards the city center and the national
airport) and (2) a horizontal scan in nine different azimuthal directions at
a fixed elevation angle (2∘ above the horizon).
Several azimuthal viewing directions were tested to obtain an optimal
horizontal sampling without any obstacles in the different viewing directions
(see Fig. 1). The selection of more azimuthal directions towards the north,
northeast, and northwest was made considering the location of the main
NO2 emission sources and, consequently, the highly variable NO2 horizontal distribution towards these directions.
The experimental setup of the BIRA-IASB dual-scan MAX-DOAS instrument. Each line is color-coded according to the different setups that were used from March 2018 to February 2020. The length of each line is equal to 20 km, which corresponds to the typical horizontal
sensitivity for the MAX-DOAS measurements in the present study (see Fig. 18). The black square shows the MAX-DOAS instrument location, the black
polygon the national airport, the black dots the NO2
hotspots emitting more than 10 kg NOxh-1
(Emission Inventory of the Belgian Interregional Environment Agency, 2017; Frederik Tack, personal communication, 2019), and the black line the Brussels Ring motorway.
The integration time for each measured radiance spectrum is 60 s, resulting
in a full scan duration of approximately 20 min.
Multi-wavelength DOAS analysis
The measured radiance spectra of a full measurement scan are analyzed using
the QDOAS spectral fitting software developed by BIRA-IASB (Fayt et al., 2017). The DOAS technique separates the narrow absorption features of trace
gases in the UV–visible spectral range from a spectral background caused
mainly by Mie and Rayleigh scattering and instrumental effects. The trace
gas concentration integrated along the light path in a measured spectrum
relative to the amount of the same absorber in a reference spectrum is the
primary product of the DOAS analysis and is called differential slant column
density (dSCD). Here, average zenith spectra before and after each
measurement scan are used as a reference.
The O4 and NO2 dSCDs are retrieved in six different wavelength
intervals: three intervals in the UV spectral range (330–361, 350–370,
and 360–383.5 nm) and three in the visible range (420–460, 450–490,
and 510–540.1 nm). These fitting windows were selected to optimize the
determination of the O4 and NO2 dSCDs at the maximum number of
different O4 absorption bands available in the wavelength domain of the
instrument. Figure 2 shows an example of the O4 and NO2 fits in
all the intervals used in the present work. In each chosen fitting window,
we select a reference wavelength, which corresponds to the maximum of an
O4 absorption peak (or close to it) in the respective wavelength
intervals (see Fig. 2), and it is subsequently used for radiative transport
calculations and further analysis. The different reference wavelengths are
343, 360, 380, 447, 477, and 530 nm (see Fig. 2). To optimize the derivation of the dSCDs at the six selected wavelengths, the fit of a
slope parameter, which accounts for the variation of the dSCD within the
fitting interval (Puķīte et al., 2010), is necessary. This is
especially important when the reference wavelength is not located in the
center of the fitting window (i.e., 330–361, 350–370, 420–460, and
450–490 nm). The DOAS settings used for each fitting interval are presented
in Table S1 in the Supplement. As shown in this table, two different O4 cross-sections
are used in this study: (1) Finkenzeller and Volkamer (2022) in the UV
fitting intervals and (2) Thalman and Volkamer (2013) in the visible fitting
intervals. The main motivation for using the O4 cross-section from
Finkenzeller (measured at 25 ∘C) in the UV fitting intervals is the
significant improvement of the fit quality and the reduction of the
uncertainties for the UV retrievals. Sensitivity tests and comparisons with
radiative transport simulations also show that the resulting O4 and
NO2 dSCDs are consistent throughout the whole wavelength range covered
by the six intervals. For NO2 and O3, which are the strongest
absorbers in all the fitting windows, a correction for the solar I0
effect (Aliwell et al., 2002) is applied. A high-resolution solar atlas
(Kurucz et al., 1984) is used for the wavelength calibration of the measured
spectra.
Results of the O4 and NO2 fit for the six selected fitting windows from the dual-scan MAX-DOAS measurements in Uccle (2 June 2019 at 07:05 UTC). The measured spectra are represented with black lines, while the fit results are shown with red lines. The blue lines represent the six reference wavelengths.
TROPOMI tropospheric NO2 measurements
In the present study, MAX-DOAS tropospheric NO2 VCDs are used to
validate co-located TROPOMI satellite observations. TROPOMI is a passive
grating imaging spectrometer flying on board the S5P satellite platform. It
covers the UV–visible (250–500 nm), near-infrared (710–770 nm), and
shortwave infrared (2314–2382 nm) spectral ranges (Veefkind et al., 2011).
TROPOMI measures in a push-broom configuration with a full swath width as
wide as 2600 km, and it provides daily global coverage at a spatial
resolution (true-nadir pixel size) of 7×3.5 km2, which is further improved to
5.5×3.5 km2 on 6 August 2019. The TROPOMI tropospheric NO2
algorithm has been developed at KNMI and uses a
retrieval assimilation modeling system that is based on the 3-D global TM5
chemistry transport model (van Geffen et al., 2019; Williams et al., 2017).
We use the reprocessed (RPRO) and offline (OFFL) datasets of the TROPOMI L2
tropospheric NO2 column product (see Table 1 for the corresponding
versions). According to the guidelines provided by van Geffen et al. (2019), RPRO datasets are available only for the first period of the present
study (see Table 1). For the remaining periods, OFFL datasets are used,
which are the main data products available within 2 weeks from the
TROPOMI measurement. To ensure the best measurement quality, only pixels with
a quality assurance value larger than 0.75 are used. This quality flagging
eliminates pixels with a cloud radiance fraction larger than 0.5, snow or
ice, and erroneous retrievals (Eskes and Eichmann, 2022).
TROPOMI NO2 processor versions used
in the present study.
DatasetVersionStarting dateEnd dateRPRO1 February 200230 April 201817 October 2018OFFL1 February 200017 October 201828 November 2018OFFL1 February 200228 November 201820 March 2019OFFL1 March 200020 March 201923 April 2019OFFL1 March 200123 April 201926 June 2019OFFL1 March 200226 June 201929 November 2020
Next to operational products, two additional TROPOMI datasets are also used
(see Sect. S2 in the Supplement). In the first one, the TROPOMI retrieval
is performed with different a priori profiles (Douros et al., 2022). The coarse TM5-MP a priori NO2 profiles, using a spatial resolution of 1∘×1∘, are replaced by NO2 profile shapes from
the CAMS (Copernicus Atmospheric Monitoring Service) regional chemistry
transport model (CTM) ensemble at a spatial resolution of 0.1∘×0.1∘ (S5P-CAMS product). The replacement of coarse a priori
information with finer information can lead to significant changes in the TROPOMI-retrieved NO2 tropospheric columns. The available dataset covers
October 2018 to March 2020 (OFFL dataset, L2, and version 01.02.00 up to
01.04.00; Eskes and Eichmann, 2022).
In the second additional product, the TROPOMI retrieval is performed with an
improved cloud product (Eskes et al., 2022; van Geffen et al., 2019).
According to van Geffen et al. (2019), the improvement of the FRESCO-S
cloud pressure retrieval scheme to the FRESCO-wide product has an impact on
the NO2 air mass factors (AMFs) and consequently on the NO2 tropospheric columns
over polluted areas. More precisely, the existing FRESCO-S product had a
negative bias in the cloud height values, which resulted in a low NO2
tropospheric column (Compernolle et al., 2021). The TROPOMI tropospheric
NO2 columns are retrieved using an improved FRESCO-S cloud retrieval
scheme, called FRESCO-wide, in v1.4 since 29 November 2020 (Eskes and
Eichmann, 2022). In the present study, the diagnostic datasets (DDSv2) are
used, which are an ensemble of reprocessed data for past periods analyzed
with new versions (van Geffen et al., 2022). Over the MAX-DOAS measurement
time period, only DDSv2 data corresponding to OFFL datasets (v1.2 and v1.3)
are available. Excluding the spin-up period needed by TM5-MP, only four data
periods are available for our comparisons (i.e., 30 June–6 July 2018,
30 December 2018–5 January 2019, 30 March–5 April 2019, and 17–23 September 2019).
Ancillary measurements
In the present study, ancillary measurements are used for two main purposes:
(1) as a priori information on the retrieval of the MAX-DOAS NO2 and
aerosol horizontal profiles as well as (2) to validate the retrieved MAX-DOAS
NO2 horizontal profiles.
First, the CIMEL Sun photometer is a multi-channel photometer, which
measured the direct solar irradiance and sky radiance at the Earth's surface
in discrete wavelengths in the UV, visible, and near-IR wavelengths of the
spectrum. Aerosol optical thickness measurements at the Brussels-Capital
Region from a co-located CIMEL Sun photometer of AERONET
(https://aeronet.gsfc.nasa.gov/, last access: 11 July 2022) are used in the retrieval of
the MAX-DOAS aerosol horizontal profiles (see Sect. 5.3).
Secondly, independent NO2 horizontal profiles, used as a priori
information in the new OEM-based (OEM: optical estimation method) horizontal distribution inversion approach,
are provided by the RIO model. RIO is a land use regression model based on
the interpolation of the hourly NO2 near-surface concentrations
measured by the in situ telemetric air quality network in Belgium
(Hooyberghs et al., 2006; Janssen et al., 2008; see https://irceline.be/en, last access: 13 July 2022). RIO provides hourly NO2 concentration maps at a 4×4 km2 spatial resolution.
To validate the retrieved MAX-DOAS NO2 horizontal profiles, independent
measurements are necessary in the Brussels-Capital Region.
For the S5P validation campaign over Belgium (S5PVAL-BE,
https://s5pcampaigns.aeronomie.be/, last access: 11 July 2022), airborne measurements in the two
largest urban regions over Belgium, i.e., Antwerp and Brussels, took place
from 26 to 29 June 2019 (Tack et al., 2021). The Airborne Prism EXperiment
(APEX) imaging spectrometer was used to measure the horizontal distribution
of tropospheric NO2 columns with a spatial resolution of approximately
75m×120m (Tack et al., 2017, 2019). During the S5PVAL-BE flight over Brussels, car-mobile DOAS observations were performed by the
BIRA-IASB mobile DOAS, the so-called AEROMOBIL (Merlaud, 2013). The
AEROMOBIL consists of a compact double Avantes spectrometer simultaneously recording
scattered light in two elevation angles (i.e., one at
30∘ elevation angle and one at zenith). The AEROMOBIL was used to
measure the spatial distribution of tropospheric NO2 columns mainly
over the Ring road of Brussels. These airborne and car-mobile measurements
are compared with the retrieved MAX-DOAS NO2 horizontal profiles (see
Sect. 6.2).
Description of the mapping MAX-DOAS technique
First, the measured radiance spectra in the UV and visible wavelength ranges
are analyzed in the six different fitting windows listed in Sect. 2.2.
Then, the optimal-estimation-method-based (OEM-based) Mexican MAX-DOAS Fit (MMF)
algorithm is applied to the O4 and NO2 dSCDs in the main azimuthal
direction (and at 477 nm) to retrieve vertical NO2 profiles and obtain
information about the vertical extent of NO2 in the troposphere via the
mixing layer height of NO2 (MLHNO2; see Sect. 5.1).
As an intermediate step, radiative transfer model (RTM) simulations are
performed (see Table 2 and Sect. 5.2) to obtain information about the
horizontal sensitivity (LNO2) and aerosol optical depth (AOD) as a function of O4 dSCDs, wavelength, and MLHNO2.
RTM inputs for the simulations of LNO2 at the six selected
wavelengths (343, 360, 380, 447, 477, and 530 nm).
Then, in the next step, a new dual-scan parameterization technique is
applied to the O4 and NO2 dSCDs at the six different wavelengths
and in all the azimuthal directions with MLHNO2, measured O4
dSCDs, and measurement geometry being the main input parameters to retrieve
the horizontal sensitivity of NO2 and, consequently, the NO2
near-surface concentrations, VCDs, and near-surface aerosol extinction as
a function of distance from the instrument (see Sect. 5.2).
In the final step, a new OEM-based horizontal distribution inversion
approach is developed using the six near-surface NO2 concentrations and
aerosol extinction values per azimuthal direction to retrieve NO2 and
aerosol extinction horizontal profiles in an output grid of 500 m thickness
(see Sect. 5.3).
A flowchart describing the mapping MAX-DOAS technique is shown in Fig. 3.
Mapping MAX-DOAS technique flowchart.
Aerosol and NO2 OEM-based profile retrievals
The optimal-estimation-based MMF inversion algorithm (Friedrich et al., 2019) is applied to retrieve the aerosol extinction coefficient and NO2 vertical profiles for each MAX-DOAS elevation scan in the main azimuthal direction at 360 and 477 nm. First, the O4 measurements are used to retrieve the aerosol extinction profile. Several studies indicated the importance of applying a scaling factor (≠1) to the observed O4 dSCDs to bring them in agreement with simulated O4 dSCDs by radiative transfer modeling (Wagner et al., 2009; Clémer et al., 2010; Merlaud et al., 2011; see also Wagner et al., 2019, Table 1 for a comprehensive list of all those studies). However,
there is no consensus on the fundamental reason for applying this scaling
(see, e.g., Ortega et al., 2016). As found by Tirpitz et al. (2021), the
choice of the scaling factor has only a small effect on the performance of
the trace gas retrieval, so we decided not to apply it in the present study.
The aerosol extinction profile retrieved from each scan is used as an input
to the radiative transfer calculations used to retrieve the NO2
profile. Further details about the MMF inversion algorithm, the
input a priori parameters, the quality check of each scan, and the estimated
uncertainties of the aerosol and NO2 vertical profile can be found in
Dimitropoulou et al. (2020).
A broken cloud-filtering approach based on Gielen et al. (2014) is applied
to the MAX-DOAS measurements to exclude MAX-DOAS aerosol and NO2 scans
influenced by the presence of clouds, which are known to potentially degrade
the quality of the retrievals (Gielen et al., 2014; Wagner et al., 2014).
Three sky conditions can be distinguished with this flagging approach: (1) clear sky, (2) homogeneous cloud coverage, and (3) broken cloud conditions. Retrievals under broken cloud conditions are rejected from the present study.
The profile retrieval was performed to estimate the mixing layer height of
NO2 (MLHNO2). The MLHNO2 is estimated per measurement scan, and it is the ratio of VCDNO2,main to the NO2 near-surface concentration (cNO2,main) as retrieved in the main azimuthal direction by the MMF inversion algorithm:
MLHNO2=VCDNO2,maincNO2,main.
Therefore, during one measurement scan, two assumptions were made: (1) the
homogeneous distribution of NO2 inside the MLHNO2 and (2) homogeneous MLHNO2 around the measurement site and its use in all the azimuthal directions. The validity of the second assumption is tested in
Sect. 5.2.3.
Dual-scan MAX-DOAS retrieval method
A complete MAX-DOAS measurement scan is composed of two different sub-scans,
as described in Sect. 2.1. The aerosol and NO2 vertical profiles are
retrieved from the elevation scan in the main azimuthal direction. In the
other azimuthal directions, measurements are performed only in a single low
elevation angle (2∘), and therefore the retrieval of aerosol and
NO2 vertical profiles is not possible. Using the fact that the lowest
elevation angles have the highest sensitivity to trace gases located near
the surface due to the long light path in this layer, a new dual-scan
MAX-DOAS retrieval strategy was developed here. This new retrieval strategy
is an extension of the work presented in Dimitropoulou et al. (2020) and
aims to retrieve the near-surface NO2 box-averaged volume mixing ratios
(VMRs) and the NO2 VCDs at six different wavelengths. In Dimitropoulou
et al. (2020), the applied dual-scan NO2 MAX-DOAS retrieval was itself
an adaptation of the parameterization technique proposed by Sinreich et al. (2013). More precisely, in the presence of sufficient aerosols in the
atmosphere (i.e., sufficient aerosols to constrain the light path in a
near-surface layer and ensure that the near-surface NO2 concentration
can be approximated by a near-surface box profile), the measured NO2
dSCDs at one low elevation angle (2∘) can be related to the
near-surface NO2 box-averaged concentration as follows:
dSCDNO2=cNO2LNO2,
where dSCDNO2 is the differential slant column density of NO2 and cNO2 its mean concentration along the differential effective light path, LNO2.
Consequently, the knowledge of the differential effective light-path
length (i.e., LNO2) is crucial to derive the near-surface NO2
concentrations. The oxygen collisional complex (O4) can be used as a
tracer for the effective light path in the atmosphere, as its concentration
is well-known (it is the square of O2 concentration). As a result,
observed changes in the O4 dSCDs can be directly attributed to
changes in the light path due to the presence of particles like aerosols and
clouds. LO4 is calculated by using Eq. (2) for O4.
However, the direct use of the O4 light-path length in the NO2
retrieval is not possible under moderate to high pollution conditions, such
as those in Brussels, because the profile shapes of O4 and NO2 are not the same. In Dimitropoulou et al. (2020), we used radiative transfer model (RTM) simulations to estimate a unitless correction factor, which accounts for these profile shape differences. This unitless correction factor
indicates that under moderate to high pollution conditions, LNO2 is equal to or smaller than LO4. For a correction factor equal or close to 1, LO4 is equal to LNO2, which means that there is a moderate to high aerosol load in the atmosphere during the measurement. On
the other hand, correction factors smaller than unity are obtained for
measurements performed under aerosol-free conditions or a thin MLH. Assuming
a homogeneous NO2 distribution inside the MLH, the MLH is derived from
the NO2 vertical profiles in the main azimuthal direction and is
defined as the ratio of the NO2 VCD to the near-surface concentration
of NO2. In Dimitropoulou et al. (2020), the RTM simulations were
performed for eight different MLH values of aerosols and NO2 in the
range of 500–2000 m (i.e., eight different combinations) as well as for different
measurement viewing geometries (solar zenith angle – SZA, relative azimuth
angle – RAA, and the corresponding elevation angle of 2∘). For every
MAX-DOAS measurement, one value of the correction factor is given according
to its viewing geometry and MLH value during the measurement. For further
information, we refer the reader to Dimitropoulou et al. (2020).
In the present study, a new dual-scan NO2 MAX-DOAS retrieval method,
which is more suitable for interpreting multi-wavelength measurements than
the previous approach (Dimitropoulou et al., 2020), is developed. It is
presented in detail in the following subsection.
Developed dual-scan MAX-DOAS retrieval method
The main advantages of the new dual-scan NO2 MAX-DOAS retrieval method
(which are also the main differences with respect to Dimitropoulou et al., 2020) are the following: (1) the direct use of the measured O4 dSCDs to estimate LNO2 for every measurement, (2) retrieval of near-surface aerosol extinction close to the ground, and (3) the exploitation of the wavelength dependency of the horizontal path representative of MAX-DOAS measurements for the retrieval of the horizontal distribution of aerosols (and therefore NO2) around the measurement site. The latter is done using O4 and NO2 dSCDs measured at six different wavelengths. This new method is described below.
Assuming that the NO2 vertical distribution can be approximated by a
box profile of height equal to mixing layer height (MLHNO2), the
following equation can be used.
cNO2=VCDNO2MLHNO2=dSCDNO2LNO2
This means that the NO2 near-surface concentration can be expressed
as a ratio of the dSCDNO2 to the LNO2 (see Eq. 2) or as a ratio of the VCDNO2 to the MLHNO2. When knowing MLHNO2 and VCDNO2, LNO2 can be simulated as
follows.
LNO2simulated=dSCDNO2simulatedMLHNO2VCDNO2
Here, NO2 dSCDs and consequently LNO2 are simulated using the
radiative transfer model VLIDORT version 2.7 (Spurr, 2006). Seasonal median
MAX-DOAS NO2 vertical profiles, as retrieved by applying the MMF
inversion algorithm in the main azimuthal direction (see Sect. 5.1), show
that the bulk (70 %) of the NO2 concentration is located inside the
MLHNO2, which is expected since MLHNO2 is estimated as the ratio of VCDNO2 to the near-surface NO2 concentration. On the other hand, this is not the case for aerosols (only 30 % of the aerosol content is seen to be located inside the MLHNO2). Considering this feature, for the VLIDORT simulations, the NO2 a priori profiles are modeled as box profiles with a constant concentration equal to 1.5×1011 molec. cm-3 from the surface to the MLHNO2. Two layers compose the aerosol a priori
profiles: (1) the MLHNO2 and (2) the free troposphere. The equation, which is applied to estimate the aerosol extinction profile a(z), is the following (see Wang et al., 2014):
a(z)=AODpMLHNO2forz≤MLHNO2
and
a(z)=bξ,MLHNO2,pexp-zξforz>MLHNO2,
where AOD is the aerosol optical depth, p is the fraction of AOD inside the
MLHNO2, b is a normalizing constant for the exponential component (Wang et al., 2014), z is the simulation altitude grid, and ξ is the scaling height for the aerosols located outside the MLHNO2, which is set to 5 km (Wang et al., 2014). In the present study, the fraction of AOD located within the MLHNO2 is set to p=0.3 (see above). The effect of the p value and the NO2 profile shape on the retrieved NO2 near-surface VMRs and VCDs was investigated and considered in the error budget (see Sect. 5.2.2).
The MLHNO2 is a known parameter, and it is estimated per measurement scan as the ratio of VCDNO2 to the NO2 near-surface concentration as retrieved in the main azimuthal direction by the MMF inversion algorithm.
The RTM simulations have nine input parameters in total, which are the
elevation angle, SZA, RAA, AOD, MLHNO2, cNO2, AOD (p and ξ), and wavelength. It should be noted that the elevation angle is kept constant (i.e., 2∘). For the six different wavelengths
(343, 360, 380, 447, 477, and 530 nm), we separately perform RTM simulations, and LNO2 (see Eq. 4) is simulated for the assumed SZA, RAA, MLHNO2, cNO2, and AOD input scenarios presented in Table 2.
The simulated O4 dSCDs are a function of the input parameter AOD. The
relation between the simulated O4 dSCDs and the input AOD values is
shown in Fig. 4a. A piecewise cubic hermite interpolating polynomial fitting
through the AOD as a function of the simulated O4 dSCDs for each SZA,
RAA, and MLHNO2 combination can be used in order to perform an inverse method (i.e., to estimate the near-surface aerosol extinction from the
measured O4 dSCDs). Additionally, in Fig. 4b, we can see that the
relation between the O4 dSCDs and the AOD values is valid for MAX-DOAS
measurements.
(a) Dots: simulated AOD for NO2 box profile of 1 km at 477 nm for an SZA of 40∘ and RAA of 30∘ as a function of the simulated O4 dSCDs for the different AOD values (1, 0.8, 0.6,
0.4, 0.3, 0.1, and 0; see Table 2). Blue line: simulated AOD by applying an
exponential fit through the data points. (b) Dots: estimated AOD in the six wavelengths used in the retrieval for 1 example day (11 September 2018). Red line: fitted line through the data points.
For every combination of all eight parameters (i.e., all the parameters of
Table 2, except the AOD values), a polynomial fit of simulated LNO2 as a function of simulated O4 dSCDs is applied. Figure 5a shows simulated LNO2 as a function of simulated O4 dSCDs, and a second-order polynomial is fitted through the data points. We also observe in Fig. 5b, in which an example day of MAX-DOAS measurements is presented, that the LNO2 as a function of the measured O4 dSCDs has the same relation as the simulated quantities. Since NO2 is an optically thin absorber, LNO2 is not a function of cNO2, and consequently an LNO2 value
can be estimated by using the measured O4 dSCD for each measurement.
Based on the corresponding SZA, RAA, measured O4 dSCD, and MLHNO2, an LNO2 is attributed to each low-elevation MAX-DOAS measurement through this polynomial fit. To express LNO2 as a function of four different parameters (i.e., O4 dSCD, SZA, RAA, and
MLHNO2), LNO2 is interpolated linearly at the O4 dSCD, SZA, RAA, and MLHNO2 of each measurement. For example, a MAX-DOAS measurement with SZA =30∘, RAA =60∘, MLHNO2=1 km, and measured O4 dSCD =6×1043 molec.2 cm-5 will have an LNO2 equal to 15 km at 477 nm (see Fig. 5a).
(a) Dots (stars): simulated LNO2 for NO2 box profile of 1 km at 477 nm (530 nm) for an SZA of 30∘ and RAA of 60∘ as a function of the simulated O4 dSCDs for the different
AOD values (1, 0.8, 0.6, 0.4, 0.3, 0.1, and 0; see Table 2). Blue (red) line:
second-order polynomial fit through the data points. (b) Dots: estimated LNO2 in the six wavelengths used in the retrieval for
1 example day (11 September 2018). Red line: fitted line through the data
points.
Based on this approach, the near-surface NO2 concentration can be
calculated at the six different wavelengths by using the measured
dSCDNO2 together with the simulated LNO2 value (Eq. 2). The corresponding near-surface NO2 VMRs are obtained by dividing the
NO2 concentrations by the air number density. To derive the air number
density, we use monthly averaged pressure and temperature profiles over a
20-year period. These profiles are extracted from the European Centre for
Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. In the last
step, the tropospheric NO2 VCD is calculated from the product of the
near-surface NO2 concentration with the MLHNO2.
Regarding aerosols, the AOD is estimated for every off-axis measurement (see
Fig. 4a). The near-surface aerosol extinction is then calculated as the
ratio between the aerosols inside the MLHNO2 (i.e., AOD times p) and MLHNO2. The near-surface aerosol extinction refers to the layer that extends from the surface to the MLHNO2. As mentioned above, around 30 % of the total aerosols are expected to be found inside this layer.
The effect of SZA, RAA, and MLHNO2 on the simulated LNO2 is
investigated in the Supplement. First, the simulated LNO2 is presented in Fig. S1 as a function of RAA for different MLHNO2 and wavelengths as well as a single AOD and SZA value. LNO2 strongly depends on MLHNO2. The lower the MLHNO2, the shorter the LNO2. The same NO2 concentration and aerosol load are used for the three different MLHNO2
scenarios. So, when aerosols are concentrated in a thin layer (i.e.,
MLHNO2=0.5 km), LNO2 becomes shorter. Secondly, we observe that LNO2 depends on RAA. The larger the RAA, the longer the LNO2. In Fig. S2, simulated LNO2 is plotted for each wavelength and each
considered MLHNO2 as a function of SZA (at a constant AOD and RAA).
LNO2 depends strongly on SZA. The highest dependency is observed for
large SZA values at which LNO2 becomes maximum. Finally, in both Figs. S1 and S2, we observe that LNO2 becomes longer with wavelength, which is expected because of the less pronounced Rayleigh and Mie scattering at longer wavelengths.
An example of dual-scan MAX-DOAS retrieval is shown in Fig. 6. Based on the
RTM simulations described above, LNO2 is derived for the wavelengths of
interest, and ultimately, near-surface NO2 concentrations and
tropospheric NO2 VCDs are estimated. In the last step, the near-surface
aerosol extinction values are assigned to the six different wavelengths.
(a) Corresponding LNO2, (b) near-surface NO2 concentrations, (c) NO2 VCDs, and (d) aerosol optical densities as a function of the six wavelengths used in the retrieval (11 September 2018, 11:51 UTC, 123.5∘ azimuthal direction).
Uncertainty budget
To estimate uncertainties in the dual-scan parameterized NO2
near-surface concentration and VCD, the standard error propagation method is
used as
σcNO22=σdSCDNO2∂cNO2∂dSCDNO22+σLNO2∂cNO2∂LNO22,
which is solved as
σcNO22=σdSCDNO2LNO22+-σLNO2dSCDNO2LNO222.
Then,
σcNO22=σdSCDNO2cNO2dSCDNO22+σLNO2cNO2LNO22,
where
σLNO22=LNO2dSCDNO2(sim)σdSCDNO22+LNO2MLHNO2σMLHNO22.
According to Kreher et al. (2020) and Bösch et al. (2018), in urban or
suburban polluted conditions, the use of the DOAS fit uncertainty of
NO2 for the dSCDNO2 uncertainty is not appropriate because the
dSCDNO2 uncertainty is mostly driven by atmospheric variability as well
as spatial and temporal fluctuations in the O4 and NO2 fields. In
this study, a conservative value of 3.5×1015 molec. cm-2 is
attributed to σdSCDNO2 (Kreher et al., 2020). This
represents an error of up to 6.0 % in the NO2 dSCDs in the visible
range (477 nm).
The second error source is related to the estimation of LNO2 from the RTM simulations. To estimate this error, sensitivity tests on the input
aerosol and NO2 vertical profiles were performed. The fraction of
aerosols located inside the MLHNO2 (40 % and 60 % instead of 30 %) and the NO2 profile shape (linearly decreasing instead of box) were modified. The error related to the RTM simulations of the simulated
dSCDNO2 is about 9.6 % in the visible range (477 nm). Additionally, according to Dimitropoulou et al. (2020), the uncertainty related to MLHNO2 is about 4 % in the visible range (477 nm).
Combining all the error sources, the total uncertainties in the NO2
near-surface concentration are about 12.7 %, 12.4 %, 11.4 %, 11.1 %, 11.3 %, and 12.6 % at 343, 360, 380, 447, 477, and 530 nm, respectively.
Finally, the total uncertainties in the NO2 VCD are about 12.0 %,
11.7 %, 10.7 %, 10.3 %, 10.6 %, and 11.9 % at 343, 360, 380, 447, 477, and 530 nm, respectively.
Verification of the dual-scan MAX-DOAS retrieval method
The consistency check and verification of the dual-scan MAX-DOAS retrieval
method in Uccle are based on two different correlative comparisons.
The consistency check compares the NO2 near-surface VMRs and
tropospheric VCDs retrieved by the dual-scan parameterization in the main
azimuthal direction to the same quantities retrieved with the MMF inversion
algorithm at the two main wavelengths (360 and 477 nm). As can be seen in
Figs. 7 and 8, both datasets are in good agreement, with correlation
coefficient values in the range of 0.86 to 0.95 and slope values close to
unity for all four comparisons.
Comparison between MMF and parameterized NO2 near-surface VMR at 477 nm (visible, a) and 360 nm (UV, b), as derived from the main azimuthal direction (i.e., 35.5∘ azimuthal direction).
Comparison between MMF and parameterized NO2 VCD at 477 nm (visible, a) and 360 nm (UV, b), as derived from the main azimuthal direction (i.e., 35.5∘ azimuthal direction).
The verification step is based on the same type of comparison as the first
one but for three additional azimuthal directions in which elevation scans,
and hence profile retrievals, are available for some periods. Onward from 3 July 2019, elevation scans were performed in these three additional azimuthal
directions to complement the already existing measurement setup. These
elevation scans were performed once per day around noon in the 11, 105, and 262.5∘ azimuthal directions. Figure 9 shows the
comparison between near-surface NO2 VMRs and tropospheric VCDs
retrieved by the dual-scan parameterization method and the corresponding
results obtained with the MMF inversion algorithm. Overall good agreement is
obtained (R=0.79 and 0.84 for near-surface VMR and VCD, respectively). We
observe that the comparison concerning the near-surface NO2 VMR seems
to be noisier than in the main azimuth direction. This is mainly due to the
use of the MLHNO2 calculated in the main azimuthal direction for all the different azimuth angles in the dual-scan method. Additionally, the
parameterization technique slightly underestimated the near-surface NO2
VMR (s=0.84), while a slope value of 1.00 is obtained for tropospheric
VCDs.
Visible range: comparison between MMF and (a) parameterized NO2 near-surface VMR as well as (b) parameterized NO2 VCD at three different azimuthal directions, as indicated in the color bar (11,
105, and 262.5∘ azimuthal directions). The elevation scans in these azimuthal directions were performed once per day from 3 July 2019.
Horizontal distribution inversion approach
The parameterized NO2 near-surface concentrations at the six different
wavelengths are used as input in a new horizontal distribution inversion
approach. As parameterized NO2 near-surface concentrations, we refer to
the conversion of the measured NO2 dSCDs (i.e., at the elevation angle
of 2∘) to near-surface NO2 concentrations by applying the
dual-scan MAX-DOAS retrieval method as described in Sect. 5.2. Figure 10
shows a sketch of the assumed horizontal box model configuration, in which
successive boxes of concentration cN between the horizontal distances
dN-1 and dN from the MAX-DOAS instrument are considered along
the light path. The index N is equal to the total number of successive
boxes.
Schematic representation of the six different LNO2 (i.e., one horizontal line for each wavelength) used in the new horizontal distribution inversion approach. The length of each line shows the sensitivity of each wavelength as a function of the horizontal distance. The shortest line represents the smallest wavelength.
The different horizontal lines illustrate the horizontal extent (or
differential effective light path as described in Sect. 5.2) in which the
NO2 near-surface concentrations are extended for the six different
wavelengths. Generally, the MAX-DOAS horizontal sensitivities are longer for
larger wavelengths because of the less pronounced Rayleigh scattering (see
also Fig. 6; Ortega et al., 2016; Dimitropoulou et al., 2020). In Fig. 10,
the shortest line represents the smallest wavelength's horizontal
sensitivity (343 nm) and the longest line the largest wavelength's
horizontal sensitivity (530 nm). As can be seen in the sketch, the effective
horizontal light path at the six different wavelengths passes through a
different number of horizontal bins.
The parameterized NO2 near-surface concentrations at the different
wavelengths are the mean concentrations along the horizontal effective light
paths (see Sect. 5.2), which are also called differential effective light
paths because they are linked to the dSCDNO2. When having information coming from one wavelength only, it is not possible to know how the NO2 is distributed along this light path. In the present work, the knowledge of mean NO2 concentrations at six different wavelengths is used to retrieve a horizontal NO2 profile, assuming the horizontal box model described in Fig. 10. This new retrieval method is described below.
The measurement vector y consists of the six retrieved surface concentrations (called c‾NO2; see the method presented in Sect. 5.2) at the six different wavelengths. These near-surface concentrations can be expressed as functions of the different effective light paths (LNO2) and correspond to the average surface concentrations along those LNO2.
y=FmeascNO2true=c‾NO2=dSCDNO2LNO2Fcalcul, which represents the forward model, can be
expressed as follows:
FcalculcNO2true=1LNO22∫0LNO2e-xLNO2cNO2(x)⋅dxcos(a),
where x is the horizontal distance, a is the elevation angle of the
measurement, and cNO2 is NO2 near-surface
concentration as a function of x, the distance from the MAX-DOAS instrument.
Our retrieval of the horizontal distribution of cNO2 is based on the inversion theory (Rodgers, 2000), in which a horizontal profile
cNO2 (state vector) is retrieved given an a priori
horizontal profile xα, the measurement vector y, the matrix of the weighting function K, the uncertainty
covariance matrix of the a priori Sα and the
uncertainty covariance matrix of the measurement Se.
cNO2=xa+KTSe-1K+Sa-1-1KTSe-1(y-Kxa)
The weighting function indicates the sensitivity of the measurement vector
to a change in the horizontal profile. Following the Beer–Lambert law, the
weighting functions follow an exponential decrease between the
instrument and LNO2. Additionally, we should consider
that the light path through the horizontal boxes is slanted and not
horizontal by including the cosine of the elevation angle (i.e., 2∘
elevation angle). The weighting functions are given by the following
equation.
Kλ,x=1LNO2e-xLNO2Δxcos(a)
An example of weighting functions is presented in Fig. 11. The sensitivity
decreases exponentially up to a distance corresponding to the differential
effective light-path length of each measurement. More precisely, each
measurement is highly sensitive to the MAX-DOAS instrument location. This
sensitivity decreases exponentially as a function of the horizontal
distance. Then, it reaches a value equal to 1/e to the horizontal distance
equal to the differential effective light-path length of each measurement.
It should be noted that since NO2 is an optically thin absorber, the
measurements depend linearly on each horizontal box's concentration. For
this reason, OEM for the linear case is considered here, and only one
inversion step is needed (see Eq. 12).
Examples of weighting functions used in the new
horizontal distribution inversion approach (11 September 2018, 11:26 UTC).
The selected output horizontal grid for the retrieval extends from the
MAX-DOAS instrument to the horizontal distance in which the weighting
function of the largest wavelength (i.e., 530 nm) decreases at 10 % of its value at the MAX-DOAS instrument's location per azimuthal direction and
consists of successive boxes of 0.5 km thickness on the horizontal axis.
Since this inversion problem is ill-conditioned, more than one horizontal
NO2 profile can be consistent with the measurement vector. To reject
unrealistic solutions, the a priori profile xa and its uncertainty covariance matrix must be included in the retrieval. In the OEM, the a priori information usually comes from an independent
source, like a model or other correlative measurements. In the present
study, RIO model data were chosen as a priori. Seasonal average maps of RIO
NO2 near-surface concentration are constructed (see Fig. S3), and then
seasonal averages of RIO NO2 near-surface concentration horizontal
profiles were calculated in each azimuthal direction and interpolated on the
retrieval's horizontal grid by regridding the initial 4×4 km2 spatial resolution to a finer one (see Fig. 12). The shape of the RIO a priori NO2 profiles per azimuthal direction stays the same during different seasons of the year, indicating that the wind effect on NO2
transportation disappears by the seasonal averaging and that the same
sources contribute to the NO2 horizontal field. A mean scaling factor
equal to the mean ratio between the measured and RIO NO2 near-surface
concentrations is applied because of the systematic underestimation of
NO2 near-surface concentrations by MAX-DOAS when compared to in situ
measurements (see Dimitropoulou et al., 2020, and Sect. S1).
Example of seasonal RIO a priori NO2 horizontal profiles for the new
horizontal distribution inversion approach as a function of the horizontal
distance from the MAX-DOAS instrument in six different azimuthal viewing
directions, before the application of the scaling factor.
For the aerosol horizontal distribution retrieval, there are no sufficient
independent measurements that provide information about the horizontal
distribution of AOD and can serve as an a priori AOD profile. Therefore, a
constant a priori AOD profile is used in the AOD retrieval based on CIMEL
observations. An AOD equal to 0.18, which is the yearly averaged AOD value
from CIMEL at 477 nm, is used. To construct the near-surface aerosol
extinction a priori profiles, 30 % of the total
amount of AOD is considered to be located inside the MLH (i.e., known for each MAX-DOAS
vertical scan from the MMF inversion algorithm; see Sect. 5.1).
The diagonal elements of the Sa matrix are set equal
to the square of a scaling factor times the NO2 concentration a priori
profile. The non-diagonal elements, which account for correlation between
the different horizontal grid cells, are set as follows (Barret et al., 2002):
Saij=SaiiSaijexp-ln(2)xi-xjγ2,
where xi and xj are the horizontal distances at the ith
and jth horizontal boxes, and γ is half of the correlation length. For NO2, γ is set equal to 3.5 km, and for aerosols,
γ is set equal to 2 km. To eliminate inversion instabilities,
Sa elements which are smaller than 0.1 % of the
maximum Sa element are set equal to zero.
To estimate the correlation length, a covariance matrix was constructed by
exploiting the airborne observations above Brussels (28 June 2019). The
airborne observations have a spatial resolution of approximately 100×100 m2. NO2 horizontal profiles were constructed in different
azimuthal directions at a spatial resolution of 500×500 m2, expanding from the MAX-DOAS position to a maximum distance of 20 km, and were used to calculate a covariance matrix. A correlation length equal to 7 km and, consequently, a gamma value equal to 3.5 km are found to be representative for the NO2 horizontal profiles in Brussels.
Using this correlation length, a variance of 45 % is used. This choice was made based on the seasonal variance of the RIO a priori profiles
compared to their seasonal mean value. It is found that the seasonal
variance of RIO observations has a mean value of 45 %. Additionally, it is found to be a good compromise for obtaining reasonable retrieval results,
e.g., in terms of information content, while avoiding unrealistic
oscillations in the retrieved aerosol and NO2 profiles. It should be
noted that for aerosols, a variance of 65 % is used.
The measurement covariance matrix Se is chosen to be
diagonal, with elements corresponding to the uncertainties of the dual-scan
parameterized NO2 near-surface concentration (see Sect. 5.2.2).
Three examples of the retrieved NO2 horizontal profile are presented in
Fig. 13, together with corresponding measured and simulated c‾NO2 at the six different wavelengths for 2 July 2018
(Fig. 13a; low NO2 abundance condition), 11 September 2018 (Fig. 13b;
medium NO2 abundance condition), and 30 September 2018 (Fig. 13c; medium NO2 abundance condition). The root mean square (rms) is calculated between measured and
simulated NO2 near-surface concentrations of the horizontal retrieval
normalized by the mean of the measured NO2 near-surface concentrations
(upper panels in Fig. 13). As the NO2 values become larger, the
agreement between measured and simulated c‾NO2,
expressed via the rms value, is improved.
Measured and retrieved NO2 near-surface concentrations (upper panels in a, b, and c) at the six different
wavelengths (i.e., horizontal distances) as a function of the estimated
horizontal distances and (lower panels in a, b, and c) the retrieved NO2 near-surface horizontal profile and a priori profile. The panels correspond to (a) 11 September 2018 at 12:91 UTC with a 265∘ azimuthal direction, (b) 2 July 2018 at 10:42 UTC with a 25∘ azimuthal direction, and (c) 30 September
2018 at 07:96 UTC with a 167.5∘ azimuthal direction.
Similarly, examples of measured and retrieved near-surface aerosol
extinction coefficients and retrieved aerosol horizontal profiles are shown in
Fig. 14 for different aerosol load conditions (low, Fig. 14a; medium, Fig. 14b; and high, Fig. 14c) over the Brussels-Capital Region. We observe that the agreement between simulated and measured near-surface aerosol extinction coefficients at the six different wavelength tends to be worse than for NO2. This could be due to the use of a constructed (constant) a
priori aerosol horizontal profile due to the lack of information on the
aerosol extinction horizontal distribution in the Brussels-Capital region.
Measured and retrieved near-surface aerosol extinction (upper panels in a, b, and c) at the six different wavelengths (i.e., horizontal distances) as a function of the estimated horizontal distances and (lower panels in a, b, and c) the retrieved near-surface aerosol extinction horizontal profile. The panels correspond to (a) 11 September 2018 at 11:48 UTC with a 167.5∘ azimuthal direction, (b) 20 September 2018 at 07:08 UTC with a 25∘ azimuthal direction, and (c) 2 June 2018 at 18:16 UTC with a 35.5∘ azimuthal direction.
An essential condition of the dual-scan MAX-DOAS retrieval and the new
horizontal inversion approach at six different wavelengths is the increasing
trend of the horizontal sensitivity as a function of wavelength.
Consequently, every wavelength is sensitive to a different horizontal region,
and the six different wavelengths can be used to retrieve the horizontal
distribution of aerosols and trace gases. Sensitivity tests were conducted
in which simulated LO4 is expressed as a function of the six different
wavelengths for different aerosol conditions. As can be seen in Fig. S4, the
linear relationship between LO4 (and LNO2) and wavelength exists
for AOD values ranging from 0 to 1. An AOD equal to unity is chosen as the
maximum AOD of the simulations because in Uccle AOD values rarely exceed 1 (see in https://aeronet.gsfc.nasa.gov/, last access: 11 July 2022, for the Brussels
measurement site). Therefore, the relation stays linear as the aerosol load
changes for the conditions observed in Uccle. The only condition leading to
nonlinearity is when clouds are present. However, as explained in Sect. 5.1, a cloud-filtering approach is applied, rejecting the broken cloud scenes, which are the more problematic ones.
Characterization of the retrieval
To characterize the retrieval, the averaging kernels, AK, play a
crucial role. The AK matrix is calculated as follows (Rodgers,
2000).
AK=dcNO2dcNO2true=KTSe-1K+Sa-1-1KTSe-1K
The AKs are the rows of the AK matrix. They present the
sensitivity of the retrieved (cNO2) to the true (cNO2true) atmospheric profile. Ideally, the AK matrix should be an identity matrix. In Fig. 15, an
example of selected AKs is shown. As can be seen that, for distances
smaller than the first measurement (e.g., near-surface NO2
concentration retrieved at 343 nm), the AKs have maximum values and decrease exponentially as a function of the horizontal distance from the
instrument. For this particular example, the AKs do not exceed the
values of 0.12.
Example of NO2 averaging kernels. They are calculated for observations on 11 September 2018 at 11:51 UTC and 300∘ azimuthal direction.
Another important point about the retrieval is the trace of the
AK matrix, which refers to the number of degrees of freedom for
signal (DOFS). The DOFS are an indication of the number of independent
pieces of information that one can retrieve from the measurements. Ideally,
the DOFS would be equal to the number of horizontal boxes for the horizontal
distribution. In reality, the DOFS are lower because of the limited
horizontal resolution of the measurements. In Fig. 15, the DOFS are close to 2, which means that two independent pieces of information are contained in
the measurements for this particular example.
In the present work, the total retrieval error is equal to the error related
to the measurement noise and the smoothing of the true atmospheric profile.
According to Rodgers (2000), the noise covariance matrix is estimated as
Smeas=GSeGT,
with G being the gain matrix:
G=KTSe-1K+Sa-1-1KTSe-1.
Then, the retrieval noise error is given as the square root of the diagonal
elements of the noise covariance matrix.
The smoothing error is calculated as follows:
Ssmooth=AK-ISxAK-IT,
with Sx being a realistic covariance metric of the
true NO2 horizontal profile.
The horizontal profiles of the measurement and smoothing error in percentage
are shown in Fig. 16. As can be seen, the smoothing error is significantly
larger than the measurement error (range of 3 %–10 % and 14 %–40 %, respectively). The smoothing error also becomes larger as the horizontal distances from the instrument become larger. This is mainly because of the exponential decrease in the sensitivity as a function of the horizontal distance (see weighting functions in Fig. 11) and, consequently, the larger impact of the difference between the a priori profile and the true state of the atmosphere.
Example of the NO2 measurement and smoothing error in percentage for 2 July 2018 at 10:42 UTC and 25∘ azimuthal direction.
To eliminate the unsuccessful retrievals, the percentage of accepted
retrievals with respect to the total number of retrievals during the four
seasons is investigated when a specific filtering on rms and DOFS is applied
(see Table 3 and Figs. S7 and S8). As we can see in Fig. S8, DOFS are in
the range of 1.0–1.6. From these tests, it is found that most of the
retrievals have DOFS larger than 1.2 (see Fig. S8). The rms is defined as the
root mean square deviation between measured and simulated cNO2
normalized by the mean of the measured cNO2 (e.g., same rms as in
Fig. 13). Table 3 and Fig. S7 indicate that rms values are in the range of
0 %–30 %, and most of the retrievals have an rms smaller than 9 % with a median rms value of around 5.6 % during all seasons. Based on these investigations, DOFS >1.2 and rms <9 % are used as
retrieval quality control criteria.
Seasonally averaged root mean square (rms) and DOFS values. The rms is calculated between measured and retrieved NO2 near-surface concentrations of the horizontal retrieval (Fig. 13). DOFS represent the degrees of freedom of the horizontal retrieval (Fig. 15). The percentage of the accepted retrievals is presented for the different selection criteria.
The variation of the MAX-DOAS horizontal distribution of tropospheric
NO2 VCDs as a function of time over the course of 28 June 2019 is
presented in Fig. 17. This particular day is chosen because airborne
measurements took place above the Brussels region (see Sect. 6.2). The
horizontal NO2 profiles are plotted per azimuthal direction with the
horizontal axis showing the time in UTC and the vertical axis the horizontal
distance in kilometers. Because of the quality check on the retrieved NO2 horizontal profiles (see Sect. 5.3), some profiles were rejected (e.g., azimuthal direction equal to 262.5 and 265∘).
Diurnal variation of the retrieved NO2 horizontal profiles per azimuthal direction as a function of time (UTC) for 28 June 2019.
During this day, maximum NO2 columns are mainly observed around 05:00 and 10:00 UTC, which correspond to 07:00 and noon local time. Early in the morning (05:00 UTC), high NO2 columns are expected to be observed
because of the low MLH (MLHNO2 in the range of 300–600 m height) in combination with the morning rush-hour NO2 emissions. Around 10:00 UTC, the maximum NO2 columns are detected in the north (N), northeast (NE), and northwest (NW) direction (see Fig. 18). The same NO2 horizontal distribution is found when investigating the NO2 near-surface
concentrations for this day (see Fig. S5). In the Brussels region, the main
emission sources are located in the N and west (W) parts of the city and are
linked to the motorway around Brussels (the so-called Ring), the Brussels
city center, and the Drogenbos power plant (NW direction). Concerning the
NO2 peaks, they are located at a distance of around 0 to 8 km from the
measurement site. It can be seen from Fig. 18 that the Ring, the Brussels
city center, and the Drogenbos power plant are located within these
distances. As measured by the meteorological station on the BIRA-IASB
rooftop, the wind was coming from the E direction during that day, resulting
in the progressive displacement of the NO2 peak from the NNE to the W
direction. On the contrary, the azimuthal directions pointing towards a
large forested area (i.e., 62.5, 75, and 105∘), the Bois de la Cambre, detect considerably lower NO2 columns than the other directions.
Maps of hourly averaged NO2 horizontal profiles per azimuthal direction for 28 June 2019 corresponding to Fig. 17. The wind direction is shown with the black arrow. The black square shows the MAX-DOAS instrument location, the black polygon the national airport, the black dots the NO2 hotspots
emitting more than 10 kg NOxh-1 (Emission
Inventory of the Belgian Interregional Environment Agency, 2017; Frederik Tack, personal communication, 2019), and the
black line the Brussels Ring road.
Maximum near-surface aerosol extinction coefficient values are observed
all day long and detected in the N, NW, and NE direction (see Fig. S6). NO2 and aerosol peaks are co-located towards the N and NW direction.
MAX-DOAS horizontal NO2 distribution versus airborne, car-mobile DOAS, and TROPOMI: 28 June 2019 case study
The APEX tropospheric NO2 columns are compared to the tropospheric
NO2 horizontal distribution as retrieved by applying our new mapping
MAX-DOAS technique to the 28 June 2019 measurements. During the same day,
TROPOMI pixels (OFFL 010302 product; see Table 1) selected over the Brussels
region are compared to MAX-DOAS observations. During this day, the TROPOMI
overpass time was at 12:19 UTC. MAX-DOAS horizontal profiles of tropospheric
NO2 VCDs are selected around the TROPOMI overpass time (±1 h). The horizontal profile of MAX-DOAS NO2 VCDs for each horizontal
line of sight has a horizontal sampling of 0.5 km (see Fig. 13). The
MAX-DOAS NO2 VCDs for the horizontal segment crossing a TROPOMI pixel
and located inside the pixel are averaged and compared to the corresponding
TROPOMI NO2 VCD. It should be noted that the MAX-DOAS segments are
weighted by their relative length inside each pixel. APEX observations
located inside each TROPOMI pixel were used to assign one APEX NO2 VCD
value per pixel. Maps of co-located TROPOMI, AEROMOBIL, averaged MAX-DOAS,
and averaged APEX NO2 VCDs for 28 June 2019 are shown in Fig. 19.
Two maps of APEX and MAX-DOAS observations are presented: one with APEX and
MAX-DOAS in their initial resolution and one with spatially averaged APEX
and MAX-DOAS observations in the area covered by a TROPOMI pixel. The
NO2 plume as detected by APEX is covering the NW, N, and NE parts of
the Brussels region. MAX-DOAS successfully detected the same NO2 plume
in the NW and N but not in the NE direction. The correlation and agreement
between APEX and MAX-DOAS observations are very good (R=0.89 and s=1.22).
(a) Tropospheric NO2 VCD as detected by the APEX instrument in its initial spatial resolution. Tropospheric NO2 VCD maps (TROPOMI pixels) as retrieved over Brussels on 28 June 2019 by (b) APEX, (d) MAX-DOAS, and (f) TROPOMI (overpass time at 12:19 UTC). Tropospheric NO2 VCD as retrieved by (c) MAX-DOAS and (e) AEROMOBIL (between 08:30 and 15:42 UTC) in its initial spatial resolution. The black square shows the MAX-DOAS instrument location, the black polygon the national airport, the black dots the NO2 hotspots emitting more than 10 kg NOxh-1 (Emission Inventory of the Belgian Interregional Environment Agency, 2017; Frederik Tack, personal communication, 2019), and the black line the Brussels Ring road.
During the S5PVAL-BE flight over Brussels, the AEROMOBIL was used to measure
the spatial distribution of tropospheric NO2 columns, mainly over the
Ring road of Brussels, with a measurement start time at 08:30 UTC and end time
at 15:42 UTC. The AEROMOBIL NO2 VCDs, which are located closest in both
time and space to each MAX-DOAS horizontal grid, are compared to the
corresponding MAX-DOAS VCDs (see Fig. 19c and e). AEROMOBIL and MAX-DOAS
agree perfectly on the location of maximum (i.e., NW direction) and minimum
(i.e., SE direction) NO2 tropospheric VCDs (Fig. 19c and e). We can
observe in Fig. 20b that the correlation coefficient is moderate (R equal
to 0.79) and the slope value is equal to 0.59. The correlation plot between
the two datasets reveals that AEROMOBIL gives higher NO2 tropospheric VCDs
compared to MAX-DOAS ones. This finding could be partly explained by the
fact that AEROMOBIL follows busy routes on which the NO2 tropospheric
VCDs reach maximum values because of the contribution of NO2 production
from vehicle engines via fossil fuel combustion.
During the TROPOMI overpass (i.e., 12:19 UTC) above the Brussels-Capital
Region, dual-scan MAX-DOAS tropospheric NO2 columns are retrieved, as can be seen in Fig. 18. The correlation between TROPOMI and MAX-DOAS
tropospheric NO2 columns during the day of the airborne measurements
above Brussels is presented in Fig. 20c. Excellent agreement is obtained,
with a correlation coefficient value equal to 0.88. The slope value is equal
to 0.70. During that day, MAX-DOAS and TROPOMI are in good agreement but
TROPOMI tends to underestimate the tropospheric NO2 columns. It should
be noted that during that day, the range of observed NO2 VCDs is from
4.4×1015 to 9.5×1015 molec. cm-2, as retrieved by the MAX-DOAS observations.
Scatter plot between (a) the tropospheric NO2 columns derived by airborne measurements (APEX) and the MAX-DOAS observations, (b) the tropospheric NO2 columns derived by car-mobile DOAS measurements (AEROMOBIL) and the MAX-DOAS observations, and (c) the tropospheric NO2 columns derived by MAX-DOAS observations and the
TROPOMI tropospheric NO2 columns over Brussels on 28 June 2019.
Comparison between MAX-DOAS horizontal NO2 distribution and TROPOMI observations
To compare the TROPOMI and MAX-DOAS tropospheric NO2 columns, a
similar approach is used as in Sect. 6.2. Additionally, TROPOMI and
MAX-DOAS tropospheric NO2 columns are compared on a seasonal basis, and
seasonally averaged maps of those VCDs in the area covered by the
TROPOMI pixels are created. To generate these maps, the ensemble of TROPOMI
pixels recorded on 28 June 2019 is chosen as a reference and TROPOMI pixels
that coincide with this reference grid are averaged. The daily horizontal
profiles of MAX-DOAS NO2 columns are averaged on the daily TROPOMI
grids and then the reference grid is used to create the seasonally averaged
MAX-DOAS maps.
The seasonally and annually averaged maps of TROPOMI and MAX-DOAS NO2
VCDs are presented in Figs. 21 and 22. Only pixels including at least 20
comparison days are taken into account in the analysis. It is found that the
locations of the NO2 peaks and dips show a reasonably high degree of
similarity between TROPOMI and MAX-DOAS during all seasons, except summer.
The NO2 peaks appear mainly above Brussels city center, the Drogenbos
power plant (W direction), and the NW part of the Ring road, which are the
main known emission sources, as mentioned earlier. These maps also indicate
that the tropospheric NO2 column over the Brussels area has a clear
seasonal cycle, with a maximum during winter.
Seasonal tropospheric NO2 VCD grids (TROPOMI grids) as retrieved over Brussels by TROPOMI and MAX-DOAS. The black square shows the MAX-DOAS position, the black polygon the national airport, the black dots the NO2 hotspots, and the black line the Brussels Ring motorway.
Annual (e.g., based over the 2 years of observations) tropospheric NO2 VCD grids (TROPOMI grids) as retrieved over Brussels by (a) TROPOMI and (b) MAX-DOAS. (c) Annual bias between tropospheric NO2 VCD as observed
by TROPOMI and MAX-DOAS (the negative values are shown with blue, zero with white, and positive with red). The black square
shows the MAX-DOAS instrument location, the black polygon the national
airport, the black dots the NO2 hotspots, and the
black line the Brussels Ring road.
Figure 22c shows the annual relative biases (e.g., 100×(TROPOMI-MAX-DOAS)/MAX-DOAS) per pixel. It is found that negative biases (i.e., MAX-DOAS larger than TROPOMI), ranging from -3 % to -38 %, are found for all the pixels.
The seasonal correlation plots for April 2018-February 2020 are displayed in
Fig. 23. The highest correlation is found during spring (R=0.70), while
lower correlations are reported in autumn, summer, and winter, with
correlation coefficient values of 0.67, 0.61, and 0.57, respectively. It
should be noted that during spring (2018 and 2019), the number of comparison
points is smaller than for the other seasons because TROPOMI data start
from the end of April 2018. During spring, the slope value is equal to 0.88,
while during winter, summer, and autumn, the slope values are smaller (0.67,
0.45, and 0.56, respectively). Similar findings have been reported in
several studies (Verhoelst et al., 2021; Tack et al., 2021; Judd et al., 2020; Dimitropoulou et al., 2020; Ialongo et al., 2019). When
seasonally averaged TROPOMI and MAX-DOAS pixels (the pixels shown in Fig. 21) are compared one by one (see seasonal, SEAS, in Fig. 23), both the
correlation coefficient (R in the range of 0.62–0.85) and slope values (s in the range of 0.36–1.84) improve considerably for spring and autumn.
Seasonal scatter plots of tropospheric
NO2 columns derived from the dual-scan MAX-DOAS and
TROPOMI measurements over Brussels. Blue line: regression analysis results
when all the MAX-DOAS and TROPOMI pixels are included in the comparison. Red
line: seasonal average analysis generated by the pixels in Fig. 21.
The seasonal regression analysis parameters between TROPOMI and dual-scan
MAX-DOAS measurements derived in the present study are compared to the same
parameters presented in Dimitropoulou et al. (2020); see Table 4. Both
studies make use of the dual-scan MAX-DOAS instrument in Uccle. In addition
to the different approach (i.e., the retrieval of NO2 horizontal
profiles), in the present study, almost 2 years of measurements are used,
while in Dimitropoulou et al. (2020), only 1 year is exploited for the
TROPOMI validation. In Table 4, for the present study, only 1 year of
measurements is used to have comparable time coverage for both studies.
As presented in Table 4, the largest slope value is found in winter in both
the present study and in Dimitropoulou et al. (2020). The season
in which the highest correlation coefficient is obtained differs between
the two studies (here in spring and in autumn in Dimitropoulou et al., 2020).
The main advantage of the new mapping MAX-DOAS technique is the larger
number of comparison points between TROPOMI and MAX-DOAS, leading to
significantly more reliable statistics. In the present study, the deviation
of the comparison points from the fitted regression line is increased, mainly
because of the uncertainties in the horizontal inversion approach. The
scatter increase is reflected in the correlation coefficient values, which
are smaller for all seasons, except spring and winter. Regarding the slope
value, it is larger during all seasons.
Summary of the regression analysis parameters (e.g.,
correlation coefficient, R, and slope, s) and the number of data points (N) derived in the present study during only 1 year of observations (i.e.,
number of pixels) and in Dimitropoulou et al. (2020). Please note that R
(seasonal) and s (seasonal) correspond to SEAS in Fig. 23.
SeasonSpringSummerAutumnWinterR0.700.650.450.67R (seasonal)0.760.620.850.75R (Dimitropoulou et al., 2020)0.690.770.850.60s0.880.640.731.71s (seasonal)1.840.430.620.36s (Dimitropoulou et al., 2020)0.470.580.610.81N1342529984N (Dimitropoulou et al., 2020)16583613Conclusions
A total of 2 years (March 2018 to February 2020) of dual-scan MAX-DOAS measurements
in Uccle (urban background site located in the south of the Brussels-Capital
Region) were used to develop a new strategy for the retrieval of
near-surface NO2 concentrations and aerosol extinction horizontal
profiles. A full dual-scan measurement is composed of one vertical scan at a
fixed azimuthal direction pointing towards the city center and horizontal
scans in 10 azimuthal directions at a fixed low elevation angle (2∘).
The first step of this new retrieval strategy is to analyze measured
radiance spectra in six different fitting windows. This provides O4 and
NO2 dSCDs at the following six wavelengths: 343, 360, 380, 447, 477, and 530 nm. Then, information about the vertical extent of NO2 in the troposphere (MLHNO2) is derived from profile retrievals
in the main azimuthal direction performed using the OEM-based MMF algorithm.
In the third step, a new parameterization technique is applied, with
MLHNO2, measured O4 dSCDs, and measurement geometry being used as
input parameters to retrieve the horizontal sensitivity of NO2 and,
consequently, the NO2 near-surface concentrations, VCDs,
near-surface aerosol extinction, and AODs in all the azimuthal directions for
the six different wavelengths. Compared to the method presented in
Dimitropoulou et al. (2020), the new retrieval method offers the
possibility of the direct determination of LNO2 and near-surface
aerosol extinction based on the measured O4 dSCDs.
The retrieved dual-scan NO2 near-surface concentrations and VCDs are
verified via comparisons to the MMF NO2 vertical profiles derived in
the main azimuthal directions and in three additional azimuthal directions.
Good overall agreement is found for the two comparisons during the 2 years of measurements.
The dependence of the horizontal sensitivity on the wavelength is then used
to develop a new OEM-based horizontal distribution inversion approach.
Considering a horizontal box model, horizontal NO2 and aerosol
extinction profiles are retrieved in an output horizontal grid of 500 m
thickness starting from the instrument to each of the measurement maximum
horizontal representative distances.
The daily variability of NO2 horizontal profiles in all the azimuthal
directions provides information about the location of the NO2 hotspots
in the Brussels-Capital Region and how the plumes are transported.
Similarly, the NO2 horizontal profiles' seasonal variability over March
2018–February 2020 reveals that the NO2 hotspots are mainly found above
the Brussels city center, the Drogenbos power plant, and the NW part of the
Ring road during all seasons.
On 28 June 2019, airborne measurements (APEX) of NO2 were performed
over Brussels. The MAX-DOAS NO2 VCD horizontal profiles are compared to
APEX, car-mobile DOAS (i.e., AEROMOBIL), and TROPOMI measurements, and
good overall agreement is found between the different datasets for this
day.
In the last part of the study, MAX-DOAS retrievals are compared to TROPOMI
tropospheric NO2 observations over the March 2018–February 2020
period. The comparison of seasonal maps shows good overall agreement
between the two datasets as to the NO2 horizontal distribution over the
Brussels area. Results also show that during all seasons, TROPOMI
underestimates the MAX-DOAS tropospheric NO2 columns.
Overall, our investigation about the spatial sampling led to the following
two important findings.
The dual-scan multi-wavelength approach allows a good identification of the main emissions sources in urban regions, in agreement with the spatial allocation of the main emission sources observed by APEX and TROPOMI.
The characterization of the NO2 concentration horizontal field using the dual scan multi-wavelength approach results in obtaining slope values closer to unity between TROPOMI and MAX-DOAS observations. The high spatial resolution of TROPOMI requires ground-based measurement that can provide information about the horizontal distribution of tropospheric NO2 columns in urban regions.
To conclude, our study presents a new horizontal distribution inversion
approach for NO2 and aerosols developed by using dual-scan
multi-wavelength MAX-DOAS measurements over an urban area. This approach
provides a better characterization of the horizontal distribution of an
important urban pollutant, NO2, which leads to improved agreement
between satellite and MAX-DOAS measurements in moderate to highly polluted
conditions. Based on our study, further modifications of the measurement
mode aiming at a better sampling of the vertical and horizontal NO2
distribution could be implemented and investigated. For instance, performing
vertical scans in several azimuthal directions throughout the day and/or
horizontal scans in more than 10 azimuthal directions could further improve
our knowledge about the tropospheric NO2 spatial variability in urban
regions and therefore the satellite validation results in those conditions.
Data availability
The datasets generated and analyzed in the present work are available from the corresponding author on request.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-15-4503-2022-supplement.
Author contributions
ED undertook the development and validation of the dual-scan
multi-wavelength MAX-DOAS retrieval strategy in Uccle, exploited the
MAX-DOAS retrievals during 2 years, performed the validation of the TROPOMI
tropospheric NO2 columns, and wrote the paper. FH supported and guided ED in
the development of the dual-scan multi-wavelength MAX-DOAS retrieval
strategy, provided general guidelines, and revised and edited the paper. MMF
provided the MMF inversion algorithm and the RTM as well as supporting and
guiding ED in the new OEM-based horizontal profile retrieval. FT provided
the airborne APEX dataset and contributed to scientific discussions. GP
provided the dataset of the TROPOMI tropospheric NO2 columns and supported
ED in the TROPOMI validation approaches. AM provided the AEROMOBIL dataset
and contributed to scientific discussions. CF and CH provided technical and
software support for the MAX-DOAS instrument in Uccle. CF developed the
QDOAS software and guided ED in the DOAS analysis. FF provided the RIO model
dataset. MVR supervised the present work, provided general guidelines and
valuable comments during the whole process of the paper preparation, and
revised and edited the paper. All authors reviewed, discussed, and commented
on the paper.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We gratefully acknowledge the Belgian Federal Science Policy Office (BELSPO). The authors would like to thank the AERONET team for providing valuable data.
Review statement
This paper was edited by Thomas von Clarmann and reviewed by three anonymous referees.
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